WOE-Univariate feature selection-7043.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "df = pd.read_csv(\"churn-7043.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['customerID'], axis=1) \n",
    "num_df = df.select_dtypes(include=['float64', 'int64']).copy()\n",
    "cat_df = df.select_dtypes(include=['object']).copy()\n",
    "\n",
    "# Categorical boolean mask\n",
    "categorical_feature_mask = cat_df.dtypes==object\n",
    "# filter categorical columns using mask and turn it into a list\n",
    "categorical_cols = cat_df.columns[categorical_feature_mask].tolist()\n",
    "\n",
    "import numpy as np\n",
    "#conData=np.log(0.00001 + 1)\n",
    "conData=0\n",
    "cat_df=cat_df.fillna(conData)\n",
    "num_df=num_df.fillna(conData)\n",
    "cat_df=cat_df.astype(str)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "cat_df[categorical_cols] = cat_df[categorical_cols].apply(lambda col: le.fit_transform(col))\n",
    "\n",
    "#cat_df[categorical_cols].head(10)\n",
    "\n",
    "\n",
    "churn=cat_df['churn']\n",
    "cat_df=cat_df.drop(['churn'], axis=1)\n",
    "\n",
    "\n",
    "num_df=num_df.fillna(conData)\n",
    "\n",
    "result_df = pd.concat([num_df, cat_df], axis=1)\n",
    "np.nan_to_num(result_df)\n",
    "\n",
    "result_df_op=result_df\n",
    "\n",
    "X=result_df_op\n",
    "y=churn\n",
    "\n",
    "from sklearn.model_selection import train_test_split  \n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "X_train=X\n",
    "X_test=X\n",
    "y_train=y\n",
    "y_test=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
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       "      <td>0.340675</td>\n",
       "      <td>0.724536</td>\n",
       "      <td>0.335507</td>\n",
       "      <td>-0.424849</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.156151</td>\n",
       "      <td>-1.197331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.019409</td>\n",
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       "      <td>-0.588090</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.156151</td>\n",
       "      <td>1.085495</td>\n",
       "      <td>0.325095</td>\n",
       "      <td>0.019602</td>\n",
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       "      <td>0.829097</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   SeniorCitizen    tenure  MonthlyCharges    gender   Partner  Dependents  \\\n",
       "0      -0.156151  1.507375        1.018243  0.019602 -0.389129    0.231141   \n",
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       "4      -0.156151  1.085495        0.325095  0.019602  0.308156    0.231141   \n",
       "\n",
       "   PhoneService  MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0     -0.084284      -0.084284        -0.434427        0.685886     -0.274938   \n",
       "1      0.008838      -0.078011        -0.434427       -0.747185      0.609808   \n",
       "2      0.008838      -0.078011        -0.434427       -0.747185     -0.274938   \n",
       "3     -0.084284      -0.084284        -0.434427       -0.747185      0.609808   \n",
       "4      0.008838      -0.078011         0.691066        0.685886      0.609808   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0          0.576292     0.680487     0.333624         0.340675  0.724536   \n",
       "1         -0.218402     0.680487     0.333624         0.340675 -1.045259   \n",
       "2          0.576292     0.680487     0.333624         0.340675  0.724536   \n",
       "3         -0.218402    -0.703371     0.333624         0.340675 -1.045259   \n",
       "4          0.576292     0.680487     0.333624         0.340675  0.724536   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  TotalCharges  \n",
       "0          0.335507       0.829097           0.0  \n",
       "1         -0.615628      -0.424849           0.0  \n",
       "2          0.335507      -0.424849           0.0  \n",
       "3         -0.615628      -0.588090           0.0  \n",
       "4          0.335507       0.829097           0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pip install mlencoders\n",
    "from mlencoders.weight_of_evidence_encoder import WeightOfEvidenceEncoder\n",
    "\n",
    "enc = WeightOfEvidenceEncoder(cols=X.columns)\n",
    "X_encoded = enc.fit_transform(X, y)\n",
    "X_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
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       "      <th>PaperlessBilling</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "   SeniorCitizen    tenure  MonthlyCharges    gender   Partner  Dependents  \\\n",
       "0            0.0  1.507375        1.018243  0.019602  0.000000    0.231141   \n",
       "1            0.0  0.000000        0.000000  0.000000  0.308156    0.231141   \n",
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       "4            0.0  1.085495        0.325095  0.019602  0.308156    0.231141   \n",
       "\n",
       "   PhoneService  MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0      0.000000            0.0         0.000000        0.685886      0.000000   \n",
       "1      0.008838            0.0         0.000000        0.000000      0.609808   \n",
       "2      0.008838            0.0         0.000000        0.000000      0.000000   \n",
       "3      0.000000            0.0         0.000000        0.000000      0.609808   \n",
       "4      0.008838            0.0         0.691066        0.685886      0.609808   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0          0.576292     0.680487     0.333624         0.340675  0.724536   \n",
       "1          0.000000     0.680487     0.333624         0.340675  0.000000   \n",
       "2          0.576292     0.680487     0.333624         0.340675  0.724536   \n",
       "3          0.000000     0.000000     0.333624         0.340675  0.000000   \n",
       "4          0.576292     0.680487     0.333624         0.340675  0.724536   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  TotalCharges  \n",
       "0          0.335507       0.829097           0.0  \n",
       "1          0.000000       0.000000           0.0  \n",
       "2          0.335507       0.000000           0.0  \n",
       "3          0.000000       0.000000           0.0  \n",
       "4          0.335507       0.829097           0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded[X_encoded < 0]=0\n",
    "X_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 19)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>2</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>1045.853995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tenure</td>\n",
       "      <td>681.896420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Contract</td>\n",
       "      <td>376.683060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>353.545901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>285.453849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>276.357326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>258.787596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TotalCharges</td>\n",
       "      <td>198.307311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>173.231172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>145.036817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>91.677563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>35.456619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>25.346342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>24.571953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Partner</td>\n",
       "      <td>23.728790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>13.152834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>0.679971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.005168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>0.000860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Feature       Scores\n",
       "2     MonthlyCharges  1045.853995\n",
       "1             tenure   681.896420\n",
       "15          Contract   376.683060\n",
       "17     PaymentMethod   353.545901\n",
       "9     OnlineSecurity   285.453849\n",
       "12       TechSupport   276.357326\n",
       "8    InternetService   258.787596\n",
       "18      TotalCharges   198.307311\n",
       "10      OnlineBackup   173.231172\n",
       "11  DeviceProtection   145.036817\n",
       "0      SeniorCitizen    91.677563\n",
       "16  PaperlessBilling    35.456619\n",
       "14   StreamingMovies    25.346342\n",
       "13       StreamingTV    24.571953\n",
       "4            Partner    23.728790\n",
       "5         Dependents    13.152834\n",
       "7      MultipleLines     0.679971\n",
       "3             gender     0.005168\n",
       "6       PhoneService     0.000860"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=X_encoded\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": [
       "(7043, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train=y\n",
    "x_train_chi = select_feature.transform(X_train)\n",
    "x_train_chi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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 24 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    7.1s\n",
      "[Parallel(n_jobs=-1)]: Done 152 tasks      | elapsed:    8.1s\n",
      "[Parallel(n_jobs=-1)]: Done 402 tasks      | elapsed:    9.4s\n",
      "[Parallel(n_jobs=-1)]: Done 752 tasks      | elapsed:   11.3s\n",
      "[Parallel(n_jobs=-1)]: Done 1202 tasks      | elapsed:   13.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'var_smoothing': 0.43287612810830584}\n",
      "GaussianNB(var_smoothing=0.43287612810830584)\n",
      "0.8158458139772459\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:   15.2s 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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.51704"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'var_smoothing': 1e-09}\n",
    "GaussianNB(priors=None, var_smoothing=1e-09)\n",
    "0.51704\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4674 500 808 1061\n",
      "pod:  0.5676832530765115\n",
      "pof:  0.09663703131039815\n",
      "AUC:  0.7355231108830567\n",
      "accuracy:  0.8142836859292915\n",
      "precision:  0.6796925048046124\n",
      "recall:  0.5676832530765115\n",
      "F1 - score:  0.6186588921282797\n"
     ]
    }
   ],
   "source": [
    "#Import Gaussian Naive Bayes model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "#Create a Gaussian Classifier\n",
    "classifier = GaussianNB(priors=None, var_smoothing=0.533669923120631)\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)\n",
    "\n",
    "precision=(tp)/(tp+fp)\n",
    "print ('precision: ',precision)\n",
    "\n",
    "recall=(tp)/(tp+fn)\n",
    "print ('recall: ',recall)\n",
    "\n",
    "F1_score=(2*precision*recall)/(precision+recall)\n",
    "print ('F1 - score: ',F1_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 24 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 152 tasks      | elapsed:    0.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 2, 'multi_class': 'ovr', 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "LogisticRegression(C=2, multi_class='ovr', penalty='l1', solver='liblinear')\n",
      "0.8216648533204383\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:    1.5s 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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4751 423 837 1032\n",
      "pod:  0.5521669341894061\n",
      "pof:  0.08175492848859683\n",
      "AUC:  0.7352060028504046\n",
      "accuracy:  0.821098963509868\n",
      "precision:  0.709278350515464\n",
      "recall:  0.5521669341894061\n",
      "F1 - score:  0.6209386281588448\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "classifier = LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
    "          intercept_scaling=1, max_iter=100, multi_class='ovr',\n",
    "          n_jobs=None, penalty='l2', random_state=None, solver='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",
    "precision=(tp)/(tp+fp)\n",
    "print ('precision: ',precision)\n",
    "\n",
    "recall=(tp)/(tp+fn)\n",
    "print ('recall: ',recall)\n",
    "\n",
    "F1_score=(2*precision*recall)/(precision+recall)\n",
    "print ('F1 - score: ',F1_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4753 421 844 1025\n",
      "pod:  0.5484216158373462\n",
      "pof:  0.08136838036335524\n",
      "AUC:  0.7335266177369956\n",
      "accuracy:  0.8203890387618913\n",
      "precision:  0.7088520055325035\n",
      "recall:  0.5484216158373462\n",
      "F1 - score:  0.6184012066365008\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",
    "precision=(tp)/(tp+fp)\n",
    "print ('precision: ',precision)\n",
    "\n",
    "recall=(tp)/(tp+fn)\n",
    "print ('recall: ',recall)\n",
    "\n",
    "F1_score=(2*precision*recall)/(precision+recall)\n",
    "print ('F1 - score: ',F1_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 24 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:    1.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'metric': 'euclidean', 'n_neighbors': 19, 'weights': 'uniform'}\n",
      "KNeighborsClassifier(metric='euclidean', n_neighbors=19)\n",
      "0.8148512351723308\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_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 24 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    2.3s\n",
      "[Parallel(n_jobs=-1)]: Done 152 tasks      | elapsed:   17.4s\n",
      "[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.1}\n",
      "SVC(C=10, gamma=0.1)\n",
      "0.8206701321727918\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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4587 587 832 1037\n",
      "pod:  0.5548421615837347\n",
      "pof:  0.11345187475840743\n",
      "AUC:  0.7206951434126636\n",
      "accuracy:  0.7985233565242085\n",
      "precision:  0.6385467980295566\n",
      "recall:  0.5548421615837347\n",
      "F1 - score:  0.5937589464643573\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "classifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
    "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
    "           weights='uniform' )  \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",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "precision=(tp)/(tp+fp)\n",
    "print ('precision: ',precision)\n",
    "\n",
    "recall=(tp)/(tp+fn)\n",
    "print ('recall: ',recall)\n",
    "\n",
    "F1_score=(2*precision*recall)/(precision+recall)\n",
    "print ('F1 - score: ',F1_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4607 567 835 1034\n",
      "pod:  0.5532370251471375\n",
      "pof:  0.1095863935059915\n",
      "AUC:  0.721825315820573\n",
      "accuracy:  0.8009371006673293\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "classifier = KNeighborsClassifier(n_neighbors=5)  \n",
    "classifier.fit(X_train, y_train) \n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "#from sklearn.model_selection import cross_val_score\n",
    "#roc_auc = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10,  scoring='roc_auc')\n",
    "#print('roc_auc: ',roc_auc.mean())\n",
    "\n",
    "#from sklearn.metrics import roc_curve, auc\n",
    "#fpr, tpr, thresholds = roc_curve(y_train, y_pred)\n",
    "#auc = auc(fpr, tpr)\n",
    "#print('auc: ',auc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 24 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 114 tasks      | elapsed:   15.6s\n",
      "[Parallel(n_jobs=-1)]: Done 317 tasks      | elapsed:   43.5s\n",
      "[Parallel(n_jobs=-1)]: Done 600 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  2.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'max_depth': 100, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "RandomForestClassifier(max_depth=100, max_features=3, min_samples_leaf=3,\n",
      "                       min_samples_split=10, n_estimators=200)\n",
      "0.8228024733206599\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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4734 440 835 1034\n",
      "pod:  0.5532370251471375\n",
      "pof:  0.08504058755315037\n",
      "AUC:  0.7340982187969936\n",
      "accuracy:  0.8189691892659378\n",
      "precision:  0.7014925373134329\n",
      "recall:  0.5532370251471375\n",
      "F1 - score:  0.6186060424768173\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier =RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=100, max_features=3, max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=3, min_samples_split=10,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=None,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)\n",
    "classifier.fit(x_train_chi, y_train) \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred  = cross_val_predict(estimator = classifier, X = x_train_chi, y = y_train, cv = 10)\n",
    "tn, fp, fn, tp  = confusion_matrix(y_train, y_pred).ravel()\n",
    "print(tn, fp, fn, tp)\n",
    "pod=tp/(tp+fn)\n",
    "\n",
    "print('pod: ',pod)\n",
    "pof=fp/(fp+tn)\n",
    "print ('pof: ',pof)\n",
    "auc_val=(1+pod-pof)/2\n",
    "print ('AUC: ',auc_val)\n",
    "\n",
    "accuracy=(tp+tn)/(tp+fn+fp+tn)\n",
    "print ('accuracy: ',accuracy)\n",
    "\n",
    "\n",
    "precision=(tp)/(tp+fp)\n",
    "print ('precision: ',precision)\n",
    "\n",
    "recall=(tp)/(tp+fn)\n",
    "print ('recall: ',recall)\n",
    "\n",
    "F1_score=(2*precision*recall)/(precision+recall)\n",
    "print ('F1 - score: ',F1_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4641 533 849 1020\n",
      "pod:  0.5457463884430177\n",
      "pof:  0.10301507537688442\n",
      "AUC:  0.7213656565330666\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#classifier =RandomForestClassifier(n_estimators=100,max_depth=2,random_state=0)\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_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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  40 | elapsed:    0.0s remaining:    0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'entropy', 'splitter': 'random'}\n",
      "DecisionTreeClassifier(criterion='entropy', splitter='random')\n",
      "0.7654426579626048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  35 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Create the parameter grid based on the results of random search \n",
    "\n",
    "\n",
    "#param_grid={\n",
    "#    'criterion': ['gini', 'entropy'],\n",
    "#    'splitter': ['best', 'random'],\n",
    "#    'min_samples_split' : range(10,500,20),\n",
    "#    'max_depth': [1, 2, 3, 4, 5, 20],\n",
    "#    'max_features': [1, 2, 3, 4],\n",
    "#    'max_leaf_nodes': list(range(2, 100)), \n",
    "#    'min_samples_split': [2, 3, 4]\n",
    "#} \n",
    "\n",
    "param_grid={\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'splitter': ['best', 'random']\n",
    "} \n",
    "\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = DecisionTreeClassifier(), param_grid = param_grid, \n",
    "                          cv = 10, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(x_train_chi, y_train)\n",
    "\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:323: FutureWarning: The parameter 'presort' is deprecated and has no effect. It will be removed in v0.24. You can suppress this warning by not passing any value to the 'presort' parameter.\n",
      "  warnings.warn(\"The parameter 'presort' is deprecated and has no \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4389 785 925 944\n",
      "pod:  0.5050829320492242\n",
      "pof:  0.15172013915732507\n",
      "AUC:  0.6766813964459496\n",
      "accuracy:  0.7572057361919636\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "classifier = DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
    "            max_features=None, max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
    "            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": [
      "4343 831 884 985\n",
      "pod:  0.527019796682718\n",
      "pof:  0.16061074603788172\n",
      "AUC:  0.6832045253224182\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": [
      "{}\n",
      "GradientBoostingClassifier()\n",
      "0.8219485412637008\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "param_grid = {\n",
    "    \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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4704 470 784 1085\n",
      "pod:  0.5805243445692884\n",
      "pof:  0.09083880943177426\n",
      "AUC:  0.744842767568757\n",
      "accuracy:  0.82195087320744\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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4717 457 779 1090\n",
      "pod:  0.5831995719636169\n",
      "pof:  0.0883262466177039\n",
      "AUC:  0.7474366626729565\n",
      "accuracy:  0.8245066023001562\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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 6s 25ms/step - loss: 0.5260 - accuracy: 0.7704 - val_loss: 0.4505 - val_accuracy: 0.8021\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4115 - accuracy: 0.7968 - val_loss: 0.4355 - val_accuracy: 0.8210\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4141 - accuracy: 0.7966 - val_loss: 0.4172 - val_accuracy: 0.8099\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4001 - accuracy: 0.8110 - val_loss: 0.4130 - val_accuracy: 0.8139\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3831 - accuracy: 0.8136 - val_loss: 0.4048 - val_accuracy: 0.8210\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3770 - accuracy: 0.8273 - val_loss: 0.4086 - val_accuracy: 0.8233\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3884 - accuracy: 0.8191 - val_loss: 0.4167 - val_accuracy: 0.8226\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3921 - accuracy: 0.8199 - val_loss: 0.4027 - val_accuracy: 0.8226\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3716 - accuracy: 0.8270 - val_loss: 0.4092 - val_accuracy: 0.8107\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3804 - accuracy: 0.8264 - val_loss: 0.3997 - val_accuracy: 0.8233\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3713 - accuracy: 0.8278 - val_loss: 0.4022 - val_accuracy: 0.8218\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3854 - accuracy: 0.8231 - val_loss: 0.4028 - val_accuracy: 0.8273\n",
      "Epoch 13/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3729 - accuracy: 0.8319 - val_loss: 0.4026 - val_accuracy: 0.8210\n",
      "y2_pred:  [[0.6764283 ]\n",
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    {
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     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 9ms/step - loss: 0.5156 - accuracy: 0.7583 - val_loss: 0.4145 - val_accuracy: 0.8091\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4044 - accuracy: 0.8130 - val_loss: 0.4115 - val_accuracy: 0.8202\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4032 - accuracy: 0.8053 - val_loss: 0.4116 - val_accuracy: 0.8139\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4020 - accuracy: 0.8052 - val_loss: 0.4140 - val_accuracy: 0.8233\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3831 - accuracy: 0.8283 - val_loss: 0.4109 - val_accuracy: 0.8218\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3835 - accuracy: 0.8219 - val_loss: 0.4035 - val_accuracy: 0.8257\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3993 - accuracy: 0.8113 - val_loss: 0.4102 - val_accuracy: 0.8281\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3945 - accuracy: 0.8045 - val_loss: 0.4093 - val_accuracy: 0.8249\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3844 - accuracy: 0.8193 - val_loss: 0.4288 - val_accuracy: 0.8162\n",
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      "pod 1st:  0.4385026737967914\n"
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    {
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     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 8ms/step - loss: 0.5273 - accuracy: 0.7609 - val_loss: 0.4387 - val_accuracy: 0.8099\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4086 - accuracy: 0.8145 - val_loss: 0.4170 - val_accuracy: 0.8107\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4021 - accuracy: 0.8185 - val_loss: 0.4113 - val_accuracy: 0.8091\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4043 - accuracy: 0.8113 - val_loss: 0.4198 - val_accuracy: 0.8044\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3809 - accuracy: 0.8222 - val_loss: 0.4093 - val_accuracy: 0.8186\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3948 - accuracy: 0.8188 - val_loss: 0.4094 - val_accuracy: 0.8147\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3745 - accuracy: 0.8276 - val_loss: 0.4037 - val_accuracy: 0.8194\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3843 - accuracy: 0.8240 - val_loss: 0.4052 - val_accuracy: 0.8131\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3791 - accuracy: 0.8246 - val_loss: 0.4148 - val_accuracy: 0.8162\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3612 - accuracy: 0.8328 - val_loss: 0.4010 - val_accuracy: 0.8194\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3754 - accuracy: 0.8238 - val_loss: 0.4031 - val_accuracy: 0.8178\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3741 - accuracy: 0.8343 - val_loss: 0.4060 - val_accuracy: 0.8131\n",
      "Epoch 13/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3662 - accuracy: 0.8328 - val_loss: 0.4016 - val_accuracy: 0.8170\n",
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      "pod 1st:  0.5989304812834224\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 8ms/step - loss: 0.5157 - accuracy: 0.7598 - val_loss: 0.4368 - val_accuracy: 0.8099\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4059 - accuracy: 0.8097 - val_loss: 0.4140 - val_accuracy: 0.8162\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3926 - accuracy: 0.8133 - val_loss: 0.4088 - val_accuracy: 0.8194\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3936 - accuracy: 0.8195 - val_loss: 0.4064 - val_accuracy: 0.8241\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3950 - accuracy: 0.8106 - val_loss: 0.4104 - val_accuracy: 0.8233\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3783 - accuracy: 0.8201 - val_loss: 0.4088 - val_accuracy: 0.8123\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3867 - accuracy: 0.8142 - val_loss: 0.4055 - val_accuracy: 0.8131\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3845 - accuracy: 0.8184 - val_loss: 0.4082 - val_accuracy: 0.8226\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3863 - accuracy: 0.8208 - val_loss: 0.4167 - val_accuracy: 0.8194\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3775 - accuracy: 0.8287 - val_loss: 0.4191 - val_accuracy: 0.8226\n",
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      "pod 1st:  0.5806451612903226\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 10ms/step - loss: 0.5365 - accuracy: 0.7561 - val_loss: 0.4555 - val_accuracy: 0.8005\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4104 - accuracy: 0.8203 - val_loss: 0.4263 - val_accuracy: 0.8131\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4080 - accuracy: 0.8160 - val_loss: 0.4159 - val_accuracy: 0.8162\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3827 - accuracy: 0.8215 - val_loss: 0.4251 - val_accuracy: 0.8186\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3940 - accuracy: 0.8248 - val_loss: 0.4126 - val_accuracy: 0.8186\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3854 - accuracy: 0.8282 - val_loss: 0.4175 - val_accuracy: 0.8226\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3977 - accuracy: 0.8116 - val_loss: 0.4190 - val_accuracy: 0.8178\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3801 - accuracy: 0.8282 - val_loss: 0.4078 - val_accuracy: 0.8241\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3864 - accuracy: 0.8194 - val_loss: 0.4099 - val_accuracy: 0.8170\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3797 - accuracy: 0.8209 - val_loss: 0.4093 - val_accuracy: 0.8084\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3762 - accuracy: 0.8268 - val_loss: 0.4072 - val_accuracy: 0.8162\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3880 - accuracy: 0.8167 - val_loss: 0.4121 - val_accuracy: 0.8218\n",
      "Epoch 13/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3757 - accuracy: 0.8292 - val_loss: 0.4160 - val_accuracy: 0.8257\n",
      "Epoch 14/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3803 - accuracy: 0.8236 - val_loss: 0.4040 - val_accuracy: 0.8218\n",
      "Epoch 15/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3775 - accuracy: 0.8261 - val_loss: 0.4098 - val_accuracy: 0.8257\n",
      "Epoch 16/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3785 - accuracy: 0.8209 - val_loss: 0.4040 - val_accuracy: 0.8241\n",
      "Epoch 17/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3686 - accuracy: 0.8228 - val_loss: 0.4239 - val_accuracy: 0.8155\n",
      "y2_pred:  [[0.01954442]\n",
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     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 8ms/step - loss: 0.5095 - accuracy: 0.7522 - val_loss: 0.4129 - val_accuracy: 0.8202\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3976 - accuracy: 0.8106 - val_loss: 0.4063 - val_accuracy: 0.8241\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3707 - accuracy: 0.8287 - val_loss: 0.4002 - val_accuracy: 0.8265\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3786 - accuracy: 0.8205 - val_loss: 0.4027 - val_accuracy: 0.8226\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3785 - accuracy: 0.8241 - val_loss: 0.3964 - val_accuracy: 0.8273\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3863 - accuracy: 0.8209 - val_loss: 0.4118 - val_accuracy: 0.8068\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - ETA: 0s - loss: 0.3695 - accuracy: 0.83 - 1s 5ms/step - loss: 0.3696 - accuracy: 0.8318 - val_loss: 0.4071 - val_accuracy: 0.8233\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3714 - accuracy: 0.8255 - val_loss: 0.4059 - val_accuracy: 0.8249\n",
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    {
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     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 8ms/step - loss: 0.5422 - accuracy: 0.7400 - val_loss: 0.4599 - val_accuracy: 0.7950\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4134 - accuracy: 0.8007 - val_loss: 0.4338 - val_accuracy: 0.8147\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4048 - accuracy: 0.8038 - val_loss: 0.4149 - val_accuracy: 0.8226\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3968 - accuracy: 0.8110 - val_loss: 0.4102 - val_accuracy: 0.8297\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3661 - accuracy: 0.8273 - val_loss: 0.4113 - val_accuracy: 0.8273\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3821 - accuracy: 0.8245 - val_loss: 0.4043 - val_accuracy: 0.8289\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3867 - accuracy: 0.8187 - val_loss: 0.4114 - val_accuracy: 0.8226\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3748 - accuracy: 0.8192 - val_loss: 0.4184 - val_accuracy: 0.8036\n",
      "Epoch 9/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3837 - accuracy: 0.8199 - val_loss: 0.3983 - val_accuracy: 0.8273\n",
      "Epoch 10/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3776 - accuracy: 0.8228 - val_loss: 0.4034 - val_accuracy: 0.8304\n",
      "Epoch 11/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3825 - accuracy: 0.8201 - val_loss: 0.3989 - val_accuracy: 0.8320\n",
      "Epoch 12/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3824 - accuracy: 0.8113 - val_loss: 0.4070 - val_accuracy: 0.8289\n",
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    {
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     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 8ms/step - loss: 0.5140 - accuracy: 0.7521 - val_loss: 0.4253 - val_accuracy: 0.8060\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4111 - accuracy: 0.8051 - val_loss: 0.4103 - val_accuracy: 0.8147\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3917 - accuracy: 0.8142 - val_loss: 0.4065 - val_accuracy: 0.8273\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3753 - accuracy: 0.8269 - val_loss: 0.4033 - val_accuracy: 0.8210\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3832 - accuracy: 0.8239 - val_loss: 0.4134 - val_accuracy: 0.8273\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3793 - accuracy: 0.8224 - val_loss: 0.4041 - val_accuracy: 0.8226\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3862 - accuracy: 0.8246 - val_loss: 0.4051 - val_accuracy: 0.8241\n",
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      "pod 1st:  0.5935828877005348\n"
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    {
     "name": "stdout",
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     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 8ms/step - loss: 0.5412 - accuracy: 0.7367 - val_loss: 0.4214 - val_accuracy: 0.8005\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4153 - accuracy: 0.8134 - val_loss: 0.4051 - val_accuracy: 0.8107\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4064 - accuracy: 0.8023 - val_loss: 0.4033 - val_accuracy: 0.8084\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3907 - accuracy: 0.8171 - val_loss: 0.3992 - val_accuracy: 0.8178\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3786 - accuracy: 0.8310 - val_loss: 0.3930 - val_accuracy: 0.8226\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3843 - accuracy: 0.8206 - val_loss: 0.3949 - val_accuracy: 0.8194\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3958 - accuracy: 0.8104 - val_loss: 0.3970 - val_accuracy: 0.8194\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3789 - accuracy: 0.8236 - val_loss: 0.3945 - val_accuracy: 0.8210\n",
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      "pod 1st:  0.5828877005347594\n"
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "159/159 [==============================] - 3s 8ms/step - loss: 0.5147 - accuracy: 0.7573 - val_loss: 0.4478 - val_accuracy: 0.7973\n",
      "Epoch 2/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4130 - accuracy: 0.7993 - val_loss: 0.4186 - val_accuracy: 0.8107\n",
      "Epoch 3/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3888 - accuracy: 0.8177 - val_loss: 0.4143 - val_accuracy: 0.8131\n",
      "Epoch 4/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.4000 - accuracy: 0.8092 - val_loss: 0.4117 - val_accuracy: 0.8044\n",
      "Epoch 5/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3827 - accuracy: 0.8137 - val_loss: 0.4052 - val_accuracy: 0.8131\n",
      "Epoch 6/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3794 - accuracy: 0.8257 - val_loss: 0.4069 - val_accuracy: 0.8162\n",
      "Epoch 7/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3828 - accuracy: 0.8175 - val_loss: 0.4134 - val_accuracy: 0.8202\n",
      "Epoch 8/30\n",
      "159/159 [==============================] - 1s 5ms/step - loss: 0.3770 - accuracy: 0.8220 - val_loss: 0.4067 - val_accuracy: 0.8186\n",
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      "pod 1st:  0.5989304812834224\n",
      "pod:  [0.6363636363636364, 0.4385026737967914, 0.5989304812834224, 0.5806451612903226, 0.6203208556149733, 0.5133689839572193, 0.6631016042780749, 0.5935828877005348, 0.5828877005347594, 0.5989304812834224]\n",
      "pof:  [0.10810810810810811, 0.04826254826254826, 0.11196911196911197, 0.08494208494208494, 0.10831721470019343, 0.09477756286266925, 0.1276595744680851, 0.09477756286266925, 0.10638297872340426, 0.0851063829787234]\n",
      "auc:  [0.7641277641277641, 0.6951200627671216, 0.7434806846571552, 0.7478515381741188, 0.75600182045739, 0.709295710547275, 0.7677210149049949, 0.7494026624189328, 0.7382523609056776, 0.7569120491523496]\n",
      "tn_list:  [462, 493, 460, 474, 461, 468, 451, 468, 462, 473]\n",
      "fp_list:  [56, 25, 58, 44, 56, 49, 66, 49, 55, 44]\n",
      "fn_list:  [68, 105, 75, 78, 71, 91, 63, 76, 78, 75]\n",
      "tp_list:  [119, 82, 112, 108, 116, 96, 124, 111, 109, 112]\n",
      "4672 502 780 1089\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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 71ms/step - loss: 0.6199 - acc: 0.6979 - val_loss: 0.5658 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 26ms/step - loss: 0.5385 - acc: 0.7451 - val_loss: 0.5105 - val_acc: 0.7547\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 26ms/step - loss: 0.5214 - acc: 0.7557 - val_loss: 0.5052 - val_acc: 0.7658\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 26ms/step - loss: 0.5211 - acc: 0.7470 - val_loss: 0.5138 - val_acc: 0.7618\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 26ms/step - loss: 0.5074 - acc: 0.7707 - val_loss: 0.5008 - val_acc: 0.7658\n",
      "y22_pred:  [[1.]\n",
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      "pod 1st:  0.5935828877005348\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 64ms/step - loss: 0.6176 - acc: 0.6911 - val_loss: 0.5692 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5560 - acc: 0.7364 - val_loss: 0.5238 - val_acc: 0.7303\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5166 - acc: 0.7532 - val_loss: 0.5045 - val_acc: 0.7658\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5009 - acc: 0.7615 - val_loss: 0.5077 - val_acc: 0.7658\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5056 - acc: 0.7589 - val_loss: 0.5005 - val_acc: 0.7689\n",
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      "pod 1st:  0.29411764705882354\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 72ms/step - loss: 0.6078 - acc: 0.7339 - val_loss: 0.5763 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5690 - acc: 0.7314 - val_loss: 0.5400 - val_acc: 0.7279\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5276 - acc: 0.7420 - val_loss: 0.5046 - val_acc: 0.7618\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5065 - acc: 0.7609 - val_loss: 0.5056 - val_acc: 0.7658\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.4996 - acc: 0.7680 - val_loss: 0.4986 - val_acc: 0.7689\n",
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      "pod 1st:  0.33689839572192515\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 63ms/step - loss: 0.6110 - acc: 0.7311 - val_loss: 0.5671 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5488 - acc: 0.7407 - val_loss: 0.5289 - val_acc: 0.7303\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5206 - acc: 0.7559 - val_loss: 0.5048 - val_acc: 0.7658\n",
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      "40/40 [==============================] - 1s 27ms/step - loss: 0.5066 - acc: 0.7620 - val_loss: 0.5001 - val_acc: 0.7689\n",
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      "pod 1st:  0.3225806451612903\n"
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    {
     "name": "stdout",
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     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 63ms/step - loss: 0.6119 - acc: 0.7385 - val_loss: 0.5696 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5443 - acc: 0.7442 - val_loss: 0.5272 - val_acc: 0.7547\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5103 - acc: 0.7628 - val_loss: 0.5086 - val_acc: 0.7618\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.4969 - acc: 0.7643 - val_loss: 0.5065 - val_acc: 0.7689\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.4982 - acc: 0.7669 - val_loss: 0.5089 - val_acc: 0.7689\n",
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      "pod 1st:  0.2887700534759358\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 64ms/step - loss: 0.6195 - acc: 0.7343 - val_loss: 0.5968 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5724 - acc: 0.7270 - val_loss: 0.5320 - val_acc: 0.7279\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5307 - acc: 0.7363 - val_loss: 0.5362 - val_acc: 0.7658\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5027 - acc: 0.7687 - val_loss: 0.4989 - val_acc: 0.7658\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5015 - acc: 0.7656 - val_loss: 0.5009 - val_acc: 0.7689\n",
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      "pod 1st:  0.3422459893048128\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 64ms/step - loss: 0.6187 - acc: 0.6821 - val_loss: 0.5664 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5484 - acc: 0.7412 - val_loss: 0.5201 - val_acc: 0.7516\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5113 - acc: 0.7603 - val_loss: 0.5254 - val_acc: 0.7547\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5066 - acc: 0.7621 - val_loss: 0.5039 - val_acc: 0.7618\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5080 - acc: 0.7630 - val_loss: 0.5144 - val_acc: 0.7571\n",
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      "pod 1st:  0.11229946524064172\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 64ms/step - loss: 0.6122 - acc: 0.7373 - val_loss: 0.5833 - val_acc: 0.7279\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5615 - acc: 0.7317 - val_loss: 0.5245 - val_acc: 0.7279\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.5215 - acc: 0.7434 - val_loss: 0.5162 - val_acc: 0.7618\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5036 - acc: 0.7588 - val_loss: 0.5024 - val_acc: 0.7658\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 27ms/step - loss: 0.4885 - acc: 0.7722 - val_loss: 0.5019 - val_acc: 0.7689\n",
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      "pod 1st:  0.3048128342245989\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 65ms/step - loss: 0.6151 - acc: 0.6881 - val_loss: 0.5730 - val_acc: 0.7248\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 30ms/step - loss: 0.5609 - acc: 0.7316 - val_loss: 0.5578 - val_acc: 0.7271\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5300 - acc: 0.7521 - val_loss: 0.5088 - val_acc: 0.7666\n",
      "Epoch 4/5\n",
      "40/40 [==============================] - 1s 29ms/step - loss: 0.5025 - acc: 0.7566 - val_loss: 0.5056 - val_acc: 0.7658\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5042 - acc: 0.7674 - val_loss: 0.5041 - val_acc: 0.7658\n",
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      "pod 1st:  0.28342245989304815\n",
      "Epoch 1/5\n",
      "40/40 [==============================] - 9s 64ms/step - loss: 0.6178 - acc: 0.6882 - val_loss: 0.5770 - val_acc: 0.7248\n",
      "Epoch 2/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5393 - acc: 0.7398 - val_loss: 0.5310 - val_acc: 0.7532\n",
      "Epoch 3/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5089 - acc: 0.7547 - val_loss: 0.5203 - val_acc: 0.7595\n",
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      "40/40 [==============================] - 1s 28ms/step - loss: 0.5104 - acc: 0.7609 - val_loss: 0.5096 - val_acc: 0.7595\n",
      "Epoch 5/5\n",
      "40/40 [==============================] - 1s 28ms/step - loss: 0.5011 - acc: 0.7581 - val_loss: 0.5103 - val_acc: 0.7595\n",
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      "pod 1st:  0.5828877005347594\n",
      "pod:  [0.5935828877005348, 0.29411764705882354, 0.33689839572192515, 0.3225806451612903, 0.2887700534759358, 0.3422459893048128, 0.11229946524064172, 0.3048128342245989, 0.28342245989304815, 0.5828877005347594]\n",
      "pof:  [0.1833976833976834, 0.0637065637065637, 0.06563706563706563, 0.07528957528957529, 0.09090909090909091, 0.08123791102514506, 0.007736943907156673, 0.07156673114119923, 0.07156673114119923, 0.16054158607350097]\n",
      "auc:  [0.7050926021514258, 0.61520554167613, 0.6356306650424297, 0.6236455349358575, 0.5989304812834225, 0.6305040391398339, 0.5522812606667425, 0.6166230515416998, 0.6059278643759245, 0.7111730572306293]\n",
      "4723 451 1222 647\n"
     ]
    }
   ],
   "source": [
    "#RNN\n",
    "def get_RNN_Predict(X2_train, X2_test, y2_train, y2_test):\n",
    "    import pandas as pd\n",
    "    from keras.models import Sequential\n",
    "    from keras.layers import Dense,Dropout, LSTM, GRU\n",
    "    from keras.layers import Embedding\n",
    "    max_features = 10000 # number of words to consider as features\n",
    "    import numpy as np\n",
    "    #create model \n",
    "    model = Sequential()\n",
    "    model.add(Embedding(max_features, 32))\n",
    "    model.add(LSTM(32))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(optimizer='rmsprop',loss='binary_crossentropy', metrics=['acc'])\n",
    "    history = model.fit(X2_train, y2_train, epochs=5, batch_size=128, validation_split=0.2)\n",
    "    y2_pred = model.predict(X2_test)\n",
    "    y22_pred=y2_pred.round()\n",
    "    print('y22_pred: ',y22_pred)\n",
    "    return y22_pred\n",
    "\n",
    "pod_list = []\n",
    "pof_list = []\n",
    "auc_val_list = []\n",
    "tn_list= []\n",
    "fp_list= []\n",
    "fn_list= []\n",
    "tp_list= []\n",
    "    \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "folds = StratifiedKFold(n_splits=10)\n",
    "for train_index, test_index in folds.split(df_x_train_chi,y_train):\n",
    "    X2_train, X2_test, y2_train, y2_test=df_x_train_chi.iloc[train_index], df_x_train_chi.iloc[test_index],y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "    y_pred = get_RNN_Predict(X2_train, X2_test, y2_train, y2_test)\n",
    "    tn, fp, fn, tp  = confusion_matrix(y2_test, y_pred).ravel()\n",
    "    tn_list.append(tn)\n",
    "    fp_list.append(fp)\n",
    "    fn_list.append(fn)\n",
    "    tp_list.append(tp)\n",
    "    \n",
    "    pod=tp/(tp+fn)\n",
    "    print('pod 1st: ',pod)\n",
    "    pof=fp/(fp+tn)\n",
    "    auc_val=(1+pod-pof)/2\n",
    "    #break\n",
    "    pod_list.append(pod)\n",
    "    pof_list.append(pof)\n",
    "    auc_val_list.append(auc_val)\n",
    "\n",
    "print('pod: ',pod_list)\n",
    "print ('pof: ',pof_list)\n",
    "print ('auc: ',auc_val_list)\n",
    "\n",
    "tn=sum(tn_list)\n",
    "fp=sum(fp_list) \n",
    "fn=sum(fn_list) \n",
    "tp=sum(tp_list)\n",
    "print(tn, fp, fn, tp)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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