{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 7 – Ensemble Learning and Random Forests**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This notebook contains all the sample code and solutions to the exercises in chapter 7._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"ensembles\"\n",
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voting classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "heads_proba = 0.51\n",
    "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
    "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure law_of_large_numbers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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bEVJSYl6n+ZXZhiUgKp3IyMaFVyA6hJhJq7J5zqe1TeRPLeP45MAO\nHisysWTXX+hf3nguNw8jHgW9ATa+BDXRxkkZARnrz3peZ43Qa0Lsx5AyQptr71vhihfAcPb3U6EI\nxIUQOE1yRpBS+m+D+Q0j62wJD448CXT0y0UFkNGiNZHz9SRNPsVp4fWiWRUbwST7ma0VLTpF+Qic\nPIfkhM1N7z9qv8Jib/HNKh7cxRusOax/8wYFzqsdtV+B9r1WTDVvxeIoxawP5rXhA8lo3dGnfY+M\nwx5xA3CNTYuZEyu9y2zCbcCMASHcVAs7hYkbeRu403oJAoFEkqMrId4dSZnTe7NWv1t3x7/G3a+M\n4NiOfDL3FHJsRz7tesfR57I2fPbUD1SFeUVJVegpnEYLTmMF0QX9qLQ4CB3QnMofcvzGdOJis+Eo\nsTKcZHc0cl+hjx9R5fc5/DtWz/PtTOQfLEYkBWNr431/049lE1rl5NF3tSSqj1zeySNuMp+5graP\nLPO53pTh7SiptPOBM7DVrDFxs7FvR4Zu15yfJydG80lucYNta7GNbM42gFILm0st3NNAu5SCbEbj\n6zRXVKRZS958882AfR544AE+/PBDigoLcdZTnsNSgvk2o9qnrCAski/61Hwl1Iibe78uJapSWzJy\nM5xQJKE2FzdtqGD2df5LOOO3Wuhw2kF+Mz0FV0SQuHo57w+6vLFb4BE3d2/4EnN4BKFGAysGjmG3\npUbIC4HtEu/W9MbEzcR8N69e04O/7ExnUXWVT11dcQMwOzOX2Zm5ULOR4Kper9GvIo3PeozEbN+P\n8+gBSo5dhDk0j4h+EnfyKPL/exS33U3SkAE48qswDvg7wlhnWdRRDS67FrunOBPevQIqfFOmeIjp\nAJHJMPQBeO/KRu+RD4HEzaiZ4HbC6d2apabzlWAOB7dLc+Ze+Cff9rVCbOf72uueLRCbCrqmLfEq\nFD8nTfa2FEKkosWiaQW+Ue6klHec53n9zHgf9jq39mUnG/h7Dv1Wj3GMG+okiiiXZqR0B+5Q9yr1\nmriB8k7RBMc3LTBd/F97kf+qb7aM7VFev4srRoSyel0lCzLnaOMLHRmX/8un/S1fvE6spZzqNl7/\nkpVFxTQT5YyM7shE28Wsz/4IEXwzQpjoHV3GSpN390mGsYB2jnjeCfIu5dQ6IQfi+sf7odPrSO2f\nSGr/RMb+SfNJevLJJ+k0oj35R4572laFe48tEcdI39mRHle3J6R3PGXLMqk6XoIBPTG3duF/yz6m\nwFKEx+heDVcymDT0DKmJUTSk0MWQwmogGI7A7BNFfD4kGmrSa9x/Oo8gCckuHa8tPUJzt6B95xiK\nHS6s9aw0lUlhdM134nZq/4j/6dSKew+fpNXhck6mhEKN/8uSDRbydaXsbmage3UEl47rwPwFH7Fm\n4EC6dtWCeP+7sxYvqMDu4KLvDjAwJ4MXBvSkTVIr1qXnc1O+z+bFRsmIS+at4cl8syoXiSRNn8M+\ng+9SyrXXXktWVhZbt25l2rRpBAUFMXXqVJgZib3mK8GE5hy/Z9944iruZFyzp1ha+hhbO5hZ2dv3\n8zlqd5VH3NSS0jOOy6dofjPtFh5lUbom+G0GQb9jVow1z96wPCcp7xYDF3P/V3spjcwn2NKZxQOM\njDm4FSkEJqeDZRcN5FR0AtdvW40OcFSUUwoMWPoJb912O5FJyXT6zmtV6R0ezM6KapqbjGy4uBN/\n2H6U4XvLGZflIMau/X2f3rWZx4DHavpIoF9N2IKNqysIdsGnLY0828W7g62txUVmmJ5t4R1ok5FF\nn+Iw3tzWHQHYSSQ/EwzuAxSYBUlWSfbjvhZHU9tIoq/vSPHHRzyJdgEiL19FWIci3PsXI9uPxWAQ\nkNwbV7WkcnseppbhlH2Vgb7tZiKGN0cXEYItoxRnTgnhnSsp3x+MdJuwHS/DmBBCtP0hpM1JSdFE\nmrXain70fci4rh6fOD/0Bug+icKUCdzz4U5m9LbSKetT9BXZiIH3woKbwWWD1y/W2l82G3rcAD+8\nDi37Q8pILY6QvRLmT4bM2lhZAqadAEOQZlE6sgwKjkCf23Bby3DbqzDMHQqmMJj4DpSeQB5YhKWs\nmKJrFzBnYzF7s8ro3DycQe1iScu38MSErlhsTr45mEfb2FDiw4NoFRNCVkkVL6w6Sr820YzqHE9c\nuLlB53rF74OmLlGNA74AdgF9gG1AO7RdThullIGDo/yKqF2iSv3mbbYXrCbiRi2ey/Hii3gsZiaX\nZaznlrav+i1HrZt6A+NnvUVlvNePo7fcygPMYR53sZkhbOpSRPPEq/2u+eWLO8k+WkpoMzOVpZqQ\nMocYuOvFhgVCfWodi0+FCA5E6Hm8h9dJMLashHs+fYtqlwWdqQsLxo7geLI3R9WDbRKJfflxCnLy\nsCa3xRkRja7KQuiJw+iFgevaPMCx8l3sKFrFwNgTtI0MJvquxRz5724Wmr1uVzcOuJGPf/jYc65z\nmpn+xDSfreoA/a9sS79x/o6fCxYs4NChQ2d8r7G5Q2l/tYOUlBQ+/vjjM7YHmF/dEzdGZolghhF4\nqWQI5R4Bc8c3ZSQXu9jW3syKPk0TmqNP2XnmoPbvVxJfTLOr+9PvRB5/P2xl9MlClpi3N9j3xhtv\nJDU1lerqaubMmeNTN8zRmVO6Io4ZCtiQ2ovu2enEWsooN4dQEhpOfHkJu1qlsreltrNv+s48nujt\n9fFZvbaCZg5odkUiIe0rkQkXoZNuREU29rJI8t/cTzPDq+h7jEF3cg2G8k3kue9F6u1I6wgAvrc4\n2Rmp591Rvn5KbfIc9D9qxeiSpOQ5+WObx+Ca1xCx7QiN8Q2CKaXk6JZc2vWOR++yIAoOQ3IfXG4d\nb/51vaddRGwQN8y4mOKcSj58cQXlUQcJd7bEUJKANbiQEEsLdOgQsSUUiiO49b6O2lnNYgmx2+iS\n2QObvhqdy4TRYObmWzo2aO2sZS9OPsdOW7ueW40m9PUejC60vDYCKDXCqEt8t/dff8LOgtb+FqPN\nqyo8VtSmkm5zkW510y7GRDvn+U+RUdU/npQr26Ovdciu4eMtJ3l0kb+fGsCrN/RifO5riO//c17m\ncIAOfMZ4v/KhbGEYW9DhRo+kSIbznPN6vnANw4EBkMQJCz0MOdjRs8uZjB5JmQxCNrDZ9+XJPX1C\nHvwikVILXNmiP1jL4NBXUHgE0tdqFry0lVq7tsNg1CxI6nV2wSzPEntWhceHsT4RY1sTPrwlCH6U\ngPwl+eDsAD6XUj4jhKhAC9B3GvgALXP3iz/lJC8EtQInLLcv2WvaEXnTAgDyiefv4g1uLZ/H2PCv\nfATO1etX8rcF8xj/zzepTPR+qf9VvsBg/R4+cl3DcsbzHpMZcPEqQkPbedpYKx2884AmTv7y5iXs\nXn2S7z4/xnUP9yWhbWCn12+LK5i0J53dg7oSbtCxOK+UsZsLqd6Rz+jhoZSYvb/OjA47jhrHzwfe\nmo5xcFdmd5vsM94rnVsxqXwbb8x8gSqXCVkTBbb2I2sODeWmp1/if3/TknNOarWXlqFllDpupsg1\nkQ+CfCMa1+Xee+8l1BzBqcPFpPZLDNjG7XZjtVp59tlnfcrHVlZScPgwhzp35raERN6oqJ/ho2Ei\ni7pTFrPXcx6kiyI4rx179HoqO0dwd2IlG/dsJtHdjLaueLq6WhJxRVuuzcpmf3NtaS+6wkVxuP8O\npMt2VNLFaOLF7r5CadvKCr+vVicu5gWtb9Kc77zzTj744APs9jNHXGjhisYqHHRwNaerS1sWjf9r\nL6p252PZmM3hcB03D2pYmP0h+yiPH6jAIVN9ygvMgstHhHnOwxwSizHwl9fwU+nMT7+TMlcSQTe8\nSXDXhgW5q8JO4XsHEAYdEZe0IihVi49kP1VB/lt70Zl06K9qh3XFcfSlNpKfGIww6rSdhhllmIIM\nPlZKq1uyt9qFWQiyHG5aR+fwgzGtocujl3pcwkWiuxnj7L3JtkuaXdaGDV9l4nQ0bmUN02nGPV10\nEJNnDeR0WimLX/LOpShMx+vjmpaItsPOUkaUuZigM5No1b5zn7VW01HoaevS0ypSzzeJRk4H6wh1\nSu7KsFOJFQN6zDXCvNQpaWbw/TdxAEagAolTVFEkbLRyR2CsSR0jziLG6RLsLMfBiSBBudUb3iII\nB1eb9xMkvGXj5Bp6iQPkE0MucaTIk6xnEL1JZ7urNaeGzOI/6zOYYXifmw2rKScMEw4OyvZ8JcY0\neU5nw0p7Kq1bt+GHzGJCsFONEVlnT6geF19eHw9HV9KidBv23IMk1KQwfPui+dx0xUicny6i7FBL\n9CE2XFXa90HY4CQir0gB3Y97oANUbsul5Avt82oId9BsqB7zmqs8WqXUcTcWl9deYBDHcctmuPF+\nznQUYdIdxSlbEK7/ghD9akSH0TjHfYCz2IqIDaYqvQzbOi0elqjZnWtKCsPUKhxniQ2dSYepVQTG\nhBB0IUaO5Vfwj8/2Mj7XwWWOpu/mNbeLxJ5lQdqa6OulF+CSIKDl7GG/GIFjAbpLKTOEEMXAMCnl\nfiHERcBSKWWrMwzxi6dW4ACUfTSJyJs0R80iYrhPzOUu+TojWeMjcF586Ql6HT3I1dNfoyzJ62cw\nVb7MlVFO/leSxOfiBt6Tk+jb4x1iYoZ62rw2ZS0OPVQE65j+/AiklFhKbIRH+wd0A8DlpMvGPRRL\nPe+0DWOTPYh3swuZnBjN08mRpOzw3U4dW5RLYYwmLMKtlVQEeR96vcoPsiuiCx93bcElxxdRvWQa\nr6dpu6na9OzD8d3eaMn3f/IVL072rvuPTkzjm1xtZ46+Y3tKdd4/vLCyDlgifR829913H9HR0Vgs\nFl566SXuvvtutm/fTklJCceOBf5V/dhjjyHz8kgfpW0VLo6K4puxjX8pjhgxAtfJeA5v1pxMLeEZ\nVIf6RtKNLL6IsmjfX6gtXTEku2PQuczcOrYdDXHL+u9IPLwcnaEVzTuM5ljzJP6Taub6E3YePKxZ\nb2yuasx6zYL2dtAan/6hx/aCEFza4nYiZAinyGND0BG/60ydeAfOL7L5Lys9ZQMcHSgWFtxCcom5\nJ+4qB+h0JE2/GJ3ZAE47EkHlzkIcWRbCJrSj5bd7/caui05KEqySKj2MzXH6pc0IxHfJLYhq24zo\nJsYRchZbyX3WN/hf6MDmVH7v70P1Y5BI5HUtWPPNWk7Y/KMne3DriC7si97dwN9YDZ0HNefQ5qbP\n8dPBYRxpYeLuFWXEVLjQu0EKmD0xCpc+8APxlg2HaVEQRlFkBeu6h5GW0HAkrLjyElqU5BNnKaNb\nppGQypaIOg9uiRunsYLSmD1+faulkWDh4LQrhlWOFGIQLK6X/s+NG1HzH8D+lEJ+OO0/1tlgFEFU\n6pwYXU4qTUFIIbAaTVSag9nStgvDj+6iSN+ZbTkOruqRxL2XtGfyGxu5TG7HJM68vN+FowRhYycX\nUds60OJbh9SLiNgbRbJuPd0MSymwz65pW4GLOJoZ3sQlm1Hhmhyg95kJvTgRWVmG7ujnhLKYqqBJ\nGPWnCK76ComRfPvzGEU6kjCs7p/0WX7eeZpq1uGg1gNPoO3qnIKZyfgHhz1bWs755QicHOBSKeVB\nIcQB4DEp5ZdCiF7At1JK/3CsvzLqCpzSj/5As5u0rd8lNONe8Q63y7cYxSofgfPSi/+iZ9ohrn38\nVUqSvdsq35Y3kRjZlU/KmvOhuJ258hYG9XiV2JgRnjavTVnLJ0PDSEsykT2ih59J3I+1T5EoxgWs\n2hxWySBL0xNKPpX2bx7r8H9s2XI9rUNCoTid0j8fJPvoYboOv5SNH89j6+LPAXhgwde8cL2/KRk0\nn4WEHm1Jt8cwduxYBg4cyObNm1m1yjeOyMyZM3nmmWew2WwBxwF48MEHKSoqIjo6mrAwzZJwqFNn\nT/2R1FR29/YNtzS2101sXXWYP8+5DL1Rx9y/aVF9B13bns0Lj1GQ2LCFKRCFoZF83ldznjW4nEza\nvha920WoXZt3cokTa8FRRiXdSrghkkpHGT8ULiWzrbYs1OxUEsJUQEmCN8K0OfckxpJ8z+/oq259\nmOCacEVu3PwvyLuMd4W9F0luX4fcpKcHUZB5HEMBhPdMwpT9PXxwjRacLjzRN9bLjBLcWTvZtiuK\nrStO8PR1UUidoHWRg/gSF/0kvN6h8Yf7Xau00ARvj/FaJL9ZayHK4fs9EXtnN4I6RCGlDPirVkpJ\n9iMN57k6G4RRR7Or2mGIDabgLX/hlvz0EERNPCK3w0VpQQmGY9XsX7GdDcYDDOkxiDV7/T8LCbFJ\n5BWeJjQ4nL5dB7Nj3xaat4wlpV0KAwcO5PjxE+j1BpY85RWio6a0p1XHOIKDgyk6bWH/+mxy0sso\nyg68Q8+hh/REI58NadpXpM7twt2EnFhxFSVM/rYYR+h+pJCcjE5g+UUDPfV3bvwKo1v7PqvNVWMx\nBzNy5CW4cvMYdcml/HfJBkqO/eDpIwG3EOjrPRMk0NmZTHdXKwyYyRdFrDbt86l3CR0JrlCKdBWc\njI5n2UV+Aer9MOsE09slkV5lY1lBKXl2J5fFRvBut7YIIXC43BSVl5GBkWt3e38MxRl1PKbL4P9s\nbXzGSyotIMxazUXZ6cRZymiMeHckV9r7+Fm4jCKD3WIb3YIyiWQ3wS5Jgf15nNJXgEokp3UlOHHR\nyh2LQOBGu8+NfZMbRBbxpr/hkG0odl2MyzXJpz4PN89SjQstDksv9BzExTZcnnTOJmA4RtohuIHA\nf896CmlmfAOBjSrXSIL1mylx3IObGAQWat1vZb3+JuMmdvRM5ZP0ajpZttJFppEno8jt9TfuvLQ7\nM77cz5rDmk/gtRjpg4Gl4WB2SExCcCJI8OykzpzMzedYTjGX57xDVPkxomwZ6KOScPW6DWPuTtzD\nZqJvnvKLEThfAsuklHOFEM8CE4H30aIK50spfxqb4wWkrsAp+eg6om7SHvAVhDNFzONW+TZjWc7T\npa9yIErzY6kVOCPf8ElyzkdyIgBrGc07YgrD5Rr+070ncbGXetp8Pmc79/bXPmSHh3SjWSO/it1S\nMnfJS7xl7kaO+cypBj7t0Y5Je9ID1n0QU8aohVdRaggnylknz+lM7xeCdLv58vkn6TlmHG179iEn\n7QgfP/5Ag9e7pVcx8ddMh47aLpj58+dz5Ii/daIhJkyYQK9e/rEiC998i4J/eyPKVgUFs2H4NMqj\nDmKwRxBVHHj7/V/evASA/OwSPnvxOy659f/ZO+/wJo60gf9GkiVb7jYuGNtgTDOml9ACBEiBQHou\nIQ3Se8+lJx8kd8ml3iW5NFJIL5dOCql0SKN3TDfYuFfJtqw23x8rW5ZVLCekz+95/Hg18+7s7Fre\nfdCsqlkAACAASURBVPedt/Tjfx96M9rG1fRH6pyQVoHF5p/5wKHTo5Nuvxt9W4YauzB6fBLPLAqQ\nS8RD1+oCrGUW0nr2omzvbm54cwE6vZ765QeoX+jN61MlLCTJGJ+brfmMLMobC/nsvw/7jHlsRhnf\nHEpFmIaSEtuXGmcG4CbeUEGdswug79CMXhav57mpvn4ymZUOzl9kYewpuWT2SyQ+1YwpSvtO1n9T\nSP03ncv5Ej0qnYYfSonoGk3adcMAKH14Nc4qG+YhKSSc0kuzPgHS6Ua6JDqTHnejg9LH1mFIjiTl\n4kGIIBYQKSWOkgaMGTEB+9tTUVHBU0891bFgALp06ULf5LGsKvjYp33mzJlUV1djs9lISUlh4EDN\nqbq6pIFDu2rJHZaCpbGWp9sU13UJwRcDRnMwyTcf0r+sxRjra9m3bx9jxowhKSmJnvkD+L6+kQs2\n76Njm0b45BfvxaE3MKlAixhsMEayKG8EJQnaS1pqfTWnlUTwTN/gStl9uhiyVpWxZ0gS90QFf3EJ\nRL86Fzvif/3CpsnWOqpifL/3ORWH6GKtZVDRHpx6PQaXi5W9BlHQtXvAMY498C3V7kQcegMbs3oH\nlAG49/s9pDlT2SDt1MRJluTGEVdfxKnlX7JF9GR5XR72Jj2pSVF0sziZd0weqWO6tS7P3vjORj5c\nX8zlE3O54ZjeuCWs3lfNk4t380NhNTLBiDM3FndyJPpCK7paO8be8RyXCNMLvmDAoBGU2U1EOC18\nWhJPQUUzBQfL6S5KaSKS0/TLOdG4GiEliRF2dHZ/Bd3usXRaRAKjx76ExZNbKae5nut3P0m01UJN\n2Tjqm8bSNWo502Of5aHc2WyK7su0vXuIianGVpSP0W0noamBA/ZhmHU12GU07rg6Lnnwgt+NgtMT\niJFSbhJCmIFHgXFoBTRv9JRI+EPjo+C8fhqJ52qWmkaiuES8zjnyZY7nE/5Z9yTbE7RQ1Edff45v\nbF35/GLf0M0WBWcV43laXA/Aun67yeh6OgDvl1az7eFNPDVDW975YXQe3aOCm/yWVNVx1ib/jL6B\nGFK/nS9OOosah5N3Nq5kjsW7hHQodgO6EefDXN9/cvpMA49TdTCqDxXx0g2XA5pvTrd++exd682R\nc1OexyzxfzWg09Hc3My//hW6gPuVV15Jampwhc1RWsqBCy8i4+GHMHRJYffEibiFnkWTHkInIxAB\nSqBNvWwAuUMDj7lp0yaysrJwN0YQHQ2R1h3sf+4CiptHUuQeS4wxkkJ9OeU6zefn5OaRJMoY9Oj8\nlpw64kb3fOJ0bd4i47rBcfdrobnWUqzvPEHt3pNYWfYBFbaDZJhzOWr6LCLTDvHSk8/T7G4MPrgH\nU8K1uJq34Gxa7NNuiByHzrGeY2b9jc9eeqVVVghNoZh1/1giE0wYOuEkKN0SR2kD7gYHVa9tQ9rD\ne+S2ta78HrDb7VRWVrJs2TIKCgo45phj+Pprb8FQs9lMY2PH1z4Yt912GxEREaxZs4bPP//cp+/y\nyy8nPj6eqKgoXFKys8FGH7MJfZgh11JKyhvKOW31Cna3KxkDcJJ8jzv75DN1Tw7VHSzD/dJ8PaIP\n3Q8dRFdTR/SIYTiKijB0zcBZVU/tJ8XY9tXzRB8T72ZHEO2UnFDs4KK9diYcHVipenJNI33r3bzR\nI4Kt8XpWJxt4b4WVrEZJsx5WZZnYMi6VhbUWapw/M/fPb8CJqQnc17sbeiGwuyXf11r59/4ydjb6\n5zL7KSQZ9Cwd3g2zrCImWlPMpJQctNlZWWVhZZ2VBIOOmkOb+ZDOO2RnVzRwICX4KkKG1FKpzJAf\n4kbH10ylaMroX1zBQUoZ8gfNlnU8kNyR7B/5B83SGvAn9sa75LWLbpLfLOopcy+9KagcIJ/Y8pn8\nZlFP+c2injKjd2JQuajpp8q0xetl2uL18u1lK0OOOfCF11tlo6afGlTO0DtPOuYkStlYI2VTbcgx\n5z08R8o5cVLOiZPzrj8xpGwLLpdTDhk8OKjcJcMipKzeJ6WUcs2aNSHHXLNmTeu4l1xySVC5YcOG\ntcodvPrqkGPOHH+DdDZYpGy2ynmXHRn6nDznLufEyWFddUHlZvQfKxdf8ZgsubWrvPWK80KOeckl\nl8gnb/1Q/veyb+TYftODn1NXnZRz4mTDncly/TV9Q445a1Rf2XhnkrTdlSRPHz4wpOwjZ0xv/emW\nGBdyni0crr/TwLQ+suqdAln3TaE8eOvy0N+9efNax5w3b15Y3z0ppRw2bNivek5Dhw6VhYWFcs6c\nOXLOnDkhx5wxY0ar3IwZMw7LOR0/PVZu2HCxXLd+tnz6mW4hx3z6mW6t953jp8cGleue37v1XpK2\neH3IMU+/Yaj8alEv+fGifHn9DV1CylYX1UqbwynLX3hR9jeZgsqdkpQmt/XtJ3eMmCw/OvrykGN+\neP7z8uCty+XBW5fLswefEFSu7T3CYtkRcsxbs7vL9QMGypUjjpB3ZoS+pm+dcYacM2eO/Pt9/5JZ\nXYKf/4ScXHnfDTfJD084Uf5n7LiQY6Y++Yrs+/GysO7l4f6d0q+6SaYtXi+PfvU9GXvjXSFlW8ZL\nX7RGGnrnBZVr+3xKevaNkGMmPftGq2zK9KM7c05rfunneofeglJKp6cKeD+0Apl/SVpWQB3u0M6Y\npyc1sa1M27YRBXSc/NkaLK2vh2KnaA1wTnDU0xREbqB1Jwai4cHuWt6JUMRleCuLBTBPBkKn06PT\nBzctu6SA756G4x8KKvNzSLvrLngyeJjqhBMS0T/kefsoDq8GrPWQCZmYBiWBI3EqbAdJMz1Jeqzk\nb8efw4PPvBZ0rMsuu4y+PfN55fZvwzq22eBgSFJoZ9ax6WUcGLeQbxY0YTB/CgQO4wXNX2rR/GfZ\n8OWnIccs3rGV9+67m8JN6ymqDu2r0EJzc3nLi0BAIrrFkPQ3LTIrbko2PBhU9A+D01lHU9N8xk94\njfS0k2lX1cKHGTNmUF5eiMt1eDP9VlYt7lgIGDniIzIzi9m67caQckZ7IS96LMwF9OWqELLp9Wn0\n6rKQbbtmodUtDs66gmFQAOSAI1tCkMC2hsFGVgybQ+7xd3JoZwF8E1gO4Lt6B8UmzZ/tgDO4JaNx\n31aWvtIHV1bHlpsIlw5HShTu4S4ampvhv8Fle/ReR7/VGzAWChZaLEHlslOSOWXhZwA4baEtLtfV\nP0FqWQ7F6/L4uGQ/64LI6d1upq9czIXCxvYuyVwSRA5gYtW3HLmlmp49NvO13E+AMrF+SBF6iXAc\ny0iVRzCdBdTJA0ETiwJcJx8mQvZHAqv4gYVB5GJtDeSWF9FkNHHkrk2hLv1hI9wlqh/QHItDfB3/\n2LRdouLdq+Bv2nq9BM7jXU7iff7G29xc+TalOhcfPncfO5oNzBlzMeef3I9nmyysGdMfXfUCtu+4\nDYDVjOIxcUuHx35pQA/yYqIY/f123hjUkyme2kjpSwLnIbhl34t8be3PtA0fcf952pOkePlE9APO\ngM1t0vT3P4mxMWeyN6obS0b2pXeki2XLBwMwfNj/SHjM4xN0ayFEhRfu2p4dy77ks6e9X9Ub+61A\n3BPeQzMgDhs4myAq0a9LSsmOPG92536bNiI+vhy2vK8lHdv4lt8+QUnNZ/f8ChxWA5UxUWzPSMYS\nZWLmhfdR/Nk6vq/4FIe7mbP+8QiJdhf7Tz+9ddfcr76kJtlEijmFT/Z8wtScqZj0vkuMT10e+sF0\n8rX96ZaXpmWz/WcqH1Vcx57KDYAbY9xsdPrkgPtd/uRR6A06fvjwHfbufYq47pVMPPYLYuJ8nSC/\n/W4KTU37ARjc/31eue4utG+z73JR+ogK0odr2Yh3vJtDfA8LZeu6AILxZ5/PESedzu7dD1J44Dm6\ndTubfn3/0eGl/SVxuV18vOdjju5+NM2uZox6I9GGaB797nZOyJ1Br5QxGHR6bLZioqK818ThqKGk\n9CO6pp9CRIT3u75m7RnU1a0NdKgOycw8D3NUDgeLXqGpyetTFRGRhMNRTf+8hzCbc7Hby+nS5Rhs\ntoMYjWno9Saam8uxWLaycdPFPmMmPm9g71Q3zjjJgIcM7LnARUNOFHl5j9I3qTcmUxp6fecLYpaU\nfMi27X8HYNiwt9m751Fq67wRbjq7gVFDPsGc3gfL0qVUPPY40WPGYOyeTcyECex57XM2x/9IUp9f\n9xFwYMnfyZ70SKf2Kf7uUvQRjaSPCKOS/E/EUjQUS/EQbDXdsdd3wxRfhNA3A4L0Ea8RmRAimi8A\na9fMoKkpDp3OhdHYhNlcR0xsFcnJBzGb6ykt7UVshBNXQwoVO47FJQV9jvsnQvg/v13NZqTUoYuw\nIV0R7PtyLhExFdTXpuKI2E9ZFzvRdht6nQu3Ww8Isov3EWVppqBfP9JLi8nfshWDw4ndGMnu3rlk\ni50cyM4mc2UlqeUVpBw9icbGA1QnbsLeW5Lwhh59tUDX6Ht/0ZnN9Hj3HXTZ2Wz89FO+37OHSquV\nmaefTt7Agb8bH5xpwAPAHGAt4JOfXkrZca753zltFZyVy87jyIneN/XZvM00PmUmr/P3ytcpQ/DC\nI7dzKLoL/xx1vk8iqb17H2fffq0QoUV04XLmdXjsx/plsaC8liXV2ltCS+2qQArOaakJPFVwP4/+\n7xDm1CbkKSaisHHmj+sxT3/eW73Yw1f9LuLmrPN5oPlMjPhbNSJFPOMmBX6P2FWzC4mkrrkOi93C\n5OzJAeXaRlmN7lLI2AHxiNxJULMfzgxu8fCj2QL/ytS259ZBfQmUbNQypPY+BhoqkZV72XHc7NZd\nEntbcTTqyRrfzlI29FwYcg6k5cPH18C2BdB9HPViEsUPzG8VK06MYWO2fwHMrr36cvKt/4fzhx8p\nvuZav/5tWTD3XK8B9MdzfuT1ba8TZ4wjxZzCpMxJbP+uhCWv7Qj//IFz7hlN2YFyiiya8lmx+ST0\nxgZiM/YzYPgsunU7GyEEZeWfs2XL1T77Tp60CyF0bN9xJ4cOhfap6oiq7QkcXJnOGfddyc79vuef\nnX0JNtshysu1t9ZeubdQWroAa0MBR477FpMpcEHRUKwtW8v5X5zPXaPu4sx+ZwKaQvvilhd5btNz\nvD1DO5+TPjrJb99Ug5s7ugZ+czaZ0mluLg17HunpJ2OxbKWhwWuCGDL4ZTZsPL8TZ/PTSHzOQNSG\n4P44i87uw4W3v4U5woxbuhn++nCcbqefnF7oeWjCQxzbQ4v9kG43NR99xKaBMfT6dDPVz8+naOxQ\n4tZvJcHVhNMscFoNmJwudG6JWyfQuwOnzbMng3WiAdOSWHSNgqT+3dGPzCH2wlM5tGsf5fu64BYO\nXOZGyrbaaCxPZeGAO7gmW4u8cUktFcqWA/ksqYlmdp+txEWFVyQ4FLV7j8RSPISGksH+18NUT5f8\nj0nstay1zdGYSHXBcQi9HVdzDF3yP0G6ddgtGZSunoXBXEPmkU+A1GOI+mkvbKVrz6F2z0RaXirM\nqdtJ6vsVUV12oY/onGN2aHSYTKmkpZ2KOWkSnx5cjc4Rgf7DXGSmhVNnDocVq6m4735cdf7ncrAL\nOPVQlCwYv81fHyi7/EQGzLqWXQ37qasuYerg0/1kXHV1uBsbcRQXEzVsGE6ppUuwyiamvDMFm0v7\n/zToDDjdTo5IP4L5U+f/bhScth6FbXcQgJQygLfnH4y2Cs6K5ecxfoL3wXyOeJ98uYk7uIfbKuZT\njJHP77mUH7sN5L7h5/DRVeMYkqW9Fa7fcD7V1ZrDbXa/Jxhf0LHD1vSUeD6r8H7xnnaUcvLRx5Kx\nzDck9rSFr3LHxRezfuECCr5fRe6MQmK7aQ6ROYWNZI98HNeCS6lKMtK1rLn1BrVn1lz27w++tNPy\nYGyLy+1iyGu+UUrvnfAefZN861i1sPHrhXzzghYtMqPbdvrGeWtsccNWiM8MeQ0Af+fntoy9Br7V\nLEX1ByMpXuUbTt339BJ0BgmXLoP0Qa21cqTbzY7+WkmEmIkTsS5b5rPfwsGBc99MFjpcD99B9DEX\ntLbF3H4j1n95c1o+fKqO1X0DP5BWnbWKOGMcTruLf9z+GqkNWqqo9FlNRG/pzv5tu0ju9wUR0RWU\nrpmFlDoWZ37C308fj3V/iLWQTpDb8yb27H20Q7n+eQ+zbfvNIWVizIOxNoaXG2XI4JdwS4dP1GAg\nCqoLuP+H+ylvLKfIqr3x6pG8NO01Pt/zKT8WvsXpiXYqHDpeqzbi8tQ4c+sh1+TimtTD85AYNvRN\nrA0FdMs4E50udH4Pi60SaT9ErWUruwruam2/91Ak1S4dMTrJecnNRAjoaerYETtxnoHIzYKaEX14\nLX0PRV0ED8/X7kMxd9+C9R/+y71bsgXPT9Xx+HPeJZmnputY1V/g1AuEhGibpE+RZPCeJLqXS1Lq\nG1mdm+E3VijSaq1kV9fTxdKEqVcuhtNPxLZhAz1vvhNDejrotEzIlU2VFNYX0uhoZN6meWysCPw9\nyU/OJz85n0Epg1h0YBFLDi4B4O/Db+DoLl0wyh4kpfRFCB06fQQVFV+zafPlZGddRM+ef0evN3p8\nKxwg9Xz9+tdYSpM48owsCuR2rl5xXeux9C5BvK0rk/acjcVUzVd9XgIh0SNxIcgsi2Lw7nhS6nz/\n3s7u8Zxzwd3sa7Cy8flamuueR+gtJPSwUFcYA1IQ1cVGfA8LSX3qMES1XxbT0zPnBrKzZ1O0o4nt\n3xezLek7yg/UUx1RxtCyKSSaEjEaDeSPz6Sx3k5s/m72brsMvc73+2IpNlO7Jw6XXUfUwGqa7Dos\nJhcDPd/7rftisH+VSWWCnS059dRHO6iNcaCT2nfAaej42f5bsuX8Lb8bBWdiqH4p5bJQ/X8E2io4\nGzccx+Ah3kRrLblvbpT/4q2qq3FbrLz90I0UJ3bj4ok3sP8Bb36ahobdfP/DcQCMHvUlZxVIfqzr\n/BuKSbppbqd03PzsXT6fh1wWvLzByHW1RDW5MLgkiyd0CSoHMG7cKkzGNCqrFtMl+SiE0PPMhmd4\neuPTfrIvHvsiR3Q9wq9dStmaEDA/voypGe3Cp099AQb9Lfgkagrh8UEh59kWS3U3ir7yfnd1EW7S\nhtST8FoJtR98SMkdd5D51JPUvvOun1IDUBkDP+T2bA3Nvu75N/li9hnsEzBpeyH6Nv8WbgEzb/Na\na45e7+bSL7Sb0ccnp/FG30qkJ1JoxE43t7zvvVHtu+0MbhUftH5O1rsZZnYxPcGbK+enEhc3lJEj\n3sPptLBsua8ympNzPT1zrqGp6SB6fTQrVo5ECCPR0bnExuZTUqKlQZgwfi0REQm4XI1UVC4iOWkC\nu3b9k5JS75wbyiLZ/Wl3Bl/kG/pf/H0qg6YOpar2S4KRlDSe7KwLsTuqsVq24XDWU1LyHnFJE7l/\nx3fsbtbT0+Ti2jCUlaYqE6Z4u6bItiMj4xy6pVzNGwfe4f1tz1Dm1HFagp3xsU7erYngO6sBN4Jv\nTnkfk7ueuvr1mM192WfX89j6p9hWtY1bRt7C2Xln+4xrd9nZVLGJC768wO+YbZk7eg6jnv8ey6ef\ntbbJCAPVeV1J3nQQd6Q2Z2kC4QRdg/Z9abjkVEbcdJ/PWNKpWWWEwRNGLyU12zZSdtpZgBZqrpNa\nSWCd5/PmzBQOJYWfjizSbsdm7Ly/0IZetdRnRHChmMbzDR8wcW0yOk8l4tIkG9/3r6bZ5KLf/ji2\n9agnpsnAo6c9y/DMkQC4XS7e+r+bMUaZOeGG25BS8tFD/+BQgX9RXoDp197MZ094UyWk9exNn9Hj\nMMfFU7pnFxu/Dubx4U98ahp15aF9iQ4nOzMt2ExuYhsMpNWYKExrYm83K5nlZqrj7Ex2D8F6sITo\n6sOZBCA0qwZWURNrpzLeTlSznix9Gtf2uZwJ409m+Rsvsf6rT9nctRJrqoHEgb1xFdWgq2hkTdJB\nTNHRWBzBfZHaIjxJgaSAKdlTmNFzBhOzJrK7ZjcZMRkUWYoYkDLg96Hg/BVoq+DU13chro0Fon39\nqa5l5bw59zqa9EZOPeF+HwUHYNFizSowZfIeVtZYOH2Db06ao2o3szRhYKfml1JZwvnvtcnjoZMM\nuST48kf+Dgtb+/nf8AYPeh67o5rt229tbevb514Kdv4fAGlpJzAg/zEe/PFBXt8eeA178+zAjq6N\ndbU8c+m5APQYNATrvg3MzlzhFTjufhgTwK3RYdOUG2sZ9D7OW3clsQdMuBkWBHaFbK7XU74xDmtx\n+D4J5fFw9ZUGxm1KpneRlkNl4ehS/jXzaS766iLy97uZ85bvDefhc8yszvYu743pOobbXmukebWv\n74Yc2Bex2asESCTNeZLqa5zoIntS3XCQBH3Hik2lU/DPEv9zSopM5KXxt1C482YGDHjSx0LS2LiP\n774/GoCxY5YTFfXzau9I6aa+sojasmKWvfomFYX7AEjJ7kHFgf0+slc8/wbmuHjcbgdLlvYLMNov\ng6NsKDk517D09XlYK+oAweBjpjH5oit44McH+HL/l1TbOrd6Pi1nGsd2P5Y7Vt5Bk9PXnT+yWTJ+\nq2RbtiC9RnLre517MC3PF0zY6r3f5n75Bcbu3bFZrTTW15KUkUlV0UHMCQlExWj/u01WC8tff4lt\nyxfRb9xEDm7ZiKX6p8V69Bo5hhNuvA2dJ5Hg+vL1pJvT6RrTlXp7PfXN9dy64lY2VWxiVrcziP76\nAE37D2/W6d8TvUeNJXf4KKLyszFHxVLZVMmc+Vcwdq3XklzcpYlvRpYjA6zX9Y/IZXfDPiLtOtw6\nOHl5Vwzun15lfdYjTxKTmMy6uk2kmdOIM8WRYIindGcBG778FIPJxPYVS1rlswcOYfAx06guOsh3\n77+Fu4NglcNJXEoqY04/m/i0dEp3FZCYmUVKZndeuOYiDCYTzhBJXfuNm4g5Lp7JF1ymFJxfi7YK\njsWSTGys9ybSXsEBrcgmwLSTH/FTcOrrN2E0phAZ2ZUN9Y1MXeu1ZrwhT6Og5GzuzTitU/PbN7Y/\nz1x4MoMvKqBkdRdiMhqJ7dZIUuI4qmtW+cl363Y2xcXegpQJCUcwbOgbCKHD5Wpm6bL+fvu0MGXy\nHm5acj0Ftbt5/8T3eXL9k7y89WUfmTtH3cnMfjNxuZrYt++/dO9+ORERcX5Zj49K3cPw5EPehhOe\ngPhu0OtoqNgJT430PfjdVfDOLCj4DO4qB4PHhFyxE5b8E054HD65TvOp8bB3w2Sad4TwdRk5GMPM\nk3nn/Xt5e6Ie4YbZX3QHYPGwcg6k+z7IDE7JOwXH4Pz4CwD6bdlMtaOOV7a9wpTsKQxOGYyjrIzd\nE48Kekh7dzeVt/r7R7Rg2iqI+UZ70NSd6iLhTT2VtzqxSCMf2Eewvjywg3lbsmKzOKHnCVwx5IoO\nZX8ObpeLfevXkJCXi8EQweLHHmPvutV+cisHVrI7s4EFJy/gw63/YUhz+G/XsbH55PV7kM1frWPD\n4vk015pwO7WHhd7oYuAF3v+hopVp1O6Jw2nruGTE4GOnM2n2JUgdDH3NP5lkQn0EupRYulsTiNtS\nz495NdiMLqTnORXZLJmyUTJ7UcfKjMUUwQ8DcrE7HWTGJDCuSya7lnxDzrnn0f2qqxF6vU/m57Wf\nfcTSV18IPJinLlxnGDjlOMbPnIXBaKJwy0aMkZFk5Q/62fWTKg/sp6KuDJM08OF9c3z6+k85hinn\nX071wQMsnv8sJbs7TvCZ1X8gRrOZPWu0gr29jxjLjOtv9YnQtFRVUrR9C4tefIZxZ57L4GOOp3Dz\nBj7+9/3oDQakWxIVG8vf7r6P2OQUdHo9jfV11JeXkZzdHYFg38a15AwZQXODlY1fLyQiMgpLVQU5\ng4eTM7Tj56rdZWdb1Tb+8f0/GJo6lAsHXMj68vXkxOfQP9l7/3S5XTyy5pHWF8JZeeeRX5xI9ept\nmuLvcjH4mGms+eRDqosPYmvQolatkU5cE3pwz4XP/GJVz11OB9XFRSRnZlO4aT0fPDA3qGxslxTy\nxk2kZPdOSnYXtCopsckpWKoqDvvc/v7OZ0rB+bVoq+BYrYnExHidVjur4LRlu7WJSau1f/rH5WV0\noRJXcxee3fcfvs0zdzivlMoSpi39gIeeep4DO5eyq8jXibhn77lcvPwBrk9zYNYF1+AnjF9PRIS3\niGdV1QrM5u58+92koPt8WWfgoVO8N6w5387hg10fECUkCQbJyORcJkdsBcBgiOfIcauor6hpLc7Z\nwpDuOqaY2y0TZQyF6n1gq/VtnxuGQ5+UsPNLeOvM1n2k2035o49S/aLmQPzi1b246MndlCXANVcY\nQEKSJYJRW5NIq9FC6AceO5XBM//G0e8d3Tr0mX3P5Kx+Z5GbENg3R0pJZeXXJCdPoual1yh/WDOf\nRw4ahG3TJlxxkrIHgltpEubrMZQJjAcDv+n127IZy6LFlNx1FzLGTOMVZ/BG8cecfuwNvPbyjazI\nFz5VhIVbcuu7bpYOElx905tUN1VzZOaRROgCV07vCCkl1S+9TPOe3cRMmEj0MVM489MzKajxfXBd\nN+w6nlz9BOd9GbgMXb3Zwdcjy7FE+yp5/+x6KTHPfckGPRx/83kcLPk3295NwtUc2I2vm0vQf9te\nDG53q0+ZW8CmrFQOJXotlOPjUul1z72tySjbY262k21tJj0phdrLT2T3199i3dtxpMvE7YVE2500\n63XopcSp01EaH822zBQAjpw5C6PTxeL33gg5zvDpJzNixinEJGnRcWs/W8DSV5/v8PgACEFi1244\nmhoxJyRy4o23E5+ajnS7EWEmCfytkW436z7/mKz8QaT26PlbT0fxM2iyWli3cAGrP36fmKRkuvbq\ny45V2v392Muvpd+YCRhMJh+lrbmxEaETVBcXsXnRl+iNEUy54PI/l4IjhEgCXgSOBSqB26WUbwaQ\nmwvcCbS1cw2SUu719A/xjJMHbAcuklJu8PQJtIivlvjLF4DbZAcn2lbBaWhIIDra++ANpeA8uiMj\nLAAAIABJREFUcMsrvHKhv09KCwUNNib+qFkXWjIcA5Rs+BvjLr+HdfUNXLVdSwQdaWvEFumr9Fz6\n+iPEW2u56X+fsr9wHnv2+DodZua/yulfXI5AcMWgi5na4xj2rve1omRknElev/sDzq9lOQ2gxCHo\nGuG9TOUOwVnHeWvAWO1Wxrw1hseyAmd6NZtzGTP6K96ecwvFO3zX1G+69lj4+u6A+wEw8VbIOwHS\nO7F0t22BpigleB+yDY11THl1HA1Rgq5VkrJEcOsEfQ7EMHaLb+j1VS++TWRMDBvKN/BV4VdcOvBS\nEiJDh8uXln3C1q1admqTMY3Ro7/EXVKLMSsLi2U7P672vfZj0z5i/2ln0DTYTUSxwFApyFmwgIaV\nKzD17YfbakEYDNQv/Jz6heFZPB69pAuDVlexMl/HPW94ldr7z9CxIVd74E3MnMhDEx4i0hCJTgR/\nCFqXLydywAAMSZrTdsndd1P77nt+cm9N1PHNEIHF7P+m2bM4mpQaE3kHOvYBiWtspt4c2pH3qFmX\n0HP4SOxLllJ9T+fD0p06gVsIlvXLxmEIP/4h2miiwf7zHJeHHX8Sxqgovn8/vCi2C/4zj6QM3+VE\n6Xbzys1X09zYwMX/fRG9IbzipgrFHwkhxJ9OwXkLzS/uImAI8BkwVkq5tZ3cXKCXlPLcAGMY0dJI\nPQY8DVwG3AT0llLahRCXATcCU9Aivr4GnpBSPhtqbm0VnMbGOMzm+ta+e/kHBcJrkpy4fgdzn7uH\nr4cK/t394ZAWHLeUZCzVogrW9NlGQYH2kK/aPo0zrnqSNXUNzFinhaRe+b/HefpMLRJA53IxdOsP\nTP52IcddcT0DjjraRxlpwZL9KHev8lUc2iogkyftDmr+bImUStC7STFIuhvdzGjn/Dpl8h6cTit2\neyU6nRGdzsSKlcEVuimTNX8jl9PJNy88xZYlWhr8saeezpjtniiHrkPYEb2L4owoJu/LRVz0VbDh\nOs3w14Zjd2v+Mj3ierC/fj8A5y/s7iN3+bzXiE7wz7XTEYH+BjEx/bFafRW6/P7/ITV1Kjqd5shp\n27YNY04OuqjA/kIuawM7R/z8//XHTtLxbf/ACs1/jvoPR3f3WqvK/vUA1a9opRz6bliPMBrZPXkK\nztLQYdXr+0bgkE6O2CnZOzSNd3MrmN1lOkNzxnHg9juoiY5kTc+uIccwOZw0e+qvTdxeyN6UBCri\nopm444BfDbDE884jbto0EOAoKibu+Gmg0yGbmii+5RZ0UWbqP/nE/yB6PfEnncRem4Ufivf6dQ/d\nX0pCYzNNRgNJDd4w88ijJrJz/272mXQ0RAZ2xJ1w7oU4bDa+e097Pzvm0msYNOU4H5mW5aiS3QW8\nead/LbcLH5tHYtef5yulUPxR+d0qOEKIKLRaVLuklIUdyXv2iUZL6ztASrnT0/YaUCylvK2d7FyC\nKzjHAi8BmS1WGSHEAeBSKeUXQohvgZellM95+i4CLpFSjg41P73ZLAf0CXwtCsmhEa9lJbWmnoyK\nIrZmC1yNPRndM3BSthYONNmxulzkRUdSW6fVb5IuPXHxfXGJKNZabJjtNlJqyilM06wRqZUlmOza\nTTerv2bVqKn9wWfcmJh+bKj0L/aYHhVPjKyhzCHo22Wkn4Jjd9nZW7cXi93rET84ZTCF9ftIprbt\nCgiJCSOpqfX3t2ihyilwSkiLkDjckJo0qrVPut1UFh3AZtWO0617BlIvoKmKeqE5f5r0CZhjA4ee\nt0dKyZqyNQAMSxmGPkBW5dWl2lzTo9PJis3C1mBtdZAFzUk2MiaG0DV/2x7TSW3dWvS6KKKiMrE2\nBEnR2obEhFEdygQ8ls2Gu7kZodej81RUd5aVoY+P13IzuFzYtm713clgwDx0KM27duGq1ayOjQNy\n2OdR7FowN0NOWXj/62UJgso4MDmgT2McOpMJZ3l5p85FREbisNsRiQnIhgYc0o3R4aLWY71JS0jC\nUaz5ZokQ9yBjjx4YQtQra8XtxlldjauuDlNOTmuagIBIib2wENnc7JsXRG9AnxCPKddXiXXa7YDE\nYAxteQoXm9WCKTraLzWDQvFXYtmyZb+4ghOW7VMI8TLwo5TyaY8F5UcgH7ALIU6RUn4ecgCNPoCz\nRbnxsBEIFoJ+ghCiGigBnpRSPuNpzwc2tVty2uRp/8Lzu20iho2etkDndSlwKRD0zTrgfj73444f\nGtlR2ltgxQELBo8bjNC7sFi3ERGRSP8IAw1lVtwuwaj4aOoPbcZqD/AAluCQmguGW0KEIXDemNKm\nOjRDGVTbqkmO8lXA2uep6JvUF6PeSO/EvpQ0lFBuLSInOhaXsy6ocpMQPwKH04rN3kiRpQijgES9\nZE3paiQQb4pHIOiV1YvSvRsxxtnZUrufrGinj27R7KolUrp9llFsThsmg8mnujbA9urtJFgjiLTr\nOVS9jbScXhg9fzfpdlPepDnCxRhjyIrNwt7UGEC5CT+UFsDh0B6ALndTq3ITFzsIvT4Km62IJltx\nq2y0uRdGY1LAccJBREaij/QtsWFI05LmtVyJyPx8hEexsx84gDEnB4TA1KcP9v37cZaXE1dqZUD2\nAOotlVRYSulRIdC5AjvJVsfpSKr37avxFOjukzGQSL02H2OPHrhqa3E3NeE4eFCbU1QUsqmdg3Z6\nOsZsTUkP9B/V9uob2y7NSInj0CFNmdHpcNXUoE9IaA2V7hCdDkOXLhi6hE6JoE1cYOzRI7xxAcNP\nCKcORWe/gwqF4qcR7uLuccATnu0T0e5T6cCFwFwgHAUnBqhv11aH7z2vhXeA59AKoIwC3hdC1Eop\n3/KM094Tte047fvrgBghhGjvh+Ox8jwH2hLVv/8d2El3rnsOu/ReK8M5C1dRanqapt46LAVzWXp/\nxxFRG745wKr3dpM2/BUSc1cGkEhkw7w8bvrfpzA3nke3j2/tuel/n7ZGPn1WF8HX9QZ0wNLZSxn4\nSsc+K0tnL6XEWkJlUyX9k/v7JfBbcOoCsmJ9U/1XVS1jw8YLg445ZfIKn89//7Af0+IdbGnS8UKl\n9yHdIKp4OlNTsMo2JJE2xD9s1+4uJq/3LWRnXsz8bS/x+LrHaZQNTI2fyCTnIPr2GU5Gbl+eumim\nz36uCMHeVAtunaTvwTi+GW4jIqUnC6a+T07XPlpEVw/t7T//qKOZesX1HV4rAJermebmEuotmz3+\nNt4EaXFxQxg5QvPJklLicjV4st4K4uOHBB7wV6JtKYuYQSOwLl4MxmxaiwOnJPPVzF6YF/3ImO2S\n1yfr+GK4wNAMLz7uYmcGPHd1T07uNo4ZPWcwKCX8vEQKhULRGX6pyLG2hKvgJAItNuqpwPtSynIh\nxNtozsDhYAXi2rXFAX6Zg6SUbR0avhVCPA6cDrwVxjjt++MAa0dOxp1B75Y4PasjQoSXe2DVe5qz\n7sFlSSQGDtAhqVsWOH3LKcTn1PPd98fR3KwlqBoc5eTrei1C5ot9X7TKjUwf2bo80x4pJSd+dCI2\nl41LBvqWbRuaOpSu0f7+EklJE3w+jxu7AiH0rFw1lm4ZZ/nJXzHuBfZvmc2AKN+k1w9met/wW5Sb\nApuOHY16Bv6YRs+pRRh1sGfPQ3z0fx/y3hFlEAOzFncjd/JyChbtYZv944DnpXdIehfHtH4+eq2m\nzHzwhW/Bwavmv01kdAyhkNKF2+2krGwBBTvn4nb7OpsmJY0nM/M8n9wzQggMhhji4/3Dj38LhBCk\n3nwz5Q8/rCk3bej22GPETT2OPICrtO/EpMKv+GzZ37GY4YzbDeTG5/LZyR/9JnNXKBSKw024i8Cl\nwAAhhB7NmtNScS0GCDcl607AIITo3aZtMLA1iHxb2lYJ3AoMEr7q36A242z1jNvZYwSlrfUGYG9G\nCp4UHQiDhTtW3EGDI7xsxW7HLnZ/Gji0NjMvH5o0JWB2Ty2JXM6xxTQ27sbl0vS3r+ojMBs0f6B5\nm7Q6V8NShzH/uPmc0usUQHOubUu1rbq1Fsjzm72hqU9MeoJXp72KQeev5woh6Nr1dCJNGUyetJvI\nyAxMpjSmTN5Dv37/9JPPTT2ydfvWoZoSdX5y4Irezvd70v3dXtQX+hrvBs7exT159VzwZRZDz99F\nXHZDm/wnXv205/DgTs7tOesfD/soN9u33+637LZv339ZvKQPS5f1Z/uO232UG7O5J1Mm72HokJc7\nLD3weyD5Iq/VLeORR8jbsZ28HduJm+rrACuE4Lgex7F59ubWn4+UcqNQKP5EhGvBmQ/8DzgEuIBF\nnvZRQFjVBKWUDUKID4B7hRAXo0VRnQSMbS8rhDgJWA7UAiOBa4E7PN1LPXO4VgjxLLRWkm95ZX0V\nuFEIsRDtqXgTHN7K7C59hOYsC/TotYpP9q6mb1JfZufPbq0iPf7MPgyapNVfslRryoWUWk4QhzXw\nZS/dvQsaNQUn2dhI3wH+q3cDs07jvPQjuXn5zbQYpV6e+jIAtx1xG0dlHdVaFPOED09gf/1+jnrn\nKL9xNs3a1KGJsH/egyH72xMXN5j6+o10rXycTyZ66yBVRR3J47vWcq/HmuOwenO0bH2jF+aUJnKO\n9fqyDL7YN+dKS0mKT1ak8tic77QxmpuZd/eVzLz5H1QeOoAtTsei2+4FIKNPHod2bmfo1BPo2tub\nWbclAupQyTuMGb0Is7kHe/c9wb59jwc8n+jo3ow6IpzV198XeTuCl/BQKBSKvwphKThSynuFEFuB\nbOBdKWXLq7kT6MxT8Eo0ZakcqAKukFJuFUKMBz6XUra8as/0yJmAIuBBKeUrnrnYhRAno+W3eQAt\nD87JbeY0D+gJtNQTeMHT9pPYu3cYtFtSajRnYPREYvfPiGZZEbik71LV2s/3tyo438xfg7Ppe/Sm\nAQC47IFzc5w25zZ2b7+fnoBu9sdE7ff3gTm25ynohLb/nro9dI/r3qqomCPMPhW/546dy/lfnO83\nhkFn+EXWP4cPe4clSzVrV9sij2eMeYUzxsDjs07A5fBep14jR5M3fhKf/PtfQHH74fy4dOaxrdsR\nJhNXP/QiAF1SMmhsLKT68j1IaSc5OZ1jut1EbGy+N2vsOt8aQ99972+NSU09nujo3mRnXYDBoBxB\nFQqF4o9M2BmkpJR+2e5alI5OjFENnBygfQXaclfLZ38nD1/59cDwIH0SuMXz87PYs2c4h4r7+yk4\nCQ1u6j1LVG6PYmNz2ti30ZvO2uW0Mf+Gy5l25Q3sXfMEyEYQmuXCZQ+8Mvjd99oyT+GELuTU+yd9\nu+FgFB8PT0YvvApSYX3wKP3haf6XaFTXUVw39LoA0j8fXYClrjGjv2ndvvblBRRuWg9C8P79/8fg\nY44nvVcfAIq/PJ5T776aFYtPxBijWbqOmriVFSuPwOXSlv9Kil+nX++7W4/jcNTS3FzODz9O8zlm\nVdUSqqq0mi0Txq9Fr4+i1hNiP2TwfD/n6fi4oYwY4Z/cTqFQKBR/XIIqOEKIWeEOIqV89fBM5/dF\naUmfgO1CSlweHWNFsRZN9MzGZxid7k34Z28spKmmiBVvvaIpN4CzSUtnLV0duz7tr/6wdXtxvYFP\n6iKQCBIjE4k3BQ4PD0W3mG68c8I7xBnb+2cfXiZP2s3iJb0AyMq6ALM5p7VP6HT0GKIpXS0FGkGL\nEmth4tFf8d33k+nX95/o9ZFMnLCe2tq1rN9wLlK6WLlqDBPGaz40y1cE1HF9aCuTlDSe5OSJTJm8\nhyVL83G7bSQljWfokJd/9nkrFAqF4vdFKAvOU+0+G4EIoCVMRofmYNyM5vfyp8MdpDJst2oXFQGS\n4LYN1LLVaW5KJbv8i89Nu/omShwXe/aBg0u7kj3Jt2pvl5QpVFRoGX6/qNeUG4BYo7Z0kpeUx/bq\n7UzvGTyLMsC8o+fx1o63eGLyE79KWJ4QgnFjV7Bz1310z740qFyLcuPXbu7OhPHrW5eIhNCTmHgE\nR477nhUrR+JwVON2N7Pq26P89h086AVstmISEkfhcjawZq1v+H5+/0datycd9bP8zhUKhULxOyeo\ngiOlbHVCEEJMR8t3cz3Qkk53FPBvoPPFYv4otI3d8nDT+0WYnWbWBEheLD2qn9O2DunWUv44A9S2\n6ZLVnTjDPSx787+Urk0BpJ+C06LcvFllxC69k2hJiPfW9LeoaKogzZwW8hTGdhvL2G5+fty/KJGR\nGQwa2F4/Dp+2RUFbaJtAb+myIbS4XEVH92bokFcxmfyz3cbGDsRi2YxOF0X37IsxGsNIAqdQKBSK\nPwXh+uA8AlwopfyuTdsqIcT1wMvApwH3+oOjszXijoohocFCbbSm70U1O0EPsfZMymUJQsKlP/wH\nZ2wTjolaxLyzaWnIcd0uF5k9zqWpaClaBH5wy0qFU+ubO2aujyOzXqcnPTr955zeH44RIz5gzZpT\nW5Wb2NiBHDEyeGhzqD6FQqFQ/LkJV8HpAQRK9NKIFln1J0VTLqZv/pY3Rmt5RAwN63HFTeTofbcw\npqgOS2SV1m6JYt6GeYxwT+pw1NQePQGt2N6e1T+QO2IUS5Zp/j7DNtayc/RwrE1a0coGtzaH0/p0\nnC35z0583GCfz0qBUSgUCkUwwk309wPwhBCitXiMZ/s/wPe/xMR+F3h8VmKbvdl4G1xrW7ejHfEk\nNXrT+AsELnvgHCQRJm9lHp2nlpBOp6f3qLHo9HoiIrQ1L6NdYjJ5LTPlTlWQry2TJ+2mf95DjBu3\n6reeikKhUCh+x4T79LwISAb2CyH2CyH2A/uBVLyJ9v7U/G3NYv726ct+7UXxXidisz0+6PLUcVeG\nrmhhNGoKjsHlRuo0BWhBbUSoXf6SaBmWTyPS9NdanlMoFApF5wg30d8eIcQg4BigJTXsduCbw1nj\n6feGO9Lcup3cUE/MIW9larerCrd9Jz2rx7S29S8fiy2IQSs9t2fIYw3eWEm5rgGjXVJYf4BYoNwh\nSIpM4srBV/68E1EoFAqF4i9GZxL9SeArz89fE48uJ6UDe72W41BnzEOnTwDA5TgQdFdzXHTIoaMO\n7aS7Z/uxA6WckKBnZ7OedbOW/fx5KxQKhULxFyNsBUcIkQhMQ3MqNrbtk1Lee5jn9bumuTZwaSu3\nfWfrtjF2Jo6GL5DuWoxxs8Dg5n9TDuIWcJndSowxhq2VW4nQR9DHnMEWo5GLu6ayvLCIKpeOl6tM\nDEkZ8mudkkKhUCgUfyrCUnCEEKOBz9CS+qWgFQ7q6vm8H/hrKDgdrMa57Jtat4UuEWgpqSAobSil\nyaQlyrHYLcQYY5j52UwATs6cxEfdNJ+S/4w9F0qWA/DIRG9iOoVCoVAoFOETrgXnYeAN4DqgHpiM\nFjb+FvDiLzO13x+CQAqOO0AbCF0UETEn4LKt48PBL2HaX9Pat3DfQnrGe31yPipa0rr9uke5AUiL\nDp3ET6FQKBQKRWDCjaIaBDzp8cNxASYpZRlwK1qG478u0oWjcTkuh3/RS50+iYjoo6mOLmFnjXf5\n6rF1j7G5crOffFuOzzn+sE9VoVAoFIq/CuEqOPY222XQ6g9rBTL8xf86uOzbcDWvwWH1Flv/YEKx\nr4zOyZf7v/Rp6ygL8az+Ydc6VSgUCoVC0Y5wFZx1wEjP9lLgn0KI2cATwKZgO/2R2b3riLDkXM1r\n/drqo53MH3krDRG1fJr3dMD99EIfsL0Fgy5s/2+FQqFQKBTtCPcpeifQUnzzLrTq4f8FdgIX/ALz\n+s2pq/cv3hg2AuwGG6+NmBNUZO53c0MO0Sexz08/vkKhUCgUf3HCsuBIKddIKZd4tiuklNOklHFS\nyhFSytDOJH9UAvgTm6JD57IJxG1H3Obz+URn4OzE7xV7q4nfMPwGhAhegFOhUCgUCkVoOlXoSAgx\nQghxphAi2vM5WgjxJ11L8Vcweh8xruO9DL5LT5sqfFfwsiwVPp+TXFqF8HiXG4PnUk7K6rhgp0Kh\nUCgUiuCEpeAIIdKEEN8DPwJvAi3xy/8GHv2F5va7Q8rAIeFtOekO32Wp5Kjk1u1BtmY2mUw+/Uc2\naoU8o7NGMzRtKACJpsSfO1WFQqFQKP7ShGt9+Q9a9FQy0LYewbtovjh/OqT0teAcO2UK1u0bOtwv\nJirO5/Pw1OFER0Tz7MZnea2kjAMGAyvM3sricyqruWTARcQedRd31u7hx9IfSYhMODwnoVAoFArF\nX5Rwl6imAHdKKWvate9BK90QFkKIJCHEh0KIBiFEoRDi7A7kjUKI7UKIojZt44UQ1nY/Ughxmqf/\nfCGEq13/UeHOsT16p5Me+/YxcvhwdLrQkU8ABqO3isWbx7/JlO5TuGrIVWyevRldj/F0dzpb+03C\ngHFuHT2OuguA3IRczup31k+dqkKhUCgUCg/hWnCi8M2F00IKYOvE8Z7yjJMGDAE+E0JslFJuDSJ/\nM1CBN4ILKeUKIKbls0d5+QT4os1+30kpj+zEvAKgWXCiGpsY9cOPGCIj0em9Ck5q7zuI0C+geIfv\n1A1GI5tnB/G7ju3q49nzwdHP/7wpKhQKhUKhCEi4FpzlwPltPkshhB4tk/GicAbwOCafBtwtpbRK\nKVcCHwPnBZHPAc4F/tXB0LOB96SUDeHMI1xayk61lGcQOh1C571c5/1zLLHJXfz2MxhNfm2t2K0A\nvHaolAWlNWSnDjp8E1YoFAqFQtFKuArOLcAlQoivAROaY/E2YBxwe5hj9AGcUsqdbdo2AvlB5P8L\n3AE0BRvQozSdDrzSrmuoEKJSCLFTCHF3sEgvIcSlQog1Qog1AXoB0HuinABGn3qmj8Qxl1zlt5c+\nInAYOADNFkjuzZCrt9Dz1iIwGIPLKhQKhUKh+MmEmwdnGzAQ+Bb4CohEczAeKqXcE+axYtAKdbal\njjbLTy0IIU4B9FLKDzsY81SgEljWpm05MABIRbMYnYW21OWHlPI5Ty6fEX59nt+DNnrDvKNifR2I\njVFmvzFDWnD2r9BGjlVFNBUKhUKh+CUJO4eNlLIUCJ6at2OsQFy7tjjA0rbBY5V5CAin2uRs4FVP\nEdCWee5t079ZCHEvmoLT0VKXL54oqrSysk7tpjcEuaQ2j25XtbtT4ykUCoVCoeg8IRUcIURYEVJS\nygMdS7ETMAghekspd3naBgPtHYx7Az2AFZ5svkYgXghRCoyWUu73zC0LOAq4rKPpEShrX5joZICU\nxsEQInAG4ppCWDP/p05BoVAoFApFJ+nIgrOfgEULWhGe/g7jp6WUDUKID4B7hRAXo0VRnQSMbSe6\nBchq83ks8CQwDC2iqoXzgG/bL5EJIaYB66SUZUKIfsDdaMtpnUKGqRPljZ/E9hVLPDsFuVSPK2di\nhUKhUCh+TTpScEa22RZovi5nA0WBxTvkSmA+UA5UAVdIKbcKIcYDn0spY6SUTqC09aBCVANuzxJZ\nW2YBDwc4xhTgZSFEDFpywteB+zs90zANN8dffRMHt27CWl0VZJx2Aw3/U9YmVSgUCoXid0VIBUdK\nubbtZyGEG9jczs8lbKSU1cDJAdp9ctu061sKZAZo7xdE/u/A33/K/HwJbsHR6X0vW87QEWxe9GVg\nYUe7ILDJd//ciSkUCoVCoeiAP2mhzF+OCx9/DlO76KmxfzsnuIKz4Q3fz5Hxv9DMFAqFQqFQtKAU\nnKAEXqNKTM/wa2uv8Piw8S3fz3p1yRUKhUKh+KUJN9FfWzoRVvQHphNxV23rT/nRmSgshUKhUCgU\nh4WOwsQ/btcUCTwvhGhs2yilPPFwT+y3RoTS46SENuHgLSUckjM9UfXVe8EYAzGpcGjdLzlNhUKh\nUCgUAehovaR9aNDrv9RE/jDUFcF/8uGUeTB4Zmvz+Y8+TXRCkvbhiaGAgLm1v80cFQqFQqH4i9NR\nFNVfN6ZZBLHgVOzQfm96x0fBabXetBJg/2GzDs/cFAqFQqFQhER5vAbB3hzMcdizNCXdHQ/i9sjo\njXB3RWhZhUKhUCgUh42f4mT8l0DKIMmZheeShaPgWD11rCbfdXgmpVAoFAqFIiyUgtNZnM3a73AU\nnMoC7XdEiDByhUKhUCgUhx2l4IQgJTLSv/GtM7XfblfgnSp3+W8bow/vxBQKhUKhUIREKTghSI/X\nsg4nX3qpf6etLvBOTpt3e6GnYoSy4CgUCoVC8auinIw9BMrrZ8jIoOsD/yL++OP9OxvKAw8UKLGf\nsuAoFAqFQvGrohScDkg42a82aGgcjf5tyoKjUCgUCsWvilqiCoFoyVbsckJDJTjt3s4WZ+P2NFT6\ntykLjkKhUCgUvypKwQmHT66Dh3NhQ5tEzo4m7/aOhbD0QW156oNL/Pc3mH75OSoUCoVCoWhFLVGF\nw9YPtN+f3uBt6zZc+91sgbfP0rbrDnqXqPpOh4LPPMKdqNypUCgUCoXiZ6MsOOEQyK/m4Pfa77ZL\nVetf8273Psa7HZv+y8xLoVAoFApFQJSC83MJ5ovT4lgc1w2iEn69+SgUCoVCoVAKzs+mbd6bttQW\nar/ri3+9uSgUCoVCoQB+ZQVHCJEkhPhQCNEghCgUQpzdgbxRCLFdCFHUrl16xrB6fl5o0yeEEA8K\nIao8Pw+K1nCozjFw4MDQAm53cAUnpa/2O3fKTzm0QqFQKBSKn8Gv7WT8FGAH0oAhwGdCiI1Syq1B\n5G8GKoDYAH2DpZS7A7RfCpwMDAYk8DWwD3i2s5PNyckJLfDeBTDu2sB9eSfCrAWQM7Gzh1UoFArF\nXwCHw0FRURE2W5AX5T8BkZGRZGZmEhER8asf+1dTcIQQ0cBpwAAppRVYKYT4GDgPuC2AfA5wLnAj\n8HwnDjUbeFRKWeQZ51HgEsJQcJqaYomKsoR/pG0fwajLAvcJAT2PCn8shUKhUPylKCoqIjY2lh49\nevATFxp+10gpqaqqoqioqGODwS/Ar7lE1QdwSil3tmnbCOQHkf8vcAfQFKR/uRCiVAjxgRCiR5v2\nfM+4HR5DCHGpEGKNEGINQHNzmBmHo5K82/YG//7I+PDGUSgUCsVfFpvNRnJy8p9SuQH327mKAAAW\na0lEQVQtWW5ycvJvZqH6NRWcGKC+XVsdAZafhBCnAHop5YdBxpoI9AD6AYeAT4UQLdaoGM+4bY8R\nE8gPR0r5nJRyhJRyRGdOBOn2bh/8wb/fpBQchUKhUHTMn1W5aeG3PL9f0wfHCsS1a4sDfNaEPEtZ\nDwEBKlxqSCmXezbtQojr0BSnPGBzgOPEAVYpA1XBbD9wuH+INkMtf9i/+9R5YY6jUCgUCoXil+DX\ntODsBAxCiN5t2gYD7R2Me6NZZ1YIIUqBD4CunuWoHkHGlnjTBW/1jBvqGEEH8cMRaIWsA0Uoe0w4\nh1MoFAqF4jelR48eDBw4kCFDhjBihLaY8e6775Kfn49Op2PNmjWtsqtWrWLQoEGMGDGCXbt2AVBb\nW8uxxx6L2+0OOP5vya+m4EgpG9CUlXuFENFCiHHAScBr7US38P/t3XuYlVWhx/Hvb2SQW15QQRhK\n1INgoI2lHc85YpyDw0GyjDyoUallQjfNzC76yPESKd7KS1dOGoia+eS1tKQSElIPkQc0L6GYJCoD\nAiowiCLr/LHWwDt79uw9A7NnD3t+n+dZz+y91rved71rvzN7zXrXehe8mzjLqhb4HFCfXr8oabik\nWkm7SOoDXA28BDyd8t8EnCOpRtJA4GvAjNYVMk/DZeNrzeOyTynOp8K7HM3MrHLMmTOHRYsWbW3M\njBgxgjvvvJOjjz66yXZXX301999/P9dccw0//nGctzN16lTOP/98qqo632P1OrpEXwR6AiuBnwNf\nCCE8KWmkpPUAIYTNIYQVjQFYA2xJ798hTjH/BfG21PPE3p7jQghvp2P8BPgV8XbVX4H7UlxRIW/P\nTJ5+nWOvaB534H+05hBmZmZ5SWoxTJ8+fet206dPL7jtjjr44IMZOnRos/jq6moaGhpoaGigurqa\npUuX8uKLLzJq1KgdPmYpdOhzcEIIa4jPqMmNn0ccHJwvz1xgUOb9g0Dzmt+WHoBvpNDGAua5MHKH\n7kyaC736Nt/upFvg0gFtPqSZmVm5SGLMmDFIYvLkyUyaNKnFbc877zxOOeUUevbsyaxZszj33HOZ\nOnVqB5a2bbyaeEa2KdNz2d/iiwU5nT8DD8ufuXsv+MRtcXVxMzOzNmrNXBiASZMmFWyItMX8+fOp\nqalh5cqV1NXVMWzYsGa3phrV1tby6KNxoemHHnqIAQMGEELgpJNOorq6mquvvpr+/fu3S7naQ+e7\naVZW23pwujWkhsqfrm199qHHwqEntnOZzMzMSqOmpgaAfv36MX78eBYsWFA0TwiBqVOnMmXKFC6+\n+GKuuOIKzjjjDK677rpSF7dN3MDJWPfG3q3f+LMPlK4gZmZmJbZhwwbWrVu39fXs2bMZMWJE0Xw3\n3XQT48aNo2/fvjQ0NFBVVUVVVRUNDQ2lLnKb+BZVxpo1NQzef1GT5/i16D1Hlrw8ZmZmpVJfX8/4\n8eMB2Lx5MxMnTmTs2LHcddddnHnmmaxatYoPf/jD1NbW8sAD8Z/6hoYGZsyYwezZswE455xzGDdu\nHN27d+fWW28t27nk4waOmZlZF3TAAQewePHiZvHjx4/f2vDJ1atXL+bMmbP1/ciRI3niiSdKVsYd\n4VtUebVxmt2JN5WmGGZmZrZd3MDJaHwOzttvdW9bxt77lKA0ZmZmtr18iyoriCVLjmT98p5UsaJp\n2kFj4QOn5c/3bo/HMTMz60zcg5OjfsUQNjX0bp5Q+8k4DTyfTviIajMzs67M38x55XnYkteXMjMz\n22n4FlVGyLdUQ6NuPZvHjZ0GL8wvXYHMzMxsu7gHp7X2z/Po6iO/ACff0vFlMTMzaweDBw/mkEMO\noba2lsMPPxyANWvWUFdXx5AhQ6irq2Pt2rUA3HHHHQwfPpyRI0eyevVqAJYuXcpJJ51UtvIX4gZO\na3Vr48wqMzOzncCcOXNYtGgRCxcuBGDatGmMHj2aZ599ltGjRzNt2jQArr/+ev785z8zefLkrQ/1\nu+CCCzrtgpu+RZXHgR/4IB+54MJyF8PMzLqIs88+m0WLFrXrPmtra7nmmmvanO+ee+5h7ty5AJx6\n6qmMGjWKyy+/nKqqKjZt2kRDQwPV1dXMmzePfffdlyFDhrRruduLGzh5VO/agz599yp3MczMzEpK\nEmPGjEESkydPZtKkSdTX1zNgwAAA9t13X+rr6wE477zzOOaYYxg4cCA333wzEyZM4Lbbbitn8Qty\nAyePJkONq3vBEaeXqyhmZtYFbE9PS3uYP38+NTU1rFy5krq6OoYNG9YkXRJKs4jr6uqoq6sDti24\nuWTJEq666ir23HNPrr32Wnr16tXh59ASj8HJZ9M6+O57Yd0K2LIZtEu5S2RmZtbuampqAOjXrx/j\nx49nwYIF9O/fn1deeQWAV155hX79+jXJ07jg5pe+9CUuvPBCZs6cyVFHHcUtt3SuSTdu4OSh1Uvg\njZfgqXthyztQ5QaOmZlVlg0bNrBu3bqtr2fPns2IESP46Ec/ysyZMwGYOXMmxx9/fJN8V155JWed\ndRbV1dVs3LgRSVRVVdHQ0NDh51CIb1EVE95xD46ZmVWc+vr6rauGb968mYkTJzJ27FiOOOIITjzx\nRG644Qb2228/br/99q15Xn75ZRYsWMCFF8aJOGeeeSZHHHEEe+yxB3fffXdZzqMlbuDkE9KTjMOW\n+NM9OGZmVmEOOOAAFi9e3Cx+r7324g9/+EPePAMHDuS+++7b+n7ChAlMmDChZGXcER16i0pSX0l3\nSdogaZmkiUW27y7paUnLM3EHSbpH0ipJayQ9IGloJv00Se9IWp8Jo1pZwqZvt2xO0W7gmJmZ7Uw6\negzOD4C3gP7AJ4EfSRpeYPuvA6ty4vYA7gWGpv0sAO7J2eaREEKfTJjblkJuXXaqsYHjxTTNzMx2\nKh32zS2pN3ACMCWEsD6EMJ/YUPl0C9vvD3wKuCwbH0JYEEK4IYSwJoTwNvA9YKikdntwTeMdqm0N\nHN/JMzOz9hdCnsWdK0g5z68juyYOAjaHEJZk4hYDLfXgXA+cD2wsst+jgRUhhNWZuMMkvSppiaQp\nkvK2UCRNkrRQ0kLINGwabXknbehbVGZm1r569OjB6tWrK7aRE0Jg9erV9OjRoyzH78iuiT7AGzlx\nrwPvyt1Q0nhglxDCXYXGz0gaRLztdU4m+iFgBLCM2Hj6BbCZnJ4ggBDCdGA6QK+hBzW/wrb24LiB\nY2Zm7WvQoEEsX76cVatyR2JUjh49ejBo0KCyHLsjGzjrgd1y4nYD1mUj0q2sK4BxhXYmaR9gNvDD\nEMLPG+NDCM9nNntC0iXEsTzNGjgteV/fN2EtcYo4uAfHzMzaXXV1Nfvvv3+5i1GxOrKBswToJmlI\nCOHZFPc+4Mmc7YYAg4F56fHQ3YHdJa0AjgwhvCBpT2Lj5t4QwneKHDfQbHpUYXv1SA0bDzI2MzPb\nKXXYN3cIYQNwJ3CJpN6S/g04HpiVs+lfgXcDtSl8DqhPr1+UtBvwAPCnEMK3co8j6VhJ/dPrYcAU\nms+yKmzdyvjzqZTNPThmZmY7lY7umvgi0BNYCfwc+EII4UlJIyWtBwghbA4hrGgMwBpgS3r/DjAe\nOAL4TM6zbt6TjjEaeFzSBuB+YqPq0rYUMvzj4fhi7Qvxp8fgmJmZ7VRUqaO320rSOuBv5S5HF7U3\n8Gq5C9GFuf7Lx3VfPq778hoaQmg2yag9+QEv2/wthHB4uQvRFUla6LovH9d/+bjuy8d1X16Nj2cp\nJY+eNTMzs4rjBo6ZmZlVHDdwtple7gJ0Ya778nL9l4/rvnxc9+VV8vr3IGMzMzOrOO7BMTMzs4rj\nBo6ZmZlVHDdwzMzMrOJ0+QaOpL6S7pK0QdIySRPLXaadlaRdJd2Q6nGdpEWSjs2kj5b0jKQGSXMk\n7ZeT90ZJb0haIemcnH23mNeakjRE0puSbs7ETUyfywZJd0vqm0kr+DtQKK81JelkSU+nuloqaWSK\n97VfQpIGS7pf0tpUh9+X1C2l1Ur6S6q/v0iqzeSTpMslrU7hcqVFEIvl7aokfVnSQkmbJM3ISSvJ\ndV4sb4tCCF06EJeM+AXQBzgKeB0YXu5y7YwB6A1cRFwstQo4jrha/GDiU0NfByYAPYArgUczeS8D\n5gF7AgcDK4CxKa1gXodmn8PsVJc3p/fD0+dwdLrObwVuy2zf4u9AsbwOTeq9DlgGHJmu/5oUfO2X\nvu7vB2akOtoXeAI4i7hY8zLgq8CuKW4Z0D3lm0x8gv2g9Fk9BXw+pRXM21UD8HHgY8CPgBmZ+JJd\n54XyFixruSurzB9Ub+At4KBM3CxgWrnLVikBeBw4AZgEPJxT9xuBYen9y8CYTPq3G79Ii+V1aFLf\nJwO3ExuajQ2cS4FbM9scmK77dxX7HSiUt9zn2tkC8DBwep54X/ulr/ungXGZ91cCPwHGAC+RZgyn\ntH9kvlgfBiZl0k5v/GItlrerB2AqTRs4JbvOC+UtFLr6LaqDgM0hhCWZuMXE/1ptBymu6n4Q8CSx\nThc3poW4uvxSYLikPYEB2XSafg4t5i1l+Xc2knYDLgFyu29z628pqVFD8d+BQnktkbQLcDiwj6Tn\nJC1Pt0l64mu/I1wDnCypl6Qa4Fjgt8R6ejykb8XkcVqoX5rXfaG81lRJrvNW5G1RV2/g9AHeyIl7\nnfifre0ASdXALcDMEMIzxLp+PWezxrruk3mfm0aRvLbNt4EbQgjLc+KL1X2h3wHXfev0B6qB/wJG\nArXAYcAF+NrvCA8Rv/DeAJYDC4G7KV5/uemvA33SOBzXfduU6jovlrdFXb2Bsx7YLSduN+KYA9tO\nkqqItzneAr6cogvV9frM+9y0YnmNOBgSOAb4Xp7kYnVfqG5d962zMf28PoTwSgjhVeC7wDh87ZdU\n+nvzW+BO4q2NvYljNS6n7df3bsD61Gvjum+bUl3nxfK2qKs3cJYA3SQNycS9j3hLxbZD+s/nBuJ/\ntCeEEN5OSU8S67Zxu97E8RxPhhDWAq9k02n6ObSYt0SnsTMaRRzM/Q9JK4BzgRMkPUbz+juAOGhy\nCcV/BwrltSRdw8uB7O2Mxte+9kurL/Ae4PshhE0hhNXAz4iNyyeBQ7Mzo4BDaaF+aV73hfJaUyW5\nzluRt2XlHqhU7gDcRpxF0hv4NzyLakfr88fAo0CfnPh9Ut2eQBwlfzlNR8lPA/5I/M9rWLqgx7Ym\nr0MA6EWcPdIYrgJ+mequset+ZLrOb6bpLKoWfweK5XVo8hlcAvwZ6Jeu43nE24a+9ktf988D3wK6\nAXsAdxFn/DXOhPoKsWH+ZZrOovo8cYByDTCQ+KWZO4sqb96uGlId9yDObJqVXncr5XVeKG/Bspa7\nssodiK3/u4ENxBHyE8tdpp01APsR/2t9k9it2Bg+mdKPAZ4hdufPBQZn8u4K3Ji+TOuBc3L23WJe\nh7yfxUWkWVTp/cR0fW8A7gH6ZtIK/g4UyuvQpJ6qgR8CrxGnsV4H9EhpvvZLW/e1qW7WAq8SZxL2\nT2mHAX9J9fcYcFgmn4ArgDUpXEHTWVMt5u2qIf1tCTnhopRWkuu8WN6WghfbNDMzs4rT1cfgmJmZ\nWQVyA8fMzMwqjhs4ZmZmVnHcwDEzM7OK4waOmZmZVRw3cMzMzKziuIFjZltJmiHp1+UuR5ak4yU9\nK2mzpBklOkanO28z2zFu4Jh1EulLNkiakhM/KsXvXa6yldkNwB3EB0l+pUTH+ArwqR3ZgaTTJK0v\nvqWZdQQ3cMw6lzeBr0vap9wFaU9pdfntybcHsBfwQAjhpRBC7orD7SKE8HoI4bVS7NvMysMNHLPO\nZQ7wAjClpQ3y9ehIGpziDs/Z5lhJf5G0UdI8SYMkfUjSYknrJf1a0l55jnGBpPq0zc8k9cykSdI3\nJC1N+31C0qfylOUTkh6UtBGY3MK57ClppqS1aV+/lzS88RyIj94HeDDtc1QL++ku6VJJyyRtkvS8\npLMy6UdL+l9Jb6bz+p6k7pn0JreoJM2V9MO0z1clrZR0VVq5Ou9nQlzgsXcqZ5B0UbFzTOm7S5qV\njvFmKvvZmfTJkpaktFclPSCpWyb9M5KeSulLJH01W85i+c0qlRs4Zp3LFuKigZ+XdGA77O9i4Gzg\nn4kL1f0C+G9gEnEF8uHEtWWyPkRcrXc0cfG7McTF7xpNBU4HvgS8l7jo3k8kfThnP5cR12Z6L3Gt\nq3xmpLIdD3wQaAB+mxpUD6fykcoxIMXlMxM4BTgHODiV7zUASTXAb4D/I64tdDrwiVS+Qj4JbAb+\nlbjQ4tnASS1s+3BKb0jlHEBc8LTYOUKsz0OA44ChwGeBl1LZDwd+QPwchxI/k982HlTSGcClxM/0\nYOBrwDeBL7Ymv1lFK/fCXQ4ODjEQvwh/nV7PIa3aTWyIBGDvfO9T3OAUd3jONv+Z2ebLKe79mbiL\ngL/mlOE1MqvBE8embCKuJt6buBjeyJyyXwPcn1OWrxU53yFpu6MzcbsTVxX+XHq/d9pmVCv2k3d1\nYeA7wLNAVSbutHROvXLrPr2fCzySs5/fAT8tUI7TgPXbcY73Aje2sM+Pp23f1UL6P4BP58SdDTzV\nmvwODpUc3E1p1jl9E3hE0pU7uJ/HM6/r088ncuL65eYJIWQHyz4CdAcOJK7q24PYA5FdqbeaeGst\na2GRsh1M7LF6pDEihPC6pCeIvT6tdVjaz5wCx3k0hLAlEzefeE7/RNM6ysqNf5nmdVVMa87xR8Av\nJX2A2Ij6VQjhjyntd8Ay4O+SHgBmA3eGENalcVrvJvae/ShzzG7EVbIL5m/jeZjtdHyLyqwTCiEs\nIM4cuiJPcuMXtTJxLQ3ifTu727Tv3Li2/B1o3PYjQG0mDCfeysra0Ib95grFN2kXhY7zds77ttZV\nq44dQvgNcYbYVcQeq/sk/SylrQPeD5xI7K05D3hG0sBMWT5P089iBOnWXpH8ZhXNDRyzzut8YCQw\nNid+Vfo5IBNX247HPURS78z7I4G3gKXAU8RbO/uFEJ7LCcvaeJyniX+D/qUxQtJuxPEoT7VhP4vS\nfv69wHGOzBkgfBTbzqm9vAXskufYRc8xhPBqCGFWCOE04hihUyXtmtI2hxAeDCGcBxxKvE14XAih\nntirdGCez+K5zL7z5m/H8zbrlHyLyqyTCiE8J2k6zZ/98hzwInCRpG8Rx7xc0I6H7gbcKOkSYCAw\nDfifEMIGAElXAVdJEvAQ0IfYCNoSQpje2oOEEJ6VdA/xFssk4tif7wBvALe2YT9LJN0O/FTSV4DH\ngEHA4BDCLOJA57OBH0q6FjggndP3QwgNrT1OK7wA9JBURxzQ3NCac0z1/BjwJLHuPw48H0LYJOk4\n4q3Bh4A1xEbcu4gNJ4ALgeslvQbcT+zJez9QE0K4rBX5zSqWe3DMOrdLiDN5tkq3mE4mflEvJs6Q\nOb8dj/lH4pftHOAu4EHgG5n0KcTByeem7X5HnOX09+041meABcSBtguAXsTBwhvbuJ9TiA2G64Bn\niIOGdwcIIbwEHEscq7MIuBH4Oe1bZ4QQHgZ+nPa9im11VuwcNxEbPYuBPxEbIB9Jaa8BHwN+n87r\nXOLg5HnpmD8lzrr6dMo/jzhD7u+tyW9WyRRCR93qNjMzM+sY7sExMzOziuMGjpmZmVUcN3DMzMys\n4riBY2ZmZhXHDRwzMzOrOG7gmJmZWcVxA8fMzMwqjhs4ZmZmVnH+H+bGBCdWAIHsAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f266806f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3.5))\n",
    "plt.plot(cumulative_heads_ratio)\n",
    "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
    "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
    "plt.xlabel(\"Number of coin tosses\")\n",
    "plt.ylabel(\"Heads ratio\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 10000, 0.42, 0.58])\n",
    "save_fig(\"law_of_large_numbers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)), ('rf', RandomFor...f',\n",
       "  max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.896\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)), ('rf', RandomFor...bf',\n",
       "  max_iter=-1, probability=True, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=1, voting='soft', weights=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging ensembles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.856\n"
     ]
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_without_and_with_bagging_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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FL7w81dlGu519tBgfa1n95HKTWFZ/28PzGo1Gs97Q2rAUrQsajceqz7HQrF1ajRlth6Gh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\nalpE5E0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26394707b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title(\"Decision Tree\", fontsize=14)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
    "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
    "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rnd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97599999999999998"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) 0.112492250999\n",
      "sepal width (cm) 0.0231192882825\n",
      "petal length (cm) 0.441030464364\n",
      "petal width (cm) 0.423357996355\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
    "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
    "    print(name, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.11249225,  0.02311929,  0.44103046,  0.423358  ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7g5eklA/Pop+jZJCPbhhtZNhZY2vyJcRx3X2gcvwA41qYxlR7W/Xc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f263b521b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "for i in range(15):\n",
    "    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
    "    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
    "    tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])\n",
    "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out-of-Bag evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90133333333333332"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    bootstrap=True, n_jobs=-1, oob_score=True, random_state=40)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "bag_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.31746032,  0.68253968],\n",
       "       [ 0.34117647,  0.65882353],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.08379888,  0.91620112],\n",
       "       [ 0.31693989,  0.68306011],\n",
       "       [ 0.02923977,  0.97076023],\n",
       "       [ 0.97687861,  0.02312139],\n",
       "       [ 0.97765363,  0.02234637],\n",
       "       [ 0.74404762,  0.25595238],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.71195652,  0.28804348],\n",
       "       [ 0.83957219,  0.16042781],\n",
       "       [ 0.97777778,  0.02222222],\n",
       "       [ 0.0625    ,  0.9375    ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.97297297,  0.02702703],\n",
       "       [ 0.95238095,  0.04761905],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.01704545,  0.98295455],\n",
       "       [ 0.38947368,  0.61052632],\n",
       "       [ 0.88700565,  0.11299435],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.96685083,  0.03314917],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.99428571,  0.00571429],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.64804469,  0.35195531],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.13402062,  0.86597938],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.36065574,  0.63934426],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.27093596,  0.72906404],\n",
       "       [ 0.34146341,  0.65853659],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.00531915,  0.99468085],\n",
       "       [ 0.98265896,  0.01734104],\n",
       "       [ 0.91428571,  0.08571429],\n",
       "       [ 0.97282609,  0.02717391],\n",
       "       [ 0.97029703,  0.02970297],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.06134969,  0.93865031],\n",
       "       [ 0.98019802,  0.01980198],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.97790055,  0.02209945],\n",
       "       [ 0.79473684,  0.20526316],\n",
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       "       [ 0.02884615,  0.97115385],\n",
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       "       [ 1.        ,  0.        ],\n",
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       "       [ 0.01604278,  0.98395722],\n",
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       "       [ 0.96987952,  0.03012048],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.05747126,  0.94252874],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 1.        ,  0.        ],\n",
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       "       [ 0.98989899,  0.01010101],\n",
       "       [ 0.01675978,  0.98324022],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.13541667,  0.86458333],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.00546448,  0.99453552],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.41836735,  0.58163265],\n",
       "       [ 0.11309524,  0.88690476],\n",
       "       [ 0.22110553,  0.77889447],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.97647059,  0.02352941],\n",
       "       [ 0.22826087,  0.77173913],\n",
       "       [ 0.98882682,  0.01117318],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.96428571,  0.03571429],\n",
       "       [ 0.33507853,  0.66492147],\n",
       "       [ 0.98235294,  0.01764706],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.99465241,  0.00534759],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.06043956,  0.93956044],\n",
       "       [ 0.97619048,  0.02380952],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.03108808,  0.96891192],\n",
       "       [ 0.57291667,  0.42708333]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91200000000000003"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "mnist = fetch_mldata('MNIST original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "rnd_clf.fit(mnist[\"data\"], mnist[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = matplotlib.cm.hot,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure mnist_feature_importance_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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ycfmrFEIFQComKrWMfKh4i8nWDvRY5NQ3DfSYFRizZN/B9bcdFWjyj97egtIW7xy8g+TZ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\nQ0OoAACGhlABAAzN/wEpoZ6UXy5rfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f266a0cae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(rnd_clf.feature_importances_)\n",
    "\n",
    "cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])\n",
    "cbar.ax.set_yticklabels(['Not important', 'Very important'])\n",
    "\n",
    "save_fig(\"mnist_feature_importance_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R',\n",
       "          base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "          learning_rate=0.5, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "    algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "ada_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zxDeq8pxKHV/Tki7tPtNuIuf6xxUwdcjtgjzEYs8VS8lLmWZx8ftNlw3pFd2y\nmzdqgrYazt9mFpZS81j+kxXH3bnT5Ny5YFEoADhOhp07my+7XwgUyGSGi+Y2IQzOnQt2Xdg2jmzr\nzoaiUZVnr3e8piYQix2q0m579dy380yvlyg4l/q4AqYOXu8Y8/PfLjO1SCnQ9cFNU6xvPdSKarSw\nJJMn0LQ+dH25OrGqBpif/4eG9//3fk9jauqP6kSn5WjW56DrWwiHby0GDCjK4KqbSbuxoWhU5bm/\n/84qTSCVOotpnisKn2bNZuu55XSvWS8l8tcTroCpw9DQvczOPoaqDgAyX7U3TSh0c88W4NV2tNYz\nMQihrwv/U6EVQSlCeFa8/ystPK36HHR9S7EVczyuEgxGunSF1TT7DLT6rDSq8lxLE0gmj+H372vJ\nbLbeW073GtfxX40rYOoQDO6nv/9uEolDxaZWweD1KIqOx7Ot6gX3+a4hlTretnBYi5eznolBUfxr\nXmZ9fNzm6NFdZf4PgO3bZ5qOmOtGRYNOqJXwmc0uMTR0iHe963NVz0mzz0A7z0qjKs+1BLLXO47P\nt7tsjJW0281Wnv9y10gefjCMW66/h4yOvqNmImA4fFvZC55InGZm5suEQjfj8+1uSziUvpymebHY\nQfLUqY+yZ8/HehYeXWunf/78I6jqUNmx3TSdSSlZWlrEtuu/qO9970VSqQkWFv4SRQmjKEEcJ47j\nRFGUdzA3d7Ht719aOommbSOTWfbJSKkQiTxHIvEQ2ewFlpb+E5FIHx5P7j4oiorX6603ZE0qEz4z\nmUtEoz9mampHTcHQ7ALdzkK+UpXnSoE8MfFwUeMpfR51fbiuD7JbJtd4PEY6nWrpnEoURaG/fxBF\nab/cYrsayWYJb85dg1uuv2fUW4ArX/BMZgZVDZLNzuD3X9nWzq3wcprmRWKxZ1EUL5o2SCYz11NN\nptZOv5fRObFYlC996YscPBjBcVZ22Pf1hRkbO0kgECeRCDI1dRWRyFPAU23P4cCBC+j6WbLZZYER\nDC7Q37/A7Owi2ayBZb3EV77yk8RiEsdRAYnPl2XLFg8PPhhsquRMJcnkibxPTy8WNoXl56TZBbrd\nhby0yvNKFLTbTGY+X7laACoez0jd57HT58ayLL71rSd4/PFDZDKd1eEVQnLttT5++Zd/paNx2sEN\nb17m8rviFqm1AJ8//0jZC27b0WJnyQKt7twKL2cqdbIYWOA4aXR9uFhdebXMDL2Kzjl48P/wxS9+\nm7NLNnIIZt6jAAAgAElEQVRoHhS54jmzwCtTV5R8YkPfTEfzsCL93HzFcaysjml5MLQsgcGLzMTD\nRFQLVIvXvu3TXFzawt7BGc4vbAVHQNJPXxAOHbqRZguRl1YUyGQuouvl1QRKn5NmF+jVKlI5NvYA\np059FCktdH0Yn+9qdH0LlhWp+Tx28txcuDDFF77w1zx3BKytC+Dr0AwlBbOH+jj+4CP81BuuqHvY\nZtE21iuugGmDyhe80L7X42n/hS+8nNnsHJo2iOOkcZw0weD1a9IFsFG15naIxaI88cTjnJ0agKtO\n4zEEuuHp1nRbIs0Wjs5rXDF4jgF/gngmQMIKkXT68HiXtSqpKPiMLB6vgm3ZOEqchcVBTHMCy9qJ\npmkNne2VFQWEmM/319lS/I7S56TZBbrRcd0MFAkG9+P1jhMO344QywK13vPYKLhipXk98cSXeekl\nHWtkGjVoYnj1GiHqzZPNWGQ981w4fQUHDx6nnqXM1TZ6i3sX26DyBdf1EUzzPH7/NUjpNL1zq3zp\nBgffhGmeJ5PJ2bqDweuLO8bVSh7rpFpzIywri22DUBRQBVtHhvnQ/R/qwoy7w/zEp3CyEdQSreC7\nXx1mxw4vVx+4g+/86DuYCQukQEqJ49jE4680dLZXVhTQ9a2k0+ewrMWaz0mzYbf1jgO6HijSqrZU\nS+NvJighm80CXoQGXr/Bf7z/txjsH2xrzgCf/ptPc+qVCRxFYNsCpQlt2aX7uAKmDSpf8EDgSoaH\n7ymLIlspHr/WS5dKfZPt2+9jYeGbxcCCQjOxdsxT7exmN0tl21YJDt3D0tQjAChqCMeOIWUW3X91\n3XMa3SuA/v7jnD69HUXJoqphjh07QDJ5B4axxAc+0FezS2izYbe1jpuYeLjrUVwboWBnLUSLNedc\nqslFy7nl+teE2gtB833Xe51F327Y82apbFugWRu7EbyW/rH7ic8/gZ2eRvWOovv3ourBumPXule2\nnebSpSeYmvpz/t2/M1EUP7q+FSEEv//7f8bOnYsoipf+/juL5zTbRmAlepE4u5EKdq4XNkt48wcf\nivInH3PL9W9IGr109cwMrWgH7e4a12tl23ZpxcZuBK/FCF5b/HvnnmDxuPjCMJm0DckQo6PTwI6q\ne2WaF1la+h6WNY+qBlFVDcuKYppZDGMUy1os+tV6Qbf6z7Ta4G015rWRcIMDllkXAkYIMQj8BfAm\nYA74sJTyb2oc99vARwCz5OMbpJSnV2Oe3aSVl64d7aDdXeN6rWzbDGb85TINJDh0D3BX2+OVLhT/\n9Y/+Ai01zR4txZ4rZzl37h5CoQNlRUETicNYVhxFMfLFNrMIoeE4SdLpSaTMEg7fWqwK0G06NWf1\nSgtdzzXDNou2sV5ZFwIG+FMgA2wDXgX8LyHEi1LKIzWO/ZKUcvWD27tMKy9dO9pBu7vGjVrZ1oy/\nzNLUIyhaGFUfwclGWJp6BDt7AKhv5qpFLbPakeO/wu7thzlw91+TyQSwrAgLC9/MV57O+d6kzKBp\nA0iZKyuUq96s5Dti6oAkFnsBKe1iE7RuCptOzVm90kLXW82wUlxto7esuYARQgSAfwNcJ6WMA08J\nIf4Z+L+B9RNm1GUaRQJVVrCNxQ5jWZFiyRqf72o8nqGG2kGlAEulzpJKHcMwdjIx8XBNE1uleWTH\njvubXgTWepcan38iJ1wKoeP5/7fM88A1LY1Vy6wWi59gbnYnpukFEmhaH1JqpFLHi62LJyYeZmHh\n21hWEstazJ9ZiF4SOI5DJjOP17u72AQtHL6VVjptrsR/+2+3MTl5R9Xn4+N23eTQwu8+O/sVdH0E\nn+9qDGMrsLIW2qzpttTMVjjn/PlHEEIHBCMjz/LqVw9yPBUmxUAbV7552Ey5OWsuYIC9gCWlfKXk\nsxeBu+sc/xYhxAJwAfiMlPKztQ4SQtwP3A8wPr6ji9PtHpW27VomirNnP0EyeRJF8aFpYWw7tzD5\n/fsIBK5sOHZBgMVih0mnJ/H799UtZdOpeWStd6l2ehq1IokxFw3WfA+YRnjUDLZTnkxRufjmNgMv\nkc2eRkoVIZx8N1AfHs8WRkcXmZ7ehmHkwm8dJ8PMzBz793dPi6ksT1OgXiBB+e8+gmVFicWeBW7B\nMLY21ELbeWZKzwEPkcjTSCkBH4aR5tXbz3N0/tqa514udJqbs54E1HoQMEGg8qojQKjGsX8PPEIu\nwfsngK8IIZaklH9beaCU8pH8sdxyy40bIgi+lokim53L7/JyFZ1ztn2TVOoY4+PvaTheQYBNTDyM\n17uzoemjG+aRtaxsq3pHq/JYHDvG6E6zKzb2rK2jKk7ZZ5WLbzC4n927P8T09F8yP/8EUtro+h5C\noRuJx5/ngQf+PxTFV4wgk9Ihk5nmmmv+qKW5dJPS393v30s0+mNAkEqdQFWNhlpoO89M6Tnx+JFi\ntWxdv0gmcwV2RueKwXPdv9DLiPWUPLoeBEwcCFd8FgZilQdKKY+W/PlDIcQfAz8HVAmYjUjtsFcT\nIVRCoZtJpU5i21E0LZyv7txanbNSKnffq+mkP/q9n+eoNcbFp8sT6TrZYdXKY3GsKP/x9wRGcKHj\nOS+l+1CEg2GkAYllRZAyXrX4BoP72bv3D4jHlwul2naabHYO207i9V6BaV5cUTtYLUp/9+XeN6+Q\nyczg8dzVUAtt55kpPce2oyhKCCFAUXJxO6blYSDYfGO4dnj6H+/h/d+vTuLciCao9c56EDCvAJoQ\n4mop5Yn8ZzcCtRz8lUiaaXG4QajlKFdVAynBMLYW7eKVZWnaGbdycVtNJ30yMsy28fmqXVYnO6xa\neSzhkV8oCzvuhLPHbiKxGOKvkg8RCqUYHBzD691ZliRZSsFkOD39KEtLT6KqAwjhQ0qLWOxZbHsf\niqKueSRV5e+u61vyLSnuKvqWmj0XVn5mSs9R1TCOk0ZKcJxci2tDyxI3A124svrE5gcYe/X62OFv\ndtb8jkopE0KIrwK/I4T4VXJRZG8F7qw8VgjxVuD7wBJwK/AbwH9u97vXMvO81nfXcpR7PMNImdsx\n13Oel46VM6FJpMw0HLdyjKGhezl79hP5nbaJqhp4PMOMjGycOIvKPJZ2qRW6mlgawAjMk0waDA5a\n7N+/F1XVGiZJBoP7MYytDA29EU3rI5O5RCp1gkxmjmx2puk2DL18TjsJzvD5ruHSpT9ESguPZwhd\n376i0Cz9Pp9vD9Hoj5BSkskMoOtpND3D0fk9LV9Hpd/h5dM/Tywax2tb7Nv1lZbHc+kOay5g8rwL\n+BxwEZgHHpBSHhFCvBZ4QkpZiDP9hfxxBjAF/IGU8i/b+cK1zDxv9N3VjvLcAt9Md0bwsLT0A4QQ\nhMO3rzButelDSonMe6ssK0U2e4KzZ/+AUOj6DVX2pVNqmUkO/sQEHmMG5leuj1UqEOLxwwSDN5Fr\n/ZzrjFnwvTQrXFp5TsfH7ZpCb3y82vEP7QdnxONHWVj4Jn7/PjKZC2Sz89h2hJ0739fw3NLvs+0Y\nfX13AIILF57FNL28dPpaUnrrUWSVfofp2CK2skRisn4l5fVKK7k5tRz6zzylM3lG5c7Xm1XHrzbr\nQsBIKReAt9X4/ElKkhiklF2zJ7Tr1O7GbrLRd+/a9cG6YZ615jE39y8IoRMIXEc6fQpNy7mzUqlT\nRWdyo3FL5+Tz7SYUupFE4jiRyI+QMks8fgRFCZBKrX3Zl41ApUAQ4hUikafp77+rmPPSiumx1ee0\nnT417QRnlAcH5KIZLStCKnWcWiWTVnpvvvOdT/L8817SY+fwD22ImJye0YofqJZDf/KMysx5tUpI\nrUXy6LoQMGtBOw7KVlraNnqZOnWol85DSokQkljsWRzHRNe3ImXOgdrKuIU55bou/ggARQngOKli\nf/a1LvuyEagUCIHAdUSjT5NIHMLjed2KJqjKZycWO0wgUH7Pm60N10vzbyvP8HqtU7dZufP1ucjJ\nT32+8+CWTmlKwAghfMAJwAGullKaJf/258B9wC9LKf+uJ7PsAe04KJvZTV68+DUmJ5ft0radqdr9\nd+pQL52Hx9OHbadRFC+2HcNxcj+NqoZbGne54dkJpHRQFB9S2iiKH0XxkslcQFU7KqxaxN83R2x+\nbF3ssLpN5cJrGFsJh28nHj9IJjPd0ARVayFOpydRFH9RS4CVf9NWFvSCIIrFDhcb5zVjEm3lGV6t\nOnV2Zo5M8gSOFWNQj5L26NSKRwsNLbrlYVaJpgSMlDIlhHgQ+HNy/pI/BBBCfBz498C7N5Jwgfac\nmyvt2uLxo5w794fYtonjmGSz86RSJ/H7ry97mTrNei/N7AcV246jqmGE0LGsKEIIAoEDLZX6X26R\nO4cQ3nzODWjaVoQwyGbnGRgor+tVuUv2+1/H1JSDlE7V+H/0R7t58cV3EkloEEzgzwcKrbfQ0HpJ\narH5BEbAglSQS5e8PP/8Yt6hneT5548Vj8tmFfr6FvF6h4qfqaqXcPgNJBJvJJGwmZ+PAj+q+g7T\n/DukTCGESiFK37aHsO3D6PpQ08/KzMw/sbRkImWsOE5fn7dqQS8IIsexSaUmEEIhm11qyiTayjO8\nGiHwdnaJdPQ5hGKgqEEUscBIX4yoka469o63PcEH7lv/Fb6bGeOZp3SOvLDcuC/U56wL30uBVkxk\njwLvAz4shPifwK+SK+XyoJTyf/Rgbj2lHefmSru2+fnHyWbjOE4KKXNZ3FJm8pEyqWLYZydZ7/H4\nUdLpSUCgaeG8xiJxnDSq6iMUuplCFJnHs63pcUtb5DqOiW1n0LQhNM2fF1oaQ0P3ls1jeZe8nenp\nY5w69b/4Pz++lYXF4arxf/TDHRgDl/D3pxEK7No9ztju2s7MtaRektroOZMrXvMZnCyIS8MYaT8C\nSKThv5fkSQ4Oatx1+4/Ys2cfw8NXYNtRLl06zTe+uY0Tp77LcumYau5+7SHiiRC5OJcCkm1bsiST\nFxkZieL376z7m0opOXLkBV588X9z8VIIWRLB7/faHDhwjp07HZR8e8eCZpFIHEFVfcU23ZnMDMHg\ngYYaRivP8GqEwA8PHebC1ChCyWnZc4sSK5tifOyVFc5cfbqRCFkY48gLHsL9yxu66FJzrbxXi6av\nSEppCyE+BHwN+Cfg9cCnpZS/06vJ9ZpWnZsr7dpyOzIHx8kiZZZcio4HKTOkUieJx48Wv6/drPf5\n+cfx+/eRTB4rZvYrig8hJNdc8ycdFyXcs+djxV1tITpIUbSq6KDC4qQoQQ4dep5Tp7MoYdh544+Z\nPn2gamz7xTgEUui6zg37rmfLUHfKo6xGWYwffsdg4fx++gIPMzE1iW3bIMEXnmP/3V8qO3Ya+Nah\na5i9OMO+ayYwzTDf+vY456RAjE80/J4l1cEYmsO0lk2RhpZhKhHka39xNXfeBu98538gGKzOgXIc\nh8ce+xyPP7HItbd58FaMY+LwzLMqx479Cb/yK7+Gz+crahaWFS1m1AthFE1lK2kYzT7Dq1Gn7tfe\n9TlUfaTY2vmZw8+wML9I0FSZmnhN175nvRHqc8qESiIumDqrrRtzX0vbRynl14UQB4E3AH8H/L+l\n/y5ySRifAX4K2EKuXtinpZSf7s5015aVdm25HZmKlBkAhFCR0kIIFSH8XbE5p9NT+Hy7UdVg25n9\njSi9RlXVGRi4q6Y9vrA4RaNLXLqUICt1UGAgmGHv1VdVjXsiHGZszMfeK/aiat1psAWd7QYbhXgC\nxCK5F/f8ZO7vi5OjDAW3MDD2Asl0isjclVXXOjVzjoWYwvefvpvI0iKmqTB1KYi48iyBkI8d2+rX\nxct6woz1PUPW8WI5Bppi4mSzHL2wDVPPcPq0xcmTx3j1q3+i6tyLF2c4evQcMbOPU5Et3LYlSr/h\nRyGLTzmPR0kzaepMT5/i7NmTXHvt9SWaRbjox5PSRFXDXdUwVqNOXa1SQYaWJTa/uQtnVprD1otz\nv0BLAkYI8X+Ry7IHiEkpK/V9DZgh19flNHAD8A0hxKyU8u87nex6oNGubWjoXubmHseyFpCSfKFD\ngcezDcMY6YrNubAodJLZvxLN7EwL81h+AgRej4XhG+c9P/fuquNPf3ewpiBYS2oJpyMveIqCJdzv\ncO6sRjqVMzVdmlHJmDCysJtAMEIm1seJf30vqqe/eP7hE4eR6jlec90T7Nr1Ih5PnL7hIU5bQbbv\n3M+v/sKvNpzTH3wow+SpeRw7iaL6WUwYTF5YwIvJ+E9+jepXroBESoFQFBaTg5zPjvCWGyTpxaeI\nJAOcngnhSJWrrnqRTOYUcH1Rs/B4RshkXi6aW73e3V3VMFYjobmyVJAm0hhalsNn9rKlshCVy6rR\ntIARQrwJ+ALwD0AW+H+EEH8opXy5cIyUMgH8l5LTXsiX3n8NuUKVm5pgcD87d76Pkyf/M1JmUdU+\nVDWEEAJd396VHeFal8WvnIfjAEgMPfdCz5nVmfQPPxiuckZCTr0fv6J2AuBacnFGJZvJCZRoRGBZ\nOQGTTtnongTBUIx4LEwyqTE88D284ZtR9ZzfaXJxgYWpMK++8Rk8HkinAxh6mptGzxNTd6/43bOz\nI+y5btmHdWbyDHOpSyTO76p7Tjx+lLm5v+Paa79HoG8Lp7MBYtkDaIbEP/QGpmLTJDOTyIyXbNYi\nk/kB8LYyzcJxkkXTWCBwZdeEwGqFKFeWCrIcLwcnr2F+cStbwr0JIllPVYvXK82GKf8E8FXgB8Av\nA2Pkerh8nBoJkiXneYDXAg93PNMNwtatuSSzc+f+EMex8Hj6iyU0Sp3k7bLWZfEr53Hy5BfxeuPM\nR4d5eXIvY7uqe5tMT2qM7LCLmkGBmfMqt9+9fiJeICf0Jk5raFq5pqCqEsty0HUFITRAIISCUAwy\nyRP49GWhEPYmMU0Dy5v728wYCMvDlsAxuk1hAbdtlXQ6iNdIc9PoFDNyK3Y6U9XCIJvVcZyLxb97\nXQF7NVtpl5YKOvncZ5hPnO3q+JU0a56tFESHX/DwzFM6/oDkupuyxc9b8Zt00olzNQXjigJGCLEf\neJxcUcq35XNgTgkh/gL4dSHEXVLKH9Q5/TPk4iS/0K0JbwS2bn0Lfv+enpkFml0Uum2aqDVef/+/\n5/BhnVemwih7zlJPR6sVOjl1Vlt3O707X28yc14lEJSE+x1OHfOQiIOmQTZbXVtVCJ1nfrCTdNYH\nwMWF68G0+OJf/Q5jO87zlrf8GZCrEmyoka7P9yMfsZia+k1sWzA/P0/aVFG9KbaMLvH2Ow/jZMu/\n0+PJoChbuz6PeqyHVtprTaUgKvx3J/6STt6b1Szn33BEIcQ48A1gEbhHSll6Vb8LvAP4JDUanwsh\nPgXcAbxBFrzelxFr2RsFum+aqDeeED/Tg9k3z2r0VFc1sCxwbIVsViEW9ZFM6vh8GaTMkEj0M7A1\nFyoaSadwsBkenmJ+fntxDEPLYtrdayxW4Nw5g/HxONlsBiEukEhp4EsRmd9R9EuoJCmYMT0eE12v\nel1bptnNy1q30u4lhw96qsy+AHXdZJchDQWMlHIS2Fnn36YBf61/E0L8EblIsjdIKec6naRLfeq9\n6N02TdQbL5H4PkJ4QDo4tuT01BkuLVwirM8V7eFm/APYmaGin6KbdLKTqyec/EFZDP/MZMDrza0Y\nGRP2XDXBrXec5ML5kVyek2OiaOW98UzLg6ZZCGFT6p+6lN634pyi8Sg/fvEojpPzTTnWPDv654ll\nPVx77TM4zl7g9uLxQiik02ex7Qxer0kmG8QRNmkT/uzv/5WQHiCoRAjqKaKLfZw8eRU339x6teJS\nWtm8FHx1f/zHP8uFC9uKuWF+/z48nr6GrZzXO8mEYPtYtQ/xwlTtKMkffscoMxMn4oL33ze4qX02\nXdeJhBB/Qi6M+fVSykvdHt9lmUYverdNE/XG0/XzHDhwLVPn4xikuXrgMAe/8w3GhlUGtrwa3bcb\n6WRIR58rc4avBc3ant9/33LEW+mikIgLdu/t58L0draOTKKoIebm7yKZ9CGUnBAykz4UT5QLMzvw\nGQpeb4K5yBAvT17D9p3V/qkCaTPNVx7/KhOXriS5NAxSwfBkGApoRJxR+gILbNli4/V+h3j8eoLB\n/cTjR7HtLI5joig6up6lLxwhmfEQSfg5efpMfvSrkUkf/rltvO4ndXbvLg+tbtWU2srmpeCrm5kJ\nsW3b8bK5+v1XMzlZ/55sNmIRpSwpEpR1mWzcTbp6ZUKIXcB7ARM4I0TRXv2klPKebn6XS+MXvdum\niXrj+f07+aVf+jX27fsyv/d7V/DSt8bwepOoioOiKvgDO5g+n1tExMw8RnB5QVntZLBmbc+lmk0u\nys0ufv7Bhxxge/5/AA7vvy+zLJAOHiK6FCc5P4BjBTh8+PUcP5fzT22nNvFEnE997lPMXVpg320v\noM5uw0Dl1Tf9EENPowid/ftHGR/PFcssLOS5XKW3Yxg7sawFvF4vmqZjmgoy3o/vXCHyTDI8mOUX\n3/0T3HrrXcVMfqhu9zA//21mZx+jv/9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h+ttGW1/AKr+nkHz2zFN6meBZ7Qg1M/4yS1OP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\niqLYgioYRVEUxRb+P5dlnMWv8mWeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f266a0919b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(ada_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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wgsJ5HzqflWtWTiuPlWZmUAptgOmhC6C1QZOfnTv38fLLx+gUQaq6mTOrnD//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\nIiIiUrL/A4RLb8sfXapqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2668fc45c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = len(X_train)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "for subplot, learning_rate in ((121, 1), (122, 0.5)):\n",
    "    sample_weights = np.ones(m)\n",
    "    for i in range(5):\n",
    "        plt.subplot(subplot)\n",
    "        svm_clf = SVC(kernel=\"rbf\", C=0.05, random_state=42)\n",
    "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
    "        y_pred = svm_clf.predict(X_train)\n",
    "        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
    "        plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n",
    "        plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
    "plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
    "plt.text(-0.5,  0.10, \"3\", fontsize=14)\n",
    "plt.text(-0.4,  0.55, \"4\", fontsize=14)\n",
    "plt.text(-0.3,  0.90, \"5\", fontsize=14)\n",
    "save_fig(\"boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['base_estimator_',\n",
       " 'classes_',\n",
       " 'estimator_errors_',\n",
       " 'estimator_weights_',\n",
       " 'estimators_',\n",
       " 'feature_importances_',\n",
       " 'n_classes_']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(m for m in dir(ada_clf) if not m.startswith(\"_\") and m.endswith(\"_\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X = np.random.rand(100, 1) - 0.5\n",
    "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg1.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = y - tree_reg1.predict(X)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg2.fit(X, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3 = y2 - tree_reg2.predict(X)\n",
    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg3.fit(X, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_new = np.array([[0.8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.75026781])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gradient_boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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l+JWIpuEnwk0v4bWV+vw/xye/d5pf9i+TFJfS6vUD/A//zuA55uFX1RrnnMssifgMMNHM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\nivdSKcV0xfQkq0W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sbIEb74FhY+pSFAmTSSBWr463HHWimC5JUZc6oKsL/v3v2h0/y0Fb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\n+GCv9V555ZWiAojf/va3vv/++/vmm2/e63UlBirJAUT8JD9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2bRonnXRSxrI2NTVpciop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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2668dcffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
    "    y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
    "    if label or data_label:\n",
    "        plt.legend(loc=\"upper center\", fontsize=16)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(11,11))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Residuals and tree predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Ensemble predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n",
    "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "plt.subplot(325)\n",
    "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n",
    "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "save_fig(\"gradient_boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=1.0, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=3, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0, random_state=42)\n",
    "gbrt.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=200, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_slow = GradientBoostingRegressor(max_depth=2, n_estimators=200, learning_rate=0.1, random_state=42)\n",
    "gbrt_slow.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gbrt_learning_rate_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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XCcRyIENE+vpsG4xd6MXfEGCWMWabs0rgfdgVJ9t6WB6llFLxpXFBKaVSkGcJhDFmD/aK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f266a105d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"Ensemble predictions\")\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n",
    "\n",
    "save_fig(\"gbrt_learning_rate_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting with Early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=55, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=49)\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "errors = [mean_squared_error(y_val, y_pred)\n",
    "          for y_pred in gbrt.staged_predict(X_val)]\n",
    "bst_n_estimators = np.argmin(errors)\n",
    "\n",
    "gbrt_best = GradientBoostingRegressor(max_depth=2,n_estimators=bst_n_estimators, random_state=42)\n",
    "gbrt_best.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "min_error = np.min(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure early_stopping_gbrt_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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BX+25zszamtlhwCnA/XGKzwCuMLNeQV/VK/ETywBgZi2CqckNyDezVmbWLPLcn5rZfmbW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\nfHcjXh/nRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2668dbb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(errors, \"b.-\")\n",
    "plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], \"k--\")\n",
    "plt.plot([0, 120], [min_error, min_error], \"k--\")\n",
    "plt.plot(bst_n_estimators, min_error, \"ko\")\n",
    "plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n",
    "plt.axis([0, 120, 0, 0.01])\n",
    "plt.xlabel(\"Number of trees\")\n",
    "plt.title(\"Validation error\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\n",
    "\n",
    "save_fig(\"early_stopping_gbrt_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbrt = GradientBoostingRegressor(max_depth=2, warm_start=True, random_state=42)\n",
    "\n",
    "min_val_error = float(\"inf\")\n",
    "error_going_up = 0\n",
    "for n_estimators in range(1, 120):\n",
    "    gbrt.n_estimators = n_estimators\n",
    "    gbrt.fit(X_train, y_train)\n",
    "    y_pred = gbrt.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    if val_error < min_val_error:\n",
    "        min_val_error = val_error\n",
    "        error_going_up = 0\n",
    "    else:\n",
    "        error_going_up += 1\n",
    "        if error_going_up == 5:\n",
    "            break  # early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "print(gbrt.n_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Coming soon**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "nav_menu": {
   "height": "252px",
   "width": "333px"
  },
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
