Formatting

import os

import logging

import deepchem

from deepchem.feat import Featurizer

from typing import Iterable

from typing import Iterable, List

logger = logging.getLogger(__name__)

  if reload:

    save_folder = os.path.join(save_dir, "mydataset-featurized")

    if not move_mean:

      save_folder = os.path.join(save_folder,

                                 str(featurizer) + "_mean_unmoved")

      save_folder = os.path.join(save_folder, str(featurizer) + "_mean_unmoved")

    else:

      save_folder = os.path.join(save_folder, str(featurizer))

    if loaded:

      return my_tasks, all_dataset, transformers

  sdf_featurizers = []  # e.g. 'CoulombMatrix' or 'MP'

  # 3D coordinate featurizers, e.g. 'CoulombMatrix' or 'MP'

  # For crystal structures, replace with json_featurizers

  sdf_featurizers = []  # type: List[Featurizer]

  # If featurizer requires a non-CSV file format, load .tar.gz file

  if featurizer in sdf_featurizers:

  else:  # only load CSV file

    dataset_file = os.path.join(data_dir, "mydataset.csv")

    if not os.path.exists(dataset_file):

      deepchem.utils.download_url(

          url=MYDATASET_CSV_URL, dest_dir=data_dir)

      deepchem.utils.download_url(url=MYDATASET_CSV_URL, dest_dir=data_dir)

  # Handle all allowed SDF featurizers

  if featurizer in sdf_featurizers:

      'random':

      deepchem.splits.RandomSplitter(),

      'stratified':

      deepchem.splits.SingletaskStratifiedSplitter(

          task_number=len(my_tasks)),

      deepchem.splits.SingletaskStratifiedSplitter(task_number=len(my_tasks)),

      'scaffold':

      deepchem.splits.ScaffoldSplitter()

  }

    test_dataset = transformer.transform(test_dataset)

  if reload:  # save to disk

    deepchem.utils.save.save_dataset_to_disk(save_folder, train_dataset,

                                             valid_dataset, test_dataset,

                                             transformers)

    deepchem.utils.save.save_dataset_to_disk(

        save_folder, train_dataset, valid_dataset, test_dataset, transformers)

  return my_tasks, (train_dataset, valid_dataset, test_dataset), transformers