From 72356268990fda806b9e931f7ff7c17ec2fa7e7f Mon Sep 17 00:00:00 2001 From: K^+pj8 Date: Wed, 6 Jul 2022 15:55:50 +0200 Subject: [PATCH] add StackingClassifier --- 07_ensemble_learning_and_random_forests.ipynb | 6469 ++++++++++------- 1 file changed, 3903 insertions(+), 2566 deletions(-) diff --git a/07_ensemble_learning_and_random_forests.ipynb b/07_ensemble_learning_and_random_forests.ipynb index 5ee246cf4..1b1f6621c 100644 --- a/07_ensemble_learning_and_random_forests.ipynb +++ b/07_ensemble_learning_and_random_forests.ipynb @@ -1,2570 +1,3907 @@ { - "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": [ - "\n", - " \n", - " \n", - "
\n", - " \"Open\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Python ≥3.5 is required\n", - "import sys\n", - "assert sys.version_info >= (3, 5)\n", - "\n", - "# Scikit-Learn ≥0.20 is required\n", - "import sklearn\n", - "assert sklearn.__version__ >= \"0.20\"\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 as mpl\n", - "import matplotlib.pyplot as plt\n", - "mpl.rc('axes', labelsize=14)\n", - "mpl.rc('xtick', labelsize=12)\n", - "mpl.rc('ytick', labelsize=12)\n", - "\n", - "# Where to save the figures\n", - "PROJECT_ROOT_DIR = \".\"\n", - "CHAPTER_ID = \"ensembles\"\n", - "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n", - "os.makedirs(IMAGES_PATH, exist_ok=True)\n", - "\n", - "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n", - " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n", - " print(\"Saving figure\", fig_id)\n", - " if tight_layout:\n", - " plt.tight_layout()\n", - " plt.savefig(path, format=fig_extension, dpi=resolution)" - ] - }, - { - "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": "markdown", - "metadata": {}, - "source": [ - "**Code to generate Figure 7–3. The law of large numbers:**" - ] - }, - { - "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": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "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": "markdown", - "metadata": {}, - "source": [ - "Let's use the moons dataset:" - ] - }, - { - "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": "markdown", - "metadata": {}, - "source": [ - "**Note**: to be future-proof, we set `solver=\"lbfgs\"`, `n_estimators=100`, and `gamma=\"scale\"` since these will be the default values in upcoming Scikit-Learn versions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Code examples:**" - ] - }, - { - "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(solver=\"lbfgs\", random_state=42)\n", - "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", - "svm_clf = SVC(gamma=\"scale\", 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(random_state=42)),\n", - " ('rf', RandomForestClassifier(random_state=42)),\n", - " ('svc', SVC(random_state=42))])" - ] - }, - "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 0.896\n", - "SVC 0.896\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": [ - "**Note**: the results in this notebook may differ slightly from the book, as Scikit-Learn algorithms sometimes get tweaked." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Soft voting:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", - " ('rf', RandomForestClassifier(random_state=42)),\n", - " ('svc', SVC(probability=True, random_state=42))],\n", - " voting='soft')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n", - "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", - "svm_clf = SVC(gamma=\"scale\", 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 0.896\n", - "SVC 0.896\n", - "VotingClassifier 0.92\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 and Pasting\n", - "## Bagging and Pasting in Scikit-Learn" - ] - }, - { - "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(), n_estimators=500,\n", - " max_samples=100, bootstrap=True, 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": "markdown", - "metadata": {}, - "source": [ - "**Code to generate Figure 7–5. A single Decision Tree (left) versus a bagging ensemble of 500 trees (right):**" - ] - }, - { - "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, 2.45, -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": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving figure decision_tree_without_and_with_bagging_plot\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", - "plt.sca(axes[0])\n", - "plot_decision_boundary(tree_clf, X, y)\n", - "plt.title(\"Decision Tree\", fontsize=14)\n", - "plt.sca(axes[1])\n", - "plot_decision_boundary(bag_clf, X, y)\n", - "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n", - "plt.ylabel(\"\")\n", - "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Out-of-Bag evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8986666666666666" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bag_clf = BaggingClassifier(\n", - " DecisionTreeClassifier(), n_estimators=500,\n", - " bootstrap=True, oob_score=True, random_state=40)\n", - "bag_clf.fit(X_train, y_train)\n", - "bag_clf.oob_score_" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.32275132, 0.67724868],\n", - " [0.34117647, 0.65882353],\n", - " [1. , 0. ],\n", - " [0. , 1. ],\n", - " [0. , 1. ],\n", - " [0.09497207, 0.90502793],\n", - " [0.31147541, 0.68852459],\n", - " [0.01754386, 0.98245614],\n", - " [0.97109827, 0.02890173],\n", - " [0.97765363, 0.02234637],\n", - " [0.74404762, 0.25595238],\n", - " [0. , 1. ],\n", - " [0.7173913 , 0.2826087 ],\n", - " [0.85026738, 0.14973262],\n", - " [0.97222222, 0.02777778],\n", - " [0.0625 , 0.9375 ],\n", - " [0. , 1. ],\n", - " [0.97837838, 0.02162162],\n", - " [0.94642857, 0.05357143],\n", - " [1. , 0. ],\n", - " [0.01704545, 0.98295455],\n", - " [0.39473684, 0.60526316],\n", - " [0.88700565, 0.11299435],\n", - " [1. , 0. ],\n", - " [0.97790055, 0.02209945],\n", - " [0. , 1. ],\n", - " [0.99428571, 0.00571429],\n", - " [1. , 0. ],\n", - 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" [1. , 0. ],\n", - " [0.96428571, 0.03571429],\n", - " [0.34554974, 0.65445026],\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", - " [0.98214286, 0.01785714],\n", - " [1. , 0. ],\n", - " [0.03108808, 0.96891192],\n", - " [0.58854167, 0.41145833]])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bag_clf.oob_decision_function_" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.912" - ] - }, - "execution_count": 17, - "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": [ - "# Random Forests" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=42)\n", - "rnd_clf.fit(X_train, y_train)\n", - "\n", - "y_pred_rf = rnd_clf.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Random Forest is equivalent to a bag of decision trees:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "bag_clf = BaggingClassifier(\n", - " DecisionTreeClassifier(max_features=\"sqrt\", max_leaf_nodes=16),\n", - " n_estimators=500, random_state=42)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "bag_clf.fit(X_train, y_train)\n", - "y_pred = bag_clf.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum(y_pred == y_pred_rf) / len(y_pred) # very similar predictions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Importance" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sepal length (cm) 0.11249225099876375\n", - "sepal width (cm) 0.02311928828251033\n", - "petal length (cm) 0.4410304643639577\n", - "petal width (cm) 0.4233579963547682\n" - ] - } - ], - "source": [ - "from sklearn.datasets import load_iris\n", - "iris = load_iris()\n", - "rnd_clf = RandomForestClassifier(n_estimators=500, 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": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.11249225, 0.02311929, 0.44103046, 0.423358 ])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rnd_clf.feature_importances_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following figure overlays the decision boundaries of 15 decision trees. As you can see, even though each decision tree is imperfect, the ensemble defines a pretty good decision boundary:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "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_train[indices_with_replacement], y_train[indices_with_replacement])\n", - " plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Code to generate Figure 7–6. MNIST pixel importance (according to a Random Forest classifier):**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this and keep the same code as in the book, we use `as_frame=False`." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import fetch_openml\n", - "\n", - "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n", - "mnist.target = mnist.target.astype(np.uint8)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(random_state=42)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rnd_clf = RandomForestClassifier(n_estimators=100, 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 = mpl.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": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "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": [ - "# Boosting\n", - "## AdaBoost" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_decision_boundary(ada_clf, X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Code to generate Figure 7–8. Decision boundaries of consecutive predictors:**" - ] - }, - { - "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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "m = len(X_train)\n", - "\n", - "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", - "for subplot, learning_rate in ((0, 1), (1, 0.5)):\n", - " sample_weights = np.ones(m) / m\n", - " plt.sca(axes[subplot])\n", - " for i in range(5):\n", - " svm_clf = SVC(kernel=\"rbf\", C=0.2, gamma=0.6, random_state=42)\n", - " svm_clf.fit(X_train, y_train, sample_weight=sample_weights * m)\n", - " y_pred = svm_clf.predict(X_train)\n", - "\n", - " r = sample_weights[y_pred != y_train].sum() / sample_weights.sum() # equation 7-1\n", - " alpha = learning_rate * np.log((1 - r) / r) # equation 7-2\n", - " sample_weights[y_pred != y_train] *= np.exp(alpha) # equation 7-3\n", - " sample_weights /= sample_weights.sum() # normalization step\n", - "\n", - " plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n", - " plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n", - " if subplot == 0:\n", - " plt.text(-0.75, -0.95, \"1\", fontsize=14)\n", - " plt.text(-1.05, -0.95, \"2\", fontsize=14)\n", - " plt.text(1.0, -0.95, \"3\", fontsize=14)\n", - " plt.text(-1.45, -0.5, \"4\", fontsize=14)\n", - " plt.text(1.36, -0.95, \"5\", fontsize=14)\n", - " else:\n", - " plt.ylabel(\"\")\n", - "\n", - "save_fig(\"boosting_plot\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gradient Boosting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let create a simple quadratic dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "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": "markdown", - "metadata": {}, - "source": [ - "Now let's train a decision tree regressor on this dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeRegressor(max_depth=2, random_state=42)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.tree import DecisionTreeRegressor\n", - "\n", - "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", - "tree_reg1.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeRegressor(max_depth=2, random_state=42)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y2 = y - tree_reg1.predict(X)\n", - "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", - "tree_reg2.fit(X, y2)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeRegressor(max_depth=2, random_state=42)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y3 = y2 - tree_reg2.predict(X)\n", - "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", - "tree_reg3.fit(X, y3)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "X_new = np.array([[0.8]])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.75026781])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Code to generate Figure 7–9. In this depiction of Gradient Boosting, the first predictor (top left) is trained normally, then each consecutive predictor (middle left and lower left) is trained on the previous predictor’s residuals; the right column shows the resulting ensemble’s predictions:**" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "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)" - ] - }, - { - "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": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "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": "markdown", - "metadata": {}, - "source": [ - "Now let's try a gradient boosting regressor:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n", - " random_state=42)" - ] - }, - "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": "markdown", - "metadata": {}, - "source": [ - "**Code to generate Figure 7–10. GBRT ensembles with not enough predictors (left) and too many (right):**" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)" - ] - }, - "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": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", - "\n", - "plt.sca(axes[0])\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", - "plt.xlabel(\"$x_1$\", fontsize=16)\n", - "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", - "\n", - "plt.sca(axes[1])\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", - "plt.xlabel(\"$x_1$\", fontsize=16)\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(max_depth=2, n_estimators=56, random_state=42)" - ] - }, - "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) + 1\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": "markdown", - "metadata": {}, - "source": [ - "**Code to generate Figure 7–11. Tuning the number of trees using early stopping:**" - ] - }, - { - "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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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 4))\n", - "\n", - "plt.subplot(121)\n", - "plt.plot(np.arange(1, len(errors) + 1), 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.ylabel(\"Error\", fontsize=16)\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", - "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", - "plt.xlabel(\"$x_1$\", fontsize=16)\n", - "\n", - "save_fig(\"early_stopping_gbrt_plot\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Early stopping with some patience (interrupts training only after there's no improvement for 5 epochs):" - ] - }, - { - "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: 0.00400040950714611\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) # Not shown\n", - " print(\"Validation MSE:\", val_error) # Not shown" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0]\tvalidation_0-rmse:0.22834\n", - "Will train until validation_0-rmse hasn't improved in 2 rounds.\n", - "[1]\tvalidation_0-rmse:0.16224\n", - "[2]\tvalidation_0-rmse:0.11843\n", - "[3]\tvalidation_0-rmse:0.08760\n", - "[4]\tvalidation_0-rmse:0.06848\n", - "[5]\tvalidation_0-rmse:0.05709\n", - "[6]\tvalidation_0-rmse:0.05297\n", - "[7]\tvalidation_0-rmse:0.05129\n", - "[8]\tvalidation_0-rmse:0.05155\n", - "[9]\tvalidation_0-rmse:0.05211\n", - "Stopping. Best iteration:\n", - "[7]\tvalidation_0-rmse:0.05129\n", - "\n", - "Validation MSE: 0.0026308690413069744\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) # Not shown\n", - " print(\"Validation MSE:\", val_error) # Not shown" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "76.1 ms ± 5.64 ms per loop (mean ± std. dev. of 7 runs, 10 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": [ - "20.8 ms ± 351 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit GradientBoostingRegressor().fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "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": "markdown", - "metadata": {}, - "source": [ - "The MNIST dataset was loaded earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "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": 57, - "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": 58, - "metadata": {}, - "outputs": [], - "source": [ - "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", - "extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\n", - "svm_clf = LinearSVC(max_iter=100, tol=20, random_state=42)\n", - "mlp_clf = MLPClassifier(random_state=42)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training the RandomForestClassifier(random_state=42)\n", - "Training the ExtraTreesClassifier(random_state=42)\n", - "Training the LinearSVC(max_iter=100, random_state=42, tol=20)\n", - "Training the MLPClassifier(random_state=42)\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": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.9692, 0.9715, 0.859, 0.9639]" - ] - }, - "execution_count": 60, - "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": 61, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import VotingClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "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": 63, - "metadata": {}, - "outputs": [], - "source": [ - "voting_clf = VotingClassifier(named_estimators)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VotingClassifier(estimators=[('random_forest_clf',\n", - " RandomForestClassifier(random_state=42)),\n", - " ('extra_trees_clf',\n", - " ExtraTreesClassifier(random_state=42)),\n", - " ('svm_clf',\n", - " LinearSVC(max_iter=100, random_state=42, tol=20)),\n", - " ('mlp_clf', MLPClassifier(random_state=42))])" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "voting_clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9711" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "voting_clf.score(X_val, y_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.9692, 0.9715, 0.859, 0.9639]" - ] - }, - "execution_count": 66, - "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": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VotingClassifier(estimators=[('random_forest_clf',\n", - " RandomForestClassifier(random_state=42)),\n", - " ('extra_trees_clf',\n", - " ExtraTreesClassifier(random_state=42)),\n", - " ('svm_clf', None),\n", - " ('mlp_clf', MLPClassifier(random_state=42))])" - ] - }, - "execution_count": 67, - "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": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('random_forest_clf', RandomForestClassifier(random_state=42)),\n", - " ('extra_trees_clf', ExtraTreesClassifier(random_state=42)),\n", - " ('svm_clf', None),\n", - " ('mlp_clf', MLPClassifier(random_state=42))]" - ] - }, - "execution_count": 68, - "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": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[RandomForestClassifier(random_state=42),\n", - " ExtraTreesClassifier(random_state=42),\n", - " LinearSVC(max_iter=100, random_state=42, tol=20),\n", - " MLPClassifier(random_state=42)]" - ] - }, - "execution_count": 69, - "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": 70, - "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": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9735" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "voting_clf.score(X_val, y_val)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A bit 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": 72, - "metadata": {}, - "outputs": [], - "source": [ - "voting_clf.voting = \"soft\"" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9693" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "voting_clf.score(X_val, y_val)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nope, hard voting wins in this case." - ] - }, - { - "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": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9706" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "voting_clf.voting = \"hard\"\n", - "voting_clf.score(X_test, y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.9645, 0.9691, 0.9624]" - ] - }, - "execution_count": 75, - "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 only very slightly reduced the error rate of the best model in this case." - ] - }, - { - "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": 76, - "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": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[5., 5., 5., 5.],\n", - " [8., 8., 8., 8.],\n", - " [2., 2., 3., 2.],\n", - " ...,\n", - " [7., 7., 7., 7.],\n", - " [6., 6., 6., 6.],\n", - " [7., 7., 7., 7.]], dtype=float32)" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_val_predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)" - ] - }, - "execution_count": 78, - "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": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9689" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "CfQoUzTufcRw" + }, + "source": [ + "**Chapter 7 – Ensemble Learning and Random Forests**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HDG-_ZmZfcRy" + }, + "source": [ + "_This notebook contains all the sample code and solutions to the exercises in chapter 7._" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TNLcqQzHfcR0" + }, + "source": [ + "\n", + " \n", + " \n", + "
\n", + " \"Open\n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bHs3iPQofcR2" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3GlFG9LjfcR2" + }, + "source": [ + "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "CdhRYNk2fcR3" + }, + "outputs": [], + "source": [ + "# Python ≥3.5 is required\n", + "import sys\n", + "assert sys.version_info >= (3, 5)\n", + "\n", + "# Scikit-Learn ≥0.20 is required\n", + "import sklearn\n", + "assert sklearn.__version__ >= \"0.20\"\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 as mpl\n", + "import matplotlib.pyplot as plt\n", + "mpl.rc('axes', labelsize=14)\n", + "mpl.rc('xtick', labelsize=12)\n", + "mpl.rc('ytick', labelsize=12)\n", + "\n", + "# Where to save the figures\n", + "PROJECT_ROOT_DIR = \".\"\n", + "CHAPTER_ID = \"ensembles\"\n", + "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n", + "os.makedirs(IMAGES_PATH, exist_ok=True)\n", + "\n", + "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n", + " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n", + " print(\"Saving figure\", fig_id)\n", + " if tight_layout:\n", + " plt.tight_layout()\n", + " plt.savefig(path, format=fig_extension, dpi=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-xsz6pGnfcR6" + }, + "source": [ + "# Voting Classifiers" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "mLafhSoofcR7" + }, + "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": "markdown", + "metadata": { + "id": "e4uoH7TjfcR9" + }, + "source": [ + "**Code to generate Figure 7–3. The law of large numbers:**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "W37MrAY6fcR-", + "outputId": "13901232-5438-45df-8a89-b73cecb85b70", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving figure law_of_large_numbers_plot\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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cDtAZvG2KP50VUH/pa6vJKLBSUmkP7nya6HQ6mjdvDsA999wTss2kWZuRUtL18R/o+vgPDHthBamTF9L+0e/rHPfb9BxSJy9k6W5taXjG8YoTzkNKyV2zNnPj++tP2E6hUCgUirPN2VxivQ8wCCFaSyn3e8q6Ur/AXInP8XMBMFgIMcpz3gDoLoToJqX8+8mHCjTFRDnrljGlbskzg4dQvs/Gww4X+dOmBdTvz9cEgNN9Iil06tx8880hy91+5qTHv93lPc4o0OJ1qhwuqh0uwozBcTPvrgqIj2ZjVjEtEi11zmH+1mzvsdPl9rq8FAqFQqH4ozlrTyQppRWYCzwhhIgUQvQHxgKf1G4rhBgrhIgTGr2Be4BvPNU3obmaunleG4HHgYfrNZFa7qTkKk2AXJwT7I4p8bw9ixoZcNuDPVcDsoM3ZHSV2bFnn9i6cSp07KiFDlVFN6PKT3t++EtWyPaZBdagMqvNSfrR0oCyB+Zsw+mq2ys35RufxlyfWfRbpqxQKBQKxRnlbP+sngSEA/nA58BdUsqdQoiBQgj/J/81wAE0V9PHwHNSyo8ApJQlUsq8mhfaku0yKWXg07kOmlYeCypLqXKjD2FMOZafrx1ILYtvbR7eoOmvZmV5FL73PgC5T68jf8YWXNbfFqNyMq688kqmTp3KhV2bEykc3BS2ge6Go9wUtoGbwjYw1rQ9oP1Vb/8aNMbfPtkUcuyF23O9S8UBckurSJ28kNTJC72rrwCufXfd73Q3CoVCoVCcPmdVxEgpi6SU46SUkVLKZlLKzzzlq6SUFr92E6SU8Z78MO2klK+eYMwhUsp36zuH2w59Q1KVT5C0L3Ohk+CqZaE5rPNtFCkFHL07dFwKwMsrZ1D81TpsfpaKkrn7kfL03Uzu6mqkX7Zg/32Yuhpyvcdxumqu69WYVyd0B8Bs0PHlhsPcPWszW4+UIKVk9QHtnsZ3b8yKfw/x9r33i610fGwJZdWa8Lrz080Bc+iQErwflEKhUCgUfzTnXIBDmNtBryKX3znoJThriZgfTOnegF43UL1rT9BYwhzFaqJJumQGYd2u5/jbPtdL1c5CiufsD+pTm/Ki6jrFTv706ezt1p2cBx/0jVtVVedYk/omclH7BNolR1FQYec/c7azcHsu415fw3Pf7/W2e3p8Z1ITItnj2euphi5TfwAg/UhJQPmnt/XxHh8trsR1hmKAFAqFQqH4LZxzIkYIMNQSDQYpA3ex9uDWaW+PWwiEPni/IsvFL57wWpUbj5H3wkYq00MvT87ZX8LHD/3Cnl9zQ9YXvqmlwSlb8K23rHfv3nVe75133uHpp59mT17Qgi/e+vkgALcPTCPcpAX8hgr8tTvduKOMVI9sjK1PIjJMT5FwUz2yMe4IPQOeW07Hx77HcYI4GoVCoVAozgbnnIgBgb7W81cfwp0E4BI1IgbQndpCLmdBFUWf70E6gx/6xzK1tDnLPg628rhtofdIslgsPProowFliYmJ9Z7P/41sF3D+/BVdSEuI9J63eWQx9n5asj0Za8I2OJkB67T52QcmA1DRKIIr1u/jx13HqLK7WJdRyP5j5dhD3KNCoVAoFGeK3/xkFkJYAOlZbfSXpHYQrxYT41MxSfedB2/9hEunWSqyI3SIUxQxNbhtLvSGQM2Yc6AkZNvcg6XkbjtKOCARSBGosPR6PVOnTmXhwoXk5+dz9dVX8/zzz3vrB7ZOYNV+Lf5lVOdkFm3P89aZas3hqp5NuapnU4qtdrq8sRJnqxPHv9h7JuCON7OuupqtCzchQixRT0uIpHuzWP4+tNUJl28rFAqFQnE61PvJLIS4G/gP0NhzfhRt1VDwJj9/YoQQ1Hai1FhinLhw4mZPprYzgkvne+A7DSbv6uxJQ//FG8tfqvMalgGJVKwOdCHJaidEGrFlZGJMSUYXHk7WNl/wcPa+YlbN3k/hUd8irUF6M3vbTOBYUi/Sco/j2Lye0rnzsK5ZQ9r8efTNyyP+1luDrv/m9T3YdKiYAa0S0OsEZdUOukz9gSt7NKlzznGRJhw9E+qsr8Ed79us0plqwXgg2HWVWWAls8DK3M3ZZD4zCiFOb0sGhUKhUChCUS93khDiIeBZ4D3gQs/rA+BZIcSpbN74hyEQQVl6DW5NxHxv2sqnYSuZs0RLSePU+eTOM5eP9x5nxjRic2KbkOO7rcfJ/ff44PJqF87iYjJGjWJv9/Nw1Yopmf/SlgABA3A8oSvHknoB8MHj29n/6DSsa9Zocxg3nvxpL1C5eQuVmzZx73XXe/vt27mNLokG735O0WFGsp4dzbQruwaM73BLyp1akHPy8q0BdcMaRJE3tBvzurfi2pQGfN8j+H5dLaORQFK0mY6NQltwWjy0CJvTFbJOoVAoFIrTob4xMXcCd0gpH5dS/uR5TQXu8rz+MsSagzdX1CFxCcjTBbp3/C0xP3bpHFC3IckXW+K2lVO1+aOQ19NZtIBgd7WT/ef3A6DaHMtbd6846Vx3t58YcJ6RNobK8MD4l0MTJ3LouuvJGzOGLgmaJWXBggVMnz79pEu8m/2cTutV27lo476guo86twDg/FgLL7VrRrfoCLb3D96r0zayMeseGs7Cewby1Z3nA3DZeY299VJC20e+J3XyQir8ctHU5r2jxxmxYS8bS60crgodD6RQKBQKhT/1FTENgQ0hytcDSb/fdM48eqH3WmL6HdceqnpJwOqkmJg8Bg76hMZpgTsiGFK6M6uHZvHYFZ+KdDux7/8B6+L7kdbQK5Aie2nBsLLK9wDf0SHYBVQfjid2Z22fqYGFDl9SvdZvBm7qfaLl2ODbN2preaW37K0Ozfm6W0uMIXblTjQZyRvajSODugSUj9u8nxVFZbxaVsJbd/Vhc2oYix8aGtS/02NLeHjedqrswZaZh/dns72iiks276f32t38Z+8Rrthy4ITzVygUCsW5TX1FzD7g2hDl1wJ7Q5T/afGPz+hX4BExbnD6lTdooO0XpDMHZt0N73MXdzYdRJRJz5jUjgidAX3DDgBIZ6BgkKVfEndFayJ7ahqv8NPdoDcBgrKYFqd1D3XZVwyuQHHw7rt15wC0uoKFxIGBnRmXFMeAuFAbi/sw6nXkDvG5ptaWWrkmPYMfC8u4KeMoe6zVfJRXRNazo5l/d/+AvrPWHab9lO/51+7DTEg/yLLCMn4pDt6m4aOcQlaXVPBFbuEJ56JQKBSKc5f6ipipwBQhxFIhxOOe11LgEeCxMza7M0RtEaAncIm12awtvHIQmBumpPHPABwfmsxTfaMpMgn0MVqwrHQGukAqlv9EZM9kdOG+2OmoMa8Rdt5NAe0Grv73b55/WVRqnXVjvllAkybanIqKiiiZNx/74cNB7Z46GJybxmIIzhtTF0II8oZ2q7P+w2wtaLlb01iynh1Nj+ZxSMDeKQ63xcBneUUsLyrn2m0ZXLZVs7gsOq81tzcJDC6+b88RXsrKqz28QqFQKBT1EzFSyrlAHyAPuMTzygN6Synnn7npnR30bp87SQgXiQ0PAWCvJWKOdfwAKXxuoQuHWrAfWauduOza/0qPeuszr7wK+5HMgDGMzc4ntkTL5Hv9g50wOqsYtuJu+q6bynlbtBVPRnuZt/3Aq1szcPUDDFjzH2/Zph7/R+Hj83EbTN6ypAe1+OqIqipuu+02b3nugw9y8MKRuCt9LiOAjaWaUFvcow1Le7Y5oSA5EUt6hg5wBi1YuMCuvV9z7urHg5N64W4cgb1/aA9kl6gInmjVOKj8+cw80n7eRvLyrSQv38rfdx2ixFF3fI1CoVAozg3qnexOSrlJSnm9lLKH53W9lHLLmZzcmUAIQbsyzZWSatVWCOmlFthrMFTTvLlvZ2oHpqD++0bcFnB+0bWau8SQFE38rZ0wRGV466q3bydz7NigMRxGC4nHt5IzcrC3LKLqOLGlBxm24m6uuSrMW95laFOMTismRwXD9z3tLU9fng2vLQAgctBAGkyciCk11Vs/oG9fhNuN2+MmK5k7L2AO2yo091e3qHA6RUVwqnR06znatR25Q7oy2OOGGtrA547qtGYH0ulm6fT1TD2YE9D38UYNaWw2MiI+mgXdW2HQCYQQHB3clSODuwYIqyq3bzXX18eK6fpLYLySQqFQKM496swTI4RoIKUsqjk+0SA17f4qjMlx4sotpa/UNJwObe+kNm1+JT7BZ0mp7U6CYFdUmWdDxkbPPEN46zjMbdKo+KlWH7sVYfJlxZWmKIylB73njV95hbJly6hcn0XcZQOIvugixjgOUu0RICnPPEPugw/S6JGHYLZvXLtD0H3Pbt95VpY272P5VM+YgezViwqLhejycnKefpa1pR3oO1gfkOulrhwuVdu3Y0hIQNpsAeLIn+PvbcC2vxoAU1oMX9zRGSEEFduP0wpf/pj/fraZN7oEC0LroXKaHyjhpRtakRjlm5PBL6g4b2g3+q/dzcFaK5ZsbsmLmXncn5Yccm7nKq8vP8C0Jb4wtaxnRwfUW21OOj62BICdj48k0mzgm63Z5JfZuLhzMk3iTl3QKhQKxdnmRMnujgshUqSU+UABoeNJhae8/sEUfzgSAZhLnOxJMdOuUmLwuJP0hsBA3lCWGGcdb5nBs7w54bbbvHse1VCx6F9EjXvbez4kIZac8B5wcC64HRjiG4DuEiL6QljPNNx2F7o1OUQAXNKC2PHjiB0/Tus8e5l3nF/mHKBRq1gSmlrQG3SEd+9O1ZYtZIydSFzvsUAei0eP4oZNpZQJM4W7ipm/w8XMi20Q1YQ7m4TerkA6nWRdeVVAWcsl32Nq3hwAV3k57spKr4ABsGeWkv3gau/5eqD3SM0i80bTwPds5dJyKvWC6+zZWIFeTy1l4yPDaRBhQgg4kF9BdLiRBIsZvU6wpm97CuxOwvWCSL2ecZv3s7bUyrSsPKZl5bGub3uahZnO6aR6pVUO0o+UBAgYgNTJC3nmss48OHd7UJ8aMVPDU4t28+qE7hwpqqTIaucfw1oRGxH8GTjrLLwftn4Okw+D/vQyZysUiv8tTvSNMAwo8js+7a2LPRadmoR5BcCDUsrPQrSbCjwM+P/87iKlzBBCtAGmAf3QxNMG4B4pZb1WSQlP3l2HdSl2/SWA9GTsDX4ArqNfUJkdc1AZgM5jkdFFRtJ2Wzp7u/gnlgt864xC0NwSDWNm4DiyluJFvtsUQkfJvLqXFrftm8zetb5A16+f2wjA3W8NI/G++zg8cSKRQx6iUpShhS2BruOlROoNNAvTcfUAn0VowO51HHC0o1WHDuRPn+4VX2FdApdQAxwceRHtPVafzHHjcWRnE3nRNHRhMaHfD+C7nyu4ZLBv24FZ1nDaHbRiahlHxL5ilhDNGMopRtLzyaVc0K4he4+Vc7TYt9Lruj7NeGp8ZxJMvj/VWV1a0HKV76HcZ602r+whXdH/DwsZh8tNkdVOhc1Jy0QLLyzZy568cpbuPhbQ7vnLuzCkXSK9n9JMgqEETF3c87nPQ/zeai2eK33KhZRVO7CYDfzt0000jg1n3pZsnhnfidFdUogO/x2FjtsNWaugWV/YOQ/m/c1X995wuOIDiG0GRRmQ0Lp+Y0oJRzdA4x6g+wv93lIoFCelThEjpfzZ73jF73S91wE7Wm6ZbsBCIUS6lDJUgMOXUsrrQ5THAguAm4FyYArwDdAuRNsgpOcZ53Yc8G4jYHNL7H7PvnWcTziVlAntAT1GzmURlwJQHUrE3DgRfQOfx01nqv+XurFpX2SVT+SUfHPwBK3hghvbM/T6drz19xVBdZF9fDtcJ0hfXMpHYT/z1uBxQe1Xb97G6s3bePTRRwOsR9XbtoW8tpQSXC4c2dnoG7RCFxaDbfcC0BkwpQ1CmDTBIsINGJMjSM4s8wqZi4qPccFlIwFw213kTPkFgG+JYhhl2IGf9uQHXXPWusPcO7w1DaN8cUKRBj0bzu/A2M37ybH5rGeNV6SzvFdb2lvCQ87/z0iR1U5suBFdiLw8AG63ZPybv3C0qJJCq91bfvvANN5ZlRnUPi7CyOU9mqDXCQ4+PYqWDy0CoGmDcMIMeiYNbclFHVMIN+n55NcsKu0ubhvYAr1O8MaKAzz/ffBvga5P/OA9jhWVbJdGTAj2Lqsy7UEAACAASURBVP6AvYuD59yoUSOGDh1K69atcbklNqeL4S/+THm1k09u68O+vHIu7Jjks/Ls+wE+u/Lkb1bOFnjVLwC9+/Uw9vUT95k/CbbO8p3/+wBY6r9hqkKh+HNTL9usEMIF1LiW/MvjgXwp5Ul/3gghIoHLgU5SygpgtRBiAXADUO+tC6SU69G8FTXjvgw8IoSIl1KeNKmIzvtLXVJh1I6rnJIqJMKlxcC8KgKXPZux4RIGXFKHjTBq0+D++xG6M7MhuHS6EX6bNgqdQK8T3DljCG/9Y4W33FpqIzzKRMtl68h/NR2BYLC9Az+bdoU0ofU74Pt1vmb6q7QYNxOAwkX3oHfYWTXwJQZc0452HcM49uyzlC/+Hkd2NsenvwpAxKAHtPk5inFkrMG+ez6GZv0xpTak+cynkS4Xh25/h+SGHVl693Xo3G4q0z4jont3dCY9xsYWHNlafphlRHMrFaSi51HCuRcrutRoNmQVA7BwWy43908LmH/TMBOb+3VkRVEZTxzIYZdVc229fjif1zo0P703/XfA5nQx46cDTOyXik5Ag0jtge2WoNcJth8t5e7PNnO4SFs1tue/FxFm1D5G1Q4XFTYnUWEGHp63g/QjwRuF1hYwL1/dlXHdGge41PQ6ERQT488N56cGnE8a0opJQ1oF3EPbR773njfSlXKhKTi7c21ycnKYNWsWn1V3x17rK2bc69q2GQ/M0c63/6cXUScSMGmD4ZpZ4LTDtFr5lbZ8Cq1GQMdx4LSBoxIW/AOa94fwuEArTg0veO6vx82QNgiWPgYTvoSkDie9L4VC8eejvg7mumz0ZjTLSn1oAzillP7fgunA4DrajxFCFAG5wGtSyjfraDcIyKuPgPFHIvmpoRHzERs2lxG3CN5TqYZKLTqFCiwh3UnVbndQmbldO2x79mA3Wljf8yEuPtmEaqKLapH9yBpSHu6DPsqEdEvKlx0momcS1rV5jB3VnG8WacvB1z72K8US+ob79OTurHnQpi1v17LCXP/r91jsvniWZWWltACKRQXzLtNWUyXmGVk9J4POw4YS1u08yhd/z8HhIwAwthrh7Zv2xRvoo024KirY17MXjsPwwZ19cdjdDFj7OjpDGELqsJuiODThWq9LKukf3XHkWTn2ymYA3sPndnq9Q1OSb+yI0+Wm1cOLefzbXUEipoYhDaIZ0juazEobwzbs4bj9j116nVdaTbzFxD2fb2HJzmO8trx+WYe/3HCEif1SeX91Jk98t6vOdrP/dj6frj3EgvQc4iKMvHJNd5rGhZ+R3cLNBj0HnhzJjh07WL58OaWlpd46p9AR3bAhM4ztaWorpOyoxC4NjDPvIEJo1rFrw7bwk70V+W4LNgyEYQ+wZA7RbSFqupZD82XH5cxwjed143SOdpzEim37eGBkWzoMGItOCPRmAVNLNTFTsFdLafDOMPhqInxVa+K7vw08n/AltBkJM3pAkcfSuekD7QXw5vm+tvemQ1zq7/H2KRSKs8AJRYwQ4l+eQwncKYTwT62qBwYCe+p5LQtQVqusFAiVHnY2MBM4hpafZo4QokRK+Xmt+TVBc1H9K3gIb5s7gDsAEtv4VrJIKYkLO0COLhydDMct6g76WSS0B/tb/INt4jwA/uEwM8OoxbJYXcEiJvXLL3BXVjL7gSXY9TH8VOYgTCfobwn9lifc2omCd3eErKv4JYeYkak4C6ooW3qYsqW+5HWX3NwefYgYml0lv1LlKifPkuItG3J0I7cfbMIqNAHTz9GWX4ya++DdsJ+CxnC7JGvf3kb+ziaUd7uP87a+AkBYJ+2Xs6V/I/TRmoVBb9EeogUJXajEAiZYPmg63dJfZWvXe7xjrrv5S5oP70r/y1thSo7E2MSCo9bGl85dRUiXxKDXLFAmYOTkxTRs3YBV+wtY99AFJEWHIZ1ubAdLMLeIIS3CzJXJDZh3rJgfCkppEmaiQz3cSocKrQyetgKA6dd0Y2w3LU9Nflk1e/LKSYkJo3VS6AzG+46VE2HSc6SoignvrPWWu1LCERUOhAD0QjO/uAN/CUgdSLOetAgzhworeWzBTh5bEHrZ+LQrunBlz6be895pDXh1QveT3lsN5eXlbNmyhYEDBwYFPx8/fpzY2FiMxuCVeN999x0bN2oxVw6dnvdCuCQBMojh4E2p2Ilg5f6ePPHtLsa41iCBuFY6uhVmcI1zAYuaDGRqxusYet/BB3v03FL6mneM6a7LAbjL8U/c+/S40/qxeqkVvl+M8Hwwn72sM31bxJMY155IswGumwOzLq/zvtNHvkF5u0vpH2vR7vuezeCogpXTYNWLWqPGPSB7k6/T9K5w82Jo3g/sVijPA2M4RDfytanZk6w4E16t/e/g92vk0QJY/QosfxL+sRniW9Y51zNGdSm8Oxyu+gSkC+JbgSF0XJ9C8VdEnGiTQCFEjc26OXAU8M9VbweygClSynUnvZAQ3YE1UsoIv7L7gSFSyjEn6TsZ6CWlvNyvLBH4GZglpXzqZNcHaNg2Rf7w72tIOHgZc7JeotMd23A7Tbya9QHbm5mYVnEvDRrkcJ2Y4+0z+cM3eGviNZTUWmX+WVUEbyYcY5U1ioFxFr7q1qr25QB4/c5lAedjY7WHRez4VtgySqlK1/ZcajSlL3kvbsRtdXrr/QN8I/umYF3ry7Jb869Wl4nsy8zn0EWN5rnrfL8y//bzfG/7wfYOtHanhBQvANHF7THbEr3z3VXlInXH++gjEzF30B5mCY/2pdrqIKpBGEd2F7Hn4Rc52GhEyPFqI3Fz1YO92bUml7iG4TRyurCtOBrQJvn/ejLyg3V8VKBZl56kiu9xcDUmnn58GLmPaXE1hqQIkv/Zg1k5hdy/90jQtfYM6ESsMbR47PnkUgoqTrzhZIvESPbr3eBwM354C2aXlPFuoxT+/sFGqkdqoseYXgR2F+4GYbhahhY9JqCbyUz6dxnYLvQl9UssdVK+1hec+8jo9vyw6xjrM4tY/9AFNIwOdmEeOXKERYsWMXr0aG+G5hoOHDjAp59+yp133kl+fj5z584NqJ8wYQJ2u52MjAy2bPEF8sbHx1NWVsbo0aM5cuQImzZpD/eS8Ei+6H3if9fzivbz0fmdSGzUnoOV1fRfF/q3TUNbIe+mTyGqqpKK6HDuazuVPs4UJg1oQaMoM20+WIOzTXCwuCh3IModGLcXe/+G+7eK5x/tq/hx0w7cjbrzf4OaYE5oxpz8Eu7ZHZyl+tKGsdzbPImOlnD25maSrbNQKnVcVLWX8Ga9YO7tWjDxmWL4VOh2vS8mpzwPlj2pWZW2felr13YUXPkR2Csgwu975/heLeg5bYgmrGJqJYbMTYfProby4GzcAQx7BNpdAg3b/w43pVCERgixSUrZ84xf52Q7HXsmsxy4TEpZfMoX0mJiioGOUsr9nrKPgRwp5QljYoQQ/wH6SCkv85zHAcuAJSfr64+/iPk660U636HFhby6bxabWxh4sfRfxMdnB4iYR96bQfUtZbwgHg4Ya2FSCjPzXuMbcTk6IKeOjLe1RcyYGAM6IWj8zAAtsdvkVQA0eXYgbrsLd4UDdAJDrJniufuxrg9Oue8QMGpwJNdnOZiYFezNm1n4PuWWhnx+6QRv2Q2/LibS7ntY31I9FB06PjSvwCmC91ECaHRsIKNiQgcp765ysc+mWaC6Dm9K+tJg8VAXVksWlRbtIZOYN8hb3ntMGp3bxVHwzslX01TpILyWAez41S25uCQ4OLiGpT3b0CkqgiKrHaNeYDEbSHtw0Umv5UoJx9ElOFWScWshjm7xJ+1fH8JKHchNBXQc04LN1ipmd21JWoQZoxCYHDZiIyJ4bfGPXNypPbM+eL/Oce644w5mzpz5u8zJDYy/7DIuLvS90d0jTIz9YSHlx3sSYZdI4MmrT5hG6ndFf6AMfaENV1IYrtQT7/FVF4kmQ5Db8T9pyVzfKJ7OazRr2M41lxLvLA3VPZBu18PIp2DXfE1krH0DmvbRLDc1gii2GZQEi6o/HcMfhwH3/dGzUPyPcLZETL1iYqSUwVsS/0aklFYhxFzgCSHEbWirk8ZC8DpmIcRYYCVQAvQC7gEe8tRFA0vQrDr1FjB+M/H81yfedFLirmN1iN1oJMpeTu1QGF1iKeF5Wur+YGdS3awod5IYrqeJx6zf5NmBvjFNenQNfDEtseNahRQx+6N0FJt1zGhr9oqYBYdfp0/iJWwv/ZWZE+8N6uMvYAB0nmTNY+29mGNei6nUBeYkxoiWzDFrhrWcpFWsdzant1OzMuWLUkpFJa3dKV4BAwQJmFY9GzLshvZYS2ys/GIvJcequGJyTww6FzP//YtXwIBP0JirEln/LfQanYYu0uC1SPlzSHecffpcLNJMb2drELoAH2Dilwd5PMXACredtQVWIhqGU9glzls/fOM+klflU1KpxWxc0UOzYPRrGc/r155H9//+CPiG/HXyMM5/dllIAQPUKWCuSo7j+pR4Lt1ygL83a0jLCDP/3HOESL0uwPU4vV0znsvMJcfmoDrGCMNS2GzVlpdflR5ilVpEEk9nFDE4uTnt8w6FvHYoAdO1a1f69evHypUr2bkz0GV1OYs40vom9mblUOrwfR183ms4pREWZnoETLTVxb3faQ91Fz2I8LxLApj4UxkfXRAdMG7jQic3/VSGTsKRztWk9Uph66qVzDuvrhA4H+cf2E7XbO3+GzZrzhNpPpeNq1U0rtBGz8DrZ1qZlpRAt2ZxvFBVzpG8cn70aJ5QcVPPZebxXKbvs9ax/wJupZz/RLcm0nKIkg3hVO0oJW5UHGFdmlGZXkrp4iwinEnEuCKwGUYTPnIi4qJnfINe+aHvOH8PvNGn7glf9TFUl0H7MfDcaQSmtxgCl76mialu10JYLMR6XJFuN2z+EL77Z+i+Sx+DXd/AmOmQEpxmIQApwe1SeXsUfzj1/gv05Ge5AmgGgVngpJS31HOYScD7QD5QCNwlpdwphBgILJZS1kQnXuNpZ0ZzYz0npfzIUzceTdh0FELc5Dd2Byll/X/u+FmgpP0okIZOBMsRm9FE6xfcWtYaP/YeeIYRrOMLbuS2xglB/eqi3A2XT+lbr7ZCJzA1jcJ+pDyg/MbzfbleXEBm2WaqXBWsyPuC7y4IXOkRg5vb3p6KtU038Kyg0ks384srGRcXQZyMZGL1YObmvEhY3L/INusCBNs2wyG6J3egOPs4C8xafESG1QGkEAqdQTB8Ygf0Rh2xSRFceq/2AMrIyODjjz+mR88mFPh5jWoEjS38OLK0DbYqJ40ePZ/K9Hwq0vMp3XWMcEw0fLQ3707zPSB2GrRBDlX35QEiiPI4GUbnOhmNDoiCXLjEUUh+qsWbpbgw1kBcuZM0p46f12fTUApeuqobUw7nel1DNTy2N5umFzTlgEemTm2axJKCMiJ2l/JTqu8jsHZJCYssh0l1xZHqiCH85mg+euUFMh54gAhP/qAJKZrgmXusmMd2ZXLJmsWMPf9+LhOpvJ+eyWOm+gck/9y2O+Hmpvx3r4ljooSclCo2F+0OaDNlyhTefvttunfvTt++2t/blRcN5MqdgdtmSAkrV7XDRDta6vPI06fw4vg4ajNpcbBV4tYXBxIWaURKSfgDP1Nt1kRlTKWLaL+0AU23h+HcXkwnOtM0bz0mRww5DQwUx+bTrEhzo9n1Rj4YMJroqgq6ZPsEXP7hQ9x5+BD3/uMevq1w8UBGtrfu+rhY5paW8X7nVJqHmfl20xFGLc4lrOZjvC+fSvKZ5Dl9BsgOF4wdZOEfe21MzLIjgf/rFsaKJM1tmlDtpsIgqDYI3iOK98ryeGyNmzE52v0XLSom8+dSTG6J2SxI3HiMyo0eV6AnYi+sbRyRfVMo/CgwQDvxjsOYXRsols2JSY5DFxENBhNlyw8jivTYDpRQPXs7YW3XENEzCWOKheLZe4k8LwZ9rIWi2QcwJEVgzywjelhDopwfUlHWj+psAwljwxEtBuG2ubVNZ/3FVA06HfS8heklA/kmPZtnh8XSw7oSR3xbwixx8N4IyNkMbw8EnQGGTIaGHWHNdBjxOKR0BUMYHPgpMBapSW+49QcoywG9Efn1zcjzbkZnCqc8ojmmNdMw750Pie2hx03akve8beQ0GcVPHZ7msW934ZYwuE0iVXYXIzokcfugFixIzyGrwErTBuH0Sm1A49hwZq7M4JO1h7ilfxpD2iaSlhB5Tie5/L2QUv5l38f6upNGA3OALUAPtARzLdEed6uklJeeyUn+XjRsmyK/fbw/jbfey+zM5+j6N+2L/52s2/m5+QheK7mH2Li8AHfSo+9Op/WRLG58/OWAsT4PewR31W5uYRbjo3N4vE1LYqK7BrRx2F3MvEdLt9NpUGN2rNS+gO98bQh6Q/2WZFftKaLwQ+3X87eNDKRZ3dzU1ydi7vvsDQxlOQjAFH0zT10bGDy4qU87vrj5ClzmcKobpeEOiyDi4A709moSzI25oNH1zMl6Gae0c0/bddh0cdhazWffrn0sMW31jhPmtFBt8AXgNm/QkevvvMx7f+HRJqorHEx8ph+RMYFmK7fbzRNPPBFQ1nL/AQ62DvxJrXOG0SK8L45GmYwaNYo336xrQZqPHFccK+ytEMBPQrMGSI+dTecXMdTfXIFtiCa8/m9uMUJKnr+8/m6Qr5cUkerZhqL6fEF2i+b8/ehx5q2yMte4mkpRd1zNQw89hMlkYuXKlSxbFuhe7OFowSZjBjaDkYOJjWifewgBFERGE2G3YXQ52Zl6HmubNmZErp1Iu4P5zX3//huWlCOARreCLqklRKVoAad6M8XfZmPdYSPBMo2q8FFElr6FQzZmt3EyMc4Cwu2a+P7K6uSbXpHsaRroOrzvm2Ly4gw0LXByjeVhEoaORz/kn+gM+qC8NrZKB9YSOw0aRWrBsy47hMVQcqySWY/5gp4vf6AHSWnRbP7hED/+tAS7uZDeLUaSnVVAQuNojm6uQiKxRmWA1FFlCbTy5UY3oFW+nqiStljDCjBXJzDoitbELT3kTWRZF/dhBQljyk1cEB0cyGxDchg3QoIr2sh1/SJDjBLI8DwHz6RXn+TKwawsdxKjF3SNODPJ93LHp3FepyT0kb77rFntVxdrHxhI/NfjMeZsPO3r2zDyMZeTHeLHzi18QQNKsaClFyiSFqY4buY7txa/F4GdNobjtNYfJ88dxU5nMjokhTLSk289mPl396db09jQk5ES/gwP6NJsOLYDUgdA+hdwdCMcXAa2cjBFgFWLj6THzdB3EiTWvbnu6SKlpOjT3VTtDL2oN/GOzpiaRiGMp/73+WeLidkEfC2lfEYIUQ50BXKAT4BfpZQvndlp/j40bJsiP38zgpYrXmXu/jfpersmYuZwFXPF1XwqL0ei4wbhW7P5/T03UmKJ5pqnXwsY62vT37HZcrmL9+jJOm5lJhcMC3QBvPX3FbicbiJjTNz03ADeuGsZUmrZdeti3Ob9FDtd/Ny7HetLKqhyuWn1ynaiRzSntasgZJ+IynJumPsWx/pdwPwW5wXU5QzpCl9ez8vzQvv3e465jKLsI2Rs3kBDcwVXp6ZjFHpybG+zwbCbrYa6LQSPPfYYRTlWDCY9MYnBK4GklDidTr777jvS09MD6m7ctJnvmzQmNSuLobNn89yLL9Z5HX9GDLqEX7/dS0XMfm9ZdHF7hDTytSmMOY8O462Xn9XK3eFc6OhKrIwk+b/9abpsK9JUt3hsUO7ihuVllJvdvD/S5yq6OMfBf7dXB7XfpT/qXd11Mu6//35erOc9XmTvxnb9YYY6OhGGkYgeScRd3prsh7RtHZ7qYGZe07oTKn62YRFtigYGlb/axsTHaT6BGW9zU2gO/X7MXjeFQdU/U9zrWeKG3wDmupdwV6zNoWT+QUxNo0i4pRO6cANum4vShRlY1+dhSIrANbQp8ou9mFvFknhbZwCcVQ6c2VaqdhQEBK0XOt0ctrtxSU2QZsVtpFRXWdflvQy3d8FUHU+2w02bK1uz4rOT57SJ1EGlG3qNSaPnqFQWvbmdrG2+z9mWNBPf9a7f8vU+P+QTG2fiwVI9Fhd84rJRapN0cOppadZzpLGRTIuO+Y2NPL6jmnalTgpEGQ1lDDoENrck3ylp6vc3WuR008Dzg2eutNFMf5xMaeESGYEJAzrEScWbPz/h4BWq6dE+0ZPpWeubpitksCkjoO1NzKYJecyTI+gj0imRseyiPd1lFo85LiGizUBW7D3GAtMjtNTlUkI0CRQxTd6OXZyZhJPLTX3p1zaZzVmF5BUUUyrD8F/e0COmgik9bMTs/pyG1r1E2LWHdLaMZ9vYpVwYV0bB+/twOALFVcJtnTE3j0YYddQ8E0/FMuGudpL/2lacBZpb2GgpJb7DJgzbtEekS8aSa/sA/PblM4gsnDI1YByj2IdbxiKEnTjjDMy6nTD6RSpNl+KMMuIy6HAsO4orpwK3ww0uN8Kkx9I3BbfdhbOgGmNyBGHtGmBMjkRn0vP4tzvZtvs404vqL0xEmAHpdIPztwRNaDR9btCfSsRU4Ev7XwQMklLuEEJ0BhZKKZud6Yn+HtSImOQdt7Jk7Wq63qY9gOZxBV+LCXwsr8SFnpvFF94+y++aQIklivHTAmMNPhfX43ZXcR9v0IY9TOLVIBHz+p3LKI7UEaXTce+0QTgdWgCtoS51W5xF8lYtsVnekK4kr9Ae/L/2ac/hJd9wdaxPmbfK3M2BtNCrC9pXHGS3RbPI5A3tBm8PZndxHIu2aIKkw8Ch7Fq1HIDht03C7Xaz7H0tY2+rqALiTZWsK2yGFDoq2gWKIqTAu+YV0Ol0PProowghWLduHYsXL+b+++/nnXfeoays9op6H1OnTmVPl65Iux1hNDJ73Fjc+ro/XPHx8dxyyy18eL9vIdzx5JVB7SLKm1MZFRgvMsDRDjtO1kSF8V6f0AEVPTL3M3D1PIxWbc6R/R/mlwQDP6QYWbqsglhH4OfEjpOPw7xJrTEfO4LeWkacO5JeTa+kgbSw0PYjx2ICPbapjZsx4aIryHl9Ex/59b/M1oeNhoOc50yjadeW2PYVEz2iOZbzPUt7beVIk4XShZlE9EgiPVwyZsuJc9AMOeZgVaIBl04wLM/BsuRg64M/z5qjubhFIokNI9HVM3lj4ae7qNoR+GtOZzFqAeq/I069G/cNTdn9yRo2GOvOah1e0YTIilQEdc/fHGEgKS2awzvrt2+tUwcvj42l7VEHozZZMXi+z8vCBdMvDXa9AaQeL2DUphxiyhuS0eQwX57fO2S7Gnpk7aE4Mop+B7bTKK8NJntMgDiRwkVJ3Hacpro/U4ts7cmXFkZg4DECN/J04UbveU8cOAP+9k4FCbiMFoTTikunxy0Ex6NiCbfb2JDWgXC7jeEuO/OzYiiwOvjuHwM4eLyCJ75cwxhz3XmQakjlCF3ZxSp6U0QcLqFDL0M/SNs3GkTrDBdtja8jkBQ5/o2efFw0RFBJtOFzKl1DccgWIfufEJ3A0jcF++49RJZ/AMKJI6wH4bbFmMReqt3nUeiYQpT+K8pd4yDEpsF/Vrbg5EmqyA+IEIV4BA8QRv/TvJezJWLqGxNTDt5UtblAK2CHp3/oT/GfGKepNMC6qPOsHHehxxViL0uzI3gFkNutKe0wqkNm8a3htUtiiZOCezmBeKlhelcYrH251AgYgDvT99Dj+8VwjU/EXNGlI8+Wh/5QJzqKKbIV4BI6+OU1yN1K+7RBhI2cSnRiQ+KbNPOKGFNYOJFxPqvDgfIEah6NQroZWvANOV1vwhDbiBEjRmAymXj++ef93gc3O3fupE2bNixerJmq67I4TJkyhaNHj9KsmaZ50+bPJ2PUKKTDwRVffc3sa64OaB8ZGUlUTneSW8Qw9u89yTvosya17tkQuXEABcmrA/rUFjAAq43acl9jNZx/oIpfW2mWgNHb1hButxFvLUMA1c3aEJ+ZjVM6uCTHySU5Tq5ZsYTvRRYVKU0Is0URUZBAVPQuDkX5fmlG7t+Gzqn9jVToHcRFR6MrhTHmkRTaypln9iaYZtjBVhx/PR0jBm6rvoAG17bD1VjgKrHRyNaOhA5p8NVNELYSzs+AbbO1pb+AuPQ1YgdfSEnOEdY/nsPfovW8fXEMlio3jYqd9Dxk53iqmR9TtC+fmjgPwCtg+u+qYtDOKn7oHsGmVtrf7eBjDl7cWg2U4yCbHE+fRk/0Qxh0iLqC3nMqggQMECBgwrslUrX1eMj+/sSMbkFY61jsuVaKvwy0bsWOb4Wlj/bLucVTLRjpdGrXXnuMFdvWUCoqSerdgg2b1lNlOUqVRYuXMuiMON3aXPr3GcyhA9kIkwu3zk6vMWMYNakz27dvp1FCKl89qSVdlEjGP9KORo018bh/4zGqK5wYv9qPdAcK2egqyaNfFuES8NkgC1nJPutYVmICb1xUEysXuNN6fHkJhVGBro9NqdquKRmJWlxWrLWcRsVFjNpcQUXMfqoNRta26MielFQA2uQdZtjemjlrjDLvRtemM52iTByNSaBttw68NOM19A6r9zouIdDJYKeMBC62dyPJHYtLCHbrDrHJ6LPMuBFIIUhxRXFMV8p3XfqRHdeQE7EHGNTZwg2xUcwsK2F2cTFc2Ibh3S5mQJwWZV1cbSMcyTNHCnn7qPZ3ogcuahDBioL9fK8LjFNrUnSMhIpSuh/eh9ml/Sjb/f/snXd4VFXawH9n+kx6I4WQACGhhIQuvWOh2F1FbFiwoLIoq64rK+6qa29YEOxdsXcQkA7SewuEGtJ7z7Tz/XGTTCZTMrjoot/9PU+e3Dn3veeeaXfe+9bcVewzgZIrAmiVshFjrRl0crajwn6j2xxl2g/Jx0APy1Ji7PlIRzJF1hc81m/HwQGRS4f1tYTKUKz8FSegsUE1o9xkqxyuWMRw3UsEaRdR7+xPluMsopwT3GTnUc9a7EggOfi3+gAAIABJREFUEkE7NGzATnmjs0wC4Qj6omU0ekb7UCaCtZ9j0a5EYqDe0Q+jZh9ldiUCTGBDQzVW6X6jK6hBJG9ldUIGBwqquOn4t2RojvCt8XwGjr+KPh3bMfzJ5dxHHRrqmIWJ/RbBNpuNOK2GYpuDtLQo/j2hIwtX7SRJU0R69nt0qVqDU+jQdBuPtEShiesJT3hd9mknUEvMV8APUsoFQognUdoHvIsSZFsopTznt13m6aHJEhOVdSnL1+4i83rF3Pw9F/ChuI7X5VU40HGLeKf5mOW3XYlDo2Hcyx+4zfVBY8maOfyHQ6IrD8gHuXPMN837nU7JvOnLebgx/TTfRwp2E4drG9jy9tXc2f0Bv3LN8iMy6bzKe4+jE6vGIBFIBEbZ+INiCoe/u37gi48fZeUHb3H+XX/HYDKzduH7/PL5x17nA7j7wmDExa+CORy73c4jjzwS0Dr1ej0Gg4Fbb72VkBDPlNh93VxfMgkUtEtjzYghOHR1hJf0Rm8L9TjmnBvTSR0QC8Avi/aSva2IlNEmFi1y+fuDK1PQN0RSFrPJ67psGi16p/fU8ibu6BXF1loD6w76rrsRdnA7PYaPZvfyJWSOPY+zb74Dp9VB/tObcFYqr70NO1bsBLVSdkPv7Mrnz8yh9KR73MfgmCLWF8VgCL2WHsE72FeruB+NopoGGYQQbVtJPh0S7BHj8pc1VXQ7aePKOQNx2JzEJCnvh5TSrQN5IJh7xVC/twRpcxJzcybGzmE4qq3kPaJYyiKu6EpQH9ePnNPqUNpnCLAX1lLw3FaipqZj7uY7LslZb0daHWhDAyvOtn79ehYvXty2oBdGjRxNwR4b+4rdX4frr7+evLw86urq6NOnD+HhivKRva2QoHAjsR1DWb58OatWuayC5eZgPj5rnNs8vbExx+RgzZrVtG/fnk6dOpGWloYIDePl44W8erwQ/5/GU6NDaQGpBSdIK8zBptFSbgnm836uJNPLDhzj69QkbD4UVKMQPJ4nGLC3kgnjQqn0UpXcFz0qHJQYBAXm36YViz9C66qpNLu7//od3U90dTkdygpxCC0OjYZ3h3ivoa532hh1aAulhnBKgsM4Gu09gSGpJJ9/ZOkx12h4M9hJuygdi+JN9CrcyllFO1hsGsShghgSwkyU1Nq4JzmG685Nw5CofOeKqxs4+9mVhJn1PHFpJr2TwtEAC1YfYfn+QjYVVOK06LANiAa7E/2uMpxRRuI7hHKr/Rg9yw+SOfYKDu3ZTLY+lR05FWw7WUNufgGpmhyOOON5Pvgd4mUhcbIIQ1AEVOW6PQcpBQ501DnD2BKWyuR+jzfvm1K8kXNPfkFtcTB55Zdid0RwUfh/0AbncX/aXcTWVtK/OJ+G+jgsVdBgCyfSXsrRhn5E649QaEvljvnjzih3UmcgWEq5UwhhAZ4BhgJZwN2nlBX0P6RJiYnOuozl63aSMVVRYhYxkffEDcyX1+FAy3ThqsOx/LYrue6cf3D84ozmMY108B6XA/Af5rBHKOmITYpKtd3B3NWHMX92gicuC0yJ6bRiG3UyMB/sm5bjTBh4AStKK1ny/VO8kagoVNfmfs2TUx70zB4AuO5bpVeMH7599jGyNii9bUZcdT2rPnired+VydtJsDRmST2kWERKSkp48cUXfc5nNpu57777/J6z9MMPqVqyhKTXXqPopZcoeXU+ZWEpbOp/KzqHxesxvmKK6uvr2bVrF/369aPgcCVxnYKxrX6eE8sWsqHyGiwhIcQ6w90Clm+sH4NE0oCdD0yr/a61JSanjfvEy4gWrjW6TYKLXgGhhZpCDj25AROJfHHseYJ0YUog9VWTKSo7wBef+K710owmBFPYNOrLPEPO9EHjiXR+S/K5U9n41ftoDfHog1x1gW5/dQx2p0Tn40fKG9LhxHqiCuuJaiq+P9z2AShFGCMuCiDn+Xekurqa4uJi3n77baKiosjIyGDFihXN+41GIw0N/gsc+mLYsGGMGTMGh8PBO++8Q1FRUfNcMTEx3HLLLUgp0ev11DgcFDXY6GjxbaltTYPTyerjP3Pt4Qicwt1ya5HV/I3HGJf5H0bsavu6/VsigGMjM6nbvgNLYnt0UVHYS0rQhoVjy62haP5uyvSC6QPMHLdoSKx1ct++BgxO6ZaY0MSQIjszDzTgEPBNez0fdTQQbnXy0/Ia6rWgkfDukAisqWG8nXtKHWbOCNIsJm5NimFKfBQFDTY0Aj7LL2Nhfin7ajzj7X4Ntye1495EPRqNEb2+MclBSrZW1HKorp59VXVECCvLDu1jo0mxcBlsEqu+7WtEUJ0TndNKRZD3z7JW2mlPDlWEcAPz+YELWDH2mt9FiUFK6fcPxWU0AYhqS/ZM/0O52ff6F3L3bPnZsr7yk2X9Zcjds33KAfLj9bfIpcs6y6XLOktjaqpPuU5DLpCxP2+TsT9vk5s3b/Y7Z+SrHzTLmide4lMuM0Ev5ZNdpKwpkdLp9Dvn/EkmKeeESjknVM5/4Sm/sk3UVlbIPn16+5Sb1lffLNvWc9q8eXOz7LRp03zK9e3bV0oppdPplHu7dvM75zVjZ0lprZWy4qScf9so/8+p8bnLOaGyb7zGp9zFPUfLd6+dLnMe7OJ3nYCcNm2anHvrIvnSLcvkkG4TfT+neI2Uc0Jl/r3x8pUrz/Y757/HRsuqf0TJLX8dLi/rl+FX9unLJzb/tY8I9bvO0/0+ZSb3kCUf7ZOF83fIE/et8v/Zmz+/ec758+cH9NmTUsq+ffv+rs+pb9++ctGiRXLOnDlyzpw5fuecNGlSs9ykSZNOy3OaMDFE5uZ+Lpcu6yxfmdfe75yvzGvffN2ZMDHEp5wptUvztST2521+57zzrjj51bJM+dOyFDnzrmi/stXldbLWZpdHr75G9jAafcr9JSxM7u3aTWaNniK/v+41v3N+d91r8sR9q+SJ+1bJKb3O9/s+NXHokP9r2ez4BLmu3wC5JaOXnJni+/oMyDlz5sgH58yRtz/xtOwQ7fv59+2ZKV+4bbr86vzz5dNDhvmds/3zbwR0Ldeldg/4fQq5e7aM/XmbHP7h123+PrWcU5fa3aeceeIlzXKRr37gd86Wv0+dJg4K6DkBm3+P3/U2Y2KklPbGInXdUGq7/GlRYmLaDhNK0RXTFJ3hTz4/QoenM+S/QycdUFMIT3aCiW1ku/S/AeT7yrYxsOqm5pDQtt0VVfkQEudf5lcghCBoxHA44Lsd15BB5fBo47lPtN17VDqhrsQA8b0hb6tXmdzaQ1yUuIuQ1JHccv7NvPbaaz7nu+WWW6g7HMqOZYFVKI4113Bb2obmWiVeZTrE81758zgdoLN8B/iuWHzbax8wb9pVbZ43N2s/i199gd3Ll5BT2nblWSmdOJ3+rRO6KBORk7u5Bn4nn/dviZR2dPrpDB8BPXu+yL/+5Vt26NChNDTUIOXpTYveu++egOR69XoDvX4+lZU7/Mp14Djz5KVYMfA95/OyH9lw3fVEV51LfUjbpb5+2ZKO025Ac70V63oBPuKri7qmsGXs5cSPW8CxrJXwjnc5UFLNj5oUt2tOne/YqZqcPax+tQvWNNnmOhF6zJZ66sbY0R0v97lOgPOPfIh5kwacsLyqyqdcyoljnP2zcr2x1fu3nFxb+DHhm3ZSWxvOt3lH8X7VAYPNxpRFX3H7+Wfz3tIl+Pnoce7x1YzYWkm3jMX8IEt43u8KAiOdnXSWy5jKaxyV1X6vUTPk0+TJ8fRjE1+xhyM+5ILrazHZGkgrOMGS07DGQAjUnbQBeEBKufS3X9JvR0t30sqN20m/WglhXc1IXhUzeE5Ox46We8SLdFh3kHffe5CHBl7Phvh0LJOSKLU5yB/dm2U/u2qxtKwp44/80b158VgBjx7OI2dkL3QaweLiCq7b5flx+GXvLM5Je4jRS7+h3bnlvCZuZ1Ltl7y+qdVHN3kYHFtDXGMwcP7o3hzI+jc5OcpVY3TP79C80lhY76EASqj74JkrJrlOGVTGZdddBCMDu/B6xW5Viml5yYCpP3CAIxcqvZnaz32B0HPOgYc8e+kEgtMuOPBZPBLIiQzhQIdYMs67kHbZ7ThWtJu95UrvpTvf/JCqhZ9T+IQSsGzu148O772DQzqotFaSV51HRkyG29yt658ARMRZKMtXUoHD2xmZMrsPQmeCbe/h/HoGL554CHutEnRoDL+rOYWz77nJbF2sxCulDojlnBvTcdjtvDJtMgnDDmLS9GLC1HfcUj6llPy8XHHjpKTcy9HVgi3ffoM30q/JQm9xUH4khMIdkUiHoK5YCUye+cFXaHW65s/00KFrMRlPv4J6KlQ0VFBQW0DH0I5ohRaN0FBUV8TWnB84p8s1aDRaHI46hNCi0bjifqzWEurrTxIa6qo263DUsWJlz1+1DoulEykp96DVWNi+Y6rbvujocej14XTuNJOGhgKCglLQaoOw26vR60Mb7xJt7D/wT/LyPnM/9jEdxffbqdispeM2QelNDipMvRmdPhu91khwcNeA4p5kiwJlDkcDa9YOwW5Xsht7ps9l954ZbvJhIoM+Iz5Fq9VTuvATZGUVmpBQgoYORRMRyff3fUDwJV6K5J0maou6YIlxz6izVkdxYuUsUib+45TmOvzjw3Q6dw5Cc+qpv4FQX5ZIadbZ1JWkYKuJBqnBEFKA3hCNg2MkjXoajTbwApVFRUns36e484VwYrKGowk/hiWonPbt91NdHUlZSXva6XXU5PWksioag7mcruOe9jqfrSYSfZCSXVdbmEbe5uvQWooorQ6jOCoHh1YQXN+ATteA3W4EJD2zdrCvc08cOh3JBYdI3Z6NFgcHUrtj0Vai7VHBSXsKGYsOElFfj6lbBFZtCUWTSzBv0mDeqsF4QCDs7u4ny+BBdJg3j+K8PJZ8+CEHbTaCgoK49957z6iYmPHA48AcYAtQ03K/lDKwXMX/MU1KTHX2SA4esNJnwnoA1jGMl8VdPCVnYEfL/eI5Oq7ez1sf/osHBk9ja2xXjj4+sXmeX6PE5I7qRUJjxlGiSc/mwel8U1jOzXuOepXNvS+JT471IuOWAyxiImP5iYmrTuLWJbeR88b8SFSQlmnV03A4XG9NQvzl5OYtpEf0NOIzvXdo2F28m64RXVlybAmDEwYTYfJMNis4fIj373f1VLkzbR2G8f+GhkpIGQtJfsqpt+bEJnhjHPS8FC57E6SE7R8q5dZNoVCRQ9n7b5P/ohJInfTy0xTNmUFcv3JMEa0uGld/AbZaSBoCTynpkzL1XCrlWHIfcSl8q9MSqTJ7BoeeffOdZIw5h6wBZ+GsrvbYf99ULUfilS/s9N7TuTztcrLKsjhRdYLzOp2HvVjL+q+y3eqKtEZohFtWS1J6JGlnxVFddYxyzRRqi7pQuONydOYyMs+rpEfmHRiN7ZBSsnbdMBoaXKXwe6a/QGysolC2/Aw24awPQWNy3U3WFJgIivV913jgs06EhmXQZ0ow+UUfAhAW2peq6j0MGriETZsvwmYrJSysHz3Tn2fHjpuIi7+E5KSbfM7pj1e2v8K8HfPYes1W9Bol46K8vpz5O+dj0Vu4vfftbMrfxE0/ec5/abiV4SHefzSMhlgarAVe93kjJDidqmr39guRkcMpLQ08JurXEnufHm2V7xgEw08fk5KkFM0sqSth1MJRXuVm9p3Jld2uxKJX4sYc1dVUnziCPTme+n89xfGCw2jzKzAfO469m0R/RECDBm3j9V7ivXmsBMrGBeMI0iDXJRNZehCtRWL5x6XIbrFk7yqnNCcGc5jEiY38PZHYogSFPR9gZKv3Z+X6ycjYXYzq7L07+6lSW5xC/qbrsFbFtVq9JCh+Jx2Gu9fyqi9vT31pR+rLOhKRuhSdqRKtoY7K4wPI33oV0T2+JTLNewPcQDmxagY1+coNjtDVkzDwDULab2/jqF+H0RhHeOhQ2ne6lV3lObA3gl1fFHHWxZFkdo8k/4HZ1G5SEhm8vb8lIRBUDyYv1Q+CHnuQ+CFjWVu+hX6WboR06OS2X9rt2PLzqdu+A337BMyZmTirq9EEB5NXV8D9q+9na6Fic+oc1pnDFYfZPXX3GaXEtFR3Wx4gAClPt331N6JJiTlypA/FRckMOOsrADYwiLniHh6VsyjP785T8TeRunw3CxY+yt+H3sKOmFSfSswc08ccamg7n377kHR6r3N9mXd3iWClPoTbW3Tb7bl/C92yd/PS088p1g8h6X2zy7UyImgmuh9nU2dWyrtb6hvflmu/ZtlR9zTC1rSuYQOwo2gHV/9wdfPjAXEDeGnMS80XxpbUVpQz72aX7KzuLS74Ny6BDv7rYABgq4dHY33vv3GpouAA+z5O8NjdfXIuaPQw/RcIiW12kVmPHiX7vPHEPfQQ+Q895HZMjUHHyu7JHnN1i4wlalQvep59BSeGjFIGh5+F3LUfUe6qxXHlvVocWu8/OruuU9w+WfuPs+R55Q6zVl/FhGv6sOLNQ6CxE9v7E0oPnI2jIQSn3cyysfN4uEcS+QVf+X4d/JDe4zn27HX1vgkN7U1lZdsXzd693vawJrRGqw3G4fBU5lqT1OFG4hP+gskYh07n30358/Gf2VOyh+8Pf8/JalfLgJ3X7iTz3UyitE4ujrDxXYWefJu79WFEsI1LIv77ejMmYwKRkcMIDx9AfPwlfmUdjgYaGgooLPyOw0fmIhuz+4rtghcKTFQ7YVSInQvDA1uXqIHIBTqMBzVkJcB7Y7XctMhBcqPnpHJ4BqGrPd2HM2/W0rFAMvNr5Tv+7VmCj0dqsOlcn0WtQ9KuTMP1a9rR+UgRVp2WjSme3xtfGGx2BhzJI6TOigYwdOwIIUFoNFqS5s8HIdCEhiKEoN5ej1M6WZ+3nuzybF7c5j2gPzE4kVp7LTP7zuTBdQ82j7854h7CqCbCMpjI6O5oNAYcjhrWrhuF3V5OSudZJCffhhACm60crTaY3MOHWPzmGtrF92fAZe25ePkFVNuqm3+hdQ49I7OvJLWkH+uTvmZH+58xCIkWqHMKoioNJOdbyMx2t+SO/tvdtIvIYO2nhyk6UkxDxTxCk6swhlkp2h2JJaqeiNQKgtvXYI70dFlrtcGkdL6b2HZTWP3xQUryqtEnWTmem4fTKjEcjwacRLUPol1yOIndI6g1PUdJ0Wcec9WXGSg+FIqhSyXtIqwcr9SREGKn6W1euy+MvOxQ8qPqORJfi0TiDPTX9n9cqfhMU2JG+tsvpfzvKif9TriUmN7otDY6JClKxTvcyE9CyeWfmf8uz8ddy5x5zzJq5yb+Nvx2gvr1Y+Gtg5vnaanEJA3cw/CNvmM4fDFu1TcsHeHerWHYxiUM3up6KWP7FRHf3/dd/thVyr76G79h7UH/Pu2xY7Kx26ux2ysxmZQLXcY7GV5lm36cW7P6o3fY+JVSzfi21PVYWlfzbctl9dYEOLbWv0wj0gn7F7pfkOP6lRN651NoB11N7j8ewJabS+ILz5M10HsvKrtG8FOGYqHpNX4SPaWBDxZ9QXxZFX2OK92ul2cIRu+SvHm2hkX9lR/RqErJvJeVhNf13QQrz41jq8Xlr5/1hYOBB5TvTbu/zWK07nlk48VCSWyH/7Svw+TFI1DjgKBTUPm7d3+C+LhLOZD1ICdPfui2b9DAxZhMCVRV7aW8YgvZ2U+i04Vgt7usMcHB3TlrwLcIISgqWoLDWY/dXsWBA/90myv3lxhstXqSx7inYZrFEOrkOp/r69r1YfT6cKKjxlBXfwKbrZzioiVYglLYV5rF3Vs+QSLIMNu5Mdp/DNOK/WF0rdJjjWogubNnfELfPh8hDB24+buxVDoExXbBE4lKvaYFRUb21muY3ms648PBYIjh2PEFpKU+SK2uA//Z9AQD4gZwedrlBBvcU3Dr7fXsLt7Nm7vfZPVJ39aYxed9ReXl1yMLlM/CxkwzPWLSmRu1hfs/ddLQVWI4Jqjr48S4T4OuTPlMRH/xITE9lB5iNbYajFojMq8AXUJCszvIabWy+darCFm32+u5bRoNJyOC2ZsY4/c1/G8oDm3AYNfw9fA8Hk6eRXJMCneu+CsXrFXSjPMi69mWVk5RRAOWei0htToKIhtoZ2rHJxcuJMqs1Juqq6rkx5efZczUWwhrF4u1vp6Xrr/c6zmHXH4V6SPG8todyvUrpf9Ahl5xDeUFeXTokcHaT95j++Lvfa5Zo9XidCjf1XE33c7BjeuIbN+BbT96d63+ejxtGztSKshpV0uD3kmXnGDqDQ6OJtRi1zrR2TVc1fNq+ji68P43z5GW00LZFxICzEQ9VQ7H1/BLz1KsOicI0DoERqnjows+ISIoim2Lv2Vhztfs4Si2MB1BphBsBWXUmhw0GH69ay7aHM2UblMYmzyW7PJs0iLS6BjW8cxRYv4stFRiOnVy3b2+yF38Ioa5yT7z/CP0PbCHv46cQVZEkpsl5ujRV8g+/Awdk6eTkjKLO/Ye47OCMr/n7hpk4kAbqXTT33mMoDqXO6jDiFyiuvtWDLoerOZAqmdJ9GFD17Nm7WC3sYEDF7Fhw3kAjB61D43G4FOJmT9uPkPaezQXB+DbZx4ha6MSC5KS3pXMys/pHNziuT9YChofv9JNsS1D/6o0lQMl7fuIZ+XdJnI3hlFx2D0l05iaSsPBgz6OgAXnaVjaWzD1x47NY7FzJrPk2BK2Fmxh4eOeFTmu+Lu2WREBGBnch9sf8KwxE/HWPMquv635sRQSa4rk0AwHIvovhJcv9LmulnxcauCXGh1aJE6gqfzYB2c/R+WRh2hoyGPE8G3NqZIAy37uQpMhdPiwDRgM7o1HpXQGFEvRRHV1Fke370KjgR9feMun3Pjb76b78NEIIcg5+aGHAvRbEt9uKvFxV/D1M3MoOawoNne89QlbynZwy9JbfB6XHpXOnhLvbox3x7+LUzqZumiqx76IKoneDkVhMG67ZNriU7uwV5sguMXXvOu2rWjMZuxWKxqttvlHV9OiOnVDbS17ViwhKCIKs8XC0kfmUKb5ddfl0ddNo++ECwE4XnmcTw8s5Lr0qei1er48+CUhhhAeWv8QBquGiTndCNtf08aMgaPV6ejUpz+HNv3StvDvRHhcAjEdO5E/PJxBSUN5dcWzhCw7Sbtyl3t5d6cKNncrb+2hQusUdMq1YNNJ9A5BuzIjaScCS5DwxXVPv0yNrOetox8wIHEgwXYT6SFd+XTO3zEFBVOam+Mmn5zZB53BSPbm3/c1jeuSxohrbiC2UwoFFfnUlpeRkNSFshMn+OblJ9FbobrEe57P4MumEJ/alc59+qtKzOmmpTupU6dtzeOvMIO1rYxNc59+iIzsA9wx7gayg3u4KTFOZwM5Oe+TmHgdGo2Owb/s5Uidcpf5qJxFR44yq+wT8iMDb1N/blkOE3atoax0I/FnFXLou2R6T1MsPPHxk8nL8yxE19r836P708THXwzA2nUjqK8/6XEMQELCFXROncOA9wdwY8aNRJujeXzj4x5y26/Zjlajpa7uBHV1x4mMHEplcSGv3e5u9ZnZbQ3apnopA26CXlOgXXelqVnOZnh9rEvYEgUzd8N/4iEoBu5pDPRzOuHT68AQBBe/6hbMazWkkv2u/4tt/NznOfLB68yP3cvadA0JRSbO2aS4rr4ZlktpqMv8r3FKEkrg2dcVZaZmZF/6z/+ALw9+yQ9HfuD+gffTOawzh8adjS0nx+v5ABpSnZTc5Tu4T39MEPq5lpK77Rh3C6QRrKmSrTVaaqKm8PlB//FUz4x8ho35G7mr310E6RVFrun7ero7zh7etom6ykrSR44l79ABPnxglodMVFoKz6T8zPXp1zA14yZ+2TARo6Mw4HMYDDEkdZhG1fEIdm1+jPzNMTjtitIVf1YhsX1cF8WCbVHkb41G2v0rZRf/fQ4RHZIo1VVTZa3imh+vcReQjTe+Goio1KNzCIoi3C1C5gaJyQrzX2q73Fxp5yQOhhiJ6NyFzl8vQueU1Ol1dJh2M9G33oJdJ7A5bFj0FqTTyeJXX2DPSu9xF1MeeYYPZ3u+zv7IHDmOjN4DqHLa2bz4OwZfOpmOvfud0hytqSkvY9OirzhckEXNtkNY6+rc9iefO5J+fcewY8kPZG/e4GMWd3QGI3arK9tt8GVXMviyKYCibJecOE72lo3s+Ol7qstKGT31FpJ6ZvLjy89SeMTl+tYbTVz6j3+T0LU7lUWFFB7NJu9QFoMvmczOZYupKMqnS//BlJw8zs9vvkr6yLHsWbmMKY8+Q3yXrn7XaLM2sD1/Gy/sfokdRTvIjMmke2R3VuWs4skRT5IZk4mm8aZg2fFlzFzuigucZBpBzDc5SLvymbGEhaPR6agu8bScJ15+NheedyOmoMB6cAWK0+FASsmB9aux1tXRpf9A3r9/JjXl/m+ofy/+tvB7VYk53fhSYl7nVpaLs91kX3l8Nt2PZXPXDSb2lz7ipsS0Jm65y6rznrwMDZK9Sx/m0bN7eMh2z9rOvjT3wndhlaUszkgmMbEDK1b1QLS6C9sSPA1Z8Cb9g/xfZFvGvVitxeSc/AirtdDDDREePpDycuViVNb+cS7rqpTMtjlt9H1P6ZVkEBK7hMUTX2bPTkVpGXjWDwQHd+WZyecr/tYW3NxlAyH6Vu6Cu/fDs93cxy59AzIu8/s8ACjYC/MarUn9b4BJz1G9ejUnpt3cLLKts6DPYclr52oIm3w5n2V9hsGq4cI18QTVKwrkVc/M5ZJVV1NlU+7iu0d2Z1/pPjZetRF9VT3a0FBEq55NDkc9dkc1mlI7h0aNpjX1PZ2UTvetvIR+ocW0U4Ou0FPRiJ5xJ9E33YT12DGyP3qDiqHp3HJCyVWO10VRXlNCrcn9OFODpHO+xNi/L6+f+wbVtmoiTYF34G6NdDqp27EDXXQ0ushIrEYtNyy+gV3FLjfi9N7T+UvaX3hm7p1Eb/V07di0Tr5ucsjjAAAgAElEQVQbmkdFcNPr0FQyXTAr7FI0B8oJ1UUS0ucd6ovDOfBlHKOn3tbco6slna0QX1lD2Ml8DF1SaDiUzaHYCA7GuT/H6x5/gaVvz+fkfs/eOwanZExcR6JGjGCz7RBvWbYwsTCDsmVb/L4WHewOMhqD61s6DI5GhXI8Oowh55xP50FD2P3+O6zJ9V8AcMa7n6E3uoqBffrIbI7vOvUgz5DoGG54bj7W+jpMwcE47Y7GzuFnVuihw25DCA0arRbpdLJj6SJ+fvNVbnrxdUJj/Lck+KPicDrQnmHvQ6BIpxPRRk80h92ORqNh+Tuvsf2n75FOp4dC2n34aDLHnkv7rj2UmBspcTod/PLFJ2SMOYfFr87l+K7tf04lRggRCbwBnAMUA/dLKT/0IvcQ8ADQsnBFppTycOP+3o3zdAf2ATdKKdu8WvhyJ52gA38X7unLCx69n9Sco/z1Zi0zxv3EpEzfAXMtlZimdgQAB/Z/yuhruzN6k6sXzJg13/HzsElux9/z6mxunPs64bFxXrNO3qwfxM6inTw/8im0jnJCKr5pVkKa6JJyL8nJnub1k/nfsX/vX32u3ZH8JOekuNb80LqH+PLgZzzboc6r/KiRuynPL+btu29zG+8+dCQTSlu1Iuh9NWx/3/U4eRhc+ZGShRQIFTmwdi6c8zDoFPOvlJJvvn+OJ068iVNAtxzJllTlizlpTRzRle5ZSLM++Q6b08YFX15AkD6ITyZ90uZFaPuOGykpWQFAr8zXMR9S0pGDBg0ia9e/OFH0brNscHA3gm89jNRISu6yEfWCHm2lIPGVl8mZfjvJH7xP/iOPEj39Nk7eOcPb6Ty4d3oQzoZ6yoJh7HbJlJUul8blf9c2B+t9feHXdA5vu6md02pFY3ClIrds9dAsI2DyfVqvgYBBdVouXd4eTRvdkjUaDVqg5+FctnX0n6Ydn9aN6MQkktdvwfnLRr+yNo0GfWPZ+5i/ziB/7oucjAimymTgWEy432N/Czr27sfR7f6Vo5bc9OLrhLVTXg9rfR16g5Fdy5ewZMGLJPboyaX/eBid/o/TOFBFJRCEEGdUA8jTxcuAFYhF6db1vRBih5TSm/P6Eynl1a0HhRAG4GvgeeAV4BbgayFEqpSy7epnyixuj0x4xqroGpuLVZkhxOT/AnNfpzieOJLvMR6a10D3YPeW9N0P7XRTYrpmK3e/5pBQGqyepsiMjFc4tuxhAGauVGqz/HDBp5RvUyxDWl0oI4ZtRKPxvsYv847yca6JEcF2dtZpuSvWvaBZms7dr/nAwAe4LC6BgqPeK5kVFHxHQvu/MP31D7FbrSyYPhWAfWtXMm5wVwzlLoVtf/WXxIXqCD/nNejpPyvEK2GJMOFJtyEhBLNL3gKL8h5uSVX+a5y4KTCd+vRnzPW3AqDX6Pnx0h8JBIejrlmBAdix8ya6dfsPel0Yv7RSMDt0uIG01AeQG+3Y8guoWrqEyA3XNbt6uu/fB0DnL78AoKRHD+r3tt3B98lXfLvOFj7u4PL7la/thV8rsQ83Z97Mgp0LmNh5Io8Ne8zN1dSksMT+czaRV12FrcB7KrJGKnNLg56iUMnDl0osDZB2UrIyQ7Kp5zEevuZdwneeYOuTj7G3fbTHHE6nEyd4VWBSCsrIjo1A73AybvcRxA7Fatgy4iR00iQc5eUY09KIueN2bLm5VP7wA8WvzGuWKXphLlogqVSxDqXnliANBn7s3sHr8+p+shinEAgp6VRcgVMoz7XkikvYvmsLVr3vS2BMx84UHXVZXybc+TfSBg1Dq1OOKctT3LWWsAifgat3vLUQo8WV7WcwKdeDzLHnkjn2XJ/nVlFRCYxfpcQIIcwovZMOSik92wZ7PyYIpXFkTyllNbBGCPENcA3gvYiJd0ahrPt5qZiR5goh/gaMARYFtJZWdVa0Xlqv6eyKElNtEWjbiD+4PakdTxzJZ1piNKEFV1NpVawPcSl6HI46eoWY2VFVx7WfvszI8yY2V9C859XZAKQNGobRYqGyytNcHRbWj4oG9+DeyhaNC9+pSmOUFwWmoqGCD/Z9wPyd8wEN31YY2Hr1Vv75bU/ODnW5QrKznyAu9nzWrhvmMUcT9+WYm7NADpbuJiHhL5hDFGvKhDtm8cNLSuXgF9e3Y9blmdjHzabi7f6cTDBzMsHMqNSxXnqDeyerLItLv7mUjyZ+RM9ozyJlLV+Lq7tfzaz+s3hj6fNUv7Giefysi/7C8CuvC/CMUF6+mS1br2DI4BXs3u1pLdm/370QV0hIBmcNcKVIC50OQ2J7oqZO9Xue5I8/4uhfLseYlkbwiBGEjj+Phqwsiue9StRNN2I9fpzce+71OM7YtSvJ777TnIX1Tu5ErktwZWws2LkAgPovvmX/tK8BCBp/Lvp4V/O6gocfQdbXU/iUUjxrxi06ikMlr811ENRCrxVWG+2K4cX5rrEbG0tvOj+4ilKgI9CxuAKrVsOWjnFIISgPMhFVVUu5xYRDqyEiPoFBi1ZTEGohpqoWrYSu+d7LSXX89FPMGZ7vtbFLF2JmzCBmxgyklFR8/jl5s/9J2KWXkPDoo26yTY5ba10tOoORwhfmUrZggcecWgmhEybQ41+PMhwlk2bL919xeMtGRk+9mbiUtGbXTZPp3W61IjQCrc79exYR7+qwPOuT7wA4uX8v1rpadi3/iUl/vc8tgFdFReX0E2iK9dvARinlK42WkC1AOopV5WIpZZu3uUKIPsBaKaWlxdjfgJFSyvNbyT4E3AU4gDzgJSnlvMZ9dwHnSCnHt5D/DlgupfRbh99gMcruaZ4+QTs6DuIeBNbjyCEMNit7kgRpYb0JM/u3xjikRCsEuQdLMce4Z84EhfShtLIcW14BweGRVEVEYS08ibGmtlmmQ48MbLZyqmsOUOsAixbsmlDCg7uwvdDTUyYAnQCbVOq7tGZTvntmTZNMpbWSw2UHMGokcXr/773J1J4qWz3HapRAsc5GJ1VOQZFNkBqRikM6CDWEUlNUTG11AUhBRbggTufuijIYYgiytO32aFp3WI0ec4MWrclIQuc0t/2b8zcjkXSN7Kqcu7zMLaK/fbd0NG34fVtTVu4ZrBgRPgApJeUVm93GTcYEzGbvd/2nA+lwUL93L6bu3WnYvx9nfT2W/o0WWaeT2s3Kesz9+3G8OofC2kK0TkgshuB67++niAhHlpW7je1JUhTzfrH9moMXpdVG3fZtHse3xtC5M7poT0uMN5y1dWjMJhCChkOHcJSWYuzaFUdJCY6KSoxpqWiCPBsCqqio/LFZuXLlGeVOOheY27h9ARACxAE3AA8Bgdjqg4HKVmMVjXO1ZiGwACgABgKfCyHKpZQfNc7TOu/Y1zwIIW4GbgbQmw3eRDwsMwBIyGofeAaIVghKc2uQ0vMHtKZqG0YBToMJu7WBJK0Dm6OA/MYlaxv94Q12RakpsmvQOkCvE5gd3vvZSBQFBsDutKPT6HBKp1KivdZ3D5JQQyi9YxWFxtuPdxMmYxxmUyJmE7QLUWpp1FXvIEwrKbIJDpa5FLVoYwjhUco6a+o1Hp8qq7UIoQ3BbAhHCD3HKo9RWFtIcmgykaZINBI0Wh3V9VVEVBkwNhY9c9Q3UFNbSW5dPuZqcDjtSLPypEMMIdga6t0UGEto2CkrMA0Nntk1FnMyoEEICA3picNRj81WisnUHq3We2ft04XQajFnKKnvpp6trBMaDfr4eGx5edTv3E2c2UxMhednt8osCKlTxsuDBCdDKkipVip1VpkFJxr1j5bZFwDCoMdylpeihU1VXu12hE53SgW0NBaXO9XYxdXtWhv261pJqKioqLQkUCUmAmi62p8HfC6lLBRCfIwSgBsI1eDRDzEU8Eh9kFK2DBxYJ4R4AbgM+OhU5mmcawGKQkS7rvHy2Wc9f4SKauKZGexesvqhR2fwz2sUC8SLQ75mWKr/O0+b1cGCGUqhuna97yMyzdN0fnRZAoPOeYj0g49ReSSM1w4p5frPv3ca8Wld2LjpAiCBe3LM2BqLISWGJdJQoSgIwfpgpWJlKwYmD+SpkU/R691eTO46mW+yv6Gz3WX56B7ZnYXne9YvOZT9FMeOubJFenR/mty8hZSXbyS9x3PExbkX42sKOl5dpePzckUhFEie61AHeN5Nf11s4MLmAmf1NFSU8M3GREq6BRMsg7lxWSI6i52G5poNwZAc7zEPeAZVd+iRQUVRIZVJyvvSZM4PBCmdbN8+ldKypsJ7CZjNydTVHaNn+gu0azfxtKcwny6k08n+HumugRbKQPtnnyF0wgRK6kpocDSwKmcVj254lM4ATklYOVw4+EoeGBToV1ZFRUXl1/F7XUMDVWLygZ5CiDwUq0xTnmswEGhd8CxA1xiA23Qb3wsIpLFGy+zHPcAsIYSQLl9YJvht1uoXDd4KWrnucEsa8thbUkiPKM+U6SaKjrmMTCdWx3hVYgxBNtIGDoUdJYTolB93fZCNExV3c6KF96dJgRmSMIR1uUq11J8u/YkYSwx93uvjMe9Px37i73VKWNHHB9zryXx2/md0DOvodc2xsec3KzFNBfDi4y+mpuYwFksnD/k+vd9l2/ZrGR5ib1RiJI8keM9iWv5BCsnVBo6llpM8Jg8AY5iNi4YepzTPzNB1cfS8PguA3e+mYq87tfCsE3td6cA3vfhG87aUTurqjnmsv7r6ABs2TvA6l9mUxOBBS0+pUNz/CqHRkPTmGxy/QWkz0X7uCwQNHow2xGWIbKqcOrnbZCZ3mwxAcV0xWWVZDEnwXsRQRUVF5Y9IoL8cbwKfALkocSpN1ZsGAgHV3JdS1gghvgD+LYS4CSU76ULA46oqhLgQWAWUAwOAGUBTdOWKxjXMEEK8CkxrHP85wOfigfAWF9RiaPYWpUhTUzn+l29VTnX7q2OaZUpOKhklTnsu0q6hcEck7Xq5KzIJg4rQGY2QcTli5eOMjs1mZ6RnvY/bet3GjqIdVFtdVpf4YMVCMWfwHDRCw4UpF5JTncOkL5VMpxsWe7YdWH3FasJNvlNQQ4K7MXTIGozGODetOSjIe/xKZOTQ5u1NVyxm/6EnKSn8FoBXCo1Mb6dYjOrLDURUK5aaskNhBMfXEdVdicnQWxzcklJNbajLDdTzWkWnLdgWxad1Gj6YsRhLaBjPT78SR0kVXYeM4MDhbZDvaWzrdfZ4wtopRe0cjgZWrFQUzZjos8nMVBS0nbumU1S02OfrMGTIcp/7zkSChgwhZekShMGAvl1g9TiizdFEmwOLY1FRUVH5oxCQEiOl/LcQYg+QBHzaIpXZDnjPxfXOdBSFqBAoAW6TUu4RQgwHfpRSNpU0nNwoZwRygCeklO80rsUqhLgIeB2ls/Y+4KLA06s9KSns4BFRUxucCniWnW/ZkdjpcKLRKnfvKz9UOlRbq5WeHY4Gz6yEhgo9Vmsx2Iowag1k3v8RZfsu9ZCb3ns64z8fT0618kN/a69bm/ddluYqFJcc6mpseLTyqNscc0fP9avANGEyeXPf+EanC8duL2ftuuHNY2ZzR16/6DNsNVUsevUKina5FLO0gcPJWrWG0qwwUi90JbJZYjzT2mP7lPDqgBVYQhQXycxXPmreNwmoqTzJD/P/ScakzqSm3YNW60qpltLZrMAAFBUv4ejRedjsFR4KjFYbRK/MBQQFpaLV/jGDSg2Jif/rJaioqKj8zwnYhi+l9KiR3qRYnMIcpcBFXsZXo7immh5f2cY824D/rtZ2I+vXXY7VboBWNeYqoyfiTYlZ+8Wh5u3ygiJKTh4mdcBgGirmucmFdPCMXTGG2VizdhDooFOijrK8Jz1k3qjpxVhoVmAAok2+76And53s4UICGBg/0Ocx/w2ZGa+wddsUt7E+vd/GbIoAUwSdOt9KQmwNO5b8AMCkv97Li9s2U5MP9TV9iTDGU6dzpQf36P4ke/e50oobypdCyPVu89vtVezcdRtlZeuJ7Acn81ZyMu+txuOVVgtFRUs81pp9+Onm7ciIYaSnP4fB8Osr3aqoqKionFn4VGKEENcGOomU8t22pc5M7Haj1zqkUuP50tTaatmx9ETz488evZ+q4kIumPWCh6zW4L8+xJFkC9oqV8fazTVa3i81kBqhPH5g4AM8ukGphdFSoWnNzH4z3ZSYzVdvpqSuBIv+t8miiYgYiNEYT0ODEucybNgGjC0aETbVZ+l19ngqigoRGg23LXifPat+pte480AIVixfg5MKhg5ZjcmUgBB66uqOc/jIcxw8+AiJ7aeg0Rix26vYtPkSamt9l3vfu+9v1NUd5/gJJS6mc+e76dTxdtatH0td3VEAoqPH0ivTs2aIioqKisofG3+WmNaBsgZAj6vIpgYlqLcB+MMqMdC6fq+C9PLSbC5wrxlSVawkbC1pUWitfbcenNy/l8Ej3mH7Hk9XUUsiIgZTXLwUgPdLFdfIiUpFSZrYeWKzEnNxl4t9ztHUGFArtGy/VqknkxDsu0XC6WDQwMWsXJUJgEEf5VUmJrkTMclKcK3eZKL3Oa6g2kGDv6aiYhsmk7LOpiyow0eeA6C8fAvh4f3Zt/8BrwpMUyZRE0eOzm3e7tTxdgCGDF5GXV0ONTUHiYwc7jGHioqKisofH59KjJSyOUpECDERpR7MTKCpuMhA4Fng4d9wff8T+h+s92qJcbSolCudrqyc8JhS6hpriZ3cv5eg8AhCI5V4lcKdkeSujyXz2mI0Zvf6LeXlilL0cqErtqPeocSKhBhC+PtZfyerLKvN/jgbpmxAr/39eq/odEGMHnUAKW2/Ko3ObO7gtWDcwIGL2LDhPLZtvwaLpRO1tUea96X3eI7wiLMwGV0l7WtqsvllwznNj5M63NjqPImYzWrsiIqKisqflUBjYp4GbpBSrm8xtlYIMRN4Gwi8SMcZjrmulvFb60G0UAokpBcMI1wbCZTgtOdjrXL1rcw7uNJtjpryMvT6CAYPXMW8BUo2uiU0lnqbuxJjtyuaz8EWQcDX9nB58a7qflVAa/6tXEf+0Gh0nO7WW0EWV2BSkwJjNndkyOBl3uWDUhjQ/0uCg7ths5VhNMae1vWoqKioqJzZBFoYoyPgrStdLUrG0h+esNqmQFwl+0hjr2Bc1nUgBbf+8gLDj/yFdY8rSoi93nel25ZYgtozZuqtmIJDyOz1WPN4v+3lHrKD4wez7Zpt/K3/3/67J/IHRggN/ft91vxYpwv1qcA0ERqaiUZjUBUYFRUVlf+HBKrEbEBptNjc8axx+zngl99iYb83F21f1bwtbQU4dWF0KemLztnCItNYDt9py/Y7V+bY85q3+5x3Pre/8RHBId2bx4I1nj+4IzuMRKfRnbGVYn8vwsL6EBOjdPft388jIU5FRUVFRaWZQJWYG4Eo4KgQ4qgQ4ihwFGiHq9jcHxqDXSk8PHTzMoTTlR6tke5ZRlJ6q+6r0H3YKAB0Bs8eTS2VE235yebt+UVKPMyxyoCagf+/IDPjFcaOyfZZdE9FRUVFRQUCVGKklNkopf0nogTzPgtMADKklIf8HftHQSslt678ij57NkKLAN5zDrhXwnU0eHaUbmLfmhUA7F+3yruAhJAqGyLR1XV6X72iJPWM7un9GBUVFRUVFRWvnEqxOwn81Pj3p0Yj7Tikg4byF4guN0F4WvM+h3Wf12OEJor+k0ax6ZvPiUtJ9Sozek0xQgJjzgW7UhJ/3rh5GLVG+sf+5h3LVVRUVFRU/lQErMQIISKA8SiBvG7+Einlv0/zuv6nCKedhvLGAnbSvTy+dBQ0b2v0XXHaDgBgCL2G3hf2ZeXm79CMScODqnw0jR0LDtUW8GCRGSEkL/UMpne73r/J81BRUVFRUfkzE5A7SQgxCDiEkmr9MHAD8ADwN+AyP4f+IamxLfU6Lls1ijQET2zeFkLDf7Y9ycLeWcze+x8AyurLGPnJSHYX72b/iz3J6JRERqckVusFlU5BhUNDelT6b/dEVFRUVFRU/sQEaol5CvgA+CtQCYxBSbn+CHjjt1na74+m1rPfUUuks8znvq/SXyCoxD2z6N5V91JaX8qV318J7V2NFp/NWQTAoPhBv2uROhUVFRUVlT8TgWYnZQIvNcbFOACjlLIAuA+lku+fAl21Z/2WJqRsAGlvflxrVKwyxrCbMIbdTH7oYWxOW/P+RUcX8Uue/+zzh4f+6Yodq6ioqKio/G4EqsRYW2wXAMmN29XAb9uo5wzAYd1HQ/nLOB15zWM7U5WXRGhCERqlAffxquPN+9/c9Wab88Za1AJtKioqKioqv5ZAlZitQFNe8ArgESHEdcBcYGegJxNCRAohvhRC1AghjgkhprQhbxBC7BNC5LQaHyOE2CqEqBRCHBZC3BzoGlqSl9fFfUB6l7PXbwPAaXcpMQc6FDZvv9X/fo9j9pXuI9IU6ff8/98L26moqKioqPw3BKrEPADkNm7PBoqAF4EI4FQUiJdRrDqxwFXAPCGEv8jWexrP1YwQQg98CcwHwoArgGeFEL1OYR0AHDo42O1x+sjRXuWkI7/xv8vdJIWTd/rNZlmXd2nQ13o9rrS+9FSXpKKioqKiohIggRa72yylXN64XSSlHC+lDJVS9pdS7gpkDiFEEHAp8E8pZbWUcg3wDXCND/lOwNXAY612RQKhwHtSYROwD+gRyDr8YbQE+d0vHYoet7J3BQB1hioOxmwJeP7nE13ZTO+Nf+9XrFBFRUVFRUWliUAtMQAIIfoLIa5oVEgQQgQJIQLNcEoD7FLKrBZjOwBflpgXgX8AdS0HGwOKPwKuF0JohRCDUWJ01pzCU/FKaHS7gOTKgt17Yb55rnv8S7LNFeCra5GWPRhXt2m1NoyKioqKisp/R6B1YmKFEL8AG4EPUdxBoLQfeCbAcwWjpGe3pAII8XK+iwGtlPJLH3N9BDwINACrgQeklCd8rP1mIcRmIcTmthYYaIxKeYjN7XGMOaZ5e/nxHI7pXWnTUytcT9mcuyOg+VVUVFRUVFTaJlBLzHMoWUlRQMsAkE+BcwKcoxrFDdSSUKCq5UCjledJYIa3SYQQ3YCPgWtRKgenA/cKISZ6k5dSLmh0e7VZ1z8xMbEtEYQmgjv63OE2ptO4jFFRDvcGkVoJXaxWxlfXIK78sM35VVRUVFRUVAIjUFfQWGCslLKslbUiG6UNQSBkATohRKqU8mDjWC9gTyu5VKAjsLrxXAYgTAiRDwwCegJZUsrFjfIHhBDfo7RE+D7AtXilQ4cObQsJHSEGl/HIoDGQGJLIrH6zGJM0BvFsBpMrq/g4VJHRIvkyahRc/CoIwceTPibKFPXfLFNFRUVFRUWFwC0xZtxrxTQRA9R7GfdASlkDfAH8uzGWZihwIdA6wnU30AHo3fh3E4oVqDdwAtgGpDamWQshRAowiVNI9W7NOYsWc0Ftnce4zjzCYywsJoSLulxE14iufHvRt2y5RgnsndpzKkkhihLUMlM7/bIP4JL50Kj8pUelExcU92uXqqKioqKiotJIoErMKmBqi8dSCKFFqdi77BTONx1FISpEiWu5TUq5RwgxXAhRDSCltEsp85v+gFLA2fjYIaXMRundNBclxmYl8Dnw+imsw42I8nLiWsXDhMd15YoHb/GQ1er1WPQWPrvgMzqGdXTfaW8AYFitotcNrKtnRKKnIqSioqKioqLy3xOoO+leYKUQYgBgRAnmTUep0zI00JNJKUuBi7yMr0YJ/PV2zAogsdXYQmBhoOcNCJ3W7WHHXqnEdQ7zENPqDB5jzTQo4T2j6upYUxtC2MhHTusSVVRUVFRUVFwEWidmL5ABrAN+AkwoQb19Gi0jf3iEVtHneowYozzWKJaZdp1S3OQsoRZ8Ym2MUb54PmG3rYMeF57+haqoqKioqKgAp1AnptGdM0dKOUlKOUFKOVtKmdf2kX8QtMpLEZPcCQCNRnk87qbpbmLmELPvOU5sVP7rjKd/fSoqKioqKipu+HUnCSECyjySUh5vW+rMpskSI5uK0wlFidEb3BUSncGPO+nLxhia8j/8y6GioqKionLG01ZMzFF8tkUEQDTu1/qROaMJrVBaCAit8hSkU6nz0pRKLjTuxiqdwYeV5bMbXdt6Py4nFRUVFRUVldNCW0rMgBbbAiUTaAqQ4138j4dosry0CuxtUmLCYuNJ6T+I7M2/AOB0ODwnmdsXSluEBmVe8ZusVUVFRUVFRcWF35gYKeWWFn+bASewq9V44B0Qz0CalJgmd1JrdHo9F90zu/nxjiU/eAqVtoptNrUuTKyioqKioqJyujmlBpB/RpqUGFuu0qE6Jqkj4JmV5JdR/3Btx2WerqWpqKioqKio+EFVYhqVmMrvvgOgU5/+XP/cfLoNcS9SZwkL9z3Jiv+4tvN/deFgFRUVFRUVlVPg1ygx/gJ9/3A0x8S0IDKhvedYe6XeXlJP1dKioqKioqJyJtBWivU3rYZMwGtCiJadrJFSXnC6F/Z74U2J8YYlRKnemzlu/G+5HBUVFRUVFZUAacsSU9Lq732UJoytx/+wBKrExKakAhAUHqEM5GwBKSF/l7vgyPtO5/JUVFRUVFRUfODXEiOlvP73WsgZxaJ/wC8vw0MVzUMDzr+EDj0yiE/tCgcWwUdXwKTn4buZ7seq1XpVVFRUVFR+F9TAXm+WmF9eVv632Cc0GkWBASg7qvwv2ANjH3Q/tsvZp3+RKioqKioqKh4E2sX6T0dtrVLLxa87yWH1blnRNBbGkw4IiVe2h8/yVGhUVFRUVFRUfjP+31piDmf3d3ts6NTJU8hh9X5wkxKz+U2w1ijbA287jatTUVFRUVFRaYvfVYkRQkQKIb4UQtQIIY4JIaa0IW8QQuwTQuS0GtcKIR4RQuQKIaqEENuEEH4KuXgiUdoKGKyKohI7+wFPIbsPJUa0aFFga0zUMqj9klRUVFRUVH5Pfm930suAFYgFegPfCyF2SCn3+JC/BygCQvIolDoAABmnSURBVFqN/wsYAgwGjgPpQP0prUQqSkz/TZsBMCQqdWCoOOmSsfuYsqWFxtqoxOjMp3R6FRUVFZX/a+/e46uq7ryPf76BYLgjKAjBEuwgIGijYh/HEcvzYBikF6UOaqm3eapg63WsnVFHx0upIupo1apDi4VirbVV1FFb6Vis4KWIHRBRB6StGoWA4U4Aifzmj7UP7BzOJQk555Cc3/v12q+csy57r73OTs4ve6+9l3P7Jm9nYiR1Bk4HrjezLWa2AHgaOCdN+YHA2cCtSekHAlcAF5rZ+xa8ZWZNC2IiHXbuDD8HDAgJn/zPnsx0QczObXteL7grzFpdUrRX5pxzzrmCyOc37+FAvZktj6UtIZxFSeVe4FpgW1L6kUA98A+SVktaLunidBuVNEnSIkmL4ukWnYkZ+OQc+t5yS7zGnpfpxsTEg5jPdoQgxjnnnHN5lc/LSV2ATUlpG9n7UhGSxgPtzGyOpFFJ2f2B7oSgaCAwCHhB0nIz+13yusxsOjAdoPfgvrFbkUKwUjZkCGVDhuxJ3lW/53XaMzF1Dd/XfZK6nHPOOedyJp9nYrYA3ZLSugGb4wnRZadpwGVp1pM4DXKzmW0zszeBR4FxLdLKxy/Y8zrdwN4P/9gim3LOOedc8+XzTMxyoL2kQWa2Ikr7ApA8qHcQUAHMlwTQAeguaTVwPJCYJjr+gJd9n5Ty34fBgL+F7Rv2pCXOxJjBqsVQ1gN6DoQPXt3nzTnnnHNu3+TtTIyZbQWeAG6W1FnS3wGnArOTir4FHEq4e6kSuACoiV5/aGYrgfnAv0o6QNJQ4CzgmSa1JzlhUzUs/VXDtMSYmPl3wvRRcE8lfPzfTdmMc84553Ik37fUfAfoCKwBfgF828yWSRopaQuAmdWb2erEAqwDdkXvP4vW8w1gAGHyyWcJdzy90KSWNObczabodut4cDN9VJM245xzzrncyOtzYsxsHXBaivT5hIG/qeq8SBjMG0/7CBibgyY29J+Xw7HnN3y4XUL5sfDRG+H1scU5T6ZzzjlXSEX8cBNlL5LQvsPeaYkABuD/Xb/vzXHOOedckxRxENMUWQKesu75aYZzzjnndivaWawBShMDYzIN1t2xueGzYxI6dIGv/xjefhLaFXU3OueccwVR1N++pYkTLKuXNsxQCdiu8HrFXGhXunflL14IQ8aFxTnnnEuyc+dOqqur2b69WbPitAplZWX079+f0tIU35N5UNRBzG7JZ1q69t1zZ9LqpdDv6IZjYABGXpWftjnnnGuVqqur6dq1KxUVFUTPPWtTzIza2lqqq6sZOHBgQdrgQQzArs8avt8Um8l6wV2p67Qvy117nHPOtXrbt29vswEMgCR69erF2rVrC9aGoh7Ye4CiMTGpxrxk4+NgnHPOZdFWA5iEQu9f0QYxBrRP3HW0/q+FbIpzzjnnmqFogxhgz53Tf3ywafV6fK7Fm+Kcc87lQkVFBUceeSSVlZWMGDECgF/96lcMGzaMkpISFi1atLvsyy+/zFFHHcWIESNYsSJMc7hhwwbGjBnDrl27CtL+TIo2iMl4AqxTr73Thp8eq5ziCb7OOefcfmrevHksXrx4d8AyfPhwnnjiCU466aQG5e68806ee+457r77bh58MPyDP2XKFK699lpKSva/kGH/a9H+4NQfwUUvN0wrKQ1TEEC4Bds555xrAklpl+nTp+8uN3369IxlW8LQoUMZPHjwXumlpaXU1dVRV1dHaWkpK1eu5MMPP2TUqFEtst2WVtSjUzuni0UGnwI1yxqmrV4K5z8Db8yEL3wj101zzjnnWoQkxowZgyQmT57MpEmT0pa95pprOPfcc+nYsSOzZ8/mqquuYsqUKXlsbdMUbRDT8YPldOtzcPoCJUkP7lmzDDr1hGs/htJOuW2cc865NsfMGlVu0qRJGQONplqwYAHl5eWsWbOGqqoqhgwZstdlpITKykpee+01AF566SX69u2LmXHmmWdSWlrKnXfeSZ8+fVqsbfuqeK+LWJYBSgcNSp3eoTO08VvmnHPOtR3l5eUA9O7dm/Hjx7Nw4cKsdcyMKVOmcP3113PTTTcxbdo0LrzwQu65555cN7dJ8hrESOopaY6krZLelzQxS/kOkt6RVJ0m/1xJJumCJrclrAA+25lu4zD0a01drXPOObff2Lp1K5s3b979eu7cuQwfPjxrvZ/97GeMGzeOnj17UldXR0lJCSUlJdTV1eW6yU2S78tJPwI+BfoAlcCzkpaY2bI05b8HrAW6JmdIOhC4FkhXN6WNGw+me/e1iXXA20+lLzzySnjn6fC6/3FN2YxzzjlXcDU1NYwfPx6A+vp6Jk6cyNixY5kzZw6XXnopa9eu5ctf/jKVlZU8//zzANTV1TFz5kzmzp0LwJVXXsm4cePo0KEDjzzySMH2JZW8BTGSOgOnA8PNbAuwQNLTwDnA1SnKDwTOBq4EfpxilbcC9wBnNLdNZpb+TAw0HPtyxuzmbsY555wriMMOO4wlS5bslT5+/PjdwU2yTp06MW/evN3vR44cydKlS1OWLbR8Xk46HKg3s+WxtCXAsDTl7yWcadmWnCHpi8AIIOtT6iRNkrRIUvQ0n+TxLBkGWsWDmG59s23KOeecc3mUzyCmC7ApKW0jqS8VjQfamdmcFHntgPuBS8yyjc4FM5tuZiPMbARAbW1/AD7dkmba8FHX7HntdyE555xz+618jonZAnRLSusGbI4nRJedpgHj0qznO8CbZvZacxrxUfUR1Kz+GzpuWQpmNDgz8+1XoPcRe96379CcTTjnnHMuD/IZxCwH2ksaZGYrorQvsPfA3EFABTA/ejJhB6C7pNXA8cBo4EuSEkFOT+BoSZVmdkn2Zoj6+gNSZ/VJurLVzoMY55xzbn+VtyDGzLZKegK4OboluhI4FTghqehbwKGx9ycA9wHHEO5UOh8oi+U/AfwamNHijU4EMV37tfiqnXPOObdv8n2L9XeAh4A1QC3wbTNbJmkk8Bsz62Jm9cDqRAVJ64BdZpZI2xBfoaRPgU1mtrFZLco0rEaCCbOg/Nhmrdo555xzuZPXh92Z2TozO83MOpvZ58zskSh9vpl1SVPnRTPrn2Gdo8zsJ81qD4B9lrnQsNOgx6GZyzjnnHP7qYqKCo488kgqKysZMWIEAOvWraOqqopBgwZRVVXF+vXrAXj88ccZNmwYI0eOpLa2FoCVK1dy5plnFqz9mRTvtAPAUaP/Pvv0A84551wrN2/ePBYvXsyiReFpI1OnTmX06NGsWLGC0aNHM3XqVADuvfdeXn/9dSZPnrz7wXbXXXfdfjsJZNFOAPndXz4TXryxInNB55xzbh9dccUVLF68uEXXWVlZyd13392suk899RQvvvgiAOeddx6jRo3itttuo6SkhB07dlBXV0dpaSnz58/nkEMOYdCgNPMJFljRBjG7dewRfk58rLDtcM4553JAEmPGjEESkydPZtKkSdTU1NC3b3iI6yGHHEJNTQ0A11xzDSeffDL9+vXj4YcfZsKECTz66KOFbH5GxRvEJKZE3xWNienxucK1xTnnXJvW3DMmLWHBggWUl5ezZs0aqqqqGDJkSIN8SUSPNKGqqoqqqipgzySQy5cv54477uDAAw/khz/8IZ067T8Pgi3eMTEzquCmHnvGxKhdYdvjnHPO5UB5eTkAvXv3Zvz48SxcuJA+ffqwatUqAFatWkXv3r0b1ElMAnnxxRdzww03MGvWLE488UR+/vOf5739mRRvEFP9eviZOBNT4kGMc865tmXr1q1s3rx59+u5c+cyfPhwvva1rzFr1iwAZs2axamnntqg3u23385ll11GaWkp27ZtQxIlJSXU1dXlfR8yKd7LSQm76sNPD2Kcc861MTU1Nbtnq66vr2fixImMHTuW4447jjPOOIMZM2YwYMAAHntsz7jQjz/+mIULF3LDDTcAcOmll3LcccfRo0cPnnzyyYLsRzoexCSeE+OXk5xzzrUxhx12GEuWLNkrvVevXrzwwgsp6/Tr149nn3129/sJEyYwYcKEnLVxXxTv5aQEv5zknHPOtUoexPiZGOecc65V8iBmV3R3kp+Jcc4518Is8TiPNqrQ++dBzO4zMd4VzjnnWk5ZWRm1tbUF/6LPFTOjtraWsrKygrXBB/b6mBjnnHM50L9/f6qrq1m7dm2hm5IzZWVl9O+fdo7mnPMgxsfEOOecy4HS0lIGDhxY6Ga0aXm9hiKpp6Q5krZKel/SxCzlO0h6R1J1LO1wSU9JWitpnaTnJQ1udqP8TIxzzjnXKuV7IMiPgE+BPsA3gQckDctQ/ntA8nm4HsDTwOBoPQuBp5rdohduCj/9TIxzzjnXquQtiJHUGTgduN7MtpjZAkIwck6a8gOBs4Fb4+lmttDMZpjZOjPbCdwFDJbUa58a6GdinHPOuVZF+Ro1Lelo4GUz6xRLuwr4kpl9NUX5Z4AZwHrgYTNLOXJI0mnAA2bWN03+JGBS9HY48NY+7YhrroOATwrdiCLlfV9Y3v+F431fOIPNrGuuN5LPgb1dgE1JaRuBvXZS0nignZnNkTQq3Qol9SdcoroyXRkzmw5Mj8ovMrMRTW+621fe94XjfV9Y3v+F431fOJIW5WM7+QxitgDdktK6AZvjCdFlp2nAuEwrk3QwMBe438x+0YLtdM4551wrkM8gZjnQXtIgM1sRpX0BWJZUbhBQAcyXBNAB6C5pNXC8mf1V0oGEAOZpM/tBXlrvnHPOuf1K3oIYM9sq6QngZkkXAJXAqcAJSUXfAg6NvT8BuA84BlgrqRvwPGF8zdVNbMb0ZjXetQTv+8Lxvi8s7//C8b4vnLz0fd4G9kJ4TgzwEFAF1AJXm9kjkkYCvzGzLinqjCI2sFfSecBMoA6IN/4IM/sgt3vgnHPOuf1FXoMY55xzzrmW4rMeOuecc65V8iDGOeecc61SUQQxTZ2zyaUn6QBJM6J+3CxpsaRTYvmjJb0rqU7SPEkDkuo+JGmTpNWSrkxad9q6riFJgyRtl/RwLG1i9LlslfRkNAYtkZfxdyBTXdeQpLOiOd22SloZjenzYz/HJFVIek7S+qgP75PUPsqrlPRG1H9vSKqM1ZOk2yTVRsttim59zVa3WEm6RNIiSTskzUzKy8lxnq1uWmbW5hfgF8AvCQ/cO5HwkL1hhW5Xa1yAzsCNhNvgS4CvEJ71U0F4OuZGYAJQBtwOvBareyswHzgQGAqsBsZGeRnr+rLX5zA36suHo/fDos/hpOg4fwR4NFY+7e9Atrq+NOj3KuB94Pjo+C+PFj/2c9/3zxFu6igDDgGWApcRHsPxPvBPwAFR2vtAh6jeZOB/gP7RZ/U2cFGUl7FusS7A14HTgAeAmbH0nB3nmepmbGuhOysPH0ZnwqSTh8fSZgNTC922trIAbxLmxZoEvJLU99uAIdH7j4ExsfzvJ74ss9X1pUF/nwU8RggmE0HMLcAjsTKfj477rtl+BzLVLfS+7m8L8ArwrRTpfuznvu/fAcbF3t8O/AcwBviI6EaVKO+D2JfnK8CkWN63El+e2eoW+wJMoWEQk7PjPFPdTEsxXE46HKg3s+WxtCWE/z7dPpLUh9DHywh9uiSRZ2ZbgZXAsOgBhX3j+TT8HNLWzWX7W5voOUk3s/dUG8n9t5IocCH770Cmui4iqR0wAjhY0nuSqqNLGh3xYz8f7gbOktRJUjlwCvBbQj+9adE3X+RN0vQve/d9prquoZwc542om1YxBDGNnrPJNY2kUuDnwCwze5fQ1xuTiiX6ukvsfXIeWeq6Pb4PzDCz6qT0bH2f6XfA+75x+gClwD8AIwkP7DwauA4/9vPhJcKX2iagGlgEPEn2/kvO3wh0icbFeN83Ta6O82x10yqGIKZRcza5ppFUQrgk8SlwSZScqa+3xN4n52Wr6wgDEIGTgbtSZGfr+0x9633fONuin/ea2Soz+wT4d8I8b37s51D09+a3wBOEyxAHEcZO3EbTj+9uwJbo7Iv3fdPk6jjPVjetYghids/ZFEtLNWeTa6ToP5gZhP9MTzeznVHWMkLfJsp1JoyvWGZm64FV8Xwafg5p6+ZoN1qjUYQB1B8ozCV2FXC6pD+xd/8dRhiouJzsvwOZ6rpIdAxX0/BJ4YnXfuznVk/gc8B9ZrbDzGqBnxICyGXAUfE7joCjSNO/7N33meq6hnJynDeibnqFHjiUp8FJjxLuzugM/B1+d9K+9ueDwGtAl6T0g6O+PZ0w+vw2Go4+nwr8gfAf1JDooB3bmLq+GEAnwl0ZieUO4NdR3yVOs4+MjvOHaXh3UtrfgWx1fWnwGdwMvA70jo7j+YRLfH7s577v/wxcTZjzrwcwh3AnXeIOo8sJwfclNLw76SLCoOByoB/hizH57qSUdYt1ifq4jHDH0OzodftcHueZ6mZsa6E7K08fSE/CtdOthJHnEwvdpta6AAMI/31uJ5wCTCzfjPJPBt4lnHp/EaiI1T2AMHfWJqAGuDJp3Wnr+pLys7iR6O6k6P3E6PjeCjwF9IzlZfwdyFTXlwb9VArcD2wg3AJ6D1AW5fmxn9u+r4z6Zj3wCeEOvT5R3tHAG1H//Qk4OlZPwDRgXbRMo+HdSGnrFusS/W2xpOXGKC8nx3m2uukWnzvJOeecc61SMYyJcc4551wb5EGMc84551olD2Kcc8451yp5EOOcc865VsmDGOecc861Sh7EOOecc65V8iDGuSIiaaakZwrdjjhJp0paIale0swcbWO/22/n3L7zIMa5PIm+SE3S9Unpo6L0gwrVtgKbATxOeJDi5TnaxuXA2fuyAknnS9qSvaRzLl88iHEuv7YD35N0cKEb0pKiGc2bU68H0At43sw+MrPkWW5bhJltNLMNuVi3c65wPIhxLr/mAX8Frk9XINWZGUkVUdqIpDKnSHpD0jZJ8yX1l/QlSUskbZH0jKReKbZxnaSaqMxPJXWM5UnSP0taGa13qaSzU7TlG5J+L2kbMDnNvhwoaZak9dG6/kvSsMQ+EB4hD/D7aJ2j0qyng6RbJL0vaYekP0u6LJZ/kqQ/Stoe7dddkjrE8htcTpL0oqT7o3V+ImmNpDui2ZJTfiaECQc7R+00STdm28cov7uk2dE2tkdtvyKWP1nS8ijvE0nPS2ofy/9HSW9H+csl/VO8ndnqO9eWeRDjXH7tIkxid5Gkz7fA+m4CrgD+D2HitF8C/wZMIsx6PYwwD0rclwgzxI4mTMY2hjAZW8IU4FvAxcARhEng/kPSl5PWcythHqEjCPMypTIzatupwBeBOuC3UdD0StQ+onb0jdJSmQWcC1wJDI3atwFAUjnwG+C/CfPgfAv4RtS+TL4J1AMnECb+uwI4M03ZV6L8uqidfQkTcGbbRwj9eSTwFWAw8P+Bj6K2jwB+RPgcBxM+k98mNirpQuAWwmc6FPgu8C/AdxpT37k2r9ATTfniS7EshC+7Z6LX84hmiiYEGwYclOp9lFYRpY1IKvP3sTKXRGnHxNJuBN5KasMGYjOQE8aK7CDMYN2ZMDnbyKS23w08l9SW72bZ30FRuZNiad0JM9leEL0/KCozqhHrSTmjLfADYAVQEks7P9qnTsl9H71/EXg1aT2/A36SoR3nA1uasY9PAw+lWefXo7Jd0+R/AJyTlHYF8HZj6vviS1tf/JSjc4XxL8Crkm7fx/W8GXtdE/1cmpTWO7mOmcUHqL4KdAA+T5hJtoxwJiE+O2wp4TJY3KIsbRtKOPP0aiLBzDZKWko4e9NYR0frmZdhO6+Z2a5Y2gLCPv0NDfsoLjn9Y/buq2was48PAL+WdCwhUPpPM/tDlPc74H3gL5KeB+YCT5jZ5mjc1KGEs2APxLbZnjAzc8b6TdwP51olv5zkXAGY2ULCHTnTUmQnvowVS0s3cHZnfLXRupPTmvJ7nij7VaAytgwjXHaK29qE9Saz7EVaRKbt7Ex639S+atS2zew3hDuv7iCceXpW0k+jvM3AMcAZhLMu1wDvSuoXa8tFNPwshhNdhstS37k2z4MY5wrnWmAkMDYpfW30s28srbIFt3ukpM6x98cDnwIrgbcJl2EGmNl7Scv7TdzOO4S/MX+bSJDUjTA+5O0mrGdxtJ7/m2E7xycNyj2RPfvUUj4F2qXYdtZ9NLNPzGy2mZ1PGLNznqQDorx6M/u9mV0DHEW4pPcVM6shnB36fIrP4r3YulPWb8H9dm6/5ZeTnCsQM3tP0nT2fjbKe8CHwI2SriaMQbmuBTfdHnhI0s1AP2Aq8GMz2wog6Q7gDkkCXgK6EAKdXWY2vbEbMbMVkp4iXA6ZRBiL8wNgE/BIE9azXNJjwE8kXQ78CegPVJjZbMLg4iuA+yX9EDgs2qf7zKyusdtphL8CZZKqCIOI6xqzj1E//wlYRuj7rwN/NrMdkr5CuIz3ErCOEKh1JQRHADcA90raADxHOCN3DFBuZrc2or5zbZqfiXGusG4m3CGzW3Q56CzCl/ESwp0n17bgNv9A+EKdB8wBfg/8cyz/esKA4Kuicr8j3D30l2Zs6x+BhYTBrQuBToQButuauJ5zCUHBPcC7hIG63QHM7CPgFMLYmcXAQ8AvaNk+w8xeAR6M1r2WPX2WbR93EAKbJcDLhCDjq1HeBuA04L+i/bqKMCB4frTNnxDuZjonqj+fcOfZXxpT37m2Tmb5ujTtnHPOOddy/EyMc84551olD2Kcc8451yp5EOOcc865VsmDGOecc861Sh7EOOecc65V8iDGOeecc62SBzHOOeeca5U8iHHOOedcq/S/uZjC4rI3CzEAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "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": "markdown", + "metadata": { + "id": "NGwv-vo8fcR_" + }, + "source": [ + "Let's use the moons dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "z9QQ1fB5fcSA" + }, + "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": "markdown", + "metadata": { + "id": "VeghGfwHfcSA" + }, + "source": [ + "**Note**: to be future-proof, we set `solver=\"lbfgs\"`, `n_estimators=100`, and `gamma=\"scale\"` since these will be the default values in upcoming Scikit-Learn versions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2vSKHJ0ffcSB" + }, + "source": [ + "**Code examples:**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "D5ybJRCxfcSB" + }, + "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(solver=\"lbfgs\", random_state=42)\n", + "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "svm_clf = SVC(gamma=\"scale\", 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": 7, + "metadata": { + "id": "QJ8elPcifcSC", + "outputId": "272ade52-ff8a-4e53-9227-d5fd201a468b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", + " ('rf', RandomForestClassifier(random_state=42)),\n", + " ('svc', SVC(random_state=42))])" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "voting_clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "Vc7eOXA-fcSD", + "outputId": "22910ee7-4dfc-4a2a-fa6f-ad6b6343f079", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.896\n", + "SVC 0.896\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": { + "id": "gfwvDg5wfcSE" + }, + "source": [ + "**Note**: the results in this notebook may differ slightly from the book, as Scikit-Learn algorithms sometimes get tweaked." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gdDOgIqbfcSE" + }, + "source": [ + "Soft voting:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "rhUZ0wvMfcSE", + "outputId": "12a73596-128e-4913-ba40-650853128ef6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", + " ('rf', RandomForestClassifier(random_state=42)),\n", + " ('svc', SVC(probability=True, random_state=42))],\n", + " voting='soft')" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n", + "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "svm_clf = SVC(gamma=\"scale\", 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": 10, + "metadata": { + "id": "Ji9TE0nXfcSF", + "outputId": "c7bcc5d1-9d00-4ff1-edc7-f8fea232eaf3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.896\n", + "SVC 0.896\n", + "VotingClassifier 0.92\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": { + "id": "nNCIl9dJfcSF" + }, + "source": [ + "# Bagging and Pasting\n", + "## Bagging and Pasting in Scikit-Learn" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "najqsAICfcSG" + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import BaggingClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "bag_clf = BaggingClassifier(\n", + " DecisionTreeClassifier(), n_estimators=500,\n", + " max_samples=100, bootstrap=True, random_state=42)\n", + "bag_clf.fit(X_train, y_train)\n", + "y_pred = bag_clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "dQAOksLyfcSG", + "outputId": "39eb48a2-9482-4803-be32-2659d9b82367", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.904\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "print(accuracy_score(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "O-lj6RTDfcSH", + "outputId": "c5f353ec-ee2f-4c4c-a299-6c62f231c3fe", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "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": "markdown", + "metadata": { + "id": "hbYuxicdfcSI" + }, + "source": [ + "**Code to generate Figure 7–5. A single Decision Tree (left) versus a bagging ensemble of 500 trees (right):**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "z7qMvnXqfcSI" + }, + "outputs": [], + "source": [ + "from matplotlib.colors import ListedColormap\n", + "\n", + "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -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": 15, + "metadata": { + "scrolled": true, + "id": "AtujfRj7fcSI", + "outputId": "ff7eb707-39e0-463d-a830-e82f331215a2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving figure decision_tree_without_and_with_bagging_plot\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fig, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", + "plt.sca(axes[0])\n", + "plot_decision_boundary(tree_clf, X, y)\n", + "plt.title(\"Decision Tree\", fontsize=14)\n", + "plt.sca(axes[1])\n", + "plot_decision_boundary(bag_clf, X, y)\n", + "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n", + "plt.ylabel(\"\")\n", + "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fONGVX5bfcSJ" + }, + "source": [ + "## Out-of-Bag evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "4IHvvpMDfcSJ", + "outputId": "1a16b689-dc1a-47ec-d90a-91160eec944e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.8986666666666666" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "bag_clf = BaggingClassifier(\n", + " DecisionTreeClassifier(), n_estimators=500,\n", + " bootstrap=True, oob_score=True, random_state=40)\n", + "bag_clf.fit(X_train, y_train)\n", + "bag_clf.oob_score_" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "djVC4mQWfcSJ", + "outputId": "a1bf2709-d4cd-4e8a-83df-a566c30cd442", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0.32275132, 0.67724868],\n", + " [0.34117647, 0.65882353],\n", + " [1. , 0. ],\n", + " [0. , 1. ],\n", + " [0. , 1. ],\n", + " [0.09497207, 0.90502793],\n", + " [0.31147541, 0.68852459],\n", + " [0.01754386, 0.98245614],\n", + " [0.97109827, 0.02890173],\n", + " [0.97765363, 0.02234637],\n", + " [0.74404762, 0.25595238],\n", + " [0. , 1. ],\n", + " [0.7173913 , 0.2826087 ],\n", + " [0.85026738, 0.14973262],\n", + " [0.97222222, 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"cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true, + "id": "fMBtsTHOfcSK", + "outputId": "384040aa-52ba-4417-b686-6b6597577653", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.912" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "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": { + "id": "ii_q-txYfcSL" + }, + "source": [ + "# Random Forests" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "J93xL0OGfcSL" + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=42)\n", + "rnd_clf.fit(X_train, y_train)\n", + "\n", + "y_pred_rf = rnd_clf.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q7NetyOKfcSM" + }, + "source": [ + "A Random Forest is equivalent to a bag of decision trees:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "2_3TdMODfcSM" + }, + "outputs": [], + "source": [ + "bag_clf = BaggingClassifier(\n", + " DecisionTreeClassifier(max_features=\"sqrt\", max_leaf_nodes=16),\n", + " n_estimators=500, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "vPX7cZ_lfcSM" + }, + "outputs": [], + "source": [ + "bag_clf.fit(X_train, y_train)\n", + "y_pred = bag_clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "xf05KdPTfcSM", + "outputId": "6c02234a-593f-4c40-da87-0d16e7a394b9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1.0" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "np.sum(y_pred == y_pred_rf) / len(y_pred) # very similar predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VLPybp7TfcSM" + }, + "source": [ + "## Feature Importance" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "egTsqZlKfcSO", + "outputId": "1f5f55d7-f4ea-4829-88cf-522c6a11aded", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "sepal length (cm) 0.11249225099876375\n", + "sepal width (cm) 0.02311928828251033\n", + "petal length (cm) 0.4410304643639577\n", + "petal width (cm) 0.4233579963547682\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "rnd_clf = RandomForestClassifier(n_estimators=500, 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": 24, + "metadata": { + "id": "nfuBtKkTfcSO", + "outputId": "010a2be7-dbd7-41e6-ab64-3f553736cca3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.11249225, 0.02311929, 0.44103046, 0.423358 ])" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "rnd_clf.feature_importances_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f8vxKBwyfcSO" + }, + "source": [ + "The following figure overlays the decision boundaries of 15 decision trees. As you can see, even though each decision tree is imperfect, the ensemble defines a pretty good decision boundary:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "Mj6wmBfCfcSO", + "outputId": "2f4d1037-213d-471e-d0b0-7a90ecb47c48", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "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_train[indices_with_replacement], y_train[indices_with_replacement])\n", + " plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gvzyBj98fcSO" + }, + "source": [ + "**Code to generate Figure 7–6. MNIST pixel importance (according to a Random Forest classifier):**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FOro4JXzfcSO" + }, + "source": [ + "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this and keep the same code as in the book, we use `as_frame=False`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "M-SNhDyPfcSQ" + }, + "outputs": [], + "source": [ + "from sklearn.datasets import fetch_openml\n", + "\n", + "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n", + "mnist.target = mnist.target.astype(np.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "hMT3zg9MfcSQ", + "outputId": "6ecd37b0-41c4-4197-c40e-e47212241f06", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestClassifier(random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ], + "source": [ + "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "rnd_clf.fit(mnist[\"data\"], mnist[\"target\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "CBAD8y33fcSQ" + }, + "outputs": [], + "source": [ + "def plot_digit(data):\n", + " image = data.reshape(28, 28)\n", + " plt.imshow(image, cmap = mpl.cm.hot,\n", + " interpolation=\"nearest\")\n", + " plt.axis(\"off\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "Pn7C_eJafcSR", + "outputId": "30795f4a-c8c6-4e50-e7e8-a9444f808947", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving figure mnist_feature_importance_plot\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "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": { + "id": "sIgLOCMGfcSR" + }, + "source": [ + "# Boosting\n", + "## AdaBoost" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "v_5uHYjHfcSS", + "outputId": "1a78f3b8-f56c-4f7c-abb6-3f9f4b9d2954", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n", + " learning_rate=0.5, n_estimators=200, random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 30 + } + ], + "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": 31, + "metadata": { + "id": "5n9sYqarfcSS", + "outputId": "592d39fb-2b10-4fae-ac5f-87e59a777034", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plot_decision_boundary(ada_clf, X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "00GuT8KqfcSS" + }, + "source": [ + "**Code to generate Figure 7–8. Decision boundaries of consecutive predictors:**" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "OZKy4jjefcST", + "outputId": "3ddcd49a-96e2-4456-bb9b-8301b11f7e7b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving figure boosting_plot\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "m = len(X_train)\n", + "\n", + "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", + "for subplot, learning_rate in ((0, 1), (1, 0.5)):\n", + " sample_weights = np.ones(m) / m\n", + " plt.sca(axes[subplot])\n", + " for i in range(5):\n", + " svm_clf = SVC(kernel=\"rbf\", C=0.2, gamma=0.6, random_state=42)\n", + " svm_clf.fit(X_train, y_train, sample_weight=sample_weights * m)\n", + " y_pred = svm_clf.predict(X_train)\n", + "\n", + " r = sample_weights[y_pred != y_train].sum() / sample_weights.sum() # equation 7-1\n", + " alpha = learning_rate * np.log((1 - r) / r) # equation 7-2\n", + " sample_weights[y_pred != y_train] *= np.exp(alpha) # equation 7-3\n", + " sample_weights /= sample_weights.sum() # normalization step\n", + "\n", + " plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n", + " plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n", + " if subplot == 0:\n", + " plt.text(-0.75, -0.95, \"1\", fontsize=14)\n", + " plt.text(-1.05, -0.95, \"2\", fontsize=14)\n", + " plt.text(1.0, -0.95, \"3\", fontsize=14)\n", + " plt.text(-1.45, -0.5, \"4\", fontsize=14)\n", + " plt.text(1.36, -0.95, \"5\", fontsize=14)\n", + " else:\n", + " plt.ylabel(\"\")\n", + "\n", + "save_fig(\"boosting_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-bzpfsRIfcSU" + }, + "source": [ + "## Gradient Boosting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2az-6uZHfcSU" + }, + "source": [ + "Let create a simple quadratic dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "MmB5EfD5fcSV" + }, + "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": "markdown", + "metadata": { + "id": "9T5ixTcVfcSV" + }, + "source": [ + "Now let's train a decision tree regressor on this dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "98vZOj0XfcSW", + "outputId": "546497a8-42f3-41c2-b5be-e6c2e4794deb", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeRegressor(max_depth=2, random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ], + "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": { + "id": "6P694Au6fcSW", + "outputId": "6ef2900c-7a7b-4def-eae8-e6993071a47c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeRegressor(max_depth=2, random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 35 + } + ], + "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": { + "id": "jksCoPqifcSW", + "outputId": "b6fb8cdf-fcb0-482c-d468-20039b3ef0f5", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeRegressor(max_depth=2, random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 36 + } + ], + "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": { + "id": "VTXAUODFfcSX" + }, + "outputs": [], + "source": [ + "X_new = np.array([[0.8]])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "2fvVn0FZfcSX" + }, + "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": { + "id": "AVCkRqFIfcSX", + "outputId": "9ec42c0e-bb46-424f-9102-adff7e807d5e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.75026781])" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U5_0j1Q8fcSY" + }, + "source": [ + "**Code to generate Figure 7–9. In this depiction of Gradient Boosting, the first predictor (top left) is trained normally, then each consecutive predictor (middle left and lower left) is trained on the previous predictor’s residuals; the right column shows the resulting ensemble’s predictions:**" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "GbgXkLZ9fcSY" + }, + "outputs": [], + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "c6F7rCI4fcSZ", + "outputId": "623aeccb-a384-4a03-d227-3ead9090be63", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 819 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving figure gradient_boosting_plot\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "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": "markdown", + "metadata": { + "id": "HBYNy1kKfcSa" + }, + "source": [ + "Now let's try a gradient boosting regressor:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "uteQr2bbfcSa", + "outputId": "27888dfd-54da-4e65-bc0a-b4183e71a022", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n", + " random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 42 + } + ], + "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": "markdown", + "metadata": { + "id": "_hPoTifJfcSa" + }, + "source": [ + "**Code to generate Figure 7–10. GBRT ensembles with not enough predictors (left) and too many (right):**" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "j7Wjx8jjfcSa", + "outputId": "463616b3-5448-4a25-a3e0-340d3163cb14", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 43 + } + ], + "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": 44, + "metadata": { + "id": "a2ETWGmPfcSb", + "outputId": "0631076b-03b8-4452-e2ff-351b55ff8a56", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving figure gbrt_learning_rate_plot\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", + "\n", + "plt.sca(axes[0])\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", + "plt.xlabel(\"$x_1$\", fontsize=16)\n", + "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", + "\n", + "plt.sca(axes[1])\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", + "plt.xlabel(\"$x_1$\", fontsize=16)\n", + "\n", + "save_fig(\"gbrt_learning_rate_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7dl71gqnfcSb" + }, + "source": [ + "**Gradient Boosting with Early stopping:**" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "TIAXTImdfcSd", + "outputId": "062b2d84-e8dd-4300-f0ee-f7739d2652fa", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GradientBoostingRegressor(max_depth=2, n_estimators=56, random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ], + "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) + 1\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": "markdown", + "metadata": { + "id": "V0DFi07XfcSd" + }, + "source": [ + "**Code to generate Figure 7–11. Tuning the number of trees using early stopping:**" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "No-vVTRufcSd" + }, + "outputs": [], + "source": [ + "min_error = np.min(errors)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "6Qvaa_SrfcSe", + "outputId": "4e82221c-6f0e-4d27-9fe5-cf8262cb7609", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving figure early_stopping_gbrt_plot\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.figure(figsize=(10, 4))\n", + "\n", + "plt.subplot(121)\n", + "plt.plot(np.arange(1, len(errors) + 1), 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.ylabel(\"Error\", fontsize=16)\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", + "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", + "plt.xlabel(\"$x_1$\", fontsize=16)\n", + "\n", + "save_fig(\"early_stopping_gbrt_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oN737K3pfcSe" + }, + "source": [ + "Early stopping with some patience (interrupts training only after there's no improvement for 5 epochs):" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "LifyW_O1fcSf" + }, + "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": 49, + "metadata": { + "id": "ODScJS0nfcSf", + "outputId": "80868ee8-a102-4eca-84c8-bd788a0df9e8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "61\n" + ] + } + ], + "source": [ + "print(gbrt.n_estimators)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "VxPXZzctfcSf", + "outputId": "6aac3c3f-63e6-4346-f3cf-fff1cf6d33ce", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Minimum validation MSE: 0.002712853325235463\n" + ] + } + ], + "source": [ + "print(\"Minimum validation MSE:\", min_val_error)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r4Vl0YjefcSg" + }, + "source": [ + "**Using XGBoost:**" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "K4k9qH27fcSg" + }, + "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": 52, + "metadata": { + "id": "6jOx47VvfcSg", + "outputId": "d3488a25-e7eb-45d6-a6ed-db2b7257e644", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "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) # Not shown\n", + " print(\"Validation MSE:\", val_error) # Not shown" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "xAAvER_5fcSh", + "outputId": "60114fa2-97a4-4cb6-e481-c0eef0e7ca4a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[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: 0.002528626115371327\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) # Not shown\n", + " print(\"Validation MSE:\", val_error) # Not shown" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "9W-ZFUxlfcSh", + "outputId": "07b81fa4-1966-4498-d481-af7ec9ca56e2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:28] WARNING: 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WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "[12:33:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n", + "100 loops, best of 5: 9.03 ms per loop\n" + ] + } + ], + "source": [ + "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "id": "8Lu3Q5U1fcSi", + "outputId": "a0bea2a5-6db9-4277-cfe3-c7a19fb71505", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "100 loops, best of 5: 19.3 ms per loop\n" + ] + } + ], + "source": [ + "%timeit GradientBoostingRegressor().fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3PncANOufcSi" + }, + "source": [ + "# Exercise solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H94hiW_lfcSi" + }, + "source": [ + "## 1. to 7." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mZ3SX8cXfcSi" + }, + "source": [ + "See Appendix A." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m6M72hwnfcSj" + }, + "source": [ + "## 8. Voting Classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MsImSGI4fcSk" + }, + "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": "markdown", + "metadata": { + "id": "jJvbOYZWfcSk" + }, + "source": [ + "The MNIST dataset was loaded earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "id": "6-a2h4NWfcSk" + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "gz4jIkCefcSk" + }, + "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": { + "id": "siouVkKufcSl" + }, + "source": [ + "Exercise: _Then train various classifiers, such as a Random Forest classifier, an Extra-Trees classifier, and an SVM._" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "id": "glHHH_NEfcSl" + }, + "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": 59, + "metadata": { + "id": "EJkYC5nMfcSm" + }, + "outputs": [], + "source": [ + "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\n", + "svm_clf = LinearSVC(max_iter=100, tol=20, random_state=42)\n", + "mlp_clf = MLPClassifier(random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "yssJ66bOfcSn", + "outputId": "611b50f5-3eaa-4641-d37a-489d568f59a3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training the RandomForestClassifier(random_state=42)\n", + "Training the ExtraTreesClassifier(random_state=42)\n", + "Training the LinearSVC(max_iter=100, random_state=42, tol=20)\n", + "Training the MLPClassifier(random_state=42)\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": 61, + "metadata": { + "id": "N0xZKPnyfcSo", + "outputId": "4fbb0c2f-2ea9-4661-e0e4-830bf0136645", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.9692, 0.9715, 0.859, 0.9639]" + ] + }, + "metadata": {}, + "execution_count": 61 + } + ], + "source": [ + "[estimator.score(X_val, y_val) for estimator in estimators]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6FaVbSbhfcSq" + }, + "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": { + "id": "gdBLvO4xfcSq" + }, + "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": 62, + "metadata": { + "id": "Ylnnlyb9fcSq" + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import VotingClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "pg8BAWY1fcSr" + }, + "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": 64, + "metadata": { + "id": "zoVe-DeBfcSr" + }, + "outputs": [], + "source": [ + "voting_clf = VotingClassifier(named_estimators)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "id": "kqg7V0cRfcSr", + "outputId": "d2567d22-65f5-4c8f-ffc2-fff5c343e29c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('random_forest_clf',\n", + " RandomForestClassifier(random_state=42)),\n", + " ('extra_trees_clf',\n", + " ExtraTreesClassifier(random_state=42)),\n", + " ('svm_clf',\n", + " LinearSVC(max_iter=100, random_state=42, tol=20)),\n", + " ('mlp_clf', MLPClassifier(random_state=42))])" + ] + }, + "metadata": {}, + "execution_count": 65 + } + ], + "source": [ + "voting_clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "FRZZY7SifcSr", + "outputId": "6db5250f-6218-45d5-8134-e81be6ee0161", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9708" + ] + }, + "metadata": {}, + "execution_count": 66 + } + ], + "source": [ + "voting_clf.score(X_val, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "id": "u03JnGtufcSs", + "outputId": "2c3b4c64-ff23-4555-a8f9-c97257e24011", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.9692, 0.9715, 0.859, 0.9639]" + ] + }, + "metadata": {}, + "execution_count": 67 + } + ], + "source": [ + "[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v8xStulFfcSs" + }, + "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": 68, + "metadata": { + "id": "lQKCIUnIfcSs", + "outputId": "55ddd98c-31c2-4144-c1d2-8671f00a2794", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('random_forest_clf',\n", + " RandomForestClassifier(random_state=42)),\n", + " ('extra_trees_clf',\n", + " ExtraTreesClassifier(random_state=42)),\n", + " ('svm_clf', None),\n", + " ('mlp_clf', MLPClassifier(random_state=42))])" + ] + }, + "metadata": {}, + "execution_count": 68 + } + ], + "source": [ + "voting_clf.set_params(svm_clf=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XbCip0rifcSs" + }, + "source": [ + "This updated the list of estimators:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "id": "CseH9CYWfcSt", + "outputId": "1e47b7cb-0288-43b3-8b84-ac2e9941c187", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[('random_forest_clf', RandomForestClassifier(random_state=42)),\n", + " ('extra_trees_clf', ExtraTreesClassifier(random_state=42)),\n", + " ('svm_clf', None),\n", + " ('mlp_clf', MLPClassifier(random_state=42))]" + ] + }, + "metadata": {}, + "execution_count": 69 + } + ], + "source": [ + "voting_clf.estimators" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rz87CNwWfcSt" + }, + "source": [ + "However, it did not update the list of _trained_ estimators:" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "id": "rB25MTo9fcSt", + "outputId": "baa35135-e733-448d-9e4c-ddaab1447fc1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[RandomForestClassifier(random_state=42),\n", + " ExtraTreesClassifier(random_state=42),\n", + " LinearSVC(max_iter=100, random_state=42, tol=20),\n", + " MLPClassifier(random_state=42)]" + ] + }, + "metadata": {}, + "execution_count": 70 + } + ], + "source": [ + "voting_clf.estimators_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6FMUzWXIfcSu" + }, + "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": 71, + "metadata": { + "id": "t-M312jRfcSu" + }, + "outputs": [], + "source": [ + "del voting_clf.estimators_[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MFMrVvn_fcSu" + }, + "source": [ + "Now let's evaluate the `VotingClassifier` again:" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "id": "b7aNxNeSfcSv", + "outputId": "91c2fce9-3b7c-4d2c-9186-7763a0f005ad", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9736" + ] + }, + "metadata": {}, + "execution_count": 72 + } + ], + "source": [ + "voting_clf.score(X_val, y_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3ZeCimRmfcSv" + }, + "source": [ + "A bit 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": 73, + "metadata": { + "id": "uAbqztekfcSw" + }, + "outputs": [], + "source": [ + "voting_clf.voting = \"soft\"" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "id": "P75jMJ8hfcSw", + "outputId": "58803dd4-b17c-4efa-e5f7-beba2ec2c0f2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.97" + ] + }, + "metadata": {}, + "execution_count": 74 + } + ], + "source": [ + "voting_clf.score(X_val, y_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMT6KzH-fcSx" + }, + "source": [ + "Nope, hard voting wins in this case." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-gC87ClLfcSx" + }, + "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": 75, + "metadata": { + "id": "vn0K97M_fcSy", + "outputId": "72b919e0-c68a-4c87-fa5f-f691c9590f39", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9704" + ] + }, + "metadata": {}, + "execution_count": 75 + } + ], + "source": [ + "voting_clf.voting = \"hard\"\n", + "voting_clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "id": "uQhSlhHFfcSy", + "outputId": "73757e7d-9bee-4a67-8b81-6079b7487722", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.9645, 0.9691, 0.9604]" + ] + }, + "metadata": {}, + "execution_count": 76 + } + ], + "source": [ + "[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ly7t8Sp4fcSz" + }, + "source": [ + "The voting classifier only very slightly reduced the error rate of the best model in this case." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MwaJBnw8fcSz" + }, + "source": [ + "## 9. Stacking Ensemble" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QeM7VG83fcSz" + }, + "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": 77, + "metadata": { + "id": "yPtojrn7fcSz" + }, + "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": 78, + "metadata": { + "id": "6enWXAzVfcS0", + "outputId": "fa8fd901-d3d5-46b2-dc1f-3732284521bd", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[5., 5., 5., 5.],\n", + " [8., 8., 8., 8.],\n", + " [2., 2., 3., 2.],\n", + " ...,\n", + " [7., 7., 7., 7.],\n", + " [6., 6., 6., 6.],\n", + " [7., 7., 7., 7.]], dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 78 + } + ], + "source": [ + "X_val_predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "id": "SppVkwXhfcS0", + "outputId": "d7676ee2-e041-4fd1-a918-6501c19d7153", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)" + ] + }, + "metadata": {}, + "execution_count": 79 + } + ], + "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": 80, + "metadata": { + "id": "WpwHEDiKfcS1", + "outputId": "6014e893-89db-46da-ab93-9a812a242f60", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9684" + ] + }, + "metadata": {}, + "execution_count": 80 + } + ], + "source": [ + "rnd_forest_blender.oob_score_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eqRWEbqXfcS1" + }, + "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": { + "id": "6CXiqwhKfcS1" + }, + "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": 81, + "metadata": { + "id": "UG0cAMAsfcS2" + }, + "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": 82, + "metadata": { + "id": "h_EvkyuufcS2" + }, + "outputs": [], + "source": [ + "y_pred = rnd_forest_blender.predict(X_test_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "id": "cKht-pNHfcS3" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "id": "AC6jrgrGfcS3", + "outputId": "49ca0238-ddda-4326-e900-c3d385e6ebf1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9671" + ] + }, + "metadata": {}, + "execution_count": 84 + } + ], + "source": [ + "accuracy_score(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Em9gTNvIfcS4" + }, + "source": [ + "This stacking ensemble does not perform as well as the voting classifier we trained earlier, it's not quite as good as the best individual classifier." + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 9.1. Stacking Ensemble using [`Sklearn`](https://scikit-learn.org/stable/modules/ensemble.html#stacked-generalization)" + ], + "metadata": { + "id": "geyiN5jehvaL" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.ensemble import GradientBoostingRegressor\n", + "from sklearn.ensemble import StackingClassifier\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "estimators = [\n", + " ('rf', RandomForestClassifier(n_estimators=100, random_state=42)),\n", + " ('et', ExtraTreesClassifier(n_estimators=100, random_state=42)),\n", + " ('svr', make_pipeline(StandardScaler(),LinearSVC(max_iter=100, tol=20, random_state=42))),\n", + " ('mlp', MLPClassifier(random_state=42))\n", + "]\n", + "\n", + "final_estimator = xgboost.XGBRFClassifier(n_estimators=100, random_state=42)\n", + "\n", + "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)" + ], + "metadata": { + "id": "xPk2vsWxhvI2" + }, + "execution_count": 85, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "id": "dDHhyw-vfcS4", + "outputId": "eda55b33-370d-4abf-f9fc-4c6627cf6f66", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "StackingClassifier(estimators=[('rf', RandomForestClassifier(random_state=42)),\n", + " ('et', ExtraTreesClassifier(random_state=42)),\n", + " ('svr',\n", + " Pipeline(steps=[('standardscaler',\n", + " StandardScaler()),\n", + " ('linearsvc',\n", + " LinearSVC(max_iter=100,\n", + " random_state=42,\n", + " tol=20))])),\n", + " ('mlp', MLPClassifier(random_state=42))],\n", + " final_estimator=XGBRFClassifier(random_state=42))" + ] + }, + "metadata": {}, + "execution_count": 87 + } + ], + "source": [ + "# train the StackingClassifier\n", + "clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "source": [ + "y_pred = clf.predict(X_test)\n", + "# evaluate the classifier\n", + "accuracy_score(y_test, y_pred)" + ], + "metadata": { + "id": "Y-ROu7jnjMys", + "outputId": "822135c0-6526-409d-9a0e-f18c824870ed", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 88, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9749" + ] + }, + "metadata": {}, + "execution_count": 88 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Multiple stacking layers " + ], + "metadata": { + "id": "w1rj6GbVnIcc" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.ensemble import AdaBoostClassifier\n", + "\n", + "final_layer = StackingClassifier(estimators=[\n", + " ('xgb', xgboost.XGBRFClassifier(n_estimators=100, random_state=42)),\n", + " ('ada', AdaBoostClassifier(n_estimators=100, random_state=42))\n", + " ],\n", + " final_estimator=LogisticRegression(solver=\"lbfgs\", random_state=42)\n", + " )\n", + "\n", + "multi_layer_clf = StackingClassifier(estimators=estimators, final_estimator=final_layer)" + ], + "metadata": { + "id": "mrfv-sxIn3cI" + }, + "execution_count": 92, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# train the multi_layer_clf\n", + "multi_layer_clf.fit(X_train, y_train)" + ], + "metadata": { + "id": "obyRzEHhi6Mt", + "outputId": "898081e3-40ef-4fdd-b695-e11cd3b5db45", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 93, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "StackingClassifier(estimators=[('rf', RandomForestClassifier(random_state=42)),\n", + " ('et', ExtraTreesClassifier(random_state=42)),\n", + " ('svr',\n", + " Pipeline(steps=[('standardscaler',\n", + " StandardScaler()),\n", + " ('linearsvc',\n", + " LinearSVC(max_iter=100,\n", + " random_state=42,\n", + " tol=20))])),\n", + " ('mlp', MLPClassifier(random_state=42))],\n", + " final_estimator=StackingClassifier(estimators=[('xgb',\n", + " XGBRFClassifier(random_state=42)),\n", + " ('ada',\n", + " AdaBoostClassifier(n_estimators=100,\n", + " random_state=42))],\n", + " final_estimator=LogisticRegression(random_state=42)))" + ] + }, + "metadata": {}, + "execution_count": 93 + } + ] + }, + { + "cell_type": "code", + "source": [ + "y_pred = multi_layer_clf.predict(X_test)\n", + "# evaluate the classifier\n", + "accuracy_score(y_test, y_pred)" + ], + "metadata": { + "id": "jT1wSB9Yo87d", + "outputId": "927274ad-a49d-4ce4-e805-33773d4dcd94", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 94, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9747" + ] + }, + "metadata": {}, + "execution_count": 94 + } + ] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "x6q3fP5UpB8O" + }, + "execution_count": null, + "outputs": [] } - ], - "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": 80, - "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": 81, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = rnd_forest_blender.predict(X_test_predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9683" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + }, + "nav_menu": { + "height": "252px", + "width": "333px" + }, + "toc": { + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 6, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + }, + "colab": { + "name": "07_ensemble_learning_and_random_forests.ipynb", + "provenance": [] } - ], - "source": [ - "accuracy_score(y_test, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This stacking ensemble does not perform as well as the voting classifier we trained earlier, it's not quite as good as the best individual classifier." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - }, - "nav_menu": { - "height": "252px", - "width": "333px" }, - "toc": { - "navigate_menu": true, - "number_sections": true, - "sideBar": true, - "threshold": 6, - "toc_cell": false, - "toc_section_display": "block", - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file