README.md

xgboost-predictor-java

Pure Java implementation of XGBoost predictor for online prediction tasks.

Getting started

Adding to dependencies

If you use Maven:

<repositories>
  <repository>
    <id>bintray-komiya-atsushi-maven</id>
    <url>http://dl.bintray.com/komiya-atsushi/maven</url>
  </repository>
</repositories>

<dependencies>
  <dependency>
    <groupId>biz.k11i</groupId>
    <artifactId>xgboost-predictor</artifactId>
    <version>0.3.0</version>
  </dependency>
</dependencies>

Or Gradle:

repositories {
    // Use jcenter instead of mavenCentral
    jcenter()
}

dependencies {
    compile group: 'biz.k11i', name: 'xgboost-predictor', version: '0.3.0'
}

Or sbt:

resolvers += Resolver.jcenterRepo

libraryDependencies ++= Seq(
  "biz.k11i" % "xgboost-predictor" % "0.3.0"
)

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
    }
}

Apache Spark integration

See detail xgboost-predictor-spark.

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"
  • "rank:pairwise"
• API
  • Predicts probability or classification
    • Predictor#predict(FVec)
  • Outputs margin
    • Predictor#predict(FVec, true /* output margin */)
  • Predicts leaf index
    • Predictor#predictLeaf(FVec)