1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
|
import torch
from torch_geometric.data import Data
from torch_geometric.testing import withPackage
from torch_geometric.transforms import LaplacianLambdaMax
@withPackage('scipy')
def test_laplacian_lambda_max():
out = str(LaplacianLambdaMax())
assert out == 'LaplacianLambdaMax(normalization=None)'
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)
edge_attr = torch.tensor([1, 1, 2, 2], dtype=torch.float)
data = Data(edge_index=edge_index, edge_attr=edge_attr, num_nodes=3)
out = LaplacianLambdaMax(normalization=None, is_undirected=True)(data)
assert len(out) == 4
assert torch.allclose(torch.tensor(out.lambda_max), torch.tensor(4.732049))
data = Data(edge_index=edge_index, edge_attr=edge_attr, num_nodes=3)
out = LaplacianLambdaMax(normalization='sym', is_undirected=True)(data)
assert len(out) == 4
assert torch.allclose(torch.tensor(out.lambda_max), torch.tensor(2.0))
data = Data(edge_index=edge_index, edge_attr=edge_attr, num_nodes=3)
out = LaplacianLambdaMax(normalization='rw', is_undirected=True)(data)
assert len(out) == 4
assert torch.allclose(torch.tensor(out.lambda_max), torch.tensor(2.0))
data = Data(edge_index=edge_index, edge_attr=torch.randn(4, 2),
num_nodes=3)
out = LaplacianLambdaMax(normalization=None)(data)
assert len(out) == 4
assert torch.allclose(torch.tensor(out.lambda_max), torch.tensor(3.0))
|
