[Code Coverage] explain/pg_explainer.py

CHANGELOG.md

@@ -101,7 +101,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Properly reset the `data_list` cache of an `InMemoryDataset` when accessing `dataset.data` ([#6685](https://github.com/pyg-team/pytorch_geometric/pull/6685))
 - Fixed a bug in `Data.subgraph()` and `HeteroData.subgraph()` ([#6613](https://github.com/pyg-team/pytorch_geometric/pull/6613))
 - Fixed a bug in `PNAConv` and `DegreeScalerAggregation` to correctly incorporate degree statistics of isolated nodes ([#6609](https://github.com/pyg-team/pytorch_geometric/pull/6609))
-- Improved code coverage ([#6523](https://github.com/pyg-team/pytorch_geometric/pull/6523), [#6538](https://github.com/pyg-team/pytorch_geometric/pull/6538), [#6555](https://github.com/pyg-team/pytorch_geometric/pull/6555), [#6558](https://github.com/pyg-team/pytorch_geometric/pull/6558), [#6568](https://github.com/pyg-team/pytorch_geometric/pull/6568), [#6573](https://github.com/pyg-team/pytorch_geometric/pull/6573), [#6578](https://github.com/pyg-team/pytorch_geometric/pull/6578), [#6597](https://github.com/pyg-team/pytorch_geometric/pull/6597), [#6600](https://github.com/pyg-team/pytorch_geometric/pull/6600), [#6618](https://github.com/pyg-team/pytorch_geometric/pull/6618), [#6619](https://github.com/pyg-team/pytorch_geometric/pull/6619), [#6621](https://github.com/pyg-team/pytorch_geometric/pull/6621), [#6623](https://github.com/pyg-team/pytorch_geometric/pull/6623), [#6637](https://github.com/pyg-team/pytorch_geometric/pull/6637), [#6638](https://github.com/pyg-team/pytorch_geometric/pull/6638), [#6640](https://github.com/pyg-team/pytorch_geometric/pull/6640), [#6645](https://github.com/pyg-team/pytorch_geometric/pull/6645), [#6648](https://github.com/pyg-team/pytorch_geometric/pull/6648), [#6647](https://github.com/pyg-team/pytorch_geometric/pull/6647), [#6653](https://github.com/pyg-team/pytorch_geometric/pull/6653), [#6657](https://github.com/pyg-team/pytorch_geometric/pull/6657), [#6662](https://github.com/pyg-team/pytorch_geometric/pull/6662), [#6664](https://github.com/pyg-team/pytorch_geometric/pull/6664), [#6667](https://github.com/pyg-team/pytorch_geometric/pull/6667), [#6668](https://github.com/pyg-team/pytorch_geometric/pull/6668), [#6669](https://github.com/pyg-team/pytorch_geometric/pull/6669), [#6670](https://github.com/pyg-team/pytorch_geometric/pull/6670), [#6671](https://github.com/pyg-team/pytorch_geometric/pull/6671), [#6673](https://github.com/pyg-team/pytorch_geometric/pull/6673), [#6675](https://github.com/pyg-team/pytorch_geometric/pull/6675), [#6676](https://github.com/pyg-team/pytorch_geometric/pull/6676), [#6677](https://github.com/pyg-team/pytorch_geometric/pull/6677), [#6678](https://github.com/pyg-team/pytorch_geometric/pull/6678), [#6681](https://github.com/pyg-team/pytorch_geometric/pull/6681), [#6683](https://github.com/pyg-team/pytorch_geometric/pull/6683), [#6703](https://github.com/pyg-team/pytorch_geometric/pull/6703), [#6720](https://github.com/pyg-team/pytorch_geometric/pull/6720), [#6735](https://github.com/pyg-team/pytorch_geometric/pull/6735), [#6736](https://github.com/pyg-team/pytorch_geometric/pull/6736), [#6763](https://github.com/pyg-team/pytorch_geometric/pull/6763), [#6781](https://github.com/pyg-team/pytorch_geometric/pull/6781), [#6797](https://github.com/pyg-team/pytorch_geometric/pull/6797), [#6799](https://github.com/pyg-team/pytorch_geometric/pull/6799))
+- Improved code coverage ([#6523](https://github.com/pyg-team/pytorch_geometric/pull/6523), [#6538](https://github.com/pyg-team/pytorch_geometric/pull/6538), [#6555](https://github.com/pyg-team/pytorch_geometric/pull/6555), [#6558](https://github.com/pyg-team/pytorch_geometric/pull/6558), [#6568](https://github.com/pyg-team/pytorch_geometric/pull/6568), [#6573](https://github.com/pyg-team/pytorch_geometric/pull/6573), [#6578](https://github.com/pyg-team/pytorch_geometric/pull/6578), [#6597](https://github.com/pyg-team/pytorch_geometric/pull/6597), [#6600](https://github.com/pyg-team/pytorch_geometric/pull/6600), [#6618](https://github.com/pyg-team/pytorch_geometric/pull/6618), [#6619](https://github.com/pyg-team/pytorch_geometric/pull/6619), [#6621](https://github.com/pyg-team/pytorch_geometric/pull/6621), [#6623](https://github.com/pyg-team/pytorch_geometric/pull/6623), [#6637](https://github.com/pyg-team/pytorch_geometric/pull/6637), [#6638](https://github.com/pyg-team/pytorch_geometric/pull/6638), [#6640](https://github.com/pyg-team/pytorch_geometric/pull/6640), [#6645](https://github.com/pyg-team/pytorch_geometric/pull/6645), [#6648](https://github.com/pyg-team/pytorch_geometric/pull/6648), [#6647](https://github.com/pyg-team/pytorch_geometric/pull/6647), [#6653](https://github.com/pyg-team/pytorch_geometric/pull/6653), [#6657](https://github.com/pyg-team/pytorch_geometric/pull/6657), [#6662](https://github.com/pyg-team/pytorch_geometric/pull/6662), [#6664](https://github.com/pyg-team/pytorch_geometric/pull/6664), [#6667](https://github.com/pyg-team/pytorch_geometric/pull/6667), [#6668](https://github.com/pyg-team/pytorch_geometric/pull/6668), [#6669](https://github.com/pyg-team/pytorch_geometric/pull/6669), [#6670](https://github.com/pyg-team/pytorch_geometric/pull/6670), [#6671](https://github.com/pyg-team/pytorch_geometric/pull/6671), [#6673](https://github.com/pyg-team/pytorch_geometric/pull/6673), [#6675](https://github.com/pyg-team/pytorch_geometric/pull/6675), [#6676](https://github.com/pyg-team/pytorch_geometric/pull/6676), [#6677](https://github.com/pyg-team/pytorch_geometric/pull/6677), [#6678](https://github.com/pyg-team/pytorch_geometric/pull/6678), [#6681](https://github.com/pyg-team/pytorch_geometric/pull/6681), [#6683](https://github.com/pyg-team/pytorch_geometric/pull/6683), [#6703](https://github.com/pyg-team/pytorch_geometric/pull/6703), [#6720](https://github.com/pyg-team/pytorch_geometric/pull/6720), [#6735](https://github.com/pyg-team/pytorch_geometric/pull/6735), [#6736](https://github.com/pyg-team/pytorch_geometric/pull/6736), [#6763](https://github.com/pyg-team/pytorch_geometric/pull/6763), [#6781](https://github.com/pyg-team/pytorch_geometric/pull/6781), [#6797](https://github.com/pyg-team/pytorch_geometric/pull/6797), [#6799](https://github.com/pyg-team/pytorch_geometric/pull/6799), [#6824](https://github.com/pyg-team/pytorch_geometric/pull/6824))
 - Fixed a bug in which `data.to_heterogeneous()` filtered attributs in the wrong dimension ([#6522](https://github.com/pyg-team/pytorch_geometric/pull/6522))
 - Breaking Change: Temporal sampling will now also sample nodes with an equal timestamp to the seed time (requires `pyg-lib>0.1.0`) ([#6517](https://github.com/pyg-team/pytorch_geometric/pull/6517))
 - Changed `DataLoader` workers with affinity to start at `cpu0` ([#6512](https://github.com/pyg-team/pytorch_geometric/pull/6512))

test/explain/algorithm/test_explain_algorithm_utils.py

@@ -1,29 +1,12 @@
 import torch
 
-from torch_geometric.data import HeteroData
 from torch_geometric.explain.algorithm.utils import (
     clear_masks,
     set_hetero_masks,
 )
 from torch_geometric.nn import GCNConv, HeteroConv, SAGEConv, to_hetero
 
 
-def get_edge_index(num_src_nodes, num_dst_nodes, num_edges):
-    row = torch.randint(num_src_nodes, (num_edges, ), dtype=torch.long)
-    col = torch.randint(num_dst_nodes, (num_edges, ), dtype=torch.long)
-    return torch.stack([row, col], dim=0)
-
-
-def get_hetero_data():
-    data = HeteroData()
-    data['paper'].x = torch.randn(8, 16)
-    data['author'].x = torch.randn(10, 8)
-    data['paper', 'paper'].edge_index = get_edge_index(8, 8, 10)
-    data['author', 'paper'].edge_index = get_edge_index(10, 8, 10)
-    data['paper', 'author'].edge_index = get_edge_index(8, 10, 10)
-    return data
-
-
 class HeteroModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -58,8 +41,7 @@ def forward(self, x, edge_index):
         return self.conv2(x, edge_index)
 
 
-def test_set_clear_mask():
-    data = get_hetero_data()
+def test_set_clear_mask(hetero_data):
     edge_mask_dict = {
         ('paper', 'to', 'paper'): torch.ones(200),
         ('author', 'to', 'paper'): torch.ones(100),
@@ -68,31 +50,32 @@ def test_set_clear_mask():
 
     model = HeteroModel()
 
-    set_hetero_masks(model, edge_mask_dict, data.edge_index_dict)
-    for edge_type in data.edge_types:  # Check that masks are correctly set:
+    set_hetero_masks(model, edge_mask_dict, hetero_data.edge_index_dict)
+    for edge_type in hetero_data.edge_types:
+        # Check that masks are correctly set:
         str_edge_type = '__'.join(edge_type)
         assert torch.allclose(model.conv1.convs[str_edge_type]._edge_mask,
                               edge_mask_dict[edge_type])
         assert model.conv1.convs[str_edge_type].explain
 
     clear_masks(model)
-    for edge_type in data.edge_types:
+    for edge_type in hetero_data.edge_types:
         str_edge_type = '__'.join(edge_type)
         assert model.conv1.convs[str_edge_type]._edge_mask is None
         assert not model.conv1.convs[str_edge_type].explain
 
-    model = GraphSAGE()
-    model = to_hetero(GraphSAGE(), data.metadata(), debug=False)
+    model = to_hetero(GraphSAGE(), hetero_data.metadata(), debug=False)
 
-    set_hetero_masks(model, edge_mask_dict, data.edge_index_dict)
-    for edge_type in data.edge_types:  # Check that masks are correctly set:
+    set_hetero_masks(model, edge_mask_dict, hetero_data.edge_index_dict)
+    for edge_type in hetero_data.edge_types:
+        # Check that masks are correctly set:
         str_edge_type = '__'.join(edge_type)
         assert torch.allclose(model.conv1[str_edge_type]._edge_mask,
                               edge_mask_dict[edge_type])
         assert model.conv1[str_edge_type].explain
 
     clear_masks(model)
-    for edge_type in data.edge_types:
+    for edge_type in hetero_data.edge_types:
         str_edge_type = '__'.join(edge_type)
         assert model.conv1[str_edge_type]._edge_mask is None
         assert not model.conv1[str_edge_type].explain

test/explain/algorithm/test_pg_explainer.py

@@ -2,7 +2,11 @@
 import torch
 
 from torch_geometric.explain import Explainer, PGExplainer
-from torch_geometric.explain.config import ModelConfig, ModelTaskLevel
+from torch_geometric.explain.config import (
+    ModelConfig,
+    ModelMode,
+    ModelTaskLevel,
+)
 from torch_geometric.nn import GCNConv, global_add_pool
 from torch_geometric.testing import withCUDA
 
@@ -12,8 +16,13 @@ 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, 7)
+        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()
@@ -24,19 +33,26 @@ def forward(self, x, edge_index, batch=None, edge_label_index=None):
 
 
 @withCUDA
-def test_pg_explainer_node(device, check_explanation):
+@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)
-    target = torch.randint(7, (x.size(0), ), device=device)
 
-    model_config = ModelConfig(
-        mode='multiclass_classification',
-        task_level='node',
-        return_type='raw',
-    )
+    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)
 
@@ -49,7 +65,7 @@ def test_pg_explainer_node(device, check_explanation):
     )
 
     with pytest.raises(ValueError, match="not yet fully trained"):
-        explanation = explainer(x, edge_index, target=target)
+        explainer(x, edge_index, target=target)
 
     explainer.algorithm.reset_parameters()
     for epoch in range(2):
@@ -64,39 +80,95 @@ def test_pg_explainer_node(device, check_explanation):
 
 
 @withCUDA
-def test_pg_explainer_graph(device, check_explanation):
+@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)
-    target = torch.randint(7, (1, ), device=device)
 
-    model_config = ModelConfig(
-        mode='multiclass_classification',
-        task_level='graph',
-        return_type='raw',
-    )
+    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=10).to(device),
+        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"):
-        explanation = explainer(x, edge_index, target=target)
+        explainer(x, edge_index, target=target)
 
     explainer.algorithm.reset_parameters()
-    for epoch in range(10):
+    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,
+        )