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Bring XGBoost4J Intro up-to-date (#3574)

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Philip Hyunsu Cho 2018-08-10 09:08:19 -07:00 committed by Nan Zhu
parent 2e7c3a0ed5
commit 9c647d8130
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doc/jvm/java_intro.rst
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@ -6,15 +6,15 @@ This tutorial introduces Java API for XGBoost.
************** **************
Data Interface Data Interface
************** **************
Like the XGBoost python module, XGBoost4J uses DMatrix to handle data, 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 LIBSVM txt format file, sparse matrix in CSR/CSC format, and dense matrix are
supported. supported.
* The first step is to import DMatrix: * The first step is to import DMatrix:
.. code-block:: java .. code-block:: java
import org.dmlc.xgboost4j.java.DMatrix; import ml.dmlc.xgboost4j.java.DMatrix;
* Use DMatrix constructor to load data from a libsvm text format file: * Use DMatrix constructor to load data from a libsvm text format file:
@ -39,7 +39,8 @@ supported.
long[] rowHeaders = new long[] {0,2,4,7}; long[] rowHeaders = new long[] {0,2,4,7};
float[] data = new float[] {1f,2f,4f,3f,3f,1f,2f}; float[] data = new float[] {1f,2f,4f,3f,3f,1f,2f};
int[] colIndex = new int[] {0,2,0,3,0,1,2}; int[] colIndex = new int[] {0,2,0,3,0,1,2};
DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR); 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: ... or in `Compressed Sparse Column (CSC) <https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS)>`_ format:
@ -48,7 +49,8 @@ supported.
long[] colHeaders = new long[] {0,3,4,6,7}; long[] colHeaders = new long[] {0,3,4,6,7};
float[] data = new float[] {1f,4f,3f,1f,2f,2f,3f}; float[] data = new float[] {1f,4f,3f,1f,2f,2f,3f};
int[] rowIndex = new int[] {0,1,2,2,0,2,1}; int[] rowIndex = new int[] {0,1,2,2,0,2,1};
DMatrix dmat = new DMatrix(colHeaders, rowIndex, data, DMatrix.SparseType.CSC); 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 * You may also load your data from a dense matrix. Let's assume we have a matrix of form
@ -66,7 +68,7 @@ supported.
int nrow = 3; int nrow = 3;
int ncol = 2; int ncol = 2;
float missing = 0.0f; float missing = 0.0f;
DMatrix dmat = new Matrix(data, nrow, ncol, missing); DMatrix dmat = new DMatrix(data, nrow, ncol, missing);
* To set weight: * To set weight:
@ -82,7 +84,7 @@ To set parameters, parameters are specified as a Map:
.. code-block:: java .. code-block:: java
Map<String, Object> params = new HashMap<>() { Map<String, Object> params = new HashMap<String, Object>() {
{ {
put("eta", 1.0); put("eta", 1.0);
put("max_depth", 2); put("max_depth", 2);
@ -101,8 +103,8 @@ With parameters and data, you are able to train a booster model.
.. code-block:: java .. code-block:: java
import org.dmlc.xgboost4j.java.Booster; import ml.dmlc.xgboost4j.java.Booster;
import org.dmlc.xgboost4j.java.XGBoost; import ml.dmlc.xgboost4j.java.XGBoost;
* Training * Training
@ -110,11 +112,13 @@ With parameters and data, you are able to train a booster model.
DMatrix trainMat = new DMatrix("train.svm.txt"); DMatrix trainMat = new DMatrix("train.svm.txt");
DMatrix validMat = new DMatrix("valid.svm.txt"); DMatrix validMat = new DMatrix("valid.svm.txt");
// Specify a watchList to see the performance // Specify a watch list to see model accuracy on data sets
// Any Iterable<Entry<String, DMatrix>> object could be used as watchList Map<String, DMatrix> watches = new HashMap<String, DMatrix>() {
List<Entry<String, DMatrix>> watches = new ArrayList<>(); {
watches.add(new SimpleEntry<>("train", trainMat)); put("train", trainMat);
watches.add(new SimpleEntry<>("test", testMat)); put("test", testMat);
}
};
int nround = 2; int nround = 2;
Booster booster = XGBoost.train(trainMat, params, nround, watches, null, null); Booster booster = XGBoost.train(trainMat, params, nround, watches, null, null);
@ -130,15 +134,16 @@ With parameters and data, you are able to train a booster model.
.. code-block:: java .. code-block:: java
String[] model_dump = booster.getModelDump(null, false) // dump without feature map
String[] model_dump = booster.getModelDump(null, false);
// dump with feature map // dump with feature map
String[] model_dump_with_feature_map = booster.getModelDump("featureMap.txt", false) String[] model_dump_with_feature_map = booster.getModelDump("featureMap.txt", false);
* Load a model * Load a model
.. code-block:: java .. code-block:: java
Booster booster = Booster.loadModel("model.bin"); Booster booster = XGBoost.loadModel("model.bin");
********** **********
Prediction Prediction