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import pytest
import torch
from torch_geometric.explain import Explainer, Explanation, GraphMaskExplainer
from torch_geometric.explain.config import (
MaskType,
ModelConfig,
ModelMode,
ModelReturnType,
ModelTaskLevel,
)
from torch_geometric.nn import GCNConv, global_add_pool
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)
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
def check_explanation(
edge_mask_type: MaskType,
node_mask_type: MaskType,
explanation: Explanation,
):
if node_mask_type == MaskType.attributes:
assert explanation.node_mask.size() == explanation.x.size()
assert explanation.node_mask.min() >= 0
assert explanation.node_mask.max() <= 1
elif node_mask_type == MaskType.object:
assert explanation.node_mask.size() == (explanation.num_nodes, 1)
assert explanation.node_mask.min() >= 0
assert explanation.node_mask.max() <= 1
elif node_mask_type == MaskType.common_attributes:
assert explanation.node_mask.size() == (1, explanation.num_features)
assert explanation.node_mask.min() >= 0
assert explanation.node_mask.max() <= 1
if edge_mask_type == MaskType.object:
assert explanation.edge_mask.size() == (explanation.num_edges, )
assert explanation.edge_mask.min() >= 0
assert explanation.edge_mask.max() <= 1
node_mask_types = [
MaskType.object,
MaskType.common_attributes,
MaskType.attributes,
]
edge_mask_types = [
MaskType.object,
None,
]
x = torch.randn(8, 3)
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],
])
batch = torch.tensor([0, 0, 0, 1, 1, 2, 2, 2])
edge_label_index = torch.tensor([[0, 1, 2], [3, 4, 5]])
@pytest.mark.parametrize('edge_mask_type', edge_mask_types)
@pytest.mark.parametrize('node_mask_type', node_mask_types)
@pytest.mark.parametrize('explanation_type', ['model', 'phenomenon'])
@pytest.mark.parametrize('task_level', ['node', 'edge', 'graph'])
@pytest.mark.parametrize('return_type', ['probs', 'raw'])
@pytest.mark.parametrize('index', [None, 2, torch.arange(3)])
def test_graph_mask_explainer_binary_classification(
edge_mask_type,
node_mask_type,
explanation_type,
task_level,
return_type,
index,
):
model_config = ModelConfig(
mode='binary_classification',
task_level=task_level,
return_type=return_type,
)
model = GCN(model_config)
target = None
if explanation_type == 'phenomenon':
with torch.no_grad():
out = model(x, edge_index, batch, edge_label_index)
if model_config.return_type == ModelReturnType.raw:
target = (out > 0).long().view(-1)
if model_config.return_type == ModelReturnType.probs:
target = (out > 0.5).long().view(-1)
explainer = Explainer(
model=model,
algorithm=GraphMaskExplainer(2, epochs=5, log=False),
explanation_type=explanation_type,
node_mask_type=node_mask_type,
edge_mask_type=edge_mask_type,
model_config=model_config,
)
explanation = explainer(
x,
edge_index,
target=target,
index=index,
batch=batch,
edge_label_index=edge_label_index,
)
check_explanation(edge_mask_type, node_mask_type, explanation)
@pytest.mark.parametrize('edge_mask_type', edge_mask_types)
@pytest.mark.parametrize('node_mask_type', node_mask_types)
@pytest.mark.parametrize('explanation_type', ['model', 'phenomenon'])
@pytest.mark.parametrize('task_level', ['node', 'edge', 'graph'])
@pytest.mark.parametrize('return_type', ['log_probs', 'probs', 'raw'])
@pytest.mark.parametrize('index', [None, 2, torch.arange(3)])
def test_graph_mask_explainer_multiclass_classification(
edge_mask_type,
node_mask_type,
explanation_type,
task_level,
return_type,
index,
):
model_config = ModelConfig(
mode='multiclass_classification',
task_level=task_level,
return_type=return_type,
)
model = GCN(model_config)
target = None
if explanation_type == 'phenomenon':
with torch.no_grad():
target = model(x, edge_index, batch, edge_label_index).argmax(-1)
explainer = Explainer(
model=model,
algorithm=GraphMaskExplainer(2, epochs=5, log=False),
explanation_type=explanation_type,
node_mask_type=node_mask_type,
edge_mask_type=edge_mask_type,
model_config=model_config,
)
explanation = explainer(
x,
edge_index,
target=target,
index=index,
batch=batch,
edge_label_index=edge_label_index,
)
check_explanation(edge_mask_type, node_mask_type, explanation)
@pytest.mark.parametrize('edge_mask_type', edge_mask_types)
@pytest.mark.parametrize('node_mask_type', node_mask_types)
@pytest.mark.parametrize('explanation_type', ['model', 'phenomenon'])
@pytest.mark.parametrize('task_level', ['node', 'edge', 'graph'])
@pytest.mark.parametrize('index', [None, 2, torch.arange(3)])
def test_graph_mask_explainer_regression(
edge_mask_type,
node_mask_type,
explanation_type,
task_level,
index,
):
model_config = ModelConfig(
mode='regression',
task_level=task_level,
)
model = GCN(model_config)
target = None
if explanation_type == 'phenomenon':
with torch.no_grad():
target = model(x, edge_index, batch, edge_label_index)
explainer = Explainer(
model=model,
algorithm=GraphMaskExplainer(2, epochs=5, log=False),
explanation_type=explanation_type,
node_mask_type=node_mask_type,
edge_mask_type=edge_mask_type,
model_config=model_config,
)
explanation = explainer(
x,
edge_index,
target=target,
index=index,
batch=batch,
edge_label_index=edge_label_index,
)
check_explanation(edge_mask_type, node_mask_type, explanation)
|
