{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RDS Lab 7\n",
    "\n",
    "Put this Jupyter notebook into the root directory of the DataSynthesizer downloaded from [GitHub](https://github.com/DataResponsibly/DataSynthesizer).\n",
    "\n",
    "## Install DataSynthesizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding into sys.path: /Users/hping/technion-files/DataSynthesizer-unstable/DataSynthesizer/\n"
     ]
    }
   ],
   "source": [
    "# Add the direcotry of DataSynthesizer into sys.path before importing the code\n",
    "import os, sys\n",
    "module_dir = os.getcwd() + '/DataSynthesizer/'\n",
    "sys.path.append(module_dir)\n",
    "\n",
    "print(f'Adding into sys.path: {module_dir}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from DataDescriber import DataDescriber\n",
    "from DataGenerator import DataGenerator\n",
    "from ModelInspector import ModelInspector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "describer = DataDescriber()\n",
    "generator = DataGenerator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modules and Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.utils import read_json_file\n",
    "\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensitive_data_file = './data/adult_reduced.csv'\n",
    "description_files = {'random mode':                   './out/description(random).json', \n",
    "                     'independent attribute mode':    './out/description(independent).json', \n",
    "                     'correlated attribute mode':     './out/description(correlated).json'}\n",
    "synthetic_data_files = {'random mode':                './out/synthetic data(random).csv', \n",
    "                        'independent attribute mode': './out/synthetic data(independent).csv', \n",
    "                        'correlated attribute mode':  './out/synthetic data(correlated).csv'}\n",
    "\n",
    "\n",
    "output_data_size = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>sex</th>\n",
       "      <th>relationship</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>11th</td>\n",
       "      <td>Male</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Female</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education     sex   relationship      marital-status income\n",
       "0   39  Bachelors    Male  Not-in-family       Never-married  <=50K\n",
       "1   50  Bachelors    Male        Husband  Married-civ-spouse  <=50K\n",
       "2   38    HS-grad    Male  Not-in-family            Divorced  <=50K\n",
       "3   53       11th    Male        Husband  Married-civ-spouse  <=50K\n",
       "4   28  Bachelors  Female           Wife  Married-civ-spouse  <=50K"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_data = pd.read_csv(sensitive_data_file)\n",
    "real_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "describer.describe_dataset_in_random_mode(sensitive_data_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "describer.save_dataset_description_to_file(description_files['random mode'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Important parameters here\n",
    "\n",
    "- __seed__: initialize the random number generator for both `random` and `np.random`\n",
    "- __mininum__, __maxmimum__: hide the min/max informtion in the sensitive data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.generate_dataset_in_random_mode(n=output_data_size, \n",
    "                                          description_file=description_files['random mode'], \n",
    "                                          seed=0, \n",
    "                                          minimum=0, \n",
    "                                          maximum=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.save_synthetic_data(synthetic_data_files['random mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "synthetic_random = pd.read_csv(synthetic_data_files['random mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>sex</th>\n",
       "      <th>relationship</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>11th</td>\n",
       "      <td>Male</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Female</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education     sex   relationship      marital-status income\n",
       "0   39  Bachelors    Male  Not-in-family       Never-married  <=50K\n",
       "1   50  Bachelors    Male        Husband  Married-civ-spouse  <=50K\n",
       "2   38    HS-grad    Male  Not-in-family            Divorced  <=50K\n",
       "3   53       11th    Male        Husband  Married-civ-spouse  <=50K\n",
       "4   28  Bachelors  Female           Wife  Married-civ-spouse  <=50K"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>sex</th>\n",
       "      <th>relationship</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>Preschool</td>\n",
       "      <td>Male</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>Female</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Separated</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Female</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Separated</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>12th</td>\n",
       "      <td>Male</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     education     sex    relationship      marital-status income\n",
       "0   44     Preschool    Male         Husband  Married-civ-spouse  <=50K\n",
       "1   47  Some-college  Female         Husband           Separated   >50K\n",
       "2   64    Assoc-acdm  Female   Not-in-family           Separated  <=50K\n",
       "3   67       Masters    Male  Other-relative  Married-civ-spouse  <=50K\n",
       "4   67          12th    Male       Own-child       Never-married   >50K"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_random.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8,3), dpi=100)\n",
    "axes[0].hist(real_data['age'])\n",
    "axes[1].hist(synthetic_random['age'])\n",
    "axes[0].set_xlabel('age')\n",
    "axes[1].set_xlabel('age')\n",
    "axes[0].set_title('real data')\n",
    "axes[1].set_title('random mode');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>relationship_real</th>\n",
       "      <th>relationship_random_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Husband</th>\n",
       "      <td>13193</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not-in-family</th>\n",
       "      <td>8305</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Own-child</th>\n",
       "      <td>5068</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unmarried</th>\n",
       "      <td>3446</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wife</th>\n",
       "      <td>1568</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-relative</th>\n",
       "      <td>981</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                relationship_real  relationship_random_mode\n",
       "Husband                     13193                       156\n",
       "Not-in-family                8305                       159\n",
       "Own-child                    5068                       189\n",
       "Unmarried                    3446                       165\n",
       "Wife                         1568                       166\n",
       "Other-relative                981                       165"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relationship_real = real_data['relationship'].value_counts()\n",
    "relationship_random = synthetic_random['relationship'].value_counts()\n",
    "relationship_both = pd.merge(relationship_real.to_frame(), \n",
    "                             relationship_random.to_frame(), \n",
    "                             left_index=True, \n",
    "                             right_index=True, \n",
    "                             suffixes=('_real', '_random_mode'))\n",
    "relationship_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(6,3), dpi=120)\n",
    "axes[0].bar(relationship_both.index, relationship_both['relationship_real'])\n",
    "axes[1].bar(relationship_both.index, relationship_both['relationship_random_mode'])\n",
    "axes[0].set_title('real data')\n",
    "axes[1].set_title('random mode')\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "description_json = read_json_file(description_files['random mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['meta', 'attribute_description'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description_json.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"name\": \"relationship\",\n",
      "    \"data_type\": \"String\",\n",
      "    \"is_categorical\": true,\n",
      "    \"is_candidate_key\": false,\n",
      "    \"min\": 4,\n",
      "    \"max\": 14,\n",
      "    \"missing_rate\": 0.0,\n",
      "    \"distribution_bins\": [\n",
      "        \"Not-in-family\",\n",
      "        \"Husband\",\n",
      "        \"Wife\",\n",
      "        \"Own-child\",\n",
      "        \"Unmarried\",\n",
      "        \"Other-relative\"\n",
      "    ],\n",
      "    \"distribution_probabilities\": [\n",
      "        0.16666666666666666,\n",
      "        0.16666666666666666,\n",
      "        0.16666666666666666,\n",
      "        0.16666666666666666,\n",
      "        0.16666666666666666,\n",
      "        0.16666666666666666\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(json.dumps(description_json['attribute_description']['relationship'], indent=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Independent attribute mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "describer.describe_dataset_in_independent_attribute_mode(sensitive_data_file, epsilon=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "describer.save_dataset_description_to_file(description_files['independent attribute mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.generate_dataset_in_independent_mode(n=output_data_size, \n",
    "                                               description_file=description_files['independent attribute mode'], \n",
    "                                               seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.save_synthetic_data(synthetic_data_files['independent attribute mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "synthetic_independent = pd.read_csv(synthetic_data_files['independent attribute mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>sex</th>\n",
       "      <th>relationship</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.0</td>\n",
       "      <td>10th</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Male</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>Male</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age education     sex   relationship      marital-status income\n",
       "0  42.0   HS-grad    Male  Not-in-family  Married-civ-spouse  <=50K\n",
       "1  45.0      10th    Male  Not-in-family  Married-civ-spouse  <=50K\n",
       "2  40.0   HS-grad    Male      Own-child            Divorced  <=50K\n",
       "3  36.0   HS-grad  Female      Unmarried  Married-civ-spouse  <=50K\n",
       "4  33.0      11th    Male      Own-child            Divorced  <=50K"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_independent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8,3), dpi=100)\n",
    "axes[0].hist(real_data['age'])\n",
    "axes[1].hist(synthetic_independent['age'])\n",
    "axes[0].set_xlabel('age')\n",
    "axes[1].set_xlabel('age')\n",
    "axes[0].set_title('real data')\n",
    "axes[1].set_title('independent attribute mode');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "relationship_real = real_data['relationship'].value_counts()\n",
    "relationship_independent = synthetic_independent['relationship'].value_counts()\n",
    "relationship_both = pd.merge(relationship_real.to_frame(), \n",
    "                             relationship_independent.to_frame(), \n",
    "                             left_index=True, \n",
    "                             right_index=True, \n",
    "                             suffixes=('_real', '_independent_attribute_mode'))\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(6,3), dpi=120)\n",
    "axes[0].bar(relationship_both.index, relationship_both['relationship_real'])\n",
    "axes[1].bar(relationship_both.index, relationship_both['relationship_independent_attribute_mode'])\n",
    "axes[0].set_title('real data')\n",
    "axes[1].set_title('independent attribute mode')\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlated attribute mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================ Constructing Bayesian Network (BN) ================\n",
      "Adding ROOT income\n",
      "Adding attribute age\n",
      "Adding attribute relationship\n",
      "Adding attribute marital-status\n",
      "Adding attribute sex\n",
      "Adding attribute education\n",
      "========================== BN constructed ==========================\n"
     ]
    }
   ],
   "source": [
    "describer.describe_dataset_in_correlated_attribute_mode(sensitive_data_file, \n",
    "                                                        epsilon=0.1, \n",
    "                                                        k=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.utils import display_bayesian_network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constructed Bayesian network:\n",
      "    age            has parents ['income'].\n",
      "    relationship   has parents ['age', 'income'].\n",
      "    marital-status has parents ['relationship', 'age'].\n",
      "    sex            has parents ['relationship', 'age'].\n",
      "    education      has parents ['marital-status', 'age'].\n"
     ]
    }
   ],
   "source": [
    "display_bayesian_network(describer.bayesian_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "describer.save_dataset_description_to_file(description_files['correlated attribute mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.generate_dataset_in_correlated_attribute_mode(n=output_data_size, \n",
    "                                                        description_file=description_files['correlated attribute mode'],\n",
    "                                                        seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.save_synthetic_data(synthetic_data_files['correlated attribute mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "synthetic_correlated = pd.read_csv(synthetic_data_files['correlated attribute mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8,3), dpi=100)\n",
    "axes[0].hist(real_data['age'])\n",
    "axes[1].hist(synthetic_correlated['age'])\n",
    "axes[0].set_xlabel('age')\n",
    "axes[1].set_xlabel('age')\n",
    "axes[0].set_title('real data')\n",
    "axes[1].set_title('correlated attribute mode');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "relationship_real = real_data['relationship'].value_counts()\n",
    "relationship_correlated = synthetic_correlated['relationship'].value_counts()\n",
    "relationship_both = pd.merge(relationship_real.to_frame(), \n",
    "                             relationship_correlated.to_frame(), \n",
    "                             left_index=True, \n",
    "                             right_index=True, \n",
    "                             suffixes=('_real', '_correlated_attribute_mode'))\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(6,3), dpi=120)\n",
    "axes[0].bar(relationship_both.index, relationship_both['relationship_real'])\n",
    "axes[1].bar(relationship_both.index, relationship_both['relationship_correlated_attribute_mode'])\n",
    "axes[0].set_title('real data')\n",
    "axes[1].set_title('correlated attribute mode')\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attribute_description = read_json_file(description_files['correlated attribute mode'])['attribute_description']\n",
    "ModelInspector(real_data, \n",
    "               synthetic_correlated, \n",
    "               attribute_description).mutual_information_heatmap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical measures\n",
    "\n",
    "### Mutual information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import normalized_mutual_info_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5249037668258572"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_mutual_info_score(real_data['marital-status'], \n",
    "                             real_data['relationship'], \n",
    "                             average_method='arithmetic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014483450445067415"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_mutual_info_score(real_data['marital-status'], \n",
    "                             real_data['education'], \n",
    "                             average_method='arithmetic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-sample Kolmogorov–Smirnov test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ks_2samp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ks_test(df_in: pd.DataFrame, df_out: pd.DataFrame, attr: str):\n",
    "    \"\"\"\n",
    "    df_in: the sensitive dataset\n",
    "    df_out: the synthetic dataset\n",
    "    attr: the attribute that will be calculated for Two-sample Kolmogorov–Smirnov test.\n",
    "    \"\"\"\n",
    "    return ks_2samp(df_in[attr], df_out[attr])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33259405423666355"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks_test(real_data, synthetic_random, 'age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04653290746598687"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks_test(real_data, synthetic_independent, 'age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.034914130401400456"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks_test(real_data, synthetic_correlated, 'age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KL-divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distribution_of_categorical_attribute(attribute: pd.Series, indicies=None):\n",
    "    distribution = attribute.dropna().value_counts()\n",
    "    if indicies is not None:\n",
    "        for idx in set(indicies) - set(distribution.index):\n",
    "            distribution.loc[idx] = 0\n",
    "    distribution.sort_index(inplace=True)\n",
    "    return distribution/sum(distribution)\n",
    "\n",
    "def kl_test(df_in: pd.DataFrame, df_out: pd.DataFrame, attr: str):\n",
    "    \"\"\"\n",
    "    df_in: the sensitive dataset\n",
    "    df_out: the synthetic dataset\n",
    "    attr: the attribute that will be calculated for KL-divergence.\n",
    "    \"\"\"\n",
    "    distribution_in = get_distribution_of_categorical_attribute(df_in[attr])\n",
    "    distribution_out = get_distribution_of_categorical_attribute(df_out[attr], distribution_in.index)\n",
    "    return entropy(distribution_out, distribution_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37194502301936416"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kl_test(real_data, synthetic_random, 'relationship')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004852757128718812"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kl_test(real_data, synthetic_independent, 'relationship')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0023945420264870447"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kl_test(real_data, synthetic_correlated, 'relationship')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>sex</th>\n",
       "      <th>relationship</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>11th</td>\n",
       "      <td>Male</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Female</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education     sex   relationship      marital-status income\n",
       "0   39  Bachelors    Male  Not-in-family       Never-married  <=50K\n",
       "1   50  Bachelors    Male        Husband  Married-civ-spouse  <=50K\n",
       "2   38    HS-grad    Male  Not-in-family            Divorced  <=50K\n",
       "3   53       11th    Male        Husband  Married-civ-spouse  <=50K\n",
       "4   28  Bachelors  Female           Wife  Married-civ-spouse  <=50K"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "sns.boxplot(x='income', y='age', data=real_data, linewidth=0.5, fliersize=2);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}