[R-package] limit number of threads used in tests and examples (fixes #5987) (#5988)

R-package/R/callback.R

@@ -24,7 +24,7 @@ CB_ENV <- R6::R6Class(
 )
 
 # Format the evaluation metric string
-format.eval.string <- function(eval_res, eval_err) {
+.format_eval_string <- function(eval_res, eval_err) {
 
   # Check for empty evaluation string
   if (is.null(eval_res) || length(eval_res) == 0L) {
@@ -40,7 +40,7 @@ format.eval.string <- function(eval_res, eval_err) {
 
 }
 
-merge.eval.string <- function(env) {
+.merge_eval_string <- function(env) {
 
   # Check length of evaluation list
   if (length(env$eval_list) <= 0L) {
@@ -63,7 +63,7 @@ merge.eval.string <- function(env) {
     }
 
     # Set error message
-    msg <- c(msg, format.eval.string(eval_res = env$eval_list[[j]], eval_err = eval_err))
+    msg <- c(msg, .format_eval_string(eval_res = env$eval_list[[j]], eval_err = eval_err))
 
   }
 
@@ -86,11 +86,11 @@ cb_print_evaluation <- function(period) {
       if ((i - 1L) %% period == 0L || is.element(i, c(env$begin_iteration, env$end_iteration))) {
 
         # Merge evaluation string
-        msg <- merge.eval.string(env = env)
+        msg <- .merge_eval_string(env = env)
 
         # Check if message is existing
         if (nchar(msg) > 0L) {
-          print(merge.eval.string(env = env))
+          print(.merge_eval_string(env = env))
         }
 
       }
@@ -270,7 +270,7 @@ cb_early_stop <- function(stopping_rounds, first_metric_only, verbose) {
 
           # Prepare to print if verbose
           if (verbose) {
-            best_msg[[i]] <<- as.character(merge.eval.string(env = env))
+            best_msg[[i]] <<- as.character(.merge_eval_string(env = env))
           }
 
         } else {

R-package/R/lgb.Booster.R

@@ -928,6 +928,7 @@ NULL
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
@@ -1086,7 +1087,10 @@ predict.lgb.Booster <- function(object,
 #' X <- as.matrix(mtcars[, -1L])
 #' y <- mtcars[, 1L]
 #' dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))
-#' params <- list(min_data_in_leaf = 2L)
+#' params <- list(
+#'   min_data_in_leaf = 2L
+#'   , num_threads = 2L
+#' )
 #' model <- lgb.train(
 #'   params = params
 #'  , data = dtrain
@@ -1231,6 +1235,7 @@ summary.lgb.Booster <- function(object, ...) {
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
@@ -1296,6 +1301,7 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
@@ -1351,6 +1357,7 @@ lgb.save <- function(booster, filename, num_iteration = NULL) {
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
@@ -1401,6 +1408,7 @@ lgb.dump <- function(booster, num_iteration = NULL) {
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(

R-package/R/lgb.cv.R

@@ -59,6 +59,7 @@ CVBooster <- R6::R6Class(
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' model <- lgb.cv(
 #'   params = params

R-package/R/lgb.importance.R

@@ -24,6 +24,7 @@
 #'   , max_depth = -1L
 #'   , min_data_in_leaf = 1L
 #'   , min_sum_hessian_in_leaf = 1.0
+#'   , num_threads = 2L
 #' )
 #' model <- lgb.train(
 #'     params = params

R-package/R/lgb.interprete.R

@@ -35,6 +35,7 @@
 #'     , max_depth = -1L
 #'     , min_data_in_leaf = 1L
 #'     , min_sum_hessian_in_leaf = 1.0
+#'     , num_threads = 2L
 #' )
 #' model <- lgb.train(
 #'     params = params

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

@@ -40,6 +40,7 @@
 #'   , max_depth = -1L
 #'   , min_data_in_leaf = 1L
 #'   , min_sum_hessian_in_leaf = 1.0
+#'   , num_threads = 2L
 #' )
 #' model <- lgb.train(params, dtrain, 10L)
 #'

R-package/R/lgb.plot.importance.R

@@ -28,6 +28,7 @@
 #'     , learning_rate = 0.1
 #'     , min_data_in_leaf = 1L
 #'     , min_sum_hessian_in_leaf = 1.0
+#'     , num_threads = 2L
 #' )
 #'
 #' model <- lgb.train(

R-package/R/lgb.plot.interpretation.R

@@ -39,6 +39,7 @@
 #'   , max_depth = -1L
 #'   , min_data_in_leaf = 1L
 #'   , min_sum_hessian_in_leaf = 1.0
+#'   , num_threads = 2L
 #' )
 #' model <- lgb.train(
 #'   params = params

R-package/R/lgb.restore_handle.R

@@ -23,7 +23,9 @@
 #'   , agaricus.train$label
 #'   , params = list(objective = "binary")
 #'   , nrounds = 5L
-#'   , verbose = 0)
+#'   , verbose = 0
+#'   , num_threads = 2L
+#' )
 #' fname <- tempfile(fileext="rds")
 #' saveRDS(model, fname)
 #'

R-package/R/lgb.train.R

@@ -30,6 +30,7 @@
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(

R-package/R/readRDS.lgb.Booster.R

@@ -23,6 +23,7 @@
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(

R-package/R/saveRDS.lgb.Booster.R

@@ -33,6 +33,7 @@
 #'   , metric = "l2"
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
+#'   , num_threads = 2L
 #' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(

R-package/man/lgb.configure_fast_predict.Rd

@@ -119,7 +119,10 @@ data(mtcars)
 X <- as.matrix(mtcars[, -1L])
 y <- mtcars[, 1L]
 dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))
-params <- list(min_data_in_leaf = 2L)
+params <- list(
+  min_data_in_leaf = 2L
+  , num_threads = 2L
+)
 model <- lgb.train(
   params = params
  , data = dtrain

R-package/man/lgb.cv.Rd

@@ -160,6 +160,7 @@ params <- list(
   , metric = "l2"
   , min_data = 1L
   , learning_rate = 1.0
+  , num_threads = 2L
 )
 model <- lgb.cv(
   params = params

R-package/man/lgb.dump.Rd

@@ -31,6 +31,7 @@ params <- list(
   , metric = "l2"
   , min_data = 1L
   , learning_rate = 1.0
+  , num_threads = 2L
 )
 valids <- list(test = dtest)
 model <- lgb.train(

R-package/man/lgb.get.eval.result.Rd

@@ -45,6 +45,7 @@ params <- list(
   , metric = "l2"
   , min_data = 1L
   , learning_rate = 1.0
+  , num_threads = 2L
 )
 valids <- list(test = dtest)
 model <- lgb.train(

R-package/man/lgb.importance.Rd

@@ -35,6 +35,7 @@ params <- list(
   , max_depth = -1L
   , min_data_in_leaf = 1L
   , min_sum_hessian_in_leaf = 1.0
+  , num_threads = 2L
 )
 model <- lgb.train(
     params = params

R-package/man/lgb.interprete.Rd

@@ -48,6 +48,7 @@ params <- list(
     , max_depth = -1L
     , min_data_in_leaf = 1L
     , min_sum_hessian_in_leaf = 1.0
+    , num_threads = 2L
 )
 model <- lgb.train(
     params = params

R-package/man/lgb.load.Rd

@@ -31,6 +31,7 @@ params <- list(
   , metric = "l2"
   , min_data = 1L
   , learning_rate = 1.0
+  , num_threads = 2L
 )
 valids <- list(test = dtest)
 model <- lgb.train(

R-package/man/lgb.model.dt.tree.Rd

@@ -51,6 +51,7 @@ params <- list(
   , max_depth = -1L
   , min_data_in_leaf = 1L
   , min_sum_hessian_in_leaf = 1.0
+  , num_threads = 2L
 )
 model <- lgb.train(params, dtrain, 10L)