def test_split_edge_case(self):
G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y = \
simple_networkx_graph()
dg = Graph(G)
dg_node = dg.split()
dg_num_nodes_reduced = dg.num_nodes - 3
self.assertEqual(
dg_node[0].node_label_index.shape[0], 1 + int(dg_num_nodes_reduced * 0.8))
self.assertEqual(
dg_node[1].node_label_index.shape[0], 1 + int(dg_num_nodes_reduced * 0.1))
self.assertEqual(
dg_node[2].node_label_index.shape[0], dg.num_nodes - 2 -
int(dg_num_nodes_reduced * 0.8) - int(dg_num_nodes_reduced * 0.1))
dg_edge = dg.split(task='edge')
dg_num_edges_reduced = dg.num_edges - 3
edge_0 = 1 + int(dg_num_edges_reduced * 0.8)
edge_1 = 1 + int(dg_num_edges_reduced * 0.1)
edge_2 = dg.num_edges - edge_0 - edge_1
self.assertEqual(dg_edge[0].edge_label_index.shape[1], edge_0)
self.assertEqual(dg_edge[1].edge_label_index.shape[1], edge_1)
self.assertEqual(dg_edge[2].edge_label_index.shape[1], edge_2)
dg_link = dg.split(task='link_pred')
dg_num_edges_reduced = dg.num_edges - 3
edge_0 = 1 + int(dg_num_edges_reduced * 0.8)
edge_1 = 1 + int(dg_num_edges_reduced * 0.1)
edge_2 = dg.num_edges - edge_0 - edge_1
self.assertEqual(dg_link[0].edge_label_index.shape[1], edge_0)
self.assertEqual(dg_link[1].edge_label_index.shape[1], edge_1)
self.assertEqual(dg_link[2].edge_label_index.shape[1], edge_2)
def test_split_edge_case(self):
G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y = (
simple_networkx_graph())
Graph.add_node_attr(G, "node_label", y)
Graph.add_edge_attr(G, "edge_label", edge_y)
dg = Graph(G)
dg_node = dg.split()
dg_num_nodes = dg.num_nodes
node_0 = int(dg_num_nodes * 0.8)
node_1 = int(dg_num_nodes * 0.1)
node_2 = dg_num_nodes - node_0 - node_1
self.assertEqual(dg_node[0].node_label_index.shape[0], node_0)
self.assertEqual(dg_node[1].node_label_index.shape[0], node_1)
self.assertEqual(dg_node[2].node_label_index.shape[0], node_2)
dg_edge = dg.split(task="edge")
dg_num_edges = dg.num_edges
edge_0 = int(dg_num_edges * 0.8)
edge_1 = int(dg_num_edges * 0.1)
edge_2 = dg_num_edges - edge_0 - edge_1
self.assertEqual(dg_edge[0].edge_label_index.shape[1], edge_0)
self.assertEqual(dg_edge[1].edge_label_index.shape[1], edge_1)
self.assertEqual(dg_edge[2].edge_label_index.shape[1], edge_2)
dg_link = dg.split(task="link_pred")
edge_0 = int(dg_num_edges * 0.8)
edge_1 = int(dg_num_edges * 0.1)
edge_2 = dg.num_edges - edge_0 - edge_1
self.assertEqual(dg_link[0].edge_label_index.shape[1], edge_0)
self.assertEqual(dg_link[1].edge_label_index.shape[1], edge_1)
self.assertEqual(dg_link[2].edge_label_index.shape[1], edge_2)
def test_split_edge_case(self):
G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y = (
simple_networkx_graph()
)
dg = Graph(
node_label=y,
edge_label=edge_y,
edge_index=edge_index,
directed=True
)
dg_node = dg.split()
dg_num_nodes = dg.num_nodes
self.assertEqual(
dg_node[0].node_label_index.shape[0],
int(dg_num_nodes * 0.8),
)
self.assertEqual(
dg_node[1].node_label_index.shape[0],
int(dg_num_nodes * 0.1),
)
self.assertEqual(
dg_node[2].node_label_index.shape[0],
dg.num_nodes
- int(dg_num_nodes * 0.8)
- int(dg_num_nodes * 0.1)
)
dg_edge = dg.split(task="edge")
dg_num_edges = dg.num_edges
edge_0 = int(dg_num_edges * 0.8)
edge_1 = int(dg_num_edges * 0.1)
edge_2 = dg.num_edges - edge_0 - edge_1
self.assertEqual(
dg_edge[0].edge_label_index.shape[1],
edge_0
)
self.assertEqual(
dg_edge[1].edge_label_index.shape[1],
edge_1
)
self.assertEqual(
dg_edge[2].edge_label_index.shape[1],
edge_2
)
dg_link = dg.split(task="link_pred")
dg_num_edges = dg.num_edges
edge_0 = int(dg_num_edges * 0.8)
edge_1 = int(dg_num_edges * 0.1)
edge_2 = dg.num_edges - edge_0 - edge_1
self.assertEqual(dg_link[0].edge_label_index.shape[1], edge_0)
self.assertEqual(dg_link[1].edge_label_index.shape[1], edge_1)
self.assertEqual(dg_link[2].edge_label_index.shape[1], edge_2)
def test_split(self):
pyg_dataset = Planetoid("./cora", "Cora")
x = pyg_dataset[0].x
y = pyg_dataset[0].y
edge_index = pyg_dataset[0].edge_index
row, col = copy.deepcopy(edge_index)
mask = row < col
row, col = row[mask], col[mask]
edge_index = torch.stack([row, col], dim=0)
edge_index = torch.cat(
[edge_index, torch.flip(edge_index, [0])], dim=1)
dg = Graph(node_feature=x,
node_label=y,
edge_index=edge_index,
directed=False)
dg_node = dg.split()
dg_num_nodes = dg.num_nodes
node_0 = int(dg_num_nodes * 0.8)
node_1 = int(dg_num_nodes * 0.1)
node_2 = dg_num_nodes - node_0 - node_1
self.assertEqual(dg_node[0].node_label_index.shape[0], node_0)
self.assertEqual(dg_node[1].node_label_index.shape[0], node_1)
self.assertEqual(dg_node[2].node_label_index.shape[0], node_2)
for split_ratio in [[0.1, 0.4, 0.5], [0.4, 0.3, 0.3], [0.7, 0.2, 0.1]]:
dg_link_custom = (dg.split(task="link_pred",
split_ratio=split_ratio))
dg_num_edges = dg.num_edges
edge_0 = 2 * int(split_ratio[0] * dg_num_edges)
edge_1 = 2 * int(split_ratio[1] * dg_num_edges)
edge_2 = 2 * (dg_num_edges - int(split_ratio[0] * dg_num_edges) -
int(split_ratio[1] * dg_num_edges))
self.assertEqual(
dg_link_custom[0].edge_label_index.shape[1],
edge_0,
)
self.assertEqual(
dg_link_custom[1].edge_label_index.shape[1],
edge_1,
)
self.assertEqual(
dg_link_custom[2].edge_label_index.shape[1],
edge_2,
)
def test_split(self):
pyg_dataset = Planetoid("./cora", "Cora")
x = pyg_dataset[0].x
y = pyg_dataset[0].y
edge_index = pyg_dataset[0].edge_index
row, col = copy.deepcopy(edge_index)
mask = row < col
row, col = row[mask], col[mask]
edge_index = torch.stack([row, col], dim=0)
edge_index = torch.cat(
[edge_index, torch.flip(edge_index, [0])], dim=1
)
dg = Graph(
node_feature=x, node_label=y,
edge_index=edge_index, directed=False
)
dg_node = dg.split()
dg_num_nodes_reduced = dg.num_nodes - 3
self.assertEqual(
dg_node[0].node_label_index.shape[0],
1 + int(dg_num_nodes_reduced * 0.8),
)
self.assertEqual(
dg_node[1].node_label_index.shape[0],
1 + int(dg_num_nodes_reduced * 0.1),
)
self.assertEqual(
dg_node[2].node_label_index.shape[0],
dg.num_nodes
- 2
- int(dg_num_nodes_reduced * 0.8)
- int(dg_num_nodes_reduced * 0.1),
)
dg_link = dg.split(task="link_pred")
dg_num_edges_reduced = dg.num_edges - 3
edge_0 = 2 * (1 + int(dg_num_edges_reduced * 0.8))
edge_1 = 2 * (1 + int(dg_num_edges_reduced * 0.1))
edge_2 = dg.num_edges * 2 - edge_0 - edge_1
self.assertEqual(dg_link[0].edge_label_index.shape[1], edge_0)
self.assertEqual(dg_link[1].edge_label_index.shape[1], edge_1)
self.assertEqual(dg_link[2].edge_label_index.shape[1], edge_2)
for message_ratio in [0.1, 0.2, 0.4, 0.8]:
dg_link_resample = (
dg_link[0].clone().resample_disjoint(
message_ratio=message_ratio,
)
)
positive_edge_num = (
int(0.5 * dg_link[0].clone().edge_label_index.shape[1])
)
self.assertEqual(
dg_link_resample.edge_label_index.shape[1],
2 * (
positive_edge_num
- 1
- int(message_ratio * (positive_edge_num - 2))
)
)
for split_ratio in [[0.1, 0.4, 0.5], [0.4, 0.3, 0.3], [0.7, 0.2, 0.1]]:
dg_link_custom = (
dg.split(task="link_pred", split_ratio=split_ratio)
)
dg_num_edges_reduced = dg.num_edges - 3
edge_0 = 2 * (1 + int(dg_num_edges_reduced * split_ratio[0]))
self.assertEqual(
dg_link_custom[0].edge_label_index.shape[1],
edge_0,
)
edge_1 = (
2 * (
1
+ int(split_ratio[0] * dg_num_edges_reduced)
+ 1
+ int(split_ratio[1] * dg_num_edges_reduced)
)
- edge_0
)
self.assertEqual(
dg_link_custom[1].edge_label_index.shape[1],
edge_1,
)
edge_2 = dg.num_edges * 2 - edge_0 - edge_1
self.assertEqual(
dg_link_custom[2].edge_label_index.shape[1],
edge_2,
)
