def test_hetero_graph_basics(self):
G = generate_simple_hete_graph()
hete = HeteroGraph(G)
hete = HeteroGraph(node_feature=hete.node_feature,
node_label=hete.node_label,
edge_feature=hete.edge_feature,
edge_label=hete.edge_label,
edge_index=hete.edge_index,
directed=True)
self.assertEqual(hete.num_node_features('n1'), 10)
self.assertEqual(hete.num_node_features('n2'), 12)
self.assertEqual(hete.num_edge_features(('n1', 'e1', 'n1')), 8)
self.assertEqual(hete.num_edge_features(('n1', 'e2', 'n2')), 12)
self.assertEqual(hete.num_nodes('n1'), 4)
self.assertEqual(hete.num_nodes('n2'), 5)
self.assertEqual(len(hete.node_types), 2)
self.assertEqual(len(hete.edge_types), 2)
message_types = hete.message_types
self.assertEqual(len(message_types), 7)
self.assertEqual(hete.num_node_labels('n1'), 2)
self.assertEqual(hete.num_node_labels('n2'), 2)
self.assertEqual(hete.num_edge_labels(('n1', 'e1', 'n1')), 2)
self.assertEqual(hete.num_edge_labels(('n1', 'e2', 'n2')), 2)
self.assertEqual(hete.num_edges(message_types[0]), 3)
self.assertEqual(len(hete.node_label_index), 2)
)
# Node feature and node label to device
for key in hetero_graph.node_feature:
hetero_graph.node_feature[key] = hetero_graph.node_feature[key].to(
args.device)
for key in hetero_graph.node_label:
hetero_graph.node_label[key] = hetero_graph.node_label[key].to(
args.device)
# Edge_index to sparse tensor and to device
for key in hetero_graph.edge_index:
edge_index = hetero_graph.edge_index[key]
adj = SparseTensor(row=edge_index[0],
col=edge_index[1],
sparse_sizes=(hetero_graph.num_nodes('paper'),
hetero_graph.num_nodes('paper')))
hetero_graph.edge_index[key] = adj.t().to(args.device)
print(hetero_graph.edge_index[message_type_1])
print(hetero_graph.edge_index[message_type_2])
# Mean aggregation
best_model = None
best_val = 0
model = HeteroGNN(hetero_graph, args, aggr="mean").to(args.device)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
for epoch in range(args.epochs):
