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
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
|
import pytest
import torch
from torch_geometric.explain import (
DummyExplainer,
Explainer,
ModelConfig,
unfaithfulness,
)
class DummyModel(torch.nn.Module):
def __init__(self, model_config: ModelConfig):
super().__init__()
self.model_config = model_config
def forward(self, x, edge_index):
if self.model_config.return_type.value == 'probs':
x = x.softmax(dim=-1)
elif self.model_config.return_type.value == 'log_probs':
x = x.log_softmax(dim=-1)
return x
@pytest.mark.parametrize('top_k', [None, 2])
@pytest.mark.parametrize('explanation_type', ['model', 'phenomenon'])
@pytest.mark.parametrize('node_mask_type', ['common_attributes', 'attributes'])
@pytest.mark.parametrize('return_type', ['raw', 'probs', 'log_probs'])
def test_unfaithfulness(top_k, explanation_type, node_mask_type, return_type):
x = torch.randn(8, 4)
edge_index = torch.tensor([
[0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7],
[1, 0, 2, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7, 6],
])
model_config = ModelConfig(
mode='multiclass_classification',
task_level='node',
return_type=return_type,
)
explainer = Explainer(
DummyModel(model_config),
algorithm=DummyExplainer(),
explanation_type=explanation_type,
node_mask_type=node_mask_type,
edge_mask_type='object',
model_config=model_config,
)
target = None
if explanation_type == 'phenomenon':
target = torch.randint(0, x.size(1), (x.size(0), ))
explanation = explainer(x, edge_index, target=target,
index=torch.arange(4))
metric = unfaithfulness(explainer, explanation, top_k)
assert metric >= 0. and metric <= 1.
|
