def test_make_mol_dict(self):
"""Test generation of molecule dictionaries."""
mols = parse_sdf_utils.get_sdf_to_mol(self.test_file_short)
mol_dicts = [
parse_sdf_utils.make_mol_dict(mol, self.hparams.max_atoms,
self.hparams.max_mass_spec_peak_loc)
for mol in mols
]
for i in range(len(self.expected_mol_dicts)):
mol_dict_key_names = [
fmap_constants.NAME, fmap_constants.INCHIKEY,
fmap_constants.SMILES, fmap_constants.MOLECULAR_FORMULA
]
for kwarg in mol_dict_key_names:
self.assertEqual(self.expected_mol_dicts[i][kwarg], mol_dicts[i][kwarg])
self.assertAlmostEqual(
self.expected_mol_dicts[i][fmap_constants.MOLECULE_WEIGHT],
mol_dicts[i][fmap_constants.MOLECULE_WEIGHT])
self.assertSequenceAlmostEqual(
self.expected_mol_dicts[i][fmap_constants.ATOM_WEIGHTS],
mol_dicts[i][fmap_constants.ATOM_WEIGHTS])
self.assertSequenceAlmostEqual(
self.expected_mol_dicts[i][fmap_constants.ADJACENCY_MATRIX],
mol_dicts[i][fmap_constants.ADJACENCY_MATRIX])
self.assertSequenceAlmostEqual(
self.expected_mol_dicts[i][fmap_constants.DENSE_MASS_SPEC],
mol_dicts[i][fmap_constants.DENSE_MASS_SPEC])
def test_record_contents(self):
"""Test the contents of the stored record file to ensure features match."""
mol_list = parse_sdf_utils.get_sdf_to_mol(self.test_file_long)
mol_dicts = [parse_sdf_utils.make_mol_dict(mol) for mol in mol_list]
parsed_smiles_tokens = [
feature_utils.tokenize_smiles(
np.array([mol_dict[fmap_constants.SMILES]]))
for mol_dict in mol_dicts
]
token_lengths = [
np.shape(token_arr)[0] for token_arr in parsed_smiles_tokens
]
parsed_smiles_tokens = [
np.pad(token_arr,
(0, ms_constants.MAX_TOKEN_LIST_LENGTH - token_length),
'constant')
for token_arr, token_length in zip(parsed_smiles_tokens, token_lengths)
]
hparams_main = tf.contrib.training.HParams(
max_atoms=ms_constants.MAX_ATOMS,
max_mass_spec_peak_loc=ms_constants.MAX_PEAK_LOC,
eval_batch_size=len(mol_list),
intensity_power=1.0)
dataset = parse_sdf_utils.get_dataset_from_record(
[os.path.join(self.test_data_directory, 'test_14_record.gz')],
hparams_main,
mode=tf.estimator.ModeKeys.EVAL)
feature_names = [
fmap_constants.ATOM_WEIGHTS,
fmap_constants.MOLECULE_WEIGHT,
fmap_constants.DENSE_MASS_SPEC,
fmap_constants.INCHIKEY, fmap_constants.NAME,
fmap_constants.MOLECULAR_FORMULA,
fmap_constants.ADJACENCY_MATRIX,
fmap_constants.ATOM_IDS, fmap_constants.SMILES
]
for fp_len in ms_constants.NUM_CIRCULAR_FP_BITS_LIST:
for rad in ms_constants.CIRCULAR_FP_RADII_LIST:
for fp_type in fmap_constants.FP_TYPE_LIST:
feature_names.append(
str(ms_constants.CircularFingerprintKey(fp_type, fp_len, rad)))
label_names = [fmap_constants.INCHIKEY]
features, _ = parse_sdf_utils.make_features_and_labels(
dataset, feature_names, label_names, mode=tf.estimator.ModeKeys.EVAL)
with tf.Session() as sess:
feature_values = sess.run(features)
# Check that the dataset was consumed
try:
sess.run(features)
raise ValueError('Dataset parsing using batch size of length of the'
' dataset resulted in more than one batch.')
except tf.errors.OutOfRangeError: # expected behavior
pass
for i in range(len(mol_list)):
self.assertAlmostEqual(
feature_values[fmap_constants.MOLECULE_WEIGHT][i],
mol_dicts[i][fmap_constants.MOLECULE_WEIGHT])
self.assertSequenceAlmostEqual(
feature_values[fmap_constants.ADJACENCY_MATRIX][i]
.flatten(),
mol_dicts[i][fmap_constants.ADJACENCY_MATRIX],
delta=0.0001)
self.assertEqual(feature_values[fmap_constants.NAME][i],
self.encode(mol_dicts[i][fmap_constants.NAME]))
self.assertEqual(feature_values[fmap_constants.INCHIKEY][i],
self.encode(mol_dicts[i][fmap_constants.INCHIKEY]))
self.assertEqual(
feature_values[fmap_constants.MOLECULAR_FORMULA][i],
self.encode(mol_dicts[i][fmap_constants.MOLECULAR_FORMULA]))
self.assertSequenceAlmostEqual(
feature_values[fmap_constants.DENSE_MASS_SPEC][i],
mol_dicts[i][fmap_constants.DENSE_MASS_SPEC],
delta=0.0001)
self.assertSequenceAlmostEqual(
feature_values[fmap_constants.ATOM_WEIGHTS][i],
mol_dicts[i][fmap_constants.ATOM_WEIGHTS],
delta=0.0001)
self.assertSequenceAlmostEqual(
feature_values[fmap_constants.ATOM_IDS][i],
mol_dicts[i][fmap_constants.ATOM_IDS],
delta=0.0001)
self.assertAllEqual(feature_values[fmap_constants.SMILES][i],
parsed_smiles_tokens[i])
self.assertAllEqual(
feature_values[fmap_constants.SMILES_TOKEN_LIST_LENGTH][i],
token_lengths[i])
for fp_len in ms_constants.NUM_CIRCULAR_FP_BITS_LIST:
for rad in ms_constants.CIRCULAR_FP_RADII_LIST:
for fp_type in fmap_constants.FP_TYPE_LIST:
fp_key = ms_constants.CircularFingerprintKey(fp_type, fp_len, rad)
self.assertSequenceAlmostEqual(
feature_values[str(fp_key)][i],
mol_dicts[i][fp_key],
delta=0.0001)