{
 "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": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "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": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Original training dataset"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Difference in mean outcomes between privileged and unprivileged groups = -0.106968\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": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "100%|██████████| 50/50 [00:00<00:00, 61.75it/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": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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": [
    {
     "output_type": "display_data",
     "data": {
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wMdDbfjtCaz3XPmYIUAjsA/yAu4CngC5a622ueXeuJ0FXgq4wSXpuIR8u28+3MfGgYEzPRjzQtynVKnv4zmkhLlJKVj4/bTzC9+sOczQ9j8gqlRjerR4jujWgfg0PmoH0BDffDEuXGjO1VZyYAX/xRXjlFfj8c7jnHvfV54isLOjQwZiN3roVQr3s0/asLKN/ca1axqz6TTfB7Nmm/wHhYNBdB2zTWo8tdm0fMEdr/WwJ498Ahmqtmxe79jnQVmvd4zyvcwJ4VmvtsbuRJehK0BXlzGbT/LQpgTcWxpKeW8iwzvV4rH8L09uDldWSJUsAuPrqq02uRHg6q02zcu9xvlsXz7I9KWigb4ua3N69IX1b1vSa5TpuExdnbEJ76iljVtEZVisMGGBsXouJKblrQHkZO9Y41GLVKujVy7w6yuLNN43/HVq2hPXrIcz8EwcvFHSVUkFALjBSa/1TsesfAe201udspFBKrQS2a60fLHbtFuB7oLLWuuis8f7ALcC3GDO628v4ttymwm+LrVGjhqwpFOUmv8jK0fR8cgstPNQqgDrVQggOPMneLevYa3ZxZTRx4kQApk6danIlwhsoYFRDGF6vMidyCjmZm8zufxLZu1URGhRA5Ur+hAYFEBLkjwd8AF+umn3wAXWUYm3nzhRexH+fAsePp+uWLVhvuIFNn36KNaT8/4iuERNDh88/5/CIEcQVFRnrWr2QX/v2NBo+nMRBg8jbvNnsck4JUEptLPbzdK319GI/RwL+QPJZj0sGSpuJiAKWlDA+wP58iQBKqfZADBAMZAM3eXLIBZnRlRldUS4ycot4689YZq6LJyI0iGcHtmZo57oe30nBGQkJCQDU95Rz64VXKbLaWLYnheWxx1l/MI0Dx43fy8GBfnSqX51LG9ege+MadGpQnZAg71q/7pSTJ40DIoYONfq1Xqy//oKrroK77jLW65antDTjVLHISKNrQSXP6fftCxyY0a0DHAX6aK1XFrv+AnC71vqcthxKqb3ATK31K8WuXQGsAOporU8F3SCgARAO3AyMBfpqrXe45M25QYWf0RXCnWw2zZzNR5iyYA/puYXc1aMRj/Vv4ZO9cCXgirII9Pfj2rbGKX8AqdkFbDh4gvWHTrD+4AneX7YPrSHAT9G+XjhdGlSnSc0qNIqoTMPIUKLDgvHz84E/HD/7zGhfVdIBEc7o1w+ee844pKF/f7jtNtfUdyF5ecba4NRUmD9fQq45UgErcHb/s9pAUimPSSplvMX+fABorQuB/fYfNymlugGPASYvCC+dzOjKjK5wkx1HM3jh1x1sPpxOl4bVeWVIW9rWCTe7LLdZuHAhAAMGDDC5EuGLMvOL2BR/kvUHjeC7/WjG6UMrAIIC/GhQo7IRfCNCi92GUr9GiHd8elJYaGx+atnS2IhWVhaL0V932zaj72vTpmV/zvPZuRNGjDB65XpAdwJf5cRmtK1a63HFru0F5p5nM9pNWusWxa5NB9pfYDPaMuCY1nrURbyVciFBV4KucLGM3CLeXhzLzLXxVK8cxLODWjO0U13fmG06D+mjK8qT1Wb07Y1PzeFQWi7xaTkcSsshPi2XQ2k55BedCcH1a4QwsF00A9tF0bF+Nc8Nvd99B6NGwR9/wKBBrnnO+HhjQ1qzZrBmDQS5oauL1vDpp0awDQuDb74xNsQJt3CivdgMjLZia4D7MWZd22qt45VS3wJore+0jz/VXuwz4FOgF0Z7sZHF2otNAf4AEoCqwG3A08B1WusFrn6friJBV4KucKHV+1J5fPYWUrMLuOOyhjx+TUufXKZQkqQk4xOxqKgokysRFZ3WmpSsAuLTctmbnMXS3cms3p9KkVVTJzyYAe2iGdQ+is4NqnvOH6BaGydw5eUZM6N+Luw88fPPMGwYTJxodBFwpbQ0uPde46jca681Qq5JJ4ZVFE4eGPEUxoERO4DHTq3ZVUotB9Ba9y02vg/wLmcOjHjjrAMjvgb6YWxcywC2AW9qrRe54n25iwRdCbrCBQotNt7+M5ZPV8bRrFYVpg7vSLu6vrtMQQhvk5FXxNLdyczfnsTKvccptNqoHVaJAW2jGNg+mm6NauBvZuhdvtxYVzt9utGWy9UeeACmTYMFC1w327p8uTEDnZICU6bAo4+6NqCLEskRwM6RoCtBV5TRgePZPPLDP+w4msnt3Rsw6bo2vr0rvBTz5s0DYPDgwSZXIsT5ZeUXsWxPCvO3J7I89jgFFhuRVYLo17IW3ZtE0L1xDepVL+d1vYMHG0f3xseDO9qB5eXBpZcaoXTrVijLJy8WC7z8Mvz3v8aSiFmzjNloUS4k6DpHgq4EXXGRtNb8uCGBl+ftolKgH28M63B6x3hFJGt0hTfKKbDwV2wKC7YnsXp/Khl5Rl/8qLBgLm1c4/RXs5pV3LfMITYWWrUyTjV76SX3vAYYSyK6dTMOb1i06OJmXw8dMjo4xMTAmDHw/vvOndwmykyCrnMk6ErQFRchPbeQZ3/ezoIdSfRsGsE7t3YkKjzY7LJMlZpqdKCJjIw0uRIhLo7NptmXks36g2mss3d3SMkqAKB65UC6NTJCb+eG1alXPYTI0EplD785OcZxv3/9BYcPG8fNutP06XDffcZSg6efdvxxWsOPPxqPBWPz2YgR7qlRnJcEXedI0JWgK5y0Ni6Nx37cwvGsAp68tiVjL2/iORtahBAuo7Xm8Inc0y3N1h86QXxa7un7A/wUtcOCiQo3vqKLfx8eTFR4CHXCg0tfAnHwINx4o9GO6/334cEHSx7n2jcFw4fD//5nHBPcvfv5xx85AjNnGpvM9uyBHj2M7hCNG7u/VlEiCbrOkaArQVc4qMhqY+qSvXy8/ACNIkJ5b0RHOtSrZnZZHuPnn38GYOjQoSZXIoT7JGfmszUhnaTMfBIz8knOMG6Nn/P+1dYMoGFEZYZ0rMuNHevQpGaxj/iXLYNbbwWrFX74wehYUF7S042WY0rBli0QftbG2dxcIwh/8w0sWWKE4969YfRo46S1ADlrykwSdJ0jQVeCrnBAUkY+98/cxJaEdG7tWo8XB7cltJL8si9O1uiKik5rTWaehcTMPJIy8jl8IpdFO5P4+0AaWkOHeuHceEkdblkzl6qTnjUOhvj1V2NDV3n7+2+44gpj2cSsWca1VauMcPvTT5CVBQ0bGsH2zjvdf9iEcJgEXedI0JWgKy5g17FM7v56A9kFFt4Y1oHrOkSbXZJHysjIACD87NkhISq45Mx85m09xu/r4xj19RRu3rGUjZ36cHTqNK7q3owqZv3R/N//wqRJxuaytWshLg5CQ+GWW4yAe8UV0i7MA0nQdY4EXQm64jz+ik3hoe82ExYSyJeju9E6OszskoQQ3ujIERg6FDZsYM2oh3imzRASMgoIDvSjf5soRnVvQPcmEeVbk9VqLJlYtgyuvNIIt0OHGmFXeCwJus6RoCtBV5Tiu3XxvPDrTlrWrsqXo7tV+K4KF/Ljjz8CMHz4cJMrEcLDrFljnEyWk2Ns7BoyBK01m+JP8suWo/y+LZH03CLu6d2Ypwa0pFJAOfbhzs+HjAw5zcyLSNB1jilB134s3ZMYx9LtBB7VWq86z/jbMI6xawFkAkuAiVrrpGJjhgGvAk2BA8BzWuv/XagWCbribDab5o1Fe/h0RRz9Wtbkg9s6m/fRoheRNbpClOCzz4xuCg0bGutx27Q5Z0h+kZXX5+/mm5h4WkeH8f6IjjSvXdWEYoU3kKDrnHIPukqp4cBMYDyw2n47BmijtT5cwvhewEpgIvALUBv4GDiptb7KPqYHsAp4EfgZGAq8DPTSWq87Xz0SdEVx+UVWnpi9lT+2JzLqsga8NLgtAf6yRs0RublG26XKlSubXIkQHsBigUcegY8/No7c/f57qF79vA9ZujuZJ+dsI6fAwvPXt+H27g3K93Q24RUk6DrHjKC7DtimtR5b7No+YI7W+tkSxk8EJmitGxa7Ngb4QGtdxf7zj0ANrXX/YmOWAMe11iPPV48EXXFKWnYBY7/dyD8J6Tw3qDX39G4s/5ERQjgvPx9GjoRffoEnn4TXXwd/x5YjpGTlM/Gnbazce5z+bWrzxrAO1AgNcnPBwptI0HVOuU5VKaWCgC7An2fd9SfQs5SHrQGilVKDlSESGAHMLzamRwnPuai051RKjVNKbVRKbbRYLM6+DeGDDhzP5qaP/2bnsUw+ub0z917eREKuk2bOnMnMmTPNLkMIc2VmwsCBRsh9/334v/9zOOQC1KoazNeju/H89W1YEXucAVNXsnpfqhsLFsK3lfdnspGAP5B81vVkIKqkB2itYzCC7XdAIXAcUMBdxYZFOfmc07XWXbXWXQOk8XWFty4ujaEf/01OgYUfxl3GgHbSPuxifP7553z++edmlyGEeVJSoF8/48SxmTNhwoSLeho/P8U9vRvzvwd7EhYSyKgv1jF5/m4KLbYLP1gI8S8ev/hQKdUG+ABjo1kXYABGgP3UzLqEb5i39Rh3fLGeyCpB/G98Lzo1OP8aOlG6xYsXs3jxYrPLEMIc8fFw+eWwe7ex6ez228v8lG3rhDPvod6MuqwB01fGcdPHa9ifku2CYoWoOMp7OjMVsGJsKCuuNpB07nAAngXWa63ftP+8TSmVA6xSSv1Ha33E/lhnnlMIvl93mOd+2U63hjX47M6uhFcONLskrxYYKP/8RAW1axdcc43RPmzxYujVy2VPHRLkz2s3tqdPi1o8NWcrg95fxRXNa3JN29pc1aoWEVUquey1hPBF5Rp0tdaFSqlNQH/gp2J39QfmlvKwyhjhuLhTP5+akY6xP8ebxcb0B/4uU8HCZ01feYDJ8/fQr2VNPhnVheDAcuxb6aO+/vprAEaPHm1qHUKUq3XrYNAgCAqCFSugQwe3vEz/NrVZ+OgVfLL8AIt3JbNkdzJ+Cro2rEH/NrXp36Y2jSJlf5IQZzOrvdgMjLZia4D7gXuAtlrreKXUtwBa6zvt40cDnwEPY2wwiwamAn5a6y72MT0xWpBNwmhBdhPwCtBb2ouJ4rTWvLN4Lx8s2891HaJ599aOBAV4/AoeryB9dEWF8+efxklitWsbM7lNmpTLy2qt2Xksk8W7kvlzVzK7EzMBaFG7Cte0iaJ/m9q0rxuOn59sqPVF0nXBOWYeGPEURmjdATymtV5pv285gNa6b7HxEzACcWMgA1gGPG1ftnBqzM3Aa0ATzhwY8fOFapGgW3HYbJpXft/F138fYkS3+vz3pvb4y38IhBAXY/ZsGDXKOABi4UKIKnHvc7lIOJHL4l3JLN6VzPpDJ7DaNFFhwQzrUpfRPRtTs6osb/AlEnSdI0cAS9CtECxWG0/P3c7czUe4t3djnruutbQPE0JcnE8+MU4769UL5s2DatXMrui0kzmFLNuTwoIdiSzdk0Kgvx/DOtdj7OWNaVKzitnlCReQoOscCboSdH1egcXKI7O2sHBnEo/3b8GEK5tJyHWDzz77DICxY8deYKQQXmz2bBg+HK67zvjeg08CPJiaw2er4piz6QhFVhvXtKnNfX2a0lm6y3g1CbrOkaArQden5RZauG/GJlbtS+WF69twd+/GZpfks66++moAlixZYnIlQrhJXh60agUREcYmNC/pNHI8q4Bv/j7EjLXxZOQV0a1Rde67oilXtqol63i9kARd50jQlaDrszLyirjn6w1sPnySKcM6cGvX+maXJITwZlOmwLPPwrJlxsEQXianwMKPGxL4YvVBjqbn0axWFcZd3oQhnepQKUA6z3gLCbrOkaArQdcnpWYXcOcX69mXksV7IzoxqL2cdiaEKIPjx6FpU+jbF377zexqyqTIamP+9kSmrYhjd2ImdauF8NpN7ejXspbZpQkHSNB1jgRdCbo+52ROIbd8GsORk7lMG9WFvvLLu1x8/PHHAIwfP97kSoRwg4cegmnTYMcOY/mCD9Bas3JfKq/+vov9KdnccEkdXhjchkg5hMKjSdB1jgRdCbo+Jb/Iyu2fr2P7kQy+uftSejSNMLukCmPgwIEALFiwwORKhHCxPXugXTu47z746COzq3G5AouVT5Yf4KO/9hNaKYDnBrXm5i71ZNOuh5Kg6xwJuhJ0fYbFauOB7zazZHcyH93WWdIsgqgAACAASURBVJYrCCFcY8gQ+Osv2L8favnuJ0T7U7J4Zu52NsafpFezCCbf1J6GEZKnPI0EXefIkVDCJ2iteeG3nSzelcyL17eRkCuEcI3ly401uc8+69MhF6BZrarMvq8Hr93Yjm0JGVzz7ko+WX6AIqvN7NKEuGgyoyszuj7hg6X7eHvxXu7v05RnBvrG+jlv89577wHwyCOPmFyJEC5is0G3bsZGtNhYCAkxu6Jyk5SRzwu/7uDPXcm0jg7jjWHt6VDPcw7GqMhkRtc5MqMrvN7sDQm8vXgvQzvV5ekBLc0up8JaunQpS5cuNbsMIVznu+9g82aYPLlChVyAqPBgpt/ZlWmjOpOWXcCNH63h1d93kZVfZHZpQjhFZnRlRterLduTzNhvN9GzaQRfju5GoL/87SaEcIG8PGjRAmrXhvXrwa/i/m7JyCvijYV7+H7dYSKrBPFY/xYM71qfAPl9awqZ0XWOBF0Jul5rS0I6I6evpVmtKswadxlVKgWYXZIQwldMngzPPWes0e3Tx+xqPMLWhHRe+2MXGw6dpEXtKjx3XRv6tKhpdlkVjgRd50jQlaDrleKOZ3PztBiqVApg7gM9qVlV+j6a7a233gJg4sSJJlciRBklJ0OzZnDVVfDLL2ZX41G01izamcTrC/YQn5bLFS1q8tyg1rSMqmp2aRWGBF3nyBSY8DopWfnc9dV6AL65+1IJuR4iJibG7BKEcI2XXoL8fHjjDbMr8ThKKQa0i6Zfq1rMiInn/aX7GPjeSoZ3a8Dj/VvI72PhcWRGV2Z0vUp2gYUR02M4kJLDrHGX0bG+7AIWQrjQrl3QoQM88AB88IHZ1Xi8kzmFvL9sHzNi4qkU4Mf4fs24p3djggP9zS7NZ8mMrnNkJbnwGgUWKw/M3MTuxCw+vr2zhFwhhOs99RSEhsILL5hdiVeoHhrEi4Pb8udjV9CzWSRvLorlyreW8+fOJLNLq/CUUuOVUgeVUvlKqU1KqcsvML6PfVy+UipOKXX/Wfc/q5TaoJTKVEodV0rNU0q1c++7KDsJusIrZOYXMearDazal8rrN7WnXyvfbtzujaZMmcKUKVPMLkOIi7d0Kfzxh7EJraZssnJGk5pV+OzOrswaexnhlYMYN2MTz/68ndxCi9mlVUhKqeHAe8BkoBPwN7BAKdWglPGNgfn2cZ2A14EPlFLDig3rC3wM9ASuBCzAEqVUDTe9DZeQpQuydMHjJWfmc9eX69mfks3/3dyBoZ3rmV2SKMGIESMA+OGHH0yuRIiLYLNBly5w8iTs2QPBwWZX5LUKLTbeXhzL9JVxNI4M5f0RnWhXN9zssnyGI0sXlFLrgG1a67HFru0D5mitny1h/BvAUK1182LXPgfaaq17lPIaVYAM4Eat9byLezfuV+GDbv369fWMGTPMLkOUosBi41BqDhabpmFEZWkhJoRwi9oLF9L6jTfYNWkSKVddZXY5PiGnwELCyTwsNk1UWCUiq8hGNVfo169fIbC92KXpWuvpp35QSgUBucBIrfVPxa5/BLTTWp/TL08ptRLYrrV+sNi1W4Dvgcpa63NOClFKRQPHgMu11qvL/s7co8IHXZnR9VwbDp3g3m82Eujvx9djusmMgBDCPaxWaNkSqlWr8IdDuNrJnEKe+Xkbi3Ym06tZBG/f0pGocJktL4sLzegqpeoAR4E+WuuVxa6/ANyutT7nCFGl1F5gptb6lWLXrgBWAHW01oklPGY20BzoqrW2luU9uZP82yw80sIdSYz6fB0RoUH8b3xPCble4NVXX+XVV181uwwhnPf773DgADz9tIRcF6seGsS0UV2YMrQ9m+PTGfDeShbJRjWvp5R6B+gNDPPkkAsSdIUHmhFziAe+20SbOmHMeaAn9WtUNrsk4YDY2FhiY2PNLkMI502dCg0awE03mV2JT1JKMeLSBvz+cG/qVQ/hPtmo5m6pgBWofdb12kBpf2UklTLeYn++05RS7wIjgSu11nFlrtbNZOmCLF3wGFpr3lwUy8fLD3B161p8MLIzIUHSi1EI4UZbtkCnTvDmmyCn+rmdbFQrOyc2o23VWo8rdm0vMPc8m9Fu0lq3KHZtOtC++GY0pdR7wHCgn9Z6d9nfjftJ0JWg6xGKrDaenruNnzcfZeSl9Xl1SDsC/OUDByGEm40eDXPmQEICVK9udjUVxt/7U3ls9hZSsgoY3KEOD1/VjGa15BhhRzgYdIcDM4DxwBrgfuAejC4K8UqpbwG01nfaxzcGdgCfAZ8CvTBaiY3UWs+1j/kIuAO4EdhV7OWytdbZrnuHriVBV4Ku6bILLDwwcxOr9qXyeP8WTLiyGUops8sSTnrB3mD/lVdeucBIITxEUhI0bAhjx8KHH5pdTYWTnlvItBVxfBtziLwiK9e1j+bhq5rTorYE3vNx9GQ0pdR44CkgGiPEPnZqc5pSajmA1rpvsfF9gHeBthjdFN7QWk8rdn9pgfFlrfVLF/NeyoMEXQm6ptJaM/bbjfwVe5zJN7VjeLcSe1kLLzBmzBgAvvrqK5MrEcJBL70EL78Me/dC8+YXHC7c40ROIZ+viuObvw+RW2RlUDsj8LaMksBbEjkC2DkSdCXommrOpiNM/Gkrk65rzb2XNzG7HCFERZGfb2xA694d5nlsr/sK5WROIZ+vjuObv+PJLrAwqH0UD1/VnFZRYWaX5lEk6DpHgq4EXdMkZuRxzbsraR0Vxg/jLsPPT5YrCCHKyVdfwd13w5IlIAdEeJT03EK+WH2Qr9ccIqvAwoC2RuBtU0cCL0jQdZYEXQm6ptBac9dXG9hw8AQLH72chhHy76y3e/ZZYyPv66+/bnIlQlyA1tCxo3G7dSvIngCPlJFbxBdrDvLV6oNkFVjo1SyCOy5rxNWta1XozcoSdJ0j56kKU/ywIYGVe4/zypC2EnJ9RFpamtklCOGYv/6Cbdvgiy8k5Hqw8MqBPN6/Bff0bszMtfF8tzae+2duok54MLd1b8CISxvIscLigmRGV2Z0y92Rk7lc++5KLqlfjZn3dJclC0KI8nXDDbB2LRw+DMFyHK23sFhtLN2TwoyYeFbvTyXQXzGofTR39mhI5wbVK0y3HpnRdY4EXQm65cpm04z6Yh1bE9JZ+OgVcuqZEKJ87dsHLVvC888bHReEVzpwPJsZMfHM3XSErAILbaLDuLNHQ4Z0rOvzBw1J0HWOBF0JuuVqRswhnv91J5Nvas9t3aWVmC+ZaD9V6q233jK5EiHOY8IE+PRTYzY3KsrsakQZ5RRY+GXLUWbExLMnKYuw4ACu61CHK1vVolezCCoH+d4KTQm6zvG9/wcIj3U4LZfJ8/dwefNIRl5a3+xyhIvl5eWZXYIQ55eebnRbGDlSQq6PCK0UwO3dG3LbpQ3YcOgkM9fG89uWo8xaf5igAD96NIngyla1uLJVLfkEsYKSGV2Z0S0XNptmxGdr2X0sk0WPXUGdaiFmlySEqGjefhsmToTNm6FTJ7OrEW5SaLGx4dAJlu1JYdmeFA6mGv+Nb16rCle2qkW/VrXo0rA6gV7auUFmdJ0jQVeCbrn4cvVBXvl9F2/e3IFbuspsrhCinFks0LQpNG4My5ebXY0oRwdTc+yhN5n1B09QZNVUDQ5gQNsonh3UmhqhQWaX6BQJus6RoCtB1+3ijmcz6P1V9Goayed3da0wO2MrmkcffRSAqVOnmlyJECWYMwduuQV++QWGDDG7GmGSrPwi1uxPZenuFH7dcozqoYFMHd6JHk0jzC7NYRJ0neOd8/bCa1htmok/baVSgD+Th7aXkCuEMMe770KTJnD99WZXIkxUNTiQAe2iefOWS/h5fE8qBwVw2+dreefPWCxWm9nlCTeQGV2Z0XWr6SsPMHn+HqYO78iNneqaXY4QoiJavx66d4epU+GRR8yuRniQnAILL/y6k7mbj9C1YXXeG9mJuh6+h0RmdJ0jQVeCrtvsS87iug9W069lTaaN6iKzuUIIc9x+O/z+Oxw5AlWrml2N8EC//HOUSb/swE/B/93cgQHtos0uqVQSdJ0jSxeEW1htmolzthEa5M9rN8qShYrgwQcf5MEHHzS7DCH+7ehRmD0b7rlHQq4o1Y2d6vLHw71pFBnK/TM3M+mX7eQXWc0uS7iABF3hFt+vP8zWhHReuqEtNavKWeQVQUhICCEhnv2Rn6iAPvoIbDbjoAghzqNhRChz7u/JfVc0Yebawwz5cA17k7PMLkuUkUNLF5Siu9asK4d6yp0sXXC91OwCrnxrOW3rhPP92O4ymyuEMMexY9C+PfTtC3Pnml2N8CIr9h7nidlbyC6w8ML1bRl5aX2P+W+ZLF1wjqMzujFKsVUpJihFdbdWJLzelAV7yCuy8uqNbT3mF4MQooJJS4NrroHCQnjxRbOrEV6mT4uazH/kcro1qsF//redge+tYvbGBAosspzB2zg6o2sDTg0sAP4HfK41f7mxtnIhM7qutfHQCW6eFsP9fZryzMBWZpcjytG4ceMAmD59usmViAovKwuuugq2bYMFC6BfP7MrEl7KZtPM3XyEz1cdJDY5i8gqQYy6rCGjLmtIZBVzluXJjK5zHJ3RfQc4CiggGBgBLFGKfUrxjFLIoeECi9XGpF92UCc8mIevamZ2OaKcRUREEBHhPU3XhY/Ky4PBg+Gff+CnnyTkijLx81Pc0rU+Cx+9nJn3dKd93XCmLtlHzynLeGrOVvYkZZpdorgAp9qLKUVvYCQwDKhlv6wBK/Ar8F+t2eLqIt1JZnRd59Qxv5/c3pmB7T23NYsQwkcVFcHQofDHH/DddzBypNkVCR+0PyWbr9YcZO7mI+QX2ejdLJJ7ejemT4ua+Pm5f7mezOg656L66CpFfeBboA9G0FX2Wwtwq9b86soi3UmCrmukZOZz5dsr6NywOt+M6SZrc4UQ5ctqhVGj4IcfYNo0uO8+sysSPi49t5Dv1x/m27/jScrMp0nNUMZd3oQRlzZw6+tK0HWOU+3FlKK/UswF9gNXnLoM/APEAYHAf11aofAKk+fvptBi4+UbZANaRTVmzBjGjBljdhmiItIaxo83Qu4bb0jIFeWiWuUgxvdtxqqn+/HeiI5UqRTAuoMnzC5LnCXAkUFK8SQwDmhy6hJgw1iu8K7WrFKKUIx1vC3cUajwXDEH0vhlyzEevrIZjSPlj8yKqn79+maXICoireHpp2H6dPjPf+Cpp8yuSFQwgf5+DOlYlxsuqUN+kc3scsRZnO26oIBM4Evgfa05dNa4PUBzrfF3fanuIUsXyqbIamPQe6vIK7Ky5PE+BAd6zf/0QghfMHkyPPccPPggfPAByCdKwsfJ0gXnODSja3cQ+AD4QmuySxlzJcbyBVFBfLn6IPtSsvn8zq4ScoUQ5evDD42QO2oUvP++hFwhxDkcDbo3Ab9pzXmnf7XmWNlLEt4iMSOP95bu4+rWtbi6TW2zyxEmGzVqFAAzZ840uRLhtTZsgE2bIDwcqlU797Zy5TNh9ttvjWN9hwyBr74CPznRXghxLkeD7nKgvlLkak3qqYtKEQlUBjK0JsMN9QkP9trvu7HaNC8Obmt2KcIDtGzZ0uwShDcrLDT63yYnlz7G3/9M8I2PNw6F+OEHCHDmw0khREXi6G+HL4EbgceA94tdHwG8h3FS2s2uLU14spV7j/PH9kSe6N+C+jUqm12O8ADPP/+82SUIbzZnjhFyZ82CSy6BjAxITzduS/p+4ECYMgWCg82uXAjhwRzdjHYEiAYaaM3RYtfrAEeAo1rjlVuuZTOa8wosVgZMXYXWmoWPXiFrc4UQZdezJ6Smwp49sgxBiPOQzWjOcfS3SU37bfpZ1zPOul9UAJ+tjONgag4vD2knIVecNmLECEaMGGF2GcIbbdoEMTFG5wQJuUIIF3J06UIWUB24BmOZwinX2G9L68IgfMzR9Dw+/Gs/A9tF0aeF/H0jzujYsaPZJQhv9eGHEBoKd91ldiVCCB/jaNDdDFwNfKkUbYHdQGvgcYz+upvcU57wNNOWH8Bq00y6vo3ZpQgP88wzz5hdgvBGqanGutwxY4yNZkII4UKOBt1pGEE3DHi52HWFEXSnubgu4YGOZxUwe2MCwzrXo261ELPLEUL4gi++gIICeOghsysRQvgghxZDac3PwDsYwbb4F8DbWv9rOYPwUV+tOUih1ca4K5pceLCocIYNG8awYcPMLkN4E4sFPv4Y+vWDttKmUAhXUkqNV0odVErlK6U2KaUuv8D4PvZx+UqpOKXU/Wfdf4VS6jel1FGllFZKjXbrG3ARh5sPas1EpfgRuAGoDSRjHCKxwV3FCc+RmV/EjJh4BrWLpknNKmaXIzxQjx49zC5BeJvff4fDh+Hdd82uRAifopQajtH+dTyw2n67QCnVRmt9uITxjYH5GO1kRwG9gY+VUse11nPtw6oAO4Bv7V9ewaH2Yr5M2os55pPlB3hj4R5+n9CbdnXDzS5HCOELrroK9u2DuDg59EEIBznSXkwptQ7YprUeW+zaPmCO1vrZEsa/AQzVWjcvdu1zoK3W+pxZDKVUNvCQ1vrri38n5cPh3yxKEQAMAloC5yzQ1JpXXFiX8CD5RVa+WH2Qy5tHSsgVQrjGrl2wbBlMniwhVwgXUkoFAV2At86660+gZykP62G/v7hFwF1KqUCtdZFrqyw/Dv12UYpaGMcAn++MT68MujVq1GD58uVml+HRTuQUclfjPJpEIv+sRKmee+45AP773/+aXInwBs2nTiU6MJCY1q0pkt8rQjgjQCm1sdjP07XW04v9HAn4YywxLS4Zo7FASaKAJSWMD7A/X+LFl+siSlnt32m0dvivY0cHvgy0Os/9Xrv+4cSJE/Tt29fsMjyWxWqj39vLiQiN5KHbeqKUuvCDRIV06623Asi/T+LCMjJgyRK47TZ63Xij2dUI4W0sWuuuZhdhgosKII4G3WswwuzXwBj7948AE+zfT7mYFxee74/tiSScyGPSdW0k5IrzeuSRR8wuQXiLb76BnBxpKSaEe6QCVozGAcXVBpJKeUxSKeMt9ufzBIe5iIlVR89arGu/Pd0RXms+BIYCLYB6zr6w8Hxaaz5ZfoDmtarQv/XZ//8XQoiLYLPBRx9B9+7QtSJOSgnhXlrrQoyDvPqfdVd/4O9SHhZTyviNHrM+V+tGaN0YrRs78zBHg+6pdRFpQBGAUtQE4u3XxznzosI7LI89zp6kLO7v0xQ/P5nNFec3cOBABg4caHYZwtMtXgx798KECWZXIoQvewcYrZS6VynVWin1HlAH+wFfSqlvlVLFW4RNA+oqpabax98LjKbYhjalVBWlVEelVEeM/NjA/nODMler1Dso9bb9+ztR6s4yP6edo0sX0jBmdcMxprfrAd8B+fb7q7uqIOE5Pl6+n7rVQrihYx2zSxFeYPDgwWaXILzBhx9CrVpw881mVyKEz9Ja/6iUigAmAdEY/W8Haa1PTVA2OGv8QaXUIOBd4AHgGPBwsR66AF2Bv4r9/LL96xuMUFwWjwI24AmMZbI2XNSr16E+ukqxGLgS6I6xNvd2/r1OYrXW9HH4RZUaDzyJ8Q9/J/Co1npVKWO/Bu4q4a7TfeSUUn359z/8U1prrfecrxbpo1uyDYdOcMu0GF4a3IbRvZz6lEAIIUoWFwfNmsGkSfCKVzbqEcJ0jvTR9TpKWTA2m1UH0jE6K/i74qkdndH9DNgPBGOk92uAmvb7jmMkcYc4e1oHRrB+5qxra4CVJYxtC5wo9vNxR+sS//bJ8gPUCA1ieLeyfyIhhBCAcdyvnx/cd5/ZlQghPEsKxua3g6evKBVXyliN1k0dfWKHgq7WzAZmn3ltmgP9MHbjrdGadEdfEHgc+Fpr/Zn95wlKqQEYU+XnnNahtc4AMs68tuoFNAHuKOG5U7TWnrI70GvtTsxk2Z4UnujfgpAgl/xBJSqAq6822jMuWXJ2K0YhgNxc+OILGDoU6ta98HghREXyFzCSM0thFdColLFOdV64YNBVikrALvuP12nNHq3JBH515oWM57qo0zrONhbYqbUuaefgRqXUqXpf01qXtJxBXMC0FQcIDfLnzh6NzC5FeJHhw4ebXYLwZN9/D+npsglNCFGSxzAOuegMNMMIsyV9yu+0CwZdrSlQigigKlDaNLKjLua0jtOUUuHArZw785uIMSO8AQjCmO1dqpTqU9LaX6XUOOydIoKCgpx8C77tcFou87Ye497LmxBeOdDscoQXGTt27IUHiYpJa/jgA+jQAXr3NrsaIYSn0ToFGAGAUjb7NZdsEHJ0je4S4CbgEowwaZZRGC0tZhS/qLWOBWKLXYpRSjXC2PB2TtC1H5U3HYzNaG6q1StNX3WAAD8/7uktG9CEEOcxfTpMmQJt20LHjme+Gjc21uEWt3o1bNtmPEYOnhFCnE2pdzDW3j7BmYPJXMLRoDsV6APMUorngC1AXvEBWjs0xXwxp3UUNxaYq7U+ccGRsI5Tfx0IhxzPKmD2xiMM61KX2mHBZpcjvMypo3+XL19uah2iHKxfb5xq1qIFHDwICxaA1d5uvWpVuOSSf4ffqVOhWjW4/XZz6xZCeKri7cW+woXtxRwNuisx0nUN4PsS7teOPJfWulApdeq0jp+K3dUfmFvyowxKqUsxZpQd7fDQEWNJg3DQl2sOYrHaGHeFw5sZhTht9OjRZpcgysPJk3DrrVCnDqxcCTVqQF4e7NwJW7ac+fr6a8jOPvO4J56AypVNK1sI4dFsgEKpMPvPLvvox9Gg68oXfQeYoZRaj9Em7H7OOq0DQGt99qkY44B9Wuvl5xSm1KPAIYyevEEYSxxuBIa5qGafl5lfxMyYeAa2j6ZxpG+15xPlQ4JuBaA1jBkDx47BqlVGyAUICTGO8y1+pK/NZvTN3bIF9u2DcXKAphCiVOa2F8M49cIlnD2tA0ApVRVjGUJpHcaDgDcxTmzLwwi812mt57uqbl/33drDZBVYeKCPzOaKi1NUZByHHhgomxh91rvvwq+/Grfdu59/rJ+fcThEs2blU5sQwpu5rb2YQyej+TI5GQ201lz19gpqVq3Ej/f1MLsc4aVkja6PW7sWLr8cBg+GuXNlU5kQJvHRk9FqAe9jtBc7NeNW+t4vJzoyOLN0QfionccyiUvNYewVTcwuRXixe++91+wShLukpRnrcuvXhy+/lJArhHCtc9uL6XJtL6YUX15giNaae1xQjzDBvG3HCPBTDGwXZXYpwouNGjXK7BKEO9hscNddkJwMa9YY3ROEEMJ9+rnyyRyd0R1N6WsilP0+CbpeyGbT/L41kSta1KRaZTk8Q1y83NxcACrLznrf8tZb8McfxoEPxTebCSGEqyhl7M/S+jCnNqSdulYSY5xDzOi6IDzIPwknOZqex8RrW5hdivBygwYNAmSNrk9ZvRr+8x+45RZ48EGzqxFC+K5DGC3GAuzfn28DmUMtbU9xdODZ6yQCgCbA80An4HpHX1B4lnlbE6kU4MfVrc8+w0MI5zzwwANmlyBc6fhxGDECGjWCzz6TdblCCHdTpXxfJg4FXa2JL+HyAaWIwTjt7AFghauKEuXDatP8vi2RK1vVomqwtIQSZTN8+HCzSxCuYrPBHXdAairExEB4uNkVCSF827ecmcUt/n2ZlbXrQgBGMQNcUIsoZ2vj0kjNLuCGS+qYXYrwARkZGQCESyjyflOmwKJF8Mkn0KmT2dUIIXyd1qNL/N4FytJ1IRjoBVQCMlxZlCgf87YeIzTIn36tapldivABQ4YMAWSNrtdbsQKef95YtnDffWZXI4QQZVLWrgun1lDICWReptBiY8GOJK5pG0VwoL/Z5Qgf8PDDD5tdgigrrWHsWGjaFKZPl3W5QojyodSF2tgWp9Ha4U5fZe26UADMAh514nmEB1i9/zgZeUUMviTa7FKEjxg6dKjZJYiyWr8e9u0zDoWoWtXsaoQQFcdoHFuX63RL24vtugBQoDVJjr6Q8CzztiYSHhJI72Y1zS5F+IjU1FQAIiMjTa5EXLRZs6BSJZA/WoQQ5c8tHyGVpeuC8FJ5hVb+3JnEDR3rEBTgZ3Y5wkfcfPPNgKzR9VpWK/z4IwwaJF0WhBDlrfhpaFWBT4F04G3gCFAPeAKIBMY688SObkYbAFwK/KM184pdvwHoCKzXmoXOvLAwz1+xKeQUWhncQbotCNd54oknzC5BlMXy5ZCUBCNHml2JEKKi0fpMi1qlPgaigN5ofbDY9RXAPmAw8JujT+3o0oUXgO7AwLOuZwMvATEgQddbzNt6jMgqlejeJMLsUoQPGTx4sNkliLKYNQuqVIHr5fwfIYSpbrXf5p11/dTPQ3FiVtfRz61b2W9jzrq+3n7b2tEXFObKyi9i2Z4Uru8Qjb+f7KgWrpOUlERSkizb90oFBTB3Ltx0E4SEmF2NEKJiq2S/nYtS16NUR5S6Hphtv+7UCVeOzuhWtt9WAbKKXa961v3Cwy3elUyBxSbdFoTLjRgxApA1ul5p4UJIT5dlC0IIT7AIY9b2MuDXs+7T9vsd5mjQTQQaAM8BDxW7/h/77TFnXlSYZ97WY9StFkLnBtXNLkX4mGeeecbsEsTFmjULIiPh6qvNrkQIISYAbYGWJdy3B3CqabujQXcJRs+yB5TiGiDWXkBTjHS9xJkXFeY4mVPIqn2p3HN5Y5Q0ghcuNmCAnATulbKz4bffYPRoCHTqE0EhhHA9rRNRqhNwJ3AlEAGkAn8B36J1vjNP5+ga3SkYG8/ACLeD7LcKyLHfL0py/DhERRmnDJls4c4kLDYt3RaEWyQkJJCQkGB2GcJZv/4KeXmybEEI4Tm0zkfr6Wg9Aq37o/VI+89OhVxwMOhqzQHgGowpY1XsaxdwjdbEOfvCFUb16kbY9YAA8NuWYzSJDKVtnTCzSxE+6I477uCOO+4wuwzhrFmzoH596NXL7EqEEMLlHD4CWGvWAm2VoilQG0i2B2BxF9kHNwAAIABJREFUPgEBxozukSOmlpGSmc/ag2lMuLK5LFsQbjFp0iSzSxDOSkuDRYvgscfATw6PEUL4HoeD7in2cCsB1xn16sHRo6aW8Mf2RLSGG6TbgnCTq2Ujk/eZMwcsFlm2IITwWQ79Ca8U3ymFVSmeP+v68/brM9xTno+oW9f0oDtv6zFaR4fRrFbVCw8W4iLExcURFyermLzKrFnQqhV07Gh2JUII4RaOflbV034786zrMzDW6vZ2WUW+yOSgm3Ail82H06V3rnCru+++m7vvvtvsMoSjjhyBlSuN2VxZziSE8FGOLl04lZDOPvYo2X4b5ZpyfFTdupCRATk5EBpa7i//+7ZEAOm2INzq5ZdfNrsE4YwffwStZdmCEMLzGZuLItH6uLMPdXRG91Q7hx5nXe9x1v2iJHXrGrcmzerO23qMjvWrUb+GHGAn3KdPnz706dPH7DKEo2bNgq5doXlzsysRQriBUmq8UuqgUipfKbVJKXX5Bcb3sY/LV0rFKaXuL+tzXmThA1Hq/1BqqP3nO4BMIAmlNqJULWeeztGgux1jicLXSjFKKbooxSjgK4wDI7Y786IVjolBd39KNrsSM7nhEpnNFe4VGxtLbGys2WUIR+zdC5s2yWyuED5KKTUceA+YDHQC/gYWKKUalDK+MTDfPq4T8DrwgVLq/9m77/goq+yP458DJPTeE7BgQRQVf4q9oIKLKDZQWMuuDQuiosiuvbKKii6KFRsqq6CACooIumJDUVRULKgLKpiAQCS0BEK4vz/uJAwhZSaZmWeS+b5fr3lN8szz3OckUXJy59xz+1V2zCoYDAwDGmJWH3gYaIjPQ/cDbo9msEhLF8YBhwGZwLNhxw2f6I6L5qYpJ8BE9/WvszCDE/ZRfa7E18UXXwzA7Nmzgw1EKvbii74ud8CAoCMRkfi4GhjnnHsi9PnlZtYbuBS4rpTzLwGynHOXhz7/3swOAq4BJldyzMraJ/T8AXAg0Aj4Ht/x60TgL9EMFlGi6xxPmdEb6FfKy5Oc4+lobppMWrRoEfdfzLXz8jgCWPT++/zWoUNc71VSg1XruGl/4/svPuH7hN5ZUk3//v0BJbpJzzkOfOopNu67L1/99BP89FPQEYlIdOqY2bywz8c654q3XzWzdGB/YFSJ62aytblASYeEXg/3FvB3M0vDT2xGO2ZltQ49/w4cH/p4NDAJWAVE9RZ1NBtGnG7GGUBfQhtGAFOd4+VobphscnJy6NGjR/xv1LQpnerWpVMi7hWyJGcD597zLrf23ZMeh+2csPtKakrI/0dSdV98AUuW0ODGG/UzE6meNjvnDijn9VZAbbY2DCiyHCir4Xk74O1Szq8TGs8qMWZlbQLqhu67D75y4Ae2rgfbFM1gUW0Y4RwvAS+FHzOjEdDPuW1KGqSkAFqMzV2cA8DBu7RM6H0lNS1YsACArl27BhyJlOvFFyEtDfqV9gadiEjgfgP2Aj7Ez946YAG+fBbgj2gGq9Sej2bUMqOPGS/iW449VZlxUkoQie6iVTRvkMbu2iRCEmDIkCEMGTIk6DCkPFu2wIQJ8Je/QEv9ASxSQ60ECvHvvodry/ZtYossK+P8zaHxKjNmZb2An0HeGT+z+1+c+5OtezZ8Ec1gUc3omtEdOAcYgJ9Shq0L0qQ8mZnw3XcJveXcxTkcuHMLatVSM3iJv3vvvTfoEKQiH37oN4q4++6gIxGROHHObTKzz4FesE15aS+2Liwr6WPg1BLHegHznHMFAJUYs7LuxifVRwCL2dploQ5+YjWq+1WY6JqxM3A2cBZQ1HAxPHPKA16N5qYpKTMTli2DwkKoXTvut8vOzeO3nA2ce+hOcb+XCED37t2DDkEq8uKL0KABnHRS0JGISHzdDzxvZp8CH+G7KmQAjwGY2XMAzrm/hc5/DBhiZqOBx/Gdts4F/hrpmDHjnAPuDT3Cjz8JPBntcGUmumZcgk9wwzeJKDk16IC2zrEu2hunnMxMn+QuXw4Z8e9pO3eRr889qFOLuN9LBGD+/PkAdOvWLeBIpFQFBfDyyz7JbdQo6GhEJI6ccxPNrCVwI3532wVAH+fcr6FTdihx/mIz6wP8G98uLAu4wjk3OYoxk1J5M7qP4BPZouR2E35F3mR8L7PZAEpyIxTeSzcBie4ni1bRpF4d9mjXJO73EgEYOnQooPZiSWvWLFi1SptEiKQI59wj+FyutNd6lHLsPeD/KjtmlZgVRnG2w7mIS28jOdEBTwPDnWO1j4e9oghIAIr65y5dCgl4i7eoPre26nMlQUaPHh10CFKeF16A5s2hd++gIxERKSluyUqkGfH5QF8zXsHP6K6MV0A1VgJ3R/tjTT6LV67nzANjvSufSNlUspDENmyAV1/1s7np6UFHIyJS0m9s29igJX5HtAL8JhEtgTRgAzFsLzYSWILPsg1oA1yE3ynjw2huIkDr1r53ZQIS3U8Wqz5XEu+zzz7js88+CzoMKc306bB+PQwcGHQkIiLbc24nnNsZ53YG+uOT3vuApjiXATTF1w+Db44QsTITXee43jl2AnrgV7nlsjXpbRAKAjOWmjEympumpFq1oH37hCS6cxetolHdOuzZXvW5kjjDhw9n+PDhQYchpZk0yf+xfdRRQUciIlKR0fjZ3Ntxzu+G5p9vxeefJbchLleFG0Y4x/vOcRG+KfDpwGv4qeSipDcD0G+3SCRo04i5i3M4YKfm1Kldqf1ARCrloYce4qGHHgo6DCkpLw9efx1OPRXqRNU6XUQkCPuHng8scfyg0PN+0QwW8b96zrEJX5872YzmwED89PGh0dwwpWVmwjffxPUWK9dt5Oc/1tHv/zrE9T4iJWnr3yQ1Y4YvWzj99KAjERGJxAqgAzANs+nA0tDnffDVBCuiGaxSU37O8adzPOochwO74qeTpSIJmNH9NFSfe7DqcyXB5syZw5w5c4IOQ0p6+WW/3W+PHkFHIiISiUfxFQN18bu1XR56rhd6/eFoBqvy+1jOsQi4o6rjpITMTFi3DtasgSbxqZ+du2gVDdJr0zWzaVzGFynL9ddfD6iPblLJz4dp0/wiNJUtiEh14NxIzOoB/2BrcguQD9yNc/dEM5z+5Uuk8F66e+4Zl1vMXZzD/js2J031uZJgjz/+eNAhSElvveX/uO7fP+hIREQi59ytmP0bvztvS3xb209wLjfaoZToJlJ4L904JLo56zfxw7K19N03/juviZTUuXPnoEOQkiZN8ptEHHNM0JGIiETHJ7UzqjqMEt1EivOmEUX1uQftrPpcSbz33nsPgKPUwio5bNwIU6dCv36+h7eISLIyuzmq8527PdJTlegmUkZopjVOie7cxauol1aLfTo0i8v4IuW55ZZbANXoJo1Zs/x6AHVbEJHkdyvb7oxWESW6Sal+fWjRIn6J7qIc/m+H5qTXUX2uJN7TTz8ddAgS7uWXoVkzOPbYoCMREYmERXheNAlx2YmuGUdGM5BzvB/N+SkrTi3GcjcU8P2yNQw9dveYjy0SiU6dOgUdghTZtAleew1OOQXS04OORkSkIueFfZwG3IZPfJ9kax/dC4HawI3RDFzejO5sIs+aXQVjSZE4Jbqf/ZKDc3CQ+udKQN5++20AevbsGXAkwttvQ26uui2ISPXg3LPFH5uNANoB/4dzX4UdfwX4HNgtmqErSk4jnUaWSGVmwpdfxnzYuYtXkV6nFt06qj5XgjFixAhAiW5SmDTJ9+ru1SvoSEREonV+6Pm3Esd/DT2fg++xG5HyEt1nS3x+HD7D/oit08iH4XubvRHpDVNehw7wxx9QUBDTldBzF+fQrWMz6qXVjtmYItF4/vnngw5BwP/b8uqrcNJJULdu0NGIiESraMbuCcxuZWvOeVvoeFQ7bpWZ6Dq3tV7CjLOAvwEDnGNS2PEzgBfxya9EIjMTnIPsbNhhh5gMuTa/gAW/5zLkmKhm80ViqmPHjkGHIAD//S/8+ae6LYhIdfUh0BO/7e+pJV5zodcjFuny/KLC35KNe6fjyxuGR3PTlBaHXrrzfv2TLQ4OVv9cCdCMGTOYMaPKvb2lqiZNgsaN4bjjgo5ERKQyLgdW4PPLko8VwBXRDBbpArKdQs+DgfA9hi8LPe8YzU1TWhwS3U8WrSKttrHfDs1jNqZItEaOHAlA7969A44khRUUwCuvQN++UK9exeeLiCQb5xZi1hW4GjiarVsAvwv8G+dWRDNcpInuj0BX4C4zhgHZQHugFX4a+cdobprS4pDozl2Uw74dmlE/XfW5EpwJEyYEHYK89x6sWqVuCyJSvflk9rpYDBVponsD8Aq+f1mr0AP8NPIW4PpYBJMSWrb0C0RilOiu37iZb37P5ZKj1MNUgtWuXbugQ5CXX4aGDUGz6iJS3ZkdDPQB2gB/AK/j3KfRDhNRja5zvA70BubiZ3At9PwJcJxz6roQMTO/FXCMEt3Pf/2Twi2Og3ZuGZPxRCpr2rRpTJs2LegwUtfmzb5s4cQT/S6MIiLVldmj+EYHNwCDQs8fY/ZwtENFvMmDc7wDvGNGA6A58KdzbIj2hoIvX1i6NCZDzV28itq1jP13VH2uBOu+++4DoG/fvgFHkqLefx9WrFC3BRGp3szOBS4u49VLMJuLc89FOlxUu5mZUQdfq9vSOd6M5loJ06EDfPZZTIaauyiHvTOb0rCuNqaTYE2aNKnikyR+Jk2CBg3g+OODjkREpCouCj3/Cvw79LwDcBW+OcLFQOwTXTNOBx5i6wK0Oma8A+wMXOIcMyMdK+VlZvqG7s75UoZKyttUyFdLV3P+4TvHMDiRymnVqlXFJ0l8FBbClClwwgk+2RURqb664vPMvji3oPio2bvA16HXIxZRja4ZR+A3hmjF1l5m4HdE2wnQEt9oZGZCfr5v6l4FX/72JwWFjoNVnytJYMqUKUyZMiXoMFLThx/C8uXqtiAiNUF66LlkjefSEq9HJNINI64LnbuwxPGiRWiHRHPTlBejFmOfLM6hlsEBO6k+V4L34IMP8uCDDwYdRmp6+WW/AK1Pn6AjERGpqiWh51GY+e2AzZoC95Z4PSKRli4cTNE0MvwUdnxR6DkzmpumvPBEd++9Kz3M3EWr2CujKY3rpcUoMJHKe+2114IOITVt2QKTJ/va3EaNgo5GRKSqXgeuBM4DzsNsHVD0j5sDomrvE+mMbsPQ828ljjcLPauXTTRiMKObX1DIl0tWc3AnbfsryaFp06Y0bdo06DBSz0cfwbJl6rYgIjXFCHy+WVQq2zjs41+Bf0UzWKSJblFGVrJE4ZrQc2x6ZaWKjAz/XIVEd/6S1WzavEX9cyVpTJw4kYkTJwYdRuqZNMlvQnPCCUFHIiJSdc6tAg4CnsLvxLsZyAKeAA7BuZxohou0dOEtfDuHV4sOmPEDsBt+GvmtaG6a8tLToU2bKvXSnbsoBzPovrNmdCU5PProowAMGDAg4EhSyJYtPtE9/nho3DjoaEREYsO55fiNIqos0kR3BL6zQkt8Ygs+yTVgFXBXLIJJKZmZVZrRnbt4FV3aNaFpfdXnSnKYPn160CGknk8+gawsdVsQESlDpFsA/w4cBswEtuAT3C2hz48IvS7RqEKiu2nzFr747U8OUn2uJJEGDRrQQD1cE8c5GDXKly1oNzoRkVJFWqOLc/zoHL3xRcEdgMbO0ds5fohbdDVZFRLdr5euJr9A9bmSXMaPH8/48eODDiN1PPoovPIK3HYbNGkSdDQiUk2ZWV0zG2NmK81svZlNNbMOEVw32MwWm1m+mX1uZkeUeP0iM3vXzFabmTOzneL1NZQn0g0jmpqxgxmtnCPfObKcI9+MVqHjUS21ruibU+LccaFvUMnH+hLnHRUaK9/MFpnZJdHElHCZmbByJWzcGPWl85esBtQ/V5LLk08+yZNPPhl0GKnhiy/gqqt8be7w4UFHIyLV22igH/BX4AigCfC6mdUu6wIzGwA8ANwJ7AfMAd40sx3CTmuAf+f/1viEHZlIZ3SfBhYDZ5Y4PjB0/KlIbxjhNyfclUD7Eo9FwEthY+4MTA+NtR++ZniMmfWLNK6EK2oxlpUV9aXfZq2hXZN6tGpUN8ZBiVTerFmzmDVrVtBh1Hxr1sAZZ0Dr1vDcc1Ar4jfmRES2YX4jhguA4c65Wc65L4BzgH2AnuVcejUwzjn3hHPue+fc5fgOCZcWneCcG+2cuwv4MH5fQcUi/RfyoNDz5BLHp+DrdQ8ichV+c8I553Kdc8uKHsAuQCd8m4kilwBZzrnLQ2M+ATzL1vZnyacKvXS/y1rDnhl6q1KSS1paGmlpWhwZV87BoEHwyy8wYQK0ahV0RCJSve0PpOFnXgFwzi0BvgcOLe0CM0sPXTezxEszy7omSJF2XWgdel5d4nhuidfLFfbNGVXipWi+OYOAb51zc8KOHcL23/C3gL+bWZpzrqBEHBcBFwFkZmYye/bsCG8dOw2zsugOfDtzJis2b474Ouegb5s1tG6cF0jcImWZMWMGAL179w44kporY+pUdn/pJRYNGsRvmzeD/g0QSUV1zGxe2OdjnXNjKzlWO6AQWFni+PLQa6VpBdQOnVPymvJmgasm/J1/50puYFamSBPdtUBz4DjglbDjx4We10U4TpW+OaEp9jOA60q81A54u5Qx64TumR3+Qug/iLEADRs2dD169Igs+ljq1g3OP5+9mjaFKO7/1ZLVjHrrIx49ax967N0+fvGJROnWW28FYOTIkcEGUlN9+SU88gj07k2nxx6jk0oWRFLVZufcAeWdYGYjgBsqGOfo2IWUEL/gW9w6Is9fIz7xC3wi+rQZe+GntLvgyxAc8Hk0kVbB2fhyi+cTdL/4adoUGjSIunThu+w1AOyVoa1WJbnoHYY4KqrLbdlSdbkiEonRQEVtcH4DDsZPQLYCVoS91hb4oIzrVuJngduWON4WWBZ1pNGxaC+INNF9DJ/oNgFuK3FDF3o9ElX95gwCJrvtt39bVsaYm9l+Oj45mFWqxdi3Wbk0rluHji3qxykwEUkqzsFFF8GiRb5UoXVElWIiksKccyuJIP8xs8+BAqAX8ELoWAf8ZOac0q5xzm0KXdcLeDnspV5sv5Yrlt5n66ZlEYt0w4gpwP34xDb8AXCfc9uUM5QzjtuEn/3tVeKlXpTxDS1iZgcC+7LtIrQiH5cx5ryS9blJpVKJ7hq6ZDTBLOo/akTi6oknnuCJJ0r731OqZOxYmDgR7rgDjiizE6OISNScc7n4zln3mFlPM9sP/67514SVhJrZD2Y2JOzS+4FzzexCM+tiZg8AGYRNfJpZOzPrBuweOrSnmXUzs8rtduVcD5w7GueiKrmIuMbBOa4xYyJwEn62dDkw1Tk+iy5S7geeN7NPgY/wHROKvzlm9py/n/tbiesuAn5yzs0uZczHgCFmNhp4HL+L27n4nnDJKzMTPvoo4tMLtzh+yF7LwAM7xjEokcqZOHEiAIMGxWR7cgH46iu48ko47ji49tqgoxGRmmko/h3wiUB94B3gb865wrBzOuPLGwBwzk00s5bAjfi2rwuAPs65X8OuuQS4JezzN0LP5wHjYvw1lCniRBcglNRGm9iWGKPCb852/XTNrDG+Z+/tZYy52Mz6AP/GtynLAq5wzsVzCr3qMjN9H13nfClDBRavXE9eQaHqcyUpvf12yfWgUiVr18Lpp/u63OefV12uiMSFc24jcHnoUdY52yUpzrlHgEfKueZWKrNZhNmR5bzqgFU4912kw0Wc6JrRGOgD7AjU2+7OrvQktDTlfXOccz1KObYWaFTBmO8B/xdpDEkhMxM2bfI7pEVQd1e0EG3P9uqhK1KjOQcXXwz/+x+8+y60aRN0RCIiiTKbimpxzX4HLsW5N8o9jwgTXTO643ceK6+uIuJEV0LCN42IINH9NiuX9Nq12LVNuTm/SCAeecT/7Tp48OCAI6kBnngCXnwRRoyAI8ub3BARqZEqepu7AzAFs+4493V5J0b6XthooCXbL0YLX5Qm0erQwT8vXRrR6d9lrWG3to1Ir6O3MCX5TJs2jWnTpgUdRvW2eTPcfDNccgn06gXXlWwZLiJS4z2LL0EF36hgAn5NF6HjbwAb8ZO1V1c0WKSlC/vgp5Hfw7eOWE8lWjxICVFsA+yc47usNRzbRW9hSnJ68803gw6helu6FM48Ez74AM47D8aMUV2uiKSid4C/AX/FuYnFR80GAv8BrgUexlcaHFXRYJEmuquBBsBpzm23DbBUVrt2/hdZBInu8jUbWbV+kxaiidREb7wBf/875Of7hWdnnx10RCIiQbkx9Fyy/vZ1fBXB9Ti3J2a5lL1NcbFIpwueCz13jfB8iUSdOtC2bUSJ7nfZuQDsmaGFaJKcHnjgAR544IGgw6heNm2Ca66BE0/0pUyff64kV0RS3Y6h5ytLbBpwSeh559DzWnxbtHJFOqP7C5ALvGbGU8BC/E4axZwrToYlGhFuGvHt777jQhd1XJAk9c477wBw5ZVXBhxJNbF4MQwcCJ9+CoMHw333Qb3tGtqIiKSahcDe+CYHQ0MdFtrj+/g6YCFmtfF7OvxS0WCRJrqPs7Umd1gprztQolspmZm+hVAFvs1aw04tG9CoblStj0USZurUqUGHUH1MngwXXOA/njQJ+vULNh4RkeRxPfAaUBvf7auo45fhZ3CvA44B0ti6SK1M0ax0KKvjgjovVEWEM7rfZa9Rfa5IdZefD5ddBv37Q+fO8OWXSnJFRMI5Nx3oBXyMn0i10PMcoBfOvQm8CzTG75pbrkinB8+rVLBSsQ4d4M8/YcMGaNCg1FPW5BfwW84GBnTX1r+SvEaNGgXANddcE3AkSWrVKujZE+bPh2HD4M47IT096KhERJKPc7OBwzBrADQHcnAuL+z1zURQnwsRJrrO8Wz0UUpEwluM7bZbqad8lxXaEU0L0SSJffzxx0GHkNzuvRe++gqmToW+fYOORkQkOZnNBp4CJuHcBmBDVYZTwWfQokh091KiK0ls8uTJQYeQvHJy4OGHYcAAJbkiIuU7EjgCGIPZROBpnJtb2cEirtE142wzvjBjvRmFJR4RTR9LKSLYNOLbrDW0alSXNo21IlukWhozBtatg+uvDzoSEZFktwlfl9sEuBCYg9kCzK7GrHW0g0WU6JpxBr6rwr5AfbQYLXYiSHT9QjTN5kpyGzlyJCNHjgw6jOSzZg088ACccgrsvXfQ0YiIJLu2wAX4HdK24HPMPYF7gaWYTYlmsEhLFy4LPefhd0hzQA7QEr9rmnZLq6zGjf2jjER34+ZCflq+lqM7R/1HjEhCzZ8/P+gQktOjj/oFpzfcEHQkIiLJz7lc4BngGczaAgOAvwIH4VuKnRzNcJEmuvvgk9ue+PYOOEdrM24ChgAqOquKclqM/bR8HZu3OC1Ek6Q3YcKEoENIPhs2+I0geveGAw4IOhoRkepmHX5i9U+gEN9bNyqR1ug2DD1/QWjjCDNqA/cBrYEHo72xhCkn0d26EE09dEWqnSeegBUr4MYbKz5XRETALA2zk0ML0ZYDzwJ/YWuS+340w0U6o7sG38fM8HsLNwaOx28LDH46WSqrQwf4739LfenbrFwaptdmxxal99gVSRZ33HEHADfddFPAkSSJjRvhnnugRw847LCgoxERqS6WA0Wze0VrwH7HJ7zP4FzF28mGiTTRzcInum2A74ED8duzFcmJ5qZSQmYmZGVBYSHU3nZW/tusNXRp34RatbTeT5LbwoULgw4huYwb5/+/fk67o4uIRKFZ6HkTPtd8GpiJc64yg0Wa6H4JdMXP3D7H9jO42lCiKjIzfZL7xx/Qvn3x4S1bHN9nr6H//h0CDE4kMuPHjw86hORRUAAjR8LBB8MxxwQdjYhIdTIfvxjtPzhX5YnUSBPdwcA/gLXOscGMpvhVcJuBV4C7qxpISgtvMRaW6P6as4H1mwq1EE2kunnhBfjlF3joITC9GyMiEjHn/i+Ww0W6BfB6YH3Y5yMBNcyMlfBEN2xlthaiSXVy8803A3D77bcHHEkZNm2CF1+Et96CO+6AXXaJz30KC+HOO6FbN+jTJz73EBGpyczqAH2Azvj9G7blXMS/aMpMdM3YIZqYnOO3aM6XMGVsGvFtVi51ahm7tW0UQFAi0VmyZEnQIZRu3Trf/eD++2HpUqhVC955B954Iz4tvyZNgh9/hJdf1myuiEi0zNoAs/FJblmqnugCvxBqJRYBV8FYUp42bfwitO0S3TXs2qYRdetE3TZOJOGeeeaZoEPY1h9/wIMPwiOP+A0bjjoKxo6FnXaC44/33RBeftl/HCtbtsCIEdClC5x2WuzGFRFJHbcBe5TzelSL0irqo1vaVr9lPaSyatf2tbklEl2/9a/KFkSismgRDB4MO+7oSwh69IBPPoHZs31S26ULfPwx7LYb9O0LsUzQp02DBQvg+uv9zLGIiETrOHwyW/SPswOuAH4CfsRvDxyx8mZh1UkhkTp08G+rhvyxNp8VazdqIZpUG9dddx0Ad911VzABfPkl3H23n6WtXRv+9jcYPhw6l/LuV/v28N570L8/nH++/yPzhhuqVmrgnJ/N7dQJBg6s/DgiIqktVM/JtcB5ADj3EGbvAt8AUbWiKjPRdS40uCRGZiZ8+23xp1sXoinRleph1apVwd18+HAYNQoaN4Zhw2DoUMjIKP+aJk3g9dfhwgvhpptgyRJ4+GGoU8kqrJkzYd48Xw9c2TFERKQQSANWAQVAHcxaA7+GXr8IGBHpYPrXOFlkZvpflCHfhhJdzehKdTF27NhgbvzWWz7JPe88v+CsWbOKrymSng7PPuvfUbnrLsjOhgkToEGUOxE65zs5dOjgZ5JFRKSyVuFndZsCy/AzuP8B8kOvN49msIgTXTM6AxdTeqsH5xzHRnNjKSEzE9au9Y/Gjfkuaw0dW9SnSb20oCMTSV6rV8MFF/i620cegXr1oh/DzNfydugRYlmvAAAgAElEQVQAQ4b4DR6mTYPWrSMf4/334aOPYMwYnzyLiEhlLcQnursA7wNnQXGO6YAvohksokTXjP3xrR5Km+YwolwBJ6UIbzG2xx5+IVp7LUST6uOaa64BYNSoUYm76ZVXwrJl8OqrlUtyww0e7Gt3zzwTDjsMZszw9baRGDEC2rb1SbeIiFTFE8DPQD18B4bjgKKZhxXA0GgGi3RG93qgYTQDS5TCEt11O+/K4pXrOXW/zPKvEUkieXl5ib3hq6/Cc8/BzTfHrh/uqafC22/7bgyHHAIXXQR16/pZ2vT0bT8ueqxY4a+5916ov31fcxERiYJzLwEvFX9uthtwNH433o9wbnU0w5lzFU/GmpENtMFvBfwofgZ3X3wx8B7AAOf4KpobJ4uGDRu69evXV3xivP30E+y+Ozz7LJ8deSKnP/YxT/39AI7t0jboyESSz4oV0LWr/wNx7lxIi3GJzw8/+KT3hx8iO79VK1i8GBppcxcRiS8z2+Cc0+RjhCKd0W0Zev4PPtHFORaYcRG+UPgq4NyYR5dKwmZ0tfWvSDmcg0sv9fW577wT+yQXYI894Pvv/b02b/bbB5d8bNy49eOMDCW5IiJJKNJENw9ohF/xlgfUCy1OWxd6/aQ4xJZaGjSA5s1h6VK+zcqlRcN02japG3RUIhEbOtSXTY0ePTq+N3rxRZg82ffM7do1vvcy84l0Who01ASKiEh1E2mi+wc+0W2B3xp4D+BdfL0EwJaYR5aKMjP9jG72GvbKaIJVpXm9SE2UlQWXXebrZ4cNCzoaERFJcpHuUflN6Hkf4HV8p4W2+PYPDphZxnUSjY4dcT//zI/L1rFne/XPlepl9OjR8Z3Ndc5v7rBxo+99W7t2/O4lIpIizKyumY0xs5Vmtt7MpppZhbuPmdlgM1tsZvlm9rmZHRH2WovQmD+YWZ6ZLTGzR82sZXljxkOkie5twJn42dwR+MS2aLrxHeDKmEeWio46Cvv2W5qv/kMbRYiU9NRT8OabcM89sNtuQUcjIlJTjAb6AX8FjgCaAK+bWZmzCWY2AHgAuBPYD5gDvGlmO4ROycBPhv4D2Bs4GzgSeDFOX0OZIuq6UOqFRjNgs3PFdbrVUtJ0XQBYsAD23ptr/zKEC5+7i13baHGLVB+XXXYZAA8//HDsB//lF9h7bzjwQJg1C2pF+je6iEjNEsuuC2bWFN+b9jzn3H9Cxzrit9s93jn3VhnXzQW+ds4NCjv2EzDJOXddGdf0wVcFNHPOrYlF/JGoyhbA6UCSZIiV16JFC2bPnh10GJ5zdG/dhotWfsZv381j6XdBByQSuZUrVwLE/v+nLVvYd9gwGm/ZwmeDBrHx/fdjO76ISPVSx8zmhX0+1jlX2T3Y9wfSCCtBdc4tMbPvgUOB7RJdM0sPXVdyd6CZoWvK0gTYCGyoZKyVUm6ia8b/AQPxu1O86hz/NeNC4C78wrSNZjziHNfEP9T4yMnJoUePHkGHUWxml8M44pMZdDrkEN+cXqSaiNv/Rw8+CPPnw1NPccjAgfG5h4hI9bHZORejXXJoBxQCK0scXx56rTStgNqhc0pe07O0C8ysGXAH8IRzbnNp58RLme//mXE48DEwDLgMmGXGSGAsPsk1fAJ8lRmXJCDWGs85x2sZ3ai/KQ80ayUCCxfCP/8JJ5wA550XdDQiItWCmY0wM1fBo0eCYmkETAN+x9fsJlR5M7rD8dPZJY+BT3JX4rN6A84BHot5dClmSU4e72TsRWF6XWq/8Qb06hV0SCIRu+iiiwAYO7aCd9C2bIFPPoGcHFi/vvTHunX++dNP/ba6Tzzhe9qKiEgkRgPjKzjnN+Bg/OxsK3ytbpG2wAdlXLcSPwtccuvWtvhNxIqFktzpoU9PdM7lVxh5jJWX6B7A1tZhrwF9geNDx850jolm/BW/W9qe8Q40FXyXnUt+Wj3WH3o4TaZPh3g33heJoZYtI+gaU9Qi7JlnSn/dzO8w1rChfzRpAg88AO3bxzZYEZEazDm3ku3LEbZjZp8DBUAv4IXQsQ5AF3wnhdLG3hS6rhfwcthLvYDJYWM3Bt7ET4j2ds4F0rygzK4LZmzEJ8LNnWONGU2BP/GJbj3nKDAjHb9b2hbnqrSwLTDJ1HXhvpkLeWT2//ih7c+kDb0SfvxRbZSkZrnxRvjXv2D4cDj99K0JbdGjXj3N3IqIlCOWXRdC4z2Kn8w8F1gF3A80B/Z3zhWGzvkBeMg591Do8wHA88Bg4CPgEuACYC/n3K+hJHcmfgHaKcDasFvmOOc2xSr+ipSXnKbhy0bXADhHbtHvH+coCD1vCh3Tb6YY+D57LZ1aNSSt74kw9Ep44w0IbasqUu098ohPcgcN8tv3KqEVEUkGQ/E73U4E6uP3R/hbUZIb0hlf3gCAc25iaPOHG4H2wAKgj3Pu19Ap++PLIgB+LHG/o4HZMf4aylTejO4W/OztbWGHby3rmHNUy22KkmlGt/fo98lsVp+nzu0OXbpAx44wU5vOSfVwXmix2DOllSVMmQL9+8OJJ/qP61TLN4BERAIX6xndmi6S3za3hH3sSjkmMZKdm88BOzX3n5xwAowZ4xflNNLGEZL8OnbsWPoL778PZ54JBx8MEyYoyRURkYSp6DeO3ltMkA2bNpObV0D7pvX9gRNOgPvug7ffhlNOCTY4kQjcfvvt2x9csABOOgl22gmmTYMGDRIel4iIpK7yEt3bynlNYiw713fcyGhWzx847DBo3BimT1eiK9XTkiXQu7dPbt96CyLpyiAiIhJDZSa6zinRTaTs1T7RLZ7RTU+H447zia5zWrgjSe/ss88GYPz48b5Hbu/esHYtfPAB7LhjwNGJiEgqKnNnNEmsrNw8ANo3rbf14AknwO+/w1dfBRSVSOQ6d+5M586dIS8PTj4Zfv4ZXn0V9tkn6NBERCRFaVVIkiia0W0Xnugef7x/nj4dunULICqRyN10001QWOi7K3z0kV94dvTRQYclIiIpTDO6SSI7N49WjdKpWyesS1u7drD//r6frkiycw6GDPGzuKNHwxlnBB2RiIikOCW6SSI7N39rfW64E06ATz6BVasSH5RIFAbuuy8DH3sM/vlPuOKKoMMRERFRopsssnPztq3PLdKnD2zZ4letiySr8ePp9s03dOvWDe66K+hoREREACW6SSN7dT4ZzUqZ0e3eHVq3VvmCJK/Zs+H887n26KO5du5cdQgREZGkoUQ3CazNL2Dtxs2lz+jWquUXpc2Y4Rf6iCST77+HU0+FXXeFyZN9WzwREZEkoUQ3CRRtFtGutEQXfPlCTg7MnZvAqEQqsHy5/2+zbl2YPp1+F15Iv379go5KRESkmNqLJYGs1b6HbqmlCwB/+QvUru3LFw49NIGRiZRh/Xro2xf++APeew922olDDjkk6KhERES2oUQ3CRTN6JZaugDQrJnfEviNN+Bf/0pgZFKtLF8OAwZAx47w3HPxq5UtLISzzoJ583wrsQMOAOCaa66Jz/1EREQqSaULSSA7Nx8zaNukjEQX/FvEX33ld0oTKWnhQjjkED+7On48vPZa/O41bJgf/4EH4KST4ncfERGRKlKimwSyV+fRpnFd0mqX8+M44QT/PH16YoKS6uPDD31Jy7p1fkeyvff2fWzXrYv9vR54wD+uugouv3ybl0466SROUuIrIiJJRIluEihzs4hwe+0FO+ygNmOyrZdfhp49oVUrv7HIoYfCo4/CkiVw222xvderr/oE99RT4d57t3v52GOP5dhjj43tPUVERKrAnHNBxxCohg0buvXr1wcawzH3zaZz28Y8evb+5Z946aXw/PN+l7S6dRMTnCQn5+D+++Gaa3z99muvQcuWW1+/8EIYNw6+/NLP8FbVp59Cjx5+rHffhQYNqj6miIhEzcw2OOcaBh1HdaEZ3YA558heHcGMLvjyhfXr4f334x+YJK/CQl+acM01cPrp8Pbb2ya5AHff7RcxXnqp31mvKhYv9h0W2rWDqVOV5IqISLWhRDdguXkF5BUUktGsnIVoRY45xs/kqnwhdW3YAP36wUMP+UVhEyZAvVL+22nZ0pcXfPQRPPNM5e+3YoVfCFlQ4OvD27Yt89Tjjz+e448/vvL3EhERiTElugHb2losghndBg3g6KO1IC1V/fGH//lPnQoPPgijRvmd88ry97/DEUfAP/4BK1dGf7+cHOjVC375xdfn7rFHuaf37duXvn37Rn8fERGROFGiG7DsXL9ZRPtIZnTBly/89JN/SOr48UffPuybb+CVV7breFCqWrX8wrQ1a3yyG43Vq+G44+CHH3z975FHVnjJ4MGDGTx4cHT3ERERiSMlugHLWu1ndDMimdEF/zYyqHwhlUya5JPctWv9QrCTT4782r32gquv9uULH3wQ2TVr1kDv3vD11zBlik94RUREqiElugHLzs2jdi2jdeMIuyh06uTfQlb5Qs23ciUMHOgXnO28M3z8MRx0UPTj3Hyzb0136aW+1rY869b5dw0+/xxeemnrH1YR6NmzJz179ow+PhERkThRohuw7NX5tG1cl9q1otiu9YQT/A5Ya9bELzAJ1quv+tnYKVNgxAjfI3eXXSo3VsOGMGYMfPst/PvfZZ+3YYPvrjBnDrzwApxySlS3GTBgAAMGDKhcjCIiInEQSKJrZoPNbLGZ5ZvZ52Z2RAXnp5vZ7aFrNprZb2Z2Rdjr55qZK+URYeFrcLJy82jfLMKyhSIDB8KmTX7lvdQsOTlw9tl+U4aMDJg3D264AerUqdq4J53kSx5uuw1+/XX71/PzfWL73nu+V/Ppp0d9i0GDBjFo0KCqxSkiIhJDCU90zWwA8ABwJ7AfMAd408x2KOeyCUBv4CKgM3A68HWJczYA7cMfzrn82EYfe35XtCjz8QMOgBNP9Kvuc3PjE5gk3uuv+1nciRPh1lv9Jg377BO78R980D9fccW2xzdu9C3LZs2Cp5+GM8+M3T1FREQCFMSM7tXAOOfcE865751zlwPZwKWlnWxmxwHHAn2cc7Occ7845+Y652aXONU555aFP+L6VcSAc47s3Hwyop3RBbj9dvjzz/LfipbqYfVqOPdcXzbQurVPcG+5BdLSYnufHXbwCfTUqb6TAvia3QEDfM3344/7OCqpR48e9OjRIxaRioiIxERCE10zSwf2B2aWeGkmcGgZl50CfAZcbWZLzewnM3vQzBqVOK++mf0aOud1M9uvnDguMrN5ZjZv8+bNlf1yqixn/SY2bd4S/YwuwH77wWmn+UQ3Jyf2wUlizJgBXbvC+PG+RGHePP+zjZehQ/39rrjCvxtw5pk+6R0zBi66qEpDn3vuuZxbhURZREQk1hI9o9sKqA0sL3F8OdCujGs6AYcD+wL9gCH4MoZxYecsBM4HTgb+CuQDH5nZbqUN6Jwb65w7wDl3QJ2q1j5WQVSbRZTm1lt9y6lRo2IXlCTOuHFw/PHQtKnvqDBiBKSnx/eeaWm+t+5vv/mEd9IkuO8+GDKkykMr0RURkWRTHbou1AIccGaoZOEtfLLbz8zaAjjnPnbOPeucm++c+wAYAPwPiKCrfnCyVoc2i6jMjC7A3nvDGWf42ssVK2IYmcRdYaFPbLt39628undP3L0PPxzOPx+WLoU77/R9dmOgoKCAgoral4mIiCRQohPdlUAh0LbE8bZAWTW12cDvzrnwVVffh55LXcDmnCsE5gGlzugmi+IZ3Uh3RSvNrbdCXh7cc09sgpLEeOMN+N//4JproF4AzUEeftjPIl93XcyG7NWrF7169YrZeCIiIlWV0ETXObcJ+Bwo+duwF777Qmk+AjJK1OTuHnoupU8SmJkB++CT5KSVlZtHWm2jVcMIN4sozR57wFln+cRlWdKvv5Mi//43dOzo66yDUK8eHHxwTIe88MILufDCC2M6poiISFUEUbpwP3CumV1oZl3M7AEgA3gMwMyeM7Pnws5/AVgFPGNme5nZYfj2ZJOcc3+ErrnFzP5iZp3MrBvwFD7RfSyBX1fUluXm065pPWpFs1lEaW6+2ffVveuu2AQm8TV/PsyeDZdfXvX+uEnk7LPP5uyzzw46DBERkWIJT3SdcxOBocCNwHz8QrM+zrmi2dkdCCtJcM6tA3oCTfHdF14C3sMvPivSDBiLL2mYCWQCRzrnPo3rF1NF2avzK78QLdyuu/q2UI895usuJbmNHu13K6ths58bNmxgw4YNQYchIiJRMLO6ZjbGzFaa2Xozm2pmHSK4rtzNv8zsCTP7n5nlmdkKM3vNzLrE7yspI07nXKLvmVQaNmzo1q9fH8i9D7/7vxywY3NGD4xBO6lffoHdd4cLLvCr6iU5LVsGO+4IgwbVuJ3tinrozp49O9A4RERqMjPb4JxrGMPxHsV3rfo7/h30+/ETiPuH1jyVds0AYDwwGPgw9HwesKdz7rfQORcD3wFLgBbArfgWszs55xK2crnmvG9azWzZ4li+Jj/67X/LstNOPsl96in45z/955J8Hn3Ub9Jw5ZVBRxJzl15a6p4vIiKSpMysKXABcJ5zblbo2Dn4NVA9gbfKuLR486/Q55ebWW/85l/XATjnHg87/xczuxH4Ct82dmGsv5ayVIf2YjXSynUbKSh0lW8tVpobboBateCOO2I3psROfr5PdE88EXZL6oYglTJgwAAGDBgQdBgiIhK5/YE0wjbycs4twZeClrqRV2U2/zKzhvgZ39+AX6oadDRSfka3RYsWgbzVmldQyLC9N9M892dmz/4lZuPuesIJZI4bx6fHHENeZmbMxpWqazd9OnusWMH8Hj1YXQPf3l+3bh0AjRqV3LRQRERiqI6ZzQv7fKxzbmwlx2qHb/u6ssTx8jbyKm/zr57hB8xsMHAP0BA/i3usc25jJWOtFNXoBlSj++Y32Vz6ny94/fLD6ZrZNHYDL1sGnTpB//7w3HMVny+J4Rzsuy+Y+a4LVsVOG0lINboiIvEXSY2umY0AbqhgqKPxXa+eA9JcWEJoZv8FfnLOXVzK2BnA78BRzrn3w47fDJzlnOscdqwp0AZoD1wDdAQOc84lbOVyys/oBqVos4iMWNXoFmnXDi67DO6/H66/3vfZleC98w588w08/XSNTHIBrrjiiqBDEBERbzR+sVh5fgMOxs/OtgLCt1htC3xQxnURb/4V2uwrF/jJzD4B/gT6Ac9X/CXEhhLdgGTn5lG3Ti2aN0iL/eD/+IevBb31VpgwIfbjS/RGj4Y2beCvfw06krg5LajNL0REZBvOuZVsX46wHTP7HCjAb9z1QuhYB6ALZWzk5ZzbFLquF/By2Eu9gMnl3S70qMIuWdHTYrSAZOXmk9GsPhaP2b3Wrf2q/okT/SyiBGvhQr/l7+DBwWz3myArV65k5coK/10VEZEkEZpxfQq4x8x6mtl++NnWr4G3i84zsx/MbEjYpRVt/rWrmf3TzPY3sx3M7FB8UrwReD0xX52nRDcg2avzaNckjknPsGHQpAncckv87iGRefBBSE+HSy4JOpK46t+/P/379w86DBERic5Q4BVgIvARsA7oW6KHbmd8eQMQ0eZfG4EewJvAz6Gx1wKHOOe2KW+IN5UuBCQ7N59DdmkZvxu0aAFXXQW33QazZkGvXvG7l5QtJwfGjYOzzoK2JcuZapZhw4YFHYKIiEQp1AXh8tCjrHO2e/vZOfcI8EgZ5y8Bjo9VjFWhGd0AbC7cwvI1+WTEYvvf8lx1FWRmwnHHwTHHwJtv+tX/kjhPPgkbNtTIDSJK6tu3L3379g06DBERkWJKdAOwYt1Gtjho3yzO9ZpNm8J338GoUfDjj9CnD+yzDzz7LGzaFN97i98BbcwY/0fGvvsGHU3cLVu2jGXLEvqOlIiISLmU6AYga3WotVi8Z3TB1+kOGwaLFvkEF+Dcc32v3VGjYM2a+MeQqiZPhqVL/cx6Chg4cCADBw4MOgwREZFiSnQDkJ2bByRgRjdcejr87W/w9dcwfTrsvjsMHw4dO/p2ZL//nrhYUsXo0X6r3z59go4kIa699lquvfbaoMMQEREppkQ3ANmhGd32TRIwo1uSGRx/PPz3v/DZZ/7j++6DnXeGyy+HjQndma/m+vhjmDvX1+bWSo3/zXr37k3v3r2DDkNERKRYavwGTjJZuXk0SK9Nk/oBN7044AC/ocRPP8H558NDD8Gxx8IffwQbV00wejQ0awZ//3vQkSTMkiVLWLJkSdBhiIiIFFOiG4Ds1fm0b1ovPptFVEanTvDYYz7p/fxzOPBAX+IglfPbb74+d9AgaNQo6GgS5pxzzuGcc84JOgwREZFi6qMbgOzcPDKaBVC2UJEBA2CXXeDkk+HQQ+E///EfS3TGjPHPQ4aUf14Nc+ONNwYdgoiIyDY0oxuA7Fw/o5uUDjjA1+7uuSeceiqMHKneu9GYOdOXLQwcCDvsEHQ0CdWzZ0969uwZdBgiIiLFlOgm2KbNW1ixbiPtE9FarLIyMuC99/wM73XX+W4N+flBR5X8vvgC+vWDvfaCR0rdLKZGW7RoEYsWLQo6DBERkWIqXUiw5WvycQ4yEtlarDLq14cXXvBJ2003wc8/wyuvQLt2QUeWnBYt8m3EWrb07duaNAk6ooQ7//zzAZg9e3awgYiIiIQo0U2w7Fw/M9oumWd0i5jBjTdCly5+Vrd7d5g6FfbbL+jIksuKFdC7t98JbfZsPyOegm677bagQxAREdmGEt0EK9osIiNZa3RL06+f78xw8slw+OHw5JP+OT0d0tL8o+jjFOkZW2z9ejjxRFiyBN55B/bYI+iIAnPUUUcFHYKIiMg2UiwrCV7R9r/tk7HrQnn22w8+/RT22QfOPNMvtGrXzr9V36QJ1KsHtWv7R/36/ljr1vDUU0FHHj+bN/tFZ/Pm+dZshx4adESBWrhwIQsXLgw6DBERkWKa0U2wZbl5NK5Xh0Z1q+G3vl07ePddX76wZo1/q76gADZt2va56OOpU/0mFBdcEHTkseccXHIJvP6670GsNmxcfPHFgGp0RUQkeVTDbKt6y8rNJ6M61OeWpV49OOOMyM7dYQe4+mr43/98f96a5Lbb/Gz1jTdCKMFLdXfeeWfQIYiIiGxDpQsJlp2bR/tk77gQK6ed5p8nTw42jlgbO9YnuuefD7ffHnQ0SePQQw/l0BQv3xARkeSiRDfBirb/TQk77ug7NUyaFHQksTN1Klx6qW8l9thjvjOFALBgwQIWLFgQdBgiIiLFlOgmUH5BIavWb0ruzSJirX9/v9Par78GHUnVffyxX3y2//7w0ku+y4QUGzJkCENSbNtjERFJbkp0E2hZqIduyszogm9NBjBlSrBxVNXXX0PfvpCZ6RegNWwYdERJ59577+Xee+8NOgwREZFiSnQTKKuoh251ay1WFbvsAt26Ve/yhU8/hR49fNu0GTOgTZugI0pK3bt3p3v37kGHISIiUkyJbgKl5Iwu+PKFOXPg99+DjiR6778PPXtC8+bwwQc1r3tEDM2fP5/58+cHHYaIiEgxJboJlF2c6KbQjC5sLV945ZVg44jWzJl+a9/MTJ/w7rRT0BEltaFDhzJ06NCgwxARESmmProJlLU6j+YN0qifXjvoUBJrjz1gr718+UJ1Waz02mu+X3CXLj7hVblChUaPHh10CCIiItvQjG4CZefm0y7VZnOL9O/vZ0WXLw86koq9+KKfhd5vP78TnJLciHTr1o1u3boFHYaIiEgxJboJlLU6j4xUq88t0q+f3zY32csXnn4azjoLDjsMZs3ytbkSkc8++4zPPvss6DBERESKKdFNoOzc/NTZFa2krl1h992Te5e0MWPgggvguOPgzTehceOgI6pWhg8fzvDhw4MOQ0REpJhqdBNkw6bN5OYVpN5CtCJmvnzh7rth5Upo1SroiLY1ciRcdx2ccgpMmAB16wYdUbXz0EMPBR2CiIjINjSjmyBFHRcyUnVGF3z5QmGhX+iVLJyDG2/0Se6ZZ/odz5TkVkrXrl3p2rVr0GGIiIgUU6KbINmrU7S1WLj99oOdd06e8oXCQrjiCvjXv+DCC+G557StbxXMmTOHOXPmBB2GiIhIMZUuJEjRrmgpt1lEuKLyhdGj4c8/g13olZ8PZ5/tk+5hw+Dee318UmnXX389ALNnzw42EBERkRDN6CZI0Yxuu1ROdMGXLxQUwLRpwcXw559+wdnkyXD//TBqlJLcGHj88cd5/PHHgw5DRESkmBLdBMnOzaNVo3Tq1kmxzSJKOvBA6NgxuPKFJUvg8MNh7lzfL/eqq4KJowbq3LkznTt3DjoMERGRYkp0EyQrNz+163OLmPlZ3bfegjVrEnvvb76BQw6BpUthxgwYODCx96/h3nvvPd57772gwxARESmmRDdBluXmpXZ9brh+/WDjRnjjjcTdc/ZsOOII32Xhgw/g6KMTd+8Uccstt3DLLbcEHYaIiEgxJboJkr06n4xmmtEF4NBDoX17mDQpMfd7+WX4y18gIwM+/hj22Scx900xTz/9NE8//XTQYYiISBTMrK6ZjTGzlWa23symmlmHCK4bbGaLzSzfzD43syPKOM/M7E0zc2bWP/ZfQfmU6CbA2vwC1m7crIVoRWrVgtNO87uPrV8f33s9+CAMGADdu8OHH8IOO8T3fimsU6dOdOrUKegwREQkOqOBfsBfgSOAJsDrZlbmoiIzGwA8ANwJ7AfMAd40s9J+yQ4DtsQ66Egp0U2Aos0iVLoQpl8/yMvzyW48bNkC//gHXHklnHoqzJoFLVrE514CwNtvv83bb78ddBgiIhIhM2sKXAAMd87Ncs59AZwD7AP0LOfSq4FxzrknnHPfO+cuB7KBS0uM3x24EjgvLl9ABNRHNwGyVvseuipdCHPEEdC6tS9f6B/FOxnr1sGqVb5F2OrVpT/+/BN+/hnmzIHLLoMHHoDaKd7tIgFGjBgBQM+e5f3bKCIiSWR/IA2YWXTAObfEzL4HDgXeKv6100AAABJXSURBVHmBmaWHrhtV4qWZoWuKzmsMvABc5Jz7wwJq45nyiW6LFi3i3uA+Z8Mmhu29mdX/m8/sXzSJXmT3Aw+kzdSpzHnrLbZUsO1uek4OOz39NO3ffBPbUvY7IJsbNmRzo0ZsbtSIZYMHs7RfP7/4TOJu8ODBgDaMEBGJszpmNi/s87HOubGVHKsdUAisLHF8eei10rQCaofOKXlN+EzHY8AM51yc3rqNTMonujk5OfTo0SOu97h/5kLGLPiZHwf2IK22Et1imzbBG29wZH6+XyxWmg0b/KYOI0f68y+9FPbfH5o12/7RpAl1atcu/o9619BDRESkBtnsnDugvBPMbARwQwXjxK39kJmdA+wLlBtnIqR8opsI2bn5tGlcV0luSUcf7bcBnjQJTj5529e2bIEXXoDrrvN9b087De6+G3ZV6pqsZsyYAUDv3r0DjkREJOWNBsZXcM5vwMH42dlWwIqw19oCZb0duhI/C9y2xPG2wLLQx8cCewLrSpQsTDSzj51zh1f0BcSKEt0EyNZmEaVLS/MJ7iuv+L66ReULH3wAV18N8+b52dv//AeOPDLYWKVCI0eOBJToiogEzTm3ku3LEbZjZp8DBUAvfD0todZiXfCdFEobe1Poul7Ay2Ev9QKKtj29ge1reL8BrgFei/gLiQElugmQlZtH57aNgw4jOfXvD+PGwTvvwO67wz//CVOmQIcO8PzzcOaZvh2ZJL0JEyYEHYKIiETBOZdrZk8B95jZH8Aq4H7ga6C4jY6Z/QA85Jx7KHTofuB5M/sU+Ai4BMjA1+XinPsd+D38XqGZ3SXOuUVx/aJKUKIbZ845slfn02P3NkGHkpx69oQmTXwbsF9/hfR0uOMOP6PboEHQ0UkU2rUra92CiIgksaHAZmAiUB94B/ibc64w7JzO+PIGAJxzE82sJXAj0B5YAPRxzv2asKgjpEQ3znLzCsgrKCSjmXrolqpuXd9Td9w4uOACuP12v2uaVDvTpk0DoG/fvgFHIiIikXLObQQuDz3KOme73mDOuUeAR6K4TyD9xcw5F8R9k0bDhg3d+jjuzrVp8xa+WrqajGb1yVQf3dKtXw85OdCxY9CRSBUUdS9RezERkfgxsw3OuYZBx1FdKNGNc6IrkipWrvTrHlq1alXBmSIiUllKdKOj0gURiQkluCIikmy0nF1EYmLKlClMmTIl6DBERESKqXRBpQsiMaEaXRGR+FPpQnSU6CrRFYmJ3NxcAJo2bRpwJCIiNZcS3eioRldEYkIJroiIJBvV6IpITEycOJGJEycGHYaIiEgxlS6odEEkJlSjKyISfypdiI4SXSW6IjGxYcMGABpo62YRkbhRohsd1eiKSEwowRURkWSjGl0RiYnx48czfvz4oMMQEREpptIFlS6IxIRqdEVE4k+lC9FRoqtEVyQmCgoKAEhLSws4EhGRmkuJbnRUoysiMaEEV0REko1qdEUkJsaNG8e4ceOCDkNERKSYShdUuiASE6rRFRGJP5UuRCflE10z2wLkVfLyOsDmGIYjsaWfT/LSzya56eeTvPSzSW6J+PnUd87pHfkIpXyiWxVmNs85d0DQcUjp9PNJXvrZJDf9fJKXfjbJTT+f5KO/CERERESkRlKiKyIiIiI1khLdqhkbdABSLv18kpd+NslNP5/kpZ9NctPPJ8moRldEREREaiTN6IqIiIhIjaREV0RERERqJCW6IiIiIlIjKdEth5kNNrPFZpZvZp+b2REVnH9U6Lx8M1tkZpckKtZUFM3Px8zam9kL/9/e3UfLUdd3HH9/eAoUpaGIAUJDaI0YHtqg1oo8RazHCkcOAgV6Tgy37cGCRQ5HUREFAspD7LEGUMCChySgBY1gWxoFTQlY0XhADA9CSmgSC5KEQFISuCS58ds/fr/lzl139t7d7O592M/rnDl3duY7M9+d3+y93zvzm1lJT0naJmluB1PtOg22zcmS7pX0gqSNkpZIOrGT+XabBtvnWEkPSnpRUm/+DF3QyXy7SaN/dwrLHSWpT9Lj7c6xmzX42ZkuKWoMb+tkzt3OhW4JSacD1wBXAocDDwLflzSpJP5AYGGOOxy4CrhO0imdybi7NNo+wDhgHXA1sKQjSXapJtrmWOA/gRNy/ELgrqH+gbfGNNE+m4BrgWOAg4EvApdJ+lgH0u0qTbRNZbk9gfnAorYn2cWabR/gEGDfwvB0O/O0gfzUhRKSlgCPRsRZhWlPAwsi4rM14mcDJ0fElMK0m4FDIuKITuTcTRptn6pl7wbWRURPe7PsTtvTNoX4nwM/johPtinNrtWi9rkT2BwRf92mNLtSs22T22MpIODUiDi07cl2oSbqgunAfcDeEbGuY4naAD6jW4OkXYB3APdWzboXeE/JYkfUiL8HeKeknVubYXdrsn2sA1rYNm8E1rcqL0ta0T6SDs+x97c2u+7WbNvkM+sTSGfarU2287PzkKTnJS2S9N62JGilXOjW9iZgR2BN1fQ1wD4ly+xTEr9TXp+1TjPtY52x3W0j6R+A/YFbW5uasR3tI+lZSZuBh4DrI+LG9qTYtRpuG0mHAZcCMyJiW3vT63rNfHaeB84BTgFOBpYBi9wtq7N2Gu4EzMwqcp/2fwROj4hVw52PDXA08Abg3cBsSSsiwv+MDBNJ44A7gAsiYsVw52O/KyKWkYrbip9Kmgx8CvjxcOTUjVzo1rYO2Ea6HFQ0AVhdsszqkvi+vD5rnWbaxzqj6baRdCrphpqZEfHv7Umv6zXdPoVi6jFJE4BZ+Kx7KzXaNvsCU4FbJN2Sp+0ASFIfcHxEVF9mt+a16u/OEuCMViVlg3PXhRoiYgvwMPD+qlnvJ91lWctPS+Ifioitrc2wuzXZPtYBzbaNpNNIRVNPRCxoX4bdrYWfnR1ITzKxFmmibZ4DDgOmFYYbgeV53L8LW6iFn51ppC4N1iE+o1vun4Bb893fPwHOBvYj/SJB0nyAiJiZ428EzpU0B/g6cCTQA/iu5PZotH2QNC2P7gH8Nr/eEhG/6mTiXaChtpF0BqnIvQB4QFKlv9uWiHipw7l3g0bb5+PACvovwR5DaqvrO5t2Vxhy2+QTKAOemStpLelpGH6Wbns0+tk5H1gJPAHsAswATiL12bUOcaFbIiLukLQX8HnSJaLHSZeCKv0GJ1XFr5B0PPAVUufz3wDnRcR3O5h212i0fbJHql5/CFgFTG5Xnt2oibY5m/S7aE4eKu4Hprc32+7TRPvsCMwmfU76gGeAC8l/3K11mvy9Zh3SRPvsQrrnYH+gl1TwnhARCzuUsuHn6JqZmZnZGOU+umZmZmY2JrnQNTMzM7MxyYWumZmZmY1JLnTNzMzMbExyoWtmZmZmY5ILXTMzMzMbk1zomo1wkqZI+qqkJyVtkrRR0lOSbpL07kLcSkkhaeUwplvJZW7OJfJ3u1emT5D0TUnPS9qW58+RNLkQP7eNeY2XNCsPJw01706RNL2w/cGGWXmZyuvFnc53MO1s10baqmq/tjQPMxvZ/IURZiOYpL8BbuB3v271oDzsTfqmndHiGuD0Ydz+eODSPD4P+N4w5mJmZm3mQtdshJJ0HHAz6cpLAFeQvl56LXAAcCrw1mFLsI6I6CF9BXa1d+SfG4ADI2JDYZ7anNag6uTdqe0vprAfJPUAt+SX83J+LSdp14h4rR3rNjMbTu66YDZyXUX/Z/TaiLg4Ip6NiC0R8XREXAWcVW8FkqZJulPSckkvS9oqaXWe9s6q2AMlzZf0a0mvSdog6fF8ifjNhbizJD0k6SVJmyU9J+mHks4sxAy4rFy5dAy8JYeMB9bn+T31LnFLerukf8nb2SJpnaT7JL0rz3+DpHmSHpP0Yn6PGyQ9IOn0wnpmASsKqz6zept1ulzsLukySU9I6pX0qqRHJH1C0k6FuAHvQ9LMvA97lbqenEkbSTpO0s/y9p6R9GlJxcJ5ViG/D0v6hqR1pK8nrcRMlXRrYX+vlbRA0p9UbWtIx0vVMqdJerTe/pB0tKR/k/RC4Xi9vXr7dfbBfjnfTfl4uAF4Y0lsw+/BzEaZiPDgwcMIG4A3k87iVoaJQ1hmZY5dWZh2RtV6isMrwNRC7BN1Yg/NMX9VJ2ZBYV1zC9MnA9PrLNeTYyqv5xbW82Fga9lyOWafOusOYGaOm1UnZm6tvPO03YGH6yy7ENghxxbfx/qS+KMaOA56au2XqpjK/HUl+2pGIXZWVfzrcXn+UcCrJXn3Akc3eLwU98fqwfYHMAPYVhL3GjC97BjL03YDnqyx7G9q7cehvAcPHjyM7sFndM1GpsmF8Zcj4rkm1/ML4APAvqR+vnsA5+R5vwf8PYCkvYCD8/RrScXdHwB/BlwM/F+ed0z+uYnUR3gcqRvFacAPypKIiMURIWBVnrQqIpSHubWWkbQbcBP9XawuASYAbyIV3P+Tp28k9fudnN/TrsB7SAUbwCdyDrOAAwubmFfIoacsd+B84O15/B7Svvwj0r4F+CDpH4pq44GPAb8PzC5M/0idbW2PvYAvAXsC5w5hewL+krTPDsvTbiIVi6tI3UzGAYcDL5D269egoeOlaAJ19oek3YHrSFcx+kj/5OwBnJ3jxpG67tQzE3hbHv8ZsD/pKsKG6sAm34OZjTLuo2s2tq0G/g6YQyoEd6uaf1D+uZ5UDIwnFW4bSWfGlkbEFwvxK/LP3YHPk850PgncGxGtLgyOJBVvAIsj4guFeQsK46+Sit87gKmky9TF/r4HsX1OKIx/NiJWA0i6nP6b2Y4HvlW13MMRcUOOvQ34TJ5+wHbmU2YNcElEbJM0D/jqINv7ckTck8cflzSF/iLxAFLbVjtM0j6kfuJDOV6KBtsfR+b1ASyMiMq+/bqks4FpwFslvSUilpds47jC+FWVfxAlfZnU371oqMe8mY1iPqNrNjKtLIzvIWm/JtfzbeDTpAKwusilMi0ifks6s/YsMAX4HHAb8Fju+/qHOf564DtAJX4O6SznGkkXNpljmQmF8V/VifsM6Uzjn5POAFbf1Lbrduaxd2H814XxVYXxWv05lxXGX2lhPmWeiYhtDWzvkarXQ+2TulcDx0vRYPujbD/D4Pv69dwK48+WjAMNHfNmNoq50DUbgSJiLfDzwqRP1Yor3ghVY96epG4LkM72HQLsCNS8qSci7gYmkc6AnghcTuoveSjp7C0R8VpEnEa6xHsU8LfAEtJl5SslTRzaOxySNYXxqXXiit0GTgLG5W4SL9aIjSbyWFsYn1QyXoyp2Lqd223U69uLiKFsr7fqdfE9/KjQreP1gdQX+Ym8jUGPl7L8qL0/yvZz9eta+7piXWF8/5Lx/iQafw9mNsq40DUbuT5HOnMKcF6+Y34/STsrfYnERaQ+lWX66C8o+oCXSZf4v1ArWNJ1wPtI/W9/AHwX2JxnT8oxp0g6F5gILCWd3V1aWQUlBUWTfkJ/sfpeSRdJ2lvSnpJOklTpL9xXWGYDsLOkixl4dq+iWPxOyf1CB/MfhfErlL70YjKpz3CtmFEpIp4G/ju/fJ+k85W+YGNXSX8q6RLg9kr8UI6XBj1I6k4A8EFJJyo9UeMsUj9hgGV1ui0A3FcYv1DSREl/DHyyVnAb3oOZjTAudM1GqIj4Eelmsa2kz+qlwHPAFlJBcgXpxqOy5TcCi/LLicD/ks6SHlyyyDnADwvbWEq6UQlS9wRIZ1avI3Ul2JiHj+Z5zwOPNvAW64qIXtLj0yqF7BWks3kvAXeRbggjj1csJhUt51HjBqSI2ES60x7SDWub8qO2euqkcg0DbzxbTeqrXHkm8PdJ/YPHgo+Snm4g4CukwrMX+CVwGQO7kwzleBmyiHgF+Djpn7udgX8lHV//nEM2039jWpn5wFN5/AhSt4TlDOwWUdTS92BmI48LXbMRLCJuJnU1uJ5U3PaS+jcuA74BXD3IKmaQirD1pLvIb6P8m8muBv6LVEz2kW7y+gWpaLwmxywi3XS1nFRQbiMVuLcDx+bitGUi4i5S39vbSY+I6iMVuvfT3293NnAlqVjpzfOOo/yu+Y8AD5DOcA8lh1dIT5u4nHSz0mZSMfhL0pnCE3N/z1EvIu4nFfDzSUXiVtL+fpT0D85FhfChHC+Nbv+bpEfR3U06+95H+ufs28C7In2hRr3le4G/AO4kfU42kL5wo+x50y1/D2Y2smhoXbnMzMzMzEYXn9E1MzMzszHJha6ZmZmZjUkudM3MzMxsTHKha2ZmZmZjkgtdMzMzMxuTXOiamZmZ2ZjkQtfMzMzMxiQXumZmZmY2Jv0/x6hZ16y/8oAAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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,
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balance accuracy: 0.1900\nBest balance accuracy: 0.8245\nCorresponding abs(1-disparate impact) value: 0.2483\nCorresponding average odds difference value: -0.0234\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": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balance accuracy: 0.1900\n        Actual label: [1.]\n        Actual label: [0.]\n"
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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.as_pyplot_figure()\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.as_pyplot_figure()\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": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "100%|██████████| 50/50 [00:00<00:00, 76.17it/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": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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,
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balance accuracy: 0.2600\nBest balance accuracy: 0.8083\nCorresponding abs(1-disparate impact) value: 0.4090\nCorresponding average odds difference value: -0.0698\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": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balance accuracy: 0.2600\n        Actual label: [1.]\n        Actual label: [0.]\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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.as_pyplot_figure()\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.as_pyplot_figure()\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
}