def test_save_load():
data = pd.DataFrame({
'continuous': np.random.random(100),
'discrete': np.random.choice(['a', 'b', 'c'], 100)
})
discrete_columns = ['discrete']
ctgan = CTGANSynthesizer(epochs=1)
ctgan.fit(data, discrete_columns)
with tf.TemporaryDirectory() as temporary_directory:
ctgan.save(temporary_directory + "test_tvae.pkl")
ctgan = CTGANSynthesizer.load(temporary_directory + "test_tvae.pkl")
sampled = ctgan.sample(1000)
assert set(sampled.columns) == {'continuous', 'discrete'}
assert set(sampled['discrete'].unique()) == {'a', 'b', 'c'}
def main():
"""CLI."""
args = _parse_args()
if args.tsv:
data, discrete_columns = read_tsv(args.data, args.metadata)
else:
data, discrete_columns = read_csv(args.data, args.metadata,
args.header, args.discrete)
if args.load:
model = CTGANSynthesizer.load(args.load)
else:
generator_dim = [int(x) for x in args.generator_dim.split(',')]
discriminator_dim = [int(x) for x in args.discriminator_dim.split(',')]
model = CTGANSynthesizer(embedding_dim=args.embedding_dim,
generator_dim=generator_dim,
discriminator_dim=discriminator_dim,
generator_lr=args.generator_lr,
generator_decay=args.generator_decay,
discriminator_lr=args.discriminator_lr,
discriminator_decay=args.discriminator_decay,
batch_size=args.batch_size,
epochs=args.epochs)
model.fit(data, discrete_columns)
if args.save is not None:
model.save(args.save)
num_samples = args.num_samples or len(data)
if args.sample_condition_column is not None:
assert args.sample_condition_column_value is not None
sampled = model.sample(num_samples, args.sample_condition_column,
args.sample_condition_column_value)
if args.tsv:
write_tsv(sampled, args.metadata, args.output)
else:
sampled.to_csv(args.output, index=False)
