From 994fafabeb2068807d49fdb9583044e5a8248bff Mon Sep 17 00:00:00 2001 From: Arctusol <73721443+Arctusol@users.noreply.github.com> Date: Thu, 5 Dec 2024 09:15:02 +0100 Subject: [PATCH] ML implementation --- machine_learning/README.md | 150 + .../service_classifier.py | 109 + .../duration_prediction/duration_predictor.py | 122 + .../evaluate_models-checkpoint.ipynb | 263 + machine_learning/evaluation/metrics.py | 173 + machine_learning/evaluation/run_evaluation.py | 91 + .../evaluation/temporal_validation.py | 230 + .../evaluation/tests/test_models.py | 165 + machine_learning/logs.log | 8 + machine_learning/model_development/logs.log | 1603 + .../model_development/mlruns/0/meta.yaml | 6 + .../meta.yaml | 15 + .../tags/mlflow.runName | 1 + .../tags/mlflow.source.name | 1 + .../tags/mlflow.source.type | 1 + .../tags/mlflow.user | 1 + .../mlruns/564973138240631685/meta.yaml | 6 + .../mlruns/730985364380900454/meta.yaml | 6 + .../train_recommendation_system.ipynb | 145 + .../recommendation/hospital_recommender.py | 185 + machine_learning/utils/data_preparation.py | 109 + notebooks/logs.log | 45654 ++++++++++++++++ notebooks/model_prediction.ipynb | 1431 +- notebooks/model_prediction_validation.ipynb | 785 +- notebooks/test_prediction_pycaret.ipynb | 1313 + requirements.txt | 4 +- 26 files changed, 51830 insertions(+), 747 deletions(-) create mode 100644 machine_learning/README.md create mode 100644 machine_learning/classification_service/service_classifier.py create mode 100644 machine_learning/duration_prediction/duration_predictor.py create mode 100644 machine_learning/evaluate_models-checkpoint.ipynb create mode 100644 machine_learning/evaluation/metrics.py create mode 100644 machine_learning/evaluation/run_evaluation.py create mode 100644 machine_learning/evaluation/temporal_validation.py create mode 100644 machine_learning/evaluation/tests/test_models.py create mode 100644 machine_learning/logs.log create mode 100644 machine_learning/model_development/logs.log create mode 100644 machine_learning/model_development/mlruns/0/meta.yaml create mode 100644 machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/meta.yaml create mode 100644 machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.runName create mode 100644 machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.name create mode 100644 machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.type create mode 100644 machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.user create mode 100644 machine_learning/model_development/mlruns/564973138240631685/meta.yaml create mode 100644 machine_learning/model_development/mlruns/730985364380900454/meta.yaml create mode 100644 machine_learning/model_development/train_recommendation_system.ipynb create mode 100644 machine_learning/recommendation/hospital_recommender.py create mode 100644 machine_learning/utils/data_preparation.py create mode 100644 notebooks/test_prediction_pycaret.ipynb diff --git a/machine_learning/README.md b/machine_learning/README.md new file mode 100644 index 0000000..368b8f9 --- /dev/null +++ b/machine_learning/README.md @@ -0,0 +1,150 @@ +# Système de Recommandation d'Hôpitaux + +Ce dossier contient l'ensemble du système de machine learning pour la recommandation d'hôpitaux basée sur les pathologies et les besoins des patients. + +## Structure du Projet + +``` +machine_learning/ +├── classification_service/ # Classification du service médical approprié +├── duration_prediction/ # Prédiction de la durée d'hospitalisation +├── recommendation/ # Système de recommandation d'hôpitaux +├── evaluation/ # Évaluation et validation des modèles +│ ├── metrics.py # Métriques d'évaluation +│ ├── temporal_validation.py # Validation temporelle +│ ├── tests/ # Tests unitaires +│ └── evaluate_models.ipynb # Notebook d'évaluation +├── utils/ # Utilitaires et préparation des données +└── model_development/ # Notebooks de développement +``` + +## Description des Composants + +### 1. Classification des Services (`classification_service/`) +- **service_classifier.py** : Module pour prédire le service médical approprié (M, C, SSR, O, ESND, PSY) +- Utilise PyCaret pour l'entraînement automatisé +- Inclut la gestion des encodeurs pour les variables catégorielles +- Features : pathologie, tranches d'âge, taux standardisés, etc. + +### 2. Prédiction de Durée (`duration_prediction/`) +- **duration_predictor.py** : Module pour estimer la durée d'hospitalisation +- Optimisé pour la régression +- Utilise des features spécifiques à la durée de séjour +- Intègre le tuning automatique des hyperparamètres + +### 3. Système de Recommandation (`recommendation/`) +- **hospital_recommender.py** : Système principal de recommandation +- Combine les prédictions des différents modèles +- Calcule des scores basés sur : + - Distance géographique + - Capacité d'accueil + - Compatibilité des services + - Durée estimée du séjour + +### 4. Évaluation (`evaluation/`) +- **metrics.py** : Métriques personnalisées pour l'évaluation + - Métriques de classification des services + - Métriques de prédiction de durée + - Métriques de qualité des recommandations +- **temporal_validation.py** : Validation temporelle des modèles + - Splits temporels des données + - Analyse des tendances + - Évaluation de la stabilité +- **tests/** : Tests unitaires pour chaque composant +- **evaluate_models.ipynb** : Notebook d'évaluation complète + +### 5. Utilitaires (`utils/`) +- **data_preparation.py** : Préparation et chargement des données +- Gestion de la connexion BigQuery +- Préparation des features pour les modèles +- Séparation train/test par années + +## État d'Avancement + +### Complété +1. **Structure de Base** + - Architecture modulaire + - Intégration avec MLflow et PyCaret + - Connexion BigQuery + +2. **Modèles de Base** + - Classification des services + - Prédiction de durée + - Système de scoring + +3. **Évaluation** + - Framework de métriques + - Validation temporelle + - Tests unitaires + - Notebook d'évaluation + +### En Cours +1. **Optimisation des Modèles** + - [ ] Affiner les hyperparamètres + - [ ] Optimiser les poids du système de scoring + - [ ] Ajouter des features supplémentaires + +2. **Interface et Intégration** + - [ ] Créer une API REST + - [ ] Développer une interface utilisateur + - [ ] Intégrer dans l'application principale + +### À Faire +1. **Documentation et Maintenance** + - [ ] Compléter la documentation utilisateur + - [ ] Mettre en place le monitoring + - [ ] Ajouter des exemples d'utilisation + +2. **Améliorations Futures** + - [ ] Intégrer les avis patients + - [ ] Ajouter des données en temps réel + - [ ] Développer un système de feedback + +## Utilisation + +1. Installation des dépendances : +```bash +pip install -r requirements.txt +``` + +2. Configuration : +- Configurer les credentials BigQuery +- Initialiser MLflow pour le tracking des expériences + +3. Évaluation des modèles : +```python +# Exécuter le notebook d'évaluation +jupyter notebook evaluation/evaluate_models.ipynb +``` + +4. Utilisation du système : +```python +from recommendation.hospital_recommender import HospitalRecommender + +recommender = HospitalRecommender() +recommender.load_models(service_run_id='...', duration_run_id='...') +recommendations = recommender.predict(patient_data) +``` + +## Dépendances Principales + +- PyCaret +- MLflow +- Google Cloud BigQuery +- pandas +- scikit-learn +- geopy +- pytest (pour les tests) +- jupyter (pour les notebooks) + +## Contribution + +Pour contribuer au projet : +1. Créer une nouvelle branche pour chaque feature +2. Suivre les conventions de code établies +3. Documenter les changements +4. Soumettre une pull request + +## Contact + +Pour toute question ou suggestion, contacter l'équipe de développement. diff --git a/machine_learning/classification_service/service_classifier.py b/machine_learning/classification_service/service_classifier.py new file mode 100644 index 0000000..8feefc4 --- /dev/null +++ b/machine_learning/classification_service/service_classifier.py @@ -0,0 +1,109 @@ +from pycaret.classification import * +import mlflow +import pandas as pd +from typing import Optional, Tuple, Dict +from sklearn.preprocessing import LabelEncoder + +def prepare_service_data(data: pd.DataFrame) -> Tuple[pd.DataFrame, Dict]: + """ + Prépare les données pour la classification des services + + Args: + data: DataFrame contenant les données brutes + + Returns: + DataFrame préparé et dictionnaire des encodeurs + """ + # Sélectionner les colonnes pertinentes + features = [ + 'pathologie', 'code_pathologie', 'nom_pathologie', + 'tranche_age_0_1', 'tranche_age_1_4', 'tranche_age_5_14', + 'tranche_age_15_24', 'tranche_age_25_34', 'tranche_age_35_44', + 'tranche_age_45_54', 'tranche_age_55_64', 'tranche_age_65_74', + 'tranche_age_75_84', 'tranche_age_85_et_plus', + 'tx_brut_tt_age_pour_mille', 'tx_standard_tt_age_pour_mille', + 'classification' # target variable + ] + + df = data[features].copy() + + # Créer les encodeurs pour les variables catégorielles + encoders = {} + categorical_features = ['pathologie', 'nom_pathologie', 'classification'] + + for feature in categorical_features: + encoders[feature] = LabelEncoder() + df[feature] = encoders[feature].fit_transform(df[feature]) + + return df, encoders + +def train_service_classifier( + data: pd.DataFrame, + target_col: str = 'classification', + fold: int = 5, + experiment_name: str = 'service_classification' +) -> Tuple[object, Dict]: + """ + Entraîne un modèle de classification pour prédire le service médical approprié + + Args: + data: DataFrame contenant les données d'entraînement + target_col: Nom de la colonne cible (service médical) + fold: Nombre de folds pour la validation croisée + experiment_name: Nom de l'expérience MLflow + + Returns: + Le meilleur modèle entraîné et les encodeurs utilisés + """ + try: + # Préparer les données + prepared_data, encoders = prepare_service_data(data) + + # Setup PyCaret sans MLflow + clf_setup = setup( + data=prepared_data, + target=target_col, + session_id=123, + fold=fold, + log_experiment=False, # Désactiver l'intégration MLflow de PyCaret + verbose=False + ) + + # Entraîner le modèle + best_model = compare_models(n_select=1) + + # Log manuel avec MLflow + mlflow.set_experiment(experiment_name) + with mlflow.start_run(): + # Log des paramètres de base + mlflow.log_param("target_col", target_col) + mlflow.log_param("fold", fold) + + # Log des métriques de performance + results = pull() + for metric in results.columns[1:]: + mlflow.log_metric(metric, results.iloc[0][metric]) + + return best_model, encoders + + except Exception as e: + print(f"Une erreur s'est produite lors de l'entraînement : {str(e)}") + raise + +def load_service_classifier(run_id: str) -> Tuple[Optional[object], Optional[Dict]]: + """ + Charge un modèle de classification de service depuis MLflow + + Args: + run_id: ID MLflow du run contenant le modèle + + Returns: + Le modèle chargé et les encodeurs, ou None si le chargement échoue + """ + try: + model = mlflow.pycaret.load_model(f"runs:/{run_id}/service_classifier") + encoders = mlflow.load_dict(f"runs:/{run_id}/label_encoders.json") + return model, encoders + except Exception as e: + print(f"Erreur lors du chargement du modèle: {str(e)}") + return None, None diff --git a/machine_learning/duration_prediction/duration_predictor.py b/machine_learning/duration_prediction/duration_predictor.py new file mode 100644 index 0000000..794f688 --- /dev/null +++ b/machine_learning/duration_prediction/duration_predictor.py @@ -0,0 +1,122 @@ +from pycaret.regression import * +import mlflow +import pandas as pd +from typing import Optional, Tuple, Dict +from sklearn.preprocessing import LabelEncoder + +def prepare_duration_data(data: pd.DataFrame) -> Tuple[pd.DataFrame, Dict]: + """ + Prépare les données pour la prédiction de durée + + Args: + data: DataFrame contenant les données brutes + + Returns: + DataFrame préparé et dictionnaire des encodeurs + """ + # Sélectionner les colonnes pertinentes + features = [ + 'pathologie', 'code_pathologie', 'nom_pathologie', + 'classification', 'sexe', + 'tranche_age_0_1', 'tranche_age_1_4', 'tranche_age_5_14', + 'tranche_age_15_24', 'tranche_age_25_34', 'tranche_age_35_44', + 'tranche_age_45_54', 'tranche_age_55_64', 'tranche_age_65_74', + 'tranche_age_75_84', 'tranche_age_85_et_plus', + 'tx_brut_tt_age_pour_mille', 'tx_standard_tt_age_pour_mille', + 'AVG_duree_hospi' # target variable + ] + + df = data[features].copy() + + # Créer les encodeurs pour les variables catégorielles + encoders = {} + categorical_features = ['pathologie', 'nom_pathologie', 'classification', 'sexe'] + + for feature in categorical_features: + encoders[feature] = LabelEncoder() + df[feature] = encoders[feature].fit_transform(df[feature]) + + return df, encoders + +def train_duration_predictor( + data: pd.DataFrame, + target_col: str = 'AVG_duree_hospi', + fold: int = 5, + experiment_name: str = 'duration_prediction' +) -> Tuple[object, Dict]: + """ + Entraîne un modèle de régression pour prédire la durée d'hospitalisation + + Args: + data: DataFrame contenant les données d'entraînement + target_col: Nom de la colonne cible (durée moyenne d'hospitalisation) + fold: Nombre de folds pour la validation croisée + experiment_name: Nom de l'expérience MLflow + + Returns: + Le meilleur modèle entraîné et les encodeurs utilisés + """ + # Préparer les données + prepared_data, encoders = prepare_duration_data(data) + + # Configurer MLflow + mlflow.set_experiment(experiment_name) + + # Setup PyCaret + reg_setup = setup( + data=prepared_data, + target=target_col, + session_id=123, + fold=fold, + log_experiment=False, + experiment_name=experiment_name, + feature_selection=True, + remove_multicollinearity=True, + normalize=True, + transformation=True, + ) + + # Comparer différents modèles + best_model = compare_models(n_select=1) + + # Tuner le meilleur modèle + tuned_model = tune_model(best_model) + + # Log du modèle et des métriques avec MLflow + with mlflow.start_run(): + # Log des paramètres + mlflow.log_params(tuned_model.get_params()) + + # Log du modèle + mlflow.pycaret.log_model(tuned_model, "duration_predictor") + + # Log des métriques de performance + results = pull() + for metric in results.columns[1:]: + mlflow.log_metric(metric, results.iloc[0][metric]) + + # Log des encodeurs + mlflow.log_dict( + {k: v.classes_.tolist() for k, v in encoders.items()}, + "label_encoders.json" + ) + + return tuned_model, encoders + +def load_duration_predictor(run_id: str) -> Tuple[Optional[object], Optional[Dict]]: + """ + Charge un modèle de prédiction de durée depuis MLflow + + Args: + run_id: ID MLflow du run contenant le modèle + + Returns: + Le modèle chargé et les encodeurs, ou None si le chargement échoue + """ + try: + model = mlflow.pycaret.load_model(f"runs:/{run_id}/duration_predictor") + encoders = mlflow.load_dict(f"runs:/{run_id}/label_encoders.json") + return model, encoders + except Exception as e: + print(f"Erreur lors du chargement du modèle: {str(e)}") + return None, None diff --git a/machine_learning/evaluate_models-checkpoint.ipynb b/machine_learning/evaluate_models-checkpoint.ipynb new file mode 100644 index 0000000..80c6079 --- /dev/null +++ b/machine_learning/evaluate_models-checkpoint.ipynb @@ -0,0 +1,263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Évaluation des Modèles de Recommandation d'Hôpitaux\n", + "\n", + "Ce notebook effectue une évaluation complète de notre système de recommandation d'hôpitaux." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": [ + "import sys\n", + "sys.path.append('../')\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from google.cloud import bigquery\n", + "import os\n", + "\n", + "from utils.data_preparation import load_data, prepare_datasets\n", + "from classification_service.service_classifier import train_service_classifier\n", + "from duration_prediction.duration_predictor import train_duration_predictor\n", + "from recommendation.hospital_recommender import HospitalRecommender\n", + "from evaluation.temporal_validation import TemporalValidator\n", + "from evaluation.metrics import (\n", + " evaluate_service_classification,\n", + " evaluate_duration_prediction,\n", + " evaluate_recommendations\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Chargement et Préparation des Données" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": [ + "# Charger les données\n", + "df_hospi = load_data()\n", + "datasets = prepare_datasets(df_hospi)\n", + "\n", + "# Afficher les informations sur les données\n", + "print(\"Taille des ensembles de données :\")\n", + "for name, df in datasets.items():\n", + " print(f\"{name}: {df.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Évaluation du Modèle de Classification des Services" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": [ + "# Initialiser le validateur temporel\n", + "validator = TemporalValidator(n_splits=3)\n", + "\n", + "# Valider le modèle de classification\n", + "service_results = validator.validate_service_classifier(\n", + " train_service_classifier,\n", + " datasets['dpt_ens']\n", + ")\n", + "\n", + "# Afficher les résultats\n", + "print(\"\\nMétriques moyennes de classification :\")\n", + "for metric, value in service_results['avg_metrics'].items():\n", + " print(f\"{metric}: {value:.4f}\")\n", + "\n", + "# Visualiser l'évolution des métriques\n", + "plt.figure(figsize=(12, 6))\n", + "metrics_to_plot = ['accuracy', 'macro_f1']\n", + "for metric in metrics_to_plot:\n", + " values = [split[metric] for split in service_results['split_metrics']]\n", + " plt.plot(values, label=metric)\n", + "plt.title('Évolution des Métriques de Classification')\n", + "plt.xlabel('Split Temporel')\n", + "plt.ylabel('Score')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Évaluation du Modèle de Prédiction de Durée" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": [ + "# Valider le modèle de prédiction de durée\n", + "duration_results = validator.validate_duration_predictor(\n", + " train_duration_predictor,\n", + " datasets['dpt_ens']\n", + ")\n", + "\n", + "# Afficher les résultats\n", + "print(\"\\nMétriques moyennes de prédiction de durée :\")\n", + "for metric, value in duration_results['avg_metrics'].items():\n", + " print(f\"{metric}: {value:.4f}\")\n", + "\n", + "# Visualiser l'évolution des métriques\n", + "plt.figure(figsize=(12, 6))\n", + "metrics_to_plot = ['mae', 'rmse']\n", + "for metric in metrics_to_plot:\n", + " values = [split[metric] for split in duration_results['split_metrics']]\n", + " plt.plot(values, label=metric)\n", + "plt.title('Évolution des Métriques de Prédiction de Durée')\n", + "plt.xlabel('Split Temporel')\n", + "plt.ylabel('Erreur')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Évaluation du Système de Recommandation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": [ + "# Créer des cas de test\n", + "test_cases = [\n", + " {\n", + " 'patient_data': {\n", + " 'age': 45,\n", + " 'sexe': 'H',\n", + " 'pathologie': 'example_pathology',\n", + " 'region': 'Île-de-France'\n", + " },\n", + " 'ground_truth': {\n", + " 'appropriate_hospitals': ['Hospital_1', 'Hospital_2'],\n", + " 'relevance_scores': {'Hospital_1': 0.9, 'Hospital_2': 0.8}\n", + " }\n", + " }\n", + " # Ajouter plus de cas de test ici\n", + "]\n", + "\n", + "# Initialiser le système de recommandation\n", + "recommender = HospitalRecommender()\n", + "\n", + "# Valider le système de recommandation\n", + "recommendation_results = validator.validate_recommendation_system(\n", + " recommender,\n", + " datasets['dpt_ens'],\n", + " test_cases\n", + ")\n", + "\n", + "# Afficher les résultats\n", + "print(\"\\nMétriques moyennes de recommandation :\")\n", + "for metric, value in recommendation_results['avg_metrics'].items():\n", + " print(f\"{metric}: {value:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Analyse des Tendances Temporelles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "source": [ + "# Analyser les tendances temporelles\n", + "service_trends = validator.analyze_temporal_trends(service_results)\n", + "duration_trends = validator.analyze_temporal_trends(duration_results)\n", + "\n", + "# Afficher les résultats\n", + "print(\"\\nTendances temporelles - Classification des Services :\")\n", + "for metric, trend in service_trends.items():\n", + " print(f\"{metric}:\")\n", + " print(f\" - Pente: {trend['slope']:.4f}\")\n", + " print(f\" - Stabilité: {trend['stability']:.4f}\")\n", + "\n", + "print(\"\\nTendances temporelles - Prédiction de Durée :\")\n", + "for metric, trend in duration_trends.items():\n", + " print(f\"{metric}:\")\n", + " print(f\" - Pente: {trend['slope']:.4f}\")\n", + " print(f\" - Stabilité: {trend['stability']:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Conclusions et Recommandations\n", + "\n", + "Basé sur les résultats ci-dessus, nous pouvons conclure :\n", + "\n", + "1. **Classification des Services** :\n", + " - Performance globale\n", + " - Points forts et faiblesses par service\n", + " - Stabilité temporelle\n", + "\n", + "2. **Prédiction de Durée** :\n", + " - Précision des prédictions\n", + " - Variations par tranche de durée\n", + " - Évolution temporelle\n", + "\n", + "3. **Système de Recommandation** :\n", + " - Pertinence des recommandations\n", + " - Diversité des suggestions\n", + " - Adaptation aux contraintes géographiques\n", + "\n", + "### Recommandations d'Amélioration :\n", + "\n", + "1. [À compléter après l'analyse des résultats]\n", + "2. [À compléter après l'analyse des résultats]\n", + "3. [À compléter après l'analyse des résultats]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/machine_learning/evaluation/metrics.py b/machine_learning/evaluation/metrics.py new file mode 100644 index 0000000..ae34fef --- /dev/null +++ b/machine_learning/evaluation/metrics.py @@ -0,0 +1,173 @@ +import numpy as np +from sklearn.metrics import ( + accuracy_score, precision_score, recall_score, f1_score, + mean_absolute_error, mean_squared_error, r2_score, + confusion_matrix +) +from typing import Dict, List, Tuple +import pandas as pd + +def evaluate_service_classification( + y_true: np.ndarray, + y_pred: np.ndarray, + labels: List[str] = None +) -> Dict[str, float]: + """ + Évalue les performances du modèle de classification des services + + Args: + y_true: Valeurs réelles + y_pred: Prédictions du modèle + labels: Liste des labels de services + + Returns: + Dictionnaire contenant les différentes métriques + """ + metrics = { + 'accuracy': accuracy_score(y_true, y_pred), + 'macro_precision': precision_score(y_true, y_pred, average='macro'), + 'macro_recall': recall_score(y_true, y_pred, average='macro'), + 'macro_f1': f1_score(y_true, y_pred, average='macro') + } + + # Calculer les métriques par service si les labels sont fournis + if labels: + conf_matrix = confusion_matrix(y_true, y_pred) + per_class_metrics = {} + + for i, label in enumerate(labels): + true_pos = conf_matrix[i, i] + false_pos = conf_matrix[:, i].sum() - true_pos + false_neg = conf_matrix[i, :].sum() - true_pos + + precision = true_pos / (true_pos + false_pos) if (true_pos + false_pos) > 0 else 0 + recall = true_pos / (true_pos + false_neg) if (true_pos + false_neg) > 0 else 0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 + + per_class_metrics[f'{label}_precision'] = precision + per_class_metrics[f'{label}_recall'] = recall + per_class_metrics[f'{label}_f1'] = f1 + + metrics.update(per_class_metrics) + + return metrics + +def evaluate_duration_prediction( + y_true: np.ndarray, + y_pred: np.ndarray +) -> Dict[str, float]: + """ + Évalue les performances du modèle de prédiction de durée + + Args: + y_true: Durées réelles + y_pred: Durées prédites + + Returns: + Dictionnaire contenant les différentes métriques + """ + metrics = { + 'mae': mean_absolute_error(y_true, y_pred), + 'rmse': np.sqrt(mean_squared_error(y_true, y_pred)), + 'r2': r2_score(y_true, y_pred), + 'mape': np.mean(np.abs((y_true - y_pred) / y_true)) * 100 + } + + # Calculer les erreurs par tranche de durée + duration_ranges = [(0, 3), (4, 7), (8, 14), (15, 30), (30, float('inf'))] + for start, end in duration_ranges: + mask = (y_true >= start) & (y_true <= end) + if mask.any(): + range_mae = mean_absolute_error(y_true[mask], y_pred[mask]) + metrics[f'mae_{start}_{end}_days'] = range_mae + + return metrics + +def evaluate_recommendations( + recommendations: List[Dict], + ground_truth: Dict, + top_n: int = 5 +) -> Dict[str, float]: + """ + Évalue la qualité des recommandations + + Args: + recommendations: Liste des recommandations générées + ground_truth: Données réelles sur les hôpitaux appropriés + top_n: Nombre de recommandations à considérer + + Returns: + Dictionnaire contenant les métriques d'évaluation + """ + metrics = {} + + # Précision@K + correct_recommendations = sum( + 1 for rec in recommendations[:top_n] + if rec['hospital_name'] in ground_truth['appropriate_hospitals'] + ) + metrics['precision_at_k'] = correct_recommendations / min(top_n, len(recommendations)) + + # Score moyen de pertinence + if 'relevance_scores' in ground_truth: + avg_relevance = np.mean([ + ground_truth['relevance_scores'].get(rec['hospital_name'], 0) + for rec in recommendations[:top_n] + ]) + metrics['avg_relevance'] = avg_relevance + + # Distance moyenne des recommandations + if all('distance_score' in rec for rec in recommendations[:top_n]): + avg_distance_score = np.mean([ + rec['distance_score'] for rec in recommendations[:top_n] + ]) + metrics['avg_distance_score'] = avg_distance_score + + # Diversité des services recommandés + unique_services = len(set( + rec['service'] for rec in recommendations[:top_n] + )) + metrics['service_diversity'] = unique_services / top_n + + return metrics + +def evaluate_temporal_stability( + model, + temporal_test_sets: List[Tuple[pd.DataFrame, pd.DataFrame]], + metric_func: callable +) -> Dict[str, List[float]]: + """ + Évalue la stabilité temporelle des prédictions + + Args: + model: Modèle à évaluer + temporal_test_sets: Liste de tuples (X, y) pour différentes périodes + metric_func: Fonction de métrique à utiliser + + Returns: + Dictionnaire contenant l'évolution des métriques dans le temps + """ + temporal_metrics = { + 'metric_values': [], + 'relative_changes': [] + } + + previous_metrics = None + for X_test, y_test in temporal_test_sets: + # Calculer les métriques pour cette période + y_pred = model.predict(X_test) + current_metrics = metric_func(y_test, y_pred) + + temporal_metrics['metric_values'].append(current_metrics) + + # Calculer le changement relatif si ce n'est pas la première période + if previous_metrics is not None: + relative_change = { + k: (v - previous_metrics[k]) / previous_metrics[k] + for k, v in current_metrics.items() + } + temporal_metrics['relative_changes'].append(relative_change) + + previous_metrics = current_metrics + + return temporal_metrics diff --git a/machine_learning/evaluation/run_evaluation.py b/machine_learning/evaluation/run_evaluation.py new file mode 100644 index 0000000..119d18d --- /dev/null +++ b/machine_learning/evaluation/run_evaluation.py @@ -0,0 +1,91 @@ +import sys +sys.path.append('../') + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from google.cloud import bigquery +import os +import mlflow + +from utils.data_preparation import ( + load_data, + prepare_datasets, + prepare_train_test_data, + prepare_features_for_service_classification, + prepare_features_for_duration_prediction +) +from classification_service.service_classifier import train_service_classifier +from duration_prediction.duration_predictor import train_duration_predictor +from recommendation.hospital_recommender import HospitalRecommender +from evaluation.metrics import ( + evaluate_service_classification, + evaluate_duration_prediction, + evaluate_recommendations +) + +def main(): + # 1. Charger les données + print("Chargement des données...") + data_dict = load_data() + + # 2. Préparer les datasets + print("Préparation des datasets...") + datasets = prepare_datasets(data_dict['morbidite'], data_dict['capacite']) + + # 3. Préparer les features pour la classification des services + print("Préparation des features pour la classification...") + features_classification = prepare_features_for_service_classification( + datasets['dpt'], + datasets['capacite'] + ) + + # 4. Préparer les features pour la prédiction de durée + print("Préparation des features pour la prédiction de durée...") + features_duration = prepare_features_for_duration_prediction( + datasets['dpt'], + datasets['capacite'] + ) + + # 5. Diviser en train/test + print("Division en train/test...") + X_train_class, X_test_class = prepare_train_test_data(features_classification) + X_train_dur, X_test_dur = prepare_train_test_data(features_duration) + + # 6. Entraîner et évaluer le modèle de classification + print("\nÉvaluation du modèle de classification des services...") + service_model = train_service_classifier(X_train_class) + service_metrics = evaluate_service_classification(service_model, X_test_class) + print("Métriques de classification:") + print(service_metrics) + + # 7. Entraîner et évaluer le modèle de prédiction de durée + print("\nÉvaluation du modèle de prédiction de durée...") + duration_model = train_duration_predictor(X_train_dur) + duration_metrics = evaluate_duration_prediction(duration_model, X_test_dur) + print("Métriques de prédiction de durée:") + print(duration_metrics) + + # 8. Évaluer le système de recommandation complet + print("\nÉvaluation du système de recommandation...") + recommender = HospitalRecommender(service_model, duration_model) + recommendation_metrics = evaluate_recommendations( + recommender, + datasets['dpt'], + datasets['capacite'] + ) + print("Métriques de recommandation:") + print(recommendation_metrics) + + # 9. Sauvegarder les résultats + results = { + 'service_classification': service_metrics, + 'duration_prediction': duration_metrics, + 'recommendation': recommendation_metrics + } + pd.DataFrame(results).to_csv('evaluation_results.csv') + print("\nRésultats sauvegardés dans evaluation_results.csv") + +if __name__ == "__main__": + main() diff --git a/machine_learning/evaluation/temporal_validation.py b/machine_learning/evaluation/temporal_validation.py new file mode 100644 index 0000000..76d3c44 --- /dev/null +++ b/machine_learning/evaluation/temporal_validation.py @@ -0,0 +1,230 @@ +import pandas as pd +import numpy as np +from typing import List, Tuple, Dict, Any +from sklearn.model_selection import TimeSeriesSplit +from .metrics import ( + evaluate_service_classification, + evaluate_duration_prediction, + evaluate_recommendations +) + +class TemporalValidator: + """ + Classe pour effectuer la validation temporelle des modèles + """ + + def __init__( + self, + n_splits: int = 3, + test_size: int = 1, + gap: int = 0 + ): + """ + Initialise le validateur temporel + + Args: + n_splits: Nombre de splits temporels + test_size: Taille de l'ensemble de test en années + gap: Écart entre train et test en années + """ + self.n_splits = n_splits + self.test_size = test_size + self.gap = gap + self.tscv = TimeSeriesSplit( + n_splits=n_splits, + test_size=test_size, + gap=gap + ) + + def prepare_temporal_splits( + self, + data: pd.DataFrame, + date_column: str = 'annee' + ) -> List[Tuple[pd.DataFrame, pd.DataFrame]]: + """ + Prépare les splits temporels des données + + Args: + data: DataFrame contenant les données + date_column: Nom de la colonne contenant les dates + + Returns: + Liste de tuples (train, test) pour chaque split + """ + splits = [] + sorted_data = data.sort_values(date_column) + + for train_idx, test_idx in self.tscv.split(sorted_data): + train_data = sorted_data.iloc[train_idx] + test_data = sorted_data.iloc[test_idx] + splits.append((train_data, test_data)) + + return splits + + def validate_service_classifier( + self, + model_trainer: callable, + data: pd.DataFrame, + target_col: str = 'classification' + ) -> Dict[str, List[Dict[str, float]]]: + """ + Valide le modèle de classification des services + + Args: + model_trainer: Fonction d'entraînement du modèle + data: DataFrame contenant les données + target_col: Nom de la colonne cible + + Returns: + Dictionnaire contenant les métriques pour chaque split + """ + splits = self.prepare_temporal_splits(data) + results = [] + + for train_data, test_data in splits: + # Entraîner le modèle + model, encoders = model_trainer(train_data) + + # Faire des prédictions + X_test = test_data.drop(columns=[target_col]) + y_test = test_data[target_col] + y_pred = model.predict(X_test) + + # Évaluer + metrics = evaluate_service_classification(y_test, y_pred) + results.append(metrics) + + return { + 'split_metrics': results, + 'avg_metrics': { + k: np.mean([r[k] for r in results]) + for k in results[0].keys() + } + } + + def validate_duration_predictor( + self, + model_trainer: callable, + data: pd.DataFrame, + target_col: str = 'AVG_duree_hospi' + ) -> Dict[str, List[Dict[str, float]]]: + """ + Valide le modèle de prédiction de durée + + Args: + model_trainer: Fonction d'entraînement du modèle + data: DataFrame contenant les données + target_col: Nom de la colonne cible + + Returns: + Dictionnaire contenant les métriques pour chaque split + """ + splits = self.prepare_temporal_splits(data) + results = [] + + for train_data, test_data in splits: + # Entraîner le modèle + model, encoders = model_trainer(train_data) + + # Faire des prédictions + X_test = test_data.drop(columns=[target_col]) + y_test = test_data[target_col] + y_pred = model.predict(X_test) + + # Évaluer + metrics = evaluate_duration_prediction(y_test, y_pred) + results.append(metrics) + + return { + 'split_metrics': results, + 'avg_metrics': { + k: np.mean([r[k] for r in results]) + for k in results[0].keys() + } + } + + def validate_recommendation_system( + self, + recommender: Any, + data: pd.DataFrame, + test_cases: List[Dict] + ) -> Dict[str, List[Dict[str, float]]]: + """ + Valide le système de recommandation + + Args: + recommender: Instance du système de recommandation + data: DataFrame contenant les données des hôpitaux + test_cases: Liste de cas de test avec vérité terrain + + Returns: + Dictionnaire contenant les métriques pour chaque cas de test + """ + splits = self.prepare_temporal_splits(data) + results = [] + + for train_data, _ in splits: + # Mettre à jour les données d'hôpitaux + recommender.load_hospital_data(train_data) + + # Tester chaque cas + split_results = [] + for test_case in test_cases: + recommendations = recommender.predict(test_case['patient_data']) + metrics = evaluate_recommendations( + recommendations, + test_case['ground_truth'] + ) + split_results.append(metrics) + + results.append(split_results) + + # Calculer les moyennes + avg_metrics = {} + for metric in results[0][0].keys(): + values = [ + result[metric] + for split in results + for result in split + ] + avg_metrics[metric] = np.mean(values) + + return { + 'split_metrics': results, + 'avg_metrics': avg_metrics + } + + def analyze_temporal_trends( + self, + validation_results: Dict[str, List[Dict[str, float]]] + ) -> Dict[str, Any]: + """ + Analyse les tendances temporelles dans les résultats + + Args: + validation_results: Résultats de la validation + + Returns: + Dictionnaire contenant l'analyse des tendances + """ + trends = {} + + # Calculer les tendances pour chaque métrique + split_metrics = validation_results['split_metrics'] + for metric in split_metrics[0].keys(): + values = [split[metric] for split in split_metrics] + + # Calculer la pente de la tendance + x = np.arange(len(values)) + slope = np.polyfit(x, values, 1)[0] + + # Calculer la stabilité (écart-type) + stability = np.std(values) + + trends[metric] = { + 'slope': slope, + 'stability': stability, + 'values': values + } + + return trends diff --git a/machine_learning/evaluation/tests/test_models.py b/machine_learning/evaluation/tests/test_models.py new file mode 100644 index 0000000..02c8ab9 --- /dev/null +++ b/machine_learning/evaluation/tests/test_models.py @@ -0,0 +1,165 @@ +import unittest +import numpy as np +import pandas as pd +from sklearn.datasets import make_classification, make_regression +from ...classification_service.service_classifier import train_service_classifier +from ...duration_prediction.duration_predictor import train_duration_predictor +from ...recommendation.hospital_recommender import HospitalRecommender +from ..metrics import ( + evaluate_service_classification, + evaluate_duration_prediction, + evaluate_recommendations +) + +class TestServiceClassifier(unittest.TestCase): + @classmethod + def setUpClass(cls): + """Prépare les données de test pour la classification""" + # Créer des données synthétiques pour les tests + X, y = make_classification( + n_samples=1000, + n_features=20, + n_informative=15, + n_redundant=5, + n_classes=6, + random_state=42 + ) + + # Convertir en DataFrame avec les noms de colonnes appropriés + feature_names = [ + 'pathologie', 'code_pathologie', 'nom_pathologie', + *[f'tranche_age_{i}' for i in range(11)], + 'tx_brut_tt_age_pour_mille', + 'tx_standard_tt_age_pour_mille', + *[f'feature_{i}' for i in range(4)] + ] + cls.data = pd.DataFrame(X, columns=feature_names) + cls.data['classification'] = y + + def test_model_training(self): + """Teste l'entraînement du modèle de classification""" + model, encoders = train_service_classifier(self.data) + self.assertIsNotNone(model) + self.assertIsNotNone(encoders) + + def test_prediction_shape(self): + """Teste la forme des prédictions""" + model, encoders = train_service_classifier(self.data) + predictions = model.predict(self.data) + self.assertEqual(len(predictions), len(self.data)) + + def test_evaluation_metrics(self): + """Teste les métriques d'évaluation""" + model, encoders = train_service_classifier(self.data) + predictions = model.predict(self.data) + metrics = evaluate_service_classification( + self.data['classification'], + predictions + ) + + required_metrics = ['accuracy', 'macro_precision', 'macro_recall', 'macro_f1'] + for metric in required_metrics: + self.assertIn(metric, metrics) + self.assertGreaterEqual(metrics[metric], 0) + self.assertLessEqual(metrics[metric], 1) + +class TestDurationPredictor(unittest.TestCase): + @classmethod + def setUpClass(cls): + """Prépare les données de test pour la régression""" + # Créer des données synthétiques pour les tests + X, y = make_regression( + n_samples=1000, + n_features=20, + noise=0.1, + random_state=42 + ) + + # Convertir en DataFrame avec les noms de colonnes appropriés + feature_names = [ + 'pathologie', 'code_pathologie', 'nom_pathologie', + 'classification', 'sexe', + *[f'tranche_age_{i}' for i in range(11)], + *[f'feature_{i}' for i in range(4)] + ] + cls.data = pd.DataFrame(X, columns=feature_names) + cls.data['AVG_duree_hospi'] = np.abs(y) # Durées toujours positives + + def test_model_training(self): + """Teste l'entraînement du modèle de régression""" + model, encoders = train_duration_predictor(self.data) + self.assertIsNotNone(model) + self.assertIsNotNone(encoders) + + def test_prediction_shape(self): + """Teste la forme des prédictions""" + model, encoders = train_duration_predictor(self.data) + predictions = model.predict(self.data) + self.assertEqual(len(predictions), len(self.data)) + + def test_evaluation_metrics(self): + """Teste les métriques d'évaluation""" + model, encoders = train_duration_predictor(self.data) + predictions = model.predict(self.data) + metrics = evaluate_duration_prediction( + self.data['AVG_duree_hospi'], + predictions + ) + + required_metrics = ['mae', 'rmse', 'r2', 'mape'] + for metric in required_metrics: + self.assertIn(metric, metrics) + self.assertGreaterEqual(metrics[metric], 0) + +class TestRecommendationSystem(unittest.TestCase): + @classmethod + def setUpClass(cls): + """Prépare les données de test pour le système de recommandation""" + cls.recommender = HospitalRecommender() + + # Créer des données synthétiques pour les hôpitaux + cls.hospital_data = pd.DataFrame({ + 'nom_region': [f'Hospital_{i}' for i in range(10)], + 'classification': ['M', 'C', 'SSR', 'O', 'PSY'] * 2, + 'lit_hospi_complete': np.random.randint(50, 500, 10), + 'hospi_total_24h': np.random.randint(10, 100, 10) + }) + + cls.patient_data = { + 'age': 45, + 'sexe': 'H', + 'pathologie': 'example_pathology', + 'region': 'Example Region' + } + + def test_recommendation_structure(self): + """Teste la structure des recommandations""" + self.recommender.load_hospital_data(self.hospital_data) + recommendations = self.recommender.predict(self.patient_data) + + self.assertIsInstance(recommendations, list) + if recommendations: + required_keys = [ + 'hospital_name', 'service', 'score', + 'distance_score', 'capacity_score', 'service_score' + ] + for key in required_keys: + self.assertIn(key, recommendations[0]) + + def test_recommendation_scoring(self): + """Teste le scoring des recommandations""" + self.recommender.load_hospital_data(self.hospital_data) + recommendations = self.recommender.predict(self.patient_data) + + if recommendations: + # Vérifier que les scores sont triés par ordre décroissant + scores = [rec['score'] for rec in recommendations] + self.assertEqual(scores, sorted(scores, reverse=True)) + + # Vérifier que les scores sont dans l'intervalle [0, 1] + for rec in recommendations: + self.assertGreaterEqual(rec['score'], 0) + self.assertLessEqual(rec['score'], 1) + +if __name__ == '__main__': + unittest.main() diff --git a/machine_learning/logs.log b/machine_learning/logs.log new file mode 100644 index 0000000..5bbffc0 --- /dev/null +++ b/machine_learning/logs.log @@ -0,0 +1,8 @@ +2024-12-04 15:46:22,490:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 15:46:22,490:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 15:46:22,490:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 15:46:22,490:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. diff --git a/machine_learning/model_development/logs.log b/machine_learning/model_development/logs.log new file mode 100644 index 0000000..79158da --- /dev/null +++ b/machine_learning/model_development/logs.log @@ -0,0 +1,1603 @@ +2024-12-04 16:35:32,023:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:35:32,023:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:35:32,023:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:35:32,023:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:38:27,807:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:38:27,808:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:38:27,808:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:38:27,808:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:00,492:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:00,492:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:00,492:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:00,492:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:34,793:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:34,793:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:34,793:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:34,793:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:40:50,835:INFO:PyCaret ClassificationExperiment +2024-12-04 16:40:50,835:INFO:Logging name: service_classification_v1 +2024-12-04 16:40:50,835:INFO:ML Usecase: MLUsecase.CLASSIFICATION +2024-12-04 16:40:50,835:INFO:version 3.3.2 +2024-12-04 16:40:50,835:INFO:Initializing setup() +2024-12-04 16:40:50,835:INFO:self.USI: 9650 +2024-12-04 16:40:50,835:INFO:self._variable_keys: {'is_multiclass', 'memory', 'html_param', 'n_jobs_param', 'gpu_param', 'fold_generator', 'data', 'X', 'exp_id', 'log_plots_param', 'seed', '_ml_usecase', 'fix_imbalance', 'logging_param', 'USI', 'pipeline', 'X_test', 'gpu_n_jobs_param', 'y', 'idx', 'target_param', 'y_train', 'fold_shuffle_param', 'X_train', 'exp_name_log', 'y_test', 'fold_groups_param', '_available_plots'} +2024-12-04 16:40:50,835:INFO:Checking environment +2024-12-04 16:40:50,835:INFO:python_version: 3.11.9 +2024-12-04 16:40:50,835:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:40:50,835:INFO:machine: AMD64 +2024-12-04 16:40:50,835:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:40:50,841:INFO:Memory: svmem(total=17007292416, available=1760534528, percent=89.6, used=15246757888, free=1760534528) +2024-12-04 16:40:50,842:INFO:Physical Core: 4 +2024-12-04 16:40:50,842:INFO:Logical Core: 8 +2024-12-04 16:40:50,842:INFO:Checking libraries +2024-12-04 16:40:50,842:INFO:System: +2024-12-04 16:40:50,842:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:40:50,842:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:40:50,842:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:40:50,842:INFO:PyCaret required dependencies: +2024-12-04 16:40:50,869:INFO: pip: 24.3.1 +2024-12-04 16:40:50,869:INFO: setuptools: 65.5.0 +2024-12-04 16:40:50,869:INFO: pycaret: 3.3.2 +2024-12-04 16:40:50,869:INFO: IPython: 8.12.3 +2024-12-04 16:40:50,869:INFO: ipywidgets: 8.1.5 +2024-12-04 16:40:50,869:INFO: tqdm: 4.67.1 +2024-12-04 16:40:50,869:INFO: numpy: 1.26.4 +2024-12-04 16:40:50,869:INFO: pandas: 2.1.4 +2024-12-04 16:40:50,869:INFO: jinja2: 3.1.4 +2024-12-04 16:40:50,869:INFO: scipy: 1.11.4 +2024-12-04 16:40:50,869:INFO: joblib: 1.3.2 +2024-12-04 16:40:50,869:INFO: sklearn: 1.4.2 +2024-12-04 16:40:50,870:INFO: pyod: 2.0.2 +2024-12-04 16:40:50,870:INFO: imblearn: 0.12.4 +2024-12-04 16:40:50,870:INFO: category_encoders: 2.6.4 +2024-12-04 16:40:50,870:INFO: lightgbm: 4.5.0 +2024-12-04 16:40:50,870:INFO: numba: 0.60.0 +2024-12-04 16:40:50,870:INFO: requests: 2.32.3 +2024-12-04 16:40:50,870:INFO: matplotlib: 3.7.5 +2024-12-04 16:40:50,870:INFO: scikitplot: 0.3.7 +2024-12-04 16:40:50,870:INFO: yellowbrick: 1.5 +2024-12-04 16:40:50,870:INFO: plotly: 5.24.1 +2024-12-04 16:40:50,870:INFO: plotly-resampler: Not installed +2024-12-04 16:40:50,870:INFO: kaleido: 0.2.1 +2024-12-04 16:40:50,870:INFO: schemdraw: 0.15 +2024-12-04 16:40:50,870:INFO: statsmodels: 0.14.4 +2024-12-04 16:40:50,870:INFO: sktime: 0.26.0 +2024-12-04 16:40:50,870:INFO: tbats: 1.1.3 +2024-12-04 16:40:50,870:INFO: pmdarima: 2.0.4 +2024-12-04 16:40:50,870:INFO: psutil: 6.1.0 +2024-12-04 16:40:50,870:INFO: markupsafe: 3.0.2 +2024-12-04 16:40:50,870:INFO: pickle5: Not installed +2024-12-04 16:40:50,870:INFO: cloudpickle: 3.1.0 +2024-12-04 16:40:50,870:INFO: deprecation: 2.1.0 +2024-12-04 16:40:50,870:INFO: xxhash: 3.5.0 +2024-12-04 16:40:50,870:INFO: wurlitzer: Not installed +2024-12-04 16:40:50,870:INFO:PyCaret optional dependencies: +2024-12-04 16:40:50,909:INFO: shap: Not installed +2024-12-04 16:40:50,909:INFO: interpret: Not installed +2024-12-04 16:40:50,909:INFO: umap: Not installed +2024-12-04 16:40:50,909:INFO: ydata_profiling: Not installed +2024-12-04 16:40:50,909:INFO: explainerdashboard: Not installed +2024-12-04 16:40:50,909:INFO: autoviz: Not installed +2024-12-04 16:40:50,909:INFO: fairlearn: Not installed +2024-12-04 16:40:50,910:INFO: deepchecks: Not installed +2024-12-04 16:40:50,910:INFO: xgboost: Not installed +2024-12-04 16:40:50,910:INFO: catboost: Not installed +2024-12-04 16:40:50,910:INFO: kmodes: Not installed +2024-12-04 16:40:50,910:INFO: mlxtend: Not installed +2024-12-04 16:40:50,910:INFO: statsforecast: Not installed +2024-12-04 16:40:50,910:INFO: tune_sklearn: Not installed +2024-12-04 16:40:50,910:INFO: ray: Not installed +2024-12-04 16:40:50,910:INFO: hyperopt: Not installed +2024-12-04 16:40:50,910:INFO: optuna: Not installed +2024-12-04 16:40:50,910:INFO: skopt: Not installed +2024-12-04 16:40:50,910:INFO: mlflow: 2.18.0 +2024-12-04 16:40:50,910:INFO: gradio: Not installed +2024-12-04 16:40:50,910:INFO: fastapi: Not installed +2024-12-04 16:40:50,910:INFO: uvicorn: 0.32.1 +2024-12-04 16:40:50,910:INFO: m2cgen: Not installed +2024-12-04 16:40:50,910:INFO: evidently: Not installed +2024-12-04 16:40:50,910:INFO: fugue: Not installed +2024-12-04 16:40:50,910:INFO: streamlit: 1.40.1 +2024-12-04 16:40:50,910:INFO: prophet: Not installed +2024-12-04 16:40:50,910:INFO:None +2024-12-04 16:40:50,910:INFO:Set up data. +2024-12-04 16:40:51,004:INFO:Set up folding strategy. +2024-12-04 16:40:51,004:INFO:Set up train/test split. +2024-12-04 16:40:51,146:INFO:Set up index. +2024-12-04 16:40:51,153:INFO:Assigning column types. +2024-12-04 16:40:51,246:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:40:51,284:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:40:51,290:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:40:51,321:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,321:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,368:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:40:51,368:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:40:51,392:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,392:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,394:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:40:51,435:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:40:51,461:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,461:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,495:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:40:51,526:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,527:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,527:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. +2024-12-04 16:40:51,592:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,592:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,664:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,668:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,670:INFO:Preparing preprocessing pipeline... +2024-12-04 16:40:51,686:INFO:Set up simple imputation. +2024-12-04 16:40:51,737:INFO:Set up encoding of categorical features. +2024-12-04 16:40:51,739:INFO:Set up removing multicollinearity. +2024-12-04 16:40:51,739:INFO:Set up column transformation. +2024-12-04 16:40:51,739:INFO:Set up feature normalization. +2024-12-04 16:40:51,739:INFO:Set up feature selection. +2024-12-04 16:40:51,807:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:51,807:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:40:59,582:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:40:59,592:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(exclude=None, + include=['pathologie', 'nom_pathologie', + 'tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74... + learning_rate=0.1, + max_depth=-1, + min_child_samples=20, + min_child_weight=0.001, + min_split_gain=0.0, + n_estimators=100, + n_jobs=None, + num_leaves=31, + objective=None, + random_state=None, + reg_alpha=0.0, + reg_lambda=0.0, + subsample=1.0, + subsample_for_bin=200000, + subsample_freq=0), + importance_getter='auto', + max_features=3, + norm_order=1, + prefit=False, + threshold=-inf)))], + verbose=False) +2024-12-04 16:40:59,592:INFO:Creating final display dataframe. +2024-12-04 16:41:01,109:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target classification +2 Target type Multiclass +3 Original data shape (283187, 17) +4 Transformed data shape (283187, 4) +5 Transformed train set shape (198230, 4) +6 Transformed test set shape (84957, 4) +7 Numeric features 15 +8 Categorical features 1 +9 Rows with missing values 3.0% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Remove multicollinearity True +17 Multicollinearity threshold 0.9 +18 Transformation True +19 Transformation method yeo-johnson +20 Normalize True +21 Normalize method zscore +22 Feature selection True +23 Feature selection method classic +24 Feature selection estimator lightgbm +25 Number of features selected 0.2 +26 Fold Generator StratifiedKFold +27 Fold Number 5 +28 CPU Jobs -1 +29 Use GPU False +30 Log Experiment MlflowLogger +31 Experiment Name service_classification_v1 +32 USI 9650 +2024-12-04 16:41:01,216:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:41:01,217:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:41:01,280:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:41:01,284:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:41:01,284:INFO:Logging experiment in loggers +2024-12-04 16:42:33,357:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:42:33,357:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:42:33,357:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:42:33,357:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:42:51,384:INFO:PyCaret ClassificationExperiment +2024-12-04 16:42:51,384:INFO:Logging name: service_classification_v1 +2024-12-04 16:42:51,384:INFO:ML Usecase: MLUsecase.CLASSIFICATION +2024-12-04 16:42:51,384:INFO:version 3.3.2 +2024-12-04 16:42:51,384:INFO:Initializing setup() +2024-12-04 16:42:51,384:INFO:self.USI: 1929 +2024-12-04 16:42:51,384:INFO:self._variable_keys: {'n_jobs_param', 'X_test', 'y', '_available_plots', 'target_param', 'data', 'USI', 'y_train', 'X_train', 'y_test', 'gpu_n_jobs_param', 'idx', 'seed', '_ml_usecase', 'fold_shuffle_param', 'X', 'fold_generator', 'is_multiclass', 'memory', 'exp_name_log', 'gpu_param', 'exp_id', 'fix_imbalance', 'log_plots_param', 'logging_param', 'html_param', 'fold_groups_param', 'pipeline'} +2024-12-04 16:42:51,384:INFO:Checking environment +2024-12-04 16:42:51,384:INFO:python_version: 3.11.9 +2024-12-04 16:42:51,384:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:42:51,384:INFO:machine: AMD64 +2024-12-04 16:42:51,384:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:42:51,390:INFO:Memory: svmem(total=17007292416, available=1642381312, percent=90.3, used=15364911104, free=1642381312) +2024-12-04 16:42:51,390:INFO:Physical Core: 4 +2024-12-04 16:42:51,390:INFO:Logical Core: 8 +2024-12-04 16:42:51,390:INFO:Checking libraries +2024-12-04 16:42:51,390:INFO:System: +2024-12-04 16:42:51,390:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:42:51,390:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:42:51,390:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:42:51,390:INFO:PyCaret required dependencies: +2024-12-04 16:42:51,414:INFO: pip: 24.3.1 +2024-12-04 16:42:51,414:INFO: setuptools: 65.5.0 +2024-12-04 16:42:51,414:INFO: pycaret: 3.3.2 +2024-12-04 16:42:51,414:INFO: IPython: 8.12.3 +2024-12-04 16:42:51,414:INFO: ipywidgets: 8.1.5 +2024-12-04 16:42:51,415:INFO: tqdm: 4.67.1 +2024-12-04 16:42:51,415:INFO: numpy: 1.26.4 +2024-12-04 16:42:51,415:INFO: pandas: 2.1.4 +2024-12-04 16:42:51,415:INFO: jinja2: 3.1.4 +2024-12-04 16:42:51,415:INFO: scipy: 1.11.4 +2024-12-04 16:42:51,415:INFO: joblib: 1.3.2 +2024-12-04 16:42:51,415:INFO: sklearn: 1.4.2 +2024-12-04 16:42:51,415:INFO: pyod: 2.0.2 +2024-12-04 16:42:51,415:INFO: imblearn: 0.12.4 +2024-12-04 16:42:51,415:INFO: category_encoders: 2.6.4 +2024-12-04 16:42:51,415:INFO: lightgbm: 4.5.0 +2024-12-04 16:42:51,415:INFO: numba: 0.60.0 +2024-12-04 16:42:51,415:INFO: requests: 2.32.3 +2024-12-04 16:42:51,415:INFO: matplotlib: 3.7.5 +2024-12-04 16:42:51,415:INFO: scikitplot: 0.3.7 +2024-12-04 16:42:51,415:INFO: yellowbrick: 1.5 +2024-12-04 16:42:51,415:INFO: plotly: 5.24.1 +2024-12-04 16:42:51,415:INFO: plotly-resampler: Not installed +2024-12-04 16:42:51,415:INFO: kaleido: 0.2.1 +2024-12-04 16:42:51,415:INFO: schemdraw: 0.15 +2024-12-04 16:42:51,415:INFO: statsmodels: 0.14.4 +2024-12-04 16:42:51,415:INFO: sktime: 0.26.0 +2024-12-04 16:42:51,415:INFO: tbats: 1.1.3 +2024-12-04 16:42:51,415:INFO: pmdarima: 2.0.4 +2024-12-04 16:42:51,415:INFO: psutil: 6.1.0 +2024-12-04 16:42:51,415:INFO: markupsafe: 3.0.2 +2024-12-04 16:42:51,415:INFO: pickle5: Not installed +2024-12-04 16:42:51,415:INFO: cloudpickle: 3.1.0 +2024-12-04 16:42:51,415:INFO: deprecation: 2.1.0 +2024-12-04 16:42:51,415:INFO: xxhash: 3.5.0 +2024-12-04 16:42:51,415:INFO: wurlitzer: Not installed +2024-12-04 16:42:51,415:INFO:PyCaret optional dependencies: +2024-12-04 16:42:51,447:INFO: shap: Not installed +2024-12-04 16:42:51,447:INFO: interpret: Not installed +2024-12-04 16:42:51,447:INFO: umap: Not installed +2024-12-04 16:42:51,447:INFO: ydata_profiling: Not installed +2024-12-04 16:42:51,447:INFO: explainerdashboard: Not installed +2024-12-04 16:42:51,447:INFO: autoviz: Not installed +2024-12-04 16:42:51,447:INFO: fairlearn: Not installed +2024-12-04 16:42:51,447:INFO: deepchecks: Not installed +2024-12-04 16:42:51,447:INFO: xgboost: Not installed +2024-12-04 16:42:51,447:INFO: catboost: Not installed +2024-12-04 16:42:51,447:INFO: kmodes: Not installed +2024-12-04 16:42:51,448:INFO: mlxtend: Not installed +2024-12-04 16:42:51,448:INFO: statsforecast: Not installed +2024-12-04 16:42:51,448:INFO: tune_sklearn: Not installed +2024-12-04 16:42:51,448:INFO: ray: Not installed +2024-12-04 16:42:51,448:INFO: hyperopt: Not installed +2024-12-04 16:42:51,448:INFO: optuna: Not installed +2024-12-04 16:42:51,448:INFO: skopt: Not installed +2024-12-04 16:42:51,448:INFO: mlflow: 2.18.0 +2024-12-04 16:42:51,448:INFO: gradio: Not installed +2024-12-04 16:42:51,448:INFO: fastapi: Not installed +2024-12-04 16:42:51,448:INFO: uvicorn: 0.32.1 +2024-12-04 16:42:51,448:INFO: m2cgen: Not installed +2024-12-04 16:42:51,448:INFO: evidently: Not installed +2024-12-04 16:42:51,448:INFO: fugue: Not installed +2024-12-04 16:42:51,448:INFO: streamlit: 1.40.1 +2024-12-04 16:42:51,448:INFO: prophet: Not installed +2024-12-04 16:42:51,448:INFO:None +2024-12-04 16:42:51,448:INFO:Set up data. +2024-12-04 16:42:51,570:INFO:Set up folding strategy. +2024-12-04 16:42:51,570:INFO:Set up train/test split. +2024-12-04 16:42:51,721:INFO:Set up index. +2024-12-04 16:42:51,727:INFO:Assigning column types. +2024-12-04 16:42:51,832:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:42:51,873:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:42:51,877:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:42:51,909:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:51,909:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:51,954:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:42:51,954:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:42:51,978:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:51,978:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:51,978:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:42:52,019:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:42:52,043:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,043:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,086:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:42:52,113:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,113:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,113:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. +2024-12-04 16:42:52,182:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,182:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,244:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,244:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,244:INFO:Preparing preprocessing pipeline... +2024-12-04 16:42:52,269:INFO:Set up simple imputation. +2024-12-04 16:42:52,321:INFO:Set up encoding of categorical features. +2024-12-04 16:42:52,321:INFO:Set up removing multicollinearity. +2024-12-04 16:42:52,321:INFO:Set up column transformation. +2024-12-04 16:42:52,321:INFO:Set up feature normalization. +2024-12-04 16:42:52,321:INFO:Set up feature selection. +2024-12-04 16:42:52,396:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:52,396:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:53,084:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:42:53,103:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(exclude=None, + include=['pathologie', 'nom_pathologie', + 'tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74... + learning_rate=0.1, + max_depth=-1, + min_child_samples=20, + min_child_weight=0.001, + min_split_gain=0.0, + n_estimators=100, + n_jobs=None, + num_leaves=31, + objective=None, + random_state=None, + reg_alpha=0.0, + reg_lambda=0.0, + subsample=1.0, + subsample_for_bin=200000, + subsample_freq=0), + importance_getter='auto', + max_features=3, + norm_order=1, + prefit=False, + threshold=-inf)))], + verbose=False) +2024-12-04 16:42:53,104:INFO:Creating final display dataframe. +2024-12-04 16:42:53,767:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target classification +2 Target type Multiclass +3 Original data shape (283187, 17) +4 Transformed data shape (283187, 4) +5 Transformed train set shape (198230, 4) +6 Transformed test set shape (84957, 4) +7 Numeric features 15 +8 Categorical features 1 +9 Rows with missing values 3.0% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Remove multicollinearity True +17 Multicollinearity threshold 0.9 +18 Transformation True +19 Transformation method yeo-johnson +20 Normalize True +21 Normalize method zscore +22 Feature selection True +23 Feature selection method classic +24 Feature selection estimator lightgbm +25 Number of features selected 0.2 +26 Fold Generator StratifiedKFold +27 Fold Number 5 +28 CPU Jobs -1 +29 Use GPU False +30 Log Experiment MlflowLogger +31 Experiment Name service_classification_v1 +32 USI 1929 +2024-12-04 16:42:53,871:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:53,871:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:53,939:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:53,939:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:42:53,939:INFO:Logging experiment in loggers +2024-12-04 16:43:37,732:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:43:37,734:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:43:37,734:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:43:37,734:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:44:46,757:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:44:46,757:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:44:46,757:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:44:46,757:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:44:59,331:INFO:PyCaret ClassificationExperiment +2024-12-04 16:44:59,332:INFO:Logging name: clf-default-name +2024-12-04 16:44:59,332:INFO:ML Usecase: MLUsecase.CLASSIFICATION +2024-12-04 16:44:59,332:INFO:version 3.3.2 +2024-12-04 16:44:59,332:INFO:Initializing setup() +2024-12-04 16:44:59,332:INFO:self.USI: c46c +2024-12-04 16:44:59,332:INFO:self._variable_keys: {'idx', 'logging_param', 'y_train', 'data', 'exp_id', 'X_test', 'n_jobs_param', 'fold_shuffle_param', 'is_multiclass', 'gpu_param', 'fold_groups_param', 'pipeline', 'log_plots_param', 'USI', 'target_param', 'X_train', 'y_test', 'X', 'fix_imbalance', 'exp_name_log', 'memory', '_available_plots', 'seed', '_ml_usecase', 'fold_generator', 'y', 'html_param', 'gpu_n_jobs_param'} +2024-12-04 16:44:59,332:INFO:Checking environment +2024-12-04 16:44:59,332:INFO:python_version: 3.11.9 +2024-12-04 16:44:59,332:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:44:59,332:INFO:machine: AMD64 +2024-12-04 16:44:59,332:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:44:59,339:INFO:Memory: svmem(total=17007292416, available=1556086784, percent=90.9, used=15451205632, free=1556086784) +2024-12-04 16:44:59,339:INFO:Physical Core: 4 +2024-12-04 16:44:59,339:INFO:Logical Core: 8 +2024-12-04 16:44:59,339:INFO:Checking libraries +2024-12-04 16:44:59,339:INFO:System: +2024-12-04 16:44:59,339:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:44:59,339:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:44:59,339:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:44:59,339:INFO:PyCaret required dependencies: +2024-12-04 16:44:59,362:INFO: pip: 24.3.1 +2024-12-04 16:44:59,362:INFO: setuptools: 65.5.0 +2024-12-04 16:44:59,362:INFO: pycaret: 3.3.2 +2024-12-04 16:44:59,362:INFO: IPython: 8.12.3 +2024-12-04 16:44:59,362:INFO: ipywidgets: 8.1.5 +2024-12-04 16:44:59,362:INFO: tqdm: 4.67.1 +2024-12-04 16:44:59,362:INFO: numpy: 1.26.4 +2024-12-04 16:44:59,362:INFO: pandas: 2.1.4 +2024-12-04 16:44:59,362:INFO: jinja2: 3.1.4 +2024-12-04 16:44:59,362:INFO: scipy: 1.11.4 +2024-12-04 16:44:59,362:INFO: joblib: 1.3.2 +2024-12-04 16:44:59,362:INFO: sklearn: 1.4.2 +2024-12-04 16:44:59,362:INFO: pyod: 2.0.2 +2024-12-04 16:44:59,362:INFO: imblearn: 0.12.4 +2024-12-04 16:44:59,362:INFO: category_encoders: 2.6.4 +2024-12-04 16:44:59,362:INFO: lightgbm: 4.5.0 +2024-12-04 16:44:59,362:INFO: numba: 0.60.0 +2024-12-04 16:44:59,362:INFO: requests: 2.32.3 +2024-12-04 16:44:59,362:INFO: matplotlib: 3.7.5 +2024-12-04 16:44:59,362:INFO: scikitplot: 0.3.7 +2024-12-04 16:44:59,362:INFO: yellowbrick: 1.5 +2024-12-04 16:44:59,363:INFO: plotly: 5.24.1 +2024-12-04 16:44:59,363:INFO: plotly-resampler: Not installed +2024-12-04 16:44:59,363:INFO: kaleido: 0.2.1 +2024-12-04 16:44:59,363:INFO: schemdraw: 0.15 +2024-12-04 16:44:59,363:INFO: statsmodels: 0.14.4 +2024-12-04 16:44:59,363:INFO: sktime: 0.26.0 +2024-12-04 16:44:59,363:INFO: tbats: 1.1.3 +2024-12-04 16:44:59,363:INFO: pmdarima: 2.0.4 +2024-12-04 16:44:59,363:INFO: psutil: 6.1.0 +2024-12-04 16:44:59,363:INFO: markupsafe: 3.0.2 +2024-12-04 16:44:59,363:INFO: pickle5: Not installed +2024-12-04 16:44:59,363:INFO: cloudpickle: 3.1.0 +2024-12-04 16:44:59,363:INFO: deprecation: 2.1.0 +2024-12-04 16:44:59,363:INFO: xxhash: 3.5.0 +2024-12-04 16:44:59,363:INFO: wurlitzer: Not installed +2024-12-04 16:44:59,363:INFO:PyCaret optional dependencies: +2024-12-04 16:44:59,394:INFO: shap: Not installed +2024-12-04 16:44:59,394:INFO: interpret: Not installed +2024-12-04 16:44:59,394:INFO: umap: Not installed +2024-12-04 16:44:59,394:INFO: ydata_profiling: Not installed +2024-12-04 16:44:59,394:INFO: explainerdashboard: Not installed +2024-12-04 16:44:59,394:INFO: autoviz: Not installed +2024-12-04 16:44:59,395:INFO: fairlearn: Not installed +2024-12-04 16:44:59,395:INFO: deepchecks: Not installed +2024-12-04 16:44:59,395:INFO: xgboost: Not installed +2024-12-04 16:44:59,395:INFO: catboost: Not installed +2024-12-04 16:44:59,395:INFO: kmodes: Not installed +2024-12-04 16:44:59,395:INFO: mlxtend: Not installed +2024-12-04 16:44:59,395:INFO: statsforecast: Not installed +2024-12-04 16:44:59,395:INFO: tune_sklearn: Not installed +2024-12-04 16:44:59,395:INFO: ray: Not installed +2024-12-04 16:44:59,395:INFO: hyperopt: Not installed +2024-12-04 16:44:59,395:INFO: optuna: Not installed +2024-12-04 16:44:59,395:INFO: skopt: Not installed +2024-12-04 16:44:59,395:INFO: mlflow: 2.18.0 +2024-12-04 16:44:59,395:INFO: gradio: Not installed +2024-12-04 16:44:59,395:INFO: fastapi: Not installed +2024-12-04 16:44:59,395:INFO: uvicorn: 0.32.1 +2024-12-04 16:44:59,395:INFO: m2cgen: Not installed +2024-12-04 16:44:59,395:INFO: evidently: Not installed +2024-12-04 16:44:59,395:INFO: fugue: Not installed +2024-12-04 16:44:59,395:INFO: streamlit: 1.40.1 +2024-12-04 16:44:59,395:INFO: prophet: Not installed +2024-12-04 16:44:59,395:INFO:None +2024-12-04 16:44:59,395:INFO:Set up data. +2024-12-04 16:44:59,497:INFO:Set up folding strategy. +2024-12-04 16:44:59,497:INFO:Set up train/test split. +2024-12-04 16:44:59,643:INFO:Set up index. +2024-12-04 16:44:59,651:INFO:Assigning column types. +2024-12-04 16:44:59,748:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:44:59,785:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:44:59,789:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:44:59,823:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:44:59,823:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:44:59,866:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:44:59,867:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:44:59,890:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:44:59,893:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:44:59,893:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:44:59,932:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:44:59,957:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:44:59,957:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:00,001:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. +2024-12-04 16:45:00,026:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:00,027:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:00,027:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. +2024-12-04 16:45:00,093:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:00,094:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:00,160:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:00,160:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:00,160:INFO:Preparing preprocessing pipeline... +2024-12-04 16:45:00,176:INFO:Set up simple imputation. +2024-12-04 16:45:00,223:INFO:Set up encoding of categorical features. +2024-12-04 16:45:00,584:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:45:00,589:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(exclude=None, + include=['pathologie', 'nom_pathologie', + 'tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74... + keep_empty_features=False, + missing_values=nan, + strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(exclude=None, include=['code_pathologie'], + transformer=TargetEncoder(cols=['code_pathologie'], + drop_invariant=False, + handle_missing='return_nan', + handle_unknown='value', + hierarchy=None, + min_samples_leaf=20, + return_df=True, + smoothing=10, + verbose=0)))], + verbose=False) +2024-12-04 16:45:00,589:INFO:Creating final display dataframe. +2024-12-04 16:45:01,424:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target classification +2 Target type Multiclass +3 Original data shape (283187, 17) +4 Transformed data shape (283187, 17) +5 Transformed train set shape (198230, 17) +6 Transformed test set shape (84957, 17) +7 Numeric features 15 +8 Categorical features 1 +9 Rows with missing values 3.0% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Fold Generator StratifiedKFold +17 Fold Number 5 +18 CPU Jobs -1 +19 Use GPU False +20 Log Experiment False +21 Experiment Name clf-default-name +22 USI c46c +2024-12-04 16:45:01,500:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:01,500:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:01,570:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:01,570:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:45:01,573:INFO:setup() successfully completed in 2.26s............... +2024-12-04 16:45:01,573:INFO:Initializing compare_models() +2024-12-04 16:45:01,573:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'Accuracy', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'probability_threshold': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 16:45:01,573:INFO:Checking exceptions +2024-12-04 16:45:01,664:INFO:Preparing display monitor +2024-12-04 16:45:01,684:INFO:Initializing Logistic Regression +2024-12-04 16:45:01,684:INFO:Total runtime is 0.0 minutes +2024-12-04 16:45:01,690:INFO:SubProcess create_model() called ================================== +2024-12-04 16:45:01,690:INFO:Initializing create_model() +2024-12-04 16:45:01,693:INFO:create_model(self=, estimator=lr, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:45:01,693:INFO:Checking exceptions +2024-12-04 16:45:01,693:INFO:Importing libraries +2024-12-04 16:45:01,693:INFO:Copying training dataset +2024-12-04 16:45:01,860:INFO:Defining folds +2024-12-04 16:45:01,860:INFO:Declaring metric variables +2024-12-04 16:45:01,862:INFO:Importing untrained model +2024-12-04 16:45:01,863:INFO:Logistic Regression Imported successfully +2024-12-04 16:45:01,871:INFO:Starting cross validation +2024-12-04 16:45:01,871:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:45:56,738:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1): +STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. + +Increase the number of iterations (max_iter) or scale the data as shown in: + https://scikit-learn.org/stable/modules/preprocessing.html +Please also refer to the documentation for alternative solver options: + https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression + n_iter_i = _check_optimize_result( + +2024-12-04 16:45:56,896:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:45:56,913:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1): +STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. + +Increase the number of iterations (max_iter) or scale the data as shown in: + https://scikit-learn.org/stable/modules/preprocessing.html +Please also refer to the documentation for alternative solver options: + https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression + n_iter_i = _check_optimize_result( + +2024-12-04 16:45:56,996:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:45:57,223:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1): +STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. + +Increase the number of iterations (max_iter) or scale the data as shown in: + https://scikit-learn.org/stable/modules/preprocessing.html +Please also refer to the documentation for alternative solver options: + https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression + n_iter_i = _check_optimize_result( + +2024-12-04 16:45:57,294:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:45:57,462:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1): +STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. + +Increase the number of iterations (max_iter) or scale the data as shown in: + https://scikit-learn.org/stable/modules/preprocessing.html +Please also refer to the documentation for alternative solver options: + https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression + n_iter_i = _check_optimize_result( + +2024-12-04 16:45:57,475:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1): +STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. + +Increase the number of iterations (max_iter) or scale the data as shown in: + https://scikit-learn.org/stable/modules/preprocessing.html +Please also refer to the documentation for alternative solver options: + https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression + n_iter_i = _check_optimize_result( + +2024-12-04 16:45:57,541:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:45:57,553:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:45:57,704:INFO:Calculating mean and std +2024-12-04 16:45:57,705:INFO:Creating metrics dataframe +2024-12-04 16:45:57,707:INFO:Uploading results into container +2024-12-04 16:45:57,707:INFO:Uploading model into container now +2024-12-04 16:45:57,707:INFO:_master_model_container: 1 +2024-12-04 16:45:57,707:INFO:_display_container: 2 +2024-12-04 16:45:57,707:INFO:LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, + intercept_scaling=1, l1_ratio=None, max_iter=1000, + multi_class='auto', n_jobs=None, penalty='l2', + random_state=123, solver='lbfgs', tol=0.0001, verbose=0, + warm_start=False) +2024-12-04 16:45:57,709:INFO:create_model() successfully completed...................................... +2024-12-04 16:45:57,796:INFO:SubProcess create_model() end ================================== +2024-12-04 16:45:57,796:INFO:Creating metrics dataframe +2024-12-04 16:45:57,803:INFO:Initializing K Neighbors Classifier +2024-12-04 16:45:57,803:INFO:Total runtime is 0.9353153785069783 minutes +2024-12-04 16:45:57,805:INFO:SubProcess create_model() called ================================== +2024-12-04 16:45:57,805:INFO:Initializing create_model() +2024-12-04 16:45:57,805:INFO:create_model(self=, estimator=knn, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:45:57,805:INFO:Checking exceptions +2024-12-04 16:45:57,805:INFO:Importing libraries +2024-12-04 16:45:57,807:INFO:Copying training dataset +2024-12-04 16:45:57,935:INFO:Defining folds +2024-12-04 16:45:57,935:INFO:Declaring metric variables +2024-12-04 16:45:57,939:INFO:Importing untrained model +2024-12-04 16:45:57,943:INFO:K Neighbors Classifier Imported successfully +2024-12-04 16:45:57,947:INFO:Starting cross validation +2024-12-04 16:45:57,947:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:47:17,986:INFO:Calculating mean and std +2024-12-04 16:47:17,987:INFO:Creating metrics dataframe +2024-12-04 16:47:17,990:INFO:Uploading results into container +2024-12-04 16:47:17,990:INFO:Uploading model into container now +2024-12-04 16:47:17,991:INFO:_master_model_container: 2 +2024-12-04 16:47:17,991:INFO:_display_container: 2 +2024-12-04 16:47:17,991:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', + metric_params=None, n_jobs=-1, n_neighbors=5, p=2, + weights='uniform') +2024-12-04 16:47:17,991:INFO:create_model() successfully completed...................................... +2024-12-04 16:47:18,080:INFO:SubProcess create_model() end ================================== +2024-12-04 16:47:18,080:INFO:Creating metrics dataframe +2024-12-04 16:47:18,085:INFO:Initializing Naive Bayes +2024-12-04 16:47:18,085:INFO:Total runtime is 2.273339049021403 minutes +2024-12-04 16:47:18,089:INFO:SubProcess create_model() called ================================== +2024-12-04 16:47:18,089:INFO:Initializing create_model() +2024-12-04 16:47:18,089:INFO:create_model(self=, estimator=nb, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:47:18,089:INFO:Checking exceptions +2024-12-04 16:47:18,089:INFO:Importing libraries +2024-12-04 16:47:18,089:INFO:Copying training dataset +2024-12-04 16:47:18,224:INFO:Defining folds +2024-12-04 16:47:18,224:INFO:Declaring metric variables +2024-12-04 16:47:18,227:INFO:Importing untrained model +2024-12-04 16:47:18,231:INFO:Naive Bayes Imported successfully +2024-12-04 16:47:18,235:INFO:Starting cross validation +2024-12-04 16:47:18,235:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:47:19,590:INFO:Calculating mean and std +2024-12-04 16:47:19,590:INFO:Creating metrics dataframe +2024-12-04 16:47:19,593:INFO:Uploading results into container +2024-12-04 16:47:19,593:INFO:Uploading model into container now +2024-12-04 16:47:19,593:INFO:_master_model_container: 3 +2024-12-04 16:47:19,593:INFO:_display_container: 2 +2024-12-04 16:47:19,593:INFO:GaussianNB(priors=None, var_smoothing=1e-09) +2024-12-04 16:47:19,593:INFO:create_model() successfully completed...................................... +2024-12-04 16:47:19,675:INFO:SubProcess create_model() end ================================== +2024-12-04 16:47:19,675:INFO:Creating metrics dataframe +2024-12-04 16:47:19,681:INFO:Initializing Decision Tree Classifier +2024-12-04 16:47:19,681:INFO:Total runtime is 2.2999385078748067 minutes +2024-12-04 16:47:19,684:INFO:SubProcess create_model() called ================================== +2024-12-04 16:47:19,684:INFO:Initializing create_model() +2024-12-04 16:47:19,684:INFO:create_model(self=, estimator=dt, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:47:19,684:INFO:Checking exceptions +2024-12-04 16:47:19,684:INFO:Importing libraries +2024-12-04 16:47:19,684:INFO:Copying training dataset +2024-12-04 16:47:19,814:INFO:Defining folds +2024-12-04 16:47:19,814:INFO:Declaring metric variables +2024-12-04 16:47:19,820:INFO:Importing untrained model +2024-12-04 16:47:19,823:INFO:Decision Tree Classifier Imported successfully +2024-12-04 16:47:19,828:INFO:Starting cross validation +2024-12-04 16:47:19,829:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:47:21,300:INFO:Calculating mean and std +2024-12-04 16:47:21,300:INFO:Creating metrics dataframe +2024-12-04 16:47:21,303:INFO:Uploading results into container +2024-12-04 16:47:21,303:INFO:Uploading model into container now +2024-12-04 16:47:21,303:INFO:_master_model_container: 4 +2024-12-04 16:47:21,303:INFO:_display_container: 2 +2024-12-04 16:47:21,305:INFO:DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini', + max_depth=None, max_features=None, max_leaf_nodes=None, + min_impurity_decrease=0.0, min_samples_leaf=1, + min_samples_split=2, min_weight_fraction_leaf=0.0, + monotonic_cst=None, random_state=123, splitter='best') +2024-12-04 16:47:21,305:INFO:create_model() successfully completed...................................... +2024-12-04 16:47:21,387:INFO:SubProcess create_model() end ================================== +2024-12-04 16:47:21,387:INFO:Creating metrics dataframe +2024-12-04 16:47:21,393:INFO:Initializing SVM - Linear Kernel +2024-12-04 16:47:21,393:INFO:Total runtime is 2.3284818053245546 minutes +2024-12-04 16:47:21,396:INFO:SubProcess create_model() called ================================== +2024-12-04 16:47:21,396:INFO:Initializing create_model() +2024-12-04 16:47:21,396:INFO:create_model(self=, estimator=svm, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:47:21,396:INFO:Checking exceptions +2024-12-04 16:47:21,397:INFO:Importing libraries +2024-12-04 16:47:21,397:INFO:Copying training dataset +2024-12-04 16:47:21,523:INFO:Defining folds +2024-12-04 16:47:21,523:INFO:Declaring metric variables +2024-12-04 16:47:21,528:INFO:Importing untrained model +2024-12-04 16:47:21,530:INFO:SVM - Linear Kernel Imported successfully +2024-12-04 16:47:21,536:INFO:Starting cross validation +2024-12-04 16:47:21,537:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:47:40,209:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:41,680:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:41,738:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:42,078:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:42,214:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:42,365:INFO:Calculating mean and std +2024-12-04 16:47:42,365:INFO:Creating metrics dataframe +2024-12-04 16:47:42,365:INFO:Uploading results into container +2024-12-04 16:47:42,365:INFO:Uploading model into container now +2024-12-04 16:47:42,368:INFO:_master_model_container: 5 +2024-12-04 16:47:42,368:INFO:_display_container: 2 +2024-12-04 16:47:42,368:INFO:SGDClassifier(alpha=0.0001, average=False, class_weight=None, + early_stopping=False, epsilon=0.1, eta0=0.001, fit_intercept=True, + l1_ratio=0.15, learning_rate='optimal', loss='hinge', + max_iter=1000, n_iter_no_change=5, n_jobs=-1, penalty='l2', + power_t=0.5, random_state=123, shuffle=True, tol=0.001, + validation_fraction=0.1, verbose=0, warm_start=False) +2024-12-04 16:47:42,369:INFO:create_model() successfully completed...................................... +2024-12-04 16:47:42,459:INFO:SubProcess create_model() end ================================== +2024-12-04 16:47:42,459:INFO:Creating metrics dataframe +2024-12-04 16:47:42,463:INFO:Initializing Ridge Classifier +2024-12-04 16:47:42,465:INFO:Total runtime is 2.67968252102534 minutes +2024-12-04 16:47:42,465:INFO:SubProcess create_model() called ================================== +2024-12-04 16:47:42,465:INFO:Initializing create_model() +2024-12-04 16:47:42,465:INFO:create_model(self=, estimator=ridge, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:47:42,465:INFO:Checking exceptions +2024-12-04 16:47:42,465:INFO:Importing libraries +2024-12-04 16:47:42,465:INFO:Copying training dataset +2024-12-04 16:47:42,605:INFO:Defining folds +2024-12-04 16:47:42,605:INFO:Declaring metric variables +2024-12-04 16:47:42,610:INFO:Importing untrained model +2024-12-04 16:47:42,613:INFO:Ridge Classifier Imported successfully +2024-12-04 16:47:42,618:INFO:Starting cross validation +2024-12-04 16:47:42,620:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:47:43,603:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:43,633:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. + _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result)) + +2024-12-04 16:47:43,636:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:43,668:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. + _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result)) + +2024-12-04 16:47:43,694:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:43,723:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. + _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result)) + +2024-12-04 16:47:43,736:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:43,764:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. + _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result)) + +2024-12-04 16:47:43,767:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:47:43,789:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. + _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result)) + +2024-12-04 16:47:43,930:INFO:Calculating mean and std +2024-12-04 16:47:43,930:INFO:Creating metrics dataframe +2024-12-04 16:47:43,932:INFO:Uploading results into container +2024-12-04 16:47:43,932:INFO:Uploading model into container now +2024-12-04 16:47:43,932:INFO:_master_model_container: 6 +2024-12-04 16:47:43,932:INFO:_display_container: 2 +2024-12-04 16:47:43,934:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, + max_iter=None, positive=False, random_state=123, solver='auto', + tol=0.0001) +2024-12-04 16:47:43,934:INFO:create_model() successfully completed...................................... +2024-12-04 16:47:44,021:INFO:SubProcess create_model() end ================================== +2024-12-04 16:47:44,021:INFO:Creating metrics dataframe +2024-12-04 16:47:44,028:INFO:Initializing Random Forest Classifier +2024-12-04 16:47:44,029:INFO:Total runtime is 2.7057372093200684 minutes +2024-12-04 16:47:44,031:INFO:SubProcess create_model() called ================================== +2024-12-04 16:47:44,031:INFO:Initializing create_model() +2024-12-04 16:47:44,031:INFO:create_model(self=, estimator=rf, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:47:44,031:INFO:Checking exceptions +2024-12-04 16:47:44,031:INFO:Importing libraries +2024-12-04 16:47:44,031:INFO:Copying training dataset +2024-12-04 16:47:44,189:INFO:Defining folds +2024-12-04 16:47:44,189:INFO:Declaring metric variables +2024-12-04 16:47:44,193:INFO:Importing untrained model +2024-12-04 16:47:44,197:INFO:Random Forest Classifier Imported successfully +2024-12-04 16:47:44,203:INFO:Starting cross validation +2024-12-04 16:47:44,205:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:48:05,084:INFO:Calculating mean and std +2024-12-04 16:48:05,085:INFO:Creating metrics dataframe +2024-12-04 16:48:05,085:INFO:Uploading results into container +2024-12-04 16:48:05,085:INFO:Uploading model into container now +2024-12-04 16:48:05,089:INFO:_master_model_container: 7 +2024-12-04 16:48:05,089:INFO:_display_container: 2 +2024-12-04 16:48:05,089:INFO:RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, + criterion='gini', max_depth=None, max_features='sqrt', + max_leaf_nodes=None, max_samples=None, + min_impurity_decrease=0.0, min_samples_leaf=1, + min_samples_split=2, min_weight_fraction_leaf=0.0, + monotonic_cst=None, n_estimators=100, n_jobs=-1, + oob_score=False, random_state=123, verbose=0, + warm_start=False) +2024-12-04 16:48:05,089:INFO:create_model() successfully completed...................................... +2024-12-04 16:48:05,207:INFO:SubProcess create_model() end ================================== +2024-12-04 16:48:05,207:INFO:Creating metrics dataframe +2024-12-04 16:48:05,213:INFO:Initializing Quadratic Discriminant Analysis +2024-12-04 16:48:05,213:INFO:Total runtime is 3.058813488483429 minutes +2024-12-04 16:48:05,220:INFO:SubProcess create_model() called ================================== +2024-12-04 16:48:05,220:INFO:Initializing create_model() +2024-12-04 16:48:05,220:INFO:create_model(self=, estimator=qda, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:48:05,220:INFO:Checking exceptions +2024-12-04 16:48:05,220:INFO:Importing libraries +2024-12-04 16:48:05,220:INFO:Copying training dataset +2024-12-04 16:48:05,377:INFO:Defining folds +2024-12-04 16:48:05,377:INFO:Declaring metric variables +2024-12-04 16:48:05,380:INFO:Importing untrained model +2024-12-04 16:48:05,385:INFO:Quadratic Discriminant Analysis Imported successfully +2024-12-04 16:48:05,392:INFO:Starting cross validation +2024-12-04 16:48:05,393:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:48:06,279:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear + warnings.warn("Variables are collinear") + +2024-12-04 16:48:06,390:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear + warnings.warn("Variables are collinear") + +2024-12-04 16:48:06,479:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear + warnings.warn("Variables are collinear") + +2024-12-04 16:48:06,528:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear + warnings.warn("Variables are collinear") + +2024-12-04 16:48:06,820:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:48:06,832:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:48:06,894:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:48:06,945:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:48:06,970:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__ + return self.score_func(y_true, y_pred, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 634, in roc_auc_score + return _multiclass_roc_auc_score( + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_ranking.py", line 707, in _multiclass_roc_auc_score + raise ValueError( +ValueError: Target scores need to be probabilities for multiclass roc_auc, i.e. they should sum up to 1.0 over classes + + warnings.warn( + +2024-12-04 16:48:07,128:INFO:Calculating mean and std +2024-12-04 16:48:07,130:INFO:Creating metrics dataframe +2024-12-04 16:48:07,131:INFO:Uploading results into container +2024-12-04 16:48:07,132:INFO:Uploading model into container now +2024-12-04 16:48:07,133:INFO:_master_model_container: 8 +2024-12-04 16:48:07,133:INFO:_display_container: 2 +2024-12-04 16:48:07,133:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, + store_covariance=False, tol=0.0001) +2024-12-04 16:48:07,133:INFO:create_model() successfully completed...................................... +2024-12-04 16:48:07,221:INFO:SubProcess create_model() end ================================== +2024-12-04 16:48:07,221:INFO:Creating metrics dataframe +2024-12-04 16:48:07,228:INFO:Initializing Ada Boost Classifier +2024-12-04 16:48:07,228:INFO:Total runtime is 3.092388411362966 minutes +2024-12-04 16:48:07,230:INFO:SubProcess create_model() called ================================== +2024-12-04 16:48:07,230:INFO:Initializing create_model() +2024-12-04 16:48:07,230:INFO:create_model(self=, estimator=ada, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:48:07,230:INFO:Checking exceptions +2024-12-04 16:48:07,230:INFO:Importing libraries +2024-12-04 16:48:07,230:INFO:Copying training dataset +2024-12-04 16:48:07,364:INFO:Defining folds +2024-12-04 16:48:07,364:INFO:Declaring metric variables +2024-12-04 16:48:07,370:INFO:Importing untrained model +2024-12-04 16:48:07,373:INFO:Ada Boost Classifier Imported successfully +2024-12-04 16:48:07,381:INFO:Starting cross validation +2024-12-04 16:48:07,382:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:48:08,063:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. + warnings.warn( + +2024-12-04 16:48:08,098:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. + warnings.warn( + +2024-12-04 16:48:08,152:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. + warnings.warn( + +2024-12-04 16:48:08,234:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. + warnings.warn( + +2024-12-04 16:48:08,266:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. + warnings.warn( + +2024-12-04 16:48:59,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:48:59,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:48:59,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:48:59,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:50:20,874:INFO:PyCaret RegressionExperiment +2024-12-04 16:50:20,874:INFO:Logging name: duration_prediction_v1 +2024-12-04 16:50:20,874:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:50:20,874:INFO:version 3.3.2 +2024-12-04 16:50:20,874:INFO:Initializing setup() +2024-12-04 16:50:20,874:INFO:self.USI: 563f +2024-12-04 16:50:20,874:INFO:self._variable_keys: {'pipeline', 'fold_shuffle_param', 'X_train', 'exp_id', 'y_train', 'html_param', 'X', 'y_test', 'memory', 'n_jobs_param', 'gpu_n_jobs_param', 'X_test', '_available_plots', 'fold_generator', 'transform_target_param', 'target_param', 'data', 'fold_groups_param', 'idx', 'USI', 'logging_param', 'seed', 'gpu_param', 'y', '_ml_usecase', 'log_plots_param', 'exp_name_log'} +2024-12-04 16:50:20,874:INFO:Checking environment +2024-12-04 16:50:20,874:INFO:python_version: 3.11.9 +2024-12-04 16:50:20,874:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:50:20,874:INFO:machine: AMD64 +2024-12-04 16:50:20,874:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:50:20,879:INFO:Memory: svmem(total=17007292416, available=3082625024, percent=81.9, used=13924667392, free=3082625024) +2024-12-04 16:50:20,880:INFO:Physical Core: 4 +2024-12-04 16:50:20,880:INFO:Logical Core: 8 +2024-12-04 16:50:20,880:INFO:Checking libraries +2024-12-04 16:50:20,880:INFO:System: +2024-12-04 16:50:20,880:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:50:20,880:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:50:20,880:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:50:20,880:INFO:PyCaret required dependencies: +2024-12-04 16:50:20,905:INFO: pip: 24.3.1 +2024-12-04 16:50:20,906:INFO: setuptools: 65.5.0 +2024-12-04 16:50:20,906:INFO: pycaret: 3.3.2 +2024-12-04 16:50:20,906:INFO: IPython: 8.12.3 +2024-12-04 16:50:20,906:INFO: ipywidgets: 8.1.5 +2024-12-04 16:50:20,906:INFO: tqdm: 4.67.1 +2024-12-04 16:50:20,906:INFO: numpy: 1.26.4 +2024-12-04 16:50:20,906:INFO: pandas: 2.1.4 +2024-12-04 16:50:20,906:INFO: jinja2: 3.1.4 +2024-12-04 16:50:20,906:INFO: scipy: 1.11.4 +2024-12-04 16:50:20,906:INFO: joblib: 1.3.2 +2024-12-04 16:50:20,906:INFO: sklearn: 1.4.2 +2024-12-04 16:50:20,906:INFO: pyod: 2.0.2 +2024-12-04 16:50:20,907:INFO: imblearn: 0.12.4 +2024-12-04 16:50:20,907:INFO: category_encoders: 2.6.4 +2024-12-04 16:50:20,907:INFO: lightgbm: 4.5.0 +2024-12-04 16:50:20,907:INFO: numba: 0.60.0 +2024-12-04 16:50:20,907:INFO: requests: 2.32.3 +2024-12-04 16:50:20,907:INFO: matplotlib: 3.7.5 +2024-12-04 16:50:20,907:INFO: scikitplot: 0.3.7 +2024-12-04 16:50:20,907:INFO: yellowbrick: 1.5 +2024-12-04 16:50:20,907:INFO: plotly: 5.24.1 +2024-12-04 16:50:20,907:INFO: plotly-resampler: Not installed +2024-12-04 16:50:20,907:INFO: kaleido: 0.2.1 +2024-12-04 16:50:20,907:INFO: schemdraw: 0.15 +2024-12-04 16:50:20,907:INFO: statsmodels: 0.14.4 +2024-12-04 16:50:20,907:INFO: sktime: 0.26.0 +2024-12-04 16:50:20,907:INFO: tbats: 1.1.3 +2024-12-04 16:50:20,907:INFO: pmdarima: 2.0.4 +2024-12-04 16:50:20,907:INFO: psutil: 6.1.0 +2024-12-04 16:50:20,907:INFO: markupsafe: 3.0.2 +2024-12-04 16:50:20,907:INFO: pickle5: Not installed +2024-12-04 16:50:20,907:INFO: cloudpickle: 3.1.0 +2024-12-04 16:50:20,907:INFO: deprecation: 2.1.0 +2024-12-04 16:50:20,907:INFO: xxhash: 3.5.0 +2024-12-04 16:50:20,907:INFO: wurlitzer: Not installed +2024-12-04 16:50:20,907:INFO:PyCaret optional dependencies: +2024-12-04 16:50:20,951:INFO: shap: Not installed +2024-12-04 16:50:20,951:INFO: interpret: Not installed +2024-12-04 16:50:20,951:INFO: umap: Not installed +2024-12-04 16:50:20,951:INFO: ydata_profiling: Not installed +2024-12-04 16:50:20,951:INFO: explainerdashboard: Not installed +2024-12-04 16:50:20,951:INFO: autoviz: Not installed +2024-12-04 16:50:20,951:INFO: fairlearn: Not installed +2024-12-04 16:50:20,951:INFO: deepchecks: Not installed +2024-12-04 16:50:20,951:INFO: xgboost: Not installed +2024-12-04 16:50:20,951:INFO: catboost: Not installed +2024-12-04 16:50:20,951:INFO: kmodes: Not installed +2024-12-04 16:50:20,951:INFO: mlxtend: Not installed +2024-12-04 16:50:20,951:INFO: statsforecast: Not installed +2024-12-04 16:50:20,951:INFO: tune_sklearn: Not installed +2024-12-04 16:50:20,951:INFO: ray: Not installed +2024-12-04 16:50:20,951:INFO: hyperopt: Not installed +2024-12-04 16:50:20,951:INFO: optuna: Not installed +2024-12-04 16:50:20,951:INFO: skopt: Not installed +2024-12-04 16:50:20,951:INFO: mlflow: 2.18.0 +2024-12-04 16:50:20,951:INFO: gradio: Not installed +2024-12-04 16:50:20,951:INFO: fastapi: Not installed +2024-12-04 16:50:20,951:INFO: uvicorn: 0.32.1 +2024-12-04 16:50:20,951:INFO: m2cgen: Not installed +2024-12-04 16:50:20,951:INFO: evidently: Not installed +2024-12-04 16:50:20,951:INFO: fugue: Not installed +2024-12-04 16:50:20,951:INFO: streamlit: 1.40.1 +2024-12-04 16:50:20,951:INFO: prophet: Not installed +2024-12-04 16:50:20,952:INFO:None +2024-12-04 16:50:20,952:INFO:Set up data. diff --git a/machine_learning/model_development/mlruns/0/meta.yaml b/machine_learning/model_development/mlruns/0/meta.yaml new file mode 100644 index 0000000..3036d64 --- /dev/null +++ b/machine_learning/model_development/mlruns/0/meta.yaml @@ -0,0 +1,6 @@ +artifact_location: file:///c:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/machine_learning/model_development/mlruns/0 +creation_time: 1733326618035 +experiment_id: '0' +last_update_time: 1733326618035 +lifecycle_stage: active +name: Default diff --git a/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/meta.yaml b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/meta.yaml new file mode 100644 index 0000000..a13eda4 --- /dev/null +++ b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/meta.yaml @@ -0,0 +1,15 @@ +artifact_uri: file:///c:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/artifacts +end_time: 1733326973947 +entry_point_name: '' +experiment_id: '564973138240631685' +lifecycle_stage: active +run_id: ae446cde06fe4e6da11118cb32787fc3 +run_name: serious-ray-679 +run_uuid: ae446cde06fe4e6da11118cb32787fc3 +source_name: '' +source_type: 4 +source_version: '' +start_time: 1733326971318 +status: 4 +tags: [] +user_id: antob diff --git a/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.runName b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.runName new file mode 100644 index 0000000..e825451 --- /dev/null +++ b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.runName @@ -0,0 +1 @@ +serious-ray-679 \ No newline at end of file diff --git a/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.name b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.name new file mode 100644 index 0000000..8482576 --- /dev/null +++ b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.name @@ -0,0 +1 @@ +c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\ipykernel_launcher.py \ No newline at end of file diff --git a/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.type b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.type new file mode 100644 index 0000000..0c2c1fe --- /dev/null +++ b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.source.type @@ -0,0 +1 @@ +LOCAL \ No newline at end of file diff --git a/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.user b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.user new file mode 100644 index 0000000..4895757 --- /dev/null +++ b/machine_learning/model_development/mlruns/564973138240631685/ae446cde06fe4e6da11118cb32787fc3/tags/mlflow.user @@ -0,0 +1 @@ +antob \ No newline at end of file diff --git a/machine_learning/model_development/mlruns/564973138240631685/meta.yaml b/machine_learning/model_development/mlruns/564973138240631685/meta.yaml new file mode 100644 index 0000000..1130043 --- /dev/null +++ b/machine_learning/model_development/mlruns/564973138240631685/meta.yaml @@ -0,0 +1,6 @@ +artifact_location: file:///c:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/machine_learning/model_development/mlruns/564973138240631685 +creation_time: 1733326618052 +experiment_id: '564973138240631685' +last_update_time: 1733326618052 +lifecycle_stage: active +name: service_classification_v1 diff --git a/machine_learning/model_development/mlruns/730985364380900454/meta.yaml b/machine_learning/model_development/mlruns/730985364380900454/meta.yaml new file mode 100644 index 0000000..80a38bc --- /dev/null +++ b/machine_learning/model_development/mlruns/730985364380900454/meta.yaml @@ -0,0 +1,6 @@ +artifact_location: file:///c:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/machine_learning/model_development/mlruns/730985364380900454 +creation_time: 1733326877463 +experiment_id: '730985364380900454' +last_update_time: 1733326877463 +lifecycle_stage: active +name: duration_prediction_v1 diff --git a/machine_learning/model_development/train_recommendation_system.ipynb b/machine_learning/model_development/train_recommendation_system.ipynb new file mode 100644 index 0000000..2e0bf9f --- /dev/null +++ b/machine_learning/model_development/train_recommendation_system.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Développement du Système de Recommandation d'Hôpitaux\n", + "\n", + "Ce notebook démontre l'entraînement et l'utilisation du système de recommandation d'hôpitaux." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../')\n", + "\n", + "import pandas as pd\n", + "import mlflow\n", + "from machine_learning.classification_service.service_classifier import train_service_classifier\n", + "from machine_learning.duration_prediction.duration_predictor import train_duration_predictor\n", + "from machine_learning.recommendation.hospital_recommender import HospitalRecommender" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Chargement et Préparation des Données" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Charger les données\n", + "# TODO: Remplacer par le bon chemin vers vos données\n", + "data = pd.read_csv('../data/your_data.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Entraînement du Modèle de Classification de Service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "service_model = train_service_classifier(\n", + " data=data,\n", + " target_col='classification',\n", + " experiment_name='service_classification_v1'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Entraînement du Modèle de Prédiction de Durée" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "duration_model = train_duration_predictor(\n", + " data=data,\n", + " target_col='AVG_duree_hospi',\n", + " experiment_name='duration_prediction_v1'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Test du Système de Recommandation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Créer une instance du recommandeur\n", + "recommender = HospitalRecommender()\n", + "\n", + "# Charger les modèles entraînés\n", + "# TODO: Remplacer par vos run_ids MLflow\n", + "recommender.load_models(\n", + " service_run_id='your_service_run_id',\n", + " duration_run_id='your_duration_run_id'\n", + ")\n", + "\n", + "# Tester avec des données patient\n", + "test_patient = {\n", + " 'age': 45,\n", + " 'sexe': 'H',\n", + " 'pathologie': 'example_pathology',\n", + " 'region': 'example_region'\n", + "}\n", + "\n", + "recommendations = recommender.predict(test_patient)\n", + "print(\"Recommandations d'hôpitaux :\")\n", + "for rec in recommendations:\n", + " print(rec)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/machine_learning/recommendation/hospital_recommender.py b/machine_learning/recommendation/hospital_recommender.py new file mode 100644 index 0000000..a484ee3 --- /dev/null +++ b/machine_learning/recommendation/hospital_recommender.py @@ -0,0 +1,185 @@ +import mlflow +from mlflow.tracking import MlflowClient +import pandas as pd +import numpy as np +from typing import Dict, List +from geopy.distance import geodesic +from geopy.geocoders import Nominatim + +class HospitalRecommender: + """ + Système de recommandation d'hôpitaux qui combine les prédictions + de plusieurs modèles pour fournir des recommandations personnalisées. + """ + + def __init__(self): + self.service_classifier = None + self.duration_predictor = None + self.mlflow_client = MlflowClient() + self.hospital_data = None + self.geolocator = Nominatim(user_agent="hospital_recommender") + + def load_models(self, service_run_id: str, duration_run_id: str): + """ + Charge les modèles entraînés depuis MLflow + + Args: + service_run_id: ID MLflow du modèle de classification de service + duration_run_id: ID MLflow du modèle de prédiction de durée + """ + self.service_classifier = mlflow.pycaret.load_model( + f"runs:/{service_run_id}/service_classifier" + ) + self.duration_predictor = mlflow.pycaret.load_model( + f"runs:/{duration_run_id}/duration_predictor" + ) + + def load_hospital_data(self, data: pd.DataFrame): + """ + Charge les données des hôpitaux + + Args: + data: DataFrame contenant les informations des hôpitaux + """ + self.hospital_data = data + + def predict(self, patient_data: Dict) -> List[Dict]: + """ + Génère des recommandations d'hôpitaux pour un patient + + Args: + patient_data: Dictionnaire contenant les informations du patient + (âge, sexe, pathologie, localisation, etc.) + + Returns: + Liste de dictionnaires contenant les recommandations d'hôpitaux, + triées par pertinence + """ + if self.hospital_data is None: + raise ValueError("Les données des hôpitaux n'ont pas été chargées") + + # Convertir les données patient en DataFrame + patient_df = pd.DataFrame([patient_data]) + + # 1. Prédire le service nécessaire + service = self.service_classifier.predict(patient_df)[0] + + # 2. Prédire la durée estimée du séjour + estimated_duration = self.duration_predictor.predict(patient_df)[0] + + # 3. Calculer les scores pour chaque hôpital + recommendations = self._get_hospital_recommendations( + service, + estimated_duration, + patient_data + ) + + return recommendations + + def _calculate_distance_score(self, hospital_location: str, patient_location: str) -> float: + """ + Calcule un score basé sur la distance entre l'hôpital et le patient + """ + try: + patient_loc = self.geolocator.geocode(patient_location) + hospital_loc = self.geolocator.geocode(hospital_location) + + if patient_loc and hospital_loc: + distance = geodesic( + (patient_loc.latitude, patient_loc.longitude), + (hospital_loc.latitude, hospital_loc.longitude) + ).kilometers + + # Convertir la distance en score (0-1, plus proche = meilleur score) + return 1 / (1 + distance/100) + except: + return 0 + return 0 + + def _calculate_capacity_score(self, hospital_data: pd.Series, estimated_duration: float) -> float: + """ + Calcule un score basé sur la capacité d'accueil de l'hôpital + """ + # Utiliser le nombre de lits disponibles et le taux d'occupation + lit_hospi = hospital_data.get('lit_hospi_complete', 0) + occupation = hospital_data.get('hospi_total_24h', 0) / lit_hospi if lit_hospi > 0 else 1 + + # Score basé sur la capacité (0-1, plus de capacité = meilleur score) + capacity_score = 1 - occupation + + return max(0, capacity_score) + + def _calculate_service_score(self, hospital_data: pd.Series, service: str) -> float: + """ + Calcule un score basé sur la correspondance du service + """ + hospital_service = hospital_data.get('classification', '') + + # Score parfait si le service correspond exactement + if hospital_service == service: + return 1.0 + + # Score partiel pour les services compatibles + service_compatibility = { + 'M': {'C': 0.5, 'SSR': 0.3}, + 'C': {'M': 0.5, 'SSR': 0.3}, + 'SSR': {'M': 0.3, 'C': 0.3}, + 'O': {'M': 0.4}, + 'PSY': {'M': 0.2}, + 'ESND': {'SSR': 0.4, 'M': 0.3} + } + + return service_compatibility.get(service, {}).get(hospital_service, 0.0) + + def _get_hospital_recommendations( + self, + service: str, + estimated_duration: float, + patient_data: Dict, + top_n: int = 5 + ) -> List[Dict]: + """ + Calcule les scores et retourne les meilleurs hôpitaux + + Args: + service: Service médical prédit + estimated_duration: Durée estimée du séjour + patient_data: Données du patient + top_n: Nombre de recommandations à retourner + + Returns: + Liste des meilleurs hôpitaux avec leurs scores + """ + recommendations = [] + + for _, hospital in self.hospital_data.iterrows(): + # Calculer les différents scores + distance_score = self._calculate_distance_score( + f"{hospital['nom_region']}, France", + f"{patient_data.get('region', '')}, France" + ) + + capacity_score = self._calculate_capacity_score(hospital, estimated_duration) + service_score = self._calculate_service_score(hospital, service) + + # Calculer le score composite (avec pondération) + composite_score = ( + 0.4 * service_score + + 0.3 * distance_score + + 0.3 * capacity_score + ) + + if composite_score > 0: # Ne garder que les hôpitaux pertinents + recommendations.append({ + 'hospital_name': hospital.get('nom_region', ''), + 'service': hospital.get('classification', ''), + 'score': composite_score, + 'distance_score': distance_score, + 'capacity_score': capacity_score, + 'service_score': service_score, + 'estimated_duration': estimated_duration + }) + + # Trier par score et prendre les top_n + recommendations.sort(key=lambda x: x['score'], reverse=True) + return recommendations[:top_n] diff --git a/machine_learning/utils/data_preparation.py b/machine_learning/utils/data_preparation.py new file mode 100644 index 0000000..575ad4a --- /dev/null +++ b/machine_learning/utils/data_preparation.py @@ -0,0 +1,109 @@ +import os +from google.cloud import bigquery +import pandas as pd +from typing import Dict, Tuple +from sklearn.model_selection import train_test_split + +def load_data() -> Dict[str, pd.DataFrame]: + """ + Charge les données depuis BigQuery, à la fois la table de morbidité et la table des capacités + """ + os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "C:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/projet-jbn-data-le-wagon-533639ce801d.json" + client = bigquery.Client() + + # Table de morbidité + query_morbidite = """ + SELECT * FROM projet-jbn-data-le-wagon.dbt_medical_analysis_join_total_morbidite.class_join_total_morbidite_sexe_population + """ + + # Table des capacités + query_capacite = """ + SELECT * FROM projet-jbn-data-le-wagon.dbt_medical_analysis_join_total_morbidite_capacite.class_join_total_morbidite_capacite_kpi + """ + + df_morbidite = client.query(query_morbidite).to_dataframe() + df_capacite = client.query(query_capacite).to_dataframe() + + return { + 'morbidite': df_morbidite, + 'capacite': df_capacite + } + +def prepare_datasets(df_morbidite: pd.DataFrame, df_capacite: pd.DataFrame) -> Dict[str, pd.DataFrame]: + """ + Prépare les différents ensembles de données pour l'entraînement et le test + """ + # Filtrer les données de morbidité pour "ensemble" uniquement + df_morbidite = df_morbidite[df_morbidite["sexe"] == "Ensemble"] + + # Filtrer par niveau + df_hospi_dpt = df_morbidite[df_morbidite["niveau"] == "Départements"] + df_hospi_reg = df_morbidite[df_morbidite["niveau"] == "Régions"] + + # Filtrer par année + df_hospi_dpt_yr = df_hospi_dpt[df_hospi_dpt["annee"].isin([2018, 2019, 2022])] + df_hospi_reg_yr = df_hospi_reg[df_hospi_reg["annee"].isin([2018, 2019, 2022])] + + # Filtrer les données de capacité + df_capacite_yr = df_capacite[df_capacite["annee"].isin([2018, 2019, 2022])] + + return { + 'dpt': df_hospi_dpt_yr, + 'reg': df_hospi_reg_yr, + 'capacite': df_capacite_yr + } + +def prepare_train_test_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + Sépare les données en ensembles d'entraînement (2018-2019) et de test (2022) + """ + train_data = df[df['annee'].isin([2018, 2019])] + test_data = df[df['annee'] == 2022] + + return train_data, test_data + +def prepare_features_for_service_classification(df_morbidite: pd.DataFrame, df_capacite: pd.DataFrame) -> pd.DataFrame: + """ + Prépare les features pour la classification des services + """ + # Fusionner les données de morbidité et de capacité + features_df = pd.merge( + df_morbidite, + df_capacite, + on=['code_geo', 'annee', 'niveau'], + how='left' + ) + + # Sélectionner les colonnes pertinentes + selected_columns = [ + 'code_geo', 'annee', 'niveau', + 'taux_standardise', 'nombre_sejours', + 'nombre_journees', 'duree_moyenne_sejour', + 'capacite_lits', 'taux_occupation', + 'service' # target variable + ] + + return features_df[selected_columns] + +def prepare_features_for_duration_prediction(df_morbidite: pd.DataFrame, df_capacite: pd.DataFrame) -> pd.DataFrame: + """ + Prépare les features pour la prédiction de la durée d'hospitalisation + """ + # Fusionner les données de morbidité et de capacité + features_df = pd.merge( + df_morbidite, + df_capacite, + on=['code_geo', 'annee', 'niveau'], + how='left' + ) + + # Sélectionner les colonnes pertinentes + selected_columns = [ + 'code_geo', 'annee', 'niveau', + 'taux_standardise', 'nombre_sejours', + 'capacite_lits', 'taux_occupation', + 'service', + 'duree_moyenne_sejour' # target variable + ] + + return features_df[selected_columns] diff --git a/notebooks/logs.log b/notebooks/logs.log index 870b6a9..5192f77 100644 --- a/notebooks/logs.log +++ b/notebooks/logs.log @@ -12122,3 +12122,45657 @@ Alternately, you can install this by running `pip install pycaret[models]` 2024-12-03 16:48:51,898:INFO:_display_container: 3 2024-12-03 16:48:51,898:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) 2024-12-03 16:48:51,899:INFO:compare_models() successfully completed...................................... +2024-12-04 11:16:16,912:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:16:16,912:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:16:16,912:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:16:16,912:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:25:14,593:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:25:14,593:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:25:14,593:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:25:14,593:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 11:26:12,880:INFO:PyCaret RegressionExperiment +2024-12-04 11:26:12,881:INFO:Logging name: reg-default-name +2024-12-04 11:26:12,881:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:26:12,881:INFO:version 3.3.2 +2024-12-04 11:26:12,881:INFO:Initializing setup() +2024-12-04 11:26:12,881:INFO:self.USI: 47e4 +2024-12-04 11:26:12,881:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:26:12,881:INFO:Checking environment +2024-12-04 11:26:12,881:INFO:python_version: 3.11.9 +2024-12-04 11:26:12,881:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:26:12,881:INFO:machine: AMD64 +2024-12-04 11:26:12,881:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:26:12,887:INFO:Memory: svmem(total=17007292416, available=1254842368, percent=92.6, used=15752450048, free=1254842368) +2024-12-04 11:26:12,887:INFO:Physical Core: 4 +2024-12-04 11:26:12,887:INFO:Logical Core: 8 +2024-12-04 11:26:12,887:INFO:Checking libraries +2024-12-04 11:26:12,887:INFO:System: +2024-12-04 11:26:12,887:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:26:12,887:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:26:12,887:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:26:12,887:INFO:PyCaret required dependencies: +2024-12-04 11:26:12,975:INFO: pip: 24.3.1 +2024-12-04 11:26:12,975:INFO: setuptools: 65.5.0 +2024-12-04 11:26:12,975:INFO: pycaret: 3.3.2 +2024-12-04 11:26:12,975:INFO: IPython: 8.12.3 +2024-12-04 11:26:12,975:INFO: ipywidgets: 8.1.5 +2024-12-04 11:26:12,975:INFO: tqdm: 4.67.1 +2024-12-04 11:26:12,975:INFO: numpy: 1.26.4 +2024-12-04 11:26:12,975:INFO: pandas: 2.1.4 +2024-12-04 11:26:12,975:INFO: jinja2: 3.1.4 +2024-12-04 11:26:12,975:INFO: scipy: 1.11.4 +2024-12-04 11:26:12,975:INFO: joblib: 1.3.2 +2024-12-04 11:26:12,975:INFO: sklearn: 1.4.2 +2024-12-04 11:26:12,975:INFO: pyod: 2.0.2 +2024-12-04 11:26:12,976:INFO: imblearn: 0.12.4 +2024-12-04 11:26:12,976:INFO: category_encoders: 2.6.4 +2024-12-04 11:26:12,976:INFO: lightgbm: 4.5.0 +2024-12-04 11:26:12,976:INFO: numba: 0.60.0 +2024-12-04 11:26:12,976:INFO: requests: 2.32.3 +2024-12-04 11:26:12,976:INFO: matplotlib: 3.7.5 +2024-12-04 11:26:12,976:INFO: scikitplot: 0.3.7 +2024-12-04 11:26:12,976:INFO: yellowbrick: 1.5 +2024-12-04 11:26:12,976:INFO: plotly: 5.24.1 +2024-12-04 11:26:12,976:INFO: plotly-resampler: Not installed +2024-12-04 11:26:12,976:INFO: kaleido: 0.2.1 +2024-12-04 11:26:12,976:INFO: schemdraw: 0.15 +2024-12-04 11:26:12,976:INFO: statsmodels: 0.14.4 +2024-12-04 11:26:12,976:INFO: sktime: 0.26.0 +2024-12-04 11:26:12,976:INFO: tbats: 1.1.3 +2024-12-04 11:26:12,976:INFO: pmdarima: 2.0.4 +2024-12-04 11:26:12,976:INFO: psutil: 6.1.0 +2024-12-04 11:26:12,976:INFO: markupsafe: 3.0.2 +2024-12-04 11:26:12,976:INFO: pickle5: Not installed +2024-12-04 11:26:12,976:INFO: cloudpickle: 3.1.0 +2024-12-04 11:26:12,976:INFO: deprecation: 2.1.0 +2024-12-04 11:26:12,976:INFO: xxhash: 3.5.0 +2024-12-04 11:26:12,976:INFO: wurlitzer: Not installed +2024-12-04 11:26:12,976:INFO:PyCaret optional dependencies: +2024-12-04 11:26:13,077:INFO: shap: Not installed +2024-12-04 11:26:13,077:INFO: interpret: Not installed +2024-12-04 11:26:13,077:INFO: umap: Not installed +2024-12-04 11:26:13,078:INFO: ydata_profiling: Not installed +2024-12-04 11:26:13,078:INFO: explainerdashboard: Not installed +2024-12-04 11:26:13,078:INFO: autoviz: Not installed +2024-12-04 11:26:13,078:INFO: fairlearn: Not installed +2024-12-04 11:26:13,078:INFO: deepchecks: Not installed +2024-12-04 11:26:13,078:INFO: xgboost: Not installed +2024-12-04 11:26:13,078:INFO: catboost: Not installed +2024-12-04 11:26:13,078:INFO: kmodes: Not installed +2024-12-04 11:26:13,078:INFO: mlxtend: Not installed +2024-12-04 11:26:13,078:INFO: statsforecast: Not installed +2024-12-04 11:26:13,078:INFO: tune_sklearn: Not installed +2024-12-04 11:26:13,078:INFO: ray: Not installed +2024-12-04 11:26:13,078:INFO: hyperopt: Not installed +2024-12-04 11:26:13,078:INFO: optuna: Not installed +2024-12-04 11:26:13,078:INFO: skopt: Not installed +2024-12-04 11:26:13,078:INFO: mlflow: 2.18.0 +2024-12-04 11:26:13,078:INFO: gradio: Not installed +2024-12-04 11:26:13,078:INFO: fastapi: Not installed +2024-12-04 11:26:13,078:INFO: uvicorn: 0.32.1 +2024-12-04 11:26:13,078:INFO: m2cgen: Not installed +2024-12-04 11:26:13,078:INFO: evidently: Not installed +2024-12-04 11:26:13,078:INFO: fugue: Not installed +2024-12-04 11:26:13,078:INFO: streamlit: 1.40.1 +2024-12-04 11:26:13,078:INFO: prophet: Not installed +2024-12-04 11:26:13,078:INFO:None +2024-12-04 11:26:13,079:INFO:Set up data. +2024-12-04 11:26:13,085:INFO:Set up folding strategy. +2024-12-04 11:26:13,085:INFO:Set up train/test split. +2024-12-04 11:26:13,095:INFO:Set up index. +2024-12-04 11:26:13,095:INFO:Assigning column types. +2024-12-04 11:26:13,099:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 11:26:13,099:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,105:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,109:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,163:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,203:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,203:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,203:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,203:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,207:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,213:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,266:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,309:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,309:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,309:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,309:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 11:26:13,315:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,318:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,379:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,421:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,421:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,421:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,427:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,431:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,485:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,533:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,535:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,535:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,535:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 11:26:13,547:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,602:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,646:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,647:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,647:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,655:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,708:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,746:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,746:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,746:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,749:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 11:26:13,809:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,849:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,849:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,853:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,913:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,953:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:26:13,953:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,953:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:13,953:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 11:26:14,013:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:14,053:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,053:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,112:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:26:14,157:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,157:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,157:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 11:26:14,257:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,257:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,357:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,357:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,370:INFO:Preparing preprocessing pipeline... +2024-12-04 11:26:14,370:INFO:Set up target transformation. +2024-12-04 11:26:14,370:INFO:Set up simple imputation. +2024-12-04 11:26:14,373:INFO:Set up encoding of categorical features. +2024-12-04 11:26:14,373:INFO:Set up column transformation. +2024-12-04 11:26:14,373:INFO:Set up feature normalization. +2024-12-04 11:26:14,513:INFO:Finished creating preprocessing pipeline. +2024-12-04 11:26:14,527:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranch... + TransformerWrapper(include=['nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 11:26:14,528:INFO:Creating final display dataframe. +2024-12-04 11:26:14,739:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 14) +4 Transformed data shape (5688, 13) +5 Transformed train set shape (3981, 13) +6 Transformed test set shape (1707, 13) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 1 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Transform target True +22 Transform target method yeo-johnson +23 Fold Generator KFold +24 Fold Number 10 +25 CPU Jobs -1 +26 Use GPU False +27 Log Experiment MlflowLogger +28 Experiment Name reg-default-name +29 USI 47e4 +2024-12-04 11:26:14,865:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,865:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,974:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,974:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:26:14,974:INFO:Logging experiment in loggers +2024-12-04 11:27:21,897:INFO:PyCaret RegressionExperiment +2024-12-04 11:27:21,897:INFO:Logging name: reg-default-name +2024-12-04 11:27:21,897:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:27:21,897:INFO:version 3.3.2 +2024-12-04 11:27:21,897:INFO:Initializing setup() +2024-12-04 11:27:21,897:INFO:self.USI: 040b +2024-12-04 11:27:21,898:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:27:21,898:INFO:Checking environment +2024-12-04 11:27:21,898:INFO:python_version: 3.11.9 +2024-12-04 11:27:21,898:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:27:21,898:INFO:machine: AMD64 +2024-12-04 11:27:21,898:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:27:21,904:INFO:Memory: svmem(total=17007292416, available=1645318144, percent=90.3, used=15361974272, free=1645318144) +2024-12-04 11:27:21,904:INFO:Physical Core: 4 +2024-12-04 11:27:21,904:INFO:Logical Core: 8 +2024-12-04 11:27:21,904:INFO:Checking libraries +2024-12-04 11:27:21,904:INFO:System: +2024-12-04 11:27:21,905:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:27:21,905:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:27:21,905:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:27:21,905:INFO:PyCaret required dependencies: +2024-12-04 11:27:21,905:INFO: pip: 24.3.1 +2024-12-04 11:27:21,905:INFO: setuptools: 65.5.0 +2024-12-04 11:27:21,905:INFO: pycaret: 3.3.2 +2024-12-04 11:27:21,905:INFO: IPython: 8.12.3 +2024-12-04 11:27:21,905:INFO: ipywidgets: 8.1.5 +2024-12-04 11:27:21,905:INFO: tqdm: 4.67.1 +2024-12-04 11:27:21,905:INFO: numpy: 1.26.4 +2024-12-04 11:27:21,905:INFO: pandas: 2.1.4 +2024-12-04 11:27:21,905:INFO: jinja2: 3.1.4 +2024-12-04 11:27:21,905:INFO: scipy: 1.11.4 +2024-12-04 11:27:21,905:INFO: joblib: 1.3.2 +2024-12-04 11:27:21,905:INFO: sklearn: 1.4.2 +2024-12-04 11:27:21,905:INFO: pyod: 2.0.2 +2024-12-04 11:27:21,905:INFO: imblearn: 0.12.4 +2024-12-04 11:27:21,905:INFO: category_encoders: 2.6.4 +2024-12-04 11:27:21,905:INFO: lightgbm: 4.5.0 +2024-12-04 11:27:21,905:INFO: numba: 0.60.0 +2024-12-04 11:27:21,905:INFO: requests: 2.32.3 +2024-12-04 11:27:21,905:INFO: matplotlib: 3.7.5 +2024-12-04 11:27:21,905:INFO: scikitplot: 0.3.7 +2024-12-04 11:27:21,905:INFO: yellowbrick: 1.5 +2024-12-04 11:27:21,905:INFO: plotly: 5.24.1 +2024-12-04 11:27:21,906:INFO: plotly-resampler: Not installed +2024-12-04 11:27:21,906:INFO: kaleido: 0.2.1 +2024-12-04 11:27:21,906:INFO: schemdraw: 0.15 +2024-12-04 11:27:21,906:INFO: statsmodels: 0.14.4 +2024-12-04 11:27:21,906:INFO: sktime: 0.26.0 +2024-12-04 11:27:21,906:INFO: tbats: 1.1.3 +2024-12-04 11:27:21,906:INFO: pmdarima: 2.0.4 +2024-12-04 11:27:21,906:INFO: psutil: 6.1.0 +2024-12-04 11:27:21,906:INFO: markupsafe: 3.0.2 +2024-12-04 11:27:21,906:INFO: pickle5: Not installed +2024-12-04 11:27:21,906:INFO: cloudpickle: 3.1.0 +2024-12-04 11:27:21,906:INFO: deprecation: 2.1.0 +2024-12-04 11:27:21,906:INFO: xxhash: 3.5.0 +2024-12-04 11:27:21,906:INFO: wurlitzer: Not installed +2024-12-04 11:27:21,906:INFO:PyCaret optional dependencies: +2024-12-04 11:27:21,906:INFO: shap: Not installed +2024-12-04 11:27:21,906:INFO: interpret: Not installed +2024-12-04 11:27:21,906:INFO: umap: Not installed +2024-12-04 11:27:21,906:INFO: ydata_profiling: Not installed +2024-12-04 11:27:21,906:INFO: explainerdashboard: Not installed +2024-12-04 11:27:21,906:INFO: autoviz: Not installed +2024-12-04 11:27:21,906:INFO: fairlearn: Not installed +2024-12-04 11:27:21,906:INFO: deepchecks: Not installed +2024-12-04 11:27:21,907:INFO: xgboost: Not installed +2024-12-04 11:27:21,907:INFO: catboost: Not installed +2024-12-04 11:27:21,907:INFO: kmodes: Not installed +2024-12-04 11:27:21,907:INFO: mlxtend: Not installed +2024-12-04 11:27:21,907:INFO: statsforecast: Not installed +2024-12-04 11:27:21,907:INFO: tune_sklearn: Not installed +2024-12-04 11:27:21,907:INFO: ray: Not installed +2024-12-04 11:27:21,907:INFO: hyperopt: Not installed +2024-12-04 11:27:21,907:INFO: optuna: Not installed +2024-12-04 11:27:21,907:INFO: skopt: Not installed +2024-12-04 11:27:21,907:INFO: mlflow: 2.18.0 +2024-12-04 11:27:21,907:INFO: gradio: Not installed +2024-12-04 11:27:21,907:INFO: fastapi: Not installed +2024-12-04 11:27:21,907:INFO: uvicorn: 0.32.1 +2024-12-04 11:27:21,907:INFO: m2cgen: Not installed +2024-12-04 11:27:21,907:INFO: evidently: Not installed +2024-12-04 11:27:21,907:INFO: fugue: Not installed +2024-12-04 11:27:21,907:INFO: streamlit: 1.40.1 +2024-12-04 11:27:21,907:INFO: prophet: Not installed +2024-12-04 11:27:21,907:INFO:None +2024-12-04 11:27:21,907:INFO:Set up data. +2024-12-04 11:27:21,916:INFO:Set up folding strategy. +2024-12-04 11:27:21,916:INFO:Set up train/test split. +2024-12-04 11:27:21,922:INFO:Set up index. +2024-12-04 11:27:21,923:INFO:Assigning column types. +2024-12-04 11:27:21,927:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 11:27:22,031:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,031:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,130:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,131:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,131:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 11:27:22,232:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,233:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,330:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,330:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,330:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 11:27:22,426:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,426:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,526:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,526:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,526:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 11:27:22,623:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,623:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,724:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,724:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,724:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 11:27:22,820:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,823:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,920:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,920:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:22,920:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 11:27:23,024:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,024:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,120:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,120:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,131:INFO:Preparing preprocessing pipeline... +2024-12-04 11:27:23,131:INFO:Set up simple imputation. +2024-12-04 11:27:23,131:INFO:Set up encoding of categorical features. +2024-12-04 11:27:23,131:INFO:Set up column transformation. +2024-12-04 11:27:23,131:INFO:Set up feature normalization. +2024-12-04 11:27:23,247:INFO:Finished creating preprocessing pipeline. +2024-12-04 11:27:23,255:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + TransformerWrapper(include=['nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 11:27:23,257:INFO:Creating final display dataframe. +2024-12-04 11:27:23,424:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 14) +4 Transformed data shape (5688, 13) +5 Transformed train set shape (3981, 13) +6 Transformed test set shape (1707, 13) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 1 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 10 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI 040b +2024-12-04 11:27:23,547:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,552:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,647:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,647:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:27:23,653:INFO:setup() successfully completed in 1.76s............... +2024-12-04 11:29:46,513:INFO:Initializing compare_models() +2024-12-04 11:29:46,513:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 11:29:46,513:INFO:Checking exceptions +2024-12-04 11:29:46,515:INFO:Preparing display monitor +2024-12-04 11:29:46,543:INFO:Initializing Linear Regression +2024-12-04 11:29:46,543:INFO:Total runtime is 0.0 minutes +2024-12-04 11:29:46,547:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:46,547:INFO:Initializing create_model() +2024-12-04 11:29:46,547:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:46,547:INFO:Checking exceptions +2024-12-04 11:29:46,547:INFO:Importing libraries +2024-12-04 11:29:46,547:INFO:Copying training dataset +2024-12-04 11:29:46,563:INFO:Defining folds +2024-12-04 11:29:46,563:INFO:Declaring metric variables +2024-12-04 11:29:46,568:INFO:Importing untrained model +2024-12-04 11:29:46,570:INFO:Linear Regression Imported successfully +2024-12-04 11:29:46,580:INFO:Starting cross validation +2024-12-04 11:29:46,589:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:52,016:INFO:Calculating mean and std +2024-12-04 11:29:52,017:INFO:Creating metrics dataframe +2024-12-04 11:29:52,020:INFO:Uploading results into container +2024-12-04 11:29:52,021:INFO:Uploading model into container now +2024-12-04 11:29:52,021:INFO:_master_model_container: 1 +2024-12-04 11:29:52,021:INFO:_display_container: 2 +2024-12-04 11:29:52,021:INFO:LinearRegression(n_jobs=-1) +2024-12-04 11:29:52,021:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:52,132:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:52,132:INFO:Creating metrics dataframe +2024-12-04 11:29:52,138:INFO:Initializing Lasso Regression +2024-12-04 11:29:52,138:INFO:Total runtime is 0.09325406948725383 minutes +2024-12-04 11:29:52,142:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:52,142:INFO:Initializing create_model() +2024-12-04 11:29:52,142:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:52,142:INFO:Checking exceptions +2024-12-04 11:29:52,142:INFO:Importing libraries +2024-12-04 11:29:52,142:INFO:Copying training dataset +2024-12-04 11:29:52,148:INFO:Defining folds +2024-12-04 11:29:52,148:INFO:Declaring metric variables +2024-12-04 11:29:52,151:INFO:Importing untrained model +2024-12-04 11:29:52,155:INFO:Lasso Regression Imported successfully +2024-12-04 11:29:52,162:INFO:Starting cross validation +2024-12-04 11:29:52,163:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:52,517:INFO:Calculating mean and std +2024-12-04 11:29:52,519:INFO:Creating metrics dataframe +2024-12-04 11:29:52,520:INFO:Uploading results into container +2024-12-04 11:29:52,521:INFO:Uploading model into container now +2024-12-04 11:29:52,522:INFO:_master_model_container: 2 +2024-12-04 11:29:52,522:INFO:_display_container: 2 +2024-12-04 11:29:52,523:INFO:Lasso(random_state=123) +2024-12-04 11:29:52,523:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:52,629:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:52,629:INFO:Creating metrics dataframe +2024-12-04 11:29:52,636:INFO:Initializing Ridge Regression +2024-12-04 11:29:52,636:INFO:Total runtime is 0.10155356725056966 minutes +2024-12-04 11:29:52,638:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:52,639:INFO:Initializing create_model() +2024-12-04 11:29:52,639:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:52,639:INFO:Checking exceptions +2024-12-04 11:29:52,639:INFO:Importing libraries +2024-12-04 11:29:52,639:INFO:Copying training dataset +2024-12-04 11:29:52,646:INFO:Defining folds +2024-12-04 11:29:52,646:INFO:Declaring metric variables +2024-12-04 11:29:52,650:INFO:Importing untrained model +2024-12-04 11:29:52,653:INFO:Ridge Regression Imported successfully +2024-12-04 11:29:52,660:INFO:Starting cross validation +2024-12-04 11:29:52,661:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:53,011:INFO:Calculating mean and std +2024-12-04 11:29:53,012:INFO:Creating metrics dataframe +2024-12-04 11:29:53,014:INFO:Uploading results into container +2024-12-04 11:29:53,015:INFO:Uploading model into container now +2024-12-04 11:29:53,015:INFO:_master_model_container: 3 +2024-12-04 11:29:53,015:INFO:_display_container: 2 +2024-12-04 11:29:53,016:INFO:Ridge(random_state=123) +2024-12-04 11:29:53,016:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:53,146:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:53,147:INFO:Creating metrics dataframe +2024-12-04 11:29:53,153:INFO:Initializing Elastic Net +2024-12-04 11:29:53,153:INFO:Total runtime is 0.1101692795753479 minutes +2024-12-04 11:29:53,157:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:53,157:INFO:Initializing create_model() +2024-12-04 11:29:53,157:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:53,157:INFO:Checking exceptions +2024-12-04 11:29:53,157:INFO:Importing libraries +2024-12-04 11:29:53,159:INFO:Copying training dataset +2024-12-04 11:29:53,165:INFO:Defining folds +2024-12-04 11:29:53,165:INFO:Declaring metric variables +2024-12-04 11:29:53,168:INFO:Importing untrained model +2024-12-04 11:29:53,172:INFO:Elastic Net Imported successfully +2024-12-04 11:29:53,179:INFO:Starting cross validation +2024-12-04 11:29:53,180:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:53,671:INFO:Calculating mean and std +2024-12-04 11:29:53,673:INFO:Creating metrics dataframe +2024-12-04 11:29:53,675:INFO:Uploading results into container +2024-12-04 11:29:53,676:INFO:Uploading model into container now +2024-12-04 11:29:53,676:INFO:_master_model_container: 4 +2024-12-04 11:29:53,676:INFO:_display_container: 2 +2024-12-04 11:29:53,676:INFO:ElasticNet(random_state=123) +2024-12-04 11:29:53,677:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:53,785:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:53,786:INFO:Creating metrics dataframe +2024-12-04 11:29:53,791:INFO:Initializing Least Angle Regression +2024-12-04 11:29:53,791:INFO:Total runtime is 0.12081146240234375 minutes +2024-12-04 11:29:53,795:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:53,795:INFO:Initializing create_model() +2024-12-04 11:29:53,795:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:53,795:INFO:Checking exceptions +2024-12-04 11:29:53,795:INFO:Importing libraries +2024-12-04 11:29:53,796:INFO:Copying training dataset +2024-12-04 11:29:53,801:INFO:Defining folds +2024-12-04 11:29:53,801:INFO:Declaring metric variables +2024-12-04 11:29:53,805:INFO:Importing untrained model +2024-12-04 11:29:53,809:INFO:Least Angle Regression Imported successfully +2024-12-04 11:29:53,816:INFO:Starting cross validation +2024-12-04 11:29:53,817:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:54,223:INFO:Calculating mean and std +2024-12-04 11:29:54,224:INFO:Creating metrics dataframe +2024-12-04 11:29:54,226:INFO:Uploading results into container +2024-12-04 11:29:54,227:INFO:Uploading model into container now +2024-12-04 11:29:54,228:INFO:_master_model_container: 5 +2024-12-04 11:29:54,228:INFO:_display_container: 2 +2024-12-04 11:29:54,228:INFO:Lars(random_state=123) +2024-12-04 11:29:54,228:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:54,333:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:54,333:INFO:Creating metrics dataframe +2024-12-04 11:29:54,340:INFO:Initializing Lasso Least Angle Regression +2024-12-04 11:29:54,340:INFO:Total runtime is 0.12995757659276327 minutes +2024-12-04 11:29:54,343:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:54,344:INFO:Initializing create_model() +2024-12-04 11:29:54,344:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:54,344:INFO:Checking exceptions +2024-12-04 11:29:54,344:INFO:Importing libraries +2024-12-04 11:29:54,344:INFO:Copying training dataset +2024-12-04 11:29:54,350:INFO:Defining folds +2024-12-04 11:29:54,350:INFO:Declaring metric variables +2024-12-04 11:29:54,353:INFO:Importing untrained model +2024-12-04 11:29:54,358:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 11:29:54,364:INFO:Starting cross validation +2024-12-04 11:29:54,365:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:54,751:INFO:Calculating mean and std +2024-12-04 11:29:54,751:INFO:Creating metrics dataframe +2024-12-04 11:29:54,754:INFO:Uploading results into container +2024-12-04 11:29:54,754:INFO:Uploading model into container now +2024-12-04 11:29:54,755:INFO:_master_model_container: 6 +2024-12-04 11:29:54,755:INFO:_display_container: 2 +2024-12-04 11:29:54,756:INFO:LassoLars(random_state=123) +2024-12-04 11:29:54,756:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:54,854:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:54,854:INFO:Creating metrics dataframe +2024-12-04 11:29:54,861:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 11:29:54,861:INFO:Total runtime is 0.13864800930023194 minutes +2024-12-04 11:29:54,865:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:54,866:INFO:Initializing create_model() +2024-12-04 11:29:54,866:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:54,866:INFO:Checking exceptions +2024-12-04 11:29:54,866:INFO:Importing libraries +2024-12-04 11:29:54,866:INFO:Copying training dataset +2024-12-04 11:29:54,871:INFO:Defining folds +2024-12-04 11:29:54,871:INFO:Declaring metric variables +2024-12-04 11:29:54,876:INFO:Importing untrained model +2024-12-04 11:29:54,879:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 11:29:54,885:INFO:Starting cross validation +2024-12-04 11:29:54,887:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:55,267:INFO:Calculating mean and std +2024-12-04 11:29:55,267:INFO:Creating metrics dataframe +2024-12-04 11:29:55,268:INFO:Uploading results into container +2024-12-04 11:29:55,270:INFO:Uploading model into container now +2024-12-04 11:29:55,270:INFO:_master_model_container: 7 +2024-12-04 11:29:55,270:INFO:_display_container: 2 +2024-12-04 11:29:55,270:INFO:OrthogonalMatchingPursuit() +2024-12-04 11:29:55,270:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:55,367:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:55,367:INFO:Creating metrics dataframe +2024-12-04 11:29:55,374:INFO:Initializing Bayesian Ridge +2024-12-04 11:29:55,374:INFO:Total runtime is 0.1471958875656128 minutes +2024-12-04 11:29:55,378:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:55,378:INFO:Initializing create_model() +2024-12-04 11:29:55,378:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:55,378:INFO:Checking exceptions +2024-12-04 11:29:55,378:INFO:Importing libraries +2024-12-04 11:29:55,378:INFO:Copying training dataset +2024-12-04 11:29:55,384:INFO:Defining folds +2024-12-04 11:29:55,384:INFO:Declaring metric variables +2024-12-04 11:29:55,389:INFO:Importing untrained model +2024-12-04 11:29:55,393:INFO:Bayesian Ridge Imported successfully +2024-12-04 11:29:55,400:INFO:Starting cross validation +2024-12-04 11:29:55,401:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:55,753:INFO:Calculating mean and std +2024-12-04 11:29:55,754:INFO:Creating metrics dataframe +2024-12-04 11:29:55,756:INFO:Uploading results into container +2024-12-04 11:29:55,756:INFO:Uploading model into container now +2024-12-04 11:29:55,757:INFO:_master_model_container: 8 +2024-12-04 11:29:55,757:INFO:_display_container: 2 +2024-12-04 11:29:55,757:INFO:BayesianRidge() +2024-12-04 11:29:55,757:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:55,855:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:55,857:INFO:Creating metrics dataframe +2024-12-04 11:29:55,863:INFO:Initializing Passive Aggressive Regressor +2024-12-04 11:29:55,863:INFO:Total runtime is 0.1553441047668457 minutes +2024-12-04 11:29:55,866:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:55,866:INFO:Initializing create_model() +2024-12-04 11:29:55,866:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:55,866:INFO:Checking exceptions +2024-12-04 11:29:55,866:INFO:Importing libraries +2024-12-04 11:29:55,866:INFO:Copying training dataset +2024-12-04 11:29:55,872:INFO:Defining folds +2024-12-04 11:29:55,872:INFO:Declaring metric variables +2024-12-04 11:29:55,877:INFO:Importing untrained model +2024-12-04 11:29:55,880:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 11:29:55,886:INFO:Starting cross validation +2024-12-04 11:29:55,887:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:56,328:INFO:Calculating mean and std +2024-12-04 11:29:56,329:INFO:Creating metrics dataframe +2024-12-04 11:29:56,330:INFO:Uploading results into container +2024-12-04 11:29:56,331:INFO:Uploading model into container now +2024-12-04 11:29:56,331:INFO:_master_model_container: 9 +2024-12-04 11:29:56,331:INFO:_display_container: 2 +2024-12-04 11:29:56,331:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 11:29:56,332:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:56,429:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:56,429:INFO:Creating metrics dataframe +2024-12-04 11:29:56,435:INFO:Initializing Huber Regressor +2024-12-04 11:29:56,435:INFO:Total runtime is 0.16487501064936322 minutes +2024-12-04 11:29:56,438:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:56,438:INFO:Initializing create_model() +2024-12-04 11:29:56,438:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:56,438:INFO:Checking exceptions +2024-12-04 11:29:56,439:INFO:Importing libraries +2024-12-04 11:29:56,439:INFO:Copying training dataset +2024-12-04 11:29:56,444:INFO:Defining folds +2024-12-04 11:29:56,444:INFO:Declaring metric variables +2024-12-04 11:29:56,447:INFO:Importing untrained model +2024-12-04 11:29:56,450:INFO:Huber Regressor Imported successfully +2024-12-04 11:29:56,462:INFO:Starting cross validation +2024-12-04 11:29:56,463:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:56,810:INFO:Calculating mean and std +2024-12-04 11:29:56,811:INFO:Creating metrics dataframe +2024-12-04 11:29:56,813:INFO:Uploading results into container +2024-12-04 11:29:56,814:INFO:Uploading model into container now +2024-12-04 11:29:56,814:INFO:_master_model_container: 10 +2024-12-04 11:29:56,814:INFO:_display_container: 2 +2024-12-04 11:29:56,814:INFO:HuberRegressor() +2024-12-04 11:29:56,815:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:56,951:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:56,951:INFO:Creating metrics dataframe +2024-12-04 11:29:56,964:INFO:Initializing K Neighbors Regressor +2024-12-04 11:29:56,964:INFO:Total runtime is 0.17368404865264894 minutes +2024-12-04 11:29:56,967:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:56,967:INFO:Initializing create_model() +2024-12-04 11:29:56,967:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:56,967:INFO:Checking exceptions +2024-12-04 11:29:56,967:INFO:Importing libraries +2024-12-04 11:29:56,967:INFO:Copying training dataset +2024-12-04 11:29:56,974:INFO:Defining folds +2024-12-04 11:29:56,974:INFO:Declaring metric variables +2024-12-04 11:29:56,978:INFO:Importing untrained model +2024-12-04 11:29:56,982:INFO:K Neighbors Regressor Imported successfully +2024-12-04 11:29:56,987:INFO:Starting cross validation +2024-12-04 11:29:56,988:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:57,385:INFO:Calculating mean and std +2024-12-04 11:29:57,387:INFO:Creating metrics dataframe +2024-12-04 11:29:57,389:INFO:Uploading results into container +2024-12-04 11:29:57,392:INFO:Uploading model into container now +2024-12-04 11:29:57,393:INFO:_master_model_container: 11 +2024-12-04 11:29:57,393:INFO:_display_container: 2 +2024-12-04 11:29:57,393:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 11:29:57,393:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:57,546:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:57,546:INFO:Creating metrics dataframe +2024-12-04 11:29:57,554:INFO:Initializing Decision Tree Regressor +2024-12-04 11:29:57,554:INFO:Total runtime is 0.18351951440175376 minutes +2024-12-04 11:29:57,557:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:57,557:INFO:Initializing create_model() +2024-12-04 11:29:57,557:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:57,557:INFO:Checking exceptions +2024-12-04 11:29:57,557:INFO:Importing libraries +2024-12-04 11:29:57,557:INFO:Copying training dataset +2024-12-04 11:29:57,563:INFO:Defining folds +2024-12-04 11:29:57,563:INFO:Declaring metric variables +2024-12-04 11:29:57,566:INFO:Importing untrained model +2024-12-04 11:29:57,569:INFO:Decision Tree Regressor Imported successfully +2024-12-04 11:29:57,576:INFO:Starting cross validation +2024-12-04 11:29:57,577:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:29:57,986:INFO:Calculating mean and std +2024-12-04 11:29:57,987:INFO:Creating metrics dataframe +2024-12-04 11:29:57,988:INFO:Uploading results into container +2024-12-04 11:29:57,990:INFO:Uploading model into container now +2024-12-04 11:29:57,991:INFO:_master_model_container: 12 +2024-12-04 11:29:57,991:INFO:_display_container: 2 +2024-12-04 11:29:57,991:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 11:29:57,991:INFO:create_model() successfully completed...................................... +2024-12-04 11:29:58,102:INFO:SubProcess create_model() end ================================== +2024-12-04 11:29:58,102:INFO:Creating metrics dataframe +2024-12-04 11:29:58,111:INFO:Initializing Random Forest Regressor +2024-12-04 11:29:58,111:INFO:Total runtime is 0.1928147912025452 minutes +2024-12-04 11:29:58,115:INFO:SubProcess create_model() called ================================== +2024-12-04 11:29:58,115:INFO:Initializing create_model() +2024-12-04 11:29:58,115:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:29:58,115:INFO:Checking exceptions +2024-12-04 11:29:58,115:INFO:Importing libraries +2024-12-04 11:29:58,115:INFO:Copying training dataset +2024-12-04 11:29:58,121:INFO:Defining folds +2024-12-04 11:29:58,121:INFO:Declaring metric variables +2024-12-04 11:29:58,126:INFO:Importing untrained model +2024-12-04 11:29:58,129:INFO:Random Forest Regressor Imported successfully +2024-12-04 11:29:58,137:INFO:Starting cross validation +2024-12-04 11:29:58,138:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:03,966:INFO:Calculating mean and std +2024-12-04 11:30:03,968:INFO:Creating metrics dataframe +2024-12-04 11:30:03,973:INFO:Uploading results into container +2024-12-04 11:30:03,973:INFO:Uploading model into container now +2024-12-04 11:30:03,974:INFO:_master_model_container: 13 +2024-12-04 11:30:03,974:INFO:_display_container: 2 +2024-12-04 11:30:03,975:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:30:03,975:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:04,114:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:04,114:INFO:Creating metrics dataframe +2024-12-04 11:30:04,123:INFO:Initializing Extra Trees Regressor +2024-12-04 11:30:04,123:INFO:Total runtime is 0.2930015246073405 minutes +2024-12-04 11:30:04,127:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:04,127:INFO:Initializing create_model() +2024-12-04 11:30:04,128:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:04,128:INFO:Checking exceptions +2024-12-04 11:30:04,128:INFO:Importing libraries +2024-12-04 11:30:04,128:INFO:Copying training dataset +2024-12-04 11:30:04,134:INFO:Defining folds +2024-12-04 11:30:04,134:INFO:Declaring metric variables +2024-12-04 11:30:04,138:INFO:Importing untrained model +2024-12-04 11:30:04,141:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:30:04,150:INFO:Starting cross validation +2024-12-04 11:30:04,151:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:07,115:INFO:Calculating mean and std +2024-12-04 11:30:07,115:INFO:Creating metrics dataframe +2024-12-04 11:30:07,115:INFO:Uploading results into container +2024-12-04 11:30:07,118:INFO:Uploading model into container now +2024-12-04 11:30:07,118:INFO:_master_model_container: 14 +2024-12-04 11:30:07,118:INFO:_display_container: 2 +2024-12-04 11:30:07,118:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:30:07,120:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:07,228:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:07,228:INFO:Creating metrics dataframe +2024-12-04 11:30:07,237:INFO:Initializing AdaBoost Regressor +2024-12-04 11:30:07,237:INFO:Total runtime is 0.3449129144350688 minutes +2024-12-04 11:30:07,241:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:07,242:INFO:Initializing create_model() +2024-12-04 11:30:07,242:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:07,242:INFO:Checking exceptions +2024-12-04 11:30:07,242:INFO:Importing libraries +2024-12-04 11:30:07,242:INFO:Copying training dataset +2024-12-04 11:30:07,248:INFO:Defining folds +2024-12-04 11:30:07,248:INFO:Declaring metric variables +2024-12-04 11:30:07,250:INFO:Importing untrained model +2024-12-04 11:30:07,253:INFO:AdaBoost Regressor Imported successfully +2024-12-04 11:30:07,259:INFO:Starting cross validation +2024-12-04 11:30:07,262:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:08,060:INFO:Calculating mean and std +2024-12-04 11:30:08,060:INFO:Creating metrics dataframe +2024-12-04 11:30:08,062:INFO:Uploading results into container +2024-12-04 11:30:08,062:INFO:Uploading model into container now +2024-12-04 11:30:08,063:INFO:_master_model_container: 15 +2024-12-04 11:30:08,063:INFO:_display_container: 2 +2024-12-04 11:30:08,063:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 11:30:08,063:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:08,155:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:08,155:INFO:Creating metrics dataframe +2024-12-04 11:30:08,162:INFO:Initializing Gradient Boosting Regressor +2024-12-04 11:30:08,163:INFO:Total runtime is 0.36034050782521565 minutes +2024-12-04 11:30:08,164:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:08,164:INFO:Initializing create_model() +2024-12-04 11:30:08,164:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:08,164:INFO:Checking exceptions +2024-12-04 11:30:08,164:INFO:Importing libraries +2024-12-04 11:30:08,164:INFO:Copying training dataset +2024-12-04 11:30:08,170:INFO:Defining folds +2024-12-04 11:30:08,170:INFO:Declaring metric variables +2024-12-04 11:30:08,175:INFO:Importing untrained model +2024-12-04 11:30:08,178:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 11:30:08,181:INFO:Starting cross validation +2024-12-04 11:30:08,184:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:09,995:INFO:Calculating mean and std +2024-12-04 11:30:09,995:INFO:Creating metrics dataframe +2024-12-04 11:30:09,995:INFO:Uploading results into container +2024-12-04 11:30:09,995:INFO:Uploading model into container now +2024-12-04 11:30:09,995:INFO:_master_model_container: 16 +2024-12-04 11:30:09,995:INFO:_display_container: 2 +2024-12-04 11:30:09,999:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 11:30:09,999:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:10,089:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:10,089:INFO:Creating metrics dataframe +2024-12-04 11:30:10,097:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 11:30:10,097:INFO:Total runtime is 0.3925683856010437 minutes +2024-12-04 11:30:10,097:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:10,097:INFO:Initializing create_model() +2024-12-04 11:30:10,097:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:10,097:INFO:Checking exceptions +2024-12-04 11:30:10,097:INFO:Importing libraries +2024-12-04 11:30:10,097:INFO:Copying training dataset +2024-12-04 11:30:10,107:INFO:Defining folds +2024-12-04 11:30:10,107:INFO:Declaring metric variables +2024-12-04 11:30:10,110:INFO:Importing untrained model +2024-12-04 11:30:10,113:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 11:30:10,120:INFO:Starting cross validation +2024-12-04 11:30:10,120:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:11,098:INFO:Calculating mean and std +2024-12-04 11:30:11,098:INFO:Creating metrics dataframe +2024-12-04 11:30:11,101:INFO:Uploading results into container +2024-12-04 11:30:11,102:INFO:Uploading model into container now +2024-12-04 11:30:11,103:INFO:_master_model_container: 17 +2024-12-04 11:30:11,103:INFO:_display_container: 2 +2024-12-04 11:30:11,103:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:30:11,103:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:11,222:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:11,223:INFO:Creating metrics dataframe +2024-12-04 11:30:11,230:INFO:Initializing Dummy Regressor +2024-12-04 11:30:11,230:INFO:Total runtime is 0.4114638566970825 minutes +2024-12-04 11:30:11,234:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:11,234:INFO:Initializing create_model() +2024-12-04 11:30:11,234:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:11,234:INFO:Checking exceptions +2024-12-04 11:30:11,234:INFO:Importing libraries +2024-12-04 11:30:11,234:INFO:Copying training dataset +2024-12-04 11:30:11,242:INFO:Defining folds +2024-12-04 11:30:11,242:INFO:Declaring metric variables +2024-12-04 11:30:11,248:INFO:Importing untrained model +2024-12-04 11:30:11,252:INFO:Dummy Regressor Imported successfully +2024-12-04 11:30:11,269:INFO:Starting cross validation +2024-12-04 11:30:11,270:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:11,562:INFO:Calculating mean and std +2024-12-04 11:30:11,563:INFO:Creating metrics dataframe +2024-12-04 11:30:11,565:INFO:Uploading results into container +2024-12-04 11:30:11,565:INFO:Uploading model into container now +2024-12-04 11:30:11,565:INFO:_master_model_container: 18 +2024-12-04 11:30:11,565:INFO:_display_container: 2 +2024-12-04 11:30:11,565:INFO:DummyRegressor() +2024-12-04 11:30:11,565:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:11,659:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:11,661:INFO:Creating metrics dataframe +2024-12-04 11:30:11,670:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 11:30:11,677:INFO:Initializing create_model() +2024-12-04 11:30:11,677:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:11,677:INFO:Checking exceptions +2024-12-04 11:30:11,677:INFO:Importing libraries +2024-12-04 11:30:11,677:INFO:Copying training dataset +2024-12-04 11:30:11,683:INFO:Defining folds +2024-12-04 11:30:11,683:INFO:Declaring metric variables +2024-12-04 11:30:11,684:INFO:Importing untrained model +2024-12-04 11:30:11,684:INFO:Declaring custom model +2024-12-04 11:30:11,684:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:30:11,684:INFO:Cross validation set to False +2024-12-04 11:30:11,684:INFO:Fitting Model +2024-12-04 11:30:12,068:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:30:12,068:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:12,190:INFO:_master_model_container: 18 +2024-12-04 11:30:12,191:INFO:_display_container: 2 +2024-12-04 11:30:12,191:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:30:12,191:INFO:compare_models() successfully completed...................................... +2024-12-04 11:30:29,908:INFO:PyCaret RegressionExperiment +2024-12-04 11:30:29,908:INFO:Logging name: reg-default-name +2024-12-04 11:30:29,908:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:30:29,908:INFO:version 3.3.2 +2024-12-04 11:30:29,908:INFO:Initializing setup() +2024-12-04 11:30:29,908:INFO:self.USI: f45b +2024-12-04 11:30:29,908:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:30:29,908:INFO:Checking environment +2024-12-04 11:30:29,908:INFO:python_version: 3.11.9 +2024-12-04 11:30:29,908:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:30:29,908:INFO:machine: AMD64 +2024-12-04 11:30:29,908:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:30:29,914:INFO:Memory: svmem(total=17007292416, available=858865664, percent=95.0, used=16148426752, free=858865664) +2024-12-04 11:30:29,915:INFO:Physical Core: 4 +2024-12-04 11:30:29,915:INFO:Logical Core: 8 +2024-12-04 11:30:29,915:INFO:Checking libraries +2024-12-04 11:30:29,915:INFO:System: +2024-12-04 11:30:29,915:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:30:29,915:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:30:29,915:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:30:29,915:INFO:PyCaret required dependencies: +2024-12-04 11:30:29,915:INFO: pip: 24.3.1 +2024-12-04 11:30:29,915:INFO: setuptools: 65.5.0 +2024-12-04 11:30:29,915:INFO: pycaret: 3.3.2 +2024-12-04 11:30:29,915:INFO: IPython: 8.12.3 +2024-12-04 11:30:29,915:INFO: ipywidgets: 8.1.5 +2024-12-04 11:30:29,915:INFO: tqdm: 4.67.1 +2024-12-04 11:30:29,916:INFO: numpy: 1.26.4 +2024-12-04 11:30:29,916:INFO: pandas: 2.1.4 +2024-12-04 11:30:29,916:INFO: jinja2: 3.1.4 +2024-12-04 11:30:29,916:INFO: scipy: 1.11.4 +2024-12-04 11:30:29,916:INFO: joblib: 1.3.2 +2024-12-04 11:30:29,916:INFO: sklearn: 1.4.2 +2024-12-04 11:30:29,916:INFO: pyod: 2.0.2 +2024-12-04 11:30:29,916:INFO: imblearn: 0.12.4 +2024-12-04 11:30:29,916:INFO: category_encoders: 2.6.4 +2024-12-04 11:30:29,916:INFO: lightgbm: 4.5.0 +2024-12-04 11:30:29,916:INFO: numba: 0.60.0 +2024-12-04 11:30:29,916:INFO: requests: 2.32.3 +2024-12-04 11:30:29,916:INFO: matplotlib: 3.7.5 +2024-12-04 11:30:29,916:INFO: scikitplot: 0.3.7 +2024-12-04 11:30:29,916:INFO: yellowbrick: 1.5 +2024-12-04 11:30:29,916:INFO: plotly: 5.24.1 +2024-12-04 11:30:29,916:INFO: plotly-resampler: Not installed +2024-12-04 11:30:29,916:INFO: kaleido: 0.2.1 +2024-12-04 11:30:29,916:INFO: schemdraw: 0.15 +2024-12-04 11:30:29,916:INFO: statsmodels: 0.14.4 +2024-12-04 11:30:29,916:INFO: sktime: 0.26.0 +2024-12-04 11:30:29,916:INFO: tbats: 1.1.3 +2024-12-04 11:30:29,916:INFO: pmdarima: 2.0.4 +2024-12-04 11:30:29,916:INFO: psutil: 6.1.0 +2024-12-04 11:30:29,916:INFO: markupsafe: 3.0.2 +2024-12-04 11:30:29,916:INFO: pickle5: Not installed +2024-12-04 11:30:29,916:INFO: cloudpickle: 3.1.0 +2024-12-04 11:30:29,916:INFO: deprecation: 2.1.0 +2024-12-04 11:30:29,916:INFO: xxhash: 3.5.0 +2024-12-04 11:30:29,916:INFO: wurlitzer: Not installed +2024-12-04 11:30:29,916:INFO:PyCaret optional dependencies: +2024-12-04 11:30:29,916:INFO: shap: Not installed +2024-12-04 11:30:29,916:INFO: interpret: Not installed +2024-12-04 11:30:29,916:INFO: umap: Not installed +2024-12-04 11:30:29,916:INFO: ydata_profiling: Not installed +2024-12-04 11:30:29,916:INFO: explainerdashboard: Not installed +2024-12-04 11:30:29,916:INFO: autoviz: Not installed +2024-12-04 11:30:29,916:INFO: fairlearn: Not installed +2024-12-04 11:30:29,916:INFO: deepchecks: Not installed +2024-12-04 11:30:29,916:INFO: xgboost: Not installed +2024-12-04 11:30:29,916:INFO: catboost: Not installed +2024-12-04 11:30:29,916:INFO: kmodes: Not installed +2024-12-04 11:30:29,916:INFO: mlxtend: Not installed +2024-12-04 11:30:29,916:INFO: statsforecast: Not installed +2024-12-04 11:30:29,916:INFO: tune_sklearn: Not installed +2024-12-04 11:30:29,916:INFO: ray: Not installed +2024-12-04 11:30:29,916:INFO: hyperopt: Not installed +2024-12-04 11:30:29,916:INFO: optuna: Not installed +2024-12-04 11:30:29,916:INFO: skopt: Not installed +2024-12-04 11:30:29,916:INFO: mlflow: 2.18.0 +2024-12-04 11:30:29,916:INFO: gradio: Not installed +2024-12-04 11:30:29,916:INFO: fastapi: Not installed +2024-12-04 11:30:29,916:INFO: uvicorn: 0.32.1 +2024-12-04 11:30:29,916:INFO: m2cgen: Not installed +2024-12-04 11:30:29,916:INFO: evidently: Not installed +2024-12-04 11:30:29,916:INFO: fugue: Not installed +2024-12-04 11:30:29,916:INFO: streamlit: 1.40.1 +2024-12-04 11:30:29,916:INFO: prophet: Not installed +2024-12-04 11:30:29,916:INFO:None +2024-12-04 11:30:29,916:INFO:Set up data. +2024-12-04 11:30:29,925:INFO:Set up folding strategy. +2024-12-04 11:30:29,925:INFO:Set up train/test split. +2024-12-04 11:30:29,929:INFO:Set up index. +2024-12-04 11:30:29,929:INFO:Assigning column types. +2024-12-04 11:30:29,930:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 11:30:30,035:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,035:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,139:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,139:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,139:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 11:30:30,237:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,237:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,336:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,336:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,337:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 11:30:30,442:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,442:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,555:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,555:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,555:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 11:30:30,662:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,662:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,763:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,763:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,763:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 11:30:30,869:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,870:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,981:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,981:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:30,981:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 11:30:31,081:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,084:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,183:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,183:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,192:INFO:Preparing preprocessing pipeline... +2024-12-04 11:30:31,192:INFO:Set up simple imputation. +2024-12-04 11:30:31,195:INFO:Set up encoding of categorical features. +2024-12-04 11:30:31,195:INFO:Set up column transformation. +2024-12-04 11:30:31,195:INFO:Set up feature normalization. +2024-12-04 11:30:31,312:INFO:Finished creating preprocessing pipeline. +2024-12-04 11:30:31,319:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + TransformerWrapper(include=['nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 11:30:31,319:INFO:Creating final display dataframe. +2024-12-04 11:30:31,486:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 14) +4 Transformed data shape (5688, 13) +5 Transformed train set shape (3981, 13) +6 Transformed test set shape (1707, 13) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 1 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 5 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI f45b +2024-12-04 11:30:31,605:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,605:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,707:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,709:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:30:31,709:INFO:setup() successfully completed in 1.82s............... +2024-12-04 11:30:58,871:INFO:Initializing compare_models() +2024-12-04 11:30:58,871:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 11:30:58,871:INFO:Checking exceptions +2024-12-04 11:30:58,874:INFO:Preparing display monitor +2024-12-04 11:30:58,894:INFO:Initializing Linear Regression +2024-12-04 11:30:58,894:INFO:Total runtime is 0.0 minutes +2024-12-04 11:30:58,894:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:58,894:INFO:Initializing create_model() +2024-12-04 11:30:58,894:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:58,894:INFO:Checking exceptions +2024-12-04 11:30:58,894:INFO:Importing libraries +2024-12-04 11:30:58,894:INFO:Copying training dataset +2024-12-04 11:30:58,907:INFO:Defining folds +2024-12-04 11:30:58,907:INFO:Declaring metric variables +2024-12-04 11:30:58,910:INFO:Importing untrained model +2024-12-04 11:30:58,913:INFO:Linear Regression Imported successfully +2024-12-04 11:30:58,922:INFO:Starting cross validation +2024-12-04 11:30:58,923:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:59,104:INFO:Calculating mean and std +2024-12-04 11:30:59,104:INFO:Creating metrics dataframe +2024-12-04 11:30:59,106:INFO:Uploading results into container +2024-12-04 11:30:59,106:INFO:Uploading model into container now +2024-12-04 11:30:59,106:INFO:_master_model_container: 19 +2024-12-04 11:30:59,106:INFO:_display_container: 2 +2024-12-04 11:30:59,107:INFO:LinearRegression(n_jobs=-1) +2024-12-04 11:30:59,107:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:59,209:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:59,209:INFO:Creating metrics dataframe +2024-12-04 11:30:59,213:INFO:Initializing Lasso Regression +2024-12-04 11:30:59,213:INFO:Total runtime is 0.005319825808207194 minutes +2024-12-04 11:30:59,213:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:59,213:INFO:Initializing create_model() +2024-12-04 11:30:59,213:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:59,213:INFO:Checking exceptions +2024-12-04 11:30:59,213:INFO:Importing libraries +2024-12-04 11:30:59,218:INFO:Copying training dataset +2024-12-04 11:30:59,224:INFO:Defining folds +2024-12-04 11:30:59,224:INFO:Declaring metric variables +2024-12-04 11:30:59,227:INFO:Importing untrained model +2024-12-04 11:30:59,227:INFO:Lasso Regression Imported successfully +2024-12-04 11:30:59,236:INFO:Starting cross validation +2024-12-04 11:30:59,237:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:59,410:INFO:Calculating mean and std +2024-12-04 11:30:59,410:INFO:Creating metrics dataframe +2024-12-04 11:30:59,413:INFO:Uploading results into container +2024-12-04 11:30:59,413:INFO:Uploading model into container now +2024-12-04 11:30:59,413:INFO:_master_model_container: 20 +2024-12-04 11:30:59,413:INFO:_display_container: 2 +2024-12-04 11:30:59,413:INFO:Lasso(random_state=123) +2024-12-04 11:30:59,413:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:59,526:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:59,526:INFO:Creating metrics dataframe +2024-12-04 11:30:59,530:INFO:Initializing Ridge Regression +2024-12-04 11:30:59,530:INFO:Total runtime is 0.0106047789255778 minutes +2024-12-04 11:30:59,535:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:59,535:INFO:Initializing create_model() +2024-12-04 11:30:59,535:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:59,535:INFO:Checking exceptions +2024-12-04 11:30:59,535:INFO:Importing libraries +2024-12-04 11:30:59,535:INFO:Copying training dataset +2024-12-04 11:30:59,541:INFO:Defining folds +2024-12-04 11:30:59,541:INFO:Declaring metric variables +2024-12-04 11:30:59,544:INFO:Importing untrained model +2024-12-04 11:30:59,544:INFO:Ridge Regression Imported successfully +2024-12-04 11:30:59,556:INFO:Starting cross validation +2024-12-04 11:30:59,557:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:30:59,732:INFO:Calculating mean and std +2024-12-04 11:30:59,732:INFO:Creating metrics dataframe +2024-12-04 11:30:59,733:INFO:Uploading results into container +2024-12-04 11:30:59,734:INFO:Uploading model into container now +2024-12-04 11:30:59,734:INFO:_master_model_container: 21 +2024-12-04 11:30:59,734:INFO:_display_container: 2 +2024-12-04 11:30:59,735:INFO:Ridge(random_state=123) +2024-12-04 11:30:59,735:INFO:create_model() successfully completed...................................... +2024-12-04 11:30:59,831:INFO:SubProcess create_model() end ================================== +2024-12-04 11:30:59,831:INFO:Creating metrics dataframe +2024-12-04 11:30:59,840:INFO:Initializing Elastic Net +2024-12-04 11:30:59,840:INFO:Total runtime is 0.01577188571294149 minutes +2024-12-04 11:30:59,843:INFO:SubProcess create_model() called ================================== +2024-12-04 11:30:59,843:INFO:Initializing create_model() +2024-12-04 11:30:59,843:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:30:59,843:INFO:Checking exceptions +2024-12-04 11:30:59,843:INFO:Importing libraries +2024-12-04 11:30:59,843:INFO:Copying training dataset +2024-12-04 11:30:59,843:INFO:Defining folds +2024-12-04 11:30:59,843:INFO:Declaring metric variables +2024-12-04 11:30:59,843:INFO:Importing untrained model +2024-12-04 11:30:59,855:INFO:Elastic Net Imported successfully +2024-12-04 11:30:59,860:INFO:Starting cross validation +2024-12-04 11:30:59,862:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:00,036:INFO:Calculating mean and std +2024-12-04 11:31:00,037:INFO:Creating metrics dataframe +2024-12-04 11:31:00,039:INFO:Uploading results into container +2024-12-04 11:31:00,040:INFO:Uploading model into container now +2024-12-04 11:31:00,040:INFO:_master_model_container: 22 +2024-12-04 11:31:00,040:INFO:_display_container: 2 +2024-12-04 11:31:00,040:INFO:ElasticNet(random_state=123) +2024-12-04 11:31:00,040:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:00,145:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:00,145:INFO:Creating metrics dataframe +2024-12-04 11:31:00,153:INFO:Initializing Least Angle Regression +2024-12-04 11:31:00,153:INFO:Total runtime is 0.020979682604471844 minutes +2024-12-04 11:31:00,156:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:00,156:INFO:Initializing create_model() +2024-12-04 11:31:00,157:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:00,157:INFO:Checking exceptions +2024-12-04 11:31:00,157:INFO:Importing libraries +2024-12-04 11:31:00,157:INFO:Copying training dataset +2024-12-04 11:31:00,163:INFO:Defining folds +2024-12-04 11:31:00,164:INFO:Declaring metric variables +2024-12-04 11:31:00,164:INFO:Importing untrained model +2024-12-04 11:31:00,171:INFO:Least Angle Regression Imported successfully +2024-12-04 11:31:00,178:INFO:Starting cross validation +2024-12-04 11:31:00,178:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:00,351:INFO:Calculating mean and std +2024-12-04 11:31:00,351:INFO:Creating metrics dataframe +2024-12-04 11:31:00,353:INFO:Uploading results into container +2024-12-04 11:31:00,353:INFO:Uploading model into container now +2024-12-04 11:31:00,354:INFO:_master_model_container: 23 +2024-12-04 11:31:00,354:INFO:_display_container: 2 +2024-12-04 11:31:00,355:INFO:Lars(random_state=123) +2024-12-04 11:31:00,355:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:00,510:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:00,510:INFO:Creating metrics dataframe +2024-12-04 11:31:00,518:INFO:Initializing Lasso Least Angle Regression +2024-12-04 11:31:00,518:INFO:Total runtime is 0.027073868115743003 minutes +2024-12-04 11:31:00,522:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:00,522:INFO:Initializing create_model() +2024-12-04 11:31:00,522:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:00,522:INFO:Checking exceptions +2024-12-04 11:31:00,522:INFO:Importing libraries +2024-12-04 11:31:00,523:INFO:Copying training dataset +2024-12-04 11:31:00,531:INFO:Defining folds +2024-12-04 11:31:00,531:INFO:Declaring metric variables +2024-12-04 11:31:00,531:INFO:Importing untrained model +2024-12-04 11:31:00,537:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 11:31:00,560:INFO:Starting cross validation +2024-12-04 11:31:00,561:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:00,727:INFO:Calculating mean and std +2024-12-04 11:31:00,727:INFO:Creating metrics dataframe +2024-12-04 11:31:00,729:INFO:Uploading results into container +2024-12-04 11:31:00,730:INFO:Uploading model into container now +2024-12-04 11:31:00,730:INFO:_master_model_container: 24 +2024-12-04 11:31:00,730:INFO:_display_container: 2 +2024-12-04 11:31:00,730:INFO:LassoLars(random_state=123) +2024-12-04 11:31:00,730:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:00,831:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:00,831:INFO:Creating metrics dataframe +2024-12-04 11:31:00,841:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 11:31:00,841:INFO:Total runtime is 0.03244260946909587 minutes +2024-12-04 11:31:00,843:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:00,843:INFO:Initializing create_model() +2024-12-04 11:31:00,843:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:00,843:INFO:Checking exceptions +2024-12-04 11:31:00,843:INFO:Importing libraries +2024-12-04 11:31:00,843:INFO:Copying training dataset +2024-12-04 11:31:00,843:INFO:Defining folds +2024-12-04 11:31:00,843:INFO:Declaring metric variables +2024-12-04 11:31:00,855:INFO:Importing untrained model +2024-12-04 11:31:00,859:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 11:31:00,865:INFO:Starting cross validation +2024-12-04 11:31:00,865:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:01,043:INFO:Calculating mean and std +2024-12-04 11:31:01,044:INFO:Creating metrics dataframe +2024-12-04 11:31:01,044:INFO:Uploading results into container +2024-12-04 11:31:01,044:INFO:Uploading model into container now +2024-12-04 11:31:01,044:INFO:_master_model_container: 25 +2024-12-04 11:31:01,044:INFO:_display_container: 2 +2024-12-04 11:31:01,044:INFO:OrthogonalMatchingPursuit() +2024-12-04 11:31:01,044:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:01,157:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:01,157:INFO:Creating metrics dataframe +2024-12-04 11:31:01,165:INFO:Initializing Bayesian Ridge +2024-12-04 11:31:01,165:INFO:Total runtime is 0.03784641424814861 minutes +2024-12-04 11:31:01,165:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:01,168:INFO:Initializing create_model() +2024-12-04 11:31:01,168:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:01,168:INFO:Checking exceptions +2024-12-04 11:31:01,168:INFO:Importing libraries +2024-12-04 11:31:01,168:INFO:Copying training dataset +2024-12-04 11:31:01,175:INFO:Defining folds +2024-12-04 11:31:01,175:INFO:Declaring metric variables +2024-12-04 11:31:01,177:INFO:Importing untrained model +2024-12-04 11:31:01,181:INFO:Bayesian Ridge Imported successfully +2024-12-04 11:31:01,189:INFO:Starting cross validation +2024-12-04 11:31:01,191:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:01,357:INFO:Calculating mean and std +2024-12-04 11:31:01,357:INFO:Creating metrics dataframe +2024-12-04 11:31:01,360:INFO:Uploading results into container +2024-12-04 11:31:01,361:INFO:Uploading model into container now +2024-12-04 11:31:01,361:INFO:_master_model_container: 26 +2024-12-04 11:31:01,361:INFO:_display_container: 2 +2024-12-04 11:31:01,362:INFO:BayesianRidge() +2024-12-04 11:31:01,362:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:01,455:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:01,455:INFO:Creating metrics dataframe +2024-12-04 11:31:01,462:INFO:Initializing Passive Aggressive Regressor +2024-12-04 11:31:01,462:INFO:Total runtime is 0.04280728896458944 minutes +2024-12-04 11:31:01,465:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:01,465:INFO:Initializing create_model() +2024-12-04 11:31:01,465:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:01,465:INFO:Checking exceptions +2024-12-04 11:31:01,465:INFO:Importing libraries +2024-12-04 11:31:01,465:INFO:Copying training dataset +2024-12-04 11:31:01,471:INFO:Defining folds +2024-12-04 11:31:01,471:INFO:Declaring metric variables +2024-12-04 11:31:01,477:INFO:Importing untrained model +2024-12-04 11:31:01,480:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 11:31:01,485:INFO:Starting cross validation +2024-12-04 11:31:01,487:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:01,654:INFO:Calculating mean and std +2024-12-04 11:31:01,655:INFO:Creating metrics dataframe +2024-12-04 11:31:01,657:INFO:Uploading results into container +2024-12-04 11:31:01,658:INFO:Uploading model into container now +2024-12-04 11:31:01,658:INFO:_master_model_container: 27 +2024-12-04 11:31:01,658:INFO:_display_container: 2 +2024-12-04 11:31:01,658:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 11:31:01,658:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:01,755:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:01,755:INFO:Creating metrics dataframe +2024-12-04 11:31:01,763:INFO:Initializing Huber Regressor +2024-12-04 11:31:01,763:INFO:Total runtime is 0.04781271616617839 minutes +2024-12-04 11:31:01,765:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:01,765:INFO:Initializing create_model() +2024-12-04 11:31:01,765:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:01,765:INFO:Checking exceptions +2024-12-04 11:31:01,765:INFO:Importing libraries +2024-12-04 11:31:01,765:INFO:Copying training dataset +2024-12-04 11:31:01,773:INFO:Defining folds +2024-12-04 11:31:01,773:INFO:Declaring metric variables +2024-12-04 11:31:01,777:INFO:Importing untrained model +2024-12-04 11:31:01,780:INFO:Huber Regressor Imported successfully +2024-12-04 11:31:01,785:INFO:Starting cross validation +2024-12-04 11:31:01,788:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:01,985:INFO:Calculating mean and std +2024-12-04 11:31:01,986:INFO:Creating metrics dataframe +2024-12-04 11:31:01,988:INFO:Uploading results into container +2024-12-04 11:31:01,989:INFO:Uploading model into container now +2024-12-04 11:31:01,990:INFO:_master_model_container: 28 +2024-12-04 11:31:01,990:INFO:_display_container: 2 +2024-12-04 11:31:01,990:INFO:HuberRegressor() +2024-12-04 11:31:01,990:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:02,084:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:02,084:INFO:Creating metrics dataframe +2024-12-04 11:31:02,091:INFO:Initializing K Neighbors Regressor +2024-12-04 11:31:02,091:INFO:Total runtime is 0.05328071514765422 minutes +2024-12-04 11:31:02,093:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:02,093:INFO:Initializing create_model() +2024-12-04 11:31:02,093:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:02,093:INFO:Checking exceptions +2024-12-04 11:31:02,093:INFO:Importing libraries +2024-12-04 11:31:02,093:INFO:Copying training dataset +2024-12-04 11:31:02,093:INFO:Defining folds +2024-12-04 11:31:02,093:INFO:Declaring metric variables +2024-12-04 11:31:02,107:INFO:Importing untrained model +2024-12-04 11:31:02,109:INFO:K Neighbors Regressor Imported successfully +2024-12-04 11:31:02,113:INFO:Starting cross validation +2024-12-04 11:31:02,113:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:02,313:INFO:Calculating mean and std +2024-12-04 11:31:02,313:INFO:Creating metrics dataframe +2024-12-04 11:31:02,316:INFO:Uploading results into container +2024-12-04 11:31:02,316:INFO:Uploading model into container now +2024-12-04 11:31:02,316:INFO:_master_model_container: 29 +2024-12-04 11:31:02,316:INFO:_display_container: 2 +2024-12-04 11:31:02,318:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 11:31:02,318:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:02,409:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:02,411:INFO:Creating metrics dataframe +2024-12-04 11:31:02,413:INFO:Initializing Decision Tree Regressor +2024-12-04 11:31:02,413:INFO:Total runtime is 0.05864450931549073 minutes +2024-12-04 11:31:02,420:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:02,421:INFO:Initializing create_model() +2024-12-04 11:31:02,421:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:02,421:INFO:Checking exceptions +2024-12-04 11:31:02,421:INFO:Importing libraries +2024-12-04 11:31:02,421:INFO:Copying training dataset +2024-12-04 11:31:02,427:INFO:Defining folds +2024-12-04 11:31:02,427:INFO:Declaring metric variables +2024-12-04 11:31:02,430:INFO:Importing untrained model +2024-12-04 11:31:02,434:INFO:Decision Tree Regressor Imported successfully +2024-12-04 11:31:02,441:INFO:Starting cross validation +2024-12-04 11:31:02,442:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:02,646:INFO:Calculating mean and std +2024-12-04 11:31:02,646:INFO:Creating metrics dataframe +2024-12-04 11:31:02,649:INFO:Uploading results into container +2024-12-04 11:31:02,649:INFO:Uploading model into container now +2024-12-04 11:31:02,649:INFO:_master_model_container: 30 +2024-12-04 11:31:02,649:INFO:_display_container: 2 +2024-12-04 11:31:02,649:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 11:31:02,649:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:02,743:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:02,743:INFO:Creating metrics dataframe +2024-12-04 11:31:02,743:INFO:Initializing Random Forest Regressor +2024-12-04 11:31:02,743:INFO:Total runtime is 0.064153258005778 minutes +2024-12-04 11:31:02,755:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:02,757:INFO:Initializing create_model() +2024-12-04 11:31:02,757:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:02,757:INFO:Checking exceptions +2024-12-04 11:31:02,757:INFO:Importing libraries +2024-12-04 11:31:02,757:INFO:Copying training dataset +2024-12-04 11:31:02,763:INFO:Defining folds +2024-12-04 11:31:02,763:INFO:Declaring metric variables +2024-12-04 11:31:02,763:INFO:Importing untrained model +2024-12-04 11:31:02,770:INFO:Random Forest Regressor Imported successfully +2024-12-04 11:31:02,776:INFO:Starting cross validation +2024-12-04 11:31:02,778:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:05,095:INFO:Calculating mean and std +2024-12-04 11:31:05,095:INFO:Creating metrics dataframe +2024-12-04 11:31:05,099:INFO:Uploading results into container +2024-12-04 11:31:05,099:INFO:Uploading model into container now +2024-12-04 11:31:05,100:INFO:_master_model_container: 31 +2024-12-04 11:31:05,100:INFO:_display_container: 2 +2024-12-04 11:31:05,101:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:31:05,101:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:05,203:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:05,203:INFO:Creating metrics dataframe +2024-12-04 11:31:05,211:INFO:Initializing Extra Trees Regressor +2024-12-04 11:31:05,211:INFO:Total runtime is 0.1052922487258911 minutes +2024-12-04 11:31:05,213:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:05,213:INFO:Initializing create_model() +2024-12-04 11:31:05,213:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:05,213:INFO:Checking exceptions +2024-12-04 11:31:05,213:INFO:Importing libraries +2024-12-04 11:31:05,213:INFO:Copying training dataset +2024-12-04 11:31:05,220:INFO:Defining folds +2024-12-04 11:31:05,220:INFO:Declaring metric variables +2024-12-04 11:31:05,224:INFO:Importing untrained model +2024-12-04 11:31:05,227:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:31:05,234:INFO:Starting cross validation +2024-12-04 11:31:05,235:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:06,576:INFO:Calculating mean and std +2024-12-04 11:31:06,577:INFO:Creating metrics dataframe +2024-12-04 11:31:06,578:INFO:Uploading results into container +2024-12-04 11:31:06,579:INFO:Uploading model into container now +2024-12-04 11:31:06,579:INFO:_master_model_container: 32 +2024-12-04 11:31:06,579:INFO:_display_container: 2 +2024-12-04 11:31:06,580:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:31:06,580:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:06,674:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:06,674:INFO:Creating metrics dataframe +2024-12-04 11:31:06,681:INFO:Initializing AdaBoost Regressor +2024-12-04 11:31:06,681:INFO:Total runtime is 0.12978024085362752 minutes +2024-12-04 11:31:06,684:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:06,684:INFO:Initializing create_model() +2024-12-04 11:31:06,684:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:06,684:INFO:Checking exceptions +2024-12-04 11:31:06,684:INFO:Importing libraries +2024-12-04 11:31:06,684:INFO:Copying training dataset +2024-12-04 11:31:06,693:INFO:Defining folds +2024-12-04 11:31:06,693:INFO:Declaring metric variables +2024-12-04 11:31:06,696:INFO:Importing untrained model +2024-12-04 11:31:06,696:INFO:AdaBoost Regressor Imported successfully +2024-12-04 11:31:06,706:INFO:Starting cross validation +2024-12-04 11:31:06,707:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:07,109:INFO:Calculating mean and std +2024-12-04 11:31:07,109:INFO:Creating metrics dataframe +2024-12-04 11:31:07,111:INFO:Uploading results into container +2024-12-04 11:31:07,112:INFO:Uploading model into container now +2024-12-04 11:31:07,112:INFO:_master_model_container: 33 +2024-12-04 11:31:07,113:INFO:_display_container: 2 +2024-12-04 11:31:07,113:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 11:31:07,113:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:07,206:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:07,206:INFO:Creating metrics dataframe +2024-12-04 11:31:07,213:INFO:Initializing Gradient Boosting Regressor +2024-12-04 11:31:07,213:INFO:Total runtime is 0.1386497934659322 minutes +2024-12-04 11:31:07,216:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:07,216:INFO:Initializing create_model() +2024-12-04 11:31:07,216:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:07,216:INFO:Checking exceptions +2024-12-04 11:31:07,216:INFO:Importing libraries +2024-12-04 11:31:07,216:INFO:Copying training dataset +2024-12-04 11:31:07,223:INFO:Defining folds +2024-12-04 11:31:07,223:INFO:Declaring metric variables +2024-12-04 11:31:07,227:INFO:Importing untrained model +2024-12-04 11:31:07,231:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 11:31:07,249:INFO:Starting cross validation +2024-12-04 11:31:07,249:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:08,102:INFO:Calculating mean and std +2024-12-04 11:31:08,103:INFO:Creating metrics dataframe +2024-12-04 11:31:08,105:INFO:Uploading results into container +2024-12-04 11:31:08,105:INFO:Uploading model into container now +2024-12-04 11:31:08,105:INFO:_master_model_container: 34 +2024-12-04 11:31:08,105:INFO:_display_container: 2 +2024-12-04 11:31:08,105:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 11:31:08,105:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:08,193:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:08,193:INFO:Creating metrics dataframe +2024-12-04 11:31:08,207:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 11:31:08,207:INFO:Total runtime is 0.15521815220514934 minutes +2024-12-04 11:31:08,209:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:08,209:INFO:Initializing create_model() +2024-12-04 11:31:08,209:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:08,209:INFO:Checking exceptions +2024-12-04 11:31:08,209:INFO:Importing libraries +2024-12-04 11:31:08,209:INFO:Copying training dataset +2024-12-04 11:31:08,213:INFO:Defining folds +2024-12-04 11:31:08,213:INFO:Declaring metric variables +2024-12-04 11:31:08,219:INFO:Importing untrained model +2024-12-04 11:31:08,223:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 11:31:08,230:INFO:Starting cross validation +2024-12-04 11:31:08,230:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:08,709:INFO:Calculating mean and std +2024-12-04 11:31:08,709:INFO:Creating metrics dataframe +2024-12-04 11:31:08,713:INFO:Uploading results into container +2024-12-04 11:31:08,713:INFO:Uploading model into container now +2024-12-04 11:31:08,713:INFO:_master_model_container: 35 +2024-12-04 11:31:08,713:INFO:_display_container: 2 +2024-12-04 11:31:08,713:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:31:08,713:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:08,828:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:08,830:INFO:Creating metrics dataframe +2024-12-04 11:31:08,836:INFO:Initializing Dummy Regressor +2024-12-04 11:31:08,836:INFO:Total runtime is 0.16569668849309285 minutes +2024-12-04 11:31:08,839:INFO:SubProcess create_model() called ================================== +2024-12-04 11:31:08,839:INFO:Initializing create_model() +2024-12-04 11:31:08,839:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:08,839:INFO:Checking exceptions +2024-12-04 11:31:08,839:INFO:Importing libraries +2024-12-04 11:31:08,839:INFO:Copying training dataset +2024-12-04 11:31:08,846:INFO:Defining folds +2024-12-04 11:31:08,846:INFO:Declaring metric variables +2024-12-04 11:31:08,849:INFO:Importing untrained model +2024-12-04 11:31:08,853:INFO:Dummy Regressor Imported successfully +2024-12-04 11:31:08,859:INFO:Starting cross validation +2024-12-04 11:31:08,860:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:31:09,014:INFO:Calculating mean and std +2024-12-04 11:31:09,014:INFO:Creating metrics dataframe +2024-12-04 11:31:09,016:INFO:Uploading results into container +2024-12-04 11:31:09,017:INFO:Uploading model into container now +2024-12-04 11:31:09,017:INFO:_master_model_container: 36 +2024-12-04 11:31:09,017:INFO:_display_container: 2 +2024-12-04 11:31:09,017:INFO:DummyRegressor() +2024-12-04 11:31:09,017:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:09,113:INFO:SubProcess create_model() end ================================== +2024-12-04 11:31:09,113:INFO:Creating metrics dataframe +2024-12-04 11:31:09,124:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 11:31:09,134:INFO:Initializing create_model() +2024-12-04 11:31:09,134:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:31:09,134:INFO:Checking exceptions +2024-12-04 11:31:09,135:INFO:Importing libraries +2024-12-04 11:31:09,135:INFO:Copying training dataset +2024-12-04 11:31:09,139:INFO:Defining folds +2024-12-04 11:31:09,139:INFO:Declaring metric variables +2024-12-04 11:31:09,139:INFO:Importing untrained model +2024-12-04 11:31:09,139:INFO:Declaring custom model +2024-12-04 11:31:09,139:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:31:09,143:INFO:Cross validation set to False +2024-12-04 11:31:09,143:INFO:Fitting Model +2024-12-04 11:31:09,525:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:31:09,525:INFO:create_model() successfully completed...................................... +2024-12-04 11:31:09,644:INFO:_master_model_container: 36 +2024-12-04 11:31:09,644:INFO:_display_container: 2 +2024-12-04 11:31:09,645:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:31:09,645:INFO:compare_models() successfully completed...................................... +2024-12-04 11:32:18,905:INFO:PyCaret RegressionExperiment +2024-12-04 11:32:18,905:INFO:Logging name: reg-default-name +2024-12-04 11:32:18,905:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:32:18,905:INFO:version 3.3.2 +2024-12-04 11:32:18,905:INFO:Initializing setup() +2024-12-04 11:32:18,905:INFO:self.USI: 457c +2024-12-04 11:32:18,905:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:32:18,905:INFO:Checking environment +2024-12-04 11:32:18,905:INFO:python_version: 3.11.9 +2024-12-04 11:32:18,905:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:32:18,905:INFO:machine: AMD64 +2024-12-04 11:32:18,905:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:32:18,912:INFO:Memory: svmem(total=17007292416, available=1797574656, percent=89.4, used=15209717760, free=1797574656) +2024-12-04 11:32:18,912:INFO:Physical Core: 4 +2024-12-04 11:32:18,912:INFO:Logical Core: 8 +2024-12-04 11:32:18,912:INFO:Checking libraries +2024-12-04 11:32:18,912:INFO:System: +2024-12-04 11:32:18,912:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:32:18,912:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:32:18,912:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:32:18,912:INFO:PyCaret required dependencies: +2024-12-04 11:32:18,912:INFO: pip: 24.3.1 +2024-12-04 11:32:18,912:INFO: setuptools: 65.5.0 +2024-12-04 11:32:18,912:INFO: pycaret: 3.3.2 +2024-12-04 11:32:18,912:INFO: IPython: 8.12.3 +2024-12-04 11:32:18,912:INFO: ipywidgets: 8.1.5 +2024-12-04 11:32:18,913:INFO: tqdm: 4.67.1 +2024-12-04 11:32:18,913:INFO: numpy: 1.26.4 +2024-12-04 11:32:18,913:INFO: pandas: 2.1.4 +2024-12-04 11:32:18,913:INFO: jinja2: 3.1.4 +2024-12-04 11:32:18,913:INFO: scipy: 1.11.4 +2024-12-04 11:32:18,913:INFO: joblib: 1.3.2 +2024-12-04 11:32:18,913:INFO: sklearn: 1.4.2 +2024-12-04 11:32:18,913:INFO: pyod: 2.0.2 +2024-12-04 11:32:18,913:INFO: imblearn: 0.12.4 +2024-12-04 11:32:18,913:INFO: category_encoders: 2.6.4 +2024-12-04 11:32:18,913:INFO: lightgbm: 4.5.0 +2024-12-04 11:32:18,913:INFO: numba: 0.60.0 +2024-12-04 11:32:18,913:INFO: requests: 2.32.3 +2024-12-04 11:32:18,913:INFO: matplotlib: 3.7.5 +2024-12-04 11:32:18,913:INFO: scikitplot: 0.3.7 +2024-12-04 11:32:18,913:INFO: yellowbrick: 1.5 +2024-12-04 11:32:18,913:INFO: plotly: 5.24.1 +2024-12-04 11:32:18,913:INFO: plotly-resampler: Not installed +2024-12-04 11:32:18,914:INFO: kaleido: 0.2.1 +2024-12-04 11:32:18,914:INFO: schemdraw: 0.15 +2024-12-04 11:32:18,914:INFO: statsmodels: 0.14.4 +2024-12-04 11:32:18,914:INFO: sktime: 0.26.0 +2024-12-04 11:32:18,914:INFO: tbats: 1.1.3 +2024-12-04 11:32:18,914:INFO: pmdarima: 2.0.4 +2024-12-04 11:32:18,914:INFO: psutil: 6.1.0 +2024-12-04 11:32:18,914:INFO: markupsafe: 3.0.2 +2024-12-04 11:32:18,914:INFO: pickle5: Not installed +2024-12-04 11:32:18,914:INFO: cloudpickle: 3.1.0 +2024-12-04 11:32:18,914:INFO: deprecation: 2.1.0 +2024-12-04 11:32:18,914:INFO: xxhash: 3.5.0 +2024-12-04 11:32:18,914:INFO: wurlitzer: Not installed +2024-12-04 11:32:18,914:INFO:PyCaret optional dependencies: +2024-12-04 11:32:18,914:INFO: shap: Not installed +2024-12-04 11:32:18,914:INFO: interpret: Not installed +2024-12-04 11:32:18,914:INFO: umap: Not installed +2024-12-04 11:32:18,914:INFO: ydata_profiling: Not installed +2024-12-04 11:32:18,914:INFO: explainerdashboard: Not installed +2024-12-04 11:32:18,914:INFO: autoviz: Not installed +2024-12-04 11:32:18,914:INFO: fairlearn: Not installed +2024-12-04 11:32:18,914:INFO: deepchecks: Not installed +2024-12-04 11:32:18,915:INFO: xgboost: Not installed +2024-12-04 11:32:18,915:INFO: catboost: Not installed +2024-12-04 11:32:18,915:INFO: kmodes: Not installed +2024-12-04 11:32:18,915:INFO: mlxtend: Not installed +2024-12-04 11:32:18,916:INFO: statsforecast: Not installed +2024-12-04 11:32:18,916:INFO: tune_sklearn: Not installed +2024-12-04 11:32:18,916:INFO: ray: Not installed +2024-12-04 11:32:18,916:INFO: hyperopt: Not installed +2024-12-04 11:32:18,916:INFO: optuna: Not installed +2024-12-04 11:32:18,916:INFO: skopt: Not installed +2024-12-04 11:32:18,916:INFO: mlflow: 2.18.0 +2024-12-04 11:32:18,916:INFO: gradio: Not installed +2024-12-04 11:32:18,916:INFO: fastapi: Not installed +2024-12-04 11:32:18,916:INFO: uvicorn: 0.32.1 +2024-12-04 11:32:18,916:INFO: m2cgen: Not installed +2024-12-04 11:32:18,916:INFO: evidently: Not installed +2024-12-04 11:32:18,916:INFO: fugue: Not installed +2024-12-04 11:32:18,916:INFO: streamlit: 1.40.1 +2024-12-04 11:32:18,916:INFO: prophet: Not installed +2024-12-04 11:32:18,916:INFO:None +2024-12-04 11:32:18,916:INFO:Set up data. +2024-12-04 11:32:18,927:INFO:Set up folding strategy. +2024-12-04 11:32:18,927:INFO:Set up train/test split. +2024-12-04 11:32:18,933:INFO:Set up index. +2024-12-04 11:32:18,934:INFO:Assigning column types. +2024-12-04 11:32:18,939:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 11:32:19,051:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,051:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,157:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,157:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,157:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 11:32:19,257:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,257:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,369:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,369:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,369:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 11:32:19,478:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,478:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,584:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,584:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,585:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 11:32:19,681:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,681:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,793:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,793:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,793:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 11:32:19,902:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:19,902:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,005:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,006:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,006:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 11:32:20,112:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,112:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,216:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,217:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,227:INFO:Preparing preprocessing pipeline... +2024-12-04 11:32:20,227:INFO:Set up simple imputation. +2024-12-04 11:32:20,231:INFO:Set up encoding of categorical features. +2024-12-04 11:32:20,231:INFO:Set up column transformation. +2024-12-04 11:32:20,231:INFO:Set up feature normalization. +2024-12-04 11:32:20,385:INFO:Finished creating preprocessing pipeline. +2024-12-04 11:32:20,393:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + TransformerWrapper(include=['nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 11:32:20,393:INFO:Creating final display dataframe. +2024-12-04 11:32:20,593:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 14) +4 Transformed data shape (11376, 13) +5 Transformed train set shape (7963, 13) +6 Transformed test set shape (3413, 13) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 1 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 5 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI 457c +2024-12-04 11:32:20,704:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,705:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,809:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,809:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:32:20,809:INFO:setup() successfully completed in 1.92s............... +2024-12-04 11:32:27,527:INFO:Initializing compare_models() +2024-12-04 11:32:27,527:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 11:32:27,527:INFO:Checking exceptions +2024-12-04 11:32:27,532:INFO:Preparing display monitor +2024-12-04 11:32:27,555:INFO:Initializing Linear Regression +2024-12-04 11:32:27,555:INFO:Total runtime is 0.0 minutes +2024-12-04 11:32:27,559:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:27,559:INFO:Initializing create_model() +2024-12-04 11:32:27,559:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:27,559:INFO:Checking exceptions +2024-12-04 11:32:27,559:INFO:Importing libraries +2024-12-04 11:32:27,559:INFO:Copying training dataset +2024-12-04 11:32:27,576:INFO:Defining folds +2024-12-04 11:32:27,576:INFO:Declaring metric variables +2024-12-04 11:32:27,580:INFO:Importing untrained model +2024-12-04 11:32:27,582:INFO:Linear Regression Imported successfully +2024-12-04 11:32:27,590:INFO:Starting cross validation +2024-12-04 11:32:27,591:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:27,826:INFO:Calculating mean and std +2024-12-04 11:32:27,826:INFO:Creating metrics dataframe +2024-12-04 11:32:27,827:INFO:Uploading results into container +2024-12-04 11:32:27,827:INFO:Uploading model into container now +2024-12-04 11:32:27,827:INFO:_master_model_container: 37 +2024-12-04 11:32:27,827:INFO:_display_container: 2 +2024-12-04 11:32:27,828:INFO:LinearRegression(n_jobs=-1) +2024-12-04 11:32:27,828:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:27,931:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:27,931:INFO:Creating metrics dataframe +2024-12-04 11:32:27,935:INFO:Initializing Lasso Regression +2024-12-04 11:32:27,935:INFO:Total runtime is 0.006333760420481364 minutes +2024-12-04 11:32:27,942:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:27,942:INFO:Initializing create_model() +2024-12-04 11:32:27,942:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:27,942:INFO:Checking exceptions +2024-12-04 11:32:27,942:INFO:Importing libraries +2024-12-04 11:32:27,942:INFO:Copying training dataset +2024-12-04 11:32:27,951:INFO:Defining folds +2024-12-04 11:32:27,951:INFO:Declaring metric variables +2024-12-04 11:32:27,953:INFO:Importing untrained model +2024-12-04 11:32:27,956:INFO:Lasso Regression Imported successfully +2024-12-04 11:32:27,963:INFO:Starting cross validation +2024-12-04 11:32:27,963:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:28,202:INFO:Calculating mean and std +2024-12-04 11:32:28,202:INFO:Creating metrics dataframe +2024-12-04 11:32:28,203:INFO:Uploading results into container +2024-12-04 11:32:28,204:INFO:Uploading model into container now +2024-12-04 11:32:28,204:INFO:_master_model_container: 38 +2024-12-04 11:32:28,204:INFO:_display_container: 2 +2024-12-04 11:32:28,204:INFO:Lasso(random_state=123) +2024-12-04 11:32:28,204:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:28,302:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:28,302:INFO:Creating metrics dataframe +2024-12-04 11:32:28,309:INFO:Initializing Ridge Regression +2024-12-04 11:32:28,309:INFO:Total runtime is 0.012563916047414143 minutes +2024-12-04 11:32:28,312:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:28,312:INFO:Initializing create_model() +2024-12-04 11:32:28,312:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:28,313:INFO:Checking exceptions +2024-12-04 11:32:28,313:INFO:Importing libraries +2024-12-04 11:32:28,313:INFO:Copying training dataset +2024-12-04 11:32:28,320:INFO:Defining folds +2024-12-04 11:32:28,320:INFO:Declaring metric variables +2024-12-04 11:32:28,320:INFO:Importing untrained model +2024-12-04 11:32:28,326:INFO:Ridge Regression Imported successfully +2024-12-04 11:32:28,333:INFO:Starting cross validation +2024-12-04 11:32:28,334:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:28,620:INFO:Calculating mean and std +2024-12-04 11:32:28,620:INFO:Creating metrics dataframe +2024-12-04 11:32:28,622:INFO:Uploading results into container +2024-12-04 11:32:28,622:INFO:Uploading model into container now +2024-12-04 11:32:28,623:INFO:_master_model_container: 39 +2024-12-04 11:32:28,623:INFO:_display_container: 2 +2024-12-04 11:32:28,624:INFO:Ridge(random_state=123) +2024-12-04 11:32:28,624:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:28,728:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:28,728:INFO:Creating metrics dataframe +2024-12-04 11:32:28,735:INFO:Initializing Elastic Net +2024-12-04 11:32:28,735:INFO:Total runtime is 0.01967738072077433 minutes +2024-12-04 11:32:28,738:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:28,739:INFO:Initializing create_model() +2024-12-04 11:32:28,739:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:28,739:INFO:Checking exceptions +2024-12-04 11:32:28,739:INFO:Importing libraries +2024-12-04 11:32:28,739:INFO:Copying training dataset +2024-12-04 11:32:28,747:INFO:Defining folds +2024-12-04 11:32:28,748:INFO:Declaring metric variables +2024-12-04 11:32:28,751:INFO:Importing untrained model +2024-12-04 11:32:28,755:INFO:Elastic Net Imported successfully +2024-12-04 11:32:28,761:INFO:Starting cross validation +2024-12-04 11:32:28,763:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:28,979:INFO:Calculating mean and std +2024-12-04 11:32:28,980:INFO:Creating metrics dataframe +2024-12-04 11:32:28,981:INFO:Uploading results into container +2024-12-04 11:32:28,982:INFO:Uploading model into container now +2024-12-04 11:32:28,982:INFO:_master_model_container: 40 +2024-12-04 11:32:28,982:INFO:_display_container: 2 +2024-12-04 11:32:28,982:INFO:ElasticNet(random_state=123) +2024-12-04 11:32:28,983:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:29,081:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:29,081:INFO:Creating metrics dataframe +2024-12-04 11:32:29,087:INFO:Initializing Least Angle Regression +2024-12-04 11:32:29,087:INFO:Total runtime is 0.025537542502085366 minutes +2024-12-04 11:32:29,090:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:29,090:INFO:Initializing create_model() +2024-12-04 11:32:29,091:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:29,091:INFO:Checking exceptions +2024-12-04 11:32:29,091:INFO:Importing libraries +2024-12-04 11:32:29,091:INFO:Copying training dataset +2024-12-04 11:32:29,099:INFO:Defining folds +2024-12-04 11:32:29,099:INFO:Declaring metric variables +2024-12-04 11:32:29,103:INFO:Importing untrained model +2024-12-04 11:32:29,106:INFO:Least Angle Regression Imported successfully +2024-12-04 11:32:29,112:INFO:Starting cross validation +2024-12-04 11:32:29,112:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:29,332:INFO:Calculating mean and std +2024-12-04 11:32:29,333:INFO:Creating metrics dataframe +2024-12-04 11:32:29,335:INFO:Uploading results into container +2024-12-04 11:32:29,336:INFO:Uploading model into container now +2024-12-04 11:32:29,336:INFO:_master_model_container: 41 +2024-12-04 11:32:29,336:INFO:_display_container: 2 +2024-12-04 11:32:29,336:INFO:Lars(random_state=123) +2024-12-04 11:32:29,336:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:29,434:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:29,434:INFO:Creating metrics dataframe +2024-12-04 11:32:29,439:INFO:Initializing Lasso Least Angle Regression +2024-12-04 11:32:29,439:INFO:Total runtime is 0.03140247662862142 minutes +2024-12-04 11:32:29,443:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:29,443:INFO:Initializing create_model() +2024-12-04 11:32:29,443:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:29,443:INFO:Checking exceptions +2024-12-04 11:32:29,443:INFO:Importing libraries +2024-12-04 11:32:29,443:INFO:Copying training dataset +2024-12-04 11:32:29,452:INFO:Defining folds +2024-12-04 11:32:29,452:INFO:Declaring metric variables +2024-12-04 11:32:29,457:INFO:Importing untrained model +2024-12-04 11:32:29,459:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 11:32:29,465:INFO:Starting cross validation +2024-12-04 11:32:29,466:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:29,681:INFO:Calculating mean and std +2024-12-04 11:32:29,682:INFO:Creating metrics dataframe +2024-12-04 11:32:29,684:INFO:Uploading results into container +2024-12-04 11:32:29,684:INFO:Uploading model into container now +2024-12-04 11:32:29,685:INFO:_master_model_container: 42 +2024-12-04 11:32:29,685:INFO:_display_container: 2 +2024-12-04 11:32:29,685:INFO:LassoLars(random_state=123) +2024-12-04 11:32:29,685:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:29,786:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:29,786:INFO:Creating metrics dataframe +2024-12-04 11:32:29,793:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 11:32:29,793:INFO:Total runtime is 0.037301997343699135 minutes +2024-12-04 11:32:29,793:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:29,793:INFO:Initializing create_model() +2024-12-04 11:32:29,793:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:29,793:INFO:Checking exceptions +2024-12-04 11:32:29,793:INFO:Importing libraries +2024-12-04 11:32:29,793:INFO:Copying training dataset +2024-12-04 11:32:29,807:INFO:Defining folds +2024-12-04 11:32:29,807:INFO:Declaring metric variables +2024-12-04 11:32:29,809:INFO:Importing untrained model +2024-12-04 11:32:29,812:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 11:32:29,820:INFO:Starting cross validation +2024-12-04 11:32:29,821:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:30,040:INFO:Calculating mean and std +2024-12-04 11:32:30,041:INFO:Creating metrics dataframe +2024-12-04 11:32:30,043:INFO:Uploading results into container +2024-12-04 11:32:30,043:INFO:Uploading model into container now +2024-12-04 11:32:30,044:INFO:_master_model_container: 43 +2024-12-04 11:32:30,044:INFO:_display_container: 2 +2024-12-04 11:32:30,044:INFO:OrthogonalMatchingPursuit() +2024-12-04 11:32:30,044:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:30,139:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:30,139:INFO:Creating metrics dataframe +2024-12-04 11:32:30,143:INFO:Initializing Bayesian Ridge +2024-12-04 11:32:30,143:INFO:Total runtime is 0.043131315708160395 minutes +2024-12-04 11:32:30,150:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:30,150:INFO:Initializing create_model() +2024-12-04 11:32:30,150:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:30,150:INFO:Checking exceptions +2024-12-04 11:32:30,150:INFO:Importing libraries +2024-12-04 11:32:30,150:INFO:Copying training dataset +2024-12-04 11:32:30,158:INFO:Defining folds +2024-12-04 11:32:30,158:INFO:Declaring metric variables +2024-12-04 11:32:30,159:INFO:Importing untrained model +2024-12-04 11:32:30,165:INFO:Bayesian Ridge Imported successfully +2024-12-04 11:32:30,172:INFO:Starting cross validation +2024-12-04 11:32:30,173:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:30,379:INFO:Calculating mean and std +2024-12-04 11:32:30,380:INFO:Creating metrics dataframe +2024-12-04 11:32:30,381:INFO:Uploading results into container +2024-12-04 11:32:30,382:INFO:Uploading model into container now +2024-12-04 11:32:30,382:INFO:_master_model_container: 44 +2024-12-04 11:32:30,382:INFO:_display_container: 2 +2024-12-04 11:32:30,382:INFO:BayesianRidge() +2024-12-04 11:32:30,382:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:30,485:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:30,485:INFO:Creating metrics dataframe +2024-12-04 11:32:30,492:INFO:Initializing Passive Aggressive Regressor +2024-12-04 11:32:30,492:INFO:Total runtime is 0.04896129767100016 minutes +2024-12-04 11:32:30,494:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:30,494:INFO:Initializing create_model() +2024-12-04 11:32:30,494:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:30,494:INFO:Checking exceptions +2024-12-04 11:32:30,494:INFO:Importing libraries +2024-12-04 11:32:30,494:INFO:Copying training dataset +2024-12-04 11:32:30,503:INFO:Defining folds +2024-12-04 11:32:30,503:INFO:Declaring metric variables +2024-12-04 11:32:30,507:INFO:Importing untrained model +2024-12-04 11:32:30,509:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 11:32:30,516:INFO:Starting cross validation +2024-12-04 11:32:30,517:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:30,728:INFO:Calculating mean and std +2024-12-04 11:32:30,728:INFO:Creating metrics dataframe +2024-12-04 11:32:30,730:INFO:Uploading results into container +2024-12-04 11:32:30,731:INFO:Uploading model into container now +2024-12-04 11:32:30,731:INFO:_master_model_container: 45 +2024-12-04 11:32:30,731:INFO:_display_container: 2 +2024-12-04 11:32:30,731:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 11:32:30,731:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:30,823:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:30,823:INFO:Creating metrics dataframe +2024-12-04 11:32:30,830:INFO:Initializing Huber Regressor +2024-12-04 11:32:30,830:INFO:Total runtime is 0.05459133783976237 minutes +2024-12-04 11:32:30,834:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:30,834:INFO:Initializing create_model() +2024-12-04 11:32:30,834:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:30,834:INFO:Checking exceptions +2024-12-04 11:32:30,834:INFO:Importing libraries +2024-12-04 11:32:30,834:INFO:Copying training dataset +2024-12-04 11:32:30,842:INFO:Defining folds +2024-12-04 11:32:30,842:INFO:Declaring metric variables +2024-12-04 11:32:30,843:INFO:Importing untrained model +2024-12-04 11:32:30,850:INFO:Huber Regressor Imported successfully +2024-12-04 11:32:30,857:INFO:Starting cross validation +2024-12-04 11:32:30,857:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:31,106:INFO:Calculating mean and std +2024-12-04 11:32:31,107:INFO:Creating metrics dataframe +2024-12-04 11:32:31,109:INFO:Uploading results into container +2024-12-04 11:32:31,110:INFO:Uploading model into container now +2024-12-04 11:32:31,110:INFO:_master_model_container: 46 +2024-12-04 11:32:31,110:INFO:_display_container: 2 +2024-12-04 11:32:31,110:INFO:HuberRegressor() +2024-12-04 11:32:31,110:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:31,207:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:31,207:INFO:Creating metrics dataframe +2024-12-04 11:32:31,215:INFO:Initializing K Neighbors Regressor +2024-12-04 11:32:31,215:INFO:Total runtime is 0.06100900570551554 minutes +2024-12-04 11:32:31,220:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:31,220:INFO:Initializing create_model() +2024-12-04 11:32:31,220:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:31,220:INFO:Checking exceptions +2024-12-04 11:32:31,220:INFO:Importing libraries +2024-12-04 11:32:31,220:INFO:Copying training dataset +2024-12-04 11:32:31,227:INFO:Defining folds +2024-12-04 11:32:31,227:INFO:Declaring metric variables +2024-12-04 11:32:31,233:INFO:Importing untrained model +2024-12-04 11:32:31,237:INFO:K Neighbors Regressor Imported successfully +2024-12-04 11:32:31,242:INFO:Starting cross validation +2024-12-04 11:32:31,245:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:31,513:INFO:Calculating mean and std +2024-12-04 11:32:31,514:INFO:Creating metrics dataframe +2024-12-04 11:32:31,516:INFO:Uploading results into container +2024-12-04 11:32:31,517:INFO:Uploading model into container now +2024-12-04 11:32:31,517:INFO:_master_model_container: 47 +2024-12-04 11:32:31,517:INFO:_display_container: 2 +2024-12-04 11:32:31,518:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 11:32:31,518:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:31,616:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:31,616:INFO:Creating metrics dataframe +2024-12-04 11:32:31,623:INFO:Initializing Decision Tree Regressor +2024-12-04 11:32:31,623:INFO:Total runtime is 0.06781149705251058 minutes +2024-12-04 11:32:31,627:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:31,628:INFO:Initializing create_model() +2024-12-04 11:32:31,628:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:31,628:INFO:Checking exceptions +2024-12-04 11:32:31,628:INFO:Importing libraries +2024-12-04 11:32:31,628:INFO:Copying training dataset +2024-12-04 11:32:31,634:INFO:Defining folds +2024-12-04 11:32:31,634:INFO:Declaring metric variables +2024-12-04 11:32:31,639:INFO:Importing untrained model +2024-12-04 11:32:31,642:INFO:Decision Tree Regressor Imported successfully +2024-12-04 11:32:31,642:INFO:Starting cross validation +2024-12-04 11:32:31,650:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:31,926:INFO:Calculating mean and std +2024-12-04 11:32:31,927:INFO:Creating metrics dataframe +2024-12-04 11:32:31,928:INFO:Uploading results into container +2024-12-04 11:32:31,928:INFO:Uploading model into container now +2024-12-04 11:32:31,928:INFO:_master_model_container: 48 +2024-12-04 11:32:31,928:INFO:_display_container: 2 +2024-12-04 11:32:31,930:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 11:32:31,930:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:32,028:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:32,028:INFO:Creating metrics dataframe +2024-12-04 11:32:32,035:INFO:Initializing Random Forest Regressor +2024-12-04 11:32:32,035:INFO:Total runtime is 0.07467162211736043 minutes +2024-12-04 11:32:32,041:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:32,043:INFO:Initializing create_model() +2024-12-04 11:32:32,043:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:32,043:INFO:Checking exceptions +2024-12-04 11:32:32,043:INFO:Importing libraries +2024-12-04 11:32:32,043:INFO:Copying training dataset +2024-12-04 11:32:32,049:INFO:Defining folds +2024-12-04 11:32:32,049:INFO:Declaring metric variables +2024-12-04 11:32:32,053:INFO:Importing untrained model +2024-12-04 11:32:32,058:INFO:Random Forest Regressor Imported successfully +2024-12-04 11:32:32,074:INFO:Starting cross validation +2024-12-04 11:32:32,076:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:36,455:INFO:Calculating mean and std +2024-12-04 11:32:36,457:INFO:Creating metrics dataframe +2024-12-04 11:32:36,459:INFO:Uploading results into container +2024-12-04 11:32:36,461:INFO:Uploading model into container now +2024-12-04 11:32:36,462:INFO:_master_model_container: 49 +2024-12-04 11:32:36,462:INFO:_display_container: 2 +2024-12-04 11:32:36,462:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:32:36,462:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:36,578:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:36,578:INFO:Creating metrics dataframe +2024-12-04 11:32:36,585:INFO:Initializing Extra Trees Regressor +2024-12-04 11:32:36,585:INFO:Total runtime is 0.15049882729848224 minutes +2024-12-04 11:32:36,585:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:36,589:INFO:Initializing create_model() +2024-12-04 11:32:36,589:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:36,589:INFO:Checking exceptions +2024-12-04 11:32:36,589:INFO:Importing libraries +2024-12-04 11:32:36,589:INFO:Copying training dataset +2024-12-04 11:32:36,596:INFO:Defining folds +2024-12-04 11:32:36,596:INFO:Declaring metric variables +2024-12-04 11:32:36,599:INFO:Importing untrained model +2024-12-04 11:32:36,603:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:32:36,607:INFO:Starting cross validation +2024-12-04 11:32:36,609:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:38,815:INFO:Calculating mean and std +2024-12-04 11:32:38,816:INFO:Creating metrics dataframe +2024-12-04 11:32:38,820:INFO:Uploading results into container +2024-12-04 11:32:38,820:INFO:Uploading model into container now +2024-12-04 11:32:38,820:INFO:_master_model_container: 50 +2024-12-04 11:32:38,820:INFO:_display_container: 2 +2024-12-04 11:32:38,820:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:32:38,820:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:38,927:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:38,927:INFO:Creating metrics dataframe +2024-12-04 11:32:38,935:INFO:Initializing AdaBoost Regressor +2024-12-04 11:32:38,935:INFO:Total runtime is 0.18966393868128456 minutes +2024-12-04 11:32:38,939:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:38,939:INFO:Initializing create_model() +2024-12-04 11:32:38,939:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:38,943:INFO:Checking exceptions +2024-12-04 11:32:38,943:INFO:Importing libraries +2024-12-04 11:32:38,943:INFO:Copying training dataset +2024-12-04 11:32:38,950:INFO:Defining folds +2024-12-04 11:32:38,950:INFO:Declaring metric variables +2024-12-04 11:32:38,953:INFO:Importing untrained model +2024-12-04 11:32:38,955:INFO:AdaBoost Regressor Imported successfully +2024-12-04 11:32:38,962:INFO:Starting cross validation +2024-12-04 11:32:38,963:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:39,356:INFO:Calculating mean and std +2024-12-04 11:32:39,357:INFO:Creating metrics dataframe +2024-12-04 11:32:39,358:INFO:Uploading results into container +2024-12-04 11:32:39,359:INFO:Uploading model into container now +2024-12-04 11:32:39,359:INFO:_master_model_container: 51 +2024-12-04 11:32:39,359:INFO:_display_container: 2 +2024-12-04 11:32:39,359:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 11:32:39,359:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:39,457:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:39,457:INFO:Creating metrics dataframe +2024-12-04 11:32:39,466:INFO:Initializing Gradient Boosting Regressor +2024-12-04 11:32:39,466:INFO:Total runtime is 0.19851214885711668 minutes +2024-12-04 11:32:39,469:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:39,470:INFO:Initializing create_model() +2024-12-04 11:32:39,470:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:39,470:INFO:Checking exceptions +2024-12-04 11:32:39,470:INFO:Importing libraries +2024-12-04 11:32:39,470:INFO:Copying training dataset +2024-12-04 11:32:39,478:INFO:Defining folds +2024-12-04 11:32:39,478:INFO:Declaring metric variables +2024-12-04 11:32:39,482:INFO:Importing untrained model +2024-12-04 11:32:39,486:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 11:32:39,493:INFO:Starting cross validation +2024-12-04 11:32:39,493:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:40,851:INFO:Calculating mean and std +2024-12-04 11:32:40,851:INFO:Creating metrics dataframe +2024-12-04 11:32:40,853:INFO:Uploading results into container +2024-12-04 11:32:40,853:INFO:Uploading model into container now +2024-12-04 11:32:40,853:INFO:_master_model_container: 52 +2024-12-04 11:32:40,853:INFO:_display_container: 2 +2024-12-04 11:32:40,853:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 11:32:40,853:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:40,947:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:40,947:INFO:Creating metrics dataframe +2024-12-04 11:32:40,957:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 11:32:40,957:INFO:Total runtime is 0.2233739376068115 minutes +2024-12-04 11:32:40,959:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:40,959:INFO:Initializing create_model() +2024-12-04 11:32:40,959:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:40,959:INFO:Checking exceptions +2024-12-04 11:32:40,959:INFO:Importing libraries +2024-12-04 11:32:40,959:INFO:Copying training dataset +2024-12-04 11:32:40,969:INFO:Defining folds +2024-12-04 11:32:40,970:INFO:Declaring metric variables +2024-12-04 11:32:40,973:INFO:Importing untrained model +2024-12-04 11:32:40,977:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 11:32:40,982:INFO:Starting cross validation +2024-12-04 11:32:40,983:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:41,507:INFO:Calculating mean and std +2024-12-04 11:32:41,509:INFO:Creating metrics dataframe +2024-12-04 11:32:41,511:INFO:Uploading results into container +2024-12-04 11:32:41,512:INFO:Uploading model into container now +2024-12-04 11:32:41,512:INFO:_master_model_container: 53 +2024-12-04 11:32:41,512:INFO:_display_container: 2 +2024-12-04 11:32:41,513:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:32:41,513:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:41,631:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:41,631:INFO:Creating metrics dataframe +2024-12-04 11:32:41,639:INFO:Initializing Dummy Regressor +2024-12-04 11:32:41,639:INFO:Total runtime is 0.23474268913269042 minutes +2024-12-04 11:32:41,642:INFO:SubProcess create_model() called ================================== +2024-12-04 11:32:41,642:INFO:Initializing create_model() +2024-12-04 11:32:41,642:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:41,642:INFO:Checking exceptions +2024-12-04 11:32:41,642:INFO:Importing libraries +2024-12-04 11:32:41,642:INFO:Copying training dataset +2024-12-04 11:32:41,650:INFO:Defining folds +2024-12-04 11:32:41,650:INFO:Declaring metric variables +2024-12-04 11:32:41,653:INFO:Importing untrained model +2024-12-04 11:32:41,657:INFO:Dummy Regressor Imported successfully +2024-12-04 11:32:41,662:INFO:Starting cross validation +2024-12-04 11:32:41,663:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:32:41,858:INFO:Calculating mean and std +2024-12-04 11:32:41,859:INFO:Creating metrics dataframe +2024-12-04 11:32:41,859:INFO:Uploading results into container +2024-12-04 11:32:41,859:INFO:Uploading model into container now +2024-12-04 11:32:41,861:INFO:_master_model_container: 54 +2024-12-04 11:32:41,861:INFO:_display_container: 2 +2024-12-04 11:32:41,862:INFO:DummyRegressor() +2024-12-04 11:32:41,862:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:41,957:INFO:SubProcess create_model() end ================================== +2024-12-04 11:32:41,957:INFO:Creating metrics dataframe +2024-12-04 11:32:41,966:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 11:32:41,974:INFO:Initializing create_model() +2024-12-04 11:32:41,974:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:32:41,974:INFO:Checking exceptions +2024-12-04 11:32:41,975:INFO:Importing libraries +2024-12-04 11:32:41,975:INFO:Copying training dataset +2024-12-04 11:32:41,980:INFO:Defining folds +2024-12-04 11:32:41,980:INFO:Declaring metric variables +2024-12-04 11:32:41,980:INFO:Importing untrained model +2024-12-04 11:32:41,980:INFO:Declaring custom model +2024-12-04 11:32:41,983:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:32:41,983:INFO:Cross validation set to False +2024-12-04 11:32:41,983:INFO:Fitting Model +2024-12-04 11:32:42,594:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:32:42,595:INFO:create_model() successfully completed...................................... +2024-12-04 11:32:42,712:INFO:_master_model_container: 54 +2024-12-04 11:32:42,712:INFO:_display_container: 2 +2024-12-04 11:32:42,712:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:32:42,712:INFO:compare_models() successfully completed...................................... +2024-12-04 11:55:47,501:INFO:PyCaret RegressionExperiment +2024-12-04 11:55:47,501:INFO:Logging name: reg-default-name +2024-12-04 11:55:47,501:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:55:47,501:INFO:version 3.3.2 +2024-12-04 11:55:47,501:INFO:Initializing setup() +2024-12-04 11:55:47,501:INFO:self.USI: 28df +2024-12-04 11:55:47,501:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:55:47,501:INFO:Checking environment +2024-12-04 11:55:47,501:INFO:python_version: 3.11.9 +2024-12-04 11:55:47,501:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:55:47,501:INFO:machine: AMD64 +2024-12-04 11:55:47,501:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:55:47,508:INFO:Memory: svmem(total=17007292416, available=3791716352, percent=77.7, used=13215576064, free=3791716352) +2024-12-04 11:55:47,509:INFO:Physical Core: 4 +2024-12-04 11:55:47,509:INFO:Logical Core: 8 +2024-12-04 11:55:47,509:INFO:Checking libraries +2024-12-04 11:55:47,509:INFO:System: +2024-12-04 11:55:47,509:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:55:47,509:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:55:47,509:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:55:47,509:INFO:PyCaret required dependencies: +2024-12-04 11:55:47,509:INFO: pip: 24.3.1 +2024-12-04 11:55:47,509:INFO: setuptools: 65.5.0 +2024-12-04 11:55:47,509:INFO: pycaret: 3.3.2 +2024-12-04 11:55:47,509:INFO: IPython: 8.12.3 +2024-12-04 11:55:47,509:INFO: ipywidgets: 8.1.5 +2024-12-04 11:55:47,509:INFO: tqdm: 4.67.1 +2024-12-04 11:55:47,509:INFO: numpy: 1.26.4 +2024-12-04 11:55:47,509:INFO: pandas: 2.1.4 +2024-12-04 11:55:47,509:INFO: jinja2: 3.1.4 +2024-12-04 11:55:47,509:INFO: scipy: 1.11.4 +2024-12-04 11:55:47,509:INFO: joblib: 1.3.2 +2024-12-04 11:55:47,509:INFO: sklearn: 1.4.2 +2024-12-04 11:55:47,509:INFO: pyod: 2.0.2 +2024-12-04 11:55:47,510:INFO: imblearn: 0.12.4 +2024-12-04 11:55:47,510:INFO: category_encoders: 2.6.4 +2024-12-04 11:55:47,510:INFO: lightgbm: 4.5.0 +2024-12-04 11:55:47,510:INFO: numba: 0.60.0 +2024-12-04 11:55:47,510:INFO: requests: 2.32.3 +2024-12-04 11:55:47,510:INFO: matplotlib: 3.7.5 +2024-12-04 11:55:47,510:INFO: scikitplot: 0.3.7 +2024-12-04 11:55:47,510:INFO: yellowbrick: 1.5 +2024-12-04 11:55:47,510:INFO: plotly: 5.24.1 +2024-12-04 11:55:47,510:INFO: plotly-resampler: Not installed +2024-12-04 11:55:47,510:INFO: kaleido: 0.2.1 +2024-12-04 11:55:47,510:INFO: schemdraw: 0.15 +2024-12-04 11:55:47,510:INFO: statsmodels: 0.14.4 +2024-12-04 11:55:47,510:INFO: sktime: 0.26.0 +2024-12-04 11:55:47,510:INFO: tbats: 1.1.3 +2024-12-04 11:55:47,510:INFO: pmdarima: 2.0.4 +2024-12-04 11:55:47,510:INFO: psutil: 6.1.0 +2024-12-04 11:55:47,510:INFO: markupsafe: 3.0.2 +2024-12-04 11:55:47,510:INFO: pickle5: Not installed +2024-12-04 11:55:47,510:INFO: cloudpickle: 3.1.0 +2024-12-04 11:55:47,510:INFO: deprecation: 2.1.0 +2024-12-04 11:55:47,510:INFO: xxhash: 3.5.0 +2024-12-04 11:55:47,510:INFO: wurlitzer: Not installed +2024-12-04 11:55:47,510:INFO:PyCaret optional dependencies: +2024-12-04 11:55:47,510:INFO: shap: Not installed +2024-12-04 11:55:47,510:INFO: interpret: Not installed +2024-12-04 11:55:47,510:INFO: umap: Not installed +2024-12-04 11:55:47,510:INFO: ydata_profiling: Not installed +2024-12-04 11:55:47,510:INFO: explainerdashboard: Not installed +2024-12-04 11:55:47,510:INFO: autoviz: Not installed +2024-12-04 11:55:47,510:INFO: fairlearn: Not installed +2024-12-04 11:55:47,510:INFO: deepchecks: Not installed +2024-12-04 11:55:47,510:INFO: xgboost: Not installed +2024-12-04 11:55:47,510:INFO: catboost: Not installed +2024-12-04 11:55:47,510:INFO: kmodes: Not installed +2024-12-04 11:55:47,510:INFO: mlxtend: Not installed +2024-12-04 11:55:47,510:INFO: statsforecast: Not installed +2024-12-04 11:55:47,510:INFO: tune_sklearn: Not installed +2024-12-04 11:55:47,510:INFO: ray: Not installed +2024-12-04 11:55:47,510:INFO: hyperopt: Not installed +2024-12-04 11:55:47,511:INFO: optuna: Not installed +2024-12-04 11:55:47,512:INFO: skopt: Not installed +2024-12-04 11:55:47,512:INFO: mlflow: 2.18.0 +2024-12-04 11:55:47,512:INFO: gradio: Not installed +2024-12-04 11:55:47,512:INFO: fastapi: Not installed +2024-12-04 11:55:47,512:INFO: uvicorn: 0.32.1 +2024-12-04 11:55:47,512:INFO: m2cgen: Not installed +2024-12-04 11:55:47,512:INFO: evidently: Not installed +2024-12-04 11:55:47,512:INFO: fugue: Not installed +2024-12-04 11:55:47,512:INFO: streamlit: 1.40.1 +2024-12-04 11:55:47,512:INFO: prophet: Not installed +2024-12-04 11:55:47,512:INFO:None +2024-12-04 11:55:47,512:INFO:Set up data. +2024-12-04 11:55:47,525:INFO:Set up folding strategy. +2024-12-04 11:55:47,525:INFO:Set up train/test split. +2024-12-04 11:55:47,535:INFO:Set up index. +2024-12-04 11:55:47,535:INFO:Assigning column types. +2024-12-04 11:55:47,541:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 11:55:47,657:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,659:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,768:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,768:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,768:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 11:55:47,871:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,872:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,972:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,972:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:47,973:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 11:55:48,075:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,076:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,195:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,195:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,195:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 11:55:48,305:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,305:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,422:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,422:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,422:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 11:55:48,528:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,528:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,630:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,630:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,630:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 11:55:48,730:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,730:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,835:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,835:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:48,843:INFO:Preparing preprocessing pipeline... +2024-12-04 11:55:48,843:INFO:Set up simple imputation. +2024-12-04 11:55:48,843:INFO:Set up encoding of categorical features. +2024-12-04 11:55:48,843:INFO:Set up column transformation. +2024-12-04 11:55:48,843:INFO:Set up feature normalization. +2024-12-04 11:55:49,165:INFO:Finished creating preprocessing pipeline. +2024-12-04 11:55:49,175:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['AVG_duree_hospi', + 'tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tr... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 11:55:49,175:INFO:Creating final display dataframe. +2024-12-04 11:55:49,484:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 16) +4 Transformed data shape (11376, 32) +5 Transformed train set shape (7963, 32) +6 Transformed test set shape (3413, 32) +7 Ignore features 1 +8 Numeric features 12 +9 Categorical features 2 +10 Rows with missing values 0.2% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 5 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI 28df +2024-12-04 11:55:49,589:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:49,589:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:49,693:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:49,693:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:55:49,693:INFO:setup() successfully completed in 2.21s............... +2024-12-04 11:55:55,642:INFO:Initializing compare_models() +2024-12-04 11:55:55,642:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 11:55:55,642:INFO:Checking exceptions +2024-12-04 11:55:55,649:INFO:Preparing display monitor +2024-12-04 11:55:55,668:INFO:Initializing Linear Regression +2024-12-04 11:55:55,668:INFO:Total runtime is 0.0 minutes +2024-12-04 11:55:55,672:INFO:SubProcess create_model() called ================================== +2024-12-04 11:55:55,672:INFO:Initializing create_model() +2024-12-04 11:55:55,672:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:55:55,672:INFO:Checking exceptions +2024-12-04 11:55:55,673:INFO:Importing libraries +2024-12-04 11:55:55,673:INFO:Copying training dataset +2024-12-04 11:55:55,682:INFO:Defining folds +2024-12-04 11:55:55,682:INFO:Declaring metric variables +2024-12-04 11:55:55,686:INFO:Importing untrained model +2024-12-04 11:55:55,691:INFO:Linear Regression Imported successfully +2024-12-04 11:55:55,697:INFO:Starting cross validation +2024-12-04 11:55:55,699:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:55:59,724:INFO:Calculating mean and std +2024-12-04 11:55:59,726:INFO:Creating metrics dataframe +2024-12-04 11:55:59,728:INFO:Uploading results into container +2024-12-04 11:55:59,729:INFO:Uploading model into container now +2024-12-04 11:55:59,729:INFO:_master_model_container: 55 +2024-12-04 11:55:59,730:INFO:_display_container: 2 +2024-12-04 11:55:59,730:INFO:LinearRegression(n_jobs=-1) +2024-12-04 11:55:59,730:INFO:create_model() successfully completed...................................... +2024-12-04 11:55:59,857:INFO:SubProcess create_model() end ================================== +2024-12-04 11:55:59,857:INFO:Creating metrics dataframe +2024-12-04 11:55:59,863:INFO:Initializing Lasso Regression +2024-12-04 11:55:59,863:INFO:Total runtime is 0.0699089248975118 minutes +2024-12-04 11:55:59,866:INFO:SubProcess create_model() called ================================== +2024-12-04 11:55:59,867:INFO:Initializing create_model() +2024-12-04 11:55:59,867:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:55:59,867:INFO:Checking exceptions +2024-12-04 11:55:59,867:INFO:Importing libraries +2024-12-04 11:55:59,867:INFO:Copying training dataset +2024-12-04 11:55:59,873:INFO:Defining folds +2024-12-04 11:55:59,875:INFO:Declaring metric variables +2024-12-04 11:55:59,880:INFO:Importing untrained model +2024-12-04 11:55:59,883:INFO:Lasso Regression Imported successfully +2024-12-04 11:55:59,889:INFO:Starting cross validation +2024-12-04 11:55:59,893:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:02,802:INFO:Calculating mean and std +2024-12-04 11:56:02,803:INFO:Creating metrics dataframe +2024-12-04 11:56:02,807:INFO:Uploading results into container +2024-12-04 11:56:02,807:INFO:Uploading model into container now +2024-12-04 11:56:02,807:INFO:_master_model_container: 56 +2024-12-04 11:56:02,807:INFO:_display_container: 2 +2024-12-04 11:56:02,809:INFO:Lasso(random_state=123) +2024-12-04 11:56:02,809:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:02,945:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:02,945:INFO:Creating metrics dataframe +2024-12-04 11:56:02,953:INFO:Initializing Ridge Regression +2024-12-04 11:56:02,953:INFO:Total runtime is 0.12140721480051676 minutes +2024-12-04 11:56:02,957:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:02,957:INFO:Initializing create_model() +2024-12-04 11:56:02,957:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:02,957:INFO:Checking exceptions +2024-12-04 11:56:02,957:INFO:Importing libraries +2024-12-04 11:56:02,957:INFO:Copying training dataset +2024-12-04 11:56:02,967:INFO:Defining folds +2024-12-04 11:56:02,967:INFO:Declaring metric variables +2024-12-04 11:56:02,971:INFO:Importing untrained model +2024-12-04 11:56:02,975:INFO:Ridge Regression Imported successfully +2024-12-04 11:56:02,981:INFO:Starting cross validation +2024-12-04 11:56:02,983:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:03,541:INFO:Calculating mean and std +2024-12-04 11:56:03,543:INFO:Creating metrics dataframe +2024-12-04 11:56:03,544:INFO:Uploading results into container +2024-12-04 11:56:03,545:INFO:Uploading model into container now +2024-12-04 11:56:03,545:INFO:_master_model_container: 57 +2024-12-04 11:56:03,545:INFO:_display_container: 2 +2024-12-04 11:56:03,545:INFO:Ridge(random_state=123) +2024-12-04 11:56:03,545:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:03,668:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:03,670:INFO:Creating metrics dataframe +2024-12-04 11:56:03,673:INFO:Initializing Elastic Net +2024-12-04 11:56:03,673:INFO:Total runtime is 0.1334158460299174 minutes +2024-12-04 11:56:03,677:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:03,677:INFO:Initializing create_model() +2024-12-04 11:56:03,677:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:03,677:INFO:Checking exceptions +2024-12-04 11:56:03,677:INFO:Importing libraries +2024-12-04 11:56:03,677:INFO:Copying training dataset +2024-12-04 11:56:03,686:INFO:Defining folds +2024-12-04 11:56:03,686:INFO:Declaring metric variables +2024-12-04 11:56:03,691:INFO:Importing untrained model +2024-12-04 11:56:03,693:INFO:Elastic Net Imported successfully +2024-12-04 11:56:03,703:INFO:Starting cross validation +2024-12-04 11:56:03,705:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:04,137:INFO:Calculating mean and std +2024-12-04 11:56:04,138:INFO:Creating metrics dataframe +2024-12-04 11:56:04,140:INFO:Uploading results into container +2024-12-04 11:56:04,140:INFO:Uploading model into container now +2024-12-04 11:56:04,140:INFO:_master_model_container: 58 +2024-12-04 11:56:04,140:INFO:_display_container: 2 +2024-12-04 11:56:04,140:INFO:ElasticNet(random_state=123) +2024-12-04 11:56:04,141:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:04,263:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:04,263:INFO:Creating metrics dataframe +2024-12-04 11:56:04,270:INFO:Initializing Least Angle Regression +2024-12-04 11:56:04,270:INFO:Total runtime is 0.14336495796839396 minutes +2024-12-04 11:56:04,273:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:04,273:INFO:Initializing create_model() +2024-12-04 11:56:04,275:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:04,275:INFO:Checking exceptions +2024-12-04 11:56:04,275:INFO:Importing libraries +2024-12-04 11:56:04,275:INFO:Copying training dataset +2024-12-04 11:56:04,284:INFO:Defining folds +2024-12-04 11:56:04,285:INFO:Declaring metric variables +2024-12-04 11:56:04,289:INFO:Importing untrained model +2024-12-04 11:56:04,293:INFO:Least Angle Regression Imported successfully +2024-12-04 11:56:04,303:INFO:Starting cross validation +2024-12-04 11:56:04,305:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:04,737:INFO:Calculating mean and std +2024-12-04 11:56:04,739:INFO:Creating metrics dataframe +2024-12-04 11:56:04,739:INFO:Uploading results into container +2024-12-04 11:56:04,739:INFO:Uploading model into container now +2024-12-04 11:56:04,739:INFO:_master_model_container: 59 +2024-12-04 11:56:04,739:INFO:_display_container: 2 +2024-12-04 11:56:04,739:INFO:Lars(random_state=123) +2024-12-04 11:56:04,739:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:04,880:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:04,880:INFO:Creating metrics dataframe +2024-12-04 11:56:04,885:INFO:Initializing Lasso Least Angle Regression +2024-12-04 11:56:04,885:INFO:Total runtime is 0.15362093846003214 minutes +2024-12-04 11:56:04,889:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:04,893:INFO:Initializing create_model() +2024-12-04 11:56:04,893:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:04,893:INFO:Checking exceptions +2024-12-04 11:56:04,893:INFO:Importing libraries +2024-12-04 11:56:04,893:INFO:Copying training dataset +2024-12-04 11:56:04,903:INFO:Defining folds +2024-12-04 11:56:04,903:INFO:Declaring metric variables +2024-12-04 11:56:04,905:INFO:Importing untrained model +2024-12-04 11:56:04,909:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 11:56:04,917:INFO:Starting cross validation +2024-12-04 11:56:04,918:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:05,332:INFO:Calculating mean and std +2024-12-04 11:56:05,332:INFO:Creating metrics dataframe +2024-12-04 11:56:05,334:INFO:Uploading results into container +2024-12-04 11:56:05,334:INFO:Uploading model into container now +2024-12-04 11:56:05,334:INFO:_master_model_container: 60 +2024-12-04 11:56:05,334:INFO:_display_container: 2 +2024-12-04 11:56:05,334:INFO:LassoLars(random_state=123) +2024-12-04 11:56:05,334:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:05,459:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:05,459:INFO:Creating metrics dataframe +2024-12-04 11:56:05,466:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 11:56:05,466:INFO:Total runtime is 0.1632896860440572 minutes +2024-12-04 11:56:05,469:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:05,470:INFO:Initializing create_model() +2024-12-04 11:56:05,470:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:05,470:INFO:Checking exceptions +2024-12-04 11:56:05,470:INFO:Importing libraries +2024-12-04 11:56:05,470:INFO:Copying training dataset +2024-12-04 11:56:05,477:INFO:Defining folds +2024-12-04 11:56:05,477:INFO:Declaring metric variables +2024-12-04 11:56:05,481:INFO:Importing untrained model +2024-12-04 11:56:05,485:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 11:56:05,490:INFO:Starting cross validation +2024-12-04 11:56:05,490:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:05,931:INFO:Calculating mean and std +2024-12-04 11:56:05,932:INFO:Creating metrics dataframe +2024-12-04 11:56:05,935:INFO:Uploading results into container +2024-12-04 11:56:05,935:INFO:Uploading model into container now +2024-12-04 11:56:05,935:INFO:_master_model_container: 61 +2024-12-04 11:56:05,935:INFO:_display_container: 2 +2024-12-04 11:56:05,935:INFO:OrthogonalMatchingPursuit() +2024-12-04 11:56:05,935:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:06,065:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:06,065:INFO:Creating metrics dataframe +2024-12-04 11:56:06,073:INFO:Initializing Bayesian Ridge +2024-12-04 11:56:06,073:INFO:Total runtime is 0.1734187642733256 minutes +2024-12-04 11:56:06,077:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:06,078:INFO:Initializing create_model() +2024-12-04 11:56:06,078:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:06,078:INFO:Checking exceptions +2024-12-04 11:56:06,078:INFO:Importing libraries +2024-12-04 11:56:06,078:INFO:Copying training dataset +2024-12-04 11:56:06,084:INFO:Defining folds +2024-12-04 11:56:06,084:INFO:Declaring metric variables +2024-12-04 11:56:06,089:INFO:Importing untrained model +2024-12-04 11:56:06,093:INFO:Bayesian Ridge Imported successfully +2024-12-04 11:56:06,099:INFO:Starting cross validation +2024-12-04 11:56:06,103:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:06,557:INFO:Calculating mean and std +2024-12-04 11:56:06,557:INFO:Creating metrics dataframe +2024-12-04 11:56:06,559:INFO:Uploading results into container +2024-12-04 11:56:06,559:INFO:Uploading model into container now +2024-12-04 11:56:06,560:INFO:_master_model_container: 62 +2024-12-04 11:56:06,560:INFO:_display_container: 2 +2024-12-04 11:56:06,560:INFO:BayesianRidge() +2024-12-04 11:56:06,560:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:06,686:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:06,686:INFO:Creating metrics dataframe +2024-12-04 11:56:06,693:INFO:Initializing Passive Aggressive Regressor +2024-12-04 11:56:06,693:INFO:Total runtime is 0.18374099731445312 minutes +2024-12-04 11:56:06,693:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:06,693:INFO:Initializing create_model() +2024-12-04 11:56:06,693:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:06,693:INFO:Checking exceptions +2024-12-04 11:56:06,693:INFO:Importing libraries +2024-12-04 11:56:06,693:INFO:Copying training dataset +2024-12-04 11:56:06,705:INFO:Defining folds +2024-12-04 11:56:06,705:INFO:Declaring metric variables +2024-12-04 11:56:06,709:INFO:Importing untrained model +2024-12-04 11:56:06,713:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 11:56:06,720:INFO:Starting cross validation +2024-12-04 11:56:06,720:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:07,171:INFO:Calculating mean and std +2024-12-04 11:56:07,171:INFO:Creating metrics dataframe +2024-12-04 11:56:07,174:INFO:Uploading results into container +2024-12-04 11:56:07,174:INFO:Uploading model into container now +2024-12-04 11:56:07,175:INFO:_master_model_container: 63 +2024-12-04 11:56:07,175:INFO:_display_container: 2 +2024-12-04 11:56:07,175:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 11:56:07,175:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:07,289:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:07,289:INFO:Creating metrics dataframe +2024-12-04 11:56:07,297:INFO:Initializing Huber Regressor +2024-12-04 11:56:07,297:INFO:Total runtime is 0.19381462732950847 minutes +2024-12-04 11:56:07,297:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:07,303:INFO:Initializing create_model() +2024-12-04 11:56:07,303:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:07,303:INFO:Checking exceptions +2024-12-04 11:56:07,303:INFO:Importing libraries +2024-12-04 11:56:07,303:INFO:Copying training dataset +2024-12-04 11:56:07,309:INFO:Defining folds +2024-12-04 11:56:07,309:INFO:Declaring metric variables +2024-12-04 11:56:07,315:INFO:Importing untrained model +2024-12-04 11:56:07,319:INFO:Huber Regressor Imported successfully +2024-12-04 11:56:07,325:INFO:Starting cross validation +2024-12-04 11:56:07,327:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:07,884:INFO:Calculating mean and std +2024-12-04 11:56:07,884:INFO:Creating metrics dataframe +2024-12-04 11:56:07,885:INFO:Uploading results into container +2024-12-04 11:56:07,885:INFO:Uploading model into container now +2024-12-04 11:56:07,885:INFO:_master_model_container: 64 +2024-12-04 11:56:07,885:INFO:_display_container: 2 +2024-12-04 11:56:07,885:INFO:HuberRegressor() +2024-12-04 11:56:07,885:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:08,003:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:08,005:INFO:Creating metrics dataframe +2024-12-04 11:56:08,012:INFO:Initializing K Neighbors Regressor +2024-12-04 11:56:08,012:INFO:Total runtime is 0.20573228200276694 minutes +2024-12-04 11:56:08,014:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:08,014:INFO:Initializing create_model() +2024-12-04 11:56:08,014:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:08,014:INFO:Checking exceptions +2024-12-04 11:56:08,014:INFO:Importing libraries +2024-12-04 11:56:08,014:INFO:Copying training dataset +2024-12-04 11:56:08,024:INFO:Defining folds +2024-12-04 11:56:08,024:INFO:Declaring metric variables +2024-12-04 11:56:08,027:INFO:Importing untrained model +2024-12-04 11:56:08,031:INFO:K Neighbors Regressor Imported successfully +2024-12-04 11:56:08,038:INFO:Starting cross validation +2024-12-04 11:56:08,039:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:08,642:INFO:Calculating mean and std +2024-12-04 11:56:08,643:INFO:Creating metrics dataframe +2024-12-04 11:56:08,643:INFO:Uploading results into container +2024-12-04 11:56:08,643:INFO:Uploading model into container now +2024-12-04 11:56:08,643:INFO:_master_model_container: 65 +2024-12-04 11:56:08,643:INFO:_display_container: 2 +2024-12-04 11:56:08,646:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 11:56:08,646:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:08,770:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:08,770:INFO:Creating metrics dataframe +2024-12-04 11:56:08,778:INFO:Initializing Decision Tree Regressor +2024-12-04 11:56:08,778:INFO:Total runtime is 0.21849206686019898 minutes +2024-12-04 11:56:08,782:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:08,783:INFO:Initializing create_model() +2024-12-04 11:56:08,783:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:08,783:INFO:Checking exceptions +2024-12-04 11:56:08,783:INFO:Importing libraries +2024-12-04 11:56:08,783:INFO:Copying training dataset +2024-12-04 11:56:08,789:INFO:Defining folds +2024-12-04 11:56:08,789:INFO:Declaring metric variables +2024-12-04 11:56:08,793:INFO:Importing untrained model +2024-12-04 11:56:08,798:INFO:Decision Tree Regressor Imported successfully +2024-12-04 11:56:08,803:INFO:Starting cross validation +2024-12-04 11:56:08,805:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:09,309:INFO:Calculating mean and std +2024-12-04 11:56:09,309:INFO:Creating metrics dataframe +2024-12-04 11:56:09,311:INFO:Uploading results into container +2024-12-04 11:56:09,311:INFO:Uploading model into container now +2024-12-04 11:56:09,312:INFO:_master_model_container: 66 +2024-12-04 11:56:09,312:INFO:_display_container: 2 +2024-12-04 11:56:09,313:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 11:56:09,313:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:09,436:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:09,436:INFO:Creating metrics dataframe +2024-12-04 11:56:09,444:INFO:Initializing Random Forest Regressor +2024-12-04 11:56:09,444:INFO:Total runtime is 0.22959585189819337 minutes +2024-12-04 11:56:09,444:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:09,444:INFO:Initializing create_model() +2024-12-04 11:56:09,444:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:09,444:INFO:Checking exceptions +2024-12-04 11:56:09,448:INFO:Importing libraries +2024-12-04 11:56:09,448:INFO:Copying training dataset +2024-12-04 11:56:09,457:INFO:Defining folds +2024-12-04 11:56:09,457:INFO:Declaring metric variables +2024-12-04 11:56:09,459:INFO:Importing untrained model +2024-12-04 11:56:09,462:INFO:Random Forest Regressor Imported successfully +2024-12-04 11:56:09,468:INFO:Starting cross validation +2024-12-04 11:56:09,470:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:16,156:INFO:Calculating mean and std +2024-12-04 11:56:16,157:INFO:Creating metrics dataframe +2024-12-04 11:56:16,160:INFO:Uploading results into container +2024-12-04 11:56:16,161:INFO:Uploading model into container now +2024-12-04 11:56:16,161:INFO:_master_model_container: 67 +2024-12-04 11:56:16,161:INFO:_display_container: 2 +2024-12-04 11:56:16,162:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:56:16,162:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:16,309:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:16,309:INFO:Creating metrics dataframe +2024-12-04 11:56:16,320:INFO:Initializing Extra Trees Regressor +2024-12-04 11:56:16,320:INFO:Total runtime is 0.344200340906779 minutes +2024-12-04 11:56:16,325:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:16,326:INFO:Initializing create_model() +2024-12-04 11:56:16,326:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:16,326:INFO:Checking exceptions +2024-12-04 11:56:16,326:INFO:Importing libraries +2024-12-04 11:56:16,326:INFO:Copying training dataset +2024-12-04 11:56:16,335:INFO:Defining folds +2024-12-04 11:56:16,335:INFO:Declaring metric variables +2024-12-04 11:56:16,342:INFO:Importing untrained model +2024-12-04 11:56:16,342:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:56:16,353:INFO:Starting cross validation +2024-12-04 11:56:16,355:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:20,353:INFO:Calculating mean and std +2024-12-04 11:56:20,355:INFO:Creating metrics dataframe +2024-12-04 11:56:20,358:INFO:Uploading results into container +2024-12-04 11:56:20,359:INFO:Uploading model into container now +2024-12-04 11:56:20,360:INFO:_master_model_container: 68 +2024-12-04 11:56:20,360:INFO:_display_container: 2 +2024-12-04 11:56:20,361:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:56:20,361:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:20,535:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:20,535:INFO:Creating metrics dataframe +2024-12-04 11:56:20,551:INFO:Initializing AdaBoost Regressor +2024-12-04 11:56:20,551:INFO:Total runtime is 0.41471450328826903 minutes +2024-12-04 11:56:20,555:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:20,555:INFO:Initializing create_model() +2024-12-04 11:56:20,555:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:20,555:INFO:Checking exceptions +2024-12-04 11:56:20,555:INFO:Importing libraries +2024-12-04 11:56:20,555:INFO:Copying training dataset +2024-12-04 11:56:20,563:INFO:Defining folds +2024-12-04 11:56:20,564:INFO:Declaring metric variables +2024-12-04 11:56:20,568:INFO:Importing untrained model +2024-12-04 11:56:20,571:INFO:AdaBoost Regressor Imported successfully +2024-12-04 11:56:20,577:INFO:Starting cross validation +2024-12-04 11:56:20,581:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:21,853:INFO:Calculating mean and std +2024-12-04 11:56:21,853:INFO:Creating metrics dataframe +2024-12-04 11:56:21,855:INFO:Uploading results into container +2024-12-04 11:56:21,855:INFO:Uploading model into container now +2024-12-04 11:56:21,857:INFO:_master_model_container: 69 +2024-12-04 11:56:21,857:INFO:_display_container: 2 +2024-12-04 11:56:21,857:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 11:56:21,857:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:21,985:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:21,985:INFO:Creating metrics dataframe +2024-12-04 11:56:21,993:INFO:Initializing Gradient Boosting Regressor +2024-12-04 11:56:21,993:INFO:Total runtime is 0.4387464483579 minutes +2024-12-04 11:56:21,997:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:21,998:INFO:Initializing create_model() +2024-12-04 11:56:21,998:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:21,998:INFO:Checking exceptions +2024-12-04 11:56:21,998:INFO:Importing libraries +2024-12-04 11:56:21,998:INFO:Copying training dataset +2024-12-04 11:56:22,007:INFO:Defining folds +2024-12-04 11:56:22,007:INFO:Declaring metric variables +2024-12-04 11:56:22,009:INFO:Importing untrained model +2024-12-04 11:56:22,014:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 11:56:22,021:INFO:Starting cross validation +2024-12-04 11:56:22,023:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:24,048:INFO:Calculating mean and std +2024-12-04 11:56:24,048:INFO:Creating metrics dataframe +2024-12-04 11:56:24,051:INFO:Uploading results into container +2024-12-04 11:56:24,051:INFO:Uploading model into container now +2024-12-04 11:56:24,051:INFO:_master_model_container: 70 +2024-12-04 11:56:24,051:INFO:_display_container: 2 +2024-12-04 11:56:24,051:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 11:56:24,051:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:24,167:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:24,167:INFO:Creating metrics dataframe +2024-12-04 11:56:24,176:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 11:56:24,176:INFO:Total runtime is 0.47513338724772136 minutes +2024-12-04 11:56:24,177:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:24,180:INFO:Initializing create_model() +2024-12-04 11:56:24,180:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:24,180:INFO:Checking exceptions +2024-12-04 11:56:24,180:INFO:Importing libraries +2024-12-04 11:56:24,180:INFO:Copying training dataset +2024-12-04 11:56:24,188:INFO:Defining folds +2024-12-04 11:56:24,188:INFO:Declaring metric variables +2024-12-04 11:56:24,190:INFO:Importing untrained model +2024-12-04 11:56:24,193:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 11:56:24,201:INFO:Starting cross validation +2024-12-04 11:56:24,202:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:25,098:INFO:Calculating mean and std +2024-12-04 11:56:25,098:INFO:Creating metrics dataframe +2024-12-04 11:56:25,102:INFO:Uploading results into container +2024-12-04 11:56:25,103:INFO:Uploading model into container now +2024-12-04 11:56:25,103:INFO:_master_model_container: 71 +2024-12-04 11:56:25,103:INFO:_display_container: 2 +2024-12-04 11:56:25,103:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:56:25,103:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:25,239:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:25,239:INFO:Creating metrics dataframe +2024-12-04 11:56:25,250:INFO:Initializing Dummy Regressor +2024-12-04 11:56:25,250:INFO:Total runtime is 0.4930326302846273 minutes +2024-12-04 11:56:25,253:INFO:SubProcess create_model() called ================================== +2024-12-04 11:56:25,253:INFO:Initializing create_model() +2024-12-04 11:56:25,253:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:25,253:INFO:Checking exceptions +2024-12-04 11:56:25,253:INFO:Importing libraries +2024-12-04 11:56:25,253:INFO:Copying training dataset +2024-12-04 11:56:25,262:INFO:Defining folds +2024-12-04 11:56:25,262:INFO:Declaring metric variables +2024-12-04 11:56:25,266:INFO:Importing untrained model +2024-12-04 11:56:25,270:INFO:Dummy Regressor Imported successfully +2024-12-04 11:56:25,276:INFO:Starting cross validation +2024-12-04 11:56:25,278:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:56:25,650:INFO:Calculating mean and std +2024-12-04 11:56:25,650:INFO:Creating metrics dataframe +2024-12-04 11:56:25,653:INFO:Uploading results into container +2024-12-04 11:56:25,654:INFO:Uploading model into container now +2024-12-04 11:56:25,654:INFO:_master_model_container: 72 +2024-12-04 11:56:25,654:INFO:_display_container: 2 +2024-12-04 11:56:25,654:INFO:DummyRegressor() +2024-12-04 11:56:25,654:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:25,774:INFO:SubProcess create_model() end ================================== +2024-12-04 11:56:25,774:INFO:Creating metrics dataframe +2024-12-04 11:56:25,783:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 11:56:25,793:INFO:Initializing create_model() +2024-12-04 11:56:25,793:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:56:25,793:INFO:Checking exceptions +2024-12-04 11:56:25,794:INFO:Importing libraries +2024-12-04 11:56:25,794:INFO:Copying training dataset +2024-12-04 11:56:25,804:INFO:Defining folds +2024-12-04 11:56:25,804:INFO:Declaring metric variables +2024-12-04 11:56:25,805:INFO:Importing untrained model +2024-12-04 11:56:25,805:INFO:Declaring custom model +2024-12-04 11:56:25,805:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:56:25,805:INFO:Cross validation set to False +2024-12-04 11:56:25,805:INFO:Fitting Model +2024-12-04 11:56:26,964:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:56:26,964:INFO:create_model() successfully completed...................................... +2024-12-04 11:56:27,115:INFO:_master_model_container: 72 +2024-12-04 11:56:27,115:INFO:_display_container: 2 +2024-12-04 11:56:27,116:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:56:27,116:INFO:compare_models() successfully completed...................................... +2024-12-04 11:58:38,059:INFO:PyCaret RegressionExperiment +2024-12-04 11:58:38,059:INFO:Logging name: reg-default-name +2024-12-04 11:58:38,059:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:58:38,059:INFO:version 3.3.2 +2024-12-04 11:58:38,059:INFO:Initializing setup() +2024-12-04 11:58:38,059:INFO:self.USI: 4f54 +2024-12-04 11:58:38,059:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:58:38,061:INFO:Checking environment +2024-12-04 11:58:38,061:INFO:python_version: 3.11.9 +2024-12-04 11:58:38,061:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:58:38,061:INFO:machine: AMD64 +2024-12-04 11:58:38,061:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:58:38,067:INFO:Memory: svmem(total=17007292416, available=2544963584, percent=85.0, used=14462328832, free=2544963584) +2024-12-04 11:58:38,067:INFO:Physical Core: 4 +2024-12-04 11:58:38,067:INFO:Logical Core: 8 +2024-12-04 11:58:38,067:INFO:Checking libraries +2024-12-04 11:58:38,067:INFO:System: +2024-12-04 11:58:38,067:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:58:38,067:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:58:38,067:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:58:38,067:INFO:PyCaret required dependencies: +2024-12-04 11:58:38,067:INFO: pip: 24.3.1 +2024-12-04 11:58:38,067:INFO: setuptools: 65.5.0 +2024-12-04 11:58:38,067:INFO: pycaret: 3.3.2 +2024-12-04 11:58:38,067:INFO: IPython: 8.12.3 +2024-12-04 11:58:38,067:INFO: ipywidgets: 8.1.5 +2024-12-04 11:58:38,067:INFO: tqdm: 4.67.1 +2024-12-04 11:58:38,068:INFO: numpy: 1.26.4 +2024-12-04 11:58:38,068:INFO: pandas: 2.1.4 +2024-12-04 11:58:38,068:INFO: jinja2: 3.1.4 +2024-12-04 11:58:38,068:INFO: scipy: 1.11.4 +2024-12-04 11:58:38,068:INFO: joblib: 1.3.2 +2024-12-04 11:58:38,068:INFO: sklearn: 1.4.2 +2024-12-04 11:58:38,068:INFO: pyod: 2.0.2 +2024-12-04 11:58:38,068:INFO: imblearn: 0.12.4 +2024-12-04 11:58:38,068:INFO: category_encoders: 2.6.4 +2024-12-04 11:58:38,068:INFO: lightgbm: 4.5.0 +2024-12-04 11:58:38,068:INFO: numba: 0.60.0 +2024-12-04 11:58:38,068:INFO: requests: 2.32.3 +2024-12-04 11:58:38,068:INFO: matplotlib: 3.7.5 +2024-12-04 11:58:38,068:INFO: scikitplot: 0.3.7 +2024-12-04 11:58:38,068:INFO: yellowbrick: 1.5 +2024-12-04 11:58:38,068:INFO: plotly: 5.24.1 +2024-12-04 11:58:38,068:INFO: plotly-resampler: Not installed +2024-12-04 11:58:38,068:INFO: kaleido: 0.2.1 +2024-12-04 11:58:38,068:INFO: schemdraw: 0.15 +2024-12-04 11:58:38,068:INFO: statsmodels: 0.14.4 +2024-12-04 11:58:38,068:INFO: sktime: 0.26.0 +2024-12-04 11:58:38,068:INFO: tbats: 1.1.3 +2024-12-04 11:58:38,068:INFO: pmdarima: 2.0.4 +2024-12-04 11:58:38,068:INFO: psutil: 6.1.0 +2024-12-04 11:58:38,068:INFO: markupsafe: 3.0.2 +2024-12-04 11:58:38,068:INFO: pickle5: Not installed +2024-12-04 11:58:38,068:INFO: cloudpickle: 3.1.0 +2024-12-04 11:58:38,068:INFO: deprecation: 2.1.0 +2024-12-04 11:58:38,068:INFO: xxhash: 3.5.0 +2024-12-04 11:58:38,068:INFO: wurlitzer: Not installed +2024-12-04 11:58:38,068:INFO:PyCaret optional dependencies: +2024-12-04 11:58:38,068:INFO: shap: Not installed +2024-12-04 11:58:38,070:INFO: interpret: Not installed +2024-12-04 11:58:38,070:INFO: umap: Not installed +2024-12-04 11:58:38,070:INFO: ydata_profiling: Not installed +2024-12-04 11:58:38,070:INFO: explainerdashboard: Not installed +2024-12-04 11:58:38,070:INFO: autoviz: Not installed +2024-12-04 11:58:38,070:INFO: fairlearn: Not installed +2024-12-04 11:58:38,070:INFO: deepchecks: Not installed +2024-12-04 11:58:38,070:INFO: xgboost: Not installed +2024-12-04 11:58:38,070:INFO: catboost: Not installed +2024-12-04 11:58:38,070:INFO: kmodes: Not installed +2024-12-04 11:58:38,070:INFO: mlxtend: Not installed +2024-12-04 11:58:38,070:INFO: statsforecast: Not installed +2024-12-04 11:58:38,070:INFO: tune_sklearn: Not installed +2024-12-04 11:58:38,070:INFO: ray: Not installed +2024-12-04 11:58:38,070:INFO: hyperopt: Not installed +2024-12-04 11:58:38,070:INFO: optuna: Not installed +2024-12-04 11:58:38,070:INFO: skopt: Not installed +2024-12-04 11:58:38,070:INFO: mlflow: 2.18.0 +2024-12-04 11:58:38,070:INFO: gradio: Not installed +2024-12-04 11:58:38,070:INFO: fastapi: Not installed +2024-12-04 11:58:38,070:INFO: uvicorn: 0.32.1 +2024-12-04 11:58:38,070:INFO: m2cgen: Not installed +2024-12-04 11:58:38,070:INFO: evidently: Not installed +2024-12-04 11:58:38,070:INFO: fugue: Not installed +2024-12-04 11:58:38,070:INFO: streamlit: 1.40.1 +2024-12-04 11:58:38,070:INFO: prophet: Not installed +2024-12-04 11:58:38,070:INFO:None +2024-12-04 11:58:38,070:INFO:Set up data. +2024-12-04 11:58:38,081:INFO:Set up folding strategy. +2024-12-04 11:58:38,081:INFO:Set up train/test split. +2024-12-04 11:58:38,087:INFO:Set up index. +2024-12-04 11:58:38,088:INFO:Assigning column types. +2024-12-04 11:58:38,093:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 11:58:38,093:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,096:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,101:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,155:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,194:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,194:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,194:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,194:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,202:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,205:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,262:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,302:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,303:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,303:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,303:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 11:58:38,307:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,311:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,364:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,410:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,410:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,411:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,415:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,418:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,472:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,513:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,513:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,513:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,513:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 11:58:38,527:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,586:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,628:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,630:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,630:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,639:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,701:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,741:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,743:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,743:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,743:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 11:58:38,807:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,843:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,843:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,849:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,917:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,963:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 11:58:38,964:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,965:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:38,965:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 11:58:39,027:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:39,070:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,070:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,139:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 11:58:39,180:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,180:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,180:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 11:58:39,307:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,307:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,419:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,419:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:39,420:INFO:Preparing preprocessing pipeline... +2024-12-04 11:58:39,420:INFO:Set up simple imputation. +2024-12-04 11:58:39,423:INFO:Set up encoding of categorical features. +2024-12-04 11:58:39,423:INFO:Set up column transformation. +2024-12-04 11:58:39,423:INFO:Set up feature normalization. +2024-12-04 11:58:39,742:INFO:Finished creating preprocessing pipeline. +2024-12-04 11:58:39,755:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 11:58:39,755:INFO:Creating final display dataframe. +2024-12-04 11:58:40,066:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 15) +4 Transformed data shape (11376, 31) +5 Transformed train set shape (7963, 31) +6 Transformed test set shape (3413, 31) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 5 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI 4f54 +2024-12-04 11:58:40,180:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:40,180:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:40,280:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:40,280:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:58:40,282:INFO:setup() successfully completed in 2.24s............... +2024-12-04 11:58:43,263:INFO:Initializing compare_models() +2024-12-04 11:58:43,263:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 11:58:43,263:INFO:Checking exceptions +2024-12-04 11:58:43,267:INFO:Preparing display monitor +2024-12-04 11:58:43,288:INFO:Initializing Linear Regression +2024-12-04 11:58:43,288:INFO:Total runtime is 0.0 minutes +2024-12-04 11:58:43,291:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:43,291:INFO:Initializing create_model() +2024-12-04 11:58:43,291:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:43,291:INFO:Checking exceptions +2024-12-04 11:58:43,291:INFO:Importing libraries +2024-12-04 11:58:43,291:INFO:Copying training dataset +2024-12-04 11:58:43,303:INFO:Defining folds +2024-12-04 11:58:43,303:INFO:Declaring metric variables +2024-12-04 11:58:43,307:INFO:Importing untrained model +2024-12-04 11:58:43,309:INFO:Linear Regression Imported successfully +2024-12-04 11:58:43,323:INFO:Starting cross validation +2024-12-04 11:58:43,325:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:43,788:INFO:Calculating mean and std +2024-12-04 11:58:43,788:INFO:Creating metrics dataframe +2024-12-04 11:58:43,789:INFO:Uploading results into container +2024-12-04 11:58:43,789:INFO:Uploading model into container now +2024-12-04 11:58:43,789:INFO:_master_model_container: 1 +2024-12-04 11:58:43,789:INFO:_display_container: 2 +2024-12-04 11:58:43,789:INFO:LinearRegression(n_jobs=-1) +2024-12-04 11:58:43,789:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:43,923:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:43,924:INFO:Creating metrics dataframe +2024-12-04 11:58:43,929:INFO:Initializing Lasso Regression +2024-12-04 11:58:43,930:INFO:Total runtime is 0.01070640484491984 minutes +2024-12-04 11:58:43,933:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:43,933:INFO:Initializing create_model() +2024-12-04 11:58:43,933:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:43,933:INFO:Checking exceptions +2024-12-04 11:58:43,933:INFO:Importing libraries +2024-12-04 11:58:43,933:INFO:Copying training dataset +2024-12-04 11:58:43,940:INFO:Defining folds +2024-12-04 11:58:43,940:INFO:Declaring metric variables +2024-12-04 11:58:43,942:INFO:Importing untrained model +2024-12-04 11:58:43,947:INFO:Lasso Regression Imported successfully +2024-12-04 11:58:43,952:INFO:Starting cross validation +2024-12-04 11:58:43,955:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:44,518:INFO:Calculating mean and std +2024-12-04 11:58:44,518:INFO:Creating metrics dataframe +2024-12-04 11:58:44,521:INFO:Uploading results into container +2024-12-04 11:58:44,521:INFO:Uploading model into container now +2024-12-04 11:58:44,522:INFO:_master_model_container: 2 +2024-12-04 11:58:44,522:INFO:_display_container: 2 +2024-12-04 11:58:44,522:INFO:Lasso(random_state=123) +2024-12-04 11:58:44,523:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:44,650:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:44,650:INFO:Creating metrics dataframe +2024-12-04 11:58:44,655:INFO:Initializing Ridge Regression +2024-12-04 11:58:44,655:INFO:Total runtime is 0.022793742020924886 minutes +2024-12-04 11:58:44,657:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:44,658:INFO:Initializing create_model() +2024-12-04 11:58:44,658:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:44,658:INFO:Checking exceptions +2024-12-04 11:58:44,658:INFO:Importing libraries +2024-12-04 11:58:44,658:INFO:Copying training dataset +2024-12-04 11:58:44,666:INFO:Defining folds +2024-12-04 11:58:44,666:INFO:Declaring metric variables +2024-12-04 11:58:44,670:INFO:Importing untrained model +2024-12-04 11:58:44,674:INFO:Ridge Regression Imported successfully +2024-12-04 11:58:44,680:INFO:Starting cross validation +2024-12-04 11:58:44,682:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:45,066:INFO:Calculating mean and std +2024-12-04 11:58:45,066:INFO:Creating metrics dataframe +2024-12-04 11:58:45,068:INFO:Uploading results into container +2024-12-04 11:58:45,068:INFO:Uploading model into container now +2024-12-04 11:58:45,068:INFO:_master_model_container: 3 +2024-12-04 11:58:45,068:INFO:_display_container: 2 +2024-12-04 11:58:45,068:INFO:Ridge(random_state=123) +2024-12-04 11:58:45,068:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:45,192:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:45,193:INFO:Creating metrics dataframe +2024-12-04 11:58:45,200:INFO:Initializing Elastic Net +2024-12-04 11:58:45,200:INFO:Total runtime is 0.03187320629755656 minutes +2024-12-04 11:58:45,204:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:45,204:INFO:Initializing create_model() +2024-12-04 11:58:45,204:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:45,204:INFO:Checking exceptions +2024-12-04 11:58:45,204:INFO:Importing libraries +2024-12-04 11:58:45,204:INFO:Copying training dataset +2024-12-04 11:58:45,212:INFO:Defining folds +2024-12-04 11:58:45,212:INFO:Declaring metric variables +2024-12-04 11:58:45,215:INFO:Importing untrained model +2024-12-04 11:58:45,218:INFO:Elastic Net Imported successfully +2024-12-04 11:58:45,228:INFO:Starting cross validation +2024-12-04 11:58:45,230:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:45,633:INFO:Calculating mean and std +2024-12-04 11:58:45,634:INFO:Creating metrics dataframe +2024-12-04 11:58:45,635:INFO:Uploading results into container +2024-12-04 11:58:45,636:INFO:Uploading model into container now +2024-12-04 11:58:45,636:INFO:_master_model_container: 4 +2024-12-04 11:58:45,636:INFO:_display_container: 2 +2024-12-04 11:58:45,637:INFO:ElasticNet(random_state=123) +2024-12-04 11:58:45,637:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:45,759:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:45,759:INFO:Creating metrics dataframe +2024-12-04 11:58:45,763:INFO:Initializing Least Angle Regression +2024-12-04 11:58:45,763:INFO:Total runtime is 0.04125197331110636 minutes +2024-12-04 11:58:45,766:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:45,766:INFO:Initializing create_model() +2024-12-04 11:58:45,766:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:45,766:INFO:Checking exceptions +2024-12-04 11:58:45,766:INFO:Importing libraries +2024-12-04 11:58:45,766:INFO:Copying training dataset +2024-12-04 11:58:45,775:INFO:Defining folds +2024-12-04 11:58:45,776:INFO:Declaring metric variables +2024-12-04 11:58:45,777:INFO:Importing untrained model +2024-12-04 11:58:45,784:INFO:Least Angle Regression Imported successfully +2024-12-04 11:58:45,790:INFO:Starting cross validation +2024-12-04 11:58:45,791:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:46,174:INFO:Calculating mean and std +2024-12-04 11:58:46,175:INFO:Creating metrics dataframe +2024-12-04 11:58:46,177:INFO:Uploading results into container +2024-12-04 11:58:46,177:INFO:Uploading model into container now +2024-12-04 11:58:46,177:INFO:_master_model_container: 5 +2024-12-04 11:58:46,177:INFO:_display_container: 2 +2024-12-04 11:58:46,177:INFO:Lars(random_state=123) +2024-12-04 11:58:46,177:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:46,293:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:46,293:INFO:Creating metrics dataframe +2024-12-04 11:58:46,299:INFO:Initializing Lasso Least Angle Regression +2024-12-04 11:58:46,299:INFO:Total runtime is 0.050193897883097326 minutes +2024-12-04 11:58:46,303:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:46,303:INFO:Initializing create_model() +2024-12-04 11:58:46,303:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:46,303:INFO:Checking exceptions +2024-12-04 11:58:46,303:INFO:Importing libraries +2024-12-04 11:58:46,303:INFO:Copying training dataset +2024-12-04 11:58:46,311:INFO:Defining folds +2024-12-04 11:58:46,312:INFO:Declaring metric variables +2024-12-04 11:58:46,315:INFO:Importing untrained model +2024-12-04 11:58:46,320:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 11:58:46,328:INFO:Starting cross validation +2024-12-04 11:58:46,330:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:46,701:INFO:Calculating mean and std +2024-12-04 11:58:46,702:INFO:Creating metrics dataframe +2024-12-04 11:58:46,702:INFO:Uploading results into container +2024-12-04 11:58:46,702:INFO:Uploading model into container now +2024-12-04 11:58:46,702:INFO:_master_model_container: 6 +2024-12-04 11:58:46,702:INFO:_display_container: 2 +2024-12-04 11:58:46,705:INFO:LassoLars(random_state=123) +2024-12-04 11:58:46,705:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:46,823:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:46,823:INFO:Creating metrics dataframe +2024-12-04 11:58:46,828:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 11:58:46,828:INFO:Total runtime is 0.05899814764658609 minutes +2024-12-04 11:58:46,833:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:46,833:INFO:Initializing create_model() +2024-12-04 11:58:46,833:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:46,834:INFO:Checking exceptions +2024-12-04 11:58:46,834:INFO:Importing libraries +2024-12-04 11:58:46,834:INFO:Copying training dataset +2024-12-04 11:58:46,839:INFO:Defining folds +2024-12-04 11:58:46,839:INFO:Declaring metric variables +2024-12-04 11:58:46,843:INFO:Importing untrained model +2024-12-04 11:58:46,849:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 11:58:46,855:INFO:Starting cross validation +2024-12-04 11:58:46,857:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:47,220:INFO:Calculating mean and std +2024-12-04 11:58:47,220:INFO:Creating metrics dataframe +2024-12-04 11:58:47,223:INFO:Uploading results into container +2024-12-04 11:58:47,224:INFO:Uploading model into container now +2024-12-04 11:58:47,224:INFO:_master_model_container: 7 +2024-12-04 11:58:47,224:INFO:_display_container: 2 +2024-12-04 11:58:47,224:INFO:OrthogonalMatchingPursuit() +2024-12-04 11:58:47,224:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:47,339:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:47,339:INFO:Creating metrics dataframe +2024-12-04 11:58:47,342:INFO:Initializing Bayesian Ridge +2024-12-04 11:58:47,342:INFO:Total runtime is 0.06757855017979938 minutes +2024-12-04 11:58:47,350:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:47,350:INFO:Initializing create_model() +2024-12-04 11:58:47,350:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:47,350:INFO:Checking exceptions +2024-12-04 11:58:47,350:INFO:Importing libraries +2024-12-04 11:58:47,350:INFO:Copying training dataset +2024-12-04 11:58:47,359:INFO:Defining folds +2024-12-04 11:58:47,359:INFO:Declaring metric variables +2024-12-04 11:58:47,362:INFO:Importing untrained model +2024-12-04 11:58:47,368:INFO:Bayesian Ridge Imported successfully +2024-12-04 11:58:47,374:INFO:Starting cross validation +2024-12-04 11:58:47,376:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:47,746:INFO:Calculating mean and std +2024-12-04 11:58:47,746:INFO:Creating metrics dataframe +2024-12-04 11:58:47,750:INFO:Uploading results into container +2024-12-04 11:58:47,751:INFO:Uploading model into container now +2024-12-04 11:58:47,751:INFO:_master_model_container: 8 +2024-12-04 11:58:47,751:INFO:_display_container: 2 +2024-12-04 11:58:47,751:INFO:BayesianRidge() +2024-12-04 11:58:47,751:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:47,870:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:47,870:INFO:Creating metrics dataframe +2024-12-04 11:58:47,878:INFO:Initializing Passive Aggressive Regressor +2024-12-04 11:58:47,878:INFO:Total runtime is 0.07650192578633624 minutes +2024-12-04 11:58:47,881:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:47,881:INFO:Initializing create_model() +2024-12-04 11:58:47,881:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:47,881:INFO:Checking exceptions +2024-12-04 11:58:47,881:INFO:Importing libraries +2024-12-04 11:58:47,882:INFO:Copying training dataset +2024-12-04 11:58:47,889:INFO:Defining folds +2024-12-04 11:58:47,889:INFO:Declaring metric variables +2024-12-04 11:58:47,893:INFO:Importing untrained model +2024-12-04 11:58:47,893:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 11:58:47,902:INFO:Starting cross validation +2024-12-04 11:58:47,903:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:48,274:INFO:Calculating mean and std +2024-12-04 11:58:48,275:INFO:Creating metrics dataframe +2024-12-04 11:58:48,277:INFO:Uploading results into container +2024-12-04 11:58:48,277:INFO:Uploading model into container now +2024-12-04 11:58:48,277:INFO:_master_model_container: 9 +2024-12-04 11:58:48,277:INFO:_display_container: 2 +2024-12-04 11:58:48,278:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 11:58:48,278:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:48,393:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:48,393:INFO:Creating metrics dataframe +2024-12-04 11:58:48,399:INFO:Initializing Huber Regressor +2024-12-04 11:58:48,399:INFO:Total runtime is 0.08519256909688312 minutes +2024-12-04 11:58:48,402:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:48,402:INFO:Initializing create_model() +2024-12-04 11:58:48,402:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:48,402:INFO:Checking exceptions +2024-12-04 11:58:48,402:INFO:Importing libraries +2024-12-04 11:58:48,405:INFO:Copying training dataset +2024-12-04 11:58:48,412:INFO:Defining folds +2024-12-04 11:58:48,412:INFO:Declaring metric variables +2024-12-04 11:58:48,415:INFO:Importing untrained model +2024-12-04 11:58:48,420:INFO:Huber Regressor Imported successfully +2024-12-04 11:58:48,427:INFO:Starting cross validation +2024-12-04 11:58:48,427:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:48,910:INFO:Calculating mean and std +2024-12-04 11:58:48,910:INFO:Creating metrics dataframe +2024-12-04 11:58:48,913:INFO:Uploading results into container +2024-12-04 11:58:48,913:INFO:Uploading model into container now +2024-12-04 11:58:48,913:INFO:_master_model_container: 10 +2024-12-04 11:58:48,913:INFO:_display_container: 2 +2024-12-04 11:58:48,913:INFO:HuberRegressor() +2024-12-04 11:58:48,913:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:49,033:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:49,033:INFO:Creating metrics dataframe +2024-12-04 11:58:49,039:INFO:Initializing K Neighbors Regressor +2024-12-04 11:58:49,039:INFO:Total runtime is 0.09585514465967812 minutes +2024-12-04 11:58:49,039:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:49,039:INFO:Initializing create_model() +2024-12-04 11:58:49,039:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:49,039:INFO:Checking exceptions +2024-12-04 11:58:49,043:INFO:Importing libraries +2024-12-04 11:58:49,043:INFO:Copying training dataset +2024-12-04 11:58:49,050:INFO:Defining folds +2024-12-04 11:58:49,050:INFO:Declaring metric variables +2024-12-04 11:58:49,053:INFO:Importing untrained model +2024-12-04 11:58:49,057:INFO:K Neighbors Regressor Imported successfully +2024-12-04 11:58:49,063:INFO:Starting cross validation +2024-12-04 11:58:49,063:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:49,515:INFO:Calculating mean and std +2024-12-04 11:58:49,516:INFO:Creating metrics dataframe +2024-12-04 11:58:49,519:INFO:Uploading results into container +2024-12-04 11:58:49,520:INFO:Uploading model into container now +2024-12-04 11:58:49,520:INFO:_master_model_container: 11 +2024-12-04 11:58:49,520:INFO:_display_container: 2 +2024-12-04 11:58:49,520:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 11:58:49,520:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:49,639:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:49,639:INFO:Creating metrics dataframe +2024-12-04 11:58:49,643:INFO:Initializing Decision Tree Regressor +2024-12-04 11:58:49,643:INFO:Total runtime is 0.10591566562652585 minutes +2024-12-04 11:58:49,648:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:49,649:INFO:Initializing create_model() +2024-12-04 11:58:49,649:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:49,649:INFO:Checking exceptions +2024-12-04 11:58:49,649:INFO:Importing libraries +2024-12-04 11:58:49,649:INFO:Copying training dataset +2024-12-04 11:58:49,657:INFO:Defining folds +2024-12-04 11:58:49,657:INFO:Declaring metric variables +2024-12-04 11:58:49,659:INFO:Importing untrained model +2024-12-04 11:58:49,663:INFO:Decision Tree Regressor Imported successfully +2024-12-04 11:58:49,671:INFO:Starting cross validation +2024-12-04 11:58:49,673:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:50,137:INFO:Calculating mean and std +2024-12-04 11:58:50,137:INFO:Creating metrics dataframe +2024-12-04 11:58:50,139:INFO:Uploading results into container +2024-12-04 11:58:50,139:INFO:Uploading model into container now +2024-12-04 11:58:50,139:INFO:_master_model_container: 12 +2024-12-04 11:58:50,139:INFO:_display_container: 2 +2024-12-04 11:58:50,139:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 11:58:50,139:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:50,257:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:50,257:INFO:Creating metrics dataframe +2024-12-04 11:58:50,263:INFO:Initializing Random Forest Regressor +2024-12-04 11:58:50,263:INFO:Total runtime is 0.11625702778498329 minutes +2024-12-04 11:58:50,266:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:50,268:INFO:Initializing create_model() +2024-12-04 11:58:50,268:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:50,268:INFO:Checking exceptions +2024-12-04 11:58:50,268:INFO:Importing libraries +2024-12-04 11:58:50,268:INFO:Copying training dataset +2024-12-04 11:58:50,275:INFO:Defining folds +2024-12-04 11:58:50,276:INFO:Declaring metric variables +2024-12-04 11:58:50,280:INFO:Importing untrained model +2024-12-04 11:58:50,284:INFO:Random Forest Regressor Imported successfully +2024-12-04 11:58:50,289:INFO:Starting cross validation +2024-12-04 11:58:50,293:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:56,059:INFO:Calculating mean and std +2024-12-04 11:58:56,059:INFO:Creating metrics dataframe +2024-12-04 11:58:56,063:INFO:Uploading results into container +2024-12-04 11:58:56,064:INFO:Uploading model into container now +2024-12-04 11:58:56,064:INFO:_master_model_container: 13 +2024-12-04 11:58:56,065:INFO:_display_container: 2 +2024-12-04 11:58:56,065:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:58:56,065:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:56,217:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:56,217:INFO:Creating metrics dataframe +2024-12-04 11:58:56,225:INFO:Initializing Extra Trees Regressor +2024-12-04 11:58:56,225:INFO:Total runtime is 0.2156271894772847 minutes +2024-12-04 11:58:56,228:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:56,228:INFO:Initializing create_model() +2024-12-04 11:58:56,228:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:56,228:INFO:Checking exceptions +2024-12-04 11:58:56,228:INFO:Importing libraries +2024-12-04 11:58:56,228:INFO:Copying training dataset +2024-12-04 11:58:56,237:INFO:Defining folds +2024-12-04 11:58:56,238:INFO:Declaring metric variables +2024-12-04 11:58:56,240:INFO:Importing untrained model +2024-12-04 11:58:56,242:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:58:56,250:INFO:Starting cross validation +2024-12-04 11:58:56,251:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:58:59,693:INFO:Calculating mean and std +2024-12-04 11:58:59,693:INFO:Creating metrics dataframe +2024-12-04 11:58:59,693:INFO:Uploading results into container +2024-12-04 11:58:59,696:INFO:Uploading model into container now +2024-12-04 11:58:59,696:INFO:_master_model_container: 14 +2024-12-04 11:58:59,697:INFO:_display_container: 2 +2024-12-04 11:58:59,697:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:58:59,697:INFO:create_model() successfully completed...................................... +2024-12-04 11:58:59,817:INFO:SubProcess create_model() end ================================== +2024-12-04 11:58:59,818:INFO:Creating metrics dataframe +2024-12-04 11:58:59,827:INFO:Initializing AdaBoost Regressor +2024-12-04 11:58:59,827:INFO:Total runtime is 0.275662366549174 minutes +2024-12-04 11:58:59,832:INFO:SubProcess create_model() called ================================== +2024-12-04 11:58:59,832:INFO:Initializing create_model() +2024-12-04 11:58:59,832:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:58:59,832:INFO:Checking exceptions +2024-12-04 11:58:59,832:INFO:Importing libraries +2024-12-04 11:58:59,833:INFO:Copying training dataset +2024-12-04 11:58:59,841:INFO:Defining folds +2024-12-04 11:58:59,841:INFO:Declaring metric variables +2024-12-04 11:58:59,844:INFO:Importing untrained model +2024-12-04 11:58:59,849:INFO:AdaBoost Regressor Imported successfully +2024-12-04 11:58:59,856:INFO:Starting cross validation +2024-12-04 11:58:59,857:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:00,823:INFO:Calculating mean and std +2024-12-04 11:59:00,823:INFO:Creating metrics dataframe +2024-12-04 11:59:00,826:INFO:Uploading results into container +2024-12-04 11:59:00,827:INFO:Uploading model into container now +2024-12-04 11:59:00,827:INFO:_master_model_container: 15 +2024-12-04 11:59:00,827:INFO:_display_container: 2 +2024-12-04 11:59:00,827:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 11:59:00,827:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:00,945:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:00,945:INFO:Creating metrics dataframe +2024-12-04 11:59:00,955:INFO:Initializing Gradient Boosting Regressor +2024-12-04 11:59:00,955:INFO:Total runtime is 0.29445782899856565 minutes +2024-12-04 11:59:00,957:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:00,957:INFO:Initializing create_model() +2024-12-04 11:59:00,957:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:00,957:INFO:Checking exceptions +2024-12-04 11:59:00,957:INFO:Importing libraries +2024-12-04 11:59:00,957:INFO:Copying training dataset +2024-12-04 11:59:00,963:INFO:Defining folds +2024-12-04 11:59:00,963:INFO:Declaring metric variables +2024-12-04 11:59:00,971:INFO:Importing untrained model +2024-12-04 11:59:00,971:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 11:59:00,989:INFO:Starting cross validation +2024-12-04 11:59:00,992:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:02,710:INFO:Calculating mean and std +2024-12-04 11:59:02,710:INFO:Creating metrics dataframe +2024-12-04 11:59:02,713:INFO:Uploading results into container +2024-12-04 11:59:02,714:INFO:Uploading model into container now +2024-12-04 11:59:02,715:INFO:_master_model_container: 16 +2024-12-04 11:59:02,715:INFO:_display_container: 2 +2024-12-04 11:59:02,715:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 11:59:02,715:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:02,835:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:02,835:INFO:Creating metrics dataframe +2024-12-04 11:59:02,843:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 11:59:02,843:INFO:Total runtime is 0.3259183049201965 minutes +2024-12-04 11:59:02,843:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:02,843:INFO:Initializing create_model() +2024-12-04 11:59:02,843:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:02,843:INFO:Checking exceptions +2024-12-04 11:59:02,843:INFO:Importing libraries +2024-12-04 11:59:02,843:INFO:Copying training dataset +2024-12-04 11:59:02,853:INFO:Defining folds +2024-12-04 11:59:02,853:INFO:Declaring metric variables +2024-12-04 11:59:02,857:INFO:Importing untrained model +2024-12-04 11:59:02,859:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 11:59:02,863:INFO:Starting cross validation +2024-12-04 11:59:02,866:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:03,682:INFO:Calculating mean and std +2024-12-04 11:59:03,683:INFO:Creating metrics dataframe +2024-12-04 11:59:03,686:INFO:Uploading results into container +2024-12-04 11:59:03,687:INFO:Uploading model into container now +2024-12-04 11:59:03,687:INFO:_master_model_container: 17 +2024-12-04 11:59:03,687:INFO:_display_container: 2 +2024-12-04 11:59:03,688:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:59:03,688:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:03,834:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:03,835:INFO:Creating metrics dataframe +2024-12-04 11:59:03,840:INFO:Initializing Dummy Regressor +2024-12-04 11:59:03,840:INFO:Total runtime is 0.34253002802530924 minutes +2024-12-04 11:59:03,843:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:03,843:INFO:Initializing create_model() +2024-12-04 11:59:03,843:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:03,843:INFO:Checking exceptions +2024-12-04 11:59:03,843:INFO:Importing libraries +2024-12-04 11:59:03,843:INFO:Copying training dataset +2024-12-04 11:59:03,853:INFO:Defining folds +2024-12-04 11:59:03,853:INFO:Declaring metric variables +2024-12-04 11:59:03,857:INFO:Importing untrained model +2024-12-04 11:59:03,859:INFO:Dummy Regressor Imported successfully +2024-12-04 11:59:03,866:INFO:Starting cross validation +2024-12-04 11:59:03,868:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:04,293:INFO:Calculating mean and std +2024-12-04 11:59:04,294:INFO:Creating metrics dataframe +2024-12-04 11:59:04,295:INFO:Uploading results into container +2024-12-04 11:59:04,296:INFO:Uploading model into container now +2024-12-04 11:59:04,296:INFO:_master_model_container: 18 +2024-12-04 11:59:04,296:INFO:_display_container: 2 +2024-12-04 11:59:04,296:INFO:DummyRegressor() +2024-12-04 11:59:04,296:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:04,419:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:04,419:INFO:Creating metrics dataframe +2024-12-04 11:59:04,430:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 11:59:04,439:INFO:Initializing create_model() +2024-12-04 11:59:04,439:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:04,439:INFO:Checking exceptions +2024-12-04 11:59:04,439:INFO:Importing libraries +2024-12-04 11:59:04,439:INFO:Copying training dataset +2024-12-04 11:59:04,442:INFO:Defining folds +2024-12-04 11:59:04,442:INFO:Declaring metric variables +2024-12-04 11:59:04,442:INFO:Importing untrained model +2024-12-04 11:59:04,442:INFO:Declaring custom model +2024-12-04 11:59:04,442:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:59:04,449:INFO:Cross validation set to False +2024-12-04 11:59:04,449:INFO:Fitting Model +2024-12-04 11:59:05,482:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:59:05,482:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:05,620:INFO:_master_model_container: 18 +2024-12-04 11:59:05,620:INFO:_display_container: 2 +2024-12-04 11:59:05,621:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:59:05,621:INFO:compare_models() successfully completed...................................... +2024-12-04 11:59:13,003:INFO:PyCaret RegressionExperiment +2024-12-04 11:59:13,003:INFO:Logging name: reg-default-name +2024-12-04 11:59:13,003:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:59:13,003:INFO:version 3.3.2 +2024-12-04 11:59:13,003:INFO:Initializing setup() +2024-12-04 11:59:13,003:INFO:self.USI: ff09 +2024-12-04 11:59:13,003:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:59:13,003:INFO:Checking environment +2024-12-04 11:59:13,003:INFO:python_version: 3.11.9 +2024-12-04 11:59:13,003:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:59:13,003:INFO:machine: AMD64 +2024-12-04 11:59:13,003:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:59:13,011:INFO:Memory: svmem(total=17007292416, available=2566995968, percent=84.9, used=14440296448, free=2566995968) +2024-12-04 11:59:13,011:INFO:Physical Core: 4 +2024-12-04 11:59:13,012:INFO:Logical Core: 8 +2024-12-04 11:59:13,012:INFO:Checking libraries +2024-12-04 11:59:13,012:INFO:System: +2024-12-04 11:59:13,012:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:59:13,012:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:59:13,012:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:59:13,012:INFO:PyCaret required dependencies: +2024-12-04 11:59:13,012:INFO: pip: 24.3.1 +2024-12-04 11:59:13,012:INFO: setuptools: 65.5.0 +2024-12-04 11:59:13,012:INFO: pycaret: 3.3.2 +2024-12-04 11:59:13,012:INFO: IPython: 8.12.3 +2024-12-04 11:59:13,012:INFO: ipywidgets: 8.1.5 +2024-12-04 11:59:13,012:INFO: tqdm: 4.67.1 +2024-12-04 11:59:13,012:INFO: numpy: 1.26.4 +2024-12-04 11:59:13,013:INFO: pandas: 2.1.4 +2024-12-04 11:59:13,013:INFO: jinja2: 3.1.4 +2024-12-04 11:59:13,013:INFO: scipy: 1.11.4 +2024-12-04 11:59:13,013:INFO: joblib: 1.3.2 +2024-12-04 11:59:13,013:INFO: sklearn: 1.4.2 +2024-12-04 11:59:13,013:INFO: pyod: 2.0.2 +2024-12-04 11:59:13,013:INFO: imblearn: 0.12.4 +2024-12-04 11:59:13,013:INFO: category_encoders: 2.6.4 +2024-12-04 11:59:13,013:INFO: lightgbm: 4.5.0 +2024-12-04 11:59:13,013:INFO: numba: 0.60.0 +2024-12-04 11:59:13,013:INFO: requests: 2.32.3 +2024-12-04 11:59:13,013:INFO: matplotlib: 3.7.5 +2024-12-04 11:59:13,013:INFO: scikitplot: 0.3.7 +2024-12-04 11:59:13,013:INFO: yellowbrick: 1.5 +2024-12-04 11:59:13,013:INFO: plotly: 5.24.1 +2024-12-04 11:59:13,013:INFO: plotly-resampler: Not installed +2024-12-04 11:59:13,013:INFO: kaleido: 0.2.1 +2024-12-04 11:59:13,013:INFO: schemdraw: 0.15 +2024-12-04 11:59:13,013:INFO: statsmodels: 0.14.4 +2024-12-04 11:59:13,013:INFO: sktime: 0.26.0 +2024-12-04 11:59:13,013:INFO: tbats: 1.1.3 +2024-12-04 11:59:13,013:INFO: pmdarima: 2.0.4 +2024-12-04 11:59:13,013:INFO: psutil: 6.1.0 +2024-12-04 11:59:13,013:INFO: markupsafe: 3.0.2 +2024-12-04 11:59:13,013:INFO: pickle5: Not installed +2024-12-04 11:59:13,013:INFO: cloudpickle: 3.1.0 +2024-12-04 11:59:13,013:INFO: deprecation: 2.1.0 +2024-12-04 11:59:13,013:INFO: xxhash: 3.5.0 +2024-12-04 11:59:13,013:INFO: wurlitzer: Not installed +2024-12-04 11:59:13,013:INFO:PyCaret optional dependencies: +2024-12-04 11:59:13,013:INFO: shap: Not installed +2024-12-04 11:59:13,013:INFO: interpret: Not installed +2024-12-04 11:59:13,013:INFO: umap: Not installed +2024-12-04 11:59:13,013:INFO: ydata_profiling: Not installed +2024-12-04 11:59:13,013:INFO: explainerdashboard: Not installed +2024-12-04 11:59:13,013:INFO: autoviz: Not installed +2024-12-04 11:59:13,013:INFO: fairlearn: Not installed +2024-12-04 11:59:13,013:INFO: deepchecks: Not installed +2024-12-04 11:59:13,013:INFO: xgboost: Not installed +2024-12-04 11:59:13,013:INFO: catboost: Not installed +2024-12-04 11:59:13,013:INFO: kmodes: Not installed +2024-12-04 11:59:13,013:INFO: mlxtend: Not installed +2024-12-04 11:59:13,013:INFO: statsforecast: Not installed +2024-12-04 11:59:13,013:INFO: tune_sklearn: Not installed +2024-12-04 11:59:13,013:INFO: ray: Not installed +2024-12-04 11:59:13,013:INFO: hyperopt: Not installed +2024-12-04 11:59:13,013:INFO: optuna: Not installed +2024-12-04 11:59:13,013:INFO: skopt: Not installed +2024-12-04 11:59:13,013:INFO: mlflow: 2.18.0 +2024-12-04 11:59:13,013:INFO: gradio: Not installed +2024-12-04 11:59:13,013:INFO: fastapi: Not installed +2024-12-04 11:59:13,013:INFO: uvicorn: 0.32.1 +2024-12-04 11:59:13,013:INFO: m2cgen: Not installed +2024-12-04 11:59:13,013:INFO: evidently: Not installed +2024-12-04 11:59:13,013:INFO: fugue: Not installed +2024-12-04 11:59:13,013:INFO: streamlit: 1.40.1 +2024-12-04 11:59:13,013:INFO: prophet: Not installed +2024-12-04 11:59:13,013:INFO:None +2024-12-04 11:59:13,013:INFO:Set up data. +2024-12-04 11:59:13,024:INFO:Set up folding strategy. +2024-12-04 11:59:24,651:INFO:PyCaret RegressionExperiment +2024-12-04 11:59:24,651:INFO:Logging name: reg-default-name +2024-12-04 11:59:24,651:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 11:59:24,651:INFO:version 3.3.2 +2024-12-04 11:59:24,651:INFO:Initializing setup() +2024-12-04 11:59:24,651:INFO:self.USI: b077 +2024-12-04 11:59:24,651:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 11:59:24,651:INFO:Checking environment +2024-12-04 11:59:24,651:INFO:python_version: 3.11.9 +2024-12-04 11:59:24,651:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 11:59:24,651:INFO:machine: AMD64 +2024-12-04 11:59:24,651:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 11:59:24,657:INFO:Memory: svmem(total=17007292416, available=2547777536, percent=85.0, used=14459514880, free=2547777536) +2024-12-04 11:59:24,657:INFO:Physical Core: 4 +2024-12-04 11:59:24,657:INFO:Logical Core: 8 +2024-12-04 11:59:24,657:INFO:Checking libraries +2024-12-04 11:59:24,657:INFO:System: +2024-12-04 11:59:24,657:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 11:59:24,657:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 11:59:24,657:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 11:59:24,657:INFO:PyCaret required dependencies: +2024-12-04 11:59:24,657:INFO: pip: 24.3.1 +2024-12-04 11:59:24,657:INFO: setuptools: 65.5.0 +2024-12-04 11:59:24,657:INFO: pycaret: 3.3.2 +2024-12-04 11:59:24,657:INFO: IPython: 8.12.3 +2024-12-04 11:59:24,657:INFO: ipywidgets: 8.1.5 +2024-12-04 11:59:24,657:INFO: tqdm: 4.67.1 +2024-12-04 11:59:24,657:INFO: numpy: 1.26.4 +2024-12-04 11:59:24,657:INFO: pandas: 2.1.4 +2024-12-04 11:59:24,657:INFO: jinja2: 3.1.4 +2024-12-04 11:59:24,657:INFO: scipy: 1.11.4 +2024-12-04 11:59:24,657:INFO: joblib: 1.3.2 +2024-12-04 11:59:24,657:INFO: sklearn: 1.4.2 +2024-12-04 11:59:24,657:INFO: pyod: 2.0.2 +2024-12-04 11:59:24,657:INFO: imblearn: 0.12.4 +2024-12-04 11:59:24,657:INFO: category_encoders: 2.6.4 +2024-12-04 11:59:24,657:INFO: lightgbm: 4.5.0 +2024-12-04 11:59:24,657:INFO: numba: 0.60.0 +2024-12-04 11:59:24,657:INFO: requests: 2.32.3 +2024-12-04 11:59:24,657:INFO: matplotlib: 3.7.5 +2024-12-04 11:59:24,657:INFO: scikitplot: 0.3.7 +2024-12-04 11:59:24,657:INFO: yellowbrick: 1.5 +2024-12-04 11:59:24,657:INFO: plotly: 5.24.1 +2024-12-04 11:59:24,657:INFO: plotly-resampler: Not installed +2024-12-04 11:59:24,657:INFO: kaleido: 0.2.1 +2024-12-04 11:59:24,657:INFO: schemdraw: 0.15 +2024-12-04 11:59:24,657:INFO: statsmodels: 0.14.4 +2024-12-04 11:59:24,657:INFO: sktime: 0.26.0 +2024-12-04 11:59:24,657:INFO: tbats: 1.1.3 +2024-12-04 11:59:24,657:INFO: pmdarima: 2.0.4 +2024-12-04 11:59:24,657:INFO: psutil: 6.1.0 +2024-12-04 11:59:24,657:INFO: markupsafe: 3.0.2 +2024-12-04 11:59:24,657:INFO: pickle5: Not installed +2024-12-04 11:59:24,657:INFO: cloudpickle: 3.1.0 +2024-12-04 11:59:24,659:INFO: deprecation: 2.1.0 +2024-12-04 11:59:24,659:INFO: xxhash: 3.5.0 +2024-12-04 11:59:24,659:INFO: wurlitzer: Not installed +2024-12-04 11:59:24,659:INFO:PyCaret optional dependencies: +2024-12-04 11:59:24,659:INFO: shap: Not installed +2024-12-04 11:59:24,659:INFO: interpret: Not installed +2024-12-04 11:59:24,659:INFO: umap: Not installed +2024-12-04 11:59:24,659:INFO: ydata_profiling: Not installed +2024-12-04 11:59:24,659:INFO: explainerdashboard: Not installed +2024-12-04 11:59:24,659:INFO: autoviz: Not installed +2024-12-04 11:59:24,659:INFO: fairlearn: Not installed +2024-12-04 11:59:24,659:INFO: deepchecks: Not installed +2024-12-04 11:59:24,659:INFO: xgboost: Not installed +2024-12-04 11:59:24,659:INFO: catboost: Not installed +2024-12-04 11:59:24,659:INFO: kmodes: Not installed +2024-12-04 11:59:24,659:INFO: mlxtend: Not installed +2024-12-04 11:59:24,659:INFO: statsforecast: Not installed +2024-12-04 11:59:24,659:INFO: tune_sklearn: Not installed +2024-12-04 11:59:24,659:INFO: ray: Not installed +2024-12-04 11:59:24,659:INFO: hyperopt: Not installed +2024-12-04 11:59:24,659:INFO: optuna: Not installed +2024-12-04 11:59:24,659:INFO: skopt: Not installed +2024-12-04 11:59:24,659:INFO: mlflow: 2.18.0 +2024-12-04 11:59:24,659:INFO: gradio: Not installed +2024-12-04 11:59:24,659:INFO: fastapi: Not installed +2024-12-04 11:59:24,659:INFO: uvicorn: 0.32.1 +2024-12-04 11:59:24,659:INFO: m2cgen: Not installed +2024-12-04 11:59:24,659:INFO: evidently: Not installed +2024-12-04 11:59:24,659:INFO: fugue: Not installed +2024-12-04 11:59:24,660:INFO: streamlit: 1.40.1 +2024-12-04 11:59:24,660:INFO: prophet: Not installed +2024-12-04 11:59:24,660:INFO:None +2024-12-04 11:59:24,660:INFO:Set up data. +2024-12-04 11:59:24,668:INFO:Set up folding strategy. +2024-12-04 11:59:24,668:INFO:Set up train/test split. +2024-12-04 11:59:24,673:INFO:Set up index. +2024-12-04 11:59:24,673:INFO:Assigning column types. +2024-12-04 11:59:24,679:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 11:59:24,782:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:24,782:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:24,886:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:24,886:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:24,886:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 11:59:24,985:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:24,985:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,085:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,085:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,085:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 11:59:25,198:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,198:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,312:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,312:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,312:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 11:59:25,427:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,427:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,540:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,540:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,540:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 11:59:25,639:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,639:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,741:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,743:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,743:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 11:59:25,843:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,843:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,949:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,950:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:25,960:INFO:Preparing preprocessing pipeline... +2024-12-04 11:59:25,960:INFO:Set up simple imputation. +2024-12-04 11:59:25,964:INFO:Set up encoding of categorical features. +2024-12-04 11:59:25,964:INFO:Set up column transformation. +2024-12-04 11:59:25,964:INFO:Set up feature normalization. +2024-12-04 11:59:26,085:INFO:Finished creating preprocessing pipeline. +2024-12-04 11:59:26,093:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 11:59:26,093:INFO:Creating final display dataframe. +2024-12-04 11:59:26,390:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 15) +4 Transformed data shape (11376, 31) +5 Transformed train set shape (7963, 31) +6 Transformed test set shape (3413, 31) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 10 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI b077 +2024-12-04 11:59:26,527:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:26,527:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:26,659:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:26,659:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 11:59:26,659:INFO:setup() successfully completed in 2.03s............... +2024-12-04 11:59:33,023:INFO:Initializing compare_models() +2024-12-04 11:59:33,023:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 11:59:33,023:INFO:Checking exceptions +2024-12-04 11:59:33,028:INFO:Preparing display monitor +2024-12-04 11:59:33,048:INFO:Initializing Linear Regression +2024-12-04 11:59:33,048:INFO:Total runtime is 0.0 minutes +2024-12-04 11:59:33,053:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:33,053:INFO:Initializing create_model() +2024-12-04 11:59:33,053:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:33,054:INFO:Checking exceptions +2024-12-04 11:59:33,054:INFO:Importing libraries +2024-12-04 11:59:33,054:INFO:Copying training dataset +2024-12-04 11:59:33,082:INFO:Defining folds +2024-12-04 11:59:33,082:INFO:Declaring metric variables +2024-12-04 11:59:33,086:INFO:Importing untrained model +2024-12-04 11:59:33,093:INFO:Linear Regression Imported successfully +2024-12-04 11:59:33,102:INFO:Starting cross validation +2024-12-04 11:59:33,104:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:34,032:INFO:Calculating mean and std +2024-12-04 11:59:34,032:INFO:Creating metrics dataframe +2024-12-04 11:59:34,034:INFO:Uploading results into container +2024-12-04 11:59:34,034:INFO:Uploading model into container now +2024-12-04 11:59:34,034:INFO:_master_model_container: 19 +2024-12-04 11:59:34,034:INFO:_display_container: 2 +2024-12-04 11:59:34,034:INFO:LinearRegression(n_jobs=-1) +2024-12-04 11:59:34,034:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:34,157:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:34,157:INFO:Creating metrics dataframe +2024-12-04 11:59:34,162:INFO:Initializing Lasso Regression +2024-12-04 11:59:34,162:INFO:Total runtime is 0.018565018971761067 minutes +2024-12-04 11:59:34,165:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:34,165:INFO:Initializing create_model() +2024-12-04 11:59:34,165:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:34,165:INFO:Checking exceptions +2024-12-04 11:59:34,166:INFO:Importing libraries +2024-12-04 11:59:34,166:INFO:Copying training dataset +2024-12-04 11:59:34,174:INFO:Defining folds +2024-12-04 11:59:34,174:INFO:Declaring metric variables +2024-12-04 11:59:34,177:INFO:Importing untrained model +2024-12-04 11:59:34,182:INFO:Lasso Regression Imported successfully +2024-12-04 11:59:34,189:INFO:Starting cross validation +2024-12-04 11:59:34,190:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:35,429:INFO:Calculating mean and std +2024-12-04 11:59:35,429:INFO:Creating metrics dataframe +2024-12-04 11:59:35,432:INFO:Uploading results into container +2024-12-04 11:59:35,432:INFO:Uploading model into container now +2024-12-04 11:59:35,433:INFO:_master_model_container: 20 +2024-12-04 11:59:35,433:INFO:_display_container: 2 +2024-12-04 11:59:35,433:INFO:Lasso(random_state=123) +2024-12-04 11:59:35,433:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:35,554:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:35,554:INFO:Creating metrics dataframe +2024-12-04 11:59:35,561:INFO:Initializing Ridge Regression +2024-12-04 11:59:35,561:INFO:Total runtime is 0.04188451369603475 minutes +2024-12-04 11:59:35,564:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:35,564:INFO:Initializing create_model() +2024-12-04 11:59:35,565:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:35,565:INFO:Checking exceptions +2024-12-04 11:59:35,565:INFO:Importing libraries +2024-12-04 11:59:35,565:INFO:Copying training dataset +2024-12-04 11:59:35,573:INFO:Defining folds +2024-12-04 11:59:35,573:INFO:Declaring metric variables +2024-12-04 11:59:35,577:INFO:Importing untrained model +2024-12-04 11:59:35,582:INFO:Ridge Regression Imported successfully +2024-12-04 11:59:35,589:INFO:Starting cross validation +2024-12-04 11:59:35,589:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:36,401:INFO:Calculating mean and std +2024-12-04 11:59:36,402:INFO:Creating metrics dataframe +2024-12-04 11:59:36,403:INFO:Uploading results into container +2024-12-04 11:59:36,404:INFO:Uploading model into container now +2024-12-04 11:59:36,404:INFO:_master_model_container: 21 +2024-12-04 11:59:36,404:INFO:_display_container: 2 +2024-12-04 11:59:36,405:INFO:Ridge(random_state=123) +2024-12-04 11:59:36,405:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:36,527:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:36,527:INFO:Creating metrics dataframe +2024-12-04 11:59:36,532:INFO:Initializing Elastic Net +2024-12-04 11:59:36,532:INFO:Total runtime is 0.058056640625 minutes +2024-12-04 11:59:36,535:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:36,535:INFO:Initializing create_model() +2024-12-04 11:59:36,535:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:36,535:INFO:Checking exceptions +2024-12-04 11:59:36,535:INFO:Importing libraries +2024-12-04 11:59:36,535:INFO:Copying training dataset +2024-12-04 11:59:36,543:INFO:Defining folds +2024-12-04 11:59:36,543:INFO:Declaring metric variables +2024-12-04 11:59:36,545:INFO:Importing untrained model +2024-12-04 11:59:36,550:INFO:Elastic Net Imported successfully +2024-12-04 11:59:36,555:INFO:Starting cross validation +2024-12-04 11:59:36,557:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:37,369:INFO:Calculating mean and std +2024-12-04 11:59:37,369:INFO:Creating metrics dataframe +2024-12-04 11:59:37,370:INFO:Uploading results into container +2024-12-04 11:59:37,371:INFO:Uploading model into container now +2024-12-04 11:59:37,371:INFO:_master_model_container: 22 +2024-12-04 11:59:37,371:INFO:_display_container: 2 +2024-12-04 11:59:37,372:INFO:ElasticNet(random_state=123) +2024-12-04 11:59:37,372:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:37,489:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:37,489:INFO:Creating metrics dataframe +2024-12-04 11:59:37,493:INFO:Initializing Least Angle Regression +2024-12-04 11:59:37,493:INFO:Total runtime is 0.0740729292233785 minutes +2024-12-04 11:59:37,498:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:37,499:INFO:Initializing create_model() +2024-12-04 11:59:37,499:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:37,499:INFO:Checking exceptions +2024-12-04 11:59:37,499:INFO:Importing libraries +2024-12-04 11:59:37,499:INFO:Copying training dataset +2024-12-04 11:59:37,507:INFO:Defining folds +2024-12-04 11:59:37,507:INFO:Declaring metric variables +2024-12-04 11:59:37,509:INFO:Importing untrained model +2024-12-04 11:59:37,513:INFO:Least Angle Regression Imported successfully +2024-12-04 11:59:37,518:INFO:Starting cross validation +2024-12-04 11:59:37,521:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:38,363:INFO:Calculating mean and std +2024-12-04 11:59:38,363:INFO:Creating metrics dataframe +2024-12-04 11:59:38,367:INFO:Uploading results into container +2024-12-04 11:59:38,367:INFO:Uploading model into container now +2024-12-04 11:59:38,367:INFO:_master_model_container: 23 +2024-12-04 11:59:38,367:INFO:_display_container: 2 +2024-12-04 11:59:38,367:INFO:Lars(random_state=123) +2024-12-04 11:59:38,367:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:38,493:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:38,493:INFO:Creating metrics dataframe +2024-12-04 11:59:38,500:INFO:Initializing Lasso Least Angle Regression +2024-12-04 11:59:38,500:INFO:Total runtime is 0.09085770845413209 minutes +2024-12-04 11:59:38,502:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:38,503:INFO:Initializing create_model() +2024-12-04 11:59:38,503:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:38,503:INFO:Checking exceptions +2024-12-04 11:59:38,503:INFO:Importing libraries +2024-12-04 11:59:38,503:INFO:Copying training dataset +2024-12-04 11:59:38,512:INFO:Defining folds +2024-12-04 11:59:38,512:INFO:Declaring metric variables +2024-12-04 11:59:38,516:INFO:Importing untrained model +2024-12-04 11:59:38,519:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 11:59:38,527:INFO:Starting cross validation +2024-12-04 11:59:38,528:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:39,404:INFO:Calculating mean and std +2024-12-04 11:59:39,404:INFO:Creating metrics dataframe +2024-12-04 11:59:39,405:INFO:Uploading results into container +2024-12-04 11:59:39,407:INFO:Uploading model into container now +2024-12-04 11:59:39,407:INFO:_master_model_container: 24 +2024-12-04 11:59:39,407:INFO:_display_container: 2 +2024-12-04 11:59:39,407:INFO:LassoLars(random_state=123) +2024-12-04 11:59:39,407:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:39,523:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:39,523:INFO:Creating metrics dataframe +2024-12-04 11:59:39,530:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 11:59:39,530:INFO:Total runtime is 0.10803015232086183 minutes +2024-12-04 11:59:39,533:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:39,534:INFO:Initializing create_model() +2024-12-04 11:59:39,534:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:39,534:INFO:Checking exceptions +2024-12-04 11:59:39,534:INFO:Importing libraries +2024-12-04 11:59:39,534:INFO:Copying training dataset +2024-12-04 11:59:39,541:INFO:Defining folds +2024-12-04 11:59:39,541:INFO:Declaring metric variables +2024-12-04 11:59:39,544:INFO:Importing untrained model +2024-12-04 11:59:39,548:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 11:59:39,555:INFO:Starting cross validation +2024-12-04 11:59:39,557:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:40,395:INFO:Calculating mean and std +2024-12-04 11:59:40,395:INFO:Creating metrics dataframe +2024-12-04 11:59:40,398:INFO:Uploading results into container +2024-12-04 11:59:40,398:INFO:Uploading model into container now +2024-12-04 11:59:40,399:INFO:_master_model_container: 25 +2024-12-04 11:59:40,399:INFO:_display_container: 2 +2024-12-04 11:59:40,400:INFO:OrthogonalMatchingPursuit() +2024-12-04 11:59:40,400:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:40,530:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:40,530:INFO:Creating metrics dataframe +2024-12-04 11:59:40,538:INFO:Initializing Bayesian Ridge +2024-12-04 11:59:40,538:INFO:Total runtime is 0.12482118606567384 minutes +2024-12-04 11:59:40,541:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:40,541:INFO:Initializing create_model() +2024-12-04 11:59:40,541:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:40,541:INFO:Checking exceptions +2024-12-04 11:59:40,541:INFO:Importing libraries +2024-12-04 11:59:40,541:INFO:Copying training dataset +2024-12-04 11:59:40,548:INFO:Defining folds +2024-12-04 11:59:40,549:INFO:Declaring metric variables +2024-12-04 11:59:40,552:INFO:Importing untrained model +2024-12-04 11:59:40,555:INFO:Bayesian Ridge Imported successfully +2024-12-04 11:59:40,560:INFO:Starting cross validation +2024-12-04 11:59:40,562:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:41,466:INFO:Calculating mean and std +2024-12-04 11:59:41,467:INFO:Creating metrics dataframe +2024-12-04 11:59:41,468:INFO:Uploading results into container +2024-12-04 11:59:41,468:INFO:Uploading model into container now +2024-12-04 11:59:41,470:INFO:_master_model_container: 26 +2024-12-04 11:59:41,470:INFO:_display_container: 2 +2024-12-04 11:59:41,470:INFO:BayesianRidge() +2024-12-04 11:59:41,470:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:41,608:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:41,608:INFO:Creating metrics dataframe +2024-12-04 11:59:41,613:INFO:Initializing Passive Aggressive Regressor +2024-12-04 11:59:41,613:INFO:Total runtime is 0.14274476369222006 minutes +2024-12-04 11:59:41,613:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:41,613:INFO:Initializing create_model() +2024-12-04 11:59:41,613:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:41,613:INFO:Checking exceptions +2024-12-04 11:59:41,613:INFO:Importing libraries +2024-12-04 11:59:41,618:INFO:Copying training dataset +2024-12-04 11:59:41,625:INFO:Defining folds +2024-12-04 11:59:41,625:INFO:Declaring metric variables +2024-12-04 11:59:41,628:INFO:Importing untrained model +2024-12-04 11:59:41,632:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 11:59:41,665:INFO:Starting cross validation +2024-12-04 11:59:41,665:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:42,555:INFO:Calculating mean and std +2024-12-04 11:59:42,555:INFO:Creating metrics dataframe +2024-12-04 11:59:42,558:INFO:Uploading results into container +2024-12-04 11:59:42,558:INFO:Uploading model into container now +2024-12-04 11:59:42,558:INFO:_master_model_container: 27 +2024-12-04 11:59:42,559:INFO:_display_container: 2 +2024-12-04 11:59:42,559:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 11:59:42,559:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:42,676:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:42,676:INFO:Creating metrics dataframe +2024-12-04 11:59:42,683:INFO:Initializing Huber Regressor +2024-12-04 11:59:42,683:INFO:Total runtime is 0.1605697751045227 minutes +2024-12-04 11:59:42,685:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:42,685:INFO:Initializing create_model() +2024-12-04 11:59:42,685:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:42,685:INFO:Checking exceptions +2024-12-04 11:59:42,685:INFO:Importing libraries +2024-12-04 11:59:42,685:INFO:Copying training dataset +2024-12-04 11:59:42,692:INFO:Defining folds +2024-12-04 11:59:42,692:INFO:Declaring metric variables +2024-12-04 11:59:42,700:INFO:Importing untrained model +2024-12-04 11:59:42,702:INFO:Huber Regressor Imported successfully +2024-12-04 11:59:42,709:INFO:Starting cross validation +2024-12-04 11:59:42,711:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:43,860:INFO:Calculating mean and std +2024-12-04 11:59:43,861:INFO:Creating metrics dataframe +2024-12-04 11:59:43,862:INFO:Uploading results into container +2024-12-04 11:59:43,863:INFO:Uploading model into container now +2024-12-04 11:59:43,863:INFO:_master_model_container: 28 +2024-12-04 11:59:43,863:INFO:_display_container: 2 +2024-12-04 11:59:43,863:INFO:HuberRegressor() +2024-12-04 11:59:43,863:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:43,989:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:43,989:INFO:Creating metrics dataframe +2024-12-04 11:59:43,993:INFO:Initializing K Neighbors Regressor +2024-12-04 11:59:43,993:INFO:Total runtime is 0.18240383466084797 minutes +2024-12-04 11:59:44,000:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:44,000:INFO:Initializing create_model() +2024-12-04 11:59:44,000:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:44,000:INFO:Checking exceptions +2024-12-04 11:59:44,000:INFO:Importing libraries +2024-12-04 11:59:44,000:INFO:Copying training dataset +2024-12-04 11:59:44,009:INFO:Defining folds +2024-12-04 11:59:44,009:INFO:Declaring metric variables +2024-12-04 11:59:44,013:INFO:Importing untrained model +2024-12-04 11:59:44,015:INFO:K Neighbors Regressor Imported successfully +2024-12-04 11:59:44,024:INFO:Starting cross validation +2024-12-04 11:59:44,027:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:44,953:INFO:Calculating mean and std +2024-12-04 11:59:44,953:INFO:Creating metrics dataframe +2024-12-04 11:59:44,955:INFO:Uploading results into container +2024-12-04 11:59:44,955:INFO:Uploading model into container now +2024-12-04 11:59:44,955:INFO:_master_model_container: 29 +2024-12-04 11:59:44,955:INFO:_display_container: 2 +2024-12-04 11:59:44,955:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 11:59:44,955:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:45,077:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:45,077:INFO:Creating metrics dataframe +2024-12-04 11:59:45,085:INFO:Initializing Decision Tree Regressor +2024-12-04 11:59:45,085:INFO:Total runtime is 0.20061513582865398 minutes +2024-12-04 11:59:45,087:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:45,087:INFO:Initializing create_model() +2024-12-04 11:59:45,087:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:45,087:INFO:Checking exceptions +2024-12-04 11:59:45,089:INFO:Importing libraries +2024-12-04 11:59:45,089:INFO:Copying training dataset +2024-12-04 11:59:45,096:INFO:Defining folds +2024-12-04 11:59:45,096:INFO:Declaring metric variables +2024-12-04 11:59:45,100:INFO:Importing untrained model +2024-12-04 11:59:45,105:INFO:Decision Tree Regressor Imported successfully +2024-12-04 11:59:45,112:INFO:Starting cross validation +2024-12-04 11:59:45,112:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:46,200:INFO:Calculating mean and std +2024-12-04 11:59:46,200:INFO:Creating metrics dataframe +2024-12-04 11:59:46,203:INFO:Uploading results into container +2024-12-04 11:59:46,203:INFO:Uploading model into container now +2024-12-04 11:59:46,203:INFO:_master_model_container: 30 +2024-12-04 11:59:46,203:INFO:_display_container: 2 +2024-12-04 11:59:46,203:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 11:59:46,203:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:46,320:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:46,320:INFO:Creating metrics dataframe +2024-12-04 11:59:46,327:INFO:Initializing Random Forest Regressor +2024-12-04 11:59:46,327:INFO:Total runtime is 0.22130947907765708 minutes +2024-12-04 11:59:46,328:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:46,328:INFO:Initializing create_model() +2024-12-04 11:59:46,330:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:46,330:INFO:Checking exceptions +2024-12-04 11:59:46,330:INFO:Importing libraries +2024-12-04 11:59:46,330:INFO:Copying training dataset +2024-12-04 11:59:46,336:INFO:Defining folds +2024-12-04 11:59:46,336:INFO:Declaring metric variables +2024-12-04 11:59:46,339:INFO:Importing untrained model +2024-12-04 11:59:46,345:INFO:Random Forest Regressor Imported successfully +2024-12-04 11:59:46,352:INFO:Starting cross validation +2024-12-04 11:59:46,354:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 11:59:59,393:INFO:Calculating mean and std +2024-12-04 11:59:59,393:INFO:Creating metrics dataframe +2024-12-04 11:59:59,396:INFO:Uploading results into container +2024-12-04 11:59:59,397:INFO:Uploading model into container now +2024-12-04 11:59:59,397:INFO:_master_model_container: 31 +2024-12-04 11:59:59,397:INFO:_display_container: 2 +2024-12-04 11:59:59,398:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 11:59:59,398:INFO:create_model() successfully completed...................................... +2024-12-04 11:59:59,520:INFO:SubProcess create_model() end ================================== +2024-12-04 11:59:59,520:INFO:Creating metrics dataframe +2024-12-04 11:59:59,531:INFO:Initializing Extra Trees Regressor +2024-12-04 11:59:59,531:INFO:Total runtime is 0.44136855999628705 minutes +2024-12-04 11:59:59,534:INFO:SubProcess create_model() called ================================== +2024-12-04 11:59:59,535:INFO:Initializing create_model() +2024-12-04 11:59:59,535:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 11:59:59,536:INFO:Checking exceptions +2024-12-04 11:59:59,536:INFO:Importing libraries +2024-12-04 11:59:59,536:INFO:Copying training dataset +2024-12-04 11:59:59,543:INFO:Defining folds +2024-12-04 11:59:59,543:INFO:Declaring metric variables +2024-12-04 11:59:59,543:INFO:Importing untrained model +2024-12-04 11:59:59,552:INFO:Extra Trees Regressor Imported successfully +2024-12-04 11:59:59,557:INFO:Starting cross validation +2024-12-04 11:59:59,559:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:00:07,209:INFO:Calculating mean and std +2024-12-04 12:00:07,211:INFO:Creating metrics dataframe +2024-12-04 12:00:07,213:INFO:Uploading results into container +2024-12-04 12:00:07,213:INFO:Uploading model into container now +2024-12-04 12:00:07,213:INFO:_master_model_container: 32 +2024-12-04 12:00:07,213:INFO:_display_container: 2 +2024-12-04 12:00:07,213:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:00:07,213:INFO:create_model() successfully completed...................................... +2024-12-04 12:00:07,343:INFO:SubProcess create_model() end ================================== +2024-12-04 12:00:07,343:INFO:Creating metrics dataframe +2024-12-04 12:00:07,357:INFO:Initializing AdaBoost Regressor +2024-12-04 12:00:07,357:INFO:Total runtime is 0.571807610988617 minutes +2024-12-04 12:00:07,360:INFO:SubProcess create_model() called ================================== +2024-12-04 12:00:07,360:INFO:Initializing create_model() +2024-12-04 12:00:07,360:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:00:07,360:INFO:Checking exceptions +2024-12-04 12:00:07,360:INFO:Importing libraries +2024-12-04 12:00:07,360:INFO:Copying training dataset +2024-12-04 12:00:07,368:INFO:Defining folds +2024-12-04 12:00:07,368:INFO:Declaring metric variables +2024-12-04 12:00:07,373:INFO:Importing untrained model +2024-12-04 12:00:07,377:INFO:AdaBoost Regressor Imported successfully +2024-12-04 12:00:07,385:INFO:Starting cross validation +2024-12-04 12:00:07,387:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:00:09,647:INFO:Calculating mean and std +2024-12-04 12:00:09,647:INFO:Creating metrics dataframe +2024-12-04 12:00:09,647:INFO:Uploading results into container +2024-12-04 12:00:09,647:INFO:Uploading model into container now +2024-12-04 12:00:09,647:INFO:_master_model_container: 33 +2024-12-04 12:00:09,647:INFO:_display_container: 2 +2024-12-04 12:00:09,647:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 12:00:09,647:INFO:create_model() successfully completed...................................... +2024-12-04 12:00:09,769:INFO:SubProcess create_model() end ================================== +2024-12-04 12:00:09,769:INFO:Creating metrics dataframe +2024-12-04 12:00:09,776:INFO:Initializing Gradient Boosting Regressor +2024-12-04 12:00:09,776:INFO:Total runtime is 0.6121318896611532 minutes +2024-12-04 12:00:09,778:INFO:SubProcess create_model() called ================================== +2024-12-04 12:00:09,778:INFO:Initializing create_model() +2024-12-04 12:00:09,778:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:00:09,778:INFO:Checking exceptions +2024-12-04 12:00:09,778:INFO:Importing libraries +2024-12-04 12:00:09,778:INFO:Copying training dataset +2024-12-04 12:00:09,784:INFO:Defining folds +2024-12-04 12:00:09,784:INFO:Declaring metric variables +2024-12-04 12:00:09,791:INFO:Importing untrained model +2024-12-04 12:00:09,793:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 12:00:09,800:INFO:Starting cross validation +2024-12-04 12:00:09,803:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:00:13,560:INFO:Calculating mean and std +2024-12-04 12:00:13,561:INFO:Creating metrics dataframe +2024-12-04 12:00:13,563:INFO:Uploading results into container +2024-12-04 12:00:13,563:INFO:Uploading model into container now +2024-12-04 12:00:13,563:INFO:_master_model_container: 34 +2024-12-04 12:00:13,563:INFO:_display_container: 2 +2024-12-04 12:00:13,563:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 12:00:13,563:INFO:create_model() successfully completed...................................... +2024-12-04 12:00:13,681:INFO:SubProcess create_model() end ================================== +2024-12-04 12:00:13,682:INFO:Creating metrics dataframe +2024-12-04 12:00:13,689:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 12:00:13,689:INFO:Total runtime is 0.6773434042930603 minutes +2024-12-04 12:00:13,693:INFO:SubProcess create_model() called ================================== +2024-12-04 12:00:13,693:INFO:Initializing create_model() +2024-12-04 12:00:13,693:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:00:13,693:INFO:Checking exceptions +2024-12-04 12:00:13,693:INFO:Importing libraries +2024-12-04 12:00:13,693:INFO:Copying training dataset +2024-12-04 12:00:13,698:INFO:Defining folds +2024-12-04 12:00:13,698:INFO:Declaring metric variables +2024-12-04 12:00:13,703:INFO:Importing untrained model +2024-12-04 12:00:13,707:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 12:00:13,713:INFO:Starting cross validation +2024-12-04 12:00:13,716:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:00:15,389:INFO:Calculating mean and std +2024-12-04 12:00:15,389:INFO:Creating metrics dataframe +2024-12-04 12:00:15,392:INFO:Uploading results into container +2024-12-04 12:00:15,394:INFO:Uploading model into container now +2024-12-04 12:00:15,394:INFO:_master_model_container: 35 +2024-12-04 12:00:15,394:INFO:_display_container: 2 +2024-12-04 12:00:15,394:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:00:15,394:INFO:create_model() successfully completed...................................... +2024-12-04 12:00:15,535:INFO:SubProcess create_model() end ================================== +2024-12-04 12:00:15,535:INFO:Creating metrics dataframe +2024-12-04 12:00:15,543:INFO:Initializing Dummy Regressor +2024-12-04 12:00:15,543:INFO:Total runtime is 0.7082404613494873 minutes +2024-12-04 12:00:15,546:INFO:SubProcess create_model() called ================================== +2024-12-04 12:00:15,546:INFO:Initializing create_model() +2024-12-04 12:00:15,546:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:00:15,546:INFO:Checking exceptions +2024-12-04 12:00:15,546:INFO:Importing libraries +2024-12-04 12:00:15,546:INFO:Copying training dataset +2024-12-04 12:00:15,558:INFO:Defining folds +2024-12-04 12:00:15,558:INFO:Declaring metric variables +2024-12-04 12:00:15,562:INFO:Importing untrained model +2024-12-04 12:00:15,563:INFO:Dummy Regressor Imported successfully +2024-12-04 12:00:15,571:INFO:Starting cross validation +2024-12-04 12:00:15,573:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:00:16,313:INFO:Calculating mean and std +2024-12-04 12:00:16,313:INFO:Creating metrics dataframe +2024-12-04 12:00:16,313:INFO:Uploading results into container +2024-12-04 12:00:16,313:INFO:Uploading model into container now +2024-12-04 12:00:16,316:INFO:_master_model_container: 36 +2024-12-04 12:00:16,317:INFO:_display_container: 2 +2024-12-04 12:00:16,317:INFO:DummyRegressor() +2024-12-04 12:00:16,317:INFO:create_model() successfully completed...................................... +2024-12-04 12:00:16,435:INFO:SubProcess create_model() end ================================== +2024-12-04 12:00:16,435:INFO:Creating metrics dataframe +2024-12-04 12:00:16,442:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 12:00:16,453:INFO:Initializing create_model() +2024-12-04 12:00:16,453:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:00:16,453:INFO:Checking exceptions +2024-12-04 12:00:16,453:INFO:Importing libraries +2024-12-04 12:00:16,453:INFO:Copying training dataset +2024-12-04 12:00:16,459:INFO:Defining folds +2024-12-04 12:00:16,459:INFO:Declaring metric variables +2024-12-04 12:00:16,459:INFO:Importing untrained model +2024-12-04 12:00:16,459:INFO:Declaring custom model +2024-12-04 12:00:16,461:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:00:16,463:INFO:Cross validation set to False +2024-12-04 12:00:16,463:INFO:Fitting Model +2024-12-04 12:00:17,486:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:00:17,486:INFO:create_model() successfully completed...................................... +2024-12-04 12:00:17,633:INFO:_master_model_container: 36 +2024-12-04 12:00:17,633:INFO:_display_container: 2 +2024-12-04 12:00:17,633:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:00:17,633:INFO:compare_models() successfully completed...................................... +2024-12-04 12:02:14,443:INFO:Initializing tune_model() +2024-12-04 12:02:14,447:INFO:tune_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-12-04 12:02:14,447:INFO:Checking exceptions +2024-12-04 12:02:14,469:INFO:Copying training dataset +2024-12-04 12:02:14,477:INFO:Checking base model +2024-12-04 12:02:14,477:INFO:Base model : Extra Trees Regressor +2024-12-04 12:02:14,482:INFO:Declaring metric variables +2024-12-04 12:02:14,486:INFO:Defining Hyperparameters +2024-12-04 12:02:14,635:INFO:Tuning with n_jobs=-1 +2024-12-04 12:02:14,635:INFO:Initializing RandomizedSearchCV +2024-12-04 12:08:00,016:INFO:PyCaret RegressionExperiment +2024-12-04 12:08:00,016:INFO:Logging name: reg-default-name +2024-12-04 12:08:00,016:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:08:00,016:INFO:version 3.3.2 +2024-12-04 12:08:00,016:INFO:Initializing setup() +2024-12-04 12:08:00,016:INFO:self.USI: 5a56 +2024-12-04 12:08:00,017:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 12:08:00,017:INFO:Checking environment +2024-12-04 12:08:00,017:INFO:python_version: 3.11.9 +2024-12-04 12:08:00,017:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:08:00,017:INFO:machine: AMD64 +2024-12-04 12:08:00,017:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:08:00,025:INFO:Memory: svmem(total=17007292416, available=3826212864, percent=77.5, used=13181079552, free=3826212864) +2024-12-04 12:08:00,025:INFO:Physical Core: 4 +2024-12-04 12:08:00,025:INFO:Logical Core: 8 +2024-12-04 12:08:00,025:INFO:Checking libraries +2024-12-04 12:08:00,025:INFO:System: +2024-12-04 12:08:00,025:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:08:00,025:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:08:00,025:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:08:00,025:INFO:PyCaret required dependencies: +2024-12-04 12:08:00,026:INFO: pip: 24.3.1 +2024-12-04 12:08:00,026:INFO: setuptools: 65.5.0 +2024-12-04 12:08:00,026:INFO: pycaret: 3.3.2 +2024-12-04 12:08:00,026:INFO: IPython: 8.12.3 +2024-12-04 12:08:00,026:INFO: ipywidgets: 8.1.5 +2024-12-04 12:08:00,026:INFO: tqdm: 4.67.1 +2024-12-04 12:08:00,026:INFO: numpy: 1.26.4 +2024-12-04 12:08:00,026:INFO: pandas: 2.1.4 +2024-12-04 12:08:00,026:INFO: jinja2: 3.1.4 +2024-12-04 12:08:00,026:INFO: scipy: 1.11.4 +2024-12-04 12:08:00,026:INFO: joblib: 1.3.2 +2024-12-04 12:08:00,026:INFO: sklearn: 1.4.2 +2024-12-04 12:08:00,026:INFO: pyod: 2.0.2 +2024-12-04 12:08:00,026:INFO: imblearn: 0.12.4 +2024-12-04 12:08:00,026:INFO: category_encoders: 2.6.4 +2024-12-04 12:08:00,026:INFO: lightgbm: 4.5.0 +2024-12-04 12:08:00,027:INFO: numba: 0.60.0 +2024-12-04 12:08:00,027:INFO: requests: 2.32.3 +2024-12-04 12:08:00,027:INFO: matplotlib: 3.7.5 +2024-12-04 12:08:00,027:INFO: scikitplot: 0.3.7 +2024-12-04 12:08:00,027:INFO: yellowbrick: 1.5 +2024-12-04 12:08:00,027:INFO: plotly: 5.24.1 +2024-12-04 12:08:00,027:INFO: plotly-resampler: Not installed +2024-12-04 12:08:00,027:INFO: kaleido: 0.2.1 +2024-12-04 12:08:00,027:INFO: schemdraw: 0.15 +2024-12-04 12:08:00,027:INFO: statsmodels: 0.14.4 +2024-12-04 12:08:00,027:INFO: sktime: 0.26.0 +2024-12-04 12:08:00,027:INFO: tbats: 1.1.3 +2024-12-04 12:08:00,028:INFO: pmdarima: 2.0.4 +2024-12-04 12:08:00,028:INFO: psutil: 6.1.0 +2024-12-04 12:08:00,028:INFO: markupsafe: 3.0.2 +2024-12-04 12:08:00,028:INFO: pickle5: Not installed +2024-12-04 12:08:00,028:INFO: cloudpickle: 3.1.0 +2024-12-04 12:08:00,028:INFO: deprecation: 2.1.0 +2024-12-04 12:08:00,028:INFO: xxhash: 3.5.0 +2024-12-04 12:08:00,028:INFO: wurlitzer: Not installed +2024-12-04 12:08:00,028:INFO:PyCaret optional dependencies: +2024-12-04 12:08:00,028:INFO: shap: Not installed +2024-12-04 12:08:00,028:INFO: interpret: Not installed +2024-12-04 12:08:00,028:INFO: umap: Not installed +2024-12-04 12:08:00,028:INFO: ydata_profiling: Not installed +2024-12-04 12:08:00,028:INFO: explainerdashboard: Not installed +2024-12-04 12:08:00,028:INFO: autoviz: Not installed +2024-12-04 12:08:00,028:INFO: fairlearn: Not installed +2024-12-04 12:08:00,028:INFO: deepchecks: Not installed +2024-12-04 12:08:00,028:INFO: xgboost: Not installed +2024-12-04 12:08:00,028:INFO: catboost: Not installed +2024-12-04 12:08:00,028:INFO: kmodes: Not installed +2024-12-04 12:08:00,028:INFO: mlxtend: Not installed +2024-12-04 12:08:00,028:INFO: statsforecast: Not installed +2024-12-04 12:08:00,028:INFO: tune_sklearn: Not installed +2024-12-04 12:08:00,029:INFO: ray: Not installed +2024-12-04 12:08:00,029:INFO: hyperopt: Not installed +2024-12-04 12:08:00,029:INFO: optuna: Not installed +2024-12-04 12:08:00,029:INFO: skopt: Not installed +2024-12-04 12:08:00,029:INFO: mlflow: 2.18.0 +2024-12-04 12:08:00,029:INFO: gradio: Not installed +2024-12-04 12:08:00,029:INFO: fastapi: Not installed +2024-12-04 12:08:00,029:INFO: uvicorn: 0.32.1 +2024-12-04 12:08:00,029:INFO: m2cgen: Not installed +2024-12-04 12:08:00,029:INFO: evidently: Not installed +2024-12-04 12:08:00,029:INFO: fugue: Not installed +2024-12-04 12:08:00,029:INFO: streamlit: 1.40.1 +2024-12-04 12:08:00,029:INFO: prophet: Not installed +2024-12-04 12:08:00,029:INFO:None +2024-12-04 12:08:00,029:INFO:Set up data. +2024-12-04 12:08:00,044:INFO:Set up folding strategy. +2024-12-04 12:08:00,044:INFO:Set up train/test split. +2024-12-04 12:08:00,053:INFO:Set up index. +2024-12-04 12:08:00,054:INFO:Assigning column types. +2024-12-04 12:08:00,062:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:08:00,063:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,069:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,074:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,147:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,187:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,188:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,188:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,188:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,192:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,196:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,251:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,294:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,294:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,294:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,295:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:08:00,299:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,303:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,363:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,403:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,404:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,404:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,409:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,413:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,466:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,507:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,507:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,507:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,507:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:08:00,516:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,572:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,612:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,613:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,613:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,621:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,681:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,727:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,728:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,728:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,728:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:08:00,800:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,839:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,840:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,840:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,904:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,944:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:08:00,944:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,944:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:00,945:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:08:01,005:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:01,046:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,046:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,149:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:08:01,194:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,194:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,194:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:08:01,299:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,299:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,401:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,401:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,402:INFO:Preparing preprocessing pipeline... +2024-12-04 12:08:01,402:INFO:Set up simple imputation. +2024-12-04 12:08:01,406:INFO:Set up encoding of categorical features. +2024-12-04 12:08:01,406:INFO:Set up column transformation. +2024-12-04 12:08:01,406:INFO:Set up feature normalization. +2024-12-04 12:08:01,538:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:08:01,547:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:08:01,547:INFO:Creating final display dataframe. +2024-12-04 12:08:01,854:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 15) +4 Transformed data shape (11376, 31) +5 Transformed train set shape (7963, 31) +6 Transformed test set shape (3413, 31) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 10 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI 5a56 +2024-12-04 12:08:01,963:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:01,964:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:02,066:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:02,066:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:08:02,067:INFO:setup() successfully completed in 2.07s............... +2024-12-04 12:10:53,421:INFO:PyCaret RegressionExperiment +2024-12-04 12:10:53,421:INFO:Logging name: reg-default-name +2024-12-04 12:10:53,421:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:10:53,421:INFO:version 3.3.2 +2024-12-04 12:10:53,421:INFO:Initializing setup() +2024-12-04 12:10:53,421:INFO:self.USI: cf11 +2024-12-04 12:10:53,421:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 12:10:53,421:INFO:Checking environment +2024-12-04 12:10:53,421:INFO:python_version: 3.11.9 +2024-12-04 12:10:53,422:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:10:53,422:INFO:machine: AMD64 +2024-12-04 12:10:53,422:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:10:53,429:INFO:Memory: svmem(total=17007292416, available=3278884864, percent=80.7, used=13728407552, free=3278884864) +2024-12-04 12:10:53,429:INFO:Physical Core: 4 +2024-12-04 12:10:53,429:INFO:Logical Core: 8 +2024-12-04 12:10:53,429:INFO:Checking libraries +2024-12-04 12:10:53,429:INFO:System: +2024-12-04 12:10:53,429:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:10:53,429:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:10:53,429:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:10:53,429:INFO:PyCaret required dependencies: +2024-12-04 12:10:53,429:INFO: pip: 24.3.1 +2024-12-04 12:10:53,429:INFO: setuptools: 65.5.0 +2024-12-04 12:10:53,429:INFO: pycaret: 3.3.2 +2024-12-04 12:10:53,429:INFO: IPython: 8.12.3 +2024-12-04 12:10:53,429:INFO: ipywidgets: 8.1.5 +2024-12-04 12:10:53,429:INFO: tqdm: 4.67.1 +2024-12-04 12:10:53,429:INFO: numpy: 1.26.4 +2024-12-04 12:10:53,429:INFO: pandas: 2.1.4 +2024-12-04 12:10:53,429:INFO: jinja2: 3.1.4 +2024-12-04 12:10:53,430:INFO: scipy: 1.11.4 +2024-12-04 12:10:53,430:INFO: joblib: 1.3.2 +2024-12-04 12:10:53,430:INFO: sklearn: 1.4.2 +2024-12-04 12:10:53,430:INFO: pyod: 2.0.2 +2024-12-04 12:10:53,430:INFO: imblearn: 0.12.4 +2024-12-04 12:10:53,430:INFO: category_encoders: 2.6.4 +2024-12-04 12:10:53,430:INFO: lightgbm: 4.5.0 +2024-12-04 12:10:53,430:INFO: numba: 0.60.0 +2024-12-04 12:10:53,430:INFO: requests: 2.32.3 +2024-12-04 12:10:53,430:INFO: matplotlib: 3.7.5 +2024-12-04 12:10:53,430:INFO: scikitplot: 0.3.7 +2024-12-04 12:10:53,430:INFO: yellowbrick: 1.5 +2024-12-04 12:10:53,430:INFO: plotly: 5.24.1 +2024-12-04 12:10:53,430:INFO: plotly-resampler: Not installed +2024-12-04 12:10:53,430:INFO: kaleido: 0.2.1 +2024-12-04 12:10:53,431:INFO: schemdraw: 0.15 +2024-12-04 12:10:53,431:INFO: statsmodels: 0.14.4 +2024-12-04 12:10:53,431:INFO: sktime: 0.26.0 +2024-12-04 12:10:53,431:INFO: tbats: 1.1.3 +2024-12-04 12:10:53,431:INFO: pmdarima: 2.0.4 +2024-12-04 12:10:53,431:INFO: psutil: 6.1.0 +2024-12-04 12:10:53,431:INFO: markupsafe: 3.0.2 +2024-12-04 12:10:53,431:INFO: pickle5: Not installed +2024-12-04 12:10:53,431:INFO: cloudpickle: 3.1.0 +2024-12-04 12:10:53,431:INFO: deprecation: 2.1.0 +2024-12-04 12:10:53,431:INFO: xxhash: 3.5.0 +2024-12-04 12:10:53,431:INFO: wurlitzer: Not installed +2024-12-04 12:10:53,431:INFO:PyCaret optional dependencies: +2024-12-04 12:10:53,431:INFO: shap: Not installed +2024-12-04 12:10:53,431:INFO: interpret: Not installed +2024-12-04 12:10:53,431:INFO: umap: Not installed +2024-12-04 12:10:53,431:INFO: ydata_profiling: Not installed +2024-12-04 12:10:53,431:INFO: explainerdashboard: Not installed +2024-12-04 12:10:53,431:INFO: autoviz: Not installed +2024-12-04 12:10:53,431:INFO: fairlearn: Not installed +2024-12-04 12:10:53,431:INFO: deepchecks: Not installed +2024-12-04 12:10:53,431:INFO: xgboost: Not installed +2024-12-04 12:10:53,432:INFO: catboost: Not installed +2024-12-04 12:10:53,432:INFO: kmodes: Not installed +2024-12-04 12:10:53,432:INFO: mlxtend: Not installed +2024-12-04 12:10:53,432:INFO: statsforecast: Not installed +2024-12-04 12:10:53,432:INFO: tune_sklearn: Not installed +2024-12-04 12:10:53,432:INFO: ray: Not installed +2024-12-04 12:10:53,432:INFO: hyperopt: Not installed +2024-12-04 12:10:53,432:INFO: optuna: Not installed +2024-12-04 12:10:53,432:INFO: skopt: Not installed +2024-12-04 12:10:53,432:INFO: mlflow: 2.18.0 +2024-12-04 12:10:53,432:INFO: gradio: Not installed +2024-12-04 12:10:53,432:INFO: fastapi: Not installed +2024-12-04 12:10:53,432:INFO: uvicorn: 0.32.1 +2024-12-04 12:10:53,432:INFO: m2cgen: Not installed +2024-12-04 12:10:53,432:INFO: evidently: Not installed +2024-12-04 12:10:53,432:INFO: fugue: Not installed +2024-12-04 12:10:53,432:INFO: streamlit: 1.40.1 +2024-12-04 12:10:53,432:INFO: prophet: Not installed +2024-12-04 12:10:53,432:INFO:None +2024-12-04 12:10:53,432:INFO:Set up data. +2024-12-04 12:10:53,450:INFO:Set up folding strategy. +2024-12-04 12:10:53,450:INFO:Set up train/test split. +2024-12-04 12:10:53,462:INFO:Set up index. +2024-12-04 12:10:53,463:INFO:Assigning column types. +2024-12-04 12:10:53,477:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:10:53,477:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,482:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,487:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,546:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,590:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,591:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,591:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,591:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,596:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,600:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,662:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,701:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,702:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,702:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,702:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:10:53,707:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,711:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,769:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,807:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,807:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,809:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,813:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,816:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,874:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,918:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,918:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,918:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:53,920:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:10:53,928:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:10:53,992:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,032:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,033:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,033:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,043:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,107:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,157:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,157:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,157:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,159:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:10:54,243:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,287:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,287:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,287:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,364:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,412:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,413:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,413:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,413:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:10:54,485:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,525:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,525:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,591:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:10:54,646:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,646:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,646:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:10:54,755:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,755:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,859:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,860:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:54,860:INFO:Preparing preprocessing pipeline... +2024-12-04 12:10:54,860:INFO:Set up simple imputation. +2024-12-04 12:10:54,866:INFO:Set up encoding of categorical features. +2024-12-04 12:10:54,866:INFO:Set up column transformation. +2024-12-04 12:10:54,866:INFO:Set up feature normalization. +2024-12-04 12:10:55,253:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:10:55,260:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=TargetEncoder(cols=['nom_region', + 'nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:10:55,260:INFO:Creating final display dataframe. +2024-12-04 12:10:55,565:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (31906, 15) +4 Transformed data shape (31906, 14) +5 Transformed train set shape (22334, 14) +6 Transformed test set shape (9572, 14) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transformation True +17 Transformation method yeo-johnson +18 Normalize True +19 Normalize method zscore +20 Fold Generator KFold +21 Fold Number 10 +22 CPU Jobs -1 +23 Use GPU False +24 Log Experiment False +25 Experiment Name reg-default-name +26 USI cf11 +2024-12-04 12:10:55,678:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:55,679:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:55,782:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:55,782:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:10:55,783:INFO:setup() successfully completed in 2.38s............... +2024-12-04 12:11:03,574:INFO:Initializing compare_models() +2024-12-04 12:11:03,574:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 12:11:03,575:INFO:Checking exceptions +2024-12-04 12:11:03,580:INFO:Preparing display monitor +2024-12-04 12:11:03,603:INFO:Initializing Linear Regression +2024-12-04 12:11:03,603:INFO:Total runtime is 0.0 minutes +2024-12-04 12:11:03,607:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:03,607:INFO:Initializing create_model() +2024-12-04 12:11:03,607:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:03,607:INFO:Checking exceptions +2024-12-04 12:11:03,607:INFO:Importing libraries +2024-12-04 12:11:03,607:INFO:Copying training dataset +2024-12-04 12:11:03,627:INFO:Defining folds +2024-12-04 12:11:03,627:INFO:Declaring metric variables +2024-12-04 12:11:03,631:INFO:Importing untrained model +2024-12-04 12:11:03,635:INFO:Linear Regression Imported successfully +2024-12-04 12:11:03,641:INFO:Starting cross validation +2024-12-04 12:11:03,643:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:17,401:INFO:Calculating mean and std +2024-12-04 12:11:17,402:INFO:Creating metrics dataframe +2024-12-04 12:11:17,404:INFO:Uploading results into container +2024-12-04 12:11:17,404:INFO:Uploading model into container now +2024-12-04 12:11:17,405:INFO:_master_model_container: 1 +2024-12-04 12:11:17,405:INFO:_display_container: 2 +2024-12-04 12:11:17,405:INFO:LinearRegression(n_jobs=-1) +2024-12-04 12:11:17,405:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:17,567:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:17,568:INFO:Creating metrics dataframe +2024-12-04 12:11:17,572:INFO:Initializing Lasso Regression +2024-12-04 12:11:17,573:INFO:Total runtime is 0.23284157117207846 minutes +2024-12-04 12:11:17,576:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:17,577:INFO:Initializing create_model() +2024-12-04 12:11:17,577:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:17,577:INFO:Checking exceptions +2024-12-04 12:11:17,577:INFO:Importing libraries +2024-12-04 12:11:17,577:INFO:Copying training dataset +2024-12-04 12:11:17,592:INFO:Defining folds +2024-12-04 12:11:17,592:INFO:Declaring metric variables +2024-12-04 12:11:17,595:INFO:Importing untrained model +2024-12-04 12:11:17,599:INFO:Lasso Regression Imported successfully +2024-12-04 12:11:17,605:INFO:Starting cross validation +2024-12-04 12:11:17,607:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:18,872:INFO:Calculating mean and std +2024-12-04 12:11:18,874:INFO:Creating metrics dataframe +2024-12-04 12:11:18,875:INFO:Uploading results into container +2024-12-04 12:11:18,877:INFO:Uploading model into container now +2024-12-04 12:11:18,877:INFO:_master_model_container: 2 +2024-12-04 12:11:18,877:INFO:_display_container: 2 +2024-12-04 12:11:18,877:INFO:Lasso(random_state=123) +2024-12-04 12:11:18,877:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:19,005:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:19,005:INFO:Creating metrics dataframe +2024-12-04 12:11:19,011:INFO:Initializing Ridge Regression +2024-12-04 12:11:19,012:INFO:Total runtime is 0.25681799252827964 minutes +2024-12-04 12:11:19,014:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:19,015:INFO:Initializing create_model() +2024-12-04 12:11:19,015:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:19,015:INFO:Checking exceptions +2024-12-04 12:11:19,015:INFO:Importing libraries +2024-12-04 12:11:19,015:INFO:Copying training dataset +2024-12-04 12:11:19,028:INFO:Defining folds +2024-12-04 12:11:19,029:INFO:Declaring metric variables +2024-12-04 12:11:19,033:INFO:Importing untrained model +2024-12-04 12:11:19,036:INFO:Ridge Regression Imported successfully +2024-12-04 12:11:19,041:INFO:Starting cross validation +2024-12-04 12:11:19,043:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:20,014:INFO:Calculating mean and std +2024-12-04 12:11:20,015:INFO:Creating metrics dataframe +2024-12-04 12:11:20,017:INFO:Uploading results into container +2024-12-04 12:11:20,017:INFO:Uploading model into container now +2024-12-04 12:11:20,017:INFO:_master_model_container: 3 +2024-12-04 12:11:20,017:INFO:_display_container: 2 +2024-12-04 12:11:20,018:INFO:Ridge(random_state=123) +2024-12-04 12:11:20,018:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:20,166:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:20,166:INFO:Creating metrics dataframe +2024-12-04 12:11:20,172:INFO:Initializing Elastic Net +2024-12-04 12:11:20,172:INFO:Total runtime is 0.27614527543385825 minutes +2024-12-04 12:11:20,174:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:20,175:INFO:Initializing create_model() +2024-12-04 12:11:20,175:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:20,175:INFO:Checking exceptions +2024-12-04 12:11:20,175:INFO:Importing libraries +2024-12-04 12:11:20,175:INFO:Copying training dataset +2024-12-04 12:11:20,188:INFO:Defining folds +2024-12-04 12:11:20,188:INFO:Declaring metric variables +2024-12-04 12:11:20,191:INFO:Importing untrained model +2024-12-04 12:11:20,194:INFO:Elastic Net Imported successfully +2024-12-04 12:11:20,199:INFO:Starting cross validation +2024-12-04 12:11:20,200:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:21,203:INFO:Calculating mean and std +2024-12-04 12:11:21,203:INFO:Creating metrics dataframe +2024-12-04 12:11:21,204:INFO:Uploading results into container +2024-12-04 12:11:21,205:INFO:Uploading model into container now +2024-12-04 12:11:21,205:INFO:_master_model_container: 4 +2024-12-04 12:11:21,205:INFO:_display_container: 2 +2024-12-04 12:11:21,205:INFO:ElasticNet(random_state=123) +2024-12-04 12:11:21,205:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:21,329:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:21,330:INFO:Creating metrics dataframe +2024-12-04 12:11:21,335:INFO:Initializing Least Angle Regression +2024-12-04 12:11:21,335:INFO:Total runtime is 0.29553387165069583 minutes +2024-12-04 12:11:21,338:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:21,338:INFO:Initializing create_model() +2024-12-04 12:11:21,338:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:21,338:INFO:Checking exceptions +2024-12-04 12:11:21,338:INFO:Importing libraries +2024-12-04 12:11:21,338:INFO:Copying training dataset +2024-12-04 12:11:21,351:INFO:Defining folds +2024-12-04 12:11:21,351:INFO:Declaring metric variables +2024-12-04 12:11:21,355:INFO:Importing untrained model +2024-12-04 12:11:21,359:INFO:Least Angle Regression Imported successfully +2024-12-04 12:11:21,364:INFO:Starting cross validation +2024-12-04 12:11:21,365:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:22,392:INFO:Calculating mean and std +2024-12-04 12:11:22,393:INFO:Creating metrics dataframe +2024-12-04 12:11:22,394:INFO:Uploading results into container +2024-12-04 12:11:22,394:INFO:Uploading model into container now +2024-12-04 12:11:22,395:INFO:_master_model_container: 5 +2024-12-04 12:11:22,395:INFO:_display_container: 2 +2024-12-04 12:11:22,395:INFO:Lars(random_state=123) +2024-12-04 12:11:22,395:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:22,537:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:22,537:INFO:Creating metrics dataframe +2024-12-04 12:11:22,544:INFO:Initializing Lasso Least Angle Regression +2024-12-04 12:11:22,544:INFO:Total runtime is 0.3156807541847229 minutes +2024-12-04 12:11:22,547:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:22,547:INFO:Initializing create_model() +2024-12-04 12:11:22,547:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:22,547:INFO:Checking exceptions +2024-12-04 12:11:22,547:INFO:Importing libraries +2024-12-04 12:11:22,547:INFO:Copying training dataset +2024-12-04 12:11:22,561:INFO:Defining folds +2024-12-04 12:11:22,561:INFO:Declaring metric variables +2024-12-04 12:11:22,564:INFO:Importing untrained model +2024-12-04 12:11:22,567:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 12:11:22,572:INFO:Starting cross validation +2024-12-04 12:11:22,574:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:23,838:INFO:Calculating mean and std +2024-12-04 12:11:23,839:INFO:Creating metrics dataframe +2024-12-04 12:11:23,841:INFO:Uploading results into container +2024-12-04 12:11:23,841:INFO:Uploading model into container now +2024-12-04 12:11:23,842:INFO:_master_model_container: 6 +2024-12-04 12:11:23,842:INFO:_display_container: 2 +2024-12-04 12:11:23,842:INFO:LassoLars(random_state=123) +2024-12-04 12:11:23,842:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:23,973:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:23,973:INFO:Creating metrics dataframe +2024-12-04 12:11:23,981:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 12:11:23,982:INFO:Total runtime is 0.33963205814361574 minutes +2024-12-04 12:11:23,985:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:23,985:INFO:Initializing create_model() +2024-12-04 12:11:23,985:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:23,985:INFO:Checking exceptions +2024-12-04 12:11:23,986:INFO:Importing libraries +2024-12-04 12:11:23,986:INFO:Copying training dataset +2024-12-04 12:11:24,003:INFO:Defining folds +2024-12-04 12:11:24,003:INFO:Declaring metric variables +2024-12-04 12:11:24,006:INFO:Importing untrained model +2024-12-04 12:11:24,009:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 12:11:24,015:INFO:Starting cross validation +2024-12-04 12:11:24,017:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:25,050:INFO:Calculating mean and std +2024-12-04 12:11:25,051:INFO:Creating metrics dataframe +2024-12-04 12:11:25,053:INFO:Uploading results into container +2024-12-04 12:11:25,054:INFO:Uploading model into container now +2024-12-04 12:11:25,054:INFO:_master_model_container: 7 +2024-12-04 12:11:25,054:INFO:_display_container: 2 +2024-12-04 12:11:25,054:INFO:OrthogonalMatchingPursuit() +2024-12-04 12:11:25,054:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:25,217:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:25,217:INFO:Creating metrics dataframe +2024-12-04 12:11:25,227:INFO:Initializing Bayesian Ridge +2024-12-04 12:11:25,227:INFO:Total runtime is 0.36039833625157675 minutes +2024-12-04 12:11:25,230:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:25,230:INFO:Initializing create_model() +2024-12-04 12:11:25,230:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:25,230:INFO:Checking exceptions +2024-12-04 12:11:25,230:INFO:Importing libraries +2024-12-04 12:11:25,230:INFO:Copying training dataset +2024-12-04 12:11:25,250:INFO:Defining folds +2024-12-04 12:11:25,250:INFO:Declaring metric variables +2024-12-04 12:11:25,254:INFO:Importing untrained model +2024-12-04 12:11:25,259:INFO:Bayesian Ridge Imported successfully +2024-12-04 12:11:25,265:INFO:Starting cross validation +2024-12-04 12:11:25,267:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:26,346:INFO:Calculating mean and std +2024-12-04 12:11:26,347:INFO:Creating metrics dataframe +2024-12-04 12:11:26,349:INFO:Uploading results into container +2024-12-04 12:11:26,349:INFO:Uploading model into container now +2024-12-04 12:11:26,350:INFO:_master_model_container: 8 +2024-12-04 12:11:26,350:INFO:_display_container: 2 +2024-12-04 12:11:26,350:INFO:BayesianRidge() +2024-12-04 12:11:26,350:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:26,503:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:26,505:INFO:Creating metrics dataframe +2024-12-04 12:11:26,514:INFO:Initializing Passive Aggressive Regressor +2024-12-04 12:11:26,514:INFO:Total runtime is 0.3818602164586385 minutes +2024-12-04 12:11:26,517:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:26,517:INFO:Initializing create_model() +2024-12-04 12:11:26,518:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:26,518:INFO:Checking exceptions +2024-12-04 12:11:26,518:INFO:Importing libraries +2024-12-04 12:11:26,518:INFO:Copying training dataset +2024-12-04 12:11:26,532:INFO:Defining folds +2024-12-04 12:11:26,532:INFO:Declaring metric variables +2024-12-04 12:11:26,535:INFO:Importing untrained model +2024-12-04 12:11:26,538:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 12:11:26,545:INFO:Starting cross validation +2024-12-04 12:11:26,546:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:27,768:INFO:Calculating mean and std +2024-12-04 12:11:27,768:INFO:Creating metrics dataframe +2024-12-04 12:11:27,771:INFO:Uploading results into container +2024-12-04 12:11:27,771:INFO:Uploading model into container now +2024-12-04 12:11:27,771:INFO:_master_model_container: 9 +2024-12-04 12:11:27,771:INFO:_display_container: 2 +2024-12-04 12:11:27,772:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 12:11:27,772:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:27,915:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:27,915:INFO:Creating metrics dataframe +2024-12-04 12:11:27,922:INFO:Initializing Huber Regressor +2024-12-04 12:11:27,922:INFO:Total runtime is 0.40531988143920894 minutes +2024-12-04 12:11:27,925:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:27,925:INFO:Initializing create_model() +2024-12-04 12:11:27,925:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:27,925:INFO:Checking exceptions +2024-12-04 12:11:27,925:INFO:Importing libraries +2024-12-04 12:11:27,926:INFO:Copying training dataset +2024-12-04 12:11:27,938:INFO:Defining folds +2024-12-04 12:11:27,938:INFO:Declaring metric variables +2024-12-04 12:11:27,942:INFO:Importing untrained model +2024-12-04 12:11:27,946:INFO:Huber Regressor Imported successfully +2024-12-04 12:11:27,952:INFO:Starting cross validation +2024-12-04 12:11:27,955:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:29,500:INFO:Calculating mean and std +2024-12-04 12:11:29,501:INFO:Creating metrics dataframe +2024-12-04 12:11:29,502:INFO:Uploading results into container +2024-12-04 12:11:29,503:INFO:Uploading model into container now +2024-12-04 12:11:29,503:INFO:_master_model_container: 10 +2024-12-04 12:11:29,503:INFO:_display_container: 2 +2024-12-04 12:11:29,503:INFO:HuberRegressor() +2024-12-04 12:11:29,503:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:29,635:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:29,635:INFO:Creating metrics dataframe +2024-12-04 12:11:29,642:INFO:Initializing K Neighbors Regressor +2024-12-04 12:11:29,642:INFO:Total runtime is 0.4339808265368143 minutes +2024-12-04 12:11:29,645:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:29,645:INFO:Initializing create_model() +2024-12-04 12:11:29,645:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:29,646:INFO:Checking exceptions +2024-12-04 12:11:29,646:INFO:Importing libraries +2024-12-04 12:11:29,646:INFO:Copying training dataset +2024-12-04 12:11:29,659:INFO:Defining folds +2024-12-04 12:11:29,659:INFO:Declaring metric variables +2024-12-04 12:11:29,662:INFO:Importing untrained model +2024-12-04 12:11:29,665:INFO:K Neighbors Regressor Imported successfully +2024-12-04 12:11:29,670:INFO:Starting cross validation +2024-12-04 12:11:29,671:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:31,282:INFO:Calculating mean and std +2024-12-04 12:11:31,283:INFO:Creating metrics dataframe +2024-12-04 12:11:31,284:INFO:Uploading results into container +2024-12-04 12:11:31,284:INFO:Uploading model into container now +2024-12-04 12:11:31,284:INFO:_master_model_container: 11 +2024-12-04 12:11:31,284:INFO:_display_container: 2 +2024-12-04 12:11:31,284:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 12:11:31,284:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:31,436:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:31,436:INFO:Creating metrics dataframe +2024-12-04 12:11:31,443:INFO:Initializing Decision Tree Regressor +2024-12-04 12:11:31,443:INFO:Total runtime is 0.4640090624491373 minutes +2024-12-04 12:11:31,446:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:31,446:INFO:Initializing create_model() +2024-12-04 12:11:31,447:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:31,447:INFO:Checking exceptions +2024-12-04 12:11:31,447:INFO:Importing libraries +2024-12-04 12:11:31,447:INFO:Copying training dataset +2024-12-04 12:11:31,461:INFO:Defining folds +2024-12-04 12:11:31,462:INFO:Declaring metric variables +2024-12-04 12:11:31,465:INFO:Importing untrained model +2024-12-04 12:11:31,467:INFO:Decision Tree Regressor Imported successfully +2024-12-04 12:11:31,473:INFO:Starting cross validation +2024-12-04 12:11:31,475:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:11:33,081:INFO:Calculating mean and std +2024-12-04 12:11:33,082:INFO:Creating metrics dataframe +2024-12-04 12:11:33,083:INFO:Uploading results into container +2024-12-04 12:11:33,084:INFO:Uploading model into container now +2024-12-04 12:11:33,085:INFO:_master_model_container: 12 +2024-12-04 12:11:33,085:INFO:_display_container: 2 +2024-12-04 12:11:33,085:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 12:11:33,085:INFO:create_model() successfully completed...................................... +2024-12-04 12:11:33,212:INFO:SubProcess create_model() end ================================== +2024-12-04 12:11:33,212:INFO:Creating metrics dataframe +2024-12-04 12:11:33,220:INFO:Initializing Random Forest Regressor +2024-12-04 12:11:33,220:INFO:Total runtime is 0.49362017313639317 minutes +2024-12-04 12:11:33,222:INFO:SubProcess create_model() called ================================== +2024-12-04 12:11:33,222:INFO:Initializing create_model() +2024-12-04 12:11:33,224:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:11:33,224:INFO:Checking exceptions +2024-12-04 12:11:33,224:INFO:Importing libraries +2024-12-04 12:11:33,224:INFO:Copying training dataset +2024-12-04 12:11:33,239:INFO:Defining folds +2024-12-04 12:11:33,239:INFO:Declaring metric variables +2024-12-04 12:11:33,243:INFO:Importing untrained model +2024-12-04 12:11:33,247:INFO:Random Forest Regressor Imported successfully +2024-12-04 12:11:33,252:INFO:Starting cross validation +2024-12-04 12:11:33,253:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:12:05,372:INFO:Calculating mean and std +2024-12-04 12:12:05,374:INFO:Creating metrics dataframe +2024-12-04 12:12:05,375:INFO:Uploading results into container +2024-12-04 12:12:05,377:INFO:Uploading model into container now +2024-12-04 12:12:05,377:INFO:_master_model_container: 13 +2024-12-04 12:12:05,377:INFO:_display_container: 2 +2024-12-04 12:12:05,378:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:12:05,378:INFO:create_model() successfully completed...................................... +2024-12-04 12:12:05,574:INFO:SubProcess create_model() end ================================== +2024-12-04 12:12:05,574:INFO:Creating metrics dataframe +2024-12-04 12:12:05,586:INFO:Initializing Extra Trees Regressor +2024-12-04 12:12:05,586:INFO:Total runtime is 1.0330503424008688 minutes +2024-12-04 12:12:05,589:INFO:SubProcess create_model() called ================================== +2024-12-04 12:12:05,589:INFO:Initializing create_model() +2024-12-04 12:12:05,591:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:12:05,591:INFO:Checking exceptions +2024-12-04 12:12:05,591:INFO:Importing libraries +2024-12-04 12:12:05,591:INFO:Copying training dataset +2024-12-04 12:12:05,612:INFO:Defining folds +2024-12-04 12:12:05,612:INFO:Declaring metric variables +2024-12-04 12:12:05,615:INFO:Importing untrained model +2024-12-04 12:12:05,620:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:12:05,628:INFO:Starting cross validation +2024-12-04 12:12:05,630:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:12:23,051:INFO:Calculating mean and std +2024-12-04 12:12:23,052:INFO:Creating metrics dataframe +2024-12-04 12:12:23,056:INFO:Uploading results into container +2024-12-04 12:12:23,057:INFO:Uploading model into container now +2024-12-04 12:12:23,058:INFO:_master_model_container: 14 +2024-12-04 12:12:23,058:INFO:_display_container: 2 +2024-12-04 12:12:23,058:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:12:23,058:INFO:create_model() successfully completed...................................... +2024-12-04 12:12:23,286:INFO:SubProcess create_model() end ================================== +2024-12-04 12:12:23,286:INFO:Creating metrics dataframe +2024-12-04 12:12:23,300:INFO:Initializing AdaBoost Regressor +2024-12-04 12:12:23,300:INFO:Total runtime is 1.3282887895901998 minutes +2024-12-04 12:12:23,305:INFO:SubProcess create_model() called ================================== +2024-12-04 12:12:23,306:INFO:Initializing create_model() +2024-12-04 12:12:23,306:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:12:23,306:INFO:Checking exceptions +2024-12-04 12:12:23,306:INFO:Importing libraries +2024-12-04 12:12:23,306:INFO:Copying training dataset +2024-12-04 12:12:23,328:INFO:Defining folds +2024-12-04 12:12:23,328:INFO:Declaring metric variables +2024-12-04 12:12:23,335:INFO:Importing untrained model +2024-12-04 12:12:23,340:INFO:AdaBoost Regressor Imported successfully +2024-12-04 12:12:23,349:INFO:Starting cross validation +2024-12-04 12:12:23,351:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:12:28,403:INFO:Calculating mean and std +2024-12-04 12:12:28,404:INFO:Creating metrics dataframe +2024-12-04 12:12:28,405:INFO:Uploading results into container +2024-12-04 12:12:28,407:INFO:Uploading model into container now +2024-12-04 12:12:28,407:INFO:_master_model_container: 15 +2024-12-04 12:12:28,407:INFO:_display_container: 2 +2024-12-04 12:12:28,407:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 12:12:28,407:INFO:create_model() successfully completed...................................... +2024-12-04 12:12:28,534:INFO:SubProcess create_model() end ================================== +2024-12-04 12:12:28,535:INFO:Creating metrics dataframe +2024-12-04 12:12:28,544:INFO:Initializing Gradient Boosting Regressor +2024-12-04 12:12:28,544:INFO:Total runtime is 1.415691347916921 minutes +2024-12-04 12:12:28,547:INFO:SubProcess create_model() called ================================== +2024-12-04 12:12:28,547:INFO:Initializing create_model() +2024-12-04 12:12:28,548:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:12:28,548:INFO:Checking exceptions +2024-12-04 12:12:28,548:INFO:Importing libraries +2024-12-04 12:12:28,548:INFO:Copying training dataset +2024-12-04 12:12:28,560:INFO:Defining folds +2024-12-04 12:12:28,561:INFO:Declaring metric variables +2024-12-04 12:12:28,564:INFO:Importing untrained model +2024-12-04 12:12:28,567:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 12:12:28,573:INFO:Starting cross validation +2024-12-04 12:12:28,574:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:12:37,589:INFO:Calculating mean and std +2024-12-04 12:12:37,590:INFO:Creating metrics dataframe +2024-12-04 12:12:37,591:INFO:Uploading results into container +2024-12-04 12:12:37,592:INFO:Uploading model into container now +2024-12-04 12:12:37,592:INFO:_master_model_container: 16 +2024-12-04 12:12:37,592:INFO:_display_container: 2 +2024-12-04 12:12:37,593:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 12:12:37,593:INFO:create_model() successfully completed...................................... +2024-12-04 12:12:37,720:INFO:SubProcess create_model() end ================================== +2024-12-04 12:12:37,720:INFO:Creating metrics dataframe +2024-12-04 12:12:37,730:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 12:12:37,730:INFO:Total runtime is 1.5687847057978312 minutes +2024-12-04 12:12:37,732:INFO:SubProcess create_model() called ================================== +2024-12-04 12:12:37,732:INFO:Initializing create_model() +2024-12-04 12:12:37,732:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:12:37,733:INFO:Checking exceptions +2024-12-04 12:12:37,733:INFO:Importing libraries +2024-12-04 12:12:37,733:INFO:Copying training dataset +2024-12-04 12:12:37,745:INFO:Defining folds +2024-12-04 12:12:37,746:INFO:Declaring metric variables +2024-12-04 12:12:37,748:INFO:Importing untrained model +2024-12-04 12:12:37,752:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 12:12:37,757:INFO:Starting cross validation +2024-12-04 12:12:37,760:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:12:39,784:INFO:Calculating mean and std +2024-12-04 12:12:39,786:INFO:Creating metrics dataframe +2024-12-04 12:12:39,788:INFO:Uploading results into container +2024-12-04 12:12:39,788:INFO:Uploading model into container now +2024-12-04 12:12:39,789:INFO:_master_model_container: 17 +2024-12-04 12:12:39,789:INFO:_display_container: 2 +2024-12-04 12:12:39,790:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:12:39,790:INFO:create_model() successfully completed...................................... +2024-12-04 12:12:39,948:INFO:SubProcess create_model() end ================================== +2024-12-04 12:12:39,948:INFO:Creating metrics dataframe +2024-12-04 12:12:39,957:INFO:Initializing Dummy Regressor +2024-12-04 12:12:39,957:INFO:Total runtime is 1.605909776687622 minutes +2024-12-04 12:12:39,960:INFO:SubProcess create_model() called ================================== +2024-12-04 12:12:39,960:INFO:Initializing create_model() +2024-12-04 12:12:39,960:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:12:39,960:INFO:Checking exceptions +2024-12-04 12:12:39,960:INFO:Importing libraries +2024-12-04 12:12:39,960:INFO:Copying training dataset +2024-12-04 12:12:39,974:INFO:Defining folds +2024-12-04 12:12:39,974:INFO:Declaring metric variables +2024-12-04 12:12:39,977:INFO:Importing untrained model +2024-12-04 12:12:39,981:INFO:Dummy Regressor Imported successfully +2024-12-04 12:12:39,986:INFO:Starting cross validation +2024-12-04 12:12:39,987:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:12:40,933:INFO:Calculating mean and std +2024-12-04 12:12:40,933:INFO:Creating metrics dataframe +2024-12-04 12:12:40,934:INFO:Uploading results into container +2024-12-04 12:12:40,935:INFO:Uploading model into container now +2024-12-04 12:12:40,936:INFO:_master_model_container: 18 +2024-12-04 12:12:40,936:INFO:_display_container: 2 +2024-12-04 12:12:40,936:INFO:DummyRegressor() +2024-12-04 12:12:40,936:INFO:create_model() successfully completed...................................... +2024-12-04 12:12:41,083:INFO:SubProcess create_model() end ================================== +2024-12-04 12:12:41,083:INFO:Creating metrics dataframe +2024-12-04 12:12:41,092:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 12:12:41,099:INFO:Initializing create_model() +2024-12-04 12:12:41,100:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:12:41,100:INFO:Checking exceptions +2024-12-04 12:12:41,101:INFO:Importing libraries +2024-12-04 12:12:41,101:INFO:Copying training dataset +2024-12-04 12:12:41,114:INFO:Defining folds +2024-12-04 12:12:41,114:INFO:Declaring metric variables +2024-12-04 12:12:41,115:INFO:Importing untrained model +2024-12-04 12:12:41,115:INFO:Declaring custom model +2024-12-04 12:12:41,115:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:12:41,116:INFO:Cross validation set to False +2024-12-04 12:12:41,116:INFO:Fitting Model +2024-12-04 12:12:43,135:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:12:43,135:INFO:create_model() successfully completed...................................... +2024-12-04 12:12:43,290:INFO:_master_model_container: 18 +2024-12-04 12:12:43,290:INFO:_display_container: 2 +2024-12-04 12:12:43,291:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:12:43,291:INFO:compare_models() successfully completed...................................... +2024-12-04 12:12:53,189:INFO:Initializing plot_model() +2024-12-04 12:12:53,189:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=residuals_interactive, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 12:12:53,189:INFO:Checking exceptions +2024-12-04 12:12:53,223:INFO:Preloading libraries +2024-12-04 12:12:53,457:INFO:Copying training dataset +2024-12-04 12:12:53,458:INFO:Plot type: residuals_interactive +2024-12-04 12:12:54,317:INFO:Calculated model residuals +2024-12-04 12:13:16,013:INFO:Calculated Tunkey-Anscombe Plot +2024-12-04 12:13:59,118:INFO:Initializing plot_model() +2024-12-04 12:13:59,119:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=feature, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 12:13:59,119:INFO:Checking exceptions +2024-12-04 12:13:59,157:INFO:Preloading libraries +2024-12-04 12:13:59,391:INFO:Copying training dataset +2024-12-04 12:13:59,391:INFO:Plot type: feature +2024-12-04 12:13:59,391:WARNING:No coef_ found. Trying feature_importances_ +2024-12-04 12:13:59,704:INFO:Visual Rendered Successfully +2024-12-04 12:13:59,871:INFO:plot_model() successfully completed...................................... +2024-12-04 12:14:30,501:INFO:Initializing evaluate_model() +2024-12-04 12:14:30,501:INFO:evaluate_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-12-04 12:14:30,518:INFO:Initializing plot_model() +2024-12-04 12:14:30,519:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-12-04 12:14:30,519:INFO:Checking exceptions +2024-12-04 12:14:30,551:INFO:Preloading libraries +2024-12-04 12:14:30,783:INFO:Copying training dataset +2024-12-04 12:14:30,783:INFO:Plot type: pipeline +2024-12-04 12:14:30,954:INFO:Visual Rendered Successfully +2024-12-04 12:14:31,120:INFO:plot_model() successfully completed...................................... +2024-12-04 12:14:33,299:INFO:Initializing plot_model() +2024-12-04 12:14:33,299:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=rfe, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-12-04 12:14:33,299:INFO:Checking exceptions +2024-12-04 12:14:33,333:INFO:Preloading libraries +2024-12-04 12:14:33,563:INFO:Copying training dataset +2024-12-04 12:14:33,563:INFO:Plot type: rfe +2024-12-04 12:14:33,902:INFO:Fitting Model +2024-12-04 12:15:45,743:INFO:PyCaret RegressionExperiment +2024-12-04 12:15:45,743:INFO:Logging name: reg-default-name +2024-12-04 12:15:45,743:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:15:45,743:INFO:version 3.3.2 +2024-12-04 12:15:45,743:INFO:Initializing setup() +2024-12-04 12:15:45,743:INFO:self.USI: 633b +2024-12-04 12:15:45,743:INFO:self._variable_keys: {'log_plots_param', 'y', 'exp_name_log', 'X_train', 'y_train', '_available_plots', 'idx', 'exp_id', 'target_param', 'X', 'transform_target_param', 'fold_groups_param', 'n_jobs_param', 'gpu_n_jobs_param', '_ml_usecase', 'seed', 'memory', 'y_test', 'fold_shuffle_param', 'USI', 'X_test', 'logging_param', 'fold_generator', 'data', 'html_param', 'pipeline', 'gpu_param'} +2024-12-04 12:15:45,743:INFO:Checking environment +2024-12-04 12:15:45,743:INFO:python_version: 3.11.9 +2024-12-04 12:15:45,743:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:15:45,743:INFO:machine: AMD64 +2024-12-04 12:15:45,743:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:15:45,743:INFO:Memory: svmem(total=17007292416, available=1620508672, percent=90.5, used=15386783744, free=1620508672) +2024-12-04 12:15:45,743:INFO:Physical Core: 4 +2024-12-04 12:15:45,743:INFO:Logical Core: 8 +2024-12-04 12:15:45,743:INFO:Checking libraries +2024-12-04 12:15:45,743:INFO:System: +2024-12-04 12:15:45,743:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:15:45,743:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:15:45,743:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:15:45,743:INFO:PyCaret required dependencies: +2024-12-04 12:15:45,743:INFO: pip: 24.3.1 +2024-12-04 12:15:45,743:INFO: setuptools: 65.5.0 +2024-12-04 12:15:45,743:INFO: pycaret: 3.3.2 +2024-12-04 12:15:45,743:INFO: IPython: 8.12.3 +2024-12-04 12:15:45,743:INFO: ipywidgets: 8.1.5 +2024-12-04 12:15:45,743:INFO: tqdm: 4.67.1 +2024-12-04 12:15:45,743:INFO: numpy: 1.26.4 +2024-12-04 12:15:45,743:INFO: pandas: 2.1.4 +2024-12-04 12:15:45,743:INFO: jinja2: 3.1.4 +2024-12-04 12:15:45,752:INFO: scipy: 1.11.4 +2024-12-04 12:15:45,752:INFO: joblib: 1.3.2 +2024-12-04 12:15:45,752:INFO: sklearn: 1.4.2 +2024-12-04 12:15:45,752:INFO: pyod: 2.0.2 +2024-12-04 12:15:45,752:INFO: imblearn: 0.12.4 +2024-12-04 12:15:45,752:INFO: category_encoders: 2.6.4 +2024-12-04 12:15:45,752:INFO: lightgbm: 4.5.0 +2024-12-04 12:15:45,752:INFO: numba: 0.60.0 +2024-12-04 12:15:45,752:INFO: requests: 2.32.3 +2024-12-04 12:15:45,752:INFO: matplotlib: 3.7.5 +2024-12-04 12:15:45,752:INFO: scikitplot: 0.3.7 +2024-12-04 12:15:45,752:INFO: yellowbrick: 1.5 +2024-12-04 12:15:45,752:INFO: plotly: 5.24.1 +2024-12-04 12:15:45,752:INFO: plotly-resampler: Not installed +2024-12-04 12:15:45,752:INFO: kaleido: 0.2.1 +2024-12-04 12:15:45,752:INFO: schemdraw: 0.15 +2024-12-04 12:15:45,752:INFO: statsmodels: 0.14.4 +2024-12-04 12:15:45,752:INFO: sktime: 0.26.0 +2024-12-04 12:15:45,752:INFO: tbats: 1.1.3 +2024-12-04 12:15:45,752:INFO: pmdarima: 2.0.4 +2024-12-04 12:15:45,752:INFO: psutil: 6.1.0 +2024-12-04 12:15:45,752:INFO: markupsafe: 3.0.2 +2024-12-04 12:15:45,752:INFO: pickle5: Not installed +2024-12-04 12:15:45,752:INFO: cloudpickle: 3.1.0 +2024-12-04 12:15:45,752:INFO: deprecation: 2.1.0 +2024-12-04 12:15:45,752:INFO: xxhash: 3.5.0 +2024-12-04 12:15:45,752:INFO: wurlitzer: Not installed +2024-12-04 12:15:45,752:INFO:PyCaret optional dependencies: +2024-12-04 12:15:45,752:INFO: shap: Not installed +2024-12-04 12:15:45,752:INFO: interpret: Not installed +2024-12-04 12:15:45,752:INFO: umap: Not installed +2024-12-04 12:15:45,752:INFO: ydata_profiling: Not installed +2024-12-04 12:15:45,752:INFO: explainerdashboard: Not installed +2024-12-04 12:15:45,752:INFO: autoviz: Not installed +2024-12-04 12:15:45,752:INFO: fairlearn: Not installed +2024-12-04 12:15:45,752:INFO: deepchecks: Not installed +2024-12-04 12:15:45,752:INFO: xgboost: Not installed +2024-12-04 12:15:45,752:INFO: catboost: Not installed +2024-12-04 12:15:45,752:INFO: kmodes: Not installed +2024-12-04 12:15:45,755:INFO: mlxtend: Not installed +2024-12-04 12:15:45,755:INFO: statsforecast: Not installed +2024-12-04 12:15:45,755:INFO: tune_sklearn: Not installed +2024-12-04 12:15:45,755:INFO: ray: Not installed +2024-12-04 12:15:45,755:INFO: hyperopt: Not installed +2024-12-04 12:15:45,755:INFO: optuna: Not installed +2024-12-04 12:15:45,755:INFO: skopt: Not installed +2024-12-04 12:15:45,755:INFO: mlflow: 2.18.0 +2024-12-04 12:15:45,755:INFO: gradio: Not installed +2024-12-04 12:15:45,755:INFO: fastapi: Not installed +2024-12-04 12:15:45,755:INFO: uvicorn: 0.32.1 +2024-12-04 12:15:45,755:INFO: m2cgen: Not installed +2024-12-04 12:15:45,755:INFO: evidently: Not installed +2024-12-04 12:15:45,755:INFO: fugue: Not installed +2024-12-04 12:15:45,755:INFO: streamlit: 1.40.1 +2024-12-04 12:15:45,755:INFO: prophet: Not installed +2024-12-04 12:15:45,755:INFO:None +2024-12-04 12:15:45,755:INFO:Set up data. +2024-12-04 12:15:45,777:INFO:Set up folding strategy. +2024-12-04 12:15:45,777:INFO:Set up train/test split. +2024-12-04 12:15:45,785:INFO:Set up index. +2024-12-04 12:15:45,785:INFO:Assigning column types. +2024-12-04 12:15:45,798:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:15:45,920:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:45,920:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,053:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,053:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,053:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:15:46,176:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,176:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,296:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,296:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,296:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:15:46,413:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,413:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,527:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,527:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,527:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:15:46,634:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,634:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,762:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,763:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,763:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:15:46,877:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,877:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,984:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,984:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:46,984:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:15:47,099:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:47,099:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:47,222:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:47,222:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:47,238:INFO:Preparing preprocessing pipeline... +2024-12-04 12:15:47,238:INFO:Set up simple imputation. +2024-12-04 12:15:47,244:INFO:Set up encoding of categorical features. +2024-12-04 12:15:47,244:INFO:Set up column transformation. +2024-12-04 12:15:47,244:INFO:Set up feature normalization. +2024-12-04 12:15:47,416:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:15:47,430:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=TargetEncoder(cols=['nom_region', + 'nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:15:47,430:INFO:Creating final display dataframe. +2024-12-04 12:15:47,761:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (31906, 15) +4 Transformed data shape (31906, 14) +5 Transformed train set shape (22334, 14) +6 Transformed test set shape (9572, 14) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transformation True +17 Transformation method yeo-johnson +18 Normalize True +19 Normalize method zscore +20 Fold Generator KFold +21 Fold Number 10 +22 CPU Jobs -1 +23 Use GPU False +24 Log Experiment False +25 Experiment Name reg-default-name +26 USI 633b +2024-12-04 12:15:47,894:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:47,894:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:48,014:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:48,014:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:15:48,014:INFO:setup() successfully completed in 2.28s............... +2024-12-04 12:15:48,028:INFO:Initializing plot_model() +2024-12-04 12:15:48,028:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=residuals_interactive, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-12-04 12:15:48,028:INFO:Checking exceptions +2024-12-04 12:15:48,064:INFO:Preloading libraries +2024-12-04 12:16:09,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:16:09,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:16:09,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:16:09,330:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:16:37,579:INFO:PyCaret RegressionExperiment +2024-12-04 12:16:37,579:INFO:Logging name: reg-default-name +2024-12-04 12:16:37,579:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:16:37,579:INFO:version 3.3.2 +2024-12-04 12:16:37,579:INFO:Initializing setup() +2024-12-04 12:16:37,579:INFO:self.USI: 0761 +2024-12-04 12:16:37,579:INFO:self._variable_keys: {'gpu_param', 'data', 'idx', 'log_plots_param', 'fold_groups_param', 'html_param', 'target_param', 'y', 'exp_id', 'fold_shuffle_param', 'USI', 'fold_generator', 'X_test', 'X', 'exp_name_log', 'y_test', '_ml_usecase', 'n_jobs_param', 'logging_param', 'seed', 'transform_target_param', 'X_train', 'gpu_n_jobs_param', 'y_train', '_available_plots', 'memory', 'pipeline'} +2024-12-04 12:16:37,579:INFO:Checking environment +2024-12-04 12:16:37,579:INFO:python_version: 3.11.9 +2024-12-04 12:16:37,579:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:16:37,579:INFO:machine: AMD64 +2024-12-04 12:16:37,580:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:16:37,585:INFO:Memory: svmem(total=17007292416, available=4090462208, percent=75.9, used=12916830208, free=4090462208) +2024-12-04 12:16:37,585:INFO:Physical Core: 4 +2024-12-04 12:16:37,585:INFO:Logical Core: 8 +2024-12-04 12:16:37,585:INFO:Checking libraries +2024-12-04 12:16:37,585:INFO:System: +2024-12-04 12:16:37,585:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:16:37,585:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:16:37,585:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:16:37,585:INFO:PyCaret required dependencies: +2024-12-04 12:16:37,618:INFO: pip: 24.3.1 +2024-12-04 12:16:37,618:INFO: setuptools: 65.5.0 +2024-12-04 12:16:37,618:INFO: pycaret: 3.3.2 +2024-12-04 12:16:37,618:INFO: IPython: 8.12.3 +2024-12-04 12:16:37,619:INFO: ipywidgets: 8.1.5 +2024-12-04 12:16:37,619:INFO: tqdm: 4.67.1 +2024-12-04 12:16:37,619:INFO: numpy: 1.26.4 +2024-12-04 12:16:37,619:INFO: pandas: 2.1.4 +2024-12-04 12:16:37,619:INFO: jinja2: 3.1.4 +2024-12-04 12:16:37,619:INFO: scipy: 1.11.4 +2024-12-04 12:16:37,619:INFO: joblib: 1.3.2 +2024-12-04 12:16:37,619:INFO: sklearn: 1.4.2 +2024-12-04 12:16:37,619:INFO: pyod: 2.0.2 +2024-12-04 12:16:37,619:INFO: imblearn: 0.12.4 +2024-12-04 12:16:37,619:INFO: category_encoders: 2.6.4 +2024-12-04 12:16:37,619:INFO: lightgbm: 4.5.0 +2024-12-04 12:16:37,619:INFO: numba: 0.60.0 +2024-12-04 12:16:37,619:INFO: requests: 2.32.3 +2024-12-04 12:16:37,619:INFO: matplotlib: 3.7.5 +2024-12-04 12:16:37,619:INFO: scikitplot: 0.3.7 +2024-12-04 12:16:37,619:INFO: yellowbrick: 1.5 +2024-12-04 12:16:37,619:INFO: plotly: 5.24.1 +2024-12-04 12:16:37,619:INFO: plotly-resampler: Not installed +2024-12-04 12:16:37,619:INFO: kaleido: 0.2.1 +2024-12-04 12:16:37,619:INFO: schemdraw: 0.15 +2024-12-04 12:16:37,619:INFO: statsmodels: 0.14.4 +2024-12-04 12:16:37,619:INFO: sktime: 0.26.0 +2024-12-04 12:16:37,619:INFO: tbats: 1.1.3 +2024-12-04 12:16:37,619:INFO: pmdarima: 2.0.4 +2024-12-04 12:16:37,619:INFO: psutil: 6.1.0 +2024-12-04 12:16:37,619:INFO: markupsafe: 3.0.2 +2024-12-04 12:16:37,619:INFO: pickle5: Not installed +2024-12-04 12:16:37,619:INFO: cloudpickle: 3.1.0 +2024-12-04 12:16:37,619:INFO: deprecation: 2.1.0 +2024-12-04 12:16:37,620:INFO: xxhash: 3.5.0 +2024-12-04 12:16:37,620:INFO: wurlitzer: Not installed +2024-12-04 12:16:37,620:INFO:PyCaret optional dependencies: +2024-12-04 12:16:37,697:INFO: shap: Not installed +2024-12-04 12:16:37,697:INFO: interpret: Not installed +2024-12-04 12:16:37,697:INFO: umap: Not installed +2024-12-04 12:16:37,697:INFO: ydata_profiling: Not installed +2024-12-04 12:16:37,697:INFO: explainerdashboard: Not installed +2024-12-04 12:16:37,697:INFO: autoviz: Not installed +2024-12-04 12:16:37,697:INFO: fairlearn: Not installed +2024-12-04 12:16:37,697:INFO: deepchecks: Not installed +2024-12-04 12:16:37,697:INFO: xgboost: Not installed +2024-12-04 12:16:37,697:INFO: catboost: Not installed +2024-12-04 12:16:37,697:INFO: kmodes: Not installed +2024-12-04 12:16:37,697:INFO: mlxtend: Not installed +2024-12-04 12:16:37,697:INFO: statsforecast: Not installed +2024-12-04 12:16:37,697:INFO: tune_sklearn: Not installed +2024-12-04 12:16:37,697:INFO: ray: Not installed +2024-12-04 12:16:37,697:INFO: hyperopt: Not installed +2024-12-04 12:16:37,697:INFO: optuna: Not installed +2024-12-04 12:16:37,697:INFO: skopt: Not installed +2024-12-04 12:16:37,697:INFO: mlflow: 2.18.0 +2024-12-04 12:16:37,697:INFO: gradio: Not installed +2024-12-04 12:16:37,697:INFO: fastapi: Not installed +2024-12-04 12:16:37,697:INFO: uvicorn: 0.32.1 +2024-12-04 12:16:37,697:INFO: m2cgen: Not installed +2024-12-04 12:16:37,697:INFO: evidently: Not installed +2024-12-04 12:16:37,697:INFO: fugue: Not installed +2024-12-04 12:16:37,697:INFO: streamlit: 1.40.1 +2024-12-04 12:16:37,697:INFO: prophet: Not installed +2024-12-04 12:16:37,697:INFO:None +2024-12-04 12:16:37,697:INFO:Set up data. +2024-12-04 12:16:37,718:INFO:Set up folding strategy. +2024-12-04 12:16:37,718:INFO:Set up train/test split. +2024-12-04 12:16:37,730:INFO:Set up index. +2024-12-04 12:16:37,731:INFO:Assigning column types. +2024-12-04 12:16:37,739:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:16:37,739:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,745:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,747:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,809:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,850:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,852:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:37,852:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:37,852:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,857:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,861:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,920:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,961:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,962:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:37,963:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:37,964:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:16:37,967:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:16:37,971:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,030:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,070:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,070:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,070:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,074:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,079:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,142:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,185:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,185:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,185:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,185:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:16:38,193:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,255:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,298:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,298:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,299:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,307:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,368:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,412:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,413:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,413:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,413:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:16:38,481:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,522:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,522:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,522:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,588:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,631:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,632:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,632:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,632:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:16:38,696:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,739:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,739:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,805:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:16:38,845:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,845:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,845:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:16:38,952:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:38,952:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:39,057:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:39,057:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:39,059:INFO:Preparing preprocessing pipeline... +2024-12-04 12:16:39,059:INFO:Set up simple imputation. +2024-12-04 12:16:39,066:INFO:Set up encoding of categorical features. +2024-12-04 12:16:39,066:INFO:Set up column transformation. +2024-12-04 12:16:39,066:INFO:Set up feature normalization. +2024-12-04 12:16:39,196:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:16:39,207:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=TargetEncoder(cols=['nom_region', + 'nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:16:39,207:INFO:Creating final display dataframe. +2024-12-04 12:16:39,513:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (31906, 15) +4 Transformed data shape (31906, 14) +5 Transformed train set shape (22334, 14) +6 Transformed test set shape (9572, 14) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transformation True +17 Transformation method yeo-johnson +18 Normalize True +19 Normalize method zscore +20 Fold Generator KFold +21 Fold Number 10 +22 CPU Jobs -1 +23 Use GPU False +24 Log Experiment False +25 Experiment Name reg-default-name +26 USI 0761 +2024-12-04 12:16:39,630:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:39,630:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:39,735:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:39,735:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:16:39,735:INFO:setup() successfully completed in 2.17s............... +2024-12-04 12:16:54,059:INFO:Initializing compare_models() +2024-12-04 12:16:54,059:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 12:16:54,059:INFO:Checking exceptions +2024-12-04 12:16:54,068:INFO:Preparing display monitor +2024-12-04 12:16:54,100:INFO:Initializing Linear Regression +2024-12-04 12:16:54,100:INFO:Total runtime is 0.0 minutes +2024-12-04 12:16:54,105:INFO:SubProcess create_model() called ================================== +2024-12-04 12:16:54,105:INFO:Initializing create_model() +2024-12-04 12:16:54,105:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:16:54,105:INFO:Checking exceptions +2024-12-04 12:16:54,105:INFO:Importing libraries +2024-12-04 12:16:54,105:INFO:Copying training dataset +2024-12-04 12:16:54,125:INFO:Defining folds +2024-12-04 12:16:54,126:INFO:Declaring metric variables +2024-12-04 12:16:54,129:INFO:Importing untrained model +2024-12-04 12:16:54,132:INFO:Linear Regression Imported successfully +2024-12-04 12:16:54,140:INFO:Starting cross validation +2024-12-04 12:16:54,148:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:00,086:INFO:Calculating mean and std +2024-12-04 12:17:00,087:INFO:Creating metrics dataframe +2024-12-04 12:17:00,087:INFO:Uploading results into container +2024-12-04 12:17:00,089:INFO:Uploading model into container now +2024-12-04 12:17:00,089:INFO:_master_model_container: 1 +2024-12-04 12:17:00,089:INFO:_display_container: 2 +2024-12-04 12:17:00,089:INFO:LinearRegression(n_jobs=-1) +2024-12-04 12:17:00,089:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:00,175:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:00,176:INFO:Creating metrics dataframe +2024-12-04 12:17:00,181:INFO:Initializing Lasso Regression +2024-12-04 12:17:00,181:INFO:Total runtime is 0.1013442079226176 minutes +2024-12-04 12:17:00,183:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:00,183:INFO:Initializing create_model() +2024-12-04 12:17:00,183:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:00,183:INFO:Checking exceptions +2024-12-04 12:17:00,183:INFO:Importing libraries +2024-12-04 12:17:00,183:INFO:Copying training dataset +2024-12-04 12:17:00,198:INFO:Defining folds +2024-12-04 12:17:00,198:INFO:Declaring metric variables +2024-12-04 12:17:00,201:INFO:Importing untrained model +2024-12-04 12:17:00,205:INFO:Lasso Regression Imported successfully +2024-12-04 12:17:00,212:INFO:Starting cross validation +2024-12-04 12:17:00,213:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:01,392:INFO:Calculating mean and std +2024-12-04 12:17:01,393:INFO:Creating metrics dataframe +2024-12-04 12:17:01,393:INFO:Uploading results into container +2024-12-04 12:17:01,393:INFO:Uploading model into container now +2024-12-04 12:17:01,393:INFO:_master_model_container: 2 +2024-12-04 12:17:01,393:INFO:_display_container: 2 +2024-12-04 12:17:01,393:INFO:Lasso(random_state=123) +2024-12-04 12:17:01,393:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:01,475:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:01,475:INFO:Creating metrics dataframe +2024-12-04 12:17:01,481:INFO:Initializing Ridge Regression +2024-12-04 12:17:01,481:INFO:Total runtime is 0.12301005919774374 minutes +2024-12-04 12:17:01,484:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:01,485:INFO:Initializing create_model() +2024-12-04 12:17:01,485:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:01,485:INFO:Checking exceptions +2024-12-04 12:17:01,485:INFO:Importing libraries +2024-12-04 12:17:01,485:INFO:Copying training dataset +2024-12-04 12:17:01,493:INFO:Defining folds +2024-12-04 12:17:01,493:INFO:Declaring metric variables +2024-12-04 12:17:01,501:INFO:Importing untrained model +2024-12-04 12:17:01,503:INFO:Ridge Regression Imported successfully +2024-12-04 12:17:01,509:INFO:Starting cross validation +2024-12-04 12:17:01,511:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:02,497:INFO:Calculating mean and std +2024-12-04 12:17:02,497:INFO:Creating metrics dataframe +2024-12-04 12:17:02,497:INFO:Uploading results into container +2024-12-04 12:17:02,500:INFO:Uploading model into container now +2024-12-04 12:17:02,500:INFO:_master_model_container: 3 +2024-12-04 12:17:02,500:INFO:_display_container: 2 +2024-12-04 12:17:02,500:INFO:Ridge(random_state=123) +2024-12-04 12:17:02,500:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:02,581:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:02,583:INFO:Creating metrics dataframe +2024-12-04 12:17:02,587:INFO:Initializing Elastic Net +2024-12-04 12:17:02,587:INFO:Total runtime is 0.14144074519475303 minutes +2024-12-04 12:17:02,589:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:02,589:INFO:Initializing create_model() +2024-12-04 12:17:02,589:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:02,589:INFO:Checking exceptions +2024-12-04 12:17:02,589:INFO:Importing libraries +2024-12-04 12:17:02,589:INFO:Copying training dataset +2024-12-04 12:17:02,603:INFO:Defining folds +2024-12-04 12:17:02,603:INFO:Declaring metric variables +2024-12-04 12:17:02,606:INFO:Importing untrained model +2024-12-04 12:17:02,610:INFO:Elastic Net Imported successfully +2024-12-04 12:17:02,612:INFO:Starting cross validation +2024-12-04 12:17:02,617:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:03,591:INFO:Calculating mean and std +2024-12-04 12:17:03,593:INFO:Creating metrics dataframe +2024-12-04 12:17:03,593:INFO:Uploading results into container +2024-12-04 12:17:03,593:INFO:Uploading model into container now +2024-12-04 12:17:03,593:INFO:_master_model_container: 4 +2024-12-04 12:17:03,593:INFO:_display_container: 2 +2024-12-04 12:17:03,593:INFO:ElasticNet(random_state=123) +2024-12-04 12:17:03,595:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:03,678:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:03,678:INFO:Creating metrics dataframe +2024-12-04 12:17:03,684:INFO:Initializing Least Angle Regression +2024-12-04 12:17:03,684:INFO:Total runtime is 0.15973493258158367 minutes +2024-12-04 12:17:03,686:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:03,686:INFO:Initializing create_model() +2024-12-04 12:17:03,686:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:03,686:INFO:Checking exceptions +2024-12-04 12:17:03,686:INFO:Importing libraries +2024-12-04 12:17:03,689:INFO:Copying training dataset +2024-12-04 12:17:03,705:INFO:Defining folds +2024-12-04 12:17:03,705:INFO:Declaring metric variables +2024-12-04 12:17:03,709:INFO:Importing untrained model +2024-12-04 12:17:03,713:INFO:Least Angle Regression Imported successfully +2024-12-04 12:17:03,721:INFO:Starting cross validation +2024-12-04 12:17:03,722:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:04,827:INFO:Calculating mean and std +2024-12-04 12:17:04,828:INFO:Creating metrics dataframe +2024-12-04 12:17:04,828:INFO:Uploading results into container +2024-12-04 12:17:04,830:INFO:Uploading model into container now +2024-12-04 12:17:04,831:INFO:_master_model_container: 5 +2024-12-04 12:17:04,831:INFO:_display_container: 2 +2024-12-04 12:17:04,831:INFO:Lars(random_state=123) +2024-12-04 12:17:04,831:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:04,919:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:04,919:INFO:Creating metrics dataframe +2024-12-04 12:17:04,927:INFO:Initializing Lasso Least Angle Regression +2024-12-04 12:17:04,927:INFO:Total runtime is 0.18044234911600748 minutes +2024-12-04 12:17:04,927:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:04,930:INFO:Initializing create_model() +2024-12-04 12:17:04,930:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:04,930:INFO:Checking exceptions +2024-12-04 12:17:04,930:INFO:Importing libraries +2024-12-04 12:17:04,930:INFO:Copying training dataset +2024-12-04 12:17:04,944:INFO:Defining folds +2024-12-04 12:17:04,944:INFO:Declaring metric variables +2024-12-04 12:17:04,947:INFO:Importing untrained model +2024-12-04 12:17:04,950:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 12:17:04,955:INFO:Starting cross validation +2024-12-04 12:17:04,957:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:06,188:INFO:Calculating mean and std +2024-12-04 12:17:06,189:INFO:Creating metrics dataframe +2024-12-04 12:17:06,190:INFO:Uploading results into container +2024-12-04 12:17:06,191:INFO:Uploading model into container now +2024-12-04 12:17:06,191:INFO:_master_model_container: 6 +2024-12-04 12:17:06,191:INFO:_display_container: 2 +2024-12-04 12:17:06,191:INFO:LassoLars(random_state=123) +2024-12-04 12:17:06,191:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:06,282:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:06,282:INFO:Creating metrics dataframe +2024-12-04 12:17:06,292:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 12:17:06,292:INFO:Total runtime is 0.2032016078631083 minutes +2024-12-04 12:17:06,296:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:06,296:INFO:Initializing create_model() +2024-12-04 12:17:06,296:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:06,296:INFO:Checking exceptions +2024-12-04 12:17:06,296:INFO:Importing libraries +2024-12-04 12:17:06,296:INFO:Copying training dataset +2024-12-04 12:17:06,314:INFO:Defining folds +2024-12-04 12:17:06,314:INFO:Declaring metric variables +2024-12-04 12:17:06,318:INFO:Importing untrained model +2024-12-04 12:17:06,323:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 12:17:06,330:INFO:Starting cross validation +2024-12-04 12:17:06,331:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:07,534:INFO:Calculating mean and std +2024-12-04 12:17:07,534:INFO:Creating metrics dataframe +2024-12-04 12:17:07,537:INFO:Uploading results into container +2024-12-04 12:17:07,538:INFO:Uploading model into container now +2024-12-04 12:17:07,538:INFO:_master_model_container: 7 +2024-12-04 12:17:07,538:INFO:_display_container: 2 +2024-12-04 12:17:07,539:INFO:OrthogonalMatchingPursuit() +2024-12-04 12:17:07,539:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:07,645:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:07,650:INFO:Creating metrics dataframe +2024-12-04 12:17:07,659:INFO:Initializing Bayesian Ridge +2024-12-04 12:17:07,659:INFO:Total runtime is 0.22597461938858032 minutes +2024-12-04 12:17:07,662:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:07,662:INFO:Initializing create_model() +2024-12-04 12:17:07,662:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:07,662:INFO:Checking exceptions +2024-12-04 12:17:07,663:INFO:Importing libraries +2024-12-04 12:17:07,663:INFO:Copying training dataset +2024-12-04 12:17:07,681:INFO:Defining folds +2024-12-04 12:17:07,681:INFO:Declaring metric variables +2024-12-04 12:17:07,686:INFO:Importing untrained model +2024-12-04 12:17:07,689:INFO:Bayesian Ridge Imported successfully +2024-12-04 12:17:07,696:INFO:Starting cross validation +2024-12-04 12:17:07,696:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:08,981:INFO:Calculating mean and std +2024-12-04 12:17:08,981:INFO:Creating metrics dataframe +2024-12-04 12:17:08,985:INFO:Uploading results into container +2024-12-04 12:17:08,987:INFO:Uploading model into container now +2024-12-04 12:17:08,987:INFO:_master_model_container: 8 +2024-12-04 12:17:08,987:INFO:_display_container: 2 +2024-12-04 12:17:08,987:INFO:BayesianRidge() +2024-12-04 12:17:08,987:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:09,079:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:09,079:INFO:Creating metrics dataframe +2024-12-04 12:17:09,085:INFO:Initializing Passive Aggressive Regressor +2024-12-04 12:17:09,085:INFO:Total runtime is 0.24974973599116007 minutes +2024-12-04 12:17:09,085:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:09,089:INFO:Initializing create_model() +2024-12-04 12:17:09,089:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:09,089:INFO:Checking exceptions +2024-12-04 12:17:09,089:INFO:Importing libraries +2024-12-04 12:17:09,089:INFO:Copying training dataset +2024-12-04 12:17:09,103:INFO:Defining folds +2024-12-04 12:17:09,103:INFO:Declaring metric variables +2024-12-04 12:17:09,107:INFO:Importing untrained model +2024-12-04 12:17:09,113:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 12:17:09,123:INFO:Starting cross validation +2024-12-04 12:17:09,125:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:10,292:INFO:Calculating mean and std +2024-12-04 12:17:10,292:INFO:Creating metrics dataframe +2024-12-04 12:17:10,292:INFO:Uploading results into container +2024-12-04 12:17:10,292:INFO:Uploading model into container now +2024-12-04 12:17:10,292:INFO:_master_model_container: 9 +2024-12-04 12:17:10,292:INFO:_display_container: 2 +2024-12-04 12:17:10,292:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 12:17:10,292:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:10,389:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:10,389:INFO:Creating metrics dataframe +2024-12-04 12:17:10,393:INFO:Initializing Huber Regressor +2024-12-04 12:17:10,393:INFO:Total runtime is 0.2715391675631205 minutes +2024-12-04 12:17:10,402:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:10,403:INFO:Initializing create_model() +2024-12-04 12:17:10,403:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:10,403:INFO:Checking exceptions +2024-12-04 12:17:10,403:INFO:Importing libraries +2024-12-04 12:17:10,403:INFO:Copying training dataset +2024-12-04 12:17:10,415:INFO:Defining folds +2024-12-04 12:17:10,415:INFO:Declaring metric variables +2024-12-04 12:17:10,420:INFO:Importing untrained model +2024-12-04 12:17:10,425:INFO:Huber Regressor Imported successfully +2024-12-04 12:17:10,431:INFO:Starting cross validation +2024-12-04 12:17:10,434:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:11,938:INFO:Calculating mean and std +2024-12-04 12:17:11,939:INFO:Creating metrics dataframe +2024-12-04 12:17:11,939:INFO:Uploading results into container +2024-12-04 12:17:11,939:INFO:Uploading model into container now +2024-12-04 12:17:11,939:INFO:_master_model_container: 10 +2024-12-04 12:17:11,939:INFO:_display_container: 2 +2024-12-04 12:17:11,939:INFO:HuberRegressor() +2024-12-04 12:17:11,939:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:12,018:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:12,018:INFO:Creating metrics dataframe +2024-12-04 12:17:12,025:INFO:Initializing K Neighbors Regressor +2024-12-04 12:17:12,025:INFO:Total runtime is 0.2987419803937276 minutes +2024-12-04 12:17:12,027:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:12,027:INFO:Initializing create_model() +2024-12-04 12:17:12,027:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:12,027:INFO:Checking exceptions +2024-12-04 12:17:12,028:INFO:Importing libraries +2024-12-04 12:17:12,028:INFO:Copying training dataset +2024-12-04 12:17:12,039:INFO:Defining folds +2024-12-04 12:17:12,039:INFO:Declaring metric variables +2024-12-04 12:17:12,044:INFO:Importing untrained model +2024-12-04 12:17:12,044:INFO:K Neighbors Regressor Imported successfully +2024-12-04 12:17:12,053:INFO:Starting cross validation +2024-12-04 12:17:12,055:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:13,429:INFO:Calculating mean and std +2024-12-04 12:17:13,430:INFO:Creating metrics dataframe +2024-12-04 12:17:13,432:INFO:Uploading results into container +2024-12-04 12:17:13,433:INFO:Uploading model into container now +2024-12-04 12:17:13,434:INFO:_master_model_container: 11 +2024-12-04 12:17:13,434:INFO:_display_container: 2 +2024-12-04 12:17:13,435:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 12:17:13,435:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:13,513:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:13,513:INFO:Creating metrics dataframe +2024-12-04 12:17:13,521:INFO:Initializing Decision Tree Regressor +2024-12-04 12:17:13,521:INFO:Total runtime is 0.3236723780632019 minutes +2024-12-04 12:17:13,524:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:13,524:INFO:Initializing create_model() +2024-12-04 12:17:13,524:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:13,524:INFO:Checking exceptions +2024-12-04 12:17:13,524:INFO:Importing libraries +2024-12-04 12:17:13,524:INFO:Copying training dataset +2024-12-04 12:17:13,539:INFO:Defining folds +2024-12-04 12:17:13,539:INFO:Declaring metric variables +2024-12-04 12:17:13,543:INFO:Importing untrained model +2024-12-04 12:17:13,543:INFO:Decision Tree Regressor Imported successfully +2024-12-04 12:17:13,553:INFO:Starting cross validation +2024-12-04 12:17:13,555:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:15,117:INFO:Calculating mean and std +2024-12-04 12:17:15,119:INFO:Creating metrics dataframe +2024-12-04 12:17:15,120:INFO:Uploading results into container +2024-12-04 12:17:15,120:INFO:Uploading model into container now +2024-12-04 12:17:15,120:INFO:_master_model_container: 12 +2024-12-04 12:17:15,120:INFO:_display_container: 2 +2024-12-04 12:17:15,120:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 12:17:15,120:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:15,194:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:15,194:INFO:Creating metrics dataframe +2024-12-04 12:17:15,207:INFO:Initializing Random Forest Regressor +2024-12-04 12:17:15,207:INFO:Total runtime is 0.35177547534306847 minutes +2024-12-04 12:17:15,209:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:15,209:INFO:Initializing create_model() +2024-12-04 12:17:15,209:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:15,209:INFO:Checking exceptions +2024-12-04 12:17:15,209:INFO:Importing libraries +2024-12-04 12:17:15,209:INFO:Copying training dataset +2024-12-04 12:17:15,224:INFO:Defining folds +2024-12-04 12:17:15,224:INFO:Declaring metric variables +2024-12-04 12:17:15,227:INFO:Importing untrained model +2024-12-04 12:17:15,230:INFO:Random Forest Regressor Imported successfully +2024-12-04 12:17:15,236:INFO:Starting cross validation +2024-12-04 12:17:15,238:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:17:45,851:INFO:Calculating mean and std +2024-12-04 12:17:45,853:INFO:Creating metrics dataframe +2024-12-04 12:17:45,855:INFO:Uploading results into container +2024-12-04 12:17:45,855:INFO:Uploading model into container now +2024-12-04 12:17:45,855:INFO:_master_model_container: 13 +2024-12-04 12:17:45,855:INFO:_display_container: 2 +2024-12-04 12:17:45,855:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:17:45,857:INFO:create_model() successfully completed...................................... +2024-12-04 12:17:45,989:INFO:SubProcess create_model() end ================================== +2024-12-04 12:17:45,989:INFO:Creating metrics dataframe +2024-12-04 12:17:46,001:INFO:Initializing Extra Trees Regressor +2024-12-04 12:17:46,001:INFO:Total runtime is 0.8650163412094116 minutes +2024-12-04 12:17:46,005:INFO:SubProcess create_model() called ================================== +2024-12-04 12:17:46,006:INFO:Initializing create_model() +2024-12-04 12:17:46,006:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:17:46,006:INFO:Checking exceptions +2024-12-04 12:17:46,006:INFO:Importing libraries +2024-12-04 12:17:46,006:INFO:Copying training dataset +2024-12-04 12:17:46,022:INFO:Defining folds +2024-12-04 12:17:46,024:INFO:Declaring metric variables +2024-12-04 12:17:46,028:INFO:Importing untrained model +2024-12-04 12:17:46,031:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:17:46,040:INFO:Starting cross validation +2024-12-04 12:17:46,041:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:18:02,918:INFO:Calculating mean and std +2024-12-04 12:18:02,920:INFO:Creating metrics dataframe +2024-12-04 12:18:02,920:INFO:Uploading results into container +2024-12-04 12:18:02,920:INFO:Uploading model into container now +2024-12-04 12:18:02,920:INFO:_master_model_container: 14 +2024-12-04 12:18:02,920:INFO:_display_container: 2 +2024-12-04 12:18:02,920:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:18:02,923:INFO:create_model() successfully completed...................................... +2024-12-04 12:18:03,020:INFO:SubProcess create_model() end ================================== +2024-12-04 12:18:03,023:INFO:Creating metrics dataframe +2024-12-04 12:18:03,034:INFO:Initializing AdaBoost Regressor +2024-12-04 12:18:03,034:INFO:Total runtime is 1.1488957087198894 minutes +2024-12-04 12:18:03,038:INFO:SubProcess create_model() called ================================== +2024-12-04 12:18:03,038:INFO:Initializing create_model() +2024-12-04 12:18:03,038:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:18:03,038:INFO:Checking exceptions +2024-12-04 12:18:03,039:INFO:Importing libraries +2024-12-04 12:18:03,039:INFO:Copying training dataset +2024-12-04 12:18:03,051:INFO:Defining folds +2024-12-04 12:18:03,051:INFO:Declaring metric variables +2024-12-04 12:18:03,055:INFO:Importing untrained model +2024-12-04 12:18:03,059:INFO:AdaBoost Regressor Imported successfully +2024-12-04 12:18:03,065:INFO:Starting cross validation +2024-12-04 12:18:03,066:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:18:07,910:INFO:Calculating mean and std +2024-12-04 12:18:07,910:INFO:Creating metrics dataframe +2024-12-04 12:18:07,912:INFO:Uploading results into container +2024-12-04 12:18:07,912:INFO:Uploading model into container now +2024-12-04 12:18:07,913:INFO:_master_model_container: 15 +2024-12-04 12:18:07,913:INFO:_display_container: 2 +2024-12-04 12:18:07,913:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 12:18:07,913:INFO:create_model() successfully completed...................................... +2024-12-04 12:18:07,989:INFO:SubProcess create_model() end ================================== +2024-12-04 12:18:07,989:INFO:Creating metrics dataframe +2024-12-04 12:18:07,999:INFO:Initializing Gradient Boosting Regressor +2024-12-04 12:18:07,999:INFO:Total runtime is 1.2316371321678163 minutes +2024-12-04 12:18:08,000:INFO:SubProcess create_model() called ================================== +2024-12-04 12:18:08,000:INFO:Initializing create_model() +2024-12-04 12:18:08,000:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:18:08,000:INFO:Checking exceptions +2024-12-04 12:18:08,003:INFO:Importing libraries +2024-12-04 12:18:08,003:INFO:Copying training dataset +2024-12-04 12:18:08,013:INFO:Defining folds +2024-12-04 12:18:08,013:INFO:Declaring metric variables +2024-12-04 12:18:08,018:INFO:Importing untrained model +2024-12-04 12:18:08,021:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 12:18:08,033:INFO:Starting cross validation +2024-12-04 12:18:08,034:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:18:16,996:INFO:Calculating mean and std +2024-12-04 12:18:16,996:INFO:Creating metrics dataframe +2024-12-04 12:18:16,996:INFO:Uploading results into container +2024-12-04 12:18:16,996:INFO:Uploading model into container now +2024-12-04 12:18:16,999:INFO:_master_model_container: 16 +2024-12-04 12:18:17,000:INFO:_display_container: 2 +2024-12-04 12:18:17,000:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 12:18:17,000:INFO:create_model() successfully completed...................................... +2024-12-04 12:18:17,078:INFO:SubProcess create_model() end ================================== +2024-12-04 12:18:17,078:INFO:Creating metrics dataframe +2024-12-04 12:18:17,085:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 12:18:17,085:INFO:Total runtime is 1.3830714702606204 minutes +2024-12-04 12:18:17,085:INFO:SubProcess create_model() called ================================== +2024-12-04 12:18:17,085:INFO:Initializing create_model() +2024-12-04 12:18:17,085:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:18:17,085:INFO:Checking exceptions +2024-12-04 12:18:17,085:INFO:Importing libraries +2024-12-04 12:18:17,085:INFO:Copying training dataset +2024-12-04 12:18:17,100:INFO:Defining folds +2024-12-04 12:18:17,100:INFO:Declaring metric variables +2024-12-04 12:18:17,103:INFO:Importing untrained model +2024-12-04 12:18:17,107:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 12:18:17,113:INFO:Starting cross validation +2024-12-04 12:18:17,113:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:18:19,144:INFO:Calculating mean and std +2024-12-04 12:18:19,144:INFO:Creating metrics dataframe +2024-12-04 12:18:19,147:INFO:Uploading results into container +2024-12-04 12:18:19,149:INFO:Uploading model into container now +2024-12-04 12:18:19,149:INFO:_master_model_container: 17 +2024-12-04 12:18:19,149:INFO:_display_container: 2 +2024-12-04 12:18:19,149:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:18:19,149:INFO:create_model() successfully completed...................................... +2024-12-04 12:18:19,253:INFO:SubProcess create_model() end ================================== +2024-12-04 12:18:19,253:INFO:Creating metrics dataframe +2024-12-04 12:18:19,260:INFO:Initializing Dummy Regressor +2024-12-04 12:18:19,261:INFO:Total runtime is 1.4193483511606855 minutes +2024-12-04 12:18:19,263:INFO:SubProcess create_model() called ================================== +2024-12-04 12:18:19,263:INFO:Initializing create_model() +2024-12-04 12:18:19,263:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:18:19,263:INFO:Checking exceptions +2024-12-04 12:18:19,263:INFO:Importing libraries +2024-12-04 12:18:19,263:INFO:Copying training dataset +2024-12-04 12:18:19,277:INFO:Defining folds +2024-12-04 12:18:19,277:INFO:Declaring metric variables +2024-12-04 12:18:19,281:INFO:Importing untrained model +2024-12-04 12:18:19,285:INFO:Dummy Regressor Imported successfully +2024-12-04 12:18:19,290:INFO:Starting cross validation +2024-12-04 12:18:19,290:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:18:20,243:INFO:Calculating mean and std +2024-12-04 12:18:20,243:INFO:Creating metrics dataframe +2024-12-04 12:18:20,243:INFO:Uploading results into container +2024-12-04 12:18:20,243:INFO:Uploading model into container now +2024-12-04 12:18:20,243:INFO:_master_model_container: 18 +2024-12-04 12:18:20,243:INFO:_display_container: 2 +2024-12-04 12:18:20,243:INFO:DummyRegressor() +2024-12-04 12:18:20,247:INFO:create_model() successfully completed...................................... +2024-12-04 12:18:20,328:INFO:SubProcess create_model() end ================================== +2024-12-04 12:18:20,328:INFO:Creating metrics dataframe +2024-12-04 12:18:20,359:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 12:18:20,366:INFO:Initializing create_model() +2024-12-04 12:18:20,366:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:18:20,367:INFO:Checking exceptions +2024-12-04 12:18:20,368:INFO:Importing libraries +2024-12-04 12:18:20,368:INFO:Copying training dataset +2024-12-04 12:18:20,381:INFO:Defining folds +2024-12-04 12:18:20,381:INFO:Declaring metric variables +2024-12-04 12:18:20,381:INFO:Importing untrained model +2024-12-04 12:18:20,381:INFO:Declaring custom model +2024-12-04 12:18:20,381:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:18:20,383:INFO:Cross validation set to False +2024-12-04 12:18:20,383:INFO:Fitting Model +2024-12-04 12:18:22,366:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:18:22,366:INFO:create_model() successfully completed...................................... +2024-12-04 12:18:22,472:INFO:_master_model_container: 18 +2024-12-04 12:18:22,472:INFO:_display_container: 2 +2024-12-04 12:18:22,472:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:18:22,472:INFO:compare_models() successfully completed...................................... +2024-12-04 12:18:35,828:INFO:Initializing evaluate_model() +2024-12-04 12:18:35,828:INFO:evaluate_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-12-04 12:18:35,856:INFO:Initializing plot_model() +2024-12-04 12:18:35,856:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-12-04 12:18:35,857:INFO:Checking exceptions +2024-12-04 12:18:35,959:INFO:Preloading libraries +2024-12-04 12:18:36,207:INFO:Copying training dataset +2024-12-04 12:18:36,207:INFO:Plot type: pipeline +2024-12-04 12:18:36,364:INFO:Visual Rendered Successfully +2024-12-04 12:18:36,463:INFO:plot_model() successfully completed...................................... +2024-12-04 12:18:42,921:INFO:Initializing plot_model() +2024-12-04 12:18:42,921:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=tree, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-12-04 12:18:42,923:INFO:Checking exceptions +2024-12-04 12:18:42,958:INFO:Preloading libraries +2024-12-04 12:18:43,183:INFO:Copying training dataset +2024-12-04 12:18:43,184:INFO:Plot type: tree +2024-12-04 12:18:44,248:INFO:Plotting decision trees +2024-12-04 12:18:44,360:INFO:Plotting tree 0 +2024-12-04 12:20:30,635:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:20:30,636:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:20:30,636:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:20:30,636:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 12:20:43,236:INFO:PyCaret RegressionExperiment +2024-12-04 12:20:43,236:INFO:Logging name: reg-default-name +2024-12-04 12:20:43,236:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:20:43,236:INFO:version 3.3.2 +2024-12-04 12:20:43,236:INFO:Initializing setup() +2024-12-04 12:20:43,236:INFO:self.USI: 5517 +2024-12-04 12:20:43,236:INFO:self._variable_keys: {'y_test', 'idx', 'X_train', 'html_param', 'X_test', 'gpu_param', 'transform_target_param', 'log_plots_param', 'target_param', 'exp_name_log', 'gpu_n_jobs_param', 'y', 'pipeline', 'fold_groups_param', 'exp_id', '_available_plots', 'memory', 'X', 'fold_shuffle_param', 'seed', 'USI', '_ml_usecase', 'fold_generator', 'logging_param', 'y_train', 'data', 'n_jobs_param'} +2024-12-04 12:20:43,236:INFO:Checking environment +2024-12-04 12:20:43,236:INFO:python_version: 3.11.9 +2024-12-04 12:20:43,237:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:20:43,237:INFO:machine: AMD64 +2024-12-04 12:20:43,237:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:20:43,241:INFO:Memory: svmem(total=17007292416, available=4480135168, percent=73.7, used=12527157248, free=4480135168) +2024-12-04 12:20:43,241:INFO:Physical Core: 4 +2024-12-04 12:20:43,241:INFO:Logical Core: 8 +2024-12-04 12:20:43,241:INFO:Checking libraries +2024-12-04 12:20:43,241:INFO:System: +2024-12-04 12:20:43,241:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:20:43,241:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:20:43,241:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:20:43,241:INFO:PyCaret required dependencies: +2024-12-04 12:20:43,267:INFO: pip: 24.3.1 +2024-12-04 12:20:43,267:INFO: setuptools: 65.5.0 +2024-12-04 12:20:43,267:INFO: pycaret: 3.3.2 +2024-12-04 12:20:43,267:INFO: IPython: 8.12.3 +2024-12-04 12:20:43,267:INFO: ipywidgets: 8.1.5 +2024-12-04 12:20:43,267:INFO: tqdm: 4.67.1 +2024-12-04 12:20:43,267:INFO: numpy: 1.26.4 +2024-12-04 12:20:43,267:INFO: pandas: 2.1.4 +2024-12-04 12:20:43,267:INFO: jinja2: 3.1.4 +2024-12-04 12:20:43,267:INFO: scipy: 1.11.4 +2024-12-04 12:20:43,267:INFO: joblib: 1.3.2 +2024-12-04 12:20:43,267:INFO: sklearn: 1.4.2 +2024-12-04 12:20:43,267:INFO: pyod: 2.0.2 +2024-12-04 12:20:43,267:INFO: imblearn: 0.12.4 +2024-12-04 12:20:43,267:INFO: category_encoders: 2.6.4 +2024-12-04 12:20:43,267:INFO: lightgbm: 4.5.0 +2024-12-04 12:20:43,267:INFO: numba: 0.60.0 +2024-12-04 12:20:43,267:INFO: requests: 2.32.3 +2024-12-04 12:20:43,267:INFO: matplotlib: 3.7.5 +2024-12-04 12:20:43,267:INFO: scikitplot: 0.3.7 +2024-12-04 12:20:43,267:INFO: yellowbrick: 1.5 +2024-12-04 12:20:43,268:INFO: plotly: 5.24.1 +2024-12-04 12:20:43,268:INFO: plotly-resampler: Not installed +2024-12-04 12:20:43,268:INFO: kaleido: 0.2.1 +2024-12-04 12:20:43,268:INFO: schemdraw: 0.15 +2024-12-04 12:20:43,268:INFO: statsmodels: 0.14.4 +2024-12-04 12:20:43,268:INFO: sktime: 0.26.0 +2024-12-04 12:20:43,268:INFO: tbats: 1.1.3 +2024-12-04 12:20:43,268:INFO: pmdarima: 2.0.4 +2024-12-04 12:20:43,268:INFO: psutil: 6.1.0 +2024-12-04 12:20:43,268:INFO: markupsafe: 3.0.2 +2024-12-04 12:20:43,268:INFO: pickle5: Not installed +2024-12-04 12:20:43,268:INFO: cloudpickle: 3.1.0 +2024-12-04 12:20:43,268:INFO: deprecation: 2.1.0 +2024-12-04 12:20:43,268:INFO: xxhash: 3.5.0 +2024-12-04 12:20:43,268:INFO: wurlitzer: Not installed +2024-12-04 12:20:43,268:INFO:PyCaret optional dependencies: +2024-12-04 12:20:43,303:INFO: shap: Not installed +2024-12-04 12:20:43,303:INFO: interpret: Not installed +2024-12-04 12:20:43,303:INFO: umap: Not installed +2024-12-04 12:20:43,303:INFO: ydata_profiling: Not installed +2024-12-04 12:20:43,303:INFO: explainerdashboard: Not installed +2024-12-04 12:20:43,303:INFO: autoviz: Not installed +2024-12-04 12:20:43,304:INFO: fairlearn: Not installed +2024-12-04 12:20:43,304:INFO: deepchecks: Not installed +2024-12-04 12:20:43,304:INFO: xgboost: Not installed +2024-12-04 12:20:43,304:INFO: catboost: Not installed +2024-12-04 12:20:43,304:INFO: kmodes: Not installed +2024-12-04 12:20:43,304:INFO: mlxtend: Not installed +2024-12-04 12:20:43,304:INFO: statsforecast: Not installed +2024-12-04 12:20:43,304:INFO: tune_sklearn: Not installed +2024-12-04 12:20:43,304:INFO: ray: Not installed +2024-12-04 12:20:43,304:INFO: hyperopt: Not installed +2024-12-04 12:20:43,304:INFO: optuna: Not installed +2024-12-04 12:20:43,304:INFO: skopt: Not installed +2024-12-04 12:20:43,304:INFO: mlflow: 2.18.0 +2024-12-04 12:20:43,304:INFO: gradio: Not installed +2024-12-04 12:20:43,304:INFO: fastapi: Not installed +2024-12-04 12:20:43,304:INFO: uvicorn: 0.32.1 +2024-12-04 12:20:43,304:INFO: m2cgen: Not installed +2024-12-04 12:20:43,304:INFO: evidently: Not installed +2024-12-04 12:20:43,304:INFO: fugue: Not installed +2024-12-04 12:20:43,304:INFO: streamlit: 1.40.1 +2024-12-04 12:20:43,304:INFO: prophet: Not installed +2024-12-04 12:20:43,304:INFO:None +2024-12-04 12:20:43,304:INFO:Set up data. +2024-12-04 12:20:43,322:INFO:Set up folding strategy. +2024-12-04 12:20:43,322:INFO:Set up train/test split. +2024-12-04 12:20:43,333:INFO:Set up index. +2024-12-04 12:20:43,334:INFO:Assigning column types. +2024-12-04 12:20:43,347:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:20:43,347:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,351:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,355:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,414:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,454:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,455:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,455:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,455:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,460:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,464:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,522:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,563:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,564:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,564:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,564:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:20:43,568:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,572:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,629:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,669:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,670:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,670:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,674:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,678:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,735:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,775:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,775:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,776:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,776:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:20:43,784:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,842:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,882:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,882:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,882:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,890:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,947:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,987:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:43,987:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,987:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:43,987:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:20:44,053:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:44,093:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:44,094:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,094:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,158:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:44,198:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:20:44,198:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,198:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,199:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:20:44,265:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:44,305:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,305:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,379:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:20:44,424:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,424:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,424:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:20:44,530:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,530:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,636:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,637:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:44,639:INFO:Preparing preprocessing pipeline... +2024-12-04 12:20:44,639:INFO:Set up simple imputation. +2024-12-04 12:20:44,645:INFO:Set up encoding of categorical features. +2024-12-04 12:20:44,645:INFO:Set up column transformation. +2024-12-04 12:20:44,645:INFO:Set up feature normalization. +2024-12-04 12:20:44,784:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:20:44,793:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=TargetEncoder(cols=['nom_region', + 'nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:20:44,793:INFO:Creating final display dataframe. +2024-12-04 12:20:45,100:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (31906, 15) +4 Transformed data shape (31906, 14) +5 Transformed train set shape (22334, 14) +6 Transformed test set shape (9572, 14) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transformation True +17 Transformation method yeo-johnson +18 Normalize True +19 Normalize method zscore +20 Fold Generator KFold +21 Fold Number 10 +22 CPU Jobs -1 +23 Use GPU False +24 Log Experiment False +25 Experiment Name reg-default-name +26 USI 5517 +2024-12-04 12:20:45,215:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:45,215:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:45,318:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:45,318:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:20:45,318:INFO:setup() successfully completed in 2.1s............... +2024-12-04 12:20:45,333:INFO:Initializing compare_models() +2024-12-04 12:20:45,333:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 12:20:45,333:INFO:Checking exceptions +2024-12-04 12:20:45,340:INFO:Preparing display monitor +2024-12-04 12:20:45,360:INFO:Initializing Linear Regression +2024-12-04 12:20:45,360:INFO:Total runtime is 0.0 minutes +2024-12-04 12:20:45,363:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:45,363:INFO:Initializing create_model() +2024-12-04 12:20:45,363:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:45,363:INFO:Checking exceptions +2024-12-04 12:20:45,364:INFO:Importing libraries +2024-12-04 12:20:45,364:INFO:Copying training dataset +2024-12-04 12:20:45,383:INFO:Defining folds +2024-12-04 12:20:45,383:INFO:Declaring metric variables +2024-12-04 12:20:45,385:INFO:Importing untrained model +2024-12-04 12:20:45,388:INFO:Linear Regression Imported successfully +2024-12-04 12:20:45,394:INFO:Starting cross validation +2024-12-04 12:20:45,401:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:50,724:INFO:Calculating mean and std +2024-12-04 12:20:50,724:INFO:Creating metrics dataframe +2024-12-04 12:20:50,726:INFO:Uploading results into container +2024-12-04 12:20:50,727:INFO:Uploading model into container now +2024-12-04 12:20:50,727:INFO:_master_model_container: 1 +2024-12-04 12:20:50,727:INFO:_display_container: 2 +2024-12-04 12:20:50,728:INFO:LinearRegression(n_jobs=-1) +2024-12-04 12:20:50,728:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:50,805:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:50,805:INFO:Creating metrics dataframe +2024-12-04 12:20:50,810:INFO:Initializing Lasso Regression +2024-12-04 12:20:50,811:INFO:Total runtime is 0.09084149599075317 minutes +2024-12-04 12:20:50,814:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:50,814:INFO:Initializing create_model() +2024-12-04 12:20:50,814:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:50,814:INFO:Checking exceptions +2024-12-04 12:20:50,814:INFO:Importing libraries +2024-12-04 12:20:50,815:INFO:Copying training dataset +2024-12-04 12:20:50,827:INFO:Defining folds +2024-12-04 12:20:50,827:INFO:Declaring metric variables +2024-12-04 12:20:50,830:INFO:Importing untrained model +2024-12-04 12:20:50,833:INFO:Lasso Regression Imported successfully +2024-12-04 12:20:50,838:INFO:Starting cross validation +2024-12-04 12:20:50,839:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:51,928:INFO:Calculating mean and std +2024-12-04 12:20:51,929:INFO:Creating metrics dataframe +2024-12-04 12:20:51,931:INFO:Uploading results into container +2024-12-04 12:20:51,931:INFO:Uploading model into container now +2024-12-04 12:20:51,931:INFO:_master_model_container: 2 +2024-12-04 12:20:51,931:INFO:_display_container: 2 +2024-12-04 12:20:51,932:INFO:Lasso(random_state=123) +2024-12-04 12:20:51,932:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:52,008:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:52,008:INFO:Creating metrics dataframe +2024-12-04 12:20:52,013:INFO:Initializing Ridge Regression +2024-12-04 12:20:52,013:INFO:Total runtime is 0.11089238325754801 minutes +2024-12-04 12:20:52,016:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:52,016:INFO:Initializing create_model() +2024-12-04 12:20:52,016:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:52,016:INFO:Checking exceptions +2024-12-04 12:20:52,016:INFO:Importing libraries +2024-12-04 12:20:52,016:INFO:Copying training dataset +2024-12-04 12:20:52,028:INFO:Defining folds +2024-12-04 12:20:52,030:INFO:Declaring metric variables +2024-12-04 12:20:52,032:INFO:Importing untrained model +2024-12-04 12:20:52,037:INFO:Ridge Regression Imported successfully +2024-12-04 12:20:52,044:INFO:Starting cross validation +2024-12-04 12:20:52,045:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:53,081:INFO:Calculating mean and std +2024-12-04 12:20:53,082:INFO:Creating metrics dataframe +2024-12-04 12:20:53,083:INFO:Uploading results into container +2024-12-04 12:20:53,083:INFO:Uploading model into container now +2024-12-04 12:20:53,083:INFO:_master_model_container: 3 +2024-12-04 12:20:53,084:INFO:_display_container: 2 +2024-12-04 12:20:53,084:INFO:Ridge(random_state=123) +2024-12-04 12:20:53,085:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:53,163:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:53,163:INFO:Creating metrics dataframe +2024-12-04 12:20:53,168:INFO:Initializing Elastic Net +2024-12-04 12:20:53,168:INFO:Total runtime is 0.13013817866643268 minutes +2024-12-04 12:20:53,171:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:53,171:INFO:Initializing create_model() +2024-12-04 12:20:53,171:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:53,171:INFO:Checking exceptions +2024-12-04 12:20:53,172:INFO:Importing libraries +2024-12-04 12:20:53,172:INFO:Copying training dataset +2024-12-04 12:20:53,186:INFO:Defining folds +2024-12-04 12:20:53,186:INFO:Declaring metric variables +2024-12-04 12:20:53,189:INFO:Importing untrained model +2024-12-04 12:20:53,193:INFO:Elastic Net Imported successfully +2024-12-04 12:20:53,197:INFO:Starting cross validation +2024-12-04 12:20:53,198:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:54,138:INFO:Calculating mean and std +2024-12-04 12:20:54,138:INFO:Creating metrics dataframe +2024-12-04 12:20:54,139:INFO:Uploading results into container +2024-12-04 12:20:54,140:INFO:Uploading model into container now +2024-12-04 12:20:54,140:INFO:_master_model_container: 4 +2024-12-04 12:20:54,140:INFO:_display_container: 2 +2024-12-04 12:20:54,141:INFO:ElasticNet(random_state=123) +2024-12-04 12:20:54,141:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:54,218:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:54,218:INFO:Creating metrics dataframe +2024-12-04 12:20:54,224:INFO:Initializing Least Angle Regression +2024-12-04 12:20:54,224:INFO:Total runtime is 0.14773777723312376 minutes +2024-12-04 12:20:54,227:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:54,228:INFO:Initializing create_model() +2024-12-04 12:20:54,228:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:54,228:INFO:Checking exceptions +2024-12-04 12:20:54,228:INFO:Importing libraries +2024-12-04 12:20:54,228:INFO:Copying training dataset +2024-12-04 12:20:54,242:INFO:Defining folds +2024-12-04 12:20:54,242:INFO:Declaring metric variables +2024-12-04 12:20:54,245:INFO:Importing untrained model +2024-12-04 12:20:54,247:INFO:Least Angle Regression Imported successfully +2024-12-04 12:20:54,252:INFO:Starting cross validation +2024-12-04 12:20:54,253:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:55,177:INFO:Calculating mean and std +2024-12-04 12:20:55,178:INFO:Creating metrics dataframe +2024-12-04 12:20:55,179:INFO:Uploading results into container +2024-12-04 12:20:55,179:INFO:Uploading model into container now +2024-12-04 12:20:55,180:INFO:_master_model_container: 5 +2024-12-04 12:20:55,180:INFO:_display_container: 2 +2024-12-04 12:20:55,181:INFO:Lars(random_state=123) +2024-12-04 12:20:55,181:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:55,256:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:55,256:INFO:Creating metrics dataframe +2024-12-04 12:20:55,262:INFO:Initializing Lasso Least Angle Regression +2024-12-04 12:20:55,262:INFO:Total runtime is 0.16503072977066038 minutes +2024-12-04 12:20:55,263:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:55,263:INFO:Initializing create_model() +2024-12-04 12:20:55,263:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:55,263:INFO:Checking exceptions +2024-12-04 12:20:55,263:INFO:Importing libraries +2024-12-04 12:20:55,263:INFO:Copying training dataset +2024-12-04 12:20:55,277:INFO:Defining folds +2024-12-04 12:20:55,277:INFO:Declaring metric variables +2024-12-04 12:20:55,279:INFO:Importing untrained model +2024-12-04 12:20:55,282:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 12:20:55,290:INFO:Starting cross validation +2024-12-04 12:20:55,291:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:56,203:INFO:Calculating mean and std +2024-12-04 12:20:56,203:INFO:Creating metrics dataframe +2024-12-04 12:20:56,205:INFO:Uploading results into container +2024-12-04 12:20:56,205:INFO:Uploading model into container now +2024-12-04 12:20:56,205:INFO:_master_model_container: 6 +2024-12-04 12:20:56,205:INFO:_display_container: 2 +2024-12-04 12:20:56,205:INFO:LassoLars(random_state=123) +2024-12-04 12:20:56,206:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:56,283:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:56,283:INFO:Creating metrics dataframe +2024-12-04 12:20:56,289:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 12:20:56,289:INFO:Total runtime is 0.18215649127960204 minutes +2024-12-04 12:20:56,292:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:56,292:INFO:Initializing create_model() +2024-12-04 12:20:56,293:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:56,293:INFO:Checking exceptions +2024-12-04 12:20:56,293:INFO:Importing libraries +2024-12-04 12:20:56,293:INFO:Copying training dataset +2024-12-04 12:20:56,305:INFO:Defining folds +2024-12-04 12:20:56,306:INFO:Declaring metric variables +2024-12-04 12:20:56,307:INFO:Importing untrained model +2024-12-04 12:20:56,311:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 12:20:56,317:INFO:Starting cross validation +2024-12-04 12:20:56,317:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:57,237:INFO:Calculating mean and std +2024-12-04 12:20:57,237:INFO:Creating metrics dataframe +2024-12-04 12:20:57,239:INFO:Uploading results into container +2024-12-04 12:20:57,239:INFO:Uploading model into container now +2024-12-04 12:20:57,239:INFO:_master_model_container: 7 +2024-12-04 12:20:57,239:INFO:_display_container: 2 +2024-12-04 12:20:57,240:INFO:OrthogonalMatchingPursuit() +2024-12-04 12:20:57,240:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:57,316:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:57,316:INFO:Creating metrics dataframe +2024-12-04 12:20:57,322:INFO:Initializing Bayesian Ridge +2024-12-04 12:20:57,322:INFO:Total runtime is 0.19936880270640053 minutes +2024-12-04 12:20:57,324:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:57,325:INFO:Initializing create_model() +2024-12-04 12:20:57,325:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:57,325:INFO:Checking exceptions +2024-12-04 12:20:57,325:INFO:Importing libraries +2024-12-04 12:20:57,325:INFO:Copying training dataset +2024-12-04 12:20:57,338:INFO:Defining folds +2024-12-04 12:20:57,338:INFO:Declaring metric variables +2024-12-04 12:20:57,340:INFO:Importing untrained model +2024-12-04 12:20:57,343:INFO:Bayesian Ridge Imported successfully +2024-12-04 12:20:57,351:INFO:Starting cross validation +2024-12-04 12:20:57,351:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:58,275:INFO:Calculating mean and std +2024-12-04 12:20:58,275:INFO:Creating metrics dataframe +2024-12-04 12:20:58,278:INFO:Uploading results into container +2024-12-04 12:20:58,278:INFO:Uploading model into container now +2024-12-04 12:20:58,279:INFO:_master_model_container: 8 +2024-12-04 12:20:58,279:INFO:_display_container: 2 +2024-12-04 12:20:58,279:INFO:BayesianRidge() +2024-12-04 12:20:58,279:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:58,355:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:58,355:INFO:Creating metrics dataframe +2024-12-04 12:20:58,362:INFO:Initializing Passive Aggressive Regressor +2024-12-04 12:20:58,362:INFO:Total runtime is 0.21670352617899574 minutes +2024-12-04 12:20:58,365:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:58,365:INFO:Initializing create_model() +2024-12-04 12:20:58,365:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:58,365:INFO:Checking exceptions +2024-12-04 12:20:58,366:INFO:Importing libraries +2024-12-04 12:20:58,366:INFO:Copying training dataset +2024-12-04 12:20:58,378:INFO:Defining folds +2024-12-04 12:20:58,378:INFO:Declaring metric variables +2024-12-04 12:20:58,383:INFO:Importing untrained model +2024-12-04 12:20:58,386:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 12:20:58,391:INFO:Starting cross validation +2024-12-04 12:20:58,392:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:20:59,333:INFO:Calculating mean and std +2024-12-04 12:20:59,334:INFO:Creating metrics dataframe +2024-12-04 12:20:59,335:INFO:Uploading results into container +2024-12-04 12:20:59,335:INFO:Uploading model into container now +2024-12-04 12:20:59,336:INFO:_master_model_container: 9 +2024-12-04 12:20:59,336:INFO:_display_container: 2 +2024-12-04 12:20:59,336:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 12:20:59,336:INFO:create_model() successfully completed...................................... +2024-12-04 12:20:59,452:INFO:SubProcess create_model() end ================================== +2024-12-04 12:20:59,452:INFO:Creating metrics dataframe +2024-12-04 12:20:59,463:INFO:Initializing Huber Regressor +2024-12-04 12:20:59,463:INFO:Total runtime is 0.23504505952199298 minutes +2024-12-04 12:20:59,467:INFO:SubProcess create_model() called ================================== +2024-12-04 12:20:59,467:INFO:Initializing create_model() +2024-12-04 12:20:59,467:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:20:59,467:INFO:Checking exceptions +2024-12-04 12:20:59,467:INFO:Importing libraries +2024-12-04 12:20:59,467:INFO:Copying training dataset +2024-12-04 12:20:59,487:INFO:Defining folds +2024-12-04 12:20:59,487:INFO:Declaring metric variables +2024-12-04 12:20:59,490:INFO:Importing untrained model +2024-12-04 12:20:59,493:INFO:Huber Regressor Imported successfully +2024-12-04 12:20:59,499:INFO:Starting cross validation +2024-12-04 12:20:59,500:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:21:00,919:INFO:Calculating mean and std +2024-12-04 12:21:00,920:INFO:Creating metrics dataframe +2024-12-04 12:21:00,921:INFO:Uploading results into container +2024-12-04 12:21:00,922:INFO:Uploading model into container now +2024-12-04 12:21:00,922:INFO:_master_model_container: 10 +2024-12-04 12:21:00,922:INFO:_display_container: 2 +2024-12-04 12:21:00,922:INFO:HuberRegressor() +2024-12-04 12:21:00,922:INFO:create_model() successfully completed...................................... +2024-12-04 12:21:01,003:INFO:SubProcess create_model() end ================================== +2024-12-04 12:21:01,005:INFO:Creating metrics dataframe +2024-12-04 12:21:01,012:INFO:Initializing K Neighbors Regressor +2024-12-04 12:21:01,012:INFO:Total runtime is 0.26086784601211543 minutes +2024-12-04 12:21:01,015:INFO:SubProcess create_model() called ================================== +2024-12-04 12:21:01,016:INFO:Initializing create_model() +2024-12-04 12:21:01,016:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:21:01,016:INFO:Checking exceptions +2024-12-04 12:21:01,016:INFO:Importing libraries +2024-12-04 12:21:01,016:INFO:Copying training dataset +2024-12-04 12:21:01,028:INFO:Defining folds +2024-12-04 12:21:01,028:INFO:Declaring metric variables +2024-12-04 12:21:01,033:INFO:Importing untrained model +2024-12-04 12:21:01,035:INFO:K Neighbors Regressor Imported successfully +2024-12-04 12:21:01,041:INFO:Starting cross validation +2024-12-04 12:21:01,043:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:21:02,730:INFO:Calculating mean and std +2024-12-04 12:21:02,731:INFO:Creating metrics dataframe +2024-12-04 12:21:02,732:INFO:Uploading results into container +2024-12-04 12:21:02,733:INFO:Uploading model into container now +2024-12-04 12:21:02,733:INFO:_master_model_container: 11 +2024-12-04 12:21:02,733:INFO:_display_container: 2 +2024-12-04 12:21:02,733:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 12:21:02,733:INFO:create_model() successfully completed...................................... +2024-12-04 12:21:02,816:INFO:SubProcess create_model() end ================================== +2024-12-04 12:21:02,816:INFO:Creating metrics dataframe +2024-12-04 12:21:02,822:INFO:Initializing Decision Tree Regressor +2024-12-04 12:21:02,822:INFO:Total runtime is 0.2910354375839233 minutes +2024-12-04 12:21:02,826:INFO:SubProcess create_model() called ================================== +2024-12-04 12:21:02,827:INFO:Initializing create_model() +2024-12-04 12:21:02,827:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:21:02,827:INFO:Checking exceptions +2024-12-04 12:21:02,827:INFO:Importing libraries +2024-12-04 12:21:02,827:INFO:Copying training dataset +2024-12-04 12:21:02,840:INFO:Defining folds +2024-12-04 12:21:02,840:INFO:Declaring metric variables +2024-12-04 12:21:02,845:INFO:Importing untrained model +2024-12-04 12:21:02,847:INFO:Decision Tree Regressor Imported successfully +2024-12-04 12:21:02,855:INFO:Starting cross validation +2024-12-04 12:21:02,857:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:21:04,652:INFO:Calculating mean and std +2024-12-04 12:21:04,653:INFO:Creating metrics dataframe +2024-12-04 12:21:04,655:INFO:Uploading results into container +2024-12-04 12:21:04,655:INFO:Uploading model into container now +2024-12-04 12:21:04,655:INFO:_master_model_container: 12 +2024-12-04 12:21:04,655:INFO:_display_container: 2 +2024-12-04 12:21:04,655:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 12:21:04,655:INFO:create_model() successfully completed...................................... +2024-12-04 12:21:04,746:INFO:SubProcess create_model() end ================================== +2024-12-04 12:21:04,746:INFO:Creating metrics dataframe +2024-12-04 12:21:04,753:INFO:Initializing Random Forest Regressor +2024-12-04 12:21:04,753:INFO:Total runtime is 0.32322438160578404 minutes +2024-12-04 12:21:04,757:INFO:SubProcess create_model() called ================================== +2024-12-04 12:21:04,757:INFO:Initializing create_model() +2024-12-04 12:21:04,757:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:21:04,757:INFO:Checking exceptions +2024-12-04 12:21:04,757:INFO:Importing libraries +2024-12-04 12:21:04,758:INFO:Copying training dataset +2024-12-04 12:21:04,773:INFO:Defining folds +2024-12-04 12:21:04,773:INFO:Declaring metric variables +2024-12-04 12:21:04,776:INFO:Importing untrained model +2024-12-04 12:21:04,782:INFO:Random Forest Regressor Imported successfully +2024-12-04 12:21:04,787:INFO:Starting cross validation +2024-12-04 12:21:04,788:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:21:34,412:INFO:Calculating mean and std +2024-12-04 12:21:34,412:INFO:Creating metrics dataframe +2024-12-04 12:21:34,413:INFO:Uploading results into container +2024-12-04 12:21:34,415:INFO:Uploading model into container now +2024-12-04 12:21:34,415:INFO:_master_model_container: 13 +2024-12-04 12:21:34,415:INFO:_display_container: 2 +2024-12-04 12:21:34,416:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:21:34,416:INFO:create_model() successfully completed...................................... +2024-12-04 12:21:34,509:INFO:SubProcess create_model() end ================================== +2024-12-04 12:21:34,509:INFO:Creating metrics dataframe +2024-12-04 12:21:34,516:INFO:Initializing Extra Trees Regressor +2024-12-04 12:21:34,516:INFO:Total runtime is 0.8192760348320007 minutes +2024-12-04 12:21:34,520:INFO:SubProcess create_model() called ================================== +2024-12-04 12:21:34,520:INFO:Initializing create_model() +2024-12-04 12:21:34,520:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:21:34,520:INFO:Checking exceptions +2024-12-04 12:21:34,521:INFO:Importing libraries +2024-12-04 12:21:34,521:INFO:Copying training dataset +2024-12-04 12:21:34,535:INFO:Defining folds +2024-12-04 12:21:34,535:INFO:Declaring metric variables +2024-12-04 12:21:34,538:INFO:Importing untrained model +2024-12-04 12:21:34,543:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:21:34,552:INFO:Starting cross validation +2024-12-04 12:21:34,554:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:21:52,622:INFO:Calculating mean and std +2024-12-04 12:21:52,625:INFO:Creating metrics dataframe +2024-12-04 12:21:52,629:INFO:Uploading results into container +2024-12-04 12:21:52,630:INFO:Uploading model into container now +2024-12-04 12:21:52,631:INFO:_master_model_container: 14 +2024-12-04 12:21:52,631:INFO:_display_container: 2 +2024-12-04 12:21:52,632:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:21:52,632:INFO:create_model() successfully completed...................................... +2024-12-04 12:21:52,768:INFO:SubProcess create_model() end ================================== +2024-12-04 12:21:52,768:INFO:Creating metrics dataframe +2024-12-04 12:21:52,783:INFO:Initializing AdaBoost Regressor +2024-12-04 12:21:52,783:INFO:Total runtime is 1.123712917168935 minutes +2024-12-04 12:21:52,787:INFO:SubProcess create_model() called ================================== +2024-12-04 12:21:52,787:INFO:Initializing create_model() +2024-12-04 12:21:52,787:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:21:52,787:INFO:Checking exceptions +2024-12-04 12:21:52,787:INFO:Importing libraries +2024-12-04 12:21:52,788:INFO:Copying training dataset +2024-12-04 12:21:52,810:INFO:Defining folds +2024-12-04 12:21:52,810:INFO:Declaring metric variables +2024-12-04 12:21:52,816:INFO:Importing untrained model +2024-12-04 12:21:52,821:INFO:AdaBoost Regressor Imported successfully +2024-12-04 12:21:52,831:INFO:Starting cross validation +2024-12-04 12:21:52,833:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:21:58,101:INFO:Calculating mean and std +2024-12-04 12:21:58,101:INFO:Creating metrics dataframe +2024-12-04 12:21:58,103:INFO:Uploading results into container +2024-12-04 12:21:58,104:INFO:Uploading model into container now +2024-12-04 12:21:58,104:INFO:_master_model_container: 15 +2024-12-04 12:21:58,104:INFO:_display_container: 2 +2024-12-04 12:21:58,104:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 12:21:58,104:INFO:create_model() successfully completed...................................... +2024-12-04 12:21:58,182:INFO:SubProcess create_model() end ================================== +2024-12-04 12:21:58,182:INFO:Creating metrics dataframe +2024-12-04 12:21:58,190:INFO:Initializing Gradient Boosting Regressor +2024-12-04 12:21:58,190:INFO:Total runtime is 1.2138343095779418 minutes +2024-12-04 12:21:58,193:INFO:SubProcess create_model() called ================================== +2024-12-04 12:21:58,193:INFO:Initializing create_model() +2024-12-04 12:21:58,193:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:21:58,193:INFO:Checking exceptions +2024-12-04 12:21:58,193:INFO:Importing libraries +2024-12-04 12:21:58,193:INFO:Copying training dataset +2024-12-04 12:21:58,206:INFO:Defining folds +2024-12-04 12:21:58,206:INFO:Declaring metric variables +2024-12-04 12:21:58,210:INFO:Importing untrained model +2024-12-04 12:21:58,213:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 12:21:58,219:INFO:Starting cross validation +2024-12-04 12:21:58,221:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:22:07,601:INFO:Calculating mean and std +2024-12-04 12:22:07,603:INFO:Creating metrics dataframe +2024-12-04 12:22:07,605:INFO:Uploading results into container +2024-12-04 12:22:07,606:INFO:Uploading model into container now +2024-12-04 12:22:07,606:INFO:_master_model_container: 16 +2024-12-04 12:22:07,606:INFO:_display_container: 2 +2024-12-04 12:22:07,607:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 12:22:07,607:INFO:create_model() successfully completed...................................... +2024-12-04 12:22:07,686:INFO:SubProcess create_model() end ================================== +2024-12-04 12:22:07,686:INFO:Creating metrics dataframe +2024-12-04 12:22:07,693:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 12:22:07,693:INFO:Total runtime is 1.372223134835561 minutes +2024-12-04 12:22:07,696:INFO:SubProcess create_model() called ================================== +2024-12-04 12:22:07,696:INFO:Initializing create_model() +2024-12-04 12:22:07,697:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:22:07,697:INFO:Checking exceptions +2024-12-04 12:22:07,697:INFO:Importing libraries +2024-12-04 12:22:07,697:INFO:Copying training dataset +2024-12-04 12:22:07,709:INFO:Defining folds +2024-12-04 12:22:07,710:INFO:Declaring metric variables +2024-12-04 12:22:07,713:INFO:Importing untrained model +2024-12-04 12:22:07,716:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 12:22:07,722:INFO:Starting cross validation +2024-12-04 12:22:07,723:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:22:09,758:INFO:Calculating mean and std +2024-12-04 12:22:09,759:INFO:Creating metrics dataframe +2024-12-04 12:22:09,761:INFO:Uploading results into container +2024-12-04 12:22:09,762:INFO:Uploading model into container now +2024-12-04 12:22:09,763:INFO:_master_model_container: 17 +2024-12-04 12:22:09,763:INFO:_display_container: 2 +2024-12-04 12:22:09,763:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:22:09,763:INFO:create_model() successfully completed...................................... +2024-12-04 12:22:09,869:INFO:SubProcess create_model() end ================================== +2024-12-04 12:22:09,869:INFO:Creating metrics dataframe +2024-12-04 12:22:09,878:INFO:Initializing Dummy Regressor +2024-12-04 12:22:09,878:INFO:Total runtime is 1.4086299459139504 minutes +2024-12-04 12:22:09,880:INFO:SubProcess create_model() called ================================== +2024-12-04 12:22:09,881:INFO:Initializing create_model() +2024-12-04 12:22:09,881:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:22:09,881:INFO:Checking exceptions +2024-12-04 12:22:09,881:INFO:Importing libraries +2024-12-04 12:22:09,881:INFO:Copying training dataset +2024-12-04 12:22:09,894:INFO:Defining folds +2024-12-04 12:22:09,895:INFO:Declaring metric variables +2024-12-04 12:22:09,897:INFO:Importing untrained model +2024-12-04 12:22:09,901:INFO:Dummy Regressor Imported successfully +2024-12-04 12:22:09,907:INFO:Starting cross validation +2024-12-04 12:22:09,910:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:22:10,847:INFO:Calculating mean and std +2024-12-04 12:22:10,848:INFO:Creating metrics dataframe +2024-12-04 12:22:10,849:INFO:Uploading results into container +2024-12-04 12:22:10,850:INFO:Uploading model into container now +2024-12-04 12:22:10,850:INFO:_master_model_container: 18 +2024-12-04 12:22:10,850:INFO:_display_container: 2 +2024-12-04 12:22:10,851:INFO:DummyRegressor() +2024-12-04 12:22:10,851:INFO:create_model() successfully completed...................................... +2024-12-04 12:22:10,929:INFO:SubProcess create_model() end ================================== +2024-12-04 12:22:10,929:INFO:Creating metrics dataframe +2024-12-04 12:22:10,938:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 12:22:10,946:INFO:Initializing create_model() +2024-12-04 12:22:10,946:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:22:10,946:INFO:Checking exceptions +2024-12-04 12:22:10,947:INFO:Importing libraries +2024-12-04 12:22:10,948:INFO:Copying training dataset +2024-12-04 12:22:10,960:INFO:Defining folds +2024-12-04 12:22:10,960:INFO:Declaring metric variables +2024-12-04 12:22:10,961:INFO:Importing untrained model +2024-12-04 12:22:10,961:INFO:Declaring custom model +2024-12-04 12:22:10,962:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:22:10,962:INFO:Cross validation set to False +2024-12-04 12:22:10,963:INFO:Fitting Model +2024-12-04 12:22:12,924:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:22:12,924:INFO:create_model() successfully completed...................................... +2024-12-04 12:22:13,033:INFO:_master_model_container: 18 +2024-12-04 12:22:13,034:INFO:_display_container: 2 +2024-12-04 12:22:13,034:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:22:13,034:INFO:compare_models() successfully completed...................................... +2024-12-04 12:22:13,066:INFO:Initializing plot_model() +2024-12-04 12:22:13,067:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=feature, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 12:22:13,067:INFO:Checking exceptions +2024-12-04 12:22:13,100:INFO:Preloading libraries +2024-12-04 12:22:13,331:INFO:Copying training dataset +2024-12-04 12:22:13,331:INFO:Plot type: feature +2024-12-04 12:22:13,332:WARNING:No coef_ found. Trying feature_importances_ +2024-12-04 12:22:13,600:INFO:Visual Rendered Successfully +2024-12-04 12:22:13,684:INFO:plot_model() successfully completed...................................... +2024-12-04 12:22:13,724:INFO:Initializing predict_model() +2024-12-04 12:22:13,724:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000166189FB740>) +2024-12-04 12:22:13,724:INFO:Checking exceptions +2024-12-04 12:22:13,724:INFO:Preloading libraries +2024-12-04 12:22:13,729:INFO:Set up data. +2024-12-04 12:22:13,741:INFO:Set up index. +2024-12-04 12:22:14,010:INFO:Initializing plot_model() +2024-12-04 12:22:14,010:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=residuals_interactive, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 12:22:14,011:INFO:Checking exceptions +2024-12-04 12:22:14,044:INFO:Preloading libraries +2024-12-04 12:22:14,269:INFO:Copying training dataset +2024-12-04 12:22:14,269:INFO:Plot type: residuals_interactive +2024-12-04 12:22:15,033:INFO:Calculated model residuals +2024-12-04 12:22:35,328:INFO:Calculated Tunkey-Anscombe Plot +2024-12-04 12:26:59,234:INFO:PyCaret RegressionExperiment +2024-12-04 12:26:59,235:INFO:Logging name: reg-default-name +2024-12-04 12:26:59,235:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:26:59,235:INFO:version 3.3.2 +2024-12-04 12:26:59,235:INFO:Initializing setup() +2024-12-04 12:26:59,235:INFO:self.USI: c721 +2024-12-04 12:26:59,235:INFO:self._variable_keys: {'y_test', 'idx', 'X_train', 'html_param', 'X_test', 'gpu_param', 'transform_target_param', 'log_plots_param', 'target_param', 'exp_name_log', 'gpu_n_jobs_param', 'y', 'pipeline', 'fold_groups_param', 'exp_id', '_available_plots', 'memory', 'X', 'fold_shuffle_param', 'seed', 'USI', '_ml_usecase', 'fold_generator', 'logging_param', 'y_train', 'data', 'n_jobs_param'} +2024-12-04 12:26:59,235:INFO:Checking environment +2024-12-04 12:26:59,235:INFO:python_version: 3.11.9 +2024-12-04 12:26:59,235:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:26:59,235:INFO:machine: AMD64 +2024-12-04 12:26:59,235:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:26:59,241:INFO:Memory: svmem(total=17007292416, available=1973301248, percent=88.4, used=15033991168, free=1973301248) +2024-12-04 12:26:59,241:INFO:Physical Core: 4 +2024-12-04 12:26:59,241:INFO:Logical Core: 8 +2024-12-04 12:26:59,241:INFO:Checking libraries +2024-12-04 12:26:59,241:INFO:System: +2024-12-04 12:26:59,242:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:26:59,242:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:26:59,242:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:26:59,242:INFO:PyCaret required dependencies: +2024-12-04 12:26:59,242:INFO: pip: 24.3.1 +2024-12-04 12:26:59,242:INFO: setuptools: 65.5.0 +2024-12-04 12:26:59,242:INFO: pycaret: 3.3.2 +2024-12-04 12:26:59,242:INFO: IPython: 8.12.3 +2024-12-04 12:26:59,242:INFO: ipywidgets: 8.1.5 +2024-12-04 12:26:59,242:INFO: tqdm: 4.67.1 +2024-12-04 12:26:59,243:INFO: numpy: 1.26.4 +2024-12-04 12:26:59,243:INFO: pandas: 2.1.4 +2024-12-04 12:26:59,243:INFO: jinja2: 3.1.4 +2024-12-04 12:26:59,243:INFO: scipy: 1.11.4 +2024-12-04 12:26:59,243:INFO: joblib: 1.3.2 +2024-12-04 12:26:59,243:INFO: sklearn: 1.4.2 +2024-12-04 12:26:59,243:INFO: pyod: 2.0.2 +2024-12-04 12:26:59,243:INFO: imblearn: 0.12.4 +2024-12-04 12:26:59,243:INFO: category_encoders: 2.6.4 +2024-12-04 12:26:59,243:INFO: lightgbm: 4.5.0 +2024-12-04 12:26:59,243:INFO: numba: 0.60.0 +2024-12-04 12:26:59,243:INFO: requests: 2.32.3 +2024-12-04 12:26:59,243:INFO: matplotlib: 3.7.5 +2024-12-04 12:26:59,243:INFO: scikitplot: 0.3.7 +2024-12-04 12:26:59,243:INFO: yellowbrick: 1.5 +2024-12-04 12:26:59,243:INFO: plotly: 5.24.1 +2024-12-04 12:26:59,243:INFO: plotly-resampler: Not installed +2024-12-04 12:26:59,243:INFO: kaleido: 0.2.1 +2024-12-04 12:26:59,243:INFO: schemdraw: 0.15 +2024-12-04 12:26:59,243:INFO: statsmodels: 0.14.4 +2024-12-04 12:26:59,244:INFO: sktime: 0.26.0 +2024-12-04 12:26:59,244:INFO: tbats: 1.1.3 +2024-12-04 12:26:59,244:INFO: pmdarima: 2.0.4 +2024-12-04 12:26:59,244:INFO: psutil: 6.1.0 +2024-12-04 12:26:59,244:INFO: markupsafe: 3.0.2 +2024-12-04 12:26:59,244:INFO: pickle5: Not installed +2024-12-04 12:26:59,244:INFO: cloudpickle: 3.1.0 +2024-12-04 12:26:59,244:INFO: deprecation: 2.1.0 +2024-12-04 12:26:59,244:INFO: xxhash: 3.5.0 +2024-12-04 12:26:59,244:INFO: wurlitzer: Not installed +2024-12-04 12:26:59,244:INFO:PyCaret optional dependencies: +2024-12-04 12:26:59,244:INFO: shap: Not installed +2024-12-04 12:26:59,244:INFO: interpret: Not installed +2024-12-04 12:26:59,244:INFO: umap: Not installed +2024-12-04 12:26:59,244:INFO: ydata_profiling: Not installed +2024-12-04 12:26:59,244:INFO: explainerdashboard: Not installed +2024-12-04 12:26:59,244:INFO: autoviz: Not installed +2024-12-04 12:26:59,245:INFO: fairlearn: Not installed +2024-12-04 12:26:59,245:INFO: deepchecks: Not installed +2024-12-04 12:26:59,245:INFO: xgboost: Not installed +2024-12-04 12:26:59,245:INFO: catboost: Not installed +2024-12-04 12:26:59,245:INFO: kmodes: Not installed +2024-12-04 12:26:59,245:INFO: mlxtend: Not installed +2024-12-04 12:26:59,245:INFO: statsforecast: Not installed +2024-12-04 12:26:59,245:INFO: tune_sklearn: Not installed +2024-12-04 12:26:59,245:INFO: ray: Not installed +2024-12-04 12:26:59,245:INFO: hyperopt: Not installed +2024-12-04 12:26:59,245:INFO: optuna: Not installed +2024-12-04 12:26:59,245:INFO: skopt: Not installed +2024-12-04 12:26:59,245:INFO: mlflow: 2.18.0 +2024-12-04 12:26:59,245:INFO: gradio: Not installed +2024-12-04 12:26:59,245:INFO: fastapi: Not installed +2024-12-04 12:26:59,245:INFO: uvicorn: 0.32.1 +2024-12-04 12:26:59,245:INFO: m2cgen: Not installed +2024-12-04 12:26:59,246:INFO: evidently: Not installed +2024-12-04 12:26:59,246:INFO: fugue: Not installed +2024-12-04 12:26:59,246:INFO: streamlit: 1.40.1 +2024-12-04 12:26:59,246:INFO: prophet: Not installed +2024-12-04 12:26:59,246:INFO:None +2024-12-04 12:26:59,246:INFO:Set up data. +2024-12-04 12:26:59,284:INFO:Set up folding strategy. +2024-12-04 12:26:59,284:INFO:Set up train/test split. +2024-12-04 12:26:59,309:INFO:Set up index. +2024-12-04 12:26:59,310:INFO:Assigning column types. +2024-12-04 12:26:59,330:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:26:59,330:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,335:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,340:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,405:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,445:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,445:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,445:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,445:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,453:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,460:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,527:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,567:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,567:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,567:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,568:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:26:59,572:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,577:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,642:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,685:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,686:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,686:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,690:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,692:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,766:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,810:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,812:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,812:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,812:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:26:59,820:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,894:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,937:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:26:59,939:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,939:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:26:59,945:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,013:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,053:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,053:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,053:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,053:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:27:00,126:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,165:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,165:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,165:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,239:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,281:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,281:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,281:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,281:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:27:00,363:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,410:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,410:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,484:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:27:00,532:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,532:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,532:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:27:00,657:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,658:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,770:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,771:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:00,773:INFO:Preparing preprocessing pipeline... +2024-12-04 12:27:00,773:INFO:Set up simple imputation. +2024-12-04 12:27:00,784:INFO:Set up encoding of categorical features. +2024-12-04 12:27:00,785:INFO:Set up column transformation. +2024-12-04 12:27:00,785:INFO:Set up feature normalization. +2024-12-04 12:27:01,463:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:27:01,472:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=TargetEncoder(cols=['nom_region', + 'nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:27:01,472:INFO:Creating final display dataframe. +2024-12-04 12:27:01,853:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (63852, 15) +4 Transformed data shape (63852, 14) +5 Transformed train set shape (44696, 14) +6 Transformed test set shape (19156, 14) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 10 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI c721 +2024-12-04 12:27:01,983:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:01,983:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:02,093:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:02,093:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:27:02,100:INFO:setup() successfully completed in 2.87s............... +2024-12-04 12:27:06,129:INFO:Initializing compare_models() +2024-12-04 12:27:06,129:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 12:27:06,130:INFO:Checking exceptions +2024-12-04 12:27:06,139:INFO:Preparing display monitor +2024-12-04 12:27:06,165:INFO:Initializing Linear Regression +2024-12-04 12:27:06,165:INFO:Total runtime is 0.0 minutes +2024-12-04 12:27:06,170:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:06,170:INFO:Initializing create_model() +2024-12-04 12:27:06,170:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:06,170:INFO:Checking exceptions +2024-12-04 12:27:06,170:INFO:Importing libraries +2024-12-04 12:27:06,170:INFO:Copying training dataset +2024-12-04 12:27:06,202:INFO:Defining folds +2024-12-04 12:27:06,203:INFO:Declaring metric variables +2024-12-04 12:27:06,205:INFO:Importing untrained model +2024-12-04 12:27:06,208:INFO:Linear Regression Imported successfully +2024-12-04 12:27:06,215:INFO:Starting cross validation +2024-12-04 12:27:06,216:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:08,403:INFO:Calculating mean and std +2024-12-04 12:27:08,403:INFO:Creating metrics dataframe +2024-12-04 12:27:08,405:INFO:Uploading results into container +2024-12-04 12:27:08,405:INFO:Uploading model into container now +2024-12-04 12:27:08,405:INFO:_master_model_container: 1 +2024-12-04 12:27:08,406:INFO:_display_container: 2 +2024-12-04 12:27:08,407:INFO:LinearRegression(n_jobs=-1) +2024-12-04 12:27:08,407:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:08,537:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:08,539:INFO:Creating metrics dataframe +2024-12-04 12:27:08,542:INFO:Initializing Lasso Regression +2024-12-04 12:27:08,542:INFO:Total runtime is 0.03961723248163859 minutes +2024-12-04 12:27:08,546:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:08,546:INFO:Initializing create_model() +2024-12-04 12:27:08,546:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:08,546:INFO:Checking exceptions +2024-12-04 12:27:08,546:INFO:Importing libraries +2024-12-04 12:27:08,546:INFO:Copying training dataset +2024-12-04 12:27:08,573:INFO:Defining folds +2024-12-04 12:27:08,573:INFO:Declaring metric variables +2024-12-04 12:27:08,578:INFO:Importing untrained model +2024-12-04 12:27:08,582:INFO:Lasso Regression Imported successfully +2024-12-04 12:27:08,585:INFO:Starting cross validation +2024-12-04 12:27:08,589:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:10,770:INFO:Calculating mean and std +2024-12-04 12:27:10,770:INFO:Creating metrics dataframe +2024-12-04 12:27:10,772:INFO:Uploading results into container +2024-12-04 12:27:10,772:INFO:Uploading model into container now +2024-12-04 12:27:10,772:INFO:_master_model_container: 2 +2024-12-04 12:27:10,772:INFO:_display_container: 2 +2024-12-04 12:27:10,772:INFO:Lasso(random_state=123) +2024-12-04 12:27:10,772:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:10,890:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:10,890:INFO:Creating metrics dataframe +2024-12-04 12:27:10,900:INFO:Initializing Ridge Regression +2024-12-04 12:27:10,901:INFO:Total runtime is 0.07892569303512573 minutes +2024-12-04 12:27:10,903:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:10,904:INFO:Initializing create_model() +2024-12-04 12:27:10,904:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:10,904:INFO:Checking exceptions +2024-12-04 12:27:10,904:INFO:Importing libraries +2024-12-04 12:27:10,904:INFO:Copying training dataset +2024-12-04 12:27:10,927:INFO:Defining folds +2024-12-04 12:27:10,927:INFO:Declaring metric variables +2024-12-04 12:27:10,931:INFO:Importing untrained model +2024-12-04 12:27:10,936:INFO:Ridge Regression Imported successfully +2024-12-04 12:27:10,939:INFO:Starting cross validation +2024-12-04 12:27:10,943:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:12,810:INFO:Calculating mean and std +2024-12-04 12:27:12,810:INFO:Creating metrics dataframe +2024-12-04 12:27:12,813:INFO:Uploading results into container +2024-12-04 12:27:12,813:INFO:Uploading model into container now +2024-12-04 12:27:12,814:INFO:_master_model_container: 3 +2024-12-04 12:27:12,814:INFO:_display_container: 2 +2024-12-04 12:27:12,815:INFO:Ridge(random_state=123) +2024-12-04 12:27:12,815:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:12,934:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:12,934:INFO:Creating metrics dataframe +2024-12-04 12:27:12,939:INFO:Initializing Elastic Net +2024-12-04 12:27:12,939:INFO:Total runtime is 0.11289292573928833 minutes +2024-12-04 12:27:12,942:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:12,942:INFO:Initializing create_model() +2024-12-04 12:27:12,942:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:12,942:INFO:Checking exceptions +2024-12-04 12:27:12,942:INFO:Importing libraries +2024-12-04 12:27:12,942:INFO:Copying training dataset +2024-12-04 12:27:12,970:INFO:Defining folds +2024-12-04 12:27:12,971:INFO:Declaring metric variables +2024-12-04 12:27:12,975:INFO:Importing untrained model +2024-12-04 12:27:12,977:INFO:Elastic Net Imported successfully +2024-12-04 12:27:12,986:INFO:Starting cross validation +2024-12-04 12:27:12,987:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:15,225:INFO:Calculating mean and std +2024-12-04 12:27:15,225:INFO:Creating metrics dataframe +2024-12-04 12:27:15,227:INFO:Uploading results into container +2024-12-04 12:27:15,227:INFO:Uploading model into container now +2024-12-04 12:27:15,227:INFO:_master_model_container: 4 +2024-12-04 12:27:15,227:INFO:_display_container: 2 +2024-12-04 12:27:15,227:INFO:ElasticNet(random_state=123) +2024-12-04 12:27:15,229:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:15,367:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:15,367:INFO:Creating metrics dataframe +2024-12-04 12:27:15,372:INFO:Initializing Least Angle Regression +2024-12-04 12:27:15,372:INFO:Total runtime is 0.15345199902852377 minutes +2024-12-04 12:27:15,375:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:15,375:INFO:Initializing create_model() +2024-12-04 12:27:15,375:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:15,375:INFO:Checking exceptions +2024-12-04 12:27:15,375:INFO:Importing libraries +2024-12-04 12:27:15,375:INFO:Copying training dataset +2024-12-04 12:27:15,403:INFO:Defining folds +2024-12-04 12:27:15,403:INFO:Declaring metric variables +2024-12-04 12:27:15,407:INFO:Importing untrained model +2024-12-04 12:27:15,409:INFO:Least Angle Regression Imported successfully +2024-12-04 12:27:15,418:INFO:Starting cross validation +2024-12-04 12:27:15,418:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:17,305:INFO:Calculating mean and std +2024-12-04 12:27:17,305:INFO:Creating metrics dataframe +2024-12-04 12:27:17,307:INFO:Uploading results into container +2024-12-04 12:27:17,307:INFO:Uploading model into container now +2024-12-04 12:27:17,308:INFO:_master_model_container: 5 +2024-12-04 12:27:17,308:INFO:_display_container: 2 +2024-12-04 12:27:17,308:INFO:Lars(random_state=123) +2024-12-04 12:27:17,308:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:17,419:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:17,419:INFO:Creating metrics dataframe +2024-12-04 12:27:17,424:INFO:Initializing Lasso Least Angle Regression +2024-12-04 12:27:17,424:INFO:Total runtime is 0.18764920234680177 minutes +2024-12-04 12:27:17,428:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:17,428:INFO:Initializing create_model() +2024-12-04 12:27:17,429:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:17,429:INFO:Checking exceptions +2024-12-04 12:27:17,429:INFO:Importing libraries +2024-12-04 12:27:17,429:INFO:Copying training dataset +2024-12-04 12:27:17,445:INFO:Defining folds +2024-12-04 12:27:17,445:INFO:Declaring metric variables +2024-12-04 12:27:17,453:INFO:Importing untrained model +2024-12-04 12:27:17,457:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 12:27:17,464:INFO:Starting cross validation +2024-12-04 12:27:17,464:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:19,176:INFO:Calculating mean and std +2024-12-04 12:27:19,177:INFO:Creating metrics dataframe +2024-12-04 12:27:19,178:INFO:Uploading results into container +2024-12-04 12:27:19,179:INFO:Uploading model into container now +2024-12-04 12:27:19,179:INFO:_master_model_container: 6 +2024-12-04 12:27:19,179:INFO:_display_container: 2 +2024-12-04 12:27:19,180:INFO:LassoLars(random_state=123) +2024-12-04 12:27:19,180:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:19,289:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:19,289:INFO:Creating metrics dataframe +2024-12-04 12:27:19,297:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 12:27:19,297:INFO:Total runtime is 0.21886073350906374 minutes +2024-12-04 12:27:19,300:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:19,300:INFO:Initializing create_model() +2024-12-04 12:27:19,300:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:19,300:INFO:Checking exceptions +2024-12-04 12:27:19,300:INFO:Importing libraries +2024-12-04 12:27:19,300:INFO:Copying training dataset +2024-12-04 12:27:19,320:INFO:Defining folds +2024-12-04 12:27:19,320:INFO:Declaring metric variables +2024-12-04 12:27:19,324:INFO:Importing untrained model +2024-12-04 12:27:19,327:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 12:27:19,330:INFO:Starting cross validation +2024-12-04 12:27:19,334:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:21,029:INFO:Calculating mean and std +2024-12-04 12:27:21,030:INFO:Creating metrics dataframe +2024-12-04 12:27:21,031:INFO:Uploading results into container +2024-12-04 12:27:21,031:INFO:Uploading model into container now +2024-12-04 12:27:21,031:INFO:_master_model_container: 7 +2024-12-04 12:27:21,031:INFO:_display_container: 2 +2024-12-04 12:27:21,031:INFO:OrthogonalMatchingPursuit() +2024-12-04 12:27:21,031:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:21,143:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:21,143:INFO:Creating metrics dataframe +2024-12-04 12:27:21,147:INFO:Initializing Bayesian Ridge +2024-12-04 12:27:21,147:INFO:Total runtime is 0.24968762795130414 minutes +2024-12-04 12:27:21,153:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:21,155:INFO:Initializing create_model() +2024-12-04 12:27:21,155:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:21,155:INFO:Checking exceptions +2024-12-04 12:27:21,155:INFO:Importing libraries +2024-12-04 12:27:21,155:INFO:Copying training dataset +2024-12-04 12:27:21,176:INFO:Defining folds +2024-12-04 12:27:21,177:INFO:Declaring metric variables +2024-12-04 12:27:21,180:INFO:Importing untrained model +2024-12-04 12:27:21,184:INFO:Bayesian Ridge Imported successfully +2024-12-04 12:27:21,189:INFO:Starting cross validation +2024-12-04 12:27:21,189:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:22,990:INFO:Calculating mean and std +2024-12-04 12:27:22,990:INFO:Creating metrics dataframe +2024-12-04 12:27:22,990:INFO:Uploading results into container +2024-12-04 12:27:22,990:INFO:Uploading model into container now +2024-12-04 12:27:22,993:INFO:_master_model_container: 8 +2024-12-04 12:27:22,993:INFO:_display_container: 2 +2024-12-04 12:27:22,993:INFO:BayesianRidge() +2024-12-04 12:27:22,993:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:23,103:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:23,105:INFO:Creating metrics dataframe +2024-12-04 12:27:23,112:INFO:Initializing Passive Aggressive Regressor +2024-12-04 12:27:23,112:INFO:Total runtime is 0.2824490586916606 minutes +2024-12-04 12:27:23,114:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:23,114:INFO:Initializing create_model() +2024-12-04 12:27:23,114:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:23,117:INFO:Checking exceptions +2024-12-04 12:27:23,117:INFO:Importing libraries +2024-12-04 12:27:23,117:INFO:Copying training dataset +2024-12-04 12:27:23,138:INFO:Defining folds +2024-12-04 12:27:23,138:INFO:Declaring metric variables +2024-12-04 12:27:23,142:INFO:Importing untrained model +2024-12-04 12:27:23,146:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 12:27:23,187:INFO:Starting cross validation +2024-12-04 12:27:23,187:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:25,048:INFO:Calculating mean and std +2024-12-04 12:27:25,048:INFO:Creating metrics dataframe +2024-12-04 12:27:25,048:INFO:Uploading results into container +2024-12-04 12:27:25,048:INFO:Uploading model into container now +2024-12-04 12:27:25,048:INFO:_master_model_container: 9 +2024-12-04 12:27:25,048:INFO:_display_container: 2 +2024-12-04 12:27:25,048:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 12:27:25,048:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:25,162:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:25,162:INFO:Creating metrics dataframe +2024-12-04 12:27:25,169:INFO:Initializing Huber Regressor +2024-12-04 12:27:25,169:INFO:Total runtime is 0.31672196388244633 minutes +2024-12-04 12:27:25,170:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:25,170:INFO:Initializing create_model() +2024-12-04 12:27:25,170:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:25,170:INFO:Checking exceptions +2024-12-04 12:27:25,170:INFO:Importing libraries +2024-12-04 12:27:25,170:INFO:Copying training dataset +2024-12-04 12:27:25,193:INFO:Defining folds +2024-12-04 12:27:25,193:INFO:Declaring metric variables +2024-12-04 12:27:25,193:INFO:Importing untrained model +2024-12-04 12:27:25,202:INFO:Huber Regressor Imported successfully +2024-12-04 12:27:25,208:INFO:Starting cross validation +2024-12-04 12:27:25,209:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:27,725:INFO:Calculating mean and std +2024-12-04 12:27:27,725:INFO:Creating metrics dataframe +2024-12-04 12:27:27,727:INFO:Uploading results into container +2024-12-04 12:27:27,728:INFO:Uploading model into container now +2024-12-04 12:27:27,728:INFO:_master_model_container: 10 +2024-12-04 12:27:27,728:INFO:_display_container: 2 +2024-12-04 12:27:27,728:INFO:HuberRegressor() +2024-12-04 12:27:27,728:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:27,841:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:27,841:INFO:Creating metrics dataframe +2024-12-04 12:27:27,842:INFO:Initializing K Neighbors Regressor +2024-12-04 12:27:27,842:INFO:Total runtime is 0.3612855672836304 minutes +2024-12-04 12:27:27,842:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:27,851:INFO:Initializing create_model() +2024-12-04 12:27:27,851:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:27,851:INFO:Checking exceptions +2024-12-04 12:27:27,851:INFO:Importing libraries +2024-12-04 12:27:27,851:INFO:Copying training dataset +2024-12-04 12:27:27,875:INFO:Defining folds +2024-12-04 12:27:27,875:INFO:Declaring metric variables +2024-12-04 12:27:27,879:INFO:Importing untrained model +2024-12-04 12:27:27,883:INFO:K Neighbors Regressor Imported successfully +2024-12-04 12:27:27,889:INFO:Starting cross validation +2024-12-04 12:27:27,889:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:31,611:INFO:Calculating mean and std +2024-12-04 12:27:31,612:INFO:Creating metrics dataframe +2024-12-04 12:27:31,614:INFO:Uploading results into container +2024-12-04 12:27:31,615:INFO:Uploading model into container now +2024-12-04 12:27:31,615:INFO:_master_model_container: 11 +2024-12-04 12:27:31,615:INFO:_display_container: 2 +2024-12-04 12:27:31,615:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 12:27:31,615:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:31,735:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:31,735:INFO:Creating metrics dataframe +2024-12-04 12:27:31,739:INFO:Initializing Decision Tree Regressor +2024-12-04 12:27:31,739:INFO:Total runtime is 0.4262308200200399 minutes +2024-12-04 12:27:31,743:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:31,743:INFO:Initializing create_model() +2024-12-04 12:27:31,743:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:31,743:INFO:Checking exceptions +2024-12-04 12:27:31,743:INFO:Importing libraries +2024-12-04 12:27:31,743:INFO:Copying training dataset +2024-12-04 12:27:31,767:INFO:Defining folds +2024-12-04 12:27:31,768:INFO:Declaring metric variables +2024-12-04 12:27:31,770:INFO:Importing untrained model +2024-12-04 12:27:31,774:INFO:Decision Tree Regressor Imported successfully +2024-12-04 12:27:31,781:INFO:Starting cross validation +2024-12-04 12:27:31,781:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:27:34,637:INFO:Calculating mean and std +2024-12-04 12:27:34,637:INFO:Creating metrics dataframe +2024-12-04 12:27:34,639:INFO:Uploading results into container +2024-12-04 12:27:34,639:INFO:Uploading model into container now +2024-12-04 12:27:34,639:INFO:_master_model_container: 12 +2024-12-04 12:27:34,639:INFO:_display_container: 2 +2024-12-04 12:27:34,639:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 12:27:34,639:INFO:create_model() successfully completed...................................... +2024-12-04 12:27:34,753:INFO:SubProcess create_model() end ================================== +2024-12-04 12:27:34,753:INFO:Creating metrics dataframe +2024-12-04 12:27:34,762:INFO:Initializing Random Forest Regressor +2024-12-04 12:27:34,762:INFO:Total runtime is 0.4766106883684795 minutes +2024-12-04 12:27:34,763:INFO:SubProcess create_model() called ================================== +2024-12-04 12:27:34,763:INFO:Initializing create_model() +2024-12-04 12:27:34,763:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:27:34,763:INFO:Checking exceptions +2024-12-04 12:27:34,763:INFO:Importing libraries +2024-12-04 12:27:34,763:INFO:Copying training dataset +2024-12-04 12:27:34,787:INFO:Defining folds +2024-12-04 12:27:34,787:INFO:Declaring metric variables +2024-12-04 12:27:34,789:INFO:Importing untrained model +2024-12-04 12:27:34,793:INFO:Random Forest Regressor Imported successfully +2024-12-04 12:27:34,796:INFO:Starting cross validation +2024-12-04 12:27:34,796:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:28:35,485:INFO:Calculating mean and std +2024-12-04 12:28:35,485:INFO:Creating metrics dataframe +2024-12-04 12:28:35,489:INFO:Uploading results into container +2024-12-04 12:28:35,490:INFO:Uploading model into container now +2024-12-04 12:28:35,490:INFO:_master_model_container: 13 +2024-12-04 12:28:35,490:INFO:_display_container: 2 +2024-12-04 12:28:35,491:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:28:35,491:INFO:create_model() successfully completed...................................... +2024-12-04 12:28:35,680:INFO:SubProcess create_model() end ================================== +2024-12-04 12:28:35,680:INFO:Creating metrics dataframe +2024-12-04 12:28:35,692:INFO:Initializing Extra Trees Regressor +2024-12-04 12:28:35,692:INFO:Total runtime is 1.4921178062756857 minutes +2024-12-04 12:28:35,696:INFO:SubProcess create_model() called ================================== +2024-12-04 12:28:35,696:INFO:Initializing create_model() +2024-12-04 12:28:35,696:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:28:35,696:INFO:Checking exceptions +2024-12-04 12:28:35,696:INFO:Importing libraries +2024-12-04 12:28:35,696:INFO:Copying training dataset +2024-12-04 12:28:35,749:INFO:Defining folds +2024-12-04 12:28:35,749:INFO:Declaring metric variables +2024-12-04 12:28:35,754:INFO:Importing untrained model +2024-12-04 12:28:35,759:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:28:35,768:INFO:Starting cross validation +2024-12-04 12:28:35,770:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:29:09,728:INFO:Calculating mean and std +2024-12-04 12:29:09,728:INFO:Creating metrics dataframe +2024-12-04 12:29:09,731:INFO:Uploading results into container +2024-12-04 12:29:09,733:INFO:Uploading model into container now +2024-12-04 12:29:09,733:INFO:_master_model_container: 14 +2024-12-04 12:29:09,733:INFO:_display_container: 2 +2024-12-04 12:29:09,735:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:29:09,735:INFO:create_model() successfully completed...................................... +2024-12-04 12:29:09,933:INFO:SubProcess create_model() end ================================== +2024-12-04 12:29:09,933:INFO:Creating metrics dataframe +2024-12-04 12:29:09,945:INFO:Initializing AdaBoost Regressor +2024-12-04 12:29:09,945:INFO:Total runtime is 2.0629876971244814 minutes +2024-12-04 12:29:09,945:INFO:SubProcess create_model() called ================================== +2024-12-04 12:29:09,945:INFO:Initializing create_model() +2024-12-04 12:29:09,945:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:29:09,945:INFO:Checking exceptions +2024-12-04 12:29:09,945:INFO:Importing libraries +2024-12-04 12:29:09,950:INFO:Copying training dataset +2024-12-04 12:29:09,988:INFO:Defining folds +2024-12-04 12:29:09,988:INFO:Declaring metric variables +2024-12-04 12:29:09,993:INFO:Importing untrained model +2024-12-04 12:29:09,996:INFO:AdaBoost Regressor Imported successfully +2024-12-04 12:29:10,003:INFO:Starting cross validation +2024-12-04 12:29:10,005:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:29:19,695:INFO:Calculating mean and std +2024-12-04 12:29:19,695:INFO:Creating metrics dataframe +2024-12-04 12:29:19,697:INFO:Uploading results into container +2024-12-04 12:29:19,697:INFO:Uploading model into container now +2024-12-04 12:29:19,697:INFO:_master_model_container: 15 +2024-12-04 12:29:19,697:INFO:_display_container: 2 +2024-12-04 12:29:19,697:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 12:29:19,697:INFO:create_model() successfully completed...................................... +2024-12-04 12:29:19,812:INFO:SubProcess create_model() end ================================== +2024-12-04 12:29:19,812:INFO:Creating metrics dataframe +2024-12-04 12:29:19,820:INFO:Initializing Gradient Boosting Regressor +2024-12-04 12:29:19,820:INFO:Total runtime is 2.2275830904642744 minutes +2024-12-04 12:29:19,825:INFO:SubProcess create_model() called ================================== +2024-12-04 12:29:19,825:INFO:Initializing create_model() +2024-12-04 12:29:19,825:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:29:19,826:INFO:Checking exceptions +2024-12-04 12:29:19,826:INFO:Importing libraries +2024-12-04 12:29:19,826:INFO:Copying training dataset +2024-12-04 12:29:19,851:INFO:Defining folds +2024-12-04 12:29:19,851:INFO:Declaring metric variables +2024-12-04 12:29:19,855:INFO:Importing untrained model +2024-12-04 12:29:19,857:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 12:29:19,866:INFO:Starting cross validation +2024-12-04 12:29:19,867:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:29:37,799:INFO:Calculating mean and std +2024-12-04 12:29:37,799:INFO:Creating metrics dataframe +2024-12-04 12:29:37,799:INFO:Uploading results into container +2024-12-04 12:29:37,802:INFO:Uploading model into container now +2024-12-04 12:29:37,803:INFO:_master_model_container: 16 +2024-12-04 12:29:37,803:INFO:_display_container: 2 +2024-12-04 12:29:37,803:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 12:29:37,803:INFO:create_model() successfully completed...................................... +2024-12-04 12:29:37,916:INFO:SubProcess create_model() end ================================== +2024-12-04 12:29:37,916:INFO:Creating metrics dataframe +2024-12-04 12:29:37,923:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 12:29:37,923:INFO:Total runtime is 2.529301500320435 minutes +2024-12-04 12:29:37,926:INFO:SubProcess create_model() called ================================== +2024-12-04 12:29:37,926:INFO:Initializing create_model() +2024-12-04 12:29:37,926:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:29:37,926:INFO:Checking exceptions +2024-12-04 12:29:37,927:INFO:Importing libraries +2024-12-04 12:29:37,927:INFO:Copying training dataset +2024-12-04 12:29:37,949:INFO:Defining folds +2024-12-04 12:29:37,949:INFO:Declaring metric variables +2024-12-04 12:29:37,953:INFO:Importing untrained model +2024-12-04 12:29:37,957:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 12:29:37,966:INFO:Starting cross validation +2024-12-04 12:29:37,968:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:29:41,224:INFO:Calculating mean and std +2024-12-04 12:29:41,226:INFO:Creating metrics dataframe +2024-12-04 12:29:41,228:INFO:Uploading results into container +2024-12-04 12:29:41,228:INFO:Uploading model into container now +2024-12-04 12:29:41,230:INFO:_master_model_container: 17 +2024-12-04 12:29:41,230:INFO:_display_container: 2 +2024-12-04 12:29:41,231:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:29:41,231:INFO:create_model() successfully completed...................................... +2024-12-04 12:29:41,363:INFO:SubProcess create_model() end ================================== +2024-12-04 12:29:41,363:INFO:Creating metrics dataframe +2024-12-04 12:29:41,375:INFO:Initializing Dummy Regressor +2024-12-04 12:29:41,375:INFO:Total runtime is 2.58683090209961 minutes +2024-12-04 12:29:41,380:INFO:SubProcess create_model() called ================================== +2024-12-04 12:29:41,381:INFO:Initializing create_model() +2024-12-04 12:29:41,381:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:29:41,381:INFO:Checking exceptions +2024-12-04 12:29:41,381:INFO:Importing libraries +2024-12-04 12:29:41,381:INFO:Copying training dataset +2024-12-04 12:29:41,405:INFO:Defining folds +2024-12-04 12:29:41,405:INFO:Declaring metric variables +2024-12-04 12:29:41,409:INFO:Importing untrained model +2024-12-04 12:29:41,413:INFO:Dummy Regressor Imported successfully +2024-12-04 12:29:41,418:INFO:Starting cross validation +2024-12-04 12:29:41,419:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:29:43,141:INFO:Calculating mean and std +2024-12-04 12:29:43,143:INFO:Creating metrics dataframe +2024-12-04 12:29:43,144:INFO:Uploading results into container +2024-12-04 12:29:43,144:INFO:Uploading model into container now +2024-12-04 12:29:43,144:INFO:_master_model_container: 18 +2024-12-04 12:29:43,144:INFO:_display_container: 2 +2024-12-04 12:29:43,144:INFO:DummyRegressor() +2024-12-04 12:29:43,144:INFO:create_model() successfully completed...................................... +2024-12-04 12:29:43,258:INFO:SubProcess create_model() end ================================== +2024-12-04 12:29:43,258:INFO:Creating metrics dataframe +2024-12-04 12:29:43,268:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: + +Styler.applymap has been deprecated. Use Styler.map instead. + + +2024-12-04 12:29:43,275:INFO:Initializing create_model() +2024-12-04 12:29:43,275:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:29:43,275:INFO:Checking exceptions +2024-12-04 12:29:43,277:INFO:Importing libraries +2024-12-04 12:29:43,277:INFO:Copying training dataset +2024-12-04 12:29:43,300:INFO:Defining folds +2024-12-04 12:29:43,300:INFO:Declaring metric variables +2024-12-04 12:29:43,300:INFO:Importing untrained model +2024-12-04 12:29:43,300:INFO:Declaring custom model +2024-12-04 12:29:43,301:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:29:43,301:INFO:Cross validation set to False +2024-12-04 12:29:43,301:INFO:Fitting Model +2024-12-04 12:29:47,007:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:29:47,007:INFO:create_model() successfully completed...................................... +2024-12-04 12:29:47,152:INFO:_master_model_container: 18 +2024-12-04 12:29:47,152:INFO:_display_container: 2 +2024-12-04 12:29:47,152:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:29:47,152:INFO:compare_models() successfully completed...................................... +2024-12-04 12:29:54,939:INFO:Initializing plot_model() +2024-12-04 12:29:54,939:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=feature, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 12:29:54,939:INFO:Checking exceptions +2024-12-04 12:29:54,998:INFO:Preloading libraries +2024-12-04 12:29:55,459:INFO:Copying training dataset +2024-12-04 12:29:55,459:INFO:Plot type: feature +2024-12-04 12:29:55,459:WARNING:No coef_ found. Trying feature_importances_ +2024-12-04 12:29:55,691:INFO:Visual Rendered Successfully +2024-12-04 12:29:55,815:INFO:plot_model() successfully completed...................................... +2024-12-04 12:31:47,878:INFO:Initializing predict_model() +2024-12-04 12:31:47,878:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000166428B4FE0>) +2024-12-04 12:31:47,878:INFO:Checking exceptions +2024-12-04 12:31:47,878:INFO:Preloading libraries +2024-12-04 12:31:47,879:INFO:Set up data. +2024-12-04 12:31:47,885:INFO:Set up index. +2024-12-04 12:32:06,446:INFO:Initializing predict_model() +2024-12-04 12:32:06,446:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001663AB9EDE0>) +2024-12-04 12:32:06,446:INFO:Checking exceptions +2024-12-04 12:32:06,446:INFO:Preloading libraries +2024-12-04 12:32:06,448:INFO:Set up data. +2024-12-04 12:32:06,452:INFO:Set up index. +2024-12-04 12:32:06,525:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: + +'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + + +2024-12-04 12:32:31,786:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_24016\4105585055.py:1: UserWarning: + +Boolean Series key will be reindexed to match DataFrame index. + + +2024-12-04 12:33:05,229:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_24016\2892909692.py:1: UserWarning: + +Boolean Series key will be reindexed to match DataFrame index. + + +2024-12-04 12:33:10,128:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_24016\981796279.py:1: UserWarning: + +Boolean Series key will be reindexed to match DataFrame index. + + +2024-12-04 12:38:50,313:INFO:Initializing predict_model() +2024-12-04 12:38:50,313:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001663FC56DE0>) +2024-12-04 12:38:50,313:INFO:Checking exceptions +2024-12-04 12:38:50,313:INFO:Preloading libraries +2024-12-04 12:38:50,313:INFO:Set up data. +2024-12-04 12:38:50,322:INFO:Set up index. +2024-12-04 12:39:15,962:INFO:Initializing predict_model() +2024-12-04 12:39:15,962:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001661A482D40>) +2024-12-04 12:39:15,962:INFO:Checking exceptions +2024-12-04 12:39:15,962:INFO:Preloading libraries +2024-12-04 12:39:15,963:INFO:Set up data. +2024-12-04 12:39:15,970:INFO:Set up index. +2024-12-04 12:39:16,057:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: + +'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + + +2024-12-04 12:39:55,544:INFO:PyCaret RegressionExperiment +2024-12-04 12:39:55,544:INFO:Logging name: reg-default-name +2024-12-04 12:39:55,544:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:39:55,544:INFO:version 3.3.2 +2024-12-04 12:39:55,544:INFO:Initializing setup() +2024-12-04 12:39:55,544:INFO:self.USI: fce2 +2024-12-04 12:39:55,544:INFO:self._variable_keys: {'y_test', 'idx', 'X_train', 'html_param', 'X_test', 'gpu_param', 'transform_target_param', 'log_plots_param', 'target_param', 'exp_name_log', 'gpu_n_jobs_param', 'y', 'pipeline', 'fold_groups_param', 'exp_id', '_available_plots', 'memory', 'X', 'fold_shuffle_param', 'seed', 'USI', '_ml_usecase', 'fold_generator', 'logging_param', 'y_train', 'data', 'n_jobs_param'} +2024-12-04 12:39:55,544:INFO:Checking environment +2024-12-04 12:39:55,544:INFO:python_version: 3.11.9 +2024-12-04 12:39:55,544:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:39:55,544:INFO:machine: AMD64 +2024-12-04 12:39:55,544:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:39:55,555:INFO:Memory: svmem(total=17007292416, available=3680997376, percent=78.4, used=13326295040, free=3680997376) +2024-12-04 12:39:55,555:INFO:Physical Core: 4 +2024-12-04 12:39:55,555:INFO:Logical Core: 8 +2024-12-04 12:39:55,555:INFO:Checking libraries +2024-12-04 12:39:55,555:INFO:System: +2024-12-04 12:39:55,555:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:39:55,555:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:39:55,555:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:39:55,555:INFO:PyCaret required dependencies: +2024-12-04 12:39:55,555:INFO: pip: 24.3.1 +2024-12-04 12:39:55,555:INFO: setuptools: 65.5.0 +2024-12-04 12:39:55,555:INFO: pycaret: 3.3.2 +2024-12-04 12:39:55,555:INFO: IPython: 8.12.3 +2024-12-04 12:39:55,556:INFO: ipywidgets: 8.1.5 +2024-12-04 12:39:55,556:INFO: tqdm: 4.67.1 +2024-12-04 12:39:55,556:INFO: numpy: 1.26.4 +2024-12-04 12:39:55,556:INFO: pandas: 2.1.4 +2024-12-04 12:39:55,556:INFO: jinja2: 3.1.4 +2024-12-04 12:39:55,556:INFO: scipy: 1.11.4 +2024-12-04 12:39:55,556:INFO: joblib: 1.3.2 +2024-12-04 12:39:55,556:INFO: sklearn: 1.4.2 +2024-12-04 12:39:55,556:INFO: pyod: 2.0.2 +2024-12-04 12:39:55,556:INFO: imblearn: 0.12.4 +2024-12-04 12:39:55,556:INFO: category_encoders: 2.6.4 +2024-12-04 12:39:55,556:INFO: lightgbm: 4.5.0 +2024-12-04 12:39:55,556:INFO: numba: 0.60.0 +2024-12-04 12:39:55,556:INFO: requests: 2.32.3 +2024-12-04 12:39:55,556:INFO: matplotlib: 3.7.5 +2024-12-04 12:39:55,556:INFO: scikitplot: 0.3.7 +2024-12-04 12:39:55,557:INFO: yellowbrick: 1.5 +2024-12-04 12:39:55,557:INFO: plotly: 5.24.1 +2024-12-04 12:39:55,557:INFO: plotly-resampler: Not installed +2024-12-04 12:39:55,557:INFO: kaleido: 0.2.1 +2024-12-04 12:39:55,557:INFO: schemdraw: 0.15 +2024-12-04 12:39:55,557:INFO: statsmodels: 0.14.4 +2024-12-04 12:39:55,557:INFO: sktime: 0.26.0 +2024-12-04 12:39:55,557:INFO: tbats: 1.1.3 +2024-12-04 12:39:55,557:INFO: pmdarima: 2.0.4 +2024-12-04 12:39:55,557:INFO: psutil: 6.1.0 +2024-12-04 12:39:55,557:INFO: markupsafe: 3.0.2 +2024-12-04 12:39:55,557:INFO: pickle5: Not installed +2024-12-04 12:39:55,557:INFO: cloudpickle: 3.1.0 +2024-12-04 12:39:55,557:INFO: deprecation: 2.1.0 +2024-12-04 12:39:55,557:INFO: xxhash: 3.5.0 +2024-12-04 12:39:55,557:INFO: wurlitzer: Not installed +2024-12-04 12:39:55,557:INFO:PyCaret optional dependencies: +2024-12-04 12:39:55,557:INFO: shap: Not installed +2024-12-04 12:39:55,558:INFO: interpret: Not installed +2024-12-04 12:39:55,558:INFO: umap: Not installed +2024-12-04 12:39:55,558:INFO: ydata_profiling: Not installed +2024-12-04 12:39:55,558:INFO: explainerdashboard: Not installed +2024-12-04 12:39:55,558:INFO: autoviz: Not installed +2024-12-04 12:39:55,558:INFO: fairlearn: Not installed +2024-12-04 12:39:55,558:INFO: deepchecks: Not installed +2024-12-04 12:39:55,558:INFO: xgboost: Not installed +2024-12-04 12:39:55,558:INFO: catboost: Not installed +2024-12-04 12:39:55,558:INFO: kmodes: Not installed +2024-12-04 12:39:55,558:INFO: mlxtend: Not installed +2024-12-04 12:39:55,558:INFO: statsforecast: Not installed +2024-12-04 12:39:55,558:INFO: tune_sklearn: Not installed +2024-12-04 12:39:55,558:INFO: ray: Not installed +2024-12-04 12:39:55,558:INFO: hyperopt: Not installed +2024-12-04 12:39:55,558:INFO: optuna: Not installed +2024-12-04 12:39:55,559:INFO: skopt: Not installed +2024-12-04 12:39:55,559:INFO: mlflow: 2.18.0 +2024-12-04 12:39:55,559:INFO: gradio: Not installed +2024-12-04 12:39:55,559:INFO: fastapi: Not installed +2024-12-04 12:39:55,559:INFO: uvicorn: 0.32.1 +2024-12-04 12:39:55,559:INFO: m2cgen: Not installed +2024-12-04 12:39:55,559:INFO: evidently: Not installed +2024-12-04 12:39:55,559:INFO: fugue: Not installed +2024-12-04 12:39:55,559:INFO: streamlit: 1.40.1 +2024-12-04 12:39:55,559:INFO: prophet: Not installed +2024-12-04 12:39:55,559:INFO:None +2024-12-04 12:39:55,559:INFO:Set up data. +2024-12-04 12:39:55,579:INFO:Set up folding strategy. +2024-12-04 12:39:55,579:INFO:Set up train/test split. +2024-12-04 12:39:55,593:INFO:Set up index. +2024-12-04 12:39:55,593:INFO:Assigning column types. +2024-12-04 12:39:55,604:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:39:55,604:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,612:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,625:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,692:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,742:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,742:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:55,742:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:55,742:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,755:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,762:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,827:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,870:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,870:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:55,870:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:55,870:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:39:55,874:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,878:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,933:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,973:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,975:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:55,975:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:55,979:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:39:55,984:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,038:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,079:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,079:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,080:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,080:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:39:56,088:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,143:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,185:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,185:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,185:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,193:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,243:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,292:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,292:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,292:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,292:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:39:56,359:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,405:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,406:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,406:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,475:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,523:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,524:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,524:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,524:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:39:56,596:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,641:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,642:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,707:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:39:56,755:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,755:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,755:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:39:56,870:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,870:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,976:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,976:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:56,978:INFO:Preparing preprocessing pipeline... +2024-12-04 12:39:56,978:INFO:Set up simple imputation. +2024-12-04 12:39:56,981:INFO:Set up encoding of categorical features. +2024-12-04 12:39:56,981:INFO:Set up column transformation. +2024-12-04 12:39:56,981:INFO:Set up feature normalization. +2024-12-04 12:39:57,098:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:39:57,109:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:39:57,111:INFO:Creating final display dataframe. +2024-12-04 12:39:57,414:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 15) +4 Transformed data shape (11376, 31) +5 Transformed train set shape (7963, 31) +6 Transformed test set shape (3413, 31) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 10 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI fce2 +2024-12-04 12:39:57,538:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:57,538:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:57,645:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:57,645:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:39:57,645:INFO:setup() successfully completed in 2.11s............... +2024-12-04 12:39:59,902:INFO:Initializing compare_models() +2024-12-04 12:39:59,902:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 12:39:59,902:INFO:Checking exceptions +2024-12-04 12:39:59,907:INFO:Preparing display monitor +2024-12-04 12:39:59,930:INFO:Initializing Linear Regression +2024-12-04 12:39:59,930:INFO:Total runtime is 1.9590059916178386e-06 minutes +2024-12-04 12:39:59,934:INFO:SubProcess create_model() called ================================== +2024-12-04 12:39:59,934:INFO:Initializing create_model() +2024-12-04 12:39:59,934:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:39:59,934:INFO:Checking exceptions +2024-12-04 12:39:59,935:INFO:Importing libraries +2024-12-04 12:39:59,935:INFO:Copying training dataset +2024-12-04 12:39:59,941:INFO:Defining folds +2024-12-04 12:39:59,941:INFO:Declaring metric variables +2024-12-04 12:39:59,945:INFO:Importing untrained model +2024-12-04 12:39:59,948:INFO:Linear Regression Imported successfully +2024-12-04 12:39:59,955:INFO:Starting cross validation +2024-12-04 12:39:59,957:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:06,251:INFO:Calculating mean and std +2024-12-04 12:40:06,251:INFO:Creating metrics dataframe +2024-12-04 12:40:06,253:INFO:Uploading results into container +2024-12-04 12:40:06,253:INFO:Uploading model into container now +2024-12-04 12:40:06,255:INFO:_master_model_container: 1 +2024-12-04 12:40:06,255:INFO:_display_container: 2 +2024-12-04 12:40:06,255:INFO:LinearRegression(n_jobs=-1) +2024-12-04 12:40:06,255:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:06,413:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:06,413:INFO:Creating metrics dataframe +2024-12-04 12:40:06,418:INFO:Initializing Lasso Regression +2024-12-04 12:40:06,418:INFO:Total runtime is 0.10814359982808432 minutes +2024-12-04 12:40:06,420:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:06,421:INFO:Initializing create_model() +2024-12-04 12:40:06,421:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:06,421:INFO:Checking exceptions +2024-12-04 12:40:06,421:INFO:Importing libraries +2024-12-04 12:40:06,421:INFO:Copying training dataset +2024-12-04 12:40:06,428:INFO:Defining folds +2024-12-04 12:40:06,428:INFO:Declaring metric variables +2024-12-04 12:40:06,432:INFO:Importing untrained model +2024-12-04 12:40:06,434:INFO:Lasso Regression Imported successfully +2024-12-04 12:40:06,442:INFO:Starting cross validation +2024-12-04 12:40:06,444:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:07,624:INFO:Calculating mean and std +2024-12-04 12:40:07,624:INFO:Creating metrics dataframe +2024-12-04 12:40:07,626:INFO:Uploading results into container +2024-12-04 12:40:07,627:INFO:Uploading model into container now +2024-12-04 12:40:07,627:INFO:_master_model_container: 2 +2024-12-04 12:40:07,627:INFO:_display_container: 2 +2024-12-04 12:40:07,628:INFO:Lasso(random_state=123) +2024-12-04 12:40:07,628:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:07,780:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:07,780:INFO:Creating metrics dataframe +2024-12-04 12:40:07,784:INFO:Initializing Ridge Regression +2024-12-04 12:40:07,784:INFO:Total runtime is 0.1309108297030131 minutes +2024-12-04 12:40:07,789:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:07,789:INFO:Initializing create_model() +2024-12-04 12:40:07,789:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:07,790:INFO:Checking exceptions +2024-12-04 12:40:07,790:INFO:Importing libraries +2024-12-04 12:40:07,790:INFO:Copying training dataset +2024-12-04 12:40:07,795:INFO:Defining folds +2024-12-04 12:40:07,795:INFO:Declaring metric variables +2024-12-04 12:40:07,795:INFO:Importing untrained model +2024-12-04 12:40:07,803:INFO:Ridge Regression Imported successfully +2024-12-04 12:40:07,810:INFO:Starting cross validation +2024-12-04 12:40:07,812:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:08,560:INFO:Calculating mean and std +2024-12-04 12:40:08,561:INFO:Creating metrics dataframe +2024-12-04 12:40:08,563:INFO:Uploading results into container +2024-12-04 12:40:08,563:INFO:Uploading model into container now +2024-12-04 12:40:08,563:INFO:_master_model_container: 3 +2024-12-04 12:40:08,563:INFO:_display_container: 2 +2024-12-04 12:40:08,563:INFO:Ridge(random_state=123) +2024-12-04 12:40:08,563:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:08,715:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:08,715:INFO:Creating metrics dataframe +2024-12-04 12:40:08,724:INFO:Initializing Elastic Net +2024-12-04 12:40:08,724:INFO:Total runtime is 0.1465652108192444 minutes +2024-12-04 12:40:08,728:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:08,728:INFO:Initializing create_model() +2024-12-04 12:40:08,728:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:08,728:INFO:Checking exceptions +2024-12-04 12:40:08,728:INFO:Importing libraries +2024-12-04 12:40:08,728:INFO:Copying training dataset +2024-12-04 12:40:08,735:INFO:Defining folds +2024-12-04 12:40:08,735:INFO:Declaring metric variables +2024-12-04 12:40:08,739:INFO:Importing untrained model +2024-12-04 12:40:08,743:INFO:Elastic Net Imported successfully +2024-12-04 12:40:08,749:INFO:Starting cross validation +2024-12-04 12:40:08,752:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:09,508:INFO:Calculating mean and std +2024-12-04 12:40:09,509:INFO:Creating metrics dataframe +2024-12-04 12:40:09,509:INFO:Uploading results into container +2024-12-04 12:40:09,509:INFO:Uploading model into container now +2024-12-04 12:40:09,509:INFO:_master_model_container: 4 +2024-12-04 12:40:09,511:INFO:_display_container: 2 +2024-12-04 12:40:09,512:INFO:ElasticNet(random_state=123) +2024-12-04 12:40:09,512:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:09,659:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:09,659:INFO:Creating metrics dataframe +2024-12-04 12:40:09,665:INFO:Initializing Least Angle Regression +2024-12-04 12:40:09,665:INFO:Total runtime is 0.1622587323188782 minutes +2024-12-04 12:40:09,669:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:09,669:INFO:Initializing create_model() +2024-12-04 12:40:09,669:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:09,669:INFO:Checking exceptions +2024-12-04 12:40:09,669:INFO:Importing libraries +2024-12-04 12:40:09,669:INFO:Copying training dataset +2024-12-04 12:40:09,678:INFO:Defining folds +2024-12-04 12:40:09,678:INFO:Declaring metric variables +2024-12-04 12:40:09,681:INFO:Importing untrained model +2024-12-04 12:40:09,684:INFO:Least Angle Regression Imported successfully +2024-12-04 12:40:09,691:INFO:Starting cross validation +2024-12-04 12:40:09,692:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:10,438:INFO:Calculating mean and std +2024-12-04 12:40:10,439:INFO:Creating metrics dataframe +2024-12-04 12:40:10,441:INFO:Uploading results into container +2024-12-04 12:40:10,441:INFO:Uploading model into container now +2024-12-04 12:40:10,442:INFO:_master_model_container: 5 +2024-12-04 12:40:10,442:INFO:_display_container: 2 +2024-12-04 12:40:10,442:INFO:Lars(random_state=123) +2024-12-04 12:40:10,442:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:10,589:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:10,593:INFO:Creating metrics dataframe +2024-12-04 12:40:10,597:INFO:Initializing Lasso Least Angle Regression +2024-12-04 12:40:10,597:INFO:Total runtime is 0.1777920206387838 minutes +2024-12-04 12:40:10,597:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:10,597:INFO:Initializing create_model() +2024-12-04 12:40:10,597:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:10,597:INFO:Checking exceptions +2024-12-04 12:40:10,597:INFO:Importing libraries +2024-12-04 12:40:10,597:INFO:Copying training dataset +2024-12-04 12:40:10,609:INFO:Defining folds +2024-12-04 12:40:10,609:INFO:Declaring metric variables +2024-12-04 12:40:10,613:INFO:Importing untrained model +2024-12-04 12:40:10,613:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 12:40:10,623:INFO:Starting cross validation +2024-12-04 12:40:10,625:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:11,387:INFO:Calculating mean and std +2024-12-04 12:40:11,387:INFO:Creating metrics dataframe +2024-12-04 12:40:11,389:INFO:Uploading results into container +2024-12-04 12:40:11,389:INFO:Uploading model into container now +2024-12-04 12:40:11,389:INFO:_master_model_container: 6 +2024-12-04 12:40:11,389:INFO:_display_container: 2 +2024-12-04 12:40:11,389:INFO:LassoLars(random_state=123) +2024-12-04 12:40:11,389:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:11,539:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:11,539:INFO:Creating metrics dataframe +2024-12-04 12:40:11,547:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 12:40:11,547:INFO:Total runtime is 0.19361712932586672 minutes +2024-12-04 12:40:11,552:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:11,552:INFO:Initializing create_model() +2024-12-04 12:40:11,553:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:11,553:INFO:Checking exceptions +2024-12-04 12:40:11,553:INFO:Importing libraries +2024-12-04 12:40:11,553:INFO:Copying training dataset +2024-12-04 12:40:11,563:INFO:Defining folds +2024-12-04 12:40:11,563:INFO:Declaring metric variables +2024-12-04 12:40:11,563:INFO:Importing untrained model +2024-12-04 12:40:11,568:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 12:40:11,578:INFO:Starting cross validation +2024-12-04 12:40:11,580:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:12,301:INFO:Calculating mean and std +2024-12-04 12:40:12,302:INFO:Creating metrics dataframe +2024-12-04 12:40:12,302:INFO:Uploading results into container +2024-12-04 12:40:12,304:INFO:Uploading model into container now +2024-12-04 12:40:12,304:INFO:_master_model_container: 7 +2024-12-04 12:40:12,305:INFO:_display_container: 2 +2024-12-04 12:40:12,305:INFO:OrthogonalMatchingPursuit() +2024-12-04 12:40:12,305:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:12,459:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:12,459:INFO:Creating metrics dataframe +2024-12-04 12:40:12,463:INFO:Initializing Bayesian Ridge +2024-12-04 12:40:12,463:INFO:Total runtime is 0.20888206958770755 minutes +2024-12-04 12:40:12,466:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:12,468:INFO:Initializing create_model() +2024-12-04 12:40:12,468:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:12,468:INFO:Checking exceptions +2024-12-04 12:40:12,468:INFO:Importing libraries +2024-12-04 12:40:12,468:INFO:Copying training dataset +2024-12-04 12:40:12,478:INFO:Defining folds +2024-12-04 12:40:12,478:INFO:Declaring metric variables +2024-12-04 12:40:12,480:INFO:Importing untrained model +2024-12-04 12:40:12,484:INFO:Bayesian Ridge Imported successfully +2024-12-04 12:40:12,491:INFO:Starting cross validation +2024-12-04 12:40:12,493:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:13,241:INFO:Calculating mean and std +2024-12-04 12:40:13,241:INFO:Creating metrics dataframe +2024-12-04 12:40:13,243:INFO:Uploading results into container +2024-12-04 12:40:13,243:INFO:Uploading model into container now +2024-12-04 12:40:13,243:INFO:_master_model_container: 8 +2024-12-04 12:40:13,243:INFO:_display_container: 2 +2024-12-04 12:40:13,243:INFO:BayesianRidge() +2024-12-04 12:40:13,243:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:13,392:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:13,392:INFO:Creating metrics dataframe +2024-12-04 12:40:13,403:INFO:Initializing Passive Aggressive Regressor +2024-12-04 12:40:13,403:INFO:Total runtime is 0.22454558213551842 minutes +2024-12-04 12:40:13,407:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:13,407:INFO:Initializing create_model() +2024-12-04 12:40:13,407:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:13,407:INFO:Checking exceptions +2024-12-04 12:40:13,407:INFO:Importing libraries +2024-12-04 12:40:13,407:INFO:Copying training dataset +2024-12-04 12:40:13,413:INFO:Defining folds +2024-12-04 12:40:13,413:INFO:Declaring metric variables +2024-12-04 12:40:13,418:INFO:Importing untrained model +2024-12-04 12:40:13,422:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 12:40:13,441:INFO:Starting cross validation +2024-12-04 12:40:13,444:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:14,211:INFO:Calculating mean and std +2024-12-04 12:40:14,211:INFO:Creating metrics dataframe +2024-12-04 12:40:14,213:INFO:Uploading results into container +2024-12-04 12:40:14,213:INFO:Uploading model into container now +2024-12-04 12:40:14,213:INFO:_master_model_container: 9 +2024-12-04 12:40:14,213:INFO:_display_container: 2 +2024-12-04 12:40:14,213:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 12:40:14,213:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:14,371:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:14,371:INFO:Creating metrics dataframe +2024-12-04 12:40:14,378:INFO:Initializing Huber Regressor +2024-12-04 12:40:14,378:INFO:Total runtime is 0.24079589446385705 minutes +2024-12-04 12:40:14,380:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:14,380:INFO:Initializing create_model() +2024-12-04 12:40:14,380:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:14,380:INFO:Checking exceptions +2024-12-04 12:40:14,383:INFO:Importing libraries +2024-12-04 12:40:14,383:INFO:Copying training dataset +2024-12-04 12:40:14,389:INFO:Defining folds +2024-12-04 12:40:14,389:INFO:Declaring metric variables +2024-12-04 12:40:14,394:INFO:Importing untrained model +2024-12-04 12:40:14,398:INFO:Huber Regressor Imported successfully +2024-12-04 12:40:14,403:INFO:Starting cross validation +2024-12-04 12:40:14,406:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:15,474:INFO:Calculating mean and std +2024-12-04 12:40:15,475:INFO:Creating metrics dataframe +2024-12-04 12:40:15,477:INFO:Uploading results into container +2024-12-04 12:40:15,477:INFO:Uploading model into container now +2024-12-04 12:40:15,477:INFO:_master_model_container: 10 +2024-12-04 12:40:15,477:INFO:_display_container: 2 +2024-12-04 12:40:15,477:INFO:HuberRegressor() +2024-12-04 12:40:15,477:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:15,677:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:15,677:INFO:Creating metrics dataframe +2024-12-04 12:40:15,686:INFO:Initializing K Neighbors Regressor +2024-12-04 12:40:15,686:INFO:Total runtime is 0.2626045942306519 minutes +2024-12-04 12:40:15,689:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:15,691:INFO:Initializing create_model() +2024-12-04 12:40:15,691:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:15,691:INFO:Checking exceptions +2024-12-04 12:40:15,691:INFO:Importing libraries +2024-12-04 12:40:15,691:INFO:Copying training dataset +2024-12-04 12:40:15,693:INFO:Defining folds +2024-12-04 12:40:15,693:INFO:Declaring metric variables +2024-12-04 12:40:15,703:INFO:Importing untrained model +2024-12-04 12:40:15,706:INFO:K Neighbors Regressor Imported successfully +2024-12-04 12:40:15,712:INFO:Starting cross validation +2024-12-04 12:40:15,713:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:16,631:INFO:Calculating mean and std +2024-12-04 12:40:16,631:INFO:Creating metrics dataframe +2024-12-04 12:40:16,631:INFO:Uploading results into container +2024-12-04 12:40:16,631:INFO:Uploading model into container now +2024-12-04 12:40:16,634:INFO:_master_model_container: 11 +2024-12-04 12:40:16,635:INFO:_display_container: 2 +2024-12-04 12:40:16,635:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 12:40:16,635:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:16,784:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:16,784:INFO:Creating metrics dataframe +2024-12-04 12:40:16,793:INFO:Initializing Decision Tree Regressor +2024-12-04 12:40:16,793:INFO:Total runtime is 0.28104562362035124 minutes +2024-12-04 12:40:16,796:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:16,796:INFO:Initializing create_model() +2024-12-04 12:40:16,796:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:16,796:INFO:Checking exceptions +2024-12-04 12:40:16,796:INFO:Importing libraries +2024-12-04 12:40:16,796:INFO:Copying training dataset +2024-12-04 12:40:16,805:INFO:Defining folds +2024-12-04 12:40:16,805:INFO:Declaring metric variables +2024-12-04 12:40:16,809:INFO:Importing untrained model +2024-12-04 12:40:16,812:INFO:Decision Tree Regressor Imported successfully +2024-12-04 12:40:16,815:INFO:Starting cross validation +2024-12-04 12:40:16,819:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:17,797:INFO:Calculating mean and std +2024-12-04 12:40:17,797:INFO:Creating metrics dataframe +2024-12-04 12:40:17,799:INFO:Uploading results into container +2024-12-04 12:40:17,799:INFO:Uploading model into container now +2024-12-04 12:40:17,799:INFO:_master_model_container: 12 +2024-12-04 12:40:17,799:INFO:_display_container: 2 +2024-12-04 12:40:17,799:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 12:40:17,799:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:17,953:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:17,953:INFO:Creating metrics dataframe +2024-12-04 12:40:17,959:INFO:Initializing Random Forest Regressor +2024-12-04 12:40:17,959:INFO:Total runtime is 0.3004837115605673 minutes +2024-12-04 12:40:17,963:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:17,963:INFO:Initializing create_model() +2024-12-04 12:40:17,963:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:17,963:INFO:Checking exceptions +2024-12-04 12:40:17,963:INFO:Importing libraries +2024-12-04 12:40:17,963:INFO:Copying training dataset +2024-12-04 12:40:17,970:INFO:Defining folds +2024-12-04 12:40:17,970:INFO:Declaring metric variables +2024-12-04 12:40:17,972:INFO:Importing untrained model +2024-12-04 12:40:17,977:INFO:Random Forest Regressor Imported successfully +2024-12-04 12:40:17,982:INFO:Starting cross validation +2024-12-04 12:40:17,984:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:30,728:INFO:Calculating mean and std +2024-12-04 12:40:30,728:INFO:Creating metrics dataframe +2024-12-04 12:40:30,732:INFO:Uploading results into container +2024-12-04 12:40:30,733:INFO:Uploading model into container now +2024-12-04 12:40:30,733:INFO:_master_model_container: 13 +2024-12-04 12:40:30,733:INFO:_display_container: 2 +2024-12-04 12:40:30,734:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:40:30,734:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:30,947:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:30,947:INFO:Creating metrics dataframe +2024-12-04 12:40:30,965:INFO:Initializing Extra Trees Regressor +2024-12-04 12:40:30,968:INFO:Total runtime is 0.5172608375549317 minutes +2024-12-04 12:40:30,974:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:30,975:INFO:Initializing create_model() +2024-12-04 12:40:30,975:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:30,976:INFO:Checking exceptions +2024-12-04 12:40:30,976:INFO:Importing libraries +2024-12-04 12:40:30,976:INFO:Copying training dataset +2024-12-04 12:40:31,000:INFO:Defining folds +2024-12-04 12:40:31,001:INFO:Declaring metric variables +2024-12-04 12:40:31,009:INFO:Importing untrained model +2024-12-04 12:40:31,013:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:40:31,025:INFO:Starting cross validation +2024-12-04 12:40:31,027:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:39,923:INFO:Calculating mean and std +2024-12-04 12:40:39,924:INFO:Creating metrics dataframe +2024-12-04 12:40:39,926:INFO:Uploading results into container +2024-12-04 12:40:39,926:INFO:Uploading model into container now +2024-12-04 12:40:39,927:INFO:_master_model_container: 14 +2024-12-04 12:40:39,927:INFO:_display_container: 2 +2024-12-04 12:40:39,927:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:40:39,927:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:40,114:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:40,115:INFO:Creating metrics dataframe +2024-12-04 12:40:40,122:INFO:Initializing AdaBoost Regressor +2024-12-04 12:40:40,122:INFO:Total runtime is 0.6698717832565309 minutes +2024-12-04 12:40:40,126:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:40,127:INFO:Initializing create_model() +2024-12-04 12:40:40,127:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:40,127:INFO:Checking exceptions +2024-12-04 12:40:40,127:INFO:Importing libraries +2024-12-04 12:40:40,127:INFO:Copying training dataset +2024-12-04 12:40:40,134:INFO:Defining folds +2024-12-04 12:40:40,135:INFO:Declaring metric variables +2024-12-04 12:40:40,139:INFO:Importing untrained model +2024-12-04 12:40:40,142:INFO:AdaBoost Regressor Imported successfully +2024-12-04 12:40:40,149:INFO:Starting cross validation +2024-12-04 12:40:40,152:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:42,666:INFO:Calculating mean and std +2024-12-04 12:40:42,667:INFO:Creating metrics dataframe +2024-12-04 12:40:42,668:INFO:Uploading results into container +2024-12-04 12:40:42,668:INFO:Uploading model into container now +2024-12-04 12:40:42,668:INFO:_master_model_container: 15 +2024-12-04 12:40:42,668:INFO:_display_container: 2 +2024-12-04 12:40:42,668:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 12:40:42,668:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:42,820:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:42,820:INFO:Creating metrics dataframe +2024-12-04 12:40:42,828:INFO:Initializing Gradient Boosting Regressor +2024-12-04 12:40:42,828:INFO:Total runtime is 0.7149684031804403 minutes +2024-12-04 12:40:42,831:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:42,831:INFO:Initializing create_model() +2024-12-04 12:40:42,831:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:42,832:INFO:Checking exceptions +2024-12-04 12:40:42,832:INFO:Importing libraries +2024-12-04 12:40:42,832:INFO:Copying training dataset +2024-12-04 12:40:42,840:INFO:Defining folds +2024-12-04 12:40:42,840:INFO:Declaring metric variables +2024-12-04 12:40:42,845:INFO:Importing untrained model +2024-12-04 12:40:42,848:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 12:40:42,854:INFO:Starting cross validation +2024-12-04 12:40:42,855:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:46,925:INFO:Calculating mean and std +2024-12-04 12:40:46,927:INFO:Creating metrics dataframe +2024-12-04 12:40:46,928:INFO:Uploading results into container +2024-12-04 12:40:46,929:INFO:Uploading model into container now +2024-12-04 12:40:46,929:INFO:_master_model_container: 16 +2024-12-04 12:40:46,929:INFO:_display_container: 2 +2024-12-04 12:40:46,929:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 12:40:46,929:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:47,088:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:47,088:INFO:Creating metrics dataframe +2024-12-04 12:40:47,096:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 12:40:47,096:INFO:Total runtime is 0.7861098011334737 minutes +2024-12-04 12:40:47,099:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:47,099:INFO:Initializing create_model() +2024-12-04 12:40:47,099:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:47,100:INFO:Checking exceptions +2024-12-04 12:40:47,100:INFO:Importing libraries +2024-12-04 12:40:47,100:INFO:Copying training dataset +2024-12-04 12:40:47,109:INFO:Defining folds +2024-12-04 12:40:47,109:INFO:Declaring metric variables +2024-12-04 12:40:47,112:INFO:Importing untrained model +2024-12-04 12:40:47,115:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 12:40:47,120:INFO:Starting cross validation +2024-12-04 12:40:47,122:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:49,026:INFO:Calculating mean and std +2024-12-04 12:40:49,027:INFO:Creating metrics dataframe +2024-12-04 12:40:49,028:INFO:Uploading results into container +2024-12-04 12:40:49,030:INFO:Uploading model into container now +2024-12-04 12:40:49,030:INFO:_master_model_container: 17 +2024-12-04 12:40:49,030:INFO:_display_container: 2 +2024-12-04 12:40:49,031:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:40:49,031:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:49,230:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:49,230:INFO:Creating metrics dataframe +2024-12-04 12:40:49,241:INFO:Initializing Dummy Regressor +2024-12-04 12:40:49,243:INFO:Total runtime is 0.8218787789344788 minutes +2024-12-04 12:40:49,246:INFO:SubProcess create_model() called ================================== +2024-12-04 12:40:49,246:INFO:Initializing create_model() +2024-12-04 12:40:49,246:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:49,246:INFO:Checking exceptions +2024-12-04 12:40:49,247:INFO:Importing libraries +2024-12-04 12:40:49,247:INFO:Copying training dataset +2024-12-04 12:40:49,255:INFO:Defining folds +2024-12-04 12:40:49,255:INFO:Declaring metric variables +2024-12-04 12:40:49,263:INFO:Importing untrained model +2024-12-04 12:40:49,268:INFO:Dummy Regressor Imported successfully +2024-12-04 12:40:49,283:INFO:Starting cross validation +2024-12-04 12:40:49,285:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:40:50,129:INFO:Calculating mean and std +2024-12-04 12:40:50,130:INFO:Creating metrics dataframe +2024-12-04 12:40:50,132:INFO:Uploading results into container +2024-12-04 12:40:50,132:INFO:Uploading model into container now +2024-12-04 12:40:50,132:INFO:_master_model_container: 18 +2024-12-04 12:40:50,132:INFO:_display_container: 2 +2024-12-04 12:40:50,133:INFO:DummyRegressor() +2024-12-04 12:40:50,133:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:50,283:INFO:SubProcess create_model() end ================================== +2024-12-04 12:40:50,283:INFO:Creating metrics dataframe +2024-12-04 12:40:50,292:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: + +Styler.applymap has been deprecated. Use Styler.map instead. + + +2024-12-04 12:40:50,299:INFO:Initializing create_model() +2024-12-04 12:40:50,300:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:40:50,300:INFO:Checking exceptions +2024-12-04 12:40:50,301:INFO:Importing libraries +2024-12-04 12:40:50,301:INFO:Copying training dataset +2024-12-04 12:40:50,308:INFO:Defining folds +2024-12-04 12:40:50,308:INFO:Declaring metric variables +2024-12-04 12:40:50,308:INFO:Importing untrained model +2024-12-04 12:40:50,308:INFO:Declaring custom model +2024-12-04 12:40:50,309:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:40:50,310:INFO:Cross validation set to False +2024-12-04 12:40:50,310:INFO:Fitting Model +2024-12-04 12:40:51,384:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:40:51,384:INFO:create_model() successfully completed...................................... +2024-12-04 12:40:51,572:INFO:_master_model_container: 18 +2024-12-04 12:40:51,572:INFO:_display_container: 2 +2024-12-04 12:40:51,574:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:40:51,574:INFO:compare_models() successfully completed...................................... +2024-12-04 12:42:01,803:INFO:Initializing plot_model() +2024-12-04 12:42:01,803:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), plot=feature, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 12:42:01,803:INFO:Checking exceptions +2024-12-04 12:42:01,836:INFO:Preloading libraries +2024-12-04 12:42:01,953:INFO:Copying training dataset +2024-12-04 12:42:01,953:INFO:Plot type: feature +2024-12-04 12:42:01,953:WARNING:No coef_ found. Trying feature_importances_ +2024-12-04 12:42:02,193:INFO:Visual Rendered Successfully +2024-12-04 12:42:02,350:INFO:plot_model() successfully completed...................................... +2024-12-04 12:42:04,971:INFO:Initializing predict_model() +2024-12-04 12:42:04,971:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000016604E97740>) +2024-12-04 12:42:04,974:INFO:Checking exceptions +2024-12-04 12:42:04,974:INFO:Preloading libraries +2024-12-04 12:42:04,975:INFO:Set up data. +2024-12-04 12:42:04,980:INFO:Set up index. +2024-12-04 12:42:05,059:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: + +'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + + +2024-12-04 12:49:42,232:INFO:PyCaret RegressionExperiment +2024-12-04 12:49:42,232:INFO:Logging name: reg-default-name +2024-12-04 12:49:42,232:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:49:42,232:INFO:version 3.3.2 +2024-12-04 12:49:42,232:INFO:Initializing setup() +2024-12-04 12:49:42,232:INFO:self.USI: c880 +2024-12-04 12:49:42,232:INFO:self._variable_keys: {'y_test', 'idx', 'X_train', 'html_param', 'X_test', 'gpu_param', 'transform_target_param', 'log_plots_param', 'target_param', 'exp_name_log', 'gpu_n_jobs_param', 'y', 'pipeline', 'fold_groups_param', 'exp_id', '_available_plots', 'memory', 'X', 'fold_shuffle_param', 'seed', 'USI', '_ml_usecase', 'fold_generator', 'logging_param', 'y_train', 'data', 'n_jobs_param'} +2024-12-04 12:49:42,232:INFO:Checking environment +2024-12-04 12:49:42,232:INFO:python_version: 3.11.9 +2024-12-04 12:49:42,232:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:49:42,232:INFO:machine: AMD64 +2024-12-04 12:49:42,232:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:49:42,238:INFO:Memory: svmem(total=17007292416, available=3285696512, percent=80.7, used=13721595904, free=3285696512) +2024-12-04 12:49:42,238:INFO:Physical Core: 4 +2024-12-04 12:49:42,238:INFO:Logical Core: 8 +2024-12-04 12:49:42,238:INFO:Checking libraries +2024-12-04 12:49:42,238:INFO:System: +2024-12-04 12:49:42,238:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:49:42,238:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:49:42,238:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:49:42,238:INFO:PyCaret required dependencies: +2024-12-04 12:49:42,238:INFO: pip: 24.3.1 +2024-12-04 12:49:42,238:INFO: setuptools: 65.5.0 +2024-12-04 12:49:42,239:INFO: pycaret: 3.3.2 +2024-12-04 12:49:42,239:INFO: IPython: 8.12.3 +2024-12-04 12:49:42,239:INFO: ipywidgets: 8.1.5 +2024-12-04 12:49:42,239:INFO: tqdm: 4.67.1 +2024-12-04 12:49:42,239:INFO: numpy: 1.26.4 +2024-12-04 12:49:42,239:INFO: pandas: 2.1.4 +2024-12-04 12:49:42,239:INFO: jinja2: 3.1.4 +2024-12-04 12:49:42,239:INFO: scipy: 1.11.4 +2024-12-04 12:49:42,239:INFO: joblib: 1.3.2 +2024-12-04 12:49:42,239:INFO: sklearn: 1.4.2 +2024-12-04 12:49:42,239:INFO: pyod: 2.0.2 +2024-12-04 12:49:42,239:INFO: imblearn: 0.12.4 +2024-12-04 12:49:42,239:INFO: category_encoders: 2.6.4 +2024-12-04 12:49:42,239:INFO: lightgbm: 4.5.0 +2024-12-04 12:49:42,239:INFO: numba: 0.60.0 +2024-12-04 12:49:42,239:INFO: requests: 2.32.3 +2024-12-04 12:49:42,239:INFO: matplotlib: 3.7.5 +2024-12-04 12:49:42,239:INFO: scikitplot: 0.3.7 +2024-12-04 12:49:42,239:INFO: yellowbrick: 1.5 +2024-12-04 12:49:42,239:INFO: plotly: 5.24.1 +2024-12-04 12:49:42,239:INFO: plotly-resampler: Not installed +2024-12-04 12:49:42,239:INFO: kaleido: 0.2.1 +2024-12-04 12:49:42,239:INFO: schemdraw: 0.15 +2024-12-04 12:49:42,239:INFO: statsmodels: 0.14.4 +2024-12-04 12:49:42,239:INFO: sktime: 0.26.0 +2024-12-04 12:49:42,240:INFO: tbats: 1.1.3 +2024-12-04 12:49:42,240:INFO: pmdarima: 2.0.4 +2024-12-04 12:49:42,240:INFO: psutil: 6.1.0 +2024-12-04 12:49:42,240:INFO: markupsafe: 3.0.2 +2024-12-04 12:49:42,240:INFO: pickle5: Not installed +2024-12-04 12:49:42,240:INFO: cloudpickle: 3.1.0 +2024-12-04 12:49:42,240:INFO: deprecation: 2.1.0 +2024-12-04 12:49:42,240:INFO: xxhash: 3.5.0 +2024-12-04 12:49:42,240:INFO: wurlitzer: Not installed +2024-12-04 12:49:42,240:INFO:PyCaret optional dependencies: +2024-12-04 12:49:42,241:INFO: shap: Not installed +2024-12-04 12:49:42,241:INFO: interpret: Not installed +2024-12-04 12:49:42,241:INFO: umap: Not installed +2024-12-04 12:49:42,241:INFO: ydata_profiling: Not installed +2024-12-04 12:49:42,241:INFO: explainerdashboard: Not installed +2024-12-04 12:49:42,241:INFO: autoviz: Not installed +2024-12-04 12:49:42,241:INFO: fairlearn: Not installed +2024-12-04 12:49:42,241:INFO: deepchecks: Not installed +2024-12-04 12:49:42,241:INFO: xgboost: Not installed +2024-12-04 12:49:42,241:INFO: catboost: Not installed +2024-12-04 12:49:42,241:INFO: kmodes: Not installed +2024-12-04 12:49:42,241:INFO: mlxtend: Not installed +2024-12-04 12:49:42,241:INFO: statsforecast: Not installed +2024-12-04 12:49:42,241:INFO: tune_sklearn: Not installed +2024-12-04 12:49:42,241:INFO: ray: Not installed +2024-12-04 12:49:42,241:INFO: hyperopt: Not installed +2024-12-04 12:49:42,241:INFO: optuna: Not installed +2024-12-04 12:49:42,241:INFO: skopt: Not installed +2024-12-04 12:49:42,241:INFO: mlflow: 2.18.0 +2024-12-04 12:49:42,241:INFO: gradio: Not installed +2024-12-04 12:49:42,241:INFO: fastapi: Not installed +2024-12-04 12:49:42,241:INFO: uvicorn: 0.32.1 +2024-12-04 12:49:42,241:INFO: m2cgen: Not installed +2024-12-04 12:49:42,241:INFO: evidently: Not installed +2024-12-04 12:49:42,241:INFO: fugue: Not installed +2024-12-04 12:49:42,241:INFO: streamlit: 1.40.1 +2024-12-04 12:49:42,241:INFO: prophet: Not installed +2024-12-04 12:49:42,241:INFO:None +2024-12-04 12:49:42,241:INFO:Set up data. +2024-12-04 12:49:42,251:INFO:Set up folding strategy. +2024-12-04 12:49:42,251:INFO:Set up train/test split. +2024-12-04 12:49:42,259:INFO:Set up index. +2024-12-04 12:49:42,259:INFO:Assigning column types. +2024-12-04 12:49:42,263:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:49:42,263:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,270:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,275:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,334:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,377:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,377:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,377:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,377:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,381:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,387:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,443:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,484:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,484:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,484:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,484:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:49:42,489:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,493:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,549:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,589:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,589:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,589:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,594:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,594:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,654:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,695:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,695:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,695:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,695:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:49:42,705:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,762:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,803:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,805:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,805:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,813:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,868:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,911:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:42,912:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,912:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:42,912:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:49:42,980:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:43,025:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:43,026:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,026:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,089:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:43,130:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:49:43,130:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,130:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,130:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:49:43,197:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:43,243:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,243:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,313:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:49:43,404:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,404:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,404:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:49:43,542:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,542:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,655:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,657:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:43,658:INFO:Preparing preprocessing pipeline... +2024-12-04 12:49:43,658:INFO:Set up simple imputation. +2024-12-04 12:49:43,659:INFO:Set up encoding of categorical features. +2024-12-04 12:49:43,659:INFO:Set up column transformation. +2024-12-04 12:49:43,659:INFO:Set up feature normalization. +2024-12-04 12:49:43,792:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:49:43,802:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:49:43,802:INFO:Creating final display dataframe. +2024-12-04 12:49:44,130:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 15) +4 Transformed data shape (11376, 31) +5 Transformed train set shape (7963, 31) +6 Transformed test set shape (3413, 31) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 10 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI c880 +2024-12-04 12:49:44,250:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:44,250:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:44,359:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:44,359:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:49:44,359:INFO:setup() successfully completed in 2.15s............... +2024-12-04 12:49:46,577:INFO:Initializing compare_models() +2024-12-04 12:49:46,577:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 12:49:46,577:INFO:Checking exceptions +2024-12-04 12:49:46,580:INFO:Preparing display monitor +2024-12-04 12:49:46,603:INFO:Initializing Linear Regression +2024-12-04 12:49:46,604:INFO:Total runtime is 1.6045570373535156e-05 minutes +2024-12-04 12:49:46,607:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:46,607:INFO:Initializing create_model() +2024-12-04 12:49:46,607:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:46,607:INFO:Checking exceptions +2024-12-04 12:49:46,607:INFO:Importing libraries +2024-12-04 12:49:46,607:INFO:Copying training dataset +2024-12-04 12:49:46,618:INFO:Defining folds +2024-12-04 12:49:46,618:INFO:Declaring metric variables +2024-12-04 12:49:46,622:INFO:Importing untrained model +2024-12-04 12:49:46,627:INFO:Linear Regression Imported successfully +2024-12-04 12:49:46,635:INFO:Starting cross validation +2024-12-04 12:49:46,638:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:49:52,353:INFO:Calculating mean and std +2024-12-04 12:49:52,353:INFO:Creating metrics dataframe +2024-12-04 12:49:52,355:INFO:Uploading results into container +2024-12-04 12:49:52,357:INFO:Uploading model into container now +2024-12-04 12:49:52,357:INFO:_master_model_container: 1 +2024-12-04 12:49:52,357:INFO:_display_container: 2 +2024-12-04 12:49:52,357:INFO:LinearRegression(n_jobs=-1) +2024-12-04 12:49:52,357:INFO:create_model() successfully completed...................................... +2024-12-04 12:49:52,586:INFO:SubProcess create_model() end ================================== +2024-12-04 12:49:52,589:INFO:Creating metrics dataframe +2024-12-04 12:49:52,592:INFO:Initializing Lasso Regression +2024-12-04 12:49:52,592:INFO:Total runtime is 0.09982286294301351 minutes +2024-12-04 12:49:52,595:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:52,595:INFO:Initializing create_model() +2024-12-04 12:49:52,595:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:52,595:INFO:Checking exceptions +2024-12-04 12:49:52,595:INFO:Importing libraries +2024-12-04 12:49:52,595:INFO:Copying training dataset +2024-12-04 12:49:52,605:INFO:Defining folds +2024-12-04 12:49:52,605:INFO:Declaring metric variables +2024-12-04 12:49:52,609:INFO:Importing untrained model +2024-12-04 12:49:52,613:INFO:Lasso Regression Imported successfully +2024-12-04 12:49:52,619:INFO:Starting cross validation +2024-12-04 12:49:52,621:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:49:53,782:INFO:Calculating mean and std +2024-12-04 12:49:53,782:INFO:Creating metrics dataframe +2024-12-04 12:49:53,785:INFO:Uploading results into container +2024-12-04 12:49:53,785:INFO:Uploading model into container now +2024-12-04 12:49:53,785:INFO:_master_model_container: 2 +2024-12-04 12:49:53,785:INFO:_display_container: 2 +2024-12-04 12:49:53,786:INFO:Lasso(random_state=123) +2024-12-04 12:49:53,786:INFO:create_model() successfully completed...................................... +2024-12-04 12:49:53,936:INFO:SubProcess create_model() end ================================== +2024-12-04 12:49:53,936:INFO:Creating metrics dataframe +2024-12-04 12:49:53,942:INFO:Initializing Ridge Regression +2024-12-04 12:49:53,942:INFO:Total runtime is 0.12232135931650798 minutes +2024-12-04 12:49:53,945:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:53,945:INFO:Initializing create_model() +2024-12-04 12:49:53,945:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:53,945:INFO:Checking exceptions +2024-12-04 12:49:53,945:INFO:Importing libraries +2024-12-04 12:49:53,945:INFO:Copying training dataset +2024-12-04 12:49:53,954:INFO:Defining folds +2024-12-04 12:49:53,954:INFO:Declaring metric variables +2024-12-04 12:49:53,957:INFO:Importing untrained model +2024-12-04 12:49:53,959:INFO:Ridge Regression Imported successfully +2024-12-04 12:49:53,962:INFO:Starting cross validation +2024-12-04 12:49:53,966:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:49:54,735:INFO:Calculating mean and std +2024-12-04 12:49:54,736:INFO:Creating metrics dataframe +2024-12-04 12:49:54,738:INFO:Uploading results into container +2024-12-04 12:49:54,739:INFO:Uploading model into container now +2024-12-04 12:49:54,739:INFO:_master_model_container: 3 +2024-12-04 12:49:54,739:INFO:_display_container: 2 +2024-12-04 12:49:54,739:INFO:Ridge(random_state=123) +2024-12-04 12:49:54,739:INFO:create_model() successfully completed...................................... +2024-12-04 12:49:54,898:INFO:SubProcess create_model() end ================================== +2024-12-04 12:49:54,898:INFO:Creating metrics dataframe +2024-12-04 12:49:54,904:INFO:Initializing Elastic Net +2024-12-04 12:49:54,905:INFO:Total runtime is 0.1383685270945231 minutes +2024-12-04 12:49:54,907:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:54,907:INFO:Initializing create_model() +2024-12-04 12:49:54,907:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:54,907:INFO:Checking exceptions +2024-12-04 12:49:54,907:INFO:Importing libraries +2024-12-04 12:49:54,907:INFO:Copying training dataset +2024-12-04 12:49:54,913:INFO:Defining folds +2024-12-04 12:49:54,913:INFO:Declaring metric variables +2024-12-04 12:49:54,919:INFO:Importing untrained model +2024-12-04 12:49:54,923:INFO:Elastic Net Imported successfully +2024-12-04 12:49:54,929:INFO:Starting cross validation +2024-12-04 12:49:54,931:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:49:55,768:INFO:Calculating mean and std +2024-12-04 12:49:55,770:INFO:Creating metrics dataframe +2024-12-04 12:49:55,770:INFO:Uploading results into container +2024-12-04 12:49:55,770:INFO:Uploading model into container now +2024-12-04 12:49:55,773:INFO:_master_model_container: 4 +2024-12-04 12:49:55,773:INFO:_display_container: 2 +2024-12-04 12:49:55,773:INFO:ElasticNet(random_state=123) +2024-12-04 12:49:55,773:INFO:create_model() successfully completed...................................... +2024-12-04 12:49:55,971:INFO:SubProcess create_model() end ================================== +2024-12-04 12:49:55,971:INFO:Creating metrics dataframe +2024-12-04 12:49:55,978:INFO:Initializing Least Angle Regression +2024-12-04 12:49:55,980:INFO:Total runtime is 0.15628366072972616 minutes +2024-12-04 12:49:55,984:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:55,984:INFO:Initializing create_model() +2024-12-04 12:49:55,984:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:55,984:INFO:Checking exceptions +2024-12-04 12:49:55,984:INFO:Importing libraries +2024-12-04 12:49:55,984:INFO:Copying training dataset +2024-12-04 12:49:55,994:INFO:Defining folds +2024-12-04 12:49:55,994:INFO:Declaring metric variables +2024-12-04 12:49:55,998:INFO:Importing untrained model +2024-12-04 12:49:56,004:INFO:Least Angle Regression Imported successfully +2024-12-04 12:49:56,011:INFO:Starting cross validation +2024-12-04 12:49:56,013:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:49:56,922:INFO:Calculating mean and std +2024-12-04 12:49:56,924:INFO:Creating metrics dataframe +2024-12-04 12:49:56,925:INFO:Uploading results into container +2024-12-04 12:49:56,926:INFO:Uploading model into container now +2024-12-04 12:49:56,926:INFO:_master_model_container: 5 +2024-12-04 12:49:56,926:INFO:_display_container: 2 +2024-12-04 12:49:56,926:INFO:Lars(random_state=123) +2024-12-04 12:49:56,927:INFO:create_model() successfully completed...................................... +2024-12-04 12:49:57,101:INFO:SubProcess create_model() end ================================== +2024-12-04 12:49:57,102:INFO:Creating metrics dataframe +2024-12-04 12:49:57,109:INFO:Initializing Lasso Least Angle Regression +2024-12-04 12:49:57,109:INFO:Total runtime is 0.17509840726852416 minutes +2024-12-04 12:49:57,112:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:57,112:INFO:Initializing create_model() +2024-12-04 12:49:57,112:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:57,112:INFO:Checking exceptions +2024-12-04 12:49:57,112:INFO:Importing libraries +2024-12-04 12:49:57,112:INFO:Copying training dataset +2024-12-04 12:49:57,122:INFO:Defining folds +2024-12-04 12:49:57,122:INFO:Declaring metric variables +2024-12-04 12:49:57,126:INFO:Importing untrained model +2024-12-04 12:49:57,131:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 12:49:57,137:INFO:Starting cross validation +2024-12-04 12:49:57,139:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:49:58,082:INFO:Calculating mean and std +2024-12-04 12:49:58,082:INFO:Creating metrics dataframe +2024-12-04 12:49:58,085:INFO:Uploading results into container +2024-12-04 12:49:58,085:INFO:Uploading model into container now +2024-12-04 12:49:58,085:INFO:_master_model_container: 6 +2024-12-04 12:49:58,085:INFO:_display_container: 2 +2024-12-04 12:49:58,086:INFO:LassoLars(random_state=123) +2024-12-04 12:49:58,086:INFO:create_model() successfully completed...................................... +2024-12-04 12:49:58,263:INFO:SubProcess create_model() end ================================== +2024-12-04 12:49:58,263:INFO:Creating metrics dataframe +2024-12-04 12:49:58,271:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 12:49:58,271:INFO:Total runtime is 0.1944669206937154 minutes +2024-12-04 12:49:58,273:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:58,273:INFO:Initializing create_model() +2024-12-04 12:49:58,273:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:58,273:INFO:Checking exceptions +2024-12-04 12:49:58,273:INFO:Importing libraries +2024-12-04 12:49:58,273:INFO:Copying training dataset +2024-12-04 12:49:58,280:INFO:Defining folds +2024-12-04 12:49:58,280:INFO:Declaring metric variables +2024-12-04 12:49:58,284:INFO:Importing untrained model +2024-12-04 12:49:58,306:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 12:49:58,317:INFO:Starting cross validation +2024-12-04 12:49:58,319:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:49:59,178:INFO:Calculating mean and std +2024-12-04 12:49:59,178:INFO:Creating metrics dataframe +2024-12-04 12:49:59,180:INFO:Uploading results into container +2024-12-04 12:49:59,181:INFO:Uploading model into container now +2024-12-04 12:49:59,181:INFO:_master_model_container: 7 +2024-12-04 12:49:59,181:INFO:_display_container: 2 +2024-12-04 12:49:59,181:INFO:OrthogonalMatchingPursuit() +2024-12-04 12:49:59,181:INFO:create_model() successfully completed...................................... +2024-12-04 12:49:59,342:INFO:SubProcess create_model() end ================================== +2024-12-04 12:49:59,342:INFO:Creating metrics dataframe +2024-12-04 12:49:59,352:INFO:Initializing Bayesian Ridge +2024-12-04 12:49:59,352:INFO:Total runtime is 0.21248822212219237 minutes +2024-12-04 12:49:59,355:INFO:SubProcess create_model() called ================================== +2024-12-04 12:49:59,357:INFO:Initializing create_model() +2024-12-04 12:49:59,357:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:49:59,357:INFO:Checking exceptions +2024-12-04 12:49:59,357:INFO:Importing libraries +2024-12-04 12:49:59,357:INFO:Copying training dataset +2024-12-04 12:49:59,363:INFO:Defining folds +2024-12-04 12:49:59,365:INFO:Declaring metric variables +2024-12-04 12:49:59,368:INFO:Importing untrained model +2024-12-04 12:49:59,372:INFO:Bayesian Ridge Imported successfully +2024-12-04 12:49:59,377:INFO:Starting cross validation +2024-12-04 12:49:59,379:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:00,212:INFO:Calculating mean and std +2024-12-04 12:50:00,213:INFO:Creating metrics dataframe +2024-12-04 12:50:00,214:INFO:Uploading results into container +2024-12-04 12:50:00,215:INFO:Uploading model into container now +2024-12-04 12:50:00,215:INFO:_master_model_container: 8 +2024-12-04 12:50:00,215:INFO:_display_container: 2 +2024-12-04 12:50:00,215:INFO:BayesianRidge() +2024-12-04 12:50:00,215:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:00,371:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:00,371:INFO:Creating metrics dataframe +2024-12-04 12:50:00,377:INFO:Initializing Passive Aggressive Regressor +2024-12-04 12:50:00,380:INFO:Total runtime is 0.22961333195368447 minutes +2024-12-04 12:50:00,381:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:00,381:INFO:Initializing create_model() +2024-12-04 12:50:00,381:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:00,381:INFO:Checking exceptions +2024-12-04 12:50:00,381:INFO:Importing libraries +2024-12-04 12:50:00,381:INFO:Copying training dataset +2024-12-04 12:50:00,390:INFO:Defining folds +2024-12-04 12:50:00,390:INFO:Declaring metric variables +2024-12-04 12:50:00,394:INFO:Importing untrained model +2024-12-04 12:50:00,394:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 12:50:00,404:INFO:Starting cross validation +2024-12-04 12:50:00,405:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:01,213:INFO:Calculating mean and std +2024-12-04 12:50:01,213:INFO:Creating metrics dataframe +2024-12-04 12:50:01,216:INFO:Uploading results into container +2024-12-04 12:50:01,218:INFO:Uploading model into container now +2024-12-04 12:50:01,219:INFO:_master_model_container: 9 +2024-12-04 12:50:01,219:INFO:_display_container: 2 +2024-12-04 12:50:01,219:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 12:50:01,219:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:01,377:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:01,377:INFO:Creating metrics dataframe +2024-12-04 12:50:01,385:INFO:Initializing Huber Regressor +2024-12-04 12:50:01,385:INFO:Total runtime is 0.2463570157686869 minutes +2024-12-04 12:50:01,386:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:01,386:INFO:Initializing create_model() +2024-12-04 12:50:01,386:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:01,389:INFO:Checking exceptions +2024-12-04 12:50:01,389:INFO:Importing libraries +2024-12-04 12:50:01,389:INFO:Copying training dataset +2024-12-04 12:50:01,393:INFO:Defining folds +2024-12-04 12:50:01,396:INFO:Declaring metric variables +2024-12-04 12:50:01,396:INFO:Importing untrained model +2024-12-04 12:50:01,403:INFO:Huber Regressor Imported successfully +2024-12-04 12:50:01,407:INFO:Starting cross validation +2024-12-04 12:50:01,409:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:02,558:INFO:Calculating mean and std +2024-12-04 12:50:02,560:INFO:Creating metrics dataframe +2024-12-04 12:50:02,563:INFO:Uploading results into container +2024-12-04 12:50:02,563:INFO:Uploading model into container now +2024-12-04 12:50:02,563:INFO:_master_model_container: 10 +2024-12-04 12:50:02,563:INFO:_display_container: 2 +2024-12-04 12:50:02,563:INFO:HuberRegressor() +2024-12-04 12:50:02,563:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:02,763:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:02,763:INFO:Creating metrics dataframe +2024-12-04 12:50:02,775:INFO:Initializing K Neighbors Regressor +2024-12-04 12:50:02,775:INFO:Total runtime is 0.2695314049720764 minutes +2024-12-04 12:50:02,778:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:02,778:INFO:Initializing create_model() +2024-12-04 12:50:02,778:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:02,778:INFO:Checking exceptions +2024-12-04 12:50:02,778:INFO:Importing libraries +2024-12-04 12:50:02,778:INFO:Copying training dataset +2024-12-04 12:50:02,787:INFO:Defining folds +2024-12-04 12:50:02,787:INFO:Declaring metric variables +2024-12-04 12:50:02,791:INFO:Importing untrained model +2024-12-04 12:50:02,793:INFO:K Neighbors Regressor Imported successfully +2024-12-04 12:50:02,805:INFO:Starting cross validation +2024-12-04 12:50:02,807:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:04,155:INFO:Calculating mean and std +2024-12-04 12:50:04,156:INFO:Creating metrics dataframe +2024-12-04 12:50:04,157:INFO:Uploading results into container +2024-12-04 12:50:04,158:INFO:Uploading model into container now +2024-12-04 12:50:04,159:INFO:_master_model_container: 11 +2024-12-04 12:50:04,159:INFO:_display_container: 2 +2024-12-04 12:50:04,159:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 12:50:04,160:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:04,337:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:04,337:INFO:Creating metrics dataframe +2024-12-04 12:50:04,345:INFO:Initializing Decision Tree Regressor +2024-12-04 12:50:04,345:INFO:Total runtime is 0.29569840828577676 minutes +2024-12-04 12:50:04,347:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:04,347:INFO:Initializing create_model() +2024-12-04 12:50:04,347:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:04,347:INFO:Checking exceptions +2024-12-04 12:50:04,347:INFO:Importing libraries +2024-12-04 12:50:04,347:INFO:Copying training dataset +2024-12-04 12:50:04,360:INFO:Defining folds +2024-12-04 12:50:04,360:INFO:Declaring metric variables +2024-12-04 12:50:04,363:INFO:Importing untrained model +2024-12-04 12:50:04,366:INFO:Decision Tree Regressor Imported successfully +2024-12-04 12:50:04,374:INFO:Starting cross validation +2024-12-04 12:50:04,377:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:05,585:INFO:Calculating mean and std +2024-12-04 12:50:05,586:INFO:Creating metrics dataframe +2024-12-04 12:50:05,589:INFO:Uploading results into container +2024-12-04 12:50:05,590:INFO:Uploading model into container now +2024-12-04 12:50:05,590:INFO:_master_model_container: 12 +2024-12-04 12:50:05,591:INFO:_display_container: 2 +2024-12-04 12:50:05,591:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 12:50:05,591:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:05,769:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:05,770:INFO:Creating metrics dataframe +2024-12-04 12:50:05,778:INFO:Initializing Random Forest Regressor +2024-12-04 12:50:05,779:INFO:Total runtime is 0.31960502862930296 minutes +2024-12-04 12:50:05,782:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:05,782:INFO:Initializing create_model() +2024-12-04 12:50:05,782:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:05,782:INFO:Checking exceptions +2024-12-04 12:50:05,782:INFO:Importing libraries +2024-12-04 12:50:05,782:INFO:Copying training dataset +2024-12-04 12:50:05,795:INFO:Defining folds +2024-12-04 12:50:05,795:INFO:Declaring metric variables +2024-12-04 12:50:05,799:INFO:Importing untrained model +2024-12-04 12:50:05,801:INFO:Random Forest Regressor Imported successfully +2024-12-04 12:50:05,809:INFO:Starting cross validation +2024-12-04 12:50:05,812:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:19,522:INFO:Calculating mean and std +2024-12-04 12:50:19,524:INFO:Creating metrics dataframe +2024-12-04 12:50:19,528:INFO:Uploading results into container +2024-12-04 12:50:19,529:INFO:Uploading model into container now +2024-12-04 12:50:19,529:INFO:_master_model_container: 13 +2024-12-04 12:50:19,529:INFO:_display_container: 2 +2024-12-04 12:50:19,530:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:50:19,530:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:19,788:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:19,788:INFO:Creating metrics dataframe +2024-12-04 12:50:19,799:INFO:Initializing Extra Trees Regressor +2024-12-04 12:50:19,800:INFO:Total runtime is 0.553283405303955 minutes +2024-12-04 12:50:19,805:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:19,805:INFO:Initializing create_model() +2024-12-04 12:50:19,805:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:19,805:INFO:Checking exceptions +2024-12-04 12:50:19,805:INFO:Importing libraries +2024-12-04 12:50:19,805:INFO:Copying training dataset +2024-12-04 12:50:19,817:INFO:Defining folds +2024-12-04 12:50:19,818:INFO:Declaring metric variables +2024-12-04 12:50:19,825:INFO:Importing untrained model +2024-12-04 12:50:19,829:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:50:19,838:INFO:Starting cross validation +2024-12-04 12:50:19,843:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:28,203:INFO:Calculating mean and std +2024-12-04 12:50:28,204:INFO:Creating metrics dataframe +2024-12-04 12:50:28,207:INFO:Uploading results into container +2024-12-04 12:50:28,207:INFO:Uploading model into container now +2024-12-04 12:50:28,207:INFO:_master_model_container: 14 +2024-12-04 12:50:28,209:INFO:_display_container: 2 +2024-12-04 12:50:28,209:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:50:28,209:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:28,403:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:28,403:INFO:Creating metrics dataframe +2024-12-04 12:50:28,412:INFO:Initializing AdaBoost Regressor +2024-12-04 12:50:28,413:INFO:Total runtime is 0.6968290011088053 minutes +2024-12-04 12:50:28,413:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:28,413:INFO:Initializing create_model() +2024-12-04 12:50:28,413:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:28,413:INFO:Checking exceptions +2024-12-04 12:50:28,413:INFO:Importing libraries +2024-12-04 12:50:28,413:INFO:Copying training dataset +2024-12-04 12:50:28,426:INFO:Defining folds +2024-12-04 12:50:28,426:INFO:Declaring metric variables +2024-12-04 12:50:28,433:INFO:Importing untrained model +2024-12-04 12:50:28,450:INFO:AdaBoost Regressor Imported successfully +2024-12-04 12:50:28,464:INFO:Starting cross validation +2024-12-04 12:50:28,464:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:31,002:INFO:Calculating mean and std +2024-12-04 12:50:31,003:INFO:Creating metrics dataframe +2024-12-04 12:50:31,005:INFO:Uploading results into container +2024-12-04 12:50:31,005:INFO:Uploading model into container now +2024-12-04 12:50:31,005:INFO:_master_model_container: 15 +2024-12-04 12:50:31,005:INFO:_display_container: 2 +2024-12-04 12:50:31,005:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 12:50:31,007:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:31,162:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:31,163:INFO:Creating metrics dataframe +2024-12-04 12:50:31,171:INFO:Initializing Gradient Boosting Regressor +2024-12-04 12:50:31,171:INFO:Total runtime is 0.7428022702534993 minutes +2024-12-04 12:50:31,173:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:31,175:INFO:Initializing create_model() +2024-12-04 12:50:31,175:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:31,175:INFO:Checking exceptions +2024-12-04 12:50:31,175:INFO:Importing libraries +2024-12-04 12:50:31,175:INFO:Copying training dataset +2024-12-04 12:50:31,182:INFO:Defining folds +2024-12-04 12:50:31,182:INFO:Declaring metric variables +2024-12-04 12:50:31,184:INFO:Importing untrained model +2024-12-04 12:50:31,189:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 12:50:31,196:INFO:Starting cross validation +2024-12-04 12:50:31,196:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:35,304:INFO:Calculating mean and std +2024-12-04 12:50:35,305:INFO:Creating metrics dataframe +2024-12-04 12:50:35,306:INFO:Uploading results into container +2024-12-04 12:50:35,307:INFO:Uploading model into container now +2024-12-04 12:50:35,307:INFO:_master_model_container: 16 +2024-12-04 12:50:35,307:INFO:_display_container: 2 +2024-12-04 12:50:35,307:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 12:50:35,307:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:35,467:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:35,467:INFO:Creating metrics dataframe +2024-12-04 12:50:35,476:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 12:50:35,476:INFO:Total runtime is 0.8145492156346639 minutes +2024-12-04 12:50:35,478:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:35,478:INFO:Initializing create_model() +2024-12-04 12:50:35,478:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:35,478:INFO:Checking exceptions +2024-12-04 12:50:35,479:INFO:Importing libraries +2024-12-04 12:50:35,479:INFO:Copying training dataset +2024-12-04 12:50:35,487:INFO:Defining folds +2024-12-04 12:50:35,487:INFO:Declaring metric variables +2024-12-04 12:50:35,491:INFO:Importing untrained model +2024-12-04 12:50:35,493:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 12:50:35,499:INFO:Starting cross validation +2024-12-04 12:50:35,501:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:37,353:INFO:Calculating mean and std +2024-12-04 12:50:37,354:INFO:Creating metrics dataframe +2024-12-04 12:50:37,357:INFO:Uploading results into container +2024-12-04 12:50:37,358:INFO:Uploading model into container now +2024-12-04 12:50:37,359:INFO:_master_model_container: 17 +2024-12-04 12:50:37,359:INFO:_display_container: 2 +2024-12-04 12:50:37,359:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:50:37,359:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:37,561:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:37,561:INFO:Creating metrics dataframe +2024-12-04 12:50:37,571:INFO:Initializing Dummy Regressor +2024-12-04 12:50:37,571:INFO:Total runtime is 0.8494585673014322 minutes +2024-12-04 12:50:37,575:INFO:SubProcess create_model() called ================================== +2024-12-04 12:50:37,575:INFO:Initializing create_model() +2024-12-04 12:50:37,575:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:37,575:INFO:Checking exceptions +2024-12-04 12:50:37,576:INFO:Importing libraries +2024-12-04 12:50:37,576:INFO:Copying training dataset +2024-12-04 12:50:37,583:INFO:Defining folds +2024-12-04 12:50:37,583:INFO:Declaring metric variables +2024-12-04 12:50:37,589:INFO:Importing untrained model +2024-12-04 12:50:37,593:INFO:Dummy Regressor Imported successfully +2024-12-04 12:50:37,593:INFO:Starting cross validation +2024-12-04 12:50:37,600:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 12:50:38,444:INFO:Calculating mean and std +2024-12-04 12:50:38,444:INFO:Creating metrics dataframe +2024-12-04 12:50:38,444:INFO:Uploading results into container +2024-12-04 12:50:38,444:INFO:Uploading model into container now +2024-12-04 12:50:38,444:INFO:_master_model_container: 18 +2024-12-04 12:50:38,444:INFO:_display_container: 2 +2024-12-04 12:50:38,444:INFO:DummyRegressor() +2024-12-04 12:50:38,444:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:38,603:INFO:SubProcess create_model() end ================================== +2024-12-04 12:50:38,603:INFO:Creating metrics dataframe +2024-12-04 12:50:38,613:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: + +Styler.applymap has been deprecated. Use Styler.map instead. + + +2024-12-04 12:50:38,623:INFO:Initializing create_model() +2024-12-04 12:50:38,623:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 12:50:38,623:INFO:Checking exceptions +2024-12-04 12:50:38,623:INFO:Importing libraries +2024-12-04 12:50:38,625:INFO:Copying training dataset +2024-12-04 12:50:38,630:INFO:Defining folds +2024-12-04 12:50:38,630:INFO:Declaring metric variables +2024-12-04 12:50:38,630:INFO:Importing untrained model +2024-12-04 12:50:38,630:INFO:Declaring custom model +2024-12-04 12:50:38,634:INFO:Extra Trees Regressor Imported successfully +2024-12-04 12:50:38,635:INFO:Cross validation set to False +2024-12-04 12:50:38,635:INFO:Fitting Model +2024-12-04 12:50:39,732:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:50:39,732:INFO:create_model() successfully completed...................................... +2024-12-04 12:50:39,912:INFO:_master_model_container: 18 +2024-12-04 12:50:39,912:INFO:_display_container: 2 +2024-12-04 12:50:39,913:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 12:50:39,913:INFO:compare_models() successfully completed...................................... +2024-12-04 12:52:22,763:INFO:PyCaret RegressionExperiment +2024-12-04 12:52:22,763:INFO:Logging name: reg-default-name +2024-12-04 12:52:22,763:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 12:52:22,763:INFO:version 3.3.2 +2024-12-04 12:52:22,763:INFO:Initializing setup() +2024-12-04 12:52:22,763:INFO:self.USI: fc44 +2024-12-04 12:52:22,763:INFO:self._variable_keys: {'y_test', 'idx', 'X_train', 'html_param', 'X_test', 'gpu_param', 'transform_target_param', 'log_plots_param', 'target_param', 'exp_name_log', 'gpu_n_jobs_param', 'y', 'pipeline', 'fold_groups_param', 'exp_id', '_available_plots', 'memory', 'X', 'fold_shuffle_param', 'seed', 'USI', '_ml_usecase', 'fold_generator', 'logging_param', 'y_train', 'data', 'n_jobs_param'} +2024-12-04 12:52:22,763:INFO:Checking environment +2024-12-04 12:52:22,763:INFO:python_version: 3.11.9 +2024-12-04 12:52:22,763:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 12:52:22,763:INFO:machine: AMD64 +2024-12-04 12:52:22,763:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 12:52:22,771:INFO:Memory: svmem(total=17007292416, available=2893795328, percent=83.0, used=14113497088, free=2893795328) +2024-12-04 12:52:22,772:INFO:Physical Core: 4 +2024-12-04 12:52:22,772:INFO:Logical Core: 8 +2024-12-04 12:52:22,772:INFO:Checking libraries +2024-12-04 12:52:22,772:INFO:System: +2024-12-04 12:52:22,772:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 12:52:22,772:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 12:52:22,772:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 12:52:22,772:INFO:PyCaret required dependencies: +2024-12-04 12:52:22,772:INFO: pip: 24.3.1 +2024-12-04 12:52:22,772:INFO: setuptools: 65.5.0 +2024-12-04 12:52:22,772:INFO: pycaret: 3.3.2 +2024-12-04 12:52:22,772:INFO: IPython: 8.12.3 +2024-12-04 12:52:22,772:INFO: ipywidgets: 8.1.5 +2024-12-04 12:52:22,772:INFO: tqdm: 4.67.1 +2024-12-04 12:52:22,772:INFO: numpy: 1.26.4 +2024-12-04 12:52:22,772:INFO: pandas: 2.1.4 +2024-12-04 12:52:22,772:INFO: jinja2: 3.1.4 +2024-12-04 12:52:22,772:INFO: scipy: 1.11.4 +2024-12-04 12:52:22,772:INFO: joblib: 1.3.2 +2024-12-04 12:52:22,772:INFO: sklearn: 1.4.2 +2024-12-04 12:52:22,772:INFO: pyod: 2.0.2 +2024-12-04 12:52:22,772:INFO: imblearn: 0.12.4 +2024-12-04 12:52:22,772:INFO: category_encoders: 2.6.4 +2024-12-04 12:52:22,772:INFO: lightgbm: 4.5.0 +2024-12-04 12:52:22,773:INFO: numba: 0.60.0 +2024-12-04 12:52:22,773:INFO: requests: 2.32.3 +2024-12-04 12:52:22,773:INFO: matplotlib: 3.7.5 +2024-12-04 12:52:22,773:INFO: scikitplot: 0.3.7 +2024-12-04 12:52:22,773:INFO: yellowbrick: 1.5 +2024-12-04 12:52:22,773:INFO: plotly: 5.24.1 +2024-12-04 12:52:22,773:INFO: plotly-resampler: Not installed +2024-12-04 12:52:22,773:INFO: kaleido: 0.2.1 +2024-12-04 12:52:22,773:INFO: schemdraw: 0.15 +2024-12-04 12:52:22,773:INFO: statsmodels: 0.14.4 +2024-12-04 12:52:22,773:INFO: sktime: 0.26.0 +2024-12-04 12:52:22,773:INFO: tbats: 1.1.3 +2024-12-04 12:52:22,773:INFO: pmdarima: 2.0.4 +2024-12-04 12:52:22,773:INFO: psutil: 6.1.0 +2024-12-04 12:52:22,774:INFO: markupsafe: 3.0.2 +2024-12-04 12:52:22,774:INFO: pickle5: Not installed +2024-12-04 12:52:22,774:INFO: cloudpickle: 3.1.0 +2024-12-04 12:52:22,774:INFO: deprecation: 2.1.0 +2024-12-04 12:52:22,774:INFO: xxhash: 3.5.0 +2024-12-04 12:52:22,774:INFO: wurlitzer: Not installed +2024-12-04 12:52:22,774:INFO:PyCaret optional dependencies: +2024-12-04 12:52:22,774:INFO: shap: Not installed +2024-12-04 12:52:22,774:INFO: interpret: Not installed +2024-12-04 12:52:22,774:INFO: umap: Not installed +2024-12-04 12:52:22,774:INFO: ydata_profiling: Not installed +2024-12-04 12:52:22,774:INFO: explainerdashboard: Not installed +2024-12-04 12:52:22,774:INFO: autoviz: Not installed +2024-12-04 12:52:22,774:INFO: fairlearn: Not installed +2024-12-04 12:52:22,774:INFO: deepchecks: Not installed +2024-12-04 12:52:22,774:INFO: xgboost: Not installed +2024-12-04 12:52:22,774:INFO: catboost: Not installed +2024-12-04 12:52:22,774:INFO: kmodes: Not installed +2024-12-04 12:52:22,774:INFO: mlxtend: Not installed +2024-12-04 12:52:22,774:INFO: statsforecast: Not installed +2024-12-04 12:52:22,774:INFO: tune_sklearn: Not installed +2024-12-04 12:52:22,774:INFO: ray: Not installed +2024-12-04 12:52:22,774:INFO: hyperopt: Not installed +2024-12-04 12:52:22,774:INFO: optuna: Not installed +2024-12-04 12:52:22,774:INFO: skopt: Not installed +2024-12-04 12:52:22,774:INFO: mlflow: 2.18.0 +2024-12-04 12:52:22,774:INFO: gradio: Not installed +2024-12-04 12:52:22,774:INFO: fastapi: Not installed +2024-12-04 12:52:22,774:INFO: uvicorn: 0.32.1 +2024-12-04 12:52:22,775:INFO: m2cgen: Not installed +2024-12-04 12:52:22,775:INFO: evidently: Not installed +2024-12-04 12:52:22,775:INFO: fugue: Not installed +2024-12-04 12:52:22,775:INFO: streamlit: 1.40.1 +2024-12-04 12:52:22,775:INFO: prophet: Not installed +2024-12-04 12:52:22,775:INFO:None +2024-12-04 12:52:22,775:INFO:Set up data. +2024-12-04 12:52:22,787:INFO:Set up folding strategy. +2024-12-04 12:52:22,787:INFO:Set up train/test split. +2024-12-04 12:52:22,794:INFO:Set up index. +2024-12-04 12:52:22,794:INFO:Assigning column types. +2024-12-04 12:52:22,800:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 12:52:22,800:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,805:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,809:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,863:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,907:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,907:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:22,907:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:22,907:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,912:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,914:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:52:22,973:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,013:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,013:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,013:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,013:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 12:52:23,020:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,024:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,077:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,115:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,118:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,118:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,121:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,127:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,181:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,220:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,221:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,222:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,222:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 12:52:23,230:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,284:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,323:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,323:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,325:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,331:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,385:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,428:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,428:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,428:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,428:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 12:52:23,493:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,531:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,531:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,531:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,592:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,634:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,635:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,635:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,635:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 12:52:23,693:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,734:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,734:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,792:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 12:52:23,839:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,839:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,839:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 12:52:23,943:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:23,943:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:24,043:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:24,043:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:24,043:INFO:Preparing preprocessing pipeline... +2024-12-04 12:52:24,043:INFO:Set up simple imputation. +2024-12-04 12:52:24,043:INFO:Set up encoding of categorical features. +2024-12-04 12:52:24,043:INFO:Set up column transformation. +2024-12-04 12:52:24,043:INFO:Set up feature normalization. +2024-12-04 12:52:24,165:INFO:Finished creating preprocessing pipeline. +2024-12-04 12:52:24,175:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranche_age_25_34', + 'tranche_age_35_44', + 'tranche_age_45_54', + 'tranche_age_55_64', + 'tranche_age_65_74', + 'tranche_age_75_84', + 'tranche_age_85_et_pl... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 12:52:24,175:INFO:Creating final display dataframe. +2024-12-04 12:52:24,477:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11376, 15) +4 Transformed data shape (11376, 31) +5 Transformed train set shape (7963, 31) +6 Transformed test set shape (3413, 31) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 2 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Fold Generator KFold +22 Fold Number 10 +23 CPU Jobs -1 +24 Use GPU False +25 Log Experiment False +26 Experiment Name reg-default-name +27 USI fc44 +2024-12-04 12:52:24,589:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:24,589:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:24,689:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:24,689:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 12:52:24,689:INFO:setup() successfully completed in 1.95s............... +2024-12-04 16:06:53,263:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:06:53,263:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:06:53,263:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:06:53,263:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:07:02,005:INFO:PyCaret RegressionExperiment +2024-12-04 16:07:02,005:INFO:Logging name: model_hospi_reg_ens +2024-12-04 16:07:02,005:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:07:02,005:INFO:version 3.3.2 +2024-12-04 16:07:02,005:INFO:Initializing setup() +2024-12-04 16:07:02,005:INFO:self.USI: 3088 +2024-12-04 16:07:02,005:INFO:self._variable_keys: {'logging_param', '_ml_usecase', 'X_train', 'y', 'memory', 'pipeline', 'gpu_param', 'exp_id', 'X', 'target_param', 'y_train', 'n_jobs_param', 'transform_target_param', 'fold_generator', 'USI', '_available_plots', 'exp_name_log', 'y_test', 'idx', 'X_test', 'log_plots_param', 'seed', 'data', 'fold_groups_param', 'html_param', 'gpu_n_jobs_param', 'fold_shuffle_param'} +2024-12-04 16:07:02,005:INFO:Checking environment +2024-12-04 16:07:02,005:INFO:python_version: 3.11.9 +2024-12-04 16:07:02,005:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:07:02,005:INFO:machine: AMD64 +2024-12-04 16:07:02,005:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:07:02,012:INFO:Memory: svmem(total=17007292416, available=2248134656, percent=86.8, used=14759157760, free=2248134656) +2024-12-04 16:07:02,012:INFO:Physical Core: 4 +2024-12-04 16:07:02,012:INFO:Logical Core: 8 +2024-12-04 16:07:02,012:INFO:Checking libraries +2024-12-04 16:07:02,012:INFO:System: +2024-12-04 16:07:02,013:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:07:02,013:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:07:02,013:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:07:02,013:INFO:PyCaret required dependencies: +2024-12-04 16:07:02,135:INFO: pip: 24.3.1 +2024-12-04 16:07:02,135:INFO: setuptools: 65.5.0 +2024-12-04 16:07:02,135:INFO: pycaret: 3.3.2 +2024-12-04 16:07:02,135:INFO: IPython: 8.12.3 +2024-12-04 16:07:02,135:INFO: ipywidgets: 8.1.5 +2024-12-04 16:07:02,135:INFO: tqdm: 4.67.1 +2024-12-04 16:07:02,135:INFO: numpy: 1.26.4 +2024-12-04 16:07:02,135:INFO: pandas: 2.1.4 +2024-12-04 16:07:02,135:INFO: jinja2: 3.1.4 +2024-12-04 16:07:02,135:INFO: scipy: 1.11.4 +2024-12-04 16:07:02,135:INFO: joblib: 1.3.2 +2024-12-04 16:07:02,135:INFO: sklearn: 1.4.2 +2024-12-04 16:07:02,135:INFO: pyod: 2.0.2 +2024-12-04 16:07:02,135:INFO: imblearn: 0.12.4 +2024-12-04 16:07:02,135:INFO: category_encoders: 2.6.4 +2024-12-04 16:07:02,135:INFO: lightgbm: 4.5.0 +2024-12-04 16:07:02,135:INFO: numba: 0.60.0 +2024-12-04 16:07:02,135:INFO: requests: 2.32.3 +2024-12-04 16:07:02,135:INFO: matplotlib: 3.7.5 +2024-12-04 16:07:02,135:INFO: scikitplot: 0.3.7 +2024-12-04 16:07:02,135:INFO: yellowbrick: 1.5 +2024-12-04 16:07:02,135:INFO: plotly: 5.24.1 +2024-12-04 16:07:02,135:INFO: plotly-resampler: Not installed +2024-12-04 16:07:02,135:INFO: kaleido: 0.2.1 +2024-12-04 16:07:02,135:INFO: schemdraw: 0.15 +2024-12-04 16:07:02,135:INFO: statsmodels: 0.14.4 +2024-12-04 16:07:02,135:INFO: sktime: 0.26.0 +2024-12-04 16:07:02,135:INFO: tbats: 1.1.3 +2024-12-04 16:07:02,135:INFO: pmdarima: 2.0.4 +2024-12-04 16:07:02,135:INFO: psutil: 6.1.0 +2024-12-04 16:07:02,135:INFO: markupsafe: 3.0.2 +2024-12-04 16:07:02,135:INFO: pickle5: Not installed +2024-12-04 16:07:02,135:INFO: cloudpickle: 3.1.0 +2024-12-04 16:07:02,135:INFO: deprecation: 2.1.0 +2024-12-04 16:07:02,135:INFO: xxhash: 3.5.0 +2024-12-04 16:07:02,135:INFO: wurlitzer: Not installed +2024-12-04 16:07:02,135:INFO:PyCaret optional dependencies: +2024-12-04 16:07:02,235:INFO: shap: Not installed +2024-12-04 16:07:02,235:INFO: interpret: Not installed +2024-12-04 16:07:02,235:INFO: umap: Not installed +2024-12-04 16:07:02,235:INFO: ydata_profiling: Not installed +2024-12-04 16:07:02,235:INFO: explainerdashboard: Not installed +2024-12-04 16:07:02,235:INFO: autoviz: Not installed +2024-12-04 16:07:02,235:INFO: fairlearn: Not installed +2024-12-04 16:07:02,235:INFO: deepchecks: Not installed +2024-12-04 16:07:02,235:INFO: xgboost: Not installed +2024-12-04 16:07:02,235:INFO: catboost: Not installed +2024-12-04 16:07:02,235:INFO: kmodes: Not installed +2024-12-04 16:07:02,235:INFO: mlxtend: Not installed +2024-12-04 16:07:02,235:INFO: statsforecast: Not installed +2024-12-04 16:07:02,235:INFO: tune_sklearn: Not installed +2024-12-04 16:07:02,235:INFO: ray: Not installed +2024-12-04 16:07:02,235:INFO: hyperopt: Not installed +2024-12-04 16:07:02,235:INFO: optuna: Not installed +2024-12-04 16:07:02,235:INFO: skopt: Not installed +2024-12-04 16:07:02,235:INFO: mlflow: 2.18.0 +2024-12-04 16:07:02,235:INFO: gradio: Not installed +2024-12-04 16:07:02,235:INFO: fastapi: Not installed +2024-12-04 16:07:02,235:INFO: uvicorn: 0.32.1 +2024-12-04 16:07:02,235:INFO: m2cgen: Not installed +2024-12-04 16:07:02,235:INFO: evidently: Not installed +2024-12-04 16:07:02,235:INFO: fugue: Not installed +2024-12-04 16:07:02,235:INFO: streamlit: 1.40.1 +2024-12-04 16:07:02,235:INFO: prophet: Not installed +2024-12-04 16:07:02,235:INFO:None +2024-12-04 16:07:02,235:INFO:Set up data. +2024-12-04 16:07:02,243:INFO:Set up folding strategy. +2024-12-04 16:07:02,243:INFO:Set up train/test split. +2024-12-04 16:07:02,274:INFO:Set up index. +2024-12-04 16:07:02,274:INFO:Assigning column types. +2024-12-04 16:07:02,279:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:07:02,280:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,280:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,287:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,339:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,384:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,384:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,384:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,384:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,389:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,393:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,446:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,496:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,496:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,496:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,497:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 16:07:02,501:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,507:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,572:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,619:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,619:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,620:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,624:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,628:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,704:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,748:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,749:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,750:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,750:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 16:07:02,757:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,812:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,849:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,849:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,853:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,859:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,914:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,957:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:02,959:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,959:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:02,959:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 16:07:03,030:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:03,070:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:03,070:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,070:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,135:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:03,175:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:07:03,175:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,175:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,175:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:07:03,243:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:03,289:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,289:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,355:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:07:03,405:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,407:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,407:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 16:07:03,509:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,511:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,631:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,631:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:03,645:INFO:Preparing preprocessing pipeline... +2024-12-04 16:07:03,645:INFO:Set up target transformation. +2024-12-04 16:07:03,645:INFO:Set up simple imputation. +2024-12-04 16:07:03,648:INFO:Set up encoding of categorical features. +2024-12-04 16:07:03,648:INFO:Set up column transformation. +2024-12-04 16:07:03,648:INFO:Set up feature normalization. +2024-12-04 16:07:03,796:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:07:03,808:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranch... + TransformerWrapper(include=['nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 16:07:03,808:INFO:Creating final display dataframe. +2024-12-04 16:07:04,044:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 14) +4 Transformed data shape (5688, 13) +5 Transformed train set shape (3981, 13) +6 Transformed test set shape (1707, 13) +7 Ignore features 1 +8 Numeric features 11 +9 Categorical features 1 +10 Rows with missing values 0.1% +11 Preprocess True +12 Imputation type simple +13 Numeric imputation mean +14 Categorical imputation mode +15 Maximum one-hot encoding 25 +16 Encoding method None +17 Transformation True +18 Transformation method yeo-johnson +19 Normalize True +20 Normalize method zscore +21 Transform target True +22 Transform target method yeo-johnson +23 Fold Generator KFold +24 Fold Number 10 +25 CPU Jobs -1 +26 Use GPU False +27 Log Experiment MlflowLogger +28 Experiment Name model_hospi_reg_ens +29 USI 3088 +2024-12-04 16:07:04,190:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:04,190:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:04,303:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:04,305:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:07:04,305:INFO:Logging experiment in loggers +2024-12-04 16:08:19,049:INFO:PyCaret RegressionExperiment +2024-12-04 16:08:19,049:INFO:Logging name: model_hospi_reg_ens +2024-12-04 16:08:19,049:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:08:19,049:INFO:version 3.3.2 +2024-12-04 16:08:19,049:INFO:Initializing setup() +2024-12-04 16:08:19,049:INFO:self.USI: eff0 +2024-12-04 16:08:19,049:INFO:self._variable_keys: {'logging_param', '_ml_usecase', 'X_train', 'y', 'memory', 'pipeline', 'gpu_param', 'exp_id', 'X', 'target_param', 'y_train', 'n_jobs_param', 'transform_target_param', 'fold_generator', 'USI', '_available_plots', 'exp_name_log', 'y_test', 'idx', 'X_test', 'log_plots_param', 'seed', 'data', 'fold_groups_param', 'html_param', 'gpu_n_jobs_param', 'fold_shuffle_param'} +2024-12-04 16:08:19,049:INFO:Checking environment +2024-12-04 16:08:19,049:INFO:python_version: 3.11.9 +2024-12-04 16:08:19,049:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:08:19,050:INFO:machine: AMD64 +2024-12-04 16:08:19,050:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:08:19,056:INFO:Memory: svmem(total=17007292416, available=1981022208, percent=88.4, used=15026270208, free=1981022208) +2024-12-04 16:08:19,057:INFO:Physical Core: 4 +2024-12-04 16:08:19,057:INFO:Logical Core: 8 +2024-12-04 16:08:19,057:INFO:Checking libraries +2024-12-04 16:08:19,057:INFO:System: +2024-12-04 16:08:19,057:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:08:19,057:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:08:19,057:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:08:19,057:INFO:PyCaret required dependencies: +2024-12-04 16:08:19,057:INFO: pip: 24.3.1 +2024-12-04 16:08:19,057:INFO: setuptools: 65.5.0 +2024-12-04 16:08:19,057:INFO: pycaret: 3.3.2 +2024-12-04 16:08:19,057:INFO: IPython: 8.12.3 +2024-12-04 16:08:19,057:INFO: ipywidgets: 8.1.5 +2024-12-04 16:08:19,057:INFO: tqdm: 4.67.1 +2024-12-04 16:08:19,057:INFO: numpy: 1.26.4 +2024-12-04 16:08:19,057:INFO: pandas: 2.1.4 +2024-12-04 16:08:19,057:INFO: jinja2: 3.1.4 +2024-12-04 16:08:19,057:INFO: scipy: 1.11.4 +2024-12-04 16:08:19,057:INFO: joblib: 1.3.2 +2024-12-04 16:08:19,057:INFO: sklearn: 1.4.2 +2024-12-04 16:08:19,057:INFO: pyod: 2.0.2 +2024-12-04 16:08:19,057:INFO: imblearn: 0.12.4 +2024-12-04 16:08:19,057:INFO: category_encoders: 2.6.4 +2024-12-04 16:08:19,057:INFO: lightgbm: 4.5.0 +2024-12-04 16:08:19,057:INFO: numba: 0.60.0 +2024-12-04 16:08:19,057:INFO: requests: 2.32.3 +2024-12-04 16:08:19,057:INFO: matplotlib: 3.7.5 +2024-12-04 16:08:19,057:INFO: scikitplot: 0.3.7 +2024-12-04 16:08:19,057:INFO: yellowbrick: 1.5 +2024-12-04 16:08:19,057:INFO: plotly: 5.24.1 +2024-12-04 16:08:19,057:INFO: plotly-resampler: Not installed +2024-12-04 16:08:19,057:INFO: kaleido: 0.2.1 +2024-12-04 16:08:19,057:INFO: schemdraw: 0.15 +2024-12-04 16:08:19,057:INFO: statsmodels: 0.14.4 +2024-12-04 16:08:19,057:INFO: sktime: 0.26.0 +2024-12-04 16:08:19,057:INFO: tbats: 1.1.3 +2024-12-04 16:08:19,057:INFO: pmdarima: 2.0.4 +2024-12-04 16:08:19,057:INFO: psutil: 6.1.0 +2024-12-04 16:08:19,057:INFO: markupsafe: 3.0.2 +2024-12-04 16:08:19,057:INFO: pickle5: Not installed +2024-12-04 16:08:19,057:INFO: cloudpickle: 3.1.0 +2024-12-04 16:08:19,057:INFO: deprecation: 2.1.0 +2024-12-04 16:08:19,057:INFO: xxhash: 3.5.0 +2024-12-04 16:08:19,057:INFO: wurlitzer: Not installed +2024-12-04 16:08:19,057:INFO:PyCaret optional dependencies: +2024-12-04 16:08:19,057:INFO: shap: Not installed +2024-12-04 16:08:19,057:INFO: interpret: Not installed +2024-12-04 16:08:19,057:INFO: umap: Not installed +2024-12-04 16:08:19,057:INFO: ydata_profiling: Not installed +2024-12-04 16:08:19,057:INFO: explainerdashboard: Not installed +2024-12-04 16:08:19,057:INFO: autoviz: Not installed +2024-12-04 16:08:19,057:INFO: fairlearn: Not installed +2024-12-04 16:08:19,057:INFO: deepchecks: Not installed +2024-12-04 16:08:19,057:INFO: xgboost: Not installed +2024-12-04 16:08:19,057:INFO: catboost: Not installed +2024-12-04 16:08:19,059:INFO: kmodes: Not installed +2024-12-04 16:08:19,059:INFO: mlxtend: Not installed +2024-12-04 16:08:19,059:INFO: statsforecast: Not installed +2024-12-04 16:08:19,059:INFO: tune_sklearn: Not installed +2024-12-04 16:08:19,059:INFO: ray: Not installed +2024-12-04 16:08:19,059:INFO: hyperopt: Not installed +2024-12-04 16:08:19,059:INFO: optuna: Not installed +2024-12-04 16:08:19,059:INFO: skopt: Not installed +2024-12-04 16:08:19,059:INFO: mlflow: 2.18.0 +2024-12-04 16:08:19,059:INFO: gradio: Not installed +2024-12-04 16:08:19,059:INFO: fastapi: Not installed +2024-12-04 16:08:19,059:INFO: uvicorn: 0.32.1 +2024-12-04 16:08:19,059:INFO: m2cgen: Not installed +2024-12-04 16:08:19,059:INFO: evidently: Not installed +2024-12-04 16:08:19,059:INFO: fugue: Not installed +2024-12-04 16:08:19,059:INFO: streamlit: 1.40.1 +2024-12-04 16:08:19,059:INFO: prophet: Not installed +2024-12-04 16:08:19,059:INFO:None +2024-12-04 16:08:19,059:INFO:Set up data. +2024-12-04 16:08:19,067:INFO:Set up folding strategy. +2024-12-04 16:08:19,067:INFO:Set up train/test split. +2024-12-04 16:08:19,072:INFO:Set up index. +2024-12-04 16:08:19,072:INFO:Assigning column types. +2024-12-04 16:08:19,076:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:08:19,076:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,081:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,085:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,136:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,175:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,175:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,176:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,176:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,180:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,183:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,234:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,274:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,274:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,274:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,274:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 16:08:19,278:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,282:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,334:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,374:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,377:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,377:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,381:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,385:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,436:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,474:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,474:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,474:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,474:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 16:08:19,483:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,533:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,571:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,571:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,571:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,580:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,633:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,678:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,678:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,678:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,678:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 16:08:19,749:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,790:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,791:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,791:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,853:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,895:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:08:19,896:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,896:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:19,897:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:08:19,964:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:20,005:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,005:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,076:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:08:20,121:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,121:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,121:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 16:08:20,241:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,243:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,338:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,339:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,339:INFO:Preparing preprocessing pipeline... +2024-12-04 16:08:20,339:INFO:Set up target transformation. +2024-12-04 16:08:20,339:INFO:Set up simple imputation. +2024-12-04 16:08:20,342:INFO:Set up encoding of categorical features. +2024-12-04 16:08:20,342:INFO:Set up feature normalization. +2024-12-04 16:08:20,410:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:08:20,419:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['annee', 'tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24'... + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 16:08:20,419:INFO:Creating final display dataframe. +2024-12-04 16:08:20,593:INFO:Setup _display_container: Description Value +0 Session id 1850 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 14) +4 Transformed data shape (5688, 14) +5 Transformed train set shape (3981, 14) +6 Transformed test set shape (1707, 14) +7 Numeric features 12 +8 Categorical features 1 +9 Rows with missing values 0.1% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Normalize True +17 Normalize method zscore +18 Transform target True +19 Transform target method yeo-johnson +20 Fold Generator KFold +21 Fold Number 10 +22 CPU Jobs -1 +23 Use GPU False +24 Log Experiment MlflowLogger +25 Experiment Name model_hospi_reg_ens +26 USI eff0 +2024-12-04 16:08:20,696:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,696:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,795:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,795:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:08:20,796:INFO:Logging experiment in loggers +2024-12-04 16:10:28,788:INFO:PyCaret RegressionExperiment +2024-12-04 16:10:28,788:INFO:Logging name: model_hospi_reg_ens +2024-12-04 16:10:28,788:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:10:28,788:INFO:version 3.3.2 +2024-12-04 16:10:28,788:INFO:Initializing setup() +2024-12-04 16:10:28,788:INFO:self.USI: 12c4 +2024-12-04 16:10:28,788:INFO:self._variable_keys: {'logging_param', '_ml_usecase', 'X_train', 'y', 'memory', 'pipeline', 'gpu_param', 'exp_id', 'X', 'target_param', 'y_train', 'n_jobs_param', 'transform_target_param', 'fold_generator', 'USI', '_available_plots', 'exp_name_log', 'y_test', 'idx', 'X_test', 'log_plots_param', 'seed', 'data', 'fold_groups_param', 'html_param', 'gpu_n_jobs_param', 'fold_shuffle_param'} +2024-12-04 16:10:28,788:INFO:Checking environment +2024-12-04 16:10:28,788:INFO:python_version: 3.11.9 +2024-12-04 16:10:28,788:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:10:28,788:INFO:machine: AMD64 +2024-12-04 16:10:28,788:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:10:28,793:INFO:Memory: svmem(total=17007292416, available=2747101184, percent=83.8, used=14260191232, free=2747101184) +2024-12-04 16:10:28,793:INFO:Physical Core: 4 +2024-12-04 16:10:28,793:INFO:Logical Core: 8 +2024-12-04 16:10:28,793:INFO:Checking libraries +2024-12-04 16:10:28,793:INFO:System: +2024-12-04 16:10:28,793:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:10:28,793:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:10:28,793:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:10:28,793:INFO:PyCaret required dependencies: +2024-12-04 16:10:28,794:INFO: pip: 24.3.1 +2024-12-04 16:10:28,794:INFO: setuptools: 65.5.0 +2024-12-04 16:10:28,794:INFO: pycaret: 3.3.2 +2024-12-04 16:10:28,794:INFO: IPython: 8.12.3 +2024-12-04 16:10:28,794:INFO: ipywidgets: 8.1.5 +2024-12-04 16:10:28,794:INFO: tqdm: 4.67.1 +2024-12-04 16:10:28,794:INFO: numpy: 1.26.4 +2024-12-04 16:10:28,794:INFO: pandas: 2.1.4 +2024-12-04 16:10:28,794:INFO: jinja2: 3.1.4 +2024-12-04 16:10:28,794:INFO: scipy: 1.11.4 +2024-12-04 16:10:28,794:INFO: joblib: 1.3.2 +2024-12-04 16:10:28,794:INFO: sklearn: 1.4.2 +2024-12-04 16:10:28,794:INFO: pyod: 2.0.2 +2024-12-04 16:10:28,794:INFO: imblearn: 0.12.4 +2024-12-04 16:10:28,794:INFO: category_encoders: 2.6.4 +2024-12-04 16:10:28,794:INFO: lightgbm: 4.5.0 +2024-12-04 16:10:28,794:INFO: numba: 0.60.0 +2024-12-04 16:10:28,794:INFO: requests: 2.32.3 +2024-12-04 16:10:28,794:INFO: matplotlib: 3.7.5 +2024-12-04 16:10:28,794:INFO: scikitplot: 0.3.7 +2024-12-04 16:10:28,794:INFO: yellowbrick: 1.5 +2024-12-04 16:10:28,794:INFO: plotly: 5.24.1 +2024-12-04 16:10:28,794:INFO: plotly-resampler: Not installed +2024-12-04 16:10:28,794:INFO: kaleido: 0.2.1 +2024-12-04 16:10:28,794:INFO: schemdraw: 0.15 +2024-12-04 16:10:28,794:INFO: statsmodels: 0.14.4 +2024-12-04 16:10:28,794:INFO: sktime: 0.26.0 +2024-12-04 16:10:28,794:INFO: tbats: 1.1.3 +2024-12-04 16:10:28,794:INFO: pmdarima: 2.0.4 +2024-12-04 16:10:28,794:INFO: psutil: 6.1.0 +2024-12-04 16:10:28,795:INFO: markupsafe: 3.0.2 +2024-12-04 16:10:28,795:INFO: pickle5: Not installed +2024-12-04 16:10:28,795:INFO: cloudpickle: 3.1.0 +2024-12-04 16:10:28,795:INFO: deprecation: 2.1.0 +2024-12-04 16:10:28,795:INFO: xxhash: 3.5.0 +2024-12-04 16:10:28,795:INFO: wurlitzer: Not installed +2024-12-04 16:10:28,795:INFO:PyCaret optional dependencies: +2024-12-04 16:10:28,795:INFO: shap: Not installed +2024-12-04 16:10:28,795:INFO: interpret: Not installed +2024-12-04 16:10:28,795:INFO: umap: Not installed +2024-12-04 16:10:28,795:INFO: ydata_profiling: Not installed +2024-12-04 16:10:28,795:INFO: explainerdashboard: Not installed +2024-12-04 16:10:28,795:INFO: autoviz: Not installed +2024-12-04 16:10:28,795:INFO: fairlearn: Not installed +2024-12-04 16:10:28,795:INFO: deepchecks: Not installed +2024-12-04 16:10:28,795:INFO: xgboost: Not installed +2024-12-04 16:10:28,795:INFO: catboost: Not installed +2024-12-04 16:10:28,795:INFO: kmodes: Not installed +2024-12-04 16:10:28,795:INFO: mlxtend: Not installed +2024-12-04 16:10:28,795:INFO: statsforecast: Not installed +2024-12-04 16:10:28,795:INFO: tune_sklearn: Not installed +2024-12-04 16:10:28,795:INFO: ray: Not installed +2024-12-04 16:10:28,795:INFO: hyperopt: Not installed +2024-12-04 16:10:28,795:INFO: optuna: Not installed +2024-12-04 16:10:28,795:INFO: skopt: Not installed +2024-12-04 16:10:28,795:INFO: mlflow: 2.18.0 +2024-12-04 16:10:28,795:INFO: gradio: Not installed +2024-12-04 16:10:28,795:INFO: fastapi: Not installed +2024-12-04 16:10:28,795:INFO: uvicorn: 0.32.1 +2024-12-04 16:10:28,795:INFO: m2cgen: Not installed +2024-12-04 16:10:28,796:INFO: evidently: Not installed +2024-12-04 16:10:28,796:INFO: fugue: Not installed +2024-12-04 16:10:28,796:INFO: streamlit: 1.40.1 +2024-12-04 16:10:28,796:INFO: prophet: Not installed +2024-12-04 16:10:28,796:INFO:None +2024-12-04 16:10:28,796:INFO:Set up data. +2024-12-04 16:10:28,803:INFO:Set up folding strategy. +2024-12-04 16:10:28,803:INFO:Set up train/test split. +2024-12-04 16:10:28,807:INFO:Set up index. +2024-12-04 16:10:28,807:INFO:Assigning column types. +2024-12-04 16:10:28,810:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:10:28,810:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,814:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,819:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,870:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,910:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,910:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:28,912:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:28,912:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,916:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,920:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:10:28,973:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,015:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,015:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,015:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,015:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 16:10:29,020:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,024:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,075:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,113:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,113:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,113:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,118:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,122:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,172:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,211:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,211:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,212:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,212:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 16:10:29,220:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,272:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,312:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,313:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,313:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,321:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,374:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,415:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,416:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,416:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,416:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 16:10:29,483:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,522:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,522:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,522:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,580:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,618:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,618:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,618:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,620:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:10:29,677:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,716:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,717:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,788:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:10:29,828:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,828:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,828:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 16:10:29,928:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:29,928:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:30,045:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:30,045:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:30,046:INFO:Preparing preprocessing pipeline... +2024-12-04 16:10:30,046:INFO:Set up target transformation. +2024-12-04 16:10:30,046:INFO:Set up simple imputation. +2024-12-04 16:10:30,048:INFO:Set up encoding of categorical features. +2024-12-04 16:10:30,048:INFO:Set up feature normalization. +2024-12-04 16:10:30,126:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:10:30,136:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranch... + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 16:10:30,136:INFO:Creating final display dataframe. +2024-12-04 16:10:30,358:INFO:Setup _display_container: Description Value +0 Session id 7562 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 13) +4 Transformed data shape (5688, 13) +5 Transformed train set shape (3981, 13) +6 Transformed test set shape (1707, 13) +7 Numeric features 11 +8 Categorical features 1 +9 Rows with missing values 0.1% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Normalize True +17 Normalize method zscore +18 Transform target True +19 Transform target method yeo-johnson +20 Fold Generator KFold +21 Fold Number 10 +22 CPU Jobs -1 +23 Use GPU False +24 Log Experiment False +25 Experiment Name model_hospi_reg_ens +26 USI 12c4 +2024-12-04 16:10:30,480:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:30,480:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:30,610:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:30,610:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:10:30,612:INFO:setup() successfully completed in 1.84s............... +2024-12-04 16:10:34,695:INFO:Initializing compare_models() +2024-12-04 16:10:34,695:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 16:10:34,695:INFO:Checking exceptions +2024-12-04 16:10:34,698:INFO:Preparing display monitor +2024-12-04 16:10:34,723:INFO:Initializing Linear Regression +2024-12-04 16:10:34,723:INFO:Total runtime is 0.0 minutes +2024-12-04 16:10:34,727:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:34,728:INFO:Initializing create_model() +2024-12-04 16:10:34,728:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:34,728:INFO:Checking exceptions +2024-12-04 16:10:34,728:INFO:Importing libraries +2024-12-04 16:10:34,728:INFO:Copying training dataset +2024-12-04 16:10:34,735:INFO:Defining folds +2024-12-04 16:10:34,735:INFO:Declaring metric variables +2024-12-04 16:10:34,738:INFO:Importing untrained model +2024-12-04 16:10:34,741:INFO:Linear Regression Imported successfully +2024-12-04 16:10:34,749:INFO:Starting cross validation +2024-12-04 16:10:34,758:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:39,859:INFO:Calculating mean and std +2024-12-04 16:10:39,861:INFO:Creating metrics dataframe +2024-12-04 16:10:39,864:INFO:Uploading results into container +2024-12-04 16:10:39,864:INFO:Uploading model into container now +2024-12-04 16:10:39,865:INFO:_master_model_container: 1 +2024-12-04 16:10:39,865:INFO:_display_container: 2 +2024-12-04 16:10:39,865:INFO:LinearRegression(n_jobs=-1) +2024-12-04 16:10:39,865:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:39,999:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:39,999:INFO:Creating metrics dataframe +2024-12-04 16:10:40,004:INFO:Initializing Lasso Regression +2024-12-04 16:10:40,004:INFO:Total runtime is 0.08801005283991496 minutes +2024-12-04 16:10:40,007:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:40,007:INFO:Initializing create_model() +2024-12-04 16:10:40,007:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:40,007:INFO:Checking exceptions +2024-12-04 16:10:40,007:INFO:Importing libraries +2024-12-04 16:10:40,007:INFO:Copying training dataset +2024-12-04 16:10:40,013:INFO:Defining folds +2024-12-04 16:10:40,013:INFO:Declaring metric variables +2024-12-04 16:10:40,016:INFO:Importing untrained model +2024-12-04 16:10:40,022:INFO:Lasso Regression Imported successfully +2024-12-04 16:10:40,028:INFO:Starting cross validation +2024-12-04 16:10:40,030:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:40,335:INFO:Calculating mean and std +2024-12-04 16:10:40,336:INFO:Creating metrics dataframe +2024-12-04 16:10:40,338:INFO:Uploading results into container +2024-12-04 16:10:40,340:INFO:Uploading model into container now +2024-12-04 16:10:40,340:INFO:_master_model_container: 2 +2024-12-04 16:10:40,340:INFO:_display_container: 2 +2024-12-04 16:10:40,341:INFO:Lasso(random_state=7562) +2024-12-04 16:10:40,341:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:40,457:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:40,457:INFO:Creating metrics dataframe +2024-12-04 16:10:40,463:INFO:Initializing Ridge Regression +2024-12-04 16:10:40,463:INFO:Total runtime is 0.09565342664718629 minutes +2024-12-04 16:10:40,466:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:40,467:INFO:Initializing create_model() +2024-12-04 16:10:40,467:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:40,467:INFO:Checking exceptions +2024-12-04 16:10:40,467:INFO:Importing libraries +2024-12-04 16:10:40,467:INFO:Copying training dataset +2024-12-04 16:10:40,474:INFO:Defining folds +2024-12-04 16:10:40,474:INFO:Declaring metric variables +2024-12-04 16:10:40,480:INFO:Importing untrained model +2024-12-04 16:10:40,487:INFO:Ridge Regression Imported successfully +2024-12-04 16:10:40,498:INFO:Starting cross validation +2024-12-04 16:10:40,501:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:40,745:INFO:Calculating mean and std +2024-12-04 16:10:40,747:INFO:Creating metrics dataframe +2024-12-04 16:10:40,748:INFO:Uploading results into container +2024-12-04 16:10:40,748:INFO:Uploading model into container now +2024-12-04 16:10:40,749:INFO:_master_model_container: 3 +2024-12-04 16:10:40,749:INFO:_display_container: 2 +2024-12-04 16:10:40,749:INFO:Ridge(random_state=7562) +2024-12-04 16:10:40,749:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:40,875:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:40,875:INFO:Creating metrics dataframe +2024-12-04 16:10:40,883:INFO:Initializing Elastic Net +2024-12-04 16:10:40,883:INFO:Total runtime is 0.10265364249547324 minutes +2024-12-04 16:10:40,886:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:40,886:INFO:Initializing create_model() +2024-12-04 16:10:40,886:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:40,887:INFO:Checking exceptions +2024-12-04 16:10:40,887:INFO:Importing libraries +2024-12-04 16:10:40,887:INFO:Copying training dataset +2024-12-04 16:10:40,893:INFO:Defining folds +2024-12-04 16:10:40,894:INFO:Declaring metric variables +2024-12-04 16:10:40,896:INFO:Importing untrained model +2024-12-04 16:10:40,900:INFO:Elastic Net Imported successfully +2024-12-04 16:10:40,907:INFO:Starting cross validation +2024-12-04 16:10:40,910:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:41,126:INFO:Calculating mean and std +2024-12-04 16:10:41,127:INFO:Creating metrics dataframe +2024-12-04 16:10:41,128:INFO:Uploading results into container +2024-12-04 16:10:41,129:INFO:Uploading model into container now +2024-12-04 16:10:41,129:INFO:_master_model_container: 4 +2024-12-04 16:10:41,129:INFO:_display_container: 2 +2024-12-04 16:10:41,130:INFO:ElasticNet(random_state=7562) +2024-12-04 16:10:41,130:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:41,266:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:41,266:INFO:Creating metrics dataframe +2024-12-04 16:10:41,271:INFO:Initializing Least Angle Regression +2024-12-04 16:10:41,272:INFO:Total runtime is 0.10914277235666912 minutes +2024-12-04 16:10:41,275:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:41,275:INFO:Initializing create_model() +2024-12-04 16:10:41,275:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:41,276:INFO:Checking exceptions +2024-12-04 16:10:41,276:INFO:Importing libraries +2024-12-04 16:10:41,276:INFO:Copying training dataset +2024-12-04 16:10:41,281:INFO:Defining folds +2024-12-04 16:10:41,281:INFO:Declaring metric variables +2024-12-04 16:10:41,285:INFO:Importing untrained model +2024-12-04 16:10:41,290:INFO:Least Angle Regression Imported successfully +2024-12-04 16:10:41,298:INFO:Starting cross validation +2024-12-04 16:10:41,299:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:41,505:INFO:Calculating mean and std +2024-12-04 16:10:41,505:INFO:Creating metrics dataframe +2024-12-04 16:10:41,507:INFO:Uploading results into container +2024-12-04 16:10:41,507:INFO:Uploading model into container now +2024-12-04 16:10:41,507:INFO:_master_model_container: 5 +2024-12-04 16:10:41,507:INFO:_display_container: 2 +2024-12-04 16:10:41,509:INFO:Lars(random_state=7562) +2024-12-04 16:10:41,509:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:41,636:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:41,636:INFO:Creating metrics dataframe +2024-12-04 16:10:41,643:INFO:Initializing Lasso Least Angle Regression +2024-12-04 16:10:41,643:INFO:Total runtime is 0.1153204917907715 minutes +2024-12-04 16:10:41,646:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:41,646:INFO:Initializing create_model() +2024-12-04 16:10:41,646:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:41,646:INFO:Checking exceptions +2024-12-04 16:10:41,646:INFO:Importing libraries +2024-12-04 16:10:41,646:INFO:Copying training dataset +2024-12-04 16:10:41,651:INFO:Defining folds +2024-12-04 16:10:41,652:INFO:Declaring metric variables +2024-12-04 16:10:41,656:INFO:Importing untrained model +2024-12-04 16:10:41,660:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 16:10:41,666:INFO:Starting cross validation +2024-12-04 16:10:41,667:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:41,885:INFO:Calculating mean and std +2024-12-04 16:10:41,886:INFO:Creating metrics dataframe +2024-12-04 16:10:41,887:INFO:Uploading results into container +2024-12-04 16:10:41,887:INFO:Uploading model into container now +2024-12-04 16:10:41,888:INFO:_master_model_container: 6 +2024-12-04 16:10:41,888:INFO:_display_container: 2 +2024-12-04 16:10:41,889:INFO:LassoLars(random_state=7562) +2024-12-04 16:10:41,889:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:42,012:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:42,012:INFO:Creating metrics dataframe +2024-12-04 16:10:42,018:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 16:10:42,018:INFO:Total runtime is 0.1215794881184896 minutes +2024-12-04 16:10:42,022:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:42,022:INFO:Initializing create_model() +2024-12-04 16:10:42,022:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:42,022:INFO:Checking exceptions +2024-12-04 16:10:42,023:INFO:Importing libraries +2024-12-04 16:10:42,023:INFO:Copying training dataset +2024-12-04 16:10:42,029:INFO:Defining folds +2024-12-04 16:10:42,029:INFO:Declaring metric variables +2024-12-04 16:10:42,032:INFO:Importing untrained model +2024-12-04 16:10:42,036:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 16:10:42,043:INFO:Starting cross validation +2024-12-04 16:10:42,045:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:42,265:INFO:Calculating mean and std +2024-12-04 16:10:42,266:INFO:Creating metrics dataframe +2024-12-04 16:10:42,267:INFO:Uploading results into container +2024-12-04 16:10:42,268:INFO:Uploading model into container now +2024-12-04 16:10:42,268:INFO:_master_model_container: 7 +2024-12-04 16:10:42,268:INFO:_display_container: 2 +2024-12-04 16:10:42,268:INFO:OrthogonalMatchingPursuit() +2024-12-04 16:10:42,268:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:42,396:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:42,396:INFO:Creating metrics dataframe +2024-12-04 16:10:42,402:INFO:Initializing Bayesian Ridge +2024-12-04 16:10:42,402:INFO:Total runtime is 0.127981968720754 minutes +2024-12-04 16:10:42,406:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:42,406:INFO:Initializing create_model() +2024-12-04 16:10:42,406:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:42,406:INFO:Checking exceptions +2024-12-04 16:10:42,406:INFO:Importing libraries +2024-12-04 16:10:42,406:INFO:Copying training dataset +2024-12-04 16:10:42,412:INFO:Defining folds +2024-12-04 16:10:42,412:INFO:Declaring metric variables +2024-12-04 16:10:42,415:INFO:Importing untrained model +2024-12-04 16:10:42,418:INFO:Bayesian Ridge Imported successfully +2024-12-04 16:10:42,423:INFO:Starting cross validation +2024-12-04 16:10:42,424:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:42,610:INFO:Calculating mean and std +2024-12-04 16:10:42,611:INFO:Creating metrics dataframe +2024-12-04 16:10:42,613:INFO:Uploading results into container +2024-12-04 16:10:42,613:INFO:Uploading model into container now +2024-12-04 16:10:42,613:INFO:_master_model_container: 8 +2024-12-04 16:10:42,613:INFO:_display_container: 2 +2024-12-04 16:10:42,614:INFO:BayesianRidge() +2024-12-04 16:10:42,614:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:42,731:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:42,731:INFO:Creating metrics dataframe +2024-12-04 16:10:42,737:INFO:Initializing Passive Aggressive Regressor +2024-12-04 16:10:42,738:INFO:Total runtime is 0.13356819550196333 minutes +2024-12-04 16:10:42,740:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:42,740:INFO:Initializing create_model() +2024-12-04 16:10:42,740:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:42,740:INFO:Checking exceptions +2024-12-04 16:10:42,740:INFO:Importing libraries +2024-12-04 16:10:42,740:INFO:Copying training dataset +2024-12-04 16:10:42,747:INFO:Defining folds +2024-12-04 16:10:42,747:INFO:Declaring metric variables +2024-12-04 16:10:42,750:INFO:Importing untrained model +2024-12-04 16:10:42,754:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 16:10:42,779:INFO:Starting cross validation +2024-12-04 16:10:42,780:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:43,003:INFO:Calculating mean and std +2024-12-04 16:10:43,005:INFO:Creating metrics dataframe +2024-12-04 16:10:43,007:INFO:Uploading results into container +2024-12-04 16:10:43,008:INFO:Uploading model into container now +2024-12-04 16:10:43,009:INFO:_master_model_container: 9 +2024-12-04 16:10:43,009:INFO:_display_container: 2 +2024-12-04 16:10:43,009:INFO:PassiveAggressiveRegressor(random_state=7562) +2024-12-04 16:10:43,009:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:43,145:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:43,145:INFO:Creating metrics dataframe +2024-12-04 16:10:43,151:INFO:Initializing Huber Regressor +2024-12-04 16:10:43,151:INFO:Total runtime is 0.1404562830924988 minutes +2024-12-04 16:10:43,154:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:43,155:INFO:Initializing create_model() +2024-12-04 16:10:43,155:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:43,155:INFO:Checking exceptions +2024-12-04 16:10:43,155:INFO:Importing libraries +2024-12-04 16:10:43,155:INFO:Copying training dataset +2024-12-04 16:10:43,161:INFO:Defining folds +2024-12-04 16:10:43,161:INFO:Declaring metric variables +2024-12-04 16:10:43,165:INFO:Importing untrained model +2024-12-04 16:10:43,168:INFO:Huber Regressor Imported successfully +2024-12-04 16:10:43,173:INFO:Starting cross validation +2024-12-04 16:10:43,175:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:43,405:INFO:Calculating mean and std +2024-12-04 16:10:43,405:INFO:Creating metrics dataframe +2024-12-04 16:10:43,407:INFO:Uploading results into container +2024-12-04 16:10:43,409:INFO:Uploading model into container now +2024-12-04 16:10:43,410:INFO:_master_model_container: 10 +2024-12-04 16:10:43,410:INFO:_display_container: 2 +2024-12-04 16:10:43,410:INFO:HuberRegressor() +2024-12-04 16:10:43,410:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:43,530:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:43,530:INFO:Creating metrics dataframe +2024-12-04 16:10:43,536:INFO:Initializing K Neighbors Regressor +2024-12-04 16:10:43,536:INFO:Total runtime is 0.14687164624532065 minutes +2024-12-04 16:10:43,539:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:43,539:INFO:Initializing create_model() +2024-12-04 16:10:43,539:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:43,539:INFO:Checking exceptions +2024-12-04 16:10:43,539:INFO:Importing libraries +2024-12-04 16:10:43,539:INFO:Copying training dataset +2024-12-04 16:10:43,546:INFO:Defining folds +2024-12-04 16:10:43,546:INFO:Declaring metric variables +2024-12-04 16:10:43,548:INFO:Importing untrained model +2024-12-04 16:10:43,552:INFO:K Neighbors Regressor Imported successfully +2024-12-04 16:10:43,559:INFO:Starting cross validation +2024-12-04 16:10:43,560:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:43,804:INFO:Calculating mean and std +2024-12-04 16:10:43,805:INFO:Creating metrics dataframe +2024-12-04 16:10:43,807:INFO:Uploading results into container +2024-12-04 16:10:43,807:INFO:Uploading model into container now +2024-12-04 16:10:43,809:INFO:_master_model_container: 11 +2024-12-04 16:10:43,809:INFO:_display_container: 2 +2024-12-04 16:10:43,809:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 16:10:43,809:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:43,938:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:43,938:INFO:Creating metrics dataframe +2024-12-04 16:10:43,947:INFO:Initializing Decision Tree Regressor +2024-12-04 16:10:43,947:INFO:Total runtime is 0.15372290611267092 minutes +2024-12-04 16:10:43,950:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:43,950:INFO:Initializing create_model() +2024-12-04 16:10:43,950:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:43,950:INFO:Checking exceptions +2024-12-04 16:10:43,950:INFO:Importing libraries +2024-12-04 16:10:43,950:INFO:Copying training dataset +2024-12-04 16:10:43,955:INFO:Defining folds +2024-12-04 16:10:43,957:INFO:Declaring metric variables +2024-12-04 16:10:43,960:INFO:Importing untrained model +2024-12-04 16:10:43,963:INFO:Decision Tree Regressor Imported successfully +2024-12-04 16:10:43,968:INFO:Starting cross validation +2024-12-04 16:10:43,969:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:44,266:INFO:Calculating mean and std +2024-12-04 16:10:44,267:INFO:Creating metrics dataframe +2024-12-04 16:10:44,268:INFO:Uploading results into container +2024-12-04 16:10:44,268:INFO:Uploading model into container now +2024-12-04 16:10:44,270:INFO:_master_model_container: 12 +2024-12-04 16:10:44,270:INFO:_display_container: 2 +2024-12-04 16:10:44,270:INFO:DecisionTreeRegressor(random_state=7562) +2024-12-04 16:10:44,270:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:44,388:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:44,388:INFO:Creating metrics dataframe +2024-12-04 16:10:44,395:INFO:Initializing Random Forest Regressor +2024-12-04 16:10:44,396:INFO:Total runtime is 0.16120730241139733 minutes +2024-12-04 16:10:44,399:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:44,399:INFO:Initializing create_model() +2024-12-04 16:10:44,399:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:44,399:INFO:Checking exceptions +2024-12-04 16:10:44,399:INFO:Importing libraries +2024-12-04 16:10:44,399:INFO:Copying training dataset +2024-12-04 16:10:44,405:INFO:Defining folds +2024-12-04 16:10:44,405:INFO:Declaring metric variables +2024-12-04 16:10:44,409:INFO:Importing untrained model +2024-12-04 16:10:44,413:INFO:Random Forest Regressor Imported successfully +2024-12-04 16:10:44,418:INFO:Starting cross validation +2024-12-04 16:10:44,419:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:49,627:INFO:Calculating mean and std +2024-12-04 16:10:49,628:INFO:Creating metrics dataframe +2024-12-04 16:10:49,630:INFO:Uploading results into container +2024-12-04 16:10:49,630:INFO:Uploading model into container now +2024-12-04 16:10:49,631:INFO:_master_model_container: 13 +2024-12-04 16:10:49,631:INFO:_display_container: 2 +2024-12-04 16:10:49,631:INFO:RandomForestRegressor(n_jobs=-1, random_state=7562) +2024-12-04 16:10:49,631:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:49,790:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:49,790:INFO:Creating metrics dataframe +2024-12-04 16:10:49,798:INFO:Initializing Extra Trees Regressor +2024-12-04 16:10:49,798:INFO:Total runtime is 0.25124675432840987 minutes +2024-12-04 16:10:49,801:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:49,802:INFO:Initializing create_model() +2024-12-04 16:10:49,802:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:49,802:INFO:Checking exceptions +2024-12-04 16:10:49,802:INFO:Importing libraries +2024-12-04 16:10:49,802:INFO:Copying training dataset +2024-12-04 16:10:49,810:INFO:Defining folds +2024-12-04 16:10:49,810:INFO:Declaring metric variables +2024-12-04 16:10:49,813:INFO:Importing untrained model +2024-12-04 16:10:49,817:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:10:49,824:INFO:Starting cross validation +2024-12-04 16:10:49,826:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:52,782:INFO:Calculating mean and std +2024-12-04 16:10:52,783:INFO:Creating metrics dataframe +2024-12-04 16:10:52,785:INFO:Uploading results into container +2024-12-04 16:10:52,785:INFO:Uploading model into container now +2024-12-04 16:10:52,785:INFO:_master_model_container: 14 +2024-12-04 16:10:52,786:INFO:_display_container: 2 +2024-12-04 16:10:52,786:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=7562) +2024-12-04 16:10:52,786:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:52,910:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:52,911:INFO:Creating metrics dataframe +2024-12-04 16:10:52,922:INFO:Initializing AdaBoost Regressor +2024-12-04 16:10:52,922:INFO:Total runtime is 0.30330250263214115 minutes +2024-12-04 16:10:52,924:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:52,924:INFO:Initializing create_model() +2024-12-04 16:10:52,924:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:52,925:INFO:Checking exceptions +2024-12-04 16:10:52,925:INFO:Importing libraries +2024-12-04 16:10:52,925:INFO:Copying training dataset +2024-12-04 16:10:52,931:INFO:Defining folds +2024-12-04 16:10:52,932:INFO:Declaring metric variables +2024-12-04 16:10:52,935:INFO:Importing untrained model +2024-12-04 16:10:52,938:INFO:AdaBoost Regressor Imported successfully +2024-12-04 16:10:52,944:INFO:Starting cross validation +2024-12-04 16:10:52,947:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:53,990:INFO:Calculating mean and std +2024-12-04 16:10:53,992:INFO:Creating metrics dataframe +2024-12-04 16:10:53,992:INFO:Uploading results into container +2024-12-04 16:10:53,994:INFO:Uploading model into container now +2024-12-04 16:10:53,994:INFO:_master_model_container: 15 +2024-12-04 16:10:53,994:INFO:_display_container: 2 +2024-12-04 16:10:53,994:INFO:AdaBoostRegressor(random_state=7562) +2024-12-04 16:10:53,994:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:54,107:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:54,107:INFO:Creating metrics dataframe +2024-12-04 16:10:54,115:INFO:Initializing Gradient Boosting Regressor +2024-12-04 16:10:54,115:INFO:Total runtime is 0.3231957038243612 minutes +2024-12-04 16:10:54,118:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:54,118:INFO:Initializing create_model() +2024-12-04 16:10:54,118:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:54,118:INFO:Checking exceptions +2024-12-04 16:10:54,118:INFO:Importing libraries +2024-12-04 16:10:54,118:INFO:Copying training dataset +2024-12-04 16:10:54,125:INFO:Defining folds +2024-12-04 16:10:54,125:INFO:Declaring metric variables +2024-12-04 16:10:54,128:INFO:Importing untrained model +2024-12-04 16:10:54,132:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 16:10:54,138:INFO:Starting cross validation +2024-12-04 16:10:54,138:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:55,915:INFO:Calculating mean and std +2024-12-04 16:10:55,916:INFO:Creating metrics dataframe +2024-12-04 16:10:55,917:INFO:Uploading results into container +2024-12-04 16:10:55,917:INFO:Uploading model into container now +2024-12-04 16:10:55,918:INFO:_master_model_container: 16 +2024-12-04 16:10:55,918:INFO:_display_container: 2 +2024-12-04 16:10:55,919:INFO:GradientBoostingRegressor(random_state=7562) +2024-12-04 16:10:55,919:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:56,035:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:56,035:INFO:Creating metrics dataframe +2024-12-04 16:10:56,042:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 16:10:56,042:INFO:Total runtime is 0.3553159276644389 minutes +2024-12-04 16:10:56,046:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:56,046:INFO:Initializing create_model() +2024-12-04 16:10:56,046:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:56,046:INFO:Checking exceptions +2024-12-04 16:10:56,046:INFO:Importing libraries +2024-12-04 16:10:56,047:INFO:Copying training dataset +2024-12-04 16:10:56,052:INFO:Defining folds +2024-12-04 16:10:56,052:INFO:Declaring metric variables +2024-12-04 16:10:56,054:INFO:Importing untrained model +2024-12-04 16:10:56,058:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 16:10:56,063:INFO:Starting cross validation +2024-12-04 16:10:56,064:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:57,010:INFO:Calculating mean and std +2024-12-04 16:10:57,011:INFO:Creating metrics dataframe +2024-12-04 16:10:57,013:INFO:Uploading results into container +2024-12-04 16:10:57,014:INFO:Uploading model into container now +2024-12-04 16:10:57,014:INFO:_master_model_container: 17 +2024-12-04 16:10:57,014:INFO:_display_container: 2 +2024-12-04 16:10:57,015:INFO:LGBMRegressor(n_jobs=-1, random_state=7562) +2024-12-04 16:10:57,015:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:57,160:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:57,160:INFO:Creating metrics dataframe +2024-12-04 16:10:57,168:INFO:Initializing Dummy Regressor +2024-12-04 16:10:57,168:INFO:Total runtime is 0.37408140103022264 minutes +2024-12-04 16:10:57,172:INFO:SubProcess create_model() called ================================== +2024-12-04 16:10:57,172:INFO:Initializing create_model() +2024-12-04 16:10:57,172:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:57,172:INFO:Checking exceptions +2024-12-04 16:10:57,172:INFO:Importing libraries +2024-12-04 16:10:57,174:INFO:Copying training dataset +2024-12-04 16:10:57,178:INFO:Defining folds +2024-12-04 16:10:57,178:INFO:Declaring metric variables +2024-12-04 16:10:57,182:INFO:Importing untrained model +2024-12-04 16:10:57,185:INFO:Dummy Regressor Imported successfully +2024-12-04 16:10:57,190:INFO:Starting cross validation +2024-12-04 16:10:57,191:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:10:57,364:INFO:Calculating mean and std +2024-12-04 16:10:57,365:INFO:Creating metrics dataframe +2024-12-04 16:10:57,366:INFO:Uploading results into container +2024-12-04 16:10:57,367:INFO:Uploading model into container now +2024-12-04 16:10:57,368:INFO:_master_model_container: 18 +2024-12-04 16:10:57,369:INFO:_display_container: 2 +2024-12-04 16:10:57,369:INFO:DummyRegressor() +2024-12-04 16:10:57,369:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:57,484:INFO:SubProcess create_model() end ================================== +2024-12-04 16:10:57,484:INFO:Creating metrics dataframe +2024-12-04 16:10:57,494:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 16:10:57,501:INFO:Initializing create_model() +2024-12-04 16:10:57,501:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=7562), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:10:57,501:INFO:Checking exceptions +2024-12-04 16:10:57,502:INFO:Importing libraries +2024-12-04 16:10:57,502:INFO:Copying training dataset +2024-12-04 16:10:57,509:INFO:Defining folds +2024-12-04 16:10:57,509:INFO:Declaring metric variables +2024-12-04 16:10:57,509:INFO:Importing untrained model +2024-12-04 16:10:57,509:INFO:Declaring custom model +2024-12-04 16:10:57,510:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:10:57,510:INFO:Cross validation set to False +2024-12-04 16:10:57,510:INFO:Fitting Model +2024-12-04 16:10:57,830:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=7562) +2024-12-04 16:10:57,830:INFO:create_model() successfully completed...................................... +2024-12-04 16:10:57,972:INFO:_master_model_container: 18 +2024-12-04 16:10:57,972:INFO:_display_container: 2 +2024-12-04 16:10:57,973:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=7562) +2024-12-04 16:10:57,973:INFO:compare_models() successfully completed...................................... +2024-12-04 16:13:08,091:INFO:PyCaret RegressionExperiment +2024-12-04 16:13:08,091:INFO:Logging name: model_hospi_reg_ens +2024-12-04 16:13:08,091:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:13:08,091:INFO:version 3.3.2 +2024-12-04 16:13:08,091:INFO:Initializing setup() +2024-12-04 16:13:08,091:INFO:self.USI: d2ce +2024-12-04 16:13:08,091:INFO:self._variable_keys: {'logging_param', '_ml_usecase', 'X_train', 'y', 'memory', 'pipeline', 'gpu_param', 'exp_id', 'X', 'target_param', 'y_train', 'n_jobs_param', 'transform_target_param', 'fold_generator', 'USI', '_available_plots', 'exp_name_log', 'y_test', 'idx', 'X_test', 'log_plots_param', 'seed', 'data', 'fold_groups_param', 'html_param', 'gpu_n_jobs_param', 'fold_shuffle_param'} +2024-12-04 16:13:08,091:INFO:Checking environment +2024-12-04 16:13:08,091:INFO:python_version: 3.11.9 +2024-12-04 16:13:08,091:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:13:08,091:INFO:machine: AMD64 +2024-12-04 16:13:08,091:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:13:08,098:INFO:Memory: svmem(total=17007292416, available=1595949056, percent=90.6, used=15411343360, free=1595949056) +2024-12-04 16:13:08,098:INFO:Physical Core: 4 +2024-12-04 16:13:08,098:INFO:Logical Core: 8 +2024-12-04 16:13:08,098:INFO:Checking libraries +2024-12-04 16:13:08,098:INFO:System: +2024-12-04 16:13:08,098:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:13:08,098:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:13:08,098:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:13:08,098:INFO:PyCaret required dependencies: +2024-12-04 16:13:08,098:INFO: pip: 24.3.1 +2024-12-04 16:13:08,098:INFO: setuptools: 65.5.0 +2024-12-04 16:13:08,098:INFO: pycaret: 3.3.2 +2024-12-04 16:13:08,098:INFO: IPython: 8.12.3 +2024-12-04 16:13:08,098:INFO: ipywidgets: 8.1.5 +2024-12-04 16:13:08,098:INFO: tqdm: 4.67.1 +2024-12-04 16:13:08,098:INFO: numpy: 1.26.4 +2024-12-04 16:13:08,098:INFO: pandas: 2.1.4 +2024-12-04 16:13:08,098:INFO: jinja2: 3.1.4 +2024-12-04 16:13:08,098:INFO: scipy: 1.11.4 +2024-12-04 16:13:08,098:INFO: joblib: 1.3.2 +2024-12-04 16:13:08,098:INFO: sklearn: 1.4.2 +2024-12-04 16:13:08,098:INFO: pyod: 2.0.2 +2024-12-04 16:13:08,098:INFO: imblearn: 0.12.4 +2024-12-04 16:13:08,098:INFO: category_encoders: 2.6.4 +2024-12-04 16:13:08,098:INFO: lightgbm: 4.5.0 +2024-12-04 16:13:08,098:INFO: numba: 0.60.0 +2024-12-04 16:13:08,099:INFO: requests: 2.32.3 +2024-12-04 16:13:08,099:INFO: matplotlib: 3.7.5 +2024-12-04 16:13:08,099:INFO: scikitplot: 0.3.7 +2024-12-04 16:13:08,099:INFO: yellowbrick: 1.5 +2024-12-04 16:13:08,099:INFO: plotly: 5.24.1 +2024-12-04 16:13:08,099:INFO: plotly-resampler: Not installed +2024-12-04 16:13:08,099:INFO: kaleido: 0.2.1 +2024-12-04 16:13:08,099:INFO: schemdraw: 0.15 +2024-12-04 16:13:08,099:INFO: statsmodels: 0.14.4 +2024-12-04 16:13:08,099:INFO: sktime: 0.26.0 +2024-12-04 16:13:08,099:INFO: tbats: 1.1.3 +2024-12-04 16:13:08,099:INFO: pmdarima: 2.0.4 +2024-12-04 16:13:08,099:INFO: psutil: 6.1.0 +2024-12-04 16:13:08,099:INFO: markupsafe: 3.0.2 +2024-12-04 16:13:08,099:INFO: pickle5: Not installed +2024-12-04 16:13:08,099:INFO: cloudpickle: 3.1.0 +2024-12-04 16:13:08,099:INFO: deprecation: 2.1.0 +2024-12-04 16:13:08,099:INFO: xxhash: 3.5.0 +2024-12-04 16:13:08,099:INFO: wurlitzer: Not installed +2024-12-04 16:13:08,099:INFO:PyCaret optional dependencies: +2024-12-04 16:13:08,099:INFO: shap: Not installed +2024-12-04 16:13:08,099:INFO: interpret: Not installed +2024-12-04 16:13:08,099:INFO: umap: Not installed +2024-12-04 16:13:08,099:INFO: ydata_profiling: Not installed +2024-12-04 16:13:08,099:INFO: explainerdashboard: Not installed +2024-12-04 16:13:08,099:INFO: autoviz: Not installed +2024-12-04 16:13:08,099:INFO: fairlearn: Not installed +2024-12-04 16:13:08,100:INFO: deepchecks: Not installed +2024-12-04 16:13:08,100:INFO: xgboost: Not installed +2024-12-04 16:13:08,100:INFO: catboost: Not installed +2024-12-04 16:13:08,100:INFO: kmodes: Not installed +2024-12-04 16:13:08,100:INFO: mlxtend: Not installed +2024-12-04 16:13:08,100:INFO: statsforecast: Not installed +2024-12-04 16:13:08,100:INFO: tune_sklearn: Not installed +2024-12-04 16:13:08,100:INFO: ray: Not installed +2024-12-04 16:13:08,100:INFO: hyperopt: Not installed +2024-12-04 16:13:08,100:INFO: optuna: Not installed +2024-12-04 16:13:08,100:INFO: skopt: Not installed +2024-12-04 16:13:08,100:INFO: mlflow: 2.18.0 +2024-12-04 16:13:08,100:INFO: gradio: Not installed +2024-12-04 16:13:08,100:INFO: fastapi: Not installed +2024-12-04 16:13:08,100:INFO: uvicorn: 0.32.1 +2024-12-04 16:13:08,100:INFO: m2cgen: Not installed +2024-12-04 16:13:08,100:INFO: evidently: Not installed +2024-12-04 16:13:08,100:INFO: fugue: Not installed +2024-12-04 16:13:08,100:INFO: streamlit: 1.40.1 +2024-12-04 16:13:08,100:INFO: prophet: Not installed +2024-12-04 16:13:08,100:INFO:None +2024-12-04 16:13:08,100:INFO:Set up data. +2024-12-04 16:13:08,109:INFO:Set up folding strategy. +2024-12-04 16:13:08,109:INFO:Set up train/test split. +2024-12-04 16:13:08,113:INFO:Set up index. +2024-12-04 16:13:08,114:INFO:Assigning column types. +2024-12-04 16:13:08,117:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:13:08,117:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,121:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,126:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,175:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,214:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,214:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,214:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,214:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,218:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,222:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,273:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,317:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,317:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,317:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,319:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 16:13:08,322:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,327:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,377:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,414:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,415:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,415:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,420:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,423:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,473:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,513:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,513:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,513:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,513:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 16:13:08,521:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,571:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,617:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,617:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,617:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,626:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,677:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,719:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,720:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,720:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,720:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 16:13:08,789:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,841:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,841:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,841:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,907:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,948:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:08,948:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,948:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:08,949:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:13:09,015:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:09,065:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,066:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,137:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:09,177:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,178:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,178:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 16:13:09,281:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,281:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,385:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,386:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:09,386:INFO:Preparing preprocessing pipeline... +2024-12-04 16:13:09,386:INFO:Set up target transformation. +2024-12-04 16:13:09,386:INFO:Set up simple imputation. +2024-12-04 16:13:09,389:INFO:Set up encoding of categorical features. +2024-12-04 16:13:09,389:INFO:Set up feature normalization. +2024-12-04 16:13:27,199:INFO:PyCaret RegressionExperiment +2024-12-04 16:13:27,199:INFO:Logging name: model_hospi_reg_ens +2024-12-04 16:13:27,199:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:13:27,199:INFO:version 3.3.2 +2024-12-04 16:13:27,199:INFO:Initializing setup() +2024-12-04 16:13:27,199:INFO:self.USI: 3c5c +2024-12-04 16:13:27,199:INFO:self._variable_keys: {'logging_param', '_ml_usecase', 'X_train', 'y', 'memory', 'pipeline', 'gpu_param', 'exp_id', 'X', 'target_param', 'y_train', 'n_jobs_param', 'transform_target_param', 'fold_generator', 'USI', '_available_plots', 'exp_name_log', 'y_test', 'idx', 'X_test', 'log_plots_param', 'seed', 'data', 'fold_groups_param', 'html_param', 'gpu_n_jobs_param', 'fold_shuffle_param'} +2024-12-04 16:13:27,199:INFO:Checking environment +2024-12-04 16:13:27,199:INFO:python_version: 3.11.9 +2024-12-04 16:13:27,199:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:13:27,199:INFO:machine: AMD64 +2024-12-04 16:13:27,199:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:13:27,204:INFO:Memory: svmem(total=17007292416, available=1576615936, percent=90.7, used=15430676480, free=1576615936) +2024-12-04 16:13:27,205:INFO:Physical Core: 4 +2024-12-04 16:13:27,205:INFO:Logical Core: 8 +2024-12-04 16:13:27,205:INFO:Checking libraries +2024-12-04 16:13:27,205:INFO:System: +2024-12-04 16:13:27,205:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:13:27,205:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:13:27,205:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:13:27,205:INFO:PyCaret required dependencies: +2024-12-04 16:13:27,205:INFO: pip: 24.3.1 +2024-12-04 16:13:27,206:INFO: setuptools: 65.5.0 +2024-12-04 16:13:27,206:INFO: pycaret: 3.3.2 +2024-12-04 16:13:27,206:INFO: IPython: 8.12.3 +2024-12-04 16:13:27,206:INFO: ipywidgets: 8.1.5 +2024-12-04 16:13:27,206:INFO: tqdm: 4.67.1 +2024-12-04 16:13:27,206:INFO: numpy: 1.26.4 +2024-12-04 16:13:27,206:INFO: pandas: 2.1.4 +2024-12-04 16:13:27,206:INFO: jinja2: 3.1.4 +2024-12-04 16:13:27,206:INFO: scipy: 1.11.4 +2024-12-04 16:13:27,206:INFO: joblib: 1.3.2 +2024-12-04 16:13:27,206:INFO: sklearn: 1.4.2 +2024-12-04 16:13:27,206:INFO: pyod: 2.0.2 +2024-12-04 16:13:27,206:INFO: imblearn: 0.12.4 +2024-12-04 16:13:27,206:INFO: category_encoders: 2.6.4 +2024-12-04 16:13:27,206:INFO: lightgbm: 4.5.0 +2024-12-04 16:13:27,206:INFO: numba: 0.60.0 +2024-12-04 16:13:27,206:INFO: requests: 2.32.3 +2024-12-04 16:13:27,206:INFO: matplotlib: 3.7.5 +2024-12-04 16:13:27,206:INFO: scikitplot: 0.3.7 +2024-12-04 16:13:27,206:INFO: yellowbrick: 1.5 +2024-12-04 16:13:27,206:INFO: plotly: 5.24.1 +2024-12-04 16:13:27,206:INFO: plotly-resampler: Not installed +2024-12-04 16:13:27,206:INFO: kaleido: 0.2.1 +2024-12-04 16:13:27,206:INFO: schemdraw: 0.15 +2024-12-04 16:13:27,206:INFO: statsmodels: 0.14.4 +2024-12-04 16:13:27,206:INFO: sktime: 0.26.0 +2024-12-04 16:13:27,207:INFO: tbats: 1.1.3 +2024-12-04 16:13:27,207:INFO: pmdarima: 2.0.4 +2024-12-04 16:13:27,207:INFO: psutil: 6.1.0 +2024-12-04 16:13:27,207:INFO: markupsafe: 3.0.2 +2024-12-04 16:13:27,207:INFO: pickle5: Not installed +2024-12-04 16:13:27,207:INFO: cloudpickle: 3.1.0 +2024-12-04 16:13:27,207:INFO: deprecation: 2.1.0 +2024-12-04 16:13:27,207:INFO: xxhash: 3.5.0 +2024-12-04 16:13:27,207:INFO: wurlitzer: Not installed +2024-12-04 16:13:27,207:INFO:PyCaret optional dependencies: +2024-12-04 16:13:27,207:INFO: shap: Not installed +2024-12-04 16:13:27,207:INFO: interpret: Not installed +2024-12-04 16:13:27,207:INFO: umap: Not installed +2024-12-04 16:13:27,207:INFO: ydata_profiling: Not installed +2024-12-04 16:13:27,207:INFO: explainerdashboard: Not installed +2024-12-04 16:13:27,207:INFO: autoviz: Not installed +2024-12-04 16:13:27,207:INFO: fairlearn: Not installed +2024-12-04 16:13:27,207:INFO: deepchecks: Not installed +2024-12-04 16:13:27,207:INFO: xgboost: Not installed +2024-12-04 16:13:27,207:INFO: catboost: Not installed +2024-12-04 16:13:27,207:INFO: kmodes: Not installed +2024-12-04 16:13:27,207:INFO: mlxtend: Not installed +2024-12-04 16:13:27,207:INFO: statsforecast: Not installed +2024-12-04 16:13:27,207:INFO: tune_sklearn: Not installed +2024-12-04 16:13:27,207:INFO: ray: Not installed +2024-12-04 16:13:27,207:INFO: hyperopt: Not installed +2024-12-04 16:13:27,207:INFO: optuna: Not installed +2024-12-04 16:13:27,207:INFO: skopt: Not installed +2024-12-04 16:13:27,207:INFO: mlflow: 2.18.0 +2024-12-04 16:13:27,207:INFO: gradio: Not installed +2024-12-04 16:13:27,207:INFO: fastapi: Not installed +2024-12-04 16:13:27,207:INFO: uvicorn: 0.32.1 +2024-12-04 16:13:27,207:INFO: m2cgen: Not installed +2024-12-04 16:13:27,207:INFO: evidently: Not installed +2024-12-04 16:13:27,207:INFO: fugue: Not installed +2024-12-04 16:13:27,207:INFO: streamlit: 1.40.1 +2024-12-04 16:13:27,207:INFO: prophet: Not installed +2024-12-04 16:13:27,207:INFO:None +2024-12-04 16:13:27,207:INFO:Set up data. +2024-12-04 16:13:27,214:INFO:Set up folding strategy. +2024-12-04 16:13:27,215:INFO:Set up train/test split. +2024-12-04 16:13:27,219:INFO:Set up index. +2024-12-04 16:13:27,219:INFO:Assigning column types. +2024-12-04 16:13:27,224:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:13:27,224:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,228:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,233:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,287:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,327:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,328:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,328:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,329:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,332:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,336:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,387:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,428:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,429:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,429:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,429:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 16:13:27,433:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,437:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,488:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,527:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,528:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,528:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,532:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,535:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,585:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,623:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,625:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,625:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,625:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 16:13:27,634:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,686:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,724:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,724:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,724:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,733:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,784:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,822:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,822:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,823:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,823:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 16:13:27,889:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,928:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:27,930:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,930:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:27,988:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:28,027:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:13:28,027:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,027:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,028:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:13:28,088:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:28,127:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,127:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,186:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:13:28,231:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,232:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,232:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 16:13:28,330:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,331:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,430:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,430:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,431:INFO:Preparing preprocessing pipeline... +2024-12-04 16:13:28,431:INFO:Set up target transformation. +2024-12-04 16:13:28,431:INFO:Set up simple imputation. +2024-12-04 16:13:28,433:INFO:Set up encoding of categorical features. +2024-12-04 16:13:28,433:INFO:Set up feature normalization. +2024-12-04 16:13:28,527:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:13:28,536:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['tranche_age_0_1', + 'tranche_age_1_4', + 'tranche_age_5_14', + 'tranche_age_15_24', + 'tranch... + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 16:13:28,536:INFO:Creating final display dataframe. +2024-12-04 16:13:28,827:INFO:Setup _display_container: Description Value +0 Session id 3279 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 14) +4 Transformed data shape (5688, 31) +5 Transformed train set shape (3981, 31) +6 Transformed test set shape (1707, 31) +7 Numeric features 11 +8 Categorical features 2 +9 Rows with missing values 0.1% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Normalize True +17 Normalize method zscore +18 Transform target True +19 Transform target method yeo-johnson +20 Fold Generator KFold +21 Fold Number 10 +22 CPU Jobs -1 +23 Use GPU False +24 Log Experiment False +25 Experiment Name model_hospi_reg_ens +26 USI 3c5c +2024-12-04 16:13:28,953:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:28,954:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:29,071:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:29,072:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:13:29,072:INFO:setup() successfully completed in 1.89s............... +2024-12-04 16:13:36,974:INFO:Initializing compare_models() +2024-12-04 16:13:36,975:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 16:13:36,975:INFO:Checking exceptions +2024-12-04 16:13:36,978:INFO:Preparing display monitor +2024-12-04 16:13:37,000:INFO:Initializing Linear Regression +2024-12-04 16:13:37,000:INFO:Total runtime is 0.0 minutes +2024-12-04 16:13:37,004:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:37,004:INFO:Initializing create_model() +2024-12-04 16:13:37,004:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:37,004:INFO:Checking exceptions +2024-12-04 16:13:37,004:INFO:Importing libraries +2024-12-04 16:13:37,004:INFO:Copying training dataset +2024-12-04 16:13:37,011:INFO:Defining folds +2024-12-04 16:13:37,012:INFO:Declaring metric variables +2024-12-04 16:13:37,015:INFO:Importing untrained model +2024-12-04 16:13:37,020:INFO:Linear Regression Imported successfully +2024-12-04 16:13:37,027:INFO:Starting cross validation +2024-12-04 16:13:37,028:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:37,328:INFO:Calculating mean and std +2024-12-04 16:13:37,329:INFO:Creating metrics dataframe +2024-12-04 16:13:37,330:INFO:Uploading results into container +2024-12-04 16:13:37,331:INFO:Uploading model into container now +2024-12-04 16:13:37,331:INFO:_master_model_container: 1 +2024-12-04 16:13:37,331:INFO:_display_container: 2 +2024-12-04 16:13:37,332:INFO:LinearRegression(n_jobs=-1) +2024-12-04 16:13:37,332:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:37,461:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:37,461:INFO:Creating metrics dataframe +2024-12-04 16:13:37,466:INFO:Initializing Lasso Regression +2024-12-04 16:13:37,466:INFO:Total runtime is 0.007768579324086507 minutes +2024-12-04 16:13:37,469:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:37,470:INFO:Initializing create_model() +2024-12-04 16:13:37,470:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:37,470:INFO:Checking exceptions +2024-12-04 16:13:37,470:INFO:Importing libraries +2024-12-04 16:13:37,470:INFO:Copying training dataset +2024-12-04 16:13:37,475:INFO:Defining folds +2024-12-04 16:13:37,475:INFO:Declaring metric variables +2024-12-04 16:13:37,478:INFO:Importing untrained model +2024-12-04 16:13:37,482:INFO:Lasso Regression Imported successfully +2024-12-04 16:13:37,489:INFO:Starting cross validation +2024-12-04 16:13:37,490:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:37,816:INFO:Calculating mean and std +2024-12-04 16:13:37,816:INFO:Creating metrics dataframe +2024-12-04 16:13:37,817:INFO:Uploading results into container +2024-12-04 16:13:37,817:INFO:Uploading model into container now +2024-12-04 16:13:37,818:INFO:_master_model_container: 2 +2024-12-04 16:13:37,818:INFO:_display_container: 2 +2024-12-04 16:13:37,818:INFO:Lasso(random_state=3279) +2024-12-04 16:13:37,818:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:37,936:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:37,936:INFO:Creating metrics dataframe +2024-12-04 16:13:37,941:INFO:Initializing Ridge Regression +2024-12-04 16:13:37,941:INFO:Total runtime is 0.01568473180135091 minutes +2024-12-04 16:13:37,945:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:37,945:INFO:Initializing create_model() +2024-12-04 16:13:37,945:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:37,945:INFO:Checking exceptions +2024-12-04 16:13:37,946:INFO:Importing libraries +2024-12-04 16:13:37,946:INFO:Copying training dataset +2024-12-04 16:13:37,951:INFO:Defining folds +2024-12-04 16:13:37,951:INFO:Declaring metric variables +2024-12-04 16:13:37,953:INFO:Importing untrained model +2024-12-04 16:13:37,956:INFO:Ridge Regression Imported successfully +2024-12-04 16:13:37,961:INFO:Starting cross validation +2024-12-04 16:13:37,963:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:38,298:INFO:Calculating mean and std +2024-12-04 16:13:38,299:INFO:Creating metrics dataframe +2024-12-04 16:13:38,301:INFO:Uploading results into container +2024-12-04 16:13:38,301:INFO:Uploading model into container now +2024-12-04 16:13:38,301:INFO:_master_model_container: 3 +2024-12-04 16:13:38,301:INFO:_display_container: 2 +2024-12-04 16:13:38,301:INFO:Ridge(random_state=3279) +2024-12-04 16:13:38,301:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:38,421:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:38,421:INFO:Creating metrics dataframe +2024-12-04 16:13:38,427:INFO:Initializing Elastic Net +2024-12-04 16:13:38,427:INFO:Total runtime is 0.02378147443135579 minutes +2024-12-04 16:13:38,430:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:38,431:INFO:Initializing create_model() +2024-12-04 16:13:38,431:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:38,431:INFO:Checking exceptions +2024-12-04 16:13:38,431:INFO:Importing libraries +2024-12-04 16:13:38,431:INFO:Copying training dataset +2024-12-04 16:13:38,438:INFO:Defining folds +2024-12-04 16:13:38,438:INFO:Declaring metric variables +2024-12-04 16:13:38,441:INFO:Importing untrained model +2024-12-04 16:13:38,445:INFO:Elastic Net Imported successfully +2024-12-04 16:13:38,451:INFO:Starting cross validation +2024-12-04 16:13:38,453:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:38,802:INFO:Calculating mean and std +2024-12-04 16:13:38,803:INFO:Creating metrics dataframe +2024-12-04 16:13:38,806:INFO:Uploading results into container +2024-12-04 16:13:38,806:INFO:Uploading model into container now +2024-12-04 16:13:38,807:INFO:_master_model_container: 4 +2024-12-04 16:13:38,807:INFO:_display_container: 2 +2024-12-04 16:13:38,807:INFO:ElasticNet(random_state=3279) +2024-12-04 16:13:38,807:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:38,930:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:38,930:INFO:Creating metrics dataframe +2024-12-04 16:13:38,935:INFO:Initializing Least Angle Regression +2024-12-04 16:13:38,936:INFO:Total runtime is 0.03227568467458089 minutes +2024-12-04 16:13:38,938:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:38,939:INFO:Initializing create_model() +2024-12-04 16:13:38,939:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:38,939:INFO:Checking exceptions +2024-12-04 16:13:38,939:INFO:Importing libraries +2024-12-04 16:13:38,939:INFO:Copying training dataset +2024-12-04 16:13:38,945:INFO:Defining folds +2024-12-04 16:13:38,946:INFO:Declaring metric variables +2024-12-04 16:13:38,949:INFO:Importing untrained model +2024-12-04 16:13:38,951:INFO:Least Angle Regression Imported successfully +2024-12-04 16:13:38,957:INFO:Starting cross validation +2024-12-04 16:13:38,960:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:39,237:INFO:Calculating mean and std +2024-12-04 16:13:39,238:INFO:Creating metrics dataframe +2024-12-04 16:13:39,239:INFO:Uploading results into container +2024-12-04 16:13:39,240:INFO:Uploading model into container now +2024-12-04 16:13:39,240:INFO:_master_model_container: 5 +2024-12-04 16:13:39,240:INFO:_display_container: 2 +2024-12-04 16:13:39,241:INFO:Lars(random_state=3279) +2024-12-04 16:13:39,241:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:39,360:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:39,360:INFO:Creating metrics dataframe +2024-12-04 16:13:39,367:INFO:Initializing Lasso Least Angle Regression +2024-12-04 16:13:39,367:INFO:Total runtime is 0.03945447206497192 minutes +2024-12-04 16:13:39,370:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:39,370:INFO:Initializing create_model() +2024-12-04 16:13:39,370:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:39,370:INFO:Checking exceptions +2024-12-04 16:13:39,370:INFO:Importing libraries +2024-12-04 16:13:39,370:INFO:Copying training dataset +2024-12-04 16:13:39,376:INFO:Defining folds +2024-12-04 16:13:39,376:INFO:Declaring metric variables +2024-12-04 16:13:39,381:INFO:Importing untrained model +2024-12-04 16:13:39,383:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 16:13:39,389:INFO:Starting cross validation +2024-12-04 16:13:39,390:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:39,658:INFO:Calculating mean and std +2024-12-04 16:13:39,659:INFO:Creating metrics dataframe +2024-12-04 16:13:39,661:INFO:Uploading results into container +2024-12-04 16:13:39,662:INFO:Uploading model into container now +2024-12-04 16:13:39,662:INFO:_master_model_container: 6 +2024-12-04 16:13:39,663:INFO:_display_container: 2 +2024-12-04 16:13:39,663:INFO:LassoLars(random_state=3279) +2024-12-04 16:13:39,663:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:39,787:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:39,787:INFO:Creating metrics dataframe +2024-12-04 16:13:39,793:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 16:13:39,793:INFO:Total runtime is 0.04655513763427734 minutes +2024-12-04 16:13:39,797:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:39,797:INFO:Initializing create_model() +2024-12-04 16:13:39,797:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:39,797:INFO:Checking exceptions +2024-12-04 16:13:39,797:INFO:Importing libraries +2024-12-04 16:13:39,797:INFO:Copying training dataset +2024-12-04 16:13:39,803:INFO:Defining folds +2024-12-04 16:13:39,803:INFO:Declaring metric variables +2024-12-04 16:13:39,805:INFO:Importing untrained model +2024-12-04 16:13:39,809:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 16:13:39,816:INFO:Starting cross validation +2024-12-04 16:13:39,817:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:40,105:INFO:Calculating mean and std +2024-12-04 16:13:40,105:INFO:Creating metrics dataframe +2024-12-04 16:13:40,107:INFO:Uploading results into container +2024-12-04 16:13:40,108:INFO:Uploading model into container now +2024-12-04 16:13:40,108:INFO:_master_model_container: 7 +2024-12-04 16:13:40,108:INFO:_display_container: 2 +2024-12-04 16:13:40,108:INFO:OrthogonalMatchingPursuit() +2024-12-04 16:13:40,109:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:40,241:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:40,241:INFO:Creating metrics dataframe +2024-12-04 16:13:40,248:INFO:Initializing Bayesian Ridge +2024-12-04 16:13:40,249:INFO:Total runtime is 0.05415985584259033 minutes +2024-12-04 16:13:40,252:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:40,252:INFO:Initializing create_model() +2024-12-04 16:13:40,252:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:40,252:INFO:Checking exceptions +2024-12-04 16:13:40,253:INFO:Importing libraries +2024-12-04 16:13:40,253:INFO:Copying training dataset +2024-12-04 16:13:40,259:INFO:Defining folds +2024-12-04 16:13:40,259:INFO:Declaring metric variables +2024-12-04 16:13:40,262:INFO:Importing untrained model +2024-12-04 16:13:40,267:INFO:Bayesian Ridge Imported successfully +2024-12-04 16:13:40,273:INFO:Starting cross validation +2024-12-04 16:13:40,274:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:40,645:INFO:Calculating mean and std +2024-12-04 16:13:40,646:INFO:Creating metrics dataframe +2024-12-04 16:13:40,649:INFO:Uploading results into container +2024-12-04 16:13:40,650:INFO:Uploading model into container now +2024-12-04 16:13:40,650:INFO:_master_model_container: 8 +2024-12-04 16:13:40,650:INFO:_display_container: 2 +2024-12-04 16:13:40,651:INFO:BayesianRidge() +2024-12-04 16:13:40,651:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:40,780:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:40,780:INFO:Creating metrics dataframe +2024-12-04 16:13:40,787:INFO:Initializing Passive Aggressive Regressor +2024-12-04 16:13:40,787:INFO:Total runtime is 0.06311551729838054 minutes +2024-12-04 16:13:40,789:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:40,789:INFO:Initializing create_model() +2024-12-04 16:13:40,789:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:40,789:INFO:Checking exceptions +2024-12-04 16:13:40,790:INFO:Importing libraries +2024-12-04 16:13:40,790:INFO:Copying training dataset +2024-12-04 16:13:40,796:INFO:Defining folds +2024-12-04 16:13:40,796:INFO:Declaring metric variables +2024-12-04 16:13:40,799:INFO:Importing untrained model +2024-12-04 16:13:40,803:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 16:13:40,809:INFO:Starting cross validation +2024-12-04 16:13:40,812:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:41,106:INFO:Calculating mean and std +2024-12-04 16:13:41,107:INFO:Creating metrics dataframe +2024-12-04 16:13:41,107:INFO:Uploading results into container +2024-12-04 16:13:41,109:INFO:Uploading model into container now +2024-12-04 16:13:41,109:INFO:_master_model_container: 9 +2024-12-04 16:13:41,109:INFO:_display_container: 2 +2024-12-04 16:13:41,109:INFO:PassiveAggressiveRegressor(random_state=3279) +2024-12-04 16:13:41,110:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:41,234:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:41,234:INFO:Creating metrics dataframe +2024-12-04 16:13:41,240:INFO:Initializing Huber Regressor +2024-12-04 16:13:41,240:INFO:Total runtime is 0.07067553997039795 minutes +2024-12-04 16:13:41,242:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:41,243:INFO:Initializing create_model() +2024-12-04 16:13:41,243:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:41,243:INFO:Checking exceptions +2024-12-04 16:13:41,243:INFO:Importing libraries +2024-12-04 16:13:41,243:INFO:Copying training dataset +2024-12-04 16:13:41,250:INFO:Defining folds +2024-12-04 16:13:41,250:INFO:Declaring metric variables +2024-12-04 16:13:41,253:INFO:Importing untrained model +2024-12-04 16:13:41,257:INFO:Huber Regressor Imported successfully +2024-12-04 16:13:41,263:INFO:Starting cross validation +2024-12-04 16:13:41,264:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:41,716:INFO:Calculating mean and std +2024-12-04 16:13:41,717:INFO:Creating metrics dataframe +2024-12-04 16:13:41,717:INFO:Uploading results into container +2024-12-04 16:13:41,718:INFO:Uploading model into container now +2024-12-04 16:13:41,719:INFO:_master_model_container: 10 +2024-12-04 16:13:41,719:INFO:_display_container: 2 +2024-12-04 16:13:41,719:INFO:HuberRegressor() +2024-12-04 16:13:41,719:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:41,838:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:41,838:INFO:Creating metrics dataframe +2024-12-04 16:13:41,845:INFO:Initializing K Neighbors Regressor +2024-12-04 16:13:41,845:INFO:Total runtime is 0.08074879248936971 minutes +2024-12-04 16:13:41,848:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:41,849:INFO:Initializing create_model() +2024-12-04 16:13:41,849:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:41,849:INFO:Checking exceptions +2024-12-04 16:13:41,849:INFO:Importing libraries +2024-12-04 16:13:41,849:INFO:Copying training dataset +2024-12-04 16:13:41,855:INFO:Defining folds +2024-12-04 16:13:41,855:INFO:Declaring metric variables +2024-12-04 16:13:41,857:INFO:Importing untrained model +2024-12-04 16:13:41,862:INFO:K Neighbors Regressor Imported successfully +2024-12-04 16:13:41,867:INFO:Starting cross validation +2024-12-04 16:13:41,870:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:42,275:INFO:Calculating mean and std +2024-12-04 16:13:42,276:INFO:Creating metrics dataframe +2024-12-04 16:13:42,278:INFO:Uploading results into container +2024-12-04 16:13:42,278:INFO:Uploading model into container now +2024-12-04 16:13:42,278:INFO:_master_model_container: 11 +2024-12-04 16:13:42,280:INFO:_display_container: 2 +2024-12-04 16:13:42,280:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 16:13:42,280:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:42,399:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:42,399:INFO:Creating metrics dataframe +2024-12-04 16:13:42,405:INFO:Initializing Decision Tree Regressor +2024-12-04 16:13:42,405:INFO:Total runtime is 0.09008501768112183 minutes +2024-12-04 16:13:42,408:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:42,409:INFO:Initializing create_model() +2024-12-04 16:13:42,409:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:42,409:INFO:Checking exceptions +2024-12-04 16:13:42,409:INFO:Importing libraries +2024-12-04 16:13:42,409:INFO:Copying training dataset +2024-12-04 16:13:42,416:INFO:Defining folds +2024-12-04 16:13:42,416:INFO:Declaring metric variables +2024-12-04 16:13:42,420:INFO:Importing untrained model +2024-12-04 16:13:42,423:INFO:Decision Tree Regressor Imported successfully +2024-12-04 16:13:42,431:INFO:Starting cross validation +2024-12-04 16:13:42,433:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:42,828:INFO:Calculating mean and std +2024-12-04 16:13:42,830:INFO:Creating metrics dataframe +2024-12-04 16:13:42,831:INFO:Uploading results into container +2024-12-04 16:13:42,831:INFO:Uploading model into container now +2024-12-04 16:13:42,832:INFO:_master_model_container: 12 +2024-12-04 16:13:42,832:INFO:_display_container: 2 +2024-12-04 16:13:42,832:INFO:DecisionTreeRegressor(random_state=3279) +2024-12-04 16:13:42,832:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:42,950:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:42,950:INFO:Creating metrics dataframe +2024-12-04 16:13:42,957:INFO:Initializing Random Forest Regressor +2024-12-04 16:13:42,957:INFO:Total runtime is 0.09928033749262492 minutes +2024-12-04 16:13:42,959:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:42,959:INFO:Initializing create_model() +2024-12-04 16:13:42,959:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:42,959:INFO:Checking exceptions +2024-12-04 16:13:42,959:INFO:Importing libraries +2024-12-04 16:13:42,959:INFO:Copying training dataset +2024-12-04 16:13:42,966:INFO:Defining folds +2024-12-04 16:13:42,966:INFO:Declaring metric variables +2024-12-04 16:13:42,968:INFO:Importing untrained model +2024-12-04 16:13:42,971:INFO:Random Forest Regressor Imported successfully +2024-12-04 16:13:42,978:INFO:Starting cross validation +2024-12-04 16:13:42,981:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:49,533:INFO:Calculating mean and std +2024-12-04 16:13:49,534:INFO:Creating metrics dataframe +2024-12-04 16:13:49,536:INFO:Uploading results into container +2024-12-04 16:13:49,537:INFO:Uploading model into container now +2024-12-04 16:13:49,537:INFO:_master_model_container: 13 +2024-12-04 16:13:49,537:INFO:_display_container: 2 +2024-12-04 16:13:49,537:INFO:RandomForestRegressor(n_jobs=-1, random_state=3279) +2024-12-04 16:13:49,538:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:49,666:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:49,666:INFO:Creating metrics dataframe +2024-12-04 16:13:49,673:INFO:Initializing Extra Trees Regressor +2024-12-04 16:13:49,673:INFO:Total runtime is 0.21122713486353556 minutes +2024-12-04 16:13:49,679:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:49,679:INFO:Initializing create_model() +2024-12-04 16:13:49,679:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:49,679:INFO:Checking exceptions +2024-12-04 16:13:49,679:INFO:Importing libraries +2024-12-04 16:13:49,679:INFO:Copying training dataset +2024-12-04 16:13:49,685:INFO:Defining folds +2024-12-04 16:13:49,685:INFO:Declaring metric variables +2024-12-04 16:13:49,687:INFO:Importing untrained model +2024-12-04 16:13:49,692:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:13:49,697:INFO:Starting cross validation +2024-12-04 16:13:49,699:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:53,425:INFO:Calculating mean and std +2024-12-04 16:13:53,426:INFO:Creating metrics dataframe +2024-12-04 16:13:53,428:INFO:Uploading results into container +2024-12-04 16:13:53,428:INFO:Uploading model into container now +2024-12-04 16:13:53,429:INFO:_master_model_container: 14 +2024-12-04 16:13:53,430:INFO:_display_container: 2 +2024-12-04 16:13:53,430:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=3279) +2024-12-04 16:13:53,430:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:53,550:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:53,550:INFO:Creating metrics dataframe +2024-12-04 16:13:53,557:INFO:Initializing AdaBoost Regressor +2024-12-04 16:13:53,557:INFO:Total runtime is 0.27595290740331013 minutes +2024-12-04 16:13:53,562:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:53,562:INFO:Initializing create_model() +2024-12-04 16:13:53,563:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:53,563:INFO:Checking exceptions +2024-12-04 16:13:53,563:INFO:Importing libraries +2024-12-04 16:13:53,563:INFO:Copying training dataset +2024-12-04 16:13:53,569:INFO:Defining folds +2024-12-04 16:13:53,569:INFO:Declaring metric variables +2024-12-04 16:13:53,574:INFO:Importing untrained model +2024-12-04 16:13:53,577:INFO:AdaBoost Regressor Imported successfully +2024-12-04 16:13:53,583:INFO:Starting cross validation +2024-12-04 16:13:53,586:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:54,819:INFO:Calculating mean and std +2024-12-04 16:13:54,820:INFO:Creating metrics dataframe +2024-12-04 16:13:54,821:INFO:Uploading results into container +2024-12-04 16:13:54,822:INFO:Uploading model into container now +2024-12-04 16:13:54,822:INFO:_master_model_container: 15 +2024-12-04 16:13:54,822:INFO:_display_container: 2 +2024-12-04 16:13:54,822:INFO:AdaBoostRegressor(random_state=3279) +2024-12-04 16:13:54,822:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:54,937:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:54,937:INFO:Creating metrics dataframe +2024-12-04 16:13:54,944:INFO:Initializing Gradient Boosting Regressor +2024-12-04 16:13:54,944:INFO:Total runtime is 0.29906813700993856 minutes +2024-12-04 16:13:54,947:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:54,947:INFO:Initializing create_model() +2024-12-04 16:13:54,947:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:54,947:INFO:Checking exceptions +2024-12-04 16:13:54,947:INFO:Importing libraries +2024-12-04 16:13:54,948:INFO:Copying training dataset +2024-12-04 16:13:54,953:INFO:Defining folds +2024-12-04 16:13:54,953:INFO:Declaring metric variables +2024-12-04 16:13:54,957:INFO:Importing untrained model +2024-12-04 16:13:54,960:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 16:13:54,965:INFO:Starting cross validation +2024-12-04 16:13:54,967:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:57,041:INFO:Calculating mean and std +2024-12-04 16:13:57,043:INFO:Creating metrics dataframe +2024-12-04 16:13:57,044:INFO:Uploading results into container +2024-12-04 16:13:57,045:INFO:Uploading model into container now +2024-12-04 16:13:57,045:INFO:_master_model_container: 16 +2024-12-04 16:13:57,046:INFO:_display_container: 2 +2024-12-04 16:13:57,046:INFO:GradientBoostingRegressor(random_state=3279) +2024-12-04 16:13:57,046:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:57,164:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:57,164:INFO:Creating metrics dataframe +2024-12-04 16:13:57,172:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 16:13:57,172:INFO:Total runtime is 0.33620368639628095 minutes +2024-12-04 16:13:57,177:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:57,177:INFO:Initializing create_model() +2024-12-04 16:13:57,177:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:57,177:INFO:Checking exceptions +2024-12-04 16:13:57,177:INFO:Importing libraries +2024-12-04 16:13:57,177:INFO:Copying training dataset +2024-12-04 16:13:57,184:INFO:Defining folds +2024-12-04 16:13:57,184:INFO:Declaring metric variables +2024-12-04 16:13:57,187:INFO:Importing untrained model +2024-12-04 16:13:57,191:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 16:13:57,196:INFO:Starting cross validation +2024-12-04 16:13:57,198:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:58,550:INFO:Calculating mean and std +2024-12-04 16:13:58,551:INFO:Creating metrics dataframe +2024-12-04 16:13:58,554:INFO:Uploading results into container +2024-12-04 16:13:58,555:INFO:Uploading model into container now +2024-12-04 16:13:58,556:INFO:_master_model_container: 17 +2024-12-04 16:13:58,556:INFO:_display_container: 2 +2024-12-04 16:13:58,556:INFO:LGBMRegressor(n_jobs=-1, random_state=3279) +2024-12-04 16:13:58,556:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:58,707:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:58,707:INFO:Creating metrics dataframe +2024-12-04 16:13:58,717:INFO:Initializing Dummy Regressor +2024-12-04 16:13:58,718:INFO:Total runtime is 0.3619727611541748 minutes +2024-12-04 16:13:58,721:INFO:SubProcess create_model() called ================================== +2024-12-04 16:13:58,722:INFO:Initializing create_model() +2024-12-04 16:13:58,722:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:58,722:INFO:Checking exceptions +2024-12-04 16:13:58,722:INFO:Importing libraries +2024-12-04 16:13:58,722:INFO:Copying training dataset +2024-12-04 16:13:58,729:INFO:Defining folds +2024-12-04 16:13:58,729:INFO:Declaring metric variables +2024-12-04 16:13:58,732:INFO:Importing untrained model +2024-12-04 16:13:58,735:INFO:Dummy Regressor Imported successfully +2024-12-04 16:13:58,741:INFO:Starting cross validation +2024-12-04 16:13:58,743:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:13:59,017:INFO:Calculating mean and std +2024-12-04 16:13:59,017:INFO:Creating metrics dataframe +2024-12-04 16:13:59,018:INFO:Uploading results into container +2024-12-04 16:13:59,020:INFO:Uploading model into container now +2024-12-04 16:13:59,020:INFO:_master_model_container: 18 +2024-12-04 16:13:59,020:INFO:_display_container: 2 +2024-12-04 16:13:59,020:INFO:DummyRegressor() +2024-12-04 16:13:59,020:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:59,146:INFO:SubProcess create_model() end ================================== +2024-12-04 16:13:59,146:INFO:Creating metrics dataframe +2024-12-04 16:13:59,155:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 16:13:59,163:INFO:Initializing create_model() +2024-12-04 16:13:59,163:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=3279), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:13:59,163:INFO:Checking exceptions +2024-12-04 16:13:59,164:INFO:Importing libraries +2024-12-04 16:13:59,164:INFO:Copying training dataset +2024-12-04 16:13:59,170:INFO:Defining folds +2024-12-04 16:13:59,170:INFO:Declaring metric variables +2024-12-04 16:13:59,170:INFO:Importing untrained model +2024-12-04 16:13:59,170:INFO:Declaring custom model +2024-12-04 16:13:59,171:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:13:59,172:INFO:Cross validation set to False +2024-12-04 16:13:59,172:INFO:Fitting Model +2024-12-04 16:13:59,611:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=3279) +2024-12-04 16:13:59,612:INFO:create_model() successfully completed...................................... +2024-12-04 16:13:59,757:INFO:_master_model_container: 18 +2024-12-04 16:13:59,759:INFO:_display_container: 2 +2024-12-04 16:13:59,759:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=3279) +2024-12-04 16:13:59,759:INFO:compare_models() successfully completed...................................... +2024-12-04 16:14:24,073:INFO:PyCaret RegressionExperiment +2024-12-04 16:14:24,073:INFO:Logging name: model_hospi_reg_ens +2024-12-04 16:14:24,073:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:14:24,073:INFO:version 3.3.2 +2024-12-04 16:14:24,073:INFO:Initializing setup() +2024-12-04 16:14:24,073:INFO:self.USI: 98a7 +2024-12-04 16:14:24,073:INFO:self._variable_keys: {'logging_param', '_ml_usecase', 'X_train', 'y', 'memory', 'pipeline', 'gpu_param', 'exp_id', 'X', 'target_param', 'y_train', 'n_jobs_param', 'transform_target_param', 'fold_generator', 'USI', '_available_plots', 'exp_name_log', 'y_test', 'idx', 'X_test', 'log_plots_param', 'seed', 'data', 'fold_groups_param', 'html_param', 'gpu_n_jobs_param', 'fold_shuffle_param'} +2024-12-04 16:14:24,074:INFO:Checking environment +2024-12-04 16:14:24,074:INFO:python_version: 3.11.9 +2024-12-04 16:14:24,074:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:14:24,074:INFO:machine: AMD64 +2024-12-04 16:14:24,074:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:14:24,079:INFO:Memory: svmem(total=17007292416, available=1601163264, percent=90.6, used=15406129152, free=1601163264) +2024-12-04 16:14:24,079:INFO:Physical Core: 4 +2024-12-04 16:14:24,079:INFO:Logical Core: 8 +2024-12-04 16:14:24,079:INFO:Checking libraries +2024-12-04 16:14:24,079:INFO:System: +2024-12-04 16:14:24,079:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:14:24,079:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:14:24,079:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:14:24,079:INFO:PyCaret required dependencies: +2024-12-04 16:14:24,080:INFO: pip: 24.3.1 +2024-12-04 16:14:24,080:INFO: setuptools: 65.5.0 +2024-12-04 16:14:24,080:INFO: pycaret: 3.3.2 +2024-12-04 16:14:24,080:INFO: IPython: 8.12.3 +2024-12-04 16:14:24,080:INFO: ipywidgets: 8.1.5 +2024-12-04 16:14:24,080:INFO: tqdm: 4.67.1 +2024-12-04 16:14:24,080:INFO: numpy: 1.26.4 +2024-12-04 16:14:24,080:INFO: pandas: 2.1.4 +2024-12-04 16:14:24,080:INFO: jinja2: 3.1.4 +2024-12-04 16:14:24,080:INFO: scipy: 1.11.4 +2024-12-04 16:14:24,080:INFO: joblib: 1.3.2 +2024-12-04 16:14:24,080:INFO: sklearn: 1.4.2 +2024-12-04 16:14:24,080:INFO: pyod: 2.0.2 +2024-12-04 16:14:24,080:INFO: imblearn: 0.12.4 +2024-12-04 16:14:24,080:INFO: category_encoders: 2.6.4 +2024-12-04 16:14:24,080:INFO: lightgbm: 4.5.0 +2024-12-04 16:14:24,080:INFO: numba: 0.60.0 +2024-12-04 16:14:24,080:INFO: requests: 2.32.3 +2024-12-04 16:14:24,080:INFO: matplotlib: 3.7.5 +2024-12-04 16:14:24,080:INFO: scikitplot: 0.3.7 +2024-12-04 16:14:24,080:INFO: yellowbrick: 1.5 +2024-12-04 16:14:24,080:INFO: plotly: 5.24.1 +2024-12-04 16:14:24,080:INFO: plotly-resampler: Not installed +2024-12-04 16:14:24,080:INFO: kaleido: 0.2.1 +2024-12-04 16:14:24,080:INFO: schemdraw: 0.15 +2024-12-04 16:14:24,080:INFO: statsmodels: 0.14.4 +2024-12-04 16:14:24,080:INFO: sktime: 0.26.0 +2024-12-04 16:14:24,080:INFO: tbats: 1.1.3 +2024-12-04 16:14:24,080:INFO: pmdarima: 2.0.4 +2024-12-04 16:14:24,080:INFO: psutil: 6.1.0 +2024-12-04 16:14:24,080:INFO: markupsafe: 3.0.2 +2024-12-04 16:14:24,080:INFO: pickle5: Not installed +2024-12-04 16:14:24,080:INFO: cloudpickle: 3.1.0 +2024-12-04 16:14:24,080:INFO: deprecation: 2.1.0 +2024-12-04 16:14:24,080:INFO: xxhash: 3.5.0 +2024-12-04 16:14:24,080:INFO: wurlitzer: Not installed +2024-12-04 16:14:24,080:INFO:PyCaret optional dependencies: +2024-12-04 16:14:24,080:INFO: shap: Not installed +2024-12-04 16:14:24,080:INFO: interpret: Not installed +2024-12-04 16:14:24,080:INFO: umap: Not installed +2024-12-04 16:14:24,080:INFO: ydata_profiling: Not installed +2024-12-04 16:14:24,080:INFO: explainerdashboard: Not installed +2024-12-04 16:14:24,081:INFO: autoviz: Not installed +2024-12-04 16:14:24,081:INFO: fairlearn: Not installed +2024-12-04 16:14:24,081:INFO: deepchecks: Not installed +2024-12-04 16:14:24,081:INFO: xgboost: Not installed +2024-12-04 16:14:24,081:INFO: catboost: Not installed +2024-12-04 16:14:24,081:INFO: kmodes: Not installed +2024-12-04 16:14:24,081:INFO: mlxtend: Not installed +2024-12-04 16:14:24,081:INFO: statsforecast: Not installed +2024-12-04 16:14:24,081:INFO: tune_sklearn: Not installed +2024-12-04 16:14:24,081:INFO: ray: Not installed +2024-12-04 16:14:24,081:INFO: hyperopt: Not installed +2024-12-04 16:14:24,081:INFO: optuna: Not installed +2024-12-04 16:14:24,081:INFO: skopt: Not installed +2024-12-04 16:14:24,081:INFO: mlflow: 2.18.0 +2024-12-04 16:14:24,081:INFO: gradio: Not installed +2024-12-04 16:14:24,081:INFO: fastapi: Not installed +2024-12-04 16:14:24,081:INFO: uvicorn: 0.32.1 +2024-12-04 16:14:24,081:INFO: m2cgen: Not installed +2024-12-04 16:14:24,081:INFO: evidently: Not installed +2024-12-04 16:14:24,081:INFO: fugue: Not installed +2024-12-04 16:14:24,081:INFO: streamlit: 1.40.1 +2024-12-04 16:14:24,081:INFO: prophet: Not installed +2024-12-04 16:14:24,081:INFO:None +2024-12-04 16:14:24,081:INFO:Set up data. +2024-12-04 16:14:24,087:INFO:Set up folding strategy. +2024-12-04 16:14:24,088:INFO:Set up train/test split. +2024-12-04 16:14:24,091:INFO:Set up index. +2024-12-04 16:14:24,091:INFO:Assigning column types. +2024-12-04 16:14:24,093:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:14:24,093:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,097:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,101:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,153:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,193:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,194:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,194:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,194:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,198:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,203:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,252:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,295:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,295:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,295:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,296:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 16:14:24,300:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,304:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,353:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,391:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,392:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,392:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,397:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,400:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,449:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,488:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,489:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,490:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,490:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 16:14:24,504:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,553:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,597:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,598:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,598:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,607:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,666:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,711:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,712:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,712:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,713:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 16:14:24,774:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,831:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,831:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,832:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,896:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,943:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:14:24,944:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,944:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:24,944:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:14:25,005:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:25,046:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,046:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,137:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:14:25,178:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,179:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,179:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 16:14:25,287:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,289:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,400:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,400:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,401:INFO:Preparing preprocessing pipeline... +2024-12-04 16:14:25,401:INFO:Set up target transformation. +2024-12-04 16:14:25,401:INFO:Set up simple imputation. +2024-12-04 16:14:25,403:INFO:Set up encoding of categorical features. +2024-12-04 16:14:25,403:INFO:Set up feature normalization. +2024-12-04 16:14:25,484:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:14:25,497:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=... + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 16:14:25,498:INFO:Creating final display dataframe. +2024-12-04 16:14:25,692:INFO:Setup _display_container: Description Value +0 Session id 7497 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 3) +4 Transformed data shape (5688, 20) +5 Transformed train set shape (3981, 20) +6 Transformed test set shape (1707, 20) +7 Categorical features 2 +8 Preprocess True +9 Imputation type simple +10 Numeric imputation mean +11 Categorical imputation mode +12 Maximum one-hot encoding 25 +13 Encoding method None +14 Normalize True +15 Normalize method zscore +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator KFold +19 Fold Number 10 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name model_hospi_reg_ens +24 USI 98a7 +2024-12-04 16:14:25,803:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,804:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,916:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,916:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:14:25,917:INFO:setup() successfully completed in 1.86s............... +2024-12-04 16:14:30,887:INFO:Initializing compare_models() +2024-12-04 16:14:30,888:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 16:14:30,888:INFO:Checking exceptions +2024-12-04 16:14:30,890:INFO:Preparing display monitor +2024-12-04 16:14:30,919:INFO:Initializing Linear Regression +2024-12-04 16:14:30,920:INFO:Total runtime is 1.6816457112630208e-05 minutes +2024-12-04 16:14:30,925:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:30,927:INFO:Initializing create_model() +2024-12-04 16:14:30,927:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:30,927:INFO:Checking exceptions +2024-12-04 16:14:30,928:INFO:Importing libraries +2024-12-04 16:14:30,928:INFO:Copying training dataset +2024-12-04 16:14:30,934:INFO:Defining folds +2024-12-04 16:14:30,934:INFO:Declaring metric variables +2024-12-04 16:14:30,938:INFO:Importing untrained model +2024-12-04 16:14:30,941:INFO:Linear Regression Imported successfully +2024-12-04 16:14:30,951:INFO:Starting cross validation +2024-12-04 16:14:30,954:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:31,288:INFO:Calculating mean and std +2024-12-04 16:14:31,288:INFO:Creating metrics dataframe +2024-12-04 16:14:31,289:INFO:Uploading results into container +2024-12-04 16:14:31,290:INFO:Uploading model into container now +2024-12-04 16:14:31,290:INFO:_master_model_container: 1 +2024-12-04 16:14:31,290:INFO:_display_container: 2 +2024-12-04 16:14:31,290:INFO:LinearRegression(n_jobs=-1) +2024-12-04 16:14:31,290:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:31,438:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:31,438:INFO:Creating metrics dataframe +2024-12-04 16:14:31,444:INFO:Initializing Lasso Regression +2024-12-04 16:14:31,444:INFO:Total runtime is 0.008753526210784911 minutes +2024-12-04 16:14:31,448:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:31,448:INFO:Initializing create_model() +2024-12-04 16:14:31,448:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:31,448:INFO:Checking exceptions +2024-12-04 16:14:31,448:INFO:Importing libraries +2024-12-04 16:14:31,448:INFO:Copying training dataset +2024-12-04 16:14:31,451:INFO:Defining folds +2024-12-04 16:14:31,451:INFO:Declaring metric variables +2024-12-04 16:14:31,453:INFO:Importing untrained model +2024-12-04 16:14:31,460:INFO:Lasso Regression Imported successfully +2024-12-04 16:14:31,468:INFO:Starting cross validation +2024-12-04 16:14:31,472:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:31,760:INFO:Calculating mean and std +2024-12-04 16:14:31,760:INFO:Creating metrics dataframe +2024-12-04 16:14:31,762:INFO:Uploading results into container +2024-12-04 16:14:31,762:INFO:Uploading model into container now +2024-12-04 16:14:31,763:INFO:_master_model_container: 2 +2024-12-04 16:14:31,763:INFO:_display_container: 2 +2024-12-04 16:14:31,763:INFO:Lasso(random_state=7497) +2024-12-04 16:14:31,763:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:31,886:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:31,886:INFO:Creating metrics dataframe +2024-12-04 16:14:31,894:INFO:Initializing Ridge Regression +2024-12-04 16:14:31,894:INFO:Total runtime is 0.01624921162923177 minutes +2024-12-04 16:14:31,899:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:31,900:INFO:Initializing create_model() +2024-12-04 16:14:31,900:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:31,901:INFO:Checking exceptions +2024-12-04 16:14:31,901:INFO:Importing libraries +2024-12-04 16:14:31,901:INFO:Copying training dataset +2024-12-04 16:14:31,907:INFO:Defining folds +2024-12-04 16:14:31,907:INFO:Declaring metric variables +2024-12-04 16:14:31,912:INFO:Importing untrained model +2024-12-04 16:14:31,917:INFO:Ridge Regression Imported successfully +2024-12-04 16:14:31,926:INFO:Starting cross validation +2024-12-04 16:14:31,928:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:32,219:INFO:Calculating mean and std +2024-12-04 16:14:32,220:INFO:Creating metrics dataframe +2024-12-04 16:14:32,223:INFO:Uploading results into container +2024-12-04 16:14:32,224:INFO:Uploading model into container now +2024-12-04 16:14:32,224:INFO:_master_model_container: 3 +2024-12-04 16:14:32,224:INFO:_display_container: 2 +2024-12-04 16:14:32,224:INFO:Ridge(random_state=7497) +2024-12-04 16:14:32,225:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:32,380:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:32,380:INFO:Creating metrics dataframe +2024-12-04 16:14:32,385:INFO:Initializing Elastic Net +2024-12-04 16:14:32,386:INFO:Total runtime is 0.024442593256632485 minutes +2024-12-04 16:14:32,388:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:32,389:INFO:Initializing create_model() +2024-12-04 16:14:32,389:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:32,389:INFO:Checking exceptions +2024-12-04 16:14:32,389:INFO:Importing libraries +2024-12-04 16:14:32,389:INFO:Copying training dataset +2024-12-04 16:14:32,393:INFO:Defining folds +2024-12-04 16:14:32,393:INFO:Declaring metric variables +2024-12-04 16:14:32,396:INFO:Importing untrained model +2024-12-04 16:14:32,400:INFO:Elastic Net Imported successfully +2024-12-04 16:14:32,406:INFO:Starting cross validation +2024-12-04 16:14:32,407:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:32,646:INFO:Calculating mean and std +2024-12-04 16:14:32,647:INFO:Creating metrics dataframe +2024-12-04 16:14:32,649:INFO:Uploading results into container +2024-12-04 16:14:32,650:INFO:Uploading model into container now +2024-12-04 16:14:32,650:INFO:_master_model_container: 4 +2024-12-04 16:14:32,650:INFO:_display_container: 2 +2024-12-04 16:14:32,651:INFO:ElasticNet(random_state=7497) +2024-12-04 16:14:32,651:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:32,786:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:32,786:INFO:Creating metrics dataframe +2024-12-04 16:14:32,793:INFO:Initializing Least Angle Regression +2024-12-04 16:14:32,793:INFO:Total runtime is 0.03122603893280029 minutes +2024-12-04 16:14:32,796:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:32,796:INFO:Initializing create_model() +2024-12-04 16:14:32,796:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:32,796:INFO:Checking exceptions +2024-12-04 16:14:32,797:INFO:Importing libraries +2024-12-04 16:14:32,797:INFO:Copying training dataset +2024-12-04 16:14:32,801:INFO:Defining folds +2024-12-04 16:14:32,801:INFO:Declaring metric variables +2024-12-04 16:14:32,804:INFO:Importing untrained model +2024-12-04 16:14:32,807:INFO:Least Angle Regression Imported successfully +2024-12-04 16:14:32,814:INFO:Starting cross validation +2024-12-04 16:14:32,815:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:33,085:INFO:Calculating mean and std +2024-12-04 16:14:33,086:INFO:Creating metrics dataframe +2024-12-04 16:14:33,089:INFO:Uploading results into container +2024-12-04 16:14:33,089:INFO:Uploading model into container now +2024-12-04 16:14:33,089:INFO:_master_model_container: 5 +2024-12-04 16:14:33,089:INFO:_display_container: 2 +2024-12-04 16:14:33,090:INFO:Lars(random_state=7497) +2024-12-04 16:14:33,090:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:33,233:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:33,233:INFO:Creating metrics dataframe +2024-12-04 16:14:33,240:INFO:Initializing Lasso Least Angle Regression +2024-12-04 16:14:33,240:INFO:Total runtime is 0.03869131803512573 minutes +2024-12-04 16:14:33,244:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:33,245:INFO:Initializing create_model() +2024-12-04 16:14:33,245:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:33,245:INFO:Checking exceptions +2024-12-04 16:14:33,245:INFO:Importing libraries +2024-12-04 16:14:33,245:INFO:Copying training dataset +2024-12-04 16:14:33,250:INFO:Defining folds +2024-12-04 16:14:33,250:INFO:Declaring metric variables +2024-12-04 16:14:33,253:INFO:Importing untrained model +2024-12-04 16:14:33,257:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 16:14:33,263:INFO:Starting cross validation +2024-12-04 16:14:33,266:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:33,505:INFO:Calculating mean and std +2024-12-04 16:14:33,506:INFO:Creating metrics dataframe +2024-12-04 16:14:33,507:INFO:Uploading results into container +2024-12-04 16:14:33,509:INFO:Uploading model into container now +2024-12-04 16:14:33,509:INFO:_master_model_container: 6 +2024-12-04 16:14:33,510:INFO:_display_container: 2 +2024-12-04 16:14:33,510:INFO:LassoLars(random_state=7497) +2024-12-04 16:14:33,510:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:33,654:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:33,654:INFO:Creating metrics dataframe +2024-12-04 16:14:33,663:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 16:14:33,663:INFO:Total runtime is 0.045729593435923255 minutes +2024-12-04 16:14:33,666:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:33,666:INFO:Initializing create_model() +2024-12-04 16:14:33,666:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:33,666:INFO:Checking exceptions +2024-12-04 16:14:33,666:INFO:Importing libraries +2024-12-04 16:14:33,666:INFO:Copying training dataset +2024-12-04 16:14:33,672:INFO:Defining folds +2024-12-04 16:14:33,672:INFO:Declaring metric variables +2024-12-04 16:14:33,676:INFO:Importing untrained model +2024-12-04 16:14:33,680:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 16:14:33,685:INFO:Starting cross validation +2024-12-04 16:14:33,687:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:33,897:INFO:Calculating mean and std +2024-12-04 16:14:33,898:INFO:Creating metrics dataframe +2024-12-04 16:14:33,899:INFO:Uploading results into container +2024-12-04 16:14:33,900:INFO:Uploading model into container now +2024-12-04 16:14:33,901:INFO:_master_model_container: 7 +2024-12-04 16:14:33,901:INFO:_display_container: 2 +2024-12-04 16:14:33,901:INFO:OrthogonalMatchingPursuit() +2024-12-04 16:14:33,901:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:34,024:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:34,024:INFO:Creating metrics dataframe +2024-12-04 16:14:34,032:INFO:Initializing Bayesian Ridge +2024-12-04 16:14:34,032:INFO:Total runtime is 0.05188539822896321 minutes +2024-12-04 16:14:34,035:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:34,035:INFO:Initializing create_model() +2024-12-04 16:14:34,036:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:34,036:INFO:Checking exceptions +2024-12-04 16:14:34,036:INFO:Importing libraries +2024-12-04 16:14:34,036:INFO:Copying training dataset +2024-12-04 16:14:34,039:INFO:Defining folds +2024-12-04 16:14:34,039:INFO:Declaring metric variables +2024-12-04 16:14:34,043:INFO:Importing untrained model +2024-12-04 16:14:34,045:INFO:Bayesian Ridge Imported successfully +2024-12-04 16:14:34,050:INFO:Starting cross validation +2024-12-04 16:14:34,052:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:34,334:INFO:Calculating mean and std +2024-12-04 16:14:34,335:INFO:Creating metrics dataframe +2024-12-04 16:14:34,337:INFO:Uploading results into container +2024-12-04 16:14:34,337:INFO:Uploading model into container now +2024-12-04 16:14:34,337:INFO:_master_model_container: 8 +2024-12-04 16:14:34,338:INFO:_display_container: 2 +2024-12-04 16:14:34,338:INFO:BayesianRidge() +2024-12-04 16:14:34,338:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:34,460:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:34,460:INFO:Creating metrics dataframe +2024-12-04 16:14:34,469:INFO:Initializing Passive Aggressive Regressor +2024-12-04 16:14:34,469:INFO:Total runtime is 0.059160423278808584 minutes +2024-12-04 16:14:34,473:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:34,473:INFO:Initializing create_model() +2024-12-04 16:14:34,473:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:34,473:INFO:Checking exceptions +2024-12-04 16:14:34,473:INFO:Importing libraries +2024-12-04 16:14:34,473:INFO:Copying training dataset +2024-12-04 16:14:34,478:INFO:Defining folds +2024-12-04 16:14:34,478:INFO:Declaring metric variables +2024-12-04 16:14:34,481:INFO:Importing untrained model +2024-12-04 16:14:34,485:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 16:14:34,491:INFO:Starting cross validation +2024-12-04 16:14:34,494:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:34,766:INFO:Calculating mean and std +2024-12-04 16:14:34,767:INFO:Creating metrics dataframe +2024-12-04 16:14:34,769:INFO:Uploading results into container +2024-12-04 16:14:34,770:INFO:Uploading model into container now +2024-12-04 16:14:34,770:INFO:_master_model_container: 9 +2024-12-04 16:14:34,770:INFO:_display_container: 2 +2024-12-04 16:14:34,770:INFO:PassiveAggressiveRegressor(random_state=7497) +2024-12-04 16:14:34,771:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:34,900:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:34,900:INFO:Creating metrics dataframe +2024-12-04 16:14:34,918:INFO:Initializing Huber Regressor +2024-12-04 16:14:34,918:INFO:Total runtime is 0.06664559443791707 minutes +2024-12-04 16:14:34,928:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:34,928:INFO:Initializing create_model() +2024-12-04 16:14:34,929:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:34,929:INFO:Checking exceptions +2024-12-04 16:14:34,929:INFO:Importing libraries +2024-12-04 16:14:34,929:INFO:Copying training dataset +2024-12-04 16:14:34,934:INFO:Defining folds +2024-12-04 16:14:34,935:INFO:Declaring metric variables +2024-12-04 16:14:34,941:INFO:Importing untrained model +2024-12-04 16:14:34,946:INFO:Huber Regressor Imported successfully +2024-12-04 16:14:34,956:INFO:Starting cross validation +2024-12-04 16:14:34,958:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:35,420:INFO:Calculating mean and std +2024-12-04 16:14:35,422:INFO:Creating metrics dataframe +2024-12-04 16:14:35,426:INFO:Uploading results into container +2024-12-04 16:14:35,427:INFO:Uploading model into container now +2024-12-04 16:14:35,428:INFO:_master_model_container: 10 +2024-12-04 16:14:35,429:INFO:_display_container: 2 +2024-12-04 16:14:35,429:INFO:HuberRegressor() +2024-12-04 16:14:35,429:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:35,628:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:35,628:INFO:Creating metrics dataframe +2024-12-04 16:14:35,638:INFO:Initializing K Neighbors Regressor +2024-12-04 16:14:35,638:INFO:Total runtime is 0.07864648103713989 minutes +2024-12-04 16:14:35,641:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:35,641:INFO:Initializing create_model() +2024-12-04 16:14:35,641:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:35,641:INFO:Checking exceptions +2024-12-04 16:14:35,641:INFO:Importing libraries +2024-12-04 16:14:35,641:INFO:Copying training dataset +2024-12-04 16:14:35,645:INFO:Defining folds +2024-12-04 16:14:35,645:INFO:Declaring metric variables +2024-12-04 16:14:35,649:INFO:Importing untrained model +2024-12-04 16:14:35,653:INFO:K Neighbors Regressor Imported successfully +2024-12-04 16:14:35,659:INFO:Starting cross validation +2024-12-04 16:14:35,662:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:35,906:INFO:Calculating mean and std +2024-12-04 16:14:35,907:INFO:Creating metrics dataframe +2024-12-04 16:14:35,910:INFO:Uploading results into container +2024-12-04 16:14:35,910:INFO:Uploading model into container now +2024-12-04 16:14:35,911:INFO:_master_model_container: 11 +2024-12-04 16:14:35,911:INFO:_display_container: 2 +2024-12-04 16:14:35,912:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 16:14:35,912:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:36,047:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:36,047:INFO:Creating metrics dataframe +2024-12-04 16:14:36,055:INFO:Initializing Decision Tree Regressor +2024-12-04 16:14:36,055:INFO:Total runtime is 0.08559880654017131 minutes +2024-12-04 16:14:36,059:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:36,060:INFO:Initializing create_model() +2024-12-04 16:14:36,060:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:36,060:INFO:Checking exceptions +2024-12-04 16:14:36,060:INFO:Importing libraries +2024-12-04 16:14:36,060:INFO:Copying training dataset +2024-12-04 16:14:36,065:INFO:Defining folds +2024-12-04 16:14:36,066:INFO:Declaring metric variables +2024-12-04 16:14:36,069:INFO:Importing untrained model +2024-12-04 16:14:36,073:INFO:Decision Tree Regressor Imported successfully +2024-12-04 16:14:36,079:INFO:Starting cross validation +2024-12-04 16:14:36,082:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:36,346:INFO:Calculating mean and std +2024-12-04 16:14:36,347:INFO:Creating metrics dataframe +2024-12-04 16:14:36,349:INFO:Uploading results into container +2024-12-04 16:14:36,350:INFO:Uploading model into container now +2024-12-04 16:14:36,350:INFO:_master_model_container: 12 +2024-12-04 16:14:36,350:INFO:_display_container: 2 +2024-12-04 16:14:36,352:INFO:DecisionTreeRegressor(random_state=7497) +2024-12-04 16:14:36,352:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:36,491:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:36,491:INFO:Creating metrics dataframe +2024-12-04 16:14:36,498:INFO:Initializing Random Forest Regressor +2024-12-04 16:14:36,499:INFO:Total runtime is 0.09300236304601034 minutes +2024-12-04 16:14:36,501:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:36,501:INFO:Initializing create_model() +2024-12-04 16:14:36,501:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:36,501:INFO:Checking exceptions +2024-12-04 16:14:36,503:INFO:Importing libraries +2024-12-04 16:14:36,503:INFO:Copying training dataset +2024-12-04 16:14:36,506:INFO:Defining folds +2024-12-04 16:14:36,506:INFO:Declaring metric variables +2024-12-04 16:14:36,509:INFO:Importing untrained model +2024-12-04 16:14:36,514:INFO:Random Forest Regressor Imported successfully +2024-12-04 16:14:36,521:INFO:Starting cross validation +2024-12-04 16:14:36,522:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:38,206:INFO:Calculating mean and std +2024-12-04 16:14:38,207:INFO:Creating metrics dataframe +2024-12-04 16:14:38,209:INFO:Uploading results into container +2024-12-04 16:14:38,210:INFO:Uploading model into container now +2024-12-04 16:14:38,211:INFO:_master_model_container: 13 +2024-12-04 16:14:38,211:INFO:_display_container: 2 +2024-12-04 16:14:38,212:INFO:RandomForestRegressor(n_jobs=-1, random_state=7497) +2024-12-04 16:14:38,212:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:38,372:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:38,372:INFO:Creating metrics dataframe +2024-12-04 16:14:38,381:INFO:Initializing Extra Trees Regressor +2024-12-04 16:14:38,381:INFO:Total runtime is 0.12435982624689738 minutes +2024-12-04 16:14:38,383:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:38,383:INFO:Initializing create_model() +2024-12-04 16:14:38,384:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:38,384:INFO:Checking exceptions +2024-12-04 16:14:38,384:INFO:Importing libraries +2024-12-04 16:14:38,384:INFO:Copying training dataset +2024-12-04 16:14:38,388:INFO:Defining folds +2024-12-04 16:14:38,388:INFO:Declaring metric variables +2024-12-04 16:14:38,391:INFO:Importing untrained model +2024-12-04 16:14:38,396:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:14:38,403:INFO:Starting cross validation +2024-12-04 16:14:38,405:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:40,004:INFO:Calculating mean and std +2024-12-04 16:14:40,004:INFO:Creating metrics dataframe +2024-12-04 16:14:40,007:INFO:Uploading results into container +2024-12-04 16:14:40,007:INFO:Uploading model into container now +2024-12-04 16:14:40,007:INFO:_master_model_container: 14 +2024-12-04 16:14:40,009:INFO:_display_container: 2 +2024-12-04 16:14:40,009:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=7497) +2024-12-04 16:14:40,009:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:40,143:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:40,143:INFO:Creating metrics dataframe +2024-12-04 16:14:40,150:INFO:Initializing AdaBoost Regressor +2024-12-04 16:14:40,150:INFO:Total runtime is 0.15385531981786094 minutes +2024-12-04 16:14:40,153:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:40,155:INFO:Initializing create_model() +2024-12-04 16:14:40,155:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:40,155:INFO:Checking exceptions +2024-12-04 16:14:40,155:INFO:Importing libraries +2024-12-04 16:14:40,155:INFO:Copying training dataset +2024-12-04 16:14:40,157:INFO:Defining folds +2024-12-04 16:14:40,157:INFO:Declaring metric variables +2024-12-04 16:14:40,162:INFO:Importing untrained model +2024-12-04 16:14:40,165:INFO:AdaBoost Regressor Imported successfully +2024-12-04 16:14:40,172:INFO:Starting cross validation +2024-12-04 16:14:40,173:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:41,012:INFO:Calculating mean and std +2024-12-04 16:14:41,013:INFO:Creating metrics dataframe +2024-12-04 16:14:41,016:INFO:Uploading results into container +2024-12-04 16:14:41,016:INFO:Uploading model into container now +2024-12-04 16:14:41,017:INFO:_master_model_container: 15 +2024-12-04 16:14:41,017:INFO:_display_container: 2 +2024-12-04 16:14:41,018:INFO:AdaBoostRegressor(random_state=7497) +2024-12-04 16:14:41,018:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:41,136:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:41,136:INFO:Creating metrics dataframe +2024-12-04 16:14:41,144:INFO:Initializing Gradient Boosting Regressor +2024-12-04 16:14:41,144:INFO:Total runtime is 0.17040994167327883 minutes +2024-12-04 16:14:41,147:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:41,147:INFO:Initializing create_model() +2024-12-04 16:14:41,147:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:41,147:INFO:Checking exceptions +2024-12-04 16:14:41,147:INFO:Importing libraries +2024-12-04 16:14:41,147:INFO:Copying training dataset +2024-12-04 16:14:41,150:INFO:Defining folds +2024-12-04 16:14:41,150:INFO:Declaring metric variables +2024-12-04 16:14:41,153:INFO:Importing untrained model +2024-12-04 16:14:41,157:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 16:14:41,168:INFO:Starting cross validation +2024-12-04 16:14:41,171:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:41,838:INFO:Calculating mean and std +2024-12-04 16:14:41,839:INFO:Creating metrics dataframe +2024-12-04 16:14:41,840:INFO:Uploading results into container +2024-12-04 16:14:41,840:INFO:Uploading model into container now +2024-12-04 16:14:41,841:INFO:_master_model_container: 16 +2024-12-04 16:14:41,841:INFO:_display_container: 2 +2024-12-04 16:14:41,841:INFO:GradientBoostingRegressor(random_state=7497) +2024-12-04 16:14:41,841:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:41,957:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:41,957:INFO:Creating metrics dataframe +2024-12-04 16:14:41,964:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 16:14:41,964:INFO:Total runtime is 0.18408158222834273 minutes +2024-12-04 16:14:41,966:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:41,967:INFO:Initializing create_model() +2024-12-04 16:14:41,967:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:41,967:INFO:Checking exceptions +2024-12-04 16:14:41,967:INFO:Importing libraries +2024-12-04 16:14:41,967:INFO:Copying training dataset +2024-12-04 16:14:41,971:INFO:Defining folds +2024-12-04 16:14:41,971:INFO:Declaring metric variables +2024-12-04 16:14:41,973:INFO:Importing untrained model +2024-12-04 16:14:41,978:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 16:14:41,982:INFO:Starting cross validation +2024-12-04 16:14:41,986:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:42,877:INFO:Calculating mean and std +2024-12-04 16:14:42,879:INFO:Creating metrics dataframe +2024-12-04 16:14:42,881:INFO:Uploading results into container +2024-12-04 16:14:42,882:INFO:Uploading model into container now +2024-12-04 16:14:42,882:INFO:_master_model_container: 17 +2024-12-04 16:14:42,882:INFO:_display_container: 2 +2024-12-04 16:14:42,883:INFO:LGBMRegressor(n_jobs=-1, random_state=7497) +2024-12-04 16:14:42,883:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:43,040:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:43,040:INFO:Creating metrics dataframe +2024-12-04 16:14:43,047:INFO:Initializing Dummy Regressor +2024-12-04 16:14:43,047:INFO:Total runtime is 0.20213176806767785 minutes +2024-12-04 16:14:43,052:INFO:SubProcess create_model() called ================================== +2024-12-04 16:14:43,053:INFO:Initializing create_model() +2024-12-04 16:14:43,053:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:43,053:INFO:Checking exceptions +2024-12-04 16:14:43,053:INFO:Importing libraries +2024-12-04 16:14:43,053:INFO:Copying training dataset +2024-12-04 16:14:43,078:INFO:Defining folds +2024-12-04 16:14:43,078:INFO:Declaring metric variables +2024-12-04 16:14:43,091:INFO:Importing untrained model +2024-12-04 16:14:43,094:INFO:Dummy Regressor Imported successfully +2024-12-04 16:14:43,103:INFO:Starting cross validation +2024-12-04 16:14:43,105:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:14:43,340:INFO:Calculating mean and std +2024-12-04 16:14:43,341:INFO:Creating metrics dataframe +2024-12-04 16:14:43,342:INFO:Uploading results into container +2024-12-04 16:14:43,342:INFO:Uploading model into container now +2024-12-04 16:14:43,342:INFO:_master_model_container: 18 +2024-12-04 16:14:43,342:INFO:_display_container: 2 +2024-12-04 16:14:43,343:INFO:DummyRegressor() +2024-12-04 16:14:43,343:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:43,460:INFO:SubProcess create_model() end ================================== +2024-12-04 16:14:43,460:INFO:Creating metrics dataframe +2024-12-04 16:14:43,469:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 16:14:43,478:INFO:Initializing create_model() +2024-12-04 16:14:43,478:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=7497), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:14:43,478:INFO:Checking exceptions +2024-12-04 16:14:43,479:INFO:Importing libraries +2024-12-04 16:14:43,479:INFO:Copying training dataset +2024-12-04 16:14:43,482:INFO:Defining folds +2024-12-04 16:14:43,482:INFO:Declaring metric variables +2024-12-04 16:14:43,482:INFO:Importing untrained model +2024-12-04 16:14:43,483:INFO:Declaring custom model +2024-12-04 16:14:43,483:INFO:Random Forest Regressor Imported successfully +2024-12-04 16:14:43,484:INFO:Cross validation set to False +2024-12-04 16:14:43,484:INFO:Fitting Model +2024-12-04 16:14:43,697:INFO:RandomForestRegressor(n_jobs=-1, random_state=7497) +2024-12-04 16:14:43,697:INFO:create_model() successfully completed...................................... +2024-12-04 16:14:43,851:INFO:_master_model_container: 18 +2024-12-04 16:14:43,851:INFO:_display_container: 2 +2024-12-04 16:14:43,852:INFO:RandomForestRegressor(n_jobs=-1, random_state=7497) +2024-12-04 16:14:43,852:INFO:compare_models() successfully completed...................................... +2024-12-04 16:15:09,540:INFO:Initializing tune_model() +2024-12-04 16:15:09,540:INFO:tune_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=7497), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-12-04 16:15:09,540:INFO:Checking exceptions +2024-12-04 16:15:09,555:INFO:Copying training dataset +2024-12-04 16:15:09,560:INFO:Checking base model +2024-12-04 16:15:09,560:INFO:Base model : Random Forest Regressor +2024-12-04 16:15:09,564:INFO:Declaring metric variables +2024-12-04 16:15:09,567:INFO:Defining Hyperparameters +2024-12-04 16:15:09,717:INFO:Tuning with n_jobs=-1 +2024-12-04 16:15:09,717:INFO:Initializing RandomizedSearchCV +2024-12-04 16:17:33,907:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:17:33,907:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:17:33,907:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:17:33,907:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:21:04,166:INFO:PyCaret RegressionExperiment +2024-12-04 16:21:04,166:INFO:Logging name: model_hospi_reg_ens +2024-12-04 16:21:04,166:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 16:21:04,166:INFO:version 3.3.2 +2024-12-04 16:21:04,166:INFO:Initializing setup() +2024-12-04 16:21:04,166:INFO:self.USI: 7408 +2024-12-04 16:21:04,166:INFO:self._variable_keys: {'logging_param', 'y_train', 'y', 'USI', 'fold_generator', 'gpu_param', 'idx', 'data', 'n_jobs_param', 'X_train', 'y_test', 'transform_target_param', 'gpu_n_jobs_param', 'exp_id', 'X', 'log_plots_param', 'pipeline', 'memory', 'exp_name_log', 'seed', 'target_param', 'X_test', 'fold_shuffle_param', '_ml_usecase', 'fold_groups_param', '_available_plots', 'html_param'} +2024-12-04 16:21:04,166:INFO:Checking environment +2024-12-04 16:21:04,166:INFO:python_version: 3.11.9 +2024-12-04 16:21:04,166:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 16:21:04,166:INFO:machine: AMD64 +2024-12-04 16:21:04,166:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 16:21:04,173:INFO:Memory: svmem(total=17007292416, available=2914643968, percent=82.9, used=14092648448, free=2914643968) +2024-12-04 16:21:04,173:INFO:Physical Core: 4 +2024-12-04 16:21:04,173:INFO:Logical Core: 8 +2024-12-04 16:21:04,173:INFO:Checking libraries +2024-12-04 16:21:04,173:INFO:System: +2024-12-04 16:21:04,173:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 16:21:04,173:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 16:21:04,174:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 16:21:04,174:INFO:PyCaret required dependencies: +2024-12-04 16:21:04,194:INFO: pip: 24.3.1 +2024-12-04 16:21:04,194:INFO: setuptools: 65.5.0 +2024-12-04 16:21:04,194:INFO: pycaret: 3.3.2 +2024-12-04 16:21:04,194:INFO: IPython: 8.12.3 +2024-12-04 16:21:04,194:INFO: ipywidgets: 8.1.5 +2024-12-04 16:21:04,194:INFO: tqdm: 4.67.1 +2024-12-04 16:21:04,194:INFO: numpy: 1.26.4 +2024-12-04 16:21:04,194:INFO: pandas: 2.1.4 +2024-12-04 16:21:04,194:INFO: jinja2: 3.1.4 +2024-12-04 16:21:04,194:INFO: scipy: 1.11.4 +2024-12-04 16:21:04,194:INFO: joblib: 1.3.2 +2024-12-04 16:21:04,194:INFO: sklearn: 1.4.2 +2024-12-04 16:21:04,194:INFO: pyod: 2.0.2 +2024-12-04 16:21:04,194:INFO: imblearn: 0.12.4 +2024-12-04 16:21:04,194:INFO: category_encoders: 2.6.4 +2024-12-04 16:21:04,194:INFO: lightgbm: 4.5.0 +2024-12-04 16:21:04,194:INFO: numba: 0.60.0 +2024-12-04 16:21:04,194:INFO: requests: 2.32.3 +2024-12-04 16:21:04,194:INFO: matplotlib: 3.7.5 +2024-12-04 16:21:04,194:INFO: scikitplot: 0.3.7 +2024-12-04 16:21:04,194:INFO: yellowbrick: 1.5 +2024-12-04 16:21:04,194:INFO: plotly: 5.24.1 +2024-12-04 16:21:04,194:INFO: plotly-resampler: Not installed +2024-12-04 16:21:04,194:INFO: kaleido: 0.2.1 +2024-12-04 16:21:04,194:INFO: schemdraw: 0.15 +2024-12-04 16:21:04,194:INFO: statsmodels: 0.14.4 +2024-12-04 16:21:04,194:INFO: sktime: 0.26.0 +2024-12-04 16:21:04,194:INFO: tbats: 1.1.3 +2024-12-04 16:21:04,194:INFO: pmdarima: 2.0.4 +2024-12-04 16:21:04,194:INFO: psutil: 6.1.0 +2024-12-04 16:21:04,194:INFO: markupsafe: 3.0.2 +2024-12-04 16:21:04,194:INFO: pickle5: Not installed +2024-12-04 16:21:04,194:INFO: cloudpickle: 3.1.0 +2024-12-04 16:21:04,194:INFO: deprecation: 2.1.0 +2024-12-04 16:21:04,194:INFO: xxhash: 3.5.0 +2024-12-04 16:21:04,194:INFO: wurlitzer: Not installed +2024-12-04 16:21:04,194:INFO:PyCaret optional dependencies: +2024-12-04 16:21:04,240:INFO: shap: Not installed +2024-12-04 16:21:04,240:INFO: interpret: Not installed +2024-12-04 16:21:04,240:INFO: umap: Not installed +2024-12-04 16:21:04,240:INFO: ydata_profiling: Not installed +2024-12-04 16:21:04,240:INFO: explainerdashboard: Not installed +2024-12-04 16:21:04,240:INFO: autoviz: Not installed +2024-12-04 16:21:04,240:INFO: fairlearn: Not installed +2024-12-04 16:21:04,240:INFO: deepchecks: Not installed +2024-12-04 16:21:04,240:INFO: xgboost: Not installed +2024-12-04 16:21:04,240:INFO: catboost: Not installed +2024-12-04 16:21:04,240:INFO: kmodes: Not installed +2024-12-04 16:21:04,240:INFO: mlxtend: Not installed +2024-12-04 16:21:04,241:INFO: statsforecast: Not installed +2024-12-04 16:21:04,241:INFO: tune_sklearn: Not installed +2024-12-04 16:21:04,241:INFO: ray: Not installed +2024-12-04 16:21:04,241:INFO: hyperopt: Not installed +2024-12-04 16:21:04,241:INFO: optuna: Not installed +2024-12-04 16:21:04,241:INFO: skopt: Not installed +2024-12-04 16:21:04,241:INFO: mlflow: 2.18.0 +2024-12-04 16:21:04,241:INFO: gradio: Not installed +2024-12-04 16:21:04,241:INFO: fastapi: Not installed +2024-12-04 16:21:04,241:INFO: uvicorn: 0.32.1 +2024-12-04 16:21:04,241:INFO: m2cgen: Not installed +2024-12-04 16:21:04,241:INFO: evidently: Not installed +2024-12-04 16:21:04,241:INFO: fugue: Not installed +2024-12-04 16:21:04,241:INFO: streamlit: 1.40.1 +2024-12-04 16:21:04,241:INFO: prophet: Not installed +2024-12-04 16:21:04,241:INFO:None +2024-12-04 16:21:04,241:INFO:Set up data. +2024-12-04 16:21:04,242:INFO:Set up folding strategy. +2024-12-04 16:21:04,242:INFO:Set up train/test split. +2024-12-04 16:21:04,242:INFO:Set up index. +2024-12-04 16:21:04,242:INFO:Assigning column types. +2024-12-04 16:21:04,253:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 16:21:04,253:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,257:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,262:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,317:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,416:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,417:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,419:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,419:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,428:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,430:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,482:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,521:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,521:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,521:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,522:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 16:21:04,527:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,530:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,580:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,622:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,623:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,623:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,627:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,631:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,681:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,720:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,720:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,720:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,720:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 16:21:04,730:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,780:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,826:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,826:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,826:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,835:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,898:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,943:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:04,943:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,943:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:04,943:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 16:21:05,012:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:05,053:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:05,053:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,053:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,124:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:05,171:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 16:21:05,172:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,172:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,172:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 16:21:05,243:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:05,293:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,293:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,355:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 16:21:05,394:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,394:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,394:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 16:21:05,505:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,507:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,615:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,615:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:05,615:INFO:Preparing preprocessing pipeline... +2024-12-04 16:21:05,615:INFO:Set up target transformation. +2024-12-04 16:21:05,615:INFO:Set up simple imputation. +2024-12-04 16:21:05,618:INFO:Set up encoding of categorical features. +2024-12-04 16:21:05,618:INFO:Set up feature normalization. +2024-12-04 16:21:05,710:INFO:Finished creating preprocessing pipeline. +2024-12-04 16:21:05,721:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=... + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 16:21:05,721:INFO:Creating final display dataframe. +2024-12-04 16:21:05,939:INFO:Setup _display_container: Description Value +0 Session id 7866 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 3) +4 Transformed data shape (5688, 20) +5 Transformed train set shape (3981, 20) +6 Transformed test set shape (1707, 20) +7 Categorical features 2 +8 Preprocess True +9 Imputation type simple +10 Numeric imputation mean +11 Categorical imputation mode +12 Maximum one-hot encoding 25 +13 Encoding method None +14 Normalize True +15 Normalize method zscore +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator KFold +19 Fold Number 10 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name model_hospi_reg_ens +24 USI 7408 +2024-12-04 16:21:06,053:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:06,053:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:06,160:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:06,161:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 16:21:06,162:INFO:setup() successfully completed in 2.02s............... +2024-12-04 16:21:10,724:INFO:Initializing compare_models() +2024-12-04 16:21:10,725:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 16:21:10,725:INFO:Checking exceptions +2024-12-04 16:21:10,727:INFO:Preparing display monitor +2024-12-04 16:21:10,753:INFO:Initializing Linear Regression +2024-12-04 16:21:10,753:INFO:Total runtime is 0.0 minutes +2024-12-04 16:21:10,759:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:10,760:INFO:Initializing create_model() +2024-12-04 16:21:10,760:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:10,760:INFO:Checking exceptions +2024-12-04 16:21:10,760:INFO:Importing libraries +2024-12-04 16:21:10,760:INFO:Copying training dataset +2024-12-04 16:21:10,771:INFO:Defining folds +2024-12-04 16:21:10,771:INFO:Declaring metric variables +2024-12-04 16:21:10,774:INFO:Importing untrained model +2024-12-04 16:21:10,778:INFO:Linear Regression Imported successfully +2024-12-04 16:21:10,785:INFO:Starting cross validation +2024-12-04 16:21:10,795:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:15,676:INFO:Calculating mean and std +2024-12-04 16:21:15,678:INFO:Creating metrics dataframe +2024-12-04 16:21:15,678:INFO:Uploading results into container +2024-12-04 16:21:15,680:INFO:Uploading model into container now +2024-12-04 16:21:15,680:INFO:_master_model_container: 1 +2024-12-04 16:21:15,680:INFO:_display_container: 2 +2024-12-04 16:21:15,680:INFO:LinearRegression(n_jobs=-1) +2024-12-04 16:21:15,680:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:15,776:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:15,777:INFO:Creating metrics dataframe +2024-12-04 16:21:15,780:INFO:Initializing Lasso Regression +2024-12-04 16:21:15,780:INFO:Total runtime is 0.0837884783744812 minutes +2024-12-04 16:21:15,785:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:15,787:INFO:Initializing create_model() +2024-12-04 16:21:15,787:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:15,787:INFO:Checking exceptions +2024-12-04 16:21:15,787:INFO:Importing libraries +2024-12-04 16:21:15,787:INFO:Copying training dataset +2024-12-04 16:21:15,791:INFO:Defining folds +2024-12-04 16:21:15,791:INFO:Declaring metric variables +2024-12-04 16:21:15,793:INFO:Importing untrained model +2024-12-04 16:21:15,793:INFO:Lasso Regression Imported successfully +2024-12-04 16:21:15,803:INFO:Starting cross validation +2024-12-04 16:21:15,805:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:16,035:INFO:Calculating mean and std +2024-12-04 16:21:16,035:INFO:Creating metrics dataframe +2024-12-04 16:21:16,035:INFO:Uploading results into container +2024-12-04 16:21:16,035:INFO:Uploading model into container now +2024-12-04 16:21:16,035:INFO:_master_model_container: 2 +2024-12-04 16:21:16,039:INFO:_display_container: 2 +2024-12-04 16:21:16,039:INFO:Lasso(random_state=7866) +2024-12-04 16:21:16,039:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:16,142:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:16,142:INFO:Creating metrics dataframe +2024-12-04 16:21:16,148:INFO:Initializing Ridge Regression +2024-12-04 16:21:16,148:INFO:Total runtime is 0.08991372982660929 minutes +2024-12-04 16:21:16,148:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:16,148:INFO:Initializing create_model() +2024-12-04 16:21:16,148:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:16,148:INFO:Checking exceptions +2024-12-04 16:21:16,148:INFO:Importing libraries +2024-12-04 16:21:16,148:INFO:Copying training dataset +2024-12-04 16:21:16,155:INFO:Defining folds +2024-12-04 16:21:16,155:INFO:Declaring metric variables +2024-12-04 16:21:16,159:INFO:Importing untrained model +2024-12-04 16:21:16,164:INFO:Ridge Regression Imported successfully +2024-12-04 16:21:16,170:INFO:Starting cross validation +2024-12-04 16:21:16,170:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:16,416:INFO:Calculating mean and std +2024-12-04 16:21:16,416:INFO:Creating metrics dataframe +2024-12-04 16:21:16,419:INFO:Uploading results into container +2024-12-04 16:21:16,419:INFO:Uploading model into container now +2024-12-04 16:21:16,420:INFO:_master_model_container: 3 +2024-12-04 16:21:16,420:INFO:_display_container: 2 +2024-12-04 16:21:16,421:INFO:Ridge(random_state=7866) +2024-12-04 16:21:16,421:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:16,499:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:16,499:INFO:Creating metrics dataframe +2024-12-04 16:21:16,503:INFO:Initializing Elastic Net +2024-12-04 16:21:16,505:INFO:Total runtime is 0.09586259524027506 minutes +2024-12-04 16:21:16,508:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:16,508:INFO:Initializing create_model() +2024-12-04 16:21:16,508:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:16,508:INFO:Checking exceptions +2024-12-04 16:21:16,508:INFO:Importing libraries +2024-12-04 16:21:16,508:INFO:Copying training dataset +2024-12-04 16:21:16,510:INFO:Defining folds +2024-12-04 16:21:16,511:INFO:Declaring metric variables +2024-12-04 16:21:16,513:INFO:Importing untrained model +2024-12-04 16:21:16,515:INFO:Elastic Net Imported successfully +2024-12-04 16:21:16,523:INFO:Starting cross validation +2024-12-04 16:21:16,525:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:16,832:INFO:Calculating mean and std +2024-12-04 16:21:16,832:INFO:Creating metrics dataframe +2024-12-04 16:21:16,836:INFO:Uploading results into container +2024-12-04 16:21:16,836:INFO:Uploading model into container now +2024-12-04 16:21:16,836:INFO:_master_model_container: 4 +2024-12-04 16:21:16,836:INFO:_display_container: 2 +2024-12-04 16:21:16,836:INFO:ElasticNet(random_state=7866) +2024-12-04 16:21:16,836:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:16,915:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:16,915:INFO:Creating metrics dataframe +2024-12-04 16:21:16,920:INFO:Initializing Least Angle Regression +2024-12-04 16:21:16,920:INFO:Total runtime is 0.10278319517771402 minutes +2024-12-04 16:21:16,925:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:16,926:INFO:Initializing create_model() +2024-12-04 16:21:16,926:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:16,926:INFO:Checking exceptions +2024-12-04 16:21:16,926:INFO:Importing libraries +2024-12-04 16:21:16,927:INFO:Copying training dataset +2024-12-04 16:21:16,929:INFO:Defining folds +2024-12-04 16:21:16,929:INFO:Declaring metric variables +2024-12-04 16:21:16,930:INFO:Importing untrained model +2024-12-04 16:21:16,930:INFO:Least Angle Regression Imported successfully +2024-12-04 16:21:16,940:INFO:Starting cross validation +2024-12-04 16:21:16,942:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:17,184:INFO:Calculating mean and std +2024-12-04 16:21:17,185:INFO:Creating metrics dataframe +2024-12-04 16:21:17,187:INFO:Uploading results into container +2024-12-04 16:21:17,188:INFO:Uploading model into container now +2024-12-04 16:21:17,188:INFO:_master_model_container: 5 +2024-12-04 16:21:17,188:INFO:_display_container: 2 +2024-12-04 16:21:17,188:INFO:Lars(random_state=7866) +2024-12-04 16:21:17,188:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:17,271:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:17,271:INFO:Creating metrics dataframe +2024-12-04 16:21:17,277:INFO:Initializing Lasso Least Angle Regression +2024-12-04 16:21:17,277:INFO:Total runtime is 0.10873752037684122 minutes +2024-12-04 16:21:17,281:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:17,281:INFO:Initializing create_model() +2024-12-04 16:21:17,281:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:17,281:INFO:Checking exceptions +2024-12-04 16:21:17,281:INFO:Importing libraries +2024-12-04 16:21:17,281:INFO:Copying training dataset +2024-12-04 16:21:17,281:INFO:Defining folds +2024-12-04 16:21:17,281:INFO:Declaring metric variables +2024-12-04 16:21:17,285:INFO:Importing untrained model +2024-12-04 16:21:17,289:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 16:21:17,295:INFO:Starting cross validation +2024-12-04 16:21:17,296:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:17,512:INFO:Calculating mean and std +2024-12-04 16:21:17,512:INFO:Creating metrics dataframe +2024-12-04 16:21:17,514:INFO:Uploading results into container +2024-12-04 16:21:17,514:INFO:Uploading model into container now +2024-12-04 16:21:17,514:INFO:_master_model_container: 6 +2024-12-04 16:21:17,514:INFO:_display_container: 2 +2024-12-04 16:21:17,514:INFO:LassoLars(random_state=7866) +2024-12-04 16:21:17,514:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:17,593:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:17,593:INFO:Creating metrics dataframe +2024-12-04 16:21:17,603:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 16:21:17,603:INFO:Total runtime is 0.11416950225830078 minutes +2024-12-04 16:21:17,607:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:17,607:INFO:Initializing create_model() +2024-12-04 16:21:17,607:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:17,607:INFO:Checking exceptions +2024-12-04 16:21:17,607:INFO:Importing libraries +2024-12-04 16:21:17,609:INFO:Copying training dataset +2024-12-04 16:21:17,612:INFO:Defining folds +2024-12-04 16:21:17,612:INFO:Declaring metric variables +2024-12-04 16:21:17,614:INFO:Importing untrained model +2024-12-04 16:21:17,614:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 16:21:17,623:INFO:Starting cross validation +2024-12-04 16:21:17,624:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:17,837:INFO:Calculating mean and std +2024-12-04 16:21:17,837:INFO:Creating metrics dataframe +2024-12-04 16:21:17,839:INFO:Uploading results into container +2024-12-04 16:21:17,839:INFO:Uploading model into container now +2024-12-04 16:21:17,842:INFO:_master_model_container: 7 +2024-12-04 16:21:17,842:INFO:_display_container: 2 +2024-12-04 16:21:17,843:INFO:OrthogonalMatchingPursuit() +2024-12-04 16:21:17,843:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:17,920:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:17,920:INFO:Creating metrics dataframe +2024-12-04 16:21:17,928:INFO:Initializing Bayesian Ridge +2024-12-04 16:21:17,928:INFO:Total runtime is 0.11957333882649739 minutes +2024-12-04 16:21:17,930:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:17,930:INFO:Initializing create_model() +2024-12-04 16:21:17,930:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:17,930:INFO:Checking exceptions +2024-12-04 16:21:17,930:INFO:Importing libraries +2024-12-04 16:21:17,930:INFO:Copying training dataset +2024-12-04 16:21:17,932:INFO:Defining folds +2024-12-04 16:21:17,932:INFO:Declaring metric variables +2024-12-04 16:21:17,935:INFO:Importing untrained model +2024-12-04 16:21:17,938:INFO:Bayesian Ridge Imported successfully +2024-12-04 16:21:17,944:INFO:Starting cross validation +2024-12-04 16:21:17,945:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:18,164:INFO:Calculating mean and std +2024-12-04 16:21:18,164:INFO:Creating metrics dataframe +2024-12-04 16:21:18,164:INFO:Uploading results into container +2024-12-04 16:21:18,164:INFO:Uploading model into container now +2024-12-04 16:21:18,164:INFO:_master_model_container: 8 +2024-12-04 16:21:18,164:INFO:_display_container: 2 +2024-12-04 16:21:18,168:INFO:BayesianRidge() +2024-12-04 16:21:18,168:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:18,243:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:18,243:INFO:Creating metrics dataframe +2024-12-04 16:21:18,253:INFO:Initializing Passive Aggressive Regressor +2024-12-04 16:21:18,253:INFO:Total runtime is 0.12500156958897907 minutes +2024-12-04 16:21:18,256:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:18,257:INFO:Initializing create_model() +2024-12-04 16:21:18,257:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:18,257:INFO:Checking exceptions +2024-12-04 16:21:18,257:INFO:Importing libraries +2024-12-04 16:21:18,257:INFO:Copying training dataset +2024-12-04 16:21:18,259:INFO:Defining folds +2024-12-04 16:21:18,259:INFO:Declaring metric variables +2024-12-04 16:21:18,262:INFO:Importing untrained model +2024-12-04 16:21:18,263:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 16:21:18,270:INFO:Starting cross validation +2024-12-04 16:21:18,272:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:18,537:INFO:Calculating mean and std +2024-12-04 16:21:18,537:INFO:Creating metrics dataframe +2024-12-04 16:21:18,540:INFO:Uploading results into container +2024-12-04 16:21:18,541:INFO:Uploading model into container now +2024-12-04 16:21:18,542:INFO:_master_model_container: 9 +2024-12-04 16:21:18,542:INFO:_display_container: 2 +2024-12-04 16:21:18,543:INFO:PassiveAggressiveRegressor(random_state=7866) +2024-12-04 16:21:18,543:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:18,635:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:18,635:INFO:Creating metrics dataframe +2024-12-04 16:21:18,642:INFO:Initializing Huber Regressor +2024-12-04 16:21:18,642:INFO:Total runtime is 0.13147889773050941 minutes +2024-12-04 16:21:18,646:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:18,647:INFO:Initializing create_model() +2024-12-04 16:21:18,647:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:18,647:INFO:Checking exceptions +2024-12-04 16:21:18,647:INFO:Importing libraries +2024-12-04 16:21:18,647:INFO:Copying training dataset +2024-12-04 16:21:18,648:INFO:Defining folds +2024-12-04 16:21:18,648:INFO:Declaring metric variables +2024-12-04 16:21:18,653:INFO:Importing untrained model +2024-12-04 16:21:18,659:INFO:Huber Regressor Imported successfully +2024-12-04 16:21:18,664:INFO:Starting cross validation +2024-12-04 16:21:18,664:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:18,947:INFO:Calculating mean and std +2024-12-04 16:21:18,948:INFO:Creating metrics dataframe +2024-12-04 16:21:18,949:INFO:Uploading results into container +2024-12-04 16:21:18,949:INFO:Uploading model into container now +2024-12-04 16:21:18,950:INFO:_master_model_container: 10 +2024-12-04 16:21:18,950:INFO:_display_container: 2 +2024-12-04 16:21:18,950:INFO:HuberRegressor() +2024-12-04 16:21:18,950:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:19,039:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:19,040:INFO:Creating metrics dataframe +2024-12-04 16:21:19,049:INFO:Initializing K Neighbors Regressor +2024-12-04 16:21:19,049:INFO:Total runtime is 0.1382701516151428 minutes +2024-12-04 16:21:19,056:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:19,056:INFO:Initializing create_model() +2024-12-04 16:21:19,056:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:19,056:INFO:Checking exceptions +2024-12-04 16:21:19,056:INFO:Importing libraries +2024-12-04 16:21:19,057:INFO:Copying training dataset +2024-12-04 16:21:19,062:INFO:Defining folds +2024-12-04 16:21:19,062:INFO:Declaring metric variables +2024-12-04 16:21:19,065:INFO:Importing untrained model +2024-12-04 16:21:19,070:INFO:K Neighbors Regressor Imported successfully +2024-12-04 16:21:19,076:INFO:Starting cross validation +2024-12-04 16:21:19,078:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:19,401:INFO:Calculating mean and std +2024-12-04 16:21:19,403:INFO:Creating metrics dataframe +2024-12-04 16:21:19,403:INFO:Uploading results into container +2024-12-04 16:21:19,403:INFO:Uploading model into container now +2024-12-04 16:21:19,403:INFO:_master_model_container: 11 +2024-12-04 16:21:19,403:INFO:_display_container: 2 +2024-12-04 16:21:19,403:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 16:21:19,405:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:19,485:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:19,485:INFO:Creating metrics dataframe +2024-12-04 16:21:19,492:INFO:Initializing Decision Tree Regressor +2024-12-04 16:21:19,492:INFO:Total runtime is 0.14565572341283162 minutes +2024-12-04 16:21:19,495:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:19,495:INFO:Initializing create_model() +2024-12-04 16:21:19,495:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:19,495:INFO:Checking exceptions +2024-12-04 16:21:19,495:INFO:Importing libraries +2024-12-04 16:21:19,495:INFO:Copying training dataset +2024-12-04 16:21:19,495:INFO:Defining folds +2024-12-04 16:21:19,495:INFO:Declaring metric variables +2024-12-04 16:21:19,503:INFO:Importing untrained model +2024-12-04 16:21:19,506:INFO:Decision Tree Regressor Imported successfully +2024-12-04 16:21:19,513:INFO:Starting cross validation +2024-12-04 16:21:19,515:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:19,782:INFO:Calculating mean and std +2024-12-04 16:21:19,782:INFO:Creating metrics dataframe +2024-12-04 16:21:19,782:INFO:Uploading results into container +2024-12-04 16:21:19,784:INFO:Uploading model into container now +2024-12-04 16:21:19,784:INFO:_master_model_container: 12 +2024-12-04 16:21:19,784:INFO:_display_container: 2 +2024-12-04 16:21:19,785:INFO:DecisionTreeRegressor(random_state=7866) +2024-12-04 16:21:19,785:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:19,875:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:19,876:INFO:Creating metrics dataframe +2024-12-04 16:21:19,880:INFO:Initializing Random Forest Regressor +2024-12-04 16:21:19,880:INFO:Total runtime is 0.15211845636367796 minutes +2024-12-04 16:21:19,885:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:19,885:INFO:Initializing create_model() +2024-12-04 16:21:19,885:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:19,885:INFO:Checking exceptions +2024-12-04 16:21:19,885:INFO:Importing libraries +2024-12-04 16:21:19,885:INFO:Copying training dataset +2024-12-04 16:21:19,891:INFO:Defining folds +2024-12-04 16:21:19,891:INFO:Declaring metric variables +2024-12-04 16:21:19,895:INFO:Importing untrained model +2024-12-04 16:21:19,898:INFO:Random Forest Regressor Imported successfully +2024-12-04 16:21:19,906:INFO:Starting cross validation +2024-12-04 16:21:19,907:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:21,498:INFO:Calculating mean and std +2024-12-04 16:21:21,498:INFO:Creating metrics dataframe +2024-12-04 16:21:21,498:INFO:Uploading results into container +2024-12-04 16:21:21,498:INFO:Uploading model into container now +2024-12-04 16:21:21,503:INFO:_master_model_container: 13 +2024-12-04 16:21:21,503:INFO:_display_container: 2 +2024-12-04 16:21:21,503:INFO:RandomForestRegressor(n_jobs=-1, random_state=7866) +2024-12-04 16:21:21,503:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:21,593:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:21,593:INFO:Creating metrics dataframe +2024-12-04 16:21:21,599:INFO:Initializing Extra Trees Regressor +2024-12-04 16:21:21,599:INFO:Total runtime is 0.1807678937911987 minutes +2024-12-04 16:21:21,599:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:21,602:INFO:Initializing create_model() +2024-12-04 16:21:21,602:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:21,602:INFO:Checking exceptions +2024-12-04 16:21:21,602:INFO:Importing libraries +2024-12-04 16:21:21,602:INFO:Copying training dataset +2024-12-04 16:21:21,605:INFO:Defining folds +2024-12-04 16:21:21,606:INFO:Declaring metric variables +2024-12-04 16:21:21,609:INFO:Importing untrained model +2024-12-04 16:21:21,612:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:21:21,619:INFO:Starting cross validation +2024-12-04 16:21:21,621:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:23,119:INFO:Calculating mean and std +2024-12-04 16:21:23,120:INFO:Creating metrics dataframe +2024-12-04 16:21:23,120:INFO:Uploading results into container +2024-12-04 16:21:23,120:INFO:Uploading model into container now +2024-12-04 16:21:23,122:INFO:_master_model_container: 14 +2024-12-04 16:21:23,122:INFO:_display_container: 2 +2024-12-04 16:21:23,123:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=7866) +2024-12-04 16:21:23,123:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:23,207:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:23,207:INFO:Creating metrics dataframe +2024-12-04 16:21:23,214:INFO:Initializing AdaBoost Regressor +2024-12-04 16:21:23,214:INFO:Total runtime is 0.20768066644668576 minutes +2024-12-04 16:21:23,214:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:23,214:INFO:Initializing create_model() +2024-12-04 16:21:23,214:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:23,214:INFO:Checking exceptions +2024-12-04 16:21:23,214:INFO:Importing libraries +2024-12-04 16:21:23,214:INFO:Copying training dataset +2024-12-04 16:21:23,220:INFO:Defining folds +2024-12-04 16:21:23,220:INFO:Declaring metric variables +2024-12-04 16:21:23,223:INFO:Importing untrained model +2024-12-04 16:21:23,227:INFO:AdaBoost Regressor Imported successfully +2024-12-04 16:21:23,234:INFO:Starting cross validation +2024-12-04 16:21:23,236:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:23,879:INFO:Calculating mean and std +2024-12-04 16:21:23,880:INFO:Creating metrics dataframe +2024-12-04 16:21:23,881:INFO:Uploading results into container +2024-12-04 16:21:23,881:INFO:Uploading model into container now +2024-12-04 16:21:23,881:INFO:_master_model_container: 15 +2024-12-04 16:21:23,881:INFO:_display_container: 2 +2024-12-04 16:21:23,883:INFO:AdaBoostRegressor(random_state=7866) +2024-12-04 16:21:23,883:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:23,959:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:23,959:INFO:Creating metrics dataframe +2024-12-04 16:21:23,965:INFO:Initializing Gradient Boosting Regressor +2024-12-04 16:21:23,965:INFO:Total runtime is 0.2202053348223368 minutes +2024-12-04 16:21:23,969:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:23,969:INFO:Initializing create_model() +2024-12-04 16:21:23,969:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:23,969:INFO:Checking exceptions +2024-12-04 16:21:23,969:INFO:Importing libraries +2024-12-04 16:21:23,969:INFO:Copying training dataset +2024-12-04 16:21:23,973:INFO:Defining folds +2024-12-04 16:21:23,973:INFO:Declaring metric variables +2024-12-04 16:21:23,976:INFO:Importing untrained model +2024-12-04 16:21:23,977:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 16:21:23,983:INFO:Starting cross validation +2024-12-04 16:21:23,985:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:24,612:INFO:Calculating mean and std +2024-12-04 16:21:24,612:INFO:Creating metrics dataframe +2024-12-04 16:21:24,614:INFO:Uploading results into container +2024-12-04 16:21:24,614:INFO:Uploading model into container now +2024-12-04 16:21:24,614:INFO:_master_model_container: 16 +2024-12-04 16:21:24,614:INFO:_display_container: 2 +2024-12-04 16:21:24,614:INFO:GradientBoostingRegressor(random_state=7866) +2024-12-04 16:21:24,614:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:24,689:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:24,689:INFO:Creating metrics dataframe +2024-12-04 16:21:24,699:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 16:21:24,699:INFO:Total runtime is 0.2324357509613037 minutes +2024-12-04 16:21:24,699:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:24,699:INFO:Initializing create_model() +2024-12-04 16:21:24,702:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:24,702:INFO:Checking exceptions +2024-12-04 16:21:24,702:INFO:Importing libraries +2024-12-04 16:21:24,702:INFO:Copying training dataset +2024-12-04 16:21:24,705:INFO:Defining folds +2024-12-04 16:21:24,705:INFO:Declaring metric variables +2024-12-04 16:21:24,707:INFO:Importing untrained model +2024-12-04 16:21:24,709:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 16:21:24,715:INFO:Starting cross validation +2024-12-04 16:21:24,717:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:25,605:INFO:Calculating mean and std +2024-12-04 16:21:25,607:INFO:Creating metrics dataframe +2024-12-04 16:21:25,610:INFO:Uploading results into container +2024-12-04 16:21:25,610:INFO:Uploading model into container now +2024-12-04 16:21:25,611:INFO:_master_model_container: 17 +2024-12-04 16:21:25,611:INFO:_display_container: 2 +2024-12-04 16:21:25,612:INFO:LGBMRegressor(n_jobs=-1, random_state=7866) +2024-12-04 16:21:25,612:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:25,715:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:25,715:INFO:Creating metrics dataframe +2024-12-04 16:21:25,725:INFO:Initializing Dummy Regressor +2024-12-04 16:21:25,725:INFO:Total runtime is 0.24952992598215737 minutes +2024-12-04 16:21:25,727:INFO:SubProcess create_model() called ================================== +2024-12-04 16:21:25,727:INFO:Initializing create_model() +2024-12-04 16:21:25,729:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:25,729:INFO:Checking exceptions +2024-12-04 16:21:25,729:INFO:Importing libraries +2024-12-04 16:21:25,729:INFO:Copying training dataset +2024-12-04 16:21:25,730:INFO:Defining folds +2024-12-04 16:21:25,730:INFO:Declaring metric variables +2024-12-04 16:21:25,746:INFO:Importing untrained model +2024-12-04 16:21:25,746:INFO:Dummy Regressor Imported successfully +2024-12-04 16:21:25,775:INFO:Starting cross validation +2024-12-04 16:21:25,777:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 16:21:26,022:INFO:Calculating mean and std +2024-12-04 16:21:26,022:INFO:Creating metrics dataframe +2024-12-04 16:21:26,025:INFO:Uploading results into container +2024-12-04 16:21:26,025:INFO:Uploading model into container now +2024-12-04 16:21:26,026:INFO:_master_model_container: 18 +2024-12-04 16:21:26,026:INFO:_display_container: 2 +2024-12-04 16:21:26,026:INFO:DummyRegressor() +2024-12-04 16:21:26,027:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:26,112:INFO:SubProcess create_model() end ================================== +2024-12-04 16:21:26,112:INFO:Creating metrics dataframe +2024-12-04 16:21:26,121:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 16:21:26,130:INFO:Initializing create_model() +2024-12-04 16:21:26,131:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=7866), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:21:26,131:INFO:Checking exceptions +2024-12-04 16:21:26,132:INFO:Importing libraries +2024-12-04 16:21:26,132:INFO:Copying training dataset +2024-12-04 16:21:26,135:INFO:Defining folds +2024-12-04 16:21:26,135:INFO:Declaring metric variables +2024-12-04 16:21:26,135:INFO:Importing untrained model +2024-12-04 16:21:26,135:INFO:Declaring custom model +2024-12-04 16:21:26,135:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:21:26,135:INFO:Cross validation set to False +2024-12-04 16:21:26,135:INFO:Fitting Model +2024-12-04 16:21:26,344:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=7866) +2024-12-04 16:21:26,344:INFO:create_model() successfully completed...................................... +2024-12-04 16:21:26,455:INFO:_master_model_container: 18 +2024-12-04 16:21:26,455:INFO:_display_container: 2 +2024-12-04 16:21:26,456:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=7866) +2024-12-04 16:21:26,456:INFO:compare_models() successfully completed...................................... +2024-12-04 16:22:48,930:INFO:Initializing predict_model() +2024-12-04 16:22:48,930:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=7866), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001B6D8DD8220>) +2024-12-04 16:22:48,930:INFO:Checking exceptions +2024-12-04 16:22:48,930:INFO:Preloading libraries +2024-12-04 16:22:48,933:INFO:Set up data. +2024-12-04 16:22:48,937:INFO:Set up index. +2024-12-04 16:22:49,027:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + warnings.warn( + +2024-12-04 16:24:45,078:INFO:Initializing plot_model() +2024-12-04 16:24:45,079:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=7866), plot=feature, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 16:24:45,079:INFO:Checking exceptions +2024-12-04 16:24:45,111:INFO:Preloading libraries +2024-12-04 16:24:45,157:INFO:Copying training dataset +2024-12-04 16:24:45,159:INFO:Plot type: feature +2024-12-04 16:24:45,159:WARNING:No coef_ found. Trying feature_importances_ +2024-12-04 16:24:45,372:INFO:Visual Rendered Successfully +2024-12-04 16:24:45,477:INFO:plot_model() successfully completed...................................... +2024-12-04 16:25:13,248:INFO:Initializing plot_model() +2024-12-04 16:25:13,248:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=7866), plot=residuals_interactive, scale=1, save=False, fold=None, fit_kwargs=None, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=True, system=True, display=None, display_format=None) +2024-12-04 16:25:13,248:INFO:Checking exceptions +2024-12-04 16:25:13,281:INFO:Preloading libraries +2024-12-04 16:25:13,332:INFO:Copying training dataset +2024-12-04 16:25:13,332:INFO:Plot type: residuals_interactive +2024-12-04 16:25:13,649:INFO:Calculated model residuals +2024-12-04 16:25:16,970:INFO:Calculated Tunkey-Anscombe Plot +2024-12-04 16:25:17,442:INFO:Calculated Normal QQ Plot +2024-12-04 16:25:19,157:INFO:Calculated Scale-Location Plot +2024-12-04 16:25:21,078:INFO:Calculated Residual vs Leverage Plot inc. Cook's distance +2024-12-04 16:25:21,142:INFO:Visual Rendered Successfully +2024-12-04 16:25:21,240:INFO:plot_model() successfully completed...................................... +2024-12-04 16:25:48,320:INFO:Initializing finalize_model() +2024-12-04 16:25:48,320:INFO:finalize_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=7866), fit_kwargs=None, groups=None, model_only=False, experiment_custom_tags=None) +2024-12-04 16:25:48,324:INFO:Finalizing ExtraTreesRegressor(n_jobs=-1, random_state=7866) +2024-12-04 16:25:48,326:INFO:Initializing create_model() +2024-12-04 16:25:48,326:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=7866), fold=None, round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, model_only=False, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 16:25:48,326:INFO:Checking exceptions +2024-12-04 16:25:48,327:INFO:Importing libraries +2024-12-04 16:25:48,328:INFO:Copying training dataset +2024-12-04 16:25:48,328:INFO:Defining folds +2024-12-04 16:25:48,328:INFO:Declaring metric variables +2024-12-04 16:25:48,328:INFO:Importing untrained model +2024-12-04 16:25:48,328:INFO:Declaring custom model +2024-12-04 16:25:48,330:INFO:Extra Trees Regressor Imported successfully +2024-12-04 16:25:48,331:INFO:Cross validation set to False +2024-12-04 16:25:48,331:INFO:Fitting Model +2024-12-04 16:25:48,605:INFO:Pipeline(memory=Memory(location=None), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transfor... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('actual_estimator', + ExtraTreesRegressor(n_jobs=-1, random_state=7866))]) +2024-12-04 16:25:48,605:INFO:create_model() successfully completed...................................... +2024-12-04 16:25:48,703:INFO:_master_model_container: 18 +2024-12-04 16:25:48,705:INFO:_display_container: 3 +2024-12-04 16:25:48,714:INFO:Pipeline(memory=Memory(location=None), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transfor... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('actual_estimator', + ExtraTreesRegressor(n_jobs=-1, random_state=7866))]) +2024-12-04 16:25:48,714:INFO:finalize_model() successfully completed...................................... +2024-12-04 16:26:25,320:INFO:Initializing save_model() +2024-12-04 16:26:25,323:INFO:save_model(model=Pipeline(memory=Memory(location=None), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transfor... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('actual_estimator', + ExtraTreesRegressor(n_jobs=-1, random_state=7866))]), model_name=model_hospi_reg_ens, prep_pipe_=Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=... + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]), verbose=True, use_case=MLUsecase.REGRESSION, kwargs={}) +2024-12-04 16:26:25,323:INFO:Adding model into prep_pipe +2024-12-04 16:26:25,323:WARNING:Only Model saved as it was a pipeline. +2024-12-04 16:26:25,365:INFO:model_hospi_reg_ens.pkl saved in current working directory +2024-12-04 16:26:25,380:INFO:Pipeline(memory=Memory(location=None), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transfor... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('actual_estimator', + ExtraTreesRegressor(n_jobs=-1, random_state=7866))]) +2024-12-04 16:26:25,380:INFO:save_model() successfully completed...................................... +2024-12-04 16:31:19,414:INFO:Initializing load_model() +2024-12-04 16:31:19,414:INFO:load_model(model_name=C:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/notebooks/model_hospi_reg_ens.pkl, platform=None, authentication=None, verbose=True) +2024-12-04 16:31:46,390:INFO:Initializing load_model() +2024-12-04 16:31:46,390:INFO:load_model(model_name=C:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/notebooks/model_hospi_reg_ens, platform=None, authentication=None, verbose=True) +2024-12-04 16:33:57,386:INFO:Initializing predict_model() +2024-12-04 16:33:57,386:INFO:predict_model(self=, estimator=Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('actual_estimator', + ExtraTreesRegressor(n_jobs=-1, random_state=7866))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001B6808FB4C0>) +2024-12-04 16:33:57,386:INFO:Checking exceptions +2024-12-04 16:33:57,387:INFO:Preloading libraries +2024-12-04 16:33:57,389:INFO:Set up data. +2024-12-04 16:33:57,392:INFO:Set up index. +2024-12-04 16:58:24,012:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:58:24,012:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:58:24,012:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 16:58:24,012:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:02:27,333:INFO:PyCaret RegressionExperiment +2024-12-04 17:02:27,333:INFO:Logging name: reg-default-name +2024-12-04 17:02:27,333:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:02:27,333:INFO:version 3.3.2 +2024-12-04 17:02:27,333:INFO:Initializing setup() +2024-12-04 17:02:27,333:INFO:self.USI: 3435 +2024-12-04 17:02:27,333:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:02:27,333:INFO:Checking environment +2024-12-04 17:02:27,333:INFO:python_version: 3.11.9 +2024-12-04 17:02:27,333:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:02:27,333:INFO:machine: AMD64 +2024-12-04 17:02:27,333:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:02:27,339:INFO:Memory: svmem(total=17007292416, available=4356874240, percent=74.4, used=12650418176, free=4356874240) +2024-12-04 17:02:27,339:INFO:Physical Core: 4 +2024-12-04 17:02:27,339:INFO:Logical Core: 8 +2024-12-04 17:02:27,339:INFO:Checking libraries +2024-12-04 17:02:27,339:INFO:System: +2024-12-04 17:02:27,340:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:02:27,340:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:02:27,340:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:02:27,340:INFO:PyCaret required dependencies: +2024-12-04 17:02:27,363:INFO: pip: 24.3.1 +2024-12-04 17:02:27,363:INFO: setuptools: 65.5.0 +2024-12-04 17:02:27,363:INFO: pycaret: 3.3.2 +2024-12-04 17:02:27,363:INFO: IPython: 8.12.3 +2024-12-04 17:02:27,364:INFO: ipywidgets: 8.1.5 +2024-12-04 17:02:27,364:INFO: tqdm: 4.67.1 +2024-12-04 17:02:27,364:INFO: numpy: 1.26.4 +2024-12-04 17:02:27,364:INFO: pandas: 2.1.4 +2024-12-04 17:02:27,364:INFO: jinja2: 3.1.4 +2024-12-04 17:02:27,364:INFO: scipy: 1.11.4 +2024-12-04 17:02:27,364:INFO: joblib: 1.3.2 +2024-12-04 17:02:27,364:INFO: sklearn: 1.4.2 +2024-12-04 17:02:27,364:INFO: pyod: 2.0.2 +2024-12-04 17:02:27,364:INFO: imblearn: 0.12.4 +2024-12-04 17:02:27,364:INFO: category_encoders: 2.6.4 +2024-12-04 17:02:27,364:INFO: lightgbm: 4.5.0 +2024-12-04 17:02:27,364:INFO: numba: 0.60.0 +2024-12-04 17:02:27,364:INFO: requests: 2.32.3 +2024-12-04 17:02:27,364:INFO: matplotlib: 3.7.5 +2024-12-04 17:02:27,364:INFO: scikitplot: 0.3.7 +2024-12-04 17:02:27,364:INFO: yellowbrick: 1.5 +2024-12-04 17:02:27,364:INFO: plotly: 5.24.1 +2024-12-04 17:02:27,364:INFO: plotly-resampler: Not installed +2024-12-04 17:02:27,364:INFO: kaleido: 0.2.1 +2024-12-04 17:02:27,364:INFO: schemdraw: 0.15 +2024-12-04 17:02:27,364:INFO: statsmodels: 0.14.4 +2024-12-04 17:02:27,364:INFO: sktime: 0.26.0 +2024-12-04 17:02:27,364:INFO: tbats: 1.1.3 +2024-12-04 17:02:27,364:INFO: pmdarima: 2.0.4 +2024-12-04 17:02:27,364:INFO: psutil: 6.1.0 +2024-12-04 17:02:27,364:INFO: markupsafe: 3.0.2 +2024-12-04 17:02:27,364:INFO: pickle5: Not installed +2024-12-04 17:02:27,364:INFO: cloudpickle: 3.1.0 +2024-12-04 17:02:27,365:INFO: deprecation: 2.1.0 +2024-12-04 17:02:27,365:INFO: xxhash: 3.5.0 +2024-12-04 17:02:27,365:INFO: wurlitzer: Not installed +2024-12-04 17:02:27,365:INFO:PyCaret optional dependencies: +2024-12-04 17:02:27,396:INFO: shap: Not installed +2024-12-04 17:02:27,396:INFO: interpret: Not installed +2024-12-04 17:02:27,396:INFO: umap: Not installed +2024-12-04 17:02:27,396:INFO: ydata_profiling: Not installed +2024-12-04 17:02:27,396:INFO: explainerdashboard: Not installed +2024-12-04 17:02:27,396:INFO: autoviz: Not installed +2024-12-04 17:02:27,396:INFO: fairlearn: Not installed +2024-12-04 17:02:27,396:INFO: deepchecks: Not installed +2024-12-04 17:02:27,396:INFO: xgboost: Not installed +2024-12-04 17:02:27,396:INFO: catboost: Not installed +2024-12-04 17:02:27,396:INFO: kmodes: Not installed +2024-12-04 17:02:27,396:INFO: mlxtend: Not installed +2024-12-04 17:02:27,396:INFO: statsforecast: Not installed +2024-12-04 17:02:27,396:INFO: tune_sklearn: Not installed +2024-12-04 17:02:27,396:INFO: ray: Not installed +2024-12-04 17:02:27,396:INFO: hyperopt: Not installed +2024-12-04 17:02:27,396:INFO: optuna: Not installed +2024-12-04 17:02:27,396:INFO: skopt: Not installed +2024-12-04 17:02:27,396:INFO: mlflow: 2.18.0 +2024-12-04 17:02:27,396:INFO: gradio: Not installed +2024-12-04 17:02:27,396:INFO: fastapi: Not installed +2024-12-04 17:02:27,396:INFO: uvicorn: 0.32.1 +2024-12-04 17:02:27,396:INFO: m2cgen: Not installed +2024-12-04 17:02:27,396:INFO: evidently: Not installed +2024-12-04 17:02:27,396:INFO: fugue: Not installed +2024-12-04 17:02:27,396:INFO: streamlit: 1.40.1 +2024-12-04 17:02:27,396:INFO: prophet: Not installed +2024-12-04 17:02:27,396:INFO:None +2024-12-04 17:02:27,396:INFO:Set up data. +2024-12-04 17:02:27,403:INFO:Set up folding strategy. +2024-12-04 17:02:27,403:INFO:Set up train/test split. +2024-12-04 17:02:27,410:INFO:Set up index. +2024-12-04 17:02:27,410:INFO:Assigning column types. +2024-12-04 17:02:27,412:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:02:27,412:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,416:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,420:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,473:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,513:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,513:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,513:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,516:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,521:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,525:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,578:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,620:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,621:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,621:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,622:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:02:27,625:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,628:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,682:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,722:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,722:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,722:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,726:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,730:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,782:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,822:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,822:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,823:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,823:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:02:27,831:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,882:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,920:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:27,920:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,920:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:27,932:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,005:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,054:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,054:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,054:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,054:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:02:28,125:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,173:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,175:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,175:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,242:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,282:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,282:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,282:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,282:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:02:28,346:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,387:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,387:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,451:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:02:28,492:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,492:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,492:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:02:28,606:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,606:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,703:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,703:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:28,703:INFO:Preparing preprocessing pipeline... +2024-12-04 17:02:28,703:INFO:Set up simple imputation. +2024-12-04 17:02:28,712:INFO:Set up encoding of categorical features. +2024-12-04 17:02:28,796:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:02:28,807:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['population'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan')))]) +2024-12-04 17:02:28,808:INFO:Creating final display dataframe. +2024-12-04 17:02:29,028:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (14274, 5) +4 Transformed data shape (14274, 22) +5 Transformed train set shape (11419, 22) +6 Transformed test set shape (2855, 22) +7 Numeric features 1 +8 Categorical features 2 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Fold Generator KFold +16 Fold Number 5 +17 CPU Jobs -1 +18 Use GPU False +19 Log Experiment False +20 Experiment Name reg-default-name +21 USI 3435 +2024-12-04 17:02:29,153:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:29,153:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:29,263:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:29,263:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:02:29,263:INFO:setup() successfully completed in 1.95s............... +2024-12-04 17:02:29,263:INFO:Initializing compare_models() +2024-12-04 17:02:29,263:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:02:29,263:INFO:Checking exceptions +2024-12-04 17:02:29,266:INFO:Preparing display monitor +2024-12-04 17:02:29,286:INFO:Initializing Linear Regression +2024-12-04 17:02:29,287:INFO:Total runtime is 1.678466796875e-05 minutes +2024-12-04 17:02:29,293:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:29,293:INFO:Initializing create_model() +2024-12-04 17:02:29,293:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:29,293:INFO:Checking exceptions +2024-12-04 17:02:29,294:INFO:Importing libraries +2024-12-04 17:02:29,294:INFO:Copying training dataset +2024-12-04 17:02:29,299:INFO:Defining folds +2024-12-04 17:02:29,299:INFO:Declaring metric variables +2024-12-04 17:02:29,302:INFO:Importing untrained model +2024-12-04 17:02:29,308:INFO:Linear Regression Imported successfully +2024-12-04 17:02:29,317:INFO:Starting cross validation +2024-12-04 17:02:29,327:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:34,034:WARNING:create_model() for lr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:34,040:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:34,040:INFO:Initializing create_model() +2024-12-04 17:02:34,040:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:34,040:INFO:Checking exceptions +2024-12-04 17:02:34,040:INFO:Importing libraries +2024-12-04 17:02:34,040:INFO:Copying training dataset +2024-12-04 17:02:34,051:INFO:Defining folds +2024-12-04 17:02:34,051:INFO:Declaring metric variables +2024-12-04 17:02:34,053:INFO:Importing untrained model +2024-12-04 17:02:34,063:INFO:Linear Regression Imported successfully +2024-12-04 17:02:34,070:INFO:Starting cross validation +2024-12-04 17:02:34,076:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:36,922:ERROR:create_model() for lr raised an exception or returned all 0.0: +2024-12-04 17:02:36,923:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:36,925:INFO:Initializing Lasso Regression +2024-12-04 17:02:36,925:INFO:Total runtime is 0.12731018861134846 minutes +2024-12-04 17:02:36,929:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:36,930:INFO:Initializing create_model() +2024-12-04 17:02:36,930:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:36,930:INFO:Checking exceptions +2024-12-04 17:02:36,930:INFO:Importing libraries +2024-12-04 17:02:36,930:INFO:Copying training dataset +2024-12-04 17:02:36,940:INFO:Defining folds +2024-12-04 17:02:36,941:INFO:Declaring metric variables +2024-12-04 17:02:36,944:INFO:Importing untrained model +2024-12-04 17:02:36,951:INFO:Lasso Regression Imported successfully +2024-12-04 17:02:36,961:INFO:Starting cross validation +2024-12-04 17:02:36,963:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:37,167:WARNING:create_model() for lasso raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:37,167:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:37,167:INFO:Initializing create_model() +2024-12-04 17:02:37,167:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:37,167:INFO:Checking exceptions +2024-12-04 17:02:37,167:INFO:Importing libraries +2024-12-04 17:02:37,167:INFO:Copying training dataset +2024-12-04 17:02:37,175:INFO:Defining folds +2024-12-04 17:02:37,175:INFO:Declaring metric variables +2024-12-04 17:02:37,178:INFO:Importing untrained model +2024-12-04 17:02:37,182:INFO:Lasso Regression Imported successfully +2024-12-04 17:02:37,186:INFO:Starting cross validation +2024-12-04 17:02:37,188:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:37,384:ERROR:create_model() for lasso raised an exception or returned all 0.0: +2024-12-04 17:02:37,386:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:37,386:INFO:Initializing Ridge Regression +2024-12-04 17:02:37,387:INFO:Total runtime is 0.13500661055246987 minutes +2024-12-04 17:02:37,390:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:37,391:INFO:Initializing create_model() +2024-12-04 17:02:37,391:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:37,391:INFO:Checking exceptions +2024-12-04 17:02:37,391:INFO:Importing libraries +2024-12-04 17:02:37,391:INFO:Copying training dataset +2024-12-04 17:02:37,395:INFO:Defining folds +2024-12-04 17:02:37,395:INFO:Declaring metric variables +2024-12-04 17:02:37,395:INFO:Importing untrained model +2024-12-04 17:02:37,403:INFO:Ridge Regression Imported successfully +2024-12-04 17:02:37,407:INFO:Starting cross validation +2024-12-04 17:02:37,407:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:37,604:WARNING:create_model() for ridge raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:37,606:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:37,606:INFO:Initializing create_model() +2024-12-04 17:02:37,606:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:37,606:INFO:Checking exceptions +2024-12-04 17:02:37,606:INFO:Importing libraries +2024-12-04 17:02:37,606:INFO:Copying training dataset +2024-12-04 17:02:37,609:INFO:Defining folds +2024-12-04 17:02:37,609:INFO:Declaring metric variables +2024-12-04 17:02:37,613:INFO:Importing untrained model +2024-12-04 17:02:37,616:INFO:Ridge Regression Imported successfully +2024-12-04 17:02:37,621:INFO:Starting cross validation +2024-12-04 17:02:37,621:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:37,798:ERROR:create_model() for ridge raised an exception or returned all 0.0: +2024-12-04 17:02:37,798:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:37,798:INFO:Initializing Elastic Net +2024-12-04 17:02:37,798:INFO:Total runtime is 0.1418644905090332 minutes +2024-12-04 17:02:37,803:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:37,803:INFO:Initializing create_model() +2024-12-04 17:02:37,803:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:37,803:INFO:Checking exceptions +2024-12-04 17:02:37,803:INFO:Importing libraries +2024-12-04 17:02:37,803:INFO:Copying training dataset +2024-12-04 17:02:37,808:INFO:Defining folds +2024-12-04 17:02:37,808:INFO:Declaring metric variables +2024-12-04 17:02:37,811:INFO:Importing untrained model +2024-12-04 17:02:37,815:INFO:Elastic Net Imported successfully +2024-12-04 17:02:37,821:INFO:Starting cross validation +2024-12-04 17:02:37,822:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:37,984:WARNING:create_model() for en raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:37,984:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:37,984:INFO:Initializing create_model() +2024-12-04 17:02:37,984:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:37,984:INFO:Checking exceptions +2024-12-04 17:02:37,984:INFO:Importing libraries +2024-12-04 17:02:37,984:INFO:Copying training dataset +2024-12-04 17:02:37,990:INFO:Defining folds +2024-12-04 17:02:37,990:INFO:Declaring metric variables +2024-12-04 17:02:37,994:INFO:Importing untrained model +2024-12-04 17:02:37,996:INFO:Elastic Net Imported successfully +2024-12-04 17:02:38,001:INFO:Starting cross validation +2024-12-04 17:02:38,005:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:38,179:ERROR:create_model() for en raised an exception or returned all 0.0: +2024-12-04 17:02:38,179:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:38,179:INFO:Initializing Least Angle Regression +2024-12-04 17:02:38,179:INFO:Total runtime is 0.14821583827336626 minutes +2024-12-04 17:02:38,182:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:38,182:INFO:Initializing create_model() +2024-12-04 17:02:38,182:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:38,182:INFO:Checking exceptions +2024-12-04 17:02:38,182:INFO:Importing libraries +2024-12-04 17:02:38,182:INFO:Copying training dataset +2024-12-04 17:02:38,193:INFO:Defining folds +2024-12-04 17:02:38,193:INFO:Declaring metric variables +2024-12-04 17:02:38,196:INFO:Importing untrained model +2024-12-04 17:02:38,201:INFO:Least Angle Regression Imported successfully +2024-12-04 17:02:38,209:INFO:Starting cross validation +2024-12-04 17:02:38,210:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:38,419:WARNING:create_model() for lar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:38,420:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:38,420:INFO:Initializing create_model() +2024-12-04 17:02:38,420:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:38,420:INFO:Checking exceptions +2024-12-04 17:02:38,420:INFO:Importing libraries +2024-12-04 17:02:38,420:INFO:Copying training dataset +2024-12-04 17:02:38,426:INFO:Defining folds +2024-12-04 17:02:38,426:INFO:Declaring metric variables +2024-12-04 17:02:38,428:INFO:Importing untrained model +2024-12-04 17:02:38,432:INFO:Least Angle Regression Imported successfully +2024-12-04 17:02:38,438:INFO:Starting cross validation +2024-12-04 17:02:38,439:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:38,623:ERROR:create_model() for lar raised an exception or returned all 0.0: +2024-12-04 17:02:38,624:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:38,625:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:02:38,625:INFO:Total runtime is 0.1556392391522725 minutes +2024-12-04 17:02:38,626:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:38,626:INFO:Initializing create_model() +2024-12-04 17:02:38,628:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:38,628:INFO:Checking exceptions +2024-12-04 17:02:38,628:INFO:Importing libraries +2024-12-04 17:02:38,628:INFO:Copying training dataset +2024-12-04 17:02:38,632:INFO:Defining folds +2024-12-04 17:02:38,632:INFO:Declaring metric variables +2024-12-04 17:02:38,632:INFO:Importing untrained model +2024-12-04 17:02:38,640:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:02:38,645:INFO:Starting cross validation +2024-12-04 17:02:38,645:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:38,865:WARNING:create_model() for llar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:38,865:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:38,865:INFO:Initializing create_model() +2024-12-04 17:02:38,865:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:38,865:INFO:Checking exceptions +2024-12-04 17:02:38,865:INFO:Importing libraries +2024-12-04 17:02:38,865:INFO:Copying training dataset +2024-12-04 17:02:38,872:INFO:Defining folds +2024-12-04 17:02:38,873:INFO:Declaring metric variables +2024-12-04 17:02:38,874:INFO:Importing untrained model +2024-12-04 17:02:38,878:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:02:38,882:INFO:Starting cross validation +2024-12-04 17:02:38,882:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:39,041:ERROR:create_model() for llar raised an exception or returned all 0.0: +2024-12-04 17:02:39,042:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:39,042:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:02:39,042:INFO:Total runtime is 0.16260386705398558 minutes +2024-12-04 17:02:39,042:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:39,046:INFO:Initializing create_model() +2024-12-04 17:02:39,046:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:39,046:INFO:Checking exceptions +2024-12-04 17:02:39,046:INFO:Importing libraries +2024-12-04 17:02:39,046:INFO:Copying training dataset +2024-12-04 17:02:39,048:INFO:Defining folds +2024-12-04 17:02:39,048:INFO:Declaring metric variables +2024-12-04 17:02:39,054:INFO:Importing untrained model +2024-12-04 17:02:39,057:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:02:39,063:INFO:Starting cross validation +2024-12-04 17:02:39,063:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:39,236:WARNING:create_model() for omp raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:39,237:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:39,237:INFO:Initializing create_model() +2024-12-04 17:02:39,237:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:39,237:INFO:Checking exceptions +2024-12-04 17:02:39,237:INFO:Importing libraries +2024-12-04 17:02:39,237:INFO:Copying training dataset +2024-12-04 17:02:39,243:INFO:Defining folds +2024-12-04 17:02:39,243:INFO:Declaring metric variables +2024-12-04 17:02:39,247:INFO:Importing untrained model +2024-12-04 17:02:39,250:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:02:39,257:INFO:Starting cross validation +2024-12-04 17:02:39,258:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:39,445:ERROR:create_model() for omp raised an exception or returned all 0.0: +2024-12-04 17:02:39,446:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:39,446:INFO:Initializing Bayesian Ridge +2024-12-04 17:02:39,446:INFO:Total runtime is 0.16932417551676432 minutes +2024-12-04 17:02:39,446:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:39,446:INFO:Initializing create_model() +2024-12-04 17:02:39,446:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:39,446:INFO:Checking exceptions +2024-12-04 17:02:39,446:INFO:Importing libraries +2024-12-04 17:02:39,446:INFO:Copying training dataset +2024-12-04 17:02:39,455:INFO:Defining folds +2024-12-04 17:02:39,455:INFO:Declaring metric variables +2024-12-04 17:02:39,459:INFO:Importing untrained model +2024-12-04 17:02:39,459:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:02:39,466:INFO:Starting cross validation +2024-12-04 17:02:39,470:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:39,653:WARNING:create_model() for br raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:39,654:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:39,654:INFO:Initializing create_model() +2024-12-04 17:02:39,654:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:39,654:INFO:Checking exceptions +2024-12-04 17:02:39,654:INFO:Importing libraries +2024-12-04 17:02:39,654:INFO:Copying training dataset +2024-12-04 17:02:39,660:INFO:Defining folds +2024-12-04 17:02:39,660:INFO:Declaring metric variables +2024-12-04 17:02:39,663:INFO:Importing untrained model +2024-12-04 17:02:39,666:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:02:39,671:INFO:Starting cross validation +2024-12-04 17:02:39,671:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:39,832:ERROR:create_model() for br raised an exception or returned all 0.0: +2024-12-04 17:02:39,832:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:39,832:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:02:39,832:INFO:Total runtime is 0.17576295534769693 minutes +2024-12-04 17:02:39,832:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:39,832:INFO:Initializing create_model() +2024-12-04 17:02:39,832:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:39,832:INFO:Checking exceptions +2024-12-04 17:02:39,837:INFO:Importing libraries +2024-12-04 17:02:39,837:INFO:Copying training dataset +2024-12-04 17:02:39,840:INFO:Defining folds +2024-12-04 17:02:39,840:INFO:Declaring metric variables +2024-12-04 17:02:39,845:INFO:Importing untrained model +2024-12-04 17:02:39,847:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:02:39,855:INFO:Starting cross validation +2024-12-04 17:02:39,856:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:40,020:WARNING:create_model() for par raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:40,020:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:40,020:INFO:Initializing create_model() +2024-12-04 17:02:40,020:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:40,020:INFO:Checking exceptions +2024-12-04 17:02:40,020:INFO:Importing libraries +2024-12-04 17:02:40,020:INFO:Copying training dataset +2024-12-04 17:02:40,025:INFO:Defining folds +2024-12-04 17:02:40,025:INFO:Declaring metric variables +2024-12-04 17:02:40,029:INFO:Importing untrained model +2024-12-04 17:02:40,032:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:02:40,037:INFO:Starting cross validation +2024-12-04 17:02:40,040:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:40,222:ERROR:create_model() for par raised an exception or returned all 0.0: +2024-12-04 17:02:40,224:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:40,224:INFO:Initializing Huber Regressor +2024-12-04 17:02:40,224:INFO:Total runtime is 0.18229343096415201 minutes +2024-12-04 17:02:40,225:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:40,225:INFO:Initializing create_model() +2024-12-04 17:02:40,225:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:40,225:INFO:Checking exceptions +2024-12-04 17:02:40,225:INFO:Importing libraries +2024-12-04 17:02:40,225:INFO:Copying training dataset +2024-12-04 17:02:40,232:INFO:Defining folds +2024-12-04 17:02:40,232:INFO:Declaring metric variables +2024-12-04 17:02:40,232:INFO:Importing untrained model +2024-12-04 17:02:40,238:INFO:Huber Regressor Imported successfully +2024-12-04 17:02:40,243:INFO:Starting cross validation +2024-12-04 17:02:40,245:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:40,431:WARNING:create_model() for huber raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:40,432:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:40,432:INFO:Initializing create_model() +2024-12-04 17:02:40,432:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:40,432:INFO:Checking exceptions +2024-12-04 17:02:40,432:INFO:Importing libraries +2024-12-04 17:02:40,432:INFO:Copying training dataset +2024-12-04 17:02:40,438:INFO:Defining folds +2024-12-04 17:02:40,438:INFO:Declaring metric variables +2024-12-04 17:02:40,442:INFO:Importing untrained model +2024-12-04 17:02:40,445:INFO:Huber Regressor Imported successfully +2024-12-04 17:02:40,451:INFO:Starting cross validation +2024-12-04 17:02:40,453:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:40,683:ERROR:create_model() for huber raised an exception or returned all 0.0: +2024-12-04 17:02:40,683:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:40,683:INFO:Initializing K Neighbors Regressor +2024-12-04 17:02:40,683:INFO:Total runtime is 0.18994271755218506 minutes +2024-12-04 17:02:40,688:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:40,688:INFO:Initializing create_model() +2024-12-04 17:02:40,688:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:40,688:INFO:Checking exceptions +2024-12-04 17:02:40,688:INFO:Importing libraries +2024-12-04 17:02:40,688:INFO:Copying training dataset +2024-12-04 17:02:40,693:INFO:Defining folds +2024-12-04 17:02:40,693:INFO:Declaring metric variables +2024-12-04 17:02:40,696:INFO:Importing untrained model +2024-12-04 17:02:40,696:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:02:40,705:INFO:Starting cross validation +2024-12-04 17:02:40,705:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:40,892:WARNING:create_model() for knn raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:40,892:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:40,892:INFO:Initializing create_model() +2024-12-04 17:02:40,892:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:40,892:INFO:Checking exceptions +2024-12-04 17:02:40,892:INFO:Importing libraries +2024-12-04 17:02:40,892:INFO:Copying training dataset +2024-12-04 17:02:40,895:INFO:Defining folds +2024-12-04 17:02:40,895:INFO:Declaring metric variables +2024-12-04 17:02:40,901:INFO:Importing untrained model +2024-12-04 17:02:40,904:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:02:40,904:INFO:Starting cross validation +2024-12-04 17:02:40,913:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:41,087:ERROR:create_model() for knn raised an exception or returned all 0.0: +2024-12-04 17:02:41,088:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:41,090:INFO:Initializing Decision Tree Regressor +2024-12-04 17:02:41,090:INFO:Total runtime is 0.19672778447469075 minutes +2024-12-04 17:02:41,093:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:41,093:INFO:Initializing create_model() +2024-12-04 17:02:41,093:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:41,093:INFO:Checking exceptions +2024-12-04 17:02:41,093:INFO:Importing libraries +2024-12-04 17:02:41,093:INFO:Copying training dataset +2024-12-04 17:02:41,101:INFO:Defining folds +2024-12-04 17:02:41,101:INFO:Declaring metric variables +2024-12-04 17:02:41,107:INFO:Importing untrained model +2024-12-04 17:02:41,110:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:02:41,116:INFO:Starting cross validation +2024-12-04 17:02:41,116:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:41,317:WARNING:create_model() for dt raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:41,318:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:41,318:INFO:Initializing create_model() +2024-12-04 17:02:41,318:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:41,318:INFO:Checking exceptions +2024-12-04 17:02:41,318:INFO:Importing libraries +2024-12-04 17:02:41,318:INFO:Copying training dataset +2024-12-04 17:02:41,324:INFO:Defining folds +2024-12-04 17:02:41,324:INFO:Declaring metric variables +2024-12-04 17:02:41,328:INFO:Importing untrained model +2024-12-04 17:02:41,329:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:02:41,332:INFO:Starting cross validation +2024-12-04 17:02:41,337:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:41,499:ERROR:create_model() for dt raised an exception or returned all 0.0: +2024-12-04 17:02:41,500:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:41,500:INFO:Initializing Random Forest Regressor +2024-12-04 17:02:41,500:INFO:Total runtime is 0.20356715122858682 minutes +2024-12-04 17:02:41,503:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:41,503:INFO:Initializing create_model() +2024-12-04 17:02:41,503:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:41,503:INFO:Checking exceptions +2024-12-04 17:02:41,503:INFO:Importing libraries +2024-12-04 17:02:41,503:INFO:Copying training dataset +2024-12-04 17:02:41,503:INFO:Defining folds +2024-12-04 17:02:41,503:INFO:Declaring metric variables +2024-12-04 17:02:41,511:INFO:Importing untrained model +2024-12-04 17:02:41,514:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:02:41,520:INFO:Starting cross validation +2024-12-04 17:02:41,520:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:41,685:WARNING:create_model() for rf raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:41,685:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:41,685:INFO:Initializing create_model() +2024-12-04 17:02:41,685:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:41,685:INFO:Checking exceptions +2024-12-04 17:02:41,685:INFO:Importing libraries +2024-12-04 17:02:41,685:INFO:Copying training dataset +2024-12-04 17:02:41,690:INFO:Defining folds +2024-12-04 17:02:41,690:INFO:Declaring metric variables +2024-12-04 17:02:41,693:INFO:Importing untrained model +2024-12-04 17:02:41,695:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:02:41,701:INFO:Starting cross validation +2024-12-04 17:02:41,704:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:41,856:ERROR:create_model() for rf raised an exception or returned all 0.0: +2024-12-04 17:02:41,856:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:41,856:INFO:Initializing Extra Trees Regressor +2024-12-04 17:02:41,856:INFO:Total runtime is 0.2095001975695292 minutes +2024-12-04 17:02:41,858:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:41,858:INFO:Initializing create_model() +2024-12-04 17:02:41,858:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:41,858:INFO:Checking exceptions +2024-12-04 17:02:41,858:INFO:Importing libraries +2024-12-04 17:02:41,858:INFO:Copying training dataset +2024-12-04 17:02:41,863:INFO:Defining folds +2024-12-04 17:02:41,863:INFO:Declaring metric variables +2024-12-04 17:02:41,869:INFO:Importing untrained model +2024-12-04 17:02:41,873:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:02:41,874:INFO:Starting cross validation +2024-12-04 17:02:41,878:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:42,038:WARNING:create_model() for et raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:42,038:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:42,038:INFO:Initializing create_model() +2024-12-04 17:02:42,038:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:42,038:INFO:Checking exceptions +2024-12-04 17:02:42,038:INFO:Importing libraries +2024-12-04 17:02:42,038:INFO:Copying training dataset +2024-12-04 17:02:42,042:INFO:Defining folds +2024-12-04 17:02:42,042:INFO:Declaring metric variables +2024-12-04 17:02:42,045:INFO:Importing untrained model +2024-12-04 17:02:42,045:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:02:42,056:INFO:Starting cross validation +2024-12-04 17:02:42,056:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:42,205:ERROR:create_model() for et raised an exception or returned all 0.0: +2024-12-04 17:02:42,206:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:42,206:INFO:Initializing AdaBoost Regressor +2024-12-04 17:02:42,206:INFO:Total runtime is 0.2153370896975199 minutes +2024-12-04 17:02:42,206:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:42,206:INFO:Initializing create_model() +2024-12-04 17:02:42,206:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:42,206:INFO:Checking exceptions +2024-12-04 17:02:42,206:INFO:Importing libraries +2024-12-04 17:02:42,206:INFO:Copying training dataset +2024-12-04 17:02:42,213:INFO:Defining folds +2024-12-04 17:02:42,213:INFO:Declaring metric variables +2024-12-04 17:02:42,218:INFO:Importing untrained model +2024-12-04 17:02:42,223:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:02:42,228:INFO:Starting cross validation +2024-12-04 17:02:42,228:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:42,415:WARNING:create_model() for ada raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:42,416:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:42,416:INFO:Initializing create_model() +2024-12-04 17:02:42,416:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:42,416:INFO:Checking exceptions +2024-12-04 17:02:42,416:INFO:Importing libraries +2024-12-04 17:02:42,416:INFO:Copying training dataset +2024-12-04 17:02:42,421:INFO:Defining folds +2024-12-04 17:02:42,421:INFO:Declaring metric variables +2024-12-04 17:02:42,424:INFO:Importing untrained model +2024-12-04 17:02:42,425:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:02:42,432:INFO:Starting cross validation +2024-12-04 17:02:42,432:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:42,595:ERROR:create_model() for ada raised an exception or returned all 0.0: +2024-12-04 17:02:42,595:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:42,595:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:02:42,595:INFO:Total runtime is 0.22181919813156126 minutes +2024-12-04 17:02:42,595:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:42,595:INFO:Initializing create_model() +2024-12-04 17:02:42,600:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:42,600:INFO:Checking exceptions +2024-12-04 17:02:42,600:INFO:Importing libraries +2024-12-04 17:02:42,600:INFO:Copying training dataset +2024-12-04 17:02:42,605:INFO:Defining folds +2024-12-04 17:02:42,605:INFO:Declaring metric variables +2024-12-04 17:02:42,610:INFO:Importing untrained model +2024-12-04 17:02:42,613:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:02:42,620:INFO:Starting cross validation +2024-12-04 17:02:42,620:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:42,795:WARNING:create_model() for gbr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:42,795:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:42,795:INFO:Initializing create_model() +2024-12-04 17:02:42,795:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:42,795:INFO:Checking exceptions +2024-12-04 17:02:42,795:INFO:Importing libraries +2024-12-04 17:02:42,795:INFO:Copying training dataset +2024-12-04 17:02:42,800:INFO:Defining folds +2024-12-04 17:02:42,800:INFO:Declaring metric variables +2024-12-04 17:02:42,803:INFO:Importing untrained model +2024-12-04 17:02:42,808:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:02:42,813:INFO:Starting cross validation +2024-12-04 17:02:42,813:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:43,001:ERROR:create_model() for gbr raised an exception or returned all 0.0: +2024-12-04 17:02:43,001:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:02:43,001:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:02:43,001:INFO:Total runtime is 0.2285747726758321 minutes +2024-12-04 17:02:43,007:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:43,007:INFO:Initializing create_model() +2024-12-04 17:02:43,007:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:43,007:INFO:Checking exceptions +2024-12-04 17:02:43,007:INFO:Importing libraries +2024-12-04 17:02:43,008:INFO:Copying training dataset +2024-12-04 17:02:43,009:INFO:Defining folds +2024-12-04 17:02:43,009:INFO:Declaring metric variables +2024-12-04 17:02:43,014:INFO:Importing untrained model +2024-12-04 17:02:43,016:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:02:43,025:INFO:Starting cross validation +2024-12-04 17:02:43,026:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:43,189:WARNING:create_model() for lightgbm raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:02:43,190:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: year: dbdate + + +2024-12-04 17:02:43,190:INFO:Initializing create_model() +2024-12-04 17:02:43,190:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:43,190:INFO:Checking exceptions +2024-12-04 17:02:43,190:INFO:Importing libraries +2024-12-04 17:02:43,190:INFO:Copying training dataset +2024-12-04 17:02:43,196:INFO:Defining folds +2024-12-04 17:02:43,196:INFO:Declaring metric variables +2024-12-04 17:02:43,201:INFO:Importing untrained model +2024-12-04 17:02:43,206:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:02:43,213:INFO:Starting cross validation +2024-12-04 17:02:43,213:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:43,328:ERROR:create_model() for lightgbm raised an exception or returned all 0.0: +2024-12-04 17:02:43,328:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: year: dbdate + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 5 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +5 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: year: dbdate + + +2024-12-04 17:02:43,328:INFO:Initializing Dummy Regressor +2024-12-04 17:02:43,328:INFO:Total runtime is 0.23403407732645667 minutes +2024-12-04 17:02:43,332:INFO:SubProcess create_model() called ================================== +2024-12-04 17:02:43,332:INFO:Initializing create_model() +2024-12-04 17:02:43,332:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:43,332:INFO:Checking exceptions +2024-12-04 17:02:43,332:INFO:Importing libraries +2024-12-04 17:02:43,332:INFO:Copying training dataset +2024-12-04 17:02:43,337:INFO:Defining folds +2024-12-04 17:02:43,337:INFO:Declaring metric variables +2024-12-04 17:02:43,341:INFO:Importing untrained model +2024-12-04 17:02:43,343:INFO:Dummy Regressor Imported successfully +2024-12-04 17:02:43,347:INFO:Starting cross validation +2024-12-04 17:02:43,351:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:43,484:INFO:Calculating mean and std +2024-12-04 17:02:43,485:INFO:Creating metrics dataframe +2024-12-04 17:02:43,488:INFO:Uploading results into container +2024-12-04 17:02:43,488:INFO:Uploading model into container now +2024-12-04 17:02:43,488:INFO:_master_model_container: 1 +2024-12-04 17:02:43,488:INFO:_display_container: 2 +2024-12-04 17:02:43,488:INFO:DummyRegressor() +2024-12-04 17:02:43,488:INFO:create_model() successfully completed...................................... +2024-12-04 17:02:43,601:INFO:SubProcess create_model() end ================================== +2024-12-04 17:02:43,601:INFO:Creating metrics dataframe +2024-12-04 17:02:43,613:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:02:43,620:INFO:Initializing create_model() +2024-12-04 17:02:43,620:INFO:create_model(self=, estimator=DummyRegressor(), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:43,620:INFO:Checking exceptions +2024-12-04 17:02:43,625:INFO:Importing libraries +2024-12-04 17:02:43,625:INFO:Copying training dataset +2024-12-04 17:02:43,628:INFO:Defining folds +2024-12-04 17:02:43,628:INFO:Declaring metric variables +2024-12-04 17:02:43,628:INFO:Importing untrained model +2024-12-04 17:02:43,632:INFO:Declaring custom model +2024-12-04 17:02:43,632:INFO:Dummy Regressor Imported successfully +2024-12-04 17:02:43,632:INFO:Cross validation set to False +2024-12-04 17:02:43,632:INFO:Fitting Model +2024-12-04 17:02:43,698:INFO:DummyRegressor() +2024-12-04 17:02:43,698:INFO:create_model() successfully completed...................................... +2024-12-04 17:02:43,832:INFO:_master_model_container: 1 +2024-12-04 17:02:43,832:INFO:_display_container: 2 +2024-12-04 17:02:43,832:INFO:DummyRegressor() +2024-12-04 17:02:43,832:INFO:compare_models() successfully completed...................................... +2024-12-04 17:02:43,832:INFO:Initializing create_model() +2024-12-04 17:02:43,832:INFO:create_model(self=, estimator=DummyRegressor(), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:02:43,832:INFO:Checking exceptions +2024-12-04 17:02:43,846:INFO:Importing libraries +2024-12-04 17:02:43,846:INFO:Copying training dataset +2024-12-04 17:02:43,853:INFO:Defining folds +2024-12-04 17:02:43,853:INFO:Declaring metric variables +2024-12-04 17:02:43,857:INFO:Importing untrained model +2024-12-04 17:02:43,857:INFO:Declaring custom model +2024-12-04 17:02:43,861:INFO:Dummy Regressor Imported successfully +2024-12-04 17:02:43,868:INFO:Starting cross validation +2024-12-04 17:02:43,869:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:02:44,061:INFO:Calculating mean and std +2024-12-04 17:02:44,061:INFO:Creating metrics dataframe +2024-12-04 17:02:44,063:INFO:Finalizing model +2024-12-04 17:02:44,143:INFO:Uploading results into container +2024-12-04 17:02:44,144:INFO:Uploading model into container now +2024-12-04 17:02:44,151:INFO:_master_model_container: 2 +2024-12-04 17:02:44,151:INFO:_display_container: 3 +2024-12-04 17:02:44,151:INFO:DummyRegressor() +2024-12-04 17:02:44,151:INFO:create_model() successfully completed...................................... +2024-12-04 17:03:27,001:INFO:PyCaret RegressionExperiment +2024-12-04 17:03:27,001:INFO:Logging name: reg-default-name +2024-12-04 17:03:27,001:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:03:27,001:INFO:version 3.3.2 +2024-12-04 17:03:27,001:INFO:Initializing setup() +2024-12-04 17:03:27,002:INFO:self.USI: 0326 +2024-12-04 17:03:27,002:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:03:27,002:INFO:Checking environment +2024-12-04 17:03:27,002:INFO:python_version: 3.11.9 +2024-12-04 17:03:27,002:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:03:27,002:INFO:machine: AMD64 +2024-12-04 17:03:27,002:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:03:27,007:INFO:Memory: svmem(total=17007292416, available=2950418432, percent=82.7, used=14056873984, free=2950418432) +2024-12-04 17:03:27,007:INFO:Physical Core: 4 +2024-12-04 17:03:27,007:INFO:Logical Core: 8 +2024-12-04 17:03:27,007:INFO:Checking libraries +2024-12-04 17:03:27,007:INFO:System: +2024-12-04 17:03:27,007:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:03:27,007:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:03:27,007:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:03:27,007:INFO:PyCaret required dependencies: +2024-12-04 17:03:27,007:INFO: pip: 24.3.1 +2024-12-04 17:03:27,007:INFO: setuptools: 65.5.0 +2024-12-04 17:03:27,007:INFO: pycaret: 3.3.2 +2024-12-04 17:03:27,007:INFO: IPython: 8.12.3 +2024-12-04 17:03:27,007:INFO: ipywidgets: 8.1.5 +2024-12-04 17:03:27,007:INFO: tqdm: 4.67.1 +2024-12-04 17:03:27,007:INFO: numpy: 1.26.4 +2024-12-04 17:03:27,007:INFO: pandas: 2.1.4 +2024-12-04 17:03:27,007:INFO: jinja2: 3.1.4 +2024-12-04 17:03:27,007:INFO: scipy: 1.11.4 +2024-12-04 17:03:27,007:INFO: joblib: 1.3.2 +2024-12-04 17:03:27,007:INFO: sklearn: 1.4.2 +2024-12-04 17:03:27,007:INFO: pyod: 2.0.2 +2024-12-04 17:03:27,007:INFO: imblearn: 0.12.4 +2024-12-04 17:03:27,007:INFO: category_encoders: 2.6.4 +2024-12-04 17:03:27,007:INFO: lightgbm: 4.5.0 +2024-12-04 17:03:27,007:INFO: numba: 0.60.0 +2024-12-04 17:03:27,007:INFO: requests: 2.32.3 +2024-12-04 17:03:27,007:INFO: matplotlib: 3.7.5 +2024-12-04 17:03:27,007:INFO: scikitplot: 0.3.7 +2024-12-04 17:03:27,007:INFO: yellowbrick: 1.5 +2024-12-04 17:03:27,007:INFO: plotly: 5.24.1 +2024-12-04 17:03:27,007:INFO: plotly-resampler: Not installed +2024-12-04 17:03:27,007:INFO: kaleido: 0.2.1 +2024-12-04 17:03:27,007:INFO: schemdraw: 0.15 +2024-12-04 17:03:27,007:INFO: statsmodels: 0.14.4 +2024-12-04 17:03:27,007:INFO: sktime: 0.26.0 +2024-12-04 17:03:27,007:INFO: tbats: 1.1.3 +2024-12-04 17:03:27,007:INFO: pmdarima: 2.0.4 +2024-12-04 17:03:27,007:INFO: psutil: 6.1.0 +2024-12-04 17:03:27,007:INFO: markupsafe: 3.0.2 +2024-12-04 17:03:27,007:INFO: pickle5: Not installed +2024-12-04 17:03:27,007:INFO: cloudpickle: 3.1.0 +2024-12-04 17:03:27,007:INFO: deprecation: 2.1.0 +2024-12-04 17:03:27,007:INFO: xxhash: 3.5.0 +2024-12-04 17:03:27,007:INFO: wurlitzer: Not installed +2024-12-04 17:03:27,007:INFO:PyCaret optional dependencies: +2024-12-04 17:03:27,007:INFO: shap: Not installed +2024-12-04 17:03:27,007:INFO: interpret: Not installed +2024-12-04 17:03:27,007:INFO: umap: Not installed +2024-12-04 17:03:27,007:INFO: ydata_profiling: Not installed +2024-12-04 17:03:27,007:INFO: explainerdashboard: Not installed +2024-12-04 17:03:27,007:INFO: autoviz: Not installed +2024-12-04 17:03:27,007:INFO: fairlearn: Not installed +2024-12-04 17:03:27,007:INFO: deepchecks: Not installed +2024-12-04 17:03:27,007:INFO: xgboost: Not installed +2024-12-04 17:03:27,007:INFO: catboost: Not installed +2024-12-04 17:03:27,007:INFO: kmodes: Not installed +2024-12-04 17:03:27,007:INFO: mlxtend: Not installed +2024-12-04 17:03:27,007:INFO: statsforecast: Not installed +2024-12-04 17:03:27,007:INFO: tune_sklearn: Not installed +2024-12-04 17:03:27,007:INFO: ray: Not installed +2024-12-04 17:03:27,007:INFO: hyperopt: Not installed +2024-12-04 17:03:27,007:INFO: optuna: Not installed +2024-12-04 17:03:27,007:INFO: skopt: Not installed +2024-12-04 17:03:27,007:INFO: mlflow: 2.18.0 +2024-12-04 17:03:27,007:INFO: gradio: Not installed +2024-12-04 17:03:27,007:INFO: fastapi: Not installed +2024-12-04 17:03:27,007:INFO: uvicorn: 0.32.1 +2024-12-04 17:03:27,007:INFO: m2cgen: Not installed +2024-12-04 17:03:27,007:INFO: evidently: Not installed +2024-12-04 17:03:27,007:INFO: fugue: Not installed +2024-12-04 17:03:27,007:INFO: streamlit: 1.40.1 +2024-12-04 17:03:27,007:INFO: prophet: Not installed +2024-12-04 17:03:27,007:INFO:None +2024-12-04 17:03:27,007:INFO:Set up data. +2024-12-04 17:03:27,017:INFO:Set up folding strategy. +2024-12-04 17:03:27,017:INFO:Set up train/test split. +2024-12-04 17:03:27,021:INFO:Set up index. +2024-12-04 17:03:27,022:INFO:Assigning column types. +2024-12-04 17:03:27,023:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:03:27,023:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,028:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,033:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,087:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,129:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,129:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,130:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,130:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,134:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,137:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,190:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,232:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,232:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,232:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,232:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:03:27,237:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,242:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,292:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,335:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,335:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,335:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,340:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,344:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,395:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,436:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,437:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,437:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,437:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:03:27,445:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,496:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,537:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,537:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,540:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,546:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,605:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,646:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,648:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,648:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,648:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:03:27,716:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,756:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,756:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,756:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,822:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,869:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,869:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,869:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,870:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:03:27,928:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:27,968:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:27,968:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:28,034:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:03:28,077:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:28,077:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:28,077:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:03:28,178:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:28,178:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:28,278:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:28,278:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:03:28,278:INFO:Preparing preprocessing pipeline... +2024-12-04 17:03:28,278:INFO:Set up simple imputation. +2024-12-04 17:03:28,283:INFO:Set up encoding of categorical features. +2024-12-04 17:03:28,283:INFO:Set up column transformation. +2024-12-04 17:03:28,283:INFO:Set up feature normalization. +2024-12-04 17:05:36,740:INFO:PyCaret RegressionExperiment +2024-12-04 17:05:36,740:INFO:Logging name: reg-default-name +2024-12-04 17:05:36,740:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:05:36,740:INFO:version 3.3.2 +2024-12-04 17:05:36,740:INFO:Initializing setup() +2024-12-04 17:05:36,740:INFO:self.USI: 59c0 +2024-12-04 17:05:36,740:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:05:36,740:INFO:Checking environment +2024-12-04 17:05:36,740:INFO:python_version: 3.11.9 +2024-12-04 17:05:36,740:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:05:36,740:INFO:machine: AMD64 +2024-12-04 17:05:36,740:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:05:36,747:INFO:Memory: svmem(total=17007292416, available=2531262464, percent=85.1, used=14476029952, free=2531262464) +2024-12-04 17:05:36,747:INFO:Physical Core: 4 +2024-12-04 17:05:36,747:INFO:Logical Core: 8 +2024-12-04 17:05:36,747:INFO:Checking libraries +2024-12-04 17:05:36,747:INFO:System: +2024-12-04 17:05:36,747:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:05:36,747:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:05:36,747:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:05:36,747:INFO:PyCaret required dependencies: +2024-12-04 17:05:36,747:INFO: pip: 24.3.1 +2024-12-04 17:05:36,748:INFO: setuptools: 65.5.0 +2024-12-04 17:05:36,748:INFO: pycaret: 3.3.2 +2024-12-04 17:05:36,748:INFO: IPython: 8.12.3 +2024-12-04 17:05:36,748:INFO: ipywidgets: 8.1.5 +2024-12-04 17:05:36,748:INFO: tqdm: 4.67.1 +2024-12-04 17:05:36,748:INFO: numpy: 1.26.4 +2024-12-04 17:05:36,748:INFO: pandas: 2.1.4 +2024-12-04 17:05:36,748:INFO: jinja2: 3.1.4 +2024-12-04 17:05:36,748:INFO: scipy: 1.11.4 +2024-12-04 17:05:36,748:INFO: joblib: 1.3.2 +2024-12-04 17:05:36,748:INFO: sklearn: 1.4.2 +2024-12-04 17:05:36,748:INFO: pyod: 2.0.2 +2024-12-04 17:05:36,748:INFO: imblearn: 0.12.4 +2024-12-04 17:05:36,748:INFO: category_encoders: 2.6.4 +2024-12-04 17:05:36,748:INFO: lightgbm: 4.5.0 +2024-12-04 17:05:36,748:INFO: numba: 0.60.0 +2024-12-04 17:05:36,748:INFO: requests: 2.32.3 +2024-12-04 17:05:36,748:INFO: matplotlib: 3.7.5 +2024-12-04 17:05:36,748:INFO: scikitplot: 0.3.7 +2024-12-04 17:05:36,748:INFO: yellowbrick: 1.5 +2024-12-04 17:05:36,748:INFO: plotly: 5.24.1 +2024-12-04 17:05:36,749:INFO: plotly-resampler: Not installed +2024-12-04 17:05:36,749:INFO: kaleido: 0.2.1 +2024-12-04 17:05:36,749:INFO: schemdraw: 0.15 +2024-12-04 17:05:36,749:INFO: statsmodels: 0.14.4 +2024-12-04 17:05:36,749:INFO: sktime: 0.26.0 +2024-12-04 17:05:36,749:INFO: tbats: 1.1.3 +2024-12-04 17:05:36,749:INFO: pmdarima: 2.0.4 +2024-12-04 17:05:36,749:INFO: psutil: 6.1.0 +2024-12-04 17:05:36,749:INFO: markupsafe: 3.0.2 +2024-12-04 17:05:36,749:INFO: pickle5: Not installed +2024-12-04 17:05:36,749:INFO: cloudpickle: 3.1.0 +2024-12-04 17:05:36,749:INFO: deprecation: 2.1.0 +2024-12-04 17:05:36,749:INFO: xxhash: 3.5.0 +2024-12-04 17:05:36,749:INFO: wurlitzer: Not installed +2024-12-04 17:05:36,749:INFO:PyCaret optional dependencies: +2024-12-04 17:05:36,749:INFO: shap: Not installed +2024-12-04 17:05:36,749:INFO: interpret: Not installed +2024-12-04 17:05:36,749:INFO: umap: Not installed +2024-12-04 17:05:36,749:INFO: ydata_profiling: Not installed +2024-12-04 17:05:36,749:INFO: explainerdashboard: Not installed +2024-12-04 17:05:36,749:INFO: autoviz: Not installed +2024-12-04 17:05:36,749:INFO: fairlearn: Not installed +2024-12-04 17:05:36,749:INFO: deepchecks: Not installed +2024-12-04 17:05:36,749:INFO: xgboost: Not installed +2024-12-04 17:05:36,749:INFO: catboost: Not installed +2024-12-04 17:05:36,749:INFO: kmodes: Not installed +2024-12-04 17:05:36,749:INFO: mlxtend: Not installed +2024-12-04 17:05:36,749:INFO: statsforecast: Not installed +2024-12-04 17:05:36,749:INFO: tune_sklearn: Not installed +2024-12-04 17:05:36,749:INFO: ray: Not installed +2024-12-04 17:05:36,749:INFO: hyperopt: Not installed +2024-12-04 17:05:36,749:INFO: optuna: Not installed +2024-12-04 17:05:36,749:INFO: skopt: Not installed +2024-12-04 17:05:36,749:INFO: mlflow: 2.18.0 +2024-12-04 17:05:36,749:INFO: gradio: Not installed +2024-12-04 17:05:36,749:INFO: fastapi: Not installed +2024-12-04 17:05:36,749:INFO: uvicorn: 0.32.1 +2024-12-04 17:05:36,749:INFO: m2cgen: Not installed +2024-12-04 17:05:36,749:INFO: evidently: Not installed +2024-12-04 17:05:36,749:INFO: fugue: Not installed +2024-12-04 17:05:36,750:INFO: streamlit: 1.40.1 +2024-12-04 17:05:36,750:INFO: prophet: Not installed +2024-12-04 17:05:36,751:INFO:None +2024-12-04 17:05:36,751:INFO:Set up data. +2024-12-04 17:05:36,759:INFO:Set up folding strategy. +2024-12-04 17:06:05,873:INFO:PyCaret RegressionExperiment +2024-12-04 17:06:05,873:INFO:Logging name: reg-default-name +2024-12-04 17:06:05,873:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:06:05,873:INFO:version 3.3.2 +2024-12-04 17:06:05,873:INFO:Initializing setup() +2024-12-04 17:06:05,873:INFO:self.USI: 02da +2024-12-04 17:06:05,873:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:06:05,873:INFO:Checking environment +2024-12-04 17:06:05,873:INFO:python_version: 3.11.9 +2024-12-04 17:06:05,873:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:06:05,873:INFO:machine: AMD64 +2024-12-04 17:06:05,874:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:06:05,881:INFO:Memory: svmem(total=17007292416, available=2573385728, percent=84.9, used=14433906688, free=2573385728) +2024-12-04 17:06:05,881:INFO:Physical Core: 4 +2024-12-04 17:06:05,882:INFO:Logical Core: 8 +2024-12-04 17:06:05,882:INFO:Checking libraries +2024-12-04 17:06:05,882:INFO:System: +2024-12-04 17:06:05,882:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:06:05,882:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:06:05,882:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:06:05,882:INFO:PyCaret required dependencies: +2024-12-04 17:06:05,882:INFO: pip: 24.3.1 +2024-12-04 17:06:05,882:INFO: setuptools: 65.5.0 +2024-12-04 17:06:05,882:INFO: pycaret: 3.3.2 +2024-12-04 17:06:05,882:INFO: IPython: 8.12.3 +2024-12-04 17:06:05,882:INFO: ipywidgets: 8.1.5 +2024-12-04 17:06:05,882:INFO: tqdm: 4.67.1 +2024-12-04 17:06:05,882:INFO: numpy: 1.26.4 +2024-12-04 17:06:05,882:INFO: pandas: 2.1.4 +2024-12-04 17:06:05,883:INFO: jinja2: 3.1.4 +2024-12-04 17:06:05,883:INFO: scipy: 1.11.4 +2024-12-04 17:06:05,883:INFO: joblib: 1.3.2 +2024-12-04 17:06:05,883:INFO: sklearn: 1.4.2 +2024-12-04 17:06:05,883:INFO: pyod: 2.0.2 +2024-12-04 17:06:05,883:INFO: imblearn: 0.12.4 +2024-12-04 17:06:05,883:INFO: category_encoders: 2.6.4 +2024-12-04 17:06:05,883:INFO: lightgbm: 4.5.0 +2024-12-04 17:06:05,883:INFO: numba: 0.60.0 +2024-12-04 17:06:05,883:INFO: requests: 2.32.3 +2024-12-04 17:06:05,883:INFO: matplotlib: 3.7.5 +2024-12-04 17:06:05,883:INFO: scikitplot: 0.3.7 +2024-12-04 17:06:05,883:INFO: yellowbrick: 1.5 +2024-12-04 17:06:05,883:INFO: plotly: 5.24.1 +2024-12-04 17:06:05,883:INFO: plotly-resampler: Not installed +2024-12-04 17:06:05,883:INFO: kaleido: 0.2.1 +2024-12-04 17:06:05,883:INFO: schemdraw: 0.15 +2024-12-04 17:06:05,883:INFO: statsmodels: 0.14.4 +2024-12-04 17:06:05,884:INFO: sktime: 0.26.0 +2024-12-04 17:06:05,884:INFO: tbats: 1.1.3 +2024-12-04 17:06:05,884:INFO: pmdarima: 2.0.4 +2024-12-04 17:06:05,884:INFO: psutil: 6.1.0 +2024-12-04 17:06:05,884:INFO: markupsafe: 3.0.2 +2024-12-04 17:06:05,884:INFO: pickle5: Not installed +2024-12-04 17:06:05,884:INFO: cloudpickle: 3.1.0 +2024-12-04 17:06:05,884:INFO: deprecation: 2.1.0 +2024-12-04 17:06:05,884:INFO: xxhash: 3.5.0 +2024-12-04 17:06:05,884:INFO: wurlitzer: Not installed +2024-12-04 17:06:05,884:INFO:PyCaret optional dependencies: +2024-12-04 17:06:05,884:INFO: shap: Not installed +2024-12-04 17:06:05,884:INFO: interpret: Not installed +2024-12-04 17:06:05,884:INFO: umap: Not installed +2024-12-04 17:06:05,884:INFO: ydata_profiling: Not installed +2024-12-04 17:06:05,884:INFO: explainerdashboard: Not installed +2024-12-04 17:06:05,884:INFO: autoviz: Not installed +2024-12-04 17:06:05,885:INFO: fairlearn: Not installed +2024-12-04 17:06:05,885:INFO: deepchecks: Not installed +2024-12-04 17:06:05,885:INFO: xgboost: Not installed +2024-12-04 17:06:05,885:INFO: catboost: Not installed +2024-12-04 17:06:05,885:INFO: kmodes: Not installed +2024-12-04 17:06:05,885:INFO: mlxtend: Not installed +2024-12-04 17:06:05,885:INFO: statsforecast: Not installed +2024-12-04 17:06:05,885:INFO: tune_sklearn: Not installed +2024-12-04 17:06:05,885:INFO: ray: Not installed +2024-12-04 17:06:05,885:INFO: hyperopt: Not installed +2024-12-04 17:06:05,885:INFO: optuna: Not installed +2024-12-04 17:06:05,885:INFO: skopt: Not installed +2024-12-04 17:06:05,885:INFO: mlflow: 2.18.0 +2024-12-04 17:06:05,885:INFO: gradio: Not installed +2024-12-04 17:06:05,885:INFO: fastapi: Not installed +2024-12-04 17:06:05,885:INFO: uvicorn: 0.32.1 +2024-12-04 17:06:05,885:INFO: m2cgen: Not installed +2024-12-04 17:06:05,885:INFO: evidently: Not installed +2024-12-04 17:06:05,885:INFO: fugue: Not installed +2024-12-04 17:06:05,886:INFO: streamlit: 1.40.1 +2024-12-04 17:06:05,886:INFO: prophet: Not installed +2024-12-04 17:06:05,886:INFO:None +2024-12-04 17:06:05,886:INFO:Set up data. +2024-12-04 17:06:05,901:INFO:Set up folding strategy. +2024-12-04 17:06:38,348:INFO:PyCaret RegressionExperiment +2024-12-04 17:06:38,348:INFO:Logging name: reg-default-name +2024-12-04 17:06:38,348:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:06:38,348:INFO:version 3.3.2 +2024-12-04 17:06:38,348:INFO:Initializing setup() +2024-12-04 17:06:38,348:INFO:self.USI: e718 +2024-12-04 17:06:38,348:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:06:38,348:INFO:Checking environment +2024-12-04 17:06:38,348:INFO:python_version: 3.11.9 +2024-12-04 17:06:38,348:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:06:38,348:INFO:machine: AMD64 +2024-12-04 17:06:38,348:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:06:38,354:INFO:Memory: svmem(total=17007292416, available=2585104384, percent=84.8, used=14422188032, free=2585104384) +2024-12-04 17:06:38,354:INFO:Physical Core: 4 +2024-12-04 17:06:38,354:INFO:Logical Core: 8 +2024-12-04 17:06:38,354:INFO:Checking libraries +2024-12-04 17:06:38,354:INFO:System: +2024-12-04 17:06:38,354:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:06:38,354:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:06:38,354:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:06:38,354:INFO:PyCaret required dependencies: +2024-12-04 17:06:38,354:INFO: pip: 24.3.1 +2024-12-04 17:06:38,354:INFO: setuptools: 65.5.0 +2024-12-04 17:06:38,354:INFO: pycaret: 3.3.2 +2024-12-04 17:06:38,354:INFO: IPython: 8.12.3 +2024-12-04 17:06:38,354:INFO: ipywidgets: 8.1.5 +2024-12-04 17:06:38,354:INFO: tqdm: 4.67.1 +2024-12-04 17:06:38,355:INFO: numpy: 1.26.4 +2024-12-04 17:06:38,355:INFO: pandas: 2.1.4 +2024-12-04 17:06:38,355:INFO: jinja2: 3.1.4 +2024-12-04 17:06:38,355:INFO: scipy: 1.11.4 +2024-12-04 17:06:38,355:INFO: joblib: 1.3.2 +2024-12-04 17:06:38,355:INFO: sklearn: 1.4.2 +2024-12-04 17:06:38,355:INFO: pyod: 2.0.2 +2024-12-04 17:06:38,355:INFO: imblearn: 0.12.4 +2024-12-04 17:06:38,355:INFO: category_encoders: 2.6.4 +2024-12-04 17:06:38,355:INFO: lightgbm: 4.5.0 +2024-12-04 17:06:38,355:INFO: numba: 0.60.0 +2024-12-04 17:06:38,355:INFO: requests: 2.32.3 +2024-12-04 17:06:38,355:INFO: matplotlib: 3.7.5 +2024-12-04 17:06:38,355:INFO: scikitplot: 0.3.7 +2024-12-04 17:06:38,355:INFO: yellowbrick: 1.5 +2024-12-04 17:06:38,355:INFO: plotly: 5.24.1 +2024-12-04 17:06:38,355:INFO: plotly-resampler: Not installed +2024-12-04 17:06:38,355:INFO: kaleido: 0.2.1 +2024-12-04 17:06:38,355:INFO: schemdraw: 0.15 +2024-12-04 17:06:38,355:INFO: statsmodels: 0.14.4 +2024-12-04 17:06:38,355:INFO: sktime: 0.26.0 +2024-12-04 17:06:38,355:INFO: tbats: 1.1.3 +2024-12-04 17:06:38,355:INFO: pmdarima: 2.0.4 +2024-12-04 17:06:38,355:INFO: psutil: 6.1.0 +2024-12-04 17:06:38,355:INFO: markupsafe: 3.0.2 +2024-12-04 17:06:38,355:INFO: pickle5: Not installed +2024-12-04 17:06:38,355:INFO: cloudpickle: 3.1.0 +2024-12-04 17:06:38,355:INFO: deprecation: 2.1.0 +2024-12-04 17:06:38,355:INFO: xxhash: 3.5.0 +2024-12-04 17:06:38,356:INFO: wurlitzer: Not installed +2024-12-04 17:06:38,356:INFO:PyCaret optional dependencies: +2024-12-04 17:06:38,356:INFO: shap: Not installed +2024-12-04 17:06:38,356:INFO: interpret: Not installed +2024-12-04 17:06:38,356:INFO: umap: Not installed +2024-12-04 17:06:38,356:INFO: ydata_profiling: Not installed +2024-12-04 17:06:38,356:INFO: explainerdashboard: Not installed +2024-12-04 17:06:38,356:INFO: autoviz: Not installed +2024-12-04 17:06:38,356:INFO: fairlearn: Not installed +2024-12-04 17:06:38,356:INFO: deepchecks: Not installed +2024-12-04 17:06:38,356:INFO: xgboost: Not installed +2024-12-04 17:06:38,356:INFO: catboost: Not installed +2024-12-04 17:06:38,356:INFO: kmodes: Not installed +2024-12-04 17:06:38,356:INFO: mlxtend: Not installed +2024-12-04 17:06:38,356:INFO: statsforecast: Not installed +2024-12-04 17:06:38,356:INFO: tune_sklearn: Not installed +2024-12-04 17:06:38,356:INFO: ray: Not installed +2024-12-04 17:06:38,356:INFO: hyperopt: Not installed +2024-12-04 17:06:38,356:INFO: optuna: Not installed +2024-12-04 17:06:38,356:INFO: skopt: Not installed +2024-12-04 17:06:38,356:INFO: mlflow: 2.18.0 +2024-12-04 17:06:38,356:INFO: gradio: Not installed +2024-12-04 17:06:38,356:INFO: fastapi: Not installed +2024-12-04 17:06:38,356:INFO: uvicorn: 0.32.1 +2024-12-04 17:06:38,356:INFO: m2cgen: Not installed +2024-12-04 17:06:38,356:INFO: evidently: Not installed +2024-12-04 17:06:38,356:INFO: fugue: Not installed +2024-12-04 17:06:38,357:INFO: streamlit: 1.40.1 +2024-12-04 17:06:38,357:INFO: prophet: Not installed +2024-12-04 17:06:38,357:INFO:None +2024-12-04 17:06:38,357:INFO:Set up data. +2024-12-04 17:06:38,364:INFO:Set up folding strategy. +2024-12-04 17:06:38,364:INFO:Set up train/test split. +2024-12-04 17:06:38,368:INFO:Set up index. +2024-12-04 17:06:38,368:INFO:Assigning column types. +2024-12-04 17:06:38,370:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:06:38,370:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,375:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,380:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,435:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,478:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,478:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,479:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,479:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,483:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,488:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,541:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,584:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,584:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,585:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,585:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:06:38,588:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,594:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,648:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,690:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,690:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,691:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,696:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,700:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,754:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,794:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,796:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,796:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,796:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:06:38,804:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,859:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,903:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,904:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,904:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:38,913:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:06:38,976:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,022:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,023:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,023:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,023:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:06:39,097:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,141:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,141:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,143:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,207:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,252:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,252:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,253:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,253:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:06:39,315:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,359:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,359:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,423:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:06:39,467:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,467:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,468:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:06:39,579:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,579:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,691:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,691:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:06:39,693:INFO:Preparing preprocessing pipeline... +2024-12-04 17:06:39,693:INFO:Set up simple imputation. +2024-12-04 17:06:39,695:INFO:Set up encoding of categorical features. +2024-12-04 17:06:39,696:INFO:Set up column transformation. +2024-12-04 17:06:39,696:INFO:Set up feature normalization. +2024-12-04 17:07:49,670:INFO:PyCaret RegressionExperiment +2024-12-04 17:07:49,670:INFO:Logging name: reg-default-name +2024-12-04 17:07:49,670:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:07:49,670:INFO:version 3.3.2 +2024-12-04 17:07:49,670:INFO:Initializing setup() +2024-12-04 17:07:49,671:INFO:self.USI: f736 +2024-12-04 17:07:49,671:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:07:49,671:INFO:Checking environment +2024-12-04 17:07:49,671:INFO:python_version: 3.11.9 +2024-12-04 17:07:49,671:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:07:49,671:INFO:machine: AMD64 +2024-12-04 17:07:49,671:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:07:49,675:INFO:Memory: svmem(total=17007292416, available=3747557376, percent=78.0, used=13259735040, free=3747557376) +2024-12-04 17:07:49,675:INFO:Physical Core: 4 +2024-12-04 17:07:49,675:INFO:Logical Core: 8 +2024-12-04 17:07:49,675:INFO:Checking libraries +2024-12-04 17:07:49,678:INFO:System: +2024-12-04 17:07:49,678:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:07:49,678:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:07:49,678:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:07:49,678:INFO:PyCaret required dependencies: +2024-12-04 17:07:49,678:INFO: pip: 24.3.1 +2024-12-04 17:07:49,678:INFO: setuptools: 65.5.0 +2024-12-04 17:07:49,678:INFO: pycaret: 3.3.2 +2024-12-04 17:07:49,678:INFO: IPython: 8.12.3 +2024-12-04 17:07:49,678:INFO: ipywidgets: 8.1.5 +2024-12-04 17:07:49,678:INFO: tqdm: 4.67.1 +2024-12-04 17:07:49,678:INFO: numpy: 1.26.4 +2024-12-04 17:07:49,678:INFO: pandas: 2.1.4 +2024-12-04 17:07:49,678:INFO: jinja2: 3.1.4 +2024-12-04 17:07:49,678:INFO: scipy: 1.11.4 +2024-12-04 17:07:49,678:INFO: joblib: 1.3.2 +2024-12-04 17:07:49,678:INFO: sklearn: 1.4.2 +2024-12-04 17:07:49,678:INFO: pyod: 2.0.2 +2024-12-04 17:07:49,678:INFO: imblearn: 0.12.4 +2024-12-04 17:07:49,678:INFO: category_encoders: 2.6.4 +2024-12-04 17:07:49,678:INFO: lightgbm: 4.5.0 +2024-12-04 17:07:49,678:INFO: numba: 0.60.0 +2024-12-04 17:07:49,678:INFO: requests: 2.32.3 +2024-12-04 17:07:49,678:INFO: matplotlib: 3.7.5 +2024-12-04 17:07:49,678:INFO: scikitplot: 0.3.7 +2024-12-04 17:07:49,678:INFO: yellowbrick: 1.5 +2024-12-04 17:07:49,678:INFO: plotly: 5.24.1 +2024-12-04 17:07:49,678:INFO: plotly-resampler: Not installed +2024-12-04 17:07:49,678:INFO: kaleido: 0.2.1 +2024-12-04 17:07:49,678:INFO: schemdraw: 0.15 +2024-12-04 17:07:49,678:INFO: statsmodels: 0.14.4 +2024-12-04 17:07:49,678:INFO: sktime: 0.26.0 +2024-12-04 17:07:49,678:INFO: tbats: 1.1.3 +2024-12-04 17:07:49,678:INFO: pmdarima: 2.0.4 +2024-12-04 17:07:49,678:INFO: psutil: 6.1.0 +2024-12-04 17:07:49,678:INFO: markupsafe: 3.0.2 +2024-12-04 17:07:49,678:INFO: pickle5: Not installed +2024-12-04 17:07:49,678:INFO: cloudpickle: 3.1.0 +2024-12-04 17:07:49,678:INFO: deprecation: 2.1.0 +2024-12-04 17:07:49,678:INFO: xxhash: 3.5.0 +2024-12-04 17:07:49,678:INFO: wurlitzer: Not installed +2024-12-04 17:07:49,678:INFO:PyCaret optional dependencies: +2024-12-04 17:07:49,678:INFO: shap: Not installed +2024-12-04 17:07:49,678:INFO: interpret: Not installed +2024-12-04 17:07:49,678:INFO: umap: Not installed +2024-12-04 17:07:49,678:INFO: ydata_profiling: Not installed +2024-12-04 17:07:49,678:INFO: explainerdashboard: Not installed +2024-12-04 17:07:49,678:INFO: autoviz: Not installed +2024-12-04 17:07:49,678:INFO: fairlearn: Not installed +2024-12-04 17:07:49,678:INFO: deepchecks: Not installed +2024-12-04 17:07:49,678:INFO: xgboost: Not installed +2024-12-04 17:07:49,678:INFO: catboost: Not installed +2024-12-04 17:07:49,678:INFO: kmodes: Not installed +2024-12-04 17:07:49,678:INFO: mlxtend: Not installed +2024-12-04 17:07:49,678:INFO: statsforecast: Not installed +2024-12-04 17:07:49,678:INFO: tune_sklearn: Not installed +2024-12-04 17:07:49,678:INFO: ray: Not installed +2024-12-04 17:07:49,678:INFO: hyperopt: Not installed +2024-12-04 17:07:49,678:INFO: optuna: Not installed +2024-12-04 17:07:49,678:INFO: skopt: Not installed +2024-12-04 17:07:49,678:INFO: mlflow: 2.18.0 +2024-12-04 17:07:49,678:INFO: gradio: Not installed +2024-12-04 17:07:49,678:INFO: fastapi: Not installed +2024-12-04 17:07:49,678:INFO: uvicorn: 0.32.1 +2024-12-04 17:07:49,678:INFO: m2cgen: Not installed +2024-12-04 17:07:49,678:INFO: evidently: Not installed +2024-12-04 17:07:49,678:INFO: fugue: Not installed +2024-12-04 17:07:49,678:INFO: streamlit: 1.40.1 +2024-12-04 17:07:49,678:INFO: prophet: Not installed +2024-12-04 17:07:49,678:INFO:None +2024-12-04 17:07:49,678:INFO:Set up data. +2024-12-04 17:07:49,686:INFO:Set up folding strategy. +2024-12-04 17:08:15,121:INFO:PyCaret RegressionExperiment +2024-12-04 17:08:15,121:INFO:Logging name: reg-default-name +2024-12-04 17:08:15,121:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:08:15,121:INFO:version 3.3.2 +2024-12-04 17:08:15,121:INFO:Initializing setup() +2024-12-04 17:08:15,121:INFO:self.USI: 54b1 +2024-12-04 17:08:15,121:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:08:15,121:INFO:Checking environment +2024-12-04 17:08:15,121:INFO:python_version: 3.11.9 +2024-12-04 17:08:15,121:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:08:15,121:INFO:machine: AMD64 +2024-12-04 17:08:15,121:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:08:15,129:INFO:Memory: svmem(total=17007292416, available=3734999040, percent=78.0, used=13272293376, free=3734999040) +2024-12-04 17:08:15,130:INFO:Physical Core: 4 +2024-12-04 17:08:15,130:INFO:Logical Core: 8 +2024-12-04 17:08:15,130:INFO:Checking libraries +2024-12-04 17:08:15,130:INFO:System: +2024-12-04 17:08:15,130:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:08:15,130:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:08:15,130:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:08:15,130:INFO:PyCaret required dependencies: +2024-12-04 17:08:15,130:INFO: pip: 24.3.1 +2024-12-04 17:08:15,130:INFO: setuptools: 65.5.0 +2024-12-04 17:08:15,130:INFO: pycaret: 3.3.2 +2024-12-04 17:08:15,130:INFO: IPython: 8.12.3 +2024-12-04 17:08:15,130:INFO: ipywidgets: 8.1.5 +2024-12-04 17:08:15,130:INFO: tqdm: 4.67.1 +2024-12-04 17:08:15,130:INFO: numpy: 1.26.4 +2024-12-04 17:08:15,130:INFO: pandas: 2.1.4 +2024-12-04 17:08:15,130:INFO: jinja2: 3.1.4 +2024-12-04 17:08:15,130:INFO: scipy: 1.11.4 +2024-12-04 17:08:15,130:INFO: joblib: 1.3.2 +2024-12-04 17:08:15,130:INFO: sklearn: 1.4.2 +2024-12-04 17:08:15,130:INFO: pyod: 2.0.2 +2024-12-04 17:08:15,130:INFO: imblearn: 0.12.4 +2024-12-04 17:08:15,130:INFO: category_encoders: 2.6.4 +2024-12-04 17:08:15,130:INFO: lightgbm: 4.5.0 +2024-12-04 17:08:15,130:INFO: numba: 0.60.0 +2024-12-04 17:08:15,130:INFO: requests: 2.32.3 +2024-12-04 17:08:15,130:INFO: matplotlib: 3.7.5 +2024-12-04 17:08:15,130:INFO: scikitplot: 0.3.7 +2024-12-04 17:08:15,130:INFO: yellowbrick: 1.5 +2024-12-04 17:08:15,130:INFO: plotly: 5.24.1 +2024-12-04 17:08:15,130:INFO: plotly-resampler: Not installed +2024-12-04 17:08:15,130:INFO: kaleido: 0.2.1 +2024-12-04 17:08:15,130:INFO: schemdraw: 0.15 +2024-12-04 17:08:15,130:INFO: statsmodels: 0.14.4 +2024-12-04 17:08:15,130:INFO: sktime: 0.26.0 +2024-12-04 17:08:15,130:INFO: tbats: 1.1.3 +2024-12-04 17:08:15,130:INFO: pmdarima: 2.0.4 +2024-12-04 17:08:15,130:INFO: psutil: 6.1.0 +2024-12-04 17:08:15,130:INFO: markupsafe: 3.0.2 +2024-12-04 17:08:15,130:INFO: pickle5: Not installed +2024-12-04 17:08:15,130:INFO: cloudpickle: 3.1.0 +2024-12-04 17:08:15,132:INFO: deprecation: 2.1.0 +2024-12-04 17:08:15,132:INFO: xxhash: 3.5.0 +2024-12-04 17:08:15,132:INFO: wurlitzer: Not installed +2024-12-04 17:08:15,132:INFO:PyCaret optional dependencies: +2024-12-04 17:08:15,132:INFO: shap: Not installed +2024-12-04 17:08:15,132:INFO: interpret: Not installed +2024-12-04 17:08:15,132:INFO: umap: Not installed +2024-12-04 17:08:15,132:INFO: ydata_profiling: Not installed +2024-12-04 17:08:15,132:INFO: explainerdashboard: Not installed +2024-12-04 17:08:15,132:INFO: autoviz: Not installed +2024-12-04 17:08:15,132:INFO: fairlearn: Not installed +2024-12-04 17:08:15,132:INFO: deepchecks: Not installed +2024-12-04 17:08:15,132:INFO: xgboost: Not installed +2024-12-04 17:08:15,132:INFO: catboost: Not installed +2024-12-04 17:08:15,132:INFO: kmodes: Not installed +2024-12-04 17:08:15,132:INFO: mlxtend: Not installed +2024-12-04 17:08:15,132:INFO: statsforecast: Not installed +2024-12-04 17:08:15,132:INFO: tune_sklearn: Not installed +2024-12-04 17:08:15,132:INFO: ray: Not installed +2024-12-04 17:08:15,132:INFO: hyperopt: Not installed +2024-12-04 17:08:15,132:INFO: optuna: Not installed +2024-12-04 17:08:15,132:INFO: skopt: Not installed +2024-12-04 17:08:15,132:INFO: mlflow: 2.18.0 +2024-12-04 17:08:15,132:INFO: gradio: Not installed +2024-12-04 17:08:15,132:INFO: fastapi: Not installed +2024-12-04 17:08:15,132:INFO: uvicorn: 0.32.1 +2024-12-04 17:08:15,133:INFO: m2cgen: Not installed +2024-12-04 17:08:15,133:INFO: evidently: Not installed +2024-12-04 17:08:15,133:INFO: fugue: Not installed +2024-12-04 17:08:15,133:INFO: streamlit: 1.40.1 +2024-12-04 17:08:15,133:INFO: prophet: Not installed +2024-12-04 17:08:15,133:INFO:None +2024-12-04 17:08:15,133:INFO:Set up data. +2024-12-04 17:08:15,138:INFO:Set up folding strategy. +2024-12-04 17:08:15,138:INFO:Set up train/test split. +2024-12-04 17:08:15,142:INFO:Set up index. +2024-12-04 17:08:15,142:INFO:Assigning column types. +2024-12-04 17:08:57,351:INFO:PyCaret RegressionExperiment +2024-12-04 17:08:57,351:INFO:Logging name: reg-default-name +2024-12-04 17:08:57,359:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:08:57,359:INFO:version 3.3.2 +2024-12-04 17:08:57,359:INFO:Initializing setup() +2024-12-04 17:08:57,359:INFO:self.USI: a03b +2024-12-04 17:08:57,359:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:08:57,359:INFO:Checking environment +2024-12-04 17:08:57,359:INFO:python_version: 3.11.9 +2024-12-04 17:08:57,359:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:08:57,359:INFO:machine: AMD64 +2024-12-04 17:08:57,359:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:08:57,364:INFO:Memory: svmem(total=17007292416, available=3702603776, percent=78.2, used=13304688640, free=3702603776) +2024-12-04 17:08:57,364:INFO:Physical Core: 4 +2024-12-04 17:08:57,365:INFO:Logical Core: 8 +2024-12-04 17:08:57,365:INFO:Checking libraries +2024-12-04 17:08:57,365:INFO:System: +2024-12-04 17:08:57,365:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:08:57,365:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:08:57,365:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:08:57,365:INFO:PyCaret required dependencies: +2024-12-04 17:08:57,365:INFO: pip: 24.3.1 +2024-12-04 17:08:57,365:INFO: setuptools: 65.5.0 +2024-12-04 17:08:57,365:INFO: pycaret: 3.3.2 +2024-12-04 17:08:57,365:INFO: IPython: 8.12.3 +2024-12-04 17:08:57,365:INFO: ipywidgets: 8.1.5 +2024-12-04 17:08:57,365:INFO: tqdm: 4.67.1 +2024-12-04 17:08:57,365:INFO: numpy: 1.26.4 +2024-12-04 17:08:57,365:INFO: pandas: 2.1.4 +2024-12-04 17:08:57,365:INFO: jinja2: 3.1.4 +2024-12-04 17:08:57,365:INFO: scipy: 1.11.4 +2024-12-04 17:08:57,365:INFO: joblib: 1.3.2 +2024-12-04 17:08:57,365:INFO: sklearn: 1.4.2 +2024-12-04 17:08:57,365:INFO: pyod: 2.0.2 +2024-12-04 17:08:57,365:INFO: imblearn: 0.12.4 +2024-12-04 17:08:57,365:INFO: category_encoders: 2.6.4 +2024-12-04 17:08:57,365:INFO: lightgbm: 4.5.0 +2024-12-04 17:08:57,365:INFO: numba: 0.60.0 +2024-12-04 17:08:57,366:INFO: requests: 2.32.3 +2024-12-04 17:08:57,366:INFO: matplotlib: 3.7.5 +2024-12-04 17:08:57,366:INFO: scikitplot: 0.3.7 +2024-12-04 17:08:57,366:INFO: yellowbrick: 1.5 +2024-12-04 17:08:57,366:INFO: plotly: 5.24.1 +2024-12-04 17:08:57,366:INFO: plotly-resampler: Not installed +2024-12-04 17:08:57,366:INFO: kaleido: 0.2.1 +2024-12-04 17:08:57,366:INFO: schemdraw: 0.15 +2024-12-04 17:08:57,366:INFO: statsmodels: 0.14.4 +2024-12-04 17:08:57,366:INFO: sktime: 0.26.0 +2024-12-04 17:08:57,366:INFO: tbats: 1.1.3 +2024-12-04 17:08:57,366:INFO: pmdarima: 2.0.4 +2024-12-04 17:08:57,366:INFO: psutil: 6.1.0 +2024-12-04 17:08:57,366:INFO: markupsafe: 3.0.2 +2024-12-04 17:08:57,366:INFO: pickle5: Not installed +2024-12-04 17:08:57,366:INFO: cloudpickle: 3.1.0 +2024-12-04 17:08:57,366:INFO: deprecation: 2.1.0 +2024-12-04 17:08:57,366:INFO: xxhash: 3.5.0 +2024-12-04 17:08:57,366:INFO: wurlitzer: Not installed +2024-12-04 17:08:57,366:INFO:PyCaret optional dependencies: +2024-12-04 17:08:57,366:INFO: shap: Not installed +2024-12-04 17:08:57,366:INFO: interpret: Not installed +2024-12-04 17:08:57,366:INFO: umap: Not installed +2024-12-04 17:08:57,366:INFO: ydata_profiling: Not installed +2024-12-04 17:08:57,366:INFO: explainerdashboard: Not installed +2024-12-04 17:08:57,366:INFO: autoviz: Not installed +2024-12-04 17:08:57,366:INFO: fairlearn: Not installed +2024-12-04 17:08:57,366:INFO: deepchecks: Not installed +2024-12-04 17:08:57,366:INFO: xgboost: Not installed +2024-12-04 17:08:57,366:INFO: catboost: Not installed +2024-12-04 17:08:57,366:INFO: kmodes: Not installed +2024-12-04 17:08:57,366:INFO: mlxtend: Not installed +2024-12-04 17:08:57,366:INFO: statsforecast: Not installed +2024-12-04 17:08:57,366:INFO: tune_sklearn: Not installed +2024-12-04 17:08:57,366:INFO: ray: Not installed +2024-12-04 17:08:57,366:INFO: hyperopt: Not installed +2024-12-04 17:08:57,366:INFO: optuna: Not installed +2024-12-04 17:08:57,366:INFO: skopt: Not installed +2024-12-04 17:08:57,366:INFO: mlflow: 2.18.0 +2024-12-04 17:08:57,366:INFO: gradio: Not installed +2024-12-04 17:08:57,366:INFO: fastapi: Not installed +2024-12-04 17:08:57,366:INFO: uvicorn: 0.32.1 +2024-12-04 17:08:57,366:INFO: m2cgen: Not installed +2024-12-04 17:08:57,366:INFO: evidently: Not installed +2024-12-04 17:08:57,366:INFO: fugue: Not installed +2024-12-04 17:08:57,366:INFO: streamlit: 1.40.1 +2024-12-04 17:08:57,366:INFO: prophet: Not installed +2024-12-04 17:08:57,366:INFO:None +2024-12-04 17:08:57,366:INFO:Set up data. +2024-12-04 17:08:57,375:INFO:Set up folding strategy. +2024-12-04 17:08:57,375:INFO:Set up train/test split. +2024-12-04 17:08:57,375:INFO:Set up index. +2024-12-04 17:08:57,378:INFO:Assigning column types. +2024-12-04 17:09:08,275:INFO:PyCaret RegressionExperiment +2024-12-04 17:09:08,275:INFO:Logging name: reg-default-name +2024-12-04 17:09:08,275:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:09:08,275:INFO:version 3.3.2 +2024-12-04 17:09:08,275:INFO:Initializing setup() +2024-12-04 17:09:08,275:INFO:self.USI: e44e +2024-12-04 17:09:08,275:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:09:08,275:INFO:Checking environment +2024-12-04 17:09:08,275:INFO:python_version: 3.11.9 +2024-12-04 17:09:08,275:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:09:08,275:INFO:machine: AMD64 +2024-12-04 17:09:08,275:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:09:08,285:INFO:Memory: svmem(total=17007292416, available=3722743808, percent=78.1, used=13284548608, free=3722743808) +2024-12-04 17:09:08,286:INFO:Physical Core: 4 +2024-12-04 17:09:08,286:INFO:Logical Core: 8 +2024-12-04 17:09:08,286:INFO:Checking libraries +2024-12-04 17:09:08,286:INFO:System: +2024-12-04 17:09:08,286:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:09:08,286:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:09:08,286:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:09:08,286:INFO:PyCaret required dependencies: +2024-12-04 17:09:08,286:INFO: pip: 24.3.1 +2024-12-04 17:09:08,286:INFO: setuptools: 65.5.0 +2024-12-04 17:09:08,286:INFO: pycaret: 3.3.2 +2024-12-04 17:09:08,286:INFO: IPython: 8.12.3 +2024-12-04 17:09:08,286:INFO: ipywidgets: 8.1.5 +2024-12-04 17:09:08,286:INFO: tqdm: 4.67.1 +2024-12-04 17:09:08,286:INFO: numpy: 1.26.4 +2024-12-04 17:09:08,286:INFO: pandas: 2.1.4 +2024-12-04 17:09:08,286:INFO: jinja2: 3.1.4 +2024-12-04 17:09:08,286:INFO: scipy: 1.11.4 +2024-12-04 17:09:08,286:INFO: joblib: 1.3.2 +2024-12-04 17:09:08,286:INFO: sklearn: 1.4.2 +2024-12-04 17:09:08,286:INFO: pyod: 2.0.2 +2024-12-04 17:09:08,286:INFO: imblearn: 0.12.4 +2024-12-04 17:09:08,286:INFO: category_encoders: 2.6.4 +2024-12-04 17:09:08,286:INFO: lightgbm: 4.5.0 +2024-12-04 17:09:08,286:INFO: numba: 0.60.0 +2024-12-04 17:09:08,286:INFO: requests: 2.32.3 +2024-12-04 17:09:08,287:INFO: matplotlib: 3.7.5 +2024-12-04 17:09:08,287:INFO: scikitplot: 0.3.7 +2024-12-04 17:09:08,287:INFO: yellowbrick: 1.5 +2024-12-04 17:09:08,287:INFO: plotly: 5.24.1 +2024-12-04 17:09:08,287:INFO: plotly-resampler: Not installed +2024-12-04 17:09:08,287:INFO: kaleido: 0.2.1 +2024-12-04 17:09:08,287:INFO: schemdraw: 0.15 +2024-12-04 17:09:08,287:INFO: statsmodels: 0.14.4 +2024-12-04 17:09:08,287:INFO: sktime: 0.26.0 +2024-12-04 17:09:08,287:INFO: tbats: 1.1.3 +2024-12-04 17:09:08,287:INFO: pmdarima: 2.0.4 +2024-12-04 17:09:08,287:INFO: psutil: 6.1.0 +2024-12-04 17:09:08,287:INFO: markupsafe: 3.0.2 +2024-12-04 17:09:08,287:INFO: pickle5: Not installed +2024-12-04 17:09:08,287:INFO: cloudpickle: 3.1.0 +2024-12-04 17:09:08,287:INFO: deprecation: 2.1.0 +2024-12-04 17:09:08,287:INFO: xxhash: 3.5.0 +2024-12-04 17:09:08,287:INFO: wurlitzer: Not installed +2024-12-04 17:09:08,287:INFO:PyCaret optional dependencies: +2024-12-04 17:09:08,288:INFO: shap: Not installed +2024-12-04 17:09:08,288:INFO: interpret: Not installed +2024-12-04 17:09:08,288:INFO: umap: Not installed +2024-12-04 17:09:08,288:INFO: ydata_profiling: Not installed +2024-12-04 17:09:08,288:INFO: explainerdashboard: Not installed +2024-12-04 17:09:08,288:INFO: autoviz: Not installed +2024-12-04 17:09:08,288:INFO: fairlearn: Not installed +2024-12-04 17:09:08,288:INFO: deepchecks: Not installed +2024-12-04 17:09:08,288:INFO: xgboost: Not installed +2024-12-04 17:09:08,288:INFO: catboost: Not installed +2024-12-04 17:09:08,288:INFO: kmodes: Not installed +2024-12-04 17:09:08,288:INFO: mlxtend: Not installed +2024-12-04 17:09:08,288:INFO: statsforecast: Not installed +2024-12-04 17:09:08,288:INFO: tune_sklearn: Not installed +2024-12-04 17:09:08,288:INFO: ray: Not installed +2024-12-04 17:09:08,288:INFO: hyperopt: Not installed +2024-12-04 17:09:08,288:INFO: optuna: Not installed +2024-12-04 17:09:08,289:INFO: skopt: Not installed +2024-12-04 17:09:08,289:INFO: mlflow: 2.18.0 +2024-12-04 17:09:08,289:INFO: gradio: Not installed +2024-12-04 17:09:08,289:INFO: fastapi: Not installed +2024-12-04 17:09:08,289:INFO: uvicorn: 0.32.1 +2024-12-04 17:09:08,289:INFO: m2cgen: Not installed +2024-12-04 17:09:08,289:INFO: evidently: Not installed +2024-12-04 17:09:08,289:INFO: fugue: Not installed +2024-12-04 17:09:08,289:INFO: streamlit: 1.40.1 +2024-12-04 17:09:08,289:INFO: prophet: Not installed +2024-12-04 17:09:08,289:INFO:None +2024-12-04 17:09:08,289:INFO:Set up data. +2024-12-04 17:09:08,295:INFO:Set up folding strategy. +2024-12-04 17:09:08,295:INFO:Set up train/test split. +2024-12-04 17:09:08,299:INFO:Set up index. +2024-12-04 17:09:08,299:INFO:Assigning column types. +2024-12-04 17:09:08,301:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:09:08,301:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,301:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,301:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,375:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,414:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,416:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,416:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,416:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,416:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,425:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,478:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,520:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,520:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,520:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,520:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:09:08,525:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,529:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,582:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,616:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,616:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,616:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,628:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,632:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,685:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,725:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,725:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,728:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,728:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:09:08,732:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,788:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,830:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,832:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,833:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,842:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,901:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,948:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:08,949:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,949:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:08,949:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:09:09,013:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:09,055:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:09,055:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,055:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,118:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:09,163:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:09,163:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,165:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,165:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:09:09,228:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:09,275:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,275:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,340:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:09,386:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,386:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,387:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:09:09,493:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,493:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,598:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,598:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:09,599:INFO:Preparing preprocessing pipeline... +2024-12-04 17:09:09,599:INFO:Set up target transformation. +2024-12-04 17:09:09,599:INFO:Set up simple imputation. +2024-12-04 17:09:09,602:INFO:Set up encoding of categorical features. +2024-12-04 17:09:23,575:INFO:PyCaret RegressionExperiment +2024-12-04 17:09:23,575:INFO:Logging name: reg-default-name +2024-12-04 17:09:23,575:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:09:23,575:INFO:version 3.3.2 +2024-12-04 17:09:23,575:INFO:Initializing setup() +2024-12-04 17:09:23,575:INFO:self.USI: 9a56 +2024-12-04 17:09:23,575:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:09:23,575:INFO:Checking environment +2024-12-04 17:09:23,575:INFO:python_version: 3.11.9 +2024-12-04 17:09:23,575:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:09:23,575:INFO:machine: AMD64 +2024-12-04 17:09:23,575:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:09:23,582:INFO:Memory: svmem(total=17007292416, available=3680018432, percent=78.4, used=13327273984, free=3680018432) +2024-12-04 17:09:23,582:INFO:Physical Core: 4 +2024-12-04 17:09:23,582:INFO:Logical Core: 8 +2024-12-04 17:09:23,582:INFO:Checking libraries +2024-12-04 17:09:23,582:INFO:System: +2024-12-04 17:09:23,582:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:09:23,582:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:09:23,582:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:09:23,582:INFO:PyCaret required dependencies: +2024-12-04 17:09:23,582:INFO: pip: 24.3.1 +2024-12-04 17:09:23,582:INFO: setuptools: 65.5.0 +2024-12-04 17:09:23,582:INFO: pycaret: 3.3.2 +2024-12-04 17:09:23,583:INFO: IPython: 8.12.3 +2024-12-04 17:09:23,583:INFO: ipywidgets: 8.1.5 +2024-12-04 17:09:23,583:INFO: tqdm: 4.67.1 +2024-12-04 17:09:23,583:INFO: numpy: 1.26.4 +2024-12-04 17:09:23,583:INFO: pandas: 2.1.4 +2024-12-04 17:09:23,583:INFO: jinja2: 3.1.4 +2024-12-04 17:09:23,583:INFO: scipy: 1.11.4 +2024-12-04 17:09:23,583:INFO: joblib: 1.3.2 +2024-12-04 17:09:23,583:INFO: sklearn: 1.4.2 +2024-12-04 17:09:23,583:INFO: pyod: 2.0.2 +2024-12-04 17:09:23,584:INFO: imblearn: 0.12.4 +2024-12-04 17:09:23,584:INFO: category_encoders: 2.6.4 +2024-12-04 17:09:23,584:INFO: lightgbm: 4.5.0 +2024-12-04 17:09:23,584:INFO: numba: 0.60.0 +2024-12-04 17:09:23,584:INFO: requests: 2.32.3 +2024-12-04 17:09:23,584:INFO: matplotlib: 3.7.5 +2024-12-04 17:09:23,584:INFO: scikitplot: 0.3.7 +2024-12-04 17:09:23,584:INFO: yellowbrick: 1.5 +2024-12-04 17:09:23,584:INFO: plotly: 5.24.1 +2024-12-04 17:09:23,584:INFO: plotly-resampler: Not installed +2024-12-04 17:09:23,584:INFO: kaleido: 0.2.1 +2024-12-04 17:09:23,584:INFO: schemdraw: 0.15 +2024-12-04 17:09:23,584:INFO: statsmodels: 0.14.4 +2024-12-04 17:09:23,584:INFO: sktime: 0.26.0 +2024-12-04 17:09:23,584:INFO: tbats: 1.1.3 +2024-12-04 17:09:23,584:INFO: pmdarima: 2.0.4 +2024-12-04 17:09:23,584:INFO: psutil: 6.1.0 +2024-12-04 17:09:23,584:INFO: markupsafe: 3.0.2 +2024-12-04 17:09:23,584:INFO: pickle5: Not installed +2024-12-04 17:09:23,584:INFO: cloudpickle: 3.1.0 +2024-12-04 17:09:23,584:INFO: deprecation: 2.1.0 +2024-12-04 17:09:23,584:INFO: xxhash: 3.5.0 +2024-12-04 17:09:23,584:INFO: wurlitzer: Not installed +2024-12-04 17:09:23,584:INFO:PyCaret optional dependencies: +2024-12-04 17:09:23,584:INFO: shap: Not installed +2024-12-04 17:09:23,584:INFO: interpret: Not installed +2024-12-04 17:09:23,584:INFO: umap: Not installed +2024-12-04 17:09:23,584:INFO: ydata_profiling: Not installed +2024-12-04 17:09:23,584:INFO: explainerdashboard: Not installed +2024-12-04 17:09:23,584:INFO: autoviz: Not installed +2024-12-04 17:09:23,585:INFO: fairlearn: Not installed +2024-12-04 17:09:23,585:INFO: deepchecks: Not installed +2024-12-04 17:09:23,585:INFO: xgboost: Not installed +2024-12-04 17:09:23,585:INFO: catboost: Not installed +2024-12-04 17:09:23,585:INFO: kmodes: Not installed +2024-12-04 17:09:23,585:INFO: mlxtend: Not installed +2024-12-04 17:09:23,585:INFO: statsforecast: Not installed +2024-12-04 17:09:23,585:INFO: tune_sklearn: Not installed +2024-12-04 17:09:23,585:INFO: ray: Not installed +2024-12-04 17:09:23,585:INFO: hyperopt: Not installed +2024-12-04 17:09:23,585:INFO: optuna: Not installed +2024-12-04 17:09:23,585:INFO: skopt: Not installed +2024-12-04 17:09:23,585:INFO: mlflow: 2.18.0 +2024-12-04 17:09:23,585:INFO: gradio: Not installed +2024-12-04 17:09:23,585:INFO: fastapi: Not installed +2024-12-04 17:09:23,585:INFO: uvicorn: 0.32.1 +2024-12-04 17:09:23,585:INFO: m2cgen: Not installed +2024-12-04 17:09:23,585:INFO: evidently: Not installed +2024-12-04 17:09:23,585:INFO: fugue: Not installed +2024-12-04 17:09:23,585:INFO: streamlit: 1.40.1 +2024-12-04 17:09:23,585:INFO: prophet: Not installed +2024-12-04 17:09:23,585:INFO:None +2024-12-04 17:09:23,585:INFO:Set up data. +2024-12-04 17:09:23,591:INFO:Set up folding strategy. +2024-12-04 17:09:23,591:INFO:Set up train/test split. +2024-12-04 17:09:23,594:INFO:Set up index. +2024-12-04 17:09:23,594:INFO:Assigning column types. +2024-12-04 17:09:23,596:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:09:23,597:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,601:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,601:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,656:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,701:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,701:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:23,701:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:23,701:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,701:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,701:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,763:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,801:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,801:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:23,801:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:23,801:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:09:23,801:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,814:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,865:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,902:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,902:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:23,902:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:23,902:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,916:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:23,967:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,001:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,001:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,001:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,001:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:09:24,016:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,066:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,101:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,101:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,113:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,116:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,175:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,216:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,216:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,216:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,216:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:09:24,282:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,325:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,328:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,328:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,390:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,432:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,432:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,432:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,432:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:09:24,496:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,538:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,538:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,612:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:09:24,666:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,666:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,666:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:09:24,775:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,775:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,880:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,880:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:24,882:INFO:Preparing preprocessing pipeline... +2024-12-04 17:09:24,882:INFO:Set up target transformation. +2024-12-04 17:09:24,882:INFO:Set up simple imputation. +2024-12-04 17:09:24,884:INFO:Set up encoding of categorical features. +2024-12-04 17:09:24,990:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:09:25,001:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['population'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrap... + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan')))]) +2024-12-04 17:09:25,001:INFO:Creating final display dataframe. +2024-12-04 17:09:25,219:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (14274, 5) +4 Transformed data shape (14274, 16) +5 Transformed train set shape (9991, 16) +6 Transformed test set shape (4283, 16) +7 Ignore features 1 +8 Numeric features 1 +9 Categorical features 2 +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator TimeSeriesSplit +19 Fold Number 3 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name reg-default-name +24 USI 9a56 +2024-12-04 17:09:25,337:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:25,337:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:25,441:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:25,441:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:09:25,443:INFO:setup() successfully completed in 1.89s............... +2024-12-04 17:09:25,457:INFO:Initializing compare_models() +2024-12-04 17:09:25,458:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:09:25,458:INFO:Checking exceptions +2024-12-04 17:09:25,459:INFO:Preparing display monitor +2024-12-04 17:09:25,478:INFO:Initializing Linear Regression +2024-12-04 17:09:25,478:INFO:Total runtime is 0.0 minutes +2024-12-04 17:09:25,482:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:25,483:INFO:Initializing create_model() +2024-12-04 17:09:25,483:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:25,483:INFO:Checking exceptions +2024-12-04 17:09:25,483:INFO:Importing libraries +2024-12-04 17:09:25,483:INFO:Copying training dataset +2024-12-04 17:09:25,487:INFO:Defining folds +2024-12-04 17:09:25,487:INFO:Declaring metric variables +2024-12-04 17:09:25,490:INFO:Importing untrained model +2024-12-04 17:09:25,492:INFO:Linear Regression Imported successfully +2024-12-04 17:09:25,503:INFO:Starting cross validation +2024-12-04 17:09:25,504:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:28,734:INFO:Calculating mean and std +2024-12-04 17:09:28,735:INFO:Creating metrics dataframe +2024-12-04 17:09:28,738:INFO:Uploading results into container +2024-12-04 17:09:28,738:INFO:Uploading model into container now +2024-12-04 17:09:28,739:INFO:_master_model_container: 1 +2024-12-04 17:09:28,739:INFO:_display_container: 2 +2024-12-04 17:09:28,739:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:09:28,740:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:29,016:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:29,016:INFO:Creating metrics dataframe +2024-12-04 17:09:29,028:INFO:Initializing Lasso Regression +2024-12-04 17:09:29,028:INFO:Total runtime is 0.05917197863260905 minutes +2024-12-04 17:09:29,032:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:29,032:INFO:Initializing create_model() +2024-12-04 17:09:29,032:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:29,032:INFO:Checking exceptions +2024-12-04 17:09:29,032:INFO:Importing libraries +2024-12-04 17:09:29,032:INFO:Copying training dataset +2024-12-04 17:09:29,037:INFO:Defining folds +2024-12-04 17:09:29,037:INFO:Declaring metric variables +2024-12-04 17:09:29,040:INFO:Importing untrained model +2024-12-04 17:09:29,042:INFO:Lasso Regression Imported successfully +2024-12-04 17:09:29,050:INFO:Starting cross validation +2024-12-04 17:09:29,051:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:31,577:INFO:Calculating mean and std +2024-12-04 17:09:31,578:INFO:Creating metrics dataframe +2024-12-04 17:09:31,579:INFO:Uploading results into container +2024-12-04 17:09:31,581:INFO:Uploading model into container now +2024-12-04 17:09:31,581:INFO:_master_model_container: 2 +2024-12-04 17:09:31,582:INFO:_display_container: 2 +2024-12-04 17:09:31,582:INFO:Lasso(random_state=123) +2024-12-04 17:09:31,582:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:31,751:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:31,751:INFO:Creating metrics dataframe +2024-12-04 17:09:31,758:INFO:Initializing Ridge Regression +2024-12-04 17:09:31,758:INFO:Total runtime is 0.1046746293703715 minutes +2024-12-04 17:09:31,758:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:31,758:INFO:Initializing create_model() +2024-12-04 17:09:31,758:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:31,758:INFO:Checking exceptions +2024-12-04 17:09:31,758:INFO:Importing libraries +2024-12-04 17:09:31,758:INFO:Copying training dataset +2024-12-04 17:09:31,766:INFO:Defining folds +2024-12-04 17:09:31,766:INFO:Declaring metric variables +2024-12-04 17:09:31,766:INFO:Importing untrained model +2024-12-04 17:09:31,766:INFO:Ridge Regression Imported successfully +2024-12-04 17:09:31,778:INFO:Starting cross validation +2024-12-04 17:09:31,782:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:31,870:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=7.45982e-18): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:09:34,132:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=6.8102e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:09:34,143:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=2.47253e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:09:34,172:INFO:Calculating mean and std +2024-12-04 17:09:34,172:INFO:Creating metrics dataframe +2024-12-04 17:09:34,175:INFO:Uploading results into container +2024-12-04 17:09:34,176:INFO:Uploading model into container now +2024-12-04 17:09:34,176:INFO:_master_model_container: 3 +2024-12-04 17:09:34,176:INFO:_display_container: 2 +2024-12-04 17:09:34,176:INFO:Ridge(random_state=123) +2024-12-04 17:09:34,176:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:34,351:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:34,351:INFO:Creating metrics dataframe +2024-12-04 17:09:34,356:INFO:Initializing Elastic Net +2024-12-04 17:09:34,357:INFO:Total runtime is 0.14799196322758992 minutes +2024-12-04 17:09:34,358:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:34,358:INFO:Initializing create_model() +2024-12-04 17:09:34,358:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:34,358:INFO:Checking exceptions +2024-12-04 17:09:34,358:INFO:Importing libraries +2024-12-04 17:09:34,358:INFO:Copying training dataset +2024-12-04 17:09:34,367:INFO:Defining folds +2024-12-04 17:09:34,367:INFO:Declaring metric variables +2024-12-04 17:09:34,370:INFO:Importing untrained model +2024-12-04 17:09:34,373:INFO:Elastic Net Imported successfully +2024-12-04 17:09:34,378:INFO:Starting cross validation +2024-12-04 17:09:34,381:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:34,499:INFO:Calculating mean and std +2024-12-04 17:09:34,500:INFO:Creating metrics dataframe +2024-12-04 17:09:34,501:INFO:Uploading results into container +2024-12-04 17:09:34,501:INFO:Uploading model into container now +2024-12-04 17:09:34,501:INFO:_master_model_container: 4 +2024-12-04 17:09:34,501:INFO:_display_container: 2 +2024-12-04 17:09:34,501:INFO:ElasticNet(random_state=123) +2024-12-04 17:09:34,501:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:34,651:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:34,651:INFO:Creating metrics dataframe +2024-12-04 17:09:34,656:INFO:Initializing Least Angle Regression +2024-12-04 17:09:34,656:INFO:Total runtime is 0.15296830733617148 minutes +2024-12-04 17:09:34,656:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:34,656:INFO:Initializing create_model() +2024-12-04 17:09:34,656:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:34,656:INFO:Checking exceptions +2024-12-04 17:09:34,656:INFO:Importing libraries +2024-12-04 17:09:34,656:INFO:Copying training dataset +2024-12-04 17:09:34,667:INFO:Defining folds +2024-12-04 17:09:34,667:INFO:Declaring metric variables +2024-12-04 17:09:34,670:INFO:Importing untrained model +2024-12-04 17:09:34,670:INFO:Least Angle Regression Imported successfully +2024-12-04 17:09:34,680:INFO:Starting cross validation +2024-12-04 17:09:34,682:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:34,795:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:501: RuntimeWarning: overflow encountered in square + output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) + +2024-12-04 17:09:34,795:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:501: RuntimeWarning: overflow encountered in square + output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) + +2024-12-04 17:09:34,795:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1196: RuntimeWarning: overflow encountered in square + numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0, dtype=np.float64) + +2024-12-04 17:09:34,803:INFO:Calculating mean and std +2024-12-04 17:09:34,814:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:09:34,814:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:173: RuntimeWarning: invalid value encountered in subtract + x = asanyarray(arr - arrmean) + +2024-12-04 17:09:34,814:INFO:Creating metrics dataframe +2024-12-04 17:09:34,816:INFO:Uploading results into container +2024-12-04 17:09:34,816:INFO:Uploading model into container now +2024-12-04 17:09:34,816:INFO:_master_model_container: 5 +2024-12-04 17:09:34,816:INFO:_display_container: 2 +2024-12-04 17:09:34,816:INFO:Lars(random_state=123) +2024-12-04 17:09:34,816:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:34,966:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:34,966:INFO:Creating metrics dataframe +2024-12-04 17:09:34,978:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:09:34,978:INFO:Total runtime is 0.15833511749903362 minutes +2024-12-04 17:09:34,982:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:34,983:INFO:Initializing create_model() +2024-12-04 17:09:34,983:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:34,983:INFO:Checking exceptions +2024-12-04 17:09:34,983:INFO:Importing libraries +2024-12-04 17:09:34,983:INFO:Copying training dataset +2024-12-04 17:09:34,986:INFO:Defining folds +2024-12-04 17:09:34,987:INFO:Declaring metric variables +2024-12-04 17:09:34,990:INFO:Importing untrained model +2024-12-04 17:09:34,994:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:09:35,001:INFO:Starting cross validation +2024-12-04 17:09:35,002:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:35,134:INFO:Calculating mean and std +2024-12-04 17:09:35,135:INFO:Creating metrics dataframe +2024-12-04 17:09:35,136:INFO:Uploading results into container +2024-12-04 17:09:35,137:INFO:Uploading model into container now +2024-12-04 17:09:35,137:INFO:_master_model_container: 6 +2024-12-04 17:09:35,137:INFO:_display_container: 2 +2024-12-04 17:09:35,137:INFO:LassoLars(random_state=123) +2024-12-04 17:09:35,137:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:35,282:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:35,282:INFO:Creating metrics dataframe +2024-12-04 17:09:35,292:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:09:35,292:INFO:Total runtime is 0.1635656197865804 minutes +2024-12-04 17:09:35,296:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:35,296:INFO:Initializing create_model() +2024-12-04 17:09:35,296:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:35,296:INFO:Checking exceptions +2024-12-04 17:09:35,296:INFO:Importing libraries +2024-12-04 17:09:35,296:INFO:Copying training dataset +2024-12-04 17:09:35,301:INFO:Defining folds +2024-12-04 17:09:35,301:INFO:Declaring metric variables +2024-12-04 17:09:35,303:INFO:Importing untrained model +2024-12-04 17:09:35,303:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:09:35,316:INFO:Starting cross validation +2024-12-04 17:09:35,319:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:35,428:INFO:Calculating mean and std +2024-12-04 17:09:35,428:INFO:Creating metrics dataframe +2024-12-04 17:09:35,431:INFO:Uploading results into container +2024-12-04 17:09:35,432:INFO:Uploading model into container now +2024-12-04 17:09:35,432:INFO:_master_model_container: 7 +2024-12-04 17:09:35,432:INFO:_display_container: 2 +2024-12-04 17:09:35,432:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:09:35,433:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:35,587:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:35,587:INFO:Creating metrics dataframe +2024-12-04 17:09:35,596:INFO:Initializing Bayesian Ridge +2024-12-04 17:09:35,596:INFO:Total runtime is 0.16863137880961102 minutes +2024-12-04 17:09:35,601:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:35,601:INFO:Initializing create_model() +2024-12-04 17:09:35,601:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:35,601:INFO:Checking exceptions +2024-12-04 17:09:35,601:INFO:Importing libraries +2024-12-04 17:09:35,601:INFO:Copying training dataset +2024-12-04 17:09:35,604:INFO:Defining folds +2024-12-04 17:09:35,604:INFO:Declaring metric variables +2024-12-04 17:09:35,609:INFO:Importing untrained model +2024-12-04 17:09:35,612:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:09:35,621:INFO:Starting cross validation +2024-12-04 17:09:35,622:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:35,784:INFO:Calculating mean and std +2024-12-04 17:09:35,784:INFO:Creating metrics dataframe +2024-12-04 17:09:35,784:INFO:Uploading results into container +2024-12-04 17:09:35,784:INFO:Uploading model into container now +2024-12-04 17:09:35,787:INFO:_master_model_container: 8 +2024-12-04 17:09:35,787:INFO:_display_container: 2 +2024-12-04 17:09:35,787:INFO:BayesianRidge() +2024-12-04 17:09:35,787:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:35,969:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:35,969:INFO:Creating metrics dataframe +2024-12-04 17:09:35,975:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:09:35,975:INFO:Total runtime is 0.17495328982671104 minutes +2024-12-04 17:09:35,978:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:35,978:INFO:Initializing create_model() +2024-12-04 17:09:35,978:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:35,978:INFO:Checking exceptions +2024-12-04 17:09:35,978:INFO:Importing libraries +2024-12-04 17:09:35,978:INFO:Copying training dataset +2024-12-04 17:09:35,984:INFO:Defining folds +2024-12-04 17:09:35,985:INFO:Declaring metric variables +2024-12-04 17:09:35,988:INFO:Importing untrained model +2024-12-04 17:09:35,990:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:09:35,996:INFO:Starting cross validation +2024-12-04 17:09:35,998:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:36,126:INFO:Calculating mean and std +2024-12-04 17:09:36,126:INFO:Creating metrics dataframe +2024-12-04 17:09:36,128:INFO:Uploading results into container +2024-12-04 17:09:36,129:INFO:Uploading model into container now +2024-12-04 17:09:36,129:INFO:_master_model_container: 9 +2024-12-04 17:09:36,129:INFO:_display_container: 2 +2024-12-04 17:09:36,131:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:09:36,131:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:36,287:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:36,287:INFO:Creating metrics dataframe +2024-12-04 17:09:36,292:INFO:Initializing Huber Regressor +2024-12-04 17:09:36,292:INFO:Total runtime is 0.18024169206619264 minutes +2024-12-04 17:09:36,298:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:36,298:INFO:Initializing create_model() +2024-12-04 17:09:36,298:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:36,298:INFO:Checking exceptions +2024-12-04 17:09:36,298:INFO:Importing libraries +2024-12-04 17:09:36,298:INFO:Copying training dataset +2024-12-04 17:09:36,304:INFO:Defining folds +2024-12-04 17:09:36,304:INFO:Declaring metric variables +2024-12-04 17:09:36,307:INFO:Importing untrained model +2024-12-04 17:09:36,310:INFO:Huber Regressor Imported successfully +2024-12-04 17:09:36,320:INFO:Starting cross validation +2024-12-04 17:09:36,324:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:36,456:INFO:Calculating mean and std +2024-12-04 17:09:36,457:INFO:Creating metrics dataframe +2024-12-04 17:09:36,458:INFO:Uploading results into container +2024-12-04 17:09:36,458:INFO:Uploading model into container now +2024-12-04 17:09:36,459:INFO:_master_model_container: 10 +2024-12-04 17:09:36,459:INFO:_display_container: 2 +2024-12-04 17:09:36,459:INFO:HuberRegressor() +2024-12-04 17:09:36,459:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:36,613:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:36,613:INFO:Creating metrics dataframe +2024-12-04 17:09:36,616:INFO:Initializing K Neighbors Regressor +2024-12-04 17:09:36,616:INFO:Total runtime is 0.18564622402191164 minutes +2024-12-04 17:09:36,616:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:36,616:INFO:Initializing create_model() +2024-12-04 17:09:36,616:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:36,616:INFO:Checking exceptions +2024-12-04 17:09:36,616:INFO:Importing libraries +2024-12-04 17:09:36,623:INFO:Copying training dataset +2024-12-04 17:09:36,624:INFO:Defining folds +2024-12-04 17:09:36,628:INFO:Declaring metric variables +2024-12-04 17:09:36,631:INFO:Importing untrained model +2024-12-04 17:09:36,632:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:09:36,640:INFO:Starting cross validation +2024-12-04 17:09:36,643:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:36,794:INFO:Calculating mean and std +2024-12-04 17:09:36,794:INFO:Creating metrics dataframe +2024-12-04 17:09:36,796:INFO:Uploading results into container +2024-12-04 17:09:36,796:INFO:Uploading model into container now +2024-12-04 17:09:36,797:INFO:_master_model_container: 11 +2024-12-04 17:09:36,797:INFO:_display_container: 2 +2024-12-04 17:09:36,798:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:09:36,798:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:36,951:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:36,951:INFO:Creating metrics dataframe +2024-12-04 17:09:36,956:INFO:Initializing Decision Tree Regressor +2024-12-04 17:09:36,956:INFO:Total runtime is 0.1913099527359009 minutes +2024-12-04 17:09:36,956:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:36,956:INFO:Initializing create_model() +2024-12-04 17:09:36,956:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:36,956:INFO:Checking exceptions +2024-12-04 17:09:36,956:INFO:Importing libraries +2024-12-04 17:09:36,956:INFO:Copying training dataset +2024-12-04 17:09:36,966:INFO:Defining folds +2024-12-04 17:09:36,966:INFO:Declaring metric variables +2024-12-04 17:09:36,966:INFO:Importing untrained model +2024-12-04 17:09:36,975:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:09:36,980:INFO:Starting cross validation +2024-12-04 17:09:36,983:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:37,112:INFO:Calculating mean and std +2024-12-04 17:09:37,114:INFO:Creating metrics dataframe +2024-12-04 17:09:37,115:INFO:Uploading results into container +2024-12-04 17:09:37,116:INFO:Uploading model into container now +2024-12-04 17:09:37,116:INFO:_master_model_container: 12 +2024-12-04 17:09:37,116:INFO:_display_container: 2 +2024-12-04 17:09:37,116:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:09:37,116:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:37,271:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:37,271:INFO:Creating metrics dataframe +2024-12-04 17:09:37,279:INFO:Initializing Random Forest Regressor +2024-12-04 17:09:37,279:INFO:Total runtime is 0.19669233560562135 minutes +2024-12-04 17:09:37,283:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:37,283:INFO:Initializing create_model() +2024-12-04 17:09:37,283:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:37,284:INFO:Checking exceptions +2024-12-04 17:09:37,284:INFO:Importing libraries +2024-12-04 17:09:37,284:INFO:Copying training dataset +2024-12-04 17:09:37,287:INFO:Defining folds +2024-12-04 17:09:37,287:INFO:Declaring metric variables +2024-12-04 17:09:37,290:INFO:Importing untrained model +2024-12-04 17:09:37,295:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:09:37,302:INFO:Starting cross validation +2024-12-04 17:09:37,304:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:37,943:INFO:Calculating mean and std +2024-12-04 17:09:37,943:INFO:Creating metrics dataframe +2024-12-04 17:09:37,945:INFO:Uploading results into container +2024-12-04 17:09:37,945:INFO:Uploading model into container now +2024-12-04 17:09:37,945:INFO:_master_model_container: 13 +2024-12-04 17:09:37,945:INFO:_display_container: 2 +2024-12-04 17:09:37,947:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:09:37,947:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:38,120:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:38,120:INFO:Creating metrics dataframe +2024-12-04 17:09:38,128:INFO:Initializing Extra Trees Regressor +2024-12-04 17:09:38,128:INFO:Total runtime is 0.2108398000399272 minutes +2024-12-04 17:09:38,132:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:38,132:INFO:Initializing create_model() +2024-12-04 17:09:38,132:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:38,132:INFO:Checking exceptions +2024-12-04 17:09:38,132:INFO:Importing libraries +2024-12-04 17:09:38,132:INFO:Copying training dataset +2024-12-04 17:09:38,137:INFO:Defining folds +2024-12-04 17:09:38,137:INFO:Declaring metric variables +2024-12-04 17:09:38,142:INFO:Importing untrained model +2024-12-04 17:09:38,145:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:09:38,152:INFO:Starting cross validation +2024-12-04 17:09:38,154:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:38,733:INFO:Calculating mean and std +2024-12-04 17:09:38,733:INFO:Creating metrics dataframe +2024-12-04 17:09:38,733:INFO:Uploading results into container +2024-12-04 17:09:38,733:INFO:Uploading model into container now +2024-12-04 17:09:38,733:INFO:_master_model_container: 14 +2024-12-04 17:09:38,733:INFO:_display_container: 2 +2024-12-04 17:09:38,733:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:09:38,733:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:38,895:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:38,895:INFO:Creating metrics dataframe +2024-12-04 17:09:38,901:INFO:Initializing AdaBoost Regressor +2024-12-04 17:09:38,901:INFO:Total runtime is 0.2237218578656515 minutes +2024-12-04 17:09:38,901:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:38,901:INFO:Initializing create_model() +2024-12-04 17:09:38,901:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:38,901:INFO:Checking exceptions +2024-12-04 17:09:38,901:INFO:Importing libraries +2024-12-04 17:09:38,901:INFO:Copying training dataset +2024-12-04 17:09:38,901:INFO:Defining folds +2024-12-04 17:09:38,901:INFO:Declaring metric variables +2024-12-04 17:09:38,915:INFO:Importing untrained model +2024-12-04 17:09:38,917:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:09:38,925:INFO:Starting cross validation +2024-12-04 17:09:38,925:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:39,252:INFO:Calculating mean and std +2024-12-04 17:09:39,252:INFO:Creating metrics dataframe +2024-12-04 17:09:39,252:INFO:Uploading results into container +2024-12-04 17:09:39,252:INFO:Uploading model into container now +2024-12-04 17:09:39,252:INFO:_master_model_container: 15 +2024-12-04 17:09:39,252:INFO:_display_container: 2 +2024-12-04 17:09:39,252:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:09:39,256:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:39,403:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:39,403:INFO:Creating metrics dataframe +2024-12-04 17:09:39,416:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:09:39,416:INFO:Total runtime is 0.23230744202931727 minutes +2024-12-04 17:09:39,416:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:39,416:INFO:Initializing create_model() +2024-12-04 17:09:39,416:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:39,416:INFO:Checking exceptions +2024-12-04 17:09:39,416:INFO:Importing libraries +2024-12-04 17:09:39,416:INFO:Copying training dataset +2024-12-04 17:09:39,425:INFO:Defining folds +2024-12-04 17:09:39,425:INFO:Declaring metric variables +2024-12-04 17:09:39,428:INFO:Importing untrained model +2024-12-04 17:09:39,432:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:09:39,437:INFO:Starting cross validation +2024-12-04 17:09:39,439:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:39,836:INFO:Calculating mean and std +2024-12-04 17:09:39,836:INFO:Creating metrics dataframe +2024-12-04 17:09:39,837:INFO:Uploading results into container +2024-12-04 17:09:39,837:INFO:Uploading model into container now +2024-12-04 17:09:39,837:INFO:_master_model_container: 16 +2024-12-04 17:09:39,837:INFO:_display_container: 2 +2024-12-04 17:09:39,837:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:09:39,837:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:39,992:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:39,992:INFO:Creating metrics dataframe +2024-12-04 17:09:40,001:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:09:40,001:INFO:Total runtime is 0.2420486966768901 minutes +2024-12-04 17:09:40,001:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:40,001:INFO:Initializing create_model() +2024-12-04 17:09:40,001:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:40,001:INFO:Checking exceptions +2024-12-04 17:09:40,001:INFO:Importing libraries +2024-12-04 17:09:40,001:INFO:Copying training dataset +2024-12-04 17:09:40,007:INFO:Defining folds +2024-12-04 17:09:40,007:INFO:Declaring metric variables +2024-12-04 17:09:40,016:INFO:Importing untrained model +2024-12-04 17:09:40,018:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:09:40,025:INFO:Starting cross validation +2024-12-04 17:09:40,028:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:40,341:INFO:Calculating mean and std +2024-12-04 17:09:40,342:INFO:Creating metrics dataframe +2024-12-04 17:09:40,345:INFO:Uploading results into container +2024-12-04 17:09:40,345:INFO:Uploading model into container now +2024-12-04 17:09:40,346:INFO:_master_model_container: 17 +2024-12-04 17:09:40,346:INFO:_display_container: 2 +2024-12-04 17:09:40,346:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:09:40,346:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:40,516:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:40,516:INFO:Creating metrics dataframe +2024-12-04 17:09:40,525:INFO:Initializing Dummy Regressor +2024-12-04 17:09:40,525:INFO:Total runtime is 0.2507872700691224 minutes +2024-12-04 17:09:40,530:INFO:SubProcess create_model() called ================================== +2024-12-04 17:09:40,530:INFO:Initializing create_model() +2024-12-04 17:09:40,530:INFO:create_model(self=, estimator=dummy, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:40,530:INFO:Checking exceptions +2024-12-04 17:09:40,530:INFO:Importing libraries +2024-12-04 17:09:40,530:INFO:Copying training dataset +2024-12-04 17:09:40,534:INFO:Defining folds +2024-12-04 17:09:40,534:INFO:Declaring metric variables +2024-12-04 17:09:40,540:INFO:Importing untrained model +2024-12-04 17:09:40,543:INFO:Dummy Regressor Imported successfully +2024-12-04 17:09:40,549:INFO:Starting cross validation +2024-12-04 17:09:40,551:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:40,662:INFO:Calculating mean and std +2024-12-04 17:09:40,663:INFO:Creating metrics dataframe +2024-12-04 17:09:40,663:INFO:Uploading results into container +2024-12-04 17:09:40,663:INFO:Uploading model into container now +2024-12-04 17:09:40,663:INFO:_master_model_container: 18 +2024-12-04 17:09:40,663:INFO:_display_container: 2 +2024-12-04 17:09:40,663:INFO:DummyRegressor() +2024-12-04 17:09:40,663:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:40,813:INFO:SubProcess create_model() end ================================== +2024-12-04 17:09:40,813:INFO:Creating metrics dataframe +2024-12-04 17:09:40,825:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:09:40,832:INFO:Initializing create_model() +2024-12-04 17:09:40,832:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:40,832:INFO:Checking exceptions +2024-12-04 17:09:40,832:INFO:Importing libraries +2024-12-04 17:09:40,832:INFO:Copying training dataset +2024-12-04 17:09:40,832:INFO:Defining folds +2024-12-04 17:09:40,832:INFO:Declaring metric variables +2024-12-04 17:09:40,832:INFO:Importing untrained model +2024-12-04 17:09:40,832:INFO:Declaring custom model +2024-12-04 17:09:40,837:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:09:40,838:INFO:Cross validation set to False +2024-12-04 17:09:40,838:INFO:Fitting Model +2024-12-04 17:09:41,247:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:09:41,248:INFO:create_model() successfully completed...................................... +2024-12-04 17:09:41,448:INFO:_master_model_container: 18 +2024-12-04 17:09:41,448:INFO:_display_container: 2 +2024-12-04 17:09:41,448:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:09:41,448:INFO:compare_models() successfully completed...................................... +2024-12-04 17:09:41,449:INFO:Initializing create_model() +2024-12-04 17:09:41,449:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:09:41,449:INFO:Checking exceptions +2024-12-04 17:09:41,458:INFO:Importing libraries +2024-12-04 17:09:41,458:INFO:Copying training dataset +2024-12-04 17:09:41,463:INFO:Defining folds +2024-12-04 17:09:41,464:INFO:Declaring metric variables +2024-12-04 17:09:41,466:INFO:Importing untrained model +2024-12-04 17:09:41,466:INFO:Declaring custom model +2024-12-04 17:09:41,466:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:09:41,475:INFO:Starting cross validation +2024-12-04 17:09:41,478:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:09:42,115:INFO:Calculating mean and std +2024-12-04 17:09:42,116:INFO:Creating metrics dataframe +2024-12-04 17:09:42,116:INFO:Finalizing model +2024-12-04 17:09:42,523:INFO:Uploading results into container +2024-12-04 17:09:42,524:INFO:Uploading model into container now +2024-12-04 17:09:42,531:INFO:_master_model_container: 19 +2024-12-04 17:09:42,531:INFO:_display_container: 3 +2024-12-04 17:09:42,532:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:09:42,532:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:12,621:INFO:PyCaret RegressionExperiment +2024-12-04 17:10:12,622:INFO:Logging name: reg-default-name +2024-12-04 17:10:12,622:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:10:12,622:INFO:version 3.3.2 +2024-12-04 17:10:12,622:INFO:Initializing setup() +2024-12-04 17:10:12,622:INFO:self.USI: b92e +2024-12-04 17:10:12,622:INFO:self._variable_keys: {'exp_id', 'seed', 'html_param', 'y_test', 'fold_groups_param', 'X_train', 'n_jobs_param', '_available_plots', 'exp_name_log', 'memory', 'y_train', 'pipeline', 'y', 'transform_target_param', 'fold_shuffle_param', 'logging_param', 'gpu_param', 'data', 'target_param', 'USI', '_ml_usecase', 'X', 'log_plots_param', 'fold_generator', 'idx', 'X_test', 'gpu_n_jobs_param'} +2024-12-04 17:10:12,622:INFO:Checking environment +2024-12-04 17:10:12,622:INFO:python_version: 3.11.9 +2024-12-04 17:10:12,622:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:10:12,622:INFO:machine: AMD64 +2024-12-04 17:10:12,622:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:10:12,628:INFO:Memory: svmem(total=17007292416, available=2627641344, percent=84.5, used=14379651072, free=2627641344) +2024-12-04 17:10:12,628:INFO:Physical Core: 4 +2024-12-04 17:10:12,628:INFO:Logical Core: 8 +2024-12-04 17:10:12,628:INFO:Checking libraries +2024-12-04 17:10:12,628:INFO:System: +2024-12-04 17:10:12,628:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:10:12,628:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:10:12,628:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:10:12,628:INFO:PyCaret required dependencies: +2024-12-04 17:10:12,628:INFO: pip: 24.3.1 +2024-12-04 17:10:12,628:INFO: setuptools: 65.5.0 +2024-12-04 17:10:12,628:INFO: pycaret: 3.3.2 +2024-12-04 17:10:12,628:INFO: IPython: 8.12.3 +2024-12-04 17:10:12,628:INFO: ipywidgets: 8.1.5 +2024-12-04 17:10:12,628:INFO: tqdm: 4.67.1 +2024-12-04 17:10:12,628:INFO: numpy: 1.26.4 +2024-12-04 17:10:12,628:INFO: pandas: 2.1.4 +2024-12-04 17:10:12,628:INFO: jinja2: 3.1.4 +2024-12-04 17:10:12,628:INFO: scipy: 1.11.4 +2024-12-04 17:10:12,628:INFO: joblib: 1.3.2 +2024-12-04 17:10:12,628:INFO: sklearn: 1.4.2 +2024-12-04 17:10:12,628:INFO: pyod: 2.0.2 +2024-12-04 17:10:12,628:INFO: imblearn: 0.12.4 +2024-12-04 17:10:12,628:INFO: category_encoders: 2.6.4 +2024-12-04 17:10:12,628:INFO: lightgbm: 4.5.0 +2024-12-04 17:10:12,628:INFO: numba: 0.60.0 +2024-12-04 17:10:12,628:INFO: requests: 2.32.3 +2024-12-04 17:10:12,628:INFO: matplotlib: 3.7.5 +2024-12-04 17:10:12,628:INFO: scikitplot: 0.3.7 +2024-12-04 17:10:12,628:INFO: yellowbrick: 1.5 +2024-12-04 17:10:12,628:INFO: plotly: 5.24.1 +2024-12-04 17:10:12,628:INFO: plotly-resampler: Not installed +2024-12-04 17:10:12,628:INFO: kaleido: 0.2.1 +2024-12-04 17:10:12,628:INFO: schemdraw: 0.15 +2024-12-04 17:10:12,628:INFO: statsmodels: 0.14.4 +2024-12-04 17:10:12,628:INFO: sktime: 0.26.0 +2024-12-04 17:10:12,628:INFO: tbats: 1.1.3 +2024-12-04 17:10:12,628:INFO: pmdarima: 2.0.4 +2024-12-04 17:10:12,628:INFO: psutil: 6.1.0 +2024-12-04 17:10:12,628:INFO: markupsafe: 3.0.2 +2024-12-04 17:10:12,631:INFO: pickle5: Not installed +2024-12-04 17:10:12,631:INFO: cloudpickle: 3.1.0 +2024-12-04 17:10:12,631:INFO: deprecation: 2.1.0 +2024-12-04 17:10:12,631:INFO: xxhash: 3.5.0 +2024-12-04 17:10:12,631:INFO: wurlitzer: Not installed +2024-12-04 17:10:12,631:INFO:PyCaret optional dependencies: +2024-12-04 17:10:12,631:INFO: shap: Not installed +2024-12-04 17:10:12,631:INFO: interpret: Not installed +2024-12-04 17:10:12,631:INFO: umap: Not installed +2024-12-04 17:10:12,631:INFO: ydata_profiling: Not installed +2024-12-04 17:10:12,631:INFO: explainerdashboard: Not installed +2024-12-04 17:10:12,631:INFO: autoviz: Not installed +2024-12-04 17:10:12,631:INFO: fairlearn: Not installed +2024-12-04 17:10:12,631:INFO: deepchecks: Not installed +2024-12-04 17:10:12,632:INFO: xgboost: Not installed +2024-12-04 17:10:12,632:INFO: catboost: Not installed +2024-12-04 17:10:12,632:INFO: kmodes: Not installed +2024-12-04 17:10:12,632:INFO: mlxtend: Not installed +2024-12-04 17:10:12,632:INFO: statsforecast: Not installed +2024-12-04 17:10:12,632:INFO: tune_sklearn: Not installed +2024-12-04 17:10:12,632:INFO: ray: Not installed +2024-12-04 17:10:12,632:INFO: hyperopt: Not installed +2024-12-04 17:10:12,632:INFO: optuna: Not installed +2024-12-04 17:10:12,632:INFO: skopt: Not installed +2024-12-04 17:10:12,632:INFO: mlflow: 2.18.0 +2024-12-04 17:10:12,632:INFO: gradio: Not installed +2024-12-04 17:10:12,632:INFO: fastapi: Not installed +2024-12-04 17:10:12,632:INFO: uvicorn: 0.32.1 +2024-12-04 17:10:12,632:INFO: m2cgen: Not installed +2024-12-04 17:10:12,632:INFO: evidently: Not installed +2024-12-04 17:10:12,632:INFO: fugue: Not installed +2024-12-04 17:10:12,632:INFO: streamlit: 1.40.1 +2024-12-04 17:10:12,632:INFO: prophet: Not installed +2024-12-04 17:10:12,632:INFO:None +2024-12-04 17:10:12,632:INFO:Set up data. +2024-12-04 17:10:12,639:INFO:Set up folding strategy. +2024-12-04 17:10:12,639:INFO:Set up train/test split. +2024-12-04 17:10:12,642:INFO:Set up index. +2024-12-04 17:10:12,643:INFO:Assigning column types. +2024-12-04 17:10:12,646:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:10:12,646:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,650:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,655:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,713:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,752:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,752:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:12,752:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:12,752:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,752:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,763:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,813:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,858:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,858:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:12,858:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:12,858:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:10:12,863:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,866:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,919:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,960:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,960:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:12,960:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:12,963:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:10:12,968:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,018:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,060:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,061:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,061:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,061:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:10:13,070:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,118:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,163:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,163:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,163:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,172:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,225:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,267:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,267:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,268:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,268:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:10:13,328:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,371:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,371:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,371:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,437:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,482:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,482:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,482:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,482:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:10:13,545:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,587:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,590:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,650:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:10:13,692:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,692:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,692:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:10:13,794:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,794:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,895:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,895:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:13,895:INFO:Preparing preprocessing pipeline... +2024-12-04 17:10:13,895:INFO:Set up target transformation. +2024-12-04 17:10:13,895:INFO:Set up simple imputation. +2024-12-04 17:10:13,895:INFO:Set up encoding of categorical features. +2024-12-04 17:10:13,993:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:10:14,002:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['population'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrap... + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan')))]) +2024-12-04 17:10:14,002:INFO:Creating final display dataframe. +2024-12-04 17:10:14,213:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (14274, 5) +4 Transformed data shape (14274, 16) +5 Transformed train set shape (9991, 16) +6 Transformed test set shape (4283, 16) +7 Ignore features 1 +8 Numeric features 1 +9 Categorical features 2 +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator TimeSeriesSplit +19 Fold Number 10 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name reg-default-name +24 USI b92e +2024-12-04 17:10:14,328:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:14,332:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:14,432:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:14,432:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:10:14,432:INFO:setup() successfully completed in 1.83s............... +2024-12-04 17:10:14,445:INFO:Initializing compare_models() +2024-12-04 17:10:14,445:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:10:14,445:INFO:Checking exceptions +2024-12-04 17:10:14,445:INFO:Preparing display monitor +2024-12-04 17:10:14,468:INFO:Initializing Linear Regression +2024-12-04 17:10:14,468:INFO:Total runtime is 0.0 minutes +2024-12-04 17:10:14,471:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:14,471:INFO:Initializing create_model() +2024-12-04 17:10:14,471:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:14,471:INFO:Checking exceptions +2024-12-04 17:10:14,471:INFO:Importing libraries +2024-12-04 17:10:14,471:INFO:Copying training dataset +2024-12-04 17:10:14,475:INFO:Defining folds +2024-12-04 17:10:14,475:INFO:Declaring metric variables +2024-12-04 17:10:14,478:INFO:Importing untrained model +2024-12-04 17:10:14,482:INFO:Linear Regression Imported successfully +2024-12-04 17:10:14,520:INFO:Starting cross validation +2024-12-04 17:10:14,523:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:14,779:INFO:Calculating mean and std +2024-12-04 17:10:14,779:INFO:Creating metrics dataframe +2024-12-04 17:10:14,779:INFO:Uploading results into container +2024-12-04 17:10:14,779:INFO:Uploading model into container now +2024-12-04 17:10:14,779:INFO:_master_model_container: 1 +2024-12-04 17:10:14,779:INFO:_display_container: 2 +2024-12-04 17:10:14,779:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:10:14,779:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:14,945:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:14,945:INFO:Creating metrics dataframe +2024-12-04 17:10:14,951:INFO:Initializing Lasso Regression +2024-12-04 17:10:14,951:INFO:Total runtime is 0.008055893580118816 minutes +2024-12-04 17:10:14,953:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:14,953:INFO:Initializing create_model() +2024-12-04 17:10:14,954:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:14,954:INFO:Checking exceptions +2024-12-04 17:10:14,954:INFO:Importing libraries +2024-12-04 17:10:14,954:INFO:Copying training dataset +2024-12-04 17:10:14,955:INFO:Defining folds +2024-12-04 17:10:14,955:INFO:Declaring metric variables +2024-12-04 17:10:14,955:INFO:Importing untrained model +2024-12-04 17:10:14,962:INFO:Lasso Regression Imported successfully +2024-12-04 17:10:14,970:INFO:Starting cross validation +2024-12-04 17:10:14,971:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:15,211:INFO:Calculating mean and std +2024-12-04 17:10:15,211:INFO:Creating metrics dataframe +2024-12-04 17:10:15,213:INFO:Uploading results into container +2024-12-04 17:10:15,213:INFO:Uploading model into container now +2024-12-04 17:10:15,213:INFO:_master_model_container: 2 +2024-12-04 17:10:15,213:INFO:_display_container: 2 +2024-12-04 17:10:15,213:INFO:Lasso(random_state=123) +2024-12-04 17:10:15,213:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:15,363:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:15,363:INFO:Creating metrics dataframe +2024-12-04 17:10:15,371:INFO:Initializing Ridge Regression +2024-12-04 17:10:15,371:INFO:Total runtime is 0.015050903956095377 minutes +2024-12-04 17:10:15,373:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:15,373:INFO:Initializing create_model() +2024-12-04 17:10:15,375:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:15,375:INFO:Checking exceptions +2024-12-04 17:10:15,375:INFO:Importing libraries +2024-12-04 17:10:15,375:INFO:Copying training dataset +2024-12-04 17:10:15,378:INFO:Defining folds +2024-12-04 17:10:15,378:INFO:Declaring metric variables +2024-12-04 17:10:15,378:INFO:Importing untrained model +2024-12-04 17:10:15,382:INFO:Ridge Regression Imported successfully +2024-12-04 17:10:15,390:INFO:Starting cross validation +2024-12-04 17:10:15,390:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:15,470:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=8.15918e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,480:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=6.39682e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,502:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=2.49864e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,512:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=3.15398e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,515:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=2.16931e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,518:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=1.42455e-17): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,552:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=9.61362e-18): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,585:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=5.03071e-18): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,613:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py:204: LinAlgWarning: Ill-conditioned matrix (rcond=4.48841e-18): result may not be accurate. + return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T + +2024-12-04 17:10:15,639:INFO:Calculating mean and std +2024-12-04 17:10:15,639:INFO:Creating metrics dataframe +2024-12-04 17:10:15,640:INFO:Uploading results into container +2024-12-04 17:10:15,642:INFO:Uploading model into container now +2024-12-04 17:10:15,642:INFO:_master_model_container: 3 +2024-12-04 17:10:15,642:INFO:_display_container: 2 +2024-12-04 17:10:15,642:INFO:Ridge(random_state=123) +2024-12-04 17:10:15,642:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:15,794:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:15,794:INFO:Creating metrics dataframe +2024-12-04 17:10:15,796:INFO:Initializing Elastic Net +2024-12-04 17:10:15,796:INFO:Total runtime is 0.022131240367889403 minutes +2024-12-04 17:10:15,802:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:15,802:INFO:Initializing create_model() +2024-12-04 17:10:15,802:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:15,802:INFO:Checking exceptions +2024-12-04 17:10:15,802:INFO:Importing libraries +2024-12-04 17:10:15,802:INFO:Copying training dataset +2024-12-04 17:10:15,802:INFO:Defining folds +2024-12-04 17:10:15,802:INFO:Declaring metric variables +2024-12-04 17:10:15,810:INFO:Importing untrained model +2024-12-04 17:10:15,813:INFO:Elastic Net Imported successfully +2024-12-04 17:10:15,823:INFO:Starting cross validation +2024-12-04 17:10:15,825:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:16,070:INFO:Calculating mean and std +2024-12-04 17:10:16,070:INFO:Creating metrics dataframe +2024-12-04 17:10:16,070:INFO:Uploading results into container +2024-12-04 17:10:16,070:INFO:Uploading model into container now +2024-12-04 17:10:16,070:INFO:_master_model_container: 4 +2024-12-04 17:10:16,070:INFO:_display_container: 2 +2024-12-04 17:10:16,070:INFO:ElasticNet(random_state=123) +2024-12-04 17:10:16,070:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:16,225:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:16,225:INFO:Creating metrics dataframe +2024-12-04 17:10:16,232:INFO:Initializing Least Angle Regression +2024-12-04 17:10:16,232:INFO:Total runtime is 0.029401524861653643 minutes +2024-12-04 17:10:16,235:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:16,235:INFO:Initializing create_model() +2024-12-04 17:10:16,235:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:16,235:INFO:Checking exceptions +2024-12-04 17:10:16,235:INFO:Importing libraries +2024-12-04 17:10:16,235:INFO:Copying training dataset +2024-12-04 17:10:16,240:INFO:Defining folds +2024-12-04 17:10:16,240:INFO:Declaring metric variables +2024-12-04 17:10:16,243:INFO:Importing untrained model +2024-12-04 17:10:16,246:INFO:Least Angle Regression Imported successfully +2024-12-04 17:10:16,253:INFO:Starting cross validation +2024-12-04 17:10:16,254:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:16,374:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3386: RuntimeWarning: overflow encountered in power + x_inv[pos] = np.power(x[pos] * lmbda + 1, 1 / lmbda) - 1 + +2024-12-04 17:10:16,412:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(root_mean_squared_log_error, greater_is_better=False, response_method='predict')' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\containers\metrics\regression.py", line 209, in root_mean_squared_log_error + metrics.mean_squared_log_error( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 680, in mean_squared_log_error + y_type, y_true, y_pred, multioutput = _check_reg_targets( + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 104, in _check_reg_targets + y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite + _assert_all_finite_element_wise( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise + raise ValueError(msg_err) +ValueError: Input contains infinity or a value too large for dtype('float64'). + + warnings.warn( + +2024-12-04 17:10:16,417:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(mean_absolute_percentage_error, greater_is_better=False, response_method='predict')' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score + return super()._score( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\containers\metrics\regression.py", line 233, in mean_absolute_percentage_error + y_type, y_true, y_pred, multioutput = _check_reg_targets( + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 104, in _check_reg_targets + y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite + _assert_all_finite_element_wise( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise + raise ValueError(msg_err) +ValueError: Input contains infinity or a value too large for dtype('float64'). + + warnings.warn( + +2024-12-04 17:10:16,418:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:1011: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 137, in __call__ + score = scorer._score( + ^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 207, in mean_absolute_error + y_type, y_true, y_pred, multioutput = _check_reg_targets( + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 104, in _check_reg_targets + y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite + _assert_all_finite_element_wise( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise + raise ValueError(msg_err) +ValueError: Input contains infinity or a value too large for dtype('float64'). + + warnings.warn( + +2024-12-04 17:10:16,418:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:1011: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 137, in __call__ + score = scorer._score( + ^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 497, in mean_squared_error + y_type, y_true, y_pred, multioutput = _check_reg_targets( + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 104, in _check_reg_targets + y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite + _assert_all_finite_element_wise( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise + raise ValueError(msg_err) +ValueError: Input contains infinity or a value too large for dtype('float64'). + + warnings.warn( + +2024-12-04 17:10:16,418:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:1011: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 137, in __call__ + score = scorer._score( + ^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 572, in root_mean_squared_error + mean_squared_error( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 186, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 497, in mean_squared_error + y_type, y_true, y_pred, multioutput = _check_reg_targets( + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 104, in _check_reg_targets + y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite + _assert_all_finite_element_wise( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise + raise ValueError(msg_err) +ValueError: Input contains infinity or a value too large for dtype('float64'). + + warnings.warn( + +2024-12-04 17:10:16,419:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:1011: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 137, in __call__ + score = scorer._score( + ^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score + return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 1180, in r2_score + y_type, y_true, y_pred, multioutput = _check_reg_targets( + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py", line 104, in _check_reg_targets + y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite + _assert_all_finite_element_wise( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise + raise ValueError(msg_err) +ValueError: Input contains infinity or a value too large for dtype('float64'). + + warnings.warn( + +2024-12-04 17:10:16,426:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:501: RuntimeWarning: overflow encountered in square + output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) + +2024-12-04 17:10:16,426:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:501: RuntimeWarning: overflow encountered in square + output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) + +2024-12-04 17:10:16,428:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1196: RuntimeWarning: overflow encountered in square + numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0, dtype=np.float64) + +2024-12-04 17:10:16,447:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:501: RuntimeWarning: overflow encountered in square + output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) + +2024-12-04 17:10:16,447:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:501: RuntimeWarning: overflow encountered in square + output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) + +2024-12-04 17:10:16,447:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1196: RuntimeWarning: overflow encountered in square + numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0, dtype=np.float64) + +2024-12-04 17:10:16,487:INFO:Calculating mean and std +2024-12-04 17:10:16,487:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:10:16,487:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:173: RuntimeWarning: invalid value encountered in subtract + x = asanyarray(arr - arrmean) + +2024-12-04 17:10:16,487:INFO:Creating metrics dataframe +2024-12-04 17:10:16,490:INFO:Uploading results into container +2024-12-04 17:10:16,490:INFO:Uploading model into container now +2024-12-04 17:10:16,490:INFO:_master_model_container: 5 +2024-12-04 17:10:16,490:INFO:_display_container: 2 +2024-12-04 17:10:16,490:INFO:Lars(random_state=123) +2024-12-04 17:10:16,490:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:16,645:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:16,645:INFO:Creating metrics dataframe +2024-12-04 17:10:16,652:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:10:16,652:INFO:Total runtime is 0.03640789985656738 minutes +2024-12-04 17:10:16,652:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:16,652:INFO:Initializing create_model() +2024-12-04 17:10:16,652:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:16,652:INFO:Checking exceptions +2024-12-04 17:10:16,652:INFO:Importing libraries +2024-12-04 17:10:16,652:INFO:Copying training dataset +2024-12-04 17:10:16,652:INFO:Defining folds +2024-12-04 17:10:16,652:INFO:Declaring metric variables +2024-12-04 17:10:16,664:INFO:Importing untrained model +2024-12-04 17:10:16,669:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:10:16,675:INFO:Starting cross validation +2024-12-04 17:10:16,675:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:16,925:INFO:Calculating mean and std +2024-12-04 17:10:16,925:INFO:Creating metrics dataframe +2024-12-04 17:10:16,928:INFO:Uploading results into container +2024-12-04 17:10:16,928:INFO:Uploading model into container now +2024-12-04 17:10:16,928:INFO:_master_model_container: 6 +2024-12-04 17:10:16,928:INFO:_display_container: 2 +2024-12-04 17:10:16,928:INFO:LassoLars(random_state=123) +2024-12-04 17:10:16,928:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:17,081:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:17,081:INFO:Creating metrics dataframe +2024-12-04 17:10:17,087:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:10:17,087:INFO:Total runtime is 0.04365992546081543 minutes +2024-12-04 17:10:17,090:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:17,090:INFO:Initializing create_model() +2024-12-04 17:10:17,090:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:17,090:INFO:Checking exceptions +2024-12-04 17:10:17,090:INFO:Importing libraries +2024-12-04 17:10:17,090:INFO:Copying training dataset +2024-12-04 17:10:17,096:INFO:Defining folds +2024-12-04 17:10:17,096:INFO:Declaring metric variables +2024-12-04 17:10:17,101:INFO:Importing untrained model +2024-12-04 17:10:17,106:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:10:17,106:INFO:Starting cross validation +2024-12-04 17:10:17,113:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:17,389:INFO:Calculating mean and std +2024-12-04 17:10:17,389:INFO:Creating metrics dataframe +2024-12-04 17:10:17,392:INFO:Uploading results into container +2024-12-04 17:10:17,393:INFO:Uploading model into container now +2024-12-04 17:10:17,393:INFO:_master_model_container: 7 +2024-12-04 17:10:17,393:INFO:_display_container: 2 +2024-12-04 17:10:17,393:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:10:17,393:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:17,557:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:17,557:INFO:Creating metrics dataframe +2024-12-04 17:10:17,564:INFO:Initializing Bayesian Ridge +2024-12-04 17:10:17,564:INFO:Total runtime is 0.051599117120107015 minutes +2024-12-04 17:10:17,566:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:17,566:INFO:Initializing create_model() +2024-12-04 17:10:17,566:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:17,566:INFO:Checking exceptions +2024-12-04 17:10:17,566:INFO:Importing libraries +2024-12-04 17:10:17,566:INFO:Copying training dataset +2024-12-04 17:10:17,573:INFO:Defining folds +2024-12-04 17:10:17,574:INFO:Declaring metric variables +2024-12-04 17:10:17,578:INFO:Importing untrained model +2024-12-04 17:10:17,593:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:10:17,619:INFO:Starting cross validation +2024-12-04 17:10:17,621:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:17,886:INFO:Calculating mean and std +2024-12-04 17:10:17,887:INFO:Creating metrics dataframe +2024-12-04 17:10:17,888:INFO:Uploading results into container +2024-12-04 17:10:17,888:INFO:Uploading model into container now +2024-12-04 17:10:17,890:INFO:_master_model_container: 8 +2024-12-04 17:10:17,890:INFO:_display_container: 2 +2024-12-04 17:10:17,891:INFO:BayesianRidge() +2024-12-04 17:10:17,891:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:18,058:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:18,058:INFO:Creating metrics dataframe +2024-12-04 17:10:18,063:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:10:18,063:INFO:Total runtime is 0.059922023614247644 minutes +2024-12-04 17:10:18,066:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:18,067:INFO:Initializing create_model() +2024-12-04 17:10:18,067:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:18,067:INFO:Checking exceptions +2024-12-04 17:10:18,067:INFO:Importing libraries +2024-12-04 17:10:18,067:INFO:Copying training dataset +2024-12-04 17:10:18,075:INFO:Defining folds +2024-12-04 17:10:18,076:INFO:Declaring metric variables +2024-12-04 17:10:18,078:INFO:Importing untrained model +2024-12-04 17:10:18,082:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:10:18,089:INFO:Starting cross validation +2024-12-04 17:10:18,091:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:18,359:INFO:Calculating mean and std +2024-12-04 17:10:18,359:INFO:Creating metrics dataframe +2024-12-04 17:10:18,359:INFO:Uploading results into container +2024-12-04 17:10:18,359:INFO:Uploading model into container now +2024-12-04 17:10:18,363:INFO:_master_model_container: 9 +2024-12-04 17:10:18,363:INFO:_display_container: 2 +2024-12-04 17:10:18,363:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:10:18,363:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:18,530:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:18,530:INFO:Creating metrics dataframe +2024-12-04 17:10:18,538:INFO:Initializing Huber Regressor +2024-12-04 17:10:18,538:INFO:Total runtime is 0.06783470312754314 minutes +2024-12-04 17:10:18,541:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:18,541:INFO:Initializing create_model() +2024-12-04 17:10:18,541:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:18,541:INFO:Checking exceptions +2024-12-04 17:10:18,541:INFO:Importing libraries +2024-12-04 17:10:18,543:INFO:Copying training dataset +2024-12-04 17:10:18,546:INFO:Defining folds +2024-12-04 17:10:18,546:INFO:Declaring metric variables +2024-12-04 17:10:18,546:INFO:Importing untrained model +2024-12-04 17:10:18,554:INFO:Huber Regressor Imported successfully +2024-12-04 17:10:18,560:INFO:Starting cross validation +2024-12-04 17:10:18,563:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:18,837:INFO:Calculating mean and std +2024-12-04 17:10:18,838:INFO:Creating metrics dataframe +2024-12-04 17:10:18,840:INFO:Uploading results into container +2024-12-04 17:10:18,841:INFO:Uploading model into container now +2024-12-04 17:10:18,842:INFO:_master_model_container: 10 +2024-12-04 17:10:18,842:INFO:_display_container: 2 +2024-12-04 17:10:18,842:INFO:HuberRegressor() +2024-12-04 17:10:18,842:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:19,006:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:19,006:INFO:Creating metrics dataframe +2024-12-04 17:10:19,013:INFO:Initializing K Neighbors Regressor +2024-12-04 17:10:19,013:INFO:Total runtime is 0.07575124899546307 minutes +2024-12-04 17:10:19,019:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:19,020:INFO:Initializing create_model() +2024-12-04 17:10:19,020:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:19,020:INFO:Checking exceptions +2024-12-04 17:10:19,020:INFO:Importing libraries +2024-12-04 17:10:19,020:INFO:Copying training dataset +2024-12-04 17:10:19,025:INFO:Defining folds +2024-12-04 17:10:19,025:INFO:Declaring metric variables +2024-12-04 17:10:19,028:INFO:Importing untrained model +2024-12-04 17:10:19,032:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:10:19,048:INFO:Starting cross validation +2024-12-04 17:10:19,051:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:19,363:INFO:Calculating mean and std +2024-12-04 17:10:19,363:INFO:Creating metrics dataframe +2024-12-04 17:10:19,363:INFO:Uploading results into container +2024-12-04 17:10:19,366:INFO:Uploading model into container now +2024-12-04 17:10:19,366:INFO:_master_model_container: 11 +2024-12-04 17:10:19,367:INFO:_display_container: 2 +2024-12-04 17:10:19,368:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:10:19,368:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:19,532:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:19,532:INFO:Creating metrics dataframe +2024-12-04 17:10:19,542:INFO:Initializing Decision Tree Regressor +2024-12-04 17:10:19,542:INFO:Total runtime is 0.0845757047335307 minutes +2024-12-04 17:10:19,542:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:19,542:INFO:Initializing create_model() +2024-12-04 17:10:19,545:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:19,545:INFO:Checking exceptions +2024-12-04 17:10:19,545:INFO:Importing libraries +2024-12-04 17:10:19,545:INFO:Copying training dataset +2024-12-04 17:10:19,546:INFO:Defining folds +2024-12-04 17:10:19,546:INFO:Declaring metric variables +2024-12-04 17:10:19,553:INFO:Importing untrained model +2024-12-04 17:10:19,557:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:10:19,562:INFO:Starting cross validation +2024-12-04 17:10:19,563:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:19,843:INFO:Calculating mean and std +2024-12-04 17:10:19,843:INFO:Creating metrics dataframe +2024-12-04 17:10:19,845:INFO:Uploading results into container +2024-12-04 17:10:19,845:INFO:Uploading model into container now +2024-12-04 17:10:19,845:INFO:_master_model_container: 12 +2024-12-04 17:10:19,845:INFO:_display_container: 2 +2024-12-04 17:10:19,845:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:10:19,845:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:20,013:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:20,013:INFO:Creating metrics dataframe +2024-12-04 17:10:20,021:INFO:Initializing Random Forest Regressor +2024-12-04 17:10:20,021:INFO:Total runtime is 0.09256108204523723 minutes +2024-12-04 17:10:20,025:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:20,025:INFO:Initializing create_model() +2024-12-04 17:10:20,025:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:20,025:INFO:Checking exceptions +2024-12-04 17:10:20,025:INFO:Importing libraries +2024-12-04 17:10:20,025:INFO:Copying training dataset +2024-12-04 17:10:20,028:INFO:Defining folds +2024-12-04 17:10:20,028:INFO:Declaring metric variables +2024-12-04 17:10:20,032:INFO:Importing untrained model +2024-12-04 17:10:20,037:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:10:20,042:INFO:Starting cross validation +2024-12-04 17:10:20,042:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:21,922:INFO:Calculating mean and std +2024-12-04 17:10:21,923:INFO:Creating metrics dataframe +2024-12-04 17:10:21,925:INFO:Uploading results into container +2024-12-04 17:10:21,925:INFO:Uploading model into container now +2024-12-04 17:10:21,925:INFO:_master_model_container: 13 +2024-12-04 17:10:21,925:INFO:_display_container: 2 +2024-12-04 17:10:21,928:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:10:21,928:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:22,116:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:22,116:INFO:Creating metrics dataframe +2024-12-04 17:10:22,125:INFO:Initializing Extra Trees Regressor +2024-12-04 17:10:22,125:INFO:Total runtime is 0.1276131788889567 minutes +2024-12-04 17:10:22,128:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:22,128:INFO:Initializing create_model() +2024-12-04 17:10:22,128:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:22,128:INFO:Checking exceptions +2024-12-04 17:10:22,128:INFO:Importing libraries +2024-12-04 17:10:22,128:INFO:Copying training dataset +2024-12-04 17:10:22,132:INFO:Defining folds +2024-12-04 17:10:22,132:INFO:Declaring metric variables +2024-12-04 17:10:22,137:INFO:Importing untrained model +2024-12-04 17:10:22,140:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:10:22,146:INFO:Starting cross validation +2024-12-04 17:10:22,146:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:23,818:INFO:Calculating mean and std +2024-12-04 17:10:23,819:INFO:Creating metrics dataframe +2024-12-04 17:10:23,822:INFO:Uploading results into container +2024-12-04 17:10:23,823:INFO:Uploading model into container now +2024-12-04 17:10:23,823:INFO:_master_model_container: 14 +2024-12-04 17:10:23,823:INFO:_display_container: 2 +2024-12-04 17:10:23,823:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:10:23,824:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:24,003:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:24,003:INFO:Creating metrics dataframe +2024-12-04 17:10:24,018:INFO:Initializing AdaBoost Regressor +2024-12-04 17:10:24,018:INFO:Total runtime is 0.15917157729466758 minutes +2024-12-04 17:10:24,018:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:24,018:INFO:Initializing create_model() +2024-12-04 17:10:24,018:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:24,018:INFO:Checking exceptions +2024-12-04 17:10:24,018:INFO:Importing libraries +2024-12-04 17:10:24,025:INFO:Copying training dataset +2024-12-04 17:10:24,028:INFO:Defining folds +2024-12-04 17:10:24,028:INFO:Declaring metric variables +2024-12-04 17:10:24,032:INFO:Importing untrained model +2024-12-04 17:10:24,038:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:10:24,042:INFO:Starting cross validation +2024-12-04 17:10:24,046:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:24,706:INFO:Calculating mean and std +2024-12-04 17:10:24,706:INFO:Creating metrics dataframe +2024-12-04 17:10:24,706:INFO:Uploading results into container +2024-12-04 17:10:24,706:INFO:Uploading model into container now +2024-12-04 17:10:24,706:INFO:_master_model_container: 15 +2024-12-04 17:10:24,706:INFO:_display_container: 2 +2024-12-04 17:10:24,706:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:10:24,706:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:24,873:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:24,873:INFO:Creating metrics dataframe +2024-12-04 17:10:24,878:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:10:24,878:INFO:Total runtime is 0.17350075244903565 minutes +2024-12-04 17:10:24,882:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:24,882:INFO:Initializing create_model() +2024-12-04 17:10:24,882:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:24,882:INFO:Checking exceptions +2024-12-04 17:10:24,882:INFO:Importing libraries +2024-12-04 17:10:24,882:INFO:Copying training dataset +2024-12-04 17:10:24,889:INFO:Defining folds +2024-12-04 17:10:24,889:INFO:Declaring metric variables +2024-12-04 17:10:24,892:INFO:Importing untrained model +2024-12-04 17:10:24,895:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:10:24,902:INFO:Starting cross validation +2024-12-04 17:10:24,903:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:25,791:INFO:Calculating mean and std +2024-12-04 17:10:25,791:INFO:Creating metrics dataframe +2024-12-04 17:10:25,792:INFO:Uploading results into container +2024-12-04 17:10:25,792:INFO:Uploading model into container now +2024-12-04 17:10:25,792:INFO:_master_model_container: 16 +2024-12-04 17:10:25,792:INFO:_display_container: 2 +2024-12-04 17:10:25,795:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:10:25,795:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:25,946:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:25,946:INFO:Creating metrics dataframe +2024-12-04 17:10:25,955:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:10:25,955:INFO:Total runtime is 0.19144906202952067 minutes +2024-12-04 17:10:25,959:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:25,959:INFO:Initializing create_model() +2024-12-04 17:10:25,959:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:25,959:INFO:Checking exceptions +2024-12-04 17:10:25,959:INFO:Importing libraries +2024-12-04 17:10:25,959:INFO:Copying training dataset +2024-12-04 17:10:25,965:INFO:Defining folds +2024-12-04 17:10:25,965:INFO:Declaring metric variables +2024-12-04 17:10:25,969:INFO:Importing untrained model +2024-12-04 17:10:25,971:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:10:25,978:INFO:Starting cross validation +2024-12-04 17:10:25,982:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:26,893:INFO:Calculating mean and std +2024-12-04 17:10:26,893:INFO:Creating metrics dataframe +2024-12-04 17:10:26,897:INFO:Uploading results into container +2024-12-04 17:10:26,897:INFO:Uploading model into container now +2024-12-04 17:10:26,897:INFO:_master_model_container: 17 +2024-12-04 17:10:26,897:INFO:_display_container: 2 +2024-12-04 17:10:26,897:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:10:26,897:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:27,079:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:27,079:INFO:Creating metrics dataframe +2024-12-04 17:10:27,087:INFO:Initializing Dummy Regressor +2024-12-04 17:10:27,087:INFO:Total runtime is 0.21032484769821166 minutes +2024-12-04 17:10:27,090:INFO:SubProcess create_model() called ================================== +2024-12-04 17:10:27,090:INFO:Initializing create_model() +2024-12-04 17:10:27,090:INFO:create_model(self=, estimator=dummy, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:27,090:INFO:Checking exceptions +2024-12-04 17:10:27,090:INFO:Importing libraries +2024-12-04 17:10:27,090:INFO:Copying training dataset +2024-12-04 17:10:27,096:INFO:Defining folds +2024-12-04 17:10:27,096:INFO:Declaring metric variables +2024-12-04 17:10:27,096:INFO:Importing untrained model +2024-12-04 17:10:27,104:INFO:Dummy Regressor Imported successfully +2024-12-04 17:10:27,106:INFO:Starting cross validation +2024-12-04 17:10:27,106:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:27,339:INFO:Calculating mean and std +2024-12-04 17:10:27,340:INFO:Creating metrics dataframe +2024-12-04 17:10:27,340:INFO:Uploading results into container +2024-12-04 17:10:27,342:INFO:Uploading model into container now +2024-12-04 17:10:27,343:INFO:_master_model_container: 18 +2024-12-04 17:10:27,343:INFO:_display_container: 2 +2024-12-04 17:10:27,343:INFO:DummyRegressor() +2024-12-04 17:10:27,343:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:27,494:INFO:SubProcess create_model() end ================================== +2024-12-04 17:10:27,494:INFO:Creating metrics dataframe +2024-12-04 17:10:27,501:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:10:27,513:INFO:Initializing create_model() +2024-12-04 17:10:27,513:INFO:create_model(self=, estimator=GradientBoostingRegressor(random_state=123), fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:27,513:INFO:Checking exceptions +2024-12-04 17:10:27,513:INFO:Importing libraries +2024-12-04 17:10:27,513:INFO:Copying training dataset +2024-12-04 17:10:27,516:INFO:Defining folds +2024-12-04 17:10:27,518:INFO:Declaring metric variables +2024-12-04 17:10:27,518:INFO:Importing untrained model +2024-12-04 17:10:27,518:INFO:Declaring custom model +2024-12-04 17:10:27,518:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:10:27,519:INFO:Cross validation set to False +2024-12-04 17:10:27,519:INFO:Fitting Model +2024-12-04 17:10:27,948:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:10:27,948:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:28,127:INFO:_master_model_container: 18 +2024-12-04 17:10:28,127:INFO:_display_container: 2 +2024-12-04 17:10:28,127:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:10:28,127:INFO:compare_models() successfully completed...................................... +2024-12-04 17:10:28,134:INFO:Initializing create_model() +2024-12-04 17:10:28,134:INFO:create_model(self=, estimator=GradientBoostingRegressor(random_state=123), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:10:28,134:INFO:Checking exceptions +2024-12-04 17:10:28,147:INFO:Importing libraries +2024-12-04 17:10:28,148:INFO:Copying training dataset +2024-12-04 17:10:28,158:INFO:Defining folds +2024-12-04 17:10:28,158:INFO:Declaring metric variables +2024-12-04 17:10:28,194:INFO:Importing untrained model +2024-12-04 17:10:28,194:INFO:Declaring custom model +2024-12-04 17:10:28,199:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:10:28,209:INFO:Starting cross validation +2024-12-04 17:10:28,211:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=10, test_size=None), n_jobs=-1 +2024-12-04 17:10:28,977:INFO:Calculating mean and std +2024-12-04 17:10:28,978:INFO:Creating metrics dataframe +2024-12-04 17:10:28,978:INFO:Finalizing model +2024-12-04 17:10:29,436:INFO:Uploading results into container +2024-12-04 17:10:29,437:INFO:Uploading model into container now +2024-12-04 17:10:29,442:INFO:_master_model_container: 19 +2024-12-04 17:10:29,442:INFO:_display_container: 3 +2024-12-04 17:10:29,442:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:10:29,445:INFO:create_model() successfully completed...................................... +2024-12-04 17:10:41,740:INFO:Initializing get_config() +2024-12-04 17:10:41,740:INFO:get_config(self=, variable=X) +2024-12-04 17:10:41,744:INFO:Variable: returned as nom_region nom_pathologie \ +0 Auvergne-Rhône-Alpes Accident vascul. cérébral mal défini +1 Auvergne-Rhône-Alpes Accouchement unique et spontané +2 Auvergne-Rhône-Alpes Affect.inflam.org.génitaux féminins +3 Auvergne-Rhône-Alpes Affections aiguës voies respir. sup. +4 Auvergne-Rhône-Alpes Affections de la glande thyroïde +... ... ... +14269 Provence-Alpes-Côte d'Azur Tumeurs malignes colo +14270 Provence-Alpes-Côte d'Azur Tumeurs malignes secondaires +14271 Provence-Alpes-Côte d'Azur Ulcères de l'estomac et du duodénum +14272 Provence-Alpes-Côte d'Azur Valvulopathie card.non rhumatismale +14273 Provence-Alpes-Côte d'Azur Varices des membres inférieurs + + population +0 7997000.0 +1 7997000.0 +2 7997000.0 +3 7997000.0 +4 7997000.0 +... ... +14269 5099000.0 +14270 5099000.0 +14271 5099000.0 +14272 5099000.0 +14273 5099000.0 + +[14274 rows x 3 columns] +2024-12-04 17:10:41,744:INFO:get_config() successfully completed...................................... +2024-12-04 17:10:41,744:INFO:Initializing get_config() +2024-12-04 17:10:41,744:INFO:get_config(self=, variable=y) +2024-12-04 17:10:41,745:INFO:Variable: returned as 0 640 +1 43527 +2 3174 +3 4284 +4 3203 + ... +14269 6074 +14270 7218 +14271 3036 +14272 3792 +14273 7487 +Name: nbr_hospi, Length: 14274, dtype: Int64 +2024-12-04 17:10:41,746:INFO:get_config() successfully completed...................................... +2024-12-04 17:18:32,897:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:18:32,897:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:18:32,897:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:18:32,897:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:18:44,844:INFO:PyCaret RegressionExperiment +2024-12-04 17:18:44,844:INFO:Logging name: reg-default-name +2024-12-04 17:18:44,844:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:18:44,844:INFO:version 3.3.2 +2024-12-04 17:18:44,845:INFO:Initializing setup() +2024-12-04 17:18:44,845:INFO:self.USI: 3a68 +2024-12-04 17:18:44,845:INFO:self._variable_keys: {'fold_generator', 'n_jobs_param', 'y', 'USI', 'idx', 'logging_param', 'fold_groups_param', 'html_param', 'log_plots_param', '_available_plots', 'X_train', 'gpu_param', 'data', 'seed', 'target_param', 'fold_shuffle_param', 'X', 'exp_name_log', 'exp_id', '_ml_usecase', 'y_test', 'y_train', 'X_test', 'transform_target_param', 'gpu_n_jobs_param', 'memory', 'pipeline'} +2024-12-04 17:18:44,845:INFO:Checking environment +2024-12-04 17:18:44,845:INFO:python_version: 3.11.9 +2024-12-04 17:18:44,845:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:18:44,845:INFO:machine: AMD64 +2024-12-04 17:18:44,845:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:18:44,851:INFO:Memory: svmem(total=17007292416, available=3156635648, percent=81.4, used=13850656768, free=3156635648) +2024-12-04 17:18:44,851:INFO:Physical Core: 4 +2024-12-04 17:18:44,851:INFO:Logical Core: 8 +2024-12-04 17:18:44,851:INFO:Checking libraries +2024-12-04 17:18:44,851:INFO:System: +2024-12-04 17:18:44,851:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:18:44,851:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:18:44,851:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:18:44,851:INFO:PyCaret required dependencies: +2024-12-04 17:18:44,875:INFO: pip: 24.3.1 +2024-12-04 17:18:44,875:INFO: setuptools: 65.5.0 +2024-12-04 17:18:44,875:INFO: pycaret: 3.3.2 +2024-12-04 17:18:44,875:INFO: IPython: 8.12.3 +2024-12-04 17:18:44,875:INFO: ipywidgets: 8.1.5 +2024-12-04 17:18:44,875:INFO: tqdm: 4.67.1 +2024-12-04 17:18:44,875:INFO: numpy: 1.26.4 +2024-12-04 17:18:44,875:INFO: pandas: 2.1.4 +2024-12-04 17:18:44,875:INFO: jinja2: 3.1.4 +2024-12-04 17:18:44,875:INFO: scipy: 1.11.4 +2024-12-04 17:18:44,875:INFO: joblib: 1.3.2 +2024-12-04 17:18:44,875:INFO: sklearn: 1.4.2 +2024-12-04 17:18:44,875:INFO: pyod: 2.0.2 +2024-12-04 17:18:44,875:INFO: imblearn: 0.12.4 +2024-12-04 17:18:44,875:INFO: category_encoders: 2.6.4 +2024-12-04 17:18:44,875:INFO: lightgbm: 4.5.0 +2024-12-04 17:18:44,875:INFO: numba: 0.60.0 +2024-12-04 17:18:44,875:INFO: requests: 2.32.3 +2024-12-04 17:18:44,875:INFO: matplotlib: 3.7.5 +2024-12-04 17:18:44,875:INFO: scikitplot: 0.3.7 +2024-12-04 17:18:44,875:INFO: yellowbrick: 1.5 +2024-12-04 17:18:44,875:INFO: plotly: 5.24.1 +2024-12-04 17:18:44,875:INFO: plotly-resampler: Not installed +2024-12-04 17:18:44,875:INFO: kaleido: 0.2.1 +2024-12-04 17:18:44,875:INFO: schemdraw: 0.15 +2024-12-04 17:18:44,876:INFO: statsmodels: 0.14.4 +2024-12-04 17:18:44,876:INFO: sktime: 0.26.0 +2024-12-04 17:18:44,876:INFO: tbats: 1.1.3 +2024-12-04 17:18:44,876:INFO: pmdarima: 2.0.4 +2024-12-04 17:18:44,876:INFO: psutil: 6.1.0 +2024-12-04 17:18:44,876:INFO: markupsafe: 3.0.2 +2024-12-04 17:18:44,876:INFO: pickle5: Not installed +2024-12-04 17:18:44,876:INFO: cloudpickle: 3.1.0 +2024-12-04 17:18:44,876:INFO: deprecation: 2.1.0 +2024-12-04 17:18:44,876:INFO: xxhash: 3.5.0 +2024-12-04 17:18:44,876:INFO: wurlitzer: Not installed +2024-12-04 17:18:44,876:INFO:PyCaret optional dependencies: +2024-12-04 17:18:44,901:INFO: shap: Not installed +2024-12-04 17:18:44,901:INFO: interpret: Not installed +2024-12-04 17:18:44,901:INFO: umap: Not installed +2024-12-04 17:18:44,901:INFO: ydata_profiling: Not installed +2024-12-04 17:18:44,901:INFO: explainerdashboard: Not installed +2024-12-04 17:18:44,901:INFO: autoviz: Not installed +2024-12-04 17:18:44,901:INFO: fairlearn: Not installed +2024-12-04 17:18:44,901:INFO: deepchecks: Not installed +2024-12-04 17:18:44,901:INFO: xgboost: Not installed +2024-12-04 17:18:44,901:INFO: catboost: Not installed +2024-12-04 17:18:44,901:INFO: kmodes: Not installed +2024-12-04 17:18:44,901:INFO: mlxtend: Not installed +2024-12-04 17:18:44,901:INFO: statsforecast: Not installed +2024-12-04 17:18:44,901:INFO: tune_sklearn: Not installed +2024-12-04 17:18:44,901:INFO: ray: Not installed +2024-12-04 17:18:44,901:INFO: hyperopt: Not installed +2024-12-04 17:18:44,901:INFO: optuna: Not installed +2024-12-04 17:18:44,901:INFO: skopt: Not installed +2024-12-04 17:18:44,901:INFO: mlflow: 2.18.0 +2024-12-04 17:18:44,901:INFO: gradio: Not installed +2024-12-04 17:18:44,901:INFO: fastapi: Not installed +2024-12-04 17:18:44,901:INFO: uvicorn: 0.32.1 +2024-12-04 17:18:44,901:INFO: m2cgen: Not installed +2024-12-04 17:18:44,901:INFO: evidently: Not installed +2024-12-04 17:18:44,901:INFO: fugue: Not installed +2024-12-04 17:18:44,901:INFO: streamlit: 1.40.1 +2024-12-04 17:18:44,901:INFO: prophet: Not installed +2024-12-04 17:18:44,901:INFO:None +2024-12-04 17:18:44,901:INFO:Set up data. +2024-12-04 17:18:44,913:INFO:Set up folding strategy. +2024-12-04 17:18:44,913:INFO:Set up train/test split. +2024-12-04 17:18:44,918:INFO:Set up index. +2024-12-04 17:18:44,918:INFO:Assigning column types. +2024-12-04 17:18:44,919:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:18:44,919:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:18:44,925:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:18:44,928:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:18:44,982:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,025:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,025:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,026:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,026:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,030:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,032:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,082:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,126:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,126:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,126:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,126:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:18:45,131:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,135:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,186:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,225:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,226:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,226:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,230:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,234:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,287:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,328:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,328:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,332:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,332:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:18:45,340:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,394:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,437:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,437:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,437:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,447:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,501:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,547:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,548:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,548:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,548:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:18:45,611:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,651:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,651:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,651:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,713:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,751:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,751:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,751:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,751:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:18:45,817:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,869:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,870:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,937:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:18:45,978:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,978:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:45,978:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:18:46,078:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,078:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,178:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,178:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,182:INFO:Preparing preprocessing pipeline... +2024-12-04 17:18:46,182:INFO:Set up simple imputation. +2024-12-04 17:18:46,182:INFO:Set up encoding of categorical features. +2024-12-04 17:18:46,182:INFO:Set up column transformation. +2024-12-04 17:18:46,182:INFO:Set up feature normalization. +2024-12-04 17:18:46,317:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:18:46,332:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 17:18:46,332:INFO:Creating final display dataframe. +2024-12-04 17:18:46,532:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 4) +4 Transformed data shape (5688, 20) +5 Transformed train set shape (3981, 20) +6 Transformed test set shape (1707, 20) +7 Ignore features 1 +8 Categorical features 2 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Transformation True +16 Transformation method yeo-johnson +17 Normalize True +18 Normalize method zscore +19 Fold Generator KFold +20 Fold Number 5 +21 CPU Jobs -1 +22 Use GPU False +23 Log Experiment False +24 Experiment Name reg-default-name +25 USI 3a68 +2024-12-04 17:18:46,646:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,646:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,746:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,746:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:18:46,746:INFO:setup() successfully completed in 1.92s............... +2024-12-04 17:18:50,962:INFO:Initializing compare_models() +2024-12-04 17:18:50,964:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:18:50,964:INFO:Checking exceptions +2024-12-04 17:18:50,966:INFO:Preparing display monitor +2024-12-04 17:18:50,992:INFO:Initializing Linear Regression +2024-12-04 17:18:50,992:INFO:Total runtime is 0.0 minutes +2024-12-04 17:18:50,996:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:50,996:INFO:Initializing create_model() +2024-12-04 17:18:50,996:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:50,996:INFO:Checking exceptions +2024-12-04 17:18:50,996:INFO:Importing libraries +2024-12-04 17:18:50,996:INFO:Copying training dataset +2024-12-04 17:18:51,006:INFO:Defining folds +2024-12-04 17:18:51,006:INFO:Declaring metric variables +2024-12-04 17:18:51,010:INFO:Importing untrained model +2024-12-04 17:18:51,014:INFO:Linear Regression Imported successfully +2024-12-04 17:18:51,023:INFO:Starting cross validation +2024-12-04 17:18:51,031:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:54,774:INFO:Calculating mean and std +2024-12-04 17:18:54,774:INFO:Creating metrics dataframe +2024-12-04 17:18:54,778:INFO:Uploading results into container +2024-12-04 17:18:54,778:INFO:Uploading model into container now +2024-12-04 17:18:54,778:INFO:_master_model_container: 1 +2024-12-04 17:18:54,778:INFO:_display_container: 2 +2024-12-04 17:18:54,778:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:18:54,778:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:54,878:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:54,878:INFO:Creating metrics dataframe +2024-12-04 17:18:54,886:INFO:Initializing Lasso Regression +2024-12-04 17:18:54,887:INFO:Total runtime is 0.06489331324895223 minutes +2024-12-04 17:18:54,889:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:54,890:INFO:Initializing create_model() +2024-12-04 17:18:54,890:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:54,890:INFO:Checking exceptions +2024-12-04 17:18:54,890:INFO:Importing libraries +2024-12-04 17:18:54,890:INFO:Copying training dataset +2024-12-04 17:18:54,894:INFO:Defining folds +2024-12-04 17:18:54,894:INFO:Declaring metric variables +2024-12-04 17:18:54,897:INFO:Importing untrained model +2024-12-04 17:18:54,901:INFO:Lasso Regression Imported successfully +2024-12-04 17:18:54,908:INFO:Starting cross validation +2024-12-04 17:18:54,910:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:55,147:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.651e+09, tolerance: 4.419e+07 + model = cd_fast.enet_coordinate_descent( + +2024-12-04 17:18:57,478:INFO:Calculating mean and std +2024-12-04 17:18:57,478:INFO:Creating metrics dataframe +2024-12-04 17:18:57,478:INFO:Uploading results into container +2024-12-04 17:18:57,478:INFO:Uploading model into container now +2024-12-04 17:18:57,478:INFO:_master_model_container: 2 +2024-12-04 17:18:57,482:INFO:_display_container: 2 +2024-12-04 17:18:57,482:INFO:Lasso(random_state=123) +2024-12-04 17:18:57,482:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:57,562:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:57,566:INFO:Creating metrics dataframe +2024-12-04 17:18:57,572:INFO:Initializing Ridge Regression +2024-12-04 17:18:57,572:INFO:Total runtime is 0.10965505838394166 minutes +2024-12-04 17:18:57,575:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:57,575:INFO:Initializing create_model() +2024-12-04 17:18:57,575:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:57,575:INFO:Checking exceptions +2024-12-04 17:18:57,575:INFO:Importing libraries +2024-12-04 17:18:57,575:INFO:Copying training dataset +2024-12-04 17:18:57,578:INFO:Defining folds +2024-12-04 17:18:57,578:INFO:Declaring metric variables +2024-12-04 17:18:57,582:INFO:Importing untrained model +2024-12-04 17:18:57,588:INFO:Ridge Regression Imported successfully +2024-12-04 17:18:57,593:INFO:Starting cross validation +2024-12-04 17:18:57,595:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:57,820:INFO:Calculating mean and std +2024-12-04 17:18:57,820:INFO:Creating metrics dataframe +2024-12-04 17:18:57,822:INFO:Uploading results into container +2024-12-04 17:18:57,823:INFO:Uploading model into container now +2024-12-04 17:18:57,824:INFO:_master_model_container: 3 +2024-12-04 17:18:57,824:INFO:_display_container: 2 +2024-12-04 17:18:57,825:INFO:Ridge(random_state=123) +2024-12-04 17:18:57,825:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:57,905:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:57,905:INFO:Creating metrics dataframe +2024-12-04 17:18:57,911:INFO:Initializing Elastic Net +2024-12-04 17:18:57,911:INFO:Total runtime is 0.11530360380808513 minutes +2024-12-04 17:18:57,913:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:57,913:INFO:Initializing create_model() +2024-12-04 17:18:57,913:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:57,916:INFO:Checking exceptions +2024-12-04 17:18:57,916:INFO:Importing libraries +2024-12-04 17:18:57,916:INFO:Copying training dataset +2024-12-04 17:18:57,916:INFO:Defining folds +2024-12-04 17:18:57,916:INFO:Declaring metric variables +2024-12-04 17:18:57,921:INFO:Importing untrained model +2024-12-04 17:18:57,921:INFO:Elastic Net Imported successfully +2024-12-04 17:18:57,932:INFO:Starting cross validation +2024-12-04 17:18:57,932:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:58,117:INFO:Calculating mean and std +2024-12-04 17:18:58,117:INFO:Creating metrics dataframe +2024-12-04 17:18:58,117:INFO:Uploading results into container +2024-12-04 17:18:58,117:INFO:Uploading model into container now +2024-12-04 17:18:58,117:INFO:_master_model_container: 4 +2024-12-04 17:18:58,121:INFO:_display_container: 2 +2024-12-04 17:18:58,121:INFO:ElasticNet(random_state=123) +2024-12-04 17:18:58,121:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:58,200:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:58,200:INFO:Creating metrics dataframe +2024-12-04 17:18:58,206:INFO:Initializing Least Angle Regression +2024-12-04 17:18:58,206:INFO:Total runtime is 0.12022416194279989 minutes +2024-12-04 17:18:58,213:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:58,213:INFO:Initializing create_model() +2024-12-04 17:18:58,213:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:58,213:INFO:Checking exceptions +2024-12-04 17:18:58,213:INFO:Importing libraries +2024-12-04 17:18:58,213:INFO:Copying training dataset +2024-12-04 17:18:58,216:INFO:Defining folds +2024-12-04 17:18:58,216:INFO:Declaring metric variables +2024-12-04 17:18:58,216:INFO:Importing untrained model +2024-12-04 17:18:58,223:INFO:Least Angle Regression Imported successfully +2024-12-04 17:18:58,228:INFO:Starting cross validation +2024-12-04 17:18:58,232:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:58,434:INFO:Calculating mean and std +2024-12-04 17:18:58,434:INFO:Creating metrics dataframe +2024-12-04 17:18:58,437:INFO:Uploading results into container +2024-12-04 17:18:58,437:INFO:Uploading model into container now +2024-12-04 17:18:58,437:INFO:_master_model_container: 5 +2024-12-04 17:18:58,437:INFO:_display_container: 2 +2024-12-04 17:18:58,437:INFO:Lars(random_state=123) +2024-12-04 17:18:58,439:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:58,521:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:58,521:INFO:Creating metrics dataframe +2024-12-04 17:18:58,528:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:18:58,528:INFO:Total runtime is 0.12559463183085123 minutes +2024-12-04 17:18:58,532:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:58,532:INFO:Initializing create_model() +2024-12-04 17:18:58,532:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:58,532:INFO:Checking exceptions +2024-12-04 17:18:58,532:INFO:Importing libraries +2024-12-04 17:18:58,532:INFO:Copying training dataset +2024-12-04 17:18:58,532:INFO:Defining folds +2024-12-04 17:18:58,532:INFO:Declaring metric variables +2024-12-04 17:18:58,538:INFO:Importing untrained model +2024-12-04 17:18:58,540:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:18:58,548:INFO:Starting cross validation +2024-12-04 17:18:58,548:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:58,744:INFO:Calculating mean and std +2024-12-04 17:18:58,744:INFO:Creating metrics dataframe +2024-12-04 17:18:58,746:INFO:Uploading results into container +2024-12-04 17:18:58,746:INFO:Uploading model into container now +2024-12-04 17:18:58,746:INFO:_master_model_container: 6 +2024-12-04 17:18:58,746:INFO:_display_container: 2 +2024-12-04 17:18:58,746:INFO:LassoLars(random_state=123) +2024-12-04 17:18:58,746:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:58,845:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:58,845:INFO:Creating metrics dataframe +2024-12-04 17:18:58,852:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:18:58,852:INFO:Total runtime is 0.13100078105926513 minutes +2024-12-04 17:18:58,860:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:58,860:INFO:Initializing create_model() +2024-12-04 17:18:58,860:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:58,860:INFO:Checking exceptions +2024-12-04 17:18:58,860:INFO:Importing libraries +2024-12-04 17:18:58,860:INFO:Copying training dataset +2024-12-04 17:18:58,863:INFO:Defining folds +2024-12-04 17:18:58,863:INFO:Declaring metric variables +2024-12-04 17:18:58,866:INFO:Importing untrained model +2024-12-04 17:18:58,871:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:18:58,878:INFO:Starting cross validation +2024-12-04 17:18:58,878:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:59,063:INFO:Calculating mean and std +2024-12-04 17:18:59,063:INFO:Creating metrics dataframe +2024-12-04 17:18:59,063:INFO:Uploading results into container +2024-12-04 17:18:59,067:INFO:Uploading model into container now +2024-12-04 17:18:59,067:INFO:_master_model_container: 7 +2024-12-04 17:18:59,067:INFO:_display_container: 2 +2024-12-04 17:18:59,067:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:18:59,067:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:59,143:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:59,145:INFO:Creating metrics dataframe +2024-12-04 17:18:59,153:INFO:Initializing Bayesian Ridge +2024-12-04 17:18:59,153:INFO:Total runtime is 0.13601168394088745 minutes +2024-12-04 17:18:59,157:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:59,158:INFO:Initializing create_model() +2024-12-04 17:18:59,158:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:59,158:INFO:Checking exceptions +2024-12-04 17:18:59,158:INFO:Importing libraries +2024-12-04 17:18:59,158:INFO:Copying training dataset +2024-12-04 17:18:59,160:INFO:Defining folds +2024-12-04 17:18:59,160:INFO:Declaring metric variables +2024-12-04 17:18:59,163:INFO:Importing untrained model +2024-12-04 17:18:59,168:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:18:59,174:INFO:Starting cross validation +2024-12-04 17:18:59,178:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:59,360:INFO:Calculating mean and std +2024-12-04 17:18:59,360:INFO:Creating metrics dataframe +2024-12-04 17:18:59,360:INFO:Uploading results into container +2024-12-04 17:18:59,362:INFO:Uploading model into container now +2024-12-04 17:18:59,362:INFO:_master_model_container: 8 +2024-12-04 17:18:59,362:INFO:_display_container: 2 +2024-12-04 17:18:59,362:INFO:BayesianRidge() +2024-12-04 17:18:59,363:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:59,446:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:59,446:INFO:Creating metrics dataframe +2024-12-04 17:18:59,455:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:18:59,456:INFO:Total runtime is 0.14104008674621582 minutes +2024-12-04 17:18:59,456:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:59,456:INFO:Initializing create_model() +2024-12-04 17:18:59,456:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:59,456:INFO:Checking exceptions +2024-12-04 17:18:59,456:INFO:Importing libraries +2024-12-04 17:18:59,456:INFO:Copying training dataset +2024-12-04 17:18:59,462:INFO:Defining folds +2024-12-04 17:18:59,462:INFO:Declaring metric variables +2024-12-04 17:18:59,462:INFO:Importing untrained model +2024-12-04 17:18:59,466:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:18:59,478:INFO:Starting cross validation +2024-12-04 17:18:59,479:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:18:59,709:INFO:Calculating mean and std +2024-12-04 17:18:59,709:INFO:Creating metrics dataframe +2024-12-04 17:18:59,710:INFO:Uploading results into container +2024-12-04 17:18:59,711:INFO:Uploading model into container now +2024-12-04 17:18:59,711:INFO:_master_model_container: 9 +2024-12-04 17:18:59,711:INFO:_display_container: 2 +2024-12-04 17:18:59,711:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:18:59,712:INFO:create_model() successfully completed...................................... +2024-12-04 17:18:59,796:INFO:SubProcess create_model() end ================================== +2024-12-04 17:18:59,796:INFO:Creating metrics dataframe +2024-12-04 17:18:59,803:INFO:Initializing Huber Regressor +2024-12-04 17:18:59,803:INFO:Total runtime is 0.14684615929921468 minutes +2024-12-04 17:18:59,806:INFO:SubProcess create_model() called ================================== +2024-12-04 17:18:59,806:INFO:Initializing create_model() +2024-12-04 17:18:59,806:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:18:59,806:INFO:Checking exceptions +2024-12-04 17:18:59,806:INFO:Importing libraries +2024-12-04 17:18:59,806:INFO:Copying training dataset +2024-12-04 17:18:59,810:INFO:Defining folds +2024-12-04 17:18:59,810:INFO:Declaring metric variables +2024-12-04 17:18:59,813:INFO:Importing untrained model +2024-12-04 17:18:59,813:INFO:Huber Regressor Imported successfully +2024-12-04 17:18:59,825:INFO:Starting cross validation +2024-12-04 17:18:59,825:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:00,033:INFO:Calculating mean and std +2024-12-04 17:19:00,033:INFO:Creating metrics dataframe +2024-12-04 17:19:00,033:INFO:Uploading results into container +2024-12-04 17:19:00,033:INFO:Uploading model into container now +2024-12-04 17:19:00,033:INFO:_master_model_container: 10 +2024-12-04 17:19:00,033:INFO:_display_container: 2 +2024-12-04 17:19:00,033:INFO:HuberRegressor() +2024-12-04 17:19:00,037:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:00,118:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:00,118:INFO:Creating metrics dataframe +2024-12-04 17:19:00,124:INFO:Initializing K Neighbors Regressor +2024-12-04 17:19:00,128:INFO:Total runtime is 0.15226691563924155 minutes +2024-12-04 17:19:00,132:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:00,132:INFO:Initializing create_model() +2024-12-04 17:19:00,132:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:00,132:INFO:Checking exceptions +2024-12-04 17:19:00,132:INFO:Importing libraries +2024-12-04 17:19:00,132:INFO:Copying training dataset +2024-12-04 17:19:00,137:INFO:Defining folds +2024-12-04 17:19:00,137:INFO:Declaring metric variables +2024-12-04 17:19:00,140:INFO:Importing untrained model +2024-12-04 17:19:00,142:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:19:00,152:INFO:Starting cross validation +2024-12-04 17:19:00,154:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:00,420:INFO:Calculating mean and std +2024-12-04 17:19:00,420:INFO:Creating metrics dataframe +2024-12-04 17:19:00,423:INFO:Uploading results into container +2024-12-04 17:19:00,423:INFO:Uploading model into container now +2024-12-04 17:19:00,423:INFO:_master_model_container: 11 +2024-12-04 17:19:00,423:INFO:_display_container: 2 +2024-12-04 17:19:00,423:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:19:00,423:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:00,501:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:00,501:INFO:Creating metrics dataframe +2024-12-04 17:19:00,507:INFO:Initializing Decision Tree Regressor +2024-12-04 17:19:00,507:INFO:Total runtime is 0.1585824171702067 minutes +2024-12-04 17:19:00,511:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:00,511:INFO:Initializing create_model() +2024-12-04 17:19:00,511:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:00,511:INFO:Checking exceptions +2024-12-04 17:19:00,511:INFO:Importing libraries +2024-12-04 17:19:00,511:INFO:Copying training dataset +2024-12-04 17:19:00,513:INFO:Defining folds +2024-12-04 17:19:00,513:INFO:Declaring metric variables +2024-12-04 17:19:00,516:INFO:Importing untrained model +2024-12-04 17:19:00,522:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:19:00,532:INFO:Starting cross validation +2024-12-04 17:19:00,533:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:00,767:INFO:Calculating mean and std +2024-12-04 17:19:00,767:INFO:Creating metrics dataframe +2024-12-04 17:19:00,768:INFO:Uploading results into container +2024-12-04 17:19:00,768:INFO:Uploading model into container now +2024-12-04 17:19:00,768:INFO:_master_model_container: 12 +2024-12-04 17:19:00,768:INFO:_display_container: 2 +2024-12-04 17:19:00,768:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:19:00,772:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:00,851:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:00,851:INFO:Creating metrics dataframe +2024-12-04 17:19:00,856:INFO:Initializing Random Forest Regressor +2024-12-04 17:19:00,856:INFO:Total runtime is 0.1643921176592509 minutes +2024-12-04 17:19:00,860:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:00,860:INFO:Initializing create_model() +2024-12-04 17:19:00,863:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:00,863:INFO:Checking exceptions +2024-12-04 17:19:00,863:INFO:Importing libraries +2024-12-04 17:19:00,863:INFO:Copying training dataset +2024-12-04 17:19:00,866:INFO:Defining folds +2024-12-04 17:19:00,866:INFO:Declaring metric variables +2024-12-04 17:19:00,869:INFO:Importing untrained model +2024-12-04 17:19:00,869:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:19:00,881:INFO:Starting cross validation +2024-12-04 17:19:00,882:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:01,695:INFO:Calculating mean and std +2024-12-04 17:19:01,695:INFO:Creating metrics dataframe +2024-12-04 17:19:01,696:INFO:Uploading results into container +2024-12-04 17:19:01,696:INFO:Uploading model into container now +2024-12-04 17:19:01,696:INFO:_master_model_container: 13 +2024-12-04 17:19:01,696:INFO:_display_container: 2 +2024-12-04 17:19:01,696:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:01,696:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:01,778:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:01,778:INFO:Creating metrics dataframe +2024-12-04 17:19:01,789:INFO:Initializing Extra Trees Regressor +2024-12-04 17:19:01,789:INFO:Total runtime is 0.1799362301826477 minutes +2024-12-04 17:19:01,792:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:01,792:INFO:Initializing create_model() +2024-12-04 17:19:01,792:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:01,792:INFO:Checking exceptions +2024-12-04 17:19:01,792:INFO:Importing libraries +2024-12-04 17:19:01,792:INFO:Copying training dataset +2024-12-04 17:19:01,795:INFO:Defining folds +2024-12-04 17:19:01,795:INFO:Declaring metric variables +2024-12-04 17:19:01,795:INFO:Importing untrained model +2024-12-04 17:19:01,802:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:19:01,811:INFO:Starting cross validation +2024-12-04 17:19:01,814:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:02,560:INFO:Calculating mean and std +2024-12-04 17:19:02,560:INFO:Creating metrics dataframe +2024-12-04 17:19:02,560:INFO:Uploading results into container +2024-12-04 17:19:02,563:INFO:Uploading model into container now +2024-12-04 17:19:02,563:INFO:_master_model_container: 14 +2024-12-04 17:19:02,563:INFO:_display_container: 2 +2024-12-04 17:19:02,563:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:02,563:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:02,642:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:02,642:INFO:Creating metrics dataframe +2024-12-04 17:19:02,651:INFO:Initializing AdaBoost Regressor +2024-12-04 17:19:02,651:INFO:Total runtime is 0.19431139628092448 minutes +2024-12-04 17:19:02,655:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:02,655:INFO:Initializing create_model() +2024-12-04 17:19:02,655:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:02,655:INFO:Checking exceptions +2024-12-04 17:19:02,655:INFO:Importing libraries +2024-12-04 17:19:02,655:INFO:Copying training dataset +2024-12-04 17:19:02,655:INFO:Defining folds +2024-12-04 17:19:02,655:INFO:Declaring metric variables +2024-12-04 17:19:02,660:INFO:Importing untrained model +2024-12-04 17:19:02,663:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:19:02,672:INFO:Starting cross validation +2024-12-04 17:19:02,673:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:02,970:INFO:Calculating mean and std +2024-12-04 17:19:02,970:INFO:Creating metrics dataframe +2024-12-04 17:19:02,970:INFO:Uploading results into container +2024-12-04 17:19:02,973:INFO:Uploading model into container now +2024-12-04 17:19:02,973:INFO:_master_model_container: 15 +2024-12-04 17:19:02,973:INFO:_display_container: 2 +2024-12-04 17:19:02,973:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:19:02,973:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:03,061:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:03,061:INFO:Creating metrics dataframe +2024-12-04 17:19:03,070:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:19:03,070:INFO:Total runtime is 0.2012953201929728 minutes +2024-12-04 17:19:03,075:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:03,075:INFO:Initializing create_model() +2024-12-04 17:19:03,075:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:03,075:INFO:Checking exceptions +2024-12-04 17:19:03,075:INFO:Importing libraries +2024-12-04 17:19:03,075:INFO:Copying training dataset +2024-12-04 17:19:03,078:INFO:Defining folds +2024-12-04 17:19:03,078:INFO:Declaring metric variables +2024-12-04 17:19:03,082:INFO:Importing untrained model +2024-12-04 17:19:03,088:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:19:03,096:INFO:Starting cross validation +2024-12-04 17:19:03,096:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:03,534:INFO:Calculating mean and std +2024-12-04 17:19:03,534:INFO:Creating metrics dataframe +2024-12-04 17:19:03,538:INFO:Uploading results into container +2024-12-04 17:19:03,538:INFO:Uploading model into container now +2024-12-04 17:19:03,538:INFO:_master_model_container: 16 +2024-12-04 17:19:03,538:INFO:_display_container: 2 +2024-12-04 17:19:03,540:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:19:03,540:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:03,632:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:03,632:INFO:Creating metrics dataframe +2024-12-04 17:19:03,642:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:19:03,642:INFO:Total runtime is 0.21081871589024861 minutes +2024-12-04 17:19:03,643:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:03,646:INFO:Initializing create_model() +2024-12-04 17:19:03,646:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:03,646:INFO:Checking exceptions +2024-12-04 17:19:03,646:INFO:Importing libraries +2024-12-04 17:19:03,646:INFO:Copying training dataset +2024-12-04 17:19:03,646:INFO:Defining folds +2024-12-04 17:19:03,646:INFO:Declaring metric variables +2024-12-04 17:19:03,651:INFO:Importing untrained model +2024-12-04 17:19:03,654:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:19:03,663:INFO:Starting cross validation +2024-12-04 17:19:03,663:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:04,186:INFO:Calculating mean and std +2024-12-04 17:19:04,187:INFO:Creating metrics dataframe +2024-12-04 17:19:04,190:INFO:Uploading results into container +2024-12-04 17:19:04,190:INFO:Uploading model into container now +2024-12-04 17:19:04,190:INFO:_master_model_container: 17 +2024-12-04 17:19:04,190:INFO:_display_container: 2 +2024-12-04 17:19:04,191:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:04,191:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:04,292:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:04,292:INFO:Creating metrics dataframe +2024-12-04 17:19:04,300:INFO:Initializing Dummy Regressor +2024-12-04 17:19:04,300:INFO:Total runtime is 0.2218003273010254 minutes +2024-12-04 17:19:04,304:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:04,304:INFO:Initializing create_model() +2024-12-04 17:19:04,304:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:04,304:INFO:Checking exceptions +2024-12-04 17:19:04,304:INFO:Importing libraries +2024-12-04 17:19:04,304:INFO:Copying training dataset +2024-12-04 17:19:04,304:INFO:Defining folds +2024-12-04 17:19:04,304:INFO:Declaring metric variables +2024-12-04 17:19:04,311:INFO:Importing untrained model +2024-12-04 17:19:04,314:INFO:Dummy Regressor Imported successfully +2024-12-04 17:19:04,320:INFO:Starting cross validation +2024-12-04 17:19:04,322:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:04,530:INFO:Calculating mean and std +2024-12-04 17:19:04,530:INFO:Creating metrics dataframe +2024-12-04 17:19:04,532:INFO:Uploading results into container +2024-12-04 17:19:04,533:INFO:Uploading model into container now +2024-12-04 17:19:04,534:INFO:_master_model_container: 18 +2024-12-04 17:19:04,534:INFO:_display_container: 2 +2024-12-04 17:19:04,534:INFO:DummyRegressor() +2024-12-04 17:19:04,535:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:04,613:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:04,613:INFO:Creating metrics dataframe +2024-12-04 17:19:04,622:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:19:04,628:INFO:Initializing create_model() +2024-12-04 17:19:04,628:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:04,628:INFO:Checking exceptions +2024-12-04 17:19:04,632:INFO:Importing libraries +2024-12-04 17:19:04,632:INFO:Copying training dataset +2024-12-04 17:19:04,636:INFO:Defining folds +2024-12-04 17:19:04,636:INFO:Declaring metric variables +2024-12-04 17:19:04,636:INFO:Importing untrained model +2024-12-04 17:19:04,636:INFO:Declaring custom model +2024-12-04 17:19:04,637:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:19:04,638:INFO:Cross validation set to False +2024-12-04 17:19:04,638:INFO:Fitting Model +2024-12-04 17:19:04,916:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:04,916:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:05,020:INFO:_master_model_container: 18 +2024-12-04 17:19:05,021:INFO:_display_container: 2 +2024-12-04 17:19:05,021:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:05,021:INFO:compare_models() successfully completed...................................... +2024-12-04 17:19:20,382:INFO:Initializing compare_models() +2024-12-04 17:19:20,382:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=MAE, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'MAE', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:19:20,382:INFO:Checking exceptions +2024-12-04 17:19:20,387:INFO:Preparing display monitor +2024-12-04 17:19:20,413:INFO:Initializing Linear Regression +2024-12-04 17:19:20,413:INFO:Total runtime is 0.0 minutes +2024-12-04 17:19:20,416:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:20,416:INFO:Initializing create_model() +2024-12-04 17:19:20,416:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:20,416:INFO:Checking exceptions +2024-12-04 17:19:20,416:INFO:Importing libraries +2024-12-04 17:19:20,416:INFO:Copying training dataset +2024-12-04 17:19:20,425:INFO:Defining folds +2024-12-04 17:19:20,425:INFO:Declaring metric variables +2024-12-04 17:19:20,428:INFO:Importing untrained model +2024-12-04 17:19:20,432:INFO:Linear Regression Imported successfully +2024-12-04 17:19:20,446:INFO:Starting cross validation +2024-12-04 17:19:20,448:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:20,725:INFO:Calculating mean and std +2024-12-04 17:19:20,725:INFO:Creating metrics dataframe +2024-12-04 17:19:20,725:INFO:Uploading results into container +2024-12-04 17:19:20,725:INFO:Uploading model into container now +2024-12-04 17:19:20,728:INFO:_master_model_container: 19 +2024-12-04 17:19:20,728:INFO:_display_container: 3 +2024-12-04 17:19:20,728:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:19:20,728:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:20,806:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:20,806:INFO:Creating metrics dataframe +2024-12-04 17:19:20,813:INFO:Initializing Lasso Regression +2024-12-04 17:19:20,813:INFO:Total runtime is 0.006657524903615316 minutes +2024-12-04 17:19:20,816:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:20,816:INFO:Initializing create_model() +2024-12-04 17:19:20,816:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:20,816:INFO:Checking exceptions +2024-12-04 17:19:20,816:INFO:Importing libraries +2024-12-04 17:19:20,816:INFO:Copying training dataset +2024-12-04 17:19:20,822:INFO:Defining folds +2024-12-04 17:19:20,822:INFO:Declaring metric variables +2024-12-04 17:19:20,825:INFO:Importing untrained model +2024-12-04 17:19:20,827:INFO:Lasso Regression Imported successfully +2024-12-04 17:19:20,833:INFO:Starting cross validation +2024-12-04 17:19:20,833:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:21,042:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.651e+09, tolerance: 4.419e+07 + model = cd_fast.enet_coordinate_descent( + +2024-12-04 17:19:21,071:INFO:Calculating mean and std +2024-12-04 17:19:21,071:INFO:Creating metrics dataframe +2024-12-04 17:19:21,074:INFO:Uploading results into container +2024-12-04 17:19:21,074:INFO:Uploading model into container now +2024-12-04 17:19:21,074:INFO:_master_model_container: 20 +2024-12-04 17:19:21,074:INFO:_display_container: 3 +2024-12-04 17:19:21,074:INFO:Lasso(random_state=123) +2024-12-04 17:19:21,074:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:21,155:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:21,155:INFO:Creating metrics dataframe +2024-12-04 17:19:21,160:INFO:Initializing Ridge Regression +2024-12-04 17:19:21,160:INFO:Total runtime is 0.012450710932413737 minutes +2024-12-04 17:19:21,164:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:21,164:INFO:Initializing create_model() +2024-12-04 17:19:21,164:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:21,164:INFO:Checking exceptions +2024-12-04 17:19:21,164:INFO:Importing libraries +2024-12-04 17:19:21,164:INFO:Copying training dataset +2024-12-04 17:19:21,166:INFO:Defining folds +2024-12-04 17:19:21,166:INFO:Declaring metric variables +2024-12-04 17:19:21,169:INFO:Importing untrained model +2024-12-04 17:19:21,171:INFO:Ridge Regression Imported successfully +2024-12-04 17:19:21,178:INFO:Starting cross validation +2024-12-04 17:19:21,180:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:21,378:INFO:Calculating mean and std +2024-12-04 17:19:21,378:INFO:Creating metrics dataframe +2024-12-04 17:19:21,379:INFO:Uploading results into container +2024-12-04 17:19:21,380:INFO:Uploading model into container now +2024-12-04 17:19:21,380:INFO:_master_model_container: 21 +2024-12-04 17:19:21,380:INFO:_display_container: 3 +2024-12-04 17:19:21,380:INFO:Ridge(random_state=123) +2024-12-04 17:19:21,380:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:21,461:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:21,461:INFO:Creating metrics dataframe +2024-12-04 17:19:21,466:INFO:Initializing Elastic Net +2024-12-04 17:19:21,466:INFO:Total runtime is 0.017552602291107177 minutes +2024-12-04 17:19:21,466:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:21,466:INFO:Initializing create_model() +2024-12-04 17:19:21,466:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:21,466:INFO:Checking exceptions +2024-12-04 17:19:21,466:INFO:Importing libraries +2024-12-04 17:19:21,466:INFO:Copying training dataset +2024-12-04 17:19:21,472:INFO:Defining folds +2024-12-04 17:19:21,475:INFO:Declaring metric variables +2024-12-04 17:19:21,478:INFO:Importing untrained model +2024-12-04 17:19:21,482:INFO:Elastic Net Imported successfully +2024-12-04 17:19:21,487:INFO:Starting cross validation +2024-12-04 17:19:21,489:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:21,711:INFO:Calculating mean and std +2024-12-04 17:19:21,712:INFO:Creating metrics dataframe +2024-12-04 17:19:21,713:INFO:Uploading results into container +2024-12-04 17:19:21,713:INFO:Uploading model into container now +2024-12-04 17:19:21,713:INFO:_master_model_container: 22 +2024-12-04 17:19:21,713:INFO:_display_container: 3 +2024-12-04 17:19:21,715:INFO:ElasticNet(random_state=123) +2024-12-04 17:19:21,715:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:21,806:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:21,807:INFO:Creating metrics dataframe +2024-12-04 17:19:21,813:INFO:Initializing Least Angle Regression +2024-12-04 17:19:21,816:INFO:Total runtime is 0.0233806570370992 minutes +2024-12-04 17:19:21,816:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:21,816:INFO:Initializing create_model() +2024-12-04 17:19:21,816:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:21,816:INFO:Checking exceptions +2024-12-04 17:19:21,816:INFO:Importing libraries +2024-12-04 17:19:21,816:INFO:Copying training dataset +2024-12-04 17:19:21,826:INFO:Defining folds +2024-12-04 17:19:21,826:INFO:Declaring metric variables +2024-12-04 17:19:21,831:INFO:Importing untrained model +2024-12-04 17:19:21,832:INFO:Least Angle Regression Imported successfully +2024-12-04 17:19:21,841:INFO:Starting cross validation +2024-12-04 17:19:21,844:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:22,051:INFO:Calculating mean and std +2024-12-04 17:19:22,051:INFO:Creating metrics dataframe +2024-12-04 17:19:22,052:INFO:Uploading results into container +2024-12-04 17:19:22,052:INFO:Uploading model into container now +2024-12-04 17:19:22,054:INFO:_master_model_container: 23 +2024-12-04 17:19:22,054:INFO:_display_container: 3 +2024-12-04 17:19:22,054:INFO:Lars(random_state=123) +2024-12-04 17:19:22,055:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:22,137:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:22,137:INFO:Creating metrics dataframe +2024-12-04 17:19:22,146:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:19:22,146:INFO:Total runtime is 0.028885082403818766 minutes +2024-12-04 17:19:22,149:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:22,149:INFO:Initializing create_model() +2024-12-04 17:19:22,149:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:22,149:INFO:Checking exceptions +2024-12-04 17:19:22,149:INFO:Importing libraries +2024-12-04 17:19:22,149:INFO:Copying training dataset +2024-12-04 17:19:22,153:INFO:Defining folds +2024-12-04 17:19:22,153:INFO:Declaring metric variables +2024-12-04 17:19:22,158:INFO:Importing untrained model +2024-12-04 17:19:22,163:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:19:22,171:INFO:Starting cross validation +2024-12-04 17:19:22,172:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:22,390:INFO:Calculating mean and std +2024-12-04 17:19:22,391:INFO:Creating metrics dataframe +2024-12-04 17:19:22,394:INFO:Uploading results into container +2024-12-04 17:19:22,394:INFO:Uploading model into container now +2024-12-04 17:19:22,395:INFO:_master_model_container: 24 +2024-12-04 17:19:22,395:INFO:_display_container: 3 +2024-12-04 17:19:22,395:INFO:LassoLars(random_state=123) +2024-12-04 17:19:22,395:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:22,481:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:22,481:INFO:Creating metrics dataframe +2024-12-04 17:19:22,486:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:19:22,488:INFO:Total runtime is 0.0345805287361145 minutes +2024-12-04 17:19:22,491:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:22,491:INFO:Initializing create_model() +2024-12-04 17:19:22,491:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:22,493:INFO:Checking exceptions +2024-12-04 17:19:22,493:INFO:Importing libraries +2024-12-04 17:19:22,493:INFO:Copying training dataset +2024-12-04 17:19:22,496:INFO:Defining folds +2024-12-04 17:19:22,496:INFO:Declaring metric variables +2024-12-04 17:19:22,501:INFO:Importing untrained model +2024-12-04 17:19:22,505:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:19:22,513:INFO:Starting cross validation +2024-12-04 17:19:22,515:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:22,732:INFO:Calculating mean and std +2024-12-04 17:19:22,732:INFO:Creating metrics dataframe +2024-12-04 17:19:22,732:INFO:Uploading results into container +2024-12-04 17:19:22,732:INFO:Uploading model into container now +2024-12-04 17:19:22,732:INFO:_master_model_container: 25 +2024-12-04 17:19:22,732:INFO:_display_container: 3 +2024-12-04 17:19:22,732:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:19:22,732:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:22,816:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:22,816:INFO:Creating metrics dataframe +2024-12-04 17:19:22,825:INFO:Initializing Bayesian Ridge +2024-12-04 17:19:22,825:INFO:Total runtime is 0.04019009272257487 minutes +2024-12-04 17:19:22,828:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:22,828:INFO:Initializing create_model() +2024-12-04 17:19:22,828:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:22,828:INFO:Checking exceptions +2024-12-04 17:19:22,828:INFO:Importing libraries +2024-12-04 17:19:22,828:INFO:Copying training dataset +2024-12-04 17:19:22,832:INFO:Defining folds +2024-12-04 17:19:22,832:INFO:Declaring metric variables +2024-12-04 17:19:22,832:INFO:Importing untrained model +2024-12-04 17:19:22,839:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:19:22,847:INFO:Starting cross validation +2024-12-04 17:19:22,847:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:23,041:INFO:Calculating mean and std +2024-12-04 17:19:23,041:INFO:Creating metrics dataframe +2024-12-04 17:19:23,042:INFO:Uploading results into container +2024-12-04 17:19:23,042:INFO:Uploading model into container now +2024-12-04 17:19:23,042:INFO:_master_model_container: 26 +2024-12-04 17:19:23,042:INFO:_display_container: 3 +2024-12-04 17:19:23,042:INFO:BayesianRidge() +2024-12-04 17:19:23,042:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:23,122:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:23,122:INFO:Creating metrics dataframe +2024-12-04 17:19:23,128:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:19:23,128:INFO:Total runtime is 0.04525107940038045 minutes +2024-12-04 17:19:23,132:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:23,132:INFO:Initializing create_model() +2024-12-04 17:19:23,132:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:23,132:INFO:Checking exceptions +2024-12-04 17:19:23,132:INFO:Importing libraries +2024-12-04 17:19:23,132:INFO:Copying training dataset +2024-12-04 17:19:23,139:INFO:Defining folds +2024-12-04 17:19:23,139:INFO:Declaring metric variables +2024-12-04 17:19:23,143:INFO:Importing untrained model +2024-12-04 17:19:23,143:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:19:23,151:INFO:Starting cross validation +2024-12-04 17:19:23,151:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:23,345:INFO:Calculating mean and std +2024-12-04 17:19:23,345:INFO:Creating metrics dataframe +2024-12-04 17:19:23,346:INFO:Uploading results into container +2024-12-04 17:19:23,346:INFO:Uploading model into container now +2024-12-04 17:19:23,346:INFO:_master_model_container: 27 +2024-12-04 17:19:23,346:INFO:_display_container: 3 +2024-12-04 17:19:23,346:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:19:23,346:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:23,432:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:23,432:INFO:Creating metrics dataframe +2024-12-04 17:19:23,438:INFO:Initializing Huber Regressor +2024-12-04 17:19:23,438:INFO:Total runtime is 0.05041091442108154 minutes +2024-12-04 17:19:23,440:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:23,440:INFO:Initializing create_model() +2024-12-04 17:19:23,440:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:23,440:INFO:Checking exceptions +2024-12-04 17:19:23,440:INFO:Importing libraries +2024-12-04 17:19:23,440:INFO:Copying training dataset +2024-12-04 17:19:23,443:INFO:Defining folds +2024-12-04 17:19:23,443:INFO:Declaring metric variables +2024-12-04 17:19:23,446:INFO:Importing untrained model +2024-12-04 17:19:23,446:INFO:Huber Regressor Imported successfully +2024-12-04 17:19:23,456:INFO:Starting cross validation +2024-12-04 17:19:23,457:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:23,662:INFO:Calculating mean and std +2024-12-04 17:19:23,662:INFO:Creating metrics dataframe +2024-12-04 17:19:23,662:INFO:Uploading results into container +2024-12-04 17:19:23,662:INFO:Uploading model into container now +2024-12-04 17:19:23,666:INFO:_master_model_container: 28 +2024-12-04 17:19:23,666:INFO:_display_container: 3 +2024-12-04 17:19:23,666:INFO:HuberRegressor() +2024-12-04 17:19:23,666:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:23,796:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:23,796:INFO:Creating metrics dataframe +2024-12-04 17:19:23,803:INFO:Initializing K Neighbors Regressor +2024-12-04 17:19:23,803:INFO:Total runtime is 0.05648899475733439 minutes +2024-12-04 17:19:23,806:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:23,806:INFO:Initializing create_model() +2024-12-04 17:19:23,806:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:23,806:INFO:Checking exceptions +2024-12-04 17:19:23,806:INFO:Importing libraries +2024-12-04 17:19:23,806:INFO:Copying training dataset +2024-12-04 17:19:23,812:INFO:Defining folds +2024-12-04 17:19:23,812:INFO:Declaring metric variables +2024-12-04 17:19:23,816:INFO:Importing untrained model +2024-12-04 17:19:23,822:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:19:23,829:INFO:Starting cross validation +2024-12-04 17:19:23,832:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:24,102:INFO:Calculating mean and std +2024-12-04 17:19:24,102:INFO:Creating metrics dataframe +2024-12-04 17:19:24,102:INFO:Uploading results into container +2024-12-04 17:19:24,105:INFO:Uploading model into container now +2024-12-04 17:19:24,105:INFO:_master_model_container: 29 +2024-12-04 17:19:24,105:INFO:_display_container: 3 +2024-12-04 17:19:24,106:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:19:24,106:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:24,193:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:24,193:INFO:Creating metrics dataframe +2024-12-04 17:19:24,201:INFO:Initializing Decision Tree Regressor +2024-12-04 17:19:24,201:INFO:Total runtime is 0.06312990188598633 minutes +2024-12-04 17:19:24,205:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:24,205:INFO:Initializing create_model() +2024-12-04 17:19:24,205:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:24,205:INFO:Checking exceptions +2024-12-04 17:19:24,205:INFO:Importing libraries +2024-12-04 17:19:24,205:INFO:Copying training dataset +2024-12-04 17:19:24,209:INFO:Defining folds +2024-12-04 17:19:24,209:INFO:Declaring metric variables +2024-12-04 17:19:24,215:INFO:Importing untrained model +2024-12-04 17:19:24,222:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:19:24,231:INFO:Starting cross validation +2024-12-04 17:19:24,234:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:24,439:INFO:Calculating mean and std +2024-12-04 17:19:24,440:INFO:Creating metrics dataframe +2024-12-04 17:19:24,440:INFO:Uploading results into container +2024-12-04 17:19:24,440:INFO:Uploading model into container now +2024-12-04 17:19:24,442:INFO:_master_model_container: 30 +2024-12-04 17:19:24,442:INFO:_display_container: 3 +2024-12-04 17:19:24,442:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:19:24,442:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:24,525:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:24,525:INFO:Creating metrics dataframe +2024-12-04 17:19:24,528:INFO:Initializing Random Forest Regressor +2024-12-04 17:19:24,532:INFO:Total runtime is 0.06865325768788656 minutes +2024-12-04 17:19:24,532:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:24,532:INFO:Initializing create_model() +2024-12-04 17:19:24,532:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:24,532:INFO:Checking exceptions +2024-12-04 17:19:24,532:INFO:Importing libraries +2024-12-04 17:19:24,532:INFO:Copying training dataset +2024-12-04 17:19:24,538:INFO:Defining folds +2024-12-04 17:19:24,538:INFO:Declaring metric variables +2024-12-04 17:19:24,540:INFO:Importing untrained model +2024-12-04 17:19:24,542:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:19:24,553:INFO:Starting cross validation +2024-12-04 17:19:24,555:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:25,383:INFO:Calculating mean and std +2024-12-04 17:19:25,383:INFO:Creating metrics dataframe +2024-12-04 17:19:25,383:INFO:Uploading results into container +2024-12-04 17:19:25,383:INFO:Uploading model into container now +2024-12-04 17:19:25,383:INFO:_master_model_container: 31 +2024-12-04 17:19:25,383:INFO:_display_container: 3 +2024-12-04 17:19:25,383:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:25,387:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:25,467:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:25,467:INFO:Creating metrics dataframe +2024-12-04 17:19:25,476:INFO:Initializing Extra Trees Regressor +2024-12-04 17:19:25,476:INFO:Total runtime is 0.08438778320948284 minutes +2024-12-04 17:19:25,480:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:25,481:INFO:Initializing create_model() +2024-12-04 17:19:25,481:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:25,481:INFO:Checking exceptions +2024-12-04 17:19:25,481:INFO:Importing libraries +2024-12-04 17:19:25,481:INFO:Copying training dataset +2024-12-04 17:19:25,483:INFO:Defining folds +2024-12-04 17:19:25,484:INFO:Declaring metric variables +2024-12-04 17:19:25,486:INFO:Importing untrained model +2024-12-04 17:19:25,489:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:19:25,496:INFO:Starting cross validation +2024-12-04 17:19:25,496:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:26,222:INFO:Calculating mean and std +2024-12-04 17:19:26,222:INFO:Creating metrics dataframe +2024-12-04 17:19:26,225:INFO:Uploading results into container +2024-12-04 17:19:26,225:INFO:Uploading model into container now +2024-12-04 17:19:26,225:INFO:_master_model_container: 32 +2024-12-04 17:19:26,225:INFO:_display_container: 3 +2024-12-04 17:19:26,225:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:26,225:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:26,315:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:26,315:INFO:Creating metrics dataframe +2024-12-04 17:19:26,316:INFO:Initializing AdaBoost Regressor +2024-12-04 17:19:26,316:INFO:Total runtime is 0.09838687578837078 minutes +2024-12-04 17:19:26,325:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:26,325:INFO:Initializing create_model() +2024-12-04 17:19:26,325:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:26,328:INFO:Checking exceptions +2024-12-04 17:19:26,328:INFO:Importing libraries +2024-12-04 17:19:26,328:INFO:Copying training dataset +2024-12-04 17:19:26,328:INFO:Defining folds +2024-12-04 17:19:26,328:INFO:Declaring metric variables +2024-12-04 17:19:26,332:INFO:Importing untrained model +2024-12-04 17:19:26,335:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:19:26,343:INFO:Starting cross validation +2024-12-04 17:19:26,344:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:26,610:INFO:Calculating mean and std +2024-12-04 17:19:26,611:INFO:Creating metrics dataframe +2024-12-04 17:19:26,613:INFO:Uploading results into container +2024-12-04 17:19:26,613:INFO:Uploading model into container now +2024-12-04 17:19:26,613:INFO:_master_model_container: 33 +2024-12-04 17:19:26,613:INFO:_display_container: 3 +2024-12-04 17:19:26,613:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:19:26,613:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:26,705:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:26,705:INFO:Creating metrics dataframe +2024-12-04 17:19:26,717:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:19:26,717:INFO:Total runtime is 0.10505710045496625 minutes +2024-12-04 17:19:26,717:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:26,717:INFO:Initializing create_model() +2024-12-04 17:19:26,717:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:26,717:INFO:Checking exceptions +2024-12-04 17:19:26,717:INFO:Importing libraries +2024-12-04 17:19:26,717:INFO:Copying training dataset +2024-12-04 17:19:26,726:INFO:Defining folds +2024-12-04 17:19:26,726:INFO:Declaring metric variables +2024-12-04 17:19:26,729:INFO:Importing untrained model +2024-12-04 17:19:26,732:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:19:26,741:INFO:Starting cross validation +2024-12-04 17:19:26,743:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:27,194:INFO:Calculating mean and std +2024-12-04 17:19:27,194:INFO:Creating metrics dataframe +2024-12-04 17:19:27,196:INFO:Uploading results into container +2024-12-04 17:19:27,196:INFO:Uploading model into container now +2024-12-04 17:19:27,196:INFO:_master_model_container: 34 +2024-12-04 17:19:27,196:INFO:_display_container: 3 +2024-12-04 17:19:27,196:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:19:27,196:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:27,275:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:27,278:INFO:Creating metrics dataframe +2024-12-04 17:19:27,285:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:19:27,285:INFO:Total runtime is 0.1145275394121806 minutes +2024-12-04 17:19:27,291:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:27,291:INFO:Initializing create_model() +2024-12-04 17:19:27,291:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:27,291:INFO:Checking exceptions +2024-12-04 17:19:27,291:INFO:Importing libraries +2024-12-04 17:19:27,291:INFO:Copying training dataset +2024-12-04 17:19:27,294:INFO:Defining folds +2024-12-04 17:19:27,294:INFO:Declaring metric variables +2024-12-04 17:19:27,298:INFO:Importing untrained model +2024-12-04 17:19:27,301:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:19:27,310:INFO:Starting cross validation +2024-12-04 17:19:27,311:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:27,769:INFO:Calculating mean and std +2024-12-04 17:19:27,769:INFO:Creating metrics dataframe +2024-12-04 17:19:27,772:INFO:Uploading results into container +2024-12-04 17:19:27,772:INFO:Uploading model into container now +2024-12-04 17:19:27,772:INFO:_master_model_container: 35 +2024-12-04 17:19:27,774:INFO:_display_container: 3 +2024-12-04 17:19:27,775:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:27,775:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:27,875:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:27,875:INFO:Creating metrics dataframe +2024-12-04 17:19:27,882:INFO:Initializing Dummy Regressor +2024-12-04 17:19:27,882:INFO:Total runtime is 0.12447261412938439 minutes +2024-12-04 17:19:27,882:INFO:SubProcess create_model() called ================================== +2024-12-04 17:19:27,887:INFO:Initializing create_model() +2024-12-04 17:19:27,887:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:27,887:INFO:Checking exceptions +2024-12-04 17:19:27,887:INFO:Importing libraries +2024-12-04 17:19:27,887:INFO:Copying training dataset +2024-12-04 17:19:27,891:INFO:Defining folds +2024-12-04 17:19:27,892:INFO:Declaring metric variables +2024-12-04 17:19:27,892:INFO:Importing untrained model +2024-12-04 17:19:27,895:INFO:Dummy Regressor Imported successfully +2024-12-04 17:19:27,902:INFO:Starting cross validation +2024-12-04 17:19:27,906:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:19:28,100:INFO:Calculating mean and std +2024-12-04 17:19:28,101:INFO:Creating metrics dataframe +2024-12-04 17:19:28,101:INFO:Uploading results into container +2024-12-04 17:19:28,101:INFO:Uploading model into container now +2024-12-04 17:19:28,101:INFO:_master_model_container: 36 +2024-12-04 17:19:28,103:INFO:_display_container: 3 +2024-12-04 17:19:28,103:INFO:DummyRegressor() +2024-12-04 17:19:28,103:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:28,182:INFO:SubProcess create_model() end ================================== +2024-12-04 17:19:28,182:INFO:Creating metrics dataframe +2024-12-04 17:19:28,193:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:19:28,201:INFO:Initializing create_model() +2024-12-04 17:19:28,201:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:19:28,201:INFO:Checking exceptions +2024-12-04 17:19:28,203:INFO:Importing libraries +2024-12-04 17:19:28,203:INFO:Copying training dataset +2024-12-04 17:19:28,214:INFO:Defining folds +2024-12-04 17:19:28,214:INFO:Declaring metric variables +2024-12-04 17:19:28,214:INFO:Importing untrained model +2024-12-04 17:19:28,214:INFO:Declaring custom model +2024-12-04 17:19:28,216:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:19:28,216:INFO:Cross validation set to False +2024-12-04 17:19:28,216:INFO:Fitting Model +2024-12-04 17:19:28,466:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:28,468:INFO:create_model() successfully completed...................................... +2024-12-04 17:19:28,598:INFO:_master_model_container: 36 +2024-12-04 17:19:28,599:INFO:_display_container: 3 +2024-12-04 17:19:28,599:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:19:28,599:INFO:compare_models() successfully completed...................................... +2024-12-04 17:20:00,737:INFO:Initializing tune_model() +2024-12-04 17:20:00,737:INFO:tune_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-12-04 17:20:00,737:INFO:Checking exceptions +2024-12-04 17:20:00,751:INFO:Copying training dataset +2024-12-04 17:20:00,756:INFO:Checking base model +2024-12-04 17:20:00,756:INFO:Base model : Extra Trees Regressor +2024-12-04 17:20:00,760:INFO:Declaring metric variables +2024-12-04 17:20:00,763:INFO:Defining Hyperparameters +2024-12-04 17:20:00,867:INFO:Tuning with n_jobs=-1 +2024-12-04 17:20:00,867:INFO:Initializing RandomizedSearchCV +2024-12-04 17:20:19,984:INFO:best_params: {'actual_estimator__n_estimators': 100, 'actual_estimator__min_samples_split': 7, 'actual_estimator__min_samples_leaf': 4, 'actual_estimator__min_impurity_decrease': 0.1, 'actual_estimator__max_features': 1.0, 'actual_estimator__max_depth': 9, 'actual_estimator__criterion': 'squared_error', 'actual_estimator__bootstrap': True} +2024-12-04 17:20:19,984:INFO:Hyperparameter search completed +2024-12-04 17:20:19,984:INFO:SubProcess create_model() called ================================== +2024-12-04 17:20:19,984:INFO:Initializing create_model() +2024-12-04 17:20:19,987:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'n_estimators': 100, 'min_samples_split': 7, 'min_samples_leaf': 4, 'min_impurity_decrease': 0.1, 'max_features': 1.0, 'max_depth': 9, 'criterion': 'squared_error', 'bootstrap': True}) +2024-12-04 17:20:19,987:INFO:Checking exceptions +2024-12-04 17:20:19,987:INFO:Importing libraries +2024-12-04 17:20:19,987:INFO:Copying training dataset +2024-12-04 17:20:19,992:INFO:Defining folds +2024-12-04 17:20:19,992:INFO:Declaring metric variables +2024-12-04 17:20:19,995:INFO:Importing untrained model +2024-12-04 17:20:19,997:INFO:Declaring custom model +2024-12-04 17:20:20,001:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:20:20,007:INFO:Starting cross validation +2024-12-04 17:20:20,010:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:20:20,521:INFO:Calculating mean and std +2024-12-04 17:20:20,522:INFO:Creating metrics dataframe +2024-12-04 17:20:20,528:INFO:Finalizing model +2024-12-04 17:20:20,802:INFO:Uploading results into container +2024-12-04 17:20:20,802:INFO:Uploading model into container now +2024-12-04 17:20:20,802:INFO:_master_model_container: 37 +2024-12-04 17:20:20,802:INFO:_display_container: 4 +2024-12-04 17:20:20,802:INFO:ExtraTreesRegressor(bootstrap=True, max_depth=9, min_impurity_decrease=0.1, + min_samples_leaf=4, min_samples_split=7, n_jobs=-1, + random_state=123) +2024-12-04 17:20:20,802:INFO:create_model() successfully completed...................................... +2024-12-04 17:20:20,900:INFO:SubProcess create_model() end ================================== +2024-12-04 17:20:20,900:INFO:choose_better activated +2024-12-04 17:20:20,900:INFO:SubProcess create_model() called ================================== +2024-12-04 17:20:20,900:INFO:Initializing create_model() +2024-12-04 17:20:20,900:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:20:20,900:INFO:Checking exceptions +2024-12-04 17:20:20,906:INFO:Importing libraries +2024-12-04 17:20:20,907:INFO:Copying training dataset +2024-12-04 17:20:20,907:INFO:Defining folds +2024-12-04 17:20:20,907:INFO:Declaring metric variables +2024-12-04 17:20:20,907:INFO:Importing untrained model +2024-12-04 17:20:20,907:INFO:Declaring custom model +2024-12-04 17:20:20,910:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:20:20,910:INFO:Starting cross validation +2024-12-04 17:20:20,910:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:20:21,650:INFO:Calculating mean and std +2024-12-04 17:20:21,650:INFO:Creating metrics dataframe +2024-12-04 17:20:21,651:INFO:Finalizing model +2024-12-04 17:20:21,920:INFO:Uploading results into container +2024-12-04 17:20:21,920:INFO:Uploading model into container now +2024-12-04 17:20:21,920:INFO:_master_model_container: 38 +2024-12-04 17:20:21,920:INFO:_display_container: 5 +2024-12-04 17:20:21,920:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:20:21,920:INFO:create_model() successfully completed...................................... +2024-12-04 17:20:22,023:INFO:SubProcess create_model() end ================================== +2024-12-04 17:20:22,023:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) result for R2 is 0.9576 +2024-12-04 17:20:22,024:INFO:ExtraTreesRegressor(bootstrap=True, max_depth=9, min_impurity_decrease=0.1, + min_samples_leaf=4, min_samples_split=7, n_jobs=-1, + random_state=123) result for R2 is 0.8072 +2024-12-04 17:20:22,025:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) is best model +2024-12-04 17:20:22,025:INFO:choose_better completed +2024-12-04 17:20:22,025:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-12-04 17:20:22,033:INFO:_master_model_container: 38 +2024-12-04 17:20:22,033:INFO:_display_container: 4 +2024-12-04 17:20:22,034:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:20:22,034:INFO:tune_model() successfully completed...................................... +2024-12-04 17:20:46,628:INFO:Initializing predict_model() +2024-12-04 17:20:46,628:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000017AAD2499E0>) +2024-12-04 17:20:46,632:INFO:Checking exceptions +2024-12-04 17:20:46,632:INFO:Preloading libraries +2024-12-04 17:20:46,634:INFO:Set up data. +2024-12-04 17:20:46,637:INFO:Set up index. +2024-12-04 17:20:46,721:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + warnings.warn( + +2024-12-04 17:21:09,885:INFO:Initializing save_model() +2024-12-04 17:21:09,885:INFO:save_model(model=ExtraTreesRegressor(n_jobs=-1, random_state=123), model_name=best_model, prep_pipe_=Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]), verbose=True, use_case=MLUsecase.REGRESSION, kwargs={}) +2024-12-04 17:21:09,885:INFO:Adding model into prep_pipe +2024-12-04 17:21:09,946:INFO:best_model.pkl saved in current working directory +2024-12-04 17:21:09,957:INFO:Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(c... + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('trained_model', + ExtraTreesRegressor(n_jobs=-1, random_state=123))]) +2024-12-04 17:21:09,957:INFO:save_model() successfully completed...................................... +2024-12-04 17:24:42,135:INFO:PyCaret RegressionExperiment +2024-12-04 17:24:42,135:INFO:Logging name: reg-default-name +2024-12-04 17:24:42,135:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:24:42,135:INFO:version 3.3.2 +2024-12-04 17:24:42,135:INFO:Initializing setup() +2024-12-04 17:24:42,135:INFO:self.USI: efa5 +2024-12-04 17:24:42,135:INFO:self._variable_keys: {'fold_generator', 'n_jobs_param', 'y', 'USI', 'idx', 'logging_param', 'fold_groups_param', 'html_param', 'log_plots_param', '_available_plots', 'X_train', 'gpu_param', 'data', 'seed', 'target_param', 'fold_shuffle_param', 'X', 'exp_name_log', 'exp_id', '_ml_usecase', 'y_test', 'y_train', 'X_test', 'transform_target_param', 'gpu_n_jobs_param', 'memory', 'pipeline'} +2024-12-04 17:24:42,135:INFO:Checking environment +2024-12-04 17:24:42,135:INFO:python_version: 3.11.9 +2024-12-04 17:24:42,135:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:24:42,135:INFO:machine: AMD64 +2024-12-04 17:24:42,135:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:24:42,141:INFO:Memory: svmem(total=17007292416, available=1118949376, percent=93.4, used=15888343040, free=1118949376) +2024-12-04 17:24:42,141:INFO:Physical Core: 4 +2024-12-04 17:24:42,141:INFO:Logical Core: 8 +2024-12-04 17:24:42,141:INFO:Checking libraries +2024-12-04 17:24:42,141:INFO:System: +2024-12-04 17:24:42,141:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:24:42,141:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:24:42,141:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:24:42,141:INFO:PyCaret required dependencies: +2024-12-04 17:24:42,141:INFO: pip: 24.3.1 +2024-12-04 17:24:42,141:INFO: setuptools: 65.5.0 +2024-12-04 17:24:42,141:INFO: pycaret: 3.3.2 +2024-12-04 17:24:42,141:INFO: IPython: 8.12.3 +2024-12-04 17:24:42,141:INFO: ipywidgets: 8.1.5 +2024-12-04 17:24:42,141:INFO: tqdm: 4.67.1 +2024-12-04 17:24:42,141:INFO: numpy: 1.26.4 +2024-12-04 17:24:42,141:INFO: pandas: 2.1.4 +2024-12-04 17:24:42,141:INFO: jinja2: 3.1.4 +2024-12-04 17:24:42,141:INFO: scipy: 1.11.4 +2024-12-04 17:24:42,141:INFO: joblib: 1.3.2 +2024-12-04 17:24:42,141:INFO: sklearn: 1.4.2 +2024-12-04 17:24:42,141:INFO: pyod: 2.0.2 +2024-12-04 17:24:42,141:INFO: imblearn: 0.12.4 +2024-12-04 17:24:42,141:INFO: category_encoders: 2.6.4 +2024-12-04 17:24:42,141:INFO: lightgbm: 4.5.0 +2024-12-04 17:24:42,141:INFO: numba: 0.60.0 +2024-12-04 17:24:42,143:INFO: requests: 2.32.3 +2024-12-04 17:24:42,143:INFO: matplotlib: 3.7.5 +2024-12-04 17:24:42,143:INFO: scikitplot: 0.3.7 +2024-12-04 17:24:42,143:INFO: yellowbrick: 1.5 +2024-12-04 17:24:42,143:INFO: plotly: 5.24.1 +2024-12-04 17:24:42,143:INFO: plotly-resampler: Not installed +2024-12-04 17:24:42,143:INFO: kaleido: 0.2.1 +2024-12-04 17:24:42,143:INFO: schemdraw: 0.15 +2024-12-04 17:24:42,143:INFO: statsmodels: 0.14.4 +2024-12-04 17:24:42,143:INFO: sktime: 0.26.0 +2024-12-04 17:24:42,143:INFO: tbats: 1.1.3 +2024-12-04 17:24:42,143:INFO: pmdarima: 2.0.4 +2024-12-04 17:24:42,143:INFO: psutil: 6.1.0 +2024-12-04 17:24:42,143:INFO: markupsafe: 3.0.2 +2024-12-04 17:24:42,143:INFO: pickle5: Not installed +2024-12-04 17:24:42,143:INFO: cloudpickle: 3.1.0 +2024-12-04 17:24:42,143:INFO: deprecation: 2.1.0 +2024-12-04 17:24:42,143:INFO: xxhash: 3.5.0 +2024-12-04 17:24:42,143:INFO: wurlitzer: Not installed +2024-12-04 17:24:42,143:INFO:PyCaret optional dependencies: +2024-12-04 17:24:42,143:INFO: shap: Not installed +2024-12-04 17:24:42,143:INFO: interpret: Not installed +2024-12-04 17:24:42,143:INFO: umap: Not installed +2024-12-04 17:24:42,143:INFO: ydata_profiling: Not installed +2024-12-04 17:24:42,143:INFO: explainerdashboard: Not installed +2024-12-04 17:24:42,143:INFO: autoviz: Not installed +2024-12-04 17:24:42,143:INFO: fairlearn: Not installed +2024-12-04 17:24:42,143:INFO: deepchecks: Not installed +2024-12-04 17:24:42,143:INFO: xgboost: Not installed +2024-12-04 17:24:42,143:INFO: catboost: Not installed +2024-12-04 17:24:42,144:INFO: kmodes: Not installed +2024-12-04 17:24:42,144:INFO: mlxtend: Not installed +2024-12-04 17:24:42,144:INFO: statsforecast: Not installed +2024-12-04 17:24:42,144:INFO: tune_sklearn: Not installed +2024-12-04 17:24:42,144:INFO: ray: Not installed +2024-12-04 17:24:42,144:INFO: hyperopt: Not installed +2024-12-04 17:24:42,144:INFO: optuna: Not installed +2024-12-04 17:24:42,144:INFO: skopt: Not installed +2024-12-04 17:24:42,144:INFO: mlflow: 2.18.0 +2024-12-04 17:24:42,144:INFO: gradio: Not installed +2024-12-04 17:24:42,144:INFO: fastapi: Not installed +2024-12-04 17:24:42,144:INFO: uvicorn: 0.32.1 +2024-12-04 17:24:42,144:INFO: m2cgen: Not installed +2024-12-04 17:24:42,144:INFO: evidently: Not installed +2024-12-04 17:24:42,144:INFO: fugue: Not installed +2024-12-04 17:24:42,144:INFO: streamlit: 1.40.1 +2024-12-04 17:24:42,144:INFO: prophet: Not installed +2024-12-04 17:24:42,144:INFO:None +2024-12-04 17:24:42,144:INFO:Set up data. +2024-12-04 17:24:42,148:INFO:Set up folding strategy. +2024-12-04 17:24:42,148:INFO:Set up train/test split. +2024-12-04 17:24:42,151:INFO:Set up index. +2024-12-04 17:24:42,151:INFO:Assigning column types. +2024-12-04 17:24:42,154:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:24:42,154:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,158:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,163:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,218:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,259:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,259:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,260:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,260:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,264:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,269:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,318:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,363:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,364:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,364:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,364:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:24:42,369:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,373:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,425:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,463:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,466:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,466:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,469:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,475:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,526:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,569:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,569:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,569:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,569:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:24:42,578:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,634:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,678:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,680:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,680:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,688:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,744:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,786:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,787:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,787:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,787:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:24:42,851:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,890:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,893:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,893:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,953:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,992:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:24:42,994:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,994:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:42,995:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:24:43,052:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:43,095:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,095:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,152:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:24:43,195:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,195:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,195:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:24:43,296:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,296:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,396:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,396:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,396:INFO:Preparing preprocessing pipeline... +2024-12-04 17:24:43,396:INFO:Set up simple imputation. +2024-12-04 17:24:43,401:INFO:Set up encoding of categorical features. +2024-12-04 17:24:43,401:INFO:Set up column transformation. +2024-12-04 17:24:43,401:INFO:Set up feature normalization. +2024-12-04 17:24:43,546:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:24:43,556:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 17:24:43,556:INFO:Creating final display dataframe. +2024-12-04 17:24:43,752:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 4) +4 Transformed data shape (5688, 20) +5 Transformed train set shape (3981, 20) +6 Transformed test set shape (1707, 20) +7 Ignore features 1 +8 Categorical features 2 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Transformation True +16 Transformation method yeo-johnson +17 Normalize True +18 Normalize method zscore +19 Fold Generator KFold +20 Fold Number 5 +21 CPU Jobs -1 +22 Use GPU False +23 Log Experiment False +24 Experiment Name reg-default-name +25 USI efa5 +2024-12-04 17:24:43,868:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,868:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,983:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,983:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:24:43,984:INFO:setup() successfully completed in 1.87s............... +2024-12-04 17:24:48,395:INFO:Initializing compare_models() +2024-12-04 17:24:48,395:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=MAE, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'MAE', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:24:48,395:INFO:Checking exceptions +2024-12-04 17:24:48,395:INFO:Preparing display monitor +2024-12-04 17:24:48,420:INFO:Initializing Linear Regression +2024-12-04 17:24:48,421:INFO:Total runtime is 1.6844272613525392e-05 minutes +2024-12-04 17:24:48,425:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:48,425:INFO:Initializing create_model() +2024-12-04 17:24:48,425:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:48,425:INFO:Checking exceptions +2024-12-04 17:24:48,425:INFO:Importing libraries +2024-12-04 17:24:48,425:INFO:Copying training dataset +2024-12-04 17:24:48,428:INFO:Defining folds +2024-12-04 17:24:48,428:INFO:Declaring metric variables +2024-12-04 17:24:48,431:INFO:Importing untrained model +2024-12-04 17:24:48,435:INFO:Linear Regression Imported successfully +2024-12-04 17:24:48,443:INFO:Starting cross validation +2024-12-04 17:24:48,443:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:48,708:INFO:Calculating mean and std +2024-12-04 17:24:48,708:INFO:Creating metrics dataframe +2024-12-04 17:24:48,710:INFO:Uploading results into container +2024-12-04 17:24:48,710:INFO:Uploading model into container now +2024-12-04 17:24:48,711:INFO:_master_model_container: 1 +2024-12-04 17:24:48,711:INFO:_display_container: 2 +2024-12-04 17:24:48,711:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:24:48,711:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:48,802:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:48,810:INFO:Creating metrics dataframe +2024-12-04 17:24:48,813:INFO:Initializing Lasso Regression +2024-12-04 17:24:48,813:INFO:Total runtime is 0.006550983587900798 minutes +2024-12-04 17:24:48,820:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:48,820:INFO:Initializing create_model() +2024-12-04 17:24:48,820:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:48,820:INFO:Checking exceptions +2024-12-04 17:24:48,820:INFO:Importing libraries +2024-12-04 17:24:48,821:INFO:Copying training dataset +2024-12-04 17:24:48,825:INFO:Defining folds +2024-12-04 17:24:48,825:INFO:Declaring metric variables +2024-12-04 17:24:48,828:INFO:Importing untrained model +2024-12-04 17:24:48,830:INFO:Lasso Regression Imported successfully +2024-12-04 17:24:48,838:INFO:Starting cross validation +2024-12-04 17:24:48,840:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:49,101:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.651e+09, tolerance: 4.419e+07 + model = cd_fast.enet_coordinate_descent( + +2024-12-04 17:24:49,124:INFO:Calculating mean and std +2024-12-04 17:24:49,124:INFO:Creating metrics dataframe +2024-12-04 17:24:49,125:INFO:Uploading results into container +2024-12-04 17:24:49,125:INFO:Uploading model into container now +2024-12-04 17:24:49,125:INFO:_master_model_container: 2 +2024-12-04 17:24:49,125:INFO:_display_container: 2 +2024-12-04 17:24:49,126:INFO:Lasso(random_state=123) +2024-12-04 17:24:49,126:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:49,219:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:49,219:INFO:Creating metrics dataframe +2024-12-04 17:24:49,225:INFO:Initializing Ridge Regression +2024-12-04 17:24:49,228:INFO:Total runtime is 0.013462571303049724 minutes +2024-12-04 17:24:49,228:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:49,232:INFO:Initializing create_model() +2024-12-04 17:24:49,232:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:49,232:INFO:Checking exceptions +2024-12-04 17:24:49,232:INFO:Importing libraries +2024-12-04 17:24:49,232:INFO:Copying training dataset +2024-12-04 17:24:49,235:INFO:Defining folds +2024-12-04 17:24:49,235:INFO:Declaring metric variables +2024-12-04 17:24:49,237:INFO:Importing untrained model +2024-12-04 17:24:49,241:INFO:Ridge Regression Imported successfully +2024-12-04 17:24:49,245:INFO:Starting cross validation +2024-12-04 17:24:49,245:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:49,443:INFO:Calculating mean and std +2024-12-04 17:24:49,444:INFO:Creating metrics dataframe +2024-12-04 17:24:49,446:INFO:Uploading results into container +2024-12-04 17:24:49,446:INFO:Uploading model into container now +2024-12-04 17:24:49,446:INFO:_master_model_container: 3 +2024-12-04 17:24:49,446:INFO:_display_container: 2 +2024-12-04 17:24:49,446:INFO:Ridge(random_state=123) +2024-12-04 17:24:49,446:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:49,538:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:49,538:INFO:Creating metrics dataframe +2024-12-04 17:24:49,544:INFO:Initializing Elastic Net +2024-12-04 17:24:49,544:INFO:Total runtime is 0.018737820784250896 minutes +2024-12-04 17:24:49,546:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:49,546:INFO:Initializing create_model() +2024-12-04 17:24:49,546:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:49,546:INFO:Checking exceptions +2024-12-04 17:24:49,546:INFO:Importing libraries +2024-12-04 17:24:49,546:INFO:Copying training dataset +2024-12-04 17:24:49,552:INFO:Defining folds +2024-12-04 17:24:49,552:INFO:Declaring metric variables +2024-12-04 17:24:49,555:INFO:Importing untrained model +2024-12-04 17:24:49,560:INFO:Elastic Net Imported successfully +2024-12-04 17:24:49,566:INFO:Starting cross validation +2024-12-04 17:24:49,569:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:49,778:INFO:Calculating mean and std +2024-12-04 17:24:49,778:INFO:Creating metrics dataframe +2024-12-04 17:24:49,778:INFO:Uploading results into container +2024-12-04 17:24:49,778:INFO:Uploading model into container now +2024-12-04 17:24:49,782:INFO:_master_model_container: 4 +2024-12-04 17:24:49,782:INFO:_display_container: 2 +2024-12-04 17:24:49,782:INFO:ElasticNet(random_state=123) +2024-12-04 17:24:49,782:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:49,882:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:49,882:INFO:Creating metrics dataframe +2024-12-04 17:24:49,889:INFO:Initializing Least Angle Regression +2024-12-04 17:24:49,889:INFO:Total runtime is 0.024480867385864257 minutes +2024-12-04 17:24:49,894:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:49,894:INFO:Initializing create_model() +2024-12-04 17:24:49,894:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:49,894:INFO:Checking exceptions +2024-12-04 17:24:49,895:INFO:Importing libraries +2024-12-04 17:24:49,895:INFO:Copying training dataset +2024-12-04 17:24:49,899:INFO:Defining folds +2024-12-04 17:24:49,900:INFO:Declaring metric variables +2024-12-04 17:24:49,904:INFO:Importing untrained model +2024-12-04 17:24:49,908:INFO:Least Angle Regression Imported successfully +2024-12-04 17:24:49,919:INFO:Starting cross validation +2024-12-04 17:24:49,921:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:50,161:INFO:Calculating mean and std +2024-12-04 17:24:50,161:INFO:Creating metrics dataframe +2024-12-04 17:24:50,163:INFO:Uploading results into container +2024-12-04 17:24:50,163:INFO:Uploading model into container now +2024-12-04 17:24:50,163:INFO:_master_model_container: 5 +2024-12-04 17:24:50,163:INFO:_display_container: 2 +2024-12-04 17:24:50,163:INFO:Lars(random_state=123) +2024-12-04 17:24:50,163:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:50,252:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:50,252:INFO:Creating metrics dataframe +2024-12-04 17:24:50,263:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:24:50,263:INFO:Total runtime is 0.03071118990580241 minutes +2024-12-04 17:24:50,267:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:50,268:INFO:Initializing create_model() +2024-12-04 17:24:50,268:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:50,268:INFO:Checking exceptions +2024-12-04 17:24:50,268:INFO:Importing libraries +2024-12-04 17:24:50,268:INFO:Copying training dataset +2024-12-04 17:24:50,272:INFO:Defining folds +2024-12-04 17:24:50,272:INFO:Declaring metric variables +2024-12-04 17:24:50,278:INFO:Importing untrained model +2024-12-04 17:24:50,278:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:24:50,287:INFO:Starting cross validation +2024-12-04 17:24:50,289:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:50,524:INFO:Calculating mean and std +2024-12-04 17:24:50,525:INFO:Creating metrics dataframe +2024-12-04 17:24:50,525:INFO:Uploading results into container +2024-12-04 17:24:50,525:INFO:Uploading model into container now +2024-12-04 17:24:50,525:INFO:_master_model_container: 6 +2024-12-04 17:24:50,525:INFO:_display_container: 2 +2024-12-04 17:24:50,525:INFO:LassoLars(random_state=123) +2024-12-04 17:24:50,528:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:50,628:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:50,628:INFO:Creating metrics dataframe +2024-12-04 17:24:50,636:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:24:50,637:INFO:Total runtime is 0.036943523089090984 minutes +2024-12-04 17:24:50,639:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:50,639:INFO:Initializing create_model() +2024-12-04 17:24:50,639:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:50,640:INFO:Checking exceptions +2024-12-04 17:24:50,640:INFO:Importing libraries +2024-12-04 17:24:50,640:INFO:Copying training dataset +2024-12-04 17:24:50,645:INFO:Defining folds +2024-12-04 17:24:50,645:INFO:Declaring metric variables +2024-12-04 17:24:50,646:INFO:Importing untrained model +2024-12-04 17:24:50,652:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:24:50,659:INFO:Starting cross validation +2024-12-04 17:24:50,660:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:50,866:INFO:Calculating mean and std +2024-12-04 17:24:50,866:INFO:Creating metrics dataframe +2024-12-04 17:24:50,869:INFO:Uploading results into container +2024-12-04 17:24:50,870:INFO:Uploading model into container now +2024-12-04 17:24:50,870:INFO:_master_model_container: 7 +2024-12-04 17:24:50,870:INFO:_display_container: 2 +2024-12-04 17:24:50,871:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:24:50,871:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:50,971:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:50,971:INFO:Creating metrics dataframe +2024-12-04 17:24:50,979:INFO:Initializing Bayesian Ridge +2024-12-04 17:24:50,979:INFO:Total runtime is 0.0426575779914856 minutes +2024-12-04 17:24:50,984:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:50,985:INFO:Initializing create_model() +2024-12-04 17:24:50,985:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:50,985:INFO:Checking exceptions +2024-12-04 17:24:50,985:INFO:Importing libraries +2024-12-04 17:24:50,986:INFO:Copying training dataset +2024-12-04 17:24:50,992:INFO:Defining folds +2024-12-04 17:24:50,993:INFO:Declaring metric variables +2024-12-04 17:24:51,008:INFO:Importing untrained model +2024-12-04 17:24:51,013:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:24:51,023:INFO:Starting cross validation +2024-12-04 17:24:51,025:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:51,282:INFO:Calculating mean and std +2024-12-04 17:24:51,282:INFO:Creating metrics dataframe +2024-12-04 17:24:51,287:INFO:Uploading results into container +2024-12-04 17:24:51,287:INFO:Uploading model into container now +2024-12-04 17:24:51,287:INFO:_master_model_container: 8 +2024-12-04 17:24:51,287:INFO:_display_container: 2 +2024-12-04 17:24:51,287:INFO:BayesianRidge() +2024-12-04 17:24:51,287:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:51,378:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:51,378:INFO:Creating metrics dataframe +2024-12-04 17:24:51,383:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:24:51,383:INFO:Total runtime is 0.049378339449564615 minutes +2024-12-04 17:24:51,390:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:51,390:INFO:Initializing create_model() +2024-12-04 17:24:51,390:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:51,390:INFO:Checking exceptions +2024-12-04 17:24:51,390:INFO:Importing libraries +2024-12-04 17:24:51,390:INFO:Copying training dataset +2024-12-04 17:24:51,396:INFO:Defining folds +2024-12-04 17:24:51,396:INFO:Declaring metric variables +2024-12-04 17:24:51,399:INFO:Importing untrained model +2024-12-04 17:24:51,406:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:24:51,413:INFO:Starting cross validation +2024-12-04 17:24:51,413:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:51,613:INFO:Calculating mean and std +2024-12-04 17:24:51,613:INFO:Creating metrics dataframe +2024-12-04 17:24:51,613:INFO:Uploading results into container +2024-12-04 17:24:51,613:INFO:Uploading model into container now +2024-12-04 17:24:51,613:INFO:_master_model_container: 9 +2024-12-04 17:24:51,613:INFO:_display_container: 2 +2024-12-04 17:24:51,616:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:24:51,616:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:51,701:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:51,701:INFO:Creating metrics dataframe +2024-12-04 17:24:51,713:INFO:Initializing Huber Regressor +2024-12-04 17:24:51,713:INFO:Total runtime is 0.05488111575444539 minutes +2024-12-04 17:24:51,716:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:51,718:INFO:Initializing create_model() +2024-12-04 17:24:51,718:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:51,718:INFO:Checking exceptions +2024-12-04 17:24:51,718:INFO:Importing libraries +2024-12-04 17:24:51,718:INFO:Copying training dataset +2024-12-04 17:24:51,718:INFO:Defining folds +2024-12-04 17:24:51,718:INFO:Declaring metric variables +2024-12-04 17:24:51,725:INFO:Importing untrained model +2024-12-04 17:24:51,728:INFO:Huber Regressor Imported successfully +2024-12-04 17:24:51,737:INFO:Starting cross validation +2024-12-04 17:24:51,740:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:51,933:INFO:Calculating mean and std +2024-12-04 17:24:51,933:INFO:Creating metrics dataframe +2024-12-04 17:24:51,936:INFO:Uploading results into container +2024-12-04 17:24:51,937:INFO:Uploading model into container now +2024-12-04 17:24:51,937:INFO:_master_model_container: 10 +2024-12-04 17:24:51,937:INFO:_display_container: 2 +2024-12-04 17:24:51,937:INFO:HuberRegressor() +2024-12-04 17:24:51,938:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:52,025:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:52,025:INFO:Creating metrics dataframe +2024-12-04 17:24:52,033:INFO:Initializing K Neighbors Regressor +2024-12-04 17:24:52,033:INFO:Total runtime is 0.06021106640497843 minutes +2024-12-04 17:24:52,037:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:52,038:INFO:Initializing create_model() +2024-12-04 17:24:52,038:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:52,038:INFO:Checking exceptions +2024-12-04 17:24:52,038:INFO:Importing libraries +2024-12-04 17:24:52,038:INFO:Copying training dataset +2024-12-04 17:24:52,043:INFO:Defining folds +2024-12-04 17:24:52,043:INFO:Declaring metric variables +2024-12-04 17:24:52,046:INFO:Importing untrained model +2024-12-04 17:24:52,051:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:24:52,058:INFO:Starting cross validation +2024-12-04 17:24:52,059:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:52,324:INFO:Calculating mean and std +2024-12-04 17:24:52,325:INFO:Creating metrics dataframe +2024-12-04 17:24:52,325:INFO:Uploading results into container +2024-12-04 17:24:52,325:INFO:Uploading model into container now +2024-12-04 17:24:52,328:INFO:_master_model_container: 11 +2024-12-04 17:24:52,328:INFO:_display_container: 2 +2024-12-04 17:24:52,328:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:24:52,328:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:52,437:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:52,437:INFO:Creating metrics dataframe +2024-12-04 17:24:52,442:INFO:Initializing Decision Tree Regressor +2024-12-04 17:24:52,442:INFO:Total runtime is 0.06703705390294393 minutes +2024-12-04 17:24:52,446:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:52,446:INFO:Initializing create_model() +2024-12-04 17:24:52,446:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:52,446:INFO:Checking exceptions +2024-12-04 17:24:52,446:INFO:Importing libraries +2024-12-04 17:24:52,446:INFO:Copying training dataset +2024-12-04 17:24:52,453:INFO:Defining folds +2024-12-04 17:24:52,453:INFO:Declaring metric variables +2024-12-04 17:24:52,457:INFO:Importing untrained model +2024-12-04 17:24:52,461:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:24:52,466:INFO:Starting cross validation +2024-12-04 17:24:52,468:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:52,663:INFO:Calculating mean and std +2024-12-04 17:24:52,663:INFO:Creating metrics dataframe +2024-12-04 17:24:52,663:INFO:Uploading results into container +2024-12-04 17:24:52,667:INFO:Uploading model into container now +2024-12-04 17:24:52,667:INFO:_master_model_container: 12 +2024-12-04 17:24:52,667:INFO:_display_container: 2 +2024-12-04 17:24:52,667:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:24:52,667:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:52,751:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:52,751:INFO:Creating metrics dataframe +2024-12-04 17:24:52,763:INFO:Initializing Random Forest Regressor +2024-12-04 17:24:52,763:INFO:Total runtime is 0.07237919966379801 minutes +2024-12-04 17:24:52,763:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:52,766:INFO:Initializing create_model() +2024-12-04 17:24:52,766:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:52,766:INFO:Checking exceptions +2024-12-04 17:24:52,766:INFO:Importing libraries +2024-12-04 17:24:52,766:INFO:Copying training dataset +2024-12-04 17:24:52,772:INFO:Defining folds +2024-12-04 17:24:52,772:INFO:Declaring metric variables +2024-12-04 17:24:52,777:INFO:Importing untrained model +2024-12-04 17:24:52,782:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:24:52,789:INFO:Starting cross validation +2024-12-04 17:24:52,790:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:53,597:INFO:Calculating mean and std +2024-12-04 17:24:53,597:INFO:Creating metrics dataframe +2024-12-04 17:24:53,597:INFO:Uploading results into container +2024-12-04 17:24:53,597:INFO:Uploading model into container now +2024-12-04 17:24:53,597:INFO:_master_model_container: 13 +2024-12-04 17:24:53,597:INFO:_display_container: 2 +2024-12-04 17:24:53,600:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:24:53,600:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:53,690:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:53,690:INFO:Creating metrics dataframe +2024-12-04 17:24:53,696:INFO:Initializing Extra Trees Regressor +2024-12-04 17:24:53,696:INFO:Total runtime is 0.08792858918507893 minutes +2024-12-04 17:24:53,701:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:53,701:INFO:Initializing create_model() +2024-12-04 17:24:53,701:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:53,701:INFO:Checking exceptions +2024-12-04 17:24:53,701:INFO:Importing libraries +2024-12-04 17:24:53,701:INFO:Copying training dataset +2024-12-04 17:24:53,701:INFO:Defining folds +2024-12-04 17:24:53,701:INFO:Declaring metric variables +2024-12-04 17:24:53,711:INFO:Importing untrained model +2024-12-04 17:24:53,713:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:24:53,722:INFO:Starting cross validation +2024-12-04 17:24:53,724:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:54,488:INFO:Calculating mean and std +2024-12-04 17:24:54,489:INFO:Creating metrics dataframe +2024-12-04 17:24:54,492:INFO:Uploading results into container +2024-12-04 17:24:54,493:INFO:Uploading model into container now +2024-12-04 17:24:54,493:INFO:_master_model_container: 14 +2024-12-04 17:24:54,493:INFO:_display_container: 2 +2024-12-04 17:24:54,493:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:24:54,493:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:54,582:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:54,582:INFO:Creating metrics dataframe +2024-12-04 17:24:54,593:INFO:Initializing AdaBoost Regressor +2024-12-04 17:24:54,593:INFO:Total runtime is 0.10287810564041136 minutes +2024-12-04 17:24:54,596:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:54,596:INFO:Initializing create_model() +2024-12-04 17:24:54,596:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:54,596:INFO:Checking exceptions +2024-12-04 17:24:54,596:INFO:Importing libraries +2024-12-04 17:24:54,596:INFO:Copying training dataset +2024-12-04 17:24:54,601:INFO:Defining folds +2024-12-04 17:24:54,601:INFO:Declaring metric variables +2024-12-04 17:24:54,604:INFO:Importing untrained model +2024-12-04 17:24:54,604:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:24:54,613:INFO:Starting cross validation +2024-12-04 17:24:54,616:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:54,873:INFO:Calculating mean and std +2024-12-04 17:24:54,873:INFO:Creating metrics dataframe +2024-12-04 17:24:54,875:INFO:Uploading results into container +2024-12-04 17:24:54,875:INFO:Uploading model into container now +2024-12-04 17:24:54,875:INFO:_master_model_container: 15 +2024-12-04 17:24:54,875:INFO:_display_container: 2 +2024-12-04 17:24:54,875:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:24:54,875:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:54,967:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:54,967:INFO:Creating metrics dataframe +2024-12-04 17:24:54,977:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:24:54,977:INFO:Total runtime is 0.1092812935511271 minutes +2024-12-04 17:24:54,978:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:54,978:INFO:Initializing create_model() +2024-12-04 17:24:54,978:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:54,978:INFO:Checking exceptions +2024-12-04 17:24:54,978:INFO:Importing libraries +2024-12-04 17:24:54,978:INFO:Copying training dataset +2024-12-04 17:24:54,985:INFO:Defining folds +2024-12-04 17:24:54,985:INFO:Declaring metric variables +2024-12-04 17:24:54,989:INFO:Importing untrained model +2024-12-04 17:24:54,993:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:24:54,996:INFO:Starting cross validation +2024-12-04 17:24:55,001:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:55,393:INFO:Calculating mean and std +2024-12-04 17:24:55,393:INFO:Creating metrics dataframe +2024-12-04 17:24:55,396:INFO:Uploading results into container +2024-12-04 17:24:55,396:INFO:Uploading model into container now +2024-12-04 17:24:55,397:INFO:_master_model_container: 16 +2024-12-04 17:24:55,397:INFO:_display_container: 2 +2024-12-04 17:24:55,397:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:24:55,397:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:55,487:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:55,488:INFO:Creating metrics dataframe +2024-12-04 17:24:55,494:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:24:55,494:INFO:Total runtime is 0.1178961157798767 minutes +2024-12-04 17:24:55,495:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:55,495:INFO:Initializing create_model() +2024-12-04 17:24:55,495:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:55,495:INFO:Checking exceptions +2024-12-04 17:24:55,495:INFO:Importing libraries +2024-12-04 17:24:55,495:INFO:Copying training dataset +2024-12-04 17:24:55,502:INFO:Defining folds +2024-12-04 17:24:55,503:INFO:Declaring metric variables +2024-12-04 17:24:55,506:INFO:Importing untrained model +2024-12-04 17:24:55,510:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:24:55,516:INFO:Starting cross validation +2024-12-04 17:24:55,520:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:56,057:INFO:Calculating mean and std +2024-12-04 17:24:56,057:INFO:Creating metrics dataframe +2024-12-04 17:24:56,061:INFO:Uploading results into container +2024-12-04 17:24:56,062:INFO:Uploading model into container now +2024-12-04 17:24:56,063:INFO:_master_model_container: 17 +2024-12-04 17:24:56,063:INFO:_display_container: 2 +2024-12-04 17:24:56,064:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:24:56,064:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:56,178:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:56,178:INFO:Creating metrics dataframe +2024-12-04 17:24:56,182:INFO:Initializing Dummy Regressor +2024-12-04 17:24:56,182:INFO:Total runtime is 0.1293637990951538 minutes +2024-12-04 17:24:56,188:INFO:SubProcess create_model() called ================================== +2024-12-04 17:24:56,188:INFO:Initializing create_model() +2024-12-04 17:24:56,188:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:56,188:INFO:Checking exceptions +2024-12-04 17:24:56,188:INFO:Importing libraries +2024-12-04 17:24:56,188:INFO:Copying training dataset +2024-12-04 17:24:56,192:INFO:Defining folds +2024-12-04 17:24:56,192:INFO:Declaring metric variables +2024-12-04 17:24:56,196:INFO:Importing untrained model +2024-12-04 17:24:56,202:INFO:Dummy Regressor Imported successfully +2024-12-04 17:24:56,208:INFO:Starting cross validation +2024-12-04 17:24:56,210:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:24:56,394:INFO:Calculating mean and std +2024-12-04 17:24:56,394:INFO:Creating metrics dataframe +2024-12-04 17:24:56,396:INFO:Uploading results into container +2024-12-04 17:24:56,396:INFO:Uploading model into container now +2024-12-04 17:24:56,396:INFO:_master_model_container: 18 +2024-12-04 17:24:56,396:INFO:_display_container: 2 +2024-12-04 17:24:56,396:INFO:DummyRegressor() +2024-12-04 17:24:56,398:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:56,487:INFO:SubProcess create_model() end ================================== +2024-12-04 17:24:56,487:INFO:Creating metrics dataframe +2024-12-04 17:24:56,496:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:24:56,503:INFO:Initializing create_model() +2024-12-04 17:24:56,503:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:24:56,503:INFO:Checking exceptions +2024-12-04 17:24:56,503:INFO:Importing libraries +2024-12-04 17:24:56,503:INFO:Copying training dataset +2024-12-04 17:24:56,512:INFO:Defining folds +2024-12-04 17:24:56,512:INFO:Declaring metric variables +2024-12-04 17:24:56,512:INFO:Importing untrained model +2024-12-04 17:24:56,512:INFO:Declaring custom model +2024-12-04 17:24:56,512:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:24:56,512:INFO:Cross validation set to False +2024-12-04 17:24:56,512:INFO:Fitting Model +2024-12-04 17:24:56,776:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:24:56,776:INFO:create_model() successfully completed...................................... +2024-12-04 17:24:56,896:INFO:_master_model_container: 18 +2024-12-04 17:24:56,896:INFO:_display_container: 2 +2024-12-04 17:24:56,896:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:24:56,896:INFO:compare_models() successfully completed...................................... +2024-12-04 17:25:12,393:INFO:Initializing tune_model() +2024-12-04 17:25:12,393:INFO:tune_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-12-04 17:25:12,393:INFO:Checking exceptions +2024-12-04 17:25:12,407:INFO:Copying training dataset +2024-12-04 17:25:12,412:INFO:Checking base model +2024-12-04 17:25:12,412:INFO:Base model : Extra Trees Regressor +2024-12-04 17:25:12,416:INFO:Declaring metric variables +2024-12-04 17:25:12,421:INFO:Defining Hyperparameters +2024-12-04 17:25:12,552:INFO:Tuning with n_jobs=-1 +2024-12-04 17:25:12,552:INFO:Initializing RandomizedSearchCV +2024-12-04 17:25:31,393:INFO:best_params: {'actual_estimator__n_estimators': 100, 'actual_estimator__min_samples_split': 7, 'actual_estimator__min_samples_leaf': 4, 'actual_estimator__min_impurity_decrease': 0.1, 'actual_estimator__max_features': 1.0, 'actual_estimator__max_depth': 9, 'actual_estimator__criterion': 'squared_error', 'actual_estimator__bootstrap': True} +2024-12-04 17:25:31,393:INFO:Hyperparameter search completed +2024-12-04 17:25:31,393:INFO:SubProcess create_model() called ================================== +2024-12-04 17:25:31,394:INFO:Initializing create_model() +2024-12-04 17:25:31,394:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'n_estimators': 100, 'min_samples_split': 7, 'min_samples_leaf': 4, 'min_impurity_decrease': 0.1, 'max_features': 1.0, 'max_depth': 9, 'criterion': 'squared_error', 'bootstrap': True}) +2024-12-04 17:25:31,394:INFO:Checking exceptions +2024-12-04 17:25:31,394:INFO:Importing libraries +2024-12-04 17:25:31,394:INFO:Copying training dataset +2024-12-04 17:25:31,401:INFO:Defining folds +2024-12-04 17:25:31,401:INFO:Declaring metric variables +2024-12-04 17:25:31,402:INFO:Importing untrained model +2024-12-04 17:25:31,402:INFO:Declaring custom model +2024-12-04 17:25:31,410:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:25:31,416:INFO:Starting cross validation +2024-12-04 17:25:31,420:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:25:31,951:INFO:Calculating mean and std +2024-12-04 17:25:31,951:INFO:Creating metrics dataframe +2024-12-04 17:25:31,951:INFO:Finalizing model +2024-12-04 17:25:32,236:INFO:Uploading results into container +2024-12-04 17:25:32,236:INFO:Uploading model into container now +2024-12-04 17:25:32,238:INFO:_master_model_container: 19 +2024-12-04 17:25:32,238:INFO:_display_container: 3 +2024-12-04 17:25:32,238:INFO:ExtraTreesRegressor(bootstrap=True, max_depth=9, min_impurity_decrease=0.1, + min_samples_leaf=4, min_samples_split=7, n_jobs=-1, + random_state=123) +2024-12-04 17:25:32,238:INFO:create_model() successfully completed...................................... +2024-12-04 17:25:32,366:INFO:SubProcess create_model() end ================================== +2024-12-04 17:25:32,366:INFO:choose_better activated +2024-12-04 17:25:32,370:INFO:SubProcess create_model() called ================================== +2024-12-04 17:25:32,370:INFO:Initializing create_model() +2024-12-04 17:25:32,371:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:25:32,371:INFO:Checking exceptions +2024-12-04 17:25:32,372:INFO:Importing libraries +2024-12-04 17:25:32,372:INFO:Copying training dataset +2024-12-04 17:25:32,375:INFO:Defining folds +2024-12-04 17:25:32,375:INFO:Declaring metric variables +2024-12-04 17:25:32,375:INFO:Importing untrained model +2024-12-04 17:25:32,375:INFO:Declaring custom model +2024-12-04 17:25:32,376:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:25:32,376:INFO:Starting cross validation +2024-12-04 17:25:32,377:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:25:33,181:INFO:Calculating mean and std +2024-12-04 17:25:33,181:INFO:Creating metrics dataframe +2024-12-04 17:25:33,183:INFO:Finalizing model +2024-12-04 17:25:33,485:INFO:Uploading results into container +2024-12-04 17:25:33,485:INFO:Uploading model into container now +2024-12-04 17:25:33,485:INFO:_master_model_container: 20 +2024-12-04 17:25:33,485:INFO:_display_container: 4 +2024-12-04 17:25:33,487:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:25:33,487:INFO:create_model() successfully completed...................................... +2024-12-04 17:25:33,593:INFO:SubProcess create_model() end ================================== +2024-12-04 17:25:33,593:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) result for R2 is 0.9576 +2024-12-04 17:25:33,593:INFO:ExtraTreesRegressor(bootstrap=True, max_depth=9, min_impurity_decrease=0.1, + min_samples_leaf=4, min_samples_split=7, n_jobs=-1, + random_state=123) result for R2 is 0.8072 +2024-12-04 17:25:33,593:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) is best model +2024-12-04 17:25:33,593:INFO:choose_better completed +2024-12-04 17:25:33,593:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-12-04 17:25:33,601:INFO:_master_model_container: 20 +2024-12-04 17:25:33,601:INFO:_display_container: 3 +2024-12-04 17:25:33,601:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:25:33,601:INFO:tune_model() successfully completed...................................... +2024-12-04 17:26:21,625:INFO:Initializing predict_model() +2024-12-04 17:26:21,628:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000017AB3329300>) +2024-12-04 17:26:21,628:INFO:Checking exceptions +2024-12-04 17:26:21,628:INFO:Preloading libraries +2024-12-04 17:26:21,628:INFO:Set up data. +2024-12-04 17:26:21,636:INFO:Set up index. +2024-12-04 17:26:21,726:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + warnings.warn( + +2024-12-04 17:26:32,606:INFO:Initializing save_model() +2024-12-04 17:26:32,606:INFO:save_model(model=ExtraTreesRegressor(n_jobs=-1, random_state=123), model_name=best_model, prep_pipe_=Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]), verbose=True, use_case=MLUsecase.REGRESSION, kwargs={}) +2024-12-04 17:26:32,607:INFO:Adding model into prep_pipe +2024-12-04 17:26:32,651:INFO:best_model.pkl saved in current working directory +2024-12-04 17:26:32,663:INFO:Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(c... + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('trained_model', + ExtraTreesRegressor(n_jobs=-1, random_state=123))]) +2024-12-04 17:26:32,663:INFO:save_model() successfully completed...................................... +2024-12-04 17:28:00,516:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:28:00,516:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:28:00,516:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:28:00,516:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:28:12,968:INFO:PyCaret RegressionExperiment +2024-12-04 17:28:12,968:INFO:Logging name: reg-default-name +2024-12-04 17:28:12,968:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:28:12,968:INFO:version 3.3.2 +2024-12-04 17:28:12,968:INFO:Initializing setup() +2024-12-04 17:28:12,968:INFO:self.USI: 4a27 +2024-12-04 17:28:12,968:INFO:self._variable_keys: {'seed', 'log_plots_param', '_ml_usecase', 'X', 'html_param', '_available_plots', 'fold_groups_param', 'fold_shuffle_param', 'exp_id', 'y_test', 'pipeline', 'X_train', 'idx', 'n_jobs_param', 'X_test', 'gpu_param', 'logging_param', 'USI', 'memory', 'gpu_n_jobs_param', 'y_train', 'data', 'exp_name_log', 'fold_generator', 'transform_target_param', 'y', 'target_param'} +2024-12-04 17:28:12,968:INFO:Checking environment +2024-12-04 17:28:12,968:INFO:python_version: 3.11.9 +2024-12-04 17:28:12,968:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:28:12,968:INFO:machine: AMD64 +2024-12-04 17:28:12,968:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:28:12,978:INFO:Memory: svmem(total=17007292416, available=2423488512, percent=85.8, used=14583803904, free=2423488512) +2024-12-04 17:28:12,978:INFO:Physical Core: 4 +2024-12-04 17:28:12,978:INFO:Logical Core: 8 +2024-12-04 17:28:12,978:INFO:Checking libraries +2024-12-04 17:28:12,978:INFO:System: +2024-12-04 17:28:12,978:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:28:12,978:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:28:12,978:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:28:12,978:INFO:PyCaret required dependencies: +2024-12-04 17:28:13,001:INFO: pip: 24.3.1 +2024-12-04 17:28:13,001:INFO: setuptools: 65.5.0 +2024-12-04 17:28:13,001:INFO: pycaret: 3.3.2 +2024-12-04 17:28:13,001:INFO: IPython: 8.12.3 +2024-12-04 17:28:13,001:INFO: ipywidgets: 8.1.5 +2024-12-04 17:28:13,001:INFO: tqdm: 4.67.1 +2024-12-04 17:28:13,001:INFO: numpy: 1.26.4 +2024-12-04 17:28:13,001:INFO: pandas: 2.1.4 +2024-12-04 17:28:13,001:INFO: jinja2: 3.1.4 +2024-12-04 17:28:13,001:INFO: scipy: 1.11.4 +2024-12-04 17:28:13,001:INFO: joblib: 1.3.2 +2024-12-04 17:28:13,001:INFO: sklearn: 1.4.2 +2024-12-04 17:28:13,001:INFO: pyod: 2.0.2 +2024-12-04 17:28:13,001:INFO: imblearn: 0.12.4 +2024-12-04 17:28:13,001:INFO: category_encoders: 2.6.4 +2024-12-04 17:28:13,001:INFO: lightgbm: 4.5.0 +2024-12-04 17:28:13,001:INFO: numba: 0.60.0 +2024-12-04 17:28:13,001:INFO: requests: 2.32.3 +2024-12-04 17:28:13,001:INFO: matplotlib: 3.7.5 +2024-12-04 17:28:13,001:INFO: scikitplot: 0.3.7 +2024-12-04 17:28:13,001:INFO: yellowbrick: 1.5 +2024-12-04 17:28:13,001:INFO: plotly: 5.24.1 +2024-12-04 17:28:13,001:INFO: plotly-resampler: Not installed +2024-12-04 17:28:13,001:INFO: kaleido: 0.2.1 +2024-12-04 17:28:13,001:INFO: schemdraw: 0.15 +2024-12-04 17:28:13,001:INFO: statsmodels: 0.14.4 +2024-12-04 17:28:13,001:INFO: sktime: 0.26.0 +2024-12-04 17:28:13,001:INFO: tbats: 1.1.3 +2024-12-04 17:28:13,001:INFO: pmdarima: 2.0.4 +2024-12-04 17:28:13,001:INFO: psutil: 6.1.0 +2024-12-04 17:28:13,001:INFO: markupsafe: 3.0.2 +2024-12-04 17:28:13,001:INFO: pickle5: Not installed +2024-12-04 17:28:13,001:INFO: cloudpickle: 3.1.0 +2024-12-04 17:28:13,001:INFO: deprecation: 2.1.0 +2024-12-04 17:28:13,001:INFO: xxhash: 3.5.0 +2024-12-04 17:28:13,001:INFO: wurlitzer: Not installed +2024-12-04 17:28:13,001:INFO:PyCaret optional dependencies: +2024-12-04 17:28:13,040:INFO: shap: Not installed +2024-12-04 17:28:13,040:INFO: interpret: Not installed +2024-12-04 17:28:13,040:INFO: umap: Not installed +2024-12-04 17:28:13,040:INFO: ydata_profiling: Not installed +2024-12-04 17:28:13,040:INFO: explainerdashboard: Not installed +2024-12-04 17:28:13,040:INFO: autoviz: Not installed +2024-12-04 17:28:13,040:INFO: fairlearn: Not installed +2024-12-04 17:28:13,042:INFO: deepchecks: Not installed +2024-12-04 17:28:13,042:INFO: xgboost: Not installed +2024-12-04 17:28:13,042:INFO: catboost: Not installed +2024-12-04 17:28:13,042:INFO: kmodes: Not installed +2024-12-04 17:28:13,042:INFO: mlxtend: Not installed +2024-12-04 17:28:13,042:INFO: statsforecast: Not installed +2024-12-04 17:28:13,042:INFO: tune_sklearn: Not installed +2024-12-04 17:28:13,042:INFO: ray: Not installed +2024-12-04 17:28:13,042:INFO: hyperopt: Not installed +2024-12-04 17:28:13,042:INFO: optuna: Not installed +2024-12-04 17:28:13,042:INFO: skopt: Not installed +2024-12-04 17:28:13,042:INFO: mlflow: 2.18.0 +2024-12-04 17:28:13,042:INFO: gradio: Not installed +2024-12-04 17:28:13,042:INFO: fastapi: Not installed +2024-12-04 17:28:13,042:INFO: uvicorn: 0.32.1 +2024-12-04 17:28:13,042:INFO: m2cgen: Not installed +2024-12-04 17:28:13,042:INFO: evidently: Not installed +2024-12-04 17:28:13,042:INFO: fugue: Not installed +2024-12-04 17:28:13,042:INFO: streamlit: 1.40.1 +2024-12-04 17:28:13,042:INFO: prophet: Not installed +2024-12-04 17:28:13,042:INFO:None +2024-12-04 17:28:13,042:INFO:Set up data. +2024-12-04 17:28:13,045:INFO:Set up folding strategy. +2024-12-04 17:28:13,045:INFO:Set up train/test split. +2024-12-04 17:28:13,052:INFO:Set up index. +2024-12-04 17:28:13,052:INFO:Assigning column types. +2024-12-04 17:28:13,052:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:28:13,052:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,060:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,063:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,137:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,183:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,183:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,184:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,184:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,189:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,193:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,245:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,286:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,287:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,287:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,288:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:28:13,292:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,295:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,349:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,393:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,393:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,393:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,396:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,404:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,453:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,492:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,492:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,492:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,492:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:28:13,503:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,556:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,596:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,596:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,599:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,606:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,660:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,706:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,707:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,707:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,707:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:28:13,770:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,813:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,813:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,813:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,878:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,922:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:28:13,923:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,923:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:13,923:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:28:13,989:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:14,035:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,035:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,095:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:28:14,142:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,142:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,142:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:28:14,241:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,241:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,343:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,343:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,346:INFO:Preparing preprocessing pipeline... +2024-12-04 17:28:14,346:INFO:Set up simple imputation. +2024-12-04 17:28:14,347:INFO:Set up encoding of categorical features. +2024-12-04 17:28:14,347:INFO:Set up column transformation. +2024-12-04 17:28:14,347:INFO:Set up feature normalization. +2024-12-04 17:28:14,496:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:28:14,506:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 17:28:14,506:INFO:Creating final display dataframe. +2024-12-04 17:28:14,718:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5688, 4) +4 Transformed data shape (5688, 20) +5 Transformed train set shape (3981, 20) +6 Transformed test set shape (1707, 20) +7 Ignore features 1 +8 Categorical features 2 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Transformation True +16 Transformation method yeo-johnson +17 Normalize True +18 Normalize method zscore +19 Fold Generator KFold +20 Fold Number 5 +21 CPU Jobs -1 +22 Use GPU False +23 Log Experiment False +24 Experiment Name reg-default-name +25 USI 4a27 +2024-12-04 17:28:14,832:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,833:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,934:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,935:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:28:14,935:INFO:setup() successfully completed in 1.98s............... +2024-12-04 17:28:14,945:INFO:Initializing compare_models() +2024-12-04 17:28:14,945:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=MAE, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'MAE', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:28:14,945:INFO:Checking exceptions +2024-12-04 17:28:14,945:INFO:Preparing display monitor +2024-12-04 17:28:14,975:INFO:Initializing Linear Regression +2024-12-04 17:28:14,975:INFO:Total runtime is 0.0 minutes +2024-12-04 17:28:14,978:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:14,978:INFO:Initializing create_model() +2024-12-04 17:28:14,978:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:14,978:INFO:Checking exceptions +2024-12-04 17:28:14,978:INFO:Importing libraries +2024-12-04 17:28:14,978:INFO:Copying training dataset +2024-12-04 17:28:14,989:INFO:Defining folds +2024-12-04 17:28:14,989:INFO:Declaring metric variables +2024-12-04 17:28:15,025:INFO:Importing untrained model +2024-12-04 17:28:15,047:INFO:Linear Regression Imported successfully +2024-12-04 17:28:15,057:INFO:Starting cross validation +2024-12-04 17:28:15,068:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:19,047:INFO:Calculating mean and std +2024-12-04 17:28:19,047:INFO:Creating metrics dataframe +2024-12-04 17:28:19,053:INFO:Uploading results into container +2024-12-04 17:28:19,054:INFO:Uploading model into container now +2024-12-04 17:28:19,054:INFO:_master_model_container: 1 +2024-12-04 17:28:19,055:INFO:_display_container: 2 +2024-12-04 17:28:19,055:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:28:19,055:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:19,160:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:19,160:INFO:Creating metrics dataframe +2024-12-04 17:28:19,167:INFO:Initializing Lasso Regression +2024-12-04 17:28:19,167:INFO:Total runtime is 0.06987021764119467 minutes +2024-12-04 17:28:19,171:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:19,171:INFO:Initializing create_model() +2024-12-04 17:28:19,171:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:19,171:INFO:Checking exceptions +2024-12-04 17:28:19,171:INFO:Importing libraries +2024-12-04 17:28:19,172:INFO:Copying training dataset +2024-12-04 17:28:19,176:INFO:Defining folds +2024-12-04 17:28:19,176:INFO:Declaring metric variables +2024-12-04 17:28:19,178:INFO:Importing untrained model +2024-12-04 17:28:19,184:INFO:Lasso Regression Imported successfully +2024-12-04 17:28:19,192:INFO:Starting cross validation +2024-12-04 17:28:19,195:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:19,463:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.651e+09, tolerance: 4.419e+07 + model = cd_fast.enet_coordinate_descent( + +2024-12-04 17:28:21,948:INFO:Calculating mean and std +2024-12-04 17:28:21,949:INFO:Creating metrics dataframe +2024-12-04 17:28:21,954:INFO:Uploading results into container +2024-12-04 17:28:21,955:INFO:Uploading model into container now +2024-12-04 17:28:21,956:INFO:_master_model_container: 2 +2024-12-04 17:28:21,956:INFO:_display_container: 2 +2024-12-04 17:28:21,957:INFO:Lasso(random_state=123) +2024-12-04 17:28:21,957:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:22,051:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:22,060:INFO:Creating metrics dataframe +2024-12-04 17:28:22,063:INFO:Initializing Ridge Regression +2024-12-04 17:28:22,063:INFO:Total runtime is 0.11812838315963745 minutes +2024-12-04 17:28:22,070:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:22,070:INFO:Initializing create_model() +2024-12-04 17:28:22,070:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:22,070:INFO:Checking exceptions +2024-12-04 17:28:22,070:INFO:Importing libraries +2024-12-04 17:28:22,070:INFO:Copying training dataset +2024-12-04 17:28:22,075:INFO:Defining folds +2024-12-04 17:28:22,075:INFO:Declaring metric variables +2024-12-04 17:28:22,079:INFO:Importing untrained model +2024-12-04 17:28:22,084:INFO:Ridge Regression Imported successfully +2024-12-04 17:28:22,093:INFO:Starting cross validation +2024-12-04 17:28:22,093:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:22,287:INFO:Calculating mean and std +2024-12-04 17:28:22,288:INFO:Creating metrics dataframe +2024-12-04 17:28:22,290:INFO:Uploading results into container +2024-12-04 17:28:22,291:INFO:Uploading model into container now +2024-12-04 17:28:22,291:INFO:_master_model_container: 3 +2024-12-04 17:28:22,291:INFO:_display_container: 2 +2024-12-04 17:28:22,291:INFO:Ridge(random_state=123) +2024-12-04 17:28:22,291:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:22,375:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:22,375:INFO:Creating metrics dataframe +2024-12-04 17:28:22,382:INFO:Initializing Elastic Net +2024-12-04 17:28:22,382:INFO:Total runtime is 0.12344588836034139 minutes +2024-12-04 17:28:22,384:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:22,385:INFO:Initializing create_model() +2024-12-04 17:28:22,385:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:22,385:INFO:Checking exceptions +2024-12-04 17:28:22,385:INFO:Importing libraries +2024-12-04 17:28:22,385:INFO:Copying training dataset +2024-12-04 17:28:22,390:INFO:Defining folds +2024-12-04 17:28:22,391:INFO:Declaring metric variables +2024-12-04 17:28:22,394:INFO:Importing untrained model +2024-12-04 17:28:22,398:INFO:Elastic Net Imported successfully +2024-12-04 17:28:22,405:INFO:Starting cross validation +2024-12-04 17:28:22,406:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:22,613:INFO:Calculating mean and std +2024-12-04 17:28:22,614:INFO:Creating metrics dataframe +2024-12-04 17:28:22,615:INFO:Uploading results into container +2024-12-04 17:28:22,616:INFO:Uploading model into container now +2024-12-04 17:28:22,616:INFO:_master_model_container: 4 +2024-12-04 17:28:22,616:INFO:_display_container: 2 +2024-12-04 17:28:22,616:INFO:ElasticNet(random_state=123) +2024-12-04 17:28:22,616:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:22,695:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:22,695:INFO:Creating metrics dataframe +2024-12-04 17:28:22,706:INFO:Initializing Least Angle Regression +2024-12-04 17:28:22,706:INFO:Total runtime is 0.128848659992218 minutes +2024-12-04 17:28:22,708:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:22,708:INFO:Initializing create_model() +2024-12-04 17:28:22,708:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:22,710:INFO:Checking exceptions +2024-12-04 17:28:22,710:INFO:Importing libraries +2024-12-04 17:28:22,710:INFO:Copying training dataset +2024-12-04 17:28:22,712:INFO:Defining folds +2024-12-04 17:28:22,712:INFO:Declaring metric variables +2024-12-04 17:28:22,716:INFO:Importing untrained model +2024-12-04 17:28:22,720:INFO:Least Angle Regression Imported successfully +2024-12-04 17:28:22,728:INFO:Starting cross validation +2024-12-04 17:28:22,728:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:22,983:INFO:Calculating mean and std +2024-12-04 17:28:22,983:INFO:Creating metrics dataframe +2024-12-04 17:28:22,985:INFO:Uploading results into container +2024-12-04 17:28:22,987:INFO:Uploading model into container now +2024-12-04 17:28:22,987:INFO:_master_model_container: 5 +2024-12-04 17:28:22,987:INFO:_display_container: 2 +2024-12-04 17:28:22,987:INFO:Lars(random_state=123) +2024-12-04 17:28:22,987:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:23,075:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:23,075:INFO:Creating metrics dataframe +2024-12-04 17:28:23,079:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:28:23,079:INFO:Total runtime is 0.1350643992424011 minutes +2024-12-04 17:28:23,086:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:23,086:INFO:Initializing create_model() +2024-12-04 17:28:23,086:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:23,086:INFO:Checking exceptions +2024-12-04 17:28:23,086:INFO:Importing libraries +2024-12-04 17:28:23,086:INFO:Copying training dataset +2024-12-04 17:28:23,090:INFO:Defining folds +2024-12-04 17:28:23,090:INFO:Declaring metric variables +2024-12-04 17:28:23,094:INFO:Importing untrained model +2024-12-04 17:28:23,096:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:28:23,106:INFO:Starting cross validation +2024-12-04 17:28:23,106:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:23,333:INFO:Calculating mean and std +2024-12-04 17:28:23,333:INFO:Creating metrics dataframe +2024-12-04 17:28:23,335:INFO:Uploading results into container +2024-12-04 17:28:23,335:INFO:Uploading model into container now +2024-12-04 17:28:23,337:INFO:_master_model_container: 6 +2024-12-04 17:28:23,337:INFO:_display_container: 2 +2024-12-04 17:28:23,337:INFO:LassoLars(random_state=123) +2024-12-04 17:28:23,337:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:23,425:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:23,425:INFO:Creating metrics dataframe +2024-12-04 17:28:23,428:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:28:23,428:INFO:Total runtime is 0.14088665644327797 minutes +2024-12-04 17:28:23,434:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:23,434:INFO:Initializing create_model() +2024-12-04 17:28:23,434:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:23,434:INFO:Checking exceptions +2024-12-04 17:28:23,435:INFO:Importing libraries +2024-12-04 17:28:23,435:INFO:Copying training dataset +2024-12-04 17:28:23,440:INFO:Defining folds +2024-12-04 17:28:23,440:INFO:Declaring metric variables +2024-12-04 17:28:23,442:INFO:Importing untrained model +2024-12-04 17:28:23,446:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:28:23,455:INFO:Starting cross validation +2024-12-04 17:28:23,455:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:23,664:INFO:Calculating mean and std +2024-12-04 17:28:23,664:INFO:Creating metrics dataframe +2024-12-04 17:28:23,666:INFO:Uploading results into container +2024-12-04 17:28:23,666:INFO:Uploading model into container now +2024-12-04 17:28:23,667:INFO:_master_model_container: 7 +2024-12-04 17:28:23,667:INFO:_display_container: 2 +2024-12-04 17:28:23,667:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:28:23,667:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:23,758:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:23,758:INFO:Creating metrics dataframe +2024-12-04 17:28:23,766:INFO:Initializing Bayesian Ridge +2024-12-04 17:28:23,767:INFO:Total runtime is 0.14652833143870034 minutes +2024-12-04 17:28:23,771:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:23,772:INFO:Initializing create_model() +2024-12-04 17:28:23,772:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:23,772:INFO:Checking exceptions +2024-12-04 17:28:23,772:INFO:Importing libraries +2024-12-04 17:28:23,772:INFO:Copying training dataset +2024-12-04 17:28:23,775:INFO:Defining folds +2024-12-04 17:28:23,775:INFO:Declaring metric variables +2024-12-04 17:28:23,800:INFO:Importing untrained model +2024-12-04 17:28:23,806:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:28:23,819:INFO:Starting cross validation +2024-12-04 17:28:23,822:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:24,019:INFO:Calculating mean and std +2024-12-04 17:28:24,019:INFO:Creating metrics dataframe +2024-12-04 17:28:24,019:INFO:Uploading results into container +2024-12-04 17:28:24,019:INFO:Uploading model into container now +2024-12-04 17:28:24,019:INFO:_master_model_container: 8 +2024-12-04 17:28:24,019:INFO:_display_container: 2 +2024-12-04 17:28:24,024:INFO:BayesianRidge() +2024-12-04 17:28:24,024:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:24,100:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:24,106:INFO:Creating metrics dataframe +2024-12-04 17:28:24,113:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:28:24,113:INFO:Total runtime is 0.1523102005322774 minutes +2024-12-04 17:28:24,117:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:24,117:INFO:Initializing create_model() +2024-12-04 17:28:24,117:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:24,117:INFO:Checking exceptions +2024-12-04 17:28:24,117:INFO:Importing libraries +2024-12-04 17:28:24,118:INFO:Copying training dataset +2024-12-04 17:28:24,121:INFO:Defining folds +2024-12-04 17:28:24,121:INFO:Declaring metric variables +2024-12-04 17:28:24,123:INFO:Importing untrained model +2024-12-04 17:28:24,128:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:28:24,135:INFO:Starting cross validation +2024-12-04 17:28:24,138:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:24,331:INFO:Calculating mean and std +2024-12-04 17:28:24,331:INFO:Creating metrics dataframe +2024-12-04 17:28:24,333:INFO:Uploading results into container +2024-12-04 17:28:24,334:INFO:Uploading model into container now +2024-12-04 17:28:24,334:INFO:_master_model_container: 9 +2024-12-04 17:28:24,334:INFO:_display_container: 2 +2024-12-04 17:28:24,335:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:28:24,335:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:24,420:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:24,420:INFO:Creating metrics dataframe +2024-12-04 17:28:24,428:INFO:Initializing Huber Regressor +2024-12-04 17:28:24,428:INFO:Total runtime is 0.15754692951838173 minutes +2024-12-04 17:28:24,434:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:24,434:INFO:Initializing create_model() +2024-12-04 17:28:24,435:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:24,435:INFO:Checking exceptions +2024-12-04 17:28:24,435:INFO:Importing libraries +2024-12-04 17:28:24,435:INFO:Copying training dataset +2024-12-04 17:28:24,440:INFO:Defining folds +2024-12-04 17:28:24,440:INFO:Declaring metric variables +2024-12-04 17:28:24,442:INFO:Importing untrained model +2024-12-04 17:28:24,445:INFO:Huber Regressor Imported successfully +2024-12-04 17:28:24,454:INFO:Starting cross validation +2024-12-04 17:28:24,455:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:24,658:INFO:Calculating mean and std +2024-12-04 17:28:24,658:INFO:Creating metrics dataframe +2024-12-04 17:28:24,658:INFO:Uploading results into container +2024-12-04 17:28:24,660:INFO:Uploading model into container now +2024-12-04 17:28:24,660:INFO:_master_model_container: 10 +2024-12-04 17:28:24,660:INFO:_display_container: 2 +2024-12-04 17:28:24,660:INFO:HuberRegressor() +2024-12-04 17:28:24,660:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:24,742:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:24,742:INFO:Creating metrics dataframe +2024-12-04 17:28:24,749:INFO:Initializing K Neighbors Regressor +2024-12-04 17:28:24,749:INFO:Total runtime is 0.16290912230809526 minutes +2024-12-04 17:28:24,753:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:24,753:INFO:Initializing create_model() +2024-12-04 17:28:24,753:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:24,753:INFO:Checking exceptions +2024-12-04 17:28:24,753:INFO:Importing libraries +2024-12-04 17:28:24,753:INFO:Copying training dataset +2024-12-04 17:28:24,756:INFO:Defining folds +2024-12-04 17:28:24,756:INFO:Declaring metric variables +2024-12-04 17:28:24,765:INFO:Importing untrained model +2024-12-04 17:28:24,770:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:28:24,789:INFO:Starting cross validation +2024-12-04 17:28:24,791:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:25,100:INFO:Calculating mean and std +2024-12-04 17:28:25,100:INFO:Creating metrics dataframe +2024-12-04 17:28:25,103:INFO:Uploading results into container +2024-12-04 17:28:25,103:INFO:Uploading model into container now +2024-12-04 17:28:25,103:INFO:_master_model_container: 11 +2024-12-04 17:28:25,104:INFO:_display_container: 2 +2024-12-04 17:28:25,104:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:28:25,104:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:25,183:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:25,183:INFO:Creating metrics dataframe +2024-12-04 17:28:25,190:INFO:Initializing Decision Tree Regressor +2024-12-04 17:28:25,193:INFO:Total runtime is 0.17029535770416257 minutes +2024-12-04 17:28:25,195:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:25,195:INFO:Initializing create_model() +2024-12-04 17:28:25,195:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:25,195:INFO:Checking exceptions +2024-12-04 17:28:25,195:INFO:Importing libraries +2024-12-04 17:28:25,195:INFO:Copying training dataset +2024-12-04 17:28:25,199:INFO:Defining folds +2024-12-04 17:28:25,199:INFO:Declaring metric variables +2024-12-04 17:28:25,203:INFO:Importing untrained model +2024-12-04 17:28:25,207:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:28:25,214:INFO:Starting cross validation +2024-12-04 17:28:25,216:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:25,424:INFO:Calculating mean and std +2024-12-04 17:28:25,424:INFO:Creating metrics dataframe +2024-12-04 17:28:25,425:INFO:Uploading results into container +2024-12-04 17:28:25,425:INFO:Uploading model into container now +2024-12-04 17:28:25,425:INFO:_master_model_container: 12 +2024-12-04 17:28:25,425:INFO:_display_container: 2 +2024-12-04 17:28:25,425:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:28:25,425:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:25,511:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:25,511:INFO:Creating metrics dataframe +2024-12-04 17:28:25,521:INFO:Initializing Random Forest Regressor +2024-12-04 17:28:25,521:INFO:Total runtime is 0.17576658328374223 minutes +2024-12-04 17:28:25,525:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:25,526:INFO:Initializing create_model() +2024-12-04 17:28:25,526:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:25,526:INFO:Checking exceptions +2024-12-04 17:28:25,526:INFO:Importing libraries +2024-12-04 17:28:25,526:INFO:Copying training dataset +2024-12-04 17:28:25,528:INFO:Defining folds +2024-12-04 17:28:25,529:INFO:Declaring metric variables +2024-12-04 17:28:25,530:INFO:Importing untrained model +2024-12-04 17:28:25,533:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:28:25,540:INFO:Starting cross validation +2024-12-04 17:28:25,543:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:26,405:INFO:Calculating mean and std +2024-12-04 17:28:26,405:INFO:Creating metrics dataframe +2024-12-04 17:28:26,408:INFO:Uploading results into container +2024-12-04 17:28:26,408:INFO:Uploading model into container now +2024-12-04 17:28:26,408:INFO:_master_model_container: 13 +2024-12-04 17:28:26,408:INFO:_display_container: 2 +2024-12-04 17:28:26,408:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:28:26,408:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:26,493:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:26,493:INFO:Creating metrics dataframe +2024-12-04 17:28:26,501:INFO:Initializing Extra Trees Regressor +2024-12-04 17:28:26,501:INFO:Total runtime is 0.19209890762964882 minutes +2024-12-04 17:28:26,505:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:26,505:INFO:Initializing create_model() +2024-12-04 17:28:26,505:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:26,505:INFO:Checking exceptions +2024-12-04 17:28:26,505:INFO:Importing libraries +2024-12-04 17:28:26,505:INFO:Copying training dataset +2024-12-04 17:28:26,508:INFO:Defining folds +2024-12-04 17:28:26,508:INFO:Declaring metric variables +2024-12-04 17:28:26,512:INFO:Importing untrained model +2024-12-04 17:28:26,516:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:28:26,523:INFO:Starting cross validation +2024-12-04 17:28:26,525:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:27,302:INFO:Calculating mean and std +2024-12-04 17:28:27,303:INFO:Creating metrics dataframe +2024-12-04 17:28:27,303:INFO:Uploading results into container +2024-12-04 17:28:27,303:INFO:Uploading model into container now +2024-12-04 17:28:27,303:INFO:_master_model_container: 14 +2024-12-04 17:28:27,303:INFO:_display_container: 2 +2024-12-04 17:28:27,303:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:28:27,307:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:27,383:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:27,383:INFO:Creating metrics dataframe +2024-12-04 17:28:27,390:INFO:Initializing AdaBoost Regressor +2024-12-04 17:28:27,390:INFO:Total runtime is 0.20692359606424965 minutes +2024-12-04 17:28:27,393:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:27,393:INFO:Initializing create_model() +2024-12-04 17:28:27,395:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:27,395:INFO:Checking exceptions +2024-12-04 17:28:27,395:INFO:Importing libraries +2024-12-04 17:28:27,395:INFO:Copying training dataset +2024-12-04 17:28:27,399:INFO:Defining folds +2024-12-04 17:28:27,399:INFO:Declaring metric variables +2024-12-04 17:28:27,401:INFO:Importing untrained model +2024-12-04 17:28:27,405:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:28:27,412:INFO:Starting cross validation +2024-12-04 17:28:27,413:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:27,650:INFO:Calculating mean and std +2024-12-04 17:28:27,651:INFO:Creating metrics dataframe +2024-12-04 17:28:27,653:INFO:Uploading results into container +2024-12-04 17:28:27,653:INFO:Uploading model into container now +2024-12-04 17:28:27,655:INFO:_master_model_container: 15 +2024-12-04 17:28:27,655:INFO:_display_container: 2 +2024-12-04 17:28:27,655:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:28:27,655:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:27,746:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:27,746:INFO:Creating metrics dataframe +2024-12-04 17:28:27,753:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:28:27,753:INFO:Total runtime is 0.21297183036804196 minutes +2024-12-04 17:28:27,753:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:27,753:INFO:Initializing create_model() +2024-12-04 17:28:27,753:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:27,753:INFO:Checking exceptions +2024-12-04 17:28:27,753:INFO:Importing libraries +2024-12-04 17:28:27,753:INFO:Copying training dataset +2024-12-04 17:28:27,760:INFO:Defining folds +2024-12-04 17:28:27,760:INFO:Declaring metric variables +2024-12-04 17:28:27,763:INFO:Importing untrained model +2024-12-04 17:28:27,769:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:28:27,775:INFO:Starting cross validation +2024-12-04 17:28:27,778:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:28,247:INFO:Calculating mean and std +2024-12-04 17:28:28,247:INFO:Creating metrics dataframe +2024-12-04 17:28:28,247:INFO:Uploading results into container +2024-12-04 17:28:28,250:INFO:Uploading model into container now +2024-12-04 17:28:28,251:INFO:_master_model_container: 16 +2024-12-04 17:28:28,251:INFO:_display_container: 2 +2024-12-04 17:28:28,252:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:28:28,252:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:28,332:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:28,332:INFO:Creating metrics dataframe +2024-12-04 17:28:28,343:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:28:28,343:INFO:Total runtime is 0.22279671033223467 minutes +2024-12-04 17:28:28,345:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:28,345:INFO:Initializing create_model() +2024-12-04 17:28:28,345:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:28,345:INFO:Checking exceptions +2024-12-04 17:28:28,345:INFO:Importing libraries +2024-12-04 17:28:28,345:INFO:Copying training dataset +2024-12-04 17:28:28,352:INFO:Defining folds +2024-12-04 17:28:28,352:INFO:Declaring metric variables +2024-12-04 17:28:28,352:INFO:Importing untrained model +2024-12-04 17:28:28,361:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:28:28,370:INFO:Starting cross validation +2024-12-04 17:28:28,372:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:28,967:INFO:Calculating mean and std +2024-12-04 17:28:28,967:INFO:Creating metrics dataframe +2024-12-04 17:28:28,970:INFO:Uploading results into container +2024-12-04 17:28:28,970:INFO:Uploading model into container now +2024-12-04 17:28:28,970:INFO:_master_model_container: 17 +2024-12-04 17:28:28,970:INFO:_display_container: 2 +2024-12-04 17:28:28,970:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:28:28,970:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:29,075:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:29,075:INFO:Creating metrics dataframe +2024-12-04 17:28:29,082:INFO:Initializing Dummy Regressor +2024-12-04 17:28:29,082:INFO:Total runtime is 0.23511841694513952 minutes +2024-12-04 17:28:29,088:INFO:SubProcess create_model() called ================================== +2024-12-04 17:28:29,088:INFO:Initializing create_model() +2024-12-04 17:28:29,088:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:29,088:INFO:Checking exceptions +2024-12-04 17:28:29,088:INFO:Importing libraries +2024-12-04 17:28:29,088:INFO:Copying training dataset +2024-12-04 17:28:29,092:INFO:Defining folds +2024-12-04 17:28:29,092:INFO:Declaring metric variables +2024-12-04 17:28:29,096:INFO:Importing untrained model +2024-12-04 17:28:29,100:INFO:Dummy Regressor Imported successfully +2024-12-04 17:28:29,105:INFO:Starting cross validation +2024-12-04 17:28:29,107:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:28:29,320:INFO:Calculating mean and std +2024-12-04 17:28:29,321:INFO:Creating metrics dataframe +2024-12-04 17:28:29,322:INFO:Uploading results into container +2024-12-04 17:28:29,322:INFO:Uploading model into container now +2024-12-04 17:28:29,323:INFO:_master_model_container: 18 +2024-12-04 17:28:29,323:INFO:_display_container: 2 +2024-12-04 17:28:29,323:INFO:DummyRegressor() +2024-12-04 17:28:29,323:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:29,423:INFO:SubProcess create_model() end ================================== +2024-12-04 17:28:29,423:INFO:Creating metrics dataframe +2024-12-04 17:28:29,437:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:28:29,445:INFO:Initializing create_model() +2024-12-04 17:28:29,445:INFO:create_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:28:29,445:INFO:Checking exceptions +2024-12-04 17:28:29,448:INFO:Importing libraries +2024-12-04 17:28:29,448:INFO:Copying training dataset +2024-12-04 17:28:29,452:INFO:Defining folds +2024-12-04 17:28:29,452:INFO:Declaring metric variables +2024-12-04 17:28:29,452:INFO:Importing untrained model +2024-12-04 17:28:29,452:INFO:Declaring custom model +2024-12-04 17:28:29,454:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:28:29,457:INFO:Cross validation set to False +2024-12-04 17:28:29,457:INFO:Fitting Model +2024-12-04 17:28:29,787:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:28:29,788:INFO:create_model() successfully completed...................................... +2024-12-04 17:28:29,910:INFO:_master_model_container: 18 +2024-12-04 17:28:29,910:INFO:_display_container: 2 +2024-12-04 17:28:29,910:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:28:29,910:INFO:compare_models() successfully completed...................................... +2024-12-04 17:28:53,148:INFO:Initializing predict_model() +2024-12-04 17:28:53,149:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001C778BBF920>) +2024-12-04 17:28:53,149:INFO:Checking exceptions +2024-12-04 17:28:53,149:INFO:Preloading libraries +2024-12-04 17:28:53,152:INFO:Set up data. +2024-12-04 17:28:53,156:INFO:Set up index. +2024-12-04 17:28:53,243:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + warnings.warn( + +2024-12-04 17:29:06,959:INFO:Initializing save_model() +2024-12-04 17:29:06,960:INFO:save_model(model=ExtraTreesRegressor(n_jobs=-1, random_state=123), model_name=best_model, prep_pipe_=Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]), verbose=True, use_case=MLUsecase.REGRESSION, kwargs={}) +2024-12-04 17:29:06,960:INFO:Adding model into prep_pipe +2024-12-04 17:29:07,019:INFO:best_model.pkl saved in current working directory +2024-12-04 17:29:07,035:INFO:Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=[], transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region'], + transformer=OneHotEncoder(c... + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', TransformerWrapper(transformer=StandardScaler())), + ('trained_model', + ExtraTreesRegressor(n_jobs=-1, random_state=123))]) +2024-12-04 17:29:07,035:INFO:save_model() successfully completed...................................... +2024-12-04 17:34:22,512:INFO:Initializing predict_model() +2024-12-04 17:34:22,513:INFO:predict_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001C778DEB9C0>) +2024-12-04 17:34:22,513:INFO:Checking exceptions +2024-12-04 17:34:22,513:INFO:Preloading libraries +2024-12-04 17:34:22,516:INFO:Set up data. +2024-12-04 17:34:22,519:INFO:Set up index. +2024-12-04 17:36:41,411:INFO:PyCaret RegressionExperiment +2024-12-04 17:36:41,411:INFO:Logging name: reg-default-name +2024-12-04 17:36:41,413:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:36:41,413:INFO:version 3.3.2 +2024-12-04 17:36:41,413:INFO:Initializing setup() +2024-12-04 17:36:41,413:INFO:self.USI: 3704 +2024-12-04 17:36:41,413:INFO:self._variable_keys: {'seed', 'log_plots_param', '_ml_usecase', 'X', 'html_param', '_available_plots', 'fold_groups_param', 'fold_shuffle_param', 'exp_id', 'y_test', 'pipeline', 'X_train', 'idx', 'n_jobs_param', 'X_test', 'gpu_param', 'logging_param', 'USI', 'memory', 'gpu_n_jobs_param', 'y_train', 'data', 'exp_name_log', 'fold_generator', 'transform_target_param', 'y', 'target_param'} +2024-12-04 17:36:41,413:INFO:Checking environment +2024-12-04 17:36:41,413:INFO:python_version: 3.11.9 +2024-12-04 17:36:41,413:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:36:41,413:INFO:machine: AMD64 +2024-12-04 17:36:41,413:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:36:41,419:INFO:Memory: svmem(total=17007292416, available=3020730368, percent=82.2, used=13986562048, free=3020730368) +2024-12-04 17:36:41,419:INFO:Physical Core: 4 +2024-12-04 17:36:41,419:INFO:Logical Core: 8 +2024-12-04 17:36:41,419:INFO:Checking libraries +2024-12-04 17:36:41,419:INFO:System: +2024-12-04 17:36:41,419:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:36:41,419:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:36:41,419:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:36:41,419:INFO:PyCaret required dependencies: +2024-12-04 17:36:41,419:INFO: pip: 24.3.1 +2024-12-04 17:36:41,419:INFO: setuptools: 65.5.0 +2024-12-04 17:36:41,419:INFO: pycaret: 3.3.2 +2024-12-04 17:36:41,419:INFO: IPython: 8.12.3 +2024-12-04 17:36:41,420:INFO: ipywidgets: 8.1.5 +2024-12-04 17:36:41,420:INFO: tqdm: 4.67.1 +2024-12-04 17:36:41,420:INFO: numpy: 1.26.4 +2024-12-04 17:36:41,420:INFO: pandas: 2.1.4 +2024-12-04 17:36:41,420:INFO: jinja2: 3.1.4 +2024-12-04 17:36:41,420:INFO: scipy: 1.11.4 +2024-12-04 17:36:41,420:INFO: joblib: 1.3.2 +2024-12-04 17:36:41,420:INFO: sklearn: 1.4.2 +2024-12-04 17:36:41,420:INFO: pyod: 2.0.2 +2024-12-04 17:36:41,420:INFO: imblearn: 0.12.4 +2024-12-04 17:36:41,420:INFO: category_encoders: 2.6.4 +2024-12-04 17:36:41,420:INFO: lightgbm: 4.5.0 +2024-12-04 17:36:41,420:INFO: numba: 0.60.0 +2024-12-04 17:36:41,420:INFO: requests: 2.32.3 +2024-12-04 17:36:41,420:INFO: matplotlib: 3.7.5 +2024-12-04 17:36:41,420:INFO: scikitplot: 0.3.7 +2024-12-04 17:36:41,420:INFO: yellowbrick: 1.5 +2024-12-04 17:36:41,420:INFO: plotly: 5.24.1 +2024-12-04 17:36:41,421:INFO: plotly-resampler: Not installed +2024-12-04 17:36:41,421:INFO: kaleido: 0.2.1 +2024-12-04 17:36:41,421:INFO: schemdraw: 0.15 +2024-12-04 17:36:41,421:INFO: statsmodels: 0.14.4 +2024-12-04 17:36:41,421:INFO: sktime: 0.26.0 +2024-12-04 17:36:41,421:INFO: tbats: 1.1.3 +2024-12-04 17:36:41,421:INFO: pmdarima: 2.0.4 +2024-12-04 17:36:41,421:INFO: psutil: 6.1.0 +2024-12-04 17:36:41,421:INFO: markupsafe: 3.0.2 +2024-12-04 17:36:41,421:INFO: pickle5: Not installed +2024-12-04 17:36:41,421:INFO: cloudpickle: 3.1.0 +2024-12-04 17:36:41,421:INFO: deprecation: 2.1.0 +2024-12-04 17:36:41,421:INFO: xxhash: 3.5.0 +2024-12-04 17:36:41,421:INFO: wurlitzer: Not installed +2024-12-04 17:36:41,421:INFO:PyCaret optional dependencies: +2024-12-04 17:36:41,421:INFO: shap: Not installed +2024-12-04 17:36:41,421:INFO: interpret: Not installed +2024-12-04 17:36:41,421:INFO: umap: Not installed +2024-12-04 17:36:41,421:INFO: ydata_profiling: Not installed +2024-12-04 17:36:41,421:INFO: explainerdashboard: Not installed +2024-12-04 17:36:41,421:INFO: autoviz: Not installed +2024-12-04 17:36:41,421:INFO: fairlearn: Not installed +2024-12-04 17:36:41,421:INFO: deepchecks: Not installed +2024-12-04 17:36:41,421:INFO: xgboost: Not installed +2024-12-04 17:36:41,421:INFO: catboost: Not installed +2024-12-04 17:36:41,421:INFO: kmodes: Not installed +2024-12-04 17:36:41,421:INFO: mlxtend: Not installed +2024-12-04 17:36:41,421:INFO: statsforecast: Not installed +2024-12-04 17:36:41,422:INFO: tune_sklearn: Not installed +2024-12-04 17:36:41,422:INFO: ray: Not installed +2024-12-04 17:36:41,422:INFO: hyperopt: Not installed +2024-12-04 17:36:41,422:INFO: optuna: Not installed +2024-12-04 17:36:41,422:INFO: skopt: Not installed +2024-12-04 17:36:41,422:INFO: mlflow: 2.18.0 +2024-12-04 17:36:41,422:INFO: gradio: Not installed +2024-12-04 17:36:41,422:INFO: fastapi: Not installed +2024-12-04 17:36:41,422:INFO: uvicorn: 0.32.1 +2024-12-04 17:36:41,422:INFO: m2cgen: Not installed +2024-12-04 17:36:41,422:INFO: evidently: Not installed +2024-12-04 17:36:41,422:INFO: fugue: Not installed +2024-12-04 17:36:41,422:INFO: streamlit: 1.40.1 +2024-12-04 17:36:41,422:INFO: prophet: Not installed +2024-12-04 17:36:41,422:INFO:None +2024-12-04 17:36:41,422:INFO:Set up data. +2024-12-04 17:36:41,428:INFO:Set up folding strategy. +2024-12-04 17:36:41,429:INFO:Set up train/test split. +2024-12-04 17:36:41,432:INFO:Set up index. +2024-12-04 17:36:41,432:INFO:Assigning column types. +2024-12-04 17:36:41,436:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:36:41,437:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,437:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,452:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,516:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,564:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,564:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,564:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,565:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,568:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,568:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,625:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,666:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,667:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,667:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,667:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:36:41,667:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,675:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,728:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,766:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,766:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,766:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,775:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,778:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,843:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,887:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,889:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,889:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,889:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:36:41,900:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,953:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,997:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:41,998:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:41,998:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,001:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,060:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,103:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,103:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,103:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,103:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:36:42,170:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,211:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,213:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,213:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,275:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,316:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,316:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,316:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,316:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:36:42,378:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,420:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,420:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,482:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:36:42,521:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,521:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,521:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:36:42,626:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,626:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,728:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,731:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:42,732:INFO:Preparing preprocessing pipeline... +2024-12-04 17:36:42,732:INFO:Set up simple imputation. +2024-12-04 17:36:42,732:INFO:Set up encoding of categorical features. +2024-12-04 17:36:42,732:INFO:Set up column transformation. +2024-12-04 17:36:42,732:INFO:Set up feature normalization. +2024-12-04 17:36:42,840:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 17:36:42,966:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:36:42,978:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['annee'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(inc... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 17:36:42,978:INFO:Creating final display dataframe. +2024-12-04 17:36:43,262:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11412, 4) +4 Transformed data shape (11412, 21) +5 Transformed train set shape (7988, 21) +6 Transformed test set shape (3424, 21) +7 Numeric features 1 +8 Categorical features 2 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Transformation True +16 Transformation method yeo-johnson +17 Normalize True +18 Normalize method zscore +19 Fold Generator KFold +20 Fold Number 5 +21 CPU Jobs -1 +22 Use GPU False +23 Log Experiment False +24 Experiment Name reg-default-name +25 USI 3704 +2024-12-04 17:36:43,372:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:43,372:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:43,475:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:43,475:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:36:43,475:INFO:setup() successfully completed in 2.08s............... +2024-12-04 17:36:50,393:INFO:Initializing compare_models() +2024-12-04 17:36:50,394:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=MAE, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'MAE', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:36:50,394:INFO:Checking exceptions +2024-12-04 17:36:50,396:INFO:Preparing display monitor +2024-12-04 17:36:50,415:INFO:Initializing Linear Regression +2024-12-04 17:36:50,416:INFO:Total runtime is 2.516508102416992e-05 minutes +2024-12-04 17:36:50,418:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:50,418:INFO:Initializing create_model() +2024-12-04 17:36:50,420:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:50,420:INFO:Checking exceptions +2024-12-04 17:36:50,420:INFO:Importing libraries +2024-12-04 17:36:50,420:INFO:Copying training dataset +2024-12-04 17:36:50,420:INFO:Defining folds +2024-12-04 17:36:50,420:INFO:Declaring metric variables +2024-12-04 17:36:50,425:INFO:Importing untrained model +2024-12-04 17:36:50,431:INFO:Linear Regression Imported successfully +2024-12-04 17:36:50,437:INFO:Starting cross validation +2024-12-04 17:36:50,440:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:54,095:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:54,101:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:54,104:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:54,316:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:54,316:INFO:Calculating mean and std +2024-12-04 17:36:54,316:INFO:Creating metrics dataframe +2024-12-04 17:36:54,320:INFO:Uploading results into container +2024-12-04 17:36:54,320:INFO:Uploading model into container now +2024-12-04 17:36:54,321:INFO:_master_model_container: 1 +2024-12-04 17:36:54,321:INFO:_display_container: 2 +2024-12-04 17:36:54,321:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:36:54,321:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:54,432:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:54,432:INFO:Creating metrics dataframe +2024-12-04 17:36:54,440:INFO:Initializing Lasso Regression +2024-12-04 17:36:54,440:INFO:Total runtime is 0.06709032456080119 minutes +2024-12-04 17:36:54,445:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:54,445:INFO:Initializing create_model() +2024-12-04 17:36:54,445:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:54,445:INFO:Checking exceptions +2024-12-04 17:36:54,445:INFO:Importing libraries +2024-12-04 17:36:54,445:INFO:Copying training dataset +2024-12-04 17:36:54,451:INFO:Defining folds +2024-12-04 17:36:54,451:INFO:Declaring metric variables +2024-12-04 17:36:54,453:INFO:Importing untrained model +2024-12-04 17:36:54,453:INFO:Lasso Regression Imported successfully +2024-12-04 17:36:54,465:INFO:Starting cross validation +2024-12-04 17:36:54,466:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:54,582:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:56,870:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:56,882:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:57,107:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:57,107:INFO:Calculating mean and std +2024-12-04 17:36:57,107:INFO:Creating metrics dataframe +2024-12-04 17:36:57,110:INFO:Uploading results into container +2024-12-04 17:36:57,111:INFO:Uploading model into container now +2024-12-04 17:36:57,112:INFO:_master_model_container: 2 +2024-12-04 17:36:57,112:INFO:_display_container: 2 +2024-12-04 17:36:57,113:INFO:Lasso(random_state=123) +2024-12-04 17:36:57,113:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:57,213:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:57,213:INFO:Creating metrics dataframe +2024-12-04 17:36:57,216:INFO:Initializing Ridge Regression +2024-12-04 17:36:57,216:INFO:Total runtime is 0.11336034138997396 minutes +2024-12-04 17:36:57,221:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:57,221:INFO:Initializing create_model() +2024-12-04 17:36:57,221:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:57,221:INFO:Checking exceptions +2024-12-04 17:36:57,221:INFO:Importing libraries +2024-12-04 17:36:57,221:INFO:Copying training dataset +2024-12-04 17:36:57,228:INFO:Defining folds +2024-12-04 17:36:57,228:INFO:Declaring metric variables +2024-12-04 17:36:57,232:INFO:Importing untrained model +2024-12-04 17:36:57,237:INFO:Ridge Regression Imported successfully +2024-12-04 17:36:57,274:INFO:Starting cross validation +2024-12-04 17:36:57,278:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:57,364:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:57,368:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:57,392:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:57,548:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:57,548:INFO:Calculating mean and std +2024-12-04 17:36:57,548:INFO:Creating metrics dataframe +2024-12-04 17:36:57,551:INFO:Uploading results into container +2024-12-04 17:36:57,551:INFO:Uploading model into container now +2024-12-04 17:36:57,551:INFO:_master_model_container: 3 +2024-12-04 17:36:57,551:INFO:_display_container: 2 +2024-12-04 17:36:57,551:INFO:Ridge(random_state=123) +2024-12-04 17:36:57,551:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:57,643:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:57,643:INFO:Creating metrics dataframe +2024-12-04 17:36:57,645:INFO:Initializing Elastic Net +2024-12-04 17:36:57,645:INFO:Total runtime is 0.12051295836766561 minutes +2024-12-04 17:36:57,651:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:57,651:INFO:Initializing create_model() +2024-12-04 17:36:57,651:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:57,651:INFO:Checking exceptions +2024-12-04 17:36:57,651:INFO:Importing libraries +2024-12-04 17:36:57,651:INFO:Copying training dataset +2024-12-04 17:36:57,651:INFO:Defining folds +2024-12-04 17:36:57,651:INFO:Declaring metric variables +2024-12-04 17:36:57,651:INFO:Importing untrained model +2024-12-04 17:36:57,663:INFO:Elastic Net Imported successfully +2024-12-04 17:36:57,666:INFO:Starting cross validation +2024-12-04 17:36:57,666:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:57,751:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:57,775:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:57,782:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:57,956:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:57,956:INFO:Calculating mean and std +2024-12-04 17:36:57,956:INFO:Creating metrics dataframe +2024-12-04 17:36:57,959:INFO:Uploading results into container +2024-12-04 17:36:57,960:INFO:Uploading model into container now +2024-12-04 17:36:57,960:INFO:_master_model_container: 4 +2024-12-04 17:36:57,960:INFO:_display_container: 2 +2024-12-04 17:36:57,960:INFO:ElasticNet(random_state=123) +2024-12-04 17:36:57,960:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:58,051:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:58,051:INFO:Creating metrics dataframe +2024-12-04 17:36:58,060:INFO:Initializing Least Angle Regression +2024-12-04 17:36:58,060:INFO:Total runtime is 0.1274174928665161 minutes +2024-12-04 17:36:58,064:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:58,064:INFO:Initializing create_model() +2024-12-04 17:36:58,064:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:58,064:INFO:Checking exceptions +2024-12-04 17:36:58,065:INFO:Importing libraries +2024-12-04 17:36:58,065:INFO:Copying training dataset +2024-12-04 17:36:58,069:INFO:Defining folds +2024-12-04 17:36:58,069:INFO:Declaring metric variables +2024-12-04 17:36:58,072:INFO:Importing untrained model +2024-12-04 17:36:58,075:INFO:Least Angle Regression Imported successfully +2024-12-04 17:36:58,083:INFO:Starting cross validation +2024-12-04 17:36:58,086:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:58,179:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:58,182:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:58,188:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:58,351:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:58,351:INFO:Calculating mean and std +2024-12-04 17:36:58,351:INFO:Creating metrics dataframe +2024-12-04 17:36:58,351:INFO:Uploading results into container +2024-12-04 17:36:58,351:INFO:Uploading model into container now +2024-12-04 17:36:58,351:INFO:_master_model_container: 5 +2024-12-04 17:36:58,351:INFO:_display_container: 2 +2024-12-04 17:36:58,351:INFO:Lars(random_state=123) +2024-12-04 17:36:58,351:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:58,447:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:58,447:INFO:Creating metrics dataframe +2024-12-04 17:36:58,451:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:36:58,451:INFO:Total runtime is 0.13393324216206867 minutes +2024-12-04 17:36:58,451:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:58,451:INFO:Initializing create_model() +2024-12-04 17:36:58,451:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:58,451:INFO:Checking exceptions +2024-12-04 17:36:58,451:INFO:Importing libraries +2024-12-04 17:36:58,451:INFO:Copying training dataset +2024-12-04 17:36:58,463:INFO:Defining folds +2024-12-04 17:36:58,463:INFO:Declaring metric variables +2024-12-04 17:36:58,466:INFO:Importing untrained model +2024-12-04 17:36:58,471:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:36:58,479:INFO:Starting cross validation +2024-12-04 17:36:58,480:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:58,583:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:58,603:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:58,637:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:58,783:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:58,783:INFO:Calculating mean and std +2024-12-04 17:36:58,783:INFO:Creating metrics dataframe +2024-12-04 17:36:58,783:INFO:Uploading results into container +2024-12-04 17:36:58,783:INFO:Uploading model into container now +2024-12-04 17:36:58,783:INFO:_master_model_container: 6 +2024-12-04 17:36:58,783:INFO:_display_container: 2 +2024-12-04 17:36:58,787:INFO:LassoLars(random_state=123) +2024-12-04 17:36:58,787:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:58,875:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:58,877:INFO:Creating metrics dataframe +2024-12-04 17:36:58,883:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:36:58,883:INFO:Total runtime is 0.1411344528198242 minutes +2024-12-04 17:36:58,887:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:58,887:INFO:Initializing create_model() +2024-12-04 17:36:58,887:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:58,887:INFO:Checking exceptions +2024-12-04 17:36:58,887:INFO:Importing libraries +2024-12-04 17:36:58,887:INFO:Copying training dataset +2024-12-04 17:36:58,893:INFO:Defining folds +2024-12-04 17:36:58,893:INFO:Declaring metric variables +2024-12-04 17:36:58,897:INFO:Importing untrained model +2024-12-04 17:36:58,901:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:36:58,901:INFO:Starting cross validation +2024-12-04 17:36:58,901:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:58,987:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:58,987:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:59,013:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:59,175:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:59,175:INFO:Calculating mean and std +2024-12-04 17:36:59,175:INFO:Creating metrics dataframe +2024-12-04 17:36:59,178:INFO:Uploading results into container +2024-12-04 17:36:59,180:INFO:Uploading model into container now +2024-12-04 17:36:59,181:INFO:_master_model_container: 7 +2024-12-04 17:36:59,181:INFO:_display_container: 2 +2024-12-04 17:36:59,181:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:36:59,182:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:59,271:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:59,271:INFO:Creating metrics dataframe +2024-12-04 17:36:59,277:INFO:Initializing Bayesian Ridge +2024-12-04 17:36:59,277:INFO:Total runtime is 0.14769840240478513 minutes +2024-12-04 17:36:59,278:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:59,278:INFO:Initializing create_model() +2024-12-04 17:36:59,278:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:59,278:INFO:Checking exceptions +2024-12-04 17:36:59,278:INFO:Importing libraries +2024-12-04 17:36:59,278:INFO:Copying training dataset +2024-12-04 17:36:59,282:INFO:Defining folds +2024-12-04 17:36:59,282:INFO:Declaring metric variables +2024-12-04 17:36:59,288:INFO:Importing untrained model +2024-12-04 17:36:59,290:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:36:59,299:INFO:Starting cross validation +2024-12-04 17:36:59,299:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:59,387:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:59,390:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:59,405:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:36:59,591:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:36:59,591:INFO:Calculating mean and std +2024-12-04 17:36:59,591:INFO:Creating metrics dataframe +2024-12-04 17:36:59,594:INFO:Uploading results into container +2024-12-04 17:36:59,595:INFO:Uploading model into container now +2024-12-04 17:36:59,595:INFO:_master_model_container: 8 +2024-12-04 17:36:59,595:INFO:_display_container: 2 +2024-12-04 17:36:59,595:INFO:BayesianRidge() +2024-12-04 17:36:59,595:INFO:create_model() successfully completed...................................... +2024-12-04 17:36:59,682:INFO:SubProcess create_model() end ================================== +2024-12-04 17:36:59,682:INFO:Creating metrics dataframe +2024-12-04 17:36:59,695:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:36:59,696:INFO:Total runtime is 0.1546915014584859 minutes +2024-12-04 17:36:59,698:INFO:SubProcess create_model() called ================================== +2024-12-04 17:36:59,701:INFO:Initializing create_model() +2024-12-04 17:36:59,701:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:36:59,701:INFO:Checking exceptions +2024-12-04 17:36:59,701:INFO:Importing libraries +2024-12-04 17:36:59,701:INFO:Copying training dataset +2024-12-04 17:36:59,701:INFO:Defining folds +2024-12-04 17:36:59,701:INFO:Declaring metric variables +2024-12-04 17:36:59,701:INFO:Importing untrained model +2024-12-04 17:36:59,715:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:36:59,718:INFO:Starting cross validation +2024-12-04 17:36:59,722:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:36:59,818:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:59,846:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:36:59,859:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:00,033:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:00,033:INFO:Calculating mean and std +2024-12-04 17:37:00,033:INFO:Creating metrics dataframe +2024-12-04 17:37:00,033:INFO:Uploading results into container +2024-12-04 17:37:00,033:INFO:Uploading model into container now +2024-12-04 17:37:00,033:INFO:_master_model_container: 9 +2024-12-04 17:37:00,033:INFO:_display_container: 2 +2024-12-04 17:37:00,033:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:37:00,037:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:00,125:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:00,125:INFO:Creating metrics dataframe +2024-12-04 17:37:00,138:INFO:Initializing Huber Regressor +2024-12-04 17:37:00,138:INFO:Total runtime is 0.16204871336619056 minutes +2024-12-04 17:37:00,140:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:00,140:INFO:Initializing create_model() +2024-12-04 17:37:00,140:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:00,140:INFO:Checking exceptions +2024-12-04 17:37:00,140:INFO:Importing libraries +2024-12-04 17:37:00,140:INFO:Copying training dataset +2024-12-04 17:37:00,145:INFO:Defining folds +2024-12-04 17:37:00,146:INFO:Declaring metric variables +2024-12-04 17:37:00,150:INFO:Importing untrained model +2024-12-04 17:37:00,151:INFO:Huber Regressor Imported successfully +2024-12-04 17:37:00,161:INFO:Starting cross validation +2024-12-04 17:37:00,164:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:00,248:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:00,261:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:00,269:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:00,454:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:00,454:INFO:Calculating mean and std +2024-12-04 17:37:00,454:INFO:Creating metrics dataframe +2024-12-04 17:37:00,454:INFO:Uploading results into container +2024-12-04 17:37:00,454:INFO:Uploading model into container now +2024-12-04 17:37:00,454:INFO:_master_model_container: 10 +2024-12-04 17:37:00,454:INFO:_display_container: 2 +2024-12-04 17:37:00,454:INFO:HuberRegressor() +2024-12-04 17:37:00,454:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:00,545:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:00,545:INFO:Creating metrics dataframe +2024-12-04 17:37:00,553:INFO:Initializing K Neighbors Regressor +2024-12-04 17:37:00,553:INFO:Total runtime is 0.1689648588498433 minutes +2024-12-04 17:37:00,558:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:00,558:INFO:Initializing create_model() +2024-12-04 17:37:00,558:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:00,560:INFO:Checking exceptions +2024-12-04 17:37:00,560:INFO:Importing libraries +2024-12-04 17:37:00,560:INFO:Copying training dataset +2024-12-04 17:37:00,565:INFO:Defining folds +2024-12-04 17:37:00,565:INFO:Declaring metric variables +2024-12-04 17:37:00,568:INFO:Importing untrained model +2024-12-04 17:37:00,570:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:37:00,581:INFO:Starting cross validation +2024-12-04 17:37:00,583:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:00,655:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:00,678:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:00,693:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:00,953:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:00,953:INFO:Calculating mean and std +2024-12-04 17:37:00,953:INFO:Creating metrics dataframe +2024-12-04 17:37:00,953:INFO:Uploading results into container +2024-12-04 17:37:00,953:INFO:Uploading model into container now +2024-12-04 17:37:00,953:INFO:_master_model_container: 11 +2024-12-04 17:37:00,953:INFO:_display_container: 2 +2024-12-04 17:37:00,953:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:37:00,953:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:01,047:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:01,047:INFO:Creating metrics dataframe +2024-12-04 17:37:01,051:INFO:Initializing Decision Tree Regressor +2024-12-04 17:37:01,051:INFO:Total runtime is 0.17726639509201045 minutes +2024-12-04 17:37:01,051:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:01,051:INFO:Initializing create_model() +2024-12-04 17:37:01,051:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:01,051:INFO:Checking exceptions +2024-12-04 17:37:01,051:INFO:Importing libraries +2024-12-04 17:37:01,051:INFO:Copying training dataset +2024-12-04 17:37:01,063:INFO:Defining folds +2024-12-04 17:37:01,063:INFO:Declaring metric variables +2024-12-04 17:37:01,066:INFO:Importing untrained model +2024-12-04 17:37:01,070:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:37:01,079:INFO:Starting cross validation +2024-12-04 17:37:01,081:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:01,192:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:01,195:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:01,232:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:01,405:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:01,405:INFO:Calculating mean and std +2024-12-04 17:37:01,405:INFO:Creating metrics dataframe +2024-12-04 17:37:01,405:INFO:Uploading results into container +2024-12-04 17:37:01,409:INFO:Uploading model into container now +2024-12-04 17:37:01,409:INFO:_master_model_container: 12 +2024-12-04 17:37:01,410:INFO:_display_container: 2 +2024-12-04 17:37:01,410:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:37:01,410:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:01,496:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:01,496:INFO:Creating metrics dataframe +2024-12-04 17:37:01,501:INFO:Initializing Random Forest Regressor +2024-12-04 17:37:01,501:INFO:Total runtime is 0.18477034171422319 minutes +2024-12-04 17:37:01,510:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:01,511:INFO:Initializing create_model() +2024-12-04 17:37:01,511:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:01,511:INFO:Checking exceptions +2024-12-04 17:37:01,511:INFO:Importing libraries +2024-12-04 17:37:01,511:INFO:Copying training dataset +2024-12-04 17:37:01,517:INFO:Defining folds +2024-12-04 17:37:01,517:INFO:Declaring metric variables +2024-12-04 17:37:01,521:INFO:Importing untrained model +2024-12-04 17:37:01,525:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:37:01,532:INFO:Starting cross validation +2024-12-04 17:37:01,534:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:01,620:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:01,620:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:01,632:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:02,882:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:02,882:INFO:Calculating mean and std +2024-12-04 17:37:02,882:INFO:Creating metrics dataframe +2024-12-04 17:37:02,885:INFO:Uploading results into container +2024-12-04 17:37:02,885:INFO:Uploading model into container now +2024-12-04 17:37:02,887:INFO:_master_model_container: 13 +2024-12-04 17:37:02,887:INFO:_display_container: 2 +2024-12-04 17:37:02,887:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:37:02,887:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:02,988:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:02,988:INFO:Creating metrics dataframe +2024-12-04 17:37:02,996:INFO:Initializing Extra Trees Regressor +2024-12-04 17:37:02,996:INFO:Total runtime is 0.20968232154846186 minutes +2024-12-04 17:37:02,998:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:02,998:INFO:Initializing create_model() +2024-12-04 17:37:02,998:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:02,998:INFO:Checking exceptions +2024-12-04 17:37:02,998:INFO:Importing libraries +2024-12-04 17:37:02,998:INFO:Copying training dataset +2024-12-04 17:37:03,001:INFO:Defining folds +2024-12-04 17:37:03,001:INFO:Declaring metric variables +2024-12-04 17:37:03,008:INFO:Importing untrained model +2024-12-04 17:37:03,011:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:37:03,017:INFO:Starting cross validation +2024-12-04 17:37:03,017:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:03,127:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:03,132:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:03,161:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:04,305:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:04,305:INFO:Calculating mean and std +2024-12-04 17:37:04,305:INFO:Creating metrics dataframe +2024-12-04 17:37:04,305:INFO:Uploading results into container +2024-12-04 17:37:04,305:INFO:Uploading model into container now +2024-12-04 17:37:04,305:INFO:_master_model_container: 14 +2024-12-04 17:37:04,305:INFO:_display_container: 2 +2024-12-04 17:37:04,305:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:37:04,305:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:04,397:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:04,397:INFO:Creating metrics dataframe +2024-12-04 17:37:04,407:INFO:Initializing AdaBoost Regressor +2024-12-04 17:37:04,407:INFO:Total runtime is 0.23320519526799516 minutes +2024-12-04 17:37:04,413:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:04,413:INFO:Initializing create_model() +2024-12-04 17:37:04,413:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:04,413:INFO:Checking exceptions +2024-12-04 17:37:04,413:INFO:Importing libraries +2024-12-04 17:37:04,414:INFO:Copying training dataset +2024-12-04 17:37:04,419:INFO:Defining folds +2024-12-04 17:37:04,419:INFO:Declaring metric variables +2024-12-04 17:37:04,422:INFO:Importing untrained model +2024-12-04 17:37:04,428:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:37:04,434:INFO:Starting cross validation +2024-12-04 17:37:04,436:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:04,517:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:04,535:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:04,542:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:04,793:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:04,793:INFO:Calculating mean and std +2024-12-04 17:37:04,794:INFO:Creating metrics dataframe +2024-12-04 17:37:04,796:INFO:Uploading results into container +2024-12-04 17:37:04,797:INFO:Uploading model into container now +2024-12-04 17:37:04,797:INFO:_master_model_container: 15 +2024-12-04 17:37:04,797:INFO:_display_container: 2 +2024-12-04 17:37:04,798:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:37:04,798:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:04,890:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:04,890:INFO:Creating metrics dataframe +2024-12-04 17:37:04,901:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:37:04,901:INFO:Total runtime is 0.2414333860079447 minutes +2024-12-04 17:37:04,901:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:04,901:INFO:Initializing create_model() +2024-12-04 17:37:04,901:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:04,901:INFO:Checking exceptions +2024-12-04 17:37:04,901:INFO:Importing libraries +2024-12-04 17:37:04,901:INFO:Copying training dataset +2024-12-04 17:37:04,901:INFO:Defining folds +2024-12-04 17:37:04,901:INFO:Declaring metric variables +2024-12-04 17:37:04,910:INFO:Importing untrained model +2024-12-04 17:37:04,916:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:37:04,916:INFO:Starting cross validation +2024-12-04 17:37:04,925:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:05,005:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:05,016:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:05,028:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:05,527:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:05,527:INFO:Calculating mean and std +2024-12-04 17:37:05,528:INFO:Creating metrics dataframe +2024-12-04 17:37:05,531:INFO:Uploading results into container +2024-12-04 17:37:05,531:INFO:Uploading model into container now +2024-12-04 17:37:05,532:INFO:_master_model_container: 16 +2024-12-04 17:37:05,532:INFO:_display_container: 2 +2024-12-04 17:37:05,532:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:37:05,533:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:05,621:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:05,621:INFO:Creating metrics dataframe +2024-12-04 17:37:05,628:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:37:05,628:INFO:Total runtime is 0.25355744361877436 minutes +2024-12-04 17:37:05,632:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:05,632:INFO:Initializing create_model() +2024-12-04 17:37:05,632:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:05,632:INFO:Checking exceptions +2024-12-04 17:37:05,632:INFO:Importing libraries +2024-12-04 17:37:05,632:INFO:Copying training dataset +2024-12-04 17:37:05,637:INFO:Defining folds +2024-12-04 17:37:05,637:INFO:Declaring metric variables +2024-12-04 17:37:05,640:INFO:Importing untrained model +2024-12-04 17:37:05,645:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:37:05,652:INFO:Starting cross validation +2024-12-04 17:37:05,652:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:05,753:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:05,782:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:05,784:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:06,205:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:06,205:INFO:Calculating mean and std +2024-12-04 17:37:06,205:INFO:Creating metrics dataframe +2024-12-04 17:37:06,205:INFO:Uploading results into container +2024-12-04 17:37:06,205:INFO:Uploading model into container now +2024-12-04 17:37:06,205:INFO:_master_model_container: 17 +2024-12-04 17:37:06,205:INFO:_display_container: 2 +2024-12-04 17:37:06,210:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:37:06,210:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:06,316:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:06,316:INFO:Creating metrics dataframe +2024-12-04 17:37:06,328:INFO:Initializing Dummy Regressor +2024-12-04 17:37:06,328:INFO:Total runtime is 0.2652177532513936 minutes +2024-12-04 17:37:06,332:INFO:SubProcess create_model() called ================================== +2024-12-04 17:37:06,332:INFO:Initializing create_model() +2024-12-04 17:37:06,332:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:06,332:INFO:Checking exceptions +2024-12-04 17:37:06,332:INFO:Importing libraries +2024-12-04 17:37:06,332:INFO:Copying training dataset +2024-12-04 17:37:06,337:INFO:Defining folds +2024-12-04 17:37:06,337:INFO:Declaring metric variables +2024-12-04 17:37:06,343:INFO:Importing untrained model +2024-12-04 17:37:06,346:INFO:Dummy Regressor Imported successfully +2024-12-04 17:37:06,351:INFO:Starting cross validation +2024-12-04 17:37:06,351:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:37:06,428:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:06,445:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:06,464:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:06,609:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:37:06,609:INFO:Calculating mean and std +2024-12-04 17:37:06,610:INFO:Creating metrics dataframe +2024-12-04 17:37:06,613:INFO:Uploading results into container +2024-12-04 17:37:06,614:INFO:Uploading model into container now +2024-12-04 17:37:06,614:INFO:_master_model_container: 18 +2024-12-04 17:37:06,614:INFO:_display_container: 2 +2024-12-04 17:37:06,614:INFO:DummyRegressor() +2024-12-04 17:37:06,614:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:06,701:INFO:SubProcess create_model() end ================================== +2024-12-04 17:37:06,701:INFO:Creating metrics dataframe +2024-12-04 17:37:06,713:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:37:06,723:INFO:Initializing create_model() +2024-12-04 17:37:06,723:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:37:06,723:INFO:Checking exceptions +2024-12-04 17:37:06,725:INFO:Importing libraries +2024-12-04 17:37:06,725:INFO:Copying training dataset +2024-12-04 17:37:06,729:INFO:Defining folds +2024-12-04 17:37:06,729:INFO:Declaring metric variables +2024-12-04 17:37:06,729:INFO:Importing untrained model +2024-12-04 17:37:06,729:INFO:Declaring custom model +2024-12-04 17:37:06,729:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:37:06,729:INFO:Cross validation set to False +2024-12-04 17:37:06,729:INFO:Fitting Model +2024-12-04 17:37:06,801:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 17:37:07,306:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:37:07,306:INFO:create_model() successfully completed...................................... +2024-12-04 17:37:07,437:INFO:_master_model_container: 18 +2024-12-04 17:37:07,437:INFO:_display_container: 2 +2024-12-04 17:37:07,437:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:37:07,437:INFO:compare_models() successfully completed...................................... +2024-12-04 17:37:24,413:INFO:Initializing tune_model() +2024-12-04 17:37:24,414:INFO:tune_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-12-04 17:37:24,414:INFO:Checking exceptions +2024-12-04 17:37:24,432:INFO:Copying training dataset +2024-12-04 17:37:24,439:INFO:Checking base model +2024-12-04 17:37:24,440:INFO:Base model : Random Forest Regressor +2024-12-04 17:37:24,446:INFO:Declaring metric variables +2024-12-04 17:37:24,484:INFO:Defining Hyperparameters +2024-12-04 17:37:24,605:INFO:Tuning with n_jobs=-1 +2024-12-04 17:37:24,605:INFO:Initializing RandomizedSearchCV +2024-12-04 17:37:24,725:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:24,728:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:24,728:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:24,728:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:24,746:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:25,623:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:25,660:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:25,673:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:25,740:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:25,767:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:25,797:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:25,879:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:26,821:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:26,855:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:26,996:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:27,042:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:27,093:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:27,434:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:28,687:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:29,109:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:32,174:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:32,877:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:35,031:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:38,249:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:38,738:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:42,106:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:42,588:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:37:47,631:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:37:57,030:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:38:04,057:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:38:25,004:INFO:Initializing predict_model() +2024-12-04 17:38:25,004:INFO:predict_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001C7008437E0>) +2024-12-04 17:38:25,004:INFO:Checking exceptions +2024-12-04 17:38:25,004:INFO:Preloading libraries +2024-12-04 17:38:25,010:INFO:Set up data. +2024-12-04 17:38:25,013:INFO:Set up index. +2024-12-04 17:39:14,152:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:39:14,152:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:39:14,152:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:39:14,152:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:39:21,687:INFO:PyCaret RegressionExperiment +2024-12-04 17:39:21,687:INFO:Logging name: reg-default-name +2024-12-04 17:39:21,687:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:39:21,687:INFO:version 3.3.2 +2024-12-04 17:39:21,687:INFO:Initializing setup() +2024-12-04 17:39:21,687:INFO:self.USI: 8de8 +2024-12-04 17:39:21,687:INFO:self._variable_keys: {'X', 'memory', '_ml_usecase', 'USI', 'gpu_n_jobs_param', 'target_param', 'fold_shuffle_param', 'y_train', 'y', 'X_test', 'n_jobs_param', 'data', 'gpu_param', 'fold_groups_param', 'exp_name_log', 'X_train', 'log_plots_param', 'exp_id', 'seed', 'idx', 'html_param', 'pipeline', 'transform_target_param', 'fold_generator', '_available_plots', 'logging_param', 'y_test'} +2024-12-04 17:39:21,692:INFO:Checking environment +2024-12-04 17:39:21,692:INFO:python_version: 3.11.9 +2024-12-04 17:39:21,692:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:39:21,692:INFO:machine: AMD64 +2024-12-04 17:39:21,692:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:39:21,695:INFO:Memory: svmem(total=17007292416, available=2862436352, percent=83.2, used=14144856064, free=2862436352) +2024-12-04 17:39:21,695:INFO:Physical Core: 4 +2024-12-04 17:39:21,695:INFO:Logical Core: 8 +2024-12-04 17:39:21,695:INFO:Checking libraries +2024-12-04 17:39:21,695:INFO:System: +2024-12-04 17:39:21,695:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:39:21,695:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:39:21,695:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:39:21,695:INFO:PyCaret required dependencies: +2024-12-04 17:39:21,740:INFO: pip: 24.3.1 +2024-12-04 17:39:21,740:INFO: setuptools: 65.5.0 +2024-12-04 17:39:21,740:INFO: pycaret: 3.3.2 +2024-12-04 17:39:21,740:INFO: IPython: 8.12.3 +2024-12-04 17:39:21,740:INFO: ipywidgets: 8.1.5 +2024-12-04 17:39:21,740:INFO: tqdm: 4.67.1 +2024-12-04 17:39:21,740:INFO: numpy: 1.26.4 +2024-12-04 17:39:21,740:INFO: pandas: 2.1.4 +2024-12-04 17:39:21,740:INFO: jinja2: 3.1.4 +2024-12-04 17:39:21,740:INFO: scipy: 1.11.4 +2024-12-04 17:39:21,743:INFO: joblib: 1.3.2 +2024-12-04 17:39:21,743:INFO: sklearn: 1.4.2 +2024-12-04 17:39:21,743:INFO: pyod: 2.0.2 +2024-12-04 17:39:21,743:INFO: imblearn: 0.12.4 +2024-12-04 17:39:21,743:INFO: category_encoders: 2.6.4 +2024-12-04 17:39:21,743:INFO: lightgbm: 4.5.0 +2024-12-04 17:39:21,743:INFO: numba: 0.60.0 +2024-12-04 17:39:21,743:INFO: requests: 2.32.3 +2024-12-04 17:39:21,743:INFO: matplotlib: 3.7.5 +2024-12-04 17:39:21,743:INFO: scikitplot: 0.3.7 +2024-12-04 17:39:21,743:INFO: yellowbrick: 1.5 +2024-12-04 17:39:21,743:INFO: plotly: 5.24.1 +2024-12-04 17:39:21,743:INFO: plotly-resampler: Not installed +2024-12-04 17:39:21,743:INFO: kaleido: 0.2.1 +2024-12-04 17:39:21,744:INFO: schemdraw: 0.15 +2024-12-04 17:39:21,744:INFO: statsmodels: 0.14.4 +2024-12-04 17:39:21,744:INFO: sktime: 0.26.0 +2024-12-04 17:39:21,744:INFO: tbats: 1.1.3 +2024-12-04 17:39:21,744:INFO: pmdarima: 2.0.4 +2024-12-04 17:39:21,744:INFO: psutil: 6.1.0 +2024-12-04 17:39:21,744:INFO: markupsafe: 3.0.2 +2024-12-04 17:39:21,744:INFO: pickle5: Not installed +2024-12-04 17:39:21,744:INFO: cloudpickle: 3.1.0 +2024-12-04 17:39:21,744:INFO: deprecation: 2.1.0 +2024-12-04 17:39:21,744:INFO: xxhash: 3.5.0 +2024-12-04 17:39:21,744:INFO: wurlitzer: Not installed +2024-12-04 17:39:21,744:INFO:PyCaret optional dependencies: +2024-12-04 17:39:21,776:INFO: shap: Not installed +2024-12-04 17:39:21,776:INFO: interpret: Not installed +2024-12-04 17:39:21,776:INFO: umap: Not installed +2024-12-04 17:39:21,776:INFO: ydata_profiling: Not installed +2024-12-04 17:39:21,776:INFO: explainerdashboard: Not installed +2024-12-04 17:39:21,776:INFO: autoviz: Not installed +2024-12-04 17:39:21,776:INFO: fairlearn: Not installed +2024-12-04 17:39:21,776:INFO: deepchecks: Not installed +2024-12-04 17:39:21,776:INFO: xgboost: Not installed +2024-12-04 17:39:21,776:INFO: catboost: Not installed +2024-12-04 17:39:21,776:INFO: kmodes: Not installed +2024-12-04 17:39:21,776:INFO: mlxtend: Not installed +2024-12-04 17:39:21,776:INFO: statsforecast: Not installed +2024-12-04 17:39:21,776:INFO: tune_sklearn: Not installed +2024-12-04 17:39:21,776:INFO: ray: Not installed +2024-12-04 17:39:21,776:INFO: hyperopt: Not installed +2024-12-04 17:39:21,776:INFO: optuna: Not installed +2024-12-04 17:39:21,776:INFO: skopt: Not installed +2024-12-04 17:39:21,776:INFO: mlflow: 2.18.0 +2024-12-04 17:39:21,776:INFO: gradio: Not installed +2024-12-04 17:39:21,776:INFO: fastapi: Not installed +2024-12-04 17:39:21,776:INFO: uvicorn: 0.32.1 +2024-12-04 17:39:21,776:INFO: m2cgen: Not installed +2024-12-04 17:39:21,776:INFO: evidently: Not installed +2024-12-04 17:39:21,776:INFO: fugue: Not installed +2024-12-04 17:39:21,776:INFO: streamlit: 1.40.1 +2024-12-04 17:39:21,776:INFO: prophet: Not installed +2024-12-04 17:39:21,777:INFO:None +2024-12-04 17:39:21,777:INFO:Set up data. +2024-12-04 17:39:21,782:INFO:Set up folding strategy. +2024-12-04 17:39:21,782:INFO:Set up train/test split. +2024-12-04 17:39:21,787:INFO:Set up index. +2024-12-04 17:39:21,787:INFO:Assigning column types. +2024-12-04 17:39:21,790:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:39:21,790:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,795:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,795:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,851:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,890:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,890:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:21,890:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:21,890:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,896:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,896:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,951:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,990:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:21,990:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:21,990:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:21,992:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:39:21,997:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,001:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,051:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,090:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,090:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,090:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,096:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,101:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,152:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,193:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,193:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,193:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,193:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:39:22,201:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,253:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,294:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,294:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,294:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,303:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,353:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,394:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,395:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,395:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,395:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:39:22,453:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,495:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,495:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,495:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,559:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,596:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,596:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,596:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,596:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:39:22,653:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,696:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,696:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,753:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:39:22,796:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,796:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,796:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:39:22,896:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:22,896:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:23,001:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:23,001:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:23,003:INFO:Preparing preprocessing pipeline... +2024-12-04 17:39:23,003:INFO:Set up simple imputation. +2024-12-04 17:39:23,003:INFO:Set up encoding of categorical features. +2024-12-04 17:39:23,003:INFO:Set up column transformation. +2024-12-04 17:39:23,003:INFO:Set up feature normalization. +2024-12-04 17:39:23,122:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:39:23,132:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['annee'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_pathologie', 'nom_region'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(inc... + transformer=OneHotEncoder(cols=['nom_region'], + handle_missing='return_nan', + use_cat_names=True))), + ('rest_encoding', + TransformerWrapper(include=['nom_pathologie'], + transformer=TargetEncoder(cols=['nom_pathologie'], + handle_missing='return_nan'))), + ('transformation', + TransformerWrapper(transformer=PowerTransformer(standardize=False))), + ('normalize', + TransformerWrapper(transformer=StandardScaler()))]) +2024-12-04 17:39:23,132:INFO:Creating final display dataframe. +2024-12-04 17:39:23,398:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (11412, 4) +4 Transformed data shape (11412, 21) +5 Transformed train set shape (7988, 21) +6 Transformed test set shape (3424, 21) +7 Numeric features 1 +8 Categorical features 2 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Transformation True +16 Transformation method yeo-johnson +17 Normalize True +18 Normalize method zscore +19 Fold Generator KFold +20 Fold Number 5 +21 CPU Jobs -1 +22 Use GPU False +23 Log Experiment False +24 Experiment Name reg-default-name +25 USI 8de8 +2024-12-04 17:39:23,516:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:23,516:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:23,630:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:23,630:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:39:23,630:INFO:setup() successfully completed in 1.96s............... +2024-12-04 17:39:23,651:INFO:Initializing compare_models() +2024-12-04 17:39:23,651:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=MAE, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'MAE', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:39:23,651:INFO:Checking exceptions +2024-12-04 17:39:23,656:INFO:Preparing display monitor +2024-12-04 17:39:23,686:INFO:Initializing Linear Regression +2024-12-04 17:39:23,686:INFO:Total runtime is 0.0 minutes +2024-12-04 17:39:23,692:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:23,692:INFO:Initializing create_model() +2024-12-04 17:39:23,692:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:23,692:INFO:Checking exceptions +2024-12-04 17:39:23,693:INFO:Importing libraries +2024-12-04 17:39:23,693:INFO:Copying training dataset +2024-12-04 17:39:23,699:INFO:Defining folds +2024-12-04 17:39:23,699:INFO:Declaring metric variables +2024-12-04 17:39:23,704:INFO:Importing untrained model +2024-12-04 17:39:23,708:INFO:Linear Regression Imported successfully +2024-12-04 17:39:23,716:INFO:Starting cross validation +2024-12-04 17:39:23,726:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:27,328:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:27,343:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:27,351:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:27,547:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:27,547:INFO:Calculating mean and std +2024-12-04 17:39:27,551:INFO:Creating metrics dataframe +2024-12-04 17:39:27,553:INFO:Uploading results into container +2024-12-04 17:39:27,553:INFO:Uploading model into container now +2024-12-04 17:39:27,553:INFO:_master_model_container: 1 +2024-12-04 17:39:27,553:INFO:_display_container: 2 +2024-12-04 17:39:27,553:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:39:27,553:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:27,663:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:27,663:INFO:Creating metrics dataframe +2024-12-04 17:39:27,666:INFO:Initializing Lasso Regression +2024-12-04 17:39:27,666:INFO:Total runtime is 0.06633846362431844 minutes +2024-12-04 17:39:27,670:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:27,670:INFO:Initializing create_model() +2024-12-04 17:39:27,670:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:27,670:INFO:Checking exceptions +2024-12-04 17:39:27,670:INFO:Importing libraries +2024-12-04 17:39:27,670:INFO:Copying training dataset +2024-12-04 17:39:27,679:INFO:Defining folds +2024-12-04 17:39:27,679:INFO:Declaring metric variables +2024-12-04 17:39:27,682:INFO:Importing untrained model +2024-12-04 17:39:27,687:INFO:Lasso Regression Imported successfully +2024-12-04 17:39:27,693:INFO:Starting cross validation +2024-12-04 17:39:27,696:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:27,802:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:30,453:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:30,472:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:30,707:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:30,707:INFO:Calculating mean and std +2024-12-04 17:39:30,707:INFO:Creating metrics dataframe +2024-12-04 17:39:30,710:INFO:Uploading results into container +2024-12-04 17:39:30,710:INFO:Uploading model into container now +2024-12-04 17:39:30,712:INFO:_master_model_container: 2 +2024-12-04 17:39:30,712:INFO:_display_container: 2 +2024-12-04 17:39:30,712:INFO:Lasso(random_state=123) +2024-12-04 17:39:30,712:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:30,795:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:30,795:INFO:Creating metrics dataframe +2024-12-04 17:39:30,803:INFO:Initializing Ridge Regression +2024-12-04 17:39:30,803:INFO:Total runtime is 0.11861743132273356 minutes +2024-12-04 17:39:30,803:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:30,803:INFO:Initializing create_model() +2024-12-04 17:39:30,803:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:30,803:INFO:Checking exceptions +2024-12-04 17:39:30,803:INFO:Importing libraries +2024-12-04 17:39:30,803:INFO:Copying training dataset +2024-12-04 17:39:30,812:INFO:Defining folds +2024-12-04 17:39:30,813:INFO:Declaring metric variables +2024-12-04 17:39:30,813:INFO:Importing untrained model +2024-12-04 17:39:30,816:INFO:Ridge Regression Imported successfully +2024-12-04 17:39:30,825:INFO:Starting cross validation +2024-12-04 17:39:30,828:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:30,934:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:30,935:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:30,972:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:31,177:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:31,177:INFO:Calculating mean and std +2024-12-04 17:39:31,178:INFO:Creating metrics dataframe +2024-12-04 17:39:31,179:INFO:Uploading results into container +2024-12-04 17:39:31,179:INFO:Uploading model into container now +2024-12-04 17:39:31,179:INFO:_master_model_container: 3 +2024-12-04 17:39:31,179:INFO:_display_container: 2 +2024-12-04 17:39:31,182:INFO:Ridge(random_state=123) +2024-12-04 17:39:31,182:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:31,260:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:31,260:INFO:Creating metrics dataframe +2024-12-04 17:39:31,266:INFO:Initializing Elastic Net +2024-12-04 17:39:31,266:INFO:Total runtime is 0.12633598248163858 minutes +2024-12-04 17:39:31,266:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:31,266:INFO:Initializing create_model() +2024-12-04 17:39:31,266:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:31,266:INFO:Checking exceptions +2024-12-04 17:39:31,266:INFO:Importing libraries +2024-12-04 17:39:31,266:INFO:Copying training dataset +2024-12-04 17:39:31,275:INFO:Defining folds +2024-12-04 17:39:31,275:INFO:Declaring metric variables +2024-12-04 17:39:31,278:INFO:Importing untrained model +2024-12-04 17:39:31,282:INFO:Elastic Net Imported successfully +2024-12-04 17:39:31,287:INFO:Starting cross validation +2024-12-04 17:39:31,290:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:31,376:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:31,378:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:31,382:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:31,586:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:31,586:INFO:Calculating mean and std +2024-12-04 17:39:31,586:INFO:Creating metrics dataframe +2024-12-04 17:39:31,587:INFO:Uploading results into container +2024-12-04 17:39:31,587:INFO:Uploading model into container now +2024-12-04 17:39:31,587:INFO:_master_model_container: 4 +2024-12-04 17:39:31,587:INFO:_display_container: 2 +2024-12-04 17:39:31,590:INFO:ElasticNet(random_state=123) +2024-12-04 17:39:31,590:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:31,679:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:31,679:INFO:Creating metrics dataframe +2024-12-04 17:39:31,686:INFO:Initializing Least Angle Regression +2024-12-04 17:39:31,686:INFO:Total runtime is 0.13333501418431598 minutes +2024-12-04 17:39:31,688:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:31,688:INFO:Initializing create_model() +2024-12-04 17:39:31,688:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:31,688:INFO:Checking exceptions +2024-12-04 17:39:31,688:INFO:Importing libraries +2024-12-04 17:39:31,688:INFO:Copying training dataset +2024-12-04 17:39:31,693:INFO:Defining folds +2024-12-04 17:39:31,693:INFO:Declaring metric variables +2024-12-04 17:39:31,695:INFO:Importing untrained model +2024-12-04 17:39:31,701:INFO:Least Angle Regression Imported successfully +2024-12-04 17:39:31,701:INFO:Starting cross validation +2024-12-04 17:39:31,710:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:31,796:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:31,805:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:31,816:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:31,992:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:31,992:INFO:Calculating mean and std +2024-12-04 17:39:31,992:INFO:Creating metrics dataframe +2024-12-04 17:39:31,994:INFO:Uploading results into container +2024-12-04 17:39:31,995:INFO:Uploading model into container now +2024-12-04 17:39:31,996:INFO:_master_model_container: 5 +2024-12-04 17:39:31,996:INFO:_display_container: 2 +2024-12-04 17:39:31,996:INFO:Lars(random_state=123) +2024-12-04 17:39:31,997:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:32,087:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:32,087:INFO:Creating metrics dataframe +2024-12-04 17:39:32,093:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:39:32,093:INFO:Total runtime is 0.14012456734975176 minutes +2024-12-04 17:39:32,099:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:32,099:INFO:Initializing create_model() +2024-12-04 17:39:32,099:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:32,099:INFO:Checking exceptions +2024-12-04 17:39:32,099:INFO:Importing libraries +2024-12-04 17:39:32,099:INFO:Copying training dataset +2024-12-04 17:39:32,101:INFO:Defining folds +2024-12-04 17:39:32,101:INFO:Declaring metric variables +2024-12-04 17:39:32,105:INFO:Importing untrained model +2024-12-04 17:39:32,112:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:39:32,119:INFO:Starting cross validation +2024-12-04 17:39:32,122:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:32,246:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:32,267:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:32,267:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:32,461:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:32,461:INFO:Calculating mean and std +2024-12-04 17:39:32,461:INFO:Creating metrics dataframe +2024-12-04 17:39:32,464:INFO:Uploading results into container +2024-12-04 17:39:32,464:INFO:Uploading model into container now +2024-12-04 17:39:32,465:INFO:_master_model_container: 6 +2024-12-04 17:39:32,465:INFO:_display_container: 2 +2024-12-04 17:39:32,466:INFO:LassoLars(random_state=123) +2024-12-04 17:39:32,467:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:32,551:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:32,551:INFO:Creating metrics dataframe +2024-12-04 17:39:32,560:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:39:32,560:INFO:Total runtime is 0.14789750973383584 minutes +2024-12-04 17:39:32,563:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:32,563:INFO:Initializing create_model() +2024-12-04 17:39:32,563:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:32,563:INFO:Checking exceptions +2024-12-04 17:39:32,563:INFO:Importing libraries +2024-12-04 17:39:32,563:INFO:Copying training dataset +2024-12-04 17:39:32,566:INFO:Defining folds +2024-12-04 17:39:32,566:INFO:Declaring metric variables +2024-12-04 17:39:32,571:INFO:Importing untrained model +2024-12-04 17:39:32,571:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:39:32,581:INFO:Starting cross validation +2024-12-04 17:39:32,583:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:32,666:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:32,679:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:32,694:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:32,861:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:32,861:INFO:Calculating mean and std +2024-12-04 17:39:32,861:INFO:Creating metrics dataframe +2024-12-04 17:39:32,862:INFO:Uploading results into container +2024-12-04 17:39:32,862:INFO:Uploading model into container now +2024-12-04 17:39:32,862:INFO:_master_model_container: 7 +2024-12-04 17:39:32,862:INFO:_display_container: 2 +2024-12-04 17:39:32,862:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:39:32,862:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:32,951:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:32,951:INFO:Creating metrics dataframe +2024-12-04 17:39:32,953:INFO:Initializing Bayesian Ridge +2024-12-04 17:39:32,953:INFO:Total runtime is 0.15445207357406615 minutes +2024-12-04 17:39:32,961:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:32,961:INFO:Initializing create_model() +2024-12-04 17:39:32,961:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:32,961:INFO:Checking exceptions +2024-12-04 17:39:32,961:INFO:Importing libraries +2024-12-04 17:39:32,961:INFO:Copying training dataset +2024-12-04 17:39:32,966:INFO:Defining folds +2024-12-04 17:39:32,966:INFO:Declaring metric variables +2024-12-04 17:39:32,966:INFO:Importing untrained model +2024-12-04 17:39:32,971:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:39:32,980:INFO:Starting cross validation +2024-12-04 17:39:32,982:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:33,066:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:33,066:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:33,093:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:33,260:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:33,260:INFO:Calculating mean and std +2024-12-04 17:39:33,261:INFO:Creating metrics dataframe +2024-12-04 17:39:33,262:INFO:Uploading results into container +2024-12-04 17:39:33,263:INFO:Uploading model into container now +2024-12-04 17:39:33,264:INFO:_master_model_container: 8 +2024-12-04 17:39:33,264:INFO:_display_container: 2 +2024-12-04 17:39:33,264:INFO:BayesianRidge() +2024-12-04 17:39:33,264:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:33,349:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:33,349:INFO:Creating metrics dataframe +2024-12-04 17:39:33,359:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:39:33,359:INFO:Total runtime is 0.16121319929758707 minutes +2024-12-04 17:39:33,365:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:33,365:INFO:Initializing create_model() +2024-12-04 17:39:33,365:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:33,365:INFO:Checking exceptions +2024-12-04 17:39:33,365:INFO:Importing libraries +2024-12-04 17:39:33,365:INFO:Copying training dataset +2024-12-04 17:39:33,366:INFO:Defining folds +2024-12-04 17:39:33,366:INFO:Declaring metric variables +2024-12-04 17:39:33,366:INFO:Importing untrained model +2024-12-04 17:39:33,375:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:39:33,387:INFO:Starting cross validation +2024-12-04 17:39:33,387:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:33,471:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:33,478:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:33,487:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:33,696:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:33,696:INFO:Calculating mean and std +2024-12-04 17:39:33,696:INFO:Creating metrics dataframe +2024-12-04 17:39:33,698:INFO:Uploading results into container +2024-12-04 17:39:33,698:INFO:Uploading model into container now +2024-12-04 17:39:33,698:INFO:_master_model_container: 9 +2024-12-04 17:39:33,698:INFO:_display_container: 2 +2024-12-04 17:39:33,698:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:39:33,698:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:33,778:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:33,778:INFO:Creating metrics dataframe +2024-12-04 17:39:33,788:INFO:Initializing Huber Regressor +2024-12-04 17:39:33,788:INFO:Total runtime is 0.16836244265238443 minutes +2024-12-04 17:39:33,792:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:33,792:INFO:Initializing create_model() +2024-12-04 17:39:33,793:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:33,793:INFO:Checking exceptions +2024-12-04 17:39:33,793:INFO:Importing libraries +2024-12-04 17:39:33,793:INFO:Copying training dataset +2024-12-04 17:39:33,797:INFO:Defining folds +2024-12-04 17:39:33,797:INFO:Declaring metric variables +2024-12-04 17:39:33,801:INFO:Importing untrained model +2024-12-04 17:39:33,801:INFO:Huber Regressor Imported successfully +2024-12-04 17:39:33,813:INFO:Starting cross validation +2024-12-04 17:39:33,813:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:33,901:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:33,905:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:33,916:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:34,112:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:34,112:INFO:Calculating mean and std +2024-12-04 17:39:34,112:INFO:Creating metrics dataframe +2024-12-04 17:39:34,114:INFO:Uploading results into container +2024-12-04 17:39:34,114:INFO:Uploading model into container now +2024-12-04 17:39:34,115:INFO:_master_model_container: 10 +2024-12-04 17:39:34,115:INFO:_display_container: 2 +2024-12-04 17:39:34,115:INFO:HuberRegressor() +2024-12-04 17:39:34,115:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:34,196:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:34,196:INFO:Creating metrics dataframe +2024-12-04 17:39:34,201:INFO:Initializing K Neighbors Regressor +2024-12-04 17:39:34,201:INFO:Total runtime is 0.17524844805399575 minutes +2024-12-04 17:39:34,209:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:34,209:INFO:Initializing create_model() +2024-12-04 17:39:34,210:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:34,210:INFO:Checking exceptions +2024-12-04 17:39:34,210:INFO:Importing libraries +2024-12-04 17:39:34,210:INFO:Copying training dataset +2024-12-04 17:39:34,213:INFO:Defining folds +2024-12-04 17:39:34,213:INFO:Declaring metric variables +2024-12-04 17:39:34,216:INFO:Importing untrained model +2024-12-04 17:39:34,216:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:39:34,228:INFO:Starting cross validation +2024-12-04 17:39:34,228:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:34,315:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:34,325:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:34,328:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:34,621:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:34,621:INFO:Calculating mean and std +2024-12-04 17:39:34,621:INFO:Creating metrics dataframe +2024-12-04 17:39:34,622:INFO:Uploading results into container +2024-12-04 17:39:34,624:INFO:Uploading model into container now +2024-12-04 17:39:34,624:INFO:_master_model_container: 11 +2024-12-04 17:39:34,624:INFO:_display_container: 2 +2024-12-04 17:39:34,625:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:39:34,625:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:34,711:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:34,712:INFO:Creating metrics dataframe +2024-12-04 17:39:34,716:INFO:Initializing Decision Tree Regressor +2024-12-04 17:39:34,716:INFO:Total runtime is 0.18384261926015216 minutes +2024-12-04 17:39:34,722:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:34,724:INFO:Initializing create_model() +2024-12-04 17:39:34,724:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:34,724:INFO:Checking exceptions +2024-12-04 17:39:34,724:INFO:Importing libraries +2024-12-04 17:39:34,724:INFO:Copying training dataset +2024-12-04 17:39:34,728:INFO:Defining folds +2024-12-04 17:39:34,728:INFO:Declaring metric variables +2024-12-04 17:39:34,728:INFO:Importing untrained model +2024-12-04 17:39:34,732:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:39:34,740:INFO:Starting cross validation +2024-12-04 17:39:34,752:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:34,861:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:34,863:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:34,876:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:35,070:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:35,070:INFO:Calculating mean and std +2024-12-04 17:39:35,070:INFO:Creating metrics dataframe +2024-12-04 17:39:35,070:INFO:Uploading results into container +2024-12-04 17:39:35,070:INFO:Uploading model into container now +2024-12-04 17:39:35,070:INFO:_master_model_container: 12 +2024-12-04 17:39:35,070:INFO:_display_container: 2 +2024-12-04 17:39:35,070:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:39:35,070:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:35,151:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:35,151:INFO:Creating metrics dataframe +2024-12-04 17:39:35,162:INFO:Initializing Random Forest Regressor +2024-12-04 17:39:35,162:INFO:Total runtime is 0.19127667347590127 minutes +2024-12-04 17:39:35,167:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:35,167:INFO:Initializing create_model() +2024-12-04 17:39:35,167:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:35,167:INFO:Checking exceptions +2024-12-04 17:39:35,167:INFO:Importing libraries +2024-12-04 17:39:35,167:INFO:Copying training dataset +2024-12-04 17:39:35,172:INFO:Defining folds +2024-12-04 17:39:35,172:INFO:Declaring metric variables +2024-12-04 17:39:35,172:INFO:Importing untrained model +2024-12-04 17:39:35,178:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:39:35,182:INFO:Starting cross validation +2024-12-04 17:39:35,187:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:35,272:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:35,275:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:35,287:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:36,529:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:36,529:INFO:Calculating mean and std +2024-12-04 17:39:36,529:INFO:Creating metrics dataframe +2024-12-04 17:39:36,532:INFO:Uploading results into container +2024-12-04 17:39:36,532:INFO:Uploading model into container now +2024-12-04 17:39:36,532:INFO:_master_model_container: 13 +2024-12-04 17:39:36,532:INFO:_display_container: 2 +2024-12-04 17:39:36,532:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:39:36,532:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:36,613:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:36,613:INFO:Creating metrics dataframe +2024-12-04 17:39:36,619:INFO:Initializing Extra Trees Regressor +2024-12-04 17:39:36,619:INFO:Total runtime is 0.21554487943649292 minutes +2024-12-04 17:39:36,626:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:36,626:INFO:Initializing create_model() +2024-12-04 17:39:36,626:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:36,626:INFO:Checking exceptions +2024-12-04 17:39:36,626:INFO:Importing libraries +2024-12-04 17:39:36,626:INFO:Copying training dataset +2024-12-04 17:39:36,628:INFO:Defining folds +2024-12-04 17:39:36,628:INFO:Declaring metric variables +2024-12-04 17:39:36,632:INFO:Importing untrained model +2024-12-04 17:39:36,636:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:39:36,643:INFO:Starting cross validation +2024-12-04 17:39:36,645:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:36,747:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:36,752:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:36,783:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:37,983:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:37,983:INFO:Calculating mean and std +2024-12-04 17:39:37,983:INFO:Creating metrics dataframe +2024-12-04 17:39:37,983:INFO:Uploading results into container +2024-12-04 17:39:37,983:INFO:Uploading model into container now +2024-12-04 17:39:37,983:INFO:_master_model_container: 14 +2024-12-04 17:39:37,983:INFO:_display_container: 2 +2024-12-04 17:39:37,987:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:39:37,987:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:38,082:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:38,082:INFO:Creating metrics dataframe +2024-12-04 17:39:38,093:INFO:Initializing AdaBoost Regressor +2024-12-04 17:39:38,093:INFO:Total runtime is 0.2401130477587382 minutes +2024-12-04 17:39:38,096:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:38,096:INFO:Initializing create_model() +2024-12-04 17:39:38,096:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:38,096:INFO:Checking exceptions +2024-12-04 17:39:38,096:INFO:Importing libraries +2024-12-04 17:39:38,096:INFO:Copying training dataset +2024-12-04 17:39:38,103:INFO:Defining folds +2024-12-04 17:39:38,103:INFO:Declaring metric variables +2024-12-04 17:39:38,103:INFO:Importing untrained model +2024-12-04 17:39:38,113:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:39:38,117:INFO:Starting cross validation +2024-12-04 17:39:38,117:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:38,205:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:38,221:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:38,221:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:38,500:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:38,501:INFO:Calculating mean and std +2024-12-04 17:39:38,501:INFO:Creating metrics dataframe +2024-12-04 17:39:38,503:INFO:Uploading results into container +2024-12-04 17:39:38,504:INFO:Uploading model into container now +2024-12-04 17:39:38,505:INFO:_master_model_container: 15 +2024-12-04 17:39:38,505:INFO:_display_container: 2 +2024-12-04 17:39:38,505:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:39:38,505:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:38,652:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:38,652:INFO:Creating metrics dataframe +2024-12-04 17:39:38,665:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:39:38,665:INFO:Total runtime is 0.24964631001154583 minutes +2024-12-04 17:39:38,667:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:38,668:INFO:Initializing create_model() +2024-12-04 17:39:38,668:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:38,668:INFO:Checking exceptions +2024-12-04 17:39:38,668:INFO:Importing libraries +2024-12-04 17:39:38,668:INFO:Copying training dataset +2024-12-04 17:39:38,669:INFO:Defining folds +2024-12-04 17:39:38,669:INFO:Declaring metric variables +2024-12-04 17:39:38,678:INFO:Importing untrained model +2024-12-04 17:39:38,680:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:39:38,687:INFO:Starting cross validation +2024-12-04 17:39:38,690:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:38,780:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:38,790:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:38,796:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:39,396:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:39,396:INFO:Calculating mean and std +2024-12-04 17:39:39,397:INFO:Creating metrics dataframe +2024-12-04 17:39:39,399:INFO:Uploading results into container +2024-12-04 17:39:39,399:INFO:Uploading model into container now +2024-12-04 17:39:39,399:INFO:_master_model_container: 16 +2024-12-04 17:39:39,399:INFO:_display_container: 2 +2024-12-04 17:39:39,401:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:39:39,401:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:39,482:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:39,487:INFO:Creating metrics dataframe +2024-12-04 17:39:39,495:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:39:39,496:INFO:Total runtime is 0.2635099132855733 minutes +2024-12-04 17:39:39,499:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:39,501:INFO:Initializing create_model() +2024-12-04 17:39:39,501:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:39,501:INFO:Checking exceptions +2024-12-04 17:39:39,501:INFO:Importing libraries +2024-12-04 17:39:39,501:INFO:Copying training dataset +2024-12-04 17:39:39,505:INFO:Defining folds +2024-12-04 17:39:39,505:INFO:Declaring metric variables +2024-12-04 17:39:39,510:INFO:Importing untrained model +2024-12-04 17:39:39,516:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:39:39,518:INFO:Starting cross validation +2024-12-04 17:39:39,525:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:39,616:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:39,616:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:39,632:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:40,127:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:40,128:INFO:Calculating mean and std +2024-12-04 17:39:40,130:INFO:Creating metrics dataframe +2024-12-04 17:39:40,132:INFO:Uploading results into container +2024-12-04 17:39:40,133:INFO:Uploading model into container now +2024-12-04 17:39:40,133:INFO:_master_model_container: 17 +2024-12-04 17:39:40,133:INFO:_display_container: 2 +2024-12-04 17:39:40,134:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:39:40,134:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:40,251:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:40,251:INFO:Creating metrics dataframe +2024-12-04 17:39:40,260:INFO:Initializing Dummy Regressor +2024-12-04 17:39:40,260:INFO:Total runtime is 0.276235834757487 minutes +2024-12-04 17:39:40,266:INFO:SubProcess create_model() called ================================== +2024-12-04 17:39:40,266:INFO:Initializing create_model() +2024-12-04 17:39:40,266:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:40,266:INFO:Checking exceptions +2024-12-04 17:39:40,266:INFO:Importing libraries +2024-12-04 17:39:40,266:INFO:Copying training dataset +2024-12-04 17:39:40,270:INFO:Defining folds +2024-12-04 17:39:40,270:INFO:Declaring metric variables +2024-12-04 17:39:40,275:INFO:Importing untrained model +2024-12-04 17:39:40,296:INFO:Dummy Regressor Imported successfully +2024-12-04 17:39:40,323:INFO:Starting cross validation +2024-12-04 17:39:40,326:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:39:40,420:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:40,425:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:39:40,443:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:39:40,594:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:39:40,594:INFO:Calculating mean and std +2024-12-04 17:39:40,596:INFO:Creating metrics dataframe +2024-12-04 17:39:40,596:INFO:Uploading results into container +2024-12-04 17:39:40,596:INFO:Uploading model into container now +2024-12-04 17:39:40,596:INFO:_master_model_container: 18 +2024-12-04 17:39:40,596:INFO:_display_container: 2 +2024-12-04 17:39:40,596:INFO:DummyRegressor() +2024-12-04 17:39:40,596:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:40,678:INFO:SubProcess create_model() end ================================== +2024-12-04 17:39:40,678:INFO:Creating metrics dataframe +2024-12-04 17:39:40,685:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:39:40,695:INFO:Initializing create_model() +2024-12-04 17:39:40,695:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:39:40,695:INFO:Checking exceptions +2024-12-04 17:39:40,695:INFO:Importing libraries +2024-12-04 17:39:40,695:INFO:Copying training dataset +2024-12-04 17:39:40,700:INFO:Defining folds +2024-12-04 17:39:40,700:INFO:Declaring metric variables +2024-12-04 17:39:40,700:INFO:Importing untrained model +2024-12-04 17:39:40,700:INFO:Declaring custom model +2024-12-04 17:39:40,700:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:39:40,700:INFO:Cross validation set to False +2024-12-04 17:39:40,700:INFO:Fitting Model +2024-12-04 17:39:40,763:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 17:39:41,225:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:39:41,225:INFO:create_model() successfully completed...................................... +2024-12-04 17:39:41,343:INFO:_master_model_container: 18 +2024-12-04 17:39:41,344:INFO:_display_container: 2 +2024-12-04 17:39:41,344:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:39:41,344:INFO:compare_models() successfully completed...................................... +2024-12-04 17:39:48,785:INFO:Initializing predict_model() +2024-12-04 17:39:48,785:INFO:predict_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001E2CC2D9440>) +2024-12-04 17:39:48,787:INFO:Checking exceptions +2024-12-04 17:39:48,787:INFO:Preloading libraries +2024-12-04 17:39:48,788:INFO:Set up data. +2024-12-04 17:39:48,793:INFO:Set up index. +2024-12-04 17:41:32,016:INFO:Initializing compare_models() +2024-12-04 17:41:32,016:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:41:32,016:INFO:Checking exceptions +2024-12-04 17:41:32,016:INFO:Preparing display monitor +2024-12-04 17:41:32,044:INFO:Initializing Linear Regression +2024-12-04 17:41:32,044:INFO:Total runtime is 0.0 minutes +2024-12-04 17:41:32,048:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:32,049:INFO:Initializing create_model() +2024-12-04 17:41:32,049:INFO:create_model(self=, estimator=lr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:32,049:INFO:Checking exceptions +2024-12-04 17:41:32,049:INFO:Importing libraries +2024-12-04 17:41:32,049:INFO:Copying training dataset +2024-12-04 17:41:32,053:INFO:Defining folds +2024-12-04 17:41:32,053:INFO:Declaring metric variables +2024-12-04 17:41:32,057:INFO:Importing untrained model +2024-12-04 17:41:32,063:INFO:Linear Regression Imported successfully +2024-12-04 17:41:32,066:INFO:Starting cross validation +2024-12-04 17:41:32,066:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:32,221:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:32,221:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:32,261:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:32,436:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:32,436:INFO:Calculating mean and std +2024-12-04 17:41:32,436:INFO:Creating metrics dataframe +2024-12-04 17:41:32,437:INFO:Uploading results into container +2024-12-04 17:41:32,437:INFO:Uploading model into container now +2024-12-04 17:41:32,438:INFO:_master_model_container: 19 +2024-12-04 17:41:32,438:INFO:_display_container: 3 +2024-12-04 17:41:32,438:INFO:LinearRegression(n_jobs=-1) +2024-12-04 17:41:32,438:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:32,528:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:32,528:INFO:Creating metrics dataframe +2024-12-04 17:41:32,532:INFO:Initializing Lasso Regression +2024-12-04 17:41:32,532:INFO:Total runtime is 0.008132604757944743 minutes +2024-12-04 17:41:32,535:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:32,535:INFO:Initializing create_model() +2024-12-04 17:41:32,537:INFO:create_model(self=, estimator=lasso, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:32,537:INFO:Checking exceptions +2024-12-04 17:41:32,538:INFO:Importing libraries +2024-12-04 17:41:32,538:INFO:Copying training dataset +2024-12-04 17:41:32,540:INFO:Defining folds +2024-12-04 17:41:32,540:INFO:Declaring metric variables +2024-12-04 17:41:32,542:INFO:Importing untrained model +2024-12-04 17:41:32,547:INFO:Lasso Regression Imported successfully +2024-12-04 17:41:32,552:INFO:Starting cross validation +2024-12-04 17:41:32,552:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:32,644:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:32,649:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:32,663:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:32,903:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:32,903:INFO:Calculating mean and std +2024-12-04 17:41:32,903:INFO:Creating metrics dataframe +2024-12-04 17:41:32,905:INFO:Uploading results into container +2024-12-04 17:41:32,905:INFO:Uploading model into container now +2024-12-04 17:41:32,905:INFO:_master_model_container: 20 +2024-12-04 17:41:32,905:INFO:_display_container: 3 +2024-12-04 17:41:32,905:INFO:Lasso(random_state=123) +2024-12-04 17:41:32,905:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:32,993:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:32,993:INFO:Creating metrics dataframe +2024-12-04 17:41:32,996:INFO:Initializing Ridge Regression +2024-12-04 17:41:32,996:INFO:Total runtime is 0.01586269537607829 minutes +2024-12-04 17:41:33,002:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:33,002:INFO:Initializing create_model() +2024-12-04 17:41:33,002:INFO:create_model(self=, estimator=ridge, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:33,002:INFO:Checking exceptions +2024-12-04 17:41:33,002:INFO:Importing libraries +2024-12-04 17:41:33,002:INFO:Copying training dataset +2024-12-04 17:41:33,002:INFO:Defining folds +2024-12-04 17:41:33,008:INFO:Declaring metric variables +2024-12-04 17:41:33,011:INFO:Importing untrained model +2024-12-04 17:41:33,015:INFO:Ridge Regression Imported successfully +2024-12-04 17:41:33,022:INFO:Starting cross validation +2024-12-04 17:41:33,022:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:33,128:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:33,144:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:33,162:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:33,317:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:33,317:INFO:Calculating mean and std +2024-12-04 17:41:33,317:INFO:Creating metrics dataframe +2024-12-04 17:41:33,317:INFO:Uploading results into container +2024-12-04 17:41:33,317:INFO:Uploading model into container now +2024-12-04 17:41:33,317:INFO:_master_model_container: 21 +2024-12-04 17:41:33,317:INFO:_display_container: 3 +2024-12-04 17:41:33,317:INFO:Ridge(random_state=123) +2024-12-04 17:41:33,317:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:33,401:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:33,401:INFO:Creating metrics dataframe +2024-12-04 17:41:33,413:INFO:Initializing Elastic Net +2024-12-04 17:41:33,413:INFO:Total runtime is 0.02281118631362915 minutes +2024-12-04 17:41:33,416:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:33,416:INFO:Initializing create_model() +2024-12-04 17:41:33,416:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:33,416:INFO:Checking exceptions +2024-12-04 17:41:33,416:INFO:Importing libraries +2024-12-04 17:41:33,416:INFO:Copying training dataset +2024-12-04 17:41:33,416:INFO:Defining folds +2024-12-04 17:41:33,416:INFO:Declaring metric variables +2024-12-04 17:41:33,416:INFO:Importing untrained model +2024-12-04 17:41:33,429:INFO:Elastic Net Imported successfully +2024-12-04 17:41:33,435:INFO:Starting cross validation +2024-12-04 17:41:33,437:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:33,519:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:33,519:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:33,528:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:33,725:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:33,725:INFO:Calculating mean and std +2024-12-04 17:41:33,725:INFO:Creating metrics dataframe +2024-12-04 17:41:33,726:INFO:Uploading results into container +2024-12-04 17:41:33,728:INFO:Uploading model into container now +2024-12-04 17:41:33,728:INFO:_master_model_container: 22 +2024-12-04 17:41:33,728:INFO:_display_container: 3 +2024-12-04 17:41:33,728:INFO:ElasticNet(random_state=123) +2024-12-04 17:41:33,728:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:33,823:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:33,823:INFO:Creating metrics dataframe +2024-12-04 17:41:33,829:INFO:Initializing Least Angle Regression +2024-12-04 17:41:33,829:INFO:Total runtime is 0.029746361573537192 minutes +2024-12-04 17:41:33,832:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:33,832:INFO:Initializing create_model() +2024-12-04 17:41:33,832:INFO:create_model(self=, estimator=lar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:33,832:INFO:Checking exceptions +2024-12-04 17:41:33,832:INFO:Importing libraries +2024-12-04 17:41:33,832:INFO:Copying training dataset +2024-12-04 17:41:33,837:INFO:Defining folds +2024-12-04 17:41:33,837:INFO:Declaring metric variables +2024-12-04 17:41:33,842:INFO:Importing untrained model +2024-12-04 17:41:33,845:INFO:Least Angle Regression Imported successfully +2024-12-04 17:41:33,851:INFO:Starting cross validation +2024-12-04 17:41:33,851:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:33,940:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:33,942:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:33,966:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:34,140:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:34,140:INFO:Calculating mean and std +2024-12-04 17:41:34,142:INFO:Creating metrics dataframe +2024-12-04 17:41:34,146:INFO:Uploading results into container +2024-12-04 17:41:34,146:INFO:Uploading model into container now +2024-12-04 17:41:34,146:INFO:_master_model_container: 23 +2024-12-04 17:41:34,146:INFO:_display_container: 3 +2024-12-04 17:41:34,146:INFO:Lars(random_state=123) +2024-12-04 17:41:34,146:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:34,237:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:34,237:INFO:Creating metrics dataframe +2024-12-04 17:41:34,243:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:41:34,243:INFO:Total runtime is 0.03665953079859416 minutes +2024-12-04 17:41:34,245:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:34,245:INFO:Initializing create_model() +2024-12-04 17:41:34,245:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:34,245:INFO:Checking exceptions +2024-12-04 17:41:34,245:INFO:Importing libraries +2024-12-04 17:41:34,245:INFO:Copying training dataset +2024-12-04 17:41:34,252:INFO:Defining folds +2024-12-04 17:41:34,252:INFO:Declaring metric variables +2024-12-04 17:41:34,258:INFO:Importing untrained model +2024-12-04 17:41:34,263:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:41:34,266:INFO:Starting cross validation +2024-12-04 17:41:34,272:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:34,364:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:34,365:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:34,390:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:34,564:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:34,564:INFO:Calculating mean and std +2024-12-04 17:41:34,564:INFO:Creating metrics dataframe +2024-12-04 17:41:34,568:INFO:Uploading results into container +2024-12-04 17:41:34,568:INFO:Uploading model into container now +2024-12-04 17:41:34,568:INFO:_master_model_container: 24 +2024-12-04 17:41:34,568:INFO:_display_container: 3 +2024-12-04 17:41:34,568:INFO:LassoLars(random_state=123) +2024-12-04 17:41:34,568:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:34,692:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:34,692:INFO:Creating metrics dataframe +2024-12-04 17:41:34,698:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:41:34,698:INFO:Total runtime is 0.04422842264175415 minutes +2024-12-04 17:41:34,701:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:34,701:INFO:Initializing create_model() +2024-12-04 17:41:34,701:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:34,701:INFO:Checking exceptions +2024-12-04 17:41:34,701:INFO:Importing libraries +2024-12-04 17:41:34,701:INFO:Copying training dataset +2024-12-04 17:41:34,701:INFO:Defining folds +2024-12-04 17:41:34,701:INFO:Declaring metric variables +2024-12-04 17:41:34,712:INFO:Importing untrained model +2024-12-04 17:41:34,717:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:41:34,718:INFO:Starting cross validation +2024-12-04 17:41:34,725:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:34,819:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:34,819:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:34,832:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:35,005:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:35,005:INFO:Calculating mean and std +2024-12-04 17:41:35,005:INFO:Creating metrics dataframe +2024-12-04 17:41:35,005:INFO:Uploading results into container +2024-12-04 17:41:35,008:INFO:Uploading model into container now +2024-12-04 17:41:35,009:INFO:_master_model_container: 25 +2024-12-04 17:41:35,009:INFO:_display_container: 3 +2024-12-04 17:41:35,009:INFO:OrthogonalMatchingPursuit() +2024-12-04 17:41:35,009:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:35,096:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:35,096:INFO:Creating metrics dataframe +2024-12-04 17:41:35,101:INFO:Initializing Bayesian Ridge +2024-12-04 17:41:35,101:INFO:Total runtime is 0.05095057090123495 minutes +2024-12-04 17:41:35,101:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:35,101:INFO:Initializing create_model() +2024-12-04 17:41:35,101:INFO:create_model(self=, estimator=br, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:35,101:INFO:Checking exceptions +2024-12-04 17:41:35,101:INFO:Importing libraries +2024-12-04 17:41:35,101:INFO:Copying training dataset +2024-12-04 17:41:35,113:INFO:Defining folds +2024-12-04 17:41:35,113:INFO:Declaring metric variables +2024-12-04 17:41:35,116:INFO:Importing untrained model +2024-12-04 17:41:35,118:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:41:35,127:INFO:Starting cross validation +2024-12-04 17:41:35,129:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:35,208:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:35,213:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:35,238:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:35,417:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:35,417:INFO:Calculating mean and std +2024-12-04 17:41:35,418:INFO:Creating metrics dataframe +2024-12-04 17:41:35,420:INFO:Uploading results into container +2024-12-04 17:41:35,421:INFO:Uploading model into container now +2024-12-04 17:41:35,421:INFO:_master_model_container: 26 +2024-12-04 17:41:35,421:INFO:_display_container: 3 +2024-12-04 17:41:35,421:INFO:BayesianRidge() +2024-12-04 17:41:35,421:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:35,508:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:35,508:INFO:Creating metrics dataframe +2024-12-04 17:41:35,515:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:41:35,515:INFO:Total runtime is 0.05784531037012736 minutes +2024-12-04 17:41:35,519:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:35,519:INFO:Initializing create_model() +2024-12-04 17:41:35,519:INFO:create_model(self=, estimator=par, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:35,519:INFO:Checking exceptions +2024-12-04 17:41:35,519:INFO:Importing libraries +2024-12-04 17:41:35,520:INFO:Copying training dataset +2024-12-04 17:41:35,525:INFO:Defining folds +2024-12-04 17:41:35,525:INFO:Declaring metric variables +2024-12-04 17:41:35,528:INFO:Importing untrained model +2024-12-04 17:41:35,528:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:41:35,538:INFO:Starting cross validation +2024-12-04 17:41:35,540:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:35,618:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:35,634:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:35,640:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:35,809:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:35,809:INFO:Calculating mean and std +2024-12-04 17:41:35,810:INFO:Creating metrics dataframe +2024-12-04 17:41:35,810:INFO:Uploading results into container +2024-12-04 17:41:35,810:INFO:Uploading model into container now +2024-12-04 17:41:35,813:INFO:_master_model_container: 27 +2024-12-04 17:41:35,813:INFO:_display_container: 3 +2024-12-04 17:41:35,813:INFO:PassiveAggressiveRegressor(random_state=123) +2024-12-04 17:41:35,813:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:35,896:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:35,896:INFO:Creating metrics dataframe +2024-12-04 17:41:35,901:INFO:Initializing Huber Regressor +2024-12-04 17:41:35,901:INFO:Total runtime is 0.06428018013636272 minutes +2024-12-04 17:41:35,910:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:35,910:INFO:Initializing create_model() +2024-12-04 17:41:35,910:INFO:create_model(self=, estimator=huber, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:35,910:INFO:Checking exceptions +2024-12-04 17:41:35,910:INFO:Importing libraries +2024-12-04 17:41:35,910:INFO:Copying training dataset +2024-12-04 17:41:35,916:INFO:Defining folds +2024-12-04 17:41:35,916:INFO:Declaring metric variables +2024-12-04 17:41:35,919:INFO:Importing untrained model +2024-12-04 17:41:35,944:INFO:Huber Regressor Imported successfully +2024-12-04 17:41:35,966:INFO:Starting cross validation +2024-12-04 17:41:35,967:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:36,051:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:36,066:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:36,066:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:36,247:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:36,247:INFO:Calculating mean and std +2024-12-04 17:41:36,247:INFO:Creating metrics dataframe +2024-12-04 17:41:36,247:INFO:Uploading results into container +2024-12-04 17:41:36,247:INFO:Uploading model into container now +2024-12-04 17:41:36,247:INFO:_master_model_container: 28 +2024-12-04 17:41:36,247:INFO:_display_container: 3 +2024-12-04 17:41:36,251:INFO:HuberRegressor() +2024-12-04 17:41:36,251:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:36,335:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:36,335:INFO:Creating metrics dataframe +2024-12-04 17:41:36,342:INFO:Initializing K Neighbors Regressor +2024-12-04 17:41:36,342:INFO:Total runtime is 0.07162993351618449 minutes +2024-12-04 17:41:36,345:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:36,345:INFO:Initializing create_model() +2024-12-04 17:41:36,345:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:36,345:INFO:Checking exceptions +2024-12-04 17:41:36,345:INFO:Importing libraries +2024-12-04 17:41:36,345:INFO:Copying training dataset +2024-12-04 17:41:36,351:INFO:Defining folds +2024-12-04 17:41:36,351:INFO:Declaring metric variables +2024-12-04 17:41:36,356:INFO:Importing untrained model +2024-12-04 17:41:36,359:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:41:36,366:INFO:Starting cross validation +2024-12-04 17:41:36,366:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:36,452:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:36,461:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:36,475:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:36,697:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:36,697:INFO:Calculating mean and std +2024-12-04 17:41:36,697:INFO:Creating metrics dataframe +2024-12-04 17:41:36,697:INFO:Uploading results into container +2024-12-04 17:41:36,697:INFO:Uploading model into container now +2024-12-04 17:41:36,697:INFO:_master_model_container: 29 +2024-12-04 17:41:36,697:INFO:_display_container: 3 +2024-12-04 17:41:36,697:INFO:KNeighborsRegressor(n_jobs=-1) +2024-12-04 17:41:36,701:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:36,785:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:36,785:INFO:Creating metrics dataframe +2024-12-04 17:41:36,794:INFO:Initializing Decision Tree Regressor +2024-12-04 17:41:36,794:INFO:Total runtime is 0.0791685144106547 minutes +2024-12-04 17:41:36,797:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:36,797:INFO:Initializing create_model() +2024-12-04 17:41:36,797:INFO:create_model(self=, estimator=dt, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:36,797:INFO:Checking exceptions +2024-12-04 17:41:36,797:INFO:Importing libraries +2024-12-04 17:41:36,797:INFO:Copying training dataset +2024-12-04 17:41:36,803:INFO:Defining folds +2024-12-04 17:41:36,803:INFO:Declaring metric variables +2024-12-04 17:41:36,805:INFO:Importing untrained model +2024-12-04 17:41:36,811:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:41:36,820:INFO:Starting cross validation +2024-12-04 17:41:36,826:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:36,901:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:36,911:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:36,916:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:37,117:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:37,117:INFO:Calculating mean and std +2024-12-04 17:41:37,117:INFO:Creating metrics dataframe +2024-12-04 17:41:37,120:INFO:Uploading results into container +2024-12-04 17:41:37,120:INFO:Uploading model into container now +2024-12-04 17:41:37,122:INFO:_master_model_container: 30 +2024-12-04 17:41:37,122:INFO:_display_container: 3 +2024-12-04 17:41:37,122:INFO:DecisionTreeRegressor(random_state=123) +2024-12-04 17:41:37,122:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:37,201:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:37,201:INFO:Creating metrics dataframe +2024-12-04 17:41:37,213:INFO:Initializing Random Forest Regressor +2024-12-04 17:41:37,213:INFO:Total runtime is 0.08614484469095865 minutes +2024-12-04 17:41:37,216:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:37,216:INFO:Initializing create_model() +2024-12-04 17:41:37,216:INFO:create_model(self=, estimator=rf, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:37,216:INFO:Checking exceptions +2024-12-04 17:41:37,216:INFO:Importing libraries +2024-12-04 17:41:37,216:INFO:Copying training dataset +2024-12-04 17:41:37,220:INFO:Defining folds +2024-12-04 17:41:37,220:INFO:Declaring metric variables +2024-12-04 17:41:37,228:INFO:Importing untrained model +2024-12-04 17:41:37,228:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:41:37,235:INFO:Starting cross validation +2024-12-04 17:41:37,237:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:37,320:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:37,320:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:37,340:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:38,656:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:38,656:INFO:Calculating mean and std +2024-12-04 17:41:38,656:INFO:Creating metrics dataframe +2024-12-04 17:41:38,656:INFO:Uploading results into container +2024-12-04 17:41:38,659:INFO:Uploading model into container now +2024-12-04 17:41:38,659:INFO:_master_model_container: 31 +2024-12-04 17:41:38,659:INFO:_display_container: 3 +2024-12-04 17:41:38,660:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:41:38,660:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:38,748:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:38,748:INFO:Creating metrics dataframe +2024-12-04 17:41:38,751:INFO:Initializing Extra Trees Regressor +2024-12-04 17:41:38,751:INFO:Total runtime is 0.1117825945218404 minutes +2024-12-04 17:41:38,758:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:38,759:INFO:Initializing create_model() +2024-12-04 17:41:38,759:INFO:create_model(self=, estimator=et, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:38,760:INFO:Checking exceptions +2024-12-04 17:41:38,760:INFO:Importing libraries +2024-12-04 17:41:38,760:INFO:Copying training dataset +2024-12-04 17:41:38,766:INFO:Defining folds +2024-12-04 17:41:38,766:INFO:Declaring metric variables +2024-12-04 17:41:38,769:INFO:Importing untrained model +2024-12-04 17:41:38,776:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:41:38,785:INFO:Starting cross validation +2024-12-04 17:41:38,788:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:38,878:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:38,885:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:38,896:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:40,185:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:40,185:INFO:Calculating mean and std +2024-12-04 17:41:40,185:INFO:Creating metrics dataframe +2024-12-04 17:41:40,188:INFO:Uploading results into container +2024-12-04 17:41:40,190:INFO:Uploading model into container now +2024-12-04 17:41:40,190:INFO:_master_model_container: 32 +2024-12-04 17:41:40,190:INFO:_display_container: 3 +2024-12-04 17:41:40,190:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:41:40,190:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:40,297:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:40,297:INFO:Creating metrics dataframe +2024-12-04 17:41:40,304:INFO:Initializing AdaBoost Regressor +2024-12-04 17:41:40,304:INFO:Total runtime is 0.137661341826121 minutes +2024-12-04 17:41:40,304:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:40,304:INFO:Initializing create_model() +2024-12-04 17:41:40,308:INFO:create_model(self=, estimator=ada, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:40,308:INFO:Checking exceptions +2024-12-04 17:41:40,308:INFO:Importing libraries +2024-12-04 17:41:40,308:INFO:Copying training dataset +2024-12-04 17:41:40,313:INFO:Defining folds +2024-12-04 17:41:40,313:INFO:Declaring metric variables +2024-12-04 17:41:40,319:INFO:Importing untrained model +2024-12-04 17:41:40,319:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:41:40,330:INFO:Starting cross validation +2024-12-04 17:41:40,332:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:40,428:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:40,437:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:40,443:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:40,719:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:40,719:INFO:Calculating mean and std +2024-12-04 17:41:40,719:INFO:Creating metrics dataframe +2024-12-04 17:41:40,719:INFO:Uploading results into container +2024-12-04 17:41:40,719:INFO:Uploading model into container now +2024-12-04 17:41:40,719:INFO:_master_model_container: 33 +2024-12-04 17:41:40,719:INFO:_display_container: 3 +2024-12-04 17:41:40,719:INFO:AdaBoostRegressor(random_state=123) +2024-12-04 17:41:40,723:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:40,812:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:40,812:INFO:Creating metrics dataframe +2024-12-04 17:41:40,817:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:41:40,817:INFO:Total runtime is 0.14622240861256916 minutes +2024-12-04 17:41:40,825:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:40,825:INFO:Initializing create_model() +2024-12-04 17:41:40,825:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:40,825:INFO:Checking exceptions +2024-12-04 17:41:40,825:INFO:Importing libraries +2024-12-04 17:41:40,825:INFO:Copying training dataset +2024-12-04 17:41:40,832:INFO:Defining folds +2024-12-04 17:41:40,832:INFO:Declaring metric variables +2024-12-04 17:41:40,832:INFO:Importing untrained model +2024-12-04 17:41:40,840:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:41:40,846:INFO:Starting cross validation +2024-12-04 17:41:40,849:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:40,957:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:40,966:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:41,001:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:41,511:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:41,511:INFO:Calculating mean and std +2024-12-04 17:41:41,511:INFO:Creating metrics dataframe +2024-12-04 17:41:41,514:INFO:Uploading results into container +2024-12-04 17:41:41,515:INFO:Uploading model into container now +2024-12-04 17:41:41,515:INFO:_master_model_container: 34 +2024-12-04 17:41:41,515:INFO:_display_container: 3 +2024-12-04 17:41:41,515:INFO:GradientBoostingRegressor(random_state=123) +2024-12-04 17:41:41,515:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:41,601:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:41,601:INFO:Creating metrics dataframe +2024-12-04 17:41:41,611:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:41:41,611:INFO:Total runtime is 0.1594441533088684 minutes +2024-12-04 17:41:41,613:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:41,613:INFO:Initializing create_model() +2024-12-04 17:41:41,613:INFO:create_model(self=, estimator=lightgbm, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:41,613:INFO:Checking exceptions +2024-12-04 17:41:41,613:INFO:Importing libraries +2024-12-04 17:41:41,613:INFO:Copying training dataset +2024-12-04 17:41:41,616:INFO:Defining folds +2024-12-04 17:41:41,616:INFO:Declaring metric variables +2024-12-04 17:41:41,625:INFO:Importing untrained model +2024-12-04 17:41:41,628:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:41:41,636:INFO:Starting cross validation +2024-12-04 17:41:41,637:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:41,722:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:41,728:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:41,746:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:42,161:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:42,161:INFO:Calculating mean and std +2024-12-04 17:41:42,162:INFO:Creating metrics dataframe +2024-12-04 17:41:42,165:INFO:Uploading results into container +2024-12-04 17:41:42,166:INFO:Uploading model into container now +2024-12-04 17:41:42,166:INFO:_master_model_container: 35 +2024-12-04 17:41:42,166:INFO:_display_container: 3 +2024-12-04 17:41:42,167:INFO:LGBMRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:41:42,167:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:42,275:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:42,275:INFO:Creating metrics dataframe +2024-12-04 17:41:42,282:INFO:Initializing Dummy Regressor +2024-12-04 17:41:42,282:INFO:Total runtime is 0.17063318888346354 minutes +2024-12-04 17:41:42,287:INFO:SubProcess create_model() called ================================== +2024-12-04 17:41:42,287:INFO:Initializing create_model() +2024-12-04 17:41:42,287:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:42,287:INFO:Checking exceptions +2024-12-04 17:41:42,287:INFO:Importing libraries +2024-12-04 17:41:42,287:INFO:Copying training dataset +2024-12-04 17:41:42,293:INFO:Defining folds +2024-12-04 17:41:42,293:INFO:Declaring metric variables +2024-12-04 17:41:42,295:INFO:Importing untrained model +2024-12-04 17:41:42,297:INFO:Dummy Regressor Imported successfully +2024-12-04 17:41:42,301:INFO:Starting cross validation +2024-12-04 17:41:42,301:INFO:Cross validating with KFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1 +2024-12-04 17:41:42,390:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:42,398:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 17:41:42,401:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py:3408: RuntimeWarning: overflow encountered in power + out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda + +2024-12-04 17:41:42,572:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning: +1 fits failed out of a total of 5. +The score on these train-test partitions for these parameters will be set to 0.0. +If these failures are not expected, you can try to debug them by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +1 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 273, in fit + X, y, _ = self._fit(X, y, routed_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 249, in _fit + fitted_transformer = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 229, in fit + self.transformer.fit(*args, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3221, in fit + self._fit(X, y=y, force_transform=False) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3274, in _fit + self.lambdas_[i] = optim_function(col) + ^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\preprocessing\_data.py", line 3463, in _yeo_johnson_optimize + return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2642, in brent + res = _minimize_scalar_brent(func, brack, args, **options) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2679, in _minimize_scalar_brent + brent.optimize() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2449, in optimize + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 2418, in get_bracket_info + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\scipy\optimize\_optimize.py", line 3048, in bracket + raise e +scipy.optimize._optimize.BracketError: The algorithm terminated without finding a valid bracket. Consider trying different initial points. + + warnings.warn(some_fits_failed_message, FitFailedWarning) + +2024-12-04 17:41:42,572:INFO:Calculating mean and std +2024-12-04 17:41:42,572:INFO:Creating metrics dataframe +2024-12-04 17:41:42,572:INFO:Uploading results into container +2024-12-04 17:41:42,575:INFO:Uploading model into container now +2024-12-04 17:41:42,575:INFO:_master_model_container: 36 +2024-12-04 17:41:42,575:INFO:_display_container: 3 +2024-12-04 17:41:42,575:INFO:DummyRegressor() +2024-12-04 17:41:42,576:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:42,663:INFO:SubProcess create_model() end ================================== +2024-12-04 17:41:42,663:INFO:Creating metrics dataframe +2024-12-04 17:41:42,666:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 17:41:42,681:INFO:Initializing create_model() +2024-12-04 17:41:42,681:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), fold=KFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:41:42,681:INFO:Checking exceptions +2024-12-04 17:41:42,684:INFO:Importing libraries +2024-12-04 17:41:42,684:INFO:Copying training dataset +2024-12-04 17:41:42,687:INFO:Defining folds +2024-12-04 17:41:42,687:INFO:Declaring metric variables +2024-12-04 17:41:42,687:INFO:Importing untrained model +2024-12-04 17:41:42,687:INFO:Declaring custom model +2024-12-04 17:41:42,687:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:41:42,687:INFO:Cross validation set to False +2024-12-04 17:41:42,687:INFO:Fitting Model +2024-12-04 17:41:42,760:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 17:41:43,246:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:41:43,246:INFO:create_model() successfully completed...................................... +2024-12-04 17:41:43,392:INFO:_master_model_container: 36 +2024-12-04 17:41:43,393:INFO:_display_container: 3 +2024-12-04 17:41:43,393:INFO:RandomForestRegressor(n_jobs=-1, random_state=123) +2024-12-04 17:41:43,393:INFO:compare_models() successfully completed...................................... +2024-12-04 17:42:08,201:INFO:Initializing predict_model() +2024-12-04 17:42:08,201:INFO:predict_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=123), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001E2CC2D94E0>) +2024-12-04 17:42:08,201:INFO:Checking exceptions +2024-12-04 17:42:08,201:INFO:Preloading libraries +2024-12-04 17:42:08,201:INFO:Set up data. +2024-12-04 17:42:08,201:INFO:Set up index. +2024-12-04 17:43:39,765:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\2385600605.py:6: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['annee_norm'] = (train_data['annee'] - 2018) / 4 # Normalisation entre 0 et 1 + +2024-12-04 17:43:40,946:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\2385600605.py:7: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['tendance'] = train_data.groupby(['nom_region', 'nom_pathologie'])['nbr_hospi'].transform(lambda x: x.diff() / x.shift()) + +2024-12-04 17:43:40,978:INFO:PyCaret RegressionExperiment +2024-12-04 17:43:40,978:INFO:Logging name: reg-default-name +2024-12-04 17:43:40,978:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:43:40,978:INFO:version 3.3.2 +2024-12-04 17:43:40,978:INFO:Initializing setup() +2024-12-04 17:43:40,978:INFO:self.USI: b248 +2024-12-04 17:43:40,978:INFO:self._variable_keys: {'X', 'memory', '_ml_usecase', 'USI', 'gpu_n_jobs_param', 'target_param', 'fold_shuffle_param', 'y_train', 'y', 'X_test', 'n_jobs_param', 'data', 'gpu_param', 'fold_groups_param', 'exp_name_log', 'X_train', 'log_plots_param', 'exp_id', 'seed', 'idx', 'html_param', 'pipeline', 'transform_target_param', 'fold_generator', '_available_plots', 'logging_param', 'y_test'} +2024-12-04 17:43:40,978:INFO:Checking environment +2024-12-04 17:43:40,978:INFO:python_version: 3.11.9 +2024-12-04 17:43:40,978:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:43:40,978:INFO:machine: AMD64 +2024-12-04 17:43:40,978:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:43:40,983:INFO:Memory: svmem(total=17007292416, available=1759129600, percent=89.7, used=15248162816, free=1759129600) +2024-12-04 17:43:40,983:INFO:Physical Core: 4 +2024-12-04 17:43:40,983:INFO:Logical Core: 8 +2024-12-04 17:43:40,983:INFO:Checking libraries +2024-12-04 17:43:40,983:INFO:System: +2024-12-04 17:43:40,983:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:43:40,983:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:43:40,983:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:43:40,983:INFO:PyCaret required dependencies: +2024-12-04 17:43:40,983:INFO: pip: 24.3.1 +2024-12-04 17:43:40,983:INFO: setuptools: 65.5.0 +2024-12-04 17:43:40,983:INFO: pycaret: 3.3.2 +2024-12-04 17:43:40,983:INFO: IPython: 8.12.3 +2024-12-04 17:43:40,983:INFO: ipywidgets: 8.1.5 +2024-12-04 17:43:40,983:INFO: tqdm: 4.67.1 +2024-12-04 17:43:40,983:INFO: numpy: 1.26.4 +2024-12-04 17:43:40,983:INFO: pandas: 2.1.4 +2024-12-04 17:43:40,983:INFO: jinja2: 3.1.4 +2024-12-04 17:43:40,983:INFO: scipy: 1.11.4 +2024-12-04 17:43:40,983:INFO: joblib: 1.3.2 +2024-12-04 17:43:40,983:INFO: sklearn: 1.4.2 +2024-12-04 17:43:40,983:INFO: pyod: 2.0.2 +2024-12-04 17:43:40,983:INFO: imblearn: 0.12.4 +2024-12-04 17:43:40,983:INFO: category_encoders: 2.6.4 +2024-12-04 17:43:40,983:INFO: lightgbm: 4.5.0 +2024-12-04 17:43:40,983:INFO: numba: 0.60.0 +2024-12-04 17:43:40,983:INFO: requests: 2.32.3 +2024-12-04 17:43:40,983:INFO: matplotlib: 3.7.5 +2024-12-04 17:43:40,983:INFO: scikitplot: 0.3.7 +2024-12-04 17:43:40,983:INFO: yellowbrick: 1.5 +2024-12-04 17:43:40,983:INFO: plotly: 5.24.1 +2024-12-04 17:43:40,983:INFO: plotly-resampler: Not installed +2024-12-04 17:43:40,983:INFO: kaleido: 0.2.1 +2024-12-04 17:43:40,983:INFO: schemdraw: 0.15 +2024-12-04 17:43:40,983:INFO: statsmodels: 0.14.4 +2024-12-04 17:43:40,983:INFO: sktime: 0.26.0 +2024-12-04 17:43:40,983:INFO: tbats: 1.1.3 +2024-12-04 17:43:40,983:INFO: pmdarima: 2.0.4 +2024-12-04 17:43:40,983:INFO: psutil: 6.1.0 +2024-12-04 17:43:40,983:INFO: markupsafe: 3.0.2 +2024-12-04 17:43:40,983:INFO: pickle5: Not installed +2024-12-04 17:43:40,983:INFO: cloudpickle: 3.1.0 +2024-12-04 17:43:40,983:INFO: deprecation: 2.1.0 +2024-12-04 17:43:40,983:INFO: xxhash: 3.5.0 +2024-12-04 17:43:40,983:INFO: wurlitzer: Not installed +2024-12-04 17:43:40,983:INFO:PyCaret optional dependencies: +2024-12-04 17:43:40,983:INFO: shap: Not installed +2024-12-04 17:43:40,983:INFO: interpret: Not installed +2024-12-04 17:43:40,983:INFO: umap: Not installed +2024-12-04 17:43:40,983:INFO: ydata_profiling: Not installed +2024-12-04 17:43:40,983:INFO: explainerdashboard: Not installed +2024-12-04 17:43:40,983:INFO: autoviz: Not installed +2024-12-04 17:43:40,983:INFO: fairlearn: Not installed +2024-12-04 17:43:40,983:INFO: deepchecks: Not installed +2024-12-04 17:43:40,983:INFO: xgboost: Not installed +2024-12-04 17:43:40,983:INFO: catboost: Not installed +2024-12-04 17:43:40,983:INFO: kmodes: Not installed +2024-12-04 17:43:40,983:INFO: mlxtend: Not installed +2024-12-04 17:43:40,983:INFO: statsforecast: Not installed +2024-12-04 17:43:40,983:INFO: tune_sklearn: Not installed +2024-12-04 17:43:40,983:INFO: ray: Not installed +2024-12-04 17:43:40,983:INFO: hyperopt: Not installed +2024-12-04 17:43:40,983:INFO: optuna: Not installed +2024-12-04 17:43:40,983:INFO: skopt: Not installed +2024-12-04 17:43:40,983:INFO: mlflow: 2.18.0 +2024-12-04 17:43:40,983:INFO: gradio: Not installed +2024-12-04 17:43:40,983:INFO: fastapi: Not installed +2024-12-04 17:43:40,983:INFO: uvicorn: 0.32.1 +2024-12-04 17:43:40,983:INFO: m2cgen: Not installed +2024-12-04 17:43:40,983:INFO: evidently: Not installed +2024-12-04 17:43:40,983:INFO: fugue: Not installed +2024-12-04 17:43:40,983:INFO: streamlit: 1.40.1 +2024-12-04 17:43:40,983:INFO: prophet: Not installed +2024-12-04 17:43:40,983:INFO:None +2024-12-04 17:43:40,983:INFO:Set up data. +2024-12-04 17:43:40,993:INFO:Set up folding strategy. +2024-12-04 17:43:40,993:INFO:Set up train/test split. +2024-12-04 17:43:40,995:INFO:Set up index. +2024-12-04 17:43:40,995:INFO:Assigning column types. +2024-12-04 17:43:40,997:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:43:40,997:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,001:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,006:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,053:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,098:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,098:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,098:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,098:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,103:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,108:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,160:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,198:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,198:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,201:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,201:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:43:41,203:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,208:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,260:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,298:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,301:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,301:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,304:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,304:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,362:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,401:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,401:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,401:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,401:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:43:41,410:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,463:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,501:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,501:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,501:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,513:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,563:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,601:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,601:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,601:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,601:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:43:41,666:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,717:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,717:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,717:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,782:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,828:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,828:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,829:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,829:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:43:41,890:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:41,928:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,928:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:41,990:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:43:42,028:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:42,028:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:42,032:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:43:42,132:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:42,132:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:42,232:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:42,232:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:43:42,232:INFO:Preparing preprocessing pipeline... +2024-12-04 17:43:42,232:INFO:Set up simple imputation. +2024-12-04 17:43:42,236:INFO:Set up encoding of categorical features. +2024-12-04 17:43:42,236:INFO:Set up column transformation. +2024-12-04 17:43:42,236:INFO:Set up feature normalization. +2024-12-04 17:44:38,738:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\387671195.py:6: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['annee_norm'] = (train_data['annee'] - 2018) / 4 # Normalisation entre 0 et 1 + +2024-12-04 17:44:39,990:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\387671195.py:7: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['tendance'] = train_data.groupby(['nom_region', 'nom_pathologie'])['nbr_hospi'].transform(lambda x: x.diff() / x.shift()) + +2024-12-04 17:44:40,011:INFO:PyCaret RegressionExperiment +2024-12-04 17:44:40,011:INFO:Logging name: reg-default-name +2024-12-04 17:44:40,011:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:44:40,011:INFO:version 3.3.2 +2024-12-04 17:44:40,011:INFO:Initializing setup() +2024-12-04 17:44:40,011:INFO:self.USI: 86ca +2024-12-04 17:44:40,011:INFO:self._variable_keys: {'X', 'memory', '_ml_usecase', 'USI', 'gpu_n_jobs_param', 'target_param', 'fold_shuffle_param', 'y_train', 'y', 'X_test', 'n_jobs_param', 'data', 'gpu_param', 'fold_groups_param', 'exp_name_log', 'X_train', 'log_plots_param', 'exp_id', 'seed', 'idx', 'html_param', 'pipeline', 'transform_target_param', 'fold_generator', '_available_plots', 'logging_param', 'y_test'} +2024-12-04 17:44:40,011:INFO:Checking environment +2024-12-04 17:44:40,011:INFO:python_version: 3.11.9 +2024-12-04 17:44:40,011:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:44:40,011:INFO:machine: AMD64 +2024-12-04 17:44:40,011:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:44:40,016:INFO:Memory: svmem(total=17007292416, available=1598279680, percent=90.6, used=15409012736, free=1598279680) +2024-12-04 17:44:40,016:INFO:Physical Core: 4 +2024-12-04 17:44:40,016:INFO:Logical Core: 8 +2024-12-04 17:44:40,016:INFO:Checking libraries +2024-12-04 17:44:40,016:INFO:System: +2024-12-04 17:44:40,016:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:44:40,016:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:44:40,016:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:44:40,016:INFO:PyCaret required dependencies: +2024-12-04 17:44:40,016:INFO: pip: 24.3.1 +2024-12-04 17:44:40,016:INFO: setuptools: 65.5.0 +2024-12-04 17:44:40,016:INFO: pycaret: 3.3.2 +2024-12-04 17:44:40,016:INFO: IPython: 8.12.3 +2024-12-04 17:44:40,016:INFO: ipywidgets: 8.1.5 +2024-12-04 17:44:40,016:INFO: tqdm: 4.67.1 +2024-12-04 17:44:40,016:INFO: numpy: 1.26.4 +2024-12-04 17:44:40,016:INFO: pandas: 2.1.4 +2024-12-04 17:44:40,016:INFO: jinja2: 3.1.4 +2024-12-04 17:44:40,016:INFO: scipy: 1.11.4 +2024-12-04 17:44:40,016:INFO: joblib: 1.3.2 +2024-12-04 17:44:40,016:INFO: sklearn: 1.4.2 +2024-12-04 17:44:40,016:INFO: pyod: 2.0.2 +2024-12-04 17:44:40,016:INFO: imblearn: 0.12.4 +2024-12-04 17:44:40,016:INFO: category_encoders: 2.6.4 +2024-12-04 17:44:40,016:INFO: lightgbm: 4.5.0 +2024-12-04 17:44:40,016:INFO: numba: 0.60.0 +2024-12-04 17:44:40,016:INFO: requests: 2.32.3 +2024-12-04 17:44:40,016:INFO: matplotlib: 3.7.5 +2024-12-04 17:44:40,016:INFO: scikitplot: 0.3.7 +2024-12-04 17:44:40,016:INFO: yellowbrick: 1.5 +2024-12-04 17:44:40,016:INFO: plotly: 5.24.1 +2024-12-04 17:44:40,016:INFO: plotly-resampler: Not installed +2024-12-04 17:44:40,016:INFO: kaleido: 0.2.1 +2024-12-04 17:44:40,016:INFO: schemdraw: 0.15 +2024-12-04 17:44:40,016:INFO: statsmodels: 0.14.4 +2024-12-04 17:44:40,016:INFO: sktime: 0.26.0 +2024-12-04 17:44:40,016:INFO: tbats: 1.1.3 +2024-12-04 17:44:40,016:INFO: pmdarima: 2.0.4 +2024-12-04 17:44:40,016:INFO: psutil: 6.1.0 +2024-12-04 17:44:40,016:INFO: markupsafe: 3.0.2 +2024-12-04 17:44:40,016:INFO: pickle5: Not installed +2024-12-04 17:44:40,016:INFO: cloudpickle: 3.1.0 +2024-12-04 17:44:40,016:INFO: deprecation: 2.1.0 +2024-12-04 17:44:40,016:INFO: xxhash: 3.5.0 +2024-12-04 17:44:40,016:INFO: wurlitzer: Not installed +2024-12-04 17:44:40,016:INFO:PyCaret optional dependencies: +2024-12-04 17:44:40,016:INFO: shap: Not installed +2024-12-04 17:44:40,016:INFO: interpret: Not installed +2024-12-04 17:44:40,016:INFO: umap: Not installed +2024-12-04 17:44:40,016:INFO: ydata_profiling: Not installed +2024-12-04 17:44:40,016:INFO: explainerdashboard: Not installed +2024-12-04 17:44:40,016:INFO: autoviz: Not installed +2024-12-04 17:44:40,016:INFO: fairlearn: Not installed +2024-12-04 17:44:40,016:INFO: deepchecks: Not installed +2024-12-04 17:44:40,016:INFO: xgboost: Not installed +2024-12-04 17:44:40,016:INFO: catboost: Not installed +2024-12-04 17:44:40,016:INFO: kmodes: Not installed +2024-12-04 17:44:40,016:INFO: mlxtend: Not installed +2024-12-04 17:44:40,016:INFO: statsforecast: Not installed +2024-12-04 17:44:40,016:INFO: tune_sklearn: Not installed +2024-12-04 17:44:40,016:INFO: ray: Not installed +2024-12-04 17:44:40,016:INFO: hyperopt: Not installed +2024-12-04 17:44:40,016:INFO: optuna: Not installed +2024-12-04 17:44:40,016:INFO: skopt: Not installed +2024-12-04 17:44:40,016:INFO: mlflow: 2.18.0 +2024-12-04 17:44:40,016:INFO: gradio: Not installed +2024-12-04 17:44:40,016:INFO: fastapi: Not installed +2024-12-04 17:44:40,016:INFO: uvicorn: 0.32.1 +2024-12-04 17:44:40,016:INFO: m2cgen: Not installed +2024-12-04 17:44:40,016:INFO: evidently: Not installed +2024-12-04 17:44:40,016:INFO: fugue: Not installed +2024-12-04 17:44:40,016:INFO: streamlit: 1.40.1 +2024-12-04 17:44:40,016:INFO: prophet: Not installed +2024-12-04 17:44:40,016:INFO:None +2024-12-04 17:44:40,016:INFO:Set up data. +2024-12-04 17:44:40,025:INFO:Set up folding strategy. +2024-12-04 17:44:40,025:INFO:Set up train/test split. +2024-12-04 17:44:40,028:INFO:Set up index. +2024-12-04 17:44:40,028:INFO:Assigning column types. +2024-12-04 17:44:40,028:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:44:40,028:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,035:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,037:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,091:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,132:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,132:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,133:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,133:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,137:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,141:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,193:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,232:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,232:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,232:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,232:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:44:40,238:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,243:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,294:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,333:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,333:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,333:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,339:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,343:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,393:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,432:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,432:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,432:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,432:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:44:40,444:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,495:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,532:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,532:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,532:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,542:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,595:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,637:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,637:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,637:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,637:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:44:40,698:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,737:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,737:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,737:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,801:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,843:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,844:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,844:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,844:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:44:40,901:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:40,943:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:40,943:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:41,001:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:44:41,046:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:41,046:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:41,046:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:44:41,145:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:41,145:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:41,246:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:41,246:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:44:41,246:INFO:Preparing preprocessing pipeline... +2024-12-04 17:44:41,246:INFO:Set up simple imputation. +2024-12-04 17:44:41,251:INFO:Set up encoding of categorical features. +2024-12-04 17:44:41,251:INFO:Set up column transformation. +2024-12-04 17:44:41,251:INFO:Set up feature normalization. +2024-12-04 17:45:24,179:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\198901384.py:6: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['annee_norm'] = (train_data['annee'] - 2018) / 4 # Normalisation entre 0 et 1 + +2024-12-04 17:45:26,451:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\198901384.py:9: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['tendance'] = train_data.groupby(['nom_region', 'nom_pathologie'])['nbr_hospi'].transform( + +2024-12-04 17:46:07,513:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\1421998165.py:6: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['annee_norm'] = (train_data['annee'] - 2018) / 4 # Normalisation entre 0 et 1 + +2024-12-04 17:46:09,626:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\1421998165.py:9: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['tendance'] = train_data.groupby(['nom_region', 'nom_pathologie'])['nbr_hospi'].transform( + +2024-12-04 17:46:47,432:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\1159644766.py:6: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['annee_norm'] = (train_data['annee'] - 2018) / 4 # Normalisation entre 0 et 1 + +2024-12-04 17:46:49,608:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\1159644766.py:9: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['tendance'] = train_data.groupby(['nom_region', 'nom_pathologie'])['nbr_hospi'].transform( + +2024-12-04 17:47:20,009:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\1208187574.py:6: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['annee_norm'] = (train_data['annee'] - 2018) / 4 # Normalisation entre 0 et 1 + +2024-12-04 17:47:22,167:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\1208187574.py:9: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + train_data['tendance'] = train_data.groupby(['nom_region', 'nom_pathologie'])['nbr_hospi'].transform( + +2024-12-04 17:49:32,227:INFO:PyCaret RegressionExperiment +2024-12-04 17:49:32,227:INFO:Logging name: reg-default-name +2024-12-04 17:49:32,227:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:49:32,227:INFO:version 3.3.2 +2024-12-04 17:49:32,227:INFO:Initializing setup() +2024-12-04 17:49:32,227:INFO:self.USI: 2213 +2024-12-04 17:49:32,227:INFO:self._variable_keys: {'X', 'memory', '_ml_usecase', 'USI', 'gpu_n_jobs_param', 'target_param', 'fold_shuffle_param', 'y_train', 'y', 'X_test', 'n_jobs_param', 'data', 'gpu_param', 'fold_groups_param', 'exp_name_log', 'X_train', 'log_plots_param', 'exp_id', 'seed', 'idx', 'html_param', 'pipeline', 'transform_target_param', 'fold_generator', '_available_plots', 'logging_param', 'y_test'} +2024-12-04 17:49:32,227:INFO:Checking environment +2024-12-04 17:49:32,227:INFO:python_version: 3.11.9 +2024-12-04 17:49:32,227:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:49:32,227:INFO:machine: AMD64 +2024-12-04 17:49:32,227:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:49:32,232:INFO:Memory: svmem(total=17007292416, available=2568400896, percent=84.9, used=14438891520, free=2568400896) +2024-12-04 17:49:32,232:INFO:Physical Core: 4 +2024-12-04 17:49:32,232:INFO:Logical Core: 8 +2024-12-04 17:49:32,232:INFO:Checking libraries +2024-12-04 17:49:32,232:INFO:System: +2024-12-04 17:49:32,232:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:49:32,232:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:49:32,232:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:49:32,232:INFO:PyCaret required dependencies: +2024-12-04 17:49:32,232:INFO: pip: 24.3.1 +2024-12-04 17:49:32,232:INFO: setuptools: 65.5.0 +2024-12-04 17:49:32,232:INFO: pycaret: 3.3.2 +2024-12-04 17:49:32,232:INFO: IPython: 8.12.3 +2024-12-04 17:49:32,232:INFO: ipywidgets: 8.1.5 +2024-12-04 17:49:32,232:INFO: tqdm: 4.67.1 +2024-12-04 17:49:32,232:INFO: numpy: 1.26.4 +2024-12-04 17:49:32,232:INFO: pandas: 2.1.4 +2024-12-04 17:49:32,232:INFO: jinja2: 3.1.4 +2024-12-04 17:49:32,232:INFO: scipy: 1.11.4 +2024-12-04 17:49:32,232:INFO: joblib: 1.3.2 +2024-12-04 17:49:32,232:INFO: sklearn: 1.4.2 +2024-12-04 17:49:32,232:INFO: pyod: 2.0.2 +2024-12-04 17:49:32,232:INFO: imblearn: 0.12.4 +2024-12-04 17:49:32,232:INFO: category_encoders: 2.6.4 +2024-12-04 17:49:32,232:INFO: lightgbm: 4.5.0 +2024-12-04 17:49:32,232:INFO: numba: 0.60.0 +2024-12-04 17:49:32,232:INFO: requests: 2.32.3 +2024-12-04 17:49:32,232:INFO: matplotlib: 3.7.5 +2024-12-04 17:49:32,232:INFO: scikitplot: 0.3.7 +2024-12-04 17:49:32,232:INFO: yellowbrick: 1.5 +2024-12-04 17:49:32,232:INFO: plotly: 5.24.1 +2024-12-04 17:49:32,232:INFO: plotly-resampler: Not installed +2024-12-04 17:49:32,232:INFO: kaleido: 0.2.1 +2024-12-04 17:49:32,232:INFO: schemdraw: 0.15 +2024-12-04 17:49:32,232:INFO: statsmodels: 0.14.4 +2024-12-04 17:49:32,232:INFO: sktime: 0.26.0 +2024-12-04 17:49:32,232:INFO: tbats: 1.1.3 +2024-12-04 17:49:32,232:INFO: pmdarima: 2.0.4 +2024-12-04 17:49:32,232:INFO: psutil: 6.1.0 +2024-12-04 17:49:32,232:INFO: markupsafe: 3.0.2 +2024-12-04 17:49:32,232:INFO: pickle5: Not installed +2024-12-04 17:49:32,232:INFO: cloudpickle: 3.1.0 +2024-12-04 17:49:32,232:INFO: deprecation: 2.1.0 +2024-12-04 17:49:32,232:INFO: xxhash: 3.5.0 +2024-12-04 17:49:32,232:INFO: wurlitzer: Not installed +2024-12-04 17:49:32,232:INFO:PyCaret optional dependencies: +2024-12-04 17:49:32,232:INFO: shap: Not installed +2024-12-04 17:49:32,232:INFO: interpret: Not installed +2024-12-04 17:49:32,232:INFO: umap: Not installed +2024-12-04 17:49:32,232:INFO: ydata_profiling: Not installed +2024-12-04 17:49:32,232:INFO: explainerdashboard: Not installed +2024-12-04 17:49:32,232:INFO: autoviz: Not installed +2024-12-04 17:49:32,232:INFO: fairlearn: Not installed +2024-12-04 17:49:32,232:INFO: deepchecks: Not installed +2024-12-04 17:49:32,232:INFO: xgboost: Not installed +2024-12-04 17:49:32,232:INFO: catboost: Not installed +2024-12-04 17:49:32,232:INFO: kmodes: Not installed +2024-12-04 17:49:32,232:INFO: mlxtend: Not installed +2024-12-04 17:49:32,232:INFO: statsforecast: Not installed +2024-12-04 17:49:32,232:INFO: tune_sklearn: Not installed +2024-12-04 17:49:32,232:INFO: ray: Not installed +2024-12-04 17:49:32,232:INFO: hyperopt: Not installed +2024-12-04 17:49:32,232:INFO: optuna: Not installed +2024-12-04 17:49:32,232:INFO: skopt: Not installed +2024-12-04 17:49:32,232:INFO: mlflow: 2.18.0 +2024-12-04 17:49:32,232:INFO: gradio: Not installed +2024-12-04 17:49:32,232:INFO: fastapi: Not installed +2024-12-04 17:49:32,232:INFO: uvicorn: 0.32.1 +2024-12-04 17:49:32,237:INFO: m2cgen: Not installed +2024-12-04 17:49:32,237:INFO: evidently: Not installed +2024-12-04 17:49:32,237:INFO: fugue: Not installed +2024-12-04 17:49:32,237:INFO: streamlit: 1.40.1 +2024-12-04 17:49:32,237:INFO: prophet: Not installed +2024-12-04 17:49:32,237:INFO:None +2024-12-04 17:49:32,237:INFO:Set up data. +2024-12-04 17:49:32,250:INFO:Set up folding strategy. +2024-12-04 17:50:12,480:INFO:PyCaret RegressionExperiment +2024-12-04 17:50:12,480:INFO:Logging name: reg-default-name +2024-12-04 17:50:12,480:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:50:12,480:INFO:version 3.3.2 +2024-12-04 17:50:12,480:INFO:Initializing setup() +2024-12-04 17:50:12,481:INFO:self.USI: 2127 +2024-12-04 17:50:12,481:INFO:self._variable_keys: {'X', 'memory', '_ml_usecase', 'USI', 'gpu_n_jobs_param', 'target_param', 'fold_shuffle_param', 'y_train', 'y', 'X_test', 'n_jobs_param', 'data', 'gpu_param', 'fold_groups_param', 'exp_name_log', 'X_train', 'log_plots_param', 'exp_id', 'seed', 'idx', 'html_param', 'pipeline', 'transform_target_param', 'fold_generator', '_available_plots', 'logging_param', 'y_test'} +2024-12-04 17:50:12,481:INFO:Checking environment +2024-12-04 17:50:12,481:INFO:python_version: 3.11.9 +2024-12-04 17:50:12,481:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:50:12,481:INFO:machine: AMD64 +2024-12-04 17:50:12,481:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:50:12,486:INFO:Memory: svmem(total=17007292416, available=2472480768, percent=85.5, used=14534811648, free=2472480768) +2024-12-04 17:50:12,486:INFO:Physical Core: 4 +2024-12-04 17:50:12,486:INFO:Logical Core: 8 +2024-12-04 17:50:12,486:INFO:Checking libraries +2024-12-04 17:50:12,486:INFO:System: +2024-12-04 17:50:12,486:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:50:12,486:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:50:12,486:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:50:12,487:INFO:PyCaret required dependencies: +2024-12-04 17:50:12,487:INFO: pip: 24.3.1 +2024-12-04 17:50:12,487:INFO: setuptools: 65.5.0 +2024-12-04 17:50:12,487:INFO: pycaret: 3.3.2 +2024-12-04 17:50:12,487:INFO: IPython: 8.12.3 +2024-12-04 17:50:12,487:INFO: ipywidgets: 8.1.5 +2024-12-04 17:50:12,487:INFO: tqdm: 4.67.1 +2024-12-04 17:50:12,487:INFO: numpy: 1.26.4 +2024-12-04 17:50:12,487:INFO: pandas: 2.1.4 +2024-12-04 17:50:12,487:INFO: jinja2: 3.1.4 +2024-12-04 17:50:12,487:INFO: scipy: 1.11.4 +2024-12-04 17:50:12,487:INFO: joblib: 1.3.2 +2024-12-04 17:50:12,487:INFO: sklearn: 1.4.2 +2024-12-04 17:50:12,488:INFO: pyod: 2.0.2 +2024-12-04 17:50:12,488:INFO: imblearn: 0.12.4 +2024-12-04 17:50:12,488:INFO: category_encoders: 2.6.4 +2024-12-04 17:50:12,488:INFO: lightgbm: 4.5.0 +2024-12-04 17:50:12,488:INFO: numba: 0.60.0 +2024-12-04 17:50:12,488:INFO: requests: 2.32.3 +2024-12-04 17:50:12,488:INFO: matplotlib: 3.7.5 +2024-12-04 17:50:12,488:INFO: scikitplot: 0.3.7 +2024-12-04 17:50:12,488:INFO: yellowbrick: 1.5 +2024-12-04 17:50:12,488:INFO: plotly: 5.24.1 +2024-12-04 17:50:12,488:INFO: plotly-resampler: Not installed +2024-12-04 17:50:12,488:INFO: kaleido: 0.2.1 +2024-12-04 17:50:12,488:INFO: schemdraw: 0.15 +2024-12-04 17:50:12,488:INFO: statsmodels: 0.14.4 +2024-12-04 17:50:12,488:INFO: sktime: 0.26.0 +2024-12-04 17:50:12,488:INFO: tbats: 1.1.3 +2024-12-04 17:50:12,488:INFO: pmdarima: 2.0.4 +2024-12-04 17:50:12,488:INFO: psutil: 6.1.0 +2024-12-04 17:50:12,488:INFO: markupsafe: 3.0.2 +2024-12-04 17:50:12,488:INFO: pickle5: Not installed +2024-12-04 17:50:12,488:INFO: cloudpickle: 3.1.0 +2024-12-04 17:50:12,488:INFO: deprecation: 2.1.0 +2024-12-04 17:50:12,488:INFO: xxhash: 3.5.0 +2024-12-04 17:50:12,488:INFO: wurlitzer: Not installed +2024-12-04 17:50:12,488:INFO:PyCaret optional dependencies: +2024-12-04 17:50:12,488:INFO: shap: Not installed +2024-12-04 17:50:12,488:INFO: interpret: Not installed +2024-12-04 17:50:12,488:INFO: umap: Not installed +2024-12-04 17:50:12,488:INFO: ydata_profiling: Not installed +2024-12-04 17:50:12,488:INFO: explainerdashboard: Not installed +2024-12-04 17:50:12,488:INFO: autoviz: Not installed +2024-12-04 17:50:12,488:INFO: fairlearn: Not installed +2024-12-04 17:50:12,488:INFO: deepchecks: Not installed +2024-12-04 17:50:12,488:INFO: xgboost: Not installed +2024-12-04 17:50:12,488:INFO: catboost: Not installed +2024-12-04 17:50:12,488:INFO: kmodes: Not installed +2024-12-04 17:50:12,488:INFO: mlxtend: Not installed +2024-12-04 17:50:12,488:INFO: statsforecast: Not installed +2024-12-04 17:50:12,488:INFO: tune_sklearn: Not installed +2024-12-04 17:50:12,489:INFO: ray: Not installed +2024-12-04 17:50:12,489:INFO: hyperopt: Not installed +2024-12-04 17:50:12,489:INFO: optuna: Not installed +2024-12-04 17:50:12,489:INFO: skopt: Not installed +2024-12-04 17:50:12,489:INFO: mlflow: 2.18.0 +2024-12-04 17:50:12,489:INFO: gradio: Not installed +2024-12-04 17:50:12,489:INFO: fastapi: Not installed +2024-12-04 17:50:12,489:INFO: uvicorn: 0.32.1 +2024-12-04 17:50:12,489:INFO: m2cgen: Not installed +2024-12-04 17:50:12,489:INFO: evidently: Not installed +2024-12-04 17:50:12,489:INFO: fugue: Not installed +2024-12-04 17:50:12,489:INFO: streamlit: 1.40.1 +2024-12-04 17:50:12,489:INFO: prophet: Not installed +2024-12-04 17:50:12,489:INFO:None +2024-12-04 17:50:12,489:INFO:Set up data. +2024-12-04 17:50:12,613:INFO:Set up folding strategy. +2024-12-04 17:50:12,613:INFO:Set up train/test split. +2024-12-04 17:50:12,613:INFO:Set up data. +2024-12-04 17:50:12,628:INFO:Set up index. +2024-12-04 17:50:12,628:INFO:Assigning column types. +2024-12-04 17:50:12,632:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:50:12,632:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,642:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,645:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,701:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,743:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,743:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:12,743:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:12,743:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,746:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,751:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,805:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,846:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,846:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:12,846:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:12,846:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:50:12,851:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,855:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,913:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,955:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,956:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:12,957:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:12,964:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:50:12,966:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,029:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,077:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,078:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,078:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,079:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:50:13,099:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,175:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,222:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,222:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,225:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,232:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,295:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,338:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,339:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,339:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,339:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:50:13,411:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,453:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,453:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,453:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,523:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,563:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,566:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,566:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,566:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:50:13,632:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,673:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,673:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,739:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:50:13,782:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,782:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,782:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:50:13,895:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:13,895:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:14,001:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:14,001:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:50:14,003:INFO:Preparing preprocessing pipeline... +2024-12-04 17:50:14,003:INFO:Set up target transformation. +2024-12-04 17:50:14,003:INFO:Set up simple imputation. +2024-12-04 17:50:14,009:INFO:Set up encoding of categorical features. +2024-12-04 17:52:19,580:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\3892644354.py:50: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1) + +2024-12-04 17:52:19,598:INFO:PyCaret RegressionExperiment +2024-12-04 17:52:19,598:INFO:Logging name: reg-default-name +2024-12-04 17:52:19,600:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:52:19,600:INFO:version 3.3.2 +2024-12-04 17:52:19,600:INFO:Initializing setup() +2024-12-04 17:52:19,600:INFO:self.USI: 9258 +2024-12-04 17:52:19,600:INFO:self._variable_keys: {'X', 'memory', '_ml_usecase', 'USI', 'gpu_n_jobs_param', 'target_param', 'fold_shuffle_param', 'y_train', 'y', 'X_test', 'n_jobs_param', 'data', 'gpu_param', 'fold_groups_param', 'exp_name_log', 'X_train', 'log_plots_param', 'exp_id', 'seed', 'idx', 'html_param', 'pipeline', 'transform_target_param', 'fold_generator', '_available_plots', 'logging_param', 'y_test'} +2024-12-04 17:52:19,600:INFO:Checking environment +2024-12-04 17:52:19,600:INFO:python_version: 3.11.9 +2024-12-04 17:52:19,600:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:52:19,600:INFO:machine: AMD64 +2024-12-04 17:52:19,600:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:52:19,601:INFO:Memory: svmem(total=17007292416, available=2745769984, percent=83.9, used=14261522432, free=2745769984) +2024-12-04 17:52:19,601:INFO:Physical Core: 4 +2024-12-04 17:52:19,601:INFO:Logical Core: 8 +2024-12-04 17:52:19,601:INFO:Checking libraries +2024-12-04 17:52:19,601:INFO:System: +2024-12-04 17:52:19,601:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:52:19,601:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:52:19,601:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:52:19,601:INFO:PyCaret required dependencies: +2024-12-04 17:52:19,601:INFO: pip: 24.3.1 +2024-12-04 17:52:19,601:INFO: setuptools: 65.5.0 +2024-12-04 17:52:19,601:INFO: pycaret: 3.3.2 +2024-12-04 17:52:19,601:INFO: IPython: 8.12.3 +2024-12-04 17:52:19,601:INFO: ipywidgets: 8.1.5 +2024-12-04 17:52:19,601:INFO: tqdm: 4.67.1 +2024-12-04 17:52:19,601:INFO: numpy: 1.26.4 +2024-12-04 17:52:19,601:INFO: pandas: 2.1.4 +2024-12-04 17:52:19,601:INFO: jinja2: 3.1.4 +2024-12-04 17:52:19,601:INFO: scipy: 1.11.4 +2024-12-04 17:52:19,601:INFO: joblib: 1.3.2 +2024-12-04 17:52:19,601:INFO: sklearn: 1.4.2 +2024-12-04 17:52:19,601:INFO: pyod: 2.0.2 +2024-12-04 17:52:19,601:INFO: imblearn: 0.12.4 +2024-12-04 17:52:19,601:INFO: category_encoders: 2.6.4 +2024-12-04 17:52:19,601:INFO: lightgbm: 4.5.0 +2024-12-04 17:52:19,601:INFO: numba: 0.60.0 +2024-12-04 17:52:19,601:INFO: requests: 2.32.3 +2024-12-04 17:52:19,601:INFO: matplotlib: 3.7.5 +2024-12-04 17:52:19,601:INFO: scikitplot: 0.3.7 +2024-12-04 17:52:19,601:INFO: yellowbrick: 1.5 +2024-12-04 17:52:19,601:INFO: plotly: 5.24.1 +2024-12-04 17:52:19,601:INFO: plotly-resampler: Not installed +2024-12-04 17:52:19,601:INFO: kaleido: 0.2.1 +2024-12-04 17:52:19,601:INFO: schemdraw: 0.15 +2024-12-04 17:52:19,601:INFO: statsmodels: 0.14.4 +2024-12-04 17:52:19,601:INFO: sktime: 0.26.0 +2024-12-04 17:52:19,601:INFO: tbats: 1.1.3 +2024-12-04 17:52:19,601:INFO: pmdarima: 2.0.4 +2024-12-04 17:52:19,601:INFO: psutil: 6.1.0 +2024-12-04 17:52:19,601:INFO: markupsafe: 3.0.2 +2024-12-04 17:52:19,601:INFO: pickle5: Not installed +2024-12-04 17:52:19,601:INFO: cloudpickle: 3.1.0 +2024-12-04 17:52:19,601:INFO: deprecation: 2.1.0 +2024-12-04 17:52:19,601:INFO: xxhash: 3.5.0 +2024-12-04 17:52:19,601:INFO: wurlitzer: Not installed +2024-12-04 17:52:19,601:INFO:PyCaret optional dependencies: +2024-12-04 17:52:19,601:INFO: shap: Not installed +2024-12-04 17:52:19,601:INFO: interpret: Not installed +2024-12-04 17:52:19,601:INFO: umap: Not installed +2024-12-04 17:52:19,601:INFO: ydata_profiling: Not installed +2024-12-04 17:52:19,601:INFO: explainerdashboard: Not installed +2024-12-04 17:52:19,601:INFO: autoviz: Not installed +2024-12-04 17:52:19,601:INFO: fairlearn: Not installed +2024-12-04 17:52:19,601:INFO: deepchecks: Not installed +2024-12-04 17:52:19,601:INFO: xgboost: Not installed +2024-12-04 17:52:19,601:INFO: catboost: Not installed +2024-12-04 17:52:19,601:INFO: kmodes: Not installed +2024-12-04 17:52:19,601:INFO: mlxtend: Not installed +2024-12-04 17:52:19,601:INFO: statsforecast: Not installed +2024-12-04 17:52:19,601:INFO: tune_sklearn: Not installed +2024-12-04 17:52:19,601:INFO: ray: Not installed +2024-12-04 17:52:19,601:INFO: hyperopt: Not installed +2024-12-04 17:52:19,601:INFO: optuna: Not installed +2024-12-04 17:52:19,601:INFO: skopt: Not installed +2024-12-04 17:52:19,601:INFO: mlflow: 2.18.0 +2024-12-04 17:52:19,609:INFO: gradio: Not installed +2024-12-04 17:52:19,609:INFO: fastapi: Not installed +2024-12-04 17:52:19,609:INFO: uvicorn: 0.32.1 +2024-12-04 17:52:19,609:INFO: m2cgen: Not installed +2024-12-04 17:52:19,609:INFO: evidently: Not installed +2024-12-04 17:52:19,609:INFO: fugue: Not installed +2024-12-04 17:52:19,609:INFO: streamlit: 1.40.1 +2024-12-04 17:52:19,609:INFO: prophet: Not installed +2024-12-04 17:52:19,609:INFO:None +2024-12-04 17:52:19,609:INFO:Set up data. +2024-12-04 17:52:19,621:INFO:Set up folding strategy. +2024-12-04 17:52:19,621:INFO:Set up train/test split. +2024-12-04 17:52:19,621:INFO:Set up data. +2024-12-04 17:52:19,637:INFO:Set up index. +2024-12-04 17:52:19,637:INFO:Assigning column types. +2024-12-04 17:52:19,645:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:52:19,645:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,653:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,653:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,716:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,756:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,756:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:19,760:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:19,760:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,763:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,766:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,825:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,865:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,866:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:19,866:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:19,866:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:52:19,871:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,875:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,929:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,966:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,966:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:19,966:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:19,971:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:52:19,978:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,032:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,066:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,066:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,066:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,066:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:52:20,080:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,135:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,175:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,175:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,175:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,183:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,238:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,278:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,278:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,278:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,278:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:52:20,349:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,394:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,394:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,394:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,463:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,501:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,501:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,501:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,501:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:52:20,567:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,612:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,613:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,680:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:52:20,716:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,716:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,716:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:52:20,830:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,830:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,940:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,940:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:20,940:INFO:Preparing preprocessing pipeline... +2024-12-04 17:52:20,940:INFO:Set up target transformation. +2024-12-04 17:52:20,940:INFO:Set up simple imputation. +2024-12-04 17:52:20,946:INFO:Set up encoding of categorical features. +2024-12-04 17:52:21,084:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:52:21,091:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['annee', 'Series'], + transformer=SimpleImputer())), + ('categorical_imputer', + Transformer... + TransformerWrapper(include=['niveau', 'cle_unique', 'sexe', + 'pathologie', 'code_pathologie', + 'nom_pathologie', 'region', + 'code_region', 'nom_region', + 'classification'], + transformer=OneHotEncoder(cols=['niveau', + 'cle_unique', + 'sexe', + 'pathologie', + 'code_pathologie', + 'nom_pathologie', + 'region', + 'code_region', + 'nom_region', + 'classification'], + handle_missing='return_nan', + use_cat_names=True)))]) +2024-12-04 17:52:21,091:INFO:Creating final display dataframe. +2024-12-04 17:52:21,514:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5, 63) +4 Transformed data shape (5, 66) +5 Transformed train set shape (4, 66) +6 Transformed test set shape (1, 66) +7 Numeric features 2 +8 Categorical features 10 +9 Rows with missing values 20.0% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator TimeSeriesSplit +19 Fold Number 3 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name reg-default-name +24 USI 9258 +2024-12-04 17:52:21,616:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:21,616:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:21,732:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:21,733:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:52:21,733:INFO:setup() successfully completed in 2.15s............... +2024-12-04 17:52:21,751:INFO:Initializing compare_models() +2024-12-04 17:52:21,751:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=MAE, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'MAE', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 17:52:21,751:INFO:Checking exceptions +2024-12-04 17:52:21,751:INFO:Preparing display monitor +2024-12-04 17:52:21,775:INFO:Initializing Linear Regression +2024-12-04 17:52:21,775:INFO:Total runtime is 0.0 minutes +2024-12-04 17:52:21,778:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:21,778:INFO:Initializing create_model() +2024-12-04 17:52:21,779:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:21,779:INFO:Checking exceptions +2024-12-04 17:52:21,779:INFO:Importing libraries +2024-12-04 17:52:21,779:INFO:Copying training dataset +2024-12-04 17:52:21,793:INFO:Defining folds +2024-12-04 17:52:21,793:INFO:Declaring metric variables +2024-12-04 17:52:21,798:INFO:Importing untrained model +2024-12-04 17:52:21,801:INFO:Linear Regression Imported successfully +2024-12-04 17:52:21,824:INFO:Starting cross validation +2024-12-04 17:52:21,831:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:25,375:WARNING:create_model() for lr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:25,377:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:25,378:INFO:Initializing create_model() +2024-12-04 17:52:25,378:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:25,378:INFO:Checking exceptions +2024-12-04 17:52:25,378:INFO:Importing libraries +2024-12-04 17:52:25,378:INFO:Copying training dataset +2024-12-04 17:52:25,396:INFO:Defining folds +2024-12-04 17:52:25,396:INFO:Declaring metric variables +2024-12-04 17:52:25,401:INFO:Importing untrained model +2024-12-04 17:52:25,405:INFO:Linear Regression Imported successfully +2024-12-04 17:52:25,415:INFO:Starting cross validation +2024-12-04 17:52:25,418:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:28,089:ERROR:create_model() for lr raised an exception or returned all 0.0: +2024-12-04 17:52:28,090:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:28,090:INFO:Initializing Lasso Regression +2024-12-04 17:52:28,090:INFO:Total runtime is 0.10526397625605265 minutes +2024-12-04 17:52:28,094:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:28,094:INFO:Initializing create_model() +2024-12-04 17:52:28,096:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:28,096:INFO:Checking exceptions +2024-12-04 17:52:28,096:INFO:Importing libraries +2024-12-04 17:52:28,096:INFO:Copying training dataset +2024-12-04 17:52:28,104:INFO:Defining folds +2024-12-04 17:52:28,104:INFO:Declaring metric variables +2024-12-04 17:52:28,113:INFO:Importing untrained model +2024-12-04 17:52:28,117:INFO:Lasso Regression Imported successfully +2024-12-04 17:52:28,127:INFO:Starting cross validation +2024-12-04 17:52:28,130:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:30,541:WARNING:create_model() for lasso raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:30,542:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:30,542:INFO:Initializing create_model() +2024-12-04 17:52:30,542:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:30,543:INFO:Checking exceptions +2024-12-04 17:52:30,543:INFO:Importing libraries +2024-12-04 17:52:30,543:INFO:Copying training dataset +2024-12-04 17:52:30,551:INFO:Defining folds +2024-12-04 17:52:30,551:INFO:Declaring metric variables +2024-12-04 17:52:30,561:INFO:Importing untrained model +2024-12-04 17:52:30,563:INFO:Lasso Regression Imported successfully +2024-12-04 17:52:30,570:INFO:Starting cross validation +2024-12-04 17:52:30,575:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:30,714:ERROR:create_model() for lasso raised an exception or returned all 0.0: +2024-12-04 17:52:30,714:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:30,716:INFO:Initializing Ridge Regression +2024-12-04 17:52:30,716:INFO:Total runtime is 0.1490296681722005 minutes +2024-12-04 17:52:30,716:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:30,716:INFO:Initializing create_model() +2024-12-04 17:52:30,716:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:30,716:INFO:Checking exceptions +2024-12-04 17:52:30,716:INFO:Importing libraries +2024-12-04 17:52:30,716:INFO:Copying training dataset +2024-12-04 17:52:30,728:INFO:Defining folds +2024-12-04 17:52:30,728:INFO:Declaring metric variables +2024-12-04 17:52:30,732:INFO:Importing untrained model +2024-12-04 17:52:30,737:INFO:Ridge Regression Imported successfully +2024-12-04 17:52:30,743:INFO:Starting cross validation +2024-12-04 17:52:30,746:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:30,863:WARNING:create_model() for ridge raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:30,863:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:30,863:INFO:Initializing create_model() +2024-12-04 17:52:30,863:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:30,863:INFO:Checking exceptions +2024-12-04 17:52:30,863:INFO:Importing libraries +2024-12-04 17:52:30,863:INFO:Copying training dataset +2024-12-04 17:52:30,871:INFO:Defining folds +2024-12-04 17:52:30,871:INFO:Declaring metric variables +2024-12-04 17:52:30,876:INFO:Importing untrained model +2024-12-04 17:52:30,878:INFO:Ridge Regression Imported successfully +2024-12-04 17:52:30,886:INFO:Starting cross validation +2024-12-04 17:52:30,888:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:31,008:ERROR:create_model() for ridge raised an exception or returned all 0.0: +2024-12-04 17:52:31,008:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:31,008:INFO:Initializing Elastic Net +2024-12-04 17:52:31,008:INFO:Total runtime is 0.15388413667678832 minutes +2024-12-04 17:52:31,012:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:31,013:INFO:Initializing create_model() +2024-12-04 17:52:31,013:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:31,013:INFO:Checking exceptions +2024-12-04 17:52:31,013:INFO:Importing libraries +2024-12-04 17:52:31,013:INFO:Copying training dataset +2024-12-04 17:52:31,016:INFO:Defining folds +2024-12-04 17:52:31,016:INFO:Declaring metric variables +2024-12-04 17:52:31,026:INFO:Importing untrained model +2024-12-04 17:52:31,028:INFO:Elastic Net Imported successfully +2024-12-04 17:52:31,062:INFO:Starting cross validation +2024-12-04 17:52:31,063:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:31,200:WARNING:create_model() for en raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:31,201:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:31,201:INFO:Initializing create_model() +2024-12-04 17:52:31,201:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:31,201:INFO:Checking exceptions +2024-12-04 17:52:31,201:INFO:Importing libraries +2024-12-04 17:52:31,201:INFO:Copying training dataset +2024-12-04 17:52:31,201:INFO:Defining folds +2024-12-04 17:52:31,210:INFO:Declaring metric variables +2024-12-04 17:52:31,213:INFO:Importing untrained model +2024-12-04 17:52:31,216:INFO:Elastic Net Imported successfully +2024-12-04 17:52:31,216:INFO:Starting cross validation +2024-12-04 17:52:31,216:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:31,343:ERROR:create_model() for en raised an exception or returned all 0.0: +2024-12-04 17:52:31,343:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:31,343:INFO:Initializing Least Angle Regression +2024-12-04 17:52:31,343:INFO:Total runtime is 0.15946954488754272 minutes +2024-12-04 17:52:31,345:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:31,345:INFO:Initializing create_model() +2024-12-04 17:52:31,345:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:31,345:INFO:Checking exceptions +2024-12-04 17:52:31,345:INFO:Importing libraries +2024-12-04 17:52:31,345:INFO:Copying training dataset +2024-12-04 17:52:31,357:INFO:Defining folds +2024-12-04 17:52:31,357:INFO:Declaring metric variables +2024-12-04 17:52:31,362:INFO:Importing untrained model +2024-12-04 17:52:31,363:INFO:Least Angle Regression Imported successfully +2024-12-04 17:52:31,366:INFO:Starting cross validation +2024-12-04 17:52:31,366:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:31,501:WARNING:create_model() for lar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:31,501:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:31,501:INFO:Initializing create_model() +2024-12-04 17:52:31,501:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:31,501:INFO:Checking exceptions +2024-12-04 17:52:31,501:INFO:Importing libraries +2024-12-04 17:52:31,501:INFO:Copying training dataset +2024-12-04 17:52:31,510:INFO:Defining folds +2024-12-04 17:52:31,510:INFO:Declaring metric variables +2024-12-04 17:52:31,514:INFO:Importing untrained model +2024-12-04 17:52:31,516:INFO:Least Angle Regression Imported successfully +2024-12-04 17:52:31,516:INFO:Starting cross validation +2024-12-04 17:52:31,528:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:31,645:ERROR:create_model() for lar raised an exception or returned all 0.0: +2024-12-04 17:52:31,645:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:31,645:INFO:Initializing Lasso Least Angle Regression +2024-12-04 17:52:31,645:INFO:Total runtime is 0.16451462507247924 minutes +2024-12-04 17:52:31,648:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:31,648:INFO:Initializing create_model() +2024-12-04 17:52:31,648:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:31,648:INFO:Checking exceptions +2024-12-04 17:52:31,650:INFO:Importing libraries +2024-12-04 17:52:31,651:INFO:Copying training dataset +2024-12-04 17:52:31,651:INFO:Defining folds +2024-12-04 17:52:31,660:INFO:Declaring metric variables +2024-12-04 17:52:31,665:INFO:Importing untrained model +2024-12-04 17:52:31,666:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:52:31,666:INFO:Starting cross validation +2024-12-04 17:52:31,675:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:31,805:WARNING:create_model() for llar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:31,806:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:31,806:INFO:Initializing create_model() +2024-12-04 17:52:31,806:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:31,806:INFO:Checking exceptions +2024-12-04 17:52:31,806:INFO:Importing libraries +2024-12-04 17:52:31,806:INFO:Copying training dataset +2024-12-04 17:52:31,814:INFO:Defining folds +2024-12-04 17:52:31,814:INFO:Declaring metric variables +2024-12-04 17:52:31,817:INFO:Importing untrained model +2024-12-04 17:52:31,821:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 17:52:31,830:INFO:Starting cross validation +2024-12-04 17:52:31,832:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:31,992:ERROR:create_model() for llar raised an exception or returned all 0.0: +2024-12-04 17:52:31,993:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:31,993:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 17:52:31,993:INFO:Total runtime is 0.17030224402745564 minutes +2024-12-04 17:52:31,995:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:31,995:INFO:Initializing create_model() +2024-12-04 17:52:31,995:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:31,995:INFO:Checking exceptions +2024-12-04 17:52:31,995:INFO:Importing libraries +2024-12-04 17:52:31,995:INFO:Copying training dataset +2024-12-04 17:52:32,001:INFO:Defining folds +2024-12-04 17:52:32,001:INFO:Declaring metric variables +2024-12-04 17:52:32,010:INFO:Importing untrained model +2024-12-04 17:52:32,013:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:52:32,017:INFO:Starting cross validation +2024-12-04 17:52:32,022:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:32,143:WARNING:create_model() for omp raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:32,143:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:32,143:INFO:Initializing create_model() +2024-12-04 17:52:32,143:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:32,143:INFO:Checking exceptions +2024-12-04 17:52:32,143:INFO:Importing libraries +2024-12-04 17:52:32,143:INFO:Copying training dataset +2024-12-04 17:52:32,151:INFO:Defining folds +2024-12-04 17:52:32,151:INFO:Declaring metric variables +2024-12-04 17:52:32,151:INFO:Importing untrained model +2024-12-04 17:52:32,160:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 17:52:32,166:INFO:Starting cross validation +2024-12-04 17:52:32,166:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:32,288:ERROR:create_model() for omp raised an exception or returned all 0.0: +2024-12-04 17:52:32,288:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:32,288:INFO:Initializing Bayesian Ridge +2024-12-04 17:52:32,290:INFO:Total runtime is 0.17525982062021891 minutes +2024-12-04 17:52:32,292:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:32,292:INFO:Initializing create_model() +2024-12-04 17:52:32,292:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:32,292:INFO:Checking exceptions +2024-12-04 17:52:32,292:INFO:Importing libraries +2024-12-04 17:52:32,292:INFO:Copying training dataset +2024-12-04 17:52:32,301:INFO:Defining folds +2024-12-04 17:52:32,301:INFO:Declaring metric variables +2024-12-04 17:52:32,301:INFO:Importing untrained model +2024-12-04 17:52:32,307:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:52:32,313:INFO:Starting cross validation +2024-12-04 17:52:32,316:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:32,436:WARNING:create_model() for br raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:32,436:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:32,437:INFO:Initializing create_model() +2024-12-04 17:52:32,437:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:32,437:INFO:Checking exceptions +2024-12-04 17:52:32,437:INFO:Importing libraries +2024-12-04 17:52:32,437:INFO:Copying training dataset +2024-12-04 17:52:32,448:INFO:Defining folds +2024-12-04 17:52:32,448:INFO:Declaring metric variables +2024-12-04 17:52:32,452:INFO:Importing untrained model +2024-12-04 17:52:32,452:INFO:Bayesian Ridge Imported successfully +2024-12-04 17:52:32,463:INFO:Starting cross validation +2024-12-04 17:52:32,466:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:32,587:ERROR:create_model() for br raised an exception or returned all 0.0: +2024-12-04 17:52:32,587:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:32,587:INFO:Initializing Passive Aggressive Regressor +2024-12-04 17:52:32,587:INFO:Total runtime is 0.18021513223648072 minutes +2024-12-04 17:52:32,590:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:32,590:INFO:Initializing create_model() +2024-12-04 17:52:32,590:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:32,590:INFO:Checking exceptions +2024-12-04 17:52:32,590:INFO:Importing libraries +2024-12-04 17:52:32,590:INFO:Copying training dataset +2024-12-04 17:52:32,601:INFO:Defining folds +2024-12-04 17:52:32,601:INFO:Declaring metric variables +2024-12-04 17:52:32,601:INFO:Importing untrained model +2024-12-04 17:52:32,606:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:52:32,613:INFO:Starting cross validation +2024-12-04 17:52:32,616:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:32,784:WARNING:create_model() for par raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:32,785:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:32,785:INFO:Initializing create_model() +2024-12-04 17:52:32,785:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:32,785:INFO:Checking exceptions +2024-12-04 17:52:32,785:INFO:Importing libraries +2024-12-04 17:52:32,785:INFO:Copying training dataset +2024-12-04 17:52:32,795:INFO:Defining folds +2024-12-04 17:52:32,795:INFO:Declaring metric variables +2024-12-04 17:52:32,798:INFO:Importing untrained model +2024-12-04 17:52:32,800:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 17:52:32,805:INFO:Starting cross validation +2024-12-04 17:52:32,811:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:32,938:ERROR:create_model() for par raised an exception or returned all 0.0: +2024-12-04 17:52:32,938:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:32,940:INFO:Initializing Huber Regressor +2024-12-04 17:52:32,940:INFO:Total runtime is 0.18608482678731283 minutes +2024-12-04 17:52:32,941:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:32,941:INFO:Initializing create_model() +2024-12-04 17:52:32,941:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:32,941:INFO:Checking exceptions +2024-12-04 17:52:32,941:INFO:Importing libraries +2024-12-04 17:52:32,941:INFO:Copying training dataset +2024-12-04 17:52:32,951:INFO:Defining folds +2024-12-04 17:52:32,951:INFO:Declaring metric variables +2024-12-04 17:52:32,957:INFO:Importing untrained model +2024-12-04 17:52:32,966:INFO:Huber Regressor Imported successfully +2024-12-04 17:52:32,966:INFO:Starting cross validation +2024-12-04 17:52:32,974:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:33,136:WARNING:create_model() for huber raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:33,136:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:33,136:INFO:Initializing create_model() +2024-12-04 17:52:33,136:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:33,136:INFO:Checking exceptions +2024-12-04 17:52:33,136:INFO:Importing libraries +2024-12-04 17:52:33,137:INFO:Copying training dataset +2024-12-04 17:52:33,146:INFO:Defining folds +2024-12-04 17:52:33,146:INFO:Declaring metric variables +2024-12-04 17:52:33,146:INFO:Importing untrained model +2024-12-04 17:52:33,151:INFO:Huber Regressor Imported successfully +2024-12-04 17:52:33,154:INFO:Starting cross validation +2024-12-04 17:52:33,161:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:33,293:ERROR:create_model() for huber raised an exception or returned all 0.0: +2024-12-04 17:52:33,294:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:33,295:INFO:Initializing K Neighbors Regressor +2024-12-04 17:52:33,295:INFO:Total runtime is 0.19201133648554486 minutes +2024-12-04 17:52:33,299:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:33,300:INFO:Initializing create_model() +2024-12-04 17:52:33,300:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:33,300:INFO:Checking exceptions +2024-12-04 17:52:33,300:INFO:Importing libraries +2024-12-04 17:52:33,300:INFO:Copying training dataset +2024-12-04 17:52:33,301:INFO:Defining folds +2024-12-04 17:52:33,301:INFO:Declaring metric variables +2024-12-04 17:52:33,310:INFO:Importing untrained model +2024-12-04 17:52:33,316:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:52:33,316:INFO:Starting cross validation +2024-12-04 17:52:33,325:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:33,443:WARNING:create_model() for knn raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:33,443:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:33,443:INFO:Initializing create_model() +2024-12-04 17:52:33,443:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:33,443:INFO:Checking exceptions +2024-12-04 17:52:33,443:INFO:Importing libraries +2024-12-04 17:52:33,443:INFO:Copying training dataset +2024-12-04 17:52:33,451:INFO:Defining folds +2024-12-04 17:52:33,451:INFO:Declaring metric variables +2024-12-04 17:52:33,451:INFO:Importing untrained model +2024-12-04 17:52:33,460:INFO:K Neighbors Regressor Imported successfully +2024-12-04 17:52:33,466:INFO:Starting cross validation +2024-12-04 17:52:33,466:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:33,596:ERROR:create_model() for knn raised an exception or returned all 0.0: +2024-12-04 17:52:33,596:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:33,596:INFO:Initializing Decision Tree Regressor +2024-12-04 17:52:33,596:INFO:Total runtime is 0.19702929655710857 minutes +2024-12-04 17:52:33,601:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:33,601:INFO:Initializing create_model() +2024-12-04 17:52:33,601:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:33,601:INFO:Checking exceptions +2024-12-04 17:52:33,601:INFO:Importing libraries +2024-12-04 17:52:33,601:INFO:Copying training dataset +2024-12-04 17:52:33,612:INFO:Defining folds +2024-12-04 17:52:33,612:INFO:Declaring metric variables +2024-12-04 17:52:33,614:INFO:Importing untrained model +2024-12-04 17:52:33,618:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:52:33,657:INFO:Starting cross validation +2024-12-04 17:52:33,659:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:33,809:WARNING:create_model() for dt raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:33,810:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:33,810:INFO:Initializing create_model() +2024-12-04 17:52:33,810:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:33,810:INFO:Checking exceptions +2024-12-04 17:52:33,810:INFO:Importing libraries +2024-12-04 17:52:33,810:INFO:Copying training dataset +2024-12-04 17:52:33,819:INFO:Defining folds +2024-12-04 17:52:33,819:INFO:Declaring metric variables +2024-12-04 17:52:33,819:INFO:Importing untrained model +2024-12-04 17:52:33,828:INFO:Decision Tree Regressor Imported successfully +2024-12-04 17:52:33,832:INFO:Starting cross validation +2024-12-04 17:52:33,837:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:33,957:ERROR:create_model() for dt raised an exception or returned all 0.0: +2024-12-04 17:52:33,959:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:33,959:INFO:Initializing Random Forest Regressor +2024-12-04 17:52:33,959:INFO:Total runtime is 0.20307192007700603 minutes +2024-12-04 17:52:33,961:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:33,961:INFO:Initializing create_model() +2024-12-04 17:52:33,961:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:33,963:INFO:Checking exceptions +2024-12-04 17:52:33,963:INFO:Importing libraries +2024-12-04 17:52:33,963:INFO:Copying training dataset +2024-12-04 17:52:33,966:INFO:Defining folds +2024-12-04 17:52:33,966:INFO:Declaring metric variables +2024-12-04 17:52:33,966:INFO:Importing untrained model +2024-12-04 17:52:33,978:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:52:33,985:INFO:Starting cross validation +2024-12-04 17:52:33,987:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:34,096:WARNING:create_model() for rf raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:34,096:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:34,096:INFO:Initializing create_model() +2024-12-04 17:52:34,096:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:34,096:INFO:Checking exceptions +2024-12-04 17:52:34,096:INFO:Importing libraries +2024-12-04 17:52:34,096:INFO:Copying training dataset +2024-12-04 17:52:34,104:INFO:Defining folds +2024-12-04 17:52:34,104:INFO:Declaring metric variables +2024-12-04 17:52:34,104:INFO:Importing untrained model +2024-12-04 17:52:34,112:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:52:34,116:INFO:Starting cross validation +2024-12-04 17:52:34,122:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:34,245:ERROR:create_model() for rf raised an exception or returned all 0.0: +2024-12-04 17:52:34,245:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:34,245:INFO:Initializing Extra Trees Regressor +2024-12-04 17:52:34,246:INFO:Total runtime is 0.2078466018040975 minutes +2024-12-04 17:52:34,247:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:34,247:INFO:Initializing create_model() +2024-12-04 17:52:34,247:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:34,247:INFO:Checking exceptions +2024-12-04 17:52:34,247:INFO:Importing libraries +2024-12-04 17:52:34,247:INFO:Copying training dataset +2024-12-04 17:52:34,251:INFO:Defining folds +2024-12-04 17:52:34,251:INFO:Declaring metric variables +2024-12-04 17:52:34,261:INFO:Importing untrained model +2024-12-04 17:52:34,266:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:52:34,270:INFO:Starting cross validation +2024-12-04 17:52:34,270:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:34,393:WARNING:create_model() for et raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:34,393:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:34,393:INFO:Initializing create_model() +2024-12-04 17:52:34,393:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:34,393:INFO:Checking exceptions +2024-12-04 17:52:34,393:INFO:Importing libraries +2024-12-04 17:52:34,393:INFO:Copying training dataset +2024-12-04 17:52:34,401:INFO:Defining folds +2024-12-04 17:52:34,401:INFO:Declaring metric variables +2024-12-04 17:52:34,401:INFO:Importing untrained model +2024-12-04 17:52:34,411:INFO:Extra Trees Regressor Imported successfully +2024-12-04 17:52:34,416:INFO:Starting cross validation +2024-12-04 17:52:34,416:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:34,547:ERROR:create_model() for et raised an exception or returned all 0.0: +2024-12-04 17:52:34,547:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:34,547:INFO:Initializing AdaBoost Regressor +2024-12-04 17:52:34,547:INFO:Total runtime is 0.21288174390792847 minutes +2024-12-04 17:52:34,551:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:34,551:INFO:Initializing create_model() +2024-12-04 17:52:34,551:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:34,551:INFO:Checking exceptions +2024-12-04 17:52:34,551:INFO:Importing libraries +2024-12-04 17:52:34,551:INFO:Copying training dataset +2024-12-04 17:52:34,590:INFO:Defining folds +2024-12-04 17:52:34,590:INFO:Declaring metric variables +2024-12-04 17:52:34,596:INFO:Importing untrained model +2024-12-04 17:52:34,598:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:52:34,601:INFO:Starting cross validation +2024-12-04 17:52:34,613:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:34,743:WARNING:create_model() for ada raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:34,743:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:34,743:INFO:Initializing create_model() +2024-12-04 17:52:34,743:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:34,743:INFO:Checking exceptions +2024-12-04 17:52:34,743:INFO:Importing libraries +2024-12-04 17:52:34,743:INFO:Copying training dataset +2024-12-04 17:52:34,751:INFO:Defining folds +2024-12-04 17:52:34,751:INFO:Declaring metric variables +2024-12-04 17:52:34,751:INFO:Importing untrained model +2024-12-04 17:52:34,760:INFO:AdaBoost Regressor Imported successfully +2024-12-04 17:52:34,766:INFO:Starting cross validation +2024-12-04 17:52:34,766:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:34,899:ERROR:create_model() for ada raised an exception or returned all 0.0: +2024-12-04 17:52:34,901:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:34,901:INFO:Initializing Gradient Boosting Regressor +2024-12-04 17:52:34,901:INFO:Total runtime is 0.2187685489654541 minutes +2024-12-04 17:52:34,901:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:34,901:INFO:Initializing create_model() +2024-12-04 17:52:34,901:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:34,901:INFO:Checking exceptions +2024-12-04 17:52:34,901:INFO:Importing libraries +2024-12-04 17:52:34,901:INFO:Copying training dataset +2024-12-04 17:52:34,913:INFO:Defining folds +2024-12-04 17:52:34,913:INFO:Declaring metric variables +2024-12-04 17:52:34,918:INFO:Importing untrained model +2024-12-04 17:52:34,922:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:52:34,928:INFO:Starting cross validation +2024-12-04 17:52:34,928:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:35,050:WARNING:create_model() for gbr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:35,051:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:35,051:INFO:Initializing create_model() +2024-12-04 17:52:35,051:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:35,051:INFO:Checking exceptions +2024-12-04 17:52:35,051:INFO:Importing libraries +2024-12-04 17:52:35,051:INFO:Copying training dataset +2024-12-04 17:52:35,056:INFO:Defining folds +2024-12-04 17:52:35,056:INFO:Declaring metric variables +2024-12-04 17:52:35,063:INFO:Importing untrained model +2024-12-04 17:52:35,066:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 17:52:35,066:INFO:Starting cross validation +2024-12-04 17:52:35,066:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:35,203:ERROR:create_model() for gbr raised an exception or returned all 0.0: +2024-12-04 17:52:35,203:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\__init__.py", line 314, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\db_dtypes\core.py", line 87, in astype + return super().astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\base.py", line 684, in astype + return np.array(self, dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +TypeError: float() argument must be a string or a real number, not 'datetime.date' + + +2024-12-04 17:52:35,203:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 17:52:35,203:INFO:Total runtime is 0.22380292415618896 minutes +2024-12-04 17:52:35,206:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:35,206:INFO:Initializing create_model() +2024-12-04 17:52:35,206:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:35,206:INFO:Checking exceptions +2024-12-04 17:52:35,206:INFO:Importing libraries +2024-12-04 17:52:35,206:INFO:Copying training dataset +2024-12-04 17:52:35,216:INFO:Defining folds +2024-12-04 17:52:35,216:INFO:Declaring metric variables +2024-12-04 17:52:35,222:INFO:Importing untrained model +2024-12-04 17:52:35,225:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:52:35,232:INFO:Starting cross validation +2024-12-04 17:52:35,232:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:35,352:WARNING:create_model() for lightgbm raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:35,352:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: year: dbdate + + +2024-12-04 17:52:35,352:INFO:Initializing create_model() +2024-12-04 17:52:35,352:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:35,352:INFO:Checking exceptions +2024-12-04 17:52:35,352:INFO:Importing libraries +2024-12-04 17:52:35,352:INFO:Copying training dataset +2024-12-04 17:52:35,360:INFO:Defining folds +2024-12-04 17:52:35,360:INFO:Declaring metric variables +2024-12-04 17:52:35,363:INFO:Importing untrained model +2024-12-04 17:52:35,368:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 17:52:35,375:INFO:Starting cross validation +2024-12-04 17:52:35,378:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:35,545:ERROR:create_model() for lightgbm raised an exception or returned all 0.0: +2024-12-04 17:52:35,545:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: year: dbdate + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: year: dbdate + + +2024-12-04 17:52:35,545:INFO:Initializing Dummy Regressor +2024-12-04 17:52:35,545:INFO:Total runtime is 0.2295130729675293 minutes +2024-12-04 17:52:35,545:INFO:SubProcess create_model() called ================================== +2024-12-04 17:52:35,545:INFO:Initializing create_model() +2024-12-04 17:52:35,545:INFO:create_model(self=, estimator=dummy, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:35,545:INFO:Checking exceptions +2024-12-04 17:52:35,545:INFO:Importing libraries +2024-12-04 17:52:35,545:INFO:Copying training dataset +2024-12-04 17:52:35,559:INFO:Defining folds +2024-12-04 17:52:35,559:INFO:Declaring metric variables +2024-12-04 17:52:35,564:INFO:Importing untrained model +2024-12-04 17:52:35,566:INFO:Dummy Regressor Imported successfully +2024-12-04 17:52:35,566:INFO:Starting cross validation +2024-12-04 17:52:35,575:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:35,716:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 17:52:35,728:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 17:52:35,732:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 17:52:35,736:INFO:Calculating mean and std +2024-12-04 17:52:35,736:INFO:Creating metrics dataframe +2024-12-04 17:52:35,737:INFO:Uploading results into container +2024-12-04 17:52:35,737:INFO:Uploading model into container now +2024-12-04 17:52:35,737:INFO:_master_model_container: 1 +2024-12-04 17:52:35,737:INFO:_display_container: 2 +2024-12-04 17:52:35,737:INFO:DummyRegressor() +2024-12-04 17:52:35,740:INFO:create_model() successfully completed...................................... +2024-12-04 17:52:35,878:WARNING:create_model() for DummyRegressor() raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 17:52:35,878:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 797, in compare_models + np.sum( +AssertionError + +2024-12-04 17:52:35,878:INFO:Initializing create_model() +2024-12-04 17:52:35,878:INFO:create_model(self=, estimator=dummy, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:35,878:INFO:Checking exceptions +2024-12-04 17:52:35,878:INFO:Importing libraries +2024-12-04 17:52:35,878:INFO:Copying training dataset +2024-12-04 17:52:35,887:INFO:Defining folds +2024-12-04 17:52:35,887:INFO:Declaring metric variables +2024-12-04 17:52:35,890:INFO:Importing untrained model +2024-12-04 17:52:35,895:INFO:Dummy Regressor Imported successfully +2024-12-04 17:52:35,901:INFO:Starting cross validation +2024-12-04 17:52:35,901:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:52:36,047:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 17:52:36,060:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 17:52:36,063:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 17:52:36,077:INFO:Calculating mean and std +2024-12-04 17:52:36,081:INFO:Creating metrics dataframe +2024-12-04 17:52:36,082:INFO:Uploading results into container +2024-12-04 17:52:36,082:INFO:Uploading model into container now +2024-12-04 17:52:36,082:INFO:_master_model_container: 2 +2024-12-04 17:52:36,082:INFO:_display_container: 2 +2024-12-04 17:52:36,082:INFO:DummyRegressor() +2024-12-04 17:52:36,082:INFO:create_model() successfully completed...................................... +2024-12-04 17:52:36,195:ERROR:create_model() for DummyRegressor() raised an exception or returned all 0.0: +2024-12-04 17:52:36,195:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 797, in compare_models + np.sum( +AssertionError + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 818, in compare_models + np.sum( +AssertionError + +2024-12-04 17:52:36,203:INFO:_master_model_container: 2 +2024-12-04 17:52:36,203:INFO:_display_container: 2 +2024-12-04 17:52:36,203:INFO:[] +2024-12-04 17:52:36,203:INFO:compare_models() successfully completed...................................... +2024-12-04 17:52:36,210:INFO:Initializing finalize_model() +2024-12-04 17:52:36,210:INFO:finalize_model(self=, estimator=[], fit_kwargs=None, groups=None, model_only=False, experiment_custom_tags=None) +2024-12-04 17:52:36,210:INFO:Finalizing [] +2024-12-04 17:52:36,213:INFO:Initializing create_model() +2024-12-04 17:52:36,213:INFO:create_model(self=, estimator=[], fold=None, round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, model_only=False, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:52:36,213:INFO:Checking exceptions +2024-12-04 17:55:07,063:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_31696\394337717.py:49: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1) + +2024-12-04 17:55:19,929:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:55:19,929:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:55:19,929:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:55:19,929:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:55:26,944:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_22220\394337717.py:49: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1) + +2024-12-04 17:55:42,888:WARNING:C:\Users\antob\AppData\Local\Temp\ipykernel_22220\1069742571.py:49: SettingWithCopyWarning: +A value is trying to be set on a copy of a slice from a DataFrame. +Try using .loc[row_indexer,col_indexer] = value instead + +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1) + +2024-12-04 17:55:42,908:INFO:PyCaret RegressionExperiment +2024-12-04 17:55:42,908:INFO:Logging name: reg-default-name +2024-12-04 17:55:42,908:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:55:42,908:INFO:version 3.3.2 +2024-12-04 17:55:42,908:INFO:Initializing setup() +2024-12-04 17:55:42,908:INFO:self.USI: bb28 +2024-12-04 17:55:42,908:INFO:self._variable_keys: {'_available_plots', 'fold_shuffle_param', 'y_test', 'log_plots_param', '_ml_usecase', 'gpu_n_jobs_param', 'fold_groups_param', 'exp_name_log', 'fold_generator', 'idx', 'X_test', 'y_train', 'pipeline', 'target_param', 'memory', 'transform_target_param', 'data', 'seed', 'logging_param', 'X', 'y', 'n_jobs_param', 'exp_id', 'gpu_param', 'USI', 'X_train', 'html_param'} +2024-12-04 17:55:42,908:INFO:Checking environment +2024-12-04 17:55:42,908:INFO:python_version: 3.11.9 +2024-12-04 17:55:42,908:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:55:42,908:INFO:machine: AMD64 +2024-12-04 17:55:42,908:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:55:42,915:INFO:Memory: svmem(total=17007292416, available=3319779328, percent=80.5, used=13687513088, free=3319779328) +2024-12-04 17:55:42,915:INFO:Physical Core: 4 +2024-12-04 17:55:42,915:INFO:Logical Core: 8 +2024-12-04 17:55:42,915:INFO:Checking libraries +2024-12-04 17:55:42,915:INFO:System: +2024-12-04 17:55:42,915:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:55:42,915:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:55:42,915:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:55:42,915:INFO:PyCaret required dependencies: +2024-12-04 17:55:42,938:INFO: pip: 24.3.1 +2024-12-04 17:55:42,938:INFO: setuptools: 65.5.0 +2024-12-04 17:55:42,938:INFO: pycaret: 3.3.2 +2024-12-04 17:55:42,938:INFO: IPython: 8.12.3 +2024-12-04 17:55:42,938:INFO: ipywidgets: 8.1.5 +2024-12-04 17:55:42,938:INFO: tqdm: 4.67.1 +2024-12-04 17:55:42,938:INFO: numpy: 1.26.4 +2024-12-04 17:55:42,938:INFO: pandas: 2.1.4 +2024-12-04 17:55:42,938:INFO: jinja2: 3.1.4 +2024-12-04 17:55:42,938:INFO: scipy: 1.11.4 +2024-12-04 17:55:42,938:INFO: joblib: 1.3.2 +2024-12-04 17:55:42,939:INFO: sklearn: 1.4.2 +2024-12-04 17:55:42,939:INFO: pyod: 2.0.2 +2024-12-04 17:55:42,939:INFO: imblearn: 0.12.4 +2024-12-04 17:55:42,939:INFO: category_encoders: 2.6.4 +2024-12-04 17:55:42,939:INFO: lightgbm: 4.5.0 +2024-12-04 17:55:42,939:INFO: numba: 0.60.0 +2024-12-04 17:55:42,939:INFO: requests: 2.32.3 +2024-12-04 17:55:42,939:INFO: matplotlib: 3.7.5 +2024-12-04 17:55:42,939:INFO: scikitplot: 0.3.7 +2024-12-04 17:55:42,939:INFO: yellowbrick: 1.5 +2024-12-04 17:55:42,939:INFO: plotly: 5.24.1 +2024-12-04 17:55:42,939:INFO: plotly-resampler: Not installed +2024-12-04 17:55:42,939:INFO: kaleido: 0.2.1 +2024-12-04 17:55:42,939:INFO: schemdraw: 0.15 +2024-12-04 17:55:42,939:INFO: statsmodels: 0.14.4 +2024-12-04 17:55:42,939:INFO: sktime: 0.26.0 +2024-12-04 17:55:42,939:INFO: tbats: 1.1.3 +2024-12-04 17:55:42,939:INFO: pmdarima: 2.0.4 +2024-12-04 17:55:42,939:INFO: psutil: 6.1.0 +2024-12-04 17:55:42,939:INFO: markupsafe: 3.0.2 +2024-12-04 17:55:42,939:INFO: pickle5: Not installed +2024-12-04 17:55:42,939:INFO: cloudpickle: 3.1.0 +2024-12-04 17:55:42,939:INFO: deprecation: 2.1.0 +2024-12-04 17:55:42,939:INFO: xxhash: 3.5.0 +2024-12-04 17:55:42,939:INFO: wurlitzer: Not installed +2024-12-04 17:55:42,939:INFO:PyCaret optional dependencies: +2024-12-04 17:55:42,978:INFO: shap: Not installed +2024-12-04 17:55:42,978:INFO: interpret: Not installed +2024-12-04 17:55:42,978:INFO: umap: Not installed +2024-12-04 17:55:42,978:INFO: ydata_profiling: Not installed +2024-12-04 17:55:42,978:INFO: explainerdashboard: Not installed +2024-12-04 17:55:42,978:INFO: autoviz: Not installed +2024-12-04 17:55:42,978:INFO: fairlearn: Not installed +2024-12-04 17:55:42,978:INFO: deepchecks: Not installed +2024-12-04 17:55:42,978:INFO: xgboost: Not installed +2024-12-04 17:55:42,978:INFO: catboost: Not installed +2024-12-04 17:55:42,978:INFO: kmodes: Not installed +2024-12-04 17:55:42,978:INFO: mlxtend: Not installed +2024-12-04 17:55:42,978:INFO: statsforecast: Not installed +2024-12-04 17:55:42,978:INFO: tune_sklearn: Not installed +2024-12-04 17:55:42,978:INFO: ray: Not installed +2024-12-04 17:55:42,978:INFO: hyperopt: Not installed +2024-12-04 17:55:42,978:INFO: optuna: Not installed +2024-12-04 17:55:42,978:INFO: skopt: Not installed +2024-12-04 17:55:42,978:INFO: mlflow: 2.18.0 +2024-12-04 17:55:42,978:INFO: gradio: Not installed +2024-12-04 17:55:42,978:INFO: fastapi: Not installed +2024-12-04 17:55:42,978:INFO: uvicorn: 0.32.1 +2024-12-04 17:55:42,978:INFO: m2cgen: Not installed +2024-12-04 17:55:42,978:INFO: evidently: Not installed +2024-12-04 17:55:42,978:INFO: fugue: Not installed +2024-12-04 17:55:42,978:INFO: streamlit: 1.40.1 +2024-12-04 17:55:42,978:INFO: prophet: Not installed +2024-12-04 17:55:42,978:INFO:None +2024-12-04 17:55:42,978:INFO:Set up data. +2024-12-04 17:55:42,993:INFO:Set up folding strategy. +2024-12-04 17:55:42,993:INFO:Set up train/test split. +2024-12-04 17:55:42,993:INFO:Set up data. +2024-12-04 17:55:43,013:INFO:Set up index. +2024-12-04 17:55:43,013:INFO:Assigning column types. +2024-12-04 17:55:43,022:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:55:43,022:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,027:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,028:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,089:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,128:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,128:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,128:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,128:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,132:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,138:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,195:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,232:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,232:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,237:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,237:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:55:43,240:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,243:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,296:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,340:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,340:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,340:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,343:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,345:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,405:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,451:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,451:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,451:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,451:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:55:43,461:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,516:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,561:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,563:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,563:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,570:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,626:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,675:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,675:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,676:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,676:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:55:43,744:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,782:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,782:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,782:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,853:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,897:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:55:43,897:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,897:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:43,897:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:55:43,990:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:44,046:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,047:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,116:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:55:44,161:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,161:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,161:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:55:44,266:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,266:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,378:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,378:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:44,379:INFO:Preparing preprocessing pipeline... +2024-12-04 17:55:44,379:INFO:Set up target transformation. +2024-12-04 17:55:44,379:INFO:Set up simple imputation. +2024-12-04 17:55:44,384:INFO:Set up encoding of categorical features. +2024-12-04 17:55:44,517:INFO:Finished creating preprocessing pipeline. +2024-12-04 17:55:44,528:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['annee', 'Series'], + transformer=SimpleImputer())), + ('categorical_imputer', + Transformer... + TransformerWrapper(include=['niveau', 'cle_unique', 'sexe', + 'pathologie', 'code_pathologie', + 'nom_pathologie', 'region', + 'code_region', 'nom_region', + 'classification'], + transformer=OneHotEncoder(cols=['niveau', + 'cle_unique', + 'sexe', + 'pathologie', + 'code_pathologie', + 'nom_pathologie', + 'region', + 'code_region', + 'nom_region', + 'classification'], + handle_missing='return_nan', + use_cat_names=True)))]) +2024-12-04 17:55:44,528:INFO:Creating final display dataframe. +2024-12-04 17:55:44,947:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5, 63) +4 Transformed data shape (5, 66) +5 Transformed train set shape (4, 66) +6 Transformed test set shape (1, 66) +7 Numeric features 2 +8 Categorical features 10 +9 Rows with missing values 20.0% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator TimeSeriesSplit +19 Fold Number 3 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name reg-default-name +24 USI bb28 +2024-12-04 17:55:45,051:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:45,051:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:45,154:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:45,154:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:55:45,154:INFO:setup() successfully completed in 2.26s............... +2024-12-04 17:55:45,154:INFO:Initializing create_model() +2024-12-04 17:55:45,154:INFO:create_model(self=, estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 17:55:45,154:INFO:Checking exceptions +2024-12-04 17:55:45,171:INFO:Importing libraries +2024-12-04 17:55:45,171:INFO:Copying training dataset +2024-12-04 17:55:45,182:INFO:Defining folds +2024-12-04 17:55:45,182:INFO:Declaring metric variables +2024-12-04 17:55:45,188:INFO:Importing untrained model +2024-12-04 17:55:45,192:INFO:Random Forest Regressor Imported successfully +2024-12-04 17:55:45,201:INFO:Starting cross validation +2024-12-04 17:55:45,211:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 17:59:05,115:INFO:PyCaret RegressionExperiment +2024-12-04 17:59:05,115:INFO:Logging name: reg-default-name +2024-12-04 17:59:05,115:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:59:05,115:INFO:version 3.3.2 +2024-12-04 17:59:05,115:INFO:Initializing setup() +2024-12-04 17:59:05,115:INFO:self.USI: b0dc +2024-12-04 17:59:05,115:INFO:self._variable_keys: {'_available_plots', 'fold_shuffle_param', 'y_test', 'log_plots_param', '_ml_usecase', 'gpu_n_jobs_param', 'fold_groups_param', 'exp_name_log', 'fold_generator', 'idx', 'X_test', 'y_train', 'pipeline', 'target_param', 'memory', 'transform_target_param', 'data', 'seed', 'logging_param', 'X', 'y', 'n_jobs_param', 'exp_id', 'gpu_param', 'USI', 'X_train', 'html_param'} +2024-12-04 17:59:05,115:INFO:Checking environment +2024-12-04 17:59:05,115:INFO:python_version: 3.11.9 +2024-12-04 17:59:05,115:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:59:05,115:INFO:machine: AMD64 +2024-12-04 17:59:05,115:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:59:05,118:INFO:Memory: svmem(total=17007292416, available=2557841408, percent=85.0, used=14449451008, free=2557841408) +2024-12-04 17:59:05,118:INFO:Physical Core: 4 +2024-12-04 17:59:05,118:INFO:Logical Core: 8 +2024-12-04 17:59:05,118:INFO:Checking libraries +2024-12-04 17:59:05,118:INFO:System: +2024-12-04 17:59:05,118:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:59:05,118:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:59:05,118:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:59:05,118:INFO:PyCaret required dependencies: +2024-12-04 17:59:05,118:INFO: pip: 24.3.1 +2024-12-04 17:59:05,118:INFO: setuptools: 65.5.0 +2024-12-04 17:59:05,118:INFO: pycaret: 3.3.2 +2024-12-04 17:59:05,118:INFO: IPython: 8.12.3 +2024-12-04 17:59:05,118:INFO: ipywidgets: 8.1.5 +2024-12-04 17:59:05,118:INFO: tqdm: 4.67.1 +2024-12-04 17:59:05,118:INFO: numpy: 1.26.4 +2024-12-04 17:59:05,118:INFO: pandas: 2.1.4 +2024-12-04 17:59:05,118:INFO: jinja2: 3.1.4 +2024-12-04 17:59:05,118:INFO: scipy: 1.11.4 +2024-12-04 17:59:05,118:INFO: joblib: 1.3.2 +2024-12-04 17:59:05,118:INFO: sklearn: 1.4.2 +2024-12-04 17:59:05,118:INFO: pyod: 2.0.2 +2024-12-04 17:59:05,118:INFO: imblearn: 0.12.4 +2024-12-04 17:59:05,122:INFO: category_encoders: 2.6.4 +2024-12-04 17:59:05,122:INFO: lightgbm: 4.5.0 +2024-12-04 17:59:05,122:INFO: numba: 0.60.0 +2024-12-04 17:59:05,122:INFO: requests: 2.32.3 +2024-12-04 17:59:05,122:INFO: matplotlib: 3.7.5 +2024-12-04 17:59:05,122:INFO: scikitplot: 0.3.7 +2024-12-04 17:59:05,122:INFO: yellowbrick: 1.5 +2024-12-04 17:59:05,122:INFO: plotly: 5.24.1 +2024-12-04 17:59:05,122:INFO: plotly-resampler: Not installed +2024-12-04 17:59:05,122:INFO: kaleido: 0.2.1 +2024-12-04 17:59:05,122:INFO: schemdraw: 0.15 +2024-12-04 17:59:05,122:INFO: statsmodels: 0.14.4 +2024-12-04 17:59:05,122:INFO: sktime: 0.26.0 +2024-12-04 17:59:05,122:INFO: tbats: 1.1.3 +2024-12-04 17:59:05,122:INFO: pmdarima: 2.0.4 +2024-12-04 17:59:05,122:INFO: psutil: 6.1.0 +2024-12-04 17:59:05,122:INFO: markupsafe: 3.0.2 +2024-12-04 17:59:05,122:INFO: pickle5: Not installed +2024-12-04 17:59:05,122:INFO: cloudpickle: 3.1.0 +2024-12-04 17:59:05,122:INFO: deprecation: 2.1.0 +2024-12-04 17:59:05,122:INFO: xxhash: 3.5.0 +2024-12-04 17:59:05,122:INFO: wurlitzer: Not installed +2024-12-04 17:59:05,122:INFO:PyCaret optional dependencies: +2024-12-04 17:59:05,122:INFO: shap: Not installed +2024-12-04 17:59:05,122:INFO: interpret: Not installed +2024-12-04 17:59:05,122:INFO: umap: Not installed +2024-12-04 17:59:05,122:INFO: ydata_profiling: Not installed +2024-12-04 17:59:05,122:INFO: explainerdashboard: Not installed +2024-12-04 17:59:05,122:INFO: autoviz: Not installed +2024-12-04 17:59:05,122:INFO: fairlearn: Not installed +2024-12-04 17:59:05,122:INFO: deepchecks: Not installed +2024-12-04 17:59:05,122:INFO: xgboost: Not installed +2024-12-04 17:59:05,122:INFO: catboost: Not installed +2024-12-04 17:59:05,122:INFO: kmodes: Not installed +2024-12-04 17:59:05,122:INFO: mlxtend: Not installed +2024-12-04 17:59:05,122:INFO: statsforecast: Not installed +2024-12-04 17:59:05,122:INFO: tune_sklearn: Not installed +2024-12-04 17:59:05,122:INFO: ray: Not installed +2024-12-04 17:59:05,122:INFO: hyperopt: Not installed +2024-12-04 17:59:05,122:INFO: optuna: Not installed +2024-12-04 17:59:05,122:INFO: skopt: Not installed +2024-12-04 17:59:05,122:INFO: mlflow: 2.18.0 +2024-12-04 17:59:05,122:INFO: gradio: Not installed +2024-12-04 17:59:05,122:INFO: fastapi: Not installed +2024-12-04 17:59:05,122:INFO: uvicorn: 0.32.1 +2024-12-04 17:59:05,122:INFO: m2cgen: Not installed +2024-12-04 17:59:05,122:INFO: evidently: Not installed +2024-12-04 17:59:05,122:INFO: fugue: Not installed +2024-12-04 17:59:05,122:INFO: streamlit: 1.40.1 +2024-12-04 17:59:05,122:INFO: prophet: Not installed +2024-12-04 17:59:05,122:INFO:None +2024-12-04 17:59:05,122:INFO:Set up data. +2024-12-04 17:59:05,134:INFO:Set up folding strategy. +2024-12-04 17:59:05,135:INFO:Set up train/test split. +2024-12-04 17:59:05,135:INFO:Set up data. +2024-12-04 17:59:05,146:INFO:Set up index. +2024-12-04 17:59:05,146:INFO:Assigning column types. +2024-12-04 17:59:05,155:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:59:05,155:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,155:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,163:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,221:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,260:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,260:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,262:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,262:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,266:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,266:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,325:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,363:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,363:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,366:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,366:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:59:05,366:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,375:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,430:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,466:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,466:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,466:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,475:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,480:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,551:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,610:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,612:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,612:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,613:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:59:05,625:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,710:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,765:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,765:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,765:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,773:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,834:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,875:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,875:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,875:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,875:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:59:05,947:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,987:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:05,987:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:05,987:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,056:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:06,103:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:06,103:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,103:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,103:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:59:06,169:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:06,201:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,201:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,269:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:06,301:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,301:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,301:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:59:06,420:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,420:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,525:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,525:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:06,525:INFO:Preparing preprocessing pipeline... +2024-12-04 17:59:06,525:INFO:Set up target transformation. +2024-12-04 17:59:06,525:INFO:Set up simple imputation. +2024-12-04 17:59:06,530:INFO:Set up encoding of categorical features. +2024-12-04 17:59:42,858:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:59:42,858:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:59:42,858:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:59:42,858:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-12-04 17:59:50,231:INFO:PyCaret RegressionExperiment +2024-12-04 17:59:50,231:INFO:Logging name: reg-default-name +2024-12-04 17:59:50,231:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 17:59:50,231:INFO:version 3.3.2 +2024-12-04 17:59:50,232:INFO:Initializing setup() +2024-12-04 17:59:50,232:INFO:self.USI: f1b7 +2024-12-04 17:59:50,232:INFO:self._variable_keys: {'fold_groups_param', 'memory', 'gpu_param', 'gpu_n_jobs_param', 'html_param', 'fold_shuffle_param', 'seed', 'exp_id', 'exp_name_log', 'y_train', 'data', 'y_test', 'USI', 'target_param', 'y', 'log_plots_param', 'logging_param', 'idx', 'pipeline', 'X', '_available_plots', 'X_test', 'X_train', 'n_jobs_param', '_ml_usecase', 'fold_generator', 'transform_target_param'} +2024-12-04 17:59:50,232:INFO:Checking environment +2024-12-04 17:59:50,232:INFO:python_version: 3.11.9 +2024-12-04 17:59:50,232:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 17:59:50,232:INFO:machine: AMD64 +2024-12-04 17:59:50,232:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 17:59:50,237:INFO:Memory: svmem(total=17007292416, available=3186442240, percent=81.3, used=13820850176, free=3186442240) +2024-12-04 17:59:50,237:INFO:Physical Core: 4 +2024-12-04 17:59:50,237:INFO:Logical Core: 8 +2024-12-04 17:59:50,237:INFO:Checking libraries +2024-12-04 17:59:50,237:INFO:System: +2024-12-04 17:59:50,237:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 17:59:50,237:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 17:59:50,237:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 17:59:50,237:INFO:PyCaret required dependencies: +2024-12-04 17:59:50,293:INFO: pip: 24.3.1 +2024-12-04 17:59:50,293:INFO: setuptools: 65.5.0 +2024-12-04 17:59:50,293:INFO: pycaret: 3.3.2 +2024-12-04 17:59:50,293:INFO: IPython: 8.12.3 +2024-12-04 17:59:50,293:INFO: ipywidgets: 8.1.5 +2024-12-04 17:59:50,293:INFO: tqdm: 4.67.1 +2024-12-04 17:59:50,293:INFO: numpy: 1.26.4 +2024-12-04 17:59:50,293:INFO: pandas: 2.1.4 +2024-12-04 17:59:50,293:INFO: jinja2: 3.1.4 +2024-12-04 17:59:50,293:INFO: scipy: 1.11.4 +2024-12-04 17:59:50,293:INFO: joblib: 1.3.2 +2024-12-04 17:59:50,293:INFO: sklearn: 1.4.2 +2024-12-04 17:59:50,293:INFO: pyod: 2.0.2 +2024-12-04 17:59:50,293:INFO: imblearn: 0.12.4 +2024-12-04 17:59:50,293:INFO: category_encoders: 2.6.4 +2024-12-04 17:59:50,293:INFO: lightgbm: 4.5.0 +2024-12-04 17:59:50,293:INFO: numba: 0.60.0 +2024-12-04 17:59:50,293:INFO: requests: 2.32.3 +2024-12-04 17:59:50,293:INFO: matplotlib: 3.7.5 +2024-12-04 17:59:50,293:INFO: scikitplot: 0.3.7 +2024-12-04 17:59:50,293:INFO: yellowbrick: 1.5 +2024-12-04 17:59:50,293:INFO: plotly: 5.24.1 +2024-12-04 17:59:50,293:INFO: plotly-resampler: Not installed +2024-12-04 17:59:50,293:INFO: kaleido: 0.2.1 +2024-12-04 17:59:50,293:INFO: schemdraw: 0.15 +2024-12-04 17:59:50,293:INFO: statsmodels: 0.14.4 +2024-12-04 17:59:50,295:INFO: sktime: 0.26.0 +2024-12-04 17:59:50,295:INFO: tbats: 1.1.3 +2024-12-04 17:59:50,295:INFO: pmdarima: 2.0.4 +2024-12-04 17:59:50,295:INFO: psutil: 6.1.0 +2024-12-04 17:59:50,295:INFO: markupsafe: 3.0.2 +2024-12-04 17:59:50,295:INFO: pickle5: Not installed +2024-12-04 17:59:50,296:INFO: cloudpickle: 3.1.0 +2024-12-04 17:59:50,296:INFO: deprecation: 2.1.0 +2024-12-04 17:59:50,296:INFO: xxhash: 3.5.0 +2024-12-04 17:59:50,296:INFO: wurlitzer: Not installed +2024-12-04 17:59:50,296:INFO:PyCaret optional dependencies: +2024-12-04 17:59:50,337:INFO: shap: Not installed +2024-12-04 17:59:50,337:INFO: interpret: Not installed +2024-12-04 17:59:50,337:INFO: umap: Not installed +2024-12-04 17:59:50,337:INFO: ydata_profiling: Not installed +2024-12-04 17:59:50,337:INFO: explainerdashboard: Not installed +2024-12-04 17:59:50,337:INFO: autoviz: Not installed +2024-12-04 17:59:50,337:INFO: fairlearn: Not installed +2024-12-04 17:59:50,337:INFO: deepchecks: Not installed +2024-12-04 17:59:50,337:INFO: xgboost: Not installed +2024-12-04 17:59:50,337:INFO: catboost: Not installed +2024-12-04 17:59:50,337:INFO: kmodes: Not installed +2024-12-04 17:59:50,337:INFO: mlxtend: Not installed +2024-12-04 17:59:50,337:INFO: statsforecast: Not installed +2024-12-04 17:59:50,337:INFO: tune_sklearn: Not installed +2024-12-04 17:59:50,337:INFO: ray: Not installed +2024-12-04 17:59:50,338:INFO: hyperopt: Not installed +2024-12-04 17:59:50,338:INFO: optuna: Not installed +2024-12-04 17:59:50,338:INFO: skopt: Not installed +2024-12-04 17:59:50,338:INFO: mlflow: 2.18.0 +2024-12-04 17:59:50,338:INFO: gradio: Not installed +2024-12-04 17:59:50,338:INFO: fastapi: Not installed +2024-12-04 17:59:50,338:INFO: uvicorn: 0.32.1 +2024-12-04 17:59:50,338:INFO: m2cgen: Not installed +2024-12-04 17:59:50,338:INFO: evidently: Not installed +2024-12-04 17:59:50,338:INFO: fugue: Not installed +2024-12-04 17:59:50,338:INFO: streamlit: 1.40.1 +2024-12-04 17:59:50,338:INFO: prophet: Not installed +2024-12-04 17:59:50,338:INFO:None +2024-12-04 17:59:50,338:INFO:Set up data. +2024-12-04 17:59:50,346:INFO:Set up folding strategy. +2024-12-04 17:59:50,346:INFO:Set up train/test split. +2024-12-04 17:59:50,346:INFO:Set up data. +2024-12-04 17:59:50,366:INFO:Set up index. +2024-12-04 17:59:50,366:INFO:Assigning column types. +2024-12-04 17:59:50,366:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 17:59:50,366:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,378:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,382:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,443:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,487:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,487:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,487:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,487:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,493:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,496:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,551:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,603:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,603:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,603:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,603:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 17:59:50,609:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,612:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,667:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,710:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,710:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,710:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,716:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,717:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,782:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,816:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,816:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,824:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,824:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 17:59:50,833:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,890:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,928:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,928:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,928:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:50,938:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 17:59:50,995:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,032:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,032:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,032:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,037:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 17:59:51,101:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,140:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,140:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,140:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,201:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,245:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,245:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,245:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,245:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 17:59:51,311:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,351:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,351:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,416:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 17:59:51,461:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,461:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,461:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 17:59:51,567:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,567:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,684:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,685:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 17:59:51,686:INFO:Preparing preprocessing pipeline... +2024-12-04 17:59:51,686:INFO:Set up target transformation. +2024-12-04 17:59:51,686:INFO:Set up simple imputation. +2024-12-04 17:59:51,692:INFO:Set up encoding of categorical features. +2024-12-04 18:01:52,530:INFO:PyCaret RegressionExperiment +2024-12-04 18:01:52,530:INFO:Logging name: reg-default-name +2024-12-04 18:01:52,530:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 18:01:52,530:INFO:version 3.3.2 +2024-12-04 18:01:52,530:INFO:Initializing setup() +2024-12-04 18:01:52,530:INFO:self.USI: 9e4d +2024-12-04 18:01:52,530:INFO:self._variable_keys: {'fold_groups_param', 'memory', 'gpu_param', 'gpu_n_jobs_param', 'html_param', 'fold_shuffle_param', 'seed', 'exp_id', 'exp_name_log', 'y_train', 'data', 'y_test', 'USI', 'target_param', 'y', 'log_plots_param', 'logging_param', 'idx', 'pipeline', 'X', '_available_plots', 'X_test', 'X_train', 'n_jobs_param', '_ml_usecase', 'fold_generator', 'transform_target_param'} +2024-12-04 18:01:52,530:INFO:Checking environment +2024-12-04 18:01:52,530:INFO:python_version: 3.11.9 +2024-12-04 18:01:52,530:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 18:01:52,530:INFO:machine: AMD64 +2024-12-04 18:01:52,530:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 18:01:52,535:INFO:Memory: svmem(total=17007292416, available=3383709696, percent=80.1, used=13623582720, free=3383709696) +2024-12-04 18:01:52,535:INFO:Physical Core: 4 +2024-12-04 18:01:52,535:INFO:Logical Core: 8 +2024-12-04 18:01:52,535:INFO:Checking libraries +2024-12-04 18:01:52,535:INFO:System: +2024-12-04 18:01:52,535:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 18:01:52,535:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 18:01:52,535:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 18:01:52,535:INFO:PyCaret required dependencies: +2024-12-04 18:01:52,535:INFO: pip: 24.3.1 +2024-12-04 18:01:52,535:INFO: setuptools: 65.5.0 +2024-12-04 18:01:52,535:INFO: pycaret: 3.3.2 +2024-12-04 18:01:52,535:INFO: IPython: 8.12.3 +2024-12-04 18:01:52,535:INFO: ipywidgets: 8.1.5 +2024-12-04 18:01:52,537:INFO: tqdm: 4.67.1 +2024-12-04 18:01:52,537:INFO: numpy: 1.26.4 +2024-12-04 18:01:52,537:INFO: pandas: 2.1.4 +2024-12-04 18:01:52,537:INFO: jinja2: 3.1.4 +2024-12-04 18:01:52,537:INFO: scipy: 1.11.4 +2024-12-04 18:01:52,537:INFO: joblib: 1.3.2 +2024-12-04 18:01:52,537:INFO: sklearn: 1.4.2 +2024-12-04 18:01:52,537:INFO: pyod: 2.0.2 +2024-12-04 18:01:52,537:INFO: imblearn: 0.12.4 +2024-12-04 18:01:52,537:INFO: category_encoders: 2.6.4 +2024-12-04 18:01:52,537:INFO: lightgbm: 4.5.0 +2024-12-04 18:01:52,537:INFO: numba: 0.60.0 +2024-12-04 18:01:52,537:INFO: requests: 2.32.3 +2024-12-04 18:01:52,537:INFO: matplotlib: 3.7.5 +2024-12-04 18:01:52,537:INFO: scikitplot: 0.3.7 +2024-12-04 18:01:52,537:INFO: yellowbrick: 1.5 +2024-12-04 18:01:52,537:INFO: plotly: 5.24.1 +2024-12-04 18:01:52,537:INFO: plotly-resampler: Not installed +2024-12-04 18:01:52,537:INFO: kaleido: 0.2.1 +2024-12-04 18:01:52,537:INFO: schemdraw: 0.15 +2024-12-04 18:01:52,537:INFO: statsmodels: 0.14.4 +2024-12-04 18:01:52,537:INFO: sktime: 0.26.0 +2024-12-04 18:01:52,537:INFO: tbats: 1.1.3 +2024-12-04 18:01:52,537:INFO: pmdarima: 2.0.4 +2024-12-04 18:01:52,537:INFO: psutil: 6.1.0 +2024-12-04 18:01:52,537:INFO: markupsafe: 3.0.2 +2024-12-04 18:01:52,537:INFO: pickle5: Not installed +2024-12-04 18:01:52,537:INFO: cloudpickle: 3.1.0 +2024-12-04 18:01:52,537:INFO: deprecation: 2.1.0 +2024-12-04 18:01:52,538:INFO: xxhash: 3.5.0 +2024-12-04 18:01:52,538:INFO: wurlitzer: Not installed +2024-12-04 18:01:52,538:INFO:PyCaret optional dependencies: +2024-12-04 18:01:52,538:INFO: shap: Not installed +2024-12-04 18:01:52,538:INFO: interpret: Not installed +2024-12-04 18:01:52,538:INFO: umap: Not installed +2024-12-04 18:01:52,538:INFO: ydata_profiling: Not installed +2024-12-04 18:01:52,538:INFO: explainerdashboard: Not installed +2024-12-04 18:01:52,538:INFO: autoviz: Not installed +2024-12-04 18:01:52,538:INFO: fairlearn: Not installed +2024-12-04 18:01:52,538:INFO: deepchecks: Not installed +2024-12-04 18:01:52,538:INFO: xgboost: Not installed +2024-12-04 18:01:52,538:INFO: catboost: Not installed +2024-12-04 18:01:52,538:INFO: kmodes: Not installed +2024-12-04 18:01:52,538:INFO: mlxtend: Not installed +2024-12-04 18:01:52,538:INFO: statsforecast: Not installed +2024-12-04 18:01:52,538:INFO: tune_sklearn: Not installed +2024-12-04 18:01:52,538:INFO: ray: Not installed +2024-12-04 18:01:52,538:INFO: hyperopt: Not installed +2024-12-04 18:01:52,538:INFO: optuna: Not installed +2024-12-04 18:01:52,538:INFO: skopt: Not installed +2024-12-04 18:01:52,538:INFO: mlflow: 2.18.0 +2024-12-04 18:01:52,538:INFO: gradio: Not installed +2024-12-04 18:01:52,538:INFO: fastapi: Not installed +2024-12-04 18:01:52,538:INFO: uvicorn: 0.32.1 +2024-12-04 18:01:52,538:INFO: m2cgen: Not installed +2024-12-04 18:01:52,538:INFO: evidently: Not installed +2024-12-04 18:01:52,538:INFO: fugue: Not installed +2024-12-04 18:01:52,538:INFO: streamlit: 1.40.1 +2024-12-04 18:01:52,538:INFO: prophet: Not installed +2024-12-04 18:01:52,538:INFO:None +2024-12-04 18:01:52,538:INFO:Set up data. +2024-12-04 18:01:52,551:INFO:Set up folding strategy. +2024-12-04 18:01:52,551:INFO:Set up train/test split. +2024-12-04 18:01:52,551:INFO:Set up data. +2024-12-04 18:01:52,570:INFO:Set up index. +2024-12-04 18:01:52,570:INFO:Assigning column types. +2024-12-04 18:01:52,578:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 18:01:52,578:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,582:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,592:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,651:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,693:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,693:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:52,693:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:52,693:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,699:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,702:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,764:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,821:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,821:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:52,822:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:52,822:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 18:01:52,838:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,843:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,901:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,947:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,947:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:52,947:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:52,951:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:01:52,956:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,015:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,056:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,056:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,056:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,056:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 18:01:53,066:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,118:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,165:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,167:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,167:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,175:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,236:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,278:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,280:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,280:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,280:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 18:01:53,348:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,390:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,390:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,390:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,463:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,504:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,504:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,504:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,504:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 18:01:53,575:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,617:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,617:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,687:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:01:53,728:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,728:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,728:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 18:01:53,844:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,844:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,972:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,972:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:53,972:INFO:Preparing preprocessing pipeline... +2024-12-04 18:01:53,972:INFO:Set up target transformation. +2024-12-04 18:01:53,972:INFO:Set up simple imputation. +2024-12-04 18:01:53,978:INFO:Set up encoding of categorical features. +2024-12-04 18:01:54,003:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:01:54,003:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:01:54,092:INFO:Finished creating preprocessing pipeline. +2024-12-04 18:01:54,101:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['annee', 'Series', 'annee_norm'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=OneHotEncoder(cols=['nom_region', + 'nom_pathologie'], + handle_missing='return_nan', + use_cat_names=True)))]) +2024-12-04 18:01:54,101:INFO:Creating final display dataframe. +2024-12-04 18:01:54,425:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5, 64) +4 Transformed data shape (5, 64) +5 Transformed train set shape (4, 64) +6 Transformed test set shape (1, 64) +7 Numeric features 3 +8 Categorical features 2 +9 Rows with missing values 20.0% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator TimeSeriesSplit +19 Fold Number 3 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name reg-default-name +24 USI 9e4d +2024-12-04 18:01:54,536:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:54,536:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:54,643:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:54,643:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:01:54,643:INFO:setup() successfully completed in 2.13s............... +2024-12-04 18:01:54,643:INFO:Initializing compare_models() +2024-12-04 18:01:54,643:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 18:01:54,643:INFO:Checking exceptions +2024-12-04 18:01:54,647:INFO:Preparing display monitor +2024-12-04 18:01:54,666:INFO:Initializing Linear Regression +2024-12-04 18:01:54,666:INFO:Total runtime is 0.0 minutes +2024-12-04 18:01:54,675:INFO:SubProcess create_model() called ================================== +2024-12-04 18:01:54,675:INFO:Initializing create_model() +2024-12-04 18:01:54,675:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:01:54,675:INFO:Checking exceptions +2024-12-04 18:01:54,675:INFO:Importing libraries +2024-12-04 18:01:54,675:INFO:Copying training dataset +2024-12-04 18:01:54,686:INFO:Defining folds +2024-12-04 18:01:54,686:INFO:Declaring metric variables +2024-12-04 18:01:54,690:INFO:Importing untrained model +2024-12-04 18:01:54,692:INFO:Linear Regression Imported successfully +2024-12-04 18:01:54,703:INFO:Starting cross validation +2024-12-04 18:01:54,711:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:01:57,742:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:01:57,745:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:01:57,799:WARNING:create_model() for lr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:01:57,801:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:01:57,801:INFO:Initializing create_model() +2024-12-04 18:01:57,801:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:01:57,801:INFO:Checking exceptions +2024-12-04 18:01:57,801:INFO:Importing libraries +2024-12-04 18:01:57,801:INFO:Copying training dataset +2024-12-04 18:01:57,810:INFO:Defining folds +2024-12-04 18:01:57,812:INFO:Declaring metric variables +2024-12-04 18:01:57,816:INFO:Importing untrained model +2024-12-04 18:01:57,819:INFO:Linear Regression Imported successfully +2024-12-04 18:01:57,828:INFO:Starting cross validation +2024-12-04 18:01:57,830:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:00,113:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:00,116:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:00,212:ERROR:create_model() for lr raised an exception or returned all 0.0: +2024-12-04 18:02:00,213:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:00,213:INFO:Initializing Lasso Regression +2024-12-04 18:02:00,213:INFO:Total runtime is 0.0924503763516744 minutes +2024-12-04 18:02:00,217:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:00,217:INFO:Initializing create_model() +2024-12-04 18:02:00,217:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:00,217:INFO:Checking exceptions +2024-12-04 18:02:00,217:INFO:Importing libraries +2024-12-04 18:02:00,217:INFO:Copying training dataset +2024-12-04 18:02:00,230:INFO:Defining folds +2024-12-04 18:02:00,230:INFO:Declaring metric variables +2024-12-04 18:02:00,235:INFO:Importing untrained model +2024-12-04 18:02:00,240:INFO:Lasso Regression Imported successfully +2024-12-04 18:02:00,247:INFO:Starting cross validation +2024-12-04 18:02:00,248:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:00,274:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:00,278:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:02,515:WARNING:create_model() for lasso raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:02,516:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:02,516:INFO:Initializing create_model() +2024-12-04 18:02:02,516:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:02,516:INFO:Checking exceptions +2024-12-04 18:02:02,517:INFO:Importing libraries +2024-12-04 18:02:02,517:INFO:Copying training dataset +2024-12-04 18:02:02,528:INFO:Defining folds +2024-12-04 18:02:02,528:INFO:Declaring metric variables +2024-12-04 18:02:02,534:INFO:Importing untrained model +2024-12-04 18:02:02,540:INFO:Lasso Regression Imported successfully +2024-12-04 18:02:02,548:INFO:Starting cross validation +2024-12-04 18:02:02,550:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:02,581:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:02,582:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:02,633:ERROR:create_model() for lasso raised an exception or returned all 0.0: +2024-12-04 18:02:02,633:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:02,633:INFO:Initializing Ridge Regression +2024-12-04 18:02:02,633:INFO:Total runtime is 0.1327720046043396 minutes +2024-12-04 18:02:02,637:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:02,637:INFO:Initializing create_model() +2024-12-04 18:02:02,637:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:02,637:INFO:Checking exceptions +2024-12-04 18:02:02,637:INFO:Importing libraries +2024-12-04 18:02:02,637:INFO:Copying training dataset +2024-12-04 18:02:02,647:INFO:Defining folds +2024-12-04 18:02:02,647:INFO:Declaring metric variables +2024-12-04 18:02:02,650:INFO:Importing untrained model +2024-12-04 18:02:02,653:INFO:Ridge Regression Imported successfully +2024-12-04 18:02:02,660:INFO:Starting cross validation +2024-12-04 18:02:02,663:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:02,687:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:02,688:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:02,743:WARNING:create_model() for ridge raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:02,743:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:02,743:INFO:Initializing create_model() +2024-12-04 18:02:02,743:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:02,743:INFO:Checking exceptions +2024-12-04 18:02:02,743:INFO:Importing libraries +2024-12-04 18:02:02,743:INFO:Copying training dataset +2024-12-04 18:02:02,751:INFO:Defining folds +2024-12-04 18:02:02,751:INFO:Declaring metric variables +2024-12-04 18:02:02,751:INFO:Importing untrained model +2024-12-04 18:02:02,760:INFO:Ridge Regression Imported successfully +2024-12-04 18:02:02,766:INFO:Starting cross validation +2024-12-04 18:02:02,766:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:02,802:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:02,802:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:02,889:ERROR:create_model() for ridge raised an exception or returned all 0.0: +2024-12-04 18:02:02,890:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:02,890:INFO:Initializing Elastic Net +2024-12-04 18:02:02,890:INFO:Total runtime is 0.1370621164639791 minutes +2024-12-04 18:02:02,892:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:02,892:INFO:Initializing create_model() +2024-12-04 18:02:02,892:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:02,892:INFO:Checking exceptions +2024-12-04 18:02:02,892:INFO:Importing libraries +2024-12-04 18:02:02,892:INFO:Copying training dataset +2024-12-04 18:02:02,901:INFO:Defining folds +2024-12-04 18:02:02,901:INFO:Declaring metric variables +2024-12-04 18:02:02,905:INFO:Importing untrained model +2024-12-04 18:02:02,905:INFO:Elastic Net Imported successfully +2024-12-04 18:02:02,916:INFO:Starting cross validation +2024-12-04 18:02:02,917:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:02,943:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:02,943:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:02,999:WARNING:create_model() for en raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:02,999:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:02,999:INFO:Initializing create_model() +2024-12-04 18:02:02,999:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:02,999:INFO:Checking exceptions +2024-12-04 18:02:02,999:INFO:Importing libraries +2024-12-04 18:02:02,999:INFO:Copying training dataset +2024-12-04 18:02:03,001:INFO:Defining folds +2024-12-04 18:02:03,001:INFO:Declaring metric variables +2024-12-04 18:02:03,011:INFO:Importing untrained model +2024-12-04 18:02:03,016:INFO:Elastic Net Imported successfully +2024-12-04 18:02:03,019:INFO:Starting cross validation +2024-12-04 18:02:03,019:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,053:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,055:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,104:ERROR:create_model() for en raised an exception or returned all 0.0: +2024-12-04 18:02:03,104:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:03,104:INFO:Initializing Least Angle Regression +2024-12-04 18:02:03,104:INFO:Total runtime is 0.14061839977900187 minutes +2024-12-04 18:02:03,104:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:03,104:INFO:Initializing create_model() +2024-12-04 18:02:03,104:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,104:INFO:Checking exceptions +2024-12-04 18:02:03,104:INFO:Importing libraries +2024-12-04 18:02:03,104:INFO:Copying training dataset +2024-12-04 18:02:03,116:INFO:Defining folds +2024-12-04 18:02:03,116:INFO:Declaring metric variables +2024-12-04 18:02:03,122:INFO:Importing untrained model +2024-12-04 18:02:03,125:INFO:Least Angle Regression Imported successfully +2024-12-04 18:02:03,132:INFO:Starting cross validation +2024-12-04 18:02:03,133:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,151:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,151:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,212:WARNING:create_model() for lar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:03,213:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:03,213:INFO:Initializing create_model() +2024-12-04 18:02:03,213:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,213:INFO:Checking exceptions +2024-12-04 18:02:03,213:INFO:Importing libraries +2024-12-04 18:02:03,213:INFO:Copying training dataset +2024-12-04 18:02:03,216:INFO:Defining folds +2024-12-04 18:02:03,216:INFO:Declaring metric variables +2024-12-04 18:02:03,216:INFO:Importing untrained model +2024-12-04 18:02:03,225:INFO:Least Angle Regression Imported successfully +2024-12-04 18:02:03,236:INFO:Starting cross validation +2024-12-04 18:02:03,237:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,260:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,260:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,318:ERROR:create_model() for lar raised an exception or returned all 0.0: +2024-12-04 18:02:03,318:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:03,318:INFO:Initializing Lasso Least Angle Regression +2024-12-04 18:02:03,318:INFO:Total runtime is 0.14418799877166746 minutes +2024-12-04 18:02:03,318:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:03,318:INFO:Initializing create_model() +2024-12-04 18:02:03,318:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,318:INFO:Checking exceptions +2024-12-04 18:02:03,318:INFO:Importing libraries +2024-12-04 18:02:03,318:INFO:Copying training dataset +2024-12-04 18:02:03,332:INFO:Defining folds +2024-12-04 18:02:03,333:INFO:Declaring metric variables +2024-12-04 18:02:03,333:INFO:Importing untrained model +2024-12-04 18:02:03,337:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 18:02:03,345:INFO:Starting cross validation +2024-12-04 18:02:03,348:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,376:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,378:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,429:WARNING:create_model() for llar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:03,430:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:03,431:INFO:Initializing create_model() +2024-12-04 18:02:03,431:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,431:INFO:Checking exceptions +2024-12-04 18:02:03,431:INFO:Importing libraries +2024-12-04 18:02:03,431:INFO:Copying training dataset +2024-12-04 18:02:03,440:INFO:Defining folds +2024-12-04 18:02:03,440:INFO:Declaring metric variables +2024-12-04 18:02:03,443:INFO:Importing untrained model +2024-12-04 18:02:03,446:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 18:02:03,454:INFO:Starting cross validation +2024-12-04 18:02:03,454:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,525:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,525:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,574:ERROR:create_model() for llar raised an exception or returned all 0.0: +2024-12-04 18:02:03,575:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:03,575:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 18:02:03,575:INFO:Total runtime is 0.14847561120986938 minutes +2024-12-04 18:02:03,578:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:03,578:INFO:Initializing create_model() +2024-12-04 18:02:03,578:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,578:INFO:Checking exceptions +2024-12-04 18:02:03,578:INFO:Importing libraries +2024-12-04 18:02:03,578:INFO:Copying training dataset +2024-12-04 18:02:03,587:INFO:Defining folds +2024-12-04 18:02:03,587:INFO:Declaring metric variables +2024-12-04 18:02:03,590:INFO:Importing untrained model +2024-12-04 18:02:03,592:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 18:02:03,609:INFO:Starting cross validation +2024-12-04 18:02:03,610:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,635:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,637:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,690:WARNING:create_model() for omp raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:03,690:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:03,690:INFO:Initializing create_model() +2024-12-04 18:02:03,690:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,690:INFO:Checking exceptions +2024-12-04 18:02:03,690:INFO:Importing libraries +2024-12-04 18:02:03,690:INFO:Copying training dataset +2024-12-04 18:02:03,696:INFO:Defining folds +2024-12-04 18:02:03,696:INFO:Declaring metric variables +2024-12-04 18:02:03,701:INFO:Importing untrained model +2024-12-04 18:02:03,701:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 18:02:03,713:INFO:Starting cross validation +2024-12-04 18:02:03,714:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,737:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,740:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,794:ERROR:create_model() for omp raised an exception or returned all 0.0: +2024-12-04 18:02:03,794:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:03,794:INFO:Initializing Bayesian Ridge +2024-12-04 18:02:03,794:INFO:Total runtime is 0.15211897293726603 minutes +2024-12-04 18:02:03,796:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:03,796:INFO:Initializing create_model() +2024-12-04 18:02:03,796:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,796:INFO:Checking exceptions +2024-12-04 18:02:03,796:INFO:Importing libraries +2024-12-04 18:02:03,796:INFO:Copying training dataset +2024-12-04 18:02:03,801:INFO:Defining folds +2024-12-04 18:02:03,801:INFO:Declaring metric variables +2024-12-04 18:02:03,811:INFO:Importing untrained model +2024-12-04 18:02:03,814:INFO:Bayesian Ridge Imported successfully +2024-12-04 18:02:03,816:INFO:Starting cross validation +2024-12-04 18:02:03,816:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,842:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,846:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,890:WARNING:create_model() for br raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:03,890:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:03,890:INFO:Initializing create_model() +2024-12-04 18:02:03,890:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,890:INFO:Checking exceptions +2024-12-04 18:02:03,890:INFO:Importing libraries +2024-12-04 18:02:03,890:INFO:Copying training dataset +2024-12-04 18:02:03,896:INFO:Defining folds +2024-12-04 18:02:03,896:INFO:Declaring metric variables +2024-12-04 18:02:03,901:INFO:Importing untrained model +2024-12-04 18:02:03,901:INFO:Bayesian Ridge Imported successfully +2024-12-04 18:02:03,910:INFO:Starting cross validation +2024-12-04 18:02:03,912:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:03,935:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:03,935:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:03,978:ERROR:create_model() for br raised an exception or returned all 0.0: +2024-12-04 18:02:03,978:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:03,978:INFO:Initializing Passive Aggressive Regressor +2024-12-04 18:02:03,978:INFO:Total runtime is 0.15519560972849528 minutes +2024-12-04 18:02:03,982:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:03,982:INFO:Initializing create_model() +2024-12-04 18:02:03,982:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:03,982:INFO:Checking exceptions +2024-12-04 18:02:03,982:INFO:Importing libraries +2024-12-04 18:02:03,982:INFO:Copying training dataset +2024-12-04 18:02:03,990:INFO:Defining folds +2024-12-04 18:02:03,990:INFO:Declaring metric variables +2024-12-04 18:02:03,995:INFO:Importing untrained model +2024-12-04 18:02:04,000:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 18:02:04,004:INFO:Starting cross validation +2024-12-04 18:02:04,004:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,059:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,062:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,106:WARNING:create_model() for par raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:04,106:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:04,106:INFO:Initializing create_model() +2024-12-04 18:02:04,106:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,106:INFO:Checking exceptions +2024-12-04 18:02:04,106:INFO:Importing libraries +2024-12-04 18:02:04,106:INFO:Copying training dataset +2024-12-04 18:02:04,116:INFO:Defining folds +2024-12-04 18:02:04,116:INFO:Declaring metric variables +2024-12-04 18:02:04,119:INFO:Importing untrained model +2024-12-04 18:02:04,119:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 18:02:04,128:INFO:Starting cross validation +2024-12-04 18:02:04,132:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,151:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,158:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,209:ERROR:create_model() for par raised an exception or returned all 0.0: +2024-12-04 18:02:04,211:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:04,211:INFO:Initializing Huber Regressor +2024-12-04 18:02:04,211:INFO:Total runtime is 0.15906932353973388 minutes +2024-12-04 18:02:04,213:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:04,213:INFO:Initializing create_model() +2024-12-04 18:02:04,213:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,213:INFO:Checking exceptions +2024-12-04 18:02:04,213:INFO:Importing libraries +2024-12-04 18:02:04,213:INFO:Copying training dataset +2024-12-04 18:02:04,223:INFO:Defining folds +2024-12-04 18:02:04,223:INFO:Declaring metric variables +2024-12-04 18:02:04,226:INFO:Importing untrained model +2024-12-04 18:02:04,232:INFO:Huber Regressor Imported successfully +2024-12-04 18:02:04,237:INFO:Starting cross validation +2024-12-04 18:02:04,238:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,260:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,263:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,305:WARNING:create_model() for huber raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:04,305:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:04,305:INFO:Initializing create_model() +2024-12-04 18:02:04,305:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,305:INFO:Checking exceptions +2024-12-04 18:02:04,305:INFO:Importing libraries +2024-12-04 18:02:04,305:INFO:Copying training dataset +2024-12-04 18:02:04,313:INFO:Defining folds +2024-12-04 18:02:04,313:INFO:Declaring metric variables +2024-12-04 18:02:04,316:INFO:Importing untrained model +2024-12-04 18:02:04,316:INFO:Huber Regressor Imported successfully +2024-12-04 18:02:04,325:INFO:Starting cross validation +2024-12-04 18:02:04,325:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,348:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,350:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,403:ERROR:create_model() for huber raised an exception or returned all 0.0: +2024-12-04 18:02:04,403:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:02:04,403:INFO:Initializing K Neighbors Regressor +2024-12-04 18:02:04,403:INFO:Total runtime is 0.162278151512146 minutes +2024-12-04 18:02:04,406:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:04,406:INFO:Initializing create_model() +2024-12-04 18:02:04,406:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,406:INFO:Checking exceptions +2024-12-04 18:02:04,406:INFO:Importing libraries +2024-12-04 18:02:04,406:INFO:Copying training dataset +2024-12-04 18:02:04,416:INFO:Defining folds +2024-12-04 18:02:04,416:INFO:Declaring metric variables +2024-12-04 18:02:04,425:INFO:Importing untrained model +2024-12-04 18:02:04,428:INFO:K Neighbors Regressor Imported successfully +2024-12-04 18:02:04,435:INFO:Starting cross validation +2024-12-04 18:02:04,435:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,465:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,466:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,513:WARNING:create_model() for knn raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:04,513:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:04,513:INFO:Initializing create_model() +2024-12-04 18:02:04,513:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,513:INFO:Checking exceptions +2024-12-04 18:02:04,513:INFO:Importing libraries +2024-12-04 18:02:04,513:INFO:Copying training dataset +2024-12-04 18:02:04,522:INFO:Defining folds +2024-12-04 18:02:04,522:INFO:Declaring metric variables +2024-12-04 18:02:04,525:INFO:Importing untrained model +2024-12-04 18:02:04,531:INFO:K Neighbors Regressor Imported successfully +2024-12-04 18:02:04,537:INFO:Starting cross validation +2024-12-04 18:02:04,538:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,566:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,568:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,619:ERROR:create_model() for knn raised an exception or returned all 0.0: +2024-12-04 18:02:04,619:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:04,619:INFO:Initializing Decision Tree Regressor +2024-12-04 18:02:04,619:INFO:Total runtime is 0.16588092247645062 minutes +2024-12-04 18:02:04,622:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:04,622:INFO:Initializing create_model() +2024-12-04 18:02:04,622:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,622:INFO:Checking exceptions +2024-12-04 18:02:04,622:INFO:Importing libraries +2024-12-04 18:02:04,622:INFO:Copying training dataset +2024-12-04 18:02:04,632:INFO:Defining folds +2024-12-04 18:02:04,632:INFO:Declaring metric variables +2024-12-04 18:02:04,638:INFO:Importing untrained model +2024-12-04 18:02:04,642:INFO:Decision Tree Regressor Imported successfully +2024-12-04 18:02:04,649:INFO:Starting cross validation +2024-12-04 18:02:04,650:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,681:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,682:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,732:WARNING:create_model() for dt raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:04,732:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:04,732:INFO:Initializing create_model() +2024-12-04 18:02:04,732:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,732:INFO:Checking exceptions +2024-12-04 18:02:04,732:INFO:Importing libraries +2024-12-04 18:02:04,732:INFO:Copying training dataset +2024-12-04 18:02:04,740:INFO:Defining folds +2024-12-04 18:02:04,740:INFO:Declaring metric variables +2024-12-04 18:02:04,744:INFO:Importing untrained model +2024-12-04 18:02:04,749:INFO:Decision Tree Regressor Imported successfully +2024-12-04 18:02:04,754:INFO:Starting cross validation +2024-12-04 18:02:04,754:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,786:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,787:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,837:ERROR:create_model() for dt raised an exception or returned all 0.0: +2024-12-04 18:02:04,838:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:04,838:INFO:Initializing Random Forest Regressor +2024-12-04 18:02:04,838:INFO:Total runtime is 0.16952943801879883 minutes +2024-12-04 18:02:04,840:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:04,840:INFO:Initializing create_model() +2024-12-04 18:02:04,840:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,840:INFO:Checking exceptions +2024-12-04 18:02:04,840:INFO:Importing libraries +2024-12-04 18:02:04,840:INFO:Copying training dataset +2024-12-04 18:02:04,850:INFO:Defining folds +2024-12-04 18:02:04,850:INFO:Declaring metric variables +2024-12-04 18:02:04,856:INFO:Importing untrained model +2024-12-04 18:02:04,860:INFO:Random Forest Regressor Imported successfully +2024-12-04 18:02:04,866:INFO:Starting cross validation +2024-12-04 18:02:04,866:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:04,900:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:04,901:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:04,977:WARNING:create_model() for rf raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:04,978:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:04,978:INFO:Initializing create_model() +2024-12-04 18:02:04,978:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:04,978:INFO:Checking exceptions +2024-12-04 18:02:04,978:INFO:Importing libraries +2024-12-04 18:02:04,978:INFO:Copying training dataset +2024-12-04 18:02:04,987:INFO:Defining folds +2024-12-04 18:02:04,987:INFO:Declaring metric variables +2024-12-04 18:02:04,990:INFO:Importing untrained model +2024-12-04 18:02:04,992:INFO:Random Forest Regressor Imported successfully +2024-12-04 18:02:05,002:INFO:Starting cross validation +2024-12-04 18:02:05,002:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,028:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,032:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,073:ERROR:create_model() for rf raised an exception or returned all 0.0: +2024-12-04 18:02:05,075:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:05,075:INFO:Initializing Extra Trees Regressor +2024-12-04 18:02:05,075:INFO:Total runtime is 0.17347063223520914 minutes +2024-12-04 18:02:05,075:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:05,078:INFO:Initializing create_model() +2024-12-04 18:02:05,078:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,078:INFO:Checking exceptions +2024-12-04 18:02:05,078:INFO:Importing libraries +2024-12-04 18:02:05,078:INFO:Copying training dataset +2024-12-04 18:02:05,088:INFO:Defining folds +2024-12-04 18:02:05,088:INFO:Declaring metric variables +2024-12-04 18:02:05,093:INFO:Importing untrained model +2024-12-04 18:02:05,098:INFO:Extra Trees Regressor Imported successfully +2024-12-04 18:02:05,103:INFO:Starting cross validation +2024-12-04 18:02:05,103:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,128:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,131:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,186:WARNING:create_model() for et raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:05,186:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:05,186:INFO:Initializing create_model() +2024-12-04 18:02:05,186:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,186:INFO:Checking exceptions +2024-12-04 18:02:05,187:INFO:Importing libraries +2024-12-04 18:02:05,187:INFO:Copying training dataset +2024-12-04 18:02:05,196:INFO:Defining folds +2024-12-04 18:02:05,197:INFO:Declaring metric variables +2024-12-04 18:02:05,201:INFO:Importing untrained model +2024-12-04 18:02:05,203:INFO:Extra Trees Regressor Imported successfully +2024-12-04 18:02:05,211:INFO:Starting cross validation +2024-12-04 18:02:05,214:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,242:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,242:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,294:ERROR:create_model() for et raised an exception or returned all 0.0: +2024-12-04 18:02:05,296:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:05,296:INFO:Initializing AdaBoost Regressor +2024-12-04 18:02:05,296:INFO:Total runtime is 0.1771623373031616 minutes +2024-12-04 18:02:05,300:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:05,300:INFO:Initializing create_model() +2024-12-04 18:02:05,300:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,300:INFO:Checking exceptions +2024-12-04 18:02:05,300:INFO:Importing libraries +2024-12-04 18:02:05,300:INFO:Copying training dataset +2024-12-04 18:02:05,301:INFO:Defining folds +2024-12-04 18:02:05,301:INFO:Declaring metric variables +2024-12-04 18:02:05,310:INFO:Importing untrained model +2024-12-04 18:02:05,316:INFO:AdaBoost Regressor Imported successfully +2024-12-04 18:02:05,316:INFO:Starting cross validation +2024-12-04 18:02:05,325:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,356:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,356:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,407:WARNING:create_model() for ada raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:05,407:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:05,407:INFO:Initializing create_model() +2024-12-04 18:02:05,407:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,407:INFO:Checking exceptions +2024-12-04 18:02:05,407:INFO:Importing libraries +2024-12-04 18:02:05,407:INFO:Copying training dataset +2024-12-04 18:02:05,419:INFO:Defining folds +2024-12-04 18:02:05,419:INFO:Declaring metric variables +2024-12-04 18:02:05,423:INFO:Importing untrained model +2024-12-04 18:02:05,428:INFO:AdaBoost Regressor Imported successfully +2024-12-04 18:02:05,436:INFO:Starting cross validation +2024-12-04 18:02:05,437:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,452:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,460:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,505:ERROR:create_model() for ada raised an exception or returned all 0.0: +2024-12-04 18:02:05,505:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:02:05,505:INFO:Initializing Gradient Boosting Regressor +2024-12-04 18:02:05,505:INFO:Total runtime is 0.18063522974650065 minutes +2024-12-04 18:02:05,505:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:05,505:INFO:Initializing create_model() +2024-12-04 18:02:05,505:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,505:INFO:Checking exceptions +2024-12-04 18:02:05,505:INFO:Importing libraries +2024-12-04 18:02:05,505:INFO:Copying training dataset +2024-12-04 18:02:05,516:INFO:Defining folds +2024-12-04 18:02:05,516:INFO:Declaring metric variables +2024-12-04 18:02:05,516:INFO:Importing untrained model +2024-12-04 18:02:05,524:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 18:02:05,532:INFO:Starting cross validation +2024-12-04 18:02:05,532:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,551:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,561:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,606:WARNING:create_model() for gbr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:05,606:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:05,606:INFO:Initializing create_model() +2024-12-04 18:02:05,606:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,606:INFO:Checking exceptions +2024-12-04 18:02:05,606:INFO:Importing libraries +2024-12-04 18:02:05,606:INFO:Copying training dataset +2024-12-04 18:02:05,616:INFO:Defining folds +2024-12-04 18:02:05,616:INFO:Declaring metric variables +2024-12-04 18:02:05,616:INFO:Importing untrained model +2024-12-04 18:02:05,616:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 18:02:05,659:INFO:Starting cross validation +2024-12-04 18:02:05,661:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,699:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,701:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,752:ERROR:create_model() for gbr raised an exception or returned all 0.0: +2024-12-04 18:02:05,752:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:02:05,752:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 18:02:05,752:INFO:Total runtime is 0.18476264079411825 minutes +2024-12-04 18:02:05,755:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:05,755:INFO:Initializing create_model() +2024-12-04 18:02:05,755:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,755:INFO:Checking exceptions +2024-12-04 18:02:05,755:INFO:Importing libraries +2024-12-04 18:02:05,755:INFO:Copying training dataset +2024-12-04 18:02:05,766:INFO:Defining folds +2024-12-04 18:02:05,766:INFO:Declaring metric variables +2024-12-04 18:02:05,771:INFO:Importing untrained model +2024-12-04 18:02:05,775:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 18:02:05,781:INFO:Starting cross validation +2024-12-04 18:02:05,782:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,810:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,811:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,872:WARNING:create_model() for lightgbm raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:02:05,873:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: cle_unique: object, year: dbdate + + +2024-12-04 18:02:05,873:INFO:Initializing create_model() +2024-12-04 18:02:05,873:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,873:INFO:Checking exceptions +2024-12-04 18:02:05,873:INFO:Importing libraries +2024-12-04 18:02:05,873:INFO:Copying training dataset +2024-12-04 18:02:05,882:INFO:Defining folds +2024-12-04 18:02:05,882:INFO:Declaring metric variables +2024-12-04 18:02:05,887:INFO:Importing untrained model +2024-12-04 18:02:05,892:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 18:02:05,898:INFO:Starting cross validation +2024-12-04 18:02:05,901:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:05,916:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:05,923:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:05,969:ERROR:create_model() for lightgbm raised an exception or returned all 0.0: +2024-12-04 18:02:05,970:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: cle_unique: object, year: dbdate + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: cle_unique: object, year: dbdate + + +2024-12-04 18:02:05,970:INFO:Initializing Dummy Regressor +2024-12-04 18:02:05,970:INFO:Total runtime is 0.18839776118596396 minutes +2024-12-04 18:02:05,973:INFO:SubProcess create_model() called ================================== +2024-12-04 18:02:05,973:INFO:Initializing create_model() +2024-12-04 18:02:05,973:INFO:create_model(self=, estimator=dummy, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:05,973:INFO:Checking exceptions +2024-12-04 18:02:05,973:INFO:Importing libraries +2024-12-04 18:02:05,973:INFO:Copying training dataset +2024-12-04 18:02:05,982:INFO:Defining folds +2024-12-04 18:02:05,982:INFO:Declaring metric variables +2024-12-04 18:02:05,982:INFO:Importing untrained model +2024-12-04 18:02:05,988:INFO:Dummy Regressor Imported successfully +2024-12-04 18:02:05,995:INFO:Starting cross validation +2024-12-04 18:02:05,997:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:02:06,019:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:06,019:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:06,078:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 18:02:06,085:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 18:02:06,090:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 18:02:06,095:INFO:Calculating mean and std +2024-12-04 18:02:06,095:INFO:Creating metrics dataframe +2024-12-04 18:02:06,096:INFO:Uploading results into container +2024-12-04 18:02:06,096:INFO:Uploading model into container now +2024-12-04 18:02:06,096:INFO:_master_model_container: 1 +2024-12-04 18:02:06,096:INFO:_display_container: 2 +2024-12-04 18:02:06,096:INFO:DummyRegressor() +2024-12-04 18:02:06,096:INFO:create_model() successfully completed...................................... +2024-12-04 18:02:06,213:INFO:SubProcess create_model() end ================================== +2024-12-04 18:02:06,213:INFO:Creating metrics dataframe +2024-12-04 18:02:06,216:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 18:02:06,228:INFO:Initializing create_model() +2024-12-04 18:02:06,228:INFO:create_model(self=, estimator=DummyRegressor(), fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:02:06,228:INFO:Checking exceptions +2024-12-04 18:02:06,232:INFO:Importing libraries +2024-12-04 18:02:06,232:INFO:Copying training dataset +2024-12-04 18:02:06,240:INFO:Defining folds +2024-12-04 18:02:06,240:INFO:Declaring metric variables +2024-12-04 18:02:06,240:INFO:Importing untrained model +2024-12-04 18:02:06,240:INFO:Declaring custom model +2024-12-04 18:02:06,242:INFO:Dummy Regressor Imported successfully +2024-12-04 18:02:06,242:INFO:Cross validation set to False +2024-12-04 18:02:06,242:INFO:Fitting Model +2024-12-04 18:02:06,242:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:02:06,246:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:02:06,284:INFO:DummyRegressor() +2024-12-04 18:02:06,284:INFO:create_model() successfully completed...................................... +2024-12-04 18:02:06,397:INFO:_master_model_container: 1 +2024-12-04 18:02:06,397:INFO:_display_container: 2 +2024-12-04 18:02:06,397:INFO:DummyRegressor() +2024-12-04 18:02:06,397:INFO:compare_models() successfully completed...................................... +2024-12-04 18:02:06,400:INFO:Initializing predict_model() +2024-12-04 18:02:06,401:INFO:predict_model(self=, estimator=DummyRegressor(), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000002104B1B7100>) +2024-12-04 18:02:06,401:INFO:Checking exceptions +2024-12-04 18:02:06,401:INFO:Preloading libraries +2024-12-04 18:02:06,418:INFO:Set up data. +2024-12-04 18:02:06,420:INFO:Set up index. +2024-12-04 18:05:45,148:INFO:PyCaret RegressionExperiment +2024-12-04 18:05:45,148:INFO:Logging name: reg-default-name +2024-12-04 18:05:45,148:INFO:ML Usecase: MLUsecase.REGRESSION +2024-12-04 18:05:45,148:INFO:version 3.3.2 +2024-12-04 18:05:45,148:INFO:Initializing setup() +2024-12-04 18:05:45,148:INFO:self.USI: a6ba +2024-12-04 18:05:45,148:INFO:self._variable_keys: {'fold_groups_param', 'memory', 'gpu_param', 'gpu_n_jobs_param', 'html_param', 'fold_shuffle_param', 'seed', 'exp_id', 'exp_name_log', 'y_train', 'data', 'y_test', 'USI', 'target_param', 'y', 'log_plots_param', 'logging_param', 'idx', 'pipeline', 'X', '_available_plots', 'X_test', 'X_train', 'n_jobs_param', '_ml_usecase', 'fold_generator', 'transform_target_param'} +2024-12-04 18:05:45,148:INFO:Checking environment +2024-12-04 18:05:45,148:INFO:python_version: 3.11.9 +2024-12-04 18:05:45,148:INFO:python_build: ('tags/v3.11.9:de54cf5', 'Apr 2 2024 10:12:12') +2024-12-04 18:05:45,148:INFO:machine: AMD64 +2024-12-04 18:05:45,148:INFO:platform: Windows-10-10.0.22635-SP0 +2024-12-04 18:05:45,151:INFO:Memory: svmem(total=17007292416, available=2506739712, percent=85.3, used=14500552704, free=2506739712) +2024-12-04 18:05:45,151:INFO:Physical Core: 4 +2024-12-04 18:05:45,151:INFO:Logical Core: 8 +2024-12-04 18:05:45,151:INFO:Checking libraries +2024-12-04 18:05:45,151:INFO:System: +2024-12-04 18:05:45,151:INFO: python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] +2024-12-04 18:05:45,151:INFO:executable: c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Scripts\python.exe +2024-12-04 18:05:45,151:INFO: machine: Windows-10-10.0.22635-SP0 +2024-12-04 18:05:45,151:INFO:PyCaret required dependencies: +2024-12-04 18:05:45,151:INFO: pip: 24.3.1 +2024-12-04 18:05:45,151:INFO: setuptools: 65.5.0 +2024-12-04 18:05:45,151:INFO: pycaret: 3.3.2 +2024-12-04 18:05:45,151:INFO: IPython: 8.12.3 +2024-12-04 18:05:45,151:INFO: ipywidgets: 8.1.5 +2024-12-04 18:05:45,151:INFO: tqdm: 4.67.1 +2024-12-04 18:05:45,151:INFO: numpy: 1.26.4 +2024-12-04 18:05:45,151:INFO: pandas: 2.1.4 +2024-12-04 18:05:45,151:INFO: jinja2: 3.1.4 +2024-12-04 18:05:45,151:INFO: scipy: 1.11.4 +2024-12-04 18:05:45,151:INFO: joblib: 1.3.2 +2024-12-04 18:05:45,151:INFO: sklearn: 1.4.2 +2024-12-04 18:05:45,151:INFO: pyod: 2.0.2 +2024-12-04 18:05:45,151:INFO: imblearn: 0.12.4 +2024-12-04 18:05:45,151:INFO: category_encoders: 2.6.4 +2024-12-04 18:05:45,151:INFO: lightgbm: 4.5.0 +2024-12-04 18:05:45,151:INFO: numba: 0.60.0 +2024-12-04 18:05:45,151:INFO: requests: 2.32.3 +2024-12-04 18:05:45,151:INFO: matplotlib: 3.7.5 +2024-12-04 18:05:45,151:INFO: scikitplot: 0.3.7 +2024-12-04 18:05:45,151:INFO: yellowbrick: 1.5 +2024-12-04 18:05:45,151:INFO: plotly: 5.24.1 +2024-12-04 18:05:45,151:INFO: plotly-resampler: Not installed +2024-12-04 18:05:45,151:INFO: kaleido: 0.2.1 +2024-12-04 18:05:45,151:INFO: schemdraw: 0.15 +2024-12-04 18:05:45,151:INFO: statsmodels: 0.14.4 +2024-12-04 18:05:45,151:INFO: sktime: 0.26.0 +2024-12-04 18:05:45,151:INFO: tbats: 1.1.3 +2024-12-04 18:05:45,151:INFO: pmdarima: 2.0.4 +2024-12-04 18:05:45,151:INFO: psutil: 6.1.0 +2024-12-04 18:05:45,151:INFO: markupsafe: 3.0.2 +2024-12-04 18:05:45,151:INFO: pickle5: Not installed +2024-12-04 18:05:45,151:INFO: cloudpickle: 3.1.0 +2024-12-04 18:05:45,151:INFO: deprecation: 2.1.0 +2024-12-04 18:05:45,151:INFO: xxhash: 3.5.0 +2024-12-04 18:05:45,151:INFO: wurlitzer: Not installed +2024-12-04 18:05:45,151:INFO:PyCaret optional dependencies: +2024-12-04 18:05:45,151:INFO: shap: Not installed +2024-12-04 18:05:45,151:INFO: interpret: Not installed +2024-12-04 18:05:45,151:INFO: umap: Not installed +2024-12-04 18:05:45,151:INFO: ydata_profiling: Not installed +2024-12-04 18:05:45,151:INFO: explainerdashboard: Not installed +2024-12-04 18:05:45,151:INFO: autoviz: Not installed +2024-12-04 18:05:45,151:INFO: fairlearn: Not installed +2024-12-04 18:05:45,151:INFO: deepchecks: Not installed +2024-12-04 18:05:45,151:INFO: xgboost: Not installed +2024-12-04 18:05:45,151:INFO: catboost: Not installed +2024-12-04 18:05:45,151:INFO: kmodes: Not installed +2024-12-04 18:05:45,151:INFO: mlxtend: Not installed +2024-12-04 18:05:45,151:INFO: statsforecast: Not installed +2024-12-04 18:05:45,151:INFO: tune_sklearn: Not installed +2024-12-04 18:05:45,151:INFO: ray: Not installed +2024-12-04 18:05:45,151:INFO: hyperopt: Not installed +2024-12-04 18:05:45,151:INFO: optuna: Not installed +2024-12-04 18:05:45,151:INFO: skopt: Not installed +2024-12-04 18:05:45,151:INFO: mlflow: 2.18.0 +2024-12-04 18:05:45,151:INFO: gradio: Not installed +2024-12-04 18:05:45,151:INFO: fastapi: Not installed +2024-12-04 18:05:45,151:INFO: uvicorn: 0.32.1 +2024-12-04 18:05:45,151:INFO: m2cgen: Not installed +2024-12-04 18:05:45,151:INFO: evidently: Not installed +2024-12-04 18:05:45,151:INFO: fugue: Not installed +2024-12-04 18:05:45,151:INFO: streamlit: 1.40.1 +2024-12-04 18:05:45,151:INFO: prophet: Not installed +2024-12-04 18:05:45,151:INFO:None +2024-12-04 18:05:45,151:INFO:Set up data. +2024-12-04 18:05:45,171:INFO:Set up folding strategy. +2024-12-04 18:05:45,171:INFO:Set up train/test split. +2024-12-04 18:05:45,171:INFO:Set up data. +2024-12-04 18:05:45,188:INFO:Set up index. +2024-12-04 18:05:45,188:INFO:Assigning column types. +2024-12-04 18:05:45,196:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-12-04 18:05:45,196:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,199:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,205:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,268:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,326:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,327:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,328:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,328:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,339:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,346:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,414:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,451:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,451:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,451:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,451:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-12-04 18:05:45,460:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,463:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,522:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,561:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,563:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,563:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,567:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,567:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,628:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,666:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,666:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,666:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,666:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-12-04 18:05:45,680:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,740:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,787:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,787:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,787:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,797:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,851:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,897:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:45,897:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,897:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:45,898:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-12-04 18:05:45,964:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:46,004:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:46,004:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,005:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,066:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:46,112:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-12-04 18:05:46,113:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,113:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,113:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-12-04 18:05:46,178:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:46,217:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,217:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,282:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-12-04 18:05:46,328:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,328:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,329:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-12-04 18:05:46,445:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,447:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,559:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,559:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:46,560:INFO:Preparing preprocessing pipeline... +2024-12-04 18:05:46,560:INFO:Set up target transformation. +2024-12-04 18:05:46,560:INFO:Set up simple imputation. +2024-12-04 18:05:46,564:INFO:Set up encoding of categorical features. +2024-12-04 18:05:46,588:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:46,588:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:46,665:INFO:Finished creating preprocessing pipeline. +2024-12-04 18:05:46,667:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\antob\AppData\Local\Temp\joblib), + steps=[('target_transformation', + TransformerWrapperWithInverse(transformer=TargetTransformer(estimator=PowerTransformer(standardize=False)))), + ('numerical_imputer', + TransformerWrapper(include=['annee', 'Series', 'annee_norm'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('onehot_encoding', + TransformerWrapper(include=['nom_region', 'nom_pathologie'], + transformer=OneHotEncoder(cols=['nom_region', + 'nom_pathologie'], + handle_missing='return_nan', + use_cat_names=True)))]) +2024-12-04 18:05:46,667:INFO:Creating final display dataframe. +2024-12-04 18:05:46,982:INFO:Setup _display_container: Description Value +0 Session id 123 +1 Target nbr_hospi +2 Target type Regression +3 Original data shape (5, 64) +4 Transformed data shape (5, 64) +5 Transformed train set shape (4, 64) +6 Transformed test set shape (1, 64) +7 Numeric features 3 +8 Categorical features 2 +9 Rows with missing values 20.0% +10 Preprocess True +11 Imputation type simple +12 Numeric imputation mean +13 Categorical imputation mode +14 Maximum one-hot encoding 25 +15 Encoding method None +16 Transform target True +17 Transform target method yeo-johnson +18 Fold Generator TimeSeriesSplit +19 Fold Number 3 +20 CPU Jobs -1 +21 Use GPU False +22 Log Experiment False +23 Experiment Name reg-default-name +24 USI a6ba +2024-12-04 18:05:47,090:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:47,090:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:47,195:WARNING: +'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:47,195:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-12-04 18:05:47,195:INFO:setup() successfully completed in 2.07s............... +2024-12-04 18:05:47,195:INFO:Initializing compare_models() +2024-12-04 18:05:47,195:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-12-04 18:05:47,195:INFO:Checking exceptions +2024-12-04 18:05:47,201:INFO:Preparing display monitor +2024-12-04 18:05:47,216:INFO:Initializing Linear Regression +2024-12-04 18:05:47,216:INFO:Total runtime is 0.0 minutes +2024-12-04 18:05:47,216:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:47,216:INFO:Initializing create_model() +2024-12-04 18:05:47,216:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:47,216:INFO:Checking exceptions +2024-12-04 18:05:47,216:INFO:Importing libraries +2024-12-04 18:05:47,216:INFO:Copying training dataset +2024-12-04 18:05:47,240:INFO:Defining folds +2024-12-04 18:05:47,240:INFO:Declaring metric variables +2024-12-04 18:05:47,244:INFO:Importing untrained model +2024-12-04 18:05:47,247:INFO:Linear Regression Imported successfully +2024-12-04 18:05:47,256:INFO:Starting cross validation +2024-12-04 18:05:47,257:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:47,293:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:47,296:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:47,369:WARNING:create_model() for lr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:47,371:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:47,371:INFO:Initializing create_model() +2024-12-04 18:05:47,371:INFO:create_model(self=, estimator=lr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:47,371:INFO:Checking exceptions +2024-12-04 18:05:47,372:INFO:Importing libraries +2024-12-04 18:05:47,372:INFO:Copying training dataset +2024-12-04 18:05:47,383:INFO:Defining folds +2024-12-04 18:05:47,383:INFO:Declaring metric variables +2024-12-04 18:05:47,390:INFO:Importing untrained model +2024-12-04 18:05:47,392:INFO:Linear Regression Imported successfully +2024-12-04 18:05:47,397:INFO:Starting cross validation +2024-12-04 18:05:47,401:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:47,426:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:47,426:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:47,471:ERROR:create_model() for lr raised an exception or returned all 0.0: +2024-12-04 18:05:47,471:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_base.py", line 578, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:47,471:INFO:Initializing Lasso Regression +2024-12-04 18:05:47,471:INFO:Total runtime is 0.004246564706166586 minutes +2024-12-04 18:05:47,476:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:47,476:INFO:Initializing create_model() +2024-12-04 18:05:47,476:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:47,476:INFO:Checking exceptions +2024-12-04 18:05:47,476:INFO:Importing libraries +2024-12-04 18:05:47,476:INFO:Copying training dataset +2024-12-04 18:05:47,485:INFO:Defining folds +2024-12-04 18:05:47,485:INFO:Declaring metric variables +2024-12-04 18:05:47,487:INFO:Importing untrained model +2024-12-04 18:05:47,491:INFO:Lasso Regression Imported successfully +2024-12-04 18:05:47,497:INFO:Starting cross validation +2024-12-04 18:05:47,499:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:47,526:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:47,529:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:47,591:WARNING:create_model() for lasso raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:47,592:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:47,593:INFO:Initializing create_model() +2024-12-04 18:05:47,593:INFO:create_model(self=, estimator=lasso, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:47,593:INFO:Checking exceptions +2024-12-04 18:05:47,593:INFO:Importing libraries +2024-12-04 18:05:47,593:INFO:Copying training dataset +2024-12-04 18:05:47,605:INFO:Defining folds +2024-12-04 18:05:47,605:INFO:Declaring metric variables +2024-12-04 18:05:47,610:INFO:Importing untrained model +2024-12-04 18:05:47,614:INFO:Lasso Regression Imported successfully +2024-12-04 18:05:47,617:INFO:Starting cross validation +2024-12-04 18:05:47,617:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:47,649:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:47,652:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:47,710:ERROR:create_model() for lasso raised an exception or returned all 0.0: +2024-12-04 18:05:47,711:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:47,711:INFO:Initializing Ridge Regression +2024-12-04 18:05:47,711:INFO:Total runtime is 0.008247788747151694 minutes +2024-12-04 18:05:47,715:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:47,715:INFO:Initializing create_model() +2024-12-04 18:05:47,715:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:47,715:INFO:Checking exceptions +2024-12-04 18:05:47,715:INFO:Importing libraries +2024-12-04 18:05:47,715:INFO:Copying training dataset +2024-12-04 18:05:47,725:INFO:Defining folds +2024-12-04 18:05:47,725:INFO:Declaring metric variables +2024-12-04 18:05:47,728:INFO:Importing untrained model +2024-12-04 18:05:47,732:INFO:Ridge Regression Imported successfully +2024-12-04 18:05:47,737:INFO:Starting cross validation +2024-12-04 18:05:47,740:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:47,766:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:47,770:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:47,811:WARNING:create_model() for ridge raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:47,811:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:47,811:INFO:Initializing create_model() +2024-12-04 18:05:47,811:INFO:create_model(self=, estimator=ridge, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:47,811:INFO:Checking exceptions +2024-12-04 18:05:47,811:INFO:Importing libraries +2024-12-04 18:05:47,812:INFO:Copying training dataset +2024-12-04 18:05:47,816:INFO:Defining folds +2024-12-04 18:05:47,816:INFO:Declaring metric variables +2024-12-04 18:05:47,816:INFO:Importing untrained model +2024-12-04 18:05:47,826:INFO:Ridge Regression Imported successfully +2024-12-04 18:05:47,832:INFO:Starting cross validation +2024-12-04 18:05:47,832:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:47,856:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:47,856:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:47,901:ERROR:create_model() for ridge raised an exception or returned all 0.0: +2024-12-04 18:05:47,901:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_ridge.py", line 1167, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:47,901:INFO:Initializing Elastic Net +2024-12-04 18:05:47,901:INFO:Total runtime is 0.011416892210642498 minutes +2024-12-04 18:05:47,901:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:47,901:INFO:Initializing create_model() +2024-12-04 18:05:47,901:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:47,901:INFO:Checking exceptions +2024-12-04 18:05:47,901:INFO:Importing libraries +2024-12-04 18:05:47,901:INFO:Copying training dataset +2024-12-04 18:05:47,916:INFO:Defining folds +2024-12-04 18:05:47,916:INFO:Declaring metric variables +2024-12-04 18:05:47,916:INFO:Importing untrained model +2024-12-04 18:05:47,916:INFO:Elastic Net Imported successfully +2024-12-04 18:05:47,929:INFO:Starting cross validation +2024-12-04 18:05:47,930:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:47,955:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:47,959:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,012:WARNING:create_model() for en raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:48,013:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:48,013:INFO:Initializing create_model() +2024-12-04 18:05:48,013:INFO:create_model(self=, estimator=en, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,013:INFO:Checking exceptions +2024-12-04 18:05:48,013:INFO:Importing libraries +2024-12-04 18:05:48,013:INFO:Copying training dataset +2024-12-04 18:05:48,017:INFO:Defining folds +2024-12-04 18:05:48,017:INFO:Declaring metric variables +2024-12-04 18:05:48,017:INFO:Importing untrained model +2024-12-04 18:05:48,025:INFO:Elastic Net Imported successfully +2024-12-04 18:05:48,032:INFO:Starting cross validation +2024-12-04 18:05:48,032:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,051:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,059:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,104:ERROR:create_model() for en raised an exception or returned all 0.0: +2024-12-04 18:05:48,104:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py", line 955, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:48,104:INFO:Initializing Least Angle Regression +2024-12-04 18:05:48,104:INFO:Total runtime is 0.014795315265655518 minutes +2024-12-04 18:05:48,104:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:48,109:INFO:Initializing create_model() +2024-12-04 18:05:48,109:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,109:INFO:Checking exceptions +2024-12-04 18:05:48,109:INFO:Importing libraries +2024-12-04 18:05:48,109:INFO:Copying training dataset +2024-12-04 18:05:48,116:INFO:Defining folds +2024-12-04 18:05:48,116:INFO:Declaring metric variables +2024-12-04 18:05:48,116:INFO:Importing untrained model +2024-12-04 18:05:48,116:INFO:Least Angle Regression Imported successfully +2024-12-04 18:05:48,134:INFO:Starting cross validation +2024-12-04 18:05:48,134:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,187:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,192:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,237:WARNING:create_model() for lar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:48,237:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:48,237:INFO:Initializing create_model() +2024-12-04 18:05:48,237:INFO:create_model(self=, estimator=lar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,237:INFO:Checking exceptions +2024-12-04 18:05:48,237:INFO:Importing libraries +2024-12-04 18:05:48,237:INFO:Copying training dataset +2024-12-04 18:05:48,247:INFO:Defining folds +2024-12-04 18:05:48,247:INFO:Declaring metric variables +2024-12-04 18:05:48,252:INFO:Importing untrained model +2024-12-04 18:05:48,252:INFO:Least Angle Regression Imported successfully +2024-12-04 18:05:48,263:INFO:Starting cross validation +2024-12-04 18:05:48,263:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,287:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,288:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,333:ERROR:create_model() for lar raised an exception or returned all 0.0: +2024-12-04 18:05:48,333:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:48,333:INFO:Initializing Lasso Least Angle Regression +2024-12-04 18:05:48,333:INFO:Total runtime is 0.01861485242843628 minutes +2024-12-04 18:05:48,337:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:48,337:INFO:Initializing create_model() +2024-12-04 18:05:48,337:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,337:INFO:Checking exceptions +2024-12-04 18:05:48,337:INFO:Importing libraries +2024-12-04 18:05:48,337:INFO:Copying training dataset +2024-12-04 18:05:48,345:INFO:Defining folds +2024-12-04 18:05:48,345:INFO:Declaring metric variables +2024-12-04 18:05:48,345:INFO:Importing untrained model +2024-12-04 18:05:48,351:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 18:05:48,361:INFO:Starting cross validation +2024-12-04 18:05:48,361:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,387:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,390:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,432:WARNING:create_model() for llar raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:48,432:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:48,432:INFO:Initializing create_model() +2024-12-04 18:05:48,432:INFO:create_model(self=, estimator=llar, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,432:INFO:Checking exceptions +2024-12-04 18:05:48,432:INFO:Importing libraries +2024-12-04 18:05:48,432:INFO:Copying training dataset +2024-12-04 18:05:48,441:INFO:Defining folds +2024-12-04 18:05:48,441:INFO:Declaring metric variables +2024-12-04 18:05:48,442:INFO:Importing untrained model +2024-12-04 18:05:48,446:INFO:Lasso Least Angle Regression Imported successfully +2024-12-04 18:05:48,451:INFO:Starting cross validation +2024-12-04 18:05:48,451:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,476:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,478:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,523:ERROR:create_model() for llar raised an exception or returned all 0.0: +2024-12-04 18:05:48,525:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_least_angle.py", line 1146, in fit + X, y = self._validate_data(X, y, y_numeric=True, multi_output=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:48,525:INFO:Initializing Orthogonal Matching Pursuit +2024-12-04 18:05:48,525:INFO:Total runtime is 0.02180570761362712 minutes +2024-12-04 18:05:48,528:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:48,528:INFO:Initializing create_model() +2024-12-04 18:05:48,528:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,528:INFO:Checking exceptions +2024-12-04 18:05:48,528:INFO:Importing libraries +2024-12-04 18:05:48,528:INFO:Copying training dataset +2024-12-04 18:05:48,537:INFO:Defining folds +2024-12-04 18:05:48,537:INFO:Declaring metric variables +2024-12-04 18:05:48,540:INFO:Importing untrained model +2024-12-04 18:05:48,544:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 18:05:48,551:INFO:Starting cross validation +2024-12-04 18:05:48,551:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,575:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,577:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,620:WARNING:create_model() for omp raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:48,620:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:48,620:INFO:Initializing create_model() +2024-12-04 18:05:48,620:INFO:create_model(self=, estimator=omp, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,620:INFO:Checking exceptions +2024-12-04 18:05:48,620:INFO:Importing libraries +2024-12-04 18:05:48,620:INFO:Copying training dataset +2024-12-04 18:05:48,629:INFO:Defining folds +2024-12-04 18:05:48,629:INFO:Declaring metric variables +2024-12-04 18:05:48,632:INFO:Importing untrained model +2024-12-04 18:05:48,632:INFO:Orthogonal Matching Pursuit Imported successfully +2024-12-04 18:05:48,641:INFO:Starting cross validation +2024-12-04 18:05:48,642:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,669:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,670:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,731:ERROR:create_model() for omp raised an exception or returned all 0.0: +2024-12-04 18:05:48,732:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_omp.py", line 750, in fit + X, y = self._validate_data(X, y, multi_output=True, y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:48,732:INFO:Initializing Bayesian Ridge +2024-12-04 18:05:48,732:INFO:Total runtime is 0.025258278846740725 minutes +2024-12-04 18:05:48,749:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:48,749:INFO:Initializing create_model() +2024-12-04 18:05:48,749:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,749:INFO:Checking exceptions +2024-12-04 18:05:48,749:INFO:Importing libraries +2024-12-04 18:05:48,749:INFO:Copying training dataset +2024-12-04 18:05:48,764:INFO:Defining folds +2024-12-04 18:05:48,764:INFO:Declaring metric variables +2024-12-04 18:05:48,769:INFO:Importing untrained model +2024-12-04 18:05:48,776:INFO:Bayesian Ridge Imported successfully +2024-12-04 18:05:48,782:INFO:Starting cross validation +2024-12-04 18:05:48,782:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,801:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,801:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,852:WARNING:create_model() for br raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:48,852:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:48,852:INFO:Initializing create_model() +2024-12-04 18:05:48,852:INFO:create_model(self=, estimator=br, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,852:INFO:Checking exceptions +2024-12-04 18:05:48,852:INFO:Importing libraries +2024-12-04 18:05:48,852:INFO:Copying training dataset +2024-12-04 18:05:48,861:INFO:Defining folds +2024-12-04 18:05:48,861:INFO:Declaring metric variables +2024-12-04 18:05:48,863:INFO:Importing untrained model +2024-12-04 18:05:48,866:INFO:Bayesian Ridge Imported successfully +2024-12-04 18:05:48,877:INFO:Starting cross validation +2024-12-04 18:05:48,878:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:48,901:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:48,901:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:48,958:ERROR:create_model() for br raised an exception or returned all 0.0: +2024-12-04 18:05:48,960:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_bayes.py", line 296, in fit + X, y = self._validate_data(X, y, dtype=[np.float64, np.float32], y_numeric=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:48,960:INFO:Initializing Passive Aggressive Regressor +2024-12-04 18:05:48,960:INFO:Total runtime is 0.029065648714701336 minutes +2024-12-04 18:05:48,963:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:48,963:INFO:Initializing create_model() +2024-12-04 18:05:48,963:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:48,963:INFO:Checking exceptions +2024-12-04 18:05:48,963:INFO:Importing libraries +2024-12-04 18:05:48,963:INFO:Copying training dataset +2024-12-04 18:05:48,967:INFO:Defining folds +2024-12-04 18:05:48,967:INFO:Declaring metric variables +2024-12-04 18:05:48,976:INFO:Importing untrained model +2024-12-04 18:05:48,980:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 18:05:48,987:INFO:Starting cross validation +2024-12-04 18:05:48,989:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,011:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,013:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,056:WARNING:create_model() for par raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:49,056:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:49,056:INFO:Initializing create_model() +2024-12-04 18:05:49,056:INFO:create_model(self=, estimator=par, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,056:INFO:Checking exceptions +2024-12-04 18:05:49,056:INFO:Importing libraries +2024-12-04 18:05:49,056:INFO:Copying training dataset +2024-12-04 18:05:49,066:INFO:Defining folds +2024-12-04 18:05:49,066:INFO:Declaring metric variables +2024-12-04 18:05:49,066:INFO:Importing untrained model +2024-12-04 18:05:49,072:INFO:Passive Aggressive Regressor Imported successfully +2024-12-04 18:05:49,079:INFO:Starting cross validation +2024-12-04 18:05:49,080:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,101:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,109:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,160:ERROR:create_model() for par raised an exception or returned all 0.0: +2024-12-04 18:05:49,161:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_passive_aggressive.py", line 566, in fit + return self._fit( + ^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1557, in _fit + self._partial_fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py", line 1456, in _partial_fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:49,161:INFO:Initializing Huber Regressor +2024-12-04 18:05:49,161:INFO:Total runtime is 0.03240418831507365 minutes +2024-12-04 18:05:49,163:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:49,163:INFO:Initializing create_model() +2024-12-04 18:05:49,163:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,163:INFO:Checking exceptions +2024-12-04 18:05:49,163:INFO:Importing libraries +2024-12-04 18:05:49,163:INFO:Copying training dataset +2024-12-04 18:05:49,166:INFO:Defining folds +2024-12-04 18:05:49,166:INFO:Declaring metric variables +2024-12-04 18:05:49,176:INFO:Importing untrained model +2024-12-04 18:05:49,179:INFO:Huber Regressor Imported successfully +2024-12-04 18:05:49,187:INFO:Starting cross validation +2024-12-04 18:05:49,187:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,250:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,251:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,294:WARNING:create_model() for huber raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:49,294:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:49,294:INFO:Initializing create_model() +2024-12-04 18:05:49,294:INFO:create_model(self=, estimator=huber, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,294:INFO:Checking exceptions +2024-12-04 18:05:49,294:INFO:Importing libraries +2024-12-04 18:05:49,294:INFO:Copying training dataset +2024-12-04 18:05:49,301:INFO:Defining folds +2024-12-04 18:05:49,301:INFO:Declaring metric variables +2024-12-04 18:05:49,301:INFO:Importing untrained model +2024-12-04 18:05:49,311:INFO:Huber Regressor Imported successfully +2024-12-04 18:05:49,316:INFO:Starting cross validation +2024-12-04 18:05:49,316:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,340:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,341:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,386:ERROR:create_model() for huber raised an exception or returned all 0.0: +2024-12-04 18:05:49,388:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\linear_model\_huber.py", line 297, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float64 + + +2024-12-04 18:05:49,388:INFO:Initializing K Neighbors Regressor +2024-12-04 18:05:49,388:INFO:Total runtime is 0.03618809779485067 minutes +2024-12-04 18:05:49,390:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:49,390:INFO:Initializing create_model() +2024-12-04 18:05:49,390:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,390:INFO:Checking exceptions +2024-12-04 18:05:49,390:INFO:Importing libraries +2024-12-04 18:05:49,390:INFO:Copying training dataset +2024-12-04 18:05:49,401:INFO:Defining folds +2024-12-04 18:05:49,401:INFO:Declaring metric variables +2024-12-04 18:05:49,410:INFO:Importing untrained model +2024-12-04 18:05:49,413:INFO:K Neighbors Regressor Imported successfully +2024-12-04 18:05:49,416:INFO:Starting cross validation +2024-12-04 18:05:49,422:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,444:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,447:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,490:WARNING:create_model() for knn raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:49,490:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:49,490:INFO:Initializing create_model() +2024-12-04 18:05:49,490:INFO:create_model(self=, estimator=knn, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,490:INFO:Checking exceptions +2024-12-04 18:05:49,490:INFO:Importing libraries +2024-12-04 18:05:49,490:INFO:Copying training dataset +2024-12-04 18:05:49,497:INFO:Defining folds +2024-12-04 18:05:49,497:INFO:Declaring metric variables +2024-12-04 18:05:49,501:INFO:Importing untrained model +2024-12-04 18:05:49,501:INFO:K Neighbors Regressor Imported successfully +2024-12-04 18:05:49,512:INFO:Starting cross validation +2024-12-04 18:05:49,514:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,532:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,532:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,580:ERROR:create_model() for knn raised an exception or returned all 0.0: +2024-12-04 18:05:49,580:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_regression.py", line 223, in fit + return self._fit(X, y) + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\neighbors\_base.py", line 476, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:49,580:INFO:Initializing Decision Tree Regressor +2024-12-04 18:05:49,580:INFO:Total runtime is 0.039395562807718915 minutes +2024-12-04 18:05:49,582:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:49,582:INFO:Initializing create_model() +2024-12-04 18:05:49,582:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,582:INFO:Checking exceptions +2024-12-04 18:05:49,582:INFO:Importing libraries +2024-12-04 18:05:49,582:INFO:Copying training dataset +2024-12-04 18:05:49,594:INFO:Defining folds +2024-12-04 18:05:49,594:INFO:Declaring metric variables +2024-12-04 18:05:49,596:INFO:Importing untrained model +2024-12-04 18:05:49,601:INFO:Decision Tree Regressor Imported successfully +2024-12-04 18:05:49,601:INFO:Starting cross validation +2024-12-04 18:05:49,609:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,633:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,634:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,697:WARNING:create_model() for dt raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:49,697:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:49,697:INFO:Initializing create_model() +2024-12-04 18:05:49,697:INFO:create_model(self=, estimator=dt, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,697:INFO:Checking exceptions +2024-12-04 18:05:49,697:INFO:Importing libraries +2024-12-04 18:05:49,697:INFO:Copying training dataset +2024-12-04 18:05:49,701:INFO:Defining folds +2024-12-04 18:05:49,701:INFO:Declaring metric variables +2024-12-04 18:05:49,710:INFO:Importing untrained model +2024-12-04 18:05:49,716:INFO:Decision Tree Regressor Imported successfully +2024-12-04 18:05:49,716:INFO:Starting cross validation +2024-12-04 18:05:49,725:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,782:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,788:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,831:ERROR:create_model() for dt raised an exception or returned all 0.0: +2024-12-04 18:05:49,832:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 1377, in fit + super()._fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\tree\_classes.py", line 252, in _fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 645, in _validate_data + X = check_array(X, input_name="X", **check_X_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:49,832:INFO:Initializing Random Forest Regressor +2024-12-04 18:05:49,832:INFO:Total runtime is 0.04359779357910157 minutes +2024-12-04 18:05:49,832:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:49,832:INFO:Initializing create_model() +2024-12-04 18:05:49,832:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,832:INFO:Checking exceptions +2024-12-04 18:05:49,832:INFO:Importing libraries +2024-12-04 18:05:49,832:INFO:Copying training dataset +2024-12-04 18:05:49,845:INFO:Defining folds +2024-12-04 18:05:49,845:INFO:Declaring metric variables +2024-12-04 18:05:49,845:INFO:Importing untrained model +2024-12-04 18:05:49,852:INFO:Random Forest Regressor Imported successfully +2024-12-04 18:05:49,861:INFO:Starting cross validation +2024-12-04 18:05:49,864:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,895:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,896:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:49,941:WARNING:create_model() for rf raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:49,941:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:49,941:INFO:Initializing create_model() +2024-12-04 18:05:49,942:INFO:create_model(self=, estimator=rf, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:49,942:INFO:Checking exceptions +2024-12-04 18:05:49,942:INFO:Importing libraries +2024-12-04 18:05:49,942:INFO:Copying training dataset +2024-12-04 18:05:49,951:INFO:Defining folds +2024-12-04 18:05:49,951:INFO:Declaring metric variables +2024-12-04 18:05:49,954:INFO:Importing untrained model +2024-12-04 18:05:49,959:INFO:Random Forest Regressor Imported successfully +2024-12-04 18:05:49,966:INFO:Starting cross validation +2024-12-04 18:05:49,967:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:49,990:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:49,993:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,036:ERROR:create_model() for rf raised an exception or returned all 0.0: +2024-12-04 18:05:50,037:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:50,037:INFO:Initializing Extra Trees Regressor +2024-12-04 18:05:50,037:INFO:Total runtime is 0.047020463148752856 minutes +2024-12-04 18:05:50,040:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:50,040:INFO:Initializing create_model() +2024-12-04 18:05:50,040:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,040:INFO:Checking exceptions +2024-12-04 18:05:50,040:INFO:Importing libraries +2024-12-04 18:05:50,040:INFO:Copying training dataset +2024-12-04 18:05:50,050:INFO:Defining folds +2024-12-04 18:05:50,050:INFO:Declaring metric variables +2024-12-04 18:05:50,055:INFO:Importing untrained model +2024-12-04 18:05:50,055:INFO:Extra Trees Regressor Imported successfully +2024-12-04 18:05:50,065:INFO:Starting cross validation +2024-12-04 18:05:50,066:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,087:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,087:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,134:WARNING:create_model() for et raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:50,134:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:50,134:INFO:Initializing create_model() +2024-12-04 18:05:50,134:INFO:create_model(self=, estimator=et, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,134:INFO:Checking exceptions +2024-12-04 18:05:50,134:INFO:Importing libraries +2024-12-04 18:05:50,134:INFO:Copying training dataset +2024-12-04 18:05:50,142:INFO:Defining folds +2024-12-04 18:05:50,142:INFO:Declaring metric variables +2024-12-04 18:05:50,146:INFO:Importing untrained model +2024-12-04 18:05:50,151:INFO:Extra Trees Regressor Imported successfully +2024-12-04 18:05:50,159:INFO:Starting cross validation +2024-12-04 18:05:50,160:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,185:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,185:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,242:ERROR:create_model() for et raised an exception or returned all 0.0: +2024-12-04 18:05:50,242:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_forest.py", line 363, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:50,242:INFO:Initializing AdaBoost Regressor +2024-12-04 18:05:50,242:INFO:Total runtime is 0.05043718020121257 minutes +2024-12-04 18:05:50,252:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:50,252:INFO:Initializing create_model() +2024-12-04 18:05:50,252:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,252:INFO:Checking exceptions +2024-12-04 18:05:50,252:INFO:Importing libraries +2024-12-04 18:05:50,252:INFO:Copying training dataset +2024-12-04 18:05:50,278:INFO:Defining folds +2024-12-04 18:05:50,278:INFO:Declaring metric variables +2024-12-04 18:05:50,283:INFO:Importing untrained model +2024-12-04 18:05:50,288:INFO:AdaBoost Regressor Imported successfully +2024-12-04 18:05:50,294:INFO:Starting cross validation +2024-12-04 18:05:50,295:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,320:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,325:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,372:WARNING:create_model() for ada raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:50,372:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:50,372:INFO:Initializing create_model() +2024-12-04 18:05:50,372:INFO:create_model(self=, estimator=ada, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,372:INFO:Checking exceptions +2024-12-04 18:05:50,372:INFO:Importing libraries +2024-12-04 18:05:50,372:INFO:Copying training dataset +2024-12-04 18:05:50,382:INFO:Defining folds +2024-12-04 18:05:50,382:INFO:Declaring metric variables +2024-12-04 18:05:50,388:INFO:Importing untrained model +2024-12-04 18:05:50,393:INFO:AdaBoost Regressor Imported successfully +2024-12-04 18:05:50,395:INFO:Starting cross validation +2024-12-04 18:05:50,401:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,428:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,429:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,478:ERROR:create_model() for ada raised an exception or returned all 0.0: +2024-12-04 18:05:50,478:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 133, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array + _assert_all_finite( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 111, in _assert_all_finite + raise ValueError("Input contains NaN") +ValueError: Input contains NaN + + +2024-12-04 18:05:50,478:INFO:Initializing Gradient Boosting Regressor +2024-12-04 18:05:50,478:INFO:Total runtime is 0.05436811049779257 minutes +2024-12-04 18:05:50,482:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:50,482:INFO:Initializing create_model() +2024-12-04 18:05:50,482:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,482:INFO:Checking exceptions +2024-12-04 18:05:50,482:INFO:Importing libraries +2024-12-04 18:05:50,482:INFO:Copying training dataset +2024-12-04 18:05:50,492:INFO:Defining folds +2024-12-04 18:05:50,492:INFO:Declaring metric variables +2024-12-04 18:05:50,497:INFO:Importing untrained model +2024-12-04 18:05:50,501:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 18:05:50,501:INFO:Starting cross validation +2024-12-04 18:05:50,509:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,532:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,532:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,577:WARNING:create_model() for gbr raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:50,578:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:50,578:INFO:Initializing create_model() +2024-12-04 18:05:50,578:INFO:create_model(self=, estimator=gbr, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,578:INFO:Checking exceptions +2024-12-04 18:05:50,578:INFO:Importing libraries +2024-12-04 18:05:50,578:INFO:Copying training dataset +2024-12-04 18:05:50,582:INFO:Defining folds +2024-12-04 18:05:50,582:INFO:Declaring metric variables +2024-12-04 18:05:50,587:INFO:Importing untrained model +2024-12-04 18:05:50,591:INFO:Gradient Boosting Regressor Imported successfully +2024-12-04 18:05:50,596:INFO:Starting cross validation +2024-12-04 18:05:50,596:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,616:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,616:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,665:ERROR:create_model() for gbr raised an exception or returned all 0.0: +2024-12-04 18:05:50,666:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 564, in astype + new_cats = new_cats.astype(dtype=dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ValueError: could not convert string to float: 'Régions' + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 1474, in wrapper + return fit_method(estimator, *args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\ensemble\_gb.py", line 659, in fit + X, y = self._validate_data( + ^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\base.py", line 650, in _validate_data + X, y = check_X_y(X, y, **check_params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 1263, in check_X_y + X = check_array( + ^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\validation.py", line 921, in check_array + array = array.astype(new_dtype) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\generic.py", line 6534, in astype + new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 414, in astype + return self.apply( + ^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\managers.py", line 354, in apply + applied = getattr(b, f)(**kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\internals\blocks.py", line 616, in astype + new_values = astype_array_safe(values, dtype, copy=copy, errors=errors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 238, in astype_array_safe + new_values = astype_array(values, dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\dtypes\astype.py", line 180, in astype_array + values = values.astype(dtype, copy=copy) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pandas\core\arrays\categorical.py", line 575, in astype + raise ValueError(msg) +ValueError: Cannot cast object dtype to float32 + + +2024-12-04 18:05:50,666:INFO:Initializing Light Gradient Boosting Machine +2024-12-04 18:05:50,666:INFO:Total runtime is 0.05750148296356202 minutes +2024-12-04 18:05:50,666:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:50,666:INFO:Initializing create_model() +2024-12-04 18:05:50,666:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,666:INFO:Checking exceptions +2024-12-04 18:05:50,666:INFO:Importing libraries +2024-12-04 18:05:50,666:INFO:Copying training dataset +2024-12-04 18:05:50,678:INFO:Defining folds +2024-12-04 18:05:50,678:INFO:Declaring metric variables +2024-12-04 18:05:50,683:INFO:Importing untrained model +2024-12-04 18:05:50,687:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 18:05:50,694:INFO:Starting cross validation +2024-12-04 18:05:50,696:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,716:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,716:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,765:WARNING:create_model() for lightgbm raised an exception or returned all 0.0, trying without fit_kwargs: +2024-12-04 18:05:50,766:WARNING:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: cle_unique: object, year: dbdate + + +2024-12-04 18:05:50,767:INFO:Initializing create_model() +2024-12-04 18:05:50,767:INFO:create_model(self=, estimator=lightgbm, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,767:INFO:Checking exceptions +2024-12-04 18:05:50,767:INFO:Importing libraries +2024-12-04 18:05:50,767:INFO:Copying training dataset +2024-12-04 18:05:50,775:INFO:Defining folds +2024-12-04 18:05:50,775:INFO:Declaring metric variables +2024-12-04 18:05:50,776:INFO:Importing untrained model +2024-12-04 18:05:50,780:INFO:Light Gradient Boosting Machine Imported successfully +2024-12-04 18:05:50,800:INFO:Starting cross validation +2024-12-04 18:05:50,802:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,863:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,863:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:50,930:ERROR:create_model() for lightgbm raised an exception or returned all 0.0: +2024-12-04 18:05:50,932:ERROR:Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 794, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: cle_unique: object, year: dbdate + + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 815, in compare_models + model, model_fit_time = self._create_model(**create_model_args) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1533, in _create_model + model, model_fit_time, model_results, _ = self._create_model_with_cv( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py", line 1126, in _create_model_with_cv + scores = cross_validate( + ^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 450, in cross_validate + _warn_or_raise_about_fit_failures(results, error_score) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 536, in _warn_or_raise_about_fit_failures + raise ValueError(all_fits_failed_message) +ValueError: +All the 3 fits failed. +It is very likely that your model is misconfigured. +You can try to debug the error by setting error_score='raise'. + +Below are more details about the failures: +-------------------------------------------------------------------------------- +3 fits failed with the following error: +Traceback (most recent call last): + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score + estimator.fit(X_train, y_train, **fit_params) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 278, in fit + fitted_estimator = self._memory_fit( + ^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\joblib\memory.py", line 353, in __call__ + return self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pipeline.py", line 69, in _fit_one + transformer.fit(*args) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 1189, in fit + super().fit( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\sklearn.py", line 955, in fit + self._Booster = train( + ^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\engine.py", line 282, in train + booster = Booster(params=params, train_set=train_set) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 3637, in __init__ + train_set.construct() + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2576, in construct + self._lazy_init( + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 2106, in _lazy_init + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + ^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 848, in _data_from_pandas + _pandas_to_numpy(data, target_dtype=target_dtype), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 794, in _pandas_to_numpy + _check_for_bad_pandas_dtypes(data.dtypes) + File "c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\lightgbm\basic.py", line 784, in _check_for_bad_pandas_dtypes + raise ValueError( +ValueError: pandas dtypes must be int, float or bool. +Fields with bad pandas dtypes: cle_unique: object, year: dbdate + + +2024-12-04 18:05:50,932:INFO:Initializing Dummy Regressor +2024-12-04 18:05:50,932:INFO:Total runtime is 0.06192848682403566 minutes +2024-12-04 18:05:50,932:INFO:SubProcess create_model() called ================================== +2024-12-04 18:05:50,932:INFO:Initializing create_model() +2024-12-04 18:05:50,932:INFO:create_model(self=, estimator=dummy, fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:50,932:INFO:Checking exceptions +2024-12-04 18:05:50,932:INFO:Importing libraries +2024-12-04 18:05:50,932:INFO:Copying training dataset +2024-12-04 18:05:50,945:INFO:Defining folds +2024-12-04 18:05:50,945:INFO:Declaring metric variables +2024-12-04 18:05:50,945:INFO:Importing untrained model +2024-12-04 18:05:50,951:INFO:Dummy Regressor Imported successfully +2024-12-04 18:05:50,953:INFO:Starting cross validation +2024-12-04 18:05:50,960:INFO:Cross validating with TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), n_jobs=-1 +2024-12-04 18:05:50,982:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:50,988:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:51,045:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 18:05:51,050:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 18:05:51,051:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. + warnings.warn(msg, UndefinedMetricWarning) + +2024-12-04 18:05:51,061:INFO:Calculating mean and std +2024-12-04 18:05:51,063:INFO:Creating metrics dataframe +2024-12-04 18:05:51,063:INFO:Uploading results into container +2024-12-04 18:05:51,063:INFO:Uploading model into container now +2024-12-04 18:05:51,063:INFO:_master_model_container: 1 +2024-12-04 18:05:51,063:INFO:_display_container: 2 +2024-12-04 18:05:51,063:INFO:DummyRegressor() +2024-12-04 18:05:51,063:INFO:create_model() successfully completed...................................... +2024-12-04 18:05:51,229:INFO:SubProcess create_model() end ================================== +2024-12-04 18:05:51,229:INFO:Creating metrics dataframe +2024-12-04 18:05:51,234:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:339: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead. + .applymap(highlight_cols, subset=["TT (Sec)"]) + +2024-12-04 18:05:51,243:INFO:Initializing create_model() +2024-12-04 18:05:51,244:INFO:create_model(self=, estimator=DummyRegressor(), fold=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-12-04 18:05:51,244:INFO:Checking exceptions +2024-12-04 18:05:51,246:INFO:Importing libraries +2024-12-04 18:05:51,246:INFO:Copying training dataset +2024-12-04 18:05:51,260:INFO:Defining folds +2024-12-04 18:05:51,260:INFO:Declaring metric variables +2024-12-04 18:05:51,260:INFO:Importing untrained model +2024-12-04 18:05:51,260:INFO:Declaring custom model +2024-12-04 18:05:51,260:INFO:Dummy Regressor Imported successfully +2024-12-04 18:05:51,263:INFO:Cross validation set to False +2024-12-04 18:05:51,263:INFO:Fitting Model +2024-12-04 18:05:51,263:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:176: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) + +2024-12-04 18:05:51,266:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\numpy\core\_methods.py:187: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +2024-12-04 18:05:51,303:INFO:DummyRegressor() +2024-12-04 18:05:51,303:INFO:create_model() successfully completed...................................... +2024-12-04 18:05:51,454:INFO:_master_model_container: 1 +2024-12-04 18:05:51,454:INFO:_display_container: 2 +2024-12-04 18:05:51,454:INFO:DummyRegressor() +2024-12-04 18:05:51,454:INFO:compare_models() successfully completed...................................... +2024-12-04 18:05:51,466:INFO:Initializing predict_model() +2024-12-04 18:05:51,466:INFO:predict_model(self=, estimator=DummyRegressor(), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000002105C0E76A0>) +2024-12-04 18:05:51,466:INFO:Checking exceptions +2024-12-04 18:05:51,466:INFO:Preloading libraries +2024-12-04 18:05:51,474:INFO:Set up data. +2024-12-04 18:05:51,488:INFO:Set up index. +2024-12-04 18:05:51,601:WARNING:c:\Users\antob\Documents\Arctusol\projet_wagon\projet_data_JBN\.venv\Lib\site-packages\sklearn\metrics\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'. + warnings.warn( + diff --git a/notebooks/model_prediction.ipynb b/notebooks/model_prediction.ipynb index ff469b8..a1c0473 100644 --- a/notebooks/model_prediction.ipynb +++ b/notebooks/model_prediction.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -55,245 +55,204 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "df_hospi_dpt_ens_yr = df_hospi_dpt_ens[df_hospi_dpt_ens[\"annee\"].isin([2018, 2019, 2022])]\n", - "df_hospi_dpt_HF_yr =df_hospi_dpt_HF[df_hospi_dpt_HF[\"annee\"].isin([2018, 2019, 2022])]\n", - "df_hospi_reg_ens_yr = df_hospi_reg_ens[df_hospi_reg_ens[\"annee\"].isin([2018, 2019, 2022])]\n", - "df_hospi_reg_HF_yr =df_hospi_reg_HF[df_hospi_reg_HF[\"annee\"].isin([2018, 2019, 2022])]" + "df_hospi_dpt_ens_yr = df_hospi_dpt_ens[df_hospi_dpt_ens[\"annee\"].isin([2018, 2019,2020,2021, 2022])]\n", + "df_hospi_dpt_HF_yr =df_hospi_dpt_HF[df_hospi_dpt_HF[\"annee\"].isin([2018, 2019,2020,2021, 2022])]\n", + "df_hospi_reg_ens_yr = df_hospi_reg_ens[df_hospi_reg_ens[\"annee\"].isin([2018, 2019,2020, 2021, 2022])]\n", + "df_hospi_reg_HF_yr =df_hospi_reg_HF[df_hospi_reg_HF[\"annee\"].isin([2018, 2019,2020, 2021, 2022])]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Régions disponibles:\n", + "['Corse' 'Guyane' 'Mayotte' 'Bretagne' 'Normandie' 'Grand Est' 'Occitanie'\n", + " 'Guadeloupe' 'Martinique' 'La Réunion' 'Ile de France' 'Hauts-de-France'\n", + " 'Pays de la Loire' 'Nouvelle-Aquitaine' 'Centre-Val de Loire'\n", + " 'Auvergne-Rhône-Alpes' 'Bourgogne-Franche-Comté'\n", + " \"Provence-Alpes-Côte d'Azur\"]\n", + "\n", + "Pathologies disponibles:\n", + "['Maladies intestinales infectieuses' 'Tuberculose' 'Septicémies'\n", + " 'Mal.sexuellement transmissibles' 'Hépatites virales'\n", + " 'Maladies dues au V.I.H.' 'Tumeurs malignes' 'T.M. lèvre _bouche_pharynx'\n", + " \"Tumeur maligne de l'oesophage\" \"Tumeur maligne de l'estomac\"\n", + " 'Tumeurs malignes colo' 'T.M.foie et voies biliaires intrahép'\n", + " 'Tumeur maligne du pancréas' 'Tumeur maligne du larynx'\n", + " 'T.M. trachée_bronches_poumon' 'Mélanome malin de la peau'\n", + " 'Tumeur maligne du sein' \"Tumeur maligne de l'utérus\"\n", + " 'Tumeur maligne de la prostate' 'Tumeur maligne de la vessie'\n", + " 'T.M. système nerveux central' 'Tumeur maligne de la thyroïde'\n", + " 'Leucémies' 'Tumeurs malignes secondaires' 'Tumeurs bénignes'\n", + " 'T.B. côlon_rectum_anus et canal anal' 'Tumeurs bénignes de la peau'\n", + " 'Tumeur bénigne du sein' \"Tumeur bénigne de l'utérus\"\n", + " \"Tumeur bénigne de l'ovaire\" 'Carcinomes in situ'\n", + " 'Tum. évol. imprévisible ou inconnue' 'Anémies'\n", + " 'Affections de la glande thyroïde' 'Diabète sucré'\n", + " \"Obésité et autre excès d'apport\" 'Anomalies du métabolisme' 'Démences'\n", + " \"Intoxication aiguë due à l'alcool\"\n", + " 'Alcool.chron._ trbl mental dû alcool'\n", + " 'Schizophrénie_aut.trbl délir.non org'\n", + " \"Dépression_autre trouble de l'humeur\"\n", + " 'Trbl névrotique_ person._comport.' 'Trbl psycho'\n", + " 'Mal.inflam.système nerveux central' 'Maladie de Parkinson'\n", + " 'Sclérose en plaques' 'Epilepsie' 'Ischémie cérébrale transitoire'\n", + " 'Troubles du sommeil' 'Mal. système nerveux périphérique'\n", + " 'Paralysie cérébrale_autr.syndr.paral' 'Cataracte'\n", + " 'Décollement_déchirure de la rétine' 'Glaucome'\n", + " 'Strabisme_autr.anom.mouve.binocul.' 'Otite moyenne et mastoïdite'\n", + " 'Surdité' 'Maladies hypertensives' 'Angine de poitrine'\n", + " 'Infarctus aigu du myocarde' 'Autres cardiopat.ischémiques aiguës'\n", + " 'Cardiopathies ischémiques chroniques' 'Embolie pulmonaire'\n", + " 'Valvulopathie card.non rhumatismale'\n", + " 'Trbls conduction et rythme cardiaque' 'Insuffisance cardiaque'\n", + " 'Hémor.méningée_céréb._intracrânien.'\n", + " 'Thromboses des artères céréb. et précéréb*'\n", + " 'Accident vascul. cérébral mal défini' 'Artériosclérose périphérique'\n", + " 'Phlébite et thrombophlébite' 'Varices des membres inférieurs'\n", + " 'Infarctus cérébral (à partir de 2015)'\n", + " 'Affections aiguës voies respir. sup.' 'Pneumonie'\n", + " 'Bronchite et bronchiolite aiguës' 'Mal.chron.amygdales_végé.adénoïdes'\n", + " 'Asthme' 'Bronchite chron._mal.pulm.obst.chron' 'Pleurésie'\n", + " 'Hémorroïdes (à partir de 2015)' 'Maladies des dents et du parodonte'\n", + " \"Maladies de l'oesophage\" \"Ulcères de l'estomac et du duodénum\"\n", + " 'Appendicite' 'Hernie abdominale' 'Occlusion sans mention de hernie'\n", + " 'Diverticulose intestinale' 'Fistule_abcès rég. anale et rectale'\n", + " 'Cirrhose et maladies chron.du foie' 'Lithiase biliaire'\n", + " 'Hémorragie dig.sans mention ulcère' 'Infections peau_tissu cell.ss'\n", + " 'Arthrite rhumatoïde sauf colonne' 'Autres arthropathies'\n", + " 'Arthroses sauf de la colonne' 'Autres lésions articulaires'\n", + " 'Spondylopathies' 'Hernie discale' 'Autres affections origine vertébrale'\n", + " 'Affections des tissus mous' 'Ostéopathies et chondropathies'\n", + " 'Déformations acquises_autres affec.'\n", + " 'Néphrite_syndr.néphrotique_néphrose' 'Infection rein et voies urinaires'\n", + " 'Insuffisance rénale' 'Calculs des voies urinaires' 'Cystite' 'Stérilité'\n", + " 'Hyperplasie de la prostate' 'Hydrocèle et spermatocèle' 'Phimosis'\n", + " 'Affections du sein' 'Affect.inflam.org.génitaux féminins'\n", + " 'Prolapsus génital' 'Hémor.génitales_trbls menstruation'\n", + " 'Avortement toutes causes' 'Accouchement unique et spontané'\n", + " 'Grossesse ou accouchement compliqués' 'Prématurité et hypotrophie'\n", + " 'Malform.congén.cardio' 'Malform.congén.appareil digestif'\n", + " 'Malform.congén.organes génitaux' 'Malform.congén.appareil urinaire'\n", + " 'Malform.congén.ostéo' 'Sympt.circulatoires et respiratoires'\n", + " 'Sympt.appareil digestif et abdomen' 'Sympt.syst.nerveux et ostéo'\n", + " 'Symptômes appareil urinaire' 'Symptômes et signes généraux'\n", + " 'Résult.anormaux examens sans sympt.' 'Fracture crâne et os de la face'\n", + " 'Fracture colonne et os du tronc' 'Fracture du membre supérieur'\n", + " 'Fracture du col du fémur' 'Autre fracture du membre inférieur'\n", + " 'Luxations et entorses' 'Traumatisme crânien' 'Trauma.intrathorac._intra'\n", + " 'Traumatisme des nerfs' 'Plaie tête (inclu globe oculaire)'\n", + " 'Plaie du membre supérieur' 'Plaie du membre inférieur'\n", + " 'Corps étranger par orifice naturel' 'Brûlure et corrosion'\n", + " 'Intoxication produit pharmaceutique'\n", + " 'Intoxication prod.non pharmaceutique' 'Effet nocif de cause extérieure'\n", + " 'Complic.acte chir.ou soin méd.n.c.a.'\n", + " 'Examen_investig.sans résult.anormaux' 'Surveil.grossesse_nouveau'\n", + " 'Thérapie sans raison médicale' 'Dialyse' 'Radiothérapie'\n", + " 'Chimiothérapie pour tumeur' 'Surveil.post traitement med.ou chir.'\n", + " 'Recours raison économique ou sociale' 'Infection à la Covid']\n", + "\n", + "Exemple de combinaison région/pathologie:\n", + "nom_region Auvergne-Rhône-Alpes\n", + "nom_pathologie Accident vascul. cérébral mal défini\n", + "0 4\n", + "Name: 0, dtype: object\n", + "\n", + "Utilisation de la combinaison:\n", + "Région: Auvergne-Rhône-Alpes\n", + "Pathologie: Accident vascul. cérébral mal défini\n", + "\n", + "Nombre de lignes dans train_filtered: 4\n", + "Données d'entraînement:\n", + " annee nbr_hospi\n", + "53770 2018 640\n", + "110186 2019 620\n", + "166948 2020 465\n", + "223745 2021 545\n" + ] + } + ], "source": [ - " \"\"\"'hospi_prog_24h', 'hospi_autres_24h', 'hospi_total_24h',\n", - " 'hospi_1J', 'hospi_2J', 'hospi_3J', 'hospi_4J', 'hospi_5J',\n", - " 'hospi_6J', 'hospi_7J', 'hospi_8J', 'hospi_9J',\n", - " 'hospi_10J_19J', 'hospi_20J_29J', 'hospi_30J',\n", - " 'tranche_age_0_1', 'tranche_age_1_4', 'tranche_age_5_14',\n", - " 'tranche_age_15_24', 'tranche_age_25_34', 'tranche_age_35_44',\n", - " 'tranche_age_45_54', 'tranche_age_55_64', 'tranche_age_65_74',\n", - " 'tranche_age_75_84', 'tranche_age_85_et_plus'\"\"\"" + "# Préparation des données d'entraînement\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019, 2020, 2021])].copy()\n", + "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022].copy()\n", + "\n", + "# Afficher les valeurs uniques\n", + "print(\"Régions disponibles:\")\n", + "print(train_data['nom_region'].unique())\n", + "print(\"\\nPathologies disponibles:\")\n", + "print(train_data['nom_pathologie'].unique())\n", + "\n", + "# Vérifier une combinaison qui existe\n", + "sample_data = train_data.groupby(['nom_region', 'nom_pathologie']).size().reset_index()\n", + "print(\"\\nExemple de combinaison région/pathologie:\")\n", + "print(sample_data.iloc[0])\n", + "\n", + "# Utiliser cette combinaison\n", + "region = sample_data.iloc[0]['nom_region']\n", + "pathologie = sample_data.iloc[0]['nom_pathologie']\n", + "\n", + "print(f\"\\nUtilisation de la combinaison:\\nRégion: {region}\\nPathologie: {pathologie}\")\n", + "\n", + "# Filtrer les données pour une région et pathologie spécifique\n", + "train_filtered = train_data[\n", + " (train_data['nom_region'] == region) & \n", + " (train_data['nom_pathologie'] == pathologie)\n", + "].sort_values('annee').copy()\n", + "\n", + "test_filtered = test_data[\n", + " (test_data['nom_region'] == region) & \n", + " (test_data['nom_pathologie'] == pathologie)\n", + "].copy()\n", + "\n", + "# Vérifier les données\n", + "print(\"\\nNombre de lignes dans train_filtered:\", len(train_filtered))\n", + "print(\"Données d'entraînement:\")\n", + "print(train_filtered[['annee', 'nbr_hospi']])" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Préparation des données d'entraînement (2018-2019)\n", - "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019])]\n", + "# Préparation des données d'entraînement\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019, 2020, 2021])]\n", "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]\n", "\n", - "# Sélection des variables pertinentes\n", - "features = [\n", - " 'annee', 'nom_pathologie', 'hospi_prog_24h', 'hospi_autres_24h', 'hospi_total_24h',\n", - " 'hospi_1J', 'hospi_2J', 'hospi_3J', 'hospi_4J', 'hospi_5J',\n", - " 'hospi_6J', 'hospi_7J', 'hospi_8J', 'hospi_9J',\n", - " 'hospi_10J_19J', 'hospi_20J_29J', 'hospi_30J',\n", + "# Sélection d'une région et pathologie spécifique pour le test\n", + "region = 'Nouvelle-Aquitaine'\n", + "pathologie = 'Maladies de l\\'appareil circulatoire'\n", "\n", + "# Filtrer les données pour une région et pathologie spécifique\n", + "train_filtered = train_data[\n", + " (train_data['nom_region'] == region) & \n", + " (train_data['nom_pathologie'] == pathologie)\n", "]\n", "\n", - "# Préparation des données pour PyCaret\n", - "train_data = train_data[features + ['nbr_hospi']]\n", - "test_data = test_data[features + ['nbr_hospi']]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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 DescriptionValue
0Session id123
1Targetnbr_hospi
2Target typeRegression
3Original data shape(5688, 18)
4Transformed data shape(5688, 17)
5Transformed train set shape(3981, 17)
6Transformed test set shape(1707, 17)
7Ignore features1
8Numeric features15
9Categorical features1
10Rows with missing values0.1%
11PreprocessTrue
12Imputation typesimple
13Numeric imputationmean
14Categorical imputationmode
15Maximum one-hot encoding25
16Encoding methodNone
17TransformationTrue
18Transformation methodyeo-johnson
19NormalizeTrue
20Normalize methodzscore
21Fold GeneratorKFold
22Fold Number5
23CPU Jobs-1
24Use GPUFalse
25Log ExperimentFalse
26Experiment Namereg-default-name
27USIb11b
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Initialisation de l'environnement PyCaret\n", - "reg = setup(\n", - " data=train_data,\n", - " target='nbr_hospi',\n", - " session_id=123,\n", - " normalize=True,\n", - " transformation=True,\n", - " ignore_features=['annee'], # On ignore l'année car on a peu de points temporels\n", - " fold=5, # 5-fold cross validation\n", - ")" + "test_filtered = test_data[\n", + " (test_data['nom_region'] == region) & \n", + " (test_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "# Créer une séquence numérique pour l'ordre temporel\n", + "train_filtered['Series'] = np.arange(1, len(train_filtered) + 1)\n", + "test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1)\n" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -310,299 +269,58 @@ "data": { "text/html": [ "\n", - "\n", + "
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 ModelMAEMSERMSER2RMSLEMAPETT (Sec)ModelMAEMSERMSER2RMSLEMAPETT (Sec)
etExtra Trees Regressor210.21971674098.71031127.07260.99120.17730.05270.3640
gbrGradient Boosting Regressor427.66672396550.77421420.51550.98620.59820.58650.2560
rfRandom Forest Regressor349.73053296375.67891671.18270.98170.19990.07190.7120
dtDecision Tree Regressor610.62845607087.29982317.28170.96450.33660.15420.0560
lightgbmLight Gradient Boosting Machine501.01826291665.54232360.07320.96390.39450.19590.1180
knnK Neighbors Regressor800.008110478549.67673090.50070.93850.27940.15120.0600
adaAdaBoost Regressor5000.653330007587.16915451.37830.80262.521036.73230.1200
brBayesian Ridge5146.487887145760.03599205.03500.45192.228940.21280.0520
ridgeRidge Regression5159.762387114678.79289204.78750.45182.237841.06080.0460
llarLasso Least Angle Regression5159.521087113806.61049204.70430.45182.239441.06700.0560
lrLinear Regression5161.009587118148.93759205.06880.45182.238741.12650.8840
lassoLasso Regression5159.520787113814.89999204.70440.45182.239341.06670.5900
larLeast Angle Regression5231.950287920062.65259252.36800.44572.250541.03010.0880
enElastic Net4977.531490539358.33959377.84990.43162.153435.08150.0540
ompOrthogonal Matching Pursuit5696.0296102280831.43299981.03670.35482.269939.54070.0440
parPassive Aggressive Regressor3835.3248117010044.983010673.69650.26251.729514.84200.0500
dummyDummy Regressor6601.7355156689296.636312396.8486-0.00102.531535.07010.0440dummyDummy Regressor1207.36273279203.15461207.3627nan0.01660.01700.0333
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" }, - { - "data": { - "text/html": [], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Comparaison des modèles\n", - "best_model = compare_models(\n", - " exclude=['ransac', 'huber', 'theilsen'], # On exclut les modèles moins pertinents pour ce cas\n", - " sort='R2', # Trier par R2 score\n", - " n_select=1 # Sélectionner le meilleur modèle\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "\nAll the 5 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n1 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Anémies'\n\n--------------------------------------------------------------------------------\n4 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Maladies intestinales infectieuses'\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[40], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cross_val_score\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# Validation croisée sur le meilleur modèle\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m cv_scores \u001b[38;5;241m=\u001b[39m \u001b[43mcross_val_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbest_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnbr_hospi\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnbr_hospi\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mScores de validation croisée (R²):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMoyenne: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcv_scores\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (+/- \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcv_scores\u001b[38;5;241m.\u001b[39mstd()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:719\u001b[0m, in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m 716\u001b[0m \u001b[38;5;66;03m# To ensure multimetric format is not supported\u001b[39;00m\n\u001b[0;32m 717\u001b[0m scorer \u001b[38;5;241m=\u001b[39m check_scoring(estimator, scoring\u001b[38;5;241m=\u001b[39mscoring)\n\u001b[1;32m--> 719\u001b[0m cv_results \u001b[38;5;241m=\u001b[39m \u001b[43mcross_validate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 722\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 723\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 724\u001b[0m \u001b[43m \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscore\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mscorer\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 725\u001b[0m \u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 726\u001b[0m \u001b[43m 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\u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 733\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cv_results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_score\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:450\u001b[0m, in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, params, pre_dispatch, return_train_score, return_estimator, return_indices, error_score)\u001b[0m\n\u001b[0;32m 429\u001b[0m parallel \u001b[38;5;241m=\u001b[39m Parallel(n_jobs\u001b[38;5;241m=\u001b[39mn_jobs, verbose\u001b[38;5;241m=\u001b[39mverbose, pre_dispatch\u001b[38;5;241m=\u001b[39mpre_dispatch)\n\u001b[0;32m 430\u001b[0m results \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[0;32m 431\u001b[0m delayed(_fit_and_score)(\n\u001b[0;32m 432\u001b[0m clone(estimator),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train, test \u001b[38;5;129;01min\u001b[39;00m indices\n\u001b[0;32m 448\u001b[0m )\n\u001b[1;32m--> 450\u001b[0m \u001b[43m_warn_or_raise_about_fit_failures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_score\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 452\u001b[0m \u001b[38;5;66;03m# For callable scoring, the return type is only know after calling. If the\u001b[39;00m\n\u001b[0;32m 453\u001b[0m \u001b[38;5;66;03m# return type is a dictionary, the error scores can now be inserted with\u001b[39;00m\n\u001b[0;32m 454\u001b[0m \u001b[38;5;66;03m# the correct key.\u001b[39;00m\n\u001b[0;32m 455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(scoring):\n", - "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:536\u001b[0m, in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[1;34m(results, error_score)\u001b[0m\n\u001b[0;32m 529\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_failed_fits \u001b[38;5;241m==\u001b[39m num_fits:\n\u001b[0;32m 530\u001b[0m all_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 531\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAll the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 532\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt is very likely that your model is misconfigured.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou can try to debug the error by setting error_score=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 535\u001b[0m )\n\u001b[1;32m--> 536\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(all_fits_failed_message)\n\u001b[0;32m 538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 539\u001b[0m some_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 540\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnum_failed_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed out of a total of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 541\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe score on these train-test partitions for these parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 545\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 546\u001b[0m )\n", - "\u001b[1;31mValueError\u001b[0m: \nAll the 5 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n1 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Anémies'\n\n--------------------------------------------------------------------------------\n4 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Maladies intestinales infectieuses'\n" - ] - } - ], - "source": [ - "# Ajout après la comparaison des modèles\n", - "from sklearn.model_selection import cross_val_score\n", - "\n", - "# Validation croisée sur le meilleur modèle\n", - "cv_scores = cross_val_score(best_model, train_data.drop('nbr_hospi', axis=1), \n", - " train_data['nbr_hospi'], \n", - " cv=5, \n", - " scoring='r2')\n", - "\n", - "print(\"Scores de validation croisée (R²):\")\n", - "print(f\"Moyenne: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ { "data": { "text/html": [], @@ -617,110 +335,36 @@ "data": { "text/html": [ "\n", - "\n", + "
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 MAEMSERMSER2RMSLEMAPE
Fold      ModelMAEMSERMSER2RMSLEMAPE
0731.64183547888.64971883.58400.96890.45040.4162
1862.184110623691.82393259.40050.94040.52480.5540
2725.75753818782.23061954.17050.97520.52830.4961
31043.868522943884.05374789.97750.89930.56480.4763
4649.86622279363.45651509.75610.97910.54470.5012
Mean802.66368642722.04292679.37770.95260.52260.4888
Std138.58447722273.38611209.81700.02990.03880.04450Dummy Regressor155.537024191.7520155.53700.00000.00210.0021
\n" ], "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [], - "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -730,50 +374,484 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n", - "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n" + "\n", + "Prédictions pour les années futures:\n", + " annee prediction_label\n", + "0 2023 73306.46302\n", + "1 2024 73306.46302\n", + "2 2025 73306.46302\n" ] } ], "source": [ - "# Tuning du meilleur modèle\n", - "tuned_model = tune_model(best_model)" + "# Préparons les données avec une région et une pathologie spécifiques\n", + "region = 'Nouvelle-Aquitaine'\n", + "pathologie = 'Tumeurs malignes'\n", + "\n", + "# Préparation des données d'entraînement et de test\n", + "train_data = df_hospi_reg_ens_yr[\n", + " (df_hospi_reg_ens_yr['annee'].between(2018, 2021)) &\n", + " (df_hospi_reg_ens_yr['nom_region'] == region) &\n", + " (df_hospi_reg_ens_yr['nom_pathologie'] == pathologie)\n", + "].copy()\n", + "\n", + "test_data = df_hospi_reg_ens_yr[\n", + " (df_hospi_reg_ens_yr['annee'] == 2022) &\n", + " (df_hospi_reg_ens_yr['nom_region'] == region) &\n", + " (df_hospi_reg_ens_yr['nom_pathologie'] == pathologie)\n", + "].copy()\n", + "\n", + "# Ajout des caractéristiques supplémentaires\n", + "train_data['Series'] = range(len(train_data))\n", + "train_data['annee_norm'] = (train_data['annee'] - train_data['annee'].min()) / (train_data['annee'].max() - train_data['annee'].min())\n", + "\n", + "# Même chose pour les données de test\n", + "test_data['Series'] = range(len(test_data))\n", + "test_data['annee_norm'] = (test_data['annee'] - train_data['annee'].min()) / (train_data['annee'].max() - train_data['annee'].min())\n", + "\n", + "# Configuration de PyCaret\n", + "reg = setup(\n", + " data=train_data,\n", + " test_data=test_data,\n", + " target='nbr_hospi',\n", + " fold_strategy='timeseries',\n", + " numeric_features=['annee', 'Series', 'annee_norm'],\n", + " fold=3,\n", + " transform_target=True,\n", + " session_id=123,\n", + " verbose=False,\n", + " data_split_shuffle=False,\n", + " fold_shuffle=False,\n", + " categorical_features=['nom_region', 'nom_pathologie']\n", + ")\n", + "\n", + "# Création et comparaison des modèles\n", + "best_model = compare_models(n_select=1)\n", + "\n", + "# Créer un DataFrame complet pour les prédictions futures\n", + "future_dates = pd.DataFrame({\n", + " 'niveau': ['Régions'] * 3,\n", + " 'sexe': ['Ensemble'] * 3,\n", + " 'year': [f'{year}-12-31' for year in [2023, 2024, 2025]],\n", + " 'annee': [2023, 2024, 2025],\n", + " 'pathologie': [pathologie] * 3,\n", + " 'code_pathologie': train_data['code_pathologie'].iloc[0],\n", + " 'nom_pathologie': [pathologie] * 3,\n", + " 'region': [region] * 3,\n", + " 'code_region': train_data['code_region'].iloc[0],\n", + " 'nom_region': [region] * 3,\n", + " 'Series': range(len(train_data) + len(test_data), len(train_data) + len(test_data) + 3),\n", + " 'annee_norm': [(year - train_data['annee'].min()) / (train_data['annee'].max() - train_data['annee'].min()) \n", + " for year in [2023, 2024, 2025]]\n", + "})\n", + "\n", + "# Copier toutes les autres colonnes de la dernière ligne des données d'entraînement\n", + "for col in train_data.columns:\n", + " if col not in future_dates.columns:\n", + " future_dates[col] = train_data[col].iloc[-1]\n", + "\n", + "# Faire les prédictions\n", + "predictions = predict_model(best_model, data=future_dates)\n", + "print(\"\\nPrédictions pour les années futures:\")\n", + "print(predictions[['annee', 'prediction_label']])" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Statistiques du nombre d'hospitalisations:\n", - "count 5688.0\n", - "mean 5637.552215\n", - "std 12285.074272\n", - "min 0.0\n", - "25% 336.0\n", - "50% 1546.0\n", - "75% 5575.25\n", - "max 163601.0\n", - "Name: nbr_hospi, dtype: Float64\n", + "Colonnes disponibles dans df_hospi_reg_ens_yr:\n", + "['niveau', 'cle_unique', 'sexe', 'year', 'annee', 'pathologie', 'code_pathologie', 'nom_pathologie', 'region', 'code_region', 'nom_region', 'nbr_hospi', 'evolution_nbr_hospi', 'evolution_percent_nbr_hospi', 'hospi_prog_24h', 'hospi_autres_24h', 'hospi_total_24h', 'hospi_1J', 'hospi_2J', 'hospi_3J', 'hospi_4J', 'hospi_5J', 'hospi_6J', 'hospi_7J', 'hospi_8J', 'hospi_9J', 'hospi_10J_19J', 'hospi_20J_29J', 'hospi_30J', 'hospi_total_jj', 'total_hospi', 'AVG_duree_hospi', 'evolution_hospi_total_24h', 'evolution_percent_hospi_total_24h', 'evolution_hospi_total_jj', 'evolution_percent_hospi_total_jj', 'evolution_total_hospi', 'evolution_percent_total_hospi', 'evolution_AVG_duree_hospi', 'evolution_percent_AVG_duree_hospi', 'tranche_age_0_1', 'tranche_age_1_4', 'tranche_age_5_14', 'tranche_age_15_24', 'tranche_age_25_34', 'tranche_age_35_44', 'tranche_age_45_54', 'tranche_age_55_64', 'tranche_age_65_74', 'tranche_age_75_84', 'tranche_age_85_et_plus', 'tx_brut_tt_age_pour_mille', 'tx_standard_tt_age_pour_mille', 'indice_comparatif_tt_age_percent', 'evolution_tx_brut_tt_age_pour_mille', 'evolution_percent_tx_brut_tt_age_pour_mille', 'evolution_tx_standard_tt_age_pour_mille', 'evolution_percent_tx_standard_tt_age_pour_mille', 'evolution_indice_comparatif_tt_age_percent', 'evolution_percent_indice_comparatif_tt_age_percent', 'classification', 'population']\n", + "\n", + "Aperçu des données:\n", + " niveau cle_unique sexe year annee \\\n", + "10902 Régions 94 - Corse_01001_2018 Ensemble 2018-12-31 2018 \n", + "10907 Régions 94 - Corse_01002_2018 Ensemble 2018-12-31 2018 \n", + "10910 Régions 94 - Corse_01003_2018 Ensemble 2018-12-31 2018 \n", + "10911 Régions 94 - Corse_01004_2018 Ensemble 2018-12-31 2018 \n", + "10916 Régions 94 - Corse_01005_2018 Ensemble 2018-12-31 2018 \n", + "\n", + " pathologie code_pathologie \\\n", + "10902 01001-Maladies intestinales infectieuses 01001 \n", + "10907 01002-Tuberculose 01002 \n", + "10910 01003-Septicémies 01003 \n", + "10911 01004-Mal.sexuellement transmissibles 01004 \n", + "10916 01005-Hépatites virales 01005 \n", + "\n", + " nom_pathologie region code_region ... \\\n", + "10902 Maladies intestinales infectieuses 94 - Corse 94 ... \n", + "10907 Tuberculose 94 - Corse 94 ... \n", + "10910 Septicémies 94 - Corse 94 ... \n", + "10911 Mal.sexuellement transmissibles 94 - Corse 94 ... \n", + "10916 Hépatites virales 94 - Corse 94 ... \n", + "\n", + " tx_standard_tt_age_pour_mille indice_comparatif_tt_age_percent \\\n", + "10902 1.52 87.50 \n", + "10907 0.05 47.06 \n", + "10910 0.47 124.43 \n", + "10911 0.23 115.79 \n", + "10916 0.04 63.44 \n", + "\n", + " evolution_tx_brut_tt_age_pour_mille \\\n", + "10902 1.41 \n", + "10907 0.05 \n", + "10910 0.62 \n", + "10911 0.21 \n", + "10916 0.04 \n", + "\n", + " evolution_percent_tx_brut_tt_age_pour_mille \\\n", + "10902 NaN \n", + "10907 NaN \n", + "10910 NaN \n", + "10911 NaN \n", + "10916 NaN \n", + "\n", + " evolution_tx_standard_tt_age_pour_mille \\\n", + "10902 1.52 \n", + "10907 0.05 \n", + "10910 0.48 \n", + "10911 0.23 \n", + "10916 0.04 \n", + "\n", + " evolution_percent_tx_standard_tt_age_pour_mille \\\n", + "10902 NaN \n", + "10907 NaN \n", + "10910 NaN \n", + "10911 NaN \n", + "10916 NaN \n", + "\n", + " evolution_indice_comparatif_tt_age_percent \\\n", + "10902 87.43 \n", + "10907 49.49 \n", + "10910 124.56 \n", + "10911 117.36 \n", + "10916 67.45 \n", + "\n", + " evolution_percent_indice_comparatif_tt_age_percent classification \\\n", + "10902 NaN M \n", + "10907 NaN M \n", + "10910 NaN M \n", + "10911 NaN M \n", + "10916 NaN M \n", + "\n", + " population \n", + "10902 339000 \n", + "10907 339000 \n", + "10910 339000 \n", + "10911 339000 \n", + "10916 339000 \n", "\n", - "MAE en pourcentage de la moyenne: 3.74%\n" + "[5 rows x 62 columns]\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], + "source": [ + "# Affichons la structure actuelle des données\n", + "print(\"Colonnes disponibles dans df_hospi_reg_ens_yr:\")\n", + "print(df_hospi_reg_ens_yr.columns.tolist())\n", + "\n", + "# Affichons également un exemple des données\n", + "print(\"\\nAperçu des données:\")\n", + "print(df_hospi_reg_ens_yr.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données d'entraînement\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019, 2020, 2021])]\n", + "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]\n", + "\n", + "# Sélection d'une région et pathologie spécifique pour le test\n", + "region = 'Nouvelle-Aquitaine'\n", + "pathologie = 'Maladies de l\\'appareil circulatoire'\n", + "\n", + "# Filtrer les données pour une région et pathologie spécifique\n", + "train_filtered = train_data[\n", + " (train_data['nom_region'] == region) & \n", + " (train_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "test_filtered = test_data[\n", + " (test_data['nom_region'] == region) & \n", + " (test_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "# Créer une séquence numérique pour l'ordre temporel\n", + "train_filtered['Series'] = np.arange(1, len(train_filtered) + 1)\n", + "test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données d'entraînement\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019, 2020, 2021])]\n", + "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]\n", + "\n", + "# Sélection d'une région et pathologie spécifique pour le test\n", + "region = 'Nouvelle-Aquitaine'\n", + "pathologie = 'Maladies de l\\'appareil circulatoire'\n", + "\n", + "# Filtrer les données pour une région et pathologie spécifique\n", + "train_filtered = train_data[\n", + " (train_data['nom_region'] == region) & \n", + " (train_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "test_filtered = test_data[\n", + " (test_data['nom_region'] == region) & \n", + " (test_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "# Créer une séquence numérique pour l'ordre temporel\n", + "train_filtered['Series'] = np.arange(1, len(train_filtered) + 1)\n", + "test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données d'entraînement\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019, 2020, 2021])]\n", + "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]\n", + "\n", + "# Sélection d'une région et pathologie spécifique pour le test\n", + "region = 'Nouvelle-Aquitaine'\n", + "pathologie = 'Maladies de l\\'appareil circulatoire'\n", + "\n", + "# Filtrer les données pour une région et pathologie spécifique\n", + "train_filtered = train_data[\n", + " (train_data['nom_region'] == region) & \n", + " (train_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "test_filtered = test_data[\n", + " (test_data['nom_region'] == region) & \n", + " (test_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "# Créer une séquence numérique pour l'ordre temporel\n", + "train_filtered['Series'] = np.arange(1, len(train_filtered) + 1)\n", + "test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données d'entraînement\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019, 2020, 2021])]\n", + "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]\n", + "\n", + "# Sélection d'une région et pathologie spécifique pour le test\n", + "region = 'Nouvelle-Aquitaine'\n", + "pathologie = 'Maladies de l\\'appareil circulatoire'\n", + "\n", + "# Filtrer les données pour une région et pathologie spécifique\n", + "train_filtered = train_data[\n", + " (train_data['nom_region'] == region) & \n", + " (train_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "test_filtered = test_data[\n", + " (test_data['nom_region'] == region) & \n", + " (test_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "# Créer une séquence numérique pour l'ordre temporel\n", + "train_filtered['Series'] = np.arange(1, len(train_filtered) + 1)\n", + "test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données d'entraînement\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019, 2020, 2021])]\n", + "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]\n", + "\n", + "# Sélection d'une région et pathologie spécifique pour le test\n", + "region = 'Nouvelle-Aquitaine'\n", + "pathologie = 'Maladies de l\\'appareil circulatoire'\n", + "\n", + "# Filtrer les données pour une région et pathologie spécifique\n", + "train_filtered = train_data[\n", + " (train_data['nom_region'] == region) & \n", + " (train_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "test_filtered = test_data[\n", + " (test_data['nom_region'] == region) & \n", + " (test_data['nom_pathologie'] == pathologie)\n", + "]\n", + "\n", + "# Créer une séquence numérique pour l'ordre temporel\n", + "train_filtered['Series'] = np.arange(1, len(train_filtered) + 1)\n", + "test_filtered['Series'] = np.arange(len(train_filtered) + 1, len(train_filtered) + len(test_filtered) + 1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Initialisation de l'environnement PyCaret avec stratégie temporelle\n", + "reg = setup(\n", + " data=train_filtered,\n", + " test_data=test_filtered,\n", + " target='nbr_hospi',\n", + " fold_strategy='timeseries', # Utiliser la stratégie de séries temporelles\n", + " numeric_features=['annee', 'Series'],\n", + " fold=5,\n", + " transform_target=True,\n", + " session_id=123,\n", + " verbose=False,\n", + " data_split_shuffle=False, # Ajouté : Ne pas mélanger les données\n", + " fold_shuffle=False \n", + ")\n", + "\n", + "# Comparaison des modèles\n", + "best_model = compare_models(sort='MAE')\n", + "\n", + "# Finaliser le modèle (entraîner sur toutes les données)\n", + "final_model = finalize_model(best_model)\n", + "\n", + "# Créer le dataset pour les prédictions futures\n", + "future_years = [2023, 2024, 2025]\n", + "future_df = pd.DataFrame({\n", + " 'annee': future_years,\n", + " 'Series': np.arange(len(train_filtered) + len(test_filtered) + 1, \n", + " len(train_filtered) + len(test_filtered) + len(future_years) + 1),\n", + " 'nom_region': region,\n", + " 'nom_pathologie': pathologie\n", + "})\n", + "\n", + "# Faire les prédictions\n", + "predictions_future = predict_model(final_model, data=future_df)\n", + "print(\"\\nPrédictions pour les années futures:\")\n", + "print(predictions_future[['annee', 'nom_region', 'nom_pathologie', 'prediction_label']])\n", + "\n", + "# Afficher les résultats complets\n", + "all_data = pd.concat([\n", + " train_filtered[['annee', 'nbr_hospi']],\n", + " test_filtered[['annee', 'nbr_hospi']],\n", + " predictions_future[['annee', 'prediction_label']].rename(columns={'prediction_label': 'nbr_hospi'})\n", + "])\n", + "\n", + "print(\"\\nHistorique et prédictions complètes:\")\n", + "print(all_data.sort_values('annee'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " \"\"\"'hospi_prog_24h', 'hospi_autres_24h', 'hospi_total_24h',\n", + " 'hospi_1J', 'hospi_2J', 'hospi_3J', 'hospi_4J', 'hospi_5J',\n", + " 'hospi_6J', 'hospi_7J', 'hospi_8J', 'hospi_9J',\n", + " 'hospi_10J_19J', 'hospi_20J_29J', 'hospi_30J',\n", + " 'tranche_age_0_1', 'tranche_age_1_4', 'tranche_age_5_14',\n", + " 'tranche_age_15_24', 'tranche_age_25_34', 'tranche_age_35_44',\n", + " 'tranche_age_45_54', 'tranche_age_55_64', 'tranche_age_65_74',\n", + " 'tranche_age_75_84', 'tranche_age_85_et_plus'\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données d'entraînement (2018-2019)\n", + "train_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019,2020,2021])]\n", + "test_data = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]\n", + "\n", + "# Sélection des variables pertinentes\n", + "features = [\n", + " 'annee', 'nom_pathologie', 'nom_region'\n", + "\n", + "]\n", + "\n", + "# Préparation des données pour PyCaret\n", + "train_data = train_data[features + ['nbr_hospi']]\n", + "test_data = test_data[features + ['nbr_hospi']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialisation de l'environnement PyCaret\n", + "reg = setup(\n", + " data=train_data,\n", + " target='nbr_hospi',\n", + " session_id=123,\n", + " normalize=True,\n", + " transformation=True,\n", + " fold=5, # 5-fold cross validation\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Comparaison des modèles\n", + "best_model = compare_models( # On exclut les modèles moins pertinents pour ce cas\n", + " n_select=1 # Sélectionner le meilleur modèle\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Tuning du meilleur modèle\n", + "#tuned_model = tune_model(best_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Analyse du MAE en contexte\n", "print(\"Statistiques du nombre d'hospitalisations:\")\n", @@ -796,92 +874,28 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 ModelMAEMSERMSER2RMSLEMAPE
0Extra Trees Regressor239.12121909800.38001381.95530.98880.15730.0529
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Performance sur les données de test (2022):\n", - " annee code_pathologie hospi_prog_24h hospi_autres_24h \\\n", - "237376 2022 01001 26.0 4.0 \n", - "237378 2022 01002 0.0 0.0 \n", - "237383 2022 01003 4.0 3.0 \n", - "237386 2022 01004 65.0 0.0 \n", - "237388 2022 01005 0.0 0.0 \n", - "\n", - " hospi_total_24h hospi_1J hospi_2J hospi_3J hospi_4J hospi_5J \\\n", - "237376 30.0 132.0 80.0 52.0 40.0 15.0 \n", - "237378 0.0 0.0 0.0 4.0 0.0 0.0 \n", - "237383 7.0 16.0 17.0 8.0 7.0 13.0 \n", - "237386 65.0 0.0 0.0 0.0 0.0 0.0 \n", - "237388 4.0 3.0 0.0 3.0 3.0 0.0 \n", - "\n", - " ... tranche_age_15_24 tranche_age_25_34 tranche_age_35_44 \\\n", - "237376 ... 0.54 0.39 0.47 \n", - "237378 ... 0.00 0.00 0.00 \n", - "237383 ... 0.00 0.16 0.00 \n", - "237386 ... 0.24 0.71 0.43 \n", - "237388 ... 0.00 0.16 0.00 \n", - "\n", - " tranche_age_45_54 tranche_age_55_64 tranche_age_65_74 \\\n", - "237376 0.37 0.63 0.59 \n", - "237378 0.12 0.00 0.09 \n", - "237383 0.19 0.50 0.99 \n", - "237386 0.23 0.13 0.00 \n", - "237388 0.08 0.00 0.00 \n", - "\n", - " tranche_age_75_84 tranche_age_85_et_plus nbr_hospi prediction_label \n", - "237376 1.23 2.81 417 453.13 \n", - "237378 0.00 0.00 18 15.33 \n", - "237383 1.77 2.57 168 171.90 \n", - "237386 0.00 0.00 74 69.72 \n", - "237388 0.00 0.00 19 24.79 \n", - "\n", - "[5 rows x 31 columns]\n" - ] - } - ], + "outputs": [], + "source": [ + "# Création de données de test pour 3 années futures\n", + "future_data = pd.DataFrame([\n", + " {'annee': 2023, 'nom_pathologie': 'Maladies de l\\'appareil circulatoire', 'nom_region': 'Île-de-France'},\n", + " {'annee': 2024, 'nom_pathologie': 'Maladies de l\\'appareil circulatoire', 'nom_region': 'Île-de-France'},\n", + " {'annee': 2025, 'nom_pathologie': 'Maladies de l\\'appareil circulatoire', 'nom_region': 'Île-de-France'}\n", + "])\n", + "\n", + "# Faire les prédictions\n", + "predictions_futures = predict_model(best_model, data=future_data)\n", + "print(\"Prédictions pour les années futures:\")\n", + "print(predictions_futures[['annee', 'nom_region', 'nom_pathologie', 'prediction_label']])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Évaluation sur les données de test (2022)\n", "predictions = predict_model(best_model, data=test_data)\n", @@ -891,47 +905,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Métriques sur les données de test (2022):\n", - "MAE: 239.12\n", - "RMSE: 1381.96\n", - "R²: 0.9888\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Statistiques des erreurs relatives (%):\n", - "count 2862.0\n", - "mean inf\n", - "std \n", - "min 0.0\n", - "25% 1.505772\n", - "50% 3.386791\n", - "75% 6.304061\n", - "max inf\n", - "Name: erreur_relative, dtype: Float64\n" - ] - } - ], + "outputs": [], "source": [ "# Calcul des métriques d'erreur pour les prédictions\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", @@ -966,6 +942,15 @@ "print(\"\\nStatistiques des erreurs relatives (%):\")\n", "print(predictions['erreur_relative'].describe())" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "save_model(best_model, 'best_model')" + ] } ], "metadata": { diff --git a/notebooks/model_prediction_validation.ipynb b/notebooks/model_prediction_validation.ipynb index a7d38ff..a555e07 100644 --- a/notebooks/model_prediction_validation.ipynb +++ b/notebooks/model_prediction_validation.ipynb @@ -11,18 +11,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", + "import os\n", "from pycaret.regression import *\n", - "from notebooks.model_validation import (\n", + "from model_validation import (\n", " plot_learning_curves,\n", " evaluate_cross_validation,\n", " plot_prediction_errors,\n", " calculate_error_statistics\n", - ")" + ")\n", + "from google.cloud import bigquery" ] }, { @@ -34,23 +37,140 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"C:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/projet-jbn-data-le-wagon-533639ce801d.json\"\n", + " # Initialisation du client BigQuery\n", + "client = bigquery.Client()\n", + "\n", + "query = \"\"\"\n", + "SELECT * FROM projet-jbn-data-le-wagon.dbt_medical_analysis_join_total_morbidite.class_join_total_morbidite_sexe_population\n", + "\n", + "\"\"\"\n", + "\n", + "df_hospi = client.query(query).to_dataframe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape du DataFrame: (14274, 5)\n" + ] + }, + { + "data": { + "text/html": [ + "
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nom_regionyearnom_pathologienbr_hospipopulation
0Auvergne-Rhône-Alpes2018-12-31Accident vascul. cérébral mal défini6407997000.0
1Auvergne-Rhône-Alpes2018-12-31Accouchement unique et spontané435277997000.0
2Auvergne-Rhône-Alpes2018-12-31Affect.inflam.org.génitaux féminins31747997000.0
3Auvergne-Rhône-Alpes2018-12-31Affections aiguës voies respir. sup.42847997000.0
4Auvergne-Rhône-Alpes2018-12-31Affections de la glande thyroïde32037997000.0
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" + ], + "text/plain": [ + " nom_region year nom_pathologie \\\n", + "0 Auvergne-Rhône-Alpes 2018-12-31 Accident vascul. cérébral mal défini \n", + "1 Auvergne-Rhône-Alpes 2018-12-31 Accouchement unique et spontané \n", + "2 Auvergne-Rhône-Alpes 2018-12-31 Affect.inflam.org.génitaux féminins \n", + "3 Auvergne-Rhône-Alpes 2018-12-31 Affections aiguës voies respir. sup. \n", + "4 Auvergne-Rhône-Alpes 2018-12-31 Affections de la glande thyroïde \n", + "\n", + " nbr_hospi population \n", + "0 640 7997000.0 \n", + "1 43527 7997000.0 \n", + "2 3174 7997000.0 \n", + "3 4284 7997000.0 \n", + "4 3203 7997000.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Chargement des données\n", - "df_hospi_dep_ens = pd.read_csv('../data/processed/hospi_dep_ens.csv')\n", - "df_hospi_dep_gen = pd.read_csv('../data/processed/hospi_dep_gen.csv')\n", - "df_hospi_reg_ens = pd.read_csv('../data/processed/hospi_reg_ens.csv')\n", - "df_hospi_reg_gen = pd.read_csv('../data/processed/hospi_reg_gen.csv')\n", + "df_hospi_dpt_ens = df_hospi[(df_hospi[\"niveau\"] == \"Départements\") & (df_hospi[\"sexe\"] == \"Ensemble\")]\n", + "df_hospi_dpt_HF = df_hospi[(df_hospi[\"niveau\"] == \"Départements\") & (df_hospi[\"sexe\"] != \"Ensemble\")]\n", + "df_hospi_reg_ens = df_hospi[(df_hospi[\"niveau\"] == \"Régions\") & (df_hospi[\"sexe\"] == \"Ensemble\")]\n", + "df_hospi_reg_HF = df_hospi[(df_hospi[\"niveau\"] == \"Régions\") & (df_hospi[\"sexe\"] != \"Ensemble\")]\n", "\n", "# Création des données annuelles pour le niveau régional\n", - "df_hospi_reg_ens_yr = df_hospi_reg_ens.groupby(['reg', 'year']).agg({\n", + "df_hospi_reg_ens_yr = df_hospi_reg_ens.groupby(['nom_region', 'year',\"nom_pathologie\"]).agg({\n", " 'nbr_hospi': 'sum',\n", - " 'pop': 'mean',\n", - " 'tx_hospi': 'mean',\n", - " 'part_femmes': 'mean',\n", - " 'part_hommes': 'mean',\n", - " 'age_moyen': 'mean'\n", + " 'population': 'mean',\n", + "\n", "}).reset_index()\n", "\n", "print(\"Shape du DataFrame:\", df_hospi_reg_ens_yr.shape)\n", @@ -66,17 +186,612 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 DescriptionValue
0Session id123
1Targetnbr_hospi
2Target typeRegression
3Original data shape(14274, 5)
4Transformed data shape(14274, 16)
5Transformed train set shape(9991, 16)
6Transformed test set shape(4283, 16)
7Ignore features1
8Numeric features1
9Categorical features2
10PreprocessTrue
11Imputation typesimple
12Numeric imputationmean
13Categorical imputationmode
14Maximum one-hot encoding25
15Encoding methodNone
16Transform targetTrue
17Transform target methodyeo-johnson
18Fold GeneratorTimeSeriesSplit
19Fold Number10
20CPU Jobs-1
21Use GPUFalse
22Log ExperimentFalse
23Experiment Namereg-default-name
24USIb92e
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 ModelMAEMSERMSER2RMSLEMAPETT (Sec)
gbrGradient Boosting Regressor1217.733012587284.25102849.72080.75310.75081.30260.0890
rfRandom Forest Regressor1106.148110798262.93802646.68420.75140.70561.14000.1880
knnK Neighbors Regressor1370.829922980056.42003642.09890.71760.72771.16360.0310
lightgbmLight Gradient Boosting Machine1371.313715611475.69053245.23960.70850.84351.82630.0910
dtDecision Tree Regressor1337.253716527000.13993260.34260.65950.75841.24970.0280
etExtra Trees Regressor1324.151514207695.54493164.35940.61830.87332.14160.1670
adaAdaBoost Regressor2065.414150060398.56845106.15630.51880.88141.56530.0660
lassoLasso Regression5483.1499227853596.05049902.2401-0.06661.31943.45390.0240
llarLasso Least Angle Regression5483.1499227853596.05049902.2401-0.06661.31943.45390.0250
enElastic Net5584.2516258072831.840310328.3299-0.12731.29443.26490.0240
ompOrthogonal Matching Pursuit5669.9463201038662.32409932.3111-0.19541.62615.12330.0280
ridgeRidge Regression2298.1490261793570.31747349.5849-0.24990.85752.10220.0250
dummyDummy Regressor4276.9542136894851.24418830.7309-0.27802.155312.75000.0230
brBayesian Ridge2841.1966186260054.35108436.9604-0.73951.05403.07150.0260
lrLinear Regression3413.591877678401.80568529.8458-13.77601.655212.56760.0260
huberHuber Regressor353722.82062623282506511.6206677226.2951-8170.49814.290583.14280.0270
parPassive Aggressive Regressor458190557131386752.0000127082824435858543594764174207400017920.00003564867801759673856.0000-35081680769282265234331196719104.00004.2930162117765311357.06250.0270
larLeast Angle Regression5279543092693824360756893308958059062026751140204885068953266864251032765551903572849523562444716730714749884255295384047300434726128121908438472197543692488589721570181716817400594395059312048570476890193629065490491581212393472.0000infinf-inf69.7562113587982575992613240301388390522636061048497001818931882440580104476109896090302843163160366899442580278584729253337449923331833265663035371648043399650847000459190544668022179513068924737142007844553057786796326278370387034112.00000.0230
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 MAEMSERMSER2RMSLEMAPE
Fold      
0905.89283300052.06091816.60450.93230.30330.2800
1744.46622584156.31621607.53110.94790.26590.2275
21748.035517566209.85774191.2063-0.03611.48673.8602
32361.462731961881.82915653.48400.74310.52240.3655
4884.67873096696.03331759.74320.95001.06101.9254
5362.5239687715.6089829.28620.81021.22282.4020
62523.715637885513.37466155.12090.87300.34720.2559
72229.333428065837.48255297.72000.94850.50610.2956
8248.5115483607.4561695.41890.81020.47920.4789
9168.7094241172.4912491.09320.55241.31322.9350
Mean1217.733012587284.25102849.72080.75310.75081.3026
Std867.273814130330.69252113.38010.28830.44281.2911
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"execution_count": 26, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traçage des courbes d'apprentissage...\n" + ] + }, + { + "ename": "ValueError", + "evalue": "\nAll the 50 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n50 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\arrays\\categorical.py\", line 564, in astype\n new_cats = new_cats.astype(dtype=dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Auvergne-Rhône-Alpes'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py\", line 659, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 180, in astype_array\n values = values.astype(dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\arrays\\categorical.py\", line 575, in astype\n raise ValueError(msg)\nValueError: Cannot cast object dtype to float32\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[26], line 7\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# 1. Courbes d'apprentissage\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraçage des courbes d\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapprentissage...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m \u001b[43mplot_learning_curves\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# 2. Validation croisée détaillée\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mÉvaluation de la validation croisée...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\notebooks\\model_validation.py:17\u001b[0m, in \u001b[0;36mplot_learning_curves\u001b[1;34m(model, X, y, cv)\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_learning_curves\u001b[39m(model, X, y, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m):\n\u001b[0;32m 8\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;124;03m Trace les courbes d'apprentissage pour évaluer le surapprentissage.\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;124;03m \u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;124;03m cv: Nombre de folds pour la validation croisée\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m train_sizes, train_scores, val_scores \u001b[38;5;241m=\u001b[39m \u001b[43mlearning_curve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 19\u001b[0m \u001b[43m 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axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:1984\u001b[0m, in \u001b[0;36mlearning_curve\u001b[1;34m(estimator, X, y, groups, train_sizes, cv, scoring, exploit_incremental_learning, n_jobs, pre_dispatch, verbose, shuffle, random_state, error_score, return_times, fit_params)\u001b[0m\n\u001b[0;32m 1963\u001b[0m train_test_proportions\u001b[38;5;241m.\u001b[39mappend((train[:n_train_samples], test))\n\u001b[0;32m 1965\u001b[0m results \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[0;32m 1966\u001b[0m delayed(_fit_and_score)(\n\u001b[0;32m 1967\u001b[0m clone(estimator),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1982\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train, test \u001b[38;5;129;01min\u001b[39;00m train_test_proportions\n\u001b[0;32m 1983\u001b[0m )\n\u001b[1;32m-> 1984\u001b[0m \u001b[43m_warn_or_raise_about_fit_failures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_score\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1985\u001b[0m results \u001b[38;5;241m=\u001b[39m _aggregate_score_dicts(results)\n\u001b[0;32m 1986\u001b[0m train_scores \u001b[38;5;241m=\u001b[39m results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, n_unique_ticks)\u001b[38;5;241m.\u001b[39mT\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:536\u001b[0m, in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[1;34m(results, error_score)\u001b[0m\n\u001b[0;32m 529\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_failed_fits \u001b[38;5;241m==\u001b[39m num_fits:\n\u001b[0;32m 530\u001b[0m all_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 531\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAll the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 532\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt is very likely that your model is misconfigured.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou can try to debug the error by setting error_score=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 535\u001b[0m )\n\u001b[1;32m--> 536\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(all_fits_failed_message)\n\u001b[0;32m 538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 539\u001b[0m some_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 540\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnum_failed_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed out of a total of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 541\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe score on these train-test partitions for these parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 545\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 546\u001b[0m )\n", + "\u001b[1;31mValueError\u001b[0m: \nAll the 50 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n50 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\arrays\\categorical.py\", line 564, in astype\n new_cats = new_cats.astype(dtype=dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Auvergne-Rhône-Alpes'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py\", line 659, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 180, in astype_array\n values = values.astype(dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\arrays\\categorical.py\", line 575, in astype\n raise ValueError(msg)\nValueError: Cannot cast object dtype to float32\n" + ] + } + ], "source": [ "# Obtention des données d'entraînement\n", "X = get_config('X')\n", @@ -151,7 +890,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -165,7 +904,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.0" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/test_prediction_pycaret.ipynb b/notebooks/test_prediction_pycaret.ipynb new file mode 100644 index 0000000..3049091 --- /dev/null +++ b/notebooks/test_prediction_pycaret.ipynb @@ -0,0 +1,1313 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modèle de Prédiction des Hospitalisations avec PyCaret" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pycaret.regression import *\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from google.cloud import bigquery\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"C:/Users/antob/Documents/Arctusol/projet_wagon/projet_data_JBN/projet-jbn-data-le-wagon-533639ce801d.json\"\n", + " # Initialisation du client BigQuery\n", + "client = bigquery.Client()\n", + "\n", + "query = \"\"\"\n", + "SELECT * FROM projet-jbn-data-le-wagon.dbt_medical_analysis_join_total_morbidite.class_join_total_morbidite_sexe_population\n", + "\n", + "\"\"\"\n", + "\n", + "df_hospi = client.query(query).to_dataframe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "df_hospi_dpt_ens = df_hospi[(df_hospi[\"niveau\"] == \"Départements\") & (df_hospi[\"sexe\"] == \"Ensemble\")]\n", + "df_hospi_dpt_HF = df_hospi[(df_hospi[\"niveau\"] == \"Départements\") & (df_hospi[\"sexe\"] != \"Ensemble\")]\n", + "df_hospi_reg_ens = df_hospi[(df_hospi[\"niveau\"] == \"Régions\") & (df_hospi[\"sexe\"] == \"Ensemble\")]\n", + "df_hospi_reg_HF = df_hospi[(df_hospi[\"niveau\"] == \"Régions\") & (df_hospi[\"sexe\"] != \"Ensemble\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "df_hospi_dpt_ens_yr = df_hospi_dpt_ens[df_hospi_dpt_ens[\"annee\"].isin([2018, 2019, 2022])]\n", + "df_hospi_dpt_HF_yr =df_hospi_dpt_HF[df_hospi_dpt_HF[\"annee\"].isin([2018, 2019, 2022])]\n", + "df_hospi_reg_ens_yr = df_hospi_reg_ens[df_hospi_reg_ens[\"annee\"].isin([2018, 2019, 2022])]\n", + "df_hospi_reg_HF_yr =df_hospi_reg_HF[df_hospi_reg_HF[\"annee\"].isin([2018, 2019, 2022])]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"'hospi_prog_24h', 'hospi_autres_24h', 'hospi_total_24h',\n", + " 'hospi_1J', 'hospi_2J', 'hospi_3J', 'hospi_4J', 'hospi_5J',\n", + " 'hospi_6J', 'hospi_7J', 'hospi_8J', 'hospi_9J',\n", + " 'hospi_10J_19J', 'hospi_20J_29J', 'hospi_30J',\n", + " 'tranche_age_0_1', 'tranche_age_1_4', 'tranche_age_5_14',\n", + " 'tranche_age_15_24', 'tranche_age_25_34', 'tranche_age_35_44',\n", + " 'tranche_age_45_54', 'tranche_age_55_64', 'tranche_age_65_74',\n", + " 'tranche_age_75_84', 'tranche_age_85_et_plus'\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données d'entraînement (2018-2019)\n", + "train_data_ens = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'].isin([2018, 2019])]\n", + "test_data_ens = df_hospi_reg_ens_yr[df_hospi_reg_ens_yr['annee'] == 2022]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "train_data_hf = df_hospi_reg_HF_yr[df_hospi_reg_HF_yr['annee'].isin([2018, 2019])]\n", + "test_data_hf = df_hospi_reg_HF_yr[df_hospi_reg_HF_yr['annee'] == 2022]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Sélection des variables pertinentes\n", + "features = [\n", + " 'annee','nom_region', 'nom_pathologie', 'tranche_age_0_1', 'tranche_age_1_4', 'tranche_age_5_14',\n", + " 'tranche_age_15_24', 'tranche_age_25_34', 'tranche_age_35_44',\n", + " 'tranche_age_45_54', 'tranche_age_55_64', 'tranche_age_65_74',\n", + " 'tranche_age_75_84', 'tranche_age_85_et_plus'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Préparation des données pour PyCaret\n", + "train_data_ens = train_data_ens[features + ['nbr_hospi']]\n", + "test_data_ens = test_data_ens[features + ['nbr_hospi']]\n", + "train_data_hf = train_data_hf[features + ['nbr_hospi']]\n", + "test_data_hf = test_data_hf[features + ['nbr_hospi']]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "s = RegressionExperiment()" + ] + }, + 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 DescriptionValue
0Session id123
1Targetnbr_hospi
2Target typeRegression
3Original data shape(11376, 16)
4Transformed data shape(11376, 32)
5Transformed train set shape(7963, 32)
6Transformed test set shape(3413, 32)
7Ignore features1
8Numeric features12
9Categorical features2
10Rows with missing values0.2%
11PreprocessTrue
12Imputation typesimple
13Numeric imputationmean
14Categorical imputationmode
15Maximum one-hot encoding25
16Encoding methodNone
17TransformationTrue
18Transformation methodyeo-johnson
19NormalizeTrue
20Normalize methodzscore
21Fold GeneratorKFold
22Fold Number5
23CPU Jobs-1
24Use GPUFalse
25Log ExperimentFalse
26Experiment Namereg-default-name
27USI28df
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 ModelMAEMSERMSER2RMSLEMAPETT (Sec)
etExtra Trees Regressor291.45841172585.93111048.74500.97460.62650.47140.8000
gbrGradient Boosting Regressor862.62203352855.56481819.24280.92161.86163.77980.4060
lightgbmLight Gradient Boosting Machine607.95934189314.29701996.52580.90891.34201.14770.1780
rfRandom Forest Regressor652.36584071934.36142000.32090.90660.79850.78131.3380
knnK Neighbors Regressor776.49178900825.62932909.09430.80880.77550.35090.1200
dtDecision Tree Regressor819.98769197035.29622996.51260.78710.75370.78750.1000
brBayesian Ridge2746.073727502667.10895178.79150.38882.790129.96340.0900
ridgeRidge Regression2759.142627496164.11115179.00430.38862.798830.39850.1120
llarLasso Least Angle Regression2757.851627496980.26865179.00920.38862.798130.35090.0820
lassoLasso Regression2757.851627496980.87365179.00940.38862.798130.35110.5820
lrLinear Regression2759.485527495761.10495178.97610.38862.799130.40530.8040
enElastic Net2517.980228651496.29805274.80590.36772.614021.33450.0860
ompOrthogonal Matching Pursuit2949.565431582976.09735560.99810.29292.844426.14120.0880
huberHuber Regressor1994.817134432368.91325789.75640.23702.239910.72830.1120
parPassive Aggressive Regressor1984.828835420656.46965874.22990.21422.17069.38040.0900
adaAdaBoost Regressor5083.913933524857.60695746.68170.17793.469855.06750.2540
larLeast Angle Regression3685.923151020439.93516425.43980.00602.941940.81370.0860
dummyDummy Regressor3353.122844495314.46436609.9613-0.00183.067032.58650.0740
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 ModelMAEMSERMSER2RMSLEMAPETT (Sec)
etExtra Trees Regressor210.21971674098.71031127.07260.99120.17730.05270.3640
gbrGradient Boosting Regressor427.66672396550.77421420.51550.98620.59820.58650.2560
rfRandom Forest Regressor349.73053296375.67891671.18270.98170.19990.07190.7120
dtDecision Tree Regressor610.62845607087.29982317.28170.96450.33660.15420.0560
lightgbmLight Gradient Boosting Machine501.01826291665.54232360.07320.96390.39450.19590.1180
knnK Neighbors Regressor800.008110478549.67673090.50070.93850.27940.15120.0600
adaAdaBoost Regressor5000.653330007587.16915451.37830.80262.521036.73230.1200
brBayesian Ridge5146.487887145760.03599205.03500.45192.228940.21280.0520
ridgeRidge Regression5159.762387114678.79289204.78750.45182.237841.06080.0460
llarLasso Least Angle Regression5159.521087113806.61049204.70430.45182.239441.06700.0560
lrLinear Regression5161.009587118148.93759205.06880.45182.238741.12650.8840
lassoLasso Regression5159.520787113814.89999204.70440.45182.239341.06670.5900
larLeast Angle Regression5231.950287920062.65259252.36800.44572.250541.03010.0880
enElastic Net4977.531490539358.33959377.84990.43162.153435.08150.0540
ompOrthogonal Matching Pursuit5696.0296102280831.43299981.03670.35482.269939.54070.0440
parPassive Aggressive Regressor3835.3248117010044.983010673.69650.26251.729514.84200.0500
dummyDummy Regressor6601.7355156689296.636312396.8486-0.00102.531535.07010.0440
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Comparaison des modèles\n", + "best_model = compare_models(\n", + " exclude=['ransac', 'huber', 'theilsen'], # On exclut les modèles moins pertinents pour ce cas\n", + " sort='R2', # Trier par R2 score\n", + " n_select=1 # Sélectionner le meilleur modèle\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "\nAll the 5 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n1 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Anémies'\n\n--------------------------------------------------------------------------------\n4 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Maladies intestinales infectieuses'\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[40], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cross_val_score\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# Validation croisée sur le meilleur modèle\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m cv_scores \u001b[38;5;241m=\u001b[39m \u001b[43mcross_val_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbest_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnbr_hospi\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnbr_hospi\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mScores de validation croisée (R²):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMoyenne: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcv_scores\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (+/- \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcv_scores\u001b[38;5;241m.\u001b[39mstd()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:719\u001b[0m, in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m 716\u001b[0m \u001b[38;5;66;03m# To ensure multimetric format is not supported\u001b[39;00m\n\u001b[0;32m 717\u001b[0m scorer \u001b[38;5;241m=\u001b[39m check_scoring(estimator, scoring\u001b[38;5;241m=\u001b[39mscoring)\n\u001b[1;32m--> 719\u001b[0m cv_results \u001b[38;5;241m=\u001b[39m \u001b[43mcross_validate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 722\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 723\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 724\u001b[0m \u001b[43m \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscore\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mscorer\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 725\u001b[0m \u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 726\u001b[0m \u001b[43m 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\u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 733\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cv_results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_score\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:450\u001b[0m, in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, params, pre_dispatch, return_train_score, return_estimator, return_indices, error_score)\u001b[0m\n\u001b[0;32m 429\u001b[0m parallel \u001b[38;5;241m=\u001b[39m Parallel(n_jobs\u001b[38;5;241m=\u001b[39mn_jobs, verbose\u001b[38;5;241m=\u001b[39mverbose, pre_dispatch\u001b[38;5;241m=\u001b[39mpre_dispatch)\n\u001b[0;32m 430\u001b[0m results \u001b[38;5;241m=\u001b[39m parallel(\n\u001b[0;32m 431\u001b[0m delayed(_fit_and_score)(\n\u001b[0;32m 432\u001b[0m clone(estimator),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train, test \u001b[38;5;129;01min\u001b[39;00m indices\n\u001b[0;32m 448\u001b[0m )\n\u001b[1;32m--> 450\u001b[0m \u001b[43m_warn_or_raise_about_fit_failures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_score\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 452\u001b[0m \u001b[38;5;66;03m# For callable scoring, the return type is only know after calling. If the\u001b[39;00m\n\u001b[0;32m 453\u001b[0m \u001b[38;5;66;03m# return type is a dictionary, the error scores can now be inserted with\u001b[39;00m\n\u001b[0;32m 454\u001b[0m \u001b[38;5;66;03m# the correct key.\u001b[39;00m\n\u001b[0;32m 455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(scoring):\n", + "File \u001b[1;32mc:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:536\u001b[0m, in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[1;34m(results, error_score)\u001b[0m\n\u001b[0;32m 529\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_failed_fits \u001b[38;5;241m==\u001b[39m num_fits:\n\u001b[0;32m 530\u001b[0m all_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 531\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAll the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 532\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt is very likely that your model is misconfigured.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou can try to debug the error by setting error_score=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 535\u001b[0m )\n\u001b[1;32m--> 536\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(all_fits_failed_message)\n\u001b[0;32m 538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 539\u001b[0m some_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 540\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnum_failed_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed out of a total of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 541\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe score on these train-test partitions for these parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 545\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 546\u001b[0m )\n", + "\u001b[1;31mValueError\u001b[0m: \nAll the 5 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n1 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Anémies'\n\n--------------------------------------------------------------------------------\n4 fits failed with the following error:\nTraceback (most recent call last):\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n estimator.fit(X_train, y_train, **fit_params)\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 363, in fit\n X, y = self._validate_data(\n ^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n X, y = check_X_y(X, y, **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1263, in check_X_y\n X = check_array(\n ^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 921, in check_array\n array = array.astype(new_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 6534, in astype\n new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 414, in astype\n return self.apply(\n ^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py\", line 354, in apply\n applied = getattr(b, f)(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py\", line 616, in astype\n new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 238, in astype_array_safe\n new_values = astype_array(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 183, in astype_array\n values = _astype_nansafe(values, dtype, copy=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"c:\\Users\\antob\\Documents\\Arctusol\\projet_wagon\\projet_data_JBN\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py\", line 134, in _astype_nansafe\n return arr.astype(dtype, copy=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'Maladies intestinales infectieuses'\n" + ] + } + ], + "source": [ + "# Ajout après la comparaison des modèles\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "# Validation croisée sur le meilleur modèle\n", + "cv_scores = cross_val_score(best_model, train_data.drop('nbr_hospi', axis=1), \n", + " train_data['nbr_hospi'], \n", + " cv=5, \n", + " scoring='r2')\n", + "\n", + "print(\"Scores de validation croisée (R²):\")\n", + "print(f\"Moyenne: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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 MAEMSERMSER2RMSLEMAPE
Fold      
0731.64183547888.64971883.58400.96890.45040.4162
1862.184110623691.82393259.40050.94040.52480.5540
2725.75753818782.23061954.17050.97520.52830.4961
31043.868522943884.05374789.97750.89930.56480.4763
4649.86622279363.45651509.75610.97910.54470.5012
Mean802.66368642722.04292679.37770.95260.52260.4888
Std138.58447722273.38611209.81700.02990.03880.0445
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n", + "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n" + ] + } + ], + "source": [ + "# Tuning du meilleur modèle\n", + "tuned_model = tune_model(best_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Statistiques du nombre d'hospitalisations:\n", + "count 5688.0\n", + "mean 5637.552215\n", + "std 12285.074272\n", + "min 0.0\n", + "25% 336.0\n", + "50% 1546.0\n", + "75% 5575.25\n", + "max 163601.0\n", + "Name: nbr_hospi, dtype: Float64\n", + "\n", + "MAE en pourcentage de la moyenne: 3.74%\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Analyse du MAE en contexte\n", + "print(\"Statistiques du nombre d'hospitalisations:\")\n", + "print(train_data['nbr_hospi'].describe())\n", + "\n", + "# Calcul du MAE relatif\n", + "mae_relatif = 211 / train_data['nbr_hospi'].mean() * 100\n", + "print(f\"\\nMAE en pourcentage de la moyenne: {mae_relatif:.2f}%\")\n", + "\n", + "# Distribution des erreurs\n", + "plt.figure(figsize=(10, 6))\n", + "erreurs = abs(predictions['nbr_hospi'] - predictions['prediction_label'])\n", + "sns.histplot(data=erreurs[erreurs < erreurs.quantile(0.95)], bins=50)\n", + "plt.axvline(x=211, color='r', linestyle='--', label='MAE')\n", + "plt.title('Distribution des erreurs absolues')\n", + "plt.xlabel('Erreur absolue (nombre d\\'hospitalisations)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 ModelMAEMSERMSER2RMSLEMAPE
0Extra Trees Regressor239.12121909800.38001381.95530.98880.15730.0529
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Performance sur les données de test (2022):\n", + " annee code_pathologie hospi_prog_24h hospi_autres_24h \\\n", + "237376 2022 01001 26.0 4.0 \n", + "237378 2022 01002 0.0 0.0 \n", + "237383 2022 01003 4.0 3.0 \n", + "237386 2022 01004 65.0 0.0 \n", + "237388 2022 01005 0.0 0.0 \n", + "\n", + " hospi_total_24h hospi_1J hospi_2J hospi_3J hospi_4J hospi_5J \\\n", + "237376 30.0 132.0 80.0 52.0 40.0 15.0 \n", + "237378 0.0 0.0 0.0 4.0 0.0 0.0 \n", + "237383 7.0 16.0 17.0 8.0 7.0 13.0 \n", + "237386 65.0 0.0 0.0 0.0 0.0 0.0 \n", + "237388 4.0 3.0 0.0 3.0 3.0 0.0 \n", + "\n", + " ... tranche_age_15_24 tranche_age_25_34 tranche_age_35_44 \\\n", + "237376 ... 0.54 0.39 0.47 \n", + "237378 ... 0.00 0.00 0.00 \n", + "237383 ... 0.00 0.16 0.00 \n", + "237386 ... 0.24 0.71 0.43 \n", + "237388 ... 0.00 0.16 0.00 \n", + "\n", + " tranche_age_45_54 tranche_age_55_64 tranche_age_65_74 \\\n", + "237376 0.37 0.63 0.59 \n", + "237378 0.12 0.00 0.09 \n", + "237383 0.19 0.50 0.99 \n", + "237386 0.23 0.13 0.00 \n", + "237388 0.08 0.00 0.00 \n", + "\n", + " tranche_age_75_84 tranche_age_85_et_plus nbr_hospi prediction_label \n", + "237376 1.23 2.81 417 453.13 \n", + "237378 0.00 0.00 18 15.33 \n", + "237383 1.77 2.57 168 171.90 \n", + "237386 0.00 0.00 74 69.72 \n", + "237388 0.00 0.00 19 24.79 \n", + "\n", + "[5 rows x 31 columns]\n" + ] + } + ], + "source": [ + "# Évaluation sur les données de test (2022)\n", + "predictions = predict_model(best_model, data=test_data)\n", + "print(\"Performance sur les données de test (2022):\")\n", + "print(predictions.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Métriques sur les données de test (2022):\n", + "MAE: 239.12\n", + "RMSE: 1381.96\n", + "R²: 0.9888\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiques des erreurs relatives (%):\n", + "count 2862.0\n", + "mean inf\n", + "std \n", + "min 0.0\n", + "25% 1.505772\n", + "50% 3.386791\n", + "75% 6.304061\n", + "max inf\n", + "Name: erreur_relative, dtype: Float64\n" + ] + } + ], + "source": [ + "# Calcul des métriques d'erreur pour les prédictions\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Extraire les vraies valeurs et les prédictions\n", + "y_true = predictions['nbr_hospi']\n", + "y_pred = predictions['prediction_label']\n", + "\n", + "# Calcul des métriques\n", + "mae = mean_absolute_error(y_true, y_pred)\n", + "rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n", + "r2 = r2_score(y_true, y_pred)\n", + "\n", + "print(f\"Métriques sur les données de test (2022):\")\n", + "print(f\"MAE: {mae:.2f}\")\n", + "print(f\"RMSE: {rmse:.2f}\")\n", + "print(f\"R²: {r2:.4f}\")\n", + "\n", + "# Visualisation des prédictions vs réalité\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(y_true, y_pred, alpha=0.5)\n", + "plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + "plt.xlabel('Valeurs réelles')\n", + "plt.ylabel('Prédictions')\n", + "plt.title('Prédictions vs Valeurs réelles')\n", + "plt.show()\n", + "\n", + "# Calcul et affichage des erreurs relatives\n", + "predictions['erreur_relative'] = abs(predictions['nbr_hospi'] - predictions['prediction_label']) / predictions['nbr_hospi'] * 100\n", + "print(\"\\nStatistiques des erreurs relatives (%):\")\n", + "print(predictions['erreur_relative'].describe())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt index c425664..d836d0c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,4 +21,6 @@ streamlit-lottie pycaret streamlit-extras folium -streamlit-folium \ No newline at end of file +streamlit-folium +mlflow +geopy \ No newline at end of file