17.6.3 Advanced Hyperparameter Customization

You can build an Unsupervised Anomaly Detection GraphWise model using rich hyperparameter customization.

This is implemented using the sub-config classes, GraphWiseConvLayerConfig and GraphWiseEmbeddingConfig, as shown in the following code.

The example also specifies a weight decay parameter of 0.001 and dropout with dropping probability 0.5 for the model to counteract overfitting. The Dominant embedding layer's alpha value is specified as 0.6 to slightly increase the importance of the feature reconstruction.

• 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)