ZScore-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>15</th>\n",
       "      <td>number_customer_service_calls</td>\n",
       "      <td>187.914323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>number_vmail_messages</td>\n",
       "      <td>83.244136</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>3</th>\n",
       "      <td>total_day_minutes</td>\n",
       "      <td>44.370315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_day_charge</td>\n",
       "      <td>42.237278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>total_eve_minutes</td>\n",
       "      <td>9.271553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>total_intl_minutes</td>\n",
       "      <td>7.387181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>total_intl_calls</td>\n",
       "      <td>6.089591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>total_eve_charge</td>\n",
       "      <td>5.255852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total_intl_charge</td>\n",
       "      <td>2.861297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>total_night_charge</td>\n",
       "      <td>2.429771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>state</td>\n",
       "      <td>2.033226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>account_length</td>\n",
       "      <td>0.898882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>area_code</td>\n",
       "      <td>0.758687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>total_night_minutes</td>\n",
       "      <td>0.494584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>total_day_calls</td>\n",
       "      <td>0.257870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>total_night_calls</td>\n",
       "      <td>0.048732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_eve_calls</td>\n",
       "      <td>0.039068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Feature      Scores\n",
       "17             international_plan  303.963357\n",
       "15  number_customer_service_calls  187.914323\n",
       "2           number_vmail_messages   83.244136\n",
       "18                voice_mail_plan   45.058340\n",
       "3               total_day_minutes   44.370315\n",
       "5                total_day_charge   42.237278\n",
       "6               total_eve_minutes    9.271553\n",
       "12             total_intl_minutes    7.387181\n",
       "13               total_intl_calls    6.089591\n",
       "8                total_eve_charge    5.255852\n",
       "14              total_intl_charge    2.861297\n",
       "11             total_night_charge    2.429771\n",
       "16                          state    2.033226\n",
       "0                  account_length    0.898882\n",
       "1                       area_code    0.758687\n",
       "9             total_night_minutes    0.494584\n",
       "4                 total_day_calls    0.257870\n",
       "10              total_night_calls    0.048732\n",
       "7                 total_eve_calls    0.039068"
      ]
     },
     "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=X\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": [
      "3991 302 416 291\n",
      "pod:  0.4115983026874116\n",
      "pof:  0.07034707663638481\n",
      "AUC:  0.6706256130255135\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:    7.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'var_smoothing': 0.0005336699231206307}\n",
      "GaussianNB(priors=None, var_smoothing=0.0005336699231206307)\n",
      "0.872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:   10.1s 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": [
      "4200 93 550 157\n",
      "pod:  0.22206506364922207\n",
      "pof:  0.02166317260656883\n",
      "AUC:  0.6002009455213266\n",
      "accuracy:  0.8714\n"
     ]
    }
   ],
   "source": [
    "#Import Gaussian Naive Bayes model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import RepeatedKFold, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "classifier = GaussianNB(priors=None, var_smoothing=0.0005336699231206307)\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "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:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 316 tasks      | elapsed:    4.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1, 'multi_class': 'ovr', 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "LogisticRegression(C=1, 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='newton-cg',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n",
      "0.8634\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:    4.6s finished\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": "stdout",
     "output_type": "stream",
     "text": [
      "4200 93 590 117\n",
      "pod:  0.16548797736916548\n",
      "pof:  0.02166317260656883\n",
      "AUC:  0.5719124023812984\n",
      "accuracy:  0.8634\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "classifier = LogisticRegression(C=1, 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='newton-cg',\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': 5, 'weights': 'uniform'}\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='manhattan',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "           weights='uniform')\n",
      "0.8676\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:    2.0s 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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4258 35 624 83\n",
      "pod:  0.1173974540311174\n",
      "pof:  0.00815280689494526\n",
      "AUC:  0.554622323568086\n",
      "accuracy:  0.8682\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='uniform' )  \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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4259 34 653 54\n",
      "pod:  0.07637906647807638\n",
      "pof:  0.00791986955508968\n",
      "AUC:  0.5342295984614933\n",
      "accuracy:  0.8626\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": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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:   16.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   53.1s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  4.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'max_depth': 90, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 8, 'n_estimators': 300}\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=90, 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=8,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "0.9446\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4250 43 231 476\n",
      "pod:  0.6732673267326733\n",
      "pof:  0.01001630561378989\n",
      "AUC:  0.8316255105594418\n",
      "accuracy:  0.9452\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier =RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=90, 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=8,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=300, 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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4291 2 697 10\n",
      "pod:  0.014144271570014143\n",
      "pof:  0.0004658746797111577\n",
      "AUC:  0.5068391984451515\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, 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": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "param_grid = {\n",
    "    \"loss\":[\"deviance\"],\n",
    "    \"learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
    "    \"min_samples_split\": np.linspace(0.1, 0.5, 12),\n",
    "    \"min_samples_leaf\": np.linspace(0.1, 0.5, 12),\n",
    "    \"max_depth\":[3,5,8],\n",
    "    \"max_features\":[\"log2\",\"sqrt\"],\n",
    "    \"criterion\": [\"friedman_mse\",  \"mae\"],\n",
    "    \"subsample\":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],\n",
    "    \"n_estimators\":[10]\n",
    "    }\n",
    "\n",
    "grid = GridSearchCV(GradientBoostingClassifier(), param_grid, cv=10, n_jobs=-1)\n",
    "\n",
    "grid.fit(x_train_chi, y_train)\n",
    "\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print(grid.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4066 227 198 509\n",
      "pod:  0.7199434229137199\n",
      "pof:  0.0528767761472164\n",
      "AUC:  0.8335333233832517\n",
      "accuracy:  0.915\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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4040 253 205 502\n",
      "pod:  0.71004243281471\n",
      "pof:  0.05893314698346145\n",
      "AUC:  0.8255546429156242\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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4236 57 227 480\n",
      "pod:  0.6789250353606789\n",
      "pof:  0.013277428371767994\n",
      "AUC:  0.8328238034944554\n",
      "accuracy:  0.9432\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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4240 53 223 484\n",
      "pod:  0.6845827439886846\n",
      "pof:  0.012345679012345678\n",
      "AUC:  0.8361185324881695\n",
      "accuracy:  0.9448\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": 22,
   "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 144us/step - loss: 0.4352 - acc: 0.8511 - val_loss: 0.4360 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 0.4030 - acc: 0.8586 - val_loss: 0.3985 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 60us/step - loss: 0.3941 - acc: 0.8586 - val_loss: 0.4116 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.3821 - acc: 0.8586 - val_loss: 0.3859 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.3749 - acc: 0.8597 - val_loss: 0.3802 - val_acc: 0.8622\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 59us/step - loss: 0.3651 - acc: 0.8608 - val_loss: 0.3739 - val_acc: 0.8622\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 72us/step - loss: 0.3572 - acc: 0.8672 - val_loss: 0.3613 - val_acc: 0.8644\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 65us/step - loss: 0.3490 - acc: 0.8675 - val_loss: 0.3695 - val_acc: 0.8567\n",
      "Epoch 9/30\n",
      "3599/3599 [==============================] - 0s 60us/step - loss: 0.3376 - acc: 0.8722 - val_loss: 0.3515 - val_acc: 0.8656\n",
      "Epoch 10/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 0.3443 - acc: 0.8666 - val_loss: 0.3503 - val_acc: 0.8744\n",
      "Epoch 11/30\n",
      "3599/3599 [==============================] - 0s 59us/step - loss: 0.3277 - acc: 0.8747 - val_loss: 0.3551 - val_acc: 0.8744\n",
      "Epoch 12/30\n",
      "3599/3599 [==============================] - 0s 75us/step - loss: 0.3234 - acc: 0.8750 - val_loss: 0.3465 - val_acc: 0.8678\n",
      "Epoch 13/30\n",
      "3599/3599 [==============================] - 0s 74us/step - loss: 0.3239 - acc: 0.8711 - val_loss: 0.3471 - val_acc: 0.8711\n",
      "Epoch 14/30\n",
      "3599/3599 [==============================] - 0s 63us/step - loss: 0.3200 - acc: 0.8755 - val_loss: 0.3354 - val_acc: 0.8778\n",
      "Epoch 15/30\n",
      "3599/3599 [==============================] - 0s 59us/step - loss: 0.3091 - acc: 0.8850 - val_loss: 0.3337 - val_acc: 0.8889\n",
      "Epoch 16/30\n",
      "3599/3599 [==============================] - 0s 67us/step - loss: 0.3058 - acc: 0.8800 - val_loss: 0.3233 - val_acc: 0.8922\n",
      "Epoch 17/30\n",
      "3599/3599 [==============================] - 0s 66us/step - loss: 0.3014 - acc: 0.8839 - val_loss: 0.3257 - val_acc: 0.8800\n",
      "Epoch 18/30\n",
      "3599/3599 [==============================] - 0s 72us/step - loss: 0.3059 - acc: 0.8827 - val_loss: 0.3361 - val_acc: 0.8744\n",
      "Epoch 19/30\n",
      "3599/3599 [==============================] - 0s 60us/step - loss: 0.2976 - acc: 0.8805 - val_loss: 0.3327 - val_acc: 0.8822\n",
      "y2_pred:  [[1.73490644e-02]\n",
      " [2.64060199e-02]\n",
      " [3.40140760e-01]\n",
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      "pod 1st:  0.4788732394366197\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 153us/step - loss: 0.4366 - acc: 0.8513 - val_loss: 0.4253 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.4144 - acc: 0.8586 - val_loss: 0.4028 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.3992 - acc: 0.8586 - val_loss: 0.3876 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.3984 - acc: 0.8586 - val_loss: 0.3846 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3912 - acc: 0.8591 - val_loss: 0.3755 - val_acc: 0.8578\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3742 - acc: 0.8644 - val_loss: 0.3872 - val_acc: 0.8600\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.3681 - acc: 0.8658 - val_loss: 0.3699 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 58us/step - loss: 0.3605 - acc: 0.8669 - val_loss: 0.3645 - val_acc: 0.8633\n",
      "Epoch 9/30\n",
      "3599/3599 [==============================] - 0s 64us/step - loss: 0.3577 - acc: 0.8677 - val_loss: 0.3573 - val_acc: 0.8656\n",
      "Epoch 10/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.3595 - acc: 0.8680 - val_loss: 0.3491 - val_acc: 0.8622\n",
      "Epoch 11/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3530 - acc: 0.8639 - val_loss: 0.3657 - val_acc: 0.8600\n",
      "Epoch 12/30\n",
      "3599/3599 [==============================] - 0s 59us/step - loss: 0.3479 - acc: 0.8711 - val_loss: 0.3582 - val_acc: 0.8589\n",
      "Epoch 13/30\n",
      "3599/3599 [==============================] - 0s 59us/step - loss: 0.3419 - acc: 0.8700 - val_loss: 0.3435 - val_acc: 0.8656\n",
      "Epoch 14/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.3334 - acc: 0.8744 - val_loss: 0.3448 - val_acc: 0.8656\n",
      "Epoch 15/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3329 - acc: 0.8716 - val_loss: 0.3415 - val_acc: 0.8633\n",
      "Epoch 16/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3328 - acc: 0.8775 - val_loss: 0.3443 - val_acc: 0.8656\n",
      "Epoch 17/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3283 - acc: 0.8752 - val_loss: 0.3404 - val_acc: 0.8656\n",
      "Epoch 18/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.3234 - acc: 0.8747 - val_loss: 0.3397 - val_acc: 0.8633\n",
      "Epoch 19/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3182 - acc: 0.8786 - val_loss: 0.3343 - val_acc: 0.8689\n",
      "Epoch 20/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.3142 - acc: 0.8822 - val_loss: 0.3293 - val_acc: 0.8733\n",
      "Epoch 21/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3138 - acc: 0.8844 - val_loss: 0.3229 - val_acc: 0.8656\n",
      "Epoch 22/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3112 - acc: 0.8794 - val_loss: 0.3367 - val_acc: 0.8733\n",
      "Epoch 23/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3033 - acc: 0.8880 - val_loss: 0.3392 - val_acc: 0.8811\n",
      "Epoch 24/30\n",
      "3599/3599 [==============================] - ETA: 0s - loss: 0.3053 - acc: 0.883 - 0s 51us/step - loss: 0.3067 - acc: 0.8825 - val_loss: 0.3334 - val_acc: 0.8756\n",
      "y2_pred:  [[6.96482956e-02]\n",
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      "pod 1st:  0.2535211267605634\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 179us/step - loss: 0.4372 - acc: 0.8530 - val_loss: 0.4092 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.4143 - acc: 0.8586 - val_loss: 0.3925 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.3906 - acc: 0.8602 - val_loss: 0.4152 - val_acc: 0.8678\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 58us/step - loss: 0.3787 - acc: 0.8600 - val_loss: 0.4033 - val_acc: 0.8633\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3673 - acc: 0.8630 - val_loss: 0.3652 - val_acc: 0.8622\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3648 - acc: 0.8636 - val_loss: 0.3474 - val_acc: 0.8700\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.3526 - acc: 0.8627 - val_loss: 0.3836 - val_acc: 0.8656\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.3428 - acc: 0.8633 - val_loss: 0.3311 - val_acc: 0.8744\n",
      "Epoch 9/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 0.3351 - acc: 0.8650 - val_loss: 0.3380 - val_acc: 0.8722\n",
      "Epoch 10/30\n",
      "3599/3599 [==============================] - 0s 65us/step - loss: 0.3235 - acc: 0.8647 - val_loss: 0.3129 - val_acc: 0.8789\n",
      "Epoch 11/30\n",
      "3599/3599 [==============================] - 0s 62us/step - loss: 0.3161 - acc: 0.8725 - val_loss: 0.3139 - val_acc: 0.8767\n",
      "Epoch 12/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.3114 - acc: 0.8714 - val_loss: 0.3069 - val_acc: 0.8767\n",
      "Epoch 13/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.3153 - acc: 0.8725 - val_loss: 0.3087 - val_acc: 0.8778\n",
      "Epoch 14/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.3012 - acc: 0.8736 - val_loss: 0.3125 - val_acc: 0.8789\n",
      "Epoch 15/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.2987 - acc: 0.8739 - val_loss: 0.2981 - val_acc: 0.8800\n",
      "Epoch 16/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.2977 - acc: 0.8700 - val_loss: 0.2927 - val_acc: 0.8789\n",
      "Epoch 17/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.2917 - acc: 0.8766 - val_loss: 0.2987 - val_acc: 0.8800\n",
      "Epoch 18/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.2860 - acc: 0.8800 - val_loss: 0.2872 - val_acc: 0.8800\n",
      "Epoch 19/30\n",
      "3599/3599 [==============================] - 0s 59us/step - loss: 0.2859 - acc: 0.8822 - val_loss: 0.2840 - val_acc: 0.8833\n",
      "Epoch 20/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 0.2799 - acc: 0.8783 - val_loss: 0.2891 - val_acc: 0.8778\n",
      "Epoch 21/30\n",
      "3599/3599 [==============================] - 0s 63us/step - loss: 0.2763 - acc: 0.8833 - val_loss: 0.2815 - val_acc: 0.8789\n",
      "Epoch 22/30\n",
      "3599/3599 [==============================] - 0s 61us/step - loss: 0.2701 - acc: 0.8869 - val_loss: 0.3167 - val_acc: 0.8767\n",
      "Epoch 23/30\n",
      "3599/3599 [==============================] - 0s 63us/step - loss: 0.2699 - acc: 0.8905 - val_loss: 0.2837 - val_acc: 0.8867\n",
      "Epoch 24/30\n",
      "3599/3599 [==============================] - 0s 65us/step - loss: 0.2742 - acc: 0.8808 - val_loss: 0.3031 - val_acc: 0.8867\n",
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      "pod 1st:  0.5915492957746479\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 198us/step - loss: 0.4439 - acc: 0.8525 - val_loss: 0.4023 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.4102 - acc: 0.8586 - val_loss: 0.3995 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.4008 - acc: 0.8575 - val_loss: 0.3905 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3882 - acc: 0.8583 - val_loss: 0.3832 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3770 - acc: 0.8597 - val_loss: 0.3754 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3721 - acc: 0.8583 - val_loss: 0.3741 - val_acc: 0.8622\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3606 - acc: 0.8603 - val_loss: 0.3624 - val_acc: 0.8689\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3513 - acc: 0.8631 - val_loss: 0.3510 - val_acc: 0.8689\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3501 - acc: 0.8636 - val_loss: 0.3445 - val_acc: 0.8678\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3510 - acc: 0.8664 - val_loss: 0.3426 - val_acc: 0.8711\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3444 - acc: 0.8625 - val_loss: 0.3499 - val_acc: 0.8744\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3392 - acc: 0.8656 - val_loss: 0.3523 - val_acc: 0.8711\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3400 - acc: 0.8689 - val_loss: 0.3354 - val_acc: 0.8756\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3302 - acc: 0.8689 - val_loss: 0.3395 - val_acc: 0.8744\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3255 - acc: 0.8703 - val_loss: 0.3361 - val_acc: 0.8700\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3261 - acc: 0.8689 - val_loss: 0.3371 - val_acc: 0.8700\n",
      "y2_pred:  [[3.09556246e-01]\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 207us/step - loss: 0.4360 - acc: 0.8531 - val_loss: 0.4067 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.4044 - acc: 0.8586 - val_loss: 0.4037 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3995 - acc: 0.8586 - val_loss: 0.3945 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3864 - acc: 0.8581 - val_loss: 0.3933 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3773 - acc: 0.8589 - val_loss: 0.3853 - val_acc: 0.8611\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3697 - acc: 0.8569 - val_loss: 0.3892 - val_acc: 0.8600\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3643 - acc: 0.8583 - val_loss: 0.3658 - val_acc: 0.8611\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3595 - acc: 0.8586 - val_loss: 0.3566 - val_acc: 0.8600\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3497 - acc: 0.8639 - val_loss: 0.3515 - val_acc: 0.8656\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3436 - acc: 0.8653 - val_loss: 0.3425 - val_acc: 0.8556\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3365 - acc: 0.8628 - val_loss: 0.3499 - val_acc: 0.8567\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3407 - acc: 0.8633 - val_loss: 0.3361 - val_acc: 0.8644\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3317 - acc: 0.8639 - val_loss: 0.3260 - val_acc: 0.8656\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3217 - acc: 0.8714 - val_loss: 0.3294 - val_acc: 0.8667\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3187 - acc: 0.8708 - val_loss: 0.3170 - val_acc: 0.8711\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3162 - acc: 0.8744 - val_loss: 0.3212 - val_acc: 0.8656\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3089 - acc: 0.8744 - val_loss: 0.3108 - val_acc: 0.8700\n",
      "Epoch 18/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3038 - acc: 0.8783 - val_loss: 0.3058 - val_acc: 0.8711\n",
      "Epoch 19/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.2987 - acc: 0.8836 - val_loss: 0.3129 - val_acc: 0.8667\n",
      "Epoch 20/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.2988 - acc: 0.8800 - val_loss: 0.3154 - val_acc: 0.8722\n",
      "Epoch 21/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.2940 - acc: 0.8806 - val_loss: 0.2982 - val_acc: 0.8778\n",
      "Epoch 22/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.2917 - acc: 0.8853 - val_loss: 0.3017 - val_acc: 0.8800\n",
      "Epoch 23/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.2859 - acc: 0.8853 - val_loss: 0.2957 - val_acc: 0.8889\n",
      "Epoch 24/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.2834 - acc: 0.8858 - val_loss: 0.3115 - val_acc: 0.8822\n",
      "Epoch 25/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.2701 - acc: 0.8928 - val_loss: 0.3064 - val_acc: 0.8822\n",
      "Epoch 26/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.2739 - acc: 0.8897 - val_loss: 0.3188 - val_acc: 0.8744\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 249us/step - loss: 0.4374 - acc: 0.8583 - val_loss: 0.4271 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.4199 - acc: 0.8586 - val_loss: 0.4121 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.4101 - acc: 0.8589 - val_loss: 0.4044 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3999 - acc: 0.8586 - val_loss: 0.3918 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 66us/step - loss: 0.3923 - acc: 0.8586 - val_loss: 0.3711 - val_acc: 0.8600\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 68us/step - loss: 0.3786 - acc: 0.8594 - val_loss: 0.3583 - val_acc: 0.8633\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3610 - acc: 0.8650 - val_loss: 0.3450 - val_acc: 0.8667\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3521 - acc: 0.8656 - val_loss: 0.3520 - val_acc: 0.8678\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3417 - acc: 0.8694 - val_loss: 0.3478 - val_acc: 0.8667\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3282 - acc: 0.8758 - val_loss: 0.3417 - val_acc: 0.8656\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3230 - acc: 0.8794 - val_loss: 0.3224 - val_acc: 0.8689\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.3180 - acc: 0.8772 - val_loss: 0.3124 - val_acc: 0.8722\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3063 - acc: 0.8833 - val_loss: 0.3119 - val_acc: 0.8633\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.2957 - acc: 0.8828 - val_loss: 0.2991 - val_acc: 0.8767\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.2900 - acc: 0.8892 - val_loss: 0.3045 - val_acc: 0.8689\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 67us/step - loss: 0.2905 - acc: 0.8864 - val_loss: 0.3170 - val_acc: 0.8700\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 67us/step - loss: 0.2806 - acc: 0.8903 - val_loss: 0.2959 - val_acc: 0.8711\n",
      "Epoch 18/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.2828 - acc: 0.8889 - val_loss: 0.3067 - val_acc: 0.8767\n",
      "Epoch 19/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.2697 - acc: 0.8956 - val_loss: 0.3042 - val_acc: 0.8789\n",
      "Epoch 20/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.2690 - acc: 0.8939 - val_loss: 0.3084 - val_acc: 0.8822\n",
      "y2_pred:  [[4.68703508e-02]\n",
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      "pod 1st:  0.323943661971831\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 239us/step - loss: 0.4376 - acc: 0.8586 - val_loss: 0.4333 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.4082 - acc: 0.8586 - val_loss: 0.3985 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3948 - acc: 0.8594 - val_loss: 0.3874 - val_acc: 0.8600\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3836 - acc: 0.8589 - val_loss: 0.4195 - val_acc: 0.8544\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3761 - acc: 0.8639 - val_loss: 0.3695 - val_acc: 0.8667\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3668 - acc: 0.8625 - val_loss: 0.3556 - val_acc: 0.8656\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3587 - acc: 0.8658 - val_loss: 0.3546 - val_acc: 0.8611\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3537 - acc: 0.8672 - val_loss: 0.3453 - val_acc: 0.8644\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3518 - acc: 0.8661 - val_loss: 0.3572 - val_acc: 0.8644\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3432 - acc: 0.8669 - val_loss: 0.3402 - val_acc: 0.8611\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3367 - acc: 0.8731 - val_loss: 0.3360 - val_acc: 0.8656\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3326 - acc: 0.8686 - val_loss: 0.3637 - val_acc: 0.8733\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3289 - acc: 0.8733 - val_loss: 0.3259 - val_acc: 0.8700\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3248 - acc: 0.8756 - val_loss: 0.3316 - val_acc: 0.8722\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3204 - acc: 0.8761 - val_loss: 0.3202 - val_acc: 0.8678\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3095 - acc: 0.8789 - val_loss: 0.3109 - val_acc: 0.8722\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3195 - acc: 0.8725 - val_loss: 0.3178 - val_acc: 0.8578\n",
      "Epoch 18/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3082 - acc: 0.8839 - val_loss: 0.3230 - val_acc: 0.8756\n",
      "Epoch 19/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.2999 - acc: 0.8831 - val_loss: 0.3104 - val_acc: 0.8700\n",
      "Epoch 20/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.2961 - acc: 0.8842 - val_loss: 0.2913 - val_acc: 0.8800\n",
      "Epoch 21/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.2876 - acc: 0.8892 - val_loss: 0.2936 - val_acc: 0.8789\n",
      "Epoch 22/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.2904 - acc: 0.8864 - val_loss: 0.2953 - val_acc: 0.8789\n",
      "Epoch 23/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.2914 - acc: 0.8842 - val_loss: 0.3192 - val_acc: 0.8700\n",
      "y2_pred:  [[7.53619611e-01]\n",
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      "pod 1st:  0.3380281690140845\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 278us/step - loss: 0.4387 - acc: 0.8517 - val_loss: 0.4148 - val_acc: 0.8590\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 68us/step - loss: 0.4104 - acc: 0.8597 - val_loss: 0.4149 - val_acc: 0.8613\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 66us/step - loss: 0.3946 - acc: 0.8606 - val_loss: 0.3809 - val_acc: 0.8602\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3865 - acc: 0.8606 - val_loss: 0.3816 - val_acc: 0.8624\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3602 - acc: 0.8636 - val_loss: 0.3475 - val_acc: 0.8602\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3556 - acc: 0.8658 - val_loss: 0.3464 - val_acc: 0.8635\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3369 - acc: 0.8719 - val_loss: 0.3535 - val_acc: 0.8613\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.3282 - acc: 0.8761 - val_loss: 0.3346 - val_acc: 0.8724\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3253 - acc: 0.8722 - val_loss: 0.3311 - val_acc: 0.8746\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3216 - acc: 0.8722 - val_loss: 0.3439 - val_acc: 0.8724\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3168 - acc: 0.8792 - val_loss: 0.3154 - val_acc: 0.8690\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3039 - acc: 0.8803 - val_loss: 0.3276 - val_acc: 0.8701\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3037 - acc: 0.8803 - val_loss: 0.3119 - val_acc: 0.8679\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.2953 - acc: 0.8844 - val_loss: 0.3136 - val_acc: 0.8701\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.2994 - acc: 0.8817 - val_loss: 0.3444 - val_acc: 0.8635\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.2878 - acc: 0.8811 - val_loss: 0.3330 - val_acc: 0.8768\n",
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      "pod 1st:  0.3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 280us/step - loss: 0.4476 - acc: 0.8492 - val_loss: 0.3978 - val_acc: 0.8646\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.4103 - acc: 0.8569 - val_loss: 0.3959 - val_acc: 0.8646\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3916 - acc: 0.8572 - val_loss: 0.3958 - val_acc: 0.8646\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3762 - acc: 0.8594 - val_loss: 0.3994 - val_acc: 0.8668\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3756 - acc: 0.8564 - val_loss: 0.4721 - val_acc: 0.8013\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3644 - acc: 0.8597 - val_loss: 0.3737 - val_acc: 0.8602\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.3512 - acc: 0.8647 - val_loss: 0.3554 - val_acc: 0.8657\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.3480 - acc: 0.8664 - val_loss: 0.3607 - val_acc: 0.8635\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.3447 - acc: 0.8706 - val_loss: 0.3596 - val_acc: 0.8657\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3382 - acc: 0.8678 - val_loss: 0.3711 - val_acc: 0.8713\n",
      "y2_pred:  [[1.16044968e-01]\n",
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      "pod 1st:  0.22857142857142856\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 303us/step - loss: 0.4376 - acc: 0.8514 - val_loss: 0.4117 - val_acc: 0.8646\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.4119 - acc: 0.8569 - val_loss: 0.4098 - val_acc: 0.8646\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3992 - acc: 0.8569 - val_loss: 0.4007 - val_acc: 0.8646\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3881 - acc: 0.8589 - val_loss: 0.3867 - val_acc: 0.8635\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3700 - acc: 0.8656 - val_loss: 0.3874 - val_acc: 0.8590\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 69us/step - loss: 0.3706 - acc: 0.8664 - val_loss: 0.3879 - val_acc: 0.8590\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3569 - acc: 0.8669 - val_loss: 0.3967 - val_acc: 0.8368\n",
      "y2_pred:  [[4.27682757e-01]\n",
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      "pod 1st:  0.24285714285714285\n",
      "pod:  [0.4788732394366197, 0.2535211267605634, 0.5915492957746479, 0.16901408450704225, 0.5211267605633803, 0.323943661971831, 0.3380281690140845, 0.3, 0.22857142857142856, 0.24285714285714285]\n",
      "pof:  [0.046511627906976744, 0.013953488372093023, 0.07209302325581396, 0.023310023310023312, 0.06293706293706294, 0.02564102564102564, 0.039627039627039624, 0.04895104895104895, 0.03496503496503497, 0.030303030303030304]\n",
      "auc:  [0.7161808057648215, 0.6197838191942352, 0.759728136259417, 0.5728520305985094, 0.7290948488131587, 0.6491513181654027, 0.6492005646935224, 0.6255244755244755, 0.5968031968031968, 0.6062770562770563]\n",
      "tn_list:  [410, 424, 399, 419, 402, 418, 412, 408, 414, 416]\n",
      "fp_list:  [20, 6, 31, 10, 27, 11, 17, 21, 15, 13]\n",
      "fn_list:  [37, 53, 29, 59, 34, 48, 47, 49, 54, 53]\n",
      "tp_list:  [34, 18, 42, 12, 37, 23, 24, 21, 16, 17]\n",
      "4122 171 463 244\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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3599/3599 [==============================] - 2s 565us/step - loss: 0.4760 - acc: 0.8586 - val_loss: 0.4051 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 89us/step - loss: 0.3978 - acc: 0.8586 - val_loss: 0.3968 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 88us/step - loss: 0.3887 - acc: 0.8586 - val_loss: 0.3873 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 92us/step - loss: 0.3840 - acc: 0.8586 - val_loss: 0.3771 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 88us/step - loss: 0.3824 - acc: 0.8586 - val_loss: 0.3875 - 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 542us/step - loss: 0.5032 - acc: 0.8575 - val_loss: 0.4198 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 86us/step - loss: 0.3989 - acc: 0.8586 - val_loss: 0.4025 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 87us/step - loss: 0.3908 - acc: 0.8586 - val_loss: 0.4046 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 90us/step - loss: 0.3859 - acc: 0.8586 - val_loss: 0.3765 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 84us/step - loss: 0.3812 - acc: 0.8586 - val_loss: 0.3743 - val_acc: 0.8589\n",
      "y22_pred:  [[0.]\n",
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      "pod 1st:  0.0\n",
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3599/3599 [==============================] - 2s 537us/step - loss: 0.4923 - acc: 0.8336 - val_loss: 0.3960 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 89us/step - loss: 0.3933 - acc: 0.8586 - val_loss: 0.4039 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 93us/step - loss: 0.3856 - acc: 0.8586 - val_loss: 0.3921 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 83us/step - loss: 0.3831 - acc: 0.8586 - val_loss: 0.3993 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 90us/step - loss: 0.3802 - acc: 0.8591 - val_loss: 0.3781 - val_acc: 0.8589\n"
     ]
    },
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     "output_type": "stream",
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      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 2s 575us/step - loss: 0.5010 - acc: 0.8361 - val_loss: 0.4168 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 85us/step - loss: 0.3979 - acc: 0.8586 - val_loss: 0.3921 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 86us/step - loss: 0.3900 - acc: 0.8586 - val_loss: 0.3987 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 83us/step - loss: 0.3833 - acc: 0.8586 - val_loss: 0.3770 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 82us/step - loss: 0.3809 - acc: 0.8586 - val_loss: 0.3763 - val_acc: 0.8589\n",
      "y22_pred:  [[0.]\n",
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      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 2s 608us/step - loss: 0.4946 - acc: 0.8331 - val_loss: 0.4221 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 86us/step - loss: 0.3970 - acc: 0.8586 - val_loss: 0.4047 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 87us/step - loss: 0.3868 - acc: 0.8586 - val_loss: 0.3955 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 89us/step - loss: 0.3833 - acc: 0.8586 - val_loss: 0.3767 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 86us/step - loss: 0.3813 - acc: 0.8586 - val_loss: 0.3779 - val_acc: 0.8589\n",
      "y22_pred:  [[0.]\n",
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      "pod 1st:  0.0\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 2s 648us/step - loss: 0.4839 - acc: 0.8433 - val_loss: 0.3984 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 92us/step - loss: 0.3967 - acc: 0.8586 - val_loss: 0.3876 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.3909 - acc: 0.8586 - val_loss: 0.3809 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.3873 - acc: 0.8586 - val_loss: 0.4085 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.3842 - acc: 0.8586 - val_loss: 0.3842 - 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 667us/step - loss: 0.4912 - acc: 0.8403 - val_loss: 0.4196 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 88us/step - loss: 0.3991 - acc: 0.8586 - val_loss: 0.3900 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.3894 - acc: 0.8586 - val_loss: 0.4027 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 98us/step - loss: 0.3849 - acc: 0.8586 - val_loss: 0.3902 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 100us/step - loss: 0.3803 - acc: 0.8586 - val_loss: 0.3776 - val_acc: 0.8589\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 720us/step - loss: 0.5004 - acc: 0.8525 - val_loss: 0.4133 - val_acc: 0.8590\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 93us/step - loss: 0.3969 - acc: 0.8583 - val_loss: 0.4247 - val_acc: 0.8590\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 94us/step - loss: 0.3890 - acc: 0.8583 - val_loss: 0.3930 - val_acc: 0.8590\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 101us/step - loss: 0.3826 - acc: 0.8583 - val_loss: 0.3865 - val_acc: 0.8590\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 96us/step - loss: 0.3821 - acc: 0.8583 - val_loss: 0.3773 - val_acc: 0.8590\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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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 809us/step - loss: 0.4895 - acc: 0.8403 - val_loss: 0.3973 - val_acc: 0.8646\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 99us/step - loss: 0.4015 - acc: 0.8569 - val_loss: 0.3803 - val_acc: 0.8646\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 105us/step - loss: 0.3914 - acc: 0.8569 - val_loss: 0.3728 - val_acc: 0.8646\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 97us/step - loss: 0.3852 - acc: 0.8569 - val_loss: 0.3854 - val_acc: 0.8646\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 101us/step - loss: 0.3839 - acc: 0.8572 - val_loss: 0.3615 - val_acc: 0.8646\n",
      "y22_pred:  [[0.]\n",
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      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/5\n",
      "3600/3600 [==============================] - 3s 782us/step - loss: 0.4900 - acc: 0.8336 - val_loss: 0.3902 - val_acc: 0.8646\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 103us/step - loss: 0.4001 - acc: 0.8569 - val_loss: 0.3839 - val_acc: 0.8646\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 98us/step - loss: 0.3914 - acc: 0.8569 - val_loss: 0.3877 - val_acc: 0.8646\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 96us/step - loss: 0.3862 - acc: 0.8569 - val_loss: 0.3828 - val_acc: 0.8646\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 99us/step - loss: 0.3826 - acc: 0.8569 - val_loss: 0.3745 - val_acc: 0.8646\n",
      "y22_pred:  [[0.]\n",
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      " [0.]]\n",
      "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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