Java library and command-line application for converting R models to PMML.
ada package:ada - Stochastic Boosting (SB) classificationadabag package:bagging - Bagging classificationboosting - Boosting classificationcaret package:preProcess - Transformation methods "range", "center", "scale" and "medianImpute"train - Selected JPMML-R model typescaretEnsemble package:caretEnsemble - Ensemble regression and classificationCHAID package:party - CHi-squared Automated Interaction Detection (CHAID) classificationearth package:earth - Multivariate Adaptive Regression Spline (MARS) regressionelmNN package:elmNN - Extreme Learning Machine (ELM) regressionevtree package:party - Evolutionary Learning of Trees (EvTree) regression and classificatione1071 package:naiveBayes - Naive Bayes (NB) classificationsvm - Support Vector Machine (SVM) regression, classification and anomaly detectiongbm package:gbm - Gradient Boosting Machine (GBM) regression and classificationglmnet package:glmnet (elnet, fishnet, lognet and multnet subtypes) - Generalized Linear Model with lasso or elasticnet regularization (GLMNet) regression and classificationcv.glmnet - Cross-validated GLMNet regression and calculationIsolationForest package:iForest - Isolation Forest (IF) anomaly detectionmlr package:WrappedModel - Selected JPMML-R model types.neuralnet package:nn - Neural Network (NN) regressionnnet package:multinom - Multinomial log-linear classificationnnet.formula - Neural Network (NNet) regression and classificationparty package:ctree - Conditional Inference Tree (CIT) classificationpartykit package:party - Recursive Partytioning (Party) regression and classificationpls package:mvr - Multivariate Regression (MVR) regressionrandomForest package:randomForest - Random Forest (RF) regression and classificationranger package:ranger - Random Forest (RF) regression and classificationrms package:lrm - Binary Logistic Regression (LR) classificationols - Ordinary Least Squares (OLS) regressionrpart package:rpart - Recursive Partitioning (RPart) regression and classificationr2pmml package:scorecard - Scorecard regressionstats package:glm - Generalized Linear Model (GLM) regression and classificationkmeans - K-Means clusteringlm - Linear Model (LM) regressionxgboost package:xgb.Booster - XGBoost (XGB) regression and classificationbase::I(..) function terms:&, | and !==, !=, <, <=, >= and >+, -, *, /, and %^ and **is.na functionabs, ceiling, exp, floor, log, log10, round and sqrtbase::cut() and base::ifelse() function termsplyr::revalue() and plyr::mapvalues() function termsEnter the project root directory and build using Apache Maven:
mvn clean installThe build produces an executable uber-JAR file target/jpmml-r-executable-1.4-SNAPSHOT.jar.
A typical workflow can be summarized as follows:
The following R script trains a Random Forest (RF) model and saves it in RDS data format to a file rf.rds:
library("randomForest")
rf = randomForest(Species ~ ., data = iris)
saveRDS(rf, "rf.rds")Converting the RDS file rf.rds to a PMML file rf.pmml:
java -jar target/jpmml-r-executable-1.4-SNAPSHOT.jar --rds-input rf.rds --pmml-output rf.pmmlGetting help:
java -jar target/jpmml-r-executable-1.4-SNAPSHOT.jar --helpThe conversion of large files (1 GB and beyond) can be sped up by increasing the JVM heap size using -Xms and -Xmx options:
java -Xms4G -Xmx8G -jar target/jpmml-r-executable-1.4-SNAPSHOT.jar --rds-input rf.rds --pmml-output rf.pmmlUp-to-date:
Slightly outdated:
JPMML-R is licensed under the terms and conditions of the GNU Affero General Public License, Version 3.0.
If you would like to use JPMML-R in a proprietary software project, then it is possible to enter into a licensing agreement which makes JPMML-R available under the terms and conditions of the BSD 3-Clause License instead.
JPMML-R is developed and maintained by Openscoring Ltd, Estonia.
Interested in using Java PMML API software in your company? Please contact [email protected]