From a67b79591cc283e944db1f096437ae4a95be0a86 Mon Sep 17 00:00:00 2001 From: Villu Ruusmann Date: Sat, 22 Jul 2023 09:53:42 +0300 Subject: [PATCH] Updated documentation --- README.md | 36 +++++++++++++++++++++++------------- 1 file changed, 23 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index fcde206..1d1ddf9 100644 --- a/README.md +++ b/README.md @@ -5,20 +5,30 @@ Java library and command-line application for converting [H2O.ai](https://www.h2 # Features # -* Supported MOJO types: - * [`DrfMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/drf/DrfMojoModel.html) - * [`GbmMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/gbm/GbmMojoModel.html) - * [`GlmMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/glm/GlmMojoModel.html) - * [`GlmMultinomialMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/glm/GlmMultinomialMojoModel.html) - * [`GlmOrdinalMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/glm/GlmOrdinalMojoModel.html) - * [`IsolationForestMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/isofor/IsolationForestMojoModel.html) - * [`StackedEnsembleMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/ensemble/StackedEnsembleMojoModel.html) - * `XGBoostJavaMojoModel` - * `XGBoostNativeMojoModel` +### Supported MOJO types + +* Supervised algorithms: + * Automated Machine Learning (AutoML) + * Distributed Random Forest (DRF): + * [`DrfMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/drf/DrfMojoModel.html) + * Gradient Boosting Machine (GBM): + * [`GbmMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/gbm/GbmMojoModel.html) + * Generalized Linear Model (GLM): + * [`GlmMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/glm/GlmMojoModel.html) + * [`GlmMultinomialMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/glm/GlmMultinomialMojoModel.html) + * [`GlmOrdinalMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/glm/GlmOrdinalMojoModel.html) + * Stacked Ensembles: + * [`StackedEnsembleMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/ensemble/StackedEnsembleMojoModel.html) + * XGBoost: + * `XGBoostJavaMojoModel` + * `XGBoostNativeMojoModel` +* Unsupervised algorithms: + * Isolation Forest: + * [`IsolationForestMojoModel`](https://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/hex/genmodel/algos/isofor/IsolationForestMojoModel.html) # Prerequisites # -* H2O 3.34(.0.1) or newer +* H2O.ai 3.34(.0.1) or newer * Java 1.8 or newer # Installation # @@ -34,11 +44,11 @@ The build produces a library JAR file `pmml-h2o/target/pmml-h2o-1.2-SNAPSHOT.jar A typical workflow can be summarized as follows: -1. Use H2O to train a model. +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 side of operations +### The H2O.ai side of operations Using the [`h2o`](https://github.com/h2oai/h2o-3/tree/master/h2o-py) package to train a regression model for the example Boston housing dataset: