4_validate_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DWkfDqIrvFHq"
   },
   "source": [
    "## Import all Essential libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Rv2WCmXyAcm-"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Basics - Data Handling\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Miscellaneous\n",
    "from collections import Counter\n",
    "\n",
    "# Scoring function\n",
    "from sklearn.metrics import f1_score, recall_score, accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Data generation\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Models to cross validate\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n",
    "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kA8zH3QRvMZU"
   },
   "source": [
    "## Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "DPi88c4BAcnO"
   },
   "outputs": [],
   "source": [
    "x_train_sm = pd.read_csv(\"datasets/train_smote.csv\", index_col=0)\n",
    "y_train_sm = x_train_sm[\"y\"]\n",
    "x_train_sm.drop([\"y\"], axis=1, inplace=True)\n",
    "\n",
    "x_val = pd.read_csv(\"datasets/validation.csv\", index_col=0)\n",
    "y_val = x_val[\"y\"]\n",
    "x_val.drop([\"y\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "E5aP9X5UAcnP"
   },
   "outputs": [],
   "source": [
    "params_list = {'knn_stdscaler': {'n_neighbors':3}, \n",
    "               'adaboost': {'algorithm': 'SAMME.R'},\n",
    "               'decisiontree': {'criterion': 'entropy', 'max_depth': None, 'min_samples_split': 2, 'splitter': 'best'},\n",
    "               'knn': {'n_neighbors': 3},\n",
    "               'rf': {'max_depth': None, 'min_samples_split': 2},\n",
    "               'mlp': {'early_stopping': True, 'hidden_layer_sizes': (200,), 'learning_rate': 'adaptive'},\n",
    "               'mlp_stdscaler': {'early_stopping': False, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'max_iter':1000},\n",
    "               'svm_stdscaler': {'C': 500, 'gamma': 'scale'}, \n",
    "               'sgd': {'eta0': 0.5, 'learning_rate': 'adaptive', 'penalty': 'l2'},\n",
    "               'svm': {'C': 50, 'gamma': 'auto'}}\n",
    "\n",
    "model_list = ['knn_stdscaler', 'adaboost', 'decisiontree', 'knn', 'rf', 'mlp', 'svm_stdscaler', 'sgd', 'svm']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rqPcPiCNvP9S"
   },
   "source": [
    "## Function to measure goodness of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ZNFS6ArPBWeK"
   },
   "outputs": [],
   "source": [
    "def get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred):\n",
    "    train_report = classification_report(y_train_sm, y_pred)\n",
    "    val_report = classification_report(y_val, y_val_pred)\n",
    "\n",
    "    print(train_report)\n",
    "    print(\"=\"*50)\n",
    "    print(val_report)\n",
    "    \n",
    "    val_report = classification_report(y_val, y_val_pred, output_dict=True)\n",
    "    return val_report\n",
    "\n",
    "# def get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred):\n",
    "#     acc = accuracy_score(y_train_sm, y_pred)\n",
    "#     f1 = f1_score(y_train_sm, y_pred)\n",
    "#     recall = recall_score(y_train_sm, y_pred)\n",
    "    \n",
    "#     val_acc = accuracy_score(y_val, y_val_pred)\n",
    "#     val_f1 = f1_score(y_val, y_val_pred)\n",
    "#     val_recall = recall_score(y_val, y_val_pred)\n",
    "    \n",
    "#     print(\"Train F1 Score:\", f1)\n",
    "#     print(\"Validation F1 Score:\", val_f1)\n",
    "    \n",
    "#     print(\"\\nTrain recall Score:\", recall)\n",
    "#     print(\"Validation recall Score:\", val_recall)\n",
    "    \n",
    "#     print(\"\\nTrain Accuracy Score:\", acc)\n",
    "#     print(\"Validation Accuracy Score:\", val_acc)\n",
    "    \n",
    "#     return f1, val_f1, acc, val_acc\n",
    "\n",
    "def get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred):\n",
    "    fpr, tpr, _ = roc_curve(y_train_sm, y_pred)\n",
    "    val_fpr, val_tpr, _ = roc_curve(y_val, y_val_pred)\n",
    "\n",
    "    train_auc = auc(fpr, tpr)\n",
    "    val_auc = auc(val_fpr, val_tpr)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot([0,1], [0,1], '--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.plot(fpr, tpr, '.-', label=f'Train ROC; Area:{round(train_auc, 4)}')\n",
    "    plt.plot(val_fpr, val_tpr, '.-', label=f'Validation ROC; Area:{round(val_auc, 4)}')\n",
    "    plt.grid(True)\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.title(\"ROC Curves\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "precision = []\n",
    "recall = []\n",
    "f1_score = []\n",
    "accuracy = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c4y1t5ikvdBf"
   },
   "source": [
    "## KNN, with StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "Ao6s0m3bICxC",
    "outputId": "9c2f58a6-9918-4d0f-eb76-9d06182d7de3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     56075\n",
      "         1.0       0.99      0.99      0.99     56075\n",
      "\n",
      "    accuracy                           0.99    112150\n",
      "   macro avg       0.99      0.99      0.99    112150\n",
      "weighted avg       0.99      0.99      0.99    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.98      0.99     18678\n",
      "         1.0       0.80      0.87      0.83      1322\n",
      "\n",
      "    accuracy                           0.98     20000\n",
      "   macro avg       0.89      0.93      0.91     20000\n",
      "weighted avg       0.98      0.98      0.98     20000\n",
      "\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"
    }
   ],
   "source": [
    "knn_ss = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=3))\n",
    "knn_ss.fit(x_train_sm, y_train_sm)\n",
    "y_pred = knn_ss.predict(x_train_sm)\n",
    "y_val_pred = knn_ss.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"KNN_SCALED\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WkKAv5gVykak"
   },
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "OP--mYODIQZG",
    "outputId": "9d06c370-941f-47fb-f2d0-2c6da1d5da7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.96      0.97     56075\n",
      "         1.0       0.96      0.99      0.98     56075\n",
      "\n",
      "    accuracy                           0.98    112150\n",
      "   macro avg       0.98      0.98      0.98    112150\n",
      "weighted avg       0.98      0.98      0.98    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.93      0.95     18678\n",
      "         1.0       0.40      0.69      0.50      1322\n",
      "\n",
      "    accuracy                           0.91     20000\n",
      "   macro avg       0.69      0.81      0.73     20000\n",
      "weighted avg       0.94      0.91      0.92     20000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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snRZ7cIaCL2P4wiDaW9a7MztwMo9Hv0hi55EcLo4O4W/XDXTKsLPmZvyCbz3ClyEd0Xraa9Z7W1BUWsH0/2xCKcUbNw7imni5mriKExV8OcIXLae9Z723BQdP5tE3xJcOHq68NWMI/bv5EegrVxZX5wQFPxdQ4OHn6J6INqKhrPeIgIh2lfXu7IpKLWFnC9an8cq0eKYMCWV0pPxBtsUJCn5lcJpL+5o+JZqPZL23XZtSs3h8SRLpWYXcNLIXl8a07Stlz5dzFHwZzhF2aijrvV+nfpL13ka8uuoAb64+SO9Abz6bPZIL+8hRfUOMX/AlOE3UQ7Le25+qsLNBPQOYPSacP0+IooOHDLnZw66Cr5S6HHgDcAUWaK1frPV4APAp0Ktyna9orT9slh5W3d5QtHsNZb3HdI6RrPc2LCu/hOe+TiYi2IeHLu3XLsLOmluDBV8p5Qq8DUwAMoBflFLLtdbJ1ZrdCyRrra9WSgUD+5VSC7XWpefdw2ITdOx13qsRzkWy3kUVrTWbjpXz8KtryS8p56FL+zm6S07LniP8EUCK1joNQCm1GJgMVC/4GvBTlsMpX+AMUN4sPZTbG7YLVVnvq0yrWPrT0nOy3gcEDZCs93bouKmIJ5fuYfVvJQzq2ZF/Xh9Hvy4yY6+p7Cn4PYAj1ZYzgJG12vwbWA4cA/yA6Vprc+0VKaXuAu4CCA4OJjExscGNj87P4sTpPFLsaOus8vPz7doXbYXWmszyTA6VHCK9JJ30knSOlR3DjOUtE5wfTLhnOOM7jyfcI5zuHt1xVa6QB+RB8qFkkmscb7RN7e19YcvvuRVsTClmSrjmqn6lHNu3nWP7HN0r52VPwbc1CKprLU8EdgIXA32AVUqp9Vrr3BpP0noeMA8gKipKJyQk1L9lsxkSCwntE0NoQ22dWGJiIg3uCydWZ9Y74OvuS2xQLFcGW9Iic/fnctUlVzm4x8bQ1t8XdUk/XcCP+04ya0wEAFMuK2P75p/b5b5obvYU/AygZ7XlUCxH8tXdDryotdZAilLqEBANbD2v3pXmAVqGdJyIvVnvVRc1RXSMqDEtMjEl0UE9F45WXmHmg58P8a8fDuDh5sLkQT0I9vPEz0siK5qLPQX/FyBSKRUOHAVuBG6q1eYwcAmwXinVBYgC0s67dxKrYHj1Zb138uxUZ9a7ENX9diKXx75IYleGiUv7d+GFawcS7Ccn4ZtbgwVfa12ulLoPWIllWuYHWuu9Sqm7Kx9/D3ge+EgptRvLENBjWuvT5907CU4zFMl6Fy2hqLSCGfM246IUb80YzFVx3eR900LsmoevtV4BrKj1vfeqfX0MuKx5u0Zljg5yhO8gkvUuWtL+E3n062IJO/v3TUPo382fzj4eju5Wm2bsK21lSKfVNDbrvatPVwf3WDirwtJy/vXDAct4fWXY2UV9JRahNThHwZfbGzarhrLeQ31DJetdtIifU04zd0kSR84UcesFvZkgYWetyjkKvldHx/bDyTUm6z0uOI7ADoEO7rFoi/71w37e+imF8CAf/nvXBYyMkPdZazN2wbfe3lCO8O0lWe/CaMxmjYuLYkjvTvxxXAQPX9oPL3d5zzmCsQt+sQncvUGGE+okWe/CqE7nl/Ds8r1EBPvy5wn9GB8VwvgoicRwJIMX/Bw5YVuNZL0LZ6C15qudR3nu62QKSyp4eIL8DhuFwQt++w5Ok6x34WyO5RTxxNLdrNmfyZBeHXlpahyREnZmGAYv+KZ2M0NHst5FW5BdWMq237N55uoYbhsVhquLvEeNxPgF3yfY0b1odrWz3jcc38CfP/uzZL0Lp5SWmc+P+05y19g+DOgewKbHL8HX09ilpb0y9k+lJBcC+zq6F+etKuu9+lWr1bPeQ91CJetdOJ3yCjPz1x/itR8P4OXmwnWDQwn285Rib2DG/sk44Q3Mtdb8nvv72bTI00kczD5Iha4AoLd/b0Z1G2W9qKlvp778vO5nEoYmOLbjQjRC8rFcHv1yF3uO5jJxQBeenyxhZ87AuAVfa6e4n609We+zYmdZ5713lIvIhJMrKq3g5gWbcXVx4d2bhzAptpujuyTsZNyCX1YE5nJDHeHbm/UeHxxPXHAc4QHhMi1StBn7jucS3dWPDh6uvH3zEGK6+dPRW8LOnIlxC74BgtMk610IKCgp5+WV+/l4UzqvXB/P1KGhXNhHws6ckfELfitNy7Q36z0+JJ74oHhC/UJlWqRo89YfzOTxJbvJyC7iD6N6M3GgpKQ6M+MWfGuOTsuMeUvWuxD1e3nlb7y9JpWIYB/+d/cohod1dnSXxHkybsE/zyGdnad2su3kNoZ1GUZ05+gGs95vjLrRelGTZL2L9qwq7GxYWGf+lAAPXBIpYWdthBMU/MYP6ew8tZM7V95JqbkUhcIFFyqwTIuUrHchbDuVV8wzy/YSGeLLny+LkrCzNsgJCn7jj/C3ndxGqbkUAI1mcJfB/GHAH4gNipWsdyFq0VrzxfYMXvh2H0VlFQzuJVOH26o2WfCHdRlm/drL1YsHhzzIoJBBzdUzIdqMjOxCHl+ym/UHTzM8rBMvTo2jT7DMNmurjF3wXdzBzavRTw0PCAdgdPfR/DH+j1LshahDblE5SRkm/jp5ALeM7I2LhJ21acYt+CWV0chNmPqYkpMCwIz+M6TYC1FLamY+Pyaf5I/j+hDT3Z+Ncy/GR/Jv2gXj/pTPI0cnNScVgL4dnT94TYjmUlZhZt66NN5YfRBvD1emDg0lyNdTin07Ytyf9Hnk6KTmpNLBrYNMrxSi0p6jJh77Mom9x3K5IrYrz10zkCBfCTtrbwxe8Jt4hG9KpU9AH8mxEQJL2Nmt72/BzdWF924ZwuUDJeysvTJwwc8F/+5NempqTioXdb+omTskhHPZc9TEgO7+dPBw5Z2bhxLTzZ8Ab7nmpD0z7iFwE4/wTSUmThedpk/HPi3QKSGML7+knKeX7eGqtzaw5NejAIzqEyjFXhj5CL9p97OtOmErBV+0R4n7T/HE0j0cMxVx+0VhXC5hZ6IaYxb88lIoL2pScFrVlEwp+KK9een733g3MZW+Ib58cfeFDO3dydFdEgZjzIJvTcps/JBOmimNDm4d6OYjJ6ZE+1Bh1ri6KC6ICMTNRXHfxX3xdJOwM3EuYxb88whOS8lJISIgQmboiDbvVG4xTy3bQ78ufjxyWRTj+gUzrl+wo7slDMyYVfE8cnTSctJkOEe0aVprPt92hEtfXUvi/kwCOsjJWGEfgx/hN67gm0pMZBZlyhW2os06csYSdrYh5TQjwjrz4tRYIiTsTNjJ2AW/kbN0qm4oLkf4oq3KKy5nzzETz187kJtH9JKwM9Eodg3pKKUuV0rtV0qlKKXm1tEmQSm1Uym1Vym19rx61cSTtjJDR7RFB0/m8U6i5b1dFXZ26wWSbCkar8EjfKWUK/A2MAHIAH5RSi3XWidXa9MReAe4XGt9WCl1frfJaeKQTlWGjszQEW1BuVnz1uqDvPVTCj6ertwwrCdBvp54exjzH3NhfPa8c0YAKVrrNACl1GJgMpBcrc1NwBKt9WEArfWp8+pVsQlQ4NG4scnUnFSZoSPahKSMHJ7bVMyRvANcHd+dZ66OkbAzcd7sKfg9gCPVljOAkbXa9APclVKJgB/whtb6k9orUkrdBdwFEBwcTGJios0N9k1NpoubNz+vW2dH985KPpVMtFd0nes1qvz8fKfrc0uRfQEl5Zo/ry3ETWkeHOLF4BATe7ZtcnS3HEreF83DnoJva6BQ21jPUOASoAOwSSm1WWt9oMaTtJ4HzAOIiorSCQkJtrd4ZhEUBFLn4zaYSkzkLs7louiLSBho//OMIDExsVGvtS1rz/tiz1ETMd38cXFRfBCRxenUJK6cMN7R3TKE9vy+aE72jH1kAD2rLYcCx2y0+V5rXaC1Pg2sA+Kb3KtiE3g2bvy+aoaOTMkUziavuIwnv9rNVW9tYOkOS9jZyIhAfNzlpKxoXvYU/F+ASKVUuFLKA7gRWF6rzTJgjFLKTSnljWXIZ1+Te1V1e8NGqApNiwiIaPJmhWhta347xWWvreOzLYeZNTqcSbESdiZaToNDOlrrcqXUfcBKwBX4QGu9Vyl1d+Xj72mt9ymlvgeSADOwQGu9p8m9KjZBx96NekrVDJ3uvk3L0Beitf3ju338Z20akSG+vHPPhQzuJWFnomXZNb9La70CWFHre+/VWn4ZeLlZetWELPyUnBTCA8Jlho4wNK01Zg2uLoqL+gTh6ebKveP7SNiZaBXGrI5NuJ9tWk6ajN8LQzthKmb2J9t5bZVlLsPYfsH8eUI/Kfai1RjvCg6zGUryGnWEn1uay6miUzJ+LwxJa83iX47w92/3UVphZlSfQEd3SbRTxiv4JbmAblTBT8uRGTrCmI6cKeTRL5LYlJbFBRGdeXFKHGFBPo7ulminjFfwmxCcJhk6wqgKSsv57UQuf78ulhuH95T8G+FQxiv4TQhOkxk6wkj2n8jjx30nuXd8X6K7+rNx7iV08JBxeuF4xiv4TQhOS81JlRk6wuFKy828k5jC22tS8PNyZ/pwS9iZFHthFAYu+PYP6aTmpDKyW+14HyFaz64jOTz6RRL7T+YxeVB3nr4qhkAJOxMGY8CC37ghnaoZOjJ+LxylsLScP3y4FS83VxbcNoxLY7o4uktC2GTAgl91hN/RruZVM3Sk4IvWlpSRw8DuAXh7uDH/tmFEdfXD30vuLyuMy3iD3tZZOn52Na/K0JGCL1pLbnEZjy/ZzTX//tkadjY8rLMUe2F4xjvCL8kFdx9wte+XJyUnBS9XL3r49mjhjgkBPyaf5ImvdpOZV8JdYyO4Ilburiach/EKfnFO4y66MqXJDB3RKv6+Yh/z1qUR3dWPebcOI76nfcOOQhiFAQt+43J0UnJSGNlVZuiIlqG1psKscXN1YUxkEL6ebtw9rg8ebnKAIZyPAQu+/Vn4eaV5nCo8RURHydARze+4qYgnl+4hupsfcyZGMyYymDGRwY7ulhBNZsCCbwLfELuaVp2wlQwd0ZzMZs2iXw7zjxW/UWHWjIkMcnSXhGgWxiz4QZF2NbXO0AmQGTqieRzOKmTOF7vYcugMF/UN5B/XxdEr0NvR3RKiWRiv4Jfk2h2clmpKtczQ8ZMZOqJ5FJaVk3Iqn5emxnLDsJ4oJWFnou0wVsHXulF3u5IMHdEcfjuRy6q9J7n/kkiiu/rz89yL8XKX/BvR9hirUpYVgrm8UQVfLrgSTVVSXsGrP+znqjc38NHGdE7nlwBIsRdtlrGO8K05Og0P6eSV5nGy8KQUfNEkvx7O5rEvkjh4Kp8pg3vw1FUxdPLxcHS3hGhRBiv49kcjp5kqM3TkhK1opMLScu746Be83V358PbhjI+yb1aYEM7OaQu+TMkUjbXjcDbxoR3x9nDj/T8MI6qrP76exvoVEKIlGWsM3xqc1nDBr8rQkbtciYaYisp47IskrntnozXsbGjvzlLsRbtjrHd8I25vmJZjydBxdZETbKJuK/ee4Kmv9pBVUMrd4/pwZZyEnYn2y1gFvzjH8tmOgp+Sk8LwrsNbuEPCmT3/TTLvbzhE/27+vP+H4cSG2h/KJ0RbZLCCb9/tDfNL82WGjrCpetjZ+KgQOnm788dxfXB3NdbopRCOYLCCnwuuHuDmVW+zVJNEKohzHc0p4omluxnQ3Z85E6MZHRnEaMnBEcLKYAW/8irbBi5nl9saiurMZs3CLb/z4ne/YdZwcbRMsxTCFuMVfDtydFJyUvB09ZS7XAnSTxfw6BdJbE0/w5jIIP5+XSw9O0vYmRC2GKvgl9iXhZ+ak0pEQITM0BGUlJtJO13Ay9fHcf3QUAk7E6IexjqTZWdwWqopVW560o7tPWbi9R8PABDV1Y8Nj41nmiRbCtEgYx3hF5vAv/5hmvzSfE4UnJArbNuh4rIK3vrpIO+tTaOTtwe3XNCbIF9PCTsTwk4GK/i5DU7JrMrQiQiQI/z2ZPvvZ3j0iyRSMwuYOiSUp67qT0dvCTsTojEMVvAbHtKRDJ32p7C0nDs/3oaPhxsf3zGCcf3kvrJCNIVxCn55KZQX2VXwZYZO+7D992wG96wKOxtOVFc/yb8R4jzYddJWKXW5Ut/yCdQAACAASURBVGq/UipFKTW3nnbDlVIVSqnrG92TqhydBoLTUkwpkqHTxpkKy5jzv11MfXcjS6xhZ52k2Atxnhr8DVJKuQJvAxOADOAXpdRyrXWyjXYvASub1BM7o5FTc1IZ2mVokzYhjG/biXLmvLaWMwWl/CmhD1dJ2JkQzcaeQ6YRQIrWOg1AKbUYmAwk12p3P/Al0LREMzuC06pm6EikQtv016+T+WBnCTHd/Plw5nAG9pCwMyGakz0FvwdwpNpyBjCyegOlVA/gOuBi6in4Sqm7gLsAgoODSUxMtD7W6cxO4oEd+1IxHU+0+fz0knQAijKKSDxju40zys/Pr7Ev2hOtNWYNri6KoNIKru6tmRxVzumDO0g86OjeOVZ7fl/UJvuiedhT8G1dzaJrLb8OPKa1rqjv4het9TxgHkBUVJROSEg4++DeHEiCwaMSoMsAm89fenApnIDJoyfT27+3HV13DomJidTYF+3EkTOF/GXpbgb2COCxy6NJoP3uC1tkX5wl+6J52FPwM4Ce1ZZDgWO12gwDFlcW+yDgCqVUudb6K7t7YscYfmpOKh4uHoT6htq9WmE8ZrPmk03p/HPlfhQwcUBXR3dJiHbBnoL/CxCplAoHjgI3AjdVb6C1Dq/6Win1EfBNo4o9VJulU/eFV6mmVJmh4+QOnS5gzv92se33bMb1C+Zv1w0ktJOEnQnRGhos+FrrcqXUfVhm37gCH2it9yql7q58/L1m6UmxCZQLePjW2SQ1J5XBIYObZXPCMcoqzPx+ppBXb4jnusE9JP9GiFZk18RmrfUKYEWt79ks9FrrmU3qSVU0sovtSwMKygo4XnCcaR2nNWn1wnH2HDWxKvkkD0/oR78ulrAzTzf5L02I1macK1mKTfXm6FTd9ERSMp1HcVkFb6w+yLx1aXT28eC2Ub0J9PWUYi+Egxio4NefhZ+SkwJIho6z+CX9DI99kUTa6QKmDQ3lyStjCPB2d3S3hGjXDFTwTeDVsc6H00xpMkPHSRSUlDP7k234errxf3eOYEykhJ0JYQTGKvidw+t8OCVHMnSM7pf0Mwzt1QkfTzc+mDmcqC5++Ej+jRCGYZw7XpXk1jslMy0nTcbvDSq7oJQ//3cn097bZA07G1JZ+IUQxmGc38h6svALywo5VnCMqR2ntnKnRH201qzYfYJnlu8hp7CMBy7uy9XxEnYmhFEZo+CbK+q9gXnVXa76dJTQNCP56zfJfPhzOrE9AvjkjpHEdK//bmVCCMcyRsEvybN8rmNaZtUMHUnJdDytNeVmjburCxP6d6GLvxezRofj5mqc0UEhhG3G+C1tIEfHmqHjJzN0HOnImUJufX8r//rhAAAX9g3i7nF9pNgL4SSMcYRvR8EPCwjDzcUY3W1vKsyajzem8/LK/bi6KK6IlXF6IZyRMSpoA8FpqTmpxIfEt2KHRJW0zHz+3/928evhHBKigvn7dbF079jB0d0SQjSBMQp+PUf4VTN0pgRMaeVOCbAc3R/NKeL16YOYPKi7hJ0J4cQMX/CrZuhIpELrScrIYVXySR65LIrILn6se1TCzoRoCwxS8CuHdGwU/NScVECmZLaG4rIKXlt1gPnr0wj282TmhWESdiZEG2KQgl95hG9jDD81JxV3F3eZodPCNqdlMffLJNKzCpkxoidzJ/UnoIOEnQnRlhin4Hv4guu53anK0JEZOi2noKScuz/djr+XO5/NGsmFfYMc3SUhRAswRhUtMdU5QyfNlEZcUFwrd6h92HroDMN6WzJvPrp9BP26+OLtYYy3hBCi+Rnjipk6cnQKywo5mn9Uxu+b2ZmCUh5avIMb/nM27GxQz45S7IVo44zxG15HwT9kOgTICdvmorXmm6TjPLt8L6aiMh68JFLCzoRoRwxS8HPBt8s537Zm6EjBbxbPfZ3MRxvTiQ8NYOHskUR3lbAzIdoTgxR8EwRFnvPtVJNlhk5Pv54O6FTboLWmrELj4ebCZQO60KNjB+4YHY6ri1xAJUR7Y+gxfMnQOT+/ZxVw0/wt/OuH/QBc2CeI2WMjpNgL0U45vuBrXW/B7xsgV9g2VoVZs2B9GhNfX8eeoyYign0c3SUhhAE4/tC5rBB0xTnTMqtm6Fzb91oHdcw5pZzK55H/7WLXkRwu7R/CC9fG0jXAy9HdEkIYgOMLfh05OlUzdCRDp3G01pzKLebNGYO5Oq6bhJ0JIawMW/BTTZYMHblxecN2HslhVfIJ5kyMJrKLH2vnjMfDzfGjdUIIYzFAwa8KTqs5pJOSk4K7izu9/Ho5oFPOoai0gldX7ef9DYcI8fPijovCCfT1lGIvhLDJAAW/6gi/Y41vp+WkyQydemxMPc3cL3dz+EwhN43sxdxJ0fh7SdiZEKJujq+mdQzppOSkEBsU64AOGV9BSTn3LvwV/w7uLJp9AaP6BDq6S0IIJ+D4gl9ybjSyzNCxbVNqFiPDO1cLO/Ojg4dk1Qsh7OP4wV4bR/iHciVDp7qs/BLuX7SDGfM3s7Qy7Cy+Z0cp9kKIRnH8EX6xCVw9wf3sXHG5y5WF1prlu47x7PK9FJRU8MiEflwd393R3RJCOCkDFPzcc2bopOak4ubi1u4zdJ5ZvpdPNv3O4F4d+efUOCK7+Dm6S0IIJ2aAgn9urEJqTiph/mG4u7S/WSdms6bcbAk7mzSwG70DfZh5YZjk3wghzptdY/hKqcuVUvuVUilKqbk2Hr9ZKZVU+bFRKRVvdw/qKPjt8QrbQ6cLmDF/M69Uhp2N6hPInZJsKYRoJg0WfKWUK/A2MAmIAWYopWJqNTsEjNNaxwHPA/Ps7kFJbo2CX1RexNH8o+3qCtsKs2beulQuf30dycdz6Rvs6+guCSHaIHuGdEYAKVrrNACl1GJgMpBc1UBrvbFa+81AqN09KDaBfw/rYpopDY1uN0f4KafyeGFzMYdyf2NCTBdeuHYgXfwl7EwI0fzsKfg9gCPVljOAkfW0vxP4ztYDSqm7gLsAgoODSUxMZFRuJlnuhRxITARga/5WALIOZJF4KNGO7jm3Y/lmckoq+FO8F8O75rHv183sc3SnHCg/P5/EyvdCeyf74izZF83DnoJvawBZ22yo1HgsBX+0rce11vOoHO6JiorSCQkJsKGY7uHRdE9IAGDH9h24Zbsx9ZKpbfak7a+Hs1mVfJLHLo8GIMR7DZdePN7BvTKGxMREEirfC+2d7IuzZF80D3tO2mYA1edHhgLHajdSSsUBC4DJWussu7ZeXgLlRTWmZablpLXZGTqFpeX89etkpr67kWU7jpKVXwKAm5yUFUK0AnuO8H8BIpVS4cBR4EbgpuoNlFK9gCXArVrrA3Zv3ZqUeTY4LSUnhQFBA+xehbPYcPA0c5ckkZFdxG2jevPo5dH4ejp+VqwQov1osOJorcuVUvcBKwFX4AOt9V6l1N2Vj78HPA0EAu9U3nCjXGs9rMGtl1QVfMssnaoZOtf0vaYpr8WwCkrKuX/Rr3T09uDzP45iRHhnR3dJCNEO2XWIqbVeAayo9b33qn09C5jV6K0X51g+VwanHTIdQqPpE9A2IhU2ppxmZEQgPp5ufHLHSCK7+OLlLvk3QgjHcOyYQq3gtKoMHWefkpmZV8Kzy/fy7e7jvDItnuuHhhIbeu5N2kXLKCsrIyMjg+LiYkd35bwEBASwb197nrN1VnvcF15eXoSGhuLu3nznMw1X8N1c3Ojp75wZOlprlu44yl+/SaawpII5E6OYPEjCzlpbRkYGfn5+hIWFOfU9ffPy8vDzk/wkaH/7QmtNVlYWGRkZhIeHN9t6HVzwa97e0NkzdJ5atodPNx9mSK+O/PP6OPqGtJ83qJEUFxc7fbEX7ZtSisDAQDIzM5t1vcY6wjel0r9zfwd2qPHMZk2Z2YynmytXxXWnb7Avt46SsDNHk2IvnF1LvIcdewOUYhMoF/Dwpai8iIy8DKcav0/NzGf6vE28stISdnZBRCAzL5KwMyGEMTm24JfkWmboKEW6Kd0yQ8cJbnpSVmHmncQUJr2xnv0n8ojq6t/wk0S7kZWVxaBBgxg0aBBdu3alR48e1uXS0lK71nH77bdz8OBBu7e5YMECgoODGTRoENHR0bz55ps1Hn/33XeJjo4mOjqakSNHsnHj2firsrIyHn30Ufr27cvAgQMZOXIkK1eutGu7AwcO5NZbb7W7n02xYsUKhgwZQt++fXn55Zdttjlz5gzXXHMNcXFxjBw5kuRkS9RXcnKydd8PGjQIPz8//v3vfwPw5JNP1vjZVL3mU6dOkZCQgI+PDw899FCLvrbW5vghncrhnJScFMD4d7k6cDKPh/+7k73Hcrl8QFf+eu0AQvwk7MzpHdkK6eshbAz0HHFeqwoMDGTnzp0APPvss/j6+vL//t//q9FGa43WGhcX28dcH374IXl5eY3a7s0338zrr79OZmYmUVFRTJs2jW7duvHVV1/x4YcfsnHjRjp37sy2bduYMmUK27dvJzg4mMcff5wzZ86QnJyMh4cHx48f5+eff25we0lJSbi5ufHTTz9RVFREhw4dzmlTXl6Om1vTy0xZWRn33XcfX3/9NX379mXYsGFMnjyZfv361Wj3/PPPM3LkSJYvX87evXt58MEHWbVqFTExMdafRXl5Od27d+faa8/eK3vOnDnnFHVvb2/+9re/sWPHDlJSUprcdyMyTMFPM6Xhptzo5d/LoV1qiItS5BSW8e7NQ5gU283R3REN+W4unNhdf5uSXDi5B7TZMsTYZaD12hCbusbCpBcb3ZWUlBSuvfZaRo8ezZYtW/jmm2947rnn+PXXXykqKmL69Ok8/fTTAIwePZqXXnqJkSNHEhQUxN133813332Ht7c3y5YtIyQkpM7tBAcHExERwfHjx+nWrRsvvfQSr7zyCp07Wy74GzZsGDfffDPvvPMOf/7zn/noo49IT0/Hw8MDgG7dunH99dcDlv80HnzwQQYNGnTOdhYtWsRtt93Gjh07+Oabb5g2bZq17+PGjWP9+vVMmTKFGTNmcM8993D48GFcXFx48803ueCCC9i8eTMPP/wwxcXFeHt789FHHxEZGVljG5s3b6Z///706tULT09PbrjhBpYtW8acOXNqtEtOTua5554DYMCAARw4cICsrCwCAwOtbX744Qf69+9PaGj9Yb6+vr5cdNFFbXIaqIPH8HNrHOH39u9tyBk6238/wz++s/zw+4b4snZOghT7tqTYZCn2YPlcNZmgBSQnJ3PnnXeyY8cOevTowYsvvsi2bdvYtWsXq1atsg5FVGcymRg3bhy7du1i1KhRfPDBB/VuIz09nYqKCgYOHGjd5tChQ2u0GTZsGHv37uXgwYOEh4fj62v7HgwffvihzWIP8PnnnzN9+nRmzJjBokWLajyWm5vLunXreOihh3jggQd49NFH2bZtG59//jmzZlmu0ezfvz8bNmxgx44dPPXUUzz55JMAHDlyhGuusVxtf/ToUXr2PDtNOzQ0lKNHj57Tl/j4eL788ksANm3aREZGBhkZGTXaLF68mBkzZtT43htvvEFcXByzZs3CZGq5n7tROP4Iv7NljmlqjvFm6BSUlPPyyv18vCmd7gEd+OPYPnT28cDN1bF/J0Uj2HMkfmQrfHwNVJSCqwdMXXDewzp16dOnD8OHD7cuL1q0iPfff5/y8nKOHTtGcnIyMTE17y/UoUMHJk2aBMDQoUNZv369zXUvXLiQVatWsX//fj788EPrEbstWuvzmgWyadMmQkND6dGjByEhIcyePRuTyURAgOUA7sYbb7S2/fHHH9m/f791OTs7m6KiInJycrjttttITU2tse6ePXuyfPlyaz9rs9XvJ554ggceeIBBgwYRHx9PfHx8jaGk4uJivv32W1599VXr9+6//36ee+45lFI8/vjjzJkzh3nz7L93kzNyfMH3CqC4vJiMvAyuirjKod2pbt2BTB5fsptjpiL+MCqMOROj8JGws7ap5wj4w/JmG8Ovj4+Pj/XrgwcP8sYbb7B161Y6duzILbfcYvPq4OqF29XVlfLycpvrrhrD37BhA9dccw0TJ04kJCSE/v37s337dsaOHWtt++uvvxITE0NkZCSHDh2ioKCgRt8asmjRIvbs2UNYWBhgOaJfunQpM2fOPOd1aq3ZunXrOX+AnnjiCSZOnMif/vQnUlJSuPzyy8/ZTmhoKEeOnL0dR0ZGBt27n3sxY0BAAB9//DEAZrOZsLAwa98Avv32W+vwWJUuXbpYv549e7Z1GKstc/wsHa+Asxk6BjlhW1BSzkP/3Ymnuwv/++Monr1mgBT7tq7nCBjzSIsW+9pyc3Px8/PD39+f48eP2z0zpiGjR49mxowZvPXWWwA8+uijPProo2RnZwOWYv/pp59yzz334Ofnx2233cZDDz1EWVkZAMeOHWPhwoWA5Y/Ir7/+WmP9FRUVfPnllyQnJ5Oenk56ejpLliw5Z1inyqWXXsrbb79tXa46iWoymejRw3K3u48++sjmcy+44AKSk5M5fPgwJSUlfP7559bhnupycnKs/f/Pf/7DpZdeWuOPzqJFi84Zzjl+/Lj166VLl1qHwNoyxxd8T39STcbI0Fl/MJMKs64MOxvBigfGMCxMki1FyxgyZAgxMTEMHDiQ2bNnc9FFFzXbuufOncuCBQsoKChgypQp3HrrrVxwwQVER0dz991389lnn1lP/L744osEBATQv39/YmNjmTJlivWxpKQkunbtWmPda9asITw8vMYR8vjx49m5cycnT548py9vv/02P//8M3FxccTExDB//nwAHnvsMebMmXPO664+hu/u7s6bb77J5MmTiYmJ4ZZbbiEqKsq63gULFgCwe/duYmJiiI6OZvXq1TWGbvLz81mzZk2N2TkAjzzyCLGxscTFxfHzzz/zyiuvWB8LDQ3l0Ucf5f333yc0NLTGkJQzU7bGyFpDdL9I/dtNp2DiP3jDs4yP9nzE1pu34u7a+idtT+UW8/SyvXy/9wT/mhbP1KH235K3OcjdfM5qjn2xb98++vc31vmgpnB0fkx2djb33HMPixcvdlgfqjh6XziKrfeyUmq7XfHzNjhsnEJVzYrwCiA1e4tlhk4rF3utNV9sz+D5b5IpLjfz2OXREnYmRKVOnToZotiL5uO4gk+F5Qsvf1JzUonqHNXqfXjiqz18tuUww8M68eLUOPoE256aJoQQbYHjzkRWHuEXu3fgSN4Rroy4slU2Wz3sbHJ8d/p39ePmkb1xkfwbIUQb57CTtlVDOukVhWg0ER0jWnybKafymPafTbz8veUEzMiIQG4dFSbFXgjRLji84KeUZAHQN6DlZuiUVZh5e00KV7yxgdTMfAb0kLAzIUT748AxfEvBTys6hZtyo7d/7xbZzoGTeTy0eCfJx3O5MrYbz14zgGA/zxbZlhBCGJnj5uFry0nblPwMevn3arEZOq4uirySMt67ZShv3zxEir1ocQkJCedcRPX666/zpz/9qd7nVeXZHDt2rM6rPhMSEti2bVu963n99dcpLCy0Ll9xxRXk5OTY0/V6Pfvss9Y44ZiYmBoXWmmteeGFF4iMjKRfv36MHz+evXv3Wh/Pz8/nj3/8I3369GHAgAGMHTuWLVu2NLjN8vJygoKCePbZZ8+7//X5+OOPiYyMJDIy0nrFbm2HDx9m/PjxDB48mLi4OFasWNHg87XWPPHEE/Tr14/+/ftbY6tffvllayzzwIEDcXV15cyZMy36Gq0dcsRHbO/OWv+tu75yyZX64TUP6+a0JS1Lv/DNXutyWXlFs66/ua1Zs8bRXTCM5tgXycnJjX7OjpM79Pyk+XrHyR3nvf333ntPz5w5s8b3Ro4cqdetW1fv83x8fGos5+bmntNm3Lhx+pdffql3Pb1799aZmZl29tZ+zzzzjH755Ze11lofOHBA+/n56dLSUq211m+99ZaeNGmSLigo0FprvXLlSh0REaGLioq01lpPnz5dz507V1dUWH4XU1NT9TfffNPgNr/99lt94YUX6rCwMG02m222KS8vP6/XlZWVpcPDw3VWVpY+c+aMDg8P12fOnDmn3ezZs/U777yjtdZ67969unfv3g0+/4MPPtC33nqr9XWfPHnynPUuX75cjx8/3mbfbL2XgW26iXXXofPwS7wCOJJ3hEnhk5plnfkl5bz03W/83+bf6dm5A/ck9JWws3bupa0v8duZ3+ptk1+az/7s/Wg0CkVUpyh8PeqeohvdOZrHRjxW5+PXX389Tz75JCUlJXh6epKens6xY8cYPXo0+fn5TJ48mezsbMrKynjhhReYPHlyjeenp6dz1VVXsWnTJoqKirj99ttJTk6mf//+FBUVWdvdc889/PLLLxQVFXH99dfz3HPP8eabb3Ls2DHGjx9PUFAQa9asISwsjG3bthEUFMSrr75qTducNWsWDz30EOnp6UyaNInRo0ezceNGevTowbJly2zm21eJjIzE29ub7OxsQkJCeOmll0hMTMTb2xuAyy67jAsvvJCFCxeSkJDAli1bWLhwoTX/PyIigogIy0SNK664ggULFtjMyFm0aBEPPvgg//73v9m8eTOjRo0CICwsjDvuuIMffviB++67j+HDh3PvvfeSmZmJt7c38+fPJzo6mq+//poXXniB0tJSAgMDWbhwYY0rhAFWrlzJhAkTrPHREyZM4Pvvvz8nikEpRW6u5T7cJpPJ2t/6nv/uu+/y2WefWV+3rVhrW7EPLcWhJ20Pefti1uZmydBZs/8Ul726lk+3/M4dF4Wz8qGxdPapOy1QiCp5ZXloLFecazR5ZY278UhtgYGBjBgxgu+//x6wxPJOnz4dpRReXl4sXbqUX3/9lTVr1vDII4/YTISs8u677+Lt7U1SUhJPPPEE27dvtz72t7/9jW3btpGUlMTatWtJSkrigQceoHv37qxZs4Y1a9bUWNf27dv58MMP2bJlC5s3b2b+/Pns2LEDsAS53Xvvvezdu5eOHTtao4br8uuvvxIZGUlISAi5ubkUFBTQp0/N3+OqCOa9e/cyaNAgXF1dba5rxYoVNot9UVERq1ev5qqrruL6668/J6vHy8uLDRs2cOONN3LXXXfx1ltvsX37dl555RXr8Nno0aPZvHkzO3bs4MYbb+Sf//wnANu2bbPGNNsbwfzss8/y6aefEhoayhVXXGHNKqrv+ampqfz3v/9l2LBhTJo06Zy7mBUWFvL9998zdepUm/umuTn0CD/V0wsook/A+RX8/JJyHvl8F4E+Hnx5z4UM6dWpeTopnF59R+JVdp7ayewfZlNmLsPdxZ0Xx7zIoBDbGfD2mjFjBosXL2by5MksXrzYelStteYvf/kL69atw8XFhaNHj3Ly5Mlz8mqqrFu3jgceeACAuLg44uLirI99/vnnzJs3j/Lyco4fP05ycnKNx2vbsGED1113nTVUbMqUKaxfv55rrrmG8PBwa+790KFDSU9Pt7mO1157jfnz55OWlmb9g1YXfZ4RzN988w3jx4/H29uba665hjFjxvDaa69Z/3BMnz4dsJwf2Lhxo/UGLAAlJSWAJV1z+vTpHD9+nNLSUsLDLXHsw4YNs+bw2PqDa6vfixYtYubMmTzyyCNs2rSJW2+9lT179tT7/JKSEry8vNi2bRtLlizhjjvuqBFv/fXXX3PRRRdZ/ztoaY47wqeCVFcX3JQbYf5hjX6+1prE/aeoMGt8Pd349M6RfPPAaCn2otEGhQxi/mXzuW/wfcy/bP55F3uAa6+9ltWrV1vvZjVkyBDAklmfmZnJ9u3b2blzJ126dLEZiVydreJz6NAhXnnlFVavXk1SUhJXXnllg+up7z8JT8+zkxnqi2B++OGH2b9/P//973+57bbbKC4uxt/fHx8fH9LS0mq0rYpgHjBgALt27cJsNtfbv9oWLVrEjz/+SFhYGOPGjSMrK6vGfy1Vf7jMZjMdO3Zk586d1o+qu1Xdf//93HfffezevZv//Oc/NveRvRHM77//PjfccAMAo0aNori4mNOnT9f7/NDQUOvR+3XXXUdSUlKNddq6KUtLcuAsHTOpLuYmzdA5lVvMH/9vOzM//IWvdlj+dYrp7o+nm+1/GYVoyKCQQcyKndUsxR4sM24SEhK44447avxCm0wmQkJCcHd3Z82aNfz+++/1rmfs2LHWqOI9e/ZYC0Zubi4+Pj4EBARw8uRJvvvuO+tz/Pz8bN4Pd+zYsXz11VcUFhZSUFDA0qVLGTNmTJNe35QpUxg2bJh1RsqcOXN44IEHrOcYfvzxRzZs2MBNN91Enz59GDZsGM8884z1j87BgwdZtmwZAJdccsk5Qyi5ubls2LCBw4cPk56ezp49e3j77bdtRjD7+/sTHh7O//73P8Dyh23Xrl1AzQjmumbfTJw4kR9++IHs7Gyys7P54YcfmDhx4jntevXqxerVqwFLqFlxcTHBwcH1Pv/aa6/lp59+AmDt2rU17sVrMplYu3btOedwWpJDx/BTdUmjxu+11nz+yxEueXUtaw9k8vgkCTsTxjVjxgx27dpV4+5PN998M9u2bWPYsGEsXLiQ6Ojoetdxzz33kJ+fT1xcHP/85z8ZMcKS1x8fH8/gwYMZMGAAd9xxR42I4bvuuotJkyYxfvz4GusaMmQIM2fOZMSIEYwcOZJZs2YxePDgJr++p59+mldffRWz2cz999/P8OHDiY2NJSoqiueff77Gid8FCxZw4sQJ+vbtS2xsLLNnz6Z79+6YzWZSUlLOGdJYsmQJF198cY3/PCZPnszy5cutwzXVLVy4kPfff5/4+HgGDBhg/WPy7LPPMm3aNMaMGVPj5ifVx/A7d+7MU089xfDhwxk+fDhPP/20tT9PP/209e5b//rXv5g/fz7x8fHMmDGDjz76CKVUvc+fO3cuX375JbGxsTz++OPWYSSwZPBfdtlljbrxzPlyWDzy0O5uuvwf/bkr/m7uHXSvXc95V2xMdwAABhBJREFUfMluFm09zIjwzrw0NY7woNbbUS1J4pHPknjks9pDJPCePXv44IMPauTX29Ie9oUtbSYeuUSBggZP2FaYNWUVZrzcXblucA8GdPfnphG9JP9GiDZg4MCBDRZ70XwcNqRTUnkiqr4hnQMn85j67kZeXmkJOxsR3plbLpBkSyGEaArHFXwXhSsuNjN0SsvNvLn6IFe+uZ7fswqICw1wQA+FM3PUUKUQzaUl3sMOHNJR9OoQhIdrzYujfjuRy0OLd/LbiTyuju/Os1fHEOgr+TfCfl5eXmRlZREYGHhe88CFcBStNVlZWXh5eTXreh1a8Pv69jrn++6uLhSVVTD/tmFMiOli45lC1C80NJSMjAwyMzMd3ZXzUlxc3Oy/8M6qPe4LLy8vQkOb9/7aDiv4pUoREWC56m1zWhY/Jp/kyati6BPsy0+PJOAq4/Siidzd3a1XVDqzxMTE85o22ZbIvmgedo3hK6UuV0rtV0qlKKXm2nhcKaXerHw8SSk1pKF1aiDUvy9PLN3NjfM280PySc4UlAJIsRdCiBbQ4BG+UsoVeBuYAGQAvyillmutk6s1mwREVn6MBN6t/Fyv/1u1kR2ZAcwaHc4jl0XRwUOulBVCiJZizxH+CCBFa52mtS4FFgO1rwWeDHxSGde8GeiolOrW0Ip/75TI65OO8+RVMVLshRCihdkzht8DOFJtOYNzj95ttekBHK/eSCl1F3AXgKuvK8l/TWVm4Z9ys0/NrpkZ2v4EAacd3QmDkH1xluyLs2RfnBXV1CfaU/BtDajXniBqTxu01vOAeQBKqW2FeeVNujy4rVFKbWvqpdJtjeyLs2RfnCX74iylVP33uKyHPUM6GUDPasuhwLEmtBFCCOFA9hT8X4BIpVS4UsoDuBFYXqvNcuC2ytk6FwAmrfXx2isSQgjhOA0O6Wity5VS9wErAVfgA631XqXU3ZWPvwesAK4AUoBC4HY7tj2vyb1ue2RfnCX74izZF2fJvjiryfvCYfHIQgghWpfj7nglhBCiVUnBF0KIdqLFC35LxDI4Kzv2xc2V+yBJKbVRKRXviH62hob2RbV2w5VSFUqp61uzf63Jnn2hlEpQSu1USu1VSq1t7T62Fjt+RwKUUl8rpXZV7gt7zhc6HaXUB0qpU0qpPXU83rS6qbVusQ8sJ3lTgQjAA9gFxNRqcwXwHZa5/BcAW1qyT476sHNfXAh0qvx6UnveF9Xa/YRlUsD1ju63A98XHYFkoFflcoij++3AffEX4KXKr4OBM4CHo/veAvtiLDAE2FPH402qmy19hN9isQxOqMF9obXeqLXOrlzcjOV6hrbInvcFwP3Al8Cp1uxcK7NnX9wELNFaHwbQWrfV/WHPvtCAn7Lc6MAXS8Evb91utjyt9Tosr60uTaqbLV3w64pcaGybtqCxr/NOLH/B26IG94VSqgdwHfBeK/bLEex5X/QDOimlEpVS25VSt7Va71qXPfvi30B/LBd27gYe1FqbW6d7htKkutnSefjNFsvQBtj9OpVS47EU/NEt2iPHsWdfvA48prWuaON3rbJnX7gBQ4FLgA7AJqXUZq31gZbuXCuzZ19MBHYCFwN9gFVKqfVa69yW7pzBNKlutnTBl1iGs+x6nUqpOGABMElrndVKfWtt9uyLYcDiymIfBFyhlCrXWn/VOl1sNfb+jpzWWhcABUqpdUA80NYKvj374nbgRW0ZyE5RSh0CooGtrdNFw2hS3WzpIR2JZTirwX2hlOoFLAFu/f/t3DEuBWEUhuH3S2i0JNprIRLrsQEdhT2oFDqNRmcdFLdTiGWIoxiFQuIPuRN3zvus4M/JzJczk5lvgdvbVz/OoqqOqmpVVSvgDjhdYNjD2D1yDxwn2Umyx9RWu575nHMYmcUL05MOSQ6ZmiOfZz3l//Cr3Nzohl+bq2XYOoOzOAf2gavPzfatFtgQODiLFkZmUVXrJA/AI/AOXFfVt5/rbbPB6+ISuEnyxPRa46yqFlebnOQWOAEOkrwCF8Au/C03rVaQpCb801aSmjDwJakJA1+SmjDwJakJA1+SmjDwJakJA1+SmvgAwrgi3Dj1vWIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(x_train_sm, y_train_sm)\n",
    "y_pred = knn.predict(x_train_sm)\n",
    "y_val_pred = knn.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"KNN\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H64iQmJjvhGw"
   },
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "u39idwkfIS1d",
    "outputId": "ca562e04-26a4-4e99-c05c-9353aa896fc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56075\n",
      "         1.0       1.00      1.00      1.00     56075\n",
      "\n",
      "    accuracy                           1.00    112150\n",
      "   macro avg       1.00      1.00      1.00    112150\n",
      "weighted avg       1.00      1.00      1.00    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.98      0.98     18678\n",
      "         1.0       0.73      0.85      0.79      1322\n",
      "\n",
      "    accuracy                           0.97     20000\n",
      "   macro avg       0.86      0.91      0.88     20000\n",
      "weighted avg       0.97      0.97      0.97     20000\n",
      "\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"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(criterion='entropy', max_depth=None, min_samples_split=2, splitter='best', random_state=42)\n",
    "dtc.fit(x_train_sm, y_train_sm)\n",
    "y_pred = dtc.predict(x_train_sm)\n",
    "y_val_pred = dtc.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"DECISIONTREE\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qH0xXvjRvmV2"
   },
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "K1RDGprnMr9C",
    "outputId": "0fc04403-ec5e-470e-f75e-5e9949b55d98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56075\n",
      "         1.0       1.00      1.00      1.00     56075\n",
      "\n",
      "    accuracy                           1.00    112150\n",
      "   macro avg       1.00      1.00      1.00    112150\n",
      "weighted avg       1.00      1.00      1.00    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.87      0.86      0.86      1322\n",
      "\n",
      "    accuracy                           0.98     20000\n",
      "   macro avg       0.93      0.93      0.93     20000\n",
      "weighted avg       0.98      0.98      0.98     20000\n",
      "\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"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth=None, min_samples_split=2, random_state=42)\n",
    "rf.fit(x_train_sm, y_train_sm)\n",
    "y_pred = rf.predict(x_train_sm)\n",
    "y_val_pred = rf.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"RF\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A3l4bljuvo3X"
   },
   "source": [
    "## MLP Classifier, with StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "3HeFCohhwIGQ",
    "outputId": "a68b4455-69c8-4a24-a668-502215e37fe2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.99      0.99     56075\n",
      "         1.0       0.99      0.98      0.99     56075\n",
      "\n",
      "    accuracy                           0.99    112150\n",
      "   macro avg       0.99      0.99      0.99    112150\n",
      "weighted avg       0.99      0.99      0.99    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.90      0.91      0.91      1322\n",
      "\n",
      "    accuracy                           0.99     20000\n",
      "   macro avg       0.95      0.95      0.95     20000\n",
      "weighted avg       0.99      0.99      0.99     20000\n",
      "\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"
    }
   ],
   "source": [
    "mlp_ss_esf = make_pipeline(StandardScaler(), MLPClassifier(early_stopping=False, hidden_layer_sizes=(100,), learning_rate='adaptive', max_iter=1000, random_state=42))\n",
    "mlp_ss_esf.fit(x_train_sm, y_train_sm)\n",
    "y_pred = mlp_ss_esf.predict(x_train_sm)\n",
    "y_val_pred = mlp_ss_esf.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"MLP_SCALED\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FNpjvln5yvRd"
   },
   "source": [
    "## MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "a1x10UAJyz97",
    "outputId": "62dff2bc-eac7-4516-aaa1-6727213c3c7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.86      0.88      0.87     56075\n",
      "         1.0       0.87      0.86      0.87     56075\n",
      "\n",
      "    accuracy                           0.87    112150\n",
      "   macro avg       0.87      0.87      0.87    112150\n",
      "weighted avg       0.87      0.87      0.87    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.88      0.93     18678\n",
      "         1.0       0.31      0.80      0.45      1322\n",
      "\n",
      "    accuracy                           0.87     20000\n",
      "   macro avg       0.65      0.84      0.69     20000\n",
      "weighted avg       0.94      0.87      0.90     20000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vc9d+gVBPs1cqC6Gxqutr3XxbknYoBEzrMrSy7YCeKbOustgCof4ipaSotqjbpF5QXdDhhmmQZxA6rY45ujkd5qyHeYXh1O59p6amkhyUbI23o7RT19jMdf/ZgVFKnr9uNFeP6b9iZ5amEr7Spv0CoTPt61lTDNLY8bUaZ1OvXBsKgQkQe2nXZfs+IeDmfcYQGg9X2XSyr2mq6ZjU282GqWu3GYeHswc6rY6kgCSujL2ywypTdcN0YMgpriY20FTsbPmiZIaHagn0sf5N+wuhEr6jaF0g1O2+nu2+1uMCodbVniO6Dq1ow0zTFftwgVB/ajY2U1BdYJ7e2H4mTHFd2wYaGqEhzCuMaN9oxgaP7ZDUgzyDBmwv0NHVNzXzwg+HWLn5KP+6bjRXj4mwibIIlqAS/kDXukCox6GVlkTe7QIh17a65KFJMGxO19krPqFnXSA0UJ2uP91hemNrbz23KpemdrN9tK5adL46JoVNapuzrtURqY202gYaSt/YebSUZZ/t59ipGq4fH8mlCba1UvZCqYRvy8wLhDrVJ28/e6XqJBjqu77W079t+mHYmK6bTviEWW2BUH9qbG4kryrPXJK3/RDM6YbT5nbOGmeifKKI1kZzSeQl5jnrOq2OQe6DrPgOlP6y/IdDLP/hMJGDPXj/tolcNDTA2iFZnEr41mBeIGRK4KEFqbBxW9dVn90uEHJvG0YJH9epR95u2b4NLBDqL1JKSupKzMMv7Rcknag+gbHd/YZAj0CitdHMjJ7ZYc56mHeYzW+gofSN1mJnSRG+/G5qDA/MHoanq31+L9jnu7Km1gVCZ7rp2WmBUDzAIWEqX6sNBd/Itu3gOtcqt7EFQv2ptqm2Q7mA1k00jlcep6apxtzO3cmdaG00if6JzIuZZ56zHq2Nxtv1zDeNFcdRVtPI31uKnd07M45LE4LtbginM5XwzyQvDfRbQHexaWPlmlM9rPBs97XuFgi5eLX1xKMndxla2Z6lZ/Ksq2x2gVB/MkqjqSRvN9Mbi2qLzO0EgjDvMKK10STHJpuHX2J8YwjyDFL1YJQeSSn5en8hj63JoqKuiXt/ZbvFzixNJfye5O6Et+eB0QCIlo9OUxGFpmVfz9CWBUIXdRxaaR03d9OesVfecKTG4ZJ9RUNFl+mN+ko9+tN6DLltJXl9XHzQ+eqYEDKhw5z1KJ8ou99AQ7G8osp6/vbFAb7PLiIpwpf3bpvI8NCBsYDPElTC78nBdS3JvkX0FBhxVcfZK15B57yvpyNpam4irzqv2znr7TfQcBbO5g00oo3RXDziYnNyH+w+WE1vVCympKqB7UdK+eu8BG69yPaLnVmaylY9ad1BSGhM9c1nPmYaV1c6aN1Ao/3UxtYbpvlV+TS3K5vg7+5PtDaaGZEzOsxZD/cJN2+gkZqayvRh0630bhR7lFtay/c/F/G7qTGMDPflp2WX4uvhWH9Rt1IJvyd15aZ56pcshSHTHD7Z1xnqyK3M7bgYqcLUW69uattj1c3JjWhtNMMGDWN29GzzLJho32i0ro7zp7Nifc1GyaqfjvHcdwdx0Wi4YnRLsTMHTfagEn7P8tJM0x6nPWjtSPqNURo5WXOyw5z11t56YU1hh7ahXqFEa6O5fMjl5lkwOl8dIV4h6oapYnWHiqpY+kkme/NOc2lCEP9z9cgBWezM0lTC705TPRTug8l/sHYkfaKqsarbOeu5lbkdNtDwcvFCp9WREpzSoSRvlDYKD2f7XH2rDHx1jc0s+s92hBC8eH0yV44OU/eBWqiE353CfaZ58hEDdxinydjEiaoT3U5vLK0vNbdzEk5E+EQQrY1mUuikDtMb/d391Q+KMmAcLqpiaJA3Hq5OrFicwvBQH/y9HWcBYm+ohN+d/DTTvzY+bi+lpKy+rNvpjflV+Rhk2yyjwe6DTWUDIi7pML0x0lttoKEMbHWNpmJnb2w5ynPXjmZBSgRT4+yvLIIlqITfnbw08Is2bY1nAxqaG8zVGttPbzxWeYyqdjXnXTWuRGmjGOo31Fw6oDW5qw00FHu0/Ugpf/ksE31pLTdMjGJmon2vlL1QKuF3JiXk7zLtstSPjNJIcW1xt9MbO2+gEewZjE6rM5UNaJfUQ71CO2ygoSj27PnvD/HShsNE+3vywe0TmRKrevVnoxJ+ZxX5plo3fTR+X91Ybe6dtyb1rIIsln6wtMMGGp7Onuh8dSQFJjE/dr45qUdro9UGGopDay12lhzpy+0Xx3D/rHg8XFVHpzd6lfCFEHOAFwEn4A0p5dOdnvcF3gOiWo75nJRylYVj7R/m8fvx530Ig9FAQXVBt7XWS+ra6tJrhIZw73C0Tlqmx0xvq7XuqyPQI1DdMFWUdkqrG3jiq2yGBHpx38xhDlHszNLOmvCFEE7Ay8AsIB/YJYT4UkqZ3a7ZH4FsKeUVQohA4KAQ4n0pZWOfRN2X8naBswcEjzxr0/L68g6VG9tvoGFoV5bBz82PaG00U8KmdJizHukTiauTq2l16YTpffimFGXgklKyvcDAn5/fRHWDgftmDrN2SANWb3r4E4AcKeVRACHEh8B8oH3Cl4CPMHVJvYEywND5QANCfhqEp7C3NIvdRbtJDkzGz83PPK2x/bz1ioa2ypguGheifKLQaXVMj5zeoda6n7ufFd+QogxchRV1/O3zA2z4pYHkSD/+uTCJYcE+1g5rwBJSyjM3EGIhMEdKeVvL4yXARCnl3e3a+ABfAgmAD7BISvl1N8e6A7gDIDAwcOxHH31kqfdhEZrmRqZuXcz3EZeyzDkHQze/s3ydfAlyDiLIxfQR7BJMkHMQg50H4yTObxyxuroab29Vpx3UtWhPXQs4XtnM02n1zI2UXD7MC40a5mTGjBl7pJTjzue1venhd3eFO/+WuAzYC1wKxALfCyG2SCkrO7xIyteB1wHi4+Pl9OnTzzngPpW7A7YYyNNFY8j/BTDVXZ+tm80tI29Bp9Xh5eJl8dOmpqZic9fCStS1aOOo10J/qoYffi7itouHALBgdhN7dvzkkNfC0npT9CQfiGz3OAIo6NTmFuAzaZIDHMPU2x9Y8kw3bMcN/bX5S25Obvxm+G8Y4T+iT5K9oigmhmYjr28+wmXLN/PihsOUVDUA4OOuFgZaSm96+LuAOCFEDHACuB64oVObXOBXwBYhRDCmXfuOWjLQfpFvWnAV7G/6XTUtYhq3jbqN5KBkKwemKPbtl5OVPPRJJvvyK5g5PJinrhpJoI8qi2BpZ034UkqDEOJuYD2maZlvSSmzhBB3tjz/GvB34G0hxH5MQ0APSSlP9WHclielaYZOzMXsKd4DwN1j7iZh8MD7Q0VRBpK6xmYWv74DjRCsWDyGy5NC1ZTkPtKrefhSynXAuk5fe63d5wXAbMuG1s8q8qD6JERMIL0oHW8Xb+L8HGevS0XpbwdPVjEs2FTs7N83pDA8VMtgL1drh2XXVOHyVnltC67Si9IZHTRalSlQlD5Q22jg72uzmfPiZj7POAHARUMDVLLvB6q0Qqt804Kr034RHKk4wq+H/Prsr1EU5Zz8lHOKZZ9lkldWx5JJ0cxSxc76lUr4rfJMC64yTh0AICU4xcoBKYp9+dd3B1nxYw4xAV78945JTBzib+2QHI5K+ABNdXAyEybfTXpxOi4aF0YGnL20gqIoZ2c0SjQaQUr0IH4/bQh/njkMdxc1XGoNagwfoGAvGA0QabphOypgFG5OakqYolyIU9UN3P1BOss3HAZgRnwQf5k7XCV7K1IJH8wVMmtDRpFdms2YoDFWDkhRBi4pJZ9n5DPz+U18l1WEh0rwNkMN6YBp/H6Qjv11hRikQY3fK8p5Kjhdx8Of72fjwRJSovx45pok4lSxM5uhEn7rDlcx00gvSkcg1MpaRTlP5bWN7D5ezmNXJHLTZB1OGrWAypaohH86F6qLTOP3xWkMGzQMravW2lEpyoBxtKSaH34u4o5LYhkR5sv2v/wKbzeVWmyRGsPP3wWAISyFfSX71HCOovSSodnIq6lHmPPiFv79Y4652JlK9rZL/c/kpYGLJ7+4OFNnqCMlSCV8RTmb7IJKln66jwMnKrlsRDB/n6+KnQ0EKuHn74KwFPac2guoBVeKcjZ1jc3c+MYOnDQaXr0xhbmjQq0dktJLjp3wWxdcTfkT6UXpRHhHEOQZZO2oFMUm/VxYSUKIDx6uTrx8YwqJoVr8PFX9m4HEscfwWxZcyfDxZBRnqN69onSjpsHA419mMe+lLXyWbip2NiU2QCX7Acixe/gtC66O+YZQ3lDO2OCxVg5IUWzLlsMl/OWz/eSX1/HbydFcNjLE2iEpF8CxE35eGgyKIb36GIBaYaso7Ty7/hde3niEIYFefHznZMbrBls7JOUCOW7Cb11wNWQ66UXpDHYfjE6rs3ZUimJ1rcXOxukG84fpcM+v4lT9GzvhuAm/dcFVxHjS8z4lJShFbaumOLTiqnoeW5NFXJA398+OZ0Z8EDPi1SQGe+K4N21bFlydDIzlRPUJdcNWcVhSSj7ences5zez4ZdivN0dtx9o7xz3fzYvDVy8yDDWAmr+veKY8str+ctn+9ly+BTjdYN4+pokYgO9rR2W0kccN+Hnm3a42lOyF09nT+IHxVs7IkXpd5V1BjLzK3hy/gh+MzEajSp2Ztccc0inqQ5O7jeN3xenMzpwNM4ax/3dpziWIyXV/GfTEQASw7RsW3YpN03WqWTvABwz4RdkgNFARehIcspz1HCO4hCamo28vDGHuS9u4dVNRzhVbSp25qWKnTkMx/yfzjMtuNrr6opEqgVXit07cKKChz7NJKugknmjQnjiypEEeKtiZ47GMRN+/i4YPIT0yhycNc6MChhl7YgUpc/UNTaz5M2dODtpeO03KcwZqYqdOSrHS/hSmnr4sTNIL0pnhP8I3J3drR2VoljcgRMVjAjT4uHqxCs3jiUxVIuvp4u1w1KsyPHG8E8fh5pi6sPGcKD0gKp/r9id6gYDj645wOUrtpqLnU2O9VfJXnHAHn6eacHVfp9BGIxqw3LFvqQeLObhzw9QUFHHLRfpmKOKnSntOF7CzzctuEpvKgdUwTTFfjzz7S+8mnqEoUHefHLnFMZGD7J2SIqNcbyEn2dacJVRso+hfkPxdfO1dkSKckGajRInjWDSEH+cNYK7Lx2Km7MqdqZ05Vhj+I21UHSA5vBx7C3Zq6ZjKgNacWU9v/+/3Sz/4RAA04YF8sDseJXslR45Vg+/ZcHVQf9wagpq1A1bZUCSUvLxnnyeWptNg8Go6tQrveZYCb9lh6t0TTOgCqYpA09emanY2dacU0zQDebpa0YxRBU7U3rJsRJ+XsuCq9OHCPMKI8RLzWBQBpaqegMHCir4+1UjuXFClKp/o5yTXo3hCyHmCCEOCiFyhBDLemgzXQixVwiRJYTYZNkwLUBKyE9Dho9nT9Ee1btXBozDRVW8kpoDtBU7WzJJVbZUzt1Ze/hCCCfgZWAWkA/sEkJ8KaXMbtfGD3gFmCOlzBVC2N42OeV6qCkhNziOsiPbVcJXbJ7BKFmx4TArfszBy82J68ZFEuDthqerY/1hrlhOb75zJgA5UsqjAEKID4H5QHa7NjcAn0kpcwGklMWWDvSCtexwle5uKhg1NkjN0FFsV2b+aZ7YXk9e1SGuGB3GY1ckqmJnygXrTcIPB/LaPc4HJnZqMwxwEUKkAj7Ai1LKdzsfSAhxB3AHQGBgIKmpqecR8vmJO/Q5IRp3vj62Dy+NF8czjpMrcvvt/GdSXV3dr9fClqlrAQ0Gyf2banEWkntT3BkTVMGB3dutHZZVqe8Ly+hNwu9uoFB2c5yxwK8AD2C7EGKHlPJQhxdJ+TrwOkB8fLycPn36OQd83g4+ClHjKXAqZEL4BGbMmNF/5z6L1NRU+vVa2DBHvhYHTlSQGKpFoxG8NaSUU0cy+fUs2/k+tSZH/r6wpN7ctM0HIts9jgAKumnzrZSyRkp5CtgMjLZMiBbQWAMnD1ASNoq8qjy14EqxKVX1Tfzti/1cvmIrn2eYip1NHOKPl4u6KatYVm8S/i4gTggRI4RwBa4HvuzUZg1wsRDCWQjhiWnI52fLhnoBCjJANpPu7QegFlwpNmPjL8XMfmEzH+zM5dGCiSIAACAASURBVLapMcwdpaYKK33nrEM6UkqDEOJuYD3gBLwlpcwSQtzZ8vxrUsqfhRDfApmAEXhDSnmgLwM/Jy07XKXLWjycPUjwT7ByQIoC//vNz/xn01Higrx55a4pjIlSxc6UvtWr+V1SynXAuk5fe63T42eBZy0XmgXl74bBsaSXZZMUmISLRtUFV6xDSolRgpNGcFFsAG7OTvxxRqyqf6P0C/svntay4KoqIoWDZQfVcI5iNScr6rn93T288L1pLsMlwwK5f9YwleyVfmP/KzhaFlzt9QtBVkm14Erpd1JKPtyVxz++/pnGZiOTY/2tHZLioOw/4bcsuMpwAWfhTFJAkpUDUhxJXlktSz/JZPvRUiYNGczTC5LQBXhZOyzFQdl/ws9LA1dv9lTnMdx/OJ4untaOSHEgNY0GfjlZyT+uHsX14yNV/RvFqux/DD8/jcbwMRw4pTYsV/rHwZNVvLzRVOwsIUTLtmW/4oaJqrKlYn32nfBbFlwdCBxCo7GRMcFq/1ql7zQajCz/4RCXr9jCm1uPcaq6AQAPV3VTVrEN9j2k07rgysMdUAuulL6zL+80Sz/J5GBRFfOTw3j08kT8VbEzxcbYd8JvWXC1p7GMIb5DGOSuFrYollfbaOC3q9Jwd3bijZvGMTMx2NohKUq37Dvh5++i2T+WfaXZXBZzmbWjUexMZv5pRob54unqzMqbxhEf4oPWXS3qU2yX/Y7hSwl5aeSEjqCqqUoN5ygWU1nfxF8+28+V//7JXOxsvG6wSvaKzbPfHn75Mag9xR4fP6hBVchULOKH7CIe/mI/JVUN3HHJEOaNCrV2SIrSa/ab8PNadriinmDPYEK91A+mcmH+se5nXt98lIQQH15fMo7RkX7WDklRzon9Jvz8NKSrN+kVRxgXMg4h1Bxo5dxJKWk2SpydNFwcF4C3mzN3TovF1dl+R0MV+2W/CT8vjfzwUZTUnVD71yrnpbCijr99foCEUB8evCyBi+MCuTgu0NphKcp5s89uSmMNFGWRPigcQBVMU86J0Sh5f+dxZj2/mW1HSglU8+kVO2GfPfwT6aYFVy6gddUS6xdr7YiUASK3tJYHP9nHzmNlXDTUn/+9Ookof1V/SbEP9pnw81t2uKotZEzQGDTCPv+QUSyvtslATnE1z1wziuvGRap7P4pdsc9MmLeLUwGx6Kvz1HCOcla/nKxkxYbDgKnY2U/LLmXR+CiV7BW7Y38Jv2WHq71BpmEcteBK6UmDoZnnvzvI5S9t5e1tenOxM3cXVexMsU/2N6RTdhRqS9nj4YF7ozsj/EdYOyLFBqXnlvPQJ5kcLq5mwZhwHrk8kUFertYOS1H6lP0l/JYdrtINpxkVOAoXJ7XcXemottHArW/vwtPFiVW3jGdGfJC1Q1KUfmF/CT8vjRo3H36pOs7tutnWjkaxIRm55YyO8MPT1Zk3fzuO+BAt3m729yOgKD2xvzH8/DT2hSVglEY1fq8AUFHXxEOfZHL1K9vMxc7GRg9WyV5xOPb1Hd9QDUVZ7EmajaaqhNFBo60dkWJl67NO8sgXByitaeTOabH8OknVVFIcl30l/IJ0kEYyaCBhcAJeLl7Wjkixor+vzebNrccYHqrlzd+OZ1SEr7VDUhSrsq+En5dGE5BZnce18ddZOxrFCtoXO5sRH8QgTxd+Py0WFyf7G71UlHNlXwk/fxdZQbE0GBtV/XsHdOJ0HQ9/vp8RYVoevCyBqXEBTI0LsHZYimIz7KfbIyXk7yLd31QwLTko2coBKf3FaJT833Y9s5/fxM6jZQRr3a0dkqLYJPvp4bcsuEp31qDT6gjwUD07R6A/VcPSTzJJ05dxcVwA/7h6FJGDVbEzRemO/ST8/F0YgYyGImbq1IbljqLBYOToqRqeXZjEwrERqv6NopyB/ST8vDSOePpS2VSj5t/buayCCr7PLuK+mcOID/Fh60MzVP0bRekF+0n4+WmkB8cCp1SFTDtV39TMih8P89qmowzydOU3k6IJ8HZTyV5Resk+En7rgqvEKQTJICK8I6wdkWJhe46XsfSTTI6U1HBNSgSPXD4cP09V7ExRzoV9JPyCdKQ0km6oZEzYRDWOa2dqGw387p3deLk6886tE5g2TO0rqyjnwz4Sfl4aBc5OFDWeVuP3dmTP8XLGRLYWOxtPfIiPqn+jKBegV/PwhRBzhBAHhRA5QohlZ2g3XgjRLIRYaLkQeyF/F+kBOgC14MoOVNQ28eDH+7jm1W18Zi52Nkgle0W5QGf9CRJCOAEvA7OAfGCXEOJLKWV2N+2eAdb3RaA9al1wFRmLj6xhqN/Qfj29Ylm7Txp48IVNlNU08ofpsVyuip0pisX0pss0AciRUh4FEEJ8CMwHsju1+xPwKTDeohGeTeuCKxFJcmAyTho1Y2OgevKrbN7a20BiqJZVN49nZLgqdqYoltSbhB8O5LV7nA9MbN9ACBEOXA1cyhkSvhDiDuAOgMDAQFJTU88x3K6CT/5IiEbD0YYyRtT4WeSY/a26unpAxm0JUkqMEpw0goDGZq6IlsyPN3DqcAaph60dnXU58vdFZ+paWEZvEn53U15kp8fLgYeklM1nmiEjpXwdeB0gPj5eTp8+vZdhnsHaNWzw9gNg4aSFA3IOfmpqKha5FgNMXlktf/18PyPDfXloTgLTcdxr0R11Ldqoa2EZvUn4+UBku8cRQEGnNuOAD1uSfQAwTwhhkFJ+YZEozyRvFxn+4bhq6hgZMLLPT6dcOKNR8u52Pf9cfxABXDYixNohKYpD6E3C3wXECSFigBPA9cAN7RtIKWNaPxdCvA2s7Zdk31AFxVmkD0tipN9IXJ3UQhxbd+xUDQ9+vI/dx8uZNiyQ/7l6JBGDVLEzRekPZ034UkqDEOJuTLNvnIC3pJRZQog7W55/rY9j7NmJdGqR/Nx0mlvUdMwBoanZyPGyWp6/bjRXjwlXi+QUpR/1amKzlHIdsK7T17pN9FLKmy88rF7KTyPTzRWDNA7IsXtHceCEqdjZn2cNY1iwqdiZm7OaTaUo/W1gr2TJ20X64DAERkYHqg3LbU19UzMvbjjM65uPMtjLlZsmR+Pv7aaSvaJYycBN+K0LrsIjiPcNwcfVx9oRKe3s0pfx0CeZHD1Vw7VjI/jbrxPx9XSxdliK4tAGbsIvPUJTXRmZRj+uVvVzbEpNg4Hb392Nt5sz//e7CVwcp4qdKYotGLgJPz+NX1xdqZMGNX5vI3bpyxgbNQgvN2feunk88cE+eKn6N4piMwbuJuZ5aaR7m5beqwqZ1lVe08j9/93Lta9tNxc7S2lJ/Iqi2I6B+xOZv4s9vv5E+vgT6KmGDKxBSsm6/Sd57MsDnK5t4p5Lh3LFaFXsTFFs1cBM+A1VGIuzyRgyhGmqd281T67NZtVPekaF+/LurRNJDNNaOyRFUc5gYCb8E3vQO2s4bWxU9e/7mZQSg1Hi4qRh1vBggrXu3DY1BmengTs6qCiOYmD+lObtYo+7G4C6YduP8spqWfJmGv/67hAAU4YGcOe0WJXsFWWAGJg9/Pw00n2D8Hf3I8onytrR2L1mo+SdbXqeXX8QJ41g3ig1Tq8oA9HAS/itC65CA0gJTlG1WPrY0ZJq/t/H+0jPPc30+ED+cfUowvw8rB2WoijnYeAl/NIcTjZWUiB9WKJu2Pa5ZqPkxOk6li9KZn5ymPoFqygD2MBL+HlppKvx+z6VmX+a77OLeGB2PHHBPmxeqoqdKYo9GHgJPz+NdC8fvFy8iB8Ub+1o7Ep9UzMvfH+IlVuOEujjxs1TdKrYmaLYkYGX8PN2scfLR21YbmE7jpay7NNM9KW1LJ4QybK5w/H1UMXOFMWeDKyEX19JxalfyPEKZ64azrGYmgYDd763B627Cx/cNpEpQwOsHZKiKH1gYCX8gnQy3EzbGI4JGmPlYAa+tGNljIs21bx5+5YJDAv2xtN1YH1LKIrSewNrxUzeLtLd3XDWODMqYJS1oxmwymoaue/DDK77T1uxs+RIP5XsFcXODayf8Pw00n38GOk/Endnd2tHM+BIKVmbWcjjX2ZRUdfEvb+KU8XOFMWBDJyELyX1+bvICtFykxq/Py9PfJXN29v0jI7w5f3bJ5IQooqdKYojGTgJvzSH/bIWAz6qYNo5kFLS1CxxddYwe0Qw4X4e3Do1BieNWkClKI5m4Izh56Wxx90NgVAblvfS8dIabli5k399dxCAKbEB3H7JEJXsFcVBDZwefn4a6Z5eDPUbiq+br7WjsWnNRsmqn47x3HcHcdFouGpMmLVDUhTFBgyYhG/IS2OflytXqPH7M8opruaBj/exL+80M4cH8dRVowjxVTe4FUUZKAm/vpKDp3Oo9QpR4/dnIaWkuLKelxaP4YqkUFXsTFEUs4GR8E/saSuYpipkdrE37zTfZ5/kwcsSiAv2YdODM3B1Hji3ZxRF6R8DI+HnmxZchXuFEuwVbO1obEZdYzPPf3+QN7ceI8jHnVsvisHf200le0VRujUgEr7M3Um6hycXBY+zdig2Y9uRUyz7dD+5ZbXcMDGKZXMT0LqrYmeKovTM9hO+0cjxk+mUBXmr+vctahoM/PH9dLQeLqy+fRKTY/2tHZKiKAOA7Sf80hzSRQOgEv72I6VMjBncrtiZDx6uqkS0oii9Y/uDvfmmBVeDXbXEaGOsHY1VlFY38KfVGSxeuYPPW4qdjY70U8leUZRzYvs9/Lw00j08GBM8zuGmGEop+XJfAY9/mUVNQzMPzBrGFaPVIipFUc6PzSf84hM7yfd04noHHM557Mss3t1+nDFRfvzzmiTign2sHZKiKAOYbSf8+grSq3PBM8BhFlwZjRKD0VTsbO7IUKL9vbh5ik7Vv1EU5YL1agxfCDFHCHFQCJEjhFjWzfM3CiEyWz62CSEsU93sxB7S3dzw0LiSMDjBIoe0ZcdO1bB45Q6eayl2NjnWn9+pypaKoljIWRO+EMIJeBmYCyQCi4UQiZ2aHQOmSSmTgL8Dr1skupYdrkYHJuGsse0/Ri5Es1Hy+uYjzFm+mezCSoYGels7JEVR7FBvsugEIEdKeRRACPEhMB/Ibm0gpdzWrv0OIMISwVXmbeeQqyt3hU6wxOFsUk5xFU/tqOdY5S/MSgzmqatGEqxVxc4URbG83iT8cCCv3eN8YOIZ2v8O+Ka7J4QQdwB3AAQGBpKamtrzUaQRWZSBDNTiVOhEavkZ2g5gBdVGTjc084fR7owPqeLn9B38bO2grKi6uvrM3xcORF2LNupaWEZvEn53A8iy24ZCzMCU8Kd297yU8nVahnvi4+Pl9OnTez5ryUGWZ0qc0bBk5hI8nD16EerAkJ5bzvfZRTw0x3RfIshzIzMvnWHlqGxDamoqZ/y+cCDqWrRR18IyenPTNh+IbPc4Aijo3EgIkQS8AcyXUpZecGR5aWS4u5HoF2s3yb620cCTX2VzzavbWJNxgtLqBgCc1U1ZRVH6QW96+LuAOCFEDHACuB64oX0DIUQU8BmwREp5yBKBNeRtZ7+bGzeGTbHE4axu6+FTLPssk/zyOm6aHM3SOQl4u9nvjWhFUWzPWTOOlNIghLgbWA84AW9JKbOEEHe2PP8a8CjgD7zSshrWIKW8oNKWBwp20eQpSLGD+fc1DQb+tDodP09XPvr9ZCbEDLZ2SIqiOKBedTGllOuAdZ2+9lq7z28DbrNYVPUVpNefBE8/xgSNsdhh+9u2nFNMHOKPl5sz7946kbhgb9xdVP0bRVGswzbHFPJ3s8fdjVjPEPzc/awdzTkrqWrg8S+z+Hp/Ic9dO5qFYyMYFaE2Xu8vTU1N5OfnU19fb+1QLoivry8//+zIc7baOOK1cHd3JyIiAhcXy+1zYZMJvzlvJ/vc3ZgbeqbZn7ZHSsnnGSd4cm02tQ3NPHhZPPOTVbGz/pafn4+Pjw86nW5AF9yrqqrCx0fVTwLHuxZSSkpLS8nPzycmxnJVgm0y4R/O+4lqjYaUsEnWDuWcPLLmAO/tyCUlyo9/LkxiaJDjfIPakvr6+gGf7BXHJoTA39+fkpISix7X9hK+0cie0wfB14OxQbZ/w9ZolDQZjbg5O3F5UhhDA71ZMlkVO7M2leyVga4vvodtbwOU0sOkOxkJddES6h1q7WjO6EhJNYte385z603FziYN8efmi1SxM0VRbJPNJXyZu5N0d3dSApOtHUqPmpqNvJKaw9wXt3DwZBXxIVprh6TYkNLSUpKTk0lOTiYkJITw8HDz48bGxl4d45ZbbuHw4cO9Pucbb7xBYGAgycnJJCQk8NJLL3V4/tVXXyUhIYGEhAQmTpzItm1t5a+amppYunQpQ4cOZeTIkUycOJH169f36rwjR45kyZIlvY7zfKxbt46UlBSGDh3Ks88+222b8vJyfv3rXzN69GhGjBjBu+++a36urKyMBQsWkJCQwPDhw0lLSwNg4cKF5v+X6Ohoxo3rOJP82LFjeHl5sXz58r57c/3M5oZ08nM3c8rZiZTIi60dSrcOFVXx5//uJaugkjkjQnjyqhEE+ahiZwNeXhrot4DuYoi8sGJ9/v7+7N27F4DHH38cb29v/t//+38d2kgpkVKi0XTf51q1ahVVVVXndN4bb7yR5cuXU1JSQnx8PNdeey2hoaF88cUXrFq1im3btjF48GB2797NggUL2LNnD4GBgfzlL3+hrKyM7OxsXF1dKSws5Keffjrr+TIzM3F2dubHH3+krq4OD4+uK+INBgPOzuefZpqamrj77rv56quvGDp0KOPGjWP+/PkMGzasQ7sVK1aQnJzM119/TVFREQkJCdxwww04Ozvzpz/9iSuvvJLPPvuMxsZG6urqAPjkk0/Mr7/33nsJDg7ucMz777+fuXPnnnfstsjmEv6e4gxwh5TgC1q31Wc0QnC6tolXb0xh7ijbHnJSgG+Wwcn9Z27TUAlFB0AaQWggeCS4neGvtpBRMPfpcw4lJyeHq666iqlTp7Jz507Wrl3LE088QXp6OnV1dSxatIhHH30UgKlTp/LMM88wceJEAgICuPPOO/nmm2/w9PRkzZo1BAUF9XiewMBAhgwZQmFhIaGhoTzzzDM899xzDB5sWvA3btw4brzxRl555RXuv/9+3n77bfR6Pa6urgCEhoaycOFCwPSXxr333ktycte/uFevXs1NN91ERkYGa9eu5dprrzXHPm3aNLZs2cKCBQtYvHgxd911F7m5uWg0Gl566SUmTZrEjh07+POf/0x9fT2enp68/fbbxMXFdTjHjh07GD58OFFRUbi5uXHdddexZs0aHnzwwQ7thBDmX5DV1dUEBATg5OREWVkZO3fu5P333wfA1dXV/D5bGY1GPv74Y7Zu3Wr+2ieffEJCQgJOTva1bsa2hnTqK0hvKsNX48oQvyHWjsZsz/Ey/vcb0xzgoUHebHpwukr29qS+wpTswfRvfUWfnSo7O5vf/e53ZGRkEB4eztNPP83u3bvZt28f33//PdnZ2V1eU1FRwbRp09i3bx+TJ0/mrbfeOuM59Ho9zc3NjBw50nzOsWM7ToAYN24cWVlZHD58mJiYGLy9u9+DYdWqVd0me4CPPvqIRYsWsXjxYlavXt3hucrKSjZv3sx9993HPffcw9KlS9m9ezcfffQRt91mWqM5fPhwtm7dSkZGBo888gh/+9vfAMjLy+PKK68E4MSJE0RGtpXyioiI4MSJE11iuffee9m7dy9hYWGMHj2aFStWIITg6NGjBAYGctNNNzFmzBjuuOMOamtrO7w2NTWVqKgohgwx5Zyqqir+9a9/meOxJ7bVw8/fTbq7G2P8hqER1v9dVNNg4Nn1B3lnu54wXw9+f0ksg71ccXayfmxKL/WmJ56XBu9cCc2N4OQK17xxwcM6PYmNjWX8+PHmx6tXr+bNN9/EYDBQUFBAdnY2iYkd9xfy8PAwDy2MHTuWLVu2dHvs999/n++//56DBw+yatWqLj3Z9qSUFzQLZPv27URERBAeHk5QUBC33347FRUV+PqaFhhef/315rY//PADBw8eND8uLy+nrq6O06dPc9NNN3HkyJEOx46MjOTLL780x9lZd3GvW7eOCRMmsGnTJg4dOsScOXPYv38/BoOB3bt3s2LFCsaOHcuf/vQnnn32WR577DHza1evXs3ixYvNjx955BEefPBBvLy8zvPq2C6bylynjm/muIsLYyOnWTsUNh8qYfYLm3lnu57fTtbx3Z8vYbBXzz9AygAWOQF++yVc+rDp3z5K9kCHJHL48GFefPFFfvzxRzIzM5kzZ063q4PbJ24nJycMBkO3x77xxhvJysoiNTWVe++9l+LiYsDUk96zZ0+Htunp6SQmJhIXF8exY8eoqak5p/exevVqDhw4gE6nIy4ujsrKSj7//PNu36eUkrS0NPbu3cvevXs5ceIEHh4ePPzww1x22WUcOHCAL774otv3HhERQV5e23Yc+fn5hIV1Xcy4atUqrrnmGoQQxMfHExkZyaFDh4iIiDDfkBVCcM0115Cenm5+XVNTE2vWrOG6664zfy0tLY37778fnU7Hv//9b5588kleffXVc7o+tsqmEn76CdPMgTHhk60aR02Dgfv+uxc3Fw0f/34yj185Ai9V2dK+RU6Aix/o02TfWWVlJT4+Pmi1WgoLC3s9M+Zspk6dyuLFi1mxYgUAS5cuZenSpZSXlwOmZP/ee+9x11134ePjw0033cR9991HU1MTAAUFBeYx7xtvvLFDggRobm7m008/JTs7G71ej16v57PPPusyrNNq5syZvPzyy+bHrTe0KyoqCA8PB+Dtt9/u9rWTJk0iOzub3NxcGhoa+Oijj8zDPe1FRUWxYcMGAAoLC8nJySEmJoaIiAiCgoLIyckBYMOGDR3+glq/fj2jRo0iNLRtiHbbtm3m93X33Xfz6KOPctddd3Ub30BjOwnfaCSj6jjuaEgc3HnL3P6x5XAJzUbZUuxsAuvuuZhxOlXZUukbKSkpJCYmMnLkSG6//XYuuugiix172bJlvPHGG9TU1LBgwQKWLFnCpEmTSEhI4M477+SDDz4w3/h9+umn8fX1Zfjw4YwaNYoFCxaYn8vMzCQkJKTDsTdu3EhMTEyHWS0zZsxg7969FBUVdYnl5Zdf5qeffiIpKYnExERWrlwJwEMPPcSDDz7Y5X23H8N3cXHhpZdeYv78+SQmJvKb3/yG+Ph483HfeOMNwDQbatOmTSQlJTFr1iyee+45Bg0aBJhm8CxatIikpCSysrJYtmyZ+Vwffvhhh+Eceye6GyPrD/Hx8bL9uB7Fv3DdF/PxGTyUNxd+3a+xFFfW8+iaLL7NOsm/rh3NNWMtsiVvr6ndfNpY4lr8/PPPDB8+3DIBWZG168eUl5dz11138eGHH1othlbWvhbW0t33shBiz/mWn7eZcYrq41s46OrCHf1YME1KySd78vn72mzqDUYempOgip0pSotBgwbZRLJXLMdmEv6+4xsxCkGKbma/nfPhLw7wwc5cxusG8fQ1ScQGdj81TVEUxR7YTMLfU/4zTq4wOqhvSyq0L3Y2f3QYw0N8uHFiNBpV/0ZRFDtnGzdt606T0VxNgqs/ni6efXaanOIqrv3Pdp791nTvYOIQf5ZM1qlkryiKQ7CJhN+Yt4P9bq6kBIzqk+M3NRt5eWMO817cypGSakaEq2JniqI4HpsY0sk++h0NGg1jY2Zb/NiHiqq478O9ZBdW8utRoTx+5QgCfdwsfh5FURRbZxM9/D3FpoUdYyIsNw+5lZNGUNXQxGu/GcvLN6aoZK/0uenTp3dZRLV8+XL+8Ic/nPF1rfVsCgoKzMXLujv27t27z3ic5cuXd6gXM2/ePE6fPt2b0M/o8ccfN5d6TkxM7LDQSkrJU089RVxcHMOGDWPGjBlkZWWZn6+urub3v/89sbGxjBgxgksuuYSdO3ee9ZwGg4GAgAAef/zxC47/TN555x3i4uKIi4vjnXfe6bZNbm4uM2bMYMyYMSQlJbFu3ToAjh8/ztixY0lOTmbEiBG89tpr5tdIKXn44YcZNmwYw4cPN5etfv/990lKSiIpKYkpU6awb9++Pn1/HQKyxsewYcOklFLK5mb5h9fi5RXvTpCWsvNoqXxqbZb5cZOh2WLH7gsbN260dgg2wxLXIjs7+5xfk1GUIVdmrpQZRRkXfP7XXntN3nzzzR2+NnHiRLl58+Yzvs7Ly6vD48rKyi5tpk2bJnft2nXG40RHR8uSkpJeRtt7jz32mHz22WellFIeOnRI+vj4yMbGRimllCtWrJBz586VNTU1Ukop169fL4cMGSLr6uqklFIuWrRILlu2TDY3m34Wjxw5IteuXXvWc3799ddyypQpUqfTSaPR2G0bg8FwQe+rtLRUxsTEyNLSUllWViZjYmJkWVlZl3a33367fOWVV6SUUmZlZcno6GgppZQNDQ2yvr5eSillVVWVjI6OlidOnJBSSvnWW2/JJUuWmN93UVGRlFLKn376yXyOdevWyQkTus9/3X0vA7vleeZdqw/pGEt+JsPFidl+cWdvfBbVDQae+eYX/m/HcSIHe3DX9KGq2JmDeybtGX4p++WMbaobqzlYfhCJRCCIHxSPt2vPU3QTBifw0ISHenx+4cKF/O1vf6OhoQE3Nzf0ej0FBQVMnTqV6upq5s+fT3l5OU1NTTz11FPMnz+/w+v1ej2XX34527dvp66ujltuuYXs7GyGDx9uruUOcNddd7Fr1y7q6upYuHAhTzzxBC+99BIFBQXMmDGDgIAANm7ciE6nY/fu3QQEBPD888+bq23edttt3Hfffej1eubOncvUqVPZtm0b4eHhrFmzptv69q3i4uLw9PSkvLycoKAgnnnmGVJTU/H0NE26mD17NlOmTOH9999n+vTp5hLFrfX/hwwZYq5OOW/ePN54441ua+SsXr2ae++9l3//+9/s2LGDyZNNZVd0Oh233nor3333OllmGAAADZ9JREFUHXfffTfjx4/nj3/8IyUlJXh6erJy5UoSEhL46quveOqpp2hsbMTf35/333+/S9379evXM2vWLHP56FmzZvHtt992WYErhKCyshIwlYVojbd9raOGhgaMRqP58auvvsoHH3xgft+tK5inTJlibjNp0iTy8/N7vNaWZPVMmJPzDVVOGlIiL7mg42w8WMzs5zfx3s7j3HpRDOvvU8XOlN6paqpCYlpxLpFUNZ3bxiOd+fv7M2HCBL799lvAtHx/0aJFCCFwd3fn888/Jz09nY0bN/LAAw90WxGy1auvvoqnpyeZmZk8/PDDHYqg/c///A+7d+8mMzOTTZs2kZmZyT333ENYWBgbN25k48aNHY61Z88eVq1axc6dO9mxYwcrV64kIyMDMBVy++Mf/0hWVhZ+fn58+umnZ3yP6enpxMXFERQURGVlJTU1NcTGxnZo01qCOSsri+Tk5B5ry69bt67bZF9XV8eGDRu4/PLLWbhwYZdaPe7u7mzdupXrr7+eO+64gxUrVrBnzx6ee+458/DZ1KlT2bFjBxkZGVx//fX885//BGD37t3mMs29LcH8+OOP89577xEREcG8efPMtYrAVA4iKSmJyMhIHnroIfP7OXLkCP/9738ZN24cc+fO7XYXszfffLPfNlqxeg8//YRpZ52UIef/hqsbDDzw0T78vVz59K4ppEQNslR4ygB3pp54q73Fe7n9u9tpMjbhonHh6YufJvkC14MsXryYDz/8kPnz5/Phhx+ae9VSSv7617+yefNmNBoNJ06coKioqEu9mlabN2/mnnvuATCP+bb66KOPeP311zEYDBQWFpKdnd3h+c62bt3K1Vdfba5kuWDBArZs2cKVV15JTEyMue792LFj0ev13R7jhRdeYOXKlRw9etT8C60n8gJLMK9du5YZM2bg6enJlVdeycUXX8wLL7xg/sWxaNEiwHR/YNu2beYNWMDU0wZTdc1FixZRWFhIY2MjMTExgOmXUWsdnu5+4XYX9+rVq7n55pt54IEH2L59O0uWLOHAgQNoNBoiIyPJzMykoKCAq666ioULFxIcHExDQwPu7u7s3r2bzz77jFtvvbVDeeuNGzfy5ptvdth8pS9ZvYefXnmMIJwJ9zm3+jVSSlIPFtNslHi7OfPe7yay9p6pKtkr5yw5KJmVs1dy95i7WTl75QUn+//f3r0HR1VfARz/noaE2ECIJEANqUNECyWGJSEP7Uh4jY2RWl46I9AySAOtDLF11BFsDQz801rIOE6pyquMDINWeYTOlBoTBEWbCmnzkohEHjGtQFhToklXDPvrHxt2E0mylyW7S3bPZyZDlv3t5exh9+zd3733/ABmzZpFWVmZezWr9PR0wHWwrqmpiYqKCiorKxkxYkS3bYE76674nDp1inXr1lFWVkZ1dTUzZszwup3evkkMHOg5maG3FsyPP/44x48f57XXXmPhwoU4HA5iY2OJiYnh5MmTXcZeacGckpJCVVVVl6kOK3bu3ElpaSmjRo1i8uTJ2O32Lt9arnxwOZ1O4uLi3O2XKysrqatzLVhUUFDA8uXLqamp4eWXX76uFsxbtmxxt1G+++67cTgcXLhwocuYxMREUlJS3EU9KSmJuXPnAjB79myqq6vdY6urq8nPz6e4uJj4+Phryo2vglrwTdvnVMhXTIxJuqY9gfMtDn6+vYJFfzrC3n+5vnqNS4xl4IDQWo5MBc6E4RPIT83vk2IPrjNupkyZwuLFi7vMBV+8eJHhw4cTGRnJ22+/zZkzZ3rdTk5OjrtVcW1trbtgtLS0EBMTw5AhQzh37hz79+93P2bw4MHdroebk5PD3r17aWtro7W1lT179jBpkm9rR8+ZM4eMjAz3GS1PPfUUjz32mPsYQ2lpKYcPH2b+/PmMHj2ajIwMVq1a5f7QOXHiBMXFxQBMnz79qimUlpYWDh8+TENDA6dPn6a2tpYNGzZ024I5NjaW5ORkXn/9dcD1wXblrJfOLZh7OvsmNzeXkpISmpubaW5upqSkhNzc3KvGdW7BXFdXh8PhYNiwYTQ2Nrqfd3NzM++99567o+esWbM4cOAAAIcOHXKvxdvQ0MCcOXPYvn37Vevz+lNQC/6/P3mL8wMGkP6dTO+Dcf1H/vnIp0wvOsShj5tYmafNztSNa968eVRVVXVZ/WnBggUcPXqUjIwMduzYwdixY3vdxqOPPsqXX37J+PHjee6558jKcvXrt9lspKWlkZKSwuLFi7u0GF66dCl5eXlMnTq1y7bS09NZtGgRWVlZZGdnk5+fT1pams/Pr7CwkKKiIpxOJwUFBWRmZpKamsqYMWNYu3ZtlwO/mzdv5uzZs9x+++2kpqayZMkSEhMTcTqd1NfXuw+YXrF7926mTZvW5ZvHzJkz2bdvn3u6prMdO3awZcsWbDYbKSkp7g+T1atX89BDDzFp0iQSEhLc4zvP4Q8dOpRnn32WzMxMMjMzKSwsdMdTWFjoXn1r/fr1bNq0CZvNxrx589i2bRsiQl1dHdnZ2dhsNiZPnsyTTz5JaqrrItIVK1awa9cuUlNTWblypXsaac2aNdjtdpYtW8aECRPIyAjMGt5BbY+8bn0Oz9jLeeO+VxgzwvsLb+XuGnZ+0EBW8lB+N3c8yQmhsQSZtkf20PbIHuHQEri2tpatW7dSVFTU67hwyEV3Qqo9coX9QwYbuGO4rccxl52Gry87iY6MYHbaSFISY5mfdav2v1EqBNx5551ei73qO0Gd0vnn1/8lLSq+xwXLPz73BXNffJ/fv+lqdpaVPJSf3KWdLZVSyhfBK/iXHZyKjOi2YdqldicvlJ1gxgvvcsbeyvikIUEIUPVnwZqqVKqv+OM1HLQpnUvOVqKIZeKorkfDPzrbwq9ereSjs1/wgC2R1Q+MI36Q9r9R1kVHR2O324mPj7+u88CVChZjDHa7nejo6D7dbtAK/lfOrxhqDONuu7fL30dGfIv/fX2ZTQszuHfciB4erVTPkpKSaGxspKmpKdihXBeHw9Hnb/j+KhxzER0dTVJS366vHbSC76CdVIkhasBAyk/aKT12jt/8aByjhw3iwBNTiNB5euWjyMhI9xWV/dnBgwev67TJUKK56BuW5vBF5D4ROS4i9SKyopv7RURe6Li/WkTSvW3TIWCLvY1f76nh4Y3llBw7x+etlwC02CullB943cMXkQhgA3Av0AgcEZF9xphjnYblAXd0/GQDL3b82SMD1Jy+xMHzDeTfk8wTPxzDTVF6paxSSvmLlSmdLKDeGHMSQEReBWYCnQv+TOCVjl7N5SISJyK3GGM+623D1XHHeX7iZ/w4Z4aP4SullLLKSsEfCXza6XYjV++9dzdmJNCl4IvIUmApQMSgCI6t+YRFbctams8vubpnaHhJAC54HRUeNBcemgsPzYXHGF8faKXgdzeh/s0TRK2MwRizEdgIICJH275oD0wDiRuciBz19VLpUKO58NBceGguPESk9zUue2HloG0j8N1Ot5OA//gwRimlVBBZKfhHgDtEJFlEooCHgX3fGLMPWNhxts5dwEVv8/dKKaUCy+uUjjGmXUSWA28CEcBWY8yHIvKLjvtfAv4K3A/UA23AIxb+7Y0+Rx16NBcemgsPzYWH5sLD51wErT2yUkqpwAr6EodKKaUCQwu+UkqFCb8XfH+0ZeivLORiQUcOqkXkfRHpeWWYfs5bLjqNyxSRyyLyYCDjCyQruRCRKSJSKSIfisihQMcYKBbeI0NE5C8iUtWRCyvHC/sdEdkqIudFpLaH+32rm8YYv/3gOsj7CXAbEAVUAeO+MeZ+YD+uc/nvAv7hz5iC9WMxFz8Abu74PS+cc9Fp3AFcJwU8GOy4g/i6iMN1ZfutHbeHBzvuIObiGeB3Hb8PAz4HooIdux9ykQOkA7U93O9T3fT3Hr67LYMx5hJwpS1DZ+62DMaYciBORG7xc1zB4DUXxpj3jTHNHTfLcV3PEIqsvC4ACoBdwPlABhdgVnIxH9htjGkAMMaEaj6s5MIAg8W10MEgXAW/PbBh+p8x5h1cz60nPtVNfxf8nlouXOuYUHCtz/NnuD7BQ5HXXIjISGA28FIA4woGK6+L7wE3i8hBEakQkYUBiy6wrOTiD8D3cV3YWQP80hjjDEx4NxSf6qa/++H3WVuGEGD5eYrIVFwF/x6/RhQ8VnLxPPC0MeZyiK9aZSUXA4CJwHTgJuDvIlJujPnY38EFmJVc5AKVwDRgNPCWiLxrjGnxd3A3GJ/qpr8LvrZl8LD0PEVkPLAZyDPG2AMUW6BZyUUG8GpHsU8A7heRdmPM3sCEGDBW3yMXjDGtQKuIvAPYgFAr+FZy8QjwW+OayK4XkVPAWOCDwIR4w/Cpbvp7SkfbMnh4zYWI3ArsBn4agntvnXnNhTEm2RgzyhgzCngDWBaCxR6svUeKgUkiMkBEvo2rW21dgOMMBCu5aMD1TQcRGYGrc+TJgEZ5Y/Cpbvp1D9/4ry1Dv2MxF4VAPPDHjj3bdhOCHQIt5iIsWMmFMaZORP4GVANOYLMxptvT9fozi6+LtcA2EanBNa3xtDEm5Nomi8hOYAqQICKNwCogEq6vbmprBaWUChN6pa1SSoUJLfhKKRUmtOArpVSY0IKvlFJhQgu+UkqFCS34SikVJrTgK6VUmPg//PLI+VpXRhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp_est = MLPClassifier(early_stopping=True, hidden_layer_sizes=(100,), learning_rate='adaptive', random_state=42)\n",
    "mlp_est.fit(x_train_sm, y_train_sm)\n",
    "y_pred = mlp_est.predict(x_train_sm)\n",
    "y_val_pred = mlp_est.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"MLP\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VHaHCjETHxJb"
   },
   "source": [
    "## AdaBoost, with StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "W2nyBNQjNcgD",
    "outputId": "bb08b387-7c9d-45c4-aa24-07ed40f65073"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.94      0.93     56075\n",
      "         1.0       0.94      0.92      0.93     56075\n",
      "\n",
      "    accuracy                           0.93    112150\n",
      "   macro avg       0.93      0.93      0.93    112150\n",
      "weighted avg       0.93      0.93      0.93    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.94      0.96     18678\n",
      "         1.0       0.50      0.80      0.62      1322\n",
      "\n",
      "    accuracy                           0.93     20000\n",
      "   macro avg       0.74      0.87      0.79     20000\n",
      "weighted avg       0.95      0.93      0.94     20000\n",
      "\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"
    }
   ],
   "source": [
    "ada_ss = make_pipeline(StandardScaler(), AdaBoostClassifier(algorithm='SAMME.R', random_state=42))\n",
    "ada_ss.fit(x_train_sm, y_train_sm)\n",
    "y_pred = ada_ss.predict(x_train_sm)\n",
    "y_val_pred = ada_ss.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"ADABOOST_SCALED\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "476ScToWy-OP"
   },
   "source": [
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "c0vabulRy88_",
    "outputId": "6cd0e654-59b1-438c-e8f6-c6f2276be7b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.94      0.93     56075\n",
      "         1.0       0.94      0.92      0.93     56075\n",
      "\n",
      "    accuracy                           0.93    112150\n",
      "   macro avg       0.93      0.93      0.93    112150\n",
      "weighted avg       0.93      0.93      0.93    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.94      0.96     18678\n",
      "         1.0       0.50      0.80      0.62      1322\n",
      "\n",
      "    accuracy                           0.93     20000\n",
      "   macro avg       0.74      0.87      0.79     20000\n",
      "weighted avg       0.95      0.93      0.94     20000\n",
      "\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"
    }
   ],
   "source": [
    "ada = AdaBoostClassifier(algorithm='SAMME.R', random_state=42)\n",
    "ada.fit(x_train_sm, y_train_sm)\n",
    "y_pred = ada.predict(x_train_sm)\n",
    "y_val_pred = ada.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"ADABOOST\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCWHOBVGWjd-"
   },
   "source": [
    "## Gradient Boosting, with StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "lH9unE3GIZQQ",
    "outputId": "91822b7d-5b4d-4516-c93e-43d0f603dbd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.98      0.96     56075\n",
      "         1.0       0.98      0.95      0.96     56075\n",
      "\n",
      "    accuracy                           0.96    112150\n",
      "   macro avg       0.96      0.96      0.96    112150\n",
      "weighted avg       0.96      0.96      0.96    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.98      0.98     18678\n",
      "         1.0       0.72      0.85      0.78      1322\n",
      "\n",
      "    accuracy                           0.97     20000\n",
      "   macro avg       0.85      0.91      0.88     20000\n",
      "weighted avg       0.97      0.97      0.97     20000\n",
      "\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"
    }
   ],
   "source": [
    "gb_ss = make_pipeline(StandardScaler(), GradientBoostingClassifier(random_state=42))\n",
    "gb_ss.fit(x_train_sm, y_train_sm)\n",
    "y_pred = gb_ss.predict(x_train_sm)\n",
    "y_val_pred = gb_ss.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"GRADIENTBOOSTING_SCALED\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7cK4GwiozGgz"
   },
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "R21sUYq5zJEG",
    "outputId": "d154c3e3-f6fa-4db6-cc88-1fbfcd8ed388"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.98      0.96     56075\n",
      "         1.0       0.98      0.95      0.96     56075\n",
      "\n",
      "    accuracy                           0.96    112150\n",
      "   macro avg       0.96      0.96      0.96    112150\n",
      "weighted avg       0.96      0.96      0.96    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.98      0.98     18678\n",
      "         1.0       0.72      0.85      0.78      1322\n",
      "\n",
      "    accuracy                           0.97     20000\n",
      "   macro avg       0.85      0.91      0.88     20000\n",
      "weighted avg       0.97      0.97      0.97     20000\n",
      "\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"
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier(random_state=42)\n",
    "gb.fit(x_train_sm, y_train_sm)\n",
    "y_pred = gb.predict(x_train_sm)\n",
    "y_val_pred = gb.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"GRADIENTBOOSTING\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e48gWV6vY03I"
   },
   "source": [
    "## SGDClassifier, with StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "Rgh8OqoKWlkB",
    "outputId": "b09133f9-03e6-4804-a0d1-90b29b142ad8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.90      0.88     56075\n",
      "         1.0       0.89      0.86      0.88     56075\n",
      "\n",
      "    accuracy                           0.88    112150\n",
      "   macro avg       0.88      0.88      0.88    112150\n",
      "weighted avg       0.88      0.88      0.88    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.89      0.94     18678\n",
      "         1.0       0.34      0.78      0.47      1322\n",
      "\n",
      "    accuracy                           0.88     20000\n",
      "   macro avg       0.66      0.83      0.70     20000\n",
      "weighted avg       0.94      0.88      0.90     20000\n",
      "\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"
    }
   ],
   "source": [
    "sgd_ss = make_pipeline(StandardScaler(), SGDClassifier(eta0=0.5, learning_rate='adaptive', penalty='l2', random_state=42))\n",
    "sgd_ss.fit(x_train_sm, y_train_sm)\n",
    "y_pred = sgd_ss.predict(x_train_sm)\n",
    "y_val_pred = sgd_ss.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"SGD_SCALED\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XAjXQZoHzOr4"
   },
   "source": [
    "## SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "aJpiB52GzRRM",
    "outputId": "5af49b1f-1dad-4b15-8a23-8b6aae0be1d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.80      0.82      0.81     56075\n",
      "         1.0       0.81      0.79      0.80     56075\n",
      "\n",
      "    accuracy                           0.81    112150\n",
      "   macro avg       0.81      0.81      0.80    112150\n",
      "weighted avg       0.81      0.81      0.80    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.82      0.89     18678\n",
      "         1.0       0.24      0.79      0.37      1322\n",
      "\n",
      "    accuracy                           0.82     20000\n",
      "   macro avg       0.61      0.81      0.63     20000\n",
      "weighted avg       0.93      0.82      0.86     20000\n",
      "\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"
    }
   ],
   "source": [
    "sgd = SGDClassifier(eta0=0.5, learning_rate='adaptive', penalty='l2', random_state=42)\n",
    "sgd.fit(x_train_sm, y_train_sm)\n",
    "y_pred = sgd.predict(x_train_sm)\n",
    "y_val_pred = sgd.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"SGD\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7tMW48cJZKc_"
   },
   "source": [
    "## GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "RAGVEg58ZC4i",
    "outputId": "c53c302d-9aaa-480e-ac69-2838471c8056"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.86      0.92      0.89     56075\n",
      "         1.0       0.91      0.85      0.88     56075\n",
      "\n",
      "    accuracy                           0.88    112150\n",
      "   macro avg       0.88      0.88      0.88    112150\n",
      "weighted avg       0.88      0.88      0.88    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.92      0.95     18678\n",
      "         1.0       0.41      0.78      0.54      1322\n",
      "\n",
      "    accuracy                           0.91     20000\n",
      "   macro avg       0.70      0.85      0.74     20000\n",
      "weighted avg       0.95      0.91      0.92     20000\n",
      "\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"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(x_train_sm, y_train_sm)\n",
    "y_pred = gnb.predict(x_train_sm)\n",
    "y_val_pred = gnb.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"GaussianNB\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XP8F9YLHajcW"
   },
   "source": [
    "## BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "1fCf6ZW0af1j",
    "outputId": "f8a9b3c1-afd7-49c8-e6eb-a4ac674c4650"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.65      0.56      0.60     56075\n",
      "         1.0       0.61      0.70      0.65     56075\n",
      "\n",
      "    accuracy                           0.63    112150\n",
      "   macro avg       0.63      0.63      0.62    112150\n",
      "weighted avg       0.63      0.63      0.62    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.56      0.70     18678\n",
      "         1.0       0.08      0.57      0.15      1322\n",
      "\n",
      "    accuracy                           0.56     20000\n",
      "   macro avg       0.52      0.57      0.42     20000\n",
      "weighted avg       0.89      0.56      0.67     20000\n",
      "\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"
    }
   ],
   "source": [
    "bnb = BernoulliNB()\n",
    "bnb.fit(x_train_sm, y_train_sm)\n",
    "y_pred = bnb.predict(x_train_sm)\n",
    "y_val_pred = bnb.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"BernoulliNB\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fMmXOicAcpsC"
   },
   "source": [
    "## LDA, with StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "O5xlGqEqavpA",
    "outputId": "f1e6da4b-9c9c-4a63-d030-ee5553957f03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.93      0.88     56075\n",
      "         1.0       0.92      0.82      0.87     56075\n",
      "\n",
      "    accuracy                           0.87    112150\n",
      "   macro avg       0.88      0.87      0.87    112150\n",
      "weighted avg       0.88      0.87      0.87    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.93      0.95     18678\n",
      "         1.0       0.41      0.71      0.52      1322\n",
      "\n",
      "    accuracy                           0.91     20000\n",
      "   macro avg       0.69      0.82      0.74     20000\n",
      "weighted avg       0.94      0.91      0.92     20000\n",
      "\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"
    }
   ],
   "source": [
    "lda_ss = make_pipeline(StandardScaler(), LinearDiscriminantAnalysis())\n",
    "lda_ss.fit(x_train_sm, y_train_sm)\n",
    "y_pred = lda_ss.predict(x_train_sm)\n",
    "y_val_pred = lda_ss.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"LDA_SCALED\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I5-nW_tkzZsB"
   },
   "source": [
    "## LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "lx7G5mzDzbAU",
    "outputId": "ea05e78b-fe3b-4c2c-b5c5-be80f1ca04a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.93      0.88     56075\n",
      "         1.0       0.92      0.82      0.87     56075\n",
      "\n",
      "    accuracy                           0.87    112150\n",
      "   macro avg       0.88      0.87      0.87    112150\n",
      "weighted avg       0.88      0.87      0.87    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.93      0.95     18678\n",
      "         1.0       0.41      0.71      0.52      1322\n",
      "\n",
      "    accuracy                           0.91     20000\n",
      "   macro avg       0.69      0.82      0.74     20000\n",
      "weighted avg       0.94      0.91      0.92     20000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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HBg9kRswMy0lMicGJbPh+A2mT0uw7+B6suLKOvzY1O7trZiLTB/ftcUs4bUng92bVJcfv6VJf2Xp77z7mEO83DAaf33JyUXBToLvb7iLPzqqjcM8qzqK06SBzZ+EuM/czR2vN5zsO8cinmZRW13PXDMdtdmZrEvg9WV1lU4hbz85zWoK9pk2li4e/Obz7xEPCtDbLLjHg6W+Xl+GomsPdEuwdhHticCIzY2ZKuDuII2U1/PmTnXyTdYTUqED+e9N4hvR33GZntiaB78waas0XuzDmdHzWaGVh6+3dvJuWWGIgelz7VrrewT3+wOip0lpzsOJg62oZCXenU1hey4b9RfzxvMHccJbjNzuzNQl8R9bYYD7Nv7Nll/JDgG7Z3sXdvLQSPACSzmuance2zNT9wiXQu+Bkwz0lxFwt4+HqYeeRi47kFlXxza4j3Dg5jqGRgfywaDqB3r3zeJIEvj2ZTFBxhIDSXZBxtP0B0rKDYGpo2V65mE/zD4qB+LT2tej+/cHF1U4vxjmdMNxd3EgMknB3Ro0mzRs/GHhq1W7cXVy4cHhTs7NeGvYggd+9tIaq4nYnF7V8nguNtYwC2Nr0GL++5gCPGgvBl7Xp6RIFbhI0p6ptuGcWmUshJdx7nj1Hynnggwy25ZUwfXA4f7tkqFM2O7M1CfzTVVPa+clFJblQV9F6e+9gc4j3TYakORAUQ0ZeGalnX2i+epFUutiEdbg395eRcO8dqusamf/vDSileP7KEVw0PMJpm53ZmgT+idRVtczGLVUuVsFeU9J6++ZKl+BYiJvaetklKAa82lcEFFelQ9igM/FqeqTmcLduHCbh3vvsPVLOwHA/vD1ceXHBKIb09yfETw6aW5PAb6gzn0TUWS165dHW27t5tQR45Jg2rXRjpdKlm3UU7llFWZTVmS8bKOHe+1TXmZudLf0+m6cuH868UVFMTux5bRFsoWcGft5PkPM9xE6ByNFQVtD+MnTNn5cV0LrSxc1c6RI0AJLObQr32JaZum+401zswtlprcmvyG/XOKxtuM8aMEvCvZfasL+IP3yUQU5RFVeNj2Fmcs8+U/Z09bzAz/sJ3roQGmoAZa5sse7pgmqpdIk7u31Pl4AIqXSxg+Zw31q5la0/bz1xuIemkBgk4d6bPfPNHl5Ys5cBIT68c/N4JiXIrP5Eel7g53xvPiEJAA0xEyH18pZgD4yWShc7aztzb76GqiXci1vCPSW0qXGYhLto0tzsbER0IDdPiePeWUl4e8gkrSu6FPhKqXOB5wFXYKnW+vE29wcC/wVimp7zKa31GzYea9fETjHP0E0N5jNLZz5iPqtU2IV1uFuqZazDvWnmPjt2NskhyVQbqrly5pUS7qKdoopaHv0si/gwX+6eOahXNDuztRMGvlLKFXgJmAXkA5uVUiu01llWm90BZGmtL1RKhQG7lVLLtNZ1HTxl94oeZ75ARmEWLPifhP0Z1JVwHxQ8yBLuHc3c0wvSJexFK1prNhQ0cM8z31FR28DdM6Wi7VR1ZYY/Dtintc4GUEq9C8wFrANfA/7KXOzqBxQDDW2f6IypLYWI0RL23ag53FuVQp5kuAtxIodKq/nzxztZ82stI6KD+MdlqQzqK038TlVXAj8SyLO6nQ+Mb7PNP4EVQAHgD8zXWpvaPpFS6hbgFoCwsDDS09NPYcgnoDWTC/dz2DWGfd3x/N2goqKie94LG9Fac6zhGHl1eeTV5ZFbl0t+XT5VJvOFwl1xJcIjgmEew4j2iybaI5r+Hv1xV+5QCxRAYUEhhRQef0c4/ntxJsl7AQfKGvlxXw3z4jQXDKqjYNfPFOyy96icV1cCv6Oict3m9jnANmA6kAB8o5T6Xmtd1upBWr8KvAqQlJSku+XKRpVF8F0VUUPPImpiNzx/N0h3oKs8aa3JL88ns7h1nXt5XTnQMnM/L+a8llLIoETcbXRxE0d6L+ytt74XOccqWb3rCDdNiQdg3ux6ft74Q698L2ytK4GfD0Rb3Y7CPJO3dj3wuNZaA/uUUgZgMPCTTUZ5MowG87/BcWd8187mROHu7uJOYnAi58Se03KZPRuGuxDWGhpNvP6DgadX7cHDzYW5IyIJ8/fE30u+32ylK4G/GUhUSsUBB4ErgavabJMLzAC+V0r1BZKAbFsOtMuKmwK/jwS+tXbhfszcFbJtuJ8be26rNXcJd3Em/Hq4jAc/yGB7fikzh/TlsYuHEuYvbRFs7YSBr7VuUErdCXyNuSzzda11plLqtqb7XwH+CryplNqBeQnoQa31sW4cd+csM/xYu+zeEbQK92MtLX8l3IUjqq5rZMGrG3FRihcXjOSC1P7S7KybdKkOX2v9BfBFm6+9YvV5ATDbtkM7RcUGc1/4XtJ10hLuRa0vs2cd7oOCB0m4C4ez+3A5g/qam53986pRDOkfQB9fqeLqTj3vTFujoceu30u4i56gqq6Bp1ftMa/XNzU7O2ugtEU4E3pe4BcbYOAMe4/itGmtySvPa3clps7CPSUkhYFBAyXchUP7Yd8xFn2UQV5xNddMGMAsaXZ2RvWswK+rgorDTjfD11qTW5bbritkeX37cG+ulpFwF87m6VW7efHbfcSF+vK/WyYwPj7E3kPqdXpW4JccMP/rwBU6bWfumUWZ7Diyg+rcasAq3OMk3EXPYDJpXFwUowYEc+vUeO6ZOQgvd2l2Zg89K/CLHatCp6Nw31W0q93MfbTvaGYOmynhLnqUYxW1LF6RSXyYH/fOGsS0pHCmJYXbe1i9Ws8KfDuedGUd7taNw6zDPSk4iTlxcywHVJvDPT09nbRBaWd8zEJ0B601n2w7yKOfZVFV28g9swLtPSTRpGcFfrEBPAPAp0+37qY53Ns2DutKuAvRkxWUVPOnj3ewdncho2KCeOLSVBKl2ZnD6FmBbzSYl3NseNKGSZvaVctYh7uHiweDggdZwj0lNIWEoATcXSTcRe9jrKpjywEjj1yYzLUTY3F1kROoHEnPCvxiA/QbesoPl3AX4uRlF1awetcRbjk7gZSIQDb8YQZ+nj0rWnqKnvO/Ymo0X5x8yIVd2/wkwr35MnsS7kK0aGg0seR7A8+u3oOXmwuXjIwizN9Twt6B9Zz/mdJ8MNVbSjK3Hd3GliNbGNN3DKlhqR1Wy1TUVwAt4X5e/HmWNXcJdyE6l1VQxgMfbmfnwTLOSenLX+dKszNn0HMC35hj/jc4jm1Ht3HD1zdQb6pHofBy9aK60Vzn7uHiQVKfJM6PP1/CXYhTUF3XyNVLN+Lq4sK/rh7FnGH97T0k0UU9KPBb2iJvOvAl9aZ6ADSagcEDuWzQZRLuQpyGXYfKGNzPH28PV166ehTJ/QMI8pFmZ86k5wR+sQFc3CEgkij/KAAUCk9XTx4Y+wAjwkfYeYBCOKfK2gae/Ho3b23I4anLhnPp6CgmJUizM2fUcwLfaICgGHBxtVwoe/7g+Zwfd76EvRCn6Pu9hfzhox3kG6v57cQBnDO0n72HJE5Dzwn8YoPlgK2h1Ly8c8+oe/Bx97HnqIRwWk9+/Ssvrd1PfJgv7982kbGx3XtCo+h+PSPwtTYftI0eD0B2aTb9fftL2AtxCpqbnY2J7cPv0mDhjERpdtZD9IzAryqG2rJWM/y4QMftmCmEIzpaXsMjn2aSGO7HvbOTpNlZD+Ri7wHYhFXTNJM2YSg1EB8Yb98xCeEktNa8vyWPWc+sY82vR/Hz6hnzQNFez/ifLW4pyTxadZTqhmqZ4QvRBfnGKv7w0Q6+33uMsbHBPH5pKglhfvYelugmPSPwLSddxZJ9dCuABL4QXVBW3UBGfil/mZvCb8YPwEWanfVoPSTwDeDfH9y9MZSZZ/sS+EJ0bH9hBauzjnDr1ASSIwL4cdF0fKX/Ta/QM/6Xiw2Wq1xll2QT4BFAiJdcL1MIa/WNJl5dl83za/bi4+HKpaOjCPXzlLDvRXrG/7TRAPHTADCUmSt0lA174gvh7HYeLOXBDzPILCjjvGH9ePSioYT6SbOz3sb5A7++GsoPWUoys0uyOTvqbDsPSgjHUV3XyDWvbcLN1YVXfjOKc4dKs7PeyvkD36pLZmltKUU1RbJ+LwTmWX1KRADeHq68fPVokvsHEOgjjQN7M+evw7cqyWxuqSA1+KI3q6ht4OFPd3LBi+v56JeDAExMCJGwFz1hht9y0pWhYB0gFTqi90rffZQ/fbyTgtJqrj8rlnOl2Zmw4vyBX2wAzwDw6YOhzIC7izuRfpH2HpUQZ9wTX/3Kv9L3MzDcjw9um8ToAcH2HpJwMM4f+MYcc0mmUhhKDAwIGICrizR6Er1Ho0nj6qKYEB+Cm4vizukD8XSTnwHRXg8IfAP0TQHMJZlJwUl2HpAQZ8bRshoe+nQng/r6c9/sJKYOCmPqoDB7D0s4MOc+aGtqBOMBCI6lrrGOvPI8Wb8XPZ7Wmve25DHzme9I311IoLccjBVd49wz/LKDYKqH4Dhyy3IxaZNU6IgeLa/Y3Oxs/b5jjIvtw+OXDiNemp2JLnLuwLcqycwuzQakQkf0bOU1DewsKOWvFw/l6nEx0uxMnJQuLekopc5VSu1WSu1TSi3qZJs0pdQ2pVSmUuo72w6zE9YlmU01+LGBsWdk10KcKXuPlPNy+j4AS7OzayZIZ0tx8k44w1dKuQIvAbOAfGCzUmqF1jrLapsg4GXgXK11rlLqzFwmp9gALu4QGEV2aTYRvhF4u3mfkV0L0d0aTJoX1+zlxW/34evpyhVjogn188THw7n/MBf205XvnHHAPq11NoBS6l1gLpBltc1VwEda61wArfVRWw+0Q0YDBMWAi6tc1lD0KBn5JTy6oYa88j1cODyCRy5MlmZn4rR1JfAjgTyr2/nA+DbbDALclVLpgD/wvNb67bZPpJS6BbgFICwsjPT09FMYcovRuTuo8whi+9pv2W/cT1+/vqf9nPZQUVHhlOPuDvJeQG2D5t7vqnBTmrtGeTEyvJSdWzbYe1h2Jd8XttGVwO9ooVB38DyjgRmAN7BBKbVRa72n1YO0fhV4FSApKUmnpaWd9ICtngw2HIMhMxgydgh1uXVMSZlCWtJpPKedpKenc1rvRQ/Sm9+LnQdLSe4fgIuL4vX4Io7tz+D8WdPsPSyH0Ju/L2ypKwdt84Foq9tRQEEH23ylta7UWh8D1gHDbTPETlQbobasVYWOlGQKZ1ReU8+fP9nBBS+u5+Ot5mZn4+ND8HWXg7LCtroS+JuBRKVUnFLKA7gSWNFmm0+BKUopN6WUD+Yln122HWobzSWZwbGWCh1ZwxfOZu2vR5n97Dre2ZTLTZPjmDNMmp2J7nPCJR2tdYNS6k7ga8AVeF1rnamUuq3p/le01ruUUl8BGYAJWKq13tmdA7cuyczO/plAz0D6ePXp1l0KYUv/9+Uu/v1dNonhfrx8+yRGxkizM9G9ulTfpbX+AviizddeaXP7SeBJ2w3tBNrM8OMC5LKGwvFprTFpcHVRnJUQiqebK3dMS5BmZ+KMcN5eOkYD+PUDDx+yS7OJD5L1e+HYDpfWcPPbP/PsN+ZahrMHhXHvrEES9uKMcd4zOIoN0Md8WcPimmLiAmT9XjgmrTXvbs7j75/voq7RxMSEEHsPSfRSzhv4RgPET2u5rKHM8IUDyiuu4oEPMtiQXcSE+D48Pi+V2FBfew9L9FLOGfj11VB+qNV1bGWGLxxRZV0Dvx4u4++XDOPKsdHS/0bYlXMGvvGA+d9gcw2+h4sHEX4R9h2TEE12Hy5n9a4j3DFtIIP7BfDjohl4e8g6vbA/5zxoa2xpi2woNTAgUC5rKOyvrsHEc6v3cMGL3/PaegPHKmoBJOyFw3DOGX6xVQ1+aTbJIcn2HY/o9bbnlfDABxnsPlLO3BERPHxBMiHS7Ew4GOcMfKMBPPyp9fTlYMVBzo8/394jEr1YVV0Dv33jJ7zcXFl67RhmJve195CE6JBzBn6xAfrEklueJ5c1FHaTkV/C0IhAfDzcWHLtGJL6+RPgJdeXFY7Ledfwg+WyhsI+ymrq+cNHO7jonz9Ymp2Nje0jYS8cnvPN8E2N5iqdwedjKDWgUAwIGGDvUYleYnXWEf70yQ4Ky2u55ex4zhvW395DEqLLnC/wyw6Cqb5phr+LCCs2W9wAACAASURBVD+5rKE4M/7+xS5eXZfN4H7+vHrNGIZHB9l7SEKcFOcL/OKWksycQ1/Kco7oVlprGk0aN1cXpiSG4ufpxm1TE/Bwc87VUNG7Od93rTEHAFPQALmOrehWh0qruemtLTy72tzsbEpiGAtnJErYC6flfDN8owFc3Dns5kZNY41U6AibM5k0yzfn8n9f/EqjSTMlMdTeQxLCJpwv8IsNEBRDdrm5vYLM8IUt5RZVcf8H29lkKOasgSH83yWpxIT42HtYQtiE8wW+0SCXNRTdpqq+gX1HK3ji0mFcMSZaLqojehTnWozUGopzLBcuD/IMkssaitP26+EyXlyzF4DB/QL4YdF05o+NkbAXPY5zzfCrjVBbCsFxGEq3yOxenJbahkZe+nYfL6fvJ9DbnQXjYwj188TLXZqdiZ7JuWb4xa27ZMoBW3Gqfsk1csEL63nh231cNDyC1fdOJVSanYkezrlm+E1tkUt8Q82XNZQZvjgFVXUN3PDmZnzcXXnj+rFMSwq395CEOCOcK/CbZvgGFxMgB2zFydmaa2R4VBA+Hm689tsxJPULwM/TuX4EhDgdzrWkY8wBv34Yqg4BEviia0qr63nwgwwueflHS7Oz0QP6SNiLXse5vuONBnOFTkk2nq6eRPjKZQ3F8X2deZiHPtlJUWUdt01N4PxUaXYmei/nCvxiA8SnYSgzMCBALmsoju+vK7N4bb2BIf0DeO23YxkWFWjvIQlhV84T+PXVUF4AwbFkF6UzNHSovUckHJB1s7NpSeEE+7hz69QE3F2da/VSiO7gPD8FRnMrhdqgaA5WHJT1e9HOwZJqrn9zs6XZ2eTEUO6cnihhL0QT55nhN5Vk5nh6odFSgy8sTCbNsk0HePzLXzFpmD5YyiyF6IjzBH5zSaZqBKRCR5jlHKvkgQ8y+CmnmCmJofz9kmFE95FmZ0J0xHkC32gAD38MNcfksobCorbBRPaxSp68LJXLRkdJ/xshjsN5Ar/YAH3MXTIj/CLwcvOy94iEnWQWlPJN1hHunjmIpH7+rH9wmvS/EaILnCfwjTkQPgRDmfTQ6a1q6ht58du9vPJdNsE+HvxmwgBpdibESXCO8gVTI5QcwBQcS05pjqzf90I/Hyjm/Be+56W1+7l4RCSr7z1bmp0JcZKcY4ZfVgCNdRzyC6HmkFzWsLepqmvgxre24Ovhxls3jGPqoDB7D0kIp+Qcgd9Ukpnt7g5IhU5v8fMBIyOjm5udjSWpn7/0vxHiNHRpSUcpda5SardSap9SatFxthurlGpUSl1muyFiVZJZDyAz/B6utKqe+9/fzqX/+pGPLM3OgiXshThNJ/wJUkq5Ai8Bs4B8YLNSaoXWOquD7Z4Avrb5KI0GcHEju9ZIsGcwQV5BNt+FcAxbDjdw/7PfUVxZx+/SErhAmp0JYTNdmTKNA/ZprbMBlFLvAnOBrDbb/R74EBhr0xGCeYYfFIOhTA7Y9mR/+SyL17fVktw/gDeuG8vQSGl2JoQtdSXwI4E8q9v5wHjrDZRSkcAlwHSOE/hKqVuAWwDCwsJIT0/v0iBH5+6g3j2QPcf2kOqT2uXHOYuKiooe95q6SmuNSYOriyK0rpELB2jmJjVwbO9W0vfae3T21Zu/L9qS98I2uhL4HZ26qNvcfg54UGvdeLwzHbXWrwKvAiQlJem0tLQT711r2HAMY9JZVBSnM2nwJNJSuvA4J5Kenk6X3oseJq+4ij9+vIOhkYE8eO5g0ui970VH5L1oIe+FbXQl8POBaKvbUUBBm23GAO82hX0ocJ5SqkFr/clpj7DaCLWlGHwCoFgO2PYEJpPm7Q05/OPr3SjgnJR+9h6SEL1CVwJ/M5ColIoDDgJXAldZb6C1tiysK6XeBFbaJOzBUpJpcDcPVdbwnZvhWCX3v7+dLQeMTB0Uxt8uGUpUsDQ7E+JMOGHga60blFJ3Yq6+cQVe11pnKqVua7r/lW4dYVNJZrauNV/W0E8ua+jM6htNHCiu4pkrhnPJyEhpdibEGdSlwmat9RfAF22+1mHQa62vO/1hWWme4dcZiQ2IxUU5RzcI0WLnQXOzs3tmDWJQX3OzM0836X8jxJnm+OlZnAN+fckuOyDr906mpr6RJ776lbkv/cA7P+VSVFELIGEvhJ04/qmLRgM1wbEUVBQwN2GuvUcjumhzTjEPfpBB9rFKLh8dxZ/PTybQx93ewxKiV3P8wC82cGDAGHTlIeKC5ICtM6isbeDmt7fg5+nGf24cx5REaXYmhCNw7MCvr4byAgzeflAJcQES+I5sc04xo2OC8fV04/XrxpLU1x9f6X8jhMNw7DV84wEAst1cUChiA2PtOx7RIWNlHff+bxuXv7LB0uxsVFPwCyEch2P/RBpzADCYaoj0i8TTVS544Ui01nyx4zCPrNhJSVU9C6cP5MLh0uxMCEfl4IHfVINfe4z4IKnQcTR/WZnFGz/kMCwykLdvGE9yRIC9hySEOA7HDvxiA40e/hyoOMjEyCn2Ho3APKtvMGncXV2YNaQvfQO8uGlyHG6ujr06KIRw+DV8A4f6xFDbWCszfAeQV1zFNa/9xNOr9gAwaWAot01NkLAXwkk49k9qsYHsgFBAeujYU6NJ8/p6A7OfXce2vBJi+kjvGyGckeMu6ZgaoeQAhshEqJeSTHvJLqzg/72/nV9yS0hLCuPvlwwjIsjb3sMSQpwCxw38sgJorMPgCn28+shlDe2k0aQ5WFLNc/NHMHdEhDQ7E8KJOW7gNzdNM1XJcs4ZlpFfwjdZR7hvdhKJff1Z94A0OxOiJ3DcNfzmtsg1hRL4Z0hNfSP/98UuLn7pB97bkifNzoToYRx4hp+D0c2Dkrpy6ZJ5BmzMLmLRhxnkFFWxYFw0i+YMIdBbmp0J0ZM4cOAbyA6OBBplht/NKmsbuO2/PxPg5c47N41n0sBQew9JCNENHDfwiw0Y/ENAH5XA7yY/GYoZM8Dc8+bN68cxqK8fPh6O+y0hhDg9jruGbzSQ7emFl6sX/X2lP4stFVfWcfe7W7ni3y3NzkZEB0nYC9HDOeZPeFUx1JRicNHEBsplDW1Fa83KjEMsXpFJaXU9d81IlGZnQvQijhn4zSWZDRWkBg6182B6jkc/y+LNH3MYHhXIspvHM7ifNDsTojdxzMAvNlCtFAV1JcyV9fvTorWmvlHj4ebC7JS+RAZ5c8PkOFxd5AQqIXobx1wrMRo44O6GRktJ5mk4UFTJVUs28fSq3QBMSgjl5rPjJeyF6KUcdIafg8FfmqadqkaT5o0fDDy1ajfuLi5cPDLC3kMSQjgAxwx8Yw7ZfsG4qCoGBAyw92icyr6jFdz3/na255Uwc0g4j108jH6BXvYelhDCATho4Bsw9A0n0jtYLmt4krTWHC2r4YUFI7kwtb80OxNCWDjeGn59DZQVkK0aZf2+i7bllfDk178CkNjXn+/un8ZFw6WzpRCiNccL/JIDNKI50FAu6/cnUF3XyN8+z2Leyz/w4c8HLc3OPNwc779VCGF/jrekU2ygwM2NOi0z/OP5cf8xFn24g9ziKq4aH8OiOYMJ8JJmZ0KIzjle4BsNGNzNw5IZfscqaxu4Y9kvBHi7s/zmCUxMCLH3kIQQTsDxAr/YgMHbD5DAb2vD/iLGx/Wxanbmj7eH9KoXQnSN4y32Gg1k+wbSx6sPgZ6B9h6NQyiqqOX3y7eyYMlGPm5qdjY8OkjCXghxUhxvhm/MwRDgLuv3mEssV2wvYPGKTCprG7lv1iAuHC4nUQkhTo1jBb7JhDYeIDsgitmynMMjKzJ5e8MBRsYE8Y9LU0ns62/vIQkhnJhjBX55AUZdT6mu77UzfJNJ02AyNzubM7Q/A0J8uW5SrPS/EUKcti6t4SulzlVK7VZK7VNKLerg/quVUhlNHz8qpYaf0miKDWR7mEsLe+MBW8OxShYs2chTTc3OJiaEcKN0thRC2MgJA18p5Qq8BMwBkoEFSqnkNpsZgKla61Tgr8CrpzQaq5LM3jTDbzRpXl23n3OfW0fWoTIGhvnZe0hCiB6oK0s644B9WutsAKXUu8BcIKt5A631j1bbbwSiTmk0xQayPTzxdvWir2/fU3oKZ7PvaDmPbazBUPYrs5L78tjFQ+kbIM3OhBC215XAjwTyrG7nA+OPs/2NwJcd3aGUugW4BSAsLIz09PRW9yfv3sR+Dx9CXENZ9926LgzN+RVUmCipbeR3w70Y26+cXb9sZJe9B2VHFRUV7b4veit5L1rIe2EbXQn8jhaQdYcbKjUNc+BP7uh+rfWrNC33JCUl6bS0tNYb7H6EA14ejIhMJe3stHaP7yl+yTXyTdYRHjx3MADhPmuZOX2anUflGNLT02n3fdFLyXvRQt4L2+jKQdt8INrqdhRQ0HYjpVQqsBSYq7UuOpXBVBsNFNDQYw/YVtU18JfPsrj0Xz/y6daWZmduclBWCHEGdGWGvxlIVErFAQeBK4GrrDdQSsUAHwHXaK33nNJIqo3kmKqAwB55wHb93mMs+iiDfGM1104cwAPnDsbP07GqYoUQPdsJE0dr3aCUuhP4GnAFXtdaZyqlbmu6/xXgYSAEeLmpB3uD1nrMSY2k2IDBvWeWZFbWNvD75b8Q5OPBe7dOZFxcH3sPSQjRC3Vpiqm1/gL4os3XXrH6/CbgptMaidFAtrs7Lqgec1nDH/cdY3x8CL6ebrx9w3gS+/rh5S79b4QQ9uE4awrF5hr8KL9IPFw97D2a01JYXsviFZl8vuMQT10+nMtGRzEsShrBnSn19fXk5+dTU1Nj76GclsDAQHbt6s01Wy1643vh5eVFVFQU7u62u86F4wS+0UC2lzfxQQPtPZJTprXm460H+cvKLKpqG7n/nCTmjpBmZ2dafn4+/v7+xMbGOvVlHsvLy/H3l/5J0PveC601RUVF5OfnExdnuyVuhwn8xmIDB1xdmOLE6/cPfbqT/27MZVRMEP+4LJWB4b3nG9SR1NTUOH3Yi95NKUVISAiFhYU2fV6HCfyDpTnUB7s63QFbk0lTbzLh6ebKBakRDAzz45qJ0uzM3iTshbPrju9hx7gASn0NhtpiwLkqdPYXVjD/1Q089bW52dmE+BCuO0uanQkhHJNjBH7JAbI9nOc6tvWNJl5O38ec579n9+FykvoF2HtIwoEUFRUxYsQIRowYQb9+/YiMjLTcrqur69JzXH/99ezdu7fL+1y6dClhYWGMGDGCwYMH88ILL7S6/1//+heDBw9m8ODBjB8/nh9/bGl/VV9fzwMPPMDAgQMZOnQo48eP5+uvv+7SfocOHco111zT5XGeii+++IJRo0YxcOBAnnzyyQ63MRqNnH/++QwfPpyUlBTefvttALKysizv/YgRI/D39+ef//wnAPfeey9JSUmkpqZy6aWXUlpaanm+bdu2MWHCBFJSUhg2bBj19fXd+hrPGK21XT4GDRqkLXZ/pR96MVZPfWeSdnS7D5fp855fpwc8uFLf+vYWfaSs+rSfc+3atac/sB7CFu9FVlbWyT8od5PW654y/2tDjzzyiH7yySfbfd1kMunGxsbjPrasrKzL+1myZIm+6667tNZaHz16VAcHB+uCggKttdYff/yxHjt2rC4qKtJaa71582YdHR2tjx49qrXW+r777tPXX3+9rq2t1VprXVBQoN9///0T7nP79u16+PDhOiIiQldVVXW4TX19fZdfQ0fq6up0XFyc3rlzp66pqdFDhw7Vu3fvbrfdo48+qv/4xz9qrbU+fPiwDgoKarfv+vp6HRYWpvPy8rTWWn/11VeWbe69917L4+vq6vTQoUN1RkaG1lrrwsLCE/5fdZeOvpeBLfoUc9cx1vCLzTX4znCGrYtSlFTV86+rRzFnWH97D0ecyJeL4PCO429TWwZHdoI2gXKBvkPB8zh/tfUbBnMeP+mh7Nu3j4svvpjJkyezadMmVq5cyaOPPsovv/xCdXU18+fP5+GHHwZg8uTJPPHEE4wfP57Q0FBuu+02vvzyS3x8fPj0008JDw/vdD9hYWHEx8dz6NAh+vfvzxNPPMFTTz1Fnz7mE/7GjBnD1Vdfzcsvv8y9997Lm2++SU5ODh4e5nLo/v37c9lllwHmvzTuuusuRowY0W4/y5cv59prr2Xr1q2sXLmSyy+/3DL2qVOn8v333zNv3jwWLFjA7bffTm5uLi4uLrzwwgtMmDCBjRs3cs8991BTU4OPjw9vvvkmiYmJrfaxceNGhgwZQkxMDJ6enlxxxRV8+umn3H///a22U0pRXl4OmButhYaG4ura+pyXVatWMWTIEKKizM18zznnHMt9EyZMYOXKlQB8+eWXjB49mmHDhgEQGhra6XvtbBxiSUcXZ2PwcCcuOPHEG9vBzweK+b8vzTXAA8P9+O7+NAn7nqSm1Bz2YP63pvT425+GrKwsbrzxRrZu3UpkZCSPP/44W7ZsYfv27XzzzTdkZWW1e0xpaSlTp05l+/btTJw4kddff/24+8jJyaGxsZGhQ4da9jl69OhW24wZM4bMzEz27t1LXFwcfn4dX4PhjTfe6DDsAd577z3mz5/PggULWL58eav7ysrKWLduHXfffTcLFy7kgQceYMuWLbz33nvcdJP5HM0hQ4awfv16tm7dykMPPcSf//xnAPLy8rjooosAOHjwINHRLa28oqKiOHjwYLux3HXXXWzbto2IiAiGDx/Oiy++2O6g57vvvsuCBQs6fC2vv/46c+bMAWDPnj1orZk9ezajRo3i6aef7vAxzsghZvjFxfsoc3EhPijB3kNppbK2gSe/3s1bG3KICPTm1rMT6OPrgZurQ/yeFF3RlZl43k/w1kXQWAeuHnDpUoge1y3DSUhIYOzYsZbby5cv57XXXqOhoYGCggKysrJITm59fSFvb29LGI0ePZrvv/++w+detmwZ33zzDbt37+aNN96wzNg7orU+rSqQDRs2EBUVRWRkJOHh4dx8882UlpYSGGg+wfDKK6+0bLt69Wp2795tuW00GqmurqakpIRrr72W/fv3t3ru6OhoVqxYYRlnWx2N+4svvmDcuHF899137Nmzh3PPPZcdO3ZYfpHV1NTw+eef88wzz7R77KOPPoqfn59lzA0NDfzwww9s2rQJLy8vpk2bxpgxY5g6derJvk0OxyGSK7v8AABxAY5zwHbdnkJmP7uOtzbk8NuJsay652z6+Dr3GcCiE9Hj4LcrYPqfzP92U9gD+Pr6Wj7fu3cvzz//PN9++y0ZGRmce+65HZ4dbB3crq6uNDQ0dPjcV199NZmZmaSnp3PXXXdx9OhRwDyT/vnnn1tt+8svv5CcnExiYiIGg4HKysqTeh3Lly9n586dxMbGkpiYSFlZGR9//HGHr1NrzU8//cS2bdvYtm0bBw8exNvbmz/96U+cc8457Ny5k08++aTD1x4VFUVeXsvlOPLz84mIaH8y4xtvvMGll16KUoqkpCSio6PZs6elj+Pnn39uWR6z9tprr7Fq1Sr+85//tNpnWloaISEh+Pr6MmfOHH755ZeTen8clf0D32TCUHMMgPggx1jDr6xt4O7/bcPT3YX3b53I4otS8JXOlj1b9DiYcl+3hn1bZWVl+Pv7ExAQwKFDh7pcGXMikydPZsGCBbz44osAPPDAAzzwwAMYjUbAHPb//e9/uf322/H39+faa6/l7rvvtlSiFBQUsGzZMsD8S6Rt2DU2NvLhhx+SlZVFTk4OOTk5fPTRR+2WdZrNnDmTl156yXJ727ZtgHmpKjIyEoA333yzw8dOmDCBrKwscnNzqa2t5b333rMs91iLiYlhzZo1ABw6dIh9+/a1OkN1+fLl7ZZzmmf8K1aswMur5Spzc+bMYevWrVRXV9PQ0MC6deva/dXlrOwf+OUFGFzBW7nT18e+lzX8fm8hjSbd1OxsHF8snMKYWOlsKbrHqFGjSE5OZujQodx8882cddZZNnvuRYsWsXTpUiorK5k3bx7XXHMNEyZMYPDgwdx222288847lgO/jz/+OIGBgQwZMoRhw4Yxb948y30ZGRn069ev1XOvXbuWuLg4+vZt+XmdNm0a27Zt48iRI+3G8tJLL/HDDz+QmppKcnIyS5YsAeDBBx/k/vvvb/e6rdfw3d3deeGFF5g7dy7Jycn85je/ISkpyfK8S5cuBWDx4sV89913pKamMmvWLJ566imCg4MB80HctWvXcvHFF7fazx133EF5eTkzZsxgxIgR3HHHHQCEhISwcOFCRo8ezYgRI5gwYUKrA7xO7VTLe073w1KWmb1O3/pygr7igzmnWcB06o6UVutb396iBzy4Un+wJe+M71/KMlvYrSzTAZ1MWWZ3KC4u1vPnz7frGJrZ+72wl55Xlmk0kO3hzig7lGRqrfng53z+ujKLmgYTD547WJqdCdEkODiYd999197DEDZk98CvKtrHITc34kOHnvF9/+mTnbyzKZexscE8fmkqCWEdl6YJIURPYPfAzyn+FYC44DNTkmnd7Gzu8AiG9PPn6vEDcJH+N0KIHs7uB20N5eaSqzNRkrnvaDmX/3sDT35lrgkeHx/CNRNjJeyFEL2C3QM/u6YIVxQxATHdto/6RhMvrd3Hec+vZ39hBSmR0uxMCNH72HdJp9qIwaWRKPeAbrus4Z4j5dz97jayDpVx/rD+LL4ohTB/z27ZlxBCODL7zvCbrmMb59t9lTGuLory2npe+c1oXrp6lIS96HZpaWntTqJ67rnn+N3vfnfcxzW3ASgoKLA0L+voubds2XLc53nuueeoqqqy3D7vvPMoKSnpytCPa/HixZZWz8nJya1OtNJa89hjj5GYmMigQYOYNm0amZmZlvsrKiq49dZbSUhIICUlhbPPPptNmzadcJ8NDQ2EhoayePHi0x7/8bz11lskJiaSmJjIW2+91eE2ubm5TJs2jZEjR5KamsoXX3wBmE8kmzhxIikpKaSmpvK///3P8hiDwcD48eNJTExk/vz5lvbYTz75pKVl89ChQ3F1daW4uLhbXyNg3zr8+oz/6ZFvpOin0/9gk5rVZpuyi/RjKzMtt+sb7NPatKukDr+Fverwtx7ZqpdkLNFbj2w97f2/8sor+rrrrmv1tfHjx+t169Yd93G+vr6tbndUez516lS9efPm4z7PgAEDdGFhYRdH23XWrZ737Nmj/f39dV1dndZa6xdffFHPmTNHV1ZWaq21/vrrr3V8fLyurja3D58/f75etGiRpc3w/v379cqVK0+4z88//1xPmjRJx8bGapPJ1OE2DQ0Np/W6ioqKdFxcnC4qKtLFxcU6Li5OFxcXt9vu5ptv1i+//LLWWuvMzEw9YMAArbXWu3fv1nv27NFaa33w4EHdr18/bTQatdZaX3755Xr58uVaa61vvfVWy+OtrVixQk+bNq3DsfWoOvyDR3dSrxTx4cNt8nwVtQ088eWv/GfjAaL7eHN72kBpdtbLPfHTE/zaVAnWmYq6CnYbd6PRKBRJwUn4eXReoju4z2AeHPdgp/dfdtll/PnPf6a2thZPT09ycnIoKChg8uTJVFRUMHfuXIxGI/X19Tz22GPMnTu31eNzcnK44IIL2LBhA9XV1Vx//fVkZWUxZMgQqqurLdvdfvvtbN68merqai677DIeffRRXnjhBQoKCpg2bRqhoaGsXbuW2NhYtmzZQmhoKM8884yl2+ZNN93E3XffTU5ODnPmzGHy5Mn8+OOPREZG8umnn+Lt7d3pa0xMTMTHxwej0Uh4eDhPPPEE6enp+Pj4ADB79mwmTZrEsmXLSEtLY9OmTSxbtgwXF/PPYnx8PPHx5nNvzjvvPJYuXdphj5zly5dz11138c9//pONGzcyceJEAGJjY7nhhhtYtWoVd955J2PHjuWOO+6gsLAQHx8flixZwuDBg/nss8947LHHqKurIyQkhGXLlrU6Qxjg66+/ZtasWZb20bNmzeKrr75q14pBKUVZWRlgbgvRPN5BgwZZtomIiCA8PJzCwkICAwP59ttveeeddwD47W9/y+LFi7n99tvbvcbOunjaml0D32A0NzeKCx182s+1dvdR/vTRDg6V1XDDWXH8v3MG4eNh96pT4QTK68vRmLsyajTl9eXHDfwTCQkJYdy4cXz11VfMnTuXd999l/nz56OUwsvLi48//piAgACOHTvGhAkTuOiiizrtXPmvf/0LHx8fMjIyyMjIYNSoUZb7/va3v9GnTx8aGxuZMWMGGRkZLFy4kGeeeYa1a9e2axT2888/88Ybb7Bp0ya01owfP56pU6cSHBzM3r17Wb58OUuWLOGKK67gww8/5De/+U2nr/GXX34hMTGR8PBwysrKqKysJCGhdWl1cwvm5itxte1P36x5aaSt6upq1qxZw7///W8OHz7M8uXLLYEP4OXlxfr16wGYMWMGr7zyComJiWzatInf/e53fPvtt0yePJmNGzeilGLp0qX84x//4Omnn2bLli288sorLF26tMstmBcvXszs2bN58cUXqaysZPXq1e22+emnn6irqyMhIYGioiKCgoJwc3Pr9Hmrqqr46quvLFfh6m52TcTsioOgTv+yhhW1Ddz33nZCfD348PZJjIoJttEIhbM73ky82baj27h51c3Um+pxd3Hn8SmPMyK84x7wXbVgwQLeffddS+A3z6q11vzxj39k3bp1uLi4cPDgQY4cOdKuX02zdevWsXDhQgBSU1NJTU213Pfee+/x6quv0tDQwKFDh8jKymp1f1vr16/nkksusXSynDdvHt9//z0XXXQRcXFxlr73o0ePJicnp8PnePbZZ1myZAnZ2dl89dVXx30P9Gm2YF65ciXTpk3Dx8eHiy66iClTpvDss89afnHMnz8fMB8f+PHHHy0XYAGora0FzN0158+fz6FDh6irq7M0VBszZoylD4/uYgvm5cuXc91113HfffexYcMGrrnmGnbu3Gn5q+XQoUNcc801vPXWW7i4uHTpJBt27gAACkBJREFUeT/77DPOOussy18X3c2uax2GWiOhyp0Aj5Mvk9Rak777KI0mjZ+nG/+9cTwrF06WsBcnbUT4CJbMXsKdI+9kyewlpx32ABdffDFr1qyxXM2qeWa+bNkyCgsL+fnnn9m2bRt9+/btsC2wtY7Cx2Aw8NRTT7FmzRoyMjI4//zzT/g8HQVQM0/PlmKG47Vgvueee9i9ezf/+9//uPbaa6mpqSEgIABfX1+ys7NbbdvcgjklJYXt27djMpmOO762li9fzurVq4mNjWXq1KkUFRWxdu1ay/3Nv7hMJhNBQUGW9svbtm1j1y7zBYt+//vfc+edd7Jjxw7+/e9/n1YL5tdee40rrrgCgIkTJ1JTU8OxY+ZOv2VlZZx//vk89thjTJgwATBfKaukpMTyXnb0vMe7KEt3sGPga7KpI97j5H+zHS2r4db//Mx1b2zmk63mP5GSIwLwdOv4T0YhTmRE+AhuGnaTTcIezBU3aWlp3HDDDa1+oEtLSwkPD8fd3Z21a9dy4MCB4z7P2WefbWlVvHPnTjIyMgBzwPj6+hIYGMiRI0f48ssvLY/x9/e3XO6v7XN98sknVFVVUVlZyccff8yUKVNO6fXNmzePMWPGWCpa7r//fhYuXGg5xrB69WrWr1/PVVddRUJCAmPGjOGRRx6x/NLZu3cvn376KWBejmm71FFWVsb69evJzc0lJyeHnTt38tJLL3XYgjkgIIC4uDjef/99wPyLbfv27UDrFsydVd+cc845rFq1CqPRiNFoZNWqVR12x7Ruwbxr1y5qamoICwujrq6OSy65hGuvvbbVXxlKKaZNm8YHH3xg2b/18ZrS0lK+++67dsdwupPdAt/FVI/B3Z04v6guP0ZrzXub85jxzHd8t6eQP8yRZmfCcS1YsIDt27e3uvrT1VdfzZYtWxgzZgzLli1j8ODjH7+6/fbbqaioIDU1lX/84x+MG2fu1z98+HBGjhxJSkoKN9xwQ6sWw7fccgtz5sxh2rRprZ5r1KhRXHfddYwbN47x48dz0003MXLkyFN+fQ8//DDPPPMMJpOJ3//+94wdO5Zhw4aRlJTEX//611YHfpcuXcrhw4cZOHAgw4YN4+abbyYiIgKTycS+ffvaLWl89NFHTJ8+vdVfHnPnzmXFihWW5Rpry5Yt47XXXmP48OGkpKRYfpksXryYyy+/nClTprQ6prFlyxbLpRb79OnDQw89xNixYxk7diwPP/ywZTwPP/yw5epbTz/9NEuWLGH48OEsWLCAN998E6UU7733HuvWrePNN9+0lFo29/x/4okneOaZZxg4cCBFRUXceOONljF8/PHHzJ49u9XFYrqbOt6fed1pcEKkdn+4D4sS/3979xYbRRmGcfz/WEuKFlGKEqESF4NCJRgVkRg1VeMB1BiiFwaUaDzEGI0mXniMGr3ReGOIp6BpjDdyoaiYeNZUiFhPCUJtPSBGbEQOq5EGRGn7ejEbt5a2+7HszHRn31+yabc7Xd59Mvsy/fabb5aw9Kx7g37n3lUbefnzLczPTeLxK+eSm5xcUHFqb2+ntbU17TLGhEpk0d3dzezZsytTUIp6e3uZMGFC2mXEqrOzk7a2tmEvPThYLWQxnOH2ZUlfmdm8cp4vtQ9t+wb+ph7ITRl9Smb/gLGvf4CG+joWnzqNk6cewZL50339G+cyYM6cOSWbvauc1IZ09ll0ObUZU0b+k/L7bb1c+ew6nng3Wuxsfm4S1yzwlS2dc64cKTb8Pg4zmHL4/tPR/ukbYPmHP3Dp8rX8nN/N3OaJKVToqllaQ5XOVUoc+3BqQzr7GCB3yPj9ppx9+9su7ly5nm9/6+XyU6by8OUtNDX6+jcuXENDA/l8nqampoOaB+5cWsyMfD7/v4urV0JqDf9vQa6hab+f19cdwl/7+nl+2TwubEn3ouauOjU3N9PT08OOHTvSLuWg7N27t+Jv+GpVi1k0NDTQ3Bw+izFEekf4ghmN0Rr4HZvzfNC1jQcua+GEoxv56K5W6nyc3pWpvr7+vzMqq1l7e/tBTZvMEs+iMoLG8CVdIuk7SZsk3TPM45K0vPD4BkmnDfc8Q02deCL3v7aRq1d08F7XNn7fHS0d6s3eOecqr+QRvqQ64GngQqAH+ELSajPrGrTZQmBm4XYm8Gzh66hWdnTzyc5Z3Hh2jrsuOonx4/xMWeeci0vIEf58YJOZbTazf4CVwNBzga8AXios19wBHCnp2FJP3D2pgycXbuWBy1q82TvnXMxCxvCnAb8Mut/D/kfvw20zDdg6eCNJNwM3A9Q11tH1yI9ct+fWXX9sv+mHAy08YyYDO9MuYozwLIo8iyLPouikcn8xpOEPN6A+dIJoyDaY2QpgBYCkL/f09pV1enDWSPqy3FOls8azKPIsijyLIkmjX+NyFCFDOj3AcYPuNwO/lrGNc865FIU0/C+AmZJyksYBVwOrh2yzGlhWmK2zAPjTzLYOfSLnnHPpKTmkY2Z9km4D3gXqgDYz+0bSLYXHnwPeAhYBm4A9wPUB//aKsqvOHs+iyLMo8iyKPIuisrNIbXlk55xzyUr1EofOOeeS4w3fOedqROwNP65lGapRQBZLCxlskLRO0uhXh6lipbIYtN0ZkvolXZVkfUkKyUJSq6T1kr6R9HHSNSYl4D0yUdKbkr4uZBHyeWHVkdQmabukzhEeL69vmllsN6IPeX8EZgDjgK+BliHbLALeJprLvwD4LM6a0roFZnEWcFTh+4W1nMWg7T4imhRwVdp1p7hfHAl0AdML949Ju+4Us7gPeLzw/dHA78C4tGuPIYtzgdOAzhEeL6tvxn2EH9uyDFWoZBZmts7M/ijc7SA6nyGLQvYLgNuBV4HtSRaXsJAslgCrzGwLgJllNY+QLAyYoOhCB41EDb8v2TLjZ2ZriF7bSMrqm3E3/JGWXDjQbbLgQF/nDUT/g2dRySwkTQMWA88lWFcaQvaLE4GjJLVL+krSssSqS1ZIFk8Bs4lO7NwI3GFmA8mUN6aU1TfjXg+/YssyZEDw65R0HlHDPzvWitITksWTwN1m1p/xq1aFZHEocDpwATAe+FRSh5l9H3dxCQvJ4mJgPXA+cALwvqS1ZrYr7uLGmLL6ZtwN35dlKAp6nZLmAi8AC80sn1BtSQvJYh6wstDsJwOLJPWZ2evJlJiY0PfITjPbDeyWtAY4Bchaww/J4nrgMYsGsjdJ+gmYBXyeTIljRll9M+4hHV+WoahkFpKmA6uAazN49DZYySzMLGdmx5vZ8cArwK0ZbPYQ9h55AzhH0qGSDiNarbY74TqTEJLFFqK/dJA0hWjlyM2JVjk2lNU3Yz3Ct/iWZag6gVk8CDQBzxSObPssgysEBmZRE0KyMLNuSe8AG4AB4AUzG3a6XjUL3C8eBV6UtJFoWONuM8vcssmSXgZagcmSeoCHgHo4uL7pSys451yN8DNtnXOuRnjDd865GuEN3znnaoQ3fOecqxHe8J1zrkZ4w3fOuRrhDd8552rEvwkfEPPPsL3sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(x_train_sm, y_train_sm)\n",
    "y_pred = lda.predict(x_train_sm)\n",
    "y_val_pred = lda.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"LDA\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jPecSoDFvXDm"
   },
   "source": [
    "## SVM, with Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "-ETD1ClvAcnY",
    "outputId": "8d421ec8-4495-431f-9a00-ab93addd9ae8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.97      0.99      0.98     56075\n",
      "         1.0       0.99      0.97      0.98     56075\n",
      "\n",
      "    accuracy                           0.98    112150\n",
      "   macro avg       0.98      0.98      0.98    112150\n",
      "weighted avg       0.98      0.98      0.98    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.88      0.88      0.88      1322\n",
      "\n",
      "    accuracy                           0.98     20000\n",
      "   macro avg       0.94      0.94      0.94     20000\n",
      "weighted avg       0.98      0.98      0.98     20000\n",
      "\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"
    }
   ],
   "source": [
    "svm_ss = make_pipeline(StandardScaler(), SVC(C=500, gamma=\"scale\", random_state=42))\n",
    "svm_ss.fit(x_train_sm, y_train_sm)\n",
    "y_pred = svm_ss.predict(x_train_sm)\n",
    "y_val_pred = svm_ss.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"SVM_SCALED\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VQZj3A6eygLa"
   },
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "UTb7PmJzAcna",
    "outputId": "6f4f0115-91d0-45c8-af4c-8c740e40a165"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.97      0.99      0.98     56075\n",
      "         1.0       0.99      0.97      0.98     56075\n",
      "\n",
      "    accuracy                           0.98    112150\n",
      "   macro avg       0.98      0.98      0.98    112150\n",
      "weighted avg       0.98      0.98      0.98    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.88      0.89      0.88      1322\n",
      "\n",
      "    accuracy                           0.98     20000\n",
      "   macro avg       0.94      0.94      0.94     20000\n",
      "weighted avg       0.98      0.98      0.98     20000\n",
      "\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"
    }
   ],
   "source": [
    "svm = make_pipeline(StandardScaler(), SVC(C=100, gamma=\"auto\", random_state=42))\n",
    "svm.fit(x_train_sm, y_train_sm)\n",
    "y_pred = svm.predict(x_train_sm)\n",
    "y_val_pred = svm.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"SVM\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wgtkHLwontJQ"
   },
   "source": [
    "## ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "IVxnqmdZc2YR",
    "outputId": "11fcba42-bcf3-4473-fbda-71784b8357c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56075\n",
      "         1.0       1.00      1.00      1.00     56075\n",
      "\n",
      "    accuracy                           1.00    112150\n",
      "   macro avg       1.00      1.00      1.00    112150\n",
      "weighted avg       1.00      1.00      1.00    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.90      0.85      0.87      1322\n",
      "\n",
      "    accuracy                           0.98     20000\n",
      "   macro avg       0.94      0.92      0.93     20000\n",
      "weighted avg       0.98      0.98      0.98     20000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "etc = ExtraTreesClassifier(random_state=42)\n",
    "etc.fit(x_train_sm, y_train_sm)\n",
    "y_pred = etc.predict(x_train_sm)\n",
    "y_val_pred = etc.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "models.append(\"ExtraTrees\")\n",
    "precision.append(report[\"1.0\"][\"precision\"])\n",
    "recall.append(report[\"1.0\"][\"recall\"])\n",
    "f1_score.append(report[\"1.0\"][\"f1-score\"])\n",
    "accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df[\"Models\"] = models\n",
    "df[\"Precision\"] = precision\n",
    "df[\"Recall\"] = recall\n",
    "df[\"F1-Score\"] = f1_score\n",
    "df[\"Accuracy\"] = accuracy\n",
    "\n",
    "df[\"Models\"] = df[\"Models\"]*100\n",
    "df[\"Precision\"] = df[\"Precision\"]*100\n",
    "df[\"Recall\"] = df[\"Recall\"]*100\n",
    "df[\"F1-Score\"] = df[\"F1-Score\"]*100\n",
    "df[\"Accuracy\"] = df[\"Accuracy\"]*100\n",
    "\n",
    "df.sort_values(by=[\"F1-Score\"], inplace=True)\n",
    "df.plot.bar(x=\"Models\", y=\"F1-Score\", color=\"dodgerblue\", alpha=0.5, edgecolor=\"darkslategray\", \n",
    "            title=\"F1 Score of the models (best parameter set) on Validation data\", legend=False, \n",
    "            figsize=[15,6], rot=0, grid=True, ylim=[0,100])\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN_SCALEDKNN_SCALEDKNN_SCALEDKNN_SCALEDKNN_SC...</td>\n",
       "      <td>79.682101</td>\n",
       "      <td>87.216339</td>\n",
       "      <td>83.279162</td>\n",
       "      <td>97.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNK...</td>\n",
       "      <td>39.590592</td>\n",
       "      <td>68.759455</td>\n",
       "      <td>50.248756</td>\n",
       "      <td>91.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DECISIONTREEDECISIONTREEDECISIONTREEDECISIONTR...</td>\n",
       "      <td>72.833118</td>\n",
       "      <td>85.173979</td>\n",
       "      <td>78.521618</td>\n",
       "      <td>96.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRF...</td>\n",
       "      <td>86.605784</td>\n",
       "      <td>86.081694</td>\n",
       "      <td>86.342944</td>\n",
       "      <td>98.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MLP_SCALEDMLP_SCALEDMLP_SCALEDMLP_SCALEDMLP_SC...</td>\n",
       "      <td>89.947878</td>\n",
       "      <td>91.376702</td>\n",
       "      <td>90.656660</td>\n",
       "      <td>98.755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPM...</td>\n",
       "      <td>31.448235</td>\n",
       "      <td>79.500756</td>\n",
       "      <td>45.068611</td>\n",
       "      <td>87.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ADABOOST_SCALEDADABOOST_SCALEDADABOOST_SCALEDA...</td>\n",
       "      <td>49.906191</td>\n",
       "      <td>80.484115</td>\n",
       "      <td>61.609728</td>\n",
       "      <td>93.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ADABOOSTADABOOSTADABOOSTADABOOSTADABOOSTADABOO...</td>\n",
       "      <td>49.906191</td>\n",
       "      <td>80.484115</td>\n",
       "      <td>61.609728</td>\n",
       "      <td>93.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GRADIENTBOOSTING_SCALEDGRADIENTBOOSTING_SCALED...</td>\n",
       "      <td>71.941063</td>\n",
       "      <td>84.947050</td>\n",
       "      <td>77.904960</td>\n",
       "      <td>96.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GRADIENTBOOSTINGGRADIENTBOOSTINGGRADIENTBOOSTI...</td>\n",
       "      <td>71.941063</td>\n",
       "      <td>84.947050</td>\n",
       "      <td>77.904960</td>\n",
       "      <td>96.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SGD_SCALEDSGD_SCALEDSGD_SCALEDSGD_SCALEDSGD_SC...</td>\n",
       "      <td>33.826917</td>\n",
       "      <td>77.760968</td>\n",
       "      <td>47.145150</td>\n",
       "      <td>88.475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDS...</td>\n",
       "      <td>23.763050</td>\n",
       "      <td>79.198185</td>\n",
       "      <td>36.557263</td>\n",
       "      <td>81.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GaussianNBGaussianNBGaussianNBGaussianNBGaussi...</td>\n",
       "      <td>40.910891</td>\n",
       "      <td>78.139183</td>\n",
       "      <td>53.704185</td>\n",
       "      <td>91.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BernoulliNBBernoulliNBBernoulliNBBernoulliNBBe...</td>\n",
       "      <td>8.400843</td>\n",
       "      <td>57.261725</td>\n",
       "      <td>14.652086</td>\n",
       "      <td>55.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LDA_SCALEDLDA_SCALEDLDA_SCALEDLDA_SCALEDLDA_SC...</td>\n",
       "      <td>40.803109</td>\n",
       "      <td>71.482602</td>\n",
       "      <td>51.951622</td>\n",
       "      <td>91.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LDALDALDALDALDALDALDALDALDALDALDALDALDALDALDAL...</td>\n",
       "      <td>40.803109</td>\n",
       "      <td>71.482602</td>\n",
       "      <td>51.951622</td>\n",
       "      <td>91.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SVM_SCALEDSVM_SCALEDSVM_SCALEDSVM_SCALEDSVM_SC...</td>\n",
       "      <td>88.106061</td>\n",
       "      <td>87.972769</td>\n",
       "      <td>88.039364</td>\n",
       "      <td>98.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMS...</td>\n",
       "      <td>88.005997</td>\n",
       "      <td>88.804841</td>\n",
       "      <td>88.403614</td>\n",
       "      <td>98.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ExtraTreesExtraTreesExtraTreesExtraTreesExtraT...</td>\n",
       "      <td>89.532117</td>\n",
       "      <td>85.400908</td>\n",
       "      <td>87.417731</td>\n",
       "      <td>98.375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Models  Precision     Recall  \\\n",
       "0   KNN_SCALEDKNN_SCALEDKNN_SCALEDKNN_SCALEDKNN_SC...  79.682101  87.216339   \n",
       "1   KNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNKNNK...  39.590592  68.759455   \n",
       "2   DECISIONTREEDECISIONTREEDECISIONTREEDECISIONTR...  72.833118  85.173979   \n",
       "3   RFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRFRF...  86.605784  86.081694   \n",
       "4   MLP_SCALEDMLP_SCALEDMLP_SCALEDMLP_SCALEDMLP_SC...  89.947878  91.376702   \n",
       "5   MLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPM...  31.448235  79.500756   \n",
       "6   ADABOOST_SCALEDADABOOST_SCALEDADABOOST_SCALEDA...  49.906191  80.484115   \n",
       "7   ADABOOSTADABOOSTADABOOSTADABOOSTADABOOSTADABOO...  49.906191  80.484115   \n",
       "8   GRADIENTBOOSTING_SCALEDGRADIENTBOOSTING_SCALED...  71.941063  84.947050   \n",
       "9   GRADIENTBOOSTINGGRADIENTBOOSTINGGRADIENTBOOSTI...  71.941063  84.947050   \n",
       "10  SGD_SCALEDSGD_SCALEDSGD_SCALEDSGD_SCALEDSGD_SC...  33.826917  77.760968   \n",
       "11  SGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDSGDS...  23.763050  79.198185   \n",
       "12  GaussianNBGaussianNBGaussianNBGaussianNBGaussi...  40.910891  78.139183   \n",
       "13  BernoulliNBBernoulliNBBernoulliNBBernoulliNBBe...   8.400843  57.261725   \n",
       "14  LDA_SCALEDLDA_SCALEDLDA_SCALEDLDA_SCALEDLDA_SC...  40.803109  71.482602   \n",
       "15  LDALDALDALDALDALDALDALDALDALDALDALDALDALDALDAL...  40.803109  71.482602   \n",
       "16  SVM_SCALEDSVM_SCALEDSVM_SCALEDSVM_SCALEDSVM_SC...  88.106061  87.972769   \n",
       "17  SVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMSVMS...  88.005997  88.804841   \n",
       "18  ExtraTreesExtraTreesExtraTreesExtraTreesExtraT...  89.532117  85.400908   \n",
       "\n",
       "     F1-Score  Accuracy  \n",
       "0   83.279162    97.685  \n",
       "1   50.248756    91.000  \n",
       "2   78.521618    96.920  \n",
       "3   86.342944    98.200  \n",
       "4   90.656660    98.755  \n",
       "5   45.068611    87.190  \n",
       "6   61.609728    93.370  \n",
       "7   61.609728    93.370  \n",
       "8   77.904960    96.815  \n",
       "9   77.904960    96.815  \n",
       "10  47.145150    88.475  \n",
       "11  36.557263    81.830  \n",
       "12  53.704185    91.095  \n",
       "13  14.652086    55.905  \n",
       "14  51.951622    91.260  \n",
       "15  51.951622    91.260  \n",
       "16  88.039364    98.420  \n",
       "17  88.403614    98.460  \n",
       "18  87.417731    98.375  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble_models = []\n",
    "ensemble_precision = []\n",
    "ensemble_recall = []\n",
    "ensemble_f1 = []\n",
    "ensemble_accuracy = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GRK093kInxcf"
   },
   "source": [
    "## Stacking Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "mNIh__geCMka",
    "outputId": "f94aa668-9f53-4d84-a6a8-507d54a5e678"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56075\n",
      "         1.0       1.00      1.00      1.00     56075\n",
      "\n",
      "    accuracy                           1.00    112150\n",
      "   macro avg       1.00      1.00      1.00    112150\n",
      "weighted avg       1.00      1.00      1.00    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.91      0.90      0.91      1322\n",
      "\n",
      "    accuracy                           0.99     20000\n",
      "   macro avg       0.95      0.95      0.95     20000\n",
      "weighted avg       0.99      0.99      0.99     20000\n",
      "\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"
    }
   ],
   "source": [
    "estimators = [('svm_ss', svm_ss), ('knn_ss', knn_ss), ('rf', rf), \n",
    "              ('mlp_ss_esf', mlp_ss_esf), ('etc', etc)]\n",
    "stack = StackingClassifier(estimators=estimators, final_estimator=mlp_ss_esf)\n",
    "stack.fit(x_train_sm, y_train_sm)\n",
    "y_pred = stack.predict(x_train_sm)\n",
    "y_val_pred = stack.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "ensemble_models.append(\"STACKING\")\n",
    "ensemble_precision.append(report[\"1.0\"][\"precision\"])\n",
    "ensemble_recall.append(report[\"1.0\"][\"recall\"])\n",
    "ensemble_f1.append(report[\"1.0\"][\"f1-score\"])\n",
    "ensemble_accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F4yJ2T6e21AT"
   },
   "source": [
    "## Voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "2hU6i_1z2zhd",
    "outputId": "79d410f3-3a18-4871-8ab2-3d0d1d663b88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56075\n",
      "         1.0       1.00      1.00      1.00     56075\n",
      "\n",
      "    accuracy                           1.00    112150\n",
      "   macro avg       1.00      1.00      1.00    112150\n",
      "weighted avg       1.00      1.00      1.00    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.89      0.89      0.89      1322\n",
      "\n",
      "    accuracy                           0.99     20000\n",
      "   macro avg       0.94      0.94      0.94     20000\n",
      "weighted avg       0.99      0.99      0.99     20000\n",
      "\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"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "estimators = [('svm_ss', make_pipeline(StandardScaler(), SVC(C=50, random_state=42, probability=True))), \n",
    "              ('knn_ss', knn_ss), ('rf', rf), \n",
    "              ('mlp_ss_esf', mlp_ss_esf), ('etc', etc)]\n",
    "voting = VotingClassifier(estimators=estimators, voting='soft')\n",
    "voting.fit(x_train_sm, y_train_sm)\n",
    "y_pred = voting.predict(x_train_sm)\n",
    "y_val_pred = voting.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "ensemble_models.append(\"VOTING_SOFT\")\n",
    "ensemble_precision.append(report[\"1.0\"][\"precision\"])\n",
    "ensemble_recall.append(report[\"1.0\"][\"recall\"])\n",
    "ensemble_f1.append(report[\"1.0\"][\"f1-score\"])\n",
    "ensemble_accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "Z9z3eGXW3L0J",
    "outputId": "30639f1f-85f1-4598-82d7-187eb9c575fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56075\n",
      "         1.0       1.00      1.00      1.00     56075\n",
      "\n",
      "    accuracy                           1.00    112150\n",
      "   macro avg       1.00      1.00      1.00    112150\n",
      "weighted avg       1.00      1.00      1.00    112150\n",
      "\n",
      "==================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99     18678\n",
      "         1.0       0.89      0.88      0.88      1322\n",
      "\n",
      "    accuracy                           0.98     20000\n",
      "   macro avg       0.94      0.94      0.94     20000\n",
      "weighted avg       0.98      0.98      0.98     20000\n",
      "\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"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "estimators = [('svm_ss', make_pipeline(StandardScaler(), SVC(C=50, random_state=42, probability=True))), \n",
    "              ('knn_ss', knn_ss), ('rf', rf), \n",
    "              ('mlp_ss_esf', mlp_ss_esf), ('etc', etc)]\n",
    "voting = VotingClassifier(estimators=estimators, voting='hard')\n",
    "voting.fit(x_train_sm, y_train_sm)\n",
    "y_pred = voting.predict(x_train_sm)\n",
    "y_val_pred = voting.predict(x_val)\n",
    "\n",
    "report = get_accuracy_vals(y_train_sm, y_pred, y_val, y_val_pred);\n",
    "ensemble_models.append(\"VOTING_HARD\")\n",
    "ensemble_precision.append(report[\"1.0\"][\"precision\"])\n",
    "ensemble_recall.append(report[\"1.0\"][\"recall\"])\n",
    "ensemble_f1.append(report[\"1.0\"][\"f1-score\"])\n",
    "ensemble_accuracy.append(report[\"accuracy\"])\n",
    "get_roc_curves(y_train_sm, y_pred, y_val, y_val_pred);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ensemble_df = pd.DataFrame()\n",
    "ensemble_df[\"Models\"] = ensemble_models\n",
    "ensemble_df[\"Precision\"] = ensemble_precision\n",
    "ensemble_df[\"Recall\"] = ensemble_recall\n",
    "ensemble_df[\"F1-Score\"] = ensemble_f1\n",
    "ensemble_df[\"Accuracy\"] = ensemble_accuracy\n",
    "\n",
    "ensemble_df[\"Models\"] = ensemble_df[\"Models\"]*100\n",
    "ensemble_df[\"Precision\"] = ensemble_df[\"Precision\"]*100\n",
    "ensemble_df[\"Recall\"] = ensemble_df[\"Recall\"]*100\n",
    "ensemble_df[\"F1-Score\"] = ensemble_df[\"F1-Score\"]*100\n",
    "ensemble_df[\"Accuracy\"] = ensemble_df[\"Accuracy\"]*100\n",
    "\n",
    "ensemble_df.sort_values(by=[\"F1-Score\"], inplace=True)\n",
    "ensemble_df.plot.bar(x=\"Models\", y=\"F1-Score\", color=\"dodgerblue\", alpha=0.5, edgecolor=\"darkslategray\", \n",
    "            title=\"F1 Score of Ensemble models on Validation data\", legend=False, \n",
    "            figsize=[15,6], rot=0, grid=True, ylim=[0,100])\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>STACKINGSTACKINGSTACKINGSTACKINGSTACKINGSTACKI...</td>\n",
       "      <td>91.347626</td>\n",
       "      <td>90.242057</td>\n",
       "      <td>90.791476</td>\n",
       "      <td>98.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VOTING_SOFTVOTING_SOFTVOTING_SOFTVOTING_SOFTVO...</td>\n",
       "      <td>89.006823</td>\n",
       "      <td>88.804841</td>\n",
       "      <td>88.905718</td>\n",
       "      <td>98.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VOTING_HARDVOTING_HARDVOTING_HARDVOTING_HARDVO...</td>\n",
       "      <td>89.170507</td>\n",
       "      <td>87.821483</td>\n",
       "      <td>88.490854</td>\n",
       "      <td>98.490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Models  Precision     Recall  \\\n",
       "0  STACKINGSTACKINGSTACKINGSTACKINGSTACKINGSTACKI...  91.347626  90.242057   \n",
       "1  VOTING_SOFTVOTING_SOFTVOTING_SOFTVOTING_SOFTVO...  89.006823  88.804841   \n",
       "2  VOTING_HARDVOTING_HARDVOTING_HARDVOTING_HARDVO...  89.170507  87.821483   \n",
       "\n",
       "    F1-Score  Accuracy  \n",
       "0  90.791476    98.790  \n",
       "1  88.905718    98.535  \n",
       "2  88.490854    98.490  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final model - Stacking Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "8_i0WQpjCcD7"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = 'finalized_model.sav'\n",
    "pickle.dump(stack, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Label\n",
       "ID          \n",
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "...      ...\n",
       "19995      1\n",
       "19996      1\n",
       "19997      1\n",
       "19998      1\n",
       "19999      0\n",
       "\n",
       "[20000 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the model from disk\n",
    "model = pickle.load(open(\"finalized_model.sav\", \"rb\"))\n",
    "\n",
    "X_test = pd.read_csv(\"datasets/test_final.csv\", index_col=0)\n",
    "\n",
    "y_test_pred = model.predict(X_test)\n",
    "y_test_pred = pd.DataFrame(y_test_pred, columns=[\"Label\"])\n",
    "y_test_pred.index.name=\"ID\"\n",
    "y_test_pred[\"Label\"] = y_test_pred[\"Label\"].astype(int)\n",
    "y_test_pred.to_csv(\"datasets/pred_y.csv\")\n",
    "y_test_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"datasets/results.csv\")\n",
    "ensemble_df.to_csv(\"datasets/results_ensemble.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"datasets/results.csv\", index_col=0)\n",
    "df[\"Models\"] = [\"KNN_SS\", \"KNN\", \"DT\", \"RF\", \"MLP_SS\", \"MLP\", \"ADA_SS\",\n",
    "                \"ADA\", \"GB_SS\", \"GB\", \"SGD_SS\", \"SGD\", \n",
    "                \"GaussianNB\", \"BernoulliNB\", \"LDA_SS\", \"LDA\", \"SVM_SS\", \"SVM\", \n",
    "                \"ExtraTrees\"]\n",
    "\n",
    "\n",
    "df.sort_values(by=[\"F1-Score\"], inplace=True)\n",
    "df.plot.bar(x=\"Models\", y=\"F1-Score\", color=\"dodgerblue\", alpha=0.5, edgecolor=\"darkslategray\", \n",
    "            title=\"F1 Score of the models (best parameter set) on Training data\", legend=False, \n",
    "            figsize=[20,6], rot=0, grid=True, ylim=[0,100])\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ensemble_df = pd.read_csv(\"datasets/results_ensemble.csv\", index_col=0)\n",
    "ensemble_df[\"Models\"] = [\"STACKING\", \"VOTING_SOFT\", \"VOTING_HARD\"]\n",
    "ensemble_df.sort_values(by=[\"F1-Score\"], inplace=True)\n",
    "ensemble_df.plot.bar(x=\"Models\", y=\"F1-Score\", color=\"dodgerblue\", alpha=0.5, edgecolor=\"darkslategray\", \n",
    "            title=\"F1 Score of the models (best parameter set) on Training data\", legend=False, \n",
    "            figsize=[10,6], rot=0, grid=True, ylim=[0,100])\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "3_validate_models_colab.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}