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)
def test_resample_disjoint_heterogeneous(self):
G = generate_dense_hete_dataset()
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)
graphs = [hete]
dataset = GraphDataset(graphs,
task="link_pred",
edge_train_mode="disjoint",
edge_message_ratio=0.8,
resample_disjoint=True,
resample_disjoint_period=1)
dataset_train, _, _ = dataset.split(split_ratio=[0.5, 0.2, 0.3])
graph_train_first = dataset_train[0]
graph_train_second = dataset_train[0]
for message_type in graph_train_first.edge_index:
self.assertEqual(
graph_train_first.edge_label_index[message_type].shape[1],
graph_train_second.edge_label_index[message_type].shape[1])
self.assertEqual(graph_train_first.edge_label[message_type].shape,
graph_train_second.edge_label[message_type].shape)
def test_hetero_graph_batch(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
)
heteGraphDataset = []
for _ in range(30):
heteGraphDataset.append(hete.clone())
dataloader = DataLoader(
heteGraphDataset,
collate_fn=Batch.collate(),
batch_size=3,
shuffle=True,
)
self.assertEqual(len(dataloader), math.ceil(30 / 3))
for data in dataloader:
self.assertEqual(data.num_graphs, 3)
def _custom_hete_split_link_pred_disjoint(self, graph_train):
objective_edges = graph_train.disjoint_split
nodes_dict = {}
for node in graph_train.G.nodes(data=True):
nodes_dict[node[0]] = node[1]["node_type"]
edges_dict = {}
objective_edges_dict = {}
for edge in graph_train.G.edges(data=True):
edge_type = edge[-1]["edge_type"]
head_type = nodes_dict[edge[0]]
tail_type = nodes_dict[edge[1]]
message_type = (head_type, edge_type, tail_type)
if message_type not in edges_dict:
edges_dict[message_type] = []
edges_dict[message_type].append(edge)
for edge in objective_edges:
edge_type = edge[-1]["edge_type"]
head_type = nodes_dict[edge[0]]
tail_type = nodes_dict[edge[1]]
message_type = (head_type, edge_type, tail_type)
if message_type not in objective_edges_dict:
objective_edges_dict[message_type] = []
objective_edges_dict[message_type].append(edge)
message_edges = []
for edge_type in edges_dict:
if edge_type in objective_edges_dict:
edges_no_info = [edge[:-1] for edge in edges_dict[edge_type]]
objective_edges_no_info = [
edge[:-1] for edge in objective_edges_dict[edge_type]
]
message_edges_no_info = set(edges_no_info) - set(
objective_edges_no_info)
message_edges += [(edge[0], edge[1],
graph_train.G.edges[edge[0], edge[1]])
for edge in message_edges_no_info]
else:
message_edges += edges_dict[edge_type]
# update objective edges
for edge_type in edges_dict:
if edge_type not in objective_edges_dict:
objective_edges += edges_dict[edge_type]
graph_train = HeteroGraph(graph_train._edge_subgraph_with_isonodes(
graph_train.G,
message_edges,
),
negative_edges=graph_train.negative_edges)
graph_train._create_label_link_pred(
graph_train, objective_edges, list(graph_train.G.nodes(data=True)))
return graph_train
def test_hetero_graph_none(self):
G = generate_simple_hete_graph(add_edge_type=False)
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)
message_types = hete.message_types
for message_type in message_types:
self.assertEqual(message_type[1], None)
def test_hetero_multigraph_split(self):
G = generate_dense_hete_multigraph()
hete = HeteroGraph(G)
# node
hete_node = hete.split(task='node')
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(len(hete_node[0].node_label_index[node_type]),
node_0)
self.assertEqual(len(hete_node[1].node_label_index[node_type]),
node_1)
self.assertEqual(len(hete_node[2].node_label_index[node_type]),
node_2)
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0)
self.assertEqual(hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1)
self.assertEqual(hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2)
# link prediction
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
# calculate the expected edge num for each splitted subgraph
hete_link_train_edge_num, hete_link_val_edge_num, hete_link_test_edge_num = 0, 0, 0
for key, val in hete.edge_label_index.items():
val_length = val.shape[1]
val_length_reduced = val_length - 3
hete_link_train_edge_num += 1 + int(0.5 * val_length_reduced)
hete_link_val_edge_num += 1 + int(0.3 * val_length_reduced)
hete_link_test_edge_num += \
val_length - 2 - int(0.5 * val_length_reduced) - int(0.3 * val_length_reduced)
self.assertEqual(len(hete_link[0].edge_label),
hete_link_train_edge_num)
self.assertEqual(len(hete_link[1].edge_label), hete_link_val_edge_num)
self.assertEqual(len(hete_link[2].edge_label), hete_link_test_edge_num)
def test_hetero_graph_batch(self):
G = generate_simple_hete_graph()
hete = HeteroGraph(G)
heteGraphDataset = []
for i in range(30):
heteGraphDataset.append(hete.clone())
dataloader = DataLoader(heteGraphDataset,
collate_fn=Batch.collate(),
batch_size=3,
shuffle=True)
self.assertEqual(len(dataloader), math.ceil(30 / 3))
for data in dataloader:
self.assertEqual(data.num_graphs, 3)
def main():
args = arg_parse()
edge_train_mode = args.mode
print('edge train mode: {}'.format(edge_train_mode))
G = nx.read_gpickle(args.data_path)
print(G.number_of_edges())
print('Each node has node ID (n_id). Example: ', G.nodes[0])
print(
'Each edge has edge ID (id) and categorical label (e_label). Example: ',
G[0][5871])
# find num edge types
max_label = 0
labels = []
for u, v, edge_key in G.edges:
l = G[u][v][edge_key]['e_label']
if not l in labels:
labels.append(l)
# labels are consecutive (0-17)
num_edge_types = len(labels)
H = WN_transform(G, num_edge_types)
# The nodes in the graph have the features: node_feature and node_type (just one node type "n1" here)
for node in H.nodes(data=True):
print(node)
break
# The edges in the graph have the features: edge_feature and edge_type ("0" - "17" here)
for edge in H.edges(data=True):
print(edge)
break
hete = HeteroGraph(H)
dataset = GraphDataset([hete], task='link_pred')
dataset_train, dataset_val, dataset_test = dataset.split(
transductive=True, split_ratio=[0.8, 0.1, 0.1])
train_loader = DataLoader(dataset_train,
collate_fn=Batch.collate(),
batch_size=1)
val_loader = DataLoader(dataset_val,
collate_fn=Batch.collate(),
batch_size=1)
test_loader = DataLoader(dataset_test,
collate_fn=Batch.collate(),
batch_size=1)
dataloaders = {
'train': train_loader,
'val': val_loader,
'test': test_loader
}
hidden_size = 32
model = HeteroNet(hete, hidden_size, 0.2).to(args.device)
optimizer = torch.optim.Adam(model.parameters(),
lr=0.001,
weight_decay=5e-4)
train(model, dataloaders, optimizer, args)
def _custom_hete_split_link_pred(self):
split_num = len(self.graphs[0].general_splits)
split_graphs = [[] for x in range(split_num)]
for i in range(len(self.graphs)):
graph = self.graphs[i]
graph_train = copy.copy(graph)
edges_train = graph_train.general_splits[0]
edges_val = graph_train.general_splits[1]
graph_train = HeteroGraph(
graph_train._edge_subgraph_with_isonodes(
graph_train.G,
edges_train,
),
disjoint_split=(graph_train.disjoint_split),
negative_edges=(graph_train.negative_edges))
graph_val = copy.copy(graph_train)
if split_num == 3:
graph_test = copy.copy(graph)
edges_test = graph.general_splits[2]
graph_test = HeteroGraph(
graph_test._edge_subgraph_with_isonodes(
graph_test.G, edges_train + edges_val),
negative_edges=(graph_test.negative_edges))
graph_train._create_label_link_pred(
graph_train, edges_train, list(graph_train.G.nodes(data=True)))
graph_val._create_label_link_pred(
graph_val, edges_val, list(graph_val.G.nodes(data=True)))
if split_num == 3:
graph_test._create_label_link_pred(
graph_test, edges_test,
list(graph_test.G.nodes(data=True)))
split_graphs[0].append(graph_train)
split_graphs[1].append(graph_val)
if split_num == 3:
split_graphs[2].append(graph_test)
return split_graphs
def test_hetero_graph_basics(self):
G = generate_simple_hete_graph()
hete = HeteroGraph(G)
self.assertEqual(hete.get_num_node_features('n1'), 10)
self.assertEqual(hete.get_num_node_features('n2'), 12)
self.assertEqual(hete.get_num_edge_features('e1'), 8)
self.assertEqual(hete.get_num_edge_features('e2'), 12)
self.assertEqual(hete.get_num_nodes('n1'), 4)
self.assertEqual(hete.get_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.get_num_node_labels('n1'), 2)
self.assertEqual(hete.get_num_node_labels('n2'), 2)
self.assertEqual(hete.get_num_edge_labels('e1'), 3)
self.assertEqual(hete.get_num_edge_labels('e2'), 3)
self.assertEqual(hete.get_num_edges(message_types[0]), 3)
self.assertEqual(len(hete.node_label_index), 2)
def test_dataset_hetero_graph_split(self):
G = generate_dense_hete_dataset()
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)
# node
dataset = GraphDataset([hete], task="node")
split_res = dataset.split()
for node_type in hete.node_label_index:
num_nodes = int(len(hete.node_label_index[node_type]))
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]),
node_2,
)
# node with specified split type
dataset = GraphDataset([hete], task="node")
node_split_types = ["n1"]
split_res = dataset.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = int(len(hete.node_label_index[node_type]))
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]),
node_2,
)
else:
num_nodes = int(len(hete.node_label_index[node_type]))
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]),
num_nodes,
)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]),
num_nodes,
)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]),
num_nodes,
)
# node with specified split type (string mode)
dataset = GraphDataset([hete], task="node")
node_split_types = "n1"
split_res = dataset.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = int(len(hete.node_label_index[node_type]))
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]),
node_2,
)
else:
num_nodes = int(len(hete.node_label_index[node_type]))
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]),
num_nodes,
)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]),
num_nodes,
)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]),
num_nodes,
)
# edge
dataset = GraphDataset([hete], task="edge")
split_res = dataset.split()
for edge_type in hete.edge_label_index:
num_edges = hete.edge_label_index[edge_type].shape[1]
edge_0 = int(num_edges * 0.8)
edge_1 = int(num_edges * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1],
edge_2,
)
# edge with specified split type
dataset = GraphDataset([hete], task="edge")
edge_split_types = [("n1", "e1", "n1"), ("n1", "e2", "n2")]
split_res = dataset.split(split_types=edge_split_types)
for edge_type in hete.edge_label_index:
if edge_type in edge_split_types:
num_edges = hete.edge_label_index[edge_type].shape[1]
edge_0 = int(num_edges * 0.8)
edge_1 = int(num_edges * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1],
edge_2,
)
else:
num_edges = hete.edge_label_index[edge_type].shape[1]
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1],
num_edges,
)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1],
num_edges,
)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1],
num_edges,
)
# link_pred
dataset = GraphDataset([hete], task="link_pred")
split_res = dataset.split(transductive=True)
for edge_type in hete.edge_label_index:
num_edges = hete.edge_label_index[edge_type].shape[1]
edge_0 = 2 * int(0.8 * num_edges)
edge_1 = 2 * int(0.1 * num_edges)
edge_2 = 2 * (num_edges - int(0.8 * num_edges) -
int(0.1 * num_edges))
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1], edge_0)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1], edge_1)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1], edge_2)
# link_pred with specified split type
dataset = GraphDataset([hete], task="link_pred")
link_split_types = [("n1", "e1", "n1"), ("n1", "e2", "n2")]
split_res = dataset.split(transductive=True,
split_types=link_split_types)
for edge_type in hete.edge_label_index:
if edge_type in link_split_types:
num_edges = hete.edge_label_index[edge_type].shape[1]
edge_0 = 2 * int(0.8 * num_edges)
edge_1 = 2 * int(0.1 * num_edges)
edge_2 = 2 * (num_edges - int(0.8 * num_edges) -
int(0.1 * num_edges))
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1],
edge_0)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1],
edge_1)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1],
edge_2)
else:
num_edges = hete.edge_label_index[edge_type].shape[1]
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1],
num_edges)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1],
num_edges)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1],
num_edges)
# link_pred + disjoint
dataset = GraphDataset(
[hete],
task="link_pred",
edge_train_mode="disjoint",
edge_message_ratio=0.5,
)
split_res = dataset.split(
transductive=True,
split_ratio=[0.6, 0.2, 0.2],
)
for edge_type in hete.edge_label_index:
num_edges = hete.edge_label_index[edge_type].shape[1]
edge_0 = int(0.6 * num_edges)
edge_0 = 2 * (edge_0 - int(0.5 * edge_0))
edge_1 = 2 * int(0.2 * num_edges)
edge_2 = 2 * (num_edges - int(0.6 * num_edges) -
int(0.2 * num_edges))
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1],
edge_2,
)
# link pred with edge_split_mode set to "exact"
dataset = GraphDataset([hete],
task="link_pred",
edge_split_mode="approximate")
split_res = dataset.split(transductive=True)
hete_link_train_edge_num = 0
hete_link_test_edge_num = 0
hete_link_val_edge_num = 0
num_edges = 0
for edge_type in hete.edge_label_index:
num_edges += hete.edge_label_index[edge_type].shape[1]
if edge_type in split_res[0][0].edge_label_index:
hete_link_train_edge_num += (
split_res[0][0].edge_label_index[edge_type].shape[1])
if edge_type in split_res[1][0].edge_label_index:
hete_link_test_edge_num += (
split_res[1][0].edge_label_index[edge_type].shape[1])
if edge_type in split_res[2][0].edge_label_index:
hete_link_val_edge_num += (
split_res[2][0].edge_label_index[edge_type].shape[1])
# num_edges_reduced = num_edges - 3
edge_0 = 2 * int(0.8 * num_edges)
edge_1 = 2 * int(0.1 * num_edges)
edge_2 = 2 * (num_edges - int(0.8 * num_edges) - int(0.1 * num_edges))
self.assertEqual(hete_link_train_edge_num, edge_0)
self.assertEqual(hete_link_test_edge_num, edge_1)
self.assertEqual(hete_link_val_edge_num, edge_2)
# link pred with specified types and edge_split_mode set to "exact"
dataset = GraphDataset(
[hete],
task="link_pred",
edge_split_mode="approximate",
)
link_split_types = [("n1", "e1", "n1"), ("n1", "e2", "n2")]
split_res = dataset.split(
transductive=True,
split_types=link_split_types,
)
hete_link_train_edge_num = 0
hete_link_test_edge_num = 0
hete_link_val_edge_num = 0
num_split_type_edges = 0
num_non_split_type_edges = 0
for edge_type in hete.edge_label_index:
if edge_type in link_split_types:
num_split_type_edges += (
hete.edge_label_index[edge_type].shape[1])
else:
num_non_split_type_edges += (
hete.edge_label_index[edge_type].shape[1])
if edge_type in split_res[0][0].edge_label_index:
hete_link_train_edge_num += (
split_res[0][0].edge_label_index[edge_type].shape[1])
if edge_type in split_res[1][0].edge_label_index:
hete_link_test_edge_num += (
split_res[1][0].edge_label_index[edge_type].shape[1])
if edge_type in split_res[2][0].edge_label_index:
hete_link_val_edge_num += (
split_res[2][0].edge_label_index[edge_type].shape[1])
# num_edges_reduced = num_split_type_edges - 3
num_edges = num_split_type_edges
edge_0 = 2 * int(0.8 * num_edges) + num_non_split_type_edges
edge_1 = 2 * int(0.1 * num_edges) + num_non_split_type_edges
edge_2 = 2 * (num_edges - int(0.8 * num_edges) -
int(0.1 * num_edges)) + num_non_split_type_edges
self.assertEqual(hete_link_train_edge_num, edge_0)
self.assertEqual(hete_link_test_edge_num, edge_1)
self.assertEqual(hete_link_val_edge_num, edge_2)
node_feature["citeseer_node"] = citeseer_x
node_label = {}
node_label["cora_node"] = cora_y
node_label["citeseer_node"] = citeseer_y
# prepare undirected edge_indx
for message_type in edge_index:
edge_index[message_type] = torch.cat([
edge_index[message_type],
torch.flip(edge_index[message_type], [0])
],
dim=1)
hete = HeteroGraph(node_feature=node_feature,
node_label=node_label,
edge_index=edge_index,
directed=False)
print(
f"Heterogeneous graph {hete.num_nodes()} nodes, {hete.num_edges()} edges"
)
dataset = GraphDataset([hete], task='node')
dataset_train, dataset_val, dataset_test = dataset.split(
transductive=True, split_ratio=[0.8, 0.1, 0.1])
train_loader = DataLoader(dataset_train,
collate_fn=Batch.collate(),
batch_size=16)
val_loader = DataLoader(dataset_val,
collate_fn=Batch.collate(),
batch_size=16)
test_loader = DataLoader(dataset_test,
pred = logits[node_type][node_idx]
pred = pred.max(1)[1]
acc += pred.eq(batch.node_label[node_type][node_idx].to(device)).sum().item()
total += pred.size(0)
acc /= total
accs.append(acc)
if accs[1] > best_val:
best_val = accs[1]
best_model = copy.deepcopy(model)
return accs
if __name__ == "__main__":
cora_pyg = Planetoid('./cora', 'Cora')
citeseer_pyg = Planetoid('./citeseer', 'CiteSeer')
G = concatenate_citeseer_cora(cora_pyg[0], citeseer_pyg[0])
hete = HeteroGraph(G)
print("Heterogeneous graph {} nodes, {} edges".format(hete.num_nodes, hete.num_edges))
dataset = GraphDataset([hete], task='node')
dataset_train, dataset_val, dataset_test = dataset.split(transductive=True,
split_ratio=[0.8, 0.1, 0.1])
train_loader = DataLoader(dataset_train, collate_fn=Batch.collate(),
batch_size=16)
val_loader = DataLoader(dataset_val, collate_fn=Batch.collate(),
batch_size=16)
test_loader = DataLoader(dataset_test, collate_fn=Batch.collate(),
batch_size=16)
loaders = [train_loader, val_loader, test_loader]
hidden_size = 32
model = HeteroNet(hete, hidden_size, 0.5).to(device)
def test_dataset_hetero_graph_split(self):
G = generate_dense_hete_dataset()
hete = HeteroGraph(G)
# node
dataset = GraphDataset([hete], task='node')
split_res = dataset.split()
for node_type in hete.node_label_index:
num_nodes = int(len(hete.node_label_index[node_type]))
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]), node_0)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]), node_1)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]), node_2)
# node with specified split type
dataset = GraphDataset([hete], task='node')
node_split_types = ['n1']
split_res = dataset.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = int(len(hete.node_label_index[node_type]))
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]), node_0)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]), node_1)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]), node_2)
else:
num_nodes = int(len(hete.node_label_index[node_type]))
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]), num_nodes)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]), num_nodes)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]), num_nodes)
# node with specified split type (string mode)
dataset = GraphDataset([hete], task='node')
node_split_types = 'n1'
split_res = dataset.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = int(len(hete.node_label_index[node_type]))
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]), node_0)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]), node_1)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]), node_2)
else:
num_nodes = int(len(hete.node_label_index[node_type]))
self.assertEqual(
len(split_res[0][0].node_label_index[node_type]), num_nodes)
self.assertEqual(
len(split_res[1][0].node_label_index[node_type]), num_nodes)
self.assertEqual(
len(split_res[2][0].node_label_index[node_type]), num_nodes)
# edge
dataset = GraphDataset([hete], task='edge')
split_res = dataset.split()
for edge_type in hete.edge_label_index:
num_edges = hete.edge_label_index[edge_type].shape[1]
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1], edge_0)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1], edge_1)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1], edge_2)
# edge with specified split type
dataset = GraphDataset([hete], task='edge')
edge_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
split_res = dataset.split(split_types=edge_split_types)
for edge_type in hete.edge_label_index:
if edge_type in edge_split_types:
num_edges = hete.edge_label_index[edge_type].shape[1]
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1], edge_0)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1], edge_1)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1], edge_2)
else:
num_edges = hete.edge_label_index[edge_type].shape[1]
self.assertEqual(
split_res[0][0].edge_label_index[edge_type].shape[1], num_edges)
self.assertEqual(
split_res[1][0].edge_label_index[edge_type].shape[1], num_edges)
self.assertEqual(
split_res[2][0].edge_label_index[edge_type].shape[1], num_edges)
# link_pred
dataset = GraphDataset([hete], task='link_pred')
split_res = dataset.split(transductive=True)
for edge_type in hete.edge_label_index:
num_edges = hete.edge_label_index[edge_type].shape[1]
num_edges_reduced = num_edges - 3
self.assertEqual(split_res[0][0].edge_label_index[edge_type].shape[1],
(2 * (1 + int(0.8 * (num_edges_reduced)))))
self.assertEqual(split_res[1][0].edge_label_index[edge_type].shape[1],
(2 * (1 + (int(0.1 * (num_edges_reduced))))))
self.assertEqual(split_res[2][0].edge_label_index[edge_type].shape[1],
2 * num_edges - 2 * (2 + int(0.1 * num_edges_reduced) +
int(0.8 * num_edges_reduced)))
# link_pred with specified split type
dataset = GraphDataset([hete], task='link_pred')
link_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
split_res = dataset.split(transductive=True, split_types=link_split_types)
for edge_type in hete.edge_label_index:
if edge_type in link_split_types:
num_edges = hete.edge_label_index[edge_type].shape[1]
num_edges_reduced = num_edges - 3
self.assertEqual(split_res[0][0].edge_label_index[edge_type].shape[1],
(2 * (1 + int(0.8 * (num_edges_reduced)))))
self.assertEqual(split_res[1][0].edge_label_index[edge_type].shape[1],
(2 * (1 + (int(0.1 * (num_edges_reduced))))))
self.assertEqual(split_res[2][0].edge_label_index[edge_type].shape[1],
2 * num_edges - 2 * (2 + int(0.1 * num_edges_reduced) +
int(0.8 * num_edges_reduced)))
else:
num_edges = hete.edge_label_index[edge_type].shape[1]
self.assertEqual(split_res[0][0].edge_label_index[edge_type].shape[1],
(1 * (0 + int(1.0 * (num_edges)))))
self.assertEqual(split_res[1][0].edge_label_index[edge_type].shape[1],
(1 * (0 + (int(1.0 * (num_edges))))))
self.assertEqual(split_res[2][0].edge_label_index[edge_type].shape[1],
1 * (0 + (int(1.0 * (num_edges)))))
# link_pred + disjoint
dataset = GraphDataset([hete], task='link_pred', edge_train_mode='disjoint', edge_message_ratio=0.5)
split_res = dataset.split(transductive=True, split_ratio=[0.6, 0.2, 0.2])
for edge_type in hete.edge_label_index:
num_edges = hete.edge_label_index[edge_type].shape[1]
num_edges_reduced = num_edges - 3
edge_0 = (1 + int(0.6 * num_edges_reduced))
edge_0 = 2 * (edge_0 - (1 + int(0.5 * (edge_0 - 2))))
self.assertEqual(split_res[0][0].edge_label_index[edge_type].shape[1], edge_0)
edge_1 = 2 * (1 + int(0.2 * num_edges_reduced))
self.assertEqual(split_res[1][0].edge_label_index[edge_type].shape[1], edge_1)
edge_2 = 2 * int(num_edges) - \
(2 * (1 + int(0.6 * num_edges_reduced))) - edge_1
self.assertEqual(split_res[2][0].edge_label_index[edge_type].shape[1], edge_2)
# link pred with edge_split_mode set to "exact"
dataset = GraphDataset([hete], task='link_pred', edge_split_mode="approximate")
split_res = dataset.split(transductive=True)
hete_link_train_edge_num = 0
hete_link_test_edge_num = 0
hete_link_val_edge_num = 0
num_edges = 0
for edge_type in hete.edge_label_index:
num_edges += hete.edge_label_index[edge_type].shape[1]
if edge_type in split_res[0][0].edge_label_index:
hete_link_train_edge_num += split_res[0][0].edge_label_index[edge_type].shape[1]
if edge_type in split_res[1][0].edge_label_index:
hete_link_test_edge_num += split_res[1][0].edge_label_index[edge_type].shape[1]
if edge_type in split_res[2][0].edge_label_index:
hete_link_val_edge_num += split_res[2][0].edge_label_index[edge_type].shape[1]
num_edges_reduced = num_edges - 3
self.assertEqual(hete_link_train_edge_num,
(2 * (1 + int(0.8 * (num_edges_reduced)))))
self.assertEqual(hete_link_test_edge_num,
(2 * (1 + (int(0.1 * (num_edges_reduced))))))
self.assertEqual(hete_link_val_edge_num,
2 * num_edges - 2 * (2 + int(0.1 * num_edges_reduced) +
int(0.8 * num_edges_reduced)))
# link pred with specified types and edge_split_mode set to "exact"
dataset = GraphDataset([hete], task='link_pred', edge_split_mode="approximate")
link_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
split_res = dataset.split(transductive=True, split_types=link_split_types)
hete_link_train_edge_num = 0
hete_link_test_edge_num = 0
hete_link_val_edge_num = 0
num_split_type_edges = 0
num_non_split_type_edges = 0
for edge_type in hete.edge_label_index:
if edge_type in link_split_types:
num_split_type_edges += hete.edge_label_index[edge_type].shape[1]
else:
num_non_split_type_edges += hete.edge_label_index[edge_type].shape[1]
if edge_type in split_res[0][0].edge_label_index:
hete_link_train_edge_num += split_res[0][0].edge_label_index[edge_type].shape[1]
if edge_type in split_res[1][0].edge_label_index:
hete_link_test_edge_num += split_res[1][0].edge_label_index[edge_type].shape[1]
if edge_type in split_res[2][0].edge_label_index:
hete_link_val_edge_num += split_res[2][0].edge_label_index[edge_type].shape[1]
num_edges_reduced = num_split_type_edges - 3
edge_0 = 2 * (1 + int(0.8 * (num_edges_reduced))) + num_non_split_type_edges
edge_1 = 2 * (1 + int(0.1 * (num_edges_reduced))) + num_non_split_type_edges
edge_2 = 2 * num_split_type_edges - 2 * (2 + int(0.1 * num_edges_reduced) + \
int(0.8 * num_edges_reduced)) + num_non_split_type_edges
self.assertEqual(hete_link_train_edge_num, edge_0)
self.assertEqual(hete_link_test_edge_num, edge_1)
self.assertEqual(hete_link_val_edge_num, edge_2)
def main():
args = arg_parse()
edge_train_mode = args.mode
print('edge train mode: {}'.format(edge_train_mode))
G = nx.read_gpickle(args.data_path)
print(G.number_of_edges())
print('Each node has node ID (n_id). Example: ', G.nodes[0])
print(
'Each edge has edge ID (id) and categorical label (e_label). Example: ',
G[0][5871])
# find num edge types
max_label = 0
labels = []
for u, v, edge_key in G.edges:
l = G[u][v][edge_key]['e_label']
if not l in labels:
labels.append(l)
# labels are consecutive (0-17)
num_edge_types = len(labels)
H = WN_transform(G, num_edge_types)
# The nodes in the graph have the features: node_feature and node_type (just one node type "n1" here)
for node in H.nodes(data=True):
print(node)
break
# The edges in the graph have the features: edge_feature and edge_type ("0" - "17" here)
for edge in H.edges(data=True):
print(edge)
break
hetero = HeteroGraph(H)
hetero = HeteroGraph(edge_index=hetero.edge_index,
edge_feature=hetero.edge_feature,
node_feature=hetero.node_feature,
directed=hetero.is_directed())
if edge_train_mode == "disjoint":
dataset = GraphDataset([hetero],
task='link_pred',
edge_train_mode=edge_train_mode,
edge_message_ratio=args.edge_message_ratio)
else:
dataset = GraphDataset(
[hetero],
task='link_pred',
edge_train_mode=edge_train_mode,
)
dataset_train, dataset_val, dataset_test = dataset.split(
transductive=True, split_ratio=[0.8, 0.1, 0.1])
train_loader = DataLoader(dataset_train,
collate_fn=Batch.collate(),
batch_size=1)
val_loader = DataLoader(dataset_val,
collate_fn=Batch.collate(),
batch_size=1)
test_loader = DataLoader(dataset_test,
collate_fn=Batch.collate(),
batch_size=1)
dataloaders = {
'train': train_loader,
'val': val_loader,
'test': test_loader
}
hidden_size = args.hidden_dim
conv1, conv2 = generate_2convs_link_pred_layers(hetero, HeteroSAGEConv,
hidden_size)
model = HeteroGNN(conv1, conv2, hetero, hidden_size).to(args.device)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
t_accu, v_accu, e_accu = train(model, dataloaders, optimizer, args)
def main():
writer = SummaryWriter()
args = arg_parse()
edge_train_mode = args.mode
print('edge train mode: {}'.format(edge_train_mode))
ppi_graph = read_ppi_data(args.ppi_path)
mode = 'mixed'
if mode == 'ppi':
message_passing_graph = ppi_graph
cmap_graph, knockout_nodes = read_cmap_data(args.data_path)
elif mode == 'mixed':
message_passing_graph, knockout_nodes = (
read_cmap_data(args.data_path, ppi_graph)
)
print('Each node has gene ID. Example: ', message_passing_graph.nodes['ADPGK'])
print('Each edge has de direction. Example', message_passing_graph['ADPGK']['IL1B'])
print('Total num edges: ', message_passing_graph.number_of_edges())
# disjoint edge label
disjoint_split_ratio = 0.1
val_ratio = 0.1
disjoint_edge_label_index = []
val_edges = []
# newly edited
train_edges = []
for u in knockout_nodes:
rand_num = np.random.rand()
if rand_num < disjoint_split_ratio:
# add all edges (cmap only) into edge label index
# cmap is not a multigraph
disjoint_edge_label_index.extend(
[
(u, v, edge_key)
for v in message_passing_graph.successors(u)
for edge_key in message_passing_graph[u][v]
if message_passing_graph[u][v][edge_key]['edge_type'] == 1
]
)
train_edges.extend(
[
(u, v, edge_key)
for v in message_passing_graph.successors(u)
for edge_key in message_passing_graph[u][v]
if message_passing_graph[u][v][edge_key]['edge_type'] == 1
]
)
elif rand_num < disjoint_split_ratio + val_ratio:
val_edges.extend(
[
(u, v, edge_key)
for v in message_passing_graph.successors(u)
for edge_key in message_passing_graph[u][v]
if message_passing_graph[u][v][edge_key]['edge_type'] == 1
]
)
else:
train_edges.extend(
[
(u, v, edge_key)
for v in message_passing_graph.successors(u)
for edge_key in message_passing_graph[u][v]
if message_passing_graph[u][v][edge_key]['edge_type'] == 1
]
)
# add default node types for message_passing_graph
for node in message_passing_graph.nodes:
message_passing_graph.nodes[node]['node_type'] = 0
print('Num edges to predict: ', len(disjoint_edge_label_index))
print('Num edges in val: ', len(val_edges))
print('Num edges in train: ', len(train_edges))
graph = HeteroGraph(
message_passing_graph,
custom={
"general_splits": [
train_edges,
val_edges
],
"disjoint_split": disjoint_edge_label_index,
"task": "link_pred"
}
)
graphs = [graph]
graphDataset = GraphDataset(
graphs,
task="link_pred",
edge_train_mode="disjoint"
)
# Transform dataset
# de direction (currently using homogeneous graph)
num_edge_types = 2
graphDataset = graphDataset.apply_transform(
cmap_transform, num_edge_types=num_edge_types, deep_copy=False
)
print('Number of node features: ', graphDataset.num_node_features())
# split dataset
dataset = {}
dataset['train'], dataset['val'] = graphDataset.split(transductive=True)
# sanity check
print(f"dataset['train'][0].edge_label_index.keys(): {dataset['train'][0].edge_label_index.keys()}")
print(f"dataset['train'][0].edge_label_index[(0, 1, 0)].shape[1]: {dataset['train'][0].edge_label_index[(0, 1, 0)].shape[1]}")
print(f"dataset['val'][0].edge_label_index.keys(): {dataset['val'][0].edge_label_index.keys()}")
print(f"dataset['val'][0].edge_label_index[(0, 1, 0)].shape[1]: {dataset['val'][0].edge_label_index[(0, 1, 0)].shape[1]}")
print(f"len(list(dataset['train'][0].G.edges)): {len(list(dataset['train'][0].G.edges))}")
print(f"len(list(dataset['val'][0].G.edges)): {len(list(dataset['val'][0].G.edges))}")
print(f"list(dataset['train'][0].G.edges)[:10]: {list(dataset['train'][0].G.edges)[:10]}")
print(f"list(dataset['val'][0].G.edges)[:10]: {list(dataset['val'][0].G.edges)[:10]}")
# node feature dimension
input_dim = dataset['train'].num_node_features()
edge_feat_dim = dataset['train'].num_edge_features()
num_classes = dataset['train'].num_edge_labels()
print(
'Node feature dim: {}; edge feature dim: {}; num classes: {}.'.format(
input_dim, edge_feat_dim, num_classes
)
)
exit()
# relation type is both used for edge features and edge labels
model = Net(input_dim, edge_feat_dim, num_classes, args).to(args.device)
optimizer = torch.optim.Adam(
model.parameters(), lr=0.001, weight_decay=5e-3
)
follow_batch = [] # e.g., follow_batch = ['edge_index']
dataloaders = {
split: DataLoader(
ds, collate_fn=Batch.collate(follow_batch),
batch_size=1, shuffle=(split == 'train')
)
for split, ds in dataset.items()
}
print('Graphs after split: ')
for key, dataloader in dataloaders.items():
for batch in dataloader:
print(key, ': ', batch)
train(model, dataloaders, optimizer, args, writer=writer)
def test_hetero_multigraph_split(self):
G = generate_dense_hete_multigraph()
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)
# node
hete_node = hete.split(task='node')
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
edge_0 = int(num_edges * 0.8)
edge_1 = int(num_edges * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
# link prediction
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
# calculate the expected edge num for each splitted subgraph
edge_0, edge_1, edge_2 = 0, 0, 0
for _, val in hete.edge_label_index.items():
num_edges = val.shape[1]
edge_0 += int(0.5 * num_edges)
edge_1 += int(0.3 * num_edges)
edge_2 += num_edges - int(0.5 * num_edges) - int(0.3 * num_edges)
train_edge_num = sum([
hete_link[0].edge_label[message_type].shape[0]
for message_type in hete_link[0].edge_label
])
val_edge_num = sum([
hete_link[1].edge_label[message_type].shape[0]
for message_type in hete_link[1].edge_label
])
test_edge_num = sum([
hete_link[2].edge_label[message_type].shape[0]
for message_type in hete_link[2].edge_label
])
self.assertEqual(train_edge_num, edge_0)
self.assertEqual(val_edge_num, edge_1)
self.assertEqual(test_edge_num, edge_2)
def test_hetero_graph_split(self):
# directed G
G = generate_dense_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,
)
# node
hete_node = hete.split()
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
# node with specified split type
node_split_types = ['n1']
hete_node = hete.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = len(hete.node_label_index[node_type])
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
else:
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
edge_0 = int(num_edges * 0.8)
edge_1 = int(num_edges * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
# edge with specified split type
edge_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
hete_edge = hete.split(task='edge', split_types=edge_split_types)
for edge_type in hete.edge_label_index:
if edge_type in edge_split_types:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
edge_0 = int(num_edges * 0.8)
edge_1 = int(num_edges * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
else:
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
# link_pred
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
for key, val in hete.edge_label_index.items():
num_edges = val.shape[1]
edge_0 = int(0.5 * num_edges)
edge_1 = int(0.3 * num_edges)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(hete_link[0].edge_label[key].shape[0], edge_0)
self.assertEqual(hete_link[1].edge_label[key].shape[0], edge_1)
self.assertEqual(hete_link[2].edge_label[key].shape[0], edge_2)
# undirected G
G = generate_dense_hete_graph(directed=False)
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=False)
# node
hete_node = hete.split()
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
# node with specified split type
node_split_types = ['n1']
hete_node = hete.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = len(hete.node_label_index[node_type])
node_0 = int(num_nodes * 0.8)
node_1 = int(num_nodes * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
else:
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.num_edges(edge_type))
edge_0 = int(num_edges * 0.8)
edge_1 = int(num_edges * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
# edge with specified split type
edge_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
hete_edge = hete.split(task='edge', split_types=edge_split_types)
for edge_type in hete.edge_label_index:
if edge_type in edge_split_types:
num_edges = int(hete.num_edges(edge_type))
edge_0 = int(num_edges * 0.8)
edge_1 = int(num_edges * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
else:
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
# link_pred
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
for key, val in hete.edge_label_index.items():
num_edges = int(val.shape[1] / 2)
edge_0 = 2 * int(0.5 * num_edges)
edge_1 = 2 * int(0.3 * num_edges)
edge_2 = 2 * (num_edges - int(0.5 * num_edges) -
int(0.3 * num_edges))
self.assertEqual(hete_link[0].edge_label[key].shape[0], edge_0)
self.assertEqual(hete_link[1].edge_label[key].shape[0], edge_1)
self.assertEqual(hete_link[2].edge_label[key].shape[0], edge_2)
def test_hetero_multigraph_split(self):
G = generate_dense_hete_multigraph()
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
)
# node
hete_node = hete.split(task='node')
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
# link prediction
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
# calculate the expected edge num for each splitted subgraph
hete_link_train_edge_num = 0
hete_link_val_edge_num = 0
hete_link_test_edge_num = 0
for _, val in hete.edge_label_index.items():
val_length = val.shape[1]
val_length_reduced = val_length - 3
hete_link_train_edge_num += 1 + int(0.5 * val_length_reduced)
hete_link_val_edge_num += 1 + int(0.3 * val_length_reduced)
hete_link_test_edge_num += (
val_length
- 2
- int(0.5 * val_length_reduced)
- int(0.3 * val_length_reduced)
)
train_edge_num = sum([
hete_link[0].edge_label[message_type].shape[0]
for message_type in hete_link[0].edge_label
])
val_edge_num = sum([
hete_link[1].edge_label[message_type].shape[0]
for message_type in hete_link[1].edge_label
])
test_edge_num = sum([
hete_link[2].edge_label[message_type].shape[0]
for message_type in hete_link[2].edge_label
])
self.assertEqual(
train_edge_num,
hete_link_train_edge_num
)
self.assertEqual(
val_edge_num,
hete_link_val_edge_num
)
self.assertEqual(
test_edge_num,
hete_link_test_edge_num,
)
def test_hetero_graph_none(self):
G = generate_simple_hete_graph(no_edge_type=True)
hete = HeteroGraph(G)
message_types = hete.message_types
for message_type in message_types:
self.assertEqual(message_type[1], None)
def test_hetero_graph_split(self):
G = generate_dense_hete_graph()
hete = HeteroGraph(G)
hete_node = hete.split()
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(len(hete_node[0].node_label_index[node_type]),
node_0)
self.assertEqual(len(hete_node[1].node_label_index[node_type]),
node_1)
self.assertEqual(len(hete_node[2].node_label_index[node_type]),
node_2)
# node with specified split type
node_split_types = ['n1']
hete_node = hete.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if (node_type in node_split_types):
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(len(hete_node[0].node_label_index[node_type]),
node_0)
self.assertEqual(len(hete_node[1].node_label_index[node_type]),
node_1)
self.assertEqual(len(hete_node[2].node_label_index[node_type]),
node_2)
else:
self.assertEqual(len(hete_node[0].node_label_index[node_type]),
len(hete.node_label_index[node_type]))
self.assertEqual(len(hete_node[1].node_label_index[node_type]),
len(hete.node_label_index[node_type]))
self.assertEqual(len(hete_node[2].node_label_index[node_type]),
len(hete.node_label_index[node_type]))
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0)
self.assertEqual(hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1)
self.assertEqual(hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2)
# edge with specified split type
edge_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
hete_edge = hete.split(task='edge', split_types=edge_split_types)
for edge_type in hete.edge_label_index:
if (edge_type in edge_split_types):
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1], edge_0)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1], edge_1)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1], edge_2)
else:
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1])
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1])
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1])
# link_pred
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
# calculate the expected edge num for each splitted subgraph
hete_link_train_edge_num, hete_link_val_edge_num, hete_link_test_edge_num = 0, 0, 0
for key, val in hete.edge_label_index.items():
val_length = val.shape[1]
val_length_reduced = val_length - 3
hete_link_train_edge_num += 1 + int(0.5 * val_length_reduced)
hete_link_val_edge_num += 1 + int(0.3 * val_length_reduced)
hete_link_test_edge_num += \
val_length - 2 - int(0.5 * val_length_reduced) - int(0.3 * val_length_reduced)
self.assertEqual(len(hete_link[0].edge_label),
hete_link_train_edge_num)
self.assertEqual(len(hete_link[1].edge_label), hete_link_val_edge_num)
self.assertEqual(len(hete_link[2].edge_label), hete_link_test_edge_num)
def test_hetero_graph_split(self):
# directed G
G = generate_dense_hete_graph()
hete = HeteroGraph(G)
hete_node = hete.split()
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
# node with specified split type
node_split_types = ['n1']
hete_node = hete.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
else:
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
# edge with specified split type
edge_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
hete_edge = hete.split(task='edge', split_types=edge_split_types)
for edge_type in hete.edge_label_index:
if edge_type in edge_split_types:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
else:
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
# link_pred
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
for key, val in hete.edge_label_index.items():
val_length = val.shape[1]
val_length_reduced = val_length - 3
hete_link_train_edge_num = 1 + int(0.5 * val_length_reduced)
hete_link_val_edge_num = 1 + int(0.3 * val_length_reduced)
hete_link_test_edge_num = (val_length - 2 -
int(0.5 * val_length_reduced) -
int(0.3 * val_length_reduced))
self.assertEqual(hete_link[0].edge_label[key].shape[0],
hete_link_train_edge_num)
self.assertEqual(hete_link[1].edge_label[key].shape[0],
hete_link_val_edge_num)
self.assertEqual(hete_link[2].edge_label[key].shape[0],
hete_link_test_edge_num)
# undirected G
G = generate_dense_hete_graph(directed=False)
hete = HeteroGraph(G)
hete_node = hete.split()
for node_type in hete.node_label_index:
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
# node with specified split type
node_split_types = ['n1']
hete_node = hete.split(split_types=node_split_types)
for node_type in hete.node_label_index:
if node_type in node_split_types:
num_nodes = len(hete.node_label_index[node_type])
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
node_0,
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
node_1,
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
node_2,
)
else:
self.assertEqual(
len(hete_node[0].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[1].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
self.assertEqual(
len(hete_node[2].node_label_index[node_type]),
len(hete.node_label_index[node_type]),
)
# edge
hete_edge = hete.split(task='edge')
for edge_type in hete.edge_label_index:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
# edge with specified split type
edge_split_types = [('n1', 'e1', 'n1'), ('n1', 'e2', 'n2')]
hete_edge = hete.split(task='edge', split_types=edge_split_types)
for edge_type in hete.edge_label_index:
if edge_type in edge_split_types:
num_edges = int(hete.edge_label_index[edge_type].shape[1])
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
edge_0,
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
edge_1,
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
edge_2,
)
else:
self.assertEqual(
hete_edge[0].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[1].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
self.assertEqual(
hete_edge[2].edge_label_index[edge_type].shape[1],
hete.edge_label_index[edge_type].shape[1],
)
hete_link = hete.split(task='link_pred', split_ratio=[0.5, 0.3, 0.2])
for key, val in hete.edge_label_index.items():
val_length = val.shape[1]
hete_link_train_edge_num = (2 *
(1 + int(0.5 *
(int(val_length / 2) - 3))))
hete_link_val_edge_num = (2 * (1 + int(0.3 *
(int(val_length / 2) - 3))))
hete_link_test_edge_num = (val_length - hete_link_train_edge_num -
hete_link_val_edge_num)
self.assertEqual(hete_link[0].edge_label[key].shape[0],
hete_link_train_edge_num)
self.assertEqual(hete_link[1].edge_label[key].shape[0],
hete_link_val_edge_num)
self.assertEqual(hete_link[2].edge_label[key].shape[0],
hete_link_test_edge_num)
def test_secure_split_heterogeneous(self):
G = generate_simple_small_hete_graph()
graph = HeteroGraph(G)
graph = HeteroGraph(node_label=graph.node_label,
edge_index=graph.edge_index,
edge_label=graph.edge_label,
directed=True)
graphs = [graph]
# node task
dataset = GraphDataset(graphs, task="node")
split_res = dataset.split()
for node_type in graph.node_label_index:
num_nodes = graph.node_label_index[node_type].shape[0]
num_nodes_reduced = num_nodes - 3
node_0 = 1 + int(num_nodes_reduced * 0.8)
node_1 = 1 + int(num_nodes_reduced * 0.1)
node_2 = num_nodes - node_0 - node_1
node_size = [node_0, node_1, node_2]
for i in range(3):
self.assertEqual(
split_res[i][0].node_label_index[node_type].shape[0],
node_size[i])
self.assertEqual(
split_res[i][0].node_label[node_type].shape[0],
node_size[i])
# edge task
dataset = GraphDataset(graphs, task="edge")
split_res = dataset.split()
for message_type in graph.edge_label_index:
num_edges = graph.edge_label_index[message_type].shape[1]
num_edges_reduced = num_edges - 3
edge_0 = 1 + int(num_edges_reduced * 0.8)
edge_1 = 1 + int(num_edges_reduced * 0.1)
edge_2 = num_edges - edge_0 - edge_1
edge_size = [edge_0, edge_1, edge_2]
for i in range(3):
self.assertEqual(
split_res[i][0].edge_label_index[message_type].shape[1],
edge_size[i])
self.assertEqual(
split_res[i][0].edge_label[message_type].shape[0],
edge_size[i])
# link_pred task
dataset = GraphDataset(graphs, task="link_pred")
split_res = dataset.split()
for message_type in graph.edge_label_index:
num_edges = graph.edge_label_index[message_type].shape[1]
num_edges_reduced = num_edges - 3
edge_0 = 2 * (1 + int(num_edges_reduced * 0.8))
edge_1 = 2 * (1 + int(num_edges_reduced * 0.1))
edge_2 = 2 * num_edges - edge_0 - edge_1
edge_size = [edge_0, edge_1, edge_2]
for i in range(3):
self.assertEqual(
split_res[i][0].edge_label_index[message_type].shape[1],
edge_size[i])
self.assertEqual(
split_res[i][0].edge_label[message_type].shape[0],
edge_size[i])
# Dictionary of node features
node_feature = {}
node_feature["paper"] = data['feature']
# Dictionary of node labels
node_label = {}
node_label["paper"] = data['label']
# Load the train, validation and test indices
train_idx = {"paper": data['train_idx'].to(args.device)}
val_idx = {"paper": data['val_idx'].to(args.device)}
test_idx = {"paper": data['test_idx'].to(args.device)}
# Construct a deepsnap tensor backend HeteroGraph
hetero_graph = HeteroGraph(node_feature=node_feature,
node_label=node_label,
edge_index=edge_index,
directed=True)
print(
f"ACM heterogeneous graph: {hetero_graph.num_nodes()} nodes, {hetero_graph.num_edges()} edges"
)
# 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
