Readmission_DT_RF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hospital Re-admission rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data set is downloaded from UC-irvine Machine learning repository https://archive.ics.uci.edu/ml/datasets/diabetes\n",
    "The dataset contains encounter information regarding patient demographic, vital(s) and re admitted coulmn indicating whether the patient is readmitted in 30 days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries and load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "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>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278392</td>\n",
       "      <td>8222157</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>?</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr             race  gender      age weight  \\\n",
       "0       2278392      8222157        Caucasian  Female   [0-10)      ?   \n",
       "1        149190     55629189        Caucasian  Female  [10-20)      ?   \n",
       "2         64410     86047875  AfricanAmerican  Female  [20-30)      ?   \n",
       "3        500364     82442376        Caucasian    Male  [30-40)      ?   \n",
       "4         16680     42519267        Caucasian    Male  [40-50)      ?   \n",
       "\n",
       "   admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                  6                        25                    1   \n",
       "1                  1                         1                    7   \n",
       "2                  1                         1                    7   \n",
       "3                  1                         1                    7   \n",
       "4                  1                         1                    7   \n",
       "\n",
       "   time_in_hospital    ...     citoglipton insulin  glyburide-metformin  \\\n",
       "0                 1    ...              No      No                   No   \n",
       "1                 3    ...              No      Up                   No   \n",
       "2                 2    ...              No      No                   No   \n",
       "3                 2    ...              No      Up                   No   \n",
       "4                 1    ...              No  Steady                   No   \n",
       "\n",
       "   glipizide-metformin  glimepiride-pioglitazone  metformin-rosiglitazone  \\\n",
       "0                   No                        No                       No   \n",
       "1                   No                        No                       No   \n",
       "2                   No                        No                       No   \n",
       "3                   No                        No                       No   \n",
       "4                   No                        No                       No   \n",
       "\n",
       "   metformin-pioglitazone  change diabetesMed readmitted  \n",
       "0                      No      No          No         NO  \n",
       "1                      No      Ch         Yes        >30  \n",
       "2                      No      No         Yes         NO  \n",
       "3                      No      Ch         Yes         NO  \n",
       "4                      No      Ch         Yes         NO  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.combine import SMOTEENN\n",
    "from collections import Counter\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc, accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "df= pd.read_csv('diabetic_data.csv', header =0)\n",
    "asdf = pd.read_csv('AdmissionSource.csv', header=0)\n",
    "atdf = pd.read_csv('AdmissionType.csv', header=0)\n",
    "dddf = pd.read_csv('DischargeDisposition.csv', header=0)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# utility functions copied from \n",
    "# http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    # print(cm)\n",
    "\n",
    "    im = plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar(im, fraction=0.046, pad=0.04)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# utility function to plot a roc curve\n",
    "\n",
    "def plot_roc_curve(y_test, y_pred):\n",
    "    fpr, tpr, thresholds = roc_curve(y_test,y_pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f'% roc_auc)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.plot([0,1],[0,1],'r--')\n",
    "    plt.xlim([-0.1,1.2])\n",
    "    plt.ylim([-0.1,1.2])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9oKoeqqpfAh+k69oFk6/hNVn5D2nrhI0rnyzuWVW1qKoWzZ8/f+O8GUmapUxU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\nqahERERERETP+T+2Y5Ms+v/KGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a3c292358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot with various axes scales\n",
    "plt.figure(1, figsize=(12,15))\n",
    "\n",
    "# weight\n",
    "plt.subplot(321)\n",
    "count_weight = pd.value_counts(df['weight'], sort = True).nlargest(10)\n",
    "count_weight.plot(kind = 'bar')\n",
    "plt.xlabel(\"Weight\")\n",
    "plt.ylabel(\"Occurrence\")\n",
    "\n",
    "#Payer code\n",
    "plt.subplot(322)\n",
    "count_pc = pd.value_counts(df['payer_code'], sort = True).nlargest(10)\n",
    "count_pc.plot(kind = 'bar')\n",
    "plt.xlabel(\"Payer code\")\n",
    "plt.ylabel(\"Occurence\")\n",
    "\n",
    "#Race\n",
    "plt.subplot(323)\n",
    "count_race = pd.value_counts(df['race'], sort = True).nlargest(10)\n",
    "count_race.plot(kind = 'bar')\n",
    "plt.xlabel(\"Race\")\n",
    "plt.ylabel(\"Occurence\")\n",
    "\n",
    "#Medical specialty\n",
    "plt.subplot(324)\n",
    "count_ms = pd.value_counts(df['medical_specialty'], sort = True).nlargest(10)\n",
    "count_ms.plot(kind = 'bar')\n",
    "plt.xlabel(\"Medical specialty\")\n",
    "plt.ylabel(\"Occurence\")\n",
    "\n",
    "\n",
    "# Diagnosis 1\n",
    "\n",
    "plt.subplot(325)\n",
    "count_ms = pd.value_counts(df['diag_1'], sort = True).nlargest(10)\n",
    "count_ms.plot(kind = 'bar')\n",
    "plt.xlabel(\"ICD 9 Primary Diagnosis\")\n",
    "plt.ylabel(\"Occurence\")\n",
    "\n",
    "plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,wspace=0.35)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Few features has missing data, represented with '?'. those need to be either imputed with some value or drop the feature <br><br>\n",
    "Let's observe few vital(s) on patient data, in the data-frame there are around 25 vital's that are collected, for brevity purpose let's observe few vitals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        citoglipton  metformin-pioglitazone   examide  \\\n",
      "Down            NaN                     NaN       NaN   \n",
      "No         101766.0                101765.0  101766.0   \n",
      "Steady          NaN                     1.0       NaN   \n",
      "Up              NaN                     NaN       NaN   \n",
      "\n",
      "        metformin-rosiglitazone  glimepiride-pioglitazone  metformin  \\\n",
      "Down                        NaN                       NaN        575   \n",
      "No                     101764.0                  101765.0      81778   \n",
      "Steady                      2.0                       1.0      18346   \n",
      "Up                          NaN                       NaN       1067   \n",
      "\n",
      "        repaglinide  nateglinide  chlorpropamide  \n",
      "Down             45           11               1  \n",
      "No           100227       101063          101680  \n",
      "Steady         1384          668              79  \n",
      "Up              110           24               6  \n"
     ]
    }
   ],
   "source": [
    "citoglipton_df = pd.DataFrame(pd.value_counts(df['citoglipton'], sort = True))\n",
    "metformin_pioglitazone_df = pd.DataFrame(pd.value_counts(df['metformin-pioglitazone'], sort = True))\n",
    "examide_df = pd.DataFrame(pd.value_counts(df['examide'], sort = True))\n",
    "metformin_rosiglitazone_df = pd.DataFrame(pd.value_counts(df['metformin-rosiglitazone'], sort = True))\n",
    "glimepiride_pioglitazone_df = pd.DataFrame(pd.value_counts(df['glimepiride-pioglitazone'], sort = True))\n",
    "max_glu_serum_df = pd.DataFrame(pd.value_counts(df['max_glu_serum'], sort = True))\n",
    "A1Cresult_df = pd.DataFrame(pd.value_counts(df['A1Cresult'], sort = True))\n",
    "metformin_df = pd.DataFrame(pd.value_counts(df['metformin'], sort = True))\n",
    "repaglinide_df = pd.DataFrame(pd.value_counts(df['repaglinide'], sort = True))\n",
    "nateglinide_df = pd.DataFrame(pd.value_counts(df['nateglinide'], sort = True))\n",
    "chlorpropamide_df = pd.DataFrame(pd.value_counts(df['chlorpropamide'], sort = True))\n",
    "\n",
    "vitals_df = pd.concat([citoglipton_df,metformin_pioglitazone_df,examide_df,metformin_rosiglitazone_df,\n",
    "                       glimepiride_pioglitazone_df,metformin_df,\n",
    "                      repaglinide_df,nateglinide_df,chlorpropamide_df], join='outer', axis=1)\n",
    "\n",
    "print(vitals_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following vitals are completely skewed and can be dropped from the data frame\n",
    "- citoglipton\n",
    "- metformin-pioglitazone\n",
    "- examide\n",
    "- metformin-rosiglitazone\n",
    "- glimepiride-pioglitazone\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace the '?' value with None value, and observe how many records are populated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[df['weight'] =='?', 'weight']=None\n",
    "df.loc[df['payer_code'] =='?', 'payer_code']=None\n",
    "df.loc[df['medical_specialty'] =='?', 'medical_specialty']=None\n",
    "df.loc[df['diag_2'] =='?', 'diag_2']=None\n",
    "df.loc[df['diag_3'] =='?', 'diag_3']=None\n",
    "df.loc[df['race'] =='?', 'race']='Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      "encounter_id                101766 non-null int64\n",
      "patient_nbr                 101766 non-null int64\n",
      "race                        101766 non-null object\n",
      "gender                      101766 non-null object\n",
      "age                         101766 non-null object\n",
      "weight                      3197 non-null object\n",
      "admission_type_id           101766 non-null int64\n",
      "discharge_disposition_id    101766 non-null int64\n",
      "admission_source_id         101766 non-null int64\n",
      "time_in_hospital            101766 non-null int64\n",
      "payer_code                  61510 non-null object\n",
      "medical_specialty           51817 non-null object\n",
      "num_lab_procedures          101766 non-null int64\n",
      "num_procedures              101766 non-null int64\n",
      "num_medications             101766 non-null int64\n",
      "number_outpatient           101766 non-null int64\n",
      "number_emergency            101766 non-null int64\n",
      "number_inpatient            101766 non-null int64\n",
      "diag_1                      101766 non-null object\n",
      "diag_2                      101408 non-null object\n",
      "diag_3                      100343 non-null object\n",
      "number_diagnoses            101766 non-null int64\n",
      "max_glu_serum               101766 non-null object\n",
      "A1Cresult                   101766 non-null object\n",
      "metformin                   101766 non-null object\n",
      "repaglinide                 101766 non-null object\n",
      "nateglinide                 101766 non-null object\n",
      "chlorpropamide              101766 non-null object\n",
      "glimepiride                 101766 non-null object\n",
      "acetohexamide               101766 non-null object\n",
      "glipizide                   101766 non-null object\n",
      "glyburide                   101766 non-null object\n",
      "tolbutamide                 101766 non-null object\n",
      "pioglitazone                101766 non-null object\n",
      "rosiglitazone               101766 non-null object\n",
      "acarbose                    101766 non-null object\n",
      "miglitol                    101766 non-null object\n",
      "troglitazone                101766 non-null object\n",
      "tolazamide                  101766 non-null object\n",
      "examide                     101766 non-null object\n",
      "citoglipton                 101766 non-null object\n",
      "insulin                     101766 non-null object\n",
      "glyburide-metformin         101766 non-null object\n",
      "glipizide-metformin         101766 non-null object\n",
      "glimepiride-pioglitazone    101766 non-null object\n",
      "metformin-rosiglitazone     101766 non-null object\n",
      "metformin-pioglitazone      101766 non-null object\n",
      "change                      101766 non-null object\n",
      "diabetesMed                 101766 non-null object\n",
      "readmitted                  101766 non-null object\n",
      "dtypes: int64(13), object(37)\n",
      "memory usage: 38.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 3197 records had the weight populated, cannot use Imputer to fill the missing values, as weight varies by age of the individual. The other two features that doesn't have values are payer_code and medical_specialty. Let's plot the Payer code and medical_specialty to see what those values are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s09aT5OgkK5NckmT3nrxLk1zZHkt70p+W5NK2z9FJMjNHJkmaTwwWJUmaeccD+w5PTLI9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+DfqurO9n2/aNv/GDgrydOATYEfThPxSuD4JEuAc6vqx0kArqmqVQBJzgD2Bu4D/gS4sn3PpsB3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\nSZJW8/+P2hBmyLTuOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a3c6e5b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(15,10))\n",
    "\n",
    "plt.subplot(221)\n",
    "count_pc = pd.value_counts(df['payer_code'], sort = True).sort_index()\n",
    "count_pc.plot(kind = 'bar')\n",
    "plt.title(\"Payer code histogram\")\n",
    "plt.xlabel(\"Payer code\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "#plt.grid(True)\n",
    "\n",
    "# medical_specialty\n",
    "\n",
    "plt.subplot(222)\n",
    "count_ms = pd.value_counts(df['medical_specialty'], sort = True).nlargest(20)\n",
    "count_ms.plot(kind = 'bar')\n",
    "plt.title(\"Medical specialty histogram\")\n",
    "plt.xlabel(\"Medical specialty\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "#plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's observe how skewed the data set is for the target feature 're-admitted'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     54864\n",
       ">30    35545\n",
       "<30    11357\n",
       "Name: readmitted, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['readmitted'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions that transform feature on the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert_age(row):\n",
    "    val = row['age']\n",
    "    if(val  =='[0-10)') or (val == '[10-20') : \n",
    "      return 'young';\n",
    "    if(val  =='[20-30)') or (val == '[30-40)') or (val == '[40-50)') :\n",
    "      return 'adult'\n",
    "    if(val  =='[50-60)') or (val == '[60-70)') or (val == '[70-80)') or (val == '[80-90)') or (val == '[90-100)'):\n",
    "      return 'old';\n",
    "    return 'old';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert_admission(row):\n",
    "    val = row['readmitted']\n",
    "    if (val == '<30'):\n",
    "        return 1;\n",
    "    if (val == '>30') :\n",
    "        return 0;\n",
    "    return 0;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the age and readmitted column values of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['age'] = df.apply(lambda row: convert_age(row), axis=1)\n",
    "df['readmitted'] = df.apply(lambda row: convert_admission(row), axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the columns from the dataframe that has insufficient values and in siginificant values <br>\n",
    "encounter_id is the index of the dataframe and patient_nbr is random number assigned to a patient.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop(['encounter_id','patient_nbr','payer_code','weight','medical_specialty'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= df.drop(['examide', 'citoglipton','metformin-pioglitazone','metformin-rosiglitazone','glimepiride-pioglitazone'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying Label Encoder for the categorical columns, we will have another notebook using one hot encoder and PCA on the same data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['race'] = df['race'].astype('category')\n",
    "df['gender'] = df['gender'].astype('category')\n",
    "df['age'] = df['age'].astype('category')\n",
    "df['diag_1'] = df['diag_1'].astype('category')\n",
    "df['diag_2'] = df['diag_2'].astype('category')\n",
    "df['diag_3'] = df['diag_3'].astype('category')\n",
    "df['max_glu_serum'] = df['max_glu_serum'].astype('category')\n",
    "df['A1Cresult'] = df['A1Cresult'].astype('category')\n",
    "df['metformin'] = df['metformin'].astype('category')\n",
    "df['repaglinide'] = df['repaglinide'].astype('category')\n",
    "df['nateglinide'] = df['nateglinide'].astype('category')\n",
    "df['chlorpropamide'] = df['chlorpropamide'].astype('category')\n",
    "df['glimepiride'] = df['glimepiride'].astype('category')\n",
    "df['acetohexamide'] = df['acetohexamide'].astype('category')\n",
    "df['glipizide'] = df['glipizide'].astype('category')\n",
    "df['glyburide'] = df['glyburide'].astype('category')\n",
    "df['tolbutamide'] = df['tolbutamide'].astype('category')\n",
    "df['pioglitazone'] = df['pioglitazone'].astype('category')\n",
    "df['rosiglitazone'] = df['rosiglitazone'].astype('category')\n",
    "df['acarbose'] = df['acarbose'].astype('category')\n",
    "df['miglitol'] = df['miglitol'].astype('category')\n",
    "df['troglitazone'] = df['troglitazone'].astype('category')\n",
    "df['tolazamide'] = df['tolazamide'].astype('category')\n",
    "# df['examide'] = df['examide'].astype('category')\n",
    "# df['citoglipton'] = df['citoglipton'].astype('category')\n",
    "df['insulin'] = df['insulin'].astype('category')\n",
    "df['glyburide-metformin'] = df['glyburide-metformin'].astype('category')\n",
    "df['glipizide-metformin'] = df['glipizide-metformin'].astype('category')\n",
    "# df['glimepiride-pioglitazone'] = df['glimepiride-pioglitazone'].astype('category')\n",
    "# df['metformin-rosiglitazone'] = df['metformin-rosiglitazone'].astype('category')\n",
    "# df['metformin-pioglitazone'] = df['metformin-pioglitazone'].astype('category')\n",
    "df['change'] = df['change'].astype('category')\n",
    "df['diabetesMed'] = df['diabetesMed'].astype('category')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['race'] = df['race'].cat.codes\n",
    "df['gender'] = df['gender'].cat.codes\n",
    "df['age'] = df['age'].cat.codes\n",
    "df['diag_1'] = df['diag_1'].cat.codes\n",
    "df['diag_2'] = df['diag_2'].cat.codes\n",
    "df['diag_3'] = df['diag_3'].cat.codes\n",
    "df['max_glu_serum'] = df['max_glu_serum'].cat.codes\n",
    "df['A1Cresult'] = df['A1Cresult'].cat.codes\n",
    "df['metformin'] = df['metformin'].cat.codes\n",
    "df['repaglinide'] = df['repaglinide'].cat.codes\n",
    "df['nateglinide'] = df['nateglinide'].cat.codes\n",
    "df['chlorpropamide'] = df['chlorpropamide'].cat.codes\n",
    "df['glimepiride'] = df['glimepiride'].cat.codes\n",
    "df['acetohexamide'] = df['acetohexamide'].cat.codes\n",
    "df['glipizide'] = df['glipizide'].cat.codes\n",
    "df['glyburide'] = df['glyburide'].cat.codes\n",
    "df['tolbutamide'] = df['tolbutamide'].cat.codes\n",
    "df['pioglitazone'] = df['pioglitazone'].cat.codes\n",
    "df['rosiglitazone'] = df['rosiglitazone'].cat.codes\n",
    "df['acarbose'] = df['acarbose'].cat.codes\n",
    "df['miglitol'] = df['miglitol'].cat.codes\n",
    "df['troglitazone'] = df['troglitazone'].cat.codes\n",
    "df['tolazamide'] = df['tolazamide'].cat.codes\n",
    "# df['examide'] = df['examide'].cat.codes\n",
    "# df['citoglipton'] = df['citoglipton'].cat.codes\n",
    "df['insulin'] = df['insulin'].cat.codes\n",
    "df['glyburide-metformin'] = df['glyburide-metformin'].cat.codes\n",
    "df['glipizide-metformin'] = df['glipizide-metformin'].cat.codes\n",
    "# df['glimepiride-pioglitazone'] = df['glimepiride-pioglitazone'].cat.codes\n",
    "# df['metformin-rosiglitazone'] = df['metformin-rosiglitazone'].cat.codes\n",
    "# df['metformin-pioglitazone'] = df['metformin-pioglitazone'].cat.codes\n",
    "df['change'] = df['change'].cat.codes\n",
    "df['diabetesMed'] = df['diabetesMed'].cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe the data type of features, they got converted from  object to either int8/int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 40 columns):\n",
      "race                        101766 non-null int8\n",
      "gender                      101766 non-null int8\n",
      "age                         101766 non-null int8\n",
      "admission_type_id           101766 non-null int64\n",
      "discharge_disposition_id    101766 non-null int64\n",
      "admission_source_id         101766 non-null int64\n",
      "time_in_hospital            101766 non-null int64\n",
      "num_lab_procedures          101766 non-null int64\n",
      "num_procedures              101766 non-null int64\n",
      "num_medications             101766 non-null int64\n",
      "number_outpatient           101766 non-null int64\n",
      "number_emergency            101766 non-null int64\n",
      "number_inpatient            101766 non-null int64\n",
      "diag_1                      101766 non-null int16\n",
      "diag_2                      101766 non-null int16\n",
      "diag_3                      101766 non-null int16\n",
      "number_diagnoses            101766 non-null int64\n",
      "max_glu_serum               101766 non-null int8\n",
      "A1Cresult                   101766 non-null int8\n",
      "metformin                   101766 non-null int8\n",
      "repaglinide                 101766 non-null int8\n",
      "nateglinide                 101766 non-null int8\n",
      "chlorpropamide              101766 non-null int8\n",
      "glimepiride                 101766 non-null int8\n",
      "acetohexamide               101766 non-null int8\n",
      "glipizide                   101766 non-null int8\n",
      "glyburide                   101766 non-null int8\n",
      "tolbutamide                 101766 non-null int8\n",
      "pioglitazone                101766 non-null int8\n",
      "rosiglitazone               101766 non-null int8\n",
      "acarbose                    101766 non-null int8\n",
      "miglitol                    101766 non-null int8\n",
      "troglitazone                101766 non-null int8\n",
      "tolazamide                  101766 non-null int8\n",
      "insulin                     101766 non-null int8\n",
      "glyburide-metformin         101766 non-null int8\n",
      "glipizide-metformin         101766 non-null int8\n",
      "change                      101766 non-null int8\n",
      "diabetesMed                 101766 non-null int8\n",
      "readmitted                  101766 non-null int64\n",
      "dtypes: int16(3), int64(12), int8(25)\n",
      "memory usage: 12.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>...</th>\n",
       "      <th>acarbose</th>\n",
       "      <th>miglitol</th>\n",
       "      <th>troglitazone</th>\n",
       "      <th>tolazamide</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   race  gender  age  admission_type_id  discharge_disposition_id  \\\n",
       "0     2       0    2                  6                        25   \n",
       "1     2       0    1                  1                         1   \n",
       "2     0       0    0                  1                         1   \n",
       "3     2       1    0                  1                         1   \n",
       "4     2       1    0                  1                         1   \n",
       "\n",
       "   admission_source_id  time_in_hospital  num_lab_procedures  num_procedures  \\\n",
       "0                    1                 1                  41               0   \n",
       "1                    7                 3                  59               0   \n",
       "2                    7                 2                  11               5   \n",
       "3                    7                 2                  44               1   \n",
       "4                    7                 1                  51               0   \n",
       "\n",
       "   num_medications     ...      acarbose  miglitol  troglitazone  tolazamide  \\\n",
       "0                1     ...             1         1             0           0   \n",
       "1               18     ...             1         1             0           0   \n",
       "2               13     ...             1         1             0           0   \n",
       "3               16     ...             1         1             0           0   \n",
       "4                8     ...             1         1             0           0   \n",
       "\n",
       "   insulin  glyburide-metformin  glipizide-metformin  change  diabetesMed  \\\n",
       "0        1                    1                    0       1            0   \n",
       "1        3                    1                    0       0            1   \n",
       "2        1                    1                    0       1            1   \n",
       "3        3                    1                    0       0            1   \n",
       "4        2                    1                    0       0            1   \n",
       "\n",
       "   readmitted  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's observe how skewed the data set is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAF/CAYAAAA2D9EJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSRgIJEkSBgJJkoSBQJIkYSCQJEnA/wPXKm66b6RS1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a3cdea6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(8,6))\n",
    "\n",
    "plt.subplot(111)\n",
    "count_pc = pd.value_counts(df['readmitted'], sort = True).sort_index()\n",
    "count_pc.plot(kind = 'bar')\n",
    "plt.title(\"Readmitted histogram\")\n",
    "plt.xlabel(\"Readmitted\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y= df['readmitted']\n",
    "X= df.drop(['readmitted'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88221421552571244"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_us_train, X_us_test, y_us_train, y_us_test = train_test_split(X,y, test_size=.30, random_state=0)\n",
    "model = tree.DecisionTreeClassifier(criterion='entropy', max_depth=12)\n",
    "model.fit(X_us_train, y_us_train)\n",
    "y_us_predict = model.predict(X_us_test)\n",
    "accuracy_score(y_us_test, y_us_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_us_test, y_us_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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OSZIkqXUL70LSuo9ekvQjX38Nx651P/cOX4iH/3ADiznlhSRJUm3wLiSSJBXG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\nXHwxtGkDHHYYPPNMefsRSZIkSfqxhiQ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le4ufR8QdwKM5hSNJkhqB2TaxbOLqksA4nKyJ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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a3caeaef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_us_test, y_us_predict)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Non-Readmitted','Readmitted'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the data is unbalanced, use the SMOTEENN method to under/over sample the data to have both classes represents significantly on the training data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smote_enn = SMOTEENN(random_state=0)\n",
    "X_rs,y_rs = smote_enn.fit_sample(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After re-sampling, count of non-readmitted and re-admitted observations are as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 49682), (1, 82927)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print(sorted(Counter(y_rs).items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#tune for parameters of decision tree: criterion, max_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'criterion': ['gini', 'entropy'], 'max_depth': [10, 12, 14]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_params ={'criterion':['gini','entropy'], 'max_depth':[10,12,14]}\n",
    "dt = tree.DecisionTreeClassifier()\n",
    "dt_gscv = GridSearchCV(dt,param_grid = dt_params, scoring='accuracy')\n",
    "dt_gscv.fit(X_rs,y_rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': 12}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.7711134 ,  0.81757752,  0.70919689,  0.80738576,  0.98968728,\n",
       "         0.91156348]),\n",
       " 'mean_score_time': array([ 0.01038424,  0.01285823,  0.01041857,  0.01041857,  0.01562754,\n",
       "         0.0052096 ]),\n",
       " 'mean_test_score': array([ 0.86173638,  0.86519769,  0.86739211,  0.84899969,  0.87170554,\n",
       "         0.86898325]),\n",
       " 'mean_train_score': array([ 0.89369166,  0.91041373,  0.92595561,  0.8908525 ,  0.90824573,\n",
       "         0.92204944]),\n",
       " 'param_criterion': masked_array(data = ['gini' 'gini' 'gini' 'entropy' 'entropy' 'entropy'],\n",
       "              mask = [False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'param_max_depth': masked_array(data = [10 12 14 10 12 14],\n",
       "              mask = [False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'criterion': 'gini', 'max_depth': 10},\n",
       "  {'criterion': 'gini', 'max_depth': 12},\n",
       "  {'criterion': 'gini', 'max_depth': 14},\n",
       "  {'criterion': 'entropy', 'max_depth': 10},\n",
       "  {'criterion': 'entropy', 'max_depth': 12},\n",
       "  {'criterion': 'entropy', 'max_depth': 14}],\n",
       " 'rank_test_score': array([5, 4, 3, 6, 1, 2]),\n",
       " 'split0_test_score': array([ 0.74196905,  0.73966157,  0.7395937 ,  0.7363587 ,  0.7430323 ,\n",
       "         0.74233101]),\n",
       " 'split0_train_score': array([ 0.96729823,  0.98090606,  0.99018155,  0.96797692,  0.98347379,\n",
       "         0.99295289]),\n",
       " 'split1_test_score': array([ 0.93808113,  0.94676832,  0.93642965,  0.92984639,  0.94421193,\n",
       "         0.93719883]),\n",
       " 'split1_train_score': array([ 0.86101622,  0.87722553,  0.89338959,  0.85412755,  0.87274619,\n",
       "         0.88740583]),\n",
       " 'split2_test_score': array([ 0.90516266,  0.90916701,  0.92615719,  0.88079725,  0.92787657,\n",
       "         0.9274241 ]),\n",
       " 'split2_train_score': array([ 0.85276053,  0.8731096 ,  0.8942957 ,  0.85045302,  0.8685172 ,\n",
       "         0.88578959]),\n",
       " 'std_fit_time': array([ 0.0406291 ,  0.09133044,  0.01532135,  0.02654321,  0.06029924,\n",
       "         0.01948659]),\n",
       " 'std_score_time': array([  4.80476689e-04,   3.02278618e-03,   7.36704434e-03,\n",
       "          7.36704434e-03,   5.94720425e-07,   7.36749390e-03]),\n",
       " 'std_test_score': array([ 0.08574936,  0.09008644,  0.09046591,  0.08212903,  0.09123133,\n",
       "         0.08964704]),\n",
       " 'std_train_score': array([ 0.05215672,  0.04987392,  0.0454161 ,  0.05455583,  0.05322228,\n",
       "         0.05014065])}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_gscv.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tuning for optimal parameter is not done, will do it a new version of notebook\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_rs,y_rs, test_size=.30, random_state=0)\n",
    "model = tree.DecisionTreeClassifier(criterion='entropy', max_depth=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=12,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = model.predict(X_test)\n",
    "accuracy_score(y_test, y_predict)\n",
    "cnf_matrix = confusion_matrix(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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gYvLkMiykzfrrl3Bijz3ag4pNNinDQgadOXNg331Lt5Mf/7jR1UiSJA18DiGR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\nMvPJzFw1M1cFvgJMAi6vPH3izOcqMy+IiBUo1r78ErAx8P8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ywOgiC4wqmTwZttgC1lsP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\nzMzMzOo9JzDMzMzMzMzMrN77P94wr7UyEIBiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a3cd3e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_test, y_predict)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Non-Readmitted','Readmitted'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generate the tree png file - Run this cell if the machine has graphviz installed, other wise ignore this cell\n",
    "\n",
    "with open(\"readmission.dot\",\"w\") as f:\n",
    "    f = tree.export_graphviz(model, out_file = f)\n",
    "    \n",
    "import pydot\n",
    "\n",
    "(graph,) = pydot.graph_from_dot_file('readmission.dot')\n",
    "graph.write_png('readmission.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging\n",
    "\n",
    "Let's ensemble the decision trees to form a Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91312872332403283"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random_forest = RandomForestClassifier(n_estimators=200, max_depth=12, random_state=0)\n",
    "random_forest.fit(X_train, y_train)\n",
    "\n",
    "y_predict = random_forest.predict(X_test)\n",
    "accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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WWZuksFnmAPT887D11sX4nl12qToaSZIk1Vk5q8jvgf9m5i9rVl0C7AH8tPx5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\nb8zkxdfe5CdXT2C3M+7ks2f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IEVVHIkmSJKkJ9KQC47sdLDustwPpzxxCIs2Fb38bLryw\nuG/yQpIkSZptUPTerS/qtAIjIj4BbAGMjohf1qwaTjGcRD1kAkPqod/8Bn72M/jGN4rmnZIkSZIK\nfXj2kN7S1RCS54D7KXpePFCzvAX4dj2D6m9MYEg9cOmlcOCBxdCRo4+uOhpJkiRJTabTBEZm3gXc\nFRFnZ+brDYyp3zGBIXXjjjtgl13gve+Fs8+GwYOrjkiSJElqOgO8AKNHTTxHR8RRwJrAwm0LM3O1\nukXVzzgLidSNyy+HUaOKKoxFF606GkmSJKnpBDBogGcwetLE83TgDxTP15bA+cC5dYyp32lpgYUW\nKm6SOnD44XDnnbDMMlVHIkmSJKlJ9SSBMTQzrwTIzEcy87vAh+sbVv/S2urwEelt3nwT9toL7rmn\neLzkktXGI0mSJDW5iN679UU9SWBMj6LV6SMRsW9EbAO8o85x9SstLSYwpDlkwn77wemnw113VR2N\nJEmSpD6gJz0wvgEMA74GHAUsDnyhnkH1NyYwpHaOPhpOOQUOPRT23LPqaCRJkqQ+wWlUu5GZt5Z3\nW4DPAUTEmHoG1d+YwJBqXHABHHJIMevID39YdTSSJElSn9CXh370li6HkETE+yJiu4gYWT5eKyLO\nBG5pSHT9hAkMqZQJZ50FH/gA/OEPMKgno9gkSZIkqYsKjIj4CbA9cA/w3Yj4C/B14GfAvo0Jr39o\naYEVVqg6CqkJRMBFFxWdbRdeuPvtJUmSJM020KdR7WoIybbA2pk5LSKWBCaXjx9qTGj9R0sLDBtW\ndRRShV58EQ48EI45BkaNgiWWqDoiSZIkqc8Z2OmLroeQvJ6Z0wAy80XgfyYv5o3TqGpAmz4dPv1p\nOO88GD++6mgkSZIk9VFdVWCsHBF/Lu8HsFLNYzLzM90dPCK2AI4DBgOnZuZPO9hmJ+AIIIF7MnO3\nnoff/DLtgaEBLBO++EW4/nr4059g442rjkhqehExvKv1mflKo2KRJEnNxVlIOrd9u8fHz82BI2Iw\ncALwcWAScFtEXJKZD9ZssyrwHeADmflSRLxjbs7RF7z+OsycaQJDA9SRR8If/wg/+hHsumvV0Uh9\nxQMUSf3av1DaHidgVyVJkgagAAYN7PxF5wmMzLxmPo+9ATAhMx8FiIhzKfpqPFizzZeAEzLzpfKc\nz83nOZtOS0vx0wSGBpzW1iJ5seeecOihVUcj9RmZuXzVMUiSJP0/e/cdLldZ7n38e6fSe2gJCGJQ\nEAQkREFEkF4DChjpRUJHRPAgR1FRDqgoKvAiRYqFdkQgQABpoqJAkCJNILRD6E2YTQhp9/vHmo2b\nkOzsMrPXnpnvh2tda+aZNTP3BLdkfvt+nicizgO2A17OzDWqYz8GtgemAU8A+2bmvyNiJeARoH3Z\niTsy86Dqc9YFLgDmByYAX83MrK61eSmwEvA0sGt7NjA39dzDcDjwbIf7k6tjHa0KrBoRt0fEHdUp\nJx8QEeMi4u6IuPuVV16pU7n1YYChlrXQQnDnnXDWWW5YLfVQRIyNiOOqt0dU/wIgSZJaUQRRw6ML\nLgBm/45+I7BGZn4CeIxiRkW7JzJz7erRcefSM4FxwMjq0f6axwI3Z+ZI4Obq/U7VM8CY059IznZ/\nEMUH2Bj4MnBuRCz2gSdlnp2ZozJz1LBhw2peaD0ZYKjlPPIIHH44TJsGSy4JQ4aUXZHUkCLidGAT\nYM/q0BTgl+VVJEmSWklm/hl4fbaxP2bmjOrdO4ARnb1GRCwHLJKZf8/MBH4N7Fh9eAxwYfX2hR3G\n56rLAUZEDO3qtVWTgY5tsCMotmKd/ZqrMnN6Zj5F0W4yspvv06+1Bxhuo6qW8PLLsO22cNll8NJL\nZVcjNboNMvNAYCq8tyOYiaAkSS0sonYHsFT7TIfqMa6b5ewHXNfh/soRcW9E3BYRn62ODaf43t+u\n48yMZTLzBYDqeZ5rYna2iCcAETEa+BWwKLBiRKwFfCUzD5/HUycCIyNiZeA5YCww+w4jV1J0XlwQ\nEUtRTCl5cl41NZK2tuJsB4aa3jvvwA47wIsvwp/+BCs4jV/qpekRMYBq92JELAnMKrckSZJUphrv\nQvJqZo7qYR3/DcwAflcdegFYMTNfq055vTIiPk7XZmZ0WVc6MH5BsXDHawCZeT9FS2unqm0lhwE3\nUCzmcVlmPhQRJ0TEDtXLbgBei4iHgVuBYzLzte5/jP7LKSRqCbNmwZ57wl13we9+B6NHl12R1AzO\nAC4HhkXE94C/Aj8styRJktTqImJvioxg9+q0EDLz3fbv8pn5D4oFPlel6LjoOM2k48yMl6pTTNqn\nmsxzU495dmAAAzLzmdmSnpldeB6ZOYFildGOY8d3uJ3AUdWjKRlgqCU8+ij88Y9wyimw005lVyM1\nhcz8dUT8A9isOrRLZj5YZk2SJKk8/WEb1erGG/8FfC4zp3QYHwa8npkzI+LDFEtDPJmZr0dEJSI+\nDdwJ7AWcVn3aeGBv4OTq+ap5vX9XAoxnq9NIMiIGAodTrDaqLjDAUEtYbTV4+GEYPvtGQ5J6aSAw\nnaLVsp4Lb0uSpAZQ45gvnBEAACAASURBVCkk83qviyk23FgqIiYD36HYdWQocGO1lvbtUjcCToiI\nGRQNDwdV1+8COJj/bKN6Hf9ZN+Nk4LKI2B/4P2CXedXUlQDjYIppJCsCLwE3VcfUBQYYamrXXw//\n+hd89aswotMFiCV1U3Vu6W7AFRS/dLkoIn6XmSeVW5kkSWoFmfnlOQz/ai7XXk4x9XVOj90NrDGH\n8deATbtTU1cCjBmZObY7L6r/qFRgvvlgUFf+pKVGcv/9sMsu8JGPwEEHFf9Dl1RLewDrtrdnRsSJ\nwD8AAwxJklpUyTNISteVr9UTI+JR4FLgD5lZqXNNTaVScQtVNaHnniu2S110UbjmGsMLqT6e4f3/\nnR5Ek+3UJUmSui4CBvThFJL+aJ4BRmauEhEbUGyD+r2IuA+4JDMvqXt1TaCtzekjajJtbbDddvDm\nm/DXv7ruhVRjEXEqxZoXU4CHIuKG6v0tKHYikSRJakldmtiQmX8D/hYR3wV+RrHXqwFGF1QqBhhq\nMjfdBA89BFddBWutVXY1UjNq32nkIeDaDuN3lFCLJEnqR1q8AWPeAUZELASMoejAWI1ia5MN6lxX\n0zDAUNPZcUd4/HH40IfKrkRqSpk5x8WxJEmSWl1XOjAeBK4GfpSZf6lzPU2nUoEllyy7CqkGzjyz\nWLBz880NL6Q+EBGrACcCqwPvLTSTmauWVpQkSSpVX26j2h91ZU/5D2fm4YYXPWMHhprClVfCoYfC\nueeWXYnUSi4AzqdYcHxr4DKcvilJUkuLqN3RiObagRERP8nMrwOXR0TO/nhmfqGulTUJdyFRw5s4\nEXbbDdZbD84/v+xqpFayQGbeEBGnZOYTwLciwl8mSJKkltXZFJJLq+fT+6KQZmUHhhra00/D9tvD\nMsvA+PGwwAJlVyS1knej6BN9IiIOAp4Dli65JkmSVJIg3EZ1bg9k5l3Vm6tl5vtCjIg4DLi5noU1\ng0y3UVWDO/98mDoVbr21CDEk9aWvAQsBR1CshbEosF+pFUmSpPI08NSPWunKGhhz+svS/rUupBm9\n8w7MmmWAoQb23e/CPffAaquVXYnUcjLzzsysZOb/ZeaemblDZt5edl2SJEll6WwNjC9RbJ26ckT8\nocNDCwP/rndhzaBSKc4GGGoomUVwseeexa4jH/5w2RVJLSUirgA+sPZUO9egkiSpdbX6LiSdrYFx\nF/AaMAI4o8N4Bbi3nkU1CwMMNaSTT4YTToChQ+G448quRmpFrj3VT6285AL8eo91yy5D6pcWX++w\nskuQWkJXplA0s87WwHgKeAq4qe/KaS4GGGo4l1xShBa77Qbf/GbZ1UgtKTNdY0qSJGkOOptCcltm\nfi4i3uD9rawBZGYuUffqGlx7gOE2qmoIt98O++wDn/0snHeeKwRJkiRJ/UjgFJLOppBsUj0v1ReF\nNCM7MNRQTjoJVlwRrriimD4iSZIkSf1IZ1NIZlVvrgA8n5nTImJD4BPAb4G3+qC+htbWVpwNMNQQ\nLrsMXnsNllyy7EokdRARQzPz3bLrkCRJ5RvQ2g0YXVoD5EogI2IV4NfAasBFda2qSdiBoX5v6lQ4\n9lh46y1YYAFYYYWyK5JUFRGjI+IB4PHq/bUi4rSSy5IkSSUaELU7GlFXAoxZmTkd+ALws8w8HBhe\n37KagwGG+rVM2G8/+OEP4bbbyq5G0gf9AtiOYkcwMvN+/jO9U5IkqeV0tgZGuxkRsQuwJ7BjdWxw\n/UpqHi7iqX7t+OPh4ovhf/4Htt++7GokfdCAzHxmtsW6ZpZVjCRJKleEi3h2JcDYDzgE+FFmPhkR\nKwMX17es5lCpwPzzw6Cu/ClLfen88+EHP4D99y+mkEjqj56NiNEU0zgHAocDj5VckyRJKlGjTv2o\nlXlOIcnMB4EjgLsj4mPAs5l5Yt0rawKVit0X6oemToXvfQ823xzOPNPtUqX+62DgKGBF4CXg09Ux\nSZKkljTP3oCI+CzwG+A5iq1nl42IPTPz9noX1+gqFde/UD8033zwl7/AIovAYGeDSf1VZr4MjC27\nDkmS1H+0+u8euzK54VRgm8x8GCAiVqMINEbVs7Bm0NZmgKF+5MUX4dxz4ZvfdLcRqQFExDlAzj6e\nmeNKKEeSJJUsgAEtnmB0JcAY0h5eAGTmIxExpI41NQ07MNRvTJkCO+wADz0Eu+wCH/1o2RVJmreb\nOtyeD9gJeLakWiRJkkrXlQDjnog4i6LrAmB34N76ldQ8KhUYNqzsKtTyZs6EPfaAu++GK64wvJAa\nRGZe2vF+RPwGuLGkciRJUj8wz0Usm1xXPv9BwBPAN4D/Ap4EDqxnUc3CDgz1C9/4RhFcnHoqjBlT\ndjWSem5l4ENlFyFJklSWTjswImJNYBXgisz8Ud+U1DwMMFS6J56AM86Aww+Hr3617GokdUNEvMF/\n1sAYALwOuO+xJEktrMWXwJh7gBERxwH7A/cA60XECZl5Xp9V1gTcRlWlW2UVmDgRVl+97EokdUNE\nBLAWxQ5gALMy8wMLekqSpNYRES2/iGdnU0h2Bz6RmbsA6+He892S6S4kKtG998Kvf13cXnNNGDiw\n3HokdUs1rLgiM2dWD8MLSZLU8jqbQvJuZr4NkJmvRESrrxfSLVOmFCGGAYb63OTJsN12RWjxhS/Y\nBiQ1rrsi4pOZeU/ZhUiSpP6hxRswOg0wPhwRf6jeDmCVDvfJzC/UtbIGV6kUZwMM9alKpQgvKhW4\n/XbDC6kBRcSgzJwBbAgcEBFPAG9T/Lc4M/OTpRYoSZJKM8AAY66+ONv90+tZSLMxwFCfmzEDdt0V\nHnwQrr22mDoiqRHdBXwS2LHsQiRJkvqTuQYYmXlzXxbSbAww1Oeuuw6uvx7OOgu23LLsaiT1XABk\n5hNlFyJJkvqPgJZfxLPTbVTVcwYY6nPbb1/sODJqVNmVSOqdYRFx1NwezMyf9mUxkiSp/2jx/MIA\no17aAwyXIFDdjR8Pw4bB+usbXkjNYSCwENVODEmSJBW6HGBExNDMfLeexTQTOzDUJ+68E770pSK8\nuPlmI1mpObyQmSeUXYQkSepnwkU857k1akSMjogHgMer99eKiNPqXlmDa2srzgYYqpunniqmjSy/\nPFx6qeGF1Dz8YZYkSaWLiPMi4uWIeLDD2BIRcWNEPF49L14dj4j4RURMioh/RsQnOzxn7+r1j0fE\n3h3G142IB6rP+UXEvL/QzDPAAH4BbAe8BpCZ9wObdP1jtyY7MFRXb7wB22xT7Dxy7bXFFBJJzWLT\nsguQJEn9U9Twny64ANhqtrFjgZszcyRwc/U+wNbAyOoxDjgTisAD+A7wKWA08J320KN6zbgOz5v9\nvT6gKwHGgMx8ZraxmV14XktzDQzV1WmnwRNPwBVXwMc+VnY1kmooM18vuwZJktT/FLuQ1O6Yl8z8\nMzD730vGABdWb1/If7Z9HwP8Ogt3AItFxHLAlsCNmfl6Zr4B3AhsVX1skcz8e2Ym8Gu6sIV8V9bA\neDYiRgMZEQOBw4HHuvC8llapwAILwMCBZVeipvTf/w1bbQWjR5ddiSRJkqTGtFRE3N3h/tmZefY8\nnrNMZr4AkJkvRMTS1fHhwLMdrptcHetsfPIcxjvVlQDjYIppJCsCLwE3VcfUiUrF6SOqg1/9qggu\nhg83vJAkSZJaTI0X8Xw1M2u1jeGcKssejHdqnlNIMvPlzBybmUtVj7GZ+eq8ntfqKhWnj6jGLroI\nvvIV+MlPyq5EkiRJUgkiomZHD71Unf5B9fxydXwysEKH60YAz89jfMQcxjs1zw6MiDiHOSQhmTlu\nXs9tZXZgqKb+8hfYd1/YeGM4+eSyq5EkSZLUmsYDewMnV89XdRg/LCIuoViw883qFJMbgP/psHDn\nFsA3M/P1iKhExKeBO4G9gHnudtqVKSQ3dbg9H7AT75/DojloazPAUI089hjsuCN8+MPwhz/AkCFl\nVyRJkiSpj7Uv4tln7xdxMbAxxVoZkyl2EzkZuCwi9gf+D9ilevkEYBtgEjAF2BeKxckj4vvAxOp1\nJ3RYsPxgip1O5geuqx6dmmeAkZmXzvYhfkOxcqg6UanAMsuUXYWawje+UawGe+21sPji875ekiRJ\nUvMJ6PnMj+7LzC/P5aEPbPle3Unk0Lm8znnAeXMYvxtYozs1daUDY3YrAx/qwfNaSqUCH/lI2VWo\nKVx4ITzzTNGBIUmSJEktqitrYLzBf9bAGECxD+yx9SyqGbgGhnpl1iw444xi0c5FF4VPfKLsiiRJ\nkiSVbEBftmD0Q50GGFEsTboW8Fx1aFa1NUTzYIChXvnWt+Ckk4rwYq+9yq5GkiRJkkrXaYCRmRkR\nV2Tmun1VUDOYNatYxNNtVNUj555bhBfjxsGee5ZdjSRJkqR+oK8X8eyPBnThmrsi4pN1r6SJvP12\ncbYDQ912441w0EGw5ZbFFJIWbxGTJEmS9B8RtTsa0Vw7MCJiUGbOADYEDoiIJ4C3KYKfzExDjblo\nayvOBhjqlunT4cADYfXV4bLLYFBP1tiVJEmSpObU2Teku4BPAjv2US1No1IpzgYY6pbBg+H662H+\n+WGRRcquRpIkSVK/EgygQVsnaqSzACMAMvOJPqqlaRhgqFvefhsuuqjYcWTVVcuuRpIkSVI/FDTu\n1I9a6SzAGBYRR83twcz8aR3qaQoGGOqymTNht93gmmtg1ChYZ52yK5IkSZKkfqmzAGMgsBC0eI9K\nD7QHGO5Conn6+tdh/Hg47TTDC0mSJElzF+5C0lmA8UJmntBnlTQROzDUJaedBj//OXz1q3DYYWVX\nI0mSJKmfG9Dic0g620a1tf9kesEAQ/M0eTIcfTSMGQM/+UnZ1UiSJElSv9dZB8amfVZFk3EbVc3T\niBFw442w7rowcGDZ1UiSJEnq51zEs5MOjMx8vS8LaSaugaG5evbZYsFOgI02ggUXLLceSZIkSWoQ\nnXVgqIcqleJ76YDOJuio9bz1Fmy7bTF95MknYbHFyq5IkiRJUgNp9TUwDDDqoFJx+ohmM3067LIL\nPPIITJhgeCFJkiSp21o8vzDAqIdKxekj6iATDj0U/vhHOPdc2HzzsiuSJEmSpIZjgFEHdmDofW66\nCc45B775Tdh//7KrkSRJktSAgs63EW0FBhh1YICh99lsM7j6athmm7IrkSRJktSoAqLF55C0eoBT\nF21tBhgC7rwTHnigmKi23Xau6ipJkiRJveA3qjqwA0M88UQRWuyzT7EGhiRJkiT1UtTwaER1DTAi\nYquIeDQiJkXEsZ1ct3NEZESMqmc9fcUAo8W9/nqxXeqsWXDxxS4VLEmSJKnXgmIb1VodjahuAUZE\nDATOALYGVge+HBGrz+G6hYEjgDvrVUtfM8BoYe++C1/4Ajz1FFx5Jay6atkVSZIkSVJTqGcHxmhg\nUmY+mZnTgEuAMXO47vvAj4Cpdaylz8yaBW+/7TaqLevnP4fbboPzz4fPfrbsaiRJkiQ1EaeQ1M9w\n4NkO9ydXx94TEesAK2TmNZ29UESMi4i7I+LuV155pfaV1lBbW3G2A6NFHXkkXHUV7LZb2ZVIkiRJ\nUlOpZ4Axp1DnvdUMI2IAcCrw9Xm9UGaenZmjMnPUsGHDalhi7VUqxdkAo8Vcdx28+ioMGQI77FB2\nNZIkSZKaUETtjkZUzwBjMrBCh/sjgOc73F8YWAP4U0Q8DXwaGN/oC3nagdGC/vQnGDMGjp3rOrWS\nJEmS1EtBRO2ORlTPAGMiMDIiVo6IIcBYYHz7g5n5ZmYulZkrZeZKwB3ADpl5dx1rqjs7MFrMv/4F\nO+0EI0fCKaeUXY0kSZIkNa1B9XrhzJwREYcBNwADgfMy86GIOAG4OzPHd/4KjckAo4W8/DJss00x\nbeTaa2GxxcquSJIkSVKTCurbgdAI6hZgAGTmBGDCbGPHz+XajetZS18xwGghX/0qvPhiMYVkpZXK\nrkaSJElSk2vUqR+10uoBTs21Bxhuo9oCfv5zuPpqGD267EokSZIkqekZYNSYHRgt4A9/gOnTYeml\nYdNNy65GkiRJUouIGh6NyACjxgwwmtzZZ8MXvwhnnVV2JZIkSZJaSeAuJGUX0Gzat1FdcMFy61Ad\nXH89HHJIsXDnQQeVXY0kSZIktZS6LuLZiiqVYv2LAUZDzeWf/4Rdd4U114RLLoFB/uhIkiRJ6jvu\nQuLnr7lKxekjTWfmTBg7FhZZBK65xn/BkiRJklQCf41cYwYYTWjgQPjd74q2muHDy65GkiRJUotq\n1LUrasUOjBprn0KiJjBzZrFNKsA668Baa5VbjyRJkqSW5i4kqik7MJpEJhx5JOywA9x+e9nVSJIk\nSVKfioiPRsR9HY63IuLIiPhuRDzXYXybDs/5ZkRMiohHI2LLDuNbVccmRcSxPa3JKSQ1VqnAiBFl\nV6Fe+/nP4fTT4aij4DOfKbsaSZIkSaIvZ5Bk5qPA2sX7xkDgOeAKYF/g1Mw85f21xerAWODjwPLA\nTRGxavXhM4DNgcnAxIgYn5kPd7cmA4waa2uzA6PhXXVVEVzstBP8+MdlVyNJkiRJ1V1ISpv8sSnw\nRGY+08k6HGOASzLzXeCpiJgEjK4+NikznwSIiEuq13Y7wHAKSY05haTBvfwy7L47rLce/Pa37ocr\nSZIkqVktFRF3dzjGdXLtWODiDvcPi4h/RsR5EbF4dWw48GyHayZXx+Y23m12YNSYAUaDW3rpIrhY\nf31YYIGyq5EkSZKk99R4CsmrmTlq3u8ZQ4AdgG9Wh84Evg9k9fwTYD/mvDZoMufGiexJwQYYNTRz\nJkyZYoDRkN58Ex54ADbcEHbcsexqJEmSJGk2QZQzhWRr4J7MfAmg/QwQEecA11TvTgZW6PC8EcDz\n1dtzG+8W++NrqK2tOLuNaoOZPh123hm23BJeeaXsaiRJkiSpP/kyHaaPRMRyHR7bCXiwens8MDYi\nhkbEysBI4C5gIjAyIlaudnOMrV7bbXZg1FClUpztwGggmXDwwXDTTXD++TBsWNkVSZIkSdIc9eUu\nJMX7xQIUu4cc2GH4RxGxNsU0kKfbH8vMhyLiMorFOWcAh2bmzOrrHAbcAAwEzsvMh3pSjwFGDRlg\nNKCTT4Zf/Qq+9S3YZ5+yq5EkSZKkfiMzpwBLzja2ZyfXnwicOIfxCcCE3tZjgFFD7VNIDDAaxO23\nw3HHwW67wQknlF2NJEmSJM1Vyduo9gsGGDVkB0aD2WADOPts2HPPvu/FkiRJkqTuCL+2GGDUkAFG\ng3jiieK8yipwwAHl1iJJkiRJ6hIDjBoywGgAr70GW28NgwYV26YOHFh2RZIkSZLUJXZgqGbaAwy3\nUe2n3n0XdtoJnnkGbrnF8EKSJElSQwnXwFCt2IHRj2XCfvvBX/4CF18Mn/lM2RVJkiRJkrrBAKOG\nKpWipWfBBcuuRB/wy1/CRRfBiSfC2LFlVyNJkiRJ3RLAgNZuwDDAqKW2tmL6SKvPS+qX9tqrOB90\nULl1SJIkSZJ6ZEDZBTSTSsXpI/3OP/5R/ItZcEE4+GDTJUmSJEkNK2r4TyMywKghA4x+5uGHYdNN\n4cADy65EkiRJknotonZHIzLAqCEDjH7kpZdg221h/vnhpJPKrkaSJEmS1EuugVFDlYpbqPYLU6bA\nDjvAyy/DbbfBhz5UdkWSJEmS1GuNOvWjVgwwaqhSgRVXLLsK8bWvwcSJcMUVMGpU2dVIkqQueG7y\ns3z14P155eUXGTBgALvvvT9fOehwAM47+wzOP+dMBg0axKabb823TjiJ6dOnc/QRB/Hg/fcyY+YM\ndv7SHhx+1DcAePPNf3P0EQfx6CMPERH85LSzGTX602V+PKnbRiyzGOd+fy+WWXIRZmVy3uW3c8bF\nf+J/jtyRbTZag2nTZ/LU5FcZ953f8mbbO3z+Ux/j+0fswJDBg5g2fQbH/exKbpv4GAstMJSbzvva\ne687fOnFuGTCRI455XJWWHZxzjlhTxZdeH4GDhjAt0+7ihv++nCJn1qdcRcSA4yacgpJP3HccfDZ\nz8KYMWVXIkmSumjQoEF85wc/ZM211qGtUmGrTT7NRhtvxiuvvMQNE67mpr/+g6FDh/LqKy8DcM2V\nlzPt3Xe5+W/38M6UKWz86bXZceddWWHFlTj+2K+zyaZbcM6FlzBt2jTeeWdKyZ9O6r4ZM2dx7E//\nwH3/msxCCwzlbxf9Fzff+S9uvuNffPu08cycOYsfHDGGY/bbgm/94ipe+3cbOx95Fi+88iarr7Ic\nV/+/Q1lly2/RNuVdPj325Pde9/bffYMrb7kPgP/6ylZcfuM9nPO/f+VjH16WK087mI9t+52yPrI0\nTwYYNdTWZoBRqr//HT71qWLKiNNGJElqKMssuxzLLLscAAstvDAjV/0YL77wHL/79XkceuQxDB06\nFIClhi0NQEQwZcrbzJgxg3emvsPgIYNZaOFFqLz1Fnf+7S/87P+dC8CQIUMYMmRIOR9K6oUXX32L\nF199C4C2Ke/yr6deZPlhi3HzHf9675q7HniKnTZbB4D7H5383vjDT7zA0CGD3+vGaLfKisNYeomF\nuf2eJwDITBZZcD4AFl1ofl545c26fy71RuPuHlIrLuJZQ3ZglGjCBNhwQ/jpT8uuRJIk9dKz//c0\nD/7zftZZdzRPTnqcu/5+O9tttiFf3HYz7rvnbgC2HfMFFlhgQdb52IcYveZHOOiwr7H44kvwzDNP\nseRSw/jaoQewxUajOfqIg5jy9tslfyKpd1ZcbgnW/ugIJj749PvG9xqzPjfc/sEpHztttjb3P/rs\n+8ILgF23Wpff//Ge9+6feNYExm4zmknXf58rTjuYo374v3WpXzVSwx1I3IWkxc2YAe+8Y4BRivvu\ngy99CdZaCw46qOxqJKlpRcTMiLgvIh6MiKsjYrEave4+EXF6N58zISIWqx6HdBhfKSJ260ENF0TE\nzt19nmrv7bY2DthrLN876RQWXmQRZs6YwZv/foOrb/wL3zrhJA7adzcyk/v+MZGBAwdyzyNPc8d9\nj3LWGT/jmaefZOaMGTxw/73std84/vjnu1hggQU4/Wc/LvtjST224PxDuPiUr3DMKZdTeXvqe+Pf\n2H9LZs6cxSUTJr7v+tU+vCw/OGIMh/3gkg+81i5brstl19/93v1dtxrFb6++g49s9W12OvxMfvWD\nvYhG/WarlmCAUSNtbcXZXUj62OTJsN12sPjicM01/guQpPp6JzPXzsw1gNeBQ8sqJDO3ycx/A4sB\nh3R4aCWg2wGG+ofp06dzwN5fYqddxrLN9jsCsNzw4Wy9/Y5EBOusux4DBgzg9dde5YrfX8LGm27B\n4MGDWWrY0qz3qQ24/957WG754Sy3/Ag+OWo0ANvu8AUeuP/eMj+W1GODBg3g4lMO4NLr7uaqW+5/\nb3z37T/FNhutwT7/fcH7rh++9GJc+tNxfOXbv+Gpya++77E1Vx3OoIEDufeRZ98b23vH9bm82pFx\n5z+fYr4hg1lqsQXr94HUa1HDoxEZYNRIpVKc7cDoQ5mw887w1ltFeLH88mVXJEmt5O/A8PY7EXFM\nREyMiH9GxPc6jF8ZEf+IiIciYlyH8X0j4rGIuA34TIfxCyLizIi4NSKejIjPRcR5EfFIRFzQ4bqn\nI2Ip4GRglWpnyI+r9z9bvf+1iBgYET/uUNuB1edHRJweEQ9HxLXA0vX7o1JXZCZfP/xAPrLqxzjw\n0CPfG99ymx24/c9/AuCJSY8xbdp0llhyKYaPWJHb//InMpMpb7/NPXffyUdGfpSll1mW5YePYNLj\njwLw1z/fyqofXa2MjyT12i+/szuPPvUiv/jtLe+Nbb7Banx9n83Y+cizeGfq9PfGF11ofv5w2kEc\nf9p4/n7/kx94rV23en/3BcCzL77OxqM/CsBHV16G+YYO5pU32ur0aaTecxHPGjHAKEEE/PjHxdyd\nT3yi7GokqWVExEBgU+BX1ftbACOB0RS/1BkfERtl5p+B/TLz9YiYH5gYEZcDQ4DvAesCbwK3Ah1/\nRb448HlgB+BqioDjK9Xnr52Z93W49lhgjcxcu1rLxsDRmbld9f444M3MXC8ihgK3R8QfgXWAjwJr\nAssADwPnzeGzjgPGAQwf4V7p9TTxjr9x+aW/Y7XV12Dzz64HwLHfPoGxe+zD1w8bx+fXX4fBQ4bw\nszPPJSLY5ysH8bXDDuDzG6xDZvKl3fZi9TXWBOD7PzqVw8ftw/Rp01hxpZX56RnnlPnRpB7ZYO0P\ns/t2n+KBx57jjkuOBeA7p4/nJ8fswtAhg7jmzMMAuOuBpznixEs4aOxGrLLCMI49YCuOPWArALY/\n+PT3Aokvbv5Jdjz8zPe9x7E/vYL/9+0vc/gem5AJBxz/mz78hOquYhvVRu2dqA0DjBoxwOhDmTBx\nIoweXWyXKknqK/NHxH0U0zT+AdxYHd+ierSHEAtRBBp/Bo6IiJ2q4ytUx5cF/pSZrwBExKXAqh3e\n5+rMzIh4AHgpMx+oXvdQ9b07BhjzsgXwiQ7rWyxarWEj4OLMnAk8HxG3zOnJmXk2cDbAWuusm914\nX3XT6PU/w3NvvDvHx047+4IPjC240EKcfcHFc7x+jTXX4rpb/17L8qQ+97f7nmT+dQ77wPgNf/3e\nHK6GH557Az8894a5vt7q23/3A2P/evJFPr/vqT2uUX2vteMLp5DUTPsaGAYYfeDUU4vtUm++uexK\nJKnVvFPtdPgQRRdF+xoYAZxUXR9j7cz8SGb+qtoNsRmwfmauRRFwzFd9TmdhQPu32Fkdbrff7+4v\nXwI4vENtK2fmH7tQgyRJ6mcMMGrEDow+csUVcPTRxdoXm2xSdjWS1JIy803gCODoiBgM3ADsFxEL\nAUTE8IhYmqLb4Y3MnBIRHwM+XX2JO4GNI2LJ6vN36UU5FWDhTu7fABxcfR8iYtWIWJCiO2RsdY2M\n5QD/oyJJ6v9afBVPp5DUiAFGH7jrLth992LqyK9/DQPM3ySpLJl5b0TcD4zNzN9ExGrA36vb77UB\newDXAwdFxD+BzP7k0QAAIABJREFUR4E7qs99ISK+S7EQ6AvAPcDAHtbxWkTcHhEPAtcBxwEzqrVd\nAPycYtrJPVEU9wqwI3AFxTobDwCPAbf15P0lSepL0ajJQ40YYNRIe4DhLp518u9/w/bbw7LLwvjx\nMP/8ZVckSS0nMxea7f72HW7/nCIsmN3Wc3mt84Hz5zC+T4fbTwNrzOWxlTrcnn3b1E1nu39c9Zjd\nByeXS5KkfssAo0bswKizxRaDk06C9deHpd3pTpIkSVLrafFNSAwwaqVSKWY0LLBA2ZU0mWnT4LHH\nYI01YL/9yq5GkiRJkkrT4vmFi3jWSqVSTB9p9USspjLhwAOLHUcmTy67GkmSJElSiQwwaqStzekj\nNXfiiXDBBXDMMTBiRNnVSJIkSVK5WnwXEgOMGqlUDDBq6qKL4Nvfhj32gO98p+xqJEmSJEklcw2M\nGjHAqKH774d994WNNoJzz3VejiRJkqSWVzROtPZ3IzswaqR9DQzVwMc/Dt/6FlxxBQwdWnY1kiRJ\nklS+KH63W6ujERlg1IgdGDXw6qvw/PMwaFAxfWSJJcquSJIkSZLUTziFpEYMMHpp6lQYM6YIMR58\nEAYPLrsiSZIkSepXGrRxomYMMGrEAKMXZs2CffaBv/0NLrvM8EKSJEmS5qTFEwynkNSI26j2wre/\nDZdeCj/8IeyyS9nVSJIkSZL6ITswamDGjGIGhAFGD1x6KfzP/8ABB8Axx5RdjSRJkiT1U+EuJGUX\n0AwqleJsgNEDW2wBxx0HZ5zRuEvhSpIkSVIfcBcS9Vp7gOE2qt3w5JNF28rii8OJJ7ruhSRJkiT1\nMxHxdEQ8EBH3RcTd1bElIuLGiHi8el68Oh4R8YuImBQR/4yIT3Z4nb2r1z8eEXv3tB4DjBqwA6Ob\nXngBNtkE9tqr7EokSZIkqSFEjY9u2CQz187MUdX7xwI3Z+ZI4ObqfYCtgZHVYxxwJhSBB/Ad4FPA\naOA77aFHdxlg1IABRje8/TZsvz289hoce+y8r5ckSZIk9SdjgAurty8Eduww/uss3AEsFhHLAVsC\nN2bm65n5BnAjsFVP3tgAowYMMLpo5kzYfXe491645BL45Cfn/RxJkiRJUqG2LRhLRcTdHY5xc3jH\nBP4YEf/o8PgymfkCQPW8dHV8OPBsh+dOro7Nbbzb3IWkBtrairMBxjwcfzxcdRX84hew3XZlVyNJ\nkiRJDaXGu5C82mFayNx8JjOfj4ilgRsj4l+dXDun4rKT8W4zwKgBOzC6aM89i5VODz+87EokSZIk\nSfOQmc9Xzy9HxBUUa1i8FBHLZeYL1SkiL1cvnwys0OHpI4Dnq+Mbzzb+p57U4xSSGjDAmIdJkyAT\nPvYx+OY3y65GkiRJkhpSX26jGhELRsTC7beBLYAHgfFA+04iewNXVW+PB/aq7kbyaeDN6hSTG4At\nImLx6uKdW1THus0AowbcRrUT99wDa60FP/pR2ZVIkiRJUkPr411IlgH+GhH3A3cB12bm9cDJwOYR\n8TiwefU+wATgSWAScA5wCEBmvg58H5hYPU6ojnWbU0hqoFKBAQNg/vnLrqSfefbZYq2LpZaCvXu8\n1a8kSZIkqY9l5pPAWnMYfw3YdA7jCRw6l9c6DzivtzUZYNRApVJMH+lKG07LeOst2HbbYtvU22+H\nZZctuyJJkiRJalzdaJ1oVgYYNdAeYKgqE3bbDR55BCZMgDXWKLsiSZIkSWp4Nd6FpOEYYNRAW5sB\nxvtEwCGHwM47w+abl12NJEmSJKkJGGDUgB0YHTz9NKy0EmyzTdmVSJIkSVLTCFy2wF1IasAAo+r3\nv4eRI+H668uuRJIkSZLUZAwwaqBScQtV7rgD9twTRo+GjTcuuxpJkiRJajp9vI1qv1PXACMitoqI\nRyNiUkQcO4fHj4qIhyPinxFxc0R8qJ711EvLd2A8+STssAMMHw5XXgnzzVd2RZIkSZLUfFo8wahb\ngBERA4EzgK2B1YEvR8Tqs112LzAqMz8B/B74Ub3qqaeWDjDa2ortUmfMgGuvhWHDyq5IkiRJktSE\n6rmI52hgUmY+CRARlwBjgIfbL8jMWztcfwewRx3rqZuWDjAWXBD237+YOvLRj5ZdjSRJkiQ1LbdR\nrZ/hwLMd7k8GPtXJ9fsD183pgYgYB4wDWHHFFWtVX01Mm1YcLRdgZMLkybDCCnD00WVXI0mSJElN\nz11I6mdOf7Q5xwsj9gBGAT+e0+OZeXZmjsrMUcP62RSFtrbi3HIBxgknwJprFutfSJIkSZJUZ/Xs\nwJgMrNDh/gjg+dkviojNgP8GPpeZ79axnrqoVIpzSwUYv/0tfPe7sPfesPLKZVcjSZIkSS2hxRsw\n6tqBMREYGRErR8QQYCwwvuMFEbEOcBawQ2a+XMda6qY9wGiZbVRvuw322w822QTOPtseJkmSJEnq\nK+5CUh+ZOQM4DLgBeAS4LDMfiogTImKH6mU/BhYC/jci7ouI8XN5uX6rpTownngCdtoJVlkFLr8c\nhgwpuyJJkiRJUouo5xQSMnMCMGG2seM73N6snu/fF1oqwBgxAnbbDY46ChZfvOxqJEmSJKllFI0T\nDdo6USN1DTBaQUsEGO+8UxxLLAGnn152NZIkSZKkFlTPNTBaQtPvQjJrVrFY54YbwtSpZVcjSZIk\nSa0piiUIa3U0IgOMXmr6DozjjoP//V/Yf3+Yb76yq5EkSZKkltXia3gaYPRWUwcY55wDP/whHHxw\nse6FJEmSJEklcQ2MXqpUYOBAGDq07Epq7Oabi+Bi663hF79o3B4jSZIkSWoWLf61zA6MXqpUiu6L\npvt+//GPwx57wKWXwiBzLkmSJEkqV9T0n0bkN9Neag8wmsZrr8Gii8Kyy8IFF5RdjSRJkiRJgB0Y\nvdZUAUZbG2y+Oey+e9mVSJIkSZJm4y4k6pW2tiYJMGbOhC9/Ge6/v9g2VZIkSZLUb9RyB5IGzS+c\nQtJbTdGBkQlHHgnXXANnnAHbbFN2RZIkSZIkvY8dGL1UqcBCC5VdRS+dfnpxHHUUHHJI2dVIkiRJ\nkuakxVswDDB6qSk6MNZfHw48EH7847IrkSRJkiRpjpxC0ksNHWC8+iostRSMGlUckiRJkqR+q1G3\nP60VOzB6qWEDjGeegTXWgB/9qOxKJEmSJEld4C4k6rF334Xp0xswwHjzTdh2W5g6FbbbruxqJEmS\nJEmaJ6eQ9EJbW3FuqABj+nTYeWd49FG4/npYffWyK5IkSZIkdUGDNk7UjAFGL1QqxbmhAoxDDoGb\nboLzz4dNNy27GkmSJElSVzTw1I9aMcDohfYAo6G2Uf3852GFFWCffcquRJIkSZKkLjPA6IWG6sB4\n4w1YfHH48pfLrkSSJEmS1COt3YLhIp690DABxt/+BiutBNddV3YlkiRJkqQeCNyFxACjFxoiwHji\nCRgzBpZZBkaPLrsaSZIkSZJ6xCkkvdDvA4zXXoNttoFMmDABllyy7IokSZIkST3UoI0TNWOA0Qv9\nehvVadPgC1+Ap5+Gm2+Gj3yk7IokSZIkSeoxp5D0Qr/uwBg0CDbYAC68EDbcsOxqJEmSJEm91Jdr\nYETEChFxa0Q8EhEPRcRXq+PfjYjnIuK+6rFNh+d8MyImRcSjEbFlh/GtqmOTIuLYnn5+OzB6oVIp\ncoIhQ8quZDZvvQWLLAInnVR2JZIkSZKkGom+nUQyA/h6Zt4TEQsD/4iIG6uPnZqZp7yvtojVgbHA\nx4HlgZsiYtXqw2cAmwOTgYkRMT4zH+5uQXZg9EKlUnRf9KsVXC+8EFZdFR5/vOxKJEmSJEkNKjNf\nyMx7qrcrwCPA8E6eMga4JDPfzcyngEnA6OoxKTOfzMxpwCXVa7vNAKMX2gOMfuPWW+GAA2CNNYpt\nUyVJkiRJzSNqeMBSEXF3h2PcXN82YiVgHeDO6tBhEfHPiDgvIhavjg0Hnu3wtMnVsbmNd5sBRi/0\nqwDjkUdgp51g5Ej4/e9h8OCyK5IkSZIk1VBt8wtezcxRHY6z5/ieEQsBlwNHZuZbwJnAKsDawAvA\nTzqUN7vsZLzbXAOjF/pNgPHyy8V2qfPNB9deC4stVnZFkiRJkqQGFxGDKcKL32XmHwAy86UOj58D\nXFO9OxlYocPTRwDPV2/Pbbxb7MDohba2fhJgLLAArLceXH21U0ckSZIkqQnVcgeSLu5CEsCvgEcy\n86cdxpfrcNlOwIPV2+OBsRExNCJWBkYCdwETgZERsXJEDKFY6HN8T/4M7MDohUoFll++xAJmzYKp\nU2GhheCyy0osRJIkSZJUb328C8lngD2BByLivurYccCXI2JtimkgTwMHAmTmQxFxGfAwxQ4mh2bm\nTICIOAy4ARgInJeZD/WkIAOMXqhUiuygNP/1X3DLLXDbbSUXIkmSJElqJpn5V+a8fsWETp5zInDi\nHMYndPa8rnIKSS+UugbGL38Jp5wC668PCy5YUhGSJEmSpD5T41U8G40BRg9llhhgXHcdHHoobLst\n/OxnXZvAJEmSJElSA3MKSQ+9+y7MmFFCgHH//bDrrrDWWnDJJTDIf4WSJEmS1Apa/VfXdmD0UKVS\nnPs8wFhkkWLayDXXuO6FJEmSJLWQvtyFpD/y1/c91NZWnPsswHjnHRg6FFZeGf74xz56U0mSJEmS\n+gc7MHqoTzswZsyAL34R9t67WHxDkiRJktRioqb/NCIDjB5qDzDqPosjE444oli4c8MNG7fXR5Ik\nSZLUY4FTSAwweqjPOjBOPRXOPBOOOQYOPLDObyZJkiRJUv9kgNFDfRJgXHEFHH10MX3k5JPr+EaS\nJEmSJPVvLuLZQ30SYCy+OGy1FfzmNzDArEmSJEmSWlmjTv2oFQOMHqprgDF1Ksw3H2y8cXFIkiRJ\nktTi/LV+D9VtG9V//xtGjYKf/azGLyxJkiRJamTuQqIeqVRgyJDiqJlp04r1Lh57DNZeu4YvLEmS\nJElSY3MKSQ9VKjXeQjWz2GXkllvgwgudOiJJkiRJ+o8G3v60VgwweqhSqfH0kRNPhAsugOOPh732\nquELS5IkSZIaXVSPVuYUkh6qeYCx5JKwzz7w3e/W8EUlSZIkSWoOBhg9VLMAY9q04nzwwXDeefYE\nSZIkSZLmLGp4NCADjB6qSYDx+OMwciTceGNx3/BCkiRJkjQX7kKiHmlr62WA8eqrsM02MGUKfPjD\nNatLkiRJkqRm5CKePdSrDoypU2HHHeHZZ4tdR1ZZpaa1SZIkSZKaT6s37Rtg9FCPt1GdNQv23Rdu\nvx0uuww22KDmtUmSJEmSmk+L5xdOIemJzF50YMyaBYsuCiefDLvsUvPaJEmSJElqRnZg9MDUqTBz\nZg8CjOnTYfBgOPPMutQlSZIkSWpiLd6CYQdGD1QqxblbAcZNN8HHPw6TJhUTl1p98pIkSZIkSd1g\ngNED3Q4wHnoIvvhFGDoUhg2rW12SJEmSpObV6tuoOoWkB9rainOXAowXX4Rtt4UFFoBrry3Wv5Ak\nSQ3nn/fd8+rwxYc+U3Ydes9SwKtlFyH1U/589C8fqsWLBDbyG2D0QJc7MKZMgR12gFdegT//GVZc\nse61SZKk+shM2yj7kYi4OzNHlV2H1B/589Gc7rnnHzfMPziWquFLNlzIZYDRA+0Bxjy3UX33XVhw\nQbj4Ylh33brXJUmSJElqTpm5Vdk1lM0Aowe61IExYwYsvjjccot9PpIkSZIk9ZKLePbAPAOMM86A\nzTaDt94yvJAkSaqPs8suQOrH/PlQUzLA6IFOA4xrr4UjjigW61xwwT6tS5IkqVVkpl/QpLnw50PN\nygCjB+YaYNx7L3zpS7DOOnDRRTBwYJ/XJkmSJElSMzLA6IG2Nhg6FAYP7jA4eTJstx0ssQRcfbXd\nF5IkSZIk1ZABRg9UKnPYgaRSgSWXLKaQLLdcKXVJkiRJktSsDDB6oFLpMH1k5kzIhNVWg/vugzXX\nLLU2SZIk9V5EjIqI08uuQypDRCwVEfOXXYc0OwOMHngvwMiEQw+FQw4pbg/wj1OSJKlJvAR8IiJO\nKbsQqS9FxErAj4DNDTHU3/iNuwfeCzBOOQXOOgsWW8ztUiVJkppARPGXusx8FjgJ2DoiflBuVVLf\nycyngQeBzYBNImJw58+Q+k5dA4yI2CoiHo2ISRFx7BweHxoRl1Yfv7Oa9vV7lQps/fbv4RvfgF13\nhRNPLLskSZIk1UBmJkBEfA3YG7gZ2CYiflFqYVIfaA/wgOeATwAnA5vaiaH+om4BRkQMBM4AtgZW\nB74cEavPdtn+wBuZ+RHgVOCH9aqnlj788h1844E9Yf314YILnDoiSZLURCJiCeCLwDcy8whgDPDx\niPC3VmpqmZkRsSdwBHAIcAewK3ZiqJ+o5zfv0cCkzHwyM6cBl1D8n39HY4ALq7d/T5Hu9fu5GEPb\nXuOlRVeFq66C+Q0jJUmSGtkc/v45q3pu33fuOeAPwLiI+F6fFSb1oQ4/B58Axmfmw5k5DngKOIFi\nOpVfflSqegYYw4FnO9yfXB2b4zWZOQN4E1hy9heKiHERcXdE3P3KK6/Uqdyu2+ncbZk8/h4YNqzs\nUiRJktQLEREdpo1sEBErA+8CFwEXR8TymTkL+DfFwobnl1etVFuzhXft3w0nAitHxCoAmfn96mOf\nAQb2bYXS+w2q42vPqZMie3ANmXk2cDbAqFGjPvB4X9txR/BnV5IkqfF1CC8OA8YCNwJbANsBywF/\niYhrgW2AzasLHEoNb7bwbndgeEQ8DDxfvWT7iLgPWBB4EjgtM9vKqVYq1LMDYzKwQof7I/jPD8MH\nromIQcCiwOt1rEmSJEl6n4j4FLATsCmwMPBqZr6Rmd+mWMjzD8CWmflUiWVKNdUhvDgQOBR4hGLa\n/8LAb4EFgKOrx3cyc3JJpUrvqWcHxkRgZLUN7zmKRHu32a4ZT/Efhb8DOwO3tP8gSZIkSfUQEUMz\n890OQ28CtwAHAWsAO1av2xb4c2ZW+r5Kqb6q00cWBj5NEeBtAdwJ/DEzZ0bE7cBQYL7M/Hd5lUr/\nUbcAIzNnVFvxbqCYb3FeZj4UEScAd2fmeOBXwG8iYhJF58XYetUjSZIkRcTCwA4R8VeK3RWGAqcB\newCDMnNk9bp9KXYiub2sWqVai4glgUWq3USjgMcoOi9+RfHdcLPqTiRHA3dk5l+BqaUVLM2mnh0Y\nZOYEYMJsY8d3uD0V2KWeNUiSJEntMrNSDTFuBSrArpn5ZkQcAhwfET+k+MXaLsC+/uZZzSIiBgIr\nAodFxHRgGHAAxaK1SwGHVsOLXYC9gKtKK1aai7oGGJIkSVJ/UA0tBmfm68BdwMPA/MCs6pSSWyPi\nWeArFNuo7pmZj5RXsVRb1Wkh91N8B9wBOCozX4+Is4GPAEdGxFLAEsBumfl4ieVKc2SAIUmSpFaw\nGbBQRHwM+CSwLXAU8DPgBIq5/4My89jySpRqrxpKfDozrwE+RzEl5GRgp4h4ozp+eESsBkwH2jLz\nxfIqlubOAEOSJElNKyLWAN4B7gcuB5YFds/MWcApETEU+O+IeBTYOyLWycznyqtYqrmpwM4R8V2K\nBWu3A2ZUbx8UEa8A8wEfzcyzS6tS6oJ6bqMqSZIklSYiFgDWpVjr4nXgVIpFOVerBhtk5onAb4D/\nAz5neKFmEREbRcS4zGwD/gx8CHghM9/JzOkUa1z8gWIR27OB28qrVuoaAwxJkiQ1pcycAvwOWJTi\nS9q9wOHAp4DtImJYRGwNPAmc7poXajKPA9dFxErANRTTqBaNiF8CZOYrwHUU06k+n5mPllSn1GUG\nGJIkSWoqEfHe33Ezc0b15mPAocDCwPHAR4FTgAuBSmZmX9cp1UMUBmTmCxSdR38HDsvM+yl+BlaO\niP8XEV+kWAvjLTuP1CgMMCRJktRUqutbEBFbRsQGwFsU00eeAY4BBgOHUXRlrJeZj/3/9u49Sqrq\nyuP49xdEUEGNoI6kVYiAAQkiEgaJGRWVoFFURIIxERQ0QNSBjI466miMDj6SwWg0RsVIfIAj4wM1\noElAA0Sj+AARX0QetjARWyWiAQX2/HFPS9FgdyHdVd3Vv89avVbVrVN1d59VVX17n3P2KVasZrVJ\nkiKzXlLziPgIOAg4VtJlEbEUOAXYjWw20jURsaaYMZttCScwzMzMzKwkSFLO7SFksytOJlvn34Ys\nYbGQbNeRr0XEnIhYUoxYzepC5UwiSWcAd0oaEhGLyQp3DpB0aUS8GxEDgf4R8XIRwzXbYk5gmJmZ\nmVmDVznynG63AdYD/xwRZwOTgYlkSYybyLZMXVasWM1qW+6yKUl9gB+Q1b0YLOl84AOgHzBc0kUA\nEfH3YsRqtjW8jaqZmZmZNWhVkhejyGZd7AislvR2RIxLkzOmAX2BX7jmhZWK9P6vXDZ1IPBl4L8j\n4kFJrwM/JFsy9UugJ7Bd0YI120pOYJiZNUKffvop5eXlrF69utihNBrNmzenrKyMpk2bFjsUs5KT\nk7w4lmyHkXOAkUAPYKmkZ1IS4xNgnZMXVkpy3v/DgcuAV4F9JE2PiKckrQUuBD4BrvP73xoyJzDM\nzBqh8vJyWrZsSdu2bclZMm51JCKoqKigvLycdu3aFTscs5Ih6ZvAyoiYL6k9MB4YHxEvpGnyFwID\nyYp2zoqIG4sYrlmtkrRLRLyXbh8KfAvoERH/J+kXwEOSBkTEs5KuBJY7eWENnWtgmJk1QqtXr6ZV\nq1ZOXhSIJFq1auUZL2a171+A30nqFBELgZ8Ap0nqExErgJ8COwH9JDUvZqBmtUnS3sA1ko5Kh4YD\nnci2BwYYA7wATJe0c0Q8FxGu+2INnhMYZmaNlJMXheX+Nqs9lQULI2IsWaHCuyV9Pc2wuBC4TtLh\nEfE+8O/ADRHhDKKVmpeB4yR9HfhXYA5wiKTOqSbGecBjZEk8s5LgBIaZmZmZNSg5BQvPJLue/Rj4\nQ0pi/Ab4OTBB0qER8UFE/K2I4ZrVmsqtgtP2v++RFau9FNiDrP7FnsDA9FlYFxEXeKtgKyVOYJiZ\nWdE88MADSOLVV1/97NgTTzzBMcccs1G7oUOHMnnyZCArQHrBBRfQoUMHunTpQs+ePZk6depWxzJ2\n7Fjat2/Pvvvuy2OPPbbZNtOnT6d79+506dKFIUOGsHbtWgDuvvtuunbtSteuXenduzdz587d6njM\nrHqSDiCbJv/TiDgYGAc8kv5xm0A282JpMWM0q205BTvPAs4AZpIV5xxNtk3wxWRLSb4jadtixWlW\nV5zAMDOzopk4cSIHH3wwkyZNyvs5l1xyCcuXL2f+/PnMnz+fhx9+mA8//HCr4liwYAGTJk3i5Zdf\nZtq0aYwaNYp169Zt1Gb9+vUMGTKESZMmMX/+fPbee28mTJgAQLt27XjyySeZN28el1xyCWeeeeZW\nxWNmm9Km67CWkU2ZXyPpSxFxFTAbmCWpY0TcExFvFjxQszomqRnQDTgnIn4F/CfwJnA+sBtwNjAh\nIj4pXpRmdcO7kJiZNXKjR8OLL9bua3brBtddV32bVatWMXv2bGbMmEH//v257LLLanzdjz/+mFtv\nvZVFixbRrFkzAHbffXcGDRq0VfE+9NBDDB48mGbNmtGuXTvat2/PM888w0EHHfRZm4qKCpo1a0bH\njh0BOPLIIxk7dizDhg2jd+/en7Xr1asX5eXlWxWPmW1MknJGnlsAHwF/B1oBg4BfpaZTgJZkI9Jm\nJSH3/Q8QEWskNQHOlnR6RCyUNBMYAJwJnBsRa4oVr1ldcgLDzMyK4sEHH6Rfv3507NiRXXbZheef\nf57u3btX+5yFCxey1157seOOO9b4+mPGjGHGjBmbHB88eDAXXHDBRsfefvttevXq9dn9srIy3n77\n7Y3atG7dmk8//ZQ5c+bQo0cPJk+ezFtvvbXJ648fP56jjjpqk+Nm9sXlJC9+BBwBvEZWvHMEcB/w\nNUnbAPsDgyLi7c97LbOGpEry7kBg24h4CriK7P3/H8CVZIm7F4GfOHlhpcwJDDOzRq6mmRJ1ZeLE\niYwePRrIkgoTJ06ke/fun7tbx5bu4jFu3Li82+YMbH3u+SQxadIkxowZw5o1a+jbty/bbLPxn9EZ\nM2Ywfvx4Zs2atUWxmlnNJI0ATgJ+BIwF+pIVL/w20AdoD1zv5IWVkpzkxRiyGRYrJb0L/DfwKDBK\n0hNAa2BwRLxbrFjNCsEJDDMzK7iKigqmT5/O/PnzkcS6deuQxDXXXEOrVq14//33N2r/3nvv0bp1\na9q3b8/SpUv58MMPadmyZbXn2JIZGGVlZRvNpigvL6dNmzabPPeggw5i5syZADz++OO8/vrrnz02\nb948hg8fztSpU2nVqlXNnWBmeZO0E9AUOA4Ykm5fSVaw8IaIuKuI4ZnVKUkDgf4R8S1JlwIjyXbe\nuTYiBkhqC6xy8sIaAxfxNDOzgps8eTKnnnoqS5YsYfHixbz11lu0a9eOWbNm0aFDB5YtW8Yrr7wC\nwJIlS5g7dy7dunVj++23Z9iwYZxzzjl88km2xH358uXcddem/7uMGzeOF198cZOfqskLgP79+zNp\n0iTWrFnDokWLeOONN+jZs+cm7d555x0A1qxZw9VXX82IESMAWLp0KQMGDODOO+/8rEaGmX1xVQt2\nRsTKiLiBbJT5aLK6F1OAlcApknbeTJFPswZpM+/l54DT07bB/wx0BLoDt6SCtYudvLDGwgkMMzMr\nuIkTJ3LCCSdsdOzEE0/knnvuoVmzZtx1112cdtppdOvWjYEDB3Lbbbex0047AXDFFVew66670rlz\nZ7p06cLxxx/PrrvuulXx7LfffgwaNIjOnTvTr18/brzxRpo0aQLA0UcfzbJlywC49tpr6dSpE127\nduXYY4+lT58+AFx++eVUVFQwatQounXrRo8ePbYqHrPGLrfmhaSxkn4tqQz4ABDwZeBEYBFwakR8\nEJtbC2bWwFSpedFJ0h7AWxGxiKzGy50R8Xfgf4E1wHvFi9as8NTQvut79OgRc+bMKXYYZmYN2iuv\nvEKnTp3dv2cDAAAQeElEQVSKHUajs7l+l/RcRDjjYVaFpFFka/5HAg8A0yLiXEnXA3uT1bwYHBEv\nFTFMszoh6Tyy2i4fkhWtvY7s83ASWbHOA4FhEbG4WDGaFYNrYJiZmZlZvSGpSUSsA9oAA4FhwGLg\nIoCIOCdtpbpNRHxQtEDNaknVbVIlHQYcGRF9Jd0LfDUiKiRNJ9s++GjgbCcvrDFyAsPMzMzMiipt\nD7kn8HpELEhbou4JPAy8BQyIiE8knUM2In2Hl4xYCdmWbDlIpWbAjDQLY0egcs3lDhFxr6T7ImJ9\noYM0qw9cA8PMrJHytX9hub/NNk/St4FJQH/gBUlHRsRa4BdkxQqfSMmLIcAIYJaTF1YqJPUFJkm6\nVNKJ6fBi4AjgWLLdR1an5N1/SdoB8PvfGi3PwDAza4SaN29ORUUFrVq1woX7615EUFFRQfPmzYsd\nilm9Iqk3cDvwvYh4UtIsYKykZyLieUmDgJslfQv4KnBSRLxRzJjNaoukfsBPgN8CuwH9JD1PNuto\nNtASOF/Se2RLqU6JiI+KFa9ZfeAEhplZI1RWVkZ5eTkrVqwodiiNRvPmzSkrKyt2GGb1Rtoqsisw\ni2zKPBFxu6QjgcMklaekRleya9YdvFWklQpJuwC/A46LiIfTLjtXAv8UEYskXQ18CzgMaAGcHBEL\nihexWf3gBIaZWSPUtGlT2rVrV+wwzKwRi4iQdCfZ9ehRkpoA3wYOAdYB35C0BJgREWOBfxQvWrPa\nFRHvSToWuEbSkxFRLmlXshlI88h2Hrk3IqZVLfJp1pg5gWFmZmZmBZf+KftI0m/IpsePAvYC2qaa\nF/sArYGKYsZpVlci4lFJ64HnJE0jK+Z5I7ALMBw4UNLoiPh7MeM0q0+cwDAzMzOzgkszML6Ukhjj\ngdXAAUAfSTMi4q/AX4sbpVndioipkkYCjwN7RMTfACTdCuzi5IXZxrwLiZmZmZnVOVWpGCwp9zq0\nRUTcAswDBgJHFzI2s2KKiD8A3wGmS9o9HVvvmi9mm1JDW04laQWwpNhxkE1p9JdKxn2xMffHBu6L\nDdwXG7gvNrZvRLQsdhBmdSl3Db+kFsCaiPg03e8N3A/0IlsucjLwUOVItFljIek44FKgR0SsL3Y8\nZvVRg0tg1BeS5kREj2LHUR+4Lzbm/tjAfbGB+2ID98XG3B9W6qokL84FDibbdeT0iFguaSJwV0Q8\nWsw4zeoDSS0iYlWx4zCrr1wDw8zMzMzqTE7yog9wDDCCrGjnU5K6A99L9TCU296sMXLywqx6TmCY\nmZmZWZ2SdChwFvDHiHgVOC8lLJ4BDgXKwckLMzOrnot4fnG3FDuAesR9sTH3xwbuiw3cFxu4Lzbm\n/rCSU7VgJ7AIWAF0krQ/QEScC0wDpklqUuAQzcysAXINDDMzMzOrNVVqXhwLrAU+AJ4DrgPeA+6L\niLmpzW4R8U6x4jUzs4bDMzDMzMzMrNZJGgVcTla083ZgNDAG2Bk4VVKX1HRFcSI0M7OGxgkMMzMz\nM9tqkvaStEMqyLkbcBJZgc6LgN7AD4GBwJVAE+D/wHUvzMwsf05g1EBSP0mvSVoo6YLNPN5M0r3p\n8b9Ialv4KAsjj774saQFkuZJ+qOkvYsRZyHU1Bc57QZKCkklvUViPv0haVB6f7ws6Z5Cx1goeXxO\n9pI0Q9IL6bNydDHiLARJt0t6R9L8z3lckq5PfTUv7UZQkvLoi1NSH8yT9OfKGgFmDYWk3YF/A0am\nbSDfAd4FPgGIiPfJZl90jYjlwHkR8W7RAjYzswbJCYxqpIJSNwJHAZ2BkyV1rtJsGPB+RLQHxgFX\nFzbKwsizL14AekREV2AycE1hoyyMPPsCSS2Bc4C/FDbCwsqnPyR1AC4EvhkR+5FNIy45eb43Lgb+\nJyIOAAYDNxU2yoK6A+hXzeNHAR3Sz5nArwoQU7HcQfV9sQg4JH1//hQX9rSGZwXwLNAGOC0V8XwT\nmCSpcte7vYGy9F25tjhhmplZQ+YERvV6Agsj4s2I+ASYBBxXpc1xwIR0ezJw+GYqb5eCGvsiImZE\nxMfp7tNAWYFjLJR83heQ/RNyDbC6kMEVQT79cQZwYxqBo4SLteXTFwHsmG7vBCwrYHwFFRF/IivW\n93mOA34bmaeBnSXtUZjoCqumvoiIP1d+Pijt708rMZI6SNo3ItYDdwMzgE7AGRFxPjAH+JOkm4HT\ngSsjYp2XjZiZ2RfhBEb1vgK8lXO/PB3bbJuIWAusBFoVJLrCyqcvcg0DptZpRMVTY19IOgDYMyIe\nKWRgRZLPe6Mj0FHSbElPS6puJLohy6cvLgO+L6kc+B1wdmFCq5e29HulsSjl708rIZJaAa8BMyX9\niKzGxaPAn4E2kn4YESPJlo5MAgZExIKiBWxmZg3eNjU3adQ2N5Oi6ohBPm1KQd6/p6TvAz2AQ+o0\nouKpti8kfYlsOdHQQgVUZPm8N7YhWyZwKNnI8kxJXSLigzqOrdDy6YuTgTsi4ueSDgLuTH2xvu7D\nq3cay/dn3iQdRpbAOLjYsZjVJCIqJB0B/IFsUGx/4F5gFVnti6+nWam/iYg1xYvUzMxKhRMY1SsH\n9sy5X8am070r25SnNZ47Uf2U6YYqn74gXchcRLaWu1QvVmrqi5ZAF+CJtJron4ApkvpHxJyCRVk4\n+X5Ono6IT4FFkl4jS2g8W5gQCyafvhhGqoUQEU9Jag60Bkp1WU118vpeaSwkdQVuA46KiIpix2OW\nj4iYLunbwPVkCYzdgT5kNX56AvsCE4FSvSYwM7MC8hKS6j0LdJDUTtK2ZH+Mp1RpMwUYkm4PBKaX\n6LrOGvsiLZv4NdC/hGscQA19ERErI6J1RLSNiLZk69lLNXkB+X1OHgQOA5DUmmxJyZsFjbIw8umL\npcDhAJI6Ac3Jit81RlOAU9NuJL2AlWl3gkZH0l7A/cAPIuL1YsdjtiUi4vfAucB84KOImAB8j6wW\nxjERsbKY8ZmZWenwDIxqRMRaSWcBj5HtV357RLws6XJgTkRMAcaTTQFfSDbzYnDxIq47efbFtUAL\n4L4082BpRPQvWtB1JM++aDTy7I/HgL6SFgDryLbPK7kR5jz74t+AWyWNIVsuMbREk55Imki2bKh1\nqvlxKdAUICJuJqsBcjSwEPgYOK04kda9PPriP8nqJ92Uvj/XRkRJb79spSUiHpW0Hnha0kGl+B1v\nZmbFpxK9bjYzMzOzApN0HFmx4gMbaW0fMzOrQ05gmJmZmVmtkdQiIlYVOw4zMys9TmCYmZmZmZmZ\nWb3nIp5mZmZmZmZmVu85gWFmZmZmZmZm9Z4TGGZmZmZmZmZW7zmBYVbHJK2T9GLOT9tq2raVNL8W\nzvmEpNckzZU0W9K+X+A1Rkg6Nd0eKqlNzmO3Sepcy3E+K6lbHs8ZLWn7rT23mZmZmZk1LE5gmNW9\nf0REt5yfxQU67ykRsT8wAbh2S58cETdHxG/T3aFAm5zHhkfEglqJckOcN5FfnKMBJzDMzKxRyxkg\nmS/pvq1J7ks6VNIj6XZ/SRdU03ZnSaO+wDkuk3RuvsertLlD0sAtOFetDAiZWf3jBIZZEaQ/rDMl\nPZ9+em+mzX6SnkkXJ/MkdUjHv59z/NeSmtRwuj8B7dNzD5f0gqSXJN0uqVk6fpWkBek8P0vHLpN0\nbrpg6AHcnc65XZo50UPSSEnX5MQ8VNINXzDOp4Cv5LzWryTNkfSypJ+kY+eQJVJmSJqRjvWV9FTq\nx/sktajhPGZmZqWgcoCkC/AJMCL3QWW2+Fo/IqZExFXVNNkZ2OIEhplZbXACw6zubZezfOSBdOwd\n4MiI6A58F7h+M88bAfwiIrqRJRDKJXVK7b+Zjq8DTqnh/McCL0lqDtwBfDcivg5sA4yUtAtwArBf\nRHQFrsh9ckRMBuaQzZToFhH/yHl4MjAg5/53gXu/YJz9gAdz7l8UET2ArsAhkrpGxPXAMuCwiDhM\nUmvgYuCI1JdzgB/XcB4zM7NSMxNonwZIXpF0E/A8sOfnJfol9ZP0qqRZ5PwtT4MRv0y3d5f0QFrq\nOTcNuFwF7JOua65N7c5LS0HnVQ46pOMXpaWifwBqXM4q6Yz0OnMl/W+VWSVHpMGf1yUdk9o3kXRt\nzrl/uLUdaWb12zbFDsCsEfhH+ic+V1Pgl6nmwzqg42ae9xRwkaQy4P6IeEPS4cCBwLOSALYjS4Zs\nzt2S/gEsBs4mu3BYFBGvp8cnAD8CfgmsBm6T9CjwSL6/WESskPSmpF7AG+kcs9PrbkmcOwBNgO45\nxwdJOpPse2oPoDMwr8pze6Xjs9N5tiXrNzMzs0ZB0jbAUcC0dGhf4LSIGFUl0f+RpPOBH6fZk7cC\nfYCFwL2f8/LXA09GxAlpJmUL4AKgS+W1jaS+QAegJyBgiqR/AT4CBgMHkP0tfx54roZf5/6IuDW9\n7hXAMOCG9Fhb4BBgH7KZmO2BU4GVEfGNNKt0tqTHgaip38ysYXICw6w4xgB/A/Ynmwm1umqDiLhH\n0l+A7wCPSRpOdmEwISIuzOMcp0TEnMo7klptrlFErJXUEzic7ELjLLILmnzdCwwCXgUeiIhQlk3I\nO05gLtmIzo3AAEntgHOBb0TE+5LuAJpv5rkCfh8RJ29BvGZmZqVgO0kvptszgfFkyyyXRMTT6fjn\nJfq/Rjao8QaApLuAMzdzjj5kSQIiYh2wUtKXq7Tpm35eSPdbkCU0WpJdF3yczjElj9+pS0pc7Jxe\n57Gcx/4nItYDb0h6M/0OfYGu2lAfY6d07tcxs5LkBIZZcewElEfEeklDyGYfbETSV4E3I+L6dLsr\n8DjwkKRxEfFOWv7RMiKW5HHOV4G2ktpHxELgB8CTaSrp9hHxO0lPk43EVPUh2YXI5twPXAQsAc5P\nx/64JXFGxKeSLgb+mpafbEs2crNS0u5kI0tPVInlXeBp4MbK3ylNNS3LmWViZmZWqjaZ4ZmSFB/l\nHmIzif40A7S2ZikIGBsRv65yjtFf4Bx3AMdHxFxJQ4FDcx6r+lqRzn12ROQmOlA1O76ZWcPmGhhm\nxXETMCQlDDqy8cVGpe8C89PoyteA36adPy4GHpc0D/g92fKKGkXEauA04D5JLwHrgZvJkgGPpNd7\nkmx2SFV3ADen9a7bVXnd94EFwN4R8Uw6tsVxptoaPwfOjYi5ZCM5LwO3ky1LqXQLMFXSjIhYQbZD\nysR0nqfJ+srMzMyyv4vfTMstkLS9pI5kgxrtJO2T2n3eTMY/AiPTc5tI2pFNBzUeA07Pqa3xFUm7\nkRURP0FZ8e+WZDW5atISWC6pKZvWzjpJ0pdSzF8FXkvnHpnaI6ljWpZqZiVKEV4iZmZmZmbWkEha\nFREtqhxrCzySdiapPNYHuBpolg5dHBFTJPUDriOb0TiLrK7FMWnmQ4+IOCvNgryFLGGwDhgZEU9J\nuodsZujUiDhP0r8Cw9PrrwK+HxF/lXQR2RKUJUA5sCAiflYl5suAVRHxM0kjgX9P7V8im705NC0l\nfZ+sqPnuwI8j4hFlu6xcQZYcEbACOB74ctV+MLPS4ASGmZmZmZmZmdV7XkJiZmZmZmZmZvWeExhm\nZmZmZmZmVu85gWFmZmZmZmZm9Z4TGGZmZmZmZmZW7zmBYWZmZmZmZmb1nhMYZmZmZmZmZlbvOYFh\nZmZmZmZmZvXe/wPM5xjBwBqBawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a3ca35d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_test, y_predict)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Non-Readmitted','Readmitted'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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/+6W0bv7bDIGtmjdiwaJlJfIsXLSMfhfdB8DmdWpyxL7dWL7i1+JtAHPnL+Gt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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a547cf080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(1, figsize=(10,6))\n",
    "plt.subplot(121)\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Non-Readmitted','Readmitted'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Non-Readmitted','Readmitted'], normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the following matrix gives the significance of each feature in classifying the observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=3, suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03 ,  0.081,  0.016,  0.022,  0.095,  0.029,  0.034,  0.008,\n",
       "        0.02 ,  0.011,  0.017,  0.031,  0.194,  0.01 ,  0.008,  0.007,\n",
       "        0.026,  0.002,  0.005,  0.058,  0.   ,  0.   ,  0.   ,  0.003,\n",
       "        0.   ,  0.02 ,  0.014,  0.   ,  0.009,  0.005,  0.   ,  0.   ,\n",
       "        0.   ,  0.   ,  0.052,  0.   ,  0.   ,  0.1  ,  0.093])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The top 5 features that impact the re-admission\n",
    "- X[12]\n",
    "- X[38]\n",
    "- X[4]\n",
    "- X[39]\n",
    "- X[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting\n",
    "<br>\n",
    "Lets do some xgb analysis on the data set and see any improvments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mingw_path = 'C:\\\\Program Files\\\\mingw-w64\\\\x86_64-7.2.0-posix-seh-rt_v5-rev0\\\\mingw64\\\\bin'\n",
    "\n",
    "os.environ['PATH'] = mingw_path + ';' + os.environ['PATH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "cv_params = {'max_depth': [10,12,14]}\n",
    "ind_params = {'learning_rate': 0.1, 'n_estimators': 200, 'seed':0, 'subsample': 0.8, 'colsample_bytree': 0.8, \n",
    "             'objective': 'binary:logistic'}\n",
    "\n",
    "optimized_GBM = GridSearchCV(xgb.XGBClassifier(**ind_params), \n",
    "                            cv_params, \n",
    "                             scoring = 'accuracy', cv = 5, n_jobs = -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=200,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0, silent=True,\n",
       "       subsample=0.8),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': [10, 12, 14]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score='warn', scoring='accuracy',\n",
       "       verbose=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_GBM.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\rmali\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 77.858,  91.806,  93.085]),\n",
       " 'mean_score_time': array([ 0.721,  1.072,  0.888]),\n",
       " 'mean_test_score': array([ 0.924,  0.924,  0.924]),\n",
       " 'mean_train_score': array([ 0.969,  0.99 ,  0.998]),\n",
       " 'param_max_depth': masked_array(data = [10 12 14],\n",
       "              mask = [False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'max_depth': 10}, {'max_depth': 12}, {'max_depth': 14}],\n",
       " 'rank_test_score': array([1, 3, 2]),\n",
       " 'split0_test_score': array([ 0.925,  0.924,  0.924]),\n",
       " 'split0_train_score': array([ 0.969,  0.989,  0.998]),\n",
       " 'split1_test_score': array([ 0.923,  0.923,  0.923]),\n",
       " 'split1_train_score': array([ 0.969,  0.99 ,  0.998]),\n",
       " 'split2_test_score': array([ 0.923,  0.923,  0.923]),\n",
       " 'split2_train_score': array([ 0.969,  0.99 ,  0.999]),\n",
       " 'split3_test_score': array([ 0.926,  0.925,  0.925]),\n",
       " 'split3_train_score': array([ 0.969,  0.99 ,  0.999]),\n",
       " 'split4_test_score': array([ 0.925,  0.925,  0.925]),\n",
       " 'split4_train_score': array([ 0.969,  0.989,  0.998]),\n",
       " 'std_fit_time': array([ 1.503,  4.923,  5.122]),\n",
       " 'std_score_time': array([ 0.009,  0.117,  0.195]),\n",
       " 'std_test_score': array([ 0.001,  0.001,  0.001]),\n",
       " 'std_train_score': array([ 0.,  0.,  0.])}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_GBM.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgd_mat= xgb.DMatrix(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tuned_params = {'eta': 0.1, 'seed':0, 'subsample': 0.8, 'colsample_bytree': 0.8, \n",
    "             'objective': 'binary:logistic', 'max_depth':14} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_xgb = xgb.cv(params = tuned_params, dtrain = xgd_mat, num_boost_round = 1000, nfold = 5,\n",
    "                metrics = ['error'],\n",
    "                early_stopping_rounds = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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-error-mean</th>\n",
       "      <th>test-error-std</th>\n",
       "      <th>train-error-mean</th>\n",
       "      <th>train-error-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>0.075636</td>\n",
       "      <td>0.002707</td>\n",
       "      <td>0.001190</td>\n",
       "      <td>0.000181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>0.075669</td>\n",
       "      <td>0.002610</td>\n",
       "      <td>0.001134</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0.075712</td>\n",
       "      <td>0.002699</td>\n",
       "      <td>0.001109</td>\n",
       "      <td>0.000169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0.075658</td>\n",
       "      <td>0.002720</td>\n",
       "      <td>0.001093</td>\n",
       "      <td>0.000157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0.075582</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.000155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     test-error-mean  test-error-std  train-error-mean  train-error-std\n",
       "212         0.075636        0.002707          0.001190         0.000181\n",
       "213         0.075669        0.002610          0.001134         0.000180\n",
       "214         0.075712        0.002699          0.001109         0.000169\n",
       "215         0.075658        0.002720          0.001093         0.000157\n",
       "216         0.075582        0.002646          0.001083         0.000155"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_xgb.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "readmit_gb = xgb.train(tuned_params, xgd_mat, num_boost_round = 216)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'f0': 1846,\n",
       " 'f1': 2018,\n",
       " 'f10': 1835,\n",
       " 'f11': 1272,\n",
       " 'f12': 2955,\n",
       " 'f13': 14173,\n",
       " 'f14': 13366,\n",
       " 'f15': 12841,\n",
       " 'f16': 4444,\n",
       " 'f17': 421,\n",
       " 'f18': 1345,\n",
       " 'f19': 1168,\n",
       " 'f2': 931,\n",
       " 'f20': 136,\n",
       " 'f21': 48,\n",
       " 'f22': 2,\n",
       " 'f23': 331,\n",
       " 'f25': 1062,\n",
       " 'f26': 866,\n",
       " 'f28': 497,\n",
       " 'f29': 437,\n",
       " 'f3': 3110,\n",
       " 'f30': 22,\n",
       " 'f33': 2,\n",
       " 'f34': 3405,\n",
       " 'f35': 33,\n",
       " 'f37': 1717,\n",
       " 'f38': 979,\n",
       " 'f4': 4619,\n",
       " 'f5': 2735,\n",
       " 'f6': 7198,\n",
       " 'f7': 12246,\n",
       " 'f8': 4362,\n",
       " 'f9': 11190}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = readmit_gb.get_score()\n",
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testdmat = xgb.DMatrix(X_test)\n",
    "y_xgd_pred = readmit_gb.predict(testdmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22a4e9119b0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Is4D9M/Ppthva0EWSpH7qrv0OMKOOGCRJahovv0uS1BAmdUmSGsKkLklSQ5jUJUlqiLob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\naoj/ByBd0FzsXv19AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a4e911128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance_frame = pd.DataFrame({'Importance': list(importances.values()), 'Feature': list(importances.keys())})\n",
    "importance_frame.sort_values(by = 'Importance', inplace = True)\n",
    "importance_frame.plot(kind = 'barh', x = 'Feature', figsize = (8,8), color = 'orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_xgd_pred[y_xgd_pred > 0.5] = 1\n",
    "y_xgd_pred[y_xgd_pred <= 0.5] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.926878314858\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_xgd_pred))\n",
    "cnf_matrix = confusion_matrix(y_test, y_xgd_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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1jIjRwIbA3cAKZXKDzJwUEcuX3UYA4yt2m1C2ddY+oZ32DpnAkHq5zGISzM4q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\nd1nnvW2zE75w4UPsdtY9rLP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w001w4YXw0Y/WOyJJkiRJNWACo0qcA0OqkzfegAcfhBNP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\n6r3/B/tp6qTQ82AiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a4e574be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plot_roc_curve(y_test, y_xgd_pred)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Non-Readmitted','Readmitted'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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
}