Java CategoricalLabel.size方法代码示例

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的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.CategoricalLabel; //导入方法依赖的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.CategoricalLabel; //导入方法依赖的package包/类
static
public NeuralOutputs createClassificationNeuralOutputs(List<? extends Entity> entities, CategoricalLabel categoricalLabel){

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

	NeuralOutputs neuralOutputs = new NeuralOutputs();

	for(int i = 0; i < categoricalLabel.size(); i++){
		Entity entity = entities.get(i);

		DerivedField derivedField = new DerivedField(OpType.CATEGORICAL, categoricalLabel.getDataType())
			.setExpression(new NormDiscrete(categoricalLabel.getName(), categoricalLabel.getValue(i)));

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

		neuralOutputs.addNeuralOutputs(neuralOutput);
	}

	return neuralOutputs;
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
@Override
public GeneralRegressionModel encodeModel(Schema schema){
	GeneralizedLinearRegressionModel model = getTransformer();

	String targetCategory = null;

	MiningFunction miningFunction = getMiningFunction();
	switch(miningFunction){
		case CLASSIFICATION:
			CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

			if(categoricalLabel.size() != 2){
				throw new IllegalArgumentException();
			}

			targetCategory = categoricalLabel.getValue(1);
			break;
		default:
			break;
	}

	GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), null, null, null)
		.setDistribution(parseFamily(model.getFamily()))
		.setLinkFunction(parseLinkFunction(model.getLink()))
		.setLinkParameter(parseLinkParameter(model.getLink()));

	GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, schema.getFeatures(), model.intercept(), VectorUtil.toList(model.coefficients()), targetCategory);

	return generalRegressionModel;
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
static
public RegressionModel createBinaryLogisticClassification(MathContext mathContext, List<? extends Feature> features, List<Double> coefficients, Double intercept, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

	if(categoricalLabel.size() != 2){
		throw new IllegalArgumentException();
	} // End if

	if(normalizationMethod != null){

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

	RegressionTable activeRegressionTable = RegressionModelUtil.createRegressionTable(features, coefficients, intercept)
		.setTargetCategory(categoricalLabel.getValue(1));

	RegressionTable passiveRegressionTable = RegressionModelUtil.createRegressionTable(Collections.<Feature>emptyList(), Collections.<Double>emptyList(), null)
		.setTargetCategory(categoricalLabel.getValue(0));

	RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), null)
		.setNormalizationMethod(normalizationMethod)
		.setMathContext(ModelUtil.simplifyMathContext(mathContext))
		.addRegressionTables(activeRegressionTable, passiveRegressionTable)
		.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);

	return regressionModel;
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
static
public void checkSize(int size, CategoricalLabel categoricalLabel){

	if(categoricalLabel.size() != size){
		throw new IllegalArgumentException("Expected " + size + " class(es), got " + categoricalLabel.size() + " class(es)");
	}
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
static
private Node encodeClassificationScore(Node node, RDoubleVector probabilities, Schema schema){
	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

	if(categoricalLabel.size() != probabilities.size()){
		throw new IllegalArgumentException();
	}

	Double maxProbability = null;

	for(int i = 0; i < categoricalLabel.size(); i++){
		String value = categoricalLabel.getValue(i);
		Double probability = probabilities.getValue(i);

		if(maxProbability == null || (maxProbability).compareTo(probability) < 0){
			node.setScore(value);

			maxProbability = probability;
		}

		ScoreDistribution scoreDistribution = new ScoreDistribution(value, probability);

		node.addScoreDistributions(scoreDistribution);
	}

	return node;
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
static
public MiningModel createClassification(List<? extends Model> models, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

	if(categoricalLabel.size() < 3 || categoricalLabel.size() != models.size()){
		throw new IllegalArgumentException();
	} // End if

	if(normalizationMethod != null){

		switch(normalizationMethod){
			case NONE:
			case SIMPLEMAX:
			case SOFTMAX:
				break;
			default:
				throw new IllegalArgumentException();
		}
	}

	MathContext mathContext = null;

	List<RegressionTable> regressionTables = new ArrayList<>();

	for(int i = 0; i < categoricalLabel.size(); i++){
		Model model = models.get(i);

		MathContext modelMathContext = model.getMathContext();
		if(modelMathContext == null){
			modelMathContext = MathContext.DOUBLE;
		} // End if

		if(mathContext == null){
			mathContext = modelMathContext;
		} else

		{
			if(!Objects.equals(mathContext, modelMathContext)){
				throw new IllegalArgumentException();
			}
		}

		Feature feature = MiningModelUtil.MODEL_PREDICTION.apply(model);

		RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null)
			.setTargetCategory(categoricalLabel.getValue(i));

		regressionTables.add(regressionTable);
	}

	RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables)
		.setNormalizationMethod(normalizationMethod)
		.setMathContext(ModelUtil.simplifyMathContext(mathContext))
		.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);

	List<Model> segmentationModels = new ArrayList<>(models);
	segmentationModels.add(regressionModel);

	return createModelChain(segmentationModels, schema);
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
@Override
public RegressionModel encodeModel(Schema schema){
	int[] shape = getCoefShape();

	int numberOfClasses = shape[0];
	int numberOfFeatures = shape[1];

	boolean hasProbabilityDistribution = hasProbabilityDistribution();

	List<? extends Number> coef = getCoef();
	List<? extends Number> intercepts = getIntercept();

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

	List<Feature> features = schema.getFeatures();

	if(numberOfClasses == 1){
		ClassifierUtil.checkSize(2, categoricalLabel);

		return RegressionModelUtil.createBinaryLogisticClassification(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, 0)), ValueUtil.asDouble(intercepts.get(0)), RegressionModel.NormalizationMethod.LOGIT, hasProbabilityDistribution, schema);
	} else

	if(numberOfClasses >= 3){
		ClassifierUtil.checkSize(numberOfClasses, categoricalLabel);

		List<RegressionTable> regressionTables = new ArrayList<>();

		for(int i = 0, rows = categoricalLabel.size(); i < rows; i++){
			RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, i)), ValueUtil.asDouble(intercepts.get(i)))
				.setTargetCategory(categoricalLabel.getValue(i));

			regressionTables.add(regressionTable);
		}

		RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables)
			.setNormalizationMethod(RegressionModel.NormalizationMethod.LOGIT)
			.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel) : null);

		return regressionModel;
	} else

	{
		throw new IllegalArgumentException();
	}
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
@Override
public Model encodeModel(Schema schema){
	RGenericVector glm = getObject();

	RDoubleVector coefficients = (RDoubleVector)glm.getValue("coefficients");
	RGenericVector family = (RGenericVector)glm.getValue("family");

	Double intercept = coefficients.getValue(getInterceptName(), true);

	RStringVector familyFamily = (RStringVector)family.getValue("family");
	RStringVector familyLink = (RStringVector)family.getValue("link");

	Label label = schema.getLabel();
	List<Feature> features = schema.getFeatures();

	if(coefficients.size() != (features.size() + (intercept != null ? 1 : 0))){
		throw new IllegalArgumentException();
	}

	List<Double> featureCoefficients = getFeatureCoefficients(features, coefficients);

	MiningFunction miningFunction = getMiningFunction(familyFamily.asScalar());

	String targetCategory = null;

	switch(miningFunction){
		case CLASSIFICATION:
			{
				CategoricalLabel categoricalLabel = (CategoricalLabel)label;

				if(categoricalLabel.size() != 2){
					throw new IllegalArgumentException();
				}

				targetCategory = categoricalLabel.getValue(1);
			}
			break;
		default:
			break;
	}

	GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, miningFunction, ModelUtil.createMiningSchema(label), null, null, null)
		.setDistribution(parseFamily(familyFamily.asScalar()))
		.setLinkFunction(parseLinkFunction(familyLink.asScalar()))
		.setLinkParameter(parseLinkParameter(familyLink.asScalar()));

	GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, intercept, featureCoefficients, targetCategory);

	switch(miningFunction){
		case CLASSIFICATION:
			generalRegressionModel.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, (CategoricalLabel)label));
			break;
		default:
			break;
	}

	return generalRegressionModel;
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
private MiningModel encodeMultinomialClassification(List<TreeModel> treeModels, Double initF, Schema schema){
	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

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

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

	for(int i = 0, columns = categoricalLabel.size(), rows = (treeModels.size() / columns); i < columns; i++){
		MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(treeModels, rows, columns, i), initF, segmentSchema)
			.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));

		miningModels.add(miningModel);
	}

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

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
static
public MiningModel createClassification(List<? extends Model> models, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
    CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

    // modified here
    if(categoricalLabel.size() != models.size()){
        throw new IllegalArgumentException();
    } // End if

    if(normalizationMethod != null){

        switch(normalizationMethod){
            case NONE:
            case SIMPLEMAX:
            case SOFTMAX:
                break;
            default:
                throw new IllegalArgumentException();
        }
    }

    MathContext mathContext = null;

    List<RegressionTable> regressionTables = new ArrayList<>();

    for(int i = 0; i < categoricalLabel.size(); i++){
        Model model = models.get(i);

        MathContext modelMathContext = model.getMathContext();
        if(modelMathContext == null){
            modelMathContext = MathContext.DOUBLE;
        } // End if

        if(mathContext == null){
            mathContext = modelMathContext;
        } else

        {
            if(!Objects.equals(mathContext, modelMathContext)){
                throw new IllegalArgumentException();
            }
        }

        Feature feature = MODEL_PREDICTION.apply(model);

        RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null)
                .setTargetCategory(categoricalLabel.getValue(i));

        regressionTables.add(regressionTable);
    }

    RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables)
            .setNormalizationMethod(normalizationMethod)
            .setMathContext(ModelUtil.simplifyMathContext(mathContext))
            .setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);

    List<Model> segmentationModels = new ArrayList<>(models);
    segmentationModels.add(regressionModel);

    return createModelChain(segmentationModels, schema);
}
 

import org.jpmml.converter.CategoricalLabel; //导入方法依赖的package包/类
@Override
public Model encodeModel(Schema schema){
	RGenericVector lrm = getObject();

	RDoubleVector coefficients = (RDoubleVector)lrm.getValue("coefficients");

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

	if(categoricalLabel.size() != 2){
		throw new IllegalArgumentException();
	}

	String targetCategory = categoricalLabel.getValue(1);

	Double intercept = coefficients.getValue(getInterceptName(), true);

	List<Feature> features = schema.getFeatures();

	if(coefficients.size() != (features.size() + (intercept != null ? 1 : 0))){
		throw new IllegalArgumentException();
	}

	List<Double> featureCoefficients = getFeatureCoefficients(features, coefficients);

	GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), null, null, null)
		.setLinkFunction(GeneralRegressionModel.LinkFunction.LOGIT)
		.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));

	GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, intercept, featureCoefficients, targetCategory);

	return generalRegressionModel;
}