07_ensemble_learning_and_random_forests.ipynb

{
 "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": {},
   "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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\n",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "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": {},
   "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": [],
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 8,
     "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": 9,
   "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": 10,
   "metadata": {},
   "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": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "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)\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": 14,
   "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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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) 0.11249225099876374\n",
      "sepal width (cm) 0.023119288282510326\n",
      "petal length (cm) 0.44103046436395765\n",
      "petal width (cm) 0.4233579963547681\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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9013333333333333"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "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",
       "       [0.41919192, 0.58080808],\n",
       "       [0.99473684, 0.00526316],\n",
       "       [0.        , 1.        ],\n",
       "       [0.67613636, 0.32386364],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.87356322, 0.12643678],\n",
       "       [1.        , 0.        ],\n",
       "       [0.56140351, 0.43859649],\n",
       "       [0.16304348, 0.83695652],\n",
       "       [0.67539267, 0.32460733],\n",
       "       [0.90673575, 0.09326425],\n",
       "       [0.        , 1.        ],\n",
       "       [0.16201117, 0.83798883],\n",
       "       [0.89005236, 0.10994764],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.995     , 0.005     ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.07272727, 0.92727273],\n",
       "       [0.05418719, 0.94581281],\n",
       "       [0.29533679, 0.70466321],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.81871345, 0.18128655],\n",
       "       [0.01092896, 0.98907104],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.22513089, 0.77486911],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.9368932 , 0.0631068 ],\n",
       "       [0.76536313, 0.23463687],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.17127072, 0.82872928],\n",
       "       [0.65306122, 0.34693878],\n",
       "       [0.        , 1.        ],\n",
       "       [0.03076923, 0.96923077],\n",
       "       [0.49444444, 0.50555556],\n",
       "       [1.        , 0.        ],\n",
       "       [0.02673797, 0.97326203],\n",
       "       [0.98870056, 0.01129944],\n",
       "       [0.23121387, 0.76878613],\n",
       "       [0.5       , 0.5       ],\n",
       "       [0.9947644 , 0.0052356 ],\n",
       "       [0.00555556, 0.99444444],\n",
       "       [0.98963731, 0.01036269],\n",
       "       [0.25641026, 0.74358974],\n",
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       "       [0.00561798, 0.99438202],\n",
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       "       [0.42708333, 0.57291667],\n",
       "       [0.86315789, 0.13684211],\n",
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       "       [0.05699482, 0.94300518],\n",
       "       [0.82802548, 0.17197452],\n",
       "       [0.01546392, 0.98453608],\n",
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       "       [0.02298851, 0.97701149],\n",
       "       [0.96721311, 0.03278689],\n",
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       "       [0.01041667, 0.98958333],\n",
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       "       [0.0326087 , 0.9673913 ],\n",
       "       [0.01020408, 0.98979592],\n",
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       "       [0.62264151, 0.37735849],\n",
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       "       [0.1978022 , 0.8021978 ],\n",
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       "       [0.00574713, 0.99425287],\n",
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       "       [0.8969697 , 0.1030303 ],\n",
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       "       [0.44329897, 0.55670103],\n",
       "       [0.22395833, 0.77604167],\n",
       "       [0.        , 1.        ],\n",
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       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02884615, 0.97115385],\n",
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       "       [1.        , 0.        ],\n",
       "       [0.49756098, 0.50243902],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
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       "       [1.        , 0.        ],\n",
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       "       [0.        , 1.        ],\n",
       "       [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",
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       "       [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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.912"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "mnist = fetch_mldata('MNIST original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "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": 27,
   "metadata": {},
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure mnist_feature_importance_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "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": 29,
   "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": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(ada_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "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",
    "    plt.subplot(subplot)\n",
    "    for i in range(5):\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",
    "    if 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",
    "\n",
    "save_fig(\"boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "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": 33,
   "metadata": {},
   "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": 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": [
    "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": 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": [
    "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": 36,
   "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": 36,
     "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array([[0.8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75026781])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gradient_boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 6 Axes>"
      ]
     },
     "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": 41,
   "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": 41,
     "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": 42,
   "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": 42,
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gbrt_learning_rate_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "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": 44,
   "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": 44,
     "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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_error = np.min(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure early_stopping_gbrt_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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TywBgZi2CqckNyDezVmbWLPLcn5rZfmbWCbg2+tx06tcPzGDJkprLiohIzWp5bHkLOMfMOppZPj6b31fOudWNV2MREQklm8b1YqA1fkbDfwAXOecWmdkRZrYpUu5u/HT17wHvA08H60LP42dPHI7vo7oFOBLAOTcLPwvhy/gZCZcBf6jb20qtVq2gd2+1QIiIpFiyx5ar8Nn7luJnJj4R+EFjV1ZERLysn4m6sQbJHXccfPMNvPVWg7+UiEiDMrO3nXPD0l2PhqZB1CIitZPs8SHZFogmr39/3wKR5fGWiIiIiEi9KIBI0oABsH49rFqV7pqIiEiyiothwgS/FBGR1EhmHgihIpXrkiXQvXt66yIiIjUrKYERI2DbNmjRAl58EQoK0l0rEZHspxaIJCmVq4hIdtm40QcPO3b4ZVFRumskIpI56tNCqxaIJPXpA3l5cN99MHCgrmKJiGS69u1h7dqKFojCwnTXSEQkMxQX16+FVi0QSXrrLSgvh1de8R+4+tOKiGS2tm39QXH8eHVfEhGJKiqqXwutWiCSVFRUkYEp/KB1MBIRyWwFBfqtFhGJVVjoWx7q2kKrACJJhYXQvDmUlakpXEQkmxUX+4tAhYUKLkSkaSoo8C2zdf0tVACRpIICGDPGN4Xfe68OOiIiGe+DD2DIkIrHffsy95cPMeLE1srMJCJNXn1aaDUGohZGjvTL/Pz01kNERJKwZQssWFBx+7//44t7Zykzk4hIPakFohYGDgQzf1FLREQy3L77wkMP+fszZsBf/8qxi2/jKvuYjdaGx/LPoLCwY3rrKCLSmBYtghtvhK1b67UbBRC10KYN7LWX/+xFRCTDtWlT0YXJOfjrX+n4ThF/ogiAq89axR4Fv09f/UREGtvf/lZxYaUeFEDU0qBBCiBERLLOQQfBPffA4sW+GXnWLPYo+yTdtRIRaVzffOOXl18Ohx9edfsPf5jUbhRA1NJ++8Ezz1SkvRIRkSxxwQV++dRTMGsWrFy5c5MyM4lIk7Bpk18ecwycfHKdd6MAopYGDfKpXJcu9fdFRCTLdO/ulytWAPWfkVVEJGts3OiX7dvXazfKwlRLYdCgbkwiIlkqDCCCFoj6zsgqIpI1FECkx8CB0KyZAggRkawVDSCc2zkja16eJgoVkRynACI9WrdWJiYRkazWpg20awelpfxl3AbAd1saP17dl0Qkx4UBRLt29dqNxkDUgTIxiYhkt60dutNq0yaOvq6Q7eNbsu9+UNAOaH8WFFyS7uqJiDSMcBB1PVsgFEDUwaBBPomHMjGJiGSnTzsfxMCvPuYg5oMDwotCn38OlyiAEJEcVF4OJSX+ftu29dqVujDVwaBBfrDdkiXpromIiNTFuskPcmTLuRzWrJijWhbzwaTn/Ybw4CoikiOKi2HCBHjzpaD1oV07P6C3HtQCUQdhJqYbboDLLlN/WRGRbHPoUS256eVv7Zz7Yb+DtsLlwJYtaa6ZiEjqRNNUT83fyEdQ7/EPoACiTsJJ/B5+GGbO1KA7EZFsVFAQ+e12LcGsIp9rXl5a6yYikgrRNNUtXGrGP4ACiDopLvZL5ypyhiuAEBHJYmbQqpVvgdi6td79g0VEMsHRh23j+LxXyXOl9M/7H5SjACJdCgt917HycuUMFxHJGa1b+wBiy5YqAURxMTu7O+mCkYhki0NfGM/T2673D8qDlZ061Xu/CiDqoKAATj8dHn0UXnhBBxMRkZzQqpVfxoyDiPYhbtFC3VZFJIssW+aXgwdDr16+e+Zll9V7t8rCVEfHHANlZf5vISIiOaB1a7+MCSCifYjDbqsiIllh61a/vOYaePppeOIJOPbYeu9WAUQd7bOPX374YXrrISIiKZIggCgs9C0PeXnqtioiWSYMIFq2TOluFUDUkQIIEZEckyCAKCjw3ZbGj1f3JRHJMqWlfhl20UwRjYGoo+7doWNHBRAiIjkjQQABMSlfRUSyRdgCkeIAQi0QdWTmWyEUQIiI5IhIABHO3Bqm7RYRyUoNFECoBaIe9tkHXnop3bUQEZGUCAKID+dvYcT1yrokIjkgnWMgzGwXM3vczErMbJmZnZGgnJnZTWa2JrjdZGYW2T7EzN42s83BckhkW0szu8vMVpjZWjN70swyOsfRwIHw5ZewaVO6ayIiIvUWCSCUdUlEckIDjYFItgvTZGAb0AM4E7jTzAbFKTcaGAkMBg4ETgYuBDCzFsBM4AGgM3AfMDNYD/BLoCB43m7AN8DttX9LjSccSL1kSXrrISIiKRAEEIP22qKsSyKSG9I1BsLM2gKnAmOdc5ucc3OAJ4Cz4xQ/F5jonPvCOfclMBEYFWwrxHeZmuScK3XO3QYYcEywvS/wnHNuhXNuK/AwEC9IyRjKxCQikkOCAKLfbluUdUlEckMax0AMAMqcc9Hr7AuAo+KUHRRsi5YbFNm20DnnItsXButnAVOBW81sN2AdvqXj2WTeRLrsvbcfTK0AQkQkB0QGUSvrkojkhDSOgWgHbIhZtx5on6Ds+phy7YJxELHbYvezFPgc+DJ4vX2B6+JVyMxGm9k8M5u3atWqJN5Cw2jVCvr0UQAhIpITYtO4fvYZjBoFJ53kbz//uR8UkSRlchKRtAvGQPz59lYp/S1KpgViE9AhZl0HYGMSZTsAm5xzzsxq2s9koCXQBSgBfoNvgfh27Is456YAUwCGDRvmYrc3poED4b//TWcNRESyk5ntgm99Pg5YDYxxzj2UoOzBwCTgYPwx4kbn3K0prVAYQNx1Fzz5pM+SsWJFpSIPbT+NvheMqLF1orgYRoxQJicRSSPndrZAXPPHluT/KXW/Rcm0QCwBmptZ/8i6wcCiOGUXBdvilVsEHBjNyoQfMB1uHwJMd86tdc6V4gdQf8vMuiZRx7TZZx8/iLq8PN01ERHJOkkl6AiOA7OAu/EXmfYGnk95bfbbzy9XrIB33vHLgQNh5kw2DDkSgGf+voIRI2puVSgqQpmcRKTRxG3xDFpMS2lBWXmzlP4W1RhAOOdKgH8D15lZWzM7DDgFuD9O8RnAFWbWKxjLcCUwPdhWBOwALgtStl4SrA9nUngLOMfMOppZPnAx8JVzbnXd3lrj2Gcf2LwZxoxRM7WISLJqmaDjCnySjQeDJBwbnXOLU16pU06BpUth3ryK24IF8P3vs7TVAQB0cauSOggXFqJMTiLSKMIWz7FjqXyBI2h9KKVlyn+Lkk3jejHQGlgJ/AO4yDm3yMyOCLomhe4GngTeA94Hng7W4Zzbhk/xeg5+kPT5wMhgPcBVwFb8WIhVwInAD+r+1hpHWZlf3nILSV2VEhERIHGCjnjZ9w4F1prZG2a2MpgnqHe8ndZ7jNzee8PQoRW3Fj7TePdB3fzSVid1EC4oQJmcRKRRRFs8t26FGTOCDcH4h9adWqX8tyipmaidc2vxJ/+x61/DD44OHzv82IXfJNjPu8DQBNvW4Juws8rXX/tleXlFM7UOFCIiNapNgo7d8WMfvoO/QHUz/mLWYbEFG2qM3B4H+d60JxyyimMmJfc7r0xOItIYCguheXMfQDgH06bBOedAQS/fApHfvhVjxqT2NZNtgZAETjrJp3I1UzO1iEgt1CZBxxbgcefcW8E8QX8EhptZxwauY4VuvgXi4N1XKSgQkYwQjnsAOO88fy4KPpAoKqLB5oCAJFsgJLGCAjj8cFi8GJ54QlebRESStDNBh3NuabAuUYKOhUC0NaHxs+8FAcSG/61i8gR/sUi/9yKSLrGZ3iZN8nFC+LiwkAabAwIUQKTEd74Dc+bA/vunuyYiItnBOVdiZmGCjgvwmfhOAYbHKf534DEzuw0fYIwF5jjnYucWSoniYn/1rlKQEAQQmxcsZfXCv/J4c+h5kZ8LKCktWsBpp0H37qmvsIg0ObGZ3tas8WMcKv12veXHQKgFIkMNHer7nL37Lhx5ZLprIyKSNS4GpuETdKwhkqADeNY51w7AOfeSmV2DT8zRBpgDnNEQFUo4f0PPnjgzdnXLmeiugO3AbbXc+QcfwOTJDVBrEWkynIPFizlxt23MbA7bHeQ3hxN3g8GtoeCEoNx84P33/X0FEJnp4IP98u23FUCIiCQr2QQdwbo7gTsbuk7x5m8oKAC6dOGj39/PrOvnUV4OzZrBqT+E3XomsdMlS+CZZ2B1RmclF5FsMHYs3HADg4H/hOtKgVHVPEcBRGbadVfYbTc/75CIiGSvcP6GSv2IA/3Hncnq48/c2UVgt2THQDz2mA8gtm9PeX1FpIl5912/3GsvaB8vaV2M5s3h5z9PeTUUQKTI0KG+BUJERLJXOH9DlTEQke21Hjydn++XCiBEpL7WrPHL+++H4fGGjDUOpXFNkaFD4b//hU2bai4rIiKZq6AAxoypOVCYMgWOP94vqxUGENu2VV9ORKQmYQDRpUtSxcNUr6me6FgtECkSDqSeP9+ndRURkdw1ZQpceKG///zzfjl6dILCwWzWaoEQkXqrRQCRMClECqgFIkWGBvNrqxuTiEjue+yx6h9Xoi5MIpIKZWXwzTd+xrjOnWssHi8pRKoogEiRnj39TQGEiEjuO/XU6h9XogBCRGohYbejb77xy06dIC+vxv2ESSHy8qomhagvdWFKob32guee839wzVAqIpK7wu5Kjz3mg4eE3ZdAAYSIJC1etyPwrQff7bOGgyDp8Q81JYWoDwUQKVJcDHPn+talESNS289MREQyz+jRNQQOoXAMhAZRi0h1Nm/mvYc/pV8p7CiHvFJ4fhLMnOmvP3yYN5/pkHQAAXXMHJcEBRApUlQE5eX+fmlpZPIhERFp2tQCISI1KS+HAw5g9Mcfs/O6RDnwCPwh+hhqFUA0FAUQKVJYCC1bwpYtFY9FREQUQIhIjUpK4OOPwYzNew5kcwm0aes3LVvmM32aQe9++bRNqumzYSmASJGwn9mECfDkkxXHCxERaeIUQIhITUpK/LJbN9p88gFtIpvWFVeMY9g3Q3q3KIBIoYICPzFgr15wxx0wbVq6ayQiImmnieREpCZhANG2bZVNDTWOoT6UxjXFOnaEs86Cf/wD1q5Nd21ERCTtNJGciNRk82a/jBNAZCIFEA3gootg61Y4++zUTx0uIiJZRl2YRKQmYQtEmzbVl8sQCiAawObN0KwZPPOMT+mqIEJEpAkLAojtW7breCAi8VXThSkTKYBoAEVFfrQ8pH7qcBERyS7/edsHEFa2XReVRCT+TNNZFkBoEHUDCKcOLy2F5s2V0lVEpCl7+bXmHAo0ZwfbS8spKmqWcQMiRaRxxJtpuqCAijEQ6sLUdBUUwBNP+PujRmXeyHkREWk8hUcb2/CtEG1bbNdFJZEcF7eFIVBU5IOHHTtieqmoBUIAjjsO+veHlSvTXRMREUmnggLY0Softm7nuae28+2Clumukog0kIQtDIGwl0q4fecFhSwLINQC0YCGDYN589JdCxERSbe8lr4F4tsHKxOTSC5L2MIQCCceHj8+JrjIsi5MaoFoQMOG+fkgVq6E7t3TXRsREUkbTSYn0iQkbGGIiDsxnFogJDRsmF++/XZ66yEiImkWM5lcdX2kRSR7JWxhqEmWBRBqgWhABx0EZr4b0wknpLs2IiKSNpHJ5GrqIy0i2S1uC0NN1IVJQu3bwz77aByEiEiTFwkg4vWRVgAhkuOcg08/9f/48axY4ZdqgRDw3ZheeindtRARkbSKBBDJ9JEWkRxzxRUwaVLN5RRACPgA4oEH4OuvoWfPdNdGREQaUnGxb1EoLIxpVQjHQGzbtrOPdNxyIpKbwgFPu+8OLROkct51VzjqqMarUz0ogGhg0YHUJ52U3rqIiEjDqXZsQ6QFAurYR1pEsteqVX754oswYEB665ICysLUwIYM8QOp//pXZdsQEcll1eZ/jwkgRKSJCQOIbt3SW48USSqAMLNdzOxxMysxs2VmdkaCcmZmN5nZmuB2k5lZZPsQM3vbzDYHyyExzz/YzF41s01mtsLMflm/t5d+Cxf65Usv+StTCiJERHJTOLYhLy/O2AYFECJNV2kpbNwIzZtDp06VNmVrSudkuzBNBrYBPYAhwNNmtsA5tyim3GhgJDAYcMALwCfAXWbWApgJTALuAC4EZppZf+fcNjPrCszi/+ZCAAAgAElEQVQCfgU8CrQAdq/Pm8sE0StQyrYhIpK7qh3boInkRJqusPWha1ffLSWQzSmdawwgzKwtcCqwv3NuEzDHzJ4Azgaujil+LjDROfdF8NyJwM+Au4DC4PUmOecccJuZXQUcgw8crgCec849GOyrFFhcv7eXfoWFfqzM1q3+O6NsGyIiuSvh2IZwEPX998Pcuf5+164werRvshCR3BUGEN27V1qdzSmdk2mBGACUOeeWRNYtAOINEx8UbIuWGxTZtjAIHkILg/WzgEOB98zsDWBvYC7wC+fcZ7EvYmaj8a0d9O7dO4m3kD4FBb770jnnwKZN8O1vp7tGIiLS6Dp39ssHHqi8vls3+OEPG78+ItJ4Eox/yOaUzskEEO2ADTHr1gPtE5RdH1OuXTAOInZb7H52Bw4GvgO8B9wM/AM4LPZFnHNTgCkAw4YNc7HbM01BAVx3HZxxBrz8sm+uEhGRJmT8eD+zaDgG4j//gdmzmXvrfyjv9cOsueooIjF27IDDD4c330xcJrx2HhNAZHNK52QCiE1Ah5h1HYCNSZTtAGxyzjkzq2k/W4DHnXNvAZjZH4HVZtbRORcbeGSdkSP9uJlp0xRAiIg0OXvtBb///c6Hi295in1nz2bHnGIuPHopM2bAQQcFGzt39t2bRCTzff21vyBQk/x8OOGEKquzNaVzMgHEEqB5MNh5abBuMBA7gJpg3WDgzTjlFgFXmplFujEdiB+gDb47U7Q1IeNbFmqjdWv4yU9g6lTo189/h7LxCyMiIvX3wpqh7AsM5w3eKx0AP4pszMvz4ySGDk1X9UQkWaWlftm3LyxdmricGTTLndkTanwnzrkS4N/AdWbW1swOA04B7o9TfAZwhZn1MrPdgCuB6cG2ImAHcJmZtTSzS4L1LwXLvwM/CFK95gNjgTm50PoQGjbM93O7/nqldBURacoO+X5P7s27kKXszUe2N1t23xv23hs6dPBdIubPT3cVRSQZW7f6ZevWPvhPdMuh4AGSn0juYqA1sBI/LuEi59wiMzsi6JoUuht4Ej+G4X3g6WAdzrlt+BSv5wDrgPOBkcF6nHMvAdcEz1mJH0gdd76JbLV8uV86F2eSIRERaTIKCmDQa3fx6I1LWfX6Ulp/vtRfvTzvPF9gQ+zQQxHJSGEA0apVeuvRyJKaB8I5txZ/8h+7/jX84OjwsQN+E9zi7eddIGGbrHPuTuDOZOqUjY4+2neB277dL7NptL2ISKqZ2S7AVOA4YDUwxjn3UDXlW+Cz+7V3zmXtPEHFxRWDJseMidnYIRgqWMcAIrpvdZMVaQQKIKShFRTAY4/BKafA6afrx11EmrxkJykN/RpYRfwsgFmhxomjggBi7uwNlB9Xu+NENk9KJZK1wgCiZcv01qOR5VaHrCxw8snw3e/6uSF27Eh3bURE0iMySelY59wm59wcIJykNF75vsBZwITGq2XqxZs4Kup/q3wA8f7r62s9Vq6mfYtIAwgHUTexFggFEGlw7rnwxRd+TggRkSYq0SSlgxKUvx0/Tm5LdTs1s9FmNs/M5q0KJ2/KIOHEUXl58SeOeu+zjgC0dxtqHQTUtG8RaQDqwiSN5ZRToGNHuOUWeOst9VUVkSYp6UlKzewHQJ5z7nEzK6xup5k+0WhNE0ftc0gH+Cd0ZEOtg4BsnpRKJGspgJDG0qqV/3GfORNmz1ZfVRFpkpKapDTo6nQzcGIj1avBVTdx1L7f9h/JoN4bePGftT8uZOukVCJZq4kGEOrClCY9e/ql+qqKSBO1c5LSyLp4k5T2B/oAr5nZcvy8RD3NbLmZ9WmEejauYBD17u3WKxAQyQYaRC2N6eyz/aSEoL6qItL01GKS0veBPfBZmoYAFwArgvufN16NG0lHPwZC80CIZIkEg6iLi2HChNydNFhdmNJk+HC4+GKYPBmmTlWTs4g0SRcD0/CTh64hMkkp8Kxzrp1zrgxYHj7BzNYC5c655XH3mO3CeSDWrfPZNgLz5jfnhfd21dgGkUwTpwtTU0iprBaINBo71mfLmD8/3TUREWl8zrm1zrmRzrm2zrne4SRyzrnXnHPtEjynKJsnkatR+2AM+aZNsMceO2/DTu6J/e6aWqd2FZEGFieAaAoplRVApFGPHnDSSXDffX52ahERaeLy8uDCC6FXr523krbdADjSFeXsyYhIpkvYJSlOANEUUiqrC1OanX++z8Y0a5afZE5ERJq4u+6q9PDDf3/Kwaf2ZXe+yNmTEZFMVm2XpDiDqJtCSmUFEGl2wgnQuTNcfTV07ZqbXzIREam7g0/aDYDdm33Fi8/voKAgL801Emla4nVJ2nm+lmAQda6nVFYXpjSbNw82boQPPoBjjlHfVhERidGiBfToQbPyHRT0TTx2PNezvoikS7VdkproPBBqgUizoiJwwVyppaUxUa2IiAj4wdQrVsDixdCmTZXNb74Jvzzlc8aVjqGDLaOkXx5t//hbOOOMNFRWJLdU2yVJAYSkQxjVbt3qA4m99053jUREJNOsbbM7uzAPvvOduNu/BbwZPnDAR8AddyiAEEmRhF2SmmgAoS5MaRZGtVdf7QOJJ55Id41ERCSTFBfDZcU/YQXd+YZOlLXvBJ0q38rad+IbOvEyR/Pz/Kn+iRs3prfiIrnAOfj8c1i2LP5t3TpfronNRK0WiAwQRrVlZfDnP/vB1Kefrq5MIiLiu038s/x0HuR08vJg/BgYM8YHFtEuFf8thv8Uwej+H8NpaDZrkVS44AKYNq3mck2sBUIBRAY5+mgfQEyaBHffnZszF4qISO2EXV3DFJKFhYnTShYUAKuD2azVAiFSf2+84Zc9e0J+fvwye+4JhxzSeHXKAAogMsj8+WDmW8uqpAkTEZEmKd4AzgkTqkkrGc5mrRYIkfpbu9Yv33kHdt01vXXJIAogMkh4lam01KcK02RBIiICVQdwxmuV2Klly4qNpaVNrm+2SMo4VxFAdO6c3rpkGA2iziAFBX5G6ubN4fvfV+uDiIjEF7ZKjB+foLurWiFE6m/jRj9AtW1bBeIx1AKRYQoL4fjjfUuZc75Lk4iISKxqZ7pt3x7WrPEnQN26NWq9RLJdmKDg+H3WcjBAly5prlHmUQCRgU44AZ5+GpYuhQED0l0bERFpTLHZler0vA7BQGq1QIjUSjRBwVPN1/A6wC67pLtaGUcBRAY64QS/fPZZBRAiIk1JouxKtX3e8v7t6QDKxCRSS0VFFQkK2rtg/EMQQNQ1uM9FGgORgfbaC/bZB555Jt01ERGRxhQ9eQmzK9Xleau2+haIxXM3MGGCP/ERkZqFCQry8qBbXhBAdOmyM0gfO9Yvm/r/lAKIDHXiifDKK1BSku6aiIhIY4mevFTJrlSL53Xcww+i/ueYBYy91umERyRJBd/awacjfsqy3Q7lnm5j/MpddqlzcJ+rFEBkqBNO8Nn3Lr5YP/oiIk1FjdmVknxe176+BeKPZb/j9vKLdcIjkqx33qH7U9Po9flcWn31iV+3//51Du5zlcZAZKgWLfxyxgz41780K7WISFNRbXalZJ9Xeibcey8AQ3lHJzwiyVqwwC9PPNH3V2rTBg44gAKrOqFjU6YAIkO98UbFrNRbt2pWahERqYXCQli4EA48kL7dS3jx/3QMEanko492JhlYsADefhs6doSBT73MIPD/Q4ceWukpdQ3uc5ECiAxVWAitWvngwTlYvhwmTFDUKyIiSWrbFoBubUropuOGSIUHH4Szztr5cHBwi/ogfzD7NWqlsosCiAwV9md9/nm45x647TZo1sxPhKjuTCIiUqMggIjNxqFUlNLkffCBX+66K8ttV77+uvLmT9iLjzYepQCiGgogMljYVLZpE9xyC5SXV4z814++iIhUK04AUdd5JkRySmmpX15xBZ8c/mtGjPCryssjF2uPTW8VM11SWZjMbBcze9zMSsxsmZmdkaCcmdlNZrYmuN1kZhbZPsTM3jazzcFySJx9tDCzxWb2Rd3fVm75f/8P8vP9/bw8DYQTEZEktGnjl5s3+zMj6j7PhEhOCQOIli139vi4/nq4+26/VGBds2RbICYD24AewBDgaTNb4JxbFFNuNDAS35XMAS8AnwB3mVkLYCYwCbgDuBCYaWb9nXPbIvv4NbAKaF+3t5R7Cgpg9mw49VRo3RqGDk13jUREJOM1a+YPGlu2+FvbtjtTUYYtELogJU1SJIAADY6uixpbIMysLXAqMNY5t8k5Nwd4Ajg7TvFzgYnOuS+cc18CE4FRwbZCfMAyyTlX6py7DTDgmMhr9QXOAibU+R3lqCOPhPvug88/h5EjNTeEiIgkIaYbU13nmRDJKTEBhNReMl2YBgBlzrklkXULwGe5ijEo2Bav3CBgoXPORbYvjNnP7cA1wJbqKmRmo81snpnNW7VqVRJvITd07uwvKD37LBxzjIIIERGpQRBA3PHnkp3HjIICGDNGwYM0YQog6i2ZAKIdsCFm3XridzFqF2yLlmsXjIOI3VZpP2b2AyDPOfd4TRVyzk1xzg1zzg3r1q1bEm8hN0T7qm7dCjff7FO7KpAQEZF4NjfzAcTdfylhxAgdL0QAfxIFCiDqIZkAYhPQIWZdB2BjEmU7AJuCVoeE+wm6Sd0MXJZMpZuqwkL/Xc/L84//7//g2mvRQUFEROJat90HEK3KSzRoWiSkFoh6SyaAWAI0N7P+kXWDgdgB1ATrBicotwg4MJqVCTgwWN8f6AO8ZmbLgX8DPc1suZn1SaKOTUK07+o55/h15eU+kH755fTWTUREMk/b7j6A6NhskwZNi4TCAKJVq/TWI4vVGEA450rwJ/TXmVlbMzsMOAW4P07xGcAVZtbLzHYDrgSmB9uKgB3AZWbW0swuCda/BLwP7IHP8DQEuABYEdz/vG5vLTeFfVd//nOfXMPMz1T94IO+NUItESIiEurY0wcQPzuzRIOmRUJqgai3ZNO4XgxMA1YCa4CLnHOLzOwI4FnnXLug3N3AXsB7weN7g3U457aZ2chg3Z+AxcDISArX5eGLmdlaoNw5t3OdVBa2Rrz8MixaBA895CdW/MtflFlDREQCwSDq004oAR0XJMM12izpCiDqLakAwjm3Fj+/Q+z61/CDo8PHDvhNcIu3n3eBGmcxcM4VAbsnU7emLMxbPGGCz85UXu7/J5r6TNXjxo3j0Ucf5f3330/6OaNGjWL16tU89dRTDVgzEZFGFqZx/ctf4N//rr5s+/a+j2yvXg1fL5EYjTpLugKIeku2BUIyWDi4essWH0QceGC6a5R6o0aN4r777uP8889n6tSplbb99re/5eabb+Z73/seTz31FFdddRWXXnpprfZ/6623UjnDsIhIDujTxy/nzfO3mvTtC2PHNmiVROKJN0t6qgOIsIXj8vWltAYFEPWgACIHhN2ZHn0UbrsN/vY3WLiwEZoAG9kee+zBI488wm233Ubb4KpaWVkZM2bMoHfv3jvLtWvXjnbt2iXaTVwdO3ZMaV1FRDLCb34DBx/srzBV55VXYPJkWLq0ceolEqOhZ0mPtnCcWV5Kb1AAUQ/JZGGSLFBQABMnwllnwaxZ8Lvf+X+UKVNyZ66IAw88kP79+/PII4/sXPf000/TqlUrCiO/NOPGjWP//fff+XjUqFGcdNJJ3HrrrfTq1YvOnTtz3nnnsXnz5iplQoWFhVx00UVceeWV7LLLLnTr1o1bb72V0tJSfvGLX9CpUyd69+7N/fdX5BL49NNPMTPmxVzlMzMeffTRSmX++c9/ctRRR9G6dWsOOuggFi5cyPvvv8/w4cNp27Ythx9+OJ988knKPjsRaaJatYKTToLTTqv+9sMf+vL/+1+Nuywuzp3jimSO6mZJT8V3LtrC0cKpC1N9KYDIMX37+qVz/oLTxRf71uhcmSvipz/9KdOmTdv5eNq0aZx33nlUzg5c1Wuvvcb777/P7Nmzefjhh3n88ce59dZbq33Ogw8+SPv27Zk7dy5XX301l19+OSNHjmTAgAHMmzePc889lwsuuICvv/661u/jD3/4A7/97W9599136dSpEz/5yU+49NJLueGGG3jzzTfZunUrl12maVFEpJH06+eXH31UbbHwKm4uHVckc8SbJT1V37mwhSMvD1qiAKK+FEDkmO98x6d3bRb8ZXfs8LfSUhg3Lvt/7M844wzmzZvH0qVLWb58ObNmzWLUqFE1Pq9Dhw7cdddd7Lvvvhx33HGcdtppvPjii9U+Z9CgQYwbN47+/ftzxRVX0LVrV/Lz8/nlL3/J3nvvze9//3ucc7z++uu1fh9XXHEFJ554IgMHDuTKK6/kgw8+4NJLL+Xoo49m0KBBXHLJJbysyT0kx5nZLmb2uJmVmNkyMzsjQblfm9n7ZrbRzD4xs183dl2zWVJXb3v18idTK1fC/vvDAQfEvfX7wQHM3XIA7+44gJe2FPDBQ/Mb7X1I0xRvbERdRFs4OrRUAFFfGgORY8J/kKIi6NIFLr3U/8OVl8Pzz8Nrr2V3mtfOnTvzgx/8gGnTptGpUycKCwsrjX9IZL/99iMvnMIb2G233Zg7d261zzkwMhrdzOjevTsHHHDAznX5+fl07tyZlStX1vp9RPfdo0cPgEr77tGjByUlJWzevJk2bdrUev8iWWIysA3ogZ/352kzW+Cci52o1IBzgIVAP+B5M/vcOffPRq1tFko6s02zZnD44b7AonjzxHrdg1to9w2P4P90Ig0jlWMjCgqg4FAHv9vqVyiAqDMFEDkoTO8K/qLRtdfCSy/5x1u3Zn+a1/PPP59zzz2Xdu3acd111yX1nPz8/EqPzYzy8vJaP6e6/TQLmn2i2Zy2b99e477D7lfx1tVUR5FsZWZtgVOB/Z1zm4A5ZvYEcDZwdbSsc+7myMMPzWwmcBigAKIGtcps88wz8OGHNe5zwQLYfM8DFLx6M7t33JTK6opUEb0wWpfkMFXmligr8/28mzWD5joNrit9cjmuoACuv95fgdq6tWJsRDYbMWIELVq0YPXq1YwcWWV6krTp1q0bQKUxEfPnq3lfJIEBQJlzbklk3QLgqOqeZD66PoJgktI420cDo4GkWidzXa2u3rZo4a861WDwAcDW/vAqUFKSmoqKVCN6YbQ6scFC3Ba4A4LuS61aNWSVc54CiCYgjN5ffNHPI3TTTbB2LZx5pt/eKLM+ppCZsXDhQpxztMyg5sfWrVtz6KGHctNNN9GvXz/Wr1/PmDFj0l0tkUzVDtgQs2490L6G543Dj9/7e7yNzrkpwBSAYcOGNfnJXep79TahcIK6TWqBkMwQL1iYMaPi4unOFrgBGv+QCgogmogwej/4YJ/Rb/JkuOMO34LnnP9nu/VWWLMmO4KJ9u1rOsdIj2nTpnHBBRdwyCGH0K9fP+644w6OPPLIdFdLJBNtAjrErOsAbEz0BDO7BD8W4gjnwjyMUpNkr97WSjjXjlogJEPEdtebMQP+/nd/jgM++1JhIZqFOkUs22ffHTZsmIvNuy+JTZjgx0Qk6lpv5lv1snmgtYhUz8zeds4NS3Md2gLfAIOcc0uDdTOAr5xzV8cpfz5wHXCkc+7jZF5Dx4cG9PLLcMwxcNRRdU+LI1Kd9et9JJCkt96CU0+taIH40Y/g/vthR7nPwnDuufDnPwOffQbDhsGee8KnnzZU7bNWsscHtUA0MYWFPujets1H42YQjvMtL68YI3H11fCnPymIEJGG4ZwrMbN/A9eZ2QX4VD6nAMNjy5rZmcCNwNHJBg/SwMIuTHVsgagysFUkaupU+NnPKpoPknAI8Fn4YCtwH/w5WuC+4BZSC0S9KIBoYmL7w0JFytfLL6/oK/jqq3DkkXDVVdC+PRx9tH7kRSTlLgamASuBNcBFzrlFZnYE8KxzLugnw/VAF+CtyKSRDzjnft7YFZZA2IWpDmMgkk4tK03XnDn+ZKRdO7Y3b8W6dRWbOnWC/CTOXreXkfh5ZnD22SmtclOjAKIJiu0PG035Om4czJ7tWyPKynwrBPjWivPOg+7d/RgK0NUjkUwUXtk96ijfUv/669CxI3zzDfz4x3DEEemuYQXn3FqgSio159xr+EHW4eO+jVkvSUI9WiASpZZVq4TsFAamU6dyy/9OZ+xY/30xgwt/DHfeWfMubpnAzufl5cH43/hZriU1FEDITgUFPoB47TX/o27m//Gc88t77/XlbrzR/zM651sAdfVIpLLoiRBUvX/kkb7r4OuvQ7dusHq1/x8qKYFXXoFvfcv/f735Jhx2mE9VPm8e9OzpA4HDDvPH16IiOOQQ//85Z46/mjt5st93vJb/adMq5oQRqZd6tEDESy2rVgmpZGOQS6FdOwoL/W9geD4ybRqcc07N349UTkAnVSmAkEpiZ7K+/PKqwQT4++DHS/zlL3DQQdC1q8/idNRR/gRmzhw/xk4HAWlM8U7eu3SpyDAWrkt0vzZlY5/3ySf+9q9/JU5UUFsTJ6ZmP+D/LzXeVVKiHlmY4qWWnTChFhPeSe4LA9P27Sko8D0g7r674oJmMt+PggKYNAkee8wPrtb3KbUUQEgVsTNZxwYT4eDrbdv8P/Ojj/pbPM2b+6xP0eg/3kmY/rEbT01Xx+tzAt3Qz+vcGb780nfDad7cX8E/8kj/PZw92383b7rJnygHE4NTVuaXZhXf3R07KraH952rfNIfdrV3LvH9sFyqk9lF91nT/mPLhq2D0SQJ5eX+PeoqnKRMixb+S7ZtW8UlXpLvhhTblVZXi6WSSAsE+BaH++6r3fejuLjivOW11/z5jM41UkcBhFQrXjARPaFbssT/Uyc6wSkr892iwJ/AmPmTmfDkJrx//vn+ByLcbyYEGA8++CC/+93v+Oyzz+jduzc33HADZ4az72WB8nJ46CF44w3fB37dOt9t+W9/q3yCHZ5Am1U+2Q63hyef4Ql2Xl7l9eE+Ej2vuvuxzwtfI1o29oS9Nu8/yrmK9xe7PaxHbPma7sd73KyZ/4yi3/Oysvj3oyf3zZvXv2yLFv6KW2xAFg3SdACVlDDzJ3fr1/tWiBYt6tUNqcEmvJPsFAYQwZxPdfl+JBprI6mhAEKSFm/wdXExPPywn5cl3slNtOtT9IQteiK3YwfcdRfcc0/VE8hwkruJE2HDhsa5yt2lC8ye/SBPPDGa0tLNACxbtoyf/nQ0M2fCr351Zr1fIxXda8rK/FX3Qw/1J5evvgpDhvhj+T/+Ae+952ccT6S6E+iwmTje9kT3U/m8aN1qEzhEA5gw0Ckrq/zdjA2EwgF2tT2hT3Ry37Jl/JP4VP/9E5WNPUDqgCkNJgwgNmyADh145SUoKwVXDttKm1FUZLX6/jXIhHeSnSJdmEK1/X6oVathZf1Ecu3bt3dDhw6ttO7000/n4osvZvPmzZx44olVnjNq1ChGjRrF6tWr+eEPf1hl+0UXXcSPfvQjPv/8c86Ok+bryiuv5OSTT+bDDz/kwgsvrLL92muv5dhjj2X+/PlcfvnlVbbfeOONDB8+nDfeeINrrrmmyvZJkyYxZMgQZs+ezfXXX19l+913380+++zDk08+ycQ4HaTvv/9+9thjDx5++GHujJOq4NFHH6Vr165Mnz6d6dOnV9n+zDPP0KZNG+644w4eeeSRKtuLgk7Ut9xyC0899RQbNvir2/n5AK2ZPv1ZAH7/+/F89tmLfPRRxZVksy6YPUZeHpSVjaG8vDhm77sDDwT3Lwfmx2wfAEwJ7o8GlsRsH4LZpOCk8yzgi5jtBeTlTQjmvDgVnzkyagQwFugDLKvy3qETzZt/E5w0FsbZfjp5eRcDm9mxo+p3z2wUzo3CbDXOVf3uwUXAj4DPgXgp5q4ETgY+BKp+9+Ba4Fj851b1uwc3kpc3HOfewDn/3Yt2y4FJODcEs9n4zJmVt5vdTfPm+1Be/iRlZRPjdOe5n/z8PSgvf5iysjurdP3Jz3+UHTu6AtMpL59eKTjw259hx442wB2Ulz8Sp8tQUdBacgvl5U/FbG9N69bPMmkS/Otf41mz5kWg4ruZn9+Fe+55DIAxY8awYUPxzu2dOkG3brtTWPgAXbrAffddzpYt8ytt32OPARx66BQKC+FPfxrNe+8toVMndpbp23cIxx47icJCmDz5LL74ovJ3r6CggAkTJgBw6qmnsmZN5e/eiBEjGDt2LAAnnHACW7ZsqbT9pJNO4qqrrgKgMM6RsDa/e926dUv7RHKNQRPJNbB99vHN0HF8an1Y+dx8vvWdjo1aJclOVbq+tW7t88pv2lSR8SsV+5UaaSI5aTQdOvgb+P/58J/0yCN9k2PbthUnYR07wnHH+X/mO+/0V9CXL684wQxveXn+ynC8riU1qSkmTm6fnyVYv65S60lt9x/WrbHi9tj+8y1awO23wzvv+Aw/UPG3AZ/d54ADYO5cePfdqtsLC+G00/wA+TvvpNIJdH4+jBzpb7Nnw9//Xnl7p05www0wf75vJXnmmYrnbd/ut0+cCP/5j7/K/tJLlV973To/weGaNfDVV378Q3R7SQlMn+6/fytW+O8eVHw3u3Sp+G6GrWfR7d26VaT4++ADX8/o9h49Krb36OEvvIY6dPCfW7h98uRk/0IiWWzkSNzEiZTvqPiRMYNmrpw+7lP6tH4PODx99ZOsUKXr23NlFGzd6pt027Sp177VqtVwsr4FQleYsl8yg3ovvzxxN6n69DOH+F1YErdA7EmLFp8mfF4q61afsrH94dUHXqKSvcKU7XR8aHgTYnPtj4cxr54As2bBU0/B976X7ipKhov9Dt18zTquGN/ZX5mJXqmRRqEWCMkaiSa2i96PZoNqnDEQN1QaAwHQsmUbvv/9G/jVr+r/Go2VYlTBgog0pLj9zBcE3ZbWr69VFxJ1N0mPdH/usd+ho4YG4x/atav2eZJeaoEQSSDbszCJJKIWCEmlKiegF14IU6bw8VV3sP/ki5LKyqSJ5NIjUz73St+hTothv/38GJv//rfxK1cEEM0AABvLSURBVNPEqQVCpJ7OPPNMBg0aBMCQIUPSXBsRkcxUpZ95MDDps4Xrkk6jqZSb6ZHWz724GF5+GYCC4MbL+Ml+oFIGJsk8CiBEqhFm0QozT4mISA06+i5M/buvr9K9KVF3GaXcTI+0fu6nnAKrViXe3q1b49VFak0BhIiIiKROEED0are+0uRfkLi7jCaSS4+0fe4lJT54yM+HID11JXl5cMYZjVQZqQsFECIiIpI6HSsGUUe7N02YUH13mYIDNlHQYRkEXUelcaQl1emKFX65665w442N/OKSCs3SXQERERHJIdHJWyLC7jJ5eQm6ywwaBPvvDx9+2Bi1lHRaudIve/TYuaq42AeZxbHzy0pGUguEiIiIpE6kBSIqbneZ+fPhvPP8jMOfBRN4fvCBz8AjuStsgQgCiEzJBiXJUwAhUo0b1bQqIlI7YQDx7rtw6KGVNu3MtjMzWPHJJxVXo0NZnl5ekhDTAqEsXNlHAYRINYYPH57uKoiIZJc994S2bf1A2blzay7frx888QRccIG/FL1lS8PXURpdpQxcYQtE9+6AsnBlo6QCCDPbBZgKHAesBsY45x6KU86APwEXBKvuBa52wWx1ZjYk2M++wGLgp865+cG2XwPnAnsGr3GHc+7PdX9rIvX3xhtvAAokRESS1rEj/O9/vnUhGQMH+nET++7rzzI3b27Y+knj+Oor3y9pxQrKymCfjRB2TCtvsdkPwg1aIJSFK/sk2wIxGdgG9ACGAE+b2QLn3KKYcqOBkcBgwAEvAJ8Ad5lZC3yj5STgDuBCYKaZ9XfObQMMOAdYCPQDnjezz51z/6zPGxSpj2uuuQbQPBAiIrXSo0elAbJJadPGL9UCkRteeWXnTNLNgV2i27YBrVrBYYftXJWWbFBSZzVmYTKztsCpwFjn3Cbn3BzgCeDsOMXPBSY6575wzn0JTARGBdsK8d+hSc65Uufcbfig4RgA59zNzrl3nHNlzrkP8cHGYbEvICIiItmp2kw7rVv7pQKI3LB6tV+efz5vzVpDr1Zr6NbML9+atQbWroVDDklvHaXOkmmBGACUOeeWRNYtAI6KU3ZQsC1ablBk28KwO1NgYbB+VnQnQVeoI4C7k6ifiIiIZLgaM+2EAUQSXZgSzWgtGSScZXr33Tnk+F149KWKv9kh+ptlvWQCiHbAhph164H2CcqujynXLggIYrdVt59x+NaRv8erkJmNxneXonfv3tXXXkRERNKuxkw7SXZhaqopPxszaErJa4UtEN26AeqilGuSCSA2AR1i1nUANiZRtgOwyTnnzCyp/ZjZJfixEEc450rjVcg5NwWYAjBs2DDlexMREclwNWbaSbILU1NM+dlgQVNREdx+u5/d75prYMiQ1L1W2ALRtWsKKiqZJpkAYgnQPBjsvDRYNxiIHUBNsG4w8GaccouAK83MIt2YDsQP0AbAzM4HrgaOdM59Uat3ItIAJk2alO4qiIjkhBoz7STZhakppvxssKDpxhvhhRcqHj/ySOpeK6YFQnJLjYOonXMlwL+B68ysrZkdBpwC3B+n+AzgCjPrZWa7AVcC04NtRcAO4DIzaxm0NAC8BGBmZwI3At9xzn1c97ckkjpDhgxhyJAh6a6GiEjWiTdguqAAxozx96sMpk6yC1MYiIwf33S6L4VBU15eioOmDZEe6m++mdrXCgMItUDkpGTTuF4MTANWAmuAi5xzi8zsCOBZ51y7oNzdwF7Ae8Hje4N1OOe2mdnIYN2f8PNAjAxSuAJcD3QB3vJDJgB4wDn387q+OZH6mj17NgDHHntsmmsiIpI9qusGk3BbLbIwxetPn8sDqxtsnoSSkor7y5bxxahrKeibz4dnwKefQp8+sMfzwPN12Pdnn/mlAoiclFQA4Zxbi5/fIXb9a/jB0eFjB/wmuMXbz7vA0ATb+iZTF5HGdP311wMKIEREaqO6bjAJt9UiC1OspjCwukEGIQef9TLbkz3dMna/7wYA9ghu9daqlQKIHJVsC4SIiIhIUqobp5BwWx0nkisuhnHjoLQUysubzsDqlAgCiPNtOoe5V8m3HRx9NBx+eIr2P3w4tGyZop1JJlEAISIiIilVXZebhNvqMJFc2PIQBg/NmjWdgdUpEXRh+qDFEF7ZXkiLFnDs9YCCL6mBAggRERFJueq63MTdVocuTGF3qDB4OPZY3xqRy60PKRvr4dzOz/rx59vy8pz67TOXx6BIVQogREREJP3q0IUptjtUKoKHTD4RTulYj+3b/UCU5s059Ih8Dj0iQ+olWUEBxP9v7+6D5ajqNI5/nxBCJCGEGN5keVE2IESTgKwQXSSsSgGaxRVLEZYVFALLUqjIexnJAgqo2bVACERebgiLgiAiEtiChUCEIEsCASIsCBEMyjvkDZIYOPvHOWMmzdy5PXPvnZ6ePJ+qqXunz+me8+vu6Znf9DndZnVceumlRTfBzGz9UDkDsWgRazYczIABMED1ZxkPLA/wjkDakA2euADGH9l0E2p9EYb2SSj69H4QlSswVRK3dmmXlYITCLM6dt5556KbYGa2fthiC1aMGseQpx5m4JpVuWcbkB6sWQUzZ8KRzScQ2S/CV10FM2bk/2W9v89e9OlN9CpdxYYMaa92WSk4gTCr4+abbwZg4sSJBbfEzKzDDRzIBUfM55zJq3j7HdhgAJx5JpxS88LwGYsXw6hR8NhjvWpC9osw5P9lvRXdeHp7P4h1EpyRKYHogzMQ/XafCmtbTiDM6pg6dSrgBMLMrBUm7CvO3mgwa1bDgEGw96eBwTlm3HFH2HRTePnleEflzTZr6vXHj4R7u2DOw5vwdxO3AtY9A1Hvl/VWdeNp9n4Q2QRn7rQVjIXcCURPZ1f65T4V1racQJiZmVlbaPqXbAk+9CG4917Yc89etWG39OBjN8NnP5u7Pe3ejSeb4CyY+2ZMIHJ0YfIgactyAmFmZoWQNAK4HNgPeAU4PYRwTY16As4DjkqTLgNOCyGEVrXVWqfpX7JPOAFefRXWrOldA1aujF2iDj0UttqK8aTbIlxJPMtxzTWxu1SNdrdzN55sgrPHrvm7MHmQtGU5gTAzs6JcBKwGtgTGAbdIWhBCWJipNwn4HDAWCMDtwCLgkha21dpEt11pvvjF+Oit1ath7Fh44glYtuzd5V1d8N3v1py1nbvxZBOcXV/On0C0+9kVaz0nEGZm1nKShgAHAx8KISwHfiPpV8DhwGmZ6l8BpoYQFqd5pwJH4wRivdNoV5qmroo0aBA89BA899y60+fMgaOOii969NFNRtCz+fPh/vthr71g9937dtnjt4bxX05PHnw2/s3Rhandz65Y6zmBMKtj5syZRTfBrFPtBKwJITxZNW0BsE+NuqNTWXW90bUWKmkS8YwF2223Xd+01NpGI11petNvf+5Dg5k9e6d1vyxvsUVMHH77W3j/+3sfTDd2T4+WyTmIup3PrljrOYEwq2PbbbctuglmnWoosDQzbQmwSTd1l2TqDZWk7DiIEMJ0YDrAHnvs4TESHaaRrjTN9tvvNvEYPhy++U244YY+iaWWN5bAG2+sfT58OAzftH9ea+UqWLFmMC+OOYRd++clrIM5gTCr49prrwXgS1/6UsEtMes4y4FhmWnDgBqdzt9Vdxiw3IOo1z+NdKVptt9+3cRj6tT46CePz4V9913b5rtm9c+v/uskSWfA/+zpswvWGCcQZnVMmzYNcAJh1g+eBAZKGhVCeCpNGwtkB1CTpo0FHuihnq0H8nalabbfftEDhitpcX+mx76qkvWWEwgzM2u5EMIKSb8AzpJ0FPEqTAcBH6tR/SrgREmziFdh+hZwYcsaa6XVTL/9IgcMz54dv9SHEP/21xf7opMkKz8nEGZmVpTjgCuAl4BXgX8NISyUtDdwawhhaKp3KfAB4NH0/LI0zaxfFDVguNkv9o1ebcpXVbLecgJhZmaFCCG8Rry/Q3b6HOLA6crzAJySHmYdq5kv9s1ebcpXVbLecAJhZmZm1iYa/WLv8QxWBCcQZnVcf/31RTfBzMw6QL1uRk3d8C7xeAYrghMIszpGjhxZdBPMzKzk6nUz6s0N78DjGawYA4pugFk76+rqoqurq+hmmJlZidXqZpSnLK/x4+H00xu40/ZcOPfc+NesGT4DYVZHJXk44ogjCm2HmZmVV71uRq3ugtTbMx5m4ATCzMzMrF9VuhlddVX3Za3qguRB19YXnECYmZmZtcCMGfFL+4wZ6/7y38pLqnrQtfUFj4EwMzMza0AzYwjyjHVoxdiEyhmPs8929yVrns9AmJmZmeXU7BiCnn7599gEKxMnEGZ1zJo1q+gmmJlZG2l2DEFPYx1aNTbBiYr1BScQZnVsvPHGRTfBzMzaSG/GENQb69CqsQkeRG19wQmEWR0XX3wxAMcdd1zBLTEzs3bQX1dNatXVmDyI2vqCEwizOq677jrACYSZma3VX1dNasXVmHznausLTiDMzMzM1iOtvGysdaZcl3GVNELSjZJWSHpW0qHd1JOk8yW9mh7nS1JV+ThJ8yS9mf6OyzuvmZmZmZkVL+99IC4CVgNbAocB0ySNrlFvEvA5YCwwBpgIHAMgaRBwE3A1sBkwA7gpTa87r5mZmZmZtYceEwhJQ4CDgckhhOUhhN8AvwIOr1H9K8DUEMLiEMLzwFTgiFQ2gdhl6kchhFUhhAsAAf+QY14zMzMzM2sDecZA7ASsCSE8WTVtAbBPjbqjU1l1vdFVZY+EEEJV+SNp+m09zLsOSZOIZywAVkl6LEccZTUSeKXoRvSjUsTXZG+6UsTWC50cXyfHBrBz0Q1ohXnz5r0i6dmi20Hn7095eT2s5XWxltfFWu2wLrbPUylPAjEUWJqZtgTYpJu6SzL1hqaxDNmy7HK6nTeTdBBCmA5MB5D0YAhhjxxxlJLjK69Ojg06O75Ojg1ifEW3oRVCCJsX3Qbo/P0pL6+Htbwu1vK6WKtM6yLPGIjlwLDMtGHAshx1hwHLUwLQ03LqzWtmZmZmZm0gTwLxJDBQ0qiqaWOBhTXqLkxlteotBMZkrqw0JlPe3bxmZmZmZtYGekwgQggrgF8AZ0kaIunjwEHAzBrVrwJOlLSNpPcB3wK6Utls4G3gBEkbSTo+Tb8zx7z1TM9Rp8wcX3l1cmzQ2fF1cmzQ+fG1G6/vyOthLa+Ltbwu1irNulCeHkKSRgBXAJ8GXgVOCyFcI2lv4NYQwtBUT8D5wFFp1suAUyvdkCTtlqbtCjwOfC2E8FCeec3MzMzMrHi5EggzMzMzMzPIfyM5MzMzMzMzJxBmZmZmZpZfaRMISSMk3ShphaRnJR1adJualQaVX57iWCbpYUkHVJV/UtITkt6UdJekXDf5aEeSRklaKenqqmmHpthXSPplGnNTKpIOkfR4iuHpND6oI7adpB0kzZL0uqQXJP1Y0sBUNk7SvBTfPEnjim5vPZKOl/SgpFWSujJl3W6r9B69QtLStA5ObHnjc+guPkl7Sbpd0muSXpb0c0lbV5VL0vmSXk2P8zNXzLOcmvlskjQoHT8Wt6KNrdLIupB0sqTH0mfgIkknt7Kt/SFv/J3+/mtgPXTcPpDV6PGhnY8NpU0ggIuA1cCWwGHANEk171xdAgOBPxLv7r0p8G3guvTFbSTxKliTgRHAg8C1RTW0D1wE/G/lSdpmlwKHE7flm8DFxTStOZI+TbwAwJHEGyN+Animg7bdxcBLwNbAOOJ+epykQcBNwNXAZsAM4KY0vV39CTiHeFGIv8qxraYAo4h36NwXOEXS/i1ob6NqxkfcPtOBHYgxLAOurCqfBHyOePnsMcBE4Jh+bmunauaz6WTg5f5uWAEaWRcC/oW4r+4PHC/pkJa0sv/kjb/T339510Mn7gNZjR4f2vfYEEIo3QMYQtwAO1VNmwmcV3Tb+jDGR4CDiQeW+zKxvwV8sOg2NhHTIcB1xC9jV6dp3wOuqaqzY9q2mxTd3gbiuo94RbHs9I7YdsQrph1Y9fwHxKRvP+B50sUYUtlzwP5FtzlHTOcAXXm3FfGL+X5V5WcDPys6jrzx1SjfHVhW9fw+YFLV868B9xcdR9kezXw2Ae9P77EDgMVFx1DkusjMfwFwYdFxtCL+Tn7/9WY/KPs+0Nt10e7HhrKegdgJWBNCeLJq2gKgrGcg1iFpS2KMC4kxLaiUhXhfjqcpWayShgFnAdmuH9n4nia9wVrXuuZJ2gDYA9hc0u8lLU5dfN5Dh2w74EfAIZI2lrQN8WB2GzGOR0I60iWPUL74oM62krQZ8ezLgqr6ZT/efIJ1b9S5TvyUP76iNPPZdCFwBjFh7SRNf06n7jt7U+6byTYSfye//5raDzpkH8hqdF209bGhrAnEUGBpZtoSYveRUpO0IfBfwIwQwhPEWJdkqpUx1rOBy0MI2X58ZY9vS2BD4AvEg904YDdiN7Syx1ZxD/EAtxRYTOze80s6Jz6oH8vQqufZstKRNAb4DvHUeEU2/iXA0E7qh90iDX02SfonYIMQwo393bAC9OZzegrx+8mVPdRrZ43E38nvv2b3gymUfx/Iyr0uynBsKGsCsRwYlpk2jNivt7QkDSCezloNVO7UXfpYFQfWfgr4zxrFZY+v8svAhSGEP4cQXgH+AziQ8sdW2SdvI44PGAKMJPZPPZ8OiK9KvViWVz3PlpWKpL8FbgW+HkKYU1WUjX8YsDxzdmm9J2m2pNDN4zc08J6QNAT4PnBC/7e87/Xlusgs93hiP/jPhBBW9U/rW6KR+Dv5/dfwftBB+0BWrnVRlmNDWROIJ4GBkkZVTRtLiU91pV8aLif+on1wCOEvqWghMbZKvSHEcQJlinUCcfDmc5JeAE4CDpY0n3fH9wFgI+I2bnshhNeJv8pXH+gr/3fCthsBbAf8OISwKoTwKvEXoQOJcYzJ/Eo2hnLFV9Httkrb+M/V5ZTweKN4Vak7gLNDCDMzxevETwnja4UQwoQQgrp5/D2NfTaNIh4X56Tj4i+ArRWv8rVD/0bSe328LgCQ9FXgNOCTNc5Wl00j8Xfy+6+h/aDD9oGsvOuiHMeGogdhNPsAfgb8lPir6MeJp4FGF92uXsRzCXA/MDQzffMU28HAYOIvv6UaXAVsDGxV9fghcH2KrdI1Zu+0La+mjQendhPfWcQrS21B/HV+DrHLVum3XYrvGeIBfSAwHLgRuAYYBDwLfJ2Y9B2fng8qus11YhmYtsW5xLN9g9O0utsKOA+4O23fDxITirYbLF4nvm2IYzpO6ma+Y4mD9bYB3kf8QDu26HjK+Mj72ZS2S/Vx8fPEwfpbEbsuFB5Lq9ZFqnsY8AKwS9HtLmBf6Oj3XwProeP2gWbWRVmODYU3oBcbYQSxH/YK4pVfDi26Tb2IZXvir9Yriae4Ko/DUvmngCeI3WVmAzsU3eZexjuFdBWm9PzQtA1XEC8LOqLoNjYYz4bES52+kQ5+FwCDO2XbEcd1zAZeB14hXklry1S2GzAvxTcf2K3o9vYQy5T0Xqt+TOlpWxETpCuIye6LwIlFx9JIfMCZ6f/q48vyqvlEPGX+Wnp8n6qra/nR0Dbo9rOJ+EPJ8m7mm0AbXmmlVesCWAT8JbOPXlJ0DP0Rf43YO/r918B66Lh9oNl1kZmnLY8NSo0zMzMzMzPrUVnHQJiZmZmZWQGcQJiZmZmZWW5OIMzMzMzMLDcnEGZmZmZmlpsTCDMzMzMzy80JhJmZmZmZ5eYEwtZrkrok/brodlSTdJCkpyStkdRVdHvMzMzMqjmBsMKkL+9B0uTM9Alp+sii2lawy4EbiDcY/HqtCpL+IOmklrbKzMzMDCcQVryVwMmSNi+6IX1J0oZNzjcceC/w3yGE50MIS3rRhgGSNmh2fjMzM7NanEBY0e4C/gBM7q5CrTMSknZI0/bI1DlA0jxJb0maI+lvJO0jaYGk5ZJ+Lem9NV7j25JeTHWulPSeqjJJOkXS02m5j0r65xpt+bKkOyW9BRzTTSybSZoh6fW0rDskja7EALyeqt6ZljmhxjJmE89O/CDVCWn6Ean9B0p6DFgN7JLKjpT0O0krJT0p6ZuSBlQtc1NJ0yW9JGmZpLsr67aqfGYqXynpGUnf6G6bmZmZWedyAmFFewc4DThW0o59sLx/B74B7AlsBlwLfAeYBEwARgNTMvPsA4wFPgkcDOwHnF9Vfg7wNeDfgF2Bc4FLJX0ms5xzgYtTnV92076u1LaDgI8CbwK3pYTlvtQ+Uju2TtOyPg8sBs5KdbauKhtMTMaOSe14VtLRwPfSetgF+BZwKnAcxAQJuAXYBvgssBtwDzGJqSz7HODDqXxn4KvA893EaGZmZh1sYNENMAshzJJ0L/Bd4JBeLm5yCGEOgKRLgAuBj4QQ5qdpM4AvZOZ5GzgyhLAceEzSqcDlkk5P5ScC+1WWCyyS9FFiQnFL1XIuDCFc313DJI0C/hHYJ4RwT5p2OPAccFgI4TJJL6Xqr4UQXqi1nBDCa5LeBpbVqLMBcHwIYV7V604GTqlq2yJJ5xETiB8D+wLjgM1DCG+lOpMlTQQOB75PPOMxP4TwQCp/trs4zczMrLM5gbB2cSowV9IPermcR6r+fzH9fTQzbYvsPCl5qJgLDAJ2BDYi/qp/W6WrULIhsetVtQd7aNsuxDMucysTQghLJD1KPFvQF9YAD1eepLEl2xLPmEyrqjcQUPr/I8DGwMvxZMRfDSauA4BpwPWSPgLcDtwcQri7j9psZmZmJeIEwtpCCOEBSTcQf+0+O1P8Tvpb/e22u0HKf6lebFp2dlojXfcqdScSzxR091oAKxpYblbouUouq0IIb1c9r7T/WGp3h6rUeRHYu0bZUoAQwq2StgcOIHb1ukXSz0MIR/ZNs83MzKwsnEBYOzkD+B2wf2b6y+nv1lX/j+vD1/2wpCEhhEoCsBdxAPLTxC/Xq4DtQwh39vJ1Hk/LG08cY4CkYcSxBVc2uKzVxO5KdYUQXpT0J2DHEMJV3VSbD2wJvBNCeKbOsl4BZgIzJd0K/FTSsSGEVQ223czMzErMCYS1jRDC7yVN5933Pvg98EdgiqTTgB2Ab/fhSw8ErpB0FvA+4DzgJ5WEQtIPgR+mwcb3AEOJScY7IYTpeV8khPCUpJuI3YkmAW8Qx30sBa5psM1/APaWdDXxrMMrdeqeCVwo6Q1gFvHsze7ANiGEc4E7gHuBmySdAjwBbEVM5O4IIcxJ62Y+sJC4vj4PPOPkwczMbP3jqzBZuzmL2I//r1IXpEOADwALiFdaOqMPX/Nu4hfju4AbgTuBU6rKJxOv3HRSqnc78SpJi5p4rSOBB4Bfpb8bA/tXDV7O6zvEsQ1Ps/asTE0hhMuIV006nLj+5hCvSrUolQfgQGLcPwH+D7iOeLWlP6XFrCImOwuIycYmxG5dZmZmtp5R/O5gZmZmZmbWM5+BMDMzMzOz3JxAmJmZmZlZbk4gzMzMzMwsNycQZmZmZmaWmxMIMzMzMzPLzQmEmZmZmZnl5gTCzMzMzMxycwJhZmZmZma5/T8LfHcjXh/nRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "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": 47,
   "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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "print(gbrt.n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum validation MSE: 0.002712853325235463\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum validation MSE:\", min_val_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import xgboost\n",
    "except ImportError as ex:\n",
    "    print(\"Error: the xgboost library is not installed.\")\n",
    "    xgboost = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MSE: 0.0028512559726563943\n"
     ]
    }
   ],
   "source": [
    "if xgboost is not None:  # not shown in the book\n",
    "    xgb_reg = xgboost.XGBRegressor(random_state=42)\n",
    "    xgb_reg.fit(X_train, y_train)\n",
    "    y_pred = xgb_reg.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    print(\"Validation MSE:\", val_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:0.286719\n",
      "Will train until validation_0-rmse hasn't improved in 2 rounds.\n",
      "[1]\tvalidation_0-rmse:0.258221\n",
      "[2]\tvalidation_0-rmse:0.232634\n",
      "[3]\tvalidation_0-rmse:0.210526\n",
      "[4]\tvalidation_0-rmse:0.190232\n",
      "[5]\tvalidation_0-rmse:0.172196\n",
      "[6]\tvalidation_0-rmse:0.156394\n",
      "[7]\tvalidation_0-rmse:0.142241\n",
      "[8]\tvalidation_0-rmse:0.129789\n",
      "[9]\tvalidation_0-rmse:0.118752\n",
      "[10]\tvalidation_0-rmse:0.108388\n",
      "[11]\tvalidation_0-rmse:0.100155\n",
      "[12]\tvalidation_0-rmse:0.09208\n",
      "[13]\tvalidation_0-rmse:0.084791\n",
      "[14]\tvalidation_0-rmse:0.078699\n",
      "[15]\tvalidation_0-rmse:0.073248\n",
      "[16]\tvalidation_0-rmse:0.069391\n",
      "[17]\tvalidation_0-rmse:0.066277\n",
      "[18]\tvalidation_0-rmse:0.063458\n",
      "[19]\tvalidation_0-rmse:0.060326\n",
      "[20]\tvalidation_0-rmse:0.0578\n",
      "[21]\tvalidation_0-rmse:0.055643\n",
      "[22]\tvalidation_0-rmse:0.053943\n",
      "[23]\tvalidation_0-rmse:0.053138\n",
      "[24]\tvalidation_0-rmse:0.052415\n",
      "[25]\tvalidation_0-rmse:0.051821\n",
      "[26]\tvalidation_0-rmse:0.051226\n",
      "[27]\tvalidation_0-rmse:0.051135\n",
      "[28]\tvalidation_0-rmse:0.05091\n",
      "[29]\tvalidation_0-rmse:0.050893\n",
      "[30]\tvalidation_0-rmse:0.050725\n",
      "[31]\tvalidation_0-rmse:0.050471\n",
      "[32]\tvalidation_0-rmse:0.050285\n",
      "[33]\tvalidation_0-rmse:0.050492\n",
      "[34]\tvalidation_0-rmse:0.050348\n",
      "Stopping. Best iteration:\n",
      "[32]\tvalidation_0-rmse:0.050285\n",
      "\n",
      "Validation MSE: 0.0025349167568108864\n"
     ]
    }
   ],
   "source": [
    "if xgboost is not None:  # not shown in the book\n",
    "    xgb_reg.fit(X_train, y_train,\n",
    "                eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n",
    "    y_pred = xgb_reg.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    print(\"Validation MSE:\", val_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.99 ms ± 26.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.3 ms ± 1.33 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit GradientBoostingRegressor().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Voting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Load the MNIST data and split it into a training set, a validation set, and a test set (e.g., use 50,000 instances for training, 10,000 for validation, and 10,000 for testing)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = fetch_mldata('MNIST original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
    "    mnist.data, mnist.target, test_size=10000, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_val, y_train_val, test_size=10000, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Then train various classifiers, such as a Random Forest classifier, an Extra-Trees classifier, and an SVM._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_clf = RandomForestClassifier(random_state=42)\n",
    "extra_trees_clf = ExtraTreesClassifier(random_state=42)\n",
    "svm_clf = LinearSVC(random_state=42)\n",
    "mlp_clf = MLPClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the 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)\n",
      "Training the ExtraTreesClassifier(bootstrap=False, 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)\n",
      "Training the LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
      "     verbose=0)\n",
      "Training the MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
      "       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
      "       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]\n",
    "for estimator in estimators:\n",
    "    print(\"Training the\", estimator)\n",
    "    estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9467, 0.9512, 0.8661, 0.95]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_val, y_val) for estimator in estimators]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear SVM is far outperformed by the other classifiers. However, let's keep it for now since it may improve the voting classifier's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Next, try to combine them into an ensemble that outperforms them all on the validation set, using a soft or hard voting classifier._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_estimators = [\n",
    "    (\"random_forest_clf\", random_forest_clf),\n",
    "    (\"extra_trees_clf\", extra_trees_clf),\n",
    "    (\"svm_clf\", svm_clf),\n",
    "    (\"mlp_clf\", mlp_clf),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf = VotingClassifier(named_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf', 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",
       "   ...       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False))],\n",
       "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9595"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9467, 0.9512, 0.8661, 0.95]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the SVM to see if performance improves. It is possible to remove an estimator by setting it to `None` using `set_params()` like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf', 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",
       "   ...       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False))],\n",
       "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.set_params(svm_clf=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This updated the list of estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('random_forest_clf',\n",
       "  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)),\n",
       " ('extra_trees_clf',\n",
       "  ExtraTreesClassifier(bootstrap=False, 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)),\n",
       " ('svm_clf', None),\n",
       " ('mlp_clf',\n",
       "  MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "         beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "         hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "         learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "         nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
       "         solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "         warm_start=False))]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('random_forest_clf',\n",
       "  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)),\n",
       " ('extra_trees_clf',\n",
       "  ExtraTreesClassifier(bootstrap=False, 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)),\n",
       " ('svm_clf', None),\n",
       " ('mlp_clf',\n",
       "  MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "         beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "         hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "         learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "         nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
       "         solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "         warm_start=False))]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it did not update the list of _trained_ estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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),\n",
       " ExtraTreesClassifier(bootstrap=False, 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),\n",
       " LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "      intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "      multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "      verbose=0),\n",
       " MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "        beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "        hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "        learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "        nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
       "        solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "        warm_start=False)]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can either fit the `VotingClassifier` again, or just remove the SVM from the list of trained estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "del voting_clf.estimators_[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's evaluate the `VotingClassifier` again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9636"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! The SVM was hurting performance. Now let's try using a soft voting classifier. We do not actually need to retrain the classifier, we can just set `voting` to `\"soft\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf.voting = \"soft\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.967"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a significant improvement, and it's much better than each of the individual classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Once you have found one, try it on the test set. How much better does it perform compared to the individual classifiers?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9645"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9434, 0.9444, 0.9508]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The voting classifier reduced the error rate from about 4.9% for our best model (the `MLPClassifier`) to just 3.5%. That's about 28% less errors, not bad!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Stacking Ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Run the individual classifiers from the previous exercise to make predictions on the validation set, and create a new training set with the resulting predictions: each training instance is a vector containing the set of predictions from all your classifiers for an image, and the target is the image's class. Train a classifier on this new training set._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val_predictions = np.empty((len(X_val), len(estimators)), dtype=np.float32)\n",
    "\n",
    "for index, estimator in enumerate(estimators):\n",
    "    X_val_predictions[:, index] = estimator.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2., 2., 2.],\n",
       "       [7., 7., 7., 7.],\n",
       "       [4., 4., 4., 4.],\n",
       "       ...,\n",
       "       [4., 4., 4., 4.],\n",
       "       [9., 9., 9., 9.],\n",
       "       [4., 4., 4., 4.]], dtype=float32)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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=200, n_jobs=1,\n",
       "            oob_score=True, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)\n",
    "rnd_forest_blender.fit(X_val_predictions, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9599"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could fine-tune this blender or try other types of blenders (e.g., an `MLPClassifier`), then select the best one using cross-validation, as always."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Congratulations, you have just trained a blender, and together with the classifiers they form a stacking ensemble! Now let's evaluate the ensemble on the test set. For each image in the test set, make predictions with all your classifiers, then feed the predictions to the blender to get the ensemble's predictions. How does it compare to the voting classifier you trained earlier?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_predictions = np.empty((len(X_test), len(estimators)), dtype=np.float32)\n",
    "\n",
    "for index, estimator in enumerate(estimators):\n",
    "    X_test_predictions[:, index] = estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rnd_forest_blender.predict(X_test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9576"
      ]
     },
     "execution_count": 86,
     "metadata": {},
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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