Baseline(RAW)-cross-validation-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",
    "num_df=num_df.fillna(conData)\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/html": [
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       "    }\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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen  tenure  MonthlyCharges  gender  Partner  Dependents  \\\n",
       "0              0       1           29.85       0        1           0   \n",
       "1              0      34           56.95       1        0           0   \n",
       "2              0       2           53.85       1        0           0   \n",
       "3              0      45           42.30       1        0           0   \n",
       "4              0       2           70.70       0        0           0   \n",
       "\n",
       "   PhoneService  MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0             0              1                0               0             2   \n",
       "1             1              0                0               2             0   \n",
       "2             1              0                0               2             2   \n",
       "3             0              1                0               2             0   \n",
       "4             1              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  TotalCharges  \n",
       "0                 1              2          2505  \n",
       "1                 0              3          1466  \n",
       "2                 1              3           157  \n",
       "3                 0              0          1400  \n",
       "4                 1              2           925  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3950 1224 496 1373\n",
      "pod:  0.7346174424826111\n",
      "pof:  0.23656745264785467\n",
      "AUC:  0.7490249949173782\n",
      "roc_auc:  0.8257459903480955\n",
      "auc:  0.7490249949173782\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_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, 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",
    "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4523 651 1092 777\n",
      "pod:  0.4157303370786517\n",
      "pof:  0.1258214147661384\n",
      "AUC:  0.6449544611562565\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "classifier = KNeighborsClassifier(n_neighbors=5)  \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, 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",
    "\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5068 106 1562 307\n",
      "pod:  0.164258962011771\n",
      "pof:  0.020487050637804406\n",
      "AUC:  0.5718859556869833\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier =RandomForestClassifier(n_estimators=100,max_depth=2,random_state=0)\n",
    "classifier.fit(X_train, 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, 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",
    "#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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc:  0.5718859556869832\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": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\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",
    "plt.plot(fpr, tpr, color='orange', label='ROC')\n",
    "plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('NO DT-ROC Curve for RF')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4610 564 855 1014\n",
      "pod:  0.5425361155698234\n",
      "pof:  0.1090065713181291\n",
      "AUC:  0.7167647721258471\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IICT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "classifier = LogisticRegression()\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, 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",
    "#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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4131 1043 921 948\n",
      "pod:  0.507223113964687\n",
      "pof:  0.20158484731349052\n",
      "AUC:  0.6528191333255983\n",
      "accuracy:  0.7211415589947465\n",
      "Time:  0.5950467586517334\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree\n",
    "import time\n",
    "start = time.time()\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "classifier = DecisionTreeClassifier(criterion='entropy', splitter='best')\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, 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)\n",
    "end = time.time()\n",
    "time_taken = end - start\n",
    "print('Time: ',time_taken)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4694 480 898 971\n",
      "pod:  0.5195291599785982\n",
      "pof:  0.09277155005798222\n",
      "AUC:  0.713378804960308\n",
      "accuracy:  0.8043447394576175\n",
      "Time:  9.094436407089233\n"
     ]
    }
   ],
   "source": [
    "#Gradient Boosting\n",
    "import time\n",
    "start = time.time()\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "#classifier = GradientBoostingClassifier(max_features=None, max_depth=3, criterion='friedman_mse')\n",
    "classifier = GradientBoostingClassifier()\n",
    "#classifier.fit(X_train, 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, 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)\n",
    "end = time.time()\n",
    "time_taken = end - start\n",
    "print('Time: ',time_taken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 23s 8ms/step - loss: 13.4467 - accuracy: 0.6108 - val_loss: 0.5873 - val_accuracy: 0.6869\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.8543 - accuracy: 0.6671 - val_loss: 0.5026 - val_accuracy: 0.7342\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.8108 - accuracy: 0.6926 - val_loss: 1.0797 - val_accuracy: 0.7437\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.6454 - accuracy: 0.7044 - val_loss: 0.4687 - val_accuracy: 0.7918\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.4899 - accuracy: 0.7658 - val_loss: 0.5371 - val_accuracy: 0.7508\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.5269 - accuracy: 0.7476 - val_loss: 0.5699 - val_accuracy: 0.7129\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5323 - accuracy: 0.7304 - val_loss: 0.5922 - val_accuracy: 0.7524\n",
      "y2_pred:  [[2.35000014e-01]\n",
      " [1.13865554e-01]\n",
      " [5.49467921e-01]\n",
      " [4.24693823e-02]\n",
      " [5.92300773e-01]\n",
      " [2.36084580e-01]\n",
      " [4.07798380e-01]\n",
      " [1.35844707e-01]\n",
      " [3.92172754e-01]\n",
      " [9.43288207e-03]\n",
      " [6.11454844e-02]\n",
      " [4.93207574e-02]\n",
      " [2.46800482e-02]\n",
      " [6.14308119e-02]\n",
      " [4.29737717e-01]\n",
      " [6.41933084e-03]\n",
      " [2.58650482e-02]\n",
      " [6.34893775e-03]\n",
      " [9.27965939e-02]\n",
      " [4.07476127e-01]\n",
      " [2.24784225e-01]\n",
      " [1.28432095e-01]\n",
      " [2.52215892e-01]\n",
      " [1.31286383e-02]\n",
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      " [7.46610463e-02]\n",
      " [2.28396147e-01]\n",
      " [3.82262468e-03]\n",
      " [4.00271624e-01]\n",
      " [4.12786007e-03]\n",
      " [6.62663817e-01]\n",
      " [1.89640045e-01]\n",
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      " [3.95649672e-03]\n",
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      " [4.35152650e-03]\n",
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      "pod 1st:  0.19786096256684493\n",
      "Epoch 1/30\n"
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    {
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     "text": [
      "159/159 [==============================] - 2s 7ms/step - loss: 10.0518 - accuracy: 0.6195 - val_loss: 0.7967 - val_accuracy: 0.7350\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 1.2538 - accuracy: 0.6665 - val_loss: 0.5550 - val_accuracy: 0.7437\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.7002 - accuracy: 0.6829 - val_loss: 1.0877 - val_accuracy: 0.7342\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.6820 - accuracy: 0.7036 - val_loss: 0.5191 - val_accuracy: 0.7539\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.5656 - accuracy: 0.7289 - val_loss: 0.7573 - val_accuracy: 0.7492\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5195 - accuracy: 0.7503 - val_loss: 0.6155 - val_accuracy: 0.7484\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5177 - accuracy: 0.7581 - val_loss: 0.5039 - val_accuracy: 0.7492\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5127 - accuracy: 0.7565 - val_loss: 0.4943 - val_accuracy: 0.7413\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4980 - accuracy: 0.7539 - val_loss: 0.6029 - val_accuracy: 0.7413\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.5181 - accuracy: 0.7577 - val_loss: 0.5305 - val_accuracy: 0.7445\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.5035 - accuracy: 0.7649 - val_loss: 0.4655 - val_accuracy: 0.7942\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.4759 - accuracy: 0.7657 - val_loss: 0.6473 - val_accuracy: 0.7405\n",
      "Epoch 13/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4949 - accuracy: 0.7630 - val_loss: 0.4688 - val_accuracy: 0.7831\n",
      "Epoch 14/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4807 - accuracy: 0.7611 - val_loss: 0.4990 - val_accuracy: 0.7500\n",
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      "pod 1st:  0.6417112299465241\n",
      "Epoch 1/30\n"
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    {
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     "text": [
      "159/159 [==============================] - 2s 5ms/step - loss: 13.1374 - accuracy: 0.6100 - val_loss: 1.7464 - val_accuracy: 0.7279\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 1.8061 - accuracy: 0.6389 - val_loss: 0.9811 - val_accuracy: 0.7287\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.7311 - accuracy: 0.6983 - val_loss: 1.1971 - val_accuracy: 0.7303\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.6266 - accuracy: 0.7034 - val_loss: 0.6819 - val_accuracy: 0.7350\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.7056 - accuracy: 0.6786 - val_loss: 0.5343 - val_accuracy: 0.7539\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.5644 - accuracy: 0.7344 - val_loss: 0.6719 - val_accuracy: 0.7342\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.5328 - accuracy: 0.7508 - val_loss: 0.6625 - val_accuracy: 0.5655\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5360 - accuracy: 0.7543 - val_loss: 0.5380 - val_accuracy: 0.7532\n",
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      "pod 1st:  0.6844919786096256\n",
      "Epoch 1/30\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 2s 5ms/step - loss: 19.4985 - accuracy: 0.6132 - val_loss: 0.9293 - val_accuracy: 0.3407\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.8164 - accuracy: 0.6416 - val_loss: 0.4939 - val_accuracy: 0.7508\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.6208 - accuracy: 0.7207 - val_loss: 0.5848 - val_accuracy: 0.7287\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.5143 - accuracy: 0.7457 - val_loss: 0.4699 - val_accuracy: 0.7863\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5036 - accuracy: 0.7569 - val_loss: 0.4708 - val_accuracy: 0.7760\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5466 - accuracy: 0.7291 - val_loss: 0.6963 - val_accuracy: 0.7390\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.5257 - accuracy: 0.7366 - val_loss: 0.5986 - val_accuracy: 0.7319\n",
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      "pod 1st:  0.010752688172043012\n",
      "Epoch 1/30\n"
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    {
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     "text": [
      "159/159 [==============================] - 3s 8ms/step - loss: 12.4453 - accuracy: 0.6172 - val_loss: 1.0239 - val_accuracy: 0.7279\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 1.0345 - accuracy: 0.6682 - val_loss: 0.7497 - val_accuracy: 0.5615\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.6896 - accuracy: 0.6924 - val_loss: 1.0458 - val_accuracy: 0.4006\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.7187 - accuracy: 0.6771 - val_loss: 0.5705 - val_accuracy: 0.7279\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.5877 - accuracy: 0.7212 - val_loss: 0.5594 - val_accuracy: 0.7271\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.6027 - accuracy: 0.7110 - val_loss: 0.6155 - val_accuracy: 0.7279\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5695 - accuracy: 0.7226 - val_loss: 0.6608 - val_accuracy: 0.7256\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.5825 - accuracy: 0.7184 - val_loss: 0.5353 - val_accuracy: 0.7287\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5136 - accuracy: 0.7414 - val_loss: 0.5469 - val_accuracy: 0.7303\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5137 - accuracy: 0.7413 - val_loss: 0.4936 - val_accuracy: 0.7532\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.4900 - accuracy: 0.7564 - val_loss: 0.5108 - val_accuracy: 0.7445\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.5011 - accuracy: 0.7498 - val_loss: 0.5272 - val_accuracy: 0.7445\n",
      "Epoch 13/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5154 - accuracy: 0.7470 - val_loss: 0.4718 - val_accuracy: 0.7894\n",
      "Epoch 14/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4628 - accuracy: 0.7780 - val_loss: 0.5726 - val_accuracy: 0.7405\n",
      "Epoch 15/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.4765 - accuracy: 0.7636 - val_loss: 0.4843 - val_accuracy: 0.7658\n",
      "Epoch 16/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.4721 - accuracy: 0.7817 - val_loss: 0.4687 - val_accuracy: 0.7768\n",
      "Epoch 17/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4812 - accuracy: 0.7654 - val_loss: 0.4899 - val_accuracy: 0.7579\n",
      "Epoch 18/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4713 - accuracy: 0.7668 - val_loss: 0.4514 - val_accuracy: 0.7981\n",
      "Epoch 19/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.4649 - accuracy: 0.7769 - val_loss: 0.4781 - val_accuracy: 0.7642\n",
      "Epoch 20/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4818 - accuracy: 0.7598 - val_loss: 0.4510 - val_accuracy: 0.7965\n",
      "Epoch 21/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4540 - accuracy: 0.7791 - val_loss: 0.4574 - val_accuracy: 0.7847\n",
      "Epoch 22/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.4675 - accuracy: 0.7744 - val_loss: 0.4759 - val_accuracy: 0.7752\n",
      "Epoch 23/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4579 - accuracy: 0.7846 - val_loss: 0.4493 - val_accuracy: 0.7879\n",
      "Epoch 24/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4682 - accuracy: 0.7682 - val_loss: 0.4899 - val_accuracy: 0.7634\n",
      "Epoch 25/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4479 - accuracy: 0.7856 - val_loss: 0.4539 - val_accuracy: 0.7831\n",
      "Epoch 26/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4576 - accuracy: 0.7722 - val_loss: 0.5296 - val_accuracy: 0.7508\n",
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      "pod 1st:  0.08021390374331551\n",
      "Epoch 1/30\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 3s 6ms/step - loss: 10.9926 - accuracy: 0.6242 - val_loss: 0.7029 - val_accuracy: 0.7366\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.6028 - accuracy: 0.7301 - val_loss: 0.9211 - val_accuracy: 0.4621\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.5959 - accuracy: 0.7250 - val_loss: 0.4727 - val_accuracy: 0.7989\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.5605 - accuracy: 0.7494 - val_loss: 0.6076 - val_accuracy: 0.7445\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.5159 - accuracy: 0.7502 - val_loss: 0.4800 - val_accuracy: 0.7658\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.4937 - accuracy: 0.7682 - val_loss: 0.4677 - val_accuracy: 0.7784\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.4772 - accuracy: 0.7634 - val_loss: 0.4618 - val_accuracy: 0.7768\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.4665 - accuracy: 0.7721 - val_loss: 0.6470 - val_accuracy: 0.7508\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4786 - accuracy: 0.7747 - val_loss: 0.5463 - val_accuracy: 0.7532\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4800 - accuracy: 0.7551 - val_loss: 0.5146 - val_accuracy: 0.7421\n",
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      " [1.]]\n",
      "pod 1st:  0.7219251336898396\n",
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 2s 5ms/step - loss: 11.4919 - accuracy: 0.5984 - val_loss: 5.8989 - val_accuracy: 0.7279\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 2.3525 - accuracy: 0.6576 - val_loss: 0.7012 - val_accuracy: 0.7413\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.6413 - accuracy: 0.7150 - val_loss: 0.4734 - val_accuracy: 0.7957\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5110 - accuracy: 0.7583 - val_loss: 0.5342 - val_accuracy: 0.7263\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5060 - accuracy: 0.7574 - val_loss: 0.5051 - val_accuracy: 0.7547\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.5263 - accuracy: 0.7554 - val_loss: 0.4530 - val_accuracy: 0.7981\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.5104 - accuracy: 0.7689 - val_loss: 0.4694 - val_accuracy: 0.7658\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4729 - accuracy: 0.7755 - val_loss: 0.4586 - val_accuracy: 0.7800\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4888 - accuracy: 0.7643 - val_loss: 0.4541 - val_accuracy: 0.7973\n",
      "y2_pred:  [[4.16494012e-02]\n",
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      "pod 1st:  0.37433155080213903\n",
      "Epoch 1/30\n"
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    {
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     "text": [
      "159/159 [==============================] - 2s 6ms/step - loss: 14.4755 - accuracy: 0.6014 - val_loss: 0.8323 - val_accuracy: 0.7279\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.8099 - accuracy: 0.6752 - val_loss: 0.8079 - val_accuracy: 0.7279\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5963 - accuracy: 0.7258 - val_loss: 0.8807 - val_accuracy: 0.7295\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5835 - accuracy: 0.7502 - val_loss: 0.5891 - val_accuracy: 0.7279\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4967 - accuracy: 0.7600 - val_loss: 0.5973 - val_accuracy: 0.7271\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5018 - accuracy: 0.7576 - val_loss: 0.5259 - val_accuracy: 0.7374\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.5053 - accuracy: 0.7497 - val_loss: 0.4919 - val_accuracy: 0.7516\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4948 - accuracy: 0.7552 - val_loss: 0.5006 - val_accuracy: 0.7350\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.5213 - accuracy: 0.7458 - val_loss: 0.5521 - val_accuracy: 0.7461\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.5160 - accuracy: 0.7600 - val_loss: 0.4805 - val_accuracy: 0.7894\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4766 - accuracy: 0.7750 - val_loss: 0.4614 - val_accuracy: 0.7729\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4770 - accuracy: 0.7577 - val_loss: 0.4816 - val_accuracy: 0.7926\n",
      "Epoch 13/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4823 - accuracy: 0.7742 - val_loss: 0.4581 - val_accuracy: 0.7997\n",
      "Epoch 14/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5015 - accuracy: 0.7633 - val_loss: 0.4558 - val_accuracy: 0.7871\n",
      "Epoch 15/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.4765 - accuracy: 0.7576 - val_loss: 0.4502 - val_accuracy: 0.7863\n",
      "Epoch 16/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4819 - accuracy: 0.7713 - val_loss: 0.4963 - val_accuracy: 0.7382\n",
      "Epoch 17/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4795 - accuracy: 0.7567 - val_loss: 0.5121 - val_accuracy: 0.7776\n",
      "Epoch 18/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.4544 - accuracy: 0.7753 - val_loss: 0.4894 - val_accuracy: 0.7626\n",
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      "pod 1st:  0.39572192513368987\n",
      "Epoch 1/30\n"
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    {
     "name": "stdout",
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     "text": [
      "159/159 [==============================] - 2s 6ms/step - loss: 18.1287 - accuracy: 0.6066 - val_loss: 0.5226 - val_accuracy: 0.7618\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.8673 - accuracy: 0.6798 - val_loss: 0.5599 - val_accuracy: 0.7382\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.6237 - accuracy: 0.7080 - val_loss: 0.5198 - val_accuracy: 0.7405\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.5629 - accuracy: 0.7303 - val_loss: 0.4839 - val_accuracy: 0.7910\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.5676 - accuracy: 0.7323 - val_loss: 1.5189 - val_accuracy: 0.7279\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 0s 2ms/step - loss: 0.6821 - accuracy: 0.7087 - val_loss: 0.5058 - val_accuracy: 0.7279\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5433 - accuracy: 0.7334 - val_loss: 0.4860 - val_accuracy: 0.7311\n",
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      "pod 1st:  0.016042780748663103\n",
      "Epoch 1/30\n"
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     "output_type": "stream",
     "text": [
      "159/159 [==============================] - 2s 5ms/step - loss: 7.6764 - accuracy: 0.6004 - val_loss: 2.6198 - val_accuracy: 0.3241\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 1.4138 - accuracy: 0.6425 - val_loss: 1.0308 - val_accuracy: 0.7263\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 1.1492 - accuracy: 0.6526 - val_loss: 0.5030 - val_accuracy: 0.7429\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.5446 - accuracy: 0.7351 - val_loss: 0.6641 - val_accuracy: 0.7319\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.6084 - accuracy: 0.7174 - val_loss: 0.4941 - val_accuracy: 0.7366\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.5467 - accuracy: 0.7396 - val_loss: 0.5364 - val_accuracy: 0.7271\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.5235 - accuracy: 0.7502 - val_loss: 0.4852 - val_accuracy: 0.7563\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.5288 - accuracy: 0.7533 - val_loss: 0.5246 - val_accuracy: 0.7468\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 8ms/step - loss: 0.4767 - accuracy: 0.7694 - val_loss: 0.4730 - val_accuracy: 0.7508\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 3ms/step - loss: 0.4963 - accuracy: 0.7530 - val_loss: 0.6976 - val_accuracy: 0.7303\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4779 - accuracy: 0.7723 - val_loss: 0.4634 - val_accuracy: 0.7863\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4953 - accuracy: 0.7536 - val_loss: 0.4647 - val_accuracy: 0.7744\n",
      "Epoch 13/30\n",
      "159/159 [==============================] - 1s 4ms/step - loss: 0.4741 - accuracy: 0.7710 - val_loss: 0.5107 - val_accuracy: 0.7445\n",
      "Epoch 14/30\n",
      "159/159 [==============================] - 0s 3ms/step - loss: 0.4751 - accuracy: 0.7707 - val_loss: 0.4725 - val_accuracy: 0.7823\n",
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      " [0.]]\n",
      "pod 1st:  0.33689839572192515\n",
      "pod:  [0.19786096256684493, 0.6417112299465241, 0.6844919786096256, 0.010752688172043012, 0.08021390374331551, 0.7219251336898396, 0.37433155080213903, 0.39572192513368987, 0.016042780748663103, 0.33689839572192515]\n",
      "pof:  [0.04054054054054054, 0.19498069498069498, 0.2084942084942085, 0.0, 0.01160541586073501, 0.26692456479690524, 0.05415860735009671, 0.09864603481624758, 0.0019342359767891683, 0.05222437137330754]\n",
      "auc:  [0.5786602110131521, 0.7233652674829145, 0.7379988850577086, 0.5053763440860215, 0.5343042439412903, 0.7275002844464672, 0.6600864717260211, 0.6485379451587211, 0.507054272385937, 0.6423370121743088]\n",
      "tn_list:  [497, 417, 410, 518, 511, 379, 489, 466, 516, 490]\n",
      "fp_list:  [21, 101, 108, 0, 6, 138, 28, 51, 1, 27]\n",
      "fn_list:  [150, 67, 59, 184, 172, 52, 117, 113, 184, 124]\n",
      "tp_list:  [37, 120, 128, 2, 15, 135, 70, 74, 3, 63]\n",
      "4693 481 1222 647\n",
      "Time:  113.40240168571472\n"
     ]
    }
   ],
   "source": [
    "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=30, 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(X_train,y_train):\n",
    "    X2_train, X2_test, y2_train, y2_test=X_train.iloc[train_index], X_train.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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 11s 57ms/step - loss: 0.6069 - acc: 0.7012 - val_loss: 0.5424 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 4s 90ms/step - loss: 0.5221 - acc: 0.7384 - val_loss: 0.5246 - val_acc: 0.7279\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 19ms/step - loss: 0.4909 - acc: 0.7405 - val_loss: 0.5078 - val_acc: 0.7366\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 8s 195ms/step - loss: 0.4758 - acc: 0.7552 - val_loss: 0.4939 - val_acc: 0.7587\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 5s 129ms/step - loss: 0.4517 - acc: 0.7862 - val_loss: 0.4887 - val_acc: 0.7634\n",
      "y22_pred:  [[0.]\n",
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      "pod 1st:  0.45454545454545453\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 10s 148ms/step - loss: 0.6121 - acc: 0.7024 - val_loss: 0.5391 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 25ms/step - loss: 0.5273 - acc: 0.7259 - val_loss: 0.5390 - val_acc: 0.7279\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5056 - acc: 0.7380 - val_loss: 0.5271 - val_acc: 0.7295\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 2s 56ms/step - loss: 0.4735 - acc: 0.7513 - val_loss: 0.5044 - val_acc: 0.7358\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 24ms/step - loss: 0.4568 - acc: 0.7702 - val_loss: 0.5231 - val_acc: 0.7382\n",
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      "pod 1st:  0.12299465240641712\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 13s 235ms/step - loss: 0.6165 - acc: 0.7039 - val_loss: 0.5504 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 4s 105ms/step - loss: 0.5298 - acc: 0.7395 - val_loss: 0.5267 - val_acc: 0.7240\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 24ms/step - loss: 0.5026 - acc: 0.7288 - val_loss: 0.5158 - val_acc: 0.7295\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 7s 163ms/step - loss: 0.4777 - acc: 0.7481 - val_loss: 0.4972 - val_acc: 0.7397\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 2s 58ms/step - loss: 0.4578 - acc: 0.7650 - val_loss: 0.4850 - val_acc: 0.7508\n",
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      "pod 1st:  0.2727272727272727\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 7s 80ms/step - loss: 0.6167 - acc: 0.7096 - val_loss: 0.5410 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 2s 46ms/step - loss: 0.5079 - acc: 0.7436 - val_loss: 0.5299 - val_acc: 0.7240\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 4s 109ms/step - loss: 0.4932 - acc: 0.7441 - val_loss: 0.5067 - val_acc: 0.7366\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 2s 41ms/step - loss: 0.4684 - acc: 0.7647 - val_loss: 0.4958 - val_acc: 0.7437\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 24ms/step - loss: 0.4549 - acc: 0.7814 - val_loss: 0.4814 - val_acc: 0.7674\n",
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      "pod 1st:  0.3387096774193548\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 5s 57ms/step - loss: 0.6121 - acc: 0.6927 - val_loss: 0.5533 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 2s 49ms/step - loss: 0.5232 - acc: 0.7418 - val_loss: 0.5550 - val_acc: 0.7224\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 23ms/step - loss: 0.4934 - acc: 0.7471 - val_loss: 0.5050 - val_acc: 0.7319\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 2s 35ms/step - loss: 0.4696 - acc: 0.7590 - val_loss: 0.5081 - val_acc: 0.7508\n",
      "Epoch 5/5\n",
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      "pod 1st:  0.1497326203208556\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 5s 45ms/step - loss: 0.6078 - acc: 0.7100 - val_loss: 0.5445 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 2s 37ms/step - loss: 0.5267 - acc: 0.7292 - val_loss: 0.5313 - val_acc: 0.7279\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.4885 - acc: 0.7434 - val_loss: 0.5197 - val_acc: 0.7342\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 24ms/step - loss: 0.4721 - acc: 0.7525 - val_loss: 0.5005 - val_acc: 0.7437\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 26ms/step - loss: 0.4569 - acc: 0.7797 - val_loss: 0.5005 - val_acc: 0.7539\n",
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      "pod 1st:  0.22459893048128343\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 7s 69ms/step - loss: 0.6115 - acc: 0.7121 - val_loss: 0.5442 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 7s 165ms/step - loss: 0.5321 - acc: 0.7240 - val_loss: 0.5274 - val_acc: 0.7295\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 6s 153ms/step - loss: 0.4997 - acc: 0.7409 - val_loss: 0.5176 - val_acc: 0.7311\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 2s 45ms/step - loss: 0.4763 - acc: 0.7513 - val_loss: 0.5037 - val_acc: 0.7311\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 3s 83ms/step - loss: 0.4594 - acc: 0.7679 - val_loss: 0.5457 - val_acc: 0.7358\n",
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      "pod 1st:  0.0427807486631016\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 11s 174ms/step - loss: 0.6224 - acc: 0.6900 - val_loss: 0.5383 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 4s 108ms/step - loss: 0.5068 - acc: 0.7340 - val_loss: 0.5224 - val_acc: 0.7311\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 4s 106ms/step - loss: 0.4971 - acc: 0.7450 - val_loss: 0.5139 - val_acc: 0.7279\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 3s 76ms/step - loss: 0.4592 - acc: 0.7584 - val_loss: 0.4925 - val_acc: 0.7555\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 6s 147ms/step - loss: 0.4533 - acc: 0.7719 - val_loss: 0.4894 - val_acc: 0.7808\n",
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      "pod 1st:  0.39037433155080214\n"
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    {
     "name": "stdout",
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     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 8s 109ms/step - loss: 0.6104 - acc: 0.7198 - val_loss: 0.5385 - val_acc: 0.7248\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 2s 36ms/step - loss: 0.5165 - acc: 0.7395 - val_loss: 0.5255 - val_acc: 0.7192\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 2s 38ms/step - loss: 0.4962 - acc: 0.7436 - val_loss: 0.5078 - val_acc: 0.7248\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 2s 41ms/step - loss: 0.4672 - acc: 0.7544 - val_loss: 0.5150 - val_acc: 0.7374\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 5s 124ms/step - loss: 0.4465 - acc: 0.7828 - val_loss: 0.4942 - val_acc: 0.7374\n",
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      "pod 1st:  0.1443850267379679\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 5s 39ms/step - loss: 0.6092 - acc: 0.7207 - val_loss: 0.5499 - val_acc: 0.7248\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 25ms/step - loss: 0.5144 - acc: 0.7392 - val_loss: 0.5413 - val_acc: 0.7169\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5019 - acc: 0.7334 - val_loss: 0.5312 - val_acc: 0.7208\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 30ms/step - loss: 0.4786 - acc: 0.7459 - val_loss: 0.5177 - val_acc: 0.7271\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 23ms/step - loss: 0.4572 - acc: 0.7621 - val_loss: 0.5030 - val_acc: 0.7342\n",
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      "pod 1st:  0.12299465240641712\n",
      "pod:  [0.45454545454545453, 0.12299465240641712, 0.2727272727272727, 0.3387096774193548, 0.1497326203208556, 0.22459893048128343, 0.0427807486631016, 0.39037433155080214, 0.1443850267379679, 0.12299465240641712]\n",
      "pof:  [0.1138996138996139, 0.015444015444015444, 0.04826254826254826, 0.05019305019305019, 0.02127659574468085, 0.05609284332688588, 0.0038684719535783366, 0.09671179883945841, 0.027079303675048357, 0.017408123791102514]\n",
      "auc:  [0.6703229203229204, 0.5537753184812009, 0.6122323622323622, 0.6442583136131523, 0.5642280122880874, 0.5842530435771988, 0.5194561383547616, 0.6468312663556719, 0.5586528615314598, 0.5527932643076573]\n",
      "4941 233 1446 423\n",
      "Time:  204.44809770584106\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=30, batch_size=128, 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(X_train,y_train): \n",
    "    X2_train, X2_test, y2_train, y2_test=X_train.iloc[train_index], X_train.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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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