Bagging_algorithms_Approach1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'--Data splitted done'\n"
     ]
    }
   ],
   "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",
    "from pprint import pprint\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\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.tree import DecisionTreeClassifier\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",
    "from time import time\n",
    "from operator import itemgetter\n",
    "from scipy.stats import randint\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.grid_search import RandomizedSearchCV\n",
    "from sklearn.cross_validation import  cross_val_score\n",
    "import os\n",
    "import subprocess\n",
    "from time import time\n",
    "from operator import itemgetter\n",
    "from scipy.stats import randint\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "filename = 'df_imputed_tot_OHE.pkl'\n",
    "\n",
    "\n",
    "\n",
    "#filename = 'df_imputed_tot_OHE.pkl'\n",
    "df = pd.read_pickle(filename)\n",
    "X = df[df.columns[:-1]]\n",
    "y = pd.Series(df['heartdisease'])\n",
    "train_X,test_X,train_y,test_y = train_test_split(X,y,test_size=0.33,shuffle = True, random_state=45)\n",
    "pprint('--Data splitted done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def report(grid_scores, n_top=3):\n",
    "    \"\"\"Report top n_top parameters settings, default n_top=3.\n",
    "\n",
    "    Args\n",
    "    ----\n",
    "    grid_scores -- output from grid or random search\n",
    "    n_top -- how many to report, of top models\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    top_params -- [dict] top parameter settings found in\n",
    "                  search\n",
    "    \"\"\"\n",
    "    top_scores = sorted(grid_scores,\n",
    "                        key=itemgetter(1),\n",
    "                        reverse=True)[:n_top]\n",
    "    for i, score in enumerate(top_scores):\n",
    "        print(\"Model with rank: {0}\".format(i + 1))\n",
    "        print((\"Mean validation score: \"\n",
    "               \"{0:.3f} (std: {1:.3f})\").format(\n",
    "               score.mean_validation_score,\n",
    "               np.std(score.cv_validation_scores)))\n",
    "        print(\"Parameters: {0}\".format(score.parameters))\n",
    "        print(\"\")\n",
    "\n",
    "    return top_scores[0].parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def run_randomsearch(X, y, clf, para_dist, cv=5,\n",
    "                     n_iter_search=100):\n",
    "    \"\"\"Run a random search for best Decision Tree parameters.\n",
    "\n",
    "    Args\n",
    "    ----\n",
    "    X -- features\n",
    "    y -- targets (classes)\n",
    "    cf -- scikit-learn Decision Tree\n",
    "    param_dist -- [dict] list, distributions of parameters\n",
    "                  to sample\n",
    "    cv -- fold of cross-validation, default 5\n",
    "    n_iter_search -- number of random parameter sets to try,\n",
    "                     default 20.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    top_params -- [dict] from report()\n",
    "    \"\"\"\n",
    "    random_search = RandomizedSearchCV(clf,\n",
    "                        param_distributions=param_dist,\n",
    "                        n_iter=n_iter_search, n_jobs= -1)\n",
    "\n",
    "    start = time()\n",
    "    random_search.fit(X, y)\n",
    "    print((\"\\nRandomizedSearchCV took {:.2f} seconds \"\n",
    "           \"for {:d} candidates parameter \"\n",
    "           \"settings.\").format((time() - start),\n",
    "                               n_iter_search))\n",
    "\n",
    "    top_params = report(random_search.grid_scores_, 3)\n",
    "    return  top_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'-- Model Linear_SVM'\n",
      "'-- Model RBF SVM'\n",
      "'-- Model Logistic Regression'\n",
      "'-- Model Decision Tree'\n",
      "'-- Model K-NN'\n",
      "'-- Model Random Forest'\n",
      "'--------FINISH MODELS CREATED'\n"
     ]
    }
   ],
   "source": [
    "pprint('-- Model Linear_SVM')\n",
    "model_Linear_SVM=svm.SVC(kernel='linear',C=0.1,gamma=0.1,probability=True )\n",
    "#model_Linear_SVM.fit(train_X,train_Y)\n",
    "pprint('-- Model RBF SVM')\n",
    "model_RBF_SVM=svm.SVC(kernel='rbf',C=1e4,gamma=1e-4, probability=True)\n",
    "#model_RBF_SVM.fit(train_X,train_Y)\n",
    "pprint('-- Model Logistic Regression')\n",
    "model_Logistic_Regression = LogisticRegression(penalty= 'l2', C = 0.01)\n",
    "#model_Logistic_Regression.fit(train_X,train_Y)\n",
    "pprint('-- Model Decision Tree')\n",
    "model_Decision_Tree=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
    "            max_features=None, max_leaf_nodes=10, min_samples_leaf=1,\n",
    "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "            presort=False, random_state=None, splitter='random')\n",
    "#model_Decision_Tree.fit(train_X,train_Y)\n",
    "pprint('-- Model K-NN')\n",
    "model_KNN = KNeighborsClassifier(n_neighbors=65)\n",
    "pprint('-- Model Random Forest')\n",
    "rfc_best = RandomForestClassifier(n_estimators= 400,\n",
    "               max_features= 'sqrt',\n",
    "               max_depth= 30,\n",
    "               min_samples_split= 2,\n",
    "               min_samples_leaf= 4,\n",
    "               bootstrap= True)\n",
    "\n",
    "pprint('--------FINISH MODELS CREATED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'----Random Search Decision Tree\\n'\n",
      "\n",
      "RandomizedSearchCV took 25.84 seconds for 10 candidates parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.764 (std: 0.037)\n",
      "Parameters: {'n_estimators': 500}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.761 (std: 0.035)\n",
      "Parameters: {'n_estimators': 600}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.761 (std: 0.038)\n",
      "Parameters: {'n_estimators': 800}\n",
      "\n",
      "'----Random Search SVM_Linear\\n'\n",
      "\n",
      "RandomizedSearchCV took 354.71 seconds for 10 candidates parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.795 (std: 0.023)\n",
      "Parameters: {'n_estimators': 200}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.792 (std: 0.022)\n",
      "Parameters: {'n_estimators': 100}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.792 (std: 0.022)\n",
      "Parameters: {'n_estimators': 700}\n",
      "\n",
      "'----Random Search SVM_RBF\\n'\n",
      "\n",
      "RandomizedSearchCV took 1122.28 seconds for 10 candidates parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.785 (std: 0.033)\n",
      "Parameters: {'n_estimators': 100}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.784 (std: 0.033)\n",
      "Parameters: {'n_estimators': 200}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.784 (std: 0.031)\n",
      "Parameters: {'n_estimators': 500}\n",
      "\n",
      "'----Random Search Logistic_Regression\\n'\n",
      "\n",
      "RandomizedSearchCV took 29.10 seconds for 10 candidates parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.789 (std: 0.023)\n",
      "Parameters: {'n_estimators': 100}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.789 (std: 0.023)\n",
      "Parameters: {'n_estimators': 200}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.789 (std: 0.023)\n",
      "Parameters: {'n_estimators': 300}\n",
      "\n",
      "'----Random Search K = 65 - NN\\n'\n",
      "\n",
      "RandomizedSearchCV took 218.96 seconds for 10 candidates parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.793 (std: 0.012)\n",
      "Parameters: {'n_estimators': 100}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.792 (std: 0.014)\n",
      "Parameters: {'n_estimators': 200}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.792 (std: 0.011)\n",
      "Parameters: {'n_estimators': 500}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# dict of parameter list/distributions to sample\n",
    "n_estimators_search=list(range(100,1100,100))\n",
    "param_dist = {\"n_estimators\": n_estimators_search}\n",
    "\n",
    "pprint('----Random Search Decision Tree\\n')\n",
    "bagged_DT = BaggingClassifier(base_estimator=model_Decision_Tree, random_state = 34)\n",
    "DT_rs = run_randomsearch(X, y, bagged_DT, param_dist, cv=10,\n",
    "                         n_iter_search=10)\n",
    "\n",
    "\n",
    "\n",
    "pprint('----Random Search SVM_Linear\\n')\n",
    "bagged_SVM_Linear = BaggingClassifier(base_estimator=model_Linear_SVM, random_state = 36)\n",
    "SVM_Linear_rs = run_randomsearch(X, y, bagged_SVM_Linear, param_dist, cv=10,\n",
    "                         n_iter_search=10)\n",
    "\n",
    "\n",
    "\n",
    "pprint('----Random Search SVM_RBF\\n')\n",
    "bagged_SVM_RBF = BaggingClassifier(base_estimator=model_RBF_SVM, random_state = 37)\n",
    "SVM_RBF_rs = run_randomsearch(X, y, bagged_SVM_RBF, param_dist, cv=10,\n",
    "                         n_iter_search=10)\n",
    "\n",
    "\n",
    "pprint('----Random Search Logistic_Regression\\n')\n",
    "bagged_Logistic_Regression = BaggingClassifier(base_estimator=model_Logistic_Regression, random_state = 38)\n",
    "Logistic_Regression_rs = run_randomsearch(X, y, bagged_Logistic_Regression, param_dist, cv=10,\n",
    "                         n_iter_search=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "pprint('----Random Search K = 65 - NN\\n')\n",
    "bagged_model_KNN= BaggingClassifier(base_estimator=model_KNN, random_state = 38)\n",
    "model_KNN_rs = run_randomsearch(X, y, bagged_model_KNN, param_dist, cv=10,\n",
    "                         n_iter_search=10)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bagged_DT_model = BaggingClassifier(base_estimator= model_Decision_Tree, n_estimators =  500, random_state = 34)\n",
    "bagged_SVM_Linear_model = BaggingClassifier(base_estimator=model_Linear_SVM, n_estimators =  200, random_state = 36)\n",
    "bagged_SVM_RBF_model =  BaggingClassifier(base_estimator=model_RBF_SVM, n_estimators =  100 ,random_state = 37)\n",
    "bagged_Logistic_Regression_model = BaggingClassifier(base_estimator=model_Logistic_Regression,  n_estimators =  100, random_state = 38)\n",
    "bagged_model_KNN= BaggingClassifier(base_estimator=model_KNN, n_estimators =  100, random_state = 38)\n",
    "rfc_best = RandomForestClassifier(n_estimators= 400,\n",
    "               max_features= 'sqrt',\n",
    "               max_depth= 30,\n",
    "               min_samples_split= 2,\n",
    "               min_samples_leaf= 4,\n",
    "               bootstrap= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: BaggingClassifier(base_estimator=SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.1, kernel='linear',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False),\n",
      "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=1.0, n_estimators=200, n_jobs=1, oob_score=False,\n",
      "         random_state=36, verbose=0, warm_start=False)\n",
      "Model: BaggingClassifier(base_estimator=SVC(C=10000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False),\n",
      "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=1.0, n_estimators=100, n_jobs=1, oob_score=False,\n",
      "         random_state=37, verbose=0, warm_start=False)\n",
      "Model: BaggingClassifier(base_estimator=LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False),\n",
      "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=1.0, n_estimators=100, n_jobs=1, oob_score=False,\n",
      "         random_state=38, verbose=0, warm_start=False)\n",
      "Model: BaggingClassifier(base_estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=65, p=2,\n",
      "           weights='uniform'),\n",
      "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=1.0, n_estimators=10, n_jobs=1, oob_score=False,\n",
      "         random_state=38, verbose=0, warm_start=False)\n",
      "Model: BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=10,\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='random'),\n",
      "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=1.0, n_estimators=500, n_jobs=1, oob_score=False,\n",
      "         random_state=34, verbose=0, warm_start=False)\n",
      "Model: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=30, max_features='sqrt', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=4, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=400, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_accuracy</th>\n",
       "      <th>Test_accuracy_std</th>\n",
       "      <th>Train_accuracy</th>\n",
       "      <th>Train_accuracy_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear SVM</th>\n",
       "      <td>0.798895</td>\n",
       "      <td>0.056832</td>\n",
       "      <td>0.822343</td>\n",
       "      <td>0.007216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial SVM</th>\n",
       "      <td>0.803243</td>\n",
       "      <td>0.061082</td>\n",
       "      <td>0.828985</td>\n",
       "      <td>0.006063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.796662</td>\n",
       "      <td>0.061527</td>\n",
       "      <td>0.808453</td>\n",
       "      <td>0.007548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.795622</td>\n",
       "      <td>0.066568</td>\n",
       "      <td>0.808575</td>\n",
       "      <td>0.006881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.778136</td>\n",
       "      <td>0.066074</td>\n",
       "      <td>0.819563</td>\n",
       "      <td>0.008026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.778206</td>\n",
       "      <td>0.072263</td>\n",
       "      <td>0.869805</td>\n",
       "      <td>0.007000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_accuracy  Test_accuracy_std  Train_accuracy  \\\n",
       "Linear SVM                0.798895           0.056832        0.822343   \n",
       "Radial SVM                0.803243           0.061082        0.828985   \n",
       "Logistic Regression       0.796662           0.061527        0.808453   \n",
       "KNN                       0.795622           0.066568        0.808575   \n",
       "Decision Tree             0.778136           0.066074        0.819563   \n",
       "Random Forest             0.778206           0.072263        0.869805   \n",
       "\n",
       "                     Train_accuracy_std  \n",
       "Linear SVM                     0.007216  \n",
       "Radial SVM                     0.006063  \n",
       "Logistic Regression            0.007548  \n",
       "KNN                            0.006881  \n",
       "Decision Tree                  0.008026  \n",
       "Random Forest                  0.007000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_F_score</th>\n",
       "      <th>Test_F_score_std</th>\n",
       "      <th>Train_F_score</th>\n",
       "      <th>Train_F_score_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear SVM</th>\n",
       "      <td>0.820913</td>\n",
       "      <td>0.050345</td>\n",
       "      <td>0.842757</td>\n",
       "      <td>0.006385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial SVM</th>\n",
       "      <td>0.824738</td>\n",
       "      <td>0.055381</td>\n",
       "      <td>0.849109</td>\n",
       "      <td>0.005175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.820425</td>\n",
       "      <td>0.049549</td>\n",
       "      <td>0.830587</td>\n",
       "      <td>0.006737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.819385</td>\n",
       "      <td>0.057464</td>\n",
       "      <td>0.830364</td>\n",
       "      <td>0.005721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.807797</td>\n",
       "      <td>0.050458</td>\n",
       "      <td>0.843687</td>\n",
       "      <td>0.007574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.805776</td>\n",
       "      <td>0.057851</td>\n",
       "      <td>0.886418</td>\n",
       "      <td>0.006314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_F_score  Test_F_score_std  Train_F_score  \\\n",
       "Linear SVM               0.820913          0.050345       0.842757   \n",
       "Radial SVM               0.824738          0.055381       0.849109   \n",
       "Logistic Regression      0.820425          0.049549       0.830587   \n",
       "KNN                      0.819385          0.057464       0.830364   \n",
       "Decision Tree            0.807797          0.050458       0.843687   \n",
       "Random Forest            0.805776          0.057851       0.886418   \n",
       "\n",
       "                     Train_F_score_std  \n",
       "Linear SVM                    0.006385  \n",
       "Radial SVM                    0.005175  \n",
       "Logistic Regression           0.006737  \n",
       "KNN                           0.005721  \n",
       "Decision Tree                 0.007574  \n",
       "Random Forest                 0.006314  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_precision</th>\n",
       "      <th>Test_precision_std</th>\n",
       "      <th>Train_precision</th>\n",
       "      <th>Train_precision_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear SVM</th>\n",
       "      <td>0.812644</td>\n",
       "      <td>0.062470</td>\n",
       "      <td>0.825734</td>\n",
       "      <td>0.006697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial SVM</th>\n",
       "      <td>0.814989</td>\n",
       "      <td>0.062753</td>\n",
       "      <td>0.829513</td>\n",
       "      <td>0.006590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.811924</td>\n",
       "      <td>0.074264</td>\n",
       "      <td>0.813256</td>\n",
       "      <td>0.007568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.809041</td>\n",
       "      <td>0.077359</td>\n",
       "      <td>0.814660</td>\n",
       "      <td>0.008653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.785623</td>\n",
       "      <td>0.074879</td>\n",
       "      <td>0.810029</td>\n",
       "      <td>0.006343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.793917</td>\n",
       "      <td>0.082720</td>\n",
       "      <td>0.856640</td>\n",
       "      <td>0.005004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_precision  Test_precision_std  Train_precision  \\\n",
       "Linear SVM                 0.812644            0.062470         0.825734   \n",
       "Radial SVM                 0.814989            0.062753         0.829513   \n",
       "Logistic Regression        0.811924            0.074264         0.813256   \n",
       "KNN                        0.809041            0.077359         0.814660   \n",
       "Decision Tree              0.785623            0.074879         0.810029   \n",
       "Random Forest              0.793917            0.082720         0.856640   \n",
       "\n",
       "                     Train_precision_std  \n",
       "Linear SVM                      0.006697  \n",
       "Radial SVM                      0.006590  \n",
       "Logistic Regression             0.007568  \n",
       "KNN                             0.008653  \n",
       "Decision Tree                   0.006343  \n",
       "Random Forest                   0.005004  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_Sensitivity</th>\n",
       "      <th>Test_Sensitivity_std</th>\n",
       "      <th>Train_Sensitivity</th>\n",
       "      <th>Train_Sensitivity_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear SVM</th>\n",
       "      <td>0.835098</td>\n",
       "      <td>0.074336</td>\n",
       "      <td>0.860508</td>\n",
       "      <td>0.006771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial SVM</th>\n",
       "      <td>0.840980</td>\n",
       "      <td>0.082327</td>\n",
       "      <td>0.869677</td>\n",
       "      <td>0.005314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.835059</td>\n",
       "      <td>0.060064</td>\n",
       "      <td>0.848722</td>\n",
       "      <td>0.008590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.837020</td>\n",
       "      <td>0.075321</td>\n",
       "      <td>0.846761</td>\n",
       "      <td>0.007043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.834980</td>\n",
       "      <td>0.043090</td>\n",
       "      <td>0.880371</td>\n",
       "      <td>0.013162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.827333</td>\n",
       "      <td>0.079625</td>\n",
       "      <td>0.918357</td>\n",
       "      <td>0.008590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_Sensitivity  Test_Sensitivity_std  \\\n",
       "Linear SVM                   0.835098              0.074336   \n",
       "Radial SVM                   0.840980              0.082327   \n",
       "Logistic Regression          0.835059              0.060064   \n",
       "KNN                          0.837020              0.075321   \n",
       "Decision Tree                0.834980              0.043090   \n",
       "Random Forest                0.827333              0.079625   \n",
       "\n",
       "                     Train_Sensitivity  Train_Sensitivity_std  \n",
       "Linear SVM                    0.860508               0.006771  \n",
       "Radial SVM                    0.869677               0.005314  \n",
       "Logistic Regression           0.848722               0.008590  \n",
       "KNN                           0.846761               0.007043  \n",
       "Decision Tree                 0.880371               0.013162  \n",
       "Random Forest                 0.918357               0.008590  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit_time</th>\n",
       "      <th>Scoring Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear SVM</th>\n",
       "      <td>9.345874</td>\n",
       "      <td>1.013471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial SVM</th>\n",
       "      <td>13.179519</td>\n",
       "      <td>0.672137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.461276</td>\n",
       "      <td>0.062951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.038244</td>\n",
       "      <td>0.396553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>1.664317</td>\n",
       "      <td>0.298784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>1.125181</td>\n",
       "      <td>0.205875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Fit_time  Scoring Time\n",
       "Linear SVM            9.345874      1.013471\n",
       "Radial SVM           13.179519      0.672137\n",
       "Logistic Regression   0.461276      0.062951\n",
       "KNN                   0.038244      0.396553\n",
       "Decision Tree         1.664317      0.298784\n",
       "Random Forest         1.125181      0.205875"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "from sklearn.model_selection import KFold,StratifiedKFold #for K-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score, cross_validate #score evaluation\n",
    "from sklearn.model_selection import cross_val_predict #prediction\n",
    "from sklearn.metrics import recall_score, f1_score, precision_score, accuracy_score\n",
    "from sklearn.metrics.scorer import make_scorer\n",
    "\n",
    "\n",
    "scoring = {'precision': make_scorer(precision_score),\n",
    "           'recall': make_scorer(recall_score),\n",
    "           'F-score': make_scorer( f1_score),\n",
    "           'accuracy': make_scorer(accuracy_score)}\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "\n",
    "classifiers=['Linear SVM','Radial SVM','Logistic Regression','KNN','Decision Tree','Random Forest']\n",
    "models=[bagged_SVM_Linear_model, bagged_SVM_RBF_model, bagged_Logistic_Regression_model, bagged_model_KNN, bagged_DT_model, rfc_best ]\n",
    "\n",
    "\n",
    "\n",
    "fit_time = []\n",
    "score_time = []\n",
    "\n",
    "test_accuracy = []\n",
    "test_accuracy_total = []\n",
    "\n",
    "test_accuracy_std = []\n",
    "train_accuracy = []\n",
    "train_accuracy_total = []\n",
    "train_accuracy_std = []\n",
    "\n",
    "\n",
    "test_F_score = []\n",
    "test_F_score_total = []\n",
    "\n",
    "\n",
    "test_F_score_std = []\n",
    "train_F_score = []\n",
    "train_F_score_total = []\n",
    "\n",
    "train_F_score_std = []\n",
    "\n",
    "\n",
    "test_precision = []\n",
    "test_precision_std = []\n",
    "train_precision = []\n",
    "train_precision_std = []\n",
    "\n",
    "\n",
    "test_recall = []\n",
    "test_recall_std = []\n",
    "train_recall = []\n",
    "train_recall_std = []\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for i in models:\n",
    "    model = i\n",
    "    scores = cross_validate(model,X,y, cv = kfold, scoring = scoring, return_train_score=True)\n",
    "    fit_time.append(scores['fit_time'].mean())\n",
    "    score_time.append(scores['score_time'].mean())\n",
    "    \n",
    "    test_accuracy.append(scores['test_accuracy'].mean())\n",
    "    test_accuracy_total.append(scores['test_accuracy'])\n",
    "    \n",
    "    test_accuracy_std.append(scores['test_accuracy'].std())\n",
    "    train_accuracy.append(scores['train_accuracy'].mean())\n",
    "    train_accuracy_total.append(scores['train_accuracy'])\n",
    "                          \n",
    "    train_accuracy_std.append(scores['train_accuracy'].std())\n",
    "    \n",
    "    test_F_score.append(scores['test_F-score'].mean())\n",
    "    test_F_score_total.append(scores['test_F-score'])\n",
    "    \n",
    "    test_F_score_std.append(scores['test_F-score'].std())\n",
    "    train_F_score.append(scores['train_F-score'].mean())\n",
    "    train_F_score_total.append(scores['train_F-score'])\n",
    "                         \n",
    "                         \n",
    "    train_F_score_std.append(scores['train_F-score'].std())\n",
    "    \n",
    "    test_precision.append(scores['test_precision'].mean())\n",
    "    test_precision_std.append(scores['test_precision'].std())\n",
    "    train_precision.append(scores['train_precision'].mean())\n",
    "    train_precision_std.append(scores['train_precision'].std())\n",
    "    \n",
    "    test_recall.append(scores['test_recall'].mean())\n",
    "    test_recall_std.append(scores['test_recall'].std())\n",
    "    train_recall.append(scores['train_recall'].mean())\n",
    "    train_recall_std.append(scores['train_recall'].std())\n",
    "    \n",
    "\n",
    "    print('Model: ' + str(i))\n",
    "\n",
    "sorted(scores.keys())     \n",
    "dct_acc = {'Test_accuracy':test_accuracy,\n",
    "               'Test_accuracy_std':test_accuracy_std,\n",
    "               'Train_accuracy':train_accuracy,\n",
    "               'Train_accuracy_std':train_accuracy_std, }\n",
    "dct_Fscore = {'Test_F_score':test_F_score,\n",
    "               'Test_F_score_std':test_F_score_std,\n",
    "               'Train_F_score': train_F_score,\n",
    "               'Train_F_score_std': train_F_score_std}\n",
    "dct_precision = {'Test_precision': test_precision,\n",
    "               'Test_precision_std': test_precision_std,\n",
    "               'Train_precision': train_precision,\n",
    "               'Train_precision_std': train_precision_std}\n",
    "dct_recall = {'Test_Sensitivity': test_recall, \n",
    "               'Test_Sensitivity_std': test_recall_std,\n",
    "               'Train_Sensitivity': train_recall,\n",
    "               'Train_Sensitivity_std': train_recall_std}\n",
    "dct_time = {'Fit_time': fit_time, 'Scoring Time':score_time}\n",
    "\n",
    "metrics_acc=pd.DataFrame(dct_acc,index=classifiers)       \n",
    "metrics_Fsc = pd.DataFrame(dct_Fscore,index=classifiers)\n",
    "metrics_precision = pd.DataFrame(dct_precision,index=classifiers) \n",
    "metrics_recall = pd.DataFrame(dct_recall,index=classifiers)\n",
    "metrics_time = pd.DataFrame(dct_time,index=classifiers)\n",
    "           \n",
    "display(metrics_acc)\n",
    "display(metrics_Fsc)\n",
    "display(metrics_precision)\n",
    "display(metrics_recall)\n",
    "display(metrics_time)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_accuracy_total' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-2115b93ec668>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'TEST accuracy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbox_test_accuracy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_accuracy_total\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mbox_test_accuracy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'test_accuracy_total' is not defined"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('TEST accuracy')\n",
    "box_test_accuracy=pd.DataFrame(test_accuracy_total,index=[classifiers])\n",
    "box_test_accuracy.T.boxplot()\n",
    "plt.show()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('TRAIN accuracy')\n",
    "box_train_accuracy=pd.DataFrame(train_accuracy_total,index=[classifiers])\n",
    "box_train_accuracy.T.boxplot()\n",
    "plt.show()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('TEST F-SCORE')\n",
    "box_test_F_score=pd.DataFrame(test_F_score_total,index=[classifiers])\n",
    "box_test_F_score.T.boxplot()\n",
    "plt.show()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('TRAIN F-SCORE')\n",
    "box_train_F_score=pd.DataFrame(train_F_score_total,index=[classifiers])\n",
    "box_train_F_score.T.boxplot()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Matrix for Decision Tree')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f,ax=plt.subplots(2,3,figsize=(12,6))\n",
    "y_pred = cross_val_predict(bagged_SVM_RBF_model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),ax=ax[0,0],annot=True,fmt='2.0f')\n",
    "ax[0,0].set_title('Matrix for rbf-SVM')\n",
    "y_pred = cross_val_predict(bagged_SVM_Linear_model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),ax=ax[0,1],annot=True,fmt='2.0f')\n",
    "ax[0,1].set_title('Matrix for Linear-SVM')\n",
    "y_pred = cross_val_predict(bagged_model_KNN,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),ax=ax[0,2],annot=True,fmt='2.0f')\n",
    "ax[0,2].set_title('Matrix for KNN')\n",
    "y_pred = cross_val_predict(rfc_best,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),ax=ax[1,0],annot=True,fmt='2.0f')\n",
    "ax[1,0].set_title('Matrix for Random-Forests')\n",
    "y_pred = cross_val_predict(bagged_Logistic_Regression_model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),ax=ax[1,1],annot=True,fmt='2.0f')\n",
    "ax[1,1].set_title('Matrix for Logistic Regression')\n",
    "y_pred = cross_val_predict(bagged_DT_model,X,y,cv=10)\n",
    "sns.heatmap(confusion_matrix(y,y_pred),ax=ax[1,2],annot=True,fmt='2.0f')\n",
    "ax[1,2].set_title('Matrix for Decision Tree')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAALJCAYAAACUZbS1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8d6eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>rbf-SVM</th>\n",
       "      <td>0.908509</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linear-SVM</th>\n",
       "      <td>0.908081</td>\n",
       "      <td>2.834096e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.906979</td>\n",
       "      <td>1.023371e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest</th>\n",
       "      <td>0.904888</td>\n",
       "      <td>3.754781e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.904768</td>\n",
       "      <td>3.408822e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.902078</td>\n",
       "      <td>6.666136e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AUC Mean           std\n",
       "rbf-SVM             0.908509  1.110223e-16\n",
       "Linear-SVM          0.908081  2.834096e-04\n",
       "KNN                 0.906979  1.023371e-03\n",
       "RandomForest        0.904888  3.754781e-03\n",
       "LogisticRegression  0.904768  3.408822e-03\n",
       "Decision Tree       0.902078  6.666136e-03"
      ]
     },
     "execution_count": 31,
     "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' : 'rbf-SVM',\n",
    "        'model': bagged_SVM_RBF_model,\n",
    "        'color': 'maroon',\n",
    "        'linestyle': 'solid', \n",
    "        'markers': 'H',\n",
    "         \n",
    "    },\n",
    "    {\n",
    "        'label' : 'Linear-SVM',\n",
    "        'model': bagged_SVM_Linear_model,\n",
    "        'color': 'navy',\n",
    "        'linestyle': 'dashed',\n",
    "        'markers': ',',\n",
    "    },\n",
    "    {\n",
    "        'label' : 'KNN',\n",
    "        'model': bagged_model_KNN, \n",
    "        'color': 'olive',\n",
    "        'linestyle': 'dashdot',\n",
    "        'markers': '',\n",
    "    },    \n",
    "    {\n",
    "        'label' : 'RandomForest',\n",
    "        'model': rfc_best,\n",
    "        'color': 'darkcyan',\n",
    "        'linestyle': 'dotted',\n",
    "        'markers': '>',\n",
    "       \n",
    "    },        \n",
    "    {\n",
    "        'label' : 'LogisticRegression',\n",
    "        'model': bagged_Logistic_Regression_model,\n",
    "        'color': 'fuchsia',\n",
    "        'linestyle': '-',\n",
    "        'markers': ''\n",
    " \n",
    "    },\n",
    "    {\n",
    "        'label' : 'Decision Tree',\n",
    "        'model': bagged_DT_model, \n",
    "        'color': 'orange',\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",
    "cv = StratifiedKFold(n_splits=10)\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 train, test in cv.split(X, y):\n",
    "        probas_ = m['model'].fit(train_X, train_y).predict_proba(test_X)\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",
    "    #plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "    #         label='Luck', alpha=.8)\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",
    "\n",
    "plt.show()\n",
    "AUCs=pd.DataFrame({'AUC Mean':aucs_mean, 'std':std_mean },index = labels)       \n",
    "AUCs\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}