ROC_CURVE_BOOSTING_methods_Approach_4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data analysis packages:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#from datetime import datetime as dt\n",
    "\n",
    "# Visualization packages:\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "filename = 'df_imputed_scaled_OHE_reducedv1.pkl'\n",
    "df = pd.read_pickle(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the required ML packages\n",
    "from sklearn.linear_model import LogisticRegression #logistic regression\n",
    "from sklearn import svm #support vector Machine\n",
    "from sklearn.ensemble import RandomForestClassifier #Random Forest\n",
    "from sklearn.neighbors import KNeighborsClassifier #KNN\n",
    "from sklearn.naive_bayes import GaussianNB #Naive bayes\n",
    "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
    "from sklearn.model_selection import train_test_split #training and testing data split\n",
    "from sklearn import metrics #accuracy measure\n",
    "from sklearn.metrics import confusion_matrix #for confusion matrix\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "X = df[df.columns[:-1]]\n",
    "y = pd.Series(df['heartdisease'])\n",
    "\n",
    "\n",
    "#train_X,test_X,train_y,test_y =train_test_split(X,y,test_size=0.2, random_state=45)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building Models according to the best hyperparameters\n",
    "#found by RandomSearchCV\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "from sklearn.model_selection import KFold,StratifiedKFold #for K-fold cross validation\n",
    "import xgboost as xg\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier\n",
    "#importing all the required ML packages\n",
    "from sklearn.linear_model import LogisticRegression #logistic regression\n",
    "from sklearn import svm #support vector Machine\n",
    "from sklearn.ensemble import RandomForestClassifier #Random Forest\n",
    "from sklearn.neighbors import KNeighborsClassifier #KNN\n",
    "from sklearn.naive_bayes import GaussianNB #Naive bayes\n",
    "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
    "from sklearn.model_selection import train_test_split #training and testing data split\n",
    "from sklearn import metrics #accuracy measure\n",
    "from sklearn.metrics import confusion_matrix #for confusion matrix\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "model_Linear_SVM=svm.SVC(kernel='linear',C=0.1,gamma=1e-05, probability=True)\n",
    "\n",
    "model_RBF_SVM = svm.SVC(kernel='rbf',C=10,gamma=0.01, probability=True)\n",
    "model_Logistic_Regression = LogisticRegression(penalty= 'l2', C = 1, solver = 'lbfgs') \n",
    "\n",
    "\n",
    "model_Decision_Tree =DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
    "            max_features=None, max_leaf_nodes=10, min_samples_leaf=10,\n",
    "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "            presort=False, random_state=None, splitter='random')\n",
    "\n",
    "'''\n",
    "-- Best Parameters: DT_randomSEARCH\n",
    "parameters: max_depth            setting: 1\n",
    "parameters: min_samples_split    setting: 4\n",
    "parameters: max_leaf_nodes       setting: 8\n",
    "parameters: min_samples_leaf     setting: 13\n",
    "parameters: criterion            setting: gini\n",
    "'''\n",
    "\n",
    "    \n",
    "    \n",
    "model_RandomForest = RandomForestClassifier(n_estimators= 600,\n",
    "               max_features= 'sqrt',\n",
    "               max_depth= 30,\n",
    "               min_samples_split= 2,\n",
    "               min_samples_leaf= 4,\n",
    "               bootstrap= True)\n",
    "\n",
    "model_KNN = KNeighborsClassifier(n_neighbors=22)\n",
    "\n",
    "XGB_clf = xg.XGBClassifier(subsample = 0.7,\n",
    "                           reg_alpha =  10.0, \n",
    "                           learning_rate =0.1, \n",
    "                           gamma=0.2, \n",
    "                           colsample_bytree =  0.8 ,\n",
    "                           objective= 'binary:logistic',\n",
    "                           nthread=4, \n",
    "                           scale_pos_weight=1, \n",
    "                           seed=27, \n",
    "                           n_estimators = 80, \n",
    "                           max_depth = 3,\n",
    "                           min_child_weight = 3)\n",
    "model_Logistic_Regression = LogisticRegression(penalty= 'l2', C = 1, solver = 'lbfgs') \n",
    "model_Decision_Tree =DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
    "            max_features=None, max_leaf_nodes=10, min_samples_leaf=10,\n",
    "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "            presort=False, random_state=None, splitter='random')\n",
    "AdaBoost_DT_model = AdaBoostClassifier(base_estimator= model_Decision_Tree, n_estimators =  900, learning_rate = 0.001, random_state = 34)\n",
    "AdaBoost_Logistic_Regression_model = AdaBoostClassifier(base_estimator=model_Logistic_Regression,  n_estimators =  1000, learning_rate = 0.01, random_state = 38)\n",
    "grad_boosting_model = GradientBoostingClassifier(learning_rate=0.1, n_estimators=70,max_depth=5, min_samples_split=600, min_samples_leaf=40, subsample=0.75, random_state=10,max_features = 3)\n",
    "\n",
    "\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=10, random_state=1)\n",
    "ensemble_lin_rbf=VotingClassifier(estimators=[('KNN',model_KNN),\n",
    "                                              ('RBF',model_RBF_SVM),\n",
    "                                              ('RFor',model_RandomForest),\n",
    "                                              ('LR',model_Logistic_Regression),\n",
    "                                              ('DT',model_Decision_Tree),\n",
    "                                              ('svm',model_Linear_SVM)\n",
    "                                             ],voting='soft')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Fold 1/10]\n",
      "[Fold 2/10]\n",
      "[Fold 3/10]\n",
      "[Fold 4/10]\n",
      "[Fold 5/10]\n",
      "[Fold 6/10]\n",
      "[Fold 7/10]\n",
      "[Fold 8/10]\n",
      "[Fold 9/10]\n",
      "[Fold 10/10]\n",
      "[Fold 1/10]\n",
      "[Fold 2/10]\n",
      "[Fold 3/10]\n",
      "[Fold 4/10]\n",
      "[Fold 5/10]\n",
      "[Fold 6/10]\n",
      "[Fold 7/10]\n",
      "[Fold 8/10]\n",
      "[Fold 9/10]\n",
      "[Fold 10/10]\n",
      "[Fold 1/10]\n",
      "[Fold 2/10]\n",
      "[Fold 3/10]\n",
      "[Fold 4/10]\n",
      "[Fold 5/10]\n",
      "[Fold 6/10]\n",
      "[Fold 7/10]\n",
      "[Fold 8/10]\n",
      "[Fold 9/10]\n",
      "[Fold 10/10]\n",
      "[Fold 1/10]\n",
      "[Fold 2/10]\n",
      "[Fold 3/10]\n",
      "[Fold 4/10]\n",
      "[Fold 5/10]\n",
      "[Fold 6/10]\n",
      "[Fold 7/10]\n",
      "[Fold 8/10]\n",
      "[Fold 9/10]\n",
      "[Fold 10/10]\n",
      "[Fold 1/10]\n",
      "[Fold 2/10]\n",
      "[Fold 3/10]\n",
      "[Fold 4/10]\n",
      "[Fold 5/10]\n",
      "[Fold 6/10]\n",
      "[Fold 7/10]\n",
      "[Fold 8/10]\n",
      "[Fold 9/10]\n",
      "[Fold 10/10]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb72919f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>AUC Mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AdaBoost - DT</th>\n",
       "      <td>0.862501</td>\n",
       "      <td>0.054032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost - LR</th>\n",
       "      <td>0.868022</td>\n",
       "      <td>0.053449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Tree Boosting</th>\n",
       "      <td>0.870521</td>\n",
       "      <td>0.054945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoosting</th>\n",
       "      <td>0.870326</td>\n",
       "      <td>0.056177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voting Classifier</th>\n",
       "      <td>0.871123</td>\n",
       "      <td>0.056156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        AUC Mean       std\n",
       "AdaBoost - DT           0.862501  0.054032\n",
       "AdaBoost - LR           0.868022  0.053449\n",
       "Gradient Tree Boosting  0.870521  0.054945\n",
       "XGBoosting              0.870326  0.056177\n",
       "Voting Classifier       0.871123  0.056156"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import interp\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "#'aqua', 'darkorange', 'cornflowerblue'\n",
    "models = [\n",
    "    {\n",
    "        'label' : 'AdaBoost - DT',\n",
    "        'model': AdaBoost_DT_model,\n",
    "        'color': '#e41a1c',\n",
    "        'linestyle': 'solid', \n",
    "        'markers': '',\n",
    "         \n",
    "    },\n",
    "    {\n",
    "        'label' : 'AdaBoost - LR',\n",
    "        'model': AdaBoost_Logistic_Regression_model,\n",
    "        'color': '#377eb8',\n",
    "        'linestyle': 'dashed',\n",
    "        'markers': ',',\n",
    "    },\n",
    "    {\n",
    "        'label' : 'Gradient Tree Boosting',\n",
    "        'model': grad_boosting_model, \n",
    "        'color': '#4daf4a',\n",
    "        'linestyle': 'dashdot',\n",
    "        'markers': '',\n",
    "    },    \n",
    "    \n",
    "    {\n",
    "        'label' : 'XGBoosting',\n",
    "        'model': XGB_clf,\n",
    "        'color': '#984ea3',\n",
    "        'linestyle': '-',\n",
    "        'markers': ''\n",
    " \n",
    "    },\n",
    "    {\n",
    "        'label' : 'Voting Classifier',\n",
    "        'model': ensemble_lin_rbf, \n",
    "        'color': '#ff7f00',\n",
    "        'linestyle': '-.',\n",
    "        'markers': ''\n",
    "    },\n",
    "\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# #############################################################################\n",
    "# Classification and ROC analysis\n",
    "random_state = np.random.RandomState(0)\n",
    "# Run classifier with cross-validation and plot ROC curves\n",
    "splits = 10\n",
    "cv = StratifiedKFold(n_splits=splits)\n",
    "\n",
    "\n",
    "tprs = []\n",
    "aucs = []\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "aucs_mean = []\n",
    "labels = []\n",
    "std_mean = []\n",
    "\n",
    "\n",
    "   \n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "for m in models:\n",
    "    i = 0\n",
    "    for i,(train_index, test_index) in enumerate(cv.split(X, y)):\n",
    "        \n",
    "        print('[Fold %d/%d]' % (i + 1, splits))\n",
    "        #print(\"TRAIN:\", train_index, \"\\nTEST:\", test_index)\n",
    "        train_X = X.iloc[train_index]\n",
    "        train_y = y.iloc[train_index]\n",
    "        test_X = X.iloc[test_index]\n",
    "        test_y = y.iloc[test_index]\n",
    "        probas_ = m['model'].fit(train_X, train_y).predict_proba(test_X)\n",
    "        \n",
    "        # Compute ROC curve and area the curve\n",
    "        fpr, tpr, thresholds = roc_curve(test_y, probas_[:, 1])\n",
    "        tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "        tprs[-1][0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        aucs.append(roc_auc)\n",
    "        #plt.plot(fpr, tpr, lw=1, alpha=0.3,\n",
    "        #         label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n",
    "\n",
    "        i += 1\n",
    "    \n",
    "\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "\n",
    "    plt.plot(mean_fpr, mean_tpr, color= m['color'], linestyle = m['linestyle'], marker= m['markers'],\n",
    "             label=r'[%s]  AUC = %0.5f $\\pm$ %0.4f ' % (m['label'] ,mean_auc, std_auc),\n",
    "             lw=2, alpha=.8)\n",
    "\n",
    "    aucs_mean.append(mean_auc)\n",
    "    std_mean.append(std_auc)\n",
    "    labels.append(m['label']) \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "    #plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n",
    "                    # label=r'$\\pm$ 1 std. dev.')\n",
    "    \n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC)')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "             label='Luck', alpha=.8)\n",
    "plt.show()\n",
    "AUCs=pd.DataFrame({'AUC Mean':aucs_mean, 'std':std_mean },index = labels)       \n",
    "AUCs\n",
    "\n"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}