cleanup

logger = logging.getLogger(__name__)

def hash_ecfp(ecfp, size):

  """

  Returns an int < size representing given ECFP fragment.

  ecfp_hash = int(digest, 16) % (size)

  return (ecfp_hash)

def hash_ecfp_pair(ecfp_pair, size):

  """Returns an int < size representing that ECFP pair.

  ecfp_hash = int(digest, 16) % (size)

  return (ecfp_hash)

def vectorize(hash_function,

              feature_dict=None,

              size=1024):

def vectorize(hash_function, feature_dict=None, size=1024):

  """Helper function to vectorize a spatial description from a hash.

  Hash functions are used to perform spatial featurizations in

  feature_vector = np.zeros(size)

  if feature_dict is not None:

    on_channels = [

        hash_function(feature, size)

        for key, feature in feature_dict.items()

        hash_function(feature, size) for key, feature in feature_dict.items()

    ]

    feature_vector[on_channels] += 1

    featurizer='smiles2img', split='index', img_spec="engd")

train_dataset, valid_dataset, test_dataset = delaney_datasets

<<<<<<< HEAD

# Get Metric

=======

# Get Metric 

>>>>>>> Model restore example

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

model = dc.models.ChemCeption(