BOX-COX-Univariate feature selection-5000.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 19)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>5</th>\n",
       "      <td>total_day_charge</td>\n",
       "      <td>405.518462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>number_vmail_messages</td>\n",
       "      <td>354.382558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_day_minutes</td>\n",
       "      <td>313.128886</td>\n",
       "    </tr>\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>222.281359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>total_eve_minutes</td>\n",
       "      <td>96.562673</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>8</th>\n",
       "      <td>total_eve_charge</td>\n",
       "      <td>34.727506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>total_intl_minutes</td>\n",
       "      <td>20.967975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>total_night_charge</td>\n",
       "      <td>19.017979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total_intl_charge</td>\n",
       "      <td>10.890831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>total_intl_calls</td>\n",
       "      <td>10.095321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>account_length</td>\n",
       "      <td>2.518905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>state</td>\n",
       "      <td>2.033226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>total_night_minutes</td>\n",
       "      <td>1.779776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>total_day_calls</td>\n",
       "      <td>0.190068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>total_night_calls</td>\n",
       "      <td>0.051027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>area_code</td>\n",
       "      <td>0.050535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_eve_calls</td>\n",
       "      <td>0.031638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Feature      Scores\n",
       "5                total_day_charge  405.518462\n",
       "2           number_vmail_messages  354.382558\n",
       "3               total_day_minutes  313.128886\n",
       "17             international_plan  303.963357\n",
       "15  number_customer_service_calls  222.281359\n",
       "6               total_eve_minutes   96.562673\n",
       "18                voice_mail_plan   45.058340\n",
       "8                total_eve_charge   34.727506\n",
       "12             total_intl_minutes   20.967975\n",
       "11             total_night_charge   19.017979\n",
       "14              total_intl_charge   10.890831\n",
       "13               total_intl_calls   10.095321\n",
       "0                  account_length    2.518905\n",
       "16                          state    2.033226\n",
       "9             total_night_minutes    1.779776\n",
       "4                 total_day_calls    0.190068\n",
       "10              total_night_calls    0.051027\n",
       "1                       area_code    0.050535\n",
       "7                 total_eve_calls    0.031638"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 15)"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3979 314 446 261\n",
      "pod:  0.36916548797736914\n",
      "pof:  0.07314232471465176\n",
      "AUC:  0.6480115816313587\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": 9,
   "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:   13.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'var_smoothing': 0.0001519911082952933}\n",
      "GaussianNB(priors=None, var_smoothing=0.0001519911082952933)\n",
      "0.8672666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:   16.3s 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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26710 23728 21590 27972\n",
      "pod:  0.5643840038739357\n",
      "pof:  0.4704389547563345\n",
      "AUC:  0.5469725245588006\n"
     ]
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4200 93 580 127\n",
      "pod:  0.17963224893917965\n",
      "pof:  0.02166317260656883\n",
      "AUC:  0.5789845381663055\n",
      "accuracy:  0.8654\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=0.0001519911082952933)\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": 11,
   "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:    0.8s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:    4.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 4, 'multi_class': 'ovr', 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "LogisticRegression(C=4, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
      "          n_jobs=None, penalty='l1', random_state=None, solver='liblinear',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n",
      "0.8658\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:    8.9s 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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4216 77 594 113\n",
      "pod:  0.15983026874115983\n",
      "pof:  0.017936175168879572\n",
      "AUC:  0.5709470467861402\n",
      "accuracy:  0.8658\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "classifier = LogisticRegression(C=4, class_weight=None, dual=False, fit_intercept=True,\n",
    "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
    "          n_jobs=None, penalty='l1', random_state=None, solver='liblinear',\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4215 78 598 109\n",
      "pod:  0.15417256011315417\n",
      "pof:  0.01816911250873515\n",
      "AUC:  0.5680017238022095\n",
      "accuracy:  0.8648\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train, y_train) \n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "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": 14,
   "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",
      "0.8772\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:    3.0s 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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4266 27 580 127\n",
      "pod:  0.17963224893917965\n",
      "pof:  0.006289308176100629\n",
      "AUC:  0.5866714703815396\n",
      "accuracy:  0.8786\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=11, 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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4203 90 572 135\n",
      "pod:  0.19094766619519093\n",
      "pof:  0.020964360587002098\n",
      "AUC:  0.5849916528040945\n",
      "accuracy:  0.8676\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": 19,
   "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:   19.1s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   56.8s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  3.7min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  5.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'max_depth': 80, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 8, '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=8,\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",
      "0.946\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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4258 35 234 473\n",
      "pod:  0.669024045261669\n",
      "pof:  0.00815280689494526\n",
      "AUC:  0.8304356191833618\n",
      "accuracy:  0.9462\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=8,\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": 22,
   "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": [
      "4254 39 268 439\n",
      "pod:  0.620933521923621\n",
      "pof:  0.009084556254367574\n",
      "AUC:  0.8059244828346267\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": 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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4075 218 204 503\n",
      "pod:  0.7114568599717115\n",
      "pof:  0.05078034008851619\n",
      "AUC:  0.8303382599415976\n",
      "accuracy:  0.9156\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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4068 225 206 501\n",
      "pod:  0.7086280056577087\n",
      "pof:  0.05241090146750524\n",
      "AUC:  0.8281085520951017\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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4240 53 225 482\n",
      "pod:  0.6817538896746818\n",
      "pof:  0.012345679012345678\n",
      "AUC:  0.834704105331168\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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4242 51 224 483\n",
      "pod:  0.6831683168316832\n",
      "pof:  0.011879804332634521\n",
      "AUC:  0.8356442562495244\n",
      "accuracy:  0.945\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": 28,
   "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 145us/step - loss: 0.5622 - acc: 0.8388 - val_loss: 0.4230 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 58us/step - loss: 0.4344 - acc: 0.8552 - val_loss: 0.4263 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.4282 - acc: 0.8555 - val_loss: 0.4168 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 0.4203 - acc: 0.8586 - val_loss: 0.4207 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.4197 - acc: 0.8586 - val_loss: 0.4104 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 58us/step - loss: 0.4105 - acc: 0.8586 - val_loss: 0.4182 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 55us/step - loss: 0.4093 - acc: 0.8586 - val_loss: 0.4172 - val_acc: 0.8578\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 59us/step - loss: 0.4076 - acc: 0.8588 - val_loss: 0.4082 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.4044 - acc: 0.8583 - val_loss: 0.4174 - val_acc: 0.8589\n",
      "Epoch 10/30\n",
      "3599/3599 [==============================] - 0s 54us/step - loss: 0.4038 - acc: 0.8583 - val_loss: 0.4092 - val_acc: 0.8578\n",
      "Epoch 11/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 0.4033 - acc: 0.8577 - val_loss: 0.4125 - val_acc: 0.8589\n",
      "y2_pred:  [[0.12980998]\n",
      " [0.06409732]\n",
      " [0.2775623 ]\n",
      " [0.24978054]\n",
      " [0.17456886]\n",
      " [0.17976207]\n",
      " [0.06431136]\n",
      " [0.13758329]\n",
      " [0.08440182]\n",
      " [0.066789  ]\n",
      " [0.14658886]\n",
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      " [0.12135997]\n",
      " [0.11492547]\n",
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      " [0.11020675]\n",
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      " [0.07946882]\n",
      " [0.18581149]\n",
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      " [0.10203543]\n",
      " [0.2152532 ]\n",
      " [0.13191897]\n",
      " [0.10344607]\n",
      " [0.09827575]\n",
      " [0.12905669]\n",
      " [0.23255125]\n",
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      " [0.12341347]\n",
      " [0.1349996 ]\n",
      " [0.06275478]\n",
      " [0.13602626]\n",
      " [0.08804765]\n",
      " [0.08267981]\n",
      " [0.1297541 ]\n",
      " [0.07135072]\n",
      " [0.15684521]\n",
      " [0.05779105]\n",
      " [0.13624331]\n",
      " [0.21179232]\n",
      " [0.04936022]\n",
      " [0.12410116]\n",
      " [0.07217997]\n",
      " [0.0547393 ]\n",
      " [0.25480735]\n",
      " [0.07776558]\n",
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      " [0.15732923]\n",
      " [0.09715813]\n",
      " [0.2285155 ]\n",
      " [0.12071937]\n",
      " [0.09833637]\n",
      " [0.16329837]\n",
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      " [0.23268366]\n",
      " [0.15329012]\n",
      " [0.2086741 ]\n",
      " [0.1365206 ]\n",
      " [0.17343047]\n",
      " [0.1724045 ]\n",
      " [0.06891564]\n",
      " [0.34137702]\n",
      " [0.09061143]\n",
      " [0.19956085]\n",
      " [0.13446578]\n",
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      " [0.18945721]\n",
      " [0.14477819]\n",
      " [0.09839305]\n",
      " [0.24840498]\n",
      " [0.1459662 ]\n",
      " [0.27576303]\n",
      " [0.11425352]\n",
      " [0.19612354]\n",
      " [0.23263833]\n",
      " [0.19143736]\n",
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      " [0.10583925]\n",
      " [0.16166097]\n",
      " [0.08547893]\n",
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      " [0.06851241]\n",
      " [0.15345293]\n",
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      " [0.10386282]\n",
      " [0.1601296 ]\n",
      " [0.14784473]\n",
      " [0.18633062]\n",
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      " [0.2933713 ]\n",
      " [0.16278389]\n",
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      " [0.21557534]\n",
      " [0.1538789 ]\n",
      " [0.06625408]\n",
      " [0.18187806]\n",
      " [0.22756562]\n",
      " [0.1597574 ]\n",
      " [0.1279003 ]\n",
      " [0.18513635]\n",
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      " [0.11030814]\n",
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      " [0.2000395 ]\n",
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      " [0.2057639 ]\n",
      " [0.10211182]\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3599/3599 [==============================] - 1s 152us/step - loss: 2.2645 - acc: 0.8547 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 53us/step - loss: 1.5611 - acc: 0.8402 - val_loss: 0.4147 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.4281 - acc: 0.8575 - val_loss: 0.4160 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 0.4218 - acc: 0.8586 - val_loss: 0.4168 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.4140 - acc: 0.8583 - val_loss: 0.4083 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3599/3599 [==============================] - 0s 51us/step - loss: 0.4106 - acc: 0.8586 - val_loss: 0.4131 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3599/3599 [==============================] - 0s 52us/step - loss: 0.4111 - acc: 0.8586 - val_loss: 0.4176 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3599/3599 [==============================] - 0s 50us/step - loss: 0.4034 - acc: 0.8577 - val_loss: 0.4106 - val_acc: 0.8589\n",
      "y2_pred:  [[0.11749226]\n",
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      "pod 1st:  0.0\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 180us/step - loss: 2.2747 - acc: 0.8525 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3599/3599 [==============================] - 0s 61us/step - loss: 2.2796 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3599/3599 [==============================] - 0s 56us/step - loss: 2.2796 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3599/3599 [==============================] - 0s 57us/step - loss: 2.2796 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "y2_pred:  [[0.0000000e+00]\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 900 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 195us/step - loss: 0.5361 - acc: 0.8403 - val_loss: 0.4425 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.4240 - acc: 0.8583 - val_loss: 0.4450 - val_acc: 0.8556\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.4197 - acc: 0.8581 - val_loss: 0.4298 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.4165 - acc: 0.8581 - val_loss: 0.4417 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.4177 - acc: 0.8586 - val_loss: 0.4226 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.4160 - acc: 0.8586 - val_loss: 0.4191 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 0.4065 - acc: 0.8583 - val_loss: 0.4258 - val_acc: 0.8578\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.4046 - acc: 0.8594 - val_loss: 0.4139 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.4003 - acc: 0.8586 - val_loss: 0.4191 - val_acc: 0.8589\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.4011 - acc: 0.8589 - val_loss: 0.4153 - val_acc: 0.8589\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3986 - acc: 0.8597 - val_loss: 0.4198 - val_acc: 0.8589\n",
      "y2_pred:  [[1.06670350e-01]\n",
      " [8.40784013e-02]\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 1/30\n",
      "3600/3600 [==============================] - 1s 221us/step - loss: 2.2762 - acc: 0.8531 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 2.2789 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 2.2789 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 53us/step - loss: 2.2789 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "y2_pred:  [[0.0000000e+00]\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 220us/step - loss: 0.8970 - acc: 0.8336 - val_loss: 0.4433 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 51us/step - loss: 0.4362 - acc: 0.8586 - val_loss: 0.4202 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.4248 - acc: 0.8586 - val_loss: 0.4208 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.4241 - acc: 0.8586 - val_loss: 0.4199 - val_acc: 0.8589\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.4157 - acc: 0.8586 - val_loss: 0.4310 - val_acc: 0.8589\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.4144 - acc: 0.8586 - val_loss: 0.4161 - val_acc: 0.8589\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.4124 - acc: 0.8586 - val_loss: 0.4172 - val_acc: 0.8589\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.4063 - acc: 0.8586 - val_loss: 0.4352 - val_acc: 0.8589\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.4096 - acc: 0.8586 - val_loss: 0.4061 - val_acc: 0.8589\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.4101 - acc: 0.8586 - val_loss: 0.4066 - val_acc: 0.8589\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.4079 - acc: 0.8586 - val_loss: 0.4009 - val_acc: 0.8589\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 0.4044 - acc: 0.8586 - val_loss: 0.4107 - val_acc: 0.8589\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.4035 - acc: 0.8586 - val_loss: 0.3982 - val_acc: 0.8589\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.3971 - acc: 0.8586 - val_loss: 0.4049 - val_acc: 0.8589\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 66us/step - loss: 0.3954 - acc: 0.8586 - val_loss: 0.4174 - val_acc: 0.8589\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3942 - acc: 0.8586 - val_loss: 0.3938 - val_acc: 0.8589\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3980 - acc: 0.8586 - val_loss: 0.3893 - val_acc: 0.8589\n",
      "Epoch 18/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3902 - acc: 0.8586 - val_loss: 0.3889 - val_acc: 0.8589\n",
      "Epoch 19/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3878 - acc: 0.8586 - val_loss: 0.3844 - val_acc: 0.8589\n",
      "Epoch 20/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3836 - acc: 0.8586 - val_loss: 0.3827 - val_acc: 0.8589\n",
      "Epoch 21/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3874 - acc: 0.8581 - val_loss: 0.3804 - val_acc: 0.8589\n",
      "Epoch 22/30\n",
      "3600/3600 [==============================] - 0s 54us/step - loss: 0.3821 - acc: 0.8589 - val_loss: 0.3768 - val_acc: 0.8589\n",
      "Epoch 23/30\n",
      "3600/3600 [==============================] - 0s 56us/step - loss: 0.3817 - acc: 0.8586 - val_loss: 0.4097 - val_acc: 0.8589\n",
      "Epoch 24/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3756 - acc: 0.8589 - val_loss: 0.3795 - val_acc: 0.8589\n",
      "Epoch 25/30\n",
      "3600/3600 [==============================] - 0s 55us/step - loss: 0.3797 - acc: 0.8583 - val_loss: 0.3917 - val_acc: 0.8578\n",
      "y2_pred:  [[1.77262932e-01]\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 1/30\n",
      "3600/3600 [==============================] - 1s 239us/step - loss: 2.2805 - acc: 0.8536 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 2.2789 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 2.2789 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 57us/step - loss: 2.2789 - acc: 0.8586 - val_loss: 2.2744 - val_acc: 0.8589\n",
      "y2_pred:  [[0.]\n",
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      " [0.]]\n",
      "pod 1st:  0.0\n",
      "Train on 3600 samples, validate on 901 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 257us/step - loss: 0.4999 - acc: 0.8469 - val_loss: 0.4281 - val_acc: 0.8590\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.4267 - acc: 0.8583 - val_loss: 0.4379 - val_acc: 0.8590\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.4270 - acc: 0.8583 - val_loss: 0.4291 - val_acc: 0.8590\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 59us/step - loss: 0.4214 - acc: 0.8581 - val_loss: 0.4212 - val_acc: 0.8590\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.4209 - acc: 0.8578 - val_loss: 0.4181 - val_acc: 0.8590\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.4101 - acc: 0.8583 - val_loss: 0.4205 - val_acc: 0.8590\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.4110 - acc: 0.8583 - val_loss: 0.4183 - val_acc: 0.8590\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 58us/step - loss: 0.4057 - acc: 0.8583 - val_loss: 0.4215 - val_acc: 0.8590\n",
      "y2_pred:  [[0.25289837]\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 901 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 279us/step - loss: 0.8148 - acc: 0.8389 - val_loss: 0.4277 - val_acc: 0.8646\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 63us/step - loss: 0.4811 - acc: 0.8431 - val_loss: 0.4325 - val_acc: 0.8646\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.4288 - acc: 0.8569 - val_loss: 0.4396 - val_acc: 0.8646\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.4340 - acc: 0.8569 - val_loss: 0.4157 - val_acc: 0.8646\n",
      "Epoch 5/30\n",
      "3600/3600 [==============================] - 0s 74us/step - loss: 0.4236 - acc: 0.8569 - val_loss: 0.4086 - val_acc: 0.8646\n",
      "Epoch 6/30\n",
      "3600/3600 [==============================] - 0s 74us/step - loss: 0.4168 - acc: 0.8569 - val_loss: 0.4083 - val_acc: 0.8646\n",
      "Epoch 7/30\n",
      "3600/3600 [==============================] - 0s 71us/step - loss: 0.4148 - acc: 0.8569 - val_loss: 0.4035 - val_acc: 0.8646\n",
      "Epoch 8/30\n",
      "3600/3600 [==============================] - 0s 75us/step - loss: 0.4145 - acc: 0.8569 - val_loss: 0.4087 - val_acc: 0.8646\n",
      "Epoch 9/30\n",
      "3600/3600 [==============================] - 0s 76us/step - loss: 0.4099 - acc: 0.8569 - val_loss: 0.4387 - val_acc: 0.8646\n",
      "Epoch 10/30\n",
      "3600/3600 [==============================] - 0s 71us/step - loss: 0.4126 - acc: 0.8569 - val_loss: 0.4005 - val_acc: 0.8646\n",
      "Epoch 11/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 0.4072 - acc: 0.8569 - val_loss: 0.4003 - val_acc: 0.8646\n",
      "Epoch 12/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.4092 - acc: 0.8569 - val_loss: 0.3982 - val_acc: 0.8646\n",
      "Epoch 13/30\n",
      "3600/3600 [==============================] - 0s 68us/step - loss: 0.4039 - acc: 0.8569 - val_loss: 0.3995 - val_acc: 0.8646\n",
      "Epoch 14/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 0.4008 - acc: 0.8572 - val_loss: 0.4079 - val_acc: 0.8646\n",
      "Epoch 15/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3991 - acc: 0.8569 - val_loss: 0.3982 - val_acc: 0.8646\n",
      "Epoch 16/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3969 - acc: 0.8572 - val_loss: 0.3954 - val_acc: 0.8646\n",
      "Epoch 17/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3959 - acc: 0.8569 - val_loss: 0.3969 - val_acc: 0.8646\n",
      "Epoch 18/30\n",
      "3600/3600 [==============================] - 0s 67us/step - loss: 0.3958 - acc: 0.8567 - val_loss: 0.4134 - val_acc: 0.8646\n",
      "Epoch 19/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3945 - acc: 0.8569 - val_loss: 0.3934 - val_acc: 0.8646\n",
      "Epoch 20/30\n",
      "3600/3600 [==============================] - 0s 60us/step - loss: 0.3920 - acc: 0.8572 - val_loss: 0.3921 - val_acc: 0.8646\n",
      "Epoch 21/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3896 - acc: 0.8572 - val_loss: 0.3892 - val_acc: 0.8646\n",
      "Epoch 22/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3895 - acc: 0.8569 - val_loss: 0.4182 - val_acc: 0.8646\n",
      "Epoch 23/30\n",
      "3600/3600 [==============================] - 0s 67us/step - loss: 0.3896 - acc: 0.8569 - val_loss: 0.4110 - val_acc: 0.8646\n",
      "Epoch 24/30\n",
      "3600/3600 [==============================] - 0s 61us/step - loss: 0.3879 - acc: 0.8569 - val_loss: 0.4065 - val_acc: 0.8646\n",
      "y2_pred:  [[1.73934430e-01]\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 901 samples\n",
      "Epoch 1/30\n",
      "3600/3600 [==============================] - 1s 299us/step - loss: 2.3539 - acc: 0.8450 - val_loss: 2.1825 - val_acc: 0.8646\n",
      "Epoch 2/30\n",
      "3600/3600 [==============================] - 0s 67us/step - loss: 2.3058 - acc: 0.8569 - val_loss: 2.1825 - val_acc: 0.8646\n",
      "Epoch 3/30\n",
      "3600/3600 [==============================] - 0s 62us/step - loss: 2.3058 - acc: 0.8569 - val_loss: 2.1825 - val_acc: 0.8646\n",
      "Epoch 4/30\n",
      "3600/3600 [==============================] - 0s 64us/step - loss: 2.3058 - acc: 0.8569 - val_loss: 2.1825 - val_acc: 0.8646\n",
      "y2_pred:  [[0.0000000e+00]\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, 0.002325581395348837, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "auc:  [0.5, 0.49883720930232556, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n",
      "tn_list:  [430, 429, 430, 429, 429, 429, 429, 429, 429, 429]\n",
      "fp_list:  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "fn_list:  [71, 71, 71, 71, 71, 71, 71, 70, 70, 70]\n",
      "tp_list:  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "4292 1 707 0\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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3599 samples, validate on 900 samples\n",
      "Epoch 1/5\n",
      "3599/3599 [==============================] - 2s 445us/step - loss: 0.4897 - acc: 0.8330 - val_loss: 0.4002 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 91us/step - loss: 0.3970 - acc: 0.8586 - val_loss: 0.4273 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 82us/step - loss: 0.3836 - acc: 0.8586 - val_loss: 0.3783 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 83us/step - loss: 0.3670 - acc: 0.8586 - val_loss: 0.3786 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 85us/step - loss: 0.3586 - acc: 0.8588 - val_loss: 0.3735 - 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 481us/step - loss: 0.5021 - acc: 0.8319 - val_loss: 0.4011 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 88us/step - loss: 0.4000 - acc: 0.8586 - val_loss: 0.3942 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 85us/step - loss: 0.3889 - acc: 0.8586 - val_loss: 0.3857 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 83us/step - loss: 0.3729 - acc: 0.8586 - val_loss: 0.3854 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 83us/step - loss: 0.3638 - acc: 0.8586 - val_loss: 0.3827 - 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 544us/step - loss: 0.4966 - acc: 0.8400 - val_loss: 0.4001 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3599/3599 [==============================] - 0s 83us/step - loss: 0.3972 - acc: 0.8586 - val_loss: 0.3919 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3599/3599 [==============================] - 0s 79us/step - loss: 0.3835 - acc: 0.8586 - val_loss: 0.3827 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3599/3599 [==============================] - 0s 81us/step - loss: 0.3706 - acc: 0.8586 - val_loss: 0.3789 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3599/3599 [==============================] - 0s 84us/step - loss: 0.3623 - acc: 0.8588 - val_loss: 0.4339 - 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 573us/step - loss: 0.4851 - acc: 0.8583 - val_loss: 0.4053 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 98us/step - loss: 0.3978 - acc: 0.8586 - val_loss: 0.3938 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 89us/step - loss: 0.3868 - acc: 0.8586 - val_loss: 0.3859 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 96us/step - loss: 0.3720 - acc: 0.8586 - val_loss: 0.3978 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 100us/step - loss: 0.3633 - acc: 0.8586 - val_loss: 0.3846 - 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 657us/step - loss: 0.4872 - acc: 0.8414 - val_loss: 0.4013 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 84us/step - loss: 0.3973 - acc: 0.8586 - val_loss: 0.4056 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 93us/step - loss: 0.3863 - acc: 0.8586 - val_loss: 0.4116 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 86us/step - loss: 0.3745 - acc: 0.8586 - val_loss: 0.3776 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 85us/step - loss: 0.3631 - acc: 0.8589 - val_loss: 0.4273 - 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 635us/step - loss: 0.4939 - acc: 0.8581 - val_loss: 0.4044 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 86us/step - loss: 0.3973 - acc: 0.8586 - val_loss: 0.4003 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 82us/step - loss: 0.3877 - acc: 0.8586 - val_loss: 0.3941 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 81us/step - loss: 0.3743 - acc: 0.8586 - val_loss: 0.3837 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 81us/step - loss: 0.3636 - acc: 0.8586 - val_loss: 0.3870 - 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 665us/step - loss: 0.4778 - acc: 0.8561 - val_loss: 0.4141 - val_acc: 0.8589\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 89us/step - loss: 0.3995 - acc: 0.8586 - val_loss: 0.4050 - val_acc: 0.8589\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.3890 - acc: 0.8586 - val_loss: 0.4143 - val_acc: 0.8589\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 88us/step - loss: 0.3765 - acc: 0.8586 - val_loss: 0.4370 - val_acc: 0.8589\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 85us/step - loss: 0.3666 - acc: 0.8586 - val_loss: 0.4077 - 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 729us/step - loss: 0.4934 - acc: 0.8292 - val_loss: 0.4045 - val_acc: 0.8590\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 85us/step - loss: 0.3963 - acc: 0.8583 - val_loss: 0.3950 - val_acc: 0.8590\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 86us/step - loss: 0.3836 - acc: 0.8583 - val_loss: 0.3867 - val_acc: 0.8590\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.3715 - acc: 0.8583 - val_loss: 0.3911 - val_acc: 0.8590\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 87us/step - loss: 0.3631 - acc: 0.8586 - val_loss: 0.4337 - 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 759us/step - loss: 0.4952 - acc: 0.8556 - val_loss: 0.3892 - val_acc: 0.8646\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 90us/step - loss: 0.3986 - acc: 0.8569 - val_loss: 0.4114 - val_acc: 0.8646\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 95us/step - loss: 0.3878 - acc: 0.8569 - val_loss: 0.3793 - val_acc: 0.8646\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 91us/step - loss: 0.3779 - acc: 0.8569 - val_loss: 0.3841 - val_acc: 0.8646\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 108us/step - loss: 0.3674 - acc: 0.8569 - val_loss: 0.3934 - 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 786us/step - loss: 0.4685 - acc: 0.8342 - val_loss: 0.3899 - val_acc: 0.8646\n",
      "Epoch 2/5\n",
      "3600/3600 [==============================] - 0s 94us/step - loss: 0.3967 - acc: 0.8569 - val_loss: 0.3969 - val_acc: 0.8646\n",
      "Epoch 3/5\n",
      "3600/3600 [==============================] - 0s 91us/step - loss: 0.3869 - acc: 0.8569 - val_loss: 0.3739 - val_acc: 0.8646\n",
      "Epoch 4/5\n",
      "3600/3600 [==============================] - 0s 96us/step - loss: 0.3742 - acc: 0.8569 - val_loss: 0.3722 - val_acc: 0.8646\n",
      "Epoch 5/5\n",
      "3600/3600 [==============================] - 0s 101us/step - loss: 0.3650 - acc: 0.8572 - val_loss: 0.3862 - val_acc: 0.8657\n",
      "y22_pred:  [[0.]\n",
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      " [0.]]\n",
      "pod 1st:  0.014285714285714285\n",
      "pod:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.014285714285714285]\n",
      "pof:  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002331002331002331]\n",
      "auc:  [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5059773559773559]\n",
      "4292 1 706 1\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-6dd550951f6e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuned_parameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train_chi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    685\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 687\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    689\u001b[0m         \u001b[1;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1146\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1147\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1148\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params)\u001b[0m\n\u001b[0;32m    664\u001b[0m                                \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    665\u001b[0m                                in product(candidate_params,\n\u001b[1;32m--> 666\u001b[1;33m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    667\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    668\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    932\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 934\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    935\u001b[0m             \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    936\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    831\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 833\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    834\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    835\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m    519\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m    520\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 521\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    522\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    425\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 427\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    428\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m    \u001b[1;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m                 \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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",
    "\n",
    "grid = GridSearchCV(svm.SVC(), tuned_parameters, 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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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