def get_parser():
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(title='Models sampler script',
description='available models')
for model in MODELS.get_model_names():
add_sample_args(subparsers.add_parser(model))
return parser
def get_parser():
parser = add_sample_args(argparse.ArgumentParser())
parser.add_argument('--dist_file', default='data/guacamol_v1_all.smiles')
parser.add_argument('--output_dir', default=None, help='Output directory')
parser.add_argument('--suite', default='v2')
return parser
def get_parser():
parser = add_sample_args(argparse.ArgumentParser())
# conditional generation
parser.add_argument('--conditional',
type=int,
default=0,
help='Conditional generation mode')
parser.add_argument(
'--condition_load',
type=str,
default='molhack_test.solution',
help='Target conditional input data in csv format to train')
parser.add_argument('--output_size',
type=int,
default=10,
help='Output size in the condition linear layer')
return parser
def get_parser():
return add_sample_args(argparse.ArgumentParser())
