:green_heart: fix ci (9af12830) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_data.py

+15 −4

Original line number	Diff line number	Diff line
		@@ -31,7 +31,7 @@ class GraphData:
		--------
		>>> import numpy as np
		>>> node_features = np.random.rand(5, 10)
		>>> edge_index = np.array([[0, 1, 2, 2, 3], [1, 2, 3, 3, 4]], dtype=np.int)
		>>> edge_index = np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]], dtype=np.int)
		>>> Graph(node_features=node_features, edge_index=edge_index)
		"""

		@@ -64,6 +64,8 @@ class GraphData:
		raise ValueError('edge_index.dtype must be np.int')
		elif edge_index.shape[0] != 2:
		raise ValueError('The shape of edge_index is [2, num_edges].')
		elif np.max(edge_index) >= len(node_features):
		raise ValueError('edge_index contains the invalid node number.')

		if edge_features is not None:
		if isinstance(edge_features, np.ndarray) is False:
		@@ -92,6 +94,10 @@ class GraphData:
		-------
		torch_geometric.data.Data
		Graph data for PyTorch Geometric

		Notes
		-----
		This method requires PyTorch Geometric to be installed.
		"""
		try:
		import torch
		@@ -114,15 +120,20 @@ class GraphData:
		-------
		dgl.DGLGraph
		Graph data for PyTorch Geometric

		Notes
		-----
		This method requires DGL to be installed.
		"""
		try:
		import torch
		from dgl import DGLGraph
		except ModuleNotFoundError:
		raise ValueError("This function requires DGL to be installed.")

		g = DGLGraph()
		g.add_nodes(self.num_nodes)
		g.add_edges(self.edge_index[0], self.edge_index[1])
		g.add_edges(torch.from_numpy(self.edge_index[0]), torch.from_numpy(self.edge_index[1]))
		g.ndata['x'] = torch.from_numpy(self.node_features)

		if self.edge_features is not None:
		@@ -144,8 +155,8 @@ class BatchGraphData(GraphData):
		>>> import numpy as np
		>>> node_features_list = np.random.rand(2, 5, 10)
		>>> edge_index_list = np.array([
		... [[0, 1, 2, 2, 3], [1, 2, 3, 3, 4]],
		... [[0, 1, 2, 2, 3], [1, 2, 3, 3, 4]],
		... [[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]],
		... [[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]],
		... ], dtype=np.int)
		>>> graphs = [Graph(node_features, edge_index) for node_features, edge_index
		... in zip(node_features_list, edge_index_list)]

deepchem/feat/material_featurizers/cgcnn_featurizer.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -40,8 +40,8 @@ class CGCNNFeaturizer(MaterialStructureFeaturizer):
		>>> featurizer = CGCNNFeaturizer()
		>>> features = featurizer.featurize([structure])

		Note
		----
		Notes
		-----
		This class requires Pymatgen to be installed.
		"""

deepchem/feat/material_featurizers/element_property_fingerprint.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -37,8 +37,8 @@ class ElementPropertyFingerprint(MaterialCompositionFeaturizer):
		>>> featurizer = ElementPropertyFingerprint()
		>>> features = featurizer.featurize([comp])

		Note
		----
		Notes
		-----
		This class requires matminer and Pymatgen to be installed.
		"""

deepchem/feat/material_featurizers/sine_coulomb_matrix.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -39,8 +39,8 @@ class SineCoulombMatrix(MaterialStructureFeaturizer):
		>>> featurizer = SineCoulombMatrix(max_atoms=2)
		>>> features = featurizer.featurize([structure])

		Note
		----
		Notes
		-----
		This class requires matminer and Pymatgen to be installed.
		"""

deepchem/feat/tests/test_graph_data.py

+16 −5

Original line number	Diff line number	Diff line
		@@ -7,7 +7,7 @@ from deepchem.feat.graph_data import GraphData, BatchGraphData
		class TestGraph(unittest.TestCase):

		def test_graph_data(self):
		num_nodes, num_node_features = 4, 32
		num_nodes, num_node_features = 5, 32
		num_edges, num_edge_features = 6, 32
		node_features = np.random.random_sample((num_nodes, num_node_features))
		edge_features = np.random.random_sample((num_edges, num_edge_features))
		@@ -33,13 +33,13 @@ class TestGraph(unittest.TestCase):
		from torch_geometric.data import Data
		assert isinstance(pyg_graph, Data)

		dgl_graph = graph.to_pyg_graph()
		dgl_graph = graph.to_dgl_graph()
		from dgl import DGLGraph
		assert isinstance(dgl_graph, DGLGraph)

		def test_invalid_graph_data(self):
		with pytest.raises(ValueError):
		invalid_node_features_type = list(np.random.random_sample((5, 5)))
		invalid_node_features_type = list(np.random.random_sample((5, 32)))
		edge_index = np.array([
		[0, 1, 2, 2, 3, 4],
		[1, 2, 0, 3, 4, 0],
		@@ -49,6 +49,17 @@ class TestGraph(unittest.TestCase):
		edge_index=edge_index,
		)

		with pytest.raises(ValueError):
		node_features = np.random.random_sample((5, 32))
		invalid_edge_index_shape = np.array([
		[0, 1, 2, 2, 3, 4],
		[1, 2, 0, 3, 4, 5],
		])
		_ = GraphData(
		node_features=node_features,
		edge_index=invalid_edge_index_shape,
		)

		with pytest.raises(ValueError):
		node_features = np.random.random_sample((5, 5))
		invalid_edge_index_shape = np.array([
		@@ -62,7 +73,7 @@ class TestGraph(unittest.TestCase):
		)

		with pytest.raises(TypeError):
		node_features = np.random.random_sample((5, 5))
		node_features = np.random.random_sample((5, 32))
		_ = GraphData(node_features=node_features)

		def test_batch_graph_data(self):
		@@ -71,7 +82,7 @@ class TestGraph(unittest.TestCase):
		edge_index_list = [
		np.array([[0, 1], [1, 2]]),
		np.array([[0, 1, 2, 3], [1, 2, 0, 2]]),
		np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5]])
		np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]])
		]

		graphs = [

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