Callback, vec.

R-package/R/callbacks.R

@@ -498,13 +498,12 @@ cb.cv.predict <- function(save_models = FALSE) {
         rep(NA_real_, N)
       }
 
-    ntreelimit <- NVL(env$basket$best_ntreelimit,
-                      env$end_iteration * env$num_parallel_tree)
+    iterationrange <- NVL(env$basket$best_iteration, env$end_iteration)
     if (NVL(env$params[['booster']], '') == 'gblinear') {
-      ntreelimit <- 0 # must be 0 for gblinear
+      iterationrange <- c(0, 0)  # must be 0 for gblinear
     }
     for (fd in env$bst_folds) {
-      pr <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit, reshape = TRUE)
+      pr <- predict(fd$bst, fd$watchlist[[2]], iterationrange = iterationrange, reshape = TRUE)
       if (is.matrix(pred)) {
         pred[fd$index, ] <- pr
       } else {

R-package/R/xgb.Booster.R

@@ -345,21 +345,25 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
     ntreelimit <- 0
   if (ntreelimit != 0 && is.null(iterationrange)) {
     ## only ntreelimit, initialize iteration range
-    iterationrange = list(begin = 0, end = 0)
+    iterationrange = c(0, 0)
   } else if (ntreelimit == 0 && !is.null(iterationrange)) {
     ## only iteration range, do nothing
   } else if (ntreelimit != 0 && !is.null(iterationrange)) {
     ## both are specified, let libgxgboost throw an error
   } else {
     ## no limit is supplied, use best
-    iterationrange = list(begin = 0, end = NVL(object$best_iteration, 0))
+    if (is.null(object$best_iteration)) {
+      iterationrange = c(0, 0)
+    } else {
+      iterationrange = c(0, object$best_iteration + 1)
+    }
   }
 
   args <- list(
     training = training,
     strict_shape = FALSE,
-    iteration_begin = iterationrange$begin,
-    iteration_end = iterationrange$end,
+    iteration_begin = iterationrange[0],
+    iteration_end = iterationrange[1],
     ntree_limit = ntreelimit,
     type = 0
   )