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JPMML-R

Java library and command-line application for converting R models to PMML.

Features
Prerequisites
Installation
Usage
- The R side of operations
- The JPMML-R side of operations
Documentation
License
Additional information

Features

Fast and memory-efficient:
- Can produce a 5 GB Random Forest PMML file in less than 1 minute on a desktop PC
Supported model and transformation types:
- ada package:
  - ada - Stochastic Boosting (SB) classification
- adabag package:
  - bagging - Bagging classification
  - boosting - Boosting classification
- apollo
  - apollo (formerly maxLik) - Discrete Choice Model (DCM) classification
- caret package:
  - preProcess - Transformation methods "range", "center", "scale" and "medianImpute"
  - train - Selected JPMML-R model types
- caretEnsemble package:
  - caretEnsemble - Ensemble regression and classification
- CHAID package:
  - party - CHi-squared Automated Interaction Detection (CHAID) classification
- earth package:
  - earth - Multivariate Adaptive Regression Spline (MARS) regression
- elmNNRcpp package:
  - elm - Extreme Learning Machine (ELM) regression
- evtree package:
  - party - Evolutionary Learning of Trees (EvTree) regression and classification
- e1071 package:
  - naiveBayes - Naive Bayes (NB) classification
  - svm - Support Vector Machine (SVM) regression, classification and anomaly detection
- gbm package:
  - gbm - Gradient Boosting Machine (GBM) regression and classification
- glmnet package:
  - glmnet (elnet, fishnet, lognet and multnet subtypes) - Generalized Linear Model with lasso or elasticnet regularization (GLMNet) regression and classification
  - cv.glmnet - Cross-validated GLMNet regression and calculation
- IsolationForest package:
  - iForest - Isolation Forest (IF) anomaly detection
- lightgbm package:
  - lgb.Booster - LightGBM regression and classification
- MASS package:
  - negbin - Generalized Linear Model (GLM) regression
  - polr - Ordinal regression
- mlr package:
  - WrappedModel - Selected JPMML-R model types
- neuralnet package:
  - nn - Neural Network (NN) regression
- nnet package:
  - multinom - Multinomial log-linear classification
  - nnet.formula - Neural Network (NNet) regression and classification
- party package:
  - ctree - Conditional Inference Tree (CIT) classification
- partykit package:
  - party - Recursive Partytioning (Party) regression and classification
- pls package:
  - mvr - Multivariate Regression (MVR) regression
- pscl package:
  - hurdle - Hurdle regression
- randomForest package:
  - randomForest - Random Forest (RF) regression and classification
- ranger package:
  - ranger - Random Forest (RF) regression and classification
- rms package:
  - lrm - Binary Logistic Regression (LR) classification
  - ols - Ordinary Least Squares (OLS) regression
  - orm - Ordinal regression
- rpart package:
  - rpart - Recursive Partitioning (RPart) regression and classification
- r2pmml package:
  - scorecard - Scorecard regression
- stats package:
  - glm - Generalized Linear Model (GLM) regression and classification:
    - binomial, gaussian, Gamma, inverse.gamma and poisson families
    - MASS::negative.binomial family
    - statmod::tweedie family
  - kmeans - K-Means clustering
  - lm - Linear Model (LM) regression
- xgboost package:
  - xgb.Booster - XGBoost (XGB) regression and classification
Data pre-processing using model formulae:
- Interaction terms
- base::I(..) function terms:
  - Logical operators &, | and !
  - Relational operators ==, !=, <, <=, >= and >
  - Arithmetic operators +, -, *, /, and %
  - Exponentiation operators ^ and **
  - The is.na function
  - Arithmetic functions abs, ceiling, exp, floor, log, log10, round and sqrt
- base::cut() and base::ifelse() function terms
- plyr::revalue() and plyr::mapvalues() function terms
Production quality:
- Complete test coverage.
- Fully compliant with the JPMML-Evaluator library.

Prerequisites

Java 1.8 or newer.

Installation

Enter the project root directory and build using Apache Maven:

mvn clean install

The build produces a library JAR file pmml-rexp/target/pmml-rexp-1.6-SNAPSHOT.jar, and an executable uber-JAR file pmml-rexp-example/target/pmml-rexp-example-executable-1.6-SNAPSHOT.jar.

Usage

A typical workflow can be summarized as follows:

Use R to train a model.
Serialize the model in RDS data format to a file in a local filesystem.
Use the JPMML-R command-line converter application to turn the RDS file to a PMML file.

The R side of operations

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")

The JPMML-R side of operations

Converting the RDS file rf.rds to a PMML file rf.pmml:

java -jar pmml-rexp-example/target/pmml-rexp-example-executable-1.6-SNAPSHOT.jar --rds-input rf.rds --pmml-output rf.pmml

Getting help:

java -jar pmml-rexp-example/target/pmml-rexp-example-executable-1.6-SNAPSHOT.jar --help

The 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 pmml-rexp-example/target/pmml-rexp-example-executable-1.6-SNAPSHOT.jar --rds-input rf.rds --pmml-output rf.pmml

Documentation

Up-to-date:

Slightly outdated:

Converting R to PMML

License

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.

Additional information

JPMML-R is developed and maintained by Openscoring Ltd, Estonia.

Interested in using Java PMML API software in your company? Please contact info@openscoring.io