{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Folding Strategy\n", "\n", "REP implements folding strategy as one more metaestimator.\n", "\n", "When we don't have enough data to split data on train/test, we're stick to k-folding cross-validation scheme.\n", "Folding becomes the only way when you use some multi-staged stacking algorithm.\n", "\n", "Usually we split training data into folds manually, but this is annoying and not reliable. REP has FoldingClassifier and FoldingRegressor, which do this automatically." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### download particle identification Data Set from UCI" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File `MiniBooNE_PID.txt' already there; not retrieving.\r\n" ] } ], "source": [ "!cd toy_datasets; wget -O MiniBooNE_PID.txt -nc --no-check-certificate https://archive.ics.uci.edu/ml/machine-learning-databases/00199/MiniBooNE_PID.txt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy, pandas\n", "from rep.utils import train_test_split\n", "from sklearn.metrics import roc_auc_score\n", "\n", "data = pandas.read_csv('toy_datasets/MiniBooNE_PID.txt', sep='\\s*', skiprows=[0], header=None, engine='python')\n", "labels = pandas.read_csv('toy_datasets/MiniBooNE_PID.txt', sep=' ', nrows=1, header=None)\n", "labels = [1] * labels[1].values[0] + [0] * labels[2].values[0]\n", "data.columns = ['feature_{}'.format(key) for key in data.columns]\n", "\n", "train_data, test_data, train_labels, test_labels = train_test_split(data, labels, train_size=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training variables" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "variables = list(data.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Folding strategy\n", "\n", "`FoldingClassifier` implements the same interface as all classifiers, but with some difference:\n", "\n", "* prediction methods have additional parameter \"vote\\_function\" (example folder.predict(X, __vote\\_function=None)__), which is used to combine all classifiers' predictions. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from rep.estimators import SklearnClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from rep.metaml import FoldingClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define folding model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 43s, sys: 3.01 s, total: 2min 46s\n", "Wall time: 47 s\n" ] } ], "source": [ "%%time\n", "n_folds = 4\n", "folder = FoldingClassifier(GradientBoostingClassifier(n_estimators=30), \n", " n_folds=n_folds, features=variables, \n", " parallel_profile='threads-4')\n", "folder.fit(train_data, train_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default prediction (predict i_th_ fold by i_th_ classifier)\n", "\n", "In this case each sample will be predict by estimator that was not using this particular sample in training. \n", "\n", "When you apply this prediction to some new data (not the same was passed in training), it will predict each sample by random estimator." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold prediction using folds column\n" ] }, { "data": { "text/plain": [ "array([[ 0.79281896, 0.20718104],\n", " [ 0.96200905, 0.03799095],\n", " [ 0.23027741, 0.76972259],\n", " ..., \n", " [ 0.9431413 , 0.0568587 ],\n", " [ 0.48068844, 0.51931156],\n", " [ 0.87707187, 0.12292813]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folder.predict_proba(train_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Voting prediction (predict i_th_ fold by all classifiers and take value, which is calculated by `vote_function`)\n", "\n", "It makes sense to use all classifier to predict new data, because averaging makes predictions more stable." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# definition of mean function, which combines all predictions\n", "def mean_vote(x):\n", " return numpy.mean(x, axis=0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold prediction with voting function\n" ] }, { "data": { "text/plain": [ "array([[ 0.51136471, 0.48863529],\n", " [ 0.96878304, 0.03121696],\n", " [ 0.20340611, 0.79659389],\n", " ..., \n", " [ 0.12145289, 0.87854711],\n", " [ 0.95466483, 0.04533517],\n", " [ 0.81167832, 0.18832168]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folder.predict_proba(test_data, vote_function=mean_vote)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of folds\n", "\n", "Again use `ClassificationReport` class to compare different results. For folding classifier this report uses only __default prediction__." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Report training dataset" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold prediction using folds column\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/axelr/.conda/envs/rep/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "from rep.data.storage import LabeledDataStorage\n", "from rep.report import ClassificationReport\n", "# add folds_column to dataset to use mask\n", "train_data[\"FOLDS\"] = folder._get_folds_column(len(train_data))\n", "lds = LabeledDataStorage(train_data, train_labels)\n", "\n", "report = ClassificationReport({'folding': folder}, lds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Signal distribution for each fold\n", "\n", "Use `mask` parameter to plot distribution for the specific fold " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/axelr/.conda/envs/rep/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXt4VNXVuN+dcAn3CYJc5DKJFFGopEoVRclRW4vW+mG1\nYrwRrf2kXjBavlqrNgPaav21GiL9ii1C0CpaFVIv1bYKJ4gaBDV4BT/NTEABBUm4QyDs3x/nzJyZ\nZJLMJJO5Zb3Pc56cs8+efdZZmdlr77UvS2mtEQRBEASAjEQLIAiCICQPYhQEQRCEAGIUBEEQhABi\nFARBEIQAYhQEQRCEAGIUBEEQhABiFARBEIQAcTUKSimfUupImOPFeMohCIIghKdLnJ93MpAZdD0U\neAd4Os5yCIIgCGGIq1HQWn8TfK2U+hmwE/h7POUQBEEQwpOwMQWllAJ+CvxNa30wUXIIgiAIDokc\naP4+4Ab+mkAZBEEQhCBUojbEU0o9AwzXWk9MiACCIAhCE+I90AyAUupo4ELghhbyyPatgiAIUaK1\nVu35fKLcR4XAAWBJS5m01nJoTXFxccJlSJZDdCF6EF00f8SCuBsFe4D5OuAprfW+eD8/FfH5fIkW\nIWkQXViIHhxEF7ElEe4jAzgWuDwBzxYEQRBaIO5GQWu9gtAFbEIrFBYWJlqEpEF0YSF6cBBdxJaE\nzT5qDaWUTlbZBEEQkhGlFLqdA80JmX3UHqwhCUFIPIlstJimiWEYCXt+MiG6iC0pZxQgsT9GQQBp\nnHQ2TNM6/Od+G2QYznm6kHLuI7t7lACJBMFBvoedF6UgWf/1sXAfSTwFQRAEIUBKuo+aEIu+XWfq\nHwopj/jRHUQXMSbRK/BaWJmnw9FcelCGlu9HQizKCOJ3v/udvu6669r02X379ukLLrhA9+vXT196\n6aUt5vV6vVoppRsaGsLeLy4u1ldeeWWb5GjM1q1b9Zlnnqn79OmjZ82aFZMy77zzTj1gwAA9ZMiQ\nVvOOHDlSv/rqq2HvrVixQg8bNixwPXbsWF1RURETGf20+j3sYFasWJHQ5ycTHamLcEU3969Phn+J\n/b1sV92bHj2FJOeOO+5o82efffZZvv76a3bs2EFGRvu8fa0Njr722mvceOONbNq0iVNPPZWysjJG\njBgRNu9f/vIXjj76aHbt2tUumfxs3LiRBx98kE2bNnHUUUe1ml8pFfFg74cffthe8ZIOaRk7dKQu\ngp0GAF6v1z7LaTVvqpK6Ywp+V0978seijA6mpqaG0aNHt9sgtMb27du5+OKL+e1vf0ttbS0TJkxg\n2rRpLcp1/PHHt+lZhw8fbpK2ceNGjjrqqIgMgiAkAq/Xy6RJ84H5QcYh/Ugro+D1emn2XxWhUYi6\njCB+//vfM2zYMPr27cuYMWNYvnw5AB6Ph6uuuiqQ77HHHmPkyJEMGDCAe++9F7fbzWuvvdakvOLi\nYu655x6efvpp+vTpw6JFi9BaBz4zaNAgpk+f3mxr3ev1kp+fT9++fTn33HPZvn17s7IvXbqUcePG\ncfHFF9OtWzc8Hg/r1q3j008/bZK3sLCQxx57jAceeIA+ffqwfPly6uvrKSoq4phjjuGYY47h1ltv\npb6+3labybBhw3jggQcYMmQIP/3pT0PKe/XVVzn33HPZvHkzffr04dprrwXg+eefZ+zYsWRnZ3PW\nWWexfv36sLLv37+fwsJC+vfvz9ixY1mzZk3IfbfbHfK/uPTSS5k+fTp9+/Zl3LhxvPPOO4G87777\nLt/5znfo27cvl156KdOmTePuu+9uVm+JwkxAAyVZ6UhdmKY120gpyM2FLVus9NxcJ91/pMu/JHWN\nQiO8Xi/zJ01iPrTZirenjA0bNvCnP/2JtWvXsmvXLv7973/jdruBULfNxx9/zI033siSJUvYsmUL\nO3fuZPPmzWFdIbNnz+bXv/41l112Gbt37+aaa65h0aJFLF68GNM0qa6uZs+ePdx0001hZbr88sv5\n7ne/yzfffMPdd9/N4sWLm3W5fPTRR4wfPz5w3bNnT4499tiwrpeysjKuuOIKbr/9dnbv3s3ZZ5/N\nvffey9tvv826detYt24db7/9Nvfee2/gM1999RW1tbVs3LiRRx55JKS8733ve7z88ssMHTqU3bt3\ns3DhQj799FMuv/xySktL2b59O+effz4/+tGPwvYyZs+ejdfrpbq6mn/9619N3rPxO7/wwgsUFBSw\nc+dOLrzwwoD+6uvrueiii7j22mupra2loKCA8vJyWZPQiTEMa/qpdeRQXT0DmIHWOUHp1pEOriNI\nZaMQbMLbasZjUYZNZmYmBw8e5KOPPuLQoUOMGDGC3NxcIHSx3bPPPsuFF17I6aefTteuXZkzZ06L\nlY52Bt4BeOKJJ/jFL36B2+2mV69e3HfffTz11FMcOXIk5HMbN25k7dq13HPPPXTt2pUzzzyTH/3o\nR83Ord+7dy99+/YNSevXrx979uxpUTY/Tz75JL/5zW8YMGAAAwYMoLi4mMcffzxwPyMjg9mzZ9O1\na1eysrJaLAvg6aef5oILLuCcc84hMzOTWbNmsX//ft58880mn33mmWe48847cblcDBs2jFtuuaXF\nNQRnnnkmU6ZMQSnFlVdeybp16wCorKykoaGBm2++mczMTC666CJOOeWUZstJJDKm4BBPXeTk5BBu\nPCGdSF2jEGrCydGaGdXVzAByGpvw5sx4LMqwGTVqFCUlJXg8HgYNGkRBQQFb/AYmiM2bNzNs2LDA\ndY8ePaLyo2/ZsoWRI0cGrkeMGMHhw4f56quvmjwnOzubHj16BNKCP9eY3r17N3FD7dq1iz59+kQk\n1+bNm5vItXnz5sD1wIED6datW0RlgfWewYPcSimGDx/Ol19+GfbZw4cPD3l2SwwaNChw3rNnTw4c\nOMCRI0fYvHkzxxxzTEje4cOHyyK1TkzgJ2+a4PGAx0MxnsB5cEMxXex06hqFMOTk5LTbhrenjIKC\nAl5//XVqampQSnH77bc3yTN06FC++OKLwPX+/fv55ptvmi2zcS9i6NChIfvHb9y4kS5duoRUdABD\nhgyhtraWffuckBV+ucIxduzYQIsZrJ7D559/ztixY5uVrTW5hg4d2ux7RFJeTU1N4FprzaZNm5pU\n2mC968aNG0Oe3RaGDBnSxOhs3LgxKd1HMqbg0JG6CFT0hhEwBLODjUKQJRCjkGii/Q8011OI0TM/\n/fRTli9fzsGDB+nevTtZWVlkZjbdIfziiy/mhRde4K233qK+vh6Px9NiS7TxvYKCAh566CF8Ph97\n9uwJjDk0np00cuRIJkyYQHFxMYcOHWLVqlW8+OKLzT7noosu4sMPP2Tp0qUcOHCAOXPmMH78eEaP\nHh2xXPfeey/bt29n+/btzJkzJ2RwPVouvfRSXnrpJZYvX86hQ4f44x//SFZWFqeffnrYvPfddx91\ndXV88cUXPPzww2165mmnnUZmZibz5s3j8OHD/OMf/2gyaC0I6Y4YhRg98+DBg9xxxx0MHDiQIUOG\nsH37du677z4gdE792LFjefjhh7nssssYOnQoffr04eijj6Z79+5hy208H//aa6/lqquuYvLkyeTm\n5tKzZ8+QSjA475NPPsnq1avp378/c+bMYfr06c3KP2DAAJ577jnuvPNO+vfvz5o1a3jqqaeazd9Y\nrrvuuosJEyZw4okncuKJJzJhwgTuuuuusHK1VKaf0aNH87e//Y2bb76ZgQMH8tJLL/HCCy/QpUvT\npTXFxcWMHDmSnJwcpkyZwtVXX93s88Ktb/Bfd+vWjaVLl/Loo4+SnZ3NE088wQUXXBCV2yteyJiC\nQ4fqwu6FBHmPyMcM5z1Km+lH6bchXix2q4rjjld79uwhOzubzz77rEWfv5AYTj31VG644YYmBlU2\nxOsk2LW/CZhAbW0tlZWVnHfeeYAVRtJolDeRdMp4CmEJ3rcoP9/5x7R176O2lhEhL7zwAueccw5a\na2bNmsWJJ54oBiFJWLlyJaNHj2bAgAE88cQTfPjhh0yZMiXRYjVB9vtxiIcuDGCkPWV9NDD9jTfs\nmUjpR3oYhVhU3HHc+O7555/n6quvRmvNd7/73RbdNEJ82bBhA5deeil79+7l2GOP5dlnn20yiC90\nIuxp66Yblrlh7XGwu3dvSq7NJRswfNYBWI3JNCD93EeCEAfke9g5MMvKMIJiQHu9XnIBHaaX0Dhv\nIpB4CoIgCB2Iae9KEOu8yUzcjYJSaohSarFS6mul1H6l1EdKqcnxlkMQUhlZp+DQkbrwDXZW33u9\nXibNnw/zw2+IF5w3lYnrmIJSygW8AawEzge2AbnA1/GUQxAEIRLMrAOE+GLsvbdyw+QdmXUgHiJ1\nOPEeaP4l8KXWujAoraaZvIIgNIPMPHLoSF24AZ997gVOXfM424Bqbmqy80HHSRFf4m0UpgIvK6We\nxtLhZmCB1vpP7SlUonEKgtAR+Op8qNl2X6EWOAL0HkLu3FzIDs078r8WxVu8jqG9oduiOYADwH7g\nt8B4oBDYDdwYJm9L4eZaCEfX4u2IiHWkRQnHGRkSjjNyJBynQ0fqYvp7i/wP0bq4WFfPnKlnzpyp\ndXGxdQQ9O5A3gZCC4TgzgLe11nfa1+uUUt8CbgSa9BYKCwsDMQlcLhd5eXnxkjOmpEI4zkOHDlFQ\nUMA777xDTU0NK1asIL+FedcSjjN00ZR/sDNe11VVVXF9Xme9dtvOI9O6iWEYlAIXhctfZUJeYVzl\n858Hb0bZbtprVaI5sNxzf2mUdhWwJ0zelixhs0GyowmqHYsyOpp77rkn4tZ9az0Fj8fTbFn19fV6\n7ty5etWqVXrIkCGttqx/+tOf6rvuuisiuRpz6NChJmmvv/56SOu+Ndxut37ttdfC3mvcU+gImvt+\nCunFCu+KJmnN/efD5Y03xKCnEO8pqW8AYxqljcYZy4mYcLPQrGli4SOmRRqiOdoygknlcJxdu3Zl\n5syZTJo0KezursFIOE6hs2C4jQ7Jm8zE2yg8BExUSv1aKTVKKfUT4GbCuI6iJRZBtdtTRqqH44wG\nCceZeGSdgkM8dGECHvvAZwbOO/7J8SeuRkFrvRZrBtKlwAfAPcBdWus/R1tWkkXjTPlwnG0huCwJ\nxymkMwZBRmHxWYFzIzHidChxX9Gstf6n1jpPa91Daz1Gaz2vLeU0iqRJW4Jqx6IMP6kejrO9SDjO\n+CLrFBzioQvTZ+IxPXhMD/kj8wPnps/s8GfHm/TYJdUmFlvZtqeMgoICCgoK2L17N9dffz233347\njz32WEieoUOHsmHDhsB1LMNxBoehDA7H2bNnT8AKx9naeEFb8ct1/PHHB+RqbzjODz74IHCtdevh\nOIOf3RaaC8c5atSoNpUnpA+G20ibMYPWSNkN8ZIs8FrKh+MEK3rcgQMHmpxHKpeE44wfMqbgILqI\nLWIUYvTMVA/HCXDcccfRs2dPNm/ezA9+8AN69erVbKtbwnEKQnqSdvEUUiwap4TjTHIkHKeQSsQi\nnkJaGIVU2/soOBznL37xC9asWRMyV15IHI3Dcd5www1UV1c3ib4mRkFIRiRGs02KReOUcJxJTKqE\n45QYzQ6ii9iSFj0FQYg3if4eSkXoILpwEPeRICQI+R4KyYjEaBYEQRBiihgFQUhBZG6+g+gitohR\nEARBEAKkxZiC6TMDe5CYPjOwHD2apemxKEPoPMiYgpCMxGJMIa5BdqI5aGs4Tk/7g5/EooxgJBxn\nZEg4TkFoH6RgOM5OSSqE46ysrOTuu+/m3XffJTMz0wo7WFrK4MGDw+aXcJyJRaZhOoguYkvKjilE\nu2VtuPyxKKOjqampYfTo0e02CK1RV1fHjBkzqKmpoaamhj59+nDNNde0KJd/V9JoCRcoZ+PGjRx1\n1FFRbSMuCELsSSuj4PV6oTby/LEoI5hUDsc5ZcoULr74Ynr37k2PHj248cYbeeONN8LmlXCciUda\nxg6ii9iSskahMV6vl0k/nwRraV84zjaWkW7hOFeuXMm4cePC3pNwnIKQvqTsmILpM1Gzg36stcAe\n6zR3bi5kh+bPH5nfIWX4CQ7HedRRR4VE/9LNhOMEmDNnDqWlpc2Wq52BdyA0HCfAfffdx7hx4ygr\nKwv5nD8c5/Lly6MOx/n+++9zzz338Pzzz7eYL7isJ598knnz5jFgwADA6uVcf/31zJkzBwgNx9m1\na9cWy4LQcJwAs2bNYu7cubz55ptMnjw5JO8zzzzDn//8Z1wuFy6Xi1tuuSXw3HD4w3ECXHnllZSU\nlACh4TiBpA7HKX50B9FFbElZo2C4DcxCMyTN6/WSOzcXXdK04vOYng4pw09wOM6PPvqIH/zgBzz4\n4IMMGTIkJF+iw3Fu2rSpxfI/++wzzj//fEpLS5k0aVLEckk4TkFID9LGfQR2KM3s1vN1VBkFBQW8\n/vrr1NTUoJTi9ttvb5Jn6NChfPHFF4HrWIbjDCY4HKcfv1zNUVNTw/e//31+85vfcMUVVzSbLxzh\n5GpvOM6amprAtY4gHGfws9tCc+E4k9F9JC1jB9FFbElZoxDtgrJw+WNRhp9UD8f55ZdfcvbZZ3PT\nTTfx3//9383ma0kuCccpCKlPXI2CUsqjlDrS6Njc+iebkmxGIdXDcS5YsACv14vH46FPnz706dOH\nvn37NptfwnEmFtnvx0F0EVvius2FUsoDXAoYQckNWusm/pM2h+OcrdDF7XunWJQRKRKOM7lJ1nCc\nMrjqILpwSLl4CrZRuFhr/e0I8kZsFFJt7yMJx5m8SDhOIZVJ1XCcuUqpL4GDwGrg11rrti0ssIlF\nxR3Pje8kHGfykirhOAWho4h3T2EK0BtYDwwC7gLGAGO11jsa5W2T+0gQ4kGiv4fiMnEQXTikXE9B\na/1K0OWHSqlKoBqYDjzUOH9hYWFgkZbL5SIvLy8eYgpCRARXRv7BznhdV1VVxfV5cp2c1/7z4Ong\n7SXh8RSUUsuBT7TWNzZKl56CkLTI91BIRlI+RrNSKgs4HtiSSDkEQRAEi3ivU/iDUmqyUipHKXUq\n8CzQA1gcTzkEIdWRufkOoovYEu/ZR8cAS4ABwDbgLWCi1rrlDXlawbQP/7lhnxtB5/EoQxCE1MFE\nfvNhaW/oto46aGs4zhbvRkasAy1KOM7IkHCcQjxZ4V2hi1cU6+IVxRr7b/GKYr3CuyLRorUZJBxn\napAK4Tg//vhjrr76aqqrqwE4+eSTKS0tbTa6moTjFFIVE7s3ELQ2aTbgaSFvZyJlN8QzY5A/FmV0\nNPEKx3nMMcewdOlSduzYwTfffMOFF17IZZdd1qJcEo4zcYgf3SFaXTTO7fV6oZmgWtGVnB6klVGI\n9p8bizKCSeVwnP369QvEIWhoaCAjI4PPP/88bF4JxymkMr46X+Dc6/Uyaf58mD8/bLTF4Lydhvb6\nnzrqoJUxheJG6dXV1XrIL3+p+eUvdXV1dZPPNc4fqzL8rF+/Xg8fPlxv2bJFa611TU2N/vzzz7XW\nWns8noAv/6OPPtK9e/fWb7zxhq6vr9ezZs3SXbt21a+99lrYcj0ej77qqqsC148++qgeNWqU9nq9\nes+ePfrHP/5x4H7jMYWJEyfqX/ziF7q+vl6vXLlS9+nTJ6SscPTr10936dJFZ2Rk6N/+9rfN5iss\nLNR333134Pruu+/Wp512mt62bZvetm2bPv300wP3V6xYobt06aJ/9atf6fr6er1///4m5ZmmGTIO\nsGHDBt2rVy/96quv6sOHD+sHHnhAjxo1Sh86dEhrrbXb7Q7o7Pbbb9eTJ0/WtbW1etOmTXrs2LF6\n+PDhgbKC8xYXF+usrCz98ssv6yNHjug77rhDT5w4UWut9cGDB/WIESN0aWmpPnz4sF66dKnu1q1b\nyHv6ae77KSQ/I70rnIqmulpz223WUV3dpCIamWLjC8RgTCGlewoq6MjFWeyQ2+ieovmeQnvL8BMc\njvPQoUOMGDGC3NxcoPlwnF27dmXOnDkt+sa1YySB0HCcvXr14r777uOpp57iyJEjIZ/zh+O85557\nogrHWVdXx86dO5k3b16rK8iDy3ryySf5zW9+w4ABAxgwYADFxcU8/vjjgfvB4TizsrJaLAtCw3Fm\nZmYya9Ys9u/fz5tvvtnks8888wx33nknLpeLYcOGccstt7T4nv5wnEoprrzyStatWweEhuPMzMxM\n6nCcQttxAxpYYcLMEuhpPg7m48wsgWKPla7tPO6ESZk4UtYoGDj/OA3onByqZ8yAGTPQOTmh9wg/\nWBSLMvwEh+McNGgQBQUFbNnSdE1eosNxRkLPnj2ZMWMGV199dYsup8bPk3Cc8UPGFByi1YWvzoea\nrfCUKajMJSNjG72z9kNlLphWupptHZ3RfZSyRiEcOTk5kJOTsDJSPRxnMA0NDezbty9sJRwOCccp\npAoGoIs1Zplm7mrN+3+vZs/f3mfuao3HtNJ1sXUYCZY1EaSsUTBikD8WZfhJ9XCcr776KlVVVTQ0\nNLBr1y5uu+02+vfv3+wMo3BySTjO+CG7gjpErYsqX8hliw3BRnk7A2IUYvTMVA/HWVdXR0FBAS6X\ni1GjRuH1ennllVeadflIOE4hZakcE3LZ0ozDxnk7AwnfJbU52hyOE8v/365nx6CMSJFwnMmNhONM\nfqLVhccw8ZgGJrCstpYFC95mX8MhZl4/iezs7JBtLvx5U4WUi6fQUZg4M4PycVYmGrRt76O2lhEp\nweE4Z82axYknnigGIUloHI7zww8/ZMqUKYkWS4ghPh8oBbhNcC8mk/fIRFP6r5OAkcz2GeAzAJje\nCX+WaWEUDNpfcceijEiRcJzJS6qE45RegkO0unDjQ6PAB14f3A98zUAepAxrZGF2IK+HRTGTM1VI\nO/eRIMQD+R6mLo1dQl6vl9xc0LrpYHNndB+l7ECzIHRmZJ2CQ7S6MNy+kOucnBwg/Oyjxnk7A2IU\nBEHoVBiF7g7Jmy6I+0gQ2oB8D1Mf07QOAHO2iVFsAGAY1pGKdNrZR7LCVBCE9mJgYvjnHOabSOw1\ni5TrKXRGZE66g+jCQvTgILpwiNtAs1IqJXsUgiAIQnRE1FNQSm0DHgMe1Vp/3OFSIT0FQRCEaInn\nlNRfA6cDHyql3lJKXaeU6t2eBwuCIAjJR0RGQWv9V631acBYYBVwL7BVKbVIKXVGWx6slLpDKXVE\nKdW2LS07ETIn3UF0YSF6cBBdxJao1ilorT/RWv8PcAxW76EAWKmUWq+U+rlSKtIxionAz4D3id/e\nc4IgCEIrRDX7SCnVHfgxcC1wFlavYSEwBJgJrNJaT2uljH7AO8BPsfad+0BrPTNMPhlTEARBiIK4\nrVNQSp2MZQgKgHqsQecbtdafBuV5Eauyb42/AM9orSuULDgQBEFIKiJ1H60BjsVy+QzXWv8y2CDY\n+IAWt/tUSv0MyAX80VekKxAB4jN1EF1YiB4cRBexJdL1Bzla65qWMmit9wKFzd1XSh0H/BY4Q2vd\n4E+2j7AUFhbidrsBcLlc5OXlBRap+L8Ict25rv0kizyJuq6qqkoqeeQ6Mdf+8+D46O0l0nUK1cB3\ntdbfNErPBt7RWudGUEYh1vhDQ1ByJlZvoQHopbU+FJRfxhQEQRCiIJ57H7mxKvDGdAeGRVjGMuDt\noGsFLAI+BX4XbBAEQRCExNDimIJS6sdKqYvtywvsa//xE6wQRb5IHqS13qm1/jjo+AjYB9TGa5V0\nqtLYddKZEV1YiB4cRBexpbWewrNB5wsa3TuEZRBua8fzNTLYLAiCkDREOqbgAyZorbd3uETOM2VM\nQRAEIQpiMaYgW2cLgiCkCR26IZ5S6jalVI+g82aP9gggtI74TB1EFxaiBwfRRWxpaUzhZmAxsB9r\nC4uWmu0PxlIoQRAEITGI+0gQBCFNiGc8hXAP79qeBwuCIAjJR6RbXd+ilLok6HohcEAp9am9fYXQ\ngYjP1EF0YSF6cBBdxJZIewozgW0ASqnJwE+Ay4H3gD92jGiCIAhCvIl0ncJ+YLTWepNS6v8BA7TW\n1yiljseKoXBUzAWTMQVBEISoiOeYwi5gkH3+feA1+/wwkNUeAQRBEITkIVKj8G/gr0qpR4FRwMt2\n+gmAtyMEExzEZ+ogurAQPTiILmJLpEbhJqzQmwOAS4K20D4ZeLIjBBMEQRDij6xTEARBSBPiGU/B\n/8ChwNE06mFord9tjxCCIAhCchDpOoXvKKU+Br4A3gXWBh1rOk48AcRnGozowkL04CC6iC2R9hT+\nAmwErgO2IDEQBEEQ0pJI1ynsBU7SWm/oeJECz5QxBUEQhCiI55jCh8BgIG5GQRAEIVpM+/CfG/a5\nEXQutEykU1LvAH6vlPq+UmqQUqp/8NGRAgriMw1GdGEhenAoCdKFAXjsoyLo3AjKbyK0RKQ9hVft\nv/8Kc08DmbERRxAEITqqwqR5vfaa2pycJvdMpNfQEpGOKRgt3ddamzGSJ/iZMqYgCEKreOzDj9fr\nZdL8+WwBqmfMIKeRYWicP52I25hCR1T6giAIscAEmqsFc8Ok5XecKGlBxEF2lFInKqX+pJR6WSk1\nxE67SCn1nSjKuFEptU4ptdM+3lRKnd8WwTsT4j92EF1YiB4c3KaJxvJja9NEL15M9YEDzDxwAL14\nMdrjsdLtPEZCpU1+IuopKKXOBV7A2gjvHKCHfetYYDowNcLnbQJ+CfwflkEqBMqVUidrrT+IXGxB\nEIQwGAYYBjlAKTA3weKkIpGOKbwNLNZa/0kptRsYr7WuVkpNAF7QWg9pswBKfQP8Smv910bpMqYg\nCEKrmFVVGHl5TdIV4VfZNpc/HYhnPIWxwEth0ncAbZqSqpTKVEpdBvQC3mxLGYIgCEZ5eZM0r9cL\n3vC7+ofLLzhEahR2AMPCpH8Haz+kiFFKfVsptQc4APwZuEhr/VE0ZXQ2xH/sILqwED04mD6fcw7c\nUlvLlUuXMmHJEm6prcWDrE2IhkjXKTwJPKCUmmZfd7Wnqf4RWBTlM9cDJwL9sGI9P6aUMsIZhsLC\nQtxuNwAul4u8vDwMwwCcH4Vcd65rP8kiT6Kuq6qqkkqehF5XVWEqv8ckHzDYiY9DdIE7rVGFKsqB\nddYgc372y31DAAAgAElEQVR+csnfjmv/uS/IMLaXSMcUumFV/pfhuOoU8ARwjdb6cJsFUOo/QI3W\n+rpG6TKmIAhC63g81gGYPhPTZ1JbW0vpc5UUX3ceAIbbwHAbTfKnG/Fcp1APXKGU+g2WyygDeE9r\n/X/tebhNJtAtBuUIgtAJKdl6gCL7PLjyL31f4TFWt5hfaEqzRkEptQhn8F41OgeYouwum9b62kge\nppS6H3gRaxyiD3A5Vn9P1iq0gGmagW5jZ0d0YSF6cCjb/lmTSn7lypXgC5+/PEuMQku01FMYSOiM\nrsnAEeADLMMwDqvHsDKK5w0C/oa14+pOYB0wRWv9nyjKEARBcOjd2zk3TVaWlXFW1TOgs1hZWMhk\ntzuwfgEAlysBQqYOkY4p3IHlNrpGa73XTusFLATe11r/NuaCyZiCIAgR4CosZGfOYifBB7yXZZ1/\n5wC4Q/P3806nrqwsPsLFmXjGU7gFOMdvEAC01nuVUnOA14CYGwVBEIRIyHO7MYtDG5ArV64kf1E+\nelHThqWRpoPMsSLSdQq9gKFh0ofY94QOpPF0zM6M6MJC9OBQF2Y65uTJk5v0EITIiNQoPAcsUkoV\nKKXc9lGA5T5a2nHiCYIgtMwZo6LbsmJqntExgqQJkY4p9AT+AFyLM330EPAoMEtrvS/mgsmYgiAI\nUVJSblJeZQJQ5fORZy9+nZpnUDTVSJhc8SIWYwoRGYWgB/bG2hkV4HOt9Z72PLyVZ4lREARBiIJ4\nbogHgNZ6j9Z6nX10mEEQQhH/sYPowkL04CC6iC1RGQVBEAQhvYnKfRRPxH0kCIIQHXF3HwmCIAjp\njRiFFEB8pg6iCwvRg4PoIraIURAEQRACyJiCIAhCmiBjCoIgCEJMEaOQAojP1EF0YdGZ9GCaTrA0\nw3DO/SroTLqIB5HukioIghA3TNMJfxAcCkEpxxj4qapy7gvtR3oKKYBE2HIQXVikux7CNf69Xi/g\nbZJeV2d0tDidCukpCIKQ1Jg+k2XvLWPhwrVwTj23LDud7OzskHjMQuyQ2UcpgMTjdRBdWKS7HvKm\nmqz7hxGU4gXmQ+/NsGcOkBO4c+ykEj5bJVGXQWYfCYKQprjyTLQGrWGFhmKdQ6HvBvjPxRTrHIrt\ndK2h96iqRIubVoj7KAVI5xZhtIguLNJdD3V2HAQAAxjp9TLp55MAmP7n8eTkOD0FGWWOLWIUBEFI\nOnYck4GabXlB8r0w/hPYuR96du1JyWW5ZPcA0w0VOTD89MWJFTbNiKv7SCl1h1JqjVJqp1Lqa6XU\n80qpsfGUIRWRedgOoguLdNdDbtcj6GKNLtZ4CjVMrEafdiv7JlwPE6vBsNJ1sab/Rx8nWty0It49\nhXxgHrAGyyDNAV5VSp2gta6NsyyCICQpdUBgtNQAMjaRtXAHWUDpFZtgcg6z7dvHhvm80HYSOvtI\nKdUL2An8l9b6pUb3ZPaRIHRSCp+aT1nBzwPXXuB+4OuBA3lw27aguUdQuOTPlF02I94iJiWxmH2U\n6DGFvlg9BuklCIIQwD14jDW1CGudgukzyaqtpRwYn50NEFin4E5zV1q8SbRRmAu8B7yVYDmSmnSf\nkx4NoguLdNeD4fM550GL1EoBT6O8rldekRlIMSRhRkEp9SBwOnBGc36iwsJC3PbUNJfLRV5eXuCH\n4B9ok+vOde0nWeRJ1HVVVVVSyRPra+rqAobPBMrs+/mAxzDwmSZ5QJFhkDd4cMLlTeTvwTRNfEFG\ntL0kZExBKfUQcClwltb602byyJiCIAjWRkjOlqjhd8oTgNiMKcTdKCil5gI/wTIIG1rIJ0ZBEAQh\nClJumwul1J+AQuAKYKdSarB99IqnHKlGY9dJZ0Z0YSF6cBBdxJZ47330c6A38BqwOej4RZzlEARB\nEMIgu6QKgiCkCSnnPhIEQRCSGzEKKYD4TB1EFxaiBwfRRWwRoyAIgiAEkDEFQRDiQkkVFOU55+V1\n1nlVHeS5rPOpLitPcF4hclJynUKkiFEQhPTCMME0QtNWrlxJPqAnT241r9A6MtDcSRCfqYPowiLV\n9WD6TArnF5L/+GJ4fDGF8wvxmB5Mnxl9WSmui2Qj0RviCYKQwpg+M7BZnX8308bp/g3tVu+vRDHR\n+qDbgBMyoHIJAItPKIDJkwMxEnrt8QHu+LyEEIK4jwRBaDMe04PH8ISkeb1ecufmoktCf78us4w6\nozAk7emnn+YyQE+bFpLuNk18sq9R1KRDPAVBENIIr9fLpJ9Pgj3WeU5OTviMpol32TJuXbuQ3oD3\nzTfJyc6WTe6SADEKKYB/C2FBdOEnWfRg+kzU7KCGaS2wxzrNnZsL2c6tvoMvQlVcY1148+GT8XAE\n6NaH3EqgB+DzQEUF4wbdFrFxSBZdpAtiFARBaDOG28AsNEPSmnMfGR43pseOpmYfNTW3UbblPYon\nTrXy2IfHY3Sk2EILiFFIAaQV5CC6sEhmPeTk5IT0EMJhACO9XuZPmsSVu7cw/f3q5l1NrZDMukhF\nxCgIgtBmXF3GBc7NqirMOmtFWv74RXj80cJcLoy8PMYdmQDKcjWZbljmhrXHQX0mlFybSzZg+Ozj\nv8bH8zWEIGSdQgog87AdRBcWyaKHusMDnAtXHWDiW19GxVaTgJPIZRmKAbkXgNagNYZXM3eF5m8L\nq/n7azB3hcazwkpHa4y8qRHLkCy6SBekpyAIQpvx1bkC54bbIGNjBudXLiQLOPuEs5kctFLZd2Bw\nk8+31WUkdByyTkEQhDaTN38962aMsS5MoKyKzCOPANCQcT248wKjxyeXvs/amSfaec2W4y4HpwkR\nI+sUBEFIKK6sLAJNNwO8I/sxYcIhdgDVa/sR3BEw+vYNujBarvTFICQMGVNIAcRn6iC6sOhIPZRs\nrQr8NXwmhs/Etb48cO6/D/B/A/ehFIEjNxd27DgKuh9Fbi4h9+wx6Jgj34nYIj0FQRBCKN/qo2hw\nHkXr6ygyTVb6fCwEytxuK4NhgD080OCqxe/l9c8+qt1vUPkFnPetGqAmMPuo8P6s+L+MEDViFFIA\nmYftILqw6FA9+Jv0hsHKjAyMxx5FA9eeHTpwDJC1bZsjU14efqmUgtWNhgTdYQaaY4F8J2KLGAVB\nEELYevgAgZHKLVvgyBEA8rdsaZJ3eNddgfPgseP8fPB4rHP/8IHU3SmC1jquBzAZeB74Amvnk+nN\n5NOCxYoVKxItQtIgurCIWg9R5M9/5v/ZqwnQ1aAvA/39/v11dWCVgXPkL3soOjk6APlOONj1Zrvq\n6ET0FHoB7wOLgccAmXcqCB2Nf4qn3Zz31tZCZSU5551n3Q9uynfpAloH9ic6uraWp76oZPG3rbyG\nfVjllsVDeiGOxN0oaK1fBl4GUEqVxfv5qYj4TB1EFxbR6sHEh4G9vcT4WtYuXEh9nz2cPn4i2dnZ\nGG6noh/30RqYCvhM8JlkA/mLZ8P0YiuD27AOYKpva3tfpd3IdyK2yJiCIKQiwYu7WlsIBpRl7MJQ\nytqIDrgf+LonFL1WirWUwB/zDD676zrrpNwF5dbn3VV54LVXL091QZF1mlc3JaavJSQB7fU/tecA\ndgNXN3Mvlq62lEZ8pg6iC4sV06c3Sauurrb8/mHw+/5XaK1n7tihs/7wK80Dt+mZO3boYjvdz8hl\ny5qUC9Vhyy0ujlLwDkC+Ew6k6JhCxBQWFuK250a7XC7y8vICXUX/ghW57lzXfpJFnkRdV23dCkHB\nZZYsWcLzN93ECGCG10tNTU1Ifu+7PlS5AvKhyg1DNoPKoPSJW8HlZnZWOQxeBzmQPea5wPNGjhzJ\npEnzgY0sWXIhBQUFIfL4nU6J1kdnvfaf+3w+YkVC9z5SSu0GbtRaPxbmnk6kbIKQ1BgGVFQELr3A\nfPt8BtB4m7mhTz3FlmnTyDdNxi9bxl8PHADgZ1lZZGdnYxoGFXaF0+3pV6m/7HsRlZyf73iuhMQj\nex8JQpITzb5uUe0B559JZJOD1UMgN5ecMI2pbuXl1jQ/w8A7ciRLTr2fbUDR6qImO5W6unfhYKCI\nHLzeGeTmgtZNdzT1r0UQ0oe4GwWlVC/gW/ZlBjBSKZUHfKO13hRveVIBU2LQBkg1XYQbD66t9VJZ\nCeedZ1Wy/vHgsrLIx45vevdT5gU/xDStNny4VWPAob3drPg2bhPcy2Dsh9Abcq8tAbLBZ1gH0Ouv\n7kYy5DRXbFKQat+JZCcRPYXvAsvtc4017WE2UAZcmwB5BCEuGAaMHOll0qT5bNkCTz01I6SVXlVn\nEpgY6jbBMKmtraXis0oMw15P4DYAg2XHdg0YBdMNpgG1W2qp7LWf875rP8/tTDPt0fMgWoOJgYlB\nbW0tpZVQfJ4VN9NwnsyU+90BeVura6UuTj8SsU7BRHZnjQppBTkkgy5Mn4nhjkyO8iqT2Sp83tzc\n0Oted/qcC7dBbb/xLFiwCsblUzv+LLKzncDHO6cXNM377v9jnzqTieNvCckLcNh/bVpHNtnkm8Bq\nO4NBwCpMPBDRq1kfC/9qcSUZvhPphIwpCEKUBBsF02di+kyrlf5FJed911716zasPFN96PLgTzfv\no++2PM8fwtjmAxgzDRSU3vEKMNlZTfDpQNRsO7PXnlF00hFrRtFZ9owitwk51mB09unPWXIZ0voX\nWkaMQgogPlOHZNCFz+UOnBtug5F6JJPun8SWPVt46pKnQlxCG/NGBc5LqqC8DvZvgT7zwTCt9Kku\nKMqDhiFdwBNkFXxAxpWgMuDUfHAeC5WPQHHQgLLXC3PnWufLboFGg8dHopgilGpftWT4TqQTYhQE\nIUpMcFrpI/NhsAFn/QQyu5K7YTHUYG0RUVNB18LVgc/luUy8nyxjwVsH2QeM79ud7CHZ5LkMwEB1\nORio6MeXVNH94094d+yboOGkj5+kh3cIdVNdrCvKQz3+F44EyeQFTn1yP9uA6luaTkl1d4AehPRE\njEIKIK0gh47SRTTTQV17BuALaqV7vV5O/c88tqGpvnh6SE+hV81qZxtqtwEbM2DfQgBKj7kcJjsu\noSx1iMN+eabWsWxkJYfnLWQvMPFXGfYeRQYG0O3k0xxX07QqmLIFHjgFumhyK9ZDRQ284oKn8wAY\ntMQdnUJSCPl9xBYxCkLaEk1Ff///LsUwftzqtFGATd98E6jo/YvB9h48SE+gpKQkZDFY7/p6sHsV\n+V4Y/wn8ZR/QNYv//mAx2T2s2UMVOdD7UpNATZ+fD4ZB1pn/Q0a3brCu3n4pD1RUMOLFtXwWsEt5\nQB5erzd0rKIQeMo+LXNHrjihUyNGIQUQn6lDyU0LKJp3XUR5o6no3x6/Dvgx1JVQe2AJf3l3H4cy\nGph4oA/ZWT2gbir+XeB09sDAfu+mG5YZMGbe4+wFuOk6yAaP25rMM1hnstvuVZjAstpaTliwgL0N\n9XD9DZCdjQcrb96qZ/HHtjTsw9vMgrRRlyyAH55slWu2vp7A7YtIZSmJ/D5iixgFIaFEM70T4JWq\nKooAs6QKs7wOX52Xrf+3h4nf/TYAxlQXRpHlMqkavdn6kNukdvwyFsxbxb4uh5jon95pz/kH2GWc\nYjXS84vA+B6cuwiA0oZrgHHMLgEusorr8Z474hZ97389B8fZed3WkQVkZAL/ustK91lH3p0/hDMu\nsT9vtrggbcqezwI6kRlFQkxp7456HXUgu6QGiGYTyIeiCITVYZtLRlFw8aLpTdKqq6t1dXX4XTmP\n/eP/BM4rKip0r4zpOovpuqKioknerstfCwoSVqEZ00Mzppt13iiImPpkjcaDdVyM5porreNinHT7\n6PHK602eVVFRoSvCfGd/8PNHwr9fmLwrrv9B2HcOi+wMKoSBdN8lVbDw+8YjcYOUl0NRUWR5y6Lo\ndpf8YQFFsyJz25T8czNF/mJtQZqL9OXD5bynz2TZW8tY+NeF1DfUM2PGDLKHZDtz/oGto9xWI31a\nFUzZAfe6Aciv3gHVZsjgasYnfcGjGN89H/R41m0uBGD81c/hGrKcuq0m6w5a8/iVWoMu1paPZ4cX\nDjxsCXVMNeickMVdXf72oiNzSRXLlnzCwqrVNKib+dmpS8juMSTQY5n4z26OYlpp/RuDJ0ak38Bn\nhOSiwza6ii9iFFIAn88EjFa3SQCoG7UH6B1R3lW9PsHZ3MD2X0OTfADz9u3yx1Vp1XVTdsopgbz+\nDdjuP/VU2LaNXz0VOo//rW+d5kzvBKgFbA9M6epSyIbZFU4AmK4DZttu9zxWrtyFscqLBiomnc3k\nyZNDBlczNygo1qwrN+GVJahD1tyedTUNcBDI88BU6/0zNqyhATDrSjCzy6mt3U8lcF72u9Zr1E3F\nsN/qKBqCFpnlAf2AKkvmtycCOcyuAG6FK44d47xba36eKCoJ8aM7JI0uwmx01WzYUzEK6U80vnGz\nvARjalHrGW0+W7W10UpXi8bbJAD0LfWh1DjG51fR3djC1z/5FpmZcNni9fSghjrTxboKqyWd9fej\nACgpN1myYhnvrV3AoYYGTjn1enpkZzM1z6DIrjS3jhvmPKQoj5qLanjioQ0cye/GkNtyGDlyZOD2\n58ceDvjczfx8ysaP58lLLF/5wZIS3NnZGKaJUVHB5g/tFjqBHRj4cNUqnvv6I4p/fD0Qui9P5mPz\nUVjz+Cl7D32SlT5z5nu4XEcC8/gBMmlA23J4saONDRzIg9u2NYk21v/jSutkahFMLSIb6BH2vwHD\nXcfyTci4b/OrlD0eaf13SuzG0PxJk2DLFmY0agwlM2IUYoRplmEUGiFpzbW8TbOsiVFoqZXe+7Iu\n6Hv9Vy1vZdxj5TZr4zNfHWWvPM27XxxBA8ePzsA9xo1RaGC4rbxd3ujhtNJ3AXuzAMXbu0qhP1Ss\nm82t66zbGef8wzFMbhMGzYOxfQAom/Yf6DGO2fZOmxmf7EHZs2XGl5fQfcUSjjScAMAnXd/BRw/K\ni6ayzjTJ2PBOQPaqPyygfNULfDBgAAoof+yfuFzZuMadgWG7rjInTqABrCXARXmsXLmSfKBq4eQm\nuuivMkIC0GfV1rK5spLF5zUNQH9K1aomac2Rt31c4DxRO4kmRcs4SUgaXZgmYVtv0LQFl5/f4eK0\nlc5nFKLotpU8PZeiabdElPefdd3w+B9RXsKyFUtYsPY9GhoOcf2pp5Cd3QMjbyrG1CJ8W7NC5PEu\nW8b8hQuh3vKj52Rnh9Qq6/NseextEuq8O+lZ2nSbBID6QX2tOfQ+A/6zF/r9HYDF//kJbP0hsw0C\ny1szjhoCHh3oVazt64XMI0zoeyw9dI+QXgXT3sGZh2lAWQbwqHU9Zia4J0MxVo26QTlZXUWU7TyJ\nt1dbsXyPP/UV3NmTMVxW1q6HuzmLu2ZdB8P7wIvWJrrrCi6HadOwPTEAZB2xvrIlW6soP1AHI6Df\ngToMn6WMqVkuigZbMo/Ytx3A6pUE70O92l5lHKTjX1V9BnlnEAmFbuc8hh4hIcUx3YXO94yWY1yY\nhWWtNj4SRaczCmaZz/mhtuL3K8vs5fjRcVwbHwHTzrAqEMM+1h87rtEGZRPJ1B8CXSmtmAiubGb7\nymHdrRx93M3NtyhKS62/sx3XRu1DVjchz2VSVVHG4vetDQ7cRzJwj3cHtkkA6MIh6v1yDAdWAj37\nw4THQAEV9gFkXLaahuBexZf+XsXaJr2KzPU6sAhrfPd8uk8yeHebC1QmJw1YTo+uy6mrNFlXUYGe\ntiZ0G4ipBkz8NaBYPHg5uJYz22fC7Aq6TF2PsxIMMM6F/gesb+Yn54LHVrwtc6/fVcEdeRQNzqM1\nB1zhnj3WSQTNdsMO+xoJ0VT0HWUUksaPngQkiy5Mn9up6J2ZHnDKKU26kCF5k4xOZxTuH9bb+We0\nMgi68eihzgfLS/jg74+wNGsCAB/878/49uhBkDcVphZxpP8xaI8/cwUrqWAKU2kAllPKZCsZgG6v\nfw9mW5W/v/W/6YPX+PKb/aw2LgBCW/97H/tfFJDvg6NW7oZ+PQHYvXI31ILHgAq3lbd3xiEOFmvM\nqirMujpqf7Cfyl1fcN4gK66R4XJh5FkF9/z4XadXUemiy5G/ooDFlT+DrXkhvYpuR7py0O/7t3cG\nndgbKr94g/O+ZbtjDA+G26DH2y+xvzi0ZbRy5UryF+WjF4Wm917+NofsJP8Adu3+bnzxyS6+fZLl\nuzKmujBMS+a8X2URKUX+Of+RkASVipDaVF7o7EZlYrCsdiQLF86nvt5gxsTpZGfnBBqRwXmTjU5n\nFNaPcwZBS/Jvocz4AesKrFb56sXrcVHDVLOcooq57Flf6bg2phbBwSHwL2s64tL/8rB02rTAUGXX\nAcPwO02OnfYs+6fUsn/bNsjQFBx1Pl3pSpdXevP505eQMcDxo+f5Snj9749QkWVFRjnqL//Dt0cP\nIi9vKuRZbeEup57MYaUw8/NZZhiMra1lX/cuDBth+fQ9Hg+GHa938FuPw7fOBFcd1Jlk94AeX5kE\nPOUuI/DsobXbsJZAmeA2Wenby0KgzF0OlBPsYR+155vA54KniIaj38f/gVN+aOm43KS8yrTS9XQM\nu8XkH8TuvaoKzj7FKrcoD6OV5n9ekPydmWRoGScLyaKLytwMxwGQDxguuHYUdIHSPtbU69keoAL6\nLctIkJSt0+mMwgGyApX3+JoSDpbfBidZFfLBilLIHkRZzVRuRdNFr3F844A5bRofHnOMPTNmGuBU\nmz0asqj3t3jNS1i2zMuCg/PYB1yy8XKrlTADjKcg86NuYYzNS0BTYwPQRSlrjZXPJNtnck4tVD73\nINnXFVsZCj326lwYbFpB1luruAHOOLTfktcwMO0flg8CYyP+dwM4Y9+uFssK5pIu7sB50VRnBlPY\nvD1HR1wuQOHEwVHlF4R44XJBnb8O8JmU/aOMxZusHsH0EW/gHu93yRq4zcTJ2RppYRRMWp8x4mf/\n0OzAgOk6imDlSfCYNWC6vvQR1k92ZrEc2eD4/av+sADzw1XsP7ybnNpazOffAgjMjGlo6OlU9AYw\nEphvqbd0BpDjTIDsonpZM2iw3UeDpvHVqcewfu/X5A/6MZih7qM+/1kJo0/B8PkHl7Phi3zrxf3P\nc1unhb66CDUBhXZcAIPW9VcYFEOgNeZdGfl023mzWntyY0wi/2+nL8niR08GkkUXWw8fcOoAnwHv\nZUCmtSPu4veuhdrJAZds98NRhLeLM2lhFMrWV2KMceaDtzS9s2FwdqD1f9OqKl48coT9kwy21dcz\n4sgRME0u6OJi3hl59Ph6Nxxn5S2adR3/5T0nsBjsterQxWADv+nGl/a5CZg5OdTOmGEtgLLzGfbR\nu5vTdcxzmdTVmXCcvdaB9+10f27otdf2P0YwYFrkjnxevH9sIdZ5BaEzMmZAlr2E0ZqgZxqT8VX1\nZfGW9yg+z2psGvaRNyDysbF4kxZGocrngzETrV0ovV7++qc/cQi44cYbyc7JCWkJN2QcDHxu3hl5\nVvDzZirazK9UxNOORy5yFm8FntfMYpUTujlfiEjcPJecfm6L90NIghZTR5IMLcJkQPTgkCy6cLmC\nLnwm+EzcQP5XJpg1VrrbALcRmjfJSIhRUErdAPwPMBj4CCjSWq9qa3lbD2RZq1zLSzj490fYb88Q\n+ved5zNo9CDK86ayzl4s1ntbHYxqobAgztjj4pXAOAGYZg61tTPC7iVUWBa5vA+MiM4vPu8MaaUL\nQrIzNcup6Vtr7AXnTTbibhSUUtOAEuDnwCrgRuBlpdQJWutNbSr0wB6YrVjX/RY4/T6ofR2A9dn3\nsb63C9aXB+bY9zrucTgtsmIn+pzK2PHchG/9R7NfvRF5ViB5fKbJgOjCQvTgkCy68C+ajHXeeJOI\neVG3AYu01o9qrTdorWcCW7CMRJuo74a1MvcRN6c8dx+ZL/2bzH++zCnP3Uf+Yg/jH3GDR1sHvSMu\nNxkWKQFUVVW1nqmTILqwED04iC5iS1yNglKqG3AS8O9Gt/4NnN7SZ71eb2AAuTGHDm9BayhZVMR5\n+au58dTnOfnIS5yXvxoj36RkUVFg5/zBvvURy5ssRqGuLvIZRemO6MJC9OCQirq46emXIs777aef\n7UBJmhJv99EAIBP4qlH611jjC2Hxer2cdLm1Hea7T77bZFaRzhgOtO7iAcjzuaMUWRAEIba82P2Q\nNckliIcftmJ43HzzzSHpn+Q1rc+ayxsLknr2kZqtrL1z6sfDhAIAcv9TAqOzrdH9GmsVb/aY5yIu\ns/CyyzpC1A7F5/MlWoSkQXRhIXpwSAddPPzww8x8b43/osXKPpq8bUHpRrv3dSS2+2gvcJnW+rmg\n9D8BJ2itzwpKi59ggiAIaYLWupmJ9JER156C1rpeKfUOcC4Q3Lz/PvBMo7ztejFBEAQhehLhPnoQ\neFwp9TbwJjADazxhfgJkEQRBEIKIu1HQWv9dKXUUcBcwBPgAOL/NaxQEQRCEmBHXMQVBEAQhuUnI\npt5KqRuUUl6l1H6l1FqlVItxEJVS31ZKVSil9imlvlBK3R0vWeNBNPpQShlKqX8opTYrpfYqpdYp\npa6Jp7wdRbTfi6DPfUsptVsptbujZYwXbdGFUqpIKbVeKXXA/n7cFw9ZO5o21Bc/UEq9pZTapZTa\nppQqV0p9K17ydhRKqclKqeftOvCIUmp6BJ+Jvu7UWsf1AKYB9cBPsfYgLQV2A8Obyd8X2Ao8BZwA\nXIwVZv62eMueJPq4A5iDtVmHG2tM5hBQkOh3iacegj7XDXgHeBHYlej3SJQusMbqNgA/sr8X44Ep\niX6XeOsCa5HSAeB+INfWw7+A/0v0u8RAF+cB99p14F7g6lbyt6nuTMSLrQYeaZT2KfC7ZvL/HKgD\nugel3Ql8keh/UiL00UwZTwPPJvpdEqEH4CHgUWA6sDvR75EIXdiVZT1wXKJlTwJdXAIcxnaN22ln\nAUeA/ol+nxjqZXcERqFNdWcqbHNxGvC61vpgo/xDlVIjm/lMStCebT8a0Q/YESu54k1b9aCU+iHw\nQx19M98AAAaYSURBVOBmIC2mMLdRF/8FVAPnK6WqbVdLmVJqYAeK2uG0URdvY/Wcf6aUylRK9cFq\nMLyttU7Z30gbaVPdGe8xhbZsczE4TP6vgu6lMm3a9iMYpdQFwNnAX2IrWlyJWg9KqaFY73yF1npf\nx4oXV9ryncjFivV3KXA1cBUwBnhBqeYigqQEUetCa70Rax3UHCw3Uh0wDsut1tloU92ZvNGjHWR6\nVDMopSYBTwA3a63XJlqeOPM48Get9ZpEC5IEZADdgau01qu0FZvkKuAUYEJCJYszSqnBWO7EMqx3\nN7BcLX9PcQPZFtpUd8bbKGwHGoBBjdIHYW2fHY6tNLVqg4LupTJt0QcA9gyMfwJ3a60f6Rjx4kZb\n9HAWUKyUOqSUOgQsAHrZ19d1nKgdTlt0sQU4rLX+LCjtM7ucETGXMH60RRc3Yo0t/UprvU5r/Tpw\nJZBPxJFU0oY21Z1xNQpa63qsmSKN40t+H2t1czjeAs5USnVvlP9LrXVN7KWMH23UB0qpyVgGoVhr\nXdpxEsaHNuphHNbMEv/xG2C/fR7fvYZjSBt1sQroopQKDhKbi+V6SdnfSBt10QNrUDkY/3UqeEZi\nSdvqzgSMml8KHMSaYnY8MBdrmtRw+/59wKtB+ftitQqWAGOBHwM7gVsTPQMgQfowsKaj/R7L6g+2\nj4GJfpd46iHM5wtJn9lH0X4nFLAWMIE84DtABfBmot8lAbo4C6t3cTfwLayB6lcAH9Aj0e/TTl30\nsv+/eXYdcLd9HtO6M1Ev93PAizUQtAY4I+jeIqC6Uf5x9pd8P/Allssk4f+kROjDvm7Aav0EH9Xx\nljvR34tGny0kTdYptEUXWA2Dv9sV5ldYYy4p3VBohy6m2UZyN9ag9D+AMYl+jxjowQj6vQfXAQtb\n0EXUdadscyEIgiAE6Gw+NkEQBKEFxCgIgiAIAcQoCIIgCAHEKAiCIAgBxCgIgiAIAcQoCIIgCAHE\nKAiCIAgBxCgIQhiUUi8qpRYFXZtKqXZtKWJvZ/1C+6UThI6jS6IFEIQkRRO6y+RUrH36W0UpZQDL\ngQE6dA//tIn7IKQvYhSEtEUp1U1bm6q1G611XVtEaFRG2sSQFtIXcR8JKYPtwvmzUmquUmqHfTzg\n3ydfKeVTShUrpRYqpWqx9v9BKXW6Hbx8rx28/H/tiFz+cnvarp3dSqmtSqk7/LcaPfvhoOtuSqnf\n2c88oJT6XCl1sx3RarmdbZsdYH2h/ZkQ95FSqrtSqsR+5n472PykoPuG/fmzlVKrbfnXKKW+E3vt\nCoKFGAUh1bjC/jsRuB74b6Ao6P5twMfAycCvlVLfxgrcXg6ciLVTZB6wMOgzfwC+Z987B2uX0cmE\nuo8au5MWYwWyuRUrytlPgVpgE1aAdLCCpQ8GbmmmjAewdgG9xpbpA+AVO1BMML8Dfom14+c3WIGV\nBKFjSPTOf3LIEemBtTX0+kZpdwKb7HMf8I9G9x8DFjRKy8PaXXIA0Btr982CoPu9sCr4hUFpK4BS\n+/xb9ufPbUZOgzCB4rGigb0Q9IyDwJVB9zOwguPc06ic7wflOd1OG5ro/4cc6XlIT0FIJTRQ2Sit\nEjjGdgdprC2TgzkZuNJ2De1WSu3GCkqjgWPtoxtWQBLrIVrvxWq1N8d3sCrmFe14l2OBrsAbQc89\nYstxQqO87wed+yOOHd2OZwtCs8hAs5BqtDZ7Z2+Y/H8FHgqTdzNwXBuf01EomkYOC5715Hc/SYNO\n6BDkiyWkEgo4tVHaRKzwgs3N7HkXGKe1rg5zHAA+x6p0A/F7lVK9sIKTNEcV1m/n7Gbu+2c8ZbZQ\nxud2vjOCnptpy/FxC58ThA5FjIKQagy1Z+wcp5S6BJiF0wsI17r/PXCKPWvpO0qpUUqpC5RS8wG0\n1nuAR4HfK6W+p5QaizUI3fi3ofzla60/xYpytkAp9WOlVI5S6kyl1JV23hqsFv0FSqmBtpEJwXZR\n/dl+7nlKqePt64HA/7ZRN4LQbsR9JKQSGvgbVgu80r5egGMUmoQR1Fp/oJSaDNyLNVCdCVQDS4Oy\nzcIa+F2G5X56GOgZ5tnB5V8N3AOUYg1YfwE8aD/zS6VUMfBbW77FwLVhyrjd/rsIcGH1aqZorb9q\n9NxwehCEDkHCcQopg1JqBfCB1npmomURhHRF3EdCKhFw4QiC0DGIURBSicbuF0EQYoy4jwRBEIQA\n0lMQBEEQAohREARBEAKIURAEQRACiFEQBEEQAohREARBEAKIURAEQRAC/H+6cZT1IzCwuAAAAABJ\nRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for fold_num in range(n_folds):\n", " report.prediction_pdf(mask=\"FOLDS == %d\" % fold_num, labels_dict={1: 'sig fold %d' % fold_num}).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background distribution for each fold" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEZCAYAAABvpam5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for fold_num in range(n_folds):\n", " report.prediction_pdf(mask=\"FOLDS == %d\" % fold_num, labels_dict={0: 'bck fold %d' % fold_num}).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ROCs (each fold used as test dataset)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEiCAYAAADTSFSPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzLrXYmURRQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for fold_num in range(n_folds):\n", " report.roc(mask=\"FOLDS == %d\" % fold_num).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Report for test dataset \n", "\n", "__NOTE__: Here vote function is None, so default prediction is used" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KFold prediction using folds column\n" ] } ], "source": [ "lds = LabeledDataStorage(test_data, test_labels)\n", "\n", "report = ClassificationReport({'folding': folder}, lds)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAEZCAYAAACAUb92AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "report.prediction_pdf().plot(new_plot=True, figsize = (9, 4))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEiCAYAAADTSFSPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xm8XPP9x/HXJ5FdFgRJCElsEUoI+kNwY6+lVfuvKWKr\nxtaUn6KKqCXVqlrboiSlaqdiid1VikYQuxBJCIktZJM99/P743smZzKZuffcWe+9834+HucxM2fO\nnPOdT27u536X8/2auyMiIpKvVpUugIiING9KJCIiUhAlEhERKYgSiYiIFESJRERECqJEIiIiBVEi\nERGRgiiRSIthZmPMrC7alprZx2b2ZzPrluXY75vZWDObZWaLzOw9M7vAzNplOXagmd1lZjOjYz80\ns9FmtmV5vplI06ZEIi2JA08CPYANgROAA4E/px9kZj8Enge+AvYANgEuAn4GPGFmbdKOPQD4L9AR\n+CmwGXAkMBP4XWm/zsrMbLVyXk8kKSUSaUkMWOzuX7r7DHd/Ergb2HvFAWYdgZuBh939eHef6O7T\n3f1OQtIZDPwi7djRwDh3P9Ddn3b3j939VXf/NfCTegtjdmZUe1lkZtPN7LJof5+o1rRtxvF1ZnZw\nxjFHmtkzZrYAGG5mC6Lklv65vc1siZl1j16vZ2Z3mtk30fawmW2cdnxvM3swqo19F9XGjsgn4CKg\nRCItj614YtYP2BdYkvb+PsBawO8zP+jurwNPEyeI1LFZax7uPjdnIcxGAb8BLgUGAIcBnzTie6SM\nAq4DNgfuBx4ChmYcMxR4wt2/jpLfs8ACYFfgfwi1p6fMrH10/J+B9kBNVLYRwOw8yiYCgKrK0tLs\na2bzgNaEX5YAv0x7f9Po8b0cn3+P0CQGocmrvmOzMrPVCb+cf+HuY6LdU4CXG3OeyDXufn/auf8B\n3Glmq7v7fDPrABwEnBQdciSAux+X9pmfA18ABwD3AhsA97n7W9EhH+dRLpEVVCORluY5YGtgB+Ba\n4JHosTFSM5lavUflNgBoR6jdFGpCxuvHCLWNH0evf0go57+i14OAvmY2L7URahvdgI2iY64GfmNm\nL5rZxZlNbCKNpUQiLc1Cd5/i7m+7+y+ATsD5ae9Pih63yPH5AcAH0fMP0vYVU130mN4M1ybHsd+l\nv3D3pYR+n1Tz1lDgfndfFL1uBUwkJNP0bVPgxugctwB9Cf0/mwIvmtmFhX0lqWZKJNLSXQScbWY9\no9dPALOAszIPjP4y3x24Pdr1OPA1cE62E2cbVhx5D1gM7Jnj/a+ix15p+wbmODabfwB7mNnmhH6c\nf6S99yqwMTArSqjp27epg9z9M3e/yd2PAC4gjFgTyYsSibRo7v4c8C6h4xt3XwCcCOxvZjdH94hs\nYGZHAmMJw4KvTjv2BEK/y8Nmtmc0mmpbM7uYlX+Bp19zXnSOUWY2zMw2MrMdor4K3H0hob/kbDMb\nYGY7AVc04ju9ROjXuIOQlNKb0G4n9Ic8aGa7mlnf6PGK1MgtM7vazPYxs35mNhD4AfBO0uuLZCpr\nIol+oMea2afR0MZjEnzme2b2XDTs8VMzO7+hz0jVcuL+jXR/BI4zs94A7v4vwoimdYBnCE1YFxKa\nfvZ292UrTug+FtiR0C/xD+B9QtPSemSp1aQ5F7ic0Kz2LqGTe72091Od4a8AfwHOy/F9crkd+B5w\np6etThclqV0Jnfv3EGpHYwh9JN9Ehxmh3+gdQg1tJtDg/0WRXKycKySa2Q+AnYHXgVuB4e5+az3H\ndyH8J68FfksYAjkaGOnuV5a8wCIi0qCyJpKVLhxGk5zSQCIZThhHv667L472nUdIQOuXp6QiIlKf\npt5HsiPwfCqJRJ4AepnZhhUqk4iIpGnqiaQHoeMw3Rdp74mISIU19URSmXY3ERFJrKlPkfI5q9Y8\n1k17byVmpsQjIpIHd893Jocmn0heAi43s3Zp/SR7AZ+5e9b5gRJnktatoU2b7FvbtvHWrl32x/re\nK9Yxq+X/zzNy5EhGjhyZ9+dbEsUipljEFIuYWd45BChzIjGzTsQT4bUCNoxuiJrl7tOjGVO3d/fU\nHcH/JIzvH2NmlxDWgjgbGJnzIltsAUuX5t6WLQvb8uVhW7Qo56kqrlWrvBPStJdfhi+/LF5i69QJ\nVl8dOnQI5WpGpk2bVukiNBmKRUyxKJ5y10i2J9wABqHycFG0jSHcoNUD6Jc62N3nmtlewPWEyeu+\nAa5w9z/lvMLbbzdcCveQTHIlmyVL4m3x4lWf53pMckzSYxcvhrq6kOjyTXaTJ+f3uYZ06hS2jh1D\nYkltqdcdO668pZJQ6jHX1qlTSFoF/nUkIuVVsftISsHMvCV9H5Yvzzsh1b71FjV9+hQnCS5aBAsW\nwPz5sHBhab9z69b1J5qGElGW/bWvvELNHnuUttzNRG1tLTU1NZUuRpOgWMTMrKA+EiUSaZy6ujip\nLFgQEkvmtmBBvH33Xdjmz48fc23ffRcSVyl07Ahdu0KXLtC588pbly5hW2MN6NYtPO/aNTzv2jV+\n3qmTakvSIimRpFEiiTXbv7aWLMmecOpLQg0kqNr586kpRtlWWy0kmzXWgDXXXPUx277U8e3aFaME\nBWu2PxcloFjECk0kTX3UllSbVIf/GmsU75zPPgvbbQdz5sC8efE2d278fM4c+PZbmD077J8zJ95m\nzw7bwoXw1Vdha6yOHXMnmvoeu3ZtdoMbpPqoRiKS1JIlIdl8803YUs+TPC5b1vD5s2nVCnr0CIml\na9fw2L172DbaCHr1gp49w+O664bh6yKNpKatNEok0iS5h2a2xiSe1OOcOcmvk0o6qcSSuW2wAay3\nXujvUV+PpFEiSaNEElP7b6xZx2LJEvj887iJ7dtv4euvw77Jk8O9QjNnwowZ8MUXIWnVoxao6dAh\nJJT0bf31V37do0eLr90065+LIlMfiUhL1rZtqEkksWRJSCYzZqy6ffYZTJ8OH38c+nomT67/PiOz\n0FTWu3cY/r333isnm/XXDzWfFp5sJBnVSESqzbx5IbGktk8/Xfn1Z5+FGk9D/5fMwjF77AGbbQYb\nbwwbbhiST+/esM46GijQTKhpK40SiUiRLF0aksm774ZRaunJJvV8xoz6z9G2bai59O4dEkz37rDD\nDmFf376hxtO6dXm+j9RLiSSNEklM7b8xxSJW1FgsWQLvvx+ayKZMgalTQ/NZaps1q/7Pt2sXajH9\n+4caTd++oRlv442hT5+S12b0cxFTH4mIVEbbtrDVVmHLZsGCkFCmToXXXguDAmbOhGnTwv4vv4R3\n3glbpi5dYMstw/0/AwaEWswuu4T90uQkqpGYWXvCsrd9gA7AV8Br7v5RSUvXSKqRiDQj8+bBBx/A\npElh+/jjsE2aFBJONj17wqabxtsmm4SE06+fhjQXoKRNW2Y2GDgd+CGh9jIHWAisCbQHPgJuAv7i\n7vPyLUSxKJGItBAzZ8LEifDmm/DQQ6F28+67YSLRbNq2he99D046Cb7//dBc1rZtecvcjJUskZjZ\nWGAQYU2Qh4AJ7r4g7f2NgF2A/wW2Bo5y9yfzLUgxKJHE1P4bUyxizToWy5eHJrEPPgjbhx/Ce+/B\nkzl+7Wy2GWy7bejg33572GabMFVNpFnHoshK2UfyOHCou2edjjVq1vqIsOjUlkDPfAshItKg1q1D\nJ3yfPuG+lhT3UHP5z3/g6qvDiLNp0+ImszvuiD+/5ZYhqeywQ2gKGzy4oJVIJdCoLRFpeRYtCp34\nr74Kr7wStrffDrWadB07hprKoEFw2GHhsUOHypS5gsoy/NfMHgT+Bjzi7nX5XqzUlEhEJKcFC+D1\n12H8+JBYXn45jCjLtN56sN9+sM8+UFMDa61V9qKWW7kSye3AQYTO9r8Dt7j7h/letFSUSGJq/40p\nFjHFIlZbW0vNZpvB44/Dc8/BmDHZD9x1V9h3XzjggNCh3wIVmkgS3fHj7kOBXsDFwJ7AJDP7t5kd\nY2bVVw8UkZahZ08YNgxGjw59LXPnwvPPwznnhBskW7eGf/8bfv3rcL/MGmvACSfAo4+G5jMB8uwj\niTrXjweGA4uAu4Cr3f3d4hav0eVSjUREimf2bHj6abjzTrj33pXf694ddtop1FSOO65ZT/dS9ilS\nzKwXcCwwDOgB3EMYsbUXcK67/yHfwhRKiURESmbZstCB/8ADcOutK/evDBgAv/oVHHlkk1lWuTHK\n0rRlZm3N7DAzGwd8AvwI+D3Q092Pc/cfAAcD5+VbECmu2traShehyVAsYopFrNGxWG012HprGDkS\nPvooNG+dfXaYH+zdd0MTWefOsPbaMG5cw7MntyBJZ0WbAdwATAa2dfcd3P0md5+fdszzwOxiF1BE\npMkxgx/8AH73u3Bz5C23hPnAli4NC4/tt19YHGzUqDBTcguXdNTWUcA97t6ke5fUtCUiFeMe+lPG\njYMrr4z3t24N//M/MHw4HHFEk7wBslzDf0cDp2fOp2VmnYBr3f24fAtQTEokItIkzJsXhhXfdhuM\nHRvv79cPzj0Xjj++SU0yWZY+EuAYwqy/mTpG70kTo7bwmGIRUyxiJY1F585w6KHw4INhrZZRo8JC\nXlOmwIknhqHEDz9cuuuXWb2JxMzWNLPUbZ1rRq9T29rAAcAXJS+liEhz1bdvuC/lgw/g4otDQnn7\nbTjwQDj55LBAWDPX0DTyDU2H4sCF7n5JUUuVJzVtiUiTt2ABXHMNnH9+GFK8zTZhOPGWW1asSKVe\nj6QmevoMcAjwbdrbS4CP3b3JDElQIhGRZuO558KQ4WnTwtopv/pVqLl06lT2opS0j8Tda929FugH\nPJh6HW0vNqUkIitTW3hMsYgpFrGKx2K33cL09z/7WWjeuuQSGDgwTH3fzORMJGa2rZml7vlfExgY\n7VtlK09RRURamM6d4YYbQqc8wOTJoSP+4othzpzKlq0R6lshsQ7o4e5fNtBX4u7eJCaZUdOWiDRb\nn38eaicPPRReb7BBeL7VViW/dCmX2u0DfOLuddHznNx9Wr4FKCYlEhFp9p56Kky98tprob/kwQdh\njz1KesmS9ZG4+7TUIlbR85xbvheX0ql4+28ToljEFItYk43FnnvCCy+EFRu/+y68bqpljSSdtPE0\nM/tplv0/NbOTG3NBMzvZzKaa2UIzm2Bmgxs4fh8ze8nM5prZV2b2LzPbpDHXFBFpVjp0CFPX77pr\neD1kCFx7bWXLVI+kU6R8BBwfjeBK378LMNrdN050MbMjgNsI65i8AJxCmJJ+gLtPz3J8X+A94Crg\nRqAzYdbhfu6+SjJR05aItCizZ8OOO8L774fXTz1Vkmaucs21tQjon9mMlfpF7+7tE13M7L/ARHc/\nKW3fB8C97v7rLMcfCtwJtEllCDMbAjwNdHf3bzKOVyIRkZZl2TI46qhQQ+nWLQwZ7t27qJco11xb\nnwPbZNm/DfB1khOYWVtgW+CJjLeeAHbK8bHxwFLgRDNrbWadCXN7jc9MIrKyJtv+WwGKRUyxiDWb\nWKy2Wpj8sXv3UEM54YRKl2gVSRPJP4FrzGxvM2sTbfsAVwO3JzxHd6A1q87N9SVhpcVVuPsnwN7A\nbwlL+s4GtgQOTHhNEZHmb7XV4L77wvMnnoCbb65seTIknRh/JNAXeAxI3VPSCrgbOL/4xQrMrAdw\nMzAGuAPoQkgqd5vZ7tnasYYNG0afPn0A6NatGwMHDqSmpgaI/wKphtc1NTVNqjx63XRepzSV8lTq\ndWpfUylPg6/r6mDQIGpefRVOOIHaDz+EfffN63y1tbWMGTMGYMXvy0I0as32aLRUqolrort/0IjP\ntgW+A4509/vS9l9P6GwfkuUzFwP7ufugtH3rAdOBwe7+Ysbx6iMRkZbLHS64IEyn0q4dTJwI/fsX\nfNpy9ZEA4O4fuvvd0ZY4iUSfXQK8SmiqSrcX8OKqnwDCGiiZd9Wn14gkh8y/PquZYhFTLGLNMhZm\nYfqUgw6CxYvDNPRNQM6mLTO7BjjX3b8zs2sJU8avchhhipTTE17vSuA2MxtPSB4/J/SP/DW65ihg\ne3ffMzr+EeCXZnY+YfRWZ+Ay4BNCUhIRqT5//jM88gg8+2zoiD/qqIoWp74pUmqBg9x9dvS8vkSy\nSrNUzguaDQd+BfQE3gJ+6e4vRO+NBnZz935pxx8BnAVsBiwEXgLOdvf3s5xbTVsiUh0uvzxMOw/w\n7bdhaHCeSjnX1obA9NQ0Kc2BEomIVI26OmgdzZc7bBiMHp33qUrZRzKFMGQXM3vGzPJPd1J2zbL9\nt0QUi5hiEWv2sWjVKp42ZcwYePTRyhWlnvfmAWtHz2uAtiUvjYiIJHfqqaHjHWD//WHRoooUo76m\nrXuBXQhzXe1K6BxfmuVQd/fdS1bCRlDTlohUnfQmruuug1NOafQpStlH0hE4DtgYOB0YTejszuTu\nflq+BSgmJRIRqUrHHAO33hqe5/E7sFyTNtYCP3b3b/O9UDkokcTS79itdopFTLGItahYLFwIHTuG\n57ffDj/5SaM+XpYbEt29pqknERGRqtWhAxx3XHh++ul51UoKUV/TViluSCwp1UhEpGp9+y2suWZ4\nfu+9cMghiT9ayhrJVkCb6Pn3GthERKSS1lgDLrssPP/lL8t66frWbK9x99lpz4dk2Woac1e7lE+z\nHyNfRIpFTLGItchYnHhieJw+Hb78smyXTbpme1sz65Blf4doVl8REam07t3j5//8Z9kum3TU1lig\n1t2vzNg/Aqhx94NKVL5GUR+JiFS9vfeGJ5+EQYNgwoREHynXNPI7AU9m2f8ksHO+FxcRkSIbOjQ8\nvv56uFmxDJImko7kuKudMLW7NDEtsv03T4pFTLGItdhYHHNMeKyrgzvvLMslkyaSt4Bsd7j8L/B2\n8YojIiIFO+KI8Jia1LHEkvaR7Ac8CNwDPB3t3hM4jHDH+0MlK2EjqI9ERAR4+mnYM1of8JtvwtDg\nepTrzvZHgQOBDYFroq03cGBTSSIiIhLZYw9YffXw/LbbSn65xOueu/tj7r6zu3eKtsHuPq6UhZP8\ntdj23zwoFjHFItbiY5GaR+y880p+qcSJJLpn5DAzOzu1yJWZbWxma5WueCIikpfrrw+P8+fDV1+V\n9FJJ+0g2Bp4CVge6AZu6+xQzuwLo5u4nlLSUCamPREQkjUXdHtdcA6flXu2jXPeRXEW4Z2RdVl6T\nZCzQJBa1EhGRDEOiGazOOaekl2nMDYl/cPflGfunA72KWyQphhbf/tsIikVMsYhVRSzuuis8LlgA\nr71Wsssk7iMh+5rtvYE5RSqLiIgU09prw9Zbh+cPPFCyyyTtI7kTWODux5nZPGBrYBbh3pIp7n5c\nyUrYCOojERHJcPHFcMEFMHgwPP981kPKtdTuesCz0cu+wETCWu5fALu6e/nmK66HEomISIaPP4Y+\nfcLz3AsZluWGxM+AgcDvgBuBCcBZwDZNJYnIyqqi/TchxSKmWMSqJhYbbBA//+abklyiMTckLnD3\nW9z9FHcf7u5/c/eFDX9SREQqxgy22y48//vfS3OJpE1BZjYIGAEMIMz6+x5wlbu/WpKS5UFNWyIi\nWZx6arhBcbvt4JVXVnm7LE1bZjYUGA/0AB4FxkXPx5vZUfleXEREymCbbcLj1KklOX3Spq1LgfPd\nfS93Pz/a9gJ+A1xckpJJQaqm/TcBxSKmWMSqKhYHRYvYzpoFc4p/x0bSRLI2cHeW/fcC6xSvOCIi\nUnRrrRXXSn7/+6KfPmkiqQWGZNm/G/Bc0UojRVOTmvlTFIs0ikWs6mKRmi7lo4+Kfuqk95GcAlwE\n3Ae8FO3eEfgxMBL4PHWsu99f9FImpM52EZEcXnoJdtoJ+veH995b6a1y3ZCYeAV5d2/MtCtFpUQS\nq62trb6/uHJQLGKKRazqYjF3LnTtCu3awaJFK71VrhsSWyXdGjqXmZ1sZlPNbKGZTTCzwQk+M8LM\n3jezRWY2w8xGJSm3iIhEOneGVq1g8WJYWNxbABPfR7LKB83auPvSRn7mCOA2YDjwAnAKcCwwwN2n\n5/jMlcD+wP8BbwFdgZ7u/liWY1UjERHJJbU+ydtvwxZbpO0uz30kvzCzQ9Ne3wIsMrMPzGyzRlzv\nDGC0u9/s7pPc/XRgJiGxZLvuZsCpwA/d/SF3n+bub2RLIiIi0oBW0a/8f/+7uKdNeNzpwFcAZrYr\ncBjwE+B14I9JTmBmbYFtgScy3nqCsN5JNj8CpgD7mdmUqElsjJmtnbDcVauqxsg3QLGIKRaxqoxF\n//7h8c03i3rapImkF+EXOsCBwL3ufhdhxNaOCc/RHWhNmDE43ZeEu+Sz6QdsCBwOHA0cBfQHHjKz\nvKthIiJV6ZBDwuPEiUU9bdJEMpewzC7AXsDT0fNlQPuilmhlrYB2wFHu/oK7v0BIJjsA25Xwus1e\nVY1GaYBiEVMsYlUZi92jldHfeSfnlPL5WC3hcU8AN5nZa4R1SMZF+wcASSdv+RpYTpyQUtYl9JNk\nMxNY5u6T0/ZNjs6zAbDK7GPDhg2jTzT3frdu3Rg4cOCKH5hUVVav9Vqv9boqX9fVUQPUzpvHmKFD\noW3bFb8vC5H0PpKuwCWEX95/SXV2m9lvgUXuflmii5m9DLzh7iel7fsAuMfdz8ty/F7A48DG7j4l\n2rcR8CGwg7tPyDheo7YitdU2Rr4eikVMsYhVbSx69IAvvoAZM6BnT6DwUVuJaiTuPgc4Lcv+Cxp5\nvSuB28xsPPAi8HNC/8hfAaL7Q7Z39z2j458CXgNuMbMRgAFXAS9nJhEREUmgbdvwOG3aikRSqJw1\nEjPr7O7zEp/IrIu7z01w3HDgV0BPwn0hv4z6PjCz0cBu7t4v7fgewDXAvsBCQjPbGe7+VZZzq0Yi\nIlKfbbYJne1jx8KBBwKlvY/kQzM738zWz3WAmbUys/3M7CnCzYUNcve/uHtfd2/v7tunkkj03rHp\nSSTa97m7H+7uXdx9XXc/KlsSERGRBL73vfA4Pes94HmpL5HsAmwFTDGzV83sBjO70MzONrPLzewh\nwlDevwL3AJcXrVRSsFRHmygW6RSLWNXGYp1o5Y8FC4p2ypx9JO7+IXCYmfUGjiAklh2ADoQRWK8D\nNwDj3H150UokIiKl07FjeJw/v2inzHuuraZIfSQiIg248ko480w47TS45hqgTHNtiYhIC9G1a3gs\n4t3tSiQtVNW2/2ahWMQUi1jVxmLZsvD4zTdFO6USiYhINUlN3PjOO0U7pfpIRESqyTffwFprhed1\ndWBWuj4SM7vFzDpHz3c1szb5XkRERJqINdeMnxepeau+pq2jgNWj57XAGkW5opRF1bb/ZqFYxBSL\nmGJBmHOrCOqba2sacJqZpRai2snMsqYvdy/uclsiIlI6220HEybA3AZntUqkvrm2fgTcDKyZ9YCY\nu3vropSmQOojERFJYK+94Kmn4P774cc/Lt3sv+7+IPCgma0BzAK2IFpuV0REmrHUNClFuru9wc52\nd/8WGAJMdvevs21FKYkUldp/Y4pFTLGIVXUsUtOkfP55UU6XtLP9GdTZLiLSMqRqIrNmFeV0STvb\nDXW2NytVufJbDopFTLGIVXUs1l47PLYpzl0d9SWS/yN0tp8Tvb4/x3EONInOdhERSWCDDcJjkUZt\n5WzacvcH3b07EN0CyRbAOlm2dYtSEimqqm7/zaBYxBSLWFXHInVT4syZRTldg2u2u/u3ZrY7obN9\naVGuKiIilbPeeuHxmWeKcrrEc21Fa6cfBfQDznf3r81sMPCZu08tSmkKpPtIREQSeO45qKmBrbeG\niRPLsx6JmQ0CJgE/AY4HukRv7QVcmu/FRUSkAlKd7YsXF+V0SaeR/yNwtbtvA6Rf+TFgcFFKIkVV\n1e2/GRSLmGIRq+pYtG8fHsucSLYFxmTZ/znqbBcRaV7atQuPU4vTK5Goj8TMvgD2d/cJZjYP2Nrd\np5jZvsBN7t67KKUpkPpIREQSmDcPunQJNZOFC8u2ZvuDwIVm1j61w8z6Ar8H7sv34iIiUgGdOoXH\nRYvC4lYFSppIziJMkfIV0BF4AZgMzAZ+U3AppOiquv03g2IRUyxiVR2LVq3ifpIFCwo+XYP3kQC4\n+xwz24UweeMgQgJ61d2fKrgEIiJSfosWhcevC593V2u2i4hUo/79YdIk+Pe/sV13LUsfiYiItCSp\nZXaLMARYiaSFqur23wyKRUyxiFV9LHbeOTwuXFjwqZRIRESqUYcO4XHKlIJPpT4SEZFqtO228Prr\ncMMN2EknlbePxMy6mdma6Vu+FxcRkQr5/vfD47JlBZ8q6aSNfczsMTNbBHwDfJ22fVVwKaToqr79\nN41iEVMsYlUfi9TqiEVIJInuIwFuAboBxwEzCasiiohIc7Va9Ot/aeHLTCVNJDsAO7r7W4Ve0MxO\nJtwp3wN4Bxjh7i8k+NwmwGsA7t650HK0dFW9HnUGxSKmWMSqPhapGkkREknSPpJpQLtCL2ZmRwBX\nAZcAA4EXgXFmVu+kj2bWFrgTeA7VhkREClfEGknSRHI6cFlUKyjEGcBod7/Z3Se5++mEprLhDXzu\ncmAicA+Q98iCalL17b9pFIuYYhGr+lh07Bge580r+FRJm7YeJNRIJpnZYiC9d8bdvUv2j8WiWsW2\nhBmD0z0B7FTP5/YH9ifUYA5PWF4REanP6quHx9ScWwVImkhOK/hK0B1oDXyRsf9LQn/JKsysF3Aj\ncJC7LzBTZSSpqm//TaNYxBSLWNXHIrW4VRGmSEk6+++Ygq+Un9uAv7j7KxW6vohIy1TuRAIQLWo1\nFNic0OH9LvBPd09aiq+B5ay6NO+6hH6SbIYAu5rZhaliAK3MbCkw3N3/lvmBYcOG0adPHwC6devG\nwIEDV/zlkWoTrYbX6e2/TaE8lXyd2tdUylPJ1xMnTmTEiBFNpjyVfH3VVVdV9e+HMWPGANDnv/+l\nUEmX2h0APAZ0Ad4i/ELfEpgD7Ovu7yW6mNnLwBvuflLavg+Ae9z9vBzXTXcQcB6wPTDD3WdnHK8p\nUiK1tbUrfoCqnWIRUyxiVR+Lhx6CH/4QDjgAe/jhgqZISZpIngQWAEe5+9xoXxfgH0B7d9870cXM\nDic0V51UozV6AAAZsUlEQVRMGPr7c+BYYAt3n25mo4Dt3X3PHJ8fBlyb6z4SJRIRkYTGjYP99oN9\n98Uee6ygRJK0aWtnYIdUEgFw97lmdh6QuF7k7neb2VqE5Xl7Emo3+7n79OiQHkC/hk6T9HoiIpJD\nBe4jWUSYIiVT1+i9xNz9L+7e193bu/v26Xe1u/ux7p4zkbj7mCRDjUVj5NMpFjHFIlb1sUglknJN\n2gg8BNxoZoPNrHW07UIYmju24FKIiEh5FXHSxqR9JGsAY4ADgbpodyvCjYrHZnZ6V4r6SEREEnr5\nZdhxR9hhB2z8+NL3kbj7t8CPoilSNo92v+fuH+Z7YRERqaD27cPjd98VfKpGLWzl7h+6+9hoUxJp\nwqq+/TeNYhFTLGJVH4vUDYmlXI/EzK4BznX378zsWrKPljLCXFunF1wSEREpn9atw2NdXf3HJZCz\nj8TMaglzXM2OnteXSIYUXJIiUB+JiEhCkyfDJptAv37YlCml6SNx95psz0VEpAUoYo0k6ZrtF5hZ\nxyz7O5jZBQWXQoqu6tt/0ygWMcUiVvWxaBX9+l++vPBTJTxuJLB6lv2dovdERKQ5SdVIipBIkt5H\nUgf0cPcvM/bvCdzh7msXXJIiUB+JiEhCM2bAeutBjx7Y55+X7j4SM0tfg3GKmaX/lm4NtAf+mu/F\nRUSkQopYI2moaes04tURf532+jTgBGCwu59ccCmk6Kq+/TeNYhFTLGJVH4tUH0kROtvrrZGkVkY0\ns6nAi+5e+DSRIiJSeRXoIzkcWOzuD2bs/xHQxt3vLbgkRaA+EhGRhObPh86doUMHbOHCgvpIGjNq\nK9uSugvQqC0RkeanU6fQvLVwYcGnSppI+gLvZ9k/Gdio4FJI0VV9+28axSKmWMSqPhZm8VTyBUqa\nSL4FNs2yfxNgXpb9IiLS1BUpkSTtI/kLsAtwiLtPivb1B+4D/uPuPytKaQqkPhIRkUZYYw2YPTs1\naWLJ+0jOAeYA75rZp2b2KfBOtO+sfC8uIiIVtFqiJakalCiRuPscYDCwL3BNtO0D7By9J01M1bf/\nplEsYopFTLGgaE1bidNR1Gb0ZLSJiEhzV6QaSdI+EgNOjrZ+wBbuPsXMzgGmuPvdRSlNgdRHIiLS\nCP37w6RJZesj+QXwG+CmjP0zgFPzvbiIiFRQOftIgOHAie5+FZC+wO9rwJZFKYkUldp/Y4pFTLGI\nKRbE820VepqEx20AvJVl/1KgQ1FKIiIi5VWkRJK0j+Rd4Dfufn80tfzWUR/JCOAodx9UlNIUSH0k\nIiKNsO228PrrBfeRJG0g+wNwnZl1INRidjKzo4FfAcfle3EREamg1AzABUp6H8lo4EJgFKEp61bC\neiSnufudRSmJFJXaf2OKRUyxiCkWFK1pqzH3kdwE3GRmawOt3P2LopRAREQqo5x9JM2F+khERBph\n553hxRdL10diZm8Bu7r7t9Hz+swnjOq62N2n51sYEREpozIM/70PWJL2vL6tFhgI3F6UUknB1P4b\nUyxiikVMsaD0fSTuPjLb81zMbCPCjMAiItIclLuPJJpvaxBhRcRH3H2+ma1OWMt9aXRMN3efXZSS\n5UF9JCIijbDnnvD00+WZa8vM1gVeAsYD/wTWid76I3BF6rikScTMTjazqWa20MwmmNngeo6tMbMH\nzWyGmX1nZm+Y2bFJriMiIvWYNq0op0lar/kT8CWwFrAgbf89hHVJEjOzI4CrgEsI/SovAuPMrHeO\nj+wIvAEcAmwB/AW40cz+tzHXrTZq/40pFjHFIqZYAL1z/dptnKT3kewB7BGN4ErfP4UwD1djnAGM\ndvebo9enm9m+hIkhf515sLuPytj1VzMbQkgsdzTy2iIiktK1a1FOk7RG0oEwQWOm7sCipBczs7bA\ntsATGW89AeyU9DxAV+CbRhxfdWpqaipdhCZDsYgpFjHFArC8u0VWkjSRPA8MW/n6thpwNvB0I67X\nHWgNZN4V/yXQI8kJzOwAYHfgxkZcV0REMhUpkSRt2joL+LeZbQ+0I3Swb0moGexclJIkYGY7E+5V\nOc3dJ2Q7ZtiwYfTp0weAbt26MXDgwBV/eaTaRKvhdXr7b1MoTyVfp/Y1lfJU8vXEiRMZMWJEkylP\nJV9fddVVVf37YcyYMfDqq/ShcI0Z/tuT0I8xCDDColbXu/vMxBcLTVvfAUe6+31p+68HBrj7kHo+\nOxh4BDjf3a/JcYyG/0Zqa2tX/ABVO8UipljEFAvg0EPhvvsKHv7bYCKJfvk/Dxzt7pPyvVDa+V4G\n3nD3k9L2fQDc4+7n5fjMrsDDwAXRKo25zq1EIiKS1GGHwb33ln49EndfYmZ9gWL9hr4SuM3MxhOG\n/v6c0D/yVwAzGwVs7+57Rq9rCDWR64A7zCzVl7Lc3b8qUplERKpPmTvbbwVOLMYF3f1uYATwG+B1\nwmit/dIme+wB9Ev7yDFAe0I/zUxgRrT9txjlaanS+weqnWIRUyxiigVl72zvCPzUzPYCXiX0cwCp\nGpGf3piLuvtfCDcWZnvv2CyvdSe7iEixtSrvmu21aS/TP5BKJDk7yctJfSQiIo3wk5/AHXeUZ812\nd6/J9wIiItJElbmPRJoZtf/GFIuYYhFTLFAiERGRAhUpkWjNdhGRanX00XDbbeVZj0RERFogNW1J\nfdT+G1MsYopFTLGgvPeRRFOUZOOEaeQ/cndN6y4i0pyUs4/EzOoISSPXVR0YC/zU3b/LcUzJqY9E\nRKQRjjsORo8uWx/JAcD7wFBgk2gbCrwDHAocTFg29/J8CyIiImVW5j6SS4BfuPsd7v5RtN0B/BI4\nz90fBE4jJJwmx8y0FXFrbtQWHlMsYooFZZ9ra3Pgsyz7ZwBbRM/fJuEqh5WgJq/iaI6JRERyKHON\n5D3gPDNrF1/f2gPnAu9Gu3oTZucVaVKqfvGiNIpFTLGg7DWS4YSFpWaY2VuETvctgeXAgdEx/YA/\nF6VUIiJSeuWskbj7f4G+wK8Ja4i8RqiN9Ivew93/7u5/KEqpRIpIbeExxSKmWFD2GgnuPh+4oShX\nFRGRyitnjSSjb2R9M7vIzP5Qz42KktCkSZMYOHAgXbp04brrrst53JgxY9hll11yvl9TU8PNN98M\nwO23384+++xT9LI2V2oLjykWMcWC8tRIzGwz4AGgv5m9Sbh35AmgC+EmxF+a2WHu/kBRSlOFfv/7\n37PHHnswceLEgs6TPjR36NChDB06tBjFE5GWrEgrJDZ0lisIQ3x/CLwFPAo8DnQFuhGaus4uSkmq\n1Mcff8yAAQMqXYwWTW3hMcUiplgAbdoU5TQNJZL/Ac5y94eBkwlDfP/s7nXuXgdcR7jHRPKw++67\nU1tby6mnnkqXLl148803Ofroo1lnnXXo06cPl156ac77X5588kn69+9Pt27dOO2001Y6LrMZrFWr\nVtxwww1suummrLHGGpx66qkr3qurq+PMM89k7bXXpl+/flx33XW0atWKurq60n1xEWkaVkvcTV6v\nhhLJWkQ3Irr7POA7IH1yxm+BzkUpSaWYFW9rpGeeeYZddtmF66+/nrlz53LFFVcwb948pk6dynPP\nPcett97K6NGjV/nc119/zSGHHMJll13GrFmz2GijjfjPf/5T77UeeeQRJkyYwJtvvsndd9/N448/\nDsCNN97IY489xhtvvMFrr73Gv/71rxZ306HawmOKRUyxoGyJpCG6XbxIli9fzl133cWoUaPo1KkT\nG264IWeeeSa33XbbKsc++uijbLnllhx88MG0bt2aESNG0KNH/ZMKnHPOOXTp0oXevXszZMgQ3njj\nDQDuvvtuRowYQa9evejWrRvnnnuuZgEQqRZlatoCuM3MxprZQ0B74EYze8jMxgKr/pZrbtyLtxXg\n66+/ZunSpWy44YYr9m2wwQZ89tmqM9PMmDGD9ddff6V9vXv3rvf86YmmY8eOzJ8/H4CZM2eu9NnM\n87YEaguPKRYxxYKyDf+9ldDZ/g0wC7gd+DR6/k303t+LUpIq1717d9q0acO0adNW7Pvkk0+y/mLv\n1asX06dPX/Ha3Vd63Rg9e/Zc6bP5nkdEqle9DWTuPqxM5ah6rVu35vDDD+e8887j1ltvZdasWfzp\nT3/irLPOWuXY/fbbj1NPPZUHHniAAw88kOuvv57PP/888bXcfUXz1eGHH87VV1/N/vvvT8eOHbn8\n8svVR9KCKRYxxQIttdsSXXvttXTq1Il+/fqxyy67MHToUI499lhg5ftEunfvzj333MM555xD9+7d\nmTx5MoMHD15xnszp3jMTQ/r7J554InvvvTdbbbUVgwYNYv/996d169a0KtL4chFp+RKtkNhc5Foh\n0czUgZzQuHHjGD58+EpNbOmaYyxra2v112dEsYgpFsBFF8HIkWVbIVFaqEWLFvHoo4+ybNkyPvvs\nMy666CIOPvjgShdLRJoR1Uiq3MKFC9ltt914//336dChAwcccABXX301q6++etbjFUuRFuS3v4UL\nLyy4RlKcu1Gk2erQoQPjx4+vdDFEpBlT05a0eLpfIKZYxBQLNGpLRESaBvWRSKMoliItyCWXwPnn\nN79RW2Z2splNNbOFZjbBzAY3cPz3zOw5M1tgZp+a2fl5XldbETYRkUxlTSRmdgRwFXAJMBB4ERhn\nZlknijKzLsCTwExgO+AXwFlmdkZjrpu6k7uatmeffbZk525u1BYeUyxiigXNto/kDGC0u9/s7pPc\n/XRCkhie4/ihhIkij3H3d939PuDy6DxSj0JXXGxJFIuYYhFTLIqnbInEzNoC2xKW6k33BLBTjo/t\nCDzv7oszju9lZhvm+IwAs2fPrnQRmgzFIqZYxBQLmmWNpDvQGvgiY/+XQK7FNHpkOf6LtPdERKTC\nmvrw3+bXIN9E5JorqxopFjHFIqZYULQaSdmG/0ZNW98BR0Z9Han91wMD3H1Ils/8HVjL3Q9I27c9\n8F+gr7t/nHG8Eo+ISB6axRQp7r7EzF4F9gbuS3trL+CeHB97CbjczNql9ZPsBXyWmUSia2h8qohI\nmZW7aetKYJiZHW9mm5vZ1YS+jr8CmNkoM3sq7fh/AguAMWa2hZkdDJwdnUdERJqAsk7a6O53m9la\nwG+AnsBbwH7unlrftQfQL+34uWa2F3A9MIGwvO8V7v6ncpZbRERya1FTpIiISPk19VFbK2nM9Cpm\n1sfM6rJse5ezzKXQ2Glmos+MMLP3zWyRmc0ws1HlKGs5NPLnYmSOn4s6M+teznIXW2N/LsxsHzN7\nyczmmtlXZvYvM9ukXOUtpTxicbiZTTSz78xsmpn9X7nKWkpmtquZjbUwvVSdmR2T4DONn5aq0lN5\nNGJajiOAJcDxwGbANcA8oHeO4/sAdYTO+XXStjaV/i7ljEP0mSuBScCBUVy2Bvat9Hep0M9Fp4yf\nh3WBZ4GnK/1dyhyHvsAi4HeE5uStgceBDyv9XSoQix8AS4GfR/8/9gM+A06p9HcpQix+QJiS6hDC\nqNmjGzi+C/A5cCcwIPrcXOCMej9X6S/aiID8F7ghY98HwGU5jk8lkkGVLnuF47BZ9J9qs0qXvSnE\nI8vnewPLCMPSK/59yvhzcWj0vS1t35Do/8yalf4+ZY7FP4H7MvadCnxS6e9S5LjMS5BIhgOzgXZp\n+84DPq3vc82iaSvP6VVS7jezL8zsBTM7pCQFLJM84/AjYAqwn5lNiar7Y8xs7RIWtSwK/LlIOZ4w\niOO+hg5sqvKMw3jCX+EnmllrM+sMHAOMd/dvSlbYEsszFm2BxRn7FgHrm9kGxS1hk5fXtFTNIpGQ\n3/Qq84AzgcMI1bungbvMbGipClkG+cShH7AhcDhwNHAU0B94yJr/vPD5xGMFM2sNHAfc5u5Li1+8\nsml0HNz9E8I9Xb8l/NKcDWxJaP5szvL5mXgc+JGZ7WVmrcxsU8LvDgijS6tJXtNStdg12919FpA+\nTPi1qDP1V8DtlSlVRbQC2gFHuftkADM7itBnsh3wSgXLVmn7AusDN1W6IOVmZj2Am4ExwB2EtvHf\nAneb2e4etWlUA3e/ycw2Ah4E2gBzCP0qIwlNfdUkr3/35lIj+RpYTugYTbcuYRr6pMYDzXlUSj5x\nmAksSyWRyOToPM292l7oz8XPgP+4+/vFLliZ5ROHU4B57n6Ou7/h7s8DPwV2IzRvNFd5/Uy4+zmE\ngRgbEP7yTv2BNaUEZWzKPmfVmse6ae9l1SwSibsvAVLTq6Tbi7A4VlIDgRnFKle55RmHF4DVzKxf\n2r5+hOr/KtPMNCeF/FyYWS/C6JxmXxvJMw4dWPWv7dTrZvF7IZtCfiY8mOnuy4D/BV6MWjaqyUvA\nLmbWLm1fzmmpVqj0SIJGjDg4nNAhdjywOXA1YVha7+j9UcBTaccfQ/hh2Jwwcun/os//otLfpcxx\nMMKsALWERLoN8BzhP0nFv0+545H2ud8A3wLtK/0dKvRzMYTwl/v5hFr6tsBjwDSgQ6W/T5ljsRZh\ntNLm0f+RqwlDZber9HcpQiw6Rd9pYPSdzo+e54pFF0LN7Q5gC+BgQlPfL+u9TqW/aCODMhyYSugc\nfAUYnPbeaGBK2uujgXeA+VEgxgM/qfR3KHccon09gLuj/0xfALcBa1f6e1QwHkZosriu0mWvcByO\nIPyRMY/QGf0g0L/S36PcsYgSyYtRHOYTRiltX+nvUKQ41BBqmnXRHw6p57fU83OxJeGPzYWE+2nO\nb+g6miJFREQK0mzbQkVEpGlQIhERkYIokYiISEGUSEREpCBKJCIiUhAlEhERKYgSiYiIFESJRJo8\nM6s1s2sqcN1hZjav3NdtjGhJgIcaOCa1Wui2jTjvVDM7o/ASSjVosbP/SvMQrYtyEWGq/56E6czf\nBn7n7k9Fhx1EWDtDVnUa4U59ICRd4C13Py3tmE8Isxs0Zt6o7YAFaeetAw519/sLKq20SEokUmn3\nAe0J64JMJsw0uhuwZuoAd59dmaI1fe7eYI3J3esIU6A05rzZkk5zX79GSkRNW1IxZtYNGAyc4+7P\nuvt0d5/g7n9097vTjqs1s2vTXq9rZmPNbIGZTTOzY83sbTO7MO2YOjM70czuMbP5ZvZR5qJmZvY7\nM3s/Os9UM7s8Y9bTJN/hJDP7wMwWmtlXZvZYtGBW6v1jzezd6P1JZjYifUGxhOW8IPqei8xsppn9\nPe29FU1bZjYG2BU4JTpvnZltkN60FS3cNN3MTs24xqbRMQOj19NSTVtmNi067J7omClmtmH0fFDG\neU6M4qA/UquIEolU0vxo+1EDv8CdlRfc+TthrfUhhKWEhxLWkcicOO4C4AFgK+Au4BYz651x/WMJ\nK0aeDBxJWJ86ETPbDrgOuBDYFNgDGJf2/onApYSZhvsTVt07O7pWonJGy0OfSZiEcGPgAMKa5Cnp\nsTmdMA34LYSmrB7Ap+kXimon/yTELN1Q4F13n5h23pTtoscTonNu72FK8ScINcl0xwG3epiKXapF\npWen1FbdG2Ga6lmEmUZfBP4A7JBxzLPANdHzzQizl+6Q9v76wDLggrR9dcClaa9bE6bRzjkDNPBz\n4MO018MIiz/VV/bZwOo53v8EGJqxbwTwTtJyAmcA7wOr5bjGGOChbLFK29cnus620eutotf90o75\nkFAzTL2eCpyRUc6DM857CGG9+3bR682j4wZU+udKW3k31Uikojx03vYirBU+DtgJeNnMzs3xkf6E\nX1YT0s7xKdkXLHsz7ZjlwFfAOql9Znaomb0QNRfNA64k1HSSeoKwONhUM/uHmR1tZqtH516bkOBu\nNLN5qY2w/kO/jPPUV867CX1IU83sb1GZ2zaijKtw9zeBt4hqJWb2/ahMjV2CeiywhJBQIdRG/uvu\n7xZSPml+lEik4tx9sbs/5e4Xu/vOhLXERxahnT1zpJcT/cyb2f8QFu8ZR2guGkhogkr8S9rd5xMW\nhDqcUPs4F3jfzHoS/986Cdg6bdsi2hKVM0qSm0XnmQv8EXjVzDomLWcO/yBu3hoKPO/u0xtzAndf\nCtwKHBf1Cx1F+LeTKqNEIk3Re4QRhe2zvPc+4ec21W6Pma1PqNU0xs6E5UMvdfdX3f0jQhNQo7j7\ncg8DBX5NaDLqBOzv7l8Qakkbu/uUzK2R11js7o+6+xnA9oREtFOOw5eQbDTmHcDGUW3kcEJiqc9S\nQrNbpr8R+qpOAVYH7kxwbWlhNLJCKsbM1gLuIfwV+xZhhbrtgF8Rlv+cnzo02nD3SWb2OPBXMxtO\nWFL1D4R7HhpapS19+OokYD0z+wnwMrAPobO9MeXfn9AB/m9CX8EQoDMhEULohL/WzGYTaj5tCDWY\nXu7+uyTlNLNhhF/g4wmDA44gJIsPc3x2GrCDmW1I6GvJeu+Iu39qZs8BNxCWV72n/m/LNGBPM3se\nWOzu30bn+cDMXgB+D9yR9m8mVUQ1EqmkeYRRRr8grCn/NmGU0z8IvzBTMkdtDSOMRqoF/kVYOvhL\nwrKq9VlxDnd/mJCArgLeIIy4uoBVk1F9yWk2YdTYk4TkcQZwvLv/J7rGzYR+g6OAiYSEcwJhmd9E\n5SSsK3989Nm3gB8TOr0/Tjs2/fgrCInmXcKyyr3Tjsv0D0It6lF3n9NAmc4kJMpPgFcz3ruF0CSo\nZq0qpaV2pdkzs+6EtaWPdPcHKl2eamNmZwPHunv/SpdFKkNNW9LsmNkQQnPMW4TRTZcSRjo9Vsly\nVRsz60ToVzoduKSypZFKUtOWNEdtgIsJw2bHEvoOdnX3hRUtVfW5ntDM9QKhr0WqlJq2RESkIKqR\niIhIQZRIRESkIEokIiJSECUSEREpiBKJiIgURIlEREQK8v/ymrhlMT06LgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "report.roc().plot(xlim=(0.5, 1))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }