Make featurizer positional.

#   2+. The cache can even happen at Travis CI to accelerate

#       CI testing.

#

def load_dataset(dataset,

                 split=None,

                 featurizer=None):

def load_dataset(dataset, featurizer, split=None):

  """

  Load specific dataset for benchmark.

      choice of which datasets to use, should be: tox21, muv, sider, 

      toxcast, pcba, delaney, kaggle, nci, clintox, hiv, pdbbind, chembl,

      qm7, qm7b, qm9, sampl

  featurizer: string or dc.feat.Featurizer.

      choice of featurization.

  split: string,  optional (default=None)

      choice of splitter function, None = using the default splitter

  featurizer: string or dc.feat.Featurizer,  optional (default=None)

      choice of featurization, None = using the default corresponding to model

      (string only applicable to deepchem models)

  """

  dataset_loading_functions = {

      'tox21': deepchem.molnet.load_tox21,

      featurizer=featurizer, split=split)

  return tasks, all_dataset, transformers

def benchmark_model(model,

                    all_dataset,

                    transformers,

                    metric,

                    test=False):

def benchmark_model(model, all_dataset, transformers, metric, test=False):

  """

  Benchmark custom model.

  train_dataset, valid_dataset, test_dataset = all_dataset

  model.fit(train_dataset)

  train_score = model.evaluate(train_dataset, metric,

                               transformers)

  valid_score = model.evaluate(valid_dataset, metric,

                               transformers)

  train_score = model.evaluate(train_dataset, metric, transformers)

  valid_score = model.evaluate(valid_dataset, metric, transformers)

  if test:

    test_score = model.evaluate(test_dataset, metric,

                                transformers)

    test_score = model.evaluate(test_dataset, metric, transformers)

  time_finish_fitting = time.time()

  time_for_running = time_finish_fitting - time_start_fitting