Loading deepchem/feat/molecule_featurizers/mat_featurizer.py +15 −35 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ class MATFeaturizer(MolecularFeaturizer): """ This class is a featurizer for the Molecule Attention Transformer [1]_. The featurizer accepts an RDKit Molecule, and 2 booleans (add_dummy_node and one_hot_formal_charge) as arguments. The returned value is a tuple which consists of molecular graph descriptions: The returned value is a numpy array which consists of molecular graph descriptions: - Node Features - Adjacency Matrix - Distance Matrix Loading Loading @@ -60,7 +60,7 @@ class MATFeaturizer(MolecularFeaturizer): """ attrib = [] attrib += one_hot_encode(atom.GetAtomicNumber(), attrib += one_hot_encode(atom.GetAtomicNum(), [5, 6, 7, 8, 9, 15, 16, 17, 35, 53, 999]) attrib += one_hot_encode(len(atom.GetNeighbors()), [0, 1, 2, 3, 4, 5]) attrib += one_hot_encode(atom.GetTotalNumHs(), [0, 1, 2, 3, 4]) Loading @@ -86,38 +86,18 @@ class MATFeaturizer(MolecularFeaturizer): Returns ------- tuple: (node_features, adjacency_matrix, distance_matrix) numpy.ndarray: (node_features, adjacency_matrix, distance_matrix) """ node_features = np.array([ self.atom_features(atom, self.one_hot_formal_charge) for atom in mol.getAtoms() ]) adjacency_matrix = Chem.rdmolops.getAdjacencyMatrix(mol) conformer = mol.GetConformer() positional_matrix = np.array([[ conformer.GetAtomPosition(k).x, conformer.GetAtomPosition(k).y, conformer.GetAtomPosition(k).z ] for k in range(mol.GetNumAtoms())]) distance_matrix = pairwise_distances(positional_matrix) if self.add_dummy_node: m = np.zeros((node_features.shape[0] + 1, node_features.shape[1] + 1)) m[1:, 1:] = node_features m[0, 0] = 1.0 node_features = m m = np.zeros((adjacency_matrix.shape[0] + 1, adjacency_matrix.shape[1] + 1)) m[1:, 1:] = adjacency_matrix adjacency_matrix = m m = np.full((distance_matrix.shape[0] + 1, distance_matrix.shape[1] + 1), 1e6) m[1:, 1:] = distance_matrix distance_matrix = m node_features = np.array( [self.atom_features(atom) for atom in mol.GetAtoms()]) adjacency_matrix = Chem.rdmolops.GetAdjacencyMatrix(mol) distance_matrix = Chem.rdmolops.GetDistanceMatrix(mol) adjacency_matrix.resize(node_features.shape) distance_matrix.resize(node_features.shape) return node_features, adjacency_matrix, distance_matrix No newline at end of file deepchem/feat/tests/test_mat_featurizer.py +22 −3 Original line number Diff line number Diff line import unittest from deepchem.feat import material_featurizers from deepchem.feat import MATFeaturizer import numpy as np class TestMATFeaturizer(unittest.TestCase): """ Test MATFeaturizer. """ def setUp(self): """ Set up tests. """ from rdkit import Chem smiles = 'CC(C)CC(=O)' self.mol = Chem.MolFromSmiles(smiles) def test_mat_featurizer(self): """ Test featurizer.py """ featurizer = MATFeaturizer() out = featurizer.featurize(self.mol) assert (type(out) == np.ndarray) assert (out.shape == (1, 3, 6, 27)) Loading
deepchem/feat/molecule_featurizers/mat_featurizer.py +15 −35 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ class MATFeaturizer(MolecularFeaturizer): """ This class is a featurizer for the Molecule Attention Transformer [1]_. The featurizer accepts an RDKit Molecule, and 2 booleans (add_dummy_node and one_hot_formal_charge) as arguments. The returned value is a tuple which consists of molecular graph descriptions: The returned value is a numpy array which consists of molecular graph descriptions: - Node Features - Adjacency Matrix - Distance Matrix Loading Loading @@ -60,7 +60,7 @@ class MATFeaturizer(MolecularFeaturizer): """ attrib = [] attrib += one_hot_encode(atom.GetAtomicNumber(), attrib += one_hot_encode(atom.GetAtomicNum(), [5, 6, 7, 8, 9, 15, 16, 17, 35, 53, 999]) attrib += one_hot_encode(len(atom.GetNeighbors()), [0, 1, 2, 3, 4, 5]) attrib += one_hot_encode(atom.GetTotalNumHs(), [0, 1, 2, 3, 4]) Loading @@ -86,38 +86,18 @@ class MATFeaturizer(MolecularFeaturizer): Returns ------- tuple: (node_features, adjacency_matrix, distance_matrix) numpy.ndarray: (node_features, adjacency_matrix, distance_matrix) """ node_features = np.array([ self.atom_features(atom, self.one_hot_formal_charge) for atom in mol.getAtoms() ]) adjacency_matrix = Chem.rdmolops.getAdjacencyMatrix(mol) conformer = mol.GetConformer() positional_matrix = np.array([[ conformer.GetAtomPosition(k).x, conformer.GetAtomPosition(k).y, conformer.GetAtomPosition(k).z ] for k in range(mol.GetNumAtoms())]) distance_matrix = pairwise_distances(positional_matrix) if self.add_dummy_node: m = np.zeros((node_features.shape[0] + 1, node_features.shape[1] + 1)) m[1:, 1:] = node_features m[0, 0] = 1.0 node_features = m m = np.zeros((adjacency_matrix.shape[0] + 1, adjacency_matrix.shape[1] + 1)) m[1:, 1:] = adjacency_matrix adjacency_matrix = m m = np.full((distance_matrix.shape[0] + 1, distance_matrix.shape[1] + 1), 1e6) m[1:, 1:] = distance_matrix distance_matrix = m node_features = np.array( [self.atom_features(atom) for atom in mol.GetAtoms()]) adjacency_matrix = Chem.rdmolops.GetAdjacencyMatrix(mol) distance_matrix = Chem.rdmolops.GetDistanceMatrix(mol) adjacency_matrix.resize(node_features.shape) distance_matrix.resize(node_features.shape) return node_features, adjacency_matrix, distance_matrix No newline at end of file
deepchem/feat/tests/test_mat_featurizer.py +22 −3 Original line number Diff line number Diff line import unittest from deepchem.feat import material_featurizers from deepchem.feat import MATFeaturizer import numpy as np class TestMATFeaturizer(unittest.TestCase): """ Test MATFeaturizer. """ def setUp(self): """ Set up tests. """ from rdkit import Chem smiles = 'CC(C)CC(=O)' self.mol = Chem.MolFromSmiles(smiles) def test_mat_featurizer(self): """ Test featurizer.py """ featurizer = MATFeaturizer() out = featurizer.featurize(self.mol) assert (type(out) == np.ndarray) assert (out.shape == (1, 3, 6, 27))
