diff --git a/README.md b/README.md index 17370bd..ca3a6f9 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,8 @@ Java library and command-line application for converting [R](https://www.r-proje * `cv.glmnet` - Cross-validated GLMNet regression and calculation * [`IsolationForest`](https://r-forge.r-project.org/R/?group_id=479) package: * `iForest` - Isolation Forest (IF) anomaly detection + * [`mlr`](https://cran.r-project.org/package=mlr) package: + * `WrappedModel` - Selected JPMML-R model types. * [`neuralnet`](https://cran.r-project.org/package=neuralnet) package: * `nn` - Neural Network (NN) regression * [`nnet`](https://cran.r-project.org/package=nnet) package: diff --git a/src/main/java/org/jpmml/rexp/ConverterFactory.java b/src/main/java/org/jpmml/rexp/ConverterFactory.java index 49e27ec..5d6d5af 100644 --- a/src/main/java/org/jpmml/rexp/ConverterFactory.java +++ b/src/main/java/org/jpmml/rexp/ConverterFactory.java @@ -91,6 +91,7 @@ public ConverterFactory newInstance(){ converters.put("scorecard", ScorecardConverter.class); converters.put("svm", SVMConverter.class); converters.put("train", TrainConverter.class); + converters.put("WrappedModel", WrappedModelConverter.class); converters.put("xgb.Booster", XGBoostConverter.class); } } \ No newline at end of file diff --git a/src/main/java/org/jpmml/rexp/DecorationUtil.java b/src/main/java/org/jpmml/rexp/DecorationUtil.java index d20ec35..653391f 100644 --- a/src/main/java/org/jpmml/rexp/DecorationUtil.java +++ b/src/main/java/org/jpmml/rexp/DecorationUtil.java @@ -33,6 +33,16 @@ public RGenericVector getGenericElement(RGenericVector model, String name){ } } + static + public RBooleanVector getBooleanElement(RGenericVector model, String name){ + + try { + return model.getBooleanElement(name, true); + } catch(IllegalArgumentException iae){ + throw toDecorationException(model, name, iae); + } + } + static public RNumberVector getNumericElement(RGenericVector model, String name){ diff --git a/src/main/java/org/jpmml/rexp/GLMConverter.java b/src/main/java/org/jpmml/rexp/GLMConverter.java index 1b7efea..b9038b3 100644 --- a/src/main/java/org/jpmml/rexp/GLMConverter.java +++ b/src/main/java/org/jpmml/rexp/GLMConverter.java @@ -44,7 +44,7 @@ public void encodeSchema(RExpEncoder encoder){ RGenericVector glm = getObject(); RGenericVector family = glm.getGenericElement("family"); - RGenericVector model = glm.getGenericElement("model"); + RGenericVector model = glm.getGenericElement("model", false); RStringVector familyFamily = family.getStringElement("family"); @@ -55,11 +55,13 @@ public void encodeSchema(RExpEncoder encoder){ case CLASSIFICATION: Label label = encoder.getLabel(); - RIntegerVector variable = model.getFactorElement((label.getName()).getValue()); + if(model != null){ + RIntegerVector variable = model.getFactorElement((label.getName()).getValue()); - DataField dataField = (DataField)encoder.toCategorical(label.getName(), RExpUtil.getFactorLevels(variable)); + DataField dataField = (DataField)encoder.toCategorical(label.getName(), RExpUtil.getFactorLevels(variable)); - encoder.setLabel(dataField); + encoder.setLabel(dataField); + } break; default: break; diff --git a/src/main/java/org/jpmml/rexp/LMConverter.java b/src/main/java/org/jpmml/rexp/LMConverter.java index 31d875a..c31a684 100644 --- a/src/main/java/org/jpmml/rexp/LMConverter.java +++ b/src/main/java/org/jpmml/rexp/LMConverter.java @@ -41,35 +41,70 @@ public void encodeSchema(RExpEncoder encoder){ RGenericVector lm = getObject(); RGenericVector xlevels = lm.getGenericElement("xlevels", false); - RGenericVector model = lm.getGenericElement("model"); + RGenericVector model = lm.getGenericElement("model", false); RGenericVector data = lm.getGenericElement("data", false); - RExp terms = model.getAttribute("terms"); + RExp terms; - FormulaContext context = new ModelFrameFormulaContext(model){ + FormulaContext context; - @Override - public List getCategories(String variable){ + if(model != null){ + terms = model.getAttribute("terms"); - if(xlevels != null && xlevels.hasElement(variable)){ - RStringVector levels = xlevels.getStringElement(variable); + context = new ModelFrameFormulaContext(model){ - return levels.getValues(); + @Override + public List getCategories(String variable){ + + if(xlevels != null && xlevels.hasElement(variable)){ + RStringVector levels = xlevels.getStringElement(variable); + + return levels.getValues(); + } + + return super.getCategories(variable); + } + + @Override + public RVector getData(String variable){ + + if(data != null && data.hasElement(variable)){ + return data.getVectorElement(variable); + } + + return super.getData(variable); } + }; + } else - return super.getCategories(variable); - } + { + terms = lm.getElement("terms"); - @Override - public RVector getData(String variable){ + context = new FormulaContext(){ - if(data != null && data.hasElement(variable)){ - return data.getVectorElement(variable); + @Override + public List getCategories(String variable){ + + if(xlevels != null && xlevels.hasElement(variable)){ + RStringVector levels = xlevels.getStringElement(variable); + + return levels.getValues(); + } + + return null; } - return super.getData(variable); - } - }; + @Override + public RVector getData(String variable){ + + if(data != null && data.hasElement(variable)){ + return data.getVectorElement(variable); + } + + return null; + } + }; + } encodeSchema(terms, context, encoder); } diff --git a/src/main/java/org/jpmml/rexp/WrappedModelConverter.java b/src/main/java/org/jpmml/rexp/WrappedModelConverter.java new file mode 100644 index 0000000..676756a --- /dev/null +++ b/src/main/java/org/jpmml/rexp/WrappedModelConverter.java @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2020 Villu Ruusmann + * + * This file is part of JPMML-R + * + * JPMML-R 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-R 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-R. If not, see . + */ +package org.jpmml.rexp; + +import java.util.List; + +import com.google.common.collect.Lists; +import org.dmg.pmml.DataField; +import org.dmg.pmml.DataType; +import org.dmg.pmml.FieldName; +import org.dmg.pmml.Model; +import org.dmg.pmml.OpType; +import org.jpmml.converter.CategoricalLabel; +import org.jpmml.converter.Label; +import org.jpmml.converter.Schema; + +public class WrappedModelConverter extends FilterModelConverter { + + public WrappedModelConverter(RGenericVector wrappedModel){ + super(wrappedModel); + } + + @Override + public void encodeSchema(RExpEncoder encoder){ + RGenericVector wrappedModel = getObject(); + + RGenericVector taskDesc = wrappedModel.getGenericElement("task.desc"); + + RStringVector type = taskDesc.getStringElement("type"); + RStringVector target = taskDesc.getStringElement("target"); + + super.encodeSchema(encoder); + + FieldName targetName = FieldName.create(target.asScalar()); + + DataField dataField = encoder.getDataField(targetName); + + switch(type.asScalar()){ + case "regr": + { + if(dataField == null){ + dataField = encoder.createDataField(targetName, OpType.CONTINUOUS, DataType.DOUBLE); + + encoder.setLabel(dataField); + } + } + break; + case "classif": + { + RVector classLevels = taskDesc.getVectorElement("class.levels"); + + List values = classLevels.getValues(); + + if(dataField == null){ + dataField = encoder.createDataField(targetName, OpType.CATEGORICAL, null, values); + + encoder.setLabel(dataField); + } // End if + + if(!(OpType.CATEGORICAL).equals(dataField.getOpType())){ + dataField = (DataField)encoder.toCategorical(targetName, values); + + encoder.setLabel(dataField); + } // End if + + if(classLevels.size() == 2){ + RBooleanVector invertLevels = DecorationUtil.getBooleanElement(wrappedModel, "invert_levels"); + + if(invertLevels.asScalar()){ + Label label = new CategoricalLabel(dataField.getName(), dataField.getDataType(), Lists.reverse(values)); + + encoder.setLabel(label); + } + } + } + break; + default: + throw new IllegalArgumentException(); + } + } + + @Override + public Model encodeModel(Schema schema){ + return super.encodeModel(schema); + } + + @Override + public ModelConverter createConverter(){ + RGenericVector wrappedModel = getObject(); + + RExp learnerModel = wrappedModel.getElement("learner.model"); + + return (ModelConverter)newConverter(learnerModel); + } +} \ No newline at end of file diff --git a/src/test/R/gbm.R b/src/test/R/gbm.R index 67b93a2..561bdef 100644 --- a/src/test/R/gbm.R +++ b/src/test/R/gbm.R @@ -1,6 +1,7 @@ library("caret") library("dplyr") library("gbm") +library("mlr") library("r2pmml") source("util.R") @@ -43,6 +44,24 @@ set.seed(42) generateGBMAdaBoostAuditNA() generateGBMBernoulliAuditNA() +generateWrappedGBMAdaBoostAudit = function(){ + audit.task = makeClassifTask(data = audit, target = "Adjusted") + classif.gbm = makeLearner("classif.gbm", distribution = "adaboost", shrinkage = 0.1, n.trees = 100, predict.type = "prob") + + audit.lmr = mlr::train(classif.gbm, audit.task) + audit.lmr = decorate(audit.lmr, invert_levels = TRUE) + print(audit.lmr) + + adjusted = as.data.frame(predict(audit.lmr, newdata = audit)) + + storeRds(audit.lmr, "WrappedGBMAdaBoostAuditNA") + storeCsv(data.frame("Adjusted" = adjusted$response, "probability(0)" = adjusted$prob.0, "probability(1)" = adjusted$prob.1, check.names = FALSE), "WrappedGBMAdaBoostAuditNA") +} + +set.seed(42) + +generateWrappedGBMAdaBoostAudit() + iris = loadIrisCsv("Iris") iris_x = iris[, -ncol(iris)] @@ -82,7 +101,7 @@ generateGBMFormulaIris() generateGBMIris() generateTrainGBMFormulaIris = function(){ - iris.train = train(Species ~ ., data = iris, method = "gbm", response.name = "Species") + iris.train = caret::train(Species ~ ., data = iris, method = "gbm", response.name = "Species") iris.train = verify(iris.train, newdata = sample_n(iris, 10)) print(iris.train) @@ -131,7 +150,7 @@ auto.caret = auto auto.caret$origin = as.integer(auto.caret$origin) generateTrainGBMFormulaAutoNA = function(){ - auto.train = train(mpg ~ ., data = auto.caret, method = "gbm", na.action = na.pass, response.name = "mpg") + auto.train = caret::train(mpg ~ ., data = auto.caret, method = "gbm", na.action = na.pass, response.name = "mpg") auto.train = verify(auto.train, newdata = sample_n(auto.caret, 50), na.action = na.pass) print(auto.train) diff --git a/src/test/R/glm.R b/src/test/R/glm.R index 65f20d8..ae17ef8 100644 --- a/src/test/R/glm.R +++ b/src/test/R/glm.R @@ -1,4 +1,5 @@ library("caret") +library("mlr") library("plyr") library("dplyr") library("recipes") @@ -38,7 +39,7 @@ generateGLMFormulaAudit() generateGLMCustFormulaAudit() generateTrainGLMFormulaAudit = function(){ - audit.train = train(audit.recipe, data = audit, method = "glm") + audit.train = caret::train(audit.recipe, data = audit, method = "glm") audit.train = verify(audit.train, newdata = sample_n(audit, 100)) print(audit.train) @@ -51,6 +52,24 @@ generateTrainGLMFormulaAudit = function(){ generateTrainGLMFormulaAudit() +audit$Deductions = NULL + +generateWrappedGLMFormulaAudit = function(){ + audit.task = makeClassifTask(data = audit, target = "Adjusted") + classif.glm = makeLearner("classif.binomial", predict.type = "prob") + + audit.lmr = mlr::train(classif.glm, audit.task) + audit.lmr = decorate(audit.lmr, invert_levels = FALSE) + print(audit.lmr) + + adjusted = as.data.frame(predict(audit.lmr, newdata = audit)) + + storeRds(audit.lmr, "WrappedGLMFormulaAudit") + storeCsv(data.frame("Adjusted" = adjusted$response, "probability(0)" = adjusted$prob.0, "probability(1)" = adjusted$prob.1, check.names = FALSE), "WrappedGLMFormulaAudit") +} + +generateWrappedGLMFormulaAudit() + auto = loadAutoCsv("Auto") auto.recipe = recipe(mpg ~ ., data = auto) @@ -79,7 +98,7 @@ generateGLMFormulaAuto() generateGLMCustFormulaAuto() generateTrainGLMFormulaAuto = function(){ - auto.train = train(auto.recipe, data = auto, method = "glm") + auto.train = caret::train(auto.recipe, data = auto, method = "glm") auto.train = verify(auto.train, newdata = sample_n(auto, 50)) print(auto.train) diff --git a/src/test/R/lm.R b/src/test/R/lm.R index c83b78c..6ee5c2c 100644 --- a/src/test/R/lm.R +++ b/src/test/R/lm.R @@ -1,3 +1,4 @@ +library("mlr") library("plyr") source("util.R") @@ -27,6 +28,21 @@ generateLMCustFormulaAuto = function(){ generateLMFormulaAuto() generateLMCustFormulaAuto() +generateWrappedLMFormulaAuto = function(){ + auto.task = makeRegrTask(data = auto, target = "mpg") + regr.lm = makeLearner("regr.lm") + + auto.mlr = train(regr.lm, auto.task) + print(auto.mlr) + + mpg = as.data.frame(predict(auto.mlr, newdata = auto)) + + storeRds(auto.mlr, "WrappedLMFormulaAuto") + storeCsv(data.frame("mpg" = mpg$response), "WrappedLMFormulaAuto") +} + +generateWrappedLMFormulaAuto() + wine_quality = loadWineQualityCsv("WineQuality") generateLMFormulaWineQuality = function(){ diff --git a/src/test/java/org/jpmml/rexp/GBMConverterTest.java b/src/test/java/org/jpmml/rexp/GBMConverterTest.java index bf7f6d7..172e00a 100644 --- a/src/test/java/org/jpmml/rexp/GBMConverterTest.java +++ b/src/test/java/org/jpmml/rexp/GBMConverterTest.java @@ -18,6 +18,7 @@ */ package org.jpmml.rexp; +import org.jpmml.evaluator.PMMLEquivalence; import org.junit.Test; public class GBMConverterTest extends ConverterTest { @@ -27,6 +28,11 @@ public void evaluateFitAdaBoostAuditNA() throws Exception { evaluate("GBMAdaBoost", "AuditNA"); } + @Test + public void evaluateWrappedAdaBoostAuditNA() throws Exception { + evaluate("WrappedGBMAdaBoost", "AuditNA", new PMMLEquivalence(5e-13, 5e-13)); + } + @Test public void evaluateFitBernoulliAuditNA() throws Exception { evaluate("GBMBernoulli", "AuditNA"); diff --git a/src/test/java/org/jpmml/rexp/GLMConverterTest.java b/src/test/java/org/jpmml/rexp/GLMConverterTest.java index 044f15f..337d985 100644 --- a/src/test/java/org/jpmml/rexp/GLMConverterTest.java +++ b/src/test/java/org/jpmml/rexp/GLMConverterTest.java @@ -28,6 +28,11 @@ public void evaluateFormulaAudit() throws Exception { evaluate("GLMFormula", "Audit"); } + @Test + public void evaluateWrappedFormulaAudit() throws Exception { + evaluate("WrappedGLMFormula", "Audit"); + } + @Test public void evaluateCustFormulaAudit() throws Exception { evaluate("GLMCustFormula", "Audit"); diff --git a/src/test/java/org/jpmml/rexp/LMConverterTest.java b/src/test/java/org/jpmml/rexp/LMConverterTest.java index f5bcede..2e29557 100644 --- a/src/test/java/org/jpmml/rexp/LMConverterTest.java +++ b/src/test/java/org/jpmml/rexp/LMConverterTest.java @@ -27,6 +27,11 @@ public void evaluateFormulaAuto() throws Exception { evaluate("LMFormula", "Auto"); } + @Test + public void evaluateMLRFormulaAuto() throws Exception { + evaluate("WrappedLMFormula", "Auto"); + } + @Test public void evaluateCustFormulaAuto() throws Exception { evaluate("LMCustFormula", "Auto"); diff --git a/src/test/resources/csv/WrappedGBMAdaBoostAuditNA.csv b/src/test/resources/csv/WrappedGBMAdaBoostAuditNA.csv new file mode 100644 index 0000000..33761eb --- /dev/null +++ b/src/test/resources/csv/WrappedGBMAdaBoostAuditNA.csv @@ -0,0 +1,1900 @@ +Adjusted,probability(0),probability(1) +0,0.934516869909134,0.0654831300908658 +0,0.972778323806244,0.027221676193756 +0,0.974764965210876,0.0252350347891244 +1,0.221655893721273,0.778344106278727 +1,0.4893091454994,0.5106908545006 +0,0.779351171203669,0.220648828796331 +1,0.121751512721628,0.878248487278372 +0,0.928824078331697,0.0711759216683034 +0,0.908766039296929,0.0912339607030707 +0,0.981437758085591,0.0185622419144086 +0,0.960667659874327,0.0393323401256733 +0,0.971468273350429,0.0285317266495715 +0,0.995071207537998,0.00492879246200206 +0,0.952110470402805,0.0478895295971953 +1,0.188530827325802,0.811469172674198 +0,0.951459916038631,0.0485400839613687 +0,0.927599557205415,0.0724004427945849 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