:zap: remove int64 (6663fdf4) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_data.py

+5 −5

Original line number	Diff line number	Diff line
		@@ -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.int64:
		raise ValueError('edge_index.dtype must be np.int64')
		elif edge_index.dtype != np.int:
		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):
		@@ -108,7 +108,7 @@ class GraphData:

		return Data(
		x=torch.from_numpy(self.node_features),
		edge_index=torch.from_numpy(self.edge_index),
		edge_index=torch.from_numpy(self.edge_index).long(),
		edge_attr=None if self.edge_features is None \
		else torch.from_numpy(self.edge_features),
		)
		@@ -196,13 +196,13 @@ class BatchGraphData(GraphData):
		batch_edge_index = np.hstack(
		[graph.edge_index + prev_num_node for prev_num_node, graph \
		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=np.int64)
		self.graph_index = np.array(graph_index)

		super().__init__(
		node_features=batch_node_features,

deepchem/feat/tests/test_graph_data.py

+20 −25

Original line number	Diff line number	Diff line
		@@ -11,11 +11,10 @@ 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(
		@@ -41,11 +40,10 @@ 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,
		@@ -53,11 +51,10 @@ 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,
		@@ -65,13 +62,11 @@ 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,
		@@ -85,9 +80,9 @@ 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]], 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),
		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]]),
		]

		graph_list = [

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