Copy_of_compare_checkpoint(2).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Zxvu1AigzsO",
        "outputId": "9591f690-771d-4144-a6ab-b5c4de461e1a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RalOTKiSf7Pz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "from collections import Counter\n",
        "from itertools import chain, combinations\n",
        "import sklearn as sk\n",
        "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqe1Ivlzf7P1"
      },
      "source": [
        "# Performance comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63N0wMytf7P2"
      },
      "source": [
        "In this notebook we compare the performance of multiple models on different subsets of our data.\n",
        "#### The models are:\n",
        "+ Logistic Regression\n",
        "+ SVM\n",
        "+ KNN\n",
        "+ Neural Network\n",
        "+ XGBoost\n",
        "\n",
        "#### The datasets:\n",
        "+ Mean/Mode imputed\n",
        "+ KNN imputed\n",
        "+ MICE imputed\n",
        "+ no imputations\n",
        "\n",
        "#### The targets:\n",
        "+ has_dep_diag\n",
        "+ a binary combination of all the target variables\n",
        "\n",
        "#### In combinations of:\n",
        "+ trained on balanced, tested on balanced\n",
        "+ trained on balanced, tested on imbalanced\n",
        "+ trained on imbalanced, tested on balanced\n",
        "+ trained on imbalanced, tested on imbalanced\n",
        "\n",
        "#### We employ  range of visualisation methods:\n",
        "+ ROC curves\n",
        "+ bar plots\n",
        "+ learning curves"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qL_g_VLVf7P3"
      },
      "source": [
        "The iputed datasets:\n",
        "+ 0 - unedited\n",
        "+ 1 - Mean/Mode\n",
        "+ 2/3? - drop NaN\n",
        "+ 4 - K-Means\n",
        "+ 5 - MICE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UV6hLOVJf7P4"
      },
      "outputs": [],
      "source": [
        "data1 = pd.read_csv(\"/content/drive/MyDrive/Yr3/Second_Term/ADS/Project/NN-Modelling/imputed_dataset_1.csv\", engine='python').drop(['Unnamed: 0'], axis = 1)\n",
        "data2 = pd.read_csv(\"/content/drive/MyDrive/Yr3/Second_Term/ADS/Project/NN-Modelling/imputed_dataset_2.csv\", engine='python').drop(['Unnamed: 0'], axis = 1)\n",
        "data3 = pd.read_csv(\"/content/drive/MyDrive/Yr3/Second_Term/ADS/Project/NN-Modelling/imputed_dataset_3.csv\", engine='python').drop(['Unnamed: 0'], axis = 1)\n",
        "data4 = pd.read_csv(\"/content/drive/MyDrive/Yr3/Second_Term/ADS/Project/NN-Modelling/imputed_dataset_4.csv\", engine='python').drop(['Unnamed: 0'], axis = 1)\n",
        "data5 = pd.read_csv(\"/content/drive/MyDrive/Yr3/Second_Term/ADS/Project/NN-Modelling/imputed_dataset_5.csv\", engine='python').drop(['Unnamed: 0'], axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6VLTYmWf7P5"
      },
      "source": [
        "### dataset 5 - combined variable (dep)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "1KNFDlSpf7P5"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/Yr3/Second_Term/ADS/Project/NN-Modelling/imputed_dataset_5.csv\", engine='python').drop(['Unnamed: 0'], axis = 1)\n",
        "dep_data = data.copy()\n",
        "\n",
        "dep_num = np.array([12, 11, 10, 4])\n",
        "no_dep_num = np.setdiff1d(range(13), dep_num)\n",
        "\n",
        "dep_data[['prim_diag', 'secd_diag']] = dep_data[['prim_diag', 'secd_diag']].replace(list(no_dep_num), 0)\n",
        "dep_data[['prim_diag', 'secd_diag']] = dep_data[['prim_diag', 'secd_diag']].replace(list(dep_num), 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YRDjsNnRf7P6"
      },
      "outputs": [],
      "source": [
        "full = data.dropna(axis = 0, how = 'any')\n",
        "dep = [x for x in data.columns if 'dep' in x or 'diag' in x or 'panic' in x]\n",
        "dep_data['dep'] = dep_data['secd_diag'] + dep_data['prim_diag'] + 0 * dep_data['has_dep_diag']\n",
        "dep_data['dep'] = dep_data['dep'].replace(range(2, 4), 1)\n",
        "full = dep_data.dropna(axis = 0, how = 'any')\n",
        "dep = [x for x in data.columns if 'dep' in x or 'diag' in x or 'panic' in x]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJYPpog5f7P6",
        "outputId": "87661a47-d53c-443d-8ded-69a09124128b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataset shape Counter({0.0: 7726, 1.0: 274})\n"
          ]
        }
      ],
      "source": [
        "has_dep = full.query('dep == 1')\n",
        "no_dep = full.query('dep == 0')\n",
        "size = int(np.round(0.8 * min(len(has_dep), len(no_dep))))\n",
        "sample = full.sample(8000)\n",
        "\n",
        "sample = sample.sort_index()\n",
        "sample = sample.reset_index(drop = True)\n",
        "\n",
        "\n",
        "X_comb = sample.drop(dep, axis = 1).drop('dep', axis = 1)\n",
        "Y_comb = sample['dep']\n",
        "print('dataset shape %s' % Counter(Y_comb))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivblK_Tlf7P7"
      },
      "source": [
        "### dataset 5 - has_dep_diag"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFeDrXggf7P8",
        "outputId": "53ff6c04-2079-4bce-a035-755d2882fba8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13734, 35)\n",
            "(13734,)\n",
            "dataset shape Counter({0.0: 13344, 1.0: 390})\n"
          ]
        }
      ],
      "source": [
        "X_hasdep = data5.drop(['has_dep_diag'],axis=1).drop(['secd_diag'],axis=1).drop(['prim_diag'],axis=1).drop(['dep_score'],axis=1).drop(['dep_thoughts'],axis=1).drop(['panic_score'], axis=1).drop(['dep_band_15'], axis=1)\n",
        "print(X_hasdep.shape)\n",
        "Y_hasdep = np.array(data5['has_dep_diag'])\n",
        "print(Y_hasdep.shape)\n",
        "print('dataset shape %s' % Counter(Y_hasdep))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FATjQcbf7P8"
      },
      "source": [
        "### Over/undersampling to obtain imbalanced and balanced datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YSVv53Gf7P9"
      },
      "source": [
        "Random undersampling, no SMOTE."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJPiW4MGf7P9",
        "outputId": "06cbf4d1-eee9-4a14-e07e-11f8bff594f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4000, 35)\n",
            "(4000,)\n",
            "Resampled dataset shape Counter({0.0: 3760, 1.0: 240})\n",
            "(500, 35)\n",
            "(500,)\n",
            "Resampled dataset shape Counter({0.0: 250, 1.0: 250})\n",
            "(4000, 35)\n",
            "(4000,)\n",
            "Resampled dataset shape Counter({0.0: 3760, 1.0: 240})\n",
            "(500, 35)\n",
            "(500,)\n",
            "Resampled dataset shape Counter({0.0: 250, 1.0: 250})\n"
          ]
        }
      ],
      "source": [
        "import imblearn\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.combine import SMOTEENN \n",
        "from imblearn import under_sampling\n",
        "\n",
        "nb = 250\n",
        "nl = 3760\n",
        "ns = 240\n",
        "\n",
        "\n",
        "#combined, imbalanced (original ratio), undersampling\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\n",
        "X_combined_imb, Y_combined_imb = rus.fit_resample(X_comb, Y_comb)\n",
        "print(X_combined_imb.shape)\n",
        "print(Y_combined_imb.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_combined_imb))\n",
        "\n",
        "#combined, balanced, undersampling\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: nb, 0.0: nb}, random_state=42)\n",
        "X_combined_b, Y_combined_b = rus.fit_resample(X_comb, Y_comb)\n",
        "print(X_combined_b.shape)\n",
        "print(Y_combined_b.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_combined_b))\n",
        "\n",
        "#has_dep_diag, imbalanced (original ratio), undersampling\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\n",
        "X_hasdep_imb, Y_hasdep_imb = rus.fit_resample(X_hasdep, Y_hasdep)\n",
        "print(X_hasdep_imb.shape)\n",
        "print(Y_hasdep_imb.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_hasdep_imb))\n",
        "\n",
        "#has_dep_diag, balanced, undersampling\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: nb, 0.0: nb}, random_state=42)\n",
        "X_hasdep_b, Y_hasdep_b = rus.fit_resample(X_hasdep, Y_hasdep)\n",
        "print(X_hasdep_b.shape)\n",
        "print(Y_hasdep_b.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_hasdep_b))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6L6PLPWff7P-"
      },
      "source": [
        "SMOTE increases the scores but can't be trusted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BJ9AVppHf7P_",
        "outputId": "e52e64aa-1999-4ae8-d68d-2f6e7b461ece"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\"\\n\\nimport imblearn\\nfrom imblearn.over_sampling import SMOTE\\nfrom imblearn.combine import SMOTEENN \\nfrom imblearn import under_sampling\\n\\nnb = 400\\nnl = 3760\\nns = 240\\n\\n\\n#combined, imbalanced (original ratio), undersampling\\nsmoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\\nrus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\\nX_combined_imb, Y_combined_imb = smoteenn.fit_resample(X_comb, Y_comb)\\nprint(X_combined_imb.shape)\\nprint(Y_combined_imb.shape)\\nprint('Resampled dataset shape %s' % Counter(Y_combined_imb))\\nX_combined_imb, Y_combined_imb = rus.fit_resample(X_combined_imb, Y_combined_imb)\\nprint(X_combined_imb.shape)\\nprint(Y_combined_imb.shape)\\nprint('Resampled dataset shape %s' % Counter(Y_combined_imb))\\n\\n#combined, imbalanced (original ratio), oversampling\\n\\n#combined, balanced, undersampling\\nsmoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\\nrus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: nb, 0.0: nb}, random_state=42)\\nX_combined_b, Y_combined_b = smoteenn.fit_resample(X_comb, Y_comb)\\nX_combined_b, Y_combined_b = rus.fit_resample(X_combined_b, Y_combined_b)\\n#rusboost.fit(X_combined_b, Y_combined_b)\\n#Y_combined_b = rusboost.predict(X_combined_b)\\nprint(X_combined_b.shape)\\nprint(Y_combined_b.shape)\\nprint('Resampled dataset shape %s' % Counter(Y_combined_b))\\n\\n#combined, balanced, oversampling\\n\\n#has_dep_diag, imbalanced (original ratio), undersampling\\nsmoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\\nrus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\\nX_hasdep_imb, Y_hasdep_imb = smoteenn.fit_resample(X_hasdep, Y_hasdep)\\nX_hasdep_imb, Y_hasdep_imb = rus.fit_resample(X_hasdep_imb, Y_hasdep_imb)\\nprint(X_hasdep_imb.shape)\\nprint(Y_hasdep_imb.shape)\\nprint('Resampled dataset shape %s' % Counter(Y_hasdep_imb))\\n\\n#has_dep_diag, imbalanced (original ratio), oversampling\\n\\n#has_dep_diag, balanced, undersampling\\nsmoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\\nrus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: nb, 0.0: nb}, random_state=42)\\nX_hasdep_b, Y_hasdep_b = smoteenn.fit_resample(X_hasdep, Y_hasdep)\\nX_hasdep_b, Y_hasdep_b = rus.fit_resample(X_hasdep_b, Y_hasdep_b)\\nprint(X_hasdep_b.shape)\\nprint(Y_hasdep_b.shape)\\nprint('Resampled dataset shape %s' % Counter(Y_hasdep_b))\\n\\n#has_dep_diag, balanced, oversampling\\n\\n\""
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\"\n",
        "\n",
        "import imblearn\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.combine import SMOTEENN \n",
        "from imblearn import under_sampling\n",
        "\n",
        "nb = 400\n",
        "nl = 3760\n",
        "ns = 240\n",
        "\n",
        "\n",
        "#combined, imbalanced (original ratio), undersampling\n",
        "smoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\n",
        "X_combined_imb, Y_combined_imb = smoteenn.fit_resample(X_comb, Y_comb)\n",
        "print(X_combined_imb.shape)\n",
        "print(Y_combined_imb.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_combined_imb))\n",
        "X_combined_imb, Y_combined_imb = rus.fit_resample(X_combined_imb, Y_combined_imb)\n",
        "print(X_combined_imb.shape)\n",
        "print(Y_combined_imb.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_combined_imb))\n",
        "\n",
        "#combined, imbalanced (original ratio), oversampling\n",
        "\n",
        "#combined, balanced, undersampling\n",
        "smoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: nb, 0.0: nb}, random_state=42)\n",
        "X_combined_b, Y_combined_b = smoteenn.fit_resample(X_comb, Y_comb)\n",
        "X_combined_b, Y_combined_b = rus.fit_resample(X_combined_b, Y_combined_b)\n",
        "#rusboost.fit(X_combined_b, Y_combined_b)\n",
        "#Y_combined_b = rusboost.predict(X_combined_b)\n",
        "print(X_combined_b.shape)\n",
        "print(Y_combined_b.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_combined_b))\n",
        "\n",
        "#combined, balanced, oversampling\n",
        "\n",
        "#has_dep_diag, imbalanced (original ratio), undersampling\n",
        "smoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\n",
        "X_hasdep_imb, Y_hasdep_imb = smoteenn.fit_resample(X_hasdep, Y_hasdep)\n",
        "X_hasdep_imb, Y_hasdep_imb = rus.fit_resample(X_hasdep_imb, Y_hasdep_imb)\n",
        "print(X_hasdep_imb.shape)\n",
        "print(Y_hasdep_imb.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_hasdep_imb))\n",
        "\n",
        "#has_dep_diag, imbalanced (original ratio), oversampling\n",
        "\n",
        "#has_dep_diag, balanced, undersampling\n",
        "smoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: nb, 0.0: nb}, random_state=42)\n",
        "X_hasdep_b, Y_hasdep_b = smoteenn.fit_resample(X_hasdep, Y_hasdep)\n",
        "X_hasdep_b, Y_hasdep_b = rus.fit_resample(X_hasdep_b, Y_hasdep_b)\n",
        "print(X_hasdep_b.shape)\n",
        "print(Y_hasdep_b.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_hasdep_b))\n",
        "\n",
        "#has_dep_diag, balanced, oversampling\n",
        "\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3pTNp-Omf7P_",
        "outputId": "eab0008e-cbc7-4ce0-faf3-d884a08b33d6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\"\\nnl = 3760\\nns = 240\\n\\nsvm.fit(X_train_comb_imb, y_train_comb_imb)\\ny_pred = svm.predict(X_test_comb_imb)\\nprint(f1_score(y_pred, y_test_comb_imb, average = 'macro'))\\nprint(f'accuracy: {svm.score(X_test_comb_imb, y_test_comb_imb)}')\\nprint(f'recall: {recall_score(y_pred, y_test_comb_imb)}')\\nprint(f'precision: {precision_score(y_pred, y_test_comb_imb)}')\\n\\n#combined, imbalanced (original ratio), undersampling\\nsmoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\\nrus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\\nX_combined_imb, Y_combined_imb = smoteenn.fit_resample(X_comb, Y_comb)\\nprint(X_combined_imb.shape)\\nprint(Y_combined_imb.shape)\\nprint('Resampled dataset shape %s' % Counter(Y_combined_imb))\\nX_combined_imb, Y_combined_imb = rus.fit_resample(X_combined_imb, Y_combined_imb)\\nprint(X_combined_imb.shape)\\nprint(Y_combined_imb.shape)\\nprint('Resampled dataset shape %s' % Counter(Y_combined_imb))\\n\\n#combined, imbalanced (original ratio), undersampling\\nX_train_comb_imb, X_test_comb_imb, y_train_comb_imb, y_test_comb_imb = split_dataset(0.2, Y_combined_imb.size, X_combined_imb, Y_combined_imb)\\nprint(X_train_comb_imb.shape); print(X_test_comb_imb.shape)\\nprint(y_train_comb_imb.shape); print(y_test_comb_imb.shape)\\n\\nsvm = SVC(C=5, gamma='auto', kernel='rbf')\\n\\n#combined variable\\n\\n#train imbalanced, test imbalanced\\nsvm.fit(X_train_comb_imb, y_train_comb_imb)\\ny_pred = svm.predict(X_test_comb_imb)\\nprint(f1_score(y_pred, y_test_comb_imb, average = 'macro'))\\nprint(f'accuracy: {svm.score(X_test_comb_imb, y_test_comb_imb)}')\\nprint(f'recall: {recall_score(y_pred, y_test_comb_imb)}')\\nprint(f'precision: {precision_score(y_pred, y_test_comb_imb)}')\\n\\n\""
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\"\n",
        "nl = 3760\n",
        "ns = 240\n",
        "\n",
        "svm.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = svm.predict(X_test_comb_imb)\n",
        "print(f1_score(y_pred, y_test_comb_imb, average = 'macro'))\n",
        "print(f'accuracy: {svm.score(X_test_comb_imb, y_test_comb_imb)}')\n",
        "print(f'recall: {recall_score(y_pred, y_test_comb_imb)}')\n",
        "print(f'precision: {precision_score(y_pred, y_test_comb_imb)}')\n",
        "\n",
        "#combined, imbalanced (original ratio), undersampling\n",
        "smoteenn = SMOTEENN(random_state = 42, sampling_strategy=0.1)\n",
        "rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\n",
        "X_combined_imb, Y_combined_imb = smoteenn.fit_resample(X_comb, Y_comb)\n",
        "print(X_combined_imb.shape)\n",
        "print(Y_combined_imb.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_combined_imb))\n",
        "X_combined_imb, Y_combined_imb = rus.fit_resample(X_combined_imb, Y_combined_imb)\n",
        "print(X_combined_imb.shape)\n",
        "print(Y_combined_imb.shape)\n",
        "print('Resampled dataset shape %s' % Counter(Y_combined_imb))\n",
        "\n",
        "#combined, imbalanced (original ratio), undersampling\n",
        "X_train_comb_imb, X_test_comb_imb, y_train_comb_imb, y_test_comb_imb = split_dataset(0.2, Y_combined_imb.size, X_combined_imb, Y_combined_imb)\n",
        "print(X_train_comb_imb.shape); print(X_test_comb_imb.shape)\n",
        "print(y_train_comb_imb.shape); print(y_test_comb_imb.shape)\n",
        "\n",
        "svm = SVC(C=5, gamma='auto', kernel='rbf')\n",
        "\n",
        "#combined variable\n",
        "\n",
        "#train imbalanced, test imbalanced\n",
        "svm.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = svm.predict(X_test_comb_imb)\n",
        "print(f1_score(y_pred, y_test_comb_imb, average = 'macro'))\n",
        "print(f'accuracy: {svm.score(X_test_comb_imb, y_test_comb_imb)}')\n",
        "print(f'recall: {recall_score(y_pred, y_test_comb_imb)}')\n",
        "print(f'precision: {precision_score(y_pred, y_test_comb_imb)}')\n",
        "\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZnVMvizaf7QA"
      },
      "outputs": [],
      "source": [
        "def plot_smote_to_F1(smote_ratio_start, smote_ratio_end, ns, nl):\n",
        "    xs = np.array([])\n",
        "    ys = np.array([])\n",
        "    for n in range(smote_ratio_start, smote_ratio_end):\n",
        "        nd = n/100\n",
        "        xs = np.append(xs, nd)\n",
        "        #combined, imbalanced (original ratio), undersampling\n",
        "        smoteenn = SMOTEENN(random_state = 42, sampling_strategy=nd)\n",
        "        rus = under_sampling.RandomUnderSampler(sampling_strategy={1.0: ns, 0.0: nl}, random_state=42)\n",
        "        X_combined_imb, Y_combined_imb = smoteenn.fit_resample(X_comb, Y_comb)\n",
        "        print('Resampled dataset shape %s' % Counter(Y_combined_imb))\n",
        "        X_combined_imb, Y_combined_imb = rus.fit_resample(X_combined_imb, Y_combined_imb)\n",
        "\n",
        "        #train-test split\n",
        "        X_train_comb_imb, X_test_comb_imb, y_train_comb_imb, y_test_comb_imb = split_dataset(0.2, Y_combined_imb.size, X_combined_imb, Y_combined_imb)\n",
        "\n",
        "        svm = SVC(C=5, gamma='auto', kernel='rbf')\n",
        "\n",
        "        #train imbalanced, test imbalanced\n",
        "        svm.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "        y_pred = svm.predict(X_test_comb_imb)\n",
        "        print(f1_score(y_pred, y_test_comb_imb, average = 'macro'))\n",
        "        ys = np.append(ys, f1_score(y_pred, y_test_comb_imb, average = 'macro'))\n",
        "        \n",
        "    plt.plot(xs, ys)\n",
        "    plt.title('SMOTE ratio to F1 score')\n",
        "    plt.xlabel('SMOTE ratio')\n",
        "    plt.ylabel('F1 score')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "V8AJms7yf7QB",
        "outputId": "a9d614fa-88dd-4eed-be71-d34d22510b77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resampled dataset shape Counter({0.0: 6506, 1.0: 467})\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-39f616a37249>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_smote_to_F1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m240\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3760\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-13-65f6c5a4b511>\u001b[0m in \u001b[0;36mplot_smote_to_F1\u001b[0;34m(smote_ratio_start, smote_ratio_end, ns, nl)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;31m#train-test split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mX_train_comb_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_comb_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_comb_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_comb_imb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msplit_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_combined_imb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_combined_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_combined_imb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0msvm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rbf'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'split_dataset' is not defined"
          ]
        }
      ],
      "source": [
        "plot_smote_to_F1(10, 50, 240, 3760)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pfm39HO5f7QC"
      },
      "source": [
        "### Train/test split function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iu37XkXTf7QC"
      },
      "outputs": [],
      "source": [
        "from random import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def shuffle_dataset(N, X, y, X_shuffled, y_shuffled):\n",
        "    ind_list = [i for i in range(N)]\n",
        "    shuffle(ind_list)\n",
        "    X_shuffled  = X.iloc[ind_list]\n",
        "    y_shuffled = y.iloc[ind_list]\n",
        "    \n",
        "def split_dataset(split, N, X, y):\n",
        "    X_shuffled = X\n",
        "    y_shuffled = y\n",
        "    shuffle_dataset(N, pd. DataFrame(X), pd. DataFrame(y), pd. DataFrame(X_shuffled), pd. DataFrame(y_shuffled))\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X_shuffled, y_shuffled,test_size=split, random_state=40)\n",
        "    return X_train, X_test, y_train, y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oI78pkGTf7QD",
        "outputId": "28e0638a-8f31-4b53-bf73-7b1f5f2025a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(3200, 35)\n",
            "(800, 35)\n",
            "(3200,)\n",
            "(800,)\n",
            "(400, 35)\n",
            "(100, 35)\n",
            "(400,)\n",
            "(100,)\n",
            "(3200, 35)\n",
            "(800, 35)\n",
            "(3200,)\n",
            "(800,)\n",
            "(400, 35)\n",
            "(100, 35)\n",
            "(400,)\n",
            "(100,)\n"
          ]
        }
      ],
      "source": [
        "#combined, imbalanced (original ratio), undersampling\n",
        "X_train_comb_imb, X_test_comb_imb, y_train_comb_imb, y_test_comb_imb = split_dataset(0.2, Y_combined_imb.size, X_combined_imb, Y_combined_imb)\n",
        "print(X_train_comb_imb.shape); print(X_test_comb_imb.shape)\n",
        "print(y_train_comb_imb.shape); print(y_test_comb_imb.shape)\n",
        "\n",
        "#combined, balanced, undersampling\n",
        "X_train_comb_b, X_test_comb_b, y_train_comb_b, y_test_comb_b = split_dataset(0.2, Y_combined_b.size, X_combined_b, Y_combined_b)\n",
        "print(X_train_comb_b.shape); print(X_test_comb_b.shape)\n",
        "print(y_train_comb_b.shape); print(y_test_comb_b.shape)\n",
        "\n",
        "#has_dep_diag, imbalanced (original ratio), undersampling\n",
        "X_train_hasdep_imb, X_test_hasdep_imb, y_train_hasdep_imb, y_test_hasdep_imb = split_dataset(0.2, Y_hasdep_imb.size, X_hasdep_imb, Y_hasdep_imb)\n",
        "print(X_train_hasdep_imb.shape); print(X_test_hasdep_imb.shape)\n",
        "print(y_train_hasdep_imb.shape); print(y_test_hasdep_imb.shape)\n",
        "\n",
        "#has_dep_diag, balanced, undersampling\n",
        "X_train_hasdep_b, X_test_hasdep_b, y_train_hasdep_b, y_test_hasdep_b = split_dataset(0.2, Y_hasdep_b.size, X_hasdep_b, Y_hasdep_b)\n",
        "print(X_train_hasdep_b.shape); print(X_test_hasdep_b.shape)\n",
        "print(y_train_hasdep_b.shape); print(y_test_hasdep_b.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEYaAh93f7QD"
      },
      "source": [
        "## Plotting Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwc08_84f7QE"
      },
      "source": [
        "Learning curve:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "wZthEr4Bf7QE"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.metrics import f1_score, make_scorer\n",
        "\n",
        "scorer = make_scorer(f1_score, average = 'macro')\n",
        "\n",
        "\n",
        "#plot learning curve function from the labs\n",
        "def plot_learning_curve(n, est, xs, ys, title):\n",
        "    train_sizes, train_scores, valid_scores = learning_curve(estimator = est, X = xs, y = ys, cv = n, scoring=scorer)\n",
        "\n",
        "    train_mean = np.mean(train_scores, axis=1)\n",
        "    valid_mean = np.mean(valid_scores, axis=1)\n",
        "\n",
        "    plt.plot(train_sizes, train_mean, color='blue', label='Training score')\n",
        "    plt.plot(train_sizes, valid_mean, color='green', label='Validation score')\n",
        "\n",
        "    plt.xlabel('Dataset size')\n",
        "    plt.ylabel('F1 score')\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKva0B7Sf7QF"
      },
      "source": [
        "ROC Curve:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "H2ENIxfuf7QF"
      },
      "outputs": [],
      "source": [
        "import sklearn.metrics\n",
        "\n",
        "def plot_roc(model, X_train, y_train, X_test, y_test, title):\n",
        "    model.fit(X_train, y_train)\n",
        "    preds = model.predict(X_test)\n",
        "    fpr, tpr, threshold = sklearn.metrics.roc_curve(y_test, preds)\n",
        "    roc_auc = sklearn.metrics.auc(fpr, tpr)\n",
        "\n",
        "\n",
        "    plt.title(title)\n",
        "    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "    plt.legend(loc = 'lower right')\n",
        "    plt.plot([0, 1], [0, 1],'r--')\n",
        "    plt.xlim([0, 1])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAD_wQvzf7QF"
      },
      "source": [
        "Confusion Matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "3HyizqyUf7QG"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "#plot_confusion_matrix(model, X_test, y_test)  \n",
        "def plot_cf(model, X_train, y_train, X_test, y_test):\n",
        "    model.fit(X_train, y_train)\n",
        "    plot_confusion_matrix(model, X_test, y_test)  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lxjWXk0f7QG"
      },
      "source": [
        "Print precision, recall, F1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ivEcHD4Jf7QG"
      },
      "outputs": [],
      "source": [
        "def print_metrics(model, X_train, y_train, X_test, y_test, model_name):\n",
        "    model.fit(X_train, y_train)\n",
        "    preds = model.predict(X_test)\n",
        "    f1 = f1_score(preds, y_test, average = 'macro')\n",
        "    print(model_name + ': ')\n",
        "    print(f'F1: {f1}')\n",
        "    recall = recall_score(y_test, preds, average = 'macro')\n",
        "    print(f'recall: {recall}')\n",
        "    precision = precision_score(y_test, preds, average = 'macro')\n",
        "    print(f'precision: {precision}')\n",
        "    print(f'accuracy: {model.score(X_test, y_test)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shOg37IOf7QG"
      },
      "source": [
        "## Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "la8pYXvkf7QH"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "szTQJT_Af7QH"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "clf = LogisticRegression(penalty = 'l2', max_iter = 1000, C = 1e-07, solver = 'newton-cg')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qyS83zFQf7QI",
        "outputId": "807076c6-8e02-42f2-8ad6-df7487a1f4ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic regression, combined, imbalanced: \n",
            "F1: 0.4842037395228885\n",
            "recall: 0.5\n",
            "precision: 0.469375\n",
            "accuracy: 0.93875\n",
            "Logistic regression, combined, balanced: \n",
            "F1: 0.5689223057644109\n",
            "recall: 0.5700000000000001\n",
            "precision: 0.5707070707070707\n",
            "accuracy: 0.57\n",
            "Logistic regression, has_dep_diag, imbalanced: \n",
            "F1: 0.4842037395228885\n",
            "recall: 0.5\n",
            "precision: 0.469375\n",
            "accuracy: 0.93875\n",
            "Logistic regression, has_dep_diag, balanced: \n",
            "F1: 0.6096486838154338\n",
            "recall: 0.61\n",
            "precision: 0.6103974307507025\n",
            "accuracy: 0.61\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/scipy/optimize/linesearch.py:327: LineSearchWarning: The line search algorithm did not converge\n",
            "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed\n",
            "  warnings.warn(\"Line Search failed\")\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/scipy/optimize/linesearch.py:327: LineSearchWarning: The line search algorithm did not converge\n",
            "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed\n",
            "  warnings.warn(\"Line Search failed\")\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/karolina/anaconda3/lib/python3.8/site-packages/scipy/optimize/linesearch.py:327: LineSearchWarning: The line search algorithm did not converge\n",
            "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed\n",
            "  warnings.warn(\"Line Search failed\")\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/scipy/optimize/linesearch.py:327: LineSearchWarning: The line search algorithm did not converge\n",
            "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed\n",
            "  warnings.warn(\"Line Search failed\")\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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f+1l1XX/mbqhGgcsHM3DejTRrdqPXYZ0Xa/IyxhiPLHh8EvHVa9J4w1eEt1CWLYNmzbyO6vxZDcUYY4JsV9QONtzwME23f0dMgQbs+/hnWnet53VYF8xqKMYYEySq8Mkn0DVsO3W2z+SPtq9y2b6FXJ4FkglYQjHGmKDYOmcToy5/j3vugYQrG7Bz0VZaTXuKXPmyTkNR1nknxhiTAZ05dYa5t42iwXeD6EEOCr3cmR5PlSVHjsxxK3BaWA3FGGMC5O+pq1lVsgUtv3uUNSXDODI/mp4Dy5Iji555s+jbMsYY75w+Da8+d5wiN7agwtE1zLv/Mxrsmkb5ppW8Di2grMnLGGPSUfS3a7h9WA1WrCxAfIsv6T2qHs2vKON1WEFhNRRjjEkHJ/afIKLxAGp2qk3Yli/5/nt4ZtZ1lMomyQSshmKMMRds+XuzKfJ4L8JP/8XsGr14cWZ7imasR5UEhdVQjDHmPB0+DFMbDqPeIy3JqfEsfe1XWqz5kKKVi3kdmieshmKMMedh2k9KnweEultDKHRVfxpOH06l0gW9DstTllCMMSYN9q3dy+q2/Vm4sTqFaw3h2QU30KTJDV6HlSFYk5cxxvhBE5T5/b4moWYtGm+cSMvwHCxdCk2aeB1ZxmE1FGOMScXOpdvZdMODNNs5mZgCIRz49Fdad6rrdVgZjtVQjDEmGaowbhx0bbmTWjt/Z1b717ls3wIus2SSJEsoxhiThC0RG3i/xtvcdx9Ig6vYu2QLLX98Ikt15pjerGSMMcbHmVNnmNPlXRpNfoY7yE2R17px++NlyZGjmNehZXhWQzHGGNf6yatYXbw54ZMfI7r01RxftIo7n8y6nTmmNysmY0y2d+oUvPTscYrd3JKyx/9m/sNf0XDHj1zcsILXoWUq1uRljMnWVv4vhu7DaxK9qgBcPZHeI+vRrGYpr8PKlKyGYozJlo7vPU5Ewyep1a0OV2//gilTYNBv11DSksl5sxqKMSbbiXo7gmJP3Uf46fXMrnk/z8+4iaJZ+1ElQWE1FGNMtnHoEExt8Bz1+7dCVFn2xu+0iBlN0UpFvQ4tS7AaijEmW/hxivLAg8KV2xtROORxGk5/nsolC3gdVpYS0BqKiLQRkbUisl5EBiYxvaiI/Cgiy0VklYjcHch4jDHZz97Ve5hXpTuRHZ7nootg8MIbaLl4BAUsmaS7gCUUEckJjALaArWA20SkVqLZHgJiVLUeEA68ISJ5AhWTMSb70ARl3kNfIbVr0nDzJFq0zsOSJdCokdeRZV2BbPJqBKxX1Q0AIjIR6ADE+MyjQGEREaAQsB+ID2BMxphsYMfiWLa2f4Dmu6cSXbAxB778iNYdansdVpYXyCav8sBWn+FYd5yvkUBNYDuwEnhUVRMSr0hEeotIpIhE7tmzJ1DxGmMyuYQEGDMGOofvocbu2UR0eJOa++dRzZJJUAQyoUgS4zTR8PVAFFAOqA+MFJEi/1lIdayqhqhqSKlSdo+4Mea/Nv+2ng8ue4s+fSBvkyvZH7WV8B/6kzNPTq9DyzYCmVBigYo+wxVwaiK+7ga+U8d6YCNweQBjMsZkMfFx8US0H0Hpa+pw+9/D+OKNXfz6K1St95/vpibAAplQFgPVRaSqe6G9GzAl0TxbgNYAIlIGqAFsCGBMxpgsZN23K1lbohnhPz3JirLXcWLxKm5/rAySVPuICbiAJRRVjQceBmYCq4GvVXWViPQRkT7ubMOBZiKyEvgNGKCqewMVkzEmazh5El4YdJzinVpR+sQmFjw6kUbbfuDikMSXaU0wBfSHjao6DZiWaNxon9fbgesCGYMxJmtZ/mU03V+sTczqAuRs/T/uf78eTS8r6XVYBut6xRiTSRzbfYyIBo9R5466XLPzC376CZ7+tTXFLZlkGNb1ijEmw1v6+m+UHHQf4fEbmVX7QYbP6EARe1RJhmM1FGNMhnXwIEy9cjBXPXUNZyQXy9+dRcvoURSpYHdwZUSWUIwxGdLk7xOoVQs+WN6MiEZPUXbncur1beF1WCYF1uRljMlQdkfvZn27R4jaWoNSdYcxbEpbQkLaeh2W8YPVUIwxGYImKPMe+IJcdWvSYOv3hF1XgMhICAnxOjLjL6uhGGM8t23hVrbd1Ifme6axslBTCnw1jqtvTNw5ucnorIZijPFMQgJ88AF0vnofl+2Zx6xb36HWvjlcaskkU7KEYozxxMaZ6xhdbQQPPggFm9fn4IqttPz2EevMMROzJi9jTFDFx8Uz95Y3aDLjObpLfi566066PVoGkcJeh2YukNVQjDFBs/br5fxVvDHhMwYSdXE7Ti6N4bZ+1pljVmEJxRgTcHFxMGzAcUp0bU2Jk9tY8MQkmmz/jjL1L/Y6NJOOrMnLGBNQUZ+t4LaX6rBmbQHyXveN05njpcW9DssEgNVQjDEBcXTnUWbVf5S6d9WnzZ7PmTEDBs5sxUWWTLIsq6EYY9Jd5Mu/UGZwb1qe2cSsOg/z/IxbKFzO66hMoFkNxRiTbg4cgB/rPUPIoOs4nTMvK0bNoeWK9yhczu7gyg78TigiUjCQgRhjMrfvJjmdOY6JDiWi6dOU2xVF3QdDvQ7LBFGqCUVEmolIDM5jfBGReiLyfsAjM8ZkCrtX7GRBhU5Edx5K2bIwPLIt4fNfIl+xfF6HZoLMnxrKW8D1wD4AVV0OWB/SxmRzmqDM7TWe3PVrceW2qTRvW4RFi+DKK72OzHjFr4vyqrpV/v3LozOBCccYkxnEztvMrg69Cd33M8uLhFJ44jhat63hdVjGY/7UULaKSDNARSSPiDyB2/xljMleEhJg5EjodM1Bqu5bzKzOI6mzbxaXWDIx+JdQ+gAPAeWBWKA+8GAAYzLGZEAbpq/lg0tep29fKNayHkdXbaHl1w+RI5fdLGoc/jR51VDV231HiEhzYF5gQjLGZCSnj59m7i0jaPrzMG6TgpR67y46P1QakUJeh2YyGH++Wrzn5zhjTBazZsIy1pdsTKufB7Gs/I2cWRFDl4dLW2eOJknJ1lBEpCnQDCglIo/5TCoC2AMLjMnC4uLgpWeP88gb13ImR24WPvktTV+71euwTAaXUpNXHqCQO4/vz1wPA50CGZQxxjvLPl5Gt1fqs+6vAhRsM4n7369Hk6oXeR2WyQSSTSiqOguYJSLjVXVzEGMyxnjgyPYjLG3zNC1XjqJ9yU9p83MPrr023OuwTCbiz0X54yLyOlAb+Oenr6p6dcCiMsYEVeQLMyg79H7CzmxlVv1HGTb9VgqV9Toqk9n4c1H+S2ANUBUYBmwCFgcwJmNMkOzbB1PrPE3I4LaczFmQVWPm0XLZ2xQqa3dwmbTzp4ZSQlU/EpFHfZrBZgU6MGNM4KjCt1+f4aFHctJgbziFQnPR9KdnyVskr9ehmUzMn4Ry2v2/Q0RuALYDFQIXkjEmkHZF7WBju4dYvaM2FRsM5+Wfr6deveu9DstkAf4klBdEpCjwOM7vT4oA/QIZlDEm/Z3tzLHO+Meop3EcvyGUp3+AXPaYPZNOUt2VVHWq+/IQ0Ar++aW8MSaT2DpnE7tvvo+w/b+yvEgYRb4ex9XXX+Z1WCaLSfaivIjkFJHbROQJEbnCHddeROYDI4MWoTHmvJ05A++8A52vO0TV/UuZ3e196uyLoKolExMAKd3l9RHQCygBvCsinwAjgNdU1a8nHohIGxFZKyLrRWRgMvOEi0iUiKyyi/3GpJ/1U2IYe8kr9OsHxVvV4/iaLbSY8IB15mgCJqUmrxCgrqomiEg+YC9QTVV3+rNiEckJjAKuxemleLGITFHVGJ95igHvA21UdYuIlD7P92GMcZ0+dop5HV6j6W/D6SKFKTXqHjo+UBp7ircJtJS+qpxS1QQAVY0D1vmbTFyNgPWqukFVTwETgQ6J5ukOfKeqW9zt7E7D+o0xiaz+PJINJRsS/ttgIivdSsLKGDo9aJ05muBIKaFcLiIr3L+VPsMrRWSFH+suD2z1GY51x/m6DLhIRCJEZImI9EhqRSLSW0QiRSRyz549fmzamOzlxAl4tv8xSve4niKn9vLnoMk03zyBUrWt0m+CJ6Umr5oXuO6kvhNpEttvALQG8gMLRGShqq7710KqY4GxACEhIYnXYUy2tuTDpdz2an3++rsgRW74nvtH1aVx5WJeh2WyoZQ6h7zQDiFjgYo+wxVwfhSZeJ69qnoMOCYis4F6wDqMMSk6HHuYZdcPpGXMB9xU6lPa/tqD1q1beB2WycYCebvHYqC6iFQVkTxAN2BKonkmA2EikktECgCNsefVG5OqxcOmcbRKbUJjxhDR4DGGRXekdWuvozLZXcB+I6uq8SLyMDAT54FcH6vqKhHp404fraqrRWQGsAJIAMapanSgYjIms9u7Fxa0HMCNMa+xPm8tVo+ZRPi9jb0OyxjAz4QiIvmBSqq6Ni0rV9VpwLRE40YnGn4deD0t6zUmu9EE5Zv/JfDwozlpsK81hVvko+mPg6wzR5OhpNrkJSI3AlHADHe4vogkbroyxgTIjshtLCp/M+u6P0flyvBa1HWEzxpmycRkOP5cQxmK85uSgwCqGgVUCVRAxhiHJiize3xIgYa1qLvzZ5rfVJIFC6BOHa8jMyZp/jR5xavqIbFfRhkTNJsjNrL/lntpcfAPlhULp/ikD2nVuprXYRmTIn9qKNEi0h3IKSLVReQ9YH6A4zImWzpzBt58Ezq1OUqlQyuYffsY6u35jcqWTEwm4E9C6YvzPPmTwFc43dj3C2BMxmRLf30fzZgqL/H441D22jrErd1Ciy96W2eOJtPwp8mrhqo+AzwT6GCMyY5OHT3F/BtfplnEi3SRopT9oBe33F8a56dZxmQe/nz1eVNE1ojIcBGpHfCIjMlGVo1fzOaSDQiPGMriyp1hVQy39rHOHE3mlGpCUdVWQDiwBxjrdg75bKADMyYrO34cBj16jLJ3t6HQ6QMsGjyF5pu+pGTNUl6HZsx586txVlV3quq7QB+c36QMCWRQxmRlkaMjqXtFAi+/W5CPb5pMgY2raPT8jV6HZcwF8+eHjTVFZKiIROM8+nc+TkePxpg0OLTlELNr3k/IAw25+dgX/PEHPDk5lKKVinodmjHpwp+L8p8AE4DrVDVxb8HGGD8sGvwjFV/qQ/OEnUSEPMHz0ztRoKTXURmTvlJNKKraJBiBGJMV7dkDf7Z4kvZrRrAuXx0OjP6B8Lsaeh2WMQGRbEIRka9VtYv7tEbfh1oJoKpaN+DRGZNJaYIy8csz9O2fi4YHr6NgqyI0nzKAPIXyeB2aMQGTUg3lUfd/+2AEYkxWsX1RLLE3PsCG3XWp1vhFRnx0LbVrX+t1WMYEXLIX5VV1h/vyQVXd7PsHPBic8IzJPBLiE5h9+xgKNa5F7d2/0+yWssybB7Xt11smm/DntuGkvlq1Te9AjMnMNv62gRUlr6bFV31Yf1Ej9v2xklbf9SVnTq8jMyZ4kk0oIvKAe/2khois8PnbiPOERWOyvfh4GDECOrc7RvnDMcy5axxX7v2FSuGXeB2aMUGX0jWUr4DpwMvAQJ/xR1R1f0CjMiYTWPftSn57ZDJPbn+WDh3qcPqNzYRdmt/rsIzxTEoJRVV1k4g8lHiCiBS3pGKyq5OHT7Kg/Ys0n/MyneQiyo3tzU29SuM8KduY7Cu1Gkp7YAnObcO+3dUpYHV6k+1Ej1tIvofvJfxkDHMvuZNaM96iQ/USXodlTIaQbEJR1fbu/6rBC8eYjOnYMXh+wDGeGnUDcTkLsnjYNEKH2L0pxvjypy+v5iJS0H19h4i8KSKVAh+aMRnD4pF/UveKBF4bVZBPbvmRwptX0dCSiTH/4c9twx8Ax0WkHvAUsBn4PKBRGZMBHNp8kDk1etGwbxM6nviCWbPgie+aUaR8Ya9DMyZD8iehxKuqAh2Ad1T1HcCOKJOl/fn0D5y4pBZN140novEAhsV0pkULr6MyJmPzp7fhIyLyNHAnECYiOYHcgQ3LGG/s2gWLwx6j/V9vsTZfPQ58+CPhdzTwOixjMgV/EkpXoDtwj6rudK+fvB7YsIwJLk1QvvzsDI8+notGh9tR6JoSNJ/8FLkL2HcnY/zlzyOAdwJfAkVFpD0Qp6qfBTwyY4Jk24ItRJa9gc13P0eNGvDmimsI/+UZSybGpJE/d3l1ARYBnYEuwJ8i0inQgRkTaAnxCczq9j5FmtWm1p5ZNO9UjjlzoGZNryMzJnPyp8nrGaChqu4GEJFSwK/ApEAGZkwgbfxlPYc73UPLw3NYUvxaykweS3hoFa/DMiZT8+curxxnk4lrn5/LGZPhxMfDq69CxxviKHtkHXPv/YSr9sykgiUTYy6YPzWUGSIyE+e58uBcpJ8WuJCMCYy1/4vij/6TGbjjOW655QoS3thEaNV8XodlTJbhzzPlnxSRW4FQnP68xqrq9wGPzJh0EncwjoU3DCd0/qtclKMkFT56gPb3lAYsmRiTnlJ6pnx1YARwKbASeEJVtwUrMGPSw8ox8ynwyL2En1rD3EvvovbMN2l/aXGvwzImS0rpWsjHwFSgI06Pw+8FJSJj0sHRo/DEA8co1+dG8p45TuQLMwhdP56LLJkYEzApNXkVVtUP3ddrRWRpMAIy5kL9+fYCur3VmM1bC3Jxx6n0fvcKKpSz3oKMCbSUaij5RORKEblKRK4C8icaTpWItBGRtSKyXkQGpjBfQxE5Y79vMRfiwIYDzKl+D437N6Prqc+ZPRsen9SUwpZMjAmKlGooO4A3fYZ3+gwrcHVKK3b7/BoFXAvEAotFZIqqxiQx36vAzLSFbsw5C578jkvefIimCXuIaPo0Q6d1JV8xr6MyJntJ6QFbrS5w3Y2A9aq6AUBEJuL0WByTaL6+wLdAwwvcnsmGdu6EyLD+tF//Nmvy1+fAR9MIv+1Kr8MyJlvy53co56s8sNVnOBZo7DuDiJQHbsGp7SSbUESkN9AboFIle7aXcTpz/OyTM/R/MheNj7an0PWlaf7dE9b/ljEeCuQv3iWJcZpo+G1ggKqeSWlFqjpWVUNUNaRUqVLpFZ/JpGLnbmJJ6TZs7zWY2rXh7ZWtCZ/xtCUTYzwWyBpKLFDRZ7gCsD3RPCHARBEBKAm0E5F4Vf0hgHGZTCohPoE53UbR4NunKYpwtOstDPgKclhHQMZkCKkmFHHO9rcDl6jq8+7zUMqq6qJUFl0MVBeRqsA2oBvOc1X+oapVfbYzHphqycQk5e8Zf3Gsy920PDKPxSXbcPEPowlvXtnrsIwxPvz5bvc+0BS4zR0+gnP3VopUNR54GOfurdXA16q6SkT6iEif84zXZDOnT8NLL0HHG09R+ujfzO39GSG7plHBkokxGY4/TV6NVfUqEVkGoKoHRCSPPytX1Wkk6khSVUcnM29Pf9Zpso/VXy1j1mOTeWbXUDp3ro2M2ERopbxeh2WMSYY/CeW0+1sRhX+eh5IQ0KhMtnbiQBx/thtG6MLXKZ6jFBU/eYgbepYCLJkYk5H50+T1LvA9UFpEXgTmAi8FNCqTba14fy47y9QjfOErLKzeg7zrY9xkYozJ6Pzpvv5LEVkCtMa5FfhmVV0d8MhMtnLkCDz3+FGe+bADx3MVYekrPxM64FqvwzLGpIE/d3lVAo4DP/qOU9UtgQzMZB8LR8yl6zvN2LqtEBW7/MR971xBxbKFvA7LGJNG/lxD+Qnn+ongPJGoKrAWqB3AuEw2sP+vfcS06U/ohs/pfvF4bpp3F02bNvE6LGPMefKnyauO77Db0/D9AYvIZHmaoCx8YhLV3n6YxrqfiLDBDJ3ajbxFvI7MGHMh0vxLeVVdKiLWkaM5Lzt2QGRof27c8A4xBRqw/5OfCe9Sz+uwjDHpwJ9rKI/5DOYArgL2BCwikyVpgjJ+XDz9n8pNk+M3UaRdOZp/+xi58gWy9x9jTDD5czT7Pp0oHueayreBCcdkRVtnb2T3Lb3Ztb8B9Vq8wrsfXs1ll6X4OB1jTCaUYkJxf9BYSFWfDFI8Jgs5c+oMc7uNJOT7QRQlJ0e7d+apz60zR2OyqmQTiojkUtV4fx/3a4yv9dPWcaJrT1oeXcDiUm0p/+MYWjaumPqCxphMK6UayiKc6yVRIjIF+AY4dnaiqn4X4NhMJnTqFLz6KkwaHs+M+M3Me+ALmo3sjuRI6vE4xpisxJ9rKMWBfThPVTz7exQFLKGYf4n5LJK5T05myO7hdOtWi5yvbaB5Ret/y5jsIqWEUtq9wyuac4nkrMRPXjTZ2In9J/izzXOELX6Di3KUpdJnj9DmTuvM0ZjsJqXLozmBQu5fYZ/XZ/+MIeqdWewqW5fwxa8z7/J7KbBhlZtMjDHZTUo1lB2q+nzQIjGZyuHDMOSxowz+6FaO5irGshG/0eJxuxXYmOwspYRiV1FNkua/Oodu7zVn245CVLltOve9XZvKpQt6HZYxxmMpJZTWQYvCZAr71u5lTZt+NN/0JXeUH0+H+XfRuHEjr8MyxmQQyV5DUdX9wQzEZFyaoMx79H8k1KxFo03/I6Llcwxd3Y3Gjb2OzBiTkVhHSiZF27bBsrBHab/xPVYVbMiBT38jvGOd1Bc0xmQ7llBMkjRB+XjMaR4bmIemcbdQqH1lwr7tR848Ob0OzRiTQVlCMf+x+fe/2d/xPvYdDOGq8NcY+WErqlVr5XVYxpgMzrrpM/84c+oMER3epFTrOlxycAlNetTgt9+gWjWvIzPGZAZWQzEArJuyhlPd7yL82CL+LHMjlaZ+QIuQ8l6HZYzJRKyGks2dOgXDhkHnjglcdGI78/tOoNH2yVxsycQYk0ZWQ8nGVn2yiHkDJjN0z4t0716LvK//TbNyebwOyxiTSVlCyYaO7z3OojZDCFvyFsVzXEyVL/px3e2lAEsmxpjzZ01e2cyyN/9gz8V1CF/yBvNq3keBjavcZGKMMRfGEko2cegQ9L37KBUf7wwiRL31By1iRlO0UlGvQzPGZBHW5JUNzH8pgi4jW7BjVyGq3zGdXm/VpnLJAl6HZYzJYiyhZGF7V+9hXZtHaLZlIndV+JRb/uxBSEhDr8MyxmRR1uSVBWmCMu+hr5DaNWmw5Tv+uHo4z63uRkiI15EZY7Iyq6FkMVu3wvKwvrTfPIqVhZqQ/8uPaHVTLa/DMsZkA5ZQsoiE+ATGjY7niUF5aHaqE4Vvrkbo//paZ47GmKAJaJOXiLQRkbUisl5EBiYx/XYRWeH+zReReoGMJ6va9MtfrCh5NYf6PkOjRvB+TDgtv7eegY0xwRWwhCIiOYFRQFugFnCbiCRue9kItFTVusBwYGyg4smK4uPiiWg/gjLX1aXqoSia9KzJL7/AJZd4HZkxJjsKZJNXI2C9qm4AEJGJQAcg5uwMqjrfZ/6FQIUAxpOlrP1hNWdu70H48Uj+LNuByj+9T9hV5bwOyxiTjQWyyas8sNVnONYdl5x7gelJTRCR3iISKSKRe/bsSccQM5+TJ2HIEOjUCYrE7WJBv//RaNv3lLVkYozxWCATiiQxTpOcUaQVTkIZkNR0VR2rqiGqGlKqVPbtJmTlhwv5rPzTDB8OV3avSf5tf9P0rS5IjqSK2hhjgiuQTV6xQEWf4QrA9sQziUhdYBzQVlX3BTCeTOvY7mMsvv5ZWkS9Q/GcFfh1wmO07lYKyO11aMYY849A1lAWA9VFpKqI5AG6AVN8ZxCRSsB3wJ2qui6AsWRaS1/7lf3lryA86m3mXPEghTevcpOJMcZkLAFLKKoaDzwMzARWA1+r6ioR6SMifdzZhgAlgPdFJEpEIgMVT2Zz8CA82OMolQZ0I15ys/y92bRcOZIi5Qt7HZoxxiRJVJO8rJFhhYSEaGRk1s47c5//nW4ftGTnnpy8decSer1Ri/zF83sdljEmExORJaoa0A6Y7JfyGcie6F2sb9uX0NhvuLvieG7+8y4aNGjgdVjGGOMX6xwyA9AEZW6fz8lZtxZXxU4m4toXGbKmO5ZLjDGZidVQPLZlC6wIfYj2Wz9gZeGmFPjqI8Lb1/Q6LGOMSTNLKB5JiE9gzMjTPDU4L83ju1K4Y03CJjxIjtzW/5YxJnOyhOKBDdPXcqRbL44dbkzTa0cwemxLqlRp6XVYxhhzQewaShDFnzhNRJtXKNeuHpWPRNP43jrMnAlVqngdmTHGXDiroQTJmm9XwZ13En5iGQvL3col00cRVres12EZY0y6sRpKgMXFwTPPQMcuOSl4cj8LnphEk23fUtqSiTEmi7EaSgCtGD2fP5+ZzEv7X6Vnz8sp+Op6mpa2IjfGZE12dguAozuPsuT6QYStGEnxnJX4/esnadW5JFbcxpiszJq80tmSl3/mYIUrCFsxkjl1H6ZYbLSbTIwxJmuzhJJO9u+HPnccpdKg2zmVMx/R78+h5fJ3KVS2kNehGWNMUFgbTDqYM+QXuo65mt37CnHFPT/T642a5CuWz+uwjDEmqCyhXIBdUTvYeMPDhG3/jnsrfUrHyB7Ur3+l12EZY4wnrMnrPGiCMrfXePJeVYv6238ios0rDFnTnfr1vY7MGGO8YzWUNNq0CaJDH6D9tjEsLxJK4YnjCG9bw+uwjMmQTp8+TWxsLHFxcV6Hkm3ky5ePChUqkDt38B8RbgnFTwnxCXzw7mkGDMlLaEJ3CnetS9gXfciRyyp5xiQnNjaWwoULU6VKFUTE63CyPFVl3759xMbGUrVq1aBv386Gfvh76mqii4cR9/ggwsJgzOoWtJz4oCUTY1IRFxdHiRIlLJkEiYhQokQJz2qEdkZMwenjp4m47iUq3FifCkfX0Pj+K5k2DSpX9joyYzIPSybB5WV5W5NXMmK+WUWOHncQHhfFggqdqTb9PUKvKON1WMYYk2FZDSWREydg4EDo1C0X+U8fYuFT39F069eUsmRiTKb1/fffIyKsWbPmn3ERERG0b9/+X/P17NmTSZMmAc4NBQMHDqR69epcccUVNGrUiOnTp19wLC+//DLVqlWjRo0azJw5M8l5hg4dSvny5alfvz7169dn2rRpaVreK1ZD8bF85Bwih0zm1QMjuPfeGhR5eR2VS1kRGZPZTZgwgdDQUCZOnMjQoUP9Wmbw4MHs2LGD6Oho8ubNy65du5g1a9YFxRETE8PEiRNZtWoV27dv55prrmHdunXkzPnfJ7X279+fJ5544ryX94KdLYEj24+w9PqBtIx+n4tyVSVi0kBadrTOHI1JT/36QVRU+q6zfn14++2U5zl69Cjz5s3jjz/+4KabbvIroRw/fpwPP/yQjRs3kjdvXgDKlClDly5dLijeyZMn061bN/LmzUvVqlWpVq0aixYtomnTpkFZPtCyfZPX4uenc7hSbcKiP2DWlf0osW2lm0yMMVnBDz/8QJs2bbjssssoXrw4S5cuTXWZ9evXU6lSJYoUKZLqvP379/+nacr375VXXvnPvNu2baNixYr/DFeoUIFt27Ylud6RI0dSt25d7rnnHg4cOJDm5b2Qbb+C79sHTz98hBcn9uBQntKs+mA+Le9r4nVYxmRZqdUkAmXChAn069cPgG7dujFhwgSuuuqqZO+GSutdUm+99Zbf86qqX9t74IEHGDx4MCLC4MGDefzxx/n444/9Xt4r2S6haIIy59mZdPnwWvYdLEy9+36l14jLyVskr9ehGWPS2b59+/j999+Jjo5GRDhz5gwiwmuvvUaJEiX++eZ/1v79+ylZsiTVqlVjy5YtHDlyhMKFC6e4jf79+/PHH3/8Z3y3bt0YOHDgv8ZVqFCBrVu3/jMcGxtLuXLl/rNsmTLnbgK67777/rl5wN/lPaOqmeqvQYMGer52LN2uCy6+WRV0SJVPdfny816VMcYPMTExnm5/9OjR2rt373+Na9Gihc6ePVvj4uK0SpUq/8S4adMmrVSpkh48eFBVVZ988knt2bOnnjx5UlVVt2/frp9//vkFxRMdHa1169bVuLg43bBhg1atWlXj4+P/M9/27dv/ef3mm29q165d07R8UuUORGqAz8/ZooaiCcqcez+h3vjHqMdJItq9xuBvu5PLepg3JkubMGHCf2oJHTt25KuvviIsLIwvvviCu+++m7i4OHLnzs24ceMoWrQoAC+88ALPPvsstWrVIl++fBQsWJDnn3/+guKpXbs2Xbp0oVatWuTKlYtRo0b9c4dWr1696NOnDyEhITz11FNERUUhIlSpUoUxY8akunxGIJpEm1xGFhISopGRkX7Pv2EDxITdT/vtY4kq2oJi34yjyrXVAxihMeas1atXU7NmTa/DyHaSKncRWaKqIYHcbpatoZw5dYb33znNwKH5CNU7KHLblYR+1tv63zLGmADJkgnl7ymrOHH7vZw+2ozwdm8yenQYFSuGeR2WMcZkaVnq6/qpo6eIaD2cih2u5OJj62n0YEOmTgWf27aNMUGW2ZrVMzsvyzvLJJToCSvZVCqE8N+HEFmpIwnRqwkddRsZ6BZtY7KdfPnysW/fPksqQaLu81Dy5fPmjqNM3+R1/DgMHQo/jsjDtBzH+XPQZJq9eJPXYRljcH43ERsby549e7wOJds4+8RGL2TqhBL1ziyWDJ3C6wff4L77alD8lbVULZ5xbqEzJrvLnTu3J08ONN4IaJOXiLQRkbUisl5EBiYxXUTkXXf6ChG5yp/1Ho49zOzaD1C/XzjXHP2B2d/tZexYKGrJxBhjPBOwGoqI5ARGAdcCscBiEZmiqjE+s7UFqrt/jYEP3P/JOrb9EEer1Kb5me1ENHiMRjOGU7lkgcC8CWOMMX4LZA2lEbBeVTeo6ilgItAh0TwdgM/cngEWAsVE5OKUVpp3xyaO5SrK6nHzCY98gwKWTIwxJkMI5DWU8sBWn+FY/lv7SGqe8sAO35lEpDfQ2x08ednJVdH0agK90jfgTKgksNfrIDIIK4tzrCzOsbI4p0agNxDIhJLUDbuJ7x30Zx5UdSwwFkBEIgPdfUBmYWVxjpXFOVYW51hZnCMi/vdZdZ4C2eQVC/j+pLACsP085jHGGJMJBDKhLAaqi0hVEckDdAOmJJpnCtDDvdurCXBIVXckXpExxpiML2BNXqoaLyIPAzOBnMDHqrpKRPq400cD04B2wHrgOHC3H6seG6CQMyMri3OsLM6xsjjHyuKcgJdFpuu+3hhjTMaUZfryMsYY4y1LKMYYY9JFhk0ogeq2JTPyoyxud8tghYjMF5F6XsQZDKmVhc98DUXkjIh0CmZ8weRPWYhIuIhEicgqEZkV7BiDxY9jpKiI/Cgiy92y8Od6baYjIh+LyG4RiU5memDPm4F+aP35/OFcxP8buATIAywHaiWapx0wHee3LE2AP72O28OyaAZc5L5um53Lwme+33Fu+ujkddwe7hfFgBigkjtc2uu4PSyLQcCr7utSwH4gj9exB6AsWgBXAdHJTA/oeTOj1lAC0m1LJpVqWajqfFU94A4uxPk9T1bkz34B0Bf4FtgdzOCCzJ+y6A58p6pbAFQ1q5aHP2WhQGEREaAQTkKJD26Ygaeqs3HeW3ICet7MqAkluS5Z0jpPVpDW93kvzjeQrCjVshCR8sAtwOggxuUFf/aLy4CLRCRCRJaISI+gRRdc/pTFSKAmzg+nVwKPqmpCcMLLUAJ63syoz0NJt25bsgC/36eItMJJKKEBjcg7/pTF28AAVT0jWftxnf6URS6gAdAayA8sEJGFqrou0MEFmT9lcT0QBVwNXAr8IiJzVPVwgGPLaAJ63syoCcW6bTnHr/cpInWBcUBbVd0XpNiCzZ+yCAEmusmkJNBOROJV9YegRBg8/h4je1X1GHBMRGYD9YCsllD8KYu7gVfUuZCwXkQ2ApcDi4ITYoYR0PNmRm3ysm5bzkm1LESkEvAdcGcW/PbpK9WyUNWqqlpFVasAk4AHs2AyAf+OkclAmIjkEpECOL19rw5ynMHgT1lswampISJlcHre3RDUKDOGgJ43M2QNRQPXbUum42dZDAFKAO+738zjNQv2sOpnWWQL/pSFqq4WkRnACiABGKeqSd5Ompn5uV8MB8aLyEqcZp8BqprlurUXkQlAOFBSRGKB54DcEJzzpnW9YowxJl1k1CYvY4wxmYwlFGOMMenCEooxxph0YQnFGGNMurCEYowxJl1YQjEZkttTcJTPX5UU5j2aDtsbLyIb3W0tFZGm57GOcSJSy309KNG0+Rcao7ues+US7faeWyyV+euLSLv02LYxqbHbhk2GJCJHVbVQes+bwjrGA1NVdZKIXAeMUNW6F7C+C44ptfWKyKfAOlV9MYX5ewIhqvpwesdiTGJWQzGZgogUEpHf3NrDShH5Ty/DInKxiMz2+QYf5o6/TkQWuMt+IyKpnehnA9XcZR9z1xUtIv3ccQVF5Cf32RrRItLVHR8hIiEi8gqQ343jS3faUff//3xrDG7NqKOI5BSR10VksTjPqbjfj2JZgNuxn4g0EudZOMvc/zXcX40/D3R1Y+nqxv6xu51lSZWjMefN6/777c/+kvoDzuB05hcFfI/Tq0MRd1pJnF/6nq1hH3X/Pw48477OCRR2550NFHTHDwCGJLG98bjPTgE6A3/idKy4EiiI0+X5KuBKoCPwoc+yRd3/ETi1gX9i8pnnbIy3AJ+6r/Pg9PyaH+gNPOuOzwtEAlWTiPOoz/v7BmjjDhcBcrmvrwG+dV/3BEb6LP8ScIf7uhhOv14Fvf687S9r/GXIrleMAU6oav2zAyKSG3hJRFrgdCNSHigD7PRZZjHwsTvvD6oaJSItgVrAPLdbmjw43+yT8rqIPAvswem1uTXwvTqdKyIi3wFhwAxghIi8itNMNicN72s68K6I5AXaALNV9YTbzFZXzj1hsihQHdiYaPn8IhIFVAGWAL/4zP+piFTH6T02dzLbvw64SUSecIfzAZXImn18mSCzhGIyi9txnrTXQFVPi8gmnJPhP1R1tptwbgA+F5HXgQPAL6p6mx/beFJVJ50dEJFrkppJVdeJSAOcPpFeFpGfVfV5f96EqsaJSAROd+pdgQlnNwf0VdWZqazihKrWF5GiwFTgIeBdnL6q/lDVW9wbGCKSWV6Ajqq61p94jUkLu4ZiMouiwG43mbQCKieeQUQqu/N8CHyE8yjUhUBzETl7TaSAiFzm5zZnAze7yxTEaa6aIyLlgOOq+gUwwt1OYqfdmlJSJuJ0yheG06Eh7v8Hzi4jIpe520ySqh4CHgGecJcpCmxzJ/f0mfUITtPfWTOBvuJW10TkyuS2YUxaWUIxmcWXQIiIROLUVtYkMU84ECUiy3Cuc7yjqntwTrATRGQFToK53J8NqupSnGsri3CuqYxT1WVAHWCR2/T0DPBCEouPBVacvSifyM84z/7+VZ1H1oLzLJsYYKmIRANjSKUFwY1lOU537a/h1Jbm4VxfOesPoNbZi/I4NZncbmzR7rAx6cJuGzbGGJMurIZijDEmXVhCMcYYky4soRhjjEkXllCMMcakC0soxhhj0oUlFGOMMenCEooxxph08X8zi4WTVvq+OgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "data": {
            "image/png": 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h9HpkSUQ8LOnMIgpjZk2kmZrGkv57yWEbcAawo7ASmVnja7bOEg5dPq+D5JnhD4opjpk1jWYJhOmL1EdHxKf7qDxm1iyaIRBKOiIiOipN2W9m1hPRWL3GlUaWdK9Ut1rSUkkfkfSB7q0vCmdmDSrHSRckDZK0QtKvJK2R9MU0vV3SPZLWp5/DSs6ZK2mDpHWSplW7R5ZnhO3ALpI1SrrfJwzghxnONbNWlV/TeB/w7oh4SVJ/4OeSfgJ8AFgWEddKmgPMAT4raSLJ+senAscDP5X05kpLelYKhCPTHuPHeS0Admug1r+Z1UROUSIigmQ9dYD+6RbADGBKmr6AZJnPz6bpiyJiH7BR0gZgMvBguXtUCoT9gKM5NAAeLFvWP4SZtaZevD4zXNJDJcfzI2L+IddKOm5XAW8CboiIX0oaFRFbASJiq6SRafYxwPKS0zenaWVVCoRbI+JLGf8gZmaHyh4Id0bEpIqXSpq1p0s6Frhd0lsrZO915a1SZ0njzKpoZvUlkl7jLFuvLhuxh6QJPB3YJmk0QPrZvYD2ZmBcyWljgS2VrlspEE7tXRHNzErkNB+hpBFpTRBJRwJ/CjwJLAVmpdlmAUvS/aXATEkDJY0HJvDaWzA9qrTA++7qRTQz61mOQ+xGAwvS54RtwOKI+DdJDwKLJV0GbAIuAoiINZIWA2tJRsPNrtRjDF7O08yKkl+v8aPA23tI30WZlmtEzAPmZb2HA6GZ5a+OpuHPwoHQzHInmm/2GTOzXnMgNDNzIDSzludAaGYtrQlnqDYz6z0HQjNrdY00MasDoZkVwk1jM2ttfqHazAwHQjNrbR5ZYmYGqKtxIqEDoZnlz88IzczcNDYzc43QzMw1QjMzB0Iza2nhIXZm1uIa7T3CSst5mpm9fhHZtiokjZP0M0lPSFoj6VNperukeyStTz+HlZwzV9IGSeskTat2DwdCMyuEItuWQQfwtxHxh8A7gdmSJgJzgGURMQFYlh6TfjcTOJVkIfgb06VAy3IgrKG2tuD6hcv5wnWPADD+zS/y1QUruHHxg1z9tUc48qiOGpfQypk05QVufuBJvvWLJ/jQVdtqXZz6k3Vx9wyBMCK2RsTD6f6LwBPAGGAGsCDNtgC4MN2fASyKiH0RsRHYAEyudI/CAqGkWyRtl/R4me8l6etp9fVRSWcUVZZ6NePDm3h241EHjz/1+bV86+tv4uMfOpt//9lIPjjr6doVzspqawtmX/NbPnfJeD425RTOm7GHEya8Wuti1R11ZduA4ZIeKtkuL3tN6SSSNY5/CYyKiK2QBEtgZJptDPBsyWmb07SyiqwR3kpSLS3nfGBCul0OfKPAstSd40a+ypl/vJO7bn/t9xl74ss8vip5zPHI8uM4Z+r2WhXPKjjl7XvZ8vQAnts0kI4Dbdy35FjOnvZ8rYtVd3oRCHdGxKSSbX6P15OOBn4A/HVEvFDp1j2kVax7FhYII+J+YHeFLDOAb0diOXCspNFFlafeXPHpddxy3QS6Sl4xePo3R/POKTsAeNd7tjF8lGsZ9ei4PzjAji0DDh7v3Nqf4aMP1LBEdSjIrbMEQFJ/kiD43Yj4YZq8rTtmpJ/dNYfNwLiS08cCWypdv5bPCDNXXyVd3l1t3t/1Sp8UrkiT37WDPbsHsOGJIYekf+0Lp/K+Dz3Ldd9dzpGDO+g44Ee49Ug91Dcy/ntuKXl1lkgS8E3giYj4XyVfLQVmpfuzgCUl6TMlDZQ0nqTVuaLSPWr5HmHm6mtaVZ4PMLT/yIb/T27i6Xt455/s4Mw/3kn/AV0MPqqDv/uHx/jK507jcx9/BwBjTniZM9+1s8YltZ7s3NqfEcfvP3g8fPQBdj3Xv4YlqlP5/Us9B/gI8Jik1Wna/wCuBRZLugzYBFwEEBFrJC0G1pL0OM+OiM5KN6hlIOx19bVZ3Hr9BG69fgIAp71jN//5o8/wlc+dxtBh+3n+dwOQgpkf28gd3x9b45JaT9atHsyY8fsZNW4fu57rz5QZe7h29om1LlZdyfOF6oj4OT1XnACmljlnHjAv6z1qGQiXAldJWgScBTzf3QPUqqZMf473/ZfkacEv7h3JPUuOr3GJrCddneKGvx/DNbc9RVs/uHtRO8/8elCti1VfIjwxK4CkhcAUkq7xzcDVQH+AiLgJuAO4gOQdn73ApUWVpZ49tqqdx1a1A7Bk4QksWXhCjUtkWay8dwgr7x1SPWMra5w4WFwgjIiLq3wfwOyi7m9mtdVIY4096YKZ5S8AN43NrOU1Thx0IDSzYrhpbGYtz73GZtbavJynmbW65IXqxomEDoRmVgyvWWJmrc41QjNrbX5GaGbmscZmZg01SaMDoZnlzwu8m5nhGqGZmTtLzKzlqatx2sYOhGaWv8AvVJtZaxPRUC9Ue71IMytGTusaS7pF0nZJj5ektUu6R9L69HNYyXdzJW2QtE7StCxFdSA0s2Lkt8D7rcD0w9LmAMsiYgKwLD1G0kRgJnBqes6NkvpVu4EDoZnlr/sZYZat2qUi7gd2H5Y8A1iQ7i8ALixJXxQR+yJiI8nicJOr3cPPCM2sEL3oNR4u6aGS4/kRMb/KOaO6l/+NiK2SRqbpY4DlJfk2p2kVORCaWQEyN3sBdkbEpJxu3NNC8FUL4qaxmeUvyPMZYU+2SRoNkH5uT9M3A+NK8o0FtlS7mAOhmRUjp2eEZSwFZqX7s4AlJekzJQ2UNB6YAKyodjE3jc2sEHm9RyhpITCF5FniZuBq4FpgsaTLgE3ARQARsUbSYmAt0AHMjojOavdwIDSzYuQUCCPi4jJfTS2Tfx4wrzf3cCA0s/xFQGfjjLFzIDSzYjTQEDsHQjMrhgOhmbW0ALxmiZm1toDwM0Iza2WBO0vMzPyM0MzMgdDMWtsbGkfc5xwIzSx/AXjxJjNrea4Rmllr8xA7M2t1AeH3CM2s5XlkiZm1PD8jNLOWFuFeYzMz1wjNrMUF0Vl1hvy64UBoZvnzNFxmZjTUNFxeztPMchdAdEWmLQtJ0yWtk7RB0py8y+tAaGb5i3Ri1ixbFZL6ATcA5wMTgYslTcyzuG4am1khcuwsmQxsiIinACQtAmaQrF2cC0UDdXEDSNoBPFPrchRkOLCz1oWwzJr59zoxIka83pMl3Uny95PFIODVkuP5ETG/5FofBKZHxF+mxx8BzoqIq15v+Q7XcDXCN/Lj1DtJD0XEpFqXw7Lx71VeREzP8XLq6RY5Xt/PCM2s7m0GxpUcjwW25HkDB0Izq3crgQmSxksaAMwEluZ5g4ZrGje5+dWzWB3x79UHIqJD0lXAXUA/4JaIWJPnPRqus8TMLG9uGptZy3MgNLOW50DYx6oNFVLi6+n3j0o6oxbltISkWyRtl/R4me/9ezUBB8I+lHGo0PnAhHS7HPhGnxbSDncrUOmdOP9eTcCBsG8dHCoUEfuB7qFCpWYA347EcuBYSaP7uqCWiIj7gd0Vsvj3agIOhH1rDPBsyfHmNK23eax++PdqAg6EfSvLUKHChxNZrvx7NQEHwr6VZahQ4cOJLFf+vZqAA2HfyjJUaCnw0bQ38p3A8xGxta8Lapn592oCHmLXh8oNFZJ0Zfr9TcAdwAXABmAvcGmtymsgaSEwBRguaTNwNdAf/Hs1Ew+xM7OW56axmbU8B0Iza3kOhGbW8hwIzazlORCaWctzIGxCkjolrZb0uKTvSRr8Bq51a7qKGJJurrSerKQpkv7oddzjaUm/t+JZufTD8rzUy3t9QdLf9baM1twcCJvTKxFxekS8FdgPXFn6ZToLTq9FxF9GRKW1ZKcAvQ6EZrXmQNj8HgDelNbWfibpNuAxSf0k/U9JK9N59K6Ag/Pr/ZOktZJ+DIzsvpCk+yRNSvenS3pY0q8kLZN0EknA/Zu0NvouSSMk/SC9x0pJ56TnHifpbkmPSPpneh6vewhJ/ypplaQ1ki4/7LuvpmVZJmlEmvYfJN2ZnvOApLfk8rdpTckjS5qYpCNI5su7M02aDLw1IjamweT5iDhT0kDgF5LuBt4OnAKcBowC1gK3HHbdEcC/AOem12qPiN2SbgJeioivpPluA/53RPxc0gkkI2r+kGR0xs8j4kuS/oxkHr9q/lt6jyOBlZJ+EBG7gKOAhyPibyV9Pr32VSQLK10ZEeslnQXcCLz7dfw1WgtwIGxOR0pane4/AHyTpMm6IiI2punvBf5j9/M/YCjJ5KLnAgsjohPYIuneHq7/TuD+7mtFRLn5+v4UmCgdrPANkXRMeo8PpOf+WNLvMvyZPinp/en+uLSsu4Au4P+m6d8Bfijp6PTP+72Sew/McA9rUQ6EzemViDi9NCENCC+XJgGfiIi7Dst3AdWnkVKGPJA8ejk7Il7poSyZx3ZKmkISVM+OiL2S7gMGlcke6X33HP53YFaOnxG2rruAv5LUH0DSmyUdBdwPzEyfIY4Gzuvh3AeBP5E0Pj23PU1/ETimJN/dJM1U0nynp7v3A5ekaecDw6qUdSjwuzQIvoWkRtqtDeiu1X6YpMn9ArBR0kXpPSTpbVXuYS3MgbB13Uzy/O9hJQsT/TNJC+F2YD3wGMn6G//v8BMjYgfJc70fSvoVrzVNfwS8v7uzBPgkMCntjFnLa73XXwTOlfQwSRN9U5Wy3gkcIelR4MvA8pLvXgZOlbSK5Bngl9L0S4DL0vKt4feXRDA7yLPPmFnLc43QzFqeA6GZtTwHQjNreQ6EZtbyHAjNrOU5EJpZy3MgNLOW9/8B4cO0w/KlAo0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "print_metrics(clf, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"Logistic regression, combined, imbalanced\")\n",
        "print_metrics(clf, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"Logistic regression, combined, balanced\")\n",
        "print_metrics(clf, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"Logistic regression, has_dep_diag, imbalanced\")\n",
        "print_metrics(clf, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"Logistic regression, has_dep_diag, balanced\")\n",
        "\n",
        "plot_learning_curve(5, clf, X_train_comb_imb, y_train_comb_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, clf, X_train_comb_b, y_train_comb_b, 'Balanced train set, balanced test set')\n",
        "plot_learning_curve(5, clf, X_train_hasdep_imb, y_train_hasdep_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, clf, X_train_hasdep_b, y_train_hasdep_b, 'Balanced train set, balanced test set')\n",
        "\n",
        "plot_roc(clf, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"Imbalanced train set, imbalanced test set, combined variable\")\n",
        "plot_roc(clf, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"Balanced train set, Balanced test set, combined variable\")\n",
        "plot_roc(clf, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"Imbalanced train set, imbalanced test set, has_dep-diag\")\n",
        "plot_roc(clf, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"Balanced train set, Balanced test set, has_dep_diag\")\n",
        "\n",
        "plot_cf(clf, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb)\n",
        "plot_cf(clf, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b)\n",
        "plot_cf(clf, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb)\n",
        "plot_cf(clf, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWp1bKBJf7QI"
      },
      "source": [
        "### KNN Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QmzsWe5qf7QI"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=5) #, metric='euclidean')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3DE35kXOf7QJ",
        "outputId": "33b2988b-ceb8-4def-de43-bf968b5c7ffd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KNN, combined, imbalanced: \n",
            "F1: 0.5683412376111094\n",
            "recall: 0.5519035843365309\n",
            "precision: 0.6224358974358974\n",
            "accuracy: 0.92875\n",
            "KNN, combined, balanced: \n",
            "F1: 0.6193910256410255\n",
            "recall: 0.62\n",
            "precision: 0.6207729468599035\n",
            "accuracy: 0.62\n",
            "KNN, has_dep_diag, imbalanced: \n",
            "F1: 0.49206349206349204\n",
            "recall: 0.4968885024049567\n",
            "precision: 0.4930007946696008\n",
            "accuracy: 0.915\n",
            "KNN, has_dep_diag, balanced: \n",
            "F1: 0.7085720028137876\n",
            "recall: 0.71\n",
            "precision: 0.7141982864137086\n",
            "accuracy: 0.71\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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o2ND6Ii65BA46KFiob1/oGUwzct55cMghcUsS4InCOedCt3ixdUYPGgR//gmVKsEVV9hfixawe9aTefPg6qvhxx/h1FPhjTcsScSZJwrnnAvBtm0RYx5GWzJo3x6eeAK6dIFy5bJ5QOvWkJZmGeWyyyIySHx5onDOuQRRtf6GAQOs/2HTJqhdGx55BC6/HGpmN6fe/PlQpw7su6/1ZjdtCgcfnNC4PVE451ycrVpl3/EDBsCcOXa0cP751jF94omwT3Yj2rZvh9694amn7AjikkusFzsEniiccy4Odu2ysQ4DB9r/tDQ4/njo1w8uvBD2izY56oQJcNVV1idxxRVw5pkJizs7niicc64AzZxpyeG992zMw8EHwx132JiH+vVjWEHv3vDww9YONXIkdMxpqvjE8UThnHN7acOGzDEPU6bYmIezzsoc81Aylm9aVeucbtoUbrrJBs9VqBDnyGPjicI55/IhPd3OVhowwM5e2r4dGjWC55+37oSqsRb0XrfOhlkfeSQ8+KBlmLPOimvseeWJwjnn8mDRoswxD0uWwP7725HDFVdA8+Z5PGP1k0/ghhssWTz4YHwCLgCeKJxzLhfbttlI6YEDbeS0CJxyip2Q1KULlC2bxxWuWAE33giffWbZZdQoaNIkHqEXCE8UzjmXDVX43/8yxzxs3gyHH259zZddlsOYh1gtX24d1U89BbffHmMnRniSOzrnnEuwlSszxzzMnWvj3DLGPLRpk8OYh1gsXmxF/G66yY4ili6FAw4oyNDjxhOFc67Y27kzc8zDsGE25uGEE+DNN6Fr11zGPOQmLQ369IH77rMsc8EFds5sIUkS4InCOVeM/fZb5jwPa9ZYfb0777QxD/XqFcAG5syxIn4TJ9p5sm+8kfDyGwXBE4VzrlhZvz5zzMPUqTbHz9ln21lLp55agN0F27ZZfY70dHjnHTtnNkFF/AqaJwrnXJGXlrbnmIcdO6BxY3jxRbj4YqhSpQA3Nncu1K1rnRvvv29nM1WrVoAbSLz8dss451zSW7gQHnrIKrR27GgnGl19NUybBtOnwy23FGCS+PtvuOcem2no/fftto4dC32SAD+icM4VMVu3Zo55GDvWWns6doRnnoHOnfMx5iEW48ZZBvr9d/vfqVMcNhIeTxTOuUJPFSZNsqalDz+0MQ9HHAGPPmpjHg47LI4b/+9/oVcvO2z57jubfaiI8UThnCu0VqzIHPMwb551C3Ttah3TbdrEue84o4hfixZWq6l3byhfPo4bDI8nCudcobJzJ3z9tSWHESOso/rf/4a777YhChUrxjmANWssMdSpYx0gZ54Z+nwR8ead2c65QmHGDPt+rl4dzjsPfvkF7rrLjiR+/NFGTsc1SajCRx9BgwZ2fm2+h2gXPn5E4ZxLWuvWweDB1jE9bZqNeejc2ZqWOnZMYImk5cvh+uvhyy+tqem77+z82mLCE4VzLqmkpcH331vT0hdf2JiHJk3gpZfgoosKeMxDrFautIEYzzwDt96a9EX8ClrxerbOuaT1xx+Z8zykpFgppB497OjhmGNCCGjhQhg61BJDs2aZk08UQ54onHOh2brV5u4ZMMCGIuyzjzUpPf+8ldUoUyaEoNLS4OWX4f77ra2rWzerz1RMkwR4onDOJZiq1cgbONDGPGzZYrOAPvaYjXmoUSPE4GbNgquusokozjwTXn+9UBbxK2ieKJxzCbF8udXGGzgQ5s+3IQcZYx5at06CennbtkHbthbIBx/YkUToQSUHTxTOubjZudPm6skY85CebkmhZ08b81ChQtgRArNnQ/36NlpvyBDrOa9aNeyokkrxORHYOZcwv/5qfcCHHmqzw02fbslh/nwYP96OIkJPEtu22UCMRo3gvffstg4dPElkw48onHMFYt06a7EZOBB+/hlKl7YxD1deCaecAiVKhB1hhLFj4f/+DxYsgGuusZ5zlyNPFM65fEtLg2+/teTwxRfW1HTMMXbS0EUXQeXKYUeYjYcfhkcesaqBo0dDu3ZhR5T0PFE45/JswQJLDu+8Y2MeDjwQrr3WmpSaNg07uhxkFPFr2RLuuMOSxb77hh1VoRDXRCEipwEvASWA/qr6ZJb7KwHvATWDWJ5V1YHxjMk5lz9btmSOeRg/3sY8nHoqvPACnHVWSGMeYrF6tc1QVLeuHU0UgyJ+BS1undkiUgLoA5wONAD+IyINsix2AzBbVZsAJwHPiUjpeMXknMsbVSu4d9VVNpzgiiusmsXjj9tA5WHDrLM6KZOEqnWa1K9vGa60f7XkVzyPKFoCC1R1IYCIDAE6A7MjllGgoogIUAFYB6TGMSbnXAyWLcsc8/D773aG0oUXWqL4978LwfCClBS47jqrR96qFbz1lk1R6vIlnomiOrA04noK0CrLMq8CQ4HlQEXgQlVNz7oiEekB9ACoWbNmXIJ1rrjbscNKGw0caHNLp6fDiSfCfffZUUPop7PmxerVVhPk+efh5puT7JSrwieeiSK73xya5fqpwHTgZOAI4FsRGa+qm/Z4kGo/oB9AixYtsq7DObcXpk+35PDee3aKa/XqcO+90L27ldYoNBYssNF9t91mp14tXQr77Rd2VEVCPBNFChA5U20N7Mgh0hXAk6qqwAIRWQTUAybHMS7nir21a635fsAASxSlS0OXLjbmoUOHQvYDPDUVXnwRHnzQOksuugiqVfMkUYDiOTJ7ClBHRGoHHdTdsGamSEuA9gAiUg2oCyyMY0zOFVtpaTB8uNVXOvRQa5HZZx945RWbe/rDD+0spkKVJH77DU44wUZYd+xoRf2qVQs7qiInbkcUqpoqIjcCI7HTYweo6iwRuTa4/3WgNzBIRH7DmqruUdU18YrJueLo998zxzwsW2aD4K67zjqmmzQJO7q9sG2bDZbbZx+r0dS1ayHoZS+cxFp9Co8WLVro1KlTww7DuaS2ZQt8/LE1Lf34o32Xnn66JYezzirkZ4rOnGlnMInYVHhNmoQ07V3hIiLTVLVFfh7rRQGdKyJUbSDclVfamIcrr4S//oInn7R+3a+/hvPOK8RJYutWuP12m6s6o4hf+/aeJBLAS3g4V8ilpGSOeViwwE5j7dbNEsXxxxeR1pjvv7cifosWwfXXW7VBlzCeKJwrhHbsgC+/tOQwapSNeWjbFh54wMY8lC8fdoQF6MEH4dFHoU4d+OEHG9zhEsoThXOFyC+/WL/DBx/YmIfDDrMBcd27WzHUIiU93TpXTjgB7r4bevWCcuXCjqpY8kThXJJbsyZzzMOvv9pQgXPOsY7p9u0L2emssfjrLzt3t25d+O9/rRf+9NPDjqpY885s55JQaqoV3LvgAhvzcMstULIk9OljYx4GD7ZhA0UqSahaJ3X9+vD5514CPIn4EYVzSWT+/MwxD8uX2wk9N9xgRw+NG4cdXRwtXWoTWgwbZj3w/ftDg6zFpl1YPFE4F7LNm+GjjyxBTJhgzfJnnGEjpjt1KsSns+bF2rX25F96yTJjkTpUKvw8UTgXgowxDwMG2MC4bdugXj146im49FI45JCwI0yA+fOtXO2dd9q0eEuXQsWKYUflsuGJwrkEWro0c8zDH3/Y9+LFF1vT0nHHFZExD7lJTYXnnrPZ5sqVs8xYrZoniSTmicK5ONu+fc8xD6pw0kn2PXnuuUVszENufv3VRgL+/LOdutWnjxfxKwQ8UTgXB6r2XThwoJ3aun69jXl44AEb83D44WFHGIJt2+x83pIlbWrS884LOyIXI08UzhWg1avh/fctQcyYYWMezj3XmpZOPrmY9tHOmAGNGtnprh9/bEX8Djww7KhcHvg4Cuf2UmoqfPON/UCuXt0mWCtdGl57zcY8fPABnHJKMUwSW7bYAJCmTeHdd+22du08SRRCfkThXD7Nm5c55mHFCqhaFW66yY4ejj467OhC9u230KMHLF4MN95o/RGu0PJE4VwebNqUOeZh4kQ7SjjjDOufPeOMYjLmITf33w+PP24lOMaPh9atw47I7aWYE4WIlFfVrfEMxrlklJ4O48ZZcvjkE+uTrV8fnn7azuw8+OCwI0wSGUX8WreGe++Fhx6CsmXDjsoVgFwThYicAPQHKgA1RaQJcI2qXh/v4JwL05Il8PbbMGgQLFxop/lfcok1LbVqVUzGPMRi5UprXmrQAB55xIv4FUGxHFG8AJwKDAVQ1V9FxAvCuyJp+3b44gs7evj2WzvNtV07K2J67rlep24PqpZJb7/dDrOOOy7siFycxNT0pKpLZc+fT2nxCce5xFOFadMyxzxs2AA1a9p8Od27Q+3aYUeYhP780zqrR42ypqb+/a1PwhVJsSSKpUHzk4pIaeBmYE58w3Iu/lavtqrWAwfCb79Zc3rkmId9/OTxnG3YAFOmwKuvwnXX+c4q4mJJFNcCLwHVgRRgFOD9E65QSk2F4cMtOXz1lV1v2RL69rV5pvffP+wIk9i8eVbE7667bNDckiU2Qbcr8mJJFHVV9eLIG0Tk38CE+ITkXMGbOzdzzMPKlTbm4ZZb7OihYcOwo0tyu3bBs89aR0358nD55XDQQZ4kipFYEsUrQLMYbnMu6Xz4oU1x8NNPNubhzDMzxzyUKhV2dIXAL7/AVVfZ//PPt6amgw4KOyqXYDkmChE5HjgBqCoit0fctR9Q3IoRuELohRfshJz69eGZZ+zUVh/zkAfbtlntkVKl4NNPrQPHFUvRjihKY2MnSgKRheI3AefHMyjn9tYrr1iSOP98m1+6pNcgiN0vv1h9pn33tRGGTZrAAQeEHZULUY4fH1X9AfhBRAap6p8JjMm5vfL663DzzdCli53u6kkiRps324jqPn1sfMRll9nEGa7Yi+UjtE1EngEaArvH46vqyXGLyrl86t/fztbs1Mn6J7wfIkYjRsA119gUfLfc4s1Mbg+xnPz8PjAXqA38F1gMTIljTM7ly9tv2xiw006zFhMv0Beje++1khvly8OECfDii35Gk9tDLEcUlVX1LRG5JaI56od4B+ZcXnzwgZ3q2r49fPaZTRjkcpGWZqeCnXSStc898IDvOJetWBLFruD/ChE5E1gO1IhfSM7lzUcfWRXXtm1tbupy5cKOKMmtWAE33GADSHr3hlNPtT/nchBL09OjIlIJuAO4E6ske2s8g3IuVp99BhddBCecYCOtvWhfFKo26rBBAxue7mcyuRjlekShql8HFzcC7WD3yGznQjV0KFx4oZXgGDbMm9WjWrwY/u//4LvvoE0b6/U/6qiwo3KFRLQBdyWArliNpxGqOlNEOgH3AeWAYxITonP/NGyYjZFo1sx+HFesmPtjirWNG+Hnn20i72uu8SJ+Lk+ivVveAq4GKgMvi8hA4FngaVWNKUmIyGkiMk9EFohIzxyWOUlEpovILO8kd7EYNcrO3mzUCEaOhEqVwo4oSc2eDU8+aZczivh5pVeXD9GanloAjVU1XUTKAmuAI1V1ZSwrDo5I+gCnYFVnp4jIUFWdHbHM/sBrwGmqukREvIiMi2r0aOjcGerVs4mFvNprNnbutHlae/e2Q60rr7T6TOXLhx2ZK6Si/bTYqarpAKq6HZgfa5IItAQWqOpCVd0JDAE6Z1nmIuAzVV0SbOevPKzfFTPjxsFZZ8ERR1iSOPDAsCNKQlOnwrHH2qxL555rRxVexM/tpWhHFPVEZEZwWYAjgusCqKo2zmXd1YGlEddTgFZZljkKKCUiY7F6Ui+p6jtZVyQiPYAeADVr1sxls64omjDBKr7WrAnff29lwl0WW7faaa5ly9p5wmefHXZEroiIlijq7+W6s5t6XrPZfnOgPdZB/pOITFLV+Xs8SLUf0A+gRYsWWdfhirhJk2zgcPXq1vRUrVrYESWZn3+2In7ly8Pnn0Pjxt4m5wpUjk1PqvpntL8Y1p0CHBZxvQY2WC/rMiNUdauqrgHGAU3y+iRc0TV1qv1IPuggSxKHHBJ2RElk0ya4/npo3tzmdAU48URPEq7AxfP0hylAHRGpHcy13Q0YmmWZL4E2IlJSRPbFmqZ8Pm4HWLXrU06xvojRo+2IwgWGDbOR1W+8YfXUzzsv7IhcERa3AsyqmioiNwIjsYmOBqjqLBG5Nrj/dVWdIyIjgBlAOtBfVWfGKyZXeMyYAR062Ek7Y8ZY34QL3HOPndXUoIFVP2yVtevPuYIVU6IQkXJATVWdl5eVq+owYFiW217Pcv0Z4Jm8rNcVbbNnW5IoV86SRK1aYUeUBFQhPd2K+LVvbx3W993nRfxcQuTa9CQiZwHTgRHB9aYikrUJybkCMXcunHyyFTMdPdpOhS32li2zWZgeftiud+wI//2vJwmXMLH0UfTCxkRsAFDV6UCteAXkiq/ff7ckoWpJotiXIlKFN9+0JqZRo6BKlbAjcsVULE1Pqaq6USS7s12dKxgLF1qS2LULxo61kdfF2qJFcNVV1vZ20kmWMI48MuyoXDEVS6KYKSIXASVEpA5wMzAxvmG54mTxYmjXDrZtsyOJhg3DjigJbNliPfpvvAFXX+31mVyoYnn33YTNl70D+AArN35rHGNyxcjSpXYksWmTleVoUpxH0cycCY8/bpcbNbIifj16eJJwoYvlHVhXVe9X1WODvweC2k/O7ZVlyyxJrF1rTfDNmoUdUUh27rTO6WbN4IUX4K+g5JnPwuSSRCyJ4nkRmSsivUXEGwVcgVi50pLEypUwYoTVsSuWpkyxkdW9esEFF3gRP5eUYpnhrp2IHIxNYtRPRPYDPlTVR+MenSuS/vrLksSyZZYkjj8+7IhCsnUrnHaaDRgZOtRK4zqXhGJq/FTVlar6MnAtNqbioXgG5YquNWtsMN3ixfDNN9C6ddgRhWDqVBs8V768VXmdNcuThEtqsQy4qy8ivURkJvAqdsZTjbhH5oqcdeusdtPvv8NXX0HbtmFHlGAbN9o0pMcem1nEr3Vrn6LPJb1YTo8dCAwGOqpq1uqvzsVkwwYbUDx7trWytG8fdkQJ9tVXcO211ilz55024bdzhUQsfRTHJSIQV3Rt2mRN8TNm2HQJp54adkQJdtdd8OyzdsrrF18U4557V1jlmChE5CNV7Soiv7HnhEOxznDnHJs326RD06ZZodMzzww7ogRRhbQ0K1rVsSPst59VfS1dOuzInMuzaEcUtwT/OyUiEFf0bN1qieF//4MPP4TOWWdML6pSUuC662ymucces46ZU04JOyrn8i3aDHcrgovXZzO73fWJCc8VVtu22Yk8EybA++8Xk3l10tOt5EaDBlaL5OCDw47IuQIRy+mx2f0UOr2gA3FFx/btVhV77Fh4+2248MKwI0qAjKqG114LLVvCb7/BTTeFHZVzBSJaH8V12JHD4SIyI+KuisCEeAfmCqcdO+Dcc+G772DAALjkkrAjSpCtW+2Urv794corwastuyIkWh/FB8Bw4AmgZ8Ttm1V1XVyjcoXSzp1WhWL4cOjXD7p3DzuiOPvtNxsw98ADdkbTn3/aKGvniphoTU+qqouBG4DNEX+IyIHxD80VJrt2QbduNlygTx/4v/8LO6I42rEDHnrIivi9/HJmET9PEq6Iyu2IohMwDTs9NvJYWoHD4xiXK0RSU+Hii22MxEsvwfVF+VSHSZNsQqHZs+HSS63aa+XKYUflXFzlmChUtVPwv3biwnGFTVoaXH45fPyxjSm7+eawI4qjjPN9y5eHYcNsgIhzxUAstZ7+LSLlg8uXiMjzIlIz/qG5ZJeWZv22H3wATzwBd9wRdkRx8r//ZRbx++orK+LnScIVI7GcHtsX2CYiTYC7gT+Bd+MalUt66elW3+6dd+CRR6Bnz9wfU+hs2GDTkB53XGYRvxNOgIoVQw3LuUSLJVGkqqoCnYGXVPUl7BRZV0ypwg03wFtvwYMP2l+R88UXNnBu0CArvXHBBWFH5FxoYqkeu1lE7gUuBdqISAmgVHzDcslK1fohXn/djiL++9+wI4qD22+3TuomTaypqXnzsCNyLlSxJIoLgYuAK1V1ZdA/8Ux8w3LJSNX6IV591b5LH3+8CI0riyzid8YZdibT3XdDKf9N5FyuTU+quhJ4H6gkIp2A7ar6Ttwjc0lF1Y4gXnjBjiiefbYIJYklS+xspocftusdOsD993uScC4Qy1lPXYHJwAXYvNn/ExGfdaUYUbV+iKeftqKoL75YRJJEejq89ho0bAg//ACHHhp2RM4lpVianu4HjlXVvwBEpCrwHfBJPANzyeORR6xa9tVXW7NTkUgSCxbYub3jx1sJ8H79oFatsKNyLinFkij2yUgSgbXEdraUKwIefxx69bK6TW+8AfsUlVd++3aYPx8GDrQRg0Ui+zkXH7EkihEiMhKbNxusc3tY/EJyyeLZZ62p/uKLrShqoU8S06dbEb+HH4ajj4bFi6Fs2bCjci7pxdKZfRfwBtAYaAL0U9V74h2YC9eLL9pUzxdeaEMJSpQIO6K9sH27ZbwWLaBv38wifp4knItJtPko6gDPAkcAvwF3quqyRAXmwtOnD9x2m81K9+67dsZooTVxohXxmzvXmpiefx4O9OLHzuVFtCOKAcDXwHlYBdlXEhKRC1W/fnDjjXD22VbDqVCfIbp1q83Hum0bjBhhh0aeJJzLs2i/FSuq6pvB5Xki8nMiAnLhGTDA6jedcQZ89BGULh12RPn000/QqpUV8fv6a+uP8PpMzuVbtCOKsiJyjIg0E5FmQLks13MlIqeJyDwRWSAiOZaNE5FjRSTNx2eE59137fTXjh3h00+hTJmwI8qH9evtlNcTTrAnBHD88Z4knNtL0Y4oVgDPR1xfGXFdgZOjrTioCdUHOAVIAaaIyFBVnZ3Nck8BI/MWuisogwfb6a/t2lktvELZx/vZZ1apcPVquPde64V3zhWIaBMXtdvLdbcEFqjqQgARGYJVoJ2dZbmbgE+BY/dyey4fPv7YJmpr3RqGDi2ks3nedpudptW0qU0odMwxYUfkXJESz/NZqgNLI66nAK0iFxCR6sA52NFJjolCRHoAPQBq1vQ5kwrKF1/ARRfZdAvffGNN+oVGZBG/Tp3goIPgzjsLee+7c8kpnkOoshvqqlmuvwjco6pp0Vakqv1UtYWqtqhatWpBxVesff01dO1qQwuGDYMKFcKOKA8WL4bTTsucCKN9e2tu8iThXFzEM1GkAIdFXK8BLM+yTAtgiIgsBs4HXhORLnGMyWFnip53nk23MGIE7Ldf2BHFKD0dXnnFzmKaOBH+9a+wI3KuWMi16UlEBLgYOFxVHwnmozhYVSfn8tApQB0RqQ0sA7ph81rspqq1I7YzCPhaVb/I0zNwefLdd9ClixVMHTUKKlUKO6IY/f47XHEFTJhgRxOvv+6JwrkEieWI4jXgeOA/wfXN2NlMUalqKnAjdjbTHOAjVZ0lIteKyLX5jNfthbFjbSDdUUfBt9/CAQeEHVEe7NwJf/xhk3QPG+ZJwrkEiqUzu5WqNhORXwBUdb2IxDQUS1WHkaWAoKq+nsOy3WNZp8uf8eNtbp7ate2oonLlsCOKwS+/WBG/Xr3sEGjx4kI6wMO5wi2WI4pdwVgHhd3zUaTHNSpXoCZOtNHWhx0G339vJwglte3brXP62GOttvnq1Xa7JwnnQhFLongZ+Bw4SEQeA34EHo9rVK7ATJ5sTfqHHAKjR8PBB4cdUS5+/NF62Z98Ei67DGbPBj/TzblQ5dr0pKrvi8g0oD12ymsXVZ0T98jcXps2zUpyVK1qSSLpZ/rcsgU6d7bTsEaNspnnnHOhi+Wsp5rANuCryNtUdUk8A3N759df7Xt2//0tSdSoEXZEUfz4o9VnqlDBRv4dfXQhG9jhXNEWS9PTN1i58W+A74GFwPB4BuX2zsyZ0KGDfdeOGZPEJwitXWvNS23aZBbxO+44TxLOJZlYmp4aRV4PKsdeE7eI3F6ZM8cGKpcubUcStWvn/piEU4VPPrGJL9atsxHW3bqFHZVzLgd5rvWkqj+LiBfwS0Lz5sHJJ9vc1qNHw5FHhh1RDm67DV56CZo3t76IJk3Cjsg5F0UsfRS3R1zdB2gGrI5bRC5fFiywJJGWZgPr6tYNO6IsVCE11eoxnX229azffnshn2fVueIhlj6KihF/ZbC+is7xDMrlzaJFliR27LAjiQYNwo4oi0WL7PSrjCJ+J58Md9/tScK5QiLqJzUYaFdBVe9KUDwuj5YssQmHtmyxJHH00WFHFCEtDV59Fe67D0qUgAsuCDsi51w+5JgoRKSkqqbGOu2pS7yUFEsSGzbYiOumTcOOKML8+TZt3k8/wemn2wjrww7L9WHOueQT7YhiMtYfMV1EhgIfA1sz7lTVz+Icm4ti+XJrwVm92mo3NW8edkRZpKbCn3/Ce+/Z7EiS3fQkzrnCIJZG4gOBtdgsdIqNzlbAE0VIVq2yU2BXrICRI6Fly7AjCkydakX8eve2jpKFC70+k3NFQLREcVBwxtNMMhNEhqwz1bkEWb3ajiSWLLFJh044IeyIgL//hocfhuees2JSN99sdUM8SThXJEQ766kEUCH4qxhxOePPJdjatTbietEim8q0TZuwIwJ++AEaN4ZnnoGrroJZs7yIn3NFTLQjihWq+kjCInFRrV9vtZvmzbMk0a5d2BFhp1qde64VlPr+ezvUcc4VOdEShfc+JomNG20YwqxZ1gXQoUPIAY0fD//+t9VkGj7cJhUqXz7koJxz8RKt6al9wqJwOdq0yeaT+PVX+PRTuxyaNWvgkkvgxBMzi/i1bOlJwrkiLscjClVdl8hA3D9t2WIz002ZAh9/DJ06hRSIKnz0Edx0k7WBPfywF/FzrhjxGgpJautWSwyTJsHgwXDOOSEGc8st8MorNjXp999Do0a5P8Y5V2R4okhCf/9tdfPGj7fxaqFUvlCFXbusXvk559ikFrfeaqU4nHPFSixFAV0Cbd8OXbrYhEODBsF//hNCEH/8YSP6HnjArrdrB3fc4UnCuWLKE0US2bEDzjvPpmjo3x8uvTTBAaSlwfPPW9PStGlJWKvcORcGb3pKErt2wYUXwrBhVj/vyisTHMDcuXD55TB5Mpx1FvTtC9WrJzgI51wy8kSRBHbtsiamL7+0qtw9eoQQRHq6VRocPNgylhfxc84FPFGELDXVmpg+/RReeAFuuCGBG5882bLTY49ZEb8//rDOa+eci+B9FCFKS7MpGz78EJ5+2k4qSoht2+DOO+H44+Htt63SIHiScM5lyxNFSNLT4eqr4f337Qf9XYmaQ3DMGOusfu45+L//8yJ+zrlcedNTCNLT4Zpr7PTXXr1sptCE2LLFBmXsv78ljJNOStCGnXOFmR9RJJgq3Hijnf56//3w0EMJ2OjYsZadMor4zZjhScI5FzNPFAmkav0QffvC3XfbRHBxPblo9Wo7napdOxviDVaGY99947hR51xR401PCaJq/RAvv2zJ4skn45gkVO0015tvhs2bLSN5ET/nXD55okgAVeuHeO45a3Z6/vk4H0ncdBP06QPHHQdvvWWnvjrnXD55okiAhx+2I4hrrrEjirgkifR0G5RRujScfz4ceaQlDK/P5JzbS3HtoxCR00RknogsEJGe2dx/sYjMCP4mikiTeMYTht697e+qq+C11+KUJH7/3aYhvf9+u37SSV7p1TlXYOKWKESkBNAHOB1oAPxHRLK2gSwC2qpqY6A30C9e8YThqafsrKbLLoN+/WCfgt7bqanw7LPQuDFMnw716xfwBpxzLr5NTy2BBaq6EEBEhgCdgdkZC6jqxIjlJwE14hhPQj3/PPTsCRddBAMGxCFJzJljGWjqVOjc2Q5XDj20gDfinHPxbXqqDiyNuJ4S3JaTq4Dh2d0hIj1EZKqITF2dUW4iib38sk3fcMEFViEjbi1Aq1ZZ/Y/PP/ck4ZyLm3gmiuxa4zXbBUXaYYninuzuV9V+qtpCVVtUTfJyE3372syh55xj5TlKFuQx26RJcO+9drl+fSvi17WrV3p1zsVVPBNFCnBYxPUawPKsC4lIY6A/0FlV18Yxnrh78024/nqbzmHIEChVqoBWvHUr3HYbnHCCZZ+Mo6oC24BzzuUsnoliClBHRGqLSGmgGzA0cgERqQl8BlyqqvPjGEvcDRpkp7+efjp8/HEBFmL97js4+mh48UXLQl7EzzmXYHHrzFbVVBG5ERgJlAAGqOosEbk2uP914CGgMvCaWPNJqqq2iFdM8fL++zYjXYcO8NlnUKZMAa14yxYbUX3ggTBuHLRpU0Ards652Ilqtt0GSatFixY6derUsMPY7cMP7cymtm3h668LqIzS6NG2whIlbO7qBg2gXLkCWLFzrrgSkWn5/SHuRQH3wqefwsUXw7//DV99VQBJYtUq65xu3z6ziF/z5p4knHOh8kSRT19+aa1CrVrBN99A+fJ7sTJVePddO3LImJr0oosKLFbnnNsbXuspH775xsZINGtm0ztUrLiXK7zhBjuv9vjjrYifj7B2ziURTxR5NHIknHuuVc0YORL22y+fK0pPh127rOf7wgstOVx/vddncs4lHW96yoPvv4cuXayFaNQom1E0X+bNs87qjCJ+bdt6pVfnXNLyRBGjH36wgXR16sC339oZq3m2a5fVG2/SBGbOhEaNCjxO55wraN70FIMff4Qzz4RatWz8W5Uq+VjJrFlw6aXwyy/WdtWnDxx8cEGH6pxzBc4TRS4mTbLR1tWrW9PTQQflc0UlSsC6dfDJJ3DeeQUao3POxZM3PUUxZQqceipUq2Zj4A45JI8rmDgR7gnqHNarBwsWeJJwzhU6nihy8PPP0LEjVK4MY8bYEUXMtmyBm2+G1q1t6PaaNXZ7gZaSdc65xPBEkY0ZM+CUU+zU19Gj4bDDcn/MbqNGWRG/V1+FG2+0Tut8dWo451xy8J+4WcyaZRU09t3XjiRq1crDg7dssZoelSvD+PFW28M55wo5P6KIMHeuJYlSpexI4vDDY3zgt99CWhpUqGBHFNOne5JwzhUZnigCv/8OJ59sl0ePtvESuVqxwjqnO3a0WuMAxxwDZcvGLU7nnEs0TxTYjKLt2kFqqp0CW69eLg9QtZmKGjSwwk9PPulF/JxzRVax76NYvNiOJP7+2/okGjaM4UHXXQdvvGFnNfXvD3XrxjtM5wqlXbt2kZKSwvbt28MOpdgoW7YsNWrUoFQBTpVcrBPF0qV2JLFpkzU3NW4cZeHIIn4XXWQLX3st7OMHZc7lJCUlhYoVK1KrVi2CWSxdHKkqa9euJSUlhdq1axfYeovtt9yyZZYk1q+3vuhjjomy8Jw5Ng3pfffZ9RNPtEqvniSci2r79u1UrlzZk0SCiAiVK1cu8CO4YvlNt2KFNTf99ZeVCm+R0+SAu3bB449D06Z2SlTUbOKcy44nicSKx/4udk1Pq1bZKbDLllmSaNUqhwVnzYJLLrFTXS+4AF55xWp5OOdcMVOsjijWrIEOHawD+5tvchnqULIkbNwIn30GH33kScK5Quzzzz9HRJg7d+7u28aOHUunTp32WK579+588skngHXE9+zZkzp16nD00UfTsmVLhg8fvtexPPHEExx55JHUrVuXkSNH5rjcK6+8Qt26dWnYsCF33303AN9++y3NmzenUaNGNG/enNGjR+91PLEoNkcU69ZZkliwwJJE27bZLDR+vM1Z/eyzdibT/Plen8m5ImDw4MG0bt2aIUOG0KtXr5ge8+CDD7JixQpmzpxJmTJlWLVqFT/88MNexTF79myGDBnCrFmzWL58OR06dGD+/PmUyDJp2ZgxY/jyyy+ZMWMGZcqU4a+//gKgSpUqfPXVVxx66KHMnDmTU089lWXLlu1VTLEoFt+CGzZY7aa5c2Ho0MyBdbtt3gw9e8Jrr0Ht2na5ShVPEs4VoFtvtZbcgtS0Kbz4YvRltmzZwoQJExgzZgxnn312TIli27ZtvPnmmyxatIgyZcoAUK1aNbp27bpX8X755Zd069aNMmXKULt2bY488kgmT57M8ccfv8dyffv2pWfPnru3fVAwv8ExEf2kDRs2ZPv27ezYsWP3cvFS5JueNm60UuEzZ1orUseOWRYYPtwGT/Tta+/k337zIn7OFSFffPEFp512GkcddRQHHnggP//8c66PWbBgATVr1mS//fbLddnbbruNpk2b/uPvySef/Meyy5Yt47CIKqM1atTI9ohg/vz5jB8/nlatWtG2bVumTJnyj2U+/fRTjjnmmLgnCSjiRxSbN9ukQz//bEnijDOyWeCyy2w2ookT4bjjQonTueIgt1/+8TJ48GBuvfVWALp168bgwYNp1qxZjmcH5fWsoRdeeCHmZVU1pu2lpqayfv16Jk2axJQpU+jatSsLFy7cveysWbO45557GDVqVJ5iza8imyi2brXpSydPtr7os84K7lC1051OOQUqVrS5TevVs4F0zrkiZe3atYwePZqZM2ciIqSlpSEiPP3001SuXJn169fvsfy6deuoUqUKRx55JEuWLGHz5s1UrFgx6jZuu+02xowZ84/bu3XrRs+ePfe4rUaNGixdunT39ZSUFA499NB/PLZGjRqce+65iAgtW7Zkn332Yc2aNVStWpWUlBTOOecc3nnnHY444oi87I78U9VC9de8eXPNzdatqiedpLrPPqpDhkTcsXy5apcuqqD69tu5rsc5t3dmz54d6vZff/117dGjxx63nXjiiTpu3Djdvn271qpVa3eMixcv1po1a+qGDRtUVfWuu+7S7t27644dO1RVdfny5fruu+/uVTwzZ87Uxo0b6/bt23XhwoVau3ZtTU1N/cdyffv21QcffFBVVefNm6c1atTQ9PR0Xb9+vTZu3Fg/+eSTqNvJbr8DUzWf37uhf/Hn9S+3RLFtm2qHDqoiqu+9F9yYnq761luqlSqpli2r+vTTqrt2RV2Pc27vhZ0o2rZtq8OHD9/jtpdeekmvvfZaVVX98ccftVWrVtqkSRNt0aKFjho1avdyO3bs0LvuukuPOOIIbdiwobZs2VJHjBix1zE9+uijevjhh+tRRx2lw4YN2337VVddpVOmTNm97YsvvlgbNmyoxxxzjH7//feqqtq7d2/dd999tUmTJrv/Vq1a9Y9tFHSiEM2mzSyZtWjRQqdOnZrtfTt2QJcu1rI0cCBcfnlwxzXXQL9+Vnqjf/8Ya4g75/bWnDlzqF+/fthhFDvZ7XcRmaaqOdWhiKrI9FHs3Annnw8jRlguuPySNNi+y+aGuOQSK7/Ro4fXZ3LOuTwqEt+au3ZBt27w9dd2lutVx82yYdcZRfzatPFKr845l0+F/pszNdWmqf78c3j1+Z1cu7q3HT0sWADHHht2eM4Ve4Wtebuwi8f+LtRNT2lpNgzi44/h7Tt/47KBF9uAuW7d4OWXoWrVsEN0rlgrW7Ysa9eu9VLjCaLBfBRlC3g65kKbKNLS4IorYPBgm4n0si6l4fNtVqvp7LPDDs85h40HSElJYfXq1WGHUmxkzHBXkAplokhPt37pP9/9gZ9OGMpx9zwH1IV58yBLcS3nXHhKlSpVoDOtuXDEtY9CRE4TkXkiskBEemZzv4jIy8H9M0SkWSzrvf3qTbQYcB0/cBLHrfzC6oeDJwnnnIuDuB1RiEgJoA9wCpACTBGRoao6O2Kx04E6wV8roG/wP0erF2zkjmkNqS7L0VtvRx7tDfvuG58n4ZxzLq5NTy2BBaq6EEBEhgCdgchE0Rl4Jxg1OElE9heRQ1R1RU4rPWDjYkpVrot8/QlyXNSc4pxzrgDEM1FUB5ZGXE/hn0cL2S1THdgjUYhID6BHcHXHIWtnzeR4r/QKVAHWhB1EkvB9kcn3RSbfF5nq5veB8UwU2Z0Ll/UE31iWQVX7Af0ARGRqfoehFzW+LzL5vsjk+yKT74tMIpJ97aMYxLMzOwU4LOJ6DWB5PpZxzjkXongmiilAHRGpLSKlgW7A0CzLDAUuC85+Og7YGK1/wjnnXOLFrelJVVNF5EZgJFACGKCqs0Tk2uD+14FhwBnAAmAbcEUMq+4Xp5ALI98XmXxfZPJ9kcn3RaZ874tCV2bcOedcYhX6ooDOOefiyxOFc865qJI2UcSr/EdhFMO+uDjYBzNEZKKINAkjzkTIbV9ELHesiKSJyPmJjC+RYtkXInKSiEwXkVki8kOiY0yUGD4jlUTkKxH5NdgXsfSHFjoiMkBE/hKRmTncn7/vzfzOoRrPP6zz+w/gcKA08CvQIMsyZwDDsbEYxwH/CzvuEPfFCcABweXTi/O+iFhuNHayxPlhxx3i+2J/rBJCzeD6QWHHHeK+uA94KrhcFVgHlA479jjsixOBZsDMHO7P1/dmsh5R7C7/oao7gYzyH5F2l/9Q1UnA/iJySKIDTYBc94WqTlTV9cHVSdh4lKIolvcFwE3Ap8BfiQwuwWLZFxcBn6nqEgBVLar7I5Z9oUBFsUkxKmCJIjWxYcafqo7DnltO8vW9mayJIqfSHnldpijI6/O8CvvFUBTlui9EpDpwDvB6AuMKQyzvi6OAA0RkrIhME5HLEhZdYsWyL14F6mMDen8DblHV9MSEl1Ty9b2ZrPNRFFj5jyIg5ucpIu2wRNE6rhGFJ5Z98SJwj6qmFfEZ1WLZFyWB5kB7oBzwk4hMUtX58Q4uwWLZF6cC04GTgSOAb0VkvKpuinNsySZf35vJmii8/EemmJ6niDQG+gOnq+raBMWWaLHsixbAkCBJVAHOEJFUVf0iIREmTqyfkTWquhXYKiLjgCZAUUsUseyLK4An1RrqF4jIIqAeMDkxISaNfH1vJmvTk5f/yJTrvhCRmsBnwKVF8NdipFz3harWVtVaqloL+AS4vggmCYjtM/Il0EZESorIvlj15jkJjjMRYtkXS7AjK0SkGlZJdWFCo0wO+freTMojCo1f+Y9CJ8Z98RBQGXgt+CWdqkWwYmaM+6JYiGVfqOocERkBzADSgf6qmu1pk4VZjO+L3sAgEfkNa365R1WLXPlxERkMnARUEZEU4GGgFOzd96aX8HDOORdVsjY9OeecSxKeKJxzzkXlicI551xUniicc85F5YnCOedcVJ4oXFIKKr9Oj/irFWXZLQWwvUEisijY1s8icnw+1tFfRBoEl+/Lct/EvY0xWE/GfpkZVEPdP5flm4rIGQWxbVd8+emxLimJyBZVrVDQy0ZZxyDga1X9REQ6As+qauO9WN9ex5TbekXkbWC+qj4WZfnuQAtVvbGgY3HFhx9RuEJBRCqIyPfBr/3fROQfVWNF5BARGRfxi7tNcHtHEfkpeOzHIpLbF/g44MjgsbcH65opIrcGt5UXkW+CuQ1misiFwe1jRaSFiDwJlAvieD+4b0vw/8PIX/jBkcx5IlJCRJ4RkSli8wRcE8Nu+YmgoJuItBSbi+SX4H/dYJTyI8CFQSwXBrEPCLbzS3b70bl/CLt+uv/5X3Z/QBpWxG068DlWRWC/4L4q2MjSjCPiLcH/O4D7g8slgIrBsuOA8sHt9wAPZbO9QQRzVwAXAP/DCur9BpTHSlPPAo4BzgPejHhspeD/WOzX++6YIpbJiPEc4O3gcmmskmc5oAfwQHB7GWAqUDubOLdEPL+PgdOC6/sBJYPLHYBPg8vdgVcjHv84cElweX+s7lP5sF9v/0vuv6Qs4eEc8LeqNs24IiKlgMdF5ESsHEV1oBqwMuIxU4ABwbJfqOp0EWkLNAAmBOVNSmO/xLPzjIg8AKzGqvC2Bz5XK6qHiHwGtAFGAM+KyFNYc9X4PDyv4cDLIlIGOA0Yp6p/B81djSVzRr5KQB1gUZbHlxOR6UAtYBrwbcTyb4tIHawaaKkctt8ROFtE7gyulwVqUjRrQLkC4onCFRYXYzOTNVfVXSKyGPuS201VxwWJ5EzgXRF5BlgPfKuq/4lhG3ep6icZV0SkQ3YLqep8EWmO1cx5QkRGqeojsTwJVd0uImOxstcXAoMzNgfcpKojc1nF36raVEQqAV8DNwAvY7WMxqjqOUHH/9gcHi/Aeao6L5Z4nQPvo3CFRyXgryBJtAP+lXUBEflXsMybwFvYlJCTgH+LSEafw74iclSM2xwHdAkeUx5rNhovIocC21T1PeDZYDtZ7QqObLIzBCvG1gYrZEfw/7qMx4jIUcE2s6WqG4GbgTuDx1QClgV3d49YdDPWBJdhJHCTBIdXInJMTttwLoMnCldYvA+0EJGp2NHF3GyWOQmYLiK/YP0IL6nqauyLc7CIzMASR71YNqiqP2N9F5OxPov+qvoL0AiYHDQB3Q88ms3D+wEzMjqzsxiFzW38ndrUnWBzicwGfhaRmcAb5HLEH8TyK1ZW+2ns6GYC1n+RYQzQIKMzGzvyKBXENjO47lxUfnqsc865qPyIwjnnXFSeKJxzzkXlicI551xUniicc85F5YnCOedcVJ4onHPOReWJwjnnXFT/Dw/fJPBWdStVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Fgt5zuHPHWIpXuCqgcVmNwhhj0oC/Vipt2wml1lbhdLkuhM55m/pl8gY6LMBqFMYYE1BRuw+z6uanmFl9AAcOQPvpD3DPpo8pkkaSBFiiMMaYgNk4YCqRpW7i5rBJVK2irF8PTZoEOqpLWdOTMcakspOb97C5cWcqb/metdlvYdOnc3no2cqBDitRVqMwxphU9Ouv0LLubq7fMofptd6nzN5l3JaGkwRYojDGmFRxfO12Jt/2KQ0bwj95b2HdT7t4aOnLBF+V9ht2LFEYY4w/xcYS1uETgqpU5L4/XuWtZ/fy999Q697AXvJ6OdJ+KjPGmHTqyNINHGjSnooHl/J73sYETxrJGw+k/p3VV8pqFMYYk8JU4bsJp4itXZdCBzcyremXVD84i8oPlAp0aMliicIYY1LQ/sUbadZUebh1bvqW/Yr9C8Jp+v2TZM8hgQ4t2SxRGGNMCtBTp1l3by8K3VGBArO+4oMPYEh4I26qVyzQoV0xO0dhjDFXaO83i4lp056bT/3LD8Xa8+qP91M2NNBRpRyrURhjTDLFxcGf973F/x69g+jTMUzvPI/7do+mbGiBQIeWoqxGYYwxyfDvJqVdeyH4t1DalupG6Oz+PBSSJ9Bh+YXVKIwx5jLE7D3I+lueZHKF/qxdCw+PvY9m2wdzbQZNEmCJwhhjfKPKzoHfcPyaEG5YNYXry2YhPBzatAFJvxc0+cSanowxJglnt+9m6z3PcePGGazOGsq+9+fxeM9KGT5BnGM1CmOM8WLlSnjyrr0U3zifr6p8SMmdf9D45cyTJMAShTHGJCgqfCs/3DmEGjXgt5PV+P2rnbRa3YMiV2e+hhhLFMYY4yk2lq1dPkIrVqTu/Dd58dG9hIfDvY8XCHRkAZP5UqMxxiTi1Ir17HugHdftW86vue4j25gRDH48/XXil9IsURhjDPDrD6eo+tAd5I0Txt09iYe/bUne4Ex0IsILa3oyxmRqx5eF066t0vDB3HS/egpbZobz9M+PWZLwYInCGJM5nTrF5qY9yVPrZmLGTaRXL/js34bUfKBIoCNLc6zpyRiT6RydvpDTTz5D2cjNfFOwI12/e5Cq9QIdVdplNQpjTKahCmHN36RA0/qcjFS+fHo+D+0ZQdV6+QMdWppmNQpjTKaw+z/l2eeEmJnVeazYS1T9oR+tb80d6LDSBb/WKESksYj8IyKbRaR3AtPzi8gPIvK3iKwXkTb+jMcYk/no/gNsqfk446/vx9y5UP/D+2gZMZAKliR85rdEISJBwDDgHiAEeExEQuLN9jwQrqqVgXrAIBHJ7q+YjDGZiCoHPp7E8ZI3cc3yqRS8Ojtr10KPHpDV2lIuiz+LqzqwWVW3AojIFKAJEO4xjwLBIiJAXuAwEOPHmIwxmUDczgh23PcsZcJ+ZEWWGmx+9XOe6VeBLHZWNln8WWwlgF0ewxHuOE9DgZuA3cA64EVVjYu/IhHpICIrRWTlgQMH/BWvMSYD2LQJ2jc5QMGwxYwoP5ii/y7hsQGWJK6EP4suobtVNN7w3cAaoDhQBRgqIvkuWUh1lKqGqmpokSJ2jbMx5lIxGzcz/4GPqFQJpm2vyo/Dd9FxQzeuvS4o0KGle/5MFBHANR7DJXFqDp7aAN+rYzOwDbjRjzEZYzKamBj2vDSQmJCbqfbjW7Ssv4/wcGj1bL5M1RW4P/kzUawAyolIGfcEdUtgZrx5dgJ3AohIMaA8sNWPMRljMpDoVev479rbuHpwTxZka8Rvn63ni1nFuPrqQEeWsfjtZLaqxohIZ2AOEASMVdX1ItLJnT4C6A+ME5F1OE1VvVT1oL9iMsZkHH/9dorr6tcnW2wWPr19Co9Na0HhIlaF8Ae/XiSmqrOAWfHGjfB4vRto5M8YjDEZS9TKMN74ugKDBufm4au+pu3HlenSqnCgw8rQ7DoAY0z6cPIkEY92J/utldgzcCJt28LIzXdytyUJv7PbTowxad6pH37l1BPPUPL4NiYEP0e7iU2o92Cgo8o8rEZhjEnTtrR6ndwPNuTw8ax88vAimu0ZRr0HL7mK3viR1SiMMWnSkUNxvNQzC3sm3Uazgi9T8du+vNAgV6DDypSsRmGMSVv27yeiTkvGXvsWX34JVfvcw5P/vU8tSxIBYzUKY0zaoMrxz75Cur1IkbORBP2vH38uhmrVAh2YsURhjAk43bmL3U06UWLNLP6QWqzpPIbnBoWQ3fqSThOs6ckYE1D//QddnzxEnjVLGFTqY/Kt+Y1nP7UkkZZYjcIYExD6zyb+fG0md//SgzNnqlD27V107RVMkPXhl+ZYjcIYk7piYjjc632iQypxw9S3qR+yj3XroMsrliTSKqtRGGNSTdzqvzn4YFuKRqxiZlBTjr43jO96FrNnRaRxliiMMali05pTFK1+J3ExWRlQZSqtZzSnVKlAR2V8YXncGONXMavW8t67SqWauWmd81sWDA3n1VWWJNITSxTGGP+IjORgqxfJcksVwl+ZwH33wah/6/PY8wXtgULpjDU9GWNS3NmffuHkEx0ofHQ7n+fqTLORTXnoyUBHZZLLEoUxJkXtbvMqxce9w1bKM7TRbzw3qTaFCgU6KnMlfE4UIpJHVU/6MxhjTPp1KjKON9/KQvj42jTO24eyX77B601zBjoskwKSPEchIreJSDiwwR2uLCLD/R6ZMSZ92LuXA/UeZmypvgwcCCWfuYfWEe9wjyWJDMOXk9kfAXcDhwBU9W+grj+DMsakA6qcHjGOk6VDCF70I8clH7/+CiNHQv78gQ7OpCSfmp5UdZdcfJlCrH/CMcakCzt2cLBZBwqvmstv1Oa31mN4cXh58uQJdGDGH3ypUewSkdsAFZHsItIDtxnKGJP5HD4Mr3c5SpZVK+hfbChZf1/EK+MtSWRkvtQoOgEfAyWACGAu8Jw/gzLGpEH//EPYOzO5a25PDhyoTNYeO+nVPy857VREhudLoiivqq08R4jI7cAS/4RkjElToqOJ7DuQ7O+9xdVxebipwlPMmlWUqlXzBjoyk0p8aXr61MdxxpgMRlet5nC5GuR95xV+0AeY0DucOauLUrVqoCMzqSnRGoWI1AJuA4qISHePSfkA6wzYmAzuv39PkbfmXZyJzkbvct/RenozmocEOioTCN5qFNmBvDjJJNjj7zjwsP9DM8YEgq5azehRSkhoblrIVKYNCOftDc0IsSSRaSVao1DVRcAiERmnqjtSMSZjTCCcOMGx5/uQf8IwfmM81eq1ZviYelx/faADM4Hmy8nsUyLyIVABOH99g6o28FtUxphUFfvTz5x8siPBR3YxPNuL3PFhM9p0wR4oZADfTmZ/BWwEygBvAduBFX6MyRiTig516EPQ/fcQcSQPPWst4YEtQ2j3Yl5LEuY8X2oUhVT1cxF50aM5apG/AzPG+Fd0VCwDPwpiybh61MuZlauHvcbANjnsWRHmEr4kimj3/x4RuQ/YDZT0X0jGGL/as4ejTzzPt+sr8Mq+/jRvfjdPDL2b//0v0IGZtMqXRDFARPIDL+HcP5EP6OrPoIwxfqBK9OhxxLzYnRxRUUTkrc3UqdC8eaADM2ldkolCVX90Xx4D6sP5O7ONMenF9u0ca/EM+VfM4w/q8ONDY+j9+Q0ULBjowEx6kOjpKhEJEpHHRKSHiFR0x90vIkuBoakWoTHmipw6BQPfOEbMilW8UmA4kT8s5INpliSM77zVKD4HrgH+BD4RkR1ALaC3qk73ZeUi0hinQ8EgYIyqvpfAPPWAIUA24KCq3uF7+MaYRIWHs3XITBrN782WLZXZ1W4n/QfnIV++QAdm0htviSIUqKSqcSKSEzgIlFXVvb6sWESCgGHAXTi9zq4QkZmqGu4xTwFgONBYVXeKSNFkvg9jzDlnzxLV/wOC3ulPvrhgCl7bljELilKvnvUDbpLHW6I4q6pxAKoaJSKbfE0SrurAZlXdCiAiU4AmQLjHPI8D36vqTnc7+y8remPMxVau5HiLduTbtpbJtGRDx49ZMKioPSvCXBFvieJGEVnrvhbgendYAFXVSkmsuwSwy2M4AqgRb54bgGwishCnH6mPVfXL+CsSkQ5AB4BSpUolsVljMqfDu06S/fa7OXE2J31KzuDJbx/ksZqBjspkBN4SxU1XuO6EbtvRBLZ/C3AnkAv4Q0SWqeqmixZSHQWMAggNDY2/DmMyt1Wr+G5LFZ7vkoebYqbRsEclBg8oQI4cgQ7MZBTeOgW80o4AI3BOhp9TEudmvfjzHFTVk8BJEVkMVAY2YYzx7vhxTr3Qm9zjP2MG4yletTUf/VyXKlUCHZjJaPzZm8sKoJyIlBGR7EBLYGa8eWYAdUQkq4jkxmmasudxG5ME/WkWJ8tUIMf4kQzJ0p1KfZuzfDmWJIxf+HJndrKoaoyIdAbm4FweO1ZV14tIJ3f6CFXdICI/A2uBOJxLaMP8FZMxGcHxZ3uRb8QHbCeEjypO5aVvanDTlTYUG+OFT4lCRHIBpVT1n8tZuarOAmbFGzci3vCHwIeXs15jMh1V4mLiGD02iNnj76R61pwEv/sKI7vlIMieN2n8LMmmJxF5AFgD/OwOVxGR+E1Ixhh/+e8/Tt71EJPKvUmnTnCiViNa/vMWXXpYkjCpw5dzFH1x7ok4CqCqa4DS/grIGONSJW7kaM6UDSHLr3MJ21uYUaNg3jy47rpAB2cyE1+anmJU9ZhYJ/XGpJ5t2zj5WDvyLF/AUuoxqd5o3pxQlpLWwb8JAF9qFGEi8jgQJCLlRORTYKmf4zIm04qOhtEfRXJ6+Vq65R7J7i9/ZdR8SxImcHypUXQBXgXOAJNwrmIa4M+gjMmUwsLYPWIm9y15hTVrbmZR050M+iw3xYoFOjCT2fmSKMqr6qs4ycIYk9LOniW6/7vIO2+TLS4/0UXa8913RWnWLHegIzMG8C1RDBaRq4FvgSmqut7PMRmTeaxYwamWbcm9NYyveJxljw5h8fAi9qwIk6YkeY5CVesD9YADwCgRWScir/k7MGMyupP7T3KybmMObz1C28IzKTT7Kz6dYknCpD0+deGhqntV9ROgE849FW/4MyhjMrSVK1nwaxyVauXh7qgZDG67niFbHqBx40AHZkzCfLnh7iYR6SsiYTiPQF2K08GfMeZyHDvGmTYd4dZb+aLhRLJkgbcX1mbw5/ntqXMmTfPlHMUXwGSgkarG7/3VGOOLH34gqk0nsh3ay0B6UOKFh/n7Xcht56tNOpBkolBVe/SJMVfgdOee5Bo2kE3czNtlptN98q3UiP8IL2PSsEQThYh8o6otRGQdFz9wyNcn3BmTealCbCxTp2fl668aEZIlH1l69+LLN7LbA4VMuuOtRvGi+//+1AjEmAwjIoKots/y085KPPLP21Srdhevjb2LypUDHZgxyZPoyWxV3eO+fE5Vd3j+Ac+lTnjGpCNxceiIkZwtF0LsL/P5fcv/ePddWL4cSxImXfPl8ti7Ehh3T0oHYky6tnUrUbc3QJ7txOKo6jxdbR0d13Whd2/I6rfHgxmTOrydo3gWp+ZwnYis9ZgUDCzxd2DGpBdxcTD1i5PUXx5Ot+xjuOmDtkzpLPasCJNheDvWmQTMBt4FenuMP6Gqh/0alTHpwbp1HBo7g+arX2PRopu5p94Ohn6ey54VYTIcb4lCVXW7iDwff4KIFLRkYTKtM2eI6/82+u67xMZdxa68HRg9uijt2uXCHttiMqKkahT3A3/hXB7r+RVQwI6bTOazbBlRT7Qj55ZwvuRJfm70EYvHFqJEiUAHZoz/JJooVPV+93+Z1AvHmLQr+uhJohvcx8HTeegZPIsmI+7hq8ewWoTJ8JK8HkNEbgfWqOpJEXkCqAYMUdWdfo/OmLRg+XJWBd1K2/Z5yH36B25odjOffhZM0aKBDsyY1OHL5bGfAadEpDLwMrADmODXqIxJC44eJaZNe6hZk0+qT2TfPnh52m2M+86ShMlcfLnCO0ZVVUSaAB+r6uci8pS/AzMmoKZP52z758hyaD/v0YvsrR4h/BO46qpAB2ZM6vOlRnFCRPoATwI/iUgQkM2/YRkTONFdukPTpoQfKkqT/y2n6s/vMWpCLksSJtPypUbxKPA40FZV94pIKeBD/4ZlTCpzO/H7dVFWxk29l1IU4kSnl5nyQTaCgwMdnDGB5Us343tF5CvgVhG5H/hTVb/0f2jGpJKdO4lu34lfDlTlvjVvU7ZsQ55Z1JC6dQMdmDFpgy9PuGsB/Ak8ArQAlovIw/4OzBi/i4uD4cOJubEC0fMWMevv4vTsCWvXYknCGA++ND29CtyqqvsBRKQIMA+Y6s/AjPGrzZuJfrIt2Zb9xnzuYmC5Ubz9VWluvTXQgRmT9vhyMjvLuSThOuTjcsakSarw8/QojizfRLssX7Dk9Tn8sM6ShDGJ8aVG8bOIzMF5bjY4J7dn+S8kY/xkzRpOTJzBU1vfZNq0itSqup0R43JSyZ7VaIxXvpzM7ikizYDaOP09jVLVaX6PzJiUEhWF9uuPvv8+p7QwK7I9y/vvF6V795z2rAhjfODteRTlgIHA9cA6oIeq/pdagRmTIpYuJbp1O7Jt2ch4nuLr6oOZ92VBypcPdGDGpB/ezjWMBX4EmuP0IPtpqkRkTAqJO3GSqEYPsGfrKZrk+JnIT8fx0x+WJIy5XN4q3sGqOtp9/Y+IrEqNgIy5Yn/8wb8Fa9C+Qx7OnvyRIvUq8skXwZQuHejAjEmfvNUocopIVRGpJiLVgFzxhpMkIo1F5B8R2Swivb3Md6uIxNr9GeaKHDlCXJu2cNttvH/zBP7+G9qPqcWM+ZYkjLkS3moUe4DBHsN7PYYVaOBtxW6fUMOAu4AIYIWIzFTV8ATmex+Yc3mhG+Ph+++J7vg8cvAA79GHo40eJXwUFC8e6MCMSf+8Pbio/hWuuzqwWVW3AojIFKAJEB5vvi7Ad4BdxW6SJfaFbgR9OoT1VKF7gVl0+KwqfR61BwoZk1L8eXFgCWCXx3AEUMNzBhEpATTFqZ0kmihEpAPQAaBUqVIpHqhJh9xO/P76OysjfryfwhRlV4sefD00G0WKBDo4YzIWf95hndDxnMYbHgL0UtVYbytS1VGqGqqqoUXsV8Bs305so8YsqPM6NWrAT1F3UmtGHyZ+bUnCGH/wZ40iArjGY7gksDvePKHAFHHaCAoD94pIjKpO92NcJr2Ki4Nhw4jt1YeoKOFrbcpTbWHQIChQINDBGZNx+fLMbAFaAdepaj/3eRT/U9U/k1h0BVBORMoA/wEtcZ5rcZ6qlvHYzjjgR0sSJkH//kts6zYELVvCXBozoMQI3hx7LY0aBTowYzI+X2oUw4E4nPMI/YAT+HDyWVVjRKQzztVMQcBYVV0vIp3c6SOuJHCTufyx6CzXr9jCS3xJgeefYM57Qt68gY7KmMzBl0RRQ1WrichqAFU9IiLZfVm5qs4iXgeCiSUIVX3al3WaTGT1aqK+nkHng335/PMKhFy/nRFf5KBOnUAHZkzm4svJ7Gj3XgeF88+jiPNrVCZzi4qCPn2IC72VYx+O5IexB3j5ZVi5zpKEMYHgS43iE2AaUFRE3gYeBl7za1Qm8/r9d2KebkfWLZsYRxs+DxnET+OvIjQ00IEZk3n50s34VyLyF3AnziWvD6nqBr9HZjIdPRHJ2XuasPdUPjoFzaXm63exoA9k96mh0xjjL75c9VQKOAX84DlOVXf6MzCTifz+O7tL38ZznfOyN/InsletyLDxebn55kAHZowB35qefsI5PyFATqAM8A9QwY9xmczg0CG0WzdkwgT65xrHHH2Kfh/UpFs37IFCxqQhvjQ9XXRc5/Yc29FvEZmMTxWmTiX2uc7oocO8w+tsqtaSv8fCDTcEOjhjTHyX3YWHqq7COvAzV0C7doMWLfj78DXUzrGSQkP78cviHJYkjEmjfDlH0d1jMAtQDTjgt4hMxqQKMTFs2paN4QseJDvFWdegO1+Pycq11wY6OGOMN760BAd7vI7BOWfxnX/CMRnStm3EPdOBFbG3UG/Ze+TM2YDBYxvw/tPWFbgx6YHXROHeaJdXVXumUjwmI4mNhaFDiev9CqfOBvF53CPc3QSGD7cHChmTniR6jkJEsrrdf/v02FNjLrJpE3G160DXrsw5cwe1C6znzikdmDbNkoQx6Y23GsWfOElijYjMBL4FTp6bqKrf+zk2k46FrYmhyModdGcitHyceZ8IhQsHOipjTHL4co6iIHAIp/fYc/dTKGCJwlxs5Uqip87gNe3PwIEhlCq2lU9G5uCBBwIdmDHmSnhLFEXdK57CuJAgzon/pDqTmZ0+DW++iQ4axGH5H2NjX6Bt+yJ8+GEOe6CQMRmAt0QRBOTFt0eamsxq0SLi2rUny5bNjOEZPr3mAyaPLUDDhoEOzBiTUrwlij2q2i/VIjHpT2QkZx9oxp7TBWjLr1To0oCl72APFDImg/GWKOwKd5Ow337jaIXbealnXtadmE10uQoM/SIPt98e6MCMMf7grQuPO1MtCpM+HDwITzwBdevy5nUTGD8e7uxdnT/WWpIwJiNLtEahqodTMxCThqnCN98Q17kLcYeOMIA3WXJNS5aPg1tuCXRwxhh/u+xOAU3moy+8CC1bsuZoaaoHrSKoX1+W/pXDkoQxmYT1+m8SpgrR0ew+mJ3hq5pymmtZUqUrX34RRMWKgQ7OGJOaLFGYS23Zgj7zDOuyh1J32QecOVOf/h/W5/eu9kAhYzIj+9qbC2Jj4eOPiXv1NU5HZ+Pj2FZUrgtjxkC5coEOzhgTKHaOwjg2bkRvuw1eeonZ0Q2pmiOc0OHtWLDAkoQxmZ3VKAwA27bEkXvNbl5kMkfvfJRfRok9UMgYA1iiyNz+/JO4aTP4sMDbvPlmCPlybeHDUdlp3doeKGSMucASRWZ06hS88Qb60UccCLqagdFdubdpEYYNy87VVwc6OGNMWmPnKDKbBQvQijfDoEGM1me4Ld96hn9ThO++w5KEMSZBVqPITCIjiW76CHtOF6A1Cyj5eD2WD8EeKGSM8coSRWawcCGnb63LG2/lZfHx2Rz6XwWGjMrN/fcHOjBjTHpgTU8Z2YED8NhjUL8+b1w3kYEDoXL7W/lrgyUJY4zvrEaREanC5MnEdXmB2KMneIv+zMzdkl9/hQYNAh2cMSa9sRpFRtSlC7RqxZrIclSJW03ki6+xKiy7JQljTLJYjSKjiIuDmBiOnMzOyM0Ps5eyzC3dhTFfBHHbbYEOzhiTnvm1RiEijUXkHxHZLCK9E5jeSkTWun9LRaSyP+PJsP79Fxo04N8WrxISAq/Nq0fuPl1Z9bclCWPMlfNbjUJEgoBhwF1ABLBCRGaqarjHbNuAO1T1iIjcA4wCavgrpgwnJgaGDEFff51TsTl4N7o1xSrDTz9BtWqBDs4Yk1H4s0ZRHdisqltV9SwwBWjiOYOqLlXVI+7gMqCkH+PJWDZsQGvVgp49mR17NxUI57r+bVmxwpKEMSZl+fMcRQlgl8dwBN5rC+2A2QlNEJEOQAeAUqVKpVR86dq+fZAlbB/P8zU7qz7CrC+EkJBAR2WMyYj8WaNIqFs5TXBGkfo4iaJXQtNVdZSqhqpqaJEiRVIwxHRm2TK0dx/GjIEbmtzE9Wyh5qAWLFlqScIY4z/+rFFEANd4DJcEdsefSUQqAWOAe1T1kB/jSb9OnoTXXkM//pgD2UvS50x3qt5RhDFjslG2bKCDM8ZkdP6sUawAyolIGRHJDrQEZnrOICKlgO+BJ1V1kx9jSb/mzUMrVoQhQxgZ9BxVsq2n/2dFmD8fSxLGmFThtxqFqsaISGdgDhAEjFXV9SLSyZ0+AngDKAQMF+cBCDGqGuqvmNKdyEhiHmnJ7qiCtGIxwXfVYflIuOaapBc1xpiU4tcb7lR1FjAr3rgRHq/bA+39GUO6NH8+MbffwcCheZlxcg478oTw3shcPPmkPVDIGJP6rAuPtGTfPmjRAu68k/7lJ9KnD5R48BZWbchlT50zxgSMdeGRFqjCxIlo167EHoukb5a3+eLU43z7LTz8cKCDM8ZkdlajSAuefx5at2bN6fLcHLuGHY+/wtoN2SxJGGPSBKtRBEpcHERHcyo2B+MOPcpGuYkZBZ/js1FB3HtvoIMzxpgLLFEEwj//QPv27CpegwarBrJ58x107HgH6z6AfPkCHZwxxlzMEkVqio6GQYPQvn05rbl47Wx74q6D+fOhfv1AB2eMMQmzcxSpZf16qFED+vRhdpb7uP7sBgp1e4q1ay1JGGPSNqtRpJKjJ4KI2XSYjkxlQ+nmfP851KoV6KiMMSZpVqPwp6VLoVcvvv8ebnzoRkqc3sxNrzZn1SpLEsaY9MMShT9ERsILL6C1a7N/6Nd0aH6Qq6+GZSuzMmAA5MwZ6ACNMcZ3lihS2ty5aMWK6NChjM7emRujw+j+dmH+/BOqVg10cMYYc/nsHEVKiowk9rFW7D5TiJb6G3FVb+f3z7FnRRhj0jWrUaSEX35BY2IZNSkv9c7MpVLsGh756HZ+/92ShDEm/bNEcSX27IHmzaFRI96v9BUdO0K26lVZGZaTrl0hKCjQARpjzJWzpqfkUIXx49Fu3YiNPM1b2d5j6K7HGTkS2reHLJZ+jTEZiCWK5Hj2WRg5kr+Da/NozBjK3luetSPsgULGxBcdHU1ERARRUVGBDiXTyJkzJyVLliRbtmwptk5LFL5yO/GLzpKDKbGPsyKoEpOydmLIhCy0amXPijAmIREREQQHB1O6dGnEviR+p6ocOnSIiIgIypQpk2LrtUThiw0boH179l9Xk8brB7F6dV0efrgu64dCsWKBDs6YtCsqKsqSRCoSEQoVKsSBAwdSdL3Wmu5NdDS88w5apQqnVm+k56Sq7N4N330H335rScIYX1iSSF3+KG9LFIlZvx6qV4dXX2VOziaUOR0OTzxBeDg0axbo4IwxJvVYokjE6eisHNp6jGZ8zzP5vmHcrGKMHw8FCwY6MmPM5Zo2bRoiwsaNG8+PW7hwIffff/9F8z399NNMnToVcE7E9+7dm3LlylGxYkWqV6/O7NmzrziWd999l7Jly1K+fHnmzJmT4Dx9+/alRIkSVKlShSpVqjBr1qzLWj6l2TkKT7/9BjNmsOC+gbRvX54dxzfxTKesrH/fHihkTHo2efJkateuzZQpU+jbt69Py7z++uvs2bOHsLAwcuTIwb59+1i0aNEVxREeHs6UKVNYv349u3fvpmHDhmzatImgBG666tatGz169Ej28inJEgXAiRPQuzcMH86B4DI8Mqg3Ba4vzLwFWalXL9DBGZMxdO0Ka9ak7DqrVIEhQ7zPExkZyZIlS1iwYAEPPvigT4ni1KlTjB49mm3btpEjRw4AihUrRosWLa4o3hkzZtCyZUty5MhBmTJlKFu2LH/++Se1fOxO+kqXTy5repo9GypUQD/7jDF5u3Jd5Dqe6l6YtWuxJGFMBjB9+nQaN27MDTfcQMGCBVm1alWSy2zevJlSpUqRz4emhG7dup1vIvL8e++99y6Z97///uMajxuuSpYsyX///ZfgeocOHUqlSpVo27YtR44cuezlU1LmrlGcOEHck63ZHVOUh3UpJ0rVZN5Y50F0xpiUldSRv79MnjyZrl27AtCyZUsmT55MtWrVEr066HKvGvroo498nldVfdres88+y+uvv46I8Prrr/PSSy8xduxYn5dPaZkvUajCnDlw1118NzeYYTqP5ZE30v21HLz2Gri1TGNMBnDo0CHmz59PWFgYIkJsbCwiwgcffEChQoXOH6mfc/jwYQoXLkzZsmXZuXMnJ06cIDg42Os2unXrxoIFCy4Z37JlS3r37n3RuJIlS7Jr167zwxERERQvXvySZYt5XHv/zDPPnD/p7uvyKU5V09XfLbfcosm2e7fqQw+pgn4SOl5BtWpV1dWrk79KY0ziwsPDA7r9ESNGaIcOHS4aV7duXV28eLFGRUVp6dKlz8e4fft2LVWqlB49elRVVXv27KlPP/20njlzRlVVd+/erRMmTLiieMLCwrRSpUoaFRWlW7du1TJlymhMTMwl8+3evfv868GDB+ujjz56WcsnVO7ASk3m727mqFGowhdfoN27E3vqDP1zfcDAtY/zzjvQowekYJcoxpg0ZPLkyZcc1Tdv3pxJkyZRp04dJk6cSJs2bYiKiiJbtmyMGTOG/PnzAzBgwABee+01QkJCyJkzJ3ny5KFfv35XFE+FChVo0aIFISEhZM2alWHDhp2/Yql9+/Z06tSJ0NBQXn75ZdasWYOIULp0aUaOHJnk8v4kmkCbV1oWGhqqK1euvLyFOnaEUaNYd1Vdmh0ZQ5Fa5Rg7Fm680T8xGmMcGzZs4Kabbgp0GJlOQuUuIn+pamhy1pdxaxSxsRAdTVz2nPwQ/AQLclRlTFQH3h6Shc6d7VkRxhjjq4yZKNavh3btOBpyG023DWbhwjo0aFCHtaPhuusCHZwxxqQvGes+irNnoX9/tGpVTodt5sWJt7JqFYwaBfPmWZIwJhDSW/N2eueP8s44iWLdOggNhTfe4Jd8zSl1cgOHGj3G+vXwzDP2vAhjAiFnzpwcOnTIkkUqUfd5FDlz5kzR9WaYpqeNW7OTf+spOjKDJfogn0yExx+3BGFMIJUsWZKIiIgUfz6CSdy5J9ylpPSdKBYtYv+YmXQ6OYhp08pTIPgfurwexLiu1surMWlBtmzZUvRJayYw/Nr0JCKNReQfEdksIr0TmC4i8ok7fa2IVPNpxcePc+CRZ6FePU5MnM6aeQd5803YuiOIfv0sSRhjTEryW41CRIKAYcBdQASwQkRmqmq4x2z3AOXcvxrAZ+7/RJ3Zf4yDxSpQMGo3Q7N351iP/qzqmZsCBfzyNowxJtPzZ9NTdWCzqm4FEJEpQBPAM1E0Ab50by9fJiIFRORqVd2T2EqDdm3nQJbyzHhmKk98UMMShDHG+Jk/E0UJYJfHcASX1hYSmqcEcFGiEJEOQAd38ExI3PowRtek/eiUDTgdKgwcDHQQaYSVxQVWFhdYWVxQPrkL+jNRJHS9Ufxr5HyZB1UdBYwCEJGVyb0NPaOxsrjAyuICK4sLrCwuEJHL7PvoAn+ezI4ArvEYLgnsTsY8xhhjAsifiWIFUE5EyohIdqAlMDPePDOB1u7VTzWBY97OTxhjjEl9fmt6UtUYEekMzAGCgLGqul5EOrnTRwCzgHuBzcApoI0Pqx7lp5DTIyuLC6wsLrCyuMDK4oJkl0W662bcGGNM6so4fT0ZY4zxC0sUxhhjvEqzicJv3X+kQz6URSu3DNaKyFIRqRyIOFNDUmXhMd+tIhIrIg+nZnypyZeyEJF6IrJGRNaLyKLUjjG1+PAdyS8iP4jI325Z+HI+NN0RkbEisl9EwhKZnrzfzeQ+bNuffzgnv7cA1wHZgb+BkHjz3AvMxrkXoyawPNBxB7AsbgOucl/fk5nLwmO++TgXSzwc6LgDuF8UwOkJoZQ7XDTQcQewLF4B3ndfFwEOA9kDHbsfyqIuUA0IS2R6sn4302qN4nz3H6p6FjjX/Yen891/qOoyoICIXJ3agaaCJMtCVZeq6hF3cBnO/SgZkS/7BUAX4Dtgf2oGl8p8KYvHge9VdSeAqmbU8vClLBQIFhEB8uIkipjUDdP/VHUxzntLTLJ+N9Nqokisa4/LnScjuNz32Q7niCEjSrIsRKQE0BQYkYpxBYIv+8UNwFUislBE/hKR1qkWXerypSyGAjfh3NC7DnhRVeNSJ7w0JVm/m2n1eRQp1v1HBuDz+xSR+jiJorZfIwocX8piCNBLVWMlYz+1ypeyyArcAtwJ5AL+EJFlqrrJ38GlMl/K4m5gDdAAuB74RUR+U9Xjfo4trUnW72ZaTRTW/ccFPr1PEakEjAHuUdVDqRRbavOlLEKBKW6SKAzcKyIxqjo9VSJMPb5+Rw6q6kngpIgsBioDGS1R+FIWbYD31Gmo3ywi24AbgT9TJ8Q0I1m/m2m16cm6/7ggybIQkVLA98CTGfBo0VOSZaGqZVS1tKqWBqYCz2XAJAG+fUdmAHVEJKuI5MbpvXlDKseZGnwpi504NStEpBhOT6pbUzXKtCFZv5tpskah/uv+I93xsSzeAAoBw90j6RjNgD1m+lgWmYIvZaGqG0TkZ2AtEAeMUdUEL5tMz3zcL/oD40RkHU7zSy9VzXDdj4vIZKAeUFhEIoA3gWxwZb+b1oWHMcYYr9Jq05Mxxpg0whKFMcYYryxRGGOM8coShTHGGK8sURhjjPHKEoVJk9yeX9d4/JX2Mm9kCmxvnIhsc7e1SkRqJWMdY0QkxH39SrxpS680Rnc958olzO0NtUAS81cRkXtTYtsm87LLY02aJCKRqpo3pef1so5xwI+qOlVEGgEDVbXSFazvimNKar0iMh7YpKpve5n/aSBUVTundCwm87AahUkXRCSviPzqHu2vE5FLeo0VkatFZLHHEXcdd3wjEfnDXfZbEUnqB3wxUNZdtru7rjAR6eqOyyMiP7nPNggTkUfd8QtFJFRE3gNyuXF85U6LdP9/7XmE79ZkmotIkIh8KCIrxHlOQEcfiuUP3A7dRKS6OM8iWe3+L+/epdwPeNSN5VE39rHudlYnVI7GXCLQ/afbn/0l9AfE4nTitgaYhtOLQD53WmGcO0vP1Ygj3f8vAa+6r4OAYHfexUAed3wv4I0EtjcO99kVwCPAcpwO9dYBeXC6pl4PVAWaA6M9ls3v/l+Ic/R+PiaPec7F2BQY777OjtOTZy6gA/CaOz4HsBIok0CckR7v71ugsTucD8jqvm4IfOe+fhoY6rH8O8AT7usCOP0+5Qn0521/afsvTXbhYQxwWlWrnBsQkWzAOyJSF6c7ihJAMWCvxzIrgLHuvNNVdY2I3AGEAEvc7k2y4xyJJ+RDEXkNOIDTC++dwDR1OtVDRL4H6gA/AwNF5H2c5qrfLuN9zQY+EZEcQGNgsaqedpu7KsmFJ/LlB8oB2+Itn0tE1gClgb+AXzzmHy8i5XB6A82WyPYbAQ+KSA93OCdQiozZB5RJIZYoTHrRCufJZLeoarSIbMf5kTtPVRe7ieQ+YIKIfAgcAX5R1cd82EZPVZ16bkBEGiY0k6puEpFbcPrMeVdE5qpqP1/ehKpGichCnG6vHwUmn9sc0EVV5ySxitOqWkVE8gM/As8Dn+D0ZbRAVZu6J/4XJrK8AM1V9R9f4jUG7ByFST/yA/vdJFEfuDb+DCJyrTvPaOBznEdCLgNuF5Fz5xxyi8gNPm5zMfCQu0wenGaj30SkOHBKVScCA93txBft1mwSMgWnM7Y6OB3Z4f5/9twyInKDu80Eqeox4AWgh7tMfuA/d/LTHrOewGmCO2cO0EXc6pWIVE1sG8acY4nCpBdfAaEishKndrExgXnqAWtEZDXOeYSPVfUAzg/nZBFZi5M4bvRlg6q6CufcxZ845yzGqOpq4GbgT7cJ6FVgQAKLjwLWnjuZHc9cnGcbz1Pn0Z3gPEskHFglImHASJKo8bux/I3TrfYHOLWbJTjnL85ZAIScO5mNU/PI5sYW5g4b45VdHmuMMcYrq1EYY4zxyhKFMcYYryxRGGOM8coShTHGGK8sURhjjPHKEoUxxhivLFEYY4zx6v/PqfhfCXapCwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "print_metrics(knn, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"KNN, combined, imbalanced\")\n",
        "print_metrics(knn, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"KNN, combined, balanced\")\n",
        "print_metrics(knn, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"KNN, has_dep_diag, imbalanced\")\n",
        "print_metrics(knn, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"KNN, has_dep_diag, balanced\")\n",
        "\n",
        "plot_learning_curve(5, knn, X_train_comb_imb, y_train_comb_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, knn, X_train_comb_b, y_train_comb_b, 'Balanced train set, balanced test set')\n",
        "plot_learning_curve(5, knn, X_train_hasdep_imb, y_train_hasdep_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, knn, X_train_hasdep_b, y_train_hasdep_b, 'Balanced train set, balanced test set')\n",
        "\n",
        "plot_roc(knn, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"Imbalanced train set, imbalanced test set, combined variable\")\n",
        "plot_roc(knn, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"Balanced train set, Balanced test set, combined variable\")\n",
        "plot_roc(knn, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"Imbalanced train set, imbalanced test set, has_dep-diag\")\n",
        "plot_roc(knn, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"Balanced train set, Balanced test set, has_dep_diag\")\n",
        "\n",
        "plot_cf(knn, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb)\n",
        "plot_cf(knn, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b)\n",
        "plot_cf(knn, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb)\n",
        "plot_cf(knn, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GsBJBqgDf7QJ"
      },
      "source": [
        "### SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dUv8Ken5f7QK"
      },
      "outputs": [],
      "source": [
        "from sklearn import svm\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svm = SVC(C=5, gamma='auto', kernel='rbf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z4_Fs8tif7QK",
        "outputId": "e4d50208-4cb6-4240-ad8f-d8be777a96c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SVM, combined, imbalanced: \n",
            "F1: 0.5188749074759438\n",
            "recall: 0.517079268458382\n",
            "precision: 0.6132228427310394\n",
            "accuracy: 0.935\n",
            "SVM, combined, balanced: \n",
            "F1: 0.629666700030027\n",
            "recall: 0.63\n",
            "precision: 0.6304696908871938\n",
            "accuracy: 0.63\n",
            "SVM, has_dep_diag, imbalanced: \n",
            "F1: 0.4828700711053653\n",
            "recall: 0.4973368841544607\n",
            "precision: 0.4692211055276382\n",
            "accuracy: 0.93375\n",
            "SVM, has_dep_diag, balanced: \n",
            "F1: 0.6989160979526294\n",
            "recall: 0.7\n",
            "precision: 0.702922077922078\n",
            "accuracy: 0.7\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Kup/69Ut+XrgjduworBusrg7+9rd9G2fJaqEMHQqzZu1fvbM4QJiZdZLevZNl2LCObZc7zpK1tExbtqz5OMuRR5ZggJA0E/gSUAbcERG3tEgfCNwJjAV2AldGxMI0bQWwBWgA6vP9oIWZWanb33GWtlog+6NoAUJSGfBV4M1ALfCkpLkR8XxOtk8BCyLiHZImpPln5KSfGRHrilVGM7NSljvOUpT9F2e3AEwDlkbE8ojYBcwBLmiR5zjgEYCIWAyMkjS0iGUyM7MCFTNADAdezXldm67L9SxwIYCkacDRwIg0LYCHJT0l6ap8B5F0laT5kuavXbu20wpvZna4K2aAyLrjt+XXVm4BBkpaAHwQeAZovAP51IiYCpwDfEDSm7IOEhG3R0RNRNRUV1d3TsnNzKyog9S1wMic1yOAlbkZImIz8B4ASQJeShciYmX6uEbS/SRdVvOKWF4zM8tRzBbEk8B4SaMl9QAuBebmZpBUmaYB/DMwLyI2S+orqV+apy/wFmBhEctqZmYtFK0FERH1kq4FHiK5zfXOiFgk6Zo0/TbgdcB3JDUAzwP/lG4+FLg/aVTQHfheRDxYrLKamVlrirZmsyoxNTU1MX/+/K4uhplZyZD0VL7vmXkmdTMzy+QAYWZmmRwgzMwskwOEmZllcoAwM7NMDhBmZpbJAcLMzDI5QJiZWSYHCDMzy+QAYWZmmRwgzMwskwOEmZllcoAwM7NMDhBmZpbJAcLMzDI5QJiZWSYHCDMzy+QAYWZmmRwgzMwskwOEmZllKmqAkDRT0ouSlkq6ISN9oKT7JT0n6c+SXl/otmZmVlxFCxCSyoCvAucAxwGzJB3XItungAURMQm4HPhSB7Y1M7MiKmYLYhqwNCKWR8QuYA5wQYs8xwGPAETEYmCUpKEFbmtmZkVUzAAxHHg153Vtui7Xs8CFAJKmAUcDIwrclnS7qyTNlzR/7dq1nVR0MzMrZoBQxrpo8foWYKCkBcAHgWeA+gK3TVZG3B4RNRFRU11dvR/FNTOzXN2LuO9aYGTO6xHAytwMEbEZeA+AJAEvpUuf9rY1M7PiKmYL4klgvKTRknoAlwJzczNIqkzTAP4ZmJcGjXa3NTOz4ipaCyIi6iVdCzwElAF3RsQiSdek6bcBrwO+I6kBeB74p7a2LVZZzcysNUVkdu2XpJqampg/f35XF8PMrGRIeioiarLS/E1qMzPL5ABhZmaZHCDMzCyTA4SZmWVygDAzs0wOEGZmlskBwszMMjlAmJlZJgcIMzPL5ABhZmaZHCDMzCyTA4SZmWVygDAzs0wOEGZmlskBwszMMjlAmJlZJgcIMzPL5ABhZmaZHCDMzCyTA4SZmWUqKEBIOk3Se9Ln1ZJGF7jdTEkvSloq6YaM9AGSfirpWUmLGo+Rpq2Q9BdJCyTNL7RCZmbWObq3l0HSjUANcCzwbaAc+C5wajvblQFfBd4M1AJPSpobEc/nZPsA8HxEvE1SNfCipHsiYleafmZErOtopczMbP8V0oJ4B3A+sA0gIlYC/QrYbhqwNCKWpxf8OcAFLfIE0E+SgAqgDqgvsOxmZlZE7bYggF0REZICQFLfAvc9HHg153UtcFKLPF8B5gKNQeeSiNiTpgXwcHrcb0TE7VkHkXQVcBXAUUcdVWDRzKxQu3fvpra2lp07d3Z1UWw/9OrVixEjRlBeXl7wNoUEiHslfQOolPRe4ErgmwVsp4x10eL12cAC4B+AscCvJP0+IjYDp0bESklD0vWLI2Jeqx0mgeN2gJqampb7N7P9VFtbS79+/Rg1ahRJY99KTUSwfv16amtrGT26oCFkoJ0uprTr5wfAj4D7SMYhPh0R/6+AfdcCI3NejyBpKeR6D/DjSCwFXgImQFNXFhGxBrifpMvKzA6wnTt3MmjQIAeHEiaJQYMGdbgV2GYLIu1aeiAiTgR+1cEyPQmMT+94+htwKXBZizyvADOA30saShKAlqfdWN0iYkv6/C3AZzt4fDPrJA4OpW9fzmEhg9R/lPSGju44IuqBa4GHgBeAeyNikaRrJF2TZvsccIqkvwCPAJ9I71oaCvxB0rPAn4GfR8SDHS2DmZW+9evXM2XKFKZMmcIRRxzB8OHDm17v2rWrzW3nz5/Pdddd1+4xTjnllM4q7iFFEW1320t6nuST/QqSO5lE0riYVPTSdVBNTU3Mn++vTJh1phdeeIHXve51XV0MAG666SYqKir46Ec/2rSuvr6e7t0LGU4tPZ1dt6xzKempiKjJyl9IC+IcYAzJQPLbgPPSRzOzLjF79mw+8pGPcOaZZ/KJT3yCP//5z5xyyimccMIJnHLKKbz44osAPProo5x33nlAElyuvPJKpk+fzpgxY/jyl7/ctL+Kioqm/NOnT+eiiy5iwoQJvOtd76LxQ/QvfvELJkyYwGmnncZ1113XtN9cixYtYtq0aUyZMoVJkyaxZMkSAL7zne8wadIkJk+ezLvf/W4AXn75ZWbMmMGkSZOYMWMGr7zySmbdli1bxsyZMznxxBM5/fTTWbx4cZHe1dbaDU0R8bKkycDp6arfR8SzxS2WmR2Mrr8eFizo3H1OmQJf/GLHt/vrX//Kr3/9a8rKyti8eTPz5s2je/fu/PrXv+ZTn/oU9913X6ttFi9ezG9/+1u2bNnCsccey/ve975Wt30+88wzLFq0iGHDhnHqqafy2GOPUVNTw9VXX828efMYPXo0s2bNyizTbbfdxoc+9CHe9a53sWvXLhoaGli0aBE333wzjz32GIMHD6aurg6Aa6+9lssvv5wrrriCO++8k+uuu44HHnigVd1mzJjBbbfdxvjx4/nTn/7E+9//fn7zm990/A3bB4V8k/pDwHuBH6ervivp9gLvZDIzK4qLL76YsrIyADZt2sQVV1zBkiVLkMTu3bszt3nrW99Kz5496dmzJ0OGDGH16tWMGDGiWZ5p06Y1rZsyZQorVqygoqKCMWPGNN0iOmvWLG6/vfVXs04++WRuvvlmamtrufDCCxk/fjy/+c1vuOiiixg8eDAAVVVVADzxxBP8+MfJZfXd7343H//4x1vVbevWrTz++ONcfPHFTWmvvfbaPr1f+6KQzq1/Ak6KiG0Akj4PPAE4QJgdZvblk36x9O279zu7//7v/86ZZ57J/fffz4oVK5g+fXrmNj179mx6XlZWRn1964kbsvK0N1bb6LLLLuOkk07i5z//OWeffTZ33HEHEVHQHUS5eRrrtmfPHiorK1nQ2c22AhUyBiGgIed1A9lfgjMz6xKbNm1i+PDhANx1112dvv8JEyawfPlyVqxYAcAPfvCDzHzLly9nzJgxXHfddZx//vk899xzzJgxg3vvvZf169cDNHUxnXLKKcyZMweAe+65h9NOO63V/vr378/o0aP54Q9/CCRfeHv22QPXw19IgPg28CdJN0m6Cfgj8K2ilsrMrAM+/vGP88lPfpJTTz2VhoaG9jfooN69e/O1r32NmTNnctpppzF06FAGDBjQKt8PfvADXv/61zNlyhQWL17M5ZdfzvHHH8+//uu/csYZZzB58mQ+8pGPAPDlL3+Zb3/720yaNIm7776bL33pS5nHvueee/jWt77F5MmTOf744/nJT37S6fXLp93bXAEkTQVOI2k5zIuIZ4pdsH3h21zNOt/BdJtrV9q6dSsVFRVEBB/4wAcYP348H/7wh7u6WB3S6be5SnojsCQivhwRXwKWSmo56Z6Z2SHtm9/8JlOmTOH4449n06ZNXH311V1dpKIrZJD668DUnNfbMtaZmR3SPvzhD5dci2F/FTRIHTn9UOl03Ifm1xbNzKxJIQFiuaTrJJWny4eA5cUumJmZda1CAsQ1wCkkM7I2/ujPVcUslJmZdb1CptpYQzJVt5mZHUYKuYvpvyT1T7uXHpG0TtI/HojCmZlNnz6dhx56qNm6L37xi7z//e9vc5vGW97PPfdcNm7c2CrPTTfdxK233trmsR944AGef/75ptef/vSn+fWvf92B0pe2QrqY3pL+BOh5JF1MxwAfK2qpzMxSs2bNavrGcaM5c+bknTCvpV/84hdUVlbu07FbBojPfvaznHXWWfu0r2LKmjKkMxQSIBqnOjwX+H5E1BWlJGZmGS666CJ+9rOfNU1St2LFClauXMlpp53G+973Pmpqajj++OO58cYbM7cfNWoU69atA+Dmm2/m2GOP5ayzzmqaEhyS7zi84Q1vYPLkybzzne9k+/btPP7448ydO5ePfexjTJkyhWXLljF79mx+9KMfAfDII49wwgknMHHiRK688sqm8o0aNYobb7yRqVOnMnHixMzpuUtlWvBCblf9qaTFwA7g/ZKqgY79sKmZHRKuf/B6Fvx9Qafuc8oRU/jizC/mTR80aBDTpk3jwQcf5IILLmDOnDlccsklSOLmm2+mqqqKhoYGZsyYwXPPPcekSdm/ZfbUU08xZ84cnnnmGerr65k6dSonnngiABdeeCHvfe97Afi3f/s3vvWtb/HBD36Q888/n/POO4+LLrqo2b527tzJ7NmzeeSRRzjmmGO4/PLL+frXv871118PwODBg3n66af52te+xq233sodd9zRbPtSmRa83RZERNwAnAzURMRuYDtwwX4d1cysA3K7mXK7l+69916mTp3KCSecwKJFi5p1B7X0+9//nne84x306dOH/v37c/755zelLVy4kNNPP52JEydyzz33sGjRojbL8+KLLzJ69GiOOeYYAK644grmzZvXlH7hhRcCcOKJJzZN8Jfr5JNP5j//8z/5/Oc/z8svv0zv3r3bnBb8sssuA5Jpwf/whz807SdrWvApU6Zw9dVXs2rVqjbrUIiCvvAWERtynm8j+Ta1mR1m2vqkX0xvf/vb+chHPsLTTz/Njh07mDp1Ki+99BK33norTz75JAMHDmT27Nns3Nl250a+abdnz57NAw88wOTJk7nrrrt49NFH29xPe3PYNU4Znm9K8VKZFryQMQgzsy5VUVHB9OnTufLKK5taD5s3b6Zv374MGDCA1atX88tf/rLNfbzpTW/i/vvvZ8eOHWzZsoWf/vSnTWlbtmzhyCOPZPfu3dxzzz1N6/v168eWLVta7WvChAmsWLGCpUuXAnD33XdzxhlnFFyfUpkWvKgBQtJMSS9KWirphoz0AZJ+KulZSYskvafQbc3s8DJr1iyeffZZLr00+VrW5MmTOeGEEzj++OO58sorOfXUU9vcfurUqVxyySVMmTKFd77znZx++ulNaZ/73Oc46aSTePOb38yECROa1l966aV84Qtf4IQTTmDZsmVN63v16sW3v/1tLr74YiZOnEi3bt245pprCq5LqUwLXtB03602kiZERJtD5JLKgL8Cbya5PfZJYFZEPJ+T51PAgIj4RDr4/SJwBMmPErW5bRZP923W+Tzd96Gj06f7zuPhAvJMA5ZGxPKI2AXMofXgdgD9lHSqVQB1QH2B25qZWRHlHaSW9OV8SUBlAfseDrya87pxHqdcXwHmAiuBfsAlEbFHUiHbNpbzKtK5oY466qgCimVmZoVoqwXxHmAh8FSLZT6wq4B9Zw3Ht+zPOhtYAAwDpgBfkdS/wG2TlRG3R0RNRNRUV1cXUCwzMytEW7e5PgksjIjHWyakv03dnlpgZM7rESQthVzvAW5Jf29iqaSXgAkFbmtmB0iht2DawWtfxpvbakFcRPLpPutAowvY95PAeEmjJfUgmRF2bos8rwAzACQNBY4l+a2JQrY1swOgV69erF+/fp8uMHZwiAjWr19Pr169OrRdWy2Iiv2Zdyki6iVdCzwElAF3RsQiSdek6bcBnwPukvQXkm6lT0TEOoCsbfe1LGa270aMGEFtbS1r167t6qLYfujVqxcjRozo0DZ5b3OV9HRETE2f3xcR79z/IhaXb3M1M+uYfb3NNbfDcUznFsnMzA52bQWIyPPczMwOA22NQUyWtJmkJdE7fU76OiKif9FLZ2ZmXSZvgIiIsgNZEDMzO7h4NlczM8vkAGFmZpkcIMzMLJMDhJmZZXKAMDOzTA4QZmaWyQHCzMwyOUCYmVkmBwgzM8vkAGFmZpkcIMzMLJMDhJmZZXKAMDOzTA4QZmaWyQHCzMwyOUCYmVmmogYISTMlvShpqaQbMtI/JmlBuiyU1CCpKk1bIekvadr8YpbTzMxaa+snR/eLpDLgq8CbgVrgSUlzI+L5xjwR8QXgC2n+twEfjoi6nN2cGRHrilVGMzPLr5gtiGnA0ohYHhG7gDnABW3knwV8v4jlMTOzDihmgBgOvJrzujZd14qkPsBM4L6c1QE8LOkpSVflO4ikqyTNlzR/7dq1nVBsMzOD4gYIZayLPHnfBjzWonvp1IiYCpwDfEDSm7I2jIjbI6ImImqqq6v3r8RmZtakmAGiFhiZ83oEsDJP3ktp0b0UESvTxzXA/SRdVmZmdoAUM0A8CYyXNFpSD5IgMLdlJkkDgDOAn+Ss6yupX+Nz4C3AwiKW1czMWijaXUwRUS/pWuAhoAy4MyIWSbomTb8tzfoO4OGI2Jaz+VDgfkmNZfxeRDxYrLKamVlrisg3LFB6ampqYv58f2XCzKxQkp6KiJqsNH+T2szMMjlAmJlZJgcIMzPL5ABhZmaZHCDMzCyTA4SZmWVygDAzs0wOEGZmlskBwszMMjlAmJlZJgcIMzPL5ABhZmaZHCDMzCyTA4SZmWVygDAzs0wOEGZmlskBwszMMjlAmJlZJgcIMzPL5ABhZmaZihogJM2U9KKkpZJuyEj/mKQF6bJQUoOkqkK2NTOz4ipagJBUBnwVOAc4Dpgl6bjcPBHxhYiYEhFTgE8Cv4uIukK2NTOz4ipmC2IasDQilkfELmAOcEEb+WcB39/Hbc3MrJMVM0AMB17NeV2brmtFUh9gJnDfPmx7laT5kuavXbt2vwttZmaJYgYIZayLPHnfBjwWEXUd3TYibo+Imoioqa6u3odimplZlmIGiFpgZM7rEcDKPHkvZW/3Uke3NTOzIihmgHgSGC9ptKQeJEFgbstMkgYAZwA/6ei2ZmZWPN2LteOIqJd0LfAQUAbcGRGLJF2Tpt+WZn0H8HBEbGtv22KV1czMWlNEvmGB0lNTUxPz58/v6mKYmZUMSU9FRE1Wmr9JbWZmmRwgzMwskwOEmZllcoAwM7NMDhBmZpbJAcLMzDI5QJiZWSYHCDMzy+QAYWZmmRwgzMwsU9HmYjIza8+uhl0s37CcpXVLWbJ+CUvqkmVXwy7GDBzD2IFjmz0O7jMYKevXAKwYHCDMrKh2N+xmxcYVycU/JwgsWb+Elze9zJ7Y05S3slcl46vG06OsBw8tfYhVW1c121e/Hv0YM3BM8+BRlTwePeBoysvKD3T1DmkOEGa23+r31PPyxpczg8CKjStoiIamvP179md81XhOGnES/zjpHxlXNY7xVeMZP2g8g3oPatZC2L57Oy9teInlG5azbMOypscX1r3AL5b8gtcaXmvK203dOGrAUZktj7FVY6nsVXkg35JDgmdzNbOCNOxp4JVNrzQLAkvrlrKkbgkvbXiJ3Xt2N+Wt6FGx98KfXvwbH6v7VHdKN9Ge2MPKLSuToFGXBI/lG/c+X7u9+U8QD+w1sKm1MaZyb8tj7MCxjOg/grJuZftdplLU1myuDhCW1876nazfvp71O9azbvs61m9PH3esp0dZD4b0HcLQvkOTx4qhVPeppmf3nl1dbNsPe2IPr256tenCn9saWL5hObsadjXl7VPeJ28QGNp3aJePFWx+bTMvbXipqeWR2wpZsXEF9Xvqm/KWdytnVOWovN1XFT0qurAmxeUAYWzfvb3VRT7zdc76bbu3tb/jFgb0HMDQiqHNg0dOEMl93b9n/y6/iByOGj95N138c4LAsrplzbptenXv1SoINL4e1m9YyZ6/+j311G6ubdb6yO3C2rhzY7P8Q/oOaRY4cp8f2e9Iuql0bwh1gDiERARbd23Nf4Hfvp51O1qv31m/M+8+K3tVMrjPYAb1HpQ89hnE4N7JY7N1aZ5BfQaxq2EXa7atYfXW1cnjttV7X29f0yxt/Y71mcftWdaTIX2HZAaPlkFlcJ/BdO/mIbNCRQSrtq7KGwR21O9oytuzrCdjq8ZmtgaG9x9e0he/fbVhx4bmQaNuGcs3Jq2QVza90mxgvVf3XoyuHJ20NnK6rsYMHMPoytH0Lu/dhTVpnwPEQSoi2PTapg59ql+3fV2zvt5cQlT1rmp2MW914W/xuqp3VdEvvLsbdrNu+7rmQSQ3qKSPjWlZ9RNiUJ9BBbVMhlYMpU95n6LW6WAQEazetrrVeMCS9cnz3BZgebdyxgwcs7cbKCcIHM797/tiV8MuXtn0SquWR+Pzrbu2Nss/rN+wVoPmjd1XnTUesz8cIA6APbGHjTs3dqgbp25HXbN+0FxlKqOqd1X2hT3PBb+yV2XJ/6M3Bs3MlklGUNn82ubM/fQt79sUONoLKgN7DzxoPyVHBGu3r231PYHGILBl15amvN27dWd05ehWQWBc1TiOGnCUW2AHQESwbvu6zHGPZXXL+NuWvzXLX9GjolWXVePzoyuPpkdZj6KXucsChKSZwJeAMuCOiLglI8904ItAObAuIs5I168AtgANQH2+CuTqrABRv6eeDTs2tHuRz11Xt6OuWbMzV3m38g59qh/UexADeg04aC9aB5Mdu3ewdvvaNoNI4/q129dmnqPu3bpT3ae6WeAY0ie7ZVKsgfj129dn3iK6tG4pm17b1JSvTGWMqhyVXPgHjms2MOzvARz8dtbvbLptt+W4x/INy5t1BXdTN0b2H5nZ8hgzcAxVvas6pUxdEiAklQF/Bd4M1AJPArMi4vmcPJXA48DMiHhF0pCIWJOmrQBqImJdocfclwAREVww5wLWbl/bFAg27NyQN3/Psp4d+lQ/qM8g+vXo1+XNSEtu06zbUdeqSytfUMntp89V2auyWeAodCB+w44NeYNA7t9c4/38WXcHjaocdUA+VdqB1zhu1BQ80nGPxq6s1dtWN8tf2auyKXgcM+gY/uMf/mOfjttVAeJk4KaIODt9/UmAiPi/OXneDwyLiH/L2H4FByBAAEy/azrlZeUFXfD7lPfxxf4wEBFs270tb1fXmu3Ng0vdjrrM/TQOxG/fvb3ZYL0QIweMzAwCoytH+3Zha2Xbrm15Wx5CLL528T7tt60AUcxOyeHAqzmva4GTWuQ5BiiX9CjQD/hSRHwnTQvgYUkBfCMibi9WQR+d/Wixdm0lShIVPSqoqKpgbNXYdvPvbtjdZldXz7KezQLB2Kqx9Ore6wDUxA4VfXv0ZeLQiUwcOrFVWrE+6BczQGR9zG5Zi+7AicAMoDfwhKQ/RsRfgVMjYqWkIcCvJC2OiHmtDiJdBVwFcNRRR3VqBcwKVV5WzrB+wxjWb1hXF8UOQ8Xq1SjmKGgtMDLn9QhgZUaeByNiW9qVNA+YDBARK9PHNcD9wLSsg0TE7RFRExE11dXVnVwFM7PDVzEDxJPAeEmjJfUALgXmtsjzE+B0Sd0l9SHpgnpBUl9J/QAk9QXeAiwsYlnNzKyFonUxRUS9pGuBh0huc70zIhZJuiZNvy0iXpD0IPAcsIfkVtiFksYA96fNpu7A9yLiwWKV1czMWvMX5czMDmNt3cXkb2KZmVkmBwgzM8vkAGFmZpkcIMzMLNMhNUgtaS3wcleXAxgMFDxFSIk41Op0qNUHXKdScbDV6eiIyPwS2SEVIA4WkuYXMvtsKTnU6nSo1Qdcp1JRSnVyF5OZmWVygDAzs0wOEMVRtJlnu9ChVqdDrT7gOpWKkqmTxyDMzCyTWxBmZpbJAcLMzDI5QOwnSSsk/UXSAknz03VVkn4laUn6OLCry9kWSXdKWiNpYc66vHWQ9ElJSyW9KOnsril12/LU6SZJf0vP1QJJ5+aklUKdRkr6raQXJC2S9KF0fUmeqzbqU7LnSVIvSX+W9Gxap8+k60vyHBERXvZjAVYAg1us+y/ghvT5DcDnu7qc7dThTcBUYGF7dQCOA54FegKjgWVAWVfXocA63QR8NCNvqdTpSGBq+rwf8Ne07CV5rtqoT8meJ5Jf0qxIn5cDfwLeWKrnyC2I4rgA+N/0+f8Cb++6orQvkp9yrWuxOl8dLgDmRMRrEfESsJQ8v/bXlfLUKZ9SqdOqiHg6fb4FeIHkt99L8ly1UZ98Dur6AERia/qyPF2CEj1HDhD7L4CHJT2V/j42wNCIWAXJPwEwpMtKt+/y1WE48GpOvlra/qc+2Fwr6bm0C6qxmV9ydZI0CjiB5BNqyZ+rFvWBEj5PksokLQDWAL+KiJI9Rw4Q++/UiJgKnAN8QNKburpARZb16+ilcq/014GxwBRgFfDf6fqSqpOkCuA+4PqI2NxW1ox1B129MupT0ucpIhoiYgowApgm6fVtZD+o6+QAsZ8iYmX6uAa4n6R5uFrSkQDp45quK+E+y1eHWmBkTr4RwMoDXLZ9EhGr03/ePcA32duUL5k6SSonuZjeExE/TleX7LnKqs+hcJ4AImIj8CgwkxI9Rw4Q+0FSX0n9Gp8DbwEWAnOBK9JsVwA/6ZoS7pd8dZgLXCqpp6TRwHjgz11Qvg5r/AdNvYPkXEGJ1EnJj7R/C3ghIv4nJ6kkz1W++pTyeZJULakyfd4bOAtYTImeoy4fJS/lBRhDcgfCs8Ai4F/T9YOAR4Al6WNVV5e1nXp8n6Qpv5vkE80/tVUH4F9J7rZ4ETinq8vfgTrdDfwFeI7kH/PIEqvTaSTdD88BC9Ll3FI9V23Up2TPEzAJeCYt+0Lg0+n6kjxHnmrDzMwyuYvJzMwyOUCYmVkmBwgzM8vkAGFmZpkcIMzMLJMDhB1WJDWkM4QuSmfc/IikNv8PJI2SdFkRynK9pD4dyH+NpMs7uxxm+fg2VzusSNoaERXp8yHA94DHIuLGNraZTjK76HmdXJYVQE1ErOvM/Zp1Frcg7LAVyfQoV5FMDKe0pfB7SU+nyylp1luA09OWx4fz5ZN0pKR5ab6Fkk5P179F0hNp3h9KqpB0HTAM+K2k37Ysm6RbJD2fTlh3a7ruJkkflTQs57cSFqStoqPTb/HeJ+nJdDn1QLyPduhyC8IOK7ktiJx1G4AJwBZgT0TslDQe+H5E1LRsQaTdQln5/gXoFRE3SyoD+pDM8/9jkm/IbpP0CaBnRHw2XwtCUhXwBDAhIkJSZURslHQTsDUibs3J+wHgjIj4P5K+B3wtIv4g6SjgoYh4Xee+g3Y46d7VBTA7CDTOqFkOfEXSFKABOCZP/nz5ngTuTCegeyAiFkg6g+RHYR5Lph6iB8nFvy2bgZ3AHZJ+Dvwss9BJC+GfgdPTVWcBx6XHAegvqV8kv7Vg1mEOEHZYkzSG5CK/BrgRWA1MJul+3Zlnsw9n5YuIeel0728F7pb0BWADyW8CzCq0TBFRL2kaMAO4FLgW+IcW5T6SZKK782PvD9R0A06OiB2FHsusLR6DsMOWpGrgNuArkfS1DgBWRTLN9LuBsjTrFpKfxGyUmU/S0cCaiPgmycV7KvBH4FRJ49I8fSQdk2e/jeWqAAZExC+A60l+FyE3vRy4F/hERPw1J+lhkmDSmK/ZdmYd5QBhh5vejbe5Ar8muah+Jk37GnCFpD+SdBttS9c/B9Snt8V+uI1804EFkp4B3gl8KSLWArOB70t6jiRgTEjz3w78MmOQuh/wszT/70haLLlOAd4AfCZnoHoYcB1Qkw5sPw9cs4/vkRngQWozM8vDLQgzM8vkAGFmZpkcIMzMLJMDhJmZZXKAMDOzTA4QZmaWyQHCzMwy/f/YCcHqo4K0KAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/home/karolina/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "print_metrics(svm, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"SVM, combined, imbalanced\")\n",
        "print_metrics(svm, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"SVM, combined, balanced\")\n",
        "print_metrics(svm, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"SVM, has_dep_diag, imbalanced\")\n",
        "print_metrics(svm, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"SVM, has_dep_diag, balanced\")\n",
        "\n",
        "plot_learning_curve(5, svm, X_train_comb_imb, y_train_comb_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, svm, X_train_comb_b, y_train_comb_b, 'Balanced train set, balanced test set')\n",
        "plot_learning_curve(5, svm, X_train_hasdep_imb, y_train_hasdep_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, svm, X_train_hasdep_b, y_train_hasdep_b, 'Balanced train set, balanced test set')\n",
        "\n",
        "plot_roc(svm, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"Imbalanced train set, imbalanced test set, combined variable\")\n",
        "plot_roc(svm, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"Balanced train set, Balanced test set, combined variable\")\n",
        "plot_roc(svm, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"Imbalanced train set, imbalanced test set, has_dep-diag\")\n",
        "plot_roc(svm, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"Balanced train set, Balanced test set, has_dep_diag\")\n",
        "\n",
        "plot_cf(svm, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb)\n",
        "plot_cf(svm, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b)\n",
        "plot_cf(svm, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb)\n",
        "plot_cf(svm, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dn96aDdff7QL"
      },
      "source": [
        "### FNN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import layers, models\n",
        "from tensorflow.keras.layers import Dropout"
      ],
      "metadata": {
        "id": "HOLGB-EegFrK"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def NN_Model_Dropout(neuron, activation, in_shape, classes, output_activation, optimizer, loss, dr):\n",
        "\n",
        "    model = models.Sequential()\n",
        "\n",
        "    ##Input Layer\n",
        "    model.add(Dense(37, activation = activation, input_shape = in_shape))\n",
        "\n",
        "    #Hidden Layers\n",
        "    for i in neuron:\n",
        "\n",
        "        model.add(Dense(i, activation = activation))\n",
        "        #model.add(Dropout(dr))\n",
        "\n",
        "    ##Ouput Layer\n",
        "    model.add(Dense(classes, activation = output_activation))\n",
        "\n",
        "    ##Compiling\n",
        "    model.compile(optimizer = optimizer,\n",
        "                  loss = loss,\n",
        "                  metrics=['acc'])\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "vYjs9DXmgPre"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "> Splitting data\n",
        "\n"
      ],
      "metadata": {
        "id": "TEKmGvq-gc_5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Z1nTHcJCgkO6"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#combined, imbalanced (original ratio), undersampling\n",
        "X_train_comb_imb, X_val_test_comb_imb, y_train_comb_imb, y_val_test_comb_imb = train_test_split(X_combined_imb, Y_combined_imb, test_size = 0.3, random_state = 40)\n",
        "X_val_comb_imb, X_test_comb_imb, y_val_comb_imb, y_test_comb_imb = train_test_split(X_val_test_comb_imb, y_val_test_comb_imb, test_size = 0.5, random_state = 40)\n",
        "\n",
        "print(\"X_train_comb_imb:\", X_train_comb_imb.shape)\n",
        "print(\"y_train_comb_imb:\", y_train_comb_imb.shape)\n",
        "print(\"X_val_comb_imb:\", X_val_comb_imb.shape)\n",
        "print(\"X_test_comb_imb:\", X_test_comb_imb.shape)\n",
        "print(\"y_val_comb_imb:\", y_val_comb_imb.shape)\n",
        "print(\"y_test_comb_imb:\", y_test_comb_imb.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfLqeXLphfLZ",
        "outputId": "f473d7a5-e45f-40b8-e9f4-bde337692f9f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_comb_imb: (2800, 35)\n",
            "y_train_comb_imb: (2800,)\n",
            "X_val_comb_imb: (600, 35)\n",
            "X_test_comb_imb: (600, 35)\n",
            "y_val_comb_imb: (600,)\n",
            "y_test_comb_imb: (600,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#combined, balanced, undersampling\n",
        "X_train_comb_b, X_val_test_comb_b, y_train_comb_b, y_val_test_comb_b = train_test_split(X_combined_b, Y_combined_b, test_size = 0.3, random_state = 40)\n",
        "X_val_comb_b, X_test_comb_b, y_val_comb_b, y_test_comb_b = train_test_split(X_val_test_comb_b, y_val_test_comb_b, test_size = 0.5, random_state = 40)\n",
        "\n",
        "print(\"X_train_comb_b:\", X_train_comb_b.shape)\n",
        "print(\"y_train_comb_b:\", y_train_comb_b.shape)\n",
        "print(\"X_val_comb_b:\", X_val_comb_b.shape)\n",
        "print(\"X_test_comb_b:\", X_test_comb_b.shape)\n",
        "print(\"y_val_comb_b:\", y_val_comb_b.shape)\n",
        "print(\"y_test_comb_b:\", y_test_comb_b.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwpVXnnJihuW",
        "outputId": "5292e289-bbba-443c-a8a7-68f6fed70424"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_comb_b: (350, 35)\n",
            "y_train_comb_b: (350,)\n",
            "X_val_comb_b: (75, 35)\n",
            "X_test_comb_b: (75, 35)\n",
            "y_val_comb_b: (75,)\n",
            "y_test_comb_b: (75,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#has_dep_diag, imbalanced (original ratio), undersampling\n",
        "X_train_hasdep_imb, X_val_test_hasdep_imb, y_train_hasdep_imb, y_val_test_hasdep_imb = train_test_split(X_hasdep_imb, Y_hasdep_imb, test_size = 0.3, random_state = 40)\n",
        "X_val_hasdep_imb, X_test_hasdep_imb, y_val_hasdep_imb, y_test_hasdep_imb = train_test_split(X_val_test_hasdep_imb, y_val_test_hasdep_imb, test_size = 0.5, random_state = 40)\n",
        "\n",
        "print(\"X_train_hasdep_imb:\", X_train_hasdep_imb.shape)\n",
        "print(\"y_train_hasdep_imb:\", y_train_hasdep_imb.shape)\n",
        "print(\"X_val_hasdep_imb:\", X_val_hasdep_imb.shape)\n",
        "print(\"X_test_hasdep_imb:\", X_test_hasdep_imb.shape)\n",
        "print(\"y_val_hasdep_imb:\", y_val_hasdep_imb.shape)\n",
        "print(\"y_test_hasdep_imb:\", y_test_hasdep_imb.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwWoPP2Ij31_",
        "outputId": "659c676d-1d59-46c6-b36c-1d4199fd8333"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_hasdep_imb: (2800, 35)\n",
            "y_train_hasdep_imb: (2800,)\n",
            "X_val_hasdep_imb: (600, 35)\n",
            "X_test_hasdep_imb: (600, 35)\n",
            "y_val_hasdep_imb: (600,)\n",
            "y_test_hasdep_imb: (600,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#has_dep_diag, balanced, undersampling\n",
        "X_train_hasdep_b, X_val_test_hasdep_b, y_train_hasdep_b, y_val_test_hasdep_b = train_test_split(X_hasdep_b, Y_hasdep_b, test_size = 0.3, random_state = 40)\n",
        "X_val_hasdep_b, X_test_hasdep_b, y_val_hasdep_b, y_test_hasdep_b = train_test_split(X_val_test_hasdep_b, y_val_test_hasdep_b, test_size = 0.5, random_state = 40)\n",
        "\n",
        "print(\"X_train_hasdep_b:\", X_train_hasdep_b.shape)\n",
        "print(\"y_train_hasdep_b:\", y_train_hasdep_b.shape)\n",
        "print(\"X_val_hasdep_b:\", X_val_hasdep_b.shape)\n",
        "print(\"X_test_hasdep_b:\", X_test_hasdep_b.shape)\n",
        "print(\"y_val_hasdep_b:\", y_val_hasdep_b.shape)\n",
        "print(\"y_test_hasdep_b:\", y_test_hasdep_b.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGrkZ0XhkezY",
        "outputId": "19aa4bec-cfcc-4223-8ee5-3c807f2d4438"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_hasdep_b: (350, 35)\n",
            "y_train_hasdep_b: (350,)\n",
            "X_val_hasdep_b: (75, 35)\n",
            "X_test_hasdep_b: (75, 35)\n",
            "y_val_hasdep_b: (75,)\n",
            "y_test_hasdep_b: (75,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apyh_oQWf7QL",
        "outputId": "c54a7924-9470-4c9c-8ae6-42113cc66f74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.1886475831270218, 0.9416666626930237]\n"
          ]
        }
      ],
      "source": [
        "##from FFNeuralNetwork import NN_models\n",
        "\n",
        "neurons = [35, 35]\n",
        "fnn = NN_Model_Dropout(neuron = neurons, activation = 'relu', in_shape = (35,), classes = 2, output_activation = 'sigmoid', optimizer = 'Adam', loss = 'sparse_categorical_crossentropy', dr = 0.4)\n",
        "final_history = fnn.fit(X_train_comb_imb, y_train_comb_imb, epochs = 100, validation_data = (X_val_comb_imb, y_val_comb_imb), verbose = 0)\n",
        "score = fnn.evaluate(X_test_comb_imb, y_test_comb_imb, verbose = 0, batch_size = 32)\n",
        "print(score)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "pVffuzKjf7QM"
      },
      "outputs": [],
      "source": [
        "from keras import backend as K\n",
        "\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    f1 = 2*((precision*recall)/(precision+recall+K.epsilon()))\n",
        "    return f1\n",
        "\n",
        "def fnn_print_metrics(model, X_train, y_train, X_val, y_val, X_test, y_test, model_name):\n",
        "    model.fit(X_train, y_train, epochs = 100, validation_data = (X_val, y_val), verbose = 0)\n",
        "    preds = model.predict(X_test)\n",
        "    preds_choice = preds.argmax(1)\n",
        "    #preds_choice = preds[:, 0]\n",
        "    #f1 = f1_m(y_test, preds_choice)\n",
        "    f1 = f1_score(y_test, preds_choice, average = 'macro')\n",
        "    recall = recall_score(y_test, preds_choice, average = 'macro')\n",
        "    precision = precision_score(y_test, preds_choice, average = 'macro')\n",
        "    print(model_name + ': ')\n",
        "    #print(f'F1: {f1}')\n",
        "    #print(f'recall: {recall_m(y_test, preds_choice)}')\n",
        "    #print(f'precision: {precision_m(y_test, preds_choice)}')\n",
        "    print(f'F1: {f1}')\n",
        "    print(f'recall: {recall}')\n",
        "    print(f'precision: {precision}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "7lbrYablf7QM",
        "outputId": "24ac8e62-5937-41b6-b33b-e4843876acc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-ca6156c1391a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds_choice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds_choice\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_comb_imb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int64'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'preds_choice' is not defined"
          ]
        }
      ],
      "source": [
        "preds_choice.shape\n",
        "type(preds_choice[0])\n",
        "type(np.array(y_test_comb_imb).astype('int64')[0])\n",
        "type(K.epsilon())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "5FtOf9utf7QN"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.metrics import f1_score, make_scorer\n",
        "\n",
        "scorer = make_scorer(f1_score, average = 'macro')\n",
        "\n",
        "#plot learning curve function from the labs\n",
        "def plot_learning_curve(n, est, xs, ys, title):\n",
        "    train_sizes, train_scores, valid_scores = learning_curve(estimator = est, X = xs, y = ys, cv = n, scoring=scorer)\n",
        "\n",
        "    train_mean = np.mean(train_scores, axis=1)\n",
        "    valid_mean = np.mean(valid_scores, axis=1)\n",
        "\n",
        "    plt.plot(train_sizes, train_mean, color='blue', label='Training score')\n",
        "    plt.plot(train_sizes, valid_mean, color='green', label='Validation score')\n",
        "\n",
        "    plt.xlabel('Dataset size')\n",
        "    plt.ylabel('F1 score')\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90RfobfRf7QN",
        "outputId": "ee27dba0-12ed-46b1-d607-c6ad8a33acd9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FNN, combined, imbalanced: \n",
            "F1: 0.5398666077943808\n",
            "recall: 0.5285393618726952\n",
            "precision: 0.723993288590604\n",
            "FNN, combined, b/imb: \n",
            "F1: 0.4614084847021906\n",
            "recall: 0.7045053711720379\n",
            "precision: 0.5433258981646079\n",
            "FNN, combined, balanced: \n",
            "F1: 0.6799431009957326\n",
            "recall: 0.6821428571428572\n",
            "precision: 0.6816239316239316\n",
            "FNN, has_dep_diag, imbalanced: \n",
            "F1: 0.5119797355394948\n",
            "recall: 0.5125060125060125\n",
            "precision: 0.5981543624161074\n",
            "FNN, has_dep_diag, balanced: \n",
            "F1: 0.6785714285714286\n",
            "recall: 0.6785714285714286\n",
            "precision: 0.6785714285714286\n"
          ]
        }
      ],
      "source": [
        "fnn_print_metrics(fnn, X_train_comb_imb, y_train_comb_imb, X_val_comb_imb, y_val_comb_imb, X_test_comb_imb, y_test_comb_imb, \"FNN, combined, imbalanced\")\n",
        "fnn_print_metrics(fnn, X_train_comb_b, y_train_comb_b, X_val_comb_b, y_val_comb_b, X_test_comb_imb, y_test_comb_imb, \"FNN, combined, b/imb\")\n",
        "fnn_print_metrics(fnn, X_train_comb_b, y_train_comb_b, X_val_comb_b, y_val_comb_b, X_test_comb_b, y_test_comb_b, \"FNN, combined, balanced\")\n",
        "fnn_print_metrics(fnn, X_train_hasdep_imb, y_train_hasdep_imb, X_val_hasdep_imb, y_val_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"FNN, has_dep_diag, imbalanced\")\n",
        "fnn_print_metrics(fnn, X_train_hasdep_b, y_train_hasdep_b, X_val_hasdep_b, y_val_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"FNN, has_dep_diag, balanced\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "TQ0kmVAlf7QO",
        "outputId": "c00e3270-f119-42f4-aff8-b58dc127782e"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mEmpty\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    821\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 822\u001b[0;31m                 \u001b[0mtasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ready_batches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    823\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEmpty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_qsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mEmpty\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-6d30b59770ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#fnn_print_metrics(fnn, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"FNN, has_dep_diag, balanced\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplot_learning_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train_comb_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_comb_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Imbalanced train set, imbalanced test set'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplot_learning_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train_comb_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_comb_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Balanced train set, balanced test set'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplot_learning_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train_hasdep_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_hasdep_imb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Imbalanced train set, imbalanced test set'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-43-b153d51dfdeb>\u001b[0m in \u001b[0;36mplot_learning_curve\u001b[0;34m(n, est, xs, ys, title)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#plot learning curve function from the labs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_learning_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mtrain_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlearning_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscorer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mtrain_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mlearning_curve\u001b[0;34m(estimator, X, y, groups, train_sizes, cv, scoring, exploit_incremental_learning, n_jobs, pre_dispatch, verbose, shuffle, random_state, error_score, return_times, fit_params)\u001b[0m\n\u001b[1;32m   1566\u001b[0m                 \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_times\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m             )\n\u001b[0;32m-> 1568\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_test_proportions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1569\u001b[0m         )\n\u001b[1;32m   1570\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_aggregate_score_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1041\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1043\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    831\u001b[0m                 \u001b[0mbig_batch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m                 \u001b[0mislice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mislice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbig_batch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    834\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mislice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1566\u001b[0m                 \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_times\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m             )\n\u001b[0;32m-> 1568\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_test_proportions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1569\u001b[0m         )\n\u001b[1;32m   1570\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_aggregate_score_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mclone\u001b[0;34m(estimator, safe)\u001b[0m\n\u001b[1;32m     78\u001b[0m                     \u001b[0;34m\"it does not seem to be a scikit-learn \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m                     \u001b[0;34m\"estimator as it does not implement a \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m                     \u001b[0;34m\"'get_params' method.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m                 )\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Cannot clone object '<keras.engine.sequential.Sequential object at 0x7f2ae99c32d0>' (type <class 'keras.engine.sequential.Sequential'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' method."
          ]
        }
      ],
      "source": [
        "#fnn_print_metrics(fnn, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"FNN, combined, imbalanced\")\n",
        "#fnn_print_metrics(fnn, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"FNN, combined, balanced\")\n",
        "#fnn_print_metrics(fnn, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"FNN, has_dep_diag, imbalanced\")\n",
        "#fnn_print_metrics(fnn, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"FNN, has_dep_diag, balanced\")\n",
        "\n",
        "plot_learning_curve(5, fnn, X_train_comb_imb, y_train_comb_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, fnn, X_train_comb_b, y_train_comb_b, 'Balanced train set, balanced test set')\n",
        "plot_learning_curve(5, fnn, X_train_hasdep_imb, y_train_hasdep_imb, 'Imbalanced train set, imbalanced test set')\n",
        "plot_learning_curve(5, fnn, X_train_hasdep_b, y_train_hasdep_b, 'Balanced train set, balanced test set')\n",
        "\n",
        "plot_roc(fnn, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb, \"Imbalanced train set, imbalanced test set, combined variable\")\n",
        "plot_roc(fnn, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b, \"Balanced train set, Balanced test set, combined variable\")\n",
        "plot_roc(fnn, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb, \"Imbalanced train set, imbalanced test set, has_dep-diag\")\n",
        "plot_roc(fnn, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b, \"Balanced train set, Balanced test set, has_dep_diag\")\n",
        "\n",
        "plot_cf(fnn, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb)\n",
        "plot_cf(fnn, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b)\n",
        "plot_cf(fnn, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb)\n",
        "plot_cf(fnn, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5Ev7srwf7QO"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XFFEj8gDf7QP",
        "outputId": "8ccdcebb-3c6a-4780-d4d3-cd27a1ab7997"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "f1 : 0.4842\n",
            "f1 : 0.3333\n",
            "f1 : 0.5115\n",
            "f1 : 0.5383\n",
            "f1 : 0.4842\n",
            "f1 : 0.3333\n",
            "f1 : 0.4415\n",
            "f1 : 0.5896\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.5896306676008407"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from xgboost import plot_importance\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn import metrics \n",
        "import os\n",
        "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
        "os.environ['CUDA_VISIBLE_DEVICES'] = '1'\n",
        "import xgboost as xgb\n",
        "from xgboost import XGBRegressor\n",
        "from xgboost import XGBClassifier\n",
        "#from xgboost import MultiOutputRegressor\n",
        "from sklearn.model_selection import GridSearchCV   #Perforing grid search\n",
        "from pandas import MultiIndex, Int64Index\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "def modelfit(alg, dtrain_X, dtrain_Y, dtest_X, dtest_Y, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
        "\n",
        "\n",
        "    xgb_param = alg.get_xgb_params()\n",
        "        \n",
        "    xgtrain = xgb.DMatrix(dtrain_X, dtrain_Y)\n",
        "    xgtest_X = xgb.DMatrix(dtest_X)\n",
        "    xgtest_Y = xgb.DMatrix(dtest_Y)\n",
        "        \n",
        "    cvresult = xgb.cv(xgb_param, \n",
        "                     xgtrain, \n",
        "                     num_boost_round=alg.get_params()['n_estimators'], \n",
        "                     nfold=cv_folds, \n",
        "                      metrics='auc',\n",
        "                    early_stopping_rounds=early_stopping_rounds)\n",
        "    alg.set_params(n_estimators=cvresult.shape[0])\n",
        "    \n",
        "    alg.fit(dtrain_X, dtrain_Y,eval_metric=['logloss','auc','error'])\n",
        "\n",
        "    # pred\n",
        "    dtrain_predictions = alg.predict(dtest_X)\n",
        "    dtrain_predprob = alg.predict_proba(dtest_X)[:,1]\n",
        "\n",
        "    print (\"f1 : %.4g\" % metrics.f1_score(dtest_Y, dtrain_predictions, average = 'macro'))\n",
        "    return metrics.f1_score(dtest_Y, dtrain_predictions, average = 'macro')\n",
        "\n",
        "\n",
        "\n",
        "xgbc = XGBClassifier(n_estimators=1000,\n",
        "                         learning_rate=0.05, \n",
        "                         n_jobs=4,\n",
        "                         max_depth = 16,\n",
        "                         min_child_weight = 1,\n",
        "                         gamma = 0.2,\n",
        "                         subsample=0.97, \n",
        "                         colsample_bytree=0.73,\n",
        "                         reg_alpha = 3.64,\n",
        "                         seed=1024,\n",
        "                         use_label_encoder=False)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#combined variable\n",
        "\n",
        "#train imbalanced, test imbalanced\n",
        "modelfit(xgbc, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb)\n",
        "\n",
        "#train imbalanced, test balanced\n",
        "modelfit(xgbc, X_train_comb_imb, y_train_comb_imb, X_test_comb_b, y_test_comb_b)\n",
        "\n",
        "#train balanced, test imbalanced\n",
        "modelfit(xgbc, X_train_comb_b, y_train_comb_b, X_test_comb_imb, y_test_comb_imb)\n",
        "\n",
        "#train balanced, test balanced\n",
        "modelfit(xgbc, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b)\n",
        "\n",
        "\n",
        "#has_dep_diag\n",
        "\n",
        "#train imbalanced, test imbalanced\n",
        "modelfit(xgbc, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_imb, y_test_hasdep_imb.reshape(800, 1))\n",
        "\n",
        "#train imbalanced, test balanced\n",
        "modelfit(xgbc, X_train_hasdep_imb, y_train_hasdep_imb, X_test_hasdep_b, y_test_hasdep_b.reshape(100, 1))\n",
        "\n",
        "#train balanced, test imbalanced\n",
        "modelfit(xgbc, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_imb, y_test_hasdep_imb.reshape(800, 1))\n",
        "\n",
        "#train balanced, test balanced\n",
        "modelfit(xgbc, X_train_hasdep_b, y_train_hasdep_b, X_test_hasdep_b, y_test_hasdep_b.reshape(100, 1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIaXwRaBf7QP"
      },
      "source": [
        "## Model comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1lVzjPobf7QQ",
        "outputId": "5f2d7a9b-7877-49e8-89db-8fa5d6f8177c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "f1 : 0.4842\n",
            "f1 : 0.3333\n",
            "f1 : 0.5115\n",
            "f1 : 0.5383\n"
          ]
        }
      ],
      "source": [
        "bar_x = np.arange(4)\n",
        "bar_y = np.array([])\n",
        "imbimb = []\n",
        "bimb = []\n",
        "imbb = []\n",
        "bb = []\n",
        "lr = []\n",
        "clf.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = clf.predict(X_test_comb_imb)\n",
        "f1 = f1_score(y_pred, y_test_comb_imb, average = \"macro\")\n",
        "imbimb = np.append(imbimb, f1)\n",
        "\n",
        "clf.fit(X_train_comb_b, y_train_comb_b)\n",
        "y_pred = clf.predict(X_test_comb_imb)\n",
        "f1 = f1_score(y_pred, y_test_comb_imb, average = \"macro\")\n",
        "bimb = np.append(bimb, f1)\n",
        "\n",
        "clf.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = clf.predict(X_test_comb_b)\n",
        "f1 = f1_score(y_pred, y_test_comb_b, average = \"macro\")\n",
        "imbb = np.append(imbb, f1)\n",
        "\n",
        "clf.fit(X_train_comb_b, y_train_comb_b)\n",
        "y_pred = clf.predict(X_test_comb_b)\n",
        "f1 = f1_score(y_pred, y_test_comb_b, average = \"macro\")\n",
        "bb = np.append(bb, f1)\n",
        "\n",
        "#bar_y = np.append(bar_y, lr)\n",
        "\n",
        "k = []\n",
        "\n",
        "knn.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = knn.predict(X_test_comb_imb)\n",
        "f1 = f1_score(y_pred, y_test_comb_imb, average = \"macro\")\n",
        "imbimb = np.append(imbimb, f1)\n",
        "\n",
        "knn.fit(X_train_comb_b, y_train_comb_b)\n",
        "y_pred = knn.predict(X_test_comb_imb)\n",
        "f1 = f1_score(y_pred, y_test_comb_imb, average = \"macro\")\n",
        "bimb = np.append(bimb, f1)\n",
        "\n",
        "knn.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = knn.predict(X_test_comb_b)\n",
        "f1 = f1_score(y_pred, y_test_comb_b, average = \"macro\")\n",
        "imbb = np.append(imbb, f1)\n",
        "\n",
        "knn.fit(X_train_comb_b, y_train_comb_b)\n",
        "y_pred = knn.predict(X_test_comb_b)\n",
        "f1 = f1_score(y_pred, y_test_comb_b, average = \"macro\")\n",
        "bb = np.append(bb, f1)\n",
        "\n",
        "#bar_y = np.append(bar_y, k)\n",
        "\n",
        "sv = []\n",
        "\n",
        "svm.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = svm.predict(X_test_comb_imb)\n",
        "f1 = f1_score(y_pred, y_test_comb_imb, average = \"macro\")\n",
        "imbimb = np.append(imbimb, f1)\n",
        "\n",
        "svm.fit(X_train_comb_b, y_train_comb_b)\n",
        "y_pred = svm.predict(X_test_comb_imb)\n",
        "f1 = f1_score(y_pred, y_test_comb_imb, average = \"macro\")\n",
        "bimb = np.append(bimb, f1)\n",
        "\n",
        "svm.fit(X_train_comb_imb, y_train_comb_imb)\n",
        "y_pred = svm.predict(X_test_comb_b)\n",
        "f1 = f1_score(y_test_comb_b, y_pred, average = \"macro\")\n",
        "imbb = np.append(imbb, f1)\n",
        "\n",
        "svm.fit(X_train_comb_b, y_train_comb_b)\n",
        "y_pred = svm.predict(X_test_comb_b)\n",
        "f1 = f1_score(y_pred, y_test_comb_b, average = \"macro\")\n",
        "bb = np.append(bb, f1)\n",
        "\n",
        "#bar_y = np.append(bar_y, sv)\n",
        "\n",
        "imbimb = np.append(imbimb, modelfit(xgbc, X_train_comb_imb, y_train_comb_imb, X_test_comb_imb, y_test_comb_imb))\n",
        "\n",
        "#train imbalanced, test balanced\n",
        "imbb = np.append(imbb, modelfit(xgbc, X_train_comb_imb, y_train_comb_imb, X_test_comb_b, y_test_comb_b))\n",
        "\n",
        "#train balanced, test imbalanced\n",
        "bimb = np.append(bimb, modelfit(xgbc, X_train_comb_b, y_train_comb_b, X_test_comb_imb, y_test_comb_imb))\n",
        "\n",
        "#train balanced, test balanced\n",
        "bb = np.append(bb, modelfit(xgbc, X_train_comb_b, y_train_comb_b, X_test_comb_b, y_test_comb_b))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AxiBNtlqf7QQ",
        "outputId": "82c59af9-f7ac-425a-cb41-908e313c0155"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.bar(bar_x-0.2, imbimb, 0.2, color='yellow')\n",
        "plt.bar(bar_x, bimb, 0.2, color='orange')\n",
        "plt.bar(bar_x+0.2, imbb, 0.2, color='green')\n",
        "plt.bar(bar_x+0.4, bb, 0.2, color='red')\n",
        "plt.title('Comparison of different models on different dataset splits')\n",
        "plt.legend([\"Imbalanced train set, imbalanced test set\", \"Balanced train set, imbalanced test set\", \"Imbalanced train set, balanced test set\", \"Balanced train set, balanced test set\"])\n",
        "plt.xticks(bar_x, ['LR', 'KNN', 'SVM', \"XGB\"])\n",
        "plt.xlabel('models')\n",
        "plt.ylabel('F1 score')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fQgpEd1rf7QR"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "Copy of compare-checkpoint.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}