h2o-tree-api

Pure Java implementation of XGBoost predictor for online prediction tasks

License	The Apache Software License, Version 2.0
Categories	H2 Data Databases H2O Business Logic Libraries Machine Learning
GroupId	ai.h2o
ArtifactId	h2o-tree-api
Last Version	0.3.18
Release Date	May 11, 2021
Type	jar
Description	h2o-tree-api Pure Java implementation of XGBoost predictor for online prediction tasks
Project URL	https://github.com/h2oai/xgboost-predictor
Project Organization	H2O.ai
Source Code Management	https://github.com/h2oai/xgboost-predictor

Download h2o-tree-api

Filename	Size
h2o-tree-api-0.3.18.pom
h2o-tree-api-0.3.18.jar	1 KB
h2o-tree-api-0.3.18-sources.jar	1 KB
h2o-tree-api-0.3.18-javadoc.jar	33 KB

How to add to project

Apache Maven

<!-- https://jarcasting.com/artifacts/ai.h2o/h2o-tree-api/ -->
<dependency>
    <groupId>ai.h2o</groupId>
    <artifactId>h2o-tree-api</artifactId>
    <version>0.3.18</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/ai.h2o/h2o-tree-api/
implementation 'ai.h2o:h2o-tree-api:0.3.18'

Gradle Kotlin

// https://jarcasting.com/artifacts/ai.h2o/h2o-tree-api/
implementation ("ai.h2o:h2o-tree-api:0.3.18")

Apache Buildr

'ai.h2o:h2o-tree-api:jar:0.3.18'

Apache Ivy

<dependency org="ai.h2o" name="h2o-tree-api" rev="0.3.18">
  <artifact name="h2o-tree-api" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='ai.h2o', module='h2o-tree-api', version='0.3.18')
)

Scala SBT

libraryDependencies += "ai.h2o" % "h2o-tree-api" % "0.3.18"

Leiningen

[ai.h2o/h2o-tree-api "0.3.18"]

Dependencies

There are no dependencies for this project. It is a standalone project that does not depend on any other jars.

Project Modules

There are no modules declared in this project.

xgboost-predictor-java

Pure Java implementation of XGBoost predictor for online prediction tasks.

Getting started

Adding to dependencies

If you use Maven:

<dependencies>
  <dependency>
    <groupId>ai.h2o</groupId>
    <artifactId>xgboost-predictor</artifactId>
    <version>0.3.1</version>
  </dependency>
</dependencies>

Or Gradle:

repositories {
    mavenCentral()
}

dependencies {
    compile group: 'ai.h2o', name: 'xgboost-predictor', version: '0.3.1'
}

Or sbt:

resolvers += DefaultMavenRepository

libraryDependencies ++= Seq(
  "ai.h2o" % "xgboost-predictor" % "0.3.1"
)

Using Predictor in Java

package biz.k11i.xgboost.demo;

import biz.k11i.xgboost.Predictor;
import biz.k11i.xgboost.util.FVec;

public class HowToUseXgboostPredictor {
    public static void main(String[] args) throws java.io.IOException {
        // If you want to use faster exp() calculation, uncomment the line below
        // ObjFunction.useFastMathExp(true);

        // Load model and create Predictor
        Predictor predictor = new Predictor(
                new java.io.FileInputStream("/path/to/xgboost-model-file"));

        // Create feature vector from dense representation by array
        double[] denseArray = {0, 0, 32, 0, 0, 16, -8, 0, 0, 0};
        FVec fVecDense = FVec.Transformer.fromArray(
                denseArray,
                true /* treat zero element as N/A */);

        // Create feature vector from sparse representation by map
        FVec fVecSparse = FVec.Transformer.fromMap(
                new java.util.HashMap<Integer, Double>() {{
                    put(2, 32.);
                    put(5, 16.);
                    put(6, -8.);
                }});

        // Predict probability or classification
        double[] prediction = predictor.predict(fVecDense);

        // prediction[0] has
        //    - probability ("binary:logistic")
        //    - class label ("multi:softmax")

        // Predict leaf index of each tree
        int[] leafIndexes = predictor.predictLeaf(fVecDense);

        // leafIndexes[i] has a leaf index of i-th tree
    }
}

Benchmark

Throughput comparison to xgboost4j 1.1 by xgboost-predictor-benchmark.

Feature	xgboost-predictor	xgboost4j
Model loading	49017.60 ops/s	39669.36 ops/s
Single prediction	6016955.46 ops/s	1018.01 ops/s
Batch prediction	44985.71 ops/s	5.04 ops/s
Leaf prediction	11115853.34 ops/s	1076.54 ops/s

Xgboost-predictor-java is about 6,000 to 10,000 times faster than xgboost4j on prediction tasks.

Supported models, objective functions and API

• Models
  • "gblinear"
  • "gbtree"
  • "dart"
• Objective functions
  • "binary:logistic"
  • "binary:logitraw"
  • "multi:softmax"
  • "multi:softprob"
  • "reg:linear"
  • "reg:logistic"
  • "rank:pairwise"
  • "rank:ndcg"
• API
  • Predicts probability or classification
    • Predictor#predict(FVec)
  • Outputs margin
    • Predictor#predict(FVec, true /* output margin */)
  • Predicts leaf index
    • Predictor#predictLeaf(FVec)

H2O.ai

Fast Scalable Machine Learning For Smarter Applications

Versions

Version
0.3.18 May 11, 2021
0.3.17 May 4, 2020
0.3.16 Apr 30, 2020
0.3.15 Mar 26, 2020
0.3.14 Feb 27, 2020
0.3.13 Jan 31, 2020
0.3.12 Jan 17, 2020
0.3.11 Jan 16, 2020
0.3.10 Jan 15, 2020
0.3.9 Sep 24, 2019
0.3.8 Aug 6, 2019
0.3.7 Mar 15, 2019