GOGO (e4cafed6) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_features.py

+8 −7

Original line number	Diff line number	Diff line
		@@ -264,10 +264,8 @@ def find_distance(a1, num_atoms, canon_adj_list, max_distance=7):
		class ConvMolFeaturizer(Featurizer):
		name = ['conv_mol']

		def __init__(self,
		master_atom=False,
		use_chirality=False,
		atom_properties=None):
		def __init__(self, master_atom=False, use_chirality=False,
		atom_properties=[]):
		"""
		Parameters
		----------
		@@ -298,9 +296,9 @@ class ConvMolFeaturizer(Featurizer):
		self.dtype = object
		self.master_atom = master_atom
		self.use_chirality = use_chirality
		self.atom_properties = atom_properties
		self.atom_properties = list(atom_properties)

		def get_atom_properties(self, atom):
		def _get_atom_properties(self, atom):
		"""
		For a given input RDKit atom return the values of the properties
		requested when initializing the featurize. See the __init__ of the
		@@ -328,7 +326,7 @@ class ConvMolFeaturizer(Featurizer):
		idx_nodes = [(a.GetIdx(),
		np.concatenate((atom_features(
		a, use_chirality=self.use_chirality),
		self.get_atom_properties(a))))
		self._get_atom_properties(a))))
		for a in mol.GetAtoms()]

		idx_nodes.sort() # Sort by ind to ensure same order as rd_kit
		@@ -358,6 +356,9 @@ class ConvMolFeaturizer(Featurizer):

		return ConvMol(nodes, canon_adj_list)

		def feature_length(self):
		return 75 + len(self.atom_properties)


		class WeaveFeaturizer(Featurizer):
		name = ['weave_mol']

deepchem/models/tensorgraph/models/test_graph_models.py

+34 −0

Original line number	Diff line number	Diff line
		@@ -9,6 +9,7 @@ from deepchem.models import TensorGraph
		from deepchem.molnet.load_function.delaney_datasets import load_delaney
		from deepchem.models.tensorgraph.layers import ReduceSum, L2Loss
		from deepchem.models import WeaveTensorGraph
		from deepchem.feat import ConvMolFeaturizer


		class TestGraphModels(unittest.TestCase):
		@@ -82,6 +83,39 @@ class TestGraphModels(unittest.TestCase):
		assert mu.shape == (len(dataset), len(tasks))
		assert sigma.shape == (len(dataset), len(tasks))

		def test_graph_conv_atom_features(self):
		tasks, dataset, transformers, metric = self.get_dataset(
		'regression', 'Raw', num_tasks=1)

		atom_feature_name = 'feature'
		y = []
		for mol in dataset.X:
		atom_features = []
		for atom in mol.GetAtoms():
		val = np.random.normal()
		mol.SetProp("atom %08d %s" % (atom.GetIdx(), atom_feature_name),
		str(val))
		atom_features.append(np.random.normal())
		y.append(np.sum(atom_features))

		featurizer = ConvMolFeaturizer(atom_properties=[atom_feature_name])
		X = featurizer.featurize(dataset.X)
		dataset = deepchem.data.NumpyDataset(X, np.array(y))
		batch_size = 50
		model = GraphConvTensorGraph(
		len(tasks),
		number_atom_features=featurizer.feature_length(),
		batch_size=batch_size,
		mode='regression')

		model.fit(dataset, nb_epoch=1)
		y_pred1 = model.predict(dataset)
		model.save()

		model2 = TensorGraph.load_from_dir(model.model_dir)
		y_pred2 = model2.predict(dataset)
		self.assertTrue(np.all(y_pred1 == y_pred2))

		def test_change_loss_function(self):
		tasks, dataset, transformers, metric = self.get_dataset(
		'regression', 'GraphConv', num_tasks=1)

.travis.yml

+1 −1

File changed.

Contains only whitespace changes.

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