--- a
+++ b/Ensemble/Ensemble-all.ipynb
@@ -0,0 +1,2232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "# Import libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import getpass\n",
+    "import pdvega\n",
+    "import plotly.graph_objs as go\n",
+    "\n",
+    "from plotly.offline import iplot, init_notebook_mode\n",
+    "import plotly.io as pio\n",
+    "from plotly.graph_objs import *\n",
+    "\n",
+    "# for configuring connection \n",
+    "from configobj import ConfigObj\n",
+    "import os\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "from sklearn import linear_model\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "#configure the notebook for use in offline mode\n",
+    "init_notebook_mode(connected=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ],
+      "text/vnd.plotly.v1+html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "df2= pd.read_csv(\"analysis.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "df2.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "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>Unnamed: 0</th>\n",
+       "      <th>hospitalid</th>\n",
+       "      <th>sodium</th>\n",
+       "      <th>electivesurgery</th>\n",
+       "      <th>vent</th>\n",
+       "      <th>dialysis</th>\n",
+       "      <th>gcs</th>\n",
+       "      <th>urine</th>\n",
+       "      <th>wbc</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>...</th>\n",
+       "      <th>m11_True</th>\n",
+       "      <th>m12_True</th>\n",
+       "      <th>m13_True</th>\n",
+       "      <th>m14_True</th>\n",
+       "      <th>m15_True</th>\n",
+       "      <th>m16_True</th>\n",
+       "      <th>m17_True</th>\n",
+       "      <th>m18_True</th>\n",
+       "      <th>m19_True</th>\n",
+       "      <th>m20_True</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>36.1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>134.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>34.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>137.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>36.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 85 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  hospitalid  sodium  electivesurgery  vent  dialysis   gcs  \\\n",
+       "0           0        59.0   139.0             -1.0   0.0       0.0  15.0   \n",
+       "1           1        73.0   134.0             -1.0   0.0       0.0  13.0   \n",
+       "2           2        73.0    -1.0              1.0   1.0       0.0  15.0   \n",
+       "3           3        63.0   137.0             -1.0   0.0       0.0  15.0   \n",
+       "4           4        63.0   135.0             -1.0   0.0       0.0  15.0   \n",
+       "\n",
+       "   urine   wbc  temperature  ...  m11_True  m12_True  m13_True  m14_True  \\\n",
+       "0   -1.0  14.7         36.1  ...         1         0         0         1   \n",
+       "1   -1.0  14.1         39.3  ...         1         0         0         1   \n",
+       "2   -1.0   8.0         34.8  ...         0         0         1         0   \n",
+       "3   -1.0  10.9         36.6  ...         1         0         1         1   \n",
+       "4   -1.0   5.9         35.0  ...         0         0         1         0   \n",
+       "\n",
+       "   m15_True  m16_True  m17_True  m18_True  m19_True  m20_True  \n",
+       "0         1         0         0         0         1         0  \n",
+       "1         1         0         0         0         1         0  \n",
+       "2         0         1         0         1         0         0  \n",
+       "3         1         0         0         1         1         0  \n",
+       "4         0         0         0         1         0         0  \n",
+       "\n",
+       "[5 rows x 85 columns]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "del df2['hospitalid']\n",
+    "\n",
+    "df2 = df2.drop(df2.columns[[0]], axis=1)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "df2.shape"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(95148, 83)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "**We moved all the pre-processing including splitting>imputation>Standardization to the CV iterations**"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "cols_to_norm=['gcs', 'urine', 'wbc', 'sodium',\n",
+    "       'temperature', 'respiratoryrate', 'heartrate', 'meanbp', 'creatinine',\n",
+    "       'ph', 'hematocrit', 'albumin', 'pao2', 'pco2', 'bun', 'glucose',\n",
+    "       'bilirubin', 'fio2', 'age', 'offset']\n",
+    "\n",
+    "X=df2.drop('destcopy', 1)\n",
+    "y=df2['destcopy']\n",
+    "df_cols = list(X)     #fancy impute removes column names."
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Load in our libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import re\n",
+    "import sklearn\n",
+    "import xgboost as xgb\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import plotly.offline as py\n",
+    "py.init_notebook_mode(connected=True)\n",
+    "import plotly.graph_objs as go\n",
+    "import plotly.tools as tls\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Going to use these 5 base models for the stacking\n",
+    "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
+    "                              GradientBoostingClassifier, ExtraTreesClassifier)\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ],
+      "text/vnd.plotly.v1+html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "from sklearn.model_selection import StratifiedKFold"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "\n",
+    "classes=['Death','Home','Nursing Home','Rehabilitation']\n",
+    "\n",
+    "kf_m = StratifiedKFold(n_splits=10)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Class to extend the Sklearn classifier\n",
+    "class SklearnHelper(object):\n",
+    "    def __init__(self, clf, seed=0, params=None):\n",
+    "        params['random_state'] = seed\n",
+    "        self.clf = clf(**params)\n",
+    "\n",
+    "    def train(self, x_train, y_train):\n",
+    "        self.clf.fit(x_train, y_train)\n",
+    "\n",
+    "    def predict(self, x):\n",
+    "        return self.clf.predict(x)\n",
+    "\n",
+    "    def fit(self,x,y):\n",
+    "        return self.clf.fit(x,y)\n",
+    "\n",
+    "    def feature_importances(self,x,y):\n",
+    "        return(self.clf.fit(x,y).feature_importances_)\n",
+    "    \n",
+    "\n",
+    "\n",
+    "\n",
+    "#-------------------------------------------------------------\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#------------------------------------------\n",
+    "\n",
+    "rf_params = {\n",
+    "    'n_jobs': -1,\n",
+    "    'n_estimators': 400,\n",
+    "     'warm_start': True, \n",
+    "     #'max_features': 0.2,\n",
+    "    'max_depth': 30,\n",
+    "    'min_samples_leaf': 2,\n",
+    "    'max_features' : 0.8,\n",
+    "    'verbose': 0,\n",
+    "     'criterion':'gini'\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# Extra Trees Parameters\n",
+    "et_params = {\n",
+    "    'n_jobs': -1,\n",
+    "    'n_estimators':500,\n",
+    "    #'max_features': 0.5,\n",
+    "    'max_depth': 8,\n",
+    "    'min_samples_leaf': 2,\n",
+    "    'verbose': 0\n",
+    "}\n",
+    "\n",
+    "# AdaBoost parameters\n",
+    "ada_params = {\n",
+    "    'n_estimators': 500,\n",
+    "    'learning_rate' : 0.75\n",
+    "}\n",
+    "\n",
+    "# Gradient Boosting parameters\n",
+    "gb_params = {\n",
+    "    'n_estimators': 500,\n",
+    "     #'max_features': 0.2,\n",
+    "    'max_depth': 5,\n",
+    "    'min_samples_leaf': 2,\n",
+    "    'verbose': 0\n",
+    "}\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Support Vector Classifier parameters \n",
+    "lr_params = {\n",
+    "    'penalty' : 'l1',\n",
+    "    'tol' : 6.75e-05,\n",
+    "    'C' : 2.5,\n",
+    "    'max_iter': 66\n",
+    "    }\n",
+    "\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "**Random Forest**"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "from collections import Counter"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn import preprocessing\n",
+    "from imblearn.over_sampling import SMOTENC\n",
+    "from sklearn.metrics import f1_score\n",
+    "from yellowbrick.classifier import ROCAUC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from numpy import loadtxt\n",
+    "import os\n",
+    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "import io \n",
+    "\n",
+    "\n",
+    "\n",
+    "for fold, (train_index, test_index) in enumerate(kf_m.split(X,y), 1):\n",
+    "    X_train = X.iloc[train_index]\n",
+    "    y_train = y.iloc[train_index]  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
+    "    X_test = X.iloc[test_index]\n",
+    "    y_test = y.iloc[test_index]  # See comment on ravel and  y_train\n",
+    "    \n",
+    "    \n",
+    "#------------------------------Standardize Testing Set------------------------------------\n",
+    "    \n",
+    "    std_scale = preprocessing.StandardScaler().fit(X_train[cols_to_norm])\n",
+    "    X_train[cols_to_norm] = std_scale.transform(X_train[cols_to_norm])\n",
+    "    X_test[cols_to_norm] = std_scale.transform(X_test[cols_to_norm])\n",
+    "#------------------------------------------------------------------------------------------\n",
+    "\n",
+    " # Hyperparameters are optimized using hyperopt\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Class to extend XGboost classifer\n",
+    "    sm = SMOTENC(random_state=50, categorical_features=[1,2,3,22,23,24,25,26,27,28,29,30,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61, 62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81])\n",
+    "    X_train_oversampled, y_train_oversampled = sm.fit_sample(X_train, y_train)\n",
+    "    print(sorted(Counter(y_train_oversampled).items()))\n",
+    "    \n",
+    "# --------------- Let's Start the fun ------------------------\n",
+    "\n",
+    "    # Some useful parameters which will come in handy later on\n",
+    "    ntrain = X_train_oversampled.shape[0]\n",
+    "    print(ntrain)\n",
+    "    ntest = X_test.shape[0]\n",
+    "    SEED = 0 # for reproducibility\n",
+    "    # set folds for out-of-fold prediction\n",
+    "    #kf = KFold(ntrain, n_split=5, random_state=SEED)\n",
+    "    \n",
+    "    def get_oof(clf, x_train, y_train, x_test):\n",
+    "        oof_train = np.zeros((ntrain,))\n",
+    "        oof_test = np.zeros((ntest,))\n",
+    "        oof_test_skf = np.empty((10, ntest))\n",
+    "\n",
+    "\n",
+    "        for i, (train_index, test_index) in enumerate(kf_m.split(x_train, y_train)):\n",
+    "            x_tr = x_train[train_index]\n",
+    "            y_tr = y_train[train_index]\n",
+    "            x_te = x_train[test_index]\n",
+    "\n",
+    "            clf.train(x_tr, y_tr)\n",
+    "            \n",
+    "            oof_train[test_index] = clf.predict(x_te)\n",
+    "            oof_test_skf[i, :] = clf.predict(x_test)\n",
+    "\n",
+    "        oof_test[:] = oof_test_skf.mean(axis=0)\n",
+    "        return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)\n",
+    "    \n",
+    "        # Create 5 objects that represent our 4 models\n",
+    "    #rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\n",
+    "    et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\n",
+    "    #ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\n",
+    "    gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\n",
+    "    #lr = SklearnHelper(clf=LogisticRegression, seed=SEED, params=lr_params)\n",
+    "\n",
+    "    #------------------------------------------\n",
+    "    # Create our OOF train and test predictions. These base results will be used as new features\n",
+    "    et_oof_train, et_oof_test = get_oof(et, X_train_oversampled, y_train_oversampled, X_test) # Extra Trees\n",
+    "    #rf_oof_train, rf_oof_test = get_oof(rf,X_train_oversampled, y_train_oversampled, X_test) # Random Forest\n",
+    "    #ada_oof_train, ada_oof_test = get_oof(ada, X_train_oversampled, y_train_oversampled, X_test) # AdaBoost \n",
+    "    gb_oof_train, gb_oof_test = get_oof(gb,X_train_oversampled, y_train_oversampled, X_test) # Gradient Boost\n",
+    "    #lr_oof_train, lr_oof_test = get_oof(lr,X_train_oversampled, y_train_oversampled, X_test) # Support Vector Classifier\n",
+    "\n",
+    "    print(\"Training is complete\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "    #rf_features = rf.feature_importances(X_train_oversampled,y_train_oversampled).tolist()\n",
+    "    et_features = et.feature_importances(X_train_oversampled, y_train_oversampled).tolist()\n",
+    "    #ada_features = ada.feature_importances(X_train_oversampled, y_train_oversampled).tolist()\n",
+    "    gb_features = gb.feature_importances(X_train_oversampled, y_train_oversampled).tolist()\n",
+    "    #lr_features=(map(abs,lr_features)) / (abs(lr_fit.coef_).max())\n",
+    "\n",
+    "\n",
+    "\n",
+    "    cols = df2.drop('destcopy', 1).columns.values\n",
+    "        # Create a dataframe with features\n",
+    "    feature_dataframe = pd.DataFrame( {'features': cols,\n",
+    "         \n",
+    "         'Extra Trees  feature importances': et_features,\n",
+    "         \n",
+    "         'Gradient Boost feature importances': gb_features,\n",
+    "         #'LR feature importances': lr_features\n",
+    "        })\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # Create a dataframe with features\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # Scatter plot \n",
+    "    trace = go.Scatter(\n",
+    "        y = feature_dataframe['Extra Trees  feature importances'].values,\n",
+    "        x = feature_dataframe['features'].values,\n",
+    "        mode='markers',\n",
+    "        marker=dict(\n",
+    "            sizemode = 'diameter',\n",
+    "            sizeref = 1,\n",
+    "            size = 25,\n",
+    "    #       size= feature_dataframe['AdaBoost feature importances'].values,\n",
+    "            #color = np.random.randn(500), #set color equal to a variable\n",
+    "            color = feature_dataframe['Extra Trees  feature importances'].values,\n",
+    "            colorscale='Portland',\n",
+    "            showscale=True\n",
+    "        ),\n",
+    "        text = feature_dataframe['features'].values\n",
+    "    )\n",
+    "    data = [trace]\n",
+    "\n",
+    "    layout= go.Layout(\n",
+    "        autosize= True,\n",
+    "        title= 'Extra Trees Feature Importance',\n",
+    "        hovermode= 'closest',\n",
+    "    #     xaxis= dict(\n",
+    "    #         title= 'Pop',\n",
+    "    #         ticklen= 5,\n",
+    "    #         zeroline= False,\n",
+    "    #         gridwidth= 2,\n",
+    "    #     ),\n",
+    "        yaxis=dict(\n",
+    "            title= 'Feature Importance',\n",
+    "            ticklen= 5,\n",
+    "            gridwidth= 2\n",
+    "        ),\n",
+    "        showlegend= False\n",
+    "    )\n",
+    "    fig = go.Figure(data=data, layout=layout)\n",
+    "    py.iplot(fig,filename='scatter2010')\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # Scatter plot \n",
+    "    trace = go.Scatter(\n",
+    "        y = feature_dataframe['Gradient Boost feature importances'].values,\n",
+    "        x = feature_dataframe['features'].values,\n",
+    "        mode='markers',\n",
+    "        marker=dict(\n",
+    "            sizemode = 'diameter',\n",
+    "            sizeref = 1,\n",
+    "            size = 25,\n",
+    "    #       size= feature_dataframe['AdaBoost feature importances'].values,\n",
+    "            #color = np.random.randn(500), #set color equal to a variable\n",
+    "            color = feature_dataframe['Gradient Boost feature importances'].values,\n",
+    "            colorscale='Portland',\n",
+    "            showscale=True\n",
+    "        ),\n",
+    "        text = feature_dataframe['features'].values\n",
+    "    )\n",
+    "    data = [trace]\n",
+    "\n",
+    "    layout= go.Layout(\n",
+    "        autosize= True,\n",
+    "        title= 'Gradient Boosting Feature Importance',\n",
+    "        hovermode= 'closest',\n",
+    "    #     xaxis= dict(\n",
+    "    #         title= 'Pop',\n",
+    "    #         ticklen= 5,\n",
+    "    #         zeroline= False,\n",
+    "    #         gridwidth= 2,\n",
+    "    #     ),\n",
+    "        yaxis=dict(\n",
+    "            title= 'Feature Importance',\n",
+    "            ticklen= 5,\n",
+    "            gridwidth= 2\n",
+    "        ),\n",
+    "        showlegend= False\n",
+    "    )\n",
+    "    fig = go.Figure(data=data, layout=layout)\n",
+    "    py.iplot(fig,filename='scatter2010')\n",
+    "\n",
+    "    feature_dataframe['mean'] = feature_dataframe.mean(axis= 1) # axis = 1 computes the mean row-wise\n",
+    "    feature_dataframe.head(3)\n",
+    "\n",
+    "    yv = feature_dataframe['mean'].values\n",
+    "    x = feature_dataframe['features'].values\n",
+    "    data = [go.Bar(\n",
+    "                x= x,\n",
+    "                y= yv,\n",
+    "                width = 0.5,\n",
+    "                marker=dict(\n",
+    "                   color = feature_dataframe['mean'].values,\n",
+    "                colorscale='Portland',\n",
+    "                showscale=True,\n",
+    "                reversescale = False\n",
+    "                ),\n",
+    "                opacity=0.6\n",
+    "            )]\n",
+    "\n",
+    "    layout= go.Layout(\n",
+    "        autosize= True,\n",
+    "        title= 'Barplots of Mean Feature Importance',\n",
+    "        hovermode= 'closest',\n",
+    "    #     xaxis= dict(\n",
+    "    #         title= 'Pop',\n",
+    "    #         ticklen= 5,\n",
+    "    #         zeroline= False,\n",
+    "    #         gridwidth= 2,\n",
+    "    #     ),\n",
+    "        yaxis=dict(\n",
+    "            title= 'Feature Importance',\n",
+    "            ticklen= 5,\n",
+    "            gridwidth= 2\n",
+    "        ),\n",
+    "        showlegend= False\n",
+    "    )\n",
+    "    fig = go.Figure(data=data, layout=layout)\n",
+    "    py.iplot(fig, filename='bar-direct-labels')\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    base_predictions_train = pd.DataFrame( {\n",
+    "     'ExtraTrees': et_oof_train.ravel(),\n",
+    "     \n",
+    "     'GradientBoost': gb_oof_train.ravel(),\n",
+    "     #'LR': lr_oof_train.ravel()\n",
+    "    })\n",
+    "    base_predictions_train.head()\n",
+    "\n",
+    "    data = [\n",
+    "    go.Heatmap(\n",
+    "        z= base_predictions_train.astype(float).corr().values ,\n",
+    "        x=base_predictions_train.columns.values,\n",
+    "        y= base_predictions_train.columns.values,\n",
+    "          colorscale='Viridis',\n",
+    "            showscale=True,\n",
+    "            reversescale = True\n",
+    "        )\n",
+    "    ]\n",
+    "    py.iplot(data, filename='labelled-heatmap')\n",
+    "\n",
+    "    x_train = np.concatenate(( et_oof_train,gb_oof_train), axis=1)\n",
+    "    x_test = np.concatenate(( et_oof_test, gb_oof_test), axis=1)\n",
+    "    \n",
+    "    gbm = RandomForestClassifier().fit(x_train,y_train_oversampled)\n",
+    "    y_pred = gbm.predict(x_test)\n",
+    "    visualizer = ROCAUC(gbm, classes=classes)\n",
+    "    visualizer.fit(x_train, y_train_oversampled)  # Fit the training data to the visualizer\n",
+    "    visualizer.score(x_test, y_test)  # Evaluate the model on the test data\n",
+    "    visualizer.poof(\"Ensembel_{}.pdf\".format(fold), clear_figure=True) \n",
+    "    print(f'For fold {fold}:')\n",
+    "    print(f'Accuracy: {gbm.score(x_test, y_test)}')\n",
+    "    f1=f1_score(y_test, y_pred, average='micro')\n",
+    "    print(f'f-score: {f1}')\n",
+    "    print(classification_report_imbalanced(y_test, y_pred))\n",
+    "    K= classification_report_imbalanced(y_test, y_pred)\n",
+    "    df = pd.read_fwf(io.StringIO(K))\n",
+    "    df.loc[\"1\":\"1\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-D.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
+    "    df.loc[\"2\":\"2\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-H.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
+    "    df.loc[\"3\":\"3\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-N.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
+    "    df.loc[\"4\":\"4\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-R.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
+    "    df.iloc[6:7,:].to_csv(\"RF-Ensemble-avg.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
+    "    \n",
+    "    "
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[(1, 59596), (2, 59596), (3, 59596), (4, 59596)]\n",
+      "238384\n",
+      "Training is complete\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "linkText": "Export to plot.ly",
+        "plotlyServerURL": "https://plot.ly",
+        "showLink": false
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+          "respiratoryrate",
+          "heartrate",
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+          "respiratoryrate",
+          "heartrate",
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+          "creatinine",
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+          "pao2",
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+      ]
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+     "metadata": {}
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+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "For fold 1:\n",
+      "Accuracy: 0.7351828499369483\n",
+      "f-score: 0.7351828499369484\n"
+     ]
+    },
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'classification_report_imbalanced' is not defined",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-12-5ccabd3c0e33>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    269\u001b[0m     \u001b[0mf1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'micro'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'f-score: {f1}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclassification_report_imbalanced\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    272\u001b[0m     \u001b[0mK\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mclassification_report_imbalanced\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m     \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_fwf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'classification_report_imbalanced' is not defined"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 576x396 with 0 Axes>"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "len(y_test)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "list(map(abs,lr_features))"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "visualizer\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "fig.write_image(\"images/fig1.png\")"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "#lr_fit= lr.fit(X_train_oversampled, y_train_oversampled).tolist()\n",
+    "lr_features = lr_fit.coef_\n",
+    "len(list(lr_features.flat))"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "  \n",
+    "    \n",
+    "    model = AdaBoostClassifier() \n",
+    "    model.fit(X_train_oversampled, y_train_oversampled)  \n",
+    "    y_pred = model.predict(X_test.values)\n",
+    "    visualizer = ROCAUC(model, classes=classes)\n",
+    "    visualizer.fit(X_train_oversampled, y_train_oversampled)  # Fit the training data to the visualizer\n",
+    "    visualizer.score(X_test.values, y_test)  # Evaluate the model on the test data\n",
+    "    visualizer.poof(\"Ada_Indicator_Replace_{}.pdf\".format(fold), clear_figure=True) \n",
+    "    print(f'For fold {fold}:')\n",
+    "    print(f'Accuracy: {model.score(X_test.values, y_test)}')\n",
+    "    f1=f1_score(y_test, y_pred, average='micro')\n",
+    "    print(f'f-score: {f1}')\n",
+    "    print(classification_report_imbalanced(y_test, y_pred))\n",
+    "    \n",
+    "    #\n",
+    "\n",
+    "    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    " feature_dataframe['mean'] = feature_dataframe.mean(axis= 1) # axis = 1 computes the mean row-wise\n",
+    "    feature_dataframe.head(3)\n",
+    "    \n",
+    "    y = feature_dataframe['mean'].values\n",
+    "    x = feature_dataframe['features'].values\n",
+    "    data = [go.Bar(\n",
+    "                x= x,\n",
+    "                 y= y,\n",
+    "                width = 0.5,\n",
+    "                marker=dict(\n",
+    "                   color = feature_dataframe['mean'].values,\n",
+    "                colorscale='Portland',\n",
+    "                showscale=True,\n",
+    "                reversescale = False\n",
+    "                ),\n",
+    "                opacity=0.6\n",
+    "            )]\n",
+    "\n",
+    "    layout= go.Layout(\n",
+    "        autosize= True,\n",
+    "        title= 'Barplots of Mean Feature Importance',\n",
+    "        hovermode= 'closest',\n",
+    "    #     xaxis= dict(\n",
+    "    #         title= 'Pop',\n",
+    "    #         ticklen= 5,\n",
+    "    #         zeroline= False,\n",
+    "    #         gridwidth= 2,\n",
+    "    #     ),\n",
+    "        yaxis=dict(\n",
+    "            title= 'Feature Importance',\n",
+    "            ticklen= 5,\n",
+    "            gridwidth= 2\n",
+    "        ),\n",
+    "        showlegend= False\n",
+    "    )\n",
+    "    fig = go.Figure(data=data, layout=layout)\n",
+    "    py.iplot(fig, filename='bar-direct-labels')\n",
+    "    \n",
+    "    base_predictions_train = pd.DataFrame( {\n",
+    "     'ExtraTrees': et_oof_train.ravel(),\n",
+    "     'GradientBoost': gb_oof_train.ravel()\n",
+    "    })\n",
+    "    base_predictions_train.head()\n",
+    "    \n",
+    "    data = [\n",
+    "    go.Heatmap(\n",
+    "        z= base_predictions_train.astype(float).corr().values ,\n",
+    "        x=base_predictions_train.columns.values,\n",
+    "        y= base_predictions_train.columns.values,\n",
+    "          colorscale='Viridis',\n",
+    "            showscale=True,\n",
+    "            reversescale = True\n",
+    "    )\n",
+    "    ]\n",
+    "    py.iplot(data, filename='labelled-heatmap')\n",
+    "    \n",
+    "    #-------------------------------------------------------------------------------------\n",
+    "    x_train = np.concatenate(( et_oof_train, rf_oof_train, ada_oof_train, gb_oof_train, lr_oof_train), axis=1)\n",
+    "    x_test = np.concatenate(( et_oof_test, rf_oof_test, ada_oof_test, gb_oof_test, lr_oof_test), axis=1)\n",
+    "    \n",
+    "    gbm = xgb.XGBClassifier(\n",
+    "    #learning_rate = 0.02,\n",
+    "     n_estimators= 2000,\n",
+    "     max_depth= 4,\n",
+    "     min_child_weight= 2,\n",
+    "     #gamma=1,\n",
+    "     gamma=0.9,                        \n",
+    "     subsample=0.8,\n",
+    "     colsample_bytree=0.8,\n",
+    "     objective= 'binary:logistic',\n",
+    "     nthread= -1,\n",
+    "     scale_pos_weight=1).fit(x_train, y_train_oversampled)\n",
+    "    predictions = gbm.predict(x_test)\n",
+    "    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "len(lr_features)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "cols = df2.drop('destcopy', 1).columns.values"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import plotly.graph_objects as go"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "fig.show()"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import numpy as np    \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x1 = np.random.randn(100)\n",
+    "x2 = np.random.randn(100)\n",
+    "x3 = np.random.randn(100)\n",
+    "\n",
+    "#Make difference in feature dependance\n",
+    "y = (3 + x1 + 2*x2 + 5*x3 + 0.2*np.random.randn()) > 0\n",
+    "\n",
+    "X = pd.DataFrame({'x1':x1,'x2':x2,'x3':x3})\n",
+    "\n",
+    "#Scale your data\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X) \n",
+    "X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
+    "\n",
+    "clf = LogisticRegression(random_state = 0)\n",
+    "clf.fit(X_scaled, y)\n",
+    "\n",
+    "feature_importance = abs(clf.coef_[0])\n",
+    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
+    "sorted_idx = np.argsort(feature_importance)\n",
+    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
+    "\n",
+    "featfig = plt.figure()\n",
+    "featax = featfig.add_subplot(1, 1, 1)\n",
+    "featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
+    "featax.set_yticks(pos)\n",
+    "featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=8)\n",
+    "featax.set_xlabel('Relative Feature Importance')\n",
+    "\n",
+    "plt.tight_layout()   \n",
+    "plt.show()"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "feature_importance"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  }
+ ],
+ "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file