{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medical Expenditure Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This tutorial demonstrates classification model learning with bias mitigation as a part of a Care Management use case using Medical Expenditure data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook demonstrates how the AIF 360 toolkit can be used to detect and reduce bias when learning classifiers using a variety of fairness metrics and algorithms . It also demonstrates how explanations can be generated for predictions made by models learnt with the toolkit using LIME.\n",
    "\n",
    "Classifiers are built using Logistic Regression as well as Random Forests.\n",
    "\n",
    "Bias detection is demonstrated using several metrics, including disparate impact, average odds difference, statistical parity difference, equal opportunity difference, and Theil index.\n",
    "\n",
    "Bias alleviation is explored via a variety of methods, including reweighing (pre-processing algorithm), prejudice remover (in-processing algorithm), and disparate impact remover (pre-processing technique).\n",
    "\n",
    "Data from the [Medical Expenditure Panel Survey](https://meps.ahrq.gov/mepsweb/) is used in this tutorial. See [Section 2](#2.-Data-used) below for more details.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To return to the table of contents, click on the number at any major section heading.\n",
    "\n",
    "[1. Use case](#1.-Use-case)\n",
    "\n",
    "[2. Data used](#2.-Data-used)\n",
    "\n",
    "[3. Training models without debiasing](#3.-Training-models-on-original-2015-Panel-19-data)\n",
    "\n",
    "[4. Reweighing (pre-processing bias mitigation)](#4.-Bias-mitigation-using-pre-processing-technique---Reweighing)\n",
    "\n",
    "[5. Prejudice Remover (in-processing bias mitigation)](#5.-Bias-mitigation-using-in-processing-technique---Prejudice-Remover-(PR))\n",
    "\n",
    "[6. Summary of results](#6.-Summary-of-Model-Learning-Results)\n",
    "\n",
    "[7. Deploying model](#7.-Deploying-model)\n",
    "\n",
    "[8. Generating explanations for model predictions using LIME](#8.-Generating-explanations-for-model-predictions-using-LIME)\n",
    "\n",
    "[9. Re-deploying Model](#9.-Re-deploying-Model)\n",
    "\n",
    "[10. Overall Summary](#10.-SUMMARY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [1.](#Table-of-Contents) Use case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to demonstrate how AIF 360 can be used to detect and mitigate bias in classfier models, we adopt the following use case:\n",
    "\n",
    "1. a data scientist develops a 'fair' healthcare utilization scoring model with respect to defined protected classes. Fairness may be dictated by legal or government regulations, such as a requirement that additional care decisions be not predicated on factors such as race of the patient.\n",
    "\n",
    "\n",
    "2. developer takes the model AND performance characteristics / specs of the model (e.g. accuracy, fairness tests, etc. basically the model factsheet) and deploys the model in an enterprise app that prioritizes cases for care management.\n",
    "\n",
    "\n",
    "3. the app is put into production and starts scoring people and making recommendations. \n",
    "\n",
    "\n",
    "4. explanations are generated for each recommendation\n",
    "\n",
    "\n",
    "5. both recommendations and associated explanations are given to nurses as a part of the care management process. The nurses can evaluate the recommendations for quality and correctness and provide feedback.\n",
    "\n",
    "\n",
    "6. nurse feedback as well as analysis of usage data with respect to specs of the model w.r.t accuracy and fairness is communicated to AI Ops specialist and LOB user periodically.\n",
    "\n",
    "\n",
    "7. when significant drift in model specs relative to the model factsheet is observed, the model is sent back for retraining."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [2.](#Table-of-Contents) Data used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The specific data used is the [2015 Full Year Consolidated Data File](https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-181) as well as the [2016 Full Year Consolidated Data File](https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-192)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 2015 file contains data from rounds 3,4,5 of panel 19 (2014) and rounds 1,2,3 of panel 20 (2015). The 2016 file contains data from rounds 3,4,5 of panel 20 (2015) and rounds 1,2,3 of panel 21 (2016).\n",
    "\n",
    "For this demonstration, three datasets were constructed: one from panel 19, round 5 (used for learning models), one from panel 20, round 3 (used for deployment/testing of model - steps); the other from panel 21, round 3 (used for re-training and deployment/testing of updated model)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each dataset, the sensitive attribute is 'RACE' constructed as follows: 'Whites' (privileged class) defined by the features RACEV2X = 1 (White) and HISPANX = 2 (non Hispanic); 'Non-Whites' that included everyone else.  \n",
    "\n",
    "Along with race as the sensitive feature, other features used for modeling include demographics  (such as age, gender, active duty status), physical/mental health assessments, diagnosis codes (such as history of diagnosis of cancer, or diabetes), and limitations (such as cognitive or hearing or vision limitation).\n",
    "\n",
    "To measure utilization, a composite feature, 'UTILIZATION', was created to measure the total number of trips requiring some sort of medical care by summing up the following features: OBTOTV15(16), the number of office based visits;  OPTOTV15(16), the number of outpatient visits; ERTOT15(16), the number of ER visits;  IPNGTD15(16), the number of inpatient nights, and  + HHTOTD16, the number of home health visits.\n",
    "\n",
    "The model classification task is to predict whether a person would have 'high' utilization (defined as UTILIZATION >= 10, roughly the average utilization for the considered population). High utilization respondents constituted around 17% of each dataset.\n",
    "\n",
    "To simulate the scenario, each dataset is split into 3 parts: a train, a validation, and a test/deployment part.\n",
    "\n",
    "We assume that the model is initially built and tuned using the 2015 Panel 19 train/test data. (Use case steps 1-2.)\n",
    "It is then put into practice and used to score people to identify potential candidates for care management (Use case steps 3-5). Initial deployment is simulated to 2015 Panel 20 deployment data. To show change in performance and/or fairness over time, (use case steps 6-7), the 2016 Panel 21 deployment data is used. Finally, if drift is observed, the 2015 train/validation data is used to learn a new model and evaluated again on the 2016 deployment data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [3.](#Table-of-Contents) Training models on original 2015 Panel 19 data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, load all necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../')\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "# Datasets\n",
    "from aif360.datasets import MEPSDataset19\n",
    "from aif360.datasets import MEPSDataset20\n",
    "from aif360.datasets import MEPSDataset21\n",
    "\n",
    "# Fairness metrics\n",
    "from aif360.metrics import BinaryLabelDatasetMetric\n",
    "from aif360.metrics import ClassificationMetric\n",
    "\n",
    "# Explainers\n",
    "from aif360.explainers import MetricTextExplainer\n",
    "\n",
    "# Scalers\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Classifiers\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Bias mitigation techniques\n",
    "from aif360.algorithms.preprocessing import Reweighing\n",
    "from aif360.algorithms.inprocessing import PrejudiceRemover\n",
    "\n",
    "# LIME\n",
    "from aif360.datasets.lime_encoder import LimeEncoder\n",
    "import lime\n",
    "from lime.lime_tabular import LimeTabularExplainer\n",
    "\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Load data & create splits for learning/validating/testing model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the dataset and split into train (50%), validate (30%), and test (20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(dataset_orig_panel19_train,\n",
    " dataset_orig_panel19_val,\n",
    " dataset_orig_panel19_test) = MEPSDataset19().split([0.5, 0.8], shuffle=True)\n",
    "\n",
    "sens_ind = 0\n",
    "sens_attr = dataset_orig_panel19_train.protected_attribute_names[sens_ind]\n",
    "\n",
    "unprivileged_groups = [{sens_attr: v} for v in\n",
    "                       dataset_orig_panel19_train.unprivileged_protected_attributes[sens_ind]]\n",
    "privileged_groups = [{sens_attr: v} for v in\n",
    "                     dataset_orig_panel19_train.privileged_protected_attributes[sens_ind]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function will be used throughout the notebook to print out some labels, names, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def describe(train=None, val=None, test=None):\n",
    "    if train is not None:\n",
    "        display(Markdown(\"#### Training Dataset shape\"))\n",
    "        print(train.features.shape)\n",
    "    if val is not None:\n",
    "        display(Markdown(\"#### Validation Dataset shape\"))\n",
    "        print(val.features.shape)\n",
    "    display(Markdown(\"#### Test Dataset shape\"))\n",
    "    print(test.features.shape)\n",
    "    display(Markdown(\"#### Favorable and unfavorable labels\"))\n",
    "    print(test.favorable_label, test.unfavorable_label)\n",
    "    display(Markdown(\"#### Protected attribute names\"))\n",
    "    print(test.protected_attribute_names)\n",
    "    display(Markdown(\"#### Privileged and unprivileged protected attribute values\"))\n",
    "    print(test.privileged_protected_attributes, \n",
    "          test.unprivileged_protected_attributes)\n",
    "    display(Markdown(\"#### Dataset feature names\"))\n",
    "    print(test.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show 2015 dataset details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Training Dataset shape"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(7915, 138)\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Validation Dataset shape"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4749, 138)\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Test Dataset shape"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(3166, 138)\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Favorable and unfavorable labels"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1.0 0.0\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Protected attribute names"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['RACE']\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Privileged and unprivileged protected attribute values"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[array([1.])] [array([0.])]\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Dataset feature names"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['AGE', 'RACE', 'PCS42', 'MCS42', 'K6SUM42', 'REGION=1', 'REGION=2', 'REGION=3', 'REGION=4', 'SEX=1', 'SEX=2', 'MARRY=1', 'MARRY=2', 'MARRY=3', 'MARRY=4', 'MARRY=5', 'MARRY=6', 'MARRY=7', 'MARRY=8', 'MARRY=9', 'MARRY=10', 'FTSTU=-1', 'FTSTU=1', 'FTSTU=2', 'FTSTU=3', 'ACTDTY=1', 'ACTDTY=2', 'ACTDTY=3', 'ACTDTY=4', 'HONRDC=1', 'HONRDC=2', 'HONRDC=3', 'HONRDC=4', 'RTHLTH=-1', 'RTHLTH=1', 'RTHLTH=2', 'RTHLTH=3', 'RTHLTH=4', 'RTHLTH=5', 'MNHLTH=-1', 'MNHLTH=1', 'MNHLTH=2', 'MNHLTH=3', 'MNHLTH=4', 'MNHLTH=5', 'HIBPDX=-1', 'HIBPDX=1', 'HIBPDX=2', 'CHDDX=-1', 'CHDDX=1', 'CHDDX=2', 'ANGIDX=-1', 'ANGIDX=1', 'ANGIDX=2', 'MIDX=-1', 'MIDX=1', 'MIDX=2', 'OHRTDX=-1', 'OHRTDX=1', 'OHRTDX=2', 'STRKDX=-1', 'STRKDX=1', 'STRKDX=2', 'EMPHDX=-1', 'EMPHDX=1', 'EMPHDX=2', 'CHBRON=-1', 'CHBRON=1', 'CHBRON=2', 'CHOLDX=-1', 'CHOLDX=1', 'CHOLDX=2', 'CANCERDX=-1', 'CANCERDX=1', 'CANCERDX=2', 'DIABDX=-1', 'DIABDX=1', 'DIABDX=2', 'JTPAIN=-1', 'JTPAIN=1', 'JTPAIN=2', 'ARTHDX=-1', 'ARTHDX=1', 'ARTHDX=2', 'ARTHTYPE=-1', 'ARTHTYPE=1', 'ARTHTYPE=2', 'ARTHTYPE=3', 'ASTHDX=1', 'ASTHDX=2', 'ADHDADDX=-1', 'ADHDADDX=1', 'ADHDADDX=2', 'PREGNT=-1', 'PREGNT=1', 'PREGNT=2', 'WLKLIM=-1', 'WLKLIM=1', 'WLKLIM=2', 'ACTLIM=-1', 'ACTLIM=1', 'ACTLIM=2', 'SOCLIM=-1', 'SOCLIM=1', 'SOCLIM=2', 'COGLIM=-1', 'COGLIM=1', 'COGLIM=2', 'DFHEAR42=-1', 'DFHEAR42=1', 'DFHEAR42=2', 'DFSEE42=-1', 'DFSEE42=1', 'DFSEE42=2', 'ADSMOK42=-1', 'ADSMOK42=1', 'ADSMOK42=2', 'PHQ242=-1', 'PHQ242=0', 'PHQ242=1', 'PHQ242=2', 'PHQ242=3', 'PHQ242=4', 'PHQ242=5', 'PHQ242=6', 'EMPST=-1', 'EMPST=1', 'EMPST=2', 'EMPST=3', 'EMPST=4', 'POVCAT=1', 'POVCAT=2', 'POVCAT=3', 'POVCAT=4', 'POVCAT=5', 'INSCOV=1', 'INSCOV=2', 'INSCOV=3']\n"
    }
   ],
   "source": [
    "describe(dataset_orig_panel19_train, dataset_orig_panel19_val, dataset_orig_panel19_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metrics for original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 0.48230522996275893\n"
    }
   ],
   "source": [
    "metric_orig_panel19_train = BinaryLabelDatasetMetric(\n",
    "        dataset_orig_panel19_train,\n",
    "        unprivileged_groups=unprivileged_groups,\n",
    "        privileged_groups=privileged_groups)\n",
    "explainer_orig_panel19_train = MetricTextExplainer(metric_orig_panel19_train)\n",
    "\n",
    "print(explainer_orig_panel19_train.disparate_impact())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Learning a Logistic Regression (LR) classifier on original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.1. Training LR model on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_orig_panel19_train\n",
    "model = make_pipeline(StandardScaler(),\n",
    "                      LogisticRegression(solver='liblinear', random_state=1))\n",
    "fit_params = {'logisticregression__sample_weight': dataset.instance_weights}\n",
    "\n",
    "lr_orig_panel19 = model.fit(dataset.features, dataset.labels.ravel(), **fit_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.2. Validating LR model on original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function will be used throughout the tutorial to find best threshold using a validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def test(dataset, model, thresh_arr):\n",
    "    try:\n",
    "        # sklearn classifier\n",
    "        y_val_pred_prob = model.predict_proba(dataset.features)\n",
    "        pos_ind = np.where(model.classes_ == dataset.favorable_label)[0][0]\n",
    "    except AttributeError:\n",
    "        # aif360 inprocessing algorithm\n",
    "        y_val_pred_prob = model.predict(dataset).scores\n",
    "        pos_ind = 0\n",
    "    \n",
    "    metric_arrs = defaultdict(list)\n",
    "    for thresh in thresh_arr:\n",
    "        y_val_pred = (y_val_pred_prob[:, pos_ind] > thresh).astype(np.float64)\n",
    "\n",
    "        dataset_pred = dataset.copy()\n",
    "        dataset_pred.labels = y_val_pred\n",
    "        metric = ClassificationMetric(\n",
    "                dataset, dataset_pred,\n",
    "                unprivileged_groups=unprivileged_groups,\n",
    "                privileged_groups=privileged_groups)\n",
    "\n",
    "        metric_arrs['bal_acc'].append((metric.true_positive_rate()\n",
    "                                     + metric.true_negative_rate()) / 2)\n",
    "        metric_arrs['avg_odds_diff'].append(metric.average_odds_difference())\n",
    "        metric_arrs['disp_imp'].append(metric.disparate_impact())\n",
    "        metric_arrs['stat_par_diff'].append(metric.statistical_parity_difference())\n",
    "        metric_arrs['eq_opp_diff'].append(metric.equal_opportunity_difference())\n",
    "        metric_arrs['theil_ind'].append(metric.theil_index())\n",
    "    \n",
    "    return metric_arrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "val_metrics = test(dataset=dataset_orig_panel19_val,\n",
    "                   model=lr_orig_panel19,\n",
    "                   thresh_arr=thresh_arr)\n",
    "lr_orig_best_ind = np.argmax(val_metrics['bal_acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot metrics with twin x-axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot(x, x_name, y_left, y_left_name, y_right, y_right_name):\n",
    "    fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "    ax1.plot(x, y_left)\n",
    "    ax1.set_xlabel(x_name, fontsize=16, fontweight='bold')\n",
    "    ax1.set_ylabel(y_left_name, color='b', fontsize=16, fontweight='bold')\n",
    "    ax1.xaxis.set_tick_params(labelsize=14)\n",
    "    ax1.yaxis.set_tick_params(labelsize=14)\n",
    "    ax1.set_ylim(0.5, 0.8)\n",
    "\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(x, y_right, color='r')\n",
    "    ax2.set_ylabel(y_right_name, color='r', fontsize=16, fontweight='bold')\n",
    "    if 'DI' in y_right_name:\n",
    "        ax2.set_ylim(0., 0.7)\n",
    "    else:\n",
    "        ax2.set_ylim(-0.25, 0.1)\n",
    "\n",
    "    best_ind = np.argmax(y_left)\n",
    "    ax2.axvline(np.array(x)[best_ind], color='k', linestyle=':')\n",
    "    ax2.yaxis.set_tick_params(labelsize=14)\n",
    "    ax2.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot $1 - \\min(\\text{disparate impact}, 1/\\text{disparate impact})$ since it's possible to overcorrect and end up with a value greater than 1, implying unfairness for the original privileged group. For shorthand, we simply call this 1-min(DI, 1/DI) from now on. We want the plotted metric to be less than 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x504 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "disp_imp = np.array(val_metrics['disp_imp'])\n",
    "disp_imp_err = 1 - np.minimum(disp_imp, 1/disp_imp)\n",
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     disp_imp_err, '1 - min(DI, 1/DI)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x504 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     val_metrics['avg_odds_diff'], 'avg. odds diff.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a function to print out accuracy and fairness metrics. This will be used throughout the tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_metrics(metrics, thresh_arr):\n",
    "    best_ind = np.argmax(metrics['bal_acc'])\n",
    "    print(\"Threshold corresponding to Best balanced accuracy: {:6.4f}\".format(thresh_arr[best_ind]))\n",
    "    print(\"Best balanced accuracy: {:6.4f}\".format(metrics['bal_acc'][best_ind]))\n",
    "#     disp_imp_at_best_ind = np.abs(1 - np.array(metrics['disp_imp']))[best_ind]\n",
    "    disp_imp_at_best_ind = 1 - min(metrics['disp_imp'][best_ind], 1/metrics['disp_imp'][best_ind])\n",
    "    print(\"Corresponding 1-min(DI, 1/DI) value: {:6.4f}\".format(disp_imp_at_best_ind))\n",
    "    print(\"Corresponding average odds difference value: {:6.4f}\".format(metrics['avg_odds_diff'][best_ind]))\n",
    "    print(\"Corresponding statistical parity difference value: {:6.4f}\".format(metrics['stat_par_diff'][best_ind]))\n",
    "    print(\"Corresponding equal opportunity difference value: {:6.4f}\".format(metrics['eq_opp_diff'][best_ind]))\n",
    "    print(\"Corresponding Theil index value: {:6.4f}\".format(metrics['theil_ind'][best_ind]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.1900\nBest balanced accuracy: 0.7627\nCorresponding 1-min(DI, 1/DI) value: 0.6066\nCorresponding average odds difference value: -0.1831\nCorresponding statistical parity difference value: -0.2643\nCorresponding equal opportunity difference value: -0.1608\nCorresponding Theil index value: 0.0936\n"
    }
   ],
   "source": [
    "describe_metrics(val_metrics, thresh_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.3. Testing LR model on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_orig_metrics = test(dataset=dataset_orig_panel19_test,\n",
    "                       model=lr_orig_panel19,\n",
    "                       thresh_arr=[thresh_arr[lr_orig_best_ind]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.1900\nBest balanced accuracy: 0.7759\nCorresponding 1-min(DI, 1/DI) value: 0.5738\nCorresponding average odds difference value: -0.2057\nCorresponding statistical parity difference value: -0.2612\nCorresponding equal opportunity difference value: -0.2228\nCorresponding Theil index value: 0.0921\n"
    }
   ],
   "source": [
    "describe_metrics(lr_orig_metrics, [thresh_arr[lr_orig_best_ind]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all the fairness metrics displayed above, the value should be close to '0' for fairness.\n",
    "\n",
    "1-min(DI, 1/DI) < 0.2 is typically desired for classifier predictions to be fair.\n",
    "\n",
    "However, for a logistic regression classifier trained with original training data, at the best classification rate, this is quite high. This implies unfairness.\n",
    "\n",
    "Similarly, $\\text{average odds difference} = \\frac{(FPR_{unpriv}-FPR_{priv})+(TPR_{unpriv}-TPR_{priv})}{2}$ must be close to zero for the classifier to be fair.\n",
    "\n",
    "Again, the results for this classifier-data combination are still high. This still implies unfairness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Learning a Random Forest (RF) classifier on original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.1. Training RF model on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_orig_panel19_train\n",
    "model = make_pipeline(StandardScaler(),\n",
    "                      RandomForestClassifier(n_estimators=500, min_samples_leaf=25))\n",
    "fit_params = {'randomforestclassifier__sample_weight': dataset.instance_weights}\n",
    "rf_orig_panel19 = model.fit(dataset.features, dataset.labels.ravel(), **fit_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.2. Validating RF model on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "val_metrics = test(dataset=dataset_orig_panel19_val,\n",
    "                   model=rf_orig_panel19,\n",
    "                   thresh_arr=thresh_arr)\n",
    "rf_orig_best_ind = np.argmax(val_metrics['bal_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "disp_imp = np.array(val_metrics['disp_imp'])\n",
    "disp_imp_err = 1 - np.minimum(disp_imp, 1/disp_imp)\n",
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     disp_imp_err, '1 - min(DI, 1/DI)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     val_metrics['avg_odds_diff'], 'avg. odds diff.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2300\nBest balanced accuracy: 0.7717\nCorresponding 1-min(DI, 1/DI) value: 0.4860\nCorresponding average odds difference value: -0.1157\nCorresponding statistical parity difference value: -0.1929\nCorresponding equal opportunity difference value: -0.1063\nCorresponding Theil index value: 0.0896\n"
    }
   ],
   "source": [
    "describe_metrics(val_metrics, thresh_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.3. Testing RF model on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_orig_metrics = test(dataset=dataset_orig_panel19_test,\n",
    "                       model=rf_orig_panel19,\n",
    "                       thresh_arr=[thresh_arr[rf_orig_best_ind]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2300\nBest balanced accuracy: 0.7638\nCorresponding 1-min(DI, 1/DI) value: 0.5141\nCorresponding average odds difference value: -0.1388\nCorresponding statistical parity difference value: -0.2190\nCorresponding equal opportunity difference value: -0.1135\nCorresponding Theil index value: 0.0936\n"
    }
   ],
   "source": [
    "describe_metrics(rf_orig_metrics, [thresh_arr[rf_orig_best_ind]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the case of the logistic regression classifier learned on the original data, the fairness metrics for the random forest classifier have values that are quite far from 0.\n",
    "\n",
    "For example, 1 - min(DI, 1/DI) has a value of over 0.5 as opposed to the desired value of < 0.2.\n",
    "\n",
    "This indicates that the random forest classifier learned on the original data is also unfair."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.](#Table-of-Contents) Bias mitigation using pre-processing technique - Reweighing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Transform data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "RW = Reweighing(unprivileged_groups=unprivileged_groups,\n",
    "                privileged_groups=privileged_groups)\n",
    "dataset_transf_panel19_train = RW.fit_transform(dataset_orig_panel19_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metrics for transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 1.0000000000000002\n"
    }
   ],
   "source": [
    "metric_transf_panel19_train = BinaryLabelDatasetMetric(\n",
    "        dataset_transf_panel19_train,\n",
    "        unprivileged_groups=unprivileged_groups,\n",
    "        privileged_groups=privileged_groups)\n",
    "explainer_transf_panel19_train = MetricTextExplainer(metric_transf_panel19_train)\n",
    "\n",
    "print(explainer_transf_panel19_train.disparate_impact())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Learning a Logistic Regression (LR) classifier on data transformed by reweighing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.1. Training LR model after reweighing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_transf_panel19_train\n",
    "model = make_pipeline(StandardScaler(),\n",
    "                      LogisticRegression(solver='liblinear', random_state=1))\n",
    "fit_params = {'logisticregression__sample_weight': dataset.instance_weights}\n",
    "lr_transf_panel19 = model.fit(dataset.features, dataset.labels.ravel(), **fit_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.2. Validating  LR model after reweighing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "val_metrics = test(dataset=dataset_orig_panel19_val,\n",
    "                   model=lr_transf_panel19,\n",
    "                   thresh_arr=thresh_arr)\n",
    "lr_transf_best_ind = np.argmax(val_metrics['bal_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "disp_imp = np.array(val_metrics['disp_imp'])\n",
    "disp_imp_err = 1 - np.minimum(disp_imp, 1/disp_imp)\n",
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     disp_imp_err, '1 - min(DI, 1/DI)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x504 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     val_metrics['avg_odds_diff'], 'avg. odds diff.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2200\nBest balanced accuracy: 0.7581\nCorresponding 1-min(DI, 1/DI) value: 0.2939\nCorresponding average odds difference value: -0.0084\nCorresponding statistical parity difference value: -0.0992\nCorresponding equal opportunity difference value: 0.0242\nCorresponding Theil index value: 0.0938\n"
    }
   ],
   "source": [
    "describe_metrics(val_metrics, thresh_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.3. Testing  LR model after reweighing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_transf_metrics = test(dataset=dataset_orig_panel19_test,\n",
    "                         model=lr_transf_panel19,\n",
    "                         thresh_arr=[thresh_arr[lr_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2200\nBest balanced accuracy: 0.7539\nCorresponding 1-min(DI, 1/DI) value: 0.2482\nCorresponding average odds difference value: -0.0151\nCorresponding statistical parity difference value: -0.0872\nCorresponding equal opportunity difference value: -0.0035\nCorresponding Theil index value: 0.0966\n"
    }
   ],
   "source": [
    "describe_metrics(lr_transf_metrics, [thresh_arr[lr_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fairness metrics for the logistic regression model learned after reweighing are well improved, and thus the model is much more fair relative to the logistic regression model learned from the original data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Learning a Random Forest (RF) classifier on data transformed by reweighing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3.1. Training  RF model after reweighing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_transf_panel19_train\n",
    "model = make_pipeline(StandardScaler(),\n",
    "                      RandomForestClassifier(n_estimators=500, min_samples_leaf=25))\n",
    "fit_params = {'randomforestclassifier__sample_weight': dataset.instance_weights}\n",
    "rf_transf_panel19 = model.fit(dataset.features, dataset.labels.ravel(), **fit_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3.2. Validating  RF model after reweighing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "val_metrics = test(dataset=dataset_orig_panel19_val,\n",
    "                   model=rf_transf_panel19,\n",
    "                   thresh_arr=thresh_arr)\n",
    "rf_transf_best_ind = np.argmax(val_metrics['bal_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "disp_imp = np.array(val_metrics['disp_imp'])\n",
    "disp_imp_err = 1 - np.minimum(disp_imp, 1/disp_imp)\n",
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     disp_imp_err, '1 - min(DI, 1/DI)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     val_metrics['avg_odds_diff'], 'avg. odds diff.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2500\nBest balanced accuracy: 0.7703\nCorresponding 1-min(DI, 1/DI) value: 0.4516\nCorresponding average odds difference value: -0.0876\nCorresponding statistical parity difference value: -0.1668\nCorresponding equal opportunity difference value: -0.0758\nCorresponding Theil index value: 0.0906\n"
    }
   ],
   "source": [
    "describe_metrics(val_metrics, thresh_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3.3. Testing  RF model after reweighing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_transf_metrics = test(dataset=dataset_orig_panel19_test,\n",
    "                         model=rf_transf_panel19,\n",
    "                         thresh_arr=[thresh_arr[rf_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2500\nBest balanced accuracy: 0.7586\nCorresponding 1-min(DI, 1/DI) value: 0.4307\nCorresponding average odds difference value: -0.0843\nCorresponding statistical parity difference value: -0.1632\nCorresponding equal opportunity difference value: -0.0611\nCorresponding Theil index value: 0.0963\n"
    }
   ],
   "source": [
    "describe_metrics(rf_transf_metrics, [thresh_arr[rf_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, the model learned from the transformed data is fairer than that learned from the original data. However, the random forest model learned from the transformed data is still relatively unfair as compared to the logistic regression model learned from the transformed data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## [5.](#Table-of-Contents) Bias mitigation using in-processing technique - Prejudice Remover (PR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. Learning a Prejudice Remover (PR) model on original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1.1. Training a PR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = PrejudiceRemover(sensitive_attr=sens_attr, eta=25.0)\n",
    "pr_orig_scaler = StandardScaler()\n",
    "\n",
    "dataset = dataset_orig_panel19_train.copy()\n",
    "dataset.features = pr_orig_scaler.fit_transform(dataset.features)\n",
    "\n",
    "pr_orig_panel19 = model.fit(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1.2. Validating PR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_arr = np.linspace(0.01, 0.50, 50)\n",
    "\n",
    "dataset = dataset_orig_panel19_val.copy()\n",
    "dataset.features = pr_orig_scaler.transform(dataset.features)\n",
    "\n",
    "val_metrics = test(dataset=dataset,\n",
    "                   model=pr_orig_panel19,\n",
    "                   thresh_arr=thresh_arr)\n",
    "pr_orig_best_ind = np.argmax(val_metrics['bal_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "disp_imp = np.array(val_metrics['disp_imp'])\n",
    "disp_imp_err = 1 - np.minimum(disp_imp, 1/disp_imp)\n",
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     disp_imp_err, '1 - min(DI, 1/DI)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x504 with 2 Axes>",
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4kZ/58+Gpp+CKK369z9y5cOKJfqRxyhTo1q3q64x3ubnwwgs+4K5YAUcf7Xskjz8+nFG1tWt9a8O4cf5xVhZs2+ZvkeW8S9Stm7/IbeBA376iFoiql5vrR+THjvVtC/fcUy1HZstLQTc2CroKuiI113ffwSmn+CA0diycemrp+37zje/5y831F6gdemjV1RnP8vJ8T+T998Py5b7NYPhw/7OK13CSl7cz9BbdsrLgyy99P/HHH/uvwJs3978EDRzof9HRxW6VLzvbT/U1aZJvP/jjH8OuKO4o6MZGQVdBV6Rm+uQTP7dmSgq88w4cfvjuj/nxR39V9ebN/h/iXr0qv87SbN8OP//sL4Rp377izpuX56dJKiz0Xx9H3xd//NlnPuD+9JMf/Rw+3AfCeA24QW3aBJMn+9A7aZJ/nprqR6cHDoTTT4d27cKusvrZssX3xH/6KYwc6VsX5FcUdGOjoKugKwE99thjANxwww0hVyJ7bOxYf3HL3nv7IBNLX+by5T7srl7t+0H7VcIijIWFfp7Y5ct33las2PX5unV+36QkuOUW3wdbnr7WImb+6vLrr4/t4rHDD/cB95RTEj/gliQvzweviRN98F282LewPPQQ3HBD9fzMYcjM9H+G5s3zM2kMHhx2RXFLQTc2CroKuhLQ6af7tUfefvvtkCuRXRQWwp//DBMm+Cl/mjb1E70X3Uc/btoUpk3z+x99NLz1lt8Wq9Wr/RXgixfDG2+U3fIQi4ICP2H93Xf7C7mi1a/vZ31o3x46dNh5++gjf8HO/vv7WQ6OLXFByLL9/DNce60Pcj16wKWX+pHuWrX8LSmp5MetWvn3qylhz8y3u9x6q/9ZnX8+PPusv5BNym/Vqp3/P40b50d1pVQKurFR0FXQFUlcZn4E8skn/T+USUl+mqENG/xt48aSjzv3XD9qtCfzX2Zmwkknwddf+1kGLrig/BcumfmR5T//GRYs8G0AQ4fuGmzT00sPlFOm+K95f/oJrrkGHnjAzxKxOwUFflL822/3PanDh/vpnJKTy/c5aorCQvjb3+DOO/3Fa+PHazaO8lq61Pdzr13rf3mojG9IqhkF3RiZWY2+1a1b10QkARUWmt14oxmY3Xyzf15cfr5ZZqbZokVmM2eavfOOvxUUVEwNGzea9e7ta+jQwezuu82WLo3tHLNnm/Xr58+x775m48aV/Fl2JyvL/zycM2vf3uzdd8ve/6uvzHr29O87YIDZ4sWxv2dN9957Zk2amKWnm739duzHFxSYTZhgdtRRZgccYDZypNmOHRVfZzzKy/M/vzZtzBo3Nvvss7ArShjANouD/JQot9ALCPumoCtBPfTQQ/bQQw+FXYaY+SD4pz/5v8J+//vyBcOKkpNjNmaM2Ykn+pDpnFn//mavv262fXvpxy1ZYnb++f4zNG9u9sQTZrm5e17PzJlmXbv68w4ZYrZu3a6vb99udscdZsnJZs2amb3ySrg/v0S3dKnZoYf6n/ddd/lfrnYnP9//+eje3R/XqZPZ4Yf7x23bmv3jH/4XlzDl55utWmU2Z47Zf/9r9swzZg89ZPb++2Zbt5bvnHl5Zh98YHbFFf7PPJi1bm02f37F1l7NKejGdlPrgloXJKBzzjkHgDfeeCPkSoS77/aLOlx9tW9biJce0WXL/FRbL77oHzdu7OcDvewyOPhgv8/69X6Z4See8C0Cf/yjv5gsSKtBUDk5fj7bESN8DY8/7hfEmDbNzxP8ww++NeKRR/yyqbJntm/3Pc4vvugXF3n1Vd8TXlxenn9txAj/36BrV982MmiQb3uZMsX/2Zg2zf93+cMf/HnT02OrZ8sW+OIL37qTn+9vBQWlP9661fedr1rl71ev9hdDFhaWfP6kJD+93rHH+tsxx5T+5yg/3/eRjx3r++gzM31P88CB8Nvf+p9X3bqxfb4aTq0LsVHQVdAVSSz33w933eXD4zPPxOcKSIWFfhW155/3/7jn5vrVvo47zi+qsHWrv+DrL3+Btm0rr46vv/Y/p9mzfdCePx86dvRTN/XvX3nvWxOZ+T+P11/vpx4bP95f2Ad+xbZRo3xf77JlfnnjO++EM88s+c/v9Ok+8E6a5H8Buu46vzpY8+a/3rew0F8gN3Omn+5t5kw/53Ms/7Y7By1aQOvW/tamTcmP69f3f5Y++cTfZs3yv1QBHHCA//N97LH+Qs8ffvBLMk+Y4H+5Kwq3553nw+2ezBBSwynoxkZBV0FXgti6Fd57Dw46yF/dLuF48EF/wdZFF/ngEI8ht7gNG/wo3vPP+6B52mk+8FTV6moFBfDYY/49L73Uj4ZrBK3yzJoF55zjw90TT/i/Ox56yI+W9uzpf0kLOhXb3Ll+9PeNN3wwvOIKuPJKf9FhUaidNcvP6wx+9L5nTz+/81FH+XCalOS/OUhOLv1xnTrluwAxJ2fX4Dt9uh9NLlK//q4jtwq3FUJBNzYKugq6Upb16/3Xvv/6F3+LXMF/a5cufqGBM8+EI4+s/LD100/+a7/TTvOjJjXVP//pv8odPBhGj068pVnNfCBp1CjsSqSyrV3r2xGmTvXP+/XzI7j9+pWvzebbb/0vKq++6n9xAf/3TvfuPtQWhdv99gv3l7+CAv8twowZfiT4pJMUbiuBgm5sFHQVdKUkK1fCo4/6r3i3bYMzz2Twpk2wZQv/adzY99Dl5/t5RAcO9KH3+OP3bLqq4hYv9qM5L7/s36tOHT+iee218TeSaea/nq1du3Jqe/JJ//XtOefA66/7OV5F4ll+vp/X+MAD/Vf5FWHpUnj3Xf8L7xFHQIMGFXNeSSgKurFR0FXQlWg//ujD5Esv+dGJ88/3k8MX/5p540b/D85bb/k+uqws34N28sk+9J5ySvkWIgD4/nsfcEeP9oHuiitg2DD/lec77/j5Yl98sXJ7O4tkZvqe0p9/9l/Bbtni74tu0c8LCvx8ryNG+J9bRQXeZ5/1P4PTT/cj26mpFXNeEZEEpKAbGwVdBV0B+Oor/9XgmDE+XF56qb8SPsjSsDk58L//+dD71luQkeG/nuzRw1/wc8IJ/gKN3fVFfvutvwDl9df9yOhVV/kaWrf2r5v50PeHP/jXn37afz1aGdatg4cf9iOp27b5q74bNvQjSNG36G316vlewrlz/bKwDz8MffqUvwYzPyJ25ZX+F4gJE/znFhGpwRR0Y6Ogq6Bbs/34ow+O//2vv3Dimmv881atfrXrfffdB8Bdd91V+vkKC/3FGe+/76+6nzHDTymUmup76E44wYffI47YefHHwoV+JoExY3w/27XX+imnWrYsveaLLvIXo1xwgb/gpXHjPf1JeGvW+ID61FN+yqTBg31vYdeuwY4vLPR9hLff7keBzzgD/v536Nw5eA0rV/oR9Rdf9J+1f3+YOLFi20JERBKUgm5sFHQVdGuu7Gw/5VNGBtx0k+8BLSMwDhkyBIDRo0cHf49t2+DTT33onTIF5s3zI5UNGkDfvj7sTpjgQ/b11/uQXdIUQsXl5/tlXocP9yO+o0b5EF1eGRm+ZePf//Yj1BdcAHfcAV26lO9827f7i8ceeMD/nK+6Cu65p/TPlpvrw+wLL8DkyT4w9+njp8Y67zyN5IqIRCjoxkZBV0G35rr6ah/spkzZs5AYi/Xr/eTpU6b48JuZuXOOzPL09M6e7Ud3Fy3y5xgxIrarnFet8gF35EgfNocM8QG3oqZQW7vWzxX7zDO+teH22+GGG3aOzi5Y4KfdGj3a/yzatvX9yMOGwb77VkwNIiLViIJubBR0FXRrprfe8heN3Xyzn+MyLGZ7vqpXdrafW/aJJ3yLwU037Zx6yy+yuXPy+OjHX33le37z831YvuOOyguX337ra5w40V+wdskl/mK+2bN9T/SZZ/q+6AEDEm/aMBGRKqSgGxsFXQXdmmfVKr/wQ4cOvs814FX8d999NwD33ntvZVZXfu+95wPk6tXB9k9Ohosvhttug06dKre2Ih995H+5+PJL/9/gsst8m4SWoRURCURBNzblWApFJIEVFvpwl50Nr70W01RVK1asqMTCKsBJJ/m5d4uCrnM7byU9b9DAz6ZQlfr186O4q1f7CeX3dDRbRESkDBrR1YhuzfLII35EceRIPzeriIhIAtGIbmwUdBV0a465c/3676ed5ud71WiiiIgkGAXd2CjoKujWDEVTiW3Z4i/CKscMB7fddhsADzzwQEVXJyIiEoiCbmzUoys1w003+Sm4Pvig3Evzrl+/voKLEhERkcqkEV2N6FZ/b74JZ53ll9N98MGwqxERESk3jejGRkFXQbd6K5pKbK+9YObMmGZZEBERiTcKurGpFXYBIpWmsBCGDvXL0cY4lVhJbr75Zm6++eYKKk5EREQqm3p0pfp69FG/zO4zz0Dnznt8uu3bt1dAUSIiIlJV1Lqg1oXq6csvoWdPTSUmIiLViloXYqPWBale1q+HBx6AU06BFi3g2WcVckVERGooBV2pHhYuhCuvhHbt4PbboUcP+O9/yz2VWEluvPFGbrzxxgo7n4iIiFQu9ehK4ioshMmT4Z//9PPj1qnjLz77/e+hW7ewqxMREZGQqUdXPbrh++ILGDsWWrWCtm39qGzbttC6dckzJWRlwcsvw2OPwfffQ5s2cN11cPnl0KxZ1dcvIiJSRdSjGxuN6FaFSZOgXz8/4ii7WrwYBgyATZtKfr1lSx96iwKwc36qsE2b4Igj/ONzz4WUlKqtW0REROKeRnQre0T3xx+hSxfo29ev0FW/fuW9V6LJyoJevfyiDl98AenpsHKlv/38c8mPN2+Gs8+GG2/0sypU4YVm1157LQBPPvlklb2niIhItKAjus65a4BbgNbAQuBGM/ukjP37AI8C3YBVwINm9u89OWc80IhuZdt3X3jhBbjkEjjpJHj3XR/oajozuOwy+OYb32e7zz5+e5MmcOCBpR9XWAi1wrmGMi0tLZT3FRERiYVzbhDwGHAN8GnkfpJzrquZLS9h/32Ad4EXgCHAMcBTzrl1ZvZGec4ZLwKN6DrHUWbMqoJ6qlyV9ei+8Qacf74Pce+9p17SBx+EP//Z399yS9jViIiIJIQgI7rOuVnAV2Z2edS2H4BxZnZbCfv/HTjbzPaL2vYc0M3MepXnnPEi6NDYTOeY7xzXO0fjSq2oujrnHN+68M030KcPrF4ddkXhef99uO02GDQItKSuiIhIhXHOpQKHAe8Xe+l9oHcph/UqYf/3gMOdcynlPGdciKV1oTvwT+DvzjEBeM6MjyqnrKrTpEkTpk6dWjVvVrcujUaM4MDbbyf38MOZ98gj5LRqVTXvHSfqrFrFYVddRc7ee/PlxRdTOG1a2CUF9vDDDwNws8K5iIiEJ9k590XU82fM7Jmo582AJGBNsePWAP1LOWcrYEoJ+ydHzufKcc64EDToPgqcB7QD6gCDgcHOsQR4HhhlRkbllFi5NmzYQN++favuDfv2hV69SDvlFHr96U8wZQrsv3/VvX+Ytm3zF58lJ5PywQcc17Fj2BXF5L333gOo2j8vIiIiu8o3s8PDLiJRBGpdMONmMzoAxwFPA+vw6b4T8FdguXOMdY4elVZpddKzJ3z0EezYAccdB19/HXZFla/o4rOFC+E//4EEC7kADzzwAA888EDYZYiIiJQlEygAWhbb3hJKHZTMKGX//Mj5ynPOuBDT5etmfGrGtcARQPR3zsnA2cAs5zijAuurvnr0gI8/hqQkP8r7xRe7PSQmZvD553DXXTB+vJ+tIEwPPwxjxsCIEXDiieHWIiIiUk2ZWS4wBxhQ7KUBwIxSDptZyv5fmFleOc8ZF2KaR9c5BgBXAafhw23RJKZzgYb4Ed5vzOhewXVWmtBXRluyBPr3h8xMP/XYMcfs2fm+/RZef90vpLB48c7t3brBnXfCb3/rw3VV+uADOPlkf0HemDFVOvdtRbrkkksAePHFF0OuREREaqqAsy4MAl7BTwE2HZ/dLsPPorDMOfcygJkNjey/D7AAeBYYCRwNPAWcX2x6sVLPWdGfs6IEGtF1jluc4wdgMnAmkAIY8CbQx4zDgB7AFqCGNJxWkI4d/chumzZ+pPOZZ2DOnNJXCivJihXw0ENwyCHQtSv89a9+XtoXXvAB+i/UCjAAACAASURBVNVX/Yju+ef7wPvKK5CfX3mfKdrSpTB4sK/rhRcSNuQCtG/fnvbt24ddhoiISJnMbAxwI3AnMA8/L+6pUYG0Q+RWtP9S4FR8i+o84A7g90UhN+A541LQeXQL8cHW4cPsC8C/zPip2H7fAfuZUcVDhuUX+ohukbVr/YIS8+bt3NakCXTq5Bed6NRp11tqqp+b97XXfFAGOPJIuOACOO88aN161/MXFvr9778fvvrKn+O22+Cii/y5KsO2bdC7NyxfDrNn+88hIiIi5RZ0ZTTxYgm6S4DHgefNyCplvzZAihlxne6jxU3QBcjL860HixfvvP34o79ftqzkPtsuXeDCC/2oaZAgWVgIEyfCfff5keMOHeDWW+HSS6F27fLVbQYbN/qlfIuW6l21Cj78EKZN8y0ZJ59cvnOLiIjILxR0YxM06J4BvG1G8IbeBBFXQbcseXk+7BYF302b4De/gYMPLl87gBlMmuQD72ef+daJ886DlJTdH5uf7xe8KAq0K1f6GSSKa9oU7rkHrr8+9vri0JAhQwAYPXp0yJWIiEhNpaAbm6BBNx1IB7LNyIza3gyoC2w2Y3OlVVmJEiboVhYzP/J6//1+loYgatWCVq2gbVsfkNu2/fXj1q2hTp3Krb2K3XfffQDcddddIVciIiI1lYJubIIG3TfwF6H9wYx/RW2/DngMmGDGuZVWZSWq8UFXREREEoaCbmyCzqN7VOT+jWLbx+MvUDsKEREREZE4EjToNo/cF5/zanOx10WqrcGDBzN48OCwyxAREZGAkgPutxVoDJwITIjaXrTEVYmzMIhUJz16aIVrERGRRBK0R/d9oD9+BPcR4FvgAOAm/EVqU8w4KfCbOncNcAvQGlgI3Ghmn5Sy7yjg4hJe2qVHxTnXB3gU6AasAh40s3/vrhb16IqIiEiiUI9ubIIG3bOBcfCr6cVcZNu5ZruM9JZxLjcIGI1fQu7TyP0lQFczW17C/ulAWrHN04GPzeySyD5FS9e9gF+y7pjI/eDoVT1KoqArIiIiiUJBNzaBgi6AczyMH8Et7mEz/hT4DZ2bBXxlZpdHbfsBGGdmtwU4/mh8QD7azGZEtv0dONvM9ova7zn8+su9yjqfgq4Edc455wDwxhtl/u4kIiJSaRR0YxO0RxczbnaOMcDpQEtgDX4RidlBz+GcSwUOAx4u9tL7QO+Ap7kcWFgUciN6Rc4R7T3gYudcipnlBa1RpDS9epX5O5OIiIjEmcBBFyASagMH2xI0A5LwITnaGnwPcJkibQznAcVHflsBU0o4Z3LkPVcXO88VwBUAqampAUuXmu7mm28OuwQRERGJQeCg6xzJwKlAZ37dM4sZ91ZgXaUZgp8S7ZU9OYmZPQM8A751oQLqEhEREZHK4lxB5JFhFji/BtrROVoAU/EhtzRBgm4mUIBvfYjWEsgIcPzlwBtmtqHY9oxSzpkfeU+RPXb66acD8Pbbb4dciYiISI3jynNQ0EQ8HOhSxuuBRkXNLNc5NwcYAIyNemkAv151bRfOuSOBg4EbS3h5JnBWsW0DgC/UnysV5YQTTgi7BBERkZpqOQHzZrSg04stBvYGRuGnAjPgBuD6yOO/mTEq0Bv66cVewU8rNh24CrgMP0PCMufcywBmNrTYcc8Bx5nZ/iWcs2h6sWeBkcDR+OnFztf0YiIiIlJdaNaF2AQd0W0bub8VH3Qx4wnn+Aj4GmgX9A3NbIxzrilwJ37BiAXAqWa2LLJLh+LHOOcaAIMppT3CzJY6504F/gFcjV8w4ve7C7kiIiIiEjLnHsX33v4R5/xAp9nLFXLqgCO624A6QAqwHR+QW0UebwF+Nvt1QE0EGtGVoE455RQAJk2aFHIlIiJSU1XLEV3nCoFCzJJ3eVwBgp5kPX5UNx1/4Vc74FVgR+T1xhVRjEg8GzhwYNgliIiIVEeFgMO5hpHn5brwrCRBR3Q/AI4HjsL35l7Irg3Bn5rRp6KKqkoa0RUREZFEUU1HdFfhZ8vahB88NWBZKXsbZp2CnjroiO6zwI/49oXhwIlA88hr6yh5JgQRERERkd35CDifnR0CDj8JQklimnkh0Ijurw5yNAT64eepnW7GpphPEic0oitB9e/vF++bMqX4InwiIiJVo5qO6LYA/gUcCuyLD7PLS93fbJ+gp97tiK5z1Aa+iTz9jRnfmbEFeCvom4hUB4MGDQq7BBERkerHbC1+dq2iC9NiCrNlCdqjuwloAKSZkVsRbxwvNKIrIiIiiaKajuhGTy92ceRxlU4vNg6/8lhPM2ZXxBvHCwVdERERSRTVNOhW2vRitQLu909gA/C6cwxyjs7O0SH6VhHFiMSzvn370rdv37DLEBERqW4qbXqxoGn5Y3xjcBPgtRJetxjOJZKQhg0bFnYJIiIi1dFa/PRiS3/Z4tySUvaNaXqxoK0LhbvZxcxICvqm8UStCyIiIpIoqmnrwqv46cWCMMwCZ86go7AvBT2hSHWVl5cHQEpKSsiViIiIVCt/AJLw04sVjdaWPr1YDMo1j251ohFdCaqoP3fq1Kmh1iEiIjVXtRzRjeYvRotp1LYs6qsVCeh3v/td2CWIiIhUd/0q8mSBgq5zvLCbXcyMyyqgHpHQmRnrtuawJHMbS9ZtY2lmFkvWbSNjy1707dycVZu206ZRWthlioiIVA/O+dm7zJZTdEFa0baS+P2CnTqGi9FK29Ghi9EkQa3evJ3ZP21kyboslv4SbLeRlZP/yz61k2uxT7N61E0q4MtlG0hKTePEri0Z2mtvenZsgnMVNguKiIhImapl68Kv59EtK5xaLHPsatYFBd0aZ8m6LCYvzOC9hWuYv2ITAM5Bm/Q0OjavR8dm9ejYvD77NKtHx+b1aJOeRq1ajr59+5KTX8jpd4xkzOwVbMrOo3PLBgztvRdnHdKWuqnqBBIRkcpVjYOu78stWgK4dDH17wYNunsV25QMdATuAg4BTjNjWtA3jScKutWfmbFw1RbeW5jB5AUZ/LA2C4CD2qVzUrdW9Nm/Ofu2qE+dlLL/vxkzZgwAgwYNYkdeAW/PX8VLM35i4aotNKiTzHmHt+einnuxd7Pq9fePiIjEj2oadEfhA+wlvzwui9klgU+9J7MuOEd9IBN404zB5T5RiBR0q6eCQuOLnzbw3sI1vLcwg5WbtlPLwZH7NOGkbq04sVsr2lZAn62Z8eXyjbw0Yxnvfr2a/EKjb+fmDD6iPX07t9hteBYREYlFtQy6lWhPg24jYDWQY0ajCquqCinoVh8btuXy8ffrmLpoLR//kMmGbbmkJtXi2P2acVK3VpxwQAua1q9d7vNv3rwZgPT09BJfX7tlB69/voJXZy1j7dYc6qYmcXyXFpx6YGv6dm6u1gYREdljCrqxCdq6UNKsC3WAo4H2wFozWlVwbVVCQTdxFRYaX63czNRFa5m6aB3zf96EGTSpl0qf/ZtzfJcW9OvSgvq1KyZgBp1HN7+gkFlLN/Du16t5b2EGmVm51EmpRb/OLTjlwNYcv4c1mRk78grZsiOPzdvz2LI9jy078tiyPZ8tO/Lo3jadQzs0Lvf5RUQkflXLoOvc7mb3imaYBZ7pa09nXSi63HyUGZcGfdN4oqCbWDZuy+XjH9YxddE6Pv5+Heu35eIcHNyuEX07N6dv5xYc1DadWrUqfiaE8ePHA3D22WcHPqag0Jj9kw+9kxZksG5rDqnJteizf3NOPbAVPTs2JTu3gE3ZeWzensum7Dw2ZuexOTuXTdvz2JSdx6bt/vmWHfm/hNq8gtL/v3UOfnfMPtx8UmdqJ6t1QkSkOqmmQXd3My38sieVdDFaaVfA5QCvAzeasSXom8YTBd3E8b/v1nDVK1+SW1BI47op9NnfB9tj92u2Ry0JVaWw0JizfKMPvV9nkLFlR6n7OgfpaSmkp6XQKC2FhmkpNKqbSsM6yTRMS6FhnRQapiWT/svjFBrWSSYtNYknP/qR0Z8tp0urBvxjUA8OaN2wCj+liIhUpmocdIOqklkXwPflZsRQWFxS0E0MP67dyplPzmCvpnW5/8zuHNSuEUmVMGpblszMTACaNWu2x+cqLDTmrtjEN6u30LBOMo3qptIoLYVGdVNolJZKgzrJezQq/dF3a7ll3Fds2Z7HzSftz++O6Vgpo9wiIlK1qmnQ7RP1rAEwEtgEPAL8DLQD/gg0Ay7H7O3Ap96Ti9GqAwXd+Lc5O48zn5rO1h15vHXdMRUyW0J5BO3RjRfrs3K4bfzXvP/NGnp2bMLDvz2Ydo3rhl2WiIjsgWoZdKM59xRwJbAvZkujtncCfgCex+zywKcLOKJ7MnAkMNeMiVHbTwd6AJ+bMTnom8YTBd34VlBoXDJqNjMXZ/La5T05Yu8modUycaL/oz9w4MDQaoiVmTF2zs8Mf3shtZzj3jO7cWaPtlrNTUQkQdWAoJsJNAbaYpYRtb0NfnR3I2ZNg56uVsD97gbuwffkRssC/oJfOEKkwv198nd8/P067j2je6ghF3zATaSQC+Cc47zD2zP5xuPo3KoBfxgzn+tem8um7NywSxMRkTjgnKvtnHvcOZfpnNvmnHvbOdcuwHHXOOeWOud2OOfmOOeOLfb6VOecFbv9J0BJRRfdvIFzp+FcD5w7Dfi/yPaUmD5fwBHdDUA60MiMrVHb6wNbgE1mhJtCykkjuvFrwtyf+cOY+QzttRf3ntE97HLIyPC/WLZqlZAz6VFQaIz8eDH/+OB7mtRL5W/nHETf/ZtrdFdEJIFU9Iiuc+5p4AzgYmA98CjQCDjMzApKOWYQMBq4Bvg0cn8J0NXMlkf2mQosAW6POnS7mW3eTUHjgLMpfRaG8Zj9Nshng+BBdwc+QbczY3XU9tbASiDXjDpB3zSeKOjGp/krNvHbkTM5tEMjXrnsKFKSgn75UHkSrUe3NAtWbubGMfP4cW0WHZvX4+xD2nLmIW3VvysikgAqMug659KBdcAlZvZqZFt7YBlwipm9V8pxs4CvLKpX1jn3AzDOzG6LPJ8KLDCz62IsqjXwP6BzCa9+B5yA2eoSXiv5dAGD7lKgA/C0GddFbX8cuBb4yYyOQd80nijoxp+1W3Yw8IlPSa5Vi4nXH0OTeqlhlwTA5Mm+Df3kk08OuZI9tyOvgDfnrmT83JV8vnQDAEft04SzD23LKQe2pmGdmL4ZiomZsWVHPmkpSaQmh/8LjIhIIqngoHs88CHQwszWRW1fiA+t95RwTCqQDZxvZmOjtj8JdDezPpHnU4Gir2PXAJOA4Wa2ld1xrg4wFDgeaApkAh8BL2NW+tycJZ0qYNB9FrgMP4y8GFiET9qdIrs8b8YVsbxxvGjfvr298sorYZchEQYsWbeNHXkFdGpenzopCkKVLa+gkI3ZfnGKnPwCajlHgzrJNK6bSv06yVRUY0N2bsEvK7jl5Ptvw2o5R1KtyC3qca2o543qppCsqdFERADo169fLvB11KZnzOyZ8pzLOXcB8DKQYlGB0Dn3P+AHM7uyhGPa4L/N72NmH0dtvxu40Mw6R55fgR8ZXgV0Ax6InPPE8tRaXkHXIf0bMAiohw+3RQHX4S9I+1vFl1Y1NmzY8MtX0hIuM+NP475i7JxtPH3hEZx8YOuwS9rFihUrAGjfvn3IlVQOM2Peik1MmLuS5+avYmN2Dk3rGQMPbsPhezemU/P67NOsHnVSgs3TnZNfwIzF63l/4Ro++GYNmVk5JNdy9OrUnN6dmlFQWMim7TuXLt4cWfVtc9Q2M+jUPJVxV/WmcZyM7IuIhCzfzA4vawfn3P3AHbs5T7+KK+nXioXvr51zS4BZzrlDzezLynzvaIHn0XWOnsDzwAFRm78BfmfGZ5VQW5VQ60L8eHH6UoZP/IbfH78vN51YUmtOuKpLj24QufmFTF20lglzV/Lht2vJLfCL1jgHbRul0al5fTo2r/fL/b7N69O8QW225uTz0Xdref+bNUz9bi3bcguol5pE3y4tOLFrS/p2bkF6WrC2iMJC47Ml6xk2ajbd2jTktd/1JC1VSxqLSM0WpHXBOdcMv7hCWZYDPamk1oUSjqsF5OJHfcfsprYKE/OCEc7RCWgJrDFjcaVUVYUUdOPD9B8zGfrC55zQpQX/HnJYXK7iNWXKFAD69+8fciVVa3tuAUsys1iybhuL1+16vz1v5wW5DWonsyO/gLwCo1n9VAZ0bcmJ3VrRu1NTaieXP6BOXrCaa179kn6dWzDyosNIjoMLE0VEwlJJF6MNM7PXItva4UPw7i5Gm29mV0Rt+x54o+hitBKOORiYR7GWh8qmldEUdEO3bP02Tn9iOi0b1mb8NUdTv3bQjhoJU2GhkbFlR1TwzaJOahIndm1Jj/aNK3SJ5tGfLePONxdw3uHt+Ps5B2lKNBGpsSpperGBwDB2Ti/WmKjpxZxz3wFPmNkTkeeDgFfw04pNB67CX8vVzcyWOb+K2YXAu/gLybril/PdDhxR2rRllSFQonCOV4HBwF/MuC9q+134BSNeM+OiSqlQqjUz45ZxXwHw7NDD4zrkLlmyBICOHRNygpEKV6uWo02jNNo0SuOY/Xb3DdmeGdJzL9ZuzeFfH/5A8wa1ueWkLpX6fiIiNciNQD4wBkjDtzIMLRZGOxPVCmFmY5xzTYE7gdbAAuBUM1sW2SUXOAG4AagPrADewc+6UGUhF2KfXmxfM5ZGbd8bPxnwMjP2qawiK5NGdMM1cf4qrn99LiPOOpALjuoQdjllqkk9uvHIzLh9wgJe/3w5fxnYlWFHJ+RfOSIie6TaLwFcEv81XjOi+oiDCjp8VnT5e0ax7Wsi94m5VJSEantuAQ+8+y3d2jRk0BHxP5PB8OHDwy6hRnPOcd8Z3cjMymH4f7+hWYPanHZQm7DLEhGRiuTcKfgZIT7DbDzOXQQ8BdTFubnAqZitDXq6oFd1FE3O26vY9l7FXhcJ7Olpi1m1eQf3DOxWof2claVPnz706VPixaRSRZKTavH4+Ydw+F6NuWnMfGYszgy7JBERqVjXAH8E6uFcGvAkfnpbBxwC3BvLyYIG3a8jbzDKOYY4x2HOMQR4ET/H/9dlHi1SzM8bsxk5bTEDD27Dkfs0CbucQBYtWsSiRYvCLqPGq5OSxHNDj2DvZnW54uU5LFxV9rLpIiKSUA6K3H8CHInv8f0W+C8+i54Uy8mCBt1Rkfu2wEvA55H79sVeFwlkxLvf4hzcdkriXFR05ZVXcuWVv1okRkKQXjeFly49koZ1khn24mxWbMgOuyQREakYzSP3K/GzNQD8E7g48jimnrVAPbpmPO8cJwPnlPDyODNeiOVNpWabsTiTd7/O4KYB+9OmUVrY5QQ2YsSIsEuQKK3T03j5siM5998zGfrC54y9qhepybXYnO1XV9u83S9r/Mvj7bls2Z5HTn4hB7drRK9OTdmvRX1NVSYiEl9ygdr4WR4OwncOfMfONtncWE4W0zy6znEefq61lvgL0d42Y2zZR8U3zbpQtfILCjnt8U/Jyslnyk19Ai8nK1KaOcs2cuFzn7Ejr7DM/VKTapFeNwUHrN2aA0DTeqn07NiUnp2a0qtjEzo1V/AVkfhW7WddcO4roBvwE370NgU/ytsU+B5YilmnoKeLadJSM/4P+L9d66E+cI4ZL8VyLqmZXv98Od9lbOXpCw9NuJC7YMECALp37x5yJRLtsL0a89rlPfnou7Wkp6XQMC2FRmkppKelkF43hUZpqaSnpVAnpdYvIXbFhmxmLlnPZ4vXM3PJet75ejUAzerXpmfHJvTq1JSeHZvSrF5t8goLyS8w8goKyS808gsKyS3w2/ILC8krMJJrOZrWr03T+qk0qJ2ssCwiUn6vASPgl2lrp2C2EefOiDz/MpaTlWtlNOeoBZwMXIQf4a1jFltojhca0a06m7Jz6fvwVA5o1ZDXLj8q4cKA5tGtnsyM5RuymRkJvTMXr/9lxLc8UpNq0bR+Kk3rp9Ksfm2a1qtNs8jzpvVq07heCo3qptK4biqN66bQsE5KXC55LSLxqQaM6DrgZuBYYClwL2brce53wFHAG5hNDny6GFsXjsCH20HsXCHD4f+tSKzhuQgF3apz91sLGP3ZMt694Vi6tGoYdjkxmz17NgBHHHFEyJVIZTIzlmZu4/OlG9iWW0BqkiM5qRbJtRwpSbVITnIk16pFSmR7Si1HbkEhG7blsj4rl8xtOazPymV9Vg7rt+WSuTWHzG255OaX3FpRy0F6WgqN66bSqG7RfSoN6iRTr3YSdVOTqZuaRL3UZNJSk3bZVjc1meYNapOellLFPyURCUu1D7oVbLejsM6xDzAEv2bxfkWbo3bZDrxZ8aVJdfJdxhZGf7aMC4/aKyFDLijg1hTOOTo2r0/H5vUr7JxmRlZOPuuzctmYncum7Dw2ZueyMTuPTdm5uzxevXkH367ewtacfLJzCygoLHswIiXJ8ZsDWzO0994c0r5Rwn1TIiJSmUod0XWOq/ABN3qRiOJ/gxqQbkZW5ZRX+TSiW/nMjAuencU3q7cw9ea+NK6XGnZJ5TJv3jwAevToEXIlUlOYGbkFhWTnFJCdV0B2Tj7bcgvIzs3/ZduXyzbyxpyf2ZqTz4Ft0xnaay8GHtwm4XrgRSSYajmi61xBDHsbZoHbZcsKuoX4IFsUbnOBKcAbwGJgKgncslBEQbfyTfp6NVe/+iX3ntGNob32DrucclOPrsSrrJx8JsxdycszfuKHtVk0rpvCoCM6MKRnB9o1rht2eSJSgapp0C172pxdGWaBs2eQoAvwAnCLGZsir3XDr4amoCtl2pFXwAmPTKNBnWT+e/0xJCcFXaMk/mhEV+KdmTFzyXpenrGM97/JAKD/AS25uPfe9O7UVG0NItVANQ26P7Ezc4KfSqw+kAesjzxPAbKBtZh1DHzqgEEXYB0wAT+im4mf3kFBV8r0rw9/4NEPvue1y4+id6dmuz9ARCrEyk3beW3WMl7/fAUbtuWyb4v6PPzbg+nRvlHYpYnIHqiWQTeac4cB/wOeAe7CbAfO1QH+ClwJDMBsZuDTlRF0RwAXAB2iNhftvB2oi4KulGHVpu0c/8hU+nVuwdNDDgu7nD2mWRckEe3IK+Cdr1bzjynfk5mVw+PnH8qAri3DLktEyqkGBN1PgN5AI8y2Rm1vAGwGZmJ2dODT7W56Mec4Dn9R2m+B9KiXig5cDYw249agbxpPFHQrR0Ghce2rX/LRorVMuakP7Zskfp+genQlkWVm5XDZqNl8vXIzw0/vxkUJ3C8vUpPVgKCbjV8C+ETMPoza3h94H9iBWeBQEXgeXedIxS8OMQQ4Fd8rUSSmkV3n3DXALUBrYCFwo5l9Usb+qcCd+Dl82+CXH37YzP4VeX0Y8GIJh6aZ2Y4Stv9CQbfiZWzewQ3/mcuspRu45aTOXNtv37BLqhBaGU0SXXZuPte/NpcPv1vLVX068aeTOmuxCpEEUwOC7jKgHZADvAv8HHl+Kj4A/4zZXoFPV86V0RoDg/Fz6/YmhqDrnBsEjAauAT6N3F8CdDWz5aUcMx7/Ie8AfgBa4kPs1Mjrw4AngV3WPjazjN3Vo6BbsT78dg03j51PTn4h953RnXMOaxd2SSISJb+gkHveXsirs5ZzRo82PHjuQdROTugONJEapQYE3VvxSwAXD6gusu02zB4MfLryBN1d66EjcKEZ9wXb380CvjKzy6O2/QCMM7PbStj/RGAs0MnMMks55zDgCTOLeYZ3Bd2KkZNfwN8nLeKF6Uvp2rohj19wCJ0qcML9eDBjxgwAevfuHXIlInvGzHh62mIenLyInh2bMPKiw7W6mkiCqPZBF8C5vwB/AupEbd0B/B2z4TGdak+Dbkxv5lsQsoHzzWxs1PYnge5m1qeEY54C9gc+B4biL4SbBNxuZlmRfYYBz+OHt5OAecBdZjZ3dzUp6O65nzK3cf3rc/l65WaG9d6bW0/pUi0nq1ePrlQ3E+b+zJ/GfcU+zerx4iVH0rZRWtglichu1IigC+BcOn7Rsqb42b4+w2xzrKcJvLJEBWmGD6Jrim1fA/Qv5ZiOwDH4Xo1zgEbA4/he3XMj+ywCLgXmAw2AG4DpzrmDzeyHivwAsqu35q3k9vFfk5xUi2cuOowTu7UKu6RKM3LkyLBLEKlQZx3SjpYN6nDlK3M4+6npvDjsSLq2ScwlukWkmvGhdvKenqaqR3TbACuBPmb2cdT2u4ELzaxzCce8DxwLtLJIko+0M7wX2VY8NOOcKxrV/cjMfl/C61cAVwCkpqYelpOTUxEfr0bJzs3nnrcWMnbOzxyxd2MeG3wIbTQaJJKQvsvYwrAXZpOVk8/TQw7l2P2ah12SiJSiWo7o+hwYnNm9gU+dAK0LLwFHm9m+UdvaA8uBI81sdinv9SI+CJ9SVk1qXYjdt6u3cN1rX7IkcxvX9duXG07YL6FXPAtq2rRpAPTp86s/piIJb/Xm7Vzy4mx+XJvFHb85gIt67lUj/r8WSTTVNOgWX6SsbDEsAVylf4uZWS4wBxhQ7KUBwIxSDpsOtHHORV/ZtH/kfllJBzi/zuVB+Dl+pQLNWbaRM56cztYd+bx62VH88cTONeYfw3vuuYd77rkn7DJEKkXr9DT+76peHLNfM4ZP/IbTHv+UmYvXh12WiNQcLuAttpNW5Ygu/DK92Cv4acWmA1cBlwHdzGyZc+5lADMbGtm/PvAt8BnwF3yP7kjgWzP7bWSfeyKv/wA0BH6Pn3P3aDP7vKx6NKIbXGGhcfqTn7I+K5eJ1x9Ds/q1wy6pSi1ZsgSAjh0DL7EtknDMjEkLMvjrO9+yctN2Tj2wFbefegDtGif+oi8i1UE1HdG9OOpZCjAcH2qfY+c8ur/DX+d1J2bPBj11qRejRVZEC8yMj3e/F5jZGOdcU/wCEK2BBcCpZlY0Otuh2P5Zzq+G8TgwG9gIvAm7rMTWCL8mciv88nBzgeN2F3IlNuPm/MyClVt4zFyJ3wAAIABJREFUbHCPGhdyQQFXagbnHKce2Jrju7TgmY+X8NTUH/nw27VceVxHru67L2mp1W9GFREJmdlLvzx27n58njsUs/lR2yfguwL2i+XUpY7oOkcs/RJmVuUzOFQIjegGs3VHHv0enkaHJmm8cXVvfHdIzTJlyhQA+vcvbYIQkepn1abtPDDpOybOX0Xr9DrcduoBDDyodY38O0AkHlTLEd1ozq3CLwzWDLONUdub4KcZW4NZ68Cn203QDSqmJYDjiYJuMH+b9B3/nraYt649moPbNwq7nFBoHl2pyT5fuoHhExeycNUWjty7CXcP7Er3tulhlyVS49SAoJuNX+p3Ar5ltah1YThwFrCdGD5/WUH3xWKbTsQPJU+PetOj8en6HTMujeVzxAsF3d1btn4bAx79mNMObs2j5/UIu5zQrFixAoD27duHXIlIOAoKjf/7YgUPvbeIjdm5DD6iA386qTON66WGXZpIjVEDgu77+LUVSusqmILZSYFPF+RiNOe4EHgZGGTGuKjt5wGvA1eY8XzQN40nCrq7d+UrX/DJD5l8dHNfWjass/sDRKRa27w9j8em/MBLM38iPS2F207pwrmHtVM7g0gVqAFBtzPwMVDShN5rgT6YLQp8uoBB91v8lF7pZmRFba8PbAG+N6NL0DeNJwq6ZZuxOJMLnp3FzSfuz3XHx9T/Xe1MnuwXaDn55JNDrkQkPny7egt3vrmAOcs2cuTeTbj/rO7s37JB2GWJVGvVPugCONccuAnox84lgD8C/oHZuphOFTDobgdSgdvMeDBq+5+BB4AcMxJyWSwF3dIVFBq/+dcnbN2Rz4d/7EOdlIRsw64w6tEV+bXCQmPsnBU8MOk7snbk87tjO/L7E/albmpCXp8sEvdqRNCtQEGD7nyge+RpJn4hhtZAs8i2BWYcXCkVVjIF3dK9OmsZd0xYwJMXHMpvDgp8gWO1lZGRAUCrVq1CrkQk/qzPyuGBSd8xbs7PtG2UxvDTu9G/a8uwyxKpdmpM0HWuJ3Aq0ALfsvBfyjFtbNCgexr+6rckdm0OdkAhcIYZ78T65vFAQbdkm7fn0e/hqezbvD5jruyp3jsRCeTzpRu4882v+X5NFid2bck9p3ejbaOE/MJPJC7ViKDr3NPAFSW88u//b+/O4+wczz+Of66ZTPZ9kUQSIkSQUCRoEKKSWvqjihZtEG0pqqil6GKrtfVrKaqoCsKvaYkWRUhIogmxtESCiCX7Olkm20xmu35/3M8kx5jlnMmZec6Z832/Xud1znme+7nPdc4zk1xzn+u5b9x/nEpXSa3d6s5zwLHALEKia9H9G8DXszXJldrdPWU+67aUcu0J+yjJjTz77LM8++yzcYchktEO3q0rz/1kBFcduxfT569m1P9O4/5pn7K1vCLu0EQkG5iNBX5Ezcv/no/ZWSl1l+oSwGa0BboA69zZktLBGUgjul/22epNfP330znlwL7cfup+cYeTMVSjK5KaJeu2cP0zHzD5w5X07NiKHx4+gDMO2YX2rVS/K9JQzX5E12wm8FVgIfD76H4X4KdAf+B13A9LurtUEl0zWgAHAt3ceSH5qDOXEt0v+8G4t5j1+VpevWIkPTrk3lK/tSksLASge/fu9bQUkUSvzV/NfVM/Zeana+jYugVnDe/P2MP65+RS4iI7KgcS3Q1AO+AruM9J2D4EmA1sxD3p1WqSTnTN+DZwD+ECNHenhRlTgN2A8915Kek3kUGU6H7R9I9Xc9Zf3uTq4/bi/CN3jzscEWlG3l28nj9N/ZRJH6ygZX4epx3Uj3NHDKBf17ZxhyaSNXIg0S0BCoBuuK9P2N4ZWAtsxT3pwv9kL0YbQZi/rKpGwt3JN+My4A7gz+41Fg1nPCW625VXVHLcXa9RWlHJSz89glYtcns6seomTpwIwMknnxxzJCLZ7dPVm3hg2mdM/O8SKh1O2K8354/cnb16dYw7NJGMlwOJ7nxgAPAwcAXu6zHrRMg3fwB8gvueSXeXZKL7POFitI+Avdie6A4CPgTmurNvym8mAyjR3e6RmQu47pm53H/mUI4ZrCm0qlONrkh6rSgq4aF/f8YTsxaxubSCowb14LLRg9i3b9LfSorknBxIdH8PXML2Wb42Ae0TWtyJ++VJd5dkorsW6ERYHW0+2xPdAmArsN6drsm+aCZRohus31LKyDumsk/vjjz+w0M000INioqKAOjUSf8Ji6RT0ZYyHntjAQ/PWMCGkjKuPWEwYw7ZRf8OidQgBxLdbsA7hAvQqlsADMN9bbLdJTW9GKEoGGBRte2do3tNkpjl7n7lEzYUl2k6sTp06tRJSa5II+jUtoCLvjaQVy4fyeF7dOdX/5jDlU/OpqRMU5KJ5Bz3NcAhwEOEBcrKgWXAg8DwVJJcgGTneFkK7AoMr7b9iuh+SSovKpll3eZSnpi1iJMO6KMauTpMmDABgNNOOy3mSESap05tC3jo7IO4a8p87poyn49WbOC+7w3VxWoiucZ9JXBuOrpKdkR3EuEitH9UbTDjI0Ki69F+yVLj31hIcVkFPzpCsyzU5b777uO+++6LOwyRZi0vz/jp6D156OxhLFyzhRPu+TfTP14dd1gikqWSTXRvAtYQShWqinoHEpLftcCt6Q9NmkJJWQWPvL6AkYN6MKhXh7jDyWjPP/88zz//fNxhiOSEo/fuybMXHU6vjq05++E3uffVT0h1gSMRqZ+ZtTKzu82s0Mw2m9kzZta3nmOOiNotNTO3sJpZ9TZmZteb2TIzKzazqWY2uNHeSC2SXQJ4KXAY8BJQSUhwK6PnI6L9koUm/mcphZtKOe+IAXGHkvHatm1L27b6ClWkqfTv3o6JFx7KCfvtzG8nzeNHj73DxpKyuMMSaW7uBE4BzgBGAB2B58ysrjlG2wNzCLMjFNfS5mfA5cBPgIOAVcDLZtako2oNWQK4NdAVWOtOSaNE1YRyedaFykpn1O+m0a5VC5656DBdhFaP8ePHAzBmzJiYIxHJLe7OX2Ys4JbnP2TXbm25f8xQBvbUN1CSm9I564KF+WlXA+e4++PRtn6EZXePc/d6S1PNbBNwkbuPS9hmhAvI7nH3m6NtbQjJ7hXufn864k9GUiO6ZnQyYxczurtT4s4yd0rM6B5t16XoWejlD1fyWeFmzjtigJLcJPz5z3/mz3/+c9xhiOQcM+MHh+/GEz88hA3F5Xzz3hk8894ylTKI7LihhFXItq1u6+6LCWskHLoD/e4G9KrWbzEwfQf7TVmy8+g+BZwE/NSdPyRsvwi4C3janVMbLcpG1K9fP3/sscfiDiMWn67eTHlFpWpzk1ReXg5AixbJTlYiIulWXuEsXLuFLaXltGqRT7f2LenStoA8/bEuOeKoo44qBd5P2PSAuz/QkL7M7LvAo0CBJySEZvYKMN/df5REHzWN6B4KzAB2dfdFCdv/AvRx92MaEm9DJPs/9iHR/VPVtk8E/pCwP+usXbt224pXueSdhWv59Yuvc/0JQxh52G5xhyMikrTS8kr++e5Sxs9axHv/XU+bgkpOOmBnvnfIrgzpoy8Ypdkrd/dhdTUws5uAX9TTz1HpC6kJmG1fQCIhea5Psoluj+h+fbXtRdX2S5a4f9pndG5bwHcO6hd3KFlj3LhxAIwdOzbWOERyXcsWeXx7WD++Pawf7y8pYvwbC3n6v0v5vzcXc8AunRlzyK58Y7/etC6o61oakWbtTmB8PW0WAV8F8oHuhFrdKj2B13bg9Vck9JOYlPZM2JeqBYSZv5zk89ekpxfbGN1/vdr2quebkn1Bid9nqzfx8ocrOfOru9K2pb6GT9a4ceO2Jbsikhn27duJ20/dj1nXjOLa/9mHouIyLv/7ewy/dQq3PP8hC9fk5sXGktvcvdDdP6rntoWw1G4ZMLrq2Ghqsb2BmTsQwueEhDax39aEWR12pF+LbskfkGSN7kvAKMII7v8SipT3Bi4DOgGT3Wmyeot0ysVZF66Z+D5P/WcJM676Gj06tIo7HBGRtHF3Xv90DeNnLWTS3JVUVDojB/XgnMN2Y8Qe3cnLUy2vZLd0zroQ9XcfcAIwlrBmwu+ALsBQd6+I2nxEmEHhnuh5e2CPqIuZwG3AM8DaqppcM7sK+DlwDvAx8EvgCGCQu1cNoKYS6FSq1nJwT7rsItlE92TgSbYvFrFtV7TtVHeeTvZFM0muJbqrN27lsNtf4ZQD+3DryfvFHY6ISKNZuaGEJ2Yt4vFZiyjctJXde7Rj7KH9OfnAvrRrpW+zJDs1QqLbCrgD+C7QBpgCXBjNvlDVxoEb3P366PlI4NUaunvE3cdGbQy4DvgRIXGeBfzY3eekK/ZkJD2Prhl3EEZwq7vDnZ+lNaomlGuJ7u9emsfdr37C5MuOZPce7eMOJ6s8+OCDAJx7blqW3xaRJlJaXsnz7y/n4Rmf896SIjq0bsF3hvXj7OH92aWbFoGR7JLuRLe5S2nBCDMOAk4kFBOvBJ5x561Giq1J5FKiu6W0nENve4WD+nflwbPqvGBTajBq1CgAJk+eHHMkItJQ/1m0jnEzFvD8+8upcOfovXpyzmH9OXT3bppPXLJCs090zY6oY68Da3D/IOnucn3C7VxKdB+ZuYDrnpnLk+cPZ1j/rnGHIyISm5UbShj/xkKemLWINZtL2bNne04d2peD+ndl8M6daNki2Wu1RZpWDiS6lXy5VLa6pcAFuP+r3u5SKF3oABwP7Aq0rr7fnRuT6ijD5EqiW15RyVH/O5Ue7Vsx8cLD4g5HRCQjlJRV8Nzs5Yyb+Tlzlm4AoHVBHl/p25mD+ndlaP8uHLhLFzq1KYg5UpEgRxLdZJQBB+E+u87ukrwY7SDgeaDWYUB3snLCwlxJdJ+bvYyLnvgvfxozlGOH9Io7nKz0xz/+EYALL7ww5khEpDGs2lDC2wvX8faCdby9cC1zl22gotIxg0E9OzCsfxeG7dqVg3brSp/ObeIOV3JUDiS6DxOmJduZMKPDIqAfcBiwDPgvYSawlsBjRBe/1dpdkonuDGB4HU1ciW7mcne+ee8MNpaUM/myI8nX9DoNctxxxwHwwgsvxByJiDSFLaXlvLtofUh+F67jPwvXsWlrWAp8n94dOXZIL44Z3Is9e7ZXfa80mRxIdMcAjwDfxX1CwvbTgceBs4FCwgDsQtzrXN412UR3I9AWmEZYBngz1eon3HkklfeRKXIh0X390zWc8eAb3PytIXzvkF3jDkdEJCtVVDrzVmxkxieFTJq7gncWrcMdduvejq8P7smxg3vxlb6dNVevNKocSHQ/AgYCnXDflLC9PbAB+Aj3fTBbB7TGvc6vV5JNdBcThpC7uX9pGeCslguJ7jkPv8nsJUXMuPprWhJTRCRNVm0s4eUPVvLinBW8/ukayiudXh1bc8zgnhwzuBcH79aVFvm6qE3SKwcS3WJCWcK1wC1UJapmVwC/Abbi3gazRUAX3DvU2V2Sie7NwNXAke78e8feQWZp7onuxys38vXfT+ey0Xty8dED4w4nq911110AXHLJJTFHIiKZpmhLGa/MC0nvtI9XU1JWSee2BQzq2YHenVrTs1NrendsTa9ObejVqTW9O7Wme/tWKiWTlOVAovsusG/0bC1hhoXeQPdo2/vAUGALsAD3QXV1l+zSMAsIy//+04yHgHmEq922cefRJPuSJvTA9M9oU5DPmV9VycKOmjJlCqBEV0S+rFPbAr51QF++dUBfiksrmPbxaqZ8uJKFa7bwzqJ1rCzaSmnFFy8mz88zdurQip4dW9O3Sxv22Kk9u/dozx47tWe37u30DZzkqp8D/wTyCZMgVE2EYEA5cA3wNaAAmFFfZ8mO6NY3p5m7J500Z5TmPKJbtKWMYTe/zBkH78KN3xwSdzgiIjnL3Vm7uZTlRSWsKCphxYZwv7yohBUbilm0dgtL1hVT9V+yGfTr0jZKftttS4IH7tSBTm011Vkua/YjugBhieGbgUOAPKASeAP4Be7TMGsBtCKUMZTX1VUqyam+X8kykz5YQVmFc+rQvnGHIiKS08yMbu1b0a19K4b06VRjm5KyCj5bvZlPV2/ik1Wbtt3/+5NCSsvDaHCewdF79+Ts4f05bA+t5ibNlPtU4DDM2gJdgLW4FyfsLyeM7tYr2UT3nBRDlAzwr9nL6de1DfvW8o+qpOaOO+4A4Iorrog5EhFpjloX5LPPzh3ZZ+eOX9heUeksXVfMp6s38eaCtUx4azEvf7CS3Xu04+xD+3PygX1p3yorv1QV+TKzqcBDwJO4byHU4ja8Oy0B3DxLF9ZtLuWgmyfzwxEDuPq4veIOp1k45ZRTAHjqqadijkREcllJWQX/mr2cR15fwOwlRbRv1YJTDuzDWYf2Z/ce7eMOTxpZsy9d2L4E8EZgAvAX3Gc1uDslus0z0Z3w1iKueup9nr3ocPbtqxFdEZHm6L+L1vHo6wt5bvYyyiqcEQO7c/bw/hy1106a0aGZyoFEt4QwvRhsvz7sQ+AvhJXQVqfUXbKJrhljgMuAQUDrart1MVqGOfOhWSxcs4VpV45UDZeISDO3euNWJry1iPFvLGLFhhL6dmnDyQf25bghvdirVwf9P9CM5ECi2wk4GfguMBK2rbzrhLrcf+F+ctLdJTnrwneAv0YvUtNvi5YAziDrNpcy7ObJnHfEAK46VmUL6XLbbbcBcPXVV8cciYhIzcoqKnn5g5U89vpC3vh8De6wa7e2HDu4F8cM6cX+Wrkt6zX7RDeRWU/gNOAMwgwMAI570jlnsqOwP47uiwlLATthEt9uwProJhli0twVVFQ639i3d9yhNCvvvvtu3CGIiNSpID+P4/ftzfH79mb1xq1M/jAsYvGXGZ9z//TP6NmxFccM7sWxWrlNssMmQr65DqiA1AdVkx3RXQd0BA4DZhKN4JrxK+Ai4GvuzE31xTNBcxzRPfOhWSxeu4VXr1DZgoiIQFFxGa9+tIoX56xg6serKCmrpEvbAkbt3ZNDBnSjfasWtGuVT9uW+bRt2eIL920K8jUKnEGa/YiuWQFwPKF04RtAm6o90f003I9KurskE91SQhbdhjCqC6FQuBUh237VnaOTfdFM0twS3TWbtnLwLVM4/8gBXHmMyhZEROSLqlZumzR3BZM/XMnGkvqnI21TkE/ntgUcM7gXpx/cj716daz3GGkcOZDorgWqrqKvSm6XAo8AD+P+aSrdJVu6sIEwYa8RpnvoABxHWBYYttdNSMwmzV0ZlS3sHHcozc6vf/1rAH71q1/FHImISMO1aZnPsUN6ceyQXpSWV7J0fTFbSsspLq1gc2kFxaXlbN5awZayCrZsLd+2bcm6Yp6YtYhxMxewf7/OnHFwP/5nv51ppzl8Jb06R/elhKWA/wK8RAOnCUv2p3MZIdHdiTDFw8HRi1dZ25AXl/T71/vL2K17O/bu3SHuUJqdefPmxR2CiEhatWyRx27dkx8cXLu5lIn/WcJf31rMVU+9z6+f+5ATvrIzZxzcj337dFK5nKTDu8DDwOO473B+mWzpwiPAmcB3gB7AvdWa3OxOVg5zNafShcJNWzn45slcOHIPrjhmUNzhiIhIM+XuvLNwHf/35mL+9f4ySsoq2ad3R844uB/fPKAPHVsXxB1is9XsSxfSLNlEtx3QHtjozhYzriZM91AOPA3c7k5Fo0baSJpTovv4rIX84uk5vHDJCPburfopERFpfEXFZTzz7lL+783FfLB8A60L8jiof1cG7tSBPXu2Z2DPDgzs2V7Jb5rkRKJr1oJwQdogtl+Mtp37jUl3pZXRmk+i+90H32DFhhKmXHakvj5qBNdeey0AN96Y9O+XiEjOcHfeX1rE399ewntL1jN/5SaKy7aPgfXu1JqBPTuw507t2TNKfvfq1ZE2LbNyGv7YNPtE12wnYCohya1ZOubRNWOXVOJyZ1Eq7SW9Vm/cyhufreGio/ZQkttIFi9eHHcIIiIZy8zYr29n9usbriWqrHSWrCvm45Ub+XjVRuav3MTHKzfy2Gdr2FpeCUCXtgX88XtDGb57tzhDl8xyA1DXtFEpjdDWOqJrRmUKnaW0BLCZXQhcCfQG5gKXuvtrdbRvCfySUCe8M7ASuMPd/5DQ5hTg18DuwKfAL9z96fpiaS4juo+9sZBf/WMOky49gkG9dCGaiIhkpopKZ/HaLcxbuZHfTprHgsLN3HTSEE4/OKXxtZyVAyO6nwL9gXHAOYRc9BLgJ9Hj23Afl2x39S2JYinckozfTgPuAm4BDiAsQPGCmdX1E/5X4FjgPMJQ9reB2Ql9DgcmAI8D+0f3fzeznJn27F+zl7HHTu3Zs2f7uEMRERGpVX6e0b97O44Z3IuJFx7KoXt05+qJ73Pjsx9QUZnb5ZQCQJ/o/uptW9zvAU4G9gT6ptJZXSO6D6fSkTvnJPWCZrOA2e5+bsK2+cCT7n5NDe2/Dvwd2N3dC2vpcwLQ1d1HJ2ybDKx29zPqiqc5jOiu2ljCIbdM4SdfG8hlo/eMO5xm65prwo/nrbfeGnMkIiLNR3lFJTc//yEPz1jAyEE9uPuMA+igC9dqlQMjupuB1kABYZGyFkCv6PEGYAnuSQ//11pukGzimoqoBGEocEe1XS8Bh9Zy2EnAW8BlZnYW4Y2+APzc3TdFbYYDd1c7bhJheeJm78U5K3CH/9mvd9yhNGtr1qyJOwQRkWanRX4e150wmD12as91/5zLyX+cyUNnH8Qu3drGHZrEYw1hVLcTsIIwgvs4UBLt75JKZ029nEl3wlLCK6ttXwmMquWYAcDhwFbgFMKKGXcTanVPjdr0qqXPXjV1aGbnEcogaNmyZUpvIBP9a/ZyBkZXsUrjeeCBB+IOQUSk2freIbuyW7d2XPD4f/jmvf/mT2OGcsgAXaSWg+YREt3dgenA94Cjo30O/CeVzuqr0d3GjEFm/M6Mf5nxSrXblFReNEV5hDf2XXef5e5VI7WnmFnPhnTo7g+4+zB3H9aiRXYvXbhqQwlvLljLNzSaKyIiWe7QPbrzjx8fRpd2LRnz0Cz+9pZmu8lBDwIPEMoXbgBWs/16sELg0lQ6SyrLM2MoYU6zmr5HMJKfnaEQqACqJ6g9CcPTNVkOLHX3ooRtH0b3uxBGblek2Gez8UJUtvCNfZXoNrYrrrgCgDvuqF55IyIi6bJb93Y8feFhXPTEf/jZU7P5ZPUmrjp2L/LzNHVmTnD/G/C3bc/NBgJHERYpm4H7+lS6S3Y48+fADhc+u3upmb0DjCZcYFZlNPBULYfNAL5tZu0TanKrrrhaGN2/HvXx22p9ztzRmDPdv2YvZ1DPDgxU2UKjKy4ujjsEEZGc0KlNAQ+PPYhfP/cBD0z/jHkrNnLknj3IM8jLM8wsPI7uzYx8M/Ly4Ct9OzOgh2YgajbcNwD/bOjhyS4BvBzYCbgQuI8wgvsV4CbCpL6nufNeUi8Yphd7LOprBnA+8ANgsLsvNLNHAdz9rKh9e8II7hvA9YQa3fuBD93921GbQwl1HL8E/gF8C7gRONzdZ9UVTzbPurCiqITht03hp6P25OKjB8YdjoiISNo99sZCfv3sB5RWVCbVPs/g1KF9uWTUnvTp/OXVY7Nds591Ic2STXRLCReRdSJM7eDu5JuxE6E84FF3xib9omHBiJ8RFoyYA/zU3adH+6YSXmBkQvtBhAvQDgfWEZLZq919Y0KbUwmJ9wC2Lxgxsb5YsjnRfXjG59zw7AdMvuxI9thJf72KiEjzVFJWQUlZBZUOle5UuuPbHodV2Nxha3kF//fmYsa/Eb7wHfPVXfnxUbvTrX2rmN9B+ijRTU2yiW4R0B5oBRQRCoT3ATYBi4H17nRtxDgbTTYnuqfeN5NNW8t58dIj4g4lJ1x6aah/v/POO2OORERE6rJ0fTF3Tf6YJ99ZQpuCfH4wYgDnjtitWczPq0Q3NcnOurAquu8KLIgev0qojQVI7vsESZsVRSW8vXCdLkITERGppk/nNvzm1K/w0k+P5MhBPfjDlPkc8ZtX+fNrn1FSVhF3eNKEkh3RnQh8EziGcJHXlXxxpoUJ7ny3USJsZNk6ovuXf3/Ojc99wJTLj2R3Fd2LiIjU6v0lRfxm0ke8Nr+Q3p1ac8nRAzl1aF9a5Cc9y2rG0IhuapJNdL9CuOjsHcJ0Xn8Hvk5IdqcA33NndSPG2WiyNdE95b6ZbCmt4IVLRsQdioiISFZ4/dM1/GbSR/x30Xo6ty2gQ+sWFOTlUZCfR4t8oyA/j4J8o0VeHgUt8ijIM1rkG5UOFZVOeaVTUVlJeYVve16e8HxY/67cevK+jfoelOimJqnpxaIZFRJnVTjWjM5AuTubajlMGsmy9cW8s3AdVx4zKO5QcsqPf/xjAO69996YIxERkYYYvns3Jl5wKJM/XMXkD1ZSWlFJWUVIVMsqKimrdMrKKymvrKS4uGLbvrw8o0WekZ9w36ogj7Z5edueF+Qbfbs0v1kest2OLAvWEsi+odBmYNrHYfD8mME1rnAsjaRNG/0DJiKS7cyM0fv0ZPQ+DVpcVbJMnYmuGQcCpxNmWfiHO6+Y8UPgVsKFaVvN+KM7VzR+qFLl/aVFdGjdgt176JuLpqQV0URERLJLrYmuGYcT6m+r2vzYjN8S5r91wtK/rYGfmvGJO39q7GAlmLu0iCE7d8JMyyGKiIiI1Kauyw2vBAoICW3V7cponwGFCY/PbKwA5YvKKir5cMVGhvTpGHcoOee8887jvPPOizsMERGRtDGzVmZ2t5kVmtlmM3vGzPrWc8wRUbulZuZmNraGNuOifYm3NxrtjdSirkR3GGHkdhJhud4XCEmtA2e4sxPwvajtPo0ZpGw3f+UmSssrGdKnU9yh5Jxu3brRrVu3uMMQERFJpzuBU4AzgBFAR+A5M8uv45j2hJVtLwGK62g3mbAKbtXt+HQEnIpapxczYyuhbKGLOxvM6ERYfteB1u6UmdESKCGswrcjF7a/5F9/AAAgAElEQVTFJtumF/vb24v52ZOzNX+uiIhIDkrn9GJm1glYDZzj7o9H2/oBC4Hj3H1SEn1sAi5y93HVto8Durv7/6Qj1oaqa0S3AMCdDdF9UdUOd8qi+9Jok4pFm8jcpUW0a5nPbt10IZqIiIjskKGEfO+lqg3uvhj4EDg0Df0fbmarzOxjM3vQzHZKQ58pqXcU1oxrk9mWrbp27crUqVPjDiNpu5Ru5vJ9Yfr0aXGHknNuv/12AK666qqYIxERkRzWwszeTnj+gLs/0MC+egEVbL/uqsrKaN+OeBGYCHwO9AduAl4xs6HuvnUH+05aMuUG1yU89hq2ZbW1a9cycuTIuMNISkWlc8F1kzjtoH58f+TguMPJOa+88gpA1vy8iIhIs1Tu7sPqamBmNwG/qKefo9IX0pe5+18Tnr5vZu8QSiK+QUiAm0R9ia5KEjLI54WbKC6r0IVoMbnxxhvjDkFERCQZdwLj62mzCPgqkA90J9TqVukJvJbOgNx9mZktAQams9/61JXo3tBkUUhS5izdAKCpxURERKRW7l7Il8sRviQaZS0DRgNPRNv6AnsDM9MZk5l1B/oAy9PZb31qTXTdlehmmjlLi2jVIo89NNtCLMaMGQPA+PH1/ZEsIiKS+dy9yMweAn5jZquANcDvgNmEqcEAMLOPgHvc/Z7oeXtgj2h3HrCLme0PrHX3RdH+64GnCIltf8KququAp5vgrW2TlVOC5ar3lxaxV++OtMiva7IMaSyDBg2KOwQREZF0uxQoByYAbQir4p7l7hUJbQYRyhuqDANeTXh+Q3R7BBhLuMBtX+AsoDMh2X0V+I67b2yUd1GLWufRzRXZMo9uZaXzlRte4psH7MxNJ+0bdzgiIiISg3TOo5sLNDSYJRat3cLGreUM2VkXoomIiIgkQ4lulpizLKzXoRkX4nP66adz+umnxx2GiIiIJEk1ulliztINFOQbA3vqQrS47L///nGHICIiIilQopsl5i4rYs+eHWjVIj/uUHLW1VdfHXcIIiIikgKVLmQBd2fO0iLV54qIiIikQIluFlhWVMK6LWVaKCJmp5xyCqecckrcYYiIiEiSVLqQBd5fEi5EG6wL0WI1fPjwuEMQERGRFCjRzQJzlxWRn2fs01sjunG64oor4g5BREREUqDShSwwZ2kRe/RoT+sCXYgmIiIikiwlullgzrINDFZ9buxOPPFETjzxxLjDEBERkSSpdCHDrdpQwuqNWzXjQgY4+uij4w5BREREUqBEN8NpRbTMcckll8QdgoiIiKRApQsZbs7SDQDss7NKF0RERERSoUQ3w81ZWsSA7u1o30qD73E77rjjOO644+IOQ0RERJKk7CnDzV22gQN37RJ3GAKccMIJcYcgIiIiKVCim8HWbi5l6fpizhq+a9yhCHDhhRfGHYKIiIikQKULGWzO0nAh2r66EE1EREQkZUp0M1jVjAuDNbVYRhg1ahSjRo2KOwwRERFJkkoXMtjcpRvo17UNndoWxB2KAKeddlrcIYiIiEgKlOhmsDnLirRQRAY599xz4w5BREREUqDShQxVVFzGwjVbtFCEiIiISAMp0c1QHywLC0UM1kIRGWPkyJGMHDky7jBEREQkSSpdyFBzdSFaxhk7dmzcIYiIiEgKlOhmqPeXFtGrY2t6dGgVdygSUaIrIiKSXVS6kKHmLC1iSB+VLWSSsrIyysrK4g5DREREkqRENwNt3lrOZ4WbdSFahhk9ejSjR4+OOwwRERFJkkoXMtCHyzfgjqYWyzA//OEP4w5BREREUqBENwNVLf2rEd3MMmbMmLhDEBERkRSodCEDzVm2ge7tW9Kzoy5EyyRbtmxhy5YtcYchIiIiSdKIbgaas7SIwTt3wsziDkUSHH/88QBMnTo13kBEREQkKUp0M0xJWQXzV23i6L13ijsUqeaCCy6IOwQRERFJgRLdDPPRio1UVLouRMtAp512WtwhiIiISApUo5thdCFa5ioqKqKoqCjuMERERCRJGtHNMHOXFdGpTQF9u7SJOxSp5pvf/CagGl0REZFsEcuIrpldaGafm1mJmb1jZiPqaDvSzLyG214JbcbW0qZ107yj9JmzdAND+nTUhWgZ6OKLL+biiy+OOwwRERFJUpOP6JrZacBdwIXAv6P7F8xsH3dfVMehg4G1Cc9XV9u/Bdg9cYO7l+x4xE2ntLySeSs2cs5h/eMORWpw8sknxx2CiIiIpCCO0oXLgHHu/mD0/CdmdixwAXBNHcetcvfCOva7u69IV5BxmL9qI6UVlQxWfW5GKiwMP37du3ePORIRERFJRpOWLphZS2Ao8FK1XS8Bh9Zz+NtmttzMppjZUTXsb2NmC81siZk9Z2YHpCPmpjR36QYAhuzcMeZIpCannnoqp556atxhiIiISJKaekS3O5APrKy2fSUwqpZjlhNGe98CWgJnAlPM7Eh3fy1qMw/4PvAe0AG4BJhhZl9x9/nVOzSz84DzAFq2bLlDbyid5iwrol3LfPp3axd3KFKDyy+/PO4QREREJAXm7k33YmY7A0uBI919esL2a4HvufugJPt5Hih39xNr2Z8PvAu86u51Xj3Url0737x5c7JvoVEdf9drdGzTgr+eNzzuUERERCQDmdkWd9eIWJKaetaFQqAC6Flte08glfraWcDA2na6ewXwdl1tMs3qjVv5YPkGDt9D9Z+ZasWKFaxYkdVl4CIiIjmlSRNddy8F3gFGV9s1GpiZQlf7E0oaamRhbq796mqTaWZ+Gi50GjGwR8yRSG1OP/10Tj/99LjDEBERkSTFMevC74DHzOxNYAZwPrAz8CcAM3sUwN3Pip5fCiwA5hJqdMcAJwGnVHVoZtcBbwDzgY7AxYRE94KmeEPpMP3jQjq3LdCKaBns6quvjjsEERERSUGTJ7ruPsHMugG/BHoDc4Dj3X1h1GSXaoe0BH4L9AWKCQnvN9z9+YQ2nYEHgF5AEfBf4Ah3f7PR3kgauTuvzV/NYXt0Jz9PC0VkqmOPPTbuEERERNLKzFoBdwBnAG2AKcCF7r6kjmOuAU4GBgFbCYON17j7nIQ2BlxHuPi/C6Hs9MfuPreR3krNsTblxWiZKBMuRvt45Ua+/vvp3Hbyvpx+cPU8XzLF4sWLAejXr1/MkYiISK5K98VoZnYf8E3gbGAN4Zv3zsDQ6Jqnmo6ZBPyVMCOWATcCw4F93H1t1OYqwqDmWMLsWNcChwOD3H1juuKvTxylC1LN9I/DIm+HD9SFaJnszDPPBGDq1KnxBiIiIpIGZtYJ+AFwjru/HG07E1hImPZ1Uk3Hufsx1fo5k/CN+mHAs9Fo7qXAbe7+VNTmbGAV8F3g/kZ5QzVQopsBXptfyIAe7ejbpW3coUgdfvnLX8YdgoiISDoNBQpIWMjL3Reb2YeEhbxqTHRr0IEwwcG66PluhHLSxH6LzWx61K8S3abStWvXWEfo3OHg1hs4do+WGinMcC1ahF8XnScREYlRCzN7O+H5A+7+QAP76kWY9rWw2vaV0b5k3UVYv+D1hH6r+qneb58UY9whOZ/orl27lpEjR8b2+jM/KeS3k2bx0NkHMHLv6tMLSyb57LPPABgwYEDMkYiISA4rd/dhdTUws5uAX9TTz1HpCMbMfkeovT28tpreOOV8ohu36fMLaZFnHDKgW9yhSD2+//3vAxrRFRGRjHcnML6eNouArwL5QHdgdcK+nsBr9b2Imf0eOB04yt0/S9hVtbpSz+h1Evtt0pWXlOjG7LX5qzlw1y60b6VTkeluuOGGuEMQERGpl7sX8uVyhC8xs3eAMsLCXU9E2/oCe1PPQl5mdhdwGiHJ/aja7s8JCe1owswMmFlrYARwZSrvZUcpu4pR4aatzF22gSu+vmfcoUgSjjzyyLhDEBERSRt3LzKzh4DfmNkqtk8vNhuYXNXOzD4C7nH3e6Ln9wJnEhbwWmdmVTW5m9x9k7u7md0J/Dw69mPCVGObiBLqpqJEN0YzPtGyv9lk3rx5AAwaNCjmSERERNLmUqAcmMD2BSPOqlZvO4hQ3lDlwuh+SrW+bgCujx7/JurvXrYvGPH1ppxDF7RgRKwLRlzx9/d4+YOV/OdXo7UiWhaoumhRNboiIhKXdC8Y0dxpRDcmVcv+Hq5lf7PGLbfcEncIIiIikgIlujGZv2oTKzdsZYRWQ8sahx56aNwhiIiISAry4g4gV702P9Tnatnf7DFnzhzmzJkTdxgiIiKSJI3oxuS1+au17G+WueiiiwDV6IqIiGQLJbox2FpewRufreG0Yf3iDkVS8Nvf/jbuEERERCQFSnRj8M6CdZSUVWpasSxz0EEHxR2CiIiIpEA1ujF47ZOw7O9Xd9eyv9nk3Xff5d133407DBEREUmSRnRjoGV/s9Oll14KqEZXREQkWyjTamJrNm1lztINXD5ay/5mmzvvvDPuEERERCQFSnSb2IxP1wAwYk/V52ab/fffP+4QREREJAWq0W1ir328mk5tCti3T6e4Q5EUvfXWW7z11ltxhyEiIiJJ0ohuEwrL/hZq2d8sdeWVVwKq0RUREckWSnSb0CerNrFiQ4mW/c1S99xzT9whiIiISAqU6DYhLfub3YYMGRJ3CCIiIpIC1eg2odfmr2ZAdy37m61mzpzJzJkz4w5DREREkqQR3SYSlv1dy3eG9Y07FGmgn//854BqdEVERLKFEt0m8s7CdRSXVWjZ3yx2//33xx2CiIiIpECJbhP593wt+5vtBg0aFHcIIiIikgLV6DaR1+YXcuAuWvY3m02bNo1p06bFHYaIiIgkSVlXE1izaStzlhVx2Sgt+5vNrrvuOkA1uiIiItlCiW4TmPHpGty17G+2+8tf/hJ3CCIiIpICJbpN4N/ztexvczBgwIC4QxAREZEUqEa3kVUt+3vYHt207G+Wmzx5MpMnT447DBEREUmSRnQb2coNW9lQXKZpxZqBm266CYBRo0bFHImIiIgkw9w97hhi1a5dO9+8eXOjvkZpeSWV7rQuyG/U15HGtXjxYgD69esXcyQiIpKrzGyLu7eLO45soUS3CRJdERERkXRQopsa1eiKJOnFF1/kxRdfjDsMERERSZJGdDWiK0kaOXIkoHl0RUQkPhrRTY0SXSW6kqQVK1YA0KtXr5gjERGRXKVENzWadUEkSUpwRUREsotqdEWS9Oyzz/Lss8/GHYaIiIgkSaULKl2QJKlGV0RE4qbShdQo0VWiK0kqLCwEoHv37jFHIiIiuUqJbmpUoyuSJCW4IiIi2UU1uiJJmjhxIhMnTow7DBEREUmSShdUuiBJUo2uiIjETaULqVGiq0RXklRUVARAp06dYo5ERERylRLd1KhGVyRJSnBFRESyi2p0RZI0YcIEJkyYEHcYIiIikiSVLqh0QZKkGl0REYmbShdSo0RXia4kacuWLQC0bds25khERCRXKdFNjWp0RZKkBFdERCS7qEZXJEnjx49n/PjxcYchIiIiSVLpgkoXJEmq0RURkbipdCE1sYzomtmFZva5mZWY2TtmNqKOtiPNzGu47VWt3Slm9oGZbY3uv9X470Ryycsvv8zLL78cdxgiIiJpY2atzOxuMys0s81m9oyZ9a3nmGvM7C0z22Bmq83sWTMbUq3NuBpytzca9918WZMnumZ2GnAXcAtwADATeMHMdqnn0MFA74Tb/IQ+hwMTgMeB/aP7v5vZIWl/A5KzCgoKKCgoiDsMERGRdLoTOAU4AxgBdASeM7P8Oo4ZCfwROBT4GlAOTDazrtXaTeaLudvxaY08CU1eumBms4DZ7n5uwrb5wJPufk0N7UcCrwI93L2wlj4nAF3dfXTCtsnAanc/o654VLogyRo3bhwAY8eOjTUOERHJXeksXTCzTsBq4Bx3fzza1g9YCBzn7pOS7Kc9UASc5O7PRtvGAd3d/X/SEWtDNemIrpm1BIYCL1Xb9RLhr4K6vG1my81sipkdVW3f8Br6nJREnyJJGzdu3LZkV0REpBkYChSQkEO5+2LgQ1LLoToQcsp11bYfbmarzOxjM3vQzHba0YBT1dTTi3UH8oGV1bavBEbVcsxy4ALgLaAlcCYwxcyOdPfXoja9aumzV00dmtl5wHnRUzez4lTeRIIWhOF6yUyNcn7MLN1d5iL97mQ2nZ/MpXOT2Zri/LQxs7cTnj/g7g80sK9eQAVQ/RvzWnOoWtwFvAu8nrDtRWAi8DnQH7gJeMXMhrr71gbGm7KMn0fX3ecB8xI2vW5m/YErgddqOiaJPh8AGvpDsY2Zve3uw3a0H2kcOj+ZS+cms+n8ZC6dm8yWKefHzG4CflFPs+rfjjf0tX4HHA4c7u4VVdvd/a8Jzd43s3cIJRHfICTATaKpE91Cwl8OPatt7wmsSKGfWcDpCc9XpKFPERERkebgTqC+id8XAV8lfNPenVCrW6UnSQwmmtnvCfnYUe7+WV1t3X2ZmS0BBtbXbzo1aaLr7qVRRj8a+HvCrtHAUyl0tT+hpKHK61Efv63W58wGhioiIiKSlaKL92u8gD9RlJOVEXKmJ6JtfYG9qSeHMrO7gNMISe5HSbxWd6APX8zfGl0cpQu/Ax4zszeBGcD5wM7AnwDM7FEAdz8ren4psACYS6jRHQOcRJgKo8pdwHQzuxr4B/AtwpD84Y38Xna4/EEalc5P5tK5yWw6P5lL5yazZdX5cfciM3sI+I2ZrQLWEPK02YSpwQAws4+Ae9z9nuj5vYRrpk4C1plZVT3vJnffFM3CcD1hEHM5oUb3VmAV8HQTvLVtYlkZzcwuBH5GmFNtDvBTd58e7ZsK4O4jo+c/A84F+gLFhIT3Vnd/vlqfpxIKnQcAnwK/cPcmqwERERERyTZm1gq4A/gu0AaYAlwYzb5Q1caBG9z9+oTnNbnB3a83szaEgccDgM6EZPdV4FeJ/TaFnF8CWERERESap1iWABYRERERaWxKdOtgZhea2edmVmJm75jZiHraHxm1KzGzz8zs/KaKNRelcn7MrLeZPWFmH5lZRbRiizSSFM/NyWb2UrRe+kYzm2VmJzZlvLkmxfNzpJnNNLM1ZlYc/Q5d0ZTx5pJU/99JOO5wMys3szmNHWMuS/F3Z6SZeQ23vZoy5lynRLcWZnYa4SK3Wwg1JjOBF8xsl1ra7wY8H7U7gFB0fbeZnVJTe9kxqZ4foBXhCtTbCNPTSSNpwLk5EniFMLfiAYTfo6eT/Q9eUtOA87MJ+ANwBLAP4VqIG6JrLSSNGnBuqo7rAjxKqK2URtLQ8wMMJlyTVHWb35hxyhepRrcWZjYLmO3u5yZsmw886e7X1ND+duBkdx+YsO3PwGB3H94UMeeSVM9PtWOfAwrdfWzjRpmbduTcJLR/E3jN3S9vpDBzVprOz0Rgq7uf0Uhh5qSGnpvofLwHGHCquw9p9GBzUAPygpGEC7B6RNN9SQw0olsDM2tJWP/5pWq7XqL2tZ+H19B+EjDMzArSG2Fua+D5kSaQxnPTgS+vmS47KB3nx8wOiNpOS290ua2h5yYaWe9JGGmXRrKDvztvm9lyM5tiZmlZjUySp0S3Zt0JK4WsrLa9rrWfe9XSvkXUn6RPQ86PNI0dPjdm9mPCdIKPpTc0YQfOj5ktMbOtwNvAH939T40TYs5K+dyY2b7AdcCYxKVXpVE05HdnOXABYd7/k4F5wBSVZTWtOBaMEBGpUVTT/lvgNHdfGHc88gUjgPaEJUNvN7PP3V1/jMQkmvt0AnCFu38edzzyZe4+j5DcVnndzPoDV5LE8rqSHkp0a1YIVBC+DkrUE1hRyzEramlfThLL8ElKGnJ+pGk0+NxEi748Cpzl7s82Tng5r8HnJyGZet/MehJWPVKimz6pnpvehGVaHzazh6NteYCZWTlwvLtX/5pdGi5d/+/MAk5PV1BSP5Uu1MDdS4F3CGs/JxpN7Ws/v15L+7fdvSy9Eea2Bp4faQINPTdm9h1C0jTW3Z9svAhzWxp/d/IIM5lImjTg3CwF9gX2T7j9Cfgkeqx/C9Mojb87+xNKGqSJaES3dr8DHouu/p4BnA/sTPiHBDN7FMDdz4ra/wm4yMzuBO4HDgPGAroquXGken4ws/2jhx2Byuh5qbt/0JSB54CUzo2ZnU5Icq8Aptv2NdNL3X1tE8eeC1I9Pz8BPmf7V7BHEM7VH5s27JyQ9LmJBlC+MGeuma0izIahuXQbR6q/O5cCC4C5QEtgDHASoWZXmogS3Vq4+wQz6wb8kvAV0RzCV0FVdYO7VGv/uZkdD/yeUHy+DLjY3Z9qwrBzRqrnJ/Lfas9PABYC/RsrzlzUgHNzPuHfojujW5VpwMjGjTb3NOD85AO3E35PyoFPgauJ/nOX9Gngv2vSRBpwfloSrjnoCxQTEt5vuPvzTRSyoHl0RURERKSZUo2uiIiIiDRLSnRFREREpFlSoisiIiIizZISXRERERFplpToioiIiEizpERXRERERJolJboiGc7MBprZPWb2oZltMrONZvaRmT1oZl9NaLfAzNzMFsQYblUs46JYPFrbvWp7TzN73MyWm1lFtP9OM+uf0H5cI8bV2cyuj24nJRt3UzGzkQmvX9/t+uiYqudTmzre+jTmeU3lXFX7XNMah4hkNi0YIZLBzOwc4D6+vNzqoOjWg7DSTra4CzgtxtfvDFwXPX4E+EeMsYiISCNToiuSoczsa8CfCd+8OHAzYXnpVcCuwKnAnrEFWAd3H0tYAru6odH9emA3d1+fsM8aOax61RF3U73+VBI+BzMbCzwcPX0kii/tzKy1u5c0Rt8iInFS6YJI5rqV7b+jf3D3X7n7Encvdff57n4rcG5dHZjZ/mY20cw+MbMNZlZmZiuibcOqtd3NzB41s0VmVmJm681sTvQV8U4J7c41s7fNbK2ZbTWzpWb2spmdndDmC18rV311DOwRNekMrIv2j63rK24zO9DM/i96nVIzKzSzV83s4Gh/ezN7xMzeN7M10Xtcb2bTzey0hH6uBz5P6Prs6q9ZR8lFOzO7wczmmlmxmW0xs/+a2WVm1iKh3Rfeh5mdFX2GxRZKT86mEZnZ18zsjej1PjWzn5lZYuJ8fUJ83zKzh8yskLA8aVWbvc3ssYTPe5WZPWlm+1V7raR+Xqod8x0zm13X52FmI8zsGTNbnfDz+tfqr1/HZ7BzFO+m6OfhPqBDLW1Tfg8ikmXcXTfddMuwG7ATYRS36tYniWMWRG0XJGw7vVo/ibfNwN4JbefW0XZI1ObbdbR5MqGvcQnb+wMj6zhubNSm6vm4hH6+BZTVdlzUplcdfTtwVtTu+jrajKsp7mhbO+CdOo59HsiL2ia+j3W1tD88hZ+DsTV9LtXaVO0vrOWzGpPQ9vpq7be1i/YfDmypJe5iYESKPy+Jn8eK+j4PYAxQUUu7EmBkbT9j0bY2wIc1HLusps8xmfegm266ZfdNI7oimal/wuMN7r60gf38BzgG6E2o8+0IXBDtawv8CMDMugH7RNv/QEjuugIHAb8CiqJ9R0T3mwg1wq0IZRTfAV6sLQh3n+ruBiyMNi10d4tu42o6xszaAA+yvcTqWqAn0J2QcH8Wbd9IqPvtH72n1sChhIQN4LIohuuB3RJe4pGEGMbWFjtwKXBg9HgS4bMcQPhsAY4j/EFRXWfgQqATcHvC9jPreK0d0Q34DdAFuCiJ1zPgWMJntm+07UFCsriQUGbSCjgAWE34XO+FlH5eEvWkjs/DzNoBdxO+xSgn/JHTETg/ateKULpTl7OAvaLHbwB9Cd8irK/esIHvQUSyjGp0RZq3FcAPgDsJiWCbavsHRffrCMlAZ0LitpEwMvaeu9+U0P7z6L4d8EvCSOeHwEvunu7E4DBC8gYw1d1/nbDvyYTHWwjJ7wRgb8LX1In1voPYMd9IeHyNu68AMLMb2X4x2/HAE9WOe8fd74vajgeuirbvuoPx1GYlcK27V5jZI8A99bze/7r7pOjxHDMbyPYkcVfCua1uXzPrRagTT+bnJVF9n8dhUX8Az7t71Wd7v5mdD+wP7Glme7j7J7W8xtcSHt9a9Qeimf0vod49UbI/8yKSxTSiK5KZFiQ87mhmOzewn78BPyMkgNWTXKq2uXslYWRtCTAQ+AUwHng/qn3tF7X/I/B3oKr9nYRRzpVmdnUDY6xNz4THH9TR7irCSOMhhBHA6he1td7BOHokPF6U8HhhwuOa6jnnJTzenMZ4avOpu1ek8Hr/rfY82ZrUbin8vCSq7/Oo7XOG+j/rbbElPF5Sy2MgpZ95EcliSnRFMpC7rwLeTNh0ZU3tEi+EqmFfF0LZAoTRvsFAPlDjRT3u/hywC2EE9ETgRkK95BDC6C3uXuLu3yF8xXs48H1gFuFr5VvMrE9y7zApKxMe711Hu8SygZOAVlGZxJoa2noD4liV8HiXWh4ntqlStoOvm6ptr+fuybxecbXnie9hckJZx7YboRZ5bvQa9f681BYfNX8etX3O1Z/X9FlXKUx43LeWx9uDSP09iEiWUaIrkrl+QRg5Bbg4umJ+ZzMrsLCIxM8JNZW1KWd7QlEObCB8xf/rmhqb2d3A0YT62xeBp4Ct0e5dojanmNlFQB/gPcLo7ntVXVBLQtFAM9ierB5lZj83sx5m1sXMTjKzqnrh8oRj1gMFZvYrvji6VyUx+R0Y1YXW518Jj2+2sOhFf0LNcE1tspK7zwc+jp4ebWaXWlhgo7WZfcXMrgX+WtU+mZ+XFM0klBMAHGdmJ1qYUeNcQp0wwLw6yhYAXk14fLWZ9TGz3YHLa2rcCO9BRDKMEl2RDOXukwkXi5URflevA5YCpYSE5GbChUe1Hb8RmBI97QMsJoyS7lPLIRcALye8xnuEC5UglCdAGFm9m1BKsDG6nRftWw7MTuEt1sndiwnTp1UlsjcTRvPWAk8TLggjelxlKiFpuZgaLkBy902EK+0hXLC2KZpqa2wdodzFFy88W0GoVa6aE/gFQn1wc3AeYY/EsycAAAFtSURBVHYDA35PSDyLgXeBG/hiOUkyPy9Jc/fNwE8If9wVAP8k/Hw9EDXZyvYL02rzKPBR9Hg4oSzhE75YFpEore9BRDKPEl2RDObufyaUGvyRkNwWE+ob5wEPAbfV08UYQhK2jnAV+XhqX5nsNuDfhGSynHCR138ISeNdUZsphIuuPiEklBWEBPevwJFRcpo27v40ofb2r4QposoJie40ttft3g7cQkhWiqN9X6P2q+bPBKYTRriTiWEzYbaJGwkXK20lJIPvEkYKT4zqPbOeu08jJPCPEpLEMsLnPZvwB87PE5on8/OS6us/TpiK7jnC6Hs54Y+zvwEHe1hQo67ji4FRwETC78l6woIbtc03nfb3ICKZxZIr5RIRERERyS4a0RURERGRZkmJroiIiIg0S0p0RURERKRZUqIrIiIiIs2SEl0RERERaZaU6IqIiIhIs6REV0RERESaJSW6IiIiItIsKdEVERERkWbp/wEAbgj7omUClQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     val_metrics['avg_odds_diff'], 'avg. odds diff.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.1200\nBest balanced accuracy: 0.6836\nCorresponding 1-min(DI, 1/DI) value: 0.2268\nCorresponding average odds difference value: 0.0254\nCorresponding statistical parity difference value: -0.0830\nCorresponding equal opportunity difference value: 0.1172\nCorresponding Theil index value: 0.1119\n"
    }
   ],
   "source": [
    "describe_metrics(val_metrics, thresh_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1.3. Testing PR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_orig_panel19_test.copy()\n",
    "dataset.features = pr_orig_scaler.transform(dataset.features)\n",
    "\n",
    "pr_orig_metrics = test(dataset=dataset,\n",
    "                       model=pr_orig_panel19,\n",
    "                       thresh_arr=[thresh_arr[pr_orig_best_ind]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.1200\nBest balanced accuracy: 0.6880\nCorresponding 1-min(DI, 1/DI) value: 0.1588\nCorresponding average odds difference value: 0.0523\nCorresponding statistical parity difference value: -0.0566\nCorresponding equal opportunity difference value: 0.1479\nCorresponding Theil index value: 0.1108\n"
    }
   ],
   "source": [
    "describe_metrics(pr_orig_metrics, [thresh_arr[pr_orig_best_ind]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the case of reweighing, prejudice remover results in a fair model. However, it has come at the expense of relatively lower balanced accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [6.](#Table-of-Contents) Summary of Model Learning Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                        bal_acc  avg_odds_diff  disp_imp  \\\nBias Mitigator    Classifier                                               \n                  Logistic Regression  0.775935      -0.205706  0.426176   \n                  Random Forest        0.763772      -0.138763  0.485869   \nReweighing        Logistic Regression  0.753893      -0.015104  0.751755   \nReweighing        Random Forest        0.758565      -0.084303  0.569260   \nPrejudice Remover                      0.688028       0.052286  0.841229   \n\n                                       stat_par_diff  eq_opp_diff  theil_ind  \nBias Mitigator    Classifier                                                  \n                  Logistic Regression      -0.261207    -0.222779   0.092122  \n                  Random Forest            -0.218998    -0.113503   0.093575  \nReweighing        Logistic Regression      -0.087196    -0.003518   0.096575  \nReweighing        Random Forest            -0.163191    -0.061108   0.096345  \nPrejudice Remover                          -0.056631     0.147869   0.110774  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>bal_acc</th>\n      <th>avg_odds_diff</th>\n      <th>disp_imp</th>\n      <th>stat_par_diff</th>\n      <th>eq_opp_diff</th>\n      <th>theil_ind</th>\n    </tr>\n    <tr>\n      <th>Bias Mitigator</th>\n      <th>Classifier</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th></th>\n      <th>Logistic Regression</th>\n      <td>0.775935</td>\n      <td>-0.205706</td>\n      <td>0.426176</td>\n      <td>-0.261207</td>\n      <td>-0.222779</td>\n      <td>0.092122</td>\n    </tr>\n    <tr>\n      <th></th>\n      <th>Random Forest</th>\n      <td>0.763772</td>\n      <td>-0.138763</td>\n      <td>0.485869</td>\n      <td>-0.218998</td>\n      <td>-0.113503</td>\n      <td>0.093575</td>\n    </tr>\n    <tr>\n      <th>Reweighing</th>\n      <th>Logistic Regression</th>\n      <td>0.753893</td>\n      <td>-0.015104</td>\n      <td>0.751755</td>\n      <td>-0.087196</td>\n      <td>-0.003518</td>\n      <td>0.096575</td>\n    </tr>\n    <tr>\n      <th>Reweighing</th>\n      <th>Random Forest</th>\n      <td>0.758565</td>\n      <td>-0.084303</td>\n      <td>0.569260</td>\n      <td>-0.163191</td>\n      <td>-0.061108</td>\n      <td>0.096345</td>\n    </tr>\n    <tr>\n      <th>Prejudice Remover</th>\n      <th></th>\n      <td>0.688028</td>\n      <td>0.052286</td>\n      <td>0.841229</td>\n      <td>-0.056631</td>\n      <td>0.147869</td>\n      <td>0.110774</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.multi_sparse', False)\n",
    "results = [lr_orig_metrics, rf_orig_metrics, lr_transf_metrics,\n",
    "           rf_transf_metrics, pr_orig_metrics]\n",
    "debias = pd.Series(['']*2 + ['Reweighing']*2\n",
    "                 + ['Prejudice Remover'],\n",
    "                   name='Bias Mitigator')\n",
    "clf = pd.Series(['Logistic Regression', 'Random Forest']*2 + [''],\n",
    "                name='Classifier')\n",
    "pd.concat([pd.DataFrame(metrics) for metrics in results], axis=0).set_index([debias, clf])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of all the models, the logistic regression model gives the best balance in terms of balanced accuracy and fairness. While the model learnt by prejudice remover is slightly fairer, it has much lower accuracy. All other models are quite unfair compared to the logistic model. Hence, we take the logistic regression model learnt from data transformed by re-weighing and 'deploy' it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [7.](#Table-of-Contents) Deploying model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1. Testing model learned on 2014 (Panel 19) on 2015 (Panel 20) deployment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_orig_panel20_deploy = MEPSDataset20()\n",
    "\n",
    "# now align it with the 2014 dataset\n",
    "dataset_orig_panel20_deploy = dataset_orig_panel19_train.align_datasets(dataset_orig_panel20_deploy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Test Dataset shape"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(17570, 138)\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Favorable and unfavorable labels"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1.0 0.0\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Protected attribute names"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['RACE']\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Privileged and unprivileged protected attribute values"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[array([1.])] [array([0.])]\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Dataset feature names"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['AGE', 'RACE', 'PCS42', 'MCS42', 'K6SUM42', 'REGION=1', 'REGION=2', 'REGION=3', 'REGION=4', 'SEX=1', 'SEX=2', 'MARRY=1', 'MARRY=2', 'MARRY=3', 'MARRY=4', 'MARRY=5', 'MARRY=6', 'MARRY=7', 'MARRY=8', 'MARRY=9', 'MARRY=10', 'FTSTU=-1', 'FTSTU=1', 'FTSTU=2', 'FTSTU=3', 'ACTDTY=1', 'ACTDTY=2', 'ACTDTY=3', 'ACTDTY=4', 'HONRDC=1', 'HONRDC=2', 'HONRDC=3', 'HONRDC=4', 'RTHLTH=-1', 'RTHLTH=1', 'RTHLTH=2', 'RTHLTH=3', 'RTHLTH=4', 'RTHLTH=5', 'MNHLTH=-1', 'MNHLTH=1', 'MNHLTH=2', 'MNHLTH=3', 'MNHLTH=4', 'MNHLTH=5', 'HIBPDX=-1', 'HIBPDX=1', 'HIBPDX=2', 'CHDDX=-1', 'CHDDX=1', 'CHDDX=2', 'ANGIDX=-1', 'ANGIDX=1', 'ANGIDX=2', 'MIDX=-1', 'MIDX=1', 'MIDX=2', 'OHRTDX=-1', 'OHRTDX=1', 'OHRTDX=2', 'STRKDX=-1', 'STRKDX=1', 'STRKDX=2', 'EMPHDX=-1', 'EMPHDX=1', 'EMPHDX=2', 'CHBRON=-1', 'CHBRON=1', 'CHBRON=2', 'CHOLDX=-1', 'CHOLDX=1', 'CHOLDX=2', 'CANCERDX=-1', 'CANCERDX=1', 'CANCERDX=2', 'DIABDX=-1', 'DIABDX=1', 'DIABDX=2', 'JTPAIN=-1', 'JTPAIN=1', 'JTPAIN=2', 'ARTHDX=-1', 'ARTHDX=1', 'ARTHDX=2', 'ARTHTYPE=-1', 'ARTHTYPE=1', 'ARTHTYPE=2', 'ARTHTYPE=3', 'ASTHDX=1', 'ASTHDX=2', 'ADHDADDX=-1', 'ADHDADDX=1', 'ADHDADDX=2', 'PREGNT=-1', 'PREGNT=1', 'PREGNT=2', 'WLKLIM=-1', 'WLKLIM=1', 'WLKLIM=2', 'ACTLIM=-1', 'ACTLIM=1', 'ACTLIM=2', 'SOCLIM=-1', 'SOCLIM=1', 'SOCLIM=2', 'COGLIM=-1', 'COGLIM=1', 'COGLIM=2', 'DFHEAR42=-1', 'DFHEAR42=1', 'DFHEAR42=2', 'DFSEE42=-1', 'DFSEE42=1', 'DFSEE42=2', 'ADSMOK42=-1', 'ADSMOK42=1', 'ADSMOK42=2', 'PHQ242=-1', 'PHQ242=0', 'PHQ242=1', 'PHQ242=2', 'PHQ242=3', 'PHQ242=4', 'PHQ242=5', 'PHQ242=6', 'EMPST=-1', 'EMPST=1', 'EMPST=2', 'EMPST=3', 'EMPST=4', 'POVCAT=1', 'POVCAT=2', 'POVCAT=3', 'POVCAT=4', 'POVCAT=5', 'INSCOV=1', 'INSCOV=2', 'INSCOV=3']\n"
    }
   ],
   "source": [
    "# describe(dataset_orig_panel20_train, dataset_orig_panel20_val, dataset_orig_panel20_deploy)\n",
    "describe(test=dataset_orig_panel20_deploy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 0.5456992351196291\n"
    }
   ],
   "source": [
    "metric_orig_panel20_deploy = BinaryLabelDatasetMetric(\n",
    "        dataset_orig_panel20_deploy, \n",
    "        unprivileged_groups=unprivileged_groups,\n",
    "        privileged_groups=privileged_groups)\n",
    "explainer_orig_panel20_deploy = MetricTextExplainer(metric_orig_panel20_deploy)\n",
    "\n",
    "print(explainer_orig_panel20_deploy.disparate_impact())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_transf_metrics_panel20_deploy = test(\n",
    "        dataset=dataset_orig_panel20_deploy,\n",
    "        model=lr_transf_panel19,\n",
    "        thresh_arr=[thresh_arr[lr_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2200\nBest balanced accuracy: 0.7311\nCorresponding 1-min(DI, 1/DI) value: 0.1943\nCorresponding average odds difference value: 0.0071\nCorresponding statistical parity difference value: -0.0596\nCorresponding equal opportunity difference value: 0.0303\nCorresponding Theil index value: 0.1019\n"
    }
   ],
   "source": [
    "describe_metrics(lr_transf_metrics_panel20_deploy, [thresh_arr[lr_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deployed model tested on the 2015 Panel 20 data still exhibits fairness as well as maintains accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [8.](#Table-of-Contents) Generating explanations for model predictions using LIME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1. Generating explanations on 2015 Panel 20 deployment data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section shows how LIME can be integrated with AIF360 to get explanations for model predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = dataset_transf_panel19_train  # data the deployed model (lr from transformed data)\n",
    "test_dataset = dataset_orig_panel20_deploy  # the data model is being tested on\n",
    "model = lr_transf_panel19  # lr_transf_panel19 is LR model learned from Panel 19 with Reweighing\n",
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "best_thresh = thresh_arr[lr_transf_best_ind]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to fit the encoder to the aif360 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "lime_data = LimeEncoder().fit(train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `transform()` method is then used to convert aif360 features to LIME-compatible features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_train = lime_data.transform(train_dataset.features)\n",
    "s_test = lime_data.transform(test_dataset.features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LimeTabularExplainer` takes as input the LIME-compatible data along with various other arguments to create a lime explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = LimeTabularExplainer(\n",
    "        s_train, class_names=lime_data.s_class_names, \n",
    "        feature_names=lime_data.s_feature_names,\n",
    "        categorical_features=lime_data.s_categorical_features, \n",
    "        categorical_names=lime_data.s_categorical_names, \n",
    "        kernel_width=3, verbose=False, discretize_continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `inverse_transform()` function is used to transform LIME-compatible data back to aif360-compatible data since that is needed by the model to make predictions. The function below is used to produce the predictions for any perturbed data that is produce by LIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def s_predict_fn(x):\n",
    "    return model.predict_proba(lime_data.inverse_transform(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `explain_instance()` method can then be used to produce explanations for any instance in the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_explanation(ind):\n",
    "    exp = explainer.explain_instance(s_test[ind], s_predict_fn, num_features=10)\n",
    "    print(\"Actual label: \" + str(test_dataset.labels[ind]))\n",
    "    exp.as_pyplot_figure()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2200\nActual label: [0.]\n"
    },
    {
     "output_type": "display_data",
     "data": {
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    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Actual label: [0.]\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "print(\"Threshold corresponding to Best balanced accuracy: {:6.4f}\".format(best_thresh))\n",
    "show_explanation(0)\n",
    "show_explanation(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the [LIME documentation](https://github.com/marcotcr/lime) for detailed description of results. In short, the left hand side shows the label predictions made by the model, the middle shows the features that are important to the instance in question and their contributions (weights) to the label prediction, while the right hand side shows the actual values of the features in the particular instance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [9.](#Table-of-Contents) Re-deploying Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1. Testing model learned on 2014 (Panel 19) data on 2016 (Panel 21) deployment data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Panel 21 data, and split it again into 3 parts: train, validate, and deploy. We test the deployed model against the deployment data. If a new model needs to be learnt, it will be learnt from the train/validate data and then tested again on the deployment data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Test Dataset shape"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(15675, 138)\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Favorable and unfavorable labels"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1.0 0.0\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Protected attribute names"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['RACE']\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Privileged and unprivileged protected attribute values"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[array([1.])] [array([0.])]\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Markdown object>",
      "text/markdown": "#### Dataset feature names"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['AGE', 'RACE', 'PCS42', 'MCS42', 'K6SUM42', 'REGION=1', 'REGION=2', 'REGION=3', 'REGION=4', 'SEX=1', 'SEX=2', 'MARRY=1', 'MARRY=2', 'MARRY=3', 'MARRY=4', 'MARRY=5', 'MARRY=6', 'MARRY=7', 'MARRY=8', 'MARRY=9', 'MARRY=10', 'FTSTU=-1', 'FTSTU=1', 'FTSTU=2', 'FTSTU=3', 'ACTDTY=1', 'ACTDTY=2', 'ACTDTY=3', 'ACTDTY=4', 'HONRDC=1', 'HONRDC=2', 'HONRDC=3', 'HONRDC=4', 'RTHLTH=-1', 'RTHLTH=1', 'RTHLTH=2', 'RTHLTH=3', 'RTHLTH=4', 'RTHLTH=5', 'MNHLTH=-1', 'MNHLTH=1', 'MNHLTH=2', 'MNHLTH=3', 'MNHLTH=4', 'MNHLTH=5', 'HIBPDX=-1', 'HIBPDX=1', 'HIBPDX=2', 'CHDDX=-1', 'CHDDX=1', 'CHDDX=2', 'ANGIDX=-1', 'ANGIDX=1', 'ANGIDX=2', 'MIDX=-1', 'MIDX=1', 'MIDX=2', 'OHRTDX=-1', 'OHRTDX=1', 'OHRTDX=2', 'STRKDX=-1', 'STRKDX=1', 'STRKDX=2', 'EMPHDX=-1', 'EMPHDX=1', 'EMPHDX=2', 'CHBRON=-1', 'CHBRON=1', 'CHBRON=2', 'CHOLDX=-1', 'CHOLDX=1', 'CHOLDX=2', 'CANCERDX=-1', 'CANCERDX=1', 'CANCERDX=2', 'DIABDX=-1', 'DIABDX=1', 'DIABDX=2', 'JTPAIN=-1', 'JTPAIN=1', 'JTPAIN=2', 'ARTHDX=-1', 'ARTHDX=1', 'ARTHDX=2', 'ARTHTYPE=-1', 'ARTHTYPE=1', 'ARTHTYPE=2', 'ARTHTYPE=3', 'ASTHDX=1', 'ASTHDX=2', 'ADHDADDX=-1', 'ADHDADDX=1', 'ADHDADDX=2', 'PREGNT=-1', 'PREGNT=1', 'PREGNT=2', 'WLKLIM=-1', 'WLKLIM=1', 'WLKLIM=2', 'ACTLIM=-1', 'ACTLIM=1', 'ACTLIM=2', 'SOCLIM=-1', 'SOCLIM=1', 'SOCLIM=2', 'COGLIM=-1', 'COGLIM=1', 'COGLIM=2', 'DFHEAR42=-1', 'DFHEAR42=1', 'DFHEAR42=2', 'DFSEE42=-1', 'DFSEE42=1', 'DFSEE42=2', 'ADSMOK42=-1', 'ADSMOK42=1', 'ADSMOK42=2', 'PHQ242=-1', 'PHQ242=0', 'PHQ242=1', 'PHQ242=2', 'PHQ242=3', 'PHQ242=4', 'PHQ242=5', 'PHQ242=6', 'EMPST=-1', 'EMPST=1', 'EMPST=2', 'EMPST=3', 'EMPST=4', 'POVCAT=1', 'POVCAT=2', 'POVCAT=3', 'POVCAT=4', 'POVCAT=5', 'INSCOV=1', 'INSCOV=2', 'INSCOV=3']\n"
    }
   ],
   "source": [
    "dataset_orig_panel21_deploy = MEPSDataset21()\n",
    "\n",
    "# now align it with the panel19 datasets\n",
    "dataset_orig_panel21_deploy = dataset_orig_panel19_train.align_datasets(dataset_orig_panel21_deploy)\n",
    "\n",
    "describe(test=dataset_orig_panel21_deploy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 0.48375589333734254\n"
    }
   ],
   "source": [
    "metric_orig_panel21_deploy = BinaryLabelDatasetMetric(\n",
    "        dataset_orig_panel21_deploy, \n",
    "        unprivileged_groups=unprivileged_groups,\n",
    "        privileged_groups=privileged_groups)\n",
    "explainer_orig_panel21_deploy = MetricTextExplainer(metric_orig_panel21_deploy)\n",
    "\n",
    "print(explainer_orig_panel21_deploy.disparate_impact())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the logistic regression classifier trained on the panel 19 data after reweighing is tested against the panel 21 deployment data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_transf_metrics_panel21_deploy = test(\n",
    "        dataset=dataset_orig_panel21_deploy,\n",
    "        model=lr_transf_panel19,\n",
    "        thresh_arr=[thresh_arr[lr_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.2200\nBest balanced accuracy: 0.7379\nCorresponding 1-min(DI, 1/DI) value: 0.2559\nCorresponding average odds difference value: -0.0143\nCorresponding statistical parity difference value: -0.0813\nCorresponding equal opportunity difference value: -0.0044\nCorresponding Theil index value: 0.0994\n"
    }
   ],
   "source": [
    "describe_metrics(lr_transf_metrics_panel21_deploy, [thresh_arr[lr_transf_best_ind]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to the 2015 panel 20 deployment data results, the $|1 - \\text{disparate impact}|$ fairness metric shows a noticable drift upwards. While still within specs, it may be worthwhile to re-learn the model. So even though the model is still relatively fair and accurate, we go ahead and re-learn the model from the 2015 Panel 20 data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.2. Re-learning model (from 2015 Panel 20 data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "(dataset_orig_panel20_train,\n",
    " dataset_orig_panel20_val,\n",
    " dataset_orig_panel20_test) = MEPSDataset20().split([0.5, 0.8], shuffle=True) \n",
    "\n",
    "# now align them with the 2014 datasets\n",
    "dataset_orig_panel20_train = dataset_orig_panel19_train.align_datasets(dataset_orig_panel20_train)\n",
    "dataset_orig_panel20_val = dataset_orig_panel19_train.align_datasets(dataset_orig_panel20_val)\n",
    "dataset_orig_panel20_test = dataset_orig_panel19_train.align_datasets(dataset_orig_panel20_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train and evaluate  new model on 'transformed' 2016 training/test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "RW = Reweighing(unprivileged_groups=unprivileged_groups,\n",
    "                privileged_groups=privileged_groups)\n",
    "RW.fit(dataset_orig_panel20_train)\n",
    "dataset_transf_panel20_train = RW.transform(dataset_orig_panel20_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 1.0000000000000002\n"
    }
   ],
   "source": [
    "metric_transf_panel20_train = BinaryLabelDatasetMetric(\n",
    "        dataset_transf_panel20_train, \n",
    "        unprivileged_groups=unprivileged_groups,\n",
    "        privileged_groups=privileged_groups)\n",
    "explainer_transf_panel20_train = MetricTextExplainer(metric_transf_panel20_train)\n",
    "\n",
    "print(explainer_transf_panel20_train.disparate_impact())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_transf_panel20_train\n",
    "model = make_pipeline(StandardScaler(),\n",
    "                      LogisticRegression(solver='liblinear', random_state=1))\n",
    "fit_params = {'logisticregression__sample_weight': dataset.instance_weights}\n",
    "lr_transf_panel20 = model.fit(dataset.features, dataset.labels.ravel(), **fit_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "val_metrics = test(dataset=dataset_orig_panel20_val,\n",
    "                   model=lr_transf_panel20,\n",
    "                   thresh_arr=thresh_arr)\n",
    "lr_transf_best_ind_panel20 = np.argmax(val_metrics['bal_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "disp_imp = np.array(val_metrics['disp_imp'])\n",
    "disp_imp_err = 1 - np.minimum(disp_imp, 1/disp_imp)\n",
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     disp_imp_err, '1 - min(DI, 1/DI)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot(thresh_arr, 'Classification Thresholds',\n",
    "     val_metrics['bal_acc'], 'Balanced Accuracy',\n",
    "     val_metrics['avg_odds_diff'], 'avg. odds diff.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.1900\nBest balanced accuracy: 0.7465\nCorresponding 1-min(DI, 1/DI) value: 0.1129\nCorresponding average odds difference value: 0.0036\nCorresponding statistical parity difference value: -0.0414\nCorresponding equal opportunity difference value: -0.0057\nCorresponding Theil index value: 0.0946\n"
    }
   ],
   "source": [
    "describe_metrics(val_metrics, thresh_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_transf_metrics_panel20_test = test(\n",
    "        dataset=dataset_orig_panel20_test,\n",
    "        model=lr_transf_panel20,\n",
    "        thresh_arr=[thresh_arr[lr_transf_best_ind_panel20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.1900\nBest balanced accuracy: 0.7490\nCorresponding 1-min(DI, 1/DI) value: 0.0533\nCorresponding average odds difference value: 0.0158\nCorresponding statistical parity difference value: -0.0184\nCorresponding equal opportunity difference value: -0.0150\nCorresponding Theil index value: 0.0988\n"
    }
   ],
   "source": [
    "describe_metrics(lr_transf_metrics_panel20_test, [thresh_arr[lr_transf_best_ind_panel20]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new model is both relatively fair as well as accurate so we deploy and test against the 2016 deployment data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.3. Testing model learned on 2015 (Panel 20) data on 2016 (Panel 21) deployment data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluate new 2015 transformed data model and evaluate again on 2016 deployment data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_transf_panel20_metrics_panel21_deploy = test(\n",
    "        dataset=dataset_orig_panel21_deploy,\n",
    "        model=lr_transf_panel20,\n",
    "        thresh_arr=[thresh_arr[lr_transf_best_ind_panel20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Threshold corresponding to Best balanced accuracy: 0.1900\nBest balanced accuracy: 0.7370\nCorresponding 1-min(DI, 1/DI) value: 0.1698\nCorresponding average odds difference value: -0.0021\nCorresponding statistical parity difference value: -0.0648\nCorresponding equal opportunity difference value: 0.0016\nCorresponding Theil index value: 0.0960\n"
    }
   ],
   "source": [
    "describe_metrics(lr_transf_panel20_metrics_panel21_deploy, [thresh_arr[lr_transf_best_ind_panel20]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new transformed 2016 data model is again within original accuracy/fairness specs so is deployed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [10.](#Table-of-Contents) SUMMARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                                              bal_acc  \\\nBias Mitigator Classifier          Training set Testing set             \n               Logistic Regression Panel19      Panel19      0.775935   \nReweighing     Logistic Regression Panel19      Panel19      0.753893   \nReweighing     Logistic Regression Panel19      Panel20      0.731136   \nReweighing     Logistic Regression Panel19      Panel21      0.737916   \nReweighing     Logistic Regression Panel20      Panel20      0.749024   \nReweighing     Logistic Regression Panel20      Panel21      0.736958   \n\n                                                             avg_odds_diff  \\\nBias Mitigator Classifier          Training set Testing set                  \n               Logistic Regression Panel19      Panel19          -0.205706   \nReweighing     Logistic Regression Panel19      Panel19          -0.015104   \nReweighing     Logistic Regression Panel19      Panel20           0.007135   \nReweighing     Logistic Regression Panel19      Panel21          -0.014340   \nReweighing     Logistic Regression Panel20      Panel20           0.015756   \nReweighing     Logistic Regression Panel20      Panel21          -0.002077   \n\n                                                             disp_imp  \\\nBias Mitigator Classifier          Training set Testing set             \n               Logistic Regression Panel19      Panel19      0.426176   \nReweighing     Logistic Regression Panel19      Panel19      0.751755   \nReweighing     Logistic Regression Panel19      Panel20      0.805724   \nReweighing     Logistic Regression Panel19      Panel21      0.744126   \nReweighing     Logistic Regression Panel20      Panel20      0.946696   \nReweighing     Logistic Regression Panel20      Panel21      0.830199   \n\n                                                             stat_par_diff  \\\nBias Mitigator Classifier          Training set Testing set                  \n               Logistic Regression Panel19      Panel19          -0.261207   \nReweighing     Logistic Regression Panel19      Panel19          -0.087196   \nReweighing     Logistic Regression Panel19      Panel20          -0.059602   \nReweighing     Logistic Regression Panel19      Panel21          -0.081262   \nReweighing     Logistic Regression Panel20      Panel20          -0.018444   \nReweighing     Logistic Regression Panel20      Panel21          -0.064846   \n\n                                                             eq_opp_diff  \\\nBias Mitigator Classifier          Training set Testing set                \n               Logistic Regression Panel19      Panel19        -0.222779   \nReweighing     Logistic Regression Panel19      Panel19        -0.003518   \nReweighing     Logistic Regression Panel19      Panel20         0.030262   \nReweighing     Logistic Regression Panel19      Panel21        -0.004405   \nReweighing     Logistic Regression Panel20      Panel20        -0.015005   \nReweighing     Logistic Regression Panel20      Panel21         0.001623   \n\n                                                             theil_ind  \nBias Mitigator Classifier          Training set Testing set             \n               Logistic Regression Panel19      Panel19       0.092122  \nReweighing     Logistic Regression Panel19      Panel19       0.096575  \nReweighing     Logistic Regression Panel19      Panel20       0.101910  \nReweighing     Logistic Regression Panel19      Panel21       0.099420  \nReweighing     Logistic Regression Panel20      Panel20       0.098818  \nReweighing     Logistic Regression Panel20      Panel21       0.095961  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>bal_acc</th>\n      <th>avg_odds_diff</th>\n      <th>disp_imp</th>\n      <th>stat_par_diff</th>\n      <th>eq_opp_diff</th>\n      <th>theil_ind</th>\n    </tr>\n    <tr>\n      <th>Bias Mitigator</th>\n      <th>Classifier</th>\n      <th>Training set</th>\n      <th>Testing set</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th></th>\n      <th>Logistic Regression</th>\n      <th>Panel19</th>\n      <th>Panel19</th>\n      <td>0.775935</td>\n      <td>-0.205706</td>\n      <td>0.426176</td>\n      <td>-0.261207</td>\n      <td>-0.222779</td>\n      <td>0.092122</td>\n    </tr>\n    <tr>\n      <th>Reweighing</th>\n      <th>Logistic Regression</th>\n      <th>Panel19</th>\n      <th>Panel19</th>\n      <td>0.753893</td>\n      <td>-0.015104</td>\n      <td>0.751755</td>\n      <td>-0.087196</td>\n      <td>-0.003518</td>\n      <td>0.096575</td>\n    </tr>\n    <tr>\n      <th>Reweighing</th>\n      <th>Logistic Regression</th>\n      <th>Panel19</th>\n      <th>Panel20</th>\n      <td>0.731136</td>\n      <td>0.007135</td>\n      <td>0.805724</td>\n      <td>-0.059602</td>\n      <td>0.030262</td>\n      <td>0.101910</td>\n    </tr>\n    <tr>\n      <th>Reweighing</th>\n      <th>Logistic Regression</th>\n      <th>Panel19</th>\n      <th>Panel21</th>\n      <td>0.737916</td>\n      <td>-0.014340</td>\n      <td>0.744126</td>\n      <td>-0.081262</td>\n      <td>-0.004405</td>\n      <td>0.099420</td>\n    </tr>\n    <tr>\n      <th>Reweighing</th>\n      <th>Logistic Regression</th>\n      <th>Panel20</th>\n      <th>Panel20</th>\n      <td>0.749024</td>\n      <td>0.015756</td>\n      <td>0.946696</td>\n      <td>-0.018444</td>\n      <td>-0.015005</td>\n      <td>0.098818</td>\n    </tr>\n    <tr>\n      <th>Reweighing</th>\n      <th>Logistic Regression</th>\n      <th>Panel20</th>\n      <th>Panel21</th>\n      <td>0.736958</td>\n      <td>-0.002077</td>\n      <td>0.830199</td>\n      <td>-0.064846</td>\n      <td>0.001623</td>\n      <td>0.095961</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "source": [
    "results = [lr_orig_metrics, lr_transf_metrics,\n",
    "           lr_transf_metrics_panel20_deploy,\n",
    "           lr_transf_metrics_panel21_deploy,\n",
    "           lr_transf_metrics_panel20_test,\n",
    "           lr_transf_panel20_metrics_panel21_deploy]\n",
    "debias = pd.Series([''] + ['Reweighing']*5, name='Bias Mitigator')\n",
    "clf = pd.Series(['Logistic Regression']*6, name='Classifier')\n",
    "tr = pd.Series(['Panel19']*4 + ['Panel20']*2, name='Training set')\n",
    "te = pd.Series(['Panel19']*2 + ['Panel20', 'Panel21']*2, name='Testing set')\n",
    "pd.concat([pd.DataFrame(m) for m in results], axis=0).set_index([debias, clf, tr, te])"
   ]
  }
 ],
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