labs/trees/partitioning-feature-space.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partitioning feature space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make sure to get latest dtreeviz**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -q -U dtreeviz\n",
    "! pip install -q graphviz==0.17 # 0.18 deletes the `run` func I need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.datasets import load_boston, load_iris, load_wine, load_digits, \\\n",
    "                             load_breast_cancer, load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from sklearn import tree\n",
    "from dtreeviz.trees import *\n",
    "from dtreeviz.models.shadow_decision_tree import ShadowDecTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_mse_leaves(X,y,max_depth):\n",
    "    t = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    t.fit(X,y)\n",
    "    shadow = ShadowDecTree.get_shadow_tree(t, X, y, feature_names=['sqfeet'], target_name='rent')\n",
    "    root, leaves, internal = shadow._get_tree_nodes()\n",
    "    # node2samples = shadow._get_tree_nodes()_samples()\n",
    "    # isleaf = shadow.get_node_type(t)\n",
    "    n_node_samples = t.tree_.n_node_samples\n",
    "\n",
    "    mse = 99.9#mean_squared_error(y, [np.mean(y)]*len(y))\n",
    "    print(f\"Root {0:3d} has {n_node_samples[0]:3d} samples with MSE ={mse:6.2f}\")\n",
    "    print(\"-----------------------------------------\")\n",
    "\n",
    "    avg_mse_per_record = 0.0\n",
    "    node2samples = shadow.get_node_samples()\n",
    "    for node in leaves:\n",
    "        leafy = y[node2samples[node.id]]\n",
    "        n = len(leafy)\n",
    "        mse = mean_squared_error(leafy, [np.mean(leafy)]*n)\n",
    "        avg_mse_per_record += mse * n\n",
    "        print(f\"Node {node.id:3d} has {n_node_samples[node.id]:3d} samples with MSE ={mse:6.2f}\")\n",
    "\n",
    "    avg_mse_per_record /= len(y)\n",
    "    print(f\"Average MSE per record is {avg_mse_per_record:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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>MPG</th>\n",
       "      <th>CYL</th>\n",
       "      <th>ENG</th>\n",
       "      <th>WGT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>3504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>3693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>3436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MPG  CYL    ENG   WGT\n",
       "0  18.0    8  307.0  3504\n",
       "1  15.0    8  350.0  3693\n",
       "2  18.0    8  318.0  3436"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cars = pd.read_csv(\"data/cars.csv\")\n",
    "X, y = df_cars[['ENG']], df_cars['MPG']\n",
    "df_cars.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5zqKFHHjmME7SgMQI177r6gnbOIF2Ljj/PFzXxTAMnIHB8Zb+ZlNots1mLK+IiIhIpRTkywSZoTKHggHOP/ccnESMGXMW8aIXvej0bZw4ATNE0onT2cSzV3qtvCIiIiKVUpQjp8keKnPG0GN5Z6bM3qZzcHfTz17ptfKKiIiIVMJwXbfRZfAUwzB2dnd3d+/cubPRRam5Ymam9NrslV4rr4iIiLSunp4e+vr6+lzX7Sl1X7Xky6SKCYa9FjBnl1fDaYqIiIhfKSdfWo6G0xQRERG/U0u+tBwNpykiIiJ+pyBfKlJKykszpMcUGk5TRERExC+UrtOCqtH5tJSUl8y2Q2MunW1GUekx2WWsZmfZzHCag/ExzFBbTYbTVOdeERERaTQF+S2kmrnomZSXgBliyImzaWsv69ddl3fbu7+8jcf7T+IYYUw3xl3/tI1PfOzDU5YxkBjGCJg4gfaq5c4fOTLA6OgYT+19AJwxLly8gDXXrazomPnKrlx/ERERaSSl67SQauWil5Ly4jgOj+97FmfOyzHmXoIz5+U8vu/ZSdNjssv4eP9J9g7NrWru/OZtvSTm9nDBq97Kua+8mmnTpzN37tyKj5s5tnL9RUREpBkoyG8R1cxFz6S8JJ04AEknTkehlBezDQwTXDf1arZNWUY3mcQxwiQJgetWJXc+tw7MYLhq+fjK9RcREZFmoiC/RZQcmE+h2BlkTdNk8bwzOHn8GYaeH+Dk8WdYPH9m3vNml9EIBDDdGAHiYBgVlzf3+FB5HdTr2CIiIiKlUk5+C1m9cgWbtqZyxjvD7qSBeTG6urpYv+66ojqZhkMhZjj9OMkQphunLTS9qDJeOH86hvkczsBgxeXNd/xqHbMexxYREREpheG6bqPL4CmGYezs7u7u3rlzZ6OLUrZ6jv7iOA4337EZo+tS3GQSIxDAHdjFJ25YXbCVu1aj6+Q7frVpdB0RERGphp6eHvr6+vpc1+0pdV/lErSgegaguSk4xaaxZJexFuWtZR0owBcREZFG80y6jmVFlgIfB7qBBUAIOADcB/y9bUf7s7a9Bbh5kkPdYNvRO2pbWsmmNBYRERGR+vJMkA8sBOYD3wIOAQngpcB1wDssK3KpbUcHcvZZBxzNWebdPJsaqEdqSSn5+yIiIiJSOc8E+bYd/T7w/dzllhX5IfCvwCrg9pzVvbYd3VfzwjWBUgPoRkzc1KwBvh4+RERExG88E+QXsD/9+nv5VlpW5AzgpG1HE/UrUv2UG6yXMmOtX2mGWhEREfErzwX5lhVpBzqBdmAZ8On0qvvybP4oMANwLCvyMPBJ247+Z10KWiflBOuFJm5qpXHd9aAjIiIifuW5IB9YA3wu6+d9wDW2HX0oa9nzwD3Aj4HfAUuBDwPfs6zItbYd3TLVSQzDuAq4Ks+qs0+cOFFOuauu3GA9M+LNkBMnYIZIOnE6W2ziJj3oiIiIiJ95McjvBX5DqjX/5cBbgbnZG9h29LO5O1lWZDOwG7jTsiLftO3oUKGTuK67Hdieu9wwjJ6ZM2fOKbfw1VRJsN7sI97UOk9eDzoiIiLiZ56fDMuyIi8Dfg7cYtvRDVNsezNwC/Am247+Vznna7bJsAYGBsaD9Y50sD537typd0xrtk6n9cyTr7TuRERERGqpksmwvNiSP4FtRx+1rMgvgb8ACgb5pFJ7AJqiJb4aKh2espkCfDiVJ28EggwlEzXNk9fQniIiIuJXng/y06YBs4rY7oL065EalqUhvBCkThVMO47D8aEYh44/i5M0MAMuC8OxmufJe6HuRERERErhmSDfsiLzbDt6OM/y1wMXAzvSPweBDtuOnsjZbhHwAeAYqQ65Uif9/f1s+fr2KVNwTNOk/9AB4vPOJRAKEXfi9B86oDx5ERERkRJ5JsgHvmBZkfnAg6TGxm8HeoB3AIPAX6W36wSetqxIL/BrTo2usya97p22HR2pb9FbUya//pE9T0BXN+csWshQMDBpCo7jOMyffxYHjz2CQ5AgCebPP0sj3oiIiIiUyEtB/r8A/wd4N6nRdFxSwf6XgL+37eiB9HYjwL8DrwJWkArsjwJR4Hbbjj5c32J7WzEpNpOt37ytl8GOZTjtL0B4FgeeOcz5554z6VCVpmkyvS0AZywB1wTDYXrbcwrwRURERErkmSDftqP/CvxrEduNkWq1lwpMNcrNVOsz49CbZ7ZhEsdxHZykQTIRKzhUpZt04MRTYITBjUHH9AnHzPdAoY6zIiIiIhN5JsiX+ppqNtip1mePQ79o6Ss4+JufQ2KEzjmLJh2T33EcksEOLui5HDeZxAgEcAZ20d9/mC1f/85pDxT1HG5TRERExEuUByGnKTQbbDHrM1avXEHn4G7CQ09xyXlz2Hjr9axfd92kY9FnHgySThwjECDpxOkIu2z5+ndSw2p2XcrQjIvZtLUXyBpuM2e5iIiISKtTkC+nyQ62gfFgO5NiM9X6jFPzrBmp/xrGlOfOPBi4A7voHNzNte+6mqExTnugiMfjRT1oiIiIiLQipetIXqtXrhifDbYzPRtsKeshK6XnzPwpPflkT1B19OgxNm/r5cknn4DBMzhn0UKCwQCdYZdQKDSeDhQwQySdeMFcfxEREZFWoiBf8urq6uKvP7QayD9Z1FSzxeZL6Rkao+jhME3THH9IWHTJEg7+5ufsP/YYlyw9ldNfzIOGX6mzsYiIiBSiIF9OU0qH1skCzeyOt/FEkgMHD8HAE3x645eL6iCb/ZDQ3nEmF/RYJI/08Tdr14w/JEz1oOFH6mwsIiIixVBug5ymWh1aM/n1B3Y9ACee4uxLrKKPly/vv7PNyPstQKsE+KDOxiIiIlIcBfkyQbEj5xQjk/Jz3uKFXNBj0dZxZknHy+2E20rpOPlU870RERERf1O6jkyQnWZTjQ6tpmnS2WaUdbxWTMcppNrvjYiIiPiXogM5TSkt6LFYrKrHy3AcZ/z/FeCfom83REREpBiGe2owcymCYRg7u7u7u3fu3NnootRcoRb0x/bsYcPGLYy5bbQZY9y4dhXLli0r+3gZ6lhaHH27ISIi4n89PT309fX1ua7bU+q+asmXSRUKIjds3MLovOUYC69gdN5ybtu4paLjZdS6Y2n2NwRepgBfREREClFOfoupRgtwLBZjzG2b0AF0zG0jkUgQDJZ/SxXqWFpp3rm+IRAREZFWoiC/RVQzyA2Hw7QZY4xmdQBtN8YqCvChth1Lx2ffNYuffVdSlBokIiLiPQryW0S1g9wb167itnROfrsxxk3rrq1KOWsxi20tvyHwM337ISIi4l0K8ltALYLcZcuWce+XbicWixEOh6tW1loMm6mhJ8ujbz9ERES8S1FOC8g3e2xHlYLcagb42bID/Exn2Uo6zWroydJo4i0RERFvU0t+i6hFGkyxym2Vz6SLHD8xQn//M8xfeDazOsNlpY1oYq3S6NsPERERb1OQ3yIaEeRWmtOdSRc5+NQPSMxbzqHYScIzFlSUNqIAv3iNfDAUERGRyijIbzH1DHJzc7q/fO+3uPEjf17Uvpl0EcMwiScDjI4lODk2ylP7n2VhOKZOs3Wgbz9ERES8S1GSD1Rrgqfc4xRz3Mm2yc7pHhsb46n9z/LL3xzits98iYGBgSmPm0kXcV2H0ZNDJJMuRsAkHphO/6EDTR3gl/t+NOtEXQrwRUREvEct+R5WrSEOc49z9Zt+n28/8D+THtdxHI4ePVbw3Nk53QefOUw8MJ1g23SGz3hp0ek2q1eu4Mv3fos2Y4yxZx6kfeY8gjGYP/+spmzJL/f90FCVIiIiUm3NFSVJSTLpMEbXpQzNuJhNW3urcpzbNm5haMbFMOdlE4575MgAG+68h5vv2MxfffwOjocvKHju1StX0PHCr0gceYTg8Uc4e+krShqlpaurixs/8ue84uJzufT1/5uLet7AeZe8jlkzpzVdgA/lvx/Veh9FREREMpovUpKiVGuIw9zjGIbJyTg8+cgP2LvrIZ585AccPzFCMpkcD0aZ8zJGQ3M4dPhYwXNngvSXv/gszrvkdbR1nFnW8J1rrvkTOgd3w9FHm3b4y3LfDw1VKSIiIrWgdB2PqtYQh7nHcV2H+MgggUXLCZghEk6c/v4duK47IRgNGg6JhAOuSzKZKHjuNdf8SUWjtNSiA2i1O5OW+35oqEoRERGpBQX5HlatIQ6zj9MRcjj3/AsZSJ7ESRiEAi7zF56DYRjjwagRCLLw/Es4vGcH7nMvTHnuTJBe6ey41QjKa5n/Xu77oaEqRUREpNoM13UbXQZPMQxjZ3d3d/fOnTsbXZRx1WqVzhxnw533pHLEA0HcZILOwd2sX3cde/bs4baNWxhz22gzxvjYh9/LhRdeOOW5m6ljaebaxlvN09dWTeW+HxqqUkRERLL19PTQ19fX57puT6n7KifAB6oVGGaOs3rlilQO/HOPTMiB//YD/8M5l61g6av+mHMuW0Hv/Q8Vde5m6Vianf/uum7N8t/LfT8U4IuIiEi1KF1HTpMvB/60DroExgPkQvnjhTqW1jvv3DRNzOQoe594kiQhAsRZ2jmq/HcRERHxHQX5MqnslmW/dCx1kw6ceArXCIMbg47pDSmHiIiISC0pyJeieb1jqeM4JIMdXNBzOW4yiREI4AzsasqJtUREREQqoSDfBwp12My3rtxRbiZL45kql7wWQ2BmK/a4pmkyPeQw3CTfKoiIiIjUioJ8Dys0ak2+dc8dPcqGrNFxbly7imXLlpV8XtM0yxoxp5QAv5jAvZQyZLb93XCC/r29zJ9/FrNmTtNwlSIiIuJLGkKzRM00hGah4SDzrXvsN08wOm/5+LL2wzu490u3V/3clSglcC+lDLnbdrzwK278yJ9XXF4RERGRWtEQmi2o0Kg1+dYNjiYZddsmLBtz20gkElU9d6WKHW6zlDLk2/ZkPFD1oTOrxXGcRhdBREREPE7pOh411ag1uetmtAdoN8YYzVrWbowRDJZ+C9RqxJxShtsspQzNNsLPZJpp0jARERHxtuaKcqQkmUmr3IFdEyatmmzdjWtX0X54B+6hH9J+eAc3rbu2JufOlWmZnqqFOhOMJ504QCqtpkAwXkoZStm2UZpl0jARERHxPuXkl6iZcvIz6jW6TqnnzrRMHx+K0X/owHhn10It1AMDA+PDbXakh9ucO3du2WWoZNt6chyHm+/YjNF16fgyd2AXn7hhddN94yAiIiL1UUlOvtJ1fKBQ0JpvXbUC/KnOnWmZPnT8WeLzzuXgsUcIL7yMTVt7J+0gW85wm6UE7c0Y4IN3UopERETEGzwT5FtWZCnwcaAbWACEgAPAfcDf23a0P8/2nwZeB4SBPuBm244+WM9yt6pMfr0RCOIkDQKhEA4hjIA5aZ59tmYNxmupWSYNExEREe/zTJAPLATmA98CDgEJ4KXAdcA7LCtyqW1HBwAsK3Ie8OP0NrcDJ4D3AQ9YVuSPbDsabUD5W8p4y3QygRlwiTtxgsRxk45aqCdR60nDREREpHV4Jsi37ej3ge/nLresyA+BfwVWkQroATYAZwI9th3dld7uq8BjwN2WFXmxbUdbtjNCuUFkqftlWqYXhmL0P3OAefMWVLXTa7MGw5WWq5TZhEVERETy8UyQX8D+9OvvAVhWpAN4K7AjE+AD2HZ0yLIiXwY+AVwGPFzncjZcuUM0lrtfpk+3mx4txw2Y6eWVPV8161CT1SpXs16fiIiIeIfnciYsK9JuWZE5lhVZaFmRPwS+lF51X/r1ZUAb8JM8u/80/XpZjYvZlModojF3vy/f+x8l7bf/yPMcj03n1wd+xyNPHuWuf9pWwVU071CTm7f1Mtj5korL1azXJyIiIt7hxZb8NcDnsn7eB1xj29GH0j8vSL8+k2ffzLKzpjqJYRhXAVflWXX2iRMniitpEylloqnJ9hsdfp6De3+BM3aS2+68hzUFWpjHO94aJsODL+AufD0G4LSHeHzff0953mpfR6319x/mkb0HSc6ejRlwOfuseWWVq1mvT0RERLzFi0F+L/AboBN4OanUnOyB1KenX8fy7Duas82kXNfdDmzPXW4YRs/MmTPnlFDeplDuEI3Z+x3c+wsSsy4hlDzJ8IwFBYfCzOw36MTBDGMYJgYOBEww2+p+HbW25evfgeA03NB0EobJ/oOHuGRO6eVq1usTERERb/Fc5GDb0UO2HY3adrTXtqM3A/8H+LRlRdanNzmZfs0XSbbnbNNSyp31dfXKFXS88CjO2ElCyZOcvXD+hBbmQvvNGN5Dh5nAGBmg3RjDjB1n6eKuioLWZpu9NtP6vujFl2Ee2wVHH4WBPq5919VlHa/Zrk9ERES8x4st+RPYdvRRy4r8EvgLUqPqPJtelS8lJ7MsXyqP75U7RGNXVxc3fuT93PaZLzF8xoKiW5gz/Wvndc1m34GHcTtmETTjvOOa9zbkOmol0/rutndwQXcEJz7GjOE9vOhFLyrreM12fSIiIuI9nmvJn8Q0YFb6/39FKlXnNXm2e3X69Rf1KFSzKjdwXHPN20pqYc50IH3upEHbhVcRnncJ51y2gt77Hyq4X7GaKQDObn2fMbynKq3vzXR9IiIi4i2eacm3rMg8244ezrP89cDFwA4YHypzO/AnlhW5xLajj6S36yTVafe3tODwmdVQSgtzdsdbh1Cq9T9hEggEfdmRVK3vIiIi0kw8E+QDX7CsyHzgQVJj47cDPcA7gEHgr7K2XQ+8Efgvy4rcCbxAasbbs4C3tPJEWNVQTBA73oHUdTCJk3DihAIuyWTC1x1JFeCLiIhIM/BSpPUvwDHg3cBG4FPAK0mNk/+ynImvngAuJzUu/keBO4Bh4M22HX2gvsVuXZkUlkWzp9N+eAcLw0fUkVRERESkDoxKZx8FsKzI3wAHbDv6L+mfZwL5Eq+fsu3oiopP2ECGYezs7u7u3rlzZ0POX890kELnisVihMPhoo4zMjLCtGnTStrHy5SyIyIiItXQ09NDX19fn+u6PaXuW3G6jmVFrgZuA96Qc9yLgSOcGpveAK6yrMgf23b0u5Wet9UcOTLA5m29DMcMOsIuqwtMRFXLcz22Zw8bNm5hzG2jzRjjxrWrWLZsWd7jZLY96YSIDx9nyTnnMP9Fs2pa9kaq53skIiIiUkg10nX+DPilbUd/kGfdStuOLkn/Www8CryzCudsOZmRaoyuSxmacTGbtvY25FwbNm5hdN5yjIVXMDpvObdt3DLpcTLbjs15FYlFb+bJg/01L3sj1fM9aiaO4zS6CCIiIpKjGh1vXwl8o8htvwO8qwrnbCmZkWoCZghgwkRU1e7AWuhciUSCMbdtwroxt41EIkEwOPFWisViqW0DIVw3gWGGSJrTwTB8ObpOPd+jZqFvLkRERJpXNaKPBcDTOcviwE5So9pkO5TeXkqQGakm6cQBSDpxOmo0Qk2hc4XDYdqMsQnr2oyx0wJ84NS2yTiGAa4TJ+CcBNetWdkbqZ7vUbNo1W8uREREvKAaEYiRexzbjr5g29HLbDuab9IpowrnbDnZky3VeoSaQue6ce0q2g/vwD30Q9oP7+CmdddOepzMtm1Hf0bw4P2cv2i+r0fXqed71GiFvrkQERGRxqtGus5h4MIit12a3l5KVM/Jlgqda9myZdz7pduLGiknd1u/jzpTz/eo0XU5Pg+CE09NdObEfT3/gYiIiNdUI8j/CfBnlhW50baj8ck2sqxImFQn3XwddKVI9QzsCp2rlKEwM9v6OcDPVsvrbKY8+NUrV7Bpa6osnWHX199ciIiIeE01gvwvkhoxZ5NlRVbnC/QtKxICNpOacfaeKpxTpCVl8uADZoghJ86mrb2sX3ddQ8pSz28uREREpDQVB/m2HX3IsiJfBN4PvMayIl8DHiHV6fYM4FJSs9QuAb5o29F8k2SJtJxSg+NmHcFHAb6IiEjzqUZLPsBfAgPAXwO3ANnT6BqkJsT6JHBrlc4nDVZKgFppTv5U+022vtB+jWx97u/vZ8vXt5eccpOdB28YJq7rKA9eRERE8qpKkG/bURe4xbIidwN/DLyEVCv+C8Bu4Hu2HX2uGueSxiolJ/zUjLdB4sO/Y8m55zJ/zsyig9qpzjXZ+kL7NTKnPXPuR/Y8AV3dnLNoIUPBQEkpN1e/6fe5LWvG4Y99+L15t1MKjYiISGurVks+AOlA/ivVPKY0l1JywsdnvB06QXLWdJ589hfMWHJ50UHtVOeabH2h/RqZ0755Wy+DHctw2l+A8CwOPHOY8889p6SUm28/8D+cc9kKjEAQN5mg9/6HuOiii8bXN1PHXBEREWmcqgT5lhW5APgQcD7wHPBV245Gq3FsaR6l5ISfmvE2iIuBYYZwjRCGESgqqJ3qXJOtj8fjk+7num7Dctoz5TXPbMMkjuM6OEmDZCJWdMpN7jUbecrfTB1zRUREpHEqjmwsK7IM+DmpvPw3AdcAD1hW5JpKjy3NpZRZXU/NeJvAwMV14hhuHNdNFjUT7FTnmmx9KBSadL9Gzkqbfe5FS1+BefSXBI7tonPosaKHnpyq/JqgSkRERDKqEd3cBEwH/i/wUuDtwDPAp6twbGkypczqOj7j7YlfpWa8XTCzpJlgpzrXZOsL7VfrWWkdx5nyetqG93HJeXPYeOv1rF93HXPnzi36+IXK38iHGBEREWkuhuu6U29VgGVFDgH32Xb0uqxl/wv4BrDMtqN7KyticzEMY2d3d3f3zp07G12UhvLD6DpQveEfS8mFr0an2MmOMTAwMD5BVUd6gqpSHiJERESkefT09NDX19fnum5PqftWIye/C/hZzrKfkho680WAr4J8SSklSK10xtup9pts/WTLa9E5tZRc+Go8WEx2DE1QJSIiIlCddJ0gMJKzbCRrnUjTcBxnPCA3ui5laMbFbNraW/ExJ3SINcyicuELpfZUSgG+iIhIa6tWED5Zzk9luUAiVZJpvR8ac3ly3yHOuXQZYbM6I+xkcuGPvfAch554hIRr0h4/ytGjR/N+Q6BhLkVERKTWqhXkb7KsyJfyLP+uZUVymytd247OrNJ5RYoynk5zZggOHWP/wUNccP55JJ14VWaNXb1yBR/5f3fgzOohFDSZP/+Vk6bsaJhLERERqbVqBPk/RC32UsDQ0BCdnZ1519WyE2r2+ux0mkUvvoyDj9gkZ7xAZxtVGWFn9uzZLD73Api7BMMwABgePP0bAsdxGBpzUw8blP9NQqYzs4iIiEg+FQf5th1dXoVyiA99977v8bnNvbihGRjxQdZeu4Irr3wLUJ2UlWKPkUmnGXLiBMwQ4fYOLll2Pn/9odVVy10fP0cygWGG8n5DkCnvk/sOwaFjLHrxZYTbO0r6JuGxPXvYsHELY24bbcYYN65dxbJly6pyDSIiIuIfGkBbauZzm3tJLr4SY9EbSS6+ko2be8fXVaPzaynHyDe+fLU7p041Bn+mvOdc+iaYeS4HH7FLHqt/w8YtjM5bjrHwCkbnLee2jVuqeg3lqGUHYhERESlPxS35lhV5sMRdXNuOvrHS80pzGxoawg3NIBBIpZQEAmHc0AxGR0cJhUKTzsxabIt2odld8x2jHkNLFjpHdnnDJqn+ADNe4G/Wrin6mmOxGGNu24RrHnPbSCQSBIP1H8hKHYhFRESaVzUig+VAHIgVub3y91tAZ2cnRnyQZDJGIBBOvcYHaW9vB5iQPlNO59fcFJxij1GPoSXznSNvedso6ZrD4TBtxhijTpxAIEQyGafdGGtIgA/qQCwitaO5PkQqV43oIEFq4qso8BXgu7YdLTxAuLSEtdeuYGM6Jz8QH2TdmrePr1u9csX4zKyd6ZlZS1WNY9RTprxDY5Td4fcD77maT911Lwmzg6AzzEc++O4alHRqpX6TIiJSDH1DKFI91QjyzwLeA6wCvgUMWFbkq8Bm245qttsWduWVb+HKK9+Sd3SdaqTPeG12V9fNvCaBAK5b+pdaP3z4MV72xneDa4Dh8oOf7ea1r31tdQtahHK/SRERKUTfEIpUT8V/kW07+pxtR//BtqMvBV4DfBu4DthjWZGfWFZkjWVFZlR6HvGuyYbPhOqkz3ghwAe4+8vbeOTJo/z22SEeefIod/3TtpL2z249DwSDE1rPG2GqjsYiIqUo9A2hiJSuqsm8th19GHjYsiIfBt4OvBf4EnCnZUU+YNvRe6t5PhGvcByHx/c9i3PW6zECQZxkgsf3/XdJ6S3N1nrutW9SRKS5NdvvOBGvq8knx7ajo7Yd3QrcDHwf6ADOrcW5RDzDbAMjHQwbZurnEjVj67kCfBGplmb8HSfiVVUflsOyIgs4laN/AfAssIFUp1xpAtVseS1mttmRkRE6OzsrnqW11HIXUzbTNOvSEm2aJkvP6WLv0HGShDCJs3RxV8ktVMW2nqt1XUS8SN8QilRPVYJ8y4qEgKtJpef8IeAA3wHWAQ9otJ3mUM1RC6Y61pEjA9y6YSO/PXAEQh0QH6S9YyYzp4dKnqW11HIXU7bN23o5fmKE/v5nmL/wbGZ1hms+isNfrv7fOaPrvKPsY032x08jU4iIHyjAF6lcxek6lhX5R6Af+AawAPgrYIFtR//MtqP/qQC/eVRjltlij7V5Wy+/PTgAS94CL3oFLP5jRpPtZc3SWmq5iynb0IyLOXjsJKPzlnMoNq/i+ihGpoXqEzesZv2665g7d27Vz5G5NuZeUpdrEhERkeZUjZb864ER4F+AvvQxV1lWZLLtXduO3lmF80oJqjmu+VTHchyHEycTED4DAiEwAhhmGDeYykkvZZbWUstdTNmGYwaGYeIQImCGcBIGgUCwbuO816qFynEcjg/FOHT8WZykgRlwWRiOaex6ERGRFlStnPxpwLvS/6biAgry66yaoxZMdSzTNJk5PQixFyAZBzeJ68QgMQauQ1sJs7SWWu5iytYRdhlyHUziJJw4oYBLMpnw/CgOpmnSf+gA8XnnEgiFiDtx+g8d8PQ1iYiISHmqEeS/vgrHkDqo5gyxUx1r9coVHNy/n98+/b1TOfmdM2k/vIOb1l1b03IXU7ZNW3tZNHs6/f07mHfW2XQOHq/bKA616lDmOA7z55/FwWOP4BAiSJz5889SS76IiEgLMsqZdbOVGYaxs7u7u3vnzp2NLkrZNLpOSn9/P1u+vn28I2ytO6nWo1PshjvvSfVHMExc16FzcHfFs0XWepQLjaIhIiKSX09PD319fX2u6/aUum/Vh9CU5lfNgGqqY5mmOT7jbSUBfjHnKnX7LV/fnpo+/cz6TJ9ej+naq/ltTa0fSjQSkIiISO14Jsi3rMiFwDWkhug8D2gHngT+DfisbUeHs7a9hdREXPncYNvRO2pbWml21eyI3Eznq+YY07V+KKnHQ4+IiEir8kyQD1wL/CWp8fe3AnFS/QH+Fvgzy4q82rajIzn7rAOO5izzbp6NVE29p09vxPkqUeuHkno/ZImIiLQaLwX53wQ22Hb0RNayL1pW5LfAx4DVwF05+/TadnRfnconHlPN1JZSzjc05tLZZjT1dO21fiip90OPiIhIq/FMkG/b0V9MsuobpIL8i/OttKzIGcBJ244malW2VlTtTrDV2C532VTHmj17dl2nT8/0cU+6LmDQ7J3ea/kQ5DhO3R+yREREWolngvwCFqZfj+RZ9ygwA3AsK/Iw8Enbjv5n3UrmQ6V2lix2+0q2c10mLLv6Tb/Ptx/4n0mP1agOn5/5wj+z+3dzcAMhjGScE5//Zz51yw01P2+5qpnfn5Gv7mfPnq3RdURERKrM09+NW1bEBD4OJIBtWaueB+4BPghcDawHzgG+Z1mRVfUtpb9kOksaXZcyNONiNm3trcr2lWyXu+y2jVsKHqvUa6gGx3HY/cRh3Gld0D4Ld1oXv3riMMlksubnrlQ1A/B8da8AX0REpPq83pL/WeDVwI22Hd2bWWjb0c/mbmhZkc3AbuBOy4p807ajQ4UObBjGVcBVeVadfeLEiTyL/a/UzpLFbl/JdkNjqdSXwJmpZYZhMua2EQgE8x6rUR0+HcfBdcZwXQfDCOK6DjhjLdXRVJ1tRURE6sezf1ktK/JJ4HrgHtuObphqe9uOHgO+CJwJvHaq7V3X3e667nW5/4ADM2fOrLD03pTpLJl04gAknTgdBTpLFrt9Jdt1thl0tjG+zHUd2owxkslE3mOVeg3VEg6HOWN6EGOgD557BGOgjzOmBwkGvf6cXbxG1b2IiEgr8uRf1/Q4+DcBXwHeX8Ku+9Kvc6pcpJaxeuUKOgd34w7sonNw95SdJbO373jh0Um3L/a4+bbLXfaxD7+34LFKvYZq+fhfXces8EmmG8PMCp/klhtKuXUnchyniiWrn0bVvYiISKsxmn2Ej1yWFbkZuAX4KvBe244WndRsWZG/JTUST8S2o98v5/yGYezs7u7u3rmztYfbL6Uz5pEjA2za+i1OxgNTdnSt5+g69RpVJ1csFit79l+/zBLbqLoXERHxkp6eHvr6+vpc1+0pdV9PteRbVuTjpAL8rzFJgG9ZkaBlRU7Lp7GsyCLgA8Ax4Mc1LqrvlRKgbd7Wy/AZLy2qo2uxx823Xe6yqY7VqCCz3AAfTnVcZc7L6tZpuBYU4IuIiNSWZxKCLSvyl8CtwAEgCrzLsiLZmxyx7agNdAJPW1akF/g18DtgKbAmve6deWbGlRpRZ8vy5ftm4viJEQ4+9QMcQpjEWTR7uupSRERETuOZIB+4LP16NvDPedb/ALCBEeDfgVcBK0gF9kdJPRjcbtvRh2teUhmnmU1LN1lKjmma9Pc/Q2LecgJmiIQTp79/h+pSRERETuOZIN+2o6uAVUVsN0aq1V5qqJSc6kpnNi32XLm57oXy9jMdV6udNlJMWafaJpOSEzBDDDlxNm3tHZ+Uav7CszkUO4mTMAgFXOYvPEct+UVSPwAREWklngnypTmU0/Gz3JlTiz3XY3v2sGHjFsbcNtqMMd7/nqt56OHH8s6Ke2xwjGcO7ifhBjANuHDxAq5/37sq7rxaTFmL2aZQepNpmszqDBOesQAjEMRNJugcPK4Afwp+6azsZXrAEhGpP0UHUpJKZost9Y98sefasHELo/OWYyy8gtF5y/n0Xfeett/dX97GI08eZc/+33FsbBqDsSDOWa/n8f6TVem8WkxZi9lmqrHkNQRl6Roxw7GkHDkywIY77+HmOzaz4c57GBgYaHSRRERahoJ8D6v3WOmFWpkbda5YLJaa4Ta9nREwSZgdGFkz3g6NuTy+71mc2S+HOZdCVw/J2DAYJo4RZmiMiq6hmLKWUneFAvlTI9666Z+9NQRuvdXznpXT6QFLRKRxlK7jQY1KP6hnJ9pizxUOh2kzxhhNb+cmHYLOMG4ygZHeryPsgtkGARPDiGMYAVwzjJtMEHRjdLZR0TUUU9ZS6q5QetN4vv6ZE/P1JT91/G4cjawlItJY+k3rQY1sHatnukix57px7SraD+/APfRD2g/vYP0H3z1hv/e9++0sPacLM3acacYY5thRgs4QwWd3sHTB9KpcQzFlLbXu8nUabsZW6WaffVcpTo0xVeqZiIjUludmvG20Rs946zgON9+xGaPr0vFl7sAuPnHD6rr+8axnR7pqjK4zMDDApq29DI1BZxu8951vZe7cuU05uk4hG+68Z3zknaQTp3Nwd8Na8r3WoVWdP+sv87nL3CNrrnkbc+fObXSxREQ8o5IZb5Wu4zHNkn5Qz2Cp2HPlziSbvV+5I/yUqphjV3L+SocjrabJhvpsVgrw669enzsRETmdgnwPaqZAz2u8Hmg0S9CkfGsphdc/dyIiXqQg34OaJdCTxqllSlCx52+Gb5REREQkPwX5HqYAX3LVM09e3yiJiIg0LwX5UnON+MZhqnNmd9Ktd/lyOwhXUz3z5L3wjVIzl01EJqfPrkjlFORLzTRi9JWpzvnYnj1s2LiFMbeNoDvConmzCXfOqUv5ss/dZoxx49pVLFu2rGrHb1SefDP+IfbayD8ikqLPrkj1KIFWaqYR4/lPdc4NG7cwOm85xsIrOHHma3jsqcN1K1/2uUfnLee2jVuqenyNS36KZloV8SZ9dkWqp/X++ktdNGLipqnOGYvFGHPbUutdIBAiaU4nmUzUvHwTzp0u25jbRiKRqOp5NPFT804aJiKF6bMrUl1K15GaaMToK1OdMxwO02aMMZpeTzJOwDlJIBCseflyz5104rQbYwSD1f0IeiFPvtY08o+IN+mzK1Jd+uRIzTSiVXmqc964dhXth3fgHvohM5//CRefN69u5cs+d/vhHdy07tqanatVA/wMv3+j4ThOo4sgUhN+/+yK1JPhum6jy+AphmHs7O7u7t65c2eji+IZGl1n8nNLbfntG43+/sNs+fp31ClRfM9vn12RcvX09NDX19fnum5PqfsqXUdqrhG/qKc6Z3aQXe/y6Q9X/filrjMjjjyy9yAEp7HoxZfhtnfUdIhUkUbyy2dXpJEU5IvUiYaGk3Jt3tbLYOdLSM6ejRuazsG9v+CC7khdhkgVERFv0l8GkTrR0HBSjsyII2YwjBlwwTBxCOEkxlp2iFQREZma/jqIZ1S7s2EsFqvJcfPJHhrOdd2ihoZT58rKeakOJytr9vwHZ581DzN2nMDIEWYM7albp0Qv1aOIiKQoXUeaXrXTXDIzz550gsSHf8eSc89l/pyZNU2fMU0TMznK3ieeJEmIAHGWdo7mbYVVWk/lvFSHxZR19coVbNray/ETIwSPPsP8s88BoNYDJ3ipHkVEZCK15EvTq3aaS2bm2bGZLyWx6M08+ewLdUmfcZMOnHgK94Wn4cRT4OZvHVVaT+W8VIfFlDUz/8HvndHOOZetoH3Rq+pyXV6qRxERmUgt+dLUCs2AWE4u8vjMs4EgLgaGGcI1QhhGoKadGB3HIRns4IKey3GTSYxAAGdg12nnq/b1tiIv1WEpZXUch5PxQN2uy0v1KCIip9Nvamlq2fnIAEknXlFnw8zMs8lkAlwH14ljuHFcN1nTTozZ12EEApNeR7WvtxV5qQ5LKWu9r8tL9SgiIqfTb+sW5pXOdNWeAfH977masce3kzzSR/Kp73L27La6zKxY7HVoxsfKeakOSynrqndcVdfr8lI9iojIRJrxtkR+mPHWq53pqjUD4s0bPsfeobk4BDFJcGHHET5x49oqlLA4xV6HZnysnJfqsFBZcz+z733nW5k3b15TlE1ERGqnkhlv1ZLfgrzama4aQYbjOOzdP4ATnoXRNhMnPIvHDxwtOJRltRV7HQqqKuelOixU1tzP7Ff+5Tt1LJm36lFERFIU5LeYQp3pWoYzdmpkG9dJ/SzSpPSZFRGRcmh0nRaT6Uw35MQJmCGSTpzOFupMZ5omFy5ewOP9v8QxwphujKVLFrTM9Yv3tPpnVkREyqO/Ei2o1TvTXf++d3HJeXO48KwZXHLeHK5/38qqHdsrnZnFW1r9MysiIqVTx9sS+aHjbUYrd6bLBOOmaValHkrpzFzrem/l93Uy2e+3l8ViMcLhcKOLISIidVJJx1ul67Qwrwc85cgOxs3kKG4yNUlVpaMMZTpGBswQQ06cTVt7Wb/uuknPXYtRjbw6alItHTkywOc3f4O9+wfAGePCxQu4/n3v8ly96L0VEZFSKV1HWkr2KCV7h+byeP/JikcZKrZjZK1HNfLqqEm1tHlbb2q41Pl/QGLB63i8/6Qn60XvrYiIlEot+dIysoNx13VJEsI1wrhuckJgXmqHxmI6RhZ6EKhGB8paH9+LHMdhaAziY2OM9P8EAmEYeY5jz0/3VL3ovRURkXLoL4S0jEwwnnTiGIZBgDimG8MwAiSdOB0VjFgyVcfI7HMDFZ8vV62P70WmadLZBqNHH8ed+3KY8zLoejmHDz/blPUyWaft7PfWTSb13oqISFHUki8tZfXKFWzamsptXto5Ch3TcQZ20Rl2KxqxpKuri/XrrivY6TX73JWerxHH96L3vvOt9P36c5yMvQCuQ+f0dubPOqepWsEz+fZDY9DZRt58+6vf9PvctnELo26YdiPGxz783gaVVkREvEJBvrSUfMF4NUejKXScYh4EKlHr43vRvHnzWLZkHnuHgji0Y5BgerB5Anwg1TF4aC5JQgQG49y96Rvcuv6DE7b5xre/T2JONwZBEiT4em+UWy+6qEElFhERL1CQLy0pOwguNyAuN5iudQCuAH8iN+nAiafACIMbg47pjS7SOMdx2Lt/AGf+UgzDwHFd9u5/bMI3DcVsIyIiksszQb5lRS4ErgH+EDgPaAeeBP4N+KxtR4dztl8KfBp4HRAG+oCbbTv6YD3LLf6j4Qy9w3FSQ6Re0HM5TiyGGQ7jDOxqrgDZGQPXASOYenXGptzGTYzWv5xSMX3LJiL15JkgH7gW+EvgO8BWIA68Hvhb4M8sK/Jq246OAFhW5Dzgx0ACuB04AbwPeMCyIn9k29FoA8ovPlHMmPjSHEzTJDZ0lMcevZekOZ2Ac5KXnDuvaQJ80zS5cPECHu//JY4RxnRjLF2yYEL5sreJJZKMDT7H9I4OPr3xy3rA9Ag1DIhIIzTHX7rifBNYaNvRlbYd/ZxtR79o29H/Dfwd8DJgdda2G4AzgTfZdnSDbUc/D/wB8Cxwt2VFjDqXvaVNNmqIF8+VGZZxqjHxpzpGrdWzzpuZ4zgcPHwMd8EVGPNfg7vgCg4ePtboYk1w/fvexSXnzeGCBZ1cct4crn/fykm3CTsvMGPxa1jSfaVnx8tvxXtT8xyISCN4piXftqO/mGTVN4CPARcDWFakA3grsMO2o7uy9h+yrMiXgU8AlwEP17TAUtfWq3qcK3OOJ598AgbP4JxFCwkGA6eNid8MZWz1FsNMPQyOJjkxAtPP6iRgBMCYRuLENBKJBMFgc/z6c93Uq2EY6Z/d07bp6urirz+0mr/5uy/wTDzIvoNHMAMuC8Ox5ko9moTjOBw9eqwl703NcyAijeKH3zAL069H0q8vA9qAn+TZ9qfp18tqXSipb+tVPc6VOceiSyw48RT7dz2Qd0z8Zihjq7cYZurBnNeD4cY5OTwEpMaabzPGmibAh6z0rxd1F3zPTNPk8DMHiAemQ9sZxAPT6T90oKkDxSNHBthw5z3cfMdm/urjd3AssLDl7k3NYSEijdI8f+nKYFkRE/g4qdz7benFC9Kvz+TZJbPsrKmObRjGVcBVeVadfeLEiRJL2nrq2XpVj3Nln6O940wu6LFIHunjb9auKeoc9S5jrc7hBbn1sOjCbp7+1f0Mts8k6AzzkQ++u8ElPKWU98xxHObPP4uDxx7BIUSQOPPnn9XU7+/4Q2cgyNjzZ3DoiUe4oDvScvem5rAQkUbwdJAPfBZ4NXCjbUf3ppdlxsfLM0QFoznbTMp13e3A9tzlhmH0zJw5c07pRW0tmdarISdOwAyRdOJFp7U047nynqPNKPocDStjC7YY5tbD8889w+8tfSPnLVkMJPnBz3bz2te+ttHFBEp7z0zTZNbMaYQXXoZhmLiuQ+fg7qZ9f3MfYIJBk7hr4rpJ3KTTUvem5rAQkUbw7G9Yy4p8ErgeuMe2oxuyVp1Mv7bl2a09ZxupodUrV9A5uBt3YFdJaS3Neq5Kz+GFMvpFph6SR/ogMcI5Zy8iYJpldZSutVLes8y2HH206d/f3DSVhfNm0x4/Cs81f9lrRQG+iNSTka+TV7OzrMgtwM3AV4DVth11s9a9htTwmX9n29GbcvazgP8Crrft6N3lnNswjJ3d3d3dO3fuLLf4LaeerVf1OFel5/BCGf3CcRxu/8dN40OeJp04nYO7m3LI01Les1gsRjgcrnGJKjcwMDCeptKRTlOZNWuW7s0a0mdfxF96enro6+vrc123p9R9PZeuY1mRm0kF+F8F1mQH+Gm/IpWq85o8u786/TrZSD1SA/X8g1OPc1V6Di+U0S9M0/RMPnQx75nXRk9Smkr9eO3eEJHa81S6jmVFPg7cAnwNeK9tR0/7zt22o0OkcumXW1bkkqx9O4E1wG/R8JkiLSMTaN76f69l/brrmDt3bqOLVDavjp6kAL/2vHpviEjteKYl37IifwncChwAosC7LCuSvckR247a6f9fD7wR+C/LitwJvEBqxtuzgLfkaf0XEZ/zeqA5PhHbmf4ePanVWv2rcb0aWUtE8vFMkM+pse3PBv45z/ofADaAbUefsKzI5cCngI8CYaAPeLNtR6N1KKuISEkKBXuVTsTmBa2WblLN69XIWiKSjyc73jZSM3W8bbUWr2pyHAdI/XH0Yz368Zr86tTsvC4z2o28wd6GO+9haMbFxEaHOfibn+PGT3Lpi89mzTVvq0v6UT06+mausdk7SFdLta83XydnL6emiUhKS3W8ldZr8aqmI0cG+Pzmb7B3/wDJ+AgBEixatIRZM6f5oh51b3jPZ77wz+z+3RzcQAgjGefE5/+ZT91yw/j67FSMk2NJnjfm4Zz4FT/6WR9XvPIlNQ3kHtuzhw0btzDmttFmjHHj2lUsW7as6udptXSTWlyvOjmLSC7//fZsAepgVb7N23rZOzQXZ/4fMDT7tbyQPJODx076ph51b3iL4zjsfuIw7rQuaJ+FO62LXz1xeMI4/tnjzT+57xmctt/DaJuJc/Yf8am77q1p+TZs3MLovOUYC69gdN5ybtu4pSbnyR1TP+nE6fBxukktr1cBvohk+PM3qI8VagGSwjIdF5OEMDDACILZRsINYgRMz9ej7g3vcRwH1xnDdVPpY66b+jn3PVu9cgXTnn+E5NHdGM89QmD2RRhmiITZQSwWq0nZYrEYY27bhPtpzG0jkUjU5HytNpFbq12viNSf0nU8Rh2symeaJp1tEBiM4+CCmwBnjKABbtLxfD3q3vCecDjMGdODvDDQh2uGMZwYZ0wPEgxO/NXc1dXFTf/3A/zxO96P86LXYZghXCdO0BmuWa58OBymzRhjNOt+ajfGTitbtbRaukmrXa+I1J/++nuQWoDKt3rlCpZ2PofZ/xCdx37MGYHnWTR7um/qUfeG91x/7Z9ijh7BHTmGOXqED63+s0m3Xf/BdxM8eD/uwQcJHryfGz/0npqW7ca1q2g/vAPnwA7aD+/gpnXX1vR80HrpJq12vfWSGVxBpJWpJd+D1AJUvq6uLm5d/0Hfjq6je8N7Hnr4MV72xneDYYLr8IOf7ea1r31t3m0vv/xytl9+OSMjI0ybNq3mZcuMvWYEDAClfknT0+ADIqeoJd/DFMSVzzTN8frzYz368Zr8KLsfRSAQKLofRT0CfDjV8Taw8HU17XgrUi0afEDkFAX5IiIN0syjytS7461URyunqWjwAZGJlK4jItJAq1euGJ/EqDM9iVEzqLTjrVLGTlfLOlGaigYfEMmlIN/D9Ee0Mqq/8nmt7pq5vJl+FENDQ3R2dha1T71y8m9cu4rbNm5h1A3TbsSK6nibCTaHxqCzjZYMNnMVG4BXcp+Op6kEggwlE2za2uvrGYMn06wPzSKNoCDfg9RiUxnVX/m8VndeKO9DP/oRt991Lwmzg6AzzEevv4bLL7+84m2r4al9T/Pcsd+RNDsYdIZ56qknefGLX1xwn89v/gZ7h+aSJERgMM7dm77Bres/WLMyekEmAA+YIYac+GkBeKX3qeM4HB+Kcej4szhJAzPgsjAc8+2MwYVo8AGRU1rr0+8T6lhUGdVf+bxWd14o7+133Uti0ZsxFr2BxKI3F5zFtpRtq+Fzm3tJLr6SwDkWycVXsnFzb8HtHcdh7/4BnPAsaDsDJzyLvfsHWjonupg88UrvU9M06T90gHhgOrSdQTwwnf5DB1ouwM+mAF9EQb7nqGNRZVR/5fNa3XmhvCMjIyTMDox0GQvNYjsyMkI8ML2obathaGgINzSDQCA12VYgEMYNzWB0dLTwjs4YpGfwxXVSP7ewqTpXV+M+dRyH+fPPInj8EdznHiF4/BHmzz+rqe51Eak/pet4jDoWVUb1Vz6v1Z0Xyjtt2jSCzjAJJw4BE5JO3llsM+kcrhMn8exPCcxehhGcVtMZbzs7OzHigySTMQKBcOo1Pkh7e/uk+5imyYWLF/B4/y9xjDCmG2PpkgVNVeeNkMkTHxpz6WwzJuSJV+M+NU2TWTOnEV54GYZh4roOnYO7W77eRVqdfgN4kGY1rYzqr3xeqzsvlPcD7/5jkk99F2f/gySf+i5/8Z63nrZNJp3jgsvegtG5iOTBHXWZ8XbttSsI7LuP5IEogX33sW7N26fc5/r3vYtLzpvDBQs6ueS8OVz/vpU1LaMXuJlZxTDSP48vIBaLVeU+HT/Gc817r4tIfakl34PUsagyqr/yea3uvFDeR/Y+wyve/F5SbS5Jdv1mN3/0R6fWZ6dz/N6sWbxy1iwShx3+7m+uq3lLbU/PZSzf+wwnRhxmTjPp7u6ecp9M/GoYgfTPboGtW8N4x9szT3W8feubfp8NG7cw5rbRZoxx49pVLF26tOz7tNCDhIi0JrXke1izBi1eoforn9fqrlnLO2HGW9PMm4+dL6f7jPZAXVIxMsFpaP4riu4QOh7QvujlTdvZuZ4my7m/7bNfYXTecoyFV4zPJlzJfap6F5FcCvJFRBqk2BlvG5F2VE6HUC90dq63fO/xtKBDjPaqzSaseheRfJSuIyLSQMVM3tOItKNyOoR6obNzI5z2Hr/n7ay76e/Lnk04l+pdRPIxlLdXGsMwdnZ3d3fv3Lmz0UURER9pxn4DAwMD48FpR/oBZO7cuVXfp1Vkv8d79uzhtqyc/JvWXTvlRGOFqN5F/Kmnp4e+vr4+13V7St1XQX6JFOSL3zRjcCn5Neq9isViJQ/VmV1W3WMp+epkZGSEadOm1eQcIuJ9lQT5StcRaVGZsdczLX+rV66gq6ur0cWSPBr1XlVyXtM0dY+lZddDIDGMETA5OZakv/8Z5i88m1md4arVjV8CfD2siFROCXsiLSozGofRdalG42hyjXqvKj2v7rGU7Hp4vP8ke4fmcvDYSUbnLedQbF5L102uI0cG2HDnPdx8x2Y23HkPAwMDjS6SiGcpyBdpQRqNwzsa9V5Vel7dYynZ9eAmkzhGGMc1cQgSMEM4SYNAINiSdZOPHgxFqkdBvkgLKnboRmm8Rr1XlZ5X91hKdj0YgQCmG8M0HEwSJJ04ZsAlmUy0ZN3k0oOhSHW19m8UkRZW7NjrjuPUuWSSqxHj5FfjvI0qd7PJrocL509n6YznWDR7Ou2Hd7AwfKSl6yZb9gOR66Zep4eSLf/wI1Iuja5TIo2uI34zWQc3dZpsPo3qjFjpedWJMiXf6Dqqm4kyQ4uedILEh3/Hueeey7w5M/X7R1pWJaPr6PFYpMVNFmAoN7b5NCoYrPS8CmJTsush8/+qm4m+/cD/cM5lKwi1d9J24VUMBJbo949ImRTki7S4fOk4yo0VkXrL/N4xDJOkEVbHZJEKaZx8kRZVKB0nkxs75MQJmCGSTpxOdQwUkRoa/73jOpjESThxQumOyfr9I1I6fWJEWtRU6TjqNFlfzdzBeWRkpOR9mvl6GiW7TmpVP7FYrCbHrZfM751THZMP6/dPicr5vIo/qSVfpAUVSsfJtJZ1dXWxft116hhYY83cwfmhH/2I2++6l4TZQdAZ5qPXX8Pll19ecJ9mvp5Gya4TMzmKm3RIBjuqWj+P7dnDho1bGHPbaDPGuHHtKpYtW1aF0tdX5vdOf38/W76+naGx1OAgGiRkauV8XsXf1JIv0oJKGcNcAX5tNXMH59vvupfEojdjLHoDiUVv5lN33TvlPs18PY2SXSd7h+byeP/JqtfPho1bGJ23HGPhFYzOW85tG7dU5biNsuXr2xmacTGBF3XrPipSOZ9X8TcF+SItSuk4jdfMHZxHRkZImB0Y6bIZZoiE2VEwHaSZr6dRJsx467okCeEYYVw3WbX6icVijLltE+p9zG0jkUhU4xLqTvdR6cr5vIr/KV1HpEUpHafxmrmD87Rp0wg6wyScOIYZwnXiBJ1hwuHwpPs08/U0Sm6dBIiDG8MwAlWrn3A4TJsxxmhWvbcbYwSD3vwTr/uodOV8XsX/9IkRaXEK8Burmb9R+ej11xA8eD/uwQcJHryfGz/0nin3aebraZTsOlna+RxLF0wneaSvqvVz49pVtB/eQfLgD2g/vIOb1l1bleM2yqp3XEXn4O6q15OfZT6vyQPfL/rzKv7mzcd8ERGfaOZvVC6//HK2X345IyMjTJs2rah9mvl6GiW7To4ePZbK0R9LratWh9LZs+fwkhefz+Coy4x2g1mzZlXluPWW6aR8/MQI/f3PMP+ss6EtrI63RTj//At47StfzgujSc5oD3Deeec1ukjSYGrJFxFpAs0cEBcb4Gdr5utpFNM0xzvhBl708qp2KM0c15zn7Y6qmes4eOwko/OWcyg+z9PXU0+ZugvO61GdCeChlnzLiqwHuoEeYAmw37ajiyfZ9hbg5kkOdYNtR++oRRlFREQmU8zQtc103HrLnvHWIZSa8TYxccZbL11PPfnlHpDq8kyQD9wGHAf6gDOL3GcdcDRn2c4qlklERKQotepQ6peOqprxtnx+uQekurwU5J9n29GnACwrshvoLGKfXtuO7qtpqURERIq0euUKNm1NTYzVGXar1qG0Vsett8x1LJo9nf7+HcxfeA6dg8c9ez315Jd7QKrH8GJnlkyQX0S6zhJSrf8nbTtalQGDDcPY2d3d3b1zp74QEJHWUErH2wx1vD1ddp3EYrGaDG84NDREZ2cxbWDNLXMdfrmPSrmOSq+5VvdWrfnlva62np4e+vr6+lzX7Sl1Xy+15JfjUWAG4FhW5GHgk7Yd/c8Gl0lExBMe+tGPUrNomh0EnWE+ev01XH755QX3yYyOMhwz6Ai7rF65gq6urjqVuDll14mZHMVNOiSDHVWtn3Leq2bkl+vIKOXzUOlnx6ufPa+W2wv8mqz1PHAP8EHgamA9cA7wPcuKrGpcsUREvOP2u+4lsejNGIveQGLRm/nUXfdOuU9mhA+j61KN8JGWXSd7h+byeP/JqtdPOe9VM/LLdWSU8nmo9LPj1c+eV8vtBb5sybft6Gdzl1lWZDOwG7jTsiLftO3oUKFjGIZxFXBVnlVnnzhxoirlFBFpViMjIyTMDoz0aB2GGSJhdhRMBdAIH6fLrhPXdUkSwjXCuG6yavVTznvVjPxyHRmlfB4q/ex49bPn1XJ7RcvUoG1HjwFfJDUyz2un2t513e2u616X+w84MHPmzBqXVkSksaZNm0bQGcZ14gC4TpygM1ww2MqM8JFM75N04nS0+Agf2XViGAYB4phuDMMIVK1+ynmvmpFfriOjlM9DpZ8dr372vFpur2i1WtyXfp3TyEKIiHjBR6+/huDB+3EPPkjw4P3c+KH3TLnP6pUr6BzcjTuwi87B3Rrhg4l1srTzOZYumF71+innvWpGudfx0euvaXSRKlLK56HSz45XP3teLbcX+HJ0nQL7/S3wMSBi29Hvl3Nuja4jIq1Go+tUR3ad1Kp+ynmvmtH+/QfY9h/3+6YzZj1H1/HqZ8+r5a61SkbX8V1LvmVFgpYVOS2fxrIii4APAMeAH9e9YCIiHlVO0Kg/1qfLrpNa1Y8fAnyAbf9xv686Y5byfld6b3j1s+fVcjczz3S8tazIu0mNkAMwFwhbVuSm9M/7bTv6tfT/dwJPW1akF/g18DtgKbAmve6dth0dqVvBRUREpGjqjClSHZ4J8oHVwOtyln0y/foDIBPkjwD/DrwKWEEqsD8KRIHbbTv6cM1LKiIiImXJdMYccuIEzBBJJ06nOmOKlMwzQb5tR5cXud0YqVZ7ERGRppE9FGQrzHhbSY71qndcxZavb+eF0SRntAfUGbMEXh1ytBpqldefOa7X6tYzQb6IiIgXPbZnDxs2bmHMbYP4CQwM3NAZtBlj3Lh2FcuWLav4HN+973t8bnMvbmgGRnyQtdeu4Mor31KF0peukhlMM/vuP/AsTx98BiN8BiH3JFe88iXMnTu3xiX3tuz7rJr3lhfUatbczHH7jxzn6f37CXXMYroZ90zd6rsvERGRGtqwcQuj85ZjLLyCE85Mnp/+MoyFVzA6bzm3bdxSlXN8bnMvycVXYix6I8nFV7Jxc29VjluOSmYwzez79KF+kouvxF34el/MfFsP2fdZNe8tL6jVrLmZ4z55sJ/EojczNudVnqpbBfkiIiI1EovFGHPbUjPeJpNgtoEZxnVdAmaIMbeNRCJR0TmGhoZwQzMIBFJpBIFAGDc0g9HR0WpcQkkKdZotdl/XcXGDnePXkz3zreSXfZ8BVbu3vKCSe66Y4+IaJM3pGGYI14VAwDt1qyBfRESkRsLhMG3GWGrG20AAnDFwYhiGQdKJ02aMEQxWljnb2dmJER8kmUwFwclkDCM+SHt7ezUuoSSVzGCa2dcwDYzE0Pj1eH3m23rIvs+Aqt1bXlCrWXMzx8VwCTgncZ04hgHJpHfqVkG+iIhIDd24dhXth3fgHvohM80TnHnyUdxDP6T98A5uWndtVc6x9toVBPbdh3vw+wT23ce6NW+vynHLUckMppl9lyycT2DffQSe2eHpGXzrKfs+q+a95QW1mjU3c9zzFs0nePB+2o7+zFN168kZbxtJM96KiEg5NLpO6fv6ZQbfevLaCDDV5MfRdTTjrYiISJPLDg5qFSg0S4APlc1gmtlXAX7pWjXAh9rNmps5rtfqVkG+iIiIiIjPKMgXEREREfEZBfkiIiIiIj6jIF9ERKQORkZGCq53HKfic/hlLPnjx48D5deJX+qhHENDQ40uQsNU4zPkJ80/yKeIiIiHPfSjH3H7XfeSMDsIOsN89PpruPzyy8fXHzkywOZtvQzHDDrCLqtXrqCrq6ukczy2Zw8bNm5hzG2jzRjjxrWrWLZsWbUvpebu+fIm/u2+H0F4JsROMO9Fc1h24flF14lf6qEc373ve3xucy9uaAZGfJC1167gyivf0uhi1UU1PkN+pJZ8ERGRGrr9rntJLHozxqI3kFj0Zj51170T1m/e1svQjIsxui5laMbFbNraW/I5Nmzcwui85RgLr2B03nJu27ilOoWvs3+770ew5Co463Ww5CoOHx0sqU78Ug/l+NzmXpKLr8RY9EaSi69k4+beRhepbqrxGfIjBfkiIiI1MjIyQsLswDBDABhmiITZMZ5O4jgOwzGDQHp9wAwxHDNIJpNFnyMWizHmtk04xpjbRiKRqPLV1Nbx48chPBMjkKmrMAQ7SSbdourEL/VQjqGhIdzQDAKB1BCPgUAYNzSD0dHRBpes9qrxGfIrBfkiIiI1Mm3aNILOMK4TB8B14gSd4fHxtk3TpCPskkyvTzpxOsIugUDxf57D4TBtxtiEY7QZYwSD3srInTVrFsRO4CYzdRWDxBCBgFFUnfilHsrR2dmJER8kmUw9PCaTMYz4IO3t7Q0uWe1V4zPkV6oBERGRGvro9dcQPHg/7sEHCR68nxs/9J4J61evXEHn4G7cgV10Du5mzTVvK/kcN65dRfvhHbiHfkj74R3ctO7aahW/rv73lZfD09vhmR/A09uZN3dmSXXil3oox9prVxDYdx/uwe8T2Hcf69a8vdFFqptqfIb8yHBdt9Fl8BTDMHZ2d3d379y5s9FFERERDxkZGSk4g6vjOBXP2BmLxTw3K2c+x48fZ9asWWXXiV/qoRxDQ0NNNfNxPVXjM9Rsenp66Ovr63Ndt6fUfdWSLyIiUgeFAnygKsGJXwLbWbNmAeXXiV/qoRytGuBDdT5DfqIgX0RERETEZxTki4iIiIj4jIJ8ERERaSp+m7G2njOxenXWVy+U2wtlzOb/caVERETEE/w2Y209Z2L16qyvXii3F8qYj1ryRUREpCn4bcbaes7E6tVZX71Qbi+UMR8F+SIiItJwfpuxtp4zsXp11lcvlNsLZZyMgnwRERFpOL/NWFvPmVi9OuurF8rthTJOpvlLKCIiIi3BbzPW1nMmVq/O+uqFcnuhjPloxtsSacZbERGR2vLbjLX1nInVq7O+eqHcjSijZrwVERER3/BTgA/1nYm12QPlyXih3F4oYzYF+SIiIiIiPqMgX0RERETEZxTki4iIiIj4jIJ8ERERn3Acp9FFqAq/XEcjeLXuvFruZubNwWdFRERk3JEjA2ze1stwzKAj7LJ65Qq6uroaXayS+eU6GsGrdefVcnuBWvJFREQ8bvO2XoZmXIzRdSlDMy5m09beRhepLH65jkbwat15tdxeoCBfRETEwxzHYThmEDBDAATMEMMxg2Qy2eCSlcYv19EIXq07r5bbKxTki4iIeJhpmnSEXZJOHICkE6cj7BIIeOtPvF+uoxG8WndeLbdXqBZFREQ8bvXKFXQO7sYd2EXn4G7WXPO2RhepLH65jkbwat15tdxeYLiu2+gyeIphGDu7u7u7d+7c2eiiiIiITOA4judm5czHL9fRCF6tO6+Wu9Z6enro6+vrc123p9R91ZIvIiLiE34JkvxyHY3g1brzarmbmWeG0LSsyHqgG+gBlgD7bTu6uMD2S4FPA68DwkAfcLNtRx+sfWlFRERERBrHSy35twFvAJ4EfldoQ8uKnAf8GHgNcDtwA9AJPGBZkUiNyykiIiIi0lBeCvLPs+3obNuOWsCzU2y7ATgTeJNtRzfYdvTzwB+k97vbsiJGbYsqIiJSGs34WT2qSxEPpevYdvSpYrazrEgH8FZgh21Hd2XtP2RZkS8DnwAuAx6uRTlFRERKoRk/q0d1KXKKl1ryi/UyoA34SZ51P02/Xla/4oiIiExOM35Wj+pS5BTPtOSXYEH69Zk86zLLzprqIIZhXAVclWfV2SdOnCizaCIiIqcUmvFTEwKVRnUpMpEfg/zp6dexPOtGc7aZlOu624HtucsNw+iZOXPmnPKLJyIikpKZ8XPIiRMwQySdOJ2a8bMsqkuRifx4559Mv7blWdees42IiEhDacbP6lFdipzix5b8zMg7+VJyMsvypfKIiIjUXVdXF+vXXacZP6tAdSlyih9b8n9FKlXnNXnWvTr9+ov6FUdERGRqCkqrR3Up4sMg37ajQ6Ry6ZdbVuSSzHLLinQCa4DfouEzRURERMTHPJOuY1mRdwPnpH+cC4QtK3JT+uf9th39Wtbm64E3Av9lWZE7gReA95FK13mLbUfdOhVbRERERKTuPBPkA6uB1+Us+2T69QfAeJBv29EnLCtyOfAp4KNAGOgD3mzb0WgdyioiIiIi0jCeCfJtO7q8xO1/DVxdm9KIiIiUTx1DRaTWPBPki4iIeN2RIwNs3tbLcMygI+yyeuUKurq6Gl0sEfEh33W8FRERaVabt/UyNONijK5LGZpxMZu29ja6SCLiUwryRURE6sBxHIZjBgEzBEDADDEcM0gmkw0umYj4kYJ8ERGROjBNk46wS9KJA5B04nSEXQIB/SkWkerTbxYREZE6Wb1yBZ2Du3EHdtE5uJs117yt0UUSEZ9Sx1sREZE66erqYv266zS6jojUnFryRURE6kwBvojUmoJ8ERERERGfUZAvIiIiIuIzCvJFRERERHxGQb6IiIiIiM8oyBcRERER8RkF+SIiIiIiPqMgX0RERETEZxTki4iIiIj4jIJ8ERERERGfUZAvIiIiIuIzCvJFRERERHxGQb6IiIiIiM8oyBcRERER8RkF+SIiIiIiPmO4rtvoMniKYRjHpk2bNuuiiy5qdFFERERExMd+/etfMzIyctx13dml7qsgv0SGYTwNnAHsSy+aCZxoWIG8S/VWPtVdeVRv5VPdlU91Vx7VW/lUd+VrxrpbDLzguu6SUndUkF8hwzDucV33ukaXw2tUb+VT3ZVH9VY+1V35VHflUb2VT3VXPr/VnXLyRURERER8RkG+iIiIiIjPKMgXEREREfEZBfkiIiIiIj6jIF9ERERExGcU5Fdue6ML4FGqt/Kp7sqjeiuf6q58qrvyqN7Kp7orn6/qTkNoioiIiIj4jFryRURERER8RkG+iIiIiIjPKMgXEREREfEZBfkiIiIiIj6jIF9ERERExGcU5IuIiIiI+Eyw0QVoVpYVmWxs0WHbjnbmbLsU+DTwOiAM9AE323b0wdqWsrEsK7Ie6AZ6gCXAftuOLi6wfdH1ZFmRALAW+HNgMfAc8K/Ax207OlzVC2mAUurOsiK3ADdPcqgbbDt6R872vq07y4pcCFwD/CFwHtAOPAn8G/DZ3OvTPZdSSr3pfpsofQ99nNTndQEQAg4A9wF/b9vR/jzb654rod50z03NsiLTgcdIXe/dth29Pme97rs8CtVbK9x3askv7CHg3Tn/VmdvYFmR84AfA68BbgduADqBBywrEqlraevvNuANpIKF3xXasIx6uhP4DLAH+CCpYORDwPb0B83riq67LOs4/X78Xp7t/Fx315KqhyeBT5C6j/YCfwv82LIi0zIb6p6boOh6y6L7LWUhMB/4FrAe+DBgA9cBOy0r0pXZUPfcBEXXWxbdc5P7BDAn3wrddwVNWm9ZfHvfqSW/sKdsO3rvFNtsAM4Eemw7ugvAsiJfJfXkeLdlRV5s21G/zjh2nm1HnwKwrMhuUr9UJlN0PVlW5CWkPkD/YdvRt2cOYFmRp4F/BN4BbKv61dRXKXWX0Wvb0X2FNmiBuvsmsMG2oyeyln3RsiK/BT5G6iH8rvRy3XOnlFJvGbrfANuOfh/4fu5yy4r8kFQr3ipSgRXonhtXYr1l6J7Lw7Ii3aQekv4a+Ic8m+i+y6OIesvw7X3XlE8ezcSyImHLiuQNwCwr0gG8FdiR+WAB2HZ0CPgycCFwWT3K2QiZIHUqZdTTOwED+GzOof4JOEkq7cDTiq27XJYVOcOyIoUezn1dd7Yd/UVOoJrxjfTrxaB7Llex9Zar1e+3KexPv/4e6J4rwYR6y6V7biLLipikru1+4D/yrNd9l8dU9ZZne1/edwryC/tfpN68QcuKDFhW5HOWFZmZtf5lQBvwkzz7/jT96tsgvwSl1tNlQBJ4OHtD246OArto3Tp9FDgBjFpW5MeWFfmjPNu0at0tTL8eSb/qnitObr1l0/2WxbIi7ZYVmWNZkYWWFflD4EvpVfelX3XP5VFEvWXTPXe6dcCLgesnWa/7Lr+p6i2bb+87BfmTexi4hVSg/3+AB0ndLA9ltewvSL8+k2f/zLKzalhGryi1nhYAR207OjbJ9nMsKxKuYvma3fPAPaS+KryaVH7rOcD3LCuyKmfblqu7dIvNx4EEp74u1T03hUnqDXS/TWYNqY52B4EHSKVHXGPb0YfS63XP5TdVvYHuubwsK7IEuBX4RIF0Et13OYqsN2iB+045+ZOw7eirchZ91bIijwJ/R6p39d8B09Pr8r3po+nX6XnWtZpS62n6JNvmbh+rvGjNz7ajn81dZlmRzcBu4E7Linwz/dUstGbdfRZ4NXCjbUf3ppfpnpvaZzm93nS/Ta4X+A2p/jMvJ5UiMTdrve65/HopXG+65yb3BeBpUp09J6P77nTF1FtL3HdqyS/N35N6A9+S/vlk+rUtz7btOdu0slLr6eQk2062fcux7egx4IukWsVem7WqperOsiKfJPUN2z22Hd2QtUr3XAEF6i0v3W9g29FDth2N2na017ajN5P6hvfTVmo4XNA9l1cR9TbZfi19z1lWJDPk7fttOxovsKnuuywl1FtefrvvFOSXIH3DPMup4ZieTb/mS8nJLMv3FVqrKbWeniX11Ve+D9RZpL4ya6qn5QbZl37NHh6sZerOSo1xfBPwFeD9Oat1z01iinorZF/6tSXvt1y2HX0U+CXwF+lFuueKkKfeCtmXfm2pey59bZ8h1W/hsGVFzresyPmkUkkAZqaXnYnuu3El1lsh+9Kvnr/vFOSXwLIi7aQ6qmU6qf2K1Nc3r8mz+avTr7+oQ9GaXan19HNS9+YrszdM1/+lqE4zLki/ZneabIm6s6zIzaQmMfkqsCbPMLW65/Ioot4Kadn7rYBpwKz0/+ueK152vRXSqvfcNFIpTW8Bfpv1b0d6/TXpn9eg+y5bKfVWiG/uOwX5eVhWZPYkqz5Jqh/Ddhgfomo7sNyyIpdk7d9J6ib6LTk9sVtRGfX0DcAlNb5ttveRynnbWsvyNhPLigRzRnTKLF8EfAA4RmoSlAzf151lRT5OqlP814D32nY0mbuN7rnTFVNvut9OZ1mReZMsfz2poUd/CrrnchVbb7rn8hoG/jTPv8y3H/enf/6O7rsJiq63VrnvDNf16zxN5bOsyJ2knoD/m9Q03J3AlcDrgZ8Br7ft6Eh62/NJfYDipGZDe4HUm/5S4C22HX2g7hdQJ5YVeTenvgb7IKmptDMTTuy37ejXsrYtqZ4sK/I5UjnD3yL11dtFpGaW+xHwhnwBipcUW3fprxWfJtV57dekZsddSuqXdyfwTtuO/lvOsX1bd5YV+UtSkzYdAP4fqSHNsh2x7aid3lb3XFqx9ab77XSWFfkWqZlbHyQ1xns70ENq8puTwPKsCYh0z6UVW2+654pnWZHFpOrqbtuOXp+1XPddAfnqrVXuO42uk98OYBmpDkKzAYfU0/DHgM+kx0UFwLajT1hW5HLgU8BHSQVrfcCbbTsarXO562018LqcZZ9Mv/6AVIshUFY9fZhUXtx1pL56Owp8Dvh4M36QylBs3Y0A/w68ClhB6pfPUSAK3G7b0XzfFH0Y/9ZdZizis4F/zrP+B4ANuudyFFtvut9O9y+k/ha8m1QqgEsqaP0S8Pe2HT2Q2VD33ATF1pvuuQrpvitLS9x3askXEREREfEZ5eSLiIiIiPiMgnwREREREZ9RkC8iIiIi4jMK8kVEREREfEZBvoiIiIiIzyjIFxERERHxGQX5IiIiIiI+oyBfRERERMRnFOSLiIiIiPiMgnwREREREZ9RkC8iIiIi4jMK8kVEREREfEZBvohIk7KsyHLLiriWFfm/BbZxLSvy3XqWS0REmp+CfBERERERn1GQLyIi4ywrMqPRZfASy4qELCvS3uhyiIjkCja6ACIiUl2WFVkB3ABckl70CHC7bUe/nbPdPmAfsA74FPBq4DiwJB24fhR4J7AIiAEHgfttO3pDznEiwF8DrwTagceBz9t29IuTnO8jwB3p7WPAduAG244O5Gw/B7gVeCvwIuAI8B3g47YdPZbe5pz0MW+17egtWfv+F2AB62w7+tms5T8DZth2dFnWsvnAx4G3APOAo8B3gZuyy2RZkVuAm4GLgdXAnwHzgTcCOxARaSIK8kVEmt/0dMA7JcuK/AVwN/Ab4G8BF1gF9FpW5M9tO3pPzi5nAw8C/wb8O9CZXn43cC3wVeBOwAQuAN6Qc77rgC8CPwX+DhgmFVx/wbIi5+U+EAALge+nz/VNoDt9nldYVuQy246eTB93JvBj4HxgM9AHvBz4APAGy4q80rajg7Yd3W9ZkadJBdq3pPcNA5cDyfTyz6aXnwH0pMubKf/ZwE+AMLAJeDJ9zg8Ar7esyCtsO3oi5xq2AiPAP6Trtx8RkSajIF9EpPndmv5XkGVFfg+4nVSg+irbjr6QXv4F4JfAP1hW5F9tO/p81m5LgPfZdvTLOYd7G/Cfth39PwXONx/4R+Drth19V9aqz1tWZCPwEcuKfNG2o09mrTuP01vXHwM+A3yI1DcKkPpm4ALgL207+vmsbXcBd6XX/7/04geB91hWpMO2o8OkvpGYDtwLXG1ZkaBtRxPA60g9rDyYVZ7PASHg5bYdPZR1nn8j9eCyjvTDQ5bngUj6mCIiTUk5+SIize8eUq3j+f5ls4AO4B8zAT5A+v8/R6qVPpKzz3HgK3nOeQJ4iWVFLi5Qrv8FtAGbLCsyJ/sfqRScAKmW9GwvAF/IWfb59PK3ZS17G/AcqWvP9iVS6TTZ2z5IKlD//fTPbwAGgI3ADOCy9PLXk2rd3wHj3xb8MakUoNGc8u8DngD+MM91f1YBvog0O7Xki4g0v9/adjSab4VlTYjZl6RfH8uz6e7067k5y5+07aiTZ/sPA18DfmVZkaeA/yYVuG+37Wgyvc1F6de8ZUt7Uc7PT9l2dCx7gW1Hx9LnyC7bEuAXucG0bUcTlhXZSyrNJyPTMv8G4IH063+TSvH5Xfrnn6RfH7Ht6PH09ktJPYisTv/L56k8yx6fZFsRkaahlnwREf8wytjnZL6F6U66i4F3kwqi3wj0AjvSOe/Z53sPk3/TsDXn0G4Vy54p62Hg16Ry9acDrwIeTD+M/AB4o2VFZgMvY2KqTuac9xYo/3vynDJvnYmINBO15IuI+Ecm9/0lpDq3ZsuMJpOvZTqvdIv3vcC9lhUxSOXL/zVwNamOur9Nb3p0sm8a8jjPsiJh247GMgssK9JGquX+N1nbPQUszcqnz2wbBC7Mcx0PkuosexWpTrSZ6/8+qZF8/ohUUJ8d5D9B6qEjXEL5RUQ8QS35IiL+YZMa3eaD2ePdp///g8BQepuCLCtiWlbkzAkHtqMuqc67ALPSr/8KjAG3WlZkWp7jzEwH8NnOAP4iZ9lfpJf3Zi3rBeYCa3K2fV96+bdylj9I6m/azcCBrM6+D5LqN7AeSAAPZV3TMeA+4E8sK/LqPOU3LCsyN3e5iIgXqCVfRMQnbDv6vGVF/prU8Jc/s6zIlvSqVaSGhfzzPMNB5jMD6LesyHdIBfYDpFraP0Aqx317+nyHLCvyAeDLwK8tK/I1YD+pIPylwApS3yDsyzr2k8DN6Q69O0kNaXktqVb8f8za7nbgT4G7LSvSnS7Hy0nlzu9Nr8/236Q61V4EZK4b247usazI4XQ5fmLb0cGc/T4A/A/wQ8uKfDV9ngCp/gFXkxpC9JYpa0xEpMmoJV9ExEfSw03+CalhHm9O/3seeFueMfInc5LU2PJLSE2q9QVSufnfITU057NZ5/sKcAWp4PjPSY2U80FSk0T9P+BwzrEPkcrvP5dUGs3bSeXtL08Pf5k57glSY91/CbiS1APAlaTGuP/93GDdtqO/A3alf8xOycn+OXc5th09SOpBY2P6Ov4B+CSpUYi2k/q2QkTEcwzXnawPlIiISPVkZry17ejyBhdFRMT31JIvIiIiIuIzCvJFRERERHxGQb6IiIiIiM8oJ19ERERExGfUki8iIiIi4jMK8kVEREREfEZBvoiIiIiIzyjIFxERERHxGQX5IiIiIiI+oyBfRERERMRnFOSLiIiIiPiMgnwREREREZ9RkC8iIiIi4jMK8kVEREREfEZBvoiIiIiIzyjIFxERERHxGQX5IiIiIiI+8/8BIcOqV0GipIoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 260,
       "width": 380
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = DecisionTreeRegressor(max_depth=1)\n",
    "dt.fit(X, y)\n",
    "\n",
    "rtreeviz_univar(dt, X, y,\n",
    "                feature_names='Horsepower',\n",
    "                markersize=5,\n",
    "                mean_linewidth=1,\n",
    "                target_name='MPG',\n",
    "                fontsize=9,\n",
    "                show={})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** What is the MSE between y and predicted $\\hat{y} = \\overline{y}$?\n",
    "\n",
    "Hints: You can use function `mean_squared_error(` $y$,$\\hat{y}$ `)`; create a vector of length $|y|$ with $\\overline{y}$ as elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "<pre>\n",
    "mean_squared_error(y, [np.mean(y)]*len(y)) # about 60.76\n",
    "</pre>\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Where would you split this if you could only split once?  Set the `split` variable to a reasonable value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "The split location that gets most pure subregion might be about split = 200 HP because the region to the right has a relatively flat MPG average.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alter the rtreeviz_univar() call to show the split with arg show={'splits'}**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Solution</summary>\n",
    "<pre>\n",
    "rtreeviz_univar(dt, X, y,\n",
    "                feature_names='Horsepower',\n",
    "                markersize=5,\n",
    "                mean_linewidth=1,\n",
    "                target_name='MPG',\n",
    "                fontsize=9,\n",
    "                show={'splits'})\n",
    "</pre>\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** What are the MSE values for the left, right partitions?\n",
    "\n",
    "Hints: Get the y values whose `X['ENG']` are less than `split` into `lefty` and those greater than or equal to `split` into `righty`.  The split introduces two new children that are leaves until we (possibly) split them; the leaves predict the mean of their samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Ellipsis, Ellipsis)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty = ...;   mleft = ...\n",
    "righty = ...; mright = ...\n",
    "\n",
    "mse_left = ...\n",
    "mse_right = ...\n",
    "\n",
    "mse_left, mse_right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "    Should be (35.68916307096633, 12.770261374699789)<p>\n",
    "<pre>\n",
    "lefty = y[X['ENG']<split]\n",
    "righty = y[X['ENG']>=split]\n",
    "mleft = np.mean(lefty)\n",
    "mright = np.mean(righty)\n",
    "\n",
    "mse_left = mean_squared_error(lefty, [mleft]\\*len(lefty))\n",
    "mse_right = mean_squared_error(righty, [mright]\\*len(righty))\n",
    "</pre>\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Compare the MSE values for overall y and the average of the left, right partition MSEs (which is about 24.2)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "After the split the MSE of the children is much lower than before the split, therefore, it is a worthwhile split.\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Set the split value to 100 and recompare MSE values for y, left, and right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "With split=100, mse_left, mse_right become 33.6 and 41.0. These are still less than the y MSE of 60.7 so worthwhile but not nearly as splitting at 200.\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Effect of deeper trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the sequence of tree depths 1..6 for horsepower vs MPG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 208,
       "width": 1000
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_cars[['ENG']].values\n",
    "y = df_cars['MPG'].values\n",
    "\n",
    "fig, axes = plt.subplots(1,6, figsize=(14,3), sharey=True)\n",
    "for i,ax in enumerate(axes.flatten()):\n",
    "    dt = DecisionTreeRegressor(max_depth=i+1)\n",
    "    dt.fit(X, y)\n",
    "    t = rtreeviz_univar(dt,\n",
    "                        X, y,\n",
    "                        feature_names='Horsepower',\n",
    "                        markersize=5,\n",
    "                        mean_linewidth=1,\n",
    "                        target_name='MPG' if i==0 else None,\n",
    "                        fontsize=9,\n",
    "                        show={'splits'},\n",
    "                        ax=ax)\n",
    "    ax.set_title(f\"Depth {i+1}\", fontsize=9)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Focusing on the orange horizontal lines, what do you notice as more splits appear?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "With depth 1, model is biased due to coarseness of the approximations (just 2 leaf means).  Depth 2 gets much better approximation, so bias is lower. As we add more depth to tree, number of splits increases and these appear to be chasing details of the data, decreasing bias on training set but also hurting generality.\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Consider the MSE for the 4 leaves of a depth 2 tree and 15 leaves of a depth 4 tree.  What happens to the average MSE per leaf?  What happens to the leaf sizes and how is it related to average MSE?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root   0 has 392 samples with MSE = 99.90\n",
      "-----------------------------------------\n",
      "Node   2 has 120 samples with MSE = 30.45\n",
      "Node   3 has 102 samples with MSE = 20.07\n",
      "Node   5 has  72 samples with MSE =  9.23\n",
      "Node   6 has  98 samples with MSE =  6.76\n",
      "Average MSE per record is 17.9\n"
     ]
    }
   ],
   "source": [
    "show_mse_leaves(df_cars[['ENG']], df_cars['MPG'], max_depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root   0 has 392 samples with MSE = 99.90\n",
      "-----------------------------------------\n",
      "Node   4 has   1 samples with MSE =  0.00\n",
      "Node   5 has   3 samples with MSE =  6.18\n",
      "Node   7 has  51 samples with MSE = 29.27\n",
      "Node   8 has  65 samples with MSE = 20.59\n",
      "Node  11 has  68 samples with MSE = 20.26\n",
      "Node  12 has  16 samples with MSE =  9.32\n",
      "Node  14 has  13 samples with MSE = 23.93\n",
      "Node  15 has   5 samples with MSE =  3.21\n",
      "Node  19 has  44 samples with MSE =  2.91\n",
      "Node  20 has  25 samples with MSE =  4.35\n",
      "Node  22 has   2 samples with MSE = 81.00\n",
      "Node  23 has   1 samples with MSE =  0.00\n",
      "Node  26 has  22 samples with MSE =  6.03\n",
      "Node  27 has  47 samples with MSE =  8.26\n",
      "Node  29 has  20 samples with MSE =  3.81\n",
      "Node  30 has   9 samples with MSE =  1.51\n",
      "Average MSE per record is 14.6\n"
     ]
    }
   ],
   "source": [
    "show_mse_leaves(df_cars[['ENG']], df_cars['MPG'], max_depth=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "The average MSE is much lower as we increase depth because that allows the tree to isolate pure/more-similar regions.  This also shrinks leaf size since we are splitting more as the tree deepens.\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the plot of the CYL feature (num cylinders) vs MPG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 172,
       "width": 496
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_cars[['CYL']].values\n",
    "y = df_cars['MPG'].values\n",
    "\n",
    "fig, axes = plt.subplots(1,3, figsize=(7,2.5), sharey=True)\n",
    "depths = [1,2,10]\n",
    "for i,ax in enumerate(axes.flatten()):\n",
    "    dt = DecisionTreeRegressor(max_depth=depths[i])\n",
    "    dt.fit(X, y)\n",
    "    t = rtreeviz_univar(dt,\n",
    "                        X, y,\n",
    "                        feature_names='Horsepower',\n",
    "                        markersize=5,\n",
    "                        mean_linewidth=1,\n",
    "                        target_name='MPG' if i==0 else None,\n",
    "                        fontsize=9,\n",
    "                        show={'splits','title'},\n",
    "                        ax=ax)\n",
    "    ax.set_title(f\"Depth {i+1}\", fontsize=9)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Explain why the graph looks like a bunch of vertical bars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "The x values are integers and will clump together. Since there are many MPG values at each int, you get vertical clumps of data.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Why don't we get many more splits for depth 10 vs depth 2?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "Once each unique x value has a \"bin\", there are no more splits to do.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Why are the orange predictions bars at the levels they are in the plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "Decision tree leaves predict the average y for all samples in a leaf.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "df_wine = pd.DataFrame(data=wine.data, columns=wine.feature_names)\n",
    "df_wine.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "feature_names = list(wine.feature_names)\n",
    "class_names = list(wine.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 141,
       "width": 237
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_wine[['flavanoids']].values\n",
    "y = wine.target\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=1)\n",
    "dt.fit(X, y)\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(4,1.8))\n",
    "ct = ctreeviz_univar(dt, X, y,\n",
    "                     feature_names = 'flavanoids',\n",
    "                     class_names=class_names,\n",
    "                     target_name='Wine',\n",
    "                     nbins=40, gtype='strip',\n",
    "                     fontsize=9,\n",
    "                     show={},\n",
    "                     colors={'scatter_marker_alpha':1, 'scatter_marker_alpha':1},\n",
    "                     ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Where would you split this (vertically) if you could only split once?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "The split location that gets most pure subregion might be about 1.5 because it nicely carves off the left green samples.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alter the code to show the split with arg show={'splits'}**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "<pre>\n",
    "X = df_wine[['flavanoids']].values\n",
    "y = wine.target\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=1)\n",
    "dt.fit(X, y)\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(4,1.8))\n",
    "ct = ctreeviz_univar(dt, X, y,\n",
    "                     feature_names = 'flavanoids',\n",
    "                     class_names=class_names,\n",
    "                     target_name='Wine',\n",
    "                     nbins=40, gtype='strip',\n",
    "                     fontsize=9,\n",
    "                     show={'splits'},\n",
    "                     colors={'scatter_marker_alpha':1, 'scatter_marker_alpha':1},\n",
    "                     ax=ax)\n",
    "plt.show()\n",
    "</pre>\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** For max_depth=2, how many splits will we get?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "3. We get one split for root and then with depth=2, we have 2 children that each get a split.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Where would you split this graph in that many places?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "Once we carve off the leftmost green, we would want to isolate the blue in between 1.3 and 2.3.  The other place to split is not obvious as there is no great choice. (sklearn will add a split point at 1.0)\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alter the code to show max_depth=2**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "<pre>\n",
    "X = df_wine[['flavanoids']].values\n",
    "y = wine.target\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=2)\n",
    "dt.fit(X, y)\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(4,1.8))\n",
    "ct = ctreeviz_univar(dt, X, y,\n",
    "                     feature_names = 'flavanoids',\n",
    "                     class_names=class_names,\n",
    "                     target_name='Wine',\n",
    "                     nbins=40, gtype='strip',\n",
    "                     fontsize=9,\n",
    "                     show={'splits'},\n",
    "                     colors={'scatter_marker_alpha':1, 'scatter_marker_alpha':1},\n",
    "                     ax=ax)\n",
    "plt.show()\n",
    "</pre>\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini impurity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compute the gini impurity for left and right sides for a depth=1 tree that splits flavanoids at 1.3. Here's a function that computes the value:\n",
    "\n",
    "$$\n",
    "Gini({\\bf p}) = \\sum_{i=1}^{k} p_i \\left[ \\sum_{j \\ne i}^k p_j \\right] = \\sum_{i=1}^{k} p_i (1 - p_i) = 1 - \\sum_{i=1}^{k} p_i^2\n",
    "$$\n",
    "\n",
    "where $p_i = \\frac{|y[y==i]|}{|y|}$. Since $\\sum_{j \\ne i}^k p_j$ is the probability of \"not $p_i$\", we can summarize that as just $1-p_i$. The gini value is then computing $p_i$ times \"not $p_i$\" for $k$ classes.  Value $p_i$ is the probability of seeing class $i$ in a list of target values, $y$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini(y):\n",
    "    \"\"\"\n",
    "    Compute gini impurity from y vector of class values (from k unique values).\n",
    "    Result is in range 0..(k-1/k) inclusive; binary range is 0..1/2.\n",
    "    See https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity\"\n",
    "    \"\"\"\n",
    "    _, counts = np.unique(y, return_counts=True)\n",
    "    p = counts / len(y)\n",
    "    return 1 - np.sum( p**2 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Using that function, what is the gini impurity for the overall y target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "gini(y) # about 0.66\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get all y values for rows where `df_wine['flavanoids']`<1.3 into variable `lefty` and `>=` into `righty`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lefty = ...\n",
    "righty = ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "<pre>\n",
    "lefty = y[df_wine['flavanoids']&lt;1.3]\n",
    "righty = y[df_wine['flavanoids']&gt;=1.3]\n",
    "</pre>\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** What are the gini values for left and right partitions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "gini(lefty), gini(righty) # about 0.27, 0.53\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** What can we conclude about the purity of left and right? Also, compare to gini  for all y values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "Left partition is much more pure than right but right is still more pure than original gini(y). We can conclude that the split is worthwhile as the partition would let us give more accurate predictions.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 210,
       "width": 273
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_wine[['alcohol','flavanoids']].values\n",
    "y = wine.target\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=1)\n",
    "dt.fit(X, y)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(4,3))\n",
    "ct = ctreeviz_bivar(dt, X, y,\n",
    "                     feature_names = ['alcohol','flavanoid'], class_names=class_names,\n",
    "                     target_name='iris',\n",
    "                     show={},\n",
    "                     colors={'scatter_marker_alpha':1, 'scatter_marker_alpha':1},\n",
    "                     ax=ax\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Which variable and split point would you choose if you could only split once?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "Because the blue dots are spread vertically, a horizontal split won't be very good. Hence, we should choose variable proline.  The best split will carve off the blue dots, leaving the yellow and green mixed up.  A split at proline=12.7 seems pretty good.\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modify the code to view the splits and compare your answer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q.** Which variable and split points would you choose next for depth=2?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "Once we carve off most of the blue vertically, we should separate the yellow by choosing flavanoid=1.7 to split horizontally. NOTICE, however, that the 2nd split will not be across entire graph since we are splitting the region on the right.  Splitting on the left can be at flavanoid=1 so we isolate the green from blue on left.\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modify the code to view the splits for depth=2 and compare your answer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini\n",
    "\n",
    "Let's examine gini impurity for a different pair of variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 210,
       "width": 280
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_wine[['proline','flavanoids']].values\n",
    "y = wine.target\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=1)\n",
    "dt.fit(X, y)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(4,3))\n",
    "ctreeviz_bivar(dt, X, y,\n",
    "               feature_names = ['proline','flavanoid'],\n",
    "               class_names=class_names,\n",
    "               target_name='iris',\n",
    "               show={'splits'},\n",
    "               colors={'scatter_marker_alpha':1, 'scatter_marker_alpha':1},\n",
    "               ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get all y values for rows where the split var is less than the split value into variable `lefty` and those `>=` into `righty`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lefty = ...\n",
    "righty = ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "<pre>\n",
    "lefty = y[df_wine['proline']&lt;750]\n",
    "righty = y[df_wine['proline']&gt;=750]\n",
    "</pre>\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Print out the gini for y, lefty, righty**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Solution</summary>\n",
    "<pre>\n",
    "gini(y), gini(lefty), gini(righty)\n",
    "</pre>\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a single tree and print out the training accuracy (num correct / total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = DecisionTreeClassifier()\n",
    "t.fit(df_wine, y)\n",
    "accuracy_score(y, t.predict(df_wine))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the feature importance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'rfpimp'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-7f9a2f257245>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mrfpimp\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mI\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimportances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_wine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplot_importances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mI\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rfpimp'"
     ]
    }
   ],
   "source": [
    "from rfpimp import *\n",
    "I = importances(t, df_wine, y)\n",
    "plot_importances(I)"
   ]
  }
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