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import pytest
import torch
from torch_geometric.explain import Explainer, PGExplainer
from torch_geometric.explain.config import (
ModelConfig,
ModelMode,
ModelTaskLevel,
)
from torch_geometric.nn import GCNConv, global_add_pool
from torch_geometric.testing import withCUDA
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)
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)
return x
@withCUDA
@pytest.mark.parametrize('mode', [
ModelMode.binary_classification,
ModelMode.multiclass_classification,
ModelMode.regression,
])
def test_pg_explainer_node(device, check_explanation, mode):
x = torch.randn(8, 3, device=device)
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],
], device=device)
if mode == ModelMode.binary_classification:
target = torch.randint(2, (x.size(0), ), device=device)
elif mode == ModelMode.multiclass_classification:
target = torch.randint(7, (x.size(0), ), device=device)
elif mode == ModelMode.regression:
target = torch.randn((x.size(0), 1), device=device)
model_config = ModelConfig(mode=mode, task_level='node', return_type='raw')
model = GCN(model_config).to(device)
explainer = Explainer(
model=model,
algorithm=PGExplainer(epochs=2).to(device),
explanation_type='phenomenon',
edge_mask_type='object',
model_config=model_config,
)
with pytest.raises(ValueError, match="not yet fully trained"):
explainer(x, edge_index, target=target)
explainer.algorithm.reset_parameters()
for epoch in range(2):
for index in range(x.size(0)):
loss = explainer.algorithm.train(epoch, model, x, edge_index,
target=target, index=index)
assert loss >= 0.0
explanation = explainer(x, edge_index, target=target, index=0)
check_explanation(explanation, None, explainer.edge_mask_type)
@withCUDA
@pytest.mark.parametrize('mode', [
ModelMode.binary_classification,
ModelMode.multiclass_classification,
ModelMode.regression,
])
def test_pg_explainer_graph(device, check_explanation, mode):
x = torch.randn(8, 3, device=device)
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],
], device=device)
if mode == ModelMode.binary_classification:
target = torch.randint(2, (1, ), device=device)
elif mode == ModelMode.multiclass_classification:
target = torch.randint(7, (1, ), device=device)
elif mode == ModelMode.regression:
target = torch.randn((1, 1), device=device)
model_config = ModelConfig(mode=mode, task_level='graph',
return_type='raw')
model = GCN(model_config).to(device)
explainer = Explainer(
model=model,
algorithm=PGExplainer(epochs=2).to(device),
explanation_type='phenomenon',
edge_mask_type='object',
model_config=model_config,
)
with pytest.raises(ValueError, match="not yet fully trained"):
explainer(x, edge_index, target=target)
explainer.algorithm.reset_parameters()
for epoch in range(2):
loss = explainer.algorithm.train(epoch, model, x, edge_index,
target=target)
assert loss >= 0.0
explanation = explainer(x, edge_index, target=target)
check_explanation(explanation, None, explainer.edge_mask_type)
def test_pg_explainer_supports():
# Test unsupported model task level:
with pytest.raises(ValueError, match="not support the given explanation"):
model_config = ModelConfig(
mode='binary_classification',
task_level='edge',
return_type='raw',
)
Explainer(
model=GCN(model_config),
algorithm=PGExplainer(epochs=2),
explanation_type='phenomenon',
edge_mask_type='object',
model_config=model_config,
)
# Test unsupported explanation type:
with pytest.raises(ValueError, match="not support the given explanation"):
model_config = ModelConfig(
mode='binary_classification',
task_level='node',
return_type='raw',
)
Explainer(
model=GCN(model_config),
algorithm=PGExplainer(epochs=2),
explanation_type='model',
edge_mask_type='object',
model_config=model_config,
)
# Test unsupported node mask:
with pytest.raises(ValueError, match="not support the given explanation"):
model_config = ModelConfig(
mode='binary_classification',
task_level='node',
return_type='raw',
)
Explainer(
model=GCN(model_config),
algorithm=PGExplainer(epochs=2),
explanation_type='model',
node_mask_type='object',
edge_mask_type='object',
model_config=model_config,
)
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