Run yapf on qm9

import shutil

import deepchem as dc

def load_qm9(featurizer=None, split='random'):

def load_qm9(featurizer=None, split='random'):

  """Load qm9 datasets."""

  # Featurize qm9 dataset

  print("About to featurize qm9 dataset.")

  current_dir = os.path.dirname(os.path.realpath(__file__))

  dataset_file = os.path.join(

      current_dir, "./gdb9.sdf")

  qm9_tasks = ["A", "B", "C", "mu", "alpha", "homo", "lumo", "gap", "r2", "zpve", "cv", 

                "u0_atom", "u298_atom", "h298_atom", "g298_atom"]

  dataset_file = os.path.join(current_dir, "./gdb9.sdf")

  qm9_tasks = [

      "A", "B", "C", "mu", "alpha", "homo", "lumo", "gap", "r2", "zpve", "cv",

      "u0_atom", "u298_atom", "h298_atom", "g298_atom"

  ]

  if featurizer is None:

    featurizer = dc.feat.CoulombMatrix(29)

  loader = dc.data.SDFLoader(tasks=qm9_tasks, smiles_field="smiles", 

                             mol_field="mol", featurizer=featurizer)

  loader = dc.data.SDFLoader(

      tasks=qm9_tasks,

      smiles_field="smiles",

      mol_field="mol",

      featurizer=featurizer)

  dataset = loader.featurize(dataset_file)

  splitters = {'index': dc.splits.IndexSplitter(),

  splitters = {

      'index': dc.splits.IndexSplitter(),

      'random': dc.splits.RandomSplitter(),

               'stratified': dc.splits.SingletaskStratifiedSplitter(task_number=11)}

      'stratified': dc.splits.SingletaskStratifiedSplitter(task_number=11)

  }

  splitter = splitters[split]

  train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(dataset)

  transformers = [dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]

  train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(

      dataset)

  transformers = [

      dc.trans.NormalizationTransformer(

          transform_y=True, dataset=train_dataset)

  ]

  for transformer in transformers:

    train_dataset = transformer.transform(train_dataset)

    valid_dataset = transformer.transform(valid_dataset)

qm9_tasks, datasets, transformers = load_qm9()

train_dataset, valid_dataset, test_dataset = datasets

fit_transformers = [dc.trans.CoulombFitTransformer(train_dataset)]

regression_metric = [dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), 

              dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")]

regression_metric = [

    dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),

    dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")

]

model = dc.models.TensorflowMultiTaskFitTransformRegressor(

    n_tasks=len(qm9_tasks), n_features=[29, 29], learning_rate=0.001 , momentum=.8, batch_size=32,

    n_tasks=len(qm9_tasks),

    n_features=[29, 29],

    learning_rate=0.001,

    momentum=.8,

    batch_size=32,

    weight_init_stddevs=[1 / np.sqrt(400), 1 / np.sqrt(100), 1 / np.sqrt(100)],

    bias_init_consts=[0.,0.,0.], layer_sizes=[400,100,100], 

    dropouts=[0.01,0.01,0.01], fit_transformers=fit_transformers, n_evals=10, seed=123)

    bias_init_consts=[0., 0., 0.],

    layer_sizes=[400, 100, 100],

    dropouts=[0.01, 0.01, 0.01],

    fit_transformers=fit_transformers,

    n_evals=10,

    seed=123)

# Fit trained model

model.fit(train_dataset, nb_epoch=50)