{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " \n", "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n", "###
Author: Korgun Dmitry, @tbb\n", " \n", "##
Tutorial\n", "###
\"Something else about ensemble learning\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal behind ensemble methods is to combine different classifiers into a meta-classifier that has a better generalization performance than each individual classifier alone. For example, assuming that we collected prediction from 10 different kaggle-kernels, ensemble method would allow us to combine these predictions to come up with a prediction that is more accurate and robust than the prediction by one each kernel. There are several ways to create an ensemble of classifiers each aimed for own purpose:\n", "\n", "* **Bagging** - decrease the variance\n", "* **Boosting** - decrease the bias\n", "* **Stacking** - improve the predictive force\n", "\n", "What is \"bagging\" and \"boosting\" you already know from lectures, but let me remind you main ideas.\n", "\n", "**_Bagging_** - generate additional data for training from the original dataset using combinations with repetitions to produce multisets of the same size as the original dataset. By increasing the size of training set you can't improve the model predictive force, but just decrease the variance, narrowly tuning the prediction to the expected outcome.\n", "\n", "**_Boosting_** - two-step approach, where first uses subsets of the original data to produce a series of averagely performing models and then \"boosts\" their performance by combining them together using a particular cost function (e.g. majority vote). Unlike bagging, in the classical boosting the subset creation is not random and depends upon the performance of the previous models: every new subset contains the elements that were misclassified by the previous model.\n", "\n", "**_Stacking (Blending)_ ** - is similar to boosting: you also apply several models to your original data. The difference here is that you don't have an empirical formula for your weight function, rather you introduce a meta-level and use another model/approach to estimate the input together with outputs of every model to estimate the weights, in other words, to determine what models perform well and what badly given these input data.\n", "\n", "### Intro\n", "Before we start, I guess, we should see a graph that demonstrates the relationship between the ensemble and individual classifier error. In other words, this graph visualizes the Condorcet’s jury theorem." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from scipy.misc import comb\n", "from itertools import product" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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utgEMfgOaXVJx11ABSZOCUl6y50hOcefyM0PaVFzn8v7V8M0dcNiaFU3XkdZQ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\nJ6vcpA9c/m+o1shjIfy27TD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# calculate ensemble error\n", "def ensemble_error(n_clf, error):\n", " k_start = math.ceil(n_clf / 2)\n", " probs = [comb(n_clf, k) * error ** k * (1 - error) ** (n_clf - k)\n", " for k in range(k_start, n_clf + 1)]\n", " return sum(probs)\n", "\n", "error_range = np.arange(0.0, 1.01, .01)\n", "errors = [ensemble_error(n_clf=11, error=error)\n", " for error in error_range]\n", "\n", "plt.plot(error_range, errors,\n", " label='Ensemble error', linewidth=2)\n", "plt.plot(error_range, error_range,\n", " linestyle='--', label='Base error',\n", " linewidth=2)\n", "plt.xlabel('Base error')\n", "plt.ylabel('Base/Ensemble error')\n", "plt.legend(loc='best')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the error probability of an ensemble is always better than the error of an individual classifier as long as the classifier performs better than random guessing.\n", "\n", "Let's start with a warm-up exercise and implement a simple ensemble classifier for majority voting like an example of simplest ensemble algorithm." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import some useful stuff\n", "from sklearn.base import BaseEstimator, ClassifierMixin, clone\n", "from sklearn import datasets\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.pipeline import Pipeline, _name_estimators\n", "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n", "from sklearn.metrics import roc_curve, auc\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# and make a small helper function to plot classifiers decision area\n", "def plot_clf_area(classifiers, labels,\n", " X, s_row=2, s_col=2, \n", " scaling=True,\n", " colors=None,\n", " markers=None):\n", " if not colors:\n", " colors = ['green', 'red', 'blue']\n", " \n", " if not markers:\n", " markers = ['^', 'o', 'x']\n", " \n", " if scaling:\n", " sc = StandardScaler()\n", " X_std = sc.fit_transform(X)\n", "\n", " # find plot boundaries\n", " x_min = X_std[:, 0].min() - 1\n", " x_max = X_std[:, 0].max() + 1\n", " y_min = X_std[:, 1].min() - 1\n", " y_max = X_std[:, 1].max() + 1\n", "\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, .1),\n", " np.arange(y_min, y_max, .1))\n", "\n", " f, axarr = plt.subplots(nrows=s_row, ncols=s_col,\n", " sharex='col',\n", " sharey='row',\n", " figsize=(12, 8))\n", " for idx, clf, tt in zip(product(range(s_row), range(s_col)),\n", " classifiers, labels):\n", " clf.fit(X_std, y_train)\n", " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", " Z = Z.reshape(xx.shape)\n", " axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=.3)\n", "\n", " for label, color, marker in zip(np.unique(y_train), colors, markers):\n", " axarr[idx[0], idx[1]].scatter(X_std[y_train==label, 0],\n", " X_std[y_train==label, 1],\n", " c=color,\n", " marker=marker,\n", " s=50)\n", " axarr[idx[0], idx[1]].set_title(tt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementing a simple majority vote classifier" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class MajorityVoteClassifier(BaseEstimator, ClassifierMixin):\n", " \"\"\"\n", " A Majority vote ensemble classifier\n", " \n", " Params\n", " -----\n", " classifiers : array, shape = [n_classifiers]\n", " Classifiers for the ensemble\n", " \n", " vote : str, {'label', 'probability'}\n", " Default: 'label'\n", " If 'label' the prediction based on the argmax\n", " of class labels. Else if 'probability', the\n", " argmax of the sum of probabilities is used to\n", " predict the class label.\n", " \n", " weights : array, shape = [n_classifiers]\n", " Optional, default: None\n", " If a list of 'int' or 'float' values are provided,\n", " the classifiers are weighted by importance;\n", " Uses uniform weights if 'None'\n", " \"\"\" \n", " def __init__(self, classifiers,\n", " vote='label', weights=None):\n", " self.classifiers = classifiers\n", " self.named_classifiers = {key: value \n", " for key, value \n", " in _name_estimators(classifiers)}\n", " self.vote = vote\n", " self.weights = weights\n", " \n", " def fit(self, X, y):\n", " \"\"\"\n", " Fit classifiers.\n", " \n", " Params\n", " -----\n", " X : {array, matrix}\n", " shape = [n_samples, n_features]\n", " Matrix of training samples.\n", " \n", " y : array, shape = [n_samples]\n", " Vector of target labels.\n", " \"\"\"\n", " \n", " # Use LabelEncoder to ensure class labels start with 0\n", " # which is important for np.argmax call in self.predict\n", " self.le_ = LabelEncoder()\n", " self.le_.fit(y)\n", " self.classes_ = self.le_.classes_\n", " self.classifiers_ = []\n", " for clf in self.classifiers:\n", " fitted_clf = clone(clf).fit(X, self.le_.transform(y))\n", " self.classifiers_.append(fitted_clf)\n", " return self\n", " \n", " def predict(self, X):\n", " \"\"\"\n", " Predict class labels for X.\n", " \n", " Params\n", " -----\n", " X : {array, matrix}\n", " shape = [n_samples, n_features]\n", " Matrix of training samples.\n", " \n", " Returns\n", " -----\n", " maj_vote : array, shape = [n_samples]\n", " Predicted class labels.\n", " \"\"\"\n", " \n", " if self.vote == 'probability':\n", " maj_vote = np.argmax(self.predict_proba(X), axis=1)\n", " else:\n", " predictions = np.asarray([clf.predict(X)\n", " for clf in self.classifiers_]).T\n", " maj_vote = np.apply_along_axis(lambda x:\n", " np.argmax(np.bincount(x, weights=self.weights)),\n", " axis=1,\n", " arr=predictions)\n", " \n", " maj_vote = self.le_.inverse_transform(maj_vote)\n", " return maj_vote\n", " \n", " def predict_proba(self, X):\n", " \"\"\"\n", " Predict class probabilities for X.\n", "\n", " Params\n", " -----\n", " X : {array, matrix}\n", " shape = [n_samples, n_features]\n", " Training vectors, where n_samples is the number\n", " of samples and n_features the number of features.\n", "\n", " Returns\n", " -----\n", " avg_proba : array\n", " shape = [n_samples, n_classes]\n", " Weighted average probability for each class per sample\n", " \"\"\"\n", " probas = np.asarray([clf.predict_proba(X)\n", " for clf in self.classifiers_])\n", " avg_proba = np.average(probas, axis=0, weights=self.weights)\n", " return avg_proba" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parent classes **_BaseEstimator_** and **_ClassifierMixin_** give some some base functionality like *get_params* and *set_params* for free.\n", "\n", "Now it's time to test out classifier." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['alcohol',\n", " 'malic_acid',\n", " 'ash',\n", " 'alcalinity_of_ash',\n", " 'magnesium',\n", " 'total_phenols',\n", " 'flavanoids',\n", " 'nonflavanoid_phenols',\n", " 'proanthocyanins',\n", " 'color_intensity',\n", " 'hue',\n", " 'od280/od315_of_diluted_wines',\n", " 'proline']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load data\n", "wine = datasets.load_wine()\n", "wine.feature_names" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# use only two feature - alco & hue\n", "X, y = wine.data[:, [0, 10]], wine.target\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", " test_size=.3, random_state=11)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ROC AUC: 0.85 (+/- 0.09 Logistic Regresion)\n", "ROC AUC: 0.85 (+/- 0.09 Decision Tree)\n", "ROC AUC: 0.85 (+/- 0.08 KNN)\n", "ROC AUC: 0.88 (+/- 0.06 Majority Vote)\n" ] }, { "data": { "image/png": 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I60obIL2qih6vvcrAGQ/T47VXSa+qCnySC4G6I0U72cShHcxZ/RAAz379oN94\nrE+2vevlJTapo4aPKt+3fW6+4sueLJvJk1sfAYKPI/O3NLenfhePlE73eS1z/LYQvyadooRUpi1o\ndrAa9FHl+15eYpNqR1XMNFujeaT0gZA1G/zr9v6mfdRpd6NYNDt5EaM4yem4ahXFp45m8F/upd9z\ncxj8l3spPnV0SC1K451ssuzH9zhYb8RqHazfx/LtvhMwPt3pP6Fu5f6lQV0z2P/g/sRwd/1O9jVW\nAt7Jcr4wl+Z+2eNXXp/RBadysOmA5XlmLU9w94Lsb9jHzNLpSSdU0ilKSFXagmZ/1bDcZ6KwJ4E0\nOZaavb9pH5UNoWk2+NbtMQVjfZ4jmp2cSPhEEpNeVcXIK39DRl1d67bGRmhsZOSVv2HFys+CEsf0\nqipySzez84IpZBw8QGNBAXV9+qLRFH78EbmlmykfP5Hm/Hzb78H0Etc2OeOxmmp49usHGdPbeknt\nhO5j2Hpwk9u22vpGVDOkNWlOLAgu2S7YpazuWT05pWAMnx5YSTOtS2fpZJCu0mnQ9cYcfMSReWK1\nNGfyfdW39M7p63cunl6QOl3Lv/e8ynEdfhJUEkki4Ok1CfTMBKGtEE3NrjlyEBVjT6dw6ccUfvxR\nS91iu3XboR3Mq54TMIbW5MSC0Wyu3exzvGho9pQel/JjbZmXbgM00hDUvF3xpdui2W0PMYqTmO7z\n3yDdRVxdSa+ro9ubb7Dzsl/5HcOq1qXSGg2glFv9yzVznufgqFG23oOrl9jE9BZbxRafPWAyZw+Y\n7LZt/ZbdZB5wkFlZz1FdA9cpDuU/+OC8oWyp/cFLWJtpwqGb3bZFmnXsz2A2+ahysVesHNCSRJIM\nQiWZ2kKqEi3NJi2NH266hVNKzvaqW2y3bi8tX8xhxyG3bf7+H5d0PZeSrudGdM1QNNusiXzB1xO8\ndNsT0ezgSCXNTvy/DcEN11i0XnPnonwcp4DCpUsCjmVV6zK9ro6Mujqv+pcjr7qS9Grr2LBw8PQS\nm5jeYjuWl6yW3Kz+g/vC31Kc89Whdd4ecWR24ytWDlqTSFyPTcT4L1+VPBJtnoJgF7HQ7Izqaobc\nd6/ldjt126EdzNr4EPX4j6GN9BqRaDb4121XEkmzzeMTTbdTTbPFU5xEeHoItK9ECxNf3eic+K11\naYXDQfc338CRlenWVrQ5P9+y3WigZbsV29+nvGan5b7ymp2s2P4+p/YZF/z8LDCX3HLScql11FBS\neG5QJc9MjKW4S9hSs5kjcgcCiu2121h+wDoOzowjO91Z+sdOAhnorp6HRM109lfJI5HmKQh2EHfN\nBnA4GHTfvTQWdo1Ys9dULWPqcbzDAAAgAElEQVRP3S7LfXZpn6t2je18Jm+Xz+fv258NWrPBW7e3\n1/6Y8JoNiVmhItU0W4ziJMHVQ2Ci/IijBirPOMPvmP5qXVqRUVvLoOn342jXzm157oebbuHIR2aE\nvGzXLa8X5w/+jd/9keC65PZI6QPsb9rHlpofQvoPPjT/aHbWb+eVXS8yudsFnNHl7KDiyKJB96ye\nXNj9Et7a+ybVzd6enxpHjVutY0is+K9AlTwSZZ6CYAex1mxfHuiM2lq6L1xAWnNzxJrduV13pvT9\nNfvKD9G5k3d3u0i1zzNMolk388CWe8lUmW7HBTLKPHV7XOG5Ca3ZZ3Q5OyHjdlNRs8UoThJC9RA4\nsrPZPfl8v8f4q3VphQbSmppIazLitMzzhtx3r5sgm9tHXnUlK5at9Jk4MrjzMAZ3Hub7HrSDRVte\nD6qhhxVuS25N+wGYt+slmnGPBfb3H9xKqIKJI4sGptAvKH/Dcn+9o86r1nEivdEHKkcXLW+NIMSD\nRNBsk7RmQ/Mi1ex+2UM4t+9YNtSUcVR/7/wNc/k/3AYVnmESj5ROB6BBN7gdF8go89Tt1497O6E1\nO1G74qWiZotRnCQE8urqtDSUw4EjMxNHRgZr/vb3gFnM5eMnMmj6A3ZPtRWHg6J33g67q1KwDT0s\nL+3xhmvG/3oaxPn1cNE6GLRvBwe2PEDhhD/iyG19bokkVP7i00w8ax0H+0Yfi25LvjpFue4XhLZC\nKmp2JMv/VrGrVloXSLPNeSSCbger2Uv2fRhSWJ85tmi2/YhRnCQE6mBUPm48DUVdQ+pxb9a6PPa3\nl5PW0OBz+c2RmQlak9bYaLnf37JdzraygPOwvKZHQw9fJdp8YRUHZZKXns/I9seh1izjvZfTSUeR\nXd9E46r/kPbi23w/bRaHhx3vt71zPJaM/L21KxSjC04DNKsOfu62L5gvhVjEssXLwy4I8SBemq0x\nNNmRkdGyqudJtDQ7kuV/f5oNkJWWzajSOr+a7TqPRNDtYDS7T05fdtT9GHLcrmh2dBCjOEnw6yFI\nS2PTn+8Jq2D7wVGjWPnhEkafeTrpDQ1e+x0ZGWy6/Q601hz51xmWAm+KsCeRtBu1augRrLc40Nt5\nU3MT2yrXsv4lyGto9Ry3q68H6hl613V8OfdDPqpZnlAJBoHe2s/uMp67Nt1MvXYv+RTqUmNbjBMT\nhFgTN83OzGT7JZfR7sB+iha961YT2SQamh2JdzYYj2pmbR3vBNBsR05uQiWGBdLscYUTW8rHhRK3\nK5odPcQojhLhZPb6w/QQWNWnjLSDUWNREav/8YLPsQ+OGkV6VRVHznwktIHDbDcaakMPTwKV46mn\njpLVdaT5SvTWDjoveZenuv0toRIMAr21L6n8IKz4r0RZahSEeNIWNbvofd/dQS2JQLMj8c4GU0Jt\nyjr8anaXpYvYM25yQiWGBeNpDUe3RbOjhxjFUcCquHqkRdR9dTAKdtktEAdHjWLFspUUvfM2OdvK\nvJb0/Am8VSZzJMIfakMPT8y3c3+l0wbug3zraBDS62op37KMPQXJlWAQTvxXIi01CkK8EM2OTLMj\n9c66apcv3T4ygGZn79yWlIlhoeq2aHZ0EaPYZqzK8ASb2esLXx2M7O5x35yX5zfBwp8I75n8c5/i\nHAqBGnoE4y02386tSqf9WFvGZwdX8kPnJqraWYtsc3YOuteRTOlhlITLrmvklFVb6V5+mN1F7fnv\nqP4JmWAQTvxXIi01CkI8EM2OXLMj9c66apenbger2XU9+7YYmFaaXZfdrk3otmh2dBGj2Gb8luEJ\nI7M3GoIdCb5EOJA4B4udDT08xcahHVzw9QSadBOvDoOZ7/k4UaXRbtwV3JCTS/t1XzH0rutAO0iv\nq6U5O4crX1/L99NKOOy7mlxSkIo1KAXBE9HsyLDbO+uq26FoduXYEobm5PKTrXU+NHsWh/snd9KY\naHb0EaPYZvyV4Qkns9duwU50otnQw1W8q7JgwqXwzktGnFp+IzRkZZKe1o7vp83CkZNLWk01Q++6\njvTa1i+39Drj79Y1sSNZScalRkGwG9HsyIhm2S7RbHdEs6OPGMU2U9ejO83t2pFuUb4snMxeuwU7\nloSTuBKooUckeIl3D/jDscYyW7fyw3Q9oph2465oEc0uSxeBr/7uzsSOvSWtxfYd2sHb5QvI33QZ\npxdXoVzSu7WGpSs7MHb0Ibft8SQVa1AKgiei2a2Eo9nRLNsVbc0GaHY4eHTRBv5YMoR0lzbcotmp\niRjFNtJx1SoGPvJXn/V8w8nsDVTrMtzyOdEmGokrkeJTvIe2/ugqp9k7t7V4GTwxEztcWbLvAx54\n+yt49W6mTK7gxmt2oZQhro8904N58wuZ/ucyTi8+ZMPdRE4q1qAUBFdEs1tJRc0GmL14K/9+fAq7\nNnzFX29ME81OcWwJPlFKlSilNiilflBK3W7HmMlGSxxZTY1X/UcNNOXmWiZZpFdV0eO1Vxk442F6\nvPYq6VVVbvvLx0+ENB9/TWGWz4k2rjF15hdDRm0tGdXVjLzqStKrvXvAJyJ1PfvSnJ1jua85O4fP\n8ytxOL0SLXUjh84nd/RzzJtfyGPP9HAT1ymTKxg7OjHEVRBSnXA12zzXl26LZsePQJpd26MPb5XP\nd9Ptj7pOhZMeY+Wi43n0me6i2SlOxEaxUiodmAWMB44GLlZKpdyrjL84Mp2Zyeabb/F62+64ahXF\npxUz6P5p9HtuDoPun0bxacV0XLWq5RizrE5TXp6RwYzhbWjKy7M9k9kugompSwYqx5aAj6SFJhz8\nqug/LN33IeCSEazAMe4GRpd8xbz5hYwuGd4irqbnWBCE+BOOZkNg3RbNjh/+NBuVxpsjMrl/85/d\ndPtg0wEo+SPpJ8/itfldRbNTHDvCJ04EftBabwFQSr0C/Az41oaxk4L0qiq6vrfIZxxZWkMDWbt3\ne50TbIZyoHqUdhedj5RkjqlzxZGbx/fTZnllMmuluOjyHKqz6nmybCandjrDLSO4Ttew5bTzYNH2\nlrFEXAUhcQhHs83zgtHtNqnZJ8R4UmHgS7NRaXz7v0/x6J4/AXjrtoLmcdfDp9e1jCWanZrYYRT3\nAn50+X07cJIN4yYFZhyWsmi3aWIVRxZqhrKv8jmJGAeWrDF1Vhwedjxfzv2QLksXkb1zG3U9+/LG\n8HYs3n4/OIz6kLO3PeZeN1LD3gV3uI3z2DM9RGQFIQEIV7MhNN0WzY4PVppdObaED2qWc+CH1i5w\nbrqtgUWPuo0jmp2a2GEUW/2T8WrGqJS6CrgKoKhn28iQtPIaWGIRR2aHNzWa9TAj8WSUj5/IoOkP\nWO+0OaZu9b6dtLMnNN4njpzcloxlh3bwqEuf+lpHDfN2vUQzzcbBTnFt/uw6ckc/x+K7f8ITz/Zi\n3vxCSms38+iN2W4ZzoKQqIhme+tUpLod7RrG4ep2UJq953DY84o1rpoNzpyP72Za67ZpEH92I5z0\nGN1/9ldO/fxr5s3vKpqdgtjxN70d6OPye2/Aq/uC1nqO1nqU1npUQafONlw2/vj1GgCOzEyfcWTm\nm7kVwb6ZRysOLJhYZ3/EIqZu9b6drN63k91Nu/mqaSPf5eznw+xy/vbFGt5ZtYnvNu2J+BpWuHUT\n0sB3k2nWza0HfD/ZENdBC6k562qW7f+IG6/ZxeiSL/nivZN4erH/L02HdrglgghCvBDN9tapSHU7\nmrG7keh2MsZBh0KLbjs1G02rI8NFsxn3R8obdnHchS+KZqcodniKvwAGKaUGADuAXwKX2DBuwuPP\nawCw7+RTWP/YE5aCYoc3NRKvhS+Pgl2ejEAxdZFgGsM9O+9nTIdObDnUCOwms0sn9narYv2h3ewo\nrYFNcNSgbhFfz8Srm9D3k+HVN+Gkx8idcDfndZ+Mo5ti2bdfs2vtJMaUz6T7yO5oHEaMcccT+bDr\nF0zVb/vsOrRk3wfcv/nP5KXnSctOQbCZSDQbItftaGi2uS9S3Y6mZscTN9120WxK/kheej4TT893\n0ezH6H3cl3TL6i6anaJEbBRrrZuUUtcD7wHpwN+11usjnlkSECgOa+8543wKivlm7hlbRlpa0G/m\n4caB+Ytpyy3dbFs3JrvaiJqs3mcsQJgG8fD2O+id28AQZ4Oi7e3XQT/YUFnB+m49eXN1LaWrDjFh\n1CBbru/VTWjofENcP7uRGhQj/uBg9Wu/YtdaZ+by5J+iFHxUudjIcD5qPgebcn32qG8p6wbSslMQ\nokAkmg2R63Y0NPvgqFG2ddGzW7MTATfddtFsgJqSm9j+nz+ya+1xLpr9U9HsFMaW5h1a63eAd+wY\nK5mI1GsQ6Zt5ONf361H4/RUcHHlcQlaOcPUOj2u/A4DeeV3plTO45Rjz5955GynIKWVdfg7rNzey\n44saeinv1p4DOnYIypNsdjbqNsK9m5DWsP1YRXnee2z66AbudO5yLeXT5Gji4S3T3GLZfImna2jG\ngcb9PoU4FBzawTt7FzCh63ki1kLKY8cKXSS6HQ3N/uGmm+nxn/8kpG7HC9dudK5d4Nw1+0b0Zzey\nEnfNdmgHT259RDQ7RZGOdhFgh7c3kjfzcK7vz6OQXlNDp88/9Xm9cLOQI0nas/QOO41hh3bwr7Xv\ncemws93Eo1fOYHr1HUy33BX0ztrM+m49OVQ13G3ciooqduyuCcqTvHRlB+64rx9TJudx4zVHu3U8\nWjG/kAfuLuPOj1qPd81Ynr3tUcPj4IKVeHqGZvgT4lCQpT1BaMUOzYbwdTsamj3owemkNzaisc56\nD0e3rTTbDmJl8LVqdgU3XmN0ggtWs5fs+4B9jZVu44lmpw5iFEdIvOOwQr2+v5g2BajmZst9QFiV\nIyIpP+QZO5ynd7h5hxdsXMHUd2fSITOXnw051ev8kwqLW7zGsNRr/4rtnVm9uogdX9QwpqC7T6/x\n2NGHmDK5gnnzCwFDQM2ORxf+rILVa92ftVnKp1k3MW/Xy17jWYmnWwKfk0g9D7K0JwjetDXNTne2\nqPZZOSxE3fal2XvvfZjK3sMDD+CHWBl84Wq2xvASN2j3cn2i2amDGMU2EO84rFCu7y+mzef47dqh\nMzNDzkIOlPyx/JOVfPLfIorPqnarBfl15U7WLe1CYfFuenVpjR3ulVPccoxDO7hryd8A+NOS55g0\nuNhSPEyvsSc7ajfSLXcvG7qs5sNvj2F5KZSuOsSAjh0s72XcmXvYd2Aw8+b3bRHaC39WgVK4dT8y\nhRcgveTm1gxnD/bU7+KTfR9xepezvBP4nETqefiocjEVDRWAfUt7gtAWaMuabXqMw/GA+9Ps8/58\nCy/MeoOlS/Po1t7jmi7hCr7q+sbS4FPKMITB0OdgNfvYC//Fngbvpi0gmp0qiFHcBggmPME8Jm/j\nJv/eYAtq+vdnzd//SUNRUUjnBUr+WPvIJu55eQTnX36AqXdWopRhEM99tBtfvjaY2+fsZ3jJOq/Y\nYTC8xBU1Rk/6ipoDLNy4wtJb7IteOYOdY66g4ITNrOjWmS1bB7CFOt/3c2k5LGktrHL8iGru+N9+\nbvForkLcPhdwmXZeej7ndp2Mcn5rdM8yar96JfC54CrEoeDQDh4pnU6j0+Nh19KeIAiRE03NVkBd\n1yL2Fxez+aZbQtJtv5qtHeyau4c/vz+Os05v4r7bD7qFks2bX8j0P5dxevEhy9OjEX/rD1OPTYMX\nAmt210GjyO2QS3Vz60uBq26LZrd9xChOcoIJT/A6JjMTDTiyskivr8eRmYlqaPC5/JZXVsbJJWeH\n3HEpUPmhc/PeZ8nlZ/LGCwXsravmZzduaTGIz7/iv/z6/K/pnVvsda7pJa5uNMaubqzz6y32x0mF\nxS1e4z29v6S8eQhaw9oP+zP8zK0tXg+t4c3pp7id+8L7NVz3uzVcekFay3GmyGYMWMEbBa+5tbFx\naAcjOxzn9UXgmghihSnEodCSPe2CeB4EIf5EW7M1kL23nK7vLaLr+4tD0m1/mp1ZV8fE7MWsvXAS\nr73Wl84F7p7WKZMrGDva2iCOVvytiZWnWmt49Okebsd99U0e0+8uY2zxIS/NPm5ENc1DPsex2b33\nmJVui2a3XcQoTmKCqU2J1t7HmO1NtabsN1dQ16cPAx+ZQUaN+1KQSVpDA2kNDSF3XPK37NeQnc2m\nwo6MnrSWvXVHsGxeL5bN6wXA+Vf8l/sf/pLeud5hD+DuJTYJx1tsYnqNd9RuBBp4/60j+McN53D5\n1au54/7lADxw5xiWvWTE093z7Fw+WdqNj1/+KUp9R6f+33FEQWvYhUM7WNzpauqCXFobmn80Q/OP\nDnnevjA9DtqjsaR4HgQhvsRCs01DOZz68oE0+1CPXtxweQX79x52CyVz9bxaEY34W1fcE+sMz++j\nT/fgtf8Y85t+dxlfr81j3vxClIKxHt5speC00Qe44OvgQiJEs9su8pSTmGBqU/o7RqenU3Pkkey4\n7Fes+dvfacrLo7ldO98XDLHjUvn4ieCrPWa6Yuv5Y8gpqOeie75z2/V/Tx72aRB7eolNTG9xJF2F\nTOP41xdkMPW6Ml549lievGcSXy4ew7/mHAvAtVPLuOmyLtw5fSkll3/Ct/89ik92FvJdVm3LZ6H+\nmEpHueU1zKW1aLKk8gMONO2P2/UFQbAmGpptdqDTlmcQkm771WyVxuZTjNrrF/9io9sufwZxoPhb\nOzrBuSbWPfZMD5as6NBiEE+ZXMHY4kPceM2ulmOWrvTOHQkmJCJaiGYnDuIpTmKC6Y6kHDqo+pVm\nRvQxN/wPhcs+CXh8MJjlh4b9/gpwNJNVV09DThZpaZpv/nkRA7utQmt4+i8lbufdfusQHnx4g6XI\nLty0kh8P7bW83o+Hylm4aSU/Gzwm6DlaoRQ8+PAGAGbP6sfsWUY5o2unlvHQDGNeJ3ct5tGZ33Dy\nmB0UjlrOjoaBLed3OljHiLJTqK7JJutgJr2y8ujcqbVOcjhLa6Gwo347GSqDJt3Usi1DZXBSx9H0\nyekX9esLgmBNNDS76J236fHaqxSsWRPwnED4Kxm34N6HacrORWuY+293p4VZvcFKs6MRf+uJr8Q6\nTw+2GSZhFeYRjZCIYBHNThzEKE5iguuOpIPuoNScl8feceMoWPVFyB2XfLH0iJ68+eYzjP3sfQbt\n3kL2kH4cmnw8Q7sOR2vDAH7j7/2Yel0ZDz68gdtvHdJihFoZxn07dGPqCZN9Xq9vB3vaOpuGsTkX\noMUgNumdO5ibLoMdtUd5nD2Q7Ud1YENlBV9sH0XlhiYGVXSxrbOePxzawZt75rmJK0CTbmJL7WYe\nHvqELMMJQpyIhmYbVSw0+Rs32qLbvkrG7dpzmHb7HTz+eCEfLOlkWb3ByjCOlbFplVjnOR+l8JkI\naHdIRLCIZicWYhQnMUF1R9I6pA5KdnR8gtYawwA9e9eSdtQA6vNOpDBnMGaO9cIFRcye1WoQe3po\ni8fs57yfuYchHNd9EMd1D924dGgHL61736vRhy9Mg90VXx5sz8oY5jajRvI6VuR0Zv0eo7Oe13Eq\nN+jOesEQC6+MIAjhEQ3NDnrcELAuGXeY1au68NprnTjr9G3ceM1BLw/tcSOqvYzOSIzNUJp9mFUw\nXPHnwU4URLMTCzGKk5hguyOF0kHJjo5Prk03zA504F1WbdJ55bw0dzWTzit3ywR+8OENFI/Zz6Tz\nrONywyFQow9XTIN49qx+lIwv59XXVnPHba0e7OkPbeCthUVu87bCtbPehi7r2dr3dK9j1m/bzY7S\n4DrrBUM8lwAFQfBPNDQ7lHEj5dhRlTzwwE66td+IUsaLvGv1Bl/VJ8Il2GYfrmXhRp94iBl/KePx\nZ1s92DdcvYtP/uu/jnK8EM1OLJTWPsPzo8aQYcP102/Mj/l12yrp1dUBuyMFc0wkx4OHd9ijJXM8\ncWgHI+b8hrKDe+jXsTvfXPUPv16HBf8p4tKLj6VkfDmL3i1i6nVlTH9oQ4thXFKyl0WLuvLS3NVe\nnmxf7KjdyPZq71joPTVVrNjemR9XF3FsfRdbvcZCdDhl3IgvtdbB1yZsA4hm20s0NDvcc4Jl/Zbd\nZB4wkuIyK+ujrlMO7eCCryewq34nPbJ68fpxb/vU7SUrjOoTo088xMrPOzBlcgU3XL2rxTAefdIh\nVn7WwW8dZaFtE6xui6e4DRBMd6RQOziFenyw3uF4EGqjD9ODfe6kci8P8ZbNuSxa1JWp15WF5Mlu\nbRbijlEGrpQDXXby4bfHtHiNg0EMaEFITqKh2eGeEyzHHNGd9Vt28+P2fZAFDfsOBD4pAr48vIR9\nDUZFhn0N+/jntgUc3/50y2MLhh7g6v9XzcgTKsn+1xHMm9+Lirp6LvjVd3xbdjQrP+vCT0t2UDB0\nG2v2RXXaQpIjRnGCEEyHIzvOsZtE9Q6bhNPoQylaPMBWVShcY6AjxQyx+KyitbPej7WBl8sOHaq1\nNexCEITQSFbNjoRjjuhOY4HhLf604oeoXUdrB/8ufYJ6beh2va7l5YonqO/eE+Vrle+MH/gc6DP1\nB47KPJGPFhzDR4uM2vdHnbeePr/7nM8SLHRCSDwkfCIBsOpwZMaC+epEFM45dmPtHTYMPa2NRDrP\nuFtf26PF/A3LuOadR9zqGue1y+bZCTcH3ehDa+iQd07L74eqFwecezj37yvEwgoJu4gfEj4hJKtm\n+0NrWPFBHsVnVXtp1otv1jNsbCV7mneT0e5HhhXsoFtu9Iz5T0q/ZcayBdQ2NbRsy8loxy2nTea0\n/p7VfrzRGs7u95eW398vuycozV7x3lCKx33vdf9W24Xk4qy+9wWl22IUx5n0qiqKTyt2615k0pSX\nZ9mJKJxzIsHVG+yJL++wGZfr6lV1TWALJR43XFxjiT0JJrYY3OdsEoynONr3v6N2I1/uLeVAbR0f\nfnsMBaUd6KVyvY4TY9l+UtEo7j50oL78uQfjPY2EIKumlj/9/Gqya+q89tXlZjPtzTk05GZHfE6s\n2fhJT/7zp2JOuHAjZ/zPmhbN+vjJkXz52mB++/hiLv35CoCorgZGqtuJqtlCfGmfO05iipOBYDoc\necaIhXNOOLh6gnsCR3To5LY/T6/zW1li6nVlbjWHTXHxF49rp4c50kYfroIYbB1lk3DvP1iswi4O\nVQ33Ou7NH0olxEKImPxMGNM/3rNIDPrOXUmGj/5xGWgu+GYl237504jPiTXF/Xbi2LKRhc8PpmcH\nuPKeNTz/F8MgnnTlRi6ZvMLSGLZ7VTAS3U5kzRaSAzGK40wwHY7sOCdUrEMjPN/cfXsLfHWF83xj\n9xTOhQuMt/Vrp5a51SkO52090kYf4dRRDvX+I+WkwuKWZD1Y6rW/Mb8zW7bm8bcv1jCmoLt4jYWw\nyE5rZEiut+cuFSncvpmMmnrLfRk19fTZvpmc3GMiPicePPzQYjpl1PDCs8ey8HlD2y+/ejV33L8c\npbpahsbZqdkQmW4ng2YLiY2ET4RANJIkerz2KoPun2Zp5DoyM9l02x3suOxXQZ/TlJPDpj/d7eUp\n9hcv5rrd1RgGWgzintmDw/IGBIrH9VyyArjtliE8PdsQpBdfXs3KFZ28hC4W2OEBCSce2U4+q1jB\nhsoKPvz2GBp3tWuJP/ZEjOXgScXwieOPH6w/WfFUvKcROodryHh9KWmbd+IY2JOmC8ZCe+8wo1DI\n+Me7ZN36DKraOxRCZ7aj/sGraLrmvODPycumfsa1NP3Gvd19pPoT7vmi2fHVbCE6BBs+Ib0Dg6Tj\nqlUUn1bMoPun0e+5OQy6fxrFpxXTcdWqiMYtHz8R0nxUQWhoYOAjM7yu4e8cX92LVnyQxz3Xd2f2\nA10w34O0htkPdOGe67uz4oM8L+/w+O4NnFRYTK+cwS3egNtvHeJ2/u23DuHSi49l4YIir2v66grn\n+h7mumTleSzAZZccGxdxhdYqFJ7X9LXdk2DuP9qcVFjMmX2P4hcnbKbPseVs6VbHh9nlbp8360p5\nZ9UmvtsknkCh7ZC2ch15Ay8h69ZnyJw5j6xbnyFv4CWkrVwX0bhNF4zF53/+hkay7n7e6xp+z1HK\n2O9BOJob6fmi2fHXbCG+iFEcBOlVVYy86koyqqtbvLMZtbVkVFcz8qorSbdIeAsWsxNRU26uV8SZ\nAjJqaryu0XJOXp6RwYzhIW7Ky/PZvaj4rGrOv/wAb7xQ0GIYz36gC2+8UMCpU3ZweMQy0jp85zNx\nzlMIPWO3POOtPPcfql7sdT4YYjX9oQ2UjC9n9qx+dMg7h6dn9+Paqe4hIMm2fBXs/ceCXjmDOa/v\nOM4f3MCEMV8y6pQqt8+wMYdZ3383yw/s5p1Vm2I3MUGIFodryJl8F6qqtsU7q6rrUFW15Ey+C6qs\nw8+Con0utfOnofNzLDVbVdd5X8P1nDwjoU7nZaPzc6idPw3yc7wuE6rmRnq+aHbiaLYQPySmOAii\nndh2cNQofrjpZgY9OJ30xsagrnFw1ChWLFsZdPcipWDqnZUAvPFCAW+8UADAqVN2cNL1K+nVxdUY\nLrY8P5R4q1Biu95aWMSid/17PW6/dYj7daKwLGonkcS2RYvW+OMGt+3b2x9kWMEOVnTrzOrVRbAK\nCbEQkpqM15fi04rRmozXl3qFK4SCY/Qw6v/yW7LumAMNTUFdwzF6GNVb5hq6tWUnjiOcumVhEEPk\nMa6i2aGRiJotxB6JKQ6CgTMept9zc3zu33rV1Wy56ZaEvwYY3xNnDR3Y8vvMdXNQiqCbbgQbbxVK\nbJfDAaeOPplvvvE2xEzvw9OzW8Uq/b/rDE+M1qjqOsPzohS186fhGD0shKcRPRKlTnOweJZ4G1jT\nx+uYyj0HJVkPiSlOBjLvep7MmfN87m+4+SIa7rsi4a8Bkce4imYHR7JpthAaMYkpVkpdqJRar5Ry\nKKXa7JdETf9+LWEKnjTl5FDbt5/lvkS7hhky4crymcMp6dYaOxzo/GDjrYKN7dIa7rhtCN9804ER\nI9zbGw8ffogHH97AQ3ZWbPUAACAASURBVDM2tCxjLZzXMXrLojYSaWxbrDFDLIZ0KeQXJ2wmY/gm\nr8+BAYda4o8FIZFxDOzZEqbgic7LxnFE4K6RiXCNSGNcRbODJ9k0W4gOkcYUrwPOBz6xYS4JSziJ\nbfG+xup9O90+X1fu5C9/zuaNFwo441fbmLluDudf8V/e+PspPHnPpIAiG614K9clq2UrP3Xbt3Zt\nB95aWNSyjPXS3NX8vPqVgMuiQvicVFjMCV0H8OtBmV4f12S9v32xRhLzhIQlnMS2RLtGpJormi0I\noRNRTLHW+jsA1cZfoczENl8tOu3oHmfnNcwqEv27tDbbWP1BV5bN68Vpl63lzmmv0ye/K7978jDd\nc8qCipeyLd7KI65s0i/G8tJcOHdSOXfc5u7RKCnZy7mTjDHNt/X0u3ZaljUCw/uQtmVn4Dk4MZfF\nzp1UzlsLW5fHfG1PFXyuGHSFbrl72dBlBR9+ewxv7qpl+RfWnQ49kbALIaY4E9t8Ldn7iuNNpGtE\nqrltWbPNJMFAP6eSbgv2YEtMsVJqCXCz1jqo+mTJFlNskl5dHXRiWzyusXqfIS5mWTXXDnRaw6eL\nenLmuMX0yW+NHY52zUtX0lZax5XVvDmNW+f/wrILkWdSSDj1Pn1h1tssGV/OoneLmHpdGdMf2sAd\ntxnXLinZy6JFXaW9pwdm/PG6A71oavSOPfZk87a9LTWS20JnPYkpTiKqaoNObEu0a8SrTrEriarZ\nU68zGoWYTUPAiGF2rZEsui24EmxMcUCjWCn1AdDdYteftNb/cR6zhABGsVLqKuAqgKKePU+Y+3Gb\njriIOZ41hgFnFzp3otWvPiCHa8gbeAnKIn7sjeyL+EXdK8H1nPczjs7PoXrL3KC/kFyvMWLEoZYY\nOdc/pZuRNTtqN7K92roVqyd7aqpYsb0zB7cOIGu3I+m9xqliFLtqdp8+RSd8u+FfcZ6REFMSXLNd\njWGwTvAT3RZMgjWKA4ZPaK3PsmNCWus5wBwwPMV2jCl4e4eDrSIRa/yVSJqc9h9eueofTHi4l5uI\nFY/Zz+ji/W71NHV+Lq/e/ApTZvwSRWRLlp7LiUBLNrUYxP7plTM46H9jO2o3eoVdlK465HXcgI4d\nktpYbmu4avbxxw8WzU4xkkWzTUzjWHRbiARp3pHEmN7htA7fMa7fOlsNYq2NpSpPTfS1PRBpm33H\nlaXV1PHzDu+5iZjZjWnlCvcQkNtvHcLF957HK08toX7GtTTcfBH1M66lesvcsEr7mIXorZj+0AYW\nLgj9XgV3euUM9uqs9+OIg14f6awnCOGTSpptGsZWiG4LkRBRop1S6ufAk0BX4G2l1Gqt9ThbZib4\nJBbeYVPggloeCwKzfJGvuDLP8kWu3ZgAr5i1SVMO0KTCL77fcm0NF11wnOU+sw6nxKbZQ6+cwS3J\nerDVbd+emirWde3F+s2N7CitgU3SLEQQQiGVNNuqvbSJ6LYQCZFWn3gTeNOmuQhB4Bo7PM4ldtjK\nII4k0SKgwAVoMepJ0wVjybrtWeP6wHwmM5n5KGgpX+Q5r0i6OQWDKa6LFnWlsLCeioostz/N2GIz\nm1qIHH9hF91yV9A7azPru/XkzdXWIRZAm0jWEwQrRLP942rkX3NtGStWdGKtM+TN/F10W4gECZ9I\nEsx6w57eYX9NN0zPgWtNSlNULr34WBYu8N2m0xQ4U2Q75J1jmVkcNM7yRTo/hzezpnA+b3JjxpM4\n8nKM7Xk5XvOyWiazM1bMLFlUMr6cioosRow4REVFFgAVFVn061fDN98YdTeF6GOGWBT33sewMYf5\nccRBDh2T4faRGslCW0Y02z+uZeZOPW0/a7/pwHBnA5Fnnu7HWpckadFtIRzEKE4CXGOHx/QPviWz\nq+fAFNlQPAd2C5xj9DCqt8xl/MxeXH/8OzzRdD3/c+lamk8ZZjmvSLs5BWLSeeW8NHc1r762mpfm\nrvYqRL9m3XJemrs6ZA+LED5mZ70zex7k7EHrOHHoUrfP6BNWcGDAIZYf2C2d9YQ2h2i2f0zNfvDh\nDS0/L/fQ7WUrPxXdFsImovAJwV7MWGFP3LzDuQ30yikOarxIl7N8CVxEb/75OTT/toQHfgOOW8uY\nPWsgs+cM9JqX55eB61Ig2ON9MIvLgyG2nvd65+3h3asd9UFTnZMKrf+N76jdSEHOZlZ068zq1UXs\n+KKGMQVWFSO9kRhlIdERzfaPq2aDtW7fcVvo9yuaLZiIpzgBWL1vJ4vKvyK7Yz1pHb7jyN673T6h\neIc9CddzEK0WocHOy1c3JnMO/pYRQ8Xue41kCVTwT6+cwZzQdQDnD26g5MzVOEY2sqZ3TcDPm3Wl\nEnYhJAWi2cFh5/2KZgsm4imOM65xwkPy1zkbbnh/cQfrHfYkXM+BbS1Cw5yXuTTm+oZuzqF4zH5b\nl8bsvle7E14Ed8xkvd55GynIWRfUObnde7FhcyPLS6F01SFJ1hMSFtHs4LDzfkWzBRNb2jyHSrK2\nebYT0xgGolZWzd9yVqDluGguJ0Uyr2gQjXt1vUcTKSofP8zW1Cu2d+bH1UV0rcvxGXYRKMwiVTra\nuZK0bZ6TDNHs0OZk5/2KZrdtbGvzHA1S3Si2qiIB9rdgdu0Tb0fdykSbV6LHgWkNHfLOafn9UPVi\nEdc4Yram3lBZwYffHkNObYHXMcG0oRajWIgWbV2zIbF1WzS77RKsUSwxxTHEjB22aroRjbbMrpm6\nnstZ8czOtWtennFgzc3wyykjufUW9ziw5ma4+KKRNDdH6468iXYWthA6Zme9IV0K+cUJmxl1SpXb\np++w3WSfXNbSWU8QYk1b12xw1+2mJkObm5rc43dFs4V4ITHFUSCoKhJ5XQF7wyU88czUDbQ9Vtg1\nL884sLKybN5+y/DwXTPViANrboaB/cdSWZnFpReP5JV5a1rOD8YzEY5XIxZZ2EL4nFRYzI7ajUCD\n+47umXy5dwfr8nOMznohVLYQBDto65oN7rr97juFlJbm0aPop9TVZTD1ujImTCznyAFjqajI4swz\nTuTjpZ+H5FEWzRYiQTzFNmOGRmR3rPf6xMI7nEp4ZjebBjHAa692x+GAIwcYBjFAn751IWcWh5OV\nHOssbCF0zP9/nh+zRvKZR68n++QyPswu57usWrePIAjh46qHpaV5ANTVZZCd3cT90zcw6AjDIG7f\nvpEvVxWEXBFCNFuIBDGKbcIMjTAbbAzJX+b1Gd+9wfZkunihtRFn5rm05Gt7tLAqE9SlSz2VlVkU\ntD+HioosunSp55qpZTwzO/SC+OEU0w92qTFRnqHgjmtnvb7DdpMxfJPbRxCSlUTRHCvdrqvLoFMH\nQ7MLC+vZtuPjsBqZRFOzIXGeoRAdxCi2Ac/EuSG5ezipsNjr05a8w6G+jUdLSKziwC68aLfb75u3\nLuXhGeG1Pw2ndaq5pOi5z3WpccF/ilqSV8xnaD6L226JXm1MEfTgML3Gvx6U6fURhGQlFN2OplZY\n6bYrP5QuJSMjvLbV0dRsMwTDfIYOh7Hd4YheTWPR7NgiRnEEeHqHo1FWLVEJ9W08GsXRrYq3mx5h\nV44cMBaHI/z2p3a3TjWfxYrlnbh2qvEMb7tlSIsx/PTs6NXGlCL1oWEVYiEIyUoouh0trfC85v5D\ni8nObnI75sgBY2luDl97o6XZt986hHMntT7DU0efzKUXH8tFFx4btZrGotmxRYziMPHlHU7GL81w\n3kRDfRsPZ0krEJ5xYA6HEUts8sKLq2nfvpGKiiwG9h/Lrbe4eyZuuyW4zGK7s5LNZ/G003i/dqrx\ns+vv0UrsiMbfgyAIsSVc72Eouh0trXDVbTOG2IwpBsjObqKiIosjB4xtqUrhSjDaGy3Nnj2rH3fc\nNoTpD21gxIhDfPNNBwAWvVsUtZrGotmxReoUh4irMQxtwzscSQ3KUOo62l0c3TOb+OKLRvLWwm50\n6VLPY098R1oaXHrxsWRnN1FXZxRauebaMkpLc3lvUVe364PvjOVoFK23ehYm0a6NKUXqIyPYepdt\nCalTnFhEWjc4WN2Ohla46vYlvzQ0u7Cwnk1blvKXewfx2MwBLZo9YEA1paV5DB9xiLXfdGBcSTnv\nLSryq9vx0GyIrm6LZkeO1CmOAp7e4fHdG5LWO+xKuG+iob6NWy1pTX9oAwsXhBcv5RkH9uLLazh3\n0h42b13K5J+Xt9yXaRBf7WIQDx9xiGuubc0s9rUUFa2sZKtnYRLt2ph2Ly0KghBbIvEehqLbvrQC\nwo9zddVtU7PNGOL7/ndTi2YXFdVTWprHCKdBDDBgQG1LyJkv3Y6HZkN0dVs0O3aIURwErrHD0WrJ\nHE/CSUywiuf1FGlf57hy0QXH2RYvlZ4Oc19dQ3q6+31dO7UMgGef7sd7i7q2iKxS8NLc1Sxf1snn\nl0m0iulrbYRvuHLt1LIWwY+mwNq9tCgIQmwJR7MhdN32pRWeScKux4ai2740e+p1ZZSXG6U0v/mm\nQ4s2PvO04Sn1p9vR1GzPZzFixCEOVgX+7osU0ezYIeETAbBqydxWjGFPQgmFCHX5zt+SlhmbZedS\nl7/7Oli1mDtui99SlOdSmGm0Pz27n9vP0WjpGq2lxVRCwieERCEUzYbQdNufVrjqVDR0xOq+IH4h\nBK7PoqRkL4uczhXze2v6QxtavlPs1m3RbHsIVrfFKKa1A93upt1e+9pS7LA/Qo1Z8oznDbQ9kBiX\njC9n0but3gU7DWKr+5r+0AY65senx735LK6dWkbxmP0tAur6xQT47bQX6bXDjUUUxCgWEoNw4kxD\n0e1gtGLF8k62G6n+7gtCewmwC9dnMf2hDby1sIhzJ5W7GcKTzisP2CE10muLZoePGMVB4uoJPqJD\nJ6/9eXpdmzaGITZvooHE+NxJ5bYbqcF4p01i7SkOtQ1pW7h2W0GMYiHeJIJmm2EIdhqpwXqnTWLp\nKRbNTm7EKA6Aq3fYNTTCirZsEEP830SjlVlrdV8OB5w6+mS++aYDJePLmff6almKEkJCjGIh3sRb\nsyE6uu3rvm67ZUiLMSwhBEI4BKvbGbGYTKLh6h0e12IMt21vsD/MxATXN04zMaF4zP6o1kH05xmA\nyDJsre7rrYVFLQbxq6+tdsvqnT2rn1s4gyAIQiIST82G6Om2r/sqHrO/JefCtaIEiG4L9pJSnmJf\n3uFUNYYTgVh7PGQpSogU8RQLqY7otpBsxCR8Qik1A5gENACbgd9qrQ8EOi8eRrFnFQlADOIEQMRO\nSDbEKBZSHdFtIdmIVfOO94FhWusRwEbgjgjHs53V+3ZallVrC003kg2rwu5K0ZK167ndtTGHIAiC\nEHtEt4VUIiKjWGu9WGvd5Pz1U6B35FOyD9MYTuvwHeP6rZNwiTizcIE9Bd8FQRCE2CC6LaQSdiba\nXQG8auN4YSOxw4mJa2tSwCuDONrJIYIgCEJoiG4LqcT/Z+/O46Ou7v2Pv072DRISEkIIuwEiIKi4\nouJaENyr3lbtcuu9tNX2V1urttXbelsV61bvtWi11d661qVKVZAqKiigaNSgIEskIRBCdpKQbbLM\n+f0x8w0zk9nnO5OZzOf5ePCAzHyX8w3wzpmz+qwUK6XWAYVu3rpVa/1P+zG3Av3AM16usxxYDlBQ\nVBRUYV0ZlV9XjitLgIwdjhauM4aNkJUldYSIPo6ZPXGitAbGK8ltEU9CXn1CKfUd4AfAOVrrLn/O\nMWOinTE0Ykre0A03enWltA5HsUC3JhUimshEOxGPJLdFLIvIRDul1BLgFuAifyvEoSpvqWVtw6ek\nZVsoyj1E8SjnXzOz3pcKcRQzxqI5chyrJoQQIrpIbot4EeqY4j8CqcBbyvaR8UOt9Q9CLpUHjqtI\nzMzaZt90o97lKKkMR6twbtQhhBDCfJLbIp6EVCnWWh9lVkG8cRwqUaRl4lyseu3VgiHbcsquREII\nEb0kt0U8ifptnt23DkuFOBYN99akQgghAiO5LeJJ1FaKpXV45DEWdvf3dSGEEMNLclvEk6isFEvr\nsBBCCCGEiKSoqhRL67AQQgghhBgOUVMpltZhIYQQQggxXIalUtw10Oe0G11dfx2AbMkshBBCCCGG\nxfC0FCd2kzB6x+CXRTBYGZbWYSGEEEIIEWnDUinOTtKcX9jr8qpUhoUQQgghxPAYlkpxSkKaVICF\nEEIIIUTUSBjuAgghhBBCCDHcpFIshBBCCCHinlSKhRBCCCFE3JNKsRBCCCGEiHtSKRZCCCGEEHFP\nKsVCCCGEECLuSaVYCCGEEELEPakUCyGEEEKIuCeVYiGEEEIIEfekUiyEEEIIIeKeVIqFEEIIIUTc\nk0qxEEIIIYSIe1IpFkIIIYQQcU8qxUIIIYQQIu6FVClWSv1OKfW5UqpcKfWmUqrIrIIJIYQQQggR\nKaG2FN+rtT5Gaz0feB34tQllEkIIIYQQIqJCqhRrrdsdvswEdGjFEUIIIYQQIvKSQr2AUupO4NtA\nG3BWyCUSQgghhBAiwny2FCul1imltrn5dTGA1vpWrfVE4BngR16us1wpVaaUKmtqajPvCYQQQphO\nMlsIEW+U1uaMeFBKTQZWa63n+HHsYWCXKTceHmOBpuEuRJBiuewQ2+WP5bJDbJffzLJP1lrnm3St\nmCCZPaxiuewQ2+WP5bKDlN+RX7kd0vAJpVSJ1rrC/uVFwE4/T92ltV4Qyr2Hk1KqLFbLH8tlh9gu\nfyyXHWK7/LFc9ighmT1MYrnsENvlj+Wyg5Q/GKGOKb5bKTUTsALVwA9CL5IQQgghhBCRFVKlWGv9\ndbMKIoQQQgghxHAZrh3tHhum+5ollssfy2WH2C5/LJcdYrv8sVz2aBDr379YLn8slx1iu/yxXHaQ\n8gfMtIl2QgghhBBCxKrhaikWQgghhBAiakilWAghhBBCxD2pFAshhBBCiLgnlWIhhBBCCBH3pFIs\nhBBCCCHinlSKhRBCCCFE3JNKsRBCCCGEiHtSKRZCCCGEEHFPKsVCCCGEECLuSaVYCCGEEELEPakU\nCyGEEEKIuCeVYiGEEEIIEfekUiyEEEIIIeKeVIqFEEIIIUTck0qxEEIIIYSIe1IpFkIIIYQQcU8q\nxUIIIYQQIu5JpVgIIYQQPimlJimlOpRSiUGe/yul1F/MLpcQZpFKsYgJSqm9SqlzHb7+hlLqkFJq\nkVJKK6VWuxz/tFLqdvufz7Qfs9LlmI1Kqe9GovxCCDGc7Bnaq5Qa6/J6uT0fp/i6htZ6n9Y6S2s9\nEEwZtNZ3aa3/w37fKfb7JgV6HaXUKUqpTqXUKDfvfaaU+pEf13D6mSIESKVYxCCl1HeAlcAyoNr+\n8slKqYVeTusEvu1P8AshxAhVBXzT+EIpNRdIj8SNg6n8eqK1/gCoAb7uco85wNHAc2bdS8QXqRSL\nmKKUWg7cDyzWWm92eOse4A4vp7YC/wf8JnylE0KIqPYU8G2Hr78DPOl4gFJqmb21tV0ptd/ocbO/\n59S6q5QqUkq9qpRqUUp9pZT6T4djb1dKvWTvtWsHvmt/7Wn7Ie/Zf2+1D8lYZL/OXIdrFCilupVS\n+W6e5W8uz4L969Va62b7+RcppbYrpVqVUuuVUqX2158CJgGv2e99s/31k5VSm+3Hb1VKnenPN1WM\nHFIpFrHkh8DvgHO01mUu760EZvjoDrsT+LpSama4CiiEEFHsQ2C0UqrUPi7434CnXY7pxFa5zMHW\nG/dDpdQlHq73HLYW2yLgcuAupdQ5Du9fDLxkv9YzLueeYf89xz4kYwPwd+Aah2O+CazTWje6ufdT\nwOlKqUkASqkE4CrslXyl1Ax7+W4A8oE12CrBKVrrbwH7gAvt975HKTUBWI2tcSUX+DnwDw8VcjFC\nSaVYxJLzsIX6F27e68FW6fXYWqy1rgP+BPw2LKUTQojoZ7QWnwfsBA44vqm1Xq+1/kJrbdVaf46t\nYrnI9SJKqYnAacAtWuserXU58BfgWw6HfaC1XmW/VrcfZfsbcJW9gov9Wk+5O1BrvR/YwJFK9DlA\nGraKLdgq/Ku11m9prfuA+7ANFTnVw72vAdZordfYy/sWUAYs9aPcYoSQSrGIJT8AZgB/UUopN+//\nGRinlLrQyzV+DyxWSs0LRwGFECLKPYWtRfW7uAydAFBKnaSUelcp1aiUasOWu2Ndj8PWOtyitT7s\n8Fo1MMHh6/2BFExrvQVbS/UipdQs4CjgVS+nOA6h+BbwrL0CbJTPmHOC1tpqL88E3JsMXGEfOtGq\nlGrFVukfH8gziNgmlWIRSxqwtQacDjzs+qY9DP8b2xALd5Vm7GPNHrQfI4QQcUVrXY1twt1S4GU3\nhzyLrSI6UWudja13zV2e1gK5LitATMK55Vl7K4qH1/+GrdX2W8BLWuseL9d4GZiglDoLuAznSn4t\ntoouAPaGlIkO5XO9/37gKa11jsOvTK313V7uL0YYqRSLmKK1rgXOBpYopf7g5pCngFRgiZfLPICt\nC63U/BIKIUTUuxY4W2vd6ea9UdhagHuUUidia1Uewj58YTOwQimVppQ6xn5d17HDnjQCVmCay+tP\nAZdiqxgPacl2KUMntjHLfwWqXeaavAAsU0qdo5RKBm4ELPYyA9S73Ptp4EKl1GKlVKL9mc5UShX7\n+TxiBJBKsYg59jA+G9vEjhUu7w1gW2Ei18v57dhWq/B4jBBCjFRa6z1uJisbrgN+q5Q6DPwaW+XS\nk28CU7C1yr4C/MY+FtefMnRhmweyyT5c4WT76zXAp9hact/341J/w9Yi7FSB1lrvwlaxfghoAi7E\nNrGu137ICuA2+71/bv+5cjHwK2wV9v3ATUg9Ka4orb31bgghhBBCgFJqGlABJOkwVh6UUk8AtVrr\n28J1DyHcMW0xbSGEEEKMaHOAvWGuEE/BNj742HDdQwhPpFtACCGEEF4ppX4GPAb8Ioz3+B2wDbhX\na10VrvsI4YkMnxBCCCGEEHFPWoqFEEIIIUTck0qxEEIIIYSIe8My0S57TK4unOBpUxkhxEjT3duH\n6gfVbyU9LXm4ixOSnRVfNmmt84e7HJEkmS3CbSRlhIg+/ub2sFSKCydM4JGXVw3HrYUQw2B7ZR0p\nrVZSmi2Ulowb7uKE5JTFx1T7PmpkkcwW4TaSMkJEH39zO+ThE/ZdXz5SSm1VSm1XSv13qNcUQggh\nhBAiksxoKbZg2y6yw76V4kal1Bta6w9NuLYQQgghhBBhF3Kl2L6Id4f9y2T7L1nnTQghhBBCxAxT\nVp9QSiUqpcqBBuAtrfUWM64rhBBCCCFEJJhSKdZaD2it5wPFwIlKqTmuxyilliulypRSZa2HWsy4\nrRAiBmyvrGN/TQsHKhuHuygiAJLZQoh4Y+o6xVrrVmA9sMTNe49prRdorRfkjMk187ZCiChlVIjT\nGvqYmj1aZpXHEMlsIUS8CXlMsVIqH+jTWrcqpdKBc4Hfh1wyIUTMcqwMp4FUiIUQQkQ9M1afGA/8\nTSmViK3l+QWt9esmXFcIEYOkdVgIIUQsMmP1ic+BY00oixBihJielUOKRRbhF0IIETtMHVMshBBC\nCCFELJJKsRBCCCGEiHtmjCkWQgi2V9YBOI0nFkIIIWKFVIqFECFznFxXmj0astNlPLEQQoiYIpVi\nIUTQ3LUOS2VYCCFELJIxxUKIkKS0Wrnk6FlSIRZCCBHTpFIshBBCCCHinlSKhRBCCCFE3JNKsRBC\nCCGEiHtSKRZCCCGEEHFPKsVCCCGEECLuSaVYCBEUYzk2gF2fVQ9jSYQQQojQyTrFQoiAOW7WYexc\nJ8uxCSGEiGVSKRZC+E026xBCCDFSSaVYCOEXo0Kc0mql1JIuWzkLIYQYUWRMsRDCb/MKCwf/LBVi\nIYQQI4lUioUQQgghRNyT4RMjWGJHBwVvrCZjbzVdUybTcP4yBrKyhrtYIobt2Fk73EUQYsSSzBZi\neEmleITKLitj3vJrwWolqbub/vR0SlbcxdbHHqdtwYLhLp6IMa6rTcjQCSHMJZktxPCT4RMjUGJH\nB/OWX0tSZydJ3d0AJHV3k9TZybzl15LY2TnMJRSxYntlHWvf+1IqxEKEkWS2ENFBKsUjUMEbq8Fq\ndf+m1UrBmtWRLZCISY6tw6WWdJYuKJEKsRBhIJktRHSQ4RMjUMbe6sHWBldJ3d2k76uWsWvCL9Oz\nckixWKQyLEQYSWYLER2kUjwCdU2ZTH96utuQ7U9PB61ZeMZCGbsmhBBRQDJbiOgQ8vAJpdREpdS7\nSqkdSqntSqmfmFEwEbyG85dBgoe/WqUofuZpGbsmhBBRQjJbiOhgxpjifuBGrXUpcDJwvVLqaBOu\nK4I0kJXF1scepz8z09bKgK21oT8zk5qrrgat3Z8oY9eEECLiJLOFiA4hD5/QWh8EDtr/fFgptQOY\nAHwZ6rVF8NoWLGDT+5spWLOa9H3VdE+aTMPSZUx5eKXPsWtCCCEiSzJbiOFn6phipdQU4Fhgi5v3\nlgPLAQqKisy8bVwJZLLFQGYmB6+40uk1X2PXuidNDku5hRCxRTLbHJLZQsQO05ZkU0plAf8AbtBa\nt7u+r7V+TGu9QGu9IGdMrlm3jSvZZWUsPGMhJXfeweS/PEbJnXew8IyFZJeV+X0Nr2PXEhJoWLrM\npNKKWGYsx3agsnG4iyKGiWR26CSzhYgtprQUK6WSsVWIn9Fav2zGNYUzx8XdDUbLwbzl17Lp/c0M\nZGb6vI4xds115yQSEtj62ON+XUOMHEbl1x3ZrEOI4ElmCxF7Qq4UK6UU8DiwQ2v9QOhFGhnMXlPS\nn8XdXbvdPPE0dk3CNfKGa+1Rx4050oCp2aOHHpSdLhViEVfM/P8omT0yJXR1krdhLWm1++gpmkTz\noiVYM+TvYaQwo6V4IfAt4AulVLn9tV9prdeYcO2YFI497P1Z3D0Q7sauicgKx78TfzhWiKUlWAgb\ns/8/SmaPPKO2fcqs264HbSWxp5uBtHSmPHofO+9YyeE5xw138YQJQh5TrLXeqLVWWutjtNbz7b/i\ntkIcrj3sjckW0RWjpAAAIABJREFU7shki9gTrn8n3myvrGPte1+S0mqVCrEQDsLx/1Eye2RJ6Opk\n1m3Xk9jdSWKP7d9IYk83id221xO6u4a5hMIMpk20Ezbh2sNeJluMLOH6d+KJY+twSrOFpQtKpEIs\nhF04/j9KZo8seRvWgvbwb0Rbbe+LmCeVYpOZ3WVm8La4u0y2iD3h+nfiSlqHhfAtHP8fJbNHlrTa\nfYMtxK4Se7pJq90X4RKJcDB1nWIR3jUlZbLFyGH2v5PtlXVuXx9sHbbYWoeFEEOFK7cls0eOnqJJ\nDKSlu60YD6Sl01M0aRhKJcwmlWKTNZy/jJIVd7l/04QuM5lsMTKY9e/EqAzvr2lhelbOkPeldVgI\n38KZ25LZI0PzoiVMefQ+92+qBJoXLYlsgURYyPAJkw1Hl1liRwfjX3ye6ffew/gXnyexo8P0ewhz\nmfHvxBgn3PhpPaWWdFKaLUN+ydhhIXyLdG5LZscea0YmO+9YyUB6JgNptn8jA2npDKTbXremZwxz\nCYUZlNY64jedOWeufuTlVRG/byQldnZGpMvM3TJCxqLu4VzWSxzhaeiCO7OnFTp9Hcy/E8fWYWkJ\njrxTFh/zidY6rv5zxUNmQ2RyWzLbve2VdaS0WklptkR1niV0dw1dp1gqxFHP39yW4RNhEokuM7N2\nTBLB8TV0wdWejlbAuWIc6L8Tx1UkSrNHywYbQpgo3LktmR37rOkZNC65bLiLIcJEKsUxzMwdk0Rg\nhlROLRaf56S19dHYUM/amhYmFucOaTX2dT+Q1mEhYplkthDRTSrFMSxSy3oJ3/ypoJYyjh0V9VQ1\ntLOfloCuL63DQsQ+yWwhoptUimNYOJd/E94Zrbz7aaGqoR0qbK/7qqyWloyDCqhqaKexoZ4J0/L9\nup+0DgsR+ySzhYhusvpEDGs4fxnKw0RJpbXsmBRms6cVMrE4l/zjxrEjtZuqtnZ2VNT7PK+0ZBxL\nF5QwNXu02xUj3P2SCrEQsU8yW4joJi3FMc7T2iGRX1MkPjmOC07JslJV2QgVfg6nkEquEHFHMluI\n6CUtxTGs4I3VoJT7N5WiYM1qv64Tb2tmGlsfB7KUmj9KZxV5HQ5h1VZeb1iFVQ+daOPtPW+CPS+S\nYqGMQkSCZHZskcyO3jKGi7QUxzAzJm24WzOzZMVdI3LNTNcVHFKyrKx978uAV4II1vqWddy559dk\nJmZyVt55fr8X7DWjRSyUUYhIkMyOLZLZ0VvGcJGW4hhmTNpwx59JG45rZhpBndTdTVJnJ/OWX0ui\nw1qasc519zdjPO/0rBz217SY0mq8tc7zNazayh+rHwDgoeoHnD6Be3vPm2DPi6RYKKMQkSKZHTsk\ns6O3jOEkleIwCncXV8P5yyDBw19hQoLPSRv+rJkZrGjp3tteWee0prAxYc0Yzzt3UqFfG2/4YrQ0\n1zTVMm3rW/Te8V/s+MuN0HkYsH3ybu2zbd7R2neIDS1vD57r7T1vvJ0XLd1fwT6bEMNBMnv4M3s4\nJHR1MnbNS/T+73WMXfMSCV2dktnEZ2bL8IkwiUQX10BWFlsfe9zjlqG+dkby1ZWX8VVFUOWKlu69\nSO/+dmpLDfN+cS3WgQFSeno4LhmSXt1AxZ2P8se+B+i2dgHQbe3ioeoHWJR7DgB/rHb/XoLy/JnV\n+DTv6bxo6P7yVUYhoolk9vBn9nAYte1TZt12PQPWPo6y9NKb+hFTHruPu76dTvd4yex4y+z4eMoI\ni2QXV9uCBWx6fzMVt/4Xe5d/n4pb/4tN72/2K8i8deVpoPjZZ8kuKwuoPNHQveerdTgcHJ87pacH\ngKw+SLP0UnLr9+nrOOR0vPEJ3PFTuet73ng7L1q6v4J9NiEiTTI7PodkJHR1Muu260ns7iTF0gtA\niqWXpO4unvxrM5kOG5VKZscHqRSHQTi7uNwZyMzk4BVXUnnjTRy84kqfrQ0Gb115CkjstQQcipF+\ndlfuxg5HYukzb8/db+3loi+cW3eMT+AP7b1/8FO563uegtH107zree82vzXs3V++yjjcXYRCOJLM\nHt7MXvvel+yvaeFAZWNY7+Uqb8Na8JBFCRr+bfuRryWz4yOzpVIcBrGylafRlTeQkuJ5jcwAQ3E4\nnz3SrcOOvD13Vh8c1Tz09TpLLfW97ifn1VsO8l7LO27fe6/lHeot7s+rs9Ryf9WKId1fkQ40b2X0\n9mxCDAfJ7OHP7FJLOksXlER0/fa02n0k9vif25LZI5+MKQ6DWNrKs23BAg5882om/e2vbt8PNBSH\n49kjPXbYHW/P3ZEMTCzhG+NPdnq9pbcFlCY3Oc/tNQtTizy+fuX4q92+t7+7mrL2j5xeM1oeIjlO\nzVsZjfeFiBaS2ZHPbGBII0ak9RRNojc1ZXDohKOulARyp53IN8aXDL4mmT3ySaU4DBrOX0bJirvc\nv+nHDONI6yw5yrRQ9PbsamCA5kVnBltMwDb+reCN1WTsraZrymRbdyIwPSuHFIsloGAtLRnHjs+q\nOdDWTk9Bssfj/FnD2NtzWxU8PquDJyffaMpkhVlZRzMr6+ih99FWLv9sKRZrj9PrwzFZwlMZhYhG\nktnhy2wYmtu7S09A9aZxydGz2GWpHrbdPRvP+BoFj/yWFDfvDSjNvMseYF5GVsj3kcyOHTJ8IgyM\nLq7+zMzBSRH96en0Z2b6NcM40hrOXwbaQ2ec1gH9QHB69hRb1AxeWSlOXnJewBNBDNllZSw8YyEl\nd97B5L88Rsmdd7DwjIW0bPxT0F1NpSXjmJo92raZR6t1yC9jDWNf6xgPZGXx5B3LaU+xtwxj+709\nBZZeDVXUh737Sbq/hAiOZHZ4Mhvc5/Z3rr6Agh3lQV/TLOt7trDsKtzm9rKrYH33h2G9v2R29DGl\npVgp9QRwAdCgtZ5jxjVjnTHDuGDNatL3VdM9aTINS5dFXbgaPGw86vF1b9oWLODDtW9xyjlnOV0j\n0WIBi20iyKb3Nwf0vXCcIW0wWkl+eu/T3HbfZI5NODOI0toqxqWMY0dF/dD3SKfq0/rBlmRvrca9\nJy/i+kebOPWDKsbVtbMnO5UNc4vJtCZw5ZiMsHc/SfeXEMGTzDY3s8FzbicBS+75OVtPPz2I0pqn\nMLWICSdfw/+b38cpZXsZ13CY+oJRfLBgChPSkiWz45BZwyf+D/gj8KRJ1xsRjBnG0a7gjdVo5T5K\ntVIUrFkd8HPkbXgXneihI8I+ESSQa3qbIZ2gQa/7X6znnRFQGV157MKrgKqGdvbT4uMKYzmx8Pv0\nXwoHgMaaFo4zxstNCX/3oHR/CREayWzzMtsop6fcHhjope+vT8KpSwO6ppmcMnOW7bdCYN5w3F9E\nBVMqxVrr95RSU8y4VqxzN+Z1ICv0MUnh5M+C8ONffD6gZzJ7RrOv1R0mNnfRkLeN2YwP6Lr+KC0Z\nBxWABao+rWfCtHy/zhvOCSRCCP/FWm77k6+BPlM4VqHwlduJHVspLfn3gK8rRLhEbKKdUmo5sByg\noGhkdgnE6q5AXmcfp6RQ/Oyz6MSEgJ7J2zU1gNXjgkIBX68jGXblDfBGGCcmDFZsK4Bmi9djDY4V\nYqu2sqbxVZbmXxQ3OwOJ2BYPmQ2xmdu+VozAqll4xsKAnsnszPZ1zY5k+EfSp3xdW6M2EyW340/E\n/pa11o9prRdorRfkjMmN1G0jJpZ3BfK2IHxiby+JvZaAn6nh/GXgoXtPAcXPPh3Q96Th/GVYPYyW\nsyp4fnZkFj031j7295fB2MIznnYGErFtpGc2xG5ue8tslKL42acDfiazM9u4prfcfrLUEtWZKLkd\nf+Sjj0mGe1egUHiaeT2QkoI1NdX9ST6eaSAri5qrrvG8wLzWHs83djhy/LX603385ce/xpKSTl9K\nGuC8ukNnavTuvBMtW3gKIZzFam57Wy2j5qprPK9M4eWZQs1sT7n95DW/wpKSRqebVXmakrujNhMl\nt+OTrFNskljZEckTdzOvMysqQlogXuF5JrSn8113pXMy6XheWPE8eTuf5vMvX6Aiz9ZC3OlQbzeW\nsTkz71yvZYskx/3kHRdll645IYZXLOe2p9Uypjy8MuhnCjWzS10zG2DeSfzxT79h56pfMa1Z85VL\nbkdjZoPkdrwya0m254AzgbFKqRrgN1rrx824dqyIpR2RPNIa0Cir7ffuSRNDeqZAvieOwZoGXieo\n7ZxyGVsXpLKvq4rOto2cmn06kzKmDL4frmVsgglD1/3kHRdlN7rmMhMzh+xcJMErRPjFfG67ZDZa\nh/RMgWY2+Lcr3c6OqXQsu4bXu6rYHOWZbZwXaG5LZo8MZq0+8U0zrhPLYm1HJFfuJpuglOduOD+e\nyd/viWvrsK/VGmZlHc2MzFmc99FCALYe/ox7Sx8KexB5q8R6O8dobTC09h3i3ea3WLnvDwBudy4K\n5l5CiMDEcm57miD4xf/80fN4Yx/PFExml2aPpnTBFK9ljaXMNs4LNLcls0cG+ThjkljbEcmRx8km\nXV0ooD8jI6hn8vU9+bz+MGvf+5LGT+sDXr7s7aZ/0WW1TfrotHbwdtObQT69f4IZX+ba2mDotnZx\nX9WKIV1zodxLCBG4WM1tbxME5/7kR3zxvyuDeiZ/MjvQRgxDLGS243mB5LZk9sghY4pNFGs7Ihm8\nTTbRSrHn5zdhTUkN6pk8fU8+rz8cVLCCLYDurvyt02u/r/wt54z9WthaHjyNL/PG2xaerf1HNgJx\n3ec+mHsJIYITi7nta4Jg2sHaoJ/JV2ZPz8qh1JIO2el+5/ZwZ3ZTbyPvNq/jnLFf83leMLktmT1y\nSKXYZLGyI5IjX5NNUuvqqLzxpoCva4w5A+D4M+B42x/3f1I9ZOxwIOOxHFscDEbLw3n5SwIupy/e\nxpcBHsvtaQvP/d3VbGnbTL/uH3zNCNJFued4vJfj9WX8mhDmibXc9meCYCjP5P7cw0zPymHupEJ2\nNVcz86h8Xm9YFROZ3ad7ub/qLs6yT+bzlp2B5va7zetYuU8ye6SQSrEIy2QTo4ttelbOkPfctQ77\nOx7LXYuDIVwtD57Gl21oeRuN9lhud1t4WrWVyz9b6hSscCRIB/SAx3s5Xl/GrwkRv6JhgmDMZXa/\nrQKrFF7LHWhu3191F90Dzn8PktmxSz6ujACJHR2Mf/F5pt97D+NffJ7Ejo6Azve6EHwQk00cx5yl\nNFuG/HKtEAcyHuud5reGtDgYOq0dvNP8VkBl9cXb+LKHqh/gob33A/6PI/PWNVdvOcj9VSs83su4\n/kgYv2bVVl5vWBWTZRciVNGW2YGKxczWaO6vuivgzAbvuX2ov4Ue7VwplsyOXdJSHOOyy8qY95/f\nQ/X1kdjXx0ByMiV33cnWPz/h9xalxuSKIatPJCSYNtnE29izQMZjbT60wet9Nh/awLljF/ssj79d\nWd7CsM5SS7JK9qvcBk9dcwA13fvY1Pqe2/cc1/J0/H4d6m3hgaoV/GzqL2OqS05aTUS8ipXM9ubT\n3o0xmdmH+ltIJsWvcjvylNs13fvY2Or++SSzY5NUimNYYkcH8679Lkk9PUde6+uDvj7mXftdNm3e\nEvKEuGDCdfa0QtsfimGHvcWYCvcVY2/jdd0Fxok5p7Kne4/He5+Yc6pfZfT3P3hhahGn5JzGh62b\nGeBI11kiSSSqRHq1xa9yG9x1zRl2dnxJcfokr2Vx/X716G7+Uf88x44+wa9JJNHAtdXE1/dMiJEi\nWjM7EFZt5YXOx6I6s68cfzX7u6uH5DZAH71+lduRp9yWzB55pFIcwwpXvUyiQ7g6SuzpYdwrL1N7\nzbd8Xiexo4OCN1aTsbearimTqf7+DwEoWPP64GsN5y9jICvL77INVoyB/bRQ1dAOFbavXccS+zOG\n1rAk/wKW5F/gdzncCeQ/+IzMWVR2fzUkWAfox6oH/C63P7xVmA3vNL855PsFDE4iiYWgkpnaIl6F\nM7MHsrIGh2UEm9v+2NDwJoet7U6vRVNmG2siX/7Z0iG57Uoy2z/xlNlSKY4xjmGYt/5dj1tyKmDs\nhvU+A9Z1AXhrSgozf/MbtAKdnOy0KPzWxx73u3vPYFSOU7KszJxUyK7PjmwT6mu8rhmfRt11uQXy\nH9xbV5zGeWMTM8vtjqfvFxyZRGK0PETrTOdAewaEiHURyez/vp36xUvIf+dt0Drk3PbEqq2s3P17\nLLgfQxsNmQ3ecztc5XYnkMw2jo+23I63zJZKcQxxDUPtaaKFwdNudHaOC8AbEnp7jxzQb/uUbcxw\nnrf8Wj5Y+xZjN7w7pCXCteXCnxYKX5POjPFYoTC63NITMui2drFk7AUB/Qe3dcVdRWXXHqZlTAeU\n3+PIzOargu7Y8hCt478C7RkQIpZFMrMLX3/NqcI9mNv/+T2+uvHnpB+sCzmzt3a8T33PQbfvmZ3Z\nmYmZLMo9h9UNq3ii5tGAKmWuuV3TvT/qMxuic9xuvGW2VIpjhLswVJ4Wbwc00HzWWV6v6XUBeDdU\nfz+nnnMmOjHRqSXiqxtv4qj77x2y3aivFgpvk86M90Ph2OV2f9VdHOpvobLrq4D+g8/KOppaSw1/\nP/g0l4y7nLPyzvNrHFk4OI6V+6B1I1ac/+66rF1Oax1DdI3/ikTPgBDRItKZ7akFOrGri5K7V5DY\n1xdyZucmF3LlpO/Q0tBO7piMIe+bmdnGEpV3Vd5OikpxOs5Xpcw1txePvSCqM/usvPOictxuPGa2\nVIpjRKAVWGtaGnWXXOb1GG8LwLuTaLE4fW2cO/O3t7tvoVh+LZve3wzAno5W2AcH2toHJ935Go9l\nLAETbFeSU5db/yEAXjj4DAM4jwX29h/cXVD5M47M1Y6KesD7Khy+OI6Vcw1XAIu1Z8hax9H0iT4S\nPQNCRItoyGywVZYT+/oA/zPb02S9yWkzuWDSInZ1VVM6xf3EadMyu+8Q91etAKBX9zod56tS5prb\nLx27OuDMNoO/mR2tu+LFY2ZLpThG+ApDnZCAslqxpqRgTUpi65+f8DkL2dsC8G7vgefWCLesVgrW\nrGb2FVcye1oh2yvr6ClIHpx056uCGEpXkusnXGP8r2uFOMsC/7YNSloOsPXDW8g6/TpK5k51KkOo\nQbWmrILy1GYAqsraA97W2tH6lnU09zZ5fN91rWN/P9FHYixbuHsGhIgm0ZDZAbNndrA74ZmZ2d3W\nLrdjcR0zu7XyLsYu/SnWDOfvWzRVMP3J7PUtbwc8blcyOzykUhwjfO1g1LD4fHoL8gNalqfh/GWU\nrLjL7zJ4myDijrHdqMGYdGesRuGtghhqV5K7cVCGzMQs5o06FrX1fdY+k0CCVmT0DdCT8i7qjfd4\n6ju3kjjvJGYelR/SBIMdFfVsbK3DUpjAxCkNZGels31PHwequqgqa2fpghK/nweOfE9cW02MZ7og\n/xJqevZR1vaR03v+/FCIxFi2YFrYhYhV0ZDZnvib2YEIZ2YDpCaksaCqh389m0giijRLP31l/yTh\n6dXsvGMlh+cc51SOaJgY5k9mK6U40LM/4HG7ktnhMbIGg4xgvnYwqvj1b6i88SYOXnGl3+tUGgvA\n92dk4G16R396OgMpKfSnpLh939O57rYbnT2tkCVnHE3+ceOYMC3f4z3dfdL3l7cZvwD9A/3sa/6C\nNc9AVq+VjD5b63Faby+pvd1c89SdHGio55nt//AYVJ7sqKhnR0U9a8oq2NhaR+vUdk49fhOXzejl\nnKI2vn78HtJOrqY8tZk1ZRWDwyr84a0rq3ugi2NGH0tl91dYtPOST667K7kaCbstCRFthiuzjdet\nKSk+j3EVyhbR4cxsgJTuHtY8A5mWAdIstkngyRYLid2dzLrtehK6u4aUwxBoecziK7Pnjz6OH0++\nkVfqX/C5k6kjyezwkZbiMAlmZq834drBqG3BAjZt/IDJj6xk0l+fQCtlm5CRloayWmlYfD6tJ51E\n06IzOWXJedA79BOvR0FuNxrqJ31fy/FY6GFJeQ8JHn4yJKCZsfcD7pv2It0MDap7dv+erqRJKDdl\n6Smw7XBXO66H7CnVfL24hePzS5mQPgOAA927gSpa82p5+8vZAbUa++rKOthTE9T4r2jqahRiuMR8\nZqeng1LUXH0NKIVlXCHT77+XpC7PFc0hojSzAa7chsfMRlvJ27CW+sWXRNXEMH+GHwQzblcyO3yk\nUhwGrsvwmLFeZGJHBxlVe6i9/EqS2lrpy8mh66gSU3YwGsjMpPLnN1P9w+u97o7kKeDdzWQOJfhD\nXQLGCCJvS6dNb4GsPvfnp/T0ML63gnbcjwNro4mDU3czL3eR0+t7LM20JTYxPv8Qp6buYWbeWIoz\npw5WiAEmpM9gwqQZbGnaRM7xe9g0Lpfy8gIow+dYY19dWTs7vgx4/Fc0dTUKMVxGamZ3zCqNqcwG\nz1snH+UlsxN7ukmr3Rd1E8P8HX4QSG5LZoeXVIpN5m4ZHn9n9nriLrDDscf9QGam1wkW3rYVrb/k\n0oC3G93T0Upam/MW0GYsAWMEkbul0/Z3V7OlbTNf5fbTkew+ZPvT0xmYWsLXZxZ7vMeCKbOZkWsb\nI13eUktdfx0pWTAvt4+5o9oozix1qgy7OmnswpBajb09dyDibQ1KIVxJZgeW2ewDx4F0ZmY2DN06\n2Z/MHkhLp6doUkxODAs0tyWzw0sqxSbzugxPEDN7wxHYofAUwr7C2ZXrpDujYmzmJ33XsLFqK5d/\ntpR+3c/zc+CBf3k4MSEBdcVyfmj/vnrrVt1eWUcyCfRaUpkxI4PiUVCc0eu1QmwIpdXYDPG4BqUQ\nriSz/WNk9p6alsHGDID63C9MbZ11zG2/M1sl0LxoCbPSM5iVdTQJXZ3kbVhLWu0+eoom0bxoyZAV\nKmKRZHb4SaXYZN6W4QlmZq/ZgR1NHCvGO+imqqwClZkatk/6jhXujlRYejWsecY2Ti2rDyxpKSQm\nJju15njrVt2cW8z+mhamZ+XQ1NlBSp6FXn2ImUPXtPfKU6uxP8vWhSLauhqFGA6S2f4zMpti2FHT\nQlpDH6p/+DK7NzWFxIRkdt6xEmu6LXhHbfuUWbddD9pKYk83A2npTHn0PqcVKmKVZHb4SaXYZD3j\nCxlITh5cLN1RMDN7zQ7sSPJn4srsaYWDaxjvp4W0hkK+lvTtsFQGXbvWWtK6+H8PpjPnvZ2MP9TB\nhOPOQV2xfLBC7K3FZ+5/fI9X7vkrae1JpFgspLZaseQl81bdOxzaeDE/t811GaQ1vPZqARde1OD0\nusG11fit0XPYuLUupOEUgX4/3L0vxEgnmX2Ev5ltcMxsMP9D/JCMGg//b34fp5TtZVzDYfKnLSR5\n8fcGK8QJXbaVKBK7j2R2Yo/t72LWbdfzyXNvDx5rGLBa+cPaXfx0yUwSHVYL0Ro2bB7NolPb3Wb2\ncJDMDj+pFJsou6yM6fffR4KbcAWCmtnra63LYJfPCbdAJ664azU2ewiB63CKHX31zJw3mS/G1NGS\nk8B4h7AH7y0+emCAJR9soeLU8yktGUdVWTs1ndtY85qFNc9/k6bPq7n7nl0oZQvXX9w8k4dXTuaZ\n58q56OIGt9c80L2b+q4ONtXk0t7eTQ7JTM0ebdrzu4rHNSiFcCSZfUSwme3Yamx275bHjJp15I+O\nCZ23YS14Wp7MvkJF4xLnXQMffnMv//ifKzm461PuuyFhMLMf/NN4Xlg1lhW/rubMhe2hP4wJJLPD\nTwafmGSwVbGra8jC6Broz8hwO8kisaOD8S8+z/R772H8i8+T2NHh9L6vtS6DWT4n3BxbWI0fDEnd\n3SR1djJv+bUkOrS8OjLWMJ5YnGvb+a6tPaB1fM3mrcUntdfCQPtHzDzKttay1lb+uf9hmLWKjNMf\n4eGVk/nFzTOdKsTXXV/NhRe5rxBvadrE2/t28FbFHPaXFzB7byH/ecK8sI8rFiJeBZvZxrmecjve\nMnv2tMIhmT1cuZ1Wu2+wZdhVYk83qQeqeb1h1eC6vlZt5Z386+CkB9m89jj+8KdCpwrxlZc0sejU\n6KgQi8gwpaVYKbUE+B8gEfiL1vpuM64bS7y2KqaksOfnNw35tO3PJ/NwrXUZTqGOqXNsNe7NSmVN\nGFqN/eGtxac3NYUnEzZylH3G7w69hc6+VlBg/dpPWDxxHg+vPJWHV9paha67/kjLsaMD3bv5pLGK\nTTW51NXPJqcqmUtzJlI6VyrDQoRTMJkNvnM7njM7nK3G/ugpmsRAWrrbivFAWjpbszucdoFb37KO\ntv5WWPJTElUyL666nhdX2Ro6rrykiRt+cDBqhk6IyAi5UqyUSgRWAucBNcDHSqlXtdZfhnrtWOKt\nVTGht5fUOufB8YHMUPa2rE40MmNMneNY4wnT8qHZYmoZd1TU21o1vtzJxOJct8dsKD2B6R42RLXQ\nx/OzYXT1A5w+5izWDTyJBdsz9wz0sWPB1+HZg4PHX3rzE3zUPPQ6u5qb+LhmAc27+plvyWPpCeEZ\nPyyEcBZoZoP/uR2vmW1IybKantn+aF60hCmP3uf2Pa0UP5nwPmDbBe70MWcdWclBwcDiH8GH1w8e\nLxXi+GRGS/GJwFda60oApdTfgYuBuKkUJ3Z0kNLUgDUpiYT+/iHvuxtHFugnc2/L55i9E1OoonlM\nndGtV9XWTk9BMhOLc53C3LC9so79LT08ec2v+PaTd6K0Jrm3h4G0dPqxcsnV0Jlqwdp3iIf3PUg3\nDl1sGmpfutXpenf9chGTvlE9JGTb28dJ67AQERZMZkNguS2ZHXnWjEx23rFyyOoTqASevem71Cb8\nFay2dX0f3vfgkfV+NbD2D07XevBP46ViHIfMqBRPAPY7fF0DnOR6kFJqObAcoKBo5MyQHOxKGxhw\nG66A23FkZs1QDsdOTBBaaDecv4ySFXe5f3MYx9QZrcM9BclMPM5WAXWtEG+vtLUO7bd3ASb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0oNx6xoIYQ5wpHZfl/XD+UttdTNLqDdTWb3ZhwkbdMoyp4/zp7Zh91m9tyztrCrdyt7Mmazsy2Z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\nlcXWxx73u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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# make base classifiers\n", "clf1 = LogisticRegression(penalty='l2', C=.001, random_state=11)\n", "clf2 = DecisionTreeClassifier(max_depth=2, criterion='entropy', random_state=11)\n", "clf3 = KNeighborsClassifier(n_neighbors=1, p=2, metric='minkowski')\n", "\n", "# LR and KNN use Euclidian distance metric so need to scale the data\n", "pipe1 = Pipeline([['sc', StandardScaler()],\n", " ['clf', clf1]])\n", "pipe3 = Pipeline([['sc', StandardScaler()],\n", " ['clf', clf3]])\n", "\n", "mv_clf = MajorityVoteClassifier(classifiers=[pipe1, clf2, pipe3])\n", "\n", "labels = ['Logistic Regresion', 'Decision Tree', 'KNN', 'Majority Vote']\n", "\n", "all_clf = [pipe1, clf2, pipe3, mv_clf]\n", "for clf, label in zip(all_clf, labels):\n", " scores = cross_val_score(estimator=clf,\n", " X=X_train,\n", " y=y_train,\n", " cv=10)\n", " print(f'ROC AUC: {scores.mean():.2f} (+/- {scores.std():.2f} {label})')\n", "\n", "\n", "plot_clf_area(all_clf, labels, X_train)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the perfomance of the MajorityVotingClassifier gas substabtially improved over the individual classifiers in the 10-fold cross-validation evaluation. Note that the decicion regions of the ensemble classifier seem to be a hybrid of the decision regions from the individual classifiers. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stacking\n", "The majority vote approach similar to stacking. However, the stacking algorithm used in combination with a model that predicts the final class label using the predictions of the individual classifiers in the ensemble as input.\n", "\n", "The basic idea behind stacked generalization is to use a pool of base classifiers, then using another classifier, that called meta-classifier, to combine their predictions, with the aim of reducing the generalization error.\n", "\n", "Let’s say you want to do 2-fold stacking:\n", "\n", "* Split the train set in 2 parts: train_a and train_b\n", "* Fit a first-stage model on train_a and create predictions for train_b\n", "* Fit the same model on train_b and create predictions for train_a\n", "* Finally fit the model on the entire train set and create predictions for the test set.\n", "* Now train a second-stage stacker model on the probabilities from the first-stage model(s).\n", "\n", "We will use only meta features and 1-block validation. You can easily add the necessary functionality if you need.\n", "Let implement Stacking based on the MajorityVoteClassifier class. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class StackingClassifier(BaseEstimator, ClassifierMixin):\n", " \"\"\"A Stacking classifier for scikit-learn estimators for classification.\n", " \n", " Params\n", " -----\n", " classifiers : array, shape = [n_classifiers]\n", " A list of classifiers for stacking.\n", " meta_classifier : object\n", " The meta-classifier to be fitted on the ensemble of\n", " classifiers\n", " use_probas : bool (default: True)\n", " If True, trains meta-classifier based on predicted probabilities\n", " instead of class labels.\n", " average_probas : bool (default: True)\n", " Averages the probabilities as meta features if True.\n", "\n", " \"\"\"\n", " def __init__(self, classifiers, meta_classifier,\n", " use_probas=True, average_probas=True):\n", "\n", " self.classifiers = classifiers\n", " self.meta_classifier = meta_classifier\n", " self.named_classifiers = {key: value for\n", " key, value in\n", " _name_estimators(classifiers)}\n", " self.named_meta_classifier = {f'meta-{key}' : value for\n", " key, value in _name_estimators([meta_classifier])}\n", " self.use_probas = use_probas\n", " self.average_probas = average_probas\n", "\n", " def fit(self, X, y):\n", " \"\"\" Fit ensemble classifers and the meta-classifier.\n", " \n", " Params\n", " -----\n", " X : {array, matrix}, shape = [n_samples, n_features]\n", " Training vectors, where n_samples is the number of samples and\n", " n_features is the number of features.\n", " y : array, shape = [n_samples] or [n_samples, n_outputs]\n", " Target values.\n", " \"\"\"\n", " self.classifiers_ = [clone(clf) for clf in self.classifiers]\n", " self.meta_clf_ = clone(self.meta_classifier)\n", "\n", " for clf in self.classifiers_:\n", " clf.fit(X, y)\n", "\n", " meta_features = self.predict_meta_features(X)\n", " self.meta_clf_.fit(meta_features, y)\n", " return self\n", "\n", " def predict(self, X):\n", " \"\"\" Predict target values for X.\n", " Params\n", " -----\n", " X : {array, matrix}, shape = [n_samples, n_features]\n", " Training vectors, where n_samples is the number of samples and\n", " n_features is the number of features.\n", "\n", " Returns\n", " -----\n", " labels : array, shape = [n_samples] or [n_samples, n_outputs]\n", " Predicted class labels.\n", " \"\"\"\n", " meta_features = self.predict_meta_features(X)\n", "\n", " return self.meta_clf_.predict(meta_features)\n", "\n", " def predict_proba(self, X):\n", " \"\"\" Predict class probabilities for X.\n", " Params\n", " -----\n", " X : {array, matrix}, shape = [n_samples, n_features]\n", " Training vectors, where n_samples is the number of samples and\n", " n_features is the number of features.\n", "\n", " Returns\n", " -----\n", " proba : array, shape = [n_samples, n_classes] or a list of \\\n", " n_outputs of such arrays if n_outputs > 1.\n", " Probability for each class per sample.\n", " \"\"\"\n", " meta_features = self.predict_meta_features(X)\n", "\n", " return self.meta_clf_.predict_proba(meta_features)\n", " \n", " def predict_meta_features(self, X):\n", " \"\"\" Get meta-features of test-data.\n", " Params\n", " -----\n", " X : array, shape = [n_samples, n_features]\n", " Test vectors, where n_samples is the number of samples and\n", " n_features is the number of features.\n", "\n", " Returns\n", " -----\n", " meta-features : array, shape = [n_samples, n_classifiers]\n", " Returns the meta-features for test data.\n", " \"\"\"\n", " if self.use_probas:\n", " probas = np.asarray([clf.predict_proba(X)\n", " for clf in self.classifiers_])\n", " if self.average_probas:\n", " vals = np.average(probas, axis=0)\n", " else:\n", " vals = np.concatenate(probas, axis=1)\n", " else:\n", " vals = np.column_stack([clf.predict(X) for clf in self.classifiers_])\n", " return vals\n", " \n", " def get_params(self, deep=True):\n", " \"\"\"Return estimator parameter names for GridSearch support.\"\"\"\n", "\n", " if not deep:\n", " return super(StackingClassifier, self).get_params(deep=False)\n", " else:\n", " out = self.named_classifiers.copy()\n", " for name, step in self.named_classifiers.items():\n", " for key, value in step.get_params(deep=True).items():\n", " out[f'{name}__{key}'] = value\n", "\n", " out.update(self.named_meta_classifier.copy())\n", " for name, step in self.named_meta_classifier.items():\n", " for key, value in step.get_params(deep=True).items():\n", " out[f'{name}__{key}'] = value\n", "\n", " for key, value in super(StackingClassifier,\n", " self).get_params(deep=False).items():\n", " out[f'{key}'] = value\n", "\n", " return out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usually, **_LogisticRegression_** is used as a meta-model and we will not change the tradition. Let's check StackingClassifier." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ROC AUC: 0.86 (+/- 0.09 Logistic Regresion C=0.1)\n", "ROC AUC: 0.85 (+/- 0.08 Logistic Regresion C=1)\n", "ROC AUC: 0.85 (+/- 0.08 Logistic Regresion C=10)\n", "ROC AUC: 0.64 (+/- 0.05 Decision Tree depth=1)\n", "ROC AUC: 0.85 (+/- 0.09 Decision Tree depth=2)\n", "ROC AUC: 0.86 (+/- 0.09 Decision Tree depth=3)\n", "ROC AUC: 0.85 (+/- 0.08 KNN 1)\n", "ROC AUC: 0.87 (+/- 0.08 KNN 2)\n", "ROC AUC: 0.89 (+/- 0.07 Stacking)\n" ] }, { "data": { "image/png": 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ObUFXLcbJBEdmFrUFXZu4RIpEJVxnla/Bo3xVRAvla+xRvkaXpG4U+813aLNRNmKk5XvN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\nTddRt7Ks6kNDE86QXsuWRL22I7Ld0PansnDnAs7slF69xp5HNNtyEL2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Jlbutx7QndEds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/PG7llUYxig1Xj4Z+VM9rTtDTj4dO4Afljegz6nrkEBP7zXl/eW9wDg8FPX0Pm3n7FU1Yxzy+cH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# make base LR classifiers\n", "lr1 = LogisticRegression(C=.1, random_state=11)\n", "lr2 = LogisticRegression(C=1, random_state=11)\n", "lr3 = LogisticRegression(C=10, random_state=11)\n", "\n", "# make base DT classifiers\n", "dt1 = DecisionTreeClassifier(max_depth=1, random_state=11)\n", "dt2 = DecisionTreeClassifier(max_depth=2, random_state=11)\n", "dt3 = DecisionTreeClassifier(max_depth=3, random_state=11)\n", "\n", "# make base KNN classifiers\n", "knn1 = KNeighborsClassifier(n_neighbors=1)\n", "knn2 = KNeighborsClassifier(n_neighbors=2)\n", "\n", "# scale data for metrics classifiers\n", "pipe1 = Pipeline([['sc', StandardScaler()],\n", " ['clf', lr1]])\n", "pipe2 = Pipeline([['sc', StandardScaler()],\n", " ['clf', lr2]])\n", "pipe3 = Pipeline([['sc', StandardScaler()],\n", " ['clf', lr3]])\n", "pipe4 = Pipeline([['sc', StandardScaler()],\n", " ['clf', knn1]])\n", "pipe5 = Pipeline([['sc', StandardScaler()],\n", " ['clf', knn2]])\n", "clfs = [pipe1, pipe2, pipe3, dt1, dt2, dt3, pipe4, pipe5]\n", "\n", "# make meta classifiers\n", "meta_clf = LogisticRegression(random_state=11)\n", "stacking = StackingClassifier(classifiers=clfs,\n", " meta_classifier=meta_clf)\n", "\n", "labels = [\n", " 'Logistic Regresion C=0.1',\n", " 'Logistic Regresion C=1',\n", " 'Logistic Regresion C=10',\n", " 'Decision Tree depth=1',\n", " 'Decision Tree depth=2',\n", " 'Decision Tree depth=3',\n", " 'KNN 1',\n", " 'KNN 2',\n", " 'Stacking',\n", "]\n", "\n", "clfs = clfs + [stacking]\n", "\n", "for clf, label in zip(clfs, labels):\n", " scores = cross_val_score(estimator=clf,\n", " X=X_train,\n", " y=y_train,\n", " cv=10)\n", " print(f'ROC AUC: {scores.mean():.2f} (+/- {scores.std():.2f} {label})')\n", "\n", "plot_clf_area(clfs, labels, X_train, s_row=3, s_col=3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Blending\n", "Blending is a word introduced by the Netflix winners. It is very close to stacked generalization, but a bit simpler and less risk of an information leak. Some researchers use “stacked ensembling” and “blending” interchangeably.\n", "\n", "With blending, instead of creating out-of-fold predictions for the train set, you create a small holdout set of say 10% of the train set. The stacker model then trains on this holdout set only.\n", "\n", "Blending has a few benefits:\n", "\n", "* It is simpler than stacking.\n", "* It wards against an information leak: The generalizers and stackers use different data.\n", "\n", "The cons are:\n", "* You use less data overall\n", "* The final model may overfit to the holdout set." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Summary\n", "\n", "Ensemble methods combine different classification models to cancel out their individual weakness, which often results in stable and well-performing models that are very attractive for machine learning competitions and sometimes for industrial applications too." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Resources\n", "\n", "1. [Ensemble Learning to Improve Machine Learning Results](https://blog.statsbot.co/ensemble-learning-d1dcd548e936)\n", "2. [KAGGLE ENSEMBLING GUIDE](https://mlwave.com/kaggle-ensembling-guide/)\n", "3. [The BigChaos Solution to the Netflix Grand Prize](https://www.netflixprize.com/assets/GrandPrize2009_BPC_BigChaos.pdf)\n", "4. [Feature-Weighted Linear Stacking](https://arxiv.org/pdf/0911.0460.pdf)\n", "5. [Stacking example](https://github.com/Dyakonov/ml_hacks/blob/master/dj_stacking.ipynb)\n", "6. [A Kaggler's Guide to Model Stacking in Practice](http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/)" ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }