Java library and command-line application for converting XGBoost models to PMML.
- Java 1.7 or newer.
Enter the project root directory and build using Apache Maven:
mvn clean install
The build produces an executable uber-JAR file target/converter-executable-1.2-SNAPSHOT.jar.
A typical workflow can be summarized as follows:
- Use XGBoost to train a model.
- Save the model and the associated feature map to files in a local filesystem.
- Use the JPMML-XGBoost command-line converter application to turn those two files to a PMML file.
Using r2pmml and xgboost packages to train a linear regression model for the example mtcars dataset:
library("r2pmml")
library("xgboost")
data(mtcars)
# Convert selected columns from numeric datatype to integer or factor datatypes
mtcars$cyl = as.integer(mtcars$cyl)
mtcars$vs = as.factor(mtcars$vs)
mtcars$am = as.factor(mtcars$am)
mtcars$gear = as.integer(mtcars$gear)
mtcars$carb = as.integer(mtcars$carb)
mpg_y = mtcars[, 1]
mpg_X = mtcars[, 2:ncol(mtcars)]
# Generate feature map
mpg.fmap = r2pmml::genFMap(mpg_X)
r2pmml::writeFMap(mpg.fmap, "xgboost.fmap")
# Generate DMatrix
mpg.dmatrix = r2pmml::genDMatrix(mpg_y, mpg_X, "xgboost.svm")
set.seed(31)
# Train a linear regression model
mpg.xgb = xgboost(data = mpg.dmatrix, objective = "reg:linear", nrounds = 7)
# Save the model in XGBoost proprietary binary format
xgb.save(mpg.xgb, "xgboost.model")
# Dump the model in text format
xgb.dump(mpg.xgb, "xgboost.model.txt", fmap = "xgboost.fmap")Converting the model file xgboost.model together with the associated feature map file xgboost.fmap to a PMML file xgboost.pmml:
java -jar target/converter-executable-1.2-SNAPSHOT.jar --model-input xgboost.model --fmap-input xgboost.fmap --target-name mpg --pmml-output xgboost.pmml
Getting help:
java -jar target/converter-executable-1.2-SNAPSHOT.jar --help
JPMML-XGBoost is licensed under the GNU Affero General Public License (AGPL) version 3.0. Other licenses are available on request.
Please contact info@openscoring.io