Getting Started with XGBoost4J

############################## 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:

  .. code-block:: java

  import ml.dmlc.xgboost4j.java.DMatrix;

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

  .. code-block:: java

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

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

  Suppose we have a sparse matrix

  .. code-block:: none

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

  We can express the sparse matrix in Compressed Sparse Row (CSR) <https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format)>_ format:

  .. code-block:: java

  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) <https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS)>_ format:

  .. code-block:: java

  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

  .. code-block:: none

  1 2 3 4 5 6

  Using row-major layout <https://en.wikipedia.org/wiki/Row-_and_column-major_order>_, we specify the dense matrix as follows:

  .. code-block:: java

  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:

  .. code-block:: java

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

---

Setting Parameters

---

To set parameters, parameters are specified as a Map:

.. code-block:: java

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:

  .. code-block:: java

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

• Training

  .. code-block:: java

  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.

  .. code-block:: java

  booster.saveModel("model.json");

• Generating model dump with feature map

  .. code-block:: java

  // 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

  .. code-block:: java

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

---

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).

.. code-block:: java

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