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
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
|
import os.path as osp
from typing import Optional, Union
import pytest
import torch
from torch_geometric.data import Data
from torch_geometric.explain import Explanation
from torch_geometric.explain.config import MaskType
from torch_geometric.testing import withPackage
def create_random_explanation(
data: Data,
node_mask_type: Optional[Union[MaskType, str]] = None,
edge_mask_type: Optional[Union[MaskType, str]] = None,
):
if node_mask_type is not None:
node_mask_type = MaskType(node_mask_type)
if edge_mask_type is not None:
edge_mask_type = MaskType(edge_mask_type)
if node_mask_type == MaskType.object:
node_mask = torch.rand(data.x.size(0), 1)
elif node_mask_type == MaskType.common_attributes:
node_mask = torch.rand(1, data.x.size(1))
elif node_mask_type == MaskType.attributes:
node_mask = torch.rand_like(data.x)
else:
node_mask = None
if edge_mask_type == MaskType.object:
edge_mask = torch.rand(data.edge_index.size(1))
else:
edge_mask = None
return Explanation( # Create explanation.
node_mask=node_mask,
edge_mask=edge_mask,
x=data.x,
edge_index=data.edge_index,
edge_attr=data.edge_attr,
)
@pytest.mark.parametrize('node_mask_type',
[None, 'object', 'common_attributes', 'attributes'])
@pytest.mark.parametrize('edge_mask_type', [None, 'object'])
def test_available_explanations(data, node_mask_type, edge_mask_type):
expected = []
if node_mask_type is not None:
expected.append('node_mask')
if edge_mask_type is not None:
expected.append('edge_mask')
explanation = create_random_explanation(
data,
node_mask_type=node_mask_type,
edge_mask_type=edge_mask_type,
)
assert set(explanation.available_explanations) == set(expected)
def test_validate_explanation(data):
explanation = create_random_explanation(data)
explanation.validate(raise_on_error=True)
with pytest.raises(ValueError, match="with 5 nodes"):
explanation = create_random_explanation(data, node_mask_type='object')
explanation.x = torch.randn(5, 5)
explanation.validate(raise_on_error=True)
with pytest.raises(ValueError, match="with 4 features"):
explanation = create_random_explanation(data, 'attributes')
explanation.x = torch.randn(4, 4)
explanation.validate(raise_on_error=True)
with pytest.raises(ValueError, match="with 7 edges"):
explanation = create_random_explanation(data, edge_mask_type='object')
explanation.edge_index = torch.randint(0, 4, (2, 7))
explanation.validate(raise_on_error=True)
def test_node_mask(data):
node_mask = torch.tensor([[1.], [0.], [1.], [0.]])
explanation = Explanation(
node_mask=node_mask,
x=data.x,
edge_index=data.edge_index,
edge_attr=data.edge_attr,
)
explanation.validate(raise_on_error=True)
out = explanation.get_explanation_subgraph()
assert out.node_mask.size() == (2, 1)
assert (out.node_mask > 0.0).sum() == 2
assert out.x.size() == (2, 3)
assert out.edge_index.size(1) <= 6
assert out.edge_index.size(1) == out.edge_attr.size(0)
out = explanation.get_complement_subgraph()
assert out.node_mask.size() == (2, 1)
assert (out.node_mask == 0.0).sum() == 2
assert out.x.size() == (2, 3)
assert out.edge_index.size(1) <= 6
assert out.edge_index.size(1) == out.edge_attr.size(0)
def test_edge_mask(data):
edge_mask = torch.tensor([1., 0., 1., 0., 0., 1.])
explanation = Explanation(
edge_mask=edge_mask,
x=data.x,
edge_index=data.edge_index,
edge_attr=data.edge_attr,
)
explanation.validate(raise_on_error=True)
out = explanation.get_explanation_subgraph()
assert out.x.size() == (4, 3)
assert out.edge_mask.size() == (3, )
assert (out.edge_mask > 0.0).sum() == 3
assert out.edge_index.size() == (2, 3)
assert out.edge_attr.size() == (3, 3)
out = explanation.get_complement_subgraph()
assert out.x.size() == (4, 3)
assert out.edge_mask.size() == (3, )
assert (out.edge_mask == 0.0).sum() == 3
assert out.edge_index.size() == (2, 3)
assert out.edge_attr.size() == (3, 3)
@withPackage('matplotlib', 'pandas')
@pytest.mark.parametrize('top_k', [2, None])
@pytest.mark.parametrize('node_mask_type', [None, 'attributes'])
def test_visualize_feature_importance(tmp_path, data, top_k, node_mask_type):
explanation = create_random_explanation(data, node_mask_type)
path = osp.join(tmp_path, 'feature_importance.png')
if node_mask_type is None:
with pytest.raises(ValueError, match="node_mask' is not"):
explanation.visualize_feature_importance(path, top_k=top_k)
else:
explanation.visualize_feature_importance(path, top_k=top_k)
assert osp.exists(path)
|
