Setting a Custom Loss Function and Batch Generator (for Anomaly Detection)

17.3.7 Setting a Custom Loss Function and Batch Generator (for Anomaly Detection)

In addition to different loss functions, it is also possible to select different batch generators by providing a batch generator type. This is useful for applications such as Anomaly Detection, which can be cast into the standard supervised framework but require different loss functions and batch generators.

SupervisedEdgeWise model can use the DevNetLoss and the StratifiedOversamplingBatchGenerator. DevNetLoss takes confidence margin and the value the anomaly takes in the target property as the two parameters.

The following example assumes that the convLayerConfig has already been defined:

opg4j> import oracle.pgx.config.mllib.loss.LossFunctions
opg4j> import oracle.pgx.config.mllib.batchgenerator.BatchGenerators
opg4j> var predictionLayerConfig = analyst.graphWisePredictionLayerConfigBuilder().
         setHiddenDimension(32).
         setActivationFunction(ActivationFunction.LINEAR).
         build()
opg4j> var model = analyst.supervisedEdgeWiseModelBuilder().
         setVertexInputPropertyNames("vertex_features").
         setEdgeInputPropertyNames("edge_features").
         setEdgeTargetPropertyName("labels").
         setConvLayerConfigs(convLayerConfig).
         setPredictionLayerConfigs(predictionLayerConfig).
         setLossFunction(LossFunctions.devNetLoss(5.0, true)).
         setBatchGenerator(BatchGenerators.STRATIFIED_OVERSAMPLING).
         build()

import oracle.pgx.config.mllib.loss.LossFunctions;
import oracle.pgx.config.mllib.batchgenerator.BatchGenerators;

GraphWisePredictionLayerConfig predictionLayerConfig = analyst.graphWisePredictionLayerConfigBuilder()
    .setHiddenDimension(32)
    .setActivationFunction(ActivationFunction.LINEAR)
    .build();

SupervisedEdgeWiseModel model = analyst.supervisedEdgeWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_features")
    .setEdgeInputPropertyNames("edge_features")
    .setEdgeTargetPropertyName("labels")
    .setConvLayerConfigs(convLayerConfig)
    .setPredictionLayerConfigs(predictionLayerConfig)
    .setLossFunction(LossFunctions.devNetLoss(5.0, true))
    .setBatchGenerator(BatchGenerators.STRATIFIED_OVERSAMPLING)
    .build();

from pypgx.api.mllib import DevNetLoss

pred_layer_config = dict(hidden_dim=32,
                         activation_fn='linear')

pred_layer = analyst.graphwise_pred_layer_config(**pred_layer_config)

params = dict(edge_target_property_name="labels",
              conv_layer_config=[conv_layer],
              pred_layer_config=[pred_layer],
              vertex_input_property_names=["vertex_features"],
              edge_input_property_names=["edge_features"],
              loss_fn=DevNetLoss(5.0, True),
              batch_gen='Stratified_Oversampling',
              seed=17)

model = analyst.supervised_edgewise_builder(**params)

Parent topic: Using the Supervised EdgeWise Algorithm (Edge Embeddings and Classification)