[R-package] [docs] Simplified examples to cut example run time (fixes #2988) (#2989)

* [R-package] [docs] Simplified examles to cut example run time (fixes #2988)

* updated learning rates

R-package/R/lgb.Booster.R

@@ -711,7 +711,6 @@ Booster <- R6::R6Class(
 #'         number of columns corresponding to the number of trees.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -723,11 +722,10 @@ Booster <- R6::R6Class(
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
-#'   , nrounds = 10L
+#'   , nrounds = 5L
 #'   , valids = valids
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
-#'   , early_stopping_rounds = 5L
 #' )
 #' preds <- predict(model, test$data)
 #' @export
@@ -769,7 +767,7 @@ predict.lgb.Booster <- function(object,
 #' @return lgb.Booster
 #'
 #' @examples
-#' library(lightgbm)
+#' \donttest{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -781,17 +779,17 @@ predict.lgb.Booster <- function(object,
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
-#'   , nrounds = 10L
+#'   , nrounds = 5L
 #'   , valids = valids
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
-#'   , early_stopping_rounds = 5L
+#'   , early_stopping_rounds = 3L
 #' )
 #' lgb.save(model, "model.txt")
 #' load_booster <- lgb.load(filename = "model.txt")
 #' model_string <- model$save_model_to_string(NULL) # saves best iteration
 #' load_booster_from_str <- lgb.load(model_str = model_string)
-#'
+#' }
 #' @export
 lgb.load <- function(filename = NULL, model_str = NULL) {
 
@@ -828,6 +826,7 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
 #' @return lgb.Booster
 #'
 #' @examples
+#' \donttest{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -847,6 +846,7 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
 #'   , early_stopping_rounds = 5L
 #' )
 #' lgb.save(model, "model.txt")
+#' }
 #' @export
 lgb.save <- function(booster, filename, num_iteration = NULL) {
 
@@ -874,6 +874,7 @@ lgb.save <- function(booster, filename, num_iteration = NULL) {
 #' @return json format of model
 #'
 #' @examples
+#' \donttest{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -893,7 +894,7 @@ lgb.save <- function(booster, filename, num_iteration = NULL) {
 #'   , early_stopping_rounds = 5L
 #' )
 #' json_model <- lgb.dump(model)
-#'
+#' }
 #' @export
 lgb.dump <- function(booster, num_iteration = NULL) {
 
@@ -922,7 +923,6 @@ lgb.dump <- function(booster, num_iteration = NULL) {
 #'
 #' @examples
 #' # train a regression model
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -934,11 +934,10 @@ lgb.dump <- function(booster, num_iteration = NULL) {
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
-#'   , nrounds = 10L
+#'   , nrounds = 5L
 #'   , valids = valids
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
-#'   , early_stopping_rounds = 5L
 #' )
 #'
 #' # Examine valid data_name values

R-package/R/lgb.Dataset.R

@@ -725,7 +725,6 @@ Dataset <- R6::R6Class(
 #' @return constructed dataset
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -770,7 +769,6 @@ lgb.Dataset <- function(data,
 #' @return constructed dataset
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -797,7 +795,6 @@ lgb.Dataset.create.valid <- function(dataset, data, info = list(), ...) {
 #' @param dataset Object of class \code{lgb.Dataset}
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -828,7 +825,6 @@ lgb.Dataset.construct <- function(dataset) {
 #' be directly used with an \code{lgb.Dataset} object.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -863,7 +859,6 @@ dim.lgb.Dataset <- function(x, ...) {
 #' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -936,7 +931,6 @@ dimnames.lgb.Dataset <- function(x) {
 #' @return constructed sub dataset
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -983,7 +977,6 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
 #' }
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -1037,7 +1030,6 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
 #' }
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -1078,7 +1070,6 @@ setinfo.lgb.Dataset <- function(dataset, name, info, ...) {
 #' @return passed dataset
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -1109,7 +1100,6 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
 #' @return passed dataset
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package ="lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -1141,7 +1131,6 @@ lgb.Dataset.set.reference <- function(dataset, reference) {
 #' @return passed dataset
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/R/lgb.cv.R

@@ -56,7 +56,6 @@ CVBooster <- R6::R6Class(
 #' @return a trained model \code{lgb.CVBooster}.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -64,11 +63,10 @@ CVBooster <- R6::R6Class(
 #' model <- lgb.cv(
 #'   params = params
 #'   , data = dtrain
-#'   , nrounds = 10L
+#'   , nrounds = 5L
 #'   , nfold = 3L
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
-#'   , early_stopping_rounds = 5L
 #' )
 #' @importFrom data.table data.table setorderv
 #' @export

R-package/R/lgb.importance.R

@@ -13,20 +13,22 @@
 #' }
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #'
 #' params <- list(
 #'   objective = "binary"
-#'   , learning_rate = 0.01
-#'   , num_leaves = 63L
+#'   , learning_rate = 0.1
 #'   , max_depth = -1L
 #'   , min_data_in_leaf = 1L
 #'   , min_sum_hessian_in_leaf = 1.0
 #' )
-#' model <- lgb.train(params, dtrain, 10L)
+#' model <- lgb.train(
+#'     params = params
+#'     , data = dtrain
+#'     , nrounds = 5L
+#' )
 #'
 #' tree_imp1 <- lgb.importance(model, percentage = TRUE)
 #' tree_imp2 <- lgb.importance(model, percentage = FALSE)

R-package/R/lgb.interprete.R

@@ -16,7 +16,6 @@
 #'         Contribution columns to each class.
 #'
 #' @examples
-#' Sigmoid <- function(x) 1.0 / (1.0 + exp(-x))
 #' Logit <- function(x) log(x / (1.0 - x))
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -27,13 +26,16 @@
 #'
 #' params <- list(
 #'     objective = "binary"
-#'     , learning_rate = 0.01
-#'     , num_leaves = 63L
+#'     , learning_rate = 0.1
 #'     , max_depth = -1L
 #'     , min_data_in_leaf = 1L
 #'     , min_sum_hessian_in_leaf = 1.0
 #' )
-#' model <- lgb.train(params, dtrain, 10L)
+#' model <- lgb.train(
+#'     params = params
+#'     , data = dtrain
+#'     , nrounds = 3L
+#' )
 #'
 #' tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
 #'

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

@@ -24,17 +24,19 @@
 #'
 #' params <- list(
 #'     objective = "binary"
-#'     , learning_rate = 0.01
-#'     , num_leaves = 63L
-#'     , max_depth = -1L
+#'     , learning_rate = 0.1
 #'     , min_data_in_leaf = 1L
 #'     , min_sum_hessian_in_leaf = 1.0
 #' )
 #'
-#' model <- lgb.train(params, dtrain, 10L)
+#' model <- lgb.train(
+#'     params = params
+#'     , data = dtrain
+#'     , nrounds = 5L
+#' )
 #'
 #' tree_imp <- lgb.importance(model, percentage = TRUE)
-#' lgb.plot.importance(tree_imp, top_n = 10L, measure = "Gain")
+#' lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
 #' @importFrom graphics barplot par
 #' @export
 lgb.plot.importance <- function(tree_imp,

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

@@ -15,28 +15,43 @@
 #' The \code{lgb.plot.interpretation} function creates a \code{barplot}.
 #'
 #' @examples
-#' library(lightgbm)
-#' Sigmoid <- function(x) {1.0 / (1.0 + exp(-x))}
-#' Logit <- function(x) {log(x / (1.0 - x))}
+#' \donttest{
+#' Logit <- function(x) {
+#'   log(x / (1.0 - x))
+#' }
 #' data(agaricus.train, package = "lightgbm")
-#' train <- agaricus.train
-#' dtrain <- lgb.Dataset(train$data, label = train$label)
-#' setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
+#' labels <- agaricus.train$label
+#' dtrain <- lgb.Dataset(
+#'   agaricus.train$data
+#'   , label = labels
+#' )
+#' setinfo(dtrain, "init_score", rep(Logit(mean(labels)), length(labels)))
+#'
 #' data(agaricus.test, package = "lightgbm")
-#' test <- agaricus.test
 #'
 #' params <- list(
 #'   objective = "binary"
-#'   , learning_rate = 0.01
-#'   , num_leaves = 63L
+#'   , learning_rate = 0.1
 #'   , max_depth = -1L
 #'   , min_data_in_leaf = 1L
 #'   , min_sum_hessian_in_leaf = 1.0
 #' )
-#' model <- lgb.train(params, dtrain, 10L)
+#' model <- lgb.train(
+#'   params = params
+#'   , data = dtrain
+#'   , nrounds = 5L
+#' )
 #'
-#' tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
-#' lgb.plot.interpretation(tree_interpretation[[1L]], top_n = 10L)
+#' tree_interpretation <- lgb.interprete(
+#'   model = model
+#'   , data = agaricus.test$data
+#'   , idxset = 1L:5L
+#' )
+#' lgb.plot.interpretation(
+#'   tree_interpretation_dt = tree_interpretation[[1L]]
+#'   , top_n = 5L
+#' )
+#' }
 #' @importFrom data.table setnames
 #' @importFrom graphics barplot par
 #' @export

R-package/R/lgb.prepare.R

@@ -8,7 +8,6 @@
 #'         for input in \code{lgb.Dataset}.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)

R-package/R/lgb.prepare2.R

@@ -11,7 +11,6 @@
 #'         for input in \code{lgb.Dataset}.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)

R-package/R/lgb.prepare_rules.R

@@ -10,7 +10,6 @@
 #'         in \code{lgb.Dataset}.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)

R-package/R/lgb.prepare_rules2.R

@@ -13,7 +13,6 @@
 #'         \code{lgb.Dataset}.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)

R-package/R/lgb.train.R

@@ -29,7 +29,6 @@
 #' @return a trained booster model \code{lgb.Booster}.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -41,11 +40,11 @@
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
-#'   , nrounds = 10L
+#'   , nrounds = 5L
 #'   , valids = valids
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
-#'   , early_stopping_rounds = 5L
+#'   , early_stopping_rounds = 3L
 #' )
 #' @export
 lgb.train <- function(params = list(),

R-package/R/lgb.unloader.R

@@ -14,7 +14,6 @@
 #' @return NULL invisibly.
 #'
 #' @examples
-#' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -26,11 +25,10 @@
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
-#'   , nrounds = 10L
+#'   , nrounds = 5L
 #'   , valids = valids
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
-#'   , early_stopping_rounds = 5L
 #' )
 #'
 #' \dontrun{

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

@@ -7,6 +7,7 @@
 #' @return \code{lgb.Booster}.
 #'
 #' @examples
+#' \donttest{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -27,7 +28,7 @@
 #' )
 #' saveRDS.lgb.Booster(model, "model.rds")
 #' new_model <- readRDS.lgb.Booster("model.rds")
-#'
+#' }
 #' @export
 readRDS.lgb.Booster <- function(file = "", refhook = NULL) {
 

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

@@ -18,6 +18,7 @@
 #' @return NULL invisibly.
 #'
 #' @examples
+#' \donttest{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -37,6 +38,7 @@
 #'     , early_stopping_rounds = 5L
 #' )
 #' saveRDS.lgb.Booster(model, "model.rds")
+#' }
 #' @export
 saveRDS.lgb.Booster <- function(object,
                                 file = "",

R-package/man/dim.Rd

@@ -22,7 +22,6 @@ Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
 be directly used with an \code{lgb.Dataset} object.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/dimnames.lgb.Dataset.Rd

@@ -24,7 +24,6 @@ Generic \code{dimnames} methods are used by \code{colnames}.
 Since row names are irrelevant, it is recommended to use \code{colnames} directly.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/getinfo.Rd

@@ -33,7 +33,6 @@ The \code{name} field can be one of the following:
 }
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.Dataset.Rd

@@ -40,7 +40,6 @@ Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
              or local file (that was created previously by saving an \code{lgb.Dataset}).
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.Dataset.construct.Rd

@@ -13,7 +13,6 @@ lgb.Dataset.construct(dataset)
 Construct Dataset explicitly
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.Dataset.create.valid.Rd

@@ -22,7 +22,6 @@ constructed dataset
 Construct validation data according to training data
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.Dataset.save.Rd

@@ -19,7 +19,6 @@ Please note that \code{init_score} is not saved in binary file.
              If you need it, please set it again after loading Dataset.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.Dataset.set.categorical.Rd

@@ -21,7 +21,6 @@ Set the categorical features of an \code{lgb.Dataset} object. Use this function
              to tell LightGBM which features should be treated as categorical.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.Dataset.set.reference.Rd

@@ -18,7 +18,6 @@ passed dataset
 If you want to use validation data, you should set reference to training data
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package ="lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.cv.Rd

@@ -100,7 +100,6 @@ a trained model \code{lgb.CVBooster}.
 Cross validation logic used by LightGBM
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -108,10 +107,9 @@ params <- list(objective = "regression", metric = "l2")
 model <- lgb.cv(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , nfold = 3L
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
 )
 }

R-package/man/lgb.dump.Rd

@@ -18,6 +18,7 @@ json format of model
 Dump LightGBM model to json
 }
 \examples{
+\donttest{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -37,5 +38,5 @@ model <- lgb.train(
   , early_stopping_rounds = 5L
 )
 json_model <- lgb.dump(model)
-
+}
 }

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

@@ -33,7 +33,6 @@ Given a \code{lgb.Booster}, return evaluation results for a
 }
 \examples{
 # train a regression model
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -45,11 +44,10 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
 )
 
 # Examine valid data_name values

R-package/man/lgb.importance.Rd

@@ -24,20 +24,22 @@ For a tree model, a \code{data.table} with the following columns:
 Creates a \code{data.table} of feature importances in a model.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 
 params <- list(
   objective = "binary"
-  , learning_rate = 0.01
-  , num_leaves = 63L
+  , learning_rate = 0.1
   , max_depth = -1L
   , min_data_in_leaf = 1L
   , min_sum_hessian_in_leaf = 1.0
 )
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = 5L
+)
 
 tree_imp1 <- lgb.importance(model, percentage = TRUE)
 tree_imp2 <- lgb.importance(model, percentage = FALSE)

R-package/man/lgb.interprete.Rd

@@ -29,7 +29,6 @@ For regression, binary classification and lambdarank model, a \code{list} of \co
 Computes feature contribution components of rawscore prediction.
 }
 \examples{
-Sigmoid <- function(x) 1.0 / (1.0 + exp(-x))
 Logit <- function(x) log(x / (1.0 - x))
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -40,13 +39,16 @@ test <- agaricus.test
 
 params <- list(
     objective = "binary"
-    , learning_rate = 0.01
-    , num_leaves = 63L
+    , learning_rate = 0.1
     , max_depth = -1L
     , min_data_in_leaf = 1L
     , min_sum_hessian_in_leaf = 1.0
 )
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = 3L
+)
 
 tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
 

R-package/man/lgb.load.Rd

@@ -19,7 +19,7 @@ Load LightGBM takes in either a file path or model string.
               If both are provided, Load will default to loading from file
 }
 \examples{
-library(lightgbm)
+\donttest{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -31,15 +31,15 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
+  , early_stopping_rounds = 3L
 )
 lgb.save(model, "model.txt")
 load_booster <- lgb.load(filename = "model.txt")
 model_string <- model$save_model_to_string(NULL) # saves best iteration
 load_booster_from_str <- lgb.load(model_str = model_string)
-
+}
 }

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

@@ -43,15 +43,17 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 
 params <- list(
     objective = "binary"
-    , learning_rate = 0.01
-    , num_leaves = 63L
-    , max_depth = -1L
+    , learning_rate = 0.1
     , min_data_in_leaf = 1L
     , min_sum_hessian_in_leaf = 1.0
 )
 
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = 5L
+)
 
 tree_imp <- lgb.importance(model, percentage = TRUE)
-lgb.plot.importance(tree_imp, top_n = 10L, measure = "Gain")
+lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
 }

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

@@ -34,26 +34,41 @@ The graph represents each feature as a horizontal bar of length proportional to
 contribution of a feature. Features are shown ranked in a decreasing contribution order.
 }
 \examples{
-library(lightgbm)
-Sigmoid <- function(x) {1.0 / (1.0 + exp(-x))}
-Logit <- function(x) {log(x / (1.0 - x))}
+\donttest{
+Logit <- function(x) {
+  log(x / (1.0 - x))
+}
 data(agaricus.train, package = "lightgbm")
-train <- agaricus.train
-dtrain <- lgb.Dataset(train$data, label = train$label)
-setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
+labels <- agaricus.train$label
+dtrain <- lgb.Dataset(
+  agaricus.train$data
+  , label = labels
+)
+setinfo(dtrain, "init_score", rep(Logit(mean(labels)), length(labels)))
+
 data(agaricus.test, package = "lightgbm")
-test <- agaricus.test
 
 params <- list(
   objective = "binary"
-  , learning_rate = 0.01
-  , num_leaves = 63L
+  , learning_rate = 0.1
   , max_depth = -1L
   , min_data_in_leaf = 1L
   , min_sum_hessian_in_leaf = 1.0
 )
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 5L
+)
 
-tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
-lgb.plot.interpretation(tree_interpretation[[1L]], top_n = 10L)
+tree_interpretation <- lgb.interprete(
+  model = model
+  , data = agaricus.test$data
+  , idxset = 1L:5L
+)
+lgb.plot.interpretation(
+  tree_interpretation_dt = tree_interpretation[[1L]]
+  , top_n = 5L
+)
+}
 }

R-package/man/lgb.prepare.Rd

@@ -19,7 +19,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              \code{\link{lgb.prepare_rules}} if you want to apply this transformation to other datasets.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.prepare2.Rd

@@ -22,7 +22,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.prepare_rules.Rd

@@ -22,7 +22,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              so you can convert other datasets using this converter.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.prepare_rules2.Rd

@@ -25,7 +25,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              Consider this as a half memory technique which is dangerous, especially for LightGBM.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.save.Rd

@@ -20,6 +20,7 @@ lgb.Booster
 Save LightGBM model
 }
 \examples{
+\donttest{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -40,3 +41,4 @@ model <- lgb.train(
 )
 lgb.save(model, "model.txt")
 }
+}

R-package/man/lgb.train.Rd

@@ -83,7 +83,6 @@ a trained booster model \code{lgb.Booster}.
 Logic to train with LightGBM
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -95,10 +94,10 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
+  , early_stopping_rounds = 3L
 )
 }

R-package/man/lgb.unloader.Rd

@@ -26,7 +26,6 @@ Attempts to unload LightGBM packages so you can remove objects cleanly without
              apparent reason and you do not want to restart R to fix the lost object.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -38,11 +37,10 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
 )
 
 \dontrun{

R-package/man/lgb_shared_params.Rd

@@ -4,7 +4,8 @@
 \alias{lgb_shared_params}
 \title{Shared parameter docs}
 \arguments{
-\item{callbacks}{List of callback functions that are applied at each iteration.}
+\item{callbacks}{list of callback functions
+List of callback functions that are applied at each iteration.}
 
 \item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}},
 may allow you to pass other types of data like \code{matrix} and then separately supply

R-package/man/lightgbm.Rd

@@ -45,7 +45,8 @@ If early stopping occurs, the model will have 'best_iter' field.}
 
 \item{init_model}{path of model file of \code{lgb.Booster} object, will continue training from this model}
 
-\item{callbacks}{List of callback functions that are applied at each iteration.}
+\item{callbacks}{list of callback functions
+List of callback functions that are applied at each iteration.}
 
 \item{...}{Additional arguments passed to \code{\link{lgb.train}}. For example
 \itemize{

R-package/man/predict.lgb.Booster.Rd

@@ -52,7 +52,6 @@ For regression or binary classification, it returns a vector of length \code{nro
 Predicted values based on class \code{lgb.Booster}
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -64,11 +63,10 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
 )
 preds <- predict(model, test$data)
 }

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

@@ -18,6 +18,7 @@ readRDS.lgb.Booster(file = "", refhook = NULL)
 Attempts to load a model stored in a \code{.rds} file, using \code{\link[base]{readRDS}}
 }
 \examples{
+\donttest{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -38,5 +39,5 @@ model <- lgb.train(
 )
 saveRDS.lgb.Booster(model, "model.rds")
 new_model <- readRDS.lgb.Booster("model.rds")
-
+}
 }

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

@@ -42,6 +42,7 @@ Attempts to save a model using RDS. Has an additional parameter (\code{raw})
              which decides whether to save the raw model or not.
 }
 \examples{
+\donttest{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -62,3 +63,4 @@ model <- lgb.train(
 )
 saveRDS.lgb.Booster(model, "model.rds")
 }
+}

R-package/man/setinfo.Rd

@@ -38,7 +38,6 @@ The \code{name} field can be one of the following:
 }
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/slice.Rd

@@ -24,7 +24,6 @@ Get a new \code{lgb.Dataset} containing the specified rows of
              original \code{lgb.Dataset} object
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)