slides_advanced.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playground for Advanced Topics Slides\n",
    "\n",
    "- Stephen W. Thomas\n",
    "- Used for MMA 869, MMAI 869, and GMMA 869"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-09 08:05:31.916023\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "INSTALLED VERSIONS\n",
      "------------------\n",
      "commit           : None\n",
      "python           : 3.6.10.final.0\n",
      "python-bits      : 64\n",
      "OS               : Windows\n",
      "OS-release       : 10\n",
      "machine          : AMD64\n",
      "processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel\n",
      "byteorder        : little\n",
      "LC_ALL           : None\n",
      "LANG             : None\n",
      "LOCALE           : None.None\n",
      "\n",
      "pandas           : 1.0.3\n",
      "numpy            : 1.18.1\n",
      "pytz             : 2019.3\n",
      "dateutil         : 2.8.1\n",
      "pip              : 20.0.2\n",
      "setuptools       : 46.1.3.post20200325\n",
      "Cython           : None\n",
      "pytest           : 5.4.1\n",
      "hypothesis       : None\n",
      "sphinx           : None\n",
      "blosc            : None\n",
      "feather          : None\n",
      "xlsxwriter       : None\n",
      "lxml.etree       : 4.5.0\n",
      "html5lib         : None\n",
      "pymysql          : None\n",
      "psycopg2         : None\n",
      "jinja2           : 2.11.2\n",
      "IPython          : 7.13.0\n",
      "pandas_datareader: None\n",
      "bs4              : 4.9.0\n",
      "bottleneck       : None\n",
      "fastparquet      : None\n",
      "gcsfs            : None\n",
      "lxml.etree       : 4.5.0\n",
      "matplotlib       : 3.0.0\n",
      "numexpr          : None\n",
      "odfpy            : None\n",
      "openpyxl         : None\n",
      "pandas_gbq       : None\n",
      "pyarrow          : None\n",
      "pytables         : None\n",
      "pytest           : 5.4.1\n",
      "pyxlsb           : None\n",
      "s3fs             : None\n",
      "scipy            : 1.3.1\n",
      "sqlalchemy       : None\n",
      "tables           : None\n",
      "tabulate         : 0.8.7\n",
      "xarray           : None\n",
      "xlrd             : None\n",
      "xlwt             : None\n",
      "xlsxwriter       : None\n",
      "numba            : 0.48.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'0.21.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.show_versions(as_json=False)\n",
    "\n",
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import sklearn.metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "import itertools\n",
    "\n",
    "import scipy\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Data\n",
    "\n",
    "For educational purposes, we'll generate a synthetic dataset, rather than using a real one, at first. We'll create a dataset that has two features, both informative, with some overlap, but not much.\n",
    "\n",
    "Later, we'll graduate to using real-world datasets with more features and less seperation between classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "import random\n",
    "\n",
    "# After experiementation, this random state generates a \"good looking\" dataset\n",
    "r = 4184\n",
    "\n",
    "X, y = make_classification(n_samples=500, n_features=2, n_redundant=0, n_informative=2, weights=[0.9, 0.1],\n",
    "                           n_clusters_per_class=1, flip_y=0.09, class_sep = 1.1, random_state=r);\n",
    "\n",
    "X1 = pd.DataFrame(X, columns=['Age', 'Income'])\n",
    "y1 = pd.Series(y, name='Default')\n",
    "df = pd.concat([X1, y1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "\n",
    "ind_d = y==1\n",
    "ind_p = y==0\n",
    "plt.scatter(X[ind_d,0], X[ind_d,1], marker='o', s=200, label='Defaulted');\n",
    "plt.scatter(X[ind_p,0], X[ind_p,1], marker='o', s=200, label=\"Paid\", alpha=0.8);\n",
    "\n",
    "plt.legend(fontsize=16);\n",
    "plt.title(\"Default Dataset\", fontsize=20);\n",
    "plt.xlabel('Age (normalized)', fontsize=22);\n",
    "plt.ylabel('Income (normalized)', fontsize=22);\n",
    "plt.xticks(fontsize=18);\n",
    "plt.yticks(fontsize=18);\n",
    "plt.tight_layout();\n",
    "#plt.savefig('out/default-data.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.58597271,  2.35036464],\n",
       "       [ 0.27982946,  0.83540131],\n",
       "       [ 0.19585293,  1.24920912],\n",
       "       [ 0.82281322,  1.24967923],\n",
       "       [ 1.9198709 ,  2.8343012 ],\n",
       "       [ 0.68073054, -0.22409896],\n",
       "       [ 1.14567439,  0.24986299],\n",
       "       [ 0.96821192,  0.57301515],\n",
       "       [ 1.56205843, -0.04601173]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.748126</td>\n",
       "      <td>1.185890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.585973</td>\n",
       "      <td>2.350365</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279829</td>\n",
       "      <td>0.835401</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.195853</td>\n",
       "      <td>1.249209</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.822813</td>\n",
       "      <td>1.249679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.919871</td>\n",
       "      <td>2.834301</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.680731</td>\n",
       "      <td>-0.224099</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.145674</td>\n",
       "      <td>0.249863</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.968212</td>\n",
       "      <td>0.573015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.562058</td>\n",
       "      <td>-0.046012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.336449</td>\n",
       "      <td>2.159094</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.535550</td>\n",
       "      <td>-0.469992</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.739327</td>\n",
       "      <td>-2.246285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.955808</td>\n",
       "      <td>0.585645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.567825</td>\n",
       "      <td>0.876146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age    Income  Default\n",
       "0   0.748126  1.185890        0\n",
       "1   0.585973  2.350365        0\n",
       "2   0.279829  0.835401        0\n",
       "3   0.195853  1.249209        0\n",
       "4   0.822813  1.249679        0\n",
       "5   1.919871  2.834301        0\n",
       "6   0.680731 -0.224099        0\n",
       "7   1.145674  0.249863        1\n",
       "8   0.968212  0.573015        0\n",
       "9   1.562058 -0.046012        0\n",
       "10  2.336449  2.159094        0\n",
       "11  1.535550 -0.469992        0\n",
       "12 -1.739327 -2.246285        1\n",
       "13  0.955808  0.585645        0\n",
       "14  1.567825  0.876146        0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape\n",
    "X[1:10,:]\n",
    "y.shape\n",
    "y[1:10]\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/GermanCredit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Duration',\n",
       " 'Amount',\n",
       " 'InstallmentRatePercentage',\n",
       " 'ResidenceDuration',\n",
       " 'Age',\n",
       " 'NumberExistingCredits',\n",
       " 'NumberPeopleMaintenance',\n",
       " 'Telephone',\n",
       " 'ForeignWorker',\n",
       " 'Class',\n",
       " 'CheckingAccountStatus.lt.0',\n",
       " 'CheckingAccountStatus.0.to.200',\n",
       " 'CheckingAccountStatus.gt.200',\n",
       " 'CheckingAccountStatus.none',\n",
       " 'CreditHistory.NoCredit.AllPaid',\n",
       " 'CreditHistory.ThisBank.AllPaid',\n",
       " 'CreditHistory.PaidDuly',\n",
       " 'CreditHistory.Delay',\n",
       " 'CreditHistory.Critical',\n",
       " 'Purpose.NewCar',\n",
       " 'Purpose.UsedCar',\n",
       " 'Purpose.Furniture.Equipment',\n",
       " 'Purpose.Radio.Television',\n",
       " 'Purpose.DomesticAppliance',\n",
       " 'Purpose.Repairs',\n",
       " 'Purpose.Education',\n",
       " 'Purpose.Vacation',\n",
       " 'Purpose.Retraining',\n",
       " 'Purpose.Business',\n",
       " 'Purpose.Other',\n",
       " 'SavingsAccountBonds.lt.100',\n",
       " 'SavingsAccountBonds.100.to.500',\n",
       " 'SavingsAccountBonds.500.to.1000',\n",
       " 'SavingsAccountBonds.gt.1000',\n",
       " 'SavingsAccountBonds.Unknown',\n",
       " 'EmploymentDuration.lt.1',\n",
       " 'EmploymentDuration.1.to.4',\n",
       " 'EmploymentDuration.4.to.7',\n",
       " 'EmploymentDuration.gt.7',\n",
       " 'EmploymentDuration.Unemployed',\n",
       " 'Personal.Male.Divorced.Seperated',\n",
       " 'Personal.Female.NotSingle',\n",
       " 'Personal.Male.Single',\n",
       " 'Personal.Male.Married.Widowed',\n",
       " 'Personal.Female.Single',\n",
       " 'OtherDebtorsGuarantors.None',\n",
       " 'OtherDebtorsGuarantors.CoApplicant',\n",
       " 'OtherDebtorsGuarantors.Guarantor',\n",
       " 'Property.RealEstate',\n",
       " 'Property.Insurance',\n",
       " 'Property.CarOther',\n",
       " 'Property.Unknown',\n",
       " 'OtherInstallmentPlans.Bank',\n",
       " 'OtherInstallmentPlans.Stores',\n",
       " 'OtherInstallmentPlans.None',\n",
       " 'Housing.Rent',\n",
       " 'Housing.Own',\n",
       " 'Housing.ForFree',\n",
       " 'Job.UnemployedUnskilled',\n",
       " 'Job.UnskilledResident',\n",
       " 'Job.SkilledEmployee',\n",
       " 'Job.Management.SelfEmp.HighlyQualified']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(1000, 62)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
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       "    }\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>Duration</th>\n",
       "      <th>Amount</th>\n",
       "      <th>InstallmentRatePercentage</th>\n",
       "      <th>ResidenceDuration</th>\n",
       "      <th>Age</th>\n",
       "      <th>NumberExistingCredits</th>\n",
       "      <th>NumberPeopleMaintenance</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>ForeignWorker</th>\n",
       "      <th>Class</th>\n",
       "      <th>...</th>\n",
       "      <th>OtherInstallmentPlans.Bank</th>\n",
       "      <th>OtherInstallmentPlans.Stores</th>\n",
       "      <th>OtherInstallmentPlans.None</th>\n",
       "      <th>Housing.Rent</th>\n",
       "      <th>Housing.Own</th>\n",
       "      <th>Housing.ForFree</th>\n",
       "      <th>Job.UnemployedUnskilled</th>\n",
       "      <th>Job.UnskilledResident</th>\n",
       "      <th>Job.SkilledEmployee</th>\n",
       "      <th>Job.Management.SelfEmp.HighlyQualified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1169</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>5951</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bad</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2096</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>7882</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>4870</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bad</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration  Amount  InstallmentRatePercentage  ResidenceDuration  Age  \\\n",
       "0         6    1169                          4                  4   67   \n",
       "1        48    5951                          2                  2   22   \n",
       "2        12    2096                          2                  3   49   \n",
       "3        42    7882                          2                  4   45   \n",
       "4        24    4870                          3                  4   53   \n",
       "\n",
       "   NumberExistingCredits  NumberPeopleMaintenance  Telephone  ForeignWorker  \\\n",
       "0                      2                        1          0              1   \n",
       "1                      1                        1          1              1   \n",
       "2                      1                        2          1              1   \n",
       "3                      1                        2          1              1   \n",
       "4                      2                        2          1              1   \n",
       "\n",
       "  Class  ...  OtherInstallmentPlans.Bank  OtherInstallmentPlans.Stores  \\\n",
       "0  Good  ...                           0                             0   \n",
       "1   Bad  ...                           0                             0   \n",
       "2  Good  ...                           0                             0   \n",
       "3  Good  ...                           0                             0   \n",
       "4   Bad  ...                           0                             0   \n",
       "\n",
       "   OtherInstallmentPlans.None  Housing.Rent  Housing.Own  Housing.ForFree  \\\n",
       "0                           1             0            1                0   \n",
       "1                           1             0            1                0   \n",
       "2                           1             0            1                0   \n",
       "3                           1             0            0                1   \n",
       "4                           1             0            0                1   \n",
       "\n",
       "   Job.UnemployedUnskilled  Job.UnskilledResident  Job.SkilledEmployee  \\\n",
       "0                        0                      0                    1   \n",
       "1                        0                      0                    1   \n",
       "2                        0                      1                    0   \n",
       "3                        0                      0                    1   \n",
       "4                        0                      0                    1   \n",
       "\n",
       "   Job.Management.SelfEmp.HighlyQualified  \n",
       "0                                       0  \n",
       "1                                       0  \n",
       "2                                       0  \n",
       "3                                       0  \n",
       "4                                       0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "      <th>Amount_Scaled</th>\n",
       "      <th>Amount_MinMax</th>\n",
       "      <th>Amount_BoxCox</th>\n",
       "      <th>Amount_YeoJohnson</th>\n",
       "      <th>Amount_Uniform</th>\n",
       "      <th>Amount_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1169</td>\n",
       "      <td>-0.745131</td>\n",
       "      <td>0.050567</td>\n",
       "      <td>-0.932601</td>\n",
       "      <td>-0.932701</td>\n",
       "      <td>0.154655</td>\n",
       "      <td>7.064759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5951</td>\n",
       "      <td>0.949817</td>\n",
       "      <td>0.313690</td>\n",
       "      <td>1.156740</td>\n",
       "      <td>1.156805</td>\n",
       "      <td>0.846847</td>\n",
       "      <td>8.691483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2096</td>\n",
       "      <td>-0.416562</td>\n",
       "      <td>0.101574</td>\n",
       "      <td>-0.157834</td>\n",
       "      <td>-0.157831</td>\n",
       "      <td>0.448448</td>\n",
       "      <td>7.648263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7882</td>\n",
       "      <td>1.634247</td>\n",
       "      <td>0.419941</td>\n",
       "      <td>1.496003</td>\n",
       "      <td>1.496009</td>\n",
       "      <td>0.927928</td>\n",
       "      <td>8.972464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4870</td>\n",
       "      <td>0.566664</td>\n",
       "      <td>0.254209</td>\n",
       "      <td>0.910973</td>\n",
       "      <td>0.911056</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>8.491055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Amount  Amount_Scaled  Amount_MinMax  Amount_BoxCox  Amount_YeoJohnson  \\\n",
       "0    1169      -0.745131       0.050567      -0.932601          -0.932701   \n",
       "1    5951       0.949817       0.313690       1.156740           1.156805   \n",
       "2    2096      -0.416562       0.101574      -0.157834          -0.157831   \n",
       "3    7882       1.634247       0.419941       1.496003           1.496009   \n",
       "4    4870       0.566664       0.254209       0.910973           0.911056   \n",
       "\n",
       "   Amount_Uniform  Amount_Log  \n",
       "0        0.154655    7.064759  \n",
       "1        0.846847    8.691483  \n",
       "2        0.448448    7.648263  \n",
       "3        0.927928    8.972464  \n",
       "4        0.810811    8.491055  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "X = df[['Amount']].copy()\n",
    "\n",
    "scaler = preprocessing.StandardScaler().fit(X[['Amount']])\n",
    "X['Amount_Scaled'] = scaler.transform(X[['Amount']])   \n",
    "\n",
    "scaler = preprocessing.MinMaxScaler().fit(X[['Amount']])\n",
    "X['Amount_MinMax'] = scaler.transform(X[['Amount']])   \n",
    "\n",
    "scaler = preprocessing.PowerTransformer(method='box-cox').fit(X[['Amount']])\n",
    "X['Amount_BoxCox'] = scaler.transform(X[['Amount']])   \n",
    "\n",
    "scaler = preprocessing.PowerTransformer(method='yeo-johnson').fit(X[['Amount']])\n",
    "X['Amount_YeoJohnson'] = scaler.transform(X[['Amount']])  \n",
    "\n",
    "scaler = preprocessing.QuantileTransformer(random_state=0).fit(X[['Amount']])\n",
    "X['Amount_Uniform'] = scaler.transform(X[['Amount']])  \n",
    "\n",
    "scaler = preprocessing.FunctionTransformer(np.log1p, validate=True).fit(X[['Amount']])\n",
    "X['Amount_Log'] = scaler.transform(X[['Amount']])  \n",
    "\n",
    "type(X)\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist(ax, feature, title):\n",
    "    ax.hist(feature, bins=20, edgecolor='black', linewidth=1.2);\n",
    "    ax.set_title(title, fontsize=20);\n",
    "    ax.tick_params(axis='both', which='major', labelsize=18);\n",
    "    ax.grid(True);\n",
    "\n",
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "plot_hist(plt.subplot(3, 2, 1), X['Amount'], 'Raw')\n",
    "plot_hist(plt.subplot(3, 2, 2), X['Amount_Log'], 'Log')\n",
    "plot_hist(plt.subplot(3, 2, 3), X['Amount_Scaled'], 'Standardized')\n",
    "plot_hist(plt.subplot(3, 2, 4), X['Amount_BoxCox'], 'BoxCox')\n",
    "plot_hist(plt.subplot(3, 2, 5), X['Amount_MinMax'], 'MinMax')\n",
    "plot_hist(plt.subplot(3, 2, 6), X['Amount_YeoJohnson'], 'YeoJohnson')\n",
    "\n",
    "\n",
    "\n",
    "#plt.title('Amount', fontsize=20)\n",
    "plt.tight_layout();\n",
    "plt.savefig('out/german_credit-transforms.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contrasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 61)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(200, 61)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.copy()\n",
    "y = X[['Class']]\n",
    "X = X.drop(['Class'], axis=1)\n",
    "\n",
    "y['Class'] = y['Class'].map({'Good': 1, 'Bad': 0})\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train.shape\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import featuretools as ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entityset: transactions\n",
       "  Entities:\n",
       "    transactions [Rows: 500, Columns: 5]\n",
       "    products [Rows: 5, Columns: 2]\n",
       "    sessions [Rows: 35, Columns: 4]\n",
       "    customers [Rows: 5, Columns: 4]\n",
       "  Relationships:\n",
       "    transactions.product_id -> products.product_id\n",
       "    transactions.session_id -> sessions.session_id\n",
       "    sessions.customer_id -> customers.customer_id"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es = ft.demo.load_mock_customer(return_entityset=True)\n",
    "es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>transaction_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>transaction_time</th>\n",
       "      <th>amount</th>\n",
       "      <th>product_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:00:00</td>\n",
       "      <td>127.64</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:01:05</td>\n",
       "      <td>109.48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:02:10</td>\n",
       "      <td>95.06</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:03:15</td>\n",
       "      <td>78.92</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>371</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:04:20</td>\n",
       "      <td>31.54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>486</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:05:25</td>\n",
       "      <td>23.76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>271</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:06:30</td>\n",
       "      <td>43.63</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:07:35</td>\n",
       "      <td>42.27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:08:40</td>\n",
       "      <td>47.68</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:09:45</td>\n",
       "      <td>57.39</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:10:50</td>\n",
       "      <td>20.91</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:11:55</td>\n",
       "      <td>141.66</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:13:00</td>\n",
       "      <td>121.75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>495</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:14:05</td>\n",
       "      <td>69.45</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>438</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:15:10</td>\n",
       "      <td>76.38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:16:15</td>\n",
       "      <td>141.49</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>353</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:17:20</td>\n",
       "      <td>42.99</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>270</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:18:25</td>\n",
       "      <td>123.53</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>453</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:19:30</td>\n",
       "      <td>9.32</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>370</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:20:35</td>\n",
       "      <td>106.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:21:40</td>\n",
       "      <td>116.02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>250</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:22:45</td>\n",
       "      <td>60.09</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:23:50</td>\n",
       "      <td>90.69</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:24:55</td>\n",
       "      <td>48.27</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>399</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:26:00</td>\n",
       "      <td>135.25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 00:27:05</td>\n",
       "      <td>13.81</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>496</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 00:28:10</td>\n",
       "      <td>137.82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>459</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 00:29:15</td>\n",
       "      <td>23.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 00:30:20</td>\n",
       "      <td>120.08</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>384</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 00:31:25</td>\n",
       "      <td>26.16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     transaction_id  session_id    transaction_time  amount product_id\n",
       "298             298           1 2014-01-01 00:00:00  127.64          5\n",
       "2                 2           1 2014-01-01 00:01:05  109.48          2\n",
       "308             308           1 2014-01-01 00:02:10   95.06          3\n",
       "116             116           1 2014-01-01 00:03:15   78.92          4\n",
       "371             371           1 2014-01-01 00:04:20   31.54          3\n",
       "486             486           1 2014-01-01 00:05:25   23.76          3\n",
       "271             271           1 2014-01-01 00:06:30   43.63          3\n",
       "192             192           1 2014-01-01 00:07:35   42.27          4\n",
       "341             341           1 2014-01-01 00:08:40   47.68          3\n",
       "10               10           1 2014-01-01 00:09:45   57.39          5\n",
       "29               29           1 2014-01-01 00:10:50   20.91          2\n",
       "158             158           1 2014-01-01 00:11:55  141.66          4\n",
       "245             245           1 2014-01-01 00:13:00  121.75          1\n",
       "495             495           1 2014-01-01 00:14:05   69.45          5\n",
       "438             438           1 2014-01-01 00:15:10   76.38          4\n",
       "175             175           1 2014-01-01 00:16:15  141.49          2\n",
       "353             353           2 2014-01-01 00:17:20   42.99          4\n",
       "270             270           2 2014-01-01 00:18:25  123.53          5\n",
       "453             453           2 2014-01-01 00:19:30    9.32          5\n",
       "370             370           2 2014-01-01 00:20:35  106.99          1\n",
       "22               22           2 2014-01-01 00:21:40  116.02          3\n",
       "250             250           2 2014-01-01 00:22:45   60.09          2\n",
       "74               74           2 2014-01-01 00:23:50   90.69          5\n",
       "207             207           2 2014-01-01 00:24:55   48.27          5\n",
       "399             399           2 2014-01-01 00:26:00  135.25          2\n",
       "122             122           2 2014-01-01 00:27:05   13.81          5\n",
       "496             496           3 2014-01-01 00:28:10  137.82          1\n",
       "459             459           3 2014-01-01 00:29:15   23.20          1\n",
       "134             134           3 2014-01-01 00:30:20  120.08          3\n",
       "384             384           3 2014-01-01 00:31:25   26.16          4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es[\"transactions\"].df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built 73 features\n",
      "Elapsed: 00:00 | Remaining: 00:00 | Progress: 100%|████████████████████████████████████████████| Calculated: 1/1 chunks\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>COUNT(sessions)</th>\n",
       "      <th>NUM_UNIQUE(sessions.device)</th>\n",
       "      <th>MODE(sessions.device)</th>\n",
       "      <th>SUM(transactions.amount)</th>\n",
       "      <th>STD(transactions.amount)</th>\n",
       "      <th>MAX(transactions.amount)</th>\n",
       "      <th>SKEW(transactions.amount)</th>\n",
       "      <th>MIN(transactions.amount)</th>\n",
       "      <th>MEAN(transactions.amount)</th>\n",
       "      <th>...</th>\n",
       "      <th>NUM_UNIQUE(sessions.MODE(transactions.product_id))</th>\n",
       "      <th>NUM_UNIQUE(sessions.DAY(session_start))</th>\n",
       "      <th>NUM_UNIQUE(sessions.YEAR(session_start))</th>\n",
       "      <th>NUM_UNIQUE(sessions.MONTH(session_start))</th>\n",
       "      <th>NUM_UNIQUE(sessions.WEEKDAY(session_start))</th>\n",
       "      <th>MODE(sessions.MODE(transactions.product_id))</th>\n",
       "      <th>MODE(sessions.DAY(session_start))</th>\n",
       "      <th>MODE(sessions.YEAR(session_start))</th>\n",
       "      <th>MODE(sessions.MONTH(session_start))</th>\n",
       "      <th>MODE(sessions.WEEKDAY(session_start))</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60091</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>mobile</td>\n",
       "      <td>9025.62</td>\n",
       "      <td>40.442059</td>\n",
       "      <td>139.43</td>\n",
       "      <td>0.019698</td>\n",
       "      <td>5.81</td>\n",
       "      <td>71.631905</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13244</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>desktop</td>\n",
       "      <td>7200.28</td>\n",
       "      <td>37.705178</td>\n",
       "      <td>146.81</td>\n",
       "      <td>0.098259</td>\n",
       "      <td>8.73</td>\n",
       "      <td>77.422366</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13244</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>desktop</td>\n",
       "      <td>6236.62</td>\n",
       "      <td>43.683296</td>\n",
       "      <td>149.15</td>\n",
       "      <td>0.418230</td>\n",
       "      <td>5.89</td>\n",
       "      <td>67.060430</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60091</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>mobile</td>\n",
       "      <td>8727.68</td>\n",
       "      <td>45.068765</td>\n",
       "      <td>149.95</td>\n",
       "      <td>-0.036348</td>\n",
       "      <td>5.73</td>\n",
       "      <td>80.070459</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60091</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>mobile</td>\n",
       "      <td>6349.66</td>\n",
       "      <td>44.095630</td>\n",
       "      <td>149.02</td>\n",
       "      <td>-0.025941</td>\n",
       "      <td>7.55</td>\n",
       "      <td>80.375443</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            zip_code  COUNT(sessions)  NUM_UNIQUE(sessions.device)  \\\n",
       "customer_id                                                          \n",
       "1              60091                8                            3   \n",
       "2              13244                7                            3   \n",
       "3              13244                6                            3   \n",
       "4              60091                8                            3   \n",
       "5              60091                6                            3   \n",
       "\n",
       "            MODE(sessions.device)  SUM(transactions.amount)  \\\n",
       "customer_id                                                   \n",
       "1                          mobile                   9025.62   \n",
       "2                         desktop                   7200.28   \n",
       "3                         desktop                   6236.62   \n",
       "4                          mobile                   8727.68   \n",
       "5                          mobile                   6349.66   \n",
       "\n",
       "             STD(transactions.amount)  MAX(transactions.amount)  \\\n",
       "customer_id                                                       \n",
       "1                           40.442059                    139.43   \n",
       "2                           37.705178                    146.81   \n",
       "3                           43.683296                    149.15   \n",
       "4                           45.068765                    149.95   \n",
       "5                           44.095630                    149.02   \n",
       "\n",
       "             SKEW(transactions.amount)  MIN(transactions.amount)  \\\n",
       "customer_id                                                        \n",
       "1                             0.019698                      5.81   \n",
       "2                             0.098259                      8.73   \n",
       "3                             0.418230                      5.89   \n",
       "4                            -0.036348                      5.73   \n",
       "5                            -0.025941                      7.55   \n",
       "\n",
       "             MEAN(transactions.amount)  ...  \\\n",
       "customer_id                             ...   \n",
       "1                            71.631905  ...   \n",
       "2                            77.422366  ...   \n",
       "3                            67.060430  ...   \n",
       "4                            80.070459  ...   \n",
       "5                            80.375443  ...   \n",
       "\n",
       "             NUM_UNIQUE(sessions.MODE(transactions.product_id))  \\\n",
       "customer_id                                                       \n",
       "1                                                            4    \n",
       "2                                                            4    \n",
       "3                                                            4    \n",
       "4                                                            5    \n",
       "5                                                            5    \n",
       "\n",
       "             NUM_UNIQUE(sessions.DAY(session_start))  \\\n",
       "customer_id                                            \n",
       "1                                                  1   \n",
       "2                                                  1   \n",
       "3                                                  1   \n",
       "4                                                  1   \n",
       "5                                                  1   \n",
       "\n",
       "             NUM_UNIQUE(sessions.YEAR(session_start))  \\\n",
       "customer_id                                             \n",
       "1                                                   1   \n",
       "2                                                   1   \n",
       "3                                                   1   \n",
       "4                                                   1   \n",
       "5                                                   1   \n",
       "\n",
       "             NUM_UNIQUE(sessions.MONTH(session_start))  \\\n",
       "customer_id                                              \n",
       "1                                                    1   \n",
       "2                                                    1   \n",
       "3                                                    1   \n",
       "4                                                    1   \n",
       "5                                                    1   \n",
       "\n",
       "             NUM_UNIQUE(sessions.WEEKDAY(session_start))  \\\n",
       "customer_id                                                \n",
       "1                                                      1   \n",
       "2                                                      1   \n",
       "3                                                      1   \n",
       "4                                                      1   \n",
       "5                                                      1   \n",
       "\n",
       "             MODE(sessions.MODE(transactions.product_id))  \\\n",
       "customer_id                                                 \n",
       "1                                                       4   \n",
       "2                                                       3   \n",
       "3                                                       1   \n",
       "4                                                       1   \n",
       "5                                                       3   \n",
       "\n",
       "             MODE(sessions.DAY(session_start))  \\\n",
       "customer_id                                      \n",
       "1                                            1   \n",
       "2                                            1   \n",
       "3                                            1   \n",
       "4                                            1   \n",
       "5                                            1   \n",
       "\n",
       "             MODE(sessions.YEAR(session_start))  \\\n",
       "customer_id                                       \n",
       "1                                          2014   \n",
       "2                                          2014   \n",
       "3                                          2014   \n",
       "4                                          2014   \n",
       "5                                          2014   \n",
       "\n",
       "             MODE(sessions.MONTH(session_start))  \\\n",
       "customer_id                                        \n",
       "1                                              1   \n",
       "2                                              1   \n",
       "3                                              1   \n",
       "4                                              1   \n",
       "5                                              1   \n",
       "\n",
       "             MODE(sessions.WEEKDAY(session_start))  \n",
       "customer_id                                         \n",
       "1                                                2  \n",
       "2                                                2  \n",
       "3                                                2  \n",
       "4                                                2  \n",
       "5                                                2  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix, features = ft.dfs(entityset=es,\n",
    "                                   target_entity=\"customers\",\n",
    "                                   verbose=True)\n",
    "feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zip_code',\n",
       " 'COUNT(sessions)',\n",
       " 'NUM_UNIQUE(sessions.device)',\n",
       " 'MODE(sessions.device)',\n",
       " 'SUM(transactions.amount)',\n",
       " 'STD(transactions.amount)',\n",
       " 'MAX(transactions.amount)',\n",
       " 'SKEW(transactions.amount)',\n",
       " 'MIN(transactions.amount)',\n",
       " 'MEAN(transactions.amount)',\n",
       " 'COUNT(transactions)',\n",
       " 'NUM_UNIQUE(transactions.product_id)',\n",
       " 'MODE(transactions.product_id)',\n",
       " 'DAY(join_date)',\n",
       " 'DAY(date_of_birth)',\n",
       " 'YEAR(join_date)',\n",
       " 'YEAR(date_of_birth)',\n",
       " 'MONTH(join_date)',\n",
       " 'MONTH(date_of_birth)',\n",
       " 'WEEKDAY(join_date)',\n",
       " 'WEEKDAY(date_of_birth)',\n",
       " 'SUM(sessions.STD(transactions.amount))',\n",
       " 'SUM(sessions.MAX(transactions.amount))',\n",
       " 'SUM(sessions.SKEW(transactions.amount))',\n",
       " 'SUM(sessions.MIN(transactions.amount))',\n",
       " 'SUM(sessions.MEAN(transactions.amount))',\n",
       " 'SUM(sessions.NUM_UNIQUE(transactions.product_id))',\n",
       " 'STD(sessions.SUM(transactions.amount))',\n",
       " 'STD(sessions.MAX(transactions.amount))',\n",
       " 'STD(sessions.SKEW(transactions.amount))',\n",
       " 'STD(sessions.MIN(transactions.amount))',\n",
       " 'STD(sessions.MEAN(transactions.amount))',\n",
       " 'STD(sessions.COUNT(transactions))',\n",
       " 'STD(sessions.NUM_UNIQUE(transactions.product_id))',\n",
       " 'MAX(sessions.SUM(transactions.amount))',\n",
       " 'MAX(sessions.STD(transactions.amount))',\n",
       " 'MAX(sessions.SKEW(transactions.amount))',\n",
       " 'MAX(sessions.MIN(transactions.amount))',\n",
       " 'MAX(sessions.MEAN(transactions.amount))',\n",
       " 'MAX(sessions.COUNT(transactions))',\n",
       " 'MAX(sessions.NUM_UNIQUE(transactions.product_id))',\n",
       " 'SKEW(sessions.SUM(transactions.amount))',\n",
       " 'SKEW(sessions.STD(transactions.amount))',\n",
       " 'SKEW(sessions.MAX(transactions.amount))',\n",
       " 'SKEW(sessions.MIN(transactions.amount))',\n",
       " 'SKEW(sessions.MEAN(transactions.amount))',\n",
       " 'SKEW(sessions.COUNT(transactions))',\n",
       " 'SKEW(sessions.NUM_UNIQUE(transactions.product_id))',\n",
       " 'MIN(sessions.SUM(transactions.amount))',\n",
       " 'MIN(sessions.STD(transactions.amount))',\n",
       " 'MIN(sessions.MAX(transactions.amount))',\n",
       " 'MIN(sessions.SKEW(transactions.amount))',\n",
       " 'MIN(sessions.MEAN(transactions.amount))',\n",
       " 'MIN(sessions.COUNT(transactions))',\n",
       " 'MIN(sessions.NUM_UNIQUE(transactions.product_id))',\n",
       " 'MEAN(sessions.SUM(transactions.amount))',\n",
       " 'MEAN(sessions.STD(transactions.amount))',\n",
       " 'MEAN(sessions.MAX(transactions.amount))',\n",
       " 'MEAN(sessions.SKEW(transactions.amount))',\n",
       " 'MEAN(sessions.MIN(transactions.amount))',\n",
       " 'MEAN(sessions.MEAN(transactions.amount))',\n",
       " 'MEAN(sessions.COUNT(transactions))',\n",
       " 'MEAN(sessions.NUM_UNIQUE(transactions.product_id))',\n",
       " 'NUM_UNIQUE(sessions.MODE(transactions.product_id))',\n",
       " 'NUM_UNIQUE(sessions.DAY(session_start))',\n",
       " 'NUM_UNIQUE(sessions.YEAR(session_start))',\n",
       " 'NUM_UNIQUE(sessions.MONTH(session_start))',\n",
       " 'NUM_UNIQUE(sessions.WEEKDAY(session_start))',\n",
       " 'MODE(sessions.MODE(transactions.product_id))',\n",
       " 'MODE(sessions.DAY(session_start))',\n",
       " 'MODE(sessions.YEAR(session_start))',\n",
       " 'MODE(sessions.MONTH(session_start))',\n",
       " 'MODE(sessions.WEEKDAY(session_start))']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(feature_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>num_unique</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Returns the number of unique categorical varia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>all</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Test if all values are 'True'.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sum</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Sums elements of a numeric or boolean feature.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>max</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Finds the maximum non-null value of a numeric ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>skew</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Computes the skewness of a data set.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>cum_min</td>\n",
       "      <td>transform</td>\n",
       "      <td>Returns the cumulative min after grouping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>equal</td>\n",
       "      <td>transform</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>greater_than_equal_to_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>isin</td>\n",
       "      <td>transform</td>\n",
       "      <td>For each value of the base feature, checks whe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>divide_by_feature</td>\n",
       "      <td>transform</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name         type  \\\n",
       "0                     num_unique  aggregation   \n",
       "1                            all  aggregation   \n",
       "2                            sum  aggregation   \n",
       "3                            max  aggregation   \n",
       "4                           skew  aggregation   \n",
       "..                           ...          ...   \n",
       "71                       cum_min    transform   \n",
       "72                         equal    transform   \n",
       "73  greater_than_equal_to_scalar    transform   \n",
       "74                          isin    transform   \n",
       "75             divide_by_feature    transform   \n",
       "\n",
       "                                          description  \n",
       "0   Returns the number of unique categorical varia...  \n",
       "1                      Test if all values are 'True'.  \n",
       "2      Sums elements of a numeric or boolean feature.  \n",
       "3   Finds the maximum non-null value of a numeric ...  \n",
       "4                Computes the skewness of a data set.  \n",
       "..                                                ...  \n",
       "71          Returns the cumulative min after grouping  \n",
       "72                                                     \n",
       "73                                                     \n",
       "74  For each value of the base feature, checks whe...  \n",
       "75                                                     \n",
       "\n",
       "[76 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ft.list_primitives() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, f1_score, log_loss, confusion_matrix\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Helper function\n",
    "def evaluate_with_dt(X_train, X_test, y_train, y_test):\n",
    "    clf_full = DecisionTreeClassifier(random_state=0)\n",
    "    clf_full.fit(X_train, y_train)\n",
    "    y_pred_dt = clf_full.predict(X_test)\n",
    "    print (\"\\n Confusion Matrix : \\n\", confusion_matrix(y_test, y_pred_dt))\n",
    "    print(\"Accuracy = {:.2f}\".format(accuracy_score(y_test, y_pred_dt)))\n",
    "    print(\"F1 Score = {:.2f}\".format(f1_score(y_test, y_pred_dt)))\n",
    "    print(\"Kappa = {:.2f}\".format(cohen_kappa_score(y_test, y_pred_dt)))\n",
    "    print(\"Log Loss = {:.2f}\".format(log_loss(y_test, y_pred_dt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Confusion Matrix : \n",
      " [[ 29  30]\n",
      " [ 34 107]]\n",
      "Accuracy = 0.68\n",
      "F1 Score = 0.77\n",
      "Kappa = 0.25\n",
      "Log Loss = 11.05\n"
     ]
    }
   ],
   "source": [
    "evaluate_with_dt(X_train, X_test, y_train['Class'], y_test['Class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variance Threshold (Filter Method, Unsupervised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 61)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "sel = VarianceThreshold(threshold=(0.1));\n",
    "sel.fit(X_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.39235975e+02, 8.38891826e+06, 1.25877500e+00, 1.22011094e+00,\n",
       "       1.30061548e+02, 3.34623437e-01, 1.23085937e-01, 2.43600000e-01,\n",
       "       3.37750000e-02, 1.95943750e-01, 1.95943750e-01, 5.74984375e-02,\n",
       "       2.40735937e-01, 4.18359375e-02, 4.52437500e-02, 2.49375000e-01,\n",
       "       8.29234375e-02, 2.06943750e-01, 1.81093750e-01, 9.39750000e-02,\n",
       "       1.41923437e-01, 2.02693750e-01, 1.35609375e-02, 2.31859375e-02,\n",
       "       4.41109375e-02, 0.00000000e+00, 8.67343750e-03, 8.89984375e-02,\n",
       "       1.23437500e-02, 2.41443750e-01, 9.19937500e-02, 6.51000000e-02,\n",
       "       4.63734375e-02, 1.51560938e-01, 1.39443750e-01, 2.23593750e-01,\n",
       "       1.44375000e-01, 1.88743750e-01, 6.29437500e-02, 4.06937500e-02,\n",
       "       2.16693750e-01, 2.47500000e-01, 8.19000000e-02, 0.00000000e+00,\n",
       "       9.00000000e-02, 4.06937500e-02, 5.41937500e-02, 1.99375000e-01,\n",
       "       1.79775000e-01, 2.23185937e-01, 1.30110937e-01, 1.17685938e-01,\n",
       "       4.18359375e-02, 1.47600000e-01, 1.44375000e-01, 2.03235937e-01,\n",
       "       9.69234375e-02, 2.07984375e-02, 1.52343750e-01, 2.32773437e-01,\n",
       "       1.34400000e-01])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "        True,  True, False,  True, False, False,  True, False,  True,\n",
       "        True, False,  True,  True, False, False, False, False, False,\n",
       "       False, False,  True, False, False, False,  True,  True,  True,\n",
       "        True,  True, False, False,  True,  True, False, False, False,\n",
       "       False, False,  True,  True,  True,  True,  True, False,  True,\n",
       "        True,  True, False, False,  True,  True,  True])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel.variances_\n",
    "sel.get_support()\n",
    "sum(sel.get_support())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Confusion Matrix : \n",
      " [[ 34  25]\n",
      " [ 30 111]]\n",
      "Accuracy = 0.72\n",
      "F1 Score = 0.80\n",
      "Kappa = 0.35\n",
      "Log Loss = 9.50\n"
     ]
    }
   ],
   "source": [
    "X_train_new = X_train.loc[:, sel.get_support()]\n",
    "X_test_new = X_test.loc[:, sel.get_support()]\n",
    "\n",
    "evaluate_with_dt(X_train_new, X_test_new, y_train['Class'], y_test['Class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Feature Selection (Filter Method, Supervised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=10, score_func=<function chi2 at 0x000001F52CDB87B8>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "\n",
    "sel = SelectKBest(chi2, k=10)\n",
    "sel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.06823246e-48, 0.00000000e+00, 4.09363596e-01, 8.53102259e-01,\n",
       "       5.61882097e-14, 6.44590179e-01, 9.63002992e-01, 5.97370086e-01,\n",
       "       6.69852691e-01, 1.54978247e-09, 5.75839949e-03, 1.38203850e-01,\n",
       "       4.52646515e-11, 4.45131086e-06, 1.90630991e-04, 3.67405390e-01,\n",
       "       9.98219008e-01, 7.21676188e-06, 8.03585798e-03, 2.69078763e-02,\n",
       "       8.92054990e-01, 2.23837345e-03, 6.52000750e-01, 8.90134400e-01,\n",
       "       1.40549331e-02,            nan, 3.61033126e-01, 3.02894981e-01,\n",
       "       1.70724193e-01, 2.01603259e-02, 3.00953646e-01, 4.53967611e-02,\n",
       "       1.85022557e-02, 1.31559791e-02, 2.85051843e-02, 6.27098026e-01,\n",
       "       3.40446647e-01, 2.27435459e-02, 1.60413012e-01, 3.02659985e-01,\n",
       "       3.42272027e-02, 6.82146244e-02, 8.59320947e-01,            nan,\n",
       "       8.64553871e-01, 5.11213923e-01, 2.15101186e-01, 1.06310578e-03,\n",
       "       9.53731444e-01, 5.09482599e-01, 8.67207881e-04, 5.99308430e-03,\n",
       "       3.65503443e-01, 1.83767520e-01, 6.31610930e-03, 1.53712588e-02,\n",
       "       5.86305068e-03, 9.48893561e-01, 2.00858431e-01, 9.32849216e-01,\n",
       "       2.14727509e-01])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False,  True, False, False, False, False,\n",
       "        True, False, False,  True,  True,  True, False, False,  True,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False,  True, False, False,  True, False, False, False,\n",
       "       False, False, False, False, False, False, False])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel.pvalues_\n",
    "sel.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Confusion Matrix : \n",
      " [[ 30  29]\n",
      " [ 22 119]]\n",
      "Accuracy = 0.74\n",
      "F1 Score = 0.82\n",
      "Kappa = 0.37\n",
      "Log Loss = 8.81\n"
     ]
    }
   ],
   "source": [
    "X_train_new = X_train.loc[:, sel.get_support()]\n",
    "X_test_new = X_test.loc[:, sel.get_support()]\n",
    "\n",
    "evaluate_with_dt(X_train_new, X_test_new, y_train['Class'], y_test['Class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination (Wrapper Method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\st50\\AppData\\Local\\Continuum\\anaconda3\\envs\\small_sklearn\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 7.31 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RFECV(cv=StratifiedKFold(n_splits=20, random_state=None, shuffle=False),\n",
       "      estimator=DecisionTreeClassifier(class_weight=None, criterion='gini',\n",
       "                                       max_depth=None, max_features=None,\n",
       "                                       max_leaf_nodes=None,\n",
       "                                       min_impurity_decrease=0.0,\n",
       "                                       min_impurity_split=None,\n",
       "                                       min_samples_leaf=1, min_samples_split=2,\n",
       "                                       min_weight_fraction_leaf=0.0,\n",
       "                                       presort=False, random_state=0,\n",
       "                                       splitter='best'),\n",
       "      min_features_to_select=1, n_jobs=None, scoring='accuracy', step=1,\n",
       "      verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "clf = DecisionTreeClassifier(random_state=0)\n",
    "rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(20), scoring='accuracy')\n",
    "%time rfecv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 49\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  9, 10, 11, 12, 13, 14, 15, 16, 18,\n",
       "       19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39,\n",
       "       40, 42, 44, 45, 46, 48, 49, 50, 52, 53, 54, 55, 56, 58, 59],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['Duration', 'Amount', 'InstallmentRatePercentage', 'ResidenceDuration',\n",
       "       'Age', 'NumberExistingCredits', 'NumberPeopleMaintenance', 'Telephone',\n",
       "       'CheckingAccountStatus.lt.0', 'CheckingAccountStatus.0.to.200',\n",
       "       'CheckingAccountStatus.gt.200', 'CheckingAccountStatus.none',\n",
       "       'CreditHistory.NoCredit.AllPaid', 'CreditHistory.ThisBank.AllPaid',\n",
       "       'CreditHistory.PaidDuly', 'CreditHistory.Delay', 'Purpose.NewCar',\n",
       "       'Purpose.UsedCar', 'Purpose.Furniture.Equipment',\n",
       "       'Purpose.Radio.Television', 'Purpose.DomesticAppliance',\n",
       "       'Purpose.Repairs', 'Purpose.Education', 'Purpose.Business',\n",
       "       'Purpose.Other', 'SavingsAccountBonds.lt.100',\n",
       "       'SavingsAccountBonds.100.to.500', 'SavingsAccountBonds.500.to.1000',\n",
       "       'SavingsAccountBonds.Unknown', 'EmploymentDuration.lt.1',\n",
       "       'EmploymentDuration.1.to.4', 'EmploymentDuration.4.to.7',\n",
       "       'EmploymentDuration.Unemployed', 'Personal.Male.Divorced.Seperated',\n",
       "       'Personal.Female.NotSingle', 'Personal.Male.Married.Widowed',\n",
       "       'OtherDebtorsGuarantors.None', 'OtherDebtorsGuarantors.CoApplicant',\n",
       "       'OtherDebtorsGuarantors.Guarantor', 'Property.Insurance',\n",
       "       'Property.CarOther', 'Property.Unknown', 'OtherInstallmentPlans.Stores',\n",
       "       'OtherInstallmentPlans.None', 'Housing.Rent', 'Housing.Own',\n",
       "       'Housing.ForFree', 'Job.UnskilledResident', 'Job.SkilledEmployee'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)\n",
    "rfecv.get_support(indices=True)\n",
    "X_train.columns[rfecv.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cO25m7wd+DvyXmd2KN+WrJHgsH/AF59xZHeemmb8EHg4+vzN4rWrxrncpsByvntB/hnZwzj1hZrcB3wE2mVk1XqC3C6+o+FK8zL5/xfv7JiIiCUQBHhERmVGcc43AejN7M960nCvwWgunAX68duQP4HX1eWaY/evM7FLgFrwPyRcCl+G1Ka8HvsoYPqROBedcS7DQ8ufxphq9BS+Ysgn4LF4gZKrH9D0zewD4MF5g5WK8D6xdQDVeRs09zrmqCRx73NfGOTdoZu/Ay3q5CXgD3n2wGfhnvJpB43UbXu2XPwuOIxkvUHh7cH0oi+VFRi92PJwKXg3m9AXHW4c3Het3wEPOuWEDR8Hlf2FmP8UL7l0KXISXEdOIVwvnAeCVIfs9YmYXAX8ffF1vwSvmW49XyPsXYxj3nXgZdFfgFTvPw+tuVwXcA/xkpHEP8zoeCN7bnwE24tWbasdra/9N59zvxnKceOacOxXMSHo/Xuexi/Hetxa87lvfYpi/Nc65H5rZDuD/4P1tuxovGNeIV7T5YeDXU/EaRERkaplzEWmMICIiIiJjYGb/hJcJ8w7n3IOxHo+IiIgkBgV4RERERKZQcBpYqnNuvIWkRUREREakAI+IiIiIiIiIyDSnLloiIiIiIiIiItOcAjwiIiIiIiIiItOcumhNUGFhoVu0aFHMzt/Z2UlmZmbMzi8zm+4/iSXdfxJLuv8klnT/SSzp/pNYmun3365du5qdc3NG204BnglatGgRzz33XMzOv3XrVjZs2BCz88vMpvtPYkn3n8SS7j+JJd1/Eku6/ySWZvr9Z2Y1Y9lOU7RERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKY5BXhERERERERERKa5uAjwmJnPzD5pZgfNrMfMjpnZnWaWOY5j5JvZV83scPAYJ83sMTNbH7bNIjNzozzeG51XKSIiIiIiIiISHcmxHkDQfwIfB+4H7gRWBH9ebWZvcM4FzrWzmZUBW4Es4AfAS0AOcBEwP2zTk8D7RzjMt4BZwKMTfhUiIiIiIiIiIjEQ8wCPma0EPgZscs7dELb8CPAN4EbgvlEO8xO813KRc65xpI2cc53BbYeOYS1eQOhXzrnmcb8IEREREREREZEYiocpWn8JGPD1Icu/D3QB7zvXzmZ2JbAO+LJzrtHMUswsY5xjuDX4fNc49xMRERERERERibl4CPC8DggAz4QvdM71AM8H15/Lnwefa83sIaAb6DSzl8zsnMEhADPLAt4D1AJ/HOfYRURERERE4tpvdtfzhYdfjPUwRCTKzDkX2wGY7QWKnHPFw6z7BfBuIM051zfC/vcD78Srr/My8G0gDfgUsBK42Tn3w3Oc/xa8zJ07nHOfG2WsHwQ+CFBcXLzmZz/72egvMEo6OjrIysqK2fllZtP9J7Gk+09iSfefxJLuP5mo/9zVw97mQf7nmgzSkm1Cx9D9J7E00++/q6++epdz7pLRtot5DR4gA+gdYV1P2DbDBniA2cHnduDqUCAoGPipBr5oZveco1DzrXgZRCMGgUKcc98DvgdwySWXuA0bNoy2S9Rs3bqVWJ5fZjbdfxJLuv8klnT/SSzp/pOJ+o892wi4NnLKL+Ly8oIJHUP3n8SS7r+xiYcpWl14GTfDSQ/bZiTdwef/Dc/ycc61AA8Cc4Flw+1oZucDlwN/dM7VjmfQIiIiIiIi00Gj3/vIVFXbEuORiEg0xUOApwEoNLPhgjzzgeaRpmcF1QWfjw+zLtRRK2+EfW8JPqu4soiIiIiIJJzuvkFau/oBqKpRgEckkcVDgOdZvHFcGr7QzNKBi4HnRtk/VJx5wTDrQsuahq4wsxTg/Xi1ex4Yx3hFRERERESmhVD2TmZqElW1rcS6BquIRE88BHh+DjjgE0OW34ZXe+enoQVmVmFmy4ds9xu8+jvvC3bECm07D6/48svOucPDnPcdwBzgx865/km/ChERERERkTjT6PfKml6zopjTnX3UnDpX9QsRmc5iHuBxzu0F/hu43sw2mdmtZnYn8DXgceC+sM03AweG7N8CfBpvOtdTZvYpM7sdeApIBT46wqk1PUtERERERBJaQ6uXwfPWi+YBsEvTtEQSVswDPEGfwAvSrMQL9twIfBN46zm6X50R7G51A9ABfB74v8AhvK5afxi6vZktAN4E7HDOHRi6XkREREREJBGEMnjWL5lDVlqyCi2LJLB4aJOOc24QuDP4ONd2i86xbhOwaYznqwOSxjFEERERERGRaafR30NhViqzUpNYXZpLVW1rrIckIlESLxk8IiIiIiIiEmGN/m7m5cwCYHVpHoeOt9HROxDjUYlINCjAIyIiIiIikqAaW3uYm5MOQGVpLgEHe44pi0ckESnAIyIiIiIikqAa/N2UBAM8q0vzAKhSoWWRhKQAj4iIiIiISALq6B2gvWeAebneFK2cWSksKcpSoWWRBKUAj4iIiIiISAI67vdapM8LZvAAVJbmUVXbSiDgYjUsEYkSBXhEREREREQSUEOr1yK9JJjBA7CmLA9/dz/VzZ2xGpaIRIkCPCIiIiIiIgmoMZjBMzc7LIOnLBdA07REEpACPCIiIiIiIgmoobUHM8500QIoL8wiOz1ZhZZFEpACPCIiIiIiIgmo0d/NnKw0UpJe/djn8xmVZXnK4BFJQArwiIiIiIiIJKBGf8+ZDlrhKkvzeLmpA393fwxGJSLRogCPiIiIiIhIAmr091ASNj0rpLI0D+fg+WOtMRiViESLAjwiIiIiIiIJxjlHY2v3a+rvhKxamIMZqsMjkmAU4BEREREREUkwbT0DdPYNUpJz9hSt2ekpLCuerTo8IglGAR4REREREZEEE2qRPi/37AwegMqyPJ6vbSUQcFM5LBGJIgV4REREREREEkxjaw8A84bJ4AGvDk977wAvN3VM5bBEJIoU4BEREREREUkwjX4vwFMyQgbPmrI8AE3TEkkgCvCIiIiIiIgkmEZ/Nz6DOVlpw65fVJBBfmYqu1RoWSRhKMAjIiIiIiJx7XtPvMLf3VcV62FM2I+ePMI//HLPlJ6zobWH4ux0kpOG/8hnZqxemKsMHpEEogCPiIiIiIjErbaefr6x+TC/faGRhtbuWA9n3Jxz/HDHUR7Y0zClBY0b/d3MG6ZFerjKsjyqT3bS0tk3RaMSkWhSgEdEREREROLWz585RkfvAACPHWqK8WjGr7q5k5pTXfQNBDjR3jNl52309zAvd/gCyyGVpV4dnt3HlMUjkggU4BERERERkbjUPxjgh08e4bLF+ZTmZ7DlwPQL8Dx28NUxH23umpJzOudo9HdTMkoGz6qFOST5jKqa1ikZl4hElwI8IiIiIiISl363t5EGfw+3rS9n4/Iith9uprtvMNbDGpfNB5rIy0gBoOZU55Scs7Wrn57+AHNHaJEekpGazIp5s1WHRyRBKMAjIiIiIiJxxznHXduOUD4nk43Li9i4vIjegQA7q5tjPbQx83f38+zR07z7koWkJBk1p6cmg6fB79UqGi2DB7xpWnuOtTIwGIj2sEQkyhTgERERERGRuPP0kdPsrfdzy7rF+HzGZeX5ZKQmseXg9Jmmte3lkwwEHG86v5gFeRlTlsHT2OrV+hmtBg94AZ7OvkEOnWiP9rBEJMoU4BERERERkbhz17Yj5GWkcEPlAgDSkpNYv6SQLQeacG7qulFNxpaDTeRmpLC6NI+yggxqTk1NBk/jODJ41pR5hZaralWHR2S6U4BHRERERETiSvXJDjYfPMH7Ly8jPSXpzPKNy4to8Pdw8Hj8Z5sMBhxbD51kw9I5JPmMRQWZ1JzqmpLgVKO/h2SfUZiVNuq2C/JmUZiVxu4a1eFJVJ29A5zq6I31MGQKKMAjIiIiIiJx5Qfbj5CS5OP9axe9ZvnVy4oApsU0reePtXK6s4+NK4oBKM3PoKN3gNOdfVE/d6O/h+LsdHw+G3VbM6OyNJddKrScsO54cD/v/u7OWA9DpoACPCIiIiIiEjdOd/bxq111XHfxfObMfm0GSlF2OhctyJkWAZ7HDjaR5DOuWjIHgEWFGQAcnYJpWg2t3ZTkjj49K2RNWR41p7poVpZHQnqupoXqk52caOuJ9VAkyhTgERERERGRuPGTp2roHQhw6/rFw66/elkRVbUtU5IJMxmbDzaxpiyPnGCL9NL8TABqT0e/0HKjv4d5o7RID1cZrMOzW3V4Ek5bTz9Hmr17rkrT8BKeAjwiIiIiIhIXevoHuXfnUTYsm8OS4tnDbnPNiiKcg62H4jeLp6G1mwONbVyzvOjMsoX5szCDo83RzeAJBBzH/T3MG0cGz4Xzc0j2GbsUAEg4Lza0nfnvKk3DS3gK8IiIiIiISFx44Pl6mjv6uG19+YjbXFCSw5zZaXE9TSs0tmtWvBrgSUtOoiRnFrWnoxvgOd3VR99ggJJxZPCkpySxcn6OAgAJaF+9H4DywkwF8GYABXhERERERCTmnHPcte0IK+Zlc0VFwYjb+XzGxmVFPP7SSfoHA1M4wrF77GATC/NnUTEn6zXLywoyOHoqulO0Glu9Oitzx9AiPVxlaS4v1LXG7XsqE7Ov3s+8nHTecH4x++rb6B0YjPWQJIoU4BERERERkZjb+tJJXm7q4Lb1izE7d/enq5cX0d4zwHNH4y8jobtvkO2Hm7lmefFZr6OsIIPaKBdZbvB3A4wrgwegsjSPnv4ABxrbRt9Ypo299X5WluRQWZpL32CA/Q26volMAR4REREREYm5u7ZVU5ydxlsvKhl123VLCklN8vFYHNbh2VndTO9AgI1h9XdCygoyOdXZR3tPf9TO39jqBXjGU4MHvE5aoEK8iaSzd4Dq5k4unJ9DZamu70ygAI+IiIiIiMTU/gY/Tx4+xU1XLCY1efSPKFlpyVxWns/mAyemYHTjs/lAExmpSVxWnn/WurJ8r1V6TRSzeBr9PaQm+yjITB3XfiW5s5ibnU6VOmkljBcb23AOLpifTVF2OgvyZqnOUoJTgEdmnPt31/F391XFehgiIiIiEvSDbUfISE3iry4tHfM+1ywv4pWTnRxtjn7b8bFyzvHYwSbWnVdIWnLSWevLCrxW6dEO8MzLSR91mttwKstyVYg3geyt8wosXzg/B/Cm4e2qacE5F8thSRQpwCMzinOOb245zG9faORYlDsYiIiIiMjojvt7eHBPA++5ZCE5GSlj3m/j8mKAuOqmdfB4Ow3+ntd0zwpXWhDM4DkdvaBUo7+budnjm54VUlmaR31rN01tPREelcTCvgY/c2anURS8HypLcznR1kuDX9c3USnAIzPKnjo/1Se9/6HurD4V49GIiIiIyI92HCXgHLesWzyu/UoLMjivKCuu6vCEgk1XLxs+wJOVlkxhVho1zdH7orGhtYeS3PEVWA6pDNXh0TSehLCv3n8mewfCrq+ytBKWAjwyo2yqqiMt2UduRgo7X1GAR0RERCSWOnsHuO/pGq69YC4Lg/VpxuOa5UU8VX2Kjt6BKIxu/LYcbOKiBTlnMiaGE81W6YMBx4k2b4rWRKwsySY1yadpWgmgu2+Qw00dXFCSfWbZinnZpKf4FMBLYArwyIzRNxDgoT0NvGnlXF5fUcjOV05p/qmIiIhIDP3iuWO09Qxw6/ryCe1/9fIi+gcd218+GeGRjd/pzj6qaltGzN4JKSvIoDZKpQKaO3oZCDjmTTCDJy05iQsX5KjQcgJ4sbGNgIMLwjJ4UpJ8XLQgVxk8CUwBHpkxth5qoqWrn+sr53N5RQHH23o4GsUCdyIiIiIyssGA4+4nj7CmLO9MC+fxWlOWR3Z6clzU4dl6qAnnGLH+Tsiigkwa/T309A8UJyHmAAAgAElEQVRGfAyNwdoqJRPM4AGvTsveej+9A5Efn0yd/Q3BAssLcl6zvLI0j/0NbVG5/yT2FOCRGWNTVT2FWWmsP6+QKyoKADRNS0RERCRGHt1/nGOnu7lt/fhq74RLSfJx1bIithw8SSAQ28zszQebmDM7jQtKcs65XVmw0HI0Gn40tnYDMHdSAZ48+gYC7G9oi9SwJAb21vkpyEw9q+D2mrI8BgKOvfX+GI1MokkBHpkRWrv62HzwBO+4uITkJB/lhZkUzU5jxyvNsR6aiIiIyIz0/W3VlOZn8Mbz507qONcsL6K5ozemH1j7BwM88dJJrl42B5/v3O3JQ63So5FJ3nAmg2diU7RAhXgTxd56PxfMz8Hstffj6tJcQNc3USnAIzPCQy800j/ouL5yPgBmxtqKAp6qPq06PCIiIiJTbFfNaXbXtnLz6xeRNEpAZDRXLZ2Dz7wMmlh57mgL7T0DZ1q3n0tZsJh0TRQKLTe2dpOe4jUUmaji7HTm585it+rwTFs9/YO83NTBBfOzz1pXmJVGWUGGCmknKAV4ZEbYVFXH8rmzOX/eq3/k1pYX0NzRy+GmjhiOTERERGTm+f4TR8hOT+bdlyyc9LHyMlOpLM3jsRgGeLYcPEFqko91SwpH3TY3I4Xs9GRqopDB0+jvoSRn1llZG+NVWZanTkvT2MHj7QwG3GtapIdbU5pHVW2rvuhOQArwSMKrPtnB7tpWrq+c/5r/2V1R4f0PeGe16vCIiIiITJWaU508+uJx3nt5GZlpyRE55sYVReyt93OirScixxuvzQebuKw8n6wxvB4zo6wgk5po1ODxdzMvd+L1d0LWlObS6O+hIVjTR6aXfcHpiitHqAe1uiyP5o5e6lp0fRONAjyS8H6zux6fwTsunv+a5QvzZzE/dxY7DivAIyIiIjJV7t5+hGSfcdMViyJ2zI3Lvc5VscjiOdrcSfXJzjNjGIuygozoTNHy9zA3e+L1d0LO1OFRFs+0tK/eT25GCgvyhr8XKoN1eDRNK/EowCMJLRBwbNpdz7olcygeUkHezLi8vICnjpyKedcFERERkZmgtauPXzxXx9tWlZz1b7PJWFY8m/m5s2LSLj10zvEGeOpbuukfDERsHAODAU609VASgQyeFfOySU/xUVWjOjzT0b4GPxcOU2A5ZFnxbDJTkxTAS0AK8EhCe/boaepaurl+9fxh16+tKKC1q5+Dx9uneGQiIiIiM89Pn66lu3+QW9eVR/S4ZsbG5UVsP9xMT/9gRI89mi0HmzivKOtMd6yxKCvIZCDgIjoFqqm9l4CDeZPooBWSkuTjovm57FIAYNrpHRjk0PH2EadnASQn+Vi1MFcBngSkAI8ktE1V9WSmJvGmlcN3NFhbUQCoDo+IiIhItPUNBLhnx1HWnVfI+SVnd/eZrI0riujqG+TpI6cjfuyRdPQO8PSRU1wzjuwdCO+kFbk6PI1+L1gUiRo84E3TerHBP+UBM5mcl0900D84coHlkMrSPA40ttPVNzBFI5OpoACPJKye/kF+u7eRN184j4zU4Qvezc+dRVlBBjtfUYBHREREJJoe3NNAU3svt65fHJXjry0vID3Fx5YDJ6Jy/OFsf/kk/YOOq8cZ4FlU6GX7RLIOT6PfKzBdEoEMHvDqtPQPujMFe2V62Bu8XsO1SA9XWZbLYMCx55iubyJRgEcS1h9ePEFH7wDXVw4/PStkbXkBTx85xaDq8IiIiIhEhXOOu7ZVs7Q4i6uWzonKOdJTklh3XiFbDjVNWfvnzQeayE5PZk2wKPFYFc1OIz3FF9kMnlYvwDM3J3IZPKBCvNPN3no/s9OTKQ1miY1k9UIV0k5ECvBIwtpUVUdJTjqXLy4453ZrKwpo7xlgf4Oi1yIiIiLR8OThUxw83s6t68pHLPwaCRuXF3PsdDeHmzqido6QQMDx2KEmrlpWRErS+D5WmRll+ZkcjWCAp8HfTWZqEtnpkWk9X5iVRllBhgIA08z+ej8XlIxcYDkkLzOV8jmZ7J4G19c5R2CKgrbTnQI8kpCa2nt44qWTXFc5H5/v3H/c1pYH6/BompaIiIhIVHx/WzWFWWm8Y3VJVM9z9XIvO2jzFHTT2lvvp7mjj43LJ5aRVBrhVumNrT3My50V0QBaZWkeVbWtU5YRJZPTPxjgwPF2Llxw7vo7IdPl+n7htwf44tM9sR7GtKAAjySkB59vIODgutULRt22KDudijmZKrQsIiIiEgUvn2jn8ZdO8oG1ZaQlJ0X1XPNyZnH+vOwpaZe++WATPoOrlo6v/k7IooIMak93EYhQmYBGfzfzIjQ9K6SyNJeT7b3UtUSu25dEz8snOugbCLByjEXM15TlcbqzL6KZZNGw5WATh1sDNLUpyDMaBXgkIW2qqmfVghzOK8oa0/ZrKwp45shp+gcDUR6ZiIiIyMxy95NHSEv28d7LSqfkfNesKGJXTQutXX1RPc+WgyeoLM0jPzN1QvuXFWTSOxDgRHtkPrQ2+nsiH+ApU52W6SRUEHu0DlohlaXB6xvHdZZOd/ZxpNnLdNMX8qNTgEcSzoHGNl5sbOP6ytGzd0LWlhfS1TfIC3WqwyMiIiISKac6etlUVc/1lfMpyEqbknNuXF7EYMDx+Esno3aOE2097KtvY+OKiWXvAJQVRK5Vet9AgJMdvcyLUAetkGXFs8lITYrrAIC8al+Dn6y0ZBYVZI5p+yVFWcxOS2ZXHAfwwmsEqaTG6OImwGNmPjP7pJkdNLMeMztmZnea2djuTu8Y+Wb2VTM7HDzGSTN7zMzWj7D9+83sSTNrM7MOM9tnZv8auVclsXD/7nqSfcbbVo19jvfl5fkAPKWosIiIiEjE/PTpWnoHAtz8+ui0Rh/OqgW5FGSmRnWa1mPBY28cZ3v0cKEP4ZGow3OirQfnoCQ3shk8yUk+Vi3IjesAgLxqb72f80uyR61BGuLzGReX5sZ1AG9XTQvJPuOCwiR2KMAzqrgJ8AD/CXwNeBH4GPBL4OPAQ2Y26jjNrAzYBfw18CvgI8AXgaPAWX2yzexu4EdAPXA78PfAw0DZpF+JxMzAYID7d9dz9fKicaXLFmSlsXzubEWFRURERCKkd2CQe3fWsGHZHJYUz56y8/p8xoZlRTz+0kkGojT9fvPBJubnzmLZJF7XvJx0kn0WkQyeRn9P8JiRzeABr07LgcZ2egfiuxDvTDcwGOBAY9uYp2eFVJbm8dKJdtp7+qM0ssmpqm3h/JJsVs1JovZ0F8dOx3e9oFiLTA+9STKzlXhBnU3OuRvClh8BvgHcCNw3ymF+gvd6LnLONY5yvluAvwE+4Jz78WTGLvHlyVdOcbK9lxsqz4rpjery8gL+95laegcGo14AUERERCTRPfh8A80dvdyybuqyd0KuWVHEr6vq2H2sldctyo/osXv6B9n+cjPvWrNgUh2rkpN8LMzPiFCAxyuCHOkMHoDKslwGA44jbapVGc9eOdlJT3+AC+aPrcBySGVZHgEHe475WbekMEqjm5iBwQB7jvn5i9ctZIl5NbV2Vp9iYX5GjEcWv+Ilg+cvAQO+PmT594Eu4H3n2tnMrgTWAV92zjWaWYqZDXvVzfsr/E9AVSi4Y2azLZL9BCVmNlXVkTMrhasnkC67tqKA3oEAz9e2RmFkIiIiIjOHc44fbD/CsuLZrDtv6j80rl9SSLLP2Hwg8tO0nj5ymu7+wUlNzwopzc+g5vTkp2iFMnjmRiGDZ/VCrxDv4ZbBiB9bImfvOAssh1y8MBez+CykffB4O939g1SW5VGSZRRmpWrGxSjiJcDzOiAAPBO+0DnXAzwfXH8ufx58rjWzh4BuoNPMXjKzocGhZUAFsMPM/tXMTgFtQKuZfcfMxtZ2SeJOR+8Aj+4/zttWzZtQBs7liwswU3V2ERERSSz+rn4++8A+/v4Xe3BuaqbZPHn4FAePt3PLusWTynKZqNnpKVy6OP9MrZxI2nLgBOkpPtZWFEz6WIsKMqhp7pr0dWls7WZ2ejJZaZGfoJGXmUr5nEwOtyqDJ57tq/eTkZrE4sLxfZzNmZXCkqKsuAzwhMZUWZqLmbG2opAdrzRP2d+x6SheAjwlQLNzrneYdfVAoZmdq6DKsuDz94F8vDo8twB9wI/N7G+G2fYv8OrufAl4F7AJ+BBezR9l80xDj+xtpKc/wHWrx949K1xORgorS7IVFRYREZGE4JzjV7vq2HjnVu7dWcOvq+p4cE/DlJz7B9urKcxK5e0Xj73pRaRtXF7EoRPtEa3Z4Zxj88Em1p1XSHrK5Kf0lxZk0t47QEvX5OqfNPh7KIlC9k7IJWV5vNw6GLWaRjJ5++r9nD8vm6QxFlgOV1maR1VNC4FAfAVOqmpaKM5OY36ud29fUVHAibZeqpsnn/WWqOKiBg+QAQwX3AHoCdumb4RtQtXN2oGrnXN9AGZ2P1ANfNHM7nHOBcK2nQO80Tn3p+DPvw4Gdv4auBZ4ZOhJzOyDwAcBiouL2bp169heXRR0dHTE9Pzx6O5nuinOMNqqn2frkYnF6Bak9PKnowP8YfNjpCYpzjcS3X8SS7r/JJZ0/0ksjef+q28PcO+LvRxqCXBero+PX5HO3fv6uOP+PaQ3v0RacvT+ndPQEeCxQ91cd14KTz25LWrnGU1WpxeM+O5DT/KGspSIHLO+PUBdSzdvKBmMyN+C9qYBADb9cRvn5U48YPRSXTc5qRa1v09zBgbo7IcfPPAYy/JVqzLeBJxjb10X6+cnT+geyOjup61ngJ/97jFKsuIlBwSePNRFWbaPxx9/nI6ODpK6DgNwzyM72Vgamd/pRBMvAZ4uYKRJrOlh24ykO/j8v6HgDoBzrsXMHgQ+gJe5cyBs2/qw4E7IPXgBng0ME+Bxzn0P+B7AJZdc4jZs2HCOIUXX1q1bieX5401dSxcHfv8Yn3rjUq6+esmEjxOYe4Lf/+g5Mssu5PUxmC8+Xej+k1jS/SexpPtPYmks919X3wDf2HyYu3ZWk5WezL9fv5L3XLIQn89YedFp3vWdnewPlPCpDcvOeZzJ+Of795KaXMf/vfEqCrPSonaesfjuga0cG8xgw4ZLI3K87zz+CnCQD719XUQ6Vi1oaufrVU9QWLacDavH3yQkpGPbH7mifC4bNlw46TENZ01PP9/Z8wdOzyphw4YVUTmHTNzhpnZ6H32Cay9byYY145/NsKCpg7v3PU5y8RI2vG5hFEY4fifbezn5+z/xwauXsuHKcrZu3cpbrrqKr+/ZQnNSLhs2rIn1EONSvITnGvCmYQ33f4D5eNO3RsreAagLPh8fZl2oo1beBLaVaeKB57104+sm8T9GgNctyifJZ5qmJSIiItPOH188wRu/9gTfefwVrls9n82fuoobLy3FF5yyccmifN6+qoTvPlEdtVbDpzv7+PWuOq5fPT/mwR3wpmntrD5FV99ARI635UAT58/Ljlg78gV5GZjB0VMTn3LS0z/Iqc4+5uVEvoNWyOz0FJbn+9gShaLVMnkTLbAcUl6YSc6slLiqw3Om/k7Zqx/NQ3V4dr5yKu6mk8WLeAnwPIs3lteE1s0sHbgYeG6U/UPFmYcLV4aWhf4a7cXL4hkuEjB0W5kGnHP8uqqOSxfnT7pl3uz0FC6cn6NCyyIiIjJt1LV0ces9z3Hbvc+RmZbELz60lq+8exUFwwRYbn/zcszgS48ciMpYfvpUDb0DAW6OQWv04VyzvIi+gQBPHp78v+1au/p4ruY016yYfPeskPSUJOZlp1M7iVbpJ9q8ihbRDPAArJqTzMtNHZMaq0THvvo20lN8VMzJnND+Pp+xujSXXTVxFOCpaSE1yXdW2/crKgpo6ern4PH2GI0svsVLgOfngAM+MWT5bXi1d34aWmBmFWa2fMh2v8Grv/O+8C5YZjYPeCfwsnPuMIBzrguvoPJcM7tuyHH+Nvj8u8m9HJlKe+r8VJ/s5IbKyWXvhKytKGDPsVY6eyPzTY+IiIhINPQNBPj21sO84WuP8+ThZv7pzcv57cfXc+ni/BH3KcmdxUc2nMfv9h6PeMZy78Ag9z5Vw5VL57C0ePboO0yBSxblMzstmS0HT0z6WI+/dJKAIyLt0cOVFWROKoOnodUL8JTkRq/IMsDFRV7tnUi8lxJZe+v9rJiXTXLSxD/erynN4+WmDvzdkyv4HSlVtS2snJ99VnfkUPe6Ha80x2JYcS8uAjzOub3AfwPXm9kmM7vVzO4EvgY8DtwXtvlmvFo64fu3AJ/Gy8p5ysw+ZWa3A08BqcBHh5zyn4ETwH1m9mUz+4iZPQxcD9zrnNsR+Vcp0bKpqo60ZB9vvnBeRI63tryAgYDj2aOnI3I8ERERkUh7qvoUb/nGNr78+0NctXQOf/r7q/jQVRWkjOED3gevLGd+7iw+99D+iHZFemhPIyfbe7k1TrJ3AFKTfaxfWsiWg02Tbq285WATBZmprFqQG6HRecoKMqidxJS5Rr9XYjTaGTxFGV6GyOYotJ6XiQsEHC82tHFBycSmZ4WEpkI9f6w1EsOalL6BAC/U+VlTenbllJLcWSwuzFRJjRHERYAn6BN4QZqVeMGeG4FvAm8Ndr86p2AB5BuADuDzwP8FDuF11frDkG1rgcvxMnn+Bvg6UAH8Q/BnmSb6BgI8tKeBN55fTHZ6ZCqpX7Ioj5Qk0zQtERERiTvNHb186hfPc+P3nqK7f5Af/PUlfPf9l5xpIzwW6SlJ/POfr+Dg8XZ+9uyxiIzLOccPth9haXEW65fEV6OKjcuLOdHWy/6GtgkfY2AwwNZDJ9mwrOhMTaNIKSvIpLmjj/aeiWVONPpDU7Sim8EDcM2KYp6uPk2HMt3jxtFTnXT0Dky4/k7IqoW5+Iy4mKb1YmMbvQOB19TfCbe2ooCnj5yOaIA6UcRNgMc5N+icu9M5t8w5l+acm++c+5RzrmPIdoucc8P+VXXObXLOXe6cy3TOzXbOvck59+QI2x51zr3XOTfHOZfqnFvhnPvqWIJJEj+2HmqipaufGyrHXy1+JBmpyVy8MJenFBUWEZEwzjkCk8wAEJmoQMDxWG0/G7+6lYf2NPB3V1fwx09exTUriid0vD+/cC6XLc7nzj8cwt81+SkZO185xYHGNm5Ztxiz6LVgn4gNy+Zg5mXgTFRVbSv+7v6I1t8JWVTg1ZCsmWBtm0Z/N7kZKcxKjX778o3Li+gbDLD9ZU2PiRf7goHLlUNq1YxXVloyy+ZmszsOCi1XBYNMlcNk8IBXh6ejd+BMcWl5Vby0SReZkE1V9RRmpUb8m6K15QV867HDtPX0RywzSEREprevPHqITc90c39l95R8Uy4S7vO/fZF7Xuzj8vJ8vvDOCzivaHI1bsyMz77tfN72ze38559e4o63r5zU8e7afoSCzFTecXFkaiJGUmFWGqsW5PK9J6p5+IWGCR2jtaufZJ+xLgrZSaXBAE/t6S4umEAWRmNrz5T9TVpTlkd2ulfT6NoL5k7JOeXc9tX7SU32RaTuVWVpLg8+38BgwJEU4Uy18aiqbWF+7izmjjDt8PLyUB2eU6weIQg0U8VNBo/IeLV29bH54AnecfH8SRUUG87aikICDp6pVh0eERGBls4+7n7yCMe7HDfd/WzcFKGUmeFkey8/fbqWdfOT+d/bLp90cCdkZUkON15ayo+fquHlExPvSPPKyQ62HGzifZeXkZ4S/SySifjkG5eyfkkhFXOyJvRYU5bH7W9eHpUv/soKvM5HEy203ODvoSTK9XdCUpJ8XLWsiC0HT6pNdZzYV+9nxdzZY6q/NZrK0jzaewd4uSm2HaqqalpYXTpyravCrDSWz52tOjzDUAaPTFsPvdBI/6Dj+gh1zwq3ujSX1GQfO6tP8YbzJ5b6LCIiieMnT9XQ0x/gfStS+flLHdx273Pce/OlcfthVhLLPTuO0j8Y4K3laRGf/vT3b1zKw3sa+P8efpF7b750Qse/e/sRUpN9vO/ysoiOLZKuWjqHq5bOifUwhpWVlkxhVuqE2483+rtZUxbZws/ncs3yIh7a08AL9X4uXjh155WzOefYV+/nratKInK8NcGaN1U1rSyfO7kpXxPV6O+mwd/DraNk5qytKOC+p2vpHRg8q9PWTKYMHpm2NlXVsXzubM6fF/k/PukpSawpzVNUWERE6Okf5J6dNWxYNoc3lKVw53su5pkjp/nkz59nUN9gS5R19g5w786jXLtyLnMzI/9P94KsND7xhqVse7mZPx0Yf42als4+fl1VxzsvLmHO7LSIj2+mmGir9O6+QVq7+qd02uhVS+fgM9hyQO3SY632dBdtPZMvsBxSVpBBfmYqVTGsw1NV43XxWjNCgeWQKyoK6R0IsLs29l2/4okCPDItVZ/sYHdtK9etnh+1Qn5rKwo4cLyNls6+qBxfRESmhwefb6C5o5fb1pcD8PZVJfzLW1bwyL7jfO6h/ZNuvSxyLj979hhtPQN88MryqJ3j/WvLOK8oiy/89kV6BwbHte99z9TS0x/glnXRG99MUJafMaEMnqlqkR4uLzOVNWV5apceB/bVewWWJ9siPcTMqCzNPVPkOBaqaltIS/axYpQv8S9dnI/PvDo88ioFeGRaun93PT6Dd66OXiG/KyoKcA6ePqI/GiIiM5Vzjru2V7NiXjZXVBScWX7r+nI+eGU59+6s4dtbX4nhCCWR9Q8G+MG2ai5bnB/VQqIpST7+9a3nU3Oqix8+eXTM+/UNBLhnx1HWLylk2dzI1AWaqcoKMmls66Gnf3wBtqlskR5u4/Ji9je0cTx4fomNvfV+UpKMpXOzInbMyrI8qps7Y/Yld1VtCxctyCE1+dyhipxZKVw4P4edr6ijWzgFeGTaCQQc9++u5/XnFVKcHb1vKy5akMuslCRN0xIRmcEef+kkL53o4Lb1Z7d+vv3a5bzz4hK+8ughfvncsRiNUBLZwy800ODv4cNXVUT9XFctncMbVhTxzc0v09Q2tg/tD7/QQFN7L7esWxzl0SW+soIMnIO6lvFl8TS0ehk8JblTl8EDnGkXP5nW8zJ5+xv8LC2eHdEaNKHW5LuPTX0WT0//IPvq/VSOMj0rZG1FIbtrW+nqG4jyyKYPBXhk2nn26GnqWrq5oXJBVM+TmuzjkkV57KxWgEdEZKa6a9sRirPTeOtFZxew9PmML79rFeuXFHL7pr08pg86EkHOOb77eDXLimezYdnUFAf+l7ecT99ggC8/emjUbZ1z3LXtCEuKsuK2ePF0UhZslX60eXwBnlAGz0jtpKNlSVEWC/NnseWg6vDEinOOvfX+iNXfCbloQQ5JPjtTC2cq7W/w0z/ozgSZRnNFRQEDAcezR2M3pSzeKMAj086Wg02kJvl408rod7daW1HASyc6ONneG/VziYhIfHmxoY3th5u56YrFI6aKpyb7+J/3rWHFvNl85KdVPH9MxR7j1e7aFh54vj7Wwxizx186ycHj7XzwyvKo1RscalFhJjevW8yvdtWxZ5R7+anq07zY2MbN687ObpPxC7VKrzk93gBPN4VZqVPeRcjMuGZ5MdsPN497WplERn1rN61d/ayMcIAnIzWZFfNmsysGdXhCQaWxBnguWZRHSpKxQ9O0zlCAR6adtp5+cjJSyEhNjvq5rqgoBOApZfGIiMw4d22vJiM1ib+6tPSc22WlJfPDmy5lzuw0bv7Rs1Sf7JiiEcpYdfQO8OGf7OL2X+8lME06n3338Wrm5aTztgi1Px6rj159HoVZadzx0P5zvlc/2F5NfmYq10WxHuJMkpeRwuz0ZGrG2UmrobVnyrN3QjYuL6KnP6ByBjGyr94PEPEMHoA1pXnsqWtlYDAQ8WOfy66aFhbmzxpzR76M1GRWL1Tn43AK8Mi00903yKyUqfmW4oKSbLLSkjVNS0RkhjnR1sNDexp4zyULyclIGXX7ObPTuOfmSzHgA3c/Q1O7Co/Gk6//8SVOtPXS3T9I7TgzJGJhz7FWdlaf4pZ1I2ePRcvs9BQ+c+0ydte28sCe4TOeqk928KcDTbzvslLSp+jfZInOzCgryKBmnJ20jvt7przAcshl5flkpCaxWdO0YmJvvZ8kn7E8CgXOK8vy6Oob5NCJ9ogfeyTOOapqW1gzzoLyaysK2Ffvx9/VH6WRTS8K8Mi0090/dQGe5CQfly7O5ylFhUVEZpQf7TjKYMCNq3js4sJM7r7pdZzu7OOmu5+lvUf/2IwHBxrb+OGOo6wuzT3zc7z73hPVzE5P5sZRssei5YbKBVy0IId/f+Qgnb1nFy/94ZNHSU3y8b61ZTEYXeIqK8gcfwaPv5uSGGXwpCUnsX5JIVsONOHc9MiMSyT76ttYUpQVlSBraIrUVLZLr2/tpqm9d8wFlkOuqCggoM7HZyjAI9NOd3+A9NSp+7ZobXkB1c2dagMpIjJDdPYO8NOnarj2grkszM8Y176rFuby7fdW8tKJdj78k130DUxteru8ViDg+Jff7CNnVgr/8941+AwOHJ+6b6Qn4mhzJ4/sa+T9l5eRlRb96ejD8fmM//e2lZxo6+XbWw+/Zl1rVx+/2lXHOy4uoWh2bAILiaosP4O6lu4xT4vp6B2gvWeAebmxyeABuGZ5MQ3+Hg7G+e9VonHOsS8KBZZDFuR506Sqaqeurlyo5s9Y6++EXFyaS3qKjx36Qh5QgEemoZ6+QWalTN2tu7aiAICd1SreJSIyE/zyuWO09Qxw6/ryCe2/YVkR/3HDRTx5+BSf/uWeaVPzJRH9qqqOXTUt3P7m5czNSWdxYSYH4zyD567t1ST7fNz0+kUxHceasjyuWz2f7287Qm3YtKH7nqmlu3+QW9arNXqkLSrIZCDgaGgd25eKjcEW6fNilMEDsGG510FN7dKn1vG2Hk519nFBlAI8ZkZlaS5VtVOXwbO7tpVZKUnjnnKWlpzE6xblq2ZqkAI8Mu1M5RQtgPPnZZMzK0XFu0REZoDBgOPuJ4+yplZYjRMAACAASURBVCxv3N8ihrthzQI+c+1yHtzTwBd/dyCCI5Sxauns40u/O8AlZXm8q3IBAMvnZcd1pkFzRy+/fK6OG9bMj4vsmM9cu5wkszP3cN9AgHt2HGXdeYUsn5sd49ElnlCr9JrTY5um1RDMLo9VDR6AotnprFqQw+YDqsMzlfbWeQWWoxXgAS+TpuZUF80dU9NNuKq2hVULc0hOGn+IYm1FAQePt0/ZWOOZAjwy7XT3DzJrCqdo+XzGZYvzVWhZRGQG+MP+49Se7uK2CGQnfPiqcm66YhF3bT/C95+ojsDoZDy+/Ogh2noG+Pw7L8Dn89p4r5g7m9rTXXFbH+neHUfpGwxMOHss0ubmpPN3V1fw+/3H2XG4md/ubeBEW++4alPJ2IVapR8dY6Hl4/7YZ/AAbFxezO5jrZzSh+sps6+hDZ95X0RHy5qyqavD0903yIsNbRP+YkWdj1+lAI9MO919g1PesWFtRQHHTndzbBp03hARkYn7/rZqygoyeOP5cyd9LDPjs289n7dcNI9/+90BHnh++I5EEnlVtS387Nla/uaKRawI+wAUyjp5aQo7w4xVZ+8A9+ys4U3nF1MxJyvWwznj1vXlLMibxeceepG7th2hYk4mVy2dE+thJaSi2Wmkp/ioaR5jBk9rD2bErE16yDUrinAOth46GdNxzCT76v2cV5QV1S+9L5ifQ0qSTUkdnhfqWhkIuDNBpfG6oCSb2WnJqsODAjwyDfVM8RQteDUqHIksnsNN7Tyt6LKISNzZVdNCVW0rN79+MUnBjI/J8vmMr71nFZeX5/PpX+5h28vT9wNQV9/AtGj/PjAY4F9/s4+i2Wl84o1LX7NuRYkX4DnQGH8Bnl88dwx/dz8fuqoi1kN5jfSUJP7lLSs4dKKd/Q1t3LKu/ExGlESWz2eU5mdQM8YvFBv93czJSiNlAlNaImllSTbF2WmqwzOF9tb7uaAketOzwPvdP78kZ0rq8ISCSKsnmMGTnOTjsvJ8ldRAAR6Zhqa6Bg/A0uIsCjJTJ9Uu3d/Vzx0P7ufPvr6N9971NI3BtFqR8dhX7+dXu+piPQyRhHTXtmpyZqXw7ksWRPS4aclJfO8Dl1AxJ4sP/XgXzx+buq4kkfSVRw/xtm9uZzDOi0b/5Kma/5+9+45vu772P/76yPKQbckznolnhp2EkAmJSUIYKRQo0NINtOy29NLb3g7uvb8u2t7b9ra03PaW25bS0LLaUijr0gIBAlmQxAlkL8/EsR1PyUuWJX1+f0gyzrBjWdPWeT4eeYTYsvxJsC3pfM95H/adsPHta+adsYWqIC0Jc5KRgy3RFbQ85HLzu411XFCSGVD2U6hcMS+Pi2ZmkZWSwEcWF0b6OFOaP6vSm632iG7Q8lFKcWlFDm8dbpPNgWFw0manrWcwpPk7PkuKMth9vJuhcW52m6jqhi5Ks1PITEmY8H2sKM+mrr2PE92x/RpLCjxiUtFahz2DBzwPXMvLstha24HW/j2xdbk1j73dwJqfvsEft9Zz/cJC3FrzyJb6kJxVTF1aa77+193c+/RuegedkT6OEFNKY0c/L+9r4cYLi0hOCP5qaktSPH+87QKyUxO5Zd02DkVx0O9oatr6aLUNhnWrir9O2uzc/8phVs3K5qrzzhyzU0pRmWeJug6el/Y009Q9wOcujo7sndMppfj1TUt48Usrwz4mH2uKM5Np7Owf1/a9E90D5FsiH8YNnhyenkEnO+o7I32UKW9Pkydg+bzpoS/wLC5Oxz7k5kAItw9qrdnV2BVwcbvKt/k4xrt4pMAjJpVBpxuticiTi+XlWTRb7TSMM/gO4J3aDq755Sa++exeZuWaefGeVdz/8fP54Px8nninkT55kS78sOFQGweabbjcmu118gRKiGD6/eY64gyKz1aVhOxz5FiSePyOC0mIM3Dzw++csnp6Mmj1buxZvz96t+X8x0sHGHS6+d5181Hq7GNEFflmDrX0RM36eq01v36zllk5qVwyJyfSxxmVOSk+otuaYkVxdgr2ITcne8YOLNZaezt4oqPAs3JmNolGA+sPyJhWqO1tsqFCHLDs4yu6hDJoubGzn44+B4uL0wO6nzm5ZjJTEmI+h0cKPGJSsQ+5AMI+ogUjqsLjyM9p6h7gi0/s5BO/fRtrv4P/+fQi/nzXcuZ6Z//vWFVKj93JUzuOhfTMYmp5cMNR8tOSSDAa2FLTHunjCDFlWPuH+MuOY1x7fiG5Ib4aPiMzmcfuuBCHy81ND7/DSVv0Z9r4tHjP+mqUrkPecrSd5949wefXlFOanTLq7SrzLfQOOmmKkjb+jUfaOdBs467Vkm0jPB08wDnHtGx2J/0OFwVRUnQzJcRRVZ7Fawdb/e52F/7Z02SlLDuFlMTgd5ueriDdRJ4lieoQBi1Xe4tHgXbwGAyKFWVZbK1pj+mvQSnwiEllwFfgCfOIFkBZdgo55sQxq8L2IRcPrD/MZfdvYP3+Vv75slm89tU1XLOg4JQriYuKMlhSnMHvN9dHfZaBiA7b6zvZXt/F51aXsbgoPeavTggRTI9va6Df4eKOIKxGH4/ZuWYeufUCOnoHufnhbXT3O8LyeQNhH3JhHRiiIC2J2rY+atp6I32kUzicbr753F6KMpO5e83YIcUVeWaAkI4c+OM3b9WQa0nkuoWSbSOgxLsq/Vwd474sx2jp4AG4tDKXho5+ase5BUxMzL4T1rDk7/gsKc4IaQfPzsYuUhONzM41B3xfK8qzOOHnxMVUIwUeMakMOCLXwaOUYkV5Fltrzszh0Vrzf7ubuez+N3lg/REuq8jlta9ezFfWzh61GHXHylIaO/t5dX9LOI4vJrkH3zhKVkoCn1hWRFV5NvubbXT1Rf+LQiGincPp5g9b6lk1K/uUddqhtnBGOg99dil1HX3csm571I/stnjHsz55QREQfWNaD22spbatj/uum3fOMe7ZuWaUio5NWnuOW9l8tIPbV5aSYJSn5QIK0pMwGhQNnWMXSZq7Pd+T0TQ2d2mFZ8TwdRnTCpn23kGarXbOC2OBZ1FROk3dA7SGqON0Z0M3C2ekB2V7pW/iIpYvhMojiZhUfB08kQr4W1GWRXvv4ClXLg802/jUQ2/zxSd2Yk4y8uSdy/nVjYuZnpE85n19YF4eMzJNPLSxLtTHFpPc/hM23jjUxq0XlQy3QGsN79TF7oOXEMHywnsnaLUNcseq8IfbVpVn86tPL2ZPk5W7Ht0xPIYcjZq9BZ7FRRnMzbewPorGtI539fPL149w5by8cWXYpCQaKc5MjopNWr95qwZzopFPeQtnQhjjDEzPMFF/jg6EE74OnrTo6eApTDdRkWfmtYPR8/NhqtnrDVieF+IV6SMtLg5dDk/voJODLTYWFwWWv+NTmp1CniUppqMM/C7wKKUMSqlZSqmVSqlrlFIXef8sQ8Mi5OwRHNECz5Nx8KSzd/U5+Naze7n6Fxs52NLD96+fz4v3rGSFt3J8LnEGxW0XlVLd0BXVG0lE5P3vmzWkJhq5eUUJAAump5OcEBfTVyeECAatNQ9trGVOrpnVs7Ijcoa1c3P56ccWsPloB196chfOEK+inSjfldu8tEQun5tLdUMXHb1jh8CGy30v7MegFN/+0Nxxf0xlvoWDEd5k1tjRz0t7mrlxeTHmpPiInkVEl6KslHOGsLdY7RgU5JgTw3Sq8bmsMoft9V1YB4YifZQpabjAUxi+jtN5BRYSjIaQvF7Zfawbt36/iBQopRRVo0xcxIpxFXiUUial1O1KqeeBTuAg8CbwHPCW98+dSqnnlVK3KaWip1dQTCkDDs8T30iMaAHMyDRRmG7ikS31rPnpBp7Y1sjNy4vZ8LU13Ly8GGOcfzXTjy+dgTnJyMObpItHnF19ex//t/sENy4vIs3keQGQYDSwrCRTCjxCBGjz0Q4OtvRw+6rSUTcuhcOHF03nvmvn8cr+Vr7x9O6o2e40ki9gOdeSxAfm5uLW8PrByI9hrN/fyqvezLuC9PE//azIs1Df0Ue/I3Kjcb/bVIvRYODWi0oidgYRnUqykqnv6BvzBeqJbju5liS/n3uG2qUVubjcmrcOt0X6KFFl3eY6/vlPuwLenrinyUppdgqWMBaFE41xnFeYxrYQbHD1FY0WzQhOgQc8m487+hwcbo2urLhwGfMnglIqXSn1X0AT8FvgGsAMNAP7gK3e31sAi/f9DwFNSqkfK6WC02slhNdABLdogacqfNHMLGra+phXYOGlL63ivuvmk56cMKH7S0k08ukLi/j7nmaOdcZuGJgY3W/eqsUYZ+D2laeGv1aVZ3H0ZO+k2sAjRLR5aGMt08yJXLewINJH4bNVJfzL2tk8s7OJ7724P+quPLZY7aQmGjEnxTOvwEKeJSniY1oDDhfffWEfs3JSuW2lfwHZFflmtCZiLwA6egf5y45jfHhR6De3icmnKDOZHruT7v7Ru2CarQNRNZ7ls3BGOpkpCVFRAI4Wf60+zn0v7Of5905w+c/f5OevHp7wSO7eJhvzCsLXvePzwfl5vHfcyhuHgvv/tbqhi5k5qaQlB69g9X4OT2yOaY1a4FFK3Q0cAb4G2IGfAR8EMrXW07XWC7TWK72/FwJZwFXAA97bfx044r0fIYLi/S1akbtace+VFfzlcyt4/I4LmZMXeNr7LVUlGJTikS31gR9OTCmtNjtPVx/nY0umk2M+9Unc8LhgrXTxCDERh1t7ePNwG59dUUyiMTIXDU53z6UzuX1lKY9sqeeB9UcifZxTtNrs5Fo8oyBKKS6fm8Nbh9sjmhv0qzeOcrxrgO9fP594P7sYKvM8L5AitUnrj1sbsA+5uXN1+LOfRPTzbdKqH2NVerPVTr4fXWvhEmdQrJkzjTcOnZRNscCmI+3869O7WTkzm7e+fglXzMvjv187wtqfv8mr+/1bKd/V56CpeyCsAcs+n1lRQtm0FL73wn4GncH5ua+1ZtexbpYEuB79dNMzkinKTI7ZTvexHg3/B6gDrgWma62/rrV+WWttPduNtdbdWut/aK2/CkwHrgfqgV8G+cwihtkdkQ1ZBshKTeSC0sygtfPnp5m4ZkE+f95+DJtd5pXF+x7eVIfT7eZzq89c+Tu3wIIlyciWo7H54CVEoH63sZakeAM3Xlgc6aMMU0rxzasr+diS6fz3a0f4fRSN77bY7OSN6BZYOzePgSFXxK6Q1rT18pu3avjIokKWl40v+26k6RkmUhONHIxAgaff4eSPW+tZOzeXmTmpYf/8IvqVZHsWdTSO0t2tteZE9wD5Udr9dVlFLt39Q+yK8YzJA802Pv9YNTNzUnnwpsXMyEzml59axBN3XkiSMY47/7iD2x7ZTv0418rvPeF5GR7OFek+CUYD3/nQPOra+/j9pvqg3Gdtex/d/UMsLg7+0E9VeRZv13bEZJFxrALPDVrrC7TWL2qt/Ur801q7tdbPa62XATcEdkQh3hfpEa1QuWNVGb2DTv687VikjyKihLV/iMffbuBD5xdQlHXmRrY4g2J5WRZbamOz/VSIQJzssfPsrhN8bMkMMlImNmIbKkopfviR87hyXh7fe3E/T+2IjseFVqv9lFGi5WWZpCTE8er+8I9haK359nN7SYqP49+uqpzQfRgMijl5Zg5EIGj5L9uP0dU/xOcvlu4dcXbTM5JRCurbz17g6e4fYtDpjsoOHoBVs7MxGhSvxfCYVrN1gFvXbSc10ci6W5edkplTVZ7NS/+8im9eXcn2+i4+8PO3uP+VQww4xu6M2eMNWJ4fxg1aI108expr5+byy9eP0GINPCKg2ruVa3GQO3gAVpRn0WN3su/EWXtTprRRCzxa678F4xNorZ8Nxv0IASNHtKZWgWd+YRrLyzJZt7kuajeoiPD6w9Z6+hwuvrDmzO4dn6ryLI51Dkh+kxB+enRrA0Nu9xnZVtHCGGfgvz+1kFWzsrn36d38Y29LRM/jdmtO9gySN6LAk2iM4+I501h/oDXsodAv7G5m89EOvnHFHKYFsEGoIs/MwWZbWPOOnC43D22sY2lxBkuKM8P2ecXkkhQfR74liYbOs3d2+FakF0RhBg+AJSmeC0ozef1AbBZ4bPYhbl23nb5BJ+tuXUZ+2pmFuPg4A3esKuP1r17MVefl8cvXj3L5z97k5X0to/5M2tdkY0amKah5Nf761tVzcbo1P/z7gYDva1djF5YkI+XTgt/JuGI4hyf2Ot2jK3ZdiHPwVbaToiQvIZjuWFnGCaudlyL8RF5EXr/DybrNdVxWkUNF3uhBelUzvTk8MfjgJcREDThcPPZ2A2srcynJTon0cUaVaIzjNzcvYeGMdL705C42HYlct1573yBOtz5lRAs8K97begbZ3RS+K6Q2+xDff3E/5xWm8ekAx+sq8i3Y7E5OBOFK9Hj9355mmroH+NzFoxfvhQAoykqmYZSNS83dnq/ZaO3gAbi0IodDrT0xdxHK4XRz92M7OXqyl/+9aQmV+WMHIudYknjgk4v4813LMScZ+dyj1dyybju1bWcGwO9pskYkf2ekoqxkPre6jOfePRHwVq2dDd0sKsrAYAj+FssccxKzclKlwDMWpdTbSqmblFLR1cssYop9yEWi0RCSHwSRdmlFDmXZKfxuY23UbU8R4fWnbZ72/bsvGfsFwKycVLJTE2J2S4AQE/HXncfp6h+aFOG2yQlG1t1yAWXTUrjr0R3D62TDrdU6CHDGtqdL5uQQZ1Cs3x++bVo/f/Uw7b2D/OD6+cQF+Fxgbr5nUUK4cni01vzmzVrKp6VwWUVOWD6nmLxKslJoGCVkuTnKO3gALqvMBQj61qVoprXmX5/Zzaaj7fzohgWsnJU97o+9sCyLF+9ZybevmcvOhi6ufGAj//WPg/Q7nIBndL+xs595ERrPGunuNTMpSEviO8/vm3DGjc0+xOGTPSEZz/KpKs9ie10nDmdsTUf408FzAfAH4LhS6odKqZKQnEiIMQwMuabceJaPwaC4bWUpu49b2V4f26F0sczhdPPQxlouKM08Z/u+UooV5dlsqemQoqAQ4+B2a36/qY7zZ6SztDh0TyqDKS05nj/efgE55kRu+f02DraEPxTY92Iy77QCT3pyAkuLM3g1TAWefSes/GFLPTdeWMT5MwIP5Zyd6y3whCmHZ9PRdvY32/jc6vIpeaFKBFdRVjLtvQ56B51nvO+E1Y7RoMhKnfiIYqiVZqdQNi2F12JoTOvn64/wzM4m/mXtbD66ZLrfH2+MM3DbylJe+9rFXHN+Pg9uqOHy+9/k73uahwOWI93BA56ojP939VwONNt44p2GCd3Hu43daA1LQvhYvKI8m4EhF+8d7w7Z54hG/hR4rgdexbMO/V48K9CfU0pdEZKTCXEWAw7XlAtYHumGxdPJSI7ndxtrQ/Y5Ht1az9eeei9k9y8C8+y7TTRb7dw9RvbOSFXlWZzsGaSmbXwbGISIZS/uaaauvY87V5UGbRNiOOSYk3j09gtJTjByzxO7wv75W22ecZDTR7TAM6Z1qLWHxlFGSYLpJy8fIj05ga9/oCIo92dOimdGpilsq9L/tP0Y2akJXLeoICyfT0xuvlXpZ+viafGGngfaxRZql1XksLWmg76zFKmmmj9vb+QXrx3hE0tncM+lMwO6rxxzEj/7+EKe+vwKLKZ4vvD4Tr7853eByGzQOpurzstjRVkWP33lMJ19Dr8/fmdjF0rB+TNC9/dZXpaJUsTcxtlxF3i8W7GuBGYBPweswIeAl5RSh5VSX1FKBX/HmRAjDAxN7QKPKSGOm5YX8+qBVurGuTLRH8+928S3ntvH8++ekI6PKORya379Zg3zCixcPHvauD6myhsit1XGtIQYVWNHP/c8uYsvPbmL8mkpXDkvL9JH8tuMzGQ+vnQ6NW29DIU5jL/FZifOoMg+S7fA2rmeMYz1B0LbxXOopYcNh9q47aKSoAaMVuRZwlLgcbs1b9d0sHrWNBKnYI6gCL6iTO+q9LMUT090D1CQHr3jWT6XVuTicLnZfHRqP0d583Ab//63vayePY0ffHh+0C4gLCvJ5MV7VnLftfOwD7kom5ZCZpRsflRKcd918+gddPLTVw75/fHVDV3MyTVjTgpdYHR6cgLzCiwxF2Xgd8iy1rpWa/1VYDpwO7ATmAn8FGhSSv1OKbUkuMcUwsM+5CJpChd4AG5eUUy8wcC6zXVBvd+3azv4+lO7iY9TOFxu7EOxNY86Gbyyr4Xatj6+sKZ83E8OijKTKUw3sbU2tq5OCDEeHb2DfPf5fVz2sw28ur+Ff7pkJn/74kUY4ybnjonCDBNuTVDW0/qjxTpIjjnxrN0CxVkpzMpJDXmB53cbazHFx3FjgMHKp6vMt1DX3od9aOz1xIE6fLKHjj7H8GYXIc6lOMtT4Kk/S4Gn2Wo/62amaLO0JANzknFKj2ntbbJy92PVzMk18+CNi4kP8uOLMc7AZ6tK2PSNS/nTXcuDet+Bmp1r5jMrinlyWyN7/Qjbd7s17x7rZnEYRqWryrPZ1dh9zhX0U8mEvwK11nat9Tqt9TLgQuAJwATcCmxTSm1RSn00SOcUApjaGTw+OeYkrl1YwFM7jtPd73/L49kcae3hrj/uoCgrmS9fPhvwhJuJ6KG15sENNZRmp/DB+fnj/jhPDk8WW2s6wr6qWIho1e9w8j+vH+Hin2zgj1vr+eiS6bz59Uv42hVzsITwamGoFXg35jR1D4T187ba7GcELI90+dxc3qnrxNofmseVkzY7z77bxMeXTicjyFevK/PMuDUcaT1zY00w+UYEpMAjxsucFE9WSgKNp61Kd7s1LVY7+ZOggyc+zsDFs6fx+qGTU/I5SlP3ALc9sp00Uzzrbl1GaqIxZJ8rLTmeHHP0/T//8uWzyUxO4DvP7xv3dMDRtl567M6QBiz7rCjPwuFyU90QO/mmAZcYlVK5wBXAxb43AT3AcuDPSqnN3tsIEbCpnsHjc8eqUgaGXDz+TmPA93XSZueWddtJjI/jkVuXDbf8WgekwBNNNh1tZ0+Tlc+tLvN7pr6qPIuu/qGwBYUKEa2cLjdPbmtkzU828NNXDrOiPItXvrKaH35kwZgFismi0FvgORHmAk+LzX5GwPJIa+fm4nJrNhwOzVX6R7bU43RrbltZGvT7rvCuMD4Q4vDqrbUdFGUmMz0jOaSfR0wtxVnJ1Lef2sHT0efA4XKTP0l+pl1WmUNbz+BwSPBUYR0Y4tZ12xgYcvHIbRdMiceYiUgzxXPvlRVUN3Txt11N4/oYX7FlcVHo012WlWRiNKiYGtOacIFHKbVaKfUnoAG4D8gF/gRUAZnAh4FdwAo8mT1CBGxgyD3lR7TAkwmwalY2f9hSH9Bqv95BJ7c+sp2ufgfrblnG9IxkLCbP1WubFHiiyoNv1JBrSeTDiwv9/ljfFeFYevASYiStNS/va+GKB97i357Zw/QME099fgUPfWYpM3PMkT5e0Ax38HSFuYPHaj9rwLLPwunpZKcmhGSbVt+gk8ffaeTKeXkUe0Nng6koMxlTfFxIc3hcbs3btR3DmWlCjFdxVgqNnacWeHwjmvnp0T+iBXDx7BwMiik1pjXodPG5R3dQ197Hb25eMryRL1Z9dMl0zp+Rzg//fpCecUwI7GzoIiM5ntLs4P9MP11qopHzZ6SzpSZ2ogz8KvAopVKVUncrpfYAbwAfB7qA7wPFWutPa63f1lq7tdbP4Rnd2g98INgHF7HJHgMjWj53rCrjZM8gL7x3YkIfP+Ry88XHd3KwpYdf3bh4OHU/zVfgkRGtqLGzsYuttR3cuapsQuGb+WkmyrJT2BpDD15C+FQ3dPKxX2/lc49Wo4Ff37SEp79QxbKSzEgfLeiS4uPITk0I64hW36CTnkHnmFenDQbFZRW5vHmoLaCLEmfz1I5jWAeGuHN1WVDv1yfOoJiTZ+Zgc+g6IPedsNJjd8p4lvBbcVYyJ6wDDDrfzw85YfV8/xdMggwegMyUBBYXZfD6walR4NFac+9fd/N2bSc/+ej5VJVnR/pIEWcwKO67dh5tPYP88vWj57z9zsYuFhdlhG2bZVV5FruPd8fMa59xF3iUUg8CTcAvgXnAduAmoEhr/V2tdcvpH6O1dgHbgNAP2ImYMOBwkRwDHTwAq2dlMysnld9tqvN745XWmm/+bS9vHm7jP66fzyVzcobfZ0nyzAfLiFb0ePCNGtKT4/nUBUUTvo8V5Vm8U9eJM8zbdYSIlKMne/ncozu44X+30tDZz39++Dxe+fJqrpyfN6lWoPurMN0U1gJPy/CK9DM3aI20dm4uPYNO3qkLXqHZ6XLz8OY6lhZnhDSroTLfzMEWW8i2S/qK7yvKpMAj/FOclYzWcKzz/e/5Zu/3/2TI4PG5tDKHPU1WWm3hDYgPhZ++cohn3z3B16+Yw/WL/O+6nqoWzkjn40un8/tNdRw9OXqmWXe/g5q2vrAELPusKM/CrWF7XWfYPmck+dPB83kgEXgcuFBrvVxr/YTW+lyvEt8C/jjRAwoxUiyELPsopbhjVSkHmm1+d2b8z+tH+fOOY9xz6Uw+eVrRYLiDZ8AZtLOKiTvU0sP6A618dkUJKQGE81WVZ9M76GSPH1sMhJiMTtrs/Nsze7jigbfYdKSdr66dzZtfX8OnLyyatNux/FEQ7gKPdxzkXPkSF83MJinewPogjmm9vK+VY50D3LEqNN07PhV5Frr6hzjZMxiS+99S08HMnFRyYjSjQ0ycbyyxoeP9oOVmq52EOANZUbIuezwuq/DEsb4xybt4nninkV+9UcOnLiji7jXlkT5O1PnGlRWYEuK474XRA5d3NXYDhCVg2WdxUQYJRkPMdLr780zoO3i6dT6jtd4+3g/SWj+itb7V/6MJcaaBGFiTPtJ1CwvJTk3goY214/6Yp6uPc/+rh/nI4kL+Ze3sM97vy+CRDp7o8Os3a0hOiOOWqpKA7md5mWccJZZm5BEmGgAAIABJREFUjEXseWVfCxf/ZANP7TjGzcuLefMbl3DPZbNITgjd5pJoU5hu4kT3QMi6TU7nK/CMFbIMYEqIY+XMaby6vzUoZ9Na89uNtZRkJbN2bmh3dVTkefIz9ocgh8fhdLO9vlPyd8SElAwXeN7P4TnhzcSaTJ2Ks3NTKUw3sX4S5/BsOHSSbz67h0vmTOP7182bVP/+4ZKdmshXLp/NxiPtvDJKsX9nYxdxBsX5M9LCdq6k+DiWFmfEzHPkcRd4tNbf11pP3u9KMem53BqH0x0TW7R8kuLjuHl5CW8cauPoyXPnA2w60s69T+/moplZ/OgjC8764BMfZyA5IU5ClqPAsc5+nn/vBJ+6oCjg1b9ZqYlU5Jlj5uqEiE2Pv9NIVmoCr331Yr577TyyU8ceG5qKCtJN2IfcdPY5wvL53h/ROnf3yQfm5nLCag9KoWRHQxfvHevm9lX+bxb0l2+TVihyeHYf76bf4ZICj5iQjOR4zInGUzp4WqwD5I/j+zGaKKW4ZkE+rx9sZe8k7DTusQ/xjb/uZnaumf/59OKY6BadqJtXFDM7N5Xvv7gf+5DrjPdXN3RRkWcO+4WZm5YX87Gl08N2cSSS/MngyfBuzioY4zaF3tuEfueZiDm+HxKmhNj6oXrT8iISjQYe3lQ35u0ONNv4/GPVzMxJ5X9vWkKCcfR/J0tSvHTwRIHfvlWLQcEdq4Kz+reqPJvt9Z2nhDEKMVVordnTZGVFWVZItilNFoUZvlXp4cmyaLXZMScZx/Vk/JKKHJSC9fsDvx7427dqyUiO56OLpwd8X+eSZoqnMN3EwRCsSt9a04FScGGpFHiE/5RSFGcn0zBik9aJbvvwRr3J5O5LZpKZksA3n92L2z25XmT/9/ojtPUO8qMbFgQ0Th8L4uMMfPfaeRzvGuA3b546geBya9471s2SMObv+Fx1Xj63XlQaE51X/rxS/mc8m7Pyx7hNnvc2/xTIoYQ4mwFfgSeGOnjA05nxkcXTeXpnEx29Z88HaLYOcOu67aQmGll36zIsSfFj3qfFZIyZJPlo1dYzyF92HOMji6aTH6RNGFXlWQw63cPzzUJMJSesdjr7HJw3PXxt3dGo0Lcqvbv/HLcMjharfdzdAtPMiSyakc6rB87Yu+GXmrZe1h9o5eYVJWHL3asI0SatLTUdVOZZAu7SFLGrODNleETL5da02sb/PRlN0kzxfPPqubx7rJsntzdG+jjjdrDFxrot9XxyWRELZ0gPw3hUlWdz9Xn5PLjhKMe73n+sOtTSQ5/DFdb8nVjkT4HnauCo1rp6tBt431cDXBPowYQ43YDDU+CJpQwen9tXluJwunns7TMfEG32IW5dt52+QSfrbl02rmJBmkk6eCLt95vrcLjcfO7i4IWHXlCWiUFJDo+YmvYc97T1zy+UAg9AUxg7eM4VsDzS2rl57G2y0WydeBD0w5vqiI8z8JkVxRO+D39V5JupaesNagekfchFdWOXjGeJgBRnJXOssx+ny0177yBOt56UBR6A6xYWUFWexY//fpC2EIWaB5PWmm8/uw9LkpFvXDEn0seZVP796kqUgv986cDw26obu4DwBizHIn8KPCXA4XHc7hAQnHkDIUZ4f0Qr9go8M3NSubQih0ffrj9lntXhdPOFx6o5erKXX9+8hEpvjsC5WJLiZYtWBNnsQzy2tYGr5udTNi01aPdrSYrnvOnpbK1pD9p9ChEt9jZZiTMo5o7z59xUlZ4cT3JCHE1d4dmk1WKznzNgeaS1c3MAJhym2tE7yNPVx7lhcWFYM5Yq8y043XrM9b7+2tnQhcPpZoUUeEQAirOScbo1zVY7J3wr0oPU+RtuSim+d918BoZc/HDEC/9o9bddTWyr7+TeKyukC89Phekm7l4zk5f2tLD5qOd56a6GLrJTE5mROTm/ficLfwo8ZmA8vas9QGxfXhMhEasjWj53rCylvdfBc+82AZ6rCv/6zG42H+3gxzcs4KKZ2eO+L+ngiaxHtzbQM+jkCyFYsVlVnsWuxm76HVLAE1PLniYrs3JSY7KLcySllHdVeuhHtJwuN209g+MKWPYpn5ZKSVYyr05wXfqjbzcw6HRz+8rQrkY/XUVe8IOWt9Z2EGdQXFCaGbT7FLHHlzlW39E3vNUuP31ydvCA56Ll5y8u55ldTWyJ4gtS1oEh/vOlAyyckc7Hl86I9HEmpbtWlzEj08R3n9/HkMvNzsYuFhelx0QOTiT5U+BpAeaP43bzgOj9bhWTlm9EK1YLPCvKs5ibb+F3G+vQWvOzVw/zzM4m/mXtbG5Y4l8IpcUULxk8EWIfcrFucx2rZ08LyahJVXkWTrdme31X0O9biEjRWrO3ycp5MT6e5eNZlR76Ea32XgdujV8jWkopLq/MZWtNOz1+Ps7Yh1z8cWsDl1fmMDMneN2N41GSlUyi0RDUoOUtNR2cV5iG+Ry5eEKMpTgrGfCsSj/hLfAUTNIOHp8vXjKTosxkvvXsXhxOd6SPc1Y/e+UQnX0OfnD9fAwh3uQ3VSXFx/Gtq+dy5GQvD6w/TH1HP4sjELAca/wp8GwG5imlrhrtBkqpDwLnAZsCPZgQp/N18CTF4IgWeJ4037GqlCMne/nqX97jl68f5ZPLZnDPpTP9vi+LKZ7eQeek22IwFTxVfZz2Xgd3h6B7B2BpcSbxcSqqr4oJ4a9mq50OCVge5ungCf2I1vCKdD8KPABr5+Yy5NJsPOLfz6Gndx6ns8/BHavC270DYIwzMDvXzIEgdfD0DTp571i35O+IgOWak0g0Gmjo6KO5e4CkeAPpyZO7aJgUH8d9182jpq2PhzbWnvsDwmxvk5VH327gpuXFMZ/7Fqi1c3NZPXsav3qjBpD8nXDwp8Dz397fn1RK3amUGn60V0olKqXuBJ4ENPCLIJ5RCGBEBk+MdvAAXLOggFxLIs/sauLi2dP4/vXzJ9TmaEkyojX02GWMJ9xeO9BK+bQULgxRy74pIY5FRRlslaBlMYXsaZKA5ZGmZ5jo7HMMd7aGSos3KNmfES2AJcUZpCfH+zWm5XZrHt5Yx4LpaSH7+XgulfnmoHXwbK/vxOnWkr8jAmYwKIoyk2no6KfZaic/zTQlRlwumZPDB+fn8YvXjnCsMzxbAcfD7dZ867m9ZCQn8NUPSLByoJRSfOdDc4mPUxgNigVyoSbkxl3g0VpvA76JJ4vn10C3UuqwUuow0O19mwX4jtZ6SygOK2JbrGfwACQYDfz7VZV8YG4uv7pxMfFx/tRo35dm8lz5kTGt8HK7NTsburigNDOkT86qyrPY22TF2i//f8XUsOe4BCyPVODN3wh1F48v78OfES3wdMNcOieH1w+exOka3/jFawdPUtvex52ryiL24rUiz0J7ryMo23221nQQH6dYWiz5OyJwxVkp3hGtgUm7Qetsvv2huRgNim8/txeto6Or/K/Vx9nV2M2/XVU5/HxZBKZ8Wir3XlnBpy8sivkcvXDw69Wh1vqHwA3AHiABmOn9leh92w1a6x8E+5BCAAw4PE8SY3GL1kjXLSzkt59ZSmqiccL3YfE+YEnQcnjVtPVisztD3p5aVZ6NW8M7ddLFI6YGCVg+VWG6J5PjRKgLPLZB4uMUWRPYHrN2bi7WgSF2NIwvD+yht2opTDfxwfl5fn+uYKnINwMEpYtnS00Hi4oyYv45iwiO4qxkGjr7aO62T9oNWmeTn2biK2tn88ahNl7e1xLp49Dd7+BH/zjIspIMblhcGOnjTCl3rCrje9eNJ85XBMrvy/9a679prRcC+cBy7698rfVCrfXfgn1AIXyGM3jkCX7ALN7AR5sUeMKq2vtCZ0mIA+YWzkgnKd7AFhnTElOAL2BZxrPeV5jheYEX6g6eVpudHHPShAJGV82eRkKcgfXjGNPa1djFtvpObl9ZinGCnanBUOndpHWgObACj7V/iL0nrJK/I4KmJCsZ+5CbFpt9uINvqrilqoTKfAv3vbCfvsHIRgf818uHsA4M8b3rJhaBIEQ0mPCjqNa6VWu9zftrYrswhfCDZPAET5p08ETEjoYuMpLjKc1OCennSTAaWFaSKTk8YkoYDliWAs+wXHMicQZFU1foR7T8zd/xSU00sqI8i1cPtJ5z9OJ3G+swJxn5+LLIriLOSEkgz5IU8Kr0d+o60BpWlEmBRwRHUdb7zxumUgcPeEY6/+PD82mx2Xlg/eGIneO9Y908ua2Rz67wFJyEmKwid5lkBKWUQSn1FaXUQaWUXSl1TCl1v1Jq3K+ClFKZSqmfKqWOeu+jTSn1hlJq1Wm3e0QppUf59dHg/+1EsAw4XMQZFPFxUlEPlMXkGe+SDJ7w2tnQxZLijLBcFaoqz+ZQa09QsiSEiCQJWD6TMc5AniUp5CNarTa73xu0Rlo7N5eGjn6Onuwd9TbHOvv5+95mbrywOKDR42CpyDdzoCWwAs+Wmg6S4g0sLEoP0qlErCvxrkoHplQGj8/iogw+uayI32+uD7iDbiJc3mDl7NREvrJ2Vtg/vxDB5PcjqXd71iXAbDyhymd7paK11t/3425/DnwJ+BtwP1Dp/fMipdTlWusxE/qUUsXABiAVeBg4DKQBC4DRBihvPsvbtvlxZhFmA0MuTPFx0jIZBNLBE36dfQ5q2/v42NLwXKH2jQa8XdvBh84vCMvnFCIU9jZJwPLZFKQncTyEBR6tNS02O2vm5Ez4Pi6rzOGbz8KrB1qZlWs+620e3lRHnEFxS1XJhD9PMFXkWdh8tJYhl3vCiwy21nSwrCSTRKN0HIvgKEw3YTQonG5N/hQb0fK598o5vLKvhf/3tz389fNVExoNnagntzWy+7iV//7kQsxJEqwsJje/CjxKqRvwbMsaayWAwrMqfVwFHqXUPOAe4Bmt9Q0j3l6HZ936J4EnznE3j+H5uyzQWjeP5/NqrR8bz+1E9BgYckn+TpCkJBgxKLANyJr0cNkZpvwdn3kFFsxJRrbUSIFHTG6+gGUJqz1VYbpp3AHGE9Ez6KTf4SIvLXHC95GfZuK8wjRe3d/K3WtmnvH+7n4Hf9lxjGvPL5zwKFiwVeabGXJpatp6qcjzv6jY3jvIodYerl0oP3dF8BjjDBRmmGjo6J9yI1o+6ckJ/NtVlXztqff4y45jfPKCorB83o7eQX7y8iFWlGVxrTxfElPAuC9NKKUuBP6Ep2vnSTxbswB+BPwVsHr//DDwPT/O8Ck8RaEHTnv7Q0A/cNM5zrUaWAn8l9a6WSkVr5RKHutjvB+nlFIWpVRUjKmJc7M7XJgS5H9XMBgMCospXka0wmhHQxdGg2LB9PCMmRjjDFxYmsXWmvawfD4hQkEClkdXkG6ixWrH5Q7NauHWCa5IP93aubm8e6ybkz32M973+DuN9Dtc3LGqNKDPEUy+7I2J5vC8XevJPpOAZRFsxVkppCTEYUmK/ChjqNywuJALSzP50T8O0tEbnhHzH//jIH2DTr533TyZEhBTgj+vlr/mvf1HtNY3AbsAtNb/T2v9CTwjWy8BV+Hp8hmvZYCb08ajtNZ24F3v+8dylff3RqXUC8AA0KeUOqyUGqs4ZPX+GlBKveotYIko5hvREsFhSYqXEa0w2tnQxbzCtLB2oVWVZ1Hf0R/yTTtChEqz1U57rwQsn01hhgmnW5+1cBIMLTbP/QaSwQNweWUuWsMbB0+e8vZBp4tHttSzalZ2VAWalmankBBn4MAEV6VvqekgNdEoX7Mi6K5ZkM9Hl0yf0kUIpRQ/uH4+vXYnP/z7wZB/vuqGTv6y4zi3rywddYxUiMnGnxJwFbBXa/1/Z3un1rpdKfVpoA64D/j8OO+3AGjXWp+tTNsEVCmlErTWjlE+fo7394eAI8BngUTgX4BHlVLxWut1I27fgifzpxroA84HvgxsVEpdpbVeP9pBlVJ3AXcB5ObmsmHDhnH+FYOvt7c3op8/Eo632Bka0jH39w4Vg9NOXVPrhP49Y/HrLxBOt2ZXYz+XzjCG9d8tvscTX7bu/zaxsnDqzJTL11/sqG71jJEOtdawYUN9ZA/jFS1ffx1tnn+bF1/fwqyM4BeONx73XABoOPgeA40T757VWpOVpHjyrf3k9tWecv9tPQ4+O0dFxb/nSHnJsGVfAxtM/i+JfW1PP+UWA5s2vhWCk0XP158IvxwgJ42YeP1xRbGRv1YfZ1ZcO3MyQ3NhzOXW3LfVTmaSYlFCCxs2yFLoaCc//8bHnwJPNrB5xJ+dAEopk9Z6AEBr3aOUegv4oB/3mwyM1oNnH3Gb0Qo8vnJrD3CJrxCklPobUAv8p1LqD76gZq31v5728c8qpZ7A0y30v8Co0ela698CvwVYunSpXrNmzRh/rdDasGEDkfz8kfDgoa0kA2vWrIj0UaaEwqNvM+BwsWbNRX5/bCx+/QXi3WPdDL2ymetWLmDNeflh+7xut+aBd9fTGT+NNWsWhu3zhpp8/cWO6lcOYVBHufHqNVGTwRMtX3+FrT38rPotckorWLNwtH0SE7fntSOw9zDXrr044M7DD/Xs48ltjVxYtQpTQhxaa/7zgbeoyEvkizesirqOhKUn32XTkXa//z83Wwdo/cfr3HnJbNasKgvJ2aLl60/EpnB9/V1Y5eLdn73J0w1xvHjtKhKMwY9oeGRzHY09+3nwxsVcGcbnZmLi5Off+Pjz3dKFpzPGp9v7+/TTbqfxFJnHq/+0+x0pacRtRuObPXhyZJeP1roLeB7I4/0un7PSWh8B/gLMVErNHs+hRfjZh1xR8wR/KkgzxWOzS8hyOFSHOWDZx2BQLC/PYmtNB1qHJqdDiFDyBCyb5Wf/WRSke4JWQzWC2WKzk54cH5Sx0ssrcxl0utl01JMJ9ubhNg639nLX6rKoK+4AzM23cLJn0O8MkK01nvydFZK/I0RATAlxfO+6eRxu7eXhTXVBv/+2nkHuf+Uwq2Zl88H5eUG/fyEiyZ8CzzFgZJz5XjzhyNf43qCUSsETeNzkx/2eALKVUmcr8hTiGd8arXsH4Lj395azvM+3UWs8r6rqvb9nj+O2IgIGHJLBE0ySwRM+1Q2dFKabAg4rnYiq8iyarXbqO8aqkwsRfSRgeWwpiUbSk+Np6gpNgafVZg84f8fngtJMzIlGXt3vear20MZaci2JXLMgOjfW+LZnHWrxL2h5S00H6cnxVE5g+5YQ4lSXVebygbm5/OK1IxzvCu5zmB++dAC708V910qwsph6/CnwbADmKaWmef/8Ip7Omh8qpX6slLrHe5ts4FU/7ne79xwXjHyjUioJWAjsOMfH+8KZT+8kGvm2k2d53+l8o1kygBmlJGQ5uCymeGxS4Ak5rTXVDV0sLQlv945PVbmnZr1FtmmJSabF5glYDtfmucmoMN3EiRB28ARrdXmC0cDFc6bx2oGT7DluZfPRDm69qDQkYxfBUJHvmf4/4EeBR2vN1poOlpdmYTDIC0YhguE7184D4LvP7w/afb5T28Ezu5q4a3UZZdNSg3a/QkQLfx5ZnwLeBBYBaK07gK8C8Xg2bD0ALMHTUfMtP+73z3jGur582tvvxJO987jvDUqpcqVUxWm3exZP/s5NSqnUEbfNB64Hjmitj3rfluItHJ1CKbUI+BhwQGtd48fZRRjZh1wkSZt+0KSZ4hl0urEPuSJ9lCmtqXuAVttg2MezfEqykslPS2KLd3RAiMliz3ErgHTwjKEg3RS6ES3rYNA6eMCzLr2jz8HXnnqPlIQ4PnVB0bk/KEKyUxPJTk3kYPP4N2kd6xygqXuAqpkyniVEsBSmm/jK2lmsP9DKK/vONqzhnyGXm28/t4/CdBP/dMmosatCTGrjDlnWWm8D1p72tt8opXYAHwUygYPAOq1191nuYrT73aOU+hXwT0qpZ/CsWq8EvoSnoPTEiJu/BhTjGQ3zfXyXUuprwG+At5VSvwcSgC94f/+nER8/C/i7UupZPBu3fFu0bgNceDdkiegkI1rBZUnyfPvb7ENhXd0da3z5O4uLIlPgUUqxojyLNw+14XZrubIsJo29TVYMypOHIs6uMN3ElqPtaK2DOmYw5HLT0TcY1LHSNbNzMBoUh1p7uH1lKWmm6N7sV5lv9mtVuq9Lskryd4QIqlsvKuXp6ibue2E/K2dlk5zgz46gU/1hSz2HWnv47c1LJNtNTFkT/w7x0lpX41k5Hogv48nAuQu4GmgHfgl827f96hxn+K1Sqh34BvB9wA1sBT6ttR65+asFWA9cAtwImPDk9PwZ+KHW+mCAfw8RIlprGdEKMov3ybVtwEmO+Rw3FhNW3dBFckIcFXmR+0euKs/mmZ1NHD7ZM5wtIUS0k4Dlc5ueYaLP4cI24CQtOXgFk5M9g2hN0Ea0ANKS47mgNJN36jq59aKSoN1vqFTmW3hkSz1Olxtj3Lkb3rfUdDDNnEi5jHwIEVTxcQb+48Pz+eivt/LlP73LoqIMEowGz6845f09bsTbPL8nnvbnfoeTn796mEvmTGPt3NxI/7WECJlxF3iUUp3AXq316mAfQmvtAu73/hrrdiVjvO8Z4JlzfHwLcPMEjigizOFy49bIE/0g8hV4JGg5tKobulhUlD6uFwih4tvosuVoR0AFHrdb88LuEwwOufn4shnBOp4QZ9Bas6fJysWz/VnKGXtGbtIKZoGnxWoHCOqIFsC/X1VJfUcf0zOSg3q/oVCRZ8bhdFPf0cfMc1wF0VqzpaaDqvIsCWwVIgSWlmRy1+oyfvtWLa/sn3hcaoLRwHclWFlMcf508CTg2aQlRNjZHZ5GLhklCh5fe7zNLgWeUOkbdHKg2cY/XTIzoucoTDdRkpXMlpoObltZOqH7qG7o4nsv7ue9Y93ExymuX1QYtQGpYvLzBSyfVygdZ2MpHFHgmVsQvH+rVpunwBPszX/zC9MmTaaSrxh+oLnnnAWemrZe2nsHZTxLiBD696squffKChxONw6nm0GXC4fTzZBLD7/N4XIx6PtvpxuH69T/XjA9neKslEj/VYQIKX8KPEeRFeIiQga8QcAyohU8liTfiJYUeELlvWPduDUsjlDA8kgryrN58b0T4x438GnqHuDHfz/I8++dIMecyIcXFfK3XU3UtvfKuJcIGV/A8nmyQWtMwx08QV4h3Ozr4AniiNZkMzMnFaNBcaDZxofOH3uduy/E3re1UAgRGnEGhSkhztvRH905XkJEij+XXx8DViulJnb5V4gADBd4EqRjIFiGO3ikwBMyOxq6UAoWRShgeaSq8ix6Bp3sOzG+0NC+QSf3v3KIS3+6gZf3tXDPpTN542truHtNOQAHm8e/PlgIf70fsCwFnrFkpyaQYDRwwluQCZZWm50Eo4GMII59TTYJRgMzc1I5OI5V6VuOdlCYbmJGpikMJxNCCCFG58+r5Z8DLwOvK6U+oZRKDNGZhDjDgEM6eILN7N2iJRk8oVPd0MXsHHNUbItZXubN4TnHunS3W/PX6uNc8tMN/PL1o1wxL4/Xv7aGr35gDimJRkqzU0iIM3DAj/XBQvhLApbHRylFYbqJpq7grkpvsdrJtSTGfE5FRZ75nKvS3W7N23UdrJD8HSGEEFHAnxGtI3jWkxfjXV2ulDoJnO1ZhdZalwd+PCE8fB08ksETPEnxcSQaDdjszkgfZUpyuzU7G7u4ZsHYrf3hMs2cyOzcVLbUtPOFNWf/8by9vpPvvbCfPU1Wzp+Rzv/etJglxZmn3MYYZ2BWbioHxnFVW4iJ8AQs27h49rRIH2VSKEw30dQd5AKPzR70gOXJqCLfwrPvnsDaPzRqiPWBFhvd/UOSvyOEECIq+FPgKRnx375LFKPtmNMTOo0Qo7BLBk9IpJnisfZLB08oHG3rpcfuZEkU5O/4VJVn86ftjTic7lMCko919vOjvx/k//Y0k2dJ4oFPLOTa8wswGM5+Nboiz8LGI23hOraIMa22Qdp7ByVgeZwK0028fuhkUO+z1WZnwfT0oN7nZFSZ7w1abrENd0Gebqu3K3KFFHiEEEJEAX8KPJK9IyJmeERL2vWDymKKly1aIbKjvgsgqgo8K8qzeGRLPe8e6+aC0kx6B508+MZRfrepDoOCL18+i7tWl5GcMPZDQ2W+mad3Hqejd5CsVJnWFcG1p0kClv1RkG6irWeQQaeLRGPgj5Faa1qsdj4wV763K/M827MONo9e4NlS00Fpdgr5aZK/I4QQIvLGXeDRWjeE8iBCjEW2aIVGmileMnhCpLqhi6yUBEqykiN9lGHLS7NQCjYdaaOuvZefvHyY9t5BPrKokK9fOWfcL1B8V7UPtvRw0czJ9SLQ5dY43e6gvBAWobHneLcELPuhMMPzfdvcbackO/D1v9aBIQad7qCvSJ+MppkTyUxJGDVo2elys62uk2sXRscorhBCCOFPB48QESMZPKFhSTLS1jsY6WNMSTsbu1hcnBFVoZtpyfHML0jjl28cRWtYXJTO7z67lIUz/BvFqPBe1T7QbOOimZNrLfD3X9zPpqPtrP+XiyN9FDGKPU1WZuakSsfmOBWkewoxTd0DQSnwtNhkRbqPUoqKPPOomWN7mqz0Djolf0cIIUTUkAKPmBSGM3jkCX9QpZniqWnri/QxppyO3kHq2vv4xLIZkT7KGT6yuJB+h5N/vnw2H1qQP6ECVFZqItPMieNaHxxN2noGeWKbJ4PIPuSSgnEU8gUsr549uQqHkTQ93dMlGKyg5RbvynUJWfaozLfw+DsNuNyauNNyyXxbCUcb3xJCCCHCbdwFHqVUrR/3K1u0RFDJmvTQsMiIVkhUN0Rf/o7PrReVcutFgUeqVeSZOdgyuValP7q1HofTDUCz1U5pELodRHC9H7As41njlZeWhFIEbVV6q7eDR0a0PCryzNiH3DR09FE2LfWU922t6WBOrplsySITQggRJQznvsmwknH8Kh7x30IEjYxohUaaKZ4e+xButyy+C6bqxi7i49SUfpE6N9/C4dZenC53pI+k8O1HAAAgAElEQVQyLgMOF398u4Fci+eFWLBeDIvg8gUsL5CA5XFLMBrIMSdyIkgdPM1WKfCMNDJzbKRBp4sdDZ2yPUsIIURU8afAUzrKr3LgUuB+YAj4AVAW3GOKWDcw5CLBaDijPVoExpIUj1tDr8MZ6aNMKTsbuphfmDalC5IV+WYcTjd17ZNjxO+v1cfo7h/i3isrAGjq7o/wicTZ7GmySsDyBBSkm4I2otVqs5OVkkCC0Z+niFPXzJxU4gyKg82ndiy+29iNfcgt+TtCCCGiyrgfvbXWDaP8qtNab9Bafx34JPDvwKyQnVjEJLvDJeNZIWAxeaY0bTKmFTQOp5v3jltZUhR941nBVJHnuao9WvhoNHG5NQ9vquP8Gel86PwCDAqauu2RPpY4i70SsDwhhemmoHXwtFjt0r0zQlJ8HGXZKexvPvVn3ZaaDpSCC0ulwCOEECJ6BPXyjNb6WWAPniKPEEEzMCQFnlBIM8UDSA5PEO07YcXhdEdl/k4wlU9LJT7uzKva0ejV/a3Ud/Rz16oy4uMM5FqSZEQrCnkClq3Mn8KjjaFSmGHiRLc9KOO2LbZB2aB1mop8yxmZY1trOphfkEZacnyETiWEEEKcKRT9t0eAxSG4XxHDBobcckU3BCxJniemtgEZ0QqWaA5YDqYEo4HyaamTYpPWQxtrmZFp4op5uYCn20FGtKJPq22Qth4JWJ6IwnQTDpeb9t7BgO+r1SYdPKeryDNzvGsAm91zMWTA4WLXsS4ZzxJCCBF1QlHgKUPWr4sgG3DISuNQsEgHT9BVN3QxI9NETgy8QKrMt3Agyjt4qhs6qW7o4vaLSjHGeR7yCtI93Q4iuvgClqXA47/CdBMQ+Kr0QaeLzj4H+dLBc4rKfDMAh70F7R0NnQy5tAQsCyGEiDpBK/AopeKUUvfi6d55L1j3KwSAfciFKV4CH4PNN6LluyopAqO1ZkdD15TP3/GpyDPTbLXT3e+I9FFG9dBbdaSZ4vnY0hnDbyvMMNFsHZDtcVFmOGC5wBLpo0w6BUEq8Jy0eTqA8mKgQO0P3yYtX0F7S00HRoNiWUlmJI8lhBBCnGHcnTZKqdfHeHcqnm1a6YAb+GGA5xLiFANDLhnRCgFfB4+ELAfH8a4B2noGp/x4lk/FiPXBy8ui70p2fXsfL+9v4QsXl5OS+P7DXWG6iSGX5mSPZI1Ek71NVsqnpZKcIE3A/irM8BR4Ag1abrF5V6TL98Up8ixJpJnih0Plt9Z0cP6M9FN+rgghhBDRwJ9HpjXjuE0N8G9a6xcndhwhzm7A4SJDggyDzpxoRCkp8ATL+/k7sXFVtzLPM7ZwsNkWlQWe32+uw2hQ3FJVcsrb3x9n6ZcCTxTZ02Rl1azsSB9jUrIkxWNONAYcHt5i9RR4pIPnVEopKvLMHGy2YbMPsft4N1+8ZGakjyWEEEKcwZ8CzyVjvM8BNGmtGwM8jxBnZR+SDJ5QMBgU5kQjNruELAdDdUMXKQlxzPEWPqa6aeZEslISojJouavPwV92HOP6hYVn5CH5uh2auu0sKY7E6cTpWm12CVgOUGGGiaYAs6VabVLgGU1lvoWndhzjndpO3BrJ3xFCCBGVxl3g0Vq/GcqDCDEWWZMeOhZTvIQsB0l1QxeLijKIM6hIHyUslFJU5JujMmj5sbcbsA+5uXN12RnvG+7gkVXpUWPPcQlYDpRnO1xgX9PNVjtJ8QYsJhk9Ol1lvpk+h4u/7DhGgtHA4hjJWhNCCDG5SGqtmBQkgyd00kzxMqIVBL2DTg622FgcI/k7PhV5Fg619uCKosBi+5CLP2ytZ82caczOPbObKiXRSHpyvKxKjyISsBy4gnQTTV2BfU232OzkWZJQKjaK1P6oyPN8ba4/0MqSogzpKhZCCBGVxl3gUUrNU0p9Wym1aIzbLPbepiI4xxPCY8AhHTyhYkmSDp5geLexG7eGpTFX4DFjH3LT0NEX6aMMe3ZXE+29Du5adWb3jk9Bmkk6eKKIBCwHrjDDhM3upCeArYitVju5Mp51VrNzzRgUaA1VMp4lhBAiSvnTwXM38C2gbYzbnAS+DXwhkEMJMZLbrRl0uuVqWYhYTEZZkx4E1Q1dKAULi9IjfZSwqhyxSSsauN2ahzbWMq/AMmZGRmGGiRMB5pWI4NndZJXxrAD5VqUH8nXdYrNL8PgoTAlxlGSnAJK/I4QQInr5U+C5BHhPa318tBt43/cucFmgBxPCx+50AciIVoikSQZPUFQ3djEn14wlKba2vc3MSSXOoKImh2fD4ZPUtPVx56qyMcdMfHklWkfPaFms8gUsz5cCT0AK0wNbla615qRtUAKWx1CZbyE5IY4F02OrkC+EEGLy8KfAUwjUjuN2dd7bChEUAw5vgUc6eELCkhSPbUC2aAXC7dbsauiKufwdgKT4OMqyUzjQHB0dPL99q5b8tCSuXpA/5u0K0030Djrlaz8KDAcsT5cCTyB8BZ7jEyzwdPY5cLjc0sEzhq9/YA4Pf3YZCUaJsBRCCBGd/HmEihvn7RWQOLHjCHGmgSEp8IRSmimegSEXDqc70keZtA6f7KFn0Blz+Ts+FfkWDrZEvoNn9/Fu3q7t5LaLSomPG/vh6v1V6ZLDE2nDAcv5ErAciBxzIvFxasIdPC2yIv2cSrJTZDxLCCFEVPOnwNMAXKiUGvVjvO+7EDgW6MGE8LF7CzxJMqIVEhaTZ6RIcngmrrqhC4AlsVrgyTNzvGsg4l9DD22sw5xo5JMXzDjnbYdXpUuBJ+J8AcspiRKwHAiDQZEfQHh4q7fAkysdPEIIIcSk5U+B52WgALh3jNt8A8941suBHEqIkQYcns4S6eAJjTRfgUdyeCasuqGL7NQEijKTI32UiPB1XhyKYNDy8a5+XtrTzKcuLMI8jhyk4Q6eANdKi8DtkYDloClIT5p4B491EJAOHiGEEGIy86fA8zOgB/iBUupxpdSlSqkC769LlFKPA//hvc39oTisiE0yohVaFpPnqrkELU/czoYuFhdljBnqO5VV5JsBOBjBoOV1m+tRwC1VJeO6fVZKAolGg3TwRNhJm52TErAcNIXpyRP+mm6xDqAUTDPLlL0QQggxWY27H1prfVwp9XHgr8CngE+edhMF9AKf0Fo3BO+IItYNF3gSJNQwFIY7eOwSNjsRbT2D1Hf086kLiiJ9lIjJsySRZornQIQ6eKwDQ/xpWyMfOr9geFX0uSilKEyXVemRtqdJApaDqTA9iVabnSGX+5w5VKdrsdnJTk30++OEEEIIET38ehTXWr8CzAN+ARwGBgG7979/AczXWv8j2IcUsc23RStJOnhCwrfWWzp4JmZnoyd/Z2lJbObvgKdYUpFnjlgHz5PbGulzuLhjValfH1eYYZrwxiERHLuPW1ESsBw0hRkm3BparP4XLltkRboQQggx6fmdaKi1PgZ8OQRnEeKs7DKiFVIWyeAJyM6GLhLiDMwriO0OhMp8C0/tOIbbrTEYwjeq5nC6Wbe5jotmZvn9/6AgzcSB5pMhOpkYDwlYDq6CEeHhM/zMBGu12v3+GCGEEEJEF+nDFVHv/REtKfCEgm9ESzp4Jqa6oYv5hZaY7zCrzDfT53BxLMyhxS+8d4JW2yB3rirz+2MLM0y09w4OF5FF+EnAcnD5tsNNJGi5xWYnL03yd4QQQojJbNwFHqXUDKXUZ5RSc8a4zRzvbaYH53hCvD+iJR08oZFoNJAQZ4j4iuvJaNDpYneTNWbXo49UkecZsTnQHL4cHq01D22s/f/s3Xl4XHd1//H3GWmkGckaSbZsy5J3O7Edh2zORhInzsaSsoQSWihLgCyUQilQfqWUAgVaaKEBSiklCQkNa0vZEvbGTpw4kMTZHLJIiRPJji1bm61dM9Jo5vv7484oiq1lRppN0uf1PHpk3blz7/GTiaw5OgsbllZw0YmL035+8s3w4Wm0s8jMJQcsK8GTOaMVPGmuSo9EY/SEo2rREhERmeXSqeD5APCtFM77L+B904pGZBzJCp75XiGRLWZGKOinN6why+l6sqWX4ZE4W1YtzHcoeXfi0grMoLE1d3N47nuuk8bWPq7dumZaG8xeXJWuOTz5oAHLmRfwF1GzoIRDPem9ppMze2orUxtSLiIiIoUpnQTPK4CnnHPPTHRC4rEngVfONDCRpEg0hplXaSLZEQoWawbPNDy63xuwfMaqqjxHkn/BkiLWLCqnMYcVPDfd28SSilJed1rdtJ5fPzqvJLdtZeJ5okUDlrOhvirIwTSTlq29iQSPKnhERERmtXTeMa8AnkvhvOeB+bsvWDIuPBwj6C+a1m/oJTWVQb9atKbhkf1drFxYxpIKvSkCb9ByQ44qeBoO97JrbydXn7ea0uLpVffVVgbwGbRoVXpeaMBydtRVBWlJcwZPWzLBoxk8IiIis1o6CZ4AkMo7wCGgfHrhiBwvHI1p/k6WhQJ+DVlOk3OOR17o0vydMTbWVrD/yCADQ9lv97t5VxNlJUW89Zzp/z7BX+RjaSigFq080YDl7KivCnKoO4xzLuXnJFu0lqqCR0REZFZLJ8HTApyRwnlbgNbphSNyvHA0pvk7WVYZ9KtFK00Hjobp6BtSgmeMjYlWm2fastum1RWJc8eeQ/zJmSuoKiuZ0bXqq4Jq0cqD9t4Ibb1DnKwET8bVVQWJROMcHRhO+TmHeyKUlxRREfBnMTIRERHJtnQSPHcDa83snROdYGZXA+uAu2YYl8ioSDSmFelZFgoWq4InTY+8cBRACZ4xNtZWAGR9Ds+d+0eIO8c1F6yZ8bWm084iMzc6YFkJnoxLDg8/lEbrYVtvhKWVqt4RERGZ7dJJ8HwJr0XrJjP7JzNbm3zAzNaY2T8BNyXO+VJmw5T5LDwco0wJnqwKBfz0RkbSKumf7x7Z38WC0mJOXFqR71AKxvLqIBWlxVndpNU/NMLdB6K8+mXLWLGwbMbXq68O0toTIRbXaz+XkgOWN9dpwHKmTWd4eGtvRAOWRURE5oCUEzzOuUbg+sSXfwvsNbMhMxvCG778t4nrvcc591TGI5V5Sy1a2VcZ9BOLOwaGY/kOZdZ4ZH83p6+sosin4d9JZsbGZRU0HM5egueHDx0gPALXb1079ckpqK8KEo05OvqGMnI9SY0GLGfPiwmeNCp4epTgERERmQvS2jvtnPs2cB7wc2AQ8Cc+wolj5znn/ivDMco8F47GNWQ5y0JBb+6C5vCkpi8S5ZnWXrVnjWNjbYjGw31Zqwb74cMHWFvp49QVmVlNr1Xp+aEBy9lTVeanrKQo5eHh8bijvW9ILVoiIiJzQFoJHgDn3MPOuSuBSqA28RFyzl3pnHso0wGKRIa1RSvbKhMJHs3hSc2eA93EnebvjGfjsgr6hkayMtemsbWXxtY+zqvLXNVHcl7JQW3Sypn2Pg1YziYzS8yWSi1p2TkwxEjcqYJHRERkDkg7wZPknIs759oTH/FMBiUyVlhDlrMuFFAFTzoe2d+FGZyWoSqSuWRjrTdTJRuDln/22CGKfMbZyzKY4KlKfyCtzMyTGrCcdd6q9NRe0209XntirSp4REREZr1pJ3hEckUzeLIvWcHTGxnJcySzwyP7u9iwtEIrhceR3KSV6Tk88bjjjj0tXHhCDaGSzM09Ki8tpqrMrxatHHriYK8GLGdZOtvhWnu9RJAqeERERGa/tH8NamZ1wOuBE4EQMN5P2s45d80MYxMB1KKVC6Gg961ALVpTi8Udj73QzZWn1+U7lIJUXlrMqkVlNLZmtoJn976jHOqJ8NFXb4TuvRm9dl1lMOV5JTJzT7T0sLamXAOWs2h5dZCjA8OEh6eugB1N8KiCR0REZNZL66crM/sg8M94g5VHDyc+uzFfO0AJHskIr0VLxWbZVKkhyyl7tq2P/qERzd+ZxMbaChoyvCr99j0tlJUUcflJS9n9+8wmeOqrg7xwRBU8ufJESzcvX7so32HMaS8ODw+zfsmCSc9t64lQ5DNqFpTmIjQRERHJopTfNZvZK4EvARHg88D9iYfeA3wRaE58/W/AuzMYo8xj0VickbhTBU+WJVuNVMEztUf2dwGwZeXCPEdSuDbWhtjXOUB4OJaR60WiMX7xh8O8anMtZSWZr/qoT7SzZGvzl7xIA5Zzo250ttTUlWmHeyIsXlBKkS9zrY8iIiKSH+mURXwArzLncufc3wN7AZxzNzvnPgqcBNyCV7nz+0wHKvNTOOq9QdQMnuwq8hkVpcX0RpTgmUw87vjRIweprwqyYmEw3+EUrE3LKog7r9opE3Y+005fZITXn16fkesdq74qSP/QCL1hzaDKtuSA5VOWa0B5NiW3w6Uyh6etN6IV6SIiInNEOgmes4CHJ1qF7pwbBt4H9AKfykBsIkQSFQDaopV9oaB/VlTw3L6nhfd9/1Fi8dxXW9zx+CH2HOjmQ5efiJl+2z2RTcsSm7Qy1Kb1s8cOUbOglPPXZaetZ3RVugYtZ91jL3RrwHIOLK3wKnJSqeBp7Y1QG1J7loiIyFyQToKnEmga8/UwgJmVJw8456LA74CLMxKdzHvJCh61aGVfRaB4VlQwfOt3+/jlHw7zvQf35/S+g8Mj/POvG3lZfSV/nKVKkrliRXUZZSVFNGRgVXrPYJS7Gtt57anLKC7KziwurUrPjQeajnDjPU1csL5GA5azrLjIR20okNLw8LaeiDZoiYiIzBHp/LTcibc1K+lo4vPqY84LAJo+KhmhBE/uVAb9BT9kuaNviMcPdlNS5OOLv32Gjr6hnN37pnubaO2N8MnXnoRPsyom5fMZG2orMlLB8+snDzMci/OGLCbVRttZulTBky2Nrb1c9+2HWbEwyFfffHq+w5kX6qoCHJyigmdgaIS+oRG1aImIiMwR6SR49gGrxny9B29j1luSB8xsCbANyO2v1mXOSg5pDahFK+tCQX/Bz+DZ+Uw7zsENf3IqkWiMz/+6ISf3PdwT5hv3PM8fnbKMs1ZruHIqNtaGaGztm/Hg4p8+1sLaxeW8LItDeReVl1Ba7EtpXomkr6U7zNW37qaspIjb3n021eUl+Q5pXqivCk7ZojW6Il0VPCIiInNCOgmeHcAmM1uZ+PqXQBfwMTP7HzO7AdgNLAB+ltkwZb5SBU/uzIYKnrsa26kNBXjNKcu4butafvJoC7ubj079xBn6wm+eIe7gb1+1Mev3mitOWlZB92B09A3kdLR0h3mw+ShXnlaf1ZlHZpZ4M6wWrUzrGhjmHbc8yOBwjNvefTbLq8vyHdK8UVcVpLUnMum8sraeRIJHFTwiIiJzQjoJnh8At5Ko4nHO9eOtQ48AbwI+BKzEq+z5x3SCMDOfmX3IzBrNLGJmB8zshrHzfVK4xkIz+1czey5xjQ4zu9vMtk7xvC+YmTOz/nRiltyIKMGTM6FAYQ9ZHh6Js2tvJxdvXIKZ8f5L1lNfFeSTtz/JSCyetfs+9kIXP32sheu2rmHFQr05TdXG5KDlGczhuWPPIQCuPC37M4/qq4NTtrNIesLDMa657SEOHA1z8zvOZGOtBivnUn11kJG4o71v4sSlKnhERETmlpQTPM65Bufcdc65XWOO3Q6cCLwX+DjwRuBs59xAmnF8GfgS8DTwl8D/4q1l/7mZTRmjma0CHgGuBn4E/AXwOby2sgnfGZjZaXiJKSV3ClR42Hvjri1a2VcZ9DMwHMtqsmQmHtp3lP6hES7duASAspJiPvGak2hs7eO2+7PTFeqc4zO/eJrFFaW8d9v6rNxjrtpQWwFAwwzm8Ny+p4UzVlaxclH2E2v1VcGUBtJKakZicf7yB4/x2IFuvvLm0zh3bXY2oMnEksPDJ3tdjyZ4VMEjIiIyJ8x4jYVzrgW4cbrPN7PNeEmdnzjn3jjmeDPwVeDNwPenuMx38f4upzjnDqd43yLgZuDXeMOjz0w/esk2tWjlTijofTvojYywsABnZOxoaKe02Mf562tGj71y81K2bVjMl+98ltecsoylGf4t9B2PH+KxF7r5wlWnsEBbf9ISCviprwpOu4Kn4XAvja19fPb1mzMc2fjqqoJ09g8RicYI6PvNjDjn+MTtT7K9oY1Pv24zV7xsWb5DmpdGEzzd4Ql/wGnriVARKKasRN/fRERE5oLs7JxNz1vwhjV/5ZjjNwODwNsme7KZXQhcAHzBOXfYzPxmlsqvez8AnISXXJIClUzw6A1X9lUG/QAFO4fnrsY2zlu36CXVXGbGP7x2M8OxOP/0y8wOXA4Px/iXXzeyuS7EVWcsz+i154tNyypoODy9Cp6fPdZCsc/4o1PqMhzV+JJvhg/3aA7PTH1l+15+sPsA77t4HVeftzrf4cxbdWMSPBM5rBXpIiIic8qECR4zy8g0UTPbNMUpZwFxvAHNo5xzEbx5PmdN8fwrEp9fMLOfA2FgwMyeNbNxk0OJlq7PAp92zmnjVwGLJLZoqUUr+0IBL8FTiHN4mjr62XdkkEsS7Vljra4p588vWscdjx/i9893ZuyeN+9q4lBPhE++RmvRp2vTshBNnQOjs7RSFY87bt9ziItOXJyzarIXV6WrTWsmvvfgfv5tx16u2rKcj7xiQ77DmdfKS4upKvNP+ppu642oPUtERGQOmawm90kz+zbwWedcc7oXNrM1wCfxKnD8k5xaB3Q654bGeawFOM/MSpxzwxM8P/kT5M3AXrw5PKXAh4HvmJnfOfetY57zn0Az3tyflJnZ9cD1AEuXLmXnzp3pPD2j+vv783r/XHn6Oe8/+4O/20WR3mRn1fNHvTfhux58hK7nJ0+o5fr195tmL+kU7G5i5859xz1+ss+xOGh85Pu7+cz5QYpn+FrpisT52q4wZy4tIvzCE+x8YUaXm7diR0eIxR3/8+udrAqlnqRtOBKjtTfClWvcuK+zbLz+Oga92VM7HnyMkZbJ/smSiTzSNsLXHhvilMVFvGrRUe655558h5QVs+nf38riGE80tbBz55FxH9/fMcjJNUWz5u8js+v1J3OPXn+ST3r9pWayBM/NwLXAO8zsbuA2YMdkM27MrA64DC/JchHggJumiKEMGC+5A96GruQ5EyV4KhKf+4CLk4kgM/sp0AR8zsxuc87FE8ffArwKuMA5NzJFbC/hnLuJxN/nzDPPdNu2bUvn6Rm1c+dO8nn/XHkw0khJczOXXnJxvkOZ85a19vH53fey+sRNbJuiLSbXr78bn32AjbXDXPXqCyc8p6iujWtue5jnilby5xetm9H9PvzDPcBhvnz1hdqcNQMrOvr5+p57KK/fwLYtqbe5/fpHf6C85BB/9caLx63ey8brLxqL89Fdv6ZiyUq2bVPlSboe2neUm7Y/yCkrqvjBdefM6Zkus+nf3xNfeJj9RwbYtu2i4x4bicXp/e2vOe3E1XrNzyKz6fUnc49ef5JPev2lZsKfwJxz7zWzbwBfxEvaXAJgZgeABuAI0Is3oHgR3jyb5E/wBvwW+Bvn3BNTxDAIHN934QmMOWciydrjH4yt8nHOdZnZHcA78Kp8GsxsId6sn1ucc7+fIi4pAOHhGAF/IYyKmvtGhyyH08p7Zl1vJMpD+45y/YVrJz3v0k1LuWzTUr66Yy+vO7VudP5Euh4/0M1PHm3hvdvWKbkzQ6sXlRPw+2hMYw5PJBrjV08c5pUn1+a0NdNf5GNpKEBLt2bwpOvZtj6u+a+HqK8K8q13njWnkzuzTX1VkN8/14lzDrOXVjZ29g8Td7BULVoiIiJzxqTvnJ1zjzvnXgGcDHwDOASsBF4J/Bnw54nPrwRWAAeAfwc2OedenUJyh8Q1a8ysdJzH6vHatyaq3gE4mPjcOs5jyWqj6sTnTwHlwM1mtj75AQQBS3y9IoWYJUci0Zjm7+TI6JDlSGHN4Nn1bCcjcTfu/J1jfeq1JxGLO/7xl09P617Jteg1C0r5i20zqwISKPIZG5ZWpLUq/e7GdvqGRnjD6fVZjGx89VVBWron+32CHOtwT5irb91Nqb+I2959dkFu4JvPllcHGRiOjZu4H12RriHLIiIic0ZKv2Zzzj0NvA94XyIhchpe1U0l0A20A49OZ1YP8BDwCuBsYFfyoJkFEve5d4rn78ZLNI1X/5881p74vAovwfPgBNfaCzyFl9CSAhCOxrQiPUeC/iKKfVZwQ5Z3NLZRVebn9JXVU567YmEZ7794PTfc+Sz3PtvBhScuTutev/jDYR7Z38W/vPFlVAQ0hyUTNtaGuLOhbdwKgvH89LEWFleUct66mhxE91J1VUEeO9CV8/vOVj2DUd5560P0RUb47+vPVcVbARq7Sauy7KXf01oTG+OWqYJHRERkzki798U595xz7kfOua875z7vnPtP59yPp5ncAfgfvFk9Hzzm+HV4s3e+lzxgZuvG2e71M7z5O28zswVjzl0GXAnsdc49lzj8L8Cbxvl4Gm/ez5uAD03z7yFZ4LVoKcGTC2ZGZdBfUGvSY3HHzmc6uHjDkpSHbF9/0VpWLyrjU3c8xdBI6tubItEY//zrRk5aFuKqLSrky5SNyyo4OjBMR/9Eo9Ze1D04zM5nOnjdqXV5GapeXx2ktSdCLO5yfu/ZJhKNcd23H6aps58b376Fk+sr8x2SjKN+klXpbYkKnqWq4BEREZkz8j7cJNHG9R/AH5vZT8zsWjO7AW/D1T3A98ecvgNv/s/Y53cBH8Fr53rAzD5sZn8LPACUAO8fc+79ieTUSz6ADiCW+PrOLP51JU1htWjlVCjoL6gKnj0Hujk6MMzFKbRnJZUWF/Hp159Mc+cAN9/blPLzvrmriZbuMJ94zUna2JZBG2tDADQe7pvy3F890cpwLM6Vp+W+PQu8N8PRmKOjb+pk1HwWizs++N972L3vKDf8yWmcvz731VaSmtEKnq7jWw9beyP4i4xFaqsTERGZM/Ke4En4IF6SZjNesufNeLN8XpPcfjWZxHarNwL9wGeBjwPP4G3V+r9sBS3ZF1GLVk6Fgn56I68qgmIAACAASURBVIUzZPnuxnaKfMZFJ6TXanXRiYt59cm1fO3u5zhwdOqZKm29Eb6+83letbmWl69bNN1wZRyblnmLDhtSGLT8sz0trFtczsn1oWyHNa4Xqx00h2cizjn+4Y6n+M1TrXziNSfxulMn37gn+VWzoISSYh+Heo4fHt7WE2FJRQCfEtoiIiJzRkEkeJxzMefcDc65Dc65UudcvXPuw865/mPOW+2cG/cnEefcT5xz5zrnyp1zFc65Vzjnfpfi/bc55xZMfabkmmbw5FYoUFxQFTw7Gts5c1X1cbMjUvGJ15yEYXzmF1MPXP7ib59hJOb42BXHdoDKTFWVlbCsMkBj6+QVPAe7BtndfJQrT6tPaVZPNtRXBxOxHN/OUig6+oZ46lBP3u7/7fv3850H9vOeC9dyzQVr8haHpMbMvOHh47ymD/dEWBoab7+FiIiIzFYFkeARmcjgcIyAWrRyJhT001cgCZ5D3WEaDvdy6abU27PGqqsK8oFLT+DOp9u4q7FtwvOeONjDjx45yLsuWM2qReXTDVcmsbG2YsoKntv3HALg9Xlqz4IXK3gOFfCq9E///Cne+J+/5+jAZMsls2NoJMbX7n6O89Yt4qOvUjJ0tvC2w40/g6dWA5ZFRETmFCV4pKBFhlXBk0uVBTSD5+5nvOV3qaxHn8g1F6xh/ZIFfOqOp4hEjx+47K1Ff4qaBSW8/+L1076PTG7jshDPd/QzPDJ+x61zjp891sKWVdWsXJS/TUzlpcVUlfkLtkVreCTOzmc6iETjfOf+/Tm//+17DtHRN8R7t61TW88sMl6CxzlHa29EA5ZFRETmGCV4pKCpRSu3QgE/vZEozuV/i9BdDe2sXFjGusXT754sKfbxmddt5sDRMP+58/njHv/VE608tK+Lv37FBq1Fz6KNtRVEY47nO/rHffzpw73sbe/nytPzV72TVFc5fjtLIXiw+Qj9QyPULCjh2/fvGzdpmS3OOW7Z1czG2gou0FDlWaWuKkhH39BLtgr2DY0wOByjVgkeERGROUUJHilo2qKVW5VBP9GYI5zDN47jCQ/HuO+5Ti7ZuGTG81jOW1/Da0+t4z/veZ79RwZGj0eiMT73qwY21lbwJ2dqLXo2nbQssUmrdfw2rdv3HKLYZ7zmZctyGda46qvHb2cpBDsa2gn4fXzxTadyZGCYHz96MGf3vndvJ8+09XHt1rV5m5Ek05OcLXV4TOthW2Loslq0RERE5hYleKRgxeOOSDROQBU8ORMKFgPQG87vJq37mzoZGonPqD1rrL//o02UFPn41B1PjVYn3XJfMy3dYT75Wq1Fz7Y1NeWUFPnGXZUeiztu39PCtg2LqS6Adc3JgbSFUMU2lnOOO59u44L1NWw7cTGnLK/km7uaicVzE+c3dzWxpKJUW7NmoboqL4kzNnHZ2usleNSiJSIiMrdMK8FjZpvN7Foz+5iZvW7McZ+Z5f8ndJkThhLzOtSilTuVQa9NqTeS3zk8dzW2U1ZSxDlrF2bkektDAT542QnsfKaD/3u6jfbeCF+/+zlecdJSzlundpNsKy7yccLSBTSMs0nrwaYjtPUOFUR7FsDy6iADw7G8JzmP1djaR0t3mMs2LcXMuG7rWpo7B9jeMPEA8UxpONzLrr2dXH3eakqK9Xuh2WZ5lTfX6iUJnkQFzzJV8IiIiMwpaf2kZmYrzewu4A/AjcA/AleOOeUvgbCZXZq5EGW+SrYJBf16Q5ErocQcmnwOWnbOcVdDO1tPqKG0OHPJvavPW82GpRV85udP84+/bGA4FufvrtiUsevL5DbWhmgcZ5PWTx9rYUFpMZdtWpqHqI5Xl9ikdbDABi1vf9pL5FyS2Cr36pNrWV4d5KZ7m7J+72/uaiboL+Kt56zM+r0k82orA5jxktlSbargERERmZNSfudsZjXAvcA24AngP4Fj+xp+CDjg9RmKT+ax0QSPZvDkzGgFTx4TPI2tfRzqiXDpxsy+4fcX+fjslSfT0h3mjscP8a7z17C6RmvRc2XTsgra+4Y40j80eiwSjfGbJ1t51cm1BdOKWair0rc3tnPqiiqWVHhvyIuLfFx7wRoe2d/FI/uPZu2+bb0R7ni8hT89awVVZSrQnY1Kin0sqSjl0DEtWlVl/oL5/05EREQyI53SiI8BK4F/AU53zr3/2BOcc4eBBuCCzIQn81l42Evw6AfQ3AkF81/Bc1ejtx5928bFGb/22WsW8pazV1BXGeD9l2gtei5tGh20/GKb1o6GdvqGRrjytMJoz4IXB9K2dBVOBU97b4THD3Rz+aaXzqR605krqAz6s1rFc9vv9xGLO959/pqs3UOy79hV6a09EW3QEhERmYPSSfC8FmgG/s5NPn3yAKApjDJjkdEWLSV4cqUQKnjuamznlOWVo5UKmfa5N7yMuz6ybbQdTXJjY20F4M1zSfrpYy0sqSjl5esW5Sus4ywqL6G02FdQm7R2JJKel5300qq28tJi3n7uKv7v6TaaJlhBPxODwyN878EXeOXmWlYuKsv49SV36qqCx1XwqD1LRERk7kknwbMCeHSK5A5AL1A9/ZBEPGrRyr2KgLdFqydPA2aPDgzz6AtdGdueNR4zU1VYHixaUMriitLRCp6ugWHuebad151aV1BbzMzsuGqHfNvR0Mby6iAbllYc99g7zluF3+fjlvuaM37f/334ID3hKNduXZvxa0tu1VcHOdQdIZ7YutbaM6QKHhERkTkonQRPGKhK4bxVQPf0whF5UbJFSxU8ueMv8lFWUpS3LVo7n2nHOTI+f0cKw8baitEKnl8+cZhozBXM9qyx6quDtBTIDJ7wcIxdeztHt2cda0lFgD8+o54fPXKQzjHzjWYqFnfccl8zZ6ysYssq/c5mtquvCjIci9M5MEQ0FufIwBBLtUFLRERkzkknwfMksMXMKic6wczqgVOBR2camEiygkfVFrlVGfTnbQbPjsZ2FleUsrkulJf7S3ZtWhZib1s/I7E4t+9pYf2SBQX537q+KviSjUP5dN9znQyNxCfdMnbt1jUMjcT5zv37M3bf/3uqlReODnKdqnfmhOTw8JauMO19QziHKnhERETmoHQSPN/Hq+C50cyOW6VhZj7gq0Ap8N3MhCfzWUQtWnkRCvjzMoMnGotz77MdXLJhCb4CatmRzNm0rILhWJxdezt5aF8Xbzi9ftyqlHyrqwrS2T80+j0on3Y0tFFRWszZaxZOeM76JRVctmkJ375/32jl40zdvKuJlQvLeMXm2oxcT/KrLpng6Q7T2uNVp9VWluYzJBEREcmCdBI83wR+B/wJ0GBmX00cP9nM/gVve9YbgHvwkkEiM6IWrfyoDPrz0qL18L4u+iIjXLIpe/N3JL821nrVOv/ym0YAXndqYc7jf3FVen6reOJxx/aGdi7csJiS4sn/ub7+wnV0DUb50aMHZ3zfR/Z38egL3bz7/NUFNR9Jpi+5He5Qd5i2Xi/BoyHLIiIic0/KCR7n3AhwBfBDYA2QXJN+JvD/gBOAnwGvT2EQs8iUwtqilRehYHFehizf1dhGSZGPC9bX5PzekhvrFi+g2Gc0tvZx1upqViwszM1ML74Zzu8cnscPdtPZP8Tlk7RnJZ21uppTV1TxzV1NxOIz+yf4m7uaCAWKedOZK2Z0HSkcoYCfikAxLV0vVvAsqwzmOSoRERHJtHQqeHDO9Tnn3gxsBj4CfB24Efh7YItz7o+dc32ZD1PmI23Ryo9QMD8tWjsa2zln7ULKS4tzfm/JjZJiH+uXLADg9acV3nDlpNF5Jd2DeY1jR0M7RT5j24bFU55rZrznwrXsPzLInU+3TvueLxwZ5LdPtfLWc1fp/8U5xtsOF6GtN0JJsY/qMn++QxIREZEMS/mnNzMLAS6R5GnAa8kSyZpIokWrdIrWBMmsfMzg2dc5QFPHAO84d1VO7yu5d9KyEM939PNHL1uW71AmVFsZwGfkfdDy9oY2zlxVTVXZcWPvxvXKzbWsXFjGjfc28crNtdOab3Tr75op8hnvPG912s+VwuYleMKUlxaxNFRakPOvREREZGbSeefcDWzPViAixwpHYwT9RfohNMcqg376hkZm3OaRjrsa2wG4ROvR57wPXnYit1x9FtXlqSUt8sFf5GNpKJDXVekHjg7S2NrH5Sel/v9Ekc+4dusaHnuhm4f3d6V9z57BKD98+ACvPbVO81nmoLqqIIe6wxzuiWiDloiIyByVToKnD9ibrUBEjhWOxtSelQehoFe235fDQct3NbazfskCVi4qzJkskjkrF5Vx4YlTtxzlm1ftkL8WrR0NbQBcmsL8nbGu2rKcqjI/N93blPY9v7d7P4PDMa69QKvR56L66iA94ShNHQNK4ImIiMxR6SR4GoDl2QpE5Fjh4bgGLOdBKOB1bvbmaNBy/9AIDzYf4dKN2p4lhaMu0c6SL9sb2lm3uJw1NeVpPa+spJh3nLuK7Q1tPN/Rn/Lzhkfi3Pb7fVywvoaT6kLphiuzQHJVemf/kCp4RERE5qh0Ejw3AxeY2ZZsBSMyViQaI+DX/J1cq0xU8PTkaA7PfXs7iMYclyjBIwWkvjrI4e5ITlsVk3ojUR5oOsJlabRnjfWO81bjL/LxzV3NKT/n548foq13iGu3rpnWPaXwJYeHgzdnSkREROaedNak34K3NetOM/uomZ1oZqXZC03mO7Vo5UeyRas3Ry1aOxraCQWK2bKqOif3E0lFfVWQkbijo28o5/e+99kORuIupfXo46lZUMobz1jOjx89mFL8zjlu3tXEiUsXcNEsaJ+T6Rmb4FGLloiIyNyUcoLHzGLA+4BK4HN4LVuDZhYb5yM3vR0yp4WHY2rRyoNkBU8uNmnF4467n2nnog1LKC5StZYUjvrq/K1K3/50GwvLSzh95fSTntduXUM0Fuc79++b8tzfPXeExtY+rr1grYbaz2FLKkrxF3n/fVXBIyIiMjel847K0vjQOzWZsXA0RkAJnpwL5bBF64mWHjr7hzV/RwpOstrhYI5XpY/E4tz9TAcXb1hCkW/6yZZ1ixdw2aalfPuB/QwOT/47l5t3NVGzoJTXn1437ftJ4fP5jGWV3utaM3hERETmpnRatHzpfGQzaJkfIlFV8ORDZQ5btHY0tuMz1BYiBSeZ4Mn1oOWH93fRE45y+UkzT3q+58K1dA9G+d+HD054zrNtfdzzbAdXv3wVpcX6fjvX1VV5iZ0lIXXYi4iIzEVKxEjB0gye/CgvKaLIZzmp4LmrsY0zVlZTXV6S9XuJpKO8tJiqMj+Hcpzg2f50GyVFPraeMPOk55ZV1Zy+sopv3tc04bDob+5qIuD38bZzV834flL41tSUUxsKKJknIiIyRynBIwVLM3jyw8wIBYqzvia9rTfCky29XLJJ7VlSmOoqg7TksEXLOcf2hjZevm4R5aXFM76emfGeC9dy4GiY3zzZetzj7X0RfvbYIa7aslxJ1nnir1+xgW9fc3a+wxAREZEsSfsnSDPzA1cB24D6xOEWYCfwI+dcblbvyJynGTz5Ewr6s17Bc3djOwCXbpzepiCRbKuvDrL/yEDO7vd8xwD7jgxyzda1Gbvm5SfVsnpRGTfd+zxXvKz2JUOUv3P/fqLxONdckLn7SWGrWVBKzQK1Z4mIiMxVaVXwmNkW4Bngu8B1wBWJj+sSxxrN7IxMBynzU0QtWnkTCvizPoNnR2M79VVBTly6IKv3EZmu+iqvgse58dubMm17QxsAl2Wwqq3IZ1yzdS2PH+xhd/PR0ePh4RjffWA/l21aypqa8ozdT0RERETyJ5016cuB3wKrgYPAP+Mldq4HPg+8AKwBfmtm9RNcRiQl0VicaMypRStPKrNcwROJxrhvbyeXbFyitcxSsJZXBxkYjmW9XTFp+9NtbK4LjW46ypSrzljOwvISbt7VNHrsR48epGswynUZrBYSERERkfxKp4Lnb4GFwFeB9c65v3PO3eKc+6Zz7uPACcC/AYsS54pMWyQaA1CCJ09CwWJ6s5jgeaDpCOFoTPN3pKDVJVeldw9m/V5H+od49IUuLtuU+ZbFYEkRbz93Fdsb2nmuvY943HHrfc2curySs1ZXZ/x+IiIiIpIf6SR4XgU0AR8ab86Oc24E+OvEOVdkJjyZr8KJBE9ALVp5URn00xvJXtXC3Y3tBP1FvHztoqzdQ2SmRlel52DQ8t3PdBB3cPlJ2ZlJ9Y6Xr6K02MfN9zazvaGN5s4Brt26VhV0IiIiInNIOkOW64GfukmGETjn4ma2G3jDjCOTeS0yHAdUwZMvoUD2WrScc+xobOf89Ys0RFsKWn21l+DJxar07U+3URsKsLkulJXrL1pQylVblvO/Dx/kiZYe6quCvPrk2qzcS0RERETyI50KnjBei9ZUqhPnikxbWC1aeRUK+hkeiY+2ymXS3vZ+DnaFuUTbs6TALSovobTYR0uWEzyRaIx793Zw6abszqS6dutaovE4Tx/u5V3nr6a4KK09CyIiIiJS4NL56e4PwDYz2zjRCWa2Abg4ca7ItI0meEr0BiQfQkE/QFbm8NyVWI9+yUbN35HCZmbeJq0sJ3geaDrC4HCMy7LUnpW0pqacV59cSyhQzJ+etSKr9xIRERGR3Evn3fMtQAlwl5m928xKkg+Ymd/M3gXsAPzAzZkNU+ab8HBiBo8qePKiMpngycKq9Cdaeli5sIzaykDGry2SafXVwazP4Nne0EZZSW5mUn3hqlP51V9tpSLgz/q9RERERCS3Up7B45z7jpm9CngLXgLnRjM7DDigDi9ZZMD3nXPfy0awMn9oi1Z+hQLet4ZszOFp6hhg3eLyjF9XJBvqq4I0HO7L2vWdc+xoaGfrCTU5SWgvKC1mQWk64/dEREREZLZIq//FOfdW4P3APqAIWA6sSPy5GXi/c+5tGY5R5qEXW7SU4MmH0QqecGY3acXjjubOftbULMjodUWypa4qSGf/UFbmUQE8daiXwz2RrKxHFxEREZH5Je1f4znnvg583czq8TZrGXDQOdeS6eBk/kq2aKmCJz+SM3gyXcHT2hshEo2zVhU8MkskV6Uf6g6zdnHmE5PbG9owg4s1k0pEREREZmjaddqJhI6SOpIV2qKVX6FAdmbwNHUMACjBI7PGi6vSI1lL8JyxspqaBaUZv7aIiIiIzC8pt2iZWamZrTSziknOqUicUzLROSKpSLZDBNSilRehYGIGz2CGEzyd/QCsVYuWzBLJCp6W7sGMX/twT5gnW3rVniUiIiIiGZHODJ6/wpuzc8Yk55yROOf9MwlKRC1a+VVaXETA78tKBU95SRFLQ6pWkNmhtjKAz8jKJq0dDe0AXLZJ7VkiIiIiMnPpJHheBxxwzt0z0QmJxw4Cr59pYDK/haMxin2GvyitOeCSQZVBf8aHLDd1DrBmcTlmltHrimSLv8jH0lCAg92ZT/Bsb2hj1aIy1i9RRZuIiIiIzFw6757XAQ0pnPc0cML0whHxhKMxbdDKs1DAn/Ehy00d2qAls099VZBDGU7wDAyN8Pvnj3DZpqVKeIqIiIhIRqST4FkIHE3hvKOJc0WmLRKNqT0rzyqD/oy2aEWiMVq6w6yt0YBlmV3qqoK0ZDjBs2tvJ8Mjcc3fEREREZGMSSfB0wmsT+G89UD39MIR8YSHVcGTb6FgZit49h8ZxDlt0JLZp746yOHuCLG4y9g1tze0EQoUc+bq6oxdU0RERETmt3QSPA8CZ5rZWROdkHjsTGD3TAOT+S2sCp68y3QFT7M2aMksVV8VZCTu6Ogbysj1YnHH3Y3tXLxxieaMiYiIiEjGpPOT5Y2AAT8zs8uPfTBx7KeJL7+RgdhkHgtH4wSU4MmrUKA4o2vSn+8YAGCNKnhklqmvzuyq9D0HujgyMKz2LBERERHJqOJUT3TO/dbMbgTeA/zGzA4CzyQe3gAsx0sA3eyc+1XGI5V5JTKsCp58CwX99A2NEI87fL6ZD4Ft6hhgaaiUBaUpf9sRKQj1VV6C52BXmC2rZn69O59up9hnXLRh8cwvJiIiIiKSkNY7Lefce83sWeBjwIrER1In8Hnn3JczGJ/MU+FojMUVpfkOY16rDPpxDvqGRqgM+md8vebOftZowLLMQskET6YGLe9oaOOctQsJBWb+/5WIiIiISFLav0p3zn3ZzL6KN2sn+bvM/cAjzrmRTAYn85dm8ORf8s1nbziakQRPU+cAV7xs2YyvI5Jr5aXFVJX5M7IqfV/nAHvb+/mzc1ZmIDIRERERkRdNq1fCORfDG7r8YGbDEfGEh2OawZNnoURSJxODlo8ODNM9GNWKdJm16iqDtHTNPMHzm6daATR/R0REREQyLiPrO8xsgZltMTP9xCoZEYnGCJZou0w+hYJe/jcTq9JHN2hpwLLMUvXVwRm3aD207yhfuvNZXr52ESsWlmUoMhERERERT8rvoM3sYjP7upmdfszxdwFteKvRD5rZP2Y4RpmH1KKVf8m2rN7wzDsvkxu0tCJdZqv6Kq+Cxzk3rec/29bHNf/1EMurg/zHW8/IcHQiIiIiIulV8FwLvBvYlzxgZmvw1qcHgZbE4Y+Z2aWZClDmH+ecEjwFYOwMnplq6hjAX2QsT6ybFpltllcHGRiOTSvheag7zNW37ibgL+K2d53NwvKSLEQoIiIiIvNdOgmes4HHnXNdY469HW+Oz0edcyuBlwMO+IvMhSjzzdBIHOcgUKIETz5VlmVuBk9zZz8rF5ZRXKS2O5md6pKr0rsH03pez2CUq2/dTX9khP9619lqzRIRERGRrEnn3dZi4OAxxy4BIsDXAJxzDwO/B05NJwgz85nZh8ys0cwiZnbAzG4ws5QHdpjZQjP7VzN7LnGNDjO728y2HnPeP5vZ782s3cyGEvf6hZltSydmyZ5INAagCp48W1BSjFlmZvA0dQywdrHas2T2Gl2Vnsag5Ug0xrXffoj9Rwa58R1bOKkulK3wRERERETS2qJVBoy+0zMzH7AF2O2cG/sT74HE8XR8GfgA8FPgBmBT4uvTzewy51x8sieb2SpgJ7AAuAV4FqgETgHqjzn95cATwI+BLqAWeBtwt5m9wzn3nTRjlwwLK8FTEHw+o6K0eMYtWrG4Y/+RQS7ZtCRDkYnkXn2ivTDVVemxuOMDP3iMh/d38e9vOZ3z1tVkMzwRERERkbQSPO3ACWO+PhcoB353zHmlQMq/4jSzzcBfAj9xzr1xzPFm4KvAm4HvT3GZ7+L9XU5xzh2e7ETn3EXjxPBV4HngY4ASPHkWHk4keNSilXeVZf4ZV/C0dIUZjsW1Il1mtUXlJZQW+1LapOWc4xO3P8n/Pd3GP7z2JF5zSl0OIhQRERGR+S6dFq37gdPM7E/MLAR8HG/ezp3HnLcJOJTGdd8CGPCVY47fDAziVddMyMwuBC4AvuCcO2xmfjNLa8iBc64fOAJUp/M8yY5kBU9AFTx5Fwr46Y3MbIvW86Mr0tWiJbOXmXmbtFJI8Pz7Xc/x/Qdf4L3b1vHO89fkIDoRERERkfQSPF8ERoAf4LU2vRrY45zbmTzBzJbjJXgeTuO6ZwFxvDXro5xzEWBP4vHJXJH4/IKZ/RyvemjAzJ41swmTQ2ZWY2ZLzOxUM/taIu5fpRG3ZIlm8BSOyqB/xi1aTaMr0lXBI7NbfXVwyhk8/737Bb5057P88Rn1/M0rN+QoMhERERGRNFq0nHO7zew1eG1MS/ASMh875rQ/BXo4vqpnMnVAp3NuaJzHWoDzzKzEOTc8wfOTP0HfDOwFrsZrE/sw8B0z8zvnvjX2CWa2AOgYcygM3JR4zoTM7HrgeoClS5eyc+fOyU7Pqv7+/rzeP5ue6vQSPI1P/YH4ISV58mmoP8Lhgfhxr7V0Xn+/e2qIsmL4w0O/x8wyH6TMO/n6/mfhIZrbYxPe+7H2Eb766BAn1xRxRU0X99xzT24DlJyYy//+SuHT60/ySa8/ySe9/lKTzgwenHN3Mknyxjl3A96Q5HSUAeMld8Db0JU8Z6IET0Xicx9wcTIRZGY/BZqAz5nZbccMag4Dl+P9/VcBb8Ub0FwGDEwUqHPuJrxEEGeeeabbtm3bpH+xbNq5cyf5vH82RZ9ug4cf5ryzz+Tk+sp8hzOv/brzDxx8tv2411o6r7+b9j7AictiXHzx+ZkPUOalfH3/+0NsL/cefJZzz996XAvpI/uP8o3tD/Ky5ZX84LpzKS9N659XmUXm8r+/Uvj0+pN80utP8kmvv9Sk06KVLYN4FTfjCYw5ZyLJevkfjK3ycc51AXfgbcl6SZ28cy7mnNvunPuNc+5G4GJgJXCXmfmn8XeQDNIMnsIRChbPeMiytyJd7Vky+yVXpR+7Seu59j6uue1hllUGuPWdZym5IyIiIiJ5UQgJnkNAjZmNl+Spx2vfmqh6B+Bg4nPrOI8lN2pNOjzZORcDvgecDFw4ebiSbRFt0SoYlUE/kWicoZHYtJ4/MDRCa29E83dkTkiuSh87aLm1J8LVtz5Esc/49rvPoWbBRL+vEBERERHJrkJI8DyEF8fZYw+aWQA4jakHNieHMy8f57HksfYU4ggmPi9M4VzJorCGLBeMUNAraOsNT2+TVnNnYsCyNmjJHHBsBU9POMo7v7Wb7sFh/utdZ7NyUVoLHEVEREREMqoQEjz/g7du/YPHHL8ObybO95IHzGydmW085ryf4c3feVtieHLy3GXAlcBe59xziWPVZlZybABmVg5cwzjbvCT3lOApHJXJBE9kem1aTaMJHlXwyOxXWxnAZ9DSFSYSjXH9tx/m+Y5+vvH2LZoXJiIiIiJ5l/dBAc65J8zsP4D3m9lP8FaVbwI+ANwDfH/M6TvwhiLbmOd3mdlHgBuBB8zsVqAEeG/i8/vHPP8i4EYz+zHwHF5iaA3wdrxqn0875/Zn5S8qKQsnWrRKiwsh/zi/hQJegme6c3iaOwYwg9WLlOCR2c9f5GNpKMCBrjAfWGIpPgAAIABJREFU/uEeHmw+yr+9+TS2nrA436GJiIiIiOQ/wZPwQWAf3gryPwI6gX8HPnnM9qtxOeduMrNO4G+Az+JV4twP/Jlz7ndjTn0C+AXeUOW34lUIHcFrE/tz59wvM/UXkumLRGME/D58Pq3UzrdQ0PsW0TvNBE9TZz91lUENzJY5o74qyB2PHyIWd/z9H23i9afV5zskERERERGgQBI8iSHHU65Yd86tnuSxnwA/meL5z+O1YkkBC0djas8qEMkWrelW8GiDlsw1dVVBHt7fxXVb13Dt1rX5DkdEREREZFRBJHhExgoPK8FTKJItWr2R9IcsO+do7hzgjWeowkHmjrees5L1Sxbw/ovX5zsUEREREZGXUIJHCk44GiOgFekF4cUtWulX8HT0DdE/NKINWjKnnLN2EeesXZTvMEREREREjpPWFFszW21mN5rZc2Y2aGaxCT6mt1NZBG8Gjyp4CkPAX0RJsW9aCZ7nO7RBS0REREREJFdSruAxs83AfUCIMVusJjp9JkHJ/KYZPIWlMuif1pr05sSK9DU1SvCIiIiIiIhkWzoVPP8EVAK/Bs4BKp1zvok+shKtzAvh4RhBtWgVjFCgeFpDlps6+gn4fdRVBrMQlYiIiIiIiIyVzgyeC/FWmb/BOTe9lToiKQhH4yxaoARPoagM+ukNp9912dw5wOpF5Vp3LyIiIiIikgPpVNqUAg8puSPZphk8hSUU9E+vgqdTK9JFRERERERyJZ0Ez7N4LVoiWaU16YUlFEh/Bs/wSJwXjg6ytkYbtERERERERHIhnQTPzcCFZrY6O6GIeMJRzeApJJXTqOA50DVILO5UwSMiIiIiIpIjKSd4nHNfB34IbDezV5uZBilLVoSHYwRUwVMwQsFiesNRnHMpP6epQxu0REREREREcimdNelNiT+uBn4BjJjZYSA+zunOObdu5uHJfDMSizMci6tFq4BUBv3EHQwMx1hQmtq3jKaOfgDWLlaLloiIiIiISC6ks0Vr9Zg/G+AHVk5wbuq/6hcZIzLi5QuDJSoQKxShgB+AnnA05QRPc+cANQtKqAz6sxmaiIiIiIiIJKST4FmTtShEEsLDMQBV8BSQZJKmNxylviqY0nOaOgbUniUiIiIiIpJDKSd4nHP7sxmICHgr0gHN4CkgoeCLFTypaurs59KNS7MVkoiIiIiIiBxDfTBSUMKJBI+2aBWOsRU8qegJR+nsH9YGLRERERERkRxKp0VrlJm9HNgG1CcOtQA7nXP3ZygumafUolV4xs7gSUVzpzZoiYiIiIiI5FpaCR4zWw18Dzg3eSjx2SUevx94m3NuX2bCk/lmtIJHCZ6CEQp63yZ6IyMpna8NWiIiIiIiIrmXzpr0auBuYBUwAPwceB4vybMGeC1wHnCXmW1xznVlPlyZ65IJnoBatApGxTQqeIp8xsqFZdkMS0RERERERMZIp4Lnb/CSOz8G/tw5d2Tsg2a2EPgGcBXw/4C/y1SQMn9E1KJVcIp8RkVpccozeJo6BlhRHaSkWCO+REREREREciWdd2CvBw7jtWAdOfZB59xR4O2Jc67MTHgy36hFqzCFgn56I6kleJ7v6Fd7loiIiIiISI6lk+BZDexyzg1NdELisV2Jc0XSpi1ahSkU9KdUwROPO/YdGWCtBiyLiIiIiIjkVDoJniiQylCNYOJckbQlt2gFVMFTUCqDxfSGpx6yfLg3QiQaZ41WpIuIiIiIiORUOgmeBuBiM6ud6ITEY5ckzhVJW0QtWgUpFPCnNGR5dINWjVq0REREREREcimdBM93gXJgu5ldcuyDZnYx8H94VT7fyUx4Mt+EozGKfIa/yPIdioxRmeIMnubOAQDWqYJHREREREQkp9LZovUN4I3ARcCdZnYIaAYc3pr0eryV6XcnzhVJW3g4TtBfhJkSPIUkFEy1gmeA8pIiFleU5iAqERERERERSUq5gsc5NwK8CvhXYAAvoXMBsBVYnjj2r8AVzrlY5kOV+SAcjWn+TgGqDPoZHI4RjcUnPS+5QUsJOhERERERkdxKp4InuSXrb8zsk8AWXqzaOQg84pyLZD5EmU8i0RjBknQ6ByUXQgHvW0VvOMqiBRNX5zR3DrBlVXWuwhIREREREZGEtBI8SYlEzu8yHIsI4eGYBiwXoFDQD0BvZGTCBE8kGqOlO8xVW5bnMjQREREREREhvSHLIlkXjirBU4gqkwmeSebw7DsygHOwdrE2aImIiIiIiOTahBU8ZnZh4o+7nXORMV+nxDl374wik3lJM3gKU7KCZ7JBy80d3gattTXaoCUiIiIiIpJrk7Vo7cTbkLUJeHbM16lwU1xbZFyRaIyF5SX5DkOOMVrBM8mq9KbEivQ1SvCIiIiIiIjk3GRJmHvxEjWDx3wtkjXh4RjBKlXwFJpQYOoKnqaOAWpDAcpLldsVERERERHJtQnfiTnntk32tUg2aAZPYXpxBs/IhOc0dfazdrGqd0RERERERPJBQ5aloESiMQIlSvAUmoDfh7/IJqzgcc7R1DGg9iwREREREZE8STnBY2a3mtm7UzjvnWZ268zCkvlKa9ILk5lRGfRPOIOnazBKTziqDVoiIiIiIiJ5kk4FzzuBC1I473zg6mlFI/Oac04tWgUsFPBPWMHT1NEPoBYtERERERGRPMlGi5YfiGfhujLHDcfixB0E1aJVkCqCfnonTPBoRbqIiIiIiEg+ZSPBsxnozsJ1ZY6LDHt5wYAqeApS5WQJns4BSop8LK8uy3FUIiIiIiIiApOvSWecWToXTDJfpxjYBJwB/DIDsck8E47GANSiVaBCgWIOHB0c97Gmjn5WLSqjyGc5jkpERERERERgigQP3tydJAesT3xMphX4+AxiknlqNMFTouVuhWiqCh61Z4mIiIiIiOTPVAmedyU+G3ArcB9wywTnDgMtwAPOueHMhCfzSXhYFTyFLBT0hiw7515yPBZ37D8ywGWbluYpMhEREREREZk0weOcuy35ZzP7B7zkzW0TP0Nk+pIVPJrBU5gqg35G4m70v1PSwa5BojGnDVoiIiIiIiJ5NFUFzyjn3OosxiFCRDN4Cloo4Ac4blW6NmiJiIiIiIjkn4adSMEYbdHSmvSCVBn0Ejy94ZGXHG/qTCR4Fi/IeUwiIiIiIiLiSbmCJ8nMAsDFwIlACG8+z7Gcc+6zM4xN5hlt0SpsoaD37eL4Cp5+qsr8LCwvyUdYIiIiIiIiQpoJHjN7I/ANYOFkp+Ft3FKCR9KiGTyFLdmi1RuOvuQbR1PHAGvUniUiIiIiIpJXKbdomdk5wH/jVe38AHgi8dA/Az8CehJf3wJ8JoMxyjyRnMFTphatgpRs0Tq2gqe5c4C1NWrPEhERERERyad0Kng+gpcQutI590sz+xbwMufcxwHMrAb4FnAFcEbGI5U5TzN4ClsoOYMnEmVR4tjA0AitvRFt0BIREREREcmzdIYsnwc86Zz75XgPOuc6gT8DSoFPZyA2mWdGW7SKleApRKGAlw8eO2S5uVMbtERERERERApBOgmeGuCZMV+PAJhZMHnAOdcH3Au8OiPRybwSjsYoLfbh8403t1vyrbjIR3lJ0UtatLRBS0REREREpDCkk+DpwqvOSepOfF5+zHkOWDKToGR+igzH1J5V4CqDfnojYxI8Hf2YwapFZXmMSkRERERERNJJ8BwAVo75+km8jVmvSR4ws3LgAqAlI9HJvBKOxrQivcCFgv6XVvB0DFBfFdTmMxERERERkTxLZ8jyTuCvzGyxc64D+AUwCHzezGqBg8A78Fq5fpLpQGXuC0fjSvAUuFDQT++YBE9z54Das0RERERERApAOhU8/wvcA5wO4Jw7Avw14MfbsPUVYAteoucTmQ1T5oPwcEyVIAUuFHixgsc5R1NHvwYsi4iIiIiIFICUK3icc7uBy485dqOZPQxcBSwEGoFvOee6x7mEyKQiUc3gKXSVQT8Nh0cAH+19QwwMx7QiXUREREREpACk06I1LufcI8AjGYhF5jnN4Cl8oWBxooKnlKaO5Ip0tWiJiIiIiIjkWzotWiJZpRatwhcK+OkfGiEWdzR19gOogkdERERERKQAFESCx8x8ZvYhM2s0s4iZHTCzGxJbuVK9xkIz+1czey5xjQ4zu9vMto45J2Bm15nZ7Wa2z8zCZtZkZj8ws03Z+dtJqtSiVfgqg34AwiPQ3DFAwO+jNhTIc1QiIiIiIiIyYYuWmcVmcF3nnEun/evLwAeAnwI3AJsSX59uZpc55+KTPdnMVuFt+VoA3AI8C1QCpwD1Y05dDdwE3Jc47xCwFngv8Mdm9irn3N1pxC0Z5LVoFUTOUSYQSiR4BkccTZ0DrKlZgM9neY5KREREREREJkvCzORdW8rPNbPNwF8CP3HOvXHM8Wbgq8Cbge9PcZnv4v1dTnHOHZ7kvA7gdOfcnmNi+B7wGPBF4MxUY5fM0gyewpes4BmMehu0NtdX5jkiERERERERgUlatJxzvmM/8CptBoEv4a1LrwaqEn++ARgAvpQ4N1VvwUsIfeWY4zcn7vW2yZ5sZhcCFwBfcM4dNjO/mZVN8Hc6cmxyJ3H8aeBJ4OQ04pYMCw/HCKhFq6CFAl5OuHfYcaArrBXpIiIiIiIiBSLlNiozuwavbeoS59yuYx5+HHjczG4H7jazZ5xzN6d46bOAOLB77EHnXMTM9iQen8wVic8vmNnPgVcDRWa2F/iMc+67UwVgZj5gGdCWYsySYfG4Y2gkrgqeAldZ5lXw7OuNE4s7DVgWEREREREpEOnMyfkLYNc4yZ1Rzrn7zGwX3kybVBM8dUCnc25onMdagPPMrMQ5NzzB8zckPt8M7AWuBkqBDwPfMTO/c+5bU8TwXrwEz2cnO8nMrof/396dh8lVlnkf/97ZSCCyy5qwCCIRRAIBkVEM6rgAo7wqisqoMwrjjIi4vM7rqIjgqKMDKgyjwLjggggOoKgjo0IQkZ0goLKJ0CGs0SQkpJvudN/vH+cUFGVVd1d3VVcv38911XXS5zx16q7uh4T69bNwDMDWW2/NkiVLhrht+6xdu7ajr99qT6xPAB5Ydi9LljzQ4WrUyJ+6i+Ww7lzRCwQru+5gyeq7O1uUppzJ9vefJhb7nzrJ/qdOsv+pk+x/w9NMwPMc4AfDaPcgsH8T990QqBfuAPRUtWkU8DyjPK4BDq4EQRFxEXAP8OmIOKfRQs0RcSDF9LJbgE8PVmhmnkWxSDOLFi3KxYsXD9a8rZYsWUInX7/V/rT2Cfj5z9lz991Y/MKdOl2OGnj8ifV88IpLue/xYpmtN7zyIDaePbPDVWmqmWx//2lisf+pk+x/6iT7nzrJ/jc8zayV8wTFWjtDWUjjwKaedRQjbuqZXdWmke7y+N3qUT6ZuRL4IbANT43yeZqI2Bf4McVuWodkZk+9dmq/7r5i07bZTtEa1zacNZ3p04I1vbDl3A0MdyRJkiRpnGgm4Pkl8JyIODki/mKXrCicBOxeth2uB4AtI6JeyLM9xfStRqN3AO4vjw/VuVbZUWuzOvXuA/wMWE0x8mf58EtWq/WUAY9r8IxvEfHkQsuuvyNJkiRJ40czAc/HKaZM/QtwR0ScFBF/Vz4+CdwOfJRiRM0JTdz3+rKOp03riojZwN7ADUM8v7I487w61yrnHqm590KKcKcyreu+JupVG3T3FjPoDHjGv8pW6e6gJUmSJEnjx7ADnsy8jWLHqgeBXSnCnP8qHx8Dnk0xiuawzLy1iRq+ByRwfM35oynW3vlO5URE7BIRu9e0u5giqDkqIuZWtd0WOBy4KzPvrjq/EPg5xZbuB2fmH5uoVW1SmaI1x23Sx72NKwGPI3gkSZIkadxoZpFlMvOKiNgVeAPwEp4aIbMcuAL4fmZ2N3p+g3veGhFnAMdGxIXAT4AFFFuyXwGcW9X8F8COQFQ9f2VEfAg4E7gmIr4GzKLYGWsWcGylbUTsSDFyZzPgNIodug6sKemizHy8mfeg0XMNnonjqRE8c4doKUmSJEkaK00FPADlQsTfLh+tcjxwL8UW5IcCK4DTgRMa7X5VU9NZEbEC+DDFVucDwNXAWzLzqqqmOwNblH8+scHtdqYY3aMx1N3rGjwTRWVh5Z0dwSNJkiRJ40bTAU87ZGY/xVblpwzRbqdBrl0IXDjE85dQNfpH40ePU7QmjI3nzGR6wA6bb9jpUiRJkiRJpXER8Ejd7qI1Ybxpv/lMX/MQM6c3s0a7JEmSJKmdGgY8EXEPxeLHL8/MP5ZfD1dm5i6jrk5ThlO0Jo6952/Kqh1ndroMSZIkSVKVwUbw7EQR8Mys+nq4coT1aIp6cpHlWY4KkSRJkiSpWYMFPDuXx+U1X0st19PXz7SAWU77kSRJkiSpaQ0Dnsy8b7CvpVbq7u1nzszpRLgGtiRJkiRJzXK4hMaF7r5+d9CSJEmSJGmEDHg0LnT39TPbBZYlSZIkSRqRwXbR+too7puZ+c5RPF9TTE9fvztoSZIkSZI0QoMtsvyOUdw3AQMeDVt3r1O0JEmSJEkaqcECnr8bsyo05TlFS5IkSZKkkRtsF61zxrIQTW3dfQNsOmdmp8uQJEmSJGlCcpFljQs9va7BI0mSJEnSSBnwaFxwm3RJkiRJkkZusDV46oqI2cDBwG7AxkDUaZaZefIoa9MU4ho8kiRJkiSNXFMBT0S8HvgKsPlgzSh20TLg0bA5RUuSJEmSpJEbdsATES8AzgMGgO8CewLPAz4L7Ar8NbAJ8FXg/pZXqkmtmKLljEFJkiRJkkaimRE8H6JYs+fwzPxxRHwdeF5mfhQgIrYEvg4cAuzT8ko1afX1D7B+IB3BI0mSJEnSCDUzZOJA4LbM/HG9i5m5AngLsAHwyRbUpimiu68fwDV4JEmSJEkaoWYCni2BO6q+Xg8QEXMqJzJzDfBL4NUtqU5TQndvEfC4i5YkSZIkSSPTTMCzkmJ0TsWq8jivpl0CW42mKE0tTwY8juCRJEmSJGlEmgl4lgE7VH19G8WOWYdVTkTERsCLgOUtqU5TQmWKlgGPJEmSJEkj08wiy0uA90XEMzPzUeBHwDrgMxGxDcXOWW+jmMp1YasL1eT15Bo8TtGSJEmSJGlEmgl4LgD2BhYC/5uZf4qIDwL/SbHDFhQjepYBH29plZrUepyiJUmSJEnSqAw74MnM64C/rjl3ZkTcALwB2By4Hfh6Zq6qcwupLqdoSZIkSZI0Os2M4KkrM28EbmxBLZqingx4nKIlSZIkSdKIDHuR5YhY0M5CNHW5i5YkSZIkSaPTzC5at0XENRHx7ojYtG0VacrpqSyybMAjSZIkSdKINBPwPArsD5wBPBgR50XEqyIi2lOapgqnaEmSJEmSNDrNBDzbAYcB/11+/Ubgx8D9EfFZp3BppLp7BwCYPaOZ7ihJkiRJkiqG/Yk6Mwcy8yeZ+UZgW+A9wPXlnz9MMYXrWqdwqVndff3Mmj6NGdMNeCRJkiRJGokRfaLOzFWZ+eXMPADYHfg34AFgP4opXA+0rkRNdj19/cyeabgjSZIkSdJIjfpTdWbemZkfAZ4FnAYEsMFo76upo7u33/V3JEmSJEkahRmjvUFE7AG8A3grsHV5unu099XU0d3X7xbpkiRJkiSNwogCnojYHHgL8HZgH4pROwC/Br4BfK8VxWlq6O7rd4t0SZIkSZJGYdgBT0RMBw6hCHUOA2ZSBDv3A98CvpGZd7WjSE1uPX1O0ZIkSZIkaTSaGcGzHHgmRajTQzFK5xvAzzIzW1+aporuXqdoSZIkSZI0Gs0EPFsB11KEOudl5uq2VKQpp7uvn03mzOx0GZIkSZIkTVjNBDwLMvOOtlWiKau7r5/ZTtGSJEmSJGnEhr1NuuGO2qXHKVqSJEmSJI3KsAMeqV3cJl2SJEmSpNEx4FHHdbuLliRJkiRJo2LAo44aGEh6+gaY7QgeSZIkSZJGzIBHHfXE+gEAp2hJkiRJkjQKBjzqqO6+fgDmzLQrSpIkSZI0Un6qVkc9GfC4Bo8kSZIkSSNmwKOO6u4tAh7X4JEkSZIkaeSGHfBExBYRsX9EbFlzfvuI+HZE3BoRl0TEwtaXqcmq58kpWgY8kiRJkiSNVDMjeD4CXA1sVzkRERsAvwLeDOwBHApcFhHzW1mkJq/KFK0NZ83ocCWSJEmSJE1czQQ8BwP3ZOYtVeeOBHYELgdeDpwGbAIc27IKNalVpmjNmeVsQUmSJEmSRqqZT9XbA3+oOXcokMDRmXlZZh4P3AO8qkX1aZKrjOBxDR5JkiRJkkaumYBnM2BFzbkXAndk5h+rzi0FnKKlYXENHkmSJEmSRq+ZgKcbeHKB5YjYgWJUz1U17XqBWaMvTVPBU1O0DHgkSZIkSRqpZgKe3wEvqtpF660U07N+WdNuPvBwC2rTFNDtCB5JkiRJkkatmYDnm8CGwA0RcSFwIrAG+EGlQUTMBvYBbm9hjZrEXINHkiRJkqTRa2Zv6rOAA4C3ATtQhDvvzMzHqtq8hiIEuqJlFWpS6+ntJwI2mOEuWpIkSZIkjdSwA57MHADeEREnAFsBt2fm2ppmdwL/B7imdSVqMuvu62fOzOlERKdLkSRJkiRpwmpmBA8AmdkFdDW4djNw82iL0tRRCXgkSZIkSdLINR3w1BMRzwb2Au7LzBtacU9NDd29A66/I0mSJEnSKA174ZOIeF1E/CQiXlBz/mPA74HzgWsj4tsjKSQipkXE+yPi9ojoiYhlEXFKRGzUxD02j4h/j4i7y3s8GhGXR8SLa9r9dUR8JSKuL9tlRCweSd0anZ6+frdIlyRJkiRplJpZ2fYo4CDg1sqJiNgTOAkYAK4CVgFvjojXjaCWLwCnUmzH/l7gAuA44JKIGLLOiNgRuBF4O/B94J+ATwP3AtvXNH8r8PfAdIpwSh3iFC1JkiRJkkavmSlaC4HfZOa6qnNHAQm8KzO/GRHPoghojgYuHO6NI2IPilDnwsx8fdX5PwKnAUcC5w5xm29TvJ+9MvPBIdp+FPiHzHwiIj4E7D3cWtVa3b0GPJIkSZIkjVYzI3i2AJbXnHsJsJYyfMnMe4BfAQuarOPNQABfrDl/NrCOIkhqKCIOAl4EfC4zH4yImRGxYaP2mbk8M59oska1QXdfP7OdoiVJkiRJ0qg0E/BsQBHCABARsyhGvlydmeur2j0EbN1kHftRTPO6rvpkZvZQ7Mq13xDPP6Q8dkXEJUA38HhE3BkRg4ZD6qyevn7mzGymG0qSJEmSpFrNTNF6EHhu1dcHUYQ+V9W0mws81mQd2wErGoyqWQ4cGBGzMrO3wfOfUx7PBu6iWIdnA+ADwLciYmZmfr3Jmv5CRBwDHAOw9dZbs2TJktHecsTWrl3b0ddvlT8/to7NYt2keC9TyWTpf5qY7H/qJPufOsn+p06y/6mT7H/D00zAcwVwVER8GPgpcDLF+js/rWm3J3B/k3VsCDSaMtVT1aZRwPOM8rgGOLgSBEXERcA9wKcj4pzMHGiyrqfJzLOAswAWLVqUixcvHs3tRmXJkiV08vVb5qqfs9P8rVi8eK9OV6ImTJr+pwnJ/qdOsv+pk+x/6iT7nzrJ/jc8zcyN+VeK9XY+AywFXgD8IjOvrzSIiN2AZwHXNlnHOooRN/XMrmrTSHd5/G71KJ/MXAn8ENiGp0b5aBzp7utntossS5IkSZI0KsMewZOZd0bEX1FMe9qKYr2cz9c0exnwG+BHTdbxAPDciNigzjSt7SmmbzUavQNPjRh6qM61yo5amzVZk8ZAj9ukS5IkSZI0as1M0SIzbwP+fpDrXwa+PII6rgdeAewPXFk5GRGzKRZy/uUQz78OeDcwr861yrlHRlCX2qivf4C+/jTgkSRJkiRplMbL9kXfo1jP5/ia80dTrL3zncqJiNglInavaXcxxfo7R0XE3Kq22wKHA3dl5t3tKFwj19PXD8Act0mXJEmSJGlUmhrBAxARM4E3AIsppk9BsdPVEuD7mdnX7D0z89aIOAM4NiIuBH4CLACOo1jc+dyq5r8AdqRqy/bMXBkRHwLOBK6JiK8Bs4B/LI/H1ryHvYDXlF/+VXn824h4Ufnn0zNzdbPvQ83pLgMe1+CRJEmSJGl0mgp4ImJf4AJqApbSu4BPRcQRmXnTCGo5HriXYhvyQ4EVwOnACcPZ/Sozz4qIFcCHKXb4GgCuBt6SmbVbue9TtqlWPfXs24ABT5v19BY/VqdoSZIkSZI0OsMOeCJiHnApsDmwjGLa1B8ogp6dgbeUx0sjYu/MXN5MIZnZD5xSPgZrt9Mg1y4ELhzGa30D+EYz9an1up2iJUmSJElSSzQzguf/UYQ7pwH/t3YqVkR8gmJXrfeVbd/bqiI1OT0Z8DiCR5IkSZKkUWlmkeVXAfcA76+3zk5mrgc+WLY5pDXlaTLr7nUNHkmSJEmSWqGZgGd74LrMzEYNyrVyrgO2G21hmvzcRUuSJEmSpNZoJuDpppiiNZTNyrbSoJyiJUmSJElSazQT8NwCLI6I3Rs1iIjnAAeXbaVBVaZoGfBIkiRJkjQ6zQQ8XwVmAZdFxN9HxKzKhYiYGRF/B/wCmAmc3doyNRlVRvDMntVMN5QkSZIkSbWGvYtWZn4rIl4FvJkiwDkzIh4EkmLNnWkUW6afm5nfaUexmlx6nKIlSZIkSVJLNDV0IjPfChwL3AtMB+YB88s//xE4NjOPanGNmqTcRUuSJEmSpNYY9gieisz8T+A/I2J7ip21Arg/M5e3ujhNbt19/cycHsyc7hQtSZIkSZJGY9gBT0QcB6zLzP8CKAMdQx2NWHdfv6N3JEmSJElqgWaGTpwKvLZdhWjq6enrd/0dSZIkSZJaoJmA51FgTbsK0dTT3dvPnFkGPJIkSZIkjVYzAc+vgP3aVYimnm5H8EiSJEmS1BLNBDyfBOZFxCcjItpVkKaO7r4B1+CRJEmSJKkFmtlFax/gW8DHgDdExA+A+4Dueo0WOsGlAAAgAElEQVQz85ujL0+TWU+vI3gkSZIkSWqFZgKebwBJsS36AmD3Idob8GhQ3X39PPMZG3S6DEmSJEmSJrxmAp5vUgQ8Uku4Bo8kSZIkSa0x7IAnM9/Rxjo0BXX39rsGjyRJkiRJLdDMIstSS/X09TNnll1QkiRJkqTRGnQET0TMBzYDHs7Mh4douw2wFfDnzLy/dSVqsnKKliRJkiRJrdEw4ImIucCNwExg32Hca0PgCmBdROyamXV315IAMtOAR5IkSZKkFhlsfsxbgS2Bf83Me4a6UdnmZGBb4M2tKU+T1RPrB8iE2bMMeCRJkiRJGq3BAp6/AZ4AvtzE/b5SPufw0RSlya+nrx/AETySJEmSJLXAYAHP84HrM/Px4d4sM9cB1wF7j7YwTW7dBjySJEmSJLXMYAHPlsBIFkteDjxzZOVoqujuLQMep2hJkiRJkjRqgwU864FZI7jnrPK5UkOVETyzHcEjSZIkSdKoDRbwPATsPoJ77g4MuqW69OQIHgMeSZIkSZJGbbCA5xpgQUTsMdybRcSewHOBq0dbmCa3J9fgcYqWJEmSJEmjNljA810ggK9ExJBTtSJiJsUuWlk+V2rIETySJEmSJLVOw4AnM38C/BI4EFgSEXs1ahsRzweuAF4I/Kp8rtSQa/BIkiRJktQ6M4a4fgTwa+AAYGlE3ApcDzxSXt8K2A94HsVon3uAN7anVE0mPU7RkiRJkiSpZQYNeDLz0YhYBJwBHAnsVT6yqlkAA8B5wLGZubJNtWoScYqWJEmSJEmtM9QIHjJzNXBURJwAHAbsCzyzvPwocCPw48z8Q9uq1KTT3TcAGPBIkiRJktQKQwY8FZl5D3BaG2vRFFJZg2eDGYOt8y1JkiRJkobDT9fqiJ6+fmbPnMa0adHpUiRJkiRJmvAMeNQR3b39Ts+SJEmSJKlFDHjUEd19BjySJEmSJLWKAY86oruvn9lukS5JkiRJUksY8KgjepyiJUmSJElSyxjwqCOcoiVJkiRJUusY8Kgjuvv6meMULUmSJEmSWsKARx3R3dvPbEfwSJIkSZLUEgY86ogep2hJkiRJktQyBjzqCNfgkSRJkiSpdQx41BHdva7BI0mSJElSqxjwqCN6+gZcg0eSJEmSpBYx4NGYW98/QG//gFO0JEmSJElqEQMejbme9QMAzJll95MkSZIkqRX8hK0x193bD8CcWTM6XIkkSZIkSZODAY/GXE9fGfA4RUuSJEmSpJYw4NGY6zbgkSRJkiSppQx4NOaemqJl95MkSZIkqRX8hK0xVxnB4zbpkiRJkiS1hgGPxpxTtCRJkiRJai0DHo25nienaBnwSJIkSZLUCgY8GnOO4JEkSZIkqbUMeDTmDHgkSZIkSWqtcRPwRMS0iHh/RNweET0RsSwiTomIjZq4x+YR8e8RcXd5j0cj4vKIeHGdti+IiJ9HxJqIeCwifhoRe7f2Xameyi5as52iJUmSJElSS8zodAFVvgAcB1wEnAIsKL9eGBEvz8yBwZ4cETsCS4C5wFeBO4FNgL2A7WvaHlC2XQ6cUJ4+FrgyIg7MzFtb85ZUT48jeCRJkiRJaqlxEfBExB7Ae4ELM/P1Vef/CJwGHAmcO8Rtvk3xfvbKzAeHaHsa0AsclJnLy9c6H/g9Rbj0ipG8Dw1Pd18/M6YFM6ePmwFkkiRJkiRNaOPlE/abgQC+WHP+bGAdcNRgT46Ig4AXAZ/LzAcjYmZEbNig7a7AfsAFlXAHoPzzBcDLI2KbEb8TDWl1dx8bOj1LkiRJkqSWGS8Bz37AAHBd9cnM7AFuLq8P5pDy2BURlwDdwOMRcWdE1IZDlXtdXec+11AETfs2UbuadOvyx1iw7cadLkOSJEmSpEljXEzRArYDVmTmE3WuLQcOjIhZmdnb4PnPKY9nA3cBbwc2AD4AfCsiZmbm16teq3Lfeq8FNWv2VETEMcAxAFtvvTVLlixp/I7abO3atR19/ZHq7U9uu38dr9xp5oSsX4WJ2v80Odj/1En2P3WS/U+dZP9TJ9n/hme8BDwbAvXCHYCeqjaNAp5nlMc1wMGVICgiLgLuAT4dEeeUCzVXpm7Ve73q1/oLmXkWcBbAokWLcvHixQ3Kab8lS5bQydcfqRvv+zP9P7ua175oLxbv4Uy4iWqi9j9NDvY/dZL9T51k/1Mn2f/USfa/4RkvU7TWUYy4qWd2VZtGusvjd6tH+WTmSuCHwDY8Ncqncp96rzec19IoLO1aBcDCHTbtcCWSJEmSJE0e4yXgeQDYMiLqhS7bU0zfajR6B+D+8vhQnWuVHbU2q3qtyn3rvRbUn76lFripayXzNpvDVs+YPXRjSZIkSZI0LOMl4Lmeopb9q09GxGxgb+CGIZ5fWZx5Xp1rlXOPVL0WwAvrtD0ASODGIV5PI7S0axULd9hs6IaSJEmSJGnYxkvA8z2KYOX4mvNHU6yH853KiYjYJSJ2r2l3McX6O0dFxNyqttsChwN3ZebdAOXxBuCIiNiuqu12wBHAZZlZbySQRunB1d08uLqHhfOdniVJkiRJUiuNi0WWM/PWiDgDODYiLgR+AiwAjgOuAM6tav4LYEeK7cwrz18ZER8CzgSuiYivAbOAfyyPx9a85PuAy4ErI+L08tx7KQKvD7b47alUWX9nnx0dwSNJkiRJUiuNi4CndDxwL8U25IcCK4DTgRPK3a8GlZlnRcQK4MPAycAAcDXwlsy8qqbtryNiMfCp8pHAr4EjMvM3rXpDerqlXSuZNWMaz912406XIkmSJEnSpDJuAp7M7AdOKR+DtdtpkGsXAhcO8/WuBl7WRIkapaVdq9hzu42ZNWO8zAyUJEmSJGly8JO2xkTv+gFuXb6afVxgWZIkSZKkljPg0Zj4/YOP8cT6AXfQkiRJkiSpDQx4NCaWdq0EYOEO7qAlSZIkSVKrGfBoTCxdtoptNp7NdpvO6XQpkiRJkiRNOgY8GhM3da109I4kSZIkSW1iwKO2e3TNEyz7c7cBjyRJkiRJbWLAo7a7edkqAHfQkiRJkiSpTQx41HY3da1kxrRgz+036XQpkiRJkiRNSgY8arulXSt57nYbM3vm9E6XIkmSJEnSpGTAo7Za3z/ALfevdnqWJEmSJEltZMCjtrrz4bWs6+13gWVJkiRJktrIgEdtdVPXSgAWzncEjyRJkiRJ7WLAo7Za2rWKLefOYv7mczpdiiRJkiRJk5YBj9pq6bKV7D1/MyKi06VIkiRJkjRpGfCobVat6+WeRx93/R1JkiRJktrMgEdts3TZKgB30JIkSZIkqc0MeNQ2S7tWMS1gr3mbdLoUSZIkSZImNQMetc3SrpU8Z5uN2WiDGZ0uRZIkSZKkSc2AR20xMJDc3LWKfVx/R5IkSZKktjPgUVv84dG1rHliPQtdf0eSJEmSpLYz4FFbLO0qFlh2By1JkiRJktrPgEdtcVPXSjaZM5Odt9io06VIkiRJkjTpGfCoLZZ2rWLhDpsybVp0uhRJkiRJkiY9Ax613JqePu58ZA0L57v+jiRJkiRJY8GARy33m2WryXT9HUmSJEmSxooBj1puaddKImBvAx5JkiRJksaEAY9abumyVez6zLlsPHtmp0uRJEmSJGlKMOBRS2UmS7tWOj1LkiRJkqQxZMCjlrr3T+tYua6PfXZwgWVJkiRJksaKAY9aamnXSgAWGvBIkiRJkjRmDHjUUjd1rWTuBjPYdau5nS5FkiRJkqQpw4BHLbW0axV7z9+U6dOi06VIkiRJkjRlGPCoZdb1ruf2h9a4wLIkSZIkSWPMgEctc+v9q+kfSAMeSZIkSZLGmAGPWuamrlUALJzvAsuSJEmSJI0lAx61zNKuley85UZsttGsTpciSZIkSdKUYsCjlshMli5bxcL5Ts+SJEmSJGmsGfCoJe5f2c2ja55g4Y5Oz5IkSZIkaawZ8Kglli6rrL/jCB5JkiRJksaaAY9aYmnXSubMnM7u2zyj06VIkiRJkjTlGPCoJW7qWsVe8zZhxnS7lCRJkiRJY81P4xq1nr5+fvfAahbu4Po7kiRJkiR1ggGPRu23DzxGX3+ycAfX35EkSZIkqRMMeDRqS7tWAhjwSJIkSZLUIQY8GrWlXauYt9kctnrG7E6XIkmSJEnSlGTAo1Fb2rXS9XckSZIkSeogAx6NykOre3hgdQ/7OD1LkiRJkqSOMeDRqDy1/o4jeCRJkiRJ6hQDHo3K0mWrmDVjGs/dduNOlyJJkiRJ0pRlwKNRWdq1kj2325hZM+xKkiRJkiR1ip/KNWK96we45f7V7OP0LEmSJEmSOsqAZwq6bflqzr9hGZk5qvvc/tBjPLF+wPV3JEmSJEnqMAOeKei867v48Pdv4cizruHuR9aO+D5Lu1YBsNAdtCRJkiRJ6igDninopNfsyWdf9zxuf2gNr/7SLzn1f++gp6+/6fvc1LWSbTaezXabzmlDlZIkSZIkabgMeKagadOCI/ffgV988CUcttd2nHbZ3bzqi7/kV3etaOo+S7tWOXpHkiRJkqRxwIBnCtty7gZ84U178+13vgCAo756Lceft5QVa58Y8rkr1j5B15/XGfBIkiRJkjQOGPCIFz17S356/EEc97Jn8+NbH+Sl/76E717XxcBA40WYby7X33EHLUmSJEmSOm9cBDwRMS0i3h8Rt0dET0Qsi4hTImKjYT4/GzzqriAcEUdExK8j4vGIWBMRV0bEIa19VxPL7JnT+cBf78b/vO8gFmy7MR+58FbeeObV3PHQmrrtb+payYxpwZ7bbzLGlUqSJEmSpFrjIuABvgCcCvwOeC9wAXAccElEDLfGK4G/rXm8s7ZRRPwzcD4wGzgB+ASwEfCjiHjr6N7GxLfrVnM575gD+Pwb9uIPj67l0NOu5N9+ejvdvU9fhHlp1yqeu93GzJ45vUOVSpIkSZKkihmdLiAi9qAIdS7MzNdXnf8jcBpwJHDuMG51T2Z+e4jX2ho4CbgNeEFm9pXnTwduAk6PiEsy87ERvZlJIiI4YtF8XrZgaz79k9/z5SV/4Ee3PMDJr92Txc/Ziv6B5Df3r+KNi+Z3ulRJkiRJksT4GMHzZiCAL9acPxtYBxw13BtFxKyImDtIkwOBWcB3KuEOQPnnc4HNgNcO9/Umu803msW/H/F8vnv0AcycPo13fP16jj33Jn519wrW9fa7wLIkSZIkSePEeAh49gMGgOuqT2ZmD3BzeX043kARCK2JiEci4vSIqF0gZoPyuK7O8yvnDhjm600ZL9xlC/7nfS/mA3+9G//7u4d5+9eKH9XC+S6wLEmSJEnSeBCZjXdKGpMCIm4FtsrMretcOx84AtggM3sHuce1FOv23A1sDBwCvAm4FTgwM9eW7Z4H3AL8IDMPr7nHxRSjdy7JzNc0eJ1jgGMAtt56633PO++8Jt9t66xdu5a5cwcbrNQeDz0+wLd/18vavuQTL5xNRIx5Deq8TvU/Cex/6iz7nzrJ/qdOsv+pk6Z6/zv44INvzMxFQ7Xr+Bo8wIbAEw2u9VS1aRjwZOYLak59MyJuAf4VeF95JDNvjYifAa+NiM8BXy/bvwN4ddVrNXqds4CzABYtWpSLFy9u1LTtlixZQqde/8hDO/KyGkc62f8k+586yf6nTrL/qZPsf+ok+9/wjIcpWut4aupUrdlVbZr1eYpQqDaOeBNwIfAhil27fge8EXhPeX1KL7AsSZIkSZImnvEQ8DwAbBkR9UKe7YEVg03PaqRcOPkBYMua8yvL3bq2BQ4C9gF2KdsC3N7sa0mSJEmSJHXSeAh4rqeoY//qkxExG9gbuGEkNy2fPw94uN71zHw4M6/MzKWZOUCxbg/AT0byepIkSZIkSZ0yHgKe7wEJHF9z/miK9XC+UzkREbtExO7VjSJiiwb3PZlijaFLhiogIhYB7wKuyMxfDb90SZIkSZKkzuv4IsvlwsdnAMdGxIUUI2gWAMcBVwDnVjX/BbAjUL1108ci4gDgcqALmEsxGudg4Frg9OrXi4iTgWdTbMu+mmKK1t8Dy4G/bfX7kyRJkiRJareOBzyl44F7KbYgPxRYQRHMnFBOnxrMEuC5wNuBLYB+4C7go8CpmdlT034p8HLgFRQjhLqA04DPZOaqFrwXSZIkSZKkMTUuAp7M7AdOKR+DtdupzrkfAD9o4rUupNhFS5IkSZIkaVIYD2vwSJIkSZIkaRQMeCRJkiRJkiY4Ax5JkiRJkqQJzoBHkiRJkiRpgjPgkSRJkiRJmuAMeCRJkiRJkiY4Ax5JkiRJkqQJzoBHkiRJkiRpgjPgkSRJkiRJmuAMeCRJkiRJkiY4Ax5JkiRJkqQJzoBHkiRJkiRpgjPgkSRJkiRJmuAMeCRJkiRJkiY4Ax5JkiRJkqQJzoBHkiRJkiRpgjPgkSRJkiRJmuAMeCRJkiRJkiY4Ax5JkiRJkqQJzoBHkiRJkiRpgovM7HQNE1JEPArc18EStgRWdPD1NbXZ/9RJ9j91kv1PnWT/UyfZ/9RJU73/7ZiZzxyqkQHPBBURN2Tmok7XoanJ/qdOsv+pk+x/6iT7nzrJ/qdOsv8Nj1O0JEmSJEmSJjgDHkmSJEmSpAnOgGfiOqvTBWhKs/+pk+x/6iT7nzrJ/qdOsv+pk+x/w+AaPJIkSZIkSROcI3gkSZIkSZImOAMeSZIkSZKkCc6AR5IkSZIkaYIz4JlAImJaRLw/Im6PiJ6IWBYRp0TERp2uTZNHRHwkIi6IiHsiIiPi3iHavyAifh4RayLisYj4aUTsPUblahKJiN0i4qSIuCYiHi371M0R8dF6f89FxHMi4uKIWBkRj0fElRHx0k7Uromv7E/fiYjfR8TqiFhX/nt7akRs26C9/U9tExEbRsQfy3+L/6POdfugWqbsZ/Uea+u0te+p5SJi84j494i4u/ys+2hEXB4RL65p52ePQczodAFqyheA44CLgFOABeXXCyPi5Zk50MniNGl8GvgzcBOw6WANI+IAYAmwHDihPH0scGVEHJiZt7axTk0+fw+8B/gh8B2gDzgY+BTwxog4IDO7ASJiF+DXwHrgc8Bq4Gjg0oh4dWb+vAP1a2KbB2xL8W/s/RR963nAMcCREbF3Zj4C9j+NmZOALetdsA+qTa7kL3cq6qv+wr6ndoiIHSk+U8wFvgrcCWwC7AVsX9XOzx5DcBetCSIi9gBuBS7KzNdXnX8vcBrw1sw8t1P1afKIiGdl5j3ln28D5mbmTg3aXgfsDizIzOXlue2B3wPXZOYrxqZqTQYRsQi4KzNX15z/FPBR4L2Z+R/lufOB1wP7ZubN5bm5wG+BHmD39B84tUBEHAGcD/xzZn6uPGf/U1tFxD7AdcCHKX6pd0ZmHlt13T6oloqIBM7JzHcM0c6+p5aLiCuBnYD9M/PBQdr52WMITtGaON4MBPDFmvNnA+uAo8a8Ik1KlXBnKBGxK7AfcEHlL9jy+cuBC4CXR8Q27alSk1Fm3lAb7pS+Vx73BCina70GWFL5n8vy+WuB/wJ2o+ibUivcVx43A/uf2i8iplP8/91PgQvrXLcPqm0iYlYZ2NS7Zt9Ty0XEQcCLgM9l5oMRMTMiNqzTzs8ew2DAM3HsBwxQ/DbnSZnZA9yMf5lq7FX63NV1rl1DEUjuO3blaBKbVx4fLo97ARvQuO+BfydqhCJidkRsGRHzIuIVwJnlpZ+UR/uf2u39FL+hPrbBdfug2uUNFL84XhMRj0TE6RGxSdV1+57a4ZDy2BURlwDdwOMRcWdEVA9i8LPHMBjwTBzbASsy84k615YDW0bErDGuSVPbduVxeZ1rlXPb17kmDVv5m+wTKOb6V6ah2vfUTu8CHgWWAZdSrEV2VGZeWV63/6ltImJn4JPASZl5b4Nm9kG1w3XAiRQhz9uBy3hqbZPKiB77ntrhOeXxbGBziv73TqAX+FZE/F153f43DC6yPHFsCNQLd6CY71pp0zs25UhUhk7W65c9NW2kkfoicADwL5l5R3nOvqd2uhi4nWKhx4UU0xGeWXXd/qd2+jLwR+DUQdrYB9VymfmCmlPfjIhbgH8F3lce7Xtqh2eUxzXAwZnZCxARFwH3AJ+OiHOw/w2LI3gmjnUUQyLrmV3VRhorlf5Wr1/aJzVqEXEyxW8Pz8rMz1Rdsu+pbTLz/sz8eWZenJmfoPhN4r9FxEfKJvY/tUU5FeEVwLszs2+QpvZBjZXPU/zy+NDya/ue2qG7PH63Eu4AZOZKip1Vt6EY5WP/GwYDnonjAYppWPU69PYU07ccvaOx9EB5rDcUsnKu3hBKaUgRcSLwMeDrwLtrLtv3NGYy8xZgKfBP5Sn7n1qu/P+7UynWenooInYtFxTdsWyySXluU+yDGiNl0PgAsGV5yr6ndri/PD5U51plR63NsP8NiwHPxHE9xc9r/+qTETEb2Bu4oRNFaUq7vjy+sM61A4AEbhy7cjRZRMQngE8A3wTeVWe71Vsphuc26nvg34lqrTkU6wKA/U/tMYdiKuChwF1VjyXl9aPKr9+FfVBjpPycMY+nNjmw76kdKpsIzatzrXLuEfzsMSwGPBPH9yg67fE154+mmGv4nTGvSFNaZt5N8Y/4ERFRWfSM8s9HAJdlZr0kXmooIk6gWOTxW8DfZeZAbZtyO9ZLgMUR8fyq586l+PBzFzU7DkpDabS1akQcDOxJuUOM/U9t8jjFv521j8rIsZ+WX//QPqhWi4gtGlw6mWLN1kvAv//UNhdTrL9zVNWC3kTEtsDhwF2ZebefPYYn/vIXoxqvIuJ0ivUoLqIYwrsAOA64CnhpvQ9CUrMi4m95akj4e4FZwCnl1/dl5req2h4IXE4xtPL0qudsDfxVZv5mTIrWpBAR7wH+A+gCPg7U/p32cGb+rGy7K8X/RPYBXwAeowi8nwccmpmXjlXdmhzKxRy3pdg55j6K+fz7AkdSzOlfnJk3l23tfxoTEbETxaLLZ2TmsVXn7YNqmYj4AsUIiMsp/g2eS7F19cHAtRQL33aXbe17armIOAY4E/gt8DWKzx//SPHv8mGZ+b9lOz97DMGAZwIptws+HjgG2AlYQTGy54QyUZdGLSKWAC9pcPmKzFxc0/6FwKeAF1CMMvs18JHMvKmNZWoSiohvUCxo28jT+l9ELAA+S9FfZwE3ASdm5s/bWKYmqYh4I0X/24tiqkxSBD0/Az6fmV017e1/artGAU95zT6oloiI11KMFtsT2ALopxiNcz5wamb21LS376nlIuJ1wIcpwsIB4Grgk5l5VU07P3sMwoBHkiRJkiRpgnMNHkmSJEmSpAnOgEeSJEmSJGmCM+CRJEmSJEma4Ax4JEmSJEmSJjgDHkmSJEmSpAnOgEeSJEmSJGmCM+CRJEmSJEma4Ax4JEmaJCLi3ojI8nHoIO1uK9ssHsPymhIRi8sal3S6lnaLiHdFxI0R8XjVz2/TYTxvdkR8NiLujognyufdPBY1S5Kk8WdGpwuQJElt8ZmI+J/MHOh0IWosIg4DzgZ6gJ8Bfy4v9Q7j6Z8CPgg8DPwAWAd0taHMQUXEicAngE9m5olj/fqSJKlgwCNJ0uSzDnge8FbgWx2uRYM7ojwel5lnj/C5L87Mu1pYkyRJmoCcoiVJ0uRzWnn8ZETM6mglGsr88jiSgGY+gOGOJEkCAx5Jkiaj/wauA3YG3j3cJ0XEksHW5omIb5TX39HofETsERH/HRGPRsTaiPhVRBxc1fawiLgiIlZHxGMR8cOIePYQdW1UrjVzT7nWzLKIOD0ithjkOfMj4ksRcUdEdJevdVVZYwz23iPioIj4cUSsiIiBiDh8qO9deY+ZEXFsRFxbvl53RPy+rH3zet8zoPK9ubxq/Z0Th3ide8vnRvl1Vj0W17R9Zfk9fjgieiPiwYj4bkQ8r8G9Xx4RZ0TEbyLiT+X3+76IOCciFtRpnxTTswA+UVPLiWWbncqv7x3kPWV5r4bnI+KdVd/bp61TVH7v3x0RV0bEyojoiYi7IuLUiHhmg9c8MiIui4g/R0Rf+fO+tXz/uzSqVZKk8copWpIkTU7/D7gM+GhEfC0z147Bay4CzgDuAX4BPBv4K+DSiHgZsDfwReAq4FJgf+BvgP0iYs/M/FOde84q77Unxfu5CXgJcCzwyoh4cWY+XP2EMlC6CNgEuBv4KTAXOAD4OvBS4G0N3sMRFKHY7yjWxNkS6BvqjUfEbOB/gMUUU+QuL48vBv4ZODIiXpqZ95RP+VV5fBWwdfn9eKg8N9RCyd8v63p7+fU5Vdcq9yAivgQcB6wHrgfuB3YFjgQOj4jXZ+ZPau79FWAe8FvgSiApvvdvA94QEa/MzF9VtT+H4uf6fOA3NbW3bMHniDgd+CeKvvMjYLeyNiJiY+DHwIuA1cCNwCpgH+D9wOsj4iWZeW/V/U6kCKb6gF8DDwCbAjuVr3Ml8IdW1S9J0pjITB8+fPjw4cPHJHgA91J86F1Ufn1p+fUnatrdVp5fXHN+Sb3zVde/UV5/R4PzCXyg5tq/lefvoPjw/eKqa7OBX5bXP17zvMVV97wD2L7q2jOAn5fXzq953rYUCxWvpwhAourafGBpg/ewpOr1jhnB9/5z5XN/X1PrHIoRVQlcXed5g37Ph3jNLP5Xru61d5fXbwN2r7l2OEWwsRLYrM61TWvOBfAP5f1+V/09La+fWF47sUEtO5XX7232vVT9TFYB+zd47nllmwuq3w8wvar/Lak6vwFF+LYG2K3O/Z4N7NyK/yZ9+PDhw4ePsXw4RUuSpMnrIxQfbj/YaJpKi12dmafWnPtsedwNOCMzr6xcyMwe4AvllwfT2Aczc3nV89ZQBBj9FKMz5le1PR7YDDglM8/JzKx63jLg6PLL9zZ4rZ9l5lmD1PIXImIO8I/ll8fV1NpNEY48DhwQEX/VzL1HIiKmAyeUX74xM2+vvp6ZFwNnUoxYOar2WmauqjmXmXkmxUiXBcBz21X7ID6XmdfVnoyI5wJvAu4D3paZK1cl7/gAAAYaSURBVCvXMrOf4r+BW4CXVE1L25giePtDZt5Ze8/MvCsz/9iG9yBJUlsZ8EiSNEll5k3A+RQjXj46Bi/50zo1rAT+1Og6Ty0uvF2De67KzB/Vue/dwDUU/y9zUNWlQ8rjBQ3udyOwFti7nFZV68IGzxvMvhRTwB7IzJ/VqXUFcEn55eIR3L9Ze1OMZPptZv6uQZsryuMLay9ExLyI+IeI+EJEfLVcL+gbwDZlk91aXvHQGv1cXl0ef1SGaU+TmQM8NR3uheW5RylGuz0/Ik6JiN1bXKskSR3hGjySJE1uHwNeD7w7Ir6Qmfe18bXub3B+LbBFg+uVtYHqhS1QfBBv5F6KNX7mVZ17Vnm8vs5ayrW2AJbXnBvJ92f78jjYqI/Kei7bD9KmVSrfgz3qLVxc42kjuyLik8C/MPj/I248itpGqtHPpfJe3xMR7xniHtXv9W0UU+c+AHwgIh6lCAwvBb6dmatHU6wkSZ1gwCNJ0iSWmXdHxH9RTGk6iacW5h2JoUb+Dozy+khVhxjTy+P3gJ4hnvdEnXN/MQpkGCpJ0mBhypBpUwtVvgfLKdYqGsyT07ci4vUUU7vWUAQflwEPVkbGRMS5wJtp8XuJiCFHlNcbnVOqvNcbKdYbGsxvq+53ZUTsBBxGMarqwPLPfwOcGBGvyMylQ9UlSdJ4YsAjSdLkdxLFiIWjIuLzg7TrLY9zG1zfsaVVDc9Ow7j2QNW5ZRQ7RZ2cmb/9i2e0R2Vk0s6DtKlcqx0x1A7LyuODmfmOJp53RHn8l8z8rzrXdx1hPe3sV5X3enlm/t9mnpiZ6yimMJ4PEBHbUqwJ9SaK3eAOHEVdkiSNOdfgkSRpksvMB4EvUfy7/+lBmlbCh79YkyQitqbYdnqsbRoRh9SejIhnUWx7nhQ7cVX8T3k8ovY5bVRZ12f7cjv4p4mILShGhkCxa1a7XUex7tHCiGgmlNm8PC6rvRARC4CFDZ5XCXAa/eLw0bLNFg0W+/6Ln28TKj/vwyNiVL+4LP87qaxV9fzR3EuSpE4w4JEkaWr4N4rtw/+GxiNNflEe31OOZgAgIjYHzqHxCIx2O6WmnrnAlymm51yUmV1VbT8PPAb8S0S8p96H/og4ICJaFgCV04e+Un75pZpaZ5e1zgWuycyrWvW6g9TTB5xM8f25OCL2r20TERtFxJvL4KaiMl3r6IiYVdV2K4qff6MApRIMLqh3saynsnvaSVG1OFJEvIhihNmIlAuJX0wxuuj8iJhX2yYito2I4yt9ISJ2jIh3RUS9tYQqQVw716qSJKktnKIlSdIUkJmrI+KzwOeADRs0O59i7ZWFwG8j4ipgFrAfxTSoi4HDx6DcaldTBBV3RsRlFCNBXkKxYO4fgKctrJuZyyLicOD7wH8AH42I31KMaNkO2KU8fo/GO22NxMeBRRTrudxV1toNvJhiR6su4K0tfL1BZeaXImJH4P3AtRFxC8X3axown2KU1oYUu1D9vnzaFymm8h0K3B0R11JsJ/4SilE9jX7+lwLrgNdFxC/L1+kHfpiZPyzbnEDxvXg3xZblv6WYmrUvxaiyj43i7b4d+CHwf4BXR8RvKAKajcv3uqB8318B1gObAWcDZ0TEzRSLY0+j2P59D6AP+PAo6pEkqSMcwSNJ0tRxOo13uiIze4GXU4w46QZeSREEnEOxHkkndhbqBV4KnAnsBbymPHcGcEBmPlT7hMy8nOKD+qeBRyimch0O7ECxLftHaPG28ZnZA7wCOA74HXAw8FqK0USfA/bJzHta+ZrDqOkDFOHMeRShxqEUAdSGFNu2v5WnRtZQ1rdP2T4oRrMsAM6i2GK87s+//BkcRjH9bC+KwOWdVE3py8xfAy+jGCU2n6emZb0tMz8+yvf5WHnvt1FM19sFeB1FeLSeIth5ZfkzgiKAej/F9K7Ny9pfRREkngXsnZk/Gk1NkiR1QmQOtXumJEmSJEmSxjNH8EiSJEmSJE1wBjySJEmSJEkTnAGPJEmSJEnSBGfAI0mSJEmSNMEZ8EiSJEmSJE1wBjySJEmSJEkTnAGPJEmSJEnSBGfAI0mSJEmSNMEZ8EiSJEmSJE1w/x8EE2BWUglihQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot number of features VS. cross-validation scores\n",
    "plt.figure(figsize=(16, 10));\n",
    "plt.grid(True);\n",
    "plt.title('German Credit, Decision Tree', fontsize=22);\n",
    "plt.xlabel(\"Number of features\", fontsize=22);\n",
    "plt.ylabel(\"Cross validation score (accuracy)\", fontsize=22);\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_);\n",
    "plt.xticks(fontsize=18);\n",
    "plt.yticks(fontsize=18);\n",
    "plt.tight_layout();\n",
    "plt.savefig('out/german_credit-rfe.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_results_to_df(cv_results):\n",
    "    results = pd.DataFrame(list(cv_results['params']))\n",
    "    results['mean_fit_time'] = cv_results['mean_fit_time']\n",
    "    results['mean_score_time'] = cv_results['mean_score_time']\n",
    "    results['mean_train_score'] = cv_results['mean_train_score']\n",
    "    results['std_train_score'] = cv_results['std_train_score']\n",
    "    results['mean_test_score'] = cv_results['mean_test_score']\n",
    "    results['std_test_score'] = cv_results['std_test_score']\n",
    "    results['rank_test_score'] = cv_results['rank_test_score']\n",
    "\n",
    "    results = results.sort_values(['mean_test_score'], ascending=False)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.24 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort=False, random_state=None,\n",
       "                                              splitter='best'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'criterion': ('gini', 'entropy'),\n",
       "                         'max_depth': [1, 5, 10, 50],\n",
       "                         'max_leaf_nodes': [5, 20, 100]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "hyperparams = {'criterion':('gini', 'entropy'), \n",
    "               'max_depth':[1, 5, 10, 50],\n",
    "               'max_leaf_nodes': [5, 20, 100]}\n",
    "clf = DecisionTreeClassifier(min_samples_split=2)\n",
    "gridsearch = GridSearchCV(clf, hyperparams, cv=5, return_train_score=True)\n",
    "%time gridsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>criterion</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>max_leaf_nodes</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.698750</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.698750</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>entropy</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.004802</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.698750</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>entropy</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.004799</td>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.698750</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>entropy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.698750</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.001401</td>\n",
       "      <td>0.698750</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.007799</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.811881</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.69500</td>\n",
       "      <td>0.012180</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gini</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>0.007198</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>0.811881</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.69500</td>\n",
       "      <td>0.012180</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0.006622</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>0.773458</td>\n",
       "      <td>0.022065</td>\n",
       "      <td>0.69000</td>\n",
       "      <td>0.029529</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>entropy</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>0.008801</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.793449</td>\n",
       "      <td>0.013994</td>\n",
       "      <td>0.68750</td>\n",
       "      <td>0.035515</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gini</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>0.005600</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.735325</td>\n",
       "      <td>0.019560</td>\n",
       "      <td>0.68500</td>\n",
       "      <td>0.024491</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.006798</td>\n",
       "      <td>0.001802</td>\n",
       "      <td>0.735325</td>\n",
       "      <td>0.019560</td>\n",
       "      <td>0.68500</td>\n",
       "      <td>0.024491</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.735325</td>\n",
       "      <td>0.019560</td>\n",
       "      <td>0.68500</td>\n",
       "      <td>0.024491</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>entropy</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>0.005200</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.722507</td>\n",
       "      <td>0.014490</td>\n",
       "      <td>0.68125</td>\n",
       "      <td>0.022286</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.005101</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.722507</td>\n",
       "      <td>0.014490</td>\n",
       "      <td>0.68125</td>\n",
       "      <td>0.022286</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0.006785</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>0.785018</td>\n",
       "      <td>0.021598</td>\n",
       "      <td>0.68125</td>\n",
       "      <td>0.035115</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.006702</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>0.722507</td>\n",
       "      <td>0.014490</td>\n",
       "      <td>0.68125</td>\n",
       "      <td>0.022286</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.793449</td>\n",
       "      <td>0.013994</td>\n",
       "      <td>0.67875</td>\n",
       "      <td>0.030188</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gini</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.009602</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.972189</td>\n",
       "      <td>0.003017</td>\n",
       "      <td>0.67875</td>\n",
       "      <td>0.016928</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.001602</td>\n",
       "      <td>0.804699</td>\n",
       "      <td>0.013557</td>\n",
       "      <td>0.67000</td>\n",
       "      <td>0.030002</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.796886</td>\n",
       "      <td>0.013378</td>\n",
       "      <td>0.67000</td>\n",
       "      <td>0.024235</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>entropy</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.011200</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.965939</td>\n",
       "      <td>0.005433</td>\n",
       "      <td>0.66625</td>\n",
       "      <td>0.021539</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0.008798</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.917195</td>\n",
       "      <td>0.014329</td>\n",
       "      <td>0.66375</td>\n",
       "      <td>0.028530</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0.008998</td>\n",
       "      <td>0.002002</td>\n",
       "      <td>0.945942</td>\n",
       "      <td>0.012989</td>\n",
       "      <td>0.66125</td>\n",
       "      <td>0.027985</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   criterion  max_depth  max_leaf_nodes  mean_fit_time  mean_score_time  \\\n",
       "0       gini          1               5       0.004400         0.001600   \n",
       "1       gini          1              20       0.005001         0.001600   \n",
       "14   entropy          1             100       0.004802         0.001406   \n",
       "13   entropy          1              20       0.004799         0.001201   \n",
       "12   entropy          1               5       0.004399         0.001200   \n",
       "2       gini          1             100       0.005400         0.001401   \n",
       "7       gini         10              20       0.007799         0.001601   \n",
       "10      gini         50              20       0.007198         0.001801   \n",
       "16   entropy          5              20       0.006622         0.001310   \n",
       "22   entropy         50              20       0.008801         0.001800   \n",
       "9       gini         50               5       0.005600         0.001600   \n",
       "3       gini          5               5       0.006798         0.001802   \n",
       "6       gini         10               5       0.006000         0.001601   \n",
       "21   entropy         50               5       0.005200         0.001200   \n",
       "15   entropy          5               5       0.005101         0.001000   \n",
       "17   entropy          5             100       0.006785         0.001413   \n",
       "18   entropy         10               5       0.006702         0.000994   \n",
       "19   entropy         10              20       0.007400         0.001400   \n",
       "11      gini         50             100       0.009602         0.001799   \n",
       "5       gini          5             100       0.006399         0.001602   \n",
       "4       gini          5              20       0.007598         0.001402   \n",
       "23   entropy         50             100       0.011200         0.001822   \n",
       "20   entropy         10             100       0.008798         0.001200   \n",
       "8       gini         10             100       0.008998         0.002002   \n",
       "\n",
       "    mean_train_score  std_train_score  mean_test_score  std_test_score  \\\n",
       "0           0.698750         0.000431          0.69875        0.001717   \n",
       "1           0.698750         0.000431          0.69875        0.001717   \n",
       "14          0.698750         0.000431          0.69875        0.001717   \n",
       "13          0.698750         0.000431          0.69875        0.001717   \n",
       "12          0.698750         0.000431          0.69875        0.001717   \n",
       "2           0.698750         0.000431          0.69875        0.001717   \n",
       "7           0.811881         0.011447          0.69500        0.012180   \n",
       "10          0.811881         0.011447          0.69500        0.012180   \n",
       "16          0.773458         0.022065          0.69000        0.029529   \n",
       "22          0.793449         0.013994          0.68750        0.035515   \n",
       "9           0.735325         0.019560          0.68500        0.024491   \n",
       "3           0.735325         0.019560          0.68500        0.024491   \n",
       "6           0.735325         0.019560          0.68500        0.024491   \n",
       "21          0.722507         0.014490          0.68125        0.022286   \n",
       "15          0.722507         0.014490          0.68125        0.022286   \n",
       "17          0.785018         0.021598          0.68125        0.035115   \n",
       "18          0.722507         0.014490          0.68125        0.022286   \n",
       "19          0.793449         0.013994          0.67875        0.030188   \n",
       "11          0.972189         0.003017          0.67875        0.016928   \n",
       "5           0.804699         0.013557          0.67000        0.030002   \n",
       "4           0.796886         0.013378          0.67000        0.024235   \n",
       "23          0.965939         0.005433          0.66625        0.021539   \n",
       "20          0.917195         0.014329          0.66375        0.028530   \n",
       "8           0.945942         0.012989          0.66125        0.027985   \n",
       "\n",
       "    rank_test_score  \n",
       "0                 1  \n",
       "1                 1  \n",
       "14                1  \n",
       "13                1  \n",
       "12                1  \n",
       "2                 1  \n",
       "7                 7  \n",
       "10                7  \n",
       "16                9  \n",
       "22               10  \n",
       "9                11  \n",
       "3                11  \n",
       "6                11  \n",
       "21               14  \n",
       "15               14  \n",
       "17               14  \n",
       "18               14  \n",
       "19               18  \n",
       "11               18  \n",
       "5                20  \n",
       "4                20  \n",
       "23               22  \n",
       "20               23  \n",
       "8                24  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = cv_results_to_df(gridsearch.cv_results_)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.85 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\st50\\AppData\\Local\\Continuum\\anaconda3\\envs\\small_sklearn\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features=None,\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    presort=False,\n",
       "                                                    random_state=None,\n",
       "                                                    splitter='best'),\n",
       "                   iid='w...\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001F52B79D4E0>,\n",
       "                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001F52B79DF60>,\n",
       "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001F52B79D860>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "\n",
    "hyperparam_dist = {\"max_depth\": sp_randint(1, 50),\n",
    "              \"max_features\": sp_randint(1, 11),\n",
    "              \"min_samples_split\": sp_randint(2, 11),\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "clf = DecisionTreeClassifier(min_samples_split=2)\n",
    "randomsearch = RandomizedSearchCV(clf, param_distributions=hyperparam_dist, \n",
    "                                  cv=5, return_train_score=True, n_iter=100, iid='warn');\n",
    "%time randomsearch.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>criterion</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>max_features</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.776252</td>\n",
       "      <td>0.018625</td>\n",
       "      <td>0.72000</td>\n",
       "      <td>0.024151</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>entropy</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.003999</td>\n",
       "      <td>0.001401</td>\n",
       "      <td>0.716256</td>\n",
       "      <td>0.011448</td>\n",
       "      <td>0.71000</td>\n",
       "      <td>0.012993</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0.004802</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.734679</td>\n",
       "      <td>0.010734</td>\n",
       "      <td>0.70375</td>\n",
       "      <td>0.021870</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.701254</td>\n",
       "      <td>0.005401</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.698750</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>gini</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.808742</td>\n",
       "      <td>0.011736</td>\n",
       "      <td>0.63125</td>\n",
       "      <td>0.011942</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>entropy</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>0.001194</td>\n",
       "      <td>0.800623</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>0.62625</td>\n",
       "      <td>0.027717</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>entropy</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.004200</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.841561</td>\n",
       "      <td>0.009583</td>\n",
       "      <td>0.62125</td>\n",
       "      <td>0.022514</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>entropy</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.62125</td>\n",
       "      <td>0.016869</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>entropy</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.004201</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.837187</td>\n",
       "      <td>0.005798</td>\n",
       "      <td>0.60875</td>\n",
       "      <td>0.027751</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   criterion  max_depth  max_features  min_samples_split  mean_fit_time  \\\n",
       "92   entropy          7             4                  2       0.005000   \n",
       "59   entropy          4             3                  5       0.003999   \n",
       "76      gini          4             8                  8       0.004802   \n",
       "42      gini          1             9                  5       0.003600   \n",
       "8       gini          1             4                  5       0.003400   \n",
       "..       ...        ...           ...                ...            ...   \n",
       "82      gini         43             1                  7       0.003606   \n",
       "97   entropy         37             1                  8       0.004005   \n",
       "38   entropy         36             2                  6       0.004200   \n",
       "64   entropy         31             2                  2       0.004399   \n",
       "17   entropy         28             1                  5       0.004201   \n",
       "\n",
       "    mean_score_time  mean_train_score  std_train_score  mean_test_score  \\\n",
       "92         0.001600          0.776252         0.018625          0.72000   \n",
       "59         0.001401          0.716256         0.011448          0.71000   \n",
       "76         0.001198          0.734679         0.010734          0.70375   \n",
       "42         0.001006          0.701254         0.005401          0.70000   \n",
       "8          0.001000          0.698750         0.000431          0.69875   \n",
       "..              ...               ...              ...              ...   \n",
       "82         0.001000          0.808742         0.011736          0.63125   \n",
       "97         0.001194          0.800623         0.005274          0.62625   \n",
       "38         0.001199          0.841561         0.009583          0.62125   \n",
       "64         0.000999          1.000000         0.000000          0.62125   \n",
       "17         0.001199          0.837187         0.005798          0.60875   \n",
       "\n",
       "    std_test_score  rank_test_score  \n",
       "92        0.024151                1  \n",
       "59        0.012993                2  \n",
       "76        0.021870                3  \n",
       "42        0.001186                4  \n",
       "8         0.001717                5  \n",
       "..             ...              ...  \n",
       "82        0.011942               96  \n",
       "97        0.027717               97  \n",
       "38        0.022514               98  \n",
       "64        0.016869               98  \n",
       "17        0.027751              100  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results_to_df(randomsearch.cv_results_)"
   ]
  }
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