def test_save_and_load(self):
path_checkpoint='.'
prefix_checkpoint='test'
n_epochs=10
cor = Corgan()
# dummy dataset
n_gen = 500
n = 1000
m = 7
x = np.random.randint(low=0, high=2, size=(n,m))
model_saved = cor.train(x=x,
n_epochs_pretrain=10,
n_epochs=10,
path_checkpoint=path_checkpoint,
prefix_checkpoint=prefix_checkpoint)
file = 'test.pkl'
cor.save_obj(obj=model_saved, file_name=file)
model_loaded = cor.load_obj(file)
x_synth = cor.generate(model = model_loaded, n_gen=n_gen)
# clean up
file_ckpt=os.path.join(path_checkpoint, prefix_checkpoint + ".model_epoch_%d.pth" % n_epochs)
os.remove(file_ckpt)
os.remove(file)
assert len(x_synth) == n_gen
r_all = obj_d['x']
n_subset_r = round(len(r_all) * args.frac_train)
idx_trn = np.random.choice(len(r_all), n_subset_r, replace=False)
idx_tst = np.setdiff1d(range(len(r_all)), idx_trn)
r_trn = r_all[idx_trn, :]
r_tst = r_all[idx_tst, :]
# train and save model
if args.train_type == 'corgan':
syn = Corgan(debug=debug, n_cpu=args.n_cpu_train)
elif args.train_type == 'ppgan':
syn = Ppgan(debug=debug, n_cpu=args.n_cpu_train)
model = syn.train(x=r_trn, n_epochs=args.n_epoch)
model['m'] = meta
model['header'] = obj_d['header']
syn.save_obj(model, outfile)
elif args.task == 'generate':
pre = Preprocessor(missing_value=args.missing_value_generate)
outfile = args.outprefix_generate + '.csv'
syn = Corgan()
model = syn.load_obj(args.file_model)
if model['parameter_dict']['model'] == 'corgan':
syn = Corgan()
elif model['parameter_dict']['model'] == 'ppgan':
syn = Ppgan(debug=False, n_cpu=1)
s = syn.generate(model, n_gen=args.generate_size)