Bioinformatics_Project.ipynb

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  "metadata": {
    "colab": {
      "name": "Bioinformatics Project.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "cmg0x-uMC7ii",
        "83MDglmLDUC8",
        "GZeot_ODDvNq",
        "yNO28eK6EGH6",
        "LvjVR_MXERZB"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMOCYukmqJVf"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssb1mxbgr7Xh"
      },
      "source": [
        "#Load breast cancer dataset\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "cancer_dataset = load_breast_cancer()\n",
        "#print(cancer_dataset['DESCR'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "jfUUUzk5tX25",
        "outputId": "b09e8361-1ad2-4f00-964a-167f7dd749b2"
      },
      "source": [
        "\n",
        "print(cancer_dataset['feature_names'])\n",
        "\n",
        "\n",
        "cancer_df = pd.DataFrame(np.c_[cancer_dataset['data'],cancer_dataset['target']],\n",
        "             columns = np.append(cancer_dataset['feature_names'], ['target']))\n",
        "\n",
        "\n",
        "#sns.countplot(cancer_df['target'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
            " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
            " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
            " 'smoothness error' 'compactness error' 'concavity error'\n",
            " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
            " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
            " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
            " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f647a6ae450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHLv2xu9xM9F"
      },
      "source": [
        "# **Heatmap of a correlation matrix**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mIV-eKKQw3MU",
        "outputId": "03b1680d-458b-44c1-dce4-f188b88528ae"
      },
      "source": [
        "\n",
        "# Heatmap of Correlation matrix of breast cancer DataFrame\n",
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(cancer_df.corr(), annot = True, cmap ='coolwarm', linewidths=2)\n",
        "\n",
        "# create second DataFrame by droping target\n",
        "cancer_df2 = cancer_df.drop(['target'], axis = 1)\n",
        "print(\"The shape of 'cancer_df2' is : \", cancer_df2.shape)\n",
        "# visualize correlation barplot\n",
        "plt.figure(figsize = (16,5))\n",
        "ax = sns.barplot(cancer_df2.corrwith(cancer_df.target).index, cancer_df2.corrwith(cancer_df.target))\n",
        "ax.tick_params(labelrotation = 90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The shape of 'cancer_df2' is :  (569, 30)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQYQAqt5yuuw"
      },
      "source": [
        "# **Split DataFrame in train and test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8DxG9SJylea"
      },
      "source": [
        "# input variable\n",
        "X = cancer_df.drop(['target'], axis = 1)\n",
        "# output variable\n",
        "y = cancer_df['target']\n",
        "# split dataset into train (80%) and test (20%)\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXgTjY54zMgK"
      },
      "source": [
        "\n",
        "# **Feature Engineering - Feature Scaling** Converting different units and magnitude data in one unit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JdQDVeJzSLT"
      },
      "source": [
        "# Feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train_sc = sc.fit_transform(X_train)\n",
        "X_test_sc = sc.transform(X_test)\n",
        "\n",
        "#convert (ndarray to dataframe)\n",
        "\n",
        "df_columns= ['mean radius','mean texture', 'mean perimeter', 'mean area',\n",
        " 'mean smoothness', 'mean compactness', 'mean concavity',\n",
        " 'mean concave points', 'mean symmetry' ,'mean fractal dimension',\n",
        " 'radius error' ,'texture error' ,'perimeter error', 'area error',\n",
        " 'smoothness error' ,'compactness error', 'concavity error',\n",
        " 'concave points error', 'symmetry error' ,'fractal dimension error',\n",
        " 'worst radius', 'worst texture' ,'worst perimeter' ,'worst area',\n",
        " 'worst smoothness', 'worst compactness', 'worst concavity',\n",
        " 'worst concave points', 'worst symmetry' ,'worst fractal dimension']\n",
        "\n",
        "X_train_sc_df = pd.DataFrame(X_train_sc, columns=df_columns )\n",
        "X_test_sc_df = pd.DataFrame(X_test_sc, columns= df_columns)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKG3UalnTxaN"
      },
      "source": [
        "# **Feature Selecting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oIWNeMrlKKY"
      },
      "source": [
        "\n",
        "**1- Feature Wrapper**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpykElGzTCCQ"
      },
      "source": [
        "**a) Step - Forward Feature selection (SFS) with SVM**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARrGbhFSlNlM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f526b6d-a5a4-464d-a2a2-1381e4a6cbd0"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS\n",
        "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "    \n",
        "# step forward feature selection\n",
        "# select from (1 to k) maximum k features based on the accuracy and SVM Classifier\n",
        "#cross validation =4\n",
        "#n_jobs=-1 means use all cores\n",
        "\n",
        "sfs1 = SFS(SVC (), \n",
        "           k_features=(1,30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=5,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(np.array(X_train_sc_df), y_train)\n",
        "\n",
        "#----------------------------\n",
        "print(\"#######################################\")\n",
        "print(\"Wrapper with SVM got maximum score of: \", sfs1.k_score_)\n",
        "print(\"Number of features are: \", len(sfs1.k_feature_idx_))\n",
        "print(\"Selected features are: \")\n",
        "print(X_train_sc_df.columns[list(sfs1.k_feature_idx_)])\n",
        "\n",
        "\n",
        "selected_feat_svm= X_train_sc_df.columns[list(sfs1.k_feature_idx_)]\n",
        "X_train_sc_df_forward_wrapper_svm = X_train_sc_df[selected_feat_svm]\n",
        "X_test_sc_df_forward_wrapper_svm = X_test_sc_df[selected_feat_svm]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:    1.5s finished\n",
            "\n",
            "[2021-04-06 15:24:20] Features: 1/30 -- score: 0.9142857142857143[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  29 out of  29 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:20] Features: 2/30 -- score: 0.9582417582417582[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:20] Features: 3/30 -- score: 0.9692307692307691[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:21] Features: 4/30 -- score: 0.9736263736263737[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  26 out of  26 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:21] Features: 5/30 -- score: 0.9758241758241759[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:21] Features: 6/30 -- score: 0.9780219780219781[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:22] Features: 7/30 -- score: 0.9780219780219781[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  23 out of  23 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:22] Features: 8/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  22 out of  22 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:22] Features: 9/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:23] Features: 10/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:23] Features: 11/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  19 out of  19 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:23] Features: 12/30 -- score: 0.9780219780219781[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:23] Features: 13/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:24:24] Features: 14/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:24] Features: 15/30 -- score: 0.9780219780219781[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:24] Features: 16/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:24] Features: 17/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  10 out of  13 | elapsed:    0.2s remaining:    0.1s\n",
            "[Parallel(n_jobs=-1)]: Done  13 out of  13 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:25] Features: 18/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:25] Features: 19/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  11 out of  11 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:25] Features: 20/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:25] Features: 21/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   6 out of   9 | elapsed:    0.1s remaining:    0.1s\n",
            "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:24:25] Features: 22/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:24:25] Features: 23/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   7 out of   7 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:24:26] Features: 24/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:24:26] Features: 25/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:24:26] Features: 26/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:24:26] Features: 27/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:24:26] Features: 28/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.0s finished\n",
            "\n",
            "[2021-04-06 15:24:26] Features: 29/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "#######################################\n",
            "Wrapper with SVM got maximum score of:  0.9824175824175825\n",
            "Number of features are:  25\n",
            "Selected features are: \n",
            "Index(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
            "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
            "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
            "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
            "       'smoothness error', 'compactness error', 'concavity error',\n",
            "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
            "       'worst texture', 'worst perimeter', 'worst smoothness',\n",
            "       'worst concavity', 'worst symmetry'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "\n",
            "[2021-04-06 15:24:26] Features: 30/30 -- score: 0.9802197802197803"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "meiEI63R4wn6"
      },
      "source": [
        "**b) Step - Forward Feature selection (SFS) with Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZUrCNycm5QB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d8f39f3-d209-462c-a8c6-3eb55a7d022d"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "\n",
        "sfs2 = SFS(LogisticRegression(random_state = 51, penalty = 'l1', solver='liblinear'), \n",
        "           k_features=(1,30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=5,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs2 = sfs2.fit(np.array(X_train_sc_df), y_train)\n",
        "\n",
        "#----------------------------\n",
        "print(\"#######################################\")\n",
        "print(\"Wrapper with Logistic Regression got maximum score of: \", sfs2.k_score_)\n",
        "print(\"Number of features are: \", len(sfs2.k_feature_idx_))\n",
        "print(\"Selected features are: \")\n",
        "print(X_train_sc_df.columns[list(sfs2.k_feature_idx_)])\n",
        "\n",
        "\n",
        "selected_feat_LG= X_train_sc_df.columns[list(sfs2.k_feature_idx_)]\n",
        "X_train_sc_df_forward_wrapper_LG = X_train_sc_df[selected_feat_LG]\n",
        "X_test_sc_df_forward_wrapper_LG = X_test_sc_df[selected_feat_LG]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:04] Features: 1/30 -- score: 0.9164835164835164[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  29 out of  29 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:04] Features: 2/30 -- score: 0.9538461538461538[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:04] Features: 3/30 -- score: 0.9736263736263737[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:05] Features: 4/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  26 out of  26 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:05] Features: 5/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    0.3s finished\n",
            "\n",
            "[2021-04-06 15:27:05] Features: 6/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:05] Features: 7/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  23 out of  23 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:06] Features: 8/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  22 out of  22 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:06] Features: 9/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:06] Features: 10/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:06] Features: 11/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  19 out of  19 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:06] Features: 12/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:07] Features: 13/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:07] Features: 14/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:07] Features: 15/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:07] Features: 16/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:07] Features: 17/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  10 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done  13 out of  13 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 18/30 -- score: 0.9780219780219781[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 19/30 -- score: 0.9780219780219781[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  11 out of  11 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 20/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 21/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   6 out of   9 | elapsed:    0.1s remaining:    0.1s\n",
            "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 22/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 23/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   7 out of   7 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 24/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:08] Features: 25/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:09] Features: 26/30 -- score: 0.9824175824175825[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "#######################################\n",
            "Wrapper with Logistic Regression got maximum score of:  0.9824175824175825\n",
            "Number of features are:  21\n",
            "Selected features are: \n",
            "Index(['mean radius', 'mean perimeter', 'mean area', 'mean smoothness',\n",
            "       'mean compactness', 'mean concavity', 'mean concave points',\n",
            "       'mean fractal dimension', 'texture error', 'smoothness error',\n",
            "       'concavity error', 'concave points error', 'symmetry error',\n",
            "       'fractal dimension error', 'worst texture', 'worst perimeter',\n",
            "       'worst area', 'worst smoothness', 'worst compactness', 'worst symmetry',\n",
            "       'worst fractal dimension'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:09] Features: 27/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    0.1s finished\n",
            "\n",
            "[2021-04-06 15:27:09] Features: 28/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.0s finished\n",
            "\n",
            "[2021-04-06 15:27:09] Features: 29/30 -- score: 0.9802197802197803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "\n",
            "[2021-04-06 15:27:09] Features: 30/30 -- score: 0.9758241758241759"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2qid47165_xK"
      },
      "source": [
        "**c) Step - Forward Feature selection (SFS) with XGBoost** *italicized text*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h51KEw9e6D-_",
        "outputId": "37f8c98f-8f5a-4136-ec1f-30eb031939cd"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "sfs3 = SFS(XGBClassifier(), \n",
        "           k_features=(1,30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=4,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs3 = sfs3.fit(np.array(X_train_sc_df), y_train)\n",
        "\n",
        "#----------------------------\n",
        "print(\"#######################################\")\n",
        "print(\"Wrapper with XGBoost got maximum score of: \", sfs3.k_score_)\n",
        "print(\"Number of features are: \", len(sfs3.k_feature_idx_))\n",
        "print(\"Selected features are: \")\n",
        "print(X_train_sc_df.columns[list(sfs3.k_feature_idx_)])\n",
        "\n",
        "\n",
        "selected_feat_xgboost= X_train_sc_df.columns[list(sfs3.k_feature_idx_)]\n",
        "X_train_sc_df_forward_wrapper_xgboost = X_train_sc_df[selected_feat_xgboost]\n",
        "X_test_sc_df_forward_wrapper_xgboost= X_test_sc_df[selected_feat_xgboost]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:    2.7s finished\n",
            "\n",
            "[2021-04-06 15:28:03] Features: 1/30 -- score: 0.90991305697873[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  26 out of  29 | elapsed:    2.3s remaining:    0.3s\n",
            "[Parallel(n_jobs=-1)]: Done  29 out of  29 | elapsed:    2.4s finished\n",
            "\n",
            "[2021-04-06 15:28:05] Features: 2/30 -- score: 0.9428466076696165[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:    2.5s finished\n",
            "\n",
            "[2021-04-06 15:28:08] Features: 3/30 -- score: 0.9560433162552399[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:    2.5s finished\n",
            "\n",
            "[2021-04-06 15:28:10] Features: 4/30 -- score: 0.9582751125601614[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  26 out of  26 | elapsed:    2.6s finished\n",
            "\n",
            "[2021-04-06 15:28:13] Features: 5/30 -- score: 0.9604680950163018[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    2.7s finished\n",
            "\n",
            "[2021-04-06 15:28:15] Features: 6/30 -- score: 0.9626610774724422[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:18] Features: 7/30 -- score: 0.9670470423847228[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  23 out of  23 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:21] Features: 8/30 -- score: 0.9670470423847228[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  22 out of  22 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:24] Features: 9/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:27] Features: 10/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:29] Features: 11/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  19 out of  19 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:32] Features: 12/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:35] Features: 13/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:    2.7s finished\n",
            "\n",
            "[2021-04-06 15:28:38] Features: 14/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:    2.8s finished\n",
            "\n",
            "[2021-04-06 15:28:40] Features: 15/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:    2.6s finished\n",
            "\n",
            "[2021-04-06 15:28:43] Features: 16/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:    2.6s finished\n",
            "\n",
            "[2021-04-06 15:28:46] Features: 17/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  13 out of  13 | elapsed:    2.5s finished\n",
            "\n",
            "[2021-04-06 15:28:48] Features: 18/30 -- score: 0.9714330072970037[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:    2.3s finished\n",
            "\n",
            "[2021-04-06 15:28:50] Features: 19/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  11 out of  11 | elapsed:    2.2s finished\n",
            "\n",
            "[2021-04-06 15:28:53] Features: 20/30 -- score: 0.9714330072970037[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:    2.1s finished\n",
            "\n",
            "[2021-04-06 15:28:55] Features: 21/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    1.9s finished\n",
            "\n",
            "[2021-04-06 15:28:57] Features: 22/30 -- score: 0.9692400248408632[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:    1.8s finished\n",
            "\n",
            "[2021-04-06 15:28:58] Features: 23/30 -- score: 0.9670470423847228[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   7 out of   7 | elapsed:    1.5s finished\n",
            "\n",
            "[2021-04-06 15:29:00] Features: 24/30 -- score: 0.9647958391554107[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    1.4s finished\n",
            "\n",
            "[2021-04-06 15:29:01] Features: 25/30 -- score: 0.9670082285359417[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    1.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    1.2s finished\n",
            "\n",
            "[2021-04-06 15:29:02] Features: 26/30 -- score: 0.9670082285359417[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    1.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    1.0s finished\n",
            "\n",
            "[2021-04-06 15:29:03] Features: 27/30 -- score: 0.9647958391554107[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    0.7s finished\n",
            "\n",
            "[2021-04-06 15:29:04] Features: 28/30 -- score: 0.9691818040676914[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    0.5s finished\n",
            "\n",
            "[2021-04-06 15:29:05] Features: 29/30 -- score: 0.9691818040676914[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "#######################################\n",
            "Wrapper with XGBoost got maximum score of:  0.9714330072970037\n",
            "Number of features are:  18\n",
            "Selected features are: \n",
            "Index(['mean texture', 'mean compactness', 'mean concavity', 'mean symmetry',\n",
            "       'mean fractal dimension', 'radius error', 'perimeter error',\n",
            "       'area error', 'smoothness error', 'compactness error',\n",
            "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
            "       'worst texture', 'worst smoothness', 'worst compactness',\n",
            "       'worst concavity', 'worst concave points'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    0.2s finished\n",
            "\n",
            "[2021-04-06 15:29:05] Features: 30/30 -- score: 0.96477643223102"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PQXQQeXx0Ro"
      },
      "source": [
        "**3- Feature Filtering**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSqkDWZNTOqm"
      },
      "source": [
        "a) Correlation method- remove features that are highly correlated with each other"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sU8YHUh2ELL4",
        "outputId": "081c0abc-94bd-4b37-bdcb-77a769d09220"
      },
      "source": [
        "def get_correlation(data, threshold):\n",
        "  corr_col=set()\n",
        "  corr_matrix=data.corr()\n",
        "  for i in range(len(corr_matrix.columns)):\n",
        "    for j in range (i):\n",
        "      if abs(corr_matrix.iloc[i,j])> threshold:\n",
        "        colname=corr_matrix.columns[i]\n",
        "        corr_col.add(colname)\n",
        "  return corr_col\n",
        "\n",
        "corr_feature= get_correlation(X_train_sc_df, 0.85)\n",
        "\n",
        "\n",
        "\n",
        "X_train_sc_df_filter_corr = X_train_sc_df.drop(labels=corr_feature , axis=1)\n",
        "X_test_sc_df_filter_corr = X_test_sc_df.drop(labels=corr_feature , axis=1)\n",
        "\n",
        "\n",
        "print(\"Threshold used: 0.85\")\n",
        "print(\"Number of correlated features: \", len(corr_feature))\n",
        "print(\"Number of remaining features:  \", len(X_train_sc_df_filter_corr.columns))\n",
        "print(\"Remaining Features are: \")\n",
        "print(X_train_sc_df_filter_corr.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Threshold used: 0.85\n",
            "Number of correlated features:  13\n",
            "Number of remaining features:   17\n",
            "Remaining Features are: \n",
            "Index(['mean radius', 'mean texture', 'mean smoothness', 'mean compactness',\n",
            "       'mean symmetry', 'mean fractal dimension', 'radius error',\n",
            "       'texture error', 'smoothness error', 'compactness error',\n",
            "       'concavity error', 'concave points error', 'symmetry error',\n",
            "       'fractal dimension error', 'worst smoothness', 'worst symmetry',\n",
            "       'worst fractal dimension'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zOwlacVTkIs"
      },
      "source": [
        "b) Mutual Information Filter- Mutual information measures how much information the presence/absence of a feature contributes to making the correct prediction on Y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0fsc7o7TonR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "4e105245-ba80-4fdb-8824-50a91a665370"
      },
      "source": [
        "# my code\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import  mutual_info_classif\n",
        "from sklearn.feature_selection import  SelectPercentile\n",
        "\n",
        "def selected_features_MI (x_train,x_test, y_train, y_test, percentile_value):\n",
        "  mi= mutual_info_classif(x_train, y_train)\n",
        "  #add the mi scores along with their corresponding features, and sort them\n",
        "  mi= pd.Series(mi , index=x_train.columns)\n",
        "  mi.sort_values(ascending=False, inplace=True)\n",
        "  mi.plot.bar()\n",
        "  #select percentile_value top percentile\n",
        "  sel= SelectPercentile(mutual_info_classif, percentile=percentile_value).fit(x_train, y_train)\n",
        "  #print selected feature\n",
        "  print(\"Selected Features using MI with %\",percentile_value)\n",
        "  print(\"Number of remaining features: \", len(x_train.columns[sel.get_support()]))\n",
        "  print(\"Remaining Features are: \")\n",
        "  print(x_train.columns[sel.get_support()])\n",
        "  #Now lets filter our training and testing dataframes to keep the required features only\n",
        "  x_train_mi = sel.transform(x_train)\n",
        "  x_test_mi = sel.transform(x_test)\n",
        "\n",
        "\n",
        "  return x_train_mi, x_test_mi\n",
        "\n",
        "\n",
        "X_train_sc_df_filter_mi, X_test_sc_df_filter_mi = selected_features_MI (X_train_sc_df,X_test_sc_df, y_train, y_test, 30)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selected Features using MI with % 30\n",
            "Number of remaining features:  9\n",
            "Remaining Features are: \n",
            "Index(['mean radius', 'mean perimeter', 'mean area', 'mean concavity',\n",
            "       'mean concave points', 'worst radius', 'worst perimeter', 'worst area',\n",
            "       'worst concave points'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WXEMl1/2XwvVwPrBZp+8J3ANsDrwCeHm1dZCfBOwV7o9fh3tk0R78fiXX2+3Ax8Pv/eZqS8hm64DS6yK2DUKR6AVV9UoREVV9CDhMRGYBX4zI3he2CZjy68Q8IvIq7EY4uIvsvCIyL7ANFgX7nIik3H9isql2F8ACrDauHVNMkbShqk9hT/5TRWRDbFR0vIhcCBypqnPq3y/83QK4QFX/0aEfL1fV00RkP1W9BrhGRGYkZN8Y/h7R0ueNWwXVrsIrgCtE5B3YCOoTInI7cJCq/nok7Qaq5G6fbJFfKSL7Qvi7BXCKqv5MRL6UaLc6Se8BzlZzwU2duJI+L6SqN7c09Xyi3b2wwcgLIvLv0CdV1ZQZY0lVPV9EPg8vuRK/kJBdB9hZRB4Cnq61HTM5llxD2feIqh4iIl8A3gXsDpwoIucDp6nqfS3i/1DVS1MfGmn7yXA/LAh8CnsYfUZEvqmqJ7SIl/x+Jdfb86p6cmaXS3RA6XXRxiAo9GeCXex3wd/9MWCRmKCqHg4gIgup6r+6tHs45jt/narOEJGVMBNBjO8AD2JP3mtFZHngyQLZfyT6u3uXPg4j2NC3wG6CFbCR0DnYCOMSbKpX8VMRuQf4N/BxEZkM/CfR9HPh7x9EZAvg99gIJNbndxT09+XALtjo43Fsqj0du5EuAFYcSbtBfsXuUi/xmIh8B5stHC0i85NeH5olIpeHvn1eRBYFXkz0oaTPfxGRlbGbtcqB9IdEu90GI608Hc511fa6JK45bOaRS8k1VHKPoKoqIn8E/og92BYHLhSRK1T1szXRq0Tka5iCe6b2/lta2xSRrYHdgNdgs/W3qOqfRGQhzAw0TKGX/H6F19tPROQTwMUtff5bpN1sHTCC6yLayJhuwNqYAl8GmAb8EFg3Ibse9sM9HPbXBE5KyL4151iHfs2TOL5iy74AqyRks+2q4fX7sanv+pHXvhk5tgQwMfy/EPDKRLtbYtPp1YGrgFnAVgnZlwHHATPD9nXgZQnZ32Lmr2Uir31upO0G+XmBfbH1lQuBvYF5E7ILYVPZVcL+q0jbxSdgU+/FaudwjR6ci5WAXwD/wgYl12FJlFLfbytsin0ssGWXa3Et4HpMiV8fznu0z7X7Yu+wrdml7axrqPAe2S9cY5dh6xvz1s79fS2yV0W2lD36DODtidc2GeXvV3K9PRDZUnb/ZTDF/6ew/TB2v4zkuoi+v/QN/dqwKWs3mZswG1eOnbLNLho7Fo5/MbYVtDsrIZttVw2vbRA5Fn0IAf8T23rwO/wQm92sFLZDgYsSsjtEjr1/tO0G+e9i9uCNwzaNWpK4FtmVgfnD/xuFG3OxhOxbgYXD/7uEG3750fQZi6A+Nvy/MAmbbk3+q9iD/sNhuwI4KiE7Edgfm02/HnsoRxVNkN8PS5Z3RNjuAPZJyPZrMfmwDuf0dSO8LicCV/XxWs6+3gr7cAU2454nbLsBV4z2ukh+3mg73IMvXDLqvin8rSvI2yPtfRp4hNrCabjIUgs+n65tB2OLLae3yKyGLVjcR21xI/xAdybanRHp720dzkXJQ+iE2nYqNrq/MCFb4oHR1r9Unwv7m91u7HdNHavaCTfLa7DR69eASxKyszGltCZwK2YzvaYH56LEm2E2MKG2P5HEAmN4vcRbYzbhgRX2F061TR8Wk8N3uaegvyWj6CtTr/Xg9yu53kpG8yV9KLouYtsg2ND/D7tIpgOo6u0i8vaE7CPBV1PDAs1+mOdGnfkwE848DF84fRJLHNaGqn69vh98Ry9rEVsVM10sBry3dvwpzDMlRpZdVUTWA9YHJovIAbWXJmE/aqzP+7S0sRhWfCTGqcBnMDsoqjpbRM7FvBFa+beIbKCq14V234rZWOuftTm2qLi0iHyzpb+phcCu7bbwgoisrGERLayBpBYCX1RbKNwWOEFVTxCRWxOyz6uqBnvsiWqLxXv0oM+3ish0bO3g6eqgqkYXwLHrqLK5viwhU3G9iJwInNfSdpudGXtY1c/TCwwtBLfS88VkVX1BrFzlcqr6cPorvcTp2Iyi8gPfFRsdbxuR/Sdwh4hcwfDzsG+i7ZLfr+R6OxlT6ifV+nwy5lHXyl9FZBfMEwxgJzpnoi25LtoYBIWOqj7Scm2kTuTHgG8AS2N2ysuBT7S0VXlxnKHmNTMSFsJsX/V2fwz8WETW0+HeG534JJZgZzUReQyzte0ckSt+CEV4mtoiZAslHhgfA84Skepi+jtDmTQrfo+NprbCbKUVT2HmgZG2W+dAbMHsfkyZLI9NXWM8JyI7YWan6mE7b0L2qeAtsivwtrAgn5It6XOJN8NXsAfAVdh3ezvQVqu3Rom3xjTgJhG5OOxvg5lIYvRlMRlbAL1TRG5muOLdKiK7sqpuV9s/XERuS7R7Ee3nUxOyUPb7lVxva6vqmrX9XwbPrhgfxmbRx4e+3tCh3dLroo1BUOg5o+6KVVV1mEIMT93rI7Lzi8gpmLfIS99TVdtuAhG5g6ELYyIWiHNEq1xgjoj8b6TdD0dkVVU3FZGFsanUUyLSpnRH8hASkZ/U+jwBCzY5PyGeO1OYCOyqqmtKiARU1TZPBlW9HbhdRM5R1dSDobjdFvk1saCzVcPhe1X1mcRbdsdu3i+r6gPhHJ+dkN0R+CDwYVX9o4gsh5loRtVnzfRmCA+QF4F1MYcAsAXkP3ZoO8tbI7R9I2Y+2SAc3l1VU7OVHTA/8WNV9QkxN9/PJGT3wB4s96vqv4LXTeo7fyGnv4GSUfRiqvqN+gER2S8mWPL7jeB6yxrNh3a/kniQtcoWXxfRdoKtZswQkSWxUfem2FPpcmA/VW2blojILaq6Vrdj4fjtwLexEeRLJ1tVZ0Vkl6/tPg88nlJUInID8KtIuz/M7O8sVX1zy7H/U9VPtSjpl4hdEGJ+6vU+P6ShBGBEdiVsprA+Nkp5ANg59vAQkRtVdd1YOzWZ81V1h5YHYb2/bT7POe22yN+sqm8pkF8QWE5VY2UNW2WXxzxifhFc3iaqxQCMuM8iMo34uWh70IvITFWdmtNukI/FZKCqbYMOEblVVd9U0PYG2LmYJua2uIiqPhCRE2x2uZKqHhEehK9U1Zsjsker6ue6HQvH34gtRr4Mu///BuwWBg2tsrH7Kfl9C3+/7OtNRDbBZkLDRvOqelVE9jpgY7V6zN3aLbouYozpCD08wb7ROuqOyBXbmMlw/heRSeGp3XozTxIRNOJXipkv2i7MlnZXwxaNXhbsuvX+LhB5SzWazM79Ekb1XQnn+BOtM4UOb8mxBVejoi1z+5vZbp1su7GIvBc7d/MBKwYlcUTiQfhRLLpwCcw7Zmnswb/JKPv809r/C2ABL79PfLdfiMiBke8Wu96oy4S2tyQ9i71SRLbDvDk6jtZE5FBgKjYqnYaZnr6HeQK1chI2gtwYm70+hXmRrB2RfSfQeo9sHjmGqt4GdBxFB3PaB7Hftl5cZ1GG7M0xSn6/7OtNLRAydzR/f2h7eku7x0VkS6+LNsZUoYcFlOVFZL4uT7CR2JhznP/PxW6OWdjoqm5kVuJRYj8Vkfeo6iUd+lu0gFqbNbwc+FmHiwMRuU5VNxCRpxg+IoxGlYVzvEH4v64YUnS1BatqZa7ZDviBqqYUV1G7LZTYjQ/D3EOvDv27LcxKYnwyyN4UZH8nIq8YbZ9bZ2gi8n3MFz1GSVRi7qJ9RRVt+LyI/IfO0YbvA96EuSCiqr8PtvEY66jqWhIWm1X172IVzOr9+ji2prWSiMyuvbQoZjuuy+6iqt9rGaAhYZ2nReHdgJkIl8S8YCqewjxDUpRcc12vNxHZWFV/2TJIA3hNGADG2i2Jbi+6LmIMgg296xOs1cYseZGi1eJH3SY47OSo6pbhb0mU2H7A/4rIM1gEZtsNoyNbQAVT/seLyLXYU/rnraYfVa2Uc0lUWdZIJYzm/6qqB2a2uygW9v+30N8LVPXxVqHSdoP8dFU9PrMfz2l72Hpqwe4ZVX22khWrgds2kh3BuWhlFSw/SWu7E7C0COeNsF2ILNrX2t5MVWNrSjGeVVWVEMIfZnApngvnpJKdTPs5PhfLO3MUwxfznoqMMqvP6nodB9PgQ8B6LeayBbEUADFzWfbvV3C9bYgl4npv5LW2B0Vo97XdLBBBthfXxUDY0A+NHdcQ5t8iux62Yr+Iqi4nImsCe6nqJ9oayPvsNtt7Sx9ibmEl7b8Wc2daSlVXF0vhu5WqplzDEFsY3hx7Wm+ABSF8pPZ6NGS/1ue26Vmw70ZEo/bdX6vqep0+I/KeNUJ/twMeVdVNR9tuoU3zNMxH+aDQh30xv+CPRWSPAZ7APGL2wUaUd6lqW76fkj5HZkx/BD7fOnIPsqU29Nii/ZHanrukyIYepverYCaSozCPjHMT7e6M/cZrYTbv7bFYhgsSbddt80tiAUxttvkS6uYyVV05mD2+raoxc1np71e0ZpPL3Lahj7lCL0FEbsIupOnVRSsiv1HV1WsyqWkRMHxUKuYeBDY1m4rlqRAs8GZm/WIQkdVU9Z7UQyBh272G4P+d6m/ie86LeR/sjoU6L1l77QGGzEPLYYucgpl3Hi6cbcQ++2TMrpxr60ZEXolFHX4Au3Fji6JF7YrI8ZhNN8eGvhAWEPYu7Fxchim8trwkYSS0R4vsd2P25pGcixxE5KvAXyLfLWorlbJF+2OxwLiuNvQg/05q50JVr+gguxq21iDAlaoatePXbfOq+loReTU2e2uzzYcH7Jcwz5afY/fe/qr6vYjsbQRzWe1+ukNV35DoR/bvV3i97YetOVTJ9NbCRteXR2TPAl6Hxdl0tKGXXhcxxlyhh6nbZ7FFxJcWDDXuXniTqq5TH4WIyO1a8wkVkcNV9dDCUelFwKGqekfYXx04TFW3r8mcoqp71h4Cre3G+jtDVddu6e9tqvrG9iZALGBnRyx8/WrMDfHy2M0rIqcCF2uw5Yf3bqOqe0VkF8CUWOs5jp2LkvP2Ccz1bTJ205yvqnclvlt2u0E++zz3i8JzcWXrSDF2LByPjVRVVaO2UhE5W1V37XYsHH8KM2e8gCnJ4ox9KYIJYSmGu+u2BQ8FxfsmLJK0uu5nJx70t6nqG0Xkfdi60wHAtTrcz7uSHXb/B3PZLbF2g3zJ71dyX9+u5g75bsxd9hAs2CrmbVdigSi6LmIMgg39HOyJtCV2cj6EJdqP0dVnXVUPDX9LMh2uWinz8N7fiMjrWtrdM/ztSwa+wP9g52Iv7bAwGlhXVV9aYFXVS8NoJ8bZWN7pd2OLPjuT8JIoPG/LAp9S81ToSGG7Rec5mLYOJC/m4K2EXCNBtlJ4bTdNTp/Dw3IhYEkRWTy0B+bRtHTsPSOYRb2+5TPnwXJwx9rOXlsJs9ijMVu/0EH5i8g+WC6UxxmKPlVsRN1KiW2+JIXvNWIxIAuGmcUngJ+khEuuucL7uh41e5Z2SMGsBRliRzu7rhoZ042Q2IpazgJCDpSI7JLYA+BxLHPZ90gkw6csR8T3seQ8G4XtVOD7CdmSPA6xDHwr9Oi8XYaNDFYI28HYlDkmWxX7mF37DtHcIxRmiAwyr8DMP8thvuCjbpeyKj0lBQeyCyrk9BkbVDyAeVJVFXceCH3aO9HuQuG3OyXsr0Iksx7weWxa/zzm0VW52P4V+GqibcGSjn0h7C+LpZmNyc4hM1lWkE0WnmiRPRBLM3E/5tX1a9IJwr4afpNbw3U5mXQ+mQmhvQuwe++j0LF4RkkOo5LrbRoWL/O78FsuSjpBX0muqqzrouO5LxHux0ZQLJiC2gKbqt3Xg3ZLMq0tgIWsXxy2/YEFErLFWdnIyMAX5NbFKjL9E3gWGwk9mZBdAgvIujVs3yBdYeXm8PdaLFvfkqTTfZZU3nlvuKifxpTYi6QTlZVmniyp0hO9mRKyUWXRg3MRVVgJ2fMwM2OlaBaic6Ky7Ix72CL8t4C7w/7ipAdI1xe0exWJdLkJ+XdiEbjHAu/sIjviFL49/P1KrrfWFMwvJ52CuSRDbNF1EdsGweTyJbFcC5/Gch5MIpEPRCykex/ap9ajyhGhtnh2fNi6kZ3HQSxh1v9U/ZUhH9tUMqETsYXFC7BFpf9heFGLep//xlCATzdOCeaAQ7DFmUVIh2eX5NzNy6cAACAASURBVH35EvYQ+oWaTfMd2OhwtO1CWZWe7IIDFBRUKOzzK0Rkoqq+ACAWKPMNjU/7V1bVHcUCZlALpU/aGbDR8UsEW/YhGrHDkuEvXmOmiJwH/Ijh5yK26Hs/cLWI/KxFNhYgg6peEZwY5gl9XiLxe4BlMl0hmJIqzmoVEpEtgSNpN5el1gdKfr/s601VXxSRx4EpLX2Oovm5qkqvizbGXKGrahVh9w+gmx3rR9i06Cek/YwrsnNEiLk/HYVNs+qLhtFyZ5Kfle0SLLfGHRn9rT5zTk0xTAs35ucjfc5eTFbV74Z/r6V7kEKJ3f85Vf2rWMXyCap6lYj8Xw/ahbIqPR8Kf5MxBzXWCX+ntsjGFltL+jwRuFlEdsem7yfSUkGnxrNiPtRVuytTU5IRNhGL/twDGw1Ow0afMXL8xSsmYebAd9WOKfHAm4fDNl/YkojIXtjs+D/hsyt7e9vvISJnYxG7tzF0HykRhY5lZt0WGznneHOU/H7Z15uIHI05L9zV0udrI+IluapKr4t2ejG1mVsbZdPlN2J2zAexoIRbSU+LSgpVb4Jd2FdjN9WDwDsSstHc4B36fC12s5yFVTbfnx4Ufi7sQ3blnSC3CKa4vo+ZfW4YbbtBPrtKDxHzWOxY1Y+cYyPs8ybYoOH3wGs6yL0zXDt/xtaEHgQ26vK77Ii5tD1Eh8pb2IL3dOBR4MvAvSSKjvRrw8xwS2bK3k0HO3iL7FXU8oX3+Fouud7uJRRUyehDybpf8XXRuo2522IJIvJBbKHgcrpPl6v35GT3m6Wqb5aaT6tEkmjV5OcnI4+DiOyP2cN/SndTQOVv/Dim1PfHFnZP0uHFoVv7/JI7mAQ3ydT3LEEy8r4EmX9jNsWdQ3/P0UhitZJ2a7LzYOdZsPP8XEKuJGlbVsK00j6L5fA/Gbth34DZrvfQRFqEMBpcN3y3G1X1Lx3aXgVbt7kD82m+CzhAE14Tku8vPhlbWFyBLplDCz2Jfo5Vtu8WzY2IXADsq0PpJDrJro2ZXK4hw+xTe1/WNVdwvV2KPST/2a3PpZRcFzHG3OQiIitqSwRZ7FjgDVge640ZmkZGp8vhxByKRVuqWMTWEQllk12oOripfaJqF/iViHxbI0Es2MLm1zAPlOrJmTIFgI3Ang1tHR6mzvMnZLMLP48Ezcv78grgD6G/Z4bp4lJ0SOCf2W4l+zxwZ+p1sYCmpTE3tjcx3GVwoRbZ0oRppX0+FrvJ7wqfty0WJr5aos2/Aj/LaBfMxLi3Wri7YL7aM2hxZ6y1fQ/mOdKNH2OZQ39B2mxYcQGWxOy7GbKfB24INvS64o2tHS0J3CWWO70uG1sX+zI2QFqALmafOrnXXLfrrca/gNtE5Eq6f78iCq+LNsZ8hF4yYhKROcAUzQujvQIzYVQRZztj05dYWPra2NRvMWwEMAn4mqreGJE9H3Mdq9r9ILba/f6I7P2Yy1jWU1ZEbgQ2rZ78IrIIFli0fkR2S+xmXJahxeTDVXV6q2yQX5/20VXMTpmNiMzEClo/G/bnwzwnejJLyPj8D2ElAKdiCq5S6E8CZ+rwqOCtsWIPWxGqYwWewhKMDUseNYK+vLQgWjv28k6zlYK2q6yg9WOvVdXfjrLdZJBbRLbjLKZF9mbMvDFs7UhVz4zIbhhrQyPZRCUjynpuEK67NmLfb65TYp/p5cbIanT+CHhFZvttrkFE3JCoFfjNbPeunGPh+OVkFL+uyWfVHwx93r+g3bOxjHUnMVSH9Js9+A1jfYvVZpyAKf5+XUvbFciu16c+FPvvF7Rd+Uj/POwnfaQL2/0S8J4uMkuE7TBsZvqq2rGUm+ythf1YHhvIQPDrTsgdQ6Iw9dzesKRgq2bITZyr/RrDE7I1tlr/1/C32r6Zuvmxhci/YT7r06stIXsc5gI4IWw7pBQ3ZQV+v4dFaVb762DRYjHZi7HFle+E7/XNTooUW4xZq7b/ZuDXCdmSwsHZC09BfvVwvv6n2hJyV2DJxuq/6ZUJ2dKb/K2EYseYK+RxpBe0vkLwCQ77iwNfSsieGZE9PSH7/kq5YC6fF9V/nxbZEp/nrwOvLzgXJT7Slb0Y7CGzFS2Bb9ispApSehFbB6n2n2yRfYDhAVP1LRXL8BUsiVaO8v8oNru6L+yv0uEa6trfyHvWx2bR3a7lkuvtvdjC6ANh/42k9dD9mNl1SuZvvQFWLAMsyGrFovumRLgfGwUjJsybo23r8uM/F7YXw7HYRXsy9nDYldpsIdHu3aGtB8P2Yjh2By0VujF3uratw/dbG5ux/Aqbss4hHfF4POYa9zZshX4t0srmAuBVmef4UMyb4HHsAftH4MKE7MqYW+bDwCPYLCDq3YHZmLcj36NhNmZCWRPzUPokcE1Ctu1hQcLDKCEbfdgwFFm7ATaY2IJ0FOOM1rZIV3f/CPbwvglLd9Gxkn1h27OwUe7S4fq8AFuoHu19WuJJVKL8b8Ps4fXvFn1YjaDP2TPTwuttFuYAkPPwXhR7aN0Q7pU9gUkJ2UOx9ZLfhv1XUxD8pToYgUXvE5E7yci2pplVeoJsSb7wkkT4mxX0ocimpqozwuJd3YMmutJOWQGIkoWn7QkXtaruLiJLMbRe0Nrf+4B1g60f7bzqXxVeeEFEcpJGPa+qGmzfJ6rqaSKyR0J2oojMr8HbKCzOphaTJ4jI4qr69yC7BGnngMomvgUWjv0zEUmlPs72eVaLC/iuiKyKZdScLSLXA6dqpIwZZT75ohaQsgfmIXWMJALqpCChGKaQWr2GYsfQspwkWfnpa/1bg/a1oFT2y6nYyDjZXo2S6y07/76aZ82pwKlhveBcrObBhVhG0LoHW0nBkSiDoNDfpaqfFcu29iA2Oq4vZiKFVXpK0bIkPl2LOMsIam7WWJuhC3YtsUoobYuXWpZM6LAC2X+rRcI9H1w+/4QtvLYR3De3oz0Stq3WZeEDFuApsai9XYC3By+keROy52Cl16aF/d0x00qMrwO/Du5ygj3AvpyQfUxEvoP5Bx8dvu+EhOwnsbqtq4nIY4S6rakvFzyYVgvbX7CYiQNEZC9V/UCL+AHYDHLloPgnY+agRNOyXvjsSiFNbBFYADPNdE0oVuJJ1PLdtqBd8cbcC6+RzIRbInI6NuC7k+FebimF/hvglXQOYKsoud7uFHOhnhhcSvelpSJTrc/VudgdOx9fx67Xt2GBh/VI8JKkZlEGQaFXJy2ZbU1HVqVnLBlJzc3SqLlsSmY2WDj4YtioYhbmJpaquvRjbKQ4iy4RbcHdbmfMJnikiCyLmYHaigwHdsRsn3uo6h/FihJ/LSaoqkeLlTyrRpZHqmq0RJuqniUisxiKSt5WEyl/Mbv1ZtjayxMi8iqGR6O2ND28bqtYqoo2xHJvb4m5NX6ldg6OFpFYkes7MfPiSz7SpB8sn8LcBi9WywK4EmZCq7NXkHs1YTQYeBIz49V5N+aosAxmV654CvjfRB9+gkWJ5kRIH4Q9eO4I/boEc42Msa6qTunSXp2SmWn29YalHzk4tPl9Qv79hOzvsPP/NR3uSXWhWOxCnfPDAGIxsWIeH8buw3x6YasazYaF3HfNtoaNMu4Z6/4WfK+JwFWF7ylavCxoNzvpV8v7ViARLRdeTybXishmJ42aS79PTobIs3OOheNtNnvSGfh2JyzARV5rs6cn2u4ahYwp/ai9NrxeklCsxJNodq5s4W92GpmLi0G+ZM1tYYYShEUXk0fQ34nAFzNlBZsJZyc1i7bTjxNf8IUnYKvQ9WxrC5PItoaNCKM3X0I+a8U4djwlG15bniE3qwVJu1ldGbtBO7SbvXhZeJ5nAq8JD82JQaFEM/iFC2uX6kIMCi+VfvUU4A2Zfbgl/K0vJEXTGoTXKk+MJ7HR3gvAPxKyJVkqtyI/Q+QtLfsTaXFRZWTut21eHIljr8Q8ne7GbKvV4vdGJAY3mI12UriP7sJSAHwmITsf+amgFyM/HfXRZLoXYjOVWzHvtY6eK5hC/gc2Q5lNxBEh8p6lwmdsSQeXZwoWkzHb/EXY7GZ2tSVkS7zRRr0YPKYmFzVb7be0VgNRLaorFdm1OGa/upnhJZraplBSK4OFeWvMi9nl28pgYal2Wxd3LiRSREBqdQ0x88gyWARdbCHpn8AdIcip3t9URFnJFLEoWEgzk35hHgEvYourR2A32A8x234rGwC7iVVaeYahNY3YGkFJ0ii0Zl4L5pqtMcUdIztLJTY17pghMthSK7tuFdAj2MPilJb2VsWUxWIMLx78FObdUG+3tBhG3dzx9Zr8k6TNHVNU9UmxGqCXYiaNWcTNBydh98VJYX9XbCb1kYjsaZhNeoea7DTs4dXKjcDFwQ4dLaReoyTh1mnhc7OS3YnIDtj3vjr04QQR+YyqXhgT1/bF5GgWVcwG/pnMflwvIieSUdoOuEVE1lbVGV3aTDIINvQrxTLJ5dRATKV8jdF1xVhGFg7+SUJdw9Du70Skrbp74CLSCzYxDssVLLS3/0ssivM2sapGfyBtgy1Jv7p5bn8xH/yLsTSzX8YWI7N+z3Bd/Cg8pA9KyOQ+sLpmiFTVo4CjROQoVY21UZf9MfBjEVlPVVNrDRUltmvUvKTOFJHtNFJsOsG8Yln9tsG8NZ6rFtkiZKeCpiAdNTaSX488Jf0IZrrL8UT5syYioRMcjH3HP8FLg4hfYIO1VmKLyal7pKQfJd5o6wA7i8hDmPLvNECKMggKPdudTVWvEUtgtYpaXouFaFnBr5GzYpw9uqqR7WalqmeKudAtp6qxxa5W+WvE3ASr0fDN1cUYocQla1fs4twbS/q1LGYmiJE9klbVh0RkTWzFHuBXqhpVCKp6TliMrJJGbaOJpFHhc+sP2AnY943ly4GyB9YTYm6WvwLOEZE/kZgRqurnRWRphvJvV8fb0qRmKHNU9RvAN0RkH1VNpdaNvS9XmYMFsT2Iec1cG+6XVGK6klTQ2emoKVPSnwUuESuo3i3h1q0ici626NotfzvY4nT9/vkro1tMrjhURL6LmVS79WMPVb2/fiC0HePdiePZjHkulxLq5g5VXTm4DH1b44V4D8Sizt6JLbx+GDg3diNljq4q2WOAJ7Bp/T6Ym9VdqnpwRPa92OLGfKq6ooi8EUsQljKhtE4R34bZP9tGFFKQpS7IZz1YwlR9R8wEdSY2kj5EVS+IyO6HPfiqC/l9mL927BxnFzoOr02r7T6PKalTYw84KctSuTD2YKi8bpIZIsWqsH+AlrzXqd+vGyKysar+suVh9RIdFNOoEJF5NF5ofBPMbHI/dj6Wx9ac2hRZuHbPxM6XYDbvD6nq7IjsGVgCukvpoqRF5HKCaZLheV/aindIeaHxr2Fujt8Ph3bEbN2fi8mH93St/Ski38PWToa5T8b6IWW5qpaLfZ5GCnEn+zYICl1EtgIqF56rdajoRavcbQRzhw5VE38p5W1E/p1Y8n7B6m1ekZA7Bstr0TW4KdgF96i3i5WgazuRYUS6cfhOVX+TCYbCdPedrVNEjVdAvwqbznW1t+c+WMJ3Wxe7WXPSr87GIn2fDvsLY6kKYtXdh13YYRZwh5a5oSUJI/TVsJnFvdohgZuYb/VbguwMVf1jQu5ezMuna5EBycgaKiKHq+qhpYqphDDD+wrwalXdXESmYL/RaQn5rFTQNfmcdNSHxo4nlHR2wi0ZQbIzMXNutW72K1W9OCG3HmajX0RVlwszz71U9RMR2XtVddW2RobLVObcYxju6joJG6S1ZcqUobgVwUy+K2K/STSrZhQd5arqaDesSOyV2Aj6w1h+kJQHxk3hb1X0eB7Sq8sHAEtn9uG28Pd92I/6MtKFJbYlP7n9jfX+hv+Tq/K0rHJj08NUzo4Sl6xYqHKq3eycK9ioaoHa/gKR79Ba6LhKv/DX2O8MfDb8PYFa/hs65MHBYhgeYajoyMPA5gnZj4TXz8BGnA8CH07IXord4DnnosRtsThhE/k5SUryvpQUPC8pup7l+RRksxNuYd5JFwDvocfuvZTV/pxGF/dJRpCrKtLGWnSpV9y6DYIN/T3AG1X1RQARORNzY4otRl0jmVFlWA6Fy0Xkb9gK8wWq+nhCtmtwU433YqG714Z2f66R6WwgO6Is8HMRuYzhU8RLY4JaFiwUC1VOTc1KFqmnATeJSDXq2QZ7INb7mb3AGKhmAzMzZCu+jlWNmgMgFoL/M+Ln7jPAmzSM9MRC6m/AKj610jXvtYxsYf0BsSIQ5wG/7HaepWwBvKQW68nke7mcTr6Xy0lh5H8GZs5KpSkA+DhwoIg8Q3ePmNcCm2IDv2+KpbI+Q1vSCMsII8s1v/bnuth1kfTu0rLF8iiqeouIrNNdcvibxnTDfDiXqO0vQXrUPQGz2V6AjSg+SpcnNWY++TIWvPSLhExWcFNNfl7Mn/kcrCRY9CmKuah9GfORnhn+jyY0qr1nW2wkdBzwvg5yJb7Xp2Gju9nYusIJ2NpDTLZKavYsGRntsFHEvmF7Uwe5CZh74BfC/rIk/NtHcA3NaNmX1mO1127ATE/V/nyky+Z9KLa1yIwka+hCmGK8CJshnAhs0OH7lZRpuxqrO1r5/a9LOslULNVxamaaldq59tprw301B/ON72naWyzS9zFsPesaRpkWGdMn62PeR/Ni1Zl+kJBdPrZ1OA9ZaZUxq0K1HRjO22VF36OXJ3mEJ3InTCmegU2BHwB27CA/Xzgpb6jfmB3kX4ktXl5P5EFBYXBT7X3zYqP1i4C/dJGdRCL4qEVuRYabMBYEVkjIlgQLFT9YMn+7devfK3zPdRKyWZGi2Ixremrr0PYlmM/2h7CSfycRyZqJjWpvxVxEDw038BnVjTSKczEihRLOw1nACx1kSrJlltTGvAVzR6z2VyKdpfLX1B46mF06mtq5JjMR86Z6DHso3VP9HsBqtf62bYn2Xo6l1ZiJzcC2xUxKUwmpbFvkVyaYR7FgrH2ppU5uke1a+5MQdUstJTDd0wOXpFU+tLYdjC3aF92ng7Io+iqGu+qlFqm2wIJ47sNGYStiCxdtU2sR+QQ2CpqM3RDnayJnh4jcqrXgpi593RwzhWyEjYbOx6oKxbwI1samqpX/+z8we+2sRNvZFYBEZKaqTpXhNUWzv0cnxIJeVqFmMtCIq56Yr/daGi6isKg6UzvU8qz3UURu15YFXxmqYLMt9jCuFqZ3Ah5X1f0jbU/r8HVUa4uNqQW7mvDhMoLkaiUL60F+Q+w62gxTUOdpwj2xZAE8yOfWxhytl8tuGnFTFcuIuDtmwrwCOE3NfPBq7CGwvIicqqofDd+tFdV4rdLfYilxp6nqoy2vfU5Vj245dhum7FfAHvg/xvLQvyd2ProhIj9V1S2DqUWBun1GVbXNHVFCnd+W6z5aKUpE3q8t3mSxYx0p0f792LAb9qOEJ3YX2Xuo5dvGnsCpEOijMNt8Th+yc3Vj9u1tyFgYxUwcb6vtb0DnRdGsCkDh+LXYbOUsbGFp/w6yJaHKH8EWO/+O+eH+G7Pz5vY31e5N2IitMgVMpsMCLPZg6HqsT9fkq8Lfkql1ycL6g1iQ1U4kcrq0yG8Y2zrIZy2gBtn5sYfPGpnX9CQ65IcJMtdgNvYFI6/tOorfRWr/d8xTE2Sqa+0zhLw1qWsuXI//i0UCn15tPbiWLsX0VNWX7YFLO/W327FO2yAsip6G+VufEBazbgWuVQvCaOUpHe5bfD9m421DwwKcWBRnfaQZ8+ksCW7aKetbGS+o6q9q771ORFILqAB/FpGtNEShieVmTtUjLQkWKglV3g+bLd2oqu8Ii35fScjeLyL7YiYPsEXq+xOysUjRQzr0Y2ERWUlDUIZY5sJoOtHw2j60p0GIuXBOxaazy7fI1he0/hD+PiT5gV4lC+traAe3v1a0YAG8ZAE1uI6+m6HztqlYuuaYv/hi2MNhBYanSm5LY6GqG9betziwrAZ/dVU9OxyP+uLX2oj55J8jIh8L32sGMElEvqGqqayIz4nITpgZrgocTKXE7VowW0TaZp4tfY6F88fSKremmtgccw5ZWkS+WXtpEuYdls2gmFwmYjfNO7AKLv9W1bZq6SJyMnYjno9dpO/HXNB+AcMvAjHf6+OwMOs/hffdrSU+nfG+rostKr4OGyFPBJ6OKX+xkPIFsVG9YlPs/xDMCK0XQHignRP6DJZYaVcNkXyR9nODha7TkII44/tVU8TbMHv4MyJyZ+y8hYflNzFfe8UWfz6VUnrh4dDVvz3IbobdCHVzwF4aSYsr5r9/Gu3BKbFCw/cSebhpJM+9lAV6HYWNzv+N2UwXA36qqm1eCmI5XfbAvGPqg41UgEzJNXc3mRHEInIJkTS3GvcXryru5BR+vhpzGpgHc5n9E2Y6PKAmU5nJXoHNKH4Z9t+BLVK3pZ6uTBViwW9rEfLUaCI0XswH/2OYmef74cG/g7aYZuptx9qpyVTmoQWwWe/t2HWxBjZ7XK/De19Kqxx5bU3MpHYE8MXaS09hGVv/3qlfw9oaa4UeXMIWxhZdfgVc10EhlNhKb8cUzbAkTKoarUIi+cFNM4kkgtKIS17CPljvbyyfA5JRAUgKolCDrXQnMkKVxVwQd8dCoTfGTC/z6gjtji1tL47NJOoj49ioppKfHwsWAjOtRYNeROSmmOJMyJY83LICvWQoIOseLCPkC+EGXlQj60FiUb73YGaRI7DFr7tVdb9W2SBfcs1lRxDX118yZNsiHjvI3hruuY9go/NDU58lFin6oaq/YutpZ6hqWxi8WGWzN2LeHyeqpcpoW4cZCWKVqG5Q1UsyZC8CDlXVO8L+6sBhqrp9RHbYzKY6HpvZiMgy2r42sGq3Adsw+QFQ6MdjWQ2fwVblr8WeqKk8EbntVouGt2PudC+mfnyxEO+1sdExmPKbmbhh+rYYWYLEo1CjUbNSEKrc8r4NMVvwz7VD5GVmf4/EvFDuY2ihMflQC+9ZHatwXx/FxkwHH8QWcS9n+AOr7WFR+HAbdj6D4r49cY5LFtYrhTdbVdcQS6b1K1WNZpMsueakLIL4aGymdHlGn/fHXGR/2tLu3yKyd2CR1GcCB6uVVkwp9LtV9XW1/QlY2uHXRWT3BT6HjYy3wFI7f09V39YqG+TfinkzVea1ypQaW7x8ChtYdvWHj81YO8xiS2Y292JuveeH/U9juWCyo6nH3IauwWtBLBPibtiq+ytJ14TMpUrCdC1dkjBRFtxUkgiqn5QEC62tXUKVY8RMFqNgB8xFLuvBIOaNshGm0C/BMjteRzyY5g3YmsLGDC9NFntY7I493OalexmzWKBXagRXEpBVeZ08ER5af8RMDylKrrnDunx2nZI0t89i5qeDqT2QMVfHVo7AUmJcF5T5SliUZ4wrI+f4FzFBVa0ihgEQkYcZqjwV4zRsfWkW6SChqu2SamizxZJzVR5MO2OOBjEWqJuaurARcIqIvB/L4343Zr7LR0e5ijvaDVvUOw8LQPgF5oO5cQ/aXRi76OfBFkX2pcWvtCZbEty0PDZinBT6ehyJSvd9Pm8lwULTKKj0UtCH7MIgWE71ZIGBiPwd4ferQtiXAq5IyM4hIyYhyN5b+B1zA72qgKzn6F6o4SOY//nbsTWCPwEf69B29jUHHJ1zLBx/ALP/5nh33Y9FofbjWn4fcHzYkud4BO0mgwMT8ktj9vy3V1tCbgHsQXFx2PYn4S8eXvso8Cq6+KwH+U9ia2cPk5kioL4NgsnlQMx2PkvTIfSVbNcESPXjwB9U9T9hf0FgKVV9MCK7E5ZT5ipslPJ24CBVPS8iuzChkHLYn4i5e3XM0JaLZBatEEsdfDDDk4QdWX3fFtm7Mc+HB+heiKKkryWZ5KZingS/Ic+X+mZVfYsM1f98CrMzxxbLfwTsqWkPlLrsNKy+Y6qOaKv8K7E81S/SIZFXv5H8BfDYb5Iyd1wLbFRdy13avRxLedyT63xuEEypE7HZVzdT3NHY7KAnmTVr7X4SC+R7guGmxpjZ5xfA77HB57LYoO1aVT0w9/MGweRybIF4dmUhbAFp/dr+C+FYW5CO2gr41bXXPtfhxr0SyydRLVguiNlu148J5yroIJvtchZurIPD1o3NMmSykZHlLzkTK02WVW2GsmLViwH3iMgMuj8suubhqAiLel/EPDCqijdHqGos70vJwvpXgGNU9YmwvzjwaVWNunFKbQEcWFEiC+Ai8nHMbXRlsSyYFYuSzh90P3C1iHRNc4uZK28LNvpoXpsBpFoon1o7ljLFbQOsqhmZNQv5NDabSrkf1zlRVX8U/n9CLANkqjJVlDFX6DmMUIHMozV7rVpRimjlnbBoeA22MHVPl+4soDXvE1X9Zxgtx9otSaoEBUUrwoj3f2l/WLQpJo245I2SkRQG+ZeaDbQrYgsDRwWF922xRFaTNJJ7O3BoXreBsodbdiKvyML6fiLyVo0nJNtcVV+6UdWqQr2HtF/+YZgt9eogf1uYgdY5FwtiOYrhVZ2e0sjCZeCBsM0Xtk78KGxdKZlJl1IyQFLVTvb1Vu7H1lV6rdDnYEneuqKqPxKRDbACPtMws1w00jjFuFDojEyBlATplAQ3PS0ia1XTNhF5M+nKLSVVhcDMEa/EFr26URIs1FN0ZJnkfiXmqz2dLtNfVVUxH+k3hP0Hu/TnGskMANKCKktYwq2633CV9jdGycL6RBGZvxoNBnNKJyeArgvgahkN/yEi3wD+psHfWUQmicg6qnpTa6Na8zcPC6OLaCLgSWteGdISLBShZCadTekASTJyw4vICaGNrpk1R0j2zEba6yDPR7oOcpRxodBHqEA+hnm3nIhNlx/B/EFj7V8V7In14KbXAzGF/ingAhH5fWj3lZjtLUaJgoayItGl9RX7wfvEfINz8pdULnZ117zU9BcKCuZKQTFgaa+y9D0RiVZZwkZXN4nIj0Nft8Y8HA6AqGliMSzHCZi7Z4pzMO+OKq5id8wkKB6k/wAAFl9JREFUlaIkDfPJDFem/4wcA0CsnFtW5KVEgoVEpDVYKHsmLYk8OXRe3ykdIJ2BKcbKLPlbzAGjnuK5StM8Cxts1Bn2OSLyk0SfTTh+n2bPbMiog9yNcaHQa2QrELXoynUlL0inNbjppcKykXZnhAu3XuUlmvyIMgUNZS5nJXUN+8W7VPWzIvI+LD/JtpibaOz3KJn+QlnB3JJiwHtgEbBVlaWjsd89ptDvC1vFj8Pf2E12FFbzctjCeuyLqerRwc5dlU48UiMRsDX2wb7jM5h732XAkQlZqSs8tfiL1H0+RVWfFIu8vDT0dxb2cGzlZUH2I8BZGoKFWmRKZtJtkaAZFA+QtEtu+GrmISL7tc7Iw8O/Tsl637D2M8mpg9yR8abQsxWIWJThdrTnnjiiVRZz/XszsDqWEfEJEekU3LQ2Q3a8tcTyX8SmfYflfrHQtxK/7xJ/6n7RNX+JiOyiqt+rRrWtJBbgoKxgbkkxYGG4T/IL4Visb20h8CkKF9ZRyxAaLV4SkS1ZAC/JrzOvWFDTNtiC3HOVMokwj1gU5w6pfpTMpEe4rlM6QHo6rHtUCnJd7P6O8SHaZ+S71Y8V3p+Ez9wSe/i2BjfFfP3PF5HvAIuJ1U/+MPDdks8bbwq9JAHSj7EfbxZdFjq0ILipxI5XegFIQc4ORhgs1GOmi8g92Izp42Fk3Oo2WY0yiqaOhTd8dqUnMqosjYSShfVgjjgaCyYSOt/kRQvgmAnlm9gCa5VfZ89EV76DDYxuB64VK7adShpWEiyUPZMuvOYPS3xeigMwM8rKInI9llFxWHi+mMvyBzHvobrJZRJD5rPWPq+Czchao5hjQVb/hw087+hmKlLVY8UqsT2JzXa+qIk6yJ0aGTcbBZWFSCSRT8hmBzdRVj0mu6pQkC8pWtGXYKGCc5ZdGCR8l/373J+sAKAgm1VlqfDz34G5OF6BjYh/COyXkJ0DvK6g7Xsx+/WKdEnjO8rvIJh32GjbKUklnH3Nj7Av82B2/dWJ1EsN53IjzOy2YW1bK3UusIjlTbCZ/fLYg+aIhOxV2Awyp6/ZQWHJNnp9UfRrK1Eg4bVTyCxWi5V7WifnYqasekzRxUrI900tSpV0/ua7sYfEveHCuoMOudb79JuUFJS+uY/9KKn0lF1laQT9mBja/zxWhSuVq//6wnavK5BdAIs2PIke5vUu7O+d4e93gc3C/0mFHv7mXPOlA6SJ2INwX2rl3TrIL4XZ9rekQ1Qzofg3teLbpAuCr43NUj7frQ/E86EX3dPjxuSitrjzLa0lJFJb2ErlZ9kA2E0yAki0LLipyI6nqnNEZKKqvgBME6vykyqWXJKzo6fBQiOkJH/J9cHj6Dxqv5l2yLZYQHYQGWVeIK8Nry2lqquLVeLZSlW/FJHNXljHgqbOw7wfcha0SxbAz8Zmse+mlskx0W6/yDHFVZRc8ycSyTrZoR8/IZIeOIZY/pRjyfCUAp4Jbp6/E5G9sRJ7iySa/jJ2jS1AwtdfhoLCVpL2oLDrO/W7ra3u9+HgICLHYjdMVwUS7IFt6CiDbGSoRFpru7Hc29diUaXfxRIw/QEr2xVN9xn6/Dj2w++PTVVP0uFFPQYGGcpQ9wJ283bKUHdVpAnVDtkWC/rRlsta0pk1Y7Kp0PhrMF//7+hQRsvfqOrqEdnsrKESTwOtms6Hnp0tUwozOeYimcFCUp5KOHbNf0sjNQCkMNNp6ndNyGalSg6vrY09JBfDFjwnYZG/bb7+qeulReZlWBBRSVBYnJLh/FhvFCRACvJrYvbxvYE1e9iP3KnZ8hQm8sLMBauO9bkeTxtmt96qtr81lhY2JnsRNgWfN2z7AT9KyM4If+sFfpOV7sPri2Juhg8Bz/To+2UnFCOYtrAHyurYjPL+DvJZ5eqImwNSZoYSU1zbOkPsWO07ZZVdDPJHY55xOf24o2V/Quux2mvvzzkWjh+T24eeXCtz64Pm9hZu1N9g084jsGnXPj1od4dws54ZLqwHgO07yGcraMx3915CBXMsr3W00v2gbJiN8tiwbdlBbilsgezSsD8Fy/Xciz6sjKWCfThsN1CrZt8i+wrgB1iGw8exkPnoQ5myepAlC+vLYFn6/hS2HwLLdPh+2QvgDGVy3JChTI57JWTPDufqJMzT5ATgmy0yq2Huv/dhC8/VthvBVh5pt6RGb+xBkbKhFw2QsEXZp7HZY7cMmF/DvHh2C9ulpLNUZtf+ZGgQ2rUPvdjGlckFihIgzcbCfKsAkoWxKfBoMwyWTM2yqwoF+eyiFYOAlBUGuZQQtaeqa4Zgl1t7+d0kI4issL2VsMX19bHKTQ8AO2u8XF1J1tArsAfJ2eHQLqHddybk+5Uts2u5OrGUGdtgD+66W99TwA9UtS1iNccUV3MX3AA7bxWTsFq8mxBBMrNOBtkHsNlaV5fBIL9t6A+Ymerilter2p87YA/vep+nqGpZ7vI+MG4WRaE4AVJ2AEkhJUEsh9E9qVKdkqIVg0BJ/pKuUXujpVeKvMZDqrqpdKgHWfvskoX1yWrJlyrOEJFPdZDPXgAPgTSHYfk/FFOWR2pIMNZC18hLHUHaDc0rFnFD+Nwlga/Xjj9FoliEZGSdbOERzH059x66HjPnKlbxqZXfY55rW2HxLfU+75/o8w+xmenPNSNN8WgZVwqdMgUyjT4EkFAWxFKqoEtydgwKuflLSqL2BoUHxDI9nsdQEeNe8FcR2YWha2gn0km/iM0IOvADzNa8XdjfGev/phHZEo+tkrw9XWfS4Ts9JJZ24Pc6vG7BMljAUyuHUTZAyk4PLBn5gNSSuN0edMrTap5rSKiJkOjDyZi78gli9V6n5cwuRky/bDn92CioLBRe73kASWg3t4pNdlWhIL8Q5uY0AxsJfJlEJZRB2DBF9BCWBOlMzCSwY4ff4npMiV+PJUpaYwz6XFJlaSFsen0RpmBOBDboQR+Wx8wXf8Zs3D/CzAi9+H5tAXWkF/c2jG0J2ZJgoa9iLpYfDtsVpAPkZlKrNoWNvmckZG8Mf+uL1J3u/0NjW0L2dmprKVjQYur73Yhlpqz2F8EKTHf6XV6GRfE+gg3SdicS6DTq37/XDfZzK1QgfQkgoSyIZVwp6BGej1dhU9CtSAR51WQ7Ru2Nsh8999ZokVkcWwR/YazPeZd+Hof5ak8I2w7AsT1otyRYaDa16EgswCdV0rHNa6hDu0UDpMLvV+LlEutz0vsJeDnmpDETe5DvGPp+da9//3FlctGyBEjZASSFlFRCKkmqVJqzY8yRsvwlC2DBExsQbLsi8m2NlMwbQT+65teRkRVJqeIOdsTs2DMxBTna/p6JuebVKxZ9XRN+6IV8FEvxXJlCJmDmrr1oX5gsyaNSEiwE+aa4kroFWVknReT/VPVTkkh3q3GTUokpNbsmQjDPrIotgL9XVav1ivNEZGbsPaNhXHm5FCqQ7ACSwj6UBLEUKWgRuZdI0QrtfcWhniAi78AKRbwNU6jJwiAicj62eFQpmg8Ci6nq+3vQj355azyIfafzMffRVFRyaX/bgmE6Bcj0i6BQ2iIvtcXJYATBQiU1elfGnBxeHWQfwWZXIw6mE5E3q+osKQgCDO/bjqFiEm1eLjW5tbG1imE1EVR1VkT2Hap61Qi+xsjo9ZC/nxtlCZCyA0gK+1ASxFKUVImCnB2DspGfv+SunGMj7ENJfp31Ctqd1KdzdjuweG1/CRLT+xG2v0a47l7yG0/IleRRyQ4WCvLZprggvwg1u3RCZmq4r2/BzC6z6XH+ImzGtkS1dZCbFzMddjQfAu8nmH6xDJgXAWv147pSHX8ml5LKQiVpREuoV0ICeBTYNSFbWlVoEIpWZCNl+UtuEZF1VfXG8N51GKoWM1r65a3xrFjV9tczPE3qaE0jXwd+HbwewG76L4+yTQBE5HTsOw1LE0A8T35JHpXsvD0lM+kgvwXhHEvnugVZZRclXQ2pajuW5mEv4HDMjPQiwXceiKXEBTOjVOlzO9VE+IKqXiBWK3RTzJPmZIYKWPeU8WZyaVUg13VQIP3uS04lpE2whdwsBS0FOTsGASnLX3I3dhM8HA4th81gnmeUQTIlU+vKZCZWJGVLLPvdtRo3mV2AmRk+SC3Rlaq2VrIZSZ+nMFR+75eqetdo2wzt3qWqUzJllyczd1BOsFBNtsQU923MeeAd2ILr9lj6gj0istep6gatxxPfCyzrJAwP4FJVbaskJSK/w2ZvKft9XfZQLOXuFOASYHNMF20fka1y6xyFzcLO7ad5bbwp9GwFMgiUKmgRuVfHvmhFMTJUGORAbHodKwyyfOuxOjqX1glE5E5VfX2YCV2oqj/vsAbSl0RX/URETsMWWLMeEFIQeVnYj4kMn0n/W1VXi8hV57b6uwiWXuFtEdnSAVJsreIWVY1l1vw5Zpr6V8Z3uwPLE3WrWtTzUsD3NBLpKyI/xbIxvhNzyPg39sCKJugbLePN5JJdWWhAKK0qdIOITOnVaK3fiKUOfRv2kH0Qy739q5hsPxV2H701qlqxT4jI6ljGzFf0tPO95yzMnPNHuqQJkMLIS8lPu1FiiqvO/b9E5NVYgNWrErKlZRdFLJL8+rCzPmmT0uex++8mhj8s9o3I/lstnffzIjIJiyVYNtHuDpiH1LGq+oRYGb/PJGRHzbhS6CUKRDLTffaZUgW9LmbPfIAe5uzoIwtgfs9d85f0maw82cFb4yeYHbPy1vgXtrAd4xQxl8JDMM+YRbBF+UHmNGxNp2sOcAoiL6Us7UZJjd6fiMhi2G9yC6agT030t3SAtAdwulh6WoAnsECnGN/BooFzztvM0OdTsRQA/8QeXm2EEf9Ftf0/kF/kupjxZnIpSYDUNrUSkVmq+uYe9GN92l0R2xZEpDCpUsosMbfMEeMVKciTPRbugXOToDjXy5S9UVXXrZ8TSeeGn83wtBsTMZNDcrDRzRQXHrDranAZFSvsvoCqRlNCiOWR/1rpDLZS6Kl2g0zWdSG2aruMqj4S9lfAvKGi+WfmNuNqhK4ZCZBkhAEkuUhBkWgKqwq54h4x/fLW+ApWuKAeAPRpVT2kh33vNbeKyLnYTKSbnbk0d1BWsFDuTDqYLb4FvCnsP0Pngu4jmsF2UuQ1LhWRPWk/b8MKTKiqisglwBvC/oMZbc81xtUIPQcZQQBJYftdg1icuUsfvTWyF9UGBSmohiQiC2GRl+/CzsNlWGbGtjUFKQsWKplJj3kVstB2zBSrqtrmtigW6Xuiqs4Y7ef2msYp9AopSPdZ2O4FwL46FMLrDAD98NYIZoa1w8ix+oyZqvr6Xn3GeCIs6FUpLm7WdNqNkjarB+zz2AJp8gE7KIRF9ddggXRPM0BrXePK5FJIUbrPAoqKRDv9p1/eGtgC4JW1Ue/uWFK4gUVElsE8fl4KYceiqR+NyGanppDCYKGMflbeJ5NjM4JRtr1tp9dj5iexItE/V9WnROQQzMXwSFW9tSZTOVW8u5f97SVNHqFnB5AUtluUH8LpP1JQ6SnirZGsshTkNweq6jlXqOplve5/L5GCakhSkDtICoKFMvs5S1Xf3A8TVsLsVJEyP1V+8BsAX8K8br6oquvUZKo+X6mJikpjTZMVenYAiTO+6be3xnhC4snj2o6F41mRlzX5rGChzLZuxNwbt8ESXQ1D4/7ffUMyIjpF5FbMNfbjwPGtbWikcMbcpskml9J0n1lIWRCLM3fol7fGtljl+FdgdtKBt+9SVg0pO3eQlAUL5bAlltvk3Qwv59ZTpJYnpjqm8Twxj4nId7CIzqODC2Wrp9QHsAfQPEBOmb25TiNH6FKY7rOw7ayUo87co4/eGnOwHNZ397H7PSV4gpwArIe5096ALeI/HJHNTk0hfUq7ISJrqpV26zlSlidmIczN+A5V/V1YAH6Dql4ekd1cVVO50seURip06F8AiRQEsTiDSa63hohcr6pvjb3WBGQEuYMkI2/PoCAFeWKaQpNNLtkBJIWUBLE4c4E+emvMFJHzsJqfA5/OGF7ykc6thpSdmiI3WGjAqGYPOXliGkGTFfpemGfLCyLSMYCkkF0xBb43FsSyLEMV1p2xIStPduA0TDGdIFYtp5O3xiTgX5gpp6JTMqhBYI1KmQOo6t9FJDV7LIm87EveHulvzqWfSnuemO/2oN2BpbEml37SjyAWZ+SMpbfGoCEitwMbqerfw/4SwDUJF84xzx0Uc1uU3uVcmr8WFDY/9lD6T3VslG1n5XOa2zR5hF4SQFLSZlEQizNX6Iu3RkmQzgCRXQ1pbiruVqTPOZcCvyYUhQ9K/BkRuYVRFoqXsnxOc5XGKvRIAEmndJ8lHEZmylFnrlGSJ7sktes0LEinKmS9SzjWFqQzKKjqWcETq6qGtG2OjXwMWBVzXVwMeG/t+FPAR0fTsIi8ElgaWDCYmyS8NAnzehktUxnQfE6NNbn0K4CkJIjFmTv0y1ujJEjHGRnSh5xLIvIh7LedCsxgSKE/CZw52kVtGeB8To0doQeyAkgKKQ1icfpPv7w1SoJ0nJHR85xLqnomcKaIbKeqP+xRP+sMbD6nJiv0o7Dc0MMCSHrQ7j5YEMsz2I1+GXBkD9p1Rk6/vDU+jNnQj2coSGf3nvXaAXiXqn5WLOfSg8C2WNDSaJPoAVR5V3qdz/6wUfesTzTW5AL9SffpDB6D4K3hjAzpY86lWMBfzKumSTQ2ICYEkGwJ/FZVp/dKmYvIVBG5SERuEZHZ1daLtp2RoaoPxbbRtisiZwY/5mp/cRE5fbTtOsOoci69GQsG7EnOpcDE4K4IvORuPOrIVhFZV0RmiMg/ReRZEXlBRJ4cbbu9oMkml5IAkhJKglic8U1JkI5TiJQX7S6lX/nss4qSjwVNN7n0PICkNIjFGb+UBOk4I6PfeZCkD/nsBzmfU2NH6CUBJIVkB7E4457sIB1nxPQr5xIAIStirzMjDmw+p8aO0KV/6T6zU4464x8RmcJQkM4vBzRIZ9wiBUW7R9B2X2oXSEFR8rlNYxV6RU4ASWF7xUEsjuPMfaSPtQsGNZ/TQEwT+oGI7C2W+vRWbJHldGDzHjR9Qxi1OY7TA0RkKxE5Nmxb9rLtMGqeqKovqOo0rIjFqAj5nG7DAqEQkTeKyPTRttsLGmtDp0/pPikLYnEcpwN9zLkE/bN1H8aA5nNqvMml13gQi+P0jn7lXApt9cXWPcj5nJo8Qu8Lrrgdp+f0POdSeDB8RVV3xgKVDu9Fu4GBzefUWBu64zjjgirn0hli5fNm0QPXUFV9AVg+mFx6zT5YLvcqn9OTwKf68DnFuMnFcZwxpV85l0TkLMxlcTrwdHVcVY/rRfuDiJtcHMcZM6SsaHcp94VtArBorxqVgqLkcxtX6I7jjCU9z7kkImer6q7AEz3I3RRjYPM5ucnFcZwxpdc5l0TkLmBTLOR/I4YqFgGgqn+LvK2k/YHN5+QK3XGcMSOSc+m60eZcEpF9gY8DKwGPMVyhq6quNMr2N8GqVw1cPidX6I7jjBn9yrkU2j5ZVT8+2nYi7Q5sPidX6I7jjDm9zrnUTwY5n5Mvijr/396d2wAAwzAQ23/rrBAXfiCQQ1zhQoY1xafdV3w/JZ8m6MCmrs2lTmf3nJxcAAou7zkJOkAIWy4AIQQdIISgA4QQdIAQgg4Q4gHAhS8G4+elsQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YKVYTWQT36v"
      },
      "source": [
        "c) Univariate **ROC-AUC**  -\n",
        "builds one decision tree per feature, to predict the target, then make predictions and ranks the features according to the machine learning metric (roc-auc )\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "-Sov41uzp35z",
        "outputId": "0b3360f0-807b-4a19-8bac-1f013f68c736"
      },
      "source": [
        "#my code:\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, mean_squared_error\n",
        "\n",
        "def selected_features_ROC_AUC(x_train,x_test, y_train, y_test):\n",
        "  roc_auc=[]\n",
        "  for feature in x_train.columns:\n",
        "    clf= RandomForestClassifier(n_estimators=100, random_state= 0)\n",
        "    clf.fit(x_train[feature].to_frame(), y_train)\n",
        "    y_predict = clf.predict(x_test[feature].to_frame())\n",
        "    roc_auc.append(roc_auc_score(y_test, y_predict))\n",
        "\n",
        "  roc_values= pd.Series(roc_auc , index=x_train.columns)\n",
        "  roc_values.sort_values(ascending=False, inplace=True)\n",
        "  roc_values.plot.bar()\n",
        "  sel= roc_values[roc_values>0.5]\n",
        "  x_train_roc = x_train[sel.index]\n",
        "  x_test_roc = x_test[sel.index]\n",
        "\n",
        "  print(\"Number of remaining features: \", len(x_train_roc.columns))\n",
        "  print(\"Remaining features are: \")\n",
        "  print(x_train_roc.columns)\n",
        "\n",
        "  return x_train_roc, x_test_roc\n",
        "\n",
        "\n",
        "X_train_sc_df_filter_roc_auc, X_test_sc_df_filter_roc_auc = selected_features_ROC_AUC (X_train_sc_df,X_test_sc_df, y_train, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of remaining features:  28\n",
            "Remaining features are: \n",
            "Index(['worst area', 'worst radius', 'worst perimeter', 'worst concave points',\n",
            "       'mean concave points', 'mean radius', 'mean area', 'worst concavity',\n",
            "       'mean perimeter', 'mean concavity', 'area error', 'worst compactness',\n",
            "       'radius error', 'perimeter error', 'mean compactness', 'worst texture',\n",
            "       'mean smoothness', 'fractal dimension error', 'mean symmetry',\n",
            "       'mean texture', 'concave points error', 'worst fractal dimension',\n",
            "       'texture error', 'worst symmetry', 'worst smoothness',\n",
            "       'compactness error', 'concavity error', 'smoothness error'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTzMrqQuziGu"
      },
      "source": [
        "# **Machine Learning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYZmviiZzg1R"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0v9-Jp8BwVU"
      },
      "source": [
        "# **Support Vector Machine (SVM)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPJ9clUAhRqb",
        "outputId": "8bec0b40-bd61-4103-d68c-2f4809740173"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "\n",
        "def run_svm (x_train, x_test, y_train, y_test):\n",
        "    svc_classifier= SVC ()\n",
        "\n",
        "\n",
        "    a = pd.DataFrame(x_train)\n",
        "    b = pd.DataFrame(x_test)\n",
        "    combined_x=a.append(b)\n",
        "\n",
        "    a1 = pd.DataFrame(y_train)\n",
        "    b1 = pd.DataFrame(y_test)\n",
        "    combined_y=a1.append(b1)\n",
        "\n",
        "    print(combined_x.shape)\n",
        "    print(combined_y.shape)\n",
        "\n",
        "\n",
        "    print(\"-------------------------------------\")\n",
        "\n",
        "    scoring = {'accuracy' : make_scorer(accuracy_score), \n",
        "           'precision' : make_scorer(precision_score),\n",
        "           'recall' : make_scorer(recall_score), \n",
        "           'f1_score' : make_scorer(f1_score)}\n",
        "    scores = cross_validate(estimator = svc_classifier, scoring=scoring, X = combined_x, y = combined_y, cv = 5, return_train_score=True)\n",
        "\n",
        "    print(\"mean accuracy\" , np.mean(scores['test_accuracy'])) \n",
        "    print(\"mean precision\" , np.mean(scores['test_precision'])) \n",
        "    print(\"mean recall\" , np.mean(scores['test_recall'])) \n",
        "    print(\"mean f1_score\" , np.mean(scores['test_f1_score'])) \n",
        "    print(\"-------------------------------------\")\n",
        "\n",
        "\n",
        "\n",
        "    #print(classification_report(y_test, y_pred))\n",
        "\n",
        "\n",
        "\n",
        "print(\"1-Train using original train data (without scale without feature selection)\")\n",
        "run_svm(X_train, X_test, y_train, y_test)\n",
        "\n",
        "\n",
        "print(\"2-Train with Standard scaled Data (without feature selection)\")\n",
        "run_svm(X_train_sc, X_test_sc, y_train, y_test)\n",
        "\n",
        "\n",
        "print(\"3-Train with Standard scaled Data +  feature selection (forward wrapper)\")\n",
        "run_svm(X_train_sc_df_forward_wrapper_svm, X_test_sc_df_forward_wrapper_svm, y_train, y_test)\n",
        "\n",
        "print(\"4-Train with Standard scaled Data +  feature selection (Filter : Correlation)\")\n",
        "run_svm(X_train_sc_df_filter_corr, X_test_sc_df_filter_corr, y_train, y_test)\n",
        "\n",
        "print(\"5-Train with Standard scaled Data +  feature selection (Filter : mi)\")\n",
        "run_svm(X_train_sc_df_filter_mi, X_test_sc_df_filter_mi, y_train, y_test)\n",
        "\n",
        "print(\"6-Train with Standard scaled Data +  feature selection (Filter : roc_auc)\")\n",
        "run_svm(X_train_sc_df_filter_roc_auc, X_test_sc_df_filter_roc_auc, y_train, y_test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1-Train using original train data (without scale without feature selection)\n",
            "(569, 30)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9139419344822233\n",
            "mean precision 0.8915083162451584\n",
            "mean recall 0.9859937402190923\n",
            "mean f1_score 0.9356723543820318\n",
            "-------------------------------------\n",
            "2-Train with Standard scaled Data (without feature selection)\n",
            "(569, 30)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9753920198726906\n",
            "mean precision 0.9726645229060817\n",
            "mean recall 0.988849765258216\n",
            "mean f1_score 0.9805539589786164\n",
            "-------------------------------------\n",
            "3-Train with Standard scaled Data +  feature selection (forward wrapper)\n",
            "(569, 25)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9753920198726906\n",
            "mean precision 0.9726645229060817\n",
            "mean recall 0.988849765258216\n",
            "mean f1_score 0.9805539589786164\n",
            "-------------------------------------\n",
            "4-Train with Standard scaled Data +  feature selection (Filter : Correlation)\n",
            "(569, 17)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9701443875174661\n",
            "mean precision 0.9623626532083633\n",
            "mean recall 0.9916666666666668\n",
            "mean f1_score 0.9766067157456589\n",
            "-------------------------------------\n",
            "5-Train with Standard scaled Data +  feature selection (Filter : mi)\n",
            "(569, 9)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9490762303990063\n",
            "mean precision 0.9441123915208423\n",
            "mean recall 0.9775821596244132\n",
            "mean f1_score 0.9602962295721591\n",
            "-------------------------------------\n",
            "6-Train with Standard scaled Data +  feature selection (Filter : roc_auc)\n",
            "(569, 28)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9736531594472908\n",
            "mean precision 0.9728147576478658\n",
            "mean recall 0.9860328638497652\n",
            "mean f1_score 0.9792319857774228\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbOopBp1C4Gv"
      },
      "source": [
        "# **Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gs7ClRWP0B8e",
        "outputId": "35b120e0-52c0-4e14-e5f8-409c06bc3ac3"
      },
      "source": [
        "# Logistic Regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "def run_logisticRegression (x_train, x_test, y_train, y_test):\n",
        "    lr_classifier = LogisticRegression(random_state = 51, penalty = 'l1', solver='liblinear')\n",
        "\n",
        "\n",
        "    a = pd.DataFrame(x_train)\n",
        "    b = pd.DataFrame(x_test)\n",
        "    combined_x=a.append(b)\n",
        "\n",
        "    a1 = pd.DataFrame(y_train)\n",
        "    b1 = pd.DataFrame(y_test)\n",
        "    combined_y=a1.append(b1)\n",
        "\n",
        "    print(combined_x.shape)\n",
        "    print(combined_y.shape)\n",
        "\n",
        "    print(\"-------------------------------------\")\n",
        "    scoring = {'accuracy' : make_scorer(accuracy_score), \n",
        "           'precision' : make_scorer(precision_score),\n",
        "           'recall' : make_scorer(recall_score), \n",
        "           'f1_score' : make_scorer(f1_score)}\n",
        "    scores = cross_validate(estimator = lr_classifier, scoring=scoring, X = combined_x, y = combined_y, cv = 5, return_train_score=True)\n",
        "\n",
        "    print(\"mean accuracy\" , np.mean(scores['test_accuracy'])) \n",
        "    print(\"mean precision\" , np.mean(scores['test_precision'])) \n",
        "    print(\"mean recall\" , np.mean(scores['test_recall'])) \n",
        "    print(\"mean f1_score\" , np.mean(scores['test_f1_score'])) \n",
        "    print(\"-------------------------------------\")\n",
        "\n",
        "\n",
        "\n",
        "print(\"1-Train using original train data (without scale without feature selection)\")\n",
        "run_logisticRegression(X_train, X_test, y_train, y_test)\n",
        "\n",
        "print(\"2-Train with Standard scaled Data (without feature selection)\")\n",
        "run_logisticRegression(X_train_sc, X_test_sc, y_train, y_test)\n",
        "\n",
        "print(\"3-Train with Standard scaled Data +  feature selection (forward wrapper)\")\n",
        "run_logisticRegression(X_train_sc_df_forward_wrapper_LG, X_test_sc_df_forward_wrapper_LG, y_train, y_test)\n",
        "\n",
        "print(\"4-Train with Standard scaled Data +  feature selection (Filter : Correlation)\")\n",
        "run_logisticRegression(X_train_sc_df_filter_corr, X_test_sc_df_filter_corr, y_train, y_test)\n",
        "\n",
        "print(\"5-Train with Standard scaled Data +  feature selection (Filter : mi)\")\n",
        "run_logisticRegression(X_train_sc_df_filter_mi, X_test_sc_df_filter_mi, y_train, y_test)\n",
        "\n",
        "print(\"6-Train with Standard scaled Data +  feature selection (Filter : roc_auc)\")\n",
        "run_logisticRegression(X_train_sc_df_filter_roc_auc, X_test_sc_df_filter_roc_auc, y_train, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1-Train using original train data (without scale without feature selection)\n",
            "(569, 30)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9525850023288308\n",
            "mean precision 0.9540591740739999\n",
            "mean recall 0.9720266040688574\n",
            "mean f1_score 0.962715314242408\n",
            "-------------------------------------\n",
            "2-Train with Standard scaled Data (without feature selection)\n",
            "(569, 30)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.971914299021891\n",
            "mean precision 0.97\n",
            "mean recall 0.9860328638497652\n",
            "mean f1_score 0.9778530829900693\n",
            "-------------------------------------\n",
            "3-Train with Standard scaled Data +  feature selection (forward wrapper)\n",
            "(569, 21)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9824406148113647\n",
            "mean precision 0.9834400698485204\n",
            "mean recall 0.988849765258216\n",
            "mean f1_score 0.9860127633415638\n",
            "-------------------------------------\n",
            "4-Train with Standard scaled Data +  feature selection (Filter : Correlation)\n",
            "(569, 17)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9631113181183046\n",
            "mean precision 0.9622673219335385\n",
            "mean recall 0.9804381846635369\n",
            "mean f1_score 0.9710085110667919\n",
            "-------------------------------------\n",
            "5-Train with Standard scaled Data +  feature selection (Filter : mi)\n",
            "(569, 9)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9543549138332557\n",
            "mean precision 0.9570592476356079\n",
            "mean recall 0.9719092331768389\n",
            "mean f1_score 0.9639619865677652\n",
            "-------------------------------------\n",
            "6-Train with Standard scaled Data +  feature selection (Filter : roc_auc)\n",
            "(569, 28)\n",
            "(569, 1)\n",
            "-------------------------------------\n",
            "mean accuracy 0.9736686849868033\n",
            "mean precision 0.9701141552511416\n",
            "mean recall 0.9888106416275431\n",
            "mean f1_score 0.9792898645992649\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzeXgEqqEd1o"
      },
      "source": [
        "# **XGBoost Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcWz904zEh8K",
        "outputId": "498fadcc-988f-4e71-df40-2b0e0182a603"
      },
      "source": [
        "# XGBoost Classifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "def run_xgboost (x_train, x_test, y_train, y_test):\n",
        "    xgb_classifier = XGBClassifier()\n",
        "\n",
        "\n",
        "    a = pd.DataFrame(x_train)\n",
        "    b = pd.DataFrame(x_test)\n",
        "    combined_x=a.append(b)\n",
        "\n",
        "    a1 = pd.DataFrame(y_train)\n",
        "    b1 = pd.DataFrame(y_test)\n",
        "    combined_y=a1.append(b1)\n",
        "\n",
        "    print(combined_x.shape)\n",
        "    print(combined_y.shape)\n",
        "\n",
        "    print(\"-------------------------------------\")\n",
        "    scoring = {'accuracy' : make_scorer(accuracy_score), \n",
        "           'precision' : make_scorer(precision_score),\n",
        "           'recall' : make_scorer(recall_score), \n",
        "           'f1_score' : make_scorer(f1_score)}\n",
        "    scores = cross_validate(estimator = xgb_classifier, scoring=scoring, X = combined_x, y = combined_y, cv = 5, return_train_score=True)\n",
        "\n",
        "    print(\"mean accuracy\" , np.mean(scores['test_accuracy'])) \n",
        "    print(\"mean precision\" , np.mean(scores['test_precision'])) \n",
        "    print(\"mean recall\" , np.mean(scores['test_recall'])) \n",
        "    print(\"mean f1_score\" , np.mean(scores['test_f1_score'])) \n",
        "    print(\"-------------------------------------\")\n",
        "\n",
        "\n",
        "print(\"1-Train using original train data (without scale without feature selection)\")\n",
        "run_xgboost(X_train, X_test, y_train, y_test)\n",
        "\n",
        "print(\"2-Train with Standard scaled Data (without feature selection)\")\n",
        "run_xgboost(X_train_sc, X_test_sc, y_train, y_test)\n",
        "\n",
        "print(\"3-Train with Standard scaled Data +  feature selection (forward wrapper)\")\n",
        "run_xgboost(X_train_sc_df_forward_wrapper_xgboost, X_test_sc_df_forward_wrapper_xgboost, y_train, y_test)\n",
        "\n",
        "print(\"4-Train with Standard scaled Data +  feature selection (Filter : Correlation)\")\n",
        "run_xgboost(X_train_sc_df_filter_corr, X_test_sc_df_filter_corr, y_train, y_test)\n",
        "\n",
        "print(\"5-Train with Standard scaled Data +  feature selection (Filter : mi)\")\n",
        "run_xgboost(X_train_sc_df_filter_mi, X_test_sc_df_filter_mi, y_train, y_test)\n",
        "\n",
        "print(\"6-Train with Standard scaled Data +  feature selection (Filter : roc_auc)\")\n",
        "run_xgboost(X_train_sc_df_filter_roc_auc, X_test_sc_df_filter_roc_auc, y_train, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1-Train using original train data (without scale without feature selection)\n",
            "(569, 30)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9718987734823784\n",
            "mean precision 0.9735218283549367\n",
            "mean recall 0.9832550860719875\n",
            "mean f1_score 0.9779405894554232\n",
            "-------------------------------------\n",
            "2-Train with Standard scaled Data (without feature selection)\n",
            "(569, 30)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9718987734823784\n",
            "mean precision 0.9735218283549367\n",
            "mean recall 0.9832550860719875\n",
            "mean f1_score 0.9779405894554232\n",
            "-------------------------------------\n",
            "3-Train with Standard scaled Data +  feature selection (forward wrapper)\n",
            "(569, 18)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9648657040832169\n",
            "mean precision 0.9672950689450787\n",
            "mean recall 0.9776604068857591\n",
            "mean f1_score 0.9721379293234401\n",
            "-------------------------------------\n",
            "4-Train with Standard scaled Data +  feature selection (Filter : Correlation)\n",
            "(569, 17)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9455829840086942\n",
            "mean precision 0.9477960950209969\n",
            "mean recall 0.9692879499217527\n",
            "mean f1_score 0.9576559775781422\n",
            "-------------------------------------\n",
            "5-Train with Standard scaled Data +  feature selection (Filter : mi)\n",
            "(569, 9)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9490762303990063\n",
            "mean precision 0.9564224528457294\n",
            "mean recall 0.9636150234741784\n",
            "mean f1_score 0.9597030397289398\n",
            "-------------------------------------\n",
            "6-Train with Standard scaled Data +  feature selection (Filter : roc_auc)\n",
            "(569, 28)\n",
            "(569, 1)\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "mean accuracy 0.9736531594472908\n",
            "mean precision 0.9708977857308939\n",
            "mean recall 0.988849765258216\n",
            "mean f1_score 0.9793755448425078\n",
            "-------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GgpZDk8uHtnA"
      },
      "source": [
        "# **Cross-validation of the ML model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kycfu0uZHvhr",
        "outputId": "a438581f-e914-4404-8de0-1bad8c767452"
      },
      "source": [
        "# Cross validation\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "cross_validation = cross_val_score(estimator = xgb_classifier8, X = X_train_sc_df_filter_mse,y = y_train, cv = 10)\n",
        "print(\"Cross validation accuracy of XGBoost model = \", cross_validation)\n",
        "print(\"\\nCross validation mean accuracy of XGBoost model = \", cross_validation.mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross validation accuracy of XGBoost model =  [0.84782609 0.84782609 0.82608696 0.91304348 0.86956522 0.77777778\n",
            " 0.82222222 0.88888889 0.86666667 0.91111111]\n",
            "\n",
            "Cross validation mean accuracy of XGBoost model =  0.8571014492753625\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}