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)
import numpy as np
import pandas as pd
import os
import sys
import tqdm
import pickle
import pathlib
from pathlib import Path
def get_domain_dims(DIR='us_import1'):
with open('./generated_data_v1/{}/domain_dims.pkl'.format(DIR),
'rb') as fh:
domain_dims = pickle.load(fh)
return domain_dims
def convert_np_to_pd(data_np, domain_dims):
columns = list(domain_dims.keys())
df = pd.DataFrame(data=data_np, columns=columns)
return df, columns
real_data = np.load('./generated_data_v1/us_import1/pos_data.npy')
domain_dims = get_domain_dims()
data_df, columns = convert_np_to_pd(real_data, domain_dims)
ctgan_obj = CTGANSynthesizer()
ctgan_obj.fit(data, columns)
ctgan_obj.save('ctgan.pkl')
