06_decision_trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, unicode_literals, division\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "\n",
    "PROJECT_ROOT_DIR = '.'\n",
    "CHAPTER_ID = 'decision_trees'\n",
    "IMAGE_FOLDER = os.path.join(PROJECT_ROOT_DIR, 'images', CHAPTER_ID)\n",
    "\n",
    "if not os.path.isdir(IMAGE_FOLDER):\n",
    "    os.makedirs(IMAGE_FOLDER)\n",
    "    \n",
    "def image_path(fig_id):\n",
    "    return os.path.join(IMAGE_FOLDER, 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'), dpi=300, format='png')\n",
    "    \n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trainning and visualizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\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=42,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data[:, 2:] # petal length and width\n",
    "y = iris.target\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(\n",
    "    tree_clf, \n",
    "    out_file=image_path('iris_tree.dot'), \n",
    "    feature_names=iris.feature_names[2:], \n",
    "    class_names=iris.target_names, \n",
    "    rounded=True, \n",
    "    filled=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_decision_boundaries_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=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)\n",
    "    if not iris:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris-Virginica\")\n",
    "        plt.axis(axes)\n",
    "    if iris:\n",
    "        plt.xlabel(\"Petal length\", fontsize=14)\n",
    "        plt.ylabel(\"Petal width\", fontsize=14)\n",
    "    else:\n",
    "        plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=14)\n",
    "        \n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.plot([2.45, 2.45], [0, 3], \"k-\", linewidth=2)\n",
    "plt.plot([2.45, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.plot([4.95, 4.95], [0, 1.75], \"k:\", linewidth=2)\n",
    "plt.plot([4.85, 4.85], [1.75, 3], \"k:\", linewidth=2)\n",
    "plt.text(1.40, 1.0, \"Depth=0\", fontsize=15)\n",
    "plt.text(3.2, 1.80, \"Depth=1\", fontsize=13)\n",
    "plt.text(4.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
    "\n",
    "save_fig(\"decision_tree_decision_boundaries_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting classes and class probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.90740741, 0.09259259]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict_proba([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity to training set details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.8, 1.8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[(X[:, 1] == X[y==1, 1].max()) & y==1]  # widest Iris-Versicolor flower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\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=40,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_wides_versicolor = (X[:, 1] != 1.8) | (y==2)\n",
    "X_tweaked = X[not_wides_versicolor]\n",
    "y_tweaked = y[not_wides_versicolor]\n",
    "\n",
    "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
    "tree_clf_tweaked.fit(X_tweaked, y_tweaked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_instability_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(tree_clf_tweaked, X_tweaked, y_tweaked, legend=False)\n",
    "plt.plot([0, 7.5], [0.8, 0.8], \"k-\", linewidth=2)\n",
    "plt.plot([0, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.text(1.0, 0.9, \"Depth=0\", fontsize=15)\n",
    "plt.text(1.0, 1.80, \"Depth=1\", fontsize=13)\n",
    "\n",
    "save_fig(\"decision_tree_instability_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure min_samples_leaf_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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v55JLFsYdsi+33PI9nnjiNdZuG0dd2yuceepbeeeb3xl3WDVjemb6y3HHEBDlBpEqsWbNam699Xqee+4gdh7YwZipWT7zsU9zwJR0N6Slid/ckJoqoHNutXPuJedcv3PuabwboL0n7rhERCQ+yg0iIsmRmopFEY7wJkGJiEg6KTeIiMQk9opFbhm0Rry7T2bMrLHYsmhm9g4za8n9fDTe3X7vjDZaERGJgnKDiEj6xF6xwLsd/C7gCrzbyu8CvmBm03PrkU/P7XcG8L9m1g38BlgCfDWOgEVEJHTKDSIiKRP75G3n3CJgUYmXx+ft91mg+mfwioiIcoOISAolocdCRERERERSThULERERERHxTRULERERERHxTRULERERERHxTRULERERERHxTRULERERERHxTRULERERERHxTRULERERERHxLfYb5EnwOjuX0N5+NT09r9LQcDBtbQtpaTkn7rBERCRGyg0iEjZVLKpMZ+cSVq36LP39uwDo6XmFVau8m9IqgYiI1CblBhGJgoZCVZn29qv3Jo4B/f27aG+/OqaIREQkbsoNIhIFVSyqTE/PqxVtFxGR6qfcICJRUMWiyjQ0HFzRdhERqX7KDSISBVUsqkxb20Lq6sbst62ubgxtbQtjiii5OjuXsGzZiSxd2sqyZSfS2bkk7pBEREKh3FAe5QURfzR5u8oMTMLTyh9D00RGEaklyg3DU14Q8U8ViyrU0nKOCsFhDDWRUddORKqRcsPQlBdE/FPFQgKRtvXRNZFRRCR8acoNygsi/mmOhfg20H3c0/MK4PZ2Hyd5bKomMoqIhCttuUF5QcQ/VSzEtzSuj56kiYwDkwVfeGEB8+b9nBkzno08BhGRoKUtNyQxL2gSuaSNhkKJb2nsPk7KRMbCyYKNjd2ccso99Dz1Btb0HBppLCIiQUpbbkhqXtAkckkTVSzEt4aGg3Nd3YO3D0jiONskTGQs1qI3alQfJ89+kjVPqmIhIumVxtyQ1LygSeSSFhoKJb4N132ctnG2USrVctc0pjviSEREgqXcMDJp6+kRyaeKhfjW0nIOM2deS0PDIYDR0HAIM2deu1+3cprG2Uap1KTArl3jIo5ERCRYyg0jo0nkkmYaCiWBGKr7WK0vpbW1LdxvLC1Ab289Dz17fIxRiYgEQ7mhcsXygu6SLmmhHgsJnVpfSits0du9exwPPPA2Vr46I+7QRERCpdxQ3HA9PSJJph4LCZ1aX4Y20KLX0bGWm2++jtWrD4BpnXGHJSISKuWG0pIwiVxkJFSxkNAlZQk/ERFJDuUGkeqjioVEQq0vIiJSSLlBpLpojoWIiIiIiPimioWIiIiIiPimoVA1Jml3ORURkfgpN4hIEGLvsTCzS8zsMTPrMbPFw+z7KTNbZ2bbzewmM2uIKMyqUEt3Oe3sXMKyZSeydGkry5adWJWfUaSaKTdEp1Zyg/KCSPhir1gAHcBXgJuG2snM3gZcAZwBHAbMAL4UenRVpFbuclorSVKkyik3RKQWcoPygkg0Yq9YOOeWOOd+AWwaZtfzgBudc8udc1uAq4Dzw46vmtTKXU5HkiTVkiWSLMoN0amF3DDSypNyg0hlYq9YVGAO8FTe86eAFjObElM8qVMrdzmtNEmqJUsk1ZQbfKqF3DCSypNyg0jl0lSxGA9sy3s+8HNTsZ3N7KLc+NzHNmwYrsGrNrS1LaSubsx+26rxLqeVJslKWrLUeiWSOMoNPtVCbhhJ5Um5QaRyaapY7AAm5D0f+Lmr2M7OuRucc/Odc/OnTUtvw1WQhVVLyznMnHktDQ2HAEZDwyHMnHlt1a38UWmSLLclS61XIomk3KDcMKyRVJ6UG0Qql6blZpcDc4Hbcs/nAp3OuaptchoorAZaTAYKK2DEBX4t3OV04POVu3RiQ8PBuYQweHu+oVqvqv2aiiSYcoNyw7AqzQug3CAyErFXLMysPhdHBsiYWSPQ55zrK9j1FmCxmf0Ib7WQLwCLo4w1amkprJK4/nklSbKtbeF+SRqKt2TVwgRHkaRQbihNuWFkKq08KTeIVC72igVeEvhi3vMPA18ys5uAZ4HZzrk1zrm7zezrwB+BMcDtBe9LjXIL2zQUVkG0nG3b9huuvfZqtmxpGfTa2LHb+MY3rgou4CLKbckqt/VKRAKh3FDjueHAAyewYcPYQdtbWrJ0dGwILtgSlBtEKhd7xcI5twhYVOLl8QX7/ivwryGHFKpKCts0FFZ+W846O5ewbt1VbNnyf4u+vnPnxEDiHE45LVnltl6JiH/KDcoNGzZcXOK1TCAxlkO5QaQyaZq8XRUqWWUiDSt1+G05a2+/Gud2D7nP6173r6xceUXFsQWtFiY4ikg8lBv2N9z9JZYuPYiHHpqdiAnSyg0i+8TeY1FrKilsRzLZLGp+W87KSTJmjnXrbgZg1qyvVRZgwKp9gqOIxEO5YX/l5IZsdgsrVlwOjHzSelCUG0Q8qlhErNLCttzCKq5Jcn67gEtdj2LWrbs1sIpF0iYVikhtU27YX/nDunoDnbSu3CDij4ZCRSyMLuw419D22wXc3HxGBWfLjijGQlpzXESSRrlhf5XkhqAmrSs3iPinHouIhdGFHffSgyPtAu7sXML69d7S883N69iy5cBB+zQ3r8t7FsyEvbivl4hIIeWGfQZyQ3Pz18rIC8FNWo/7eolUA1UsYhD0WMw0LD1YTH4hvmTJQXu37+nLMCqTxWz//Q888MOBnDet10tEqptyg2cgN+TnhdJGBTZpPa3XSyRJNBSqCpRqrUnS0oPFlCqsR2Wy/O8rh9PfbzgHzhkHHnheYPMr0nq9REQqkdaybqg/5Ovrm/f+nMk0c9RR3w6sMpbW6yWSJKpYVIFiY3PB6Ol5hWXLTkzs+NBShXVXzxjuX3UC/37zJ/nFLy7iuec+HehqUGlYqrFcnZ1LWLbsRJYubU30/7WIRK94boBstjvRZUXpP/AP4aSTnuXUU1/j1FNf4+STnw20h6dacoPygsRJFYsqsP8kOQADHECiJ58VK8R7+zI8vPqYUM8bx5rjYRT0mmgoIkMZKOsymeb9tvf1bUl0WRHXH/jVkBuUFyRummNRJQbG5i5bduKgJQuTOvlsIJ4XX/wKvb2v0d3dxAPPHccL3QdEcu6orkcld9SthCYaishwWlrOob39arLZLfttT3JZEed9OtKeG5QXJG6qWFSZOCef7dnTw913/4Jnn30a51zZ73PuHXR1ZXlh7RT6D+0gM6YPRz9uwla2bJvIunUbufrqz4cYeThGjx7NW9/61+ze/dWyCvrt2+9m3ryfc9JJ3XTtGseja48d8viaaCgi5Yi7rHjttVe4446fsG3b1grfeWbez4/nHunX0nIQ55zzwbIqAZXeVyPu/2sRVSyqTKU3WQrqZkDPPfc0d9xxOyteGM0Oa2RgKFa5XGMPtL1MXT0cceQRbN60hY39m9g+dif/+2oz1h/MUrORytbx4upf8773vjpohSvYv6Dv7FzChg1fpbFxNwATxnZz2pGP0rftbmi5oOjh/d7ZVkRqw0jKiiByw549e7jnnjt54IH/5aV1Y+kf1Vhx7NXHUf/cDlav/iZ/9VevDJkbRtKjobwgcVPFospUcrfToLphH330Ie66606eXXEg2bY12Oheb5pHBQwYP248Hzr7A8yZNYe+vj7uvu8e7n34j2RbN1R2sKRwsG7zRLq7mxg/vmvQy/kFfXv71Ti3e7/XR2Wy9G38T5hVvGLh9862IlIbKi0rgsgN2WyWxYuvY/nyzazd1ogd9opmdQI46O03nn1pOm/aNY6xY7sH7TKQG0YyrEl5QeKmikWVqWRsalBjMV95pZ3e3nr6M1A3up8pB0zmbW96W0Vxj6ofxTGzjmH06NEA1NfX8zdv/WvecMICXlzzYkXHSoKu7i5+9+Dv2dXTwyOPnM6b3/wbMpl9dw4vLOhLdVO7vs6S54hzHLKIpEelZUUQuWH37l1s3bqZXbvGYuN2kRmV4cS58zjisCP8fZiUe/r5p3lmxXKyjX08/fQbOPHE+6mr69v7en5uGMmwJuUFiZsqFlWo3MlnwY/FNAyjubmZBcctGOEx9jd18lSmTp4ayLGitH3Hdu59+F4weOGFYzj44M3MmbOK/v4NRQv6Ut3XVt8y5HminGgoIulVSVkReG4ww8w49uhjOeaocFf9S7qePT08s2I5YKxdO5OWljqOOuq5opWAkQ5rUl6QOKliUcM0FjM6GzYcTkvLVcyc+bqir7e1LWTlys/sNxyqN5th7IEfjypEERFAuSFKW7cexYIFtxR9TcOaJI1UsahhKrTCc9rMWWxaf+t+2774RWhpydLRMXjOSEvLOWzZsok1a66hoWHfqlBnzX57VCGLiADKDWH6x78+h22bP7Tftk99qnhu0LAmSSNVLGqYCq3wbFo/quj2zs7Sq1tNmPB2Hn/8RZ577gC6p3XSOLU3rPBEREpSbgjPts2D74QOpXODhjVJ2qhiEbGglncNigotEZH4KTeISDVQxSJCYd2BWURE0ku5QUSqhSoWEQpqeVcZ3rzWVjYU6Vqe1pLl8Y6OGCISESlOuSE6yg0i4dLtaiIU/PKuUkqxxDHUdhGRuCg3REe5QSRcqlhEqNRSfVrCr/pMOaD4xOuWlmzR7SJSu5QbasfEybuKbldukGqhoVAR0hJ+teP+VSu5+ntX070JGtZMZ/bstbzvfReUvI+FH0mb9CkilVFuqB3X3bWEJf9zB9k1B3PIuD0ceWQ9l132+VDOpdwgcVDFIkJawk+CpkmfIumn3CBBU26QuKhiETEt4SdB0qRPkeqg3CBBUm6QuKhiIVVpWku25Mof1USTPiunVWFEapdyg3JDMcoLwVHFQqpSrRQEDQ0H09PzStHtUpxWhRGpXcoNyg3FKC8ER6tCiaRYW9tC6urG7LdNkz5FRGqbcoPERT0WIimmSZ8iIlJIuUHiooqFSMoFPekzjCUKix1TRETCE2RuCGvp2sLjNjVdGEC0EidVLERkrzCWKCx1zHHj3g5MDSRuEREJR1hL1xY77p49X2Pq1BOBg3zHLfFIxBwLM5tsZneYWbeZvWxmHyyx3yIz6zWzHXmPGVHHK1KthlqiMOhjNjf/YcTH9KPU6i/VtipMNVBuEIlfGHmh1HGd66Gt7Ulfxx0J5YXgJKXH4jpgD9ACHAfcZWZPOeeWF9n3Z865D0caXRG6o6VUozCWKCz13kxm24iP6Uf+qjD5Swxu6MwwPXMoUNkSg1qmMFSpyg3KC1KNwlq6ttT7Gxq6fR13JArL6oFyPT8vQPnlei3nhdh7LMxsHHAucKVzbodz7kHgl8BH4o2stIHuO28pN7e3W7Czc0ncoYn4UmopQj9LFJZ6bzY7ccTHDEoQSwxqmcJwpC03KC9ItQojLwz1/p6ecb6OGwS/5Xot54XYKxbALKDPObcyb9tTwJwS+59lZpvNbLmZfaLUQc3sIjN7zMwe27BhU5DxhtYtKBK3MJYoLHXMLVveMuJjymDzWluZnjl00GNea2vcoY1UqnKD8oJUq7CWri12XLMG2tuP93Vc2V/UuSEJQ6HGA9sLtm0DmorsextwA9AJLABuN7OtzrmfFO7onLshty/z5891QQasO1pKJY48cjmvf/29vPbaj9i8OdnDI8JYorDUMVev7gReCyLs1Amjm7wKW8hSlRuUF6RShx66itmzH2Xp0usSPXQurKVrix23qelCNm58yXfMaVUNuSEJFYsdwISCbROArsIdnXPP5j192Mz+DXgPMCh5hEl3tJRyzTroJU475lFGjeoDyltN4/bbr2T3bu9vp59+YWDroTSMm8c7zv9s2CEHvnxt6WN+L9BzpEkVVgLCkKrcoLwglTjq8BXMm3c/9fXl5YbW1ml0FikfWlqydHRsCDdYwskLxY67Zs1q4PrAz5MW1ZAbkjAUaiVQb2Yz87bNBYpNzivkAAslqiHojpZSrpOP+sveSsWA4YZHDFQqCvV0Two0NpGES1VuUF6QSpx8wsN7KxUDhsoNxSoVQ20XiUvsFQvnXDewBPiymY0zs5OBdwE/LNzXzN5lZs3meT1wGXBntBF7NeyZM6+loeEQwGhoOISZM69NZBemxKtpzM76pZJeAAAgAElEQVSi2weGR3R2LmHZshNZurSV9va/ZerU1VGGV/OCWGJQyxSGI225QXlBKtE0blDHG+Dlhvy8sGzZiVoAIAZ+y/VazgtJGAoFcDFwE7Ae2AR8wjm33MxOAf7HOTc+t9/7c/s1AK8A1zjnbo4j4LC6BaW6dO0ay4SxgysXDQ0HD7o5UF/fOo48MvwubdkniGX/qn3pwJilKjcoL0i5urqbmDB+cOUik5lU4mZ0F0ccYW3zW67Xcl4oq2JhZmOAVUA/MNM515P32veBjwIfcs79dCRBOOc2A2cX2f4A3gS+gecfGMnxReKyev3BzD1sFZY3KGNgeESxVWQymepvzag2SVmvfFpLtmQcYVFuEKnczAPWUF+/B+cYlBvMIJsdvLqYpE+t5oayKhbOuV1m9kXg+3jV5m8BmNnVwIXAP440caSFbnwklZox5SXmHLJ6v8QBxgEHvJeWlnNYseKSuEKTAI1ksl0YBX0cLWTKDcoNUpkxfQ9xxtFPMKqgEam+vpkjjviK8kIVqdXcUMlQqMXAp4CFZvZfwMeAK4AvOuf+I4TYIjNcYigcslLOyj5SmaTU7IN04qF/YVR9YWHg2LLlXqD0KjKTJnWydWvLoO0N47aGEabEIK3f6RIWU4W5oZwKg3JDuKoxL0zou436Ij3Tmcw4WlrOyX3nBueFyZPXs3nzAYO2t9TAmP1aktbvdb6yKxbOuayZXQH8Cm9S3JuBf3fOfTms4KJQTmIY6sZHSh7BSNsSa+UkvPGjh5643da2cL/vHkA2m+HSSy/k6a4pNE7t5fILPklrSysrV6/k+h/fQO/64itGicSlGnNDuRUG5YZwVWNeyFD8poxD5YW6ujE888zP9Z2SVKhoVSjn3K+BJ4G3AD8DPpn/upk1mNl/mdlqM+sys5Vmdmlw4QavnLulhn3jI60AkT7lJLwde8YW3WdgXfvCVWTq6w/khRdOYvXq2YHHKxKmassN5d5FO8zcoLyQPuXkhSxTiu5TKi9odTFJm4pWhTKz9+GtIw7Q5ZwrvGtpPbAOOBNYDRwL3GNmnc652/wGG4ZyEkOYNz5SV3r1+vPa4zilbdl+w6EK17XPX0Wmo2Mt999/XeRxivhVbbmh3ApDWLlBeaF6ba9/L009N+w3x2KovCCSNmX3WJjZmcAtwB3AT4ELzOx1+fs457qdc1c6515wzvU75/4C/BJ4U5BBB6lUAsjfHuaNj8ptGZP0Wb3pcH7/9AK6uibgHNTVHaCWpypUy+uVQ3XmhnLyAoSXG5QXqteu+pO59/kT2L6jCedgz54m5YUqVau5odzlZhfg3ajoIeBDwCHAucDVFFkKMO99o4BTgGt9RxqSUuMZC1sPgFBW/gh7mJUEp9T42aGsfO1wXl52GrNnr+V977uAlpbXDfueGTOeZf4b7qNpTDe7X/wjXSwE5owwaglbNUy2G6lqzQ3l5AUILzcoL6RLpblh1frpPP/YGzlk3B6OPLKet761vO+LViBLl1rNDcNWLMxsNvAbYCVwdm6d8hfN7Ebg42Z2snPuoRJv/y7QhdealUjlJoawuibDHGaVJnGswV+pKCYMTp26mhkz/sSoUX0AuL51bFh1BTbx8tDPnSTVuBpMtanm3FBJhSGM3KC84ElDXoBocoOGxykvpMWQFQszmw7cA2wB3uGc25738lXAecDXgZOLvPdfgTcCb3HO7Qks4hDEOZ6xVMtYc/MZLFt2Ys20TCStUBhJ7wT4T3iHHfbE3krFANe/C9t+I3C6r2OnSdpWg6k1tZAbkpgX2toW1lSrtfLCPlqBTHkhLYasWDjn1gCHlnitAyi67I2ZfRs4Ay9xbPQbZDUr1jLW3HwG69ffVtMtE3GrtKBak1075OvTpr1EZ+f7ee21DUP+MdDQ0F38ANkNFcUTtmJ/3EA4wwUleZQbwlWqxwSo+VbrOI3kD9ihcsOhh65i9uxHWbr0umHLzDQMjytV6a2lyrBUuCpUOczsO3hLDr7ZOZesv4YSqrBlbNmyE2u+ZaKaHHnkcmbNeoT+fq/Vaqg/Bnp6xtHYWKRykZkWepzlKtYlv2LF5ZiBc717t+kPHsmn3FCZYj0myg3V46jDVzBv3v3U13s91MOVmUkfHldqqNa2bY+qobTGVHQfi+GY2WHApcCRwEtmtiP3+J8gz1PNOjuXFC08IFktE1K+BQvuI1Nwp9VSK7y8/PIJ9PbuX9+3ujG4CReGGmMlinXJQ+/eSsUArWIjA5Qb/Bm4p4VyQ/U4+YSH91YqBgxVZoa5OmUQSg3VWrfuVq1wVmMC7bFwzr0MWJDHrCUDNf5SktIyIfsbbvzs+PHbi24v9sfAxo0zePXVCXtXhaobdSDTjljIa91zgBeDCNe3Sv6I0R88AsoNfhS2BBej3JBMQ+WGpnFdRbeXKjPDXJ0yCKXL+uLXQLmhegU+FEpGrnhLsCdJLROw/1jK5uaJHHTQsax4OTnDdcI03HyKQjt2TKCpaXDlotQfA6tXz+bprik0Tu3l8gs+SVNLK6+tXjmiWMNQqku+1L4jkZbVYETCNlRegGTlhvy8MHr0QUyZMosXXjg27rAiUWle6OpuYsL4wZWLocrMJN84r3ReyFCscjGS3KC8kA6BDoUSf4aqwSfpBjoDLWheIeLIZLYyZ85DzDri2bhDC0yQN7ZZtux0stn9C8Mk/TFQqWJd8jAK79YE+/j5jI93dLAmu3bQI2mrxIiEbai80NBwSGJyQ2Fe2LOngxkzHqCtbUXcoQUmyLzw0BMn0de3f9tuteWFuroxHHjghwMbwqW8kA7qsUiQ0pOzDklE4hhQrAWtvj7LyQuW8sKyt8cUVbCCLKheeGEOBx+8iTlzVtHfP/SqUGkw1Io1Se2mF0mrofLCggV/jiGi4orlhUwmy3HHPczjHYfFFFWwgswLK146iskNfRx//KOMHr0j9WXmUEO1Jk58vXJDDVHFIkHKvdtr3Eq1oDWVmEtQiahvgBPV+TZsOJyWlquYOXP4O2+nQakueSULkWClPS+MKzGXoFJR5oaozrV27UwaGl7HZZd9PrBjxmmovKDcUDs0FCpBWlrOYebMa2loOASwRHVz5ys1NrJrxwTfx476Bji64Y6IJFna80J3d1Mgx4+yrFZeEBk59VgkTBpq9sVa0Pr6Mjy07NQYoxIRqU5pzQvZbIa//OWkGKMSkaipxyIkA+uOL13ayrJlJ9LZuSTukAJT2IKWzU5i+fKTWfni7LhDExFJrFrKC6NHt7J69Sm0tx8Vd2giEiH1WISg1B0ooXrGoOe3oC1Z8iNee215zBFJLYt6bo5IpWotL3R372Dp0mtijkhqmfJCPNRjEYJSd6CM6k6T1dwqJlKMxkRL0sWdF0C5QWqL8kI81GMRglKrY0Rxp8m4WsW++91v0d09ce/zb/y992+lLQNR3wBHN9wpLf9mV36WCAzqOCJpFmdegHhyw7/8y7Xs2LEvL/x8kffvSFqMoyyrlReGFmZugOOCD1gipYpFCEqvOz6yuxBXYqhWsTD/mMuvVOSrtGUg6u5JdYcWF9QfIUMdJ07qIpeoxZkXIJ7ckF+pyDeSFuMofy9VBpQWdm6YPPlzwQddAeUG/zQUKgSl7kAZxbrjcbeKycht33438+b9nAsv/AYf/avbOWJqe2yxBDVsIwnDP4oZqot8euZQpmcOZV5ra8RRSTWLMy+AckOaJWkIW9i5YcuW//Qdox/KDf6pYhGCONcdL9X6FVWrmIxMZ+cSNmz4Ko2N3ZjBhLHdnHbko/RtuzuWeIL6IyTNf8xoHK4EKe77USg3pNNAy77X2+X2tuzHVbkIOzdks+srjilqyg1D01CokMS17nha7tIq+2tvvxrndu+3bVQmS9/G/4RZF0QeT1DDNqIa/qEx0ZIGcd6PQrkhneIa3lxK2LkhkzlgxLEVUl6Ih3osqkzcrWIyMqVab1xfZ8SReIIathHV8I/HOzpYk1076KExsSIe5YZ0Slqvb9i5obn5475jHKC8EA/1WFShOFrFxo3bVnQCt1oGHP3O0d/veOaZJ1m7tr3oXpnMZMw2Ddpu9S37b6hz9Pd7a8T/7GeLgw83T2PjWTQ1/ZZMZivZ7CS6us7k1Ve3A5Wdt9RxNm3aQH+/4aw/lPhFZH9R54bx47cVncCtvFC+uCf9Fxr4/vhdFarUcXp6jgOuDzpsiZAqFiVoiczKXHLJp1jx8jTqjmjniKMO49IPXRp3SLEa0zCGMWPG0j16E3sOXsOzL07l5ZdfBl4uuv/hh8/npJPupb6+b++2vmyGyW2fBqB5YjP19aPobdrKNpfl6ecmsWrVupA/xUHAeQXbRnLO4sfpy45me8NOrPVVrK6Olqktxd4cilJd5FHRyiPppdxQvs9//rO88MJYNo3eTX3LVi547/kcc9QxcYeVKkkcwhZUBbXYcdasWe37uH7EmRuqJS+oYlFELdwhVcI1atQoPnX+5Sz+xc28sOpFehtfZUt/6ZGHW3ZOoHv5CZw862maGnexs6+JqYdfwbTp7wdg2pRpfPKjl3LTfy9mva1nZ9MOdg5xvCjc/d0b6OmeNGh7w7itvP2Si8o4gsPq+xkzZiwfOPu9zD16bvBBlpBfSA9VmIdFN25KJ+UGiVpQPQRRam2dRmeRsqylJUtHx4YYIipfnLmhWvJCIioWZjYZuBE4E9gILHTO/bjIfgZ8DfhYbtP3gSuccy7IeKKcLKXWr+rVNL6JSz98CctXLueO391J7549Q+6/oe8YfrVyLu849e3MP3Y+3td9n9aWVv7vxVdw3yP38cCfH6S/P94hRMUqFQPbJ09rKusYR884mne//WwaGxqDDK0iaWoJqjXKDcoNEu+k/5EoVqkYantSKTeMTCIqFsB1wB6gBe+2i3eZ2VPOueUF+10EnA3MBRzwO+AlINCFj6OaLKXWr9owZ9Yc5syaE8ix6urqeMtJb+EtJ70lkOP58YMh7nP3pU8tiiwOqWrKDSg3iEh6xL4qlJmNA84FrnTO7XDOPQj8EvhIkd3PA77pnHvFOfcq8E3g/KBjimq976TePExEJG7KDcoNIpI+sVcsgFlAn3NuZd62p4BiTbxzcq8Ntx9mdpGZPWZmj23YMHi1naFEtURm0paRExFJEOWGMreLiCRFEioW44HtBdu2AcUGaY/PvZa/33grHIwOOOducM7Nd87NnzZtSkUBRbXet+6EKlKbSk3+0zKc+1FuKHO7iKRfteSFJMyx2AFMKNg2AegqY98JwI6gJ+hBNJOlkriMnEi5dFfTkdOkwLIoNyg3SAq1tGRLrgolpVVLXkhCxWIlUG9mM51zq3Lb5gKFk/PIbZsLPDrMfqmQxmXkRAZUSyEoiaXcoNwgKZT0JWUlXLFXLJxz3Wa2BPiymX0Mb+WPdwEnFdn9FuDTZvYbvJU/PgP8e2TBhiCI1i8tSygi1Ua5wV9uUF4QkTgkYY4FwMXAGGA98BPgE8655WZ2ipntyNvveuBXwNPAM8Bd1Pi93weWJezpeQVwe5cl7OxcEndoIiJ+KTeMgPKCiMQl9h4LAOfcZrw1yAu3P4A3KW/guQM+l3tUeg4/ISZWe/tXSyxL+FUOOODdEUWRnms71J00yxna4/f9IlI+5YaRSUZeSJc4c0M1fgdHSpci/RJRsQhbR8erfOUrn487jFCceuqrDF73BHbvju4z9/c7Nmwcj5u4BWf91NWV7giL+w/zYuceanvQ75dgxf19knSr1tyQhLwAsGtXlm27mmCSN99euQHqLHcNJm5h06YpTFi/uSq/g35s317PTtsNo3uA+n3XrAJxf59qWU1ULHbtqePJNY1xhxGK47vHM2H8jkHbu7rHR/iZ+2HyBqyhl7Fjx/PON72z5J76w1yCpO+T+LGzSnNDMvICUL8Hpq+hrh4On3EERx52ZMlda+V3+djXHcuDTz5ER/9r7GrcxbMbJ0O2uj6jb2N2YYetpa6+jjcct4ApzZUtCw21831KopqoWDC6F2vtjDuKUDzUfjRvfd2TjMrsW8atN5vhofajI/3MdXV1nHjs6zn3nefQ2FB9iVpEqo9VaW5ISl4woKFxLB846+84bs5xFLmtSM2ZMH4Cn/vYP7H00Qf41e/uordlY9whJY4Bzc2T+eh7zuOwQw6LOxypUE1ULKY2T+Wjf3de3GGEprfnfjK7foj1b8TVTaV33Ec4+dTTODnCGFqmtdDa0hrhGUVE/JnSPKVqc0MS8kJdpo5Zh89iTOOY4XeuIXV1dZz+htOYd8wJvLjmRVy/JhbkG90wmqNnHE0mo96FNKqJisWYMWM4/pjj4w4jRMcDl8cdhMREY0lFRmbsmLFVnBuUF5KuaXwTx80+LrTj12pumNeqRs44JWW5WZFIlLordLl3i/b7/jBoLKmIiD/KDdWj2j9f0tVEj4Ukx7SWbMkWlCj4baWp5lYeEZG4KDdIFOKs6NUKVSwkUip8RUSkkHKDREHfs/CpYiE1JY1jTtMYs4hImqStnE1bvFI7NMdCakoax5ymMWYRkTRJWzmbtnildqhiIZJySZw0GJVa/uwiIkOp1fKxVj93UmgolEjK1XK39+MdHUWHBGzozDCvtTX2a6PhCiISl1otYwY+d2H5u6Ezw/TMoYkof6s5N6jHQkRSLclDApIcm4hINUty+Zvk2PxSj4VISNLaIpHWuEVE0iCNZWwaY5Z4qMdCakqUYy+DapGIerxoNbekiIgUk7bcoLwgSaUeC6kpaWxZSWPMIiJpkrZyNm3xSu1Qj4WIiIiIiPimHguRGEzPHLr35zDHqNbCuNhpLdmSnzFuSY5NRJJHuSE4SS5/kxybX6pYiMQszDGqtTAuNookONIkXC0JWkSip9zgT9jlr5/KWTXnBlUsREJSqkUi6dLckhJWK1wtJGERiUYac4PywmDKC8WpYiESksICK7+LO8nS3JKigl5Eki6NuUF5QcqlydsiIiIiIuKbKhYiIiIiIuKbKhYiVSzqmyiJiEjyKTdIWDTHQqSKpXlcbJKkeeKiiEgh5Qb/lBeKU8VCJCIqhMJX7jWudJUQJWERCYtyQ7gqub6V5AblheJUsRCJSC0WQl2dd7C5/ev09XRQ39DK5LbP0dTy7tDOV+411iohIpIUtZYbkpoXQLkhCKpYiEhgent7+e0Dv2Ptq2s5cOwzzJ5yF5m6PgD6el7ltec+w70P/oF1O4+JOdKvlHzlP394fYRxiIjUjq7OO9iw6gpc/y7AywsbVl0BEGrlQqKjioWIBGLFiyv44R0/Yvv27dBvLDjlt3srFQMydX0cNv63/OHJ5BY9z654Pu4QRESq0ub2r++tVAxw/bvY3P51VSyqRHKzu4ikxtJHl3LH3XeS3dOP29JMY9cEmhp3Fd23qXEXjWunRxxh+ZIcm4hImvX1FB+WVGq7pI8qFiLi28rVK3FZYO10Zkzr4uAjd9PXN5FRo7YN2revbyJnvmV89EHm+emtpV+LO7aoDHUNRETCUN/QSl/Pq0W3S3WIvWJhZpOBG4EzgY3AQufcj0vsuwj4PNCTt/lY59zqsOMUkTJk6xk9up+DDjqEY475KqtWfZb+vG7vuroxHHPMVznjjHNiDBIWLszSWWQyXktLlvPOuziGiKJ3wQVXxR1CScoLItVpctvn9ptjAWB1Y5jc9rkYo9pHK3T5F3vFArgO2AO0AMcBd5nZU8655SX2/5lz7sORRSciI9LS4lUe2tuvpqfnVRoaDqatbeHe7XHq6NgQdwgyNOUFkSo0MI8iylWhKlFrK3SFIdaKhZmNA84FjnHO7QAeNLNfAh8BrogzNpFKVXpvhFrQ0nJOIioSkh7KC1JNlBcGa2p5d2IqEhK8upjPPwvoc86tzNv2FDBniPecZWabzWy5mX0i3PBEyjfU+tfTM4cyPXMo81o1jlRkGMoLUjXKyQvKDVJN4h4KNR7YXrBtG9BUYv/bgBuATmABcLuZbXXO/aRwRzO7CLgI4ODpBwcWsIgfab3JjlrdJEKh5QVQbpBkUm6QahFqj4WZ3WdmrsTjQWAHMKHgbROArmLHc84965zrcM5lnXMPA/8GvKfEvjc45+Y75+ZPnjY5yI8l4su81tb9WqrS0GKlu5FKUOLMC7n9lRskkZQbpBqE2mPhnDt9qNdzY2nrzWymc25VbvNcoNQEvUGnAGzkEYpETwWx1DLlBZHilBukGsQ6x8I51w0sAb5sZuPM7GTgXcAPi+1vZu8ys2bzvB64DLgzuohFRCRMygsiIukV9+RtgIuBMcB64CfAJwaWFDSzU8xsR96+7wdewOsSvwW4xjl3c8TxihSlda5FAqO8IFVBeUFqTdyTt3HObQbOLvHaA3gT+QaefyCquEQqlT9RbXrm0BgjEUk35QWpFoUTmJUbpNolocdCRBKuVKubWuNERGqXcoMUir3HQqTWTGvJllyeL6m0bKCISLiUG6QaqGIhEoKhEkTaC+Li65Z/iYZxW3nH3341lphERNKg9nJDdXw2KZ8qFiIhqLZCtFTCyNfTPSmiaERE0qkWc4OWy60tmmMhIsNSYhARkULKDVJIFQsREREREfFNFQsREREREfFNFQsREREREfFNFQsRCUTDuK1xhyAiIgmT5OVyJXhaFUpEhjXcEonf/+n3eea55+h/sS364EREJBbVvHyujIwqFiIyLCUIEREppNwghTQUSkREREREfFPFQkREREREfFPFQkREREREfFPFQkREREREfFPFQkREREREfFPFQkREREREfFPFQkREREREfFPFQkREREREfNMN8kQqNK+1VXcaFRGRvZQXRDzqsRCpULHkMdR2ERGpbsoLIh5VLERERERExDdVLERERERExDdVLERERERExDdVLERERERExDdVLEQqNK0lW9F2ERGpbsoLIh4tNytSIS0dKCIi+ZQXRDzqsRAREREREd9UsRAREREREd9UsRAREREREd9UsRAREREREd9UsRAREREREd9irViY2SVm9piZ9ZjZ4jL2/5SZrTOz7WZ2k5k1RBCmiIhESLlBRCSd4u6x6AC+Atw03I5m9jbgCuAM4DBgBvClUKMTEZE4KDeIiKRQrBUL59wS59wvgE1l7H4ecKNzbrlzbgtwFXB+mPGJSHkmTZjEqLpxNI4exejRGerq4m6zkDRTbhARSac03SBvDnBn3vOngBYzm+KcG5R8zOwi4KLc057pmenPRBCjH1OBjXEHMYykx5j0+KCGYvzABxYGEEpJNXMdQ3RU3AEEpJpzQ9K/Q6AYg6IY/Ut6fJCOGH3lhjRVLMYD2/KeD/zcRJFWLefcDcANAGb2mHNufugR+qAY/Ut6fKAYg6IY/TOzx+KOISBVmxuSHh8oxqAoRv+SHh+kJ0Y/7w9tvIKZ3WdmrsTjwREccgcwIe/5wM9d/qMVEZEoKDeIiFSv0HosnHOnB3zI5cBc4Lbc87lAZ7GubhERSSblBhGR6hX3crP1ZtYIZICMmTWaWanKzi3AhWY228wmAV8AFpd5qhv8Rxs6xehf0uMDxRgUxehfYuNTbtgr6fGBYgyKYvQv6fFBDcRozrmgAqn85GaLgC8WbP6Sc26RmU0HngVmO+fW5Pb/NPDPwBjgduDjzrmeCEMWEZGQKTeIiKRTrBULERERERGpDlpsXkREREREfFPFQkREREREfKvKioWZXWJmj5lZj5ktHmbf880sa2Y78h6nJynG3P6fMrN1ZrbdzG4ys4YIYpxsZneYWbeZvWxmHxxi30Vm1ltwHWfEFZN5rjGzTbnHNWZmQcfjM8ZIrlmR81by+xH5966SGGP8/W0wsxtz/79dZvYXM3vHEPvH8ftbdoxxXccoKS8EFqPyQrgxxpIXcudOdG5Iel7Inbvmc0NVViyADuArwE1l7v8n59z4vMd94YW2V9kxmtnbgCuAM4DDgBnAl0KNznMdsAdoAT4EfM/M5gyx/88KruPqGGO6CDgbb+nJY4GzgH8IIR4/MUI016xQWd+9GL93UNnvcBy/v/XAWuA0YCLeSkS3mVlb4Y4xXseyY8yJ4zpGSXkhGMoL4cYI8eQFSH5uSHpeAOWG6qxYOOeWOOd+QZG7riZFhTGeB9zonFvunNsCXAWcH2Z8ZjYOOBe40jm3wzn3IPBL4CNhnjfAmM4Dvumce8U59yrwTUK+ZiOIMRYVfPci/94NSPrvsHOu2zm3yDnX7pzrd879GngJmFdk91iuY4UxVr2kf6dAeSGCmJQXhpD03JCS3+Gazw1VWbEYgePNbKOZrTSzK630eulxmQM8lff8KaDFzKaEeM5ZQJ9zbmXBeYdqmTrLzDab2XIz+0TMMRW7ZkPFHpRKr1vY18yPOL53IxH776+ZteD93y8v8nIiruMwMUICrmPCJP16KC9UHpPyQjASUaYNIxG/v7WYG5JWUMZhKXAM8DLef/LPgD7g6jiDKjAe2Jb3fODnJsKruY8Hthds25Y7ZzG34d1UpRNYANxuZludcz+JKaZi12y8mZkLd43lSmKM4pr5Ecf3rlKx//6a2SjgR8DNzrnni+wS+3UsI8bYr2PCpOF6KC9UHpPyQjBiL9OGkYjf31rNDanrsTCz+8zMlXg8WOnxnHOrnXMv5bqDnga+DLwnSTECO4AJec8Hfu4KMcbCcw6ct+g5nXPPOuc6nHNZ59zDwL/h8zoWUUlMxa7ZjpCTR7HzDpx7UIwRXTM/Av/eBS2M399KmFkd8EO8sdOXlNgt1utYToxxX0e/lBcA5YVyYlJeCEaic0MSyrNazg2pq1g45053zlmJx5uCOAXga5WIEGJcjjfZbMBcoNM5N+IabRkxrgTqzWxmwXlLdZUNOgU+r2MRlcRU7JqVG7sffq5bGNfMj8C/dxGI7BqamQE34k3GPNc511ti19iuYwUxFkrad3FIyguA8oLyQnTSlhsivYa1nhtSV7Eoh5nVm1kjkAEyZtZYakyYmb0jN74MM3/7TVQAAAOFSURBVDsauBK4M0kxArcAF5rZbDObhDeDf3GY8TnnuoElwJfNbJyZnQy8C692O4iZvcvMms3zeuAyAr6OFcZ0C/BpMzvYzFqBzxDyNas0xiiuWTEVfPci/95VGmNcv7853wNeB5zlnNs1xH6xXUfKjDHm6xgJ5QX/lBfCjzGuvJA7d6JzQ0ryAtR6bnDOVd0DWIRXq8p/LMq9Nh2v+2l67vm1eGMZu4HVeN08o5IUY27bp3Nxbgd+ADREEONk4Be5a7MG+GDea6fgdSEPPP8J3pjAHcDzwGVRxlQkHgO+DmzOPb4OWETfv3JjjOSalfvdS8r3rpIYY/z9PSwX0+5cPAOPDyXlOlYSY1zXMcpHqe9U7rVEXI9KYozxe6W8EG6MseSFob5/CfrulRVfnOUZyg3eL5SIiIiIiIgfVTkUSkREREREoqWKhYiIiIiI+KaKhYiIiIiI+KaKhYiIiIiI+KaKhYiIiIiI+KaKhYiIiIiI+KaKhYiIiIiI+KaKhYiIiIiI+KaKhYiIiIiI+KaKhUiAzGyMmb1iZmvMrKHgte+bWdbM3h9XfCIiEj3lBqkVqliIBMg5twv4InAocPHAdjO7GrgQuNQ599OYwhMRkRgoN0itMOdc3DGIVBUzywBPAQcAM4CPAd8Cvuic+3KcsYmISDyUG6QWqGIhEgIz+xvgV8AfgDcD33XOXRZvVCIiEiflBql2qliIhMTMngCOB34KfNAV/LKZ2XuBy4DjgI3OubbIgxQRkUgpN0g10xwLkRCY2fuAubmnXYWJI2cL8F3g85EFJiIisVFukGqnHguRgJnZmXhd3b8CeoG/A/6Pc+65EvufDXxbrVIiItVLuUFqgXosRAJkZguAJcBDwIeALwD9wNVxxiUiIvFRbpBaoYqFSEDMbDbwG2AlcLZzrsc59yJwI/AuMzs51gBFRCRyyg1SS1SxEAmAmU0H7sEbG/sO59z2vJevAnYBX48jNhERiYdyg9Sa+rgDEKkGzrk1eDc+KvZaBzA22ohERCRuyg1Sa1SxEIlJ7mZJo3IPM7NGwDnneuKNTERE4qLcIGmmioVIfD4C/CDv+S7gZaAtlmhERCQJlBsktbTcrIiIiIiI+KbJ2yIiIiIi4psqFiIiIiIi4psqFiIiIiIi4psqFiIiIiIi4psqFiIiIiIi4psqFiIiIiIi4psqFiIiIiIi4tv/Dz0UDbfR3f1JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
    "deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
    "deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
    "deep_tree_clf1.fit(Xm, ym)\n",
    "deep_tree_clf2.fit(Xm, ym)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title('No restrictions', fontsize=16)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title('min_sample_leaf={}'.format(deep_tree_clf2.min_samples_leaf), fontsize=16)\n",
    "\n",
    "save_fig(\"min_samples_leaf_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle = np.pi / 180 * 20\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xr = X.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_r = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_r.fit(Xr, y)\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "plot_decision_boundary(tree_clf_r, Xr, y, axes=[0.5, 7.5, -1.0, 1], iris=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_rotation_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(6)\n",
    "Xs = np.random.rand(100, 2) - 0.5\n",
    "ys = (Xs[:, 0] > 0).astype(np.float32) * 2\n",
    "\n",
    "angle = np.pi / 4\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xsr = Xs.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_s = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_s.fit(Xs, ys)\n",
    "tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_sr.fit(Xsr, ys)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "\n",
    "save_fig(\"sensitivity_to_rotation_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quadratic training set + noise\n",
    "np.random.seed(42)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5) ** 2 + np.random.randn(m, 1) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure tree_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=3, random_state=42)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel('$x_1$', fontsize=18)\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
    "    plt.plot(x1, y_pred, 'r-', linewidth=2, label='$\\hat{y}$')\n",
    "    plt.plot(X, y, 'b.')\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_regression_predictions(tree_reg1, X, y)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
    "plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
    "plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"max_depth=2\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "for split in (0.0458, 0.1298, 0.2873, 0.9040):\n",
    "    plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n",
    "plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\", fontsize=14)\n",
    "\n",
    "save_fig(\"tree_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(\n",
    "    tree_reg1, \n",
    "    out_file=image_path('regression_tree.dot'), \n",
    "    feature_names=['x1'], \n",
    "#     class_names=tree_reg1.class_names, \n",
    "    rounded=True, \n",
    "    filled=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure tree_regression_regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(min_samples_leaf=10, random_state=42)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n",
    "y_pred1 = tree_reg1.predict(x1)\n",
    "y_pred2 = tree_reg2.predict(x1)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X, y, 'b.')\n",
    "plt.plot(x1, y_pred1, 'r.-', linewidth=2, label=r'$\\hat{y}$')\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"No restrictions\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X, y, 'b.')\n",
    "plt.plot(x1, y_pred2, 'r.-', linewidth=2)\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.title(\"min_leaf_sample = {}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n",
    "\n",
    "save_fig(\"tree_regression_regularization_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. \n",
    "What is the approximate depth of a Decision Tree trained (without restrictions) on a training set with 1 million instances?\n",
    "\n",
    "The depth of a well-balanced binary tree containing $m$ leaves is equal to $log_2 (m)^3$, round up. A binary Decision Tree will end up more or less well balanced at the end of training, with one leaf per training instance if it is trained without restrictions. Thus, if the training set containes one million instances, the Decision Tree will have a depth of $log_2 (10^6) \\approx 20$ (actually a bit more since the tree will generally not be perfectly well balanced.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.\n",
    "Is a node’s Gini impurity generally lower or greater than its parent’s? Is it generally lower/greater, or always lower/greater?\n",
    "\n",
    "A node's Gini impurity is enerally lower than its parent's. This is ensured by the CART training algorithm's cost function, which splits each node in a way that minimizes the weighted sum of its children's Gini impurities. However, if one child is smaller than the other, it is possible for it to have a highter Gini impurity than its parents, as long as this increase is more than compensated for by a decrease of the other child's impurity.\n",
    "\n",
    "For example, consider a node containing 4 instances of class A and 1 instance of class B. The Gini impurity is $1 - (\\frac{1}{5})^2 - (\\frac{4}{5})^2 = 0.32$. Now suppose the dataset is one-dimensional and the instances are lined up in the order: A, B, A, A, A. If the algorithm split the node after the second instances, producing the child node with instances A,B, and the other child node is A, A, A. The 1st child node has Gini impurity: $1 - \\frac{1}{2}^2 - \\frac{1}{2}^2 = 0.5$, which is higher than its parents. The 2nd child is pure. So the overall weighted Gini impurity is $\\frac{2}{5} \\times 0.5 + \\frac{3}{5} \\times 0 = 0.2$, which is lower than the parent's Gini impurity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.\n",
    "If a Decision Tree is overfitting the training set, is it a good idea to try decreasing max_depth?\n",
    "\n",
    "Yes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.\n",
    "If a Decision Tree is underfitting the training set, is it a good idea to try scaling the input features?\n",
    "\n",
    "No."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.\n",
    "If it takes one hour to train a Decision Tree on a training set containing 1 million instances, roughly how much time will it take to train another Decision Tree on a training set containing 10 million instances?\n",
    "\n",
    "The training complexity $O(n \\times m log(m))$. $10 \\times \\frac{log10m}{logm} \\approx 11.7$ hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.\n",
    "If your training set contains 100,000 instances, will setting presort=True speed up training?\n",
    "\n",
    "No. Scikit-Learn can speed up training by presorting the data (set presort=True) only if the dataset is smaller than a few thousand instances. But this slows down training considerably for larger training sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.\n",
    "Train and fine-tune a Decision Tree for the moons dataset.\n",
    "- a. Generate a moons dataset using make_moons(n_samples=10000, noise=0.4).\n",
    "- b. Split it into a training set and a test set using train_test_split().\n",
    "- c. Use grid search with cross-validation (with the help of the GridSearchCV class) to find good hyperparameter values for a DecisionTreeClassifier. *Hint: try various values for max_leaf_nodes.*\n",
    "- d. Train it on the full training set using these hyperparameters, and measure your model’s performance on the test set. You should get roughly 85% to 87% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=10000, noise=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 294 candidates, totalling 882 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 882 out of 882 | elapsed:   11.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\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=42,\n",
       "            splitter='best'),\n",
       "       fit_params=None, iid='warn', n_jobs=1,\n",
       "       param_grid={'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'min_samples_split': [2, 3, 4]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {'max_leaf_nodes': list(range(2, 100)), 'min_samples_split': [2, 3, 4]}\n",
    "grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), param_grid=params, n_jobs=1, verbose=1, cv=3)\n",
    "\n",
    "grid_search_cv.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=29,\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=42,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, GridSearchcv trains the best model found on the whole training set (you main change this by setting refit=False), so we don't need to do it again. We can simply evaluate the model's accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8735"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = grid_search_cv.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.\n",
    "Grow a forest.\n",
    "- a. Continuing the previous exercise, generate 1,000 subsets of the training set, each containing 100 instances selected randomly. Hint: you can use Scikit-Learn’s ShuffleSplit class for this.\n",
    "- b. Train one Decision Tree on each subset, using the best hyperparameter values found above. Evaluate these 1,000 Decision Trees on the test set. Since they were trained on smaller sets, these Decision Trees will likely perform worse than the first Decision Tree, achieving only about 80% accuracy.\n",
    "- c. Now comes the magic. For each test set instance, generate the predictions of the 1,000 Decision Trees, and keep only the most frequent prediction (you can use SciPy’s mode() function for this). This gives you majority-vote predictions over the test set.\n",
    "- d. Evaluate these predictions on the test set: you should obtain a slightly higher accuracy than your first model (about 0.5 to 1.5% higher). Congratulations, you have trained a Random Forest classifier!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "n_trees = 1000\n",
    "n_instances = 100\n",
    "\n",
    "mini_set = []\n",
    "\n",
    "rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances, random_state=42)\n",
    "for mini_train_index, mini_test_index in rs.split(X_train):\n",
    "    X_mini_train = X_train[mini_train_index]\n",
    "    y_mini_train = y_train[mini_train_index]\n",
    "    mini_set.append([X_mini_train, y_mini_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7981179999999999"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "forest = [clone(grid_search_cv.best_estimator_) for _ in range(n_trees)]\n",
    "\n",
    "accuracy_scores = []\n",
    "\n",
    "for tree, (X_mini_train, y_mini_train) in zip(forest, mini_set):\n",
    "    tree.fit(X_mini_train, y_mini_train)\n",
    "    \n",
    "    y_pred = tree.predict(X_test)\n",
    "    accuracy_scores.append(accuracy_score(y_test, y_pred))\n",
    "    \n",
    "np.mean(accuracy_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# easy to reference later on to put tree in the first dimension\n",
    "Y_pred = np.empty([n_trees, len(X_test)], dtype=np.uint8)  \n",
    "\n",
    "for tree_index, tree in enumerate(forest):\n",
    "    Y_pred[tree_index] = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode\n",
    "\n",
    "y_pred_majority_votes, n_votes = mode(Y_pred, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8735"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y_test.shape is (2000,)\n",
    "# y_pred_majority_votes.shape is (1, 2000)\n",
    "# y_pred_majority_votes.reshape([-1]).shape is (2000,)\n",
    "\n",
    "accuracy_score(y_test, y_pred_majority_votes.reshape([-1]))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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