/**
* evaluate with given dmatrixs.
*
* @param evalMatrixs dmatrixs for evaluation
* @param evalNames name for eval dmatrixs, used for check results
* @param iter current eval iteration
* @return eval information
* @throws XGBoostError native error
*/
public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter) throws XGBoostError {
long[] handles = dmatrixsToHandles(evalMatrixs);
String[] evalInfo = new String[1];
JNIErrorHandle.checkCall(
XgboostJNI.XGBoosterEvalOneIter(handle, iter, handles, evalNames, evalInfo));
return evalInfo[0];
}
/**
* update with give grad and hess
*
* @param dtrain training data
* @param grad first order of gradient
* @param hess seconde order of gradient
* @throws XGBoostError native error
*/
public void boost(DMatrix dtrain, float[] grad, float[] hess) throws XGBoostError {
if (grad.length != hess.length) {
throw new AssertionError(
String.format("grad/hess length mismatch %s / %s", grad.length, hess.length));
}
JNIErrorHandle.checkCall(
XgboostJNI.XGBoosterBoostOneIter(handle, dtrain.getHandle(), grad, hess));
}
/**
* get the dump of the model as a string array
*
* @param withStats Controls whether the split statistics are output.
* @return dumped model information
* @throws XGBoostError native error
*/
private String[] getDumpInfo(boolean withStats) throws XGBoostError {
int statsFlag = 0;
if (withStats) {
statsFlag = 1;
}
String[][] modelInfos = new String[1][];
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterDumpModel(handle, "", statsFlag, modelInfos));
return modelInfos[0];
}
private void init(DMatrix[] dMatrixs) throws XGBoostError {
long[] handles = null;
if (dMatrixs != null) {
handles = dmatrixsToHandles(dMatrixs);
}
long[] out = new long[1];
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterCreate(handles, out));
handle = out[0];
}
/**
* base function for Predict
*
* @param data data
* @param outPutMargin output margin
* @param treeLimit limit number of trees
* @param predLeaf prediction minimum to keep leafs
* @return predict results
*/
private synchronized float[][] pred(
DMatrix data, boolean outPutMargin, int treeLimit, boolean predLeaf) throws XGBoostError {
int optionMask = 0;
if (outPutMargin) {
optionMask = 1;
}
if (predLeaf) {
optionMask = 2;
}
float[][] rawPredicts = new float[1][];
JNIErrorHandle.checkCall(
XgboostJNI.XGBoosterPredict(handle, data.getHandle(), optionMask, treeLimit, rawPredicts));
int row = (int) data.rowNum();
int col = rawPredicts[0].length / row;
float[][] predicts = new float[row][col];
int r, c;
for (int i = 0; i < rawPredicts[0].length; i++) {
r = i / col;
c = i % col;
predicts[r][c] = rawPredicts[0][i];
}
return predicts;
}
/**
* set parameter
*
* @param key param name
* @param value param value
* @throws XGBoostError native error
*/
public final void setParam(String key, String value) throws XGBoostError {
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterSetParam(handle, key, value));
}
/**
* Save the booster model into thread-local rabit checkpoint. This is only used in distributed
* training.
*
* @throws XGBoostError
*/
void saveRabitCheckpoint() throws XGBoostError {
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterSaveRabitCheckpoint(this.handle));
}
/**
* Load the booster model from thread-local rabit checkpoint. This is only used in distributed
* training.
*
* @return the stored version number of the checkpoint.
* @throws XGBoostError
*/
int loadRabitCheckpoint() throws XGBoostError {
int[] out = new int[1];
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterLoadRabitCheckpoint(this.handle, out));
return out[0];
}
/**
* save model to modelPath
*
* @param modelPath model path
*/
public void saveModel(String modelPath) throws XGBoostError {
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterSaveModel(handle, modelPath));
}
/**
* Update (one iteration)
*
* @param dtrain training data
* @param iter current iteration number
* @throws XGBoostError native error
*/
public void update(DMatrix dtrain, int iter) throws XGBoostError {
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterUpdateOneIter(handle, iter, dtrain.getHandle()));
}