15.3.3 Advanced Hyperparameter Customization

You can build an Unsupervised GraphWise model with only vertex properties or only edge properties or both using rich hyperparameter customization.

This is implemented using the sub-config class, GraphWiseConvLayerConfig.

The following code describes the implementation of the configuration in a Unsupervised GraphWise model. The example also specifies a weight decay parameter of 0.001 and dropout with dropping probability 0.5 for the model to counteract overfitting.

• 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 dgiLayerConfig = analyst.graphWiseDgiLayerConfigBuilder().
                setCorruptionFunction(new PermutationCorruption()).
                setDiscriminator(GraphWiseDgiLayerConfig.Discriminator.BILINEAR).
                setReadoutFunction(GraphWiseDgiLayerConfig.ReadoutFunction.MEAN).
                build()
opg4j> var model = analyst.unsupervisedGraphWiseModelBuilder().
                setVertexInputPropertyNames("vertex_features").
                setEdgeInputPropertyNames("edge_features").
                setConvLayerConfigs(convLayerConfig).
                setDgiLayerConfig(dgiLayerConfig).
                setLossFunction(UnsupervisedGraphWiseModelConfig.LossFunction.SIGMOID_CROSS_ENTROPY).
                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();

GraphWiseDgiLayerConfig dgiLayerConfig = analyst.graphWiseDgiLayerConfigBuilder()
    .setCorruptionFunction(new PermutationCorruption())
    .setDiscriminator(GraphWiseDgiLayerConfig.Discriminator.BILINEAR)
    .setReadoutFunction(GraphWiseDgiLayerConfig.ReadoutFunction.MEAN)
    .build();

UnsupervisedGraphWiseModel model = analyst.unsupervisedGraphWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_features")
    .setEdgeInputPropertyNames("edge_features")
    .setDgiLayerConfig(dgiLayerConfig)
    .setLossFunction(UnsupervisedGraphWiseModelConfig.LossFunction.SIGMOID_CROSS_ENTROPY)
    .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)

dgi_layer_config = dict(corruption_function=None, 
                        readout_function="mean", 
                        discriminator="bilinear")
dgi_layer = analyst.graphwise_dgi_layer_config(**dgi_layer_config)

params = dict(conv_layer_config=[conv_layer],
              dgi_layer_config=dgi_layer,
              loss_fn="sigmoid_cross_entropy",
              vertex_input_property_names=["vertex_features"],
              edge_input_property_names=["edge_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_graphwise_builder(**params)

See UnsupervisedGraphWiseModelBuilder and GraphWiseConvLayerConfigBuilder in Javadoc for full description of all available hyperparameters and their default values.