[R] Fix number of threads used in examples.

R-package/.Rbuildignore

@@ -4,3 +4,5 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 README.md
+^doc$
+^Meta$

R-package/R/callbacks.R

@@ -557,14 +557,18 @@ cb.cv.predict <- function(save_models = FALSE) {
 #'
 #' @examples
 #' #### Binary classification:
-#' #
+#'
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
+#'
 #' # In the iris dataset, it is hard to linearly separate Versicolor class from the rest
 #' # without considering the 2nd order interactions:
 #' x <- model.matrix(Species ~ .^2, iris)[,-1]
 #' colnames(x)
-#' dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"), nthread = 2)
+#' dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"), nthread = nthread)
 #' param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
-#' lambda = 0.0003, alpha = 0.0003, nthread = 2)
+#' lambda = 0.0003, alpha = 0.0003, nthread = nthread)
 #' # For 'shotgun', which is a default linear updater, using high eta values may result in
 #' # unstable behaviour in some datasets. With this simple dataset, however, the high learning
 #' # rate does not break the convergence, but allows us to illustrate the typical pattern of
@@ -594,9 +598,9 @@ cb.cv.predict <- function(save_models = FALSE) {
 #'
 #' #### Multiclass classification:
 #' #
-#' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1, nthread = 1)
+#' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1, nthread = nthread)
 #' param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
-#' lambda = 0.0003, alpha = 0.0003, nthread = 1)
+#' lambda = 0.0003, alpha = 0.0003, nthread = nthread)
 #' # For the default linear updater 'shotgun' it sometimes is helpful
 #' # to use smaller eta to reduce instability
 #' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 50, eta = 0.5,

R-package/R/xgb.Booster.R

@@ -267,11 +267,16 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
+#'
+#' ## Keep the number of threads to 2 for examples
+#' nthread <- 2
+#' data.table::setDTthreads(nthread)
+#'
 #' train <- agaricus.train
 #' test <- agaricus.test
 #'
 #' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#' eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic")
+#' eta = 0.5, nthread = nthread, nrounds = 5, objective = "binary:logistic")
 #' # use all trees by default
 #' pred <- predict(bst, test$data)
 #' # use only the 1st tree
@@ -337,8 +342,14 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
  reshape = FALSE, training = FALSE, iterationrange = NULL, strict_shape = FALSE, ...) {
  object <- xgb.Booster.complete(object, saveraw = FALSE)
 
- if (!inherits(newdata, "xgb.DMatrix"))
- newdata <- xgb.DMatrix(newdata, missing = missing, nthread = NVL(object$params[["nthread"]], -1))
+ if (!inherits(newdata, "xgb.DMatrix")) {
+ config <- jsonlite::fromJSON(xgb.config(object))
+ nthread <- strtoi(config$learner$generic_param$nthread)
+ newdata <- xgb.DMatrix(
+ newdata,
+ missing = missing, nthread = NVL(nthread, -1)
+ )
+ }
  if (!is.null(object[["feature_names"]]) &&
  !is.null(colnames(newdata)) &&
  !identical(object[["feature_names"]], colnames(newdata)))
@@ -628,10 +639,15 @@ xgb.attributes <- function(object) {
 #'
 #' @examples
 #' data(agaricus.train, package='xgboost')
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
 #' train <- agaricus.train
 #'
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+#' bst <- xgboost(
+#' data = train$data, label = train$label, max_depth = 2,
+#' eta = 1, nthread = nthread, nrounds = 2, objective = "binary:logistic"
+#' )
 #' config <- xgb.config(bst)
 #'
 #' @rdname xgb.config

R-package/R/xgb.DMatrix.R

@@ -18,7 +18,12 @@
 #'
 #' @examples
 #' data(agaricus.train, package='xgboost')
-#' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
+#' dtrain <- with(
+#' agaricus.train, xgb.DMatrix(data, label = label, nthread = nthread)
+#' )
 #' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
 #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
 #' if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')

R-package/R/xgb.load.R

@@ -22,14 +22,23 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
+#'
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
+#'
 #' train <- agaricus.train
 #' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
+#' bst <- xgboost(
+#' data = train$data, label = train$label, max_depth = 2, eta = 1,
+#' nthread = nthread,
+#' nrounds = 2,
+#' objective = "binary:logistic"
+#' )
+#'
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
 #' if (file.exists('xgb.model')) file.remove('xgb.model')
-#' pred <- predict(bst, test$data)
 #' @export
 xgb.load <- function(modelfile) {
  if (is.null(modelfile))

R-package/R/xgb.model.dt.tree.R

@@ -46,9 +46,12 @@
 #' # Basic use:
 #'
 #' data(agaricus.train, package='xgboost')
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
 #'
 #' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
-#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
+#' eta = 1, nthread = nthread, nrounds = 2,objective = "binary:logistic")
 #'
 #' (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
 #'

R-package/R/xgb.plot.deepness.R

@@ -45,10 +45,13 @@
 #' @examples
 #'
 #' data(agaricus.train, package='xgboost')
+#' ## Keep the number of threads to 2 for examples
+#' nthread <- 2
+#' data.table::setDTthreads(nthread)
 #'
-#' # Change max_depth to a higher number to get a more significant result
+#' ## Change max_depth to a higher number to get a more significant result
 #' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 6,
-#' eta = 0.1, nthread = 2, nrounds = 50, objective = "binary:logistic",
+#' eta = 0.1, nthread = nthread, nrounds = 50, objective = "binary:logistic",
 #' subsample = 0.5, min_child_weight = 2)
 #'
 #' xgb.plot.deepness(bst)

R-package/R/xgb.plot.importance.R

@@ -45,9 +45,14 @@
 #'
 #' @examples
 #' data(agaricus.train)
+#' ## Keep the number of threads to 2 for examples
+#' nthread <- 2
+#' data.table::setDTthreads(nthread)
 #'
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
-#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+#' bst <- xgboost(
+#' data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
+#' eta = 1, nthread = nthread, nrounds = 2, objective = "binary:logistic"
+#' )
 #'
 #' importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
 #'

R-package/R/xgb.plot.multi.trees.R

@@ -43,10 +43,15 @@
 #' @examples
 #'
 #' data(agaricus.train, package='xgboost')
-#'
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
-#' eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
-#' min_child_weight = 50, verbose = 0)
+#' ## Keep the number of threads to 2 for examples
+#' nthread <- 2
+#' data.table::setDTthreads(nthread)
+#'
+#' bst <- xgboost(
+#' data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
+#' eta = 1, nthread = nthread, nrounds = 30, objective = "binary:logistic",
+#' min_child_weight = 50, verbose = 0
+#' )
 #'
 #' p <- xgb.plot.multi.trees(model = bst, features_keep = 3)
 #' print(p)

R-package/R/xgb.plot.shap.R

@@ -74,9 +74,14 @@
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #'
-#' bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
+#' nrounds <- 20
+#'
+#' bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = nrounds,
 #' eta = 0.1, max_depth = 3, subsample = .5,
-#' method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
+#' method = "hist", objective = "binary:logistic", nthread = nthread, verbose = 0)
 #'
 #' xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
 #' contr <- predict(bst, agaricus.test$data, predcontrib = TRUE)
@@ -85,12 +90,11 @@
 #'
 #' # multiclass example - plots for each class separately:
 #' nclass <- 3
-#' nrounds <- 20
 #' x <- as.matrix(iris[, -5])
 #' set.seed(123)
 #' is.na(x[sample(nrow(x) * 4, 30)]) <- TRUE # introduce some missing values
 #' mbst <- xgboost(data = x, label = as.numeric(iris$Species) - 1, nrounds = nrounds,
-#' max_depth = 2, eta = 0.3, subsample = .5, nthread = 2,
+#' max_depth = 2, eta = 0.3, subsample = .5, nthread = nthread,
 #' objective = "multi:softprob", num_class = nclass, verbose = 0)
 #' trees0 <- seq(from=0, by=nclass, length.out=nrounds)
 #' col <- rgb(0, 0, 1, 0.5)

R-package/R/xgb.save.R

@@ -25,14 +25,22 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
+#'
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
+#'
 #' train <- agaricus.train
 #' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
+#' bst <- xgboost(
+#' data = train$data, label = train$label, max_depth = 2, eta = 1,
+#' nthread = nthread,
+#' nrounds = 2,
+#' objective = "binary:logistic"
+#' )
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
 #' if (file.exists('xgb.model')) file.remove('xgb.model')
-#' pred <- predict(bst, test$data)
 #' @export
 xgb.save <- function(model, fname) {
  if (typeof(fname) != "character")

R-package/R/xgb.save.raw.R

@@ -16,13 +16,18 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
+#'
+#' ## Keep the number of threads to 2 for examples
+#' nthread <- 2
+#' data.table::setDTthreads(nthread)
+#'
 #' train <- agaricus.train
 #' test <- agaricus.test
 #' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
-#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
+#' eta = 1, nthread = nthread, nrounds = 2,objective = "binary:logistic")
+#'
 #' raw <- xgb.save.raw(bst)
 #' bst <- xgb.load.raw(raw)
-#' pred <- predict(bst, test$data)
 #'
 #' @export
 xgb.save.raw <- function(model, raw_format = "deprecated") {

R-package/R/xgb.train.R

@@ -168,7 +168,8 @@
 #' than the \code{xgboost} interface.
 #'
 #' Parallelization is automatically enabled if \code{OpenMP} is present.
-#' Number of threads can also be manually specified via \code{nthread} parameter.
+#' Number of threads can also be manually specified via the \code{nthread}
+#' parameter.
 #'
 #' The evaluation metric is chosen automatically by XGBoost (according to the objective)
 #' when the \code{eval_metric} parameter is not provided.
@@ -237,17 +238,25 @@
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #'
-#' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
-#' dtest <- with(agaricus.test, xgb.DMatrix(data, label = label, nthread = 2))
+#' ## Keep the number of threads to 1 for examples
+#' nthread <- 1
+#' data.table::setDTthreads(nthread)
+#'
+#' dtrain <- with(
+#' agaricus.train, xgb.DMatrix(data, label = label, nthread = nthread)
+#' )
+#' dtest <- with(
+#' agaricus.test, xgb.DMatrix(data, label = label, nthread = nthread)
+#' )
 #' watchlist <- list(train = dtrain, eval = dtest)
 #'
 #' ## A simple xgb.train example:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
 #' objective = "binary:logistic", eval_metric = "auc")
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
 #'
-#'
-#' ## An xgb.train example where custom objective and evaluation metric are used:
+#' ## An xgb.train example where custom objective and evaluation metric are
+#' ## used:
 #' logregobj <- function(preds, dtrain) {
 #' labels <- getinfo(dtrain, "label")
 #' preds <- 1/(1 + exp(-preds))
@@ -263,12 +272,12 @@
 #'
 #' # These functions could be used by passing them either:
 #' # as 'objective' and 'eval_metric' parameters in the params list:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
 #' objective = logregobj, eval_metric = evalerror)
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
 #'
 #' # or through the ... arguments:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2)
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread)
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 #' objective = logregobj, eval_metric = evalerror)
 #'
@@ -278,7 +287,7 @@
 #'
 #'
 #' ## An xgb.train example of using variable learning rates at each iteration:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
 #' objective = "binary:logistic", eval_metric = "auc")
 #' my_etas <- list(eta = c(0.5, 0.1))
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
@@ -290,7 +299,7 @@
 #'
 #' ## An 'xgboost' interface example:
 #' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
-#' max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
+#' max_depth = 2, eta = 1, nthread = nthread, nrounds = 2,
 #' objective = "binary:logistic")
 #' pred <- predict(bst, agaricus.test$data)
 #'