mini-ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "The goal of this tutorial is to demonstrate the key components of a end-to-end data science/machine learning project. Note that the focus is to show the overall workflow not to build the best performing model.\n",
    "\n",
    "The following shows the key steps:\n",
    "\n",
    "- Load and split train/test data\n",
    "- Exploratory Data Analysis (EDA)\n",
    "- Data pre-processing and pipeline\n",
    "- Model building, evaluation, tuning, and selection\n",
    "- Feature importance analysis and feature selection\n",
    "- Model persistence\n",
    "- Web app development and deployment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn')  # change the default style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv data into pandas dataframe\n",
    "df = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# basic shape, data type, null values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 5 lines of data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    891 non-null    int64  \n",
      " 1   Sex       891 non-null    object \n",
      " 2   Age       714 non-null    float64\n",
      " 3   SibSp     891 non-null    int64  \n",
      " 4   Parch     891 non-null    int64  \n",
      " 5   Fare      891 non-null    float64\n",
      " 6   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(3), object(2)\n",
      "memory usage: 48.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Prepare the data by separating X and y\n",
    "# dropping unimportant features, such as passenger id, name, ticket number and cabin number\n",
    "# note that interesting features might be engineered from the dropped features above\n",
    "\n",
    "# axis = 1 below means dropping by columns, 0 means by rows\n",
    "X = df.drop(['Survived', 'PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)\n",
    "y = df['Survived']\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(712, 7)\n",
      "(179, 7)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into a training set and a test set. \n",
    "# Any number for the random_state is fine, see 42: https://en.wikipedia.org/wiki/42_(number) \n",
    "# We choose to use 20% (test_size=0.2) of the data set as the test set.\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic EDA\n",
    "You can show basic descriptive statistics using pandas easily. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>712.000000</td>\n",
       "      <td>712</td>\n",
       "      <td>572.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.330056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.498846</td>\n",
       "      <td>0.553371</td>\n",
       "      <td>0.379213</td>\n",
       "      <td>32.586276</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.824584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.500059</td>\n",
       "      <td>1.176404</td>\n",
       "      <td>0.791669</td>\n",
       "      <td>51.969529</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.925000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pclass   Sex         Age       SibSp       Parch        Fare  \\\n",
       "count   712.000000   712  572.000000  712.000000  712.000000  712.000000   \n",
       "unique         NaN     2         NaN         NaN         NaN         NaN   \n",
       "top            NaN  male         NaN         NaN         NaN         NaN   \n",
       "freq           NaN   467         NaN         NaN         NaN         NaN   \n",
       "mean      2.330056   NaN   29.498846    0.553371    0.379213   32.586276   \n",
       "std       0.824584   NaN   14.500059    1.176404    0.791669   51.969529   \n",
       "min       1.000000   NaN    0.420000    0.000000    0.000000    0.000000   \n",
       "25%       2.000000   NaN   21.000000    0.000000    0.000000    7.925000   \n",
       "50%       3.000000   NaN   28.000000    0.000000    0.000000   14.454200   \n",
       "75%       3.000000   NaN   38.000000    1.000000    0.000000   30.500000   \n",
       "max       3.000000   NaN   80.000000    8.000000    6.000000  512.329200   \n",
       "\n",
       "       Embarked  \n",
       "count       710  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        525  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# basic stats\n",
    "X_train.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual inspection is important\n",
    "Simple descriptive statistics may not be enough and visual inspection of data is very important, i.e., check out Anscombe's quartet: https://en.wikipedia.org/wiki/Anscombe%27s_quartet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500909\n",
      "std     3.316625   2.031568\n",
      "min     4.000000   4.260000\n",
      "25%     6.500000   6.315000\n",
      "50%     9.000000   7.580000\n",
      "75%    11.500000   8.570000\n",
      "max    14.000000  10.840000\n",
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500909\n",
      "std     3.316625   2.031657\n",
      "min     4.000000   3.100000\n",
      "25%     6.500000   6.695000\n",
      "50%     9.000000   8.140000\n",
      "75%    11.500000   8.950000\n",
      "max    14.000000   9.260000\n",
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500000\n",
      "std     3.316625   2.030424\n",
      "min     4.000000   5.390000\n",
      "25%     6.500000   6.250000\n",
      "50%     9.000000   7.110000\n",
      "75%    11.500000   7.980000\n",
      "max    14.000000  12.740000\n",
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500909\n",
      "std     3.316625   2.030579\n",
      "min     8.000000   5.250000\n",
      "25%     8.000000   6.170000\n",
      "50%     8.000000   7.040000\n",
      "75%     8.000000   8.190000\n",
      "max    19.000000  12.500000\n"
     ]
    }
   ],
   "source": [
    "anscombe = pd.read_csv(\"anscombe.csv\")\n",
    "anscombe_grouped = anscombe.groupby('dataset')\n",
    "\n",
    "# `key` contains the name of the grouped element \n",
    "# `group_df` is a normal dataframe containing only the data referring to the key\n",
    "for key, group_df in anscombe.groupby('dataset'):\n",
    "    print(group_df.describe()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17a6bdbe0>"
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     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1qpMuAAAAgOPX1t4d6ze/Gv3FYlRnMnHBgmnROLku6bIAKk64BQAAkCIDhUKseWRbbGlui46uvsHxDVt3RdOcxlix9PSoynpIBxg/hFsAAAApsuaRbfHE1t2HjXd09R0cz2TiimVzE6gMIBnCLQAAgJRoa++OLc1twx6zpXlvtLV3e0QRKKvR9Gi0cAsAACAl1m1qGfIo4pF0dPbFuk2vxKUfem+FqgLGk9H4aLRwCwAAICU6e/pHdFxXz/ABGMDbNRofjbbLIAAAQErkake2PmFSbU2ZKwHGo+N5NLqShFsAAAApsWThjMhPGj64yudqYsnC6RWqCBhPjufR6EoSbgEAAKRE4+S6aJrTOOwxTbMbbSYPlMVofTTanlsAAAApsmLp6RGZTGxp3hsdnW+9gcznaqJp9sHNnAHKYbQ+Gi3cAgAASJGqbDauWDY32tq7Y/3mV2OgGFGdjbhgwfRoyNcmXR4whi1ZOCOefH7PsI8mJvFotHALAIBj2rVrV9x6663xq1/9Kk488cT4wz/8w1ixYkXSZcG41ji5Lj594WlRX5+L11/vjP7+QtIlAWPcoUejj/RpiYck8Wi0cAsAgGO6/vrr45RTTomHHnooduzYETfddFNMmzYtlixZknRpAEAFjcZHo4VbAAAMq729PZ555plYvXp1zJo1K2bNmhXnnHNObNiwQbgFAOPMaHw02qclAgAwrNra2qirq4uHHnoo+vr6orm5OX7961/H3Llzky4NAEjIoUejP3/pWXHZktMS3fPPyi0AAIY1ceLEuOWWW2L16tVx7733xsDAQCxfvjw+/vGPj/gc2WwmstlMWeqrqsoO+Upl6X+y9D957kGy9D9Zo6X/wi0AAI7pxRdfjPPOOy8+85nPxPbt22P16tVx9tlnx8c+9rERvb6hIReZTHnCrUPy+cpuXstQ+p8s/U+ee5As/U9W0v0XbgEAMKwNGzbEgw8+GI8//njU1tZGU1NT7NmzJ773ve+NONzat6+zrCu38vm66OjojoEBnxZXafqfLP1PnnuQLP1PViX6X1+fO+Yxwi0AAIa1devWmDlzZtTWvrWXxrx58+LOO+8c8TkKhWIUCsVylDdoYKAQ/f3e2CRF/5Ol/8lzD5Kl/8lKuv8eSgUAYFhTp06NnTt3Rm9v7+BYc3NzTJ8+PcGqAAAOEm4BADCs888/P2pqamLVqlXx0ksvxaOPPhp33nlnXH755UmXBgDgsUQAAIb3rne9K9asWRN/+Zd/GZdcckk0NDTEVVddFZ/85CeTLg0AQLgFAMCxvec974l77rkn6TIAAA7jsUQAAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBaJQ232tra4rrrrouFCxfGkiVL4qGHHirl6QEAAABgiOpSnahYLMbVV18dhUIh7r333tizZ0988YtfjBNOOCEuvPDCUl0GAAAAAAaVLNzaunVrPP3007F+/fqYMWNGzJs3L1auXBl33323cAsAAACAsijZY4ktLS3R0NAQM2bMGBx73/veF1u3bo2+vr5SXQYAAAAABpVs5daUKVPijTfeiO7u7qirq4uIiN27d0d/f3+88cYb0dDQcMxzZLOZyGYzpSppiKqq7JCvVJb+J0v/k6X/yXMPkqX/AACUU8nCrTPOOCOmTp0aq1evjlWrVkVra2vcc889EREjXrnV0JCLTKY84dYh+XxdWc/P8PQ/WfqfLP1PnnuQLP0HAKAcShZuTZw4Me644464/vrrY8GCBdHY2BgrV66M22+/PU444YQRnWPfvs6yrtzK5+uio6M7BgYKZbkGR6f/ydL/ZOl/8tyDZFWi//X1ubKcFwCA0a9k4VZExPz58+PRRx+N1tbWqK+vjyeeeCLq6+sjlxvZhLNQKEahUCxlSYcZGChEf783NknR/2Tpf7L0P3nuQbL0HwCAcijZ5hf79++PT33qU/H666/HSSedFNXV1fHYY4/FokWLSnUJAAAAABiiZOHWiSeeGF1dXfG1r30tWlpa4oEHHoi1a9fGypUrS3UJAAAAABiipB9b9M1vfjNaWlriox/9aPzgBz+Ib33rWzF//vxSXgIAAAAABpV0z605c+bEfffdV8pTAgAAAMBRlXTlFgAAAABUUklXbgGUWlt7d6zb1BKdPf2Rq62OJQtnROPkuqTLAgAAYJQQbgGj0kChEGse2RZbmtuio6tvcPzJ5/dE05zGWLH09KjKWnwKAAAw3gm3gFFpzSPb4omtuw8b7+jqOzieycQVy+YmUBkAAACjiWUPwKjT1t4dW5rbhj1mS/PeaGvvrlBFAAAAjFbCLWDUWbepZcijiEfS0dkX6za9UqGKAAAAGK2EW8Co09nTP6LjunqGD8AAAAAY+4RbwKiTqx3ZdoCTamvKXAkAAACjnXALGHWWLJwR+UnDB1f5XE0sWTi9QhUBAAAwWvm0RGDUaZxcF01zGo/4aYmHNM1ujMbJdRWsCgAYa9rau2Pdppbo7OmPXG11LFk4w/wCIIWEW8CotGLp6RGZTGxp3hsdnW/trZXP1UTT7MaDPwcAeBsGCoVY88i22NLcNuRDbJ58fk80zTk4z6jKesgFIC2EW8CoVJXNxhXL5g7+RrWrpz9ytTWx5IMzoiFfm3R5AECKrXlk2xFXiHd09R0cz2TiimVzE6gMgLdDuAWMao2T6+LSD52WdBkAwBjR1t4dW5rbhj1mS/PeaGvv9ogiQEpYawsAAIwb6za1DHkU8Ug6Ovti3aZXKlQRAO+UcAsAABg3Onv6R3RcV8/wARgAo4dwCwAAGDdytSPbmWVSbU2ZKwGgVIRbAADAuLFk4YzITxo+uMrnamLJwukVqgiAd0q4BQAAjBuNk+uiaU7jsMc0zW60mTxAivi0RAAAYFxZsfT0iEwmtjTvjY7Ot/bWyudqoml248GfA5Aawi0AAGBcqcpm44plc6OtvTvWbWqJrp7+yNXWxJIPzoiGfG3S5QFwnIRbAADAuNQ4uS4u/dBpSZcBwDtkzy0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWtVJFwAAwOj30EMPxc0333zYeCaTiW3btiVQEQDAQcItAACOaenSpXHOOecMft/f3x9/9Ed/FOeee25yRQEAhHALAIARqK2tjdra2sHvv//970exWIybbropwaoAAOy5BQDAcdq/f3/8zd/8Tdx4440xYcKEpMsBAMY5K7cAADguP/rRj2Lq1KnxkY98ZMSvyWYzkc1mylJPVVV2yFcqS/+Tpf/Jcw+Spf/JGi39F24BADBixWIxHnjggVi5cuVxva6hIReZTHnCrUPy+bqynp/h6X+y9D957kGy9D9ZSfdfuAUAwIht2bIl9uzZE8uWLTuu1+3b11nWlVv5fF10dHTHwEChLNfg6PQ/WfqfPPcgWfqfrEr0v74+d8xjhFsADKutvTvWbWqJzp7+yNVWx5KFM6Jxst+MwXj1i1/8IhYuXBiTJ08+rtcVCsUoFIplquqggYFC9Pd7Y5MU/U+W/ifPPUiW/icr6f4LtwA4ooFCIdY8si22NLdFR1ff4PiTz++JpjmNsWLp6VGVtbcBjDfPPfdcfOADH0i6DACAQd6VAHBEax7ZFk9s3T0k2IqI6Ojqiye27o41//xCQpUBSdq+fXu85z3vSboMAIBBwi0ADtPW3h1bmtuGPWZL895oa++uUEXAaLF3797I5/NJlwEAMEi4BcBh1m1qOWzF1n/U0dkX6za9UqGKgNHiueeei3POOSfpMgAABgm3ADhMZ0//iI7r6hk+AAMAACg34RYAh8nVjuzzRibV1pS5EgAAgOEJtwA4zJKFMyI/afjgKp+riSULp1eoIgAAgCMTbgFwmMbJddE0p3HYY5pmN0bj5LoKVQQAAHBkI3vuBIBxZ8XS0yMymdjSvDc6Ot/aWyufq4mm2Y0Hfw4AAJAw4RYAR1SVzcYVy+ZGW3t3rNvUEl09/ZGrrYklH5wRDfnapMsDAACICOEWAMfQOLkuLv3QaUmXAQAAcET23AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWqky4AYCxra++O9Ztfjf5iMaozmbhgwbRonFyXdFkAAABjhnALoAwGCoVY88i22NLcFh1dfYPjG7buiqY5jbFi6elRlbV4FgAA4J0SbgGUwZpHtsUTW3cfNt7R1XdwPJOJK5bNTaAyAACAsaWkywZ27doVV155ZXzgAx+I888/P9asWVPK0wOkQlt7d2xpbhv2mC3Ne6OtvbtCFQEAAIxdJQ23rr/++pg0aVI89NBD8ed//udxxx13xLp160p5CYBRb92mliGPIh5JR2dfrNv0SoUqAgAAGLtKFm61t7fHM888E1dddVXMmjUrLrjggjjnnHNiw4YNpboEQCp09vSP6LiunuEDMAAAAI6tZOFWbW1t1NXVxUMPPRR9fX3R3Nwcv/71r2PuXHvKAONLrnZk2xlOqq0pcyUAAABjX8k2lJ84cWLccsstsXr16rj33ntjYGAgli9fHh//+MdHfI5sNhPZbKZUJQ1RVZUd8pXK0v9k6X9l/f7/MzOe/Nc90dF59JVZ+VxN/P7/8/+L6mr3pBL8HUiW/gMAUE4l/bTEF198Mc4777z4zGc+E9u3b4/Vq1fH2WefHR/72MdG9PqGhlxkMuUJtw7J5+vKen6Gp//J0v/KqK/PxQfn/U48+quWKB7h55mI+OC834n3zJpS6dLGPX8HkqX/AACUQ8nCrQ0bNsSDDz4Yjz/+eNTW1kZTU1Ps2bMnvve974043Nq3r7OsK7fy+bro6OiOgYFCWa7B0el/svS/8v7fJe+N3gP98eyLe4es4MrnamL+qVPi/13y3nj99c4EKxxf/B1IViX6X1+fK8t5AQAY/UoWbm3dujVmzpwZtbW1g2Pz5s2LO++8c8TnKBSKUSgcaZ1D6QwMFKK/3xubpOh/svS/sj6zdG60tXfH+s2vxkAxojobccGC6dGQr41iIaK/4F5Umr8DydJ/AADKoWTh1tSpU2Pnzp3R29sbEyZMiIiI5ubmmD59eqkuAZA6jZPr4tMXnhb19bl4/fVOb+wBAABKrGQ7u55//vlRU1MTq1atipdeeikeffTRuPPOO+Pyyy8v1SUAAAAAYIiSrdx617veFWvWrIm//Mu/jEsuuSQaGhriqquuik9+8pOlugQAAAAADFHST0t8z3veE/fcc08pTwkAAAAAR1WyxxIBAAAAoNKEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1KpOugAA4Oja2rtj3aaW6Ozpj1xtdSxZOCMaJ9clXRYAAIwawi0AGIUGCoVY88i22NLcFh1dfYPjTz6/J5rmNMaKpadHVdYCbAAAEG4BwCi05pFt8cTW3YeNd3T1HRzPZOKKZXMTqAwAAEYXv/IFgFGmrb07tjS3DXvMlua90dbeXaGKAABg9BJuAcAos25Ty5BHEY+ko7Mv1m16pUIVAQDA6CXcAoBRprOnf0THdfUMH4ABAMB4YM8tABhlcrUj++d5Um1NmSsBGF5be3es3/xq9BeLUZ3JxAULpvlEVwAqTrgFAKPMkoUz4snn9wz7aGI+VxNLFk6vYFUAbznaJ7pu2LrLJ7oCUHH+xQGAUaZxcl00zWkc9pim2Y1WRwCJOfSJrv8xhD/0ia5r/vmFhCoDYDyycgsARqEVS0+PyGRiS/Pe6Oh8681jPlcTTbMProoASMLxfKKrEB6AShBuAcAoVJXNxhXL5kZbe3es29QSXT39kautiSUfnBEN+dqkywPGseP5RNdLP/TeClUFwHgm3AKAUaxxcl1c+qHTki4DYJBPdAVgtLHnFgAAMGI+0RWA0Ua4BQAAjNiShTMiP2n44MonugJQScItAABgxHyiKwCjjT23AACA4+ITXQEYTYRbAAAcU29vb9x+++3xT//0T1FTUxOXXHJJfOELX4hMJpN0aSTgtz/Rdf3mV2OgGFGdjbhgwXSf6ApAxQm3AAA4pttuuy02btwYd999d3R2dsYXvvCFOOWUU+LSSy9NujQS1Di5Lj594WlRX5+L11/vjP7+QtIlATAO2XMLAIBh7d+/P9auXRurV6+O+fPnx9lnnx2f/exn49lnn026NAAAK7cAABje5s2b44QTTohFixYNjn3uc59LsCIAgLcItwAAGFZLS0tMmzYt/vEf/zHuvPPO6Ovri+XLl8dVV10V2ezIHgTIZjORzZZnf66qquyQr1SW/idL/5PnHiRL/5M1Wvov3AIAYFhdXV2xc+fOuP/+++P222+P1tbWuOWWW6Kuri4++9nPjugcDQ25sm8+n8/XlfX8DE//k6X/yXMPkqX/yUq6/8ItAACGVV1dHW+++WZ84xvfiGnTpkVExGuvvRY/+tGPRhxu7dvXWdaVW/l8XXR0dMfAgA3NK03/k6X/yXMPkqX/yapE/+vrc8c8RrgFAMCwTjrppJg4ceJgsBURMXv27Ni1a9eIz1EoFKNQKJajvEEDAwWf1pcg/U+W/ifPPUiW/icr6f4Ltyi7tvbuWL/51egvFqM6k4kLFkyLxsmWjAJAWpxxxhlx4MCBeOmll2L27NkREdHc3Dwk7AIASIpwi7IZKBRizSPbYktzW3R09Q2Ob9i6K5rmNMaKpadH1Qg3oQUAkjNnzpw499xz4+abb45bb701Wltb46677oqrrroq6dIAAIRblM+aR7bFE1t3Hzbe0dV3cDyTiSuWzU2gMgDgeH3961+P1atXx6c+9amoq6uLT3/603H55ZcnXRYAgHCL8mhr744tzW3DHrOleW+0tXd7RBEAUuBd73pXfPWrX026DACAw3gmjLJYt6llyKOIR9LR2RfrNr1SoYoAAACAsUi4RVl09vSP6LiunuEDMAAAAIDhCLcoi1ztyJ54nVRbU+ZKAAAAgLFMuEVZLFk4I/KThg+u8rmaWLJweoUqAgAAAMYi4RZl0Ti5LprmNA57TNPsRpvJAwAAAO+IT0ukbFYsPT0ik4ktzXujo/OtvbXyuZpomt148OcAAAAA74Bwi7KpymbjimVzo629O9ZvfjUGihHV2YgLFkyPhnxt0uUBAAAAY4Bwi7JrnFwXn77wtKivz8Xrr3dGf38h6ZIAAACAMcKeWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEit6lKe7KGHHoqbb775sPFMJhPbtm0r5aUAAAAAoLTh1tKlS+Occ84Z/L6/vz/+6I/+KM4999xSXgYAAAAAIqLE4VZtbW3U1tYOfv/9738/isVi3HTTTaW8DAAAAABERInDrd+2f//++Ju/+Zu47bbbYsKECeW6DHAMbe3dsX7zq9FfLEZ1JhMXLJgWjZPrki4LAAAASqJs4daPfvSjmDp1anzkIx8Z8Wuy2Uxks5my1FNVlR3ylcrS/8obKBTi7n/6t3juxb3R0dk3OL7h+V0x/9QpccVFc6Mq635Ugj//yXMPkqX/AACUU1nCrWKxGA888ECsXLnyuF7X0JCLTKY84dYh+bwVK0nS/8q54/5fxxPP7Yrifxjv6OyLJ57bFbUTq+Pzl34gkdrGK3/+k+ceJEv/AQAoh7KEW1u2bIk9e/bEsmXLjut1+/Z1lnXlVj5fFx0d3TEwUCjLNTg6/a+stvbu+NW/7j4s2DqkGBFP/evu2PGbvR5RrAB//pPnHiSrEv2vr8+V5bwAAIx+ZQm3fvGLX8TChQtj8uTJx/W6QqEYhcLR3o6XxsBAIfr7vbFJiv5Xxj8/uXPIo4hH0tHZF//85Mtx6YfeW6Gq8Oc/ee5BsvQfAIByKMvmF88991x84AMed4KkdPb0j+i4rp7hAzAAAAAY7coSbm3fvj3e8573lOPUwAjkake2KHNSbU2ZKwEAAIDyKku4tXfv3sjn8+U4NTACSxbOiPyk4YOrfK4mliycXqGKAAAAoDzKsufWc889V47TAiPUOLkumuY0xhNbdx/1mKbZjTaTBwAAIPXKEm4ByVux9PSITCa2NO8dsrl8PlcTTbMbD/4cAAAAUk64BWNUVTYbVyybG23t3bF+86sxUIyozkZcsGB6NORrky4PAAAASkK4BWNc4+S6+PSFp0V9fS5ef70z+vsLSZcEAAAAJVOWDeUBAAAAoBKEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAARmTdunXxvve9b8h/1113XdJlAQDjXHXSBQAAkA47duyI8847L1avXj04NnHixAQrAgAQbgEAMEIvvvhinHbaaXHSSSclXQoAwCCPJQIAMCIvvvhizJo1K+kyAACGsHILAIBjKhaL8dJLL8Uvf/nL+P73vx8DAwPxkY98JK677rqYMGHCMV+fzWYim82UpbaqquyQr1SW/idL/5PnHiRL/5M1Wvov3AIA4Jhee+216O7ujgkTJsQdd9wRr7zyStx2223R09MTq1atOubrGxpykcmUJ9w6JJ+vK+v5GZ7+J0v/k+ceJEv/k5V0/4VbAAAc07Rp02Ljxo0xefLkyGQyMXfu3CgUCvGnf/qncfPNN0dVVdWwr9+3r7OsK7fy+bro6OiOgYFCWa7B0el/svQ/ee5BsvQ/WZXof3197pjHCLcAABiRE088ccj3p556ahw4cCDa29ujoaFh2NcWCsUoFIplrC5iYKAQ/f3e2CRF/5Ol/8lzD5Kl/8lKuv8eSgUA4Jh+8YtfxOLFi6O7u3tw7N/+7d/ixBNPPGawBQBQTsItAACO6ayzzoqJEyfGqlWrorm5OR5//PH46le/GitXrky6NABgnPNYIgAAx3TCCSfE3XffHX/1V38Vf/AHfxC5XC4uvfRS4RYAkDjhFgAAI/Le97437rnnnqTLAAAYwmOJAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWqky4AAADGq7b27li3qSU6e/ojV1sdSxbOiMbJdUmXBQCpUtJwq7e3N26//fb4p3/6p6ipqYlLLrkkvvCFL0QmkynlZQAAINUGCoVY88i22NLcFh1dfYPjTz6/J5rmNMaKpadHVdZDFgAwEiUNt2677bbYuHFj3H333dHZ2Rlf+MIX4pRTTolLL720lJcBAIBUW/PItnhi6+7Dxju6+g6OZzJxxbK5CVQGAOlTsl8H7d+/P9auXRurV6+O+fPnx9lnnx2f/exn49lnny3VJQAAIPXa2rtjS3PbsMdsad4bbe3dFaoIANKtZCu3Nm/eHCeccEIsWrRocOxzn/tcqU4PAABjwrpNLUMeRTySjs6+WLfplbj0Q++tUFUAkF4lC7daWlpi2rRp8Y//+I9x5513Rl9fXyxfvjyuuuqqyI5wv4BsNhPZbHn256qqyg75SmXpf7L0P1n6nzz3IFn6D0N19vSP6LiunuEDMADgoJKFW11dXbFz5864//774/bbb4/W1ta45ZZboq6uLj772c+O6BwNDbmybz6fz/v0mSTpf7L0P1n6nzz3IFn6Dwflakc2BZ9UW1PmSgBgbChZuFVdXR1vvvlmfOMb34hp06ZFRMRrr70WP/rRj0Ycbu3b11nWlVv5fF10dHTHwEChLNfg6PQ/WfqfLP1PnnuQrEr0v74+V5bzQjksWTgjnnx+z7CPJuZzNbFk4fQKVgUA6VWycOukk06KiRMnDgZbERGzZ8+OXbt2jfgchUIxCoViqUo6ooGBQvT3e2OTFP1Plv4nS/+T5x4kS//hoMbJddE0p/GIn5Z4SNPsxmicbLUjAIxEyTa/OOOMM+LAgQPx0ksvDY41NzcPCbsAAICIFUtPj99renfkc0MfPcznauL33v87sWLp6QlVBgDpU7KVW3PmzIlzzz03br755rj11lujtbU17rrrrrjqqqtKdQkAABgTqrLZuGLZ3Ghr7451m1qiq6c/crU1seSDM6IhX5t0eQCQKiULtyIivv71r8fq1avjU5/6VNTV1cWnP/3puPzyy0t5CQAAGDMaJ9fFpR86LekyACDVShpuvetd74qvfvWrpTwlAAAAABxVyfbcAgAAAIBKE24BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUqs66QI4trb27li3qSU6e/ojV1sdSxbOiMbJdUmXBQAAAJA44dYoNlAoxJpHtsWW5rbo6OobHH/y+T3RNKcxViw9PaqyFt8BAAAA45dwaxRb88i2eGLr7sPGO7r6Do5nMnHFsrkJVAYAAAAwOlj2M0q1tXfHlua2YY/Z0rw32tq7K1QRAAAAwOgj3Bql1m1qGfIo4pF0dPbFuk2vVKgiAAAAgNFHuDVKdfb0j+i4rp7hAzAAAACAsUy4NUrlake2Hdqk2poyVwIAAAAwegm3RqklC2dEftLwwVU+VxNLFk6vUEUAAAAAo49wa5RqnFwXTXMahz2maXZjNE6uq1BFAAAAAKPPyJ59IxErlp4ekcnElua90dH51t5a+VxNNM1uPPhzAAAAgHFMuDWKVWWzccWyudHW3h3rNrVEV09/5GprYskHZ0RDvjbp8gAAAAASJ9xKgcbJdXHph05LugwAAACAUceeWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAx+Vzn/tc/Nmf/VnSZQAARIRwCwCA4/Dwww/H448/nnQZAACDhFsAAIzI/v3746tf/Wo0NTUlXQoAwKDqpAsAACAdvvKVr8TFF18c//7v/550KQAAg4RbAAAc04YNG2LTpk3xs5/9LG699dbjfn02m4lsNlP6wiKiqio75CuVpf/J0v/kuQfJ0v9kjZb+C7cAABjWgQMH4stf/nLccsstUVtb+7bO0dCQi0ymPOHWIfl8XVnPz/D0P1n6nzz3IFn6n6yk+y/cAgBgWN/+9rfj/e9/f5xzzjlv+xz79nWWdeVWPl8XHR3dMTBQKMs1ODr9T5b+J889SJb+J6sS/a+vzx3zGOEWAADDevjhh2Pv3r1x1llnRUREb29vRET8/Oc/j6effnpE5ygUilEoFMtWY0TEwEAh+vu9sUmK/idL/5PnHiRL/5OVdP+FWwAADOu+++6L/v7+we+//vWvR0TETTfdlFRJAACDhFsAAAxr2rRpQ77P5Q4+HjBz5swkygEAGMLHCQAAAACQWlZuAQBwXP76r/866RIAAAZZuQUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1ShpurVu3Lt73vvcN+e+6664r5SUAAAAAYFBJPy1xx44dcd5558Xq1asHxyZOnFjKSwAAAADAoJKGWy+++GKcdtppcdJJJ5XytAAAAABwRCV9LPHFF1+MWbNmlfKUAAAAAHBUJVu5VSwW46WXXopf/vKX8f3vfz8GBgbiIx/5SFx33XUxYcKEEZ0jm81ENpspVUlDVFVlh3ylsvQ/WfqfLP1PnnuQLP0HAKCcShZuvfbaa9Hd3R0TJkyIO+64I1555ZW47bbboqenJ1atWjWiczQ05CKTKU+4dUg+X1fW8zM8/U+W/idL/5PnHiRL/wEAKIeShVvTpk2LjRs3xuTJkyOTycTcuXOjUCjEn/7pn8bNN98cVVVVxzzHvn2dZV25lc/XRUdHdwwMFMpyDY5O/5Ol/8nS/+S5B8mqRP/r63NlOS8AAKNfSTeUP/HEE4d8f+qpp8aBAweivb09Ghoajvn6QqEYhUKxlCUdZmCgEP393tgkRf+Tpf/J0v/kuQfJ0n8AAMqhZJtf/OIXv4jFixdHd3f34Ni//du/xYknnjiiYAsAAAAAjlfJwq2zzjorJk6cGKtWrYrm5uZ4/PHH46tf/WqsXLmyVJcAAAAAgCFK9ljiCSecEHfffXf81V/9VfzBH/xB5HK5uPTSS4VbAAAAAJRNSffceu973xv33HNPKU8JAAAAAEdVsscSAQAAAKDSSrpyC8aitvbuWLepJTp7+iNXWx1LFs6Ixsl1SZcFAAAAhHALjmqgUIg1j2yLLc1t0dHVNzj+5PN7omlOY6xYenpUZS1+BAAAgCQJt+Ao1jyyLZ7Yuvuw8Y6uvoPjmUxcsWxuApUBAAAAh1h2AkfQ1t4dW5rbhj1mS/PeaGvvrlBFAAAAwJEIt+AI1m1qGfIo4pF0dPbFuk2vVKgiAAAA4EiEW3AEnT39Izquq2f4AAwAAAAoL+EWHEGudmTb0U2qrSlzJQAAAMBwhFtwBEsWzoj8pOGDq3yuJpYsnF6higAAAIAjEW7BETROroumOY3DHtM0uzEaJ9dVqCIAAADgSEb27BWMQyuWnh6RycSW5r3R0fnW3lr5XE00zW48+HMAAAAgUcItOIqqbDauWDY32tq7Y92mlujq6Y9cbU0s+eCMaMjXJl0eAAAAEMItOKbGyXVx6YdOS7oMAAAA4AjsuQUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1qpMuoBLa2rtj/eZXo79YjOpMJi5YMC0aJ9clXRYAAO+QeR4AMKbDrYFCIdY8si22NLdFR1ff4PiGrbuiaU5jrFh6elRlLV4DAEgb8zwA4JAxHW6teWRbPLF192HjHV19B8czmbhi2dwEKgMA4J0wzwMADhmzv85qa++OLc1twx6zpXlvtLV3V6giAABKwTwPAPhtYzbcWrepZcgS9SPp6OyLdZteqVBFAACUgnkeAPDbxmy41dnTP6LjunqGnxgBADC6mOcBAL9tzIZbudqRbSc2qbamzJUAAFBK5nkAwG8bs+HWkoUzIj9p+AlNPlcTSxZOr1BFAACUgnkeAPDbxmy41Ti5LprmNA57TNPsxmicXFehigAA0mvnzp1xxRVXxFlnnRXnnntu/O3f/m1itZjnAQC/bWRrulNqxdLTIzKZ2NK8Nzo639pzIZ+riabZjQd/DgDAsAqFQnzuc5+Lpqam+MlPfhI7d+6MG264IU4++eT46Ec/mkhN5nkAwCFjOtyqymbjimVzo629O9ZvfjUGihHV2YgLFkyPhnxt0uUBAKTC3r17Y+7cuXHrrbfGCSecELNmzYqzzz47Nm/enFi4ZZ4HABwypsOtQxon18WnLzwt6utz8frrndHfX0i6JACA1Jg6dWrccccdERFRLBbj17/+dfzqV7+KL3/5y8kWFuZ5AMA4CbcAACiN888/P1577bU477zz4sMf/vCIX5fNZiKbzZSlpqqq7JCvVJb+J0v/k+ceJEv/kzVa+i/cAgBgxP73//7fsXfv3rj11lvj9ttvj1WrVo3odQ0NuchkyhNuHZLP20A+SfqfLP1PnnuQLP1PVtL9F24BADBiTU1NERFx4MCBuOmmm+K///f/HhMmTDjm6/bt6yzryq18vi46OrpjYMBjiZWm/8nS/+S5B8nS/2RVov/19bljHiPcAgBgWHv37o1nnnkmLrjggsGx97znPdHX1xdvvvlmNDQ0HPMchUIxCoViOcuMgYGCPbcSpP/J0v/kuQfJ0v9kJd1/D6UCADCsV155Ja655prYs2fP4NjWrVujoaFhRMEWAEA5CbcAABhWU1NT/O7v/m78+Z//eezYsSMef/zx+NrXvhb/7b/9t6RLAwAQbgEAMLyqqqr47ne/G3V1dfHJT34yvvSlL8Xll18ef/iHf5h0aQAA5dtz63Of+1w0NDTEX//1X5frEgAAVMjJJ58c3/72t5MuAwDgMGVZufXwww/H448/Xo5TAwAAAMCgkodb+/fvj69+9auDHxMNAAAAAOVS8scSv/KVr8TFF18c//7v/17qUwMAAADAECUNtzZs2BCbNm2Kn/3sZ3Hrrbce9+uz2Uxks5lSljSoqio75CuVpf/J0v9k6X/y3INk6T8AAOVUsnDrwIED8eUvfzluueWWqK2tfVvnaGjIRSZTnnDrkHy+rqznZ3j6nyz9T5b+J889SJb+AwBQDiULt7797W/H+9///jjnnHPe9jn27ess68qtfL4uOjq6Y2CgUJZrcHT6nyz9T5b+J889SFYl+l9fnyvLeQEAGP1KFm49/PDDsXfv3jjrrLMiIqK3tzciIn7+85/H008/PaJzFArFKBSKpSrpiAYGCtHf741NUvQ/WfqfLP1PnnuQLP0HAKAcShZu3XfffdHf3z/4/de//vWIiLjppptKdQkAAAAAGKJk4da0adOGfJ/LHXw8YObMmaW6BAAAAAAMUdJPS3ynTjrpXWW/hj05kqX/ydL/ZOl/8tyDZOn/+GaeN/bpf7L0P3nuQbL0P1lJ9z9TLBbLu8kVAAAAAJRJNukCAAAAAODtEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1Bp34dbnPve5+LM/+7OkyxhXent74y/+4i/igx/8YPyn//Sf4n/9r/8VxWIx6bLGjV27dsWVV14ZH/jAB+L888+PNWvWJF3SuNDb2xsXXXRRbNy4cXCspaUlVqxYEWeeeWYsXbo0fvnLXyZY4dh2pP4/88wzcemll8ZZZ50VH/7wh+OBBx5IsMKx7Uj9P+SNN96Ic845Jx566KEEKmOsM8+rPPO8ZJnnJcM8L1nmeckbjXO9cRVuPfzww/H4448nXca4c9ttt8W//Mu/xN133x3f+MY34h/+4R/ixz/+cdJljRvXX399TJo0KR566KH48z//87jjjjti3bp1SZc1ph04cCBuuOGG2L59++BYsViMq6++OqZMmRJr166Niy++OK655pp47bXXEqx0bDpS/1tbW+OP//iPY9GiRfGTn/wkrrvuuli9enU89thjyRU6Rh2p/7/ta1/7Wvz7v/97hatiPDDPS4Z5XrLM8yrPPC9Z5nnJG61zvXETbu3fvz+++tWvRlNTU9KljCv79++PtWvXxurVq2P+/Plx9tlnx2c/+9l49tlnky5tXGhvb49nnnkmrrrqqpg1a1ZccMEFcc4558SGDRuSLm3M2rFjR3ziE5+Il19+ecj4k08+GS0tLfE//+f/jFNPPTWuvPLKOPPMM2Pt2rUJVTo2Ha3/69evjylTpsQNN9wQs2bNimXLlsV/+S//JX72s58lVOnYdLT+H7Jp06Z48skn46STTqpwZYx15nnJMM9Llnle5ZnnJcs8L3mjea43bsKtr3zlK3HxxRfHe97znqRLGVc2b94cJ5xwQixatGhw7HOf+1zcfvvtCVY1ftTW1kZdXV089NBD0dfXF83NzfHrX/865s6dm3RpY9ZTTz0VixcvPuy31s8++2zMmzcvJk2aNDi2YMGCeOaZZypc4dh2tP6fc845R/z/nTfffLNSpY0LR+t/xMHl6//jf/yPuOWWW2LChAkJVMdYZp6XDPO8ZJnnVZ55XrLM85I3mud61RW/YgI2bNgQmzZtip/97Gdx6623Jl3OuNLS0hLTpk2Lf/zHf4w777wz+vr6Yvny5XHVVVdFNjtustXETJw4MW655ZZYvXp13HvvvTEwMBDLly+Pj3/840mXNmZddtllRxxvbW2NqVOnDhlrbGyM3bt3V6KsceNo/Z8+fXpMnz598Pu2trZ4+OGH49prr61UaePC0fofEXHnnXfGvHnz4j//5/9cwYoYD8zzkmOelyzzvMozz0uWeV7yRvNcb8yHWwcOHIgvf/nLccstt0RtbW3S5Yw7XV1dsXPnzrj//vvj9ttvj9bW1rjllluirq4uPvvZzyZd3rjw4osvxnnnnRef+cxnYvv27bF69eo4++yz42Mf+1jSpY0r3d3dh/0GY8KECdHb25tQReNXT09PXHvttTFlypT45Cc/mXQ548KOHTvi/vvvj5/+9KdJl8IYY56XLPO85JnnjQ7meaOHeV4yRsNcb8yHW9/+9rfj/e9/f5xzzjlJlzIuVVdXx5tvvhnf+MY3Ytq0aRER8dprr8WPfvQjk54K2LBhQzz44IPx+OOPR21tbTQ1NcWePXvie9/7nklPhU2cODH2798/ZKy3t9ebsQrr7OyMP/mTP4nf/OY38fd///dRV1eXdEljXrFYjFWrVsV1110XU6ZMSbocxhjzvGSZ5yXLPG/0MM8bHczzkjFa5npjPtx6+OGHY+/evXHWWWdFRAym5z//+c/j6aefTrK0ceGkk06KiRMnDk54IiJmz54du3btSrCq8WPr1q0xc+bMIf+wzps3L+68884EqxqfTj755NixY8eQsb179x62hJ3yefPNN2PlypXx8ssvxw9+8IOYNWtW0iWNC6+99lo8/fTT8cILL8RXvvKViDj4G+4vf/nL8cgjj8Tf/u3fJlwhaWaelyzzvGSZ540e5nnJM89LzmiZ6435cOu+++6L/v7+we+//vWvR0TETTfdlFRJ48oZZ5wRBw4ciJdeeilmz54dERHNzc1DJkGUz9SpU2Pnzp3R29s7uFS6ubl5yDPpVMYZZ5wRd911V/T09AxOQjdv3hwLFixIuLLxoVAoxDXXXBOvvPJK3HfffXHqqacmXdK4cfLJJ8f/+T//Z8jY5ZdfHpdffrmVBbxj5nnJMs9Llnne6GGelyzzvGSNlrnemN/pcdq0aTFz5szB/3K5XORyuZg5c2bSpY0Lc+bMiXPPPTduvvnm2LZtW/ziF7+Iu+66Kz71qU8lXdq4cP7550dNTU2sWrUqXnrppXj00UfjzjvvjMsvvzzp0sadRYsWxbvf/e64+eabY/v27XHXXXfFc889F5dccknSpY0LDz74YGzcuDFuu+22yOfz0draGq2trYc9QkDpVVdXD/l3eObMmVFdXR2NjY1x8sknJ10eKWeelyzzvGSZ540e5nnJMs9L1miZ6435lVsk7+tf/3qsXr06PvWpT0VdXV18+tOf9o9uhbzrXe+KNWvWxF/+5V/GJZdcEg0NDXHVVVfZXDEBVVVV8d3vfje+9KUvxfLly2PmzJnxne98J0455ZSkSxsXfv7zn0ehUIgrr7xyyPiiRYvivvvuS6gqgPQzz0uOed7oYZ6XLPM8IiIyxWKxmHQRAAAAAPB2jPnHEgEAAAAYu4RbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACkVnXSBfy21tY3ynbubDYTDQ252LevMwqFYtmuw5Hpf7L0P1n6nzz3IFmV6P9JJ72rLOeldMzz0kt/y0dvy0dvy0t/y0dvDzeSed64WbmVzWYik8lENptJupRxSf+Tpf/J0v/kuQfJ0n/KzZ+x8tLf8tHb8tHb8tLf8tHbt+dth1u9vb1x0UUXxcaNGwfHfvGLX8THPvaxmD9/fnzsYx+Lxx9/vCRFAgAAAMCRvK1w68CBA3HDDTfE9u3bB8d27twZ11xzTSxfvjwefvjh+K//9b/G1VdfHa+88krJigUAAACA33bc4daOHTviE5/4RLz88stDxnfv3h2f+MQnYsWKFTFjxoz4zGc+E5MmTYrnnnuuZMUCAAAAwG877nDrqaeeisWLF8ePf/zjIeOLFy+OL33pSxER0dfXFw888ED09vbG/PnzS1MpAAAAAPwHx/1piZdddtmwP9+5c2f8/u//fgwMDMSNN94Y06dPH/G5s9nybZpWVZUd8pXK0v9k6X+y9D957kGy9B8AgHI67nDrWBoaGuLBBx+Mp59+Ov76r/86Zs6cGR/+8IdH+NpcZDLl/USAfL6urOdnePqfLP1Plv4nzz1Ilv4DAFAOJQ+33vWud8W8efNi3rx58eKLL8YPf/jDEYdb+/Z1lnXlVj5fFx0d3TEwUCjLNTg6/U+W/idL/5PnHiSrEv2vr8+V5bwAAIx+JQu3tm/fHu3t7bFw4cLBsVNPPTWeeuqpEZ+jUChGoVAsVUlHNDBQiP5+b2ySov/J0v9k6X/y3INk6T8AAOVQss0v/u///b+xatWqKBbfCqeef/75mDNnTqkuAQAAAABDlCzc+tjHPhatra3x9a9/PX7zm9/E3/3d38VPf/rTuPLKK0t1CQAAAAAYomTh1u/8zu/E3XffHb/61a/i4osvjr/7u7+Lb33rW/G7v/u7pboEAAAAAAzxjvbceuGFF4Z8f+aZZ8Y//MM/vKOCAAAAAGCkSrZyCwAAAAAqTbgFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtd7RpyUCAOXV1t4d6za1RGdPf+Rqq2PJwhnROLku6bIAABjnRtM8VbgFAKPQQKEQax7ZFlua26Kjq29w/Mnn90TTnMZYsfT0qMpagA0AQGWNxnmqcAsARqE1j2yLJ7buPmy8o6vv4HgmE1csm5tAZQAAjGejcZ7qV74AMMq0tXfHlua2YY/Z0rw32tq7K1QRAACM3nmqcAsARpl1m1qGLPE+ko7Ovli36ZUKVQQAAKN3nircAoBRprOnf0THdfUMP7EAAIBSGq3zVOEWAIwyudqRbYk5qbamzJUAAMBbRus8VbgFAKPMkoUzIj9p+AlBPlcTSxZOr1BFAAAweuepwi0AGGUaJ9dF05zGYY9pmt0YjZPrKlQRAACM3nnqyNaTAQAVtWLp6RGZTGxp3hsdnW/tWZDP1UTT7MaDPwcAgAobjfNU4RYAjEJV2WxcsWxutLV3x7pNLdHV0x+52ppY8sEZ0ZCvTbo8AADGqdE4TxVuAcAo1ji5Li790GlJlwEAAEOMpnmqPbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAwRG9vb1x00UWxcePGwbFnnnkmLr300jjrrLPiwx/+cDzwwAMJVggA8BbhFgAAgw4cOBA33HBDbN++fXCstbU1/viP/zgWLVoUP/nJT+K6666L1atXx2OPPZZcoQAA/3/VSRcAAMDosGPHjrjxxhujWCwOGV+/fn1MmTIlbrjhhoiImDVrVmzcuDF+9rOfxbnnnptApQAAbxFuAQAQERFPPfVULF68OL7whS/EmWeeOTh+zjnnxNy5cw87/s0336xgdQAARybcAgAgIiIuu+yyI45Pnz49pk+fPvh9W1tbPPzww3HttdeO+NzZbCay2cw7rvFIqqqyQ75SWvpbPnpbPnpbXvpbPnr79gi3AAAYsZ6enrj22mtjypQp8clPfnLEr2toyEUmU55w65B8vq6s5x/v9Ld89LZ89La89Ld89Pb4CLcAABiRzs7O+JM/+ZP4zW9+E3//938fdXUjn3jv29dZ1pVb+XxddHR0x8BAoSzXGM/0t3z0tnz0trz0t3z09nD19bljHiPcAgDgmN58881YuXJlvPzyy/GDH/wgZs2adVyvLxSKUSgUj33gOzAwUIj+fm8EykV/y0dvy0dvy0t/y0dvj49wCwCAYRUKhbjmmmvilVdeifvuuy9OPfXUpEsCABgk3AIAYFgPPvhgbNy4Mb73ve9FPp+P1tbWiIioqamJE088MdniAIBxT7gFAMCwfv7zn0ehUIgrr7xyyPiiRYvivvvuS6gqAICDhFsAABzmhRdeGPzfd999d4KVAAAML5t0AQAAAADwdgm3AAAAAEgt4RYAAAAAqfW2w63e3t646KKLYuPGjYNjzzzzTFx66aVx1llnxYc//OF44IEHSlIkAAAAABzJ2wq3Dhw4EDfccENs3759cKy1tTX++I//OBYtWhQ/+clP4rrrrovVq1fHY489VqpaAQAAAGCI4/60xB07dsSNN94YxWJxyPj69etjypQpccMNN0RExKxZs2Ljxo3xs5/9LM4999ySFAsAAAAAv+24w62nnnoqFi9eHF/4whfizDPPHBw/55xzYu7cuYcd/+abb76jAgEAAADgaI473LrsssuOOD59+vSYPn364PdtbW3x8MMPx7XXXjvic2ezmchmM8db0ohUVWWHfKWy9D9Z+p8s/U+ee5As/QcAoJyOO9waiZ6enrj22mtjypQp8clPfnLEr2toyEUmU55w65B8vq6s52d4+p8s/U+W/ifPPUiW/gMAUA4lD7c6OzvjT/7kT+I3v/lN/P3f/33U1Y18IrtvX2dZV27l83XR0dEdAwOFslyDo9P/ZOl/svQ/ee5BsirR//r6XFnOCwDA6FfScOvNN9+MlStXxssvvxw/+MEPYtasWcf1+kKhGIVC8dgHvgMDA4Xo7/fGJin6nyz9T5b+J889SJb+AwBQDiULtwqFQlxzzTXxyiuvxH333RennnpqqU4NAAAAAEdUsnDrwQcfjI0bN8b3vve9yOfz0draGhERNTU1ceKJJ5bqMgAAAAAwqGTh1s9//vMoFApx5ZVXDhlftGhR3HfffaW6DAAAAAAMekfh1gsvvDD4v+++++53XAwAAAAAHI9s0gUAAAAAwNsl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAA4TG9vb1x00UWxcePGIeM7d+6M+fPnJ1QVAMDhhFsAAAxx4MCBuOGGG2L79u1Dxnft2hVXXnllHDhwIKHKAAAOJ9wCAGDQjh074hOf+ES8/PLLQ8bXr18fy5cvjwkTJiRUGQDAkQm3AAAY9NRTT8XixYvjxz/+8ZDxxx57LD7/+c/Hl770pYQqAwA4suqkCwAAYPS47LLLjjh+2223RUQctgfXSGWzmchmM2+7ruFUVWWHfKW09Ld89LZ89La89Ld89PbtEW4BAFB2DQ25yGTKE24dks/XlfX8453+lo/elo/elpf+lo/eHh/hFgAAZbdvX2dZV27l83XR0dEdAwOFslxjPNPf8tHb8tHb8tLf8tHbw9XX5455jHALAICyKxSKUSgUy3qNgYFC9Pd7I1Au+ls+els+elte+ls+ent8PMQJAAAAQGoJtwAAAABILeEWAAAAAKllzy0AAI7ohRdeOGxs8eLFRxwHAEiKlVsAAAAApJZwCwAAAIDUekfhVm9vb1x00UWxcePGIeM7d+6M+fPnv6PCAAAAAOBY3na4deDAgbjhhhti+/btQ8Z37doVV155ZRw4cOAdFwcAAAAAw3lb4daOHTviE5/4RLz88stDxtevXx/Lly+PCRMmlKQ4AAAAABjO2wq3nnrqqVi8eHH8+Mc/HjL+2GOPxec///n40pe+VJLiAAAAAGA41W/nRZdddtkRx2+77baIiMP24BqpbDYT2Wzmbb32WKqqskO+Uln6nyz9T5b+J889SJb+AwBQTm8r3CqXhoZcZDLlCbcOyefrynp+hqf/ydL/ZOl/8tyDZOk/AADlMKrCrX37Osu6ciufr4uOju4YGCiU5Rocnf4nS/+Tpf/Jcw+SVYn+19fnynJeAABGv1EVbhUKxSgUimW9xsBAIfr7vbFJiv4nS/+Tpf/Jcw+Spf8AAJSDzS8AAAAASC3hFgAAAACpJdwCAAAAILXe8Z5bL7zwwmFjixcvPuI4AAAAAJSSlVsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAADNHb2xsXXXRRbNy4cXCspaUlVqxYEWeeeWYsXbo0fvnLXyZYIQDAW4RbAAAMOnDgQNxwww2xffv2wbFisRhXX311TJkyJdauXRsXX3xxXHPNNfHaa68lWCkAwEHVSRcAAMDosGPHjrjxxhujWCwOGX/yySejpaUl7r///pg0aVKceuqpsWHDhli7dm1ce+21CVULAHCQlVsAAERExFNPPRWLFy+OH//4x0PGn3322Zg3b15MmjRpcGzBggXxzDPPVLhCAIDDWbkFAEBERFx22WVHHG9tbY2pU6cOGWtsbIzdu3eP+NzZbCay2cw7qu9oqqqyQ75SWvpbPnpbPnpbXvpbPnr79gi3AAAYVnd3d0yYMGHI2IQJE6K3t3fE52hoyEUmU55w65B8vq6s5x/v9Ld89LZ89La89Ld89Pb4CLcAABjWxIkTY//+/UPGent7o7a2dsTn2Levs6wrt/L5uujo6I6BgUJZrjGe6W/56G356G156W/56O3h6utzxzxGuAUAwLBOPvnk2LFjx5CxvXv3Hvao4nAKhWIUCsVjH/gODAwUor/fG4Fy0d/y0dvy0dvy0t/y0dvj4yFOAACGdcYZZ8Tzzz8fPT09g2ObN2+OM844I8GqAAAOEm4BADCsRYsWxbvf/e64+eabY/v27XHXXXfFc889F5dccknSpQEACLcAABheVVVVfPe7343W1tZYvnx5/PSnP43vfOc7ccoppyRdGgCAPbcAADjcCy+8MOT7mTNnxg9/+MOEqgEAODortwAAAABIrbcdbvX29sZFF10UGzduHBxraWmJFStWxJlnnhlLly6NX/7ylyUpEgAAAACO5G2FWwcOHIgbbrghtm/fPjhWLBbj6quvjilTpsTatWvj4osvjmuuuSZee+21khULAAAAAL/tuPfc2rFjR9x4441RLBaHjD/55JPR0tIS999/f0yaNClOPfXU2LBhQ6xduzauvfbakhUMAAAAAIcc98qtp556KhYvXhw//vGPh4w/++yzMW/evJg0adLg2IIFC+KZZ555x0UCAAAAwJEc98qtyy677Ijjra2tMXXq1CFjjY2NsXv37hGfO5vNRDabOd6SRqSqKjvkK5Wl/8nS/2Tpf/Lcg2TpPwAA5XTc4dbRdHd3x4QJE4aMTZgwIXp7e0d8joaGXGQy5Qm3Dsnn68p6foan/8nS/2Tpf/Lcg2TpPwAA5VCycGvixImxf//+IWO9vb1RW1s74nPs29dZ1pVb+XxddHR0x8BAoSzX4Oj0P1n6nyz9T557kKxK9L++PleW8wIAMPqVLNw6+eSTY8eOHUPG9u7de9ijisMpFIpRKBSPfeA7MDBQiP5+b2ySov/J0v9k6X/y3INk6T8AAOVQss0vzjjjjHj++eejp6dncGzz5s1xxhlnlOoSAAAAADBEycKtRYsWxbvf/e64+eabY/v27XHXXXfFc889F5dcckmpLgEAAAAAQ5Qs3Kqqqorvfve70draGsuXL4+f/vSn8Z3vfCdOOeWUUl0CAAAAAIZ4R3tuvfDCC0O+nzlzZvzwhz98RwUBAAAAwEiVbOUWAAAAAFSacAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAAACklnALAAAAgNQSbgEAAACQWsItAAAAAFJLuAUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwCAY2pra4vrrrsuFi5cGEuWLImHHnoo6ZIAACIiojrpAgAAGN2KxWJcffXVUSgU4t577409e/bEF7/4xTjhhBPiwgsvTLo8AGCcE24BADCsrVu3xtNPPx3r16+PGTNmxLx582LlypVx9913C7cAgMR5LBEAgGG1tLREQ0NDzJgxY3Dsfe97X2zdujX6+voSrAwAwMotAACOYcqUKfHGG29Ed3d31NXVRUTE7t27o7+/P954441oaGg45jmy2Uxks5my1FdVlR3yldLS3/LR2/LR2/LS3/LR27dHuAUAwLDOOOOMmDp1aqxevTpWrVoVra2tcc8990REjHjlVkNDLjKZ8oRbh+TzdWU9/3inv+Wjt+Wjt+Wlv+Wjt8dHuAUAwLAmTpwYd9xxR1x//fWxYMGCaGxsjJUrV8btt98eJ5xwwojOsW9fZ1lXbuXzddHR0R0DA4WyXGM809/y0dvy0dvy0t/y0dvD1dfnjnmMcAsAgGOaP39+PProo9Ha2hr19fXxxBNPRH19feRyx55wRkQUCsUoFIplrXFgoBD9/d4IlIv+lo/elo/elpf+lo/eHh8PcQIAMKz9+/fHpz71qXj99dfjpJNOiurq6njsscdi0aJFSZcGACDcAgBgeCeeeGJ0dXXF1772tWhpaYkHHngg1q5dGytXrky6NAAAjyUCAHBs3/zmN+PLX/5yfPSjH43p06fHt771rZg/f37SZUVbe3es3/xq9BeLUZ3JxAULpkXjZJvwAsB4ItwCAOCY5syZE/fdd1/SZQwaKBRizSPbYktzW3R0vfWJjRu27oqmOY2xYunpUZX1kAIAjAcl/Re/ra0trrvuuli4cGEsWbIkHnrooVKeHgAAIiJizSPb4omtu4cEWxERHV198cTW3bHmn19IqDIAoNJKtnKrWCzG1VdfHYVCIe69997Ys2dPfPGLX4wTTjghLrzwwlJdBgCAca6tvTu2NLcNe8yW5r3R1t7tEUUAGAdKtnJr69at8fTTT8c3vvGNmDdvXpx33nmxcuXKuPvuu0t1CQAAiHWbWg5bsfUfdXT2xbpNr1SoIgAgSSULt1paWqKhoSFmzJgxOPa+970vtm7dGn19w08+AABgpDp7+kd0XFePOSgAjAcleyxxypQp8cYbb0R3d3fU1R1c/r179+7o7++PN954IxoaGo55jmw2E9lsplQlDVFVlR3ylcrS/2Tpf7L0P3nuQbL0n1LL1Y5sCjuptqbMlQAAo0HJwq0zzjgjpk6dGqtXr45Vq1ZFa2tr3HPPPRERI1651dCQi0ymPOHWIfm8fReSpP/J0v9k6X/y3INk6T+lsmThjHjy+T3DPpqYz9XEkoXTK1gVAJCUkoVbEydOjDvuuCOuv/76WLBgQTQ2NsbKlSvj9ttvjxNOOGFE59i3r7OsK7fy+bro6OiOgYFCWa7B0el/svQ/WfqfPPcgWZXof319riznZXRqnFwXTXMa44mtu496TNPsRpvJA8A4UbJwKyJi/vz58eijj0Zra2vU19fHE088EfX19ZHLjWzCWSgUo1AolrKkwwwMFKK/3xubpOh/svQ/WfqfPPcgWfpPKa1YenpEJhNbmvdGR+dbK7jyuZpomt148OcAwLhQsnBr//79cdVVV8V3v/vdOOmkkyIi4rHHHotFixaV6hIAABAREVXZbFyxbG60tXfH+s2vxkAxojobccGC6dGQr026PACggkoWbp144onR1dUVX/va1+Kqq66KJ598MtauXRs//OEPS3UJAAAYonFyXXz6wtOivj4Xr7/eaXUgAIxDJX0s8Zvf/GZ8+ctfjo9+9KMxffr0+Na3vhXz588v5SUAgBQ5tKqmv1iM6kwmLlgwzT5IAACUVEnDrTlz5sR9991XylMCACk0UCjEmke2xZbmtiGfaLdh665omnNwP6SqbDbBCgEAGCtKGm4BAERErHlk2xE/ya6jq+/geCYTVyybm0BlAACMNX5lCgCUVFt7d2xpbhv2mC3Ne6OtvbtCFQEAMJYJtwCAklq3qWXIo4hH0tHZF+s2vVKhigAAGMuEWwBASXX29I/ouK6e4QMwAAAYCeEWAFBSudqRbek5qbamzJUAADAeCLcAgJJasnBG5CcNH1zlczWxZOH0ClUEAMBYJtwCAEqqcXJdNM1pHPaYptmN0Ti5rkIVAQAwlo3suQEAgOOwYunpEZlMbGneGx2db+2tlc/VRNPsxoM/BwCAEhBuAQAlV5XNxhXL5kZbe3es3/xqDBQjqrMRFyyYHg352qTLAwBgDBFuAQBl0zi5Lj594WlRX5+L11/vjP7+QtIlAQAwxgi3AABIrUOrA/uLxajOZOKCBdPs5wYA44xwCwCA1BkoFGLNI9tiS3NbdHS9ta/bhq27omnOwX3dqrI+OwkAxgPhFgAAqbPmkW3xxNbdh413dPUdHM9k4oplcxOoDACoNL/OAgAgVdrau2NLc9uwx2xp3htt7d0VqggASJJwCwCAVFm3qWXIo4hH0tHZF+s2vVKhigCAJAm3AABIlc6e/hEd19UzfAAGAIwNwi0AAFIlVzuybWMn1daUuRIAYDQQbgEAkCpLFs6I/KThg6t8riaWLJxeoYoAgCQJtwAASJXGyXXRNKdx2GOaZjdG4+S6ClUEACRpZGu6AQBgFFmx9PSITCa2NO+Njs639tbK52qiaXbjwZ8DAOOCcAsAgNSpymbjimVzo629O9ZvfjUGihHV2YgLFkyPhnxt0uUBABUk3AIAILUaJ9fFpy88Lerrc/H6653R319IuiQAoMLsuQUAAABAagm3AAAAAEgt4RYAAAAAqSXcAgAAACC1hFsAAAAApJZwCwAAAIDUEm4BAAAAkFrCLQAAAABSS7gFAAAAQGoJtwAAAABILeEWAAAAAKkl3AIAAAAgtYRbAAAAAKSWcAsAAACA1BJuAQAAAJBawi0AAAAAUku4BQAAAEBqCbcAAAAASC3hFgAAAACpJdwCAAAAILWEWwAAHNOuXbviyiuvjA984ANx/vnnx5o1a5IuCQAgIiKqky4AAMqprb071m9+NfqLxajOZOKCBdOicXJd0mVB6lx//fVxyimnxEMPPRQ7duyIm266KaZNmxZLlixJujQAYJwTbgEwJg0UCrHmkW2xpbktOrr6Bsc3bN0VTXMaY8XS06MqawEzjER7e3s888wzsXr16pg1a1bMmjUrzjnnnNiwYYNwCwBInHALgDFpzSPb4omtuw8b7+jqOzieycQVy+YmUBmkT21tbdTV1cVDDz0UN954Y7S0tMSvf/3ruP7660d8jmw2E9lspiz1VVVlh3yltPS3fPS2fPS2vPS3fPT27RFuATDmtLV3x5bmtmGP2dK8N9rauz2iCCMwceLEuOWWW2L16tVx7733xsDAQCxfvjw+/vGPj/gcDQ25yGTKE24dks/7+1xO+ls+els+elte+ls+ent8hFsAjDnrNrUMeRTxSDo6+2Ldplfi0g+9t0JVQbq9+OKLcd5558VnPvOZ2L59e6xevTrOPvvs+NjHPjai1+/b11mWlVtt7d3xf37VEr0DxZhQlYkLPzhDaF1iVVXZyOfroqOjOwYGCkmXM6bobfnobXnpb/no7eHq63PHPEa4BcCY09nTP6LjunqGD8CAgzZs2BAPPvhgPP7441FbWxtNTU2xZ8+e+N73vjficKtQKEahUCxZTUfbV++J5+yrVy4DA4Xo7/dGqxz0tnz0trz0t3z09vj4Fx+AMSdXO7Lf3UyqrSlzJTA2bN26NWbOnBm1tbWDY/PmzYvXXnstsZoO7av3H1dpHtpXb80/v5BQZQBApQm3ABhzliycEflJwwdX+VxNLFk4vUIVQbpNnTo1du7cGb29vYNjzc3NMX16Mn+HjmdfPQBg7BNuATDmNE6ui6Y5jcMe0zS70b48MELnn39+1NTUxKpV/1979x8dVX3nf/w1k4TMZGBCMoCVQCERMUFHUAI96NKDNrFu4tEe1h+wLGsWqErXctquW5ddlsqGllOLbvdXTWk9ZvXsYqugrUe6bTiepUiRbmKBxO/GBYalAQIlI8kskwlkfnz/4CSQJoSgc+eTO/N8nOPJyeXO3Pd5T46fO6/5fD6zTkePHtU777yjuro6LV++3Eg917KvHgAASH/suQUASEs1VaWSw6HmQIdC4Utvgr2eHPmLL+7HA2Bkxo0bp/r6en3zm9/Ugw8+qMLCQq1evVqPPPKIkXrYVw8AAFwuqeFWe3u7nnnmGf3Xf/2Xxo8frz/90z9VTU1NMi8BAMCIZDmdWlldpmBXRDubTiiWkLKdUsXcKSr0uq7+BAAGmDFjhl566SXTZUhiXz0AADBQUsOtr3zlK5o8ebK2b9+uw4cP66mnnlJRUZEqKyuTeRkAAEbMl+/WsntmqqDAo7Nnw3zrDJAGKsun6r0PTg+7NJF99QAAyBxJ23Orq6tL+/fv1+rVqzV9+nRVVFRo4cKF2rt3b7IuAQAAALCvHgAAGCBp4ZbL5ZLb7db27dvV29urQCCg999/X2VlZcm6BAAAACDp4r56d/qvl9czcOmh15OjO2/5FPvqAQCQQZK2LDE3N1fr169XbW2tXn75ZcViMS1evFgPPfTQiJ/D6XTI6XQkq6QBsrKcA34itei/WfTfLPpvHq+BWfQfVmBfPQAA0Cepe24dOXJEd911l/7sz/5Mhw4dUm1trRYsWKD7779/RI8vLPTI4bAm3Orj9TI93ST6bxb9N4v+m8drYBb9hxXYVw8AACQt3Nq7d69ef/117dq1Sy6XS36/X6dPn9YLL7ww4nDro4/Cls7c8nrdCoUiisW46Uk1+m8W/TeL/pvHa2BWKvpfUOCx5HkBAAAw+iUt3GppadG0adPkcl2aBj5r1izV1dWN+Dni8YTi8USyShpSLBbnEz2D6L9Z9N8s+m8er4FZ9B9W6FuWGE0klO1wqGJuERvJAwCQYZIWbk2aNEnHjh3ThQsXNGbMGElSIBDQlCl8BTMAAACSKxaPq35Hq5oDQYW6e/uP721pl7/Ep5qqUmU52ecNAIBMkLQR/+6771ZOTo7WrVuno0eP6p133lFdXZ2WL1+erEsAAAAAkqT6Ha3a03JqQLAlSaHuXu1pOaX6n31oqDIAAJBqSQu3xo0bp/r6ep05c0YPPvigNm3apNWrV+uRRx5J1iUAAAAABbsiag4Ehz2nOdChYFckRRUBAACTkvptiTNmzNBLL72UzKcEAAAABmhobBs0Y+v3hcK9amg8riWfuzFFVQEAAFOSGm4BANJPsCuihsY2hXui8riyVVk+lc2aARgV7omO6LzunuEDMAAAkB4ItwAAQ7rSZs3vfXCazZoBGOVxjewWNs+VY3ElAABgNOBdCQBgSGzWDGC0qiyfKm/e8MGV15OjynK+tRsAgExAuAUAGITNmgGMZr58t/wlvmHP8Rf7WEINAECGINwCAAxyLZs1A4AJNVWlutN/vbyegTO4vJ4c3XnLp1RTVWqoMgAAkGrsuQUAGITNmgGMdllOp1ZWlynYFdHOphOKJaRsp1Qxd4oKvS7T5QEAgBQi3AIADMJmzQDswpfv1rJ7ZqqgwKOzZ8OKRuOmSwIAACnGskQAwCBs1gwAAADALgi3AACDsFkzAAAAALtgWSIAYEg1VaWSw6HmQIdC4Ut7a3k9OfIX+9isGQAAAMCoQLgFABjS5Zs1NzS2qbsnKo8rR5XzprJZMwAAAIBRg3ALADAsX75bSz4303QZAAAAADAk9twCAAAAAACAbRFuAQAAAAAAwLYItwAAAAAAAGBbhFsAAAAAAACwLcItAAAAAAAA2BbhFgAAAAAAAGyLcAsAAAAAAAC2RbgFAAAAAAAA2yLcAgAAAAAAgG0RbgEAAAAAAMC2CLcAAAAAAABgW9mmCwCAdBbsimhn0wlFEwllOxyqmFskX77bdFkAAAAAkDYItwDAArF4XPU7WtUcCCrU3dt/fG9Lu/wlPtVUlSrLyeRZAAAAAPikCLcAwAL1O1q1p+XUoOOh7t6Lxx0OrawuM1AZAAAAAKQXpg0AQJIFuyJqDgSHPac50KFgVyRFFQEAAABA+iLcAoAka2hsG7AUcSihcK8aGo+nqCIAAAAASF+EWwCQZOGe6IjO6+4ZPgADAAAAAFwd4RYAJJnHNbLtDPNcORZXAgAAAADpj3ALAJKssnyqvHnDB1deT44qy6ekqCIAAAAASF+EWwCQZL58t/wlvmHP8Rf75Mt3p6giAAAAAEhfI1s7AwC4JjVVpZLDoeZAh0LhS3treT058hf7Lv47AAAAAOATI9wCAAtkOZ1aWV2mYFdEO5tOKJaQsp1SxdwpKvS6TJcHAAAAAGmDcAsALOTLd2vZPTNVUODR2bNhRaNx0yUBAAAAQFphzy0AAAAAAADYFuEWAAAAAAAAbItwCwAAAAAAALZFuAUAAAAAAADbItwCAAAAAACAbfFtiQAAALCtYFdEO5tOKJpIKNvhUMXcIvny3abLAgAAKUS4BQAAANuJxeOq39Gq5kBQoe7e/uN7W9rlL/GppqpUWU4WKQAAkAkItwAAAHBV27dv19q1awcddzgcam1tTXk99Ttatafl1KDjoe7ei8cdDq2sLkt5XQAAIPUItwAAAHBVVVVVWrhwYf/v0WhUjz76qBYtWpTyWoJdETUHgsOe0xzoULArwhJFAAAyAHO1AQAAcFUul0sTJ07s/++nP/2pEomEnnrqqZTX0tDYNmAp4lBC4V41NB5PUUUAAMAkwi0AAABck87OTv3gBz/QX/zFX2jMmDEpv364Jzqi87p7hg/AAABAemBZIoBRLdgVUUNjm8I9UXlc2aosn8oSEwAwbOvWrZo0aZLuvffeET/G6XTI6XQk5fpj3SML1MbmjVF2Np/lflJZWc4BP5E89NY69NZa9Nc69PbjIdwCMCpd6Vuw3vvgNN+CBQAGJRIJvfbaa1q1atU1Pa6w0COHIznh1iP33KR9/++0Os+dv+I548fl6uHKm1RQkJeUa0LyevlwySr01jr01lr01zr09toQbgEYlfgWLAAYnZqbm3X69GlVV1df0+M++iictJlb2ZJuKSnUuwfbr3jOLcWFylZCZ8+Gk3LNTJaV5ZTX61YoFFEsFjddTlqht9aht9aiv9aht4MVFHiueg7hFoBRh2/BAoDRa/fu3SovL1d+fv41PS4eTygeTyStjkfvvUmJhLT/0O8U7on1H/e4sjRnxkQ9eu9NikZ5U5BMsVicnlqE3lqH3lqL/lqH3l4b1vQAGHX4FiwAGL0OHjyo22+/3XQZFyUSg5Y6Ohzc3gIAkGkY/QGMOnwLFgCMXocOHdKMGTNMl9G/fP1cZOCYcS5ycfl6/c8+NFQZAABINcItAKOOxzWyFdN5rhyLKwEA/L6Ojg55vV6jNVzL8nUAAJD+krrn1vbt27V27dpBxx0Oh1pbW5N5KQBprLJ8qt774PSwSxO9nhxVlk9JYVUAAOniskTTrmX5+pLP3ZiiqgAAgClJDbeqqqq0cOHC/t+j0ageffRRLVq0KJmXAZDmfPlu+Ut8Q35bYh9/sY/N5AEgQ7F8HQAAXC6p4ZbL5ZLL5er//fvf/74SiYSeeuqpZF4GQAaoqSqVHA41BzoUCl96c+L15Mhf7Lv47wCAjMTydQAAcLmkhluX6+zs1A9+8ANt3LhRY8aMseoyANJUltOpldVlCnZF1NDYpu6eqDyuHFXOm6pCr+vqTwAASFssXwcAAJezLNzaunWrJk2apHvvvXfEj3E6HXI6HVc/8WPIynIO+InUov9m2bn/1/k8+pPP23uWlp37ny54Dcyi/0g2lq8DAIDLWRJuJRIJvfbaa1q1atU1Pa6w0COHw5pwq4/Xy02OSfTfLPpvFv03j9fALPqPZGL5OgAA6GNJuNXc3KzTp0+rurr6mh730UdhS2dueb1uhUIRxWJxS66BK6P/ZtF/s+i/ebwGZqWi/wUFHkueF6PX5cvXdzadUCwhZTulirlTWL4OAECGsSTc2r17t8rLy5Wfn39Nj4vHE4rHE1aU1C8Wiysa5Y2NKfTfLPpvFv03j9fALPoPK/jy3Vp2z0wVFHh09myYvzEAADKQJZtfHDx4ULfffrsVTw0AAAAAAAD0syTcOnTokGbMmGHFUwMAAAAAAAD9LAm3Ojo65PV6rXhqAAAAAAAAoJ8le24dPHjQiqcFAAAAAAAABrBk5hYAAAAAAACQCoRbAAAAAAAAsC3CLQAAAAAAANiWJXtuARg9gl0R7Ww6oWgioWyHQxVzi+TLd5suCwAAAACApCDcAtJULB5X/Y5WNQeCCnX39h/f29Iuf4lPNVWlynIyeRMAAAAAYG+EW0Caqt/Rqj0tpwYdD3X3XjzucGhldZmBygAASB5mKAMAAMItIA0FuyJqDgSHPac50KFgV4Q3AAAAW2KGMgAA6MOID6Shhsa2ATf6QwmFe9XQeDxFFQEAkFx9M5R/f7zrm6Fc/7MPDVUGAABSjXALSEPhnuiIzuvuGT4AAwBgNLqWGcoAACD9EW4BacjjGtmK4zxXjsWVAACQfMxQBgAAlyPcAtJQZflUefOGD668nhxVlk9JUUUAACQPM5QBAMDlCLeANOTLd8tf4hv2HH+xj83kAQC2xAxlAABwOcItIE3VVJXqTv/18noG3th7PTm685ZPqaaq1FBlAAB8MsxQBgAAlxvZx14AbCfL6dTK6jIFuyLa2XRCsYSU7ZQq5k5RoddlujwAAD62vhnKe1pOXfEcZigDAJA5CLeANOfLd2vZPTNVUODR2bNhRaNx0yUBAPCJ1VSVSg6HmgMdCoUv7a3l9eTIX+xjhjIAABmEcAsAAAC2wwxlAADQh3ALAAAAtsUMZQAAwIbyAAAAAAAAsC3CLQAAAAAAANgW4RYAAAAAAABsi3ALAAAAAAAAtkW4BQAAAAAAANsi3AIAAAAAAIBtEW4BAAAAAADAtgi3AAAAAAAAYFuEWwAAAAAAALCtbNMFAKNdsCuihsY2hXui8riyVVk+Vb58t+myAAAAAACACLeAK4rF46rf0armQFCh7t7+4+99cFr+Ep9qqkqV5WTyIwAAAAAAJhFuAVdQv6NVe1pODToe6u69eNzh0MrqMgOVAQAAAACAPkw7AYYQ7IqoORAc9pzmQIeCXZEUVQQAAAAAAIZCuAUMoaGxbcBSxKGEwr1qaDyeoooAAAAAAMBQCLeAIYR7oiM6r7tn+AAMAAAAAABYi3ALGILHNbLt6PJcORZXAgAAAAAAhkO4BQyhsnyqvHnDB1deT44qy6ekqCIAAAAAADAUwi1gCL58t/wlvmHP8Rf75Mt3p6giAAAAAAAwlJGtvQIyUE1VqeRwqDnQoVD40t5aXk+O/MW+i/8OAAAAAACMItwCriDL6dTK6jIFuyJqaGxTd09UHleOKudNVaHXZbo8AAAAAAAgwi3gqnz5bi353EzTZQAAAAAAgCGw5xYAAAAAAABsi3ALAAAAAAAAtkW4BQAAAAAAANsi3AIAAAAAAIBtEW4BAAAAAADAtgi3AAAAcFUXLlzQhg0bNG/ePN1xxx16/vnnlUgkTJcFAACgbNMFAAAAYPTbuHGj9u3bpxdffFHhcFhf/epXNXnyZC1ZssR0aQAAIMMxcwsAAADD6uzs1LZt21RbW6tbb71VCxYs0IoVK3TgwAHTpQEAADBzCwAAAMNramrS2LFjNX/+/P5jjz32mMGKAAAALiHcAgAAwLDa2tpUVFSkN998U3V1dert7dXixYu1evVqOZ0jWwjgdDrkdDosqS8ryzngJ5KL/lqH3lqH3lqL/lqH3n48hFuwXLArop1NJxRNJJTtcKhibpF8+W7TZQEAgBHq7u7WsWPH9Oqrr2rTpk06c+aM1q9fL7fbrRUrVozoOQoLPXI4rAm3+ni93F9Yif5ah95ah95ai/5ah95eG8ItWCYWj6t+R6uaA0GFunv7j+9taZe/xKeaqlJljfDTXgAAYE52drbOnTun5557TkVFRZKkkydPauvWrSMOtz76KGzpzC2v161QKKJYLG7JNTIZ/bUOvbUOvbUW/bUOvR2soMBz1XMIt2CZ+h2t2tNyatDxUHfvxeMOh1ZWlxmoDAAAXIuJEycqNze3P9iSpOLiYrW3t4/4OeLxhOLxhBXl9YvF4opGeSNgFfprHXprHXprLfprHXp7bZg2A0sEuyJqDgSHPac50KFgVyRFFQEAgI9r9uzZOn/+vI4ePdp/LBAIDAi7AAAATCHcgiUaGtsGLEUcSijcq4bG4ymqCAAAfFwlJSVatGiR1q5dq9bWVu3evVtbtmzR0qVLTZcGAACQ3HDrwoUL2rBhg+bNm6c77rhDzz//vBIJa6efY3QK90RHdF53z/ABGAAAGB02b96sT3/601q6dKmefvppLVu2TMuXLzddFgAAQHL33Nq4caP27dunF198UeFwWF/96lc1efJkLVmyJJmXgQ14XCP708pz5VhcCQAASIZx48bp2WefNV0GAADAIEmbudXZ2alt27aptrZWt956qxYsWKAVK1bowIEDyboEbKSyfKq8ecMHV15PjirLp6SoIgAAAAAAkI6SNnOrqalJY8eO1fz58/uPPfbYY8l6etiML98tf4lvyG9L7OMv9smX705hVQAAAAAAIN0kLdxqa2tTUVGR3nzzTdXV1am3t1eLFy/W6tWr5XSObIKY0+mQ0+lIVkkDZGU5B/yE9VbdP0tOp0MHjnQoFL60t5bXk6Nbb5iglfeVKWuEfxv4ZPj7N4v+m8drYBb9BwAAgJWSFm51d3fr2LFjevXVV7Vp0yadOXNG69evl9vt1ooVK0b0HIWFHjkc1oRbfbxeZgql0tcfna8zZ7v1k18eUTgS1di8HD3w2RJNGJ9nurSMxN+/WfTfPF4Ds+g/AAAArJC0cCs7O1vnzp3Tc889p6KiIknSyZMntXXr1hGHWx99FLZ05pbX61YoFFEsFrfkGhhatqSH75oxoP9nz4ZNl5VR+Ps3i/6bx2tgVir6X1DgseR5MfoFuyLa2XRC0URC2Q6HKuYWse0BAAAZJmnh1sSJE5Wbm9sfbElScXGx2tvbR/wc8XhC8XgiWSUNKRaLKxrljY0p9N8s+m8W/TeP18As+o9kisXjqt/RquZAUKHuS9sf7G1pl7/Ep5qqUrY/AAAgQyRtxJ89e7bOnz+vo0eP9h8LBAIDwi4AAAAgGep3tGpPy6kBwZYkhbp7tafllOp/9qGhygAAQKolLdwqKSnRokWLtHbtWrW2tmr37t3asmWLli5dmqxLAAAAAAp2RdQcCA57TnOgQ8GuSIoqAgAAJiV1rvbmzZv16U9/WkuXLtXTTz+tZcuWafny5cm8BAAAADJcQ2PboBlbvy8U7lVD4/EUVQQAAExK2p5bkjRu3Dg9++yzyXxKAAAAYIBwT3RE53X3DB+AAQCA9MAumwAAALAVj2tkn8/muXIsrgQAAIwGhFsAAACwlcryqfLmDR9ceT05qiyfkqKKAACASYRbAAAAsBVfvlv+Et+w5/iLffLlu1NUEQAAMCmpe24BAAAAqVBTVSo5HGoOdCgUvrS3lteTI3+x7+K/AwCAjEC4BQAAANvJcjq1srpMwa6IdjadUCwhZTulirlTVOh1mS4PAACkEOGWDQS7ImpobFO4JyqPK1uV5VOZZg8AAKCLSxSX3TNTBQUenT0bVjQaN10SAABIMcKtUSwWj6t+R6uaA0GFui9Nt3/vg9Pyl1ycbp/lZNs0AAAAAACQuQi3RrH6Ha3a03Jq0PFQd+/F4w6HVlaXGagMAABgdOhblhhNJJTtcKhibhEz3AEAyDCEW6NUsCui5kBw2HOaAx0KdkW4gQMAABnnSjPc97a0M8MdAIAMw4g/SjU0tg24URtKKNyrhsbjKaoIAABg9Oib4f7790t9M9zrf/ahocoAAECqEW6NUuGe6IjO6+4ZPgADAABIN9cywx0AAKQ/wq1RyuMa2YrRPFeOxZUAAACMLsxwBwAAlyPcGqUqy6fKmzd8cOX15KiyfEqKKgIAABgdmOEOAAAuR7g1Svny3fKX+IY9x1/sYzN5AACQcZjhDgAALke4NYrVVJXqTv/18noG3ph5PTm685ZPqaaq1FBlAAAA5jDDHQAAXG5kH3vBiCynUyuryxTsiqihsU3dPVF5XDmqnDdVhV6X6fIAAACM6Jvhvqfl1BXPYYY7AACZg3DLBnz5bi353EzTZQAAAIwaNVWlksOh5kCHQuFLe2t5PTnyF/uY4Q4AQAYh3AIAAIDtXD7DfWfTCcUSUrZTqpg7hRnuAABkGMItAAAA2JYv361l98xUQYFHZ8+GFY3GTZcEAABSjA3lAQAAAAAAYFuEWwAAAAAAALAtwi0AAAAAAADYFuEWAAAAAAAAbItwCwAAAAAAALZFuAUAAAAAAADbItwCAAAAAACAbRFuAQAAAAAAwLayTReQCsGuiHY2nVA0kVC2w6GKuUXy5btNlwUAAIBPiPs8AACQ1uFWLB5X/Y5WNQeCCnX39h/f29Iuf4lPNVWlynIyeQ0AAMBuuM8DAAB90jrcqt/Rqj0tpwYdD3X3XjzucGhldZmBygAAAPBJcJ8HAAD6pO3HWcGuiJoDwWHPaQ50KNgVSVFFAAAASAbu8wAAwOXSNtxqaGwbMEV9KKFwrxoaj6eoIgAAACQD93kAAOByaRtuhXuiIzqvu2f4GyMAAACMLtznAQCAy6VtuOVxjWw7sTxXjsWVAAAAIJm4zwMAAJdL23CrsnyqvHnD39B4PTmqLJ+SoooAAACQDNznAQCAy6VtuOXLd8tf4hv2HH+xT758d4oqAgAAQDJwnwcAAC43sjndNlVTVSo5HGoOdCgUvrTngteTI3+x7+K/AwAAwHa4zwMAAH3SOtzKcjq1srpMwa6IdjadUCwhZTulirlTVOh1mS4PAAAAHxP3eQAAoE9ah1t9fPluLbtnpgoKPDp7NqxoNG66JAAAACQB93kAACBt99wCAAAAAABA+iPcAgAAAAAAgG0RbgEAAAAAAMC2CLcAAAAAAABgW4RbAAAAAAAAsC3CLQAAAIxIQ0ODbrrppgH/rVmzxnRZAAAgw2WbLgAAAAD2cPjwYd11112qra3tP5abm2uwIgAAAMItAAAAjNCRI0c0c+ZMTZw40XQpAAAA/ViWCAAAgBE5cuSIpk+fbroMAACAAZi5BQAAgKtKJBI6evSo3n33XX3/+99XLBbTvffeqzVr1mjMmDFXfbzT6ZDT6bCktqws54CfSC76ax16ax16ay36ax16+/EQbgEAAOCqTp48qUgkojFjxui73/2ujh8/ro0bN6qnp0fr1q276uMLCz1yOKwJt/p4vW5Lnz/T0V/r0Fvr0Ftr0V/r0NtrQ7gFAACAqyoqKtK+ffuUn58vh8OhsrIyxeNx/eVf/qXWrl2rrKysYR//0UdhS2dueb1uhUIRxWJxS66RyeivdeitdeitteivdejtYAUFnqueQ7gFAACAERk/fvyA32+44QadP39eXV1dKiwsHPax8XhC8XjCwuqkWCyuaJQ3Alahv9aht9aht9aiv9aht9eGRZwAAAC4qt27d+szn/mMIpFI/7H//u//1vjx468abAEAAFiJcAsAAABXddtttyk3N1fr1q1TIBDQrl279Oyzz2rVqlWmSwMAABmOZYkAAAC4qrFjx+rFF1/Ut771Lf3RH/2RPB6PlixZQrgFAAC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      "text/plain": [
       "<Figure size 1500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(15, 15))\n",
    "ax[0,0].scatter(anscombe[anscombe['dataset']=='I']['x'], anscombe[anscombe['dataset']=='I']['y'])\n",
    "ax[0,1].scatter(anscombe[anscombe['dataset']=='II']['x'], anscombe[anscombe['dataset']=='II']['y'])\n",
    "ax[1,0].scatter(anscombe[anscombe['dataset']=='III']['x'], anscombe[anscombe['dataset']=='III']['y'])\n",
    "ax[1,1].scatter(anscombe[anscombe['dataset']=='IV']['x'], anscombe[anscombe['dataset']=='IV']['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram\n",
    "Use the histogram to check the followings:\n",
    "\n",
    "- The distributions of the data \n",
    "- center and spread of the data\n",
    "- skewness of the data\n",
    "- presence of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Pclass'}>,\n",
       "        <AxesSubplot:title={'center':'Age'}>],\n",
       "       [<AxesSubplot:title={'center':'SibSp'}>,\n",
       "        <AxesSubplot:title={'center':'Parch'}>],\n",
       "       [<AxesSubplot:title={'center':'Fare'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms for all numerical features\n",
    "X_train.hist(figsize=(15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# key findings with potential processing\n",
    "# long tail (skewed left): log transformation\n",
    "# some outliers: outlier removal\n",
    "X_train['Fare'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot\n",
    "\n",
    "A boxplot displays the dataset based on a five-number summary:\n",
    "\n",
    "- Median (Q2 / 50th Percentile) : the middle value of the dataset.\n",
    "\n",
    "- First quartile (Q1 / 25th Percentile) : the middle value between the smallest number and the median of the dataset.\n",
    "\n",
    "- Third quartile (Q3 / 75th Percentile) : the middle value between the largest number and the median of the dataset.\n",
    "\n",
    "Interquartile Range (IQR) is the distance between the upper and lower quartile: IQR=Q3-Q1, \n",
    "IQR is used to determine outliers, which are points that are more than 1.5IQR from the median\n",
    "\n",
    "- Minimum (NOT the smallest): the lowest data point excluding any outliers.\n",
    "\n",
    "- Maximum (NOT the largest): the largest data point excluding any outliers.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### A box plot identifies the middle 50% of the data (the box), the median (the line in the box), and the outliers (the dots outside the max and min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train['Age'].plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot\n",
    "\n",
    "Scatter plot is often used for **correlation analysis** between different features. Correlation coefficient is between -1 and 1, representing negative and positive correlations. 0 means there is no liner correlation. Correlation is said to be linear if the ratio of change is constant, otherwise is non-linear. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fare')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x=X_train['Age'], y=X_train['Fare'], alpha=0.2) # alpha=0.2 specifies the opacity\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x17c1f1d30>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1250x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pairplot example using seaborn\n",
    "sns.pairplot(data=X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing\n",
    "We will build a pipeline to do some of the following tasks:\n",
    "\n",
    "- Missing data\n",
    "- Feature scaling (important for certain model such as Gradient Descent based models)\n",
    "- Categorical feature encoding\n",
    "- Outlier removal\n",
    "- Transformation\n",
    "- Custom processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        0\n",
       "Sex           0\n",
       "Age         140\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# any missing values?\n",
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will train our decision tree classifier with the following features:\n",
    "# Numerical Features: ['Age', 'SibSp', 'Fare', 'Parch']\n",
    "# Categorical Features:['Sex', 'Embarked', 'Pclass'\n",
    "\n",
    "num_features = ['Age', 'SibSp', 'Parch', 'Fare']\n",
    "cat_features = ['Sex', 'Embarked', 'Pclass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "\n",
    "# Create the preprocessing pipeline for numerical features\n",
    "# There are two steps in this pipeline\n",
    "# Pipeline(steps=[(name1, transform1), (name2, transform2), ...]) \n",
    "# NOTE the step names can be arbitrary\n",
    "\n",
    "# Step 1 is what we discussed before - filling the missing values if any using mean\n",
    "# Step 2 is feature scaling via standardization - making features look like normal-distributed \n",
    "# see standardization: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
    "num_pipeline = Pipeline(\n",
    "    steps=[\n",
    "        ('num_imputer', SimpleImputer()),  # we will tune different strategies later\n",
    "        ('scaler', StandardScaler()),\n",
    "        ]\n",
    ")\n",
    "\n",
    "# Create the preprocessing pipelines for the categorical features\n",
    "# There are two steps in this pipeline:\n",
    "# Step 1: filling the missing values if any using the most frequent value\n",
    "# Step 2: one hot encoding\n",
    "\n",
    "cat_pipeline = Pipeline(\n",
    "    steps=[\n",
    "        ('cat_imputer', SimpleImputer(strategy='most_frequent')),\n",
    "        ('onehot', OneHotEncoder()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Assign features to the pipelines and Combine two pipelines to form the preprocessor\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num_pipeline', num_pipeline, num_features),\n",
    "        ('cat_pipeline', cat_pipeline, cat_features),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline prediction\n",
    "\n",
    "It's always helpful to have some baseline predictions based on heuristics/rules so that you can benchmark your model performance. The following shows that female has much higher survival rate than male so that we can have a rule-based baseline to predict all female survivied and male died. For regression problem, an easy baseline could be using the training sample mean for all predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  1           0.742038\n",
       "        0           0.257962\n",
       "male    0           0.811092\n",
       "        1           0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the survival rates by gender\n",
    "# female survival rate: 74.2%\n",
    "# male survival rate: 18.9%\n",
    "group_norm = df.groupby('Sex')['Survived'].value_counts(normalize=True)\n",
    "group_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "709       3    male   NaN      1      1  15.2458        C\n",
       "439       2    male  31.0      0      0  10.5000        S\n",
       "840       3    male  20.0      0      0   7.9250        S\n",
       "720       2  female   6.0      0      1  33.0000        S\n",
       "39        3  female  14.0      1      0  11.2417        C"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rule-based prediction\n",
    "baseline_pred = X_test['Sex'].apply(lambda x: 0 if x == 'male' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy Score : 0.7821229050279329\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(f'Baseline Accuracy Score : {accuracy_score(y_test, baseline_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model traning, tuning, evaluation and selection\n",
    "\n",
    "Next, I attach three different models (Decision Tree, SVC, Random Forest) to the same pre-processing pipeline and tune the some parameters using GridSearch with cross validation. Then, we compare their performance and choose the best model to proceed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the model to use, which is DecisionTreeClassifier\n",
    "# Make a full pipeline by combining preprocessor and the model\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "pipeline_dt = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('clf_dt', DecisionTreeClassifier()),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we show how to use GridSearch with K-fold cross validation (K=10) to fine tune the model\n",
    "# we use the accuracy as the scoring metric with training score return_train_score=True\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# set up the values of hyperparameters you want to evaluate\n",
    "# here you must use the step names as the prefix followed by two under_scores to specify the parameter names and the \"full path\" of the steps\n",
    "\n",
    "# we are trying 2 different imputer strategies \n",
    "# 2x5 different decision tree models with different parameters\n",
    "# in total we are trying 2x2x5 = 20 different combinations\n",
    "\n",
    "param_grid_dt = [\n",
    "    {\n",
    "        'preprocessor__num_pipeline__num_imputer__strategy': ['mean', 'median'],\n",
    "        'clf_dt__criterion': ['gini', 'entropy'], \n",
    "        'clf_dt__max_depth': [3, 4, 5, 6, 7],\n",
    "    }\n",
    "]\n",
    "\n",
    "# set up the grid search \n",
    "grid_search_dt = GridSearchCV(pipeline_dt, param_grid_dt, cv=10, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                                                         Pipeline(steps=[('num_imputer',\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         ('scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['Age',\n",
       "                                                                          'SibSp',\n",
       "                                                                          'Parch',\n",
       "                                                                          'Fare']),\n",
       "                                                                        ('cat_pipeline',\n",
       "                                                                         Pipeline(steps=[('cat_imputer',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('onehot',\n",
       "                                                                                          OneHotEncoder())]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Embarked',\n",
       "                                                                          'Pclass'])])),\n",
       "                                       ('clf_dt', DecisionTreeClassifier())]),\n",
       "             param_grid=[{'clf_dt__criterion': ['gini', 'entropy'],\n",
       "                          'clf_dt__max_depth': [3, 4, 5, 6, 7],\n",
       "                          'preprocessor__num_pipeline__num_imputer__strategy': ['mean',\n",
       "                                                                                'median']}],\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model using the full pipeline\n",
    "grid_search_dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf_dt__criterion': 'entropy',\n",
       " 'clf_dt__max_depth': 3,\n",
       " 'preprocessor__num_pipeline__num_imputer__strategy': 'mean'}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the best performing parameter combination\n",
    "grid_search_dt.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mean_fit_time',\n",
       " 'mean_score_time',\n",
       " 'mean_test_score',\n",
       " 'param_clf_dt__criterion',\n",
       " 'param_clf_dt__max_depth',\n",
       " 'param_preprocessor__num_pipeline__num_imputer__strategy',\n",
       " 'params',\n",
       " 'rank_test_score',\n",
       " 'split0_test_score',\n",
       " 'split1_test_score',\n",
       " 'split2_test_score',\n",
       " 'split3_test_score',\n",
       " 'split4_test_score',\n",
       " 'split5_test_score',\n",
       " 'split6_test_score',\n",
       " 'split7_test_score',\n",
       " 'split8_test_score',\n",
       " 'split9_test_score',\n",
       " 'std_fit_time',\n",
       " 'std_score_time',\n",
       " 'std_test_score']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build-in CV results keys\n",
    "sorted(grid_search_dt.cv_results_.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82443271, 0.82443271, 0.81177621, 0.81177621, 0.81318466,\n",
       "       0.81320423, 0.80610329, 0.80330595, 0.79775039, 0.80758998,\n",
       "       0.82584116, 0.82584116, 0.82304382, 0.82304382, 0.80758998,\n",
       "       0.80618153, 0.80618153, 0.80755086, 0.79348592, 0.79628326])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test score for the 20 decision tree models\n",
    "grid_search_dt.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8258411580594679"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best decision tree model test score\n",
    "grid_search_dt.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try SVM classifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# SVC pipeline\n",
    "pipeline_svc = Pipeline([\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('clf_svc', SVC(probability=True)),  # we need the probability scores later\n",
    "])\n",
    "\n",
    "# here we are trying three different kernel and three degree values for polynomail kernel\n",
    "# in total 5 different combinations\n",
    "param_grid_svc = [\n",
    "    {\n",
    "        'clf_svc__kernel': ['linear', 'poly', 'rbf'], \n",
    "        'clf_svc__degree': [3, 4, 5],  # only for poly kernel\n",
    "    }\n",
    "]\n",
    "\n",
    "# set up the grid search \n",
    "grid_search_svc = GridSearchCV(pipeline_svc, param_grid_svc, cv=10, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                                                         Pipeline(steps=[('num_imputer',\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         ('scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['Age',\n",
       "                                                                          'SibSp',\n",
       "                                                                          'Parch',\n",
       "                                                                          'Fare']),\n",
       "                                                                        ('cat_pipeline',\n",
       "                                                                         Pipeline(steps=[('cat_imputer',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('onehot',\n",
       "                                                                                          OneHotEncoder())]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Embarked',\n",
       "                                                                          'Pclass'])])),\n",
       "                                       ('clf_svc', SVC(probability=True))]),\n",
       "             param_grid=[{'clf_svc__degree': [3, 4, 5],\n",
       "                          'clf_svc__kernel': ['linear', 'poly', 'rbf']}],\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model using the full pipeline\n",
    "grid_search_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8230829420970267"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best test score\n",
    "grid_search_svc.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try random forest classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# rf pipeline\n",
    "pipeline_rf = Pipeline([\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('clf_rf', RandomForestClassifier()),\n",
    "])\n",
    "\n",
    "# here we are trying 2x3 different rf models\n",
    "param_grid_rf = [\n",
    "    {\n",
    "        'clf_rf__criterion': ['gini', 'entropy'], \n",
    "        'clf_rf__n_estimators': [50, 100, 150],  \n",
    "    }\n",
    "]\n",
    "\n",
    "# set up the grid search \n",
    "grid_search_rf = GridSearchCV(pipeline_rf, param_grid_rf, cv=10, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.16 s, sys: 14.5 ms, total: 4.18 s\n",
      "Wall time: 4.18 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                                                         Pipeline(steps=[('num_imputer',\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         ('scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['Age',\n",
       "                                                                          'SibSp',\n",
       "                                                                          'Parch',\n",
       "                                                                          'Fare']),\n",
       "                                                                        ('cat_pipeline',\n",
       "                                                                         Pipeline(steps=[('cat_imputer',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('onehot',\n",
       "                                                                                          OneHotEncoder())]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Embarked',\n",
       "                                                                          'Pclass'])])),\n",
       "                                       ('clf_rf', RandomForestClassifier())]),\n",
       "             param_grid=[{'clf_rf__criterion': ['gini', 'entropy'],\n",
       "                          'clf_rf__n_estimators': [50, 100, 150]}],\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# train the model using the full pipeline\n",
    "grid_search_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best dt score is:  0.8258411580594679\n",
      "best svc score is:  0.8230829420970267\n",
      "best rf score is:  0.7950117370892019\n"
     ]
    }
   ],
   "source": [
    "# best test score\n",
    "print('best dt score is: ', grid_search_dt.best_score_)\n",
    "print('best svc score is: ', grid_search_svc.best_score_)\n",
    "print('best rf score is: ', grid_search_rf.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Age', 'SibSp', 'Parch',\n",
       "                                                   'Fare']),\n",
       "                                                 ('cat_pipeline',\n",
       "                                                  Pipeline(steps=[('cat_imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehot',\n",
       "                                                                   OneHotEncoder())]),\n",
       "                                                  ['Sex', 'Embarked',\n",
       "                                                   'Pclass'])])),\n",
       "                ('clf_dt',\n",
       "                 DecisionTreeClassifier(criterion='entropy', max_depth=3))])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the best model\n",
    "# the best parameters are shown, note SimpleImputer() implies that mean strategy is used\n",
    "clf_best = grid_search_dt.best_estimator_\n",
    "clf_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.7988826815642458\n"
     ]
    }
   ],
   "source": [
    "# final test on the testing set\n",
    "# To predict on new data: simply calling the predict method \n",
    "# the full pipeline steps will be applied to the testing set followed by the prediction\n",
    "y_pred = clf_best.predict(X_test)\n",
    "\n",
    "# calculate accuracy, Note: y_test is the ground truth for the testing set\n",
    "# we have similar score for the testing set as the cross validation score - good\n",
    "\n",
    "print(f'Accuracy Score : {accuracy_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " [[51 23]\n",
      " [13 92]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the confusion matrix\n",
    "plt.style.use('default') # use default style for confusion matrix plots\n",
    "\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "# the default confusion matrix with default label order (ascending order, 0, 1, etc.)\n",
    "# by default the default True Positive is in the bottom right quadrant\n",
    "#matrix = plot_confusion_matrix(clf_best, X_test, y_test) # the default confusion matrix\n",
    "\n",
    "# plot the confusion matrix with preferred label order and label name\n",
    "# true positive in the left upper quadrant\n",
    "class_names = ['1: Survived', '0: Died']\n",
    "matrix = plot_confusion_matrix(clf_best, X_test, y_test, labels=[1, 0], display_labels=class_names)\n",
    "\n",
    "# disp.confusion_matrix returns confusion matrix as an array\n",
    "print(f'Confusion Matrix: \\n {matrix.confusion_matrix}' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy Score : 0.7821229050279329\n",
      "Our Best Accuracy Score : 0.7988826815642458\n"
     ]
    }
   ],
   "source": [
    "# our model is better than the base line - good\n",
    "print(f'Baseline Accuracy Score : {accuracy_score(y_test, baseline_pred)}')\n",
    "print(f'Our Best Accuracy Score : {accuracy_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision-Recall Trade-off and ROC/AUC\n",
    "I chose the best model based on accuracy score above. In classification, the final prediction for a data point actually has probability scores. For example, for one person in this Titanic dataset, a prediction looks like `[0.35, 0.65]`, which means the predicted probability for this person to be 0 (died) is 0.35 and the predicted probability to be 1 (survived) is 0.65. The default decision threshold is 0.5 for decision tree classifier, therefore this person is predicted to be 1 (survived). However, if we change the threshold to be 0.7, then the same person would be predicted to be 0 (died). \n",
    "\n",
    "Changing the decision threshold often leads to changes in precision and recall. Increasing precision often decreases recall and vice versa, which is called precision-recall trade-off. Given a specific context, you may favor precision over recall or the other way around. \n",
    "\n",
    "Receiver Operating Characteristic (ROC) is another metric to evaluate classifier output quality using Recall (True Positive Rate) and FPR (False Positive Rate). For classification problems with very imbalanced data (such as the current COVID-19 testing data, way more people are negative), the default threshold can result in poor model performance. **ROC/AUC is often a better metric than accuracy for imbalancd data.**\n",
    "\n",
    "Next, I show the AUC scores for the three different models and plot the ROC curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.88135593 0.11864407]\n"
     ]
    }
   ],
   "source": [
    "# get the probability score the decision tree model\n",
    "# for each prediction, we have two probabilities for two labels 0 means died, 1 means survived\n",
    "y_pred_proba_dt = grid_search_dt.best_estimator_.predict_proba(X_test)\n",
    "print(y_pred_proba_dt[0])  # 0.881 died, 0.118 survivied\n",
    "y_scores_dt = y_pred_proba_dt[:, 1]  # this is the score of positive class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the probability scores for svc and random forest\n",
    "y_pred_proba_svc = grid_search_svc.best_estimator_.predict_proba(X_test)\n",
    "y_scores_svc = y_pred_proba_svc[:, 1]  # this is the score of positive class\n",
    "\n",
    "y_pred_proba_rf = grid_search_rf.best_estimator_.predict_proba(X_test)\n",
    "y_scores_rf = y_pred_proba_rf[:, 1]  # this is the score of positive class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score for dt is 0.8463320463320464\n",
      "AUC score for svc is 0.8648648648648648\n",
      "AUC score for rf is 0.8880952380952379\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17c27eac0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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41Ib+ZRgGrowM8g8eIP/AQRwHD5B/4AD5KQdx5+Ud+waACRO7rGacTjcGfp9yI4Db7WZz+lZcRtk611ZZLTS/bBRN27ep5Mp8ExBdTrm5uQQHBxc5FhwcjMPh8Ok+cXHh3l7cownOzS/TeRXNZDIRExNGeGjZxnZaLGbCw0OIjQ0vcjwtLQ2AhIR4DhzYg9vt5pJL+hQ5x+12k55+gH37dtGqVUvi4w8PmB45cgQAycl7sFg8A7gvv3wQs2d/wqBB/WjXrh0XXHABl112GdHR4YSHh3jP27NnB61bt6JmzWjv/c46qytPP/0kFouL0NBg6tc/hRo1oryfR0ZGEBJiKfZ1FFb4GQWys0MBaN68sff4jh3bWLToOz7/fLb3vPz8fLp160ZwMOzfv49HHhmN2Xz4f4h5eXkcOLC31OdHRYWWWpdUf2q/wKc2rByGy4UjJYW8/cnYk5Ox7z/0KzmZvP3JOA4cwO3j/2clMNT16ex8dn29iC7dj391o4oUEAE2JCSkWFh1OBzYbDaf7pOSkl2mHliAKSPPYs/B8g8hMJtNhIeHkJ1tx+0u278668SH48hz4Mgr218YLpeb7Gw7qalF61y2bDkACQn12LBhPREREbz11nvFro+Pr4XLZeBwOIvdAyA7247L5SY1NRurNYwPPviUP/74nV9++ZnXX3+DDz/8iHfeeb/IeWAmP99V5H5paZ7XKSmZ5OY6AHORz10uN1lZeSXWUFItBdLTcwHIyzt83OHIZ+jQ6+jT56Ii14eE2Dh40LMKxYQJk4oNV4iKii72fIvFTFRUKBkZubjKOAxEqg+1X+BTGx4ft8NBfspB8g8cPNSLeoD8gwe9Par5qSng9uHP1WwmKDaWoPgamMPDMHHs/5+aTCasQWac+W6qwaJHAmTn57AhbTMAp0QmYrOEAOA2DFIz7aRm2nG6DrdVUGgkHS4dcNT/R1e0o3VoFQiIAJuQkMCBAweKHDtw4AC1atXy6T5ut1HmMBlsNdMg4djLOJSmvEtP+LpMhdttFLvmiy/m0rx5C2rVqkNOTh5ZWVm43ZCYWA+ATZs28sYbr/LQQ+OoW/cUliz5hfx8l7fXeezY0SQltSAmJtZb06+//sK+fXsZOPAyunY9i+uuu5H+/Xuzbt0G75+p0+mmXr36LF68iLw8h3dM6fLly4mJiSUsLBK328Awitdc0tdx5OdH/vkU/A/N5XJ7j59ySgN27dpJnTr1vOe9/PJUTjmlAf36DSA2No7k5GS6dj0L8PTOjhv3EFdfPZRWrUr+8Ujh+0vgUfsFPrVhyVw5OTgLAmnKQZwHPQG14JgrI8On+5mCgrDGxRMUH481Pp6g+BqHXh/6PSYWk8Xi0z21jFb1syV9O18umwbAfR2vp2ZwHRb+tYOFf+0kJ9gJ8Z7zGteNYsA5jTi3U33S0nKqXfsFRIBt27Ytr7/+OoZhYDKZMAyDv//+m1tuucXfpfldVlYWBw8ewDAgPT2NL7/8nEWLvuW5514CPBO6unY9k0cffZi77/4fZrOFSZOeICoqisjISHr16sMbb7zKyy+/wCWXDOS//5bzyy8/MnTodWzYsN77HLfbzUsvPU9cXDzNmjVn4cIF2Gw2TjmlPtu2bfGe16tXH9588zWefnoCV189jB07tjFjxnQGDry8SoZlXHHF1YwceSNJSS0588xuLFnyEx999D5Tp74CwJVXXs1rr71CTEwcDRo05O233+C//5ZTv/4jlV6biEhZHR5/ehBnSkEwLehB9bx25+b6dE9zaKg3jBYJpnGe3y1RUX4ZPif+s3jZTv76dwt2h8t7LKl+DP3ObEhSg1iCgizV9r+Jahtgk5OTiYyMxGaz0bt3b6ZMmcKECRMYPHgwH374Ibm5ufTp0+fYNzrBvfDCFF54Ycqh8bOxNGuWxNSpr9K2bTvvOY888hjPPfc0d955GxaLha5dz+Duu/8HQGRkJM888zxTp07h008/pG7dRMaNe4KmTZsXCbDdup3D8OG38OKLz5KScpD69Rvy1FNTiIqKKlJPWFg4U6a8wNSpU7jhhiHExMRy+eVXMXTo9VXy59GqVWseeeQxZsx4jZdfnkpiYj3GjZtAu3YdALjqqqHk5OTwzDMTyM7OJinpNJ599sViX4eIVB1XTg7pP/9YrMfQbDaRbgsiLy+/zD89C1xGoR7VgzhTDmLk5/t0B0tk1KGeU0/vqbVGDYLiDr2Oj8cSFlZJtVcPZZ1Zf7LbeGCH9/Wvq/ZiOGIAaNUojovPaEizU2L8U5iPqsVOXADNmzfnnXfe8e7E1bx5c5566invMlkrVqxg3LhxbNq0iebNm/Poo49y2mmn+fSME20nLqlcasPApvYLHAe/msfBOZ/5u4zqzWzGGhNb5Mf7RX7MHxeP+YjJztVBVX0f5jpzeeTXieQ6feuVPtnlrTqddnWbcPGZDTm1TvGOnOq8E1e16YFdt27dUd+3adOGOXPmVGVJIiJSBbw9ryYT1piYIp+ZzaaToPfVwxQcUqHjT08me7OTFV59ZDaCGX3ZOTSpU8PfpZRLtQmwIiJycguqUYNTn3rG+1696FIeQ5Iuo054bX+X4Xf7U3NY8t8eVm9N9a7AazaZaN0ojjNb1aZl3fqEWgN3iToFWBERETlh1AmvzanR9f1dht9s2ZPBl79u5Z8NBas3xWC1mOjWpi59utanZkzghtbCFGBFRAKYO9+BY9cu8g8kE6gbHTn27fV3CSIByTAMsvOcpGTkkZyWx4//7mLllhTv58FWM+e0q0ufrg2IjQzxY6UVTwFWRCRAuPNyse/YQd72bdi3bcO+Yxv23bvB5Tr2xSISUNyGQWa2g5RMO2mZdlIObTKQmplHaqH3+SUMrwkJttC9QyK9OtcnOrz6Te6rCAqwIiLVkCsryxNUD/3K276N/H37oHosHFMpIjp09HcJIlXC5XaTnuXw7nyVcmQwzbCTlmXH5eMExrAQKxd0qscFnU4hoozb0gcqBVgRET8yDANXepq3V7UgtDoPHjzqdUE1axHSoAG2+g0Iqd+A4Lp1A3qWuslixRIR4e8yRI6b0+U+osfUTsqhcFrwKy3LXq5/i0aEBhEXGUJsZAixUTZiI0MOv48MoUZ0KEFWc8V/UdWQAqyISBUxDIP8A8meXtVCYfWoW36aTATXqVskrIacUv+EX5RepDqy57sKhdPDPaaFA2tGtsPn+5qAqPBgbxCNi7QRGxVSLKAGWQP3H6kVTQFWRMQHrpxsXFnZZTrXcNix79xxOKzu2I47J6fU801WK8GJ9bA1aEDIKQ0IadCAkMR6mENOrMkXItVRrt1Zao9pSoYnsGbnOX2+r9lkIiayIJzaigTSuEhPL2p0RDBWy8nRc1pRFGADnNPpZObMN/nmm/kcOLCf2Ng4zj+/B8OHj2DWrHf4+usv+eyzL4vtZbx//z4uvfRipk9/i9NOawXAr7/+wgcfvMv69WsJCgqidet23HzzbZx6aiN/fGki1U7O+nXsnDwJ3Me/JqkpJISQU+p7e1VtDRoQXKcuJqv+WhapSIZhkGN3kppRwljTQhOjcu2+T4a0WkzERBzqJT30I/3DvaaHwml4MGaz6dg3E5/ob8oA98orL7B06R888MAYEhPrsWvXTqZOncyOHTu49dbbmTnzTVavXkXLlq2KXPf99wtJTKznDa8ff/wBr732EsOH38J9943G4XDw/vvvMHLkTbz66gzq12/gjy9PpFpJW/RducKrOSzc06taKKwG1UrAZFaPi8jxcBsGWTn57D14+Ccb3/+zk4WZWd6gmpplx5Hv+/dtsNVMbNSRPaYhxBTqOY0IC8JsUjj1BwXYADd//peMHj2WTp26AFCnTl3uu+8hRo68kfvvH0Pjxk358cdFxQLs4sULueCCCwHYtWsnr7zyAg8++AgXXtjXe84jjzzGiBHX8dZbrzNu3BNV90WJVEPuvFyyVywHILJzFyJPP/OY15gsZoLr1MUaF1/spyAicnRut0F6tsPbQ1qkxzTD8z4ty47TZWAKT8PW0nPdkv/2YmTnHfXeoSEWbw9p0XGmhwJrVAhhIVZ931ZjCrABzmw28fffS+nW7RzMh3pzWrVqzbvvfkxMTAw9e17IvHlzue22O73X7N27l9WrV/LQQ+MAWLhwAVFR0fTs2fuIe5sZM+ZRgoNPzDXkRHyR9e8/GPn5AMT0vJDQRo39XJFI4HK63KRl2YuMMz1yOam0TAfuckzVDwuxEhce4Q2nR443jY0MITRE8SfQqQVLkevMZW92crmvt1pMHHCHkpmRi9NVtm/A2uE1fd6X+PLLr+KNN17lp59+4Mwzu9GpUxe6dDnDO271ggsuZPr0l9iwYT1NmzYDPMMHmjZtRoMGDQHYuHEDzZu38Abgwho2PNWnekROVJl//gFAUI2a2DQuvNx8+bu1PH+Piv85XS4yc/LJzHGQlevE7jJITskmPctBZo6DjJx8snPzj71xXJhndn5h4aFBRIUFERUWTGRYEFHhwUSFBeOwmll86D+re65sd1JvJXuyUIAtQa4zl0d+nUiuM7dKnxtqDeXxMx/0KcRed92N1K2byJw5n/DFF3OYO/czwsLCufPOe7nookuoXbsOrVq14aefvvcG2MWLv6Nnzz7ee2RlZRIbG1fhX4/IicKVlUX2qpUARHbpqh8rlpO//m6VasAMRB36BZR3XQ0XkHroFwD2Q7/kpKMZBCeAXr368MorM5g371vGjn2CU09txMSJj7N27RrA0wv7ww+LANi7dw/r16/lggt6ea+PioomM/Mo61CKnOSy/l7m3a41snNXP1cTuPZmJyu8SqUKtYZSO7ymv8uQKqAe2BIU9IQe7xCCyKjKHUKwceMGvv76S26//W4AoqNj6NWrN+ef34MrrxzA338vJSmpBd279+SFF6awffs2fvnlR9q0aUfNmrW892nevAUfffQehmEU61latOg7/vjjV+94WZGTUeZSz/CB4Dp1Ca5Xz8/VnBiGJF1GnfDaRz2nPH+PSgkOLSOVmZNPxqEf4Wfm2MnMzicju+C9A4fT95n6VovZ8yP9cM+P9CPDPD/SjwoLIvLQwvwJNSPJzsqrkjYsz1A8CUwKsKUItYYe1xgaq9VMbGw4qeZsnOX4S6EsXC4XH300iwsv7EOzZkne40FBQdhsNmJiYgGIjY2lY8cu/PLLj/z00w/07duvyH26d7+A119/me++W0CvXr2L3P/DD9+jdu06lVK/SCBwpqeRc+inGRo+UHHqhNc+5t+xVfH3aKBzGwYZ3pn6RyzCn3F4YpTTVdqfn+XQL1uxT0KCLUWWkCq2CH+UjXDb0Wfqe9vQqjaUiqUAG8CaN0/izDO78eCD93LLLbfTunUbDh48yDfffInD4eC887p7z+3Z80I+/PA9du7cwXnnPV/kPrVr1+H6629i4sTHSU09yJlnnk1mZgbvvvsWu3btZPz4CVX8lYlUH5nL/qJg0/LIzl2O+37HO0E0kO3J3uvvEgKKy+0mPctxePmojLxii++nZTlwuX3v2Qy3Wb2htMgyUlGHg6pm6kt1pv86A9xjj01k5sw3mTHjNfbv34vNFkqXLqczbdrrhIWFe88799zzeeaZp+jSpStRUVHF7jNs2A3UqpXAp59+xJtvvkZISAht2rTl1VffJDFRPzKVk1fB6gMh9RsQfJw/jdAkJimQ7zy8jFRJPaapmXmkZzsoxypSRIYFFVs2yhtSo2zERoQQEmyp+C9KpAopwAY4m83GiBEjGTFi5FHPCwsLZ/HiJUc9p3fvi+jd+6KKLE8koOUfPEjexg1AxUze0iQmjxN9oo0931W8xzTrcEBNzcwjIyff5/uagOiI4KI/yo8KITai4LWN2IhggqwKp3LiU4AVESlF5l9/el9Hdu5cofcuyySmE1UgT7TJtTsPL7ZfpMf08AL82XlOn+9rMZuIORROi/WYHnodFR6M1aLFg0RAAVZEpFQFwwdsjZsQVKNiewzLMolJqo5hGGTnOYtuW1qox7QgpOY5XD7f22oxExcZQkyRLUsPjTWN8ryOCgvGbNYEQZGyUoAVESmBY99e7Nu2Alr7NdC5DYPMnHxvr+mRPaYpmXbSMu3lWkYqOMjsHWsaV2gSVOGJURGhQVq9QqSCKcCKyEnBlZtL3sYN5G3ZjDv/2OMP7Tu2e16YTER28n34QEmrDWgWfsVzuw3Ssx2eiVAZJSwldehXeWbqh4ZYj+gx9SwdVfhH/KEhR19GSkQqhwKsiJyQnJmZHFy7kv1/Lyd77VpPIC3HlO7Q5klYY2J8ukarDVQMp8vNvpQctu5M40BaLikZxXtO07McuMvRrhGhQYUmQh0KpRGeHtS4yBBiIrSMlEh1pu9OETkh5KemkrthHbnr15O7fh2O3btKPM8UHIw5tGwTiMyhocRfMsDnWo612sCJPgu/LBz5riIz8wv3mBb8iD8j2+HzfU1A1KEdoLxLSUWFFPmRfkxECMFBmqkvEsgUYEUk4BiGQf6BZHLXHw6s+cn7SzzXHBpGaNOmhDZrTmiz5tjqN8Bkrbq/+kpabSCQZ+GXRZ7DeTiIZhTtMS0IqVm5vi8jZTaZiIkMPrxsVMFY06jDP+KPiQjRTH2Rk4ACrIhUe4Zh4Niz+3Bg3bAOZ2pqiedaIqMIbdaMiKQkandujz0qnlJ30awCJ9JqA4ZhkGN3FpoIVXScaUFIzbWXbxmpI5eOqhFt45Q60QSbISosmOhwzdQXEQ8FWBHxu7zt20rsQXWmpBwKrOtxZWWWfHFMNKbGDTE3aoCpUUOoGY/dZMJlMeGOMsjM2IHTVY7tjI5DIE7WMgyDzNz8EpeOKrw7lCO/HDP1reYSl44qvFtURFgQ5iMmQ1mtZmJjw0lNzcZZjhUCROTEpQAbwC67rB979+7xvjeZTERERNK2bTvuvvt+EhIqf5H0PXt2c/nll/DJJ19Qp07dSn3WhAnj+frrL0v87IcffsdahT8WNgyDOXM+ZdCgy6vsmSciwzA4OOczUuaX3K4lSYuysrOmlV21gthVK5jMcAuwx/Nrx++wo9LKDVhut0FGjsMTRDOK9pwe7kl14CxHV3VoiKX44vuRRX/EH27TTH0RqVgKsAHujjvupUePngC43W62bt3MM888xYQJ43nhhVf9XF3F6969J3feeW+x41UZXgH+/fdvnn12kgLscTAMg+SPPiBt4bdHPS84sR6hzZoT1qw5ybXDmblhZhVVePyqYrKW0+UmPctRykQoz/v0LEe5lpEKt1mL9ZgW7jWNjdRMfRHxD/3NE+AiIiKIj6/hfV+zZi1uvPEWHnvsEbKysoiIiPBjdRUvJCSkyNfrL0Y5lu2Rwwy3m/3vv0f6D4sBCK53CnVvuQ1TiK3IeeaQECxhYd73B9K3e18faytWq8VEZFQomRm5VT6EoMDxTtbKd7pIzXKQmlF8hn7Bj/gzshyU56uLCi80GSqqcM+pzbtrVIhm6otINaUAewIKCgoCwGz2zMTdsmUzL774LP/9twKXy0lS0mncf/8YGjY8lb///osnn3yU6667kddeexmHw8HQodfRsmVrnn56AsnJyZxzznmMGTMes9mM0+nkxRefZcGC+YSGhnHNNdcVeXZGRgavvPIiv/zyIw6HnW7dzuHOO/9HVFSUz88qjyVLfubNN19l69at1K1bl5tuupVzz+0OwKhRN9O4cRN+/XUJLpeT9977mMzMTJ59dhJ//fUnsbFx9O3bj2uvHY7FYsHpzOfhhx/m22+/w+Gw06FDJ+67bzROp5M77rgFgG7dOvHCC6/SoUOncrbWycdwu9n3zltk/PIzACENGlLv7vuw+PiPrWNNjvKOnzRXz/GTdoeLlMw80o6YnV+4JzUzx/eZ+iYTxESU3mMaFxlCdEQIQVbN1BeRwKUAWwpXTg6OQuNLfWWxmLFG2sjJzMNVxnFlwbXrFOltKo9du3by7rtv07XrmYSFheF2u3nggbvp3Lkr9977IFlZWTz77CReeeUFJk16DoADB5L56acfmDZtOr/88jOvvvoiTZo05aGHxpOensbDD9/POeecz7nnns+bb05nyZKfmTjxWaxWKxMmjC/y/Iceug+7PY+nn34OwzCYMmUSTz45nokTn/X5Wb5atmwpY8b8j9tuu4PTTz+LX3/9mbFjRzN9+tskJbUAYP78eTz77DSCgoIJDQ3jjjtupUmTprz11iwOHDjAM888idls5rrrbuSTTz5i6dKlTJ36EkFBIUye/BQvvPAs48dPYMKEpxkz5n4+//wboqKij6vNAklJu0v5wnC5cH04B+PvFQCYGtTDddNVbHelQHrKMa8PhMlRhmGQa3eVsHRUoUlRGXZyyjlTPyai5B7TgoAaHRGMpZz/ABQRCRQKsCVw5eSw5cH7cOfkVOlzzWFhnDpxsk8hdvLkp3juuacBcLlcWK1BnH32Odxxh2ecqN1uZ8CASxk48HJCDy3e3qfPxbz//jveezidTkaNuov69Rtw6aW1efnlqQwadAWtWrUGoEmTZmzfvhXDMJg3by6jRt1Fu3YdALjjjnv43//uAmDjxg38++/fvP/+Z9Sv3wCAsWMfZ8iQy9i+fatPzyrNt99+zQ8/LCpy7PHHJ3H66Wfy2Wcfc955PbjiiqsBqF+/AWvWrOKDD97l0UefBODMM7vRunVbAP7660/27t3Da6+9jdlspn79howceZe3l3jPnj2EhIRQp05dwsMjGTNmPOnp6VgsFiIjowCqxXCGqnK8u0uZ3Qa9l2TQdIcdgF01g/i8Sx75q9+syDIrlWEYZOc5SSn2I/2i7+0Ol8/3Djo0Uz/uiAlQhXtOI8ODi83UFxE5GSnABrjhw0dw7rndycnJZsaM19izZw8jRowiOjoGgNDQUAYMuIxvvvmKtWtXs337VtatW0dcXFyR+9StmwhAyKExiIVXFAgJCcHhcJCWlkZaWipNmzb3fpaU1NL7etu2LURERHrDK0CDBg2JjIxi69at3vG4ZXlWabp1O4dbb72jyLEaNWp6n9+//6VFPmvVqi1fffWF933t2oeftW3bFjIy0rnwwnO9x9xuN3a7nfT0NAYMGMTChQu46KKetG/fkXPOOZ++fS8utbYT3bF2lzoai8ugzy/pNN7ladvtCUHMOzcGp7V8YawyJke5DYPMbMcRS0fleXtMU7M8x/LLMRwhJMhyxESoor2mcVE2zdQXEfGBAmwJLId6Qo93CEFUpI2MSh5CEBsbR716pwCensgbbxzGgw/ey2uvvY3VaiUnJ4ebbhpGdHQM3bqdwwUXXMj27Vv54IP3itznyFn8R/sfaeEJTAXjbQGCg4NLPN/tduF2H+6R8uVZRwoLC/d+vUcq6flHPrvwOS6Xi/r1GzJx4pRi14WHRxAfH8fixYv56qsF/PzzT0yfPo3vvvuGl156vcz1nqiONYGqMCM/H9fbH2Ds8gw9MLVoSqNrB3NXof92fOXr5CiX+/BM/SN7TQt2i0rLspd7pn5MkSWkbMV6UkNDLAqnIiIVSAG2FJawMEIbNS739VarmcjYcJxVuAB3UFAQDz74MCNGXM9HH81iyJBr+eefZRw4kMzMmR96g+PSpb+XaxZ9TEwMcXHxrF27iiZNmgKwfv1a7+f16zckKyuT7du3Ur9+Q8AzgSw7O5v69RuQlpZ23F/j0dSv34BVq/4DrvIeW7nyvyI9woWdckoD9u3bS0xMrLd3eOnS35k//0sefvhR5s//ktjYSHr06Mm55/Zg5cr/uOWW60lNTTnpw0hZd5dy2+3sev15nGs3AhDevgN1br4V83GE1yPlO92kZRWfBJWe5SAjJ5/9qTmkZdkpz8IRkWFBxSZBFdktKiKEkGDN1BcRqWoKsCeYFi1actFF/Xn77Te58MK+REdHk5uby88//0BS0mn89deffPbZx4SH+768lslkYtCgy3njjekkJNQhMjKSF1981vt5gwYNOf30M3n88XHcc8/9GIbBs88+Tbt2HWjUqAl///1XxX2hJbjiiiHcdttwPv74A844wzOJ66efvufZZ6eVeH6XLqdTu3ZtHnvsEUaMGElWViZPP/0knTp1wWKxkJWVxcsvv8C4cTYSEury3XdfU6tWAtHRMd7xxGvXruHUUxsREhJSqV9bZSvL5CxfJ1C5cnPZ/cJz5G5YD0BEpy7UufFmTD6s2WvPdxWapV+0x7RgYlRGeWbqA1ERwaX0mBaE02CCrAqnIiLVkQLsCWjEiJH88MMiXn75BcaOfZzrrruRKVMm4XA4aNy4Cffc8wATJz5Ocglbdx7LsGE3kJeXx7hxD2GxWLj++pt49tlJ3s8ffvgxnnvuae688zbMZjNnn30ut99+T0V+eaVq2bIVjzzyGDNmvMYrr7xA/foNeOyxp+jYsXOJ51ssFiZOfJbnn3+Gm2++ltDQMM4//wJGjboTgMsuu4KMjBQeffQRMjIyaN68BRMnTsFisdCoURM6d+7KrbfewPjxE7xLdVWU/IMH2P3yNJwHD1bofUtiYJCdn4NxjNVEw4CbCur7fCKbTEcPd+78fAx7HgCRZ5xJ7euGY7IcvibX7iw+1rTg/aHdorLzfJ+pbzaZiI0MJi7KRkJ8OBE2KzHhwd4e04KZ+laLZuqLiAQqk3ESrcienFzKXuqVQHt4Bz5/tmHqwu9I/nBWlT6zsmSf1omtHXqTmpNfKKTmkWv3faa+1WIqcRJU4d2iosKCMZtN+h48AagNA5/aMLD5q/1q1ow85jnqgRWpjgpNPIu7uF+lPiotL4Pf9iwFoHWNFkQElT68xMAgmFCcTk8Pap7DRZ7DSa696O8Fk6GSg2NYa28Iv28v9Z4FgoPMxWfnF15OKiqEyNCgk378sYiIKMCKVG9mMzUGXHrs845DZvp2fl+2BoC2LfpgNmoVXXQ/005qhud9Wpa99G1ZrYd+hRf/KDTEekSPqWfpqMJBNTREy0iJiEjZKMCKnKAKT8xyud1k5eaTke0gM6fQ7zl2UhwH4NCywFM/XYGRHePTcyJCg47oMT3Ua3pot6iYCE84FRERqSj6v4pIgFuzNYXF/+zCnn942IHL5GB77Oe4zUeZoW8GjrIYhQmICg8uFE49oTQ2MsQzGSrK83twkGbqi4hI1VKAFamG7Lt3ARx1ySnDMJj/+zZm/7i52PoBpvA0bPG+LS8VRAhXde9EQnQUsYd6TjVTX0REqiMFWJFqxrFvHxm/LgEgslOXEs/JtTt548vV/LPhAODZDerUulHez/OsbnYdet2Mc6gdlkBUWDCR4UFEhQUTHmrFbCoaTn3d3UpERMRfFGBFqpmDX34ObjdYLMT361/s810Hsnlp9n/sTckBoH5CBKMGtqZGzOHwuSV9O5OXfQvAJR3blGnXLBERkUChACtSjdh37yLz998AiD77XIJq1izy+V9r9/Pm/DXYHZ7xrme1qs3QC5trHKqIiJxUFGBFqpGDX8wFw8BktRJ30eH1X11uN7N/3MzXf23CZMvGGmKiV+dT6NgsnF05u4rdx9dtX0VERAKJAqxINZG3fRtZf3k2FIg+vwdBsbEAZOQ4mP75Ktbs3I+t7Y+YrJ7tVb/P+o3v//ZbuSIiIn6jACtSTRz8fA4ApuBg4vpcBMCWPRm8NOc/UjLsmMKzveG1rEKtodQOr3nsE0VERAKIAqxINZC7aSPZy/8FIPaCXlijovhp+W7e+3add+erri0SWH7o/CFJl1EnvPYx76uVBURE5ESkACtSDRyc6+l9NYeGEtGjFzO/WcuP/+4GINhq5rq+SSQkOli+zHN+nfDaWllAREROWgqwIsfJnZdHzrq14HYd++QS5B9MIWfNKgBs51zAM3PXs2VPBljyia/p5LLzGlMr1qGJWSIiIocowIocB8feveyc8jTO1JTjvpcRGs7zO2NIsXvCa3j7n8kxO3hn848VUKmIiMiJQ/tEipSTffcudjzzVIWEV4BvI1uTYjcBcG7XaNxmR4nnaWKWiIic7NQDK1IO9h3b2TnlGVxZmQDUvPoaItq28/0+DheffL+Rv7akk2uxERpi5aZ+pxFdM4c/D413PXLCliZmiYjIyU4BVsRHeVu3sPPZybhzssFkIuG6G4g+62yf77M3JYeXvvqPXQfsYLFRr2Y4Iwe1JiE2jC3p273nacKWiIhIUQqwIj7I3bSRXc9PwZ2bC2YztYffRFTXM3y+zz8bknnjy9Xk2l1gyad1ixAuOqMWOeYDbEnXTloiIiJHowArUkaO/fvZ+exkDHseWCzUufkWIjt29ukebrfB3F+28OWvWwEwW51EdPiFjdiZuvzo14qIiIiHAqxIGR38fI43vNa97Xafx7xm5ebz2rxVrNzsmfQVFRbEwN61+HinvdRrNGFLRESkOAVYkTKw79pF5p+/AxBz7nk+h9ft+zKZNvs/DqTnAdC4bhS3DWxNmnsf7PScU9LuWpqwJSIiUpwCrEgZHPxiDhgGpqAg4vr28+na31bu5e1v1pLvdANwfodErurRFKvFTFr64fM0WUtERKRsFGBFjiF361aylv0FQEz3HlhjYsp0ndPl5qNFG1n0t6eL1WoxM+zC5nRrU6eyShURETkp+HUjA7vdzkMPPUSnTp3o1q0bM2bMKPXc7777jj59+tC+fXuuuuoqVq1aVYWVysksefZnAJhCbMT27luma1Iz7Tz9/j/e8BofZWPM0I4KryIiIhXArz2wTz/9NCtXrmTmzJns3r2bBx54gLp169K7d+8i523YsIF7772Xxx57jA4dOvD2228zYsQIvvvuO0JDNT5QKk/GmrVkrfAsDxDbsyfWyKhjXrN+RxqvzF1JerZnJ62WDWMZ0b8VEaFBlVqriIjIycJvATYnJ4dPPvmE119/nZYtW9KyZUs2bNjArFmzigXYJUuW0KRJEwYMGADAPffcw6xZs9i4cSOtW7f2Q/Vystj+/ocAmMPCiO3V+6jnGobBomU7+WjxRlxuA4CLzmjAwLMbYTabKr1WERGRk4XfhhCsXbsWp9NJ+/btvcc6duzI8uXLcbvdRc6NiYlh48aNLFu2DLfbzezZs4mIiKB+fU14kcqTvXo16Sv+AyC2V28sYeGlnmvPd/HGl6t5f+EGXG4DW7CFkQNbc+m5jRVeRUREKpjfemCTk5OJjY0lODjYe6xGjRrY7XbS0tKIi4vzHu/bty+LFy/m6quvxmKxYDabmT59OtHR0T4902w2VVmYsFjMRX6XwGIYBslzPGNfLZGR1LjwQizWkttyf2oOL3y6gu37sgCoEx/GnZe3pW6N0gNvAavFVOS1tZRniO/0PRj41IaBT20Y2Kpz+/ktwObm5hYJr4D3vcPhKHI8NTWV5ORkxo4dS9u2bfnggw8YPXo0c+bMIT4+vszPjIsLx2Sq2t6wqCiN0a3ukn/8meSffwbj8DG3w0HOhg0AnHLZIGrUrVHitX+t2ceUWcvIys0H4Mw2dbjzyvaE2Uoe75rjyGVX5uFtYtOMVO/ryKhQYmOPHXrFN/oeDHxqw8CnNgxs1bH9/BZgQ0JCigXVgvc2m63I8cmTJ9OsWTOGDBkCwOOPP06fPn347LPPuPnmm8v8zJSU7CrtgY2KCiUjIxeXy33sC8QvDLebDdNexjjiv8UCQbGxhJ91Dqmp2UWOuw2Deb9sYfaPmzEAkwmuOL8Jfc9ogD3XgT23+P1y83N56OcnyXHmlviszIxcUs3ZJX4mvtP3YOBTGwY+tWFg81f7laUzx28BNiEhgdTUVJxOJ1arp4zk5GRsNhtRUUVneq9atYqhQ4d635vNZpKSkti9e7dPz3S7Ddxu49gnViCXy43TqW/a6spwOr3hNbh2HayFhq6Yg4JocNkADGtQkTbMyXPyxper+XfjAQAiQoO4pX9LTmsYh8tlUKQrt5CdGftKDa+h1lBq2Grov5VKoO/BwKc2DHxqw8BWHdvPbwG2RYsWWK1W/v33Xzp16gTAsmXLaN26NWZz0bEWtWrVYtOmTUWObdmyRSsQSIWK6dGTmPO7e99brWZiYsOL9L7uTM7ipdn/sS/VE0Qb1I5k5MBW1Ij27ccrR24bqy1jRUREys5vATY0NJQBAwYwfvx4nnzySfbv38+MGTN46qmnAE9vbGRkJDabjSuuuIIHH3yQVq1a0b59ez755BN2797NwIED/VW+nIT+XLOPt+avxZ7vAqBbmzoM7dWMIKvF53tp21gREZHy8+tGBqNHj2b8+PFce+21REREcPvtt9OrVy8AunXrxlNPPcWgQYPo27cv2dnZTJ8+nb1799KiRQtmzpzp0wQuCSyO5P1k/vkHhtNZruudbidZ+WUYT1poSMmBvBRS07d731stJg64Q0lLz+G7pTv4fdU+CIYgm4leXevToUkYO7N3lbmmPdl7j32SiIiIHJPJMIyqHRTqR8nJmVX2LKvVTOyhHz9Xt3EjgWDHpCfJ3bC+Sp+5sEskq5pUzY/x7+s4Sj2wlUzfg4FPbRj41IaBzV/tV7Nm5DHP8WsPrEhpnGlpAJisVkzBIT5d6zbc5LnyfLomPcLC1rrBxz6xAoRaQ6kdXrNKniUiInIiUoCVai36nPOodfU1Pl2zJX07U5dNA4pPlipNJHBrwRvD4O+NB/j2j+04Dw0xOL1lAt07JGI2Hf9izpqwJSIicnwUYOWE5utkqXyni3e/Xc8vK9KBaGzBFm64qAWdmteqvCJFRETEJwqwIoccTM/jpTn/sXWvZ6x0QlwYj9zQlSibRWO3REREqhEFWKl28lMO4sqqugl3AKu3pvDq56u8W8K2a1KDWwa0IrFOVLFduERERMS/FGClWslPTmbHlEm4cz0bBYS1aFGpzzMMg2/+2M6nP27CMMAEDDj7VC46syHBQb6v7yoiIiKVTwFWqg3Hvr3snPw0ztQUAGoMuoyI9h0r7Xm5dicz5q9h2bpkAMJtVm7q15I2jbW+sIiISHWmACvVgn33LnZOeRpXejoANa+8itieF1ba8/YczGba7P/YczAHgFNqRTByUGtqxWh1ABERkepOAVb8zr5jBzuffRpXpmfca60hw4g5v3ulPW/ZumTe/Go1eQ7PlrBntExgWO8kQjRkQEREJCAowIpf5W3dys7nnsGdnQ0mEwnXXk90t3Mq5Vlut8Gcnzfz1W/bALCYTQzu0ZTuHRIxmUyV8kwRERGpeAqw4je5mzay6/kpnglbJhO1h99E1OlnVsqzsnLzmf75SlZtTQUgOjyYWwe0otkpMZXyPBEREak8CrDiFznr17Fr6nMY9jywWKhz0y1EdupcKc/atjeTabP/42CGZ3vZJvWiuW1AK2IifNuiVkRERKoHBVipctmrV7F72lQMhwOT1UqdW0YS0a59pTxrxaYDzF+8mfxDGxH06FiPK7s3wWo5/i1hRURExD8UYKVKZa1Yzp6XX8RwOjEFBVF35O2Et2pT5utznbnszU4+6jl7svd6X3+xZCuGM4Zgq5lreydxRqva5a5dREREqgcFWKkyuZs3sfulF8DlwhQcTOLtdxHW4rSyX+/M5ZFfJ5LrzPXpuTWibYwa1Jr6CZG+liwiIiLVkAKsVJmM3349HF7vupewZs19un5vdrJP4dVwWkmqXY9bL25PRGiQr+WKiIhINaUAK1XH5Vl3NahGDZ/D65GGJF1GnfBCwwEMgz/W7mfRXztxGwYAvdu04NJLkzCbtUSWiIjIiUQBVgJSnfDanBpdHwC7w8Xb36zlj9WZQDShIRZuvPg02jet6d8iRUREpFIowEpA25eaw0uz/2NncjYAiTXCGTmoNbXjwvxcmYiIiFSWcgVYu93OypUrOXDgAGazmVq1atG8eXNsNltF1ycBqqTVAlyOLAAcrny2pG/3+Z6FVxcAWL7xAK/NW02u3QlA56RaXN83CVuw/l0mIiJyIvPp//R//PEHb731Fr/++isOh6PIZzabjTPOOINrr72W008/vUKLlMBS2moB3Q9k0Bo4mJfCe8umHdczflq+mx9/zQHAbDJx2XmNubDLKdoSVkRE5CRQpgC7d+9exowZw7p167jwwgt56aWXaNasGbGxsbjdbg4ePMjq1av5448/uOuuu2jevDlPPfUUdevWrez6pRrydbUAX5ndwfz4RzoQRGRYELf0b0WLBrGV9jwRERGpXsoUYIcPH85NN93E66+/jtlcfAejxMREEhMT6dmzJ/fffz9z585l+PDhfP311xVesASWwqsFuDZ/gXvjX8Tb4riv4yif77U/NYdPf9jEwWQruII4tU4UIwe2Ii5KQ1dEREROJmUKsJ999lmZx7cGBwdzxRVX0K9fv+MqTE4MhVcL2BccQToQbAmi4aFjZfX7qr28/fVmHM5QAM5rV5erLmhGkFVbwoqIiJxsyhRgyzM5KzQ01OdrRI7kdLn55PtNfPfXDgCsFjPX9GrGOW01PEVERORkVaYAO2zYsDLf8J133il3MXLicmZmkLthveeN2VKma9Kz7Lzy+SrW70gDIC4qhJEDW3NqnahKqlJEREQCQZkCbN26dTW7W8rNmZbGzmefxrFnNwBRXc845jUbd6Xz8pz/SMvyrHbRokEsI/q3JCosuFJrFRERkeqvTAF24sSJlV2HnKCMtHR2vPYS+fv2ARDbuy+xvfuUfr5h8MM/u3h/4QZcbs+WsH261mfQuY2wlDCBUERERE4+ZQqwS5cuLfMNO3fuXO5i5MQSmeXC+dIMSEkFIK5ff+IvGVBqb74j38W7C9axZKVnw4KQYAvD+7agU1KtKqtZREREqr8yBdihQ4diMpkwDOOo55lMJtasWVMhhUngOHLXrT3Ze4nOdHLpojTIcQMQP/BS4i8qfWWKA2m5TJvzH9v3eXbrSogLY9Sg1iTWCK/U2kVERCTwlCnALlq0qLLrkABV0q5bselOLlucRkSuJ7zWvOIqYntdWOo9Vm45yPTPV5Gd59kStn3TGtx48WmEhmhLWBERESmuTAkhMTGxTDez2+3HVYwEniN33aqR6mTg4lTC7J7e+pjBg4m9oOTwahgG83/fxuwfN2MAJmDQuY3oc3oDzJo0KCIiIqXwuYsrNTWVV199lfXr1+NyuQBPEMnPz2fjxo389ddfFV6kBIZhUecSO/dLsBtgMhF7zRBqnntBiefm2p288eVq/tlwAIBwm5UR/VvS6tT4qixZREREApDP07offfRR5s6dS2xsLH/99RcJCQlkZ2fz77//cvPNN1dGjRIAEg7kE/v2PMjJBZOJ2jfcVGp43XUgm8dn/uUNr/UTIhh3XWeFVxERESkTn3tgf/vtNyZNmsR5553HunXrGD58OElJSTzyyCNs3LixMmoUPzhyYlZp9mTvpe5+B/1/SAenARYLdW4aQWSnLiWe/9fa/bw5fw12h6f3/qxWtRl6YXOCg8q2uYGIiIiIzwE2Ozub5s2bA9CoUSPWrl1LUlIS11xzjXpgTxAlTcwqTb29Dgb8mEaQC7BYqHvLSCLadyjx3B//3cXMb9YBYDGbuPqCppzXPlGbZIiIiIhPfA6wCQkJ7Nq1izp16tCwYUPWrfMEktDQUNLT0yu8QKl6R07MKk2D3XYu/jkdqwucZqh96y1EtCs5vO5Py+WDRRsAiIkI5raBrWmSGF2hdYuIiMjJwecA26tXL0aPHs3EiRM588wzufvuu2nbti0LFy6kQYMGlVGj+NGQpMuoE1672HH3yrW4fv4IXEBQEHVG3kZsq/Yl3sNtGLw9fw2OfDcWs4m7Lm9L/YTISq5cRERETlQ+B9i7776b/Px8du/eTb9+/ejVqxd33XUXkZGRvPDCC5VRo/hRnfDanBpdv8ixzL+Wsuedj8DlwhRiI/HOuwlr1rzUe/zwzy7Wbk8DoO/pDRReRURE5Lj4HGCDg4N5+OGHyc/PB+Cxxx7jlltuoVatWlitWni+OvNlYlZpMn7/lb1vvg6GgTk0lMS77iW0cZNSzz+Qlssn328CoF7NcPqd1dDnukVEREQK8zlxpqSkcNddd9G+fXvuvvtuAC677DKSkpJ47rnniI7WuMbqqLSJWeE5LvosySAy2+U9Fgxcf+h1/tdT2Gw6/J+JMzXFE17Dw6l39/+wNWxY6jMNw+Ctr9diz3dhNpm44aIWWC0+r9wmIiIiUoTPaWLChAnk5uZy0UUXeY+9/vrrZGZmMmnSpAotTipOaROzGu+0k5icT1SOu8RfpKbjTDno/YVhYImM5JT7HjxqeAX48d/drNmWCkDfM+rTsHZUZXxpIiIicpLxuQf2l19+YebMmTRr1sx7rGXLlowbN07LaAWIwhOzXNm/4v7rGwDM555Z5LywoFCCTEX/EzHZbESf1Y2gGjWP+owD6bl89L1nXeDEGuH0O/PUiipfRERETnI+B1iXy4VhGMWOBwUFkZt77KWXxP8KT8xKta0hGcBkosnQivkHiGEYzPx6LXaHC5MJbrioBUFWDR0QERGRiuFzqujcuTPPPvssWVlZ3mNZWVlMnTqVzp07V2hxEph+XrGHVVs9Qwf6dG3AqXU0dEBEREQqjs89sKNHj2bIkCGcc845NDw0BnLr1q3ExMTwxhtvVHR9EmAOpufx4aENC+rEh9G/W0P/FiQiIiInHJ8DbP369Zk/fz5fffUVGzZswGq1ctVVV9GvXz9sNltl1CgBwjAMZn6zlrwiQwcs/i5LRERETjDlWrg1MjKSwYMH43A4CAoK0l72AsAvK/awcksKAL271KdxXS2pJiIiIhWvXDNrPvjgA7p37067du3YuXMn48aN4+WXX67o2iSApGTk8eFiz9CB2nFhDDhbqw6IiIhI5fA5wM6bN48pU6YwcOBAgoKCAGjcuDGvvvoqM2bMqPACpfozDIN3Fqwj1+7ChIYOiIiISOXyeQjBjBkzGDNmDAMHDvQG1mHDhhEWFsbrr7/ODTfcUOFFSsUIz3HRbLsdV9pPpNhiAMjdsP647/vryr2s2HQQgF5dTqFJooYOiIiISOXxOcBu2bKFTp06FTvetWtXHnvssQopSipH96WZNNrlwM1CDhz5obl867SmZtp5f6Fn6EBCXBgDz250fEWKiIiIHIPPqaVGjRps2bKl2PF//vmHWrVqVUhRUjkictyeF2YzpuDgw79CbMRe0NPn+xWsOpBrd3qGDvRNIjhIQwdERESkcvncA3vllVfy2GOPMXr0aAA2b97ML7/8wvPPP8+1115b4QVKxTN1aEPTW+467vv8turw0IELOp1C03oxx31PERERkWPxOcDedNNNZGZmcs8992C32xkxYgRWq5XBgwczYsSIyqhRqqG0LDsfHBo6UCsmlEHnauiAiIiIVI1yrQN7zz33cOutt7Jx40YMw6BRo0ZERERUdG1STRmGwTvfrCM7z+lddSBEQwdERESkivg0BjYrK4u8vDwAQkNDad26NW3atCEiIoL9+/czcuTISilSjp+RlU1YnrtC7vXH6n38u9EzDaxHx3o0OyWmQu4rIiIiUhZlCrApKSmMGDGCzp0706FDB+666y5vkAX46KOPuOiii1iyZEmlFSrl50xPx/nKW0TkegKsuVHDct8rPcvOrO88S2/VjLFx6bmNK6JEERERkTIrU4CdOHEif//9N6NGjeLuu+9m2bJlTJ06ldzcXG655RbGjRtH8+bNmTt3biWXK77KT01lxzNPwd79APydFIqpS/ty3cswDN79dj3ZeU4Aru/TgpBgDR0QERGRqlWmMbC//vor48aN4+KLLwagY8eO3HXXXWzfvp0//viDsWPHcvXVV1dqoeK7/IMH2Tl5EvnJnvD6Z8swfmsTTleTqVz3W7p2P3+vTwage4dEkhrEVlitIiIiImVVpgCblpZG+/aHe+06dOjAwYMHWbNmDZ9++imNG+vHyNWNI3k/OydPwnnQs8yVuXd3fotbWe77ZWQ7eO9bz9CBGtE2LjtPbS4iIiL+UaYhBE6nE5vNVuRYcHAwY8eOVXithhx797Lz6ae84bXGZVdg6Xnecd3zvW/XkZWbD8D1fZKwBZdrAQsRERGR41a+/UMPUXitfuy7drHj6SdxpqYCUPOqIcT17ntc91y6dj9/rfMMHTi/fSItGsYdd50iIiIi5VWmbjSTyYSpnOMmperYd2xn55RncGVlgslErWuuJebc847rnhk5Dt5dsA6A+CgNHRARERH/K1OANQyDkSNHEhQU5D1mt9u57777CAkJKXLuO++8U7EVSpnkbd3Czmcn487JBpOJhOuGE31Wt+O+7/vfrfcOHbiubxKhIRo6ICIiIv5VpjQyYMCAYj2wiYmJx/1wu93Oo48+yrfffovNZuOGG27ghhtuKPHcdevWMX78eFatWkWDBg0YM2YMp59++nHXcCLI3bSRXc9PwZ2bC2YztYffTFTX4/+z+Wvtfv5c41nB4Nx2dWmpoQMiIiJSDZQpwE6cOLFSHv7000+zcuVKZs6cye7du3nggQeoW7cuvXv3LnJeZmYmN9xwA927d2fixIl8/vnnjBo1igULFhAfH18ptVVHaT9+T+q3C8DtKnLcmZqK4XSCxUKdm28lsmOn435WZo6D9771DB2IiwrhivObHPc9RURERCpCmSZxLVy40Ocbf/vtt0f9PCcnh08++YQxY8bQsmVLevbsyY033sisWbOKnTtnzhzCwsIYP348DRo04I477qBBgwasXFn+ZaECUcqX88jft5f85OQivwynE5PVSt2Rt1dIeAV4f+EGMnIODR3oo6EDIiIiUn2UKZXMmTOHd955hxtvvJEzzzwTq7Xky1wuF4sWLeKtt94iOjqaXr16lXrPtWvX4nQ6i6wv27FjR1599VXcbjdm8+Fs/eeff9KjRw8slsO7Pn322WdlKf2EYjg9O2CFNGhIaJOm3uMmi4XILqdja9iwQp7z9/pk/li9D4Cz29Sh1aknTy+3iIiIVH9lCrAvvfQSX375JY8//jgZGRmceeaZNGvWjPj4eFwuFykpKaxevZq//vqLyMhIRo0axYABA456z+TkZGJjYwkODvYeq1GjBna7nbS0NOLiDo+33LFjB23atOGRRx5h8eLFJCYm8sADD9CxY0efvliz2YTZXDWrKVgs5iK/V4hDpUe0bk3CZZf7dKnVYiry2motua6s3HzvqgNxkSEM6dW81HNPdJXShlJl1H6BT20Y+NSGga06t1+Zfy588cUX07dvXxYsWMCiRYuYPXs2Bw4cwGQyUbNmTdq2bcvjjz9O9+7dS+2hLSw3N7dIeAW87x0OR5HjOTk5vPbaawwbNozXX3+dr776iuHDh/P1119Tp06dsn4JxMWFV/lyYFFRoRV2L7PJhAsItQURGxvu07UH3IfriIwKLfX6GV8vIz3b8+d/+5XtSawTXe56TxQV2YZS9dR+gU9tGPjUhoGtOrafTwMbzWYzffr0oU+fPsf94JCQkGJBteD9kbt+WSwWWrRowR133AHAaaedxpIlS/j888+55ZZbyvzMlJTsKu2BjYoKJSMjF5fLXSH3dBsGALl5+aSmZvt0bWZGbpHXqebi1/+9Ppkflu0E4Oy2dWhcO8Ln55xIKqMNpeqo/QKf2jDwqQ0Dm7/aryyddH6bmZOQkEBqaipOp9PbY5ucnIzNZiMqKqrIuTVr1qRRo0ZFjjVs2JA9e/b49Ey328DtNo6vcB+5XG6czgpq9EOlG27D53s6XUaR10den5Wbz1tfrQEgJiKYK89vUnF1B7gKbUOpcmq/wKc2DHxqw8BWHdvPb4MaWrRogdVq5d9///UeW7ZsGa1bty4ygQugXbt2rFu3rsixzZs3V8hatOLx4aIN3qED1/ZOIswWdIwrRERERPzDbwE2NDSUAQMGMH78eFasWMHChQuZMWMGw4YNAzy9sXl5eQAMHjyYdevW8eKLL7Jt2zamTp3Kjh076N+/v7/KP6Es33iAX1fuBeDMVrVp26SGnysSERERKZ1fp5WNHj2ali1bcu211/Loo49y++23e5fe6tatG/Pnzwc8u3698cYbfP/991x88cV8//33vPbaayQkJPiz/BNCTl4+M79ZC0B0RDBXXdD0GFeIiIiI+Fe5x8A6HA527txJ/fr1MQyDoCDff+QcGhrKpEmTmDRpUrHPjhwy0LFjR2bPnl3eck86uc5c9mYne9/vyd5b4nkfLtpIWtahoQMXJhGuoQMiIiJSzfkcYA3DYMqUKbz77rvk5+ezYMECnnvuOUJDQxk/fny5gqxUrFxnLo/8OpFcZ+5Rz1ux6SC//OeZCHd6ywTaNdXQAREREan+fB5C8O677/L5558zbtw477qtF1xwAQsXLmTatGkVXqD4bm92cqnhNdQaSu3wmuTkOb1DB6LCg7n6gmZVWaKIiIhIufncA/vRRx8xduxYevbsyeOPPw5A3759CQoK4qmnnuLuu++u8CKl/IYkXUad8Nre97XDaxJqDeXt79aQmmkHYNiFzYkIVc+5iIiIBAafA+zOnTtp0aJFseNJSUkkJyeXcIX4U53w2pwaXb/IsZVbDvLTcs/Qga6nJdChWU1/lCYiIiJSLj4PIUhMTOS///4rdvynn37ilFNOqZCipPLk2p28/fWhoQNhQVytVQdEREQkwPjcAzt8+HAeffRRkpOTMQyD3377jY8++oh3332XBx98sDJqlAr08fcbScnwDB24pldzIsOC/VyRiIiIiG98DrCXXnopTqeTV155hby8PMaOHUtcXBx33XUXV111VWXUKBVk1dYUfvx3NwCdk2rRKamWnysSERER8Z3PAXb37t1cfvnlXHnllaSkpGAYBvHx8TidTlasWEGbNm0qo045Trl2J2/P9wwdiAgNYkgvrTogIiIigcnnMbA9evQgLS0NgLi4OOLj4wHP5K6hQ4dWaHFScT79YRMHMzxb8w69sDlRGjogIiIiAapMPbCzZs1ixowZgGcjg0svvRSzuWj2zcjIoG7duhVfoRzTsXbdWrM1he//2QVAp+Y16ayhAyIiIhLAyhRgBw0aRGpqKoZh8NJLL9G7d2/Cw8OLnBMeHk6vXr0qpUgp3bF23XI4Xbz19eGhA9f0al6V5YmIiIhUuDIF2NDQUEaNGgWAyWRi+PDhhIaGVmphUjbH2nXr92XZHEj3DB0Y0rMZUeEaOiAiIiKBzedJXKNGjcLpdLJv3z5cLhfgGVbgcDj477//uOSSSyq8SCmbI3fdykwJ4vmP1gDQoVlNurTQ0AEREREJfD4H2F9++YUHHniAlJSUYp/ZbDYFWD8qvOuW3eFi7II/AAi3WRnaqxkmk8mf5YmIiIhUCJ9XIXj22Wc57bTTmD59OjabjWnTpvHQQw8RERHBM888Uxk1Sjl89uMmktMODx2Ijgjxc0UiIiIiFcPnHtiNGzfy5JNPkpSURIsWLQgLC2Po0KGEhYXx5ptvcsEFF1RGneKD9TvSWLhsJwDtmtSg62kJfq5IREREpOL43ANrsViIjIwEoEGDBqxfvx6A008/nU2bNlVsdeIze76LGfM9417DQqwM691cQwdERETkhOJzgG3atCmLFy8GoFGjRixbtgyAvXv3Hu0yqSJzftrM/lTPqgRX92xKjIYOiIiIyAnG5yEEN998M3fccQdBQUFcfPHFvPjii9x8882sW7eO008/vTJqlDLasT+T75buA6Bt43jOaFn7GFeIiIiIBB6fe2AvuOACPvnkE9q1a0edOnV44403sFgs9OjRg8cee6wyapQymrdkKwYFQweSNHRARERETkg+98ACtGzZ0vu6S5cudOnSBYBVq1YRExNTIYWJ71Iy7UAog3s0JTZSQwdERETkxFTmALtixQq+/vprrFYrF110EUlJSd7P7HY7zz//PO+++y4rV66slEKlbFo3iues1ho6ICIiIieuMgXY+fPnc9999xEcHIzVauWtt97irbfeonPnzvzzzz/cf//97Nixg0GDBlV2vXIEp9PlfW0LNnOtVh0QERGRE1yZxsC+/vrrXHDBBfz555/8/vvvDB48mOeff55FixYxdOhQDMPgrbfe4sknn6zseuUIPy3f433ds+MpxEXZ/FiNiIiISOUrU4DdunUrt956q7cH9o477mD58uU8/PDDXHLJJXzxxRecccYZlV2rHGHT7nR+W314+bK2TWr4sRoRERGRqlGmIQS5ubnUrFnT+z4qKso7Fvbhhx+utOKkdPlOFzO+WoNhFDqooQMiIiJyEijzMlpHjqs0mUxceeWVFV6QlM0XS7ay52COv8sQERERqXI+rwNbmM2m8Zb+kJZlZ/7v2wA4tU6kn6sRERERqVplXkbrn3/+ITo62vveMAxWrFhRbAvZzp07V1x1UqLlGw9ghNUhJNjCxWeewutr/V2RiIiISNUpc4C9/fbbMYoMuIR77723yHuTycSaNWsqpjIpVWZuPoTBlec3ITrcdewLRERERE4gZQqwixYtquw6pAycbjcmwBRkp1FjFw1OdbIne5+/yxIRERGpUmUKsImJiZVdhxxDrjOXXGcOYYC1xi72xH/H5L+/83dZIiIiIlXuuCZxSdXZk7UfTEapn4daQ6kdXrPUz0VEREROFGUeAyv+lZ2X731dN+RU7utYdAmz2uE1CbWGVnVZIiIiIlVOATZAZGTnE37odXRIBKdG1/drPSIiIiL+oiEEASIj2+F9HRJk8WMlIiIiIv5VrgC7f/9+pk2bxj333MPBgwf55ptv2Lx5c0XXJoWkZ9u9r4OC9O8OEREROXn5nIS2bdtGv379mDNnDt9++y05OTnMnz+fSy+9lOXLl1dGjQJk5BweA2s+YltfERERkZOJzwF24sSJXHDBBSxcuJCgoCAAnn32Wbp3787kyZMrvEDxyCjUAysiIiJyMvM5wP79999cf/31mAr1AlqtVm677TZWr15docXJYenZ+cc+SUREROQk4HOAdbvduN3uYsezs7OxWDS5qLJk5DiOfZKIiIjIScDnANutWzemT59eJMSmpaXxzDPPcPrpp1doceLhdLnJzlEPrIiIiAiUI8A++OCDrFy5km7dumG327n11ls5//zz2blzJw888EBl1HjSS820U/oeXCIiIiInF583MkhISGDu3Ll8+eWXrFmzBrfbzVVXXUX//v2JiIiojBpPeikZef4uQURERKTa8DnATp06lUGDBnH55ZdXRj1SgpQMrUAgIiIiUsDnIQTz5s2jV69eDBkyhM8++4zs7OzKqEsKSclUD6yIiIhIAZ8D7MKFC5k1axbNmjVj8uTJdOvWjfvvv5/ffvutMuoT1AMrIiIiUli59iTt0KED48aN4+eff+a5557DMAxGjhxJ9+7dK7o+AQ5qDKyIiIiIV7kCbIGUlBS2bNnCjh07sNvtNGjQoKLqkkLUAysiIiJymM+TuLKysliwYAHz5s1j6dKl1K1bl4EDB/Lcc89Rp06dyqjxpJeamQfaI0JEREQEKEeAPfPMMwkKCqJXr17MnDmTTp06VUZdckiew0l2nhNTuL8rEREREakefA6wjz76KL179yY0NLQy6pEjaPiAiIiISFFlCrBLly6lffv2WK1W6tWrx8qVK0s9t3PnzhVWnGgJLREREZEjlSnADh06lCVLlhAfH8/QoUMxmUwYRvHNTU0mE2vWrKnwIk9m6oEVERERKapMAXbRokXExsZ6X0vVKdhGNiTouBaMEBERETlhlCkVJSYmYjZ7Tp02bRrR0dEkJiYW+RUeHs6ECRMqtdiTUUEPbFR4iJ8rEREREakeytQDu2zZMnbs2AHA3LlzadmyJREREUXO2bRpk3bjqgQFmxhEhQX5uRIRERGR6qFMAdZkMvHggw96Xz/xxBPFzgkLC2P48OEVW52QklnQAxvs50pEREREqocyBdgOHTqwdu1aAJKSkvjll1+oUaNGpRYmYBgGqQU9sAqwIiIiIkA51oEtCLJS+bJy83E43QBEhSnAioiIiEAZA+ywYcOYNm0aUVFRDBs27KjnvvPOOxVSmBRdQitaPbAiIiIiQBkDbOFVCOrWrYvJZKrUosSj8CYGGkIgIiIi4lGmAPvUU095X0+cOLHSipGiCvfARoUFke3HWkRERESqi3Ktjv/333+TkpICeJbVGjFiBNOnTy9xdy4pv4JNDCLDgrBaLX6uRkRERKR68DnAfvjhhwwZMoR169axdu1aRo8eTX5+Pm+//TYvvfRSZdR40ipYQisu0ubnSkRERESqD58D7MyZM3n44Yc544wzmD9/Pk2bNmXGjBk8/fTTzJ4926d72e12HnroITp16kS3bt2YMWPGMa/ZuXMn7du3548//vC19IBTsIlBXJR24RIREREp4PMyWjt37qR79+4ALFmyhHPOOQeAxo0bc+DAAZ/u9fTTT7Ny5UpmzpzJ7t27eeCBB6hbty69e/cu9Zrx48eTk5Pja9kBKdUbYNUDKyIiIlLA5x7Y+Ph49u/fT3JyMmvWrOGss84CPOvD+rK5QU5ODp988gljxoyhZcuW9OzZkxtvvJFZs2aVes0XX3xBdvbJMZXJ7TZIzXQA6oEVERERKcznAHvRRRdx3333MXz4cGrXrk2XLl2YP38+Y8aM4aKLLirzfdauXYvT6aR9+/beYx07dmT58uW43e5i56empvLMM8/w2GOP+VpyQErLsuM+NCkuXj2wIiIiIl4+DyG49957qV27Njt27GDIkCFYLBYOHjzI4MGDuf3228t8n+TkZGJjYwkOPry+aY0aNbDb7aSlpREXF1fk/IkTJzJw4ECaNm3qa8leZrMJs7lq1rC1WMxFfvdVeo7D+7pmTChWy+H3ZpMJq7V895WyO942FP9S+wU+tWHgUxsGturcfj4HWLPZzNChQ4scO/J9WeTm5hYJr4D3vcPhKHL8119/ZdmyZXz55Zc+P6ewuLjwKt+EISoqtFzX2beleV+fekocaW4HBw+9Dw6xEhsbfvzFSZmUtw2lelD7BT61YeBTGwa26th+PgdYgEWLFjF9+nTWr1+P1WqlSZMmDB8+nJ49e5b5HiEhIcWCasF7m+3wj8zz8vIYO3Ys48aNK3K8PFJSsqu0BzYqKpSMjFxcruJDIo5l++50wNPbanI7yczI9X7msDtJTT05xgL70/G2ofiX2i/wqQ0Dn9owsPmr/crSSedzgP3222+588476dGjBxdddBGGYbB06VLuvPNOXnzxRXr06FGm+yQkJJCamorT6cRq9ZSRnJyMzWYjKirKe96KFSvYsWMHd9xxR5Hrb7rpJgYMGODTmFi328DtrtrNFlwuN06n741+IM0TWGMigzHc4HQdrtttGOW6p5RPedtQqge1X+BTGwY+tWFgq47t53OAffnllxk5ciSjRo3yHrvuuuuYNm0ar776apkDbIsWLbBarfz777906tQJgGXLltG6dWvM5sNjLdq0acO3335b5NpevXrxxBNPeFdAOBEduYmBkZ1DsONQiDVrVy4RERE5efk8Knfz5s3069ev2PGLL76Y9evXl/k+oaGhDBgwgPHjx7NixQoWLlzIjBkzGDZsGODpjc3Ly8Nms9GgQYMiv8DTgxsfH+9r+QHjyE0M3N//gvXQP35MpzX3V1kiIiIifudzgK1Vqxbbtm0rdnzbtm1ERkb6dK/Ro0fTsmVLrr32Wh599FFuv/12evXqBUC3bt2YP3++r+WdMApvYuBMT8P9i2fnsU31gjHXT/RnaSIiIiJ+5fMQgosvvpjx48czbtw4OnbsCHh+9P/oo4/St29fn+4VGhrKpEmTmDRpUrHP1q1bV+p1R/vsRJDvdJGRkw9AXGQIKV9/Bfme97+3jiDJn8WJiIiI+JnPAfbWW29l/fr1jBgxwrsklWEYnHfeedxzzz0VXuDJKCXTDpZ8TLZscGwj7fvFAKyrH8KB2HItHCEiIiJywvA5DYWEhPDyyy+zadMm1q9fj2EYNG/enMaNG1dGfSelPanp2Nr+iMnqJP2nDHC5cJvgj9Za+1VERESkzAF27969fPfddwQHB3PuuefSuHFjhdZKsi1tDyark6gsF6dt8oyFXdvQRmq0lVBrKLXDa/q5QhERERH/KVOA/euvv7jxxhvJy/OEqbCwMF544QW6detWqcWdrDKyPRs6dP0vG4sBmM20ueoW2sbHUTu8JqHW6rcjhoiIiEhVKdMqBFOnTuWMM87gp59+YsmSJZx99tlMnDixsms7aWXkOIhNd5K01fMPhuizz6FRo3acGl1f4VVEREROemXqgV29ejUfffQRtWrVAuChhx7ivPPOIysri4iIiEot8GSUnu2g68pszAZgtRJ30SX+LklERESk2ihTD2xOTg4xMTHe9wkJCQQFBZGenl5ZdZ3cDqTSfLtnJy7zmZ0Jiovzc0EiIiIi1UeZAqxhGN4lswpYLBbc7uq1L+6JwpKW4n1t7tLBj5WIiIiIVD8+78QllSsnz0l+vnH4gMXiv2JEREREqqEyL6M1Y8YMQkMPTyByOp288847REdHFzlv1KhRFVfdSSglM8/fJYiIiIhUa2UKsHXr1uXrr78ucqxmzZosWrSoyDGTyaQAe5xSMuz+LkFERESkWitTgF28eHFl1yGHpGSoB1ZERETkaDQGtprREAIRERGRo1OArWY0hEBERETk6BRgqxkNIRARERE5ujKvQiCVJ9eZy97sZACSHXuICcn1c0UiIiIi1ddxBViHw0FwcHBF1XJSynXm8sivE8l1Hgqt9aGG1Q5r/VuXiIiISHVVriEEH3zwAd27d6ddu3bs2LGDcePG8fLLL1d0bSeFvdnJh8NrCWqExlZhNSIiIiLVn88Bdt68eUyZMoWBAwcSFBQEQOPGjXn11VeZMWNGhRd4MrmwzkXkrTqd/G0tvMdsVpsfKxIRERGpfnwOsDNmzGDMmDHcfvvtmM2ey4cNG8bYsWP56KOPKrzAk0mQMwYjOwYjL9zfpYiIiIhUWz4H2C1bttCpU6dix7t27cqePXsqpKiTVUa2AwCTn+sQERERqc58DrA1atRgy5YtxY7/888/1KpVq0KKOlll5HgCbJgtyM+ViIiIiFRfPgfYK6+8kscee4xFixYBsHnzZj744AMmTJjAoEGDKrzAk0lBD2xkuAKsiIiISGl8XkbrpptuIjMzk3vuuQe73c6IESOwWq0MHjyYW265pTJqPGl4AmwQkWFamkxERESkNOVaB/aee+7h1ltvZePGjRiGQaNGjYiIiKjo2k463gAbqh5YERERkdL4HGB3797tfR0fHw9ARkYGGRkZANStW7eCSjv5ZObmAxAZpgArIiIiUhqfA2z37t0xmUqfJ79mzZrjKuhEVnjL2AJ7svd6XxuG53cNIRAREREpnc8B9p133iny3uVysWXLFt5++20efPDBCivsRFNsy9ijiAwLwl0FNYmIiIgEIp8DbJcuXYodO+OMMzjllFN48cUX6d69e4UUdqI51paxwaYQcg9tYBAZFkx6VRUmIiIiEmDKNYmrJA0bNmTt2rUVdbsT2pCky6gTXrvIsRWr8/jctROL2USYzaoAKyIiIlKK45rEVSArK4vp06dTr169CinqRFcnvDanRtcvcmxJ5joAYiNDMB9ljLGIiIjIya5CJnEZhkFYWBjPPPNMhRV2sknJsAMQFxni50pEREREqrfjnsQFEBQURLNmzQgPD6+Qok5GKRl5AMRF20BTuERERERKVa4Ae/fdd9O4cePKqOeklZJZ0ANrA3L8W4yIiIhINWb29YLff/+dkBD9mLsi2fNdZB3axCAuSn+2IiIiIkfjc4AdOHAgkydPZsOGDTgcjsqo6aRTMHwAIC7K5sdKRERERKo/n4cQ/Pjjj2zfvp0FCxaU+Ll24vJdwfABODSJK+8oJ4uIiIic5HwOsLfeemtl1HFSS0k/ogc2+Sgni4iIiJzkyhRgW7RowS+//EJ8fDwDBw6s7JpOOgU9sCFBFsJtVk3hEhERETmKMo2BNQyjsus4qXmX0IoKKbbGroiIiIgU5fMkLql4hwOsJnCJiIiIHEuZx8B+/fXXREREHPO8AQMGHE89J6XDa8BqCS0RERGRYylzgH3iiSeOeY7JZFKA9ZFhGIe3kVUPrIiIiMgxlTnALlmyhPj4+Mqs5aSUnefEnu8CtImBiIiISFmUaQysJhZVHm1iICIiIuIbrULgZ8U2MRARERGRoypTgB04cCAhIQpXlUE9sCIiIiK+KdMY2Keeeqqy6zhpFUzgiggNIiTI4udqRERERKo/rQPrZymZh9aALTR8wHC5Cp2h8cciIiIihSnA+llKevFNDPL37/O8MJmwxsX5oywRERGRaksB1s+8mxgUWkLLsWc3AEE1amIODvZLXSIiIiLVlQKsH7ndBqmZxTcxsO/2BNjgOnX8UpeIiIhIdaYA60fp2Q5cbs8SZQU9sIZh4CgIsHUT/VabiIiISHWlAOtHBRO4AOIiPT2wrowM3DnZAATXqeuXukRERESqMwVYP0rNKLSJwaEe2ILxr6AAKyIiIlISBVg/OnhoEwOTCWIiDgXY3bu8n4fU1RhYERERkSMpwPpRwSYGMREhWC2eprDv2QOANS4Osy3Ub7WJiIiIVFcKsH5U0iYGBUMINHxAREREpGQKsH6UklF8E4OCIQQKsCIiIiIlU4D1o4IhBAUTuFxZWbgyMgAIrqsAKyIiIlISBVg/yXe6Sc92AIeX0HIcGv8KEFJHa8CKiIiIlEQB1k9SswovoeUJsPY9h1cg0C5cIiIiIiVTgPWT1IxCmxh414D19MBaoqKwRET4pS4RERGR6k4B1k9SMor3wHoncGkLWREREZFSKcD6ScEmBlaLmciwIKDwEloaPiAiIiJSGr8GWLvdzkMPPUSnTp3o1q0bM2bMKPXcH374gf79+9O+fXv69evHokWLqrDSipeSeWgFgsgQzCYT7rxcnCkpAIRoCS0RERGRUvk1wD799NOsXLmSmTNnMm7cOKZNm8Y333xT7Ly1a9cyatQoLr30UubOncvgwYO58847Wbt2rR+qrhiH14AtOv4VNIRARERE5Gis/npwTk4On3zyCa+//jotW7akZcuWbNiwgVmzZtG7d+8i53755ZecfvrpDBs2DIAGDRqwePFivv76a5KSkvxR/nE7chMD++7d3s80hEBERESkdH4LsGvXrsXpdNK+fXvvsY4dO/Lqq6/idrsxmw93Dg8cOJD8/Pxi98jMzKySWivDkZsYFIx/NYeFY4mK9ltdIiIiItWd3wJscnIysbGxBAcHe4/VqFEDu91OWloacXFx3uONGzcucu2GDRv47bffGDx4sE/PNJtNmM2m4yu8jCwWc5HfrZbDz3W63eTYnQDUjAnFajWTv9czhCAksS5BQZYqqVGO7sg2lMCi9gt8asPApzYMbNW5/fwWYHNzc4uEV8D73uFwlHpdSkoKt99+Ox06dKBHjx4+PTMuLhyTqWoCbIGoqFAADrhDvcdcHK6hft0YYmPD2XwowEad2oDY2PAqrVGOrqANJTCp/QKf2jDwqQ0DW3VsP78F2JCQkGJBteC9zWYr8ZoDBw5w/fXXYxgGL7zwQpFhBmWRkpJdpT2wUVGhZGTk4nK5yczI9X62e//hoQ8hZji4L5W8ffs8B+JrkZqaXSU1ytEd2YYSWNR+gU9tGPjUhoHNX+1Xlo48vwXYhIQEUlNTcTqdWK2eMpKTk7HZbERFRRU7f9++fd5JXO+8806RIQZl5XYbuN3G8RXuI5fLjdPpxuk6/Ny0zMObGESHB5O7azcYns+tCXVwOvVNXp0UtKEEJrVf4FMbBj61YWCrju3nt0ENLVq0wGq18u+//3qPLVu2jNatWxfrWc3JyeHGG2/EbDbz3nvvkZCQUMXVVqyMbE9Pc2iIhdAQa9EVCOpqDVgRERGRo/FbgA0NDWXAgAGMHz+eFStWsHDhQmbMmOHtZU1OTiYvz7PU1PTp09m+fTuTJk3yfpacnBywqxAUBNi4yENbyO7xbCFrCrFhjfW9Z1lERETkZOK3IQQAo0ePZvz48Vx77bVERERw++2306tXLwC6devGU089xaBBg1iwYAF5eXlcfvnlRa4fOHAgEydO9Efpx8UTYC3eNWAduw9vIVvVk8xEREREAo1fA2xoaCiTJk3y9qwWtm7dOu/rknbnCmTp2Q4gtNguXCEaPiAiIiJyTNVvYa+TQEaOZ1OGuMgQDKcTx37PCgTBdRRgRURERI7Frz2wJ7Lc/FwOHNxPZkYuTpfBnuy93s+ch5aiiIuyecKrywUowIqIiIiUhQJsJch15jL214nkOHOPel5clA3Hno3e98F1Eyu7NBEREZGApyEElWBvdnKp4TXYFIKR51mgNy4qxDuByxQURFCNGlVWo4iIiEigUg9sJRt62uUkhB5et3btegefuLYDnjGwB/YcWoGgdm1MPu4sJiIiInIyUoCtZIkRtTkl4hTv+6VZniEDUWFBBFktOAoCbB0NHxAREREpC3X5VbGUDM/mDLFRNgy327uElnbgEhERESkbBdgqlpJhByA+ykZ+cjKG0wl4NjEQERERkWNTgK1iKZmeHti4yBDv8AHQEAIRERGRslKArUIut5u0TAdQsITWoQBrsRBcq5YfKxMREREJHAqwVSg9y4HbMICiS2gF10rAZNV8OhEREZGyUICtQgXjX8HTA2svWIFAE7hEREREykwBtgoVjH8FiI0ILrSElgKsiIiISFkpwFahg4eW0DKbTEQ4sjHsnh5Z9cCKiIiIlJ0CbBUqGEIQGxmMc9/hFQhC1AMrIiIiUmYKsFWoYBODuCgbjt2eDQwwmQhKqO3HqkREREQCiwJsFSrogfVM4NoFQFDNWpiDg/1ZloiIiEhAUYCtQkU2MShYQks7cImIiIj4RAG2ijjyXWTm5ANFd+HSCgQiIiIivlGArSKpmYfXgI23OHDn5AAQUldbyIqIiIj4QgG2ihRM4AKIzkn1vtYQAhERERHfKMBWkYOFduEKyzzofa0AKyIiIuIbBdgqUjCBK9hqhgP7ALDGxWO2hfqzLBEREZGAowBbRbybGETZCk3gUu+riIiIiK8UYKtIwRjY+KhCS2hpApeIiIiIzxRgq0jKoVUIagUbuDIzAG0hKyIiIlIeCrBVwDAMDh7qgU1wZ3iPB9dVgBURERHxlQJsFci1O7E7XADE2dO8x7WJgYiIiIjvFGCrQEqhJbQisjxLaFmio7GEh/urJBEREZGApQBbBQqW0AIITjvg+V29ryIiIiLlogBbBQpvYkDyXkABVkRERKS8FGCrQMESWjFBblxpnm1kQzSBS0RERKRcFGCrQMEY2AaWXO8x9cCKiIiIlI8CbBUo6IFNJNN7TAFWREREpHwUYKtAwSSuGvnpAJjDw7FERfmzJBEREZGApQBbydxA6qFduKJyCsa/JmIymfxYlYiIiEjgUoCtZNm5+ThdBgChGVpCS0REROR4KcBWsoxsBwBWtxNzuqcHNrhOHX+WJCIiIhLQFGArWfqhABufn44JT09scN1Ef5YkIiIiEtAUYCtZetahAOtI9x7TEAIRERGR8lOArWQFQwgSyQLAbLNhjY31Z0kiIiIiAU0BtpJlZHtWIEhwZgCe3letQCAiIiJSfgqwlaxgDGxsXhqg4QMiIiIix8vq7wJOdBnZDsyGmbCcghUIFGBFREREjocCbCXLzMknzpGPyShYgUABVkREROR4aAhBJTPwLKFVQAFWRERE5PgowFaBGo40AEzBwQTF1/BvMSIiIiIBTgG2ChSsARucUBuTWX/kIiIiIsdDaaoK1CwIsBo+ICIiInLcFGArmclwE5t/eA1YERERETk+CrCVLMaRg9VwAQqwIiIiIhVBAbaSxedlel+HaAiBiIiIyHFTgK1k8XlZnhcWC0E1a/m3GBEREZETgAJsJath9/TABickYLJq3wgRERGR46UAW8kKhhBo/KuIiIhIxVCArUyGQbzdM4QguG6in4sREREROTEowFaiyBw3wW7PCgQh6oEVERERqRAKsJUoLt3pfa0hBCIiIiIVQwG2EsWle3pfMZkIqp3g32JEREREThAKsJUoLsPTAxtUqxbmoGA/VyMiIiJyYlCArUQFQwg0fEBERESk4ijAVgbD8KxAkK4tZEVEREQqmgJsJch1uAjLcxOSbwDaQlZERESkIinAVoKMbIe39xXUAysiIiJSkRRgK0FGtqPoElq16/ixGhEREZETi18DrN1u56GHHqJTp05069aNGTNmlHru6tWrufzyy2nbti2XXnopK1eurMJKfZNjzycu41APbGw0ZpvNvwWJiIiInED8GmCffvppVq5cycyZMxk3bhzTpk3jm2++KXZeTk4ON998M506dWL27Nm0b9+eESNGkJOT44eqy8bbA5tQ07+FiIiIiJxg/BZgc3Jy+OSTTxgzZgwtW7akZ8+e3HjjjcyaNavYufPnzyckJIT777+fxo0bM2bMGMLDw0sMu9WFN8DWUoAVERERqUh+C7Br167F6XTSvn1777GOHTuyfPly3G53kXOXL19Ox44dMZlMAJhMJjp06MC///5blSWXmSU3lzC7ZwUC9cCKiIiIVCyrvx6cnJxMbGwswcGHd6iqUaMGdrudtLQ04uLiipzbpEmTItfHx8ezYcMGn55pNpswm03HV3gZ2NLTvK8ttWthtWquXCCyWMxFfpfAovYLfGrDwKc2DGzVuf38FmBzc3OLhFfA+97hcJTp3CPPO5a4uHBvL25lqmuLBMBlhhbtOhAbG17pz5TKExUV6u8S5Dio/QKf2jDwqQ0DW3VsP78F2JCQkGIBtOC97YhZ+6Wde+R5x5KSkl0lPbDNu3XltxWriW56KtbgKFJTsyv9mVLxLBYzUVGhZGTk4nK5j32BVCtqv8CnNgx8asPA5q/2K0vHn98CbEJCAqmpqTidTqxWTxnJycnYbDaioqKKnXvgwIEixw4cOECtWrV8eqbbbeB2G8dXeBlYQ2yc/7+RxMaGk5qajdOpb9pA5nK51YYBTO0X+NSGgU9tGNiqY/v5bVBDixYtsFqtRSZiLVu2jNatW2M2Fy2rbdu2/PPPPxiGJ3wahsHff/9N27Ztq7JkEREREakG/BZgQ0NDGTBgAOPHj2fFihUsXLiQGTNmMGzYMMDTG5uXlwdA7969ycjIYMKECWzcuJEJEyaQm5tLnz59/FW+iIiIiPiJX6eVjR49mpYtW3Lttdfy6KOPcvvtt9OrVy8AunXrxvz58wGIiIhg+vTpLFu2jEGDBrF8+XJee+01wsLC/Fm+iIiIiPiBySj4ufxJIDk5s8qeZbWaNQY2wKkNA5vaL/CpDQOf2jCw+av9ataMPOY51W9hLxERERGRo1CAFREREZGAogArIiIiIgFFAVZEREREAooCrIiIiIgEFAVYEREREQkoCrAiIiIiElAUYEVEREQkoCjAioiIiEhAUYAVERERkYCiACsiIiIiAUUBVkREREQCigKsiIiIiAQUk2EYhr+LEBEREREpK/XAioiIiEhAUYAVERERkYCiACsiIiIiAUUBVkREREQCigKsiIiIiAQUBVgRERERCSgKsCIiIiISUBRgRURERCSgKMCKiIiISEBRgD0Odrudhx56iE6dOtGtWzdmzJhR6rmrV6/m8ssvp23btlx66aWsXLmyCiuVkvjSfj/88AP9+/enffv29OvXj0WLFlVhpVIaX9qwwM6dO2nfvj1//PFHFVQox+JLG65bt46rrrqKNm3a0K9fP37//fcqrFRK40sbfvfdd/Tp04f27dtz1VVXsWrVqiqsVI7G4XBw8cUXH/XvxuqUZRRgj8PTTz/NypUrmTlzJuPGjWPatGl88803xc7Lycnh5ptvplOnTsyePZv27dszYsQIcnJy/FC1FChr+61du5ZRo0Zx6aWXMnfuXAYPHsydd97J2rVr/VC1FFbWNixs/Pjx+t6rRsrahpmZmdxwww00adKEefPm0bNnT0aNGsXBgwf9ULUUVtY23LBhA/feey8jRozg888/p0WLFowYMYLc3Fw/VC2F2e127rnnHjZs2FDqOdUuyxhSLtnZ2Ubr1q2N33//3XvspZdeMq655ppi537yySdG9+7dDbfbbRiGYbjdbqNnz57GZ599VmX1SlG+tN8zzzxjDB8+vMixG264wXj22WcrvU4pnS9tWODzzz83Bg8ebDRr1qzIdeIfvrThzJkzjQsuuMBwOp3eY4MGDTJ++OGHKqlVSuZLG7711lvGwIEDve8zMzONZs2aGStWrKiSWqVkGzZsMC655BKjX79+R/27sbplGfXAltPatWtxOp20b9/ee6xjx44sX74ct9td5Nzly5fTsWNHTCYTACaTiQ4dOvDvv/9WZclSiC/tN3DgQO67775i98jMzKz0OqV0vrQhQGpqKs888wyPPfZYVZYpR+FLG/7555/06NEDi8XiPfbZZ59x7rnnVlm9UpwvbRgTE8PGjRtZtmwZbreb2bNnExERQf369au6bCnkzz//pGvXrnz00UdHPa+6ZRmrX556AkhOTiY2Npbg4GDvsRo1amC320lLSyMuLq7IuU2aNClyfXx8/FG76qVy+dJ+jRs3LnLthg0b+O233xg8eHCV1SvF+dKGABMnTmTgwIE0bdq0qkuVUvjShjt27KBNmzY88sgjLF68mMTERB544AE6duzoj9LlEF/asG/fvixevJirr74ai8WC2Wxm+vTpREdH+6N0OeTqq68u03nVLcuoB7accnNzi3zDAt73DoejTOceeZ5UHV/ar7CUlBRuv/12OnToQI8ePSq1Rjk6X9rw119/ZdmyZdx2221VVp8cmy9tmJOTw2uvvUbNmjV5/fXX6dy5M8OHD2fPnj1VVq8U50sbpqamkpyczNixY/n444/p378/o0eP1jjmAFHdsowCbDmFhIQUa7SC9zabrUznHnmeVB1f2q/AgQMHuPbaazEMgxdeeAGzWd8+/lTWNszLy2Ps2LGMGzdO33PVjC/fhxaLhRYtWnDHHXdw2mmn8b///Y+GDRvy+eefV1m9UpwvbTh58mSaNWvGkCFDaNWqFY8//jihoaF89tlnVVavlF91yzL6P3A5JSQkkJqaitPp9B5LTk7GZrMRFRVV7NwDBw4UOXbgwAFq1apVJbVKcb60H8C+ffsYMmQIDoeDd955p9iPp6XqlbUNV6xYwY4dO7jjjjto3769d6zeTTfdxNixY6u8bjnMl+/DmjVr0qhRoyLHGjZsqB5YP/OlDVetWkVSUpL3vdlsJikpid27d1dZvVJ+1S3LKMCWU4sWLbBarUUGLy9btozWrVsX65lr27Yt//zzD4ZhAGAYBn///Tdt27atypKlEF/aLycnhxtvvBGz2cx7771HQkJCFVcrJSlrG7Zp04Zvv/2WuXPnen8BPPHEE9x5551VXLUU5sv3Ybt27Vi3bl2RY5s3byYxMbEqSpVS+NKGtWrVYtOmTUWObdmyhXr16lVFqXKcqluWUYAtp9DQUAYMGMD48eNZsWIFCxcuZMaMGQwbNgzw/As0Ly8PgN69e5ORkcGECRPYuHEjEyZMIDc3lz59+vjzSzip+dJ+06dPZ/v27UyaNMn7WXJyslYh8LOytqHNZqNBgwZFfoGnNyE+Pt6fX8JJz5fvw8GDB7Nu3TpefPFFtm3bxtSpU9mxYwf9+/f355dw0vOlDa+44go+/vhj5s6dy7Zt25g8eTK7d+9m4MCB/vwS5CiqdZbxy+JdJ4icnBzj/vvvN9q1a2d069bNeOutt7yfNWvWrMjaaMuXLzcGDBhgtG7d2rjsssuMVatW+aFiKays7XfhhRcazZo1K/brgQce8FPlUsCX78HCtA5s9eFLG/7111/GwIEDjVatWhn9+/c3/vzzTz9ULEfypQ0//vhjo3fv3ka7du2Mq666yli5cqUfKpbSHPl3Y3XOMibDONQXLCIiIiISADSEQEREREQCigKsiIiIiAQUBVgRERERCSgKsCIiIiISUBRgRURERCSgKMCKiIiISEBRgBURERGRgKIAKyIBa+jQoTRv3rzEXwU7px3LH3/8QfPmzdm5c2el1Lhz585itZ122mmcccYZ3HXXXRW6D3z37t158cUXAc82j3PmzOHgwYMAzJ49m+bNm1fYs45UcP/Cv1q0aEHnzp25/vrrWb16tU/32717N1999VUlVSsigc7q7wJERI5Hnz59GDNmTLHjoaGhfqimdC+++CLt27cHwO12s2PHDsaMGcOIESP44osvMJlMx/2MTz/9lJCQEACWLl3Kgw8+yKJFiwDo27cvZ5999nE/41h++eUX72uXy8WWLVt48sknGT58OAsXLiQ8PLxM93nggQdITEzkoosuqqxSRSSAKcCKSECz2WzUrFnT32UcU3R0dJE6ExISGDVqFPfddx/r1q0jKSnpuJ8RFxfnfX3kJos2mw2bzXbczziWI9uidu3ajB07lmuuuYbff/+dHj16VHoNInLi0xACETmhpaen8/DDD3P22WfTsmVLzjjjDB5++GFyc3NLPH/r1q0MHz6cjh070r59e4YPH866deu8n2dmZvLII49w+umn07FjR4YNG8Z///1XrtosFgsAQUFBAOzZs4f77ruPs846i3bt2jF8+HDWrl3rPf/gwYPccccddO3alTZt2jB48GD+/PNP7+cFQwj++OMPhg0bBkCPHj2YPXt2kSEEDz74IJdffnmRWnbt2kVSUhK//vorAH///TdDhgyhTZs2nHfeeTz66KNkZWWV6+ss6BW2Wj19Jm63m+nTp3PhhRfSqlUrOnTowI033sj27dsBz9CQP//8kzlz5tC9e3cAHA4HzzzzDGeffTbt27fniiuuKNLbKyInFwVYETmhPfjgg6xevZpp06axYMECRo8ezdy5c/noo49KPP+ee+4hISGBzz77jE8++QSz2cyoUaMAT6/mTTfdxI4dO5g+fToff/wx7dq146qrrvJpjKfb7WbNmjW88sorJCUlceqpp5KVlcVVV13Fvn37eOWVV/jwww+x2Wxcc8017Nq1C4Dx48djt9t57733mDdvHqeeeiq33XYbOTk5Re7fvn1771jYTz75hL59+xb5fNCgQaxYscIbGAHmzZtH7dq1Of3001m7di3XX389Z599Nl988QWTJ09m1apV3HDDDcV6do9lx44dPPPMM9StW5fOnTsD8M477/Dmm2/y4IMPsmDBAl566SW2bt3KxIkTgcPDLfr06cOnn34KwOjRo1myZAmTJ09mzpw59OnTh1tuuYUffvjBp3pE5MSgIQQiEtDmzZvHggULihzr2LEjb7zxBgBnnXUWnTt39vY+1qtXj/fee4/169eXeL/t27dz5plnkpiYSFBQEE8++SSbN2/G7Xbzxx9/8O+///L7778TExMDeALv33//zTvvvOMNYCW56aabvD2uDocDwzDo1KkTjz/+OGazmS+++ILU1FRmz57tHQowZcoULrjgAmbNmsX999/P9u3badasGaeccgo2m40xY8bQr18/730LBAcHEx0dDXiGFRw5dKBz586ccsopfPHFF95wPm/ePPr374/ZbObNN9/krLPO4pZbbgGgYcOG3lr+/PNPunbtWurXWTDOFyA/P5+goCC6devGU089RVhYGAD169dn0qRJnH/++QAkJibSu3dvvvnmGwBiYmIICgrCZrMRFxfHtm3b+PLLL5k7dy4tWrQA4Prrr2ft2rW8+eabnHfeeaXWIyInJgVYEQlo3bt357777ityrHBgu/rqq1m8eDFz5sxh69atbNy4kZ07d9KoUaMS73f33Xfz5JNP8v7779OlSxfOPvtsLr74YsxmM6tWrcIwDG/wKuBwOLDb7Uet84knnqBt27aA50fp8fHxRepcv349DRs2LDKO1Waz0aZNG2/YHjVqFP/73/9YsGABHTt2pFu3blx88cXeH9GXlclkYsCAAcybN49Ro0axevVqNm7cyMsvvwzA6tWr2bZtW5EwWmDTpk1HDbBz584FPMMdnn/+eQ4ePMhdd91FvXr1vOd0796d5cuXM3XqVLZs2cKWLVvYuHEjCQkJJd6zoHf76quvLnI8Pz+fqKgon752ETkxKMCKSEALDw+nQYMGJX7mdrsZMWIEGzZs4OKLL6Zv3760bNmSRx55pNT7DRkyhN69e/Pjjz/y22+/8cILL/DKK68wd+5c3G43ERERzJ49u9h1wcHBR60zISGh1Dqh+KSrwl9DwdjRnj178vPPP/Pzzz/z66+/8tZbbzFt2jQ+/vhjmjZtetTnH2ngwIFMmzaN//77j/nz59OhQwdvfW63m379+nl7YAsrHLBLUnCPBg0aMH36dC6//HKGDx/OnDlziI2NBeC1117jpZdeYuDAgZxxxhlcd911LFq0qNRlswr+bGbNmlVsFQOzWSPhRE5G+s4XkRPWmjVr+Omnn5g6dSr33Xcfl1xyCfXr12f79u0lBsaDBw/y2GOPkZ+fz6BBg3jmmWf44osvSE5O5s8//6RZs2ZkZWWRn59PgwYNvL9ef/1173JV5dW8eXO2bt3qXbcVwG63s3LlSpo0aYLD4eCpp55ix44d9O3blyeeeIKFCxdiNptLHAd6rGW5EhMT6dq1KwsWLODrr79m0KBB3s+aNm3Kxo0bi3yNTqeTp556ij179pT5awoNDWXy5MkcOHCAxx57zHv81VdfZeTIkYwfP54rr7ySdu3asXXr1lJDfEE4T05OLlJTweQ0ETn5KMCKyAmrRo0aWK1Wvv76a3bs2MF///3HXXfdRXJyMg6Ho9j50dHR/PDDDzz88MOsWbOGHTt28OGHHxIUFESrVq04++z/t3f/II1DARzHf/7BxT+4FYcW/6AoODi4OBwiiNWKheCgiEMQl7oY0VWlgyhSyOJSK9KhqFNxcnDIpggOioqWoqKo4CK4WLrI3XSFQ4+jOBxPvp/xEZIXsnxJHi8/1NbWppmZGR0dHen+/l7Ly8tKp9Nqamr60lyHhoZUW1srx3F0dnamTCajubk55XI5jYyMqKKiQufn55qfn9fp6akeHx+VTqeVy+U+/dT/e71pJpPR29vbp9e0LEtbW1t6fX3VwMBAYXxiYkKXl5eKRqO6ubnRycmJZmdndXd3p/r6+qLuq7W1VZOTk9rb25PneZKkuro6HRwc6Pr6Wre3t3JdV/v7+388k8rKSj09Pen5+VnNzc3q6enR4uKiPM/Tw8ODEomE4vG4AoFAUfMB8D0QsAC+LZ/Pp5WVFXmep1AopOnpafl8Ptm2rYuLiw/Hl5eXK5FIqLS0VLZta3BwUIeHh1pfX1cgEFBZWZk2NzfV3t4ux3EUDod1fHystbU1dXV1fWmu1dXVSqVSqqmpkW3bGhsbUz6f1/b2tvx+vyTJdV35/X5FIhH19/drZ2dHsVhMnZ2dH87X0tKi7u5uOY7z1x0XgsGgJKm3t1dVVVWF8Y6ODm1sbOjq6kqWZSkSiaihoUHJZPKfSyU+MzU1pcbGxsJWXKurq8rn8xoeHtb4+Liy2ayi0aheXl4KfyYbHR1VNptVOBzW+/u7XNdVX1+fFhYWFAqFtLu7q6WlJVmWVfR8AJiv5Gexe6IAAAAA/xFvYAEAAGAUAhYAAABGIWABAABgFAIWAAAARiFgAQAAYBQCFgAAAEYhYAEAAGAUAhYAAABGIWABAABgFAIWAAAARiFgAQAAYBQCFgAAAEb5BU+yJbgwgLxXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fpt: false positive rate, tpr: true positive rate (recall)\n",
    "# random forest is the best model according to AUC score and its ROC curve is closer to the top-left corner\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "fpr_dt, tpr_dt, thresholds_dt = roc_curve(y_test, y_scores_dt)\n",
    "fpr_svc, tpr_svc, thresholds_svc = roc_curve(y_test, y_scores_svc)\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, y_scores_rf)\n",
    "\n",
    "print(f'AUC score for dt is {roc_auc_score(y_test, y_scores_dt)}')\n",
    "print(f'AUC score for svc is {roc_auc_score(y_test, y_scores_svc)}')\n",
    "print(f'AUC score for rf is {roc_auc_score(y_test, y_scores_rf)}')\n",
    "\n",
    "# plot the ROC Curve\n",
    "plt.style.use('seaborn')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(fpr_dt, tpr_dt, label=\"Decision Tree\")\n",
    "ax.plot(fpr_svc, tpr_svc, label=\"SVC\")\n",
    "ax.plot(fpr_rf, tpr_rf, label=\"Ramdom Forest\")\n",
    "ax.set_title('ROC Curve')\n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate (Recall)')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance\n",
    "\n",
    "Given that we are using pipeline and one-hot encoding, the feature importance scores are not very straightforward to get. The following code shows how to get the feature importance scores from the decision tree model and create a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'preprocessor': ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                  Pipeline(steps=[('num_imputer',\n",
       "                                                   SimpleImputer()),\n",
       "                                                  ('scaler', StandardScaler())]),\n",
       "                                  ['Age', 'SibSp', 'Parch', 'Fare']),\n",
       "                                 ('cat_pipeline',\n",
       "                                  Pipeline(steps=[('cat_imputer',\n",
       "                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                  ('onehot', OneHotEncoder())]),\n",
       "                                  ['Sex', 'Embarked', 'Pclass'])]),\n",
       " 'clf_dt': DecisionTreeClassifier(criterion='entropy', max_depth=3)}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best.named_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                 Pipeline(steps=[('num_imputer',\n",
       "                                                  SimpleImputer()),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 ['Age', 'SibSp', 'Parch', 'Fare']),\n",
       "                                ('cat_pipeline',\n",
       "                                 Pipeline(steps=[('cat_imputer',\n",
       "                                                  SimpleImputer(strategy='most_frequent')),\n",
       "                                                 ('onehot', OneHotEncoder())]),\n",
       "                                 ['Sex', 'Embarked', 'Pclass'])])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best.named_steps['preprocessor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08110601, 0.05513595, 0.        , 0.0608736 , 0.        ,\n",
       "       0.54533576, 0.        , 0.        , 0.        , 0.05311821,\n",
       "       0.        , 0.20443047])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = clf_best['clf_dt'].feature_importances_\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('num_pipeline',\n",
       "  Pipeline(steps=[('num_imputer', SimpleImputer()), ('scaler', StandardScaler())]),\n",
       "  ['Age', 'SibSp', 'Parch', 'Fare']),\n",
       " ('cat_pipeline',\n",
       "  Pipeline(steps=[('cat_imputer', SimpleImputer(strategy='most_frequent')),\n",
       "                  ('onehot', OneHotEncoder())]),\n",
       "  ['Sex', 'Embarked', 'Pclass'])]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best['preprocessor'].transformers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                 Pipeline(steps=[('num_imputer',\n",
       "                                                  SimpleImputer()),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 ['Age', 'SibSp', 'Parch', 'Fare']),\n",
       "                                ('cat_pipeline',\n",
       "                                 Pipeline(steps=[('cat_imputer',\n",
       "                                                  SimpleImputer(strategy='most_frequent')),\n",
       "                                                 ('onehot', OneHotEncoder())]),\n",
       "                                 ['Sex', 'Embarked', 'Pclass'])])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get columnTransformer\n",
    "clf_best[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('num_pipeline',\n",
       "  Pipeline(steps=[('num_imputer', SimpleImputer()), ('scaler', StandardScaler())]),\n",
       "  ['Age', 'SibSp', 'Parch', 'Fare']),\n",
       " ('cat_pipeline',\n",
       "  Pipeline(steps=[('cat_imputer', SimpleImputer(strategy='most_frequent')),\n",
       "                  ('onehot', OneHotEncoder())]),\n",
       "  ['Sex', 'Embarked', 'Pclass'])]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best[0].transformers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_original_feature_names = clf_best[0].transformers_[0][2]\n",
    "num_original_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex', 'Embarked', 'Pclass']"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_original_feature_names = clf_best[0].transformers_[1][2]\n",
    "cat_original_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex_female',\n",
       " 'Sex_male',\n",
       " 'Embarked_C',\n",
       " 'Embarked_Q',\n",
       " 'Embarked_S',\n",
       " 'Pclass_1',\n",
       " 'Pclass_2',\n",
       " 'Pclass_3']"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_new_feature_names = list(clf_best[0].transformers_[1][1]['onehot'].get_feature_names(cat_original_feature_names))\n",
    "cat_new_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'SibSp',\n",
       " 'Parch',\n",
       " 'Fare',\n",
       " 'Sex_female',\n",
       " 'Sex_male',\n",
       " 'Embarked_C',\n",
       " 'Embarked_Q',\n",
       " 'Embarked_S',\n",
       " 'Pclass_1',\n",
       " 'Pclass_2',\n",
       " 'Pclass_3']"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = num_original_feature_names + cat_new_feature_names\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.081106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.055136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.060874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>0.545336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_S</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_1</th>\n",
       "      <td>0.053118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_3</th>\n",
       "      <td>0.204430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            importance\n",
       "Age           0.081106\n",
       "SibSp         0.055136\n",
       "Parch         0.000000\n",
       "Fare          0.060874\n",
       "Sex_female    0.000000\n",
       "Sex_male      0.545336\n",
       "Embarked_C    0.000000\n",
       "Embarked_Q    0.000000\n",
       "Embarked_S    0.000000\n",
       "Pclass_1      0.053118\n",
       "Pclass_2      0.000000\n",
       "Pclass_3      0.204430"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = pd.DataFrame(i, index=feature_names, columns=['importance'])\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>0.545336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_3</th>\n",
       "      <td>0.204430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.081106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.060874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.055136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_1</th>\n",
       "      <td>0.053118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_S</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            importance\n",
       "Sex_male      0.545336\n",
       "Pclass_3      0.204430\n",
       "Age           0.081106\n",
       "Fare          0.060874\n",
       "SibSp         0.055136\n",
       "Pclass_1      0.053118\n",
       "Parch         0.000000\n",
       "Sex_female    0.000000\n",
       "Embarked_C    0.000000\n",
       "Embarked_Q    0.000000\n",
       "Embarked_S    0.000000\n",
       "Pclass_2      0.000000"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.sort_values('importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.sort_values('importance', ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best dt score is:  0.8258411580594679\n",
      "best dt score after feature selection is:  0.7164123630672926\n"
     ]
    }
   ],
   "source": [
    "# we remove the most important feature Sex and see how the model is affected\n",
    "# result: accuracy drops from ~0.826 to ~0.716\n",
    "num_features = ['Age', 'SibSp', 'Parch', 'Fare']\n",
    "cat_features = ['Embarked', 'Pclass']\n",
    "\n",
    "# you must update preprocess and pipeline after changing the feature list\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num_pipeline', num_pipeline, num_features),\n",
    "        ('cat_pipeline', cat_pipeline, cat_features),\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipeline_dt = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('clf_dt', DecisionTreeClassifier()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# update the grid search \n",
    "grid_search_dt_updated = GridSearchCV(pipeline_dt, param_grid_dt, cv=10, scoring='accuracy')\n",
    "\n",
    "# train the model using the updated full pipeline\n",
    "grid_search_dt_updated.fit(X_train, y_train)  # # note here X_train is still having 7 features only 6 is used\n",
    "\n",
    "print('best dt score is: ', grid_search_dt.best_score_)\n",
    "print('best dt score after feature selection is: ', grid_search_dt_updated.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best dt score is:  0.8258411580594679\n",
      "best dt score after feature selection is:  0.8258411580594679\n"
     ]
    }
   ],
   "source": [
    "# we remove unimportant features: Parch and Embarked and see the model is affected\n",
    "# result: no difference with less features!!\n",
    "num_features = ['Age', 'SibSp', 'Fare']\n",
    "cat_features = ['Sex', 'Pclass']\n",
    "\n",
    "# you must update preprocess and pipeline after changing the feature list\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num_pipeline', num_pipeline, num_features),\n",
    "        ('cat_pipeline', cat_pipeline, cat_features),\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipeline_dt = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('clf_dt', DecisionTreeClassifier()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# update the grid search \n",
    "grid_search_dt_updated = GridSearchCV(pipeline_dt, param_grid_dt, cv=10, scoring='accuracy')\n",
    "\n",
    "# train the model using the updated full pipeline\n",
    "grid_search_dt_updated.fit(X_train, y_train) # note here X_train is still having 7 features only 5 is used\n",
    "\n",
    "print('best dt score is: ', grid_search_dt.best_score_)\n",
    "print('best dt score after feature selection is: ', grid_search_dt_updated.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best dt score is:  0.8258411580594679\n",
      "best dt score after feature selection is:  0.8258411580594679\n"
     ]
    }
   ],
   "source": [
    "# we need to split the data to make X_train expect 5 features instead of 7\n",
    "\n",
    "# drop 'Parch', 'Embarked'\n",
    "X = df.drop(['Survived', 'PassengerId', 'Name', 'Ticket', 'Cabin', 'Parch', 'Embarked'], axis=1)\n",
    "y = df['Survived']\n",
    "\n",
    "# re-split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# updated feature lists\n",
    "num_features = ['Age', 'SibSp', 'Fare']\n",
    "cat_features = ['Sex', 'Pclass']\n",
    "\n",
    "# updated preprocess and pipeline\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num_pipeline', num_pipeline, num_features),\n",
    "        ('cat_pipeline', cat_pipeline, cat_features),\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipeline_dt = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('clf_dt', DecisionTreeClassifier()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# updated the grid search \n",
    "grid_search_dt_updated = GridSearchCV(pipeline_dt, param_grid_dt, cv=10, scoring='accuracy')\n",
    "\n",
    "# train the model using the updated full pipeline\n",
    "grid_search_dt_updated.fit(X_train, y_train) # note here X_train is still having 7 features only 5 is used\n",
    "\n",
    "print('best dt score is: ', grid_search_dt.best_score_)\n",
    "print('best dt score after feature selection is: ', grid_search_dt_updated.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reassign the best model to have only 5 features\n",
    "clf_best = grid_search_dt_updated.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Persist the Model\n",
    "The following code shows how to save the trained model as a pickle file, which can be loaded in to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clf-best.pickle']"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model as a pickle file\n",
    "import joblib\n",
    "joblib.dump(clf_best, \"clf-best.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num_pipeline',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Age', 'SibSp', 'Fare']),\n",
       "                                                 ('cat_pipeline',\n",
       "                                                  Pipeline(steps=[('cat_imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehot',\n",
       "                                                                   OneHotEncoder())]),\n",
       "                                                  ['Sex', 'Pclass'])])),\n",
       "                ('clf_dt',\n",
       "                 DecisionTreeClassifier(criterion='entropy', max_depth=3))])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the model from a pickle file\n",
    "saved_tree_clf = joblib.load(\"clf-best.pickle\")\n",
    "saved_tree_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Sex  Age  SibSp  Fare\n",
       "0       3  male   23      0   5.5"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passenger1 = pd.DataFrame(\n",
    "    {\n",
    "        'Pclass': [3],\n",
    "        'Sex': ['male'], \n",
    "        'Age': [23],\n",
    "        'SibSp': [0],\n",
    "        'Fare': [5.5],\n",
    "    }\n",
    ")\n",
    "passenger1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex  Age  SibSp  Fare\n",
       "0       1  female   21      0    80"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passenger2 = pd.DataFrame(\n",
    "    {\n",
    "        'Pclass': [1],\n",
    "        'Sex': ['female'], \n",
    "        'Age': [21],\n",
    "        'SibSp': [0],\n",
    "        'Fare': [80],\n",
    "    }\n",
    ")\n",
    "passenger2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# died\n",
    "pred1 = saved_tree_clf.predict(passenger1)\n",
    "pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# survived\n",
    "pred2 = saved_tree_clf.predict(passenger2)\n",
    "pred2"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.8.1 64-bit ('venv': venv)",
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