Loading deepchem/dock/docking.py +1 −1 Original line number Diff line number Diff line Loading @@ -109,7 +109,7 @@ class Docker(object): if self.scoring_model is not None: for posed_complex in complexes: # TODO: How to handle the failure here? features, _ = self.featurizer.featurize_complexes([molecular_complex]) features, _ = self.featurizer.featurize([molecular_complex]) dataset = NumpyDataset(X=features) score = self.scoring_model.predict(dataset) yield (posed_complex, score) Loading deepchem/dock/tests/test_docking.py +1 −1 Original line number Diff line number Diff line Loading @@ -105,7 +105,7 @@ class TestDocking(unittest.TestCase): class DummyFeaturizer(ComplexFeaturizer): def featurize_complexes(self, complexes, *args, **kwargs): def featurize(self, complexes, *args, **kwargs): return np.zeros((len(complexes), 5)), None class DummyModel(Model): Loading deepchem/feat/__init__.py +1 −1 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ from deepchem.feat.base_classes import UserDefinedFeaturizer from deepchem.feat.graph_features import ConvMolFeaturizer from deepchem.feat.graph_features import WeaveFeaturizer 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 CoulombMatrixEig from deepchem.feat.coulomb_matrices import BPSymmetryFunctionInput Loading deepchem/feat/atomic_coordinates.py +2 −3 Original line number Diff line number Diff line Loading @@ -3,7 +3,6 @@ Atomic coordinate featurizer. """ import logging import numpy as np from deepchem.utils.save import log from deepchem.feat import Featurizer from deepchem.feat import ComplexFeaturizer from deepchem.utils import rdkit_util, pad_array Loading Loading @@ -162,7 +161,7 @@ class NeighborListComplexAtomicCoordinates(ComplexFeaturizer): self.dtype = object 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. Loading Loading @@ -218,7 +217,7 @@ class ComplexNeighborListFragmentAtomicCoordinates(ComplexFeaturizer): self.neighborlist_featurizer = NeighborListComplexAtomicCoordinates( 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: frag1_coords, frag1_mol = rdkit_util.load_molecule( mol_pdb_file, is_protein=False, sanitize=True, add_hydrogens=False) Loading deepchem/feat/base_classes.py +38 −85 Original line number Diff line number Diff line Loading @@ -12,11 +12,6 @@ logger = logging.getLogger(__name__) 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): """Abstract class for calculating a set of features for a datapoint. Loading Loading @@ -63,18 +58,45 @@ class Featurizer(object): Parameters ---------- datapoints: object Any blob of data you like. Subclasss should instantiate this. Any blob of data you like. Subclasss should instantiate this. """ 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. """ def featurize_complexes(self, mol_files, protein_pdbs): def featurize(self, mol_files, protein_pdbs): """ Calculate features for mol/protein complexes. Loading @@ -92,12 +114,13 @@ class ComplexFeaturizer(object): failures: list Indices of complexes that failed to featurize. """ pool = multiprocessing.Pool() results = [] for i, (mol_file, protein_pdb) in enumerate(zip(mol_files, protein_pdbs)): log_message = "Featurizing %d / %d" % (i, len(mol_files)) results.append( pool.apply_async(_featurize_complex, pool.apply_async(_featurize_callback, (self, mol_file, protein_pdb, log_message))) pool.close() features = [] Loading @@ -112,7 +135,7 @@ class ComplexFeaturizer(object): features = np.asarray(features) 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. Loading Loading @@ -187,28 +210,6 @@ class MolecularFeaturizer(Featurizer): features = np.asarray(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): """ Loading Loading @@ -282,30 +283,6 @@ class StructureFeaturizer(Featurizer): features = np.asarray(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): """ Loading Loading @@ -377,30 +354,6 @@ class CompositionFeaturizer(Featurizer): features = np.asarray(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): """Directs usage of user-computed featurizations.""" Loading Loading
deepchem/dock/docking.py +1 −1 Original line number Diff line number Diff line Loading @@ -109,7 +109,7 @@ class Docker(object): if self.scoring_model is not None: for posed_complex in complexes: # TODO: How to handle the failure here? features, _ = self.featurizer.featurize_complexes([molecular_complex]) features, _ = self.featurizer.featurize([molecular_complex]) dataset = NumpyDataset(X=features) score = self.scoring_model.predict(dataset) yield (posed_complex, score) Loading
deepchem/dock/tests/test_docking.py +1 −1 Original line number Diff line number Diff line Loading @@ -105,7 +105,7 @@ class TestDocking(unittest.TestCase): class DummyFeaturizer(ComplexFeaturizer): def featurize_complexes(self, complexes, *args, **kwargs): def featurize(self, complexes, *args, **kwargs): return np.zeros((len(complexes), 5)), None class DummyModel(Model): Loading
deepchem/feat/__init__.py +1 −1 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ from deepchem.feat.base_classes import UserDefinedFeaturizer from deepchem.feat.graph_features import ConvMolFeaturizer from deepchem.feat.graph_features import WeaveFeaturizer 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 CoulombMatrixEig from deepchem.feat.coulomb_matrices import BPSymmetryFunctionInput Loading
deepchem/feat/atomic_coordinates.py +2 −3 Original line number Diff line number Diff line Loading @@ -3,7 +3,6 @@ Atomic coordinate featurizer. """ import logging import numpy as np from deepchem.utils.save import log from deepchem.feat import Featurizer from deepchem.feat import ComplexFeaturizer from deepchem.utils import rdkit_util, pad_array Loading Loading @@ -162,7 +161,7 @@ class NeighborListComplexAtomicCoordinates(ComplexFeaturizer): self.dtype = object 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. Loading Loading @@ -218,7 +217,7 @@ class ComplexNeighborListFragmentAtomicCoordinates(ComplexFeaturizer): self.neighborlist_featurizer = NeighborListComplexAtomicCoordinates( 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: frag1_coords, frag1_mol = rdkit_util.load_molecule( mol_pdb_file, is_protein=False, sanitize=True, add_hydrogens=False) Loading
deepchem/feat/base_classes.py +38 −85 Original line number Diff line number Diff line Loading @@ -12,11 +12,6 @@ logger = logging.getLogger(__name__) 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): """Abstract class for calculating a set of features for a datapoint. Loading Loading @@ -63,18 +58,45 @@ class Featurizer(object): Parameters ---------- datapoints: object Any blob of data you like. Subclasss should instantiate this. Any blob of data you like. Subclasss should instantiate this. """ 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. """ def featurize_complexes(self, mol_files, protein_pdbs): def featurize(self, mol_files, protein_pdbs): """ Calculate features for mol/protein complexes. Loading @@ -92,12 +114,13 @@ class ComplexFeaturizer(object): failures: list Indices of complexes that failed to featurize. """ pool = multiprocessing.Pool() results = [] for i, (mol_file, protein_pdb) in enumerate(zip(mol_files, protein_pdbs)): log_message = "Featurizing %d / %d" % (i, len(mol_files)) results.append( pool.apply_async(_featurize_complex, pool.apply_async(_featurize_callback, (self, mol_file, protein_pdb, log_message))) pool.close() features = [] Loading @@ -112,7 +135,7 @@ class ComplexFeaturizer(object): features = np.asarray(features) 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. Loading Loading @@ -187,28 +210,6 @@ class MolecularFeaturizer(Featurizer): features = np.asarray(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): """ Loading Loading @@ -282,30 +283,6 @@ class StructureFeaturizer(Featurizer): features = np.asarray(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): """ Loading Loading @@ -377,30 +354,6 @@ class CompositionFeaturizer(Featurizer): features = np.asarray(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): """Directs usage of user-computed featurizations.""" Loading
