def main():
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:
model = CTGANSynthesizer()
model.fit(data, discrete_columns, args.epochs)
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)
def main():
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)
model = CTGANSynthesizer()
model.fit(data, discrete_columns, args.epochs)
num_samples = args.num_samples or len(data)
sampled = model.sample(num_samples)
if args.tsv:
write_tsv(sampled, args.metadata, args.output)
else:
sampled.to_csv(args.output, index=False)
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)