make it more easier to save and reload featurized and splitted dataset

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

def load_pdbbind(featurizer="grid",

def load_pdbbind(reload=True,

                 data_dir=None,

                 subset="core",

                 load_binding_pocket=False,

                 featurizer="grid",

                 split="random",

                 subset="core",

                 reload=True):

                 save_dir=None):

  """Load and featurize raw PDBBind dataset.

  Parameters

    Only "core" or "refined" for now.

  """

  pdbbind_tasks = ["-logKd/Ki"]

  data_dir = deepchem.utils.get_data_dir()

    deepchem_dir = deepchem.utils.get_data_dir()

    if data_dir == None:

        data_dir = deepchem_dir

    data_folder = os.path.join(data_dir, "pdbbind", "v2015")

    if save_dir == None:

        save_dir = os.path.join(deepchem_dir, "from-pdbbind")

    if load_binding_pocket:

        save_folder = os.path.join(

            save_dir, "protein_pocket-%s-%s-%s" % (subset, featurizer, split))

    else:

        save_folder = os.path.join(

            save_dir, "full_protein-%s-%s-%s" % (subset, featurizer, split))

    if reload:

    save_dir = os.path.join(data_dir,

                            "pdbbind/" + featurizer + "/" + str(split))

        if not os.path.exists(save_folder):

            raise IOError("Cannot find saved dataset from %s!" % save_folder)

        print("\nLoading featurized and splitted dataset from:\n%s\n" %

              save_folder)

        loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(

        save_dir)

            save_folder)

        if loaded:

            return pdbbind_tasks, all_dataset, transformers

        else:

            raise IOError(

                "Failed to load featurized and splitted dataset from:\n%s\n" %

                save_folder)

    dataset_file = os.path.join(data_dir, "pdbbind_v2015.tar.gz")

  data_folder = os.path.join(data_dir, "v2015")

    if not os.path.exists(dataset_file):

    logger.warning("About to download PDBBind full dataset. Large file, 2GB")

        logger.warning(

            "About to download PDBBind full dataset. Large file, 2GB")

        deepchem.utils.download_url(

            'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/' +

        "pdbbind_v2015.tar.gz")

            "pdbbind_v2015.tar.gz",

            dest_dir=data_dir)

    if os.path.exists(data_folder):

    logger.info("Data directory for %s already exists" % subset)

        logger.info("PDBBind full dataset already exists.")

    else:

    print("Untarring full dataset")

    deepchem.utils.untargz_file(dataset_file, dest_dir=data_dir)

        print("Untarring full dataset...")

        deepchem.utils.untargz_file(

            dataset_file, dest_dir=os.path.join(data_dir, "pdbbind"))

    print("\nRaw dataset:\n%s" % data_folder)

    print("\nFeaturized and splitted dataset:\n%s" % save_folder)

  if subset == "core":

    index_file = os.path.join(data_folder, "INDEX_core_name.2013")

    labels_file = os.path.join(data_folder, "INDEX_core_data.2013")

  splitter = splitters[split]

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

  all_dataset = (train, valid, test)

  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

  print("\nSaving dataset to \"%s\" ..." % save_folder)

  deepchem.utils.save.save_dataset_to_disk(save_folder, train, valid, test,

                                             transformers)

  return pdbbind_tasks, all_dataset, transformers