{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm\n",
    "# NOTE TO PUT UTILITY.PY FILE IN THE SAME FOLDER OF THIS NOTEBOOK\n",
    "from nutrition_label_utility import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data and Automated Decision System (ADS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### College Ranking\n",
    "CS Rankings (CSR) (https://csrankings.org), with additional attributes from the NRC assessment dataset (http://www.nap.edu/rdp ). This dataset has the following attributes: \n",
    "- Average Count (CSR) computes the geometric mean of the adjusted number of publications in each area by institution\n",
    "- Faculty (CSR) is the number of faculty in the department\n",
    "- GRE (NRC) is the average GRE scores (2004-2006)\n",
    "- Department size (CSR) is a binary attribute derived from Faculty. Small department has the number of faculty less or equal than 30.\n",
    "- Region (NRC) is one of Northeast(NE), Midwest(MW), South Atlantic(SA), South Central(SC), West(W) regions in the US\n",
    "- Pub Count (NRC) is the average number of publications per faculty (2000-2006)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>College Name</th>\n",
       "      <th>Average Count</th>\n",
       "      <th>Faculty</th>\n",
       "      <th>GRE</th>\n",
       "      <th>Department Size</th>\n",
       "      <th>Regional Code</th>\n",
       "      <th>Pub Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carnegie Mellon University</td>\n",
       "      <td>18.3</td>\n",
       "      <td>122</td>\n",
       "      <td>791.376</td>\n",
       "      <td>Large</td>\n",
       "      <td>NE</td>\n",
       "      <td>2.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>15.0</td>\n",
       "      <td>64</td>\n",
       "      <td>771.894</td>\n",
       "      <td>Large</td>\n",
       "      <td>NE</td>\n",
       "      <td>2.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>14.3</td>\n",
       "      <td>55</td>\n",
       "      <td>800.000</td>\n",
       "      <td>Large</td>\n",
       "      <td>W</td>\n",
       "      <td>4.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>University of California--Berkeley</td>\n",
       "      <td>11.4</td>\n",
       "      <td>50</td>\n",
       "      <td>789.451</td>\n",
       "      <td>Large</td>\n",
       "      <td>W</td>\n",
       "      <td>3.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>University of Illinois--Urbana-Champaign</td>\n",
       "      <td>10.5</td>\n",
       "      <td>55</td>\n",
       "      <td>771.894</td>\n",
       "      <td>Large</td>\n",
       "      <td>MW</td>\n",
       "      <td>2.704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               College Name  Average Count  Faculty      GRE  \\\n",
       "0                Carnegie Mellon University           18.3      122  791.376   \n",
       "1     Massachusetts Institute of Technology           15.0       64  771.894   \n",
       "2                       Stanford University           14.3       55  800.000   \n",
       "3        University of California--Berkeley           11.4       50  789.451   \n",
       "4  University of Illinois--Urbana-Champaign           10.5       55  771.894   \n",
       "\n",
       "  Department Size Regional Code  Pub Count  \n",
       "0           Large            NE      2.319  \n",
       "1           Large            NE      2.667  \n",
       "2           Large             W      4.504  \n",
       "3           Large             W      3.198  \n",
       "4           Large            MW      2.704  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"CSranking.csv\")\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ADS - Algorithmic rankers\n",
    "- Ranking methodology is inspired by US World & News Report and CS Rankings.\n",
    "- A rule-based system (ranker). Scores are generated through a rule: f(x) = w_1 * Faculty(x) + w_2 * Average Count(x) + w_3 * GRE(x). \n",
    "- Generate a ranking of CS departments based on above scores.\n",
    "- Other possible rankers: [Learning to Rank methods](http://www.morganclaypool.com/doi/abs/10.2200/S00607ED2V01Y201410HLT026)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>College Name</th>\n",
       "      <th>Average Count</th>\n",
       "      <th>Faculty</th>\n",
       "      <th>GRE</th>\n",
       "      <th>Department Size</th>\n",
       "      <th>Regional Code</th>\n",
       "      <th>Pub Count</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carnegie Mellon University</td>\n",
       "      <td>18.3</td>\n",
       "      <td>122</td>\n",
       "      <td>791.376</td>\n",
       "      <td>Large</td>\n",
       "      <td>NE</td>\n",
       "      <td>2.319</td>\n",
       "      <td>931.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>15.0</td>\n",
       "      <td>64</td>\n",
       "      <td>771.894</td>\n",
       "      <td>Large</td>\n",
       "      <td>NE</td>\n",
       "      <td>2.667</td>\n",
       "      <td>850.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>14.3</td>\n",
       "      <td>55</td>\n",
       "      <td>800.000</td>\n",
       "      <td>Large</td>\n",
       "      <td>W</td>\n",
       "      <td>4.504</td>\n",
       "      <td>869.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>University of California--Berkeley</td>\n",
       "      <td>11.4</td>\n",
       "      <td>50</td>\n",
       "      <td>789.451</td>\n",
       "      <td>Large</td>\n",
       "      <td>W</td>\n",
       "      <td>3.198</td>\n",
       "      <td>850.851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>University of Illinois--Urbana-Champaign</td>\n",
       "      <td>10.5</td>\n",
       "      <td>55</td>\n",
       "      <td>771.894</td>\n",
       "      <td>Large</td>\n",
       "      <td>MW</td>\n",
       "      <td>2.704</td>\n",
       "      <td>837.394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               College Name  Average Count  Faculty      GRE  \\\n",
       "0                Carnegie Mellon University           18.3      122  791.376   \n",
       "1     Massachusetts Institute of Technology           15.0       64  771.894   \n",
       "2                       Stanford University           14.3       55  800.000   \n",
       "3        University of California--Berkeley           11.4       50  789.451   \n",
       "4  University of Illinois--Urbana-Champaign           10.5       55  771.894   \n",
       "\n",
       "  Department Size Regional Code  Pub Count    Score  \n",
       "0           Large            NE      2.319  931.676  \n",
       "1           Large            NE      2.667  850.894  \n",
       "2           Large             W      4.504  869.300  \n",
       "3           Large             W      3.198  850.851  \n",
       "4           Large            MW      2.704  837.394  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = [1, 1, 1]\n",
    "chosen_atts = [\"Average Count\", \"Faculty\", \"GRE\"]\n",
    "\n",
    "data[\"Score\"] = sum([weights[idx]*data[atti] for idx, atti in enumerate(chosen_atts)])\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Questions considering interpretability\n",
    "1) (Data) What would be a good feature set for the decisions? \n",
    "\n",
    "2) (Outcomes) Is 10.5 a high score or a low score? \n",
    "   - The score generation rule alone does not indicate the relative rank of an item. \n",
    "   - It depends on how 10.5 compares to the scores of other items.\n",
    "   \n",
    "3) (Outcomes) Is the generated scores stable? \n",
    "   - Unless raw scores are disclosed, the user has no information about the magnitude of the difference in scores   between items that appear in consecutive ranks. \n",
    "   - In Table 1, CMU (18.3) has a much higher score than the immediately following MIT (15). This is in contrast to UIUC (10.5, rank 5) and UW (10.3, rank 6), which are nearly tied.\n",
    "   \n",
    "4) (Outcomes) Is there any unfair treatment*?\n",
    "\n",
    "5) (Outcomes) How are departments represented in the ranking?\n",
    "\n",
    "6) (Rankers) How to interpret the weight? \n",
    "   - The weight of an attribute in the score generation rule does not determine its impact on the outcome.\n",
    "   - For example, given f(x) = 0.2 * Faculty(x) + 0.3 * Average Count(x) + 0.5 * GRE(x), yet for data in Table 1, attribute Faculty will be the deciding factor that sets apart top-ranked departments from those in lower ranks. \n",
    "   - This is because the value of Faculty changes most dramatically in the data, and because it correlates with     Average Count (in effect, double-counting).\n",
    "    \n",
    "7) (Rankers) Is the ranking methodology stable? \n",
    "   - For example, a score generation rule:  f(x) = Pub Count(x) + GRE(x) would be unstable, because the values of these attributes are both very close for many of the items and induce different rankings.\n",
    "   - Prioritizing one attribute over the other slightly would cause significant re-shuffling.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Analysis\n",
    "- Pre-process data set as need.\n",
    "- Explore the data set before choose the ranker."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>College Name</th>\n",
       "      <th>Average Count</th>\n",
       "      <th>Faculty</th>\n",
       "      <th>GRE</th>\n",
       "      <th>Department Size</th>\n",
       "      <th>Regional Code</th>\n",
       "      <th>Pub Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carnegie Mellon University</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.795577</td>\n",
       "      <td>Large</td>\n",
       "      <td>NE</td>\n",
       "      <td>0.338280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>0.804734</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.333776</td>\n",
       "      <td>Large</td>\n",
       "      <td>NE</td>\n",
       "      <td>0.443671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>0.763314</td>\n",
       "      <td>0.379630</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Large</td>\n",
       "      <td>W</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>University of California--Berkeley</td>\n",
       "      <td>0.591716</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.749947</td>\n",
       "      <td>Large</td>\n",
       "      <td>W</td>\n",
       "      <td>0.604482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>University of Illinois--Urbana-Champaign</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.379630</td>\n",
       "      <td>0.333776</td>\n",
       "      <td>Large</td>\n",
       "      <td>MW</td>\n",
       "      <td>0.454876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               College Name  Average Count   Faculty  \\\n",
       "0                Carnegie Mellon University       1.000000  1.000000   \n",
       "1     Massachusetts Institute of Technology       0.804734  0.462963   \n",
       "2                       Stanford University       0.763314  0.379630   \n",
       "3        University of California--Berkeley       0.591716  0.333333   \n",
       "4  University of Illinois--Urbana-Champaign       0.538462  0.379630   \n",
       "\n",
       "        GRE Department Size Regional Code  Pub Count  \n",
       "0  0.795577           Large            NE   0.338280  \n",
       "1  0.333776           Large            NE   0.443671  \n",
       "2  1.000000           Large             W   1.000000  \n",
       "3  0.749947           Large             W   0.604482  \n",
       "4  0.333776           Large            MW   0.454876  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur_data = data.iloc[:,:-1] # remove the above generated score column by original data\n",
    "ignore_atts = [\"College Name\",\"Department Size\", \"Regional Code\"] # exclude the categorical data during preprocessing\n",
    "norm_data = normalizeDataset(cur_data, ignore_atts)\n",
    "# standarded_data = standardizeData(norm_data, ignore_atts)\n",
    "norm_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_att_dist(_data, _att, _category=False):\n",
    "    plt.figure(figsize=[6,5],dpi=100)\n",
    "    sns.set(style=\"darkgrid\")  \n",
    "    sns.set(font_scale = 2)\n",
    "    if _category: # for categorical attribute\n",
    "        vis_data = _data[_att].value_counts()\n",
    "        ax = sns.barplot(x=vis_data.index, y=vis_data.values);\n",
    "        ax.set_yticks([x for x in range(max(vis_data.values)+1) if x%2==0]);\n",
    "        ax.set_xlabel(_att)\n",
    "    else:\n",
    "        ax = sns.distplot(_data[_att], kde=False, color='steelblue');\n",
    "    ax.set_ylabel(\"Count\")\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iIiLJyXpSK1FRlEoFlEqF1GW8dLRaHbRandRlEJEVYiAhq6NUKuDiYg+lUiV1KS8drVaDpKQshhIieuEYSMjq5J8dUeHObyuRlfBQ6nJeGvaVa6D+gPegVCoYSIjohWMgIauVlfAQWY/vSV0GERGBk1qJiIjoJcBAQkRERJJjICEiIiLJMZAQERGR5BhIiIiISHIMJERERCQ52QeSmTNnwtPTEwsWLCi2bVpaGn7++WeMHDkSPj4+aNasGdq3b49hw4Zh2bJlSE5OfgEVExERWR9Zr0Ny4MABbNmyxaS2Z8+exeTJk/HkyZMC309JSUFUVBSioqKwbt06LFiwAB07drREuURERFZLtoEkPDwcU6ZMMantrVu38P777yM9PR0KhQIDBw5E79694ebmhvj4eISFhWHXrl1ITk5GYGAgNm7ciKZNm1r4JyAiIrIesgwka9aswbx586BWq01qHxISgvT0dADA3LlzMXDgwAKPd+/eHR07dsS0adOQnZ2N2bNnY9OmTaLXTUREZK1kNYfk7t27CAgIQEhICNRqNVSq4m+e9vjxY0RERAAAunXr9lwY0Rs8eDC6desGADh//jz++usv8QonIiKycrIJJBs2bMCAAQNw5MgRAICHhwdmzZpV7H5nz56FTpd/IzFfX1+jbTt16iRsX7t2zYxqiYiIyJBshmwuXboEtVqNcuXKYfTo0QgKCkJUVFSx+9WtWxcTJkzAkydPip0Xog8uAJCTk2N2zURERJRPNoHEzs4Ofn5+CAwMhLu7u8n7NWvWDM2aNTOp7alTp4TtkjwHERERGSebQBIcHAyl0nIjUNevXxeGgypVqgQvLy+LPRcREZG1kU0gsWQYyc7OxrRp06DRaAAAo0aNgo2N+V1nY1N0zSqVbKb3WIQ5/cO+NY79Q0RSkE0gsZS8vDx8+OGHuHr1KgCgYcOGGDNmjNnHVSoVcHFxMPs41srJyV7qEmSLfUtEUmAgMSI3Nxf/+c9/cOjQIQCAo6MjFi9ejAoVKph9bK1Wh9TUzCIfV6mUfGMwIjU1CxqNtlT7sm+NM6dvicg6OTnZm312lYGkCOnp6Zg4cSJOnDgBAKhQoQJWrFgBDw8P0Z4jL48v+qWl0WjZfxbCviUiKTCQFCI2NhYBAQG4fv06AKBixYpYsWIF2rRpI3FlRERE8sRA8oyLFy8iMDAQ8fHxAAA3NzesXLkSTZo0kbgyIiIi+eJ0egMRERF49913hTDSoEEDbNq0iWGEiIjIwkQ5Q3L69GkAQJ06dVCtWrUS7Xvr1i1EREQgKysLAQEBYpRTKhEREQgMDERubi4AoH379li6dCkqVaokWU1ERETWQpQzJCNHjsSoUaOwZ8+eEu+7b98+hISESHr33Dt37mDixIlCGOnZsydWrVrFMEJERPSCSD6HRK1WAwASExMleX6NRoP//Oc/yMzMvwS3c+fOWLhwoSgLnxEREZFpTH7X1Wg0OH/+fIEbzD3r3r17wvCNKceLjY3FL7/8AiD/ShYp7NmzB1euXAGQf2nv2LFjcePGjWL3q1KlCtzc3CxdHhERkVUwOZCoVCqsW7cOBw4cKPRxnU6HTZs2lWroRaFQSHZJrWG9mZmZGD16tEn7/fvf/8bEiRMtVRYREZFVKdEcks8++wz29vbQ6XQFvvSe/b6pX05OTpg8ebLoP5wp9EvCExERkXRKNFGiWrVq+PLLL/HHH38U+H5oaCgUCgWaNWuGRo0amXQslUoFR0dH1KpVC3369EGVKlVKUopJvL29ERMTY7TN+fPnRX9eIiIiKpkSz9zs168f+vXrV+B7oaGhAID+/fubPORBREREpCfKpSTt2rUDAFSvXl2MwxEREZGVESWQ/O9//xPjMERERGSluHQ8ERERSU701b/UajUuXryI+Ph45OTkQKs1/TbmgwcPFrscIiIiKgNECyR5eXlYsmQJ1q9fL6x6WhIKhYKBhIiIyEqJFkgmTZqEI0eOGF3JlYiIiKgwogSSgwcP4vDhw1AoFACA2rVro23btnB1dYW9vb0YT0FEREQyJkog2bFjh7A9fvx4TJ48WQgnRERERMURJZBcuHABCoUCTZo0wZQpU8Q4JBEREVkRUS77TU5OBgB06dJFjMMRERGRlRElkLi4uAAAypcvL8bhiIiIyMqIEkheeeUVAMDNmzfFOBwRERFZGVECyYABA6DT6XD48GHExcWJcUjRzJw5E56enliwYIFJ7Q8ePIj33nsPHTp0wKuvvorXXnsNgYGBOHz4sIUrJSIisl6iBJJBgwahbdu2yMrKwuTJk5GamirGYc124MABbNmyxaS2ubm5mDx5MoKCgnDs2DEkJiZCrVbj8ePHOHz4MAIDA/Hhhx9CrVZbuGoiIiLrI8pVNgqFAkuXLsWECRNw9uxZ9OzZE3369IGXlxeqVKli8lok+rsGiyE8PLxEV/z897//xd69ewEAXl5e8Pf3h7u7O+7cuYNVq1bh+vXr+P333+Ho6IjZs2eLVicRERGJFEi8vLwAAFqtFgqFAikpKdiyZYvJZyeA/FBz9epVMcrBmjVrMG/ePJPPZpw9e1aotWPHjli5ciVsbPK7xsvLC3379sW4ceMQGRmJzZs3w8/PD82bNxelViIiIhJpyCYnJwc5OTlQq9XC0vE6na7EX+a6e/cuAgICEBISArVaDZVKZdJ+q1evBgCoVCrMnDlTCCN6dnZ2+Prrr2FrawsA+Omnn8yulYiIiP4hyhmSIUOGiHEYs2zYsEEIIgDg4eEBf39/zJgxw+h+2dnZCA8PB5B/NqRBgwaFtqtduza8vb0RERGB8PBwZGVlcVl8IiIikYgSSEJCQsQ4jFkuXboEtVqNcuXKYfTo0QgKCkJUVJRJ++Xm5gIAfHx8jLZt3749IiIikJWVhaioKHTo0EGU2omIiKydaHf7lZqdnR38/PwQGBgId3d3k/czXDulXr16RtvWrl1b2L5x4wYDCRERkUhkE0iCg4OhVJZ8SsyjR4+E7Zo1axptW6NGjUL3IyIiIvPIJpCUJowAQEpKirDt4OBgtG2FChWE7bS0tFI9nyEbm6JrVqlEmW8sW+b0D/vWODH6R6FQQKnkHb+fpdWKM4GfSI5ECSSjRo0y+xgKhQJr164VoZqS0c8fAfKHfYwxvFeP4X6loVQq4OJiPABR0ZycOKHYUsToW61WW+oPCXLGfiEqmiiBJDIyEgpF6T4N6T8tlHZ/cxleGlxcDYafbMx9UdFqdUhNzTRSl5JvukakpmZBo9GWal/2rXHm9C3wT/+uCF+HBymPRaysbKtZqRrGdx1ldv8SvYycnOzNPrsq2pBNaU5DKhQK1K5dW9K7BBsOw+Tk5Bhta/i4fk0Sc+Tl8UWptDQaLfvPQsTq2wcpj/FXwt8iVCQv/LdLVDhRAsmhQ4eKbaPT6ZCTk4O4uDhcunQJGzZswKNHj1CpUiX8+OOPcHV1FaOUEjOcN5KZWfQZi2cfd3Z2tlhNRERE1kaUQFKSy2wbNmwIHx8fvP322xg/fjzOnDmDiRMnYv369ZIM2xjWXtyVMw8fPhS2q1WrZrGaiIiIrI1ks6sqVKiAhQsXws7ODufOncPOnTslqaNRo0bC9r1794y2vX//vrDt4eFhsZqIiIisjaTTvStXrgxfX1/odDrs2LFDkho8PT2FeSRnzpwx2jYyMhIAUK5cObRo0cLitREREVkLya8/0987xnDF1BfJzs4O3bt3BwCcOnWqwFkQQ/fv38fJkycBAF27duV9bIiIiEQkeSBJT08HIM5CY6X1zjvvQKFQQK1W49NPP33uapucnBx8+umnyMvLAwCMHj1aijKJiIhkS9JAotVqcfjwYQD5wzdSadWqFYYOHQogf1jmrbfews6dO3HhwgXs3LkTfn5+wnDNsGHD0KZNG8lqJSIikiPJlo7PyMjA7Nmz8ddff0GhUMDb21uqUgAAM2fORGpqKvbu3Yvr169j6tSpz7Xp168fZsyYIUF1RERE8iZKIPnkk09Mbpubm4vExERcuHABWVlZwveHDx8uRimlZmtri4ULF2LAgAH49ddfcfnyZSQlJcHR0RHNmzfH0KFD0atXL0lrJCIikitRAkloaGip1hDRr+7q7+8PLy8vMUopwNvbGzExMSXap0ePHujRo4fotRAREVHRJF06vmHDhvD394efn59YZRAREVEZJEogWbduncltlUolypcvj6pVq6Jq1apiPD0RERGVcaIEkvbt24txGCIiIrJSkq9DQkRERGTRy37v3buH+/fvIyUlBQqFAk5OTqhduzbq1KljyaclIiKiMkb0QJKSkoI1a9Zg27ZtiIuLK7SNq6srBgwYgPHjx8PV1VXsEoiIiKiMEXXI5syZMxgwYACWL1+OuLg46HS6Qr8SEhKwbt06DBw4ULg/DBEREVkv0c6QXL58GePGjUNOTo5wCbCzszMaN24MZ2dnaLVaJCUlISYmBqmpqdDpdIiPj8f48eOxZcsWeHp6ilUKERERlTGiBJK8vDxMmTIF2dnZAICmTZvi448/RocOHQptf/z4cXz33Xe4cuUKcnJyMHnyZOzevRs2NpKtZE9EREQSEmXIZseOHbh//z4UCgW6du2KzZs3FxlGAKBTp07YvHkzXn/9dQDA3bt38dtvv4lRChEREZVBogSSgwcPAgAqVqyIb775Bra2tsXuY2Njg2+++QZOTk4AgLCwMDFKISIiojJIlEASHR0NhUIBX19fODs7m7xfpUqV4OvrC51Oh8uXL4tRChEREZVBogSSpKQkAEDdunVLvK9+n+TkZDFKISIiojJIlEBiZ2cHAMjMzCzxvvp9KlSoIEYpREREVAaJcllLzZo1ERMTg1OnTpV4X/0+1atXF6MUs2VlZWHz5s04cOAAbty4gczMTDg5OaFp06YYNGgQ+vXrB5VKJXWZREREsiLKGRL9zfUuXryIw4cPm7zfoUOHcOHCBSgUCnh7e4tRilnu3r2LIUOGICQkBGfOnEFKSgrUajUSEhLwxx9/4KOPPoK/vz9SUlKkLpWIiEhWRAkkw4cPh0KhAAB8/PHHOHr0aLH7HDlyBFOnTgUAKBQKvPnmm2KUUmqZmZkYN24c7ty5AwBo164d5s+fj19++QVff/01PDw8AACRkZH497//LSz+RkREROYTZcimYcOGGDZsGDZt2oTMzEwEBgaiXbt26N27t7BSKwBhpdawsDCcOXMGOp1OCCONGzcWo5RSW79+Pe7fvw8AGDhwIObOnSuErDZt2uBf//oXxo8fjxMnTiAyMhL79u1Dnz59pCyZiIhINkRbGvWTTz7B33//jYiICCgUCpw+fRqnT58usr3+DIO3tzc+//xzscootfDwcGF7+vTpQhjRK1euHKZOnYrBgwcDyB9uYiAhIiISh2g317Ozs8OKFSsQGBgIe3v7Im+sp/+yt7dHQEAAVq1aZdJCapYWHx8PAHByckLlypULbVO/fn1hu6g7GRMREVHJiXrzGJVKhQ8++ACjRo3CkSNHcPLkSfz9999ITk6GTqdDpUqVULduXbRt2xa9e/cWVml9GVStWhV3795Famoq4uLi4Obm9lyb27dvC9svy1VBREREcmCRu9m5uLjgjTfewBtvvGGJw1uEr68vIiMjAQDz58/HnDlzCjyu0Wgwb9484e/9+vV7ofURERHJmSiBJDY2Fu7u7sW2W7lyJRo0aICuXbu+dHf2HTFiBA4dOoTIyEiEhobi4cOHGDp0KGrUqIH79+9j7dq1uHLlCoD8q4pee+01iSsmIiKSD7NSwZ49e7Bo0SKoVCrs2bPHaFu1Wo3vv/8e2dnZqFmzJj744AMMHDjQnKcXlZ2dHVauXIlVq1Zh9erVOHnyJE6ePFmgjZubG6ZOnSpa3TY2RU/hUalEm94jS+b0D/vWOHP7h/1rHPuHqHClCiQ5OTn4+OOPceDAAeHS3cTERLi6uha5z4ULF5CVlQWFQoHY2FhMmzYN4eHhmDNnzksxqRUAbt68iejo6CKXwI+Pj8fevXvRrFkzNGzY0KznUioVcHFxMOsY1szJyV7qEmSLfWtZ7F+iwpU4kGg0GgQFBeH48ePC93Q6HWJiYtChQ4ci97O1tUWnTp1w6tQp5OXlQafTYc+ZeW6VAAAgAElEQVSePVCr1Vi8eHHpqhfR0aNH8cEHHyA7Oxuurq6YNGmScPfiBw8eYNeuXVi5ciUOHz6Mc+fOYdWqVXj11VdL/XxarQ6pqUXf+0elUvKFy4jU1CxoNNpS7cu+Nc6cvgXYv8Uxt3+JXkZOTvZmn/0rcSBZsWKFsNaITqfDwIEDMWHCBNSrV8/ofl5eXli1ahUePnyIBQsWYNeuXdDpdDhw4AD+97//YeTIkaX9Gcz25MkTTJkyBdnZ2XBxccHmzZtRp04d4fF69eph0qRJ8Pb2xtixY5GcnIyJEyciLCxMuLFgaeTl8UWptDQaLfvPQti3lsX+JSpcieJMUlISfvzxRwD5l/jOnz8fc+fOLTaMGKpRowbmzp2LmTNnAsg/u7Jo0SJkZGSUpBRR7dixQximmTRpUoEwYsjb2xtvv/02AODBgwc4dOjQC6uRiIhIzkoUSHbt2oXs7GwoFApMmjQJ/fv3L/UTv/322xgxYgQAICMjA7t37y71scx18eJFYdvX19do2549ewrbUVFRFquJiIjImpQokPz5558A8tcZGTNmjNlP/sEHH6B8+fIAUGBOyotmOIm1YsWKRtsaruKalpZmsZqIiIisSYkCSUxMDBQKBTp16iTKOiLOzs7w8fGBTqfD1atXzT5eabm4uAjb9+7dM9r28ePHwnZRS8wTERFRyZQokCQnJwNAieaMFMfT0xMAkJiYKNoxS6p9+/bC9s6dO422NRxaateuncVqIiIisiYlCiR5eXkAIOq6IQ4O+WtxqNVq0Y5ZUv379xfWUFm7di2OHDlSaLvdu3dj27ZtAIAGDRqgU6dOL6xGIiIiOSvRuIuTkxMSExORlJQkWgGpqakA/gkmUnB0dMRXX32FoKAgaDQaBAYGon///ujbty+qVq2KJ0+eYO/evfj999+h0+lQvnx5zJkz56Vb/p6IiKisKtE7av369ZGQkIDo6GjRCrh8+TIA6e+e2717dyxevBjTp09Heno6fvvtN/z222/PtXNzc8OCBQvg5eUlQZVERETyVKJA0qpVK5w5cwZnzpxBQkKC2ZM64+LicObMGSgUCjRu3NisY4mhZ8+eaNOmDTZu3Ihjx47hzp07yMjIQMWKFdGoUSN0794dQ4cOlfRsDhERkRyVKJD06tULK1euhEajwaJFizB79myznnzp0qXIy8uDQqFA165dzTqWWFxdXREUFISgoCCpSyEiIrIaJZrU2rx5c7Rs2RI6nQ5bt27Fjh07Sv3Eu3fvxubNm6FQKFC5cuViFyQjIiIi+SrxnXCmTp0KhUIBAPj000+xcOFC5Obmmrx/dnY2FixYgOnTpwvf++CDD8y6JwwRERGVbSW+TKR169YIDAzE999/DyD/ZntbtmxB//794ePjg9atWxdYaEyn0yEhIQFnz57F8ePHceDAASQnJ0On0wEABgwYAD8/P5F+HCIiIiqLSnXd6qRJk5CVlYXVq1dDoVAgMTER69evx/r16/MPamMDFxcX5ObmIjU1VQgfAAps+/n5YdasWWb+CERERFTWlXohjWnTpsHLywtff/01njx5Ap1OB4VCAZ1OB7VajSdPnhS5b506dfDRRx+hV69epX16IiKrpVQqoFQqpC7jpaPV6qDV6opvSC8ls1b26tOnD7p3744dO3Zg9+7dOH/+vLCa67MqVaoEb29vDBw4EN26dYNKpTLnqYmIrJJSqYCLsz2UfA19jlajQVJyFkNJGWX2UqPlypXD0KFDMXToUOTk5ODWrVt4+PAhMjMzoVKpULFiRdStWxe1a9cWJsMSEVHpKJUKKFUqRP2wAukPHkpdzkvDsWYNtAwcD6VSwUBSRom69rmdnR2aNm2Kpk2binlYIiJ6RvqDh0j96y+pyyASTYkv+yUiIiISGwMJERERSY6BhIiIiCTHQEJERESSYyAhIiIiyYl6lY1c3LhxA5s2bcKJEyfw6NEjaLVauLu7o0uXLvD390eNGjWkLpGIiEhWGEie8f3332PZsmXPLfB269Yt3Lp1C7/++iu+++47dO3aVaIKiYiI5IeBxMDSpUuxZMkSAICLiwvGjBmDVq1aIS8vD2FhYdiyZQvS09MxadIkbN++HQ0bNpS4YiIiInlgIHkqOjoaP/zwAwDA3d0da9euRe3atYXHO3TogKZNm+Lzzz9HdnY2Fi1ahMWLF0tVLhERkaxwUutTixcvRl5eHhQKBRYuXFggjOgNGzYMr7zyCgDg8OHDyM7OftFlEhERyRLPkABISkrCH3/8AQDo3bs3WrRoUWTbsWPH4uzZs3BxcUFmZibKly//osokIiKSLQYSACdOnIBarQYADBgwwGjbwYMHY/DgwS+iLCIiIqvBQALg2rVrwrbh2RGtVou4uDhkZGSgWrVqcHBwkKI8IiIi2WMgQf66IwBga2uLqlWrIj4+HkuWLMHevXuRkpICAFCpVGjXrh0mTpyItm3bSlkuERGR7DCQAEhOTgYAODo6IioqCgEBAcL39DQaDU6ePIlTp05h6tSpGDNmjNnPa2NT9JxilYrzjY0xp3/Yt8aZ2z/sX+PYv5bF/im7GEgAZGRkAABycnIQEBCAlJQUjBw5EsOHD0edOnWQmJiIvXv3YvHixcjMzMQ333yD6tWro1+/fqV+TqVSARcXDgGVlpOTvdQlyBb71rLYv5bF/i27GEgAZGVlAQAyMzORmZmJL7/8En5+fsLj1atXx+jRo9GqVSu88847UKvVmDNnDnx9fWFnZ1eq59RqdUhNzSzycZVKyf9YRqSmZkGj0ZZqX/atceb0LcD+LQ7717LM7V8qHScne7PPTjGQAAUu3e3QoUOBMGKoZcuWeOutt7Bx40Y8fvwYJ06cQLdu3Ur9vHl5/E9TWhqNlv1nIexby2L/Whb7t+ziYBvy547o9erVy2jb7t27C9tRUVEWq4mIiMiaMJAAcHNzE7arV69utG3NmjWF7aSkJIvVREREZE0YSAB4enoK2/rLfIuSm5srbDs5OVmsJiIiImvCQIL8uSF6Z8+eNdpWv2YJANSqVctiNREREVkTBhLkT2TVD9vs3bsX8fHxRbYNDQ0FkL9QmuF8EiIiIio9BhLkh4uxY8cCANLT0/HRRx8Ja5MYWrt2Lf78808AQM+ePVG1atUXWicREZFc8bLfp959910cPXoUJ0+exJ9//okhQ4bg3XffRZMmTZCWloZdu3bht99+AwC4urri888/l7hiIiIi+WAgeUqpVGLFihWYNm0awsLC8Ndff2H27NnPtatXrx6WLVuGypUrS1AlERGRPDGQGChfvjwWLVqEEydOYNu2bTh37hzi4+NRsWJF1K1bFwMGDMDgwYN5118iIiKRMZAUomPHjujYsaPUZRAREVkNTmolIiIiyTGQEBERkeQYSIiIiEhyDCREREQkOQYSIiIikhwDCREREUmOgYSIiIgkx0BCREREkmMgISIiIskxkBAREZHkGEiIiIhIcgwkREREJDkGEiIiIpIcA4kJUlNT0aVLF3h6euKjjz6SuhwiIiLZYSAxwX//+188efJE6jKIiIhki4GkGAcPHsSuXbukLoOIiEjWGEiMSExMRHBwsNRlEBERyR4DiRGzZs1CfHw8XF1dpS6FiIhI1hhIirBnzx6EhYVBqVRixowZUpdDREQkawwkhYiPj8fs2bMBAP7+/vDy8pK4IiIiInljIClEcHAwkpKSUL9+fUyePFnqcoiIiGSPgeQZO3bswMGDB6FUKhESEgI7OzupSyIiIpI9G6kLeJk8fvwYX331FYD8oZpWrVpZ9PlsbIrOgyoVs6Ix5vQP+9Y4c/uH/Wsc+9ey2D9lFwOJgRkzZiA1NRX16tXDBx98YNHnUioVcHFxsOhzyJmTk73UJcgW+9ay2L+Wxf4tuxhIntq6dSuOHTsmDNWUL1/eos+n1eqQmppZ5OMqlZL/sYxITc2CRqMt1b7sW+PM6VuA/Vsc9q9lmdu/VDpOTvZmn51iIAHw4MEDzJkzBwDw7rvvonXr1i/kefPy+J+mtDQaLfvPQti3lsX+tSz2b9ll9YNtOp0On332GdLT01GvXj1eVUNERCQBqz9DsnnzZpw4cQIAMGrUKNy5c+e5NoY31ktNTUV0dDQAoEqVKnBzc3sxhRIREcmY1QeSqKgoYVu/GJox4eHhCA8PBwD8+9//xsSJEy1WGxERkbWw+iEbIiIikp7VnyGZM2eOMKG1KH///Td8fX0BAP/6178wb968F1EaERGR1eAZEiIiIpIcAwkRERFJjoGEiIiIJMdAQkRERJKz+kmtpqhVqxZiYmKkLoOIiEi2eIaEiIiIJMdAQkRERJJjICEiIiLJMZAQERGR5BhIiIiISHK8yoaIiOgppVIBpVIhdRkvHa1WB61WZ9HnYCAhIiJCfhhxdq4AlYqDB8/SaLRITs60aChhICEiIkJ+IFGplNi55RTi49KkLuelUcWtIgYN9YZSqWAgISIielHi49Lw+EGy1GVYHZ6XIiIiIskxkBAREZHkGEiIiIhIcgwkREREJDkGEiIiIpIcr7IpRHx8PDZu3IiIiAjcuXMHmZmZcHR0RKNGjeDr64uhQ4eiQoUKUpdJREQkGwwkzzh48CCmT5+OtLSC16AnJSUhMjISkZGRWLduHZYtW4YmTZpIVCUREZG8MJAYiIyMxOTJk6FWq2Fra4uhQ4fi9ddfh7OzMx4+fIjQ0FAcOXIEsbGxGDNmDLZv344aNWpIXTYREVGZx0DylE6nw6xZs4QwsmrVKnh7ewuPt2jRAr1798ayZcuwePFiJCYmYt68eZg/f76EVRMREckDJ7U+FRUVhZs3bwIAhg8fXiCMGJowYQJeeeUVAMD+/fuRmZn5wmokIiKSKwaSp06fPi1s+/r6FtlOoVCgU6dOAIDc3Fzcvn3b4rURERHJHYdsnmrRogUCAgLw+PFj1K9f32hbne6fmwvl5ORYujQiIiLZYyB5ysfHBz4+Pia1PXXqlLDt7u5uqZKIiIisBodsSig8PBzR0dEAgFdeeQXVq1eXuCIiIqKyj2dISiAxMRHBwcHC38eOHWvW8Wxsis6DKhWzojHm9A/71jhz+4f9axz717L42mA5lu4fBhITZWRkIDAwEA8fPgQAtG/fHgMHDiz18ZRKBVxcHMQqz+o4OdlLXYJssW8ti/1rWexfy7F03zKQmCAtLQ3vv/8+oqKiAADVq1fHd999B6Wy9GlRq9UhNbXoS4ZVKiX/YxmRmpoFjUZbqn3Zt8aZ07cA+7c47F/L4muD5RjrWycne7PPoDCQFOPJkyd4//33hXkjVapUwc8//ww3Nzezj52XV/oXJWun0WjZfxbCvrUs9q9lsX8tx9J9ywEzI65duwY/Pz8hjFSvXh3r1q1Dw4YNJa6MiIhIXhhIihAeHo4RI0bg0aNHAIAGDRrgl19+YRghIiKyAAaSQoSGhmLChAnCsvCtW7fGxo0bueYIERGRhTCQPGP79u345JNPkJeXBwDo27cv1q5dC2dnZ4krIyIiki8GEgOnT5/GjBkzhKXh33nnHSxYsADlypWTuDIiIiJ541U2T6Wnp+Pjjz+GRqMBALz55puYOXOmxFURERFZBwaSp9avXy8seubm5oahQ4cKV9cYU6NGDQ7nEBERmYmB5KlNmzYJ23FxcRg2bJhJ+4WEhOCNN96wVFlERERWgXNIkH+PGv3ZESIiInrxeIYEgKurK2JiYqQug4iIyGrxDAkRERFJjoGEiIiIJMdAQkRERJJjICEiIiLJMZAQERGR5BhIiIiISHIMJERERCQ5BhIiIiKSHAMJERERSY6BhIiIiCTHQEJERESSYyAhIiIiyTGQEBERkeR4t99naLVahIaGYseOHYiJiUFmZibc3NzQunVrDB8+HO3atZO6RCIiItlhIDGQlpaGCRMmIDIyssD3Hzx4gAcPHuD333+Hv78/pk+fLlGFRERE8sRA8pROp8PkyZOFMNK5c2eMGDECVapUQXR0NFauXInY2FisXr0arq6ueP/99yWumIiISD44h+Sp3bt3IyIiAgDwxhtvYNWqVejRowdatmyJESNGYPv27fDw8AAALF26FI8ePZKyXCIiIllhIHlq9erVAABHR0dMmzbtucednZ0xa9YsAEBOTg7WrVv3QusjIiKSMwYSAPfv38fVq1cBAN26dYOzs3Oh7dq2bYv69esDAMLCwl5YfURERHLHQALg7NmzwraPj4/Rtu3btwcAxMbG4t69exati4iIyFowkAC4efOmsF2vXj2jbWvXri1s37hxw1IlERERWRUGEqDABNWaNWsabVujRo1C9yMiIqLS42W/AFJSUoRtBwcHo20rVKggbKelpZX6OZVKBVxdi34uhSL/z2lju0Oj0Zb6eeRGpcrP0JUq2UOnK90x9H3b6K3J0Gk1IlVW9imUKgDm9S3wT//+p2cA8ti/AhuR+7fdxx9Cl8f+1VPYmN+/+r4d/m5nvu4aMOV1V6lUmP08DCQAcnNzhe3y5csbbWv4uOF+JaVQKKBSFf8LrORovB5rpVSaf3LP1sFJhErkR4y+BQAn+4qiHEduxOpfOyf++y2MGP3rwNfdQon1b7fI41v06GWESqUSthUK4yFBZxAPLf3LISIishZ8R0XBYZjs7GyjbXNycoTtcuXKWawmIiIia8JAgoLzRrKysoy2zczMFLYrVapksZqIiIisCQMJAHd3d2H74cOHRtsaPl6tWjWL1URERGRNGEgANGrUSNgubrGz+/fvC9v6e9sQERGReRhIALRs2VKYzHrmzBmjbfV3A65RowZq1apl8dqIiIisAQMJ8sNFy5YtAQD79u1Denp6oe3OnDmDO3fuAAB69+79wuojIiKSOwaSp0aOHAkASE5ORnBwMLTagovipKSkIDg4GABga2uLd95554XXSEREJFcKnc6cNQPlZezYsYiIiACQf2ffUaNGoVq1aoiJicGKFSsQGxsLAPjwww8xfvx4KUslIiKSFQYSA+np6QgICMDp06eLbOPv74/p06cXu4AaERERmY6B5BlarRY7duzArl27cO3aNaSlpcHFxQWtWrXC22+/DR8fH6lLJCIikh0GEiIiIpIcJ7USERGR5Hi3XxmbPn06QkNDAQD9+vXDggULTNrvr7/+Qq9evQAAQ4YMwZw5c4THlixZgqVLl5a6pmXLlqFHjx6l3v9ltnTpUixZsgQA8PHHH2PcuHFG22/cuBFffPGF8Pc5c+ZgyJAhRveZN28eVq5cCQCYO3cuBg0aZF7REhs5cqSwtg8AeHt7Y926dSbvr9Fo8NprryE+Pl743qFDh1C1alW0a9cO2dnZcHBwwOnTpwvcRLMwnTt3RlxcHID81ZsPHz5stH1mZibatWuHvLw81KlTBwcOHDC57pdNYmIifvvtNxw7dgy3b99GQkIClEolXF1dUbduXXTu3Bl9+/ZFjRo1SnTcn376Cd9++y2A/Ht/HTt2DC4uLpb4EV5KlujX9PR07N27F8eOHUN0dDTi4+ORl5cHJycnNGzYEN7e3hgyZEiBFcjLCp4hsRJ79uzBvn37pC5D1jp06CBsnzt3rtj2f/zxh9G/F8Zw4b6OHTuWoLqy4cyZMwXCRXFOnTpVaPty5cqhdevWAICMjAxcv37d6HGio6OFMAIAsbGxuH37ttF9zp07h7y8PABl+3exYcMG9OrVC1999RX++OMPxMbGIjs7G5mZmfj7779x/PhxfPPNN+jduzfmz58v/Mym2L59u7Cdm5tb4O9yJ3a/qtVq/Pjjj3j99dcxY8YM7N+/H/fv30dWVhbUajUSEhIQGRmJJUuWoHfv3pg3b16JflcvA54hsSJffPEF2rVrB1dXV1GON3HiRPj6+pZoHzmvbuvl5QUHBwdkZGTg/PnzRtuq1WqcPHkSQP6bZ25uLo4fPw6tVgulsvDPCdnZ2bh8+TIAwNPTE25ubuL+AC8BjUaD/fv34//+7/9Mav/7778X+ViHDh1w4sQJAPnhoUmTJkW21YdB/e9C/70GDRoUuY/h1XidO3c2qd6XzZo1axASEgIAcHNzw5tvvgkvLy9UrlwZABAXF4fIyEhs374daWlp+PHHH/Ho0SPMnTu32CsNz58/j1u3bgHI/3//999/Y8uWLRgzZozsr1IUu19TUlIQFBQk/JsrX748+vbti44dO8Ld3R22trZ4+PAhjh8/jp07dyI7OxsrV65EdHQ0fvjhhzJzZ3qeIbEiiYmJmD17tmjHq1mzJpo0aVKir4oVK4r2/C8bGxsbtGvXDkB+X+tX9S3M+fPnkZGRAQB48803AeQvynfp0iWj+6jVagBAp06dxCr7pVGlShUAwN69e01qr1arcfDgQQAo9AW3JGes9IGkQ4cOqFmzZoHvFUV/tkqlUpXJq+8ePHiAefPmAQBeffVV7NmzB1OmTEH37t3h5eUFLy8v9OjRA59++il+//131K9fHwCwa9cuhIWFFXv8bdu2Aci/K/rYsWMBAHfv3sWff/5poZ/o5SB2v+bm5mLChAlCGPHy8kJYWBjmzJmDgQMHok2bNmjRogV69+6N2bNnY/fu3cIxIyIi8NVXX72gn9x8DCRWwtbWFkD+i70pLyZUOqa+Cerf7BwdHYUXa8PvF8ZwuKasfiI3Rj9vydRhm4iICCQnJ8PW1rZAv+s1a9YMlSpVAmD8d2F4Rqt9+/bCsU6fPo2cnJxC98nJycHFixcBAC1atCiTQXvr1q1CwA0ODoaTk1ORbatVqybMBQGAtWvXGj12VlaWECzbtm2LXr16CXN4Nm3aZG7pLzWx+3XFihXC//1WrVrh559/NjrnpE6dOli5ciUcHBwAAJs3bxbOrL7sGEisRFBQkLA9a9YsJCYmSliNfJU0kLRv3x61a9dGw4YNC3y/MPrJn3Z2dmjbtq0Y5b5U+vXrByB/LSBT5jvph2s6d+5c6Iu+UqmEt7c3gPxPrY8ePSr0OH/++afwBtKxY0ch7GVnZxeYcGvowoULwtBOWQ2HN27cELaNDU3pNW/eHK+88goAICYmxmhbw3uC9ejRA1WqVBH+bxw6dAhPnjwpbdkvPTH7NSkpCatWrQKQ/+/5q6++gqOjY7HHrF27Nvz9/QEAOp2uRBPFpcRAYiX69esn3BAwMTERs2bNkrgieXrllVeEoYeiAkl8fDyuXbsG4J83M/0QzKVLl5CcnPzcPrm5ucIn8rZt28LOzk702qXWsmVL4ZNfcWfxcnJyhKtgBgwYUGQ7w6GUon4f+ttFVK5cGU2aNEHHjh2FeTxFBUTD+SNleUKrXnF3OdebPn06Fi5ciOXLlz93vy9D+uEaW1tbdO/eHQCEK8Ly8vKwdetWMysuG8zt1507dyIrKwtA/mu4/oOLKYYNG4ZBgwZh9uzZCAgIKFnhEmEgsSLBwcHCJXdhYWEmj9WT6RQKhfAmePv2bSQlJT3XJiIiAvr1CLt06QLgn2Ci0WiEiZiGLl26hOzsbADynD8C5Pddnz59AOS/kBte9fKso0ePIiMjA/b29sIbXmEMw0JxgaRTp05QKBRwdnZGs2bNABQdSPRvNBUrVoSXl5eRn+rlpf8ZAWDGjBlCPxjTqVMn9O3bF97e3kVOvr53754Q2Lp27QpnZ2cAQM+ePYVhhK1bt0Kj0Zj7I7yUxOzXo0ePCtvG/p0Xplq1apg7dy6GDRtm0pmalwEDiRWpXLkyPv/8c+Hvs2fP5tCNBRh+Ki/sahv9m1zt2rVRp04dAPlDN/qJmYW9CRp+0pJrIAEKDtvs37+/yHb64Zpu3bqhQoUKRbarX7++cNalsEBy584d3L9/H0DBftUHxNu3bws31dTLy8tDVFQUgPzfdXHrm7yshg4dKpz+j4uLw9ixY9G7d2/MnTsXx44dE4ZcSmrbtm1C4DZcV8fe3l4InA8fPkR4eLiZP8HLScx+jY6OFrblOEz7LAYSK/Ps0I3hwlwl9eDBA0RHR5v8ZewTr5wY+1Su0+lw/PhxAAXnHtjb2wvrZhQWSPRzGdzc3NC4cWPRa35ZtGjRQljQqahhm4yMDOHNrH///sUeUz934dq1a8KVTXr6T68KhaLA78Nw+9nfx+XLl5GZmQmgbIfDypUrY/HixShfvrzwvbt372LVqlV477330L59e2FhxIiICGGejTFarRY7d+4EALi4uKBr164FHjcMKHKd3CpWv6anpwvDt7a2tqhWrdoLqV9KDCRW6IsvvhCGbvbt24c9e/aU6jhLlizB4MGDTf6S6wvQs9zd3YUzH88GkkuXLgnDOM9OhtT/PS4uTphjAuQP4+jPtMhhvkJx+vbtC6DoYZtDhw4hOzsbFStWxGuvvVbs8fRnrDQajTAPR08fNho3bizM/QHy57PoP+U+G0jksP6IXqdOnRAaGipM/jWk0Whw9epVrF69GmPHjkWXLl2wcuVKo8EkIiICDx8+BJA/t0d/dZ9e27ZtUbt2bQAQFguTIzH61TA864e95I6BxAq5uro+N3STkJAgYUXyo/9UfunSJeFqDOCfNzdbW9vn1q4wfHMznEdy9epV4cWpLH8iN1VxV9voA3TPnj1NWvDJ8Mqns2fPCtu5ubnCmadng4WNjY3wZnLq1KkC8x30w2d16tQR3lzLsgYNGmDdunXYs2cPJk2ahNatWz8XJID8Kz7mzZuHN998s8ihXsOVWAu7DYJCocDgwYMB5P9+t2zZItJP8fIxt18N55KYcnZKDhhIrJTh0E1SUlKprroJCQlBTEyMyV8TJ04U+8d4aenfBHNzc3HlyhXh+/ohAsNP4HqGn9JPnTolfF/uy8U/q1mzZqhbty6A54dtUlNThT40dnWNoapVq8LDwwNAwTk9Z86cEa5gKOxMhz78paWl4erVqwDy30T1Z73k9rto2LAhgoKCsHHjRpw+fRqrV69GQEAAWrRoUWD10JiYGIwbN+65q2ySk5Nx6NAhAPlXmxlO7iwMk88AAB5WSURBVDQ0aNAg4Xi//vqr7N9sS9uvlSpVEh5PSUmR7SRgQwwkVuyLL74QlpE3Z+iGnufj4yO8mOg/laelpQlDBoW9ASoUCuFN7syZM8ILkP5TvFyXiy+Mftjm7NmzBdas2LdvH9RqNSpXrlyi1VH1AfH8+fNCv+rPVlWoUEGYv2NIfwUU8E9AvHbtGlJTUwGU/eEaY+zt7dGxY0dMmTIFW7duxYEDB4QzGwBw5cqV58Li7t27hbOB169fh6enZ6FfPXr0ECa9xsfHl+mbEpZUSfq1XLlywqrBOp1O1mu36DGQWDEO3ViOi4uLcO8U/afyEydOCDe7KurNTP+pPD09HVeuXIFOpxM+kVvDcI2ePpA8e7WN/lL1Pn36lOjqFn0gycjIEBau0p9p8fb2LnTox3BIRh9Iyvpy8Xq5ubmIjY3FxYsXn5voW5jatWvjm2++ERbbAp6fW6Nfe6Sk5DS3TOx+bdmypbBteNbUVAsXLsSaNWtw5coVo+vGvCwYSKxc3759hUvxkpKSzLrqhgrSv2Hp70+jv4eHq6trkaezO3fuLJxZOXfuHG7evCnMtJfzJ/JnNW7cWFg7QR9CEhMThRsSmnJ1jSFvb28hwFy6dAkJCQnCHYCN9av+MX2o1AeSsrpcvN7SpUvRvXt3+Pn5FbruTVHGjBkjbD9+/FjY1l9JB+SvPLpo0aJiv/RDlqdOnSr2zsplhdj9+vrrrwvbptwN3FBqaip++uknhISEYOjQoUhJSSnR/lJgICEEBwcLQzf79+83egdVMp1++OXx48eIi4sThm46duxY5N1Oq1SpAk9PTwD5Y8r6N0C5LhdvjP4syblz5xAfH499+/ZBo9GgRo0ahQ6xGOPo6IjmzZsDyA8kpt4XSP9YWloaYmNjhd9hWQ+H9erVE7ZLsh6I4ZovhpehGp4dGTZsGPr06VPsl37yMpB/vxU5ELtfe/bsKdyPaf/+/UXe/qAwmzZtEubndOnSRbiy8mXGQELPDd18//33ElYjH23bthVm1UdERAhDBYZzEwqjf7OLiYkRFuCS63LxxhhebXPgwAHhTEm/fv1Kdft6/Rmry5cvC5fu1qpVq8CbSGH72NjYAAAOHjwo3PSvrE9off3114V/mzt37iz23jR6+uX6AQhXIeXm5mL37t0A8oOz/oxrcfR3uQaA0NDQIm9kWJaI2a9A/pwT/c03c3NzMX36dJMmAd++fRs//PCD8PcJEyaYVIfUGEgIQMGhG7nPen9R7O3t0apVKwD5d/HUT+Qrbi6IPpDcuHHDKueP6Hl4eKBRo0YA8q/G0J/VMPXqmmfpQ8T169eF4bPiznQ4OjoKS8Pr779SlpeL13N1dRWGCXJzczFmzBgcO3bM6D5nz54VbmVfq1YtYdjs4MGDwrBit27dTB7KatmypXBvlpSUFFlMqhezX/XGjBkjDPH++eefCAgIKDCs86wLFy7A399fWLxv1KhRaNGiRal/phfJRuoC6OURHByMyMhIk5eT16/UWhJ2dnZl5r4KYvDx8UFkZKTQT40bNy72Spk2bdrA3t4eWVlZuHfvHgDrDCRAflC+ceOGcPv0evXqoWnTpqU6VqtWrYR+vXnzJoDiz1YB+aHl7NmzwhmusrxcvKHJkyfj3r172Lt3L+Lj4/Hee++hVatW6NmzJzw8PODi4oKMjAz89ddfOHr0KI4ePQqdToeKFSvi+++/F84EGA7XDBw4sEQ1vPHGG/j2228B5A8xFLZ2SVkjVr/q2draYvny5Rg5ciTu3r2LiIgI9OrVC/3790fnzp3h7v7/7d15UBRXHgfw7wyCgoB4ABJFsOTSqIBHRBl00UAkajBGvLMRcQ1eGF0TjwqySlkYiXEjMR64iqsxjLfRuIqLYlSMAuKBSgQvYBQZkBGBAWeG3j8oXk07BwMCs5Lfp8qqbvr1m9c9XfZv3tkNKpUKjx8/RnJyMpKTk1kH1sDAQHz11VfGuA2NQgEJYTp16oSoqCgsWrTIoPRxcXGIi4tr0Gd4eHiwqaX/DIYNG4ZNmzaxfUP6HpiZmWHw4MHsl1Vrny5enw8//JB3/xpbOwLU3teBAwey0TVt2rQxaKSMSCTC999/z/ZbS3AoFAoRGxuLPn36YMuWLaisrERmZqbW9ZfqeHt7Y82aNXBzcwMAFBYWss6bNjY2Bs2cq278+PHYuHEjWx8oOzv7rX/Wm+K+vs7Ozg4HDhxAdHQ0jh8/jqqqKhw6dEjnyKa2bdti3rx5mDNnjs5FEP8fUUBCeEaPHo2goCBaCbiJ9OvXD5aWlmxBLUM7Q4pEIhaQvO39Fd5Ez5490bt3b1bDpN4RsjF8fHxYQOLp6akxOZ02ffv2hY2NTasc7WRqaoo5c+Zg4sSJSE5OZn2dZDIZysrK0L59e9ja2sLb2xuBgYG8UWBAbd+Pul/jQUFBWmci1adLly4YPnw460Px888/N2qSxv83b3pftbG2tkZsbCzCw8Px66+/Ii0tDQ8fPkRZWRlqamrQoUMHuLq6YtiwYZgwYQJvKYS3hYCra9gmhBBCCDGSt6cuhxBCCCGtFgUkhBBCCDE6CkgIIYQQYnQUkBBCCCHE6CggIYQQQojRUUBCCCGEEKOjgIQQQgghRkcBCSGEEEKMjgISQgghhBgdBSSEEEIIMToKSAghhBBidLS4HiENcPjwYaxYscKgtEKhEGZmZrCxsYGjoyO8vLwwceJEODs7N28hm5j6NS9YsAALFy40colaTnNee25uLk6dOoX09HTcv38fL168AAC2SNp7772H4OBgODg4NNlnNiV3d3cAQLdu3djieIS8CQpICGkmNTU1qKqqQmFhIQoLC5GWloaEhASEh4djwYIFxi4eMZLc3FysW7cOFy5c0Hq8qKgIRUVFuHTpEuLi4jBt2jQsXrwYFhYWLVxSQloWBSSENJKjoyOmTp2q87hKpcLLly+RnZ2Ny5cvQ6FQQKFQIC4uDh06dMCnn37agqUl/w/EYjGio6OhUCgAAAKBAJ6enujXrx86d+4MlUoFiUSC1NRUFBYWQqlU4t///jcyMjIQHx+Pzp07G/kKCGk+FJAQ0kgODg4ICwszKO3Tp0+xaNEi3LhxAwCwYcMGjBs3DjY2Ns1ZxCYxYcIETJgwwdjFeOv99NNPWLNmDdt///33sWzZMvTo0UMjrUqlwuHDh7F27VrI5XLcvn0b8+bNw549e2BmZtaSxSakxVCnVkJagIODA7Zu3Qpra2sAgFwux9GjR41cKtJSbty4gbVr17L9hQsXYvPmzVqDEQAwMTFBSEgI4uPjYWpqCgC4fv06du3a1SLlJcQYKCAhpIV06tQJo0aNYvvZ2dlGLA1pSatXr4ZKpQIAjB071uA+RIMHD8bs2bPZ/vbt2yGXy5uljIQYGzXZENKCunTpwralUqnetDKZDImJiTh//jweP36MsrIydOjQAS4uLhg5ciQmTZoEc3Pzej+zqqoKYrEYp0+fRk5ODqqqqmBrawtfX1989tlncHFxwapVqyAWiwEAycnJ6N69Ozu/ISNNLl68iGPHjuH69euQSqUQCATo0qULBgwYgLFjx8LPz0/nueqfIxaL4eXlhbS0NOzfvx8ZGRkoLi6Gubk5XFxcMHr0aEyePLne5guFQoGTJ0/i4sWLuHnzJkpLS1FRUQFLS0tWruDgYAwaNKje+9hYly5dwu3btwEApqamWLZsWYPODwsLQ0JCAuRyOaytrZGdnQ1vb2+taSUSCcRiMVJTU5GXl4fKykrY2NjAxcUF/v7+CAkJMahzbHl5ORITE5GUlITc3FyoVCq88847CAwMxGeffYZOnToZXH65XI4DBw7g7NmzyM3NhUwmg6WlJZydnTFixAhMnTr1rWi6JM2PAhJCWtCjR4/Ytr4OisePH8eaNWtQVlbG+3txcTGKi4vx+++/Iz4+Hhs3bsTgwYN15pOfn49Zs2YhLy+P93eJRIL9+/fjyJEjWLlyZeMuRs2TJ0+wbNkyXL16VeNYXl4e8vLycPToUQwbNgwbNmyo94XGcRyio6Oxd+9e3t+rq6uRnp6O9PR07N27FwkJCTqHxaanp+PLL7/EkydPNI7JZDLIZDLk5uZi//79mDBhAqKjo9GmTdP/l3j8+HG2LRKJYGdn16DzrayssGPHDnTr1k3ntapUKvz444/YunUrlEol75hUKoVUKsXly5exfft2xMTEYPjw4To/Lzs7G59//jkKCwt5f3/w4AG2bt2KgwcP4scffzSo7Kmpqfjqq680gu/S0lKUlpYiMzMTO3fuxJo1axAUFGRQnqT1ooCEkBaSl5eHlJQUtu/j46M13eudH93d3eHr6wsbGxtIpVKcP38eeXl5kEqlCA0NxbZt2+Dr66uRj0QiwdSpU9nLwNLSEoGBgejRowekUimSkpIglUqxevVqvPPOO42+rmfPnmH69Onsxd+mTRv4+fmhT58+EAgEuH37Ni5cuAClUonU1FSEhIRg//79egOyjRs34sqVKxAIBPDx8YG3tzeEQiFu3ryJCxcugOM4PHr0CIsXL0ZiYqLG+Xfu3EFYWBiqqqoA1I6I8vPzg729PWpqalBQUICUlBSUlJQAqK2dcXFxMbiTckOkpqaybV3feX3qq8GJiorCgQMH2L6rqytEIhFsbGwgkUhw9uxZFsyGh4fj22+/xYcffqiRT3Z2NmbMmIGXL18CqG1mDAgIgIODA54+fYqkpCQUFxdjzpw59ZY5OTkZixYtYiOKnJycMHz4cNja2kImk+HixYu4d+8eysrKsHjxYsjlcuo8/WfHEUIMdujQIc7NzY1zc3PjZsyYYfB5WVlZXGBgIDvX39+fq66u1kh369Yt7t133+Xc3Ny4/v37c8ePH9dIo1QquS1btnDu7u6cm5sb5+Pjw5WUlGikmz9/Pvu8SZMmcUVFRbzjFRUV3JIlS1iaun/5+fk6r3nTpk0anzNt2jR2PDAwkMvJydFIk52dzY0aNYqlmzlzpkYa9c9xc3PjfH19uWvXrmmkS0lJ4Xr37s3SZWRkaKSZPn06Ox4ZGckplUqNNJWVldzChQtZuoCAAL1l0nbt9ZHJZLxrunHjRoPzqM+BAwdY/n369OF+/vlnrqamhpemoqKCW758OUvn6enJPXjwQCOvKVOmsDSzZ8/mysrKeMdfvHjB/e1vf+Ndk7+/v0Y+EomEGzRoEOfm5sZ5eHhwu3bt4lQqlday1z3v/fr143Jzc9/wbpC3GdWQENJIT58+xb/+9S+txziOQ2VlJUpLS3Hr1i1kZWWB4zgAtVXw//znP7X2f/j+++/ZL8ro6GiMHTtWI42JiQnCw8Px5MkTiMViPH/+HHv37kVERARLc/36dZw5cwYAYGtri23btmm001tYWCA2NhZPnz5FRkZGo+7BhQsXkJ6eDgCwtrbW2YTi7u6OXbt2Yfz48SgvL0dqaipSU1MxbNgwnXnHxsZq7SsxYsQIfPTRRzhy5AgA4PLlyxgwYAA7XlBQgLS0NAC11/7111/DxMREIx9zc3NERUUhKSkJHMfh8ePHePnyJaysrBp2E/R4+vQpb9/e3r7J8gZq+8hs3ryZ7S9fvhxTpkzRSGdhYYGYmBiUlpbi3LlzkMvl2Lx5M7799luWJiUlBdeuXQMAdO/eHZs2bdLoo2RtbY0ffvgB48ePx/3793WWa/v27ay5MSIiAjNnztSabuLEiSgpKcF3332H6upqbN++Hd98843B109aFxplQ0gj5efnY/369Vr/xcbGYvPmzdi3bx9u3brFgpGhQ4dCLBajf//+Gvk9e/aMzd7p6OiIjz76SO/nz58/n20fO3aMd+zUqVNsOzQ0VGenQaFQiCVLlhh2wVqcOHGCbf/1r3/VO825o6MjbzK4Q4cO6Uzr7OyMoUOH6jw+ZMgQtl1cXMw7ZmpqiqioKMyaNQuLFi3S2/G1c+fOvI7GdU0VTaWiooK337FjxybNPyMjgzWVde/eHdOnT9ebfsWKFRAIBABqn5HKykp2LDk5mW2Hhobq7DBtZmaGuXPn6vyMV69esefR3NwcoaGhessUGhrKOtr+5z//watXr/SmJ60XBSSENDNfX18sWbIEJ06cQEJCAnr16qU1XVpaGgtc+vTpU2++9vb26NatG4DaWoFnz56xY+fPn2fb6kONtRk0aBDvpdwQv//+O9sODAysN716x0VtHWDreHp66s1HvVNsdXU175i9vT2mTZuGZcuWISQkRG8+9+/fZ8NxAWh0CH1Tr9fMNPXLVv3+BwQEQCjU/1+6k5MTe7YUCgUyMzPZsUuXLrFtfZ1eAcDf358FNq+7ffs2C3R69eqFdu3a6c3LzMwMffv2BVD7XWZlZelNT1ovarIhpJHee+897Nmzh+1zHAe5XI67d+9iy5YtrLbj3r17mDp1KlxdXfXml5uby7ZPnz7NFi8z1JMnT1iTQH5+PoDa/+ydnJzqPdfd3V2jpqE+SqWSjcQwNTWFi4tLvee4urrC1NQUCoUCRUVFePXqldYajPoCJPWXXF0QpwvHcSgoKGCjffLy8pCTk4M7d+6wTq2G5tVQrzf/lJaWwtLSssnyLygoYNseHh4GnePh4cGGIdc9JzU1Nbzv0tHRUW8elpaW6NatG+/z66g/x1lZWY16jtWb4MifBwUkhDQRgUAACwsLDBw4EPHx8YiJicHu3bshlUqxYMECxMTE6B1FULfaa2PVtdnLZDLWD8XKykrnL1l1jZkHQiaTsW1LS0ut/TReJxQKYWVlhefPn7M8tA2DNWR+lTq6goj8/HzEx8fj1KlTeu+tUChETU2NwZ/XEI6Ojrz8i4uL633ZN4T6d2Dod6ieru6+yGQyVlNk6DPTsWNHrQHJmz7Hb3o+eXtRQEJIMxAIBFixYgXy8/PZ0uyRkZGwt7fXOkQXAK/pYNSoURg4cGCDPtPZ2RkAv1nA0BdtY2oG1M8x5AWmrUz1NTE01pkzZ/D3v/9doznH3NwcPXv2hIeHB7y9vSESiTBjxgxIJJJmKYeZmRlcXV3xxx9/AKjt86FrUjN97t27h6KiIgwcOJAXrDXmO1C//w353l5XN6X969Sf40GDBmHkyJENytfLy6vRZSJvNwpICGkmAoEAMTExGDduHIqKiqBUKrFs2TL88ssvWicGq1vnBsAbzYmh/gu4vLwcNTU19b74X5+AzRAdOnRg2y9fvoRKpaq3lkShUPA6jjbliJY6Dx8+xNKlS1kwMmTIEEyePBn9+/dH9+7dNV7CzT0Vu6+vLwtILl26xJsK3lB79+6FWCyGqakpJk2ahFWrVgHgf9elpaUG5aWeru6Z69ixI2tKe/nypUHPTHl5uda/qz/HXbt2bZa5XUjrRJ1aCWlGNjY2vEnOpFIpoqKitKZVr8q/efOmQfnLZDKN2g0zMzOWl0Kh4M0Oq4t6u7+hzMzM2KgahUJhUB45OTnsF7SdnR3atm3b4M+tT0JCApsQzd/fH7t378aYMWPg6OioEYwolUpeMNbUfUgAYNy4cWw7LS1NYyhwfSorK9moKYVCwVuQT33b0LWR7t69y7brnhOBQMD7Lh88eKA3D6VSicePH2s91pjn+MWLF83WbEbeHhSQENLM/P39ebNiJiUl4dy5cxrp1GfjTE9P542a0aakpAQikQienp4ICgpi/TKA2g63ddRH3GiTnZ2tMU24odSblU6fPl1vevU0zdVx8caNG2w7JCREb7NERkYGb2RNc7wU+/Tpw74PhULBm/vDEPHx8axfRfv27XnDwdWfmaSkpHrL//DhQ1ZbY2JiwhvNNGLECLZdN4eNLleuXNFoDqvj7e3NpuDPy8urNyh59eoVgoKC0L9/fwQGBtKik39iFJAQ0gJWrlzJa56Ijo7WaCro1asXm59EoVBg3bp1evOsm0Sturoa7du35zUDqXeeTUhI0Fm9DgCbNm1q0LWo+/jjj9n2nj179P76l0gkvLVptE361hTU+9Doa8ZQKpUawUFTD/uts3TpUtacdeLECWzbts2g85KTk7F9+3a2P2vWLN73PGTIEFazIZFI8NNPP+nNb/369Wx7xIgRvBE/Y8aMYdu7d+/WOeqqpqaGNxnb69q3b4+AgAC2HxMTw+tX8rpdu3ahpKQECoUClZWVBo3WIq0TBSSEtABbW1ssXryY7UskEvzwww8a6SIiItgv+pMnTyIqKoo1P9RRqVSIj49nq/MC0FiBd9CgQazzbGFhIebOncurQQFqg561a9fyJsRqKJFIxGo6ysrKMHPmTK1NNzk5OQgNDWWB0dChQ+udH6Wx1Ie/xsfHa1w3UDuDamhoqMav99fvdVPx9PTkfUffffcdIiIidDZ7VFRUIC4uDhERESxIGjBgAMLDw3np2rRpw5sgb926dUhMTNRoepLL5fj6669ZB2tzc3MsXbqUl8bb2xsffPABgNpAbvbs2RoLE1ZVVWHlypX1zuw7d+5cNpz72rVriIiI0Dp65ujRo4iLi+Od1xwLHJK3A33zhLSQqVOn4vDhw2zip4SEBAQHB8PNzY2l8fPzQ3h4OLZs2QIASExMRHJyMkaOHAkHBwdIpVJcunSJ1y9kxowZvOr2OqtXr8akSZPw/PlzXL16FR988AECAgLQo0cPPH/+HMnJySgoKICJiQmEQiEbKtzQkS8bN27ExIkTIZVK8ejRIwQHB8PPzw/vvvsuBAIBsrKy2OJ6QG1Hx/Xr1zfbCJsZM2bg5MmTbAG+gIAABAYGonv37qioqMC9e/eQmprKfrXXdeYEDO8Y2hhz585FeXk5duzYAaC2+SopKQleXl7w8vJCp06dIJfLcf/+faSmpvI6/3p7e2Pr1q1aX9YhISFIS0vDsWPHoFQqERUVhb1798LPzw8dOnTAkydPcPbsWbbIolAoRFRUlNYJ+iIjI5GVlQWJRIK7d+9izJgxCAwMhLOzM0pKSnDmzBkUFhbC0tIS7dq101mL4u7ujlWrViEyMhIcx+G///0vrl69ilGjRsHJyQmlpaVIS0vDnTt32Dn+/v6YNm3aG91j8najgISQFiIUCrF69WqEhISgpqaGvTz27dvH6+fwxRdfoHPnztiwYQPkcjmkUimvNqSOiYkJZs+ezat5Uefo6IidO3di/vz5kEgkKCsr05iuvV27doiJicH69etZc4u+qda16dq1Kw4ePIgvvvgCmZmZUCqVOHfunNZ+MsOHD8c333yjdZRRUxkwYAAiIyOxdu1aqFQqlJeX4/Dhwxrp2rRpgzlz5kAgELAmiNu3b2sN7prKl19+CS8vL6xbtw4FBQXgOA6ZmZm8GVPVmZqaIiwsDPPmzdPbAXjdunVwcHDAjh07oFQqkZOTg5ycHI10tra2iI2N1Tktv62tLRITExEeHs5mXD169CgvjYWFBTZu3IgNGzbonUwvJCQEVlZWiIqKgkwmQ1lZGVt/6HWffPIJ/vGPf7zRMGTy9qOAhJAW1LdvX0ybNo31pbh27RoOHjyoMcX5p59+iqCgIIjFYlYj8uLFC7Rt2xaOjo5sKKuuaejr9O7dGydOnIBYLMbp06fx8OFDVFRUwM7ODn5+fggLC0OPHj2wdu1adk5DJiWr07VrVyQmJuLs2bM4efIkMjMzUVJSAqVSCXt7ewwYMADBwcEQiUQNzrsxpk+fDm9vb+zZswdpaWl49uwZOI6DtbU1nJ2d4e3tjcmTJ6NHjx64ceMGC0h+/fVXzJ07t1lfjAEBAfjLX/6C3377DefOncOdO3cgkUhQUVEBoVAIGxsbuLu7w8fHBx9//LFBwZtQKMTixYvxySefQCwW4/Lly5BIJGyxQA8PD7z//vuYMGEC2rdvrzcvOzs77N+/H0eOHMHx48dx9+5dyOVy2NnZQSQSISwsDE5OTtiwYUO95Ro9ejREIhEOHDiA3377DTk5OZDJZDA1NYWDgwMGDhyIkJAQrWs7kT8fAdcc49wIIW+V/v37o7q6GhYWFjp/rRNCSHOiGhJCWqGUlBRIJBI4OzvD09NT7/opeXl5bAhnz549W6qIhBDCQ6NsCGmF/vjjD6xZswazZs2qdyio+gKB6vNaEEJIS6KAhJBWyMfHh23v3LlT62RTHMdh3759LCAxMzPDlClTWqyMhBCijvqQENJKff7550hJSQFQOzW4SCSCq6srrKys8OzZM1y5cgUPHz5k6ZcvX47Q0FAjlZYQ8mdHAQkhrVRFRQWWLl3KJsPSpW3btoiMjNQY6UMIIS2JAhJCWrnLly/jl19+wfXr11FYWAiFQgFbW1s4ODhg5MiRCA4Ohq2trbGLSQj5k6OAhBBCCCFGR51aCSGEEGJ0FJAQQgghxOgoICGEEEKI0VFAQgghhBCjo4CEEEIIIUZHAQkhhBBCjI4CEkIIIYQYHQUkhBBCCDG6/wHNqN+C6HxP6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_att_dist(data, \"GRE\")\n",
    "visualize_att_dist(data, \"Regional Code\", _category=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- CORRELATIONS ---------------\n",
      "               Average Count   Faculty       GRE  Pub Count     Score\n",
      "Average Count       1.000000  0.864353  0.157443   0.445279  0.835806\n",
      "Faculty             0.864353  1.000000  0.107261   0.195689  0.890569\n",
      "GRE                 0.157443  0.107261  1.000000   0.360715  0.541750\n",
      "Pub Count           0.445279  0.195689  0.360715   1.000000  0.365228\n",
      "Score               0.835806  0.890569  0.541750   0.365228  1.000000\n",
      "--------------- CREATE A HEATMAP ---------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_df = data.corr(method='pearson')\n",
    "print(\"--------------- CORRELATIONS ---------------\")\n",
    "print(corr_df.head(len(data)))\n",
    "print(\"--------------- CREATE A HEATMAP ---------------\")\n",
    "# Create a mask to display only the lower triangle of the matrix \n",
    "mask = np.zeros_like(corr_df)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corr_df, cmap='RdYlGn_r', vmax=1.0, vmin=-1.0 , mask = mask, linewidths=2.5)\n",
    "plt.yticks(rotation=0) \n",
    "plt.xticks(rotation=90) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Nutritional Labels\n",
    "- Web application of Nutritional Labels for Rankings is live at http://demo.dataresponsibly.com/rankingfacts/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Available tools\n",
    "   - AI Fairness 360 for fairness validation\n",
    "   - Data profiling for input data analysis\n",
    "   - Data cleaning\n",
    "   - Pyplot and Seaborn for visulizations\n",
    "   - ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recipe and Ingredients\n",
    "- These two labels help to explain the ranking methodology. \n",
    "- The Recipe label succinctly describes the ranking algorithm. It states the explicit intentions of the designer of the score generation rule about which attributes matter and to what extent.\n",
    "    - For example, for a linear score generation rule, each attribute would be listed together with its weight. \n",
    "- The Ingredients label lists attributes most material to the ranked outcome, in order of importance. \n",
    "    - It may show additional attributes associated with high rank. Such associations can be derived with linear models or with other Black-box methods.\n",
    "    - For example, for a linear model, this list could present the attributes with the highest learned weights. \n",
    "- The Recipe and Ingredients labels also list statistics of the attributes in the Recipe and in the Ingredients.\n",
    "    - minimum, maximum and median values at the top-10 and over-all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_statistic(_data, _atts):\n",
    "    \"\"\"\n",
    "    Compute the statistics of input attributes.\n",
    "\n",
    "    Attributes:\n",
    "        _data: dataframe that stored the data\n",
    "        _atts: array that stores the attributes to be computed\n",
    "    Return:  json data of computed statistics\n",
    "    \"\"\"\n",
    "    output_df = pd.DataFrame(columns=[\"attribute\", \"median\", \"mean\", \"min\", \"max\"])\n",
    "    for atti in _atts:\n",
    "        atti_stats = _data.describe().loc[[\"50%\", \"mean\", \"min\", \"max\"], atti].tolist()\n",
    "        output_df.loc[output_df.shape[0]] = [atti] + atti_stats\n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recipe for top-10 ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- RANKER USE FOLLOWING ATTRIBUTES ---------------\n",
      "['Average Count', 'Faculty', 'GRE']\n",
      "--------------- STATISTICALS OF ATTRIBUTES FOR TOP-10 RANKING ---------------\n"
     ]
    },
    {
     "data": {
      "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>attribute</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Average Count</td>\n",
       "      <td>10.400</td>\n",
       "      <td>11.0200</td>\n",
       "      <td>6.800</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Faculty</td>\n",
       "      <td>55.000</td>\n",
       "      <td>63.4000</td>\n",
       "      <td>45.000</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GRE</td>\n",
       "      <td>796.254</td>\n",
       "      <td>791.4109</td>\n",
       "      <td>771.894</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       attribute   median      mean      min    max\n",
       "0  Average Count   10.400   11.0200    6.800   18.3\n",
       "1        Faculty   55.000   63.4000   45.000  122.0\n",
       "2            GRE  796.254  791.4109  771.894  800.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"--------------- RANKER USE FOLLOWING ATTRIBUTES ---------------\")\n",
    "print(chosen_atts)\n",
    "print(\"--------------- STATISTICALS OF ATTRIBUTES FOR TOP-10 RANKING ---------------\")\n",
    "compute_statistic(data.head(10), chosen_atts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recipe for overall ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- STATISTICALS OF ATTRIBUTES FOR OVERALL RANKING ---------------\n"
     ]
    },
    {
     "data": {
      "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>attribute</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Average Count</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.447059</td>\n",
       "      <td>1.400</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Faculty</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.470588</td>\n",
       "      <td>14.000</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GRE</td>\n",
       "      <td>790.0</td>\n",
       "      <td>787.264255</td>\n",
       "      <td>757.813</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       attribute  median        mean      min    max\n",
       "0  Average Count     2.9    4.447059    1.400   18.3\n",
       "1        Faculty    32.0   36.470588   14.000  122.0\n",
       "2            GRE   790.0  787.264255  757.813  800.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"--------------- STATISTICALS OF ATTRIBUTES FOR OVERALL RANKING ---------------\")\n",
    "compute_statistic(data, chosen_atts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ingredients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_correlation(_data, _y_col=\"Score\", top_threshold=0.5, round_default=2):\n",
    "    \"\"\"\n",
    "    Compute the correlation between attributes and generated scores using linear regression.\n",
    "\n",
    "    Attributes:\n",
    "        _data: dataframe that stored the data\n",
    "        _y_col: column name of Y variable\n",
    "        top_threshold: threshold of attribute coefficient\n",
    "        round_default: threshold of round function for the returned coefficient\n",
    "    Return:  list of correlated attributes and its coefficients\n",
    "    \"\"\"\n",
    "    num_atts = list(_data.iloc[:,:-1].describe().columns)\n",
    "    X = data[num_atts].values\n",
    "    y = data[_y_col].values\n",
    "\n",
    "    regr = linear_model.LinearRegression(normalize=False)\n",
    "    regr.fit(X, y)\n",
    "    for i in range(len(regr.coef_)):\n",
    "        regr.coef_[i] = round(regr.coef_[i], round_default)\n",
    "    # normalize coefficients to [-1,1]\n",
    "    max_coef = max(regr.coef_)\n",
    "    min_coef = min(regr.coef_)\n",
    "    abs_max = max(abs(max_coef),abs(min_coef))\n",
    "    norm_coef = []\n",
    "    for ci in regr.coef_:\n",
    "        new_ci = round(ci/abs_max,round_default)\n",
    "        norm_coef.append(new_ci)\n",
    "    coeff_zip = zip(norm_coef, num_atts)\n",
    "    return_coeff = {}\n",
    "    for ci, atti in coeff_zip:\n",
    "        if ci > top_threshold:\n",
    "            return_coeff[atti] = ci\n",
    "    return return_coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- IMPORTANT ATTRIBUTES ---------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Average Count': 0.6, 'GRE': 1.0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the ranker on the normalized data\n",
    "weights = [0.3, 0.2, 0.5]\n",
    "chosen_atts = [\"Average Count\", \"Faculty\", \"GRE\"]\n",
    "data[\"Score\"] = sum([weights[idx]*data[atti] for idx, atti in enumerate(chosen_atts)])\n",
    "print(\"--------------- IMPORTANT ATTRIBUTES ---------------\")\n",
    "lg_weights = linear_correlation(data)\n",
    "lg_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More details in Ingredients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- STATISTICS OF IMPORTANT ATTRIBUTES for TOP-10 RANKING ---------------\n",
      "       attribute   median      mean      min    max\n",
      "0  Average Count   10.400   11.0200    6.800   18.3\n",
      "1            GRE  796.254  791.4109  771.894  800.0\n"
     ]
    }
   ],
   "source": [
    "learned_atts = lg_weights.keys()\n",
    "print(\"--------------- STATISTICS OF IMPORTANT ATTRIBUTES for TOP-10 RANKING ---------------\")\n",
    "if len(set(learned_atts).intersection(set(chosen_atts))) != len(chosen_atts):\n",
    "    print (compute_statistic(data.head(10), learned_atts))\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- STATISTICS OF IMPORTANT ATTRIBUTES FOR OVERALL RANKING ---------------\n",
      "       attribute  median        mean      min    max\n",
      "0  Average Count     2.9    4.447059    1.400   18.3\n",
      "1            GRE   790.0  787.264255  757.813  800.0\n"
     ]
    }
   ],
   "source": [
    "print(\"--------------- STATISTICS OF IMPORTANT ATTRIBUTES FOR OVERALL RANKING ---------------\")\n",
    "if len(set(learned_atts).intersection(set(chosen_atts))) != len(chosen_atts):\n",
    "    print (compute_statistic(data, learned_atts))\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stability\n",
    "- The Stability label explains whether the ranking methodology is robust on this particular dataset. \n",
    "    - An unstable ranking is one where slight changes to the data (e.g., due to uncertainty and noise), or to the methodology (e.g., by slightly adjusting the weights in a score-based ranker) could lead to a significant change in the output. \n",
    "- This Stability label reports a stability score, as a single number that indicates the extent of the change required for the ranking to change.\n",
    "    - The stability of the ranking is quantified as the slope of the line that is fit to the score distribution, at the top-10 and over-all.\n",
    "    - A score distribution is unstable if scores of items in adjacent ranks are close to each other (|slope| <= 0.25), and so a very small change in scores will lead to a change in the ranking. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_stability(_data, _y_col=\"Score\", _top_n=100):\n",
    "    plt.figure(figsize=[6,5],dpi=100)\n",
    "    sns.set(style=\"darkgrid\")  \n",
    "    sns.set(font_scale = 2)\n",
    "    vis_data = _data.head(_top_n)\n",
    "    vis_x = [x+1 for x in vis_data.index]\n",
    "    vis_y = sorted(vis_data[_y_col], reverse=True)\n",
    "    ax = sns.scatterplot(vis_x, vis_y, color=\"steelblue\");\n",
    "    ax.set_xlabel(\"Position\")\n",
    "    ax.set_ylabel(_y_col)\n",
    "    plt.tight_layout()\n",
    "def compute_slope_scores(_data, _y_col=\"Score\", round_default=2):\n",
    "    \"\"\"\n",
    "    Compute the slop of a list of scores.\n",
    "\n",
    "    Attributes:\n",
    "        _data: file name that stored the data\n",
    "        _y_col: column name of Y variable\n",
    "        round_default: threshold of round function for the returned stability\n",
    "    Return:  slope of scores in the input _data\n",
    "    \"\"\"\n",
    "    xd = [i for i in range(1,len(_data)+1)]\n",
    "    yd = _data[_y_col].values\n",
    "    par = np.polyfit(xd, yd, 1, full=True)\n",
    "    slope = par[0][0]\n",
    "    return abs(round(slope, round_default))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- UNSTABLE AT TOP 10 (STABILITY AS 0.18) ---------------\n",
      "--------------- STABLE OVERALL (STABILITY AS 0.35) ---------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10_stability = compute_slope_scores(data.head(10))\n",
    "if top10_stability <= 0.25:\n",
    "    print(\"--------------- UNSTABLE AT TOP 10 (STABILITY AS \"+str(top10_stability)+\") ---------------\") \n",
    "else:\n",
    "    print(\"--------------- STABLE AT TOP 10 (STABILITY AS \"+str(top10_stability)+\") ---------------\") \n",
    "all_stability = compute_slope_scores(data)\n",
    "if all_stability <= 0.25:\n",
    "    print(\"--------------- UNSTABLE OVERALL (STABILITY AS \"+str(all_stability)+\") ---------------\") \n",
    "else:\n",
    "    print(\"--------------- STABLE OVERALL (STABILITY AS \"+str(all_stability)+\") ---------------\")     \n",
    "visualize_stability(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fairness\n",
    "- The Fairness label quantifies whether the ranked output exhibits statistical parity with respect to one or more sensitive attributes, such as gender or race of individuals. \n",
    "- All these measures are statistical tests, and whether a result is fair is determined by the computed p-value.\n",
    "- NULL hypothesis is that ranking process is fair for protected group.\n",
    "- The results fo three measures of statistical parity are shown: [FA*IR](https://dl.acm.org/citation.cfm?doid=3132847.3132938), [proportion](https://doi.org/10.1007/s10618-017-0506-1), and [pairwise comparison](https://arxiv.org/pdf/1804.07890.pdf).\n",
    "- A ranking is considered unfair when the p-value of the corresponding statistical test falls below 0.05 or based on the adjusted alpha for FA*IR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FA* IR measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_p_FAIR(_data, _att, _protected_group, _y_col=\"Score\", top_k = 100, round_default = 2):\n",
    "    \"\"\"\n",
    "    Compute p-value using FA*IR algorithm\n",
    "\n",
    "    Attributes:\n",
    "        _data: dataframe that stored the data\n",
    "        _att: sensitive attribute name\n",
    "        _protected_group: the value of sensitive attribute for protected group\n",
    "        _y_col: the column that stores the values of ranking\n",
    "        top_k: the top ranking to verify group fairness\n",
    "        round_default: threshold of round function\n",
    "    Return:  rounded p-value and adjusted significance level in FA*IR\n",
    "    \"\"\"\n",
    "    _data.sort_values(by=_y_col, ascending=False, inplace=True)\n",
    "    _data.reset_index(drop=True, inplace=True)\n",
    "    if len(_data)/2 < top_k:\n",
    "        top_k = int(len(_data)/2)\n",
    "    pos_protected = _data[_data[_att]==_protected_group].index+1\n",
    "    pro_prob = len(pos_protected)/len(_data)\n",
    "\n",
    "    # transform ranking to a ranking of tuples with (id,\"pro\")/(id,\"unpro\") to run FA*IR\n",
    "    transformed_ranking = []\n",
    "    for index, row in _data.head(top_k).iterrows():\n",
    "        if row[_att] == _protected_group:\n",
    "            transformed_ranking.append([index,\"pro\"])\n",
    "        else:\n",
    "            transformed_ranking.append([index,\"unpro\"])\n",
    "\n",
    "    p_value, isFair, posiFail, alpha_c, pro_needed_list = computeFairRankingProbability(top_k, pro_prob, transformed_ranking)\n",
    "    return p_value, isFair, posiFail, round(alpha_c,round_default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- GROUP FAIRNESS VERIFICATION CONSIDERING Department Size ---------------\n",
      "--------------- FAIR FOR Large (p=0.9860885630366442, alpha=0.87) ---------------\n",
      "--------------- UNFAIR FOR Small (p=0.038976119236976886, alpha=0.65) FAIL AT RANK POSITION 5 ---------\n"
     ]
    }
   ],
   "source": [
    "sensi_att = \"Department Size\"\n",
    "protected_groups = data[sensi_att].unique()\n",
    "print(\"--------------- GROUP FAIRNESS VERIFICATION CONSIDERING \"+sensi_att+\" ---------------\") \n",
    "for vi in protected_groups:\n",
    "    vi_p, fair_res, pos_fail, vi_alpha = compute_p_FAIR(data, sensi_att, vi)\n",
    "    if fair_res:\n",
    "        print(\"--------------- FAIR FOR \"+vi+\" (p=\"+str(vi_p)+\", alpha=\"+str(vi_alpha)+\") ---------------\") \n",
    "    else:\n",
    "        print(\"--------------- UNFAIR FOR \"+vi+\" (p=\"+str(vi_p)+\", alpha=\"+str(vi_alpha)+\") FAIL AT RANK POSITION \"+str(pos_fail)+\" ---------\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairwise comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_p_pairs(_data, _att, _protected_group, _y_col=\"Score\", run_time = 100, round_default = 2):\n",
    "    \"\"\"\n",
    "    Compute p-value using Pairwise oracle\n",
    "\n",
    "    Attributes:\n",
    "        _data: dataframe that stored the data\n",
    "        _att: sensitive attribute name\n",
    "        _protected_group: the value of sensitive attribute for protected group\n",
    "        _y_col: the column that stores the values of ranking\n",
    "        run_time: simulation times for pairwise comparison\n",
    "        round_default: threshold of round function\n",
    "    Return:  rounded p-value\n",
    "    \"\"\"\n",
    "    _data.sort_values(by=_y_col, ascending=False, inplace=True)\n",
    "    _data.reset_index(drop=True, inplace=True)\n",
    "    pos_protected = _data[_data[_att]==_protected_group].index+1\n",
    "    pro_prob = len(pos_protected)/len(_data)\n",
    "    total_n = len(_data)\n",
    "    pro_n = len(pos_protected)\n",
    "    seed_random_ranking = [x for x in range(total_n)]  # list of IDs\n",
    "    seed_f_index = [x for x in range(pro_n)]  # list of IDs\n",
    "    \n",
    "    sim_df = pd.DataFrame(columns=[\"Run\", \"pair_n\"])\n",
    "    # run the simulation of ranking generation, in each simulation, generate a fair ranking with input N and size of sensitive group\n",
    "    for ri in range(run_time):\n",
    "        output_ranking = mergeUnfairRanking(seed_random_ranking, seed_f_index, pro_prob)\n",
    "        position_pro_list = [i for i in range(total_n) if output_ranking[i] in seed_f_index]\n",
    "        count_sensi_prefered_pairs = 0\n",
    "        for i in range(len(position_pro_list)):\n",
    "            cur_position = position_pro_list[i]\n",
    "            left_sensi = pro_n - (i + 1)\n",
    "            count_sensi_prefered_pairs = count_sensi_prefered_pairs + (total_n - cur_position - left_sensi)\n",
    "        cur_row = [ri + 1, count_sensi_prefered_pairs]\n",
    "        sim_df.loc[sim_df.shape[0]] = cur_row\n",
    "\n",
    "    input_pair_n, _, _ = computePairN(_att, _protected_group, _data)\n",
    "    pair_samples = list(sim_df[\"pair_n\"].dropna())\n",
    "    return round(Cdf(pair_samples, input_pair_n), round_default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- GROUP FAIRNESS VERIFICATION CONSIDERING Department Size ---------------\n",
      "--------------- FAIR FOR Large (p=0.96, alpha=0.05) ---------------\n",
      "--------------- UNFAIR FOR Small (p=0.04, alpha=0.05) --------------\n"
     ]
    }
   ],
   "source": [
    "sensi_att = \"Department Size\"\n",
    "protected_groups = data[sensi_att].unique()\n",
    "print(\"--------------- GROUP FAIRNESS VERIFICATION CONSIDERING \"+sensi_att+\" ---------------\") \n",
    "for vi in protected_groups:\n",
    "    vi_p = compute_p_pairs(data, sensi_att, vi)\n",
    "    if vi_p > 0.05:\n",
    "        print(\"--------------- FAIR FOR \"+vi+\" (p=\"+str(vi_p)+\", alpha=0.05) ---------------\") \n",
    "    else:\n",
    "        print(\"--------------- UNFAIR FOR \"+vi+\" (p=\"+str(vi_p)+\", alpha=0.05) --------------\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_p_proportion(_data, _att, _protected_group, _y_col=\"Score\", top_k = 100, round_default = 2):\n",
    "    \"\"\"\n",
    "    Compute p-value using Proportion oracle, i.e., z-test method of 4.1.3 in \"A survey on measuring indirect discrimination in machine learning\".\n",
    "\n",
    "    Attributes:\n",
    "        _data: dataframe that stored the data\n",
    "        _att: sensitive attribute name\n",
    "        _protected_group: the value of sensitive attribute for protected group\n",
    "        _y_col: the column that stores the values of ranking\n",
    "        top_k: the top ranking to verify group fairness\n",
    "        round_default: threshold of round function\n",
    "    Return:  rounded p-value\n",
    "    \"\"\"\n",
    "    \n",
    "    _data.sort_values(by=_y_col, ascending=False, inplace=True)\n",
    "    _data.reset_index(drop=True, inplace=True)\n",
    "    if len(_data)/2 < top_k:\n",
    "        top_k = int(len(_data)/2)\n",
    "    total_n = len(_data)\n",
    "    pro_n = len(_data[_data[_att]==_protected_group])\n",
    "    unpro_n = total_n - pro_n\n",
    "    \n",
    "    top_data = _data.head(top_k)\n",
    "    pro_k = len(top_data[top_data[_att]==_protected_group])\n",
    "    unpro_k = top_k - pro_k\n",
    "    \n",
    "\n",
    "    pooledSE = math.sqrt((pro_k / pro_n * (1-pro_k/pro_n) / pro_n) + (unpro_k/unpro_n * (1-unpro_k/unpro_n) / unpro_n))\n",
    "    \n",
    "    z_test = (unpro_k/unpro_n - pro_k/pro_n) / pooledSE\n",
    "    p_value = norm.sf(z_test)\n",
    "\n",
    "    return round(p_value,round_default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- GROUP FAIRNESS VERIFICATION CONSIDERING Department Size ---------------\n",
      "--------------- FAIR FOR Large (p=1.0, alpha=0.05) ---------------\n",
      "--------------- UNFAIR FOR Small (p=0.0, alpha=0.05) --------------\n"
     ]
    }
   ],
   "source": [
    "sensi_att = \"Department Size\"\n",
    "protected_groups = data[sensi_att].unique()\n",
    "print(\"--------------- GROUP FAIRNESS VERIFICATION CONSIDERING \"+sensi_att+\" ---------------\") \n",
    "for vi in protected_groups:\n",
    "    vi_p = compute_p_proportion(data, sensi_att, vi)\n",
    "    if vi_p > 0.05:\n",
    "        print(\"--------------- FAIR FOR \"+vi+\" (p=\"+str(vi_p)+\", alpha=0.05) ---------------\") \n",
    "    else:\n",
    "        print(\"--------------- UNFAIR FOR \"+vi+\" (p=\"+str(vi_p)+\", alpha=0.05) --------------\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diversity\n",
    "- The Diversity label shows diversity with respect to a set of demographic categories of individuals, or a set of categorical attributes of other kinds of items. \n",
    "- This label displays the proportion of each category in the top-10 ranked list and over-all.\n",
    "- Like other labels, it is updated as the user selects different ranking methods or sets different weights. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_diversity(_data, _att, _y_col=\"Score\"):\n",
    "    plt.figure(figsize=[6,5],dpi=100)\n",
    "    sns.set(font_scale = 2)\n",
    "    sns.set_palette(palette=\"pastel\")\n",
    "    sort_data = _data.sort_values(by=_y_col, ascending=False)\n",
    "    vis_count = sort_data[_att].value_counts()\n",
    "    plt.pie(list(vis_count.values), labels=list(vis_count.index));\n",
    "    plt.title(_att)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diversity for top-10 ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chosen_atts = [\"Regional Code\", \"Department Size\"]\n",
    "for atti in chosen_atts:\n",
    "    visualize_diversity(data.head(10), atti)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diversity for overall ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for atti in chosen_atts:\n",
    "    visualize_diversity(data, atti)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}