{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# This notebook demonstrates how LIME - Local Interpretable Model-Agnostic Explanations can be used with models learnt with the AIF 360 toolkit to generate explanations for model predictions.\n", "\n", "For more information on LIME, see [https://github.com/marcotcr/lime](https://github.com/marcotcr/lime)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "%matplotlib inline\n", "\n", "import sklearn.model_selection\n", "import sklearn.metrics\n", "import sklearn.datasets\n", "import sklearn.ensemble\n", "import sklearn.preprocessing\n", "import numpy as np\n", "import lime\n", "import lime.lime_tabular\n", "from IPython.display import Markdown, display\n", "import matplotlib.pyplot as plt\n", "import sys\n", "sys.path.append(\"../\")\n", "import numpy as np\n", "from aif360.datasets import BinaryLabelDataset\n", "from aif360.metrics.binary_label_dataset_metric import BinaryLabelDatasetMetric\n", "from aif360.metrics.classification_metric import ClassificationMetric\n", "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n", "from aif360.algorithms.preprocessing.reweighing import Reweighing\n", "\n", "\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import accuracy_score\n", "\n", "from IPython.display import Markdown, display\n", "import matplotlib.pyplot as plt\n", "\n", "from aif360.datasets.lime_encoder import LimeEncoder \n", "\n", "\n", "from aif360.datasets.adult_dataset import AdultDataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Load dataset and display statistics**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Missing Data: 3620 rows removed from AdultDataset.\n" ] } ], "source": [ "np.random.seed(1)\n", "\n", "dataset_orig = AdultDataset()\n", "dataset_orig_train, dataset_orig_test = dataset_orig.split([0.7], shuffle=True)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "#### Original training dataset" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Difference in mean outcomes between privileged and unprivileged groups = -0.101638\n" ] } ], "source": [ "# Metric for the original dataset\n", "sens_attr = dataset_orig_train.protected_attribute_names[0]\n", "sens_idx = dataset_orig_train.protected_attribute_names.index(sens_attr)\n", "privileged_groups = [{sens_attr:dataset_orig_train.privileged_protected_attributes[sens_idx][0]}] \n", "unprivileged_groups = [{sens_attr:dataset_orig_train.unprivileged_protected_attributes[sens_idx][0]}] \n", "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n", " unprivileged_groups=unprivileged_groups,\n", " privileged_groups=privileged_groups)\n", "display(Markdown(\"#### Original training dataset\"))\n", "print(\"Difference in mean outcomes between privileged and unprivileged groups = %f\" % metric_orig_train.mean_difference())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Transform the data using the Re-Weighing (pre-processing) algorithm**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RW = Reweighing(unprivileged_groups=unprivileged_groups,\n", " privileged_groups=privileged_groups)\n", "RW.fit(dataset_orig_train)\n", "dataset_transf_train = RW.transform(dataset_orig_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Learn and test models from the transformed data using Logistic Regression**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Train model on given dataset\n", "\n", "dataset = dataset_transf_train # data to train on\n", "\n", "scale = StandardScaler().fit(dataset.features) # remember the scale\n", "\n", "model = LogisticRegression() # model to learn\n", "\n", "X_train = scale.transform(dataset.features) #apply the scale\n", "y_train = dataset.labels.ravel()\n", "\n", "\n", "model.fit(X_train, y_train, sample_weight=dataset.instance_weights)\n", "\n", "#save model\n", "lr_orig = model\n", "lr_scale_orig = scale" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████| 50/50 [00:00<00:00, 67.47it/s]\n" ] } ], "source": [ "#Test model on given dataset and find threshold for best balanced accuracy\n", "import numpy as np\n", "from tqdm import tqdm\n", "thresh_arr = np.linspace(0.01, 0.5, 50)\n", "\n", "scale = lr_scale_orig\n", "\n", "model = lr_orig #model to test\n", "dataset = dataset_orig_test #data to test on\n", "\n", "X_test = scale.transform(dataset.features) #apply the same scale as applied to the training data\n", "y_test = dataset.labels.ravel()\n", "y_test_pred_prob = model.predict_proba(X_test)\n", "\n", "\n", "bal_acc_arr = []\n", "disp_imp_arr = []\n", "avg_odds_diff_arr = []\n", " \n", "for thresh in tqdm(thresh_arr):\n", " y_test_pred = (y_test_pred_prob[:,1] > thresh).astype(np.double)\n", "\n", " dataset_pred = dataset.copy()\n", " dataset_pred.labels = y_test_pred\n", "\n", " classified_metric = ClassificationMetric(dataset, \n", " dataset_pred,\n", " unprivileged_groups=unprivileged_groups,\n", " privileged_groups=privileged_groups)\n", " metric_pred = BinaryLabelDatasetMetric(dataset_pred,\n", " unprivileged_groups=unprivileged_groups,\n", " privileged_groups=privileged_groups)\n", " \n", " TPR = classified_metric.true_positive_rate()\n", " TNR = classified_metric.true_negative_rate()\n", " bal_acc = 0.5*(TPR+TNR)\n", " \n", " acc = accuracy_score(y_true=dataset.labels,\n", " y_pred=dataset_pred.labels)\n", " bal_acc_arr.append(bal_acc)\n", " avg_odds_diff_arr.append(classified_metric.average_odds_difference())\n", " disp_imp_arr.append(metric_pred.disparate_impact())\n", " \n", "thresh_arr_best_ind = np.where(bal_acc_arr == np.max(bal_acc_arr))[0][0]\n", "thresh_arr_best = np.array(thresh_arr)[thresh_arr_best_ind]\n", "\n", "best_bal_acc = bal_acc_arr[thresh_arr_best_ind]\n", "disp_imp_at_best_bal_acc = np.abs(1.0-np.array(disp_imp_arr))[thresh_arr_best_ind]\n", "\n", "avg_odds_diff_at_best_bal_acc = avg_odds_diff_arr[thresh_arr_best_ind]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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eSHaeiCU1w/RuN6xanvb1AmlXL5B2dStTt0q5iz95iYszYXfVKnPbvNksL+bh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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot balanced accuracy, abs(1-disparate impact)\n", "\n", "fig, ax1 = plt.subplots(figsize=(10,7))\n", "ax1.plot(thresh_arr, bal_acc_arr)\n", "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n", "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n", "ax1.xaxis.set_tick_params(labelsize=14)\n", "ax1.yaxis.set_tick_params(labelsize=14)\n", "\n", "\n", "ax2 = ax1.twinx()\n", "ax2.plot(thresh_arr, np.abs(1.0-np.array(disp_imp_arr)), color='r')\n", "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n", "\n", "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], \n", " color='k', linestyle=':')\n", "ax2.yaxis.set_tick_params(labelsize=14)\n", "ax2.grid(True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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p7GitZs+39MWt1d7HaGDQC6O12s1WrdXeBm4Drgf2W50uQ2udUXXvsGpJGLaB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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot average odds difference\n", "fig, ax1 = plt.subplots(figsize=(10,7))\n", "ax1.plot(thresh_arr, bal_acc_arr)\n", "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n", "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n", "ax1.xaxis.set_tick_params(labelsize=14)\n", "ax1.yaxis.set_tick_params(labelsize=14)\n", "\n", "\n", "ax2 = ax1.twinx()\n", "ax2.plot(thresh_arr, avg_odds_diff_arr, color='r')\n", "ax2.set_ylabel('avg. odds diff.', color='r', fontsize=16, fontweight='bold')\n", "\n", "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], color='k', linestyle=':')\n", "ax2.yaxis.set_tick_params(labelsize=14)\n", "ax2.grid(True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold corresponding to Best balance accuracy: 0.2300\n", "Best balance accuracy: 0.8182\n", "Corresponding abs(1-disparate impact) value: 0.3014\n", "Corresponding average odds difference value: -0.0216\n" ] } ], "source": [ "rf_thresh_arr_orig_best = thresh_arr_best\n", "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n", "rf_best_bal_acc_arr_orig = best_bal_acc\n", "print(\"Best balance accuracy: %6.4f\" % rf_best_bal_acc_arr_orig)\n", "rf_disp_imp_at_best_bal_acc_orig = disp_imp_at_best_bal_acc\n", "print(\"Corresponding abs(1-disparate impact) value: %6.4f\" % rf_disp_imp_at_best_bal_acc_orig)\n", "rf_avg_odds_diff_at_best_bal_acc_orig = avg_odds_diff_at_best_bal_acc\n", "print(\"Corresponding average odds difference value: %6.4f\" % rf_avg_odds_diff_at_best_bal_acc_orig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Use LIME to generate explanations for predictions made using the learnt Logistic Regression model**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold corresponding to Best balance accuracy: 0.2300\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Actual label: [0.]\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Actual label: [1.]\n" ] } ], "source": [ "limeData = LimeEncoder().fit(dataset_orig_train)\n", "s_train = limeData.transform(dataset_orig_train.features)\n", "s_test = limeData.transform(dataset_orig_test.features)\n", "\n", "scale = lr_scale_orig\n", "\n", "model = lr_orig #model to test\n", "\n", "\n", "\n", "\n", "explainer = lime.lime_tabular.LimeTabularExplainer(s_train ,class_names=limeData.s_class_names, \n", " feature_names = limeData.s_feature_names,\n", " categorical_features=limeData.s_categorical_features, \n", " categorical_names=limeData.s_categorical_names, \n", " kernel_width=3, verbose=False,discretize_continuous=True)\n", "\n", "s_predict_fn = lambda x: model.predict_proba(scale.transform(limeData.inverse_transform(x)))\n", "\n", "import random\n", "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n", "i1 = 1\n", "exp = explainer.explain_instance(s_test[i1], s_predict_fn, num_features=5)\n", "exp.show_in_notebook(show_all=False)\n", "print(\" Actual label: \" + str(dataset_orig_test.labels[i1]))\n", "\n", "i2 = 100\n", "exp = explainer.explain_instance(s_test[i2], s_predict_fn, num_features=5)\n", "exp.show_in_notebook(show_all=False)\n", "print(\" Actual label: \" + str(dataset_orig_test.labels[i2]))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Learn and test models from the transformed data using Random Forests**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Train model on given dataset\n", "\n", "dataset = dataset_transf_train # data to train on\n", "\n", "scale = StandardScaler().fit(dataset.features) # remember the scale\n", "\n", "model = sklearn.ensemble.RandomForestClassifier(n_estimators=500) # model to learn\n", "\n", "X_train = scale.transform(dataset.features) #apply the scale\n", "y_train = dataset.labels.ravel()\n", "\n", "\n", "model.fit(X_train, y_train, sample_weight=dataset.instance_weights)\n", "\n", "#save model\n", "rf_orig = model\n", "rf_scale_orig = scale" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████| 50/50 [00:00<00:00, 77.39it/s]\n" ] } ], "source": [ "#Test model on given dataset and find threshold for best balanced accuracy\n", "import numpy as np\n", "from tqdm import tqdm\n", "thresh_arr = np.linspace(0.01, 0.5, 50)\n", "\n", "scale = rf_scale_orig\n", "\n", "model = rf_orig #model to test\n", "dataset = dataset_orig_test #data to test on\n", "\n", "X_test = scale.transform(dataset.features) #apply the same scale as applied to the training data\n", "y_test = dataset.labels.ravel()\n", "y_test_pred_prob = model.predict_proba(X_test)\n", "\n", "\n", "bal_acc_arr = []\n", "disp_imp_arr = []\n", "avg_odds_diff_arr = []\n", " \n", "for thresh in tqdm(thresh_arr):\n", " y_test_pred = (y_test_pred_prob[:,1] > thresh).astype(np.double)\n", "\n", " dataset_pred = dataset.copy()\n", " dataset_pred.labels = y_test_pred\n", "\n", " classified_metric = ClassificationMetric(dataset, \n", " dataset_pred,\n", " unprivileged_groups=unprivileged_groups,\n", " privileged_groups=privileged_groups)\n", " metric_pred = BinaryLabelDatasetMetric(dataset_pred,\n", " unprivileged_groups=unprivileged_groups,\n", " privileged_groups=privileged_groups)\n", " \n", " TPR = classified_metric.true_positive_rate()\n", " TNR = classified_metric.true_negative_rate()\n", " bal_acc = 0.5*(TPR+TNR)\n", " \n", " acc = accuracy_score(y_true=dataset.labels,\n", " y_pred=dataset_pred.labels)\n", " bal_acc_arr.append(bal_acc)\n", " avg_odds_diff_arr.append(classified_metric.average_odds_difference())\n", " disp_imp_arr.append(metric_pred.disparate_impact())\n", " \n", "thresh_arr_best_ind = np.where(bal_acc_arr == np.max(bal_acc_arr))[0][0]\n", "thresh_arr_best = np.array(thresh_arr)[thresh_arr_best_ind]\n", "\n", "best_bal_acc = bal_acc_arr[thresh_arr_best_ind]\n", "disp_imp_at_best_bal_acc = np.abs(1.0-np.array(disp_imp_arr))[thresh_arr_best_ind]\n", "\n", "avg_odds_diff_at_best_bal_acc = avg_odds_diff_arr[thresh_arr_best_ind]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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DzJ5t5rTao7jYhM2VKyEx8WRYbd/eBNnqKiw0o7alI7Zt2pgQ3ah+ZjsJrrLlqwRXUaNS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\nuvtI4BUgdZbW3b04+S9CLL8B2hMS1WTPE5LYGu9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Qwo1a64Dncc+oM1VGxYNmNCPUuu7Z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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot balanced accuracy, abs(1-disparate impact)\n", "\n", "fig, ax1 = plt.subplots(figsize=(10,7))\n", "ax1.plot(thresh_arr, bal_acc_arr)\n", "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n", "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n", "ax1.xaxis.set_tick_params(labelsize=14)\n", "ax1.yaxis.set_tick_params(labelsize=14)\n", "\n", "\n", "ax2 = ax1.twinx()\n", "ax2.plot(thresh_arr, np.abs(1.0-np.array(disp_imp_arr)), color='r')\n", "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n", "\n", "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], \n", " color='k', linestyle=':')\n", "ax2.yaxis.set_tick_params(labelsize=14)\n", "ax2.grid(True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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pMBR4FAjDWFy3Chigtc6wOmcc8FeM2eKjwEyMBXdOJTPDZs0Mv/46PPggnDwJ\nkZHOH7+J0Vrz1Dc7+WzDMX7fJ46nR3eVHeWcZNgwY3HwsmXLTI5ECMfTWvPphmP8/dvdBPt58+9x\nPbi0k/wbLVyHbMdsf5IMm5UMf/MNXH89bNoEvXs7f/wm5q0fD/Li4n3cO7QDfxrV2exwhBDN3L6M\nAu7/bAsHswq5+7IOPDoiURbXCZcgybD9yd9ss8THG4+pqebG0QTM23KcFxfv47qesTw2IsnscIQQ\nbiApOpgF9w9mfN8E3ll5iJveWUdqbrHZYQkhHECSYbNY70In6vTTgWwe/2oHAzu05IUbe8iCFhO8\n/fbbvP3222aHIYTT+ft48n83XMRbN1/MoaxCrnptNQu3p5kdlhDCziQZNktEBPj5ycxwPXan5XPP\nJ5vpEBnEO7f2lo4RJlm4cCELFy40OwwhTHN19xh+ePBSEqOD+ePnW/nTVzs4U1bZ8IVCiCZBaobN\nqhkGSEyEXr1g9mxzxndhaafPcP3ba1Ao5k0dSKzsKCeEMFlFZRWvLjvAWysO0ikqiLdvuZiOUdLf\nXDiX1Azbn0y1mSk+XmaGa5F3ppxJH/5McWkls+7oK4mwEMIleHl6MG1kEh/d0Y+cwjJGv7GGeVuO\nmx2WEOICSTJsJtmF7hylFZXc/fEmDmcX8e6tvekcHdLwRcKhXnvtNV577TWzwxDCZVzaKZLvH7yU\n7nGhPPLldh7/aruUTQjRhEkybKb4eEhPh/JysyNxCVVVmsfm7GB9Si4v3tiDgR0b2lZdOMPy5ctZ\nvny52WEI4VJahfjx6Z39eeB3HZmz+TjXvbWGgycLzA5LCHEeJBk2U0ICVFVBmqxOBnhh8T4WbE/j\nsZFJXNertdnhCIsFCxawYMECs8MQwuV4eXrwyAijbCK7sFTKJoRooiQZNpP0Gv7VR+uO8M7KQ9zS\nP4GpQzuYHY4QQthMyiaEaNokGTaT9BoGYPmeTP66YBfDkqN45tquKCW9hF3JSy+9xEsvvWR2GEK4\ntOqyifsvty6bKDQ7LCGEDbzMDsCtycwwOYWlPPbVDrrEhPD6hF54yXanLmfdunVmhyBEk1DdbaJv\nu3Aenr2Na9/8if7twgny8ybI14tgPy+CfC0/fl4EWx6DfL1ICA+gZZCv2W9BCLckfYbN7DMMEB4O\nEybAW2+ZF4OJ7vtsC0t3ZbLwj4NJipZ+nUKI5iEjr4TnvtvN0ZxiCksrKCipoLC0nJLyqlrP9/JQ\nDEtuxYT+CVzaMUJ22xR1kj7D9iczw2Zz417D3/+Sznc70nlsZJIkwkKIZiU61I83b774nOPllVUU\n/ZocV1gS5XLWp+Ty1ebjLNqLtdcGAAAgAElEQVSVQVwLfyb0S+Cm3nFEhfiZEL0Q7kVmhs2eGR49\n2kiGt20zLwYT5BSWMuLfq4gN8+frqQOlPMKF/fOf/wTgiSeeMDkSIZq30opKluzK5POfj7H2UA6e\nHophyVFM6JfAZZ0iZbZYADIz7AgyM2y2+HhYs8bsKJzurwt2kV9Szqc39ZdE2MVtc7MPakKYxdfL\nk9E9YhndI5aUrEJmb0xlzubjLN6VSeswfyb0i+emPvG0ktliIexKZobNnhn+5z/hySehoACCgsyL\nw4l++CWdez/dwqPDE/njFZ3MDkcIIVxWaUUlS3dn8tmGs7PFo7pFM3lgW3q3aSHdd9yQzAzbn8wM\nm826o0RysrmxOEFuURnT5++ka2wI90g/YSGEqJevlyfXdI/lmu6xHMku4tMNR5m9MZXvdqRzUetQ\nJg1syzU9YvD18jQ7VCGaLJkZNntmePVquOwyWLwYRowwLw4neeDzrfywM50F9w8mOSbE7HCEDf7+\n978DMH36dJMjEUIAFJdV8PXWE8xac4QDJwuJCPLh5n4J3HJJGymhcAMyM2x/MjNsNjfaeGPRzgwW\nbE/jkeGJkgg3Ifv27TM7BCGElQAfL27p34ab+yWw9lAOH645whs/HuTtFYe46qIYJg1qS6/4MCmh\nEMJGMjNs9sxweTn4+sL06fDMM+bF4WCnisoY/u9VRAX7Mv/+QXjLojkhhLCbYznF/HfdEb7cmEpB\naQU94kK5sU88I7u2IipYZoubE5kZtj9TMhKl1FSl1GGlVIlSarNS6tJ6zp2llNK1/BTVOO9mpdQ2\npVSxUipDKfWJUira8e/mAnl7Q2xss58ZfmbhLk4Xl/HSTT0kERZCCDtLaBnA9Gu6sP7PV/D3MV0p\nLqtk+jc76f/8cm56Zy3vrU7h+Klis8MUwiU5fWZYKTUO+ASYCvxkeZwMdNFan5MRKqVCAf8ah9cA\nq7TWky3nDAJWAdOAb4BWwNvAKa31FfXFY/rMMMCAARAYCMuWmRuHgyzZlcFdH2/moWGdeGhYotnh\niEZ6+umnAXj22WdNjkQIYSutNQdOFrJoZwY/7MxgT3o+AN3jQhnZNZoru0XTPtI9Ohg1NzIzbH9m\nJMMbgB1a6ylWxw4AX2mtn7Th+kEYSfQgrfVay7FpwB+11m2szpsMvKG1rvdvu0skw+PGwdatsH+/\nuXE4wOliozwiIsiX+fcNwsdLZoWbmsmTJwPw4YcfmhyJEOJ8HckuYtGuDBbtzGBb6mkAkloFM7Jb\nNNf2iKVjlCTGTYUkw/bn1GRYKeUDFAMTtNZzrI6/BXTTWg+x4R6zgD5a625WxwYAK4GxwLdAS+BT\nIE9r/fv67ucSyfC0afDWW1BcDM1swcMjs7exYHsa8+8fRNfYULPDEUIIt5d2+gyLLYnxxiO5AIzr\nG88jw5OIDPY1OTrREEmG7c/Z03QRgCeQWeN4JtBgfa+lZOImYKb1ca31OmACRgJcBmQBCri9jvvc\npZTapJTaVFFR0dj3YH8JCVBSAtnZZkdiV8t2ZzJv6wmmXt5REmEhhHARsWH+TB7Ujtl3D2DDn4cx\naWA75mw6ztAXf+StHw9SUl5pdohCOJVZ31nXnI5WtRyrzUSMZPrj31ysVBfgdeDvQG9gFEZy/W6t\ng2s9Q2vdR2vdx8vLBbrLWW+80UwUlVbw1De/0Dk6mPsv72h2OOICPPnkkzz5ZIMVTEKIJigy2Jen\nR3dhycOXMaBDBC8u3scVL69k4fY03L3blHAfzk6Gs4FKzp0FjuLc2eLaTAHmaq1zaxx/EvhZa/2i\n1nqH1noxxsK8W5VS8RcatMM1w17DM1alkJlfyj+uv0jqhJu4nJwccnJyzA5DCOFA7SODeO/2Pnx2\nZ39C/L354+dbufGddb/WFwvRnDk1S9FalwGbgeE1XhoOrK3vWqVUf6AHNUokLAIwkmxr1c9dvwi3\nmc0MZ+aXMGNVCld3j6F3mxZmhyMu0IwZM5gxY4bZYQghnGBgxwi+/eNg/jX2Io7mFHPdW2t46Iut\npJ0+Y3ZoQjiMGVN2rwCTlFJ3KqWSlVKvAbHAOwBKqY+UUh/Vct0U4ADGQrmaFgJjlFL3KqXaWzpO\nvA5sqa1dm8uJjDQ23mgmM8MvL9lHZZXmTyM7mx2KEEKIRvL0UIzrm8CKx4Zy3+Ud+H5nBpe/tIKX\nl+zjTJnUE4vmx+nJsNZ6NvAQ8BdgGzAYuEprfdRySoLl51dKqWBgPPCerqWISWs9C3gEuB/YCXyF\nkTiPccy7sDOljNnhZjAzvDstnzmbj3P7wDYktAwwOxxhB9OmTWPatGlmhyGEcLIgXy8eG9mZ/z06\nhJFdo3njfwcZ+eoq1hxsXou9hZDtmF2htRrAFVfAmTOwtt5qEZemtebW939mZ1oeK6ddTmiAt9kh\nCTu47777AHjrrbdMjkQIYaZ1h3L489e/cDi7iBt7x/HUVcm0CPQxOyy3I63V7E+SYVdJhidNguXL\nm/Ts8I/7TjL5w408fU0X7hjczuxwhBBC2FlJeSWvLz/AjFUphPp789druzK6ewyqmfXId2WSDNuf\nLPN3FQkJkJYGrtD3+DxUVFbx/Hd7aNsygImXtGn4AiGEEE2On7cnj4/qzIL7BxPXwp8HPt/KHbM2\nckIW2IkmTJJhVxEfD1VVRkLcBH256TgHThbyxJXJ0kqtmXnooYd46KGHzA5DCOFCusSGMG/qIKZf\n04X1KbkMf2UlH645TGWVe3/bLJomyVpcRRPuNVxYWsErS/fRr204I7u2MjscIYQQTuDpofjD4HYs\nefgy+rYN55mFuxn7n7Xszcg3OzQhGkVqhl2lZnj3bujaFT77DCZMMDuaRnlp8T7e/PEg8+8bRI/4\nMLPDEUII4WRaaxZsT+OZhbvJP1PODRe35rperbmkXUs8PKSe2J6kZtj+ZGbYVVRvvNHEZobT884w\nc3UKY3rGSiIshBBuSinFmJ6tWfbIEG7qE893O9K5eeYGBv7zf/zju93sPJEn2zu7KKWUr1LqDaVU\ntlKqSCm1QCkVZ8N1U5VSh5VSJUqpzUqpS+s4TymlFimltFLqxhqvtVBKfayUyrP8fKyUcnoyIcmw\nqwgOhrCwJtdN4sXF+9DAYyOTzA5FOMh99933a3s1IYSoT3igD/93w0Vs+stw3pjQi26tQ/hwzRGu\neeMnhr2ykteXH+Bojgt8GyusvQqMBSYAlwIhwLdKKc+6LlBKjQNeA54HemHsIvyDUiqhltMf5dxd\ngqt9BlwMXAmMsvz+8fm9jfPn5ewBRT0SEprUzPDOE3nM23KCe4Z0IK6FbLDRXPn7+5sdghCiifH3\n8WR0j1hG94jlVFEZP+zM4JttJ3hl6X5eWbqfXglhjOkRy9XdY4kM9jU7XLellAoF/gBM1lovtRy7\nFTgKDAMW13HpI8AsrfVMy/M/KqVGAfcCT1rdvw/wINAbyKwxdjJGAjxYa73WcuxuYLVSKklrvc8+\n77JhNtUMK0V/rdnghHiczmVqhgGuvRaOHIEdO8yOpEFaaybMXM/+zEJWPDaUED/ZYEMIIUT90k6f\nYcH2NOZvS2NPej4eCi5p35JruscysmsrWgZJYtwQe9YMK6V+BywHorTWWVbHdwFfaa3/Wss1PkAx\nMEFrPcfq+FtAN631EMvzYGAL8KDW+nullAZu0lp/ZXn9DozZ5ZDq3YWV0bC6APij1vpDe7xHW9g6\nM7xOKXYCM4FPtOaUA2NyqvDwcFasWGF2GAB08PMjdv9+Vv/vf+Dh2hUsBSUVDAos4qaL/dmyfo3Z\n4QghhGgiOgOde0BpV39OF5eTdyabnIOZfH5QEejrSViANyF+3njKwru6eCmlNlk9n6G1nnGe94rG\nKGGoucd2puW12kQAntSY6bU8H2b1/B1gkdb6+3rGztJWs7Jaa62UOlnP2A7RmDKJrhh1Jf9Siq+B\n97TmR8eE5Ty5ubkMHTrU7DAM+/bBnDkM7dTp7II6F1ReWcXIV1cBoSwefxnenq6duIsLc9dddwEw\nY8b5/lsrhBB101qzJ72A735JY86OdI7mFOPpUcGgjhFcc1EMI7q2IixAtn22UqG17lPfCUqp54Cn\nGrjP5fXdAmiodKDm679eYym16AHUG2cdY9gytl3Zmgz/G7gRiAf8gPHAeKVIAd4HZmlNhmNCdCOJ\nicbj/v0unQx/8fMxUrKKmHlbH0mE3UDLli3NDkEI0YwppegSG0KX2BCmjUhiV1o+3/2Szrc70nh8\n7g7+/LVicKcIxveNZ1hyK7zk/3ds8SrwSQPnHAMuwZjljQCyrF6LAlbVcV02xmxyzdnbKM7OFl8B\ndAEKa2zVPVsptU5rPRjIAKKUUqpGmUQk5846O1Sj+gwrxWCM1YZjMd40GNl7JTAf+IfWbLN3kI7k\nUjXDJ05AXBy8/Tbce6/Z0dSqoKScIS+uILFVEJ9PuUT2oxdCCOEQWmt+OZHHdzvSWbA9jfS8EqJD\n/JjQL4EJ/eKJCvEzO0RT2LlmOBQjCZ6ktf7MciwOI1G+Umtd6wI6pdQGYLvW+i6rY/uBuVrrJ5VS\nrYEWNS77BWPh3XytdYplAd1uYJDVArqBwBqgs8stoDvnIkU88BEwBCMZrp7SrgB+rzXz7RmkI7lU\nMqw1BAXBXXfBv/9tdjS1+nDNYZ5ZuFs22BBCCOE0FZVV/Lgvi4/XH2XV/iy8PBQju0Vz6yVt6N8u\n3K0mZuy96YZS6j/AtcDtQA7wCkYi21trXWk5Zy/wptb6TcvzcRgt0KZiJK/3YHSl6Kq1PlrHOL9Z\nQGc59gMQB0zByCVnAEe01qPt9f5s0ajWakoxHOMNj8aYVgcj+K0Yfek6AP+AppMMuxSljFKJ/fvN\njqROX20+zkWtQyURdiOTJ08G4MMPnbawVwghfsPL04PhXVoxvEsrDmcX8en6o8zZfJzvdqTTKSqI\nWwe04fperQmWzkbn42GMyczZgD9Gd4nbqhNhiySMUgoAtNazlVItgb8AMcBO4Kq6EuF63AK8Diyx\nPF8A3H8+b+JC2Npa7THgLqB99SGgCiPof2vNaqUIBE4AAVrTZCrdXWpmGGDcONiyBQ4cMDuSc+zN\nyGfUq6v52+guTBrUzuxwhJM8/fTTADz77LMmRyKEEGedKatk4Y40Pl53lF9O5BHg48n1vVpz56Xt\naRfRfHcrlu2Y7c/WZLiKs+UQ+cAHwOtac6TGeXuBTlpT564lrsblkuHp0+H//g+Ki8HHtT5T/OO7\n3cxae4QNfx5GeKBrxSaEEMJ9bU89zcfrj7JgexpouO/yjtwztD2+Xk0mHbGZJMP215glmYcxptLj\ntOaRmomwxe84O3sszkdiIlRWwuHDZkfyGxWVVXy9NY3fdY6SRFgIIYRL6REfxks39eCnP13OiK6t\n+Pey/Vz12mp+PpxrdmiiCbA1Gb4eY8b3Na0prOskrUnTmsbWiwhr1e3V9jltEaVNVh3IIruwlLEX\nx5kdinCyiRMnMnHiRLPDEEKIBkUF+/HmzRfz4eS+lJRX8ft31/Gnr3ZwurjM7NCEC7M1GV4BxCt1\ntngaQCkilCJBKULtHpm76tTJeHSxRXRzN5+gZaAPl3eOavhk0awkJSWRlJRkdhhCCGGzy5OiWPrI\nZdx9WXu+2nKcYa+sZP62E5xPBy3R/NmaDH+AUSZxc43j4y3H37dnUG4tPBwiIlwqGT5dXMbS3Zlc\n2zNWNtlwQ9OnT2f69OlmhyGEEI0S4OPFk1cls+D+QbQO8+fBL7Zx+4cbOZZTbHZowsXYmtn0tzzO\nrXF8Hsaiuv4I+3Gx9moLd6RTVlklJRJCCCGanK6xocybOoi/je7C5iO5jHh1Jf9ZcYjyyiqzQxMu\nwtZkONLyeLrG8bwarwt7cLFk+KvNx+kcHUzX2BCzQxEmGD9+POPHjzc7DCGEOG+eHopJg9qx7NEh\nDEmM5F+L9nL922vILiw1OzThAmxNhgssjyNqHK9+XueiOnEeEhMhPR0KCho+18EOnixke+ppbuwd\n51Y7/IizevbsSc+ePc0OQwghLlhMqD/v3tqHdyZezMGThUx8bwO5RbK4zt3ZugPdFmAY8IFSdAX2\nAMkYe0xrYLNjwnNT1R0lDhyAiy82NZS5W47j6aEY07O1qXEI8zzxxBNmhyCEEHY1qlsM7/t5c8es\njdzy3gY+n9KfsABpG+qubJ0ZfsfyGAI8A3xpeazek/c/do7LvVUnwyaXSlRWaeZtOc7QxEgig31N\njUUIIYSwp0EdI5h5Wx8OZRUy8f0N5BWXmx2SMIlNybDWzANewVgsZ/0D8LLWfOOY8NxUx47Go8nJ\n8JqD2WTmlzK2tyycc2djx45l7NixZochhBB2d1liJO/e2pv9GYXc+sEG8s5IQuyObO6TpTXTMLpG\n/AN4z/LYX2sed1Bs7svfHxISTE+G5245Tqi/N1ckS29hdzZgwAAGDBhgdhhCCOEQlydF8Z+JF7Mn\nPZ/bP/iZghJJiN2NcvcG1IGBgbqoqMjsMM41fDjk5cHPP5syfH5JOX2fW8bv+8Tz9+u6mRKDEEII\n4SxLdmUw9dMtdI8L5aM/9CfI19ZlVc6llCrWWgeaHUdzYvOftFJ4AVcBSYB/zde15lk7xiUSE+HT\nT0FrMKGLw/c70imtqJISCSGEEG5hRNdo3ry5F/d9tpXJH/7MrMn9CHTRhFjUQalKy28arW3+w7Pp\nRKWIwtiSub49WSUZtqekJGNmOCsLopxfpjB3y3E6RAbSI0522nZ31157LQALFiwwORIhhHCsUd1i\neH08PPDFVibP2sisyX0J8JGEuAk5r9lDW/+EnwE61/O6e9daOIJ1RwknJ8NHsovYeOQUfxrVWXoL\nC6644gqzQxBCCKe5unsMFVVVPDx7G3+YtYkPJvXF38fT7LCEbY5xHjmprQvoRlhu/qHluQYeAA4A\n+4E/NHZg0QAT26vN23IcDwXX95LewgIefPBBHnzwQbPDEEIIpxnTszUv/74H6w/nMOWjTZSUVzZ8\nkTCf1m3Ruh1at2vMZbYmw9VZ0a/d97XmTeAGIBGQwlJ7a9MGvL2dngxXVWnmbjnBoI4RRIf6OXVs\nIYQQwlVc3yuOF2/swZpD2YyfsZ6TBSVmh+TelHoFpV62/H4bSt1mr1vbmgxXfyTKAcqNOIgEjlqO\n32WvgISFp6fRb9jJyfD6wzmcOH2GG2XhnLC48sorufLKK80OQwghnO7G3nG8M7E3+zIKuO7NNexK\nyzM7JHf2EFD9NeUs4AN73djWZDjH8hgKZFh+/xT4zPJ7C3sFJKwkJjo9GZ67+QTBvl6M7Brt1HGF\n6xo9ejSjR482OwwhhDDFyK7RzLlnABq46Z11LNmV0eA1wiGqAIVS1Sv77baoydZkeJ/lsQOwyhLA\nFcDVGPXDW+wVkLCSmAgHD0Klc2qVikor+GFnOld3j8HPWxYLCMPUqVOZOnWq2WEIIYRpurUOZf59\ng+jUKpi7P9nMOysP4e77NJjgpOUx5dcjSqXU8XOoMTe2NRmeCcwA/DA6S2RxdkvmbIypa2FviYlQ\nWgqpqU4Z7oedGRSXVUqJhBBCCFFDVIgfs++6hKsviuGfP+zlsa92UFohC+uc6EeMvLO6GkEBbev5\nsZlNrdW05kvgy+rnStEJuByoANZozenGDCpsZN1Rom1bhw83d/Nx2rYMoHcbqXoRZw0bNgyAZcuW\nmRyJEEKYy8/bkzcm9KJDZBCvLT/AsZxi3rm1N+GBPmaH5g4eBjyBi4GOGJUJx+xx4waTYaXwBXZb\nnl6tNXu1Jh+Yb48ARD2sk+ERIxw6VGpuMetScnhkeKL0Fha/MW7cOLNDEEIIl6GU4uHhiXSICuKx\nOdsZ89ZPfHB7Xzq1CjY7tOZN65PAeACUqrIca1QLtbo0mAxrTalStASCsa7TEI7XqhUEBztlEd3X\nW08AcMPF0ltY/NaUKVPMDkEIIVzOtT1iiW/hz5SPNnPD22t54+ZeDE1y/o6xbkOpVzC2WX4UmIwd\nN3yztWa4+vvRHvYaWNhAKad1lPhuRzr92oUT1yLA4WMJIYQQzUGvhBYsuH8QceEB3DFrIx+uOSwL\n6xzHurXah5jQWu1VIBf4XCnGKUWSUiRY/9grIFGDE5LhlKxC9mUWcGU3aacmzjV06FCGDh1qdhhC\nCOGSYsP8+eqeAQxLbsW+jAKzw2nOHNZazaYFdBjt1DQQztnewtZ0I+4lGiMxEb74AkpKwM8xO8It\nsvRMlN7CojaTJk0yOwQhhHBpgb5evDOxN5Vay7obxzkJtKJma7XaabTuYOuNG5PAyp+uGRITQWs4\ndAi6dnXIEIt3ZtAjLpTYMH+H3F80bZIMCyFEwzw8FB6SKjnSj8AEzm2tVptG1arYmgz/tzE3FXZk\n3VHCAcnwidNn2H48j8dHJdn93qJ5KC8vB8Db29vkSIQQQrgx69Zq1bO+zmmtBqA1k+0xmDgPnToZ\njw6qG67eVnKUlEiIOgwfPhyAFStWmBuIEEII93VuazXttNZqwmShoUaLNQclw4t2ZpDUKpj2kUEO\nub9o+u68806zQxBCCCGsXW7Pm9mUDCvVYPsKrTV/sEM8ojYO6iiRXVjKxiO53P+7Tna/t2g+Jk6c\naHYIQggh3J1SRucyrY8Bh39zrDbGeTaxdWZ4EnUXIyvLa5IMO0piIixcaPfbLtudSZWWEglRv+Li\nYgACAqQHtRBCCNMcwWiv5mX5vb5Fco3qcibdJJqCxEQ4eRJOn4awMLvd9oedGSSEB5AcI1tIirpd\nddVVgNQMCyGEMJ2q4/cLYmsyXLNA2QtoD0wHegHX2CsgUYvqjhIHDkDfvna5Zd6ZctYeymbyoHbS\nE1HU69577zU7BCGEEOIjzs4GW/9+wWztJnG0lsOHlGIdkA3cC6y0V1CiBuv2anZKhn/ce5LySi0b\nbYgGjRs3zuwQhBBCOIhSyhd4CaOHrz+wHJiqtT7ewHVTgceAGGAX8JDWenUt5yngB2AkcJPW+iur\n144AbWpc8i+t9RPnDKj1pFp/twNbt2OuixdGZj7KDrGIunToAErZdRHdop0ZtArxpVe8/couRPOU\nl5dHXl6e2WEIIYRwjFeBsRjJ8KVACPCtUsqzrguUUuOA14DnMSoE1gI/qNoXtD0KVNYz/rMYCXX1\nz3Pn8R4uyIV0k/ADBgG+QKP+n9LWTxOWc2cBt9fyUrHWOtDqPB/gL8CtQCyQCbyktX69MbG5JF9f\naNvWbsnwmbJKVuw/ye/7xOPhISUSon5jxowBpGZYCCGaG6VUKEYDhMla66WWY7cCR4FhwOI6Ln0E\nmKW1nml5/kel1CiMSoEnre7fB3gQ6I2Rl9WmQGudYUOwDXU2s6bR2ubGDhfaTaI6k/re1gGtPk1M\nBX6yPP6glOqia2+D8SBQc7p8DbCqxrHPgXjgLuAAxv7VzWd/4aQkuyXDK/dnUVJeJV0khE0eeOAB\ns0MQQgjhGL0Bb2BJ9QGtdapSag8wkFqSYcvkY2+M0gprSyzXVJ8XjJGb3a21PlnP+qRpSqkngVRg\nDvCi1rqslvMmYVudcKO7nF1oN4lSjDf6UCPuY9OniWpa6zysZp6VUoMwFu/danVsBMYnmA5a62zL\n4SONiMn1JSbCTz+B1kbJxAVYvCuDsABv+rULt1Nwojm74YYbzA5BCCGEY0RjlDBk1zieaXmtNhEY\n2yLXnOnNxMjFqr0DLNJa1zdh+jqwFcgB+gH/xGjaUNduTw75Ovt8u0kAlGpNw9PaVmz9NNGAKcAu\nrfVaq2PXARuBR5RStwFnMIq1/6y1LqzvZuHh4U3i699YILGwkLXz5lHWsuV530cDnary6X2RNz+t\nrjm5LsS5quuFQ0NDTY5ECCEE4KWU2mT1fIbWeob1CUqp54CnGrhPfbu4Vc+u1qfm679eYym16AH0\nqfcGWr9i9XSHUiofmK2U+pPWOqeeeIOBd4HTwMvAcSAOoz45AiNXtNmFdJM4H7Z+mqiVpbblJuDP\nNV5qDwzGmKkeC4QBb2DkkDfWcp+7MMop8PHxYejQoY15D+YoL4fXX2dgRAQMGXLet1mx7yQvLNrI\n+7dfzNDkVnYMUDRX1X8/msKHRiGEcAMVWut6k0yMRXGfNHDOMeASjLwsAsiyei2Kc8tRq2VjzCbX\nnDmO4mx+dwXQBSisUR4xWym1Tms9uI57b7A8dsSYLT5L67Ndy5R62zL+YLQ+bHV8JUap7GhgQR1j\nnMPWBXSjMKavt2rNQqvj1wI9gZ+1ZpGtg1LPp4kGTMT4Q/u4xnEPy/U3W8oqUErdDyxWSrXSWv8m\n+bZ8gpoBEBgYaLc+dQ5l3V7tApLhxbsyCPTxZFDHCDsFJpq7Rx991OwQhBBCNIKlZLRm6cM5lFKb\ngXJgOPCZ5VgckIzRIaK2e5dZrhuOUeNbbTgw1/L7U5xbBfALMA2YX09IPS2P6Q2E/nvL45kax6uf\n30AjZodtLZN4GugPXFnjeCHwN2Ad2JQM2/Jpoj5TgLla69wax9OBE9WJsMUey2OCjfd2bfHxRleJ\nC1hEV1mlWbIrk8s7R+HnXWfHFCF+Y/To0WaHIIQQwgG01nlKqfeBF5VSJzFmY18BdgDLqs9TSu0F\n3tRav2k59ArwsVLqZ4ymBvdgfBv/juW+J4AT1mNZZohTtdYplucDMGamf8RYG9YX+DewoI6GCtZ8\nLY9zUer/OFsmUd1wwbsR/zPYnAx3tjyuq3H8Z8tjsi03sfHTRK2UUv0x6k9qW6y3BrhJKRVkVSNs\nmUq1W4mHuTw8oFOnC0qGNx3JJaeojFHdpIuEsF1GhrE0IDpa/rsRQohm6GGgApjN2U03btNaW/cG\nTsIopQBAaz1bKdUSo6VtDLATuEpr3ZicqxQYB/wVI7k9CswEXrDh2sUYs7+XcO5Ms6bulnC1Ulo3\nXCWgFCUYWXac1menrpUiBiPzL9MaP5sGNFqrfYzRUq3608QfgK5a66NKqY8AtNa31bjuPeAyIEnX\nCFopFYQxE7weY6Y6DAfAUUUAACAASURBVKOweo/W+qb64gkMDNRFRUW2hG6+sWNh927Ys6fhc2vx\nzMJdfLrhGFunDyfQtzGNRP6fvTuPs3reHzj+erfvu1Taw1TIkpAsuYoWEVFxRbpCC4X8rqWLbFlC\nilBRlHvr2ktIRdaQpRIKt1LRTMvUtE7TNJ/fH+9zdDrNzPmemXPOd87M+/l4nMd35pzv8pma5X0+\n3/fn/TYlmeUMG2NM0SEiB/VZKJFE6gMfokF6uBXAuTgXKdXiL14jog1ousFdwNCQ54ML2f70ekEP\n7yYO6V4SqFXXF7gvPBAOnHOniHRCF80tBrYCb3FofeLkdvTRMHs2ZGdDmeiCWeccc5enctZRh1kg\nbKJy++3F68fIGGNMknNuAyInAlcBfwNqo6m4HwEv41xmNKfzOjM8CZ29dcD/gJVoNN4isMsLzml1\nhmSTVDPDU6bAgAHw22/aojkKy9Zv48KnP2fMZcdzaduGcRqgMcYYY+LJZoZjr5TH/R5GF8uBBsDd\nAlsBdgVeN/EWWlEiSu8vT6V0KaFTq7oxHpQp7tatW8e6dev8HoYxxhgTF56CYef4H3AemochIY+f\ngPOcY1XcRmgOKGAw7Jzj/eWptG9emxqVysVhYKY469evH/369Yu8ozHGGJOEPCePOseXwDEitAAO\nB9ICQbJJlDp1oEaNqIPhXzfuZNXmXVxzRm6NBI3J38iRI/0egjHGGBM3Ua+kCgTAFgT7QURnh1eu\njOqw95enIgLnt7aOcyZ6nTpFbA5pjDHGJC1PaRIiTBdhvwh3hz0/MvB8pJZ/JlaOPjrqmeH3l6dy\nUuOa1K3mqfqdMQdZtWoVq1ZZJpQxxpjiyesCug6BbXgb5Olo7nAHTGIcfTSsWwe7d3vafe2W3fy0\nYTtdrdGGKaABAwYwYMAAv4dhjDHG5E1EEDmsIId6TZOoH9imhj0fbHNskVaiBBfR/fYbtGkTcfe5\nP+p/2fnH2H+RKZhRo0b5PQRjjDHmAJGuwDnAlzj3BiL9gAlAJUS+B7rh3Eavp/M6MxwsXtw+7Pn2\nYa+beIuyosT7P6ZyTINqNKpVKY6DMsXZ2Wefzdlnn+33MIwxxpigwcCtQGVEKgLPAJXRbIUTgfui\nOZnXYPiHwAWminClCG1FuBKYgjbi+CGai5pCOOoo3XoIhrftzuK7tVvpbAvnTCGsXLmSlVEu2jTG\nGGPiKHhr/DPgFKAK8DPwDhqvnh/NybymSUxF84KPAF4KeV7QYHhqNBc1hVClCjRo4CkYXrxmK85B\n++a1EzAwU1xdf/31ACxcuNDfgRhjjDEqmBv8B9Al8PFY4DVgC9AgmpN5Coad4wURugC9cnn5Ned4\nMZqLmkLyWFFi8Zp0ypUuxfGNaiRgUKa4euihh/wegjHGGBMqCygP1EZniR2wkgNpu1nRnCyaphuX\nidAb6EGg6QYwyzlejeaCJgaOPhpefz3ibl+vTqdNw+pUKFs6AYMyxdXpp5/u9xCMMcaYUGuBY9A0\niQYcSNk9IvC658Vz4D1nGADn+K9z9HOO8wLbV0WoIsLV0ZzHFNLRR8OWLfrIw+6sbJb/kcEpzWol\ncGCmOFq+fDnLly/3exjGGGNM0L/RVN1m6Azxhzi3FTgj8Pp30Zws6g50ACKUQnM0rgQuBCpwcC6x\niaeUFN3+8gu0Dy/woZas3UZ2jqOdBcOmkIYOHQpYzrAxxpgi4xFgP3AmsJoD1SPKAC8AkW+fh4gq\nGBahHRoA9wXqBJ9Gp6dNogSD4ZUr8wyGv1qdjgi0bVIzgQMzxdFjjz3m9xCMMcaYA5xzwGOBR+jz\nk4HJ0Z4uYjAsQjPg72gQfFTw6ZBd9gBvRXthUwjNmkHZsrBiRZ67LF6TTqt61ahWoWwCB2aKo3bt\n2vk9BGOMMSZu8gyGRbge6MfBjTYkbDcHHO4cO+MwNpOXMmWgRQudGc7Fvv05fL92G33aNUrwwExx\ntGTJEgBOOOEEn0dijDGmxBLZH8XeDuc8Zz/kt+OzaLAbDICzgPloHsb/gIUAFgj7pGXLPIPh5X9k\nsGfffls8Z2Ji+PDhgOUMG2OM8VX4hGzMeImaHfAicJtzbAMQ4Zh4Dch4lJICc+ZAdrbOFIdYvCYd\ngHZNLRg2hTd27Fi/h2CMMcas5eA1arXRznP70EYbtYGywG7iVFptALBChGdF6BS4mPFTSgrs2wer\nVx/y0ter02lWpzKHVS3vw8BMcXPCCSdYioQxxhh/OdcU55rhXDPgUjQwfhyojnMNgOrAk4G9/x7N\nqfMLhh8B1qHT0gLUBa4D5qJFjo2fWrbUbViqRE6OY/GarbRralUkTGwsXryYxYsX+z0MY4wxJmgs\nOit8H85p1znd3gtUAsZEc7I8g2HnuMM5mgId0Zpt2zgQGFciMFUtwjoRRkf3NZhCC5ZXC6so8evG\nnWTs2WcpEiZmbrvtNm677Ta/h2GMMcYEtQ1sTwl7/tTA9sRoThYxZ9g5PgE+EWEI2oq5H9pwo1xg\nlyOA/wPuiObCppBq1YI6dQ6ZGf46kC98arPafozKFENPP/2030MwxhhjQm0CGgKzEXkXWB/4vBs6\nWbspmpN5LjvhHFloJYnXRaiJNt64koNLr5lEyqWixOLV6RxerTyNalX0aVCmuDn22GP9HoIxxhgT\n6lngIbQV88UhzwcbwT0Tzcm8LqA7iHNsdY5nnaMDcCSao2ESLSXloDQJ5xxfr06nXdNaiMStAokp\nYb744gu++OILv4dhjDHGKOceRlsw7+VACq8AmWge8aPRnC6qdsy5j4dVwP2FPY8pgJQU2LQJtm6F\nmjVZv3UPqdszrb6wiak777wTsDrDxhhjihDn7kXkSTRDoTawGfgS5zKiPVWhg2Hjo9CKEqedxter\nrb6wib3nn3/e7yEYY4wxh9LA9/3CnsaC4WQWrCgRCIYXr0mnWoUypBxe1d9xmWIlJfh9ZowxxvhF\n5O6o9nfuPq+7WjCczJo10+5zgbzhr9dovnCpUpYvbGLn448/BuDss8/2eSTGGGNKsHs5uANdJBYM\nlwhly8KRR8LKlWzasZdVm3bR++RGfo/KFDP33HMPYDnDxhhjfOd1ti+aoNmC4aSXkgIrV/LNGssX\nNvHx4osv+j0EY4wx5pqQj8sCo9DgeDIH6gxfC5QGRkZz4jyDYRHOiuZEgeYcJtFSUuDdd1n8v41U\nKFuK446o7veITDHTvHlzv4dgjDGmpHPupb8+FnkAqAechHNLQ55/E/gWOCqaU+c3M7wQ79PMLsK5\nTLy0bAn79rHuu584sVEzypUpUOloY/I0f/58ADp16uTzSIwxxhgABgS2a8Oe/z2w7Yd2R/YkUgBr\nK7GKusBK/5yfV9LuzLYRdjYmeg888ABgwbAxxpgio0ZgOwmRezmQJjEq8Hy1aE6WXzD8Utjn56FT\n0p+HXLQDsAV4J5qLmhgKBMPNtqzjFMsXNnEwbdo0v4dgjDHGhPoM6IS2Yr447DUXeN2zPO+pO8c1\nwQcwHw2E+zjHWc5xhXOcBVyOdv34PJqLmhiqXZvd1Wty5NY/OLFxjcj7GxOlRo0a0aiRVSkxxpji\nSETKi8h4EdksIrtEZJaINPRw3GARWS0imSLyrYicGfb6QhFxYY8ZYfvUFJFpIpIReEwTES/BzI3A\nJg5uxRx8bAJu8vjl6zici5wWLMLPwNFAdefYGfJ8FWA78ItztIzmwkVF5cqV3a5du/weRqGsOPJ4\nskU49tclfg/FFEPvv6/Nfbp06eLzSIwxxojIbudc5Rie71ngIuBq9G7/E2gaQlvn3P48jukDTAcG\no7Owg9FqD62dc2sD+ywEVgF3hhy6x4W0SxaR94DGwEB0RncysMo518PDwA8DbgHO4UA75o+AJ3Fu\nk7evPnAqj8HwHqAccIdzPBry/D+B0cBe56gYzYWLimQPhjP37Wd2u250/f1bqmzd7PdwTDHUsWNH\nwOoMG2NMURDLYFhEqqMzqdc4514JPNcIXYjW1Tk3N4/jvgKWOecGhjz3K/Cac+6OwOcLgeXOuaF5\nnKMV8BNwhnPu88BzZwCfAi2dcytj8TV64bX0wC+B7WgR0kRYIkIa8BAayf+S96Emnpatz+DXmkdQ\nZdsW2LbN7+GYYmjGjBnMmDEj8o7GGGOSTVu0Zu8HwSecc+uAn4HTcztARMoFjvsg7KUPcjmmbyD9\n4kcRGSMiVUNeaw/sBL4Iee5zYFde185lMKchch8izwW2p3g6LozXcmh3AW+ihYzrBB6guRk5HDwF\nnlRq1aqV1DNem3bspc2J9WEhfPvvf7OjdWu/h2SKqRWBtt/GGGN8VUZEvgn5fKJzbmIBz1UP2I+m\nGIRKC7yWmzpoPJiWyzGhZYf+jc4w/wkcg2YSHA90Drn2JheSouCccyKyMZ9rH6DpHdeFPXsXIs/h\n3JCIx4fwFAw7xzsidAEeANqhM8o5wFfASOf4MJqLFiXp6el/3QZORle/+DWlyrXkAqBt5cqQxF+L\nKZpmz54NQI8ekVO4jDHGxF22c+7k/HYQbUpxV4TznJPfKYjcayL89YOOCQvQfxCRVcBXInKSc+67\nPM7h7doi/YHr83j1BkS+wrmX8z1HCM+NMpxjAbBAhEpATWCrc+z2eryJvf05ju9+38pFbY+BMmVg\nZcLSa0wJ8vjjjwMWDBtjTBIZiy5wy89a4DQO3PUPXXRWF/LsLLwZnU0On72ty6GzxaG+CRx3FPAd\nkArUFREJzg6LiACHRTgPHJgR/h14MrBtDNwMNEUD5dgHwzpIygDHArWd471ojjWx9/OG7ezYm027\now6HFi0sGDZx8dprr/k9BGOMMVFwzm3m0NSHQ4jIt8A+NHXh34HnGgKtODiXN/TcWYHjOgOvhrzU\nGXg9n8sdhwbeGwKfLwKqoLnDwWu1Byrnde0Qx6Kzxz1wbnnIF/QRsCzwumeeg2ERLgOeRt89OKCM\nCAuAZsANzh2SSG3i7OvV6QC0a1pLm29YTqeJgzp16kTeyRhjTNJxzmWIyAvAY4Fc3WBptWVojwkA\nRGQF8LRz7unAU08A00Tka3TR2w1AA+C5wP4tgL8D76JBeWvgceD7wP44534WkfeB50VkIJoe8Tzw\njodKEuUC2/Vhz68Pe90TT9UkRDgT+A8aCAeLGgPMQaejL43moiY2Fq9J54gaFWlQo6IGw7/9Bvtz\nLQloTIG98cYbvPHGG34PwxhjTHzcDLwBzEQD1Z1Aj7AawykcKJ6Ac24mMBwYCSwBzgC6Oed+D+yS\nBZwLzAVWAuPQahOdws77d2Bp4LW5gY/7eRjzusB2DMEmHVom7rGw1z3xOjN8Bxo4r4CDmmsE3zW0\nj+aipvCccyxek86ZRx2mT7RsCVlZsGaNpkwYEyPjxo0D4JJLLvF5JMYYY2LNOZeJdnS7MZ99JJfn\nJgAT8th/HXC2h2unA1d6HuwB7wDD0EYf1yCyE025AM1emB3NybwGw6cRzM2AX0OeXxXYHhHNRU3h\nrd68i807szRFAnRmGDRVwoJhE0Nvv/2230MwxhhjQj0AXIwumgMIrV+8BngwmpN5bboR7HSyNuz5\nimFbkyCL12i+8CnNwoJhW0RnYqx69epUr17d72EYY4wxyrktwKnAC+iCvOzAdhLQHp1x9szrzPAf\nQBMOTYcYEdiGJzCbOPtqdTq1K5ejxWGB9yl16kDt2hYMm5ibOXMmAH369PF5JMYYY0yAc2nAwIj7\neeA1GJ6L1mx7K/iECCvQWnEu8LpJoMVr0jm5aU20JF+AVZQwcfDss88CFgwbY4wpnrwGww+gFSNq\nc6AryFFoVYktaIs9kyCpGZmsS9/D1e2bHvxCSgq8+64vYzLF17v2PWWMMaYY85Qz7Bx/AB3Q0hc5\naBCcE/j8zMDrJkEOyRcOatkS0tJg2zYfRmWKq0qVKlGpUiW/h2GMMcbERTTtmH8BuohQAagFpDtH\nZtxGZvK0dN02ypcpRav61Q5+IXQR3amnJn5gpliaPl07el55ZUGq3xhjjDFFm9emG9VFaCxCHefI\ndI4/nSNThDqB522peQItW59B6wbVKFs67L/PKkqYOJg8eTKTJ0/2exjGGGNMXHgtrfYisBq4Iuz5\nvoHnX4jmoiIyWERWi0imiHwrImfms+9UEXG5PHblsf8ZIpItIstzez3Z7c9xLP8zg+Mb1jj0xRYt\noEwZC4ZNTM2bN4958+b5PQxjjDEmLrwGw8F77q+HPf8Gmj/s+Z68iPQBngIeAk4EvgDeE5HGeRwy\nDKgf9lgF/DeXc9cEXgYWeB1Psvnfpp3sztrPcUfkMhlftiw0b24VJUxMlS1blrJly/o9DGOMMSZ/\nIo3/ekTBazAc6PlL+MqsjLDXvbgFmOqcm+Sc+9k5dyNaKHlQbjs75zKcc6nBB9ACaI4WVg73AvAS\nsCiK8SSVpev0v+D4RnlkpqSk2MywiampU6cydepUv4dhjDHGRLIGzVhYFWG/g3gNhncEtueFPR/8\nfKeXk4hIOaAtWoUi1AfA6R7HMhD40Tn3Rdi5BwP10DJwxday9RlUKV+G5nWq5L5Dy5bw66+wf39i\nB2aKLQuGjTHGJBEJPDzzWk3iO6AT8KIIxwA/A63QWV4HfOvxPHWA0kBa2PNpgfPnS0SqA5cBd4Y9\nfxxwD3Cac27/QY0ocj/PdcB1AOXKlfM49KJh2fptHHtENUqVyuNrTEmBrCxYs0ZziI0ppIULF/o9\nBGOMMcaLTzjQD8Mzr8Hwc2iwWg0YFfK8BC76bJTXDR+o5PJcbq5Eg+lpfx0oUh6YAYxwzq32dHHn\nJgITASpXrhz1P5pfsrJz+HnDDq7p0DTvnUIrSlgwbIwxxpiSwrmOBTnMa9ONN4AnODD1HDoF/bhz\nB9o0R7AZ2I+mM4Sqy6GzxbkZCLzunEsPea4+0BqYEqgikQ3cDRwT+Dw8tSNprUjdTtb+HNrkVkki\nqGVL3VresImRSZMmMWlSbin6xhhjTPKLpunGCBFmAhcCh6PB6yznWOz9HC5LRL4FOgOvhrzUmUMr\nVRxERE4FjgeGh730B3Bc2HODA+e8GE2mLhaWrdf1im0a5lPWuU4dqFXLKkqYmJk5cyYAAwcO9Hkk\nxhhjDCByVj6vOmALzv3k9XSeg2GAQODrOfjNwxPANBH5GvgcuAFogKZiICIv67XcVWHHDQR+BT4+\neExuH3BQTWER2Qjsdc4Vq1rDy9Zvo2alsjSsWTH/Ha2ihImh+fPn+z0EY4wxJtRCIqXXivwJ3IBz\ncyKdzHMwLEJVoBvQBKgQ/rpz3OflPM65mSJSGxiJpjgsB7o5534P7HJIbTgRqYo2+LjPOZc0Ob6x\ntmx9Bm0a1iDSAkFatoT33kvMoIwxxhhjEi9SxYgjgDcQaYdzy/Lb0VMwLEI74F2gVj67eQqGAZxz\nE4AJebzWMZfndgB51BLL9Rz3Avd63T8Z7M7K5pe0HZzX+vDIO6ekwJQpkJEB1a1TtimcCRP0R3Xw\n4ME+j8QYY4wBtKdEZzSz4AtgLdAILdO7AfgeLfxQDq181j+/k3mtMzwWqM2hC+iiruVmCubHP7eT\n48h/8VxQaEUJYwpp9uzZzJ492+9hGGOMMUEL0OyCvjh3Bs5dgXNnAn8PPD8TXTcmwNmRTuY1TaIN\nmpvxMbrQbRcFqONmCs7T4rmg0IoSp5wSx1GZkuA9S7kxxhhTtIwMbN8Ne/4dNAC+E+daI5LBoRXM\nDuE1GN4GVAIuce6QlswmAZat30a9ahWoW+2QdO1DNW8OpUvbzLAxxhhjiqMmge0wRB7iwHqyGwLb\nZoHtDjzEul7TJF4ObI/1uL+JMV085zH/t1w5DYitvJqJgaeeeoqnnnrK72EYY4wxQcHZvvuAjYgs\nQSQNeATNXFiJSGm0FPCfkU7mdWZ4DZABvC3CC4FB7Avdwbm/AmYTYxl79rF68y4ubdvQ+0FWXs3E\nyIIFCwAYNmyYzyMxxhhjALgTeBvtSlyLAwUeBMgG7gD+BpRFy/jmy2sw/DwHcoRvzeV1BxYMx8sP\n0eQLB7VsCfPmwf79mjJhTAHNmjXL7yEYY4wxBzj3LiKdgQeBU9FMhxxgEXAXzn2MSBmgKrA30umi\nabphVSN8snS9pmm3OcJDJYmglBTYuxd+/11TJowxxvjPOdi8Gdau1cfvv+v2xBOhXz+/R2dM8nBu\nIdABkYrozHA6zu0JeT0bnSWOyGswfE2UQzQx9MP6DJrUrkT1SmW9HxRaXs2CYVMIY8aMAWDEiBE+\nj8SYImzvXkhP18eWLQe269cfCHyDj8zMg48VgSpVoG9fKBvF73ljSiqRhcALwGuBAPiPwpzOUzDs\nHC8V5iKmcJat30bbpvn1O8lFaHm1rl1jPyhTYixatMjvIRhTdPz6K4waBampBwe9u3blvr8I1K8P\njRvDCSfAhRfqx6GPTz6BSy6BRYvgrLMS+/UYk5zOAs4ExiMyE3gR574q6MmiSZMwPti0Yy9/ZmQy\nIJp8YYA6daBmTasoYQrt9ddf93sIxhQN27dDjx7w559w3HHQsCEcfzzUqgW1a+sj+HFwW6+eVvjJ\nz9/+BmXKwNy5Fgwb400W2l2uGnAtcC0iK9DZ4mk4tymak3kOhkXoB9wMpADhxW6dcxZYx8OyYL6w\nl85zoUSsooQxxsSKc9C/P/z2GyxYAGdHbGrlXfXq0L49vP8+PPhg7M5rTPF1OHAJcAXQEa0q0Qp4\nDBiNyBycu8TryTzVGRahN9oH+nigItaSOWGWrs+glMCxR1SL/uCWLS0YNoX28MMP8/DDD/s9DGP8\n9cgj8Oab8NhjsQ2Eg7p0ge++g40bY39uY4ob5zJwbgrOdQYaAsOBr9B4tCxwUTSn89p0Y0hgG1yl\n54AtgY+3Ab9Hc1Hj3Q/rt3FU3apUKleAifeUFNiwQW/tGVNAS5YsYcmSJX4Pwxj/fPAB3HWXLnAb\nPjw+1zj//APXMsZEYyeQDmwF9hfkBF6D4TZoANwp+IRzHAbcgzbf6FGQi5v8OedYtj6D46LNFw4K\nrShhTAHNmDGDGTNm+D0MY/yxZg1cfjm0bg2TJ2sKWjyceCIcdpjmDRtj8idSFpGLAovnNqLZC+ej\n6RIO+CSa03kNhisHtt8FLoIIpYHHgcOAcdFc1Hjzx7Y9bNmVxfEFDYZDK0oYY4yJzp490KuXNi96\n802oXDnyMQVVqhScd54Gwzk58buOMcVDGvAGcCkH0nf/AB4CjsK5c6I5mddgOHifXYAdgY+7Am0D\nH58azUWNN8v+6jwX5eK5oBYt9Jf3uHGwY0fk/Y3Jxf3338/999/v9zCMSSznYNAgzeOdPh2OPDL+\n1+zSBTZtAktLMiaSGmhMmgW8isakTXBuJM6tivZkXoPhPwPbusDPgY/fBhYGPk6P9sImsqXrt1G2\ntNCyftWCnaBcOXjlFf1lftFFhxZ6N8aDlStXstLuLpiS5rnn4KWX4J574IILEnPN887T7fvvJ+Z6\nxiSvJcAwoAHO9cG5uTjnCnoy8XKsCC8B/YDeaFrEM2G7POgc/yroIPxUuXJltyuvYuk+u3zil+zK\nymbW0DMKd6Lp07XNZ48e8Prr1uHIGGPys2iRVozo3Blmz9YUhkRp21bv6H0SVcqjKUFEZLdzLo45\nOyWP15/wwUB94F3neBa4E1gKfAuMBO6Ny+hKsJwcx/I/MmhT0HzhUFdeCc88o7/Ur75a89+MMcYc\nKjUVLr0UGjXSiYREBsKgVSUWLYKMjMRe15RYIlJeRMaLyGYR2SUis0SkoYfjBovIahHJFJFvReTM\nsNcXiogLe8wI22dNLvt4q+UpUgaRCxG5DZG7D3lEwWs75l3ArpDPHwas8Ggcrd6yix17s2lzRAHz\nhcMNHqwl1u64A6pW1VuA8VoVbYqVu+/W3yn33XefzyMxJs727YPevWHrVvjyS+3imWhdusDo0fDh\nh3DxxYm/vimJxqJ1eS9Hy+Y+AbwjIm2dc7nOnolIH+ApdLL0s8D2PRFp7ZxbG7LrFHQCNWgPh7oP\neDbk850RRyxSF03VTclnL89/tPIMhkVo7PUkAM6xNvJexqu/Os81isHMcNDtt+tsw8MPa8ejRx6x\ngNhEtG7dOr+HYExi3HYbfPqprrVo08afMbRvrxMWc+daMFxU7NtXbNMLRaQ68A/gGufcvMBz/dD+\nEZ2AvGr93QJMdc5NCnx+o4h0AQYBd4Tst9s5lxphGDs87BNuFNAyn9ejyh/O7/7PGmC1x0fUK/dM\n/pauy6Bi2dIceViV2J74oYd0lvixx/RjYyKYMmUKU6ZM8XsYxsTXjBnw1FMwbBhccYV/4yhbFs49\nVxfRFXw9kImVH3+EGjU0zbB4aot2bPur24tzbh1aLOH03A4QkXKB48I7xHyQyzF9A+kXP4rIGBHJ\nrSLACBHZIiJLROSuwPkjOQ8NeIN/nBxwE/Ar8Asa4HsWKU2i2E8b1qpVi4ULF/o9jEM02ruL29rA\nZ5/GYRFFr160/PVX6o0cya9pafxxief23cYYU/zs389pw4aR1bIl319wAc7nvwkNmjXj6Lfe4qtp\n09jTOKqbtCbGjr3rLurs3s2mRx/lx6oFrOwUe2VE5JuQzyc65yYW8Fz10K5tm8OeTwu8lps6aHOL\ntFyO6RTy+b/RGeY/gWOA0cDxQOeQfcYB36PpGaegKbjNgGsjjPuIwPZ24BoAnHsakY+AH9AWzZ7l\nWU1ChKimgpwLDCbJFMVqEvv253DsPXO58rQm/OuC1vG5SHa2LhJ5+22YOlUX1hmTizvu0Dteo0eP\n9nkkxsTJnDlaPu3VV/X3ot9Wr4bmzWHsWJ2pNv749FM46yyoW1dTDDduhGrV/B6Vp2oSIvIAcFeE\nU50DNABeBsq6kIBQNKhc6Zy7IZdzN0AbXJzlnPs05Pl7gMudc7mmL4jIKcBXQFvn3Hd57NMbmAnU\ncc5tyecL3AVUQGe196CTu/UCH28H1uOc53eSec4MJ2twWxz8mraTvdk5sakkkZcyZfS24AUXwIAB\nmqNmM8QmF1u2jXXNggAAIABJREFU5P37yJhi4fnnoV49rcdeFDRrBkcfrXnDFgz7wzn45z+hQQOt\nN925M8yapdWZksNYYHqEfdYCp6GzvHWATSGv1SXvlsab0dnk8Jnjuhw6Wxzqm8BxR6EdjXPzVWB7\nJDpbnJct6OxwdSAVnQl+BQg2VIhq9WuCa8YYL/5aPFfQznNeVagAb70Fp54KffvqD7rlqJkwEydO\nZOLEgt6BM6aIW7dOZ4YHDChai6S6dIGFC61Zkl/efltL3I0aBX/7GzRsCP/9r9+j8sw5t9k5tyLC\nYzdaIncfIakLgbJqrYAv8jh3VuC4zmEvdc7rmIDj0MB7Qz77nBDY5rcPQLATVAs0aBfgXKA7mj+c\nV7CdK8/BsAgpIjwhwhwRPgx7LIjmoiZ/S9dnUK1CGZrWrhT/i1Wpon8IWrfWWZG2bWHiRNgZubKJ\nMcYkvcmTdRJg4EC/R3Kw88+HPXv0Vr1JrOxsLUPasiX076+1pi+7TBc1btvm9+hiyjmXAbwAPCYi\nnUTkRGAasAyYH9xPRFaIyNCQQ58A+ovItSLSSkSeQlMungvs30JE7haRk0WkqYh0A2ag+cGfB/Zp\nLyI3i8gJItIskCIxAZgVVp4tN5OAiWiqxCh0VlsCj83A8Gj+HTwFwyK0Rae3hwFdgLNDHh0DDxMj\ny9Zvo03DGkiiyp7VrAmffQYTJugvgeuv11tDgwfDsmWJGYMpskaMGMGIESP8HoYxsZedrcHw+edD\n06Z+j+ZgZ58N5ctba2Y/TJ0KK1ZovecygWzSPn20xNrbb/s6tDi5GXgDzdX9HK3z2yOsxnAKmkoB\ngHNuJhpwjkRbI58BdHPO/R7YJQudqZ2LzuKOQ6tNdAo5716gD1ov+Ce0LvAktN5x/pz7L84NwrnP\ncO43NPXiYqAHkIJz30fzD+C1HfPrgYvkPSxH6WguXFQUtQV0mfv2c+w9c7nurOb8X5f8SujFiXNa\nbP6552DmTNi7V+te3nCDvjOuWDHxYzK+GjJkCADPPBPehd2YJPf229Czp6aLFZV84VCdO8OGDbB8\nud8jKTl274ajjoImTeDzzw/U4ndOc7lbt4Z33/V1iNaOOfa8pkmcjuZgDAp87oA2wCy0nttJsR9a\nyfTzhu1k57j45wvnRUSD35degj//hCeegC1btNrEEUfALbfAypWRz2OKjWeeecYCYVM8Pfec/l7r\n3t3vkeSuSxetc2uNbxLnqaf0b194UyoR7U44bx6kp/s3PhMXXoPh2oHtK8EnnGM5cB1wNDrFbmJg\n2XrtRx/XShJe1aoFN9+st4s+/FBnKcaP13fGb73l9+iMMabg1qzRag3/+MeBW+FFzfnn6/aD8N4G\nJi62bNEOrT16wJlnHvp6796aWvPmm4kfm4krr8FwsJd0ZvBjEVLQ+m4AF8Z4XCXW0vXbqFOlPPWr\nV/B7KAeIwDnnaNrE+vVw8slaXsbyiUuE4cOHM3x4VGsRjCn6Jk3S323XRqrt76NjjtGZa8sbToyH\nHtLF43l1Z23bVus/J1FVCeON12B4Y2BbC23TDPARsCjwcU4Mx1SiLVufwfENqydu8Vy0Dj9cZ4Vr\n1IALL9Qi5MYYk0z27YMXXtD0iEaN/B5N3kR0dnj+fJ2RNPHz++/w9NOaEnjssbnvE0yVWLAANm3K\nfR+TlLwGwz8Etm2Ad9DSFYejBY8dh/anNgWwc282/9u00798Ya/q19eAOC0NevWCrCy/R2TiaOzY\nsYwdO9bvYRgTO2+/rb+/rr/e75FE1qWLlvP6+mu/R1K83X23llAbNSr//fr0gf37LVWimPEaDI8C\nrkBnhR9Ag9/g1OUCtOSaKaQf1mfgHLRpVATyhSM5+WQtP/PZZzBokDXrMMYkj+efh8aNNdAs6jp1\n0iBt7ly/R1J8LVsG06bBTTdFvlNw/PFabcJSJYoVT8Gwcyx1jpnO8Ztz7HCOLmjKRHXnOM857H5B\nDPzwR6Dz3BFJEAyDvkMeORJefFFX4JpiaciQIX+VVzPmL2lpMHu236OI3m+/adrBtddC6SSoCFqz\npnYJtbzh+LnjDqheHW6/PfK+Ivq376OP9GfAFAuFacdcDi2YbGJk2foMjqhRkdpVyvs9FO9GjYKL\nL4Zbb7WZi2KqYsWKVLT60ibcE0/ouoGlS/0eSXQmTtQg+B//8Hsk3p1/PixerNUOTGwtXKh1g++8\nU994eNGnD+TkwBtvxHVoJnHyDYZFOEmER0UYJ8LfAs9dK8ImtG/0NhHGJGKgJcG6rXtofliS1dEu\nVQpefhmOO05/QaxY4feITIyNGTOGMWPsx9yE+T7Q4CmZ8sn37oUpUzSIb9DA79F416WLpqLNm+f3\nSIoX5+Cf/4SGDWHo0Mj7Bx1zDLRqpRWWTLGQZzAswhlotYhbgSHAPBEeRntB10JzhisCN4twQwLG\nWuylZWRSr1oRKqnmVZUquiClXDn9I7N1q98jMsbEk3OwZIm+Gf73vyE11e8RefPmm7B5c3IsnAt1\n8sla991SJWLrjTd0YeJ990XXXTWYKvHJJ9oh0CS9/GaGb0PrCEvI47bAawJsDvm4X7wGWFJk789h\n08691CtK9YWj0aSJ/qFZs+ZAYXJTLFx33XVcd911fg/DFCWpqVpaasgQLVM2YYLfI/Lm+ee1pW7n\nzn6PJDqlS+uY5861xcqxsm+f5gofcwxcdVX0x/furf8Xr70W+7GZhMsvGD4ZLZs2FxgMvIcGvg64\n3DnqAn8P7Ns6noMsCTbvzGJ/juPwZJwZDurQQf/YzJ+vbZtNsVC7dm1q164deUdTcixZottLL9Vu\nXRMmwJ49+R/jtxUrND/0uut0RjvZnH++vgmxZkexMX48/PorjB5dsIWUrVppeqBVlSgW8vuNUCew\n7eMcz6Gl1YKCWeOvB7ZVYz2wkiZ1eyZA0eo8VxDXXKOB8PjxulDFJL3Ro0czevRov4dhipLgornj\nj9ef9y1btDRVUTZxorZdvuYav0dSMMHWzLZQufA++URzhS+8EC64oODn6d1by4uuXx+7sRlf5BcM\nlwVwju2BbUbwBefYF9gGuy0U0XZpySM1Q4PhpJ4ZDnr0UV3wMWQIfPqp36MxxsTakiXQtKmWozrr\nLDjpJF1Il1NEm5FmZsJLL2nlm8MP93s0BdOggc5E5pc3vGcP/PEHLF8On38Ou3cnbnzJYt06vaPR\nooUu/i5Mt9fevXVrqRJJr0ykHUS428tzpnDSAjPDSZszHKp0aZgxQxd99O8PP/wAlSr5PSpTQNcE\nZtKmTJni80hMkbF0KZxwgn4sAjffDP366axl167+ji03r70G6enJt3AuXJcu+qbj6qt1ofLWrfp1\nBbd7w6qdDh8OTz7pz1iLoj179A1RZqZ2Ua1eyJr+Rx+tPwczZ+q/tUlaEYNh4J6Qj10uz5kYSN2e\nSdnSQq1K5fweSmxUrw6TJsE558D992telklKjSJ1ZDIly+7d8Msvupo+qHdvve385JNFMxh+7jnt\nGnbOOX6PpHD69tUZ7oULtSZurVrQsqVua9Y88FzNmvD001rl54knCjf7WVw4BzfcAN9+q/8uLVvG\n5rx9+uhCvN9/14XkJilFCobtJyhB0jIyqVu1AqVKFaN/8o4dNT9vzBi44gq9xWeSzn333RfbE65b\np3+o779fy/GZ5LJ8uaZDBGeGQf8fhw7VxgU//FC0ftZ//FFTBh57LDkXzoU66STvXc/S02HQIF04\n2KpVfMeVDMaP17SIe+/VXOFY6d1bg+FXX4URI2J3XpNQ4vIo0yIS3eyvc4yKyYgSrHLlym7Xrl1+\nD4MrJn3J3uwcXh90ut9Dia0tW/Qd+JFH6h+kZP9jZApv4ECYPBneeQe6d/d7NCZaEydqusGqVVqm\nLCg9HRo10pmyF1/0b3zhbrpJq9z88QfUqRN5/+Ji7VqdqXz0Ubjttsj7F2cLF0KnTrpY7o03Yv93\nqF07nX3/+uvYnjcPIrLbOZdkHbqKtjxnhpM1uE1WqRmZtKpfze9hxF7t2nrrtF8/vVU5eLDfIzJR\nuvLKKwGYPn164U+2ZQsEzzN7tgXDyWjJEqhWTRfQhapVS9cITJ6saVHxWqgWbPjx4Yewa5embYQ/\n9uw58PGPP+qCqZIUCAM0bgxt2sCcOSU7GF67Fi67TNNkXn45PhMyvXvD//0frF598BtEkzRsmq4I\ncM6Ruj2zeFSSyM3f/64F4++4A/780+/RmCilpKSQkpISm5NNmqSLV9q00ZlhayCQfJYu1ZJqueWh\nDhsGWVnxacKRlqb5r8cfr+kCI0bAPffogrLp07XKwjffaOOfHTugfHk44gi45BIYOTL240kG3btr\n6a+S2hU0uGAuK0sXzFWL04RTsKqE1RxOWnmmSZQURSFNYnvmPtrc+wF3dWvFwLOa+zqWuPnf/+DY\nY/WXs5WhKZmys3XWJCUFrrxS88m//VYDG5MccnI0oBgwAMaNy32fCy+EL7/UBUXRtLjNzd69+qbp\npZfg3Xdh/3449VStptCrl955KkjDhJLiiy+0GdKMGQcveCwJnNPOcq+8ArNmFa6esBennaZd7b79\nNr7XwdIk4sFmhouAtGCN4eJQVi0vLVrA3XfD66/r7XFT8rz5phanv+km6NZNZxbteyG5rFqlqQnH\nH5/3PjffrK2aX3mlYNdwTgOKG2/U2rqXXqqfjxgBP/2kgfagQVC3rgXCkZx6qr5heOcdv0eSeE89\npXcMRo2KfyAM+mbju+/gt9/ify0TcxYMFwHB7nP1imuaRNCIETo7PGQI7Nzp92iMR3379qVv376F\nP9G4cdC8ud4dqFtXZ1IsGE4uwTbMoZUkwnXsqK8/+WR0aTDOwQsvaCWKk0/W3OPzztP0h7Vr4eGH\nrSpCtEqX1lJ3772ns+olxYcf6t+biy+Gu+5KzDUvvVS3liqRlCwYLgKC3eeKfTBctqyuRF+/Hv71\nL79HYzw64YQTOCG/4MeL777T3MWhQw/M5vXooTN+lkeePJYu1f+/Y47Jex8RbdH800/wwQfezrtp\nE1x0EVx7rTboef552LAB/vMfbUNsM8AFd8EFunD1q6/8HklirFmjObwpKZpek6gKRo0aaUrKhx8m\n5nompnwJhkVksIisFpFMEflWRM7MZ9+pIuJyeewK2ecSEflARDaJyA4R+UpEYlhIML6C3efqVivv\n80gSoH17LXw+blxCcqtM4d1+++3cfvvthTvJuHFQubLmmgb16KHbkngLN1bWrNEV8ola97BkiZZK\nrBDhjXufPlC/vi54i2TePF1QOXeu3tr+6iu47jqoUSM2Yy7pgm8m5szxeyTx55zWtM/O1gVzVasm\n9vr//a9+H5ukk/BgWET6AE8BDwEnAl8A74lI4zwOGQbUD3usAkLvRZwNfAh0D5zzXeDN/ILsomRD\nRiY1K5WlQtkSMvsRLLs0cKD+0jLFW1qazvD1739w+9NjjtHyXJYqUXDDh+tisiZNtLLCpk3xvV5o\nG+b8BJtwfPCBNunITVaWlvw67zwty7Z4seaTW7e02KpRA844o2S86fz4Y1i0CB55REupJVqDBnYX\nI0n5MTN8CzDVOTfJOfezc+5GYAMwKLednXMZzrnU4ANoATQHJoXsM8w597Bz7mvn3G/OuVHAt0DP\n+H85hZdWnMuq5aZ6de0G9P33OhNkirRevXrRq1evgp9g4kQNfG688eDnRXR2eP58rQdrorNxo872\nXXqpBjv33ae1ZYcM0YVusbZli3YPzG/xXKjrr9dqEmPHHvraypV6l2jMGF0Mt3ixzg6b+OjeHZYt\n0/+/4mzMGF2PcPXVfo/EJJmEBsMiUg5oC4Qnkn0AeG29NhD40Tn3RYT9qgJJUVwxdXsm9YpzJYnc\nXHKJBkJ33623ek2R1b59e9q3b1+wg7Oy4NlnoUsXzeEL16OH1h1esKBwgyyJpk/XOyv33qu3hH/6\nSWt6T5qks2J9+8Y2FWnpUt16zR+vXVuDkunTNXCHA4vkTjpJf+7fektrEleqFLt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XUB06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\ns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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot average odds difference\n", "fig, ax1 = plt.subplots(figsize=(10,7))\n", "ax1.plot(thresh_arr, bal_acc_arr)\n", "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n", "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n", "ax1.xaxis.set_tick_params(labelsize=14)\n", "ax1.yaxis.set_tick_params(labelsize=14)\n", "\n", "\n", "ax2 = ax1.twinx()\n", "ax2.plot(thresh_arr, avg_odds_diff_arr, color='r')\n", "ax2.set_ylabel('avg. odds diff.', color='r', fontsize=16, fontweight='bold')\n", "\n", "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], color='k', linestyle=':')\n", "ax2.yaxis.set_tick_params(labelsize=14)\n", "ax2.grid(True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold corresponding to Best balance accuracy: 0.1800\n", "Best balance accuracy: 0.8028\n", "Corresponding abs(1-disparate impact) value: 0.3793\n", "Corresponding average odds difference value: -0.0603\n" ] } ], "source": [ "rf_thresh_arr_orig_best = thresh_arr_best\n", "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n", "rf_best_bal_acc_arr_orig = best_bal_acc\n", "print(\"Best balance accuracy: %6.4f\" % rf_best_bal_acc_arr_orig)\n", "rf_disp_imp_at_best_bal_acc_orig = disp_imp_at_best_bal_acc\n", "print(\"Corresponding abs(1-disparate impact) value: %6.4f\" % rf_disp_imp_at_best_bal_acc_orig)\n", "rf_avg_odds_diff_at_best_bal_acc_orig = avg_odds_diff_at_best_bal_acc\n", "print(\"Corresponding average odds difference value: %6.4f\" % rf_avg_odds_diff_at_best_bal_acc_orig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Use LIME to generate explanations for predictions made using the learnt Logistic Regression model**" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold corresponding to Best balance accuracy: 0.1800\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Actual label: [0.]\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Actual label: [1.]\n" ] } ], "source": [ "limeData = LimeEncoder().fit(dataset_orig_train)\n", "s_train = limeData.transform(dataset_orig_train.features)\n", "s_test = limeData.transform(dataset_orig_test.features)\n", "\n", "scale = rf_scale_orig\n", "\n", "model = rf_orig #model to test\n", "\n", "\n", "\n", "\n", "explainer = lime.lime_tabular.LimeTabularExplainer(s_train ,class_names=limeData.s_class_names, \n", " feature_names = limeData.s_feature_names,\n", " categorical_features=limeData.s_categorical_features, \n", " categorical_names=limeData.s_categorical_names, \n", " kernel_width=3, verbose=False,discretize_continuous=True)\n", "\n", "s_predict_fn = lambda x: model.predict_proba(scale.transform(limeData.inverse_transform(x)))\n", "\n", "import random\n", "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n", "\n", "exp = explainer.explain_instance(s_test[i1], s_predict_fn, num_features=5)\n", "exp.show_in_notebook(show_all=False)\n", "print(\" Actual label: \" + str(dataset_orig_test.labels[i1]))\n", "\n", "\n", "exp = explainer.explain_instance(s_test[i2], s_predict_fn, num_features=5)\n", "exp.show_in_notebook(show_all=False)\n", "print(\" Actual label: \" + str(dataset_orig_test.labels[i2]))\n", "\n" ] } ], "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }