Merge branch 'master' into upgrade_cuda124

.gitignore

@@ -84,6 +84,7 @@ target
 *.gcov
 *.gcda
 *.gcno
+*.ubj
 build_tests
 /tests/cpp/xgboost_test
 

R-package/R/utils.R

@@ -199,8 +199,7 @@ xgb.iter.update <- function(bst, dtrain, iter, obj) {
       bst,
       dtrain,
       outputmargin = TRUE,
-      training = TRUE,
-      reshape = TRUE
+      training = TRUE
     )
     gpair <- obj(pred, dtrain)
     n_samples <- dim(dtrain)[1]
@@ -246,7 +245,7 @@ xgb.iter.eval <- function(bst, evals, iter, feval) {
     res <- sapply(seq_along(evals), function(j) {
       w <- evals[[j]]
       ## predict using all trees
-      preds <- predict(bst, w, outputmargin = TRUE, reshape = TRUE, iterationrange = "all")
+      preds <- predict(bst, w, outputmargin = TRUE, iterationrange = "all")
       eval_res <- feval(preds, w)
       out <- eval_res$value
       names(out) <- paste0(evnames[j], "-", eval_res$metric)
@@ -411,7 +410,7 @@ xgb.createFolds <- function(y, k) {
 #' At this time, some of the parameter names were changed in order to make the code style more uniform.
 #' The deprecated parameters would be removed in the next release.
 #'
-#' To see all the current deprecated and new parameters, check the \code{xgboost:::depr_par_lut} table.
+#' To see all the current deprecated and new parameters, check the `xgboost:::depr_par_lut` table.
 #'
 #' A deprecation warning is shown when any of the deprecated parameters is used in a call.
 #' An additional warning is shown when there was a partial match to a deprecated parameter
@@ -420,70 +419,79 @@ xgb.createFolds <- function(y, k) {
 #' @name xgboost-deprecated
 NULL
 
-#' @title Model Serialization and Compatibility
-#' @description
+#' Model Serialization and Compatibility
 #'
+#' @description
 #' When it comes to serializing XGBoost models, it's possible to use R serializers such as
-#' \link{save} or \link{saveRDS} to serialize an XGBoost R model, but XGBoost also provides
+#' [save()] or [saveRDS()] to serialize an XGBoost R model, but XGBoost also provides
 #' its own serializers with better compatibility guarantees, which allow loading
 #' said models in other language bindings of XGBoost.
 #'
-#' Note that an `xgb.Booster` object, outside of its core components, might also keep:\itemize{
-#' \item Additional model configuration (accessible through \link{xgb.config}),
-#' which includes model fitting parameters like `max_depth` and runtime parameters like `nthread`.
-#' These are not necessarily useful for prediction/importance/plotting.
-#' \item Additional R-specific attributes  - e.g. results of callbacks, such as evaluation logs,
-#' which are kept as a `data.table` object, accessible through `attributes(model)$evaluation_log`
-#' if present.
-#' }
+#' Note that an `xgb.Booster` object, outside of its core components, might also keep:
+#' - Additional model configuration (accessible through [xgb.config()]), which includes
+#'   model fitting parameters like `max_depth` and runtime parameters like `nthread`.
+#'   These are not necessarily useful for prediction/importance/plotting.
+#' - Additional R specific attributes  - e.g. results of callbacks, such as evaluation logs,
+#'   which are kept as a `data.table` object, accessible through
+#'   `attributes(model)$evaluation_log` if present.
 #'
 #' The first one (configurations) does not have the same compatibility guarantees as
-#' the model itself, including attributes that are set and accessed through \link{xgb.attributes} - that is, such configuration
-#' might be lost after loading the booster in a different XGBoost version, regardless of the
-#' serializer that was used. These are saved when using \link{saveRDS}, but will be discarded
-#' if loaded into an incompatible XGBoost version. They are not saved when using XGBoost's
-#' serializers from its public interface including \link{xgb.save} and \link{xgb.save.raw}.
+#' the model itself, including attributes that are set and accessed through
+#' [xgb.attributes()] - that is, such configuration might be lost after loading the
+#' booster in a different XGBoost version, regardless of the serializer that was used.
+#' These are saved when using [saveRDS()], but will be discarded if loaded into an
+#' incompatible XGBoost version. They are not saved when using XGBoost's
+#' serializers from its public interface including [xgb.save()] and [xgb.save.raw()].
 #'
-#' The second ones (R attributes) are not part of the standard XGBoost model structure, and thus are
-#' not saved when using XGBoost's own serializers. These attributes are only used for informational
-#' purposes, such as keeping track of evaluation metrics as the model was fit, or saving the R
-#' call that produced the model, but are otherwise not used for prediction / importance / plotting / etc.
+#' The second ones (R attributes) are not part of the standard XGBoost model structure,
+#' and thus are not saved when using XGBoost's own serializers. These attributes are
+#' only used for informational purposes, such as keeping track of evaluation metrics as
+#' the model was fit, or saving the R call that produced the model, but are otherwise
+#' not used for prediction / importance / plotting / etc.
 #' These R attributes are only preserved when using R's serializers.
 #'
-#' Note that XGBoost models in R starting from version `2.1.0` and onwards, and XGBoost models
-#' before version `2.1.0`; have a very different R object structure and are incompatible with
-#' each other. Hence, models that were saved with R serializers live `saveRDS` or `save` before
-#' version `2.1.0` will not work with latter `xgboost` versions and vice versa. Be aware that
-#' the structure of R model objects could in theory change again in the future, so XGBoost's serializers
+#' Note that XGBoost models in R starting from version `2.1.0` and onwards, and
+#' XGBoost models before version `2.1.0`; have a very different R object structure and
+#' are incompatible with each other. Hence, models that were saved with R serializers
+#' like [saveRDS()] or [save()] before version `2.1.0` will not work with latter
+#' `xgboost` versions and vice versa. Be aware that the structure of R model objects
+#' could in theory change again in the future, so XGBoost's serializers
 #' should be preferred for long-term storage.
 #'
-#' Furthermore, note that using the package `qs` for serialization will require version 0.26 or
-#' higher of said package, and will have the same compatibility restrictions as R serializers.
+#' Furthermore, note that using the package `qs` for serialization will require
+#' version 0.26 or higher of said package, and will have the same compatibility
+#' restrictions as R serializers.
 #'
 #' @details
-#' Use \code{\link{xgb.save}} to save the XGBoost model as a stand-alone file. You may opt into
+#' Use [xgb.save()] to save the XGBoost model as a stand-alone file. You may opt into
 #' the JSON format by specifying the JSON extension. To read the model back, use
-#' \code{\link{xgb.load}}.
+#' [xgb.load()].
 #'
-#' Use \code{\link{xgb.save.raw}} to save the XGBoost model as a sequence (vector) of raw bytes
+#' Use [xgb.save.raw()] to save the XGBoost model as a sequence (vector) of raw bytes
 #' in a future-proof manner. Future releases of XGBoost will be able to read the raw bytes and
-#' re-construct the corresponding model. To read the model back, use \code{\link{xgb.load.raw}}.
-#' The \code{\link{xgb.save.raw}} function is useful if you'd like to persist the XGBoost model
+#' re-construct the corresponding model. To read the model back, use [xgb.load.raw()].
+#' The [xgb.save.raw()] function is useful if you would like to persist the XGBoost model
 #' as part of another R object.
 #'
-#' Use \link{saveRDS} if you require the R-specific attributes that a booster might have, such
+#' Use [saveRDS()] if you require the R-specific attributes that a booster might have, such
 #' as evaluation logs, but note that future compatibility of such objects is outside XGBoost's
 #' control as it relies on R's serialization format (see e.g. the details section in
-#' \link{serialize} and \link{save} from base R).
+#' [serialize] and [save()] from base R).
 #'
 #' For more details and explanation about model persistence and archival, consult the page
 #' \url{https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html}.
 #'
 #' @examples
-#' data(agaricus.train, package='xgboost')
-#' bst <- xgb.train(data = xgb.DMatrix(agaricus.train$data, label = agaricus.train$label),
-#'                  max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
-#'                  objective = "binary:logistic")
+#' data(agaricus.train, package = "xgboost")
+#'
+#' bst <- xgb.train(
+#'   data = xgb.DMatrix(agaricus.train$data, label = agaricus.train$label),
+#'   max_depth = 2,
+#'   eta = 1,
+#'   nthread = 2,
+#'   nrounds = 2,
+#'   objective = "binary:logistic"
+#' )
 #'
 #' # Save as a stand-alone file; load it with xgb.load()
 #' fname <- file.path(tempdir(), "xgb_model.ubj")