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import os.path as osp
from typing import Optional, Union
import pytest
import torch
from torch_geometric.data import HeteroData
from torch_geometric.explain import HeteroExplanation
from torch_geometric.explain.config import MaskType
from torch_geometric.testing import withPackage
def create_random_explanation(
hetero_data: HeteroData,
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)
out = HeteroExplanation()
for key in ['paper', 'author']:
out[key].x = hetero_data[key].x
if node_mask_type == MaskType.object:
out[key].node_mask = torch.rand(hetero_data[key].num_nodes, 1)
elif node_mask_type == MaskType.common_attributes:
out[key].node_mask = torch.rand(1, hetero_data[key].num_features)
elif node_mask_type == MaskType.attributes:
out[key].node_mask = torch.rand_like(hetero_data[key].x)
for key in [('paper', 'paper'), ('paper', 'author')]:
out[key].edge_index = hetero_data[key].edge_index
out[key].edge_attr = hetero_data[key].edge_attr
if edge_mask_type == MaskType.object:
out[key].edge_mask = torch.rand(hetero_data[key].num_edges)
return out
@pytest.mark.parametrize('node_mask_type',
[None, 'object', 'common_attributes', 'attributes'])
@pytest.mark.parametrize('edge_mask_type', [None, 'object'])
def test_available_explanations(hetero_data, node_mask_type, edge_mask_type):
expected = []
if node_mask_type:
expected.append('node_mask')
if edge_mask_type:
expected.append('edge_mask')
explanation = create_random_explanation(
hetero_data,
node_mask_type=node_mask_type,
edge_mask_type=edge_mask_type,
)
assert set(explanation.available_explanations) == set(expected)
def test_validate_explanation(hetero_data):
explanation = create_random_explanation(hetero_data)
explanation.validate(raise_on_error=True)
with pytest.raises(ValueError, match="with 8 nodes"):
explanation = create_random_explanation(hetero_data)
explanation['paper'].node_mask = torch.rand(5, 5)
explanation.validate(raise_on_error=True)
with pytest.raises(ValueError, match="with 5 features"):
explanation = create_random_explanation(hetero_data, 'attributes')
explanation['paper'].x = torch.randn(8, 5)
explanation.validate(raise_on_error=True)
with pytest.raises(ValueError, match="with 10 edges"):
explanation = create_random_explanation(hetero_data)
explanation['paper', 'paper'].edge_mask = torch.randn(5)
explanation.validate(raise_on_error=True)
def test_node_mask():
explanation = HeteroExplanation()
explanation['paper'].node_mask = torch.tensor([[1.], [0.], [1.], [1.]])
explanation['author'].node_mask = torch.tensor([[1.], [0.], [1.], [1.]])
with pytest.warns(UserWarning, match="are isolated"):
explanation.validate(raise_on_error=True)
out = explanation.get_explanation_subgraph()
assert out['paper'].node_mask.size() == (3, 1)
assert out['author'].node_mask.size() == (3, 1)
out = explanation.get_complement_subgraph()
assert out['paper'].node_mask.size() == (1, 1)
assert out['author'].node_mask.size() == (1, 1)
def test_edge_mask():
explanation = HeteroExplanation()
explanation['paper'].num_nodes = 4
explanation['author'].num_nodes = 4
explanation['paper', 'author'].edge_index = torch.tensor([
[0, 1, 2, 3],
[0, 1, 2, 3],
])
explanation['paper', 'author'].edge_mask = torch.tensor([1., 0., 1., 1.])
out = explanation.get_explanation_subgraph()
assert out['paper'].num_nodes == 4
assert out['author'].num_nodes == 4
assert out['paper', 'author'].edge_mask.size() == (3, )
assert torch.equal(out['paper', 'author'].edge_index,
torch.tensor([[0, 2, 3], [0, 2, 3]]))
out = explanation.get_complement_subgraph()
assert out['paper'].num_nodes == 4
assert out['author'].num_nodes == 4
assert out['paper', 'author'].edge_mask.size() == (1, )
assert torch.equal(out['paper', 'author'].edge_index,
torch.tensor([[1], [1]]))
@withPackage('matplotlib')
@pytest.mark.parametrize('top_k', [2, None])
@pytest.mark.parametrize('node_mask_type', [None, 'attributes'])
def test_visualize_feature_importance(
top_k,
node_mask_type,
tmp_path,
hetero_data,
):
explanation = create_random_explanation(
hetero_data,
node_mask_type=node_mask_type,
)
path = osp.join(tmp_path, 'feature_importance.png')
if node_mask_type is None:
with pytest.raises(KeyError, match="Tried to collect 'node_mask'"):
explanation.visualize_feature_importance(path, top_k=top_k)
else:
explanation.visualize_feature_importance(path, top_k=top_k)
assert osp.exists(path)
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