XGBoost4J Java API

This tutorial introduces

Data Interface

Like the xgboost python module, xgboost4j uses DMatrix to handle data, libsvm txt format file, sparse matrix in CSR/CSC format, and dense matrix is supported.

  • To import DMatrix :
import org.dmlc.xgboost4j.DMatrix;
  • To load libsvm text format file, the usage is like :
DMatrix dmat = new DMatrix("train.svm.txt");
  • To load sparse matrix in CSR/CSC format is a little complicated, the usage is like : suppose a sparse matrix : 1 0 2 0 4 0 0 3 3 1 2 0

    for CSR format

long[] rowHeaders = new long[] {0,2,4,7};
float[] data = new float[] {1f,2f,4f,3f,3f,1f,2f};
int[] colIndex = new int[] {0,2,0,3,0,1,2};
DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR);

for CSC format

long[] colHeaders = new long[] {0,3,4,6,7};
float[] data = new float[] {1f,4f,3f,1f,2f,2f,3f};
int[] rowIndex = new int[] {0,1,2,2,0,2,1};
DMatrix dmat = new DMatrix(colHeaders, rowIndex, data, DMatrix.SparseType.CSC);
  • To load 3*2 dense matrix, the usage is like : suppose a matrix : 1 2 3 4 5 6
float[] data = new float[] {1f,2f,3f,4f,5f,6f};
int nrow = 3;
int ncol = 2;
float missing = 0.0f;
DMatrix dmat = new Matrix(data, nrow, ncol, missing);
  • To set weight :
float[] weights = new float[] {1f,2f,1f};

Setting Parameters

  • in xgboost4j any Iterable<Entry<String, Object>> object could be used as parameters.
  • to set parameters, for non-multiple value params, you can simply use entrySet of an Map:
Map<String, Object> paramMap = new HashMap<>() {
    put("eta", 1.0);
    put("max_depth", 2);
    put("silent", 1);
    put("objective", "binary:logistic");
    put("eval_metric", "logloss");
Iterable<Entry<String, Object>> params = paramMap.entrySet();
  • for the situation that multiple values with same param key, List<Entry<String, Object>> would be a good choice, e.g. :
List<Entry<String, Object>> params = new ArrayList<Entry<String, Object>>() {
        add(new SimpleEntry<String, Object>("eta", 1.0));
        add(new SimpleEntry<String, Object>("max_depth", 2.0));
        add(new SimpleEntry<String, Object>("silent", 1));
        add(new SimpleEntry<String, Object>("objective", "binary:logistic"));

Training Model

With parameters and data, you are able to train a booster model.

  • Import Trainer and Booster :
import org.dmlc.xgboost4j.Booster;
import org.dmlc.xgboost4j.util.Trainer;
  • Training
DMatrix trainMat = new DMatrix("train.svm.txt");
DMatrix validMat = new DMatrix("valid.svm.txt");
//specify a watchList to see the performance
//any Iterable<Entry<String, DMatrix>> object could be used as watchList
List<Entry<String, DMatrix>> watchs =  new ArrayList<>();
watchs.add(new SimpleEntry<>("train", trainMat));
watchs.add(new SimpleEntry<>("test", testMat));
int round = 2;
Booster booster = Trainer.train(params, trainMat, round, watchs, null, null);
  • Saving model After training, you can save model and dump it out.
  • Dump Model and Feature Map
booster.dumpModel("modelInfo.txt", false)
//dump with featureMap
booster.dumpModel("modelInfo.txt", "featureMap.txt", false)
  • Load a model
Params param = new Params() {
    put("silent", 1);
    put("nthread", 6);
Booster booster = new Booster(param, "model.bin");


after training and loading a model, you use it to predict other data, the predict results will be a two-dimension float array (nsample, nclass), for predict leaf, it would be (nsample, nclass*ntrees)

DMatrix dtest = new DMatrix("test.svm.txt");
float[][] predicts = booster.predict(dtest);
//predict leaf
float[][] leafPredicts = booster.predict(dtest, 0, true);