Java ContinuousLabel类代码示例

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
	Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());

	List<MiningModel> miningModels = new ArrayList<>();

	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

	for(int i = 0, rows = categoricalLabel.size(), columns = (trees.size() / rows); i < rows; i++){
		MiningModel miningModel = createMiningModel(FortranMatrixUtil.getRow(trees, rows, columns, i), numIteration, segmentSchema)
			.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));

		miningModels.add(miningModel);
	}

	return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
	GBTClassificationModel model = getTransformer();

	String lossType = model.getLossType();
	switch(lossType){
		case "logistic":
			break;
		default:
			throw new IllegalArgumentException("Loss function " + lossType + " is not supported");
	}

	Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());

	List<TreeModel> treeModels = TreeModelUtil.encodeDecisionTreeEnsemble(model, segmentSchema);

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(segmentSchema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.WEIGHTED_SUM, treeModels, Doubles.asList(model.treeWeights())))
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbtValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 2d, 0d, RegressionModel.NormalizationMethod.LOGIT, false, schema);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<RegTree> regTrees, float base_score, Integer ntreeLimit, Schema schema){
	Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.FLOAT), schema.getFeatures());

	List<MiningModel> miningModels = new ArrayList<>();

	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

	for(int i = 0, columns = categoricalLabel.size(), rows = (regTrees.size() / columns); i < columns; i++){
		MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(regTrees, rows, columns, i), base_score, ntreeLimit, segmentSchema)
			.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.FLOAT));

		miningModels.add(miningModel);
	}

	return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
static
public NeuralOutputs createRegressionNeuralOutputs(List<? extends Entity> entities, ContinuousLabel continuousLabel){

	if(entities.size() != 1){
		throw new IllegalArgumentException();
	}

	Entity entity = Iterables.getOnlyElement(entities);

	DerivedField derivedField = new DerivedField(OpType.CONTINUOUS, continuousLabel.getDataType())
		.setExpression(new FieldRef(continuousLabel.getName()));

	NeuralOutput neuralOutput = new NeuralOutput()
		.setOutputNeuron(entity.getId())
		.setDerivedField(derivedField);

	NeuralOutputs neuralOutputs = new NeuralOutputs()
		.addNeuralOutputs(neuralOutput);

	return neuralOutputs;
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
	Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());

	MiningModel miningModel = createMiningModel(trees, numIteration, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue"), OpType.CONTINUOUS, DataType.DOUBLE, new SigmoidTransformation(-1d * BinomialLogisticRegression.this.sigmoid_)));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.NONE, true, schema);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public Label encodeLabel(FieldName targetField, List<String> targetCategories, PMMLEncoder encoder){

	if(targetCategories != null){
		throw new IllegalArgumentException();
	}

	DataField dataField = encoder.createDataField(targetField, OpType.CONTINUOUS, DataType.DOUBLE);

	return new ContinuousLabel(dataField);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public RegressionModel encodeModel(TensorFlowEncoder encoder){
	DataField dataField = encoder.createDataField(FieldName.create("_target"), OpType.CONTINUOUS, DataType.FLOAT);

	Label label = new ContinuousLabel(dataField);

	RegressionModel regressionModel = encodeRegressionModel(encoder)
		.setMiningFunction(MiningFunction.REGRESSION)
		.setMiningSchema(ModelUtil.createMiningSchema(label));

	return regressionModel;
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<RegTree> regTrees, float base_score, Integer ntreeLimit, Schema schema){
	Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.FLOAT), schema.getFeatures());

	MiningModel miningModel = createMiningModel(regTrees, base_score, ntreeLimit, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public Label encodeLabel(FieldName targetField, List<String> targetCategories, PMMLEncoder encoder){

	if(targetCategories != null){
		throw new IllegalArgumentException();
	}

	DataField dataField = encoder.createDataField(targetField, OpType.CONTINUOUS, DataType.FLOAT);

	return new ContinuousLabel(dataField);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
static
protected MiningModel createMiningModel(List<RegTree> regTrees, float base_score, Integer ntreeLimit, Schema schema){
	ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();

	Schema segmentSchema = schema.toAnonymousSchema();

	List<TreeModel> treeModels = new ArrayList<>();

	if(ntreeLimit != null){

		if(ntreeLimit > regTrees.size()){
			throw new IllegalArgumentException("Tree limit " + ntreeLimit + " is greater than the number of trees");
		}

		regTrees = regTrees.subList(0, ntreeLimit);
	}

	for(RegTree regTree : regTrees){
		TreeModel treeModel = regTree.encodeTreeModel(segmentSchema);

		treeModels.add(treeModel);
	}

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel))
		.setMathContext(MathContext.FLOAT)
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, treeModels))
		.setTargets(ModelUtil.createRescaleTargets(null, ValueUtil.floatToDouble(base_score), continuousLabel));

	return miningModel;
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
static
public RegressionModel createRegression(MathContext mathContext, List<? extends Feature> features, List<Double> coefficients, Double intercept, RegressionModel.NormalizationMethod normalizationMethod, Schema schema){
	ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();

	if(normalizationMethod != null){

		switch(normalizationMethod){
			case NONE:
			case SOFTMAX:
			case LOGIT:
			case PROBIT:
			case CLOGLOG:
			case EXP:
			case LOGLOG:
			case CAUCHIT:
				break;
			default:
				throw new IllegalArgumentException();
		}
	}

	RegressionModel regressionModel = new RegressionModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel), null)
		.setNormalizationMethod(normalizationMethod)
		.setMathContext(ModelUtil.simplifyMathContext(mathContext))
		.addRegressionTables(createRegressionTable(features, coefficients, intercept));

	return regressionModel;
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
static
public <E extends Estimator & HasEstimatorEnsemble<DecisionTreeRegressor> & HasTreeOptions> MiningModel encodeGradientBoosting(E estimator, Number initialPrediction, Number learningRate, Schema schema){
	ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();

	List<TreeModel> treeModels = TreeModelUtil.encodeTreeModelSegmentation(estimator, MiningFunction.REGRESSION, schema);

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, treeModels))
		.setTargets(ModelUtil.createRescaleTargets(learningRate, initialPrediction, continuousLabel));

	return TreeModelUtil.transform(estimator, miningModel);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
private MiningModel encodeBinaryClassification(List<TreeModel> treeModels, Double initF, double coefficient, Schema schema){
	Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());

	MiningModel miningModel = createMiningModel(treeModels, initF, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, -coefficient, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
static
private MiningModel createMiningModel(List<TreeModel> treeModels, Double initF, Schema schema){
	ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, treeModels))
		.setTargets(ModelUtil.createRescaleTargets(null, initF, continuousLabel));

	return miningModel;
}
 

import org.jpmml.converter.ContinuousLabel; //导入依赖的package包/类
@Override
public NeuralNetwork encodeModel(TensorFlowEncoder encoder){
	DataField dataField = encoder.createDataField(FieldName.create("_target"), OpType.CONTINUOUS, DataType.FLOAT);

	NeuralNetwork neuralNetwork = encodeNeuralNetwork(encoder);

	List<NeuralLayer> neuralLayers = neuralNetwork.getNeuralLayers();

	NeuralLayer neuralLayer = Iterables.getLast(neuralLayers);

	neuralLayer.setActivationFunction(NeuralNetwork.ActivationFunction.IDENTITY);

	List<Neuron> neurons = neuralLayer.getNeurons();

	ContinuousLabel continuousLabel = new ContinuousLabel(dataField);

	neuralNetwork
		.setMiningFunction(MiningFunction.REGRESSION)
		.setMiningSchema(ModelUtil.createMiningSchema(continuousLabel))
		.setNeuralOutputs(NeuralNetworkUtil.createRegressionNeuralOutputs(neurons, continuousLabel));

	return neuralNetwork;
}