removed print and deprecated functions

      reader = csv.reader(f, delimiter=delimiter)

      return [row for row in reader]

  elif input_type == "pandas":

    print input_file

    with gzip.open(input_file) as f:

      df = pickle.load(f)

    return df.iterrows()

              entry["feature_types"].append(feature_type)

  return molecules 

'''

def load_pdbbind_molecules(paths, dir_name="fingerprints"):

  """Load dataset fingerprints and return fingerprints.

  """

  # TODO(rbharath): This is a total kludge. Clean up later.

  dir_name = "targets"

  molecules = {}

  for dataset_path in paths:

    pickle_dir = os.path.join(dataset_path, dir_name)

    pickle_files = os.listdir(pickle_dir)

    if len(pickle_files) == 0:

      raise ValueError("No Pickle Files found to load molecules")

    for pickle_file in pickle_files:

      with gzip.open(os.path.join(pickle_dir, pickle_file), "rb") as f:

        contents = pickle.load(f)

        smiles, fingerprints, scaffolds, mol_ids = (

            contents["smiles"], contents["features"],

            None, None)

        for mol in range(len(contents["smiles"])):

          molecules[smiles[mol]] = {"fingerprint": fingerprints[mol],

                                    "scaffold": None,

                                    "mol_id": None}

  return molecules 

'''

def get_target_names(paths, target_dir_name="targets"):

  """Get names of targets in provided collections.

  else:

    raise ValueError("Unsupported datatype.")

'''

def load_pdbbind_datasets(paths, prediction_endpoint, target_dir_name="targets",

    feature_types=["grid"]):

  """Load pdbbind datasets.

  TODO(rbharath): This uses smiles as unique identifier. FIX BEFORE RELEASE!

  Parameters

  ----------

  pdbbind_path: list 

    List of Pdbbind data files.

  """

  data = {}

  if feature_types != ["grid"]:

    raise ValueError("Only grid features are supported for PDB-Bind data.")

  molecules = load_pdbbind_molecules(paths)

  labels, _ = load_assays(paths, prediction_endpoint, target_dir_name)

  # TODO(rbharath): Why are there fewer descriptors than labels at times?

  # What accounts for the descrepency. Please investigate.

  for ind, smiles in enumerate(molecules):

    if smiles not in labels:

      continue

    mol = molecules[smiles]

    data[ind] = {"fingerprint": mol["fingerprint"],

                 "scaffold": mol["scaffold"],

                 "labels": labels[smiles]}

  return data

'''

def load_vs_datasets(paths, prediction_endpoint, split_endpoint, target_dir_name="targets",

    feature_types=["fingerprints"]):