def test_gae():
model = GAE(encoder=lambda x: x)
model.reset_parameters()
x = torch.Tensor([[1, -1], [1, 2], [2, 1]])
z = model.encode(x)
assert z.tolist() == x.tolist()
adj = model.decoder.forward_all(z)
assert adj.tolist() == torch.sigmoid(
torch.Tensor([[+2, -1, +1], [-1, +5, +4], [+1, +4, +5]])).tolist()
edge_index = torch.tensor([[0, 1], [1, 2]])
value = model.decode(z, edge_index)
assert value.tolist() == torch.sigmoid(torch.Tensor([-1, 4])).tolist()
edge_index = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
data = Data(edge_index=edge_index)
data.num_nodes = edge_index.max().item() + 1
data = train_test_split_edges(data, val_ratio=0.2, test_ratio=0.3)
z = torch.randn(11, 16)
loss = model.recon_loss(z, data.train_pos_edge_index)
assert loss.item() > 0
auc, ap = model.test(z, data.val_pos_edge_index, data.val_neg_edge_index)
assert auc >= 0 and auc <= 1 and ap >= 0 and ap <= 1
def test_gae():
model = GAE(encoder=lambda x: x)
model.reset_parameters()
x = torch.Tensor([[1, -1], [1, 2], [2, 1]])
z = model.encode(x)
assert z.tolist() == x.tolist()
adj = model.decode(z)
assert adj.tolist() == torch.sigmoid(
torch.Tensor([[+2, -1, +1], [-1, +5, +4], [+1, +4, +5]])).tolist()
edge_index = torch.tensor([[0, 1], [1, 2]])
value = model.decode_indices(z, edge_index)
assert value.tolist() == torch.sigmoid(torch.Tensor([-1, 4])).tolist()
edge_index = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
data = Data(edge_index=edge_index)
data = model.split_edges(data, val_ratio=0.2, test_ratio=0.3)
assert data.val_pos_edge_index.size() == (2, 2)
assert data.val_neg_edge_index.size() == (2, 2)
assert data.test_pos_edge_index.size() == (2, 3)
assert data.test_neg_edge_index.size() == (2, 3)
assert data.train_pos_edge_index.size() == (2, 5)
assert data.train_neg_adj_mask.size() == (11, 11)
assert data.train_neg_adj_mask.sum().item() == (11**2 - 11) / 2 - 4 - 6 - 5
z = torch.randn(11, 16)
loss = model.recon_loss(z, data.train_pos_edge_index)
assert loss.item() > 0
auc, ap = model.test(z, data.val_pos_edge_index, data.val_neg_edge_index)
assert auc >= 0 and auc <= 1 and ap >= 0 and ap <= 1
def test_gae():
model = GAE(encoder=lambda x: x)
model.reset_parameters()
x = torch.Tensor([[1, -1], [1, 2], [2, 1]])
z = model.encode(x)
assert z.tolist() == x.tolist()
adj = model.decoder.forward_all(z)
assert adj.tolist() == torch.sigmoid(
torch.Tensor([[+2, -1, +1], [-1, +5, +4], [+1, +4, +5]])).tolist()
edge_index = torch.tensor([[0, 1], [1, 2]])
value = model.decode(z, edge_index)
assert value.tolist() == torch.sigmoid(torch.Tensor([-1, 4])).tolist()
edge_index = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
data = Data(edge_index=edge_index, num_nodes=11)
transform = RandomLinkSplit(split_labels=True,
add_negative_train_samples=False)
train_data, val_data, test_data = transform(data)
z = torch.randn(11, 16)
loss = model.recon_loss(z, train_data.pos_edge_label_index)
assert loss.item() > 0
auc, ap = model.test(z, val_data.pos_edge_label_index,
val_data.neg_edge_label_index)
assert auc >= 0 and auc <= 1 and ap >= 0 and ap <= 1
