TRGAN_uk.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRGAN\n",
    "\n",
    "### Kirill Zakharov\n",
    "### Supervisors: Elizaveta Stavinova, Anton Lysenko"
   ]
  },
  {
   "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 seaborn as sns\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\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, wasserstein_distance\n",
    "from rdt.transformers.numerical import GaussianNormalizer\n",
    "import scipy.stats as sts\n",
    "from tqdm import tqdm\n",
    "\n",
    "from TRGAN.TRGAN_main import *\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",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Data/data_uk_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>account_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>date</th>\n",
       "      <th>description</th>\n",
       "      <th>flag</th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>age</th>\n",
       "      <th>datetime</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dow</th>\n",
       "      <th>year</th>\n",
       "      <th>dtme</th>\n",
       "      <th>tcode</th>\n",
       "      <th>td</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28979</td>\n",
       "      <td>0</td>\n",
       "      <td>0014d0ef29aa9f93</td>\n",
       "      <td>-45.66</td>\n",
       "      <td>228.34</td>\n",
       "      <td>2017-04-24 11:00:00.000000</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Utility Bill</td>\n",
       "      <td>38a21d894127b49d</td>\n",
       "      <td>Debit</td>\n",
       "      <td>-1</td>\n",
       "      <td>2017-04-24 11:00:00.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>Energy__Utility Bill__Debit</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30006</td>\n",
       "      <td>1</td>\n",
       "      <td>0014d0ef29aa9f93</td>\n",
       "      <td>2842.27</td>\n",
       "      <td>3070.61</td>\n",
       "      <td>2017-04-24 17:36:55.000000</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Income</td>\n",
       "      <td>3941e50e4613a49a</td>\n",
       "      <td>Credit</td>\n",
       "      <td>-1</td>\n",
       "      <td>2017-04-24 17:36:55.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>Monthly__Income__Credit</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32220</td>\n",
       "      <td>2</td>\n",
       "      <td>0014d0ef29aa9f93</td>\n",
       "      <td>-167.25</td>\n",
       "      <td>2903.36</td>\n",
       "      <td>2017-04-25 16:00:00.000000</td>\n",
       "      <td>Credit Card Payment</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>c09e8105dcfc85b7</td>\n",
       "      <td>Debit</td>\n",
       "      <td>-1</td>\n",
       "      <td>2017-04-25 16:00:00.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>Credit Card Payment__Credit Card__Debit</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35526</td>\n",
       "      <td>3</td>\n",
       "      <td>0014d0ef29aa9f93</td>\n",
       "      <td>-40.58</td>\n",
       "      <td>2862.78</td>\n",
       "      <td>2017-04-27 16:00:00.000000</td>\n",
       "      <td>Water Bill</td>\n",
       "      <td>Utility Bill</td>\n",
       "      <td>81e4ffc8df86920e</td>\n",
       "      <td>Debit</td>\n",
       "      <td>-1</td>\n",
       "      <td>2017-04-27 16:00:00.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>Water Bill__Utility Bill__Debit</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94259</td>\n",
       "      <td>4</td>\n",
       "      <td>0014d0ef29aa9f93</td>\n",
       "      <td>-43.16</td>\n",
       "      <td>2819.62</td>\n",
       "      <td>2017-05-23 17:00:00.000000</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Utility Bill</td>\n",
       "      <td>c0ddd8f8a923d40f</td>\n",
       "      <td>Debit</td>\n",
       "      <td>-1</td>\n",
       "      <td>2017-05-23 17:00:00.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>Energy__Utility Bill__Debit</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unnamed: 0  index        account_id   amount  balance  \\\n",
       "0       28979      0  0014d0ef29aa9f93   -45.66   228.34   \n",
       "1       30006      1  0014d0ef29aa9f93  2842.27  3070.61   \n",
       "2       32220      2  0014d0ef29aa9f93  -167.25  2903.36   \n",
       "3       35526      3  0014d0ef29aa9f93   -40.58  2862.78   \n",
       "4       94259      4  0014d0ef29aa9f93   -43.16  2819.62   \n",
       "\n",
       "                         date          description          flag  \\\n",
       "0  2017-04-24 11:00:00.000000               Energy  Utility Bill   \n",
       "1  2017-04-24 17:36:55.000000              Monthly        Income   \n",
       "2  2017-04-25 16:00:00.000000  Credit Card Payment   Credit Card   \n",
       "3  2017-04-27 16:00:00.000000           Water Bill  Utility Bill   \n",
       "4  2017-05-23 17:00:00.000000               Energy  Utility Bill   \n",
       "\n",
       "                 id    type  age                    datetime  month  day  dow  \\\n",
       "0  38a21d894127b49d   Debit   -1  2017-04-24 11:00:00.000000      4   24    0   \n",
       "1  3941e50e4613a49a  Credit   -1  2017-04-24 17:36:55.000000      4   24    0   \n",
       "2  c09e8105dcfc85b7   Debit   -1  2017-04-25 16:00:00.000000      4   25    1   \n",
       "3  81e4ffc8df86920e   Debit   -1  2017-04-27 16:00:00.000000      4   27    3   \n",
       "4  c0ddd8f8a923d40f   Debit   -1  2017-05-23 17:00:00.000000      5   23    1   \n",
       "\n",
       "   year  dtme                                    tcode    td  \n",
       "0  2017     6              Energy__Utility Bill__Debit   0.0  \n",
       "1  2017     6                  Monthly__Income__Credit   0.0  \n",
       "2  2017     5  Credit Card Payment__Credit Card__Debit   0.0  \n",
       "3  2017     3          Water Bill__Utility Bill__Debit   2.0  \n",
       "4  2017     8              Energy__Utility Bill__Debit  26.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 19)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter data for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.rename(columns={\"account_id\": 'customer', 'tcode': 'mcc', 'datetime': 'transaction_date'})\n",
    "\n",
    "data = data[['customer', 'mcc', 'transaction_date', 'amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['transaction_date'] = pd.to_datetime(data['transaction_date'], format='mixed')\n",
    "data['transaction_date'] = pd.to_datetime(data['transaction_date'].dt.date)\n",
    "data = data.sort_values(by='transaction_date')\n",
    "data = data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>mcc</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06c93a19ec4ed51c</td>\n",
       "      <td>O2 Mobile__Utility Bill__Debit</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9ca357e3f3fe1404</td>\n",
       "      <td>BT Mobile__Utility Bill__Debit</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4606058106f78471</td>\n",
       "      <td>Mortgage__Utility Bill__Debit</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>664e5cba1356175d</td>\n",
       "      <td>Mortgage__Utility Bill__Debit</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-1243.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cdb6e18de44ba73f</td>\n",
       "      <td>Water Bill__Utility Bill__Debit</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-36.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           customer                              mcc transaction_date  amount\n",
       "0  06c93a19ec4ed51c   O2 Mobile__Utility Bill__Debit       2017-04-01   -33.0\n",
       "1  9ca357e3f3fe1404   BT Mobile__Utility Bill__Debit       2017-04-01   -32.0\n",
       "2  4606058106f78471    Mortgage__Utility Bill__Debit       2017-04-01  -978.0\n",
       "3  664e5cba1356175d    Mortgage__Utility Bill__Debit       2017-04-01 -1243.0\n",
       "4  cdb6e18de44ba73f  Water Bill__Utility Bill__Debit       2017-04-01   -36.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "data['mcc'] = le.fit_transform(data['mcc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_banksformer = pd.read_csv('Data/synth_banksformer_uk.csv')\n",
    "synth_banksformer['date'] = pd.to_datetime(synth_banksformer['date'])\n",
    "\n",
    "synth_banksformer = synth_banksformer.rename(columns={'tcode': 'mcc', 'date': 'transaction_date', 'account_id': 'customer'})\n",
    "synth_banksformer['mcc'] = le.fit_transform(synth_banksformer['mcc'])\n",
    "\n",
    "synth_banksformer = synth_banksformer[['mcc', 'transaction_date', 'customer', 'amount']]\n",
    "\n",
    "deltas_by_clients_banksformer = synth_banksformer.groupby('customer')['transaction_date'].apply(lambda x: (x - x.shift()).dt.days.values[1:])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize all constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "FEATURES NAMES\n",
    "'''\n",
    "cat_features = ['mcc']\n",
    "cont_features = ['amount']\n",
    "date_feature = ['transaction_date']\n",
    "client_info = ['customer']\n",
    "\n",
    "# data_cat = data[cat_features] \n",
    "\n",
    "'''\n",
    "DIMENSIONS\n",
    "'''\n",
    "dim_X_cat = len(cat_features)\n",
    "dim_cont_emb = 1\n",
    "dim_X_cont = dim_cont_emb * len(cont_features)\n",
    "dim_X_date = 4\n",
    "dim_Xoh = 30 # dimension of X one hot embeddings\n",
    "dim_Xcl = 4  # dimension of client's info embeddings\n",
    "dim_Vc_h = 10 # dimension of conditional vector\n",
    "dim_bce = 5 # dimension of the behaviour client encoding\n",
    "dim_Vc = dim_Vc_h + dim_X_date\n",
    "dim_X_emb = dim_Xoh + dim_X_cont + dim_Xcl\n",
    "dim_noise = 20\n",
    "# data_dim = len(data_transformed[0])\n",
    "\n",
    "'''\n",
    "LEARNING RATES\n",
    "'''\n",
    "lr_E_oh = 3e-4\n",
    "lr_E_cl = 3e-4\n",
    "lr_E_Vc = 3e-4\n",
    "lr_E_cont = 3e-4\n",
    "\n",
    "'''\n",
    "SAVE DIRECTORY\n",
    "'''\n",
    "DIRECTORY = 'Pretrained_model/'\n",
    "DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create OneHot representation of all categorical features $X_{oh}$ of dimension $d_{X_{oh}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_id = 'uk_10'\n",
    "load = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_oh = onehot_emb_categorical(data, cat_features)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create embeddings of the $X_{oh}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_oh with (2, 0.1)-Differential Privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss E_oh: 0.07103096693754196 || Privacy Loss E_oh: 0.07103096693754196: 100%|██████████| 20/20 [00:26<00:00,  1.33s/it]      \n"
     ]
    }
   ],
   "source": [
    "X_oh_emb, encoder_onehot, decoder_onehot = trgan_train.create_cat_emb(X_oh, dim_Xoh, lr_E_oh, epochs=20,\\\n",
    "            batch_size=2**7, load=load, directory=DIRECTORY, names=[f'TRGAN_E_oh_exp_{experiment_id}.pkl',\\\n",
    "            f'TRGAN_D_oh_exp_{experiment_id}.pkl', f'X_oh_emb_exp_{experiment_id}.npy'], device=DEVICE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create embeddings of the $X_{cont}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cont, scaler_cont = trgan_train.create_cont_emb(dim_X_cont, data, cont_features, lr_E_cont, epochs=20, batch_size=2**7,\\\n",
    "            load=load, directory='Pretrained_model/', names=f'scaler_cont_{experiment_id}.npy', type_scale='CBNormalize', device=DEVICE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create embeddings of the $X_{cl}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_cl with (0.5, 0.1)-Differential Privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss E_cl: 5.700495648852666e-07 || Privacy Loss E_cl: 0.004498223774135113: 100%|██████████| 20/20 [00:31<00:00,  1.57s/it]  \n"
     ]
    }
   ],
   "source": [
    "X_cl, encoder_cl_emb, decoder_cl_emb, scaler_cl_emb, label_encoders = trgan_train.create_client_emb(dim_Xcl, data, client_info,\\\n",
    "            dim_Xcl, lr_E_cl, epochs=20, batch_size=2**7, load=load, directory=DIRECTORY, names=[f'TRGAN_E_cl_{experiment_id}.pkl',\\\n",
    "            f'TRGAN_D_cl_{experiment_id}.pkl', f'X_cl_{experiment_id}.npy', f'scaler_{experiment_id}.joblib', f'label_enc_{experiment_id}.joblib'],\\\n",
    "            device=DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create embeddings $X_{emb}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_emb, scaler_emb = create_embeddings(X_cont, X_oh_emb, X_cl)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create conditional vector $v$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E_cv with (0.5, 0.1)-Differential Privacy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss E_cv: 0.000077304 || Privacy Loss E_cv: 0.033041507: 100%|██████████| 15/15 [00:26<00:00,  1.75s/it] \n"
     ]
    }
   ],
   "source": [
    "cond_vector, synth_time, date_transformations, behaviour_cl_enc, encoder, deltas_by_clients, synth_deltas_by_clients, xiP_array, idx_array =\\\n",
    "            trgan_train.create_conditional_vector(data, X_emb, date_feature, 'synth', dim_Vc_h, dim_bce, \\\n",
    "            name_client_id='customer', name_agg_feature='amount', lr_E_Vc=lr_E_Vc, epochs=15, batch_size=2**7, model_time='poisson', n_splits=4, load=load,\\\n",
    "            directory=DIRECTORY, names=[f'TRGAN_E_Vc_{experiment_id}.pkl', f'Vc_{experiment_id}.npy', f'BCE_{experiment_id}.npy'], opt_time=True, device=DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dim X_emb: (100000, 35) || Dim V_c: (100000, 14)\n"
     ]
    }
   ],
   "source": [
    "print(f'Dim X_emb: {X_emb.shape} || Dim V_c: {cond_vector.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_emb_df = pd.DataFrame(X_emb, columns=np.arange(len(X_emb[0])).astype(str))\n",
    "\n",
    "# transformer_arr = []\n",
    "\n",
    "# for col in X_emb_df.columns.values:\n",
    "#     transformer = GaussianNormalizer()\n",
    "#     X_emb_df = transformer.fit_transform(X_emb_df, column=col)\n",
    "    \n",
    "#     transformer_arr.append(transformer)\n",
    "\n",
    "# X_emb = X_emb_df.values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synth time comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_deltas = np.hstack(deltas_by_clients.values)\n",
    "synth_pois_deltas = np.hstack(synth_deltas_by_clients)\n",
    "synth_deltas_banksformer = np.hstack(deltas_by_clients_banksformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(20, 5), dpi=100)\n",
    "\n",
    "axs[0].hist(real_deltas, alpha=0.6, bins=np.arange(-1, 12) + 1, label='Real', density=True, color='black')\n",
    "axs[0].hist(synth_pois_deltas, alpha=0.6, bins=np.arange(-1, 12) + 1, label='TRGAN', density=True, color='red')\n",
    "\n",
    "axs[0].legend(fontsize=20)\n",
    "axs[0].set_xlabel('$\\Delta$', fontsize=26)\n",
    "axs[0].set_ylabel('Density', fontsize=26)\n",
    "axs[0].tick_params(labelsize=24)\n",
    "axs[0].locator_params(axis='x', integer=True)\n",
    "axs[0].set_ylim((0, 0.4))\n",
    "axs[0].set_xlim((-0.2, 12.5))\n",
    "\n",
    "axs[1].hist(real_deltas, alpha=0.6, bins=np.arange(-1, 12) + 1, label='Real', density=True, color='black')\n",
    "axs[1].hist(synth_deltas_banksformer, alpha=0.6, bins=np.arange(-1, 12) + 1, label='Banksformer', density=True, color='green')\n",
    "\n",
    "axs[1].legend(fontsize=20)\n",
    "axs[1].set_xlabel('$\\Delta$', fontsize=26)\n",
    "# axs[1].set_ylabel('Density', fontsize=22)\n",
    "axs[1].tick_params(labelsize=24)\n",
    "axs[1].locator_params(axis='x', integer=True)\n",
    "axs[1].set_ylim((0, 0.4))\n",
    "axs[1].set_xlim((-0.2, 12.5))\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0.1)\n",
    "plt.tight_layout(pad=0.5)\n",
    "# plt.savefig('synth_deltas_uk.pdf', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>2.455463</td>\n",
       "      <td>3.045768</td>\n",
       "      <td>8.310360</td>\n",
       "      <td>2.468173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN</th>\n",
       "      <td>2.311105</td>\n",
       "      <td>2.974915</td>\n",
       "      <td>9.538331</td>\n",
       "      <td>2.666412</td>\n",
       "      <td>0.050756</td>\n",
       "      <td>0.035032</td>\n",
       "      <td>0.144463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banksformer</th>\n",
       "      <td>2.310874</td>\n",
       "      <td>2.981118</td>\n",
       "      <td>8.931828</td>\n",
       "      <td>2.154999</td>\n",
       "      <td>0.153325</td>\n",
       "      <td>0.146063</td>\n",
       "      <td>0.346484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Mean       Std  Kurtosis  Skewness      D_JS   KS2test  \\\n",
       "Real         2.455463  3.045768  8.310360  2.468173  0.000000  0.000000   \n",
       "TRGAN        2.311105  2.974915  9.538331  2.666412  0.050756  0.035032   \n",
       "Banksformer  2.310874  2.981118  8.931828  2.154999  0.153325  0.146063   \n",
       "\n",
       "             Wassertein distance  \n",
       "Real                    0.000000  \n",
       "TRGAN                   0.144463  \n",
       "Banksformer             0.346484  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_numerical([pd.Series(real_deltas), pd.Series(synth_pois_deltas), pd.Series(synth_deltas_banksformer)], index=['Real', 'TRGAN', 'Banksformer'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train TRGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Discriminator Loss: -0.00369 || Discriminator 2 Loss: -0.00154 || Generator Loss: 0.03165 || Supervisor Loss: 0.02674: 100%|██████████| 40/40 [03:32<00:00,  5.31s/it]\n"
     ]
    }
   ],
   "source": [
    "h_dim = 2**6\n",
    "num_blocks_gen = 1\n",
    "num_blocks_dis = 1\n",
    "gauss_filter_dim = 20\n",
    "\n",
    "if load:\n",
    "    generator = Generator(dim_noise + dim_Vc, dim_X_emb, h_dim, num_blocks_gen, gauss_filter_dim, DEVICE).to(DEVICE)\n",
    "    supervisor = Supervisor(dim_X_emb + dim_Vc, dim_X_emb, h_dim, num_blocks_gen, gauss_filter_dim, DEVICE).to(DEVICE)\n",
    "\n",
    "    generator.load_state_dict(torch.load(f'{DIRECTORY}TRGAN_generator_exp_{experiment_id}.pkl'))\n",
    "    supervisor.load_state_dict(torch.load(f'{DIRECTORY}TRGAN_supervisor_exp_{experiment_id}.pkl'))\n",
    "\n",
    "    generator.eval()\n",
    "    supervisor.eval()\n",
    "\n",
    "    loss_array = np.load(f'{DIRECTORY}loss_array_exp_{experiment_id}.npy')\n",
    "\n",
    "else:\n",
    "    generator, supervisor, loss_array, discriminator, discriminator2 = train_generator(X_emb, cond_vector, dim_Vc, dim_X_emb, dim_noise, batch_size=2**8,\\\n",
    "                            lr_rates=[3e-4, 3e-4, 3e-4, 3e-4], num_epochs=40, num_blocks_gen=num_blocks_gen, num_blocks_dis=num_blocks_dis, h_dim=h_dim,\\\n",
    "                            lambda1=3, alpha=0.75, window_size=gauss_filter_dim, device=DEVICE)\n",
    "    \n",
    "    torch.save(generator.state_dict(), f'{DIRECTORY}TRGAN_generator_exp_{experiment_id}.pkl')\n",
    "    torch.save(supervisor.state_dict(), f'{DIRECTORY}TRGAN_supervisor_exp_{experiment_id}.pkl')\n",
    "\n",
    "    np.save(f'{DIRECTORY}loss_array_exp_{experiment_id}.npy', loss_array)\n",
    "\n",
    "    generator.eval()\n",
    "    supervisor.eval()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(10, 5), dpi=100)\n",
    "\n",
    "axs[0, 0].plot(np.array(loss_array).T[0], label='discriminator 1 loss')\n",
    "axs[0, 1].plot(np.array(loss_array).T[1], label='discriminator 2 loss')\n",
    "axs[1, 0].plot(np.array(loss_array).T[2], label='generator loss')\n",
    "axs[1, 1].plot(np.array(loss_array).T[3], label='supervisor loss')\n",
    "\n",
    "axs[0,0].legend()\n",
    "axs[0,1].legend()\n",
    "axs[1,0].legend()\n",
    "axs[1,1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------\n",
      "      Layer (type)         Input Shape         Param #     Tr. Param #\n",
      "=======================================================================\n",
      "          Linear-1           [512, 34]           2,240           2,240\n",
      "       LeakyReLU-2           [512, 64]               0               0\n",
      "       LayerNorm-3           [512, 64]             128             128\n",
      "          Linear-4           [512, 64]           4,160           4,160\n",
      "       LeakyReLU-5           [512, 64]               0               0\n",
      "         Dropout-6           [512, 64]               0               0\n",
      "          Linear-7           [512, 64]           4,160           4,160\n",
      "       LeakyReLU-8           [512, 64]               0               0\n",
      "         Dropout-9           [512, 64]               0               0\n",
      "      LayerNorm-10           [512, 64]             128             128\n",
      "         Linear-11           [512, 64]           2,275           2,275\n",
      "           Tanh-12           [512, 35]               0               0\n",
      "=======================================================================\n",
      "Total params: 13,091\n",
      "Trainable params: 13,091\n",
      "Non-trainable params: 0\n",
      "-----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from pytorch_model_summary import summary\n",
    "\n",
    "print(summary(generator, torch.zeros((512, dim_noise+dim_Vc)), show_input=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(summary(discriminator, torch.zeros((512, dim_X_emb+dim_Vc)), show_input=True))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate synthetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = len(X_emb)\n",
    "synth_data, synth_time = sample(n_samples, generator, supervisor, dim_noise, cond_vector, X_emb, encoder, data, behaviour_cl_enc,\\\n",
    "                                date_feature, 'customer', time='synth', model_time='poisson', n_splits=4, opt_time=False,\\\n",
    "                                xi_array=xiP_array, q_array=idx_array, device=DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# synth_data_temp = pd.DataFrame(synth_data, columns=np.arange(len(X_emb[0])).astype(str))\n",
    "\n",
    "# for tr in transformer_arr:\n",
    "#     synth_data_temp = tr.reverse_transform(synth_data_temp)\n",
    "    \n",
    "# synth_data = synth_data_temp.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_df, synth_df_cat = inverse_transforms(n_samples, synth_data, synth_time, client_info, cont_features, X_oh, scaler_emb, scaler_cl_emb, scaler_cont,\\\n",
    "                    label_encoders, decoder_cl_emb, decoder_onehot, dim_Xcl, dim_X_cont, type_scale_cont='CBNormalize', device=DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>customer</th>\n",
       "      <th>amount</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>mcc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9184d7d2d3260cb5</td>\n",
       "      <td>-31.13</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7a765b6b96c9a920</td>\n",
       "      <td>-36.07</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dc083531e69efddb</td>\n",
       "      <td>-1072.11</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7e551ec3a4ea11ec</td>\n",
       "      <td>-1158.43</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f96f823e378c47ab</td>\n",
       "      <td>-36.19</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>547c067f866a068c</td>\n",
       "      <td>-35.02</td>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>eef97c240fec9f99</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>ab991ff0080b2375</td>\n",
       "      <td>-116.10</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>79bc355e47031aef</td>\n",
       "      <td>-25.59</td>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>a85ba4f0b9807d31</td>\n",
       "      <td>-9.70</td>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               customer   amount transaction_date  mcc\n",
       "0      9184d7d2d3260cb5   -31.13       2017-04-01   23\n",
       "1      7a765b6b96c9a920   -36.07       2017-04-01    2\n",
       "2      dc083531e69efddb -1072.11       2017-04-01   19\n",
       "3      7e551ec3a4ea11ec -1158.43       2017-04-01   19\n",
       "4      f96f823e378c47ab   -36.19       2017-04-01   41\n",
       "...                 ...      ...              ...  ...\n",
       "99995  547c067f866a068c   -35.02       2017-05-31   23\n",
       "99996  eef97c240fec9f99     1.68       2017-05-31   14\n",
       "99997  ab991ff0080b2375  -116.10       2017-06-01   14\n",
       "99998  79bc355e47031aef   -25.59       2017-06-02   39\n",
       "99999  a85ba4f0b9807d31    -9.70       2017-06-05   14\n",
       "\n",
       "[100000 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# synth_df['mcc'] = le.inverse_transform(synth_df['mcc'])\n",
    "# data['mcc'] = le.inverse_transform(data['mcc'])\n",
    "synth_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>customer</th>\n",
       "      <th>mcc</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06c93a19ec4ed51c</td>\n",
       "      <td>23</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9ca357e3f3fe1404</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4606058106f78471</td>\n",
       "      <td>19</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-978.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>664e5cba1356175d</td>\n",
       "      <td>19</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-1243.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cdb6e18de44ba73f</td>\n",
       "      <td>41</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-36.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>5cc65a8436a49ede</td>\n",
       "      <td>23</td>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>-33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>5ccfeea958173e95</td>\n",
       "      <td>14</td>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>5cdcbcad0d28da5d</td>\n",
       "      <td>14</td>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>-105.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>5c4bd7ec970c328f</td>\n",
       "      <td>39</td>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>-22.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>ffd5620bcbf69023</td>\n",
       "      <td>14</td>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>-8.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               customer  mcc transaction_date   amount\n",
       "0      06c93a19ec4ed51c   23       2017-04-01   -33.00\n",
       "1      9ca357e3f3fe1404    2       2017-04-01   -32.00\n",
       "2      4606058106f78471   19       2017-04-01  -978.00\n",
       "3      664e5cba1356175d   19       2017-04-01 -1243.00\n",
       "4      cdb6e18de44ba73f   41       2017-04-01   -36.50\n",
       "...                 ...  ...              ...      ...\n",
       "99995  5cc65a8436a49ede   23       2017-05-25   -33.00\n",
       "99996  5ccfeea958173e95   14       2017-05-25    -0.15\n",
       "99997  5cdcbcad0d28da5d   14       2017-05-25  -105.81\n",
       "99998  5c4bd7ec970c328f   39       2017-05-25   -22.95\n",
       "99999  ffd5620bcbf69023   14       2017-05-25    -8.10\n",
       "\n",
       "[100000 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_gr = []\n",
    "X_gr.append(X_emb[:, :21].T[:7])\n",
    "X_gr.append(X_emb[:, :21].T[7:14])\n",
    "X_gr.append(X_emb[:, :21].T[14:21])\n",
    "X_gr = np.array(X_gr)\n",
    "\n",
    "X_gr_s = []\n",
    "X_gr_s.append(synth_data[:, :21].T[:7])\n",
    "X_gr_s.append(synth_data[:, :21].T[7:14])\n",
    "X_gr_s.append(synth_data[:, :21].T[14:21])\n",
    "X_gr_s = np.array(X_gr_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(7, 3, figsize=(15, 15), dpi=100)\n",
    "\n",
    "for i in range(7):\n",
    "    for j in range(3):\n",
    "        axs[i, j].hist(X_gr[j, i], bins=30, label='Real', alpha=0.6)\n",
    "        axs[i, j].hist(X_gr_s[j, i], bins=30, label='Synth', alpha=0.6)\n",
    "\n",
    "        axs[i, j].legend()\n",
    "\n",
    "plt.subplots_adjust(hspace=0.35)\n",
    "# plt.savefig('results_synth_exp4.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10, 5), dpi=100)\n",
    "\n",
    "plt.hist(np.log1p(abs(data['amount'])), bins=40, label='Real', alpha=0.7)\n",
    "plt.hist(np.log1p(abs(synth_df['amount'])), bins=40, label='Synth', alpha=0.7)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Numerical distribution')\n",
    "plt.xlabel('Amount')\n",
    "plt.ylabel('Density')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare mcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10, 5), dpi=100)\n",
    "\n",
    "plt.bar(np.sort(data['mcc'].value_counts().index.values).astype(str), data['mcc'].value_counts().values/(np.sum(data['mcc'].value_counts().values)),\\\n",
    "                                                    color='black', alpha=0.6, label='Real')\n",
    "plt.bar(np.sort(synth_df['mcc'].value_counts().index.values).astype(str), synth_df['mcc'].value_counts().values/(np.sum(synth_df['mcc'].value_counts().values)),\\\n",
    "                                                    color='red', alpha=0.6, label='TRGAN')\n",
    "\n",
    "# plt.xticks(data['mcc'].value_counts().index.values.astype(str)[::2], rotation=45)\n",
    "plt.tick_params(axis='x', which='both', bottom=False, labelbottom=False)\n",
    "plt.legend()\n",
    "plt.title('Caregorical distribution')\n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('MCC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = SingleTableMetadata()\n",
    "metadata.add_column(\n",
    "    column_name='amount',\n",
    "    sdtype='numerical',\n",
    "    computer_representation='Float')\n",
    "\n",
    "metadata.add_column(\n",
    "    column_name='mcc',\n",
    "    sdtype='categorical')\n",
    "\n",
    "\n",
    "metadata.add_column(\n",
    "    column_name='customer',\n",
    "    sdtype='categorical')\n",
    "\n",
    "\n",
    "metadata.add_column(\n",
    "    column_name='transaction_date',\n",
    "    sdtype='datetime',\n",
    "    datetime_format='%Y %m %d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9726"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sdmetrics.single_table import NewRowSynthesis\n",
    "\n",
    "NewRowSynthesis.compute(\n",
    "    # real_data=data.drop('index', axis=1),\n",
    "    real_data=data,\n",
    "    synthetic_data=synth_df,\n",
    "    metadata=metadata,\n",
    "    numerical_match_tolerance=0.01,\n",
    "    synthetic_sample_size=10_000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>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>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN</th>\n",
       "      <td>0.95964</td>\n",
       "      <td>0.54853</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.132959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Variation  Contingency Similarity  Values count      D_JS\n",
       "Real           1.00000                 1.00000          44.0  0.000000\n",
       "TRGAN          0.95964                 0.54853          42.0  0.132959"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_categorical(data_array=[data['mcc'], synth_df['mcc']], index=['Real', 'TRGAN'], data_cont_array=[data[['mcc', 'transaction_date']], synth_df[['mcc', 'transaction_date']]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare clients' features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(10, 5), dpi=100)\n",
    "\n",
    "plt.hist(data['customer'], alpha=0.6, label='Real', bins=np.arange(0, 5000, 70), color='black', rwidth=0.8)\n",
    "plt.hist(synth_df['customer'], alpha=0.6, label='Synth', bins=np.arange(0, 5000, 70), color='red', rwidth=0.8)\n",
    "\n",
    "plt.legend()\n",
    "plt.xticks(np.arange(0, len(data['customer'].value_counts()), 500), np.arange(0, len(data['customer'].value_counts()), 500))\n",
    "plt.title('Client id')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "pca1 = PCA(n_components=2)\n",
    "# pca2 = PCA(n_components=2)\n",
    "\n",
    "data_transformed_pca = pca1.fit_transform(X_oh)\n",
    "synth_pca = pca1.transform(synth_df_cat)\n",
    "# data_transformed_pca = pca1.fit_transform(X_emb)\n",
    "# synth_pca = pca2.fit_transform(synth_data)\n",
    "\n",
    "tsne1 = TSNE(n_components=2, perplexity = 80)\n",
    "tsne2 = TSNE(n_components=2, perplexity = 80)\n",
    "\n",
    "idx_random = np.random.randint(0, len(X_emb), 5000)\n",
    "\n",
    "data_transformed_tsne = tsne1.fit_transform(X_emb[idx_random])\n",
    "synth_tsne = tsne2.fit_transform(synth_data[idx_random])\n",
    "\n",
    "\n",
    "figure, axs = plt.subplots(1, 2, figsize=(15, 5), dpi=100)\n",
    "\n",
    "axs[0].scatter(data_transformed_pca.T[0], data_transformed_pca.T[1], label='Real', alpha=0.4, s=20)\n",
    "axs[0].scatter(synth_pca.T[0], synth_pca.T[1], label='Synth', alpha=0.4, s=10)\n",
    "\n",
    "axs[0].legend()\n",
    "axs[0].set_xlabel('$X_1$')\n",
    "axs[0].set_ylabel('$X_2$')\n",
    "axs[0].set_title('PCA')\n",
    "\n",
    "\n",
    "axs[1].scatter(data_transformed_tsne.T[0], data_transformed_tsne.T[1], label='Real', s=3, alpha=1)\n",
    "axs[1].scatter(synth_tsne.T[0], synth_tsne.T[1], label='Synth', s=3, alpha=1)\n",
    "\n",
    "axs[1].legend()\n",
    "axs[1].set_xlabel('$X_1$')\n",
    "axs[1].set_ylabel('$X_2$')\n",
    "axs[1].set_title('t-SNE')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with other models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare metadata for methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sdv.metadata import SingleTableMetadata\n",
    "\n",
    "metadata = SingleTableMetadata()\n",
    "metadata.add_column(\n",
    "    column_name='amount',\n",
    "    sdtype='numerical',\n",
    "    computer_representation='Float')\n",
    "\n",
    "metadata.add_column(\n",
    "    column_name='mcc',\n",
    "    sdtype='categorical')\n",
    "\n",
    "metadata.add_column(\n",
    "    column_name='transaction_date',\n",
    "    sdtype='datetime',\n",
    "    datetime_format='%Y %m %d')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose categorical columns for metrics calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_columns = ['mcc']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def copulagan(data: pd.DataFrame, metadata, cat_columns=['mcc'], epochs=10, n_samples=10_000, load=False):\n",
    "    \n",
    "    if load:\n",
    "        synthesizer = CopulaGANSynthesizer(metadata, epochs=epochs)\n",
    "        synthesizer = synthesizer.load(f'{DIRECTORY}CopulaGAN_uk2.pkl')\n",
    "    else:\n",
    "        synthesizer = CopulaGANSynthesizer(metadata, epochs=epochs)\n",
    "        synthesizer.fit(data)\n",
    "        synthesizer.save(f'{DIRECTORY}CopulaGAN_uk2.pkl')\n",
    "\n",
    "    synth_copulagan = synthesizer.sample(num_rows=n_samples)\n",
    "\n",
    "    synth_df_cat_copulagan = pd.get_dummies(synth_copulagan[cat_columns], columns=cat_columns)\n",
    "\n",
    "    return synth_copulagan, synth_df_cat_copulagan\n",
    "\n",
    "def ctgan(data: pd.DataFrame, metadata, cat_columns=['mcc'], epochs=10, n_samples=10_000, load=False):\n",
    "\n",
    "    if load:\n",
    "        synthesizer = CTGANSynthesizer(metadata, epochs=epochs)\n",
    "        synthesizer = synthesizer.load(f'{DIRECTORY}CTGAN_uk2.pkl')\n",
    "    else:\n",
    "        synthesizer = CTGANSynthesizer(metadata, epochs=epochs)\n",
    "        synthesizer.fit(data)\n",
    "        synthesizer.save(f'{DIRECTORY}CTGAN_uk2.pkl')\n",
    " \n",
    "    synth_ctgan = synthesizer.sample(num_rows=n_samples)\n",
    "\n",
    "    synth_df_cat_ctgan = pd.get_dummies(synth_ctgan[cat_columns], columns=cat_columns)\n",
    "\n",
    "    return synth_ctgan, synth_df_cat_ctgan\n",
    "\n",
    "def compare_categorical_w_banksformer(data, synth_df, synth_df_cat, X_oh, metadata, cat_columns, synth_banks,\\\n",
    "                                    epochs=2, n_samples=10_000, comp_col='mcc', contig_cols=['mcc', 'customer'], load=False):\n",
    "    synth_copulagan, synth_copulagan_cat = copulagan(data, metadata, cat_columns, epochs, n_samples, load)\n",
    "    synth_ctgan, synth_ctgan_cat = ctgan(data, metadata, cat_columns, epochs, n_samples, load)\n",
    "    # synth_tvae, synth_tvae_cat = tvae(data, metadata, cat_columns, epochs, n_samples, load)\n",
    "    synth_banksformer = synth_banks\n",
    "\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(20, 10), dpi=100)\n",
    "\n",
    "    axs[0, 0].bar(np.sort(data['mcc'].value_counts().index.values).astype(str),\\\n",
    "            data['mcc'].value_counts().values/np.sum(data['mcc'].value_counts().values), color='black', alpha=0.6, label='Real')\n",
    "    axs[0, 0].bar(np.sort(synth_df['mcc'].value_counts().index.values).astype(str),\\\n",
    "            synth_df['mcc'].value_counts().values/np.sum(synth_df['mcc'].value_counts().values), color='red', alpha=0.6, label='TRGAN')\n",
    "    \n",
    "    # axs[0, 0].set_xticks(data['mcc'].value_counts().index.values.astype(str)[::2])\n",
    "    # axs[0, 0].set_xticklabels(data['mcc'].value_counts().index.values.astype(str)[::2], rotation=45)\n",
    "    axs[0, 0].tick_params(axis='x', which='both', bottom=False, labelbottom=False, labelsize=13)\n",
    "    axs[0, 0].tick_params(axis='y', labelsize=20)\n",
    "    axs[0, 0].legend(fontsize=18)\n",
    "    axs[0, 0].set_xlabel('Category', fontsize=22)\n",
    "    axs[0, 0].set_ylabel('Density', fontsize=22)\n",
    "#     axs[0, 0].set_title('TRGAN')\n",
    "\n",
    "\n",
    "    axs[0, 1].bar(np.sort(data['mcc'].value_counts().index.values).astype(str),\\\n",
    "            data['mcc'].value_counts().values/np.sum(data['mcc'].value_counts().values), color='black', alpha=0.6, label='Real')\n",
    "    axs[0, 1].bar(np.sort(synth_ctgan['mcc'].value_counts().index.values).astype(str),\\\n",
    "            synth_ctgan['mcc'].value_counts().values/np.sum(synth_ctgan['mcc'].value_counts().values), color='lightblue', alpha=0.6, label='CTGAN')\n",
    "\n",
    "    # axs[0, 1].set_xticks(data['mcc'].value_counts().index.values.astype(str)[::2])\n",
    "    # axs[0, 1].set_xticklabels(data['mcc'].value_counts().index.values.astype(str)[::2], rotation=45)\n",
    "    axs[0, 1].tick_params(axis='x', which='both', bottom=False, labelbottom=False, labelsize=13)\n",
    "    axs[0, 1].tick_params(axis='y', labelsize=20)\n",
    "    axs[0, 1].legend(fontsize=18)\n",
    "    axs[0, 1].set_xlabel('Category', fontsize=22)\n",
    "    # axs[0, 1].set_ylabel('Density', fontsize=16)\n",
    "#     axs[0, 1].set_title('CTGAN')\n",
    "\n",
    "\n",
    "    axs[1, 0].bar(np.sort(data['mcc'].value_counts().index.values).astype(str),\\\n",
    "            data['mcc'].value_counts().values/np.sum(data['mcc'].value_counts().values), color='black', alpha=0.6, label='Real')\n",
    "    axs[1, 0].bar(np.sort(synth_copulagan['mcc'].value_counts().index.values).astype(str),\\\n",
    "            synth_copulagan['mcc'].value_counts().values/np.sum(synth_copulagan['mcc'].value_counts().values), color='orange', alpha=0.6, label='CopulaGAN')\n",
    "\n",
    "    # axs[1, 0].set_xticks(data['mcc'].value_counts().index.values.astype(str)[::2])\n",
    "    # axs[1, 0].set_xticklabels(data['mcc'].value_counts().index.values.astype(str)[::2], rotation=45)\n",
    "    axs[1, 0].tick_params(axis='x', which='both', bottom=False, labelbottom=False, labelsize=13)\n",
    "    axs[1, 0].tick_params(axis='y', labelsize=20)\n",
    "    axs[1, 0].legend(fontsize=18)\n",
    "    axs[1, 0].set_xlabel('Category', fontsize=22)\n",
    "    axs[1, 0].set_ylabel('Density', fontsize=22)\n",
    "#     axs[1, 0].set_title('CopulaGAN')\n",
    "\n",
    "    \n",
    "    axs[1, 1].bar(np.sort(data['mcc'].value_counts().index.values).astype(str),\\\n",
    "            data['mcc'].value_counts().values/np.sum(data['mcc'].value_counts().values), color='black', alpha=0.6, label='Real')\n",
    "    axs[1, 1].bar(np.sort(synth_banksformer['mcc'].value_counts().index.values).astype(str),\\\n",
    "            synth_banksformer['mcc'].value_counts().values/np.sum(synth_banksformer['mcc'].value_counts().values), color='green', alpha=0.6, label='Banksformer')\n",
    "    \n",
    "    # axs[1, 1].set_xticks(data['mcc'].value_counts().index.values.astype(str)[::2])\n",
    "    # axs[1, 1].set_xticklabels(data['mcc'].value_counts().index.values.astype(str)[::2], rotation=45)\n",
    "    axs[1, 1].tick_params(axis='x', which='both', bottom=False, labelbottom=False, labelsize=13)\n",
    "    axs[1, 1].tick_params(axis='y', labelsize=20)\n",
    "    axs[1, 1].legend(fontsize=18)\n",
    "    axs[1, 1].set_xlabel('Category', fontsize=22)\n",
    "    # axs[1, 1].set_ylabel('Density', fontsize=16)\n",
    "#     axs[1, 1].set_title('Banksformer')\n",
    "\n",
    "    plt.subplots_adjust(hspace=0.15, wspace=0.12)\n",
    "    plt.tight_layout(pad=0.5)\n",
    "    plt.savefig('synth_mcc_uk.pdf', dpi=300)\n",
    "    plt.show()\n",
    "\n",
    "    # display(evaluate_new_rows([data, synth_df, synth_ctgan, synth_copulagan, synth_banksformer],\\\n",
    "                # index=['Real', 'TRGAN', 'CTGAN', 'CopulaGAN', 'Banksformer'], metadata=metadata))\n",
    "\n",
    "    eval_cat = evaluate_categorical([data[comp_col], synth_df[comp_col], synth_ctgan[comp_col], synth_copulagan[comp_col], synth_banksformer[comp_col]],\\\n",
    "                                     index=['Real', 'TRGAN', 'CTGAN', 'CopulaGAN', 'Banksformer'],\\\n",
    "    data_cont_array=[data[contig_cols], synth_df[contig_cols], synth_ctgan[contig_cols], synth_copulagan[contig_cols], synth_banksformer[contig_cols]])\n",
    "\n",
    "    return eval_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>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>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN</th>\n",
       "      <td>0.99500</td>\n",
       "      <td>0.56333</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.005094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTGAN</th>\n",
       "      <td>0.97975</td>\n",
       "      <td>0.50470</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.335866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CopulaGAN</th>\n",
       "      <td>0.97965</td>\n",
       "      <td>0.53082</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.335745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banksformer</th>\n",
       "      <td>0.77099</td>\n",
       "      <td>0.60382</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.353622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total Variation  Contingency Similarity  Values count      D_JS\n",
       "Real                 1.00000                 1.00000          44.0  0.000000\n",
       "TRGAN                0.99500                 0.56333          44.0  0.005094\n",
       "CTGAN                0.97975                 0.50470          44.0  0.335866\n",
       "CopulaGAN            0.97965                 0.53082          44.0  0.335745\n",
       "Banksformer          0.77099                 0.60382          44.0  0.353622"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_categorical_w_banksformer(data[['mcc', 'amount', 'transaction_date']], synth_df[['mcc', 'amount', 'transaction_date']], synth_df_cat, X_oh, metadata, \\\n",
    "            cat_columns, synth_banksformer.sample(len(data))[['mcc', 'amount', 'transaction_date']],\\\n",
    "            epochs=3, n_samples=len(data), comp_col='mcc', contig_cols=['mcc', 'transaction_date'], load=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_numerical_w_banksformer(data, synth_df, metadata, cat_columns, synth_banks, epochs=10, n_samples=10_000,\\\n",
    "                                    comp_col='amount', load=False, type_comp='log_amount', data_customer=''):\n",
    "    \n",
    "    \"\"\"_summary_\n",
    "\n",
    "    Returns:\n",
    "       Object: Return graphs and table with metrics\n",
    "\n",
    "    Types of visuals:\n",
    "        - Log amount: log_amount\n",
    "        - Cash flow: cash_flow\n",
    "\n",
    "    \"\"\"\n",
    "    synth_copulagan, synth_copulagan_cat = copulagan(data, metadata, cat_columns, epochs, n_samples, load)\n",
    "    synth_ctgan, synth_ctgan_cat = ctgan(data, metadata, cat_columns, epochs, n_samples, load)\n",
    "    # synth_tvae, synth_tvae_cat = tvae(data, metadata, cat_columns, epochs, n_samples, load)\n",
    "    synth_banksformer = synth_banks\n",
    "\n",
    "    if type_comp == 'log_amount':\n",
    "\n",
    "        data = copy.deepcopy(data)\n",
    "        synth_df = copy.deepcopy(synth_df)\n",
    "        data[comp_col] = abs(data[comp_col])\n",
    "        synth_df[comp_col] = abs(synth_df[comp_col])\n",
    "        synth_ctgan[comp_col] = abs(synth_ctgan[comp_col])\n",
    "        synth_copulagan[comp_col] = abs(synth_copulagan[comp_col])\n",
    "        synth_banksformer[comp_col] = abs(synth_banksformer[comp_col])\n",
    "\n",
    "        hist_real = np.histogram(np.log1p(data[comp_col]), normed=True, bins=40)\n",
    "\n",
    "        fig, axs = plt.subplots(2, 2, figsize=(20, 10), dpi=100)\n",
    "\n",
    "        axs[0, 0].hist(np.log1p(data[comp_col]), bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[0, 0].hist(np.log1p(synth_df[comp_col]), bins=40, label='TRGAN', alpha=0.7, density=True, color='red')\n",
    "        axs[0, 0].legend(fontsize=18)\n",
    "        axs[0, 0].set_xlabel('Log amount', fontsize=22)\n",
    "        axs[0, 0].set_ylabel('Density', fontsize=22)\n",
    "        axs[0, 0].set_xlim((-0.5, np.max(hist_real[1]) + 1e-2))\n",
    "        axs[0, 0].set_ylim((0, np.max(hist_real[0]) + 1e-2))\n",
    "        axs[0, 0].tick_params(labelsize=20)\n",
    "        # axs[0, 0].set_title('TRGAN')\n",
    "\n",
    "        axs[0, 1].hist(np.log1p(data[comp_col]), bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[0, 1].hist(np.log1p(synth_ctgan[comp_col]), bins=40, label='CTGAN', alpha=0.7, density=True, color='lightblue')\n",
    "        axs[0, 1].legend(fontsize=18)\n",
    "        axs[0, 1].set_xlabel('Log amount', fontsize=22)\n",
    "        # axs[0, 1].set_ylabel('Density', fontsize=16)\n",
    "        axs[0, 1].set_xlim((-0.5, np.max(hist_real[1]) + 1e-2))\n",
    "        axs[0, 1].set_ylim((0, np.max(hist_real[0]) + 1e-2))\n",
    "        axs[0, 1].tick_params(labelsize=20)\n",
    "        # axs[0, 1].set_title('CTGAN')\n",
    "\n",
    "        axs[1, 0].hist(np.log1p(data[comp_col]), bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[1, 0].hist(np.log1p(synth_copulagan[comp_col]), bins=40, label='CopulaGAN', alpha=0.7, density=True, color='orange')\n",
    "        axs[1, 0].legend(fontsize=18)\n",
    "        axs[1, 0].set_xlabel('Log amount', fontsize=22)\n",
    "        axs[1, 0].set_ylabel('Density', fontsize=22)\n",
    "        axs[1, 0].set_xlim((-0.5, np.max(hist_real[1]) + 1e-2))\n",
    "        axs[1, 0].set_ylim((0, np.max(hist_real[0]) + 1e-2))\n",
    "        axs[1, 0].tick_params(labelsize=20)\n",
    "        # axs[1, 0].set_title('CopulaGAN')\n",
    "\n",
    "        axs[1, 1].hist(np.log1p(data[comp_col]), bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[1, 1].hist(np.log1p(synth_banksformer[comp_col]), bins=40, label='Banksformer', alpha=0.7, density=True, color='green')\n",
    "        axs[1, 1].legend(fontsize=18)\n",
    "        axs[1, 1].set_xlabel('Log amount', fontsize=22)\n",
    "        # axs[1, 1].set_ylabel('Density', fontsize=16)\n",
    "        axs[1, 1].set_xlim((-0.5, np.max(hist_real[1]) + 1e-2))\n",
    "        axs[1, 1].set_ylim((0, np.max(hist_real[0]) + 1e-2))\n",
    "        axs[1, 1].tick_params(labelsize=20)\n",
    "        # axs[1, 1].set_title('Banksformer')\n",
    "\n",
    "        plt.subplots_adjust(hspace=0.25, wspace=0.12)\n",
    "        plt.tight_layout(pad=0.5)\n",
    "        plt.savefig('synth_amount_uk.pdf', dpi=300)\n",
    "        plt.show()\n",
    "\n",
    "        eval_num = evaluate_numerical([np.log1p(data[comp_col]), np.log1p(synth_df[comp_col]), np.log1p(synth_ctgan[comp_col]),\\\n",
    "                                       np.log1p(synth_copulagan[comp_col]), np.log1p(synth_banksformer[comp_col])],\\\n",
    "                    ['Real', 'TRGAN', 'CTGAN', 'CopulaGAN', 'Banksformer'])\n",
    "        \n",
    "        return eval_num\n",
    "    \n",
    "    elif type_comp == 'cash_flow':\n",
    "\n",
    "        data = copy.deepcopy(data)\n",
    "        synth_df = copy.deepcopy(synth_df)\n",
    "        data['month'] = data['transaction_date'].dt.month\n",
    "        synth_banksformer['month'] = synth_banksformer['transaction_date'].dt.month\n",
    "        synth_df['month'] = synth_df['transaction_date'].dt.month\n",
    "        synth_ctgan['month'] = synth_ctgan['transaction_date'].dt.month\n",
    "        synth_copulagan['month'] = synth_copulagan['transaction_date'].dt.month\n",
    "        # synth_tvae['month'] = synth_tvae['transaction_date'].dt.month\n",
    "\n",
    "        data = pd.concat([data, data_customer], axis=1)\n",
    "        synth_ctgan = pd.concat([synth_ctgan, data_customer], axis=1)\n",
    "        synth_copulagan = pd.concat([synth_copulagan, data_customer], axis=1)\n",
    "\n",
    "        \n",
    "        cash_flow_real = (data.groupby(by=['customer', 'month'])['amount'].apply(lambda x: x - x.shift())).dropna()\n",
    "        cash_flow_banks = (synth_banksformer.groupby(by=['customer', 'month'])['amount'].apply(lambda x: x - x.shift())).dropna()\n",
    "        cash_flow_trgan = (synth_df.groupby(by=['customer', 'month'])['amount'].apply(lambda x: x - x.shift())).dropna()\n",
    "        cash_flow_ctgan = (synth_ctgan.groupby(by=['customer', 'month'])['amount'].apply(lambda x: x - x.shift())).dropna()\n",
    "        cash_flow_copulagan = (synth_copulagan.groupby(by=['customer', 'month'])['amount'].apply(lambda x: x - x.shift())).dropna()\n",
    "\n",
    "        q = np.quantile(cash_flow_real, 0.95)\n",
    "\n",
    "        cash_flow_real = cash_flow_real.iloc[np.where((cash_flow_real <= q) & (cash_flow_real >= -q))[0]]\n",
    "        cash_flow_banks = cash_flow_banks.iloc[np.where((cash_flow_banks <= q) & (cash_flow_banks >= -q))[0]]\n",
    "        cash_flow_trgan = cash_flow_trgan.iloc[np.where((cash_flow_trgan <= q) & (cash_flow_trgan >= -q))[0]]\n",
    "        cash_flow_ctgan = cash_flow_ctgan.iloc[np.where((cash_flow_ctgan <= q) & (cash_flow_ctgan >= -q))[0]]\n",
    "        cash_flow_copulagan = cash_flow_copulagan.iloc[np.where((cash_flow_copulagan <= q) & (cash_flow_copulagan >= -q))[0]]\n",
    "        \n",
    "        min_length = np.min(list(map(lambda x: len(x), [cash_flow_real, cash_flow_trgan, cash_flow_banks, cash_flow_ctgan, cash_flow_copulagan])))\n",
    "\n",
    "        cash_flow_real = pd.Series(random.sample(cash_flow_real.values.tolist(), min_length))\n",
    "        cash_flow_banks = pd.Series(random.sample(cash_flow_banks.values.tolist(), min_length))\n",
    "        cash_flow_trgan = pd.Series(random.sample(cash_flow_trgan.values.tolist(), min_length))\n",
    "        cash_flow_ctgan = pd.Series(random.sample(cash_flow_ctgan.values.tolist(), min_length))\n",
    "        cash_flow_copulagan = pd.Series(random.sample(cash_flow_copulagan.values.tolist(), min_length))\n",
    "\n",
    "        hist_real = np.histogram(cash_flow_real, normed=True, bins=40)\n",
    "\n",
    "        fig, axs = plt.subplots(2, 2, figsize=(20, 10), dpi=100)\n",
    "\n",
    "        axs[0, 0].hist(cash_flow_real, bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[0, 0].hist(cash_flow_trgan, bins=40, label='TRGAN', alpha=0.7, density=True, color='red')\n",
    "        axs[0, 0].legend(fontsize=18)\n",
    "        axs[0, 0].set_xlabel('Monthly cash flow', fontsize=22)\n",
    "        axs[0, 0].set_ylabel('Density', fontsize=22)\n",
    "        axs[0, 0].set_xlim((-np.max(hist_real[1]), np.max(hist_real[1])))\n",
    "        axs[0, 0].set_ylim((0, np.max(hist_real[0]) + 3e-4))\n",
    "        axs[0, 0].tick_params(labelsize=20)\n",
    "        # axs[0, 0].set_title('TRGAN')\n",
    "\n",
    "        axs[0, 1].hist(cash_flow_real, bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[0, 1].hist(cash_flow_ctgan, bins=40, label='CTGAN', alpha=0.7, density=True, color='lightblue')\n",
    "        axs[0, 1].legend(fontsize=18)\n",
    "        axs[0, 1].set_xlabel('Monthly cash flow', fontsize=22)\n",
    "        # axs[0, 1].set_ylabel('Density', fontsize=16)\n",
    "        axs[0, 1].set_xlim((-np.max(hist_real[1]), np.max(hist_real[1])))\n",
    "        axs[0, 1].set_ylim((0, np.max(hist_real[0]) + 3e-4))\n",
    "        axs[0, 1].tick_params(labelsize=20)\n",
    "        # axs[0, 1].set_title('CTGAN')\n",
    "\n",
    "        axs[1, 0].hist(cash_flow_real, bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[1, 0].hist(cash_flow_copulagan, bins=40, label='CopulaGAN', alpha=0.7, density=True, color='orange')\n",
    "        axs[1, 0].legend(fontsize=18)\n",
    "        axs[1, 0].set_xlabel('Monthly cash flow', fontsize=22)\n",
    "        axs[1, 0].set_ylabel('Density', fontsize=22)\n",
    "        axs[1, 0].set_xlim((-np.max(hist_real[1]), np.max(hist_real[1])))\n",
    "        axs[1, 0].set_ylim((0, np.max(hist_real[0]) + 3e-4))\n",
    "        axs[1, 0].tick_params(labelsize=20)\n",
    "        # axs[1, 0].set_title('CopulaGAN')\n",
    "\n",
    "        axs[1, 1].hist(cash_flow_real, bins=40, label='Real', alpha=0.7, density=True, color='black')\n",
    "        axs[1, 1].hist(cash_flow_banks, bins=40, label='Banksformer', alpha=0.7, density=True, color='green')\n",
    "        axs[1, 1].legend(fontsize=18)\n",
    "        axs[1, 1].set_xlabel('Monthly cash flow', fontsize=22)\n",
    "        # axs[1, 1].set_ylabel('Density', fontsize=16)\n",
    "        axs[1, 1].set_xlim((-np.max(hist_real[1]), np.max(hist_real[1])))\n",
    "        axs[1, 1].set_ylim((0, np.max(hist_real[0]) + 3e-4))\n",
    "        axs[1, 1].tick_params(labelsize=20)\n",
    "        # axs[1, 1].set_title('Banksformer')\n",
    "\n",
    "        plt.subplots_adjust(hspace=0.25, wspace=0.15)\n",
    "        plt.tight_layout(pad=0.5)\n",
    "        # plt.savefig('synth_cashflow_uk.pdf', dpi=300)\n",
    "        plt.show()\n",
    "\n",
    "        eval_num = evaluate_numerical_cashflow([cash_flow_real, cash_flow_trgan, cash_flow_ctgan, cash_flow_copulagan, cash_flow_banks],\\\n",
    "                    ['Real', 'TRGAN', 'CTGAN', 'CopulaGAN', 'Banksformer'])\n",
    "        \n",
    "        return eval_num\n",
    "    \n",
    "    else:\n",
    "        print('Choose the type of comparison')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>3.802952</td>\n",
       "      <td>1.692282</td>\n",
       "      <td>0.558295</td>\n",
       "      <td>1.106460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN</th>\n",
       "      <td>3.801549</td>\n",
       "      <td>1.694241</td>\n",
       "      <td>0.550787</td>\n",
       "      <td>1.101023</td>\n",
       "      <td>0.005956</td>\n",
       "      <td>0.02712</td>\n",
       "      <td>0.009624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTGAN</th>\n",
       "      <td>3.942693</td>\n",
       "      <td>1.820859</td>\n",
       "      <td>-0.315520</td>\n",
       "      <td>0.621497</td>\n",
       "      <td>0.037617</td>\n",
       "      <td>0.10047</td>\n",
       "      <td>0.258855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CopulaGAN</th>\n",
       "      <td>3.433930</td>\n",
       "      <td>1.752087</td>\n",
       "      <td>1.244690</td>\n",
       "      <td>1.173119</td>\n",
       "      <td>0.042094</td>\n",
       "      <td>0.14444</td>\n",
       "      <td>0.380257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banksformer</th>\n",
       "      <td>4.256315</td>\n",
       "      <td>1.633994</td>\n",
       "      <td>0.296985</td>\n",
       "      <td>1.140948</td>\n",
       "      <td>0.033222</td>\n",
       "      <td>0.17040</td>\n",
       "      <td>0.453363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Mean       Std  Kurtosis  Skewness      D_JS  KS2test  \\\n",
       "Real         3.802952  1.692282  0.558295  1.106460  0.000000  0.00000   \n",
       "TRGAN        3.801549  1.694241  0.550787  1.101023  0.005956  0.02712   \n",
       "CTGAN        3.942693  1.820859 -0.315520  0.621497  0.037617  0.10047   \n",
       "CopulaGAN    3.433930  1.752087  1.244690  1.173119  0.042094  0.14444   \n",
       "Banksformer  4.256315  1.633994  0.296985  1.140948  0.033222  0.17040   \n",
       "\n",
       "             Wassertein distance  \n",
       "Real                    0.000000  \n",
       "TRGAN                   0.009624  \n",
       "CTGAN                   0.258855  \n",
       "CopulaGAN               0.380257  \n",
       "Banksformer             0.453363  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_numerical_w_banksformer(data[['mcc', 'amount', 'transaction_date']], synth_df, metadata, cat_columns,\\\n",
    "                                synth_banksformer.sample(len(data)), epochs=5, n_samples=len(data), comp_col='amount',\\\n",
    "                                load=True, type_comp='log_amount', data_customer=data[['customer']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>5.157611</td>\n",
       "      <td>484.636446</td>\n",
       "      <td>5.186215</td>\n",
       "      <td>0.046623</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN</th>\n",
       "      <td>5.560675</td>\n",
       "      <td>467.120014</td>\n",
       "      <td>5.841214</td>\n",
       "      <td>0.117483</td>\n",
       "      <td>0.027672</td>\n",
       "      <td>0.015368</td>\n",
       "      <td>13.372970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTGAN</th>\n",
       "      <td>-2.321514</td>\n",
       "      <td>615.893787</td>\n",
       "      <td>1.971213</td>\n",
       "      <td>-0.011149</td>\n",
       "      <td>0.194283</td>\n",
       "      <td>0.113765</td>\n",
       "      <td>130.426918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CopulaGAN</th>\n",
       "      <td>0.405268</td>\n",
       "      <td>431.744952</td>\n",
       "      <td>7.617448</td>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.115757</td>\n",
       "      <td>0.049010</td>\n",
       "      <td>44.950955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banksformer</th>\n",
       "      <td>-0.406081</td>\n",
       "      <td>531.579370</td>\n",
       "      <td>4.718494</td>\n",
       "      <td>-0.014739</td>\n",
       "      <td>0.083204</td>\n",
       "      <td>0.055331</td>\n",
       "      <td>45.400944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Mean         Std  Kurtosis  Skewness      D_JS   KS2test  \\\n",
       "Real         5.157611  484.636446  5.186215  0.046623  0.000000  0.000000   \n",
       "TRGAN        5.560675  467.120014  5.841214  0.117483  0.027672  0.015368   \n",
       "CTGAN       -2.321514  615.893787  1.971213 -0.011149  0.194283  0.113765   \n",
       "CopulaGAN    0.405268  431.744952  7.617448  0.002917  0.115757  0.049010   \n",
       "Banksformer -0.406081  531.579370  4.718494 -0.014739  0.083204  0.055331   \n",
       "\n",
       "             Wassertein distance  \n",
       "Real                    0.000000  \n",
       "TRGAN                  13.372970  \n",
       "CTGAN                 130.426918  \n",
       "CopulaGAN              44.950955  \n",
       "Banksformer            45.400944  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_numerical_w_banksformer(data[['mcc', 'amount', 'transaction_date']], synth_df, metadata, cat_columns,\\\n",
    "                                synth_banksformer.sample(len(data)), epochs=5, n_samples=len(data), comp_col='amount',\\\n",
    "                                load=True, type_comp='cash_flow', data_customer=data[['customer']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>D_JS</th>\n",
       "      <th>W_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Real</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRGAN</th>\n",
       "      <td>0.129384</td>\n",
       "      <td>0.004925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banksformer</th>\n",
       "      <td>0.311156</td>\n",
       "      <td>0.025640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 D_JS       W_1\n",
       "Real         0.000000  0.000000\n",
       "TRGAN        0.129384  0.004925\n",
       "Banksformer  0.311156  0.025640"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_categorical_w_banksformer_3grams(data, synth_df, synth_banksformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# synth_df.to_csv(f'Synthetic_data_{experiment_id}.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}