BOX-COX-Univariate feature selection-7043.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"churn-7043.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['customerID'], axis=1) \n",
    "num_df = df.select_dtypes(include=['float64', 'int64']).copy()\n",
    "cat_df = df.select_dtypes(include=['object']).copy()\n",
    "\n",
    "# Categorical boolean mask\n",
    "categorical_feature_mask = cat_df.dtypes==object\n",
    "# filter categorical columns using mask and turn it into a list\n",
    "categorical_cols = cat_df.columns[categorical_feature_mask].tolist()\n",
    "\n",
    "import numpy as np\n",
    "#conData=np.log(0.00001 + 1)\n",
    "conData=0\n",
    "cat_df=cat_df.fillna(conData)\n",
    "num_df=num_df.fillna(conData)\n",
    "cat_df=cat_df.astype(str)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "cat_df[categorical_cols] = cat_df[categorical_cols].apply(lambda col: le.fit_transform(col))\n",
    "\n",
    "#cat_df[categorical_cols].head(10)\n",
    "\n",
    "\n",
    "churn=cat_df['churn']\n",
    "cat_df=cat_df.drop(['churn'], axis=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#BOX-COX\n",
    "lemda=0.5\n",
    "num_df=(num_df**lemda)\n",
    "num_df=num_df-1\n",
    "num_df[num_df < 0]=0\n",
    "num_df=num_df.div(lemda)\n",
    "#End BOX-COX\n",
    "#Z-score\n",
    "#num_df=(num_df-num_df.min())/(num_df.std(ddof=0)) ##Z-score\n",
    "\n",
    "#Log\n",
    "#num_df=round(np.log(num_df.add(1)),2)\n",
    "#num_df=num_df.replace([np.inf, -np.inf], np.nan)\n",
    "#End Log\n",
    "\n",
    "\n",
    "result_df = pd.concat([num_df, cat_df], axis=1)\n",
    "np.nan_to_num(result_df)\n",
    "\n",
    "\n",
    "result_df = pd.concat([num_df, cat_df], axis=1)\n",
    "np.nan_to_num(result_df)\n",
    "\n",
    "result_df_op=result_df\n",
    "\n",
    "X=result_df_op\n",
    "y=churn\n",
    "\n",
    "from sklearn.model_selection import train_test_split  \n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "X_train=X\n",
    "X_test=X\n",
    "y_train=y\n",
    "y_test=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 19)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Feature</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tenure</td>\n",
       "      <td>2911.079764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TotalCharges</td>\n",
       "      <td>1616.483021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Contract</td>\n",
       "      <td>1115.780167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>551.611529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>523.303866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>362.118432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>230.086520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>191.303140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>133.036443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>105.680863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Partner</td>\n",
       "      <td>82.412083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>58.492250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>9.821028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>9.746921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>8.235399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>7.490203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.258699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>0.097261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Feature       Scores\n",
       "1             tenure  2911.079764\n",
       "18      TotalCharges  1616.483021\n",
       "15          Contract  1115.780167\n",
       "9     OnlineSecurity   551.611529\n",
       "12       TechSupport   523.303866\n",
       "2     MonthlyCharges   362.118432\n",
       "10      OnlineBackup   230.086520\n",
       "11  DeviceProtection   191.303140\n",
       "5         Dependents   133.036443\n",
       "16  PaperlessBilling   105.680863\n",
       "4            Partner    82.412083\n",
       "17     PaymentMethod    58.492250\n",
       "8    InternetService     9.821028\n",
       "7      MultipleLines     9.746921\n",
       "14   StreamingMovies     8.235399\n",
       "13       StreamingTV     7.490203\n",
       "3             gender     0.258699\n",
       "6       PhoneService     0.097261\n",
       "0      SeniorCitizen          NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=X\n",
    "y_train=y\n",
    "#Univariate Selection\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "select_feature = SelectKBest(chi2, k=15).fit(X_train, y_train)\n",
    "selected_features_df = pd.DataFrame({'Feature':list(X_train.columns),\n",
    "                                     'Scores':select_feature.scores_})\n",
    "selected_features_df.sort_values(by='Scores', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train=y\n",
    "x_train_chi = select_feature.transform(X)\n",
    "x_train_chi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3952 1222 493 1376\n",
      "pod:  0.7362225789192082\n",
      "pof:  0.23618090452261306\n",
      "AUC:  0.7500208371982976\n"
     ]
    }
   ],
   "source": [
    "#Import Gaussian Naive Bayes model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "#Create a Gaussian Classifier\n",
    "classifier = GaussianNB()\n",
    "\n",
    "classifier.fit(X,y_train)\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = X, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 100 candidates, totalling 1500 fits\n",
      "{'var_smoothing': 1.519911082952933e-06}\n",
      "GaussianNB(var_smoothing=1.519911082952933e-06)\n",
      "0.7864527886207712\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import RepeatedKFold, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "cv_method = RepeatedKFold(n_splits=5, \n",
    "                          n_repeats=3, \n",
    "                          random_state=999)\n",
    "\n",
    "classifier=GaussianNB();\n",
    "#params = {\n",
    "#          \"priors\" : \"None\",\n",
    "#          \"var_smoothing\" : 1e-9\n",
    "#}\n",
    "#create an list of var_smoothing to cross validate\n",
    "#steps = [1e-8, 1e-7, 1e-6, 1e-5, 1e-4]\n",
    "\n",
    "params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
    "\n",
    "grid = GridSearchCV(estimator=classifier, \n",
    "                     param_grid=params_NB, \n",
    "                     cv=cv_method,\n",
    "                     verbose=1, \n",
    "                     scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "grid.fit(x_train_chi, y_train)\n",
    "\n",
    " \n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print(grid.best_score_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4266 908 673 1196\n",
      "pod:  0.6399143927233815\n",
      "pof:  0.17549284885968303\n",
      "AUC:  0.7322107719318492\n",
      "accuracy:  0.7755217946897629\n"
     ]
    }
   ],
   "source": [
    "#Import Gaussian Naive Bayes model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "#Create a Gaussian Classifier\n",
    "classifier = GaussianNB(priors=None, var_smoothing=3.5111917342151277e-07)\n",
    "\n",
    "classifier.fit(x_train_chi,y_train)\n",
    "\n",
    "\n",
    "cv_method = RepeatedKFold(n_splits=10, \n",
    "                          n_repeats=3, \n",
    "                          random_state=999)\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = x_train_chi, y = y_train, cv = 5)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.54812\n"
     ]
    }
   ],
   "source": [
    " #accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "accuracy=(26836+27976)/(27976+22462+22726+26836)\n",
    "print ('accuracy: ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 36 candidates, totalling 360 fits\n",
      "{'C': 5, 'multi_class': 'ovr', 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "LogisticRegression(C=5, multi_class='ovr', penalty='l1', solver='liblinear')\n",
      "0.8022144987105093\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Specify parameters\n",
    "c_values = list(np.arange(1, 10))\n",
    "\n",
    "\n",
    "param_grid = [\n",
    "    {'C': c_values, 'penalty': ['l1'], 'solver' : ['liblinear'], 'multi_class' : ['ovr']},    \n",
    "    {'C': c_values, 'penalty': ['l2'], 'solver' : ['liblinear', 'newton-cg', 'lbfgs'], 'multi_class' : ['ovr']}\n",
    "]\n",
    " \n",
    "\n",
    "grid = GridSearchCV(LogisticRegression(), param_grid, verbose=1, cv=10, n_jobs=-1)\n",
    "grid.fit(x_train_chi, y_train)\n",
    "\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4682 492 901 968\n",
      "pod:  0.5179240235420011\n",
      "pof:  0.09509083880943177\n",
      "AUC:  0.7114165923662846\n",
      "accuracy:  0.8022149652136874\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "classifier = LogisticRegression(C=2, class_weight=None, dual=False, fit_intercept=True,\n",
    "                   intercept_scaling=1, max_iter=100,\n",
    "                   multi_class='ovr', n_jobs=None, penalty='l1',\n",
    "                   random_state=None, solver='liblinear', verbose=0,\n",
    "                   warm_start=False)\n",
    "classifier.fit(x_train_chi, y_train) \n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = x_train_chi, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "#from sklearn.model_selection import cross_val_score\n",
    "#roc_auc = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10,  scoring='roc_auc')\n",
    "#print('roc_auc: ',roc_auc.mean())\n",
    "\n",
    "#from sklearn.metrics import roc_curve, auc\n",
    "#fpr, tpr, thresholds = roc_curve(y_train, y_pred)\n",
    "#auc = auc(fpr, tpr)\n",
    "#print('auc: ',auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 16 candidates, totalling 160 fits\n",
      "{'metric': 'manhattan', 'n_neighbors': 19, 'weights': 'distance'}\n",
      "KNeighborsClassifier(metric='manhattan', n_neighbors=19, weights='distance')\n",
      "0.7583403852353321\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "\n",
    "# Specify parameters\n",
    "c_values = list(np.arange(1, 10))\n",
    "param_grid ={\n",
    "   'n_neighbors': [3,5,11,19],\n",
    "   'weights': ['uniform', 'distance'],\n",
    "   'metric': ['euclidean', 'manhattan']\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(KNeighborsClassifier(), param_grid, verbose=1, cv=10, n_jobs=-1)\n",
    "grid.fit(x_train_chi, y_train)\n",
    "\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print(grid.best_score_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4833 341 1361 508\n",
      "pod:  0.27180310326377743\n",
      "pof:  0.06590645535369154\n",
      "AUC:  0.6029483239550429\n",
      "accuracy:  0.7583416157887264\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "classifier = KNeighborsClassifier(metric='manhattan', weights='distance', n_neighbors=19 )  \n",
    "classifier.fit(X_train, y_train) \n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = x_train_chi, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "#from sklearn.model_selection import cross_val_score\n",
    "#roc_auc = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10,  scoring='roc_auc')\n",
    "#print('roc_auc: ',roc_auc.mean())\n",
    "\n",
    "#from sklearn.metrics import roc_curve, auc\n",
    "#fpr, tpr, thresholds = roc_curve(y_train, y_pred)\n",
    "#auc = auc(fpr, tpr)\n",
    "#print('auc: ',auc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 288 candidates, totalling 2880 fits\n",
      "{'bootstrap': True, 'max_depth': 110, 'max_features': 3, 'min_samples_leaf': 5, 'min_samples_split': 12, 'n_estimators': 100}\n",
      "RandomForestClassifier(max_depth=110, max_features=3, min_samples_leaf=5,\n",
      "                       min_samples_split=12)\n",
      "0.805620164410058\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [80, 90, 100, 110],\n",
    "    'max_features': [2, 3],\n",
    "    'min_samples_leaf': [3, 4, 5],\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100, 200, 300, 1000]\n",
    "}\n",
    "\n",
    "\n",
    "#clf = RandomForestClassifier()\n",
    "\n",
    "# Choose some parameter combinations to try\n",
    "#parameters = {'n_estimators': [4, 6, 9], \n",
    "#              'max_features': ['log2', 'sqrt','auto'], \n",
    "#              'criterion': ['entropy', 'gini'],\n",
    "#              'max_depth': [2, 3, 5, 10], \n",
    "#              'min_samples_split': [2, 3, 5],\n",
    "#              'min_samples_leaf': [1,5,8]\n",
    "#             }\n",
    "\n",
    "#model_params = {\n",
    "#    'n_estimators': [50, 150, 250],\n",
    "#    'max_features': ['sqrt', 0.25, 0.5, 0.75, 1.0],\n",
    "#    'min_samples_split': [2, 4, 6]\n",
    "#}\n",
    "\n",
    "# Instantiate the grid search model\n",
    "grid = GridSearchCV(estimator = RandomForestClassifier(), param_grid = param_grid, \n",
    "                          cv = 10, n_jobs = -1, verbose = 2)\n",
    "grid.fit(x_train_chi, y_train)\n",
    "\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4728 446 953 916\n",
      "pod:  0.4901016586409845\n",
      "pof:  0.08620023192887515\n",
      "AUC:  0.7019507133560547\n",
      "accuracy:  0.8013630555161153\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier =RandomForestClassifier(max_depth=80, max_features=3, min_samples_leaf=5,\n",
    "                       min_samples_split=8, n_estimators=300)\n",
    "classifier.fit(x_train_chi, y_train) \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = x_train_chi, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "\n",
    "#from sklearn.model_selection import cross_val_score\n",
    "#roc_auc = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10,  scoring='roc_auc')\n",
    "#print('roc_auc: ',roc_auc.mean())\n",
    "\n",
    "#from sklearn.metrics import roc_curve, auc\n",
    "#fpr, tpr, thresholds = roc_curve(y_train, y_pred)\n",
    "#auc = auc(fpr, tpr)\n",
    "#print('auc: ',auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
      "{'criterion': 'entropy', 'splitter': 'random'}\n",
      "DecisionTreeClassifier(criterion='entropy', splitter='random')\n",
      "0.7356223807221148\n",
      "Time:  0.33646106719970703\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree\n",
    "import time\n",
    "start = time.time()\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Create the parameter grid based on the results of random search \n",
    "\n",
    "\n",
    "#param_grid={\n",
    "#    'criterion': ['gini', 'entropy'],\n",
    "#    'splitter': ['best', 'random'],\n",
    "#    'min_samples_split' : range(10,500,20),\n",
    "#    'max_depth': [1, 2, 3, 4, 5, 20],\n",
    "#    'max_features': [1, 2, 3, 4],\n",
    "#    'max_leaf_nodes': list(range(2, 100)), \n",
    "#    'min_samples_split': [2, 3, 4]\n",
    "#} \n",
    "\n",
    "param_grid={\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'splitter': ['best', 'random']\n",
    "} \n",
    "\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = DecisionTreeClassifier(), param_grid = param_grid, \n",
    "                          cv = 10, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(x_train_chi, y_train)\n",
    "\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "print(grid_search.best_score_)\n",
    "end = time.time()\n",
    "time_taken = end - start\n",
    "print('Time: ',time_taken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4176 998 900 969\n",
      "pod:  0.5184590690208668\n",
      "pof:  0.1928875144955547\n",
      "AUC:  0.6627857772626561\n",
      "accuracy:  0.7305125656680392\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier =DecisionTreeClassifier(criterion='entropy', splitter='random')\n",
    "classifier.fit(x_train_chi, y_train) \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = x_train_chi, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "\n",
    "#from sklearn.model_selection import cross_val_score\n",
    "#roc_auc = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10,  scoring='roc_auc')\n",
    "#print('roc_auc: ',roc_auc.mean())\n",
    "\n",
    "#from sklearn.metrics import roc_curve, auc\n",
    "#fpr, tpr, thresholds = roc_curve(y_train, y_pred)\n",
    "#auc = auc(fpr, tpr)\n",
    "#print('auc: ',auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'friedman_mse', 'learning_rate': 0.2, 'loss': 'deviance', 'max_depth': 8, 'max_features': 'sqrt', 'min_samples_leaf': 0.1, 'min_samples_split': 0.28181818181818186, 'n_estimators': 10, 'subsample': 0.95}\n",
      "GradientBoostingClassifier(learning_rate=0.2, max_depth=8, max_features='sqrt',\n",
      "                           min_samples_leaf=0.1,\n",
      "                           min_samples_split=0.28181818181818186,\n",
      "                           n_estimators=10, subsample=0.95)\n",
      "0.7949721953578337\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "param_grid = {\n",
    "    \"loss\":[\"deviance\"],\n",
    "    \"learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
    "    \"min_samples_split\": np.linspace(0.1, 0.5, 12),\n",
    "    \"min_samples_leaf\": np.linspace(0.1, 0.5, 12),\n",
    "    \"max_depth\":[3,5,8],\n",
    "    \"max_features\":[\"log2\",\"sqrt\"],\n",
    "    \"criterion\": [\"friedman_mse\",  \"mae\"],\n",
    "    \"subsample\":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],\n",
    "    \"n_estimators\":[10]\n",
    "    }\n",
    "\n",
    "grid = GridSearchCV(GradientBoostingClassifier(), param_grid, cv=10, n_jobs=-1)\n",
    "\n",
    "grid.fit(x_train_chi, y_train)\n",
    "\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4895 279 1234 635\n",
      "pod:  0.33975387907972177\n",
      "pof:  0.053923463471202165\n",
      "AUC:  0.6429152078042598\n",
      "accuracy:  0.7851767712622462\n"
     ]
    }
   ],
   "source": [
    "#  Gradient Boosting\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "classifier =GradientBoostingClassifier(learning_rate=0.2, max_depth=8, max_features='sqrt',\n",
    "                           min_samples_leaf=0.1,\n",
    "                           min_samples_split=0.28181818181818186,\n",
    "                           n_estimators=10, subsample=0.95)\n",
    "classifier.fit(x_train_chi, y_train) \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = x_train_chi, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "\n",
    "#from sklearn.model_selection import cross_val_score\n",
    "#roc_auc = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10,  scoring='roc_auc')\n",
    "#print('roc_auc: ',roc_auc.mean())\n",
    "\n",
    "#from sklearn.metrics import roc_curve, auc\n",
    "#fpr, tpr, thresholds = roc_curve(y_train, y_pred)\n",
    "#auc = auc(fpr, tpr)\n",
    "#print('auc: ',auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.793269 using {'batch_size': 10, 'epochs': 100}\n",
      "0.735189 (0.047563) with: {'batch_size': 10, 'epochs': 10}\n",
      "0.792986 (0.002098) with: {'batch_size': 10, 'epochs': 50}\n",
      "0.793269 (0.003560) with: {'batch_size': 10, 'epochs': 100}\n",
      "0.758908 (0.019927) with: {'batch_size': 20, 'epochs': 10}\n",
      "0.776512 (0.019441) with: {'batch_size': 20, 'epochs': 50}\n",
      "0.778215 (0.022630) with: {'batch_size': 20, 'epochs': 100}\n",
      "0.735907 (0.005486) with: {'batch_size': 40, 'epochs': 10}\n",
      "0.772685 (0.018790) with: {'batch_size': 40, 'epochs': 50}\n",
      "0.762598 (0.029318) with: {'batch_size': 40, 'epochs': 100}\n",
      "0.742583 (0.006696) with: {'batch_size': 60, 'epochs': 10}\n",
      "0.676002 (0.103338) with: {'batch_size': 60, 'epochs': 50}\n",
      "0.662370 (0.123155) with: {'batch_size': 60, 'epochs': 100}\n",
      "0.599139 (0.187533) with: {'batch_size': 80, 'epochs': 10}\n",
      "0.686468 (0.143346) with: {'batch_size': 80, 'epochs': 50}\n",
      "0.671456 (0.137360) with: {'batch_size': 80, 'epochs': 100}\n",
      "0.736901 (0.004456) with: {'batch_size': 100, 'epochs': 10}\n",
      "0.761747 (0.019903) with: {'batch_size': 100, 'epochs': 50}\n",
      "0.678693 (0.085042) with: {'batch_size': 100, 'epochs': 100}\n",
      "Time:  479.10054874420166\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start = time.time()\n",
    "\n",
    "# Use scikit-learn to grid search the batch size and epochs\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import GridSearchCV\n",
    " \n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(): \n",
    "    #model = Sequential()\n",
    "    #model.add(Dense(12, input_shape=(8,), activation='relu'))\n",
    "    #model.add(Dense(1, activation='sigmoid'))\n",
    "    #model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    #model = Sequential()\n",
    "    #get number of columns in training data\n",
    "    n_cols = x_train_chi.shape[1]\n",
    "    model.add(Dense(10, activation='relu', input_shape=(n_cols,)))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "def nn_cl_fun():\n",
    "        #opt = Adam(lr = learning_rate)\n",
    "        n_cols = x_train_chi.shape[1]\n",
    "        nn = Sequential()\n",
    "        nn.add(Dense(10, input_dim=n_cols, activation='relu'))\n",
    "        nn.add(Dense(250, activation='relu'))\n",
    "        nn.add(Dense(1, activation='sigmoid'))\n",
    "        nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "        return nn\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "tf.random.set_seed(seed)\n",
    "\n",
    "# create model\n",
    "#model = KerasClassifier(model=create_model, verbose=0)\n",
    "model = KerasClassifier(build_fn=nn_cl_fun, verbose=0)\n",
    "\n",
    "# define the grid search parameters\n",
    "batch_size = [10, 20, 40, 60, 80, 100]\n",
    "epochs = [10, 50, 100]\n",
    "param_grid = dict(batch_size=batch_size, epochs=epochs)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)\n",
    "grid_result = grid.fit(x_train_chi, y_train)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "\n",
    "end = time.time()\n",
    "time_taken = end - start\n",
    "print('Time: ',time_taken)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 5s 17ms/step - loss: 11.0715 - accuracy: 0.5869 - val_loss: 0.9696 - val_accuracy: 0.2950\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 1.0970 - accuracy: 0.6205 - val_loss: 0.5980 - val_accuracy: 0.7279\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 1s 2ms/step - loss: 0.6383 - accuracy: 0.7168 - val_loss: 0.5451 - val_accuracy: 0.7579\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.5671 - accuracy: 0.7258 - val_loss: 0.5168 - val_accuracy: 0.7681\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 1s 4ms/step - loss: 0.5505 - accuracy: 0.7324 - val_loss: 0.5020 - val_accuracy: 0.7390\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.5448 - accuracy: 0.7319 - val_loss: 0.6756 - val_accuracy: 0.5836\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.5576 - accuracy: 0.7281 - val_loss: 0.5445 - val_accuracy: 0.7319\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.5150 - accuracy: 0.7523 - val_loss: 0.5040 - val_accuracy: 0.7863\n",
      "y2_pred:  [[5.2908355e-01]\n",
      " [2.7727109e-01]\n",
      " [4.3982214e-01]\n",
      " [1.5798509e-01]\n",
      " [5.2887982e-01]\n",
      " [5.1109028e-01]\n",
      " [4.0123028e-01]\n",
      " [4.2700705e-01]\n",
      " [3.8938862e-01]\n",
      " [2.2405288e-01]\n",
      " [4.5840028e-01]\n",
      " [3.0102730e-01]\n",
      " [3.2649207e-01]\n",
      " [3.4745395e-01]\n",
      " [3.7990788e-01]\n",
      " [2.4591658e-01]\n",
      " [2.4901032e-03]\n",
      " [2.7797514e-01]\n",
      " [4.2292839e-01]\n",
      " [3.9848113e-01]\n",
      " [5.4056752e-01]\n",
      " [3.0220097e-01]\n",
      " [4.3549007e-01]\n",
      " [2.4200824e-01]\n",
      " [2.3865604e-01]\n",
      " [2.9710746e-01]\n",
      " [3.5950640e-01]\n",
      " [4.7473037e-01]\n",
      " [2.4637908e-01]\n",
      " [4.0474343e-01]\n",
      " [2.5145999e-01]\n",
      " [5.7055473e-01]\n",
      " [2.1369967e-01]\n",
      " [4.2879307e-01]\n",
      " [5.8864242e-01]\n",
      " [2.5058234e-01]\n",
      " [5.7097036e-01]\n",
      " [3.5893583e-01]\n",
      " [3.7986481e-01]\n",
      " [4.4472963e-01]\n",
      " [4.2709249e-01]\n",
      " [2.4626017e-01]\n",
      " [3.2835710e-01]\n",
      " [2.2956952e-01]\n",
      " [4.5288557e-01]\n",
      " [3.7572604e-01]\n",
      " [5.2779651e-01]\n",
      " [5.1698458e-01]\n",
      " [2.9654047e-01]\n",
      " [1.9673505e-01]\n",
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    {
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     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 2s 4ms/step - loss: 11.8292 - accuracy: 0.6062 - val_loss: 0.6968 - val_accuracy: 0.7279\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 1.0879 - accuracy: 0.6281 - val_loss: 0.7854 - val_accuracy: 0.7279\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.6655 - accuracy: 0.7089 - val_loss: 0.6001 - val_accuracy: 0.7287\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.6567 - accuracy: 0.6990 - val_loss: 0.8303 - val_accuracy: 0.7287\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.6110 - accuracy: 0.7180 - val_loss: 0.6120 - val_accuracy: 0.7279\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.5945 - accuracy: 0.7323 - val_loss: 0.5861 - val_accuracy: 0.7279\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.6006 - accuracy: 0.7153 - val_loss: 0.6349 - val_accuracy: 0.7279\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.6121 - accuracy: 0.7349 - val_loss: 0.5819 - val_accuracy: 0.7279\n",
      "Epoch 9/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.6001 - accuracy: 0.7282 - val_loss: 0.5844 - val_accuracy: 0.7279\n",
      "Epoch 10/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.5681 - accuracy: 0.7438 - val_loss: 0.5839 - val_accuracy: 0.7279\n",
      "Epoch 11/100\n",
      "254/254 [==============================] - 1s 3ms/step - loss: 0.5817 - accuracy: 0.7305 - val_loss: 0.5849 - val_accuracy: 0.7279\n",
      "y2_pred:  [[4.2624772e-03]\n",
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      "pod 1st:  0.0\n",
      "Epoch 1/100\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "254/254 [==============================] - 3s 6ms/step - loss: 10.0647 - accuracy: 0.6181 - val_loss: 1.6353 - val_accuracy: 0.7279\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 1s 4ms/step - loss: 0.9186 - accuracy: 0.6586 - val_loss: 0.7248 - val_accuracy: 0.7279\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 1s 4ms/step - loss: 0.7477 - accuracy: 0.6848 - val_loss: 0.5601 - val_accuracy: 0.7279\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 1s 4ms/step - loss: 0.5729 - accuracy: 0.7270 - val_loss: 0.6466 - val_accuracy: 0.7326\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 1s 5ms/step - loss: 0.5593 - accuracy: 0.7245 - val_loss: 0.4755 - val_accuracy: 0.7768\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 1s 6ms/step - loss: 0.5225 - accuracy: 0.7431 - val_loss: 0.4771 - val_accuracy: 0.7382\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 2s 7ms/step - loss: 0.5071 - accuracy: 0.7475 - val_loss: 0.4578 - val_accuracy: 0.7926\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.4863 - accuracy: 0.7668 - val_loss: 0.4661 - val_accuracy: 0.7618\n",
      "Epoch 9/100\n",
      "254/254 [==============================] - 2s 6ms/step - loss: 0.4891 - accuracy: 0.7591 - val_loss: 0.4942 - val_accuracy: 0.7500\n",
      "Epoch 10/100\n",
      "254/254 [==============================] - 2s 7ms/step - loss: 0.5118 - accuracy: 0.7486 - val_loss: 0.6004 - val_accuracy: 0.7390\n",
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      "pod 1st:  0.0106951871657754\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 16s 27ms/step - loss: 10.2016 - accuracy: 0.6248 - val_loss: 1.5936 - val_accuracy: 0.7279\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.8730 - accuracy: 0.6614 - val_loss: 0.5332 - val_accuracy: 0.7279\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.7569 - accuracy: 0.7064 - val_loss: 0.5056 - val_accuracy: 0.7445\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 3s 11ms/step - loss: 0.5773 - accuracy: 0.7264 - val_loss: 0.5377 - val_accuracy: 0.7271\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 3s 12ms/step - loss: 0.5592 - accuracy: 0.7353 - val_loss: 0.4922 - val_accuracy: 0.7271\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.5419 - accuracy: 0.7327 - val_loss: 0.5426 - val_accuracy: 0.7271\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.5474 - accuracy: 0.7223 - val_loss: 0.4693 - val_accuracy: 0.7808\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5636 - accuracy: 0.7227 - val_loss: 0.5855 - val_accuracy: 0.7279\n",
      "Epoch 9/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.5037 - accuracy: 0.7514 - val_loss: 0.5180 - val_accuracy: 0.7271\n",
      "Epoch 10/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5203 - accuracy: 0.7369 - val_loss: 0.5470 - val_accuracy: 0.7279\n",
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      "Epoch 1/100\n",
      "254/254 [==============================] - 9s 17ms/step - loss: 11.8718 - accuracy: 0.6079 - val_loss: 1.2941 - val_accuracy: 0.7279\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.9589 - accuracy: 0.6478 - val_loss: 0.6792 - val_accuracy: 0.5363\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.6975 - accuracy: 0.6935 - val_loss: 0.6786 - val_accuracy: 0.5363\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.6286 - accuracy: 0.7013 - val_loss: 0.5874 - val_accuracy: 0.7271\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5992 - accuracy: 0.7261 - val_loss: 0.6187 - val_accuracy: 0.7279\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.6053 - accuracy: 0.7269 - val_loss: 0.6567 - val_accuracy: 0.5852\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 2s 7ms/step - loss: 0.6107 - accuracy: 0.7233 - val_loss: 0.5448 - val_accuracy: 0.7279\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.6168 - accuracy: 0.7016 - val_loss: 0.5796 - val_accuracy: 0.7279\n",
      "Epoch 9/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5831 - accuracy: 0.7377 - val_loss: 0.6448 - val_accuracy: 0.7271\n",
      "Epoch 10/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5841 - accuracy: 0.7309 - val_loss: 0.5828 - val_accuracy: 0.7279\n",
      "y2_pred:  [[2.9699174e-01]\n",
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     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 21s 35ms/step - loss: 13.4586 - accuracy: 0.6069 - val_loss: 1.2658 - val_accuracy: 0.3115\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.9046 - accuracy: 0.6652 - val_loss: 0.5884 - val_accuracy: 0.7287\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.6656 - accuracy: 0.7118 - val_loss: 0.6346 - val_accuracy: 0.7311\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.6037 - accuracy: 0.7380 - val_loss: 0.5784 - val_accuracy: 0.7287\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5959 - accuracy: 0.7231 - val_loss: 0.5782 - val_accuracy: 0.7295\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.5785 - accuracy: 0.7369 - val_loss: 0.5830 - val_accuracy: 0.7295\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5754 - accuracy: 0.7439 - val_loss: 0.5819 - val_accuracy: 0.7271\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5820 - accuracy: 0.7308 - val_loss: 0.5765 - val_accuracy: 0.7279\n",
      "Epoch 9/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.6918 - accuracy: 0.7127 - val_loss: 0.5824 - val_accuracy: 0.7271\n",
      "Epoch 10/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5863 - accuracy: 0.7314 - val_loss: 0.5828 - val_accuracy: 0.7271\n",
      "Epoch 11/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5634 - accuracy: 0.7460 - val_loss: 0.5798 - val_accuracy: 0.7303\n",
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      "pod 1st:  0.0\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 26s 26ms/step - loss: 14.1722 - accuracy: 0.5987 - val_loss: 1.1208 - val_accuracy: 0.3068\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.9472 - accuracy: 0.6384 - val_loss: 0.5936 - val_accuracy: 0.7279\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.6109 - accuracy: 0.7205 - val_loss: 1.1052 - val_accuracy: 0.3502\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.6663 - accuracy: 0.6892 - val_loss: 0.5275 - val_accuracy: 0.7287\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5949 - accuracy: 0.7170 - val_loss: 0.6573 - val_accuracy: 0.7295\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.5753 - accuracy: 0.7329 - val_loss: 0.5466 - val_accuracy: 0.7326\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5255 - accuracy: 0.7426 - val_loss: 0.4776 - val_accuracy: 0.7358\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5595 - accuracy: 0.7244 - val_loss: 0.5495 - val_accuracy: 0.7382\n",
      "Epoch 9/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5030 - accuracy: 0.7450 - val_loss: 0.5283 - val_accuracy: 0.7334\n",
      "Epoch 10/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.5388 - accuracy: 0.7310 - val_loss: 0.4892 - val_accuracy: 0.7390\n",
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      "pod 1st:  0.03208556149732621\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 16s 23ms/step - loss: 12.8117 - accuracy: 0.5966 - val_loss: 1.4467 - val_accuracy: 0.3036\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.8328 - accuracy: 0.6457 - val_loss: 0.5821 - val_accuracy: 0.7279\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.6860 - accuracy: 0.7101 - val_loss: 0.7702 - val_accuracy: 0.4345\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.6897 - accuracy: 0.6902 - val_loss: 0.5834 - val_accuracy: 0.7571\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.6043 - accuracy: 0.7182 - val_loss: 0.6469 - val_accuracy: 0.7279\n",
      "y2_pred:  [[1.61039531e-01]\n",
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      "pod 1st:  0.0\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 14s 17ms/step - loss: 7.6026 - accuracy: 0.6075 - val_loss: 0.6067 - val_accuracy: 0.7248\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.8367 - accuracy: 0.6535 - val_loss: 0.6622 - val_accuracy: 0.7248\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.6653 - accuracy: 0.7084 - val_loss: 0.8936 - val_accuracy: 0.4109\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 3s 11ms/step - loss: 0.6361 - accuracy: 0.6990 - val_loss: 0.7069 - val_accuracy: 0.7256\n",
      "y2_pred:  [[7.76504576e-02]\n",
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      "pod 1st:  0.0\n"
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     "text": [
      "Epoch 1/100\n",
      "254/254 [==============================] - 7s 14ms/step - loss: 16.7268 - accuracy: 0.6197 - val_loss: 1.0083 - val_accuracy: 0.2973\n",
      "Epoch 2/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 1.0350 - accuracy: 0.6389 - val_loss: 1.0164 - val_accuracy: 0.7248\n",
      "Epoch 3/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.8060 - accuracy: 0.6741 - val_loss: 0.9622 - val_accuracy: 0.3730\n",
      "Epoch 4/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.7052 - accuracy: 0.6815 - val_loss: 0.5950 - val_accuracy: 0.7248\n",
      "Epoch 5/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.7290 - accuracy: 0.6923 - val_loss: 0.7471 - val_accuracy: 0.7256\n",
      "Epoch 6/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.6188 - accuracy: 0.7235 - val_loss: 0.5853 - val_accuracy: 0.7256\n",
      "Epoch 7/100\n",
      "254/254 [==============================] - 3s 10ms/step - loss: 0.5740 - accuracy: 0.7395 - val_loss: 0.5864 - val_accuracy: 0.7248\n",
      "Epoch 8/100\n",
      "254/254 [==============================] - 2s 10ms/step - loss: 0.5727 - accuracy: 0.7374 - val_loss: 0.5850 - val_accuracy: 0.7256\n",
      "Epoch 9/100\n",
      "254/254 [==============================] - 2s 9ms/step - loss: 0.6479 - accuracy: 0.7304 - val_loss: 0.5856 - val_accuracy: 0.7256\n",
      "Epoch 10/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5785 - accuracy: 0.7322 - val_loss: 0.5865 - val_accuracy: 0.7248\n",
      "Epoch 11/100\n",
      "254/254 [==============================] - 2s 8ms/step - loss: 0.5633 - accuracy: 0.7503 - val_loss: 0.5856 - val_accuracy: 0.7248\n",
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      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "pod 1st:  0.0053475935828877\n",
      "pod:  [0.40641711229946526, 0.0, 0.0106951871657754, 0.005376344086021506, 0.0, 0.0, 0.03208556149732621, 0.0, 0.0, 0.0053475935828877]\n",
      "pof:  [0.06563706563706563, 0.0, 0.003861003861003861, 0.0, 0.0, 0.0038684719535783366, 0.01160541586073501, 0.0, 0.0019342359767891683, 0.0]\n",
      "auc:  [0.6703900233311998, 0.5, 0.5034170916523858, 0.5026881720430108, 0.5, 0.49806576402321084, 0.5102400728182955, 0.5, 0.4990328820116054, 0.5026737967914439]\n",
      "tn_list:  [484, 518, 516, 518, 517, 515, 511, 517, 516, 517]\n",
      "fp_list:  [34, 0, 2, 0, 0, 2, 6, 0, 1, 0]\n",
      "fn_list:  [111, 187, 185, 185, 187, 187, 181, 187, 187, 186]\n",
      "tp_list:  [76, 0, 2, 1, 0, 0, 6, 0, 0, 1]\n",
      "5129 45 1783 86\n",
      "Time:  301.6154525279999\n"
     ]
    }
   ],
   "source": [
    "df_x_train_chi = pd.DataFrame(x_train_chi)\n",
    "df_x_train_chi.head()\n",
    "\n",
    "import time\n",
    "start = time.time()\n",
    "\n",
    "#FNN\n",
    "def get_FNN_Predict(X2_train, X2_test, y2_train, y2_test):\n",
    "    from keras.models import Sequential\n",
    "    from keras.layers import Dense\n",
    "    #create model\n",
    "    model = Sequential()\n",
    "    #get number of columns in training data\n",
    "    n_cols = X2_train.shape[1]\n",
    "    model.add(Dense(10, activation='relu', input_shape=(n_cols,)))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    #compile model using mse as a measure of model performance\n",
    "    #model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    #model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])  \n",
    "    from keras.callbacks import EarlyStopping\n",
    "    #set early stopping monitor so the model stops training when it won't improve anymore\n",
    "    early_stopping_monitor = EarlyStopping(patience=3)\n",
    "    #train model\n",
    "    model.fit(X2_train, y2_train, validation_split=0.2,  epochs=100, batch_size= 20, callbacks=[early_stopping_monitor])\n",
    "    y2_pred = model.predict(X2_test)\n",
    "    print('y2_pred: ',y2_pred)\n",
    "    y22_pred=y2_pred.round()\n",
    "    print('y22_pred: ',y22_pred)\n",
    "    return y22_pred\n",
    "\n",
    "pod_list = []\n",
    "pof_list = []\n",
    "auc_val_list = []\n",
    "tn_list= []\n",
    "fp_list= []\n",
    "fn_list= []\n",
    "tp_list= []\n",
    "    \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "folds = StratifiedKFold(n_splits=10)\n",
    "for train_index, test_index in folds.split(df_x_train_chi,y_train):\n",
    "    X2_train, X2_test, y2_train, y2_test=df_x_train_chi.iloc[train_index], df_x_train_chi.iloc[test_index],y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "    y_pred = get_FNN_Predict(X2_train, X2_test, y2_train, y2_test)\n",
    "    tn, fp, fn, tp  = confusion_matrix(y2_test, y_pred).ravel()\n",
    "    tn_list.append(tn)\n",
    "    fp_list.append(fp)\n",
    "    fn_list.append(fn)\n",
    "    tp_list.append(tp)\n",
    "    pod=tp/(tp+fn)\n",
    "    print('pod 1st: ',pod)\n",
    "    pof=fp/(fp+tn)\n",
    "    auc_val=(1+pod-pof)/2\n",
    "    #break\n",
    "    pod_list.append(pod)\n",
    "    pof_list.append(pof)\n",
    "    auc_val_list.append(auc_val)\n",
    "\n",
    "print('pod: ',pod_list)\n",
    "print ('pof: ',pof_list)\n",
    "print ('auc: ',auc_val_list)\n",
    "\n",
    "print('tn_list: ',tn_list)\n",
    "print ('fp_list: ',fp_list)\n",
    "print ('fn_list: ',fn_list)\n",
    "print ('tp_list: ',tp_list)\n",
    "\n",
    "\n",
    "tn=sum(tn_list)\n",
    "fp=sum(fp_list) \n",
    "fn=sum(fn_list) \n",
    "tp=sum(tp_list)\n",
    "print(tn, fp, fn, tp)\n",
    " \n",
    "\n",
    "end = time.time()\n",
    "time_taken = end - start\n",
    "print('Time: ',time_taken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##RNN Hyperparemeter tuning\n",
    "import time\n",
    "start = time.time()\n",
    "\n",
    "# Use scikit-learn to grid search the batch size and epochs\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import GridSearchCV\n",
    " \n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout, LSTM, GRU\n",
    "from keras.layers import Embedding\n",
    "# Function to create model, required for KerasClassifier\n",
    "def create_model(): \n",
    "    #model = Sequential()\n",
    "    #model.add(Dense(12, input_shape=(8,), activation='relu'))\n",
    "    #model.add(Dense(1, activation='sigmoid'))\n",
    "    #model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    #model = Sequential()\n",
    "    #get number of columns in training data\n",
    "    #n_cols = X2_train.shape[1]\n",
    "    model.add(Dense(10, activation='relu', input_shape=(n_cols,)))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(250, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "def nn_cl_fun():\n",
    "        #opt = Adam(lr = learning_rate)\n",
    "        #n_cols = X2_train.shape[1]\n",
    "        #nn = Sequential()\n",
    "        #nn.add(Dense(10, input_dim=n_cols, activation='relu'))\n",
    "        #nn.add(Dense(250, activation='relu'))\n",
    "        #nn.add(Dense(1, activation='sigmoid'))\n",
    "        #nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "        model = Sequential()\n",
    "        model.add(Embedding(10000, 32))\n",
    "        model.add(LSTM(32))\n",
    "        model.add(Dense(1, activation='sigmoid'))\n",
    "        model.compile(optimizer='rmsprop',loss='binary_crossentropy', metrics=['acc'])\n",
    "        return model\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "tf.random.set_seed(seed)\n",
    "\n",
    "# create model\n",
    "#model = KerasClassifier(model=create_model, verbose=0)\n",
    "model = KerasClassifier(build_fn=nn_cl_fun, verbose=0)\n",
    "\n",
    "# define the grid search parameters\n",
    "batch_size = [10, 20, 40, 60, 80, 100]\n",
    "epochs = [10, 50, 100]\n",
    "param_grid = dict(batch_size=batch_size, epochs=epochs)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)\n",
    "grid_result = grid.fit(x_train_chi, y_train)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "\n",
    "end = time.time()\n",
    "time_taken = end - start\n",
    "print('Time: ',time_taken)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "51/51 [==============================] - 7s 28ms/step - loss: 0.6135 - acc: 0.6969 - val_loss: 0.5156 - val_acc: 0.7287\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4990 - acc: 0.7350 - val_loss: 0.4909 - val_acc: 0.7453\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4534 - acc: 0.7701 - val_loss: 0.4785 - val_acc: 0.7737\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4557 - acc: 0.7846 - val_loss: 0.4899 - val_acc: 0.7626\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4451 - acc: 0.7855 - val_loss: 0.4639 - val_acc: 0.7776\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4390 - acc: 0.7912 - val_loss: 0.4586 - val_acc: 0.7744\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4165 - acc: 0.7996 - val_loss: 0.4599 - val_acc: 0.7815\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4051 - acc: 0.8064 - val_loss: 0.4712 - val_acc: 0.7776\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.3662 - acc: 0.8311 - val_loss: 0.4605 - val_acc: 0.7800\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.3236 - acc: 0.8541 - val_loss: 0.4576 - val_acc: 0.7926\n",
      "y22_pred:  [[0.]\n",
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      "pod 1st:  0.45454545454545453\n",
      "Epoch 1/10\n",
      "51/51 [==============================] - 3s 28ms/step - loss: 0.6019 - acc: 0.7135 - val_loss: 0.5078 - val_acc: 0.7295\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4914 - acc: 0.7450 - val_loss: 0.4831 - val_acc: 0.7500\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4595 - acc: 0.7730 - val_loss: 0.4760 - val_acc: 0.7737\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.4583 - acc: 0.7796 - val_loss: 0.4765 - val_acc: 0.7744\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.4491 - acc: 0.7799 - val_loss: 0.4639 - val_acc: 0.7752\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.4335 - acc: 0.7899 - val_loss: 0.4569 - val_acc: 0.7808\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.4134 - acc: 0.7969 - val_loss: 0.4644 - val_acc: 0.7744\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3956 - acc: 0.8064 - val_loss: 0.4579 - val_acc: 0.7808\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3506 - acc: 0.8406 - val_loss: 0.4656 - val_acc: 0.7847\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.2983 - acc: 0.8694 - val_loss: 0.4674 - val_acc: 0.7839\n",
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      "pod 1st:  0.40106951871657753\n"
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    {
     "name": "stdout",
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     "text": [
      "Epoch 1/10\n",
      "51/51 [==============================] - 3s 28ms/step - loss: 0.6058 - acc: 0.7334 - val_loss: 0.5194 - val_acc: 0.7287\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4936 - acc: 0.7407 - val_loss: 0.4831 - val_acc: 0.7476\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4667 - acc: 0.7656 - val_loss: 0.4766 - val_acc: 0.7721\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4584 - acc: 0.7851 - val_loss: 0.4816 - val_acc: 0.7658\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4464 - acc: 0.7858 - val_loss: 0.4624 - val_acc: 0.7729\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4291 - acc: 0.7970 - val_loss: 0.4618 - val_acc: 0.7744\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4171 - acc: 0.8085 - val_loss: 0.4592 - val_acc: 0.7752\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3987 - acc: 0.8087 - val_loss: 0.4583 - val_acc: 0.7752\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3622 - acc: 0.8351 - val_loss: 0.4622 - val_acc: 0.7808\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3082 - acc: 0.8598 - val_loss: 0.4640 - val_acc: 0.7744\n",
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      "pod 1st:  0.29411764705882354\n",
      "Epoch 1/10\n",
      "51/51 [==============================] - 4s 30ms/step - loss: 0.5894 - acc: 0.7215 - val_loss: 0.5056 - val_acc: 0.7271\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4954 - acc: 0.7442 - val_loss: 0.4914 - val_acc: 0.7524\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 20ms/step - loss: 0.4524 - acc: 0.7778 - val_loss: 0.4823 - val_acc: 0.7539\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4469 - acc: 0.7905 - val_loss: 0.4659 - val_acc: 0.7768\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 20ms/step - loss: 0.4362 - acc: 0.7911 - val_loss: 0.4640 - val_acc: 0.7760\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 20ms/step - loss: 0.4228 - acc: 0.8058 - val_loss: 0.4754 - val_acc: 0.7752\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4078 - acc: 0.8138 - val_loss: 0.5201 - val_acc: 0.7650\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.3906 - acc: 0.8185 - val_loss: 0.4657 - val_acc: 0.7792\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.3533 - acc: 0.8418 - val_loss: 0.4630 - val_acc: 0.7768\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.3166 - acc: 0.8666 - val_loss: 0.4654 - val_acc: 0.7784\n",
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      "pod 1st:  0.3602150537634409\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "51/51 [==============================] - 3s 29ms/step - loss: 0.6006 - acc: 0.7293 - val_loss: 0.5104 - val_acc: 0.7326\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.5010 - acc: 0.7401 - val_loss: 0.4969 - val_acc: 0.7445\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4542 - acc: 0.7747 - val_loss: 0.4780 - val_acc: 0.7618\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4526 - acc: 0.7858 - val_loss: 0.4695 - val_acc: 0.7760\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4415 - acc: 0.7896 - val_loss: 0.4623 - val_acc: 0.7776\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4281 - acc: 0.8028 - val_loss: 0.4691 - val_acc: 0.7776\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.4070 - acc: 0.8088 - val_loss: 0.4810 - val_acc: 0.7689\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3989 - acc: 0.8139 - val_loss: 0.4539 - val_acc: 0.7815\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3583 - acc: 0.8254 - val_loss: 0.4744 - val_acc: 0.7721\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3241 - acc: 0.8477 - val_loss: 0.4757 - val_acc: 0.7847\n",
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      "pod 1st:  0.37967914438502676\n",
      "Epoch 1/10\n",
      "51/51 [==============================] - 4s 30ms/step - loss: 0.6022 - acc: 0.7288 - val_loss: 0.5134 - val_acc: 0.7287\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4929 - acc: 0.7442 - val_loss: 0.4932 - val_acc: 0.7508\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4518 - acc: 0.7773 - val_loss: 0.4758 - val_acc: 0.7610\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4445 - acc: 0.7931 - val_loss: 0.4682 - val_acc: 0.7760\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4372 - acc: 0.7867 - val_loss: 0.4614 - val_acc: 0.7768\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4178 - acc: 0.8054 - val_loss: 0.4612 - val_acc: 0.7815\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4001 - acc: 0.8160 - val_loss: 0.4597 - val_acc: 0.7808\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.3909 - acc: 0.8168 - val_loss: 0.4588 - val_acc: 0.7831\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3406 - acc: 0.8419 - val_loss: 0.4627 - val_acc: 0.7768\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3029 - acc: 0.8668 - val_loss: 0.4817 - val_acc: 0.7800\n",
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      "pod 1st:  0.40641711229946526\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "51/51 [==============================] - 3s 28ms/step - loss: 0.5973 - acc: 0.6998 - val_loss: 0.5066 - val_acc: 0.7382\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4926 - acc: 0.7481 - val_loss: 0.4916 - val_acc: 0.7587\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4455 - acc: 0.7814 - val_loss: 0.4740 - val_acc: 0.7744\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4444 - acc: 0.7908 - val_loss: 0.4704 - val_acc: 0.7792\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4404 - acc: 0.7918 - val_loss: 0.4634 - val_acc: 0.7784\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4278 - acc: 0.7936 - val_loss: 0.4589 - val_acc: 0.7808\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 19ms/step - loss: 0.4103 - acc: 0.8123 - val_loss: 0.4691 - val_acc: 0.7689\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.3954 - acc: 0.8145 - val_loss: 0.4593 - val_acc: 0.7784\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3569 - acc: 0.8332 - val_loss: 0.4609 - val_acc: 0.7768\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.3319 - acc: 0.8524 - val_loss: 0.4711 - val_acc: 0.7823\n",
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      "pod 1st:  0.4117647058823529\n",
      "Epoch 1/10\n",
      "51/51 [==============================] - 4s 29ms/step - loss: 0.6012 - acc: 0.6995 - val_loss: 0.5047 - val_acc: 0.7295\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4859 - acc: 0.7495 - val_loss: 0.4914 - val_acc: 0.7587\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4395 - acc: 0.7866 - val_loss: 0.4738 - val_acc: 0.7760\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.4406 - acc: 0.7973 - val_loss: 0.4682 - val_acc: 0.7737\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4370 - acc: 0.7943 - val_loss: 0.4759 - val_acc: 0.7674\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4149 - acc: 0.8063 - val_loss: 0.4659 - val_acc: 0.7855\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.4047 - acc: 0.8190 - val_loss: 0.4562 - val_acc: 0.7823\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.3809 - acc: 0.8276 - val_loss: 0.4550 - val_acc: 0.7847\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.3387 - acc: 0.8484 - val_loss: 0.4607 - val_acc: 0.7894\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.2944 - acc: 0.8781 - val_loss: 0.4678 - val_acc: 0.7815\n",
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     "text": [
      "Epoch 1/10\n",
      "51/51 [==============================] - 3s 27ms/step - loss: 0.5976 - acc: 0.7279 - val_loss: 0.5086 - val_acc: 0.7232\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 15ms/step - loss: 0.4855 - acc: 0.7449 - val_loss: 0.4920 - val_acc: 0.7618\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 16ms/step - loss: 0.4422 - acc: 0.7806 - val_loss: 0.4767 - val_acc: 0.7831\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 13ms/step - loss: 0.4426 - acc: 0.7947 - val_loss: 0.4731 - val_acc: 0.7855\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 14ms/step - loss: 0.4358 - acc: 0.7930 - val_loss: 0.4869 - val_acc: 0.7689\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 14ms/step - loss: 0.4118 - acc: 0.8090 - val_loss: 0.4699 - val_acc: 0.7815\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 13ms/step - loss: 0.3994 - acc: 0.8195 - val_loss: 0.4616 - val_acc: 0.7855\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 15ms/step - loss: 0.3767 - acc: 0.8323 - val_loss: 0.4619 - val_acc: 0.7871\n",
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      "51/51 [==============================] - 1s 17ms/step - loss: 0.3290 - acc: 0.8608 - val_loss: 0.4660 - val_acc: 0.7879\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 17ms/step - loss: 0.2540 - acc: 0.9134 - val_loss: 0.4781 - val_acc: 0.7879\n",
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      "pod 1st:  0.44385026737967914\n",
      "Epoch 1/10\n",
      "51/51 [==============================] - 4s 29ms/step - loss: 0.6032 - acc: 0.7043 - val_loss: 0.5231 - val_acc: 0.7256\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 1s 15ms/step - loss: 0.4873 - acc: 0.7441 - val_loss: 0.5079 - val_acc: 0.7539\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 1s 14ms/step - loss: 0.4415 - acc: 0.7796 - val_loss: 0.4862 - val_acc: 0.7595\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 1s 14ms/step - loss: 0.4429 - acc: 0.7962 - val_loss: 0.4840 - val_acc: 0.7650\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 1s 14ms/step - loss: 0.4376 - acc: 0.7894 - val_loss: 0.5042 - val_acc: 0.7681\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 1s 14ms/step - loss: 0.4141 - acc: 0.8086 - val_loss: 0.4786 - val_acc: 0.7674\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 1s 15ms/step - loss: 0.4026 - acc: 0.8172 - val_loss: 0.4739 - val_acc: 0.7674\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 1s 15ms/step - loss: 0.3824 - acc: 0.8257 - val_loss: 0.4771 - val_acc: 0.7681\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.3444 - acc: 0.8465 - val_loss: 0.4768 - val_acc: 0.7713\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 1s 18ms/step - loss: 0.2889 - acc: 0.8871 - val_loss: 0.4895 - val_acc: 0.7642\n",
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      "pod 1st:  0.45989304812834225\n",
      "pod:  [0.45454545454545453, 0.40106951871657753, 0.29411764705882354, 0.3602150537634409, 0.37967914438502676, 0.40641711229946526, 0.4117647058823529, 0.39572192513368987, 0.44385026737967914, 0.45989304812834225]\n",
      "pof:  [0.10424710424710425, 0.05405405405405406, 0.04826254826254826, 0.08108108108108109, 0.10638297872340426, 0.1218568665377176, 0.08897485493230174, 0.08704061895551257, 0.11992263056092843, 0.07736943907156674]\n",
      "auc:  [0.6751491751491752, 0.6735077323312617, 0.6229275493981377, 0.6395669863411799, 0.6366480828308112, 0.6422801228808739, 0.6613949254750255, 0.6543406530890886, 0.6619638184093753, 0.6912618045283878]\n",
      "4714 460 1120 749\n",
      "Time:  125.21548461914062\n"
     ]
    }
   ],
   "source": [
    "#RNN\n",
    "import time\n",
    "start = time.time()\n",
    "\n",
    "def get_RNN_Predict(X2_train, X2_test, y2_train, y2_test):\n",
    "    import pandas as pd\n",
    "    from keras.models import Sequential\n",
    "    from keras.layers import Dense,Dropout, LSTM, GRU\n",
    "    from keras.layers import Embedding\n",
    "    max_features = 10000 # number of words to consider as features\n",
    "    import numpy as np\n",
    "    #create model \n",
    "    model = Sequential()\n",
    "    model.add(Embedding(max_features, 32))\n",
    "    model.add(LSTM(32))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(optimizer='rmsprop',loss='binary_crossentropy', metrics=['acc'])\n",
    "    history = model.fit(X2_train, y2_train, epochs=10, batch_size=100, validation_split=0.2)\n",
    "    y2_pred = model.predict(X2_test)\n",
    "    y22_pred=y2_pred.round()\n",
    "    print('y22_pred: ',y22_pred)\n",
    "    return y22_pred\n",
    "\n",
    "pod_list = []\n",
    "pof_list = []\n",
    "auc_val_list = []\n",
    "tn_list= []\n",
    "fp_list= []\n",
    "fn_list= []\n",
    "tp_list= []\n",
    "    \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "folds = StratifiedKFold(n_splits=10)\n",
    "for train_index, test_index in folds.split(df_x_train_chi,y_train):\n",
    "    X2_train, X2_test, y2_train, y2_test=df_x_train_chi.iloc[train_index], df_x_train_chi.iloc[test_index],y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "    y_pred = get_RNN_Predict(X2_train, X2_test, y2_train, y2_test)\n",
    "    tn, fp, fn, tp  = confusion_matrix(y2_test, y_pred).ravel()\n",
    "    tn_list.append(tn)\n",
    "    fp_list.append(fp)\n",
    "    fn_list.append(fn)\n",
    "    tp_list.append(tp)\n",
    "    \n",
    "    pod=tp/(tp+fn)\n",
    "    print('pod 1st: ',pod)\n",
    "    pof=fp/(fp+tn)\n",
    "    auc_val=(1+pod-pof)/2\n",
    "    #break\n",
    "    pod_list.append(pod)\n",
    "    pof_list.append(pof)\n",
    "    auc_val_list.append(auc_val)\n",
    "\n",
    "print('pod: ',pod_list)\n",
    "print ('pof: ',pof_list)\n",
    "print ('auc: ',auc_val_list)\n",
    "\n",
    "tn=sum(tn_list)\n",
    "fp=sum(fp_list) \n",
    "fn=sum(fn_list) \n",
    "tp=sum(tp_list)\n",
    "print(tn, fp, fn, tp)\n",
    "\n",
    "end = time.time()\n",
    "time_taken = end - start\n",
    "print('Time: ',time_taken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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