8.3.10 Unsupervised GraphWiseモデルの予測の説明
Unsupervised GraphWiseモデルの予測に重要な特徴と頂点を理解するために、YingなどによるGNNExplainer
と同様の手法を使用してUnsupervisedGnnExplanation
を生成できます。
説明には、次に関連する情報が保持されます。
- グラフ構造: 各頂点の重要度スコア
- 特徴: 各グラフ・プロパティの重要度スコア
ノート:
説明されている頂点には、常に重要度1が割り当てられます。さらに、特徴の重要度は、最も重要な特徴の重要度が1となるようにスケーリングされます。また、UnsupervisedGnnExplanation
には、推測された埋込みが含まれます。inferAndGetExplanation
メソッドは、適合したすべてのUnsupervisedGraphWiseModel
モデルで使用できます。最適な結果を得るには、特徴は0を中心とする必要があります。
たとえば、k
個の密に接続されたコンポーネントを含む(つまり、同じコンポーネントの頂点の間にエッジが多数あり、任意の2つのコンポーネントの間にエッジが少数あります)、単純なグラフcomponentGraph
があるとします。このグラフでUnsupervised GraphWiseモデルをトレーニングすると、密に接続されたコンポーネントの頂点に対して同様の埋込みを生成するモデルになると予想できます。
次の例では、推論componentGraph
で説明を生成する方法を示します。同じコンポーネントの頂点は、異なるコンポーネントの頂点よりも重要度が高くなると予想されます。なお、この例では、特徴の重要度は関連ありません。
opg4j> var componentGraph = session.readGraphWithProperties("<path_to_component_graph.json>")
// explain prediction of vertex 0
opg4j> var feat1Property = componentGraph.getVertexProperty("feat1")
opg4j> var feat2Property = componentGraph.getVertexProperty("feat2")
// build and train an Unsupervised GraphWise model
// explain prediction of vertex 0
// setting the numClusters argument to the expected number of clusters may improve
// explanation results
opg4j> var explanation = model.inferAndGetExplanation(componentGraph, componentGraph.getVertex(0), 10)
// retrieve computation graph with importance
opg4j> var importanceGraph = explanation.getImportanceGraph()
// retrieve importance of vertices
// vertex 1 is in the same densely connected component as vertex 0
// vertex 2 is in a different component
opg4j> var importanceProperty = explanation.getVertexImportanceProperty()
opg4j> var importanceVertex0 = importanceProperty.get(0) // has importance 1
opg4j> var importanceVertex1 = importanceProperty.get(1) // high importance
opg4j> var importanceVertex2 = importanceProperty.get(2) // low importance
opg4j> var featureImportances = explanation.getVertexFeatureImportance()
opg4j> var importanceConstProp = featureImportances.get(constProperty) // small as unimportant
opg4j> var importanceLabelProp = featureImportances.get(labelProperty) // large (1) as important
// optionally retrieve feature importance
opg4j> var featureImportances = explanation.getVertexFeatureImportance()
opg4j> var importanceFeat1Prop = featureImportances.get(feat1Property)
opg4j> var importanceFeat2Prop = featureImportances.get(feat2Property)
PgxGraph componentGraph = session.readGraphWithProperties("<path_to_component_graph.json>") // load component graph
VertexProperty<Integer, Float> feat1Property = componentGraph.getVertexProperty("feat1");
VertexProperty<Integer, Float> feat2Property = componentGraph.getVertexProperty("feat2");
// build and train an Unsupervised GraphWise model
// explain prediction of vertex 0
// setting the numClusters argument to the expected number of clusters may improve
// explanation results
UnsupervisedGnnExplanation<Integer> explanation = model.inferAndGetExplanation(componentGraph, componentGraph.getVertex(0), 10);
// retrieve computation graph with importances
PgxGraph importanceGraph = explanation.getImportanceGraph();
// retrieve importance of vertices
// vertex 1 is in the same densely connected component as vertex 0
// vertex 2 is in a different component
VertexProperty<Integer, Float> importanceProperty = explanation.getVertexImportanceProperty();
float importanceVertex0 = importanceProperty.get(0); // has importance 1
float importanceVertex1 = importanceProperty.get(1); // high importance
float importanceVertex2 = importanceProperty.get(2); // low importance
// retrieve feature importance (not relevant for this example)
Map<VertexProperty<Integer, ?>, Float> featureImportances = explanation.getVertexFeatureImportance();
float importanceFeat1Prop = featureImportances.get(feat1Property);
float importanceFeat2Prop = featureImportances.get(feat2Property);
# load 'component_graph' with vertex features 'feat1' and 'feat2'
feat1_property = component_graph.get_vertex_property("feat1")
feat2_property = component_graph.get_vertex_property("feat2")
# build and train an Unsupervised GraphWise model
# explain prediction of vertex 0
# setting the num_clusters argument to the expected number of clusters may improve
# explanation results
explanation = model.infer_and_get_explanation(
graph=component_graph,
vertex=component_graph.get_vertex(0),
num_clusters=10,
)
# retrieve computation graph with importances
importance_graph = explanation.get_importance_graph()
# retrieve importance of vertices
# vertex 1 is in the same densely connected component as vertex 0
# vertex 2 is in a different component
importance_property = explanation.get_vertex_importance_property()
importance_vertex_0 = importance_property[0] # has importance 1
importance_vertex_1 = importance_property[1] # high importance
importance_vertex_2 = importance_property[2] # low importance
# retrieve feature importance (not relevant for this example)
feature_importances = explanation.get_vertex_feature_importance()
importance_feat1_prop = feature_importances[feat1_property]
importance_feat2_prop = feature_importances[feat2_property]