def test_gnn_explainer_explain_node(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) assert explainer.__repr__() == 'GNNExplainer()' x = torch.randn(8, 3) y = torch.tensor([0, 1, 1, 0, 1, 0, 1, 0]) 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]]) node_feat_mask, edge_mask = explainer.explain_node(2, x, edge_index) if feat_mask_type == 'scalar': _, _ = explainer.visualize_subgraph(2, edge_index, edge_mask, y=y, 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(2, edge_index, edge_mask, y=y, edge_y=edge_y, 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.min() >= 0 and edge_mask.max() <= 1 if not allow_edge_mask: assert edge_mask[:8].tolist() == [1.] * 8 assert edge_mask[8:].tolist() == [0.] * 6
def test_gnn_explainer(model): explainer = GNNExplainer(model, log=False) assert explainer.__repr__() == 'GNNExplainer()' 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]]) node_feat_mask, edge_mask = explainer.explain_node(2, x, edge_index) 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.min() >= 0 and edge_mask.max() <= 1
def test_gnn_explainer(model): explainer = GNNExplainer(model, log=False) assert explainer.__repr__() == 'GNNExplainer()' 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]]) y = torch.randint(0, 6, (8, ), dtype=torch.long) node_feat_mask, edge_mask = explainer.explain_node(2, x, edge_index) 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.min() >= 0 and edge_mask.max() <= 1 explainer.visualize_subgraph(2, edge_index, edge_mask, threshold=None) explainer.visualize_subgraph(2, edge_index, edge_mask, threshold=0.5) explainer.visualize_subgraph(2, edge_index, edge_mask, y=y, threshold=None) explainer.visualize_subgraph(2, edge_index, edge_mask, y=y, threshold=0.5)