How to use the deepchem.feat.CircularFingerprint function in deepchem

if reload:
    save_dir = os.path.join(
        data_dir,
        assay_file_name.split(".")[0] + featurizer + "/" + str(split))

  dataset_file = os.path.join(data_dir, assay_file_name)

  if not os.path.exists(dataset_file):
    deepchem.utils.download_url(
        "http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/{0}".
        format(assay_file_name))

  # Featurize PCBA dataset
  logger.info("About to featurize PCBA dataset.")
  if featurizer == 'ECFP':
    featurizer = deepchem.feat.CircularFingerprint(size=1024)
  elif featurizer == 'GraphConv':
    featurizer = deepchem.feat.ConvMolFeaturizer()
  elif featurizer == 'Weave':
    featurizer = deepchem.feat.WeaveFeaturizer()
  elif featurizer == 'Raw':
    featurizer = deepchem.feat.RawFeaturizer()

  with gzip.GzipFile(dataset_file, "r") as fin:
    header = fin.readline().rstrip().decode("utf-8")
    columns = header.split(",")
    columns.remove("mol_id")
    columns.remove("smiles")
    PCBA_tasks = columns

  if reload:
    loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(

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")
  delaney_tasks = ['measured log solubility in mols per litre']
  if featurizer == 'ECFP':
    featurizer_func = dc.feat.CircularFingerprint(size=1024)
  elif featurizer == 'GraphConv':
    featurizer_func = dc.feat.ConvMolFeaturizer()
  loader = dc.load.DataLoader(
      tasks=delaney_tasks, smiles_field="smiles",
      featurizer=featurizer_func, verbosity = 'high')
  dataset = loader.featurize(
      dataset_file, shard_size=8192)

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

  print("About to transform data")
  for transformer in transformers:
      dataset = transformer.transform(dataset)

if split == "year":
        train_datasets, valid_datasets, test_datasets = [], [], []
        train_files = os.path.join(current_dir,
                                   "year_sets/chembl_%s_ts_train.csv.gz" % set)
        valid_files = os.path.join(current_dir,
                                   "year_sets/chembl_%s_ts_valid.csv.gz" % set)
        test_files = os.path.join(current_dir,
                                  "year_sets/chembl_%s_ts_test.csv.gz" % set)
    else:
        dataset_path = os.path.join(
            current_dir, "../datasets/chembl_%s.csv.gz" % set)

    # Featurize ChEMBL dataset
    print("About to featurize ChEMBL dataset.")
    if featurizer == 'ECFP':
        featurizer = dc.feat.CircularFingerprint(size=1024)
    elif featurizer == 'GraphConv':
        featurizer = dc.feat.ConvMolFeaturizer()

    loader = dc.data.CSVLoader(
        tasks=chembl_tasks, smiles_field="smiles", featurizer=featurizer)

    if split == "year":
        print("Featurizing train datasets")
        train_dataset = loader.featurize(
            train_files, shard_size=shard_size)

        print("Featurizing valid datasets")
        valid_dataset = loader.featurize(
            valid_files, shard_size=shard_size)

        print("Featurizing test datasets")

if not os.path.exists(save_dir):
      logger.warning("{} does not exist. Creating one.".format(save_dir))
    else:
      logger.info("{} exists. Loading featurized datasets.".format(save_dir))
    loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
        save_dir)
    if loaded:
      return thermosol_tasks, all_dataset, transformers

  dataset_file = os.path.join(data_dir, "thermosol.csv")
  if not os.path.exists(dataset_file):
    logger.info("{} does not exist. Downloading it.".format(dataset_file))
    deepchem.utils.download_url(url=THERMOSOL_URL, dest_dir=data_dir)

  if featurizer == 'ECFP':
    featurizer = deepchem.feat.CircularFingerprint(size=1024)
  elif featurizer == 'GraphConv':
    featurizer = deepchem.feat.ConvMolFeaturizer()
  elif featurizer == 'Weave':
    featurizer = deepchem.feat.WeaveFeaturizer()
  elif featurizer == 'Raw':
    featurizer = deepchem.feat.RawFeaturizer()
  elif featurizer == 'AdjacencyConv':
    featurizer = deepchem.feat.AdjacencyFingerprint(
        max_n_atoms=150, max_valence=6)

  logger.info("Featurizing datasets.")
  loader = deepchem.data.CSVLoader(
      tasks=thermosol_tasks, smiles_field='smile', featurizer=featurizer)
  dataset = loader.featurize(input_files=[dataset_file], shard_size=2000)

  logger.info("Removing missing entries...")

delim = "_CV" + delim
      save_dir = os.path.join(data_dir, featurizer + delim + mode + "/" + split)
      loaded, all_dataset, transformers = dcCustom.utils.save.load_cv_dataset_from_disk(
          save_dir, K)
    else:
      save_dir = os.path.join(data_dir, featurizer + delim + mode + "/" + split)
      loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
          save_dir)
    if loaded:
      return tasks, all_dataset, transformers
  
  dataset_file = os.path.join(data_dir, file_name)
  if featurizer == 'Weave':
    featurizer = dcCustom.feat.WeaveFeaturizer()
  elif featurizer == 'ECFP':
    featurizer = deepchem.feat.CircularFingerprint(size=1024)
  elif featurizer == 'GraphConv':
    featurizer = dcCustom.feat.ConvMolFeaturizer()

  loader = dcCustom.data.CSVLoader(
      tasks = tasks, smiles_field="smiles", protein_field = "proteinName",
      featurizer=featurizer)
  dataset = loader.featurize(dataset_file, shard_size=8192)
  
  if mode == 'regression':
    transformers = [
          deepchem.trans.NormalizationTransformer(
              transform_y=True, dataset=dataset)
    ]
  elif mode == 'classification':
    transformers = [
        deepchem.trans.BalancingTransformer(transform_w=True, dataset=dataset)

if split == "year":
        train_datasets, valid_datasets, test_datasets = [], [], []
        train_files = os.path.join(current_dir,
                                   "year_sets/chembl_%s_ts_train.csv.gz" % set)
        valid_files = os.path.join(current_dir,
                                   "year_sets/chembl_%s_ts_valid.csv.gz" % set)
        test_files = os.path.join(current_dir,
                                  "year_sets/chembl_%s_ts_test.csv.gz" % set)
    else:
        dataset_path = os.path.join(
            current_dir, "../../datasets/chembl_%s.csv.gz" % set)

    # Featurize ChEMBL dataset
    print("About to featurize ChEMBL dataset.")
    if featurizer == 'ECFP':
        featurizer = dc.feat.CircularFingerprint(size=1024)
    elif featurizer == 'GraphConv':
        featurizer = dc.feat.ConvMolFeaturizer()

    loader = dc.data.CSVLoader(
        tasks=chembl_tasks, smiles_field="smiles", featurizer=featurizer)

    if split == "year":
        print("Featurizing train datasets")
        train_dataset = loader.featurize(
            train_files, shard_size=shard_size)

        print("Featurizing valid datasets")
        valid_dataset = loader.featurize(
            valid_files, shard_size=shard_size)

        print("Featurizing test datasets")

featurizer = deepchem.feat.CoulombMatrix(29)
    elif featurizer == 'BPSymmetryFunctionInput':
      featurizer = deepchem.feat.BPSymmetryFunctionInput(29)
    elif featurizer == 'Raw':
      featurizer = deepchem.feat.RawFeaturizer()
    elif featurizer == 'MP':
      featurizer = deepchem.feat.WeaveFeaturizer(
          graph_distance=False, explicit_H=True)
    loader = deepchem.data.SDFLoader(
        tasks=qm9_tasks,
        smiles_field="smiles",
        mol_field="mol",
        featurizer=featurizer)
  else:
    if featurizer == 'ECFP':
      featurizer = deepchem.feat.CircularFingerprint(size=1024)
    elif featurizer == 'GraphConv':
      featurizer = deepchem.feat.ConvMolFeaturizer()
    elif featurizer == 'Weave':
      featurizer = deepchem.feat.WeaveFeaturizer()
    elif featurizer == "smiles2img":
      img_spec = kwargs.get("img_spec", "std")
      img_size = kwargs.get("img_size", 80)
      featurizer = deepchem.feat.SmilesToImage(
          img_size=img_size, img_spec=img_spec)
    loader = deepchem.data.CSVLoader(
        tasks=qm9_tasks, smiles_field="smiles", featurizer=featurizer)

  dataset = loader.featurize(dataset_file)
  if split == None:
    raise ValueError()

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

  if not os.path.exists(dataset_file):
    deepchem.utils.download_url(
        'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/bace.csv'
    )

  bace_tasks = ["pIC50"]
  if reload:
    loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
        save_dir)
    if loaded:
      return bace_tasks, all_dataset, transformers

  if featurizer == 'ECFP':
    featurizer = deepchem.feat.CircularFingerprint(size=1024)
  elif featurizer == 'GraphConv':
    featurizer = deepchem.feat.ConvMolFeaturizer()
  elif featurizer == 'Weave':
    featurizer = deepchem.feat.WeaveFeaturizer()
  elif featurizer == 'Raw':
    featurizer = deepchem.feat.RawFeaturizer()
  elif featurizer == 'UserDefined':
    featurizer = deepchem.feat.UserDefinedFeaturizer(
        bace_user_specified_features)

  loader = deepchem.data.CSVLoader(
      tasks=bace_tasks, smiles_field="mol", featurizer=featurizer)

  dataset = loader.featurize(dataset_file, shard_size=8192)
  if split is None:
    # Initialize transformers

dataset_file = os.path.join(data_dir, "HIV.csv")
  if not os.path.exists(dataset_file):
    deepchem.utils.download_url(
        'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/HIV.csv'
    )

  hiv_tasks = ["HIV_active"]

  if reload:
    loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
        save_dir)
    if loaded:
      return hiv_tasks, all_dataset, transformers

  if featurizer == 'ECFP':
    featurizer = deepchem.feat.CircularFingerprint(size=1024)
  elif featurizer == 'GraphConv':
    featurizer = deepchem.feat.ConvMolFeaturizer()
  elif featurizer == 'Weave':
    featurizer = deepchem.feat.WeaveFeaturizer()
  elif featurizer == 'Raw':
    featurizer = deepchem.feat.RawFeaturizer()
  elif featurizer == "smiles2img":
    img_spec = kwargs.get("img_spec", "std")
    featurizer = deepchem.feat.SmilesToImage(img_spec=img_spec)

  loader = deepchem.data.CSVLoader(
      tasks=hiv_tasks, smiles_field="smiles", featurizer=featurizer)
  dataset = loader.featurize(dataset_file, shard_size=8192)

  if split is None:
    transformers = [

def load_muv_ecfp():
  """Load MUV datasets. Does not do train/test split"""
  # Load MUV dataset
  print("About to load MUV dataset.")
  current_dir = os.path.dirname(os.path.realpath(__file__))
  dataset_file = os.path.join(
      current_dir, "../../datasets/muv.csv.gz")
  # Featurize MUV dataset
  print("About to featurize MUV dataset.")
  featurizer = dc.feat.CircularFingerprint(size=1024)
  MUV_tasks = sorted(['MUV-692', 'MUV-689', 'MUV-846', 'MUV-859', 'MUV-644',
                      'MUV-548', 'MUV-852', 'MUV-600', 'MUV-810', 'MUV-712',
                      'MUV-737', 'MUV-858', 'MUV-713', 'MUV-733', 'MUV-652',
                      'MUV-466', 'MUV-832'])

  loader = dc.data.CSVLoader(
      tasks=MUV_tasks, smiles_field="smiles", featurizer=featurizer)
  dataset = loader.featurize(dataset_file)

  # Initialize transformers 
  transformers = [
      dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)]
  print("About to transform data")
  for transformer in transformers:
      dataset = transformer.transform(dataset)