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

* 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>

R-package/R/utils.R

@@ -145,7 +145,7 @@ xgb.iter.update <- function(booster_handle, dtrain, iter, obj = NULL) {
   if (is.null(obj)) {
     .Call(XGBoosterUpdateOneIter_R, booster_handle, as.integer(iter), dtrain)
   } else {
-    pred <- predict(booster_handle, dtrain)
+    pred <- predict(booster_handle, dtrain, training = TRUE)
     gpair <- obj(pred, dtrain)
     .Call(XGBoosterBoostOneIter_R, booster_handle, dtrain, gpair$grad, gpair$hess)
   }

R-package/R/xgb.Booster.R

@@ -288,7 +288,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' @export
 predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
                                 predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
-                                reshape = FALSE, ...) {
+                                reshape = FALSE, training = FALSE, ...) {
 
   object <- xgb.Booster.complete(object, saveraw = FALSE)
   if (!inherits(newdata, "xgb.DMatrix"))
@@ -307,7 +307,8 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) +
     8L * as.logical(approxcontrib) + 16L * as.logical(predinteraction)
 
-  ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1], as.integer(ntreelimit))
+  ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1],
+               as.integer(ntreelimit), as.integer(training))
 
   n_ret <- length(ret)
   n_row <- nrow(newdata)

R-package/src/init.c

@@ -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},

R-package/src/xgboost_R.cc

@@ -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];

R-package/src/xgboost_R.h

@@ -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

R-package/tests/testthat/test_helpers.R

@@ -166,7 +166,7 @@ test_that("SHAPs sum to predictions, with or without DART", {
       nrounds = nrounds)
 
     pr <- function(...)
-      predict(fit, newdata = d, ntreelimit = nrounds, ...)
+      predict(fit, newdata = d, ...)
     pred <- pr()
     shap <- pr(predcontrib = T)
     shapi <- pr(predinteraction = T)