Java library and command-line application for converting H2O.ai models to PMML.

• Supported MOJO types:
  • DrfMojoModel
  • GbmMojoModel
  • GlmMojoModel
  • GlmMultinomialMojoModel
  • StackedEnsembleMojoModel
  • XGBoostJavaMojoModel
  • XGBoostNativeMojoModel

• H2O 3.16.0 or newer
• Java 1.8 or newer

Enter the project root directory and build using Apache Maven:

mvn clean install

The build produces an executable uber-JAR file target/jpmml-h2o-executable-1.0-SNAPSHOT.jar.

A typical workflow can be summarized as follows:

1. Use H2O to train a model.
2. Download the model in Model ObJect, Optimized (MOJO) data format to a file in a local filesystem.
3. Use the JPMML-H2O command-line converter application to turn the MOJO file to a PMML file.

Using the h2o package to train a regression model for the example Boston housing dataset:

from h2o import H2OFrame
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from pandas import DataFrame, Series
from sklearn.datasets import load_boston

import h2o
import pandas

boston = load_boston()

df = pandas.concat((DataFrame(data = boston.data, columns = boston.feature_names), Series(boston.target, name = "MEDV")), axis = 1)

h2o.connect()

df = H2OFrame(df)

glm = H2OGeneralizedLinearEstimator(family = "gaussian")
glm.train(boston.feature_names.tolist(), "MEDV", df)

glm.download_mojo(path = "mojo.zip")

Converting the MOJO file mojo.zip to a PMML file mojo.pmml:

java -jar target/jpmml-h2o-executable-1.0-SNAPSHOT.jar --mojo-input mojo.zip --pmml-output mojo.pmml

Getting help:

java -jar target/jpmml-h2o-executable-1.0-SNAPSHOT.jar --help

JPMML-H2O is dual-licensed under the GNU Affero General Public License (AGPL) version 3.0, and a commercial license.

JPMML-H2O is developed and maintained by Openscoring Ltd, Estonia.

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