Loading deepchem/feat/base_classes.pydeleted 100644 → 0 +0 −132 Original line number Diff line number Diff line """ Feature calculations. """ import logging import types import numpy as np import multiprocessing __author__ = "Steven Kearnes" __copyright__ = "Copyright 2014, Stanford University" __license__ = "BSD 3-clause" 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 ComplexFeaturizer(object): """" Abstract class for calculating features for mol/protein complexes. """ def featurize_complexes(self, mol_files, protein_pdbs): """ Calculate features for mol/protein complexes. Parameters ---------- mols: list List of PDB filenames for molecules. protein_pdbs: list List of PDB filenames for proteins. Returns ------- features: np.array Array of features 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, (self, mol_file, protein_pdb, log_message))) pool.close() features = [] failures = [] for ind, result in enumerate(results): new_features = result.get() # Handle loading failures which return None if new_features is not None: features.append(new_features) else: failures.append(ind) features = np.asarray(features) return features, failures def _featurize_complex(self, mol_pdb, complex_pdb): """ Calculate features for single mol/protein complex. Parameters ---------- mol_pdb: list Should be a list of lines of the PDB file. complex_pdb: list Should be a list of lines of the PDB file. """ raise NotImplementedError('Featurizer is not defined.') class Featurizer(object): """ Abstract class for calculating a set of features for a molecule. Child classes implement the _featurize method for calculating features for a single molecule. """ def featurize(self, mols, verbose=True, log_every_n=1000): """ Calculate features for molecules. Parameters ---------- mols : iterable RDKit Mol objects. """ mols = list(mols) features = [] for i, mol in enumerate(mols): if mol is not None: features.append(self._featurize(mol)) else: features.append(np.array([])) 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, mols): """ Calculate features for molecules. Parameters ---------- mols : iterable RDKit Mol objects. """ return self.featurize(mols) class UserDefinedFeaturizer(Featurizer): """Directs usage of user-computed featurizations.""" def __init__(self, feature_fields): """Creates user-defined-featurizer.""" self.feature_fields = feature_fields Loading
deepchem/feat/base_classes.pydeleted 100644 → 0 +0 −132 Original line number Diff line number Diff line """ Feature calculations. """ import logging import types import numpy as np import multiprocessing __author__ = "Steven Kearnes" __copyright__ = "Copyright 2014, Stanford University" __license__ = "BSD 3-clause" 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 ComplexFeaturizer(object): """" Abstract class for calculating features for mol/protein complexes. """ def featurize_complexes(self, mol_files, protein_pdbs): """ Calculate features for mol/protein complexes. Parameters ---------- mols: list List of PDB filenames for molecules. protein_pdbs: list List of PDB filenames for proteins. Returns ------- features: np.array Array of features 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, (self, mol_file, protein_pdb, log_message))) pool.close() features = [] failures = [] for ind, result in enumerate(results): new_features = result.get() # Handle loading failures which return None if new_features is not None: features.append(new_features) else: failures.append(ind) features = np.asarray(features) return features, failures def _featurize_complex(self, mol_pdb, complex_pdb): """ Calculate features for single mol/protein complex. Parameters ---------- mol_pdb: list Should be a list of lines of the PDB file. complex_pdb: list Should be a list of lines of the PDB file. """ raise NotImplementedError('Featurizer is not defined.') class Featurizer(object): """ Abstract class for calculating a set of features for a molecule. Child classes implement the _featurize method for calculating features for a single molecule. """ def featurize(self, mols, verbose=True, log_every_n=1000): """ Calculate features for molecules. Parameters ---------- mols : iterable RDKit Mol objects. """ mols = list(mols) features = [] for i, mol in enumerate(mols): if mol is not None: features.append(self._featurize(mol)) else: features.append(np.array([])) 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, mols): """ Calculate features for molecules. Parameters ---------- mols : iterable RDKit Mol objects. """ return self.featurize(mols) class UserDefinedFeaturizer(Featurizer): """Directs usage of user-computed featurizations.""" def __init__(self, feature_fields): """Creates user-defined-featurizer.""" self.feature_fields = feature_fields
