xgboost4j.md

xgboost4j : java wrapper for xgboost

This page will introduce xgboost4j, the java wrapper for xgboost, including:

• Building
• Data Interface
• Setting Parameters
• Train Model
• Prediction

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Build xgboost4j

• Build native library
  first make sure you have installed jdk and JAVA_HOME has been setted properly, then simply run ./create_wrap.sh.

• Package xgboost4j
  to package xgboost4j, you can run mvn package in xgboost4j folder or just use IDE(eclipse/netbeans) to open this maven project and build.

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Data Interface

Like the xgboost python module, xgboost4j use 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};
dmat.setWeight(weights);

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");
//specifiy 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.

booster.saveModel("model.bin");

• 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");

####Prediction 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");
//predict
float[][] predicts = booster.predict(dtest);
//predict leaf
float[][] leafPredicts = booster.predict(dtest, 0, true);