Project1_SysBio_NF_PPCM_classifiers.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPP0blfZXted"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sayalaruano/Project3Period-SysBioMaster/blob/main/Project1_SysBio_NF_PPCM_classifiers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihD1ikv4A_U-"
      },
      "source": [
        "# **Predicting cardiomyopathies using RNAseq data with machine learning models**\n",
        "\n",
        "This notebook is part of a group project for the Systems Biology masters at Maastricht University.\n",
        "\n",
        "You can find more details about my capstone project in this [GitHub repository](https://github.com/sayalaruano/Project3Period-SysBioMaster).\n",
        "\n",
        "## Dataset description\n",
        "We will be using an [RNA-sequencing dataset from the MAGNet consortium](https://github.com/mpmorley/MAGNet). To generate this dataset, leftventricular free-wall tissue was harvested at the time of cardiac surgery fromsubjects with heart failure undergoing transplantation and from unused donor hearts with apparently normal function.  This dataset is a spectacular resource of left ventricular transcriptomes of **non-failing healthy donors**, \n",
        "**Peripartum cardiomyopathy (PPCM)**, **Hypertrophic cardiomyopathy (HCM)** and **Dilated cardiomyopathy (DCM)** patients. Next to the RNA-sequencing data (in log2-transformed CPM values), extensive participant information is available (age, sex, ethnicity, disease state, and so on).\n",
        "\n",
        "**Note:** `mkdir`, `wget`, `unzip`, `make`, `cd`, `grep`, and `cat` are Unix based OS commands, so if you are in Windows, maybe these terminal tools won't work."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2E2XwwoUWbtw"
      },
      "source": [
        "## **Installation of libraries**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xLMn-W76a5gD"
      },
      "source": [
        "### **[LazyPredict](https://github.com/shankarpandala/lazypredict)**\n",
        "Library to run many sklearn's binary classifiers with default parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DRjGMT2FVNZm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46a8da17-5c0a-4da4-9dcb-8bff0831a080"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting lazypredict\n",
            "  Downloading lazypredict-0.2.12-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.2.0)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.8/dist-packages (from lazypredict) (2.2.3)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from lazypredict) (7.1.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.3.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.0.2)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.8/dist-packages (from lazypredict) (0.90)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from lazypredict) (4.64.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from lightgbm->lazypredict) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from lightgbm->lazypredict) (1.7.3)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->lazypredict) (2022.7)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->lazypredict) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lazypredict) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->lazypredict) (1.15.0)\n",
            "Installing collected packages: lazypredict\n",
            "Successfully installed lazypredict-0.2.12\n"
          ]
        }
      ],
      "source": [
        "!pip install lazypredict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZYPeKm97sFk"
      },
      "source": [
        "## **Imports, data obtention, and pre-processing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9Ql8qTq3dLSg"
      },
      "outputs": [],
      "source": [
        "# Download and unzip \n",
        "from urllib.request import urlopen\n",
        "from io import BytesIO\n",
        "from zipfile import ZipFile\n",
        "\n",
        "# Data manipulation and EDA\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Plots\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Machine learning classifers\n",
        "import lazypredict\n",
        "import pickle\n",
        "from lazypredict.Supervised import LazyClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score, f1_score, matthews_corrcoef, balanced_accuracy_score, roc_curve\n",
        "from sklearn.metrics import make_scorer\n",
        "from matplotlib_venn import venn3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "FA6Vue0LD0_t"
      },
      "outputs": [],
      "source": [
        "# Download and unzip data\n",
        "def download_and_unzip(url, extract_to='.'):\n",
        "    http_response = urlopen(url)\n",
        "    zipfile = ZipFile(BytesIO(http_response.read()))\n",
        "    zipfile.extractall(path=extract_to)\n",
        "\n",
        "url = \"https://surfdrive.surf.nl/files/index.php/s/mZrXMDlQ1Z1LvTn/download\"\n",
        "\n",
        "download_and_unzip(url, extract_to='.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQHwqXaV5eTe"
      },
      "source": [
        "## **Feature matrices preparation**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HSFMjPtd-G-"
      },
      "source": [
        "### **Load molecular descriptors (features)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-DkYKw0yLmjL"
      },
      "outputs": [],
      "source": [
        "# Load gene expression and sampleinfo data \n",
        "gxData_cpm = pd.read_table(\"MAGNET_GeneExpressionData_CPM_19112020.txt\", index_col=0)\n",
        "\n",
        "sampleInfo = pd.read_csv(\"MAGNET_SampleData_18112022.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transpose the gx dataframe\n",
        "gxData_cpm = gxData_cpm.transpose()\n",
        "\n",
        "# Create a new column with index values \n",
        "gxData_cpm = gxData_cpm.rename_axis('sample_name').reset_index()\n",
        "\n",
        "# Rename the index name\n",
        "gxData_cpm = gxData_cpm.rename_axis(None, axis=1)"
      ],
      "metadata": {
        "id": "fddqwDY69xTj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a df with the etiology and sample name columns \n",
        "columns = [\"etiology\", \"sample_name\"]\n",
        "\n",
        "etiology = sampleInfo[columns]"
      ],
      "metadata": {
        "id": "Wjr7PTapF9IE"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Join the etiology and gx dataframes\n",
        "gxData_cpm_labeled = pd.merge(gxData_cpm, etiology, on='sample_name', how='inner')"
      ],
      "metadata": {
        "id": "psfJU_KUF9SI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prin the number of samples per etiology\n",
        "gxData_cpm_labeled[\"etiology\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTo2PxW9ID8U",
        "outputId": "6e0f7f07-4b72-4b72-8344-4845311d2b45"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NF      166\n",
              "DCM     166\n",
              "HCM      28\n",
              "PPCM      6\n",
              "Name: etiology, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the NF and DCM patients\n",
        "gxData_cpm_lab_NFPPCM = gxData_cpm_labeled[(gxData_cpm_labeled[\"etiology\"] == \"NF\") | \n",
        "                                          (gxData_cpm_labeled[\"etiology\"] == \"PPCM\")]"
      ],
      "metadata": {
        "id": "_iDBzqjoID-p"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the y label dataset\n",
        "y = gxData_cpm_lab_NFPPCM['etiology'].copy()\n",
        "\n",
        "# Encoding the target class label\n",
        "y = y.map({\"PPCM\": 1, \"NF\": 0}).to_numpy()"
      ],
      "metadata": {
        "id": "UkHK2_iPKSux"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the feature matrix by deleting the y column and the sample name\n",
        "final_df_NFPPCM = gxData_cpm_lab_NFPPCM.drop(['etiology', 'sample_name'], axis=1)\n",
        "X = final_df_NFPPCM.to_numpy()"
      ],
      "metadata": {
        "id": "R51pwPQLLbtN"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9MRC7xR8eJi"
      },
      "source": [
        "## **Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6rVorK_rVnt"
      },
      "source": [
        "### **Target variable**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "bByC_71PtrBN",
        "outputId": "5c929bcb-03cb-42c4-99f3-69a44460706f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Bar plot of active and inactive AMPs\n",
        "\n",
        "# Data\n",
        "n_healthy = (gxData_cpm_lab_NFPPCM[\"etiology\"] == \"NF\").sum()\n",
        "n_disease = (gxData_cpm_lab_NFPPCM[\"etiology\"] == \"PPCM\").sum()\n",
        "\n",
        "x_label = ['Healthy', 'PPCM']\n",
        "y_label = [n_healthy, n_disease]\n",
        "\n",
        "# Setup plot\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Make bar plot\n",
        "p = ax.bar(x_label, y_label, color = ['#F8766D', '#00BFC4'], ec = 'black')\n",
        "\n",
        "ax.set_title('Disease status of samples', fontsize=14, fontweight='bold', pad=15)\n",
        "\n",
        "ax.set_ylim(0,200)\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RewWucDQbTo3"
      },
      "source": [
        "## **Machine learning models**\n",
        "First, we tested more than 30 ML binary classifiers using the [LazyPredict](https://github.com/shankarpandala/lazypredict) Python library. We chose the best model evaluating some performance metrics (e.g., accuracy, ROC AUC, F1 score, and Matthews Correlation Coefficient) on the test dataset. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01B14Lz2bdMn",
        "outputId": "370f4f7d-8978-4af2-c369-45d510b96e13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:56<00:00,  1.94s/it]\n"
          ]
        }
      ],
      "source": [
        "# Run the ML classifiers with LazyPredict     \n",
        "# Data splitting\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, \n",
        "                                                    random_state =42, \n",
        "                                                    stratify=y)\n",
        "\n",
        "# Define and build the Lazyclassifier\n",
        "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=matthews_corrcoef)\n",
        "\n",
        "models, predictions = clf.fit(X_train, X_test, y_train, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List of additional performance metrics\n",
        "add_perf_metrics = [precision_score, recall_score]\n",
        "\n",
        "# Loop by getting the performance metrics of all the ML models\n",
        "for i in add_perf_metrics:\n",
        "\n",
        "  clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric= i)\n",
        "\n",
        "  new_models, predictions = clf.fit(X_train, X_test, y_train, y_test)\n",
        "\n",
        "  cols_to_use = new_models.columns.difference(models.columns)\n",
        "\n",
        "  models = pd.merge(models, new_models[cols_to_use], left_index=True, right_index=True, how='outer')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wx64OIdC7Kwp",
        "outputId": "9f2b104b-2a78-42d8-c4d9-fa9ea2224480"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:57<00:00,  1.99s/it]\n",
            "100%|██████████| 29/29 [01:00<00:00,  2.08s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Delete Time column\n",
        "models.drop(['Time Taken', 'ROC AUC'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "UshUlk-27Lfo"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Jtf5g08QR48h",
        "outputId": "1a6eb5b4-21c1-4028-dee1-59dfc5d6103b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Accuracy  Balanced Accuracy  F1 Score  \\\n",
              "Model                                                                  \n",
              "LGBMClassifier                     1.00               1.00      1.00   \n",
              "LinearDiscriminantAnalysis         1.00               1.00      1.00   \n",
              "RidgeClassifierCV                  1.00               1.00      1.00   \n",
              "RidgeClassifier                    1.00               1.00      1.00   \n",
              "LogisticRegression                 1.00               1.00      1.00   \n",
              "AdaBoostClassifier                 0.97               0.99      0.98   \n",
              "BernoulliNB                        0.97               0.99      0.98   \n",
              "DecisionTreeClassifier             0.94               0.97      0.96   \n",
              "NearestCentroid                    0.91               0.96      0.94   \n",
              "SGDClassifier                      0.77               0.88      0.85   \n",
              "Perceptron                         0.77               0.88      0.85   \n",
              "PassiveAggressiveClassifier        0.71               0.85      0.81   \n",
              "LinearSVC                          0.71               0.85      0.81   \n",
              "BaggingClassifier                  0.97               0.50      0.96   \n",
              "DummyClassifier                    0.97               0.50      0.96   \n",
              "LabelSpreading                     0.97               0.50      0.96   \n",
              "ExtraTreesClassifier               0.97               0.50      0.96   \n",
              "LabelPropagation                   0.97               0.50      0.96   \n",
              "RandomForestClassifier             0.97               0.50      0.96   \n",
              "CalibratedClassifierCV             0.97               0.50      0.96   \n",
              "KNeighborsClassifier               0.97               0.50      0.96   \n",
              "GaussianNB                         0.97               0.50      0.96   \n",
              "SVC                                0.97               0.50      0.96   \n",
              "XGBClassifier                      0.97               0.50      0.96   \n",
              "ExtraTreeClassifier                0.94               0.49      0.94   \n",
              "QuadraticDiscriminantAnalysis      0.34               0.18      0.50   \n",
              "\n",
              "                               matthews_corrcoef  precision_score  \\\n",
              "Model                                                               \n",
              "LGBMClassifier                              1.00             1.00   \n",
              "LinearDiscriminantAnalysis                  1.00             1.00   \n",
              "RidgeClassifierCV                           1.00             1.00   \n",
              "RidgeClassifier                             1.00             1.00   \n",
              "LogisticRegression                          1.00             1.00   \n",
              "AdaBoostClassifier                          0.70             0.50   \n",
              "BernoulliNB                                 0.70             0.50   \n",
              "DecisionTreeClassifier                      0.56             0.33   \n",
              "NearestCentroid                             0.48             0.25   \n",
              "SGDClassifier                               0.29             0.11   \n",
              "Perceptron                                  0.29             0.11   \n",
              "PassiveAggressiveClassifier                 0.25             0.09   \n",
              "LinearSVC                                   0.25             0.09   \n",
              "BaggingClassifier                           0.00             0.00   \n",
              "DummyClassifier                             0.00             0.00   \n",
              "LabelSpreading                              0.00             0.00   \n",
              "ExtraTreesClassifier                        0.00             0.00   \n",
              "LabelPropagation                            0.00             0.00   \n",
              "RandomForestClassifier                      0.00             0.00   \n",
              "CalibratedClassifierCV                      0.00             0.00   \n",
              "KNeighborsClassifier                        0.00             0.00   \n",
              "GaussianNB                                  0.00             0.00   \n",
              "SVC                                         0.00             0.00   \n",
              "XGBClassifier                               0.00             0.00   \n",
              "ExtraTreeClassifier                        -0.03             0.00   \n",
              "QuadraticDiscriminantAnalysis              -0.22             0.00   \n",
              "\n",
              "                               recall_score  \n",
              "Model                                        \n",
              "LGBMClassifier                         1.00  \n",
              "LinearDiscriminantAnalysis             1.00  \n",
              "RidgeClassifierCV                      1.00  \n",
              "RidgeClassifier                        1.00  \n",
              "LogisticRegression                     1.00  \n",
              "AdaBoostClassifier                     1.00  \n",
              "BernoulliNB                            1.00  \n",
              "DecisionTreeClassifier                 1.00  \n",
              "NearestCentroid                        1.00  \n",
              "SGDClassifier                          1.00  \n",
              "Perceptron                             1.00  \n",
              "PassiveAggressiveClassifier            1.00  \n",
              "LinearSVC                              1.00  \n",
              "BaggingClassifier                      0.00  \n",
              "DummyClassifier                        0.00  \n",
              "LabelSpreading                         0.00  \n",
              "ExtraTreesClassifier                   0.00  \n",
              "LabelPropagation                       0.00  \n",
              "RandomForestClassifier                 0.00  \n",
              "CalibratedClassifierCV                 0.00  \n",
              "KNeighborsClassifier                   0.00  \n",
              "GaussianNB                             0.00  \n",
              "SVC                                    0.00  \n",
              "XGBClassifier                          0.00  \n",
              "ExtraTreeClassifier                    0.00  \n",
              "QuadraticDiscriminantAnalysis          0.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40761e72-a138-49a1-9e5f-33ea760ca10d\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>matthews_corrcoef</th>\n",
              "      <th>precision_score</th>\n",
              "      <th>recall_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.09</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.09</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-40761e72-a138-49a1-9e5f-33ea760ca10d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-40761e72-a138-49a1-9e5f-33ea760ca10d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export results as a csv\n",
        "models.to_csv(\"Results_allMLclassifiers_PPCM.csv\")"
      ],
      "metadata": {
        "id": "v6b-Gnwb7iLy"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "4ONmPLKdcKP4",
        "outputId": "67d9cf31-39ee-477c-eee3-759796dfb6d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot of Accuracy\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=models.index, x=\"Accuracy\", \n",
        "                 data=models)\n",
        "ax.set(xlim=(0, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "Bse08KSUW1FJ",
        "outputId": "8cc6b340-7dac-44f2-978d-001cc09995fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot of MCC\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=models.index, \n",
        "                 x=\"matthews_corrcoef\", data=models)\n",
        "ax.set(xlim=(0, 1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvITCk5anJ96"
      },
      "source": [
        "---\n",
        "We did not fine-tuned the hyperparameters of the best model because the performance metrics with the default parameters were very high, so hyperparameter tuning was not necessary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZK0-qLC2EfYh"
      },
      "source": [
        "### **Evaluation of the best model on the test dataset**\n",
        "According to the summary tables presented above, there were some models with a perfect prediction capcity considering all of the performance metrics (values of 1 in the previous table). However, these methods are based on regression classifiers, which cannot show the feature importance of the model. So, we used the **LGBMClassifier** that also have high performance metrics values and keep track of the feature importance (in this case, the gene expression values).\n",
        "\n",
        "Thus, we trained this model again on the entire training dataset (without creating the validation partition) and evaluated it on the **test dataset**. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Implementation of the best models\n",
        "best_model1 = make_pipeline(StandardScaler(), LGBMClassifier(random_state=42))\n",
        "best_model1.fit(X_train, y_train)\n",
        "\n",
        "best_model2 = make_pipeline(StandardScaler(), AdaBoostClassifier(random_state=42))\n",
        "best_model2.fit(X_train, y_train)\n",
        "\n",
        "best_model3 = make_pipeline(StandardScaler(), DecisionTreeClassifier(random_state=42))\n",
        "best_model3.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPklLxPX20AR",
        "outputId": "a269e52d-b790-41b7-b7bb-41e05b808f97"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('decisiontreeclassifier',\n",
              "                 DecisionTreeClassifier(random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models_names = [best_model1, best_model2, best_model3]"
      ],
      "metadata": {
        "id": "Gc__rLWDOxyc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate performance metrics\n",
        "def performance_metrics(y, y_pred):\n",
        "  acc = accuracy_score(y, y_pred)\n",
        "\n",
        "  bal_acc = balanced_accuracy_score(y, y_pred)\n",
        "\n",
        "  prec = precision_score(y, y_pred)\n",
        "\n",
        "  recall = recall_score(y, y_pred)\n",
        "\n",
        "  f1score = f1_score(y, y_pred)\n",
        "\n",
        "  mcc = matthews_corrcoef(y, y_pred)\n",
        "\n",
        "  results = [acc, bal_acc, prec, recall, f1score, mcc]\n",
        "\n",
        "  return results"
      ],
      "metadata": {
        "id": "Navl-KBoObkg"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset to store performance metrics of test dataset\n",
        "results_bestMLmodels_test = pd.DataFrame(index=range(6),columns=range(0))\n",
        "\n",
        "# Create indice list\n",
        "idxs = pd.Index(['Accuracy', 'Balanced accuracy', 'Precision', \n",
        "                                              'Recall', 'F1score', 'MCC'])\n",
        "# Set indices\n",
        "results_bestMLmodels_test.set_index(idxs, inplace=True)"
      ],
      "metadata": {
        "id": "-NJJ87HfO87I"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in models_names:  \n",
        "  # Predicting values with the best model on test and external datasets\n",
        "  y_test_pred = i.predict(X_test)\n",
        "\n",
        "  # Calculate performance metrics of test dataset\n",
        "  results_bestMLmodels_test[type(i[1]).__name__] = performance_metrics(y_test, y_test_pred)"
      ],
      "metadata": {
        "id": "uXdP-lt4O8_Y"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_bestMLmodels_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "YV_koh7yO9B5",
        "outputId": "6c822a2f-c9b3-487c-c052-5b7965976982"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   LGBMClassifier  AdaBoostClassifier  DecisionTreeClassifier\n",
              "Accuracy                     1.00                0.97                    0.94\n",
              "Balanced accuracy            1.00                0.99                    0.97\n",
              "Precision                    1.00                0.50                    0.33\n",
              "Recall                       1.00                1.00                    1.00\n",
              "F1score                      1.00                0.67                    0.50\n",
              "MCC                          1.00                0.70                    0.56"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1abe003-d1c9-45c2-b4ec-f703ca5e5365\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Balanced accuracy</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1score</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MCC</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1abe003-d1c9-45c2-b4ec-f703ca5e5365')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a1abe003-d1c9-45c2-b4ec-f703ca5e5365 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a1abe003-d1c9-45c2-b4ec-f703ca5e5365');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "WjxlipmUQ1N5"
      },
      "outputs": [],
      "source": [
        "# Export results as a csv\n",
        "results_bestMLmodels_test.to_csv(\"Results_best_ensembleclassifiers_PPCM_test_datatset.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ROC curves"
      ],
      "metadata": {
        "id": "dQpnj-0lDrch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#set up plotting area\n",
        "plt.figure(0).clf()\n",
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "#fit best model1 and plot ROC curve, test dataset\n",
        "y_pred1 = best_model1.predict_proba(X_test)[:, 1]\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred1)\n",
        "auc1 = round(roc_auc_score(y_test, y_pred1), 4)\n",
        "plt.plot(fpr,tpr,label=\"LightGBM test_dataset, AUC=\"+str(auc1))\n",
        "\n",
        "#fit best model2 and plot ROC curve, test dataset\n",
        "y_pred2 = best_model2.predict_proba(X_test)[:, 1]\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred2)\n",
        "auc2 = round(roc_auc_score(y_test, y_pred2), 4)\n",
        "plt.plot(fpr,tpr,label=\"AdaBoost, test_dataset, AUC=\"+str(auc2))\n",
        "\n",
        "#fit best model3 and plot ROC curve, test dataset\n",
        "y_pred3 = best_model3.predict_proba(X_test)[:, 1]\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred3)\n",
        "auc3 = round(roc_auc_score(y_test, y_pred3), 4)\n",
        "plt.plot(fpr,tpr,label=\"Decision Tree, test_dataset, AUC=\"+str(auc3))\n",
        "\n",
        "# Add random guess line\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k', label='Random guess')\n",
        "\n",
        "#add legend and titles\n",
        "#plt.title('ROC curve')\n",
        "plt.grid(None)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig('ROC_curve_PPCM_bestmodels.png', bbox_inches='tight')\n",
        "plt.savefig('ROC_curve_PPCM_bestmodels.svg', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "K9mc2hFMDpgb",
        "outputId": "151598f3-4d88-4439-9a53-42affce67941"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-EaFk3SnLvc"
      },
      "source": [
        "### **Feature importance of the best model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "ZFdeHIRXuqo_"
      },
      "outputs": [],
      "source": [
        "# Retrieve feature importance from the best models\n",
        "importance_bestmodel1 = pd.Series(best_model1[1].feature_importances_, name = 'Importance (split)')\n",
        "importance_bestmodel2 = pd.Series(best_model2[1].feature_importances_, name = 'Gini')\n",
        "importance_bestmodel3 = pd.Series(best_model3[1].feature_importances_, name = 'Gini')\n",
        "\n",
        "# Retrieve feature names\n",
        "feature_names = pd.Series(final_df_NFPPCM.columns, name = 'Feature')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "Cq4r6KsC_tb5"
      },
      "outputs": [],
      "source": [
        "# Combine feature names and Gini values into a Dataframe\n",
        "feat_import_bestmodel1 = pd.concat([feature_names, importance_bestmodel1], axis=1, names=['Feature', 'Importance (split)'])\n",
        "feat_import_bestmodel2 = pd.concat([feature_names, importance_bestmodel2], axis=1, names=['Feature', 'Gini'])\n",
        "feat_import_bestmodel3 = pd.concat([feature_names, importance_bestmodel3], axis=1, names=['Feature', 'Gini'])\n",
        "\n",
        "# Sort the dataframe by Gini in descending order\n",
        "featimp_bestmodel1_sort = feat_import_bestmodel1.sort_values('Importance (split)', ascending=False)\n",
        "featimp_bestmodel2_sort = feat_import_bestmodel2.sort_values('Gini', ascending=False)\n",
        "featimp_bestmodel3_sort = feat_import_bestmodel3.sort_values('Gini', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export sorted feature importance datasets\n",
        "featimp_bestmodel1_sort.to_csv(\"Important_genes_PPCM_LGBM_sorted.csv\")\n",
        "featimp_bestmodel2_sort.to_csv(\"Important_genes_PPCM_AdaBoost_sorted.csv\")\n",
        "featimp_bestmodel3_sort.to_csv(\"Important_genes_PPCM_DecisionTree_sorted.csv\")"
      ],
      "metadata": {
        "id": "mOwz9iXF-TZB"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "xWHo58SIu1RU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "outputId": "8744621f-fb3f-45cb-e28b-9450a4eae159"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2376x1080 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot of feature importance of the 2 algorithms\n",
        "#plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(33, 15))\n",
        "\n",
        "sns.barplot(ax = ax1, x = 'Importance (split)', y = 'Feature', data = featimp_bestmodel1_sort.iloc[0:19])\n",
        "ax1.title.set_text('LGBMClassifier')\n",
        "sns.barplot(ax = ax2, x = 'Gini', y = 'Feature', data = featimp_bestmodel2_sort.iloc[0:19])\n",
        "ax2.title.set_text('AdaBoostClassifier')\n",
        "sns.barplot(ax = ax3, x = 'Gini', y = 'Feature', data = featimp_bestmodel3_sort.iloc[0:19])\n",
        "ax3.title.set_text('DecisionTreeClassifier')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The feature importance results show that most of the genes have low Gini impurity values, which means that all elements belong to a certain class and there are not highly important genes that helps to the classification task."
      ],
      "metadata": {
        "id": "SiQd88ltDLcf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the top 50 most important genes for each method\n",
        "imp_genes_LGBM = featimp_bestmodel1_sort.iloc[0:500]\n",
        "imp_genes_AB = featimp_bestmodel2_sort.iloc[0:500]\n",
        "imp_genes_DT = featimp_bestmodel3_sort.iloc[0:500]"
      ],
      "metadata": {
        "id": "w9-UIRMB2RWx"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtain the top 50 most important genes that are present in at least two methods\n",
        "consensus_genes1 = imp_genes_LGBM.merge(imp_genes_AB,on='Feature')\n",
        "consensus_genes2 = imp_genes_LGBM.merge(imp_genes_DT,on='Feature')\n",
        "consensus_genes3 = imp_genes_AB.merge(imp_genes_DT,on='Feature')"
      ],
      "metadata": {
        "id": "0ornah5c-ie1"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate the results\n",
        "df_final = pd.concat([consensus_genes1, consensus_genes2, consensus_genes3], axis=0, ignore_index=True)"
      ],
      "metadata": {
        "id": "X1UVvW9_1ul8"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicates \n",
        "df_final.drop_duplicates(\"Feature\", inplace=True)"
      ],
      "metadata": {
        "id": "EQ1iqnlWPr7D"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "2lCqqMrn7-Lb",
        "outputId": "c42a8348-e4c7-4553-c941-ac840b7e79be"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Feature  Importance (split)  Gini  Gini_x  Gini_y\n",
              "0    ENSG00000198768                3.00  0.00     NaN     NaN\n",
              "1    ENSG00000000003                3.00  0.00     NaN     NaN\n",
              "2    ENSG00000177464                1.00  0.06     NaN     NaN\n",
              "3    ENSG00000197165                1.00  0.00     NaN     NaN\n",
              "4    ENSG00000196267                1.00  0.00     NaN     NaN\n",
              "..               ...                 ...   ...     ...     ...\n",
              "916  ENSG00000197701                 NaN   NaN    0.00    0.00\n",
              "917  ENSG00000197696                 NaN   NaN    0.00    0.00\n",
              "918  ENSG00000197694                 NaN   NaN    0.00    0.00\n",
              "919  ENSG00000197681                 NaN   NaN    0.00    0.00\n",
              "920  ENSG00000197653                 NaN   NaN    0.00    0.00\n",
              "\n",
              "[491 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38964a1d-46c3-42e3-b6aa-f9f30b113744\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Importance (split)</th>\n",
              "      <th>Gini</th>\n",
              "      <th>Gini_x</th>\n",
              "      <th>Gini_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ENSG00000198768</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENSG00000000003</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENSG00000177464</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENSG00000197165</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENSG00000196267</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>ENSG00000197701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>ENSG00000197696</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>918</th>\n",
              "      <td>ENSG00000197694</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>919</th>\n",
              "      <td>ENSG00000197681</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>920</th>\n",
              "      <td>ENSG00000197653</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>491 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38964a1d-46c3-42e3-b6aa-f9f30b113744')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-38964a1d-46c3-42e3-b6aa-f9f30b113744 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-38964a1d-46c3-42e3-b6aa-f9f30b113744');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export consensus genes\n",
        "df_final.to_csv(\"Consensus3methods_500important_genes_PPCM.csv\")"
      ],
      "metadata": {
        "id": "6E97wtsx4Zs2"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "consensus_genes"
      ],
      "metadata": {
        "id": "3Dnag_532Rgq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "52837987-3bc3-4d44-bdc0-f4b7aac2a1f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Feature  Importance (split)  Gini\n",
              "0   ENSG00000000003                   3  0.00\n",
              "1   ENSG00000177464                   1  0.06\n",
              "2   ENSG00000197165                   1  0.00\n",
              "3   ENSG00000196267                   1  0.00\n",
              "4   ENSG00000196700                   0  0.00\n",
              "5   ENSG00000196756                   0  0.00\n",
              "6   ENSG00000196757                   0  0.00\n",
              "7   ENSG00000196666                   0  0.00\n",
              "8   ENSG00000196689                   0  0.00\n",
              "9   ENSG00000196693                   0  0.00\n",
              "10  ENSG00000196696                   0  0.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa8e7927-e4da-4387-976b-27dd9598ba74\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Importance (split)</th>\n",
              "      <th>Gini</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ENSG00000000003</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENSG00000177464</td>\n",
              "      <td>1</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENSG00000197165</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENSG00000196267</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENSG00000196700</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ENSG00000196756</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ENSG00000196757</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ENSG00000196666</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ENSG00000196689</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ENSG00000196693</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ENSG00000196696</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa8e7927-e4da-4387-976b-27dd9598ba74')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fa8e7927-e4da-4387-976b-27dd9598ba74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fa8e7927-e4da-4387-976b-27dd9598ba74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export consensus genes\n",
        "consensus_genes.to_csv(\"Consensus3methods_500important_genes_PPCM.csv\")"
      ],
      "metadata": {
        "id": "YiaxsLoQ47RL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ven diagram"
      ],
      "metadata": {
        "id": "e-cdyDW8W3Kc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imp_genes_LGBM_set = set(imp_genes_LGBM['Feature'])\n",
        "imp_genes_AB_set = set(imp_genes_AB['Feature'])\n",
        "imp_genes_DT_set = set(imp_genes_DT['Feature'])"
      ],
      "metadata": {
        "id": "20w-qTtTW2gb"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Ven diagram\n",
        "plt.figure(figsize=(10, 10))\n",
        "venn3([imp_genes_LGBM_set, imp_genes_AB_set, imp_genes_DT_set], ('LightGBM', 'AdaBoost', 'Decision Tree'))\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig('Ven_diagram_PPCM_bestmodels.svg', bbox_inches='tight')\n",
        "plt.savefig('Ven_diagram_PPCM_bestmodels.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "5P_DE4mOW2kz",
        "outputId": "490822c2-c416-4b16-bd7a-10d469e78001"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIshhKeCVdxz"
      },
      "source": [
        "### **Save the best model into a file**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B9hQ5sd0Vh8g"
      },
      "outputs": [],
      "source": [
        "# Define the output file's name\n",
        "output_file = f'RandomForestClassifier_NF_DCM'\n",
        "output_file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7-6gKurIVpWI"
      },
      "outputs": [],
      "source": [
        "# Export the binary file of the best model\n",
        "with open(output_file, \"wb\") as f_out:\n",
        "    pickle.dump(best_model[1], f_out)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuPnx5mWeizm"
      },
      "source": [
        "---"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.11"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}