Merge pull request #2340 from MariBerry/master

          batch_size=self.batch_size,

          deterministic=deterministic,

          pad_batches=pad_batches):

        if self.mode == 'classification':

        if y_b is not None and self.mode == 'classification':

          y_b = to_one_hot(y_b.flatten(), self.n_classes).reshape(

              -1, self.n_tasks, self.n_classes)

        multiConvMol = ConvMol.agglomerate_mols(X_b)

  assert mean_std < mean_value

def test_graph_conv_model_no_task():

  tasks, dataset, _, __ = get_dataset('classification', 'GraphConv')

  batch_size = 10

  model = GraphConvModel(

      len(tasks),

      batch_size=batch_size,

      batch_normalize=False,

      mode='classification')

  model.fit(dataset, nb_epoch=20)

  # predict datset with no y (ensured by tasks = [])

  bace_url = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv"

  dc.utils.data_utils.download_url(url=bace_url, name="bace_tmp.csv")

  loader = dc.data.CSVLoader(

      tasks=[], smiles_field='mol', featurizer=dc.feat.ConvMolFeaturizer())

  td = loader.featurize(

      os.path.join(dc.utils.data_utils.get_data_dir(), "bace_tmp.csv"))

  model.predict(td)

def test_graph_conv_atom_features():

  tasks, dataset, transformers, metric = get_dataset(

      'regression', 'Raw', num_tasks=1)