:green_heart: fix ci (62620841) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_data.py

+24 −19

Original line number	Diff line number	Diff line
		@@ -31,8 +31,8 @@ class GraphData:
		--------
		>>> import numpy as np
		>>> node_features = np.random.rand(5, 10)
		>>> 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)
		>>> edge_index = np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]], dtype=np.int64)
		>>> GraphData(node_features=node_features, edge_index=edge_index)
		"""

		def __init__(
		@@ -60,8 +60,8 @@ class GraphData:

		if isinstance(edge_index, np.ndarray) is False:
		raise ValueError('edge_index must be np.ndarray.')
		elif edge_index.dtype != np.int:
		raise ValueError('edge_index.dtype must be np.int')
		elif edge_index.dtype != np.int64:
		raise ValueError('edge_index.dtype must be np.int64')
		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):
		@@ -84,7 +84,7 @@ class GraphData:
		self.graph_features = graph_features
		self.num_nodes, self.num_node_features = self.node_features.shape
		self.num_edges = edge_index.shape[1]
		if self.node_features is not None:
		if self.edge_features is not None:
		self.num_edge_features = self.edge_features.shape[1]

		def to_pyg_graph(self):
		@@ -133,7 +133,9 @@ class GraphData:

		g = DGLGraph()
		g.add_nodes(self.num_nodes)
		g.add_edges(torch.from_numpy(self.edge_index[0]), torch.from_numpy(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:
		@@ -153,51 +155,54 @@ class BatchGraphData(GraphData):
		Examples
		--------
		>>> import numpy as np
		>>> from deepchem.feat.graph_data import GraphData
		>>> node_features_list = np.random.rand(2, 5, 10)
		>>> edge_index_list = np.array([
		... [[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
		>>> graph_list = [GraphData(node_features, edge_index) for node_features, edge_index
		... in zip(node_features_list, edge_index_list)]
		>>> BatchGraphData(graphs=graphs)
		>>> BatchGraphData(graph_list=graph_list)
		"""

		def __init__(self, graphs: Sequence[GraphData]):
		def __init__(self, graph_list: Sequence[GraphData]):
		"""
		Parameters
		----------
		graphs: Sequence[GraphData]
		graph_list: Sequence[GraphData]
		List of GraphData
		"""
		# stack features
		batch_node_features = np.vstack([graph.node_features for graph in graphs])
		batch_node_features = np.vstack(
		[graph.node_features for graph in graph_list])

		# before stacking edge_features or graph_features,
		# we should check whether these are None or not
		if graphs[0].edge_features is not None:
		batch_edge_features = np.vstack([graph.edge_features for graph in graphs])
		if graph_list[0].edge_features is not None:
		batch_edge_features = np.vstack(
		[graph.edge_features for graph in graph_list])
		else:
		batch_edge_features = None

		if graphs[0].graph_features is not None:
		if graph_list[0].graph_features is not None:
		batch_graph_features = np.vstack(
		[graph.graph_features for graph in graphs])
		[graph.graph_features for graph in graph_list])
		else:
		batch_graph_features = None

		# create new edge index
		num_nodes_list = [graph.num_nodes for graph in graphs]
		num_nodes_list = [graph.num_nodes for graph in graph_list]
		batch_edge_index = np.hstack(
		[graph.edge_index + prev_num_node for prev_num_node, graph \
		in zip([0] + num_nodes_list[:-1], graphs)]
		).astype(int)
		in zip([0] + num_nodes_list[:-1], graph_list)]
		).astype(np.int64)

		# graph_index indicates which nodes belong to which graph
		graph_index = []
		for i, num_nodes in enumerate(num_nodes_list):
		graph_index.extend([i] * num_nodes)
		self.graph_index = np.array(graph_index, dtype=int)
		self.graph_index = np.array(graph_index, dtype=np.int64)

		super().__init__(
		node_features=batch_node_features,

deepchem/feat/tests/test_graph_data.py

+18 −13

Original line number	Diff line number	Diff line
		@@ -11,10 +11,11 @@ class TestGraph(unittest.TestCase):
		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))
		edge_index = np.array([
		edge_index = np.array(
		[
		[0, 1, 2, 2, 3, 4],
		[1, 2, 0, 3, 4, 0],
		])
		], dtype=np.int64)
		graph_features = None

		graph = GraphData(
		@@ -40,10 +41,11 @@ class TestGraph(unittest.TestCase):
		def test_invalid_graph_data(self):
		with pytest.raises(ValueError):
		invalid_node_features_type = list(np.random.random_sample((5, 32)))
		edge_index = np.array([
		edge_index = np.array(
		[
		[0, 1, 2, 2, 3, 4],
		[1, 2, 0, 3, 4, 0],
		])
		], dtype=np.int64)
		_ = GraphData(
		node_features=invalid_node_features_type,
		edge_index=edge_index,
		@@ -51,10 +53,11 @@ class TestGraph(unittest.TestCase):

		with pytest.raises(ValueError):
		node_features = np.random.random_sample((5, 32))
		invalid_edge_index_shape = np.array([
		invalid_edge_index_shape = np.array(
		[
		[0, 1, 2, 2, 3, 4],
		[1, 2, 0, 3, 4, 5],
		])
		], dtype=np.int64)
		_ = GraphData(
		node_features=node_features,
		edge_index=invalid_edge_index_shape,
		@@ -62,11 +65,13 @@ class TestGraph(unittest.TestCase):

		with pytest.raises(ValueError):
		node_features = np.random.random_sample((5, 5))
		invalid_edge_index_shape = np.array([
		invalid_edge_index_shape = np.array(
		[
		[0, 1, 2, 2, 3, 4],
		[1, 2, 0, 3, 4, 0],
		[2, 2, 1, 4, 0, 3],
		])
		],
		dtype=np.int64)
		_ = GraphData(
		node_features=node_features,
		edge_index=invalid_edge_index_shape,
		@@ -80,12 +85,12 @@ class TestGraph(unittest.TestCase):
		num_nodes_list, num_edge_list = [3, 4, 5], [2, 4, 5]
		num_node_features, num_edge_features = 32, 32
		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, 0]])
		np.array([[0, 1], [1, 2]], dtype=np.int64),
		np.array([[0, 1, 2, 3], [1, 2, 0, 2]], dtype=np.int64),
		np.array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]], dtype=np.int64),
		]

		graphs = [
		graph_list = [
		GraphData(
		node_features=np.random.random_sample((num_nodes_list[i],
		num_node_features)),
		@@ -94,7 +99,7 @@ class TestGraph(unittest.TestCase):
		num_edge_features)),
		graph_features=None) for i in range(len(num_edge_list))
		]
		batch = BatchGraphData(graphs)
		batch = BatchGraphData(graph_list)

		assert batch.num_nodes == sum(num_nodes_list)
		assert batch.num_node_features == num_node_features

docs/featurizers.rst

+2 −2

Original line number	Diff line number	Diff line
		@@ -180,10 +180,10 @@ SineCoulombMatrix
		.. autoclass:: deepchem.feat.SineCoulombMatrix
		:members:

		StructureGraphFeaturizer
		CGCNNFeaturizer
		^^^^^^^^^^^^^^^^^^^^^^^^

		.. autoclass:: deepchem.feat.StructureGraphFeaturizer
		.. autoclass:: deepchem.feat.CGCNNFeaturizer
		:members:

		MaterialCompositionFeaturizer

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