SophiaCase2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSDS 7331 - Case Study 2 - Logistic regression-Hospital Readmittance\n",
    "Daniel Crouthamel,\n",
    "Sophia Wu,\n",
    "Fabio Savorgnan,\n",
    "Bo Yun\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "In this study, we will build a classifier using logistic regression to predict hospital readmittance. There is missing data that must be imputed. We'll discuss variable importances for the target variable,which is hospital readmittance.\n",
    "There are total six sections in this case analysis:\n",
    "\n",
    "1. Business Understanding\n",
    "2. Data Evaluation / Engineering\n",
    "3. Modeling Preparations\n",
    "4. Model Building & Evaluation\n",
    "5. Model Interpretability & Explainability\n",
    "6. Case Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn import metrics as mt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve\n",
    "from matplotlib import pyplot\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import make_column_selector as selector\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "# Imbalanced Data\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.pipeline import Pipeline as imbpipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n",
    "\n",
    "# Pre-Processing\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  **Objective:**\n",
    "The objective of this case study is to build a classifier using logistic regression to predict hospital readmittance, it will help identify the hospital readmission of patients with Diabetes within 30 days.\n",
    "*  **Problem:**\n",
    "When patients are readmitted in the hospital within a short period, this can be very expensive to the individual household also our hospital system, as patients'wages can be lost,and the medical bills can be huge.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Evaluation and Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/diabetic_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get some info from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      " #   Column                    Non-Null Count   Dtype \n",
      "---  ------                    --------------   ----- \n",
      " 0   encounter_id              101766 non-null  int64 \n",
      " 1   patient_nbr               101766 non-null  int64 \n",
      " 2   race                      101766 non-null  object\n",
      " 3   gender                    101766 non-null  object\n",
      " 4   age                       101766 non-null  object\n",
      " 5   weight                    101766 non-null  object\n",
      " 6   admission_type_id         101766 non-null  int64 \n",
      " 7   discharge_disposition_id  101766 non-null  int64 \n",
      " 8   admission_source_id       101766 non-null  int64 \n",
      " 9   time_in_hospital          101766 non-null  int64 \n",
      " 10  payer_code                101766 non-null  object\n",
      " 11  medical_specialty         101766 non-null  object\n",
      " 12  num_lab_procedures        101766 non-null  int64 \n",
      " 13  num_procedures            101766 non-null  int64 \n",
      " 14  num_medications           101766 non-null  int64 \n",
      " 15  number_outpatient         101766 non-null  int64 \n",
      " 16  number_emergency          101766 non-null  int64 \n",
      " 17  number_inpatient          101766 non-null  int64 \n",
      " 18  diag_1                    101766 non-null  object\n",
      " 19  diag_2                    101766 non-null  object\n",
      " 20  diag_3                    101766 non-null  object\n",
      " 21  number_diagnoses          101766 non-null  int64 \n",
      " 22  max_glu_serum             101766 non-null  object\n",
      " 23  A1Cresult                 101766 non-null  object\n",
      " 24  metformin                 101766 non-null  object\n",
      " 25  repaglinide               101766 non-null  object\n",
      " 26  nateglinide               101766 non-null  object\n",
      " 27  chlorpropamide            101766 non-null  object\n",
      " 28  glimepiride               101766 non-null  object\n",
      " 29  acetohexamide             101766 non-null  object\n",
      " 30  glipizide                 101766 non-null  object\n",
      " 31  glyburide                 101766 non-null  object\n",
      " 32  tolbutamide               101766 non-null  object\n",
      " 33  pioglitazone              101766 non-null  object\n",
      " 34  rosiglitazone             101766 non-null  object\n",
      " 35  acarbose                  101766 non-null  object\n",
      " 36  miglitol                  101766 non-null  object\n",
      " 37  troglitazone              101766 non-null  object\n",
      " 38  tolazamide                101766 non-null  object\n",
      " 39  examide                   101766 non-null  object\n",
      " 40  citoglipton               101766 non-null  object\n",
      " 41  insulin                   101766 non-null  object\n",
      " 42  glyburide-metformin       101766 non-null  object\n",
      " 43  glipizide-metformin       101766 non-null  object\n",
      " 44  glimepiride-pioglitazone  101766 non-null  object\n",
      " 45  metformin-rosiglitazone   101766 non-null  object\n",
      " 46  metformin-pioglitazone    101766 non-null  object\n",
      " 47  change                    101766 non-null  object\n",
      " 48  diabetesMed               101766 non-null  object\n",
      " 49  readmitted                101766 non-null  object\n",
      "dtypes: int64(13), object(37)\n",
      "memory usage: 38.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101766, 50)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at the hist from the numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'encounter_id'}>,\n",
       "        <AxesSubplot:title={'center':'patient_nbr'}>,\n",
       "        <AxesSubplot:title={'center':'admission_type_id'}>,\n",
       "        <AxesSubplot:title={'center':'discharge_disposition_id'}>],\n",
       "       [<AxesSubplot:title={'center':'admission_source_id'}>,\n",
       "        <AxesSubplot:title={'center':'time_in_hospital'}>,\n",
       "        <AxesSubplot:title={'center':'num_lab_procedures'}>,\n",
       "        <AxesSubplot:title={'center':'num_procedures'}>],\n",
       "       [<AxesSubplot:title={'center':'num_medications'}>,\n",
       "        <AxesSubplot:title={'center':'number_outpatient'}>,\n",
       "        <AxesSubplot:title={'center':'number_emergency'}>,\n",
       "        <AxesSubplot:title={'center':'number_inpatient'}>],\n",
       "       [<AxesSubplot:title={'center':'number_diagnoses'}>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FxyLiKklH97PNPUkFZENSpJ5vS9o5r2ci6e7AzcAnIuJhVXhIZR/fw0vz5pau/yM15T5DGrB3Cn6gqvVD0nzy8dzsvJhZc+Tz473AK4qDxAdyTjKrl3xT+ilgQkQ81OS89OAyMCCuCJlZ23BFyKy75ZuBx5PCBX+0bFoPvgi0BpD0XuBqUpe440gtFG+IJl9UuwwMnLvGmZnViaStlB4yV+nlsXBmZZQe7lipvOxbYd71SL0I3sma4wAHk4feyuyb67WNRpP0mfzd3inpvNy1faSkK5UeKnpl7ppemv9ISfOUHqS5WyF9kqS5edpJ0ksPIV1H6YHQ8yTdpMKDervEnqQuzI+Suo9O768SpDQOr9JxdlcjMtzKmvnduEXI6kbS8l4mvTsiftfQzJiZmXUhpQH/1wPbRsQ/JF1IGsS+LbAkImYphUHfJCKOkLQtafxx6dEdVwHbRMRKSTcDh5PGuVxOep7OHEn/TYqydoik6cC/R8Q+Dd9Zs0Fyi5DVTaQHsVZ6uRJkZmbWOMOBEXlM7stJLRd7kgJNkP/uld/vCZwf6QGVD5ECx+wkaTSpC+INubXjzLJlSuu6CNi11Fpk1k5cETIzMzPrEPl5Nd8GHiZFIHs6In4DjIqIRXmeRaTIYJAedll8tt/CnDaG1SOXldJXWyYHOXqaXp57Y9bKenuQZsvbbLPNYvz48RWnPfvss6y33noVpzWK89D87Q9lHm699dYnImIgz2hqGX2VHWiN/1slztfAtGq+2rnsQP/lp95a9f9Y5DzWT1/5rLbs5LE/ewKvIkUz+6mkD/e1SIW06CO9r2XK83IwcDDAiBEjJo0bN44XX3yRtdbqrPvwnbhP0Dn79Ze//KX3shMRbfmaNGlS9Obaa6/tdVqjOA/N3/5Q5gG4JVqgHNTy6qvsRLTG/60S52tgWjVf7Vx2ooryU2+t+n8sch7rp698Vlt2gL2B0wuf9wdOJT2+Y3ROGw3cl98fCRxZmP/XwJvyPPcW0j8I/KA4T34/nPT8GfWVr1LZaZf/xUB04j5FdM5+9VV22r+aZ2ZmZmYlDwNTJL08j9vZlfTgzUuBA/I8BwCX5PeXAtNzJLhXkaKg3Ryp+9wySVPyevYvW6a0rvcD1+QLTrO20rZd48zMzMxsdRFxk6SLgNuAFcCfgNOA9YELJR1Eqiztnee/K0eWuzvPf2hErMyr+wQwGxgBzMkvgNOBsyTNA5YA0xuwa2Z154qQmZmZWQeJiK+w5rOWnie1DlWa/xjgmArptwDbV0h/jlyRMmtn7hpnZmZmZmZdxxUhMzMzMzPrOu4a1wLGz/xVTcvNn7V7nXNi9SRpHOkBdK8AXgROi4jvSBoJXACMB+YDH4iIpXmZI4GDgJXAYRHx65w+iVX9tC8HDo+IkLRO3sYk4Elgn4iYP5h8z33kaQ6s4Zj08Wjdzr/lZrWpteyAy48NTr8tQpLGSbpW0j2S7pJ0eE4fKelKSffnv5sUljlS0jxJ90narZA+SdLcPO2k0lOIc6SSC3L6TZLGD8G+mjXaCmBGRLwOmAIcKmlbYCZwdURMAK7On8nTpgPbAdOAUyUNy+v6HulZDBPya1pOPwhYGhFbAycAxzZix8zMzMzaXTVd43wxZ1aDiFgUEbfl98tI4UvHkB50d0ae7Qxgr/x+T+D8iHg+Ih4C5gE7SRoNbBgRN+TwpGeWLVNa10XArqUbDGZmZmbWu367xuU48ovy+2WSihdzU/NsZwA9wBEULuaAh3JoxZ0kzSdfzAFIKl3MzcnLHJ3XdRFwiiQ5Jr11itzKuSNwEzAqlysiYpGkLfJsY4AbC4stzGkv5Pfl6aVlFuR1rZD0NLAp6eF2xe2/9HTvUaNG0dPT02teR42AGRNXDHgf+1pnPSxfvnzIt1EL58vMzKw9DWiMULMv5szakaT1gZ8Bn46IZ/posKk0IfpI72uZ1RMiTiM9R4LJkyfH1KlTe83vyedcwnFzBz58cP6+va+zHnp6eugr383ifJmZmbWnqq92WuFirtq72q1wJ3Qgeajl7jv0fwe+2d9Ds7ffCnmQtDap3JwTET/PyY9LGp1vIIwGFuf0hcC4wuJjgUdz+tgK6cVlFkoaDmxEeridmZmZmfWhqopQq1zMVXtXuxXuhA4kD7VE6IL+78A3+3to9vabnYc8Vud04J6IOL4w6VLgAGBW/ntJIf1cSccDW5LG0d0cESslLZM0hdQauz9wctm6bgDeD1zjLqVmZmZm/asmalx/F3Ow5sXc9BwJ7lWsuphbBCyTNCWvc/+yZUrr8sWcdYpdgP2At0u6Pb/eQ6oAvVPS/cA782ci4i7gQuBu4Arg0IhYmdf1CeBHpAAKD5DG1kEqm5vmsXifJQctMTMzM7O+VdMiVLqYmyvp9px2FOni7UJJBwEPA3tDupiTVLqYW8GaF3OzSc9CmcPqF3Nn5Yu5JaSoc2ZtLSKup3K3T4Bde1nmGOCYCum3ANtXSH+OXPbMOk0OsrOM9FytFRExudWfw2VmZu2jmqhxvpgzs375gXg2RN4WEcXAOaVHN8ySNDN/PqLs0Q1bAldJ2ibfiCs9uuFGUkVoGulG3EuPbpA0nfTohn0atWNmZtZc1TxHyMzMrFX4OVxmZlYXA4+Ra2Zm1hgB/EZSAD/IAXNa9jlcQxEBtNmRL6vhPNZPu+TTrFO4ImRmZq1ql4h4NFd2rpR0bx/zNv05XEMRAbQVom/2x3msn3bJp1mncNc4MzNrSRHxaP67GLgY2In86AaAOj66AT+Hy8ys+7giZGZmLUfSepI2KL0H3gXciR/dYGZmdeKucWZm1opGARfn2AXDgXMj4gpJf8SPbjAzszpwRcjMzFpORDwIvL5C+pP40Q1mZlYH7hpnZmZmZmZdxxUhMzMzMzPrOu4aZ2ZdaXytoY5n7V7nnJiZmVkzuEXIzMzMzMy6jitCZmZmZmbWdVwRMjMzMzOzruOKkJmZmZmZdR1XhMzMzMzMrOu4ImRmZmZmZl3HFSEzMzMzM+s6rgiZmZmZmVnXcUXIzMzMrINI2ljSRZLulXSPpDdJGinpSkn357+bFOY/UtI8SfdJ2q2QPknS3DztJEnK6etIuiCn3yRpfBN202zQhjc7A63GT5s3MzOzNvcd4IqIeL+klwEvB44Cro6IWZJmAjOBIyRtC0wHtgO2BK6StE1ErAS+BxwM3AhcDkwD5gAHAUsjYmtJ04FjgX0au4tmg+cWITMzM7MOIWlD4C3A6QAR8c+IeArYEzgjz3YGsFd+vydwfkQ8HxEPAfOAnSSNBjaMiBsiIoAzy5YpresiYNdSa5FZO3GLkJk1XTUtsTMmruDAsvncEmtmtoZXA38DfiLp9cCtwOHAqIhYBBARiyRtkecfQ2rxKVmY017I78vTS8ssyOtaIelpYFPgiSHZI7Mh4oqQmZmZWecYDrwB+FRE3CTpO6RucL2p1JITfaT3tczqK5YOJnWtY9SoUfT09LB8+XJ6enpWm2/GxBV9ZK9v5etqhkr71Ak6db+KXBGqk/I72pXuXpuZmZkNsYXAwoi4KX++iFQRelzS6NwaNBpYXJh/XGH5scCjOX1shfTiMgslDQc2ApaUZyQiTgNOA5g8eXJMnTqVnp4epk6dutp8g7lemr/v1H7nGWqV9qkTdOp+FXmMkJmZmVmHiIjHgAWSXpuTdgXuBi4FDshpBwCX5PeXAtNzJLhXAROAm3M3umWSpuTxP/uXLVNa1/uBa/I4IrO24oqQmZm1JEnDJP1J0mX5s8P/mlXnU8A5ku4AdgC+AcwC3inpfuCd+TMRcRdwIamydAVwaI4YB/AJ4EekAAoPkCLGQQrEsKmkecBn6bvrnVnLckXIbAhJ+rGkxZLuLKQdLekRSbfn13sK03wxZ7bK4cA9hc8zSeF/JwBX58+Uhf+dBpwqaVhephT+d0J+TcvpL4X/BU4ghf816wgRcXtETI6If42IvSJiaUQ8GRG7RsSE/HdJYf5jIuI1EfHaiJhTSL8lIrbP0z5ZavWJiOciYu+I2DoidoqIB5uxn2aD1W9FyBdyZoMym1UXXkUnRMQO+XU5+GLOrEjSWGB30t3oEof/NTOzuqkmWMJs4BTSCaTohIj4djHBD+UyW11EXDeAyv1LF3PAQ7nLwU6S5pMv5gAklS7m5uRljs7LXwScIknuq20d4ETgC8AGhbSmhP+tFPmqklojX/UVlakdojY5j/XTLvk06xT9VoR8IWc2JD4paX/gFmBGRCzFz3IwA0DSHsDiiLhV0tRqFqmQVpfwv1A58lUltUa+6ivqVTtEbXIe66dd8mnWKQYTPtsXcma1+R7wNdJF19eA44CP0uBnOfRm1IjBPdNhqFTK12DunNbr7n2r3sFt1XxVaRfgfbnb9brAhpLOpgnhf83MrHPVWhFq+IUcVH8xN5gLgHpdADbiYrK/fWz2hVCzt98qeSgXEY+X3kv6IXBZ/tjQZzn05uRzLuG4ua33iLEZE1eska/BPD+iXnfvW/UObqvmqxoRcSRwJEBuEfpcRHxY0v+RQvbOYs3wv+dKOp7ULbsU/nelpGWSpgA3kcL/nlxY5gDgBhz+18ysK9V0tdOMC7m83aou5gZzAVCvh6BWumirt/4uApt9IdTs7bdKHsqV7mjnj/8OlAKR+GLOrG+zgAslHQQ8DOwNKfyvpFL43xWsGf53NjCC1B27GP73rNyFewlpfKuZmXWRmq7UfSFnVh1J5wFTgc0kLQS+AkyVtAOp5XM+8F/gizmzSiKiB+jJ758kPRyy0nzHAMdUSL8F2L5C+nPkipSZmXWnfitCvpAzq11EfLBC8ul9zO+LOTMzM7MGqCZqnC/kzMzMzMyso/T7QFUzMzMzM7NO44qQmZmZmZl1HVeEzMzMzMys67giZGZmZmZmXccVITMzMzMz6zquCJmZmZmZWddxRcjMzMzMzLqOK0JmZmZmZtZ1XBEyMzMzM7OuM7zZGTAzayfjZ/5qtc8zJq7gwLK03syftftQZMnMzMxq4BYhMzMzMzPrOq4ImZmZmZlZ13FFyMzMzMzMuo4rQmZmZmZm1nVcETIzMzMzs67jipCZmZmZmXUdV4TMzKylSFpX0s2S/izpLkn/m9NHSrpS0v357yaFZY6UNE/SfZJ2K6RPkjQ3TztJknL6OpIuyOk3SRrf8B01M7OmckXIzMxazfPA2yPi9cAOwDRJU4CZwNURMQG4On9G0rbAdGA7YBpwqqRheV3fAw4GJuTXtJx+ELA0IrYGTgCObcB+mZlZC3FFyMzMWkoky/PHtfMrgD2BM3L6GcBe+f2ewPkR8XxEPATMA3aSNBrYMCJuiIgAzixbprSui4BdS61FZmbWHVwRMjOzliNpmKTbgcXAlRFxEzAqIhYB5L9b5NnHAAsKiy/MaWPy+/L01ZaJiBXA08CmQ7IzZmbWkoY3OwNmZmblImIlsIOkjYGLJW3fx+yVWnKij/S+lllz5dLBpO51jBo1ip6enoqZmDFxRR9Z7F1v6wNYvnx5n9NbgfNYP+2ST7NO4YqQmZm1rIh4SlIPaWzP45JGR8Si3O1tcZ5tITCusNhY4NGcPrZCenGZhZKGAxsBS3rJw2nAaQCTJ0+OqVOnVszrgTN/NdDdA2D+vpXXB6mS1Nv2WoXzWD/1ymceI3cL8EhE7CFpJHABMB6YD3wgIpbmeY8kjZlbCRwWEb/O6ZOA2cAI4HLg8IgISeuQuplOAp4E9omI+YPOtFkTuCJkZm1rfI0XntbaJG0OvJArQSOAd5CCGVwKHADMyn8vyYtcCpwr6XhgS1JQhJsjYqWkZTnQwk3A/sDJhWUOAG4A3g9ck8cRmXWCw4F7gA3z51KgkVmSZubPR5QFGtkSuErSNrlFthRo5EZSRWgaMIdCoBFJ00llc5/G7ZpZ/XiMkJmZtZrRwLWS7gD+SBojdBmpAvROSfcD78yfiYi7gAuBu4ErgEPzhRzAJ4AfkQIoPEC6kAM4HdhU0jzgs+QIdGbtTtJYYHfScV/iQCNmFbhFyMzMWkpE3AHsWCH9SWDXXpY5BjimQvotwBrjiyLiOWDvQWfWrPWcCHwB2KCQtlqgEUnFQCM3FuYrBRR5gSoDjUgqBRp5ojwjlcbXVRoHVev4Ouh7jF2jdOrYrk7dryJXhNpYf92CZkxc0Wuf9fmzdh+KLJmZmVmTSNoDWBwRt0qaWs0iFdLqFmik0vi6SuOgah1fB32PsWuUdhmDNlCdul9F7hpnNoQk/VjSYkl3FtJGSrpS0v357yaFaUfmJ93fJ2m3QvokSXPztJNK3RAkrSPpgpx+k6TxDd1BMzNrJbsA75M0HzgfeLuks8mBRgDqGGiE/gKNmLW6fluEJP0YKN1h2D6ntXT0kbmPPD2ouwtmdTQbOIV0jJd40KqZmdVdRBwJHAmQW4Q+FxEflvR/ONCI2RqqaRGaTbroKipdyE0Ars6fKbuQmwacmkM4wqoLuQn5VVrnSxdywAmkCzmzjhAR17HmnTIPWjUzs0ZyoBGzCvptEYqI6yp0t9kTmJrfnwH0AEdQuJADHsqFZKfcRLthRNwAIKl0ITcnL3N0XtdFwCmS5LsL1sGaMmjVzMy6R0T0kK7PHGjErBe1BktomegjFTM3YnARSOqh1fPQiCggrRBtpBXyMABDNmi12rIDrXHsVtIJ+Wrksdhmx76ZmVnD1TtqXMOjj1Ry8jmXcNzc5gbEmzFxRWvnYe6zNa1zINHmWiHaSCvkoYLHJY3ONxHqNWh1YV+DVqstO9Aa5aeSVihTlQwkX42MbtSix76ZmVnLqDVqnKOPmNWuNNAU1hy0Oj1HgnsVqwatLgKWSZqSx//sX7ZMaV0etGpmZmZWpVorQr6QM6uCpPNIkXVeK2mhpIPwoFUzMzOzpqsmfPZ5pMAIm0laCHyFdOF2Yb6oe5g8aC4i7pJUupBbwZoXcrNJ4bPnsPqF3Fn5Qm4JKeqcWUeIiA/2MsmDVs3MzMyaqJqocb6QMzMzMzOzjlJr1zgzMzMzM7O25YqQmZmZmZl1HVeEzMzMzMys67giZGZmZmZmXccVITMzMzMz6zquCJmZmZmZWddxRcjMzMzMzLqOK0JmZmZmZtZ1XBEyM7OWImmcpGsl3SPpLkmH5/SRkq6UdH/+u0lhmSMlzZN0n6TdCumTJM3N006SpJy+jqQLcvpNksY3fEfNzKypXBEyM7NWswKYERGvA6YAh0raFpgJXB0RE4Cr82fytOnAdsA04FRJw/K6vgccDEzIr2k5/SBgaURsDZwAHNuIHTMzs9bhipCZmbWUiFgUEbfl98uAe4AxwJ7AGXm2M4C98vs9gfMj4vmIeAiYB+wkaTSwYUTcEBEBnFm2TGldFwG7llqLzMysOwxvdgbMzMx6k7us7QjcBIyKiEWQKkuStsizjQFuLCy2MKe9kN+Xp5eWWZDXtULS08CmwBMV8nAwqVWJUaNG0dPTUzGvMyauGPD+Ab2uD2D58uV9Tm8FzmP9tEs+zTqFK0JmZtaSJK0P/Az4dEQ800eDTaUJ0Ud6X8usmRhxGnAawOTJk2Pq1KkVM3HgzF/1lr8+zd+38vogVZJ6216rcB7rp13yadYp3DXOzMxajqS1SZWgcyLi5zn58dzdjfx3cU5fCIwrLD4WeDSnj62QvtoykoYDGwFL6r8nZmbWqtwiZGZmLSWP1TkduCciji9MuhQ4AJiV/15SSD9X0vHAlqSgCDdHxEpJyyRNIXWt2x84uWxdNwDvB67J44jayvhaW6Fm7V7nnJiZtR9XhMzMrNXsAuwHzJV0e047ilQBulDSQcDDwN4AEXGXpAuBu0kR5w6NiJV5uU8As4ERwJz8glTROkvSPFJL0PQh3qde9VWZmTFxRc1d7szMrG+uCNmADOTuY/kJ3HcgzawaEXE9lcfwAOzayzLHAMdUSL8F2L5C+nPkipSZmXUnjxEyMzMzM7Ou44qQmZmZmZl1HVeEzMzMzMys67giZGZmZmZmXccVITMzMzMz6zquCJmZmZmZWddxRcjMzMzMzLqOK0JmZmZmHULSOEnXSrpH0l2SDs/pIyVdKen+/HeTwjJHSpon6T5JuxXSJ0mam6edJEk5fR1JF+T0mySNb/iOmtWBK0JmZmZmnWMFMCMiXgdMAQ6VtC0wE7g6IiYAV+fP5GnTge2AacCpkobldX0POBiYkF/TcvpBwNKI2Bo4ATi2ETtmVm+uCJk1iaT5+U7b7ZJuyWl1u2NnZmbdJyIWRcRt+f0y4B5gDLAncEae7Qxgr/x+T+D8iHg+Ih4C5gE7SRoNbBgRN0REAGeWLVNa10XArj73WDsaPpiFJc0HlgErgRURMVnSSOACYDwwH/hARCzN8x9JuouwEjgsIn6d0ycBs4ERwOXA4bnQmXW6t0XEE4XPpTt2syTNzJ+PKLtjtyVwlaRtImIlq+7Y3UgqP9OAOY3cCTMzaz25y9qOwE3AqIhYBKmyJGmLPNsY0vmjZGFOeyG/L08vLbMgr2uFpKeBTYHi+QxJB5POT4waNYqenh6WL19OT0/PavmcMXFFzftYvq5mqLRPnaBT96toUBWhzBdyZvWzJzA1vz8D6AGOoHDHDnhIUumO3XzyHTsASaU7di4/ZmZdTNL6wM+AT0fEM3002FSaEH2k97XM6gkRpwGnAUyePDmmTp1KT08PU6dOXW2+A2f+qre89Wv+vlP7nWeoVdqnTtCp+1U0FF3j6tn0atbJAviNpFvzXTMou2MHFO/YLSgsW7ozN4be79iZmVkXkrQ2qRJ0TkT8PCc/nq+5yH8X5/SFwLjC4mOBR3P62Arpqy0jaTiwEbCk/ntiNrQG2yJUupAL4Ae55l/PptfVVGpirWTUiME1s9aD87Dm9pvRvNrizbq7RMSjuYxcKenePuat5Y7dqoWrLDvQ/OOmN52Qr0Yeiy1+7JvZEMljdU4H7omI4wuTLgUOAGblv5cU0s+VdDypx84E4OaIWClpmaQppK51+wMnl63rBuD9wDUe0mDtaLAVoYZdyEHlJtZKTj7nEo6bW49ef7WbMXFF1+ehfPvNaL5u5WbdiHg0/10s6WJgJ/Idu3wTYbB37IrbqqrsQGuUn0qafTz3ZiD5amQZaOVj38yG1C7AfsBcSbfntKNIFaALJR0EPAzsDRARd0m6ELibFHHu0DxsAeATrBrDPYdV3a5PB87K3bSXkIY+mLWdQV1VNPJCzqyTSFoPWCsiluX37wK+Sn3v2JmZWZeJiOupfJMZYNdeljkGOKZC+i3A9hXSnyNXpMzaWc1jhCStJ2mD0nvShdydrLqQgzUv5Kbnh3C9ilUXcouAZZKm5Obc/QvLmHWqUcD1kv4M3Az8KiKuIFWA3inpfuCd+TMRcRdQumN3BWvesfsRadzdAzhQgpmZmVm/BtMiNAq4OEchGQ6cGxFXSPoj9Wt6NetIEfEg8PoK6U9Spzt2ZmZmZta7mitCvpAzM7OhIunHwB7A4ojYPqfV7Tl1ktYhRSmdBDwJ7BMR8xu0e2Zm1gKGIny2mZnZYM0mPVOuqPScugnA1fkzZc+pmwacKmlYXqb0nLoJ+VVa50HA0ojYGjgBOHbI9sTMzFpS64VgMjOzrhcR10kaX5ZczwcO7wkcndd1EXCKJDkEsFl7GV/jw1jnz9q9zjmxduSKkJmZtYt6PqfupYcUR8QKSU8DmwJPlG+02udwDcVzrobq+Vn1fMZUOzyzqh3yCO2TT7NO4YqQmZm1u1qeU1f3Z9gdWOOd6b4M1fOz6vlMq3Z4ZlU75BHaJ59mncJjhMzMrF08np9PRx2eU/fSMpKGAxuRHgxpZmZdwi1C1jDN6Mc795Gna7pL677DZi2png8cLq3rBuD9wDUeH2Rm1l1cEbKWV2sFCmDGxDpmxMwaRtJ5pMAIm0laCHyFVAGq13PqTgfOyoEVlpCizpmZWRdxRcjMzFpORHywl0l1eU5dRDxHrkiZmVl38hghMzMzMzPrOq4ImZmZmZlZ13FFyMzMzMzMuo4rQmZmZmZm1nVcETIzMzMzs67jipCZmZmZmXUdV4TMzMzMzKzr+DlCZmZm1pFKD+SeMXEFBw7w4dzzZ+0+FFkysxbiFiEzMzMzM+s6bhEyMzOzITd+gC0yRW6dMbOh4IqQmVmD1Hoh6ItAMzOz+nPXODMzMzMz6zquCJmZmZmZWddx1zgzsxZXS5e6UpQsd6szMzOrzC1CZmZmZmbWddwiZGZmZmbWgQYTrXH2tPXqmJPW5IqQmZmtwaGOzcys07lrnJmZmZmZdR23CJmZmZmZ2WrmPvI0B9bQO6CdegW0TEVI0jTgO8Aw4EcRMavJWTJrGy4/1pvBdHHrBi47ZrXp5rJT/rtaitLZn3aqIAxGO3WtbomKkKRhwHeBdwILgT9KujQi7m5uzsxan8uPWW1cdsxq0wllpxk3idqpgtAtWqIiBOwEzIuIBwEknQ/sCbRNgTJrIpcfs9q47FjddUmULpcd6wiKiGbnAUnvB6ZFxMfy5/2AnSPik2XzHQwcnD++Frivl1VuBjwxRNmtlvPQ/O0PZR5eGRGbD8F6B6ya8jOAsgOt8X+rxPkamFbNV1uVnZw+kPJTb636fyxyHuunr3x2Stlpl//FQHTiPkHn7FevZadVWoRUIW2NGlpEnAac1u/KpFsiYnI9MlYr56H522+VPDRAv+Wn2rIDrfudOV8D06r5ajF1PfcMhXb4PzqP9dMu+WQQZaeN9rFqnbhP0Ln7VdQq4bMXAuMKn8cCjzYpL2btxuXHrDYuO2a1cdmxjtAqFaE/AhMkvUrSy4DpwKVNzpNZu3D5MauNy45ZbVx2rCO0RNe4iFgh6ZPAr0lhGH8cEXcNYpVN6cJQxnlo/vahNfIwpDq0/FTifA1Mq+arZQxB2RkK7fB/dB7rpy3yOciy0xb7OECduE/Qufv1kpYIlmBmZmZmZtZIrdI1zszMzMzMrGFcETIzMzMzs67jipB1LEmVwnuamVmd+HfWrPMUy3Wnl/GOqQhJGpX/Nu0fJqnp36ekTZu8/bGS1mlyHqYARER0egHuFpKGSdqs2fnoTSuUfesczf4dr0Y7/M5KOlzSVq2avxJJO0oaL+nlzc7LUGmFa7RG6KBzwaskjZS0aSuX8XroiGAJkk4GXgP8HbgWuCYi7mlSXoZFxMombft40vcAcGxE/KHB2/8O8CrSQ9W+EhG3N3L7OQ+zgP2Bb0bEyTmtaf+TdiLp86RIkg9HxDnNzk+RpB8BcyLiZ83OS7uQ9NaI+G2z82ED0+zf8Wq0w++spNOBzSJiz2bnpS/5+mUisBg4PSJ+3eQs1V0rXaPVm6Q3AOsCIyLi6pzWUmVhoCS9F/gGcAMwATgxIi7J0xSdUHEoaPuaq6QPAW8G3gdcBmwCHC7prQ3MwwxJ3weIiJWShjVq24U8fB7YAfgQ8Cfg65I2bOD2jwBeB/w7sAB4r6SXNeHuyO3ANcD2kr4L6X/S4Dy0HUknAe8E5gNHSDpB0tbNzVWSLwxfVqkS1OQW4OMknSTpCkmvb6U7Zvk726PZ+bCBafbv+ADcTgv/zubWqm1KlSBJO+cy+sYmZ201kg4CJkbEVNJzed4iaTNJWzU3Z/XTCtdoQ0XStsD5wIHAIZKulbRRs64D6yH3vDgG+BTwaeB44IeSjoLWbgGuVdtXhIB/AFdHxIqImA38DLgb2EvSq4d645J2Az4LjJU0R9KoXAga9t1KGg3sCnwmIp6NiKOBx4CG/OjnJu93AYfkk+EjwDTgEmBmg3/U7wM2AGYDL0iaLelsSdt3WuGtByWbke76HBIR5wG7AC8Hvixpy9J8TcrfNNKP8Vfz549I+rykH0naoFk/ypI+DLwV+BZwHfAD4KOS1s/Tm1lB+yywXUR8vpC2fuG9y0ELavbv+AC1+u/sA8AdAJIOAE4gnae/JOm/m5mxkvw9bQ5clZNeIJ03ZwNflLRXc3JWd029Rhsq+f83AzgzIg6OiL1Jx9ydknZopRsDAxERTwB/AB6PiL9HxC+BKcCHJX0jz+MWoRZzC7CrpI8D5ObWOcBGwNsbsP2HgS9GxB7AncBlkiZHxIuQmkiHOgMRsQj4DPAXSaWH5C4lXdAiacN8kh2q7T8OvC8iHpT0KuBj+fU/wCuAfYdq2xXy8idgLqkydgSwI/AWYHinFd56iOQJ4GbgA5I2iYhlEfFfwDPARZLWa+J39xfgWFIr1beBg0gXOZsAvytVhpqQry1JJ/eFEfEN4Chgb+AT0LwTRb4p8V+kCw0k7SXpe8DvJR3ezLxZ35r9Oz4QbfA7+09gYm6tehvwH8ChwKmkVqymX/vk7+kq4LOSzgZmAXuS7sTfAbypidmrp2Zfow2J/P+7lnSsldIOJ/0fz5L0+mblrVaStpC0NqnyemgpPSIeJPUw2Kp0c7STNP3HYLAiYgHwBWCKpE/ltPuBM4CpktYb4u3fA5yd33+edDfnJ5JK3VL2k7TxUOYhuy8i/hERK/Lnm0jjPQB+zhDfVYyIZ/Pfh4ApEXFPRNxGajbeVtLLh/pOYWH9Qbqz9mnSXbZvASflZmwrKHxn15O6Nu4kaQOAiPgkcA+wXZOyV/oB/k7Ox97AJyLi5xHxn6SL/Tc0KWu/BjaXNFGSIuIa4DDgQKWnrTfLEuAQYF1JlwNfA36a83aopP9pYt6sf039Ha9GO/zORsTTpBtwI4GdgecjYjnwZ9Lv3LhWaLmKiFtIlcgzgAvzjZWHSN0OJ0l6RSvkczCafY02xBaSWkr2KiVExHeBi4BtmpWpWih1p/4R8GPgCuBfJF1Xupmfz8Ub0Wb7VY22rwhlV5MOvNdJOi/XxA8DlpYu0IdSRLxQeP9dUqH/kqTHgF0i4qkG5OHFsqS7gDGSzgIejIhLhzoPBU8U3n8UeCI3sQ7pncLC+k8F3kNqldojIk4BPh8Rdw/l9ttZRFxJqgwdRuqyMClP+ldS60fDlS4AIuIx0g/0uyJibmGW0UCzxk/MB54FDgC2kfTyiPgL8ElSa1VT5N+i3wHfB54CvhYR1+SgCQcD7XzR0fFa7He8onb5nc0X4F8ldS+8TNKOwLeBOyPiry3SckXOy5XAMknfkLQ5qRvfPRHxWKvkc5Caeo1WT0pjwn8AEBE9pN4AP5Y0szDbSlI317agVWMTPwj8lXSu2I3U++KPkj4q6YvAeNLNhI7SEVHj4KWLprHAx0mRy57Od7SblZ/zgFdExNuasG2Rau33AJdFxPuakIfhwDdJYxXe0+BtC5gOzI2IOxu57XahNJbt8dyaEYX0acB7SV0aRwILI2K/FsnbS58l/Rh4MSI+1sB8rVW8UFUaxP4tYG3SHdxbgS8Cf4+IgxuVr0p5y2mb5W6Ppc+nA8tz9w1rca3wO96XdvqdzePmNgLWjoijmp2fSvLNp0NI4zUXRsSHm5ylumq1a7RaKI0J/zEpkMnawEci4lFJrwMuIAURWULqRrZPRNzarLxWK3e3/QlwRET8Oaf9FDg+Im6QtC/pZuho0nio25uW2SHSMRWhIklrF1tpmrD9rYEfAntGxDNNzMfngNOakQelwdl7ApdGxLJGb7+QjzUuELud1gxlenVE3FuYvgmptXh8o3/I+8pbPpGuRerz/4FGVzZyHoZFDoYSq8YBfgIYR7qj9lREfKjR+SrmrZdpRwLviIi2uUtpSTN/x6vl39n6yL9xm5FaSlb0N3+7avY1Wq1yhWdKRPxE0v+RzkWfiogb8vTppJb4f0QbPbog79d84IWIWJHH1j0ZEV/Ox+T43GWzI3VkRagV5K4yf29yHjou3rsNjlIo0y8Ak4EPk+7QjQXOK/1wSxoZEUtaNG8bRsQzjTyRSpoBTIiIQ/LnUmVotYqHpBHAyoj4Z2/ralTeyuYZReqi+oNm/F9tcPw7btY6iuceSYeSWvG+HBEXSzoQOLeR54B6qNDbYX9g61wRugo4JSJ+0bQMDrFOGSPUcppdCcp58MnTyvUVynScpDHAwZI2atG8fVIpUlyjKkF9hccvddMbJWl0pEHujawE9Zq3nD8kbUH6Xo91Jag9+XfcrHVE5THhMyU9ThoT3laVIKhqbOIvGp+rxnFFyKy79BXKdDdSAICLI0VdasW8/azBXS17DY8fES8qRTz6AM0JkFBN3j4IjHW3JTOz+ouIOcCDwN0R8fFm52ewcle45cBHgI2a0QW90dw1zqzLSHoXsA9we0ScnNPeSooUc0Az+6a3Yt566QpxZERcpvRg1TubNYC0lfNmZtbpWmVMeL21w9jEenFFyLpKjja2B7A4IrbvZ96tSM862BgYBsyMiMuHPJNDTOm5AO8iRYfbhPQAuC8Dj0bEp5y3vkl6N3A0KZToxaXxOa2glfNmZtaJWmFMeL1109hEV4Ssq0h6C6nZ98wqKkKnAX+KiO8pPSTw8ogY34BsDrlWDmXaynkrURPD4/enlfNmZmbWSob3P4tZ54iI6ySNL6ZJeg3wXWBzUtjmj+eQzcGqB3ZuBDzawKwOqXynZwHw5VYLZdrKeYOXukK8ghQevqW0ct7MzMxajVuErOvkitBlpRYhSVcDh0TE/ZJ2Br4ZEW/PDxr7DamL1nqk57C0/APSbOi1cleIVs6bmZlZK3GLkHW1/ODX/wf8NPXIAmCd/PeDwOyIOE7Sm4CzJG3vCFzWyhWNVs6bmZlZK3FFyLrdWsBTEbFDhWkHAdMAIuIGSeuSnvq9uHHZMzMzM7Oh4OcIWVfLoSEfkrQ3pIH6kl6fJz8M7JrTXwesC/ytKRk1MzMzs7pyRci6So6odQPwWkkLJR0E7AscJOnPpCcqlwaazwA+ntPPAw7slnCSZuUk/VjSYkl3VjHvVpKulfQnSXdIek8j8mhmZjYQDpZgZmb9cuh5MzPrNG4RMrOmyS0Hy/ODVBu1ze9L+tIg1zFb0tfrlacqtzlH0gE1LjteUkiqeVxoRFwHLClb72skXSHpVkm/k/Qvpdnp0NDznULSfEnvqHLeyKHZ214zyq5ZK6rHeaETuCI0AIM9aPIF36sHsf27JE2tdflO1dcFogt66ylegEXEwxGxfkSsbNT2I+KQiPhao7ZXLxHx7og4A0DSgZKub3aegNOAT0XEJOBzwKk5/Wjgw5IWApcDn2pO9szMzHrni8MGioj1B7n8dvXKSyeJiHc3Ow9m3cah561elA4gtfrxIWlYI2/aWHeSNDwiVjQ7H31phzxWyy1C1lBumeluks4CtgJ+mVtIv1BssZPUI+nrkv6Qp/9S0qaSzpH0jKQ/5gfiltb3L5KulLRE0n2SPlBFHl7qGiNpag6aMSMHAlgk6SNV7s4mkn4laZmkmyS9prCN/5fz+nT++/8K0w6U9GBe7iFJ+xbSfy/p5LzcvZJ2LSzXI+ljOYLh94E35e/oqTx99xyc4BlJCyQdXeV+1Oql0POF1+vytIOACyGFnidFXNxsiPPTVnLL6OdyMImnJV0gad1KrX3Frmn5+D01t4Qvz8fMKySdKGlpPm52HGBedpJ0g6Snchk4RdLLymZ7Tz5un5D0f5L6vH6o8ng+RtLvgb8Dr+6n3IyU9BNJj+b9/EVh2h6Sbs/5/4Okfy1M21HSbbm8XUA6Fot57O+7/p6kyyU9C7xN0paSfibpb7n8Hlb2Pd6Sy+Djko6v7j9gQ63Z5S1v/0hJd+flfqL0SI7ieegISY8BP5G0Tt7Go/l1oqR1CuvbMx/zz0h6QNK0nL6RpNNzOX5E6Xw6LE8bJunbuQw/COxeIY/vKHw+WtLZ+X2pd81Bkh4GrsnpH5V0T96nX0t6ZU6XpBOUzqtP5++9z7GlzeKKECBpZj6QluWD9N9zen8HzUAv2oqF6z15W8vywfq5nL6ZpMuUftCXKPW7XytPe+kg7auQqMaLu97ylKd9XNK8nKdLJW2Z09foepa/l4/l96WT4QmSlgBHSxoh6ThJf80F5HpJI/L8U/L3+ZSkP6uKroBl2+vzf2bNFRH7kcKSvze3kF5YYbbpwH7AGOA1pCh/PwFGAvcAXwGQtB5wJXAusAWpFeJUSQNtOX0FaRzLGNIF/HclbVLFch8E/hfYBJgHHJPzNRL4FXASsClwPPCr/NuwXk5/d0RsQGpRub2wzp2BB0mVhq8AP8/re0lE3AMcAtyQuxVunCc9C+wPbEw67j8haa9qv4SBcuj5uvgA6VllrwL+FThwAMt9kXScPE8qI7flzxeRjrmBWAl8Ji//JtL/7r/L5vl3YDLwBlJkzY9Wsd7+juf9gIOBDYBl9FJu8rxnAS8HtiOV9xMAJL0B+DHwX3m5HwCX5nPky4Bf5GVHAj8F/rOKfBd9iFS2NwD+APwS+DPp92JX4NOSdsvzfgf4TkRsSPrtqvT7Zs3T7PK2L7Ab6djYJq+z5BWkY/SVpDLxP8AUYAfg9cBOpfkl7QScCXye9Hv/FmB+Xs8ZwApga2BH4F3Ax/K0jwN75PTJwPurzHfRW4HXAbvl88tRwH8AmwO/I0XYJW/3LXk/Nwb2AZ6sYXtDLyK6/gXsDWxJqhjuQ7qgGE262LgXGEc6QK8lDQIenpfrIV0AvYZ0IXU38BfgHaRuh2cCPylsJ4Ct8/tFwJvz+02AN+T33yTd7V07v97Mquh+84F35PdfBW4knRA2J/1Afy1Pm0oqCF/N63gP6Y7bJv18D73l6e3AE6QT4DrAycB1edr44ndS+F4+lt8fmPPyqfydjAC+m+cZAwwjXQyukz8/mfO7FvDO/HnzfvJd3F6f/zO/mv8qO45XO37y//J/CvMeB8wpfH4vcHt+vw/wu7J1/wD4Sj/bnw18Pb+fCvyj7PhdDEypYh0/Knx+D3Bvfr8fcHPZ/DfksrAe8BTpYmxE2TwHkoIKqJB2M7Bf4bsplqvr+8njicAJlb7nGv9v55F+I14AFpIqja8CriBdGN4NfDnPuy3w+5x+O/CuZh93rfbK5eDDhc/fIv32r/G/ZfVzx2zgh4VpnwLuKXyeSGqpq7ocVpj2aeDisu1PK3z+b+DqftZfzfH81cK0vsrNaOBFKpzDgO+Rz32FtPtIF2xvqZCHP7Cq/FfzXZ9ZmLYz8HDZ/EeSz/PAdaSbI5s1+/jyq+Lx3uzydkjh83uAB/L7qcA/gXUL0x8A3lP4vBswP7//Afm3vWwbo0gVtRGFtA8C1+b315Tl4V2sfv5d7TeBNNbz7Px+fJ731YXpc4CDCp/XIl1rvpJ03fgXUmVurWb///t6uUUIiIifRsSjEfFiRFwA3E+qfX8AODEiFkTEElIlpdxPIuKBiHiadFA8EBFXReo7+VNSzbuSF4BtJW0YEUsj4rZC+mjglRHxQkT8LvIRVmZf0klkcUT8jfTju1/Z+r+a13E5Kezta/v5KnrL077AjyPitoh4nvTD/6Zia1c/Ho2Ik/N38jzpTuLhEfFIRKyMiD/k9X6YFGb38vy/uBK4hfSDUa1q/mfW2h4vvP9Hhc+lsXavBHbOrYdPKXUR25d0Z20gnozV+zr/vbCNvjzWyzJbAn8tm/evwJiIeJZUgTsEWKTUte5fCvM9Ulbe/5rX1y9JOys9u+dvkp7O26hbd7SI+GBEjI6ItSNibEScHhEPRcS0iHh9RGwbEV/N894dEbvk9B0i4jf1ykeH6e0Y6k+1ZaQqkrbJPREek/QM8A3WPHYWFN5Xe1z2dzwX19lruSHd2FoSEUsrbOOVwIyy34FxeX1b9pKHgSjm8ZXAlmXbOop0AQrp5sA2wL25R8geA9yWDa1ml7e+ytDfIuK5wufy8lCcfxypolTulaSb34sKx+cPSDfMS+ssz8NAlZeH7xS2tQQQ6Vx3DXAK6cb345JOk7ThGmtrAa4IAZL216r+xU8B25NOAtUcNLUWkP8kXeD/VdJvlQYUA/wfqZXpN0r9sWf2snxfhQRqu7jrLU+rbSsilpNaasb0s76S4ne4GambTG+FeO+yk8y/kSqG1apHQbehValiX4sFwG8jYuPCa/2I+ESd1l+rR0nHctFWwCMAEfHriHgn6bi+F/hhYb4x0qrIA3m5SqGnK32H5wKXAuMiYiPS3U5VmM9a27OkLmAASBpoxb4W3yMdixMides6ijWPnXGF970dl+X6O56Lx3Ff5WYBMFLSxhW2sQA4pux34OURUWrBrJSHkmq+62IeFwAPlW1rg4h4D0BE3B8RHyRdeB4LXJS7w1rramR566sMlf+ml5eH4vwLSD2Ryi0g3WzerHB8bhirAm0tqpCHotW+CyrfVCwvD/9VVh5GRMQfACLipEgRRbcj3SD4fIX1NV3XV4SUBnb9EPgksGmk/vZ3kk4C/R00NYuIP0bEnqQfzF+wamDxsoiYERGvJnUD+qwKA0wL+iokdc1T+bbyD/umpBPUszm5r8JTLDhPAM/ReyE+q6xQrRcRswawG0P2P7O6eRyoOYx8wWXANpL2k7R2fr0xj0lppstzvj4kabikfUhdxS6TNErS+3IZep7UUluMQrUFcFjel71JfbEvr7CNx4GxWn1A+waku+bP5T7kHxqCfbOh92dgO0k7KA2mProB29wAeAZYnlsoK91M+LykTSSNAw4HLqhivdUez9BHuYmIRaQeF6fmPKyt9IBfSOfvQ3KLqCStpxQ4ZANS17oVOQ/DJf0HqbdHyUC/65uBZ5QGtY9QGpO6vaQ3Akj6sKTNI0W/eyov4yhzra2R5e1QSWPzOLmj6LsMnQd8UdLmkjYDvgycnaedDnxE0q6S1pI0RtK/5HLyG+A4SRvmaa+R9Na83IWksjBWaRxs+Y3224HpuXxVM4bo+8CRyuNylQI1lMaMvjGXybVJ14nP0aJloesrQqQ++0EeyKsUVKAU2aK/g6Ymkl4maV9JG0XEC6QT0Mo8bQ9JW+c7WKX0SgdPX4Wkrnki3Wn+SP6hWIfUbeKmiJifu+U9QnpmyDBJH6VyJQeAfIL4MXC8UvSdYZLelNd7NvBeSbvl9HWVAj+MHcCuDMn/zOrqm6Rj9ylqG6wJpJsGpD7O00mV9cdId2HX6Wu5oRYRT5IGpM4gtZx+AdgjIp4g/ebOIOV3CWkcQ3FQ+k3ABNINg2OA9+f1lbsGuAt4TNITOe2/ga9KWkb6PfBA7TYUEX8hje+8itRNuxHPi/ocqeK8jFSxqHSBdglwK+li6Veki7H+VHs891duIHX9foHUcrWYNI6JiLiFNAj8FGApqUfFgXnaP0kDuQ/M0/YBfl7Y5oC+60ihs99LGsD+UN6vH5HGCEMaiH+XpOWkwAnTy7o7WYtpcHk7l1RReTC/+nqw79dJQwPuAOaSgjN8HSAibgY+QgoY8jTwW1bdrN4feBlpzOZSUjCHUq+aHwK/JlX+bqNQFrIvka7flpKGW5zb185ExMWkc+75Sl1q7wRKjzPZMG9vKalnzpPAt/taX9NECwxUavaL9AO9hPSjdjzpoPoYaXD/CaR/4EPAoaw5sPtjhfV8nfTsjNLndwDzCp+DFMnjZaQBxktJFY4/Av+W5/kMacDas6QByV8qLD+fVYPM1yVF11mUXyeRB9qRBt4tLNvHl5bt5TvoNU95+iGk7mxLSHfixxamvTt/P0+RBrf/lj4GdZMCJpxIqkA9TRpgOiJP2zkvv4RUOf0VsFU//7+ewvb6/J/55VerviqVFb/8ateXj2e//Fr16u8azK/mvUrRyMzMrIkkHUiq0P9bs/NiNlg+ns1WkTSfVB6uanZebHXuGmdmHUfSXUrP9ip/7dvIdZg1i6Stejl+l0uqy9hJSd/vZf3fr8f6zdpFI8qbDQ23CHUZSXexZmQeSJE/zml0fqqV+1xX8u6I+F1DM2NmZmZmbc8VITMzMzMz6zrDm52BWm222Wax+eabs9563RWi/9lnn+2qfW7V/b311lufiIjNm52PWmy22WYxfvz4Zmejblr1GKmXTtu/di470Hv56bT/U5H3rTV0atmB9vo/VNLu+Yf234e+8t9X2WnbitD48eP59re/zdSpU5udlYbq6enpqn1u1f2V1LYPah0/fjy33HJLs7NRN616jNRLp+1fO5cd6L38dNr/qcj71ho6texAe/0fKmn3/EP770Nf+e+r7DhYgpmZmZmZdR1XhMzMzMzMrOtUVRGStLGkiyTdK+keSW+SNFLSlZLuz383Kcx/pKR5ku6TtFshfZKkuXnaSZKU09eRdEFOv0nS+LrvqZmZmZmZWVZti9B3gCsi4l+A1wP3ADOBqyNiAnB1/oykbYHpwHbANOBUScPyer4HHAxMyK9pOf0gYGlEbA2cABw7yP0yMzMzMzPrVb8VIUkbAm8BTgeIiH9GxFPAnsAZebYzgL3y+z2B8yPi+Yh4CJgH7CRpNLBhRNwQKWb3mWXLlNZ1EbBrqbXIzMzMzMys3qqJGvdq4G/ATyS9HrgVOBwYFRGLACJikaQt8vxjgBsLyy/MaS/k9+XppWUW5HWtkPQ0sCnwRDEjkg4mtSgxatQoli9fTk9PT3V72iG6bZ+7bX/NzMzMrDGqqQgNB94AfCoibpL0HXI3uF5UasmJPtL7Wmb1hIjTgNMAJk+eHOuvv37LhPobP/NXNS87f9buVc/b7uENB6rb9rcV1HosD+Q4NrPmGkg5nzFxBQcW5ndZt3qb+8jTqx1jA+Hj0QajmjFCC4GFEXFT/nwRqWL0eO7uRv67uDD/uMLyY4FHc/rYCumrLSNpOLARsGSgO2NmZmZmZlaNfitCEfEYsEDSa3PSrsDdwKXAATntAOCS/P5SYHqOBPcqUlCEm3M3umWSpuTxP/uXLVNa1/uBa/I4IjMzMzMzs7qrNmrcp4BzJN0B7AB8A5gFvFPS/cA782ci4i7gQlJl6Qrg0IhYmdfzCeBHpAAKDwBzcvrpwKaS5gGfpe+ud2ZtQdK6km6W9GdJd0n635zu0PNmZmZmTVbNGCEi4nZgcoVJu/Yy/zHAMRXSbwG2r5D+HLB3NXkxayPPA2+PiOWS1gaulzQH+A9S6PlZkmaSKv5HlIWe3xK4StI2+UZCKfT8jcDlpNDzcyiEnpc0nRR6fp/G7qaZmZlZ+6m2RcjMBiiS5fnj2vkVOPS8mZmZWdO5ImQ2hCQNk3Q7KZjIlTnoyGqh54Fi6PkFhcVLIebHUGXoeaAUet7MzMzM+lBV1zgzq03u1raDpI2BiyWt0TW0YMhCz5c/g6uvZzPNmLiijyz2rlnPe+r0Z011+v6ZmZk1iytCZg0QEU9J6iGN7Xlc0uj8IOJ6hZ5f2Ffo+fJncPX1bKaan+Wwb+/rHEqd/qypTt8/MzOzZnHXOLMhImnz3BKEpBHAO4B7ceh5MzMzs6Zzi5DZ0BkNnCFpGOmmw4URcZmkG4ALJR0EPEyOmBgRd0kqhZ5fwZqh52cDI0jR4oqh58/KoeeXkKLOmZmZmVk/XBEyGyIRcQewY4X0J3HoebNeSRpHio74CuBF4LSI+I6ko4GPA3/Lsx4VEZfnZY4khZNfCRwWEb/O6ZNYdRPhcuDwiAhJ6+RtTAKeBPaJiPkN2UEzM2sJ7hpnZmatZgUwIyJeB0wBDs3P2QI4ISJ2yK9SJaj4DK5pwKm5JRZWPYNrQn5Ny+kvPYMLOIH0DC4zM+sirgiZmVlLiYhFEXFbfr8MuIdVIeMr8TO4zMxswFwRMjOzliVpPKmL6U056ZOS7pD0Y0mb5DQ/g8vMzAbMY4TMzKwlSVof+Bnw6Yh4RtL3gK+RnpX1NeA44KMM4TO4cj76fQ5Xuz3vaSDPCxs1YvX522k/+9Nu/zczqy9XhMzMrOVIWptUCTonIn4OEBGPF6b/ELgsfxyyZ3Dl7fb7HK52e97TQJ4XNmPiCo6bu+pyoVnPDBsK7fZ/M7P6ctc4MzNrKXmszunAPRFxfCF9dGG2fwfuzO/9DC4zMxswV4TMzKzV7ALsB7xd0u359R7gW5LmSroDeBvwGUjP4AJKz+C6gjWfwfUjUgCFB1j9GVyb5mdwfRaY2ZhdMxt6koZJ+pOky/LnkZKulHR//rtJYd4jJc2TdJ+k3Qrpk3J5myfppFIwkXzD4YKcflMex2fWltw1zszMWkpEXE/lMTyX97GMn8FltsrhpGiLG+bPM4GrI2KWpJn58xFloee3BK6StE2+kVAKPX8jqexNI91IeCn0vKTppNDz+zRu18zqxy1CZmZmZh1C0lhgd1JLaEkxXPwZrB5G3qHnrWtV1SIkaT6wjPTE7hURMVnSSOACYDwwH/hARCzN8/sJ32ZmZmaNdyLwBWCDQtqoPGaOiFgkaYucPobU4lNSCjH/AlWGnpdUCj3/RHlGqom4CGtGJhyIVoj61wnRB9t9H2rN/0C6xr0tIooHuZtZzczMzFqEpD2AxRFxq6Sp1SxSIa1uoeeribgIcPI5l6wWmXAgWiGKYSdEH2z3fag1/4PpGudmVjMzM7PWsQvwvtyT53xSwJGzgcdLURfz38V5/sGEnqe/0PNmra7a6ncAv5EUwA9yDb/hzazlTayt1IxXa5MuDKxZt5X2uRG6bX/NzMxqFRFHAkcC5Bahz0XEhyX9Hylc/Kz8txhG/lxJx5N68ZRCz6+UtEzSFOAmUuj5kwvLHADcgEPPW5urtiK0S0Q8mis7V0q6t495h6yZtbyJdf3112+ZZryBPJyu3ECaddu96XKgum1/zczMhsAs4EJJBwEPkyMmRsRdkkqh51ewZuj52aRx3XNYPfT8WTn0/BLScAiztlRVRSgiHs1/F0u6GNiJ3MyaW4Pq1cza7xO+zczMzKxvEdED9OT3TwK79jKfQ89b1+p3jJCk9SRtUHoPvIv0NO/iU7nLm1n9hG/repLGSbpW0j2S7pJ0eE4/WtIjZQ+KLC3jB9uZmZmZNUA1LUKjgIvzdddw4NyIuELSH3Ezq1lfVgAzIuK2fDPhVklX5mknRMS3izM74qKZmZlZ4/RbEYqIB4HXV0h3M6tZH3IraCmgyDJJ97AqQEglL0VcBB7KNwZ2ytF/NoyIGwAklSIuzsnLHJ2Xvwg4RZLcompmZmbWt9qCtpvZgOQuazuSou/sAnxS0v7ALaRWo6U0MOJiX5H42u2hdp0eWbDT98/MzKxZXBEyG2KS1gd+Bnw6Ip6R9D3ga6TIiF8DjgM+SgMjLvYVia/WCIjNeqhdp0cW7PT9MzMzaxZXhMyGkKS1SZWgcyLi5wAR8Xhh+g+By/JHR1w0swEZP4hHN5iZdbt+o8aZWW1yZLfTgXsi4vhC+ujCbP9OisIIjrhoZmZm1jBuETIbOrsA+wFzJd2e044CPihpB1IXtvnAf4EjLpqZmZk1kitCZkMkIq6n8hiey/tYxhEXzczMzBrAXePMzMzMzKzruCJkZmZmZmZdx13jzMyspUgaB5wJvAJ4ETgtIr4jaSRwATCeNL7uA/kZXEg6EjgIWAkcFhG/zumTWDW+7nLg8IgISevkbUwCngT2iYj5DdrFtlZrpLr5s3avc07MzAbHLUJmZtZqVpAeNPw6YApwqKRtgZnA1RExAbg6fyZPmw5sB0wDTpU0LK/re6SHCU/Ir2k5/SBgaURsDZwAHNuIHTMzs9bhipCZmbWUiFgUEbfl98uAe4AxwJ7AGXm2M4C98vs9gfMj4vmIeAiYB+yUQ9VvGBE35LDyZ5YtU1rXRcCuOTy9mZl1CXeNMzOzliVpPLAjcBMwKj9Xi4hYJGmLPNsY4MbCYgtz2gv5fXl6aZkFeV0rJD0NbAo8USEPB5NalRg1ahQ9PT1r5HP58uUV04fajIkrhnwbo0bUZzvN+H7606z/m5m1BleEzMysJUlaH/gZ8OmIeKaPBptKE6KP9L6WWTMx4jTgNIDJkyfH1KlT15inp6eHSulD7cAax+sMxIyJKzhu7uAvF+bvO3XwmamzZv3fzKw1uGucmZm1HElrkypB50TEz3Py47m7G/nv4py+EBhXWHws8GhOH1shfbVlJA0HNiI9lNjMzLqEK0JmZtZS8lid04F7IuL4wqRLgQPy+wOASwrp0yWtI+lVpKAIN+dudMskTcnr3L9smdK63g9ck8cRmZlZl6i6IiRpmKQ/Sbosfx4p6UpJ9+e/mxTmPVLSPEn3SdqtkD5J0tw87aTSwNR88rogp9+U+4SbmVl32gXYD3i7pNvz6z3ALOCdku4H3pk/ExF3ARcCdwNXAIdGxMq8rk8APyIFUHgAmJPTTwc2lTQP+Cw5Ap2ZmXWPgXT6PZwUuWfD/LkUxnSWpJn58xFlYUy3BK6StE0+KZXCmN5Iep7DNNJJ6aUwppKmk8KY7jPovTMzs7YTEddTeQwPwK69LHMMcEyF9FuA7SukPwfsPYhsmplZm6uqRUjSWGB30l21EocxNTMzMzOztlRt17gTgS+QnvBdsloYU6AYxnRBYb5SuNIxVBnGFCiFMTUzMzMzM6u7frvGSdoDWBwRt0qaWsU6hyyMafmzHFop/v9gnrEwkH1opX1uhG7bXzMzs8GQtC5wHbAO6Trvooj4iqSRwAXAeGA+8IGIWJqXOZI0TGElcFhE/DqnTwJmAyNIQxoOj4iQtA6pZ88k4Elgn4iY36BdNKubasYI7QK8Lw9UXRfYUNLZ5DCm+aF29QpjurCvMKblz3JYf/31Wyb+/2Ce5TCQZyt02zMP2nl/JY0jnSheQWpNPS0ivuOTkZmZDaHngbdHxPIchv56SXOA/8Bju81W02/XuIg4MiLGRsR4UkG5JiI+jMOYmvVnBTAjIl4HTAEOzSecUqCRCcDV+TNlJ6NpwKmShuV1lU5GE/JrWk5/6WQEnEA6GZmZWZeKZHn+uHZ+BR7bbbaGwTxHyGFMzfoQEYsi4rb8fhkp6uIYfDIyM7MhlB95cjupt86VEXETHttttoaBhM8mInqAnvz+SRzG1Kwq+dlYOwJrnIwkFU9GNxYWK510XqDKk5Gk0snoiaHZEzMza3X5BvQOkjYGLpa0xrVXQcPGdvc25nfUiNrHWrfCOOJOGM/c7vtQa/4HVBEys4GTtD7wM+DTEfFMHw02TT8ZQfudjNr9x7s/nb5/ZjZ0IuIpST2k7tRNH9vd25jfk8+5hOPm1nZJOpBx1kOlncczl7T7PtSaf1eEzIZQHqj6M+CciPh5Tm7ZkxHUHvijWSejdv/x7k+n75+Z1ZekzYEXciVoBPAO0vjR0njsWaw5tvtcSceTgiWUxnavlLRM0hRSb4b9gZMLyxwA3IDHdlsbG8wYITPrQx6rczpwT0QcX5jkQCNmZjZURgPXSroD+CNpjNBleGy32RrcImQ2dHYB9gPm5kGrAEeRTj4XSjoIeJg8Pi4i7pJUOhmtYM2T0WxS+Ow5rH4yOiufjJaQos6ZmVmXiog7SGNSy9M9ttusjCtCZkMkIq6n8hge8MnIzMzMrKncNc7MzMzMzLqOK0JmZmZmZtZ1XBEyMzMzM7Ou44qQmZmZmZl1HVeEzMzMzMys6zhqnJnVxfgaH8QKMH/W7nXMiZmZmVn/XBEys7Y1fuavmDFxBQfWUAlz5au1SfoxsAewOCK2z2lHAx8H/pZnOyoiLs/TjgQOAlYCh0XEr3P6JFY9g+ty4PCICEnrAGcCk4AngX0iYn5Dds7MzFqCu8aZmVkrmg1Mq5B+QkTskF+lStC2pIcJb5eXOVXSsDz/94CDgQn5VVrnQcDSiNgaOAE4dqh2xMzMWpMrQmZm1nIi4jpgSZWz7wmcHxHPR8RDwDxgJ0mjgQ0j4oaICFIL0F6FZc7I7y8CdpXU2wOQzcysA7lrnJmZtZNPStofuAWYERFLgTHAjYV5Fua0F/L78nTy3wUAEbFC0tPApsAT5RuUdDCpVYlRo0bR09OzRqaWL19eMX2ozZi4Ysi3MWpEfbbTjO+nP836v5lZa3BFyMzM2sX3gK8Bkf8eB3wUqNSSE32k08+01RMjTgNOA5g8eXJMnTp1jXl6enqolD7UahkfN1AzJq7guLmDv1yYv+/UwWemzpr1fzOz1tBv1zhJ60q6WdKfJd0l6X9z+khJV0q6P//dpLDMkZLmSbpP0m6F9EmS5uZpJ5W6IUhaR9IFOf0mSeOHYF/NzKyNRcTjEbEyIl4EfgjslCctBMYVZh0LPJrTx1ZIX20ZScOBjai+K56ZmXWAam7xPA+8PSKWS1obuF7SHOA/gKsjYpakmcBM4IiyQatbAldJ2iYiVrJq0OqNpOg904A5FAatSppOGrS6T6075TC+ZmadR9LoiFiUP/47cGd+fylwrqTjSeedCcDNEbFS0jJJU4CbgP2BkwvLHADcALwfuCaPIzIzsy7Rb0UonxiW549r51eQBppOzelnAD3AERQGrQIPSSoNWp1PHrQKIKk0aHVOXubovK6LgFMkySclM7PuJOk80jlmM0kLga8AUyXtQDoHzQf+CyAi7pJ0IXA3sAI4NN98A/gEq8Jnz8kvgNOBs/I5agnpBp6ZmXWRqjr95jCktwJbA9+NiJskjSrdmYuIRZK2yLMP6aBVMzPrfBHxwQrJp/cx/zHAMRXSbwG2r5D+HLD3YPJoZmbtraqKUL6ztoOkjYGLJa1xUikYskGr5ZF7eov2MpjoNrVGj2nUNrstwk27768fCmlmZmbWmgYUBiYinpLUQxrb83ipv3Z+VsPiPNtgBq0u7GvQannknvXXX79itJfBRNGpNapNo7bZbRFuOmB/ZwOnkCorRSdExLeLCa0wvs7MzMysW1QTNW7z3BKEpBHAO4B7WTXQlPz3kvz+UmB6jgT3KlYNWl0ELJM0JUeL279smdK6PGjVOoYfCmlmZmbWmqppERoNnJHHCa0FXBgRl0m6AbhQ0kHAw+S+1h60alaVhj4UspoHQpY04gGN5QbTJbXWhz22S5fLdu8eamZm1qqqiRp3B7BjhfQngV17WcaDVs161/CHQlbzQMiSRjygsdxguqTW+rDHVny4YyUd0D3UzMysJfXbNc7M6ssPhTQzMzNrPleEzBosj/kpKX8opMfXmZmZmTWAK0JmQyg/FPIG4LWSFuYxdd+SNFfSHcDbgM9AGl8HlMbXXcGa4+t+RAqg8ACrj6/bNI+v+ywwszF7ZmZmrUjSOEnXSrpH0l2SDs/pIyVdKen+/HeTwjJHSpon6T5JuxXSJ+Xz1TxJJ5WC8eQbdhfk9JskjW/4jprVwcA71ptZ1fxQyOqMb8K4JDOzDrWCFITnNkkbALdKuhI4ELg6ImZJmkm6cXZEuz+6odbzx/xZu9c5J9aO3CJkZmZm1iEiYlFE3JbfLwPuIUUYLT5u4QxWfwyDH91gXcktQmZmZmYdKHdZ2xG4CRiVx5wSEYskbZFna/qjG2p9DMJg1POxBJ3wmIN234da8++KkJmZmVmHkbQ+8DPg0xHxTB8NNk1/dMPJ51xS02MQBqOej1DohMcctPs+1Jp/d40zMzMz6yCS1iZVgs6JiJ/n5MdLUUvz38U53Y9usK7lipCZmZlZh8hjdU4H7omI4wuTio9bOIDVH8PgRzdYV3LXODMzM7POsQuwHzBX0u057ShgFnBhfozDw+SIoxFxl6TSoxtWsOajG2YDI0jR4oqPbjgrP7phCSnqnFnbcUXIzMzMrENExPVUHsMDsGsvy3TloxvM3DXOzMzMzMy6jitCZmbWciT9WNJiSXcW0kZKulLS/fnvJoVpR+an3N8nabdC+iRJc/O0k0rPOsnjIS7I6TflMMNmZtZFXBEyM7NWNJv0FPuimcDVETEBuDp/RtK2pDEK2+VlTpU0LC/zPdJzTCbkV2mdBwFLI2Jr4ATg2CHbEzMza0keI2RmZi0nIq6r0EqzJzA1vz8D6AGOyOnnR8TzwEN5APdOkuYDG0bEDQCSzgT2Ig343hM4Oq/rIuAUSRpM5KvxM39V03LzZ+1e6ybNzGwQXBEyM7N2MSqH9CUiFknaIqePAW4szLcwp72Q35enl5ZZkNe1QtLTwKbAE0OX/cpqrUCZmdng9FsRkjQOOBN4BfAicFpEfEfSSOACYDwwH/hARCzNyxxJ6nawEjgsIn6d0yexKgzj5cDhERGS1snbmAQ8CewTEfPrtpdmZtbJenvKfW/pfS2z5sqlg0nd6xg1ahQ9PT1rzLN8+XJmTFy5RnonGDUCZkxcMej1VPremm358uUtmS8za4xqWoRWADMi4jZJGwC3SroSOJDUV3uWpJmkvtpHlPXV3hK4StI2OSZ9qa/2jaSK0DRSF4WX+mpLmk7qq71PPXfUzMza3uOSRufWoNHA4pz+0lPus7HAozl9bIX04jILJQ0HNiI9D2UNEXEacBrA5MmTY+rUqWvM09PTw3HXP1vjbrW2GRNXcNzcwXcgmb/v1MFnps56enqo9P80s+7Qb7CEiFgUEbfl98uAe0hdCvYk9dEm/90rv3+pr3ZEPASU+mqPJvfVzn2wzyxbprSui4BdS5F9zNqZI1+Z1VXxafYHsPpT7qfn8vAqUlCEm3M3umWSpuQys3/ZMqV1vR+4ZjDjg8zMrP0M6BZPvsjaEbiJJvTVLu+e0FuT9mCa8GttIm/UNrutGb8D9nc2cAqp4l9Sinzl1lSzXkg6jxQYYTNJC4GvALOACyUdBDxMfqBjRNwl6ULgblIvhkNzuQH4BKu6ZM/JL4DTgbNyYIUlpLJnZmZdpOqKkKT1gZ8Bn46IZ/posBmyvtrl3RPWX3/9ik3aBw5i4GmtTfeN2ma3NeO3+/62Y+Qrs1YQER/sZdKuvcx/DHBMhfRbgO0rpD9HrkiZmVl3quo5QpLWJlWCzomIn+fkx3N3N+rYV5v++mqbdYDVWlOBYmvqgsJ8pVbTMVTZmgqUWlPNzMzMrA/VRI0TqQvBPRFxfGFSqX/1LNbsq32upONJ3XtKfbVXSlomaQqpa93+wMll67oB99W27jVkranVRL0qqUd0qEaqNaJVu3S57IDuoWbA4MKE+1lLZjYUqukatwuwHzBX0u057SjcV9usVg2PfFVN1KuSwXTzbIZaI1q1YgSrStq9e6iZmVmr6vfqISKup/JdZ3BfbbNauDXVzMzMrMkG/2AAM+uVI1+ZmZmZtSZXhMyGkCNfmZmZmbWmqqLGmZmZmZmZdRK3CJmZmZmZVak8AuKMiSuqCjTk6Ietxy1CZmZmZmbWdVwRMjMzMzOzruOKkJmZmZmZdR1XhMzMzMzMrOu4ImRmZmZmZl3HFSEzMzOzDiHpx5IWS7qzkDZS0pWS7s9/NylMO1LSPEn3SdqtkD5J0tw87SRJyunrSLogp98kaXxDd9CsjlwRMjMzM+scs4FpZWkzgasjYgJwdf6MpG2B6cB2eZlTJQ3Ly3wPOBiYkF+ldR4ELI2IrYETgGOHbE/MhpgrQmZmZmYdIiKuA5aUJe8JnJHfnwHsVUg/PyKej4iHgHnATpJGAxtGxA0REcCZZcuU1nURsGuptcis3fiBqmZmZmadbVRELAKIiEWStsjpY4AbC/MtzGkv5Pfl6aVlFuR1rZD0NLAp8ET5RiUdTGpVYtSoUfT09FTO3Ij0UNJG6i0v1SjPa7X5H8w2h9ry5ctbOn/9qTX/rgiZmZmZdadKLTnRR3pfy6yZGHEacBrA5MmTY+rUqRUzcfI5l3Dc3MZeks7ft3JeqnHgzF+t9nnGxBVV5X8w2xxqPT099Pb/aQe15t8VITMzayuS5gPLgJXAioiYLGkkcAEwHpgPfCAilub5jySNa1gJHBYRv87pk0jjKUYAlwOH525AZp3mcUmjc2vQaGBxTl8IjCvMNxZ4NKePrZBeXGahpOHARqzZFc/qbHxZ5ata82ftXuecdBaPETIzs3b0tojYISIm58/1HAxu1mkuBQ7I7w8ALimkT8+R4F5FKgc35250yyRNyeN/9i9bprSu9wPX+AaCtat+W4Qk/RjYA1gcEdvntLrdeZO0DmkQ3iTgSWCfiJhftz00M6vAd9c6zp7A1Pz+DKAHOILCYHDgIUmlweDzyYPBASSVBoPPaWiuzepM0nmksrCZpIXAV4BZwIWSDgIeBvYGiIi7JF0I3A2sAA6NiJV5VZ9g1XXbHFaVjdOBs3JZWkK60WDWlqrpGjcbOIVUWSkp3XmbJWlm/nxE2Z23LYGrJG2TC1XpztuNpIrQNFKheikMo6TppDCM+9Rj58xambv3mNUsgN9ICuAHeRxCPQeDr6aaAd/Lly9nxsSVa6R3gmYMZC83VIO4232AeCUR8cFeJu3ay/zHAMdUSL8F2L5C+nPkipRZu+u3IhQR11V4WFY977ztCRyd13URcIok+ULOusTbIqIYaaeeNxnMOtUuEfForuxcKenePuatZTD46olVDPju6enhuOuf7S/fbanageBDaagGmbf7AHEzG5xaf9laIgxjb3dyBnPnqtY7Q43aZifevepLt+0v7t7T0Wrpjjdj4oqXDghLIuLR/HexpIuBnajvYHAzM+sC9b7F09AwjOuvv37FOznlYQ0Hota7To3aZrfdverw/W1Y955qn+UAze8CM1CN7rZTz2dPVGPUiNZ+9kSjSVoPWCsiluX37wK+yqoB3LNYczD4uZKOJ7WmlgaDr5S0TNIU4CbSYPCTG7s3ZmbWTLVWhByG0WzwGta9p9pnOcDgKvXN0OhuO/V89kQ1ZkxcwQc692ZALUYBF+cH2Q8Hzo2IKyT9kfoNBjczsy5Q69VDPe+8ldZ1Aw7DaF3E3XvMBi4iHgReXyH9Seo0GNzMzLpDNeGzHYbRrM7cvad91Rp228zMzFpLNVHjHIbRrP7cvcfMzMysiZobD9OsS7l7j5mZmVlzrdXsDJiZmZmZmTWaW4RsQAYzPmL+rN3rmBMzMzMzs9q5ImRmZmZmZk1X6w332dPWq2k5d40zMzMzM7Ou44qQmZmZmZl1HXeNMzMzMzPrQNV2NZsxcQUHls3bDWO73SJkZmZmZmZdxy1CZma2BkeINDOzTueKUJnBnPzNzMys/mo9N7tSbtZ47XQt7YqQmVmLc+uMmZlZ/XmMkJmZmZmZdR23CJmZdbB26qJgZmbWSG4RMjMzMzOzruOKkJmZmZmZdZ2WqQhJmibpPknzJM1sdn7M2onLj1ltXHbMauOyY52gJSpCkoYB3wXeDWwLfFDSts3NlVl7cPkxq43LjlltXHasU7RKsISdgHkR8SCApPOBPYG7m5ors/bg8mNWG5edDtdfsJAZE1dwYC/zOPR8n1x2rCO0SkVoDLCg8HkhsHOT8tJwA4nqVPzRbrcf6VqiV82YuIKp9c9Kp+nq8mM2CC47ZrVx2bGO0CoVIVVIizVmkg4GDs4fl7/tbW97EnhiKDPWag6Dzcj7rGObnJkGOAw2O+zDLfk/fmWzM1DQb/kpLzuS7hvyXDVIsUx0onbcv35+m9qq7EDV5aft/k/VasdjsFp97VsLnmM7texAE46xev5/qy0jLXhMvaTSPrRyfsu97dg+/we9lp1WqQgtBMYVPo8FHi2fKSJOA04rfZZ0S0RMHvrstY5u2+du298a9Vt+ystOJ+n0Y6TT96/Jajr3VNLJ/yfvm1VQt7ID7f9/aPf8Q/vvQ635b4lgCcAfgQmSXiXpZcB04NIm58msXbj8mNXGZcesNi471hFaokUoIlZI+iTwa2AY8OOIuKvJ2TJrCy4/ZrVx2TGrjcuOdYqWqAgBRMTlwOUDXKwju/r0o9v2udv2tyY1lp9O0enHSKfvX1PVsex08v/J+2ZrqPN5p93/D+2ef2j/fagp/4pYY2ybmZmZmZlZR2uVMUJmZmZmZmYN44qQmZmZmVkXklQpFHrXaLuKkKRR+W9X/+M6XfH/6/+1lZP0vmbnwazTz0f+Hbah1O7lR9Kmzc7DYEiaAhAR0cb/g8MlbTWY/LfVGCFJJwOvAf4OXAtcExH3NDdXjSdprYh4sdn5GEqSXg08RTpGn5SkaKeD1YaMpFOBURHxn83OS6N0Q5lvN91wPuq232GXs8Zp9/Ij6XhS/gGOjYg/NDM/AyVpFrA/8M2IODmnDYuIlc3NWfUknQ5sFhF7DmY9bdMiJOlDwJuB9wGXAZsAh0t6a1Mz1gCS3iDp/0naFSAiXpQ0rNn5GiqS3gtcAswCLpK0Z+nk2653Law+JH0eGFuqBEkaJWmkpLWbnLW66rYy32664XzULb/DkjaRtK2krVwJaox2Lz/5PLQD8CHgT8DXJW3Y1EwN3O3ANcD2kr4L0GaVoCnANqVKkKSdJb1e0hsHuq62qQgB/wCujogVETEb+BlwN7BXvmvVkSRtC5wPHAgcIulaSRtFxMpOvDCStBlwDPAp4NPA8cAPJR0F7d2Ea4MjaSRwOHBv/vwx4Luk51j8j6RxfSzeNrqtzLepjj4fSdqcLvgdlrQucANwBHCdpM9I+tcmZ6sbtG35kTQa2BX4TEQ8GxFHA48BA74Ab7L7gA2A2cALkmZLOlvS9m1Sth8A7gCQdABwAvBZ4EuS/nsgK2qnitAtwK6SPg6Qm1DnABsBb29mxoZKPhhnAGdGxMERsTfpH3+npB3aqfZerYh4AvgD8HhE/D0ifglMAT4s6Rt5no7tmmG9i4glpBPQVElXAR8nXcAcTrqj+KEmZq8uurHMt6mOPh9FxN9IFYTHOvx3+L+BKyPiAOA/gW2AfSXtDKmrXDMz18HatvxExCLgM8BfJJWexbkU2AVA0oa5stTSIuJPwFzgEdJ5dEfgLcDwNinb/wQm5tastwH/ARwKnEpq5aq67LZNIY+IBcAXgCmSPpXT7gfOIF0YrdfM/A2FfDBeS/qHl9IOJ3VVOEvS65uVt6EgaYvcxekfpAMagIh4ENgD2ErSls3KnzVfRNwHTAWWAMdFxAO5b/ZFwHaSXt4md7Mq6rYy3646+Xwk6d/z23+QLviAjv0dfgZ4laQREXEr8CUggI9KGu6uckOjA8rPfRHxj4hYkT/fBJQqRT+nxVuHCufIAKaRWn1fAL4FnJR7JbS0iHga2BcYCewMPB8Ry4E/A68DxlV7LdA2FaHsatIFz+sknZcvCg4DlkbEs83N2pBZSLoLt1cpISK+S/oetmlWpuotDzz8EfBj4ArgXyRdV+oKlE/CG9FB+2y1yXeoPxARFxaSDwCezNPa4W5WX7qizHeAjjsfKQUi2S9/nAlsnbtmdurv8LnAAuC9kjbJPRKOAkYB32hqzjpf25afChXku4Axks4CHoyIS5uQraoVzpGnAu8BPgbsERGnAJ+PiLublrkByBXqr5K6Jl4maUfg28CdEfHXaq8F2ipqHLxUkx1L6hbzKuDpiPhkc3NVX5JmkAaB/Vf+vAdwJvCtiJiV075IGjR+SPNyWh954OG7gfcCR5Jq8x8gVYxeD5wCbAnsA7wlIpY2KavWgiR9G9guIt7d7LzUqtvKfKfopPNR/h1+c0S8L39+GSDSeLXNgR8C42jz32FJbwBGAOtGxNW5BWwPoAe4MSLul/QWUlebz3TAjZWW1QnlJ+/DNsA9wGWl8tMOct6nA3Mj4s5m52cwJH2WdJNm7Yg4akDLtnMZl7R2RLzQ7HzUk6TdSK0ifwLWBj4SEY9Keh1wASnSxxLSD/c+uTm/beW+tD8BjoiIP+e0nwLHR8QNkvYlVYJGk8ZN3N60zFrLyV0p9wR+HRHLmp2fWnRbme9U7Xw+yoFI7gDOjYgv5EAk7wU2Jh2DE0hjCV4JnN6uv8O5y88vSJWeTYGXkyo8O5EuCNcn7edepK63P2xGPrtRO5cfAEmfA06LiGeanZdaqItDx7d1RagT5YufKRHxE0n/RxoE9qmIuCFPn056rsM/IuK3zctp/eR9ng+8EBEr8uC3JyPiy/mOxfiIeKipmTQbIt1Y5q31SHotcBZp3MwGpOAjY0gVhb9ExKlNzN6g5XPJj0hdl47JaaeQeiO8C/gb8K+kQe/zIuJnzcqrtR+ps5+x1cnabYxQx8vRU87O7z9Pai05rTCAdV3Sg8c66YKo0sDDkitJ3ePMOlKXlnlrMb0EIrkOuJg0qH39DglE8kIh7ZOksLsXA6+MiOsj4lhXgmygXAlqX64ItaBi83AeJP0FYKakx4FdIuKfvS7chqoYePiLxufKrHG6rcxba+olEMl+pBb65R1wsVcpEMkppEhf/9KsTJlZ87gi1AYiYg7wIHB3RHy82fkZSvmO43LgI8BGEXFwk7Nk1nDdVOatdeVAJGMi4jP9ztyiJM2Q9AOAiOghRYX7saSZhdlWkJ5RZmZdZnj/s1izSdoaeAVpUHhHy3cc75P0BeC0ZufHrBm6qcxba8qBSG4E/rfZealVDkTyWeBPkn5NCkRymaRdgAty8ISXApE0Matm1iQOltAmJL08Iv7e7Hw0igceWrfrtjJvVm8ORGJm/XFFyMzMzDpSMSyzpEOBQ4AvR8TFkg4khQz3GDyzLuWKkJmZmXUFSe8GjgbGA5d6DJ5Zd3NFyMzMzLqGpPOAV0TE25qdFzNrLkeNMzMzs67gQCRmVuQWITMzM+saDkRiZiWuCJmZmZmZWddx1zgzMzMzM+s6rgiZmZmZmVnXcUXIzNqOpPmS3tHsfDSTpH0l/abZ+bD24DJjVn+NKleS7pI0dai308u2l0t6dTO23QiuCBmSjpZ0dn6/VT7ohw3Bdjq6MJkVSYocoaoe6xqf1ze8lBYR50TEu+q0/rrl1czM6isitouInqHejqQeSR8r2/b6EfFgHdY9W9LXB7ueenNFyFYTEQ/ng37lYNYzlIXJrF6KFQsz61+rlplWzZdZNXz8No8rQmZWN7mbwOck3SHpaUkXSFpX0oGSri+b96VWiHyn6FRJc3LL4e8lvULSiZKWSrpX0o5lm3ujpLvz9J9IWrew7j0k3S7pKUl/kPSvZXk8QtIdwLN9nYAkvS5X6p/KXRPeV5i2WmW/uI+SrsvJf877s4+kqZIWSjpK0hM5H/sWlt9d0p8kPSNpgaSjC1kpre+pvL43lX+nkv5F0pWSlki6T9IHCtNmS/qupF9JWibpJkmv6S2vvX0fVn8dWGa2lPQzSX+T9JCkwwrTjpb0U0ln5+NwrqRtJB0paXE+7t9VmH8jSadLWiTpEUlfV+6tkL+f30s6QdIS4GhJm0r6ZS5Df8zzD7qM5OnbFZZ9PJfjV0j6u6RNC/NNyvu+dj//ehtCHViuXuqCl8vRhZLOzMfqXZIml817ZKU8SdpE0mX5GF2a34/N044B3gyckvf9lArfzzqSvi3p4VwOvi9pRJ5WOsfNyOV5kaSP5GkHA/sCX8jr/uXA/qNDKCL8quIFzAc+B9wBPA1cAKwLHAhcXzZvAFvn97OBU4E5wHLg96SHuZ0ILAXuBXascvufz9t/FjgdGJXXuwy4CtikMP8U4A/AU8CfgamFaa8CfpuXuxI4BTg7Txuf8z88fx4J/AR4NOf3Fzl9E+Ay4G85/TJgbJ52DLASeC7v8ykVvpeNgDPz8n8FvgislacdCFwPfDuv+yHg3YX8Hwg8mPP/ELBvs48Pv1Y7Tm8GtszHzj3AIVWWkyeASblcXZP/t/sDw4CvA9eWbedOYFzezu+Br+dpbwAWAzvnZQ/I869TWPb2vOyIPvZlbWAecBTwMuDt+Zh7bZ7eA3ys7Li8vtL+5c9TgRXA8cA6wFtJZfm1hekTSTeo/hV4HNgrTxtPoVyWbw9YD1gAfAQYnr+DJ4DtCt/vEmCnPP0c4Pze8uqXy0yNZWYt4Fbgy7nMvJr0W71bnn406bywWz4Oz8x5/h9Sefs48FBhfb8AfpCP7y3y9/RfheN/BfCpvK4RwPn59XJg21wmBl1GgA2ARcCM/F1vAOycp10OfKKQ5xOAk5t9XHX7q5PKVWHed5SVo/fk9X4TuLHKPG0K/GcuIxsAPyVf1+XpPRTOaxW+nxOBS/N6NwB+CXwzT5tKKpNfJZXn9wB/J1+b5u/2680+Ntb4bpudgXZ5NapQ9bP9G0mVnzG5cN0G7Ei6qLoG+EqedwzwZD4I1wLemT9vnqffwKqLsbeQLu56qwj9ilTp2yQf2G/N6YMtTGcCl+RlxwN/AQ7K0w4EXiCdFIcBnyBVxEQ6mT3DqovH0eQTmV/Nf+Xj9MOFz98Cvl9lOflhYdqngHsKnycCT5Vt55DC5/cAD+T33wO+Vrat+wrH7nzgo1Xsy5uBx8gV9Jx2HnB0fr/aMV6+j/ReEVqvkHYh8KVetn8icEJ+v1q5LN8esA/wu7Llf8Cq34TZwI/Kvq97e8urXy4zNZaZnYGHy9KOBH6S3x8NXFmY9l7SzbJh+fMGeR83Jp3rnqdwgQh8kHy+zN/Pw4Vpw0jnjdcW0r5ejzKSt/unXvZ5H+D3hTw8BuzU7OOq21+dVK4K8xYrQlcVpm0L/KOaPFVY7w7A0sLnHnq5diNdgz0LvKYw7U3kmxekc9w/WP08tRiYUvhuW64i5D6JA3NSRDwKkJv1diBVTvpzcUTcmpe7GPjviDgzf74A+GSV2z85Ih7Py/0OWBwRfyqsd9c834eByyPi8vz5Skm3AO+RdC3wRlKBeh64rrcmSkmjgXcDm0bE0pz8W4CIeBL4WWHeY4Brq9mJ3LVhH1JL2DJgmaTjgP1ILV0Af42IH+b5zyC1qo0iVdpeBLaX9HBELCLdqbPW8Vjh/d9JNw+q8Xjh/T8qfF6/bP4Fhfd/LWznlcABkj5VmP6ysnwUl+3NlsCCiHixbDtjqli2N0sj4tmy9W0JIGlnYBawfc7vOqQbDNV4JbCzpKcKacOBswqfy/8v5d+nNU+nlJlXAluWHYfDgN/1kecnYtWY1H/kv+vnba8NLJJUmn+tsnwU329OOuZ7mz6YMjIOeIDKLgG+rxQIaBvg6Yi4uZd5rbE6pVxVUr5v60oaHhEr+sqTpJeTWi2nkW5wA2wgaVj0PzZ8c9LN71sLZVKkMl7yZCEPpby19LnGFaGBaVShGux6XgnsLem9helrkyoqW1L5Ymxche2NA5YUKkEvGWRh2oz0Y/DXsjwULzBf+q4j4u+50K0fEY8pjWH4HHC6pN8DMyLi3n62ac31LOkHFABJr6jDOovH7FakVkNIJ4BjIuKYPpaNKtb/KDBO0lqFytBWpNZLKNsnUpfX/mwiab1C+duK1IUB4FxSN9V3R8Rzkk4klZVq8rsA+G1EvLOKPFh7aMcys4B0d3hCjfkrX9fzwGZlF1a95elvpBbXsawqo8X9HUwZWUBqFVozA6msXkga//AvrF6xstbTjuWqnnmaAbyW1LXzMUk7AH8iVWj6y88TpGvN7SLikRryNFT7OigOljB4Q1GoBmsBcFZEbFx4rRcRs0itJ5tIWq8w/1Z9rGekpI0rTCsWpg1JXeyg+sL0AqnCVsxDVQUrIn6dT2ajSWOsfljNctZUfwa2k7RDHrR5dB3WeaiksZJGksbxXJDTfwgcImlnJespBSLYYIDrv4lUvr8gaW2lZzi8lzQGAVLf7v+Q9PI8kPSgsuUfJ42RKPe/kl4m6c3AHqxq9dmAdOPhOUk7AR8qLPM3Uktob+HnLwO2kbRfzuvakt4o6XVV7mtvebXmaccyczPwTB4APkLSMEnbS3rjQDOaW/t/AxwnaUNJa0l6jaS39jL/SuDnpKAJL5f0L6Qu6CWDKSOXAa+Q9Ok8WHyD3IJbciapy9X7gLMHuq/WUO1YruqZpw1IlZmn8rSvlC3X67kg3xD8IXCCpC0AJI2RtFuVeWrJ84wrQoM3FIVqsM4G3itpt3wiWlcpmsfYiPgrcAurLsb+jXRxt4Z8IpoDnKoUaWRtSaUKz2AK00rS2Ihj8gnllcBnqeIEImmUpPflitzzpP7lgwr1bUMvIv5CGkB5FXA/KRjGYJ1LulB6ML++nrd1C2l82SmkYBvzSBcpA83zP0kXNu8mVd5PBfYvtD6eAPyTdKyfQRpcXXQ0cIZStKBSdKrHcp4ezfMfUljffwNflbSMNNj8wkJe/k4KQvL7vL4pZXldBrwLmJ7X/RhwLKl7XTUq5dWaqE3LzErS+WQH0ljYJ4AfkYLj1GJ/Uu+Bu3O+LiLdAOvNJ/O2HiO1zJxHOk8MqozkZd9J2rfHSP+PtxWm/550o+K2iJg/gP2zBmvHclXPPJHGno4glc0bgSvKlvsO8H6liHInVVjvEaT9uFHSM6Tv8bVV5ul0YNt8nvlF9bsytJQHMFk/JM0nDSC7Kn8+mjS47sOS/gf4DKlicCTpB3hCRMyTNBtYGBFfzMt9jDSAb2r+vDVpQGaf3RQrbP9sYF5EHF1Y7/SIKIVX3Jk0OHAiqaJwMymyzcNKfZnPIAVauIE0eG/jvC/jSSewtSNiRa7klLrAvYw0UPU/JG1JKmiTSSeV40gDEUvLvSlvY3NS69RhkqLwvWwCnEyKHvQc6S7D1yPiRUkH5n39t8L+BzCBdIf+fNKJNkh35f87Iu7u6/sza7bconR2RIxtclbMuoKkY4FXRMQBDdjWNcC5EfGjod6WWV/Krxetb64ImZk1gCtCZkMrd4d7GTCXFBToctIF4S+GeLtvJD2KYlxuPTJrGleEBsbBEsysa0naitTtppJtI+LhRubHrNW1eJnZgNQdbktS2N7jSFHdhoxSVNO9gMNdCbJatXi56mhuEWoRLgRmZmZmZo3jipCZmZmZmXWdtu0at9lmm8X48ePXSH/22WdZb7311lygSZyfvrVrfm699dYnImLzBmSp7norOyWt8j9plXxA6+SlVfIBteelncsO9F1+Wun/U+R8DUyr5stlpzFaJS+tkg9onbwMyXknItryNWnSpKjk2muvrZjeLM5P39o1P8At0QLloJZXb2VnoN/BUGuVfES0Tl5aJR8RteelnctO9FN+Wun/U+R8DUyr5stlpzFaJS+tko+I1snLUJx3/BwhMzMzMzPrOq4ImZmZmZlZ13FFyMzMzMzMuo4rQmaDJOnHkhZLurOQNlLSlZLuz383KUw7UtI8SfdJ2q2QPknS3DztJEnK6etIuiCn3yRpfGGZA/I27pc05E9PNzMzM+sUrgiZDd5sYFpZ2kzg6oiYAFydPyNpW2A6sF1e5lRJw/Iy3wMOBibkV2mdBwFLI2Jr4ATg2LyukcBXgJ2BnYCvFCtcZmZmZtY7V4TMBikirgOWlCXvCZyR35eePF5KPz8ino+Ih4B5wE6SRgMbRsQNOcLJmWXLlNZ1EbBrbi3aDbgyIpZExFLgStaskJm1LLemmplZM7Xtc4SGyviZv6ppufmzdq9zTqzNjYqIRQARsUjSFjl9DHBjYb6FOe2F/L48vbTMgryuFZKeBjYtpldYZjWSDia1NjFq1Ch6enp6zfjiJU9z8jmX9L+HZSaO2WjAy/Rl+fLlfeazkVolL62SD6hbXmYDp5Aq/iWl1tRZkmbmz0eUtaZuCVwlaZuIWMmq1tQbgctJNwTmUGhNlTSd1Jq6T6E1dTIQwK2SLs03FGo295GnObCGc4jPH9btai074PJjg+OKkFljqUJa9JFe6zKrJ0acBpwGMHny5Jg6dWqvGTz5nEs4bu7Afxrm79v7OmvR09NDX/lspFbJS6vkA+qTl4i4rthKk+0JlFZ8BtADHEGhNRV4SFKpNXU+uTUVQFKpNXVOXubovK6L/n979x4vWVnf+f7zDY2AF1BQ25YmaRLQCDJe6EFOnJxp0zG00YiZ8dKJF8gQSRyMZEISGueciZkJc5qZeL/NEFEuXqAleiAiKqI7OSZcBGNEQIZWGGkhEC4irYJ0+zt/rGfHYvfu3bt379q1dtfn/XrVa1c9tZ5Vv1W7nqr6redSwHum9qa2OpO9qR/bpQOSJC0qJkLScNyZZFnrDVoG3NXKNwEHDWy3HLi9lS+fpnywzqYkS4D96IbibeInXxgn60zM72FIC65Xvakw+x7VpfvAKUds2fERTjHsXr4+9SQOMi5Jo2YiJA3HxcBxwPr296KB8o8meRvd8J5DgauramuSB5IcDVwFvA5495R9XQG8HPhCVVWSzwL/dWAOxa8Apw3/0KSRGElvKsy+R7UvvalT9akncZBxSRo1EyFpFyX5GF3PzBOTbKKbe7Ae2JDkBODbwCsAqur6JBuAG4AtwEltjgPAG+jmTOxDN6zn0lZ+FnBeGwp0L908Carq3iT/Bfhy2+4/Tw71kRYxe1MlSQvCREjaRVX1G9u5a/V2tj8dOH2a8muAZ05T/iAtkZrmvg8CH5x1sFL/2ZsqSVoQJkKSpJGwN1WSNEomQpKkkbA3VZI0Sv6gqiRJkqSxYyIkSZIkaeyYCEmSJEkaOyZCkiRJksaOiZAkSZKksTPrRCjJHkn+Psmn2u39k1yW5Ob29wkD256WZGOSm5IcM1B+ZJLr2n3vSpJWvleSC1r5VUlWzOMxSpIkSdIj7EyP0MnAjQO31wGXV9WhwOXtNkkOo/uthsOBNcD7kuzR6rwfOJHuh/AObfcDnADcV1WHAG8HzpjT0UiSJEnSLMwqEUqyHHgx8IGB4mOBc9r1c4CXDZSfX1UPVdUtwEbgqCTLgH2r6oqqKuDcKXUm93UhsHqyt0iSJEmS5ttsf1D1HcAfA48bKFtaVXcAVNUdSZ7cyg8ErhzYblMre7hdn1o+Wee2tq8tSe4HDgDuHgwiyYl0PUosXbqUiYmJbQLdvHnztOWzdcoRW+ZUb3uPuavxzDfjmVnf4pEkSdJw7DARSvIS4K6qujbJqlnsc7qenJqhfKY6jyyoOhM4E2DlypW1atW24UxMTDBd+Wwdv+6SOdW79dXTP+auxjPfjGdmfYtHkiRJwzGbHqHnAy9N8qvA3sC+ST4M3JlkWesNWgbc1bbfBBw0UH85cHsrXz5N+WCdTUmWAPsB987xmCRJkiRpRjucI1RVp1XV8qpaQbcIwheq6jXAxcBxbbPjgIva9YuBtW0luIPpFkW4ug2jeyDJ0W3+z+um1Jnc18vbY2zTIyRJkqSZJfkPSa5P8vUkH0uyt6v9Stvald8RWg+8MMnNwAvbbarqemADcAPwGeCkqtra6ryBbsGFjcA3gUtb+VnAAUk2An9AW4FOkiRJs5fkQOBNwMqqeiawB92JbFf7laaY7WIJAFTVBDDRrt8DrN7OdqcDp09Tfg3wzGnKHwResTOxSJIkaVpLgH2SPAw8mm4qwmnAqnb/OXTf505lYLVf4JZ2UvqoJLfSVvsFSDK52u+lrc5b2r4uBN6TJI7m0WKzKz1CkiRJ6pGq+g7w58C3gTuA+6vqc0xZ7RcYXO33toFdTK7qeyCzXO0XmFztV1pUdqpHSJIkSf3V5v4cCxwMfBf4eJLXzFRlmrJ5We13Nj97ArB0n/n/+ZK56svPaPQlDuhPLMOIw0RIkiRp9/HLwC1V9U8AST4B/AIjWO13Nj97AvDuj1zEW6+b21fS7f18yVz15Wc0+hIH9CeWYcTh0DhJkqTdx7eBo5M8uq3ythq4EVf7lbZhIiQNkUuYSpIWUlVdRbeAwVeA6+i+652Jq/1K23BonDQkA0uYHlZVP0yygW6J0sPoljBdn2Qd3QfIqVOWMH0q8PkkT2sfSJNLmF4JfJpuCdNLGVjCNMlauiVMX7WgBypJ6pWq+hPgT6YUP4Sr/UqPYI+QNFyTS5gu4SdLmB5Lt3Qp7e/L2vV/XsK0qm6hOwN3VBvLvW9VXdGGHpw7pc7kvi4EVk/2FkmLmb2pkqRhMxGShsQlTKW58QchJUkLwaFx0pAsxiVMYe7LmO6uS5hCf2LpSxywILH4g5CSpKEyEZKGZ9EtYQpzX8Z0d13CFPoTS1/igOHGUlXfSTLZm/pD4HNV9bkkj+hNTTLYm3rlwC4me00fZpa9qUkme1PvnhrPsH8LZdjJbZ8S6EHGJWnUTISk4fnnJUzpvsytBq4Bvk+37Oh6tl3C9KNJ3ka3WMLkEqZbkzyQ5GjgKrolTN89UOc44ApcwlS7iT71psLwfwtlvk8iTNWnBHqQcUkaNRMhaUiq6qokk0uYbgH+nu7L1GOBDUlOoEuWXtG2v76tLHdD237qEqZnA/vQDesZXML0vDYU6F66eRLSYteb3lRJ0u7LREgaIpcwlebE3lRJ0tCZCEmSesXeVEnSQjARkiT1jr2pkqRh83eEJEmSJI0dEyFJkiRJY8dESJIkSdLYMRGSJEmSNHZMhCRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjZ0dJkJJ9k5ydZJ/SHJ9kj9t5fsnuSzJze3vEwbqnJZkY5KbkhwzUH5kkuvafe9Kkla+V5ILWvlVSVYM4VglSZIkCZhdj9BDwC9V1bOAZwNrkhwNrAMur6pDgcvbbZIcBqwFDgfWAO9Lskfb1/uBE4FD22VNKz8BuK+qDgHeDpyx64cmSZIkSdPbYSJUnc3t5p7tUsCxwDmt/BzgZe36scD5VfVQVd0CbASOSrIM2LeqrqiqAs6dUmdyXxcCqyd7iyRJkiRpvi2ZzUatR+da4BDgvVV1VZKlVXUHQFXdkeTJbfMDgSsHqm9qZQ+361PLJ+vc1va1Jcn9wAHA3VPiOJGuR4mlS5cyMTGxTaybN2+etny2Tjliy5zqbe8xdzWe+WY8M+tbPJIkSRqOWSVCVbUVeHaSxwOfTPLMGTafrienZiifqc7UOM4EzgRYuXJlrVq1aptKExMTTFc+W8evu2RO9W599fSPuavxzDfjmVnf4pEkSdJw7NSqcVX1XWCCbm7PnW24G+3vXW2zTcBBA9WWA7e38uXTlD+iTpIlwH7AvTsTmyRJkiDJ45NcmOQbSW5M8n+4yJW0rdmsGvek1hNEkn2AXwa+AVwMHNc2Ow64qF2/GFjbGsnBdIsiXN2G0T2Q5OjWkF43pc7kvl4OfKHNI5IkSdLOeSfwmar6eeBZwI24yJW0jdn0CC0Dvpjka8CXgcuq6lPAeuCFSW4GXthuU1XXAxuAG4DPACe1oXUAbwA+QLeAwjeBS1v5WcABSTYCf0BrnJIkSZq9JPsC/yfddyuq6kdtRI+LXElT7HCOUFV9DXjONOX3AKu3U+d04PRpyq8BtplfVFUPAq+YRbySJEnavp8F/gn4UJJn0S12dTLQy0WuAJbuM/+LVc1VXxZN6ksc0J9YhhHHrBZLkCRJ0qKwBHgu8Httld93MvNIm5EucgXw7o9cxFuvm9tX0u0tVjVXfVk0qS9xQH9iGUYcJkLzZMV2Vps75YgtO1yJ7tb1Lx5GSOqBNr/uA3Q9oQX8O+Am4AJgBXAr8Mqquq9tfxrd2OutwJuq6rOt/EjgbGAf4NPAyVVVSfaiG65wJHAP8KqqunVBDk6S1EebgE1VdVW7fSFdInRnkmWtN2i+Frna5CJXWsx2atU4STvNCavSHLjqlTQ3VfWPwG1Jnt6KVtPN23aRK2kKEyFpSJywKu0STyJIc/d7wEfaQlfPBv4rLnIlbcOhcdLwLLoJqzD3Sau764RV6E8sfYkDhhvLwEmE46E7iQD8KMmxwKq22Tl0v2t3KgMnEYBb2pezo5LcSjuJ0PY7eRLh0lbnLW1fFwLvSRLPamt3UFVfBVZOc5eLXEkDTISk4Vl0E1Zh7pNWd9cJq9CfWPoSBww9lt6cRIDhr3w17OS2Twn0IOOSNGomQtLwOGFVmpvenESA4a98Nd8nEabqUwI9yLgkjZpzhKQhccKqNGfTnUR4Lu0kAsA8nkTAkwiSNJ5MhKThcsKqtJM8iSBJWggOjZOGyAmr0pxNnkR4FPAt4LfoTt5tSHIC8G3aa7+qrk8yeRJhC9ueRDib7je4LuWRJxHOaycR7qVbdU6SNEZMhCRJveNJBEnSsDk0TpIkSdLYMRGSJEmSNHZMhCRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY8dESJIkSdLY2WEilOSgJF9McmOS65Oc3Mr3T3JZkpvb3ycM1DktycYkNyU5ZqD8yCTXtfvelSStfK8kF7Tyq5KsGMKxSpIkSRIwux6hLcApVfUM4GjgpCSHAeuAy6vqUODydpt231rgcGAN8L4ke7R9vR84ETi0Xda08hOA+6rqEODtwBnzcGySJEmSNK0dJkJVdUdVfaVdfwC4ETgQOBY4p212DvCydv1Y4PyqeqiqbgE2AkclWQbsW1VXVFUB506pM7mvC4HVk71FkiRJkjTfluzMxm3I2nOAq4ClVXUHdMlSkie3zQ4ErhyotqmVPdyuTy2frHNb29eWJPcDBwB3T3n8E+l6lFi6dCkTExPbxLh58+Zpy2frlCO2zLnudJbus+N97kq8O2tXn5/5ZjySJM2vNhLnGuA7VfWSJPsDFwArgFuBV1bVfW3b0+hG5mwF3lRVn23lRwJnA/sAnwZOrqpKshfdyewjgXuAV1XVrQt2cNI8mnUilOSxwF8Cv19V35uhw2a6O2qG8pnqPLKg6kzgTICVK1fWqlWrtqk0MTHBdOWzdfy6S+ZcdzqnHLGFt14389N866tXzetjzmRXn5/5ZjySJM27k+lG8Ozbbk9OZ1ifZF27feqU6QxPBT6f5GlVtZWfTGe4ki4RWgNcysB0hiRr6aYzvGrhDk2aP7NaNS7JnnRJ0Eeq6hOt+M423I32965Wvgk4aKD6cuD2Vr58mvJH1EmyBNgPuHdnD0aSJGmcJVkOvBj4wECx0xmkaeywR6i9uM8Cbqyqtw3cdTFwHLC+/b1ooPyjSd5Gd3bhUODqqtqa5IEkR9MNrXsd8O4p+7oCeDnwhdbwpEXPIQqSpAX0DuCPgccNlC34dAaY3ZQGmN00gu2Z7+HsfRki35c4oD+xDCOO2QyNez7wWuC6JF9tZW+mS4A2JDkB+DbwCoCquj7JBuAGuhXnTmpdrABv4Cdf5i5tF+gSrfOSbKTrCVq7a4cl9YpDFKQ58CSCtHOSvAS4q6quTbJqNlWmKZuX6QwwuykNAO/+yEU7nEawPfM9vaAvQ+T7Egf0J5ZhxDGbVeO+VFWpqn9RVc9ul09X1T1VtbqqDm1/7x2oc3pV/VxVPb2qLh0ov6aqntnue+Nkr09VPVhVr6iqQ6rqqKr61rwepTQiDlGQdsnkSYRJ/myDNLPnAy9NcitwPvBLST6M0xmkac1qjpCkOXsH3RCFHw+UPWKIAjA4ROG2ge0mhyIcyCyHKACTQxSkRc2TCNLOq6rTqmp5Va2gOznwhap6DT+ZggDbTmdY237Y/mB+Mp3hDuCBJEe3dvG6KXUm9+V0Bi1qc+uHlLRDfRqiMNtx2jD3sdq76zht6E8sfYkDFiSWdzAm8xyG/T/t0+tmkHEtKKczSNMwEZKGZ3KIwq8CewP7Dg5RaF/k5muIwqaZhijMdpw2zH2s9u46Thv6E0tf4oDhxtKnkwgw/HkOw/4JhT69bgYZ13BV1QQw0a7fA6zeznanA6dPU34N8Mxpyh+kJVLSYufQOGlIHKIgzZnzHCRJQ2ciJC289cALk9wMvLDdpqquByaHKHyGbYcofIBu7sM3eeQQhQPaEIU/oE0elxYzTyJIkhaCQ+OkBeAQBWleOM9BkjRvTIQkSb3lSQRJ0rA4NE6SJEnS2Nkte4RWrLtk1CFIkiRJ6jF7hCRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY8dESJIkSdLYMRGSJEmSNHZMhCRJkiSNHRMhSZIkSWPHREiSJEnS2NlhIpTkg0nuSvL1gbL9k1yW5Ob29wkD952WZGOSm5IcM1B+ZJLr2n3vSpJWvleSC1r5VUlWzPMxSpIkSdIjzKZH6GxgzZSydcDlVXUocHm7TZLDgLXA4a3O+5Ls0eq8HzgROLRdJvd5AnBfVR0CvB04Y64HI0mSNM6SHJTki0luTHJ9kpNbuSexpSl2mAhV1d8A904pPhY4p10/B3jZQPn5VfVQVd0CbASOSrIM2LeqrqiqAs6dUmdyXxcCqycbmiRJknbKFuCUqnoGcDRwUjtR7UlsaYolc6y3tKruAKiqO5I8uZUfCFw5sN2mVvZwuz61fLLObW1fW5LcDxwA3D31QZOcSNcgWbp0KRMTE9sEtnnzZk45YuscD2v+Ld0HTjliy4zbTHccw7J58+YFfbwd2Z3jSXIQXdL/FODHwJlV9c4k+wMXACuAW4FXVtV9rc5pdB8wW4E3VdVnW/mRdL2z+wCfBk6uqkqyV3uMI4F7gFdV1a3zcgDSiNh2pLlr388mv6M9kORGuu9axwKr2mbnABPAqQycxAZuSTJ5EvtW2klsgCSTJ7EvbXXe0vZ1IfCeJGknu6VFY66J0PZM15NTM5TPVGfbwqozgTMBVq5cWatWrdpmm4mJCd76pe/PJtYFccoRW3jrdTM/zbe+etXCBEP3/Ez3vI3Kbh7P5Fm5ryR5HHBtksuA4+nOyq1Pso7urNypU87KPRX4fJKnVdVWfnJW7kq6L3Nr6D6M/vmsXJK1dGflXjVfByCNiG1HmgdtyNpzgKsYwUns2ZzAhtmdNN6e+T6Z2pcTtH2JA/oTyzDimGsidGeSZa0hLQPuauWbgIMGtlsO3N7Kl09TPlhnU5IlwH5sOxRvt7Zi3SVzqnfr+hfPcySaT56Vk+bGtiPtuiSPBf4S+P2q+t4Msw6GdhJ7NiewAd79kYt2eNJ4e+b7ZHJfTtD2JQ7oTyzDiGOuidDFwHHA+vb3ooHyjyZ5G91ZuUOBq6tqa5IHkhxNd1bidcC7p+zrCuDlwBf8INLuZrGclYO5n5nbXc/KQX9i6UscsHCxjLrtSItRkj3pkqCPVNUnWrEnsaUpdpgIJfkY3Rm4JybZBPwJXQK0IckJwLeBVwBU1fVJNgA30A1tOKkNTQB4Az8Zp31puwCcBZzXzuDdSze8QdptLKazcjD3M3O761k56E8sfYkDFiaWPrSdFsdQh/cMO6HsUwI9yLiGoy04dRZwY1W9beAuT2JLU+zw205V/cZ27lq9ne1PB06fpvwa4JnTlD9IS6Sk3Y1n5aS56VPbGfbwnmHPE+1TAj3IuIbm+cBrgeuSfLWVvRlPYkvbmO/FEiQ1npWT5sa2I81dVX2J6Xs8wZPY0iOYCEnD41k5aW5sO5KkoTMRkobEs3LS3Nh2JEkL4adGHYAkSZIkLTQTIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY8dESJIkSdLYMRGSJEmSNHZMhCRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0thZMuoANHcr1l2y03VOOWILx6+7hFvXv3gIEUmSJEmLg4mQJEmSFqW5nBQGPCEswKFxkiRJksaQiZAkSZKksWMiJEmSJGns9CYRSrImyU1JNiZZN+p4pMXE9iPNjW1HmhvbjnYHvVgsIckewHuBFwKbgC8nubiqbhhtZLsvJxfuPmw/0tzYdqS5se1od9GLRAg4CthYVd8CSHI+cCxgg5J2rBftZ67JNZhga2R60XakRWjRt53tfWZN/szITPzM2n30JRE6ELht4PYm4HkjikUz2JUvu9PxDWdeLPr2M93ryteGFkAv2o4nEbQI9aLtjMp8fxeaztTPQNv6cPQlEco0ZbXNRsmJwInt5uYkN01T74nA3fMY2y55k/HMaDbx5IwFCqYz2+fnZ4YdyE7YYfuZZduZ1IvXSM9eG714TuhPHDD3WBZV24Gdaj8L/v+ZZRvo0+tmkHHtHNvOAujL96SpcSzwd6GpevGcMITPnb4kQpuAgwZuLwdun7pRVZ0JnDnTjpJcU1Ur5ze8uTOemRnPvNhh+5lN25nUl+egL3FAf2LpSxzQr1h2wbx99kB/nxPj2jl9jatndtu205dY+hIH9CeWYcTRl1XjvgwcmuTgJI8C1gIXjzgmabGw/UhzY9uR5sa2o91CL3qEqmpLkjcCnwX2AD5YVdePOCxpUbD9SHNj25Hmxraj3UUvEiGAqvo08Ol52NWshv8sIOOZmfHMg3lsP9Cf56AvcUB/YulLHNCvWOZsN207UxnXzulrXL2yG7edvsTSlzigP7HMexyp2mZumyRJkiTt1voyR0iSJEmSFoyJkCRJkqSxs+gToSRL29/p1rQfmcF4+hCb8cysb/EspL62oT7oy+vCOPqrz+2nr/8v4xL0q+0keemoY9D2DbNtLuo5QkneDfwc8APgi8AXqurG0UbVSfKzwHfpnuN7kqRG+GQbz+KKZ6H0uQ0BJPmpqvrxCB+/F68L4+inRdB+evn/Mi71qe0keR+wtKr+7SgefyY9+Ax8dFX9YFSPPxDH0Nrmou0RSvKbwC8CLwU+BTwBODnJvx5pYECSXwMuAtYDFyY5dvIfNoozH8azuOJZKH1sQ0mem+QXkqwGqKofJ9ljRLH04nVhHP3Ux/YzqK//L+NSn9pOkj8Clk8mQUmWJtk/yZ4LHUt7/D59Bv4KcPyoHn8gjuG2zapalBfg14G3Dtx+BvAm4O3Az44wricCXwNWAY8Gfg24C3jzwDYZ83i+3qN4ntSn52chL31rQ8BhwP+iWx7z43RnCfdr9+2xwLH0ot0YR38vfWs/i+H/1fO4evO5tLtf+tJ2gP2BTcB/a7d/G7iQ7sdi3wIctMDPS58+A58x2Vanue+nFjCOob9nLNoeIeAaYHWS1wNU16V6KbAf8EujCqqq7gb+Drizqn5QVX8FHA28Jsl/bdssWFd7i+cq4B/7EA9wL/DXfYmnqv4J+Bvgrj7Es8B604baWZ1TgHOr6sSqegXdm9/Xkzy7qrYuZDx9acctjr/tSRx9eh/pg960n6n68vrdTlx9fB316nNpDPSi7VTVvcBqYFWSzwOvB04FTqbrpfrNhYqlT5+BSfYGzgMuqqqJ1kN2TJLfTHJILeBQvYV4L1u0iVBV3Qb8MXB0kt9rZTcD59C9qB+z0DEleXLrTv0hcNJArN8CXgL8dJKnLmA8v9Cu3gP8UQ/i+QPgYGAL8B96EM+RSQ4FHkUP/l8LrU9tqL2RfRH40UDZyXRd4ecledZCxdKXdpzkp5PsS/ecvGmEcfTqfaQv+tR+BvXl9TtNXL18HfXtc2kc9KntVNVNdL0N99L1Un2zqv6Ormfo8CSPXoihkX36DAQOAa4H7knyr4CPAa+hS1I/MdCWh2qh3ssWbSLUXE73Yn1Gko+1F8qbgPuq6vsLGUiStwEfAD4IfAb4+SR/Mzm2sv3j9gOetkDxrAf+MsnxwJuBZUm+NMJ4zqLrYv0msA44OMllI4zn3cDbWiyfBp6W5IujimeEetOG6IYovCbJyyYLquq9Lb6Fel30oh0neSfw3nb5ON3r869HEEev3kd6qE/tpzev32ni6uXrqG+fS2OmN22n9TS8sqo2DBQfB9zT7luo3sCRfwa2x/w68G66XrF3At+oqtdW1W8DFwBDT8oW8r1sya7uYJSqamuSz9CN7X098IfAHVX1ewsZR7rJds+mG7t4GnAicAzdP/HLSd4DPBVYAfzDAoX1VWA53YTEQ6rqV5O8F/hikg8CBy5UPEmOBp5WVb/Yig4C/j1dAvLZJGfTnZFbqHhOAI6oqn+d5DS6sx8vBT4LXN6en59ZqHhGadRtKMkpdK+N32ld8G8Gzk3y81W1vm22lW74wseHHEsv2nGSU+nGZ7+ILhF6dlWtTnI+8NdJ/gL46WHH0XyVnryP9NGo28+gvrx+t+Or9Ox11LfPpXHTp7YzVZI/Bw6sqhctwGP16TPwFODpbWjeNUnuATZO87hPH3IcC/petqiXz54qyZ5V9fACP+Yy4EPAqVX1D63s48DbquqKJK+m+4ctoxv7+dUFius5dJP91tN1aQZ4EHgYuI/ug+eshYgnyZOAt1TVSUmOA95IN0Z4CXA4sAH4WeCDw46ndXGfCiypqj9L8vvAvwO+AdxEN1nxb+ka2NDj6ZuFbENJjqE72/P3wJ7Ab1XV7UmeQXfW6at0wxVeAryqqq4dYiy9aMfpflfjo8Drq+pbLVH/deA2ui9ozwK+TZeoD7399ul9ZDEYxWdQe9xevH5niK93r6M+fS5pdG1nujiAY4HPVtUDQ36sPn0GDsaypMVyR5JHAQ9XVSV5Lt3wxdcO8TNwwd/LdqtEaFTai/ZWuhfLlnam656q+k/ti/eKqrplBHH9Gd3qI3cC1wKPA365jcVdyDj2A/4KuA54DN0QhAeB5wGrq+qPFzielcDn6L5YvpyuR2hv4FeA/avqzxYynnHV2s3RVfWhJP8deAHwe1V1Rbt/Ld3vBvywqv56geK5lRG34ySPqarvJzkY+Dzdh+DjgFcDN1fVe4Ydw5R4evE+opn15fU7Q3y9eh317XNJ46dPn4HTxLIK+PdV9eV2/88B7wI+VFUXLkAst7JA72WLfY5QX9xUVT+sqi3t9lUD913GAoynHNReKAAFrAF+n+4N/q3AWUmOGNhm6KrqfrovcfvTfcg8VFXfpesSPyrJwUkW7LVYVdcAz6E7s7GhqjZV1Ua6VYNWJXnqQsYzrqpbKejD7fof0Z0FOjPJr7dN9qb7kb2hJ0FNL9rx5Pj49kZ/dFXdWFVX0w1PODrJYxfi9dm39xHtUC9ev1P19XXUt88ljZ8+fQZOE8vZwAeTvKRt8jzgvw87CWoW9L3MHqEhSHIk3VjjR9Fl8ieOKI5lwPvpuvl/sar+McnzquqqHVQdVjzPAN5H97y8kW5M8L2jHBPczjTcT/f7Bf8P3f9r5GOUx1WSF9ENoVkBXFxVrx9hLCNvx8lPfj07yQeAB6rqP+yg2nzH0Kv3Ec1OH16/U+Lp5euoj59LGl89+wycjOVg4C+r6g0jimOo72UmQvOsndl6GnAj8KmqeumIY1kLXFfdKiC9kG650v2APavqzSOO5Ujgd4FDgU1V9ZpRxiNI8jHgKVX1ghHG0Kd2vIQuST+8qn51BI/fy/cRbV+fXr+T+v466tPnksZbHz4D+xLLQryXmQgNSZI/BM6squ+NOhaAJD9VC/gjWItJa2hPpFu2c8uOttfwJDkE+Avg2D60nT604ySPpZu8e/GwJ+/OIhbfRxaRPrx+p+PrSJpenz4DexbL0N7LTISGZHBIi6TZS/LoqvrBqOMA27EWN1+/0uLTs8/AXsQyzPcyEyFJkiRJY8cVUSRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0tgxERqxJLcm+eURPO7ZSf6sXf/FJDctdAySJEnSqCwZdQAavar6/4CnjzoOSZIkaaHYI7SbSGJSK0mSJM2SidB2tCFrf5jka0nuT3JBkr2THJ/kS1O2rSSHtOtnJ3lfkkuTbE7yt0mekuQdSe5L8o0kz5nycP8yyQ3t/g8l2Xtg3y9J8tUk303yd0n+xZQYT03yNeD7MyVDSZ6T5CtJHkhyATD4GKuSbBq4vS7JN9u2NyT59YH79kjy1iR3J7klyRvb8S9p908k+S/tuB9I8rkkTxyo/9Ik17fjmUjyjIH7Tk3ynVbvpiSrW/lPDcR0T5INSfZv9+2d5MOt/LtJvpxk6Q7/wZIkSRprJkIzeyWwBjgY+BfA8TtR7/8Cngg8BFwBfKXdvhB425TtXw0cA/wc8LRWlyTPBT4I/A5wAPA/gYuT7DVQ9zeAFwOPr6ot0wWT5FHA/wucB+wPfBz4tzPE/03gF4H9gD8FPpxkWbvv9cCLgGcDzwVeNk393wR+C3gy8CjgD1scTwM+Bvw+8CTg08BfJXlUkqcDbwT+ZVU9rj0ft7b9vak9zr8GngrcB7y33Xdci/Mguufod4EfznBskiRJkonQDryrqm6vqnuBv6L78j8bn6yqa6vqQeCTwINVdW5VbQUuAKb2CL2nqm5rj3M6XXIDXdLxP6vqqqraWlXn0CVWR0+J8baqmunL/9HAnsA7qurhqroQ+PL2Nq6qj7fj/nFVXQDcDBzV7n4l8M6q2lRV9wHrp9nFh6rqf7WYNvCT5+1VwCVVdVlVPQz8ObAP8AvAVmAv4LAke1bVrVX1zVbvd4D/2B7zIeAtwMtbL9TDdAnQIe05uraqvjfDcyFJkiSZCO3APw5c/wHw2FnWu3Pg+g+nuT11P7cNXP/fdL0eAD8DnNKGfH03yXfpej6eup262/NU4DtVVVMeZ1pJXjcwHO+7wDPperMm9zX4mNM9/vaet6cOPm5V/bjVP7CqNtL1FL0FuCvJ+UkGn4dPDsRzI13itJSul+uzwPlJbk/y35Lsub1jkyRJksBEaC6+Dzx68kaSp8zDPg8auP7TwO3t+m3A6VX1+IHLo6vqYwPbDyY323MHcGCSTHmcbST5GeAv6IapHVBVjwe+DkzWvQNYvp3Yd+R2uqRm8rHS6n8HoKo+WlX/qm1TwBlt09uAF015Hvauqu+0Hq4/rarD6HqWXgK8bidikiRJ0hgyEdp5/wAcnuTZbVGDt8zDPk9KsrwtAPBmuuFz0CUkv5vkeek8JsmLkzxuJ/d/BbAFeFOSJUn+DT8Z6jbVY+iSkH8CSPJbdD1CkzYAJyc5MMnjgVN3Io4NwIuTrG69NqfQDfX7uyRPT/JLbf7Tg3Q9Z1tbvf8BnN6SNJI8Kcmx7foLkhyRZA/ge3RD5bYiSZIkzcBEaCdV1f8C/jPwebq5M1+aucasfBT4HPCtdvmz9ljX0M0Teg/dAgEbmf2CDYMx/wj4N63ufXRzdT6xnW1vAN5KlzzdCRwB/O3AJn/RYv0a8Pd0Cx5sYRbJR1XdBLwGeDdwN/BrwK+1+Paim290N93QuifTJYUA7wQuBj6X5AHgSuB57b6n0C1A8T26IXN/DXx4R7FIkiRpvOWR00aknZPkRcD/qKqf2eHGkiRJUk/YI6SdkmSfJL/ahtgdCPwJ3cp4kiRJ0qJhj9BuIslPAzds5+7Dqurb8/Q4j6YbfvbzdPN4LgFOdslqSZIkLSYmQpIkSZLGjkPjJEmSJI2dJaMOYK6e+MQn1ooVK0Ydxk77/ve/z2Me85hRhzFniz1+mJ9juPbaa++uqifNU0iSJElaYIs2EVqxYgXXXHPNqMPYaRMTE6xatWrUYczZYo8f5ucYkvzv+YlGkiRJo+DQOEmSJEljZ1aJUJJbk1yX5KtJrmll+ye5LMnN7e8TBrY/LcnGJDclOWag/Mi2n41J3pUkrXyvJBe08quSrJjn45QkSZKkf7YzPUIvqKpnV9XKdnsdcHlVHQpc3m6T5DBgLXA4sAZ4X5I9Wp33AycCh7bLmlZ+AnBfVR0CvB04Y+6HJEmSJEkz25WhcccC57Tr5wAvGyg/v6oeqqpbgI3AUUmWAftW1RXVrdl97pQ6k/u6EFg92VskSZIkSfNttoslFPC5JAX8z6o6E1haVXcAVNUdSZ7ctj0QuHKg7qZW9nC7PrV8ss5tbV9bktwPHADcPRhEkhPpepRYunQpExMTswy/PzZv3rwo45602OOH3eMYJEmStGtmmwg9v6pub8nOZUm+McO20/Xk1AzlM9V5ZEGXgJ0JsHLlylqMq5ct9lXXFnv8sHscgyRJknbNrBKhqrq9/b0rySeBo4A7kyxrvUHLgLva5puAgwaqLwdub+XLpykfrLMpyRJgP+DeuR2SZmPFukvmVO/sNYv7N4QkSZIkmMUcoSSPSfK4yevArwBfBy4GjmubHQdc1K5fDKxtK8EdTLcowtVtGN0DSY5u839eN6XO5L5eDnyhzSOSJEmSpHk3mx6hpcAn29oFS4CPVtVnknwZ2JDkBODbwCsAqur6JBuAG4AtwElVtbXt6w3A2cA+wKXtAnAWcF6SjXQ9QWvn4dgkSZIkaVo7TISq6lvAs6YpvwdYvZ06pwOnT1N+DfDMacofpCVSkiRJkjRsu7J8tiRJkiQtSiZCkiRJksaOiZAkSZKksWMiJEmSJGnsmAhJkiRJGjsmQpIkSZLGjomQJEmSpLFjIiRJkiRp7JgISZIkSRo7JkKSJEmSxo6JkCRJkqSxYyIkSZIkaeyYCEmSJEkaOyZCkiRJksaOiZAkSZKksWMiJEmSJGnsmAhJkiRJGjsmQpIkSZLGjomQJEmSpLFjIiRJkiRp7JgISZIkSRo7s06EkuyR5O+TfKrd3j/JZUlubn+fMLDtaUk2JrkpyTED5Ucmua7d964kaeV7JbmglV+VZMU8HqMkSZIkPcLO9AidDNw4cHsdcHlVHQpc3m6T5DBgLXA4sAZ4X5I9Wp33AycCh7bLmlZ+AnBfVR0CvB04Y05HI0mSJEmzMKtEKMly4MXABwaKjwXOadfPAV42UH5+VT1UVbcAG4GjkiwD9q2qK6qqgHOn1Jnc14XA6sneIkmSJEmab0tmud07gD8GHjdQtrSq7gCoqjuSPLmVHwhcObDdplb2cLs+tXyyzm1tX1uS3A8cANw9GESSE+l6lFi6dCkTExOzDL8/Nm/e3Iu4Tzliy5zq9SX+XbE7HIMkSZJ2zQ4ToSQvAe6qqmuTrJrFPqfryakZymeq88iCqjOBMwFWrlxZq1bNJpx+mZiYoA9xH7/ukjnVO3vNY3oR/67oy/9AkiRJozObHqHnAy9N8qvA3sC+ST4M3JlkWesNWgbc1bbfBBw0UH85cHsrXz5N+WCdTUmWAPsB987xmCRJkiRpRjucI1RVp1XV8qpaQbcIwheq6jXAxcBxbbPjgIva9YuBtW0luIPpFkW4ug2jeyDJ0W3+z+um1Jnc18vbY2zTIyRJkiRJ82G2c4Smsx7YkOQE4NvAKwCq6vokG4AbgC3ASVW1tdV5A3A2sA9wabsAnAWcl2QjXU/Q2l2IS5IkSZJmtFOJUFVNABPt+j3A6u1sdzpw+jTl1wDPnKb8QVoiJUmSJEnDtjO/IyRJkiRJuwUTIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY8dESJIkSdLYMRGSJEmSNHZMhCRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY8dESJIkSdLYMRGSJEmSNHZMhCRJkiSNnR0mQkn2TnJ1kn9Icn2SP23l+ye5LMnN7e8TBuqclmRjkpuSHDNQfmSS69p970qSVr5Xkgta+VVJVgzhWCVJkiQJmF2P0EPAL1XVs4BnA2uSHA2sAy6vqkOBy9ttkhwGrAUOB9YA70uyR9vX+4ETgUPbZU0rPwG4r6oOAd4OnLHrhyZJkiRJ09thIlSdze3mnu1SwLHAOa38HOBl7fqxwPlV9VBV3QJsBI5KsgzYt6quqKoCzp1SZ3JfFwKrJ3uLJEmSJGm+LZnNRq1H51rgEOC9VXVVkqVVdQdAVd2R5Mlt8wOBKweqb2plD7frU8sn69zW9rUlyf3AAcDdU+I4ka5HiaVLlzIxMTHLw+yPzZs39yLuU47YMqd6fYl/V+wOxyBJkqRdM6tEqKq2As9O8njgk0meOcPm0/Xk1AzlM9WZGseZwJkAK1eurFWrVs0QRj9NTEzQh7iPX3fJnOqdveYxvYh/V/TlfyBJkqTR2alV46rqu8AE3dyeO9twN9rfu9pmm4CDBqotB25v5cunKX9EnSRLgP2Ae3cmNkmSJEmardmsGvek1hNEkn2AXwa+AVwMHNc2Ow64qF2/GFjbVoI7mG5RhKvbMLoHkhzd5v+8bkqdyX29HPhCm0ckSZIkSfNuNkPjlgHntHlCPwVsqKpPJbkC2JDkBODbwCsAqur6JBuAG4AtwEltaB3AG4CzgX2AS9sF4CzgvCQb6XqC1s7HwUmSJEnSdHaYCFXV14DnTFN+D7B6O3VOB06fpvwaYJv5RVX1IC2RkiRJkqRh26k5QpIkSZK0OzARkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY8dESJIkSdLYMRGSJEmSNHZMhCRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY2eHiVCSg5J8McmNSa5PcnIr3z/JZUlubn+fMFDntCQbk9yU5JiB8iOTXNfue1eStPK9klzQyq9KsmIIxypJkiRJACyZxTZbgFOq6itJHgdcm+Qy4Hjg8qpan2QdsA44NclhwFrgcOCpwOeTPK2qtgLvB04ErgQ+DawBLgVOAO6rqkOSrAXOAF41nwe6O1qx7pJRhyBJkiQtSjvsEaqqO6rqK+36A8CNwIHAscA5bbNzgJe168cC51fVQ1V1C7AROCrJMmDfqrqiqgo4d0qdyX1dCKye7C2SJEmSpPm2U3OE2pC15wBXAUur6g7okiXgyW2zA4HbBqptamUHtutTyx9Rp6q2APcDB+xMbJIkSZI0W7MZGgdAkscCfwn8flV9b4YOm+nuqBnKZ6ozNYYT6YbWsXTpUiYmJnYQdf9s3rx53uI+5Ygt87KfnTGf8Y/K7nAMkiRJ2jWzSoSS7EmXBH2kqj7Riu9Msqyq7mjD3u5q5ZuAgwaqLwdub+XLpykfrLMpyRJgP+DeqXFU1ZnAmQArV66sVatWzSb8XpmYmGC+4j5+BHOEzl7zmHmLf1Tm838gSZKkxWk2q8YFOAu4sareNnDXxcBx7fpxwEUD5WvbSnAHA4cCV7fhcw8kObrt83VT6kzu6+XAF9o8IkmSJEmad7PpEXo+8FrguiRfbWVvBtYDG5KcAHwbeAVAVV2fZANwA92Kcye1FeMA3gCcDexDt1rcpa38LOC8JBvpeoLW7tphSZIkSdL27TARqqovMf0cHoDV26lzOnD6NOXXAM+cpvxBWiIlSZIkScO2U6vGSZIkSdLuwERIkiRJ0tgxEZIkSZI0dkyEJEmSJI2dWf+gqma2Ypa/6XPKEVu2+f2fW9e/eBghSZIkSdoOe4QkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY8dESJIkSdLYMRGSJEmSNHZMhCRJkiSNHRMhSZIkSWPHREiSJEnS2DERkiRJkjR2TIQkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dnaYCCX5YJK7knx9oGz/JJclubn9fcLAfacl2ZjkpiTHDJQfmeS6dt+7kqSV75XkglZ+VZIV83yMkiRJkvQIs+kROhtYM6VsHXB5VR0KXN5uk+QwYC1weKvzviR7tDrvB04EDm2XyX2eANxXVYcAbwfOmOvBSJIkSdJs7DARqqq/Ae6dUnwscE67fg7wsoHy86vqoaq6BdgIHJVkGbBvVV1RVQWcO6XO5L4uBFZP9hZJkiRJ0jAsmWO9pVV1B0BV3ZHkya38QODKge02tbKH2/Wp5ZN1bmv72pLkfuAA4O45xrborFh3yahDkCRJksbKXBOh7ZmuJ6dmKJ+pzrY7T06kG17H0qVLmZiYmEOIw3HKEVtmtd3SfWa/bR9t3ry5V8/7XOwOxyBJkqRdM9dE6M4ky1pv0DLgrla+CThoYLvlwO2tfPk05YN1NiVZAuzHtkPxAKiqM4EzAVauXFmrVq2aY/jz7/hZ9uqccsQW3nrdfOefC+fsNY+hT8/7XExMTCz6Y5AkSdKumevy2RcDx7XrxwEXDZSvbSvBHUy3KMLVbRjdA0mObvN/XjelzuS+Xg58oc0jkiRJkqSh2GHXRJKPAauAJybZBPwJsB7YkOQE4NvAKwCq6vokG4AbgC3ASVW1te3qDXQr0O0DXNouAGcB5yXZSNcTtHZejkySJEmStmOHiVBV/cZ27lq9ne1PB06fpvwa4JnTlD9IS6QkSZIkaSHMdWicJEmSJC1aJkKSJEmSxo6JkCRJkqSxYyIkSZIkaeyYCEmSJEkaOyZCkiRJksaOiZAkSZKksWMiJEmSJGnsmAhJkiRJGjsmQpIkSZLGjomQJEmSpLFjIiRJkiRp7JgISZIkSRo7JkKSJEmSxo6JkCRJkqSxYyIkSZIkaewsGXUAfbNi3SWjDkGSJEnSkNkjJEmSJGnsmAhJkiRJGjsmQpIkSZLGjomQJEmSpLHTm8USkqwB3gnsAXygqtbPdV8ueCBJkiRpJr3oEUqyB/Be4EXAYcBvJDlstFFJkiRJ2l31IhECjgI2VtW3qupHwPnAsSOOSZIkSdJuqi9D4w4Ebhu4vQl43ohi0W5kumGSpxyxheN3MHzy1vUvHlZIkiRJ6oG+JEKZpqy22Sg5ETix3dyc5KahRjUEb4InAnePOo65esEZizt+mN3/IGfscDc/M1/xSJIkaeH1JRHaBBw0cHs5cPvUjarqTODMhQpqGJJcU1UrRx3HXC32+GH3OAZJkiTtmr7MEfoycGiSg5M8ClgLXDzimCRJkiTtpnrRI1RVW5K8Efgs3fLZH6yq60ccliRJkqTdVC8SIYCq+jTw6VHHsQAW9dA+Fn/8sHscgyRJknZBqrZZk0CSJEmSdmt9mSMkSZIkSQvGREiSJEnS2DERWgBJXjrqGCRJkiT9hInQkCV5H3DcqOOYb0kW3WsnyaNHHYMkSZL6YdF9mV1MkvwRsLyq/m27vTTJ/kn2HHFoOy3Jc5P8QpLVAFX14yR7jDqu2UryK8DxiylmSZIkDY+J0JAk2R84GfhGu/3bwHvpfivpPyY5aITh7ZQkhwHnA8cDv5vki0n2q6qtiyGxSPIM4M+BG6pq65T7bAOSJEljyOWzhyjJ04HzgO8BjwN+E1gKvAq4varOGGF4s5IkwAeAW6rqz1rZO4F/A/xaVX11hOHtUJK9gS8Bl1bV/90S1H8JHABcXVUbRxqgJEmSRsKz4UNUVTcBq4B7gbdW1Ter6u+AC4HDkzy6JRq9VV2m/EXgRwNlJwPrgfOSPGtUsc3SIcD1wD1J/hXwMeA1wC8Bn0jyC6MMTpIkSaNhIjRkVfWDqnplVW0YKD4OuKfdtxi65DYBr0nyssmCqnovXUL3tFEFNRtV9XXg3cATgHcC36iq11bVbwMXAH1P5CRJkjQES0YdwLhJ8ufAgVX1olHHMpMkpwBPq6rfqaqJJG8Gzk3y81W1vm22FVgNfHxkgW5Hi//pVXViVV2T5B5gI9vG+vSFj06SJEmj5hyhBdRWizsW+GxVPTDqeLYnyTHAB4G/B/YEfquqbm+LDlwAfJVuuN9LgFdV1bWjinU6U+JfQhf/HUkeBTxcVZXkucA5wGv7Ps9JkiRJ889ESNtoCc/RVfWhJP8deAHwe1V1Rbt/LfBd4IdV9deji3R608S/Cvj3VfXldv/PAe8CPlRVF44uUkmSJI2KiZCmlWTPqnq4XT8J+F3gP1XVJ5McD3y0qn400z5GaTvxn1ZVn0rym3Sr9k2MMkZJkiSNjomQZiXJi4C3ACuAi6vq9SMNaCcNxH8w8JdV9YbRRiRJkqRRMhHSrCX5GPCUqnrBqGOZi8UevyRJkuaPy2drVpIcAjyFbrGHRWexxy9JkqT5ZY+QZi3Jo6vqB6OOY64We/ySJEmaPyZCkiRJksaOQ+MkSZIkjR0TIUmSJEljx0RIkiRJ0tgxEZIkSZI0dkyEJEmSJI0dEyFJkiRJY+f/B5tiYJuO3IgKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1008x1008 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(14,14), xrot=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False]\n",
      "==========\n",
      "[False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False]\n"
     ]
    }
   ],
   "source": [
    "# check if there are any duplicate columns:\n",
    "print(df.columns.duplicated())\n",
    "print('='*10)\n",
    "print(df.columns.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert ids to ints\n",
    "df_ids = ['admission_type_id', 'discharge_disposition_id', 'admission_source_id']\n",
    "for i in df_ids:\n",
    "  df[i] = df[i].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if there are any missing data:\n",
    "missing_data = list(df.columns[df.isnull().any()])\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check multicollinearity within the continous variables\n",
    "df_continous = df.loc[:, df.dtypes != object]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking multicollinearity within the continous variables\n",
    "After running a correlation heatmap to all of continuous attributes to test for potential multicollinearity, we found that there was very weak correlation among the attributes with the highest correlation of .51 between [**patient_nbr**, **encounter_id**] and the second highest correlation of .47 between [**num_medications**, **time_in_hospital**]. As none of the correlations were strong, we decided to keep all attributes in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 277 ms, sys: 43.4 ms, total: 320 ms\n",
      "Wall time: 319 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "#Correlation check\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True) # one of the many color mappings\n",
    "\n",
    "# show the heatmap\n",
    "sns.set(style=\"darkgrid\") # one of the many styles to plot using\n",
    "f, ax = plt.subplots(figsize=(18, 9))\n",
    "chart=sns.heatmap(df_continous.corr(), cmap=cmap, annot=True)\n",
    "chart.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get detail imformation from each column so we can replace miss values with imputation and or delated columns or row as appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race\n",
      "Caucasian          76099\n",
      "AfricanAmerican    19210\n",
      "?                   2273\n",
      "Hispanic            2037\n",
      "Other               1506\n",
      "Asian                641\n",
      "Name: race, dtype: int64\n",
      "-------------\n",
      "gender\n",
      "Female             54708\n",
      "Male               47055\n",
      "Unknown/Invalid        3\n",
      "Name: gender, dtype: int64\n",
      "-------------\n",
      "age\n",
      "[70-80)     26068\n",
      "[60-70)     22483\n",
      "[50-60)     17256\n",
      "[80-90)     17197\n",
      "[40-50)      9685\n",
      "[30-40)      3775\n",
      "[90-100)     2793\n",
      "[20-30)      1657\n",
      "[10-20)       691\n",
      "[0-10)        161\n",
      "Name: age, dtype: int64\n",
      "-------------\n",
      "weight\n",
      "?            98569\n",
      "[75-100)      1336\n",
      "[50-75)        897\n",
      "[100-125)      625\n",
      "[125-150)      145\n",
      "[25-50)         97\n",
      "[0-25)          48\n",
      "[150-175)       35\n",
      "[175-200)       11\n",
      ">200             3\n",
      "Name: weight, dtype: int64\n",
      "-------------\n",
      "admission_type_id\n",
      "1    53990\n",
      "3    18869\n",
      "2    18480\n",
      "6     5291\n",
      "5     4785\n",
      "8      320\n",
      "7       21\n",
      "4       10\n",
      "Name: admission_type_id, dtype: int64\n",
      "-------------\n",
      "discharge_disposition_id\n",
      "1     60234\n",
      "3     13954\n",
      "6     12902\n",
      "18     3691\n",
      "2      2128\n",
      "22     1993\n",
      "11     1642\n",
      "5      1184\n",
      "25      989\n",
      "4       815\n",
      "7       623\n",
      "23      412\n",
      "13      399\n",
      "14      372\n",
      "28      139\n",
      "8       108\n",
      "15       63\n",
      "24       48\n",
      "9        21\n",
      "17       14\n",
      "16       11\n",
      "19        8\n",
      "10        6\n",
      "27        5\n",
      "12        3\n",
      "20        2\n",
      "Name: discharge_disposition_id, dtype: int64\n",
      "-------------\n",
      "admission_source_id\n",
      "7     57494\n",
      "1     29565\n",
      "17     6781\n",
      "4      3187\n",
      "6      2264\n",
      "2      1104\n",
      "5       855\n",
      "3       187\n",
      "20      161\n",
      "9       125\n",
      "8        16\n",
      "22       12\n",
      "10        8\n",
      "11        2\n",
      "14        2\n",
      "25        2\n",
      "13        1\n",
      "Name: admission_source_id, dtype: int64\n",
      "-------------\n",
      "time_in_hospital\n",
      "3     17756\n",
      "2     17224\n",
      "1     14208\n",
      "4     13924\n",
      "5      9966\n",
      "6      7539\n",
      "7      5859\n",
      "8      4391\n",
      "9      3002\n",
      "10     2342\n",
      "11     1855\n",
      "12     1448\n",
      "13     1210\n",
      "14     1042\n",
      "Name: time_in_hospital, dtype: int64\n",
      "-------------\n",
      "payer_code\n",
      "?     40256\n",
      "MC    32439\n",
      "HM     6274\n",
      "SP     5007\n",
      "BC     4655\n",
      "MD     3532\n",
      "CP     2533\n",
      "UN     2448\n",
      "CM     1937\n",
      "OG     1033\n",
      "PO      592\n",
      "DM      549\n",
      "CH      146\n",
      "WC      135\n",
      "OT       95\n",
      "MP       79\n",
      "SI       55\n",
      "FR        1\n",
      "Name: payer_code, dtype: int64\n",
      "-------------\n",
      "medical_specialty\n",
      "?                                   49949\n",
      "InternalMedicine                    14635\n",
      "Emergency/Trauma                     7565\n",
      "Family/GeneralPractice               7440\n",
      "Cardiology                           5352\n",
      "                                    ...  \n",
      "Perinatology                            1\n",
      "SportsMedicine                          1\n",
      "Dermatology                             1\n",
      "Psychiatry-Addictive                    1\n",
      "Surgery-PlasticwithinHeadandNeck        1\n",
      "Name: medical_specialty, Length: 73, dtype: int64\n",
      "-------------\n",
      "num_lab_procedures\n",
      "1      3208\n",
      "43     2804\n",
      "44     2496\n",
      "45     2376\n",
      "38     2213\n",
      "       ... \n",
      "107       1\n",
      "118       1\n",
      "129       1\n",
      "120       1\n",
      "121       1\n",
      "Name: num_lab_procedures, Length: 118, dtype: int64\n",
      "-------------\n",
      "num_procedures\n",
      "0    46652\n",
      "1    20742\n",
      "2    12717\n",
      "3     9443\n",
      "6     4954\n",
      "4     4180\n",
      "5     3078\n",
      "Name: num_procedures, dtype: int64\n",
      "-------------\n",
      "num_medications\n",
      "13    6086\n",
      "12    6004\n",
      "11    5795\n",
      "15    5792\n",
      "14    5707\n",
      "      ... \n",
      "70       2\n",
      "75       2\n",
      "74       1\n",
      "79       1\n",
      "81       1\n",
      "Name: num_medications, Length: 75, dtype: int64\n",
      "-------------\n",
      "number_outpatient\n",
      "0     85027\n",
      "1      8547\n",
      "2      3594\n",
      "3      2042\n",
      "4      1099\n",
      "5       533\n",
      "6       303\n",
      "7       155\n",
      "8        98\n",
      "9        83\n",
      "10       57\n",
      "11       42\n",
      "13       31\n",
      "12       30\n",
      "14       28\n",
      "15       20\n",
      "16       15\n",
      "17        8\n",
      "21        7\n",
      "20        7\n",
      "22        5\n",
      "18        5\n",
      "19        3\n",
      "24        3\n",
      "27        3\n",
      "23        2\n",
      "25        2\n",
      "26        2\n",
      "29        2\n",
      "33        2\n",
      "35        2\n",
      "36        2\n",
      "40        1\n",
      "28        1\n",
      "34        1\n",
      "37        1\n",
      "38        1\n",
      "39        1\n",
      "42        1\n",
      "Name: number_outpatient, dtype: int64\n",
      "-------------\n",
      "number_emergency\n",
      "0     90383\n",
      "1      7677\n",
      "2      2042\n",
      "3       725\n",
      "4       374\n",
      "5       192\n",
      "6        94\n",
      "7        73\n",
      "8        50\n",
      "10       34\n",
      "9        33\n",
      "11       23\n",
      "13       12\n",
      "12       10\n",
      "22        6\n",
      "18        5\n",
      "16        5\n",
      "19        4\n",
      "20        4\n",
      "14        3\n",
      "15        3\n",
      "21        2\n",
      "25        2\n",
      "76        1\n",
      "54        1\n",
      "24        1\n",
      "28        1\n",
      "29        1\n",
      "37        1\n",
      "42        1\n",
      "46        1\n",
      "64        1\n",
      "63        1\n",
      "Name: number_emergency, dtype: int64\n",
      "-------------\n",
      "number_inpatient\n",
      "0     67630\n",
      "1     19521\n",
      "2      7566\n",
      "3      3411\n",
      "4      1622\n",
      "5       812\n",
      "6       480\n",
      "7       268\n",
      "8       151\n",
      "9       111\n",
      "10       61\n",
      "11       49\n",
      "12       34\n",
      "13       20\n",
      "14       10\n",
      "15        9\n",
      "16        6\n",
      "19        2\n",
      "17        1\n",
      "18        1\n",
      "21        1\n",
      "Name: number_inpatient, dtype: int64\n",
      "-------------\n",
      "diag_1\n",
      "428    6862\n",
      "414    6581\n",
      "786    4016\n",
      "410    3614\n",
      "486    3508\n",
      "       ... \n",
      "915       1\n",
      "839       1\n",
      "974       1\n",
      "207       1\n",
      "347       1\n",
      "Name: diag_1, Length: 717, dtype: int64\n",
      "-------------\n",
      "diag_2\n",
      "276    6752\n",
      "428    6662\n",
      "250    6071\n",
      "427    5036\n",
      "401    3736\n",
      "       ... \n",
      "475       1\n",
      "529       1\n",
      "235       1\n",
      "192       1\n",
      "223       1\n",
      "Name: diag_2, Length: 749, dtype: int64\n",
      "-------------\n",
      "diag_3\n",
      "250     11555\n",
      "401      8289\n",
      "276      5175\n",
      "428      4577\n",
      "427      3955\n",
      "        ...  \n",
      "E825        1\n",
      "E853        1\n",
      "148         1\n",
      "893         1\n",
      "361         1\n",
      "Name: diag_3, Length: 790, dtype: int64\n",
      "-------------\n",
      "number_diagnoses\n",
      "9     49474\n",
      "5     11393\n",
      "8     10616\n",
      "7     10393\n",
      "6     10161\n",
      "4      5537\n",
      "3      2835\n",
      "2      1023\n",
      "1       219\n",
      "16       45\n",
      "10       17\n",
      "13       16\n",
      "11       11\n",
      "15       10\n",
      "12        9\n",
      "14        7\n",
      "Name: number_diagnoses, dtype: int64\n",
      "-------------\n",
      "max_glu_serum\n",
      "None    96420\n",
      "Norm     2597\n",
      ">200     1485\n",
      ">300     1264\n",
      "Name: max_glu_serum, dtype: int64\n",
      "-------------\n",
      "A1Cresult\n",
      "None    84748\n",
      ">8       8216\n",
      "Norm     4990\n",
      ">7       3812\n",
      "Name: A1Cresult, dtype: int64\n",
      "-------------\n",
      "metformin\n",
      "No        81778\n",
      "Steady    18346\n",
      "Up         1067\n",
      "Down        575\n",
      "Name: metformin, dtype: int64\n",
      "-------------\n",
      "repaglinide\n",
      "No        100227\n",
      "Steady      1384\n",
      "Up           110\n",
      "Down          45\n",
      "Name: repaglinide, dtype: int64\n",
      "-------------\n",
      "nateglinide\n",
      "No        101063\n",
      "Steady       668\n",
      "Up            24\n",
      "Down          11\n",
      "Name: nateglinide, dtype: int64\n",
      "-------------\n",
      "chlorpropamide\n",
      "No        101680\n",
      "Steady        79\n",
      "Up             6\n",
      "Down           1\n",
      "Name: chlorpropamide, dtype: int64\n",
      "-------------\n",
      "glimepiride\n",
      "No        96575\n",
      "Steady     4670\n",
      "Up          327\n",
      "Down        194\n",
      "Name: glimepiride, dtype: int64\n",
      "-------------\n",
      "acetohexamide\n",
      "No        101765\n",
      "Steady         1\n",
      "Name: acetohexamide, dtype: int64\n",
      "-------------\n",
      "glipizide\n",
      "No        89080\n",
      "Steady    11356\n",
      "Up          770\n",
      "Down        560\n",
      "Name: glipizide, dtype: int64\n",
      "-------------\n",
      "glyburide\n",
      "No        91116\n",
      "Steady     9274\n",
      "Up          812\n",
      "Down        564\n",
      "Name: glyburide, dtype: int64\n",
      "-------------\n",
      "tolbutamide\n",
      "No        101743\n",
      "Steady        23\n",
      "Name: tolbutamide, dtype: int64\n",
      "-------------\n",
      "pioglitazone\n",
      "No        94438\n",
      "Steady     6976\n",
      "Up          234\n",
      "Down        118\n",
      "Name: pioglitazone, dtype: int64\n",
      "-------------\n",
      "rosiglitazone\n",
      "No        95401\n",
      "Steady     6100\n",
      "Up          178\n",
      "Down         87\n",
      "Name: rosiglitazone, dtype: int64\n",
      "-------------\n",
      "acarbose\n",
      "No        101458\n",
      "Steady       295\n",
      "Up            10\n",
      "Down           3\n",
      "Name: acarbose, dtype: int64\n",
      "-------------\n",
      "miglitol\n",
      "No        101728\n",
      "Steady        31\n",
      "Down           5\n",
      "Up             2\n",
      "Name: miglitol, dtype: int64\n",
      "-------------\n",
      "troglitazone\n",
      "No        101763\n",
      "Steady         3\n",
      "Name: troglitazone, dtype: int64\n",
      "-------------\n",
      "tolazamide\n",
      "No        101727\n",
      "Steady        38\n",
      "Up             1\n",
      "Name: tolazamide, dtype: int64\n",
      "-------------\n",
      "examide\n",
      "No    101766\n",
      "Name: examide, dtype: int64\n",
      "-------------\n",
      "citoglipton\n",
      "No    101766\n",
      "Name: citoglipton, dtype: int64\n",
      "-------------\n",
      "insulin\n",
      "No        47383\n",
      "Steady    30849\n",
      "Down      12218\n",
      "Up        11316\n",
      "Name: insulin, dtype: int64\n",
      "-------------\n",
      "glyburide-metformin\n",
      "No        101060\n",
      "Steady       692\n",
      "Up             8\n",
      "Down           6\n",
      "Name: glyburide-metformin, dtype: int64\n",
      "-------------\n",
      "glipizide-metformin\n",
      "No        101753\n",
      "Steady        13\n",
      "Name: glipizide-metformin, dtype: int64\n",
      "-------------\n",
      "glimepiride-pioglitazone\n",
      "No        101765\n",
      "Steady         1\n",
      "Name: glimepiride-pioglitazone, dtype: int64\n",
      "-------------\n",
      "metformin-rosiglitazone\n",
      "No        101764\n",
      "Steady         2\n",
      "Name: metformin-rosiglitazone, dtype: int64\n",
      "-------------\n",
      "metformin-pioglitazone\n",
      "No        101765\n",
      "Steady         1\n",
      "Name: metformin-pioglitazone, dtype: int64\n",
      "-------------\n",
      "change\n",
      "No    54755\n",
      "Ch    47011\n",
      "Name: change, dtype: int64\n",
      "-------------\n",
      "diabetesMed\n",
      "Yes    78363\n",
      "No     23403\n",
      "Name: diabetesMed, dtype: int64\n",
      "-------------\n",
      "readmitted\n",
      "NO     54864\n",
      ">30    35545\n",
      "<30    11357\n",
      "Name: readmitted, dtype: int64\n",
      "-------------\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns[2:]:\n",
    "    print(i)\n",
    "    print(df[i].value_counts())\n",
    "    print(\"-------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First drop the unnecesary columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First delate the columns that are id of the patients or encounter, so they are usefull for the model, and the columns of weight which is important but the amount of missing values is too much to make it an useful variable, there is 96% of missing values. We also delete the examide medication and citoglipton medication because there is only the no variable, there is not any yes value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "?            96.858479\n",
       "[75-100)      1.312816\n",
       "[50-75)       0.881434\n",
       "[100-125)     0.614154\n",
       "[125-150)     0.142484\n",
       "[25-50)       0.095317\n",
       "[0-25)        0.047167\n",
       "[150-175)     0.034393\n",
       "[175-200)     0.010809\n",
       ">200          0.002948\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=df[\"weight\"].value_counts(normalize=True)*100\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['encounter_id', 'patient_nbr', 'race', 'gender', 'age', 'weight',\n",
       "       'admission_type_id', 'discharge_disposition_id', 'admission_source_id',\n",
       "       'time_in_hospital', 'payer_code', 'medical_specialty',\n",
       "       'num_lab_procedures', 'num_procedures', 'num_medications',\n",
       "       'number_outpatient', 'number_emergency', 'number_inpatient', 'diag_1',\n",
       "       'diag_2', 'diag_3', 'number_diagnoses', 'max_glu_serum', 'A1Cresult',\n",
       "       'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide',\n",
       "       'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',\n",
       "       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',\n",
       "       'tolazamide', 'examide', 'citoglipton', 'insulin',\n",
       "       'glyburide-metformin', 'glipizide-metformin',\n",
       "       'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
       "       'metformin-pioglitazone', 'change', 'diabetesMed', 'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>tolazamide</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>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</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>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>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</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>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>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</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>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>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</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>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>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</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>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 × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              race  gender      age  admission_type_id  \\\n",
       "0        Caucasian  Female   [0-10)                  6   \n",
       "1        Caucasian  Female  [10-20)                  1   \n",
       "2  AfricanAmerican  Female  [20-30)                  1   \n",
       "3        Caucasian    Male  [30-40)                  1   \n",
       "4        Caucasian    Male  [40-50)                  1   \n",
       "\n",
       "   discharge_disposition_id  admission_source_id  time_in_hospital  \\\n",
       "0                        25                    1                 1   \n",
       "1                         1                    7                 3   \n",
       "2                         1                    7                 2   \n",
       "3                         1                    7                 2   \n",
       "4                         1                    7                 1   \n",
       "\n",
       "   num_lab_procedures  num_procedures  num_medications  ...  tolazamide  \\\n",
       "0                  41               0                1  ...          No   \n",
       "1                  59               0               18  ...          No   \n",
       "2                  11               5               13  ...          No   \n",
       "3                  44               1               16  ...          No   \n",
       "4                  51               0                8  ...          No   \n",
       "\n",
       "   insulin  glyburide-metformin glipizide-metformin glimepiride-pioglitazone  \\\n",
       "0       No                   No                  No                       No   \n",
       "1       Up                   No                  No                       No   \n",
       "2       No                   No                  No                       No   \n",
       "3       Up                   No                  No                       No   \n",
       "4   Steady                   No                  No                       No   \n",
       "\n",
       "  metformin-rosiglitazone  metformin-pioglitazone change diabetesMed  \\\n",
       "0                      No                      No     No          No   \n",
       "1                      No                      No     Ch         Yes   \n",
       "2                      No                      No     No         Yes   \n",
       "3                      No                      No     Ch         Yes   \n",
       "4                      No                      No     Ch         Yes   \n",
       "\n",
       "  readmitted  \n",
       "0         NO  \n",
       "1        >30  \n",
       "2         NO  \n",
       "3         NO  \n",
       "4         NO  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced= df.drop(columns=['encounter_id','patient_nbr', 'payer_code', 'medical_specialty', 'examide','citoglipton'])\n",
    "df_reduced.shape\n",
    "df_reduced.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binning of the dig 1 diag 2 and dig 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to Categorize DX based on URL ...\n",
    "def bin_dx(code):\n",
    "    try:\n",
    "        code = float(code)\n",
    "    except:\n",
    "        code = 0\n",
    "\n",
    "    # Circulatory\n",
    "    if code in range(390,460) or code == 785:\n",
    "        return(\"Circulatory\")\n",
    "\n",
    "    # Respiratory\n",
    "    elif code in range(460,520) or code == 786:\n",
    "        return(\"Respiratory\")\n",
    "\n",
    "    # Digestive\n",
    "    elif code in range(520,580) or code == 787:\n",
    "        return(\"Digestive\")\n",
    "\n",
    "    # Diabetes\n",
    "    elif code >= 250 and code < 251:\n",
    "        return(\"Diabetes\")\n",
    "\n",
    "    # Injury\n",
    "    elif code in range(800,1000):\n",
    "        return(\"Injury\")\n",
    "\n",
    "    # Musculoskeletal\n",
    "    elif code in range(710,740):\n",
    "        return(\"Musculoskeletal\")\n",
    "\n",
    "    # Genitourinary\n",
    "    elif code in range(580,630) or code == 788:\n",
    "        return(\"Genitourinary\")\n",
    "\n",
    "    # Neoplasms\n",
    "    elif code in range(140,240):\n",
    "        return(\"Neoplasms\")\n",
    "    elif code in range(780,783) or code == 784:\n",
    "        return(\"Neoplasms\")\n",
    "    elif code in range(790,800):\n",
    "        return(\"Neoplasms\")\n",
    "    elif code in range(240,250):\n",
    "        return(\"Neoplasms\")\n",
    "    elif code in range(251,280):\n",
    "        return(\"Neoplasms\")\n",
    "    elif code in range(680,710):\n",
    "        return(\"Neoplasms\")\n",
    "    elif code in range(1,140):\n",
    "        return(\"Neoplasms\")\n",
    "    elif code in range(290,320):\n",
    "        return(\"Neoplasms\")\n",
    "\n",
    "    # Other\n",
    "    else:\n",
    "        return(\"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circulatory        30437\n",
      "Neoplasms          16318\n",
      "Respiratory        14423\n",
      "Digestive           9475\n",
      "Diabetes            8757\n",
      "Injury              6974\n",
      "Other               5308\n",
      "Genitourinary       5117\n",
      "Musculoskeletal     4957\n",
      "Name: Diag1, dtype: int64\n",
      "\n",
      "Circulatory        31881\n",
      "Neoplasms          21462\n",
      "Diabetes           12794\n",
      "Respiratory        10895\n",
      "Genitourinary       8376\n",
      "Other               7996\n",
      "Digestive           4170\n",
      "Injury              2428\n",
      "Musculoskeletal     1764\n",
      "Name: Diag2, dtype: int64\n",
      "\n",
      "Circulatory        30306\n",
      "Neoplasms          20985\n",
      "Diabetes           17157\n",
      "Other              11489\n",
      "Respiratory         7358\n",
      "Genitourinary       6680\n",
      "Digestive           3930\n",
      "Injury              1946\n",
      "Musculoskeletal     1915\n",
      "Name: Diag3, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_reduced[\"Diag1\"] = df_reduced[\"diag_1\"].apply(bin_dx)\n",
    "df_reduced[\"Diag2\"] = df_reduced[\"diag_2\"].apply(bin_dx)\n",
    "df_reduced[\"Diag3\"] = df_reduced[\"diag_3\"].apply(bin_dx)\n",
    "\n",
    "\n",
    "print(df_reduced[\"Diag1\"].value_counts())\n",
    "print(\"\")\n",
    "print(df_reduced[\"Diag2\"].value_counts())\n",
    "print(\"\")\n",
    "print(df_reduced[\"Diag3\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reduced = df_reduced.drop(columns=['diag_1', 'diag_2', 'diag_3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will bin the age variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[70-80)     26068\n",
      "[60-70)     22483\n",
      "[50-60)     17256\n",
      "[80-90)     17197\n",
      "[40-50)      9685\n",
      "[30-40)      3775\n",
      "[90-100)     2793\n",
      "[20-30)      1657\n",
      "[10-20)       691\n",
      "[0-10)        161\n",
      "Name: age, dtype: int64\n",
      "Old       68541\n",
      "Midage    26941\n",
      "Young      6284\n",
      "Name: age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_reduced[\"age\"].value_counts())\n",
    "di = {'[70-80)': 'Old', '[60-70)': 'Old', '[80-90)': 'Old', '[90-100)': 'Old', '[50-60)': 'Midage', '[40-50)': 'Midage', '[30-40)': 'Young', '[20-30)': 'Young', '[10-20)': 'Young', '[0-10)': 'Young'}\n",
    "\n",
    "df_reduced[\"age\"].replace(di, inplace=True)\n",
    "print(df_reduced[\"age\"].value_counts())\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will replace the missing values from the reminder column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I there is a discrete or categorical values we will replace with the mode, if there is a continous variable we will replace with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reagenge the id coluns can be useful in a way that all the missing values is place in one place and make it categorical\n",
    "# admission_type_id\n",
    "df_reduced.admission_type_id.replace(to_replace = 5, value = 6, inplace = True)\n",
    "df_reduced.admission_type_id.replace(to_replace = 8, value = 6, inplace = True)\n",
    "df_reduced.admission_type_id = df_reduced.admission_type_id.astype(str)\n",
    "\n",
    "# admission_source_id\n",
    "df_reduced.admission_source_id.replace(to_replace = 9, value = 17, inplace = True)\n",
    "df_reduced.admission_source_id.replace(to_replace = 15, value = 17, inplace = True)\n",
    "df_reduced.admission_source_id.replace(to_replace = 20, value = 17, inplace = True)\n",
    "df_reduced.admission_source_id.replace(to_replace = 21, value = 17, inplace = True)\n",
    "df_reduced.admission_source_id= df_reduced.admission_source_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next, we remove observations where the discharge disposition is related to hospice or death since these will not add to the possibility of being readmitted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     60234\n",
      "3     13954\n",
      "6     12902\n",
      "18     3691\n",
      "2      2128\n",
      "22     1993\n",
      "5      1184\n",
      "25      989\n",
      "4       815\n",
      "7       623\n",
      "23      412\n",
      "28      139\n",
      "8       108\n",
      "15       63\n",
      "24       48\n",
      "9        21\n",
      "17       14\n",
      "16       11\n",
      "10        6\n",
      "27        5\n",
      "12        3\n",
      "Name: discharge_disposition_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "indexNames = df_reduced[df_reduced['discharge_disposition_id'].isin([11,13,14,19,20,21])].index\n",
    "df_reduced.drop(indexNames, inplace=True)\n",
    "\n",
    "print(df_reduced['discharge_disposition_id'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling Preparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make the result binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    88029\n",
      "1    11314\n",
      "Name: readmitted, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "di = {'NO': 0, '>30': 0, '<30': 1}\n",
    "\n",
    "df_reduced[\"readmitted\"].replace(di, inplace=True)\n",
    "print(df_reduced[\"readmitted\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One Hot encode the categorical values and scale the continous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"categorical_values= ['race', 'gender', 'max_glu_serum', 'A1Cresult', 'metformin', 'repaglinide', 'nateglinide','chlorpropamide',\\n                     'glimepiride', 'acetohexamide', 'glipizide','glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', 'acarbose','miglitol', 'troglitazone', 'tolazamide', 'insulin',\\n                    'glyburide-metformin', 'glipizide-metformin','glimepiride-pioglitazone', 'metformin-rosiglitazone',\\n                    'metformin-pioglitazone', 'change', 'diabetesMed', 'Diag1', 'Diag2','Diag3', 'Age', 'admission_type_id', 'discharge_disposition_id','admission_source_id']\\n\\n\\n\\nnumeric_value= ['time_in_hospital', 'num_lab_procedures','num_procedures', 'num_medications', 'number_outpatient',\\n                'number_emergency', 'number_inpatient', 'number_diagnoses',]\\n\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"categorical_values= ['race', 'gender', 'max_glu_serum', 'A1Cresult', 'metformin', 'repaglinide', 'nateglinide','chlorpropamide',\n",
    "                     'glimepiride', 'acetohexamide', 'glipizide','glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', 'acarbose','miglitol', 'troglitazone', 'tolazamide', 'insulin',\n",
    "                    'glyburide-metformin', 'glipizide-metformin','glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
    "                    'metformin-pioglitazone', 'change', 'diabetesMed', 'Diag1', 'Diag2','Diag3', 'Age', 'admission_type_id', 'discharge_disposition_id','admission_source_id']\n",
    "\n",
    "\n",
    "\n",
    "numeric_value= ['time_in_hospital', 'num_lab_procedures','num_procedures', 'num_medications', 'number_outpatient',\n",
    "                'number_emergency', 'number_inpatient', 'number_diagnoses',]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"from sklearn.preprocessing import OneHotEncoder\\n# categorical\\nohe = OneHotEncoder()\\nX_object = df_reduced.select_dtypes('object')\\nohe.fit(X_object)\\ncodes = ohe.transform(X_object).toarray()\\nfeature_names = ohe.get_feature_names(X_object.columns)\\n\\n# integer scaled\\nx_int= df_reduced.select_dtypes(exclude='object')\\nmin_max_scaler = RobustScaler()\\nx_int = min_max_scaler.fit_transform(x_int)\\nfeature_names_i =df_reduced.select_dtypes(exclude='object').columns \\n\\nfr = pd.concat([pd.DataFrame(x_int ,columns=feature_names_i), \\n               pd.DataFrame(codes,columns=feature_names).astype(int)], axis=1)\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"from sklearn.preprocessing import OneHotEncoder\n",
    "# categorical\n",
    "ohe = OneHotEncoder()\n",
    "X_object = df_reduced.select_dtypes('object')\n",
    "ohe.fit(X_object)\n",
    "codes = ohe.transform(X_object).toarray()\n",
    "feature_names = ohe.get_feature_names(X_object.columns)\n",
    "\n",
    "# integer scaled\n",
    "x_int= df_reduced.select_dtypes(exclude='object')\n",
    "min_max_scaler = RobustScaler()\n",
    "x_int = min_max_scaler.fit_transform(x_int)\n",
    "feature_names_i =df_reduced.select_dtypes(exclude='object').columns \n",
    "\n",
    "fr = pd.concat([pd.DataFrame(x_int ,columns=feature_names_i), \n",
    "               pd.DataFrame(codes,columns=feature_names).astype(int)], axis=1)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bulding the model with the pipeline using one hot encoder inside the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99343, 43)\n",
      "(99343, 42)\n",
      "(99343,)\n"
     ]
    }
   ],
   "source": [
    "categorical_features = ['race', 'gender', 'max_glu_serum', 'A1Cresult', 'metformin', 'repaglinide', 'nateglinide','chlorpropamide',\n",
    "                        'glimepiride', 'acetohexamide', 'glipizide','glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', \n",
    "                        'acarbose','miglitol', 'troglitazone', 'tolazamide', 'insulin', 'glyburide-metformin', 'glipizide-metformin',\n",
    "                        'glimepiride-pioglitazone', 'metformin-rosiglitazone', 'metformin-pioglitazone', 'change', 'diabetesMed',\n",
    "                        'Diag1', 'Diag2','Diag3', 'age', 'admission_type_id', 'discharge_disposition_id','admission_source_id']\n",
    "\n",
    "numeric_features = ['time_in_hospital', 'num_lab_procedures','num_procedures', 'num_medications', 'number_outpatient', \n",
    "                    'number_emergency', 'number_inpatient', 'number_diagnoses']\n",
    "\n",
    "X = df_reduced[categorical_features + numeric_features]\n",
    "y = df_reduced['readmitted']\n",
    "\n",
    "# Verify shapes\n",
    "print(df_reduced.shape)\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sophiawu/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation score: 0.6446633628806032\n",
      "Test score: 0.6508134127937846\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    stratify=y,\n",
    "                                                    random_state=11)\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(missing_values=np.nan, strategy=\"median\")),\n",
    "    ('scaler', RobustScaler(with_centering=False))])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(missing_values=np.nan, strategy=\"most_frequent\")),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "\n",
    "\n",
    "pipeline = imbpipeline(steps = [['preprocessor', preprocessor],\n",
    "                                ['smote', SMOTE(random_state=11)],\n",
    "                                ##['scaler', RobustScaler(with_centering=False)],\n",
    "                                ['classifier', LogisticRegression(random_state=11,\n",
    "                                                                  max_iter=1000, solver= \"saga\")]])\n",
    "\n",
    "stratified_kfold = StratifiedKFold(n_splits=3,\n",
    "                                       shuffle=True,\n",
    "                                       random_state=11)\n",
    "\n",
    "param_grid = {'classifier__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
    "              'classifier__penalty': ['l1', 'l2']}\n",
    "\n",
    "scorers = {\n",
    "    'precision_score': make_scorer(precision_score),\n",
    "    'recall_score': make_scorer(recall_score),\n",
    "    'accuracy_score': make_scorer(accuracy_score)\n",
    "}\n",
    "              \n",
    "grid_search = GridSearchCV(estimator=pipeline,\n",
    "                           param_grid=param_grid,\n",
    "                           scoring='roc_auc',\n",
    "                           #scoring=scorers,\n",
    "                           cv=stratified_kfold,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "cv_score = grid_search.best_score_\n",
    "test_score = grid_search.score(X_test, y_test)\n",
    "print(f'Cross-validation score: {cv_score}\\nTest score: {test_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>classifier__C</th>\n",
       "      <th>classifier__penalty</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001</td>\n",
       "      <td>l1</td>\n",
       "      <td>0.644475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.643333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010</td>\n",
       "      <td>l1</td>\n",
       "      <td>0.644663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.010</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.640416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.100</td>\n",
       "      <td>l1</td>\n",
       "      <td>0.639719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.100</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.638522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000</td>\n",
       "      <td>l1</td>\n",
       "      <td>0.637907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.637669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.000</td>\n",
       "      <td>l1</td>\n",
       "      <td>0.637558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.000</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.637535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100.000</td>\n",
       "      <td>l1</td>\n",
       "      <td>0.637523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100.000</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.637521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1000.000</td>\n",
       "      <td>l1</td>\n",
       "      <td>0.637520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1000.000</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.637519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    classifier__C classifier__penalty  test_score\n",
       "0           0.001                  l1    0.644475\n",
       "1           0.001                  l2    0.643333\n",
       "2           0.010                  l1    0.644663\n",
       "3           0.010                  l2    0.640416\n",
       "4           0.100                  l1    0.639719\n",
       "5           0.100                  l2    0.638522\n",
       "6           1.000                  l1    0.637907\n",
       "7           1.000                  l2    0.637669\n",
       "8          10.000                  l1    0.637558\n",
       "9          10.000                  l2    0.637535\n",
       "10        100.000                  l1    0.637523\n",
       "11        100.000                  l2    0.637521\n",
       "12       1000.000                  l1    0.637520\n",
       "13       1000.000                  l2    0.637519"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_LR = pd.DataFrame(grid_search.cv_results_['params'])\n",
    "results_LR['test_score'] = grid_search.cv_results_['mean_test_score']\n",
    "results_LR "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print the grid best stimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'classifier__C': 0.01, 'classifier__penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the grid best stimator to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat= grid_search.best_estimator_.predict(X_test)\n",
    "y_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best classifier coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"classifier = ovr.named_steps['classifier']\\nclassifier.coef_.shape\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"classifier = ovr.named_steps['classifier']\n",
    "classifier.coef_.shape\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'eli5'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-015193e64272>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0meli5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0monehot_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'preprocessor'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_transformers_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'onehot'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_feature_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mnumeric_features_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnumeric_features_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monehot_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'eli5'"
     ]
    }
   ],
   "source": [
    "import eli5\n",
    "\n",
    "onehot_columns = list(grid_search.best_estimator_.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot'].get_feature_names(input_features=categorical_features))\n",
    "numeric_features_list = list(numeric_features)\n",
    "numeric_features_list.extend(onehot_columns)\n",
    "\n",
    "eli5.explain_weights(grid_search.best_estimator_.named_steps['classifier'], top=50, feature_names=numeric_features_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance of the modeel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassified samples: 6651\n",
      "Accuracy: 0.67\n",
      "Precision= 0.6652574362071568\n",
      "Recal= 0.6652574362071568\n",
      "fscore= 0.7243598423040026\n",
      "Confusion Matrix = [[12000  5606]\n",
      " [ 1045  1218]]\n"
     ]
    }
   ],
   "source": [
    "# how did our model perform?\n",
    "count_misclassified = (y_test != y_hat).sum()\n",
    "print('Misclassified samples: {}'.format(count_misclassified))\n",
    "accuracy = mt.accuracy_score(y_test, y_hat)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))\n",
    "precision = mt.precision_score(y_test, y_hat, average='micro')\n",
    "print(\"Precision=\", precision)\n",
    "recal = mt.recall_score(y_test, y_hat, average='micro')\n",
    "print(\"Recal=\", recal)\n",
    "fscore = mt.f1_score(y_test, y_hat, average='weighted')\n",
    "print(\"fscore=\",fscore )\n",
    "# Confusion matrix\n",
    "cm= confusion_matrix(y_test, y_hat)\n",
    "print(\"Confusion Matrix =\", cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[12000  5606]\n",
      " [ 1045  1218]]\n",
      "Normalized confusion matrix\n",
      "[[0.68 0.32]\n",
      " [0.46 0.54]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "class_names = [0,1]\n",
    "\n",
    "classifier = grid_search\n",
    "\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
    "                  (\"Normalized confusion matrix\", 'true')]\n",
    "for title, normalize in titles_options:\n",
    "    disp = plot_confusion_matrix(classifier, X_test, y_test,\n",
    "                                 display_labels=class_names,\n",
    "                                 cmap=plt.cm.Blues,\n",
    "                                 normalize=normalize)\n",
    "    disp.ax_.set_title(title)\n",
    "\n",
    "    print(title)\n",
    "    print(disp.confusion_matrix)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.68      0.78     17606\n",
      "           1       0.18      0.54      0.27      2263\n",
      "\n",
      "    accuracy                           0.67     19869\n",
      "   macro avg       0.55      0.61      0.53     19869\n",
      "weighted avg       0.84      0.67      0.72     19869\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.36      0.52     17606\n",
      "           1       0.14      0.81      0.24      2263\n",
      "\n",
      "    accuracy                           0.41     19869\n",
      "   macro avg       0.54      0.58      0.38     19869\n",
      "weighted avg       0.84      0.41      0.49     19869\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "target_names = y_test.unique().astype(str)\n",
    "probs_y = grid_search.predict_proba(X_test)\n",
    "y_pred = grid_search.best_estimator_.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=target_names))\n",
    "\n",
    "vals = []\n",
    "\n",
    "for val in probs_y[:,1]:\n",
    "    if val > 0.4:\n",
    "        vals.append(1)\n",
    "    else:\n",
    "        vals.append(0)\n",
    "\n",
    "print(classification_report(y_test, vals, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fa765ff6e90>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.plot_roc_curve(grid_search.best_estimator_, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.65 0.35]\n",
      " [0.28 0.72]\n",
      " [0.68 0.32]\n",
      " ...\n",
      " [0.74 0.26]\n",
      " [0.63 0.37]\n",
      " [0.62 0.38]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.35, 0.72, 0.32, ..., 0.26, 0.37, 0.38])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs_y = grid_search.predict_proba(X_test)\n",
    "print(probs_y)\n",
    "\n",
    "probs_y[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, probs_y[:,1]) \n",
    "\n",
    "plt.title(\"Precision-Recall vs Threshold Chart\")\n",
    "plt.plot(thresholds, precision[: -1], \"b--\", label=\"Precision\")\n",
    "plt.plot(thresholds, recall[: -1], \"r--\", label=\"Recall\")\n",
    "plt.ylabel(\"Precision, Recall\")\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.ylim([0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fabiosavorgnan/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# classifier\n",
    "classifier = grid_search.fit(X_train, y_train)\n",
    "y_pred_proba = classifier.predict_proba(X_test)[::,1]\n",
    "fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
    "auc = roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tunning the threshold for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******** For i = 0.01 ******\n",
      "0.11389601892395189\n",
      "Precision= 0.11389601892395189\n",
      "Recal= 0.11389601892395189\n",
      "fscore= 0.023291766747235054\n",
      "Confusion Matrix = [[    0 17606]\n",
      " [    0  2263]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******** For i = 0.1 ******\n",
      "0.11389601892395189\n",
      "Precision= 0.11389601892395189\n",
      "Recal= 0.11389601892395189\n",
      "fscore= 0.023291766747235054\n",
      "Confusion Matrix = [[    0 17606]\n",
      " [    0  2263]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******** For i = 0.2 ******\n",
      "0.11746942473199457\n",
      "Precision= 0.11746942473199457\n",
      "Recal= 0.11746942473199457\n",
      "fscore= 0.030849612188469076\n",
      "Confusion Matrix = [[   75 17531]\n",
      " [    4  2259]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******** For i = 0.25 ******\n",
      "0.13473249786098948\n",
      "Precision= 0.13473249786098948\n",
      "Recal= 0.13473249786098948\n",
      "fscore= 0.06615734732176949\n",
      "Confusion Matrix = [[  434 17172]\n",
      " [   20  2243]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******** For i = 0.3 ******\n",
      "0.18390457496602747\n",
      "Precision= 0.18390457496602747\n",
      "Recal= 0.18390457496602747\n",
      "fscore= 0.16017375568506537\n",
      "Confusion Matrix = [[ 1470 16136]\n",
      " [   79  2184]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_proba_df = pd.DataFrame(grid_search.predict_proba(X_test))\n",
    "threshold_list = [0.01, 0.1,0.2, 0.25, 0.3]\n",
    "for i in threshold_list:\n",
    "    print ('\\n******** For i = {} ******'.format(i))\n",
    "    Y_test_pred = pred_proba_df.applymap(lambda x: 1 if x>i else 0)\n",
    "    accuracy = mt.accuracy_score(y_test, Y_test_pred.iloc[:,1])\n",
    "    print(accuracy)\n",
    "    precision = mt.precision_score(y_test, Y_test_pred.iloc[:,1], average='micro')\n",
    "    print(\"Precision=\", precision)\n",
    "    recal = mt.recall_score(y_test, Y_test_pred.iloc[:,1], average='micro')\n",
    "    print(\"Recal=\", recal)\n",
    "    fscore = mt.f1_score(y_test, Y_test_pred.iloc[:,1], average='weighted')\n",
    "    print(\"fscore=\",fscore )\n",
    "    # Confusion matrix\n",
    "    cm= confusion_matrix(y_test, Y_test_pred.iloc[:,1])\n",
    "    print(\"Confusion Matrix =\", cm)\n",
    "    fpr, tpr, _ = roc_curve(y_test, Y_test_pred.iloc[:,1])\n",
    "    auc = roc_auc_score(y_test, Y_test_pred.iloc[:,1])\n",
    "    plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n",
    "    plt.legend(loc=4)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4faa8b0ece9f8873c1a04fffa10a5434a4872acd3a2f14e33f0a5b1cc21cdef2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}