yapf

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

import numpy as np

from deepchem.utils import hash_utils

def random_string(length, chars=None):

  import string

  if chars is None:

    chars = list(string.ascii_letters + string.ascii_letters + '()[]+-.=#@/\\')

  return ''.join(np.random.choice(chars, length))

class TestHashUtils(unittest.TestCase):

  def test_hash_ecfp(self):

    for power in (2, 16, 64):

      for _ in range(10):