Merge pull request #2017 from nd-02110114/fix-feat (4eefa98d) · Commits · 钟慕尧 / deepchem

deepchem/dock/docking.py

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -109,7 +109,7 @@ class Docker(object):
	if self.scoring_model is not None:		if self.scoring_model is not None:
	for posed_complex in complexes:		for posed_complex in complexes:
	# TODO: How to handle the failure here?		# TODO: How to handle the failure here?
	features, _ = self.featurizer.featurize_complexes([molecular_complex])		features, _ = self.featurizer.featurize([molecular_complex])
	dataset = NumpyDataset(X=features)		dataset = NumpyDataset(X=features)
	score = self.scoring_model.predict(dataset)		score = self.scoring_model.predict(dataset)
	yield (posed_complex, score)		yield (posed_complex, score)

deepchem/dock/tests/test_docking.py

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -105,7 +105,7 @@ class TestDocking(unittest.TestCase):

	class DummyFeaturizer(ComplexFeaturizer):		class DummyFeaturizer(ComplexFeaturizer):

	def featurize_complexes(self, complexes, args, *kwargs):		def featurize(self, complexes, args, *kwargs):
	return np.zeros((len(complexes), 5)), None		return np.zeros((len(complexes), 5)), None

	class DummyModel(Model):		class DummyModel(Model):

deepchem/feat/init.py

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -10,7 +10,7 @@ from deepchem.feat.base_classes import UserDefinedFeaturizer
	from deepchem.feat.graph_features import ConvMolFeaturizer		from deepchem.feat.graph_features import ConvMolFeaturizer
	from deepchem.feat.graph_features import WeaveFeaturizer		from deepchem.feat.graph_features import WeaveFeaturizer
	from deepchem.feat.fingerprints import CircularFingerprint		from deepchem.feat.fingerprints import CircularFingerprint
	from deepchem.feat.basic import RDKitDescriptors		from deepchem.feat.rdkit_descriptors import RDKitDescriptors
	from deepchem.feat.coulomb_matrices import CoulombMatrix		from deepchem.feat.coulomb_matrices import CoulombMatrix
	from deepchem.feat.coulomb_matrices import CoulombMatrixEig		from deepchem.feat.coulomb_matrices import CoulombMatrixEig
	from deepchem.feat.coulomb_matrices import BPSymmetryFunctionInput		from deepchem.feat.coulomb_matrices import BPSymmetryFunctionInput

deepchem/feat/atomic_coordinates.py

+2 −3

Original line number	Original line	Diff line number	Diff line
	@@ -3,7 +3,6 @@ Atomic coordinate featurizer.
	"""		"""
	import logging		import logging
	import numpy as np		import numpy as np
	from deepchem.utils.save import log
	from deepchem.feat import Featurizer		from deepchem.feat import Featurizer
	from deepchem.feat import ComplexFeaturizer		from deepchem.feat import ComplexFeaturizer
	from deepchem.utils import rdkit_util, pad_array		from deepchem.utils import rdkit_util, pad_array
	@@ -162,7 +161,7 @@ class NeighborListComplexAtomicCoordinates(ComplexFeaturizer):
	self.dtype = object		self.dtype = object
	self.coordinates_featurizer = AtomicCoordinates()		self.coordinates_featurizer = AtomicCoordinates()

	def _featurize_complex(self, mol_pdb_file, protein_pdb_file):		def _featurize(self, mol_pdb_file, protein_pdb_file):
	"""		"""
	Compute neighbor list for complex.		Compute neighbor list for complex.

	@@ -218,7 +217,7 @@ class ComplexNeighborListFragmentAtomicCoordinates(ComplexFeaturizer):
	self.neighborlist_featurizer = NeighborListComplexAtomicCoordinates(		self.neighborlist_featurizer = NeighborListComplexAtomicCoordinates(
	self.max_num_neighbors, self.neighbor_cutoff)		self.max_num_neighbors, self.neighbor_cutoff)

	def _featurize_complex(self, mol_pdb_file, protein_pdb_file):		def _featurize(self, mol_pdb_file, protein_pdb_file):
	try:		try:
	frag1_coords, frag1_mol = rdkit_util.load_molecule(		frag1_coords, frag1_mol = rdkit_util.load_molecule(
	mol_pdb_file, is_protein=False, sanitize=True, add_hydrogens=False)		mol_pdb_file, is_protein=False, sanitize=True, add_hydrogens=False)

deepchem/feat/base_classes.py

+38 −85

Original line number	Original line	Diff line number	Diff line
	@@ -12,11 +12,6 @@ logger = logging.getLogger(__name__)
	JSON = Dict[str, Any]		JSON = Dict[str, Any]


	def _featurize_complex(featurizer, mol_pdb_file, protein_pdb_file, log_message):
	logging.info(log_message)
	return featurizer._featurize_complex(mol_pdb_file, protein_pdb_file)


	class Featurizer(object):		class Featurizer(object):
	"""Abstract class for calculating a set of features for a datapoint.		"""Abstract class for calculating a set of features for a datapoint.

	@@ -63,18 +58,45 @@ class Featurizer(object):
	Parameters		Parameters
	----------		----------
	datapoints: object		datapoints: object
	Any blob of data you like. Subclasss should instantiate		Any blob of data you like. Subclasss should instantiate this.
	this.
	"""		"""
	return self.featurize(datapoints)		return self.featurize(datapoints)

			def _featurize(self, datapoint):
			"""Calculate features for a single datapoint.

			Parameters
			----------
			datapoint: object
			a single datapoint in a sequence of objects
			"""
			raise NotImplementedError('Featurizer is not defined.')


			def _featurize_callback(
			featurizer,
			mol_pdb_file,
			protein_pdb_file,
			log_message,
			):
			"""Callback function for apply_async in ComplexFeaturizer.

			This callback function must be defined globally
			because `apply_async` doesn't execute a nested function.

			See the details from the following link.
			https://stackoverflow.com/questions/56533827/pool-apply-async-nested-function-is-not-executed
			"""
			logging.info(log_message)
			return featurizer._featurize(mol_pdb_file, protein_pdb_file)

	class ComplexFeaturizer(object):
			class ComplexFeaturizer(Featurizer):
	""""		""""
	Abstract class for calculating features for mol/protein complexes.		Abstract class for calculating features for mol/protein complexes.
	"""		"""

	def featurize_complexes(self, mol_files, protein_pdbs):		def featurize(self, mol_files, protein_pdbs):
	"""		"""
	Calculate features for mol/protein complexes.		Calculate features for mol/protein complexes.

	@@ -92,12 +114,13 @@ class ComplexFeaturizer(object):
	failures: list		failures: list
	Indices of complexes that failed to featurize.		Indices of complexes that failed to featurize.
	"""		"""

	pool = multiprocessing.Pool()		pool = multiprocessing.Pool()
	results = []		results = []
	for i, (mol_file, protein_pdb) in enumerate(zip(mol_files, protein_pdbs)):		for i, (mol_file, protein_pdb) in enumerate(zip(mol_files, protein_pdbs)):
	log_message = "Featurizing %d / %d" % (i, len(mol_files))		log_message = "Featurizing %d / %d" % (i, len(mol_files))
	results.append(		results.append(
	pool.apply_async(_featurize_complex,		pool.apply_async(_featurize_callback,
	(self, mol_file, protein_pdb, log_message)))		(self, mol_file, protein_pdb, log_message)))
	pool.close()		pool.close()
	features = []		features = []
	@@ -112,7 +135,7 @@ class ComplexFeaturizer(object):
	features = np.asarray(features)		features = np.asarray(features)
	return features, failures		return features, failures

	def _featurize_complex(self, mol_pdb, complex_pdb):		def _featurize(self, mol_pdb, complex_pdb):
	"""		"""
	Calculate features for single mol/protein complex.		Calculate features for single mol/protein complex.

	@@ -187,28 +210,6 @@ class MolecularFeaturizer(Featurizer):
	features = np.asarray(features)		features = np.asarray(features)
	return features		return features

	def _featurize(self, mol):
	"""
	Calculate features for a single molecule.

	Parameters
	----------
	mol : RDKit Mol
	Molecule.
	"""
	raise NotImplementedError('Featurizer is not defined.')

	def __call__(self, molecules):
	"""
	Calculate features for molecules.

	Parameters
	----------
	molecules: iterable
	An iterable yielding RDKit Mol objects or SMILES strings.
	"""
	return self.featurize(molecules)


	class StructureFeaturizer(Featurizer):		class StructureFeaturizer(Featurizer):
	"""		"""
	@@ -282,30 +283,6 @@ class StructureFeaturizer(Featurizer):
	features = np.asarray(features)		features = np.asarray(features)
	return features		return features

	def _featurize(self, structure: "pymatgen.Structure"):
	"""Calculate features for a single crystal structure.

	Parameters
	----------
	structure: pymatgen.Structure object
	Structure object with 3D coordinates and periodic lattice.

	"""

	raise NotImplementedError('Featurizer is not defined.')

	def __call__(self, structures: Iterable[dict]):
	"""Calculate features for crystal structures.

	Parameters
	----------
	structures: Iterable[dict]
	An iterable of crystal structure dictionaries.

	"""

	return self.featurize(structures)


	class CompositionFeaturizer(Featurizer):		class CompositionFeaturizer(Featurizer):
	"""		"""
	@@ -377,30 +354,6 @@ class CompositionFeaturizer(Featurizer):
	features = np.asarray(features)		features = np.asarray(features)
	return features		return features

	def _featurize(self, composition: "pymatgen.Composition"):
	"""Calculate features for a single crystal composition.

	Parameters
	----------
	composition: pymatgen.Composition object
	Composition object for 3D inorganic crystal.

	"""

	raise NotImplementedError('Featurizer is not defined.')

	def __call__(self, compositions: Iterable[str]):
	"""Calculate features for crystal compositions.

	Parameters
	----------
	compositions: Iterable[str]
	An iterable of crystal compositions.

	"""

	return self.featurize(compositions)


	class UserDefinedFeaturizer(Featurizer):		class UserDefinedFeaturizer(Featurizer):
	"""Directs usage of user-computed featurizations."""		"""Directs usage of user-computed featurizations."""

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