diff --git a/README.md b/README.md index 3e06a6c..6c5185c 100644 --- a/README.md +++ b/README.md @@ -79,7 +79,10 @@ Java library and command-line application for converting [R](https://www.r-proje * [`r2pmml`](https://github.com/jpmml/r2pmml) package: * `scorecard` - Scorecard regression * `stats` package: - * `glm` - Generalized Linear Model (GLM) regression and classification + * `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`](https://cran.r-project.org/package=xgboost) package: @@ -106,6 +109,7 @@ Java library and command-line application for converting [R](https://www.r-proje # Installation # Enter the project root directory and build using [Apache Maven](https://maven.apache.org/): + ``` mvn clean install ``` @@ -123,6 +127,7 @@ A typical workflow can be summarized as follows: ### 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`: + ```R library("randomForest") @@ -134,16 +139,19 @@ 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 ```