yapfing

import shutil

import deepchem as dc

def load_delaney(featurizer='ECFP', split='index'):

  """Load delaney datasets."""

  # Featurize Delaney dataset

  print("About to featurize Delaney dataset.")

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

  dataset_file = os.path.join(

      current_dir, "../../datasets/delaney-processed.csv")

  dataset_file = os.path.join(current_dir,

                              "../../datasets/delaney-processed.csv")

  delaney_tasks = ['measured log solubility in mols per litre']

  if featurizer == 'ECFP':

    featurizer = dc.feat.CircularFingerprint(size=1024)

    featurizer = dc.feat.ConvMolFeaturizer()

  loader = dc.data.CSVLoader(

      tasks=delaney_tasks, smiles_field="smiles", featurizer=featurizer)

  dataset = loader.featurize(

      dataset_file, shard_size=8192)

  dataset = loader.featurize(dataset_file, shard_size=8192)

  # Initialize transformers

  transformers = [

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

      dc.trans.NormalizationTransformer(transform_y=True, dataset=dataset)

  ]

  print("About to transform data")

  for transformer in transformers:

    dataset = transformer.transform(dataset)

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

  splitters = {

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

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

               'scaffold': dc.splits.ScaffoldSplitter()}

      'scaffold': dc.splits.ScaffoldSplitter()

  }

  splitter = splitters[split]

  train, valid, test = splitter.train_valid_test_split(dataset)

  return delaney_tasks, (train, valid, test), transformers