Cleanup a bit

  return np.squeeze(np.array(features), axis=1), valid_inds

#def _featurize_smiles_np(arr, featurizer, log_every_N=1000):

#  """Featurize individual compounds in a numpy array.

#

#  Given a featurizer that operates on individual chemical compounds

#  or macromolecules, compute & add features for that compound to the

#  features array

#  """

#  features = []

#  from rdkit import Chem

#  from rdkit.Chem import rdmolfiles

#  from rdkit.Chem import rdmolops

#  for ind, elem in enumerate(arr.tolist()):

#    mol = Chem.MolFromSmiles(elem)

#    if mol:

#      new_order = rdmolfiles.CanonicalRankAtoms(mol)

#      mol = rdmolops.RenumberAtoms(mol, new_order)

#    if ind % log_every_N == 0:

#      logger.info("Featurizing sample %d" % ind)

#    features.append(featurizer.featurize([mol]))

#

#  valid_inds = np.array(

#      [1 if elt.size > 0 else 0 for elt in features], dtype=bool)

#  features = [elt for (is_valid, elt) in zip(valid_inds, features) if is_valid]

#  features = np.squeeze(np.array(features))

#  return features.reshape(-1,)

def _get_user_specified_features(df, featurizer):

  """Extract and merge user specified features.