import org.jpmml.converter.Schema; //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.Schema; //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.Schema; //导入依赖的package包/类
static
public TreeModel encodeTreeModel(org.apache.spark.ml.tree.Node node, PredicateManager predicateManager, MiningFunction miningFunction, Schema schema){
	Node root = encodeNode(node, predicateManager, Collections.<FieldName, Set<String>>emptyMap(), miningFunction, schema)
		.setPredicate(new True());

	TreeModel treeModel = new TreeModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);

	String compact = TreeModelOptions.COMPACT;
	if(compact != null && Boolean.valueOf(compact)){
		Visitor visitor = new TreeModelCompactor();

		visitor.applyTo(treeModel);
	}

	return treeModel;
}
 

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

	List<Cluster> clusters = new ArrayList<>();

	Vector[] clusterCenters = model.clusterCenters();
	for(int i = 0; i < clusterCenters.length; i++){
		Cluster cluster = new Cluster()
			.setId(String.valueOf(i))
			.setArray(PMMLUtil.createRealArray(VectorUtil.toList(clusterCenters[i])));

		clusters.add(cluster);
	}

	ComparisonMeasure comparisonMeasure = new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE)
		.setCompareFunction(CompareFunction.ABS_DIFF)
		.setMeasure(new SquaredEuclidean());

	return new ClusteringModel(MiningFunction.CLUSTERING, ClusteringModel.ModelClass.CENTER_BASED, clusters.size(), ModelUtil.createMiningSchema(schema.getLabel()), comparisonMeasure, ClusteringModelUtil.createClusteringFields(schema.getFeatures()), clusters);
}
 

import org.jpmml.converter.Schema; //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.Schema; //导入依赖的package包/类
static
public MiningModel createModelChain(List<? extends Model> models, Schema schema){

	if(models.size() < 1){
		throw new IllegalArgumentException();
	}

	Segmentation segmentation = createSegmentation(Segmentation.MultipleModelMethod.MODEL_CHAIN, models);

	Model lastModel = Iterables.getLast(models);

	MiningModel miningModel = new MiningModel(lastModel.getMiningFunction(), ModelUtil.createMiningSchema(schema.getLabel()))
		.setMathContext(ModelUtil.simplifyMathContext(lastModel.getMathContext()))
		.setSegmentation(segmentation);

	return miningModel;
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
	List<? extends Classifier> estimators = getEstimators();
	List<List<Integer>> estimatorsFeatures = getEstimatorsFeatures();

	Segmentation.MultipleModelMethod multipleModelMethod = Segmentation.MultipleModelMethod.AVERAGE;

	for(Classifier estimator : estimators){

		if(!estimator.hasProbabilityDistribution()){
			multipleModelMethod = Segmentation.MultipleModelMethod.MAJORITY_VOTE;

			break;
		}
	}

	MiningModel miningModel = BaggingUtil.encodeBagging(estimators, estimatorsFeatures, multipleModelMethod, MiningFunction.CLASSIFICATION, schema)
		.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, (CategoricalLabel)schema.getLabel()));

	return miningModel;
}
 

import org.jpmml.converter.Schema; //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;
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
static
public <E extends Estimator & HasTree> TreeModel encodeTreeModel(E estimator, PredicateManager predicateManager, MiningFunction miningFunction, Schema schema){
	Tree tree = estimator.getTree();

	int[] leftChildren = tree.getChildrenLeft();
	int[] rightChildren = tree.getChildrenRight();
	int[] features = tree.getFeature();
	double[] thresholds = tree.getThreshold();
	double[] values = tree.getValues();

	Node root = new Node()
		.setId("1")
		.setPredicate(new True());

	encodeNode(root, predicateManager, 0, leftChildren, rightChildren, features, thresholds, values, miningFunction, schema);

	TreeModel treeModel = new TreeModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);

	return treeModel;
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
static
public Schema toTreeModelSchema(final DataType dataType, Schema schema){
	Function<Feature, Feature> function = new Function<Feature, Feature>(){

		@Override
		public Feature apply(Feature feature){

			if(feature instanceof BinaryFeature){
				BinaryFeature binaryFeature = (BinaryFeature)feature;

				return binaryFeature;
			} else

			{
				ContinuousFeature continuousFeature = feature.toContinuousFeature(dataType);

				return continuousFeature;
			}
		}
	};

	return schema.toTransformedSchema(function);
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
@Override
public SupportVectorMachineModel encodeModel(Schema schema){
	int[] shape = getSupportVectorsShape();

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

	List<Integer> support = getSupport();
	List<? extends Number> supportVectors = getSupportVectors();
	List<? extends Number> dualCoef = getDualCoef();
	List<? extends Number> intercept = getIntercept();

	SupportVectorMachineModel supportVectorMachineModel = LibSVMUtil.createRegression(new CMatrix<>(ValueUtil.asDoubles(supportVectors), numberOfVectors, numberOfFeatures), SupportVectorMachineUtil.formatIds(support), ValueUtil.asDouble(Iterables.getOnlyElement(intercept)), ValueUtil.asDoubles(dualCoef), schema)
		.setKernel(SupportVectorMachineUtil.createKernel(getKernel(), getDegree(), getGamma(), getCoef0()));

	return supportVectorMachineModel;
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
	RGenericVector ranger = getObject();

	RStringVector treetype = (RStringVector)ranger.getValue("treetype");

	switch(treetype.asScalar()){
		case "Regression":
			return encodeRegression(ranger, schema);
		case "Classification":
			return encodeClassification(ranger, schema);
		case "Probability estimation":
			return encodeProbabilityForest(ranger, schema);
		default:
			throw new IllegalArgumentException();
	}
}
 

import org.jpmml.converter.Schema; //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;
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
private List<TreeModel> encodeForest(RGenericVector forest, MiningFunction miningFunction, ScoreEncoder scoreEncoder, Schema schema){
	RNumberVector<?> numTrees = (RNumberVector<?>)forest.getValue("num.trees");
	RGenericVector childNodeIDs = (RGenericVector)forest.getValue("child.nodeIDs");
	RGenericVector splitVarIDs = (RGenericVector)forest.getValue("split.varIDs");
	RGenericVector splitValues = (RGenericVector)forest.getValue("split.values");
	RGenericVector terminalClassCounts = (RGenericVector)forest.getValue("terminal.class.counts", true);

	Schema segmentSchema = schema.toAnonymousSchema();

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

	for(int i = 0; i < ValueUtil.asInt(numTrees.asScalar()); i++){
		TreeModel treeModel = encodeTreeModel(miningFunction, scoreEncoder, (RGenericVector)childNodeIDs.getValue(i), (RNumberVector<?>)splitVarIDs.getValue(i), (RNumberVector<?>)splitValues.getValue(i), (terminalClassCounts != null ? (RGenericVector)terminalClassCounts.getValue(i) : null), segmentSchema);

		treeModels.add(treeModel);
	}

	return treeModels;
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
	RGenericVector randomForest = getObject();

	RStringVector type = (RStringVector)randomForest.getValue("type");
	RGenericVector forest = (RGenericVector)randomForest.getValue("forest");

	switch(type.asScalar()){
		case "regression":
			return encodeRegression(forest, schema);
		case "classification":
			return encodeClassification(forest, schema);
		default:
			throw new IllegalArgumentException();
	}
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
private Schema filter(Schema schema){
	Function<Feature, Feature> function = new Function<Feature, Feature>(){

		@Override
		public Feature apply(Feature feature){
			Expression expression = encodeExpression(feature);

			if(expression == null){
				return feature;
			}

			DerivedField derivedField = createDerivedField(FeatureUtil.createName("preProcess", feature), OpType.CONTINUOUS, DataType.DOUBLE, expression);

			return new ContinuousFeature(PreProcessEncoder.this, derivedField);
		}
	};

	return schema.toTransformedSchema(function);
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
static
public MiningModel createModelChain(List<? extends Model> models, Schema schema){

    if(models.size() < 1){
        throw new IllegalArgumentException();
    }

    Segmentation segmentation = createSegmentation(Segmentation.MultipleModelMethod.MODEL_CHAIN, models);

    Model lastModel = Iterables.getLast(models);

    MiningModel miningModel = new MiningModel(lastModel.getMiningFunction(), ModelUtil.createMiningSchema(schema.getLabel()))
            .setMathContext(ModelUtil.simplifyMathContext(lastModel.getMathContext()))
            .setSegmentation(segmentation);

    return miningModel;
}
 

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

	Booster booster = model.booster();

	return BoosterUtil.encodeBooster(booster, schema);
}
 

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

	Booster booster = model.booster();

	return BoosterUtil.encodeBooster(booster, schema);
}
 

import org.jpmml.converter.Schema; //导入依赖的package包/类
static
public MiningModel encodeBooster(Booster booster, Schema schema){
	byte[] bytes = booster.toByteArray();

	Learner learner;

	try(InputStream is = new ByteArrayInputStream(bytes)){
		learner = XGBoostUtil.loadLearner(is);
	} catch(IOException ioe){
		throw new RuntimeException(ioe);
	}

	Function<Feature, Feature> function = new Function<Feature, Feature>(){

		@Override
		public Feature apply(Feature feature){

			if(feature instanceof BinaryFeature){
				BinaryFeature binaryFeature = (BinaryFeature)feature;

				return binaryFeature;
			} else

			{
				ContinuousFeature continuousFeature = feature.toContinuousFeature(DataType.FLOAT);

				return continuousFeature;
			}
		}
	};

	Schema xgbSchema = schema.toTransformedSchema(function);

	return learner.encodeMiningModel(null, false, xgbSchema);
}