diff --git a/pmml-h2o/src/main/java/org/jpmml/h2o/DrfMojoModelConverter.java b/pmml-h2o/src/main/java/org/jpmml/h2o/DrfMojoModelConverter.java index aa7e7ea..afe60a6 100644 --- a/pmml-h2o/src/main/java/org/jpmml/h2o/DrfMojoModelConverter.java +++ b/pmml-h2o/src/main/java/org/jpmml/h2o/DrfMojoModelConverter.java @@ -29,7 +29,6 @@ import org.dmg.pmml.OpType; import org.dmg.pmml.mining.MiningModel; import org.dmg.pmml.mining.Segmentation; -import org.dmg.pmml.mining.Segmentation.MultipleModelMethod; import org.dmg.pmml.regression.RegressionModel; import org.dmg.pmml.tree.TreeModel; import org.jpmml.converter.CMatrixUtil; @@ -48,7 +47,7 @@ public DrfMojoModelConverter(DrfMojoModel model){ } @Override - public MiningModel encodeModel(Schema schema){ + public Model encodeModel(Schema schema){ DrfMojoModel model = getModel(); boolean binomialDoubleTrees = getBinomialDoubleTrees(model); @@ -62,20 +61,27 @@ public MiningModel encodeModel(Schema schema){ if(model._nclasses == 1){ ContinuousLabel continuousLabel = (ContinuousLabel)label; - MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) - .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels)); + return encodeTreeEnsemble(treeModels, (List ensembleTreeModels) -> { + MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) + .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, ensembleTreeModels)); - return miningModel; + return miningModel; + }); } else if(model._nclasses == 2 && !binomialDoubleTrees){ ContinuousLabel continuousLabel = new ContinuousLabel(DataType.DOUBLE); - MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) - .setSegmentation(MiningModelUtil.createSegmentation(MultipleModelMethod.AVERAGE, treeModels)) - .setOutput(ModelUtil.createPredictedOutput("drfValue", OpType.CONTINUOUS, DataType.DOUBLE)); + Model pmmlModel = encodeTreeEnsemble(treeModels, (List ensembleTreeModels) -> { + MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) + .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, ensembleTreeModels)); - return MiningModelUtil.createBinaryLogisticClassification(miningModel, -1d, 1d, RegressionModel.NormalizationMethod.NONE, true, schema); + return miningModel; + }); + + pmmlModel.setOutput(ModelUtil.createPredictedOutput("drfValue", OpType.CONTINUOUS, DataType.DOUBLE)); + + return MiningModelUtil.createBinaryLogisticClassification(pmmlModel, -1d, 1d, RegressionModel.NormalizationMethod.NONE, true, schema); } else { @@ -84,11 +90,16 @@ public MiningModel encodeModel(Schema schema){ List models = new ArrayList<>(); for(int i = 0; i < categoricalLabel.size(); i++){ - MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(null)) - .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, CMatrixUtil.getRow(treeModels, ntreesPerGroup, ntreeGroups, i))) - .setOutput(ModelUtil.createPredictedOutput(FieldNameUtil.create("drfValue", categoricalLabel.getValue(i)), OpType.CONTINUOUS, DataType.DOUBLE)); + Model pmmlModel = encodeTreeEnsemble(CMatrixUtil.getRow(treeModels, ntreesPerGroup, ntreeGroups, i), (List ensembleTreeModels) -> { + MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(null)) + .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, ensembleTreeModels)); + + return miningModel; + }); + + pmmlModel.setOutput(ModelUtil.createPredictedOutput(FieldNameUtil.create("drfValue", categoricalLabel.getValue(i)), OpType.CONTINUOUS, DataType.DOUBLE)); - models.add(miningModel); + models.add(pmmlModel); } return MiningModelUtil.createClassification(models, RegressionModel.NormalizationMethod.SIMPLEMAX, true, schema); diff --git a/pmml-h2o/src/main/java/org/jpmml/h2o/GbmMojoModelConverter.java b/pmml-h2o/src/main/java/org/jpmml/h2o/GbmMojoModelConverter.java index 5dff314..816fc73 100644 --- a/pmml-h2o/src/main/java/org/jpmml/h2o/GbmMojoModelConverter.java +++ b/pmml-h2o/src/main/java/org/jpmml/h2o/GbmMojoModelConverter.java @@ -29,7 +29,6 @@ import org.dmg.pmml.OpType; import org.dmg.pmml.mining.MiningModel; import org.dmg.pmml.mining.Segmentation; -import org.dmg.pmml.mining.Segmentation.MultipleModelMethod; import org.dmg.pmml.regression.RegressionModel; import org.dmg.pmml.tree.TreeModel; import org.jpmml.converter.CMatrixUtil; @@ -48,7 +47,7 @@ public GbmMojoModelConverter(GbmMojoModel model){ } @Override - public MiningModel encodeModel(Schema schema){ + public Model encodeModel(Schema schema){ GbmMojoModel model = getModel(); int ntreeGroups = getNTreeGroups(model); @@ -61,33 +60,50 @@ public MiningModel encodeModel(Schema schema){ if(model._family == DistributionFamily.gaussian){ ContinuousLabel continuousLabel = (ContinuousLabel)label; - MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) - .setSegmentation(MiningModelUtil.createSegmentation(MultipleModelMethod.SUM, treeModels)) - .setTargets(ModelUtil.createRescaleTargets(null, model._init_f, continuousLabel)); + Model pmmlModel = encodeTreeEnsemble(treeModels, (List ensembleTreeModels) -> { + MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) + .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, ensembleTreeModels)); - return miningModel; + return miningModel; + }); + + pmmlModel.setTargets(ModelUtil.createRescaleTargets(null, model._init_f, continuousLabel)); + + return pmmlModel; } else if((model._family == DistributionFamily.poisson) || (model._family == DistributionFamily.gamma) || (model._family == DistributionFamily.tweedie)){ ContinuousLabel continuousLabel = new ContinuousLabel(DataType.DOUBLE); - MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) - .setSegmentation(MiningModelUtil.createSegmentation(MultipleModelMethod.SUM, treeModels)) + Model pmmlModel = encodeTreeEnsemble(treeModels, (List ensembleTreeModels) -> { + MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) + .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, ensembleTreeModels)); + + return miningModel; + }); + + pmmlModel .setTargets(ModelUtil.createRescaleTargets(null, model._init_f, continuousLabel)) .setOutput(ModelUtil.createPredictedOutput("gbmValue", OpType.CONTINUOUS, DataType.DOUBLE)); - return MiningModelUtil.createRegression(miningModel, RegressionModel.NormalizationMethod.EXP, schema); + return MiningModelUtil.createRegression(pmmlModel, RegressionModel.NormalizationMethod.EXP, schema); } else if(model._family == DistributionFamily.bernoulli){ ContinuousLabel continuousLabel = new ContinuousLabel(DataType.DOUBLE); - MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) - .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, treeModels)) + Model pmmlModel = encodeTreeEnsemble(treeModels, (List ensembleTreeModels) -> { + MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel)) + .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, ensembleTreeModels)); + + return miningModel; + }); + + pmmlModel .setTargets(ModelUtil.createRescaleTargets(null, model._init_f, continuousLabel)) .setOutput(ModelUtil.createPredictedOutput("gbmValue", OpType.CONTINUOUS, DataType.DOUBLE)); - return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema); + return MiningModelUtil.createBinaryLogisticClassification(pmmlModel, 1d, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema); } else if(model._family == DistributionFamily.multinomial){ @@ -96,11 +112,16 @@ public MiningModel encodeModel(Schema schema){ List models = new ArrayList<>(); for(int i = 0; i < categoricalLabel.size(); i++){ - MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(null)) - .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, CMatrixUtil.getRow(treeModels, ntreesPerGroup, ntreeGroups, i))) - .setOutput(ModelUtil.createPredictedOutput(FieldNameUtil.create("gbmValue", categoricalLabel.getValue(i)), OpType.CONTINUOUS, DataType.DOUBLE)); + Model pmmlModel = encodeTreeEnsemble(CMatrixUtil.getRow(treeModels, ntreesPerGroup, ntreeGroups, i), (List ensembleTreeModels) -> { + MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(null)) + .setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, ensembleTreeModels)); + + return miningModel; + }); + + pmmlModel.setOutput(ModelUtil.createPredictedOutput(FieldNameUtil.create("gbmValue", categoricalLabel.getValue(i)), OpType.CONTINUOUS, DataType.DOUBLE)); - models.add(miningModel); + models.add(pmmlModel); } return MiningModelUtil.createClassification(models, RegressionModel.NormalizationMethod.SOFTMAX, true, schema); diff --git a/pmml-h2o/src/main/java/org/jpmml/h2o/SharedTreeMojoModelConverter.java b/pmml-h2o/src/main/java/org/jpmml/h2o/SharedTreeMojoModelConverter.java index e6d7bb9..dba7a4d 100644 --- a/pmml-h2o/src/main/java/org/jpmml/h2o/SharedTreeMojoModelConverter.java +++ b/pmml-h2o/src/main/java/org/jpmml/h2o/SharedTreeMojoModelConverter.java @@ -22,18 +22,22 @@ import java.util.ArrayList; import java.util.List; import java.util.concurrent.atomic.AtomicInteger; +import java.util.function.Function; import java.util.stream.Collectors; import java.util.stream.Stream; +import com.google.common.collect.Iterables; import hex.genmodel.algos.tree.NaSplitDir; import hex.genmodel.algos.tree.SharedTreeMojoModel; import hex.genmodel.utils.ByteBufferWrapper; import hex.genmodel.utils.GenmodelBitSet; import org.dmg.pmml.DataType; import org.dmg.pmml.MiningFunction; +import org.dmg.pmml.Model; import org.dmg.pmml.Predicate; import org.dmg.pmml.SimplePredicate; import org.dmg.pmml.True; +import org.dmg.pmml.mining.MiningModel; import org.dmg.pmml.tree.BranchNode; import org.dmg.pmml.tree.LeafNode; import org.dmg.pmml.tree.Node; @@ -73,6 +77,16 @@ public List encodeTreeModels(Schema schema){ return result; } + static + public Model encodeTreeEnsemble(List treeModels, Function, MiningModel> ensembleFunction){ + + if(treeModels.size() == 1){ + return Iterables.getOnlyElement(treeModels); + } + + return ensembleFunction.apply(treeModels); + } + static public TreeModel encodeTreeModel(byte[] compressedTree, PredicateManager predicateManager, Schema schema){ Label label = new ContinuousLabel(DataType.DOUBLE); diff --git a/pmml-h2o/src/test/java/org/jpmml/h2o/testing/SegmentationInspector.java b/pmml-h2o/src/test/java/org/jpmml/h2o/testing/SegmentationInspector.java new file mode 100644 index 0000000..acaae2b --- /dev/null +++ b/pmml-h2o/src/test/java/org/jpmml/h2o/testing/SegmentationInspector.java @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2022 Villu Ruusmann + * + * This file is part of JPMML-H2O + * + * JPMML-H2O 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-H2O 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-H2O. If not, see . + */ +package org.jpmml.h2o.testing; + +import java.util.List; + +import org.dmg.pmml.VisitorAction; +import org.dmg.pmml.mining.Segment; +import org.dmg.pmml.mining.Segmentation; +import org.jpmml.model.InvalidElementException; +import org.jpmml.model.visitors.AbstractVisitor; + +public class SegmentationInspector extends AbstractVisitor { + + @Override + public VisitorAction visit(Segmentation segmentation){ + Segmentation.MultipleModelMethod multipleModelMethod = segmentation.getMultipleModelMethod(); + + switch(multipleModelMethod){ + case MAJORITY_VOTE: + case WEIGHTED_MAJORITY_VOTE: + case AVERAGE: + case WEIGHTED_AVERAGE: + case MEDIAN: + case WEIGHTED_MEDIAN: + case SUM: + case WEIGHTED_SUM: + { + List segments = segmentation.getSegments(); + + if(segments.size() <= 1){ + throw new InvalidElementException(segmentation); + } + } + break; + default: + break; + } + + return super.visit(segmentation); + } +} \ No newline at end of file diff --git a/pmml-h2o/src/test/java/org/jpmml/h2o/testing/TreeMojoModelConverterTest.java b/pmml-h2o/src/test/java/org/jpmml/h2o/testing/TreeMojoModelConverterTest.java new file mode 100644 index 0000000..410b5a0 --- /dev/null +++ b/pmml-h2o/src/test/java/org/jpmml/h2o/testing/TreeMojoModelConverterTest.java @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2022 Villu Ruusmann + * + * This file is part of JPMML-H2O + * + * JPMML-H2O 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-H2O 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-H2O. If not, see . + */ +package org.jpmml.h2o.testing; + +import java.util.function.Predicate; + +import com.google.common.base.Equivalence; +import org.jpmml.converter.testing.Datasets; +import org.jpmml.converter.testing.Fields; +import org.jpmml.evaluator.ResultField; +import org.jpmml.evaluator.testing.PMMLEquivalence; +import org.jpmml.model.visitors.VisitorBattery; +import org.junit.Test; + +public class TreeMojoModelConverterTest extends H2OEncoderBatchTest implements Datasets, Fields { + + public TreeMojoModelConverterTest(){ + super(new PMMLEquivalence(1e-13, 1e-13)); + } + + @Override + public H2OEncoderBatch createBatch(String algorithm, String dataset, Predicate columnFilter, Equivalence equivalence){ + H2OEncoderBatch result = new H2OEncoderBatch(algorithm, dataset, columnFilter, equivalence){ + + @Override + public TreeMojoModelConverterTest getArchiveBatchTest(){ + return TreeMojoModelConverterTest.this; + } + + @Override + public VisitorBattery getValidators(){ + VisitorBattery visitorBattery = super.getValidators(); + + visitorBattery.add(SegmentationInspector.class); + + return visitorBattery; + } + }; + + return result; + } + + @Test + public void evaluateAudit() throws Exception { + evaluate("DecisionTree", AUDIT); + } + + @Test + public void evaluateAuditNA() throws Exception { + evaluate("DecisionTree", AUDIT_NA, excludeFields(AUDIT_ADJUSTED)); + } + + @Test + public void evaluateAuto() throws Exception { + evaluate("DecisionTree", AUTO); + } + + @Test + public void evaluateAutoNA() throws Exception { + evaluate("DecisionTree", AUTO_NA); + } + + @Test + public void evaluateIris() throws Exception { + evaluate("DecisionTree", IRIS); + } +} \ No newline at end of file diff --git a/pmml-h2o/src/test/resources/csv/DecisionTreeAudit.csv b/pmml-h2o/src/test/resources/csv/DecisionTreeAudit.csv new file mode 100644 index 0000000..864dac1 --- /dev/null +++ b/pmml-h2o/src/test/resources/csv/DecisionTreeAudit.csv @@ -0,0 +1,1900 @@ 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a/pmml-h2o/src/test/resources/main.py b/pmml-h2o/src/test/resources/main.py index 34b9024..e766a4f 100644 --- a/pmml-h2o/src/test/resources/main.py +++ b/pmml-h2o/src/test/resources/main.py @@ -49,11 +49,13 @@ def build_audit(df, classifier, name): build_audit(audit_df, H2OGradientBoostingEstimator(ntrees = 31, seed = 42), "GBMAudit") build_audit(audit_df, H2OGeneralizedLinearEstimator(family = "binomial", lambda_ = 0, seed = 42), "GLMAudit") +build_audit(audit_df, H2ORandomForestEstimator(ntrees = 1, sample_rate = 1, mtries = -2, max_depth = 6, seed = 42), "DecisionTreeAudit") build_audit(audit_df, H2ORandomForestEstimator(ntrees = 31, binomial_double_trees = True, seed = 42), "RandomForestAudit") build_audit(audit_df, H2OXGBoostEstimator(ntrees = 31, seed = 42), "XGBoostAudit") audit_df = load_audit("AuditNA") +build_audit(audit_df, H2OGradientBoostingEstimator(ntrees = 1, sample_rate = 1, col_sample_rate = 1, max_depth = 6, seed = 42), "DecisionTreeAuditNA") build_audit(audit_df, H2OGradientBoostingEstimator(ntrees = 31, seed = 42), "GBMAuditNA") build_audit(audit_df, H2OGeneralizedLinearEstimator(family = "binomial", seed = 42), "GLMAuditNA") build_audit(audit_df, H2ORandomForestEstimator(ntrees = 31, binomial_double_trees = False, seed = 42), "RandomForestAuditNA") @@ -78,6 +80,7 @@ def build_iris(df, classifier, name): build_iris(iris_df, H2OGradientBoostingEstimator(ntrees = 11, seed = 42), "GBMIris") build_iris(iris_df, H2OGeneralizedLinearEstimator(family = "multinomial", seed = 42), "GLMIris") +build_iris(iris_df, H2ORandomForestEstimator(ntrees = 1, sample_rate = 1, mtries = -2, max_depth = 2, seed = 42), "DecisionTreeIris") build_iris(iris_df, H2ORandomForestEstimator(ntrees = 11, seed = 42), "RandomForestIris") build_iris(iris_df, H2OStackedEnsembleEstimator(base_models = train_stack(iris_df, 5), seed = 42), "StackedEnsembleIris") build_iris(iris_df, H2OXGBoostEstimator(ntrees = 11, seed = 42), "XGBoostIris") @@ -100,11 +103,13 @@ def build_auto(df, regressor, name): build_auto(auto_df, H2OGradientBoostingEstimator(ntrees = 17, seed = 42), "GBMAuto") build_auto(auto_df, H2OGeneralizedLinearEstimator(family = "gaussian", lambda_ = 0, seed = 42), "GLMAuto") +build_auto(auto_df, H2ORandomForestEstimator(ntrees = 1, sample_rate = 1, mtries = -2, max_depth = 4, seed = 42), "DecisionTreeAuto") build_auto(auto_df, H2ORandomForestEstimator(ntrees = 17, seed = 42), "RandomForestAuto") build_auto(auto_df, H2OXGBoostEstimator(ntrees = 17, seed = 42), "XGBoostAuto") auto_df = load_auto("AutoNA") +build_auto(auto_df, H2OGradientBoostingEstimator(ntrees = 1, sample_rate = 1, col_sample_rate = 1, max_depth = 4, seed = 42), "DecisionTreeAutoNA") build_auto(auto_df, H2OGradientBoostingEstimator(ntrees = 17, seed = 42), "GBMAutoNA") build_auto(auto_df, H2OGeneralizedLinearEstimator(family = "gaussian", seed = 42), "GLMAutoNA") build_auto(auto_df, H2ORandomForestEstimator(ntrees = 17, seed = 42), "RandomForestAutoNA") diff --git a/pmml-h2o/src/test/resources/mojo/DecisionTreeAudit.zip b/pmml-h2o/src/test/resources/mojo/DecisionTreeAudit.zip new file mode 100644 index 0000000..e670148 Binary files /dev/null and b/pmml-h2o/src/test/resources/mojo/DecisionTreeAudit.zip differ diff --git a/pmml-h2o/src/test/resources/mojo/DecisionTreeAuditNA.zip b/pmml-h2o/src/test/resources/mojo/DecisionTreeAuditNA.zip new file mode 100644 index 0000000..cf5a85e Binary files /dev/null and b/pmml-h2o/src/test/resources/mojo/DecisionTreeAuditNA.zip differ diff --git a/pmml-h2o/src/test/resources/mojo/DecisionTreeAuto.zip b/pmml-h2o/src/test/resources/mojo/DecisionTreeAuto.zip new file mode 100644 index 0000000..34c71f1 Binary files /dev/null and b/pmml-h2o/src/test/resources/mojo/DecisionTreeAuto.zip differ diff --git a/pmml-h2o/src/test/resources/mojo/DecisionTreeAutoNA.zip b/pmml-h2o/src/test/resources/mojo/DecisionTreeAutoNA.zip new file mode 100644 index 0000000..db40570 Binary files /dev/null and b/pmml-h2o/src/test/resources/mojo/DecisionTreeAutoNA.zip differ diff --git a/pmml-h2o/src/test/resources/mojo/DecisionTreeIris.zip b/pmml-h2o/src/test/resources/mojo/DecisionTreeIris.zip new file mode 100644 index 0000000..d45c500 Binary files /dev/null and b/pmml-h2o/src/test/resources/mojo/DecisionTreeIris.zip differ