def setUp(self): parser = ArgumentParser() add_train_args(parser) args = parser.parse_args([]) args.data_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'delaney_toy.csv') args.dataset_type = 'regression' args.batch_size = 2 args.hidden_size = 5 args.epochs = 1 args.quiet = True self.temp_dir = TemporaryDirectory() args.save_dir = self.temp_dir.name logger = create_logger(name='train', save_dir=args.save_dir, quiet=args.quiet) modify_train_args(args) cross_validate(args, logger) clear_cache() parser = ArgumentParser() add_predict_args(parser) args = parser.parse_args([]) args.batch_size = 2 args.checkpoint_dir = self.temp_dir.name args.preds_path = NamedTemporaryFile().name args.test_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'delaney_toy_smiles.csv') self.args = args
def test_hyperopt(self): try: parser = ArgumentParser() add_train_args(parser) parser.add_argument('--num_iters', type=int, default=20, help='Number of hyperparameter choices to try') parser.add_argument('--config_save_path', type=str, help='Path to .json file where best hyperparameter settings will be written') parser.add_argument('--log_dir', type=str, help='(Optional) Path to a directory where all results of the hyperparameter optimization will be written') args = parser.parse_args([]) args.data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'delaney_toy.csv') args.dataset_type = 'regression' args.batch_size = 2 args.hidden_size = 5 args.epochs = 1 args.quiet = True temp_file = NamedTemporaryFile() args.config_save_path = temp_file.name args.num_iters = 3 modify_train_args(args) grid_search(args) clear_cache() except: self.fail('hyperopt')
def clear_cache(self): clear_cache()
def tearDown(self): self.temp_dir.cleanup() os.remove(self.args.preds_path) self.args = None clear_cache()
def tearDown(self): clear_cache()
def tearDown(self): self.args = None self.logger = None clear_cache()