242_Final 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Product_Info_1</th>\n",
       "      <th>Product_Info_2</th>\n",
       "      <th>Product_Info_3</th>\n",
       "      <th>Product_Info_4</th>\n",
       "      <th>Product_Info_5</th>\n",
       "      <th>Product_Info_6</th>\n",
       "      <th>Product_Info_7</th>\n",
       "      <th>Ins_Age</th>\n",
       "      <th>Ht</th>\n",
       "      <th>...</th>\n",
       "      <th>Medical_Keyword_40</th>\n",
       "      <th>Medical_Keyword_41</th>\n",
       "      <th>Medical_Keyword_42</th>\n",
       "      <th>Medical_Keyword_43</th>\n",
       "      <th>Medical_Keyword_44</th>\n",
       "      <th>Medical_Keyword_45</th>\n",
       "      <th>Medical_Keyword_46</th>\n",
       "      <th>Medical_Keyword_47</th>\n",
       "      <th>Medical_Keyword_48</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>D3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.641791</td>\n",
       "      <td>0.581818</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>A1</td>\n",
       "      <td>26</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059701</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>E1</td>\n",
       "      <td>26</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029851</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>D4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.164179</td>\n",
       "      <td>0.672727</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>D2</td>\n",
       "      <td>26</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417910</td>\n",
       "      <td>0.654545</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Product_Info_1 Product_Info_2  Product_Info_3  Product_Info_4  \\\n",
       "0   2               1             D3              10        0.076923   \n",
       "1   5               1             A1              26        0.076923   \n",
       "2   6               1             E1              26        0.076923   \n",
       "3   7               1             D4              10        0.487179   \n",
       "4   8               1             D2              26        0.230769   \n",
       "\n",
       "   Product_Info_5  Product_Info_6  Product_Info_7   Ins_Age        Ht  ...  \\\n",
       "0               2               1               1  0.641791  0.581818  ...   \n",
       "1               2               3               1  0.059701  0.600000  ...   \n",
       "2               2               3               1  0.029851  0.745455  ...   \n",
       "3               2               3               1  0.164179  0.672727  ...   \n",
       "4               2               3               1  0.417910  0.654545  ...   \n",
       "\n",
       "   Medical_Keyword_40  Medical_Keyword_41  Medical_Keyword_42  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "4                   0                   0                   0   \n",
       "\n",
       "   Medical_Keyword_43  Medical_Keyword_44  Medical_Keyword_45  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "4                   0                   0                   0   \n",
       "\n",
       "   Medical_Keyword_46  Medical_Keyword_47  Medical_Keyword_48  Response  \n",
       "0                   0                   0                   0         8  \n",
       "1                   0                   0                   0         4  \n",
       "2                   0                   0                   0         8  \n",
       "3                   0                   0                   0         8  \n",
       "4                   0                   0                   0         8  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59381, 128)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    19489\n",
       "6    11233\n",
       "7     8027\n",
       "2     6552\n",
       "1     6207\n",
       "5     5432\n",
       "4     1428\n",
       "3     1013\n",
       "Name: Response, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reset response data to get balance\n",
    "data['Response'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['risk'] = pd.Series([1 if x == 8 else 0 for x in data['Response']], index = data['Response'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.drop('Response',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medical_History_10     0.990620\n",
      "Medical_History_32     0.981358\n",
      "Medical_History_24     0.935990\n",
      "Medical_History_15     0.751015\n",
      "Family_Hist_5          0.704114\n",
      "Family_Hist_3          0.576632\n",
      "Family_Hist_2          0.482579\n",
      "Insurance_History_5    0.427679\n",
      "Family_Hist_4          0.323066\n",
      "Employment_Info_6      0.182786\n",
      "Medical_History_1      0.149694\n",
      "Employment_Info_4      0.114161\n",
      "Employment_Info_1      0.000320\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "missing = data.isnull().sum()/len(data)\n",
    "print(missing[missing>0].sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop missing data\n",
    "data = data.dropna(thresh=data.shape[0]*0.6,how='all',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59381, 120)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna(data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unimportant data\n",
    "data.drop('Product_Info_2',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BMI is related to weight and height\n",
    "data.drop('BMI',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Id',axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((41566, 117), (17815, 117))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(data, test_size=0.3, random_state=99)\n",
    "train = train.reset_index(drop = True)\n",
    "test = test.reset_index(drop = True)\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6707830479932642"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#baseline model accuracy\n",
    "ACC = 1 - np.sum(test['risk'])/len(test['risk'])\n",
    "ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['risk']\n",
    "X_train = train.drop(['risk'],axis = 1)\n",
    "y_test = test['risk']\n",
    "X_test = test.drop(['risk'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use LDA, CART, Random Forest, XGBoost Model in this case\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import HalvingRandomSearchCV, HalvingGridSearchCV\n",
    "from sklearn.metrics import accuracy_score, recall_score, confusion_matrix, precision_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import warnings\n",
    "# Defining the metrics function to return the result of the model\n",
    "def metrics(y_true,y_pred,y_prob):\n",
    "    cm = confusion_matrix(y_true,y_pred)\n",
    "    accuracy = accuracy_score(y_true,y_pred)\n",
    "    recall = recall_score(y_true,y_pred)\n",
    "    precision = precision_score(y_true,y_pred)\n",
    "    FPR = cm[0,1]/sum(cm[0])\n",
    "    fpr, tpr, _ = roc_curve(y_true, y_prob)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    return [accuracy,recall,FPR,precision,roc_auc,fpr,tpr]\n",
    "# Defining the check_importance function to return the feature importance\n",
    "def check_importance(model, X_train):\n",
    "  #Checking importance of features\n",
    "    importances = model.feature_importances_\n",
    "  #List of columns and their importances\n",
    "    importance_dict = {'Feature' : list(X_train.columns),\n",
    "                    'Feature Importance' : importances}\n",
    "    importance_df = pd.DataFrame(importance_dict)\n",
    "  #Rounding it off to 2 digits as we might get exponential numbers\n",
    "    importance_df['Feature Importance'] = round(importance_df['Feature Importance'],2)\n",
    "    return importance_df.sort_values(by=['Feature Importance'],ascending=False)\n",
    "# Defining the model\n",
    "def model(x_train, y_train, x_test, y_test, model_name = 'LDA',scoring = 'accuracy'):\n",
    "    if model_name == 'LDA':\n",
    "        clf = LinearDiscriminantAnalysis()\n",
    "        clf.fit(x_train,y_train)\n",
    "        pred = clf.predict(x_test)\n",
    "        prob = clf.predict_proba(x_test)[:,1]\n",
    "        return metrics(y_test,pred,prob)\n",
    "    if model_name == 'CART':\n",
    "        clf = DecisionTreeClassifier()\n",
    "        # Tuning the hyper parameter ccp_alpha\n",
    "        grid_values = {'ccp_alpha': np.linspace(0.0, 0.002, 21),\n",
    "#                'min_samples_leaf': [3,4,5,6,7],\n",
    "#                'min_samples_split': [10,15,20,25,30],\n",
    "               'random_state': [1]}\n",
    "        # Using HalvingGridSearchCV method to tune the parameter. Faster than GridSearch\n",
    "        cv = HalvingGridSearchCV(clf,param_grid = grid_values,scoring=scoring,cv=5)\n",
    "        cv.fit(x_train,y_train)\n",
    "        # printing the best parameters of the estimator\n",
    "        print(cv.best_params_,flush=True)\n",
    "        pred = cv.best_estimator_.predict(x_test)\n",
    "        prob = cv.best_estimator_.predict_proba(x_test)[:,1]\n",
    "        # return the metrics and feature importance of the model\n",
    "        return metrics(y_test,pred,prob), check_importance(cv.best_estimator_,x_train)\n",
    "    if model_name == 'Random Forest':\n",
    "        clf = RandomForestClassifier()\n",
    "        # Tuning the hyperparameter min_sample_leaf. n_estimators and max_features can also be tuned\n",
    "        grid_values = {#'n_estimators': np.linspace(100,1000,10,dtype=int),\n",
    "#                       'max_features': np.linspace(5,50,10),\n",
    "                      'min_samples_leaf': np.linspace(1,5,5,dtype=int),\n",
    "                      'random_state': [1],\n",
    "                      'verbose': [0]}\n",
    "        cv = HalvingGridSearchCV(clf, param_grid= grid_values, scoring = scoring,cv=5)\n",
    "        cv.fit(x_train,y_train)\n",
    "        # printing the best parameters of the estimator\n",
    "        print(cv.best_params_,flush=True)\n",
    "        pred = cv.best_estimator_.predict(x_test)\n",
    "        prob = cv.best_estimator_.predict_proba(x_test)[:,1]\n",
    "        return metrics(y_test,pred,prob),check_importance(cv.best_estimator_,x_train)\n",
    "    if model_name == 'XGB':\n",
    "        clf = XGBClassifier(use_label_encoder=False)\n",
    "        # Tuning the learning rate in this case\n",
    "        grid_values = {#'n_estimators': np.linspace(100,1000,10,dtype=int),\n",
    "#                       'max_features': np.linspace(5,100,20),\n",
    "#                       'min_samples_leaf': np.linspace(2,20,10),\n",
    "                      'learning_rate': np.linspace(0.02,0.2,10),\n",
    "                      'random_state': [1],\n",
    "                      'verbosity':[0]}\n",
    "        cv = HalvingGridSearchCV(clf, param_grid=grid_values,scoring=scoring, cv=5)\n",
    "        cv.fit(x_train,y_train)\n",
    "        # printing the best parameters of the estimator\n",
    "        print(cv.best_params_,flush=True)\n",
    "        pred = cv.best_estimator_.predict(x_test)\n",
    "        prob = cv.best_estimator_.predict_proba(x_test)[:,1]\n",
    "        return metrics(y_test,pred,prob),check_importance(cv.best_estimator_,x_train)\n",
    "def plot_feature_importance(model, X_train):\n",
    "  # PLotting features vs their importance factors\n",
    "    fig = plt.figure(figsize = (15, 8))\n",
    "  # Extracting importance values\n",
    "    values =check_importance(model, X_train)[check_importance(model, X_train)['Feature Importance']>0]['Feature Importance'].values\n",
    "  # Extracting importance features\n",
    "    features = check_importance(model, X_train)[check_importance(model, X_train)['Feature Importance']>0]['Feature'].values\n",
    "    plt.bar(features, values, color ='blue',\n",
    "          width = 0.4)\n",
    "    plt.xticks( rotation='vertical')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ccp_alpha': 0.0011, 'random_state': 1}\n",
      "{'min_samples_leaf': 2, 'random_state': 1, 'verbose': 0}\n",
      "{'learning_rate': 0.12000000000000002, 'random_state': 1, 'verbosity': 0}\n"
     ]
    }
   ],
   "source": [
    "# Run the models and fit the data then make prediction and lastly evaluate\n",
    "ld_me = model(X_train,y_train,X_test,y_test,'LDA')\n",
    "cart_me, cart_fi = model(X_train,y_train,X_test,y_test,'CART')\n",
    "rf_me, rf_fi = model(X_train,y_train,X_test,y_test,'Random Forest')\n",
    "xgb_me, xgb_fi = model(X_train,y_train,X_test,y_test,'XGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>LDA</th>\n",
       "      <th>CART</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>XGBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.805782</td>\n",
       "      <td>0.802582</td>\n",
       "      <td>0.815605</td>\n",
       "      <td>0.825091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.659335</td>\n",
       "      <td>0.740494</td>\n",
       "      <td>0.657971</td>\n",
       "      <td>0.742882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FPR</th>\n",
       "      <td>0.122343</td>\n",
       "      <td>0.166946</td>\n",
       "      <td>0.107029</td>\n",
       "      <td>0.134561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.725652</td>\n",
       "      <td>0.685232</td>\n",
       "      <td>0.751070</td>\n",
       "      <td>0.730427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC</th>\n",
       "      <td>0.871432</td>\n",
       "      <td>0.855899</td>\n",
       "      <td>0.890339</td>\n",
       "      <td>0.904031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                LDA      CART  Random Forest   XGBoost\n",
       "Accuracy   0.805782  0.802582       0.815605  0.825091\n",
       "Recall     0.659335  0.740494       0.657971  0.742882\n",
       "FPR        0.122343  0.166946       0.107029  0.134561\n",
       "Precision  0.725652  0.685232       0.751070  0.730427\n",
       "AUC        0.871432  0.855899       0.890339  0.904031"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out the metrics for each model\n",
    "metric_all = pd.DataFrame()\n",
    "col_name = ['LDA','CART','Random Forest','XGBoost']\n",
    "for me,name in zip([ld_me,cart_me,rf_me,xgb_me],col_name):\n",
    "    metric_all[name] = me[0:5]\n",
    "metric_all.index = ['Accuracy','Recall','FPR','Precision','AUC']\n",
    "metric_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list=[221,222,223,224]\n",
    "plt.figure(figsize=(16, 12)) \n",
    "for me,name,i in zip([ld_me,cart_me,rf_me,xgb_me],col_name,range(4)):\n",
    "    tpr = me[-1]\n",
    "    fpr = me[-2]\n",
    "    roc_auc = me[-3]\n",
    "    a = list[i]\n",
    "    plt.subplot(a)\n",
    "    plt.title(name+' '+'ROC Curve', fontsize=18)\n",
    "    plt.xlabel('FPR', fontsize=16)\n",
    "    plt.ylabel('TPR', fontsize=16)\n",
    "    plt.xlim([-0.01, 1.01])\n",
    "    plt.ylim([-0.01, 1.01])\n",
    "    plt.plot(fpr, tpr, lw=3, label=name+'(area = {:0.2f})'.format(roc_auc))\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n",
    "    plt.legend(loc='lower right', fontsize=14)\n",
    "    plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bootstrap for final model\n",
    "import time\n",
    "def bootstrap_validation(test_data, test_label, model, metrics, sample=100, random_state=99):\n",
    "    tic = time.time()\n",
    "    n_sample = sample\n",
    "    n_metrics = 3\n",
    "    output_array=np.zeros([n_sample, n_metrics])\n",
    "    output_array[:]=np.nan\n",
    "    print(output_array.shape)\n",
    "    for bs_iter in range(n_sample):\n",
    "        bs_index = np.random.choice(test_data.index, len(test_data.index), replace=True)\n",
    "        bs_data = test_data.loc[bs_index]\n",
    "        bs_label = test_label.loc[bs_index]\n",
    "        bs_predicted = model.predict(bs_data)\n",
    "        bs_prob = model.predict_proba(bs_data)[:,1]\n",
    "        output_array[bs_iter,:] = metrics(bs_label,bs_predicted,bs_prob)[0:3]\n",
    "        if bs_iter % 20 == 0:\n",
    "            print(bs_iter, time.time()-tic)\n",
    "    output_df = pd.DataFrame(output_array)\n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 3)\n",
      "0 0.06268477439880371\n",
      "20 1.1571509838104248\n",
      "40 2.2557969093322754\n",
      "60 3.368259906768799\n",
      "80 4.476579904556274\n"
     ]
    }
   ],
   "source": [
    "final_model = XGBClassifier(use_label_encoder=False,learning_rate=0.12, random_state=1, verbosity=0)\n",
    "final_model.fit(X_train,y_train)\n",
    "bs_df = bootstrap_validation(X_test,y_test,final_model,metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of the outcome\n",
    "fig, ax = plt.subplots(1,3,figsize = (16,6))\n",
    "for name, i in zip(['Accuracy','TPR','FPR'], range(3)):\n",
    "    ax[i].set_xlabel('Final XGBoost Model',fontsize=16)\n",
    "    ax[i].set_ylabel('count',fontsize=16)\n",
    "    ax[i].hist(bs_df[i],bins=20,edgecolor = 'black')\n",
    "    ax[i].set_title(name,fontsize=16)\n",
    "    ax[i].grid()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 95-percent confidence interval of acc is [-0.00633315  0.00628964]\n",
      "The 95-percent confidence interval of acc is [-0.01323232  0.01209476]\n",
      "The 95-percent confidence interval of acc is [-0.00710744  0.00552891]\n"
     ]
    }
   ],
   "source": [
    "#the confidence interval of three factors\n",
    "CI1= np.quantile(bs_df.iloc[:,0]-xgb_me[0],np.array([0.025,0.975]))\n",
    "print(\"The 95-percent confidence interval of acc is %s\" % CI1)\n",
    "CI2= np.quantile(bs_df.iloc[:,1]-xgb_me[1],np.array([0.025,0.975]))\n",
    "print(\"The 95-percent confidence interval of acc is %s\" % CI2)\n",
    "CI3= np.quantile(bs_df.iloc[:,2]-xgb_me[2],np.array([0.025,0.975]))\n",
    "print(\"The 95-percent confidence interval of acc is %s\" % CI3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Feature Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Medical_History_23</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Medical_Keyword_15</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Medical_History_4</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Medical_Keyword_3</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wt</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Medical_Keyword_23</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Medical_History_30</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Medical_History_5</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>InsuredInfo_7</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>InsuredInfo_2</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Medical_History_40</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Product_Info_4</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ht</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Medical_Keyword_41</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Medical_Keyword_24</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Feature  Feature Importance\n",
       "51   Medical_History_23                0.18\n",
       "82   Medical_Keyword_15                0.08\n",
       "34    Medical_History_4                0.07\n",
       "70    Medical_Keyword_3                0.05\n",
       "8                    Wt                0.05\n",
       "90   Medical_Keyword_23                0.03\n",
       "57   Medical_History_30                0.03\n",
       "35    Medical_History_5                0.02\n",
       "21        InsuredInfo_7                0.02\n",
       "16        InsuredInfo_2                0.02\n",
       "66   Medical_History_40                0.02\n",
       "2        Product_Info_4                0.02\n",
       "7                    Ht                0.02\n",
       "108  Medical_Keyword_41                0.02\n",
       "91   Medical_Keyword_24                0.01"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the Top 15 features with greatest importance of the final model\n",
    "check_importance(final_model,X_train).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the feature importance\n",
    "plot_feature_importance(final_model,X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}