Merged changes from master branch

]

intervals = get_intervals(reference_lists)

possible_bond_stereo = ["STEREONONE", "STEREOANY", "STEREOZ", "STEREOE"]

bond_fdim_base = 6

def get_feature_list(atom):

  ]

  if use_chirality:

    bond_feats = bond_feats + one_of_k_encoding_unk(

        str(bond.GetStereo()),

        ["STEREONONE", "STEREOANY", "STEREOZ", "STEREOE"])

        str(bond.GetStereo()), possible_bond_stereo)

  return np.array(bond_feats)

    self.explicit_H = explicit_H

    # If uses use_chirality

    self.use_chirality = use_chirality

    if self.use_chirality:

      self.bt_len = bond_fdim_base + len(possible_bond_stereo)

    else:

      self.bt_len = bond_fdim_base

  def _featurize(self, mol):

    """Encodes mol as a WeaveMol object."""

        mol,

        edge_list,

        canon_adj_list,

        bt_len=6,

        bt_len=self.bt_len,

        graph_distance=self.graph_distance)

    return WeaveMol(nodes, pairs)

  Parameters

  ----------

  model: Model

    This can be any model that accepts inputs of the required shape.

    This can be any model that accepts inputs of the required shape and produces

    an output of shape (N_sequences, N_tasks).

  X: ndarray

    Shape (N_sequences, N_letters, sequence_length, 1)

  def split(self,

            dataset,

            seed=None,

            frac_train=.8,

            frac_valid=.1,

            frac_test=.1,

    log("About to generate scaffolds", self.verbose)

    for ind, smiles in enumerate(dataset.ids):

      if ind % log_every_n == 0:

        log(f"Generating scaffold {ind} {data_len}", self.verbose)

        log("Generating scaffold %d/%d" % (ind, data_len), self.verbose)

      scaffold = generate_scaffold(smiles)

      if scaffold not in scaffolds:

        scaffolds[scaffold] = [ind]