models_hyperparameters_tuning_and_comclusion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "7527b3fc-26e8-4f9f-b623-3849fb213f24"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from xgboost import XGBClassifier\n",
    "from imblearn.over_sampling import SMOTE, SMOTENC\n",
    "from dython.nominal import associations\n",
    "from dython.nominal import identify_nominal_columns\n",
    "\n",
    "from sklearn.metrics import roc_auc_score,recall_score,f1_score, roc_curve, accuracy_score, auc, precision_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from imblearn.combine import SMOTEENN\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "9f6ea65e-99c3-432d-94ea-57e6531321c8",
    "outputId": "0f3a60bc-f1b4-4a45-b5f8-f2eb5a03178d"
   },
   "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>Diabetes_012</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>HeartDiseaseorAttack</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>HvyAlcoholConsump</th>\n",
       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Diabetes_012  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "0           0.0     1.0       1.0        1.0  40.0     1.0     0.0   \n",
       "1           0.0     0.0       0.0        0.0  25.0     1.0     0.0   \n",
       "2           0.0     1.0       1.0        1.0  28.0     0.0     0.0   \n",
       "3           0.0     1.0       0.0        1.0  27.0     0.0     0.0   \n",
       "4           0.0     1.0       1.0        1.0  24.0     0.0     0.0   \n",
       "\n",
       "   HeartDiseaseorAttack  PhysActivity  Fruits  Veggies  HvyAlcoholConsump  \\\n",
       "0                   0.0           0.0     0.0      1.0                0.0   \n",
       "1                   0.0           1.0     0.0      0.0                0.0   \n",
       "2                   0.0           0.0     1.0      0.0                0.0   \n",
       "3                   0.0           1.0     1.0      1.0                0.0   \n",
       "4                   0.0           1.0     1.0      1.0                0.0   \n",
       "\n",
       "   AnyHealthcare  NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex  \\\n",
       "0            1.0          0.0      5.0      18.0      15.0       1.0  0.0   \n",
       "1            0.0          1.0      3.0       0.0       0.0       0.0  0.0   \n",
       "2            1.0          1.0      5.0      30.0      30.0       1.0  0.0   \n",
       "3            1.0          0.0      2.0       0.0       0.0       0.0  0.0   \n",
       "4            1.0          0.0      2.0       3.0       0.0       0.0  0.0   \n",
       "\n",
       "    Age  Education  Income  \n",
       "0   9.0        4.0     3.0  \n",
       "1   7.0        6.0     1.0  \n",
       "2   9.0        4.0     8.0  \n",
       "3  11.0        3.0     6.0  \n",
       "4  11.0        5.0     4.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read dataset\n",
    "df = pd.read_csv('diabetes_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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34VyE+bdqWEO13346j/32bNU4w8VgTL+b9d3qz30R2j9itdaS3W1/sWfnyHqWF7fRMZFKXTpX7055VSPixpV1iCVmtVoLCqWSZLfbZSu0jVWrVk2HDx+Rw+EcAmRl2WV17RdWq1WdO3fSzJmf+C7oYlitVmW57S9ZWdnF5HK4IBe7/XQuDRuGq0qVKvr004+UlJSoPn16+yx2d1abTXv3np6lbrfbixQaC+eRlZUlq81W4vV9wWqzKSvLbdtyi9GjTaFYT/XHyPh4vfDsszLz/f9wGluhXLK85GLzkkvhv3udunXVtGlTrVu37pzGW5zyMv6SJKvNqqwst793VpashfZ3q83qZfuyqV79+jqQm6vnJk5UYsoSjXvheVWqVMlnsRdWyxqqfW7Hrn32bNUq4T5bu149HTxwQMNffFZTk+bryWfHqaIfc6luraX9brkcsOec8XzfsV9vrfUynqxVJ0wNG1+pbes3eVnr3LParLK7bV/ZxWxfdi/bl5Op6R9/pPmLk3XrAP/eciPEWlOHsk/vu4ey9yk4tGaRdld1aKuHF32ogW8+p4WjT88H6/LUw1r+yrvnxbFYco3BstzGYPZ9Cj3DNUthoaE1FR0ToVmzFp6D6M6ONbSm7G7H1ezs/bJai/bN36lYsYLatr1eaWmryzK8//fMfJMvt68LEYXjsnHqlhEtXbeUGF24gWEYVSUFm6b5peulwleG6aZpHjJN84SkLZLcP/cS5bodxdWSJhuGUdn1eiVXofpbSbskeX6+2vn/3msYxreGYXz74+85hReXmGEYJW575Q3Xqm2fnpr3sudtmwOCAtUiqp2+XXZ+v4PnbcKaYRhe5xeW/K9yjnjpF28z7jakr9aEngM17ZE4dXt42Fmt6wvetq+ShlKadc8Fr7tKMfE0a3Wdovv11oyJr5/TmP6p8tUvXjrmLALKz8/XHd16qeeN7dWkRXM1usJPH48+i/326lbXKeaW3vrwpfNz+5LOLp/irEhbpa5dbtFDDz6p/zx6zu7Y9LdKksuZ2owePVLPP/+i8s+DC8qS5VJ0vVNtAgMD1axZU911170aMuRuPfzwg2rYMPxchHpGpe2Tstg+y4LX00oJ84jq0EG5ubkFM3XPR2fTJ5J08cUX66133tG4sWN19OjRIm19opyMv5xKEE8xMQcGBKhJs2b69OOP1Se2m47/cVzDHvR+D1uf8BZnCT8hFBAYoCuaNtGimbM0rGdfHT/+h26/f1hZR1hiRkn6xaVZq+vUsW9vffTyJI/XK15cSU9Omqj3n3tZx48dOydx/p2S5HGmfX5An77q0627hg0ZqoF33KHrbrjh3ARaIiUbS/6Y8bkm9xqiWY+OUoeHnc/MuKJ9ax07cFBZP/x0roMssbPZxryJG/GoXp741nk8bjn73xMZeZPWrt3EbSqAQgL9HcD/I39XY/zT7XuHvPSNaZo7DMPIltRE0hq5CtZn+qWmaU6RNEWS7m7S+h+PSn+356i67fS73NVsoTqYs69Iu7pXXKahY0fotfse07FCH4e+ut2N2rVlqw776SOrJWWrESL7/tO3CrDnHlZotWCdzHPInnv69ezcQwqtHuLtV/jMwewcVXXrl6rWWjq8b3+x7Xd8t14169XWJVWrnPW651K23S5b7bCCn61hNu3LKdn9o0uz7rmQa8/xuDVADatVB7zsKw2uvFwPjx+tscMe1pGD58etKQorT/2Sk2VXaNjpWW2hNpv2ZZ/9m2lHjxzR91+vUeuIdvr5p21lGWKJ7Ldnq6bb7LyaNu/bV/iVl+vhCfEac89D5932dfvAW3Trrb0lSRs3bvGYyWqzhirHSz4l8e23a1W/Xh1Vq1ZFv//um5wHDx6kAQNulSStX79Rtd22eZvNpuxC29iBAwcUEhKsgIAAORwOhYXZlJPjbNO8eTO98cZrkpyzYCMjI+Vw5Gn58hU+ymWgbrvNmcuGDRsV5ra/hIVZveTyu0JCQgpysdlO55KVZdeBA7/r+PHjOn78uNas+Z8aN75Kv/zy67nP4447NOC22yRJ6zdsUO3atQuW2Ww2Zed465MQtz4JU06281hlz8r62/V9wW63KyzMbdtyi7GgTVaWwgrFmpOTo66xseoYHa2IyEhVqFBBlYOD9fJrr+nxRx/1VfiecRbKJcxLLllecjn1dw8MDNRb77yjpIULtWzpUt8E7UV5GX9JzvN1WJjb3zssTDmF9vfsLLuX7StbpulctsE183tZSopfC8f77Nmq5XbsqmWzan8Jz/X7srK1z56tH9Y7P1GUmbrcr4Xj3Owc1XTLpbot1Pt48orL9cC4URp/77911O18HxAYqCcnTdRnySn6Js1/k3bsdrtsbtuX1cv2Zc+yy+Zl+5JUcF45kJurtGXL1LxlC327Zo0PIi/qcPY+VXGb9V3FWktH9hV/a6md321Q9Xq1dXHVENVv2UxXRt6ky9u2UmCFi1ThkovV59kRShzh24fk3X57H/W7xXmv6E0bf5QtzG0MZqulfTklPxY1a3aVXn7F+UHrqlWrqH37m+TIcyg93TezdW8b0Fv9+nWTJG3a9KNsbsdVq7Wmcs4il1O6do3iNhWAF8w49hHTNH+XdMQwjNaul247299hGEaopIaSdpZlbCXxy6YfZG1QTzXrhCkgKFA3dI3RupWeD++pHmbVg5Oe07ThY5S9c3eR39EqtpO+Oc9vUyFJHa5vrIWr1so0Ta3bukvBF1dUaPUQXX1ZHf2atV+7sw/or5N5WvL5BnW4vrFfY9216UfVql9P1euEKSAwUNd2jdbGlZ4fU6tZr07B93UbX6GAoCAdO3ioROv6yub1G1Q/PFy169ZVYFCQOvforsy09HO+7rmwbeNmhYXXV2jd2goMClTbbp21JmOVR5uaYTYNf2OiXn1qlPb+en48IMOb8tQvP2zYqHrh4QpzxRPTo5s+W1GyeKpWr6bKwc4nK1eoUEHXt71JO3f8fC7DLda2jZtVO7y+rK7tq123zvqm0MNGa4bZFDf5Zb365Mjzcvv6ZOZc9e41UL17DdSKFavU+2bnoL9Fi2Y6cvSo9p3hIqyw+vXrFnzfpMmVCrooyGdFY0n66KOPFRvbU7GxPbV8eZr69LlZknTNNS115MgR7dtX9CL/q6++UWxsF0lS3743FxSG27WLUtu2kWrbNlKpqUs1alS8z4rGzlxmqlu3XurWrZeWL19RkEvLli105MhRr7l8/fXX6tr1dC5prn08LS1d119/nQICAlSxYkW1bNlC2330kKmPZsxQbGysYmNjtXz5cvXp21eSdM011zj7xEvh96uvvlJsbKwrj75anpbmzGPFihKtf65tWL9eDRo2VN169RQUFKRuPXoo3RXjKekrVuhmV6wt3WKd+OKLatu6tSLbttWj//63vvryS78VjSVnLuFuuXTv0UMrSpiLJD3/4ovasX27pk+b5vPY3ZWX8ZckbVy/Xg0ahqtOvboKCgpSbI8eyijUJxkr0tSrbx9JUouCPtmn/fv2KStrrxo2cj6k9cY2bbRjm+/fVD1l64aNqhveQLa6dRQYFKQO3WP1ZfrKEq17YP9+5WRlqZ7r0xH/uqm1dvrxQX/bN25WWIN6Cq3jGk/Gdta3GUXP90++MVGTnh6lrELn+wfHj9aeHb8o+YOZvgy7iI3r1yu8YbjquravbsVsX729bF+VKlXSJZdcIsn5HIM27dtp21b/zdjdu/lHVW9QR1Xr2BQQGKhmXTrox1VferSpXu90ATys8eUKCAzUHwcPa8WkaXol5la91nWA5j01Vr+sWevzorEkffJJovrcPFR9bh6q9PTV6tXLeQ5v0aKpjhw5dlZjsJjoforu2FfRHftq+fKVGjt2os+KxpI069OFBQ+0y0j/Qj17Ou+d3bx5Yx09ekz795/dZLXKlS/Rdde30MqM8/MWgoA/MePYt+6WNNUwjGOSVkkq6dXtSsMwHJKCJA03TdPn0/byHQ7NnDBRj019XRaLRZ8vWKy9239RRH/nhWXm7AXq8cDdqlyligaNdj6RNT/PoXG33ilJuqhiBTW56QbNSHje16EX8d9XZmnNpl/0+5Fjan/P8/r3bdHKc93bcEDnVor415XK/H6rYh58WZUqBOnZh50XL4EBARp9T0/dM/Z9OfJN9e34L11e33/3bJWc/TLv2Vf04LuvyBJg0dcLlsi+4xe1ubWXJOmLOYvUMiZS1/fsIkdenk6e+FMfPBF/xnX9weFw6PnRY/T2jA9kCbBo0Zx52rFtm/oNdN7LbN7MT1WjVk19krRQl1SuLNM0NfCuoeoT00XHjh71uq6/5Dscmjr2BcVPe0sBARatmL9Iu7f/rM639ZMkLZs1T/0fulfBVavq/vi4gvyf6DtQkvTfl59Tsxv+pZBqVTUtc6lmvfGOVsxb6JdcylO/OBwOTYwfq9dnTJfFEqDFc+fpl23bdfPtzvfwFnwyS9Vr1tQHSYm6pHJl5Zv5uu3OobqtU1fVDA3VqIkvKCDAIsOwKH1Jqr4o9GaAr+Q7HHp37PNKmP62LAEWrZi3SLu371AX1/a1dNY83fawa/tKGOHMPS9Pj7u2rydeeU7NbrhOIdWq6r3Vy/TppLeV5qftS5IyV32hiIg2SluxQMePn9CIuNOPCJgy9TWNfGa8cnL2a/Dg/rpn2GDVrFlDSUmfKnP1Fxr5zAR17txBvXp3U15enk6cOKHHHh3ht1xWrlylqKhIZWZm6Pjx43ryyacLlr3//jQ9/fQI5eTk6PnnX9Qbb7ymxx//rzZv3qI5c+b6LebiOHOJ0KpVK3T8+HE99VRcwbL33puq4cOfceUyUW+88aoef/xRbdlyOpcdO3Zo9erVSk1NVn5+vmbPnquf/DBDf2VGhqKiopS5erWzT554omDZ+x98oKefesqZx3PP6Y3Jk/X4E09o8+bNmjN79t+u70sOh0NjRo/W+zNmKCAgQHPnzNG2bds0YKBzv/505kytyshQZFSUMlyxPu2nWP+Ow+F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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check correlation\n",
    "complete_correlation= associations(df, figsize=(25,25))\n",
    "df_complete_corr = complete_correlation['corr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "b0dded7a-8d40-446b-a7e3-103be086f22c"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>HeartDiseaseorAttack</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>HvyAlcoholConsump</th>\n",
       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>BMI</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>Diabetes_012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>7</td>\n",
       "      <td>6</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HighBP  HighChol  CholCheck  Smoker  Stroke  HeartDiseaseorAttack  \\\n",
       "0       1         1          1       1       0                     0   \n",
       "1       0         0          0       1       0                     0   \n",
       "2       1         1          1       0       0                     0   \n",
       "3       1         0          1       0       0                     0   \n",
       "4       1         1          1       0       0                     0   \n",
       "\n",
       "   PhysActivity  Fruits  Veggies  HvyAlcoholConsump  AnyHealthcare  \\\n",
       "0             0       0        1                  0              1   \n",
       "1             1       0        0                  0              0   \n",
       "2             0       1        0                  0              1   \n",
       "3             1       1        1                  0              1   \n",
       "4             1       1        1                  0              1   \n",
       "\n",
       "   NoDocbcCost  GenHlth  DiffWalk  Sex  Age  Education  Income  BMI  MentHlth  \\\n",
       "0            0        5         1    0    9          4       3   40        18   \n",
       "1            1        3         0    0    7          6       1   25         0   \n",
       "2            1        5         1    0    9          4       8   28        30   \n",
       "3            0        2         0    0   11          3       6   27         0   \n",
       "4            0        2         0    0   11          5       4   24         3   \n",
       "\n",
       "   PhysHlth  Diabetes_012  \n",
       "0        15             0  \n",
       "1         0             0  \n",
       "2        30             0  \n",
       "3         0             0  \n",
       "4         0             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proceed data\n",
    "cat_feat = ['HighBP', 'HighChol', 'CholCheck', 'Smoker', 'Stroke', 'HeartDiseaseorAttack',\n",
    "           'PhysActivity', 'Fruits', 'Veggies', 'HvyAlcoholConsump', 'AnyHealthcare', \n",
    "           'NoDocbcCost', 'GenHlth', 'DiffWalk', 'Sex', 'Age', 'Education', 'Income']\n",
    "num_feat = ['BMI','MentHlth', 'PhysHlth']\n",
    "\n",
    "target = ['Diabetes_012']\n",
    "# We tried to use one-hot encoding to transfer 'MentHlth', 'PhysHlth' into categorical feature,\n",
    "# but we found out that it lower our model's efficiency without improving performance.\n",
    "#enc1 = OneHotEncoder(handle_unknown='ignore')\n",
    "#X1 = df[['MentHlth']]\n",
    "#df['MentHlth_OH'] = list(enc1.fit_transform(X1).toarray())\n",
    "#enc2 = OneHotEncoder(handle_unknown='ignore')\n",
    "#X2 = df[['PhysHlth']]\n",
    "#df['PhysHlth_OH'] = list(enc2.fit_transform(X2).toarray())\n",
    "\n",
    "# make categorical data first and bmi last\n",
    "df2 = pd.DataFrame()\n",
    "df2[cat_feat]=df[cat_feat] \n",
    "df2[num_feat]=df[num_feat]\n",
    "df2[target] = df[target]\n",
    "\n",
    "for cat in cat_feat+target+num_feat:\n",
    "    df2[cat] = df2[cat].astype('int')\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "463493b9-273a-4627-bfca-6d980615a161",
    "outputId": "ccb30fde-8bcc-48aa-f629-10d4a014c78b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diabetes_012</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>HeartDiseaseorAttack</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>HvyAlcoholConsump</th>\n",
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       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Income</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
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       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Diabetes_012  HighBP  HighChol  CholCheck  BMI  Smoker  Stroke  \\\n",
       "0             0       1         1          1   40       1       0   \n",
       "1             0       0         0          0   25       1       0   \n",
       "2             0       1         1          1   28       0       0   \n",
       "3             0       1         0          1   27       0       0   \n",
       "4             0       1         1          1   24       0       0   \n",
       "\n",
       "   HeartDiseaseorAttack  PhysActivity  Veggies  HvyAlcoholConsump  GenHlth  \\\n",
       "0                     0             0        1                  0        5   \n",
       "1                     0             1        0                  0        3   \n",
       "2                     0             0        0                  0        5   \n",
       "3                     0             1        1                  0        2   \n",
       "4                     0             1        1                  0        2   \n",
       "\n",
       "   MentHlth  PhysHlth  DiffWalk  Age  Education  Income  \n",
       "0        18        15         1    9          4       3  \n",
       "1         0         0         0    7          6       1  \n",
       "2        30        30         1    9          4       8  \n",
       "3         0         0         0   11          3       6  \n",
       "4         3         0         0   11          5       4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add conditions\n",
    "col_name = df_complete_corr[abs(df_complete_corr['Diabetes_012'])>=0.05].index\n",
    "df3 = df2[col_name]\n",
    "df3 = df3[df3['Diabetes_012'] != 1] #remove case 1\n",
    "df3['Diabetes_012'] = df3['Diabetes_012'].map({2:1, 0:0})\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df3[df3.columns[1:]]\n",
    "y = df3[target]\n",
    "\n",
    "scaler=StandardScaler()\n",
    "normalized_x=scaler.fit_transform(X.values)\n",
    "X_train,X_test,y_train,y_test=train_test_split(normalized_x,y,test_size=0.3,random_state=6,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler()\n",
    "X_train2, y_train2 = rus.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GaussianNB_best': GaussianNB(),\n",
       " 'RandomForest_best': RandomForestClassifier(bootstrap=False, class_weight='balanced',\n",
       "                        criterion='entropy', max_depth=10, max_features='sqrt',\n",
       "                        n_estimators=70),\n",
       " 'DecisionTree_best': DecisionTreeClassifier(class_weight='balanced', max_depth=10,\n",
       "                        max_features='auto'),\n",
       " 'Logistic_best': LogisticRegression(n_jobs=-1),\n",
       " 'LDA_best': LinearDiscriminantAnalysis(n_components=1, shrinkage=1.0, solver='lsqr',\n",
       "                            tol=1.0),\n",
       " 'QDA_best': QuadraticDiscriminantAnalysis(),\n",
       " 'KNN_best': KNeighborsClassifier(n_neighbors=39),\n",
       " 'SVC': SVC(probability=True)}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "models_dic = {'GaussianNB_best': GaussianNB(),\n",
    "              'RandomForest_best': RandomForestClassifier(bootstrap = False,\n",
    "                                                         class_weight = 'balanced', criterion='entropy',\n",
    "                                                         max_depth = 10, max_features = 'sqrt',\n",
    "                                                          n_estimators= 70),\n",
    "              'DecisionTree_best':DecisionTreeClassifier(class_weight= 'balanced', criterion =  'gini',\n",
    "                                                          max_depth = 10, max_features = 'auto'),\n",
    "              'Logistic_best': LogisticRegression(n_jobs=-1),\n",
    "              'LDA_best': LDA(n_components=1, shrinkage=1.0, solver='lsqr', tol=1.0),\n",
    "              'QDA_best':QDA(),\n",
    "              'KNN_best':KNeighborsClassifier(n_neighbors=39,\n",
    "                               weights='uniform', # 'use uniform or distance'\n",
    "                                algorithm='auto',\n",
    "                                leaf_size=30,\n",
    "                                p=2, # Euclidean distance\n",
    "                                metric='minkowski',\n",
    "                                metric_params=None,\n",
    "                                n_jobs=None),\n",
    "              'SVC':SVC(probability=True)\n",
    "             }\n",
    "models_dic          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "def evaluate_performance(model_dict, x, y, t):\n",
    "    out = {\"threshold\": t}\n",
    "    print('Start evaluating...')\n",
    "    predict_time = []\n",
    "    for model_name, model in model_dict.items():\n",
    "        print(model_name)\n",
    "        now = datetime.now()\n",
    "        current_time = now.strftime(\"%H:%M:%S\")\n",
    "        print(\"Current Time =\", current_time)\n",
    "        predict_start = time.time()\n",
    "        metrics = [\"acc\", \"precision\", \"recall\", \"f1\", \"auc\"]\n",
    "        probs = model.predict_proba(x)[:,1]\n",
    "        #y_pred = probs>= t\n",
    "        y_pred =  model.predict(x)\n",
    "        predict_end = time.time()\n",
    "        predict_time.append(predict_end - predict_start)\n",
    "        y_true = y\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        precision = precision_score(y_true, y_pred)\n",
    "        recall = recall_score(y_true, y_pred)\n",
    "        f1 = f1_score(y_true, y_pred)\n",
    "        auc = roc_auc_score(y_true, probs)\n",
    "        metrics_dict = dict(zip(metrics,np.array([acc,precision,recall,f1,auc])))\n",
    "        out[model_name] = metrics_dict\n",
    "    return out, predict_time\n",
    "\n",
    "def roc_cal(y_true, probas):\n",
    "    fpr, tpr, theshold = roc_curve(y_true, probas)\n",
    "    return fpr, tpr\n",
    "\n",
    "def plot_roc(models_dict, X_test_t, y_test, name):\n",
    "    sns.set_theme(style=\"whitegrid\")\n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "    for model_name, model in models_dict.items():\n",
    "        \n",
    "        probs = model.predict_proba(X_test)[:,1]\n",
    "        fpr, tpr = roc_cal(y_test, probs)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr,label = '{} AUC = {:.2f}'.format(model_name, roc_auc))\n",
    "\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.savefig(\"{}\".format(name))\n",
    "    plt.show()##added by Ziyi\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start fitting\n",
      "GaussianNB_best\n",
      "Current Time = 22:40:20\n",
      "RandomForest_best\n",
      "Current Time = 22:40:20\n",
      "DecisionTree_best\n",
      "Current Time = 22:40:22\n",
      "Logistic_best\n",
      "Current Time = 22:40:22\n",
      "LDA_best\n",
      "Current Time = 22:40:23\n",
      "QDA_best\n",
      "Current Time = 22:40:23\n",
      "KNN_best\n",
      "Current Time = 22:40:23\n",
      "SVC\n",
      "Current Time = 22:40:23\n",
      "Start evaluating...\n",
      "GaussianNB_best\n",
      "Current Time = 22:51:38\n",
      "RandomForest_best\n",
      "Current Time = 22:51:38\n",
      "DecisionTree_best\n",
      "Current Time = 22:51:39\n",
      "Logistic_best\n",
      "Current Time = 22:51:39\n",
      "LDA_best\n",
      "Current Time = 22:51:39\n",
      "QDA_best\n",
      "Current Time = 22:51:39\n",
      "KNN_best\n",
      "Current Time = 22:51:39\n",
      "SVC\n",
      "Current Time = 22:53:17\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24min 7s, sys: 3min 19s, total: 27min 27s\n",
      "Wall time: 23min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import warnings\n",
    "import time\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "sampling_time = time.time()\n",
    "\n",
    "train_records = {}\n",
    "record_df = pd.DataFrame()\n",
    "print('Start fitting')\n",
    "for model_name, model in models_dic.items():\n",
    "    print(model_name)\n",
    "    now = datetime.now()\n",
    "    current_time = now.strftime(\"%H:%M:%S\")\n",
    "    print(\"Current Time =\", current_time)\n",
    "    train_records[model_name] = 0\n",
    "    model_start = time.time()\n",
    "    model.fit(X_train2, y_train2)\n",
    "    model_end = time.time()\n",
    "    train_records[model_name] = model_end - model_start\n",
    "\n",
    "performance_df = pd.DataFrame()\n",
    "performance_csv_name = \"performance.csv\"\n",
    "record_name = \"time_record.txt\"\n",
    "\n",
    "f = open(record_name,\"w\")\n",
    "f.write(str(train_records))\n",
    "f.close()\n",
    "\n",
    "#for model_name, model in models_dic.items():\n",
    "    #thresholds = [0.4, 0.5, 0.6, 0.7, 0.8]\n",
    "    #for t in thresholds:\n",
    "t = 'undefined'\n",
    "performance_result,predict_time = evaluate_performance(models_dic, X_test, y_test, t) ####note: remove indent\n",
    "    #performance_df = pd.concat([performance_df, pd.DataFrame.from_dict(performance_result)])\n",
    "performance_df = pd.DataFrame.from_dict(performance_result).T.iloc[1:]\n",
    "\n",
    "performance_df.to_csv(performance_csv_name)\n",
    "\n",
    "figure_name = \"AUC.png\"\n",
    "plot_roc(models_dic, X_test, y_test, figure_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>acc</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>auc</th>\n",
       "      <th>train_time</th>\n",
       "      <th>predict_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVC_best</th>\n",
       "      <td>0.711397</td>\n",
       "      <td>0.304938</td>\n",
       "      <td>0.807808</td>\n",
       "      <td>0.442745</td>\n",
       "      <td>0.818521</td>\n",
       "      <td>674.642445</td>\n",
       "      <td>382.396422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN_best</th>\n",
       "      <td>0.703152</td>\n",
       "      <td>0.297165</td>\n",
       "      <td>0.799604</td>\n",
       "      <td>0.433298</td>\n",
       "      <td>0.818462</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>97.878076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QDA_best</th>\n",
       "      <td>0.686783</td>\n",
       "      <td>0.283978</td>\n",
       "      <td>0.793286</td>\n",
       "      <td>0.418237</td>\n",
       "      <td>0.784127</td>\n",
       "      <td>0.036154</td>\n",
       "      <td>0.029693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest_best</th>\n",
       "      <td>0.72446</td>\n",
       "      <td>0.313757</td>\n",
       "      <td>0.793003</td>\n",
       "      <td>0.449619</td>\n",
       "      <td>0.831344</td>\n",
       "      <td>1.638924</td>\n",
       "      <td>0.793561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTree_best</th>\n",
       "      <td>0.706873</td>\n",
       "      <td>0.297014</td>\n",
       "      <td>0.779423</td>\n",
       "      <td>0.430122</td>\n",
       "      <td>0.807748</td>\n",
       "      <td>0.027780</td>\n",
       "      <td>0.012291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic_best</th>\n",
       "      <td>0.736131</td>\n",
       "      <td>0.321346</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.453923</td>\n",
       "      <td>0.827844</td>\n",
       "      <td>0.936724</td>\n",
       "      <td>0.004051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB_best</th>\n",
       "      <td>0.724875</td>\n",
       "      <td>0.302289</td>\n",
       "      <td>0.717465</td>\n",
       "      <td>0.425361</td>\n",
       "      <td>0.788483</td>\n",
       "      <td>0.013829</td>\n",
       "      <td>0.022791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDA_best</th>\n",
       "      <td>0.751482</td>\n",
       "      <td>0.323305</td>\n",
       "      <td>0.687099</td>\n",
       "      <td>0.43971</td>\n",
       "      <td>0.807442</td>\n",
       "      <td>0.076563</td>\n",
       "      <td>0.002063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        acc precision    recall        f1       auc  \\\n",
       "SVC_best           0.711397  0.304938  0.807808  0.442745  0.818521   \n",
       "KNN_best           0.703152  0.297165  0.799604  0.433298  0.818462   \n",
       "QDA_best           0.686783  0.283978  0.793286  0.418237  0.784127   \n",
       "RandomForest_best   0.72446  0.313757  0.793003  0.449619  0.831344   \n",
       "DecisionTree_best  0.706873  0.297014  0.779423  0.430122  0.807748   \n",
       "Logistic_best      0.736131  0.321346  0.772727  0.453923  0.827844   \n",
       "GaussianNB_best    0.724875  0.302289  0.717465  0.425361  0.788483   \n",
       "LDA_best           0.751482  0.323305  0.687099   0.43971  0.807442   \n",
       "\n",
       "                   train_time  predict_time  \n",
       "SVC_best           674.642445    382.396422  \n",
       "KNN_best             0.003280     97.878076  \n",
       "QDA_best             0.036154      0.029693  \n",
       "RandomForest_best    1.638924      0.793561  \n",
       "DecisionTree_best    0.027780      0.012291  \n",
       "Logistic_best        0.936724      0.004051  \n",
       "GaussianNB_best      0.013829      0.022791  \n",
       "LDA_best             0.076563      0.002063  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df['train_time'] = train_records.values()\n",
    "performance_df['predict_time'] = predict_time\n",
    "performance_df.index = list(performance_df.index[:-1]) + ['SVC_best']\n",
    "performance_df.sort_values('recall', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df.sort_values('recall', ascending=False).to_csv('best_model.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Without undersample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "import warnings\n",
    "import time\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "sampling_time = time.time()\n",
    "\n",
    "train_records = {}\n",
    "record_df = pd.DataFrame()\n",
    "print('Start fitting')\n",
    "for model_name, model in models_dic.items():\n",
    "    print(model_name)\n",
    "    now = datetime.now()\n",
    "    current_time = now.strftime(\"%H:%M:%S\")\n",
    "    print(\"Current Time =\", current_time)\n",
    "    train_records[model_name] = 0\n",
    "    model_start = time.time()\n",
    "    model.fit(X_train, y_train)\n",
    "    model_end = time.time()\n",
    "    train_records[model_name] = model_end - model_start\n",
    "\n",
    "performance_df = pd.DataFrame()\n",
    "performance_csv_name = \"performance.csv\"\n",
    "record_name = \"time_record.txt\"\n",
    "\n",
    "f = open(record_name,\"w\")\n",
    "f.write(str(train_records))\n",
    "f.close()\n",
    "\n",
    "#for model_name, model in models_dic.items():\n",
    "    #thresholds = [0.4, 0.5, 0.6, 0.7, 0.8]\n",
    "    #for t in thresholds:\n",
    "t = 'undefined'\n",
    "performance_result = evaluate_performance(models_dic, X_test, y_test, t) ####note: remove indent\n",
    "    #performance_df = pd.concat([performance_df, pd.DataFrame.from_dict(performance_result)])\n",
    "performance_df = pd.DataFrame.from_dict(performance_result).T.iloc[1:]\n",
    "\n",
    "performance_df.to_csv(performance_csv_name)\n",
    "\n",
    "figure_name = \"AUC.png\"\n",
    "plot_roc(models_dic, X_test, y_test, figure_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning:\n",
    "## Models:\n",
    "    - GaussianNB\n",
    "    - KNeighborsClassifier\n",
    "    - Logistic Regression\n",
    "    - LDA \n",
    "    - QDA\n",
    "    - SVC\n",
    "    - RandomForestClassifier\n",
    "    - DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'var_smoothing': 9.111627561154887e-05}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example: GaussianNB\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "nb_classifier = GaussianNB()\n",
    "\n",
    "params_NB = {'var_smoothing': np.logspace(0,-20, num=100)}\n",
    "gs_NB = GridSearchCV(estimator=nb_classifier, \n",
    "                     param_grid=params_NB, \n",
    "                     cv=5,   # use any cross validation technique \n",
    "                     verbose=1, \n",
    "                     scoring='balanced_accuracy') \n",
    "gs_NB.fit(X_train, y_train)\n",
    "\n",
    "gs_NB.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(gs_NB.cv_results_).sort_values('rank_test_score').to_csv('gaussian_tune.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "315f3b2f-d10d-49b2-b582-b0101d94343c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GaussianNB': GaussianNB(),\n",
       " 'GaussianNB_var_9.111627561154887e-05': GaussianNB(var_smoothing=9.111627561154887e-05),\n",
       " 'GaussianNB_var_0.1': GaussianNB(var_smoothing=0.1),\n",
       " 'GaussianNB_var_0.01': GaussianNB(var_smoothing=0.01),\n",
       " 'GaussianNB_var_0.001': GaussianNB(var_smoothing=0.001),\n",
       " 'GaussianNB_var_0.0001': GaussianNB(var_smoothing=0.0001)}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_smoothing_lst = [9.111627561154887e-05, 0.1, 0.01,0.001,0.0001]\n",
    "models_dic = {'GaussianNB': GaussianNB()}\n",
    "for value in var_smoothing_lst:\n",
    "    models_dic['GaussianNB_var_' + str(value)] = GaussianNB(var_smoothing = value)\n",
    "    \n",
    "models_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "7cd7b5e4"
   },
   "outputs": [],
   "source": [
    "def evaluate_performance(model_dict, x, y, t):\n",
    "    out = {\"threshold\": t}\n",
    "    print('Start evaluating...')\n",
    "    for model_name, model in model_dict.items():\n",
    "        print(model_name)\n",
    "        metrics = [\"acc\", \"precision\", \"recall\", \"f1\", \"auc\"]\n",
    "        probs = model.predict_proba(x)[:,1]\n",
    "        #y_pred = probs>= t\n",
    "        y_pred =  model.predict(x)\n",
    "        y_true = y\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        precision = precision_score(y_true, y_pred)\n",
    "        recall = recall_score(y_true, y_pred)\n",
    "        f1 = f1_score(y_true, y_pred)\n",
    "        auc = roc_auc_score(y_true, probs)\n",
    "        metrics_dict = dict(zip(metrics,np.array([acc,precision,recall,f1,auc])))\n",
    "        out[model_name] = metrics_dict\n",
    "    return out\n",
    "\n",
    "def roc_cal(y_true, probas):\n",
    "    fpr, tpr, theshold = roc_curve(y_true, probas)\n",
    "    return fpr, tpr\n",
    "\n",
    "def plot_roc(models_dict, X_test_t, y_test, name):\n",
    "    sns.set_theme(style=\"whitegrid\")\n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "    for model_name, model in models_dict.items():\n",
    "        \n",
    "        probs = model.predict_proba(X_test)[:,1]\n",
    "        fpr, tpr = roc_cal(y_test, probs)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr,label = '{} AUC = {:.2f}'.format(model_name, roc_auc))\n",
    "\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.savefig(\"{}\".format(name))\n",
    "    plt.show()##added by Ziyi\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "bd5bac40-fd5c-4e49-bbe7-25bdb08c37b1",
    "outputId": "d5e294f9-c489-4df9-adfe-1a9f6ebf5480"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C+t9ly5ZNct5sIYQQIr2Fh4Xz7cwZaPqDuDsMRjTNBUUzYpcjF4WKF8cjfyFKV6mIu6fH81/sJZiePOHqwsWE/Haa16ZMxK1SRezd3FLltWUWGyGEENmGpmlEmCKJscQSFBmCf3ggt4Lv8mjDCez0IBQFNN0eo5KXLqOG4/5Pe+y0Fnr+An6+CzCFPqFYvz64VkzdKbCl2AshhMiQNE0j1hJLSPQTwmMjiDHHEGGKwmQxYbKYeRQZhKPREU3XrP8sWtz/PwjzJ6chBw+DH2AMsxD++DFaWDR2MTrOkSr2JhVFV1AARY3GXo0EBRTNi77jx+PskrhpVFq5u3krt779Hsf8+ag4dyYu/zSzSk1S7IUQQqSYrutYdI1oczQx5lhMmhmzZuZu1ENyBFzFrJl5HP0EVVEwWcyYNBOxFhN3Qh9gQEGPiMYUGcWj0Ee46I5o0SaiwiJQzQoGs44araNY4v7Zm1W0f85HU3QVUFB18z8nwAGKGXQ17vw4VUdHR1Vi0DFip0QRjYL7PyfJJbpYzfD//6lZnFEteej44XDyFiyQxlswMUcvTzwbNqD4u/0wJNEl8r+SYi+EENlERGwkkaYookzRRJmjMVlMBEaGxN2OjQKzBVNkFIEBDzHGKJhDwjFFmTBHx2KJNqGbNVQNDJoOxI2KQcegaaBYuKHvtxZiRYkB3R5UEwpmdEXFokRbszx9xDvJMbQh8V26bkRHBV2Jy6DGomtOKP/8T9cdUHUdBXd0xYKd4oiGRg6DI6q9ETsHBxxdcpHD3Z36rZvjlNMlNTbtCws68SumsDDyvdWEvHXrkLdunTRdnxR7IYTIRDRdw2QxE2WKItIczeOoJ5g1M7GWWAIjQ7gZfBtVV3gSEozmH47+JAbT42iMsRZUTUHViPuHhqopoJpBMYGioaABZhRFx/TP+ow8o1D8M4Gb/k/BxQCKosUVYt0+7j4MKEokmp4TBRMGzQ5NyY2RSBTFBYOqY1AUUFR0LRZ7Jzfs7Qw4ODnh5uWFvZMTDs4u5C2YDwcXZ3LkcMQhRw4c02jkm14sMTHc/PpbHu7ZS86yZfBq0gjlJWbEe1FS7IUQIoPQdZ0oUzT3w/wJjnpMWHQY0WFPCHlwnwfX7xATGIFdrIK9SUFX4kqqooOiWFAwx+3WVswoWHBUtGevJK4Oo+sKikFHt+RE0R1RLSqKakBVVAyKEVQD6LE4OeXCYO+Ee153PAoXws0rL15Fi2LvlCNBp9L4eVNE0iLv3OXK3E+JvHWbAm1aUaR713Qp9CDFXggh0pSu64TGhPEw7BGPo0OJNscQERtJlCma4Dv3ibrujykoAnO0jtFiwaAbUIlG0RVQzKiGcABy/PMPsP7l1nWV+GG2ruVAteTAgAFVVVEUFXs7O+zsHHB2c8fJNReuXnkpULIkngUL4OCUuUfImU3s41D+HPkxBgd7yk34BPdqVdN1/VLshRDiP9A0jYfhAUSYorj1+B5PokK58cfvcD8KwmKxWMCoaRgtCrpqse4uV5RoFMWCgacOTaug66DjgIIJNBdUswe6AjkcXHB2dcUltzuFypambOVKUrAzAc1sRjUasXdzpfi7fXGvUgX73O7pnkOKvRBCJCM46jG3H9/nQsAVHkeHcv/BTdTAGAxBseQMUTBqGqqmgyEaVY0m0aSx/wzAdd0IFhcUjCjkwF61x86g4J6/CF7FClGwdCk8CxfEIZMflxZxwq744ffZAkq+PwjXCuXxatzIZlmk2AshBBBrjiU46jF3nzzk6sNr3D11EdPDCByjYzFqFhQlBl3RURQzBRTz/z8x/hi4qqJYcqFodqiKA44OuShYoiQ5vTzIUyAfRcqWyrJzx4uEdE3j3uat3PphDQ5586JmgM9dir0QItt4HP2EiwF+BIYHc+8vP8L9bqNGWFDNKqChoKIoMahKBDkULe4YuQq6YgQUFM0IujNGHFFUFdec7ngULUKFN+rjWTCfbd+cyBBiQ0Lw811I6J/nyFO3DiXfG4gxHSfoSYoUeyFElubvf59ff97OoU0/YDQZ4o6FGyJRFdP/n/BmwHrZmK47olo8sDfkIE9uFxp060KeQuk/0YrInAKPnSDs0mVKDB6E15uNX7pLXWqTYi+EyDLCosI4emgvN0/9SWxkBOg6BkMoQNxxdCNouiOKORd2BjtcnBzxKlqK1+rVJn+JYjbNLjIvzWQi6u49nIsVJX8zH3JXr4pjvoy1p0eKvRAiU4o0RXH2wm+c3XuQ2CcRYDFhUMOsPcQN/1y+bNFyYlByUrJkGep3bo9TBtilKrKOqAcP8JvnS/RDf6otW4LRxTnDFXqQYi+EyAQ0XePPmxc4s+1HIgKCsFgsqJgwqnGjdiOAIW6Oc0VxIlfO3NRq3pLi5ctgtDPKhC8iTQQc+oVrS5ehGAyU+mBwhjg2nxQp9kKIDEfXdR4E3mfLsuVooY/RVTOKEoWi6HHXpaug6XboJi/cnXJQ8X/1Kfl6dZxcE7U7ESLVaWYz1xYvJeDAIXKVe5XSwz/EwSP1+9unJin2Qgibexwewk9bNnL3778xRJpAjUJRYlAUHcUIWHKgaLlxMjpTuV4dXqtfF3sX5wxz8pPIXhSDAV2Hwp06ULhTBxTDMzr2ZDBS7IUQ6S7SFMW586f5dduPEBmDooahqjHYwf9PBWt2xeCYB++2rShetZIt4woRt7dp1x7cKlXEqXAhSn34fqb6sinFXgiR5sJjI/j515+4vec4mllHJQZFjcCgaGAEiyU3dmY3nHPmotIbDahQv66tIwthZXryhKsLFxPy22kKtG5JsT69M1WhByn2Qog08jDgLttWrCQqNByVaFRDeNyssfF7PM25yOVWhPrtmlP01TK2jCpEkkLPX8DvswWYnjyhWL8+5G/RzNaRXooUeyFEqtB1nd9uneXk6k0QEoVqDAbA+E9x1y25yOuchxYffkhON1cbJhUiZULO/M7FqTNwzJ+fiuPH4FK8uK0jvTQp9kKIl/YkOowzV09zYfdBLEHhqEoMqhqJblDQzG7YGxx4vUlzqjb5n62jCpFiuq6jKAquFStQuFMHCrZphSGTNyeSYi+EeCGPIoI4+NvPPNjzG6oehWKIRCFuBK9rDjiohWnctRvFX5Nd8yLzCTpxkjsbNlF+yiSMLs680qWTrSOlCin2QohkxZpj+fnXn/j78G9oIVEYDMEYVDOa5ohicSKH0YWqPq2oIifWiUzKEhPDza+/4eGeH3EpWQJLVFSGniTnRUmxF0I8k1mzcPzCcU5t3INddCQGQ2jcCXZGsGiu5HMtTMfRwzPdWclC/Fvk7TtcmfcZkbduU6BNK4p074pqZ2frWKlKir0QwirWYuLAqZ/5a9dh7GKiUQyhOCqgqQ5YTIVxc3Wnfjtvir1WztZRhUg1N1d9g+nxY8pNHId71Sq2jpMmpNgLIXgU8pC1cxegmSNR1XAc/rn+HXMuPFy8aDViKDlcnGwdU4hUYw6PQDObsXdzpcTg91AUBfvc7raOlWak2AuRjR06/CPndu5DNQajAIriABZXcufMTePevfF6pZCtIwqR6p5cvoLfp/NxKlyQchPG4ZAnt60jpTkp9kJkM+GxEezZtZkHx8+CIQzVaELTHbBXnOg9cRqOTjKCF1mTrmnc27yVWz+swSFvXgp36mjrSOlGir0Q2cRV/7/58bsfMPtHYjQGoBpB1414eFSi0/Chto4nRJqKfRyK32fzCf3zHHnr1aXEewMwOmeds+2TI8VeiCzMYrHw8/YNXD5+9p9mM5EYDKBZ3ClTvjrePbvaOqIQ6UIxqMQGBVHy/UF4Nmmc7a4ikWIvRBb0x/0LHFq3DsODUFRDGAYjaJac2CkFqO7dnGpv1LF1RCHSnGYy8XDPj+Rr6o1dzpxUWeibKdrRpgUp9kJkEeGxEWzZvpaQkxfAEIOdGgUG0HR7ipWsTct+vW0dUYh0E/XgAVfm+hJx7RoOHh7kqV0z2xZ6kGIvRKYWGBnMts0/8OT8LVTdHDfxjR3omj2qxYPir5bH552eto4pRLoKOHSYa0uXoxqNlB39EXlq17R1JJuTYi9EJqPrOj+f+JE/d+3DLkZFNQZhp/7zmKUApUsUx3tAX9uGFMJGbq9Zx52168lV7lVKD/8QBw8PW0fKEKTYC5FJxJhj+e6bzwm/cgej4TGOAHagmz0pX60Ob3RubeuIQthcntq1ACjc8e1svdv+36TYC5HBaZrG2p3f8ejIOQzG4Ljucjp4uZah2eD3cMmVy9YRhbAZXdd5sHMXkXfuUfK9ATgXLYJz0SK2jpXhSLEXIoPSdZ0T107x64r12KnBGIygm3JTtU4D6rZrZet4Qtic6ckTri78nJDfzuBeozqayZTlGtikFin2QmQwmq7xzb5vCD50ETstAjs1Cs2Sk6KFy9Pqg3dtHU+IDCH0/AX8PluA6ckTivXvS/7mTbPdtfMvQoq9EBnE4+gnrP9uOdFXHqIYQ3BQNDTFATstP13HfEROdzdbRxQiQzBHRnJ51lzsXHPx6vixuBQvZutIGZ4UeyFs7GFEAJ/OHochKAqjIfifS+ec8HQtTZvhA7F3cLB1RCEyhNiQEOzc3DA6OVFuwic4vVIYQ44cto6VKUixF8JGboTcYdcXX6E9foSDGgEG0M1eVKtXnzqtm9k6nhAZStCJk1xdtIQi3bqQv3lTcpYpbetImYoUeyHS2e93zvHz12uwDzdjUANBccDekp+6rVvxWt1ato4nRIZiiYnh5lereLh3Hy6lSuJWtYqtI2VKUuyFSCePIoJYPfNTVLM/TooGBrCYC1D+f3Vo3Kq5reMJkeFE3r7DlXmfEXnrNgXbtuaVbl3kbPuXJMVeiDSm6zorl8T9wTIYnqDhiKOei7ZDhpO3gBdnzpyxdUQhMqTY4GBMT55QbuI43GVE/59IsRciDV2/f50tn6/AXnuIagCDpQC9xoySM+uFSII5PILHf54jb93auFWuRLVlSzDISar/mRR7IdLAjeA7bFuwFDUmGAclBk1zpWHztynfoJ6towmRYT25fAW/T32JDXlMzrJlcMiTWwp9KpFiL0Qq27RnLXcOnsJoCEHTnXBUXqHz6GG45HazdTQhMiTdYuHu5q3cXr0Wh7x5qTB9Cg55cts6VpYixV6IVKLrOit8ZxH7yA+jAbC40mvkOFw989o6mhAZlq7rXJo+k5Azf5C3Xl1KvDcAo7OzrWNlOVLshUgFj8KC+H7mTIzaY3SMuLu9So/Rw20dS4gMT1EU3KpWJU/tWng2aSxT3qYRKfZC/Aeh0U/46vslqH7+2Bkeo+k5qNuoA9W8G9o6mhAZlmYycevb78n1Wjny1KpJgRYyiVRak2IvxEvQdZ2Nv23n9uajOCiBcbPfWdzpP2k6OZxk+k4hkhJ1/z5X5vkSce06qr09eWrVtHWkbEGKvRAvKCI2ksWfTscx5AkOhnA0LQflytenSY/Oto4mRIYWcOgw15YuRzUaKTv6I/LUlkKfXqTYC/ECImOjWDZxEk48QleN5FIK03P2FFvHEiLDC/3rL676LiRXuVcpPfxDHDw8bB0pW5FiL0QKbfpmJXf+uoC94TG62ZXWfQbzyqulbB1LiAzNHBmJ0cmJXOXKUeajkeSp9TqKwWDrWNmOausAQmR0mqaxbP5c7l86jtHwGMXsSt9xk6XQC/Ecuq5zf/tOTvcbSOSduyiKQt66taXQ20iajux37NjB0qVLMZvN9OrVi27duiV4/K+//mLChAmYTCby58/P3LlzyZUrV1pGEuKF3Lx9na2fL8WgBqFjpFixurR4t4+tYwmRoZlCQ7m6cDEhp8+Q+/Ua2Lm62jpStpdmxd7f3x9fX182b96Mvb09nTt3pmbNmpQsWdK6zPTp0xkyZAgNGjRg1qxZrFy5kmHDhqVVJCFSTNd1Fs2aDI8fYTREYjF70KhlWyrUr23raEJkaI/PncfvswWYw8Mp/m5f8jVrKtfOZwBpVuyPHz9OrVq1cHNzA8Db25u9e/fy/vvvW5fRNI2IiAgAoqKicJVvfyIDOH3gAMf27EE1BKIrDrjYFaP3zAm2jiVEphD86ymMTjl4beI4nIsVtXUc8Y80K/YBAQF4PHW2paenJ+fOnUuwzOjRo+nTpw8zZswgR44crF+/Pq3iCJEix3/ex+mftmIwRqFZ8vD2wMEUKF7M1rGEyNCiAwLQHvoDULRXD/Qe3TA4Oto4lXhamhV7TdMS7LrRdT3B7ejoaD755BNWrVpFxYoV+frrr/n4449Zvnx5itdx4cKFVM0sEstOvdb/+vscIWf+xGCIwpCjCLWbt+RBSDAPzgSn+bqz03a2FdnGacNy6TKmHbtR3Fw57eUpu+wzqDQr9vny5eP06dPW248ePcLT09N628/PDwcHBypWrAhAp06dWLBgwQuto3z58jhI+8M0c+bMGapVq2brGOli77atPP79NwyGKBzVgvSbMCnd1p2dtrOtyDZOfZaYGG6sXIX/j/twKVWSWO83qV69uq1jZVkxMTH/aYCbZpfe1alThxMnThAcHExUVBT79u2jfv361seLFCnCw4cPuX79OgD79++nQoUKaRVHiCR9s2wRfif2oCjROBoL02/6NFtHEiJDiw0O4dzIj/H/cR8F27WhwsxpqLndbR1LPEeajey9vLwYNmwYPXv2xGQy8fbbb1OxYkX69+/PkCFDqFChAjNnzmTo0KHouk6ePHmYMWNGWsUR4pm+nDKZqMi7KGjky1eFDkM/sHUkITI8O9dc5ChcmKLv9MK9ahVbxxEpkKbX2bds2ZKWLVsmuG/FihXW/27QoAENGjRIywhCPNOju/dYu3AOiuEJYKREuYY069nd1rGEyLDM4eHc/OY7XunaGXt3d8p+NMLWkcQLkOlyRbZz88JFtn+7FNUQjmbJRZMu71CuSmVbxxIiw3py6TJ+n/oSGxyCW+VK5K1bx9aRxAuSYi+ylWt//cWub5ejqBFYDPn4YPp0VFVmjRbiWXSLhbubt3J79VocPPJSYeY0cpYpbetY4iVIsRfZxuovFhJ48y9UQyzo+fhg6nRURQq9EEm5s2ETd9asI2+9upR4bwBGZ2dbRxIvSYq9yPJ0XWfp3KlYgu8AKk5upejz8VhbxxIiw9JMJlQ7O/I3b4qjlycebzSQ6+czOSn2IkuLiAhj5eQpcVPf4kCVWs35X5uWyT9RiGxIM5m49e33PLl0hQozp2KXMyeeDd+wdSyRCqTYiyzL/8F91vnORjU8QbO48nqD5tRq/qatYwmRIUXdv8+Veb5EXLtO/uZNQddtHUmkIin2Iku6E3CbLfNnoBpiUMx5GDRjJnZ2draOJUSGFHDwENe+WIFqZ6Ts2I/JU/N1W0cSqUyKvchyHoQGsHHeYuwMMajm3AyaM1eONwqRBM1k4u7GLbiUKE7p4UNxyJvH1pFEGpBiL7KU4ycPcXrzRuwMERDryXufzrZ1JCEypPDrN8hRID8GR0demzIRezdXFIPB1rFEGpHrjkSWsefHLZzesh7VEIHRlIv+0ybbOpIQGY6u69zfvpNzo0Zze806ABzy5JZCn8XJyF5kCV8t9SX85hVUNYac9iXoNXOcrSMJkeGYQkO5uuBzQs78Tu6aNSjUvp2tI4l0IsVeZHorpkwiOvIuiqLg5lyK7uPkGnoh/i3sih+XZs7BHB5O8Xf7ka+Zj5zLko1IsReZ2tLpMzBH3kbXnaj3ZnuqNmlo60hCZEh2bq44eOTltYnjcC5W1NZxRDqTY/Yi0/p65kzMYX+j6zmo366bFHoh/iXaP4BbP6xB13UcvbyoOGemFPpsSkb2IlP6ZtYMIp5cRdedqP1WRyrXrG3rSEJkKIHHjvP34qWg6Xg2bECOAgVkt302JsVeZDqrFn5OeOhVdN1IhVpvUaNxA1tHEiLDsMTEcGPl1/j/+BMupUpRZuRQHPPls3UsYWNS7EWmsm/LJsLu/4miQP6CFWjYtrWtIwmRoVyaNpPQc+cp2K4Nr3TtjCozRwqk2ItM5KftW7h88iCKopGvSE3eHjjQ1pGEyBB0XQddR1FVCnVoT8G2rXGvWsXWsUQGIsVeZAqrfD8jzP8iimLB2f4VKfRC/MMcHs7fny/FqcgrvNKlE24VK9g6ksiApNiLDG/phEmYY++g63bkf6UaHQYPsnUkITKEJ5cu4/epL7HBIeQsW8bWcUQGJsVeZGjfzZ+HOfY2iqJTuU4L/teqha0jCWFzusXC3U1buL1mHY6eHlSYNZ2cpUvZOpbIwKTYiwxr06IVhDz0Q1V0qtXrTO3m3raOJESGEHn3HnfWridvvTqUGDQAo5OTrSOJDE6KvciQdqz4ivv3TqGgU6TU/6TQCwFE3r6D0yuFcS7yCpXnzyNH4cJy7bxIEZlBT2Q4x08d5sbfZ9Ax4FG8Gi379LF1JCFsSjOZuP7l1/zxwVBC/jgLgNMrr0ihFykmI3uRoWza+C33Tx9FVU24uJSi87tyMp7I3qLu3efKp75EXLtO/ubNcH2tnK0jiUxIir3IMHZuWM2D3w+iKOBoKEzvT6R7ncjeHv1yhL8Xf4FqZ6Ts2NHkqVnD1pFEJiXFXmQIJ347zPXTx1AUldxupek2+mNbRxLC5rRYEy4lilN6+FAc8uaxdRyRiUmxFzZ3/vzv/LZxI6oaiYN9ESn0IlsL//sa0f4B5K1bG8/GDfFs2ADFYLB1LJHJSbEXNhUYGsjB775BUcNxNLxCv8mTbB1JCJvQNY37O3Zy69sfcPTyJE+t1+OKvBR6kQqk2AubiQgLY/W0WajGJ6jko9+0ybaOJIRNxD4O5e+FnxNy5ndy16xByfcHy2hepCop9sImTCYTK6dORDWGoJnzMnDmVFtHEsImzOHhnB06AnN4OMXf7Ue+Zj5ySZ1IdVLsRboLiwrnq4lTMRhCsFhy896MGRiN8qMoshdd11EUBaOLCwVatcC9SmWcixW1dSyRRcmkOiJd3bl7g5UTp2IwBGAxe/Du1OnYSb9tkc1E+wdw4ZMJhPldBaBQuzZS6EWakuGUSDe//X6c42vXYTQ8wWL2YtCUSdg7ONo6lhDpKvDYcf5evBR0MD1+bOs4IpuQYi/SxbFTBzmzcQsGQxiq7sn7c2fZOpIQ6coSE8ONL7/Cf9/PuJQuRZkRQ3HMl8/WsUQ2IcVepLmfD+3hr90/YjCEYaAgg2ZNs3UkIdJdwM/78d/3MwXbteGVbl1Q5TwVkY7kp02kqcs3/uLi7j0YDGE4UIj+cta9yEZ0XSc2MAgHj7zk8/HGuUQJcpUtY+tYIhuSE/REmnkUFsSPS75CNYThYCwihV5kK+bwcK7MnsefIz7C9OQJisEghV7YjIzsRZqIiYlm9ZQZGIzBYHan/8xJto4kRLp5cukyfp/6EhscQpEe3TC6uNg6ksjmpNiLVKfrOitmTcdgDEYzefHu5Im2jiREutA1jbsbN3N7zTocPT2oMHsGOUuVtHUsIaTYi9S3Ys5MiL6LpuWg28jhODrnsHUkIdKHohD+9zXy1qtLiUHvYnRysnUiIQAp9iKV7dy8hujgO6gqVH39LfLk87R1JCHSXPBvp3EqXAjHfPkoM2o4itEoU96KDEVO0BOp5sTJQ1z/9QiKEoOHewXqtW9j60hCpCnNZOL6l19xadpM7qzfBIBqZyeFXmQ4MrIXqeJW4B1Obd6Kqkbj7l6OTh8Nt3UkIdJU1L37XJn3GRHXb5C/eTOK9u5h60hCJEmKvfjPdF1nx8wFGOxDMVjy0/3jkbaOJESaenLxEn9NnoZqZ6Ts2NHkqVnD1pGEeC4p9uI/+2LqZLAPQrPkot/kCbaOI0Sacy5ejLz16vBKl8445M1j6zhCJCvZY/YRERFMnjyZXr168fjxYyZMmEBERER6ZBOZwIGd2zFH3kPTHOn64cc45pDGNiJrCrv6NxenzcASHY3B0ZFSHwyWQi8yjWSL/bRp08iVKxdBQUE4ODgQHh7OhAkyehNw/twZLhzZB1goVb4ueQsWsHUkIVKdrmnc27qd86M/IeLGLWICHtk6khAvLNlif+nSJYYNG4bRaCRHjhzMmzePS5cupUc2kYGZLWYOfL8WVY3A2bEYTXt0t3UkIVJd7ONQLk6dwc2vv8G9WlUqz5+H0yuFbR1LiBeW7DF7VU34fcBisSS6T2Q/y+ZOR1WD0Syu9Jk03tZxhEgT1xYvJfT8BYoP6E++pt5ySZ3ItJIt9jVq1GDu3LlER0dz5MgRfvjhB15//fX0yCYyqANbtmB5fA90e+p6t7N1HCFSlWY2o8XGYnRyoli/d3glKgrnokVtHUuI/yTZIfrIkSNxcnIiZ86c+Pr6UqZMGUaPHp0e2UQGdeHELyiYKVXjDao1rm/rOEKkmmj/AC6MHc/V+QvRdR1HLy8p9CJLSHZkf/jwYQYPHszgwYOt923dupU2bdqkZS6RQa2cPBnV8BgsHjR9u5Ot4wiRagKPHefvxUtBhxItW8gue5GlJFnsDxw4gNlsZs6cOei6jq7rAJjNZhYtWiTFPht6cO8eEVEPQXem58cf2TqOEKnCEhPDjS+/wn/fz7iULkWZkcNw9PKydSwhUlWSxf7SpUucPHmSoKAgvv322/9/gtFI79690yObyGA2LvwcVY3G3qk4bnny2jqOEKnCEhVF8G+nKdi+La907YxqlLnGRNaT5E91/K77H374gW7duqVnJpEBHf95DyiBaBZX3hkj52yIzE3XdYJOnCRPzdexd3Oj6uKFGJ2dbR1LiDST7FfYDh068NNPP1lnzbNYLNy+fZthw4aleTiRMei6zqmfDmJUzRQqXhE7OztbRxLipZnCwvj786UEn/yVUkM/wLPhG1LoRZaXbLEfNmwYd+7c4dGjR5QrV44///wzxZfe7dixg6VLl2I2m+nVq1eiPQTXr19n4sSJhIaG4uHhwWeffYarq+vLvRORZhZPm4JRfYRmyUWbAe/YOo4QL+3JxUtc+XQ+ppAQir7TC48GcjWJyB5SNIPe5s2bady4MWPHjmXNmjWEhoYm+8L+/v74+vqyevVqtm7dyrp16/j777+tj+u6zqBBg+jfvz/bt2/n1VdfZfny5f/t3YhUt2nHWrSIu2iaI617D5IzlEWm9WDPj5z/ZAKq0UCF2TMo2KYVikwQJrKJZH/SPT09MRqNFC1aFD8/P0qVKkVYWFiyL3z8+HFq1aqFm5sbTk5OeHt7s3fvXuvjf/31F05OTtSvH/fNeuDAgXJuQAYTbo7k7i+/oWDm1fINKFKurK0jCfHSXEoUx6NBfSr5ziNnqZK2jiNEukp2N76TkxM7duygbNmyrF+/nuLFixMZGZnsCwcEBODh4WG97enpyblz56y3b9++Td68eRk7diyXLl2iePHijB8v065mFLquc3rnDhyMwWhmD97s0dnWkYR4YcGnfiPs6t9QtjQ5S5ciZ+lSto4khE0kW+wnTJjA+vXrGTVqFBs3bqR79+4MHz482RfWNC3BLl9d1xPcNpvNnDp1iu+//54KFSowf/58Zs2axaxZs1Ic/sKFCyleVryYX47vxt4UgqbnoEKjNzhz5oytI2Vpsn1Tl242Y/75AJZTp1HyeWFfophs43Qg2zjjSrbYFy1alI8+iptAZf78+QAJjr0nJV++fJw+fdp6+9GjR3h6elpve3h4UKRIESpUqABAixYtGDJkyAuFL1++PA4ODi/0HJG8PYe2Y7h7HxSdSlXfpEHTZraOlKWdOXOGatWq2TpGlhF59x5+83yJuXGD/C2bU7RXD/44d062cRqTn+O0FRMT858GuEkes7979y4jRoxg6tSpREVFARAREcGsWbNSNHtenTp1OHHiBMHBwURFRbFv3z7r8XmAKlWqEBwczOXLl4G4Gftee+21l34jInVcD7zF1V0HUdVonBxK0KBTW1tHEiLFLDExXBg7npjAQF4dN4bi/fqgyqWiQiQ9sh87diylS5cmICCAZcuW0ahRI4YMGYKzszNffvllsi/s5eXFsGHD6NmzJyaTibfffpuKFSvSv39/hgwZQoUKFVi8eDHjxo0jKiqKfPnyMWfOnFR9c+LF7fRdgmp8jMHkSeW337J1HCFSxBITg2pvj8HBgZJDBuNcrCgOefLYOpYQGUaSxf7hw4d8++23REdH065dO9avX0/v3r3p06cPxhROJ9myZUtatmyZ4L4VK1ZY/7tSpUps3LjxJaOL1LZ92xoUSyCa7kyv8WPxS8HhGiFsLezq3/jN86VQh/Z4NWlE7uqyK1mIf0uyajs5OQHg6OhIaGgoc+bMoV69eukWTKSvi7cvcfPYSVSDRvFS1ckpkxuJDE7XNO5v38mt737Azs2NHAXy2zqSEBlWiobouXPnlkKfhUWbotn3+dcYDU9wUAvRvG9vW0cS4rliH4dydcEiHv/+B7lr1aTk+4Owy5nT1rGEyLCSLPZPXyaX0t32InP6/JtPcVCD0c256DNrkq3jCJGscD8/nlz4i+ID+5PPx1tmdhQiGUlW8StXrlC1alUAoqOjrf8df73877//nj4JRZo6cHo/9n7+oELVWk0wGAy2jiTEM2lmM2FXruD62mvkfr0G1ZYtwT63u61jCZEpJFnsf/rpp/TMIWzAbDHz5/ofsTOEkdOhFHXbt0z+SULYQLS/P36fzif872tUXfo5jl6eUuiFeAFJFvuCBQumZw5hAysWzMKoPkI356bXzLG2jiPEMwUePcbfi78AoPTwD3H08kzmGUKIf5OD8dnUsaMHMfnfQ1GMNG7b0dZxhEhE13Wuf7Gch3v3kbNMaUqPGIqjl5etYwmRKUmxz4YCQwI5s30bqiGavG7lKVenpq0jCZGIoijYubtT6O12FO7SCVVOFBbipclvTza0bsHnqIZQDBSi88cjbB1HCCtd13m490cc8+XDvUplXukse52ESA3J9rN/9OgR7777Lt7e3gQGBtK3b18CAgLSI5tIA6f+PI456hGabk+P0R/ZOo4QVqawMC7Pmsv1L1YQ+MsRW8cRIktJtthPnjyZJk2a4ODggKurK2XLlmXcuHHpkU2kssCIYE58vwmDGomTXSFcXGUSEpExhP51kbNDRxJy+gxF+/Si5AeDbR1JiCwl2WJ/7949OnbsiKqq2NnZMWrUKB48eJAe2UQqijZF8+2s2RiMwagWT/pNHW/rSEIAcXPbXxg3EdVopMKs6RRs3QpFTfZPkxDiBSR7zF5RFDRNs94ODw9PcFtkDl9+/RlGUwia7kjXUaNsHUcIdIsFxWDApWQJivXphWfjRhj/6ckhhEhdyX59fuuttxg5ciRhYWGsXbuWXr160bRp0/TIJlLJg7AANL8gFEy8+loDcnvktXUkkc0F/fobv7/3AdH+/iiKQoGWLaTQC5GGkh3ZDxw4kK1bt6JpGsePH6dTp0506NAhPbKJVLJl1QoUYzCqJQ9v9uxs6zgiG9NiY7m56jse7NqNc/Fi6LKXUIh0kWyxX7t2LS1atKBNmzbpEEektuCwEMx3HqKoCnWatrZ1HJGNRd69h988XyJu3CB/y+YU7dUD1c7O1rGEyBaS3Y3/66+/0qRJE8aOHcvZs2fTIZJITas//RTVEI4D+ajS6H+2jiOysQc7dxETGMir48ZQvF8fKfRCpKNkR/a+vr6Ehoayc+dOpk2bRnR0NB06dKBXr17pkU/8B0f/Oo4WFQyKAy3e7W/rOCIbMkdGYgoNJUf+/BTt3ZNCHdrjkCePrWMJke2k6PoWV1dXOnXqxIABA3BycmLFihVpnUv8R9GmaM58swlVjSKPY2EKFC9m60gimwm7+jd/DhvJ5Zlz0C0WDI6OUuiFsJFkR/YXL15k06ZN7N27l3LlytGvXz8aNWqUHtnES9I0jU/nTsHVEAzmPHSb+YmtI4lsRNc07m/bwa3vfsA+tzslBg1AMRhsHUuIbC3ZYv/ee+/Rvn17NmzYQIECBdIjk/iPDlw6TM7H4eiqSvMefW0dR2Qj5vAIrnzqy+Pf/yBP7ZqUfP89jC4uto4lRLaXbLE/ePAgiqKkRxaRCjRd49y3u7E3hJHTWJziFV+1dSSRjagO9mgxMRQf+C75fN6Svx1CZBBJFvsuXbqwZs0aqlatmuAXVtd1FEXh999/T5eA4sXs2b8VOyUEzexGzxnSw0CkPc1s5t7mreTz8cYuV07KT5ss090KkcEkWewXLFgAwM6dOxM9put62iUSLy3GHMu1vcdQ7SyULFlRRlUizUX7+3Nlni/hflexc81FPu+3pNALkQEl+Vvp6ekJwMSJEylYsGCCf8OHD0+3gCLlPp//KapdMLrZnaYD3rF1HJHFPTpyjLNDRxJ17x5lPhpBPu+3bB1JCJGEJEf2Q4YM4caNG9y5c4eWLVta7zebzdjb26dLOJFyVwKvYfR/hG5Q8Onc09ZxRBZ3f+dubqxYSc4yZSg9YiiOXp62jiSEeI4ki/1HH33EvXv3GD9+POPH/387VIPBQMmSJdMlnEi57UtXkMMYgsHsSalqlW0dR2RR8efs5K1XB0tkJAXbtUE1JnuerxDCxpL8LS1UqBCFChXixx9/lGO/Gdy6fWtxiAhBw46WMrOhSAO6rvNw916CfztNufFjsXdzo3DHt20dSwiRQnI2fiYXHPWYBz//htEQi1fuShQpX87WkUQWYwoL4+9FSwj+9RTu1apgiYmRdrRCZDIvdTa+yBjMmoWli+b8M1NeXjqMGmrrSCKLCf3rIn6fzscUGkrRPr0o0LKFnG0vRCaU7Nn4uXPn5tGjRxQsWJBffvmFxYsXy279DGLt4XXkDHqCptvxVhfZfS9Sl26x8PfnS1Dt7agwazoFW7eSQi9EJpXsmTVjxoyhcOHCqKrKl19+SZs2bRg/fjwrV65Mj3wiCX53/QjacwqDIQIXu5KUrlre1pFEFhETFIRdzpyo9va8+slo7HPnweiUw9axhBD/QbJf0+/cucOIESM4ePAgbdu25YMPPuDx48fpEE0kJTQ6jL3zl2EwhOKgFaD3FGl0I1JH0K+/cfbD4dz6fjUAToUKSaEXIgtIttibzWYAjh49Sq1atbBYLERGRqZ5MJG0r6dMQ7ULBrMr70yfbOs4IgvQYmO5vnwll2fMwsHDQybIESKLSXY3fpUqVWjWrBkGg4GqVavSq1cv6tSpkx7ZxDOsXf0lBi0ETXem37ipGOUaZ/EfRd27z5W5nxJx4yb5W7agaK/uqHZ2to4lhEhFyVaK8ePH88cff1C2bFlUVaVv377Ur18/PbKJf4mMjcL/7EVU1ULFqg1xcs1p60giC9A1DXNEBK+OG0PuGtVtHUcIkQaSLfYGg4GAgAA2bdqEyWSibt26qHJGrk18u3wBBkMIqpaPNzq1t3UckYmZIyN5dPgX8vl441S4EFWXfi4z4QmRhSX7271y5Uq2b99O27Zt0XWdVatW8eDBA9577730yCf+EWWKJvb2AxTVwFudOts6jsjEwvyu4vepL9EBj8j16qs4Fy0ihV6ILC7Z3/CtW7eyZs0aXFxcAHj77bfp2LGjFPt09t2n81ANTzBYPClZtZKt44hMSNc07m3dzu3vV2Of250KM6biXLSIrWMJIdJBir7Oxxd6gJw5c8pJYenM78ZlokPuoShG2rw32NZxRCbl57uAwF+Okqd2LUq+PwjjU7/XQoisLdmqXbBgQb755hu6du0KwA8//ECBAgXSPJj4f3tWfYdBjSaHsRD5i75i6zgik8pbrx6ur72Gl/ebMgumENlMssV+8uTJjBw5kjlz5gBQqVIl5s2bl+bBRJxHYUEQFYGuGuk8YpSt44hMRDObuf3DGuzcXCnYuhV5atawdSQhhI0kW+y9vLz47rvviIqKQtM0nJ2d0yOX+MdqX18MhlCMen6c3XLZOo7IJKIfPuTKvPmEX71K/uZNbR1HCGFjSV5Dd/PmTdq3b0/VqlV57733iIyMlEKfzu6GPoCwCDTdnreHDbV1HJFJPPrlKGeHjSLq/j3KfDSC4u/2s3UkIYSNJVnsp0yZQtu2bdmwYQNFihSx7sYX6UPTNdas+gLVEIrR4kpeL09bRxKZQNS9+/j5LsCpcGEq+35K3roy26UQ4jm78QMDA+nevTsAI0eOpHXr1ukWSsCKlZ/i+OA2YKDGG41tHUdkcLGPQ7F3cyVHwQK8Nmk8uV4rJ9fOCyGskhzZP315ncFgkMvt0tGtwDvE+N1GUeCVAtWp0dzb1pFEBqXrOg927eFM/4E8/vMcAG6VKkqhF0IkkORfBF3XE9yWS3XSz9ZPF2EwhGOnedJ6yEBbxxEZlCksjL8XLSH411O4V6sqE+QIIZKUZLF/+PAh06ZNS/L2uHHj0jZZNnXg6I8o+hM03Yk+T21vIZ4W+tdF/D6djyk0lKJ9elOgZXMU6VkhhEhCksW+W7duz70t0sb5bfswGGMoVKAWdtJmVCQh4sZNVHs7Ks6egUvJEraOI4TI4JIs9u+//3565hDAT7u3ohpC0M25aTNkgK3jiAwmJjCIqHv3cKtUkfzNm+LVpBEGR0dbxxJCZAJyFk8GEWWK5q9DxzAadF6tVsvWcUQGE/TrKf5etBjV3p5qy5ag2tlJoRdCpJgU+wziu8/mYTQEopvdady5g63jiAxCi43l5qpvebBrD84lilNm5DBUObwjhHhBUuwzgGt3/yYm+D4o0PjtrraOIzIIc2QUF8aOI+LGTQq0akGRnt2l0AshXkqyp+9qmsaXX37Jxx9/THh4OMuWLcNisaRHtmxB13V2fLECRY3CWclPuZrVbR1JZBBGpxy4VqrIq+PHUqzvO1LohRAvLdliP2fOHPz8/Dh3Lm7CjiNHjjBz5sw0D5ZdHPM7iWoKR9Mc6TxmjK3jCBszR0RwdcEiIm7eBKDYO73IXb2abUMJITK9ZIv9iRMnmDVrFg4ODri4uPDVV19x7NixFL34jh07aNasGW+99RY//PBDkssdOnSIRo0apTx1FvL7qi2oaiR5nAvjlCunreMIGwrzu8rZYSMJOPQLYX5/2zqOECILSfaYvdFoRH1qsg57e/sUTZ3r7++Pr68vmzdvxt7ens6dO1OzZk1KliyZYLnAwEBmz579EtEzv0chAaCFYdFz0XGM9KrPrnRd5+7mrdz+fjX2ud2pMGMquV4ta+tYQogsJNmRfenSpfnhhx+wWCxcv36dCRMmULZs8n+Ijh8/Tq1atXBzc8PJyQlvb2/27t2baLlx48Zl22v613y2AFWNJqdTXplAJxuz/H6WW998R+7Xa1B5/qdS6IUQqS7ZYv/JJ5/w119/ERQURJcuXYiIiGDs2LHJvnBAQAAeHh7W256envj7+ydY5ttvv6VcuXJUqlTpJaJnbtfvXIPYYDTNge4fy6g+O7JERQFgqFyRMqOGU+bjkRhdXGycSgiRFSW7P97FxYUZM2a88AtrmpageY6u6wlu+/n5sW/fPlatWsXDhw9f+PUBLly48FLPywgOblmPoxqNg6Ew5y/+Zes4STpz5oytI2Q5usWC+eBhLH9dwuHdPig5cnArh4Fbv/9u62hZmvwspz3ZxhlXssV+WhLNWJJrhJMvXz5Onz5tvf3o0SM8PT2tt/fu3cujR49o3749JpOJgIAAunbtyurVq1OanfLly+Pg4JDi5TOKsKhwjq2LQFNy0P2Tj8nh4mzrSM905swZqlWTM8FTU/TDh1yZN5+Yq1fx8n6LYjVqcPbCBdnOaUx+ltOebOO0FRMT858GuMnuxndzc7P+c3Z25tSpUyl64Tp16nDixAmCg4OJiopi37591K9f3/r4kCFD+PHHH9m2bRvLly/H09PzhQp9Zvb97FmohghyGPNk2EIvUt+jX45ydtgoou7fo8xHIyn53gAMmfDLqhAi80l2ZP/vk+f69+/PoEGDkn1hLy8vhg0bRs+ePTGZTLz99ttUrFiR/v37M2TIECpUqPDyqTOxiIgnmCJDUVQ72r6X/HYUWYOu6wTsP4DTK4UpPWIojk/t5RJCiLT2wtPluri4EBAQkKJlW7ZsScuWLRPct2LFikTLFSpUiAMHDrxolExp1ezZqIZw7JVC5C1YwNZxRBqLuHETY66cOOTJQ5lRw1EdHVFTcOmqEEKkpmT/6kydOtV6Yp2u6/z1118UL148zYNlRSERoVhiggEnuo39yNZxRBrSdZ2Hu/dw46tvyFOrJmVGDZcz7YUQNpNssXd3d09wu1WrVrRq1SrNAmVla75YhKpGY6e8grOrzJaXVZmehPH3osUEn/oN9+rVKP5uX1tHEkJkc8kW+9u3bzNnzpz0yJKlWTQLpochqAYFnx5dbB1HpJGIm7e4OGUaptAnFOv3DvlbNE9wyakQQthCssX+8uXLia6RFy/uh4WfYTAGgzkPRcrJDGlZlYNHXpxeeYUiPbrhUkIOdwkhMoZki72HhwfNmzenUqVKODv//2ViyV1nLxJ6fP8eiqrQvNc7to4iUlnMo0DubNhE8X7vYHR25rVJ420dSQghEkiy2MfGxmJvb0+VKlWoUqVKembKcg78vh9FjQQtJ8XLv2brOCIVBf16ir8XLUYzmfFq0oicpUvZOpIQQiSSZLHv1KkTW7ZsybZNalLTubU/YzSY8PSQQp9VaLGx3Pj6Gx7u3otziRKUGTmUHAXkUkohRMaUZLHXdT09c2RZZ/4+g6qEomkutB8uX5yyiquLlhD4yxEKtG5JkR7dUKVroRAiA0uy2MfExHDx4sUki/5rr8koNSWOLl+LwRBFTofSGAwGW8cR/4Gu6+hmM6qdHYU7vo1Hg/+Ru7rMBS6EyPiSLPZ37tzhgw8+eGaxVxSF/fv3p2mwrODE8YOoahCaxY3u42USnczMHBHBtSXLUAwGSg0bglPhQjgVLmTrWEIIkSJJFvuSJUuydevWdIyS9Zze9ROKolOgcCkZ1WdiYVf8uPKpLzGPAnmla2dbxxFCiBcmk3SnkaCwIHRTBLriQMv+MoNaZqRrGve2bOP2D2uwz5ObCjOmkutVmSNBCJH5JFnsq1evnp45spx1vr6ohifYUwB7R2ljmhmZHodyb/NWctd8nZKDB2GUdsRCiEwqyWIvk+a8vLCoMMwRQagqdBoxwtZxxAsK87uKS8kS2Od2p9Jnc3Hw9JAZJIUQmZpq6wBZ0eqF81HVaAxablzz5rZ1HJFCmsnEja+/4dyo0fj/HNdy2dHLUwq9ECLTk2P2aSA68DGqwUDrAR/YOopIoagHD/H71Jfwq3+Tz+ctPBr8z9aRhBAi1UixT2Xrv1uBaghBseShQPGito4jUiDoxK9cXbAIVJUyH48kb53ato4khBCpSop9KtJ1nQfnL6GqOvVatLJ1HJFCxpwuOBcvRulhQ3Dw8LB1HCGESHVyzD4V7fxpM6oaChZ3Kstu4Awt/PoN7u/YBYBr+dcoP32KFHohRJYlI/tUdP3n4xgMGiVLlbd1FJEEXdd5sGsPN7/+BjtXVzwbN8LolENOwhNCZGlS7FPJzi3rUNVgdLMrPu/2sXUc8QymJ2H8vWgxwad+w71GNUoNeR+jUw5bxxJCiDQnxT4V6LrOjWO/odhB+Vpv2DqOeAbNZOLcqNHEBAZSrN875G/RXEbzQohsQ4p9Kth+cAsYH6ObXXmjfRtbxxFP0TUNRVXjOtV16YRT4UK4lChu61hCCJGu5AS9/8isWbi55ziKYqFIkTK2jiOeEvMokAufTCDw2HEAPN+oL4VeCJEtycj+P9qw63tUQyi6JRet3h9k6zjZlslk4u7du0RHRwNgiYnBHBYGTb156OTEo0uXbJwwaUajkUsZOF9WINs47ck2Tj2Ojo4UKlQIOzu7VHtNKfb/kf+R8xgNZsq+JhOx2NLdu3fJmTMnRV55hdigYMxPQlHz5MXRyxPV3t7W8Z4rIiICZ2dpspOWZBunPdnGqUPXdYKCgrh79y7FihVLtdeV3fj/wR9+ZzCoT9DMuWnSS/qc21J0dDR58uRBi4rG/CQUOzc3chQskOELvRBCPE1RFPLkyWPdS5laZGT/H5xYuxVFMZHbPZ+to2Rruq6jmc0oioLRxZkchQtjcJC2wkKIzCktrhSSYv+STBYT5rAnYLCnxcB+to6TbZkjIvh78ReYatVAi41FtbfPEIXebDazYsUKtm/fjqIoWCwW2rZty4ABA9L8kr8FCxZQvnx5Gjdu/MLP7dGjB15eXsybN89636JFiwD44IMPaNSoEY6OjtjZ2WE2mylWrBjTp0/H1dU10WuFhIRQv359hg0bRp8+/z/3xOjRo3n99ddp167dM9cBsHXrVr777jvMZjOaptGhQwd69uz5wu8H4r4M+vr6cvToUVRVZerUqVSrVi3BMhs2bOD777+33r579y6tW7dmwoQJbN68mS+//BKDwUDNmjUZPXo0RqP86RSZi+zGf0nffu6LanyCveaGa253W8fJlsKu+HF26EiCTpzE4OyMkoons/xXkydP5ty5c6xbt47du3ezadMmTpw4werVq9N83R9++OFLFfp4e/fu5eeff07y8eXLl7Nt2zZ27dpF/vz5WbZs2TOX27FjB40aNWLdunXoup7i9a9bt45vvvmGpUuXsm3bNn744Qe2b9/Ohg0bXvi9APz444/cuHGD3bt3s3jxYsaMGYPZbE6wTIcOHdi2bRvbtm1j3rx55MmTh/fff5/r168zf/58Vq1axY4dOzCbzXz33XcvlUMIW5Ji/xIiTVGE332Irhto1LmrreNkS3c3b+X8mHGAToWZ0zA6OWWYSXIePnzI9u3bmTVrFrly5QLAxcWFCRMmkDdvXgD8/Pzo0aMH7du3p1mzZqxZswaIG+HGj3IBGjVqxN27d7l8+TIdO3akXbt2dOnShZs3b2IymRg1ahRt2rShTZs2rF+/HogbOW/evBkAX19fOnbsiLe3Nz169CAwMBCAevXqMXXqVNq0aUP79u25c+eOdZ2DBg1i8uTJPH78+LnvU9M0IiIirO/p3zZv3kzXrl2xt7fn5MmTKd5+S5cuZdSoUXh6egKQK1cuZs+eTenSpRMsZ7FYaN26daJ/169fT7Dc4cOHeeutt1BVlWLFipE/f37++OOPJNc/adIkhg0bRu7cubly5QqVK1e2ZmnYsOFzvwgJkVHJvqiXsO6bL1ANj1EteShdtZKt42RLptBQcteqScn3BmJ0cebeU5f8HDh9m59O3U6T9b75+is0qv7Kc5c5d+4cJUqUSLRru0SJEpQoUQKI22383nvvUbt2ba5cuULnzp3p0qVLkq/5zTff8M4779C0aVO2bNnC2bNnCQgIIDQ0lK1bt+Lv78+nn35Kx44drc+5desW169fZ+3ataiqykcffcT27dvp06cPjx49onbt2owfP55Zs2bxww8/MHr0aACqV6/O48ePmTZtWoLd+fHeffdd7OzsCAoKwmAw8P777yda5vLlywQGBlK9enWaNm3KunXrqF07+StWgoODefDgAeXKlUu07f7NYDCwbdu2ZF8zICAAj6eaHHl4ePDw4cNnLnv8+HGio6Np2rQpAGXLlmXWrFk8ePAAT09P9u7da/3CJERmIsX+BZk1C08u38Vgp1OtfiNbx8lWQn7/A0OOHOR6tSxFe3YHVc0wo/l/ezrX3r17Wbp0KZqmYW9vz6ZNmxg9ejRHjhxh2bJlXLx4kcjIyOe+XoMGDZgyZQpHjhyhUaNGNGzYkCdPnnDjxg369u1L/fr1+eijjxI8p0iRInz88cds2LCBGzducPbsWV555f+/qPzvf3GdGUuVKsXp06cTPHf48OG0bt36maPY5cuXU6hQIQC++uor+vbty+7duxO8540bN+Lj44PBYKBZs2YsWbKEwMBA8ubN+8zPTNd1VFVFVeN2Njqk4LwLi8WS4Lh/PF9fX4oX///JkzRNe+a6nmXt2rW888471tvFihVjxIgRDBo0CEdHR3x8fDh//nyy2YTIaKTYv6B9h3agGkPRza7UatnU1nGyBc1k4tb3q7m/dTvu1apSbsInKAZDkss3qp786DstlS9fnmvXrhEeHo6Liws+Pj74+Phw9+5d60lmQ4cOJVeuXDRs2JCGDRuyd+9eIO5LwtPFyWQyAeDj40OVKlU4ePAgq1at4tChQ0ybNo1du3Zx7NgxDh8+TNu2bdm1a5f1uRcuXGDEiBH07t0bb29vVFVNcOw8vqAqipLomHqOHDmYMWMGw4YNw9vb+5kn4EHcse7Zs2cTEhJC7ty5AYiNjWXnzp0YjUYOHDhgXXbz5s28++67uLq68uTJkwSvExQURMmSJXFzc6Nw4cJcuHCBGjVqWB8/deoUv/zyCyNHjrTel9KRfb58+RKMxgMDA6275Z8WGxvLb7/9xqxZs6z3xcTEULFiRbZu3QrAnj17KFy4cLLrFCKjkWP2L+jv/SdRFAslSlS0dZRsIerBQ86PGcf9rdvJ19SbMh+PTP5JNlagQAFatWrFxx9/bC1qZrOZQ4cOWUeUx44dY8iQITRp0oRjx44BcSNVd3d3/v77byDucMCjR4+AuC8H58+fp3Pnznz44YdcvHiR/fv3M2rUKN544w3GjRuHk5MTDx48sOb47bffeP311+nSpQtFixbl0KFDWCyWFL+P6tWr4+Pjw9q1a5Nc5sSJE+TPn99a6AEOHjyIu7s7R48e5cCBAxw4cIApU6ZYT9SrXbs2u3fvtu7NePToEYcOHaJWrVoA9O3bl1mzZlnfe3BwMLNmzaJIkSIpzv60+vXrs2fPHiwWC7du3eLmzZtUqFAh0XJXrlyhaNGiODk5We+LjIykd+/ehIeHExsby/fff0+zZs1eKocQtiQj+xcQERsJsTFoBjsa9kz6+KpIHZG3b3Puo7GgqpQdPYo8tWvZOlKKTZo0ia+//pqePXtisViIiIigZs2arFixAoi7xKxr1644ODhQsmRJChYsyN27d2nWrBk//vgjzZo147XXXrMeux44cCCffPIJixcvxs7OjkmTJvHqq6+yb98+mjdvjoODA61ataJMmf/vz9CsWTPef/99WrZsCcTtcbh79+4LvY/hw4dz+PDhBPfFH7NXVRWDwcBnn32W4PH4E/Oe1qJFCz777DOOHDlCgwYNrCccKoqCqqqMGjWKUqVKAdClSxfMZjN9+vSx7nXo1KkTHTp0eKHs8Xx8fDhz5gytWrUCYPr06Tg6OuLv78+7775r3Ttw584d8uVLOGeGu7s7gwcPplOnTpjNZlq0aGHdnkJkJor+ItfEZBAxMTFcuHCB8uXLp+jYXmr5ftc3hBw5gmp2Z/C8uem2Xls5c+ZMouuR05Ouadz67gfyN/PB4akTrJ7l0qVLvPrqq+mULHXJNKNpT7Zx2pNtnLr+/Tftv9Y92Y2fQrquE3j4PGChbMUqto6TZYVfv8G50Z8QExSEoqoU7dUj2UIvhBDi+WQ3fgrt3b0JgxqCrrnSuKdcW5/adF3nwc7d3Fz1LXa5chEbHIJDnjy2jiWEEFmCFPsU0HQNv0MnUQ065V573dZxshzTkydcXbiYkN9O416jGqWGvI/dP5PRCCGE+O+k2KfAb38cRzU8Bos7TXrJqD613V69jsd/nKVYv3fI36J5hr12XgghMisp9ilwcsMODIqFAgUTz+IlXo5usWAKC8PezY0iPbri9VYTXIqnXu9mIYQQ/0+KfTKCw4JRtHA03YVW779r6zhZQsyjR/h9tgBLVDQV583C6OwshV4IIdKQFPtkrJvti6pG4mgsIm0tU0HQiV/5+/MlaGYzJQYNQJVtKoQQaU7+0j7H4yePMZkCAUd6jB1t6ziZmhYby42vVvFwz4+4lCxB6ZHDyJE/v61jpRnpZ5/2zp07x+TJk4mNjaVAgQJMmzYtQcObp508eZJvv/2Wb775xnqfv78/7du35+jRo9b7TCYT/fr147333qNmzZoAXL9+nYkTJxIaGoqHhwefffYZrq6unDlzhpkzZ2IymXBzc2PGjBkULFiQdu3aWWcqjI6O5s6dO/zyyy/ExMTQokULa3+CvHnzsnLlSnRdZ8mSJfz0009ERUUxaNAg2rRp88I5P//8czZt2mTttNixY0e6detmfc7Fixfp2LEjFy5cACA0NJSRI0fi7++Pvb09U6dO5dVXXyU2NpYxY8bg5+eHqqp8/PHH1KlThwkTJvDnn39aX8/Pzw9fX198fHxS9Hnpus6cOXM4ePAgqqoydepU6zwejRs3xsXFxbrsF198Qf5n/H1o164dnp6efPHFF9b7Nm/ezKlTpxJMc/zrr7/y+eefW9sRnz17Fl9fX0JCQtA0jerVqzN69GgcHR1TlP3fduzYwdKlSzGbzfTq1SvBdoa4a+TjG0tB3CyQrq6u7Ny584V+blOVnglFR0frp0+f1qOjo9N0PUvGj9MXje6tLxs3IU3Xk1GdPn061V7LHB2t//HhcP36V6t0S2xsqr1uvIsXL6b6a/4X48aN0wcOHKiHhobquq7rYWFhes+ePfXvv/8+0bLh4eHpHS9J3bt311977TX9p59+st63cOFCfeHChbqu63rDhg31O3fuWB+bNm2aPnv27HTPqWma3qBBA/3EiRO6ruv6rl279AEDBiRazmKx6CtXrtRr1Kihd+/e3Xr/oUOH9LfeeksvXbq09b5r167pnTp10itUqKCfPHnSup633npLP3z4sK7ruj537lx9zpw5uq7HbYtLly7puq7rGzZs0AcOHJho/aNGjdKXLl2q67qu7927Vx8/fnyiZbZu3ap37dpVj4mJ0QMCAvTatWtbf25SmlPXdX3AgAH677///sztFRkZqXfu3DnB6/j6+lrfy/79+/XOnTvruq7r69ev14cOHarruq5fvnxZ/9///pfo9TZs2KD36dNH1zTNel9yP8d79uzR+/fvr1ssFv369ev6m2++qZtMJj04OFj39vZ+7nN1XdcvXbqkd+rUSW/QoIF+//596/2bNm3SP/744wTLnjx50vp5X7p0Sa9bt65+9uxZXdd13WQy6RMmTNBHjhyZ7Dqf5eHDh3rDhg31kJAQPSIiQm/ZsqV+9erVJJePjIzUmzdvrv/2228p/rnV9cR/0/5r3ZNJdZKg6zqm6DA03Z4eY8fYOk6mpOs6AYd+wRwZhcHBgYpzZlLsnV6odna2jpampJ99Qm3btrWOJi0WC/Xr1ycoKIg9e/bQsWNHWrVqhY+PD7///jsQt3fh/fffx9vbm0tPtS5+WkhICNHR0db59Bs2bMjRo0eJjY1NsNy1a9e4du0a48aNS3D/xo0bE2zn+Pv69etHpUr/37b6r7/+wsnJifr16wNx0xZ369aN2NhYPvzwQ8qWLQtAmTJlEvQlgLi+AZcvX6Z///4AnD9/Hj8/P1q3bk3Pnj25cuUKENdcp0+fPtjb2+Ph4cHq1autI86U5oS4xkfLli2jZcuWTJkyhZiYGOtjs2bNolevXgmWj//8AKKioqzr1DSNqKgoLBZLgvuf3vYLFy5kypQp1r1WM2fOpGvXrrRq1YpVq1bxLIcPH6ZZs2aoqkqxYsXInz8/f/zxB+fPn0fXdTp37kzbtm3Zs2fPM5+/efNm6tatS+PGja0/6ymxcuVK2rdvb91eRqORUaNG0aRJk0TLjhkzhtatWyf4t3379gTLHD9+nFq1auHm5oaTkxPe3t7WRlbPsmzZMmrUqEH16tVT/HObFmQ3fhIO/LwTRQ1FseQlR46X29WTnZnDI/h7yRcEHTtOkV49KNSuDaq9fbqsO+zcIcL+PJD8gi8hZ6VG5Kz4xnOXkX72CbVu3Zpdu3ZRvnx5Tp48SdmyZXF3d2ft2rV88cUX5M6dm40bN7J8+XLr7tkyZcrw+eefJ/ma7u7uODk5cfToUerVq8euXbswmUyEhITg5eVlXa5UqVJMnz490fz+/y6ggLVF8NO7+m/fvk3evHkZO3Ysly5donjx4owfPx57e3tat24NxBXHzz//PFHxWLhwIcOGDcPwT4fG+P4FnTt35siRIwwePJjdu3dz69Ytrl27xjfffENYWBj9+/enaNGiL5QzIiKCV199lVGjRlGkSBFGjx7NkiVLGDZsGPv37yc6OjrR7vY+ffrQqVMn6tWrR0REBF999RUQ9+Vsy5Yt/O9//+PJkyeJeh+sWrWK5s2bU7BgQQBr4V29ejV2dnb07duX8uXLU7169QTPCwgISNBt0MPDg4cPH5IjRw7+97//MXLkSAIDA+nWrRulS5e2/q5A3GGLHTt28N133/H48WOGDRvG4MGDU3Qe1aVLl3jzzTcT3Ofi4oK3t3eiZWfOnJns6wUEBCTY7e7p6cm5c+eeuWxYWBjr169nx44dQMp/btOCjOyTcOHnIygKlKpQ1dZRMp2wK36cHTaS4JO/UqRndwq2aWXrSOnu3/3sW7duTcuWLWnfvj0QN/qOiYlh2bJlLFmyJEX97KdOncrYsWPJmTMnLVu2pFSpUtZ+9nv37n1uP/tZs2Zx9uzZBOt5up99aGhogucOHz6cc+fOJdnPftu2bRw9epRevXrRt2/fRC1yn9a8eXN+/PFHdF1n586dtGrVClVVWbx4MUePHmXBggVs2bLFOsoEqFjx+V0lFUVh4cKFLFu2jDZt2hAWFoabmxt2qbzXyGw2c+rUKbp06cKWLVsoXLhwgmPDsbGxjBw5ErPZzIABA6z3X716lZCQEBo2bGi9L775kaqqNGjQACcnJ65fv47FYuHKlSusXLmSJUuWMGfOHG7evPlCOZ2dnVmxYgUlSpTAaDTSp08fDh8+zKNHj1i6dCnjx49P9JypU6fSrVs3jh49yldffcWwYcOIiIjg888/p3Llyhw7dowdO3Ywffp07t27B8R9sdm0aVOCvQQnTpzgwIEDdO7cmQ4dOvDw4UPrXounaZqW4PdC13VUVaVJkyaMGzcOR0dHChUqxJtvvpng/ASAQ4cO4eHhQcmSJalWrRqqqnLw4EEAayfJp+m6bl2Xoigpnks+JSP7Z72PpM7D2b59O02aNCHPP7OBptfP7bPIyP4Zok3RKISjWXLhLd3tXsijX47g57sQh7x5qTBzGjnLlE73DDkrvpHs6DstZfd+9v/m4eFBsWLF+PXXXzlx4gQTJkwgIiKCt99+m1atWlGjRg3KlCnDDz/8YH1OSk6cMhqN1hOwgoKCWLJkCW5ubsk+70V4eHhQpEgRa0vcFi1aMGTIECBuND1o0CDc3NxYunRpgj/YP//8c6JWuN999x0tWrTA3d0diCsSRqORvHnz4uPjg52dHfnz56dSpUpcvHjROrpPifv373P8+HHefvvtBK996NAhHj9+nOAEstatW/PDDz+wf/9+pkyZAkCVKlXIkycP165dY//+/fj6+qIoCsWKFaNSpUqcO3eOggUL8scff1C0aNEE3QEtFgujRo2ibt26ODs7ExwcjLOzMwsWLODAgbg9bEOGDCFfvnwEBARYnxcYGIinpycHDx4kb968CdoO/3vEvmnTJh48eECjRo0ACA8PZ+3atbz55pvkypXL2ko6XvwJcRD3+3j+/HkaNGhgfTw8PJyRI0eycOFC7J/a45iSkX2+fPk4ffq09fajR48S7LF42s8//5zgS2D8e0vrn9tnkZH9M/zw+SJUNQp7g0vyC4sEcr1aFs9GDansO88mhT4jyO797J+ldevWzJ49m5o1a5IjRw5u3ryJoigMHDiQmjVr8tNPP71QNoCxY8dad59+/fXX+Pj4PHOU919UqVKF4OBgLl++DMCBAwd47bXXAKy7zOfPn5+gYEDc2d//3o3922+/sXHjRgBOnTqFpmkUL16chg0bsmfPHnRdJyQkhHPnzr1wB0dHR0fmzp3LnTt30HWdH374gTfffJMOHTrw888/s23bNmsr323btuHi4kLZsmWte25u3rxJQEAAxYoVS3B/cHAwFy5csOY5e/Zsok6YtWrVYv369ZhMJiIiIujatStnz57lww8/tK63cePG1K9fnx07dmCxWLh16xY3b96kQoUK3Lt3j8WLF6NpGoGBgRw4cIA33njD+vqBgYEcP36cnTt3cuDAAQ4cOMDWrVs5efIkd+7coXLlypw7d47bt28DcXtbtmzZQu3atQHo3bs3a9assf6smEwmZs2ahYuLS6LPLSXq1KnDiRMnCA4OJioqin379lnP6Xiaruv89ddfVKmSsHFaevzcPouM7P9F0zQi7t9DMRho/s47to6TKYT8/gePfjlKqSGDcfDwoNQH79k6ks1l5372z/Lmm28yceJERo4cCUDZsmV59dVXadq0KYqiUK9ePc6cOfNC2SZNmsTEiROJioqiTJkyTJ8+HYA1a9YQEBDAhx9++EKv9yyOjo4sXryYcePGERUVRb58+ZgzZ471y1bJkiVp27YtEHfsNv7zvXPnTqJjsJ988gmjR49m27ZtODg48Omnn6KqKr1792bu3Lm0aNECi8XCe++9R7FiLzbJVO7cuZkyZQqDBg3CZDJRtWpV3knm79esWbOYMGECK1aswN7entmzZ5MzZ07GjBnD+PHjad68OaqqMnz4cOtehjt37iT4GQPo3Lkzt27domvXrmiaRrt27ayXAz7Nx8eHc+fO0apV3GG96dOn4+joSOfOnbly5QotWrRA0zRGjhxpPR8A4r6cNGjQIMH2LFy4MI0aNWLdunWMHDmSqVOnMnToUCwWC7Gxsbz11lt06tQJiDv/Y+7cuUyfPp2oqChMJhN16tRJdNJmSnl5eTFs2DB69uyJyWTi7bffth526t+/P0OGDKFChQoEBwdjZ2eX6BBCUj+3aU362f/L9l3ruH10L5jz8v7crN+z/nmS62evmUzc+n4197dux6nIK5SfOgk7G1xvDdLPXjyfbOO0J9s4daV2P3sZ2f/L9cO/YTRA2UpyYt7zRD14gN88X8L/vka+pj4UfacnhlT+4iUyj927d7Ns2bJnPha/+/hl9OjRI9HxWCDZqxeEEAlJsX+K/2N/VCUSzexKk+7yhyQpuqZxadpMYkMeU3b0R+SpnXiXnchemjVrluiEtNQQfyKTEOK/kWL/lC2Lv0BVo3CwT4epCzMhS1QUip0dqtFIqaFDsHdzxSE9pnkUQgjxn6TpKYA7duygWbNmvPXWWwkuq4n3888/07p1a1q1asV7772X6Frf9BYbGoiuq7To28+mOTKi8OvXOTv8I+6sWQdAzlIlpdALIUQmkWbF3t/fH19fX1avXs3WrVtZt26d9ZIiiLvOcdKkSSxfvpzt27dTpkyZZ84WlV7OnDqOaggHLRf5ixa2WY6MRtd17u/YyblRY9BionGrXCn5JwkhhMhQ0qzYJzd/sMlkYuLEidbLKZ41t3R6OrYl7iSifIWL2yxDRqNHRHBp2kxufPk1blUqU3n+p7hWKG/rWEIIIV5QmhX7Z80f7O/vb73t7u5una84Ojqa5cuXP7MxQXowW8xgiUTTXOjwwQc2yZAR6WHhPLl4iWL9+/LqJ6Ox+6epixBCiMwlzU7QS+n8wWFhYQwePJiyZctaJ6dIqfhOWv/VuXMnUdRwVLPHC0/skdXomoZ29W8MZUqj5vNCeX8gDxwdefBPR7KMymg0Jphb3dbMZjOrVq1i9+7d1ilwW7RoQZ8+fZ75e5Ca2ZcuXUq5cuUSTA+aUv3798fT0zPBRB/xzWkGDhxI8+bNE/SzL1KkCBMnTrR290tPDx48YNy4cYSEhFCkSBGmT5+Ok5PTM5c9efIkX3/9dZKXBz5+/BgfHx8GDx5Mjx49rPdPnDiRatWqWSeCgYTbA2Dnzp2sXbsWs9mMpmm0bdv2pS8L1HWd+fPnc+TIERRFYfz48VSuXDnBMlu2bGHdunXW2/fv36dZs2aMHj2a7du3880332AwGKhRowbDhg1LUbOY1JKRfgczu9jY2FStR2n2U5CS+YMDAgLo27cvtWrVYuzYsS+8jtSaVOfYujWoBij3es3nTiKT1cU8esSVT+cTdukyr86dhV/YE6rXrWvrWCly6dKlDDWhx/jx4wkMDGTDhg3kypWL8PBwBg8eTJ48eRLMUw6pPxlJ/Cx1L8NgMPDzzz/TokUL6562+ClFnZ2dUVWVL7/8kkKFCgFxs6B99913iZrwpIe5c+fSo0cPmjdvzuLFi1m1ahWjRo1KsIymaaxatYovvviCMmXKJLmdN23aRKNGjdiyZQsDBgywfiEzGo04ODgkeN7T22PdunWsXbuWZcuW4enpyZMnT+jTpw+urq506NDhhd/T3r17uX37Nnv27OHWrVsMGDCA3bt3JyjY3bt3p3v37kBcw53BgwczbNgw/P39Wbp0KRs3bsTT05NJkyaxefPmZGfSSy0yqU7qsre3T9DGOH5SnZeVZsW+Tp06LFq0iODgYHLkyMG+ffuYOnWq9XGLxcLAgQNp2rQp771nu+lVA8ODgVg0izMNO7a3WQ5bCzpxkquLloCmUXr4UHKWLgWZdC/H4RsnOXjjeJq8dsNidWhQrNZzl4nvZ//LL78k6mcff5Kqn58fU6dOJTIyksDAQAYOHEiXLl2sJ6l+8M/hpEaNGvHtt98SHh7OhAkTMJvNODg4MHPmTAoWLMjYsWO5evUqAF27dqVjx46MHj2a119/nXbt2uHr68uJEycIDQ3F09MTX19f8ubNS7169fD29ubMmTMYDAbmz59P4cJxJ6bG97OvXr36cxt0xPdDL1my5HO3R9u2bZk6dSrly5fHYrHQsGFDtmzZwqlTp/j666+Jjo4mNjaWGTNmULVqVXr06IGrqytXr15l/vz5z5wZ0WQy8dtvv7F48WIA2rVrR/fu3RMV+6f72W/YsCHJjJs3b2bMmDFMmzaNkydPWudVT87SpUuZMWOGdSCTK1cuZs+eTXh4eILlLBYL7dq1S/R8X19fihf///OEkur5XqNGjWeuf9KkSQwbNozcuXPz66+/UrlyZWuWhg0bsnz58nQr9iJjS7Nin9T8wfFzBz98+JCLFy9isVj48ccfgbiRenrNExzvhy8XYTCEYU+BdF1vRnJz1bfc27INl5IlKD1yODny50v+SSJJ0s8+obToZx8SEoKLi4t1xOvh4ZHgnKB4SfWzf9rly5cJDAykevXqNG3alHXr1qWo2AcHB/PgwQNr/4J4T/dhj2cwGFI0k2BSPd+f5fjx40RHR9O0aVMgrt/ArFmzePDgAZ6enuzdu5fAwMBk1ymyhzQ9mNOyZUtrE4548Y0iKlSoYO0kZUvKvSfoBoWGHTomv3AW5VysGAXatKJI966o6dBXOa01KFYr2dF3Wvt3P/ulS5eiaRr29vZs2rSJ0aNHc+TIEZYtW8bFixdT1M9+ypQpHDlyhEaNGtGwYUOePHli7Wdfv3795/azv3HjBmfPnuWVV16xPv50P/unD7lBXAOc1q1bJ9nPPn43/ldffUXfvn2t5yY8S/PmzenUqRMfffRRon72Bw4c4MaNG5w6dSpB56/k+tk/6xygpNafnI0bN+Lj44PBYKBZs2YsWbKEwMBA8ubN+8zXjO/DHp83JYcSUzqyT6rn+7OsXbs2wai9WLFijBgxgkGDBuHo6IiPjw/nz59PNpvIHrJ1i9vfTxxFMYSAxZ1S1bLP9eO6rvNw30/4/xT3h9yjwf8o9k6vLFHoM4Kn+9lDXLevbdu2sXTpUkJCQoC4lrU//fQTJUqUYPDgwdbn/ru3/NP97Lds2ULFihVZtWoVEydOxN3dnV27dtG9e3du3LhB27ZtE8wjf+HCBfr27YumaXh7e9OkSZMX7mc/efLk50521aFDB65fv259X8/y7372jRs3tvazv3v3LjVq1EhwUhwk388+d+7chIWFWdviPq+n+PPExsayc+dO9u7dS6NGjejTpw8Qt1sfwNXVNdHc/EFBQbi6uuLm5kbhwoUTHUc9depUoj0i8SP7f/97utADSfZ8f1bu3377zdrfHeKO6VasWJGtW7eydu1avLy8rIdmhMjWxf7XrTtQFJ2Cxcskv3AWYQ6P4MrcT7m2+AuCfv0t0R958d9JP/vEUrufvZ2dHdWrV2f37t0AbN269Zk9xZNz8OBB3N3dOXr0qLVX+pQpU1i3bh26rlO7dm12795t3fPy6NEjDh06RK1acXuO+vbty6xZs6yfU3BwMLNmzaJIkSIvnAVIsuf7v125coWiRYsmuPogMjKS3r17Ex4eTmxsLN9//32a9CsQmVO2nRvfolmI1WNRNAdavNPL1nHSRdgVP67M8yU2KIgiPbtTsG3rl971KZ5P+tknlBb97CdOnMjo0aNZunQp+fPnt+Z4kX72mzdvpmvXrgnua9GiBZ999hlHjhyhQYMGXL58mY4dO6IoCqqqMmrUKEqVKgVAly5dMJvN1ksqdV2nU6dOL3UmPiTd893f3593333Xetz/zp075MuX8Nwad3d3Bg8eTKdOnTCbzbRo0SLRYVSRfWXbfvZHTv/Cn5u+BnMe3p+b+CSkrCY6IIDfB32Afe7clBk5jJxlSif7nOT62Wck0s9ePI9s47Qn2zh1ST/7VPLnpl0AeHrmt3GStKXFxqLa2+Po6UmpIe/jXr0qRvmFFKlM+tkLkbFly2If+DgITQ8F3Yl2w5Lf1ZdZBZ8+w9+fL6Hsx6PI9WpZPBr8z9aRRBYl/eyFyNiyZbHftvJLVCWGHA5F0nUqyfSimUzc+vZ77m/fiVPRIhhdXGwdSQghhA1lvUqXDF3XCfcPRFUVGnfsZOs4qS7q/n2uzPMl4tp18jXzibuk7p/pPYUQQmRP2a7YX7hyFlUNQbe4UvS1zHlC1/MEnTxFjH8AZcd8RJ5aNW0dRwghRAaQ7Yr9sbVbURQLud2zzol5lqgoou7dx6VkCQq2aYXnGw2wz+1u61hCCCEyiGw3qU5MZBi6rtK8f19bR0kV4deuc3b4KC5OmY4lJgZFVaXQZwBms5mlS5fStGlTmjVrhre3N1988UW6TGK0YMEC9u/f/1LP7dGjR6KueYsWLbI26GnUqBHNmjWjdevWNG/enPfff/+5M+ylpfv379OtWzd8fHwYNGjQM9urxsbGMmrUKNq1a0fbtm25du1agsevXLlC8+bNk1yH2WymXr16CZp4QcJtEm/z5s3W/gIAhw4donPnzrRq1YoWLVowf/58NE17mbcKxE1N7OPjg7e3N/v27Uv0+OHDh2ndurX1X61atRgwYID1sfjpy0eMGCGtaLOhbFXs74TcQ1Ej0LWcuHnksXWc/0TXde5v38m5j8agxcRQ5qMRGFKh3a9IHZMnT+bcuXOsW7eO3bt3s2nTJk6cOMHq1avTfN0ffvghjRs3funn792795lz4sdbvnw527ZtY9euXeTPnz/JS+7S2uTJk+natSt79+6lfPnyLFmyJNEy3333HTly5GDz5s2MHTuWMWPGWB/bunUr/fr1IyoqKsl1HD58mAoVKrBnz57nLvdvv/zyC1OmTGHmzJls376djRs3cvnyZRYuXPhib/If586dY/v27Wzbto3Vq1czZ84cHj9+nGCZBg0aWKfh/fLLL3FxcWHMmDE8efKE0aNH4+vry44dOyhbtiy+vr4vlUNkXtmq2O9d9R2qEoujXeYe+VpiYrg0fSY3Vn6NW5XKVJ7/Ga7lX7N1LPGP+Ba3s2bNStTiNm/evEBci9sePXrQvn17mjVrxpo1a4DEI8ZGjRpx9+5d6yxu7dq1o0uXLty8eROTycSoUaNo06YNbdq0Yf369QCMHj3aOre7r68vHTt2xNvbmx49eli7oMWPVtu0aUP79u25c+eOdZ3xLW7/XUz+Lb7Fbfx7Skrbtm2t88dbLBbq169PUFAQe/bsoWPHjrRq1QofHx9+//13IG7vwvvvv4+3tzeXLl165mvGt7j19vYG4lrc7t27N9Fyhw4dss5GV6NGDYKDg7l//z5hYWHs378/2dn/Nm/ezJtvvknFihXZtWvXc5d92hdffMGgQYMoVqwYEDfX/6RJk3j99dcTLdu3b98EI/LWrVtz8uTJBMv88ssvvPnmmzg4OJAnTx5ef/11Dh06lOT658yZQ+fOnSlatCg3b96kQIEC1lbEDRs2fO6XOZE1Zatj9pH3H6EaFLy7dbZ1lP9EtbfH6OxCsf59yd+8qUx5+y8BBw7hv/9Amry2V+NGeDZ647nLSIvbhGzZ4jYgIAAPDw/r7fiWsQUKFGDRokXPnT44ODiY48ePM2PGDAwGA99//z1vv/32c99rvEuXLvHJJ58kuC9fvnyJprgFWLlyZbKvFxAQkGCO/Oe1vr158yanTp2ytgsvWrQoDx8+5PLly5QtW5Y9e/ZI69tsKNsU+4jI8Lhd+BZXipTLfI1vdIuFOxs24fG/euQoWIBSQz+QIp+BSYvb/2fLFrf/Xu55LWP/bfv27dSqVQtXV1caN27M+PHjuXjxIuXKlXtmp8Cn16UoSoqnNO3bt2+i4jtmzBhrsx3gmcf6k3of69ato2vXrtj/c8ltrly5mD17NuPHj0fTNDp27IiddLjMdrJNsd+y/AtUNQaj3fN3OWZEMY8eceXT+YRduoxiMFC4Q3sp9M/h2eiNZEffaenpFrcuLi74+Pjg4+PD3bt36dmzJxDXxS5Xrlw0bNiQhg0bWndBK4qS4A/70y1uq1SpwsGDB1m1ahWHDh1i2rRp7Nq1i2PHjnH48GHatm2bYFfzhQsXGDFiBL1798bb2xtVVV+4xe2wYcPw9vZOtJciXocOHZg9ezYhISFJdr77d4vbCRMmWFvctmrViho1alCmTBl++OEH63NepMWtwWBIssWtl5cXAQEB5MkTd45OUi1jn2Xz5s0EBARY28iqqsratWuZMmUKrq6u3L59O8Hy8a1vIe5n4MKFC9Zd5wA3btxg6dKlzJkzJ8HzUjKyz5cvn7WzHsR134s/RPBv+/fvT/CaFouFfPnysWHDBiBuz5O0vs1+ss0x++B7cbv43mjX3sZJXkzQiZP88eEIIm/eovSIoRTukLnyZ0fS4jYxW7W4jT9pDeD06dM4ODhQoECBZF//woULPHz4kEOHDllb3y5btowdO3YQHh5OzZo1OXToEMHBwQCEhYWxe/duateuDUC/fv34/PPPuXnzJhDXJGbWrFnkz/9yl/zWr1+fffv2ERUVRXBwMCdPnrSu62nBwcFER0cnKOaKotCnTx/8/f3RdZ1Vq1ZJ69tsKFuM7K/fvw6GUDSzO2WrVrF1nBR7dOQYfvM+w6VUSUqPGEaO/ImP94mMSVrcJmSrFrc9evRgwoQJvP322zg6OiYaVSdl8+bNtGvXLsEehpo1a1KsWDF27NhBly5dGDBgAL179wbivqh16NCBBg0aAHHFediwYQwbNgyLxYLZbMbHxyfZ8xuSUrFiRVq1asXbb7+N2WxmyJAheHl5AXFfpJYvX46Xlxd3795NdF6AqqpMmTKFfv36ERsbS+3atenbN2tceixSLlu0uF0yeQJa9B3ccpal+9iP0yHhf6NbLCgGA5aYGB7u3kv+Fs1QbXCMTVrcpg9pDZr2ZBunPdnGqUta3L4EU3gEqsFI+yEf2DrKc+m6jv++n3iwey8VZk7H6JSDgm1b2zqWEMmSFrdCZGxZvtjfCbiDYggDS05yuDjZOk6SzOHh/L34C4KOn8CtciV0UyyQw9axhEgRaXErRMaW5Yv91hVfoiomcjpl3Il0nly6jN+nvsQGh1CkVw8KtmmFksLLg4QQQojkZOlib9Es6I8fg8GOlgPetXWcZ9J1nVvfrwZVpcLMaeQsU9rWkYQQQmQxWbrY79y/GdX4BNXsQe4CXraOk0BMUDCq0YCdqytlRgxDdbDHKCe3CCGESANZel/xzYNxl/J45c9YE0gEnz7D2aEjuLZ0OQD2ud2l0AshhEgzWXZkf/fBbRQtGE13ptmAjHFNqWYycevb77m/fSdORYvwSjc5m1gIIUTay7LFftuyFaiKCRfnouRwtv1Z+NH+AVyePZeIa9fJ18yHYu/0Qv1n7mqR9ZjNZlasWMH27dtRFAWLxULbtm0ZMGBAmk91vGDBAsqXL/9SbW579OiBl5dXggY48V34PvjgAxo1aoSjoyN2dnaYzWaKFSvG9OnTk5xONy3dv3+fUaNGERQURLFixZg3b16i67xjY2P55JNPOHfuHE5OTsybN48SJUqg6zpz5szh4MGDqKrK1KlTE8wp4e/vT/v27Tl69GiS62/Xrh2enp7W5j0QNxnPqVOnmDVrlvW+X3/9lc8//9x6ZcHZs2fx9fUlJCQETdOoXr06o0ePTnaK4KTs2LGDpUuXYjab6dWrF926dUvw+KVLl6xNjiBulj1XV1d27tzJuXPnmDx5MrGxsRQoUIBp06YlaBwkso4suxvfHBGJrhvo+OGHto4CgCGHI7rZTNkxH1FiQH8p9Fmc9LNPe/+ln/2PP/7ItWvX2L17N4sXL2bMmDGYzWYgrod9z549E8xF/2+XL1/G3t6ey5cvJ5iiODmXL1/m/fffZ/jw4Wzfvp2tW7ei6zrjx49/wXcfx9/fH19fX1avXs3WrVtZt26ddbrleK+++qq1z/3atWtxdXVl0qRJ6LrOkCFDGDVqFDt27KB169YvnUNkfFlyZP84IhTVEIZuyYVTLtsdC7dERXF/xy4KtmuDXa5cVJ7/qVxSlw7+PH2Xs6duJ7/gS6j8+itUql7oucvE97P/5ZdfEvWzj/9D7Ofnx9SpU4mMjCQwMJCBAwfSpUuXBKNoiOtn/+233xIeHs6ECRMwm804ODgwc+ZMChYsyNixY7l69SoAXbt2pWPHjowePZrXX3+ddu3a4evry4kTJwgNDcXT0xNfX1/y5s1LvXr18Pb25syZMxgMBubPn2+dTz2+n3316tVxc3NL8n3G97N/utnLs7Rt25apU6dSvnx5LBYLDRs2ZMuWLZw6dYqvv/6a6OhoYmNjmTFjBlWrVqVHjx64urpy9epV5s+f/8yZEeP72S9evBiIG2V3796dUaNGJVju0KFDfPjPF/6n+9kfPnyYZs2aoaoqxYoVI3/+/Pzxxx/UqFGDjRs3smjRIus0w8+yefNm6taty+PHj1m/fr11HclZuXIl7du3p1KlSgAYjUZGjRpl7Y/wtDFjxnDx4sUE9/Xt25dWrVpZbx8/fpxatWpZPydvb2/27t2b5LS8y5Yto0aNGlSvXt06j358d72GDRvy0UcfERsba+2YJ7KOLFl59m/ciKKYcLC3XaEPv3ads8NHcXvNOp78FfcLK4U+e3heP3tvb2/g//vZb9q0ieXLlyc7Z3t8P/vNmzfTsWNHzp49yx9//GHtZ79s2bJEbWqf7mf/448/kj9/frZv3w5g7We/detWatSokaDjXHwDnGnTpj0zy7vvvkvr1q2pX78+x44dw8fH57nZ4/vZA8/sZ799+3b69evH8uXLrc8pU6YMP/74Y5JTIP/XfvYBAQEJut893R9+0aJFlC6d9CWwJpOJHTt20LRpU5o2bcrGjRutewWSc+nSJV577bUE97m4uFh/Lp42c+ZM64g8/t/Thf5Z78/T0/OZ2wHimvWsX7/e+kXA3d0dJycn66GKXbt2YTKZCAkJSdF7EZlLlhzZ37v4N6oK5V5/Pd3Xres6D3bs4uY332HnmovyUyfhWv615J8oUk2l6oWSHX2nNeln//8yYj97TdNeus/9oUOH8PDwoGTJktbnHTx4kDfffPOZr/Gyfe5TMrJ/1vtI6nPYvn07TZo0sbb7VRSFhQsXMnv2bObNm0fr1q1xc3OTXvdZVJYbasbExgBPsGi5+F/rpHfDpZUbK77ixsqvca9ahcrzP5NCnw093c8e4nrRb9u2jaVLl1pHTUOHDuWnn36iRIkSDB482Prcf/eWf7qf/ZYtW6hYsSKrVq1i4sSJuLu7s2vXLrp3786NGzdo27ZtgnnkL1y4QN++fdE0DW9vb5o0afLC/ewnT55MaGhoku+1Q4cOXL9+/bmjwX/3s2/cuLG1n/3du3epUaMGPXr0SPCcF+lnDyTbzz5efD/7fPnyPfP+lNi0aRMPHjygUaNGNG7cmPDwcGsb4Fy5ciWayz/+hDiI+9k4f/58gsfDw8MZOHAgsbGxCe5Pycj+WX3uk3ofP//8c6IpjY1GI9999x1bt26lVatWaJr23EM3IvPKcsV+05fLUNVIHIwu6bre+D+Wnk0aUvzdvpQd+zF2uXKmawaRMUg/+8QyWj/7+vXrs2PHDiwWC7du3eLmzZtUqFAh2fUGBgZy/Phxdu7cae1zv3XrVk6ePMmdO3eoXLky586d4/btuHNGYmNj2bJli7X3fO/evVmzZg3nzp0D4r7MzZo1CxcXl5c6Tl6nTh1OnDhBcHAwUVFR7Nu375nbQdd1/vrrL6pUSdjie+zYsdYsX3/9NT4+PinewyEylyy3Gz/w1l1UFarUb5Au69MtFm6vWYc5PJwSA9/FpXhxXIoXT5d1i4xL+tknlNH62fv4+HDu3DnrSHn69OkpuvRt27ZtNGjQwNpLHqBw4cI0atSIdevWMXLkSKZOncrQoUOxWCzExsby1ltv0alTJyDuXIS5c+cyffp0oqKiMJlM1KlTh3Hjxr3Qe4/n5eXFsGHD6NmzJyaTibffftt6CKR///4MGTKEChUqEBwcjJ2dXaJDCJMmTWLixIlERUVRpkwZpk+f/lI5RMaXpfrZx8TG8MX4kSg6DJmzKM1zRPsH4PfpfMKuXMGzSSNKvjcQxWBI8/WmF+lnnz6kD3jak22c9mQbpy7pZ/8cm79cjkENR9HT/uSswGMn+HvxEtB0So8Yhkf9emm+TiEyKulnL0TGlqWK/aMbN1EMUK/l8y8F+q9MoaH8vWgxOQoVoszIoTjmy5em6xMio5N+9kJkbFmm2Ac/CUYxPEHX3Klct26arCM6IAAHDw/sXF0pP20yTkWLoBqzzCYUQgiRRWWZ0y63Lv0CRTHj7JT6c3Trus7Dvfv4Y/CHBPy8HwCXkiWk0AshhMgUsky1igh6gGKAN7t3S37hF2AOD+fvz5cSdOIkbpUr4V6jeqq+vhBCCJHWskSxj4mNQVcsoDnwSjLzdL+IJ5ev4DfvM2KDQyjSqwcF27SSKW+FEEJkOlmicm1d+SWqGoVqSNkMWClliYhAMRipMHMahdq1kUIvUsxsNrN06VKaNm1Ks2bN8Pb25osvvkg0U11aWLBgAfv373+p5/bo0cN6LXy8RYsWWRv0NGrUiGbNmtG6dWuaN2/O+++//9wZ9tLS/fv36datGz4+PgwaNIiIiIhEy8TGxjJq1CjatWtH27ZtuXbtGhB3aG727Nn4+PjQrFmzBNf4f/XVV/j4+ODt7c2+ffsSvF54eDgtWrR47nwFH3zwQaImOr/++muiWQLv3r1Lo0aNrLevX7/OwIEDadmyJS1btmTEiBEEBwenfIP8y/Hjx2nZsiVvvfUWvr6+iR4PCgqidevW1n+NGjWyTrpz8+ZNunfvTsuWLenRowc3btx46RwiY8gS1cv/+i10XaVBi+b/+bVigoJ59MsRANyrVaXK5/PJWSbpphhCPIu0uE17adHi9ty5c2zfvp1t27axevVq5syZw+PHjwH4888/6dKlCzdv3kwyU3BwMBcvXsTZ2Znff/89xe/F39+fnj170rFjR3bs2MH27dspVapUkt3rkhMdHc3YsWNZsmQJu3fv5sKFC4kmR8qTJ491Gt4tW7ZQsGBBpkyZAsTNy9+uXTt27NjBiBEjGDp06EvlEBlHpi/2MbEx6GoEupaL8nVq/qfXCj59hrNDR3Bt6XJMYWEAqNIUQryg+Ba3s2bNStTiNm/evEBci9sePXrQvn17mjVrxpo1a4CEo2iIG0nfvXuXy5cv07FjR9q1a2ctOCaTiVGjRtGmTRvatGnD+vXrARg9ejSbN28GwNfXl44dO+Lt7U2PHj0IDAwEoF69ekydOpU2bdrQvn177ty5Y11nfIvb+CKXlPgWt/HvKSlt27blwoULQNyUwPXr1ycoKIg9e/bQsWNHWrVqhY+Pj7U49ujRg/fffx9vb28uXbr0zNeMb3Eb3y2uXbt27N27N9Fyhw4dss6Sl5IWt7/88gtvvvkmDg4O5MmTh9dff51Dhw4BsH79eiZOnPjcOfR37NhBjRo1eOutt547zfC/rVmzhlq1allH+oqi0L9/f7p27Zqoo56vr2+CEXnr1q358sv/a+/u42q8/z+Av05JVJiMbNYwHkW5axjStPqNQqec7gy/TYgRIkKraBZJmhiGbGazY25ydKP9po3KPfvNEhb2/aGb0Y0ilercfX5/9OialM4pnU7X8X4+Hv7onHNd533ezuPxPtd1rvN5fVPnMZmZmejduzfMzc3Rrl07CIXCBvtT6+jRo+jYsSN3RiIrK4tLMxw2bBgKCwvrvEcI//D+O3vJnljo6z2FnqDxtbkbo5TJkP3Dj7ifeBxGfXrDMnAZDDrRuvZ8dfPKOfz1v2c0sm+rEe9jwLuN/7SzsYjbfv36Afg34nbMmDG4deuWykViaiNuJ06ciGPHjiEjIwOFhYVcxG1BQQG+/PJLeHt7c9s8G3Grp6eHlStXIjExEbNnz+YiblevXo3IyEiIxWIEBQUBqFkT//Hjx1i3bh2io6Pr1VK7XG5xcTH09fVVHn3WRtwOGjSowYhbU1NTxMXFITY2Frt27QJQs6zs9u3bX7hPTUXcFhYW1lkj/9noW3WWkpVIJFi2bBksLCywdetWBAcHqxUsk5WVxeXK19LX14eLi0u9xwYEBCAgIKDR/TUl+lahUGDXrl11zoxYWVkhOTkZXl5euHDhAh4/foyioiKYm5urfC2kbeL9sC/KqVkL317k1qztmUKBa8GrUX77b7wxeSL6+HwCvWYEUhDyLIq4/RefIm6VSmW9fagbDJOVlYX8/HzY2trCwMAAAwcORHx8PHx8fNSKvlU3CCcmJoY721BLKBTC19eX+7sp0bdnzpxBnz596uQqREZGIjw8HPv378e4ceMwYMAAir7lOd4PezAplEojWDfzJ3ECfX10H/c+3vJ0R7dR77VwcUQbBrw7VuXRtyY9G3FrYmICZ2dnODs7Iy8vD5988gmAmhS7zp07w8HBAQ4ODtwpVoFAUGfgPBtxa2Njg9TUVOzbtw9paWlYt24dkpOTce7cOaSnp0MkEiE5OZnb9vr161i+fDl8fHzg5OQEPT29JkfcBgQEwMnJqd5ZilpeXl7YuHEjHj169MLku+cjbtesWcNF3Lq6umLkyJGwtLSEWCzmtmlKxK2+vr7KiNvaDHdVEbcNRcb27du30VpqHT16FFKplPtqoaKiAgcPHoSPj49a0be1X3XUUiqV8Pf3x+eff17nqxJ1juxfNvpWLpdjx44daN++PWQyGQ4dOsR9wCP8xOvv7Ivy8yHQKwOUHZu0nfxpJW5v2YaS32uOZt4UTqZBT1oMRdzWx5eI23HjxiElJQWVlZUoKSnBxYsXuXjaxkilUiQlJWHfvn1c9O3JkydRVFSES5cuoX///igtLcXVq1cB1AzyI0eOcPueOnUq0tPTuYvoGGP4+uuvUVxcrPKaiIYMHToUd+/eRXZ2NhQKBY4fP95gfwAgIyMDI0bUPViKiYnhftERFxeHwYMHo2vXrk2ug7QdvD6yP3c8CQKBAsZdXlN7m/L//B9uRW9GVUEhjPv20Vht5NVGEbd18SXidsiQIXB1dYWnpyfkcjn8/f3rxNm+yKlTp9CrVy8MHTqUu83ExAReXl44ePAgRo0ahS1btiAiIgJVVVWoqqrC6NGjuesdunfvjj179iAqKgrR0dFQKBSwsrLCjh07mtSTWoaGhoiMjMTixYtRXV0Ne3t77oK7kJAQODo6cr/YyM3NRc/n8j0CAwOxatUqbN++HWZmZtiwYUOz6iBtB68jbv/8+WdUPb2DEY7TMHr8hEa3YUol7icdR/YPYhh06QKL5UvRxdqqlSrmJ4q4bR0UDap51GPNox63LIq4fYZCUQml0ljloAeAR1f+xL2938N01Hvov9iPrrYnpAVRxC0hbRuvh71ArwIC1vj39bInT2DQuTO6Dn8XVmtC8Nq7Ni+8KpUQ0jwUcUtI28brC/QEAobX3+zV4H1KuRzZ+8X449OFqLx/HwKBAF2Hv0uDnhBCyCuH10f2jOlBOMe33u1VBYW4/eUWlN26hR4f/hfaq7hSmOiGxn5LTAghfKGJS+n4PeyVRjDpZFLntofnzuM/O3YCDLBYHoDu4+y0VB1pTR06dEBxcTG6detGA58QwluMMRQXF6tca6KpeD3s9QT1r0gszbyGjm/2gmXgUnR47uckRHe99dZbyMvLq7OQCF9IpVK1V08jzUM91jzqccvp0KFDiy9ipNFhn5SUhJ07d0Iul2PmzJmYMWNGnfuzsrIQEhKCiooKjBgxAmvXruXWulaHoUnNUX1Fdg6gVMK4bx/0nTML0NODXhP2Q/jPwMBA7ZXO2po//vijzu+zScujHmse9bht09gFegUFBYiJicGBAwcQHx+PQ4cOcSuD1VqxYgXWrFmDEydOgDHGpXap642+ffDgf04gM3AV7sTWpD7ptW9Pg54QQgh5hsaG/fnz5zF69Gi89tprMDIygpOTU52IxX/++QdVVVUYNmwYgBdHVDam591c3NkVi85WA2G5KrAlyyeEEEJ0hsYOgRuKWMzMzHzh/S+KqGxI7ZWKZdnZeGu2D8wmfAimp4fq6uoWqp7Uop62Duqz5lGPNY96rDlSqRRA86/U19iwVxWx2JQIxufVJoEZzvxvPATw8K+/WqZoUs/zSVxEM6jPmkc91jzqsebJZLJmXamvsWHfs2fPOhnZz0csPh/BWBsxqQ5jY2NYWFjAwMCAfmZFCCFE5zHGIJPJmp0/oLFhb2tri23btqGkpAQdO3ZESkoKwsPDuft79eoFQ0NDLmwlISHhhRGMz9PT00MnWtueEELIK+Rlfnuv0dS7pKQk7N69GzKZDJ6enpg7dy7mzp0Lf39/DB48GDdv3kRoaCjKy8thbW2NDRs20O80CSGEkBbGy4hbQgghhKiP10E4hBBCCFGNhj0hhBCi42jYE0IIITqOhj0hhBCi42jYE0IIITquzQ/7pKQkTJo0CRMmTIBYLK53f1ZWFtzd3eHk5ISQkBDI5XItVMlvqnr822+/wc3NDa6urvDz80NpaakWquQ3VT2ulZaWBkdHx1asTLeo6vOdO3fw8ccfw9XVFXPmzKH3cjOo6vGNGzfg4eEBV1dXfPrpp3jy5IkWquS/8vJyuLi4IC8vr959zZp7rA3Lz89nDg4O7NGjR6yiooIJhUL2999/13nM5MmT2Z9//skYY+yzzz5jYrFYC5Xyl6oel5WVsbFjx7L8/HzGGGNbtmxh4eHh2iqXl9R5HzPGWFFREXN2dmYODg5aqJL/VPVZqVSyCRMmsPT0dMYYY5s2bWJRUVHaKpeX1HkvT5s2jaWlpTHGGNuwYQPbvHmzNkrltYyMDObi4sKsra1Zbm5uvfubM/fa9JF9ayTnvepU9VgmkyEsLAxmZmYAAEtLSzx48EBb5fKSqh7XCg0NxaJFi7RQoW5Q1ecbN27AyMiIW6lz/vz5mDFjhrbK5SV13stKpRIVFRUAgMrKypda9e1VdfjwYYSFhTW4hHxz516bHvYNJec9m4z3Msl5pIaqHnft2hXjx48HAFRVVSE2NhYffvhhq9fJZ6p6DAA//PADrKysMHTo0NYuT2eo6nNOTg5ef/11BAcHQyQSISwsDEZGRtoolbfUeS8HBQUhNDQUdnZ2OH/+PD766KPWLpP31q9fjxEjRjR4X3PnXpse9ppMziM11O1hWVkZ5s2bhwEDBkAkErVmibynqse3b99GSkoK/Pz8tFGezlDVZ7lcjsuXL2PatGk4duwYzM3NERkZqY1SeUtVj6uqqhASEoJ9+/bh7NmzmD59OlatWqWNUnVWc+demx72zyfjtWRyHqmhqsdAzSfJ6dOnw9LSEuvXr2/tEnlPVY9/+eUXFBUVwcPDA/PmzeP6TZpGVZ+7d++O3r17Y/DgwQAAFxcXZGZmtnqdfKaqx7dv34ahoSGGDBkCAJg6dSouX77c6nXqsubOvTY97G1tbXHhwgWUlJSgsrISKSkpdZLxnk3OA9Ck5DxSQ1WPFQoF5s+fj4kTJyIkJITOnDSDqh77+/vjxIkTSEhIQGxsLHr06IEDBw5osWJ+UtVnGxsblJSU4ObNmwCAU6dOwdraWlvl8pKqHvfu3Rv5+fm4c+cOAODkyZPchyvSMpo991rs8kENSUxMZJMnT2YTJkxgsbGxjDHGfH19WWZmJmOMsaysLObh4cGcnJzYsmXLWHV1tTbL5aXGepySksIsLS2Zq6sr9y84OFjLFfOPqvdxrdzcXLoa/yWo6nNGRgbz8PBgkyZNYrNnz2YPHz7UZrm8pKrHaWlpTCgUMhcXFzZz5kyWk5OjzXJ5zcHBgbsa/2XnHqXeEUIIITquTZ/GJ4QQQsjLo2FPCCGE6Dga9oQQQoiOo2FPCCGE6Dga9oQQQoiOa6ftAgh5VVhaWsLCwgJ6ev9+xh40aFCjCxVJJBKcOHECu3fvfunn37ZtG8RiMczMzCAQCKBQKNCtWzeEhYWhb9++Td5fQUEBlixZgoMHDyI3NxdRUVHYtm1bndtfVl5eHsaPHw8LCwvutqdPn6Jnz56IiIiAubl5o9tv374dAwYMoCWeySuPhj0hrej777+Hqamp1p5/0qRJWLNmDff3/v37sXz5ckgkkibvy8zMjBvo9+/fx927d+vd3hI6dOiAhIQE7m/GGNatW4eYmBhs3ry50W0vXbqE/v37t1gthPAVncYnpA2Ii4uDl5cXpkyZAgcHhwZX0EtJSYFIJIK7uzu8vLzw+++/A6jJLQgKCoK7uzuEQiEiIiLUy7cGMGbMGG5I5+fnY/78+RAKhXBxccE333wDoGZN+bCwMAiFQri7u8Pf3x8VFRXIy8uDjY0NFAoFQkNDkZOTgzlz5tS53d7eHtevX+eeb+nSpdxr27lzJ0QiEdzc3ODn56d2iFV1dTUKCwvRpUsXAMDdu3cxa9YseHt7w8HBAQsWLEB1dTXEYjGuX7+OqKgo/Prrr5BKpYiIiIBIJIKrqyuCgoJQXl6u1nMSwnc07AlpRTNnzoSbmxv3r7i4GBUVFThy5AhiY2MRHx+PmJgYbNq0qd62UVFRCAsLg0QiwZIlS3Dp0iUAQEREBKytrSGRSBAfH49Hjx7hu+++U1mLXC5HXFwcRo0aBQAIDAzEqFGjkJSUhJ9++gmJiYlITk5GRkYGLl++jMTEREgkEpibm+PWrVvcfvT19bFu3Tq8/fbb+Pbbb+vc7uHhwZ01KC0txYULFyAUChEfH4/bt2/jyJEjSEhIgL29PUJDQxuss6qqCm5ubhAKhbC1tYVIJMI777yDwMBAADVxoFOmTMHhw4eRkpKCvLw8pKWlYcaMGRg0aBBWrlyJ8ePHIzY2Fvr6+pBIJEhMTESPHj0QHR2t5v8cIfxGp/EJaUUvOo2/a9cupKen4969e7h58yaePn1a7zGTJ0/GokWLYG9vj7Fjx2Lu3LkAgLS0NFy7dg1xcXEAaobji/z888/cmtoymQzW1tYIDw/H06dPceXKFezduxcA0KlTJ7i7u+P06dMICQmBvr4+vLy8YGdnBycnJwwZMgR5eXkqX6+Hhwc8PT0RFBSE48ePw9HREZ06dUJqaiquXbsGDw8PADVJXpWVlQ3u49nT+GfOnMGKFSvg4OAAY2NjAMCKFStw7tw57NmzB/fu3UNhYWGD/UtLS0NZWRnOnz/Pvf5u3bqpfA2E6AIa9oRoWX5+PqZOnQpvb28MHz4czs7OSE1Nrfe4gIAAeHh44Ny5c5BIJNi7dy/i4uKgVCqxdetW9OvXDwDw5MmTFwYWPf+dfa3y8nI8v3K2UqmEXC5H586dkZCQgCtXruDixYtYunQp5syZA3t7e5WvrVevXrCyskJaWhokEgmCg4O5ffv6+nLpflKpFKWlpSr39/7772PWrFlYsmQJkpOTYWJigmXLlkGhUGDixIn44IMP8ODBg3qvpfY5g4ODuborKipQXV2t8jkJ0QV0Gp8QLbt+/TpMTU3h5+cHOzs7btArFAruMXK5HI6OjqisrMS0adMQFhaGW7duQSqVws7ODvv27QNjDFKpFAsWLMCPP/7YpBpMTEwwdOhQiMViADXXAcTHx8PW1hapqanw8fGBjY0NFi9ejClTptT5Hh6oOWUvk8ka3Le3tzf27NmDyspKDB8+HABgZ2eHuLg47jvzrVu3YuXKlWrVOnv2bBgbG+Orr74CAJw9exYLFy7EpEmTAABXr17leqevr89dv2BnZwexWAypVAqlUonVq1ervMCPEF1BR/aEaNnYsWMRFxcHZ2dnCAQCvPfeezA1NUV2djb3mHbt2iE4OBiBgYFo164dBAIBIiIi0L59e4SEhGD9+vUQCoWQyWSwtbWFr69vk+uIjo7GF198AYlEAqlUyl2Qp1Qqcfr0abi4uMDIyAhdunRBeHh4nW379+8PQ0NDeHp6IiYmps59jo6OWLt2Lfe1AwB4eXmhoKAA3t7eEAgEeOONNxAZGalWnQYGBli9ejV8fX3h6emJgIAALFy4EEZGRjAxMcHIkSORk5PDPffmzZshk8ng5+eHjRs3QiQSQaFQYODAgQgKCmpynwjhI0q9I4QQQnQcncYnhBBCdBwNe0IIIUTH0bAnhBBCdBwNe0IIIUTH0bAnhBBCdBwNe0IIIUTH0bAnhBBCdBwNe0IIIUTH/T+MtXPPBalHMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 882 ms, sys: 12.5 ms, total: 895 ms\n",
      "Wall time: 895 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import warnings\n",
    "import time\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "sampling_time = time.time()\n",
    "\n",
    "train_records = {}\n",
    "record_df = pd.DataFrame()\n",
    "\n",
    "for model_name, model in models_dic.items():\n",
    "    train_records[model_name] = 0\n",
    "    model_start = time.time()\n",
    "    model.fit(X_train, y_train)\n",
    "    model_end = time.time()\n",
    "    train_records[model_name] = model_end - model_start\n",
    "\n",
    "performance_df = pd.DataFrame()\n",
    "performance_csv_name = \"performance.csv\"\n",
    "record_name = \"time_record.txt\"\n",
    "\n",
    "f = open(record_name,\"w\")\n",
    "f.write(str(train_records))\n",
    "f.close()\n",
    "\n",
    "#for model_name, model in models_dic.items():\n",
    "    #thresholds = [0.4, 0.5, 0.6, 0.7, 0.8]\n",
    "    #for t in thresholds:\n",
    "t = 'undefined'\n",
    "performance_result = evaluate_performance(models_dic, X_test, y_test, t)\n",
    "    #performance_df = pd.concat([performance_df, pd.DataFrame.from_dict(performance_result)])\n",
    "performance_df = pd.DataFrame.from_dict(performance_result).T.iloc[1:]\n",
    "\n",
    "performance_df.to_csv(performance_csv_name)\n",
    "\n",
    "figure_name = \"AUC.png\"\n",
    "plot_roc(models_dic, X_test, y_test, figure_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>acc</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.77544</td>\n",
       "      <td>0.333117</td>\n",
       "      <td>0.581101</td>\n",
       "      <td>0.423476</td>\n",
       "      <td>0.788795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB_var_9.111627561154887e-05</th>\n",
       "      <td>0.77544</td>\n",
       "      <td>0.333117</td>\n",
       "      <td>0.581101</td>\n",
       "      <td>0.423476</td>\n",
       "      <td>0.788796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB_var_0.1</th>\n",
       "      <td>0.784126</td>\n",
       "      <td>0.339809</td>\n",
       "      <td>0.552622</td>\n",
       "      <td>0.420841</td>\n",
       "      <td>0.789996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB_var_0.01</th>\n",
       "      <td>0.776404</td>\n",
       "      <td>0.333896</td>\n",
       "      <td>0.578367</td>\n",
       "      <td>0.423374</td>\n",
       "      <td>0.78892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB_var_0.001</th>\n",
       "      <td>0.77552</td>\n",
       "      <td>0.333153</td>\n",
       "      <td>0.580724</td>\n",
       "      <td>0.423405</td>\n",
       "      <td>0.788808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB_var_0.0001</th>\n",
       "      <td>0.775427</td>\n",
       "      <td>0.333081</td>\n",
       "      <td>0.581007</td>\n",
       "      <td>0.423422</td>\n",
       "      <td>0.788796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           acc precision    recall        f1  \\\n",
       "GaussianNB                             0.77544  0.333117  0.581101  0.423476   \n",
       "GaussianNB_var_9.111627561154887e-05   0.77544  0.333117  0.581101  0.423476   \n",
       "GaussianNB_var_0.1                    0.784126  0.339809  0.552622  0.420841   \n",
       "GaussianNB_var_0.01                   0.776404  0.333896  0.578367  0.423374   \n",
       "GaussianNB_var_0.001                   0.77552  0.333153  0.580724  0.423405   \n",
       "GaussianNB_var_0.0001                 0.775427  0.333081  0.581007  0.423422   \n",
       "\n",
       "                                           auc  \n",
       "GaussianNB                            0.788795  \n",
       "GaussianNB_var_9.111627561154887e-05  0.788796  \n",
       "GaussianNB_var_0.1                    0.789996  \n",
       "GaussianNB_var_0.01                    0.78892  \n",
       "GaussianNB_var_0.001                  0.788808  \n",
       "GaussianNB_var_0.0001                 0.788796  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions:\n",
    " - GaussianNB: best params is the default setting as the default uses the smallest possible variance smoothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler()\n",
    "X_train2, y_train2 = rus.fit_resample(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "model = SVC()\n",
    "model.fit(X_train2,y_train2)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 547 µs, sys: 146 µs, total: 693 µs\n",
      "Wall time: 590 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'C': [0.1, 0.5, 1],\n",
    "              'gamma': ['scale', 'auto'],\n",
    "              'kernel': ['linear','poly','rbf','sigmoid'],\n",
    "            'class_weight':['balanced']}\n",
    " \n",
    "clf = SVC(gamma='scale')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n",
      "[CV 1/5] END C=0.1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.749 total time= 1.1min\n",
      "[CV 2/5] END C=0.1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.748 total time= 1.0min\n",
      "[CV 3/5] END C=0.1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.753 total time=  55.6s\n",
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      "[CV 1/5] END C=0.1, class_weight=balanced, gamma=scale, kernel=poly;, score=0.744 total time= 1.2min\n",
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      "[CV 1/5] END C=0.1, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.750 total time= 1.7min\n",
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      "[CV 3/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=poly;, score=0.745 total time= 1.2min\n",
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      "[CV 2/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.746 total time= 1.9min\n",
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      "[CV 4/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.751 total time= 1.7min\n",
      "[CV 5/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.752 total time= 1.6min\n",
      "[CV 1/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.665 total time=  52.4s\n",
      "[CV 2/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.660 total time=  48.2s\n",
      "[CV 3/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.667 total time=  48.9s\n",
      "[CV 4/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.659 total time=  47.6s\n",
      "[CV 5/5] END C=0.5, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.662 total time=23.5min\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.749 total time=13.5min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/5] END C=1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.748 total time= 1.4min\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.753 total time= 1.3min\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.746 total time= 1.3min\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=scale, kernel=linear;, score=0.751 total time= 1.2min\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=scale, kernel=poly;, score=0.744 total time= 1.3min\n",
      "[CV 2/5] END C=1, class_weight=balanced, gamma=scale, kernel=poly;, score=0.742 total time= 1.3min\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=scale, kernel=poly;, score=0.745 total time= 1.2min\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=scale, kernel=poly;, score=0.744 total time= 1.2min\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=scale, kernel=poly;, score=0.744 total time= 1.1min\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=scale, kernel=rbf;, score=0.748 total time= 2.0min\n",
      "[CV 2/5] END C=1, class_weight=balanced, gamma=scale, kernel=rbf;, score=0.745 total time=17.5min\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=scale, kernel=rbf;, score=0.752 total time=27.6min\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=scale, kernel=rbf;, score=0.752 total time=34.1min\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=scale, kernel=rbf;, score=0.752 total time=17.6min\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=scale, kernel=sigmoid;, score=0.666 total time=  51.7s\n",
      "[CV 2/5] END C=1, class_weight=balanced, gamma=scale, kernel=sigmoid;, score=0.661 total time= 1.2min\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=scale, kernel=sigmoid;, score=0.670 total time=  46.8s\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=scale, kernel=sigmoid;, score=0.661 total time=  48.5s\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=scale, kernel=sigmoid;, score=0.664 total time=  42.3s\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=auto, kernel=linear;, score=0.749 total time= 1.4min\n",
      "[CV 2/5] END C=1, class_weight=balanced, gamma=auto, kernel=linear;, score=0.748 total time= 1.4min\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=auto, kernel=linear;, score=0.753 total time= 1.3min\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=auto, kernel=linear;, score=0.746 total time= 1.3min\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=auto, kernel=linear;, score=0.751 total time= 1.2min\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=auto, kernel=poly;, score=0.745 total time= 1.5min\n",
      "[CV 2/5] END C=1, class_weight=balanced, gamma=auto, kernel=poly;, score=0.742 total time= 1.4min\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=auto, kernel=poly;, score=0.745 total time= 1.3min\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=auto, kernel=poly;, score=0.745 total time= 1.3min\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=auto, kernel=poly;, score=0.745 total time= 1.2min\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.749 total time= 2.0min\n",
      "[CV 2/5] END C=1, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.745 total time= 2.0min\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.752 total time= 1.9min\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.751 total time= 1.9min\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=auto, kernel=rbf;, score=0.753 total time= 1.7min\n",
      "[CV 1/5] END C=1, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.665 total time=  51.2s\n",
      "[CV 2/5] END C=1, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.659 total time=  48.9s\n",
      "[CV 3/5] END C=1, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.666 total time= 1.2min\n",
      "[CV 4/5] END C=1, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.659 total time=  48.9s\n",
      "[CV 5/5] END C=1, class_weight=balanced, gamma=auto, kernel=sigmoid;, score=0.662 total time=  45.7s\n",
      "{'C': 0.5, 'class_weight': 'balanced', 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "CPU times: user 2h 31min 4s, sys: 9.71 s, total: 2h 31min 14s\n",
      "Wall time: 5h 21min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "model = SVC()\n",
    "gridSVC = GridSearchCV(estimator=model, \n",
    "                         param_grid=param_grid, \n",
    "                         cv=5,   # use any cross validation technique \n",
    "                         verbose=3, \n",
    "                         scoring='balanced_accuracy') \n",
    "\n",
    "gridSVC.fit(X_train2, y_train2)\n",
    "# print best parameter after tuning\n",
    "print(gridSVC.best_params_)\n",
    " \n",
    "# print how our model looks after hyper-parameter tuning\n",
    "#print(gridSVC.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(gridSVC.cv_results_).sort_values('rank_test_score').to_csv('SVC_tune.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1, 'class_weight': 'balanced', 'gamma': 'scale', 'kernel': 'sigmoid'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_tune = pd.DataFrame(gridSVC.cv_results_).sort_values('rank_test_score')\n",
    "#svc_tune['']\n",
    "svc_tune['params'][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_dic = {'SVC': SVC(probability=True),\n",
    "             '0.5_scale_rbf': SVC(C = 0.5, class_weight='balanced', gamma='scale', kernel='rbf',probability=True),\n",
    "              '0.1_scale_poly': SVC(C = 0.5, class_weight='balanced', gamma='auto', kernel='rbf',probability=True),\n",
    "              '0.1_scale_rbf': SVC(C = 0.1, class_weight='balanced', gamma='scale', kernel='rbf',probability=True),\n",
    "              '0.1_scale_sigmoid': SVC(C = 1, class_weight='balanced', gamma='auto', kernel='rbf',probability=True)\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_performance(model_dict, x, y, t):\n",
    "    out = {\"threshold\": t}\n",
    "    print('start evaluate')\n",
    "    for model_name, model in model_dict.items():\n",
    "        print(model_name)\n",
    "        metrics = [\"acc\", \"precision\", \"recall\", \"f1\", \"auc\"]\n",
    "        probs = model.predict_proba(x)[:,1]\n",
    "        #y_pred = probs>= t\n",
    "        y_pred =  model.predict(x)\n",
    "        y_true = y\n",
    "        acc = accuracy_score(y_true, y_pred)\n",
    "        precision = precision_score(y_true, y_pred)\n",
    "        recall = recall_score(y_true, y_pred)\n",
    "        f1 = f1_score(y_true, y_pred)\n",
    "        auc = roc_auc_score(y_true, probs)\n",
    "        metrics_dict = dict(zip(metrics,np.array([acc,precision,recall,f1,auc])))\n",
    "        out[model_name] = metrics_dict\n",
    "    return out\n",
    "\n",
    "def roc_cal(y_true, probas):\n",
    "    fpr, tpr, theshold = roc_curve(y_true, probas)\n",
    "    return fpr, tpr\n",
    "\n",
    "def plot_roc(models_dict, X_test_t, y_test, name):\n",
    "    sns.set_theme(style=\"whitegrid\")\n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "\n",
    "    for model_name, model in models_dict.items():\n",
    "        \n",
    "        probs = model.predict_proba(X_test)[:,1]\n",
    "        fpr, tpr = roc_cal(y_test, probs)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr,label = '{} AUC = {:.2f}'.format(model_name, roc_auc))\n",
    "\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.savefig(\"{}\".format(name))\n",
    "    plt.show()##added by Ziyi\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start fitting\n",
      "SVC\n",
      "0.5_scale_rbf\n",
      "0.1_scale_poly\n",
      "0.1_scale_rbf\n",
      "0.1_scale_sigmoid\n",
      "start evaluate\n",
      "SVC\n",
      "0.5_scale_rbf\n",
      "0.1_scale_poly\n",
      "0.1_scale_rbf\n",
      "0.1_scale_sigmoid\n",
      "start evaluate\n",
      "SVC\n",
      "0.5_scale_rbf\n",
      "0.1_scale_poly\n",
      "0.1_scale_rbf\n",
      "0.1_scale_sigmoid\n",
      "start evaluate\n",
      "SVC\n",
      "0.5_scale_rbf\n",
      "0.1_scale_poly\n",
      "0.1_scale_rbf\n",
      "0.1_scale_sigmoid\n",
      "start evaluate\n",
      "SVC\n",
      "0.5_scale_rbf\n",
      "0.1_scale_poly\n",
      "0.1_scale_rbf\n",
      "0.1_scale_sigmoid\n",
      "start evaluate\n",
      "SVC\n",
      "0.5_scale_rbf\n",
      "0.1_scale_poly\n",
      "0.1_scale_rbf\n",
      "0.1_scale_sigmoid\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3h 47min 40s, sys: 12.4 s, total: 3h 47min 52s\n",
      "Wall time: 8h 28min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import warnings\n",
    "import time\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "sampling_time = time.time()\n",
    "\n",
    "train_records = {}\n",
    "record_df = pd.DataFrame()\n",
    "print('start fitting')\n",
    "for model_name, model in models_dic.items():\n",
    "    print(model_name)\n",
    "    train_records[model_name] = 0\n",
    "    model_start = time.time()\n",
    "    model.fit(X_train2, y_train2)\n",
    "    model_end = time.time()\n",
    "    train_records[model_name] = model_end - model_start\n",
    "\n",
    "performance_df = pd.DataFrame()\n",
    "performance_csv_name = \"performance.csv\"\n",
    "record_name = \"time_record.txt\"\n",
    "\n",
    "f = open(record_name,\"w\")\n",
    "f.write(str(train_records))\n",
    "f.close()\n",
    "\n",
    "#for model_name, model in models_dic.items():\n",
    "    #thresholds = [0.4, 0.5, 0.6, 0.7, 0.8]\n",
    "    #for t in thresholds:\n",
    "t = 'undefined'\n",
    "performance_result = evaluate_performance(models_dic, X_test, y_test, t) ####note: remove indent\n",
    "    #performance_df = pd.concat([performance_df, pd.DataFrame.from_dict(performance_result)])\n",
    "performance_df = pd.DataFrame.from_dict(performance_result).T.iloc[1:]\n",
    "\n",
    "performance_df.to_csv(performance_csv_name)\n",
    "\n",
    "figure_name = \"AUC.png\"\n",
    "plot_roc(models_dic, X_test, y_test, figure_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>acc</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>0.710968</td>\n",
       "      <td>0.304521</td>\n",
       "      <td>0.807337</td>\n",
       "      <td>0.442235</td>\n",
       "      <td>0.8181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5_scale_rbf</th>\n",
       "      <td>0.710553</td>\n",
       "      <td>0.304352</td>\n",
       "      <td>0.808469</td>\n",
       "      <td>0.442226</td>\n",
       "      <td>0.820767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1_scale_poly</th>\n",
       "      <td>0.709523</td>\n",
       "      <td>0.303467</td>\n",
       "      <td>0.808091</td>\n",
       "      <td>0.441235</td>\n",
       "      <td>0.819742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1_scale_rbf</th>\n",
       "      <td>0.707957</td>\n",
       "      <td>0.302924</td>\n",
       "      <td>0.812901</td>\n",
       "      <td>0.441372</td>\n",
       "      <td>0.825872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1_scale_sigmoid</th>\n",
       "      <td>0.710808</td>\n",
       "      <td>0.304377</td>\n",
       "      <td>0.807243</td>\n",
       "      <td>0.442069</td>\n",
       "      <td>0.816941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        acc precision    recall        f1       auc\n",
       "SVC                0.710968  0.304521  0.807337  0.442235    0.8181\n",
       "0.5_scale_rbf      0.710553  0.304352  0.808469  0.442226  0.820767\n",
       "0.1_scale_poly     0.709523  0.303467  0.808091  0.441235  0.819742\n",
       "0.1_scale_rbf      0.707957  0.302924  0.812901  0.441372  0.825872\n",
       "0.1_scale_sigmoid  0.710808  0.304377  0.807243  0.442069  0.816941"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion: Using default SVC is sufficient. Using a smaller C is too time-consuming while the performance does not have siginificant impove."
   ]
  }
 ],
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