MetaprivBIDS_CoreLogic_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25dad8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11b9578",
   "metadata": {},
   "source": [
    "## Importing library for MetaprivBIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7130c337",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Core logic imported and initialized successfully!\n"
     ]
    }
   ],
   "source": [
    "from metaprivBIDS.corelogic.metapriv_corelogic import metaprivBIDS_core_logic\n",
    "\n",
    "metapriv = metaprivBIDS_core_logic()\n",
    "print(\"Core logic imported and initialized successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86f60a82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Types: \n",
      "\n",
      "[('age', 51, 'Continuous'), ('education', 14, 'Categorical'), ('marital-status', 7, 'Categorical'), ('occupation', 12, 'Categorical'), ('relationship', 6, 'Categorical'), ('sex', 2, 'Categorical'), ('salary-class', 2, 'Categorical'), ('disease', 5, 'Categorical'), ('exercise_weekly_hours', 6, 'Categorical')] \n",
      "\n",
      "age                       int64\n",
      "education                object\n",
      "marital-status           object\n",
      "occupation               object\n",
      "relationship             object\n",
      "sex                      object\n",
      "salary-class             object\n",
      "disease                  object\n",
      "exercise_weekly_hours     int64\n",
      "dtype: object \n",
      "\n",
      "None\n",
      "Find Influential Columns: \n",
      "\n",
      "{'age': {'difference': 58,\n",
      "         'normalized_difference': 1.1,\n",
      "         'unique_count_after_removal': 132},\n",
      " 'education': {'difference': 14,\n",
      "               'normalized_difference': 1.0,\n",
      "               'unique_count_after_removal': 176},\n",
      " 'marital-status': {'difference': 2,\n",
      "                    'normalized_difference': 0.3,\n",
      "                    'unique_count_after_removal': 188},\n",
      " 'occupation': {'difference': 16,\n",
      "                'normalized_difference': 1.3,\n",
      "                'unique_count_after_removal': 174},\n",
      " 'relationship': {'difference': 2,\n",
      "                  'normalized_difference': 0.3,\n",
      "                  'unique_count_after_removal': 188},\n",
      " 'salary-class': {'difference': 0,\n",
      "                  'normalized_difference': 0.0,\n",
      "                  'unique_count_after_removal': 190},\n",
      " 'sex': {'difference': 2,\n",
      "         'normalized_difference': 1.0,\n",
      "         'unique_count_after_removal': 188}}\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "data_info = metapriv.load_data('Use_Case_Data/adult_mini.csv')\n",
    "\n",
    "# Inspect {column, unique value count, column type}\n",
    "data = data_info[\"data\"]\n",
    "print(\"Column Types:\",'\\n')\n",
    "print(data_info[\"column_types\"],'\\n')\n",
    "\n",
    "print(print(data.dtypes,'\\n'))\n",
    "\n",
    "# Select Quasi-Identifiers\n",
    "selected_columns = [\"age\", \"education\", \"marital-status\", \"occupation\", \"relationship\",\"sex\",\"salary-class\"]\n",
    "results = metapriv.find_lowest_unique_columns(data, selected_columns)\n",
    "print('Find Influential Columns:','\\n')\n",
    "pprint.pprint(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5866c1a",
   "metadata": {},
   "source": [
    "## Dataset Information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0995444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_rows': 200,\n",
       " 'total_columns': 9,\n",
       " 'num_selected_columns': 7,\n",
       " 'num_unique_rows': 190,\n",
       " 'k_anonymity': 1,\n",
       " 'l_diversity': 1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metapriv.calculate_unique_rows(data, selected_columns, sensitive_attr='disease')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2dc78e",
   "metadata": {},
   "source": [
    "## Personal information Factor (PIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a55d835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a mask value (or leave blank to skip):nan\n",
      "Enter percentile (0-100): 95\n",
      "PIF Value: 21.8882683519949\n",
      "CIG DataFrame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>RIG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>7.643856</td>\n",
       "      <td>4.473931</td>\n",
       "      <td>6.058894</td>\n",
       "      <td>2.888969</td>\n",
       "      <td>2.029146</td>\n",
       "      <td>1.736966</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>25.218230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>5.643856</td>\n",
       "      <td>3.836501</td>\n",
       "      <td>6.058894</td>\n",
       "      <td>5.321928</td>\n",
       "      <td>3.395929</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>25.158149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5.321928</td>\n",
       "      <td>1.785875</td>\n",
       "      <td>7.643856</td>\n",
       "      <td>3.473931</td>\n",
       "      <td>4.058894</td>\n",
       "      <td>1.736966</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>24.407918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>5.321928</td>\n",
       "      <td>6.643856</td>\n",
       "      <td>1.029146</td>\n",
       "      <td>5.321928</td>\n",
       "      <td>4.836501</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>24.054402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.836501</td>\n",
       "      <td>5.321928</td>\n",
       "      <td>6.058894</td>\n",
       "      <td>3.184425</td>\n",
       "      <td>2.029146</td>\n",
       "      <td>1.736966</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>23.554328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2.874467</td>\n",
       "      <td>1.785875</td>\n",
       "      <td>1.029146</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.268817</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>10.859346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2.874467</td>\n",
       "      <td>1.785875</td>\n",
       "      <td>1.029146</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.268817</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>10.859346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.874467</td>\n",
       "      <td>1.785875</td>\n",
       "      <td>1.029146</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.268817</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>10.859346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2.874467</td>\n",
       "      <td>1.785875</td>\n",
       "      <td>1.029146</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.268817</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>10.859346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.643856</td>\n",
       "      <td>1.785875</td>\n",
       "      <td>1.029146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.268817</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>10.628736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  education  marital-status  occupation  relationship       sex  \\\n",
       "169  7.643856   4.473931        6.058894    2.888969      2.029146  1.736966   \n",
       "167  5.643856   3.836501        6.058894    5.321928      3.395929  0.514573   \n",
       "37   5.321928   1.785875        7.643856    3.473931      4.058894  1.736966   \n",
       "195  5.321928   6.643856        1.029146    5.321928      4.836501  0.514573   \n",
       "6    4.836501   5.321928        6.058894    3.184425      2.029146  1.736966   \n",
       "..        ...        ...             ...         ...           ...       ...   \n",
       "136  2.874467   1.785875        1.029146    3.000000      1.268817  0.514573   \n",
       "132  2.874467   1.785875        1.029146    3.000000      1.268817  0.514573   \n",
       "29   2.874467   1.785875        1.029146    3.000000      1.268817  0.514573   \n",
       "65   2.874467   1.785875        1.029146    3.000000      1.268817  0.514573   \n",
       "128  5.643856   1.785875        1.029146    0.000000      1.268817  0.514573   \n",
       "\n",
       "     salary-class        RIG  \n",
       "169      0.386468  25.218230  \n",
       "167      0.386468  25.158149  \n",
       "37       0.386468  24.407918  \n",
       "195      0.386468  24.054402  \n",
       "6        0.386468  23.554328  \n",
       "..            ...        ...  \n",
       "136      0.386468  10.859346  \n",
       "132      0.386468  10.859346  \n",
       "29       0.386468  10.859346  \n",
       "65       0.386468  10.859346  \n",
       "128      0.386468  10.628736  \n",
       "\n",
       "[200 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pif_value, cig_df = metapriv.compute_cig(data, selected_columns)\n",
    "print(\"PIF Value:\", pif_value)\n",
    "print(\"CIG DataFrame:\")\n",
    "cig_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53003759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               997.647833\n",
       "education         569.388028\n",
       "marital-status    354.753831\n",
       "occupation        629.661075\n",
       "relationship      431.933808\n",
       "sex               174.258180\n",
       "salary-class      157.325222\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_sum = [col for col in cig_df.columns if col != 'RIG']\n",
    "column_sums = cig_df[columns_to_sum].sum()\n",
    "column_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95c48aac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detected in column 'RIG' above threshold 2.5:\n",
      "Index: 169, Value: 25.218230041679757, Z-Score: 2.809188988099669\n",
      "Index: 167, Value: 25.158149437675746, Z-Score: 2.7901779948713976\n",
      "Index: 37, Value: 24.4079182979435, Z-Score: 2.5527862549991385\n"
     ]
    },
    {
     "data": {
      "image/png": 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UL17cud7QoUNNaGioc9/2799vJJmZM2c61xk8eLDJ6qvCsW7RokXN6dOnncsXL15sJJkvv/wyxxovfQ989dVXxmazmYMHDxpjjBk+fLgpX768McaY6OhoU6NGjUz379Chg2nSpInz7xkzZhgvLy9z/Phxl/VGjx5tJLl8Hi71999/G0nmvvvuy7FeY4yJjIw0ksyiRYucy+Lj403JkiVNnTp1nMtSUlJMenq6y333799vfH19zbhx45zLOnbsmOW+Xapfv36mZMmS5uTJky7LH3roIVOoUCGTnJxsjDFm0qRJRpL59NNPneskJSWZihUr5voZPnPmjJFkOnbsmGMt9957r5FkEhISjDH293dkZGSm9RzP+aUCAwNN7969M63r+A7dv3+/c1l0dLSJjo52/j1nzhzj4eFh1q5d63Lf999/30gy69atcy6TZDw8PMy2bdty3JesZFejMRc/y5e+fsYYU6dOHVOvXj3n347PRUhISKb3YsuWLU3NmjVNSkqKc1lGRoa58847TaVKlZzLatWqZdq3b59jrXmt50p+dyIjI132f9iwYUaS+eWXX5zLjh8/bgoVKpTpNctKjx49TNGiRTMtv/Szf/bsWRMdHW3CwsLM5s2bs1wvL1Nutbz88stGkvn2229zXM8Y+2dpyJAhZu7cuWbhwoXmiSeeMF5eXqZSpUomPj4+0/rz5s3L9DzdSji0nU+mTp2qFStWaMWKFfr444/VokUL9e/f36XlaMmSJfL09HS2Bjk8/fTTMsa4XHU6ZswY1ahRQ71799agQYMUHR2d6X4OQ4YMcc47Wi1SU1O1cuXKLNdPT0/X8uXL1alTJ5dzhEqWLKmHH35YP/zwgxISEq74OTh69KhiY2PVp08fl1bC22+/Xf/4xz+0ZMmSK97m5dauXeu8+OeFF17I8/1q1Kih2NhY9ejRQ3FxcZo8ebI6deqk4sWL64MPPnCut2LFCp09e1YjR47MdE6V41Ddhg0bdPz4cQ0aNMhlnfbt26tq1apZtthc3lqxYMECFSpUSP/4xz908uRJ51SvXj0FBQVp1apVki6eQ/rVV18pLS0tz/sbHh6uqlWr6vvvv5ckrVu3Tp6enho+fLiOHTum3bt3S7I/n02bNs3ycHVePfjggypSpIjzb0eL1r59+/K8jdatWys0NFTz58+XMUbz589Xt27dsl3/1KlTWrZsmcs6DzzwgGw2mz799NMrqt9xMv3Zs2fztH5ERITLBSchISHq1auXNm/erL/++kuS5Ovr62wdSU9P16lTpxQUFKQqVapo06ZNzvsWLlxYhw4dyvZUAGOMFi1apHvuuUfGGJf3Sps2bRQfH+/c3pIlS1SyZEl17tzZef+AgACXC0ey49j34ODgHNdz3G7l++FqLFiwQNWqVVPVqlVdngPH0QXH58UhOjpa1atXvya1XH6+X7NmzbJ8rz/wwAMupwicPn1a3333nbp27aqzZ8869+HUqVNq06aNdu/e7Tw1pnDhwtq2bZvzc3o19VzJ787llixZojvuuEMNGzZ0LgsPD3eenpObU6dOuXw3XC4+Pl6tW7fWzp07tXr1atWuXdvl9lq1ajl/V3ObcrqA5vvvv9fYsWPVtWvXXI9ISfZzgt999109/PDDeuCBBzRp0iTNnj1bu3fv1nvvvZdpfcc+njx5Mtdt34w4tJ1PGjZs6HKxTbdu3VSnTh0NGTJEHTp0kI+Pjw4cOKCIiIhMX9aOQ8UHDhxwLvPx8dG///1vNWjQQH5+fpo5c2aWP/YeHh6ZThivXLmyJGXbZc+JEyeUnJysKlWqZLqtWrVqysjI0J9//qkaNWrkbef/n6P+7La7bNmyq7rQ5NSpU+rWrZuKFCmiuXPnZnkYw/FD7lCoUCHnYZjKlStrzpw5Sk9P1/bt2/XVV1/pzTff1GOPPaZy5cqpVatW2rt3ryRl291MbvtZtWpV/fDDDy7LvLy8VLp0aZdlu3fvVnx8fJbnZ0oXLwCKjo7WAw88oLFjx2rixImKiYlRp06d9PDDD8vX1zfbGiX7D4ojvK9du1b169dX/fr1FRoaqrVr16p48eLasmXLFR8KvlzZsmVd/nZ8qWZ3gUxWvL291aVLF82bN08NGzbUn3/+mWNd//3vf5WWlqY6depoz549zuWNGjXS3LlzNXjw4Dw/tqNHAMfn0nEIzMHT09MlEFSsWDHTZ/HSz1yJEiWUkZGhyZMn67333tP+/ftdzlkrWrSoc37EiBFauXKlGjZsqIoVK6p169Z6+OGH1aRJE0n2z+qZM2c0Y8YMzZgxI8v6He+VAwcOZFlbVu/Tyzn2PbcwndfAmd92796tHTt2ZDp30+HyC+bKlSt3Terw8/PLVEORIkWyfK9fXsOePXtkjNGLL76oF198McvtHz9+XKVKldK4cePUsWNHVa5cWbfddpvuvvtu9ezZU7fffvsV13MlvzuXO3DggBo1apRpeV7eUw4mh/MLhw0bppSUFG3evDnL35siRYrk6fzKnOzcuVP33XefbrvtNn344YeWt/Pwww/r6aef1sqVKzVy5EiX2xz7eDX/IS/ICJLXiIeHh1q0aKHJkydr9+7dVxzKJGnZsmWSpJSUFO3evfuafTkWBMYY9e7dW0eOHNGXX37pPMfsciVLlnT5e+bMmZlO6vf09FTNmjVVs2ZNNW7cWC1atNDcuXOv+gsrO5e2TjlkZGSoWLFimjt3bpb3cfw4ODrt/fnnn/Xll186u6x5++239fPPP+fYNUnTpk31wQcfaN++fVq7dq2aNWsmm82mpk2bau3atYqIiFBGRka258TlVXYXw+T0A5KVhx9+WO+//77GjBmjWrVq5dii5HjeHIHrcvv27cvzlcWOzoQrVqwoSZowYYJLd1CRkZFX3I/qa6+9phdffFF9+/bVyy+/7Dx/a9iwYS4XKlSrVk27du3SV199pW+++UaLFi3Se++9p5deekljx451rtujRw/17t07y8e6PFxYUahQIZUsWTLb81EdfvvtN5UqVcp5Hlh2P5yXX+xxtTIyMlSzZk3nOeOXu7zrpryew3elsnuvZ+XyGhyv5TPPPKM2bdpkeR/He7B58+bau3evFi9erOXLl+vDDz/UxIkT9f7777t0l3Yl9bhD0aJFc/wPZceOHTV//ny98cYb+s9//pPpezI1NVWnT5/O02OFh4dnej7+/PNPtW7dWoUKFdKSJUuu+j9AZcqUybIexz5eyfmXNxOC5DV04cIFSRdbPBwXfJw9e9blDb1z507n7Q6//fabxo0bp0ceeUSxsbHq37+/fv/9dxUqVMjlMTIyMrRv3z5ni4gk/fHHH5KU7Qnm4eHhCggI0K5duzLdtnPnTnl4eDi/mK/kf1iO+rPbblhYmOXWyH/961/6+uuv9eSTT6p9+/bZrnf5VY25BXhHK7Kjo1rHBQBbt251fqlf7tL9vPwwya5du7LtWP1SFSpU0MqVK9WkSZM8/ejdcccduuOOO/Tqq69q3rx56t69u+bPn59jH5yOgLhixQqtX7/e+b/o5s2ba9q0aYqIiFBgYKDq1auX42Nfr/9lN23aVGXLltXq1as1fvz4bNfbv3+/fvzxRw0ZMsR59b9DRkaGevbsqXnz5uX51AfHRUSOH/devXqpadOmztsvf30cLUuXPi+Xf+YWLlyoFi1a6KOPPnK575kzZzL92AQGBurBBx/Ugw8+qNTUVN1///169dVX9dxzzyk8PFzBwcFKT0/P9T86kZGR2rp1a6basvo8ZqVDhw764IMP9MMPP7jsv8PatWsVFxenAQMGOJcVKVJEZ86cybRuVq1cV/M+qlChgrZs2aKWLVsW2FYfx39svL298/Sf1tDQUD3yyCN65JFHlJiYqObNm2vMmDG59rt7uSv53cnqvlkdXs/re6pq1aqaO3eu4uPjM/12Sfau3Vq3bq0+ffooODjY2XuEw48//pjn/if379/v8pt36tQptW7dWufPn9e3336bqZHhShljFBcXl+WFQ/v375eHh4fL7/CthHMkr5G0tDQtX75cPj4+zkMI7dq1U3p6uqZMmeKy7sSJE2Wz2ZydKaelpalPnz6KiIjQ5MmTNWvWLB07dkxPPvlklo916faMMZoyZYq8vb3VsmXLLNf39PRU69attXjxYpeWlmPHjmnevHlq2rSps8XBEfyy+rG4XMmSJVW7dm3Nnj3bZf2tW7dq+fLlateuXa7byMr69ev13HPPqV69enrjjTdyXLdVq1Yuk+PLY+3atVmeY+g49Os4VNO6dWsFBwfr9ddfV0pKisu6jha2+vXrq1ixYnr//fdduh5ZunSpduzYkWPQdejatavS09P18ssvZ7rtwoULzufv77//ztSy5ziPKLduXcqVK6dSpUpp4sSJSktLc7beNWvWTHv37tXChQt1xx13ZOqi6nJX8h64Go6uiUaPHq2ePXtmu56jNfLZZ59V586dXaauXbsqOjo625bey82bN08ffvihGjdu7Py8lC9f3uU9dHmr55EjR/TZZ585/05ISNB//vMf1a5d23melqenZ6bXbcGCBZm6hzp16pTL3z4+PqpevbqMMUpLS5Onp6ceeOABLVq0KMth2E6cOOGcb9eunY4cOeIy7JyjO5+8GD58uPz9/TVgwIBMdZ0+fVoDBw5UQECAhg8f7lxeoUIFxcfHu7RkHj161OX5cQgMDLT8HuratasOHz7scj6zw7lz55SUlGRpu9dTsWLFFBMTo+nTp2c5wsqlr+Xlz39QUJAqVqxoaRSmvP7uZHffn3/+Wb/++qtLnXn9fDVu3FjGGG3cuDHbdXr16qV33nlH77//vkaMGOFym9VzJJOSktSuXTsdPnxYS5YsUaVKlbJ9/IMHDzpD9aX7eLlp06bpxIkTuvvuuzPdtnHjRtWoUSPLsHwroEUynyxdutT5Zjx+/LjmzZun3bt3a+TIkc5Qds8996hFixZ6/vnnFRcXp1q1amn58uVavHixhg0b5mwNe+WVVxQbG6tvv/1WwcHBuv322/XSSy/phRdeUOfOnV0CmZ+fn7755hv17t1bjRo10tKlS/X1119r1KhR2Z5P5HiMFStWqGnTpho0aJC8vLw0ffp0nT9/3qWfuNq1a8vT01Pjx49XfHy8fH19ddddd2V7bt9bb72ltm3bqnHjxurXr5+z+59ChQpZGhklOTlZDz74oNLS0tShQ4dsL6QoXrx4jl23jB8/Xhs3btT999/vPBS4adMm/ec//1FoaKizC5WQkBBNnDhR/fv3V4MGDfTwww+rSJEi2rJli5KTkzV79mx5e3tr/PjxeuSRRxQdHa1u3bo5u/+JiorKNvBfKjo6WgMGDNDrr7+u2NhYtW7dWt7e3tq9e7cWLFigyZMnq3Pnzpo9e7bee+893XfffapQoYLOnj2rDz74QCEhIXkK5s2aNdP8+fNVs2ZN57mLdevWVWBgoP744488nR/paLEcOnSo2rRpI09PTz300EO53s+Kjh07qmPHjjmuM3fuXNWuXTvbkWjuvfdePf7449q0aZPq1q3rXL5w4UIFBQUpNTXVObLNunXrVKtWrUx9WOakcuXK6tevn9avX6/ixYvr3//+t44dO+bSn2mHDh2cRxTuvPNO/f7775o7d26mw+2tW7dWiRIl1KRJExUvXlw7duzQlClT1L59e2fr0RtvvKFVq1apUaNGevTRR1W9enWdPn1amzZt0sqVK52H2h599FFNmTJFvXr10saNG1WyZEnNmTPH2d1UbipVqqTZs2ere/fuqlmzZqaRbU6ePKlPPvnEpduehx56SCNGjNB9992noUOHKjk5WdOmTVPlypVdLiqS7O+jlStX6l//+pciIiJUrly5LM+/y0rPnj316aefauDAgVq1apWaNGmi9PR07dy5U59++qmWLVvmco76jWrq1Klq2rSpatasqUcffVTly5fXsWPH9NNPP+nQoUPOfkarV6+umJgY1atXT6GhodqwYYMWLlzocmFlXuX1dycrzz77rObMmaO7775bTzzxhLP7n8jIyFxPg5DsRxmKFi2qlStX5niRy5AhQ5SQkKDnn39ehQoVcg6naPUcye7du+vXX39V3759tWPHDpe+I4OCglwGuejVq5fWrFnj8h+/yMhIPfjgg6pZs6b8/Pz0ww8/aP78+apdu7ZLi7xkb/hZs2aNBg0adMV13jSu70XiN5+suv/x8/MztWvXNtOmTXN2q+Jw9uxZ8+STT5qIiAjj7e1tKlWqZN566y3nehs3bjReXl4uXfoYY8yFCxdMgwYNTEREhPn777+NMfbuHwIDA83evXtN69atTUBAgClevLgZPXp0pq5HdFn3P8YYs2nTJtOmTRsTFBRkAgICTIsWLcyPP/6YaR8/+OADU758eePp6ZmnroBWrlxpmjRpYvz9/U1ISIi55557zPbt213WyWv3P46uNHKbLu0qJCvr1q0zgwcPNrfddpspVKiQ8fb2NmXLljV9+vRx6QLJ4YsvvjB33nmncx8aNmxoPvnkE5d1/vvf/5o6deoYX19fExoaarp3724OHTrkso7jNcrOjBkzTL169Yy/v78JDg42NWvWNM8++6w5cuSIMcb+GnXr1s2ULVvW+Pr6mmLFipkOHTqYDRs25Li/DlOnTjWSzD//+U+X5a1atcqyK4ysuv+5cOGCefzxx014eLix2WzObl0c67711luZHjer99vl8voeuLT7n40bNxpJ5sUXX8x2/bi4OCPJPPnkk8aYi13RXPr5LF26tOnQoYP597//7dIVS24iIyNN+/btzbJly8ztt99ufH19TdWqVTPtQ0pKinn66adNyZIljb+/v2nSpIn56aefMnVrM336dNO8eXNTtGhR4+vraypUqGCGDx+eqYuRY8eOmcGDB5syZcoYb29vU6JECdOyZUszY8YMl/UOHDhg7r33XhMQEGDCwsLME0884exSKq9deP3222+mW7dupmTJks7H6tatm/n999+zXH/58uXmtttuMz4+PqZKlSrm448/zrL7n507d5rmzZs7u+FydDOTl+5/jLF3uzV+/HhTo0YN4+vra4oUKWLq1atnxo4d6/J8STKDBw/O075eLrfuf7L6LF++rzl9LowxZu/evaZXr16mRIkSxtvb25QqVcp06NDBLFy40LnOK6+8Yho2bGgKFy5s/P39TdWqVc2rr75qUlNTr7geY3L/3XG4vPsfY+zvh+joaOPn52dKlSplXn75ZfPRRx/lqcsdY+xdjFWsWNFlWXaf/WeffdZIMlOmTMl1uzlxdNOV1XR5d1XR0dGZnq/+/fub6tWrm+DgYOPt7W0qVqxoRowY4ez26lJLly41kszu3buvquaCzGbMFZ4RjxtGnz59tHDhwmzHoQYAwJ327dunqlWraunSpdmeblWQderUSTabLcvTOW4VHNoGAADXRPny5dWvXz+98cYbN12Q3LFjh7766ivFxsa6uxS3IkgCAIBr5vKrsW8W1apVc/bOcivjqm0AAABYwjmSAAAAsIQWSQAAAFhCkAQAAIAl1/1im4yMDB05ckTBwcEFdqgrAACAm5kxRmfPnlVERESmcdAvdd2D5JEjR7IdkQIAAAA3jj///FOlS5fO9vbrHiQdw379+eefzqEDAQAA3C4jQ/rzT/t8mTJSDi1xN7uEhASVKVPGmduyc92DpONwdkhICEESAADcOJKSpNtvt88nJkqBge6t5waQ22mIt27UBgAAwFUhSAIAAMASgiQAAAAsuSHH2k5PT1daWpq7ywAkSZ6envLy8qK7KgAALnPDBcnExEQdOnRIjNyIG0lAQIBKliwpHx8fd5cCAMAN44YKkunp6Tp06JACAgIUHh5OCxDczhij1NRUnThxQvv371elSpVy7JgVAIBbyQ0VJNPS0mSMUXh4uPz9/d1dDiBJ8vf3l7e3tw4cOKDU1FT5+fm5uyQAwLXg5SUNGnRxHrm6IZ8lWiJxo6EVEgBuAb6+0tSp7q6iQOHXEQAAAJbckC2SAAAA150x0smT9vmwMIkjpLmiRfI6WL16tWw2m86cOXNdH3fWrFkqXLjwVW0jLi5ONptNsbGx2a6T1/379ttvVa1aNaWnp19VTfkpNTVVUVFR2rBhg7tLAQC4W3KyVKyYfUpOdnc1BQJB8irZbLYcpzFjxri7xBvGs88+qxdeeEGenp5XfN+0tDSNGDFCNWvWVGBgoCIiItSrVy8dOXIkx/uNGTMm02tStWpV5+0+Pj565plnNGLEiCuuCQCAWx1B8iodPXrUOU2aNEkhISEuy5555hlL201NTc3nSt3rhx9+0N69e/XAAw9ku86YMWPUp0+fLG9LTk7Wpk2b9OKLL2rTpk363//+p127dunee+/N9bFr1Kjh8pr88MMPLrd3795dP/zwg7Zt23ZF+wQAwK2uYATJpKTsp5SUvK977lze1r0CJUqUcE6FChWSzWZzWRYUFORcd+PGjapfv74CAgJ05513ateuXc7bxowZo9q1a+vDDz9UuXLlnF3MnDlzRv3791d4eLhCQkJ01113acuWLc77bdmyRS1atFBwcLBCQkJUr169TIdply1bpmrVqikoKEh33323jh496rwtIyND48aNU+nSpeXr66vatWvrm2++yXGflyxZosqVK8vf318tWrRQXFxcrs/T/Pnz9Y9//MNy1zmFChXSihUr1LVrV1WpUkV33HGHpkyZoo0bN+rgwYM53tfLy8vlNQkLC3O5vUiRImrSpInmz59vqTYAAG5VBSNIBgVlP13ewlWsWPbrtm3rum5UVNbrXSPPP/+83n77bW3YsEFeXl7q27evy+179uzRokWL9L///c95TmKXLl10/PhxLV26VBs3blTdunXVsmVLnT59WpK9Na106dJav369Nm7cqJEjR8rb29u5zeTkZE2YMEFz5szR999/r4MHD7q0kk6ePFlvv/22JkyYoN9++01t2rTRvffeq927d2e5D3/++afuv/9+3XPPPYqNjVX//v01cuTIXPd97dq1ql+//pU+ZTmKj4+XzWbL9TzQ3bt3KyIiQuXLl1f37t2zDJ4NGzbU2rVr87U+AABudly1fR29+uqrio6OliSNHDlS7du3V0pKirOVLjU1Vf/5z38UHh4uyX44+Ndff9Xx48fl6+srSZowYYI+//xzLVy4UI899pgOHjyo4cOHO8/7q1SpkstjpqWl6f3331eFChUkSUOGDNG4ceOct0+YMEEjRozQQw89JEkaP368Vq1apUmTJmlqFn1pTZs2TRUqVNDbb78tSapSpYp+//13jR8/Psd9P3DggCIiIq7sCctBSkqKRowYoW7duikkJCTb9Ro1aqRZs2apSpUqOnr0qMaOHatmzZpp69atCg4Odq4XERGhAwcO5Ft9AADcCgpGkExMzP62yy/cOH48+3Uv71Q6D4dk89Ptt9/unC9ZsqQk6fjx4ypbtqwkKTIy0hkiJfth68TERBUtWtRlO+fOndPevXslSU899ZT69++vOXPmqFWrVurSpYszNEr2MaIv/btkyZI6/v/PUUJCgo4cOaImTZq4bL9JkyYuh88vtWPHDjVq1MhlWePGjXPd93PnzmU6rL127Vq1vaSVODU1VcYYLVy40Lls+vTp6t69u8v90tLS1LVrVxljNG3atBwf99Lt33777WrUqJEiIyP16aefql+/fs7b/P39lcwVegAAXJGCESQDA92/bj649JCzY/SejIyMS8pxrScxMVElS5bU6tWrM23LcTh3zJgxevjhh/X1119r6dKlGj16tObPn6/77rsv02M6HtcYkx+7c0XCwsL0999/uyyrX7++S7dC77zzjg4fPuzSulm8eHGX+zhC5IEDB/Tdd9/l2BqZlcKFC6ty5cras2ePy/LTp0+7hHgAwC3Iy0vq3fviPHLFs3QDq1u3rv766y95eXkpKioq2/UqV66sypUr68knn1S3bt00c+ZMZ5DMSUhIiCIiIrRu3TrnIXdJWrdunRo2bJjlfapVq6YvvvjCZdnPP/+c62PVqVNH27dvd1nm7++vihUrOv8ODQ1VQkKCy7JLOULk7t27tWrVqkwttXmRmJiovXv3qmfPni7Lt27dqjp16lzx9gAANxFfX2nWLHdXUaAUjIttblGtWrVS48aN1alTJy1fvlxxcXH68ccf9fzzz2vDhg06d+6chgwZotWrV+vAgQNat26d1q9fr2rVquX5MYYPH67x48frv//9r3bt2qWRI0cqNjZWTzzxRJbrDxw4ULt379bw4cO1a9cuzZs3T7Py8KFr06ZNpm53rkRaWpo6d+6sDRs2aO7cuUpPT9dff/2lv/76y6WrpJYtW2rKlCnOv5955hmtWbPG+dzdd9998vT0VLdu3Vy2v3btWrVu3dpyfQAA3IpokbyB2Ww2LVmyRM8//7weeeQRnThxQiVKlFDz5s1VvHhxeXp66tSpU+rVq5eOHTumsLAw3X///Ro7dmyeH2Po0KGKj4/X008/rePHj6t69er64osvMl2041C2bFktWrRITz75pN599101bNhQr732WqYr0C/XvXt3Pfvss9q1a5eqVKlyRc+DJB0+fNjZElq7dm2X21atWqWYmBhJ0t69e3XSMbyVpEOHDqlbt246deqUwsPD1bRpU/38888uh7F/+uknxcfHq3PnzldcFwDgJmLMxRFtAgIYIjEPbOY6nzCXkJCgQoUKKT4+PtP5bSkpKdq/f79LP4q4eQwfPlwJCQmaPn26u0tx8eCDD6pWrVoaNWpUtuvw3gSAW0BS0sVuABMTr/u1FDeSnPLapTi0jevm+eefV2RkpMsFRu6WmpqqmjVr6sknn3R3KQAAFDgc2sZ1U7hw4Rxb/dzBx8dHL7zwgrvLAACgQKJFEgAAAJYQJAEAAGDJDRkk3dFhNpAT3pMAAGR2QwVJz/8f7vDSfgGBG4Fj+MTLRwoCAOBWdkNdbOPl5aWAgACdOHFC3t7e8rh8bGzgOjPGKDk5WcePH1fhwoWd/9kBANyEPD0lR5/CfN/nyQ0VJG02m0qWLKn9+/frwIED7i4HcCpcuLBKlCjh7jIAANeSn5+0YIG7qyhQbqggKdm7Y6lUqRKHt3HD8Pb2piUSAIAs3HBBUpI8PDwYPQQAAOAGx0mIAAAAkn2IRJvNPiUlubuaAoEgCQAAAEsIkgAAALCEIAkAAABLCJIAAACwhCAJAAAASwiSAAAAsOSG7EcSAADguvP0lNq1uziPXBEkAQAAJPsQiV9/7e4qChQObQMAAMASgiQAAAAsIUgCAABI9mERAwPtE0Mk5gnnSAIAADgkJ7u7ggKFFkkAAABYQpAEAACAJQRJAAAAWEKQBAAAgCUESQAAAFjCVdsAAACS5OEhRUdfnEeuCJIAAACS5O8vrV7t7ioKFOI2AAAALCFIAgAAwBKCJAAAgGQfFjE83D4xRGKecI4kAACAw8mT7q6gQKFFEgAAAJYQJAEAAGAJQRIAAACWECQBAABgCUESAAAAlnDVNgAAgGQfFrF+/YvzyBVBEgAAQLIPkbh+vburKFCI2wAAALCEIAkAAABLCJIAAACSlJwsRUXZp+Rkd1dTIHCOJAAAgCQZIx04cHEeuaJFEgAAAJYQJAEAAGAJQRIAAACWECQBAABgCUESAAAAlnDVNgAAgCTZbFL16hfnkSuCJAAAgCQFBEjbtrm7igKFQ9sAAACwhCAJAAAASwiSAAAAkn1YxBo17BNDJOYJ50gCAABI9mERt2+/OI9c0SIJAAAASwiSAAAAsIQgCQAAAEsIkgAAALCEIAkAAABLuGobAABAsg+LGBl5cR65IkgCAABI9iES4+LcXUWBwqFtAAAAWEKQBAAAgCUESQAAAEk6d05q0MA+nTvn7moKBM6RBAAAkKSMDGnDhovzyBUtkgAAALCEIAkAAABLCJIAAACwhCAJAAAASwiSAAAAsISrtgEAABzCwtxdQYFCkAQAAJCkwEDpxAl3V1GgcGgbAAAAlhAkAQAAYAlBEgAAQLIPixgTY58YIjFPOEcSAABAsg+LuGbNxXnkihZJAAAAWEKQBAAAgCUESQAAAFhCkAQAAIAlBEkAAABYwlXbAAAADgEB7q6gQCFIAgAASPYhEpOS3F1FgcKhbQAAAFhCkAQAAIAlBEkAAABJSkmR2re3Tykp7q6mQOAcSQAAAElKT5eWLLk4j1zRIgkAAABLCJIAAACwhCAJAAAASwiSAAAAsIQgCQAAAEsIkgAAALCE7n8AAAAk+xCJxri7igKFFkkAAABYQpAEAACAJQRJAAAAyT4sYpcu9okhEvOEIAkAACDZh0VcuNA+MURinhAkAQAAYAlBEgAAAJYQJAEAAGAJQRIAAACWECQBAABgCUESAAAAljBEIgAAgCQFBEiJiRfnkSuCJAAAgCTZbPbxtpFnHNoGAACAJQRJAAAASTp/XurTxz6dP+/uagoEgiQAAIAkXbggzZ5tny5ccHc1BQJBEgAAAJYQJAEAAGAJQRIAAACWECQBAABgCUESAAAAlhAkAQAAYAkj2wAAAEj2YRGPH784j1wRJAEAACT7EInh4e6uokDh0DYAAAAsIUgCAABI9mERBw+2TwyRmCcESQAAAMk+LOJ779knhkjME4IkAAAALCFIAgAAwBKCJAAAACwhSAIAAMASgiQAAAAsIUgCAADAEka2AQAAkCR/f2n//ovzyBVBEgAAQJI8PKSoKHdXUaBwaBsAAACWECQBAAAkKTVVGj7cPqWmuruaAsFmjDHX8wETEhJUqFAhxcfHKyQk5Ho+NAAAQPaSkqSgIPt8YqIUGOjeetwor3mNFkkAAABYQpAEAACAJQRJAAAAWEKQBAAAgCX0Iwn8v4MHpZMn3V1FwWbLSJZf6k6l+FSV8QhwdzkFWliYVLasu6sAgJwRJAHZQ2S1akbJyTZ3l1Kg1YnaqU2v1lPd5zdqc1xdd5dToAUEGO3YYSNMArihESQB2Vsik5NteuKtv1W6/AV3l1NgFcv4W0qVhr31t457nHB3OQXWoX1emjy8iE6epFUSuK78/aWtWy/OI1cESeASpctfUPkaBEmrCidfkHZIpStcUFAAzyOAAsbDQ6pRw91VFChcbAMAAABLaJEEAACQ7MMivvaafX7UKMnHx731FAAESQAAAElKS5PGjrXPDx9OkMwDDm0DAADAEoIkAAAALCFIAgAAwBKCJAAAACwhSAIAAMASgiQAAAAsofsfAAAASfLzk3799eI8ckWQBAAAkCRPT6lBA3dXUaDc9Ie2k5OTtWnTJiUnJ7u7FAAAcIMjN1yZmz5I7ty5U/Xq1dPOnTvdXQoAALiRpabq9KhR+qRePe36/Xd3V1Mg3PRBEgAAIE/S0lR68mS9Jcl24YK7qykQCJIAAACwhCAJAAAASwiSAAAAsIQgCQAAAEsIkgAAALCEIAkAAABLCJIAAACS5OenP6ZPV4ykDB8fd1dTIDBEIgAAgCR5eiqxfn2t+f95d8rIyNCePXv0999/68yZMypcuLCKFCmiihUrysPjxmkHvOIg+f333+utt97Sxo0bdfToUX322Wfq1KnTNSgNAADg1rN582YtWLBAu3bt0r59+5SSkiI/Pz+VL19eVapUUZcuXVSnTh13lynJwqHtpKQk1apVS1OnTr0W9QAAALhHWprCPv1Ug/5/3h02b96s6dOnKyMjQ76+vurdu7emTZumPn36yNfXVxkZGZo+fbo2b97slvoud8Utkm3btlXbtm2vRS0AAADuk5qqsuPHa6qkWDcMkZiRkaEFCxbotttu0+HDh9WkSRMNGjRINptN0dHReu+993T48GHVqFFDCxcuVK1atdx+mPuanyN5/vx5nT9/3vl3QkLCtX5IF+fOnZMk7dix47o+LgoWx9sj9XxRSZxgDfdKPZ8saZP42gKuL49z51T7/+cd+eF62rNnj06dOqWWLVvq999/16OPPiqbzSZJstlsatu2rcaPH6+WLVtq69at2rNnjypXrnzd67zUNQ+Sr7/+usaOHXutHyZbcXFxkqQePXq4rQYUHMcPr1TVutXdXQZucccP75HUSnxtAddXgKSk/58/cOCAGl/nx4+Pj5ck+fn5SZJKlSrlcntERIQkydfX12V9d7rmQfK5557TU0895fw7ISFBZcqUudYP6xQVFSVJ+vjjj1WtWrXr9rgoWHbskHr0kIqVKuruUgAVK1VR0kZ9/LHE1xZw/XicOyc1bSpJioyMvO6PX6hQIUlSSkqKJOnw4cMqX7688/YjR45IkvNIr2N9d7rmQdLX19eZnN3B399fklStWjXVrVvXbXWgYPDxPSHp+p8XA1zKxzdAUqSqVZP42gKuo6Qk56wjP1xPFStWVNGiRbVjxw6FhoZq6dKlznMkjTFaunSpihYtqu3btyssLEwVK1a87jVejn4kAQAAbgAeHh7q0qWLpk+frsKFC2vdunVKS0tT7dq1FRsbq19//VVly5bVtm3bNGDAALdfaCNZCJKJiYnas2eP8+/9+/crNjZWoaGhKlu2bL4WBwAAcCupU6eOBgwYoAULFuj8+fOaPXu2pk+f7uxH0tPTUwMGDLhh+pG84iC5YcMGtWjRwvm34/zH3r17a9asWflWGAAAwHXl66s9kybpiWHD9LK3t9vKqFOnjmrVqnVzjmwTExMjY8y1qAUAAMB9vLyU0KyZlkh62cu9Z/95eHi4vWufvLhxIi0AAAAKFIIkAACAJKWlKfSLL9T7/+eRO4IkAACAJKWmKmrsWM2S5OGGIRILIoIkAAAALCFIAgAAwBKCJAAAACwhSAIAAMASgiQAAAAsIUgCAADAEvd2234dVK1aVRs3blTVqlXdXQoAALiR+frq/Jw5Onz4sCrXrOnuagqEmz5IBgQEqG7duu4uAwAA3Oi8vOTbo4fKu7uOAoRD2wAAALDkpm+RBAAAyJMLF6TPPrPP33ef5EVMyg3PEAAAgCSdPy917WqfT0wkSOYBh7YBAABgCUESAAAAlhAkAQAAYAlBEgAAAJYQJAEAAGAJQRIAAACWcF07AACAJPn4SDNnXpxHrgiSwCUO7eMjcTWKZdifv0N7vXTcg+fSKt6HgJt4e0t9+ri7igKFbytAUliYFBBgNHl4EXeXUqDViSqiXq9Kk4YX0ea4cHeXU6AFBBiFhdncXQYA5IggCUgqW1bascOmkyfdXUnBZsuoqh2pG/XRgqoynIF9VcLCbCpb1t1VALeYCxekZcvs823aMLJNHvAMAf+vbFnxw33VAiTVdXcRAGDN+fNShw72eYZIzBPaDAAAAGAJQRIAAACWECQBAABgCUESAAAAlhAkAQAAYAlBEgAAAJZwXTsAAIBkHxZxypSL88gVQRIAAECyD5E4eLC7qyhQOLQNAAAAS2iRBAAAkKT0dGntWvt8s2aSp6d76ykACJIAAACSlJIitWhhn09MlAID3VtPAcChbQAAAFhCkAQAAIAlBEkAAABYQpAEAACAJQRJAAAAWEKQBAAAgCV0/wMAACDZR7Z5882L88gVQRIAAECyj689fLi7qyhQOLQNAAAAS2iRBAAAkOxDJG7aZJ+vW5chEvOAIAkAACDZh0hs2NA+zxCJecKhbQAAAFhCkAQAAIAlBEkAAABYQpAEAACAJQRJAAAAWEKQBAAAgCV0/wMAACDZh0UcPfriPHJFkAQAAJDsQySOGePuKgoUDm0DAADAElokAQAAJCkjQ9qxwz5frZrkQXtbbgiSAAAAknTunHTbbfZ5hkjME6I2AAAALCFIAgAAwBKCJAAAACwhSAIAAMASgiQAAAAsIUgCAADAErr/AQAAkOzDIj7zzMV55IogCQAAINmHSHzrLXdXUaBwaBsAAACW0CIJAAAg2YdIPHjQPl+2LEMk5gFBEgAAQLIPkViunH2eIRLzhKgNAAAASwiSAAAAsIQgCQAAAEsIkgAAALCEIAkAAABLCJIAAACwhO5/AAAAJMnLSxo06OI8csWzBAAAIEm+vtLUqe6uokDh0DYAAAAsoUUSAABAkoyRTp60z4eFSTabe+spAAiSAAAAkpScLBUrZp9niMQ84dA2AAAALCFIAgAAwBKCJAAAACwhSAIAAMASgiQAAAAsIUgCAADAErr/AQAAkOzDIvbufXEeueJZAgAAkOxDJM6a5e4qChQObQMAAMASWiQBAAAk+xCJycn2+YAAhkjMA1okAQAAJHuIDAqyT45AiRwRJAEAAGAJQRIAAACWECQBAABgCUESAAAAlhAkAQAAYAlBEgAAAJbQjyQAAIAkeXpKnTtfnEeuCJIAAACS5OcnLVjg7ioKFA5tAwAAwBKCJAAAACwhSAIAAEhSUpJ9fG2bzT6PXBEkAQAAYAlBEgAAAJYQJAEAAGAJQRIAAACWECQBAABgCUESAAAAljCyDQAAgGQfFrFdu4vzyBVBEgAAQLIPkfj11+6uokDh0DYAAAAsIUgCAADAEoIkAACAZB8WMTDQPjFEYp5wjiQAAIBDcrK7KyhQaJEEAACAJQRJAAAAWEKQBAAAgCUESQAAAFhCkAQAAIAlXLUNAAAgSR4eUnT0xXnkiiAJAAAgSf7+0urV7q6iQCFuAwAAwBKCJAAAACwhSAIAAEj2YRHDw+0TQyTmCedIAgAAOJw86e4KChRaJAEAAGAJQRIAAACWECQBAABgCUESAAAAlhAkAQAAYAlXbQMAAEj2YRHr1784j1wRJAEAACT7EInr17u7igKFuA0AAABLCJIAAACwhCAJAAAgScnJUlSUfUpOdnc1BQLnSAIAAEiSMdKBAxfnkStaJAEAAGAJQRIAAACWECQBAABgCUESAAAAlhAkAQAAYAlXbQMAAEiSzSZVr35xHrkiSAIAAEhSQIC0bZu7qyhQOLQNAAAASwiSAAAAsIQgCQAAINmHRaxRwz4xRGKecI4kAACAZB8Wcfv2i/PIFS2SAAAAsIQgCQAAAEsIkgAAALCEIAkAAABLCJIAAACwhKu2AQAAJPuwiJGRF+eRK4IkAACAZB8iMS7O3VUUKBzaBgAAgCUESQAAAFhCkAQAAJCkc+ekBg3s07lz7q6mQOAcSQAAAEnKyJA2bLg4j1zRIgkAAABLCJIAAACwhCAJAAAASwiSAAAAsIQgCQAAAEu4ahsAAMAhLMzdFRQoBEkAAABJCgyUTpxwdxUFCoe2AQAAYAlBEgAAAJYQJAEAACT7sIgxMfaJIRLzhHMkAQAAJPuwiGvWXJxHrmiRBAAAgCUESQAAAFhCkAQAAIAlBEkAAABYQpAEAACAJVy1DQAA4BAQ4O4KChSCJAAAgGQfIjEpyd1VFCgc2gYAAIAlBEkAAABYQpAEAACQpJQUqX17+5SS4u5qCgTOkQQAAJCk9HRpyZKL88gVLZIAAACwhCAJAAAASwiSAAAAsIQgCQAAAEsIkgAAALDkul+1bYyRJCUkJFzvhwYAAMjepaPaJCTc0lduO3KaI7dl57oHybNnz0qSypQpc70fGgAAIG8iItxdwQ3h7NmzKlSoULa320xuUTOfZWRk6MiRIwoODpbNZlNCQoLKlCmjP//8UyEhIdezFFxHvM63Bl7nWwev9a2B1/nWkNXrbIzR2bNnFRERIQ+P7M+EvO4tkh4eHipdunSm5SEhIbxJbwG8zrcGXudbB6/1rYHX+dZw+eucU0ukAxfbAAAAwBKCJAAAACxxe5D09fXV6NGj5evr6+5ScA3xOt8aeJ1vHbzWtwZe51vD1bzO1/1iGwAAANwc3N4iCQAAgIKJIAkAAABLCJIAAACwhCAJAAAAS26oIBkXF6d+/fqpXLly8vf3V4UKFTR69Gilpqa6uzTks1dffVV33nmnAgICVLhwYXeXg3wydepURUVFyc/PT40aNdKvv/7q7pKQz77//nvdc889ioiIkM1m0+eff+7ukpDPXn/9dTVo0EDBwcEqVqyYOnXqpF27drm7LOSzadOm6fbbb3d2Qt64cWMtXbr0irdzQwXJnTt3KiMjQ9OnT9e2bds0ceJEvf/++xo1apS7S0M+S01NVZcuXfTPf/7T3aUgn/z3v//VU089pdGjR2vTpk2qVauW2rRpo+PHj7u7NOSjpKQk1apVS1OnTnV3KbhG1qxZo8GDB+vnn3/WihUrlJaWptatWyspKcndpSEflS5dWm+88YY2btyoDRs26K677lLHjh21bdu2K9rODd/9z1tvvaVp06Zp37597i4F18CsWbM0bNgwnTlzxt2l4Co1atRIDRo00JQpUyRJGRkZKlOmjB5//HGNHDnSzdXhWrDZbPrss8/UqVMnd5eCa+jEiRMqVqyY1qxZo+bNm7u7HFxDoaGheuutt9SvX7883+eGapHMSnx8vEJDQ91dBoAcpKamauPGjWrVqpVzmYeHh1q1aqWffvrJjZUBuFrx8fGSxG/xTSw9PV3z589XUlKSGjdufEX39bpGNeWLPXv26N1339WECRPcXQqAHJw8eVLp6ekqXry4y/LixYtr586dbqoKwNXKyMjQsGHD1KRJE912223uLgf57Pfff1fjxo2VkpKioKAgffbZZ6pevfoVbeO6tEiOHDlSNpstx+nyH5vDhw/r7rvvVpcuXfToo49ejzJxlay8zgCAG9fgwYO1detWzZ8/392l4BqoUqWKYmNj9csvv+if//ynevfure3bt1/RNq5Li+TTTz+tPn365LhO+fLlnfNHjhxRixYtdOedd2rGjBnXuDrklyt9nXHzCAsLk6enp44dO+ay/NixYypRooSbqgJwNYYMGaKvvvpK33//vUqXLu3ucnAN+Pj4qGLFipKkevXqaf369Zo8ebKmT5+e521clyAZHh6u8PDwPK17+PBhtWjRQvXq1dPMmTPl4XHDn8aJ/3clrzNuLj4+PqpXr56+/fZb54UXGRkZ+vbbbzVkyBD3Fgfgihhj9Pjjj+uzzz7T6tWrVa5cOXeXhOskIyND58+fv6L73FDnSB4+fFgxMTGKjIzUhAkTdOLECedttGrcXA4ePKjTp0/r4MGDSk9PV2xsrCSpYsWKCgoKcm9xsOSpp55S7969Vb9+fTVs2FCTJk1SUlKSHnnkEXeXhnyUmJioPXv2OP/ev3+/YmNjFRoaqrJly7qxMuSXwYMHa968eVq8eLGCg4P1119/SZIKFSokf39/N1eH/PLcc8+pbdu2Klu2rM6ePat58+Zp9erVWrZs2ZVtyNxAZs6caSRlOeHm0rt37yxf51WrVrm7NFyFd99915QtW9b4+PiYhg0bmp9//tndJSGfrVq1KsvPbu/evd1dGvJJdr/DM2fOdHdpyEd9+/Y1kZGRxsfHx4SHh5uWLVua5cuXX/F2bvh+JAEAAHBj4gREAAAAWEKQBAAAgCUESQAAAFhCkAQAAIAlBEkAAABYQpAEAACAJQRJAAAAWEKQBAAAgCUESQAAAFhCkARwU1u9erVsNlu2U4sWLbK8X3Jysp577jlVqFBBfn5+Cg8PV3R0tBYvXnyd9wAAblxe7i4AAK6lO++8U0ePHs20/IsvvtDAgQM1aNCgLO83cOBA/fLLL3r33XdVvXp1nTp1Sj/++KNOnTp1zWpNTU2Vj4/PNds+AOQ3WiQB3NR8fHxUokQJl+nvv//WM888o1GjRqlLly5Z3u+LL77QqFGj1K5dO0VFRalevXp6/PHH1bdvX+c658+f14gRI1SmTBn5+vqqYsWK+uijj5y3r1mzRg0bNpSvr69KliypkSNH6sKFC87bY2JiNGTIEA0bNkxhYWFq06aNJGnr1q1q27atgoKCVLx4cfXs2VMnT568Rs8QAFhHkARwSzlz5ow6duyomJgYvfzyy9muV6JECS1ZskRnz57Ndp1evXrpk08+0TvvvKMdO3Zo+vTpCgoKkiQdPnxY7dq1U4MGDbRlyxZNmzZNH330kV555RWXbcyePVs+Pj5at26d3n//fZ05c0Z33XWX6tSpow0bNuibb77RsWPH1LVr1/x5AgAgH9mMMcbdRQDA9ZCRkaEOHTooLi5Ov/zyi4KDg7Nd9/vvv1f37t117Ngx1apVS02bNlXnzp3VpEkTSdIff/yhKlWqaMWKFWrVqlWm+z///PNatGiRduzYIZvNJkl67733NGLECMXHx8vDw0MxMTFKSEjQpk2bnPd75ZVXtHbtWi1btsy57NChQypTpox27dqlypUr59fTAQBXjRZJALeMUaNG6aefftLixYudIfLgwYMKCgpyTq+99pokqXnz5tq3b5++/fZbde7cWdu2bVOzZs2crZixsbHy9PRUdHR0lo+1Y8cONW7c2BkiJalJkyZKTEzUoUOHnMvq1avncr8tW7Zo1apVLjVVrVpVkrR37978ezIAIB9wsQ2AW8L8+fM1YcIEff3116pUqZJzeUREhGJjY51/h4aGOue9vb3VrFkzNWvWTCNGjNArr7yicePGacSIEfL398+XugIDA13+TkxM1D333KPx48dnWrdkyZL58pgAkF8IkgBuerGxserXr5/eeOMN5wUtDl5eXqpYsWKetlO9enVduHBBKSkpqlmzpjIyMrRmzZosD21Xq1ZNixYtkjHG2Sq5bt06BQcHq3Tp0tk+Rt26dbVo0SJFRUXJy4uvaAA3Ng5tA7ipnTx5Up06dVJMTIx69Oihv/76y2U6ceJElveLiYnR9OnTtXHjRsXFxWnJkiUaNWqUWrRooZCQEEVFRal3797q27evPv/8c+3fv1+rV6/Wp59+KkkaNGiQ/vzzTz3++OPauXOnFi9erNGjR+upp56Sh0f2X72DBw/W6dOn1a1bN61fv1579+7VsmXL9Mgjjyg9Pf2aPEcAYBX/3QVwU/v666914MABHThwIMtDw5GRkYqLi8u0vE2bNpo9e7ZGjRql5ORkRUREqEOHDnrppZec60ybNk2jRo3SoEGDdOrUKZUtW1ajRo2SJJUqVUpLlizR8OHDVatWLYWGhqpfv3564YUXcqw3IiJC69at04gRI9S6dWudP39ekZGRuvvuu3MMoADgDly1DQAAAEv47y0AAAAsIUgCAADAEoIkAAAALCFIAgAAwBKCJAAAACwhSAIAAMASgiQAAAAsIUgCAADAEoIkAAAALCFIAgAAwBKCJAAAACz5PxDNbEJJiLpSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metapriv.save_boxplot(cig_df, column_name=\"RIG\", k=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ba12d2",
   "metadata": {},
   "source": [
    "## Sample Unique Detection Algorithm (SUDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a04daae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data with Scores & Dis-Scores:\n",
      "\n",
      "Attribute Contributions:\n",
      "         variable  contribution\n",
      "0             age         73.74\n",
      "3      occupation         38.36\n",
      "1       education         33.39\n",
      "4    relationship         20.74\n",
      "2  marital-status         17.61\n",
      "5             sex          9.71\n",
      "6    salary-class          7.43\n",
      "\n",
      "Attribute Level Contributions:\n",
      "        variable  attribute  contribution\n",
      "13           age       31.0          4.16\n",
      "25           age       43.0          3.39\n",
      "28           age       46.0          3.29\n",
      "27           age       45.0          3.25\n",
      "22           age       40.0          3.20\n",
      "..           ...        ...           ...\n",
      "90  relationship        5.0         12.16\n",
      "94  salary-class        1.0         65.02\n",
      "93  salary-class        0.0         34.98\n",
      "91           sex        0.0         62.57\n",
      "92           sex        1.0         37.43\n",
      "\n",
      "[95 rows x 3 columns]\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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>dis-score</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9716</td>\n",
       "      <td>1440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9636</td>\n",
       "      <td>1128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9628</td>\n",
       "      <td>1104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>66</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9624</td>\n",
       "      <td>1092.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9622</td>\n",
       "      <td>1086.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>44</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>44</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  education  marital-status  occupation  relationship  sex  \\\n",
       "160   68          2               0          -1             1    0   \n",
       "106   17          0               4          -1             3    0   \n",
       "167   46         11               6           8             4    1   \n",
       "169   66          7               6           7             1    0   \n",
       "37    19         10               1           0             5    0   \n",
       "..   ...        ...             ...         ...           ...  ...   \n",
       "138   20         13               4           9             3    1   \n",
       "47    44         11               0           2             4    0   \n",
       "124   19         13               4           6             3    1   \n",
       "162   44         11               0           2             4    0   \n",
       "153   21         10               4           1             3    1   \n",
       "\n",
       "     salary-class  dis-score   score  \n",
       "160             0     0.9716  1440.0  \n",
       "106             0     0.9636  1128.0  \n",
       "167             0     0.9628  1104.0  \n",
       "169             0     0.9624  1092.0  \n",
       "37              0     0.9622  1086.0  \n",
       "..            ...        ...     ...  \n",
       "138             0     0.0000     0.0  \n",
       "47              0     0.0000     0.0  \n",
       "124             0     0.0000     0.0  \n",
       "162             0     0.0000     0.0  \n",
       "153             0     0.0000     0.0  \n",
       "\n",
       "[200 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_suda = metapriv.compute_suda2(data, selected_columns, sample_fraction=0.2, missing_value= np.nan)\n",
    "# Print the results\n",
    "print(\"Data with Scores & Dis-Scores:\")\n",
    "print(\"\\nAttribute Contributions:\")\n",
    "print(results_suda[\"attribute_contributions\"])\n",
    "\n",
    "print(\"\\nAttribute Level Contributions:\")\n",
    "pprint.pprint(results_suda[\"attribute_level_contributions\"])\n",
    "results_suda = results_suda[\"data_with_scores\"].sort_values(by=\"dis-score\", ascending=False)\n",
    "results_suda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64f650ed",
   "metadata": {},
   "source": [
    "##### Here you can choose to find outliers for the raw score \"score\" or the normalised score \"dis-score\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40c7f784",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detected in column 'score' above threshold 2.5:\n",
      "Index: 160, Value: 1440.0, Z-Score: 6.023441860465116\n",
      "Index: 106, Value: 1128.0, Z-Score: 4.392093023255814\n",
      "Index: 167, Value: 1104.0, Z-Score: 4.26660465116279\n",
      "Index: 169, Value: 1092.0, Z-Score: 4.203860465116279\n",
      "Index: 37, Value: 1086.0, Z-Score: 4.1724883720930235\n",
      "Index: 77, Value: 1080.0, Z-Score: 4.1411162790697675\n",
      "Index: 6, Value: 984.0, Z-Score: 3.6391627906976742\n",
      "Index: 157, Value: 984.0, Z-Score: 3.6391627906976742\n",
      "Index: 61, Value: 966.0, Z-Score: 3.545046511627907\n",
      "Index: 144, Value: 888.0, Z-Score: 3.1372093023255814\n",
      "Index: 27, Value: 840.0, Z-Score: 2.8862325581395347\n",
      "Index: 195, Value: 840.0, Z-Score: 2.8862325581395347\n",
      "Index: 148, Value: 840.0, Z-Score: 2.8862325581395347\n",
      "Index: 74, Value: 816.0, Z-Score: 2.760744186046512\n",
      "Index: 21, Value: 792.0, Z-Score: 2.6352558139534885\n",
      "Index: 100, Value: 774.0, Z-Score: 2.541139534883721\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metapriv.save_boxplot(results_suda, column_name=\"score\", k=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6ea061",
   "metadata": {},
   "source": [
    "## Overview of result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9fa79d8",
   "metadata": {},
   "source": [
    "- k-Anonymity = 1 \n",
    "\n",
    "- L-diversity = 1\n",
    "\n",
    "Number of unique rows 190\n",
    "_________________\n",
    "\n",
    "- According to K-global\n",
    "{age (1.1) , education (1.0), occupation (0.5)} are the variables that has the highest normalized effect on k-anonymity. \n",
    "\n",
    "- PIF \n",
    "\n",
    "95% of rows have row information gain under og equal to 21.9\n",
    "from outlier detection we find 3 rows of high risk. \n",
    "\n",
    "    - Index: 169, Value: 25.218230041679757, Z-Score: 2.809188988099669\n",
    "    - Index: 167, Value: 25.158149437675746, Z-Score: 2.7901779948713976\n",
    "    - Index: 37, Value: 24.4079182979435, Z-Score: 2.5527862549991385\n",
    "\n",
    "PIF Identifies \n",
    "\n",
    "- age               997.647833\n",
    "- occupation        629.661075\n",
    "- education         569.388028\n",
    "\n",
    "\n",
    "As the 3 higest contributing variables to re-identifcation. \n",
    "\n",
    "\n",
    "\n",
    "- SUDA \n",
    "\n",
    "From suda we find 16 outliers \n",
    "\n",
    "    - Index: 160, Value: 1440.0, Z-Score: 6.023441860465116\n",
    "    - Index: 106, Value: 1128.0, Z-Score: 4.392093023255814\n",
    "    - Index: 167, Value: 1104.0, Z-Score: 4.26660465116279\n",
    "    - Index: 169, Value: 1092.0, Z-Score: 4.203860465116279\n",
    "    - Index: 37, Value: 1086.0, Z-Score: 4.1724883720930235\n",
    "    - Index: 77, Value: 1080.0, Z-Score: 4.1411162790697675\n",
    "    - Index: 6, Value: 984.0, Z-Score: 3.6391627906976742\n",
    "    - Index: 157, Value: 984.0, Z-Score: 3.6391627906976742\n",
    "    - Index: 61, Value: 966.0, Z-Score: 3.545046511627907\n",
    "    - Index: 144, Value: 888.0, Z-Score: 3.1372093023255814\n",
    "    - Index: 27, Value: 840.0, Z-Score: 2.8862325581395347\n",
    "    - Index: 195, Value: 840.0, Z-Score: 2.8862325581395347\n",
    "    - Index: 148, Value: 840.0, Z-Score: 2.8862325581395347\n",
    "    - Index: 74, Value: 816.0, Z-Score: 2.760744186046512\n",
    "    - Index: 21, Value: 792.0, Z-Score: 2.6352558139534885\n",
    "    - Index: 100, Value: 774.0, Z-Score: 2.541139534883721\n",
    "\n",
    "SUDA identifies\n",
    "\n",
    "- age         73.74\n",
    "- occupation         38.36\n",
    "- education         33.39\n",
    "\n",
    "As the 3 higest contributing variables to re-identifcation. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f16fa8f",
   "metadata": {},
   "source": [
    "## Lowering the risk \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac2574bf",
   "metadata": {},
   "outputs": [
    {
     "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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>disease</th>\n",
       "      <th>exercise_weekly_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   education       marital-status          occupation    relationship  \\\n",
       "0   39   Bachelors        Never-married        Adm-clerical   Not-in-family   \n",
       "1   50   Bachelors   Married-civ-spouse     Exec-managerial         Husband   \n",
       "2   38     HS-grad             Divorced   Handlers-cleaners   Not-in-family   \n",
       "3   53        11th   Married-civ-spouse   Handlers-cleaners         Husband   \n",
       "4   28   Bachelors   Married-civ-spouse      Prof-specialty            Wife   \n",
       "\n",
       "       sex salary-class       disease  exercise_weekly_hours  \n",
       "0     Male        <=50K  Hypertension                      2  \n",
       "1     Male        <=50K        Asthma                      0  \n",
       "2     Male        <=50K      Diabetes                      6  \n",
       "3     Male        <=50K      Diabetes                     10  \n",
       "4   Female        <=50K        Cancer                      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cce5039a",
   "metadata": {},
   "source": [
    "## Generalizing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5476f643",
   "metadata": {},
   "source": [
    "The function below can be re-run as many times as needed. Here \n",
    "('HS-grad', '11th', '9th', '7th-8th', '5th-6th', '10th', '1st-4th') has been made into 'K-12 Education'.\n",
    "Additionally 'Assoc-acdm' and 'Assoc-voc' has been made into 'Assoc'.\n",
    "\n",
    "The function keep track of your changes into the column, hence it does not overwrite if you keep combinging values within the same column.\n",
    "\n",
    "If you change column use new safed dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a774cbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in 'education': ['Bachelors' 'K-12 Education' 'Masters' 'Some-college' 'Assoc-acdm'\n",
      " 'Assoc-voc' 'Doctorate' 'Prof-school']\n",
      "Enter values to combine in 'education' (comma-separated): 'Assoc-acdm','Assoc-voc'\n",
      "Enter the replacement value: Assoc-Degree\n"
     ]
    }
   ],
   "source": [
    "data_mod = metapriv.combine_values(data_mod, 'education')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc79b20b",
   "metadata": {},
   "source": [
    "Making 'Widowed','Separated', 'Divorced','Married-spouse-absent','Never-married' into 'Single' \n",
    "and 'Married-civ-spouse','Married-AF-spouse' into 'Married'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd2f8fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in 'marital-status': ['Single' 'Married-civ-spouse' 'Married-AF-spouse']\n",
      "Enter values to combine in 'marital-status' (comma-separated): 'Married-civ-spouse','Married-AF-spouse'\n",
      "Enter the replacement value: Married\n"
     ]
    }
   ],
   "source": [
    "data_mod = metapriv.combine_values(data_mod, 'marital-status')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ec3cfe",
   "metadata": {},
   "source": [
    "Changing 'Handlers-cleaners','Craft-repair', 'Transport-moving','Farming-fishing','Machine-op-inspct' into 'Manual/Blue-Collar' & 'Other-service' ,'Sales' ,'Tech-support' ,'Protective-serv' into 'Services'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e0e3946b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in 'occupation': ['Adm-clerical' 'Exec-managerial' 'Manual/Blue-Collar' 'Prof-specialty'\n",
      " 'Other-service' 'Sales' 'Tech-support' nan 'Protective-serv']\n",
      "Enter values to combine in 'occupation' (comma-separated):  'Other-service' ,'Sales' ,'Tech-support' ,'Protective-serv'\n",
      "Enter the replacement value: Services\n"
     ]
    }
   ],
   "source": [
    "data_mod = metapriv.combine_values(data_mod, 'occupation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a7dacf",
   "metadata": {},
   "source": [
    "In extension we see that both marital-status and relationship is available hence we remove relationship. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "70cf4af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mod = data_mod.drop(columns=['relationship'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7d7afbfd",
   "metadata": {},
   "outputs": [
    {
     "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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>disease</th>\n",
       "      <th>exercise_weekly_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.430742</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Single</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.238954</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.541960</td>\n",
       "      <td>K-12 Education</td>\n",
       "      <td>Single</td>\n",
       "      <td>Manual/Blue-Collar</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.473662</td>\n",
       "      <td>K-12 Education</td>\n",
       "      <td>Married</td>\n",
       "      <td>Manual/Blue-Collar</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.590703</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Female</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age       education marital-status          occupation      sex  \\\n",
       "0  37.430742       Bachelors         Single        Adm-clerical     Male   \n",
       "1  51.238954       Bachelors        Married     Exec-managerial     Male   \n",
       "2  38.541960  K-12 Education         Single  Manual/Blue-Collar     Male   \n",
       "3  52.473662  K-12 Education        Married  Manual/Blue-Collar     Male   \n",
       "4  28.590703       Bachelors        Married      Prof-specialty   Female   \n",
       "\n",
       "  salary-class       disease  exercise_weekly_hours  \n",
       "0        <=50K  Hypertension                      2  \n",
       "1        <=50K        Asthma                      0  \n",
       "2        <=50K      Diabetes                      6  \n",
       "3        <=50K      Diabetes                     10  \n",
       "4        <=50K        Cancer                      2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mod.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9bcf03",
   "metadata": {},
   "source": [
    "## Adding noise to 'age' variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c6402ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mod_noise = metapriv.add_noise(data_mod, 'age', 'normal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "62e57e61",
   "metadata": {},
   "outputs": [
    {
     "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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>disease</th>\n",
       "      <th>exercise_weekly_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.375526</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Single</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.563593</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.991865</td>\n",
       "      <td>K-12 Education</td>\n",
       "      <td>Single</td>\n",
       "      <td>Manual/Blue-Collar</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age       education marital-status          occupation    sex  \\\n",
       "0  36.375526       Bachelors         Single        Adm-clerical   Male   \n",
       "1  49.563593       Bachelors        Married     Exec-managerial   Male   \n",
       "2  37.991865  K-12 Education         Single  Manual/Blue-Collar   Male   \n",
       "\n",
       "  salary-class       disease  exercise_weekly_hours  \n",
       "0        <=50K  Hypertension                      2  \n",
       "1        <=50K        Asthma                      0  \n",
       "2        <=50K      Diabetes                      6  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mod_noise.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c789ec3",
   "metadata": {},
   "source": [
    "## Modified Dataset Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8231eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_rows': 200,\n",
       " 'total_columns': 8,\n",
       " 'num_selected_columns': 5,\n",
       " 'num_unique_rows': 47,\n",
       " 'k_anonymity': 1,\n",
       " 'l_diversity': 1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = [\"education\", \"marital-status\", \"occupation\",\"sex\",\"salary-class\"]\n",
    "metapriv.calculate_unique_rows(data_mod_noise, selected_columns, sensitive_attr='disease')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b51647c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find Influential Columns: \n",
      "\n",
      "{'education': {'difference': 39,\n",
      "               'normalized_difference': 5.6,\n",
      "               'unique_count_after_removal': 8},\n",
      " 'marital-status': {'difference': 13,\n",
      "                    'normalized_difference': 6.5,\n",
      "                    'unique_count_after_removal': 34},\n",
      " 'occupation': {'difference': 34,\n",
      "                'normalized_difference': 6.8,\n",
      "                'unique_count_after_removal': 13},\n",
      " 'salary-class': {'difference': 18,\n",
      "                  'normalized_difference': 9.0,\n",
      "                  'unique_count_after_removal': 29},\n",
      " 'sex': {'difference': 21,\n",
      "         'normalized_difference': 10.5,\n",
      "         'unique_count_after_removal': 26}}\n"
     ]
    }
   ],
   "source": [
    "results = metapriv.find_lowest_unique_columns(data_mod_noise, selected_columns)\n",
    "print('Find Influential Columns:','\\n')\n",
    "pprint.pprint(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a54e84bb",
   "metadata": {},
   "outputs": [
    {
     "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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>disease</th>\n",
       "      <th>exercise_weekly_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.375526</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Single</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.563593</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.991865</td>\n",
       "      <td>K-12 Education</td>\n",
       "      <td>Single</td>\n",
       "      <td>Manual/Blue-Collar</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.403259</td>\n",
       "      <td>K-12 Education</td>\n",
       "      <td>Married</td>\n",
       "      <td>Manual/Blue-Collar</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.676917</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Female</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age       education marital-status          occupation      sex  \\\n",
       "0  36.375526       Bachelors         Single        Adm-clerical     Male   \n",
       "1  49.563593       Bachelors        Married     Exec-managerial     Male   \n",
       "2  37.991865  K-12 Education         Single  Manual/Blue-Collar     Male   \n",
       "3  53.403259  K-12 Education        Married  Manual/Blue-Collar     Male   \n",
       "4  28.676917       Bachelors        Married      Prof-specialty   Female   \n",
       "\n",
       "  salary-class       disease  exercise_weekly_hours  \n",
       "0        <=50K  Hypertension                      2  \n",
       "1        <=50K        Asthma                      0  \n",
       "2        <=50K      Diabetes                      6  \n",
       "3        <=50K      Diabetes                     10  \n",
       "4        <=50K        Cancer                      2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data_mod_noise.to_csv('data_mod_noise.csv')\n",
    "data_mod_noise = pd.read_csv('Use_Case_Data/data_mod_noise.csv', index_col=0)\n",
    "selected_columns = [\"education\", \"marital-status\", \"occupation\",\"sex\",\"salary-class\"]\n",
    "data_mod_noise.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8e6bcdc",
   "metadata": {},
   "source": [
    "## PIF for Modified Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d11a23aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a mask value (or leave blank to skip):nan\n",
      "Enter percentile (0-100): 95\n",
      "PIF Value: 8.46248663983755\n",
      "CIG DataFrame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>RIG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.643856</td>\n",
       "      <td>1.014500</td>\n",
       "      <td>2.888969</td>\n",
       "      <td>0.125769</td>\n",
       "      <td>2.089267</td>\n",
       "      <td>12.762361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2.625824</td>\n",
       "      <td>0.985645</td>\n",
       "      <td>2.888969</td>\n",
       "      <td>1.736966</td>\n",
       "      <td>2.089267</td>\n",
       "      <td>10.326671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2.343309</td>\n",
       "      <td>1.014500</td>\n",
       "      <td>2.888969</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>2.089267</td>\n",
       "      <td>8.850618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2.343309</td>\n",
       "      <td>1.014500</td>\n",
       "      <td>2.888969</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>2.089267</td>\n",
       "      <td>8.850618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.343309</td>\n",
       "      <td>1.014500</td>\n",
       "      <td>2.888969</td>\n",
       "      <td>0.514573</td>\n",
       "      <td>2.089267</td>\n",
       "      <td>8.850618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.124624</td>\n",
       "      <td>0.017799</td>\n",
       "      <td>0.430256</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>0.972567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.124624</td>\n",
       "      <td>0.017799</td>\n",
       "      <td>0.430256</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>0.972567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.124624</td>\n",
       "      <td>0.017799</td>\n",
       "      <td>0.430256</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>0.972567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.124624</td>\n",
       "      <td>0.017799</td>\n",
       "      <td>0.430256</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>0.972567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>0.124624</td>\n",
       "      <td>0.017799</td>\n",
       "      <td>0.430256</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>0.386468</td>\n",
       "      <td>0.972567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     education  marital-status  occupation       sex  salary-class        RIG\n",
       "52    6.643856        1.014500    2.888969  0.125769      2.089267  12.762361\n",
       "89    2.625824        0.985645    2.888969  1.736966      2.089267  10.326671\n",
       "63    2.343309        1.014500    2.888969  0.514573      2.089267   8.850618\n",
       "189   2.343309        1.014500    2.888969  0.514573      2.089267   8.850618\n",
       "20    2.343309        1.014500    2.888969  0.514573      2.089267   8.850618\n",
       "..         ...             ...         ...       ...           ...        ...\n",
       "31    0.124624        0.017799    0.430256  0.013419      0.386468   0.972567\n",
       "124   0.124624        0.017799    0.430256  0.013419      0.386468   0.972567\n",
       "157   0.124624        0.017799    0.430256  0.013419      0.386468   0.972567\n",
       "64    0.124624        0.017799    0.430256  0.013419      0.386468   0.972567\n",
       "144   0.124624        0.017799    0.430256  0.013419      0.386468   0.972567\n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pif_value, cig_df = metapriv.compute_cig(data_mod_noise, selected_columns)\n",
    "print(\"PIF Value:\", pif_value)\n",
    "print(\"CIG DataFrame:\")\n",
    "cig_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ebb24439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education         173.840952\n",
       "marital-status     96.814132\n",
       "occupation        172.934479\n",
       "sex                84.993907\n",
       "salary-class       87.717054\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_sum = [col for col in cig_df.columns if col != 'RIG']\n",
    "column_sums = cig_df[columns_to_sum].sum()\n",
    "column_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d782eab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detected in column 'RIG' above threshold 2.5:\n",
      "Index: 52, Value: 12.762361168676165, Z-Score: 8.354017992793137\n",
      "Index: 89, Value: 10.326670676488622, Z-Score: 6.398413080654351\n",
      "Index: 63, Value: 8.850617558582824, Z-Score: 5.2132966887264685\n",
      "Index: 189, Value: 8.850617558582824, Z-Score: 5.2132966887264685\n",
      "Index: 20, Value: 8.850617558582824, Z-Score: 5.2132966887264685\n",
      "Index: 96, Value: 8.850617558582824, Z-Score: 5.2132966887264685\n",
      "Index: 19, Value: 8.803950787734188, Z-Score: 5.175828148861503\n",
      "Index: 197, Value: 8.803950787734188, Z-Score: 5.175828148861503\n",
      "Index: 125, Value: 8.607318207170971, Z-Score: 5.017952736665517\n",
      "Index: 143, Value: 8.475271180981114, Z-Score: 4.911932774819593\n",
      "Index: 111, Value: 8.461813769251048, Z-Score: 4.901127879629335\n",
      "Index: 67, Value: 7.785192289670676, Z-Score: 4.357871544980021\n",
      "Index: 8, Value: 7.593273869608209, Z-Score: 4.203781110991016\n",
      "Index: 102, Value: 6.637093179377927, Z-Score: 3.436067946816699\n",
      "Index: 4, Value: 6.490125391729949, Z-Score: 3.3180681728830144\n",
      "Index: 50, Value: 6.405814182804719, Z-Score: 3.2503750834484624\n",
      "Index: 137, Value: 6.3985981302174695, Z-Score: 3.2445813473678147\n",
      "Index: 53, Value: 6.136740617770261, Z-Score: 3.0343371315018306\n",
      "Index: 5, Value: 6.079477984303663, Z-Score: 2.988361221324217\n",
      "Index: 84, Value: 5.9168118404928745, Z-Score: 2.857757308021032\n",
      "Index: 87, Value: 5.71204992704209, Z-Score: 2.6933548911098204\n",
      "Index: 27, Value: 5.707607418719048, Z-Score: 2.6897880212112746\n",
      "Index: 135, Value: 5.572735901528146, Z-Score: 2.5815002882779927\n",
      "Index: 198, Value: 5.572735901528146, Z-Score: 2.5815002882779927\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metapriv.save_boxplot(cig_df, column_name=\"RIG\", k=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da91d00",
   "metadata": {},
   "source": [
    "## SUDA for Modified Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1721fa91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data with Scores & Dis-Scores:\n",
      "\n",
      "Attribute Contributions:\n",
      "         variable  contribution\n",
      "0       education         89.58\n",
      "2      occupation         71.53\n",
      "3             sex         52.78\n",
      "4    salary-class         44.44\n",
      "1  marital-status         41.67\n",
      "\n",
      "Attribute Level Contributions:\n",
      "          variable  attribute  contribution\n",
      "6        education        6.0         18.60\n",
      "0        education        0.0         17.83\n",
      "1        education        1.0         17.05\n",
      "4        education        4.0         16.28\n",
      "3        education        3.0         11.63\n",
      "2        education        2.0          9.30\n",
      "5        education        5.0          9.30\n",
      "7   marital-status        0.0         50.00\n",
      "8   marital-status        1.0         50.00\n",
      "11      occupation        1.0         21.36\n",
      "9       occupation       -1.0         20.39\n",
      "10      occupation        0.0         19.42\n",
      "13      occupation        3.0         15.53\n",
      "14      occupation        4.0         12.62\n",
      "12      occupation        2.0         10.68\n",
      "18    salary-class        1.0         70.31\n",
      "17    salary-class        0.0         29.69\n",
      "15             sex        0.0         65.79\n",
      "16             sex        1.0         34.21\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>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary-class</th>\n",
       "      <th>dis-score</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6741</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5267</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4825</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4825</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4825</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     education  marital-status  occupation  sex  salary-class  dis-score  \\\n",
       "89           2               1           3    0             1     0.6741   \n",
       "52           5               0           3    0             1     0.5267   \n",
       "187          1               1          -1    0             0     0.4825   \n",
       "167          4               1           4    1             0     0.4825   \n",
       "143          0               1           3    0             1     0.4825   \n",
       "..         ...             ...         ...  ...           ...        ...   \n",
       "86           3               0           4    1             1     0.0000   \n",
       "88           3               1           4    1             0     0.0000   \n",
       "11           1               0           3    1             1     0.0000   \n",
       "90           0               0           3    1             0     0.0000   \n",
       "199          3               1           4    1             0     0.0000   \n",
       "\n",
       "     score  \n",
       "89    12.0  \n",
       "52     7.0  \n",
       "187    6.0  \n",
       "167    6.0  \n",
       "143    6.0  \n",
       "..     ...  \n",
       "86     0.0  \n",
       "88     0.0  \n",
       "11     0.0  \n",
       "90     0.0  \n",
       "199    0.0  \n",
       "\n",
       "[200 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_suda = metapriv.compute_suda2(data_mod_noise, selected_columns, sample_fraction=0.2, missing_value= np.nan)\n",
    "# Print the results\n",
    "print(\"Data with Scores & Dis-Scores:\")\n",
    "print(\"\\nAttribute Contributions:\")\n",
    "print(results_suda[\"attribute_contributions\"])\n",
    "\n",
    "print(\"\\nAttribute Level Contributions:\")\n",
    "pprint.pprint(results_suda[\"attribute_level_contributions\"])\n",
    "results_suda = results_suda[\"data_with_scores\"].sort_values(by=\"dis-score\", ascending=False)\n",
    "results_suda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a40d32a1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAD is zero, recalculating without zero entries.\n",
      "Outliers detected in column 'dis-score' above threshold 2.5:\n",
      "Index: 89, Value: 0.6741, Z-Score: 3.0632170731707316\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metapriv.save_boxplot(results_suda, column_name=\"dis-score\", k=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8914f35",
   "metadata": {},
   "source": [
    "## Overview of result after modification \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c20800",
   "metadata": {},
   "source": [
    "- k-Anonymity = 1 \n",
    "\n",
    "- L-diversity = 1\n",
    "\n",
    "Number of unique rows 41\n",
    "_________________\n",
    "\n",
    "- According to K-global\n",
    "{sex (1.1) , salary-class (1.0), marital-status (0.5)} are the variables that has the highest normalized effect on k-anonymity. \n",
    "This change makes sense because we have generalised catgorical variables into larger groups e.g. 'husband' and 'wife' became spouse.\n",
    "\n",
    "- PIF \n",
    "\n",
    "95% of rows have row information gain under og equal to 8.46 (This means that our changes have resulted in a large reduction of information gain an adversary can obtain, subsequently making it much safer) \n",
    "\n",
    "We identify 24 rows as outliers. The increase in outliers happens because some subject have been partioned into large groups due to the generalisation technique applied. Meaning that other subject now stand more out.   \n",
    "\n",
    " \n",
    "PIF Identifies \n",
    "\n",
    "- education         173.84\n",
    "- occupation        172.93\n",
    "- marital-status     96.81\n",
    "\n",
    "\n",
    "As the 3 higest contributing variables to re-identifcation. \n",
    "\n",
    "\n",
    "\n",
    "- SUDA \n",
    "\n",
    "From suda we find 7 outlier. \n",
    "\n",
    "\n",
    "\n",
    "SUDA identifies\n",
    "\n",
    "- education         89.58\n",
    "- occupation         71.53\n",
    "- sex         52.78\n",
    "\n",
    "As the 3 higest contributing variables to re-identifcation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36316cd3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}