Added support for the 'ObliqueRandomForestClassifier' model type

README.md

@@ -329,6 +329,7 @@ Java library and command-line application for converting [Scikit-Learn](https://
 
   Examples: [extensions/sktree.py](https://github.com/jpmml/jpmml-sklearn/blob/master/pmml-sklearn-extension/src/test/resources/extensions/sktree.py)
 
+  * [`sktree.ensemble.ObliqueRandomForestClassifier`](https://docs.neurodata.io/scikit-tree/dev/generated/sktree.ObliqueRandomForestClassifier.html)
   * [`sktree.ensemble.ObliqueRandomForestRegressor`](https://docs.neurodata.io/scikit-tree/dev/generated/sktree.ObliqueRandomForestRegressor.html)
   * [`sktree.tree.ObliqueDecisionTreeClassifier`](https://docs.neurodata.io/scikit-tree/dev/generated/sktree.tree.ObliqueDecisionTreeClassifier.html)
   * [`sktree.tree.ObliqueDecisionTreeRegressor`](https://docs.neurodata.io/scikit-tree/dev/generated/sktree.tree.ObliqueDecisionTreeRegressor.html)

pmml-sklearn-extension/src/main/java/sktree/ensemble/forest/ObliqueForestUtil.java

@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2024 Villu Ruusmann
+ *
+ * This file is part of JPMML-SkLearn
+ *
+ * JPMML-SkLearn is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Affero General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * JPMML-SkLearn is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU Affero General Public License for more details.
+ *
+ * You should have received a copy of the GNU Affero General Public License
+ * along with JPMML-SkLearn.  If not, see <http://www.gnu.org/licenses/>.
+ */
+package sktree.ensemble.forest;
+
+import java.util.List;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+import org.dmg.pmml.MiningFunction;
+import org.dmg.pmml.mining.MiningModel;
+import org.dmg.pmml.mining.Segmentation;
+import org.dmg.pmml.tree.TreeModel;
+import org.jpmml.converter.ModelUtil;
+import org.jpmml.converter.Schema;
+import org.jpmml.converter.mining.MiningModelUtil;
+import sklearn.Estimator;
+import sklearn.HasEstimatorEnsemble;
+import sklearn.tree.HasTree;
+
+public class ObliqueForestUtil {
+
+	private ObliqueForestUtil(){
+	}
+
+	static
+	public <E extends Estimator & HasEstimatorEnsemble<T>, T extends Estimator & HasTree> MiningModel encodeBaseObliqueForest(E estimator, MiningFunction miningFunction, Segmentation.MultipleModelMethod multipleModelMethod, Schema schema){
+		List<? extends T> estimators = estimator.getEstimators();
+
+		Schema segmentSchema = schema.toAnonymousSchema();
+
+		Function<T, TreeModel> function = new Function<T, TreeModel>(){
+
+			@Override
+			public TreeModel apply(T estimator){
+				int i = estimators.indexOf(estimator);
+
+				if(i < 0){
+					throw new IllegalArgumentException();
+				}
+
+				return (TreeModel)estimator.encode((i + 1), segmentSchema);
+			}
+		};
+
+		List<TreeModel> treeModels = estimators.stream()
+			.map(function)
+			.collect(Collectors.toList());
+
+		MiningModel miningModel = new MiningModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()))
+			.setSegmentation(MiningModelUtil.createSegmentation(multipleModelMethod, Segmentation.MissingPredictionTreatment.RETURN_MISSING, treeModels));
+
+		return miningModel;
+
+	}
+}

pmml-sklearn-extension/src/main/java/sktree/ensemble/forest/ObliqueRandomForestClassifier.java

@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2024 Villu Ruusmann
+ *
+ * This file is part of JPMML-SkLearn
+ *
+ * JPMML-SkLearn is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Affero General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * JPMML-SkLearn is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU Affero General Public License for more details.
+ *
+ * You should have received a copy of the GNU Affero General Public License
+ * along with JPMML-SkLearn.  If not, see <http://www.gnu.org/licenses/>.
+ */
+package sktree.ensemble.forest;
+
+import java.util.List;
+
+import org.dmg.pmml.DataType;
+import org.dmg.pmml.MiningFunction;
+import org.dmg.pmml.mining.MiningModel;
+import org.dmg.pmml.mining.Segmentation;
+import org.jpmml.converter.CategoricalLabel;
+import org.jpmml.converter.Schema;
+import sklearn.Classifier;
+import sklearn.HasEstimatorEnsemble;
+import sktree.tree.ObliqueDecisionTreeClassifier;
+
+public class ObliqueRandomForestClassifier extends Classifier implements HasEstimatorEnsemble<ObliqueDecisionTreeClassifier> {
+
+	public ObliqueRandomForestClassifier(String module, String name){
+		super(module, name);
+	}
+
+	@Override
+	public DataType getDataType(){
+		return DataType.FLOAT;
+	}
+
+	@Override
+	public MiningModel encodeModel(Schema schema){
+		CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
+
+		MiningModel miningModel = ObliqueForestUtil.encodeBaseObliqueForest(this, MiningFunction.CLASSIFICATION, Segmentation.MultipleModelMethod.AVERAGE, schema);
+
+		encodePredictProbaOutput(miningModel, DataType.DOUBLE, categoricalLabel);
+
+		return miningModel;
+	}
+
+	@Override
+	public List<ObliqueDecisionTreeClassifier> getEstimators(){
+		return getList("estimators_", ObliqueDecisionTreeClassifier.class);
+	}
+}

pmml-sklearn-extension/src/main/java/sktree/ensemble/forest/ObliqueRandomForestRegressor.java

@@ -18,17 +18,13 @@
  */
 package sktree.ensemble.forest;
 
-import java.util.ArrayList;
 import java.util.List;
 
+import org.dmg.pmml.DataType;
 import org.dmg.pmml.MiningFunction;
-import org.dmg.pmml.Model;
 import org.dmg.pmml.mining.MiningModel;
 import org.dmg.pmml.mining.Segmentation;
-import org.dmg.pmml.tree.TreeModel;
-import org.jpmml.converter.ModelUtil;
 import org.jpmml.converter.Schema;
-import org.jpmml.converter.mining.MiningModelUtil;
 import sklearn.HasEstimatorEnsemble;
 import sklearn.Regressor;
 import sktree.tree.ObliqueDecisionTreeRegressor;
@@ -40,25 +36,13 @@ public ObliqueRandomForestRegressor(String module, String name){
 	}
 
 	@Override
-	public Model encodeModel(Schema schema){
-		List<ObliqueDecisionTreeRegressor> estimators = getEstimators();
-
-		List<TreeModel> treeModels = new ArrayList<>();
-
-		Schema segmentSchema = schema.toAnonymousSchema();
-
-		for(int i = 0; i < estimators.size(); i++){
-			ObliqueDecisionTreeRegressor estimator = estimators.get(i);
-
-			TreeModel treeModel = (TreeModel)estimator.encode((i + 1), segmentSchema);
-
-			treeModels.add(treeModel);
-		}
-
-		MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()))
-			.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, Segmentation.MissingPredictionTreatment.RETURN_MISSING, treeModels));
+	public DataType getDataType(){
+		return DataType.FLOAT;
+	}
 
-		return miningModel;
+	@Override
+	public MiningModel encodeModel(Schema schema){
+		return ObliqueForestUtil.encodeBaseObliqueForest(this, MiningFunction.REGRESSION, Segmentation.MultipleModelMethod.AVERAGE, schema);
 	}
 
 	@Override

pmml-sklearn-extension/src/main/resources/META-INF/sklearn2pmml.properties

@@ -53,6 +53,7 @@ sklego.preprocessing.identitytransformer.IdentityTransformer = sklearn.IdentityT
 sktree._lib.sklearn.tree._criterion.Entropy = sklearn.tree.ClassificationCriterion
 sktree._lib.sklearn.tree._criterion.Gini = sklearn.tree.ClassificationCriterion
 sktree._lib.sklearn.tree._tree.DepthFirstTreeBuilder = sklearn.tree.DepthFirstTreeBuilder
+sktree.ensemble._supervised_forest.ObliqueRandomForestClassifier = sktree.ensemble.forest.ObliqueRandomForestClassifier
 sktree.ensemble._supervised_forest.ObliqueRandomForestRegressor = sktree.ensemble.forest.ObliqueRandomForestRegressor
 sktree.tree._classes.ObliqueDecisionTreeClassifier = sktree.tree.ObliqueDecisionTreeClassifier
 sktree.tree._classes.ObliqueDecisionTreeRegressor = sktree.tree.ObliqueDecisionTreeRegressor

pmml-sklearn-extension/src/test/java/org/jpmml/sklearn/extension/testing/SkTreeTest.java

@@ -39,8 +39,18 @@ public void evaluateObliqueDecisionTreeIris() throws Exception {
 		evaluate("ObliqueDecisionTree", IRIS);
 	}
 
+	@Test
+	public void evaluateObliqueRandomForestAudit() throws Exception {
+		evaluate("ObliqueRandomForest", AUDIT);
+	}
+
 	@Test
 	public void evaluateObliqueRandomForestAuto() throws Exception {
 		evaluate("ObliqueRandomForest", AUTO);
 	}
+
+	@Test
+	public void evaluateObliqueRandomForestIris() throws Exception {
+		evaluate("ObliqueRandomForest", IRIS);
+	}
 }