Awesome
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):
- Gradient Boosting Machine (GBM):
- Generalized Linear Model (GLM):
- Stacked Ensembles:
- XGBoost:
XGBoostJavaMojoModel
XGBoostNativeMojoModel
- Unsupervised algorithms:
- Isolation Forest:
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:
- Use H2O.ai to train a model.
- Download the model in Model ObJect, Optimized (MOJO) data format to a file in a local filesystem.
- 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
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 info@openscoring.io