Updated JPMML-Python dependency

pmml-sklearn-extension/src/main/java/interpret/glassbox/ebm/ExplainableBoostingUtil.java

@@ -42,6 +42,7 @@
 import org.jpmml.converter.FieldNameUtil;
 import org.jpmml.converter.IndexFeature;
 import org.jpmml.converter.Label;
+import org.jpmml.converter.ModelEncoder;
 import org.jpmml.converter.PMMLEncoder;
 import org.jpmml.converter.PMMLUtil;
 import org.jpmml.converter.Schema;
@@ -66,7 +67,7 @@ public <E extends Estimator & HasExplainableBooster> List<Feature> encodeExplain
 		ClassDictUtil.checkSize(bins, featureTypesIn);
 		ClassDictUtil.checkSize(termFeatures, termScores);
 
-		PMMLEncoder encoder = schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		Label label = schema.getLabel();
 		List<? extends Feature> features = schema.getFeatures();
 
@@ -95,7 +96,7 @@ public <E extends Estimator & HasExplainableBooster> List<Feature> encodeExplain
 	}
 
 	static
-	private List<CategoricalFeature> encodeBinLevelFeatures(Feature feature, List<?> binLevels, String featureType, PMMLEncoder encoder){
+	private List<CategoricalFeature> encodeBinLevelFeatures(Feature feature, List<?> binLevels, String featureType, ModelEncoder encoder){
 		List<CategoricalFeature> result = new ArrayList<>();
 
 		for(int i = 0; i < binLevels.size(); i++){
@@ -121,7 +122,7 @@ private List<CategoricalFeature> encodeBinLevelFeatures(Feature feature, List<?>
 	}
 
 	static
-	private IndexFeature binContinuous(Feature feature, HasArray binLevel, Integer binLevelIndex, PMMLEncoder encoder){
+	private IndexFeature binContinuous(Feature feature, HasArray binLevel, Integer binLevelIndex, ModelEncoder encoder){
 		ContinuousFeature continuousFeature = feature.toContinuousFeature();
 
 		Discretize discretize = new Discretize(continuousFeature.getName());

pmml-sklearn-extension/src/main/java/sktree/tree/ProjectionManager.java

@@ -134,7 +134,7 @@ private Feature encodeFeature(String name, List<Vector> key, SkLearnEncoder enco
 
 		{
 			if(minusExpression != null){
-				expression = ExpressionUtil.createApply(PMMLFunctions.MULTIPLY, ExpressionUtil.createConstant(-1d), minusExpression);
+				expression = ExpressionUtil.toNegative(minusExpression);
 			} else
 
 			{

pmml-sklearn-h2o/src/main/java/h2o/estimators/H2OEstimator.java

@@ -39,8 +39,8 @@
 import org.jpmml.converter.FeatureList;
 import org.jpmml.converter.FeatureUtil;
 import org.jpmml.converter.Label;
+import org.jpmml.converter.ModelEncoder;
 import org.jpmml.converter.OrdinalLabel;
-import org.jpmml.converter.PMMLEncoder;
 import org.jpmml.converter.ScalarLabelUtil;
 import org.jpmml.converter.Schema;
 import org.jpmml.converter.TypeUtil;
@@ -200,7 +200,7 @@ public Label encodeLabel(List<String> names, SkLearnEncoder encoder){
 	public Model encodeModel(Schema schema){
 		Converter<?> converter = createConverter();
 
-		PMMLEncoder encoder = schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		Label label = schema.getLabel();
 		List<? extends Feature> features = schema.getFeatures();
 

pmml-sklearn-lightgbm/src/main/java/lightgbm/sklearn/Booster.java

@@ -18,6 +18,7 @@
  */
 package lightgbm.sklearn;
 
+import java.io.Reader;
 import java.io.StringReader;
 import java.util.List;
 
@@ -55,7 +56,7 @@ public GBDT getGBDT(){
 	private GBDT loadGBDT(){
 		String handle = getHandle();
 
-		try(StringReader reader = new StringReader(handle)){
+		try(Reader reader = new StringReader(handle)){
 			List<String> lines = CharStreams.readLines(reader);
 
 			return LightGBMUtil.loadGBDT(lines.iterator());

pmml-sklearn-lightgbm/src/main/java/lightgbm/sklearn/BoosterUtil.java

@@ -55,7 +55,7 @@ public <E extends Estimator & HasBooster> ObjectiveFunction getObjectiveFunction
 	public <E extends Estimator & HasBooster & HasLightGBMOptions> MiningModel encodeModel(E estimator, Schema schema){
 		GBDT gbdt = getGBDT(estimator);
 
-		ModelEncoder encoder = (ModelEncoder)schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 
 		Map<String, ?> options = getOptions(gbdt, estimator);
 

pmml-sklearn-statsmodels/src/main/java/sklearn2pmml/statsmodels/StatsModelsUtil.java

@@ -24,8 +24,8 @@
 import org.dmg.pmml.PMML;
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.Label;
+import org.jpmml.converter.ModelEncoder;
 import org.jpmml.converter.Schema;
-import org.jpmml.sklearn.SkLearnEncoder;
 import org.jpmml.statsmodels.InterceptFeature;
 import org.jpmml.statsmodels.StatsModelsEncoder;
 import sklearn.Estimator;
@@ -47,7 +47,7 @@ public <E extends Estimator & HasResults> PMML encodePMML(E estimator){
 
 	static
 	public Schema addConstant(Schema schema){
-		SkLearnEncoder encoder = (SkLearnEncoder)schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		Label label = schema.getLabel();
 		List<Feature> features = (List)schema.getFeatures();
 

pmml-sklearn/src/main/java/sklearn/Calibrator.java

@@ -26,7 +26,6 @@
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.FieldNameUtil;
 import org.jpmml.converter.Label;
-import org.jpmml.converter.PMMLEncoder;
 import org.jpmml.converter.Schema;
 import org.jpmml.converter.regression.RegressionModelUtil;
 import org.jpmml.sklearn.SkLearnEncoder;
@@ -47,11 +46,11 @@ public Calibrator(String module, String name){
 
 	@Override
 	public RegressionModel encodeModel(Schema schema){
-		PMMLEncoder encoder = schema.getEncoder();
+		SkLearnEncoder encoder = (SkLearnEncoder)schema.getEncoder();
 		Label label = schema.getLabel();
 		List<? extends Feature> features = schema.getFeatures();
 
-		features = encodeFeatures((List)features, (SkLearnEncoder)encoder);
+		features = encodeFeatures((List)features, encoder);
 
 		Feature feature = Iterables.getOnlyElement(features);
 

pmml-sklearn/src/main/java/sklearn/Estimator.java

@@ -195,7 +195,7 @@ public void addFeatureImportances(Model model, Schema schema){
 			featureImportances = getFeatureImportances();
 		}
 
-		ModelEncoder encoder = (ModelEncoder)schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		List<? extends Feature> features = schema.getFeatures();
 
 		if(featureImportances != null){

pmml-sklearn/src/main/java/sklearn/calibration/SigmoidCalibration.java

@@ -49,8 +49,7 @@ public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encod
 
 		Feature feature = features.get(0);
 
-		Apply apply = ExpressionUtil.createApply(PMMLFunctions.MULTIPLY,
-			ExpressionUtil.createConstant(-1),
+		Apply apply = (Apply)ExpressionUtil.toNegative(
 			ExpressionUtil.createApply(PMMLFunctions.ADD,
 				ExpressionUtil.createApply(PMMLFunctions.MULTIPLY, ExpressionUtil.createConstant(a), feature.ref()),
 				ExpressionUtil.createConstant(b)

pmml-sklearn/src/main/java/sklearn/ensemble/iforest/IsolationForestUtil.java

@@ -88,7 +88,7 @@ public boolean isFinalResult(){
 			public Expression createExpression(FieldRef fieldRef){
 				Number offset = estimator.getOffset();
 
-				return ExpressionUtil.createApply(PMMLFunctions.SUBTRACT, ExpressionUtil.createConstant(-offset.doubleValue()), ExpressionUtil.createApply(PMMLFunctions.POW, ExpressionUtil.createConstant(2d), ExpressionUtil.createApply(PMMLFunctions.MULTIPLY, ExpressionUtil.createConstant(-1d), fieldRef)));
+				return ExpressionUtil.createApply(PMMLFunctions.SUBTRACT, ExpressionUtil.createConstant(-offset.doubleValue()), ExpressionUtil.createApply(PMMLFunctions.POW, ExpressionUtil.createConstant(2d), ExpressionUtil.toNegative(fieldRef)));
 			}
 		};
 

pmml-sklearn/src/main/java/sklearn/linear_model/logistic/LogisticRegression.java

@@ -36,8 +36,8 @@
 import org.jpmml.converter.CategoricalLabel;
 import org.jpmml.converter.ContinuousFeature;
 import org.jpmml.converter.Feature;
+import org.jpmml.converter.ModelEncoder;
 import org.jpmml.converter.ModelUtil;
-import org.jpmml.converter.PMMLEncoder;
 import org.jpmml.converter.Schema;
 import org.jpmml.converter.SchemaUtil;
 import org.jpmml.converter.mining.MiningModelUtil;
@@ -109,7 +109,7 @@ private Model encodeMultinomialModel(Schema schema){
 		List<Number> coef = getCoef();
 		List<Number> intercept = getIntercept();
 
-		PMMLEncoder encoder = schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
 		List<? extends Feature> features = schema.getFeatures();
 

pmml-sklearn/src/main/java/sklearn/preprocessing/PowerTransformer.java

@@ -162,10 +162,9 @@ private Apply encodeYeoJohnsonTransform(ContinuousFeature continuousFeature, Num
 
 		{
 			// "-ln(-$name + 1)"
-			falseApply = ExpressionUtil.createApply(PMMLFunctions.MULTIPLY,
-				ExpressionUtil.createConstant(-1d),
+			falseApply = (Apply)ExpressionUtil.toNegative(
 				ExpressionUtil.createApply(PMMLFunctions.LN1P,
-					ExpressionUtil.createApply(PMMLFunctions.MULTIPLY, ExpressionUtil.createConstant(-1d), continuousFeature.ref())
+					ExpressionUtil.toNegative(continuousFeature.ref())
 				)
 			);
 		}

pmml-sklearn/src/main/java/sklearn2pmml/CustomizationUtil.java

@@ -45,7 +45,6 @@
 
 import com.google.common.collect.Iterables;
 import jakarta.xml.bind.Binder;
-import jakarta.xml.bind.JAXBContext;
 import jakarta.xml.bind.JAXBElement;
 import jakarta.xml.bind.Marshaller;
 import jakarta.xml.bind.annotation.XmlRootElement;
@@ -68,8 +67,6 @@ private CustomizationUtil(){
 
 	static
 	public void customize(Model model, List<? extends Customization> customizations) throws Exception {
-		JAXBContext jaxbContext = JAXBUtil.getContext();
-
 		DocumentBuilderFactory documentBuilderFactory = DocumentBuilderFactory.newInstance();
 		documentBuilderFactory.setNamespaceAware(true);
 
@@ -81,7 +78,7 @@ public void customize(Model model, List<? extends Customization> customizations)
 
 		NamespaceContext namespaceContext = new DocumentNamespaceContext(document);
 
-		Binder<Node> binder = jaxbContext.createBinder(Node.class);
+		Binder<Node> binder = JAXBUtil.createBinder(Node.class);
 		binder.setProperty(Marshaller.JAXB_FORMATTED_OUTPUT, Boolean.FALSE);
 		binder.setProperty(Marshaller.JAXB_FRAGMENT, Boolean.TRUE);
 
@@ -302,28 +299,14 @@ private Field findField(PMMLObject parent, PMMLObject child){
 
 	static
 	private void addListElement(Field field, PMMLObject parent, PMMLObject child) throws ReflectiveOperationException {
-		Class<? extends PMMLObject> parentClazz = parent.getClass();
-		Class<? extends PMMLObject> childClazz = child.getClass();
+		@SuppressWarnings("unused")
+		List<?> fieldValue = (List<?>)ReflectionUtil.getFieldValue(field, parent);
 
 		ParameterizedType listType = (ParameterizedType)field.getGenericType();
 
 		Class<?> listElementType = (Class<?>)listType.getActualTypeArguments()[0];
 
-		Method getterMethod = ReflectionUtil.getGetterMethod(field);
-
-		String name = getterMethod.getName();
-		if(name.startsWith("get")){
-			name = "add" + name.substring(3);
-		} else
-
-		{
-			throw new IllegalArgumentException();
-		}
-
-		// See https://stackoverflow.com/a/1679444
-		Class<?> valueArrayClazz = Class.forName("[L" + childClazz.getName() + ";");
-
-		Method appenderMethod = parentClazz.getMethod(name, valueArrayClazz);
+		Method appenderMethod = ReflectionUtil.getAppenderMethod(field);
 
 		// See https://stackoverflow.com/a/36125994
 		Object[] valueArray = (Object[])Array.newInstance(listElementType, 1);

pmml-sklearn/src/main/java/sklearn2pmml/ensemble/GBDTUtil.java

@@ -145,7 +145,7 @@ public VisitorAction visit(Node node){
 			treeModelVisitor.applyTo(treeModel);
 		}
 
-		ModelEncoder encoder = (ModelEncoder)schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		Label label = schema.getLabel();
 
 		ContinuousLabel continuousLabel;

pmml-sklearn/src/main/java/sklearn2pmml/expression/ExpressionClassifier.java

@@ -35,8 +35,8 @@
 import org.jpmml.converter.CategoricalLabel;
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.FieldNameUtil;
+import org.jpmml.converter.ModelEncoder;
 import org.jpmml.converter.ModelUtil;
-import org.jpmml.converter.PMMLEncoder;
 import org.jpmml.converter.Schema;
 import org.jpmml.converter.regression.RegressionModelUtil;
 import org.jpmml.python.DataFrameScope;
@@ -55,7 +55,7 @@ public RegressionModel encodeModel(Schema schema){
 		Map<?, ?> classExprs = getClassExprs();
 		RegressionModel.NormalizationMethod normalizationMethod = parseNormalizationMethod(getNormalizationMethod());
 
-		PMMLEncoder encoder = schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
 		List<? extends Feature> features = schema.getFeatures();
 

pmml-sklearn/src/main/java/sklearn2pmml/expression/ExpressionRegressor.java

@@ -28,8 +28,8 @@
 import org.jpmml.converter.ContinuousLabel;
 import org.jpmml.converter.Feature;
 import org.jpmml.converter.FieldNameUtil;
+import org.jpmml.converter.ModelEncoder;
 import org.jpmml.converter.ModelUtil;
-import org.jpmml.converter.PMMLEncoder;
 import org.jpmml.converter.Schema;
 import org.jpmml.converter.regression.RegressionModelUtil;
 import org.jpmml.python.DataFrameScope;
@@ -48,7 +48,7 @@ public RegressionModel encodeModel(Schema schema){
 		Object expr = getExpr();
 		RegressionModel.NormalizationMethod normalizationMethod = parseNormalizationMethod(getNormalizationMethod());
 
-		PMMLEncoder encoder = schema.getEncoder();
+		ModelEncoder encoder = schema.getEncoder();
 		ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();
 		List<? extends Feature> features = schema.getFeatures();
 

pom.xml

@@ -98,42 +98,42 @@
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-evaluator-testing</artifactId>
-				<version>1.6.4</version>
+				<version>1.6.5</version>
 			</dependency>
 
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-h2o</artifactId>
-				<version>1.2.12</version>
+				<version>1.2.13</version>
 			</dependency>
 
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-lightgbm</artifactId>
-				<version>1.5.3</version>
+				<version>1.5.4</version>
 			</dependency>
 
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-python</artifactId>
-				<version>1.2.2</version>
+				<version>1.2.4</version>
 			</dependency>
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-python-testing</artifactId>
-				<version>1.2.2</version>
+				<version>1.2.4</version>
 			</dependency>
 
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-statsmodels</artifactId>
-				<version>1.1.0</version>
+				<version>1.1.1</version>
 			</dependency>
 
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-xgboost</artifactId>
-				<version>1.8.5</version>
+				<version>1.8.6</version>
 			</dependency>
 
 			<dependency>