Getting Started with XGBoost4J¶

This tutorial introduces Java API for XGBoost.

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 are supported.

• The first step is to import DMatrix:

import ml.dmlc.xgboost4j.java.DMatrix;

• Use DMatrix constructor to load data from a libsvm text format file:

DMatrix dmat = new DMatrix("train.svm.txt");

• Pass arrays to DMatrix constructor to load from sparse matrix.

Suppose we have a sparse matrix

1 0 2 0
4 0 0 3
3 1 2 0

We can express the sparse matrix in Compressed Sparse Row (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};
int numColumn = 4;
DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR, numColumn);

… or in Compressed Sparse Column (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};
int numRow = 3;
DMatrix dmat = new DMatrix(colHeaders, rowIndex, data, DMatrix.SparseType.CSC, numRow);

• You may also load your data from a dense matrix. Let’s assume we have a matrix of form

1    2
3    4
5    6

Using row-major layout, we specify the dense matrix as follows:

float[] data = new float[] {1f,2f,3f,4f,5f,6f};
int nrow = 3;
int ncol = 2;
float missing = 0.0f;
DMatrix dmat = new DMatrix(data, nrow, ncol, missing);

• To set weight:

float[] weights = new float[] {1f,2f,1f};
dmat.setWeight(weights);

Setting Parameters¶

To set parameters, parameters are specified as a Map:

Map<String, Object> params = new HashMap<String, Object>() {
{
put("eta", 1.0);
put("max_depth", 2);
put("objective", "binary:logistic");
put("eval_metric", "logloss");
}
};

Training Model¶

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

• Import Booster and XGBoost:

import ml.dmlc.xgboost4j.java.Booster;
import ml.dmlc.xgboost4j.java.XGBoost;

• Training

DMatrix trainMat = new DMatrix("train.svm.txt");
DMatrix validMat = new DMatrix("valid.svm.txt");
// Specify a watch list to see model accuracy on data sets
Map<String, DMatrix> watches = new HashMap<String, DMatrix>() {
{
put("train", trainMat);
put("test", testMat);
}
};
int nround = 2;
Booster booster = XGBoost.train(trainMat, params, nround, watches, null, null);

• Saving model

After training, you can save model and dump it out.

booster.saveModel("model.bin");

• Generaing model dump with feature map

// dump without feature map
String[] model_dump = booster.getModelDump(null, false);
// dump with feature map
String[] model_dump_with_feature_map = booster.getModelDump("featureMap.txt", false);

• Load a model

Booster booster = XGBoost.loadModel("model.bin");

Prediction¶

After training and loading a model, you can use it to make prediction for other data. The result will be a two-dimension float array (nsample, nclass); for predictLeaf(), the result would be of shape (nsample, nclass*ntrees).

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