README.md

JPMML-H2O

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

Features

Supported MOJO types

• Supervised algorithms:
  • Automated Machine Learning (AutoML)
  • Distributed Random Forest (DRF):
    • DrfMojoModel
  • Gradient Boosting Machine (GBM):
    • GbmMojoModel
  • Generalized Linear Model (GLM):
    • GlmMojoModel
    • GlmMultinomialMojoModel
    • GlmOrdinalMojoModel
  • Stacked Ensembles:
    • StackedEnsembleMojoModel
  • XGBoost:
    • XGBoostJavaMojoModel
    • XGBoostNativeMojoModel
• Unsupervised algorithms:
  • Isolation Forest:
    • IsolationForestMojoModel
    • ExtendedIsolationForestMojoModel

Prerequisites

• H2O.ai 3.34(.0.1) or newer
• Java 1.8 or newer

Installation

Enter the project root directory and build using Apache Maven:

mvn clean install

The build produces a library JAR file pmml-h2o/target/pmml-h2o-1.2-SNAPSHOT.jar, and an executable uber-JAR file pmml-h2o-example/target/pmml-h2o-example-executable-1.2-SNAPSHOT.jar.

Usage

A typical workflow can be summarized as follows:

1. Use H2O.ai 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.

The H2O.ai side of operations

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")

The Java side of operations

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

java -jar pmml-h2o-example/target/pmml-h2o-example-executable-1.2-SNAPSHOT.jar --mojo-input mojo.zip --pmml-output mojo.pmml

Getting help:

java -jar pmml-h2o-example/target/pmml-h2o-example-executable-1.2-SNAPSHOT.jar --help

Documentation

• Converting Scikit-Learn H2O.ai pipelines to PMML
• Training Scikit-Learn H2O.ai pipelines

License

JPMML-H2O is licensed under the terms and conditions of the GNU Affero General Public License, Version 3.0.

If you would like to use JPMML-H2O in a proprietary software project, then it is possible to enter into a licensing agreement which makes JPMML-H2O available under the terms and conditions of the BSD 3-Clause License instead.

Additional information

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

Interested in using Java PMML API software in your company? Please contact [email protected]