import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
/**
 * Validates that the encoded PMML model received matches expected schema.
 *
 * @param pmml {@link PMML} encoding of KMeans Clustering
 * @param schema expected schema attributes of KMeans Clustering
 */
public static void validatePMMLVsSchema(PMML pmml, InputSchema schema) {
  List<Model> models = pmml.getModels();
  Preconditions.checkArgument(models.size() == 1,
      "Should have exactly one model, but had %s", models.size());

  Model model = models.get(0);
  Preconditions.checkArgument(model instanceof ClusteringModel);
  Preconditions.checkArgument(model.getMiningFunction() == MiningFunction.CLUSTERING);

  DataDictionary dictionary = pmml.getDataDictionary();
  Preconditions.checkArgument(
      schema.getFeatureNames().equals(AppPMMLUtils.getFeatureNames(dictionary)),
      "Feature names in schema don't match names in PMML");

  MiningSchema miningSchema = model.getMiningSchema();
  Preconditions.checkArgument(schema.getFeatureNames().equals(
      AppPMMLUtils.getFeatureNames(miningSchema)));

}
 

import org.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //导入依赖的package包/类
@Override
public List<OutputField> registerOutputFields(Label label, SparkMLEncoder encoder){
	List<OutputField> result = super.registerOutputFields(label, encoder);

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

			result = new ArrayList<>(result);
			result.addAll(ModelUtil.createProbabilityFields(DataType.DOUBLE, categoricalLabel.getValues()));
			break;
		default:
			break;
	}

	return result;
}
 

import org.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //导入依赖的package包/类
@Override
public void encodeSchema(RExpEncoder encoder){
	RGenericVector glm = getObject();

	RGenericVector family = (RGenericVector)glm.getValue("family");
	RGenericVector model = (RGenericVector)glm.getValue("model");

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

	super.encodeSchema(encoder);

	MiningFunction miningFunction = getMiningFunction(familyFamily.asScalar());
	switch(miningFunction){
		case CLASSIFICATION:
			Label label = encoder.getLabel();

			RIntegerVector variable = (RIntegerVector)model.getValue((label.getName()).getValue());

			DataField dataField = (DataField)encoder.toCategorical(label.getName(), RExpUtil.getFactorLevels(variable));

			encoder.setLabel(dataField);
			break;
		default:
			break;
	}
}
 

import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
static
private MiningFunction getMiningFunction(String family){
	GeneralRegressionModel.Distribution distribution = parseFamily(family);

	switch(distribution){
		case BINOMIAL:
			return MiningFunction.CLASSIFICATION;
		case NORMAL:
		case GAMMA:
		case IGAUSS:
		case POISSON:
			return MiningFunction.REGRESSION;
		default:
			throw new IllegalArgumentException();
	}
}
 

import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
/**
 * @return A synthetic {@link DataField} element describing the default target field.
 */
protected DataField getDataField(){
	MiningFunction miningFunction = getMiningFunction();

	switch(miningFunction){
		case REGRESSION:
			MathContext mathContext = getMathContext();

			switch(mathContext){
				case FLOAT:
					return ModelEvaluator.DEFAULT_TARGET_CONTINUOUS_FLOAT;
				default:
					return ModelEvaluator.DEFAULT_TARGET_CONTINUOUS_DOUBLE;
			}
		case CLASSIFICATION:
		case CLUSTERING:
			return ModelEvaluator.DEFAULT_TARGET_CATEGORICAL_STRING;
		default:
			return null;
	}
}
 

import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
static
private TreeModelEvaluator createTreeModelEvaluator(MiningFunction miningFunction, MathContext mathContext, Target target){
	Node root = new Node()
		.setPredicate(new False());

	Targets targets = new Targets()
		.addTargets(target);

	TreeModel treeModel = new TreeModel(miningFunction, new MiningSchema(), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
		.setMathContext(mathContext)
		.setTargets(targets);

	PMML pmml = new PMML(Version.PMML_4_3.getVersion(), new Header(), new DataDictionary())
		.addModels(treeModel);

	return new TreeModelEvaluator(pmml);
}
 

import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
@Override
public VisitorAction visit(Node node){
	TreeModel treeModel = getTreeModel();

	MiningFunction miningFunction = treeModel.getMiningFunction();
	switch(miningFunction){
		case REGRESSION:
			if(node.hasScoreDistributions()){
				List<ScoreDistribution> scoreDistributions = node.getScoreDistributions();

				scoreDistributions.clear();
			}
			break;
		default:
			break;
	}

	return super.visit(node);
}
 

import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
@Test
public void clean(){
	Node node = new Node()
		.setPredicate(new True())
		.setScore("1")
		.addScoreDistributions(new ScoreDistribution("0", 0), new ScoreDistribution("1", 100));

	TreeModel treeModel = new TreeModel(MiningFunction.CLASSIFICATION, new MiningSchema(), node);

	ScoreDistributionCleaner cleaner = new ScoreDistributionCleaner();
	cleaner.applyTo(treeModel);

	assertTrue(node.hasScoreDistributions());

	treeModel.setMiningFunction(MiningFunction.REGRESSION);

	cleaner.applyTo(treeModel);

	assertFalse(node.hasScoreDistributions());
}
 

import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
public TreeModel encodeTreeModel(PredicateManager predicateManager, Schema schema){
	Node root = new Node()
		.setPredicate(new True());

	encodeNode(root, predicateManager, Collections.<FieldName, Set<String>>emptyMap(), 0, schema);

	TreeModel treeModel = new TreeModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
		.setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD);

	return treeModel;
}
 

import org.dmg.pmml.MiningFunction; //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.dmg.pmml.MiningFunction; //导入依赖的package包/类
/**
 * Validates that the encoded PMML model received matches expected schema.
 *
 * @param pmml {@link PMML} encoding of a decision forest
 * @param schema expected schema attributes of decision forest
 */
public static void validatePMMLVsSchema(PMML pmml, InputSchema schema) {
  List<Model> models = pmml.getModels();
  Preconditions.checkArgument(models.size() == 1,
                              "Should have exactly one model, but had %s", models.size());

  Model model = models.get(0);
  MiningFunction function = model.getMiningFunction();
  if (schema.isClassification()) {
    Preconditions.checkArgument(function == MiningFunction.CLASSIFICATION,
                                "Expected classification function type but got %s",
                                function);
  } else {
    Preconditions.checkArgument(function == MiningFunction.REGRESSION,
                                "Expected regression function type but got %s",
                                function);
  }

  DataDictionary dictionary = pmml.getDataDictionary();
  Preconditions.checkArgument(
      schema.getFeatureNames().equals(AppPMMLUtils.getFeatureNames(dictionary)),
      "Feature names in schema don't match names in PMML");

  MiningSchema miningSchema = model.getMiningSchema();
  Preconditions.checkArgument(schema.getFeatureNames().equals(
      AppPMMLUtils.getFeatureNames(miningSchema)));

  Integer pmmlIndex = AppPMMLUtils.findTargetIndex(miningSchema);
  if (schema.hasTarget()) {
    int schemaIndex = schema.getTargetFeatureIndex();
    Preconditions.checkArgument(
        pmmlIndex != null && schemaIndex == pmmlIndex,
        "Configured schema expects target at index %s, but PMML has target at index %s",
        schemaIndex, pmmlIndex);
  } else {
    Preconditions.checkArgument(pmmlIndex == null);
  }
}
 

import org.dmg.pmml.MiningFunction; //导入依赖的package包/类
private static PMML buildDummyModel() {
  Node node = new Node().setRecordCount(123.0);
  TreeModel treeModel = new TreeModel(MiningFunction.CLASSIFICATION, null, node);
  PMML pmml = PMMLUtils.buildSkeletonPMML();
  pmml.addModels(treeModel);
  return pmml;
}