Synthesize tabular data

Using CWGAN-GP to generate tabular synthetic data:

Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

CWGAN GP is a variant of GAN that incorporates conditional information to generate data samples, while leveraging the Wasserstein distance to improve training stability and sample quality:

• 📑 Paper: Conditional Wasserstein Generative Adversarial Networks

Here’s an example of how to synthetize tabular data with CWGAN-GP using the Credit Card dataset:

"""
    CramerGAN python file example
"""
#Install ydata-synthetic lib
# pip install ydata-synthetic
import sklearn.cluster as cluster
import numpy as np
import pandas as pd

from ydata_synthetic.utils.cache import cache_file
from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
from ydata_synthetic.synthesizers.regular import RegularSynthesizer

#Read the original data and have it preprocessed
data_path = cache_file('creditcard.csv', 'https://datahub.io/machine-learning/creditcard/r/creditcard.csv')
data = pd.read_csv(data_path, index_col=[0])

#Data processing and analysis
num_cols = list(data.columns[ data.columns != 'Class' ])
cat_cols = ['Class']

print('Dataset columns: {}'.format(num_cols))
sorted_cols = ['V14', 'V4', 'V10', 'V17', 'V12', 'V26', 'Amount', 'V21', 'V8', 'V11', 'V7', 'V28', 'V19', 'V3', 'V22', 'V6', 'V20', 'V27', 'V16', 'V13', 'V25', 'V24', 'V18', 'V2', 'V1', 'V5', 'V15', 'V9', 'V23', 'Class']
processed_data = data[ sorted_cols ].copy()

#For the purpose of this example we will only synthesize the minority class
train_data = processed_data.loc[processed_data['Class'] == 1].copy()

#Create a new class column using KMeans - This will mainly be useful if we want to leverage conditional GAN
print("Dataset info: Number of records - {} Number of variables - {}".format(train_data.shape[0], train_data.shape[1]))
algorithm = cluster.KMeans
args, kwds = (), {'n_clusters':2, 'random_state':0}
labels = algorithm(*args, **kwds).fit_predict(train_data[ num_cols ])

print( pd.DataFrame( [ [np.sum(labels==i)] for i in np.unique(labels) ], columns=['count'], index=np.unique(labels) ) )

fraud_w_classes = train_data.copy()
fraud_w_classes['Class'] = labels

# GAN training
#Define the GAN and training parameters
noise_dim = 32
dim = 128
batch_size = 128

log_step = 100
epochs = 500+1
learning_rate = 5e-4
beta_1 = 0.5
beta_2 = 0.9
models_dir = '../cache'

model_parameters = ModelParameters(batch_size=batch_size,
                           lr=learning_rate,
                           betas=(beta_1, beta_2),
                           noise_dim=noise_dim,
                           layers_dim=dim)

train_args = TrainParameters(epochs=epochs,
                             sample_interval=log_step)

#Training the CRAMERGAN model
synth = RegularSynthesizer(modelname='cramer', model_parameters=model_parameters)
synth.fit(data=train_data, train_arguments = train_args, num_cols = num_cols, cat_cols = cat_cols)

#Saving the synthesizer to later generate new events
synth.save(path='creditcard_cramergan_model.pkl')

#########################################################
#    Loading and sampling from a trained synthesizer    #
#########################################################
synth = RegularSynthesizer.load(path='creditcard_cramergan_model.pkl')
#Sampling the data
#Note that the data returned it is not inverse processed.
data_sample = synth.sample(100000)