JPMML-R

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

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
  • 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
  • elmNN package:
    • elmNN - 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
  • 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
  • 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
  • rpart package:
    • rpart - Recursive Partitioning (RPart) regression and classification
  • r2pmml package:
    • scorecard - Scorecard regression
  • stats package:
    • glm - Generalized Linear Model (GLM) regression and classification
    • kmeans - K-Means clustering
    • lm - Linear Model (LM) regression
  • xgboost package:
    • xgb.Booster - XGBoost (XGB) regression and classification
• 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 an executable uber-JAR file target/jpmml-r-executable-1.3-SNAPSHOT.jar.

Usage

A typical workflow can be summarized as follows:

1. Use R to train a model.
2. Serialize the model in RDS data format to a file in a local filesystem.
3. 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 target/jpmml-r-executable-1.3-SNAPSHOT.jar --rds-input rf.rds --pmml-output rf.pmml

Getting help:

java -jar target/jpmml-r-executable-1.3-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 target/jpmml-r-executable-1.3-SNAPSHOT.jar --rds-input rf.rds --pmml-output rf.pmml

License

JPMML-R is dual-licensed under the GNU Affero General Public License (AGPL) version 3.0, and a commercial license.

Additional information

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

Interested in using JPMML software in your application? Please contact [email protected]