Experimental_study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experimental study for the article\n",
    "## Kirill Zakharov\n",
    "2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/torch/_functorch/deprecated.py:58: UserWarning: We've integrated functorch into PyTorch. As the final step of the integration, functorch.vmap is deprecated as of PyTorch 2.0 and will be deleted in a future version of PyTorch >= 2.3. Please use torch.vmap instead; see the PyTorch 2.0 release notes and/or the torch.func migration guide for more details https://pytorch.org/docs/master/func.migrating.html\n",
      "  warn_deprecated('vmap', 'torch.vmap')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "%matplotlib inline\n",
    "\n",
    "import torch\n",
    "from torch import optim\n",
    "from torch.autograd import Variable, grad\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from scipy.special import rel_entr, kl_div\n",
    "from scipy.spatial.distance import jensenshannon\n",
    "from scipy.stats import kstest, ks_2samp\n",
    "import scipy.stats as sts\n",
    "from tqdm import tqdm\n",
    "from sdv.metadata import SingleTableMetadata\n",
    "\n",
    "from TRGAN.TRGAN_main import *\n",
    "import TRGAN.Privacy_modules as privacy_trgan\n",
    "from TRGAN.encoders import *\n",
    "import TRGAN.TRGAN_train_load_modules as trgan_train\n",
    "from TRGAN.evaluation_metrics import *\n",
    "from TRGAN.methods_comparison import *\n",
    "from TRGAN.dpgan import *\n",
    "from train import train_all_models\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "#set params for the article\n",
    "# mpl.rcParams['xtick.labelsize'] = 16\n",
    "# mpl.rcParams['ytick.labelsize'] = 16\n",
    "# mpl.rcParams['legend.fontsize'] = 14\n",
    "# mpl.rcParams['axes.labelsize'] = 18\n",
    "\n",
    "#set params for the notebook\n",
    "mpl.rcParams['xtick.labelsize'] = 12\n",
    "mpl.rcParams['ytick.labelsize'] = 12\n",
    "mpl.rcParams['legend.fontsize'] = 10\n",
    "mpl.rcParams['axes.labelsize'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_oh with (5, 0.1)-Differential Privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss E_oh: 5.542875533559588e-11 || Privacy Loss E_oh: 0.013647045008838177: 100%|██████████| 20/20 [00:32<00:00,  1.63s/it]  \n",
      "  0%|          | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_cl with (0.01, 0.1)-Differential Privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss E_cl: 5.339708764040552e-07 || Privacy Loss E_cl: 0.507098913192749: 100%|██████████| 20/20 [00:42<00:00,  2.12s/it]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_cv with (0.01, 0.1)-Differential Privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss E_cv: 0.000092855 || Privacy Loss E_cv: 0.084764495: 100%|██████████| 15/15 [00:32<00:00,  2.16s/it] \n",
      "Discriminator Loss: -0.00440 || Discriminator 2 Loss: -0.00483 || Generator Loss: -0.00841 || Supervisor Loss: -0.00599: 100%|██████████| 30/30 [05:38<00:00, 11.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRGAN with (0.3155519726287818, 0.1)-differential privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Discriminator Loss: -0.00073 || Discriminator 2 Loss: -0.00038 || Generator Loss: 0.00923 || Supervisor Loss: 0.01209: 100%|██████████| 30/30 [03:12<00:00,  6.42s/it]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRGAN with (0.5, 0.1)-differential privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Discriminator Loss: -0.00050 || Discriminator 2 Loss: -0.00643 || Generator Loss: 0.00498 || Supervisor Loss: -0.00577: 100%|██████████| 30/30 [04:04<00:00,  8.15s/it]\n",
      "Discriminator Loss: -0.00112 || Discriminator 2 Loss: -0.00135 || Generator Loss: 0.00819 || Supervisor Loss: -0.00901: 100%|██████████| 30/30 [05:56<00:00, 11.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRGAN with (0.31596858456647237, 0.1)-differential privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Discriminator Loss: -0.00018 || Discriminator 2 Loss: -0.00034 || Generator Loss: 0.00952 || Supervisor Loss: 0.01283: 100%|██████████| 30/30 [03:24<00:00,  6.82s/it]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRGAN with (0.5, 0.1)-differential privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Discriminator Loss: -0.00194 || Discriminator 2 Loss: -0.00446 || Generator Loss: -0.00499 || Supervisor Loss: 0.01069: 100%|██████████| 30/30 [04:07<00:00,  8.24s/it]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRGAN with (0.5, 0.1)-differential privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Discriminator Loss: 0.00021 || Generator Loss: 0.01000: 100%|██████████| 30/30 [01:08<00:00,  2.29s/it]\n",
      "  0%|          | 0/30 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRGAN with (0.5, 0.1)-differential privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Discriminator Loss: -0.00172 || Discriminator 2 Loss: -0.00023 || Generator Loss: -0.00999 || Supervisor Loss: -0.00777: 100%|██████████| 30/30 [03:35<00:00,  7.18s/it]\n",
      "Student Loss: nan || Generator Loss: -0.68187: 100%|██████████| 30/30 [03:40<00:00,  7.35s/it]\n"
     ]
    }
   ],
   "source": [
    "data, synth_df, synth_df_rdp, synth_df_ae, synth_df_dpgan, synth_df_trgan_dpgan, synth_df_pategan = train_all_models(load=False, experiment_id='Privacy_RAIF_2_eps=0.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 2, figsize=(10, 8), dpi=100)\n",
    "\n",
    "\n",
    "axs[0, 0].hist(np.log1p(abs(data['amount'])), bins=40, label='Real', alpha=0.6, color='black', density=True)\n",
    "axs[0, 0].hist(np.log1p(abs(synth_df['amount'])), bins=40, label='TRGAN with TDDP', alpha=0.6, color='tab:red', density=True)\n",
    "axs[0, 0].set_xlabel('Amount')\n",
    "axs[0, 0].set_ylabel('Density')\n",
    "axs[0, 0].legend()\n",
    "\n",
    "axs[0, 1].hist(np.log1p(abs(data['amount'])), bins=40, label='Real', alpha=0.6, color='black', density=True)\n",
    "axs[0, 1].hist(np.log1p(abs(synth_df_rdp['amount'])), bins=40, label='TRGAN with RDP', alpha=0.6, color='tab:blue', density=True)\n",
    "axs[0, 1].set_xlabel('Amount')\n",
    "axs[0, 1].set_ylabel('Density')\n",
    "axs[0, 1].legend()\n",
    "\n",
    "axs[1, 0].hist(np.log1p(abs(data['amount'])), bins=40, label='Real', alpha=0.6, color='black', density=True)\n",
    "axs[1, 0].hist(np.log1p(abs(synth_df_ae['amount'])), bins=40, label='TRGAN with AE RDP', alpha=0.6, color='green', density=True)\n",
    "axs[1, 0].set_xlabel('Amount')\n",
    "axs[1, 0].set_ylabel('Density')\n",
    "axs[1, 0].legend()\n",
    "\n",
    "axs[1, 1].hist(np.log1p(abs(data['amount'])), bins=40, label='Real', alpha=0.6, color='black', density=True)\n",
    "axs[1, 1].hist(np.log1p(abs(synth_df_trgan_dpgan['amount'])), bins=40, label='TRGAN with DPGAN', alpha=0.6, color='lightblue', density=True)\n",
    "axs[1, 1].set_xlabel('Amount')\n",
    "axs[1, 1].set_ylabel('Density')\n",
    "axs[1, 1].legend()\n",
    "\n",
    "axs[2, 0].hist(np.log1p(abs(data['amount'])), bins=40, label='Real', alpha=0.6, color='black', density=True)\n",
    "axs[2, 0].hist(np.log1p(abs(synth_df_dpgan['amount'])), bins=40, label='DPGAN', alpha=0.6, color='darkorchid', density=True)\n",
    "axs[2, 0].set_xlabel('Amount')\n",
    "axs[2, 0].set_ylabel('Density')\n",
    "axs[2, 0].legend()\n",
    "\n",
    "axs[2, 1].hist(np.log1p(abs(data['amount'])), bins=40, label='Real', alpha=0.6, color='black', density=True)\n",
    "axs[2, 1].hist(np.log1p(abs(synth_df_pategan['amount'])), bins=40, label='PATEGAN', alpha=0.6, color='orange', density=True)\n",
    "axs[2, 1].set_xlabel('Amount')\n",
    "axs[2, 1].set_ylabel('Density')\n",
    "axs[2, 1].legend()\n",
    "\n",
    "plt.tight_layout(pad=0.5)\n",
    "plt.subplots_adjust(hspace=0.50, wspace=0.20)\n",
    "# plt.savefig('privacy_amount.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "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>Mean</th>\n",
       "      <th>Std</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>D_JS</th>\n",
       "      <th>KS2test</th>\n",
       "      <th>Wassertein distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Real</th>\n",
       "      <td>3.202157</td>\n",
       "      <td>0.832387</td>\n",
       "      <td>-0.951807</td>\n",
       "      <td>-0.028051</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with TDDP</th>\n",
       "      <td>3.205330</td>\n",
       "      <td>0.832787</td>\n",
       "      <td>-0.952207</td>\n",
       "      <td>-0.028733</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.006448</td>\n",
       "      <td>0.003560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with RDP-GAN</th>\n",
       "      <td>3.210400</td>\n",
       "      <td>0.832025</td>\n",
       "      <td>-0.947473</td>\n",
       "      <td>-0.028119</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>0.012540</td>\n",
       "      <td>0.008489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with DP AE</th>\n",
       "      <td>3.203981</td>\n",
       "      <td>0.833656</td>\n",
       "      <td>-0.951416</td>\n",
       "      <td>-0.030591</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0.006195</td>\n",
       "      <td>0.002877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with DPGAN</th>\n",
       "      <td>3.198981</td>\n",
       "      <td>0.833123</td>\n",
       "      <td>-0.950738</td>\n",
       "      <td>-0.030508</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.006127</td>\n",
       "      <td>0.003301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPGAN</th>\n",
       "      <td>3.224998</td>\n",
       "      <td>0.834956</td>\n",
       "      <td>-0.921014</td>\n",
       "      <td>-0.031455</td>\n",
       "      <td>0.003281</td>\n",
       "      <td>0.032579</td>\n",
       "      <td>0.028708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATEGAN</th>\n",
       "      <td>3.181954</td>\n",
       "      <td>0.841412</td>\n",
       "      <td>-0.912143</td>\n",
       "      <td>-0.044390</td>\n",
       "      <td>0.004752</td>\n",
       "      <td>0.032203</td>\n",
       "      <td>0.031842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Mean       Std  Kurtosis  Skewness      D_JS  \\\n",
       "Real                3.202157  0.832387 -0.951807 -0.028051  0.000000   \n",
       "TRGAN with TDDP     3.205330  0.832787 -0.952207 -0.028733  0.000381   \n",
       "TRGAN with RDP-GAN  3.210400  0.832025 -0.947473 -0.028119  0.000681   \n",
       "TRGAN with DP AE    3.203981  0.833656 -0.951416 -0.030591  0.000377   \n",
       "TRGAN with DPGAN    3.198981  0.833123 -0.950738 -0.030508  0.000401   \n",
       "DPGAN               3.224998  0.834956 -0.921014 -0.031455  0.003281   \n",
       "PATEGAN             3.181954  0.841412 -0.912143 -0.044390  0.004752   \n",
       "\n",
       "                     KS2test  Wassertein distance  \n",
       "Real                0.000000             0.000000  \n",
       "TRGAN with TDDP     0.006448             0.003560  \n",
       "TRGAN with RDP-GAN  0.012540             0.008489  \n",
       "TRGAN with DP AE    0.006195             0.002877  \n",
       "TRGAN with DPGAN    0.006127             0.003301  \n",
       "DPGAN               0.032579             0.028708  \n",
       "PATEGAN             0.032203             0.031842  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_numerical([data['amount'], synth_df['amount'], synth_df_rdp['amount'], synth_df_ae['amount'],\\\n",
    "                    synth_df_trgan_dpgan['amount'], synth_df_dpgan['amount'], synth_df_pategan['amount']],\\\n",
    "                    ['Real', 'TRGAN with TDDP', 'TRGAN with RDP-GAN', 'TRGAN with DP AE', 'TRGAN with DPGAN', 'DPGAN', 'PATEGAN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Total Variation</th>\n",
       "      <th>Contingency Similarity</th>\n",
       "      <th>Values count</th>\n",
       "      <th>D_JS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Real</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with TDDP</th>\n",
       "      <td>0.999133</td>\n",
       "      <td>0.675548</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.002905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with RDP-GAN</th>\n",
       "      <td>0.998887</td>\n",
       "      <td>0.675609</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.003177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with DP AE</th>\n",
       "      <td>0.999249</td>\n",
       "      <td>0.672372</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.002715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with DPGAN</th>\n",
       "      <td>0.999536</td>\n",
       "      <td>0.677085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.002185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPGAN</th>\n",
       "      <td>0.548066</td>\n",
       "      <td>0.269539</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.053898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATEGAN</th>\n",
       "      <td>0.284783</td>\n",
       "      <td>0.084897</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.059386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Total Variation  Contingency Similarity  Values count  \\\n",
       "Real                       1.000000                1.000000          13.0   \n",
       "TRGAN with TDDP            0.999133                0.675548          13.0   \n",
       "TRGAN with RDP-GAN         0.998887                0.675609          13.0   \n",
       "TRGAN with DP AE           0.999249                0.672372          13.0   \n",
       "TRGAN with DPGAN           0.999536                0.677085          13.0   \n",
       "DPGAN                      0.548066                0.269539          13.0   \n",
       "PATEGAN                    0.284783                0.084897          13.0   \n",
       "\n",
       "                        D_JS  \n",
       "Real                0.000000  \n",
       "TRGAN with TDDP     0.002905  \n",
       "TRGAN with RDP-GAN  0.003177  \n",
       "TRGAN with DP AE    0.002715  \n",
       "TRGAN with DPGAN    0.002185  \n",
       "DPGAN               0.053898  \n",
       "PATEGAN             0.059386  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contig_cols = ['customer', 'mcc']\n",
    "\n",
    "evaluate_categorical([data['mcc'], synth_df['mcc'], synth_df_rdp['mcc'], synth_df_ae['mcc'],\\\n",
    "        synth_df_trgan_dpgan['mcc'], synth_df_dpgan['mcc'], synth_df_pategan['mcc']],\\\n",
    "        index=['Real', 'TRGAN with TDDP', 'TRGAN with RDP-GAN', 'TRGAN with DP AE', 'TRGAN with DPGAN', 'DPGAN', 'PATEGAN'],\\\n",
    "        data_cont_array=[data[contig_cols], synth_df[contig_cols], synth_df_rdp[contig_cols],\\\n",
    "        synth_df_ae[contig_cols], synth_df_trgan_dpgan[contig_cols], synth_df_dpgan[contig_cols], synth_df_pategan[contig_cols]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>t-closeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Real</th>\n",
       "      <td>1.048541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with TDDP</th>\n",
       "      <td>1.031266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with RDP-GAN</th>\n",
       "      <td>1.034054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with DP AE</th>\n",
       "      <td>1.034209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN with DPGAN</th>\n",
       "      <td>1.037046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPGAN</th>\n",
       "      <td>1.024282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATEGAN</th>\n",
       "      <td>1.020091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    t-closeness\n",
       "Real                   1.048541\n",
       "TRGAN with TDDP        1.031266\n",
       "TRGAN with RDP-GAN     1.034054\n",
       "TRGAN with DP AE       1.034209\n",
       "TRGAN with DPGAN       1.037046\n",
       "DPGAN                  1.024282\n",
       "PATEGAN                1.020091"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quasi_id = ['transaction_date']\n",
    "\n",
    "pd.DataFrame(np.array([\n",
    "    # [k_anonymity(data, quasi_id, 'mcc'), k_anonymity(synth_df, quasi_id, 'mcc'), k_anonymity(synth_df_rdp, quasi_id, 'mcc'),\\\n",
    "    #                     k_anonymity(synth_df_ae, quasi_id, 'mcc'), k_anonymity(synth_df_trgan_dpgan, quasi_id, 'mcc'), k_anonymity(synth_df_dpgan, quasi_id, 'mcc'),\\\n",
    "    #                     k_anonymity(synth_df_pategan, quasi_id, 'mcc')],\\\n",
    "    #                     [l_diversity(data, quasi_id, 'mcc'), l_diversity(synth_df, quasi_id, 'mcc'), l_diversity(synth_df_rdp, quasi_id, 'mcc'),\n",
    "    #                      l_diversity(synth_df_ae, quasi_id, 'mcc'), l_diversity(synth_df_trgan_dpgan, quasi_id, 'mcc'), l_diversity(synth_df_dpgan, quasi_id, 'mcc'),\n",
    "    #                      l_diversity(synth_df_pategan, quasi_id, 'mcc')],\\\n",
    "                        [t_closeness(data, quasi_id, 'amount'), t_closeness(synth_df, quasi_id, 'amount'), t_closeness(synth_df_rdp, quasi_id, 'amount'),\\\n",
    "                        t_closeness(synth_df_ae, quasi_id, 'amount'), t_closeness(synth_df_trgan_dpgan, quasi_id, 'amount'), t_closeness(synth_df_dpgan, quasi_id, 'amount'),\n",
    "                        t_closeness(synth_df_pategan, quasi_id, 'amount')]]).T,\\\n",
    "                        # [l_diversity_cont(data, quasi_id, 'amount'), l_diversity_cont(synth_df, quasi_id, 'amount'), l_diversity_cont(synth_df_dpgan, quasi_id, 'amount')]]).T,\\\n",
    "            columns=['t-closeness'], index=['Real', 'TRGAN with TDDP', 'TRGAN with RDP-GAN', 'TRGAN with DP AE',\\\n",
    "            'TRGAN with DPGAN', 'DPGAN', 'PATEGAN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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