8.3.3 Advanced Hyperparameter Customization

You can build a 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:

1. Build a Unsupervised GraphWise model as shown in the following code:
   Building a Customized Unsupervised GraphWise Model Using JShell

   opg4j> var weightProperty = analyst.pagerank(trainGraph).getName();
   opg4j> var convLayerConfig = analyst.graphWiseConvLayerConfigBuilder().
                   setNumSampledNeighbors(25).
                   setActivationFunction(ActivationFunction.TANH).
                   setWeightInitScheme(WeightInitScheme.XAVIER).
                   setWeightedAggregationProperty(weightProperty).
                   build();
   
   opg4j> var model = analyst.unsupervisedGraphWiseModelBuilder().
                   setVertexInputPropertyNames("features").
                   setConvLayerConfigs(convLayerConfig).
                   build();

   Building a Customized Unsupervised GraphWise Model Using Java

   String weightProperty = analyst.pagerank(trainGraph).getName();
   GraphWiseConvLayerConfig convLayerConfig = analyst.graphWiseConvLayerConfigBuilder()
       .setNumSampledNeighbors(25)
       .setActivationFunction(ActivationFunction.TANH)
       .setWeightInitScheme(WeightInitScheme.XAVIER)
       .setWeightedAggregationProperty(weightProperty)
       .build();
   
   UnsupervisedGraphWiseModel model = analyst.unsupervisedGraphWiseModelBuilder()
       .setVertexInputPropertyNames("features")
       .setConvLayerConfigs(convLayerConfig)
       .build();
   

   Building a Customized Unsupervised GraphWise Model Using Python

   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)
   conv_layer = analyst.graphwise_conv_layer_config(**conv_layer_config)
   params = dict(conv_layer_config=[conv_layer],
                 vertex_input_property_names=["features"])
   
   model = analyst.unsupervised_graphwise_builder(**params)

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