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()
