def test_graph_data(self):
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))
edge_index = np.array([
[0, 1, 2, 2, 3, 4],
[1, 2, 0, 3, 4, 0],
])
node_pos_features = None
graph = GraphData(node_features=node_features,
edge_index=edge_index,
edge_features=edge_features,
node_pos_features=node_pos_features)
assert graph.num_nodes == num_nodes
assert graph.num_node_features == num_node_features
assert graph.num_edges == num_edges
assert graph.num_edge_features == num_edge_features
# check convert function
pyg_graph = graph.to_pyg_graph()
from torch_geometric.data import Data
assert isinstance(pyg_graph, Data)
dgl_graph = graph.to_dgl_graph()
from dgl import DGLGraph
assert isinstance(dgl_graph, DGLGraph)
def test_graph_data(self):
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))
edge_index = np.array([
[0, 1, 2, 2, 3, 4],
[1, 2, 0, 3, 4, 0],
])
node_pos_features = None
# z is kwargs
z = np.random.random(5)
graph = GraphData(node_features=node_features,
edge_index=edge_index,
edge_features=edge_features,
node_pos_features=node_pos_features,
z=z)
assert graph.num_nodes == num_nodes
assert graph.num_node_features == num_node_features
assert graph.num_edges == num_edges
assert graph.num_edge_features == num_edge_features
assert graph.z.shape == z.shape
assert str(
graph
) == 'GraphData(node_features=[5, 32], edge_index=[2, 6], edge_features=[6, 32], z=[5])'
# check convert function
pyg_graph = graph.to_pyg_graph()
from torch_geometric.data import Data
assert isinstance(pyg_graph, Data)
assert tuple(pyg_graph.z.shape) == z.shape
dgl_graph = graph.to_dgl_graph()
from dgl import DGLGraph
assert isinstance(dgl_graph, DGLGraph)