def get_input_features(self, mol):
"""get input features
Args:
mol (Mol):
Returns:
"""
type_check_num_atoms(mol, self.max_atoms)
atom_array = construct_atomic_number_array(mol, out_size=self.out_size)
return atom_array
def get_input_features(self, mol):
"""get input features for WeaveNet
WeaveNetPreprocessor automatically add `H` to `mol`
Args:
mol (Mol):
"""
type_check_num_atoms(mol, self.max_atoms)
# TODO(Nakago): support original paper feature extraction
# currently only embed id is supported.
atom_array = construct_atomic_number_array(mol, self.max_atoms)
pair_feature = construct_pair_feature(mol,
num_max_atoms=self.max_atoms)
return atom_array, pair_feature
def test_normal_truncated(self, sample_molecule):
with pytest.raises(ValueError):
adj = common.construct_atomic_number_array(sample_molecule, 3)
def test_padding(self, sample_molecule):
actual = common.construct_atomic_number_array(sample_molecule, 5)
assert actual.shape == (5, )
expect = numpy.array([6, 7, 6, 8, 0], dtype=numpy.int32)
numpy.testing.assert_equal(actual, expect)
