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https://github.com/jpmml/r2pmml

R library for converting R models to PMML
https://github.com/jpmml/r2pmml

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R library for converting R models to PMML

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README 

        
R2PMML

======



R package for converting [R](https://www.r-project.org/) models to PMML



# Features #



This library is a thin R wrapper around the [JPMML-R](https://github.com/jpmml/jpmml-r#features) library.



# Prerequisites #



* Java 1.8 or newer. The Java executable must be available on system path.

* R 3.3, 4.0 or newer.



# Installation #



Installing a release version from CRAN:



```R

install.packages("r2pmml")

```



Alternatively, installing the latest snapshot version from GitHub using the [`devtools`](https://cran.r-project.org/package=devtools) package:



```R

library("devtools")



install_github("jpmml/r2pmml")

```



# Usage #



### Base functionality



Loading the package:



```R

library("r2pmml")

```



Training and exporting a simple `randomForest` model:



```R

library("randomForest")

library("r2pmml")



data(iris)



# Train a model using raw Iris dataset

iris.rf = randomForest(Species ~ ., data = iris, ntree = 7)

print(iris.rf)



# Export the model to PMML

r2pmml(iris.rf, "iris_rf.pmml")

```



### Data pre-processing



The `r2pmml` function takes an optional argument `preProcess`, which associates the model with data pre-processing transformations.



Training and exporting a more sophisticated `randomForest` model:



```R

library("caret")

library("randomForest")

library("r2pmml")



data(iris)



# Create a preprocessor

iris.preProcess = preProcess(iris, method = c("range"))



# Use the preprocessor to transform raw Iris dataset to pre-processed Iris dataset

iris.transformed = predict(iris.preProcess, newdata = iris)



# Train a model using pre-processed Iris dataset

iris.rf = randomForest(Species ~., data = iris.transformed, ntree = 7)

print(iris.rf)



# Export the model to PMML.

# Pass the preprocessor as the `preProcess` argument

r2pmml(iris.rf, "iris_rf.pmml", preProcess = iris.preProcess)

```



### Model formulae



Alternatively, it is possible to associate `lm`, `glm` and `randomForest` models with data pre-processing transformations using [model formulae](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html).



Training and exporting a `glm` model:



```R

library("plyr")

library("r2pmml")



# Load and prepare the Auto-MPG dataset

auto = read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data", quote = "\"", header = FALSE, na.strings = "?", row.names = NULL, col.names = c("mpg", "cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "origin", "car_name"))

auto$origin = as.factor(auto$origin)

auto$car_name = NULL

auto = na.omit(auto)



# Train a model

auto.glm = glm(mpg ~ (. - horsepower - weight - origin) ^ 2 + I(displacement / cylinders) + cut(horsepower, breaks = c(0, 50, 100, 150, 200, 250)) + I(log(weight)) + revalue(origin, replace = c("1" = "US", "2" = "Europe", "3" = "Japan")), data = auto)



# Export the model to PMML

r2pmml(auto.glm, "auto_glm.pmml")

```



### Package `ranger`



Training and exporting a `ranger` model:



```R

library("ranger")

library("r2pmml")



data(iris)



# Train a model.

# Keep the forest data structure by specifying `write.forest = TRUE`

iris.ranger = ranger(Species ~ ., data = iris, num.trees = 7, write.forest = TRUE)

print(iris.ranger)



# Export the model to PMML.

# Pass the training dataset as the `data` argument

r2pmml(iris.ranger, "iris_ranger.pmml", data = iris)

```



### Package `xgboost`



Training and exporting an `xgb.Booster` model:



```R

library("xgboost")

library("r2pmml")



data(iris)



iris_X = iris[, 1:4]

iris_y = as.integer(iris[, 5]) - 1



# Generate R model matrix

iris.matrix = model.matrix(~ . - 1, data = iris_X)



# Generate XGBoost DMatrix and feature map based on R model matrix

iris.DMatrix = xgb.DMatrix(iris.matrix, label = iris_y)

iris.fmap = as.fmap(iris.matrix)



# Train a model

iris.xgb = xgboost(data = iris.DMatrix, missing = NULL, objective = "multi:softmax", num_class = 3, nrounds = 13)



# Export the model to PMML.

# Pass the feature map as the `fmap` argument.

# Pass the name and category levels of the target field as `response_name` and `response_levels` arguments, respectively.

# Pass the value of missing value as the `missing` argument

# Pass the optimal number of trees as the `ntreelimit` argument (analogous to the `ntreelimit` argument of the `xgb::predict.xgb.Booster` function)

r2pmml(iris.xgb, "iris_xgb.pmml", fmap = iris.fmap, response_name = "Species", response_levels = c("setosa", "versicolor", "virginica"), missing = NULL, ntreelimit = 7, compact = TRUE)

```



### Advanced functionality



Tweaking JVM configuration:



```R

Sys.setenv(JAVA_TOOL_OPTIONS = "-Xms4G -Xmx8G")



r2pmml(iris.rf, "iris_rf.pmml")

```



Employing a custom converter class:



```R

r2pmml(iris.rf, "iris_rf.pmml", converter = "com.mycompany.MyRandomForestConverter", converter_classpath = "/path/to/myconverter-1.0-SNAPSHOT.jar")

```



# De-installation #



Removing the package:



```R

remove.packages("r2pmml")

```



# Documentation #



Up-to-date:



* [Converting logistic regression models to PMML documents](https://openscoring.io/blog/2020/01/19/converting_logistic_regression_pmml/#r)

* [Deploying R language models on Apache Spark ML](https://openscoring.io/blog/2019/02/09/deploying_rlang_model_sparkml/)



Slightly outdated:



* [Converting R to PMML](https://www.slideshare.net/VilluRuusmann/converting-r-to-pmml-82182483)



# License #



R2PMML is licensed under the terms and conditions of the [GNU Affero General Public License, Version 3.0](https://www.gnu.org/licenses/agpl-3.0.html).



If you would like to use R2PMML in a proprietary software project, then it is possible to enter into a licensing agreement which makes R2PMML available under the terms and conditions of the [BSD 3-Clause License](https://opensource.org/license/bsd-3-clause/) instead.



# Additional information #



R2PMML is developed and maintained by Openscoring Ltd, Estonia.



Interested in using [Java PMML API](https://github.com/jpmml) software in your company? Please contact [[email protected]](mailto:[email protected])