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[R] Fix CRAN test notes. (#8428)

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- Limit the number of used CPU cores in examples.
- Add a note for the constraint.
- Bring back the cleanup script.
This commit is contained in:
Jiaming Yuan
2022-11-09 02:03:30 +08:00
committed by GitHub
parent 8e76f5f595
commit 0b36f8fba1
22 changed files with 81 additions and 49 deletions
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12
R-package/R/callbacks.R
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@@ -544,9 +544,11 @@ cb.cv.predict <- function(save_models = FALSE) {
#'
#' @return
#' Results are stored in the \code{coefs} element of the closure.
#' The \code{\link{xgb.gblinear.history}} convenience function provides an easy way to access it.
#' The \code{\link{xgb.gblinear.history}} convenience function provides an easy
#' way to access it.
#' With \code{xgb.train}, it is either a dense of a sparse matrix.
#' While with \code{xgb.cv}, it is a list (an element per each fold) of such matrices.
#' While with \code{xgb.cv}, it is a list (an element per each fold) of such
#' matrices.
#'
#' @seealso
#' \code{\link{callbacks}}, \code{\link{xgb.gblinear.history}}.
@@ -558,7 +560,7 @@ cb.cv.predict <- function(save_models = FALSE) {
#' # without considering the 2nd order interactions:
#' x <- model.matrix(Species ~ .^2, iris)[,-1]
#' colnames(x)
#' dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
#' dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"), nthread = 2)
#' param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
#' lambda = 0.0003, alpha = 0.0003, nthread = 2)
#' # For 'shotgun', which is a default linear updater, using high eta values may result in
@@ -583,14 +585,14 @@ cb.cv.predict <- function(save_models = FALSE) {
#'
#' # For xgb.cv:
#' bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
#' callbacks = list(cb.gblinear.history()))
#' callbacks = list(cb.gblinear.history()))
#' # coefficients in the CV fold #3
#' matplot(xgb.gblinear.history(bst)[[3]], type = 'l')
#'
#'
#' #### Multiclass classification:
#' #
#' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
#' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1, nthread = 2)
#' param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
#' lambda = 0.0003, alpha = 0.0003, nthread = 2)
#' # For the default linear updater 'shotgun' it sometimes is helpful