R2PMML
R package for converting R models to PMML
Features
This package supersedes the standard pmml package:
- It produces valid and standards-compliant PMML markup.
- It supports several model types (eg.
gbm,iForest,ranger,xgb.Booster) that are not supported by the standardpmmlpackage. - It is extremely fast and memory efficient. For example, it can convert a typical
randomForestmodel to a PMML file in a few seconds time, whereas the standardpmmlpackage requires several hours to do the same.
Prerequisites
- Java 1.7 or newer. The Java executable must be available on system path.
Installation
Installing the package from its GitHub repository using the devtools package:
library("devtools")
install_git("git://github.com/jpmml/r2pmml.git")Usage
Base functionality
Loading the package:
library("r2pmml")Training and exporting a simple randomForest model:
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:
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 via model formulae.
Supported model formula features:
- Interaction terms.
-
base::I(..)function terms:- Logical operators
&,|and!. - Relational operators
==,!=,<,<=,>=and>. - Arithmetic operators
+,-,*,/, and%. - Exponentiation operators
^and**. - The
is.nafunction. - Arithmetic functions
abs,ceiling,exp,floor,log,log10,roundandsqrt.
- Logical operators
-
base::cut()andbase::ifelse()function terms. -
plyr::revalue()andplyr::mapvalues()function terms.
Training and exporting a glm model:
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:
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:
library("xgboost")
library("r2pmml")
data(iris)
iris_X = iris[, 1:4]
iris_y = as.integer(iris[, 5]) - 1
# Generate XGBoost feature map
iris.fmap = genFMap(iris_X)
# Generate XGBoost DMatrix
iris.DMatrix = genDMatrix(iris_y, iris_X)
# 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:
Sys.setenv(JAVA_TOOL_OPTIONS = "-Xms4G -Xmx8G")
r2pmml(iris.rf, "iris_rf.pmml")Employing a custom converter class:
r2pmml(iris.rf, "iris_rf.pmml", converter = "com.mycompany.MyRandomForestConverter", converter_classpath = "/path/to/myconverter-1.0-SNAPSHOT.jar")Please refer to the following resources for more ideas and code examples:
De-installation
Removing the package:
remove.packages("r2pmml")License
R2PMML is dual-licensed under the GNU Affero General Public License (AGPL) version 3.0, and a commercial license.
Additional information
R2PMML is developed and maintained by Openscoring Ltd, Estonia.
Interested in using JPMML software in your application? Please contact info@openscoring.io
