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Java MiningModelUtil类代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Java中org.jpmml.converter.mining.MiningModelUtil类的典型用法代码示例。如果您正苦于以下问题：Java MiningModelUtil类的具体用法？Java MiningModelUtil怎么用？Java MiningModelUtil使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。



MiningModelUtil类属于org.jpmml.converter.mining包，在下文中一共展示了MiningModelUtil类的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Java代码示例。

示例1: encodeMiningModel

import org.jpmml.converter.mining.MiningModelUtil; //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);
}
开发者ID:jpmml，项目名称:jpmml-lightgbm，代码行数:18，代码来源:MultinomialLogisticRegression.java


示例2: encodeModel

import org.jpmml.converter.mining.MiningModelUtil; //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);
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:23，代码来源:GBTClassificationModelConverter.java


示例3: encodeMiningModel

import org.jpmml.converter.mining.MiningModelUtil; //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);
}
开发者ID:jpmml，项目名称:jpmml-xgboost，代码行数:18，代码来源:MultinomialLogisticRegression.java


示例4: encodeModel

import org.jpmml.converter.mining.MiningModelUtil; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
	List<? extends Regressor> estimators = getEstimators();
	List<? extends Number> estimatorWeights = getEstimatorWeights();

	Schema segmentSchema = schema.toAnonymousSchema();

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

	for(Regressor estimator : estimators){
		Model model = estimator.encodeModel(segmentSchema);

		models.add(model);
	}

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(MultipleModelMethod.WEIGHTED_MEDIAN, models, estimatorWeights));

	return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:21，代码来源:AdaBoostRegressor.java


示例5: encodeRegression

import org.jpmml.converter.mining.MiningModelUtil; //导入依赖的package包/类
private MiningModel encodeRegression(RGenericVector ranger, Schema schema){
	RGenericVector forest = (RGenericVector)ranger.getValue("forest");

	ScoreEncoder scoreEncoder = new ScoreEncoder(){

		@Override
		public void encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount){
			node.setScore(ValueUtil.formatValue(splitValue));
		}
	};

	List<TreeModel> treeModels = encodeForest(forest, MiningFunction.REGRESSION, scoreEncoder, schema);

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels));

	return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:19，代码来源:RangerConverter.java


示例6: createMiningModel

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

    Schema segmentSchema = schema.toAnonymousSchema();

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

    for(RegressionTree 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;
}
开发者ID:cheng-li，项目名称:pyramid，代码行数:22，代码来源:PMMLConverter.java


示例7: encodeMiningModel

import org.jpmml.converter.mining.MiningModelUtil; //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);
}
开发者ID:jpmml，项目名称:jpmml-lightgbm，代码行数:10，代码来源:BinomialLogisticRegression.java


示例8: encodeMiningModel

import org.jpmml.converter.mining.MiningModelUtil; //导入依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
	Schema segmentSchema = schema.toAnonymousSchema();

	MiningModel miningModel = super.encodeMiningModel(trees, numIteration, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createRegression(miningModel, RegressionModel.NormalizationMethod.EXP, schema);
}
开发者ID:jpmml，项目名称:jpmml-lightgbm，代码行数:10，代码来源:PoissonRegression.java


示例9: encodeModel

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

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

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.WEIGHTED_SUM, treeModels, Doubles.asList(model.treeWeights())));

	return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:12，代码来源:GBTRegressionModelConverter.java


示例10: encodeModel

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

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

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels));

	return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:12，代码来源:RandomForestRegressionModelConverter.java


示例11: encodeModel

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

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

	MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(schema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels));

	return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:12，代码来源:RandomForestClassificationModelConverter.java


示例12: encodeMiningModel

import org.jpmml.converter.mining.MiningModelUtil; //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);
}
开发者ID:jpmml，项目名称:jpmml-xgboost，代码行数:10，代码来源:BinomialLogisticRegression.java


示例13: encodeMiningModel

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

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

	return MiningModelUtil.createRegression(miningModel, getNormalizationMethod(), schema);
}
开发者ID:jpmml，项目名称:jpmml-xgboost，代码行数:10，代码来源:GeneralizedLinearRegression.java


示例14: createMiningModel

import org.jpmml.converter.mining.MiningModelUtil; //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;
}
开发者ID:jpmml，项目名称:jpmml-xgboost，代码行数:31，代码来源:ObjFunction.java


示例15: encodeBaseForest

import org.jpmml.converter.mining.MiningModelUtil; //导入依赖的package包/类
static
public <E extends Estimator & HasEstimatorEnsemble<T> & HasTreeOptions, T extends Estimator & HasTree> MiningModel encodeBaseForest(E estimator, Segmentation.MultipleModelMethod multipleModelMethod, MiningFunction miningFunction, Schema schema){
	List<TreeModel> treeModels = TreeModelUtil.encodeTreeModelSegmentation(estimator, miningFunction, schema);

	MiningModel miningModel = new MiningModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(multipleModelMethod, treeModels));

	return TreeModelUtil.transform(estimator, miningModel);
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:10，代码来源:BaseForestUtil.java


示例16: encodeGradientBoosting

import org.jpmml.converter.mining.MiningModelUtil; //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);
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:13，代码来源:GradientBoostingUtil.java


示例17: encodePMML

import org.jpmml.converter.mining.MiningModelUtil; //导入依赖的package包/类
@Override
public PMML encodePMML(Model model){

	if(this.transformers.size() > 0){
		List<Model> models = new ArrayList<>(this.transformers);
		models.add(model);

		Schema schema = new Schema(null, Collections.<Feature>emptyList());

		model = MiningModelUtil.createModelChain(models, schema);
	}

	PMML pmml = super.encodePMML(model);

	DataDictionary dataDictionary = pmml.getDataDictionary();

	List<DataField> dataFields = dataDictionary.getDataFields();
	for(DataField dataField : dataFields){
		UnivariateStats univariateStats = getUnivariateStats(dataField.getName());

		if(univariateStats == null){
			continue;
		}

		ModelStats modelStats = model.getModelStats();
		if(modelStats == null){
			modelStats = new ModelStats();

			model.setModelStats(modelStats);
		}

		modelStats.addUnivariateStats(univariateStats);
	}

	return pmml;
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:37，代码来源:SkLearnEncoder.java


示例18: encodeClassification

import org.jpmml.converter.mining.MiningModelUtil; //导入依赖的package包/类
private MiningModel encodeClassification(RGenericVector ranger, Schema schema){
	RGenericVector forest = (RGenericVector)ranger.getValue("forest");

	final
	RStringVector levels = (RStringVector)forest.getValue("levels");

	ScoreEncoder scoreEncoder = new ScoreEncoder(){

		@Override
		public void encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount){
			int index = ValueUtil.asInt(splitValue);

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

			node.setScore(levels.getValue(index - 1));
		}
	};

	List<TreeModel> treeModels = encodeForest(forest, MiningFunction.CLASSIFICATION, scoreEncoder, schema);

	MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(schema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.MAJORITY_VOTE, treeModels));

	return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:28，代码来源:RangerConverter.java


示例19: encodeBinaryClassification

import org.jpmml.converter.mining.MiningModelUtil; //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);
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:9，代码来源:GBMConverter.java


示例20: createMiningModel

import org.jpmml.converter.mining.MiningModelUtil; //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;
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:11，代码来源:GBMConverter.java



注：本文中的org.jpmml.converter.mining.MiningModelUtil类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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