Loading deepchem/feat/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -23,3 +23,4 @@ from deepchem.feat.atomic_coordinates import AtomicCoordinates from deepchem.feat.atomic_coordinates import NeighborListComplexAtomicCoordinates from deepchem.feat.adjacency_fingerprints import AdjacencyFingerprint from deepchem.feat.smiles_featurizers import SmilesToSeq, SmilesToImage from deepchem.feat.materials_featurizers import ChemicalFingerprint, SineCoulombMatrix, StructureGraphFeaturizer deepchem/feat/materials_featurizers.py 0 → 100644 +178 −0 Original line number Diff line number Diff line """ Featurizers for inorganic crystals. """ import numpy as np from deepchem.feat import Featurizer from deepchem.utils import pad_array from matminer.featurizers.composition import ElementProperty from pymatgen import Composition, Structure from pymatgen.analysis.graphs import StructureGraph from pymatgen.analysis.local_env import MinimumDistanceNN class ChemicalFingerprint(Featurizer): """ Chemical fingerprint of elemental properties from composition. Parameters ---------- data_source : str, optional (default "matminer") Source for element property data ("matminer", "magpie", "deml") """ def __init__(self, data_source='matminer'): self.data_source = data_source def _featurize(self, comp): """ Calculate chemical fingerprint from crystal composition. Parameters ---------- comp : Reduced formula of crystal. """ # Get pymatgen Composition object c = Composition(comp) ep = ElementProperty.from_preset(self.data_source) try: feats = ep.featurize(c) except: feats = [] return feats class SineCoulombMatrix(Featurizer): """ Calculate sine Coulomb matrix for crystals. Variant of Coulomb matrix for periodic crystals Faber et al. (Inter. J. Quantum Chem. 115, 16, 2015). Parameters ---------- max_atoms : int Maximum number of atoms for any crystal in the dataset. Used to pad the Coulomb matrix. eig : bool (default True) Return flattened vector of matrix eigenvalues """ def __init__(self, max_atoms, eig=True): self.max_atoms = int(max_atoms) self.eig = eig def _featurize(self, struct): """ Calculate sine Coulomb matrix from pymatgen structure. Parameters ---------- struct : pymatgen structure dictionary """ s = Structure.from_dict(struct) features = self.sine_coulomb_matrix(s) features = np.asarray(features) return features def sine_coulomb_matrix(self, s): """ Generate sine Coulomb matrices for each crystal. Parameters ---------- s : pymatgen structure """ sites = s.sites atomic_numbers = np.array([site.specie.Z for site in sites]) sine_mat = np.zeros((len(sites), len(sites))) coords = np.array([site.frac_coords for site in sites]) lattice = s.lattice.matrix # Conversion factor ang_to_bohr = 1.8897543760313331 for i in range(len(sine_mat)): for j in range(len(sine_mat)): if i == j: sine_mat[i][i] = 0.5 * atomic_numbers[i]**2.4 elif i < j: vec = coords[i] - coords[j] coord_vec = np.sin(np.pi * vec)**2 trig_dist = np.linalg.norm( (np.matrix(coord_vec) * lattice).A1) * ang_to_bohr sine_mat[i][j] = atomic_numbers[i] * atomic_numbers[j] / \ trig_dist else: sine_mat[i][j] = sine_mat[j][i] if self.eig: # flatten array to eigenvalues eigs, _ = np.linalg.eig(sine_mat) zeros = np.zeros((self.max_atoms,)) zeros[:len(eigs)] = eigs return zeros else: sine_mat = pad_array(sine_mat, self.max_atoms) return sine_mat class StructureGraphFeaturizer(Featurizer): """ Calculate structure graph for crystals. Parameters ---------- strategy : pymatgen.analysis.local_env.NearNeighbors An instance of NearNeighbors that determines how graph is constructed. #TODO (@ncfrey) process graph features for models """ def __init__(self, strategy=MinimumDistanceNN()): self.strategy = strategy def _featurize(self, struct): """ Calculate structure graph from pymatgen structure. Parameters ---------- struct : pymatgen structure dictionary. """ # Get pymatgen structure object s = Structure.from_dict(struct) features = self._get_structure_graph_features(s) return features def _get_structure_graph_features(self, struct): """ Calculate structure graph features from pymatgen structure. Parameters ---------- struct : pymatgen structure """ atom_features = np.array([site.specie.Z for site in struct], dtype='int32') sg = StructureGraph.with_local_env_strategy(struct, self.strategy) nodes = np.asarray(sg.graph.nodes) edges = np.asarray(sg.graph.edges) return (atom_features, nodes, edges) deepchem/feat/tests/test_materials_featurizers.py 0 → 100644 +66 −0 Original line number Diff line number Diff line """ Test featurizers for inorganic crystals. """ import numpy as np import unittest from deepchem.feat.materials_featurizers import ChemicalFingerprint, SineCoulombMatrix, StructureGraphFeaturizer class TestMaterialFeaturizers(unittest.TestCase): """ Test material featurizers. """ def setUp(self): """ Set up tests. """ self.formula = 'MoS2' self.struct_dict = { '@module': 'pymatgen.core.structure', '@class': 'Structure', 'charge': None, 'lattice': { 'matrix': [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], 'a': 1.0, 'b': 1.0, 'c': 1.0, 'alpha': 90.0, 'beta': 90.0, 'gamma': 90.0, 'volume': 1.0 }, 'sites': [{ 'species': [{ 'element': 'Fe', 'occu': 1 }], 'abc': [0.0, 0.0, 0.0], 'xyz': [0.0, 0.0, 0.0], 'label': 'Fe', 'properties': {} }] } def testCF(self): """ Test CF featurizer. """ featurizer = ChemicalFingerprint(data_source='matminer') assert isinstance(featurizer, ChemicalFingerprint) def testSCM(self): """ Test SCM featurizer. """ featurizer = SineCoulombMatrix(1) features = featurizer.featurize([self.struct_dict]) assert np.isclose(features[0], 1244, atol=.5) Loading
deepchem/feat/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -23,3 +23,4 @@ from deepchem.feat.atomic_coordinates import AtomicCoordinates from deepchem.feat.atomic_coordinates import NeighborListComplexAtomicCoordinates from deepchem.feat.adjacency_fingerprints import AdjacencyFingerprint from deepchem.feat.smiles_featurizers import SmilesToSeq, SmilesToImage from deepchem.feat.materials_featurizers import ChemicalFingerprint, SineCoulombMatrix, StructureGraphFeaturizer
deepchem/feat/materials_featurizers.py 0 → 100644 +178 −0 Original line number Diff line number Diff line """ Featurizers for inorganic crystals. """ import numpy as np from deepchem.feat import Featurizer from deepchem.utils import pad_array from matminer.featurizers.composition import ElementProperty from pymatgen import Composition, Structure from pymatgen.analysis.graphs import StructureGraph from pymatgen.analysis.local_env import MinimumDistanceNN class ChemicalFingerprint(Featurizer): """ Chemical fingerprint of elemental properties from composition. Parameters ---------- data_source : str, optional (default "matminer") Source for element property data ("matminer", "magpie", "deml") """ def __init__(self, data_source='matminer'): self.data_source = data_source def _featurize(self, comp): """ Calculate chemical fingerprint from crystal composition. Parameters ---------- comp : Reduced formula of crystal. """ # Get pymatgen Composition object c = Composition(comp) ep = ElementProperty.from_preset(self.data_source) try: feats = ep.featurize(c) except: feats = [] return feats class SineCoulombMatrix(Featurizer): """ Calculate sine Coulomb matrix for crystals. Variant of Coulomb matrix for periodic crystals Faber et al. (Inter. J. Quantum Chem. 115, 16, 2015). Parameters ---------- max_atoms : int Maximum number of atoms for any crystal in the dataset. Used to pad the Coulomb matrix. eig : bool (default True) Return flattened vector of matrix eigenvalues """ def __init__(self, max_atoms, eig=True): self.max_atoms = int(max_atoms) self.eig = eig def _featurize(self, struct): """ Calculate sine Coulomb matrix from pymatgen structure. Parameters ---------- struct : pymatgen structure dictionary """ s = Structure.from_dict(struct) features = self.sine_coulomb_matrix(s) features = np.asarray(features) return features def sine_coulomb_matrix(self, s): """ Generate sine Coulomb matrices for each crystal. Parameters ---------- s : pymatgen structure """ sites = s.sites atomic_numbers = np.array([site.specie.Z for site in sites]) sine_mat = np.zeros((len(sites), len(sites))) coords = np.array([site.frac_coords for site in sites]) lattice = s.lattice.matrix # Conversion factor ang_to_bohr = 1.8897543760313331 for i in range(len(sine_mat)): for j in range(len(sine_mat)): if i == j: sine_mat[i][i] = 0.5 * atomic_numbers[i]**2.4 elif i < j: vec = coords[i] - coords[j] coord_vec = np.sin(np.pi * vec)**2 trig_dist = np.linalg.norm( (np.matrix(coord_vec) * lattice).A1) * ang_to_bohr sine_mat[i][j] = atomic_numbers[i] * atomic_numbers[j] / \ trig_dist else: sine_mat[i][j] = sine_mat[j][i] if self.eig: # flatten array to eigenvalues eigs, _ = np.linalg.eig(sine_mat) zeros = np.zeros((self.max_atoms,)) zeros[:len(eigs)] = eigs return zeros else: sine_mat = pad_array(sine_mat, self.max_atoms) return sine_mat class StructureGraphFeaturizer(Featurizer): """ Calculate structure graph for crystals. Parameters ---------- strategy : pymatgen.analysis.local_env.NearNeighbors An instance of NearNeighbors that determines how graph is constructed. #TODO (@ncfrey) process graph features for models """ def __init__(self, strategy=MinimumDistanceNN()): self.strategy = strategy def _featurize(self, struct): """ Calculate structure graph from pymatgen structure. Parameters ---------- struct : pymatgen structure dictionary. """ # Get pymatgen structure object s = Structure.from_dict(struct) features = self._get_structure_graph_features(s) return features def _get_structure_graph_features(self, struct): """ Calculate structure graph features from pymatgen structure. Parameters ---------- struct : pymatgen structure """ atom_features = np.array([site.specie.Z for site in struct], dtype='int32') sg = StructureGraph.with_local_env_strategy(struct, self.strategy) nodes = np.asarray(sg.graph.nodes) edges = np.asarray(sg.graph.edges) return (atom_features, nodes, edges)
deepchem/feat/tests/test_materials_featurizers.py 0 → 100644 +66 −0 Original line number Diff line number Diff line """ Test featurizers for inorganic crystals. """ import numpy as np import unittest from deepchem.feat.materials_featurizers import ChemicalFingerprint, SineCoulombMatrix, StructureGraphFeaturizer class TestMaterialFeaturizers(unittest.TestCase): """ Test material featurizers. """ def setUp(self): """ Set up tests. """ self.formula = 'MoS2' self.struct_dict = { '@module': 'pymatgen.core.structure', '@class': 'Structure', 'charge': None, 'lattice': { 'matrix': [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], 'a': 1.0, 'b': 1.0, 'c': 1.0, 'alpha': 90.0, 'beta': 90.0, 'gamma': 90.0, 'volume': 1.0 }, 'sites': [{ 'species': [{ 'element': 'Fe', 'occu': 1 }], 'abc': [0.0, 0.0, 0.0], 'xyz': [0.0, 0.0, 0.0], 'label': 'Fe', 'properties': {} }] } def testCF(self): """ Test CF featurizer. """ featurizer = ChemicalFingerprint(data_source='matminer') assert isinstance(featurizer, ChemicalFingerprint) def testSCM(self): """ Test SCM featurizer. """ featurizer = SineCoulombMatrix(1) features = featurizer.featurize([self.struct_dict]) assert np.isclose(features[0], 1244, atol=.5)
