handle complex feat failures (d6606e8d) · Commits · 钟慕尧 / deepchem

deepchem/feat/base_classes.py

+17 −6

Original line number	Diff line number	Diff line
		@@ -174,16 +174,27 @@ class ComplexFeaturizer(Featurizer):

		if not isinstance(complexes, Iterable):
		complexes = [cast(Tuple[str, str], complexes)]
		features = []
		for i, point in enumerate(complexes):
		if i % log_every_n == 0:
		logger.info("Featurizing datapoint %i" % i)
		features, failures = [], []
		for idx, point in enumerate(complexes):
		if idx % log_every_n == 0:
		logger.info("Featurizing datapoint %i" % idx)
		try:
		features.append(self._featurize(point))
		except:
		logger.warning(
		"Failed to featurize datapoint %i. Appending empty array." % i)
		features.append(np.array([]))
		"Failed to featurize datapoint %i. Appending empty array." % idx)
		features.append(np.zeros(1))
		failures.append(idx)

		# Find a successful featurization
		i = np.argmax([f.shape[0] for f in features])
		dtype = features[i].dtype
		shape = features[i].shape
		dummy_array = np.zeros(shape, dtype=dtype)

		# Replace failed featurizations with appropriate array
		for idx in failures:
		features[idx] = dummy_array

		return np.asarray(features)

deepchem/feat/complex_featurizers/rdkit_grid_featurizer.py

+1 −3

Original line number	Diff line number	Diff line
		@@ -449,7 +449,5 @@ class RdkitGridFeaturizer(ComplexFeaturizer):
		else:
		features_dict[system_id] = np.concatenate(feature_arrays, axis=-1)

		features = np.array(list(features_dict.values()))
		if self.nb_rotations == 0: # squeeze out axis with dimension 1
		features = np.squeeze(features, axis=0)
		features = np.concatenate(list(features_dict.values()))
		return features

deepchem/feat/tests/test_rdkit_grid_features.py

+69 −6

Original line number	Diff line number	Diff line
		@@ -36,7 +36,8 @@ class TestRdkitGridFeaturizer(unittest.TestCase):
		def test_example_featurizer(self):
		# check if use-case from examples works
		featurizer = RdkitGridFeaturizer(
		voxel_width=16.0,
		voxel_width=1.0,
		box_width=75.0,
		feature_types=['ecfp', 'splif', 'hbond', 'salt_bridge'],
		ecfp_power=9,
		splif_power=9,
		@@ -85,6 +86,7 @@ class TestRdkitGridFeaturizer(unittest.TestCase):
		# test flat features
		featurizer = RdkitGridFeaturizer(
		voxel_width=1.0,
		box_width=75.0,
		feature_types=['flat_combined'],
		ecfp_power=ecfp_power,
		splif_power=splif_power,
		@@ -100,7 +102,8 @@ class TestRdkitGridFeaturizer(unittest.TestCase):

		# check if aromatic features are ignored if sanitize=False
		featurizer = RdkitGridFeaturizer(
		voxel_width=16.0,
		voxel_width=1.0,
		box_width=75.0,
		feature_types=['all_combined'],
		ecfp_power=ecfp_power,
		splif_power=splif_power,
		@@ -112,8 +115,7 @@ class TestRdkitGridFeaturizer(unittest.TestCase):
		feature_tensor = featurizer.featurize([(self.ligand_file,
		self.protein_file)])
		self.assertIsInstance(feature_tensor, np.ndarray)
		total_len = voxel_total_len + flat_total_len - 3 - 2**ecfp_power
		self.assertEqual(feature_tensor.shape, (1, total_len))
		self.assertEqual(feature_tensor.shape, (1, 56109538))

		def test_custom_cutoffs(self):
		custom_cutoffs = {
		@@ -134,11 +136,72 @@ class TestRdkitGridFeaturizer(unittest.TestCase):
		def test_rotations(self):
		featurizer = RdkitGridFeaturizer(
		nb_rotations=3,
		box_width=16.,
		box_width=75.,
		voxel_width=1.,
		feature_types=['voxel_combined'],
		flatten=False,
		sanitize=True)
		feature_tensors = featurizer.featurize([(self.ligand_file,
		self.protein_file)])
		self.assertEqual(feature_tensors.shape, (1, 4, 16, 16, 16, 40))
		self.assertEqual(feature_tensors.shape, (1, 300, 75, 75, 40))

		featurizer = RdkitGridFeaturizer(
		nb_rotations=3,
		box_width=75.,
		voxel_width=1.,
		feature_types=['flat_combined'],
		flatten=True,
		sanitize=True)
		feature_tensors = featurizer.featurize([(self.ligand_file,
		self.protein_file)])
		self.assertEqual(feature_tensors.shape, (1, 204))

		def test_failures(self):
		# test flattened voxel features
		featurizer = RdkitGridFeaturizer(
		nb_rotations=0,
		box_width=75.,
		voxel_width=1.,
		feature_types=['voxel_combined'],
		flatten=True,
		sanitize=True)

		features = featurizer.featurize([(self.ligand_file, self.protein_file),
		('nan', 'nan')])
		self.assertEqual(features.shape, (2, 16875000))

		# test voxel features
		featurizer = RdkitGridFeaturizer(
		nb_rotations=0,
		box_width=75.,
		voxel_width=1.,
		feature_types=['voxel_combined'],
		flatten=False,
		sanitize=True)
		features = featurizer.featurize([(self.ligand_file, self.protein_file),
		('nan', 'nan')])
		self.assertEqual(features.shape, (2, 75, 75, 75, 40))

		# test flat features
		featurizer = RdkitGridFeaturizer(
		nb_rotations=0,
		box_width=75.,
		voxel_width=1.,
		feature_types=['flat_combined'],
		flatten=True,
		sanitize=True)
		features = featurizer.featurize([(self.ligand_file, self.protein_file),
		('nan', 'nan')])
		self.assertEqual(features.shape, (2, 51))

		# test rotations
		featurizer = RdkitGridFeaturizer(
		nb_rotations=5,
		box_width=75.,
		voxel_width=1.,
		feature_types=['flat_combined'],
		flatten=True,
		sanitize=True)
		features = featurizer.featurize([(self.ligand_file, self.protein_file),
		('nan', 'nan')])
		self.assertEqual(features.shape, (2, 306))

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