Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import HDFStore,read_hdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_train=read_hdf('/Users/saisantosh/Desktop/Facebook/Storage_sample_stage.h5','final_train',mode='r')\n",
    "df_final_test=read_hdf(\"/Users/saisantosh/Desktop/Facebook/Storage_sample_stage.h5\",'final_test',mode='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['source_node', 'destination_node', 'indicator_link',\n",
       "       'Jaccard_followers', 'Jaccard_followees', 'Cosine_followers',\n",
       "       'Cosine_followees', 'adar_index', 'follows_back', 'same_comp',\n",
       "       'shortest_path', 'page_rank_s', 'page_rank_d', 'katz_s', 'katz_d',\n",
       "       'hubs_s', 'hubs_d', 'authorities_s', 'authorities_d', 'P_A_followee',\n",
       "       'P_A_followers', 'num_followers_s', 'num_followers_d',\n",
       "       'num_followees_s', 'num_followees_d', 'inter_followers',\n",
       "       'inter_followees', 'weight_in', 'weight_out', 'weight_f1', 'weight_f2',\n",
       "       'weight_f3', 'weight_f4', 'svd_u_s_1', 'svd_u_s_2', 'svd_u_s_3',\n",
       "       'svd_u_s_4', 'svd_u_s_5', 'svd_u_s_6', 'svd_u_d_1', 'svd_u_d_2',\n",
       "       'svd_u_d_3', 'svd_u_d_4', 'svd_u_d_5', 'svd_u_d_6', 'svd_v_s_1',\n",
       "       'svd_v_s_2', 'svd_v_s_3', 'svd_v_s_4', 'svd_v_s_5', 'svd_v_s_6',\n",
       "       'svd_v_d_1', 'svd_v_d_2', 'svd_v_d_3', 'svd_v_d_4', 'svd_v_d_5',\n",
       "       'svd_v_d_6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['source_node', 'destination_node', 'indicator_link',\n",
       "       'Jaccard_followers', 'Jaccard_followees', 'Cosine_followers',\n",
       "       'Cosine_followees', 'adar_index', 'follows_back', 'same_comp',\n",
       "       'shortest_path', 'page_rank_s', 'page_rank_d', 'katz_s', 'katz_d',\n",
       "       'hubs_s', 'hubs_d', 'authorities_s', 'authorities_d', 'P_A_followee',\n",
       "       'P_A_followers', 'num_followers_s', 'num_followers_d',\n",
       "       'num_followees_s', 'num_followees_d', 'inter_followers',\n",
       "       'inter_followees', 'weight_in', 'weight_out', 'weight_f1', 'weight_f2',\n",
       "       'weight_f3', 'weight_f4', 'svd_u_s_1', 'svd_u_s_2', 'svd_u_s_3',\n",
       "       'svd_u_s_4', 'svd_u_s_5', 'svd_u_s_6', 'svd_u_d_1', 'svd_u_d_2',\n",
       "       'svd_u_d_3', 'svd_u_d_4', 'svd_u_d_5', 'svd_u_d_6', 'svd_v_s_1',\n",
       "       'svd_v_s_2', 'svd_v_s_3', 'svd_v_s_4', 'svd_v_s_5', 'svd_v_s_6',\n",
       "       'svd_v_d_1', 'svd_v_d_2', 'svd_v_d_3', 'svd_v_d_4', 'svd_v_d_5',\n",
       "       'svd_v_d_6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=df_final_train.indicator_link\n",
    "y_test=df_final_test.indicator_link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping source_node,destination_node, indicator_links\n",
    "\n",
    "df_final_train.drop(['source_node','destination_node','indicator_link'],axis=1,inplace=True)\n",
    "df_final_test.drop(['source_node','destination_node','indicator_link'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimators =  10 Train Score 0.9188062968263321 test Score 0.9114352861468102\n",
      "Estimators =  50 Train Score 0.9249151792299749 test Score 0.9176625137723536\n",
      "Estimators =  100 Train Score 0.9250105529759391 test Score 0.9178297204428796\n",
      "Estimators =  250 Train Score 0.9236672518738895 test Score 0.916058934195785\n",
      "Estimators =  450 Train Score 0.9237135055660577 test Score 0.9157639108858621\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Estimators vs score at depth of 5')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "estimators = [10,50,100,250,450]\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in estimators:\n",
    "    clf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=52, min_samples_split=120,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=i,random_state=25,verbose=0,warm_start=False)\n",
    "    \n",
    "    \n",
    "    \n",
    "    clf.fit(df_final_train,y_train)\n",
    "    \n",
    "    train_sc = f1_score(y_train,clf.predict(df_final_train))\n",
    "    \n",
    "    test_sc = f1_score(y_test,clf.predict(df_final_test))\n",
    "    \n",
    "    test_scores.append(test_sc)\n",
    "    \n",
    "    train_scores.append(train_sc)\n",
    "    \n",
    "    print('Estimators = ',i,'Train Score',train_sc,'test Score',test_sc)\n",
    "plt.plot(estimators,train_scores,label='Train Score')\n",
    "plt.plot(estimators,test_scores,label='Test Score')\n",
    "plt.xlabel('Estimators')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Estimators vs score at depth of 5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth= 3 Train score 0.916261278117334 and Test scores 0.9094447001715698\n",
      "depth= 9 Train score 0.9554083703299306 and Test scores 0.9223292729510632\n",
      "depth= 11 Train score 0.9592841071373531 and Test scores 0.9229887497346636\n",
      "depth= 15 Train score 0.9617618935008585 and Test scores 0.9245876551606869\n",
      "depth= 20 Train score 0.9631508157335441 and Test scores 0.9253712319163853\n",
      "depth= 35 Train score 0.9628602819620286 and Test scores 0.9253484024473146\n",
      "depth= 50 Train score 0.9628602819620286 and Test scores 0.9253484024473146\n",
      "depth= 70 Train score 0.9628602819620286 and Test scores 0.9253484024473146\n",
      "depth= 130 Train score 0.9628602819620286 and Test scores 0.9253484024473146\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "depths=[3,9,11,15,20,35,50,70,130]\n",
    "train_scores=[]\n",
    "test_scores=[]\n",
    "for i in depths:\n",
    "    clf=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=i, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=52, min_samples_split=120,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=115, random_state=25,verbose=0,warm_start=False)\n",
    "    clf.fit(df_final_train,y_train)\n",
    "    \n",
    "    train_sc=f1_score(y_train,clf.predict(df_final_train))\n",
    "    test_sc=f1_score(y_test,clf.predict(df_final_test))\n",
    "    test_scores.append(test_sc)\n",
    "    train_scores.append(train_sc)\n",
    "    print('depth=',i,\"Train score\",train_sc, \"and Test scores\",test_sc)\n",
    "plt.plot(depths,train_scores,label='Train score')\n",
    "plt.plot(depths,test_scores,label='Test score')\n",
    "plt.xlabel(\"Depth\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean test scores [0.96039905 0.96047066 0.95840575 0.96014103 0.96195597]\n",
      "********************\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=14, max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=28, min_samples_split=111,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=121,\n",
      "                       n_jobs=None, oob_score=False, random_state=25, verbose=0,\n",
      "                       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "from scipy.stats import uniform\n",
    "\n",
    "\n",
    "param_dist={\"n_estimators\":sp_randint(105,125),\n",
    "            \"max_depth\":sp_randint(10,15),\n",
    "            \"min_samples_split\":sp_randint(110,190),\n",
    "            \"min_samples_leaf\":sp_randint(25,65)}\n",
    "\n",
    "clf=RandomForestClassifier(random_state=25)\n",
    "\n",
    "rf_random=RandomizedSearchCV(clf,param_distributions=param_dist,n_iter=5,cv=10,scoring='f1',random_state=25)\n",
    "\n",
    "rf_random.fit(df_final_train,y_train)\n",
    "print(\"mean test scores\",rf_random.cv_results_[\"mean_test_score\"])\n",
    "# print(\"mean train score\",rf_random.cv_results_[\"mean_train_score\"])\n",
    "# print('mean train scores',rf_random.cv_results_['mean_train_score'])\n",
    "\n",
    "print(\"*\"*20)\n",
    "print(rf_random.best_estimator_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=14, max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=28, min_samples_split=111,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=121,\n",
      "                       n_jobs=None, oob_score=False, random_state=25, verbose=0,\n",
      "                       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(\"*\"*80)\n",
    "print(rf_random.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train f1 score 0.9629910682240019\n",
      "Test f1 score 0.9248238390355717\n"
     ]
    }
   ],
   "source": [
    "#now with best estimator\n",
    "\n",
    "clf=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "                       max_depth=14, max_features='auto', max_leaf_nodes=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=28, min_samples_split=111,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=121,\n",
    "                       n_jobs=None, oob_score=False, random_state=25, verbose=0,\n",
    "                       warm_start=False)\n",
    "\n",
    "clf.fit(df_final_train,y_train)\n",
    "y_train_pred=clf.predict(df_final_train)\n",
    "y_test_pred=clf.predict(df_final_test)\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "print('Train f1 score',f1_score(y_train,y_train_pred))\n",
    "print('Test f1 score',f1_score(y_test,y_test_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "import seaborn as sns#Plots\n",
    "def plot_confusion_matrix(test_y, predict_y):\n",
    "    C = confusion_matrix(test_y, predict_y)\n",
    "    \n",
    "    recall =(((C.T)/(C.sum(axis=1))).T)\n",
    "    \n",
    "    precision =(C/C.sum(axis=0))\n",
    "    \n",
    "    plt.figure(figsize=(20,4))\n",
    "    \n",
    "    labels = [0,1]\n",
    "    \n",
    "    # representing A in heatmap format\n",
    "    \n",
    "    cmap=sns.light_palette(\"blue\")\n",
    "    plt.subplot(1, 3, 1)\n",
    "    sns.heatmap(C, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Confusion matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 2)\n",
    "    sns.heatmap(precision, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Precision matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 3)\n",
    "    # representing B in heatmap format\n",
    "    sns.heatmap(recall, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Recall matrix\")\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_confusion_matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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HMSvhvwmwaVW+NuUL/mYN67yZEls2rbavYWwe48S/KJfQHdZjm6MoSaP31uv1dSjTcByMRpyWJXki4s0RcXhEnBYRp1bP39Kq9xsC0yhz2ECZb+A+Zt3O9WTKfDKNZ8c+CvyYWYmfJ3rZ5paUWwd230b8hYZl3ZN/9bQNJRP/DGU0zrWUg7ZVgKUo15EmpbPvvtPWTswa7XMBze/ApXlQq9XGUc6YnN1QtiAlgffZxrr1ev2her1+J2XkVl/bWwVYql6v31ydfR3I33Un4MJ6vZ71ev0WYJlqO9KQmg/ixJzakDJXwYOUERiXUv5/GyWlbwdYmlmjO5MSJ0ZRzsq+yqwY0l02inIXxLm9i6LaZ0Ngcr1ef7Ber/f1WWjs8y8HtqzValGv12+v1+vdf+N7gEVrtdoi/cQPaVgyTrzOQOLEBMrJPii36u5ensCizJrIeCHKXDMvV/WotvknYFxrmq9BNC9x4h/1ev13vH5Ub1SPxasrAZbCY4YRqSVJnog4nPJBC+APwK3V8+9HRCeOGlmDkvn+PWWkzqPAn3vU6aKMsPk1ZWLivXvZzm6UO141+hJlwuQ96H0kz9hqebcpVdlYZg3NbCwHWImSpKL6uWJvO6VBcQolmdOYuDkYuKJer0/rfZWmmv5du7dZ/VyxYZ3ePiPSkJkP48RADOR/9RhmDb2/CvhEVX458A9Kn/4w5azsM5R49PWqbBplMstftaT1GkwD+SzMrFOv16dT/rY9J+/cBbi9Xq+/QvP4IQ07xoleDaRv+DPlfx/gg5S7BC5POfF7AyUWTAOuocdIc8oo8w8wK0mk4Wuw4sRM9Xr9Ncodhu+iJHcmUO4GphGmVXPyTATempmvNRZGxEmUs0o9rx3tXj6JMowY+M76M58OY4svDr/5DXzpS/DLX/L8DTfA1lvDCy9wyN/+BhtswJNPPw3f/CZssAFsuSUsthjcfDMXvP/9XPDAA2U7Cy0EU6fCW9/Kfk88wTd7vs8RRzBu0UV54phjZi8/7DBYZBH40pc4HuDzn4eXX4Ybb4QTToCttiqjf/7jP+Czn4UddySffRaWXXbWqKBnnoHllut1lNCw8OijQ92CuXPzzTew446786lPHbPLHXf8nh/84FzuvPPxXHvt9TjllIuYOpWTFl30DUydOvvvfpttPsi73rX5PlN7yZufeeYP+e53T5q5zqmnXsyll57N1Knk4osvOdu2llhiKaZOJTfaaDP22GPS+7u3t+66G/Oxj33m1t62PxKMGUPMy/oRA/+sZ87be6mp+SZODNSHPgTbbAMHHMBEgD33hA03hEMO4aDuOp/6FETASSfBxhvDOedw29prw7veBf/937DvvrDssvDb3/Kd7bbjO88+Cz/6EXzkI/Dccyz+wx/y0csv56MXt+Pm4200UuNEX44++hRuvfV3TJ1aPgtHHnki999/F1OnzvosrL76Wnzta+c80t2XjxmzKmec8cMnu1//7W8PMGbMqnz1q+cydSrZLH60e/9azTjRMYwTPQwkTqyyCnzrW+y+5pqccuONsMsu8Na38tQKK8Cpp5Z4AHDttXzu8MP53G9/W14vuCD8/OdwzTVw6qn8tf1711rGidfHicMPP4F6/e6Z60yf/hrrrrsxhx56PGPGrLrkaacdv/Jyy42ePlK/MzTT6XGiVUmefwNjmHXZUbdVaHIpSmaeRZnAeI5+cUNl1Khy8HzxxfCTn8Daa8Oaa8KfqzE848bBn/5UOt8pU+Cpp0oCpjsJ8/a3Q3eSZ7vtSt0neruIC7jkErjySuiZ5JkyBTbffNbrcePg178u5ePGzV7e/Q/6+OOw8srw2GPlZ1/vqXlz991/4v/+73p+//sbefXVV3j55ZfYb78dWGihhdljj60BeOWVf7LHHltx8cUDuxPwCiuszJNPPjbz9ZNPPsbyy5cBO8sttzxPP/0Eyy+/Ik8//QTLLrvczHWeeGLWOk899RijRzt4S0NuvogTc2LKFFh11VmvG/vtbhMnwrbVLCq33AKLLgqjR8NHPwq//CVMnw5PPgk33VROLGTC3/5W4g/Aj38Mm2xS4paGr5799pNPPj6zr5+9zjRWWGFlZsyYzksvvchSSy1T1X+Mo446mCOOOJGxY1ebWb+v+CENU8aJHgYSJ6ZNK4kdKCejd9kFXngBJk0qceMf/yjLrr66nCzoTvKcdVb5XnLqqWgEmNc40ZvJk8vAru64sfnm2/H975/Vgtar1Vo1J88ngesi4uqIOKt6/JIy9O9/WvSebXfOOXDffXDyyeX13XfDSiuVRM+aa5aOeL31SlLlZz+Dd7+7ZMkXWww22qis22333eH7PS7UWmutWc933BHuv//1bbjmmjJyaJllymPrrUvZY4/Biy+W9wHYe+/SBoArroB99inP99lnVrkG1wEHHMoPf3gjl156PUcddRLrrrsxP//5rfz4xzdx6aXXc+ml17PIIosNOMEDsPzyK/KGNyzOvffeQWbyq1/9lE033RKATTbZgmuu+SkA11zzUzbZZFb5r371UzKTe++9g8UXX9IDew0H80WcmBO33grjx8Maa5TRnbvtVvrrRg8/XEaEArz5zSXJ8+STpXyLLUr5G95QDtzvv7+Ub7xxiTtQ1r2v5+B8DTtvfvPbePTRh5g27RFee+1Vrr/+SjbZZIvZ6pQ+v9yn4Te/uYZ1192YiOCll17giCMmsf/+n+Ztb1t/Zv1m8UMapowTPQwkTiy/fBnxCXDkkXDuueX5ww/DZpuV7yKjRpXn3fHg+ONh6aXhk59s265oHs1LnOjL6NEr8fe//5XnnnsGgD/+8SZWW+1NrdsJtUxktibBHRELUCaEGku5fnYKcGtmzhjY+sM7877ppvC738Gdd8K/q3MJn/tcyYp3qy7X4umny+vDDoP99iv1zz57VqZ8scXgkUfgjW8smfZul18OtVqp//e/w8c/XrL1669fnh9wQKm3337lvaFcNnb++eX5+uuX54stVtr1iWrmhuWWg8sug9VWKx3+rrvCs8P4BuqdMLyy+3KtE074zmzl2223LldffTsA999/J1/4wsG89NILLLzwIiy77GjOP/9KAPbffyfOPrtk4+r1u/jKV47k1Vf/xYYbvodDDvkCEcHzzz/Lscd+kieemMaKK67CMcecylJLLUNmcuqpx3Hrrb9lkUUW4/DDv0yt9rb2/gIGUacPr5yfdHqcmBvbbQennFIOws89F778ZTj2WLjttjKM/i1vge9+F5ZYoozS+exn4dpry9na886DCRPKwf1558HXv162ecwxZXj+9Olw++2w//7w6qtDupuDrhPiRE+33PIbTj/9y/z73zPYbrtd2HPPAzn33FOp1dZm00235NVXX+HLX/4MDzxwH0sttTRf+MLJjBmzKhdddAaXXHIWY8euPnNbX/vauSy77PJ9xo9OY5zoHMaJ1+svTuyyS5myIbNcOXDQQaXPX2ABOOMMeM97yrJf/hIOPRTGji0npu+7D155pbzHt75VTmZ3EuPErDgBsNtuW/Dyyy/x2muvscQSS/K1r53LGmusxRVXfJ8f/ehCRo0axUorjeXww09g6aWX7aclI0+nx4mWJXnmVSd2ypo7ndgpa+51eqesgTNOqJtxQo2ME+pmnFA344QadXqcaNkt1CVJkiRJktQ+JnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOY5JEkSZIkSeoAJnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOY5JEkSZIkSeoAJnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOY5JEkSZIkSeoAJnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOY5JEkSZIkSeoAJnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOY5JEkSZIkSeoAJnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOY5JEkSZIkSeoAJnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOY5JEkSZIkSeoAJnkkSZIkSZI6gEkeSZIkSZKkDmCSR5IkSZIkqQOMGuoGSFI7rbPOULdAkjScGSckSc0M9zjhSB5JkiSpRSJi24ioR8TkiDiil+WrRcQNEXF7RNwZEdsPRTslSe3XihhhkkeS5lJErFp1uvdFxD0R8T9V+XIRcW1EPFD9XLYqj4g4rerE74yI9Rq2tU9V/4GI2KehfP2IuKta57SIiPbvqSRpbkTEgsDpwHbABGD3iJjQo9rngcsyc11gN+CM9rZSkjQUWhUjTPJI0tybDhyamW8BNgYOqjrmI4DrMnM8cF31GkoHPr56TALOhJIUAo4GNgI2BI7uTgxVdSY1rLdtG/ZLkjQ4NgQmZ+aDmfkqcCmwU486CSxVPV8amNrG9kmShk5LYoRJHkmaS5k5LTP/VD1/EbgPGEvpnC+oql0A7Fw93wm4MItbgGUiYhVgG+DazHwmM58FrgW2rZYtlZk3Z2YCFzZsS5I0xCJiUkTc1vCY1KPKWOCRhtdTqrJGxwB7RsQU4CrgEy1rsCSprfqJEy2JEU68LEl9qDrhxo74rMw8q4+6awDrAr8HVsrMaVASQRGxYlWtr468WfmUXsolScNAFRN6jQuV3i6xzR6vdwfOz8xvRMS7gIsiYu3M/PdgtVOSNDT6iRMtiREmeSSpDwM4eAcgIpYAfgR8MjNfaDJtTl8d+ZyWS5JGhinAqg2vx/H6ofYTqS7FzcybI2JRYDTwRFtaKEkaKi2JEV6uJUnzICIWoiR4Ls7MH1fFj1eXWlH97O6E++rIm5WP66VckjQy3AqMj4g1I2JhyqSZV/So8zCwJUBEvAVYFHiyra2UJA2FlsQIkzySNJeqO12dA9yXmSc1LLoC6L5D1j7AzxrK967usrUx8Hx1Wdc1wNYRsWw14fLWwDXVshcjYuPqvfZu2JYkaZjLzOnAwZR+/j7KHVLuiYjjImLHqtqhwAER8Wfg+8C+1TxskqQO1qoY4eVakjT3NgX2Au6KiDuqss8BXwEui4iJlOz7rtWyq4DtgcnAy8B+AJn5TEQcT8nmAxyXmc9Uzw8EzgcWA66uHpKkESIzr6L0/41lRzU8v5cSTyRJ85lWxAiTPJI0lzLzd/Q+bw5Uwyp71E/goD62dS5wbi/ltwFrz0MzJUmSJM0nvFxLkiRJkiSpA5jkkSRJkiRJ6gAmeSRJkiRJkjqASR5JkiRJkqQOYJJHkiRJkiSpA5jkkSRJkiRJ6gAmeSRJkiRJkjqASR5JkiRJkqQOYJJHkiRJkiSpA5jkkSRJkiRJ6gAmeSRJkiRJkjqASR5JkiRJkqQOYJJHkiRJkiSpA5jkkSRJkiRJ6gAmeSRJkiRJkjqASR5JkiRJkqQOYJJHkiRJkiSpA5jkkSRJkiRJ6gAmeSRJkiRJkjqASR5JkiRJkqQOYJJHkiRJkiSpA5jkkSRJkiRJ6gAmeSRJkiRJkjqASR5JkiRJkqQOYJJHkiRJkiSpA4wa6gZIUjuts85Qt0CSNJwZJyRJzQz3OOFIHkmSJEmSpA5gkkeSJEmSJKkD9JvkiYjFI2KB6nlXROwYEQu1vmmSpJHAOCFJasY4IUntM5CRPDcCi0bEWOA6YD/g/FY2SpI0ohgnJEnNGCckqU0GkuSJzHwZ+E/gm5n5QWBCa5slSRpBjBOSpGaME5LUJgNK8kTEu4A9gCurMu/KJUnqZpyQJDVjnJCkNhlIkueTwJHATzLznoh4I3BDa5slSRpBjBOSpGaME5LUJv1m0DPzN8BvAKoJ057KzENa3TBJ0shgnJAkNWOckKT2GcjdtS6JiKUiYnHgXqAeEZ9pfdMkSSOBcUKS1IxxQpLaZyCXa03IzBeAnYGrgNWAvVraKknSSGKckKQ+RMS2EVGPiMkRcUQfdT4cEfdGxD0RcUm729gGxglJ6kUrYsRAkjwLRcRClE75Z5n5GpBz1nRJ6jwRcW5EPBERdzeUHRMRj0bEHdVj+4ZlR1YdeD0itmko77Vzj4g1I+L3EfFARPwgIhZu397NEeOEJPUiIhYETge2o9xNaveImNCjznjKfDWbZuZbKfPXdBrjhCT10KoYMZAkz3eAh4DFgRsjYnXghTuczB4AACAASURBVDlqvSR1pvOBbXspPzkz31E9rgKoOuzdgLdW65wREQv207mfWG1rPPAsMLGlezP3jBOS1LsNgcmZ+WBmvgpcCuzUo84BwOmZ+SxAZj7R5ja2g3FCkl6vJTGi3yRPZp6WmWMzc/ss/g68d87bL0mdJTNvBJ4ZYPWdgEsz85XM/BswmdKx99q5R0QAWwCXV+tfQDkDOuwYJyTNryJiUkTc1vCY1KPKWOCRhtdTqrJGXUBXRNwUEbdERG8nD0Y044Qk9aolMaLfu2sBRMT7KWefF20oPm4g60rSSFUdrDcesJ+VmWcNYNWDI2Jv4Dbg0CrzPha4paFOYyfes3PfCFgeeC4zp/dSf9gxTkiaH1UxoVlciN5W6/F6FDAe2BwYB/w2ItbOzOcGpZHDhHFC0vyon+8TLYkR/SZ5IuLbwBso2fazgQ8Bf+hvPUka6QZw8N6bM4HjKR308cA3gP+i7068txGV2aT+sGOckKQ+TQFWbXg9DpjaS51bqnlq/hYRdcoB/a3taWLrGSckza/6+T7RkhgxkDl5NsnMvYFnM/NY4F09GiJJqmTm45k5IzP/DXyXcjkW9N2J91X+FLBMRIzqUT4cGSckqXe3AuOrifQXpszNdkWPOj+lunQpIkZThuY/2NZWtp5xQpJeryUxYiBJnn9WP1+OiDHAa8Cac9BwSZpvRMQqDS8/CHTfeesKYLeIWCQi1qRk4P9AH517ZiZwA+VsJ8A+wM/asQ9zwTghSb2oLrk9GLgGuA+4LDPviYjjImLHqto1wNMRcS+l3/9MZj49NC1uGeOEJPXQqhgxkDl5fhERywBfA/5EuVzg7LncD0nqGBHxfcr1saMjYgpwNLB5RLyD0lc+BHwMoOqwLwPuBaYDB2XmjGo73Z37gsC5mXlP9RaHA5dGxBeB24Fz2rRrc8o4IUl9qO6yeFWPsqManifw6erRqYwTktSLVsSIKOsMsHLEIsCimfn8gFeaSxHDc+4Jtd+jjw51CzScjBnT61w1A7bXXgPvWy66aN7ea35knNBQME6okXFieDNOaCgYJ9So0+NEnyN5IuI/mywjM3/cmiZJkkYC44QkqRnjhCS1X7PLtT7QZFkCdsqSNH8zTkiSmjFOSFKb9Znkycz92tkQSdLIYpyQJDVjnJCk9uvz7loR8emImNhL+Sci4pOtbZYkabgzTkiSmjFOSFL7NbuF+n8BF/VSfla1TJI0fzNOSJKaMU5IUps1S/JkZr7aS+Er4J0EJEnGCUlSU8YJSWqzZkkeImKlgZRJkuZPxglJUjPGCUlqr2ZJnq8BV0bEZhGxZPXYHPg58PW2tE6SNJwZJyRJzRgnJKnNmt1d68KIeBI4DlibcpvDe4CjM/PqNrVPkjRMGSckSc0YJySp/fpM8gBUna8dsCSpV8YJSVIzxglJaq+mc/JIkiRJkiRpZDDJI0mSJEmS1AFM8kiSJEmSJHWAPufkiYhPN1sxM08a/OZIkkYK44QkqRnjhCS1X7OJl5dsWyskSSORcUKS1IxxQpLarNkt1I9tZ0MkSSOLcUKS1IxxQpLar+kt1AEiYlFgIvBWYNHu8sz8rxa2i0cfbeXWNZKMHTvULdBwkjnULVBPxgkNNeOEGhknhh/jhIaacUKNOj1O9JvkAS4C7ge2AY4D9gDua2WjJKlV1llnqFvQkYwTkjqGcaIljBOSOsZwjxMDubvWWpn5BeAfmXkB8H7gba1tliRpBDFOSJKaMU5IUpsMJMnzWvXzuYhYG1gaWKNlLZIkjTTGCUlSM8YJSWqTgVyudVZELAt8AbgCWAI4qqWtkiSNJMYJSVIzxglJapN+kzyZeXb19DfAG1vbHEnSSGOckCQ1Y5yQpPYZyN21FgF2oQypnFk/M49rXbMkSSOFcUKS1IxxQpLaZyCXa/0MeB74I/BKa5sjSRqBjBOSpGaME5LUJgNJ8ozLzG1b3hJJ0khlnJAkNWOckKQ2Gcjdtf4vIrzFoSSpL8YJSVIzxglJapOBjOT5D2DfiPgbZXhlAJmZ67S0ZZKkkcI4IUlqxjghSW0ykCTPdi1vhSRpJDNOSJKaMU5IUpv0meSJiKUy8wXgxTa2R5I0QhgnJEnNGCckqf2ajeS5BNiBMgt+UoZVdkvgjS1slyRp+DNOSJKaMU5IUpv1meTJzB2qn2u2rzmSpJHCOCFJasY4IUnt1++cPBGxXi/FzwN/z8zpg98kSdJIYpyQJDVjnJCk9hnIxMtnAOsBd1KGWL4N+DOwfER8PDN/1cL2SZKGP+OEJKkZ44QktckCA6jzELBuZm6QmesD7wDuBt4HfLWFbZMkjQwPYZyQJPXtIYwTktQWA0nyvDkz7+l+kZn3UjrpB1vXLEnSCGKckCQ1Y5yQpDYZSJKnHhFnRsRm1eMM4C8RsQjwWovbJ0ka/owTkqRmjBOS1IuI2DYi6hExOSKOaFLvQxGREbFBf9scSJJnX2Ay8EngU8CDVdlrwHsH0nBJ6kQRcW5EPBERdzeULRcR10bEA9XPZavyiIjTqg78zsZJKCNin6r+AxGxT0P5+hFxV7XOaRERDE/7YpyQpF614gB+BNoX44QkzSYiFgROB7YDJgC7R8SEXuotCRwC/H4g2+03yZOZ/8zMb2TmBzNz58z8ema+nJn/zsyX5mw3JKmjnA9s26PsCOC6zBwPXFe9htJ5j68ek4AzoSSFgKOBjYANgaO7E0NVnUkN6/V8r2HBOCFJvWvVAfxIY5yQpF5tCEzOzAcz81XgUmCnXuodT5m/7F8D2WifSZ6IuKz6eVd11nm2x5y3X5I6S2beCDzTo3gn4ILq+QXAzg3lF2ZxC7BMRKwCbANcm5nPZOazwLXAttWypTLz5sxM4MKGbQ0LxglJ6ldLDuBHCuOEJDU1Fnik4fWUqmymiFgXWDUzfzHQjTa7hfr/VD93GOjGJKmTRMQkykiabmdl5ln9rLZSZk4DyMxpEbFiVd5XJ96sfEov5cOJcULSfG0AcaK3Pn6jHtuYeQAfEYe1rLFDwzghab7WT5zobSqGbFh3AeBkyuWtA9Znkqf6crIgcE5mvm9ONipJnaDqgPtL6gxUX534nJYPG8YJSfO7AcSJlhzAjxTGCUnzu37ixBRg1YbX44CpDa+XBNYGfl1NzbkycEVE7JiZt/X1nk3n5MnMGcDLEbF0/82XJAGPV5daUf18oirvqxNvVj6ul/JhxTghSU3NyQH8Q8DGlAP4jpl82TghSX26FRgfEWtGxMLAbsAV3Qsz8/nMHJ2Za2TmGsAtQNMEDzS/XKvbv4C7IuJa4B8Nb3jIXOyEJHW6K4B9gK9UP3/WUH5wRFxKGar/fHWG8xrgyw2TLW8NHJmZz0TEixGxMWUizr2Bb7ZzR+aAcUKSejfzAB54lHIA/9HuhZn5PDC6+3VE/Bo4rL8D+BHIOCFJPWTm9Ig4GLgGWBA4NzPviYjjgNsy84rmW+jdQJI8V1YPSVKDiPg+sDkwOiKmUO6S9RXgsoiYCDwM7FpVvwrYnnIL2ZeB/QCqZM7xlC8CAMdlZvdkzgdS7uC1GHB19RiOjBOS1ItWHcCPQMYJSepFZl5F+Z7QWHZUH3U3H8g2B5Lk+QGwFuX64b9mZkfN+i9Jcyszd+9j0Za91E3goD62cy5wbi/lt1GG8Q93xglJ6kMrDuBHIOOEJLVJs1uoj4qIr1KuJb4A+B7wSER8NSIWalcDJUnDk3FCktSMcUKS2q/ZxMtfA5YD1szM9TNzXeBNwDLA19vROEnSsGackCQ1Y5yQpDZrluTZATggM1/sLsjMFyhzRGzf6oZJkoY944QkqRnjhCS1WbMkT1ZzSPQsnEG5nlaSNH8zTkiSmjFOSFKbNUvy3BsRe/csjIg9gftb1yRJ0ghhnJAkNWOckKQ2a3Z3rYOAH0fEfwF/pGTb30m5le8H29A2SdLwZpyQJDVjnJCkNuszyZOZjwIbRcQWwFuBAK7OzOva1ThJ0vBlnJAkNWOckKT2azaSB4DMvB64vg1tkSSNQMYJSVIzxglJap9+kzyS1EnWWWeoWyBJGs6ME5KkZoZ7nGg28bIkSZIkSZJGCJM8kiRJkiRJHcAkjyRJkiRJUgcwySNJkiRJktQBTPJIkiRJkiR1AJM8kiRJkiRJHcAkjyRJkiRJUgcwySNJkiRJktQBTPJIkiRJkiR1AJM8kiRJkiRJHcAkjyRJkiRJUgcwySNJkiRJktQBTPJIkiRJkiR1AJM8kiRJkiRJHcAkjyRJkiRJUgcwySNJkiRJktQBTPJIkiRJkiR1AJM8kiRJkiRJHcAkjyRJkiRJUgcwySNJkiRJktQBTPJIkiRJkiR1AJM8kiRJkiRJHcAkjyRJkiRJUgcwySNJkiRJktQBTPJIkiRJkiR1AJM8kiRJkiRJHcAkjyRJkiRJUgcwySNJ8yAiHoqIuyLijoi4rSpbLiKujYgHqp/LVuUREadFxOSIuDMi1mvYzj5V/QciYp+h2h9J0uCKiG0jol71/Uf0svzTEXFvFReui4jVh6KdkqT2a0WMMMkjSfPuvZn5jszcoHp9BHBdZo4HrqteA2wHjK8ek4AzoSSFgKOBjYANgaO7E0OSpJErIhYETqf0/xOA3SNiQo9qtwMbZOY6wOXAV9vbSknSUGhVjDDJI0mDbyfggur5BcDODeUXZnELsExErAJsA1ybmc9k5rPAtcC27W60JGnQbQhMzswHM/NV4FJKLJgpM2/IzJerl7cA49rcRknS0GhJjDDJI0l9iIhJEXFbw2NSL9US+FVE/LFh+UqZOQ2g+rliVT4WeKRh3SlVWV/lkqRhbABxYk7794nA1YPdTknS0OgnTrQkRoyau6ZKUufLzLOAs/qptmlmTo2IFYFrI+L+JnWjt7dpUi5JGsYGECcG3L9HxJ7ABsBmg9A0SdIw0E+caEmMcCSPJM2DzJxa/XwC+All2OXj1WVYVD+fqKpPAVZtWH0cMLVJuSRpZBtQ/x4R7wP+H7BjZr7SprZJkoZWS2KESR5JmksRsXhELNn9HNgauBu4Aui+Q9Y+wM+q51cAe1d32doYeL66nOsaYOuIWLaacHnrqkySNLLdCoyPiDUjYmFgN0osmCki1gW+Qzl4f6KXbUiSOlNLYoSXa0nS3FsJ+ElEQOlPL8nMX0bErcBlETEReBjYtap/FbA9MBl4GdgPIDOfiYjjKR09wHGZ+Uz7dkOS1AqZOT0iDqYk7hcEzs3MeyLiOOC2zLwC+BqwBPDDKp48nJk7DlmjJUlt0aoYEZnDc9qHqVOdj0LFWKefVYPMXq9dHbA56VvGjJm391JrGSfUzTihRsYJdTNOqJtxQo06PU54uZYkSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPJIkSZIkSR3AJI8kSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPJIkSZIkSR3AJI8kSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPJIkSZIkSR3AJI8kSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPJIkSZIkSR3AJI8kSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPJIkSZIkSR3AJI8kSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPJIkSZIkSR3AJI8kSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPJIkSZIkSR3AJI8kSZIkSVIHMMkjSZIkSZLUAUzySJIkSZIkdQCTPIPg1Vdf4cADP8TEiTuy777v57zzTgPgJz/5HnvssRXvfW+N559/Zmb9zOS0077IHntsxcSJH+Avf7ln5rLPfnYiO+ywAUce+bEm7/cqxx77SfbYYysOPHBXHntsysxlF1/8HfbYYyv23nsb/vCH384s/8MfbmTvvbdhjz224pJLzhrM3Z/vjRsH118P994Ld98NhxxSyj/0ofJ6xgxYf/1Z9T/6Ubj99lmPGTPg7W8vy3bbDe68E/78Z7j6alh++VJ+6aWz6v/tb+Vnb7bZBu6/Hx54AA4/fFb5GmvALbfAX/5StrXQQqV84YXL6wceKMtXX31QfzWS5lB/ffWf/3wrkyZ9kC23nMBvfvPL2Zb1FT+++MVD2Xvvbdhvvx048cQjmT79tZbugwZHX/15t9VWg//93xIvbrgBxo6dtezEE0v8ufdeOPXUWeXrrVdizAMPzF4uaeSY2zgxefJ9HHTQR9h33/czceIHuP76q2Yu+8pXjmD33bdg//13Yv/9d2Ly5Pvasi+aN/3FiVVXLd9R/vSnEiu2266UN/sucsMNZZvdy1ZYoX37o8FjkmcQLLTQwpx00gWcc84VnH32T/nDH37Lvffewdprr8c3vnEeK600drb6v//9jTz66EN873u/4tBDj+fkk4+ZuewjH9mfz33uq03f76qrfsiSSy7FxRdfy6677st3vvN1AB56aDLXX38l5513JSeeeDannnosM2bMYMaMGZx66nF85Stnc/75V3Lddb/goYcmD/rvYX41fToceihMmAAbbwwHHQRveUs5wP7P/4Qbb5y9/iWXwLrrlsdee8FDD5WOd8EFy0H3e99bOto774SDDy7r7LbbrHV+9CP48Y9f344FFoDTTy8d+IQJsPvupR1QDvhPPhm6uuDZZ2HixFI+cWJ5PX58WX7iiS37NUnqx0D66pVWWoXDDz+BLbfc4XXr9xU/3ve+Hbnggl9y7rk/59VXX+HKK3/Ysn3Q4GjWn3f7+tfhwgtLvDjuODjhhFL+rnfBppvCOuvA2mvDO98Jm21Wlp15JkyaVPr88eNh223bu1+S5s28xIlFFlmUI488kfPPL98TTj/9y7z00gszl3/845/l7LN/xtln/4y11urR4WjYGUic+Pzn4bLLSoJ/t93gjDNKeV/fRbrtsces5U8+2bZd0iAyyTMIIoLFFlscgOnTpzNjxnQgGD9+AiuvPO519W+66Tq23npnIoIJE97BP/7xAk8//QQA66//Lt7whsWbvt9NN13PNtt8EIDNNtuGP/3pZjKTm266ji22eD8LL7wwq6yyKmPGrM7999/J/fffyZgxqzNmzKostNDCbLHF+7nppusG95cwH3vssVkja156Ce67r5xRvf/+MnKmmd13h+9/vzyPKI/Fqz//UkvB1KmvX+fDH561TqMNN4TJk8tIn9deKyN0dtqpLNtiC7j88vL8ggtg553L8512Kq+hLN9yy4Hvt6TBNZC+euWVx/GmN72ZBRZ4ffjuK35svPFmRAQRwZvfvA5PPvl4y/ZBg6NZf95twgS4rvp43HDDrOWZsOiiZaTmIouUkZuPPw4rr1ziyi23lHoXXjgrFkgaGeYlTqy66pqMG7cGAKNHr8QyyyzHc889g0amgcSJzNLvAyy9dO/fKxq/i6hztD3JExH7tfs922HGjBnsv/9OfPCDm7D++pswYcLb+6z71FOPs+KKK898PXr0yjz11MAPusv6qwCw4IKjWGKJJXnhhWdft90VVliJp556vM9yDb7VVy9Z79//fmD1P/KRWR3r9Olw4IFw112lE54wAc45Z/b67353OVif3MtArLFj4ZFHZr2eMqWULb88PPdcGYrZWN5znRkz4PnnZ10iJg2VTo0T/Wl1Xz19+mtce+3P2HDDdw/aNtUaffXnjf78Z9hll/L8gx8sB/LLLVeSODfcANOmlcc115STDmPHlu0026Y0UhgnirmNE/fddyfTp7/GmDGrzSw755yTmTjxA5x++pd59dVXB6W9ap2BxIljjoE99yz1rroKPvGJ12+n8btIt/POKyewP//5QW+22iUz2/oAHm6ybBJwW/WY1O62Dcajq6trma6urhu6urrWbih7qKura3TD6yu7urr+o+H1dV1dXes3vN68q6vrF92/k17e456urq5xDa//2tXVtXxXV9fpXV1dezaUn9PV1bVLV1fXrl1dXWc3lO/V1dX1zaH+XXXgY4nM/GNm/meP8l9n5ga91N8oM+9qeL1QZl6XmW/KzMjMb2Xm57uXV5+FMzPz0D7ef9fMPLvh9V6Z+c3MXCEzJzeUr9rwvvdk5riGZX/NzOWHwe/Sx3z86PQ40ddjTvrqrq6u87u6uj7U/TtpKJ8ZP3pZ57tdXV2nDPV++hjQo6/+vLHOmMz8cWbenpmnZuaUN77xjf+TmWtl5pVZYtISmXlzZr4nM9+Zmf/bsP67M/Pnw2BfffiY44dxYubruYkTq3R1ddW7uro27lEWXV1di3R1dV3Q1dV11FDvq49+HwOJE5/OWd8b3pWZ944aNepjDct7fhchM8dWP5fMzF9l5t7DYF99zOFjVCsSRxFxZ1+LgJX6Wi8zzwJG9KzA9Xr9uVqt9mtgW+DuPqpNAVZteD0O6GUAHVACVc/fSff6U2q12ihgaeCZfrY70PfT3FkI+BFwMdDLjDm92g1ozJ2/o/r51+rnZcAR3QtHjRo1ifJ3bJjGeTZ9/f2fApYBRgHTmf3vP/OzVC3v/ixJLTU/x4km5iQ2NOotTsymVqsdDawA9D2rv4aTgXwWpgL/WT1fAtjlwQcf3AtYGLgFeKladjWwMXBRtZ1m25SGDeNEr+YpTtRqtaWAK4HP1+v1W7oX1uv1adXTV2q12nnAYYPUXrXOQD4LEynfSQFuBhZdccUVDwS+U5X1/C4C8Gj180XgEmBD4MJBarPapCVJHkrHuw3wbI/yAP6vRe85ZGq12grAa1WCZzHgfUCzKWyvAA6u1WqXAhsBzzd0rgNxBbAP5Z/1Q8D19Xo9a7XaFcAltVrtJGAMMB74A+X3Pr5Wq61J+cfdDfjoHO2kmgngHOA+4KQBrrMAsCvwnoayR4EJlC9iTwJbVdsEYKeddloKuJ/SqffmVsrfvOffOYEbKJ+VSymfnZ9V67zus1TVl1ptvooTA3QrLeira7Xa/pTf9Zb1ev3f87o9tUVf/Xmj0ZSk/L+BI4Fzge2Bh4EDgBMo/0+bAacA0ygH7RsDvwf2Br7Z4v2Q5oVx4vXmOk7UarWFgZ8AF9br9R/2WLZKvV6fVqvVAtiZvk9Ua/gYSJx4GNgSOB94C7DotGnTuk/m9vZdZBTlxPBTlBPYOwD/25rmq5ValeT5BbBEZt7Rc0FE/LpF7zmUVgEuqNVqC1L+YS6r1+u/qNVqhwCfBVYG7qzValfV6/X9gasoB2KTgZeBmdcV12q13wJvBpao1WpTllhiiZer8uOA2+r1+hWUhMJFtVptMuUAbzeAer1+T61Wuwy4lzJi46B6vT6jWv9g4BpgQeDcer0+677tmlebAnsBdwHdn/nPAYtQDqBXoJw1uYNysAKlQ50CPNiwnanAscCNwGvA34F9uxfuvvvuy/H6JNIY4GzK52k6MNvfGej+Ox9OSfB8Ebid8hmi+nkR5bM487MktcH8Fif6Va/Xp/fWVzf2/7Va7Z2Ug/RlgQ/UarVjgX/C6+MHMLFer18DfJvSn9xcq9UAflyv149r9/5pjvTVnx9HuQTlCmBzSiInKXHjIEosuBzYghKTEvgl8PNquwdSDvYXo4zwuboN+yLNLeNED/MYJz5MOf5cvlar7Vttct96vX4HcHF10joox6sfb+uOaW4MJE4cCnwX+BQlHuybmV+u1u/tu8gi1fYWqrb5v9X6GmEi05P2w1lETKqGnWo+52dBUm/sG9TNz4Kk3tg3qJufhfmDSR5JkiRJkqQO0PZbqEuSJEmSJGnwmeTR/2/vfkPvLOs4jr8/TdKJ1R6lYKNk2paT2lxKFFqGjK3SKHzgQEoSIitMYsYiC6MHroKCMCNHMsKwPw+EkYloRebYYjXX/ugs+2MOH5QP+mcL2fj24L5+63T6ndN+TrvPznm/4AfnXOe67+u6Dz/uD3zv+zq3JEmSJEmaAhZ5JlSSdUkeT/JEkk3/ewtNqyR3JvljEp90IOkYc0JzzAlJ8zEnNMecmC0WeSZQkkXAV4H1dI/U3pDk/H5npR5tBdb1PQlJk8Oc0JCtmBOSBpgTGrIVc2JmWOSZTBcDT1TVb6vqObpHX7+75zmpJ1X1EN3jzSVpjjmhY8wJSfMwJ3SMOTFbLPJMprOBpwbeH2ptkiSBOSFJGs+ckGaURZ7JlHnafNa9JGmOOSFJGseckGaURZ7JdAhYOvD+VcDTPc1FkjR5zAlJ0jjmhDSjLPJMpl3AeUnOSfJS4GpgW89zkiRNDnNCkjSOOSHNKIs8E6iqjgAfBe4HHgO+W1UH+p2V+pLkbmAHsDzJoSTX9T0nSf0yJzTInJA0zJzQIHNitqTKpZmSJEmSJEknO+/kkSRJkiRJmgIWeSRJkiRJkqaARR5JkiRJkqQpYJFHkiRJkiRpCljkkSRJkiRJmgIWefQfkhxNsifJ/iTfS3L6CezrbUm+315fmWTTmL5Lknz4eYxxS5KNIz57XzuOA0keneuXZGuSqxY6liTJnJAkjWdOSP2yyKNhh6tqVVVdADwHfGjww3QW/H9TVduqavOYLkuABZ+UR0myHrgRWFtVK4ELgb+8UPuXpBlmTkiSxjEnpB5Z5NE4PwXOTfKaJI8luR3YDSxNsjbJjiS7W4X+DIAk65IcTPIw8N65HSW5Nslt7fWZSe5J8sv292ZgM7CsVf2/2PrdlGRXkr1JPjuwr08leTzJg8DyEXP/JLCxqp4GqKp/VtWW4U5JPtPG2J/kjiRp7Te0av3eJN9ubW9t89uT5JEkLzvB71eSTnbmhDkhSeOYE+aE/s8s8mheSU4B1gP7WtNy4JtVtRp4FrgZuLyqLgR+Dnw8yWnAFuAK4BLgrBG7/wrwk6p6A11F/ACwCfhNq/rflGQtcB5wMbAKWJPk0iRrgKuB1XQn/YtGjHEB8IvjONTbquqidqVhMfCu1r4JWF1Vr+ffVx82Ah+pqlXt+A4fx/4laSqZE+aEJI1jTpgT6odFHg1bnGQP3Yn2D8A3WvuTVbWzvX4TcD6wI3u8HwAAAjtJREFUvfV9P/BqYAXwu6r6dVUVcNeIMd4OfA2gqo5W1Xy3Pa5tf4/QVftX0J2kLwHuqap/VNVfgW0ndLRwWZKfJdnX5rWyte8FvpXkGuBIa9sOfCnJDcCSqjry37uTpKlnTnTMCUmanznRMSfUi1P6noAmzuFWWT6m3XH47GAT8EBVbRjqtwqoF2geAW6tqq8PjXHjcY5xAFgD/GjkAN2VgtuBN1bVU0luAU5rH78TuBS4Evh0kpVVtTnJvcA7gJ1JLq+qgws8Lkk62ZkTHXNCkuZnTnTMCfXCO3n0fOwE3pLkXIAkpyd5LXAQOCfJstZvw4jtfwhc37ZdlOTlwN+AwTWp9wMfGFibe3aSVwIPAe9JsritYb1ixBi3Al9Iclbb/tRWMR80dwJ+po1zVev7EmBpVf0Y+ATdj7idkWRZVe2rqs/TXZlYMe5LkqQZZk6YE5I0jjlhTuhF4p08WrCq+lOSa4G7k5zamm+uql8l+SBwb5JngIfp1rIO+xhwR5LrgKPA9VW1I8n2JPuB+9o62tcBO1rl/+/ANVW1O8l3gD3Ak3Q/5jbfHH+Q5EzgwXQ7KODOoT5/TrKFbp3w74Fd7aNFwF1JXkF3BeDLre/nklzW5vwocN/CvjlJmg3mhDkhSeOYE+aEXjzpljpKkiRJkiTpZOZyLUmSJEmSpClgkUeSJEmSJGkKWOSRJEmSJEmaAhZ5JEmSJEmSpoBFHkmSJEmSpClgkUeSJEmSJGkKWOSRJEmSJEmaAv8Cw8KRKQAURjAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Test_confusion_matrix\")\n",
    "plot_confusion_matrix(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr,tpr,ths = roc_curve(y_test,y_test_pred)\n",
    "auc_sc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='navy',label='ROC curve (area = %0.2f)' % auc_sc)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic with test data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}