17.6.3 高度なハイパーパラメータ・カスタマイズ

豊富なハイパーパラメータ・カスタマイズを使用して、Unsupervised Anomaly Detection GraphWiseモデルを作成できます。

これは、次のコードに示すように、サブ構成クラスGraphWiseConvLayerConfigおよびGraphWiseEmbeddingConfigを使用して実装されます。

この例では、過剰適合に打ち消すために、モデルに対して重み減衰パラメータ0.001およびドロップ確率0.5のドロップアウトも指定しています。Dominant埋込み層のアルファ値は0.6と指定され、フィーチャ再構築の重要性が若干向上します。

• JShell
• Java
• Python

opg4j> var weightProperty = analyst.pagerank(trainGraph).getName()
opg4j> var convLayerConfig = analyst.graphWiseConvLayerConfigBuilder().
         setNumSampledNeighbors(25).
         setActivationFunction(ActivationFunction.TANH).
         setWeightInitScheme(WeightInitScheme.XAVIER).
         setWeightedAggregationProperty(weightProperty).
         setDropoutRate(0.5).
         build()

opg4j> var predictionLayerConfig = analyst.graphWisePredictionLayerConfigBuilder().
        setHiddenDimension(8).
        setActivationFunction(ActivationFunction.RELU).
        build()

opg4j> var dominantConfig = analyst.graphWiseDominantLayerConfigBuilder().
        setDecoderLayerConfigs(predictionLayerConfig).
        setAlpha(0.6).
        build()

opg4j> var model = analyst.unsupervisedAnomalyDetectionGraphWiseModelBuilder().
         setVertexInputPropertyNames("vertex_features").
         setConvLayerConfigs(convLayerConfig).
         setEmbeddingConfig(dominantConfig).
         setWeightDecay(0.001).
         setEmbeddingDim(256).
         setLearningRate(0.05).
         setNumEpochs(30).
         setSeed(42).
         setShuffle(false).
         setStandardize(true).
         setBatchSize(64).
         build()

String weightProperty = analyst.pagerank(trainGraph).getName()
GraphWiseConvLayerConfig convLayerConfig = analyst.graphWiseConvLayerConfigBuilder()
    .setNumSampledNeighbors(25)
    .setActivationFunction(ActivationFunction.TANH)
    .setWeightInitScheme(WeightInitScheme.XAVIER)
    .setWeightedAggregationProperty(weightProperty)
    .setDropoutRate(0.5)
    .build();

GraphWisePredictionLayerConfig predictionLayerConfig = analyst.graphWisePredictionLayerConfigBuilder()
    .setHiddenDimension(8)
    .setActivationFunction(ActivationFunction.RELU)
    .build();

GraphWiseEmbeddingConfig dominantConfig = analyst.graphWiseDominantLayerConfigBuilder()
    .setDecoderLayerConfigs(predictionLayerConfig)
    .setAlpha(0.6)
    .build();

UnsupervisedAnomalyDetectionGraphWiseModel model = analyst.unsupervisedAnomalyDetectionGraphWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_features")
    .setEmbeddingConfig(dominantConfig)
    .setConvLayerConfigs(convLayerConfig)
    .setWeightDecay(0.001)
    .setEmbeddingDim(256)
    .setLearningRate(0.05)
    .setNumEpochs(30)
    .setSeed(42)
    .setShuffle(false)
    .setStandardize(true)
    .setBatchSize(64)
    .build();

weightProperty = analyst.pagerank(train_graph).name

conv_layer_config = dict(num_sampled_neighbors=25,
                         activation_fn='tanh',
                         weight_init_scheme='xavier',
                         neighbor_weight_property_name=weightProperty,
                         dropout_rate=0.5)
conv_layer = analyst.graphwise_conv_layer_config(**conv_layer_config)

dominant_config = dict(alpha=0.6)

dominant_layer = analyst.graphwise_dominant_layer_config(**dominant_config)

params = dict(conv_layer_config=[conv_layer],
              embedding_config=dominant_layer,
              vertex_input_property_names=["vertex_features"],
              weight_decay=0.001,
              layer_size=256,
              learning_rate=0.05,
              num_epochs=30,
              seed=42,
              standardize=true,
              batch_size=64
)
model = analyst.unsupervised_anomaly_detection_graphwise_builder(**params)