fixed pytorch graph conv issues (875e0ac5) · Commits · 钟慕尧 / deepchem

contrib/torch/pytorch_graphconv.py

+15 −15

Original line number	Diff line number	Diff line
		@@ -6,7 +6,7 @@ import torch.optim as optim
		import random
		import numpy as np
		from sklearn.metrics import roc_auc_score

		import scipy


		def symmetric_normalize_adj(adj):

deepchem/feat/adjacency_fingerprints.py

+134 −128

Original line number	Diff line number	Diff line
		@@ -16,6 +16,7 @@ from deepchem.feat.base_classes import Featurizer
		from deepchem.feat.graph_features import atom_features
		from scipy.sparse import csr_matrix


		def get_atom_type(atom):
		elem = atom.GetAtomicNum()
		hyb = str(atom.GetHybridization).lower()
		@@ -70,10 +71,14 @@ def get_atom_type(atom):
		return (21)
		return (22)

		def get_atom_adj_matrices(mol, n_atom_types, max_n_atoms=200,

		def get_atom_adj_matrices(mol,
		n_atom_types,
		max_n_atoms=200,
		max_valence=4,
		graph_conv_features=True,
		nxn=True):
		if not graph_conv_features:
		bond_matrix = np.zeros((max_n_atoms, 4 * max_valence)).astype(np.uint8)

		if nxn:
		@@ -82,8 +87,9 @@ def get_atom_adj_matrices(mol, n_atom_types, max_n_atoms=200,
		adj_matrix = np.zeros((max_n_atoms, max_valence)).astype(np.uint8)
		adj_matrix += (adj_matrix.shape[0] - 1)

		#atom_matrix = np.zeros((max_n_atoms, n_atom_types+3)).astype(np.uint8)
		#atom_matrix[:,atom_matrix.shape[1]-1] = 1
		if not graph_conv_features:
		atom_matrix = np.zeros((max_n_atoms, n_atom_types + 3)).astype(np.uint8)
		atom_matrix[:, atom_matrix.shape[1] - 1] = 1

		atom_arrays = []
		for a_idx in range(0, mol.GetNumAtoms()):
		@@ -102,7 +108,8 @@ def get_atom_adj_matrices(mol, n_atom_types, max_n_atoms=200,
		adj_matrix[a_idx][a_idx] = 1
		else:
		adj_matrix[a_idx][n_idx] = neighbor.GetIdx()
		"""

		if not graph_conv_features:
		bond = mol.GetBondBetweenAtoms(a_idx, neighbor.GetIdx())
		bond_type = str(bond.GetBondType()).lower()
		if "single" in bond_type:
		@@ -114,7 +121,6 @@ def get_atom_adj_matrices(mol, n_atom_types, max_n_atoms=200,
		elif "aromatic" in bond_type:
		bond_order = 3
		bond_matrix[a_idx][(4 * n_idx) + bond_order] = 1
		"""

		if graph_conv_features:
		n_feat = len(atom_arrays[0])
		@@ -122,21 +128,23 @@ def get_atom_adj_matrices(mol, n_atom_types, max_n_atoms=200,
		for idx, atom_array in enumerate(atom_arrays):
		atom_matrix[idx, :] = atom_array
		else:
		atom_matrix = np.concatenate([atom_matrix, bond_matrix], axis=1).astype(np.uint8)
		atom_matrix = np.concatenate(
		[atom_matrix, bond_matrix], axis=1).astype(np.uint8)

		return (adj_matrix.astype(np.uint8), atom_matrix.astype(np.uint8))


		def featurize_mol(mol,
		n_atom_types,
		max_n_atoms,
		max_valence):
		def featurize_mol(mol, n_atom_types, max_n_atoms, max_valence):

		adj_matrix, atom_matrix = get_atom_adj_matrices(mol, n_atom_types, max_n_atoms, max_valence)
		adj_matrix, atom_matrix = get_atom_adj_matrices(mol, n_atom_types,
		max_n_atoms, max_valence)
		return ((adj_matrix, atom_matrix))


		class AdjacencyFingerprint(Featurizer):
		def __init__(self, n_atom_types=23,

		def __init__(self,
		n_atom_types=23,
		max_n_atoms=200,
		add_hydrogens=False,
		max_valence=4):
		@@ -151,9 +159,7 @@ class AdjacencyFingerprint(Featurizer):
		for idx, mol in enumerate(rdkit_mols):
		if self.add_hydrogens:
		mol = Chem.AddHs(mol)
		featurized_mol = featurize_mol(mol,
		self.n_atom_types,
		self.max_n_atoms,
		featurized_mol = featurize_mol(mol, self.n_atom_types, self.max_n_atoms,
		self.max_valence)
		featurized_mols[idx] = featurized_mol
		return (featurized_mols)

deepchem/molnet/load_function/tox21_datasets.py

+2 −2

File changed.

Contains only whitespace changes.

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