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from typing import Optional
import pytest
import torch
from torch_geometric.explain import Explainer, Explanation
from torch_geometric.explain.algorithm import CaptumExplainer
from torch_geometric.explain.config import (
MaskType,
ModelConfig,
ModelMode,
ModelReturnType,
ModelTaskLevel,
)
from torch_geometric.nn import GCNConv, global_add_pool
from torch_geometric.testing import withPackage
methods = [
'Saliency',
'InputXGradient',
'Deconvolution',
'ShapleyValueSampling',
'IntegratedGradients',
'GuidedBackprop',
]
unsupported_methods = [
'FeatureAblation',
'Occlusion',
'DeepLift',
'DeepLiftShap',
'GradientShap',
'KernelShap',
'Lime',
]
class GCN(torch.nn.Module):
def __init__(self, model_config: ModelConfig):
super().__init__()
self.model_config = model_config
if model_config.mode == ModelMode.multiclass_classification:
out_channels = 7
else:
out_channels = 1
self.conv1 = GCNConv(3, 16)
self.conv2 = GCNConv(16, out_channels)
# Add unused parameter:
self.param = torch.nn.Parameter(torch.empty(1))
def forward(self, x, edge_index, batch=None, edge_label_index=None):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index)
if self.model_config.task_level == ModelTaskLevel.graph:
x = global_add_pool(x, batch)
elif self.model_config.task_level == ModelTaskLevel.edge:
assert edge_label_index is not None
x = x[edge_label_index[0]] * x[edge_label_index[1]]
if self.model_config.mode == ModelMode.binary_classification:
if self.model_config.return_type == ModelReturnType.probs:
x = x.sigmoid()
elif self.model_config.mode == ModelMode.multiclass_classification:
if self.model_config.return_type == ModelReturnType.probs:
x = x.softmax(dim=-1)
elif self.model_config.return_type == ModelReturnType.log_probs:
x = x.log_softmax(dim=-1)
return x
node_mask_types = [MaskType.attributes, None]
edge_mask_types = [MaskType.object, None]
task_levels = [ModelTaskLevel.node, ModelTaskLevel.edge, ModelTaskLevel.graph]
indices = [1, torch.arange(2)]
def check_explanation(
explanation: Explanation,
node_mask_type: Optional[MaskType],
edge_mask_type: Optional[MaskType],
):
if node_mask_type == MaskType.attributes:
assert explanation.node_mask.size() == explanation.x.size()
elif node_mask_type is None:
assert 'node_mask' not in explanation
if edge_mask_type == MaskType.object:
assert explanation.edge_mask.size() == (explanation.num_edges, )
elif edge_mask_type is None:
assert 'edge_mask' not in explanation
@withPackage('captum')
@pytest.mark.parametrize('method', unsupported_methods)
def test_unsupported_methods(method):
model_config = ModelConfig(mode='regression', task_level='node')
with pytest.raises(ValueError, match="does not support attribution"):
Explainer(
GCN(model_config),
algorithm=CaptumExplainer(method),
explanation_type='model',
edge_mask_type='object',
node_mask_type='attributes',
model_config=model_config,
)
@withPackage('captum')
@pytest.mark.parametrize('method', ['IntegratedGradients'])
@pytest.mark.parametrize('node_mask_type', node_mask_types)
@pytest.mark.parametrize('edge_mask_type', edge_mask_types)
@pytest.mark.parametrize('task_level', task_levels)
@pytest.mark.parametrize('index', indices)
def test_captum_explainer_binary_classification(
method,
data,
node_mask_type,
edge_mask_type,
task_level,
index,
):
if node_mask_type is None and edge_mask_type is None:
return
batch = torch.tensor([0, 0, 1, 1])
edge_label_index = torch.tensor([[0, 1, 2], [2, 3, 1]])
model_config = ModelConfig(
mode='binary_classification',
task_level=task_level,
return_type='probs',
)
explainer = Explainer(
GCN(model_config),
algorithm=CaptumExplainer(method),
explanation_type='model',
edge_mask_type=edge_mask_type,
node_mask_type=node_mask_type,
model_config=model_config,
)
explanation = explainer(
data.x,
data.edge_index,
index=index,
batch=batch,
edge_label_index=edge_label_index,
)
check_explanation(explanation, node_mask_type, edge_mask_type)
@withPackage('captum')
@pytest.mark.parametrize('method', methods)
@pytest.mark.parametrize('node_mask_type', node_mask_types)
@pytest.mark.parametrize('edge_mask_type', edge_mask_types)
@pytest.mark.parametrize('task_level', task_levels)
@pytest.mark.parametrize('index', indices)
def test_captum_explainer_multiclass_classification(
method,
data,
node_mask_type,
edge_mask_type,
task_level,
index,
):
if node_mask_type is None and edge_mask_type is None:
return
batch = torch.tensor([0, 0, 1, 1])
edge_label_index = torch.tensor([[0, 1, 2], [2, 3, 1]])
model_config = ModelConfig(
mode='multiclass_classification',
task_level=task_level,
return_type='probs',
)
explainer = Explainer(
GCN(model_config),
algorithm=CaptumExplainer(method),
explanation_type='model',
edge_mask_type=edge_mask_type,
node_mask_type=node_mask_type,
model_config=model_config,
)
explanation = explainer(
data.x,
data.edge_index,
index=index,
batch=batch,
edge_label_index=edge_label_index,
)
check_explanation(explanation, node_mask_type, edge_mask_type)
@withPackage('captum')
@pytest.mark.parametrize(
'method',
[m for m in methods if m != 'ShapleyValueSampling'],
)
@pytest.mark.parametrize(
'node_mask_type',
[nm for nm in node_mask_types if nm is not None],
)
@pytest.mark.parametrize(
'edge_mask_type',
[em for em in edge_mask_types if em is not None],
)
@pytest.mark.parametrize('index', [1, torch.arange(2)])
def test_captum_hetero_data(method, node_mask_type, edge_mask_type, index,
hetero_data, hetero_model):
model_config = ModelConfig(mode='regression', task_level='node')
explainer = Explainer(
hetero_model(hetero_data.metadata()),
algorithm=CaptumExplainer(method),
edge_mask_type=edge_mask_type,
node_mask_type=node_mask_type,
model_config=model_config,
explanation_type='model',
)
explanation = explainer(hetero_data.x_dict, hetero_data.edge_index_dict,
index=index)
explanation.validate(raise_on_error=True)
@withPackage('captum')
@pytest.mark.parametrize('node_mask_type', [
MaskType.object,
MaskType.common_attributes,
])
def test_captum_explainer_supports(node_mask_type):
model_config = ModelConfig(
mode='multiclass_classification',
task_level='node',
return_type='probs',
)
with pytest.raises(ValueError, match="not support the given explanation"):
Explainer(
GCN(model_config),
algorithm=CaptumExplainer('IntegratedGradients'),
edge_mask_type=MaskType.object,
node_mask_type=node_mask_type,
model_config=model_config,
explanation_type='model',
)
|
