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[Breaking] Don't drop trees during DART prediction by default (#5115)

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* Simplify DropTrees calling logic

* Add `training` parameter for prediction method.

* [Breaking]: Add `training` to C API.

* Change for R and Python custom objective.

* Correct comment.

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Kodi Arfer
2020-01-13 08:48:30 -05:00
committed by Jiaming Yuan
parent 7b65698187
commit f100b8d878
23 changed files with 214 additions and 140 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
R-package/src/init.c
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@@ -24,7 +24,7 @@ extern SEXP XGBoosterGetAttr_R(SEXP, SEXP);
extern SEXP XGBoosterLoadModelFromRaw_R(SEXP, SEXP);
extern SEXP XGBoosterLoadModel_R(SEXP, SEXP);
extern SEXP XGBoosterModelToRaw_R(SEXP);
extern SEXP XGBoosterPredict_R(SEXP, SEXP, SEXP, SEXP);
extern SEXP XGBoosterPredict_R(SEXP, SEXP, SEXP, SEXP, SEXP);
extern SEXP XGBoosterSaveModel_R(SEXP, SEXP);
extern SEXP XGBoosterSetAttr_R(SEXP, SEXP, SEXP);
extern SEXP XGBoosterSetParam_R(SEXP, SEXP, SEXP);
@@ -50,7 +50,7 @@ static const R_CallMethodDef CallEntries[] = {
{"XGBoosterLoadModelFromRaw_R", (DL_FUNC) &XGBoosterLoadModelFromRaw_R, 2},
{"XGBoosterLoadModel_R", (DL_FUNC) &XGBoosterLoadModel_R, 2},
{"XGBoosterModelToRaw_R", (DL_FUNC) &XGBoosterModelToRaw_R, 1},
{"XGBoosterPredict_R", (DL_FUNC) &XGBoosterPredict_R, 4},
{"XGBoosterPredict_R", (DL_FUNC) &XGBoosterPredict_R, 5},
{"XGBoosterSaveModel_R", (DL_FUNC) &XGBoosterSaveModel_R, 2},
{"XGBoosterSetAttr_R", (DL_FUNC) &XGBoosterSetAttr_R, 3},
{"XGBoosterSetParam_R", (DL_FUNC) &XGBoosterSetParam_R, 3},

20
R-package/src/xgboost_R.cc
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@@ -295,24 +295,26 @@ SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evnames) {
vec_sptr.push_back(vec_names[i].c_str());
}
CHECK_CALL(XGBoosterEvalOneIter(R_ExternalPtrAddr(handle),
asInteger(iter),
BeginPtr(vec_dmats),
BeginPtr(vec_sptr),
len, &ret));
asInteger(iter),
BeginPtr(vec_dmats),
BeginPtr(vec_sptr),
len, &ret));
R_API_END();
return mkString(ret);
}
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask, SEXP ntree_limit) {
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask,
SEXP ntree_limit, SEXP training) {
SEXP ret;
R_API_BEGIN();
bst_ulong olen;
const float *res;
CHECK_CALL(XGBoosterPredict(R_ExternalPtrAddr(handle),
R_ExternalPtrAddr(dmat),
asInteger(option_mask),
asInteger(ntree_limit),
&olen, &res));
R_ExternalPtrAddr(dmat),
asInteger(option_mask),
asInteger(ntree_limit),
0,
&olen, &res));
ret = PROTECT(allocVector(REALSXP, olen));
for (size_t i = 0; i < olen; ++i) {
REAL(ret)[i] = res[i];

4
R-package/src/xgboost_R.h
View File

@@ -148,8 +148,10 @@ XGB_DLL SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evn
* \param dmat data matrix
* \param option_mask output_margin:1 predict_leaf:2
* \param ntree_limit limit number of trees used in prediction
* \param training Whether the prediction value is used for training.
*/
XGB_DLL SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask, SEXP ntree_limit);
XGB_DLL SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask,
SEXP ntree_limit, SEXP training);
/*!
* \brief load model from existing file
* \param handle handle