Another attempt at getting metric to work

  return y

def _ensure_output_shape(y, expected_dims):

  """Ensure an output array has the right number of dimensions."""

  dims = len(y.shape)

  if dims < expected_dims:

    if all(i == 1 for i in y.shape[dims:]):

      y = y.reshape(list(y.shape) + [1] * (expected_dims - dims))

  if dims > expected_dims:

    if all(i == 1 for i in y.shape[expected_dims:]):

      y = y.reshape(y.shape[:expected_dims])

  return y

def roc_auc_score(y, y_pred):

  """Area under the receiver operating characteristic curve."""

  if y.shape != y_pred.shape:

    expected_dims = (3 if self.mode == "classification" else 2)

    if len(y_pred.shape) < expected_dims:

      n_tasks = 1

      y_true = np.expand_dims(y_true, 1)

      y_pred = np.expand_dims(y_pred, 1)

    else:

      n_tasks = y_pred.shape[1]

    y_true = _ensure_output_shape(y_true, expected_dims)

    y_pred = _ensure_output_shape(y_pred, expected_dims)

    if w is None or len(w) == 0:

      w = np.ones((n_samples, n_tasks))

    computed_metrics = []

                                                      protein_files)

  # Delete labels for failing elements

  labels = np.delete(labels, failures)

  labels = labels.reshape((len(labels), 1))

  dataset = deepchem.data.DiskDataset.from_numpy(features, labels)

  print('Featurization complete.')

  # No transformations of data