Package oracle.pgx.api.mllib

Class SupervisedGraphWiseModelBuilder

java.lang.Object

oracle.pgx.api.mllib.WiseModelBuilder<Config,Self>

oracle.pgx.api.mllib.GraphWiseModelBuilder<SupervisedGraphWiseModel,SupervisedGraphWiseModelConfig,SupervisedGraphWiseModelBuilder>

oracle.pgx.api.mllib.SupervisedGraphWiseModelBuilder

public class SupervisedGraphWiseModelBuilder
extends GraphWiseModelBuilder<SupervisedGraphWiseModel,SupervisedGraphWiseModelConfig,SupervisedGraphWiseModelBuilder>

Builder for SupervisedGraphWiseModel The builder can be used to set the configuration of the model and create the model object.

Since:

19.4

Constructor Summary

Constructors

Constructor	Description
SupervisedGraphWiseModelBuilder(PgxSession session, oracle.pgx.api.internal.Core core, java.util.function.Supplier<java.lang.String> keystorePathSupplier, java.util.function.Supplier<char[]> keystorePasswordSupplier, java.util.function.BiFunction<PgxSession,oracle.pgx.api.internal.Graph,PgxGraph> graphConstructor)

Method Summary

Modifier and Type	Method	Description
SupervisedGraphWiseModel	build()	Builds the SupervisedGraphWise model with the specified parameters.
SupervisedGraphWiseModelBuilder	setBatchGenerator(BatchGenerator batchGenerator)	Sets the batch generator.
SupervisedGraphWiseModelBuilder	setClassWeights(java.util.Map<?,java.lang.Float> classWeights)	Set the class weights to be used in the loss function.
SupervisedGraphWiseModelBuilder	setLossFunction(LossFunction lossFunction)	Sets the loss function for the algorithm.
SupervisedGraphWiseModelBuilder	setPredictionLayerConfigs(GraphWisePredictionLayerConfig... layerConfigs)	Set the prediction layer configurations (See GraphWisePredictionLayerConfig.
SupervisedGraphWiseModelBuilder	setVertexTargetPropertyName(java.lang.String propertyName)	Set the target (labels) for the algorithm in the form of a property name of the graph.

Methods inherited from class oracle.pgx.api.mllib.GraphWiseModelBuilder

setTargetVertexLabels, setTargetVertexLabels

Methods inherited from class oracle.pgx.api.mllib.WiseModelBuilder

setBatchSize, setConvLayerConfigs, setEdgeInputPropertyConfigs, setEdgeInputPropertyNames, setEdgeInputPropertyNames, setEmbeddingDim, setEnableAccelerator, setLearningRate, setNormalize, setNumEpochs, setSeed, setShuffle, setStandardize, setVertexInputPropertyConfigs, setVertexInputPropertyNames, setVertexInputPropertyNames, setWeightDecay

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SupervisedGraphWiseModelBuilder

public SupervisedGraphWiseModelBuilder(PgxSession session,
                                       oracle.pgx.api.internal.Core core,
                                       java.util.function.Supplier<java.lang.String> keystorePathSupplier,
                                       java.util.function.Supplier<char[]> keystorePasswordSupplier,
                                       java.util.function.BiFunction<PgxSession,oracle.pgx.api.internal.Graph,PgxGraph> graphConstructor)

Method Detail

setVertexTargetPropertyName

public SupervisedGraphWiseModelBuilder setVertexTargetPropertyName(java.lang.String propertyName)

Set the target (labels) for the algorithm in the form of a property name of the graph.

Supported property types are given by SupervisedGraphWiseModelConfig.SUPPORTED_LABEL_TYPES.

Parameters:

propertyName - property name

Returns:

this

Since:

19.4

setPredictionLayerConfigs

public SupervisedGraphWiseModelBuilder setPredictionLayerConfigs(GraphWisePredictionLayerConfig... layerConfigs)

Set the prediction layer configurations (See GraphWisePredictionLayerConfig. You must pass at least one prediction layer config (or leave it as default).

Note that an additional layer will be inserted in the end for which:

• the activation function will be replaced with the activation function of the loss function, e.g. softmax or sigmoid
• the hidden dimension will be equal to the number of classes
• the weight initialization scheme will be copied from the previous layer

default: SupervisedGraphWiseModelConfig.DEFAULT_PREDICTION_LAYER_CONFIGS

Parameters:

layerConfigs - layer configs

Returns:

this

Since:

19.4

setClassWeights

public SupervisedGraphWiseModelBuilder setClassWeights(java.util.Map<?,java.lang.Float> classWeights)

Set the class weights to be used in the loss function. The loss for the corresponding class will be multiplied by the factor given in this map. If null, uniform class weights will be used.

default: SupervisedGraphWiseModelConfig.DEFAULT_CLASS_MAP

Parameters:

classWeights - map from classes to weights

Returns:

this

Since:

19.4

setLossFunction

public SupervisedGraphWiseModelBuilder setLossFunction(LossFunction lossFunction)

Sets the loss function for the algorithm. See LossFunction

default: SoftmaxCrossEntropyLoss

Parameters:

lossFunction - loss function

Returns:

this

Since:

21.3

setBatchGenerator

public SupervisedGraphWiseModelBuilder setBatchGenerator(BatchGenerator batchGenerator)

Sets the batch generator. See BatchGenerator

default: StandardBatchGenerator

Parameters:

batchGenerator - batch generator

Returns:

this

Since:

21.3

build

public SupervisedGraphWiseModel build()
                               throws java.lang.InterruptedException,
                                      java.util.concurrent.ExecutionException

Builds the SupervisedGraphWise model with the specified parameters.

Specified by:

build in class GraphWiseModelBuilder<SupervisedGraphWiseModel,SupervisedGraphWiseModelConfig,SupervisedGraphWiseModelBuilder>

Returns:

SupervisedGraphWise model

Throws:

java.lang.InterruptedException

java.util.concurrent.ExecutionException

Since:

19.4