def __init__(self,
input_file=None,
data_file=None,
save_tensors_dir=None,
keep_tensors=False,
out_dir=None,
normalize=False):
self.model = None
self.input_file = input_file
self.data_file = data_file
self.save_tensors_dir = save_tensors_dir
self.keep_tensors = keep_tensors
self.out_dir = out_dir
self.normalize = normalize
self.datasets = None
self.add_extra_atom_attribute = None
self.add_extra_bond_attribute = None
self.differentiate_atom_type = None
self.differentiate_bond_type = None
self.padding = None
self.padding_final_size = None
self.prediction_task = None
self.y_mean = None
self.y_std = None
if self.input_file is not None:
read_input_file(self.input_file, self)
self.get_data_from_file = False
if self.data_file is not None:
if self.data_file.endswith('.csv'):
self.get_data_from_file = True
else:
self.specify_datasets(self.data_file)
def load_input(self, input_file):
"""
This method is intended to provide a way to build model from an input file
"""
if input_file:
self.input_file = input_file
read_input_file(self.input_file, self)
def test_read_input_file(self):
predictor_test = Predictor()
path = os.path.join(os.path.dirname(dde.__file__), 'test_data',
'minimal_predictor', 'predictor_input.py')
read_input_file(path, predictor_test)
predictor_model = predictor_test.model
self.assertEqual(len(predictor_model.layers), 4)
self.assertTrue(isinstance(predictor_model.layers[1], MoleculeConv))
self.assertTrue(isinstance(predictor_model.layers[2], Dense))
self.assertEqual(predictor_model.layers[1].inner_dim, 38)
self.assertEqual(predictor_model.layers[1].units, 512)