From 5fe6a1591d682242821f8e519b3c8700db40f86d Mon Sep 17 00:00:00 2001 From: Riccardo Date: Mon, 13 Sep 2021 22:01:58 +0200 Subject: [PATCH] no neural network removed from MLJAR and AutoGluon --- app/algorithms/auto_gluon.py | 2 +- app/algorithms/mljar_supervised.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/app/algorithms/auto_gluon.py b/app/algorithms/auto_gluon.py index 0a99a1b..c66a9d6 100644 --- a/app/algorithms/auto_gluon.py +++ b/app/algorithms/auto_gluon.py @@ -47,7 +47,7 @@ def autogluon(df, task, options, time_start): time_limit=options['time']*60, presets=['best_quality'], #auto_stack=True - #hyperparameters=hyperparameters # -> Questo aggiunge le NN + hyperparameters=hyperparameters # -> Comment this line to add te Neural Network ) predictor.fit_summary() diff --git a/app/algorithms/mljar_supervised.py b/app/algorithms/mljar_supervised.py index a2ece90..ade6f33 100644 --- a/app/algorithms/mljar_supervised.py +++ b/app/algorithms/mljar_supervised.py @@ -41,7 +41,7 @@ def do_mljar(df, options, task, time_start): else: ml_task = 'binary_classification' else: ml_task=task - automl = AutoML(mode="Compete", total_time_limit=60*options['time'], ml_task=ml_task) + automl = AutoML(mode="Compete", total_time_limit=60*options['time'], ml_task=ml_task, algorithms=["Baseline", "Linear", "Decision Tree", "Random Forest", "Extra Trees", "LightGBM", "Xgboost", "CatBoost"]) automl.fit(X_train, y_train) y_pred = automl.predict_all(X_test)