LOG-Univariate feature selection-5000.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "df = pd.read_csv(\"churn-5000.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "#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",
    "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",
    "#result_df_op=result_df_op.drop(['churn'], axis=1)\n",
    "#result_df_op=np.nan_to_num(result_df_op)\n",
    "\n",
    "X=result_df_op\n",
    "#y=result_df['churn'] \n",
    "y=churn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 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>17</th>\n",
       "      <td>international_plan</td>\n",
       "      <td>303.963357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>number_vmail_messages</td>\n",
       "      <td>144.279348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>voice_mail_plan</td>\n",
       "      <td>45.058340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>number_customer_service_calls</td>\n",
       "      <td>38.021508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>total_intl_calls</td>\n",
       "      <td>2.181351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>state</td>\n",
       "      <td>2.033226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_day_charge</td>\n",
       "      <td>1.716429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_day_minutes</td>\n",
       "      <td>1.584154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>total_intl_minutes</td>\n",
       "      <td>1.416559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>total_eve_minutes</td>\n",
       "      <td>0.861565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>total_night_charge</td>\n",
       "      <td>0.411195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total_intl_charge</td>\n",
       "      <td>0.380779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>total_eve_charge</td>\n",
       "      <td>0.150612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>account_length</td>\n",
       "      <td>0.139868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>total_day_calls</td>\n",
       "      <td>0.001722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>total_night_calls</td>\n",
       "      <td>0.001319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>area_code</td>\n",
       "      <td>0.000749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_eve_calls</td>\n",
       "      <td>0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>total_night_minutes</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Feature      Scores\n",
       "17             international_plan  303.963357\n",
       "2           number_vmail_messages  144.279348\n",
       "18                voice_mail_plan   45.058340\n",
       "15  number_customer_service_calls   38.021508\n",
       "13               total_intl_calls    2.181351\n",
       "16                          state    2.033226\n",
       "5                total_day_charge    1.716429\n",
       "3               total_day_minutes    1.584154\n",
       "12             total_intl_minutes    1.416559\n",
       "6               total_eve_minutes    0.861565\n",
       "11             total_night_charge    0.411195\n",
       "14              total_intl_charge    0.380779\n",
       "8                total_eve_charge    0.150612\n",
       "0                  account_length    0.139868\n",
       "4                 total_day_calls    0.001722\n",
       "10              total_night_calls    0.001319\n",
       "1                       area_code    0.000749\n",
       "7                 total_eve_calls    0.000406\n",
       "9             total_night_minutes    0.000294"
      ]
     },
     "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": [
       "(5000, 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": [
      "4024 269 509 198\n",
      "pod:  0.28005657708628007\n",
      "pof:  0.06266014442115071\n",
      "AUC:  0.6086982163325647\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    7.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'var_smoothing': 0.0006579332246575676}\n",
      "GaussianNB(priors=None, var_smoothing=0.0006579332246575676)\n",
      "0.8625333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:   10.0s finished\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": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4252 41 639 68\n",
      "pod:  0.09618104667609619\n",
      "pof:  0.009550430934078733\n",
      "AUC:  0.5433153078710087\n",
      "accuracy:  0.864\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=0.0006579332246575676)\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": 1,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 36 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    3.2s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:    9.2s\n",
      "[Parallel(n_jobs=-1)]: Done 345 out of 360 | elapsed:   11.8s remaining:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:   11.9s finished\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 7, 'multi_class': 'ovr', 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "LogisticRegression(C=7, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n",
      "0.8638\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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4228 65 612 95\n",
      "pod:  0.13437057991513437\n",
      "pof:  0.015140927090612625\n",
      "AUC:  0.5596148264122609\n",
      "accuracy:  0.8646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "classifier = LogisticRegression(C=7, class_weight=None, dual=False, fit_intercept=True,\n",
    "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
    "          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
    "          tol=0.0001, verbose=0, 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": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='manhattan',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=11, p=2,\n",
      "           weights='distance')\n",
      "0.8602\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:    8.6s finished\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=3, 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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 42 candidates, totalling 420 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed: 410.7min\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed: 1993.6min\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import svm\n",
    "\n",
    "# Specify parameters\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "gammas = [0.001, 0.01, 0.1, 1, 1e-3, 1e-4]\n",
    "param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "\n",
    "tuned_parameters = [{'kernel': ['rbf', 'linear'], \n",
    "                     'gamma': [0.001, 0.01, 0.1, 1, 1e-3, 1e-4], \n",
    "                     'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]} ]\n",
    "\n",
    "grid = GridSearchCV(svm.SVC(), 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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4236 57 653 54\n",
      "pod:  0.07637906647807638\n",
      "pof:  0.013277428371767994\n",
      "AUC:  0.5315508190531542\n",
      "accuracy:  0.858\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "classifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='manhattan',\n",
    "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
    "           weights='distance' )  \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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4247 46 681 26\n",
      "pod:  0.036775106082036775\n",
      "pof:  0.010715117633356627\n",
      "AUC:  0.51302999422434\n",
      "accuracy:  0.8546\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, 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 288 candidates, totalling 864 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   39.8s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  4.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='manhattan',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=11, p=2,\n",
      "           weights='distance')\n",
      "0.8602\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_search = GridSearchCV(estimator = RandomForestClassifier(), param_grid = param_grid, \n",
    "                          cv = 3, 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_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'max_depth': 80, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 1000}\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=80, max_features=3, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=3, min_samples_split=10,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "0.9442\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4256 37 236 471\n",
      "pod:  0.6661951909476662\n",
      "pof:  0.008618681574656418\n",
      "AUC:  0.8287882546865049\n",
      "accuracy:  0.9454\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier =RandomForestClassifier( bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=80, max_features=3, max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=3, min_samples_split=10,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=None,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)\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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4246 47 260 447\n",
      "pod:  0.6322489391796322\n",
      "pof:  0.010948054973212206\n",
      "AUC:  0.81065044210321\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier =RandomForestClassifier()\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, 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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4064 229 213 494\n",
      "pod:  0.6987270155586988\n",
      "pof:  0.05334265082692756\n",
      "AUC:  0.8226921823658856\n",
      "accuracy:  0.9116\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree\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_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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4051 242 208 499\n",
      "pod:  0.7057991513437057\n",
      "pof:  0.056370836245050085\n",
      "AUC:  0.8247141575493279\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "classifier = DecisionTreeClassifier()\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4239 54 224 483\n",
      "pod:  0.6831683168316832\n",
      "pof:  0.012578616352201259\n",
      "AUC:  0.835294850239741\n",
      "accuracy:  0.9444\n"
     ]
    }
   ],
   "source": [
    "#Gradient Boosting\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_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",
    "#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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4242 51 221 486\n",
      "pod:  0.6874115983026874\n",
      "pof:  0.011879804332634521\n",
      "AUC:  0.8377658969850265\n",
      "accuracy:  0.9456\n"
     ]
    }
   ],
   "source": [
    "#Gradient Boosting\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3599/3599 [==============================] - 1s 147us/step - loss: 0.4381 - acc: 0.8558 - val_loss: 0.4085 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 60us/step - loss: 0.4116 - acc: 0.8586 - val_loss: 0.4043 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.4067 - acc: 0.8586 - val_loss: 0.4088 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.4038 - acc: 0.8586 - val_loss: 0.4027 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.4046 - acc: 0.8586 - val_loss: 0.4017 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3912 - acc: 0.8586 - val_loss: 0.3859 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3897 - acc: 0.8586 - val_loss: 0.4046 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 58us/step - loss: 0.3898 - acc: 0.8586 - val_loss: 0.3799 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3838 - acc: 0.8586 - val_loss: 0.3761 - val_acc: 0.8589\n",
      "Epoch 10/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3779 - acc: 0.8586 - val_loss: 0.4059 - val_acc: 0.8589\n",
      "Epoch 11/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3859 - acc: 0.8586 - val_loss: 0.3721 - val_acc: 0.8589\n",
      "Epoch 12/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3738 - acc: 0.8586 - val_loss: 0.3692 - val_acc: 0.8589\n",
      "Epoch 13/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3770 - acc: 0.8583 - val_loss: 0.3792 - val_acc: 0.8622\n",
      "Epoch 14/30\n",
      "3599/3599 [==============================] - 0s 61us/step - loss: 0.3718 - acc: 0.8583 - val_loss: 0.3759 - val_acc: 0.8589\n",
      "Epoch 15/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3743 - acc: 0.8586 - val_loss: 0.3947 - val_acc: 0.8589\n",
      "y2_pred:  [[1.62458539e-01]\n",
      " [1.42594695e-01]\n",
      " [1.74076587e-01]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3599/3599 [==============================] - 1s 170us/step - loss: 0.4267 - acc: 0.8550 - val_loss: 0.4100 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 0.4157 - acc: 0.8586 - val_loss: 0.4157 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 0.4053 - acc: 0.8586 - val_loss: 0.4009 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.4007 - acc: 0.8586 - val_loss: 0.3918 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3962 - acc: 0.8586 - val_loss: 0.3860 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.3960 - acc: 0.8586 - val_loss: 0.3868 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3923 - acc: 0.8588 - val_loss: 0.3893 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3855 - acc: 0.8586 - val_loss: 0.3831 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.3814 - acc: 0.8580 - val_loss: 0.3754 - val_acc: 0.8589\n",
      "Epoch 10/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3796 - acc: 0.8600 - val_loss: 0.3643 - val_acc: 0.8633\n",
      "Epoch 11/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.3736 - acc: 0.8605 - val_loss: 0.3659 - val_acc: 0.8644\n",
      "Epoch 12/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.3740 - acc: 0.8608 - val_loss: 0.3599 - val_acc: 0.8656\n",
      "Epoch 13/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.3699 - acc: 0.8641 - val_loss: 0.3694 - val_acc: 0.8667\n",
      "Epoch 14/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.3672 - acc: 0.8616 - val_loss: 0.3755 - val_acc: 0.8622\n",
      "Epoch 15/30\n",
      "3599/3599 [==============================] - 0s 46us/step - loss: 0.3699 - acc: 0.8630 - val_loss: 0.3564 - val_acc: 0.8644\n",
      "Epoch 16/30\n",
      "3599/3599 [==============================] - 0s 46us/step - loss: 0.3664 - acc: 0.8633 - val_loss: 0.3677 - val_acc: 0.8622\n",
      "Epoch 17/30\n",
      "3599/3599 [==============================] - 0s 45us/step - loss: 0.3685 - acc: 0.8647 - val_loss: 0.3675 - val_acc: 0.8678\n",
      "Epoch 18/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.3695 - acc: 0.8594 - val_loss: 0.3670 - val_acc: 0.8644\n",
      "y2_pred:  [[8.51899087e-02]\n",
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      "pod 1st:  0.014084507042253521\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3599/3599 [==============================] - 1s 173us/step - loss: 0.4281 - acc: 0.8530 - val_loss: 0.4039 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.4063 - acc: 0.8586 - val_loss: 0.3920 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3946 - acc: 0.8586 - val_loss: 0.3925 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3978 - acc: 0.8586 - val_loss: 0.3817 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.3889 - acc: 0.8586 - val_loss: 0.3759 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3885 - acc: 0.8586 - val_loss: 0.3729 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.3842 - acc: 0.8586 - val_loss: 0.3683 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.3809 - acc: 0.8586 - val_loss: 0.3771 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3769 - acc: 0.8586 - val_loss: 0.3745 - val_acc: 0.8589\n",
      "Epoch 10/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.3792 - acc: 0.8586 - val_loss: 0.3668 - val_acc: 0.8589\n",
      "Epoch 11/30\n",
      "3599/3599 [==============================] - 0s 58us/step - loss: 0.3766 - acc: 0.8586 - val_loss: 0.3596 - val_acc: 0.8589\n",
      "Epoch 12/30\n",
      "3599/3599 [==============================] - 0s 67us/step - loss: 0.3691 - acc: 0.8586 - val_loss: 0.3766 - val_acc: 0.8589\n",
      "Epoch 13/30\n",
      "3599/3599 [==============================] - 0s 60us/step - loss: 0.3701 - acc: 0.8591 - val_loss: 0.3654 - val_acc: 0.8589\n",
      "Epoch 14/30\n",
      "3599/3599 [==============================] - 0s 60us/step - loss: 0.3697 - acc: 0.8588 - val_loss: 0.3680 - val_acc: 0.8589\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 192us/step - loss: 0.4185 - acc: 0.8558 - val_loss: 0.4074 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 47us/step - loss: 0.4081 - acc: 0.8586 - val_loss: 0.4059 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 51us/step - loss: 0.4020 - acc: 0.8586 - val_loss: 0.3894 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3955 - acc: 0.8586 - val_loss: 0.3814 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3860 - acc: 0.8589 - val_loss: 0.3770 - val_acc: 0.8600\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3835 - acc: 0.8592 - val_loss: 0.3668 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3783 - acc: 0.8569 - val_loss: 0.3676 - val_acc: 0.8600\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3788 - acc: 0.8594 - val_loss: 0.3605 - val_acc: 0.8578\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3733 - acc: 0.8617 - val_loss: 0.3763 - val_acc: 0.8667\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3731 - acc: 0.8592 - val_loss: 0.3856 - val_acc: 0.8644\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3713 - acc: 0.8597 - val_loss: 0.3556 - val_acc: 0.8600\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 51us/step - loss: 0.3654 - acc: 0.8619 - val_loss: 0.3556 - val_acc: 0.8622\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3661 - acc: 0.8611 - val_loss: 0.3571 - val_acc: 0.8600\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3665 - acc: 0.8617 - val_loss: 0.3543 - val_acc: 0.8578\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3612 - acc: 0.8628 - val_loss: 0.3619 - val_acc: 0.8611\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.3642 - acc: 0.8606 - val_loss: 0.3755 - val_acc: 0.8611\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3667 - acc: 0.8586 - val_loss: 0.3603 - val_acc: 0.8600\n",
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      "pod 1st:  0.028169014084507043\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 205us/step - loss: 0.4355 - acc: 0.8586 - val_loss: 0.4130 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.4171 - acc: 0.8586 - val_loss: 0.4162 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.4139 - acc: 0.8586 - val_loss: 0.4111 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.4081 - acc: 0.8586 - val_loss: 0.4010 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.3994 - acc: 0.8586 - val_loss: 0.3831 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.3935 - acc: 0.8586 - val_loss: 0.3872 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 52us/step - loss: 0.3865 - acc: 0.8586 - val_loss: 0.3709 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3809 - acc: 0.8583 - val_loss: 0.3731 - val_acc: 0.8600\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3758 - acc: 0.8597 - val_loss: 0.3614 - val_acc: 0.8656\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3858 - acc: 0.8589 - val_loss: 0.3759 - val_acc: 0.8600\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.3746 - acc: 0.8600 - val_loss: 0.3579 - val_acc: 0.8633\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.3698 - acc: 0.8622 - val_loss: 0.3607 - val_acc: 0.8633\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.3690 - acc: 0.8619 - val_loss: 0.3653 - val_acc: 0.8678\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3681 - acc: 0.8606 - val_loss: 0.3658 - val_acc: 0.8667\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 261us/step - loss: 0.4497 - acc: 0.8514 - val_loss: 0.4095 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.4174 - acc: 0.8586 - val_loss: 0.4034 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.4049 - acc: 0.8586 - val_loss: 0.3957 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.4059 - acc: 0.8586 - val_loss: 0.4046 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3985 - acc: 0.8586 - val_loss: 0.3836 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3918 - acc: 0.8586 - val_loss: 0.3813 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3878 - acc: 0.8586 - val_loss: 0.3723 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 67us/step - loss: 0.3892 - acc: 0.8572 - val_loss: 0.3752 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3784 - acc: 0.8594 - val_loss: 0.3778 - val_acc: 0.8678\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3742 - acc: 0.8628 - val_loss: 0.3603 - val_acc: 0.8644\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 75us/step - loss: 0.3698 - acc: 0.8625 - val_loss: 0.3799 - val_acc: 0.8678\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3694 - acc: 0.8631 - val_loss: 0.3597 - val_acc: 0.8656\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3674 - acc: 0.8647 - val_loss: 0.3610 - val_acc: 0.8667\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3638 - acc: 0.8639 - val_loss: 0.3617 - val_acc: 0.8633\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3623 - acc: 0.8639 - val_loss: 0.3549 - val_acc: 0.8633\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 75us/step - loss: 0.3673 - acc: 0.8667 - val_loss: 0.3646 - val_acc: 0.8656\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 73us/step - loss: 0.3634 - acc: 0.8664 - val_loss: 0.3677 - val_acc: 0.8678\n",
      "Epoch 18/30\n",
      "3600/3600 [==============================] - 0s 73us/step - loss: 0.3590 - acc: 0.8619 - val_loss: 0.3806 - val_acc: 0.8667\n",
      "y2_pred:  [[2.74150670e-01]\n",
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     "output_type": "stream",
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      "pod 1st:  0.09859154929577464\n",
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 252us/step - loss: 0.4256 - acc: 0.8586 - val_loss: 0.4211 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.4101 - acc: 0.8586 - val_loss: 0.4001 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.4069 - acc: 0.8586 - val_loss: 0.4005 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.4008 - acc: 0.8586 - val_loss: 0.3922 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.4043 - acc: 0.8586 - val_loss: 0.3903 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3931 - acc: 0.8586 - val_loss: 0.3935 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3951 - acc: 0.8586 - val_loss: 0.3863 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3862 - acc: 0.8586 - val_loss: 0.3797 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3854 - acc: 0.8586 - val_loss: 0.3798 - val_acc: 0.8589\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3834 - acc: 0.8603 - val_loss: 0.3728 - val_acc: 0.8589\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3802 - acc: 0.8583 - val_loss: 0.3680 - val_acc: 0.8578\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3794 - acc: 0.8597 - val_loss: 0.4044 - val_acc: 0.8578\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3828 - acc: 0.8603 - val_loss: 0.3652 - val_acc: 0.8600\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3798 - acc: 0.8586 - val_loss: 0.3666 - val_acc: 0.8589\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3749 - acc: 0.8600 - val_loss: 0.3618 - val_acc: 0.8578\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3728 - acc: 0.8603 - val_loss: 0.3729 - val_acc: 0.8578\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.3744 - acc: 0.8603 - val_loss: 0.3612 - val_acc: 0.8600\n",
      "Epoch 18/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3724 - acc: 0.8594 - val_loss: 0.3802 - val_acc: 0.8567\n",
      "Epoch 19/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3731 - acc: 0.8592 - val_loss: 0.3618 - val_acc: 0.8600\n",
      "Epoch 20/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3699 - acc: 0.8594 - val_loss: 0.3833 - val_acc: 0.8600\n",
      "y2_pred:  [[0.30787742]\n",
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      "pod 1st:  0.014084507042253521\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 270us/step - loss: 0.4306 - acc: 0.8578 - val_loss: 0.4130 - val_acc: 0.8590\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.4142 - acc: 0.8583 - val_loss: 0.4055 - val_acc: 0.8590\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.4089 - acc: 0.8583 - val_loss: 0.4024 - val_acc: 0.8590\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.4042 - acc: 0.8583 - val_loss: 0.4111 - val_acc: 0.8590\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.4018 - acc: 0.8583 - val_loss: 0.4025 - val_acc: 0.8590\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 65us/step - loss: 0.3943 - acc: 0.8583 - val_loss: 0.4018 - val_acc: 0.8590\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3951 - acc: 0.8583 - val_loss: 0.3877 - val_acc: 0.8590\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3850 - acc: 0.8586 - val_loss: 0.4094 - val_acc: 0.8613\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3771 - acc: 0.8628 - val_loss: 0.3977 - val_acc: 0.8668\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3830 - acc: 0.8600 - val_loss: 0.3703 - val_acc: 0.8624\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 67us/step - loss: 0.3730 - acc: 0.8619 - val_loss: 0.3772 - val_acc: 0.8679\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 66us/step - loss: 0.3685 - acc: 0.8642 - val_loss: 0.3785 - val_acc: 0.8557\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3693 - acc: 0.8631 - val_loss: 0.3865 - val_acc: 0.8635\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [1.]]\n",
      "pod 1st:  0.07142857142857142\n",
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 290us/step - loss: 0.4255 - acc: 0.8506 - val_loss: 0.3922 - val_acc: 0.8646\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 72us/step - loss: 0.4062 - acc: 0.8569 - val_loss: 0.4016 - val_acc: 0.8646\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 70us/step - loss: 0.4032 - acc: 0.8569 - val_loss: 0.3780 - val_acc: 0.8646\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 68us/step - loss: 0.4045 - acc: 0.8572 - val_loss: 0.3868 - val_acc: 0.8646\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 73us/step - loss: 0.3916 - acc: 0.8569 - val_loss: 0.3715 - val_acc: 0.8646\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3897 - acc: 0.8569 - val_loss: 0.3676 - val_acc: 0.8646\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3788 - acc: 0.8583 - val_loss: 0.3710 - val_acc: 0.8657\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3814 - acc: 0.8603 - val_loss: 0.3677 - val_acc: 0.8668\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3732 - acc: 0.8631 - val_loss: 0.3745 - val_acc: 0.8646\n",
      "y2_pred:  [[0.02679774]\n",
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      "pod 1st:  0.014285714285714285\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 304us/step - loss: 0.4383 - acc: 0.8519 - val_loss: 0.4102 - val_acc: 0.8646\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.4164 - acc: 0.8569 - val_loss: 0.3973 - val_acc: 0.8646\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.4044 - acc: 0.8569 - val_loss: 0.3885 - val_acc: 0.8646\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 69us/step - loss: 0.3990 - acc: 0.8569 - val_loss: 0.3926 - val_acc: 0.8646\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.4003 - acc: 0.8569 - val_loss: 0.3909 - val_acc: 0.8646\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3895 - acc: 0.8569 - val_loss: 0.3792 - val_acc: 0.8646\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3871 - acc: 0.8569 - val_loss: 0.3741 - val_acc: 0.8646\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - ETA: 0s - loss: 0.3884 - acc: 0.857 - 0s 63us/step - loss: 0.3880 - acc: 0.8572 - val_loss: 0.3798 - val_acc: 0.8646\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 68us/step - loss: 0.3818 - acc: 0.8572 - val_loss: 0.3935 - val_acc: 0.8635\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3745 - acc: 0.8594 - val_loss: 0.3703 - val_acc: 0.8635\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3710 - acc: 0.8606 - val_loss: 0.3662 - val_acc: 0.8646\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.3694 - acc: 0.8631 - val_loss: 0.3617 - val_acc: 0.8657\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3697 - acc: 0.8622 - val_loss: 0.3762 - val_acc: 0.8657\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 66us/step - loss: 0.3730 - acc: 0.8583 - val_loss: 0.3606 - val_acc: 0.8635\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3754 - acc: 0.8614 - val_loss: 0.3670 - val_acc: 0.8646\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3649 - acc: 0.8619 - val_loss: 0.4222 - val_acc: 0.8424\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3659 - acc: 0.8614 - val_loss: 0.3719 - val_acc: 0.8590\n",
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      "pod 1st:  0.11428571428571428\n",
      "pod:  [0.0, 0.014084507042253521, 0.0, 0.028169014084507043, 0.014084507042253521, 0.09859154929577464, 0.014084507042253521, 0.07142857142857142, 0.014285714285714285, 0.11428571428571428]\n",
      "pof:  [0.0, 0.0, 0.0, 0.002331002331002331, 0.002331002331002331, 0.016317016317016316, 0.0, 0.023310023310023312, 0.0, 0.016317016317016316]\n",
      "auc:  [0.5, 0.5070422535211268, 0.5, 0.5129190058767523, 0.5058767523556256, 0.5411372664893792, 0.5070422535211268, 0.524059274059274, 0.5071428571428571, 0.548984348984349]\n",
      "tn_list:  [430, 430, 430, 428, 428, 422, 429, 419, 429, 422]\n",
      "fp_list:  [0, 0, 0, 1, 1, 7, 0, 10, 0, 7]\n",
      "fn_list:  [71, 70, 71, 69, 70, 64, 70, 65, 69, 62]\n",
      "tp_list:  [0, 1, 0, 2, 1, 7, 1, 5, 1, 8]\n",
      "4267 26 681 26\n"
     ]
    }
   ],
   "source": [
    "df_x_train_chi = pd.DataFrame(x_train_chi)\n",
    "df_x_train_chi.head()\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",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3599/3599 [==============================] - 2s 449us/step - loss: 0.4788 - acc: 0.8413 - val_loss: 0.4044 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 76us/step - loss: 0.4057 - acc: 0.8586 - val_loss: 0.4113 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 77us/step - loss: 0.4013 - acc: 0.8586 - val_loss: 0.3986 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 80us/step - loss: 0.3993 - acc: 0.8586 - val_loss: 0.4119 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 75us/step - loss: 0.3942 - acc: 0.8586 - val_loss: 0.4202 - val_acc: 0.8589\n",
      "y22_pred:  [[0.]\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3599/3599 [==============================] - 2s 478us/step - loss: 0.4841 - acc: 0.8583 - val_loss: 0.4049 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 87us/step - loss: 0.4045 - acc: 0.8586 - val_loss: 0.4005 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 80us/step - loss: 0.4003 - acc: 0.8586 - val_loss: 0.3969 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 81us/step - loss: 0.3969 - acc: 0.8586 - val_loss: 0.4096 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 83us/step - loss: 0.3933 - acc: 0.8586 - val_loss: 0.4247 - val_acc: 0.8589\n",
      "y22_pred:  [[0.]\n",
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      " [0.]\n",
      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3599/3599 [==============================] - 2s 521us/step - loss: 0.4821 - acc: 0.8558 - val_loss: 0.4046 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 81us/step - loss: 0.4035 - acc: 0.8586 - val_loss: 0.4186 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 81us/step - loss: 0.4001 - acc: 0.8586 - val_loss: 0.3960 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 86us/step - loss: 0.3952 - acc: 0.8586 - val_loss: 0.3926 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 82us/step - loss: 0.3907 - acc: 0.8586 - val_loss: 0.3883 - val_acc: 0.8589\n"
     ]
    },
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 2s 543us/step - loss: 0.4899 - acc: 0.8439 - val_loss: 0.4181 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 78us/step - loss: 0.4049 - acc: 0.8586 - val_loss: 0.4025 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 80us/step - loss: 0.3997 - acc: 0.8586 - val_loss: 0.4168 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 79us/step - loss: 0.3961 - acc: 0.8586 - val_loss: 0.4102 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 80us/step - loss: 0.3940 - acc: 0.8586 - val_loss: 0.3948 - val_acc: 0.8589\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 2s 555us/step - loss: 0.4928 - acc: 0.8519 - val_loss: 0.4223 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 80us/step - loss: 0.4030 - acc: 0.8586 - val_loss: 0.4006 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 79us/step - loss: 0.4008 - acc: 0.8586 - val_loss: 0.3967 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 82us/step - loss: 0.3971 - acc: 0.8586 - val_loss: 0.4035 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 79us/step - loss: 0.3929 - acc: 0.8586 - val_loss: 0.3921 - val_acc: 0.8589\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 3s 714us/step - loss: 0.4928 - acc: 0.8500 - val_loss: 0.4054 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 91us/step - loss: 0.4057 - acc: 0.8586 - val_loss: 0.4154 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.4013 - acc: 0.8586 - val_loss: 0.4154 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 100us/step - loss: 0.3990 - acc: 0.8586 - val_loss: 0.3984 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 88us/step - loss: 0.3949 - acc: 0.8586 - val_loss: 0.4289 - val_acc: 0.8589\n",
      "y22_pred:  [[0.]\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 2s 679us/step - loss: 0.5133 - acc: 0.8325 - val_loss: 0.4236 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.4062 - acc: 0.8586 - val_loss: 0.4092 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 85us/step - loss: 0.4020 - acc: 0.8586 - val_loss: 0.4360 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 85us/step - loss: 0.3988 - acc: 0.8586 - val_loss: 0.3934 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 93us/step - loss: 0.3941 - acc: 0.8586 - val_loss: 0.3982 - val_acc: 0.8589\n",
      "y22_pred:  [[0.]\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 3s 733us/step - loss: 0.4803 - acc: 0.8578 - val_loss: 0.4145 - val_acc: 0.8590\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 95us/step - loss: 0.4070 - acc: 0.8583 - val_loss: 0.4037 - val_acc: 0.8590\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 94us/step - loss: 0.4024 - acc: 0.8583 - val_loss: 0.3985 - val_acc: 0.8590\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 100us/step - loss: 0.3983 - acc: 0.8583 - val_loss: 0.4028 - val_acc: 0.8590\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 95us/step - loss: 0.3968 - acc: 0.8583 - val_loss: 0.4050 - val_acc: 0.8590\n"
     ]
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 3s 749us/step - loss: 0.4932 - acc: 0.8450 - val_loss: 0.4042 - val_acc: 0.8646\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 91us/step - loss: 0.4104 - acc: 0.8569 - val_loss: 0.3918 - val_acc: 0.8646\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 93us/step - loss: 0.4051 - acc: 0.8569 - val_loss: 0.3994 - val_acc: 0.8646\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 101us/step - loss: 0.4017 - acc: 0.8569 - val_loss: 0.4175 - val_acc: 0.8646\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 92us/step - loss: 0.3993 - acc: 0.8569 - val_loss: 0.3921 - val_acc: 0.8646\n",
      "y22_pred:  [[0.]\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 3s 795us/step - loss: 0.4866 - acc: 0.8569 - val_loss: 0.3948 - val_acc: 0.8646\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 91us/step - loss: 0.4086 - acc: 0.8569 - val_loss: 0.4045 - val_acc: 0.8646\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 92us/step - loss: 0.4065 - acc: 0.8569 - val_loss: 0.3881 - val_acc: 0.8646\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 94us/step - loss: 0.4020 - acc: 0.8569 - val_loss: 0.3864 - val_acc: 0.8646\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 93us/step - loss: 0.3966 - acc: 0.8569 - val_loss: 0.3813 - val_acc: 0.8646\n",
      "y22_pred:  [[0.]\n",
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      "pod 1st:  0.0\n",
      "pod:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "pof:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "auc:  [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n",
      "4293 0 707 0\n"
     ]
    }
   ],
   "source": [
    "#RNN\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=5, 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(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",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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