def test_gnn_explainer_explain_graph(model, return_type, allow_edge_mask,
feat_mask_type):
explainer = GNNExplainer(model, log=False, return_type=return_type,
allow_edge_mask=allow_edge_mask,
feat_mask_type=feat_mask_type)
x = torch.randn(8, 3)
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 4, 5, 5, 6, 6, 7],
[1, 0, 2, 1, 3, 2, 5, 4, 6, 5, 7, 6]])
node_feat_mask, edge_mask = explainer.explain_graph(x, edge_index)
if feat_mask_type == 'scalar':
pass
_, _ = explainer.visualize_subgraph(-1, edge_index, edge_mask,
y=torch.tensor(2), threshold=0.8,
node_alpha=node_feat_mask)
else:
edge_y = torch.randint(low=0, high=30, size=(edge_index.size(1), ))
_, _ = explainer.visualize_subgraph(-1, edge_index, edge_mask,
edge_y=edge_y, y=torch.tensor(2),
threshold=0.8)
if feat_mask_type == 'individual_feature':
assert node_feat_mask.size() == x.size()
elif feat_mask_type == 'scalar':
assert node_feat_mask.size() == (x.size(0), )
else:
assert node_feat_mask.size() == (x.size(1), )
assert node_feat_mask.min() >= 0 and node_feat_mask.max() <= 1
assert edge_mask.size() == (edge_index.size(1), )
assert edge_mask.max() <= 1 and edge_mask.min() >= 0
def test_graph_explainer(model):
x = torch.randn(8, 3)
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 4, 5, 5, 6, 6, 7],
[1, 0, 2, 1, 3, 2, 5, 4, 6, 5, 7, 6]])
explainer = GNNExplainer(model, log=False)
node_feat_mask, edge_mask = explainer.explain_graph(x, edge_index)
assert_edgemask_clear(model)
assert node_feat_mask.size() == (x.size(1), )
assert node_feat_mask.min() >= 0 and node_feat_mask.max() <= 1
assert edge_mask.shape[0] == edge_index.shape[1]
assert edge_mask.max() <= 1 and edge_mask.min() >= 0
def test_gnn_explainer_explain_graph(model):
explainer = GNNExplainer(model, log=False)
x = torch.randn(8, 3)
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 4, 5, 5, 6, 6, 7],
[1, 0, 2, 1, 3, 2, 5, 4, 6, 5, 7, 6]])
node_feat_mask, edge_mask = explainer.explain_graph(x, edge_index)
_, _ = explainer.visualize_subgraph(-1,
edge_index,
edge_mask,
y=torch.tensor(2),
threshold=0.8)
assert node_feat_mask.size() == (x.size(1), )
assert node_feat_mask.min() >= 0 and node_feat_mask.max() <= 1
assert edge_mask.size() == (edge_index.size(1), )
assert edge_mask.max() <= 1 and edge_mask.min() >= 0
