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())