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[R-package] move all examples to dontrun() to fix R CMD CHECK notes #3270

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merged 9 commits into from
Aug 6, 2020
Merged

[R-package] move all examples to dontrun() to fix R CMD CHECK notes #3270

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ba1f02d
Move all examples to dontrun
jameslamb Aug 4, 2020
71aec40
update docs
jameslamb Aug 4, 2020
fc53184
fix nested dontrun
jameslamb Aug 4, 2020
b3ffb1c
remove :: in examples
jameslamb Aug 4, 2020
7feb4c2
Merge branch 'master' into docs/fix-sample-runtime
jameslamb Aug 5, 2020
249f010
run_dontrun in pkgdown
jameslamb Aug 5, 2020
4d38cde
Merge branch 'master' into docs/fix-sample-runtime
StrikerRUS Aug 5, 2020
74daf5f
Merge branch 'master' into docs/fix-sample-runtime
jameslamb Aug 6, 2020
da860c8
Merge branch 'docs/fix-sample-runtime' of github.com:microsoft/LightG…
jameslamb Aug 6, 2020
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1 change: 1 addition & 0 deletions .ci/test_r_package.sh
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Expand Up @@ -173,6 +173,7 @@ check_succeeded="yes"
(
R CMD check ${PKG_TARBALL} \
--as-cran \
--run-dontrun \
|| check_succeeded="no"
) &

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2 changes: 1 addition & 1 deletion .ci/test_r_package_windows.ps1
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Expand Up @@ -160,7 +160,7 @@ if ($env:COMPILER -ne "MSVC") {
}

Write-Output "Running R CMD check as CRAN"
Run-R-Code-Redirect-Stderr "result <- processx::run(command = 'R.exe', args = c('CMD', 'check', '--no-multiarch', '--as-cran', '$PKG_FILE_NAME'), echo = TRUE, windows_verbatim_args = FALSE)" ; $check_succeeded = $?
Run-R-Code-Redirect-Stderr "result <- processx::run(command = 'R.exe', args = c('CMD', 'check', '--no-multiarch', '--as-cran', '--run-dontrun', '$PKG_FILE_NAME'), echo = TRUE, windows_verbatim_args = FALSE)" ; $check_succeeded = $?

Write-Output "R CMD check build logs:"
$INSTALL_LOG_FILE_NAME = "lightgbm.Rcheck\00install.out"
Expand Down
10 changes: 7 additions & 3 deletions R-package/R/lgb.Booster.R
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Expand Up @@ -718,6 +718,7 @@ Booster <- R6::R6Class(
#' number of columns corresponding to the number of trees.
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -735,6 +736,7 @@ Booster <- R6::R6Class(
#' , learning_rate = 1.0
#' )
#' preds <- predict(model, test$data)
#' }
#' @export
predict.lgb.Booster <- function(object,
data,
Expand Down Expand Up @@ -774,7 +776,7 @@ predict.lgb.Booster <- function(object,
#' @return lgb.Booster
#'
#' @examples
#' \donttest{
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand Down Expand Up @@ -834,7 +836,7 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
#' @return lgb.Booster
#'
#' @examples
#' \donttest{
#' \dontrun{
#' library(lightgbm)
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
Expand Down Expand Up @@ -882,7 +884,7 @@ lgb.save <- function(booster, filename, num_iteration = NULL) {
#' @return json format of model
#'
#' @examples
#' \donttest{
#' \dontrun{
#' library(lightgbm)
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
Expand Down Expand Up @@ -930,6 +932,7 @@ lgb.dump <- function(booster, num_iteration = NULL) {
#' @return vector of evaluation result
#'
#' @examples
#' \dontrun{
#' # train a regression model
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
Expand All @@ -956,6 +959,7 @@ lgb.dump <- function(booster, num_iteration = NULL) {
#'
#' # Get L2 values for "test" dataset
#' lgb.get.eval.result(model, "test", "l2")
#' }
#' @export
lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) {

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32 changes: 22 additions & 10 deletions R-package/R/lgb.Dataset.R
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Expand Up @@ -725,14 +725,15 @@ Dataset <- R6::R6Class(
#' @return constructed dataset
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' data_file <- tempfile(fileext = ".data")
#' lgb.Dataset.save(dtrain, data_file)
#' dtrain <- lgb.Dataset(data_file)
#' lgb.Dataset.construct(dtrain)
#'
#' }
#' @export
lgb.Dataset <- function(data,
params = list(),
Expand Down Expand Up @@ -770,13 +771,14 @@ lgb.Dataset <- function(data,
#' @return constructed dataset
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' data(agaricus.test, package = "lightgbm")
#' test <- agaricus.test
#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
#'
#' }
#' @export
lgb.Dataset.create.valid <- function(dataset, data, info = list(), ...) {

Expand All @@ -796,11 +798,12 @@ lgb.Dataset.create.valid <- function(dataset, data, info = list(), ...) {
#' @param dataset Object of class \code{lgb.Dataset}
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' lgb.Dataset.construct(dtrain)
#'
#' }
#' @export
lgb.Dataset.construct <- function(dataset) {

Expand All @@ -826,14 +829,15 @@ lgb.Dataset.construct <- function(dataset) {
#' be directly used with an \code{lgb.Dataset} object.
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#'
#' stopifnot(nrow(dtrain) == nrow(train$data))
#' stopifnot(ncol(dtrain) == ncol(train$data))
#' stopifnot(all(dim(dtrain) == dim(train$data)))
#'
#' }
#' @rdname dim
#' @export
dim.lgb.Dataset <- function(x, ...) {
Expand All @@ -860,6 +864,7 @@ dim.lgb.Dataset <- function(x, ...) {
#' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -868,7 +873,7 @@ dim.lgb.Dataset <- function(x, ...) {
#' colnames(dtrain)
#' colnames(dtrain) <- make.names(seq_len(ncol(train$data)))
#' print(dtrain, verbose = TRUE)
#'
#' }
#' @rdname dimnames.lgb.Dataset
#' @export
dimnames.lgb.Dataset <- function(x) {
Expand Down Expand Up @@ -932,14 +937,15 @@ dimnames.lgb.Dataset <- function(x) {
#' @return constructed sub dataset
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#'
#' dsub <- lightgbm::slice(dtrain, seq_len(42L))
#' lgb.Dataset.construct(dsub)
#' labels <- lightgbm::getinfo(dsub, "label")
#'
#' }
#' @export
slice <- function(dataset, ...) {
UseMethod("slice")
Expand Down Expand Up @@ -978,6 +984,7 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
#' }
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -988,7 +995,7 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
#'
#' labels2 <- lightgbm::getinfo(dtrain, "label")
#' stopifnot(all(labels2 == 1 - labels))
#'
#' }
#' @export
getinfo <- function(dataset, ...) {
UseMethod("getinfo")
Expand Down Expand Up @@ -1031,6 +1038,7 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
#' }
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -1041,7 +1049,7 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
#'
#' labels2 <- lightgbm::getinfo(dtrain, "label")
#' stopifnot(all.equal(labels2, 1 - labels))
#'
#' }
#' @export
setinfo <- function(dataset, ...) {
UseMethod("setinfo")
Expand Down Expand Up @@ -1071,14 +1079,15 @@ setinfo.lgb.Dataset <- function(dataset, name, info, ...) {
#' @return passed dataset
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' data_file <- tempfile(fileext = ".data")
#' lgb.Dataset.save(dtrain, data_file)
#' dtrain <- lgb.Dataset(data_file)
#' lgb.Dataset.set.categorical(dtrain, 1L:2L)
#'
#' }
#' @rdname lgb.Dataset.set.categorical
#' @export
lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
Expand All @@ -1102,14 +1111,15 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
#' @return passed dataset
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package ="lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' data(agaricus.test, package = "lightgbm")
#' test <- agaricus.test
#' dtest <- lgb.Dataset(test$data, test = train$label)
#' lgb.Dataset.set.reference(dtest, dtrain)
#'
#' }
#' @rdname lgb.Dataset.set.reference
#' @export
lgb.Dataset.set.reference <- function(dataset, reference) {
Expand All @@ -1133,10 +1143,12 @@ lgb.Dataset.set.reference <- function(dataset, reference) {
#' @return passed dataset
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' lgb.Dataset.save(dtrain, tempfile(fileext = ".bin"))
#' }
#' @export
lgb.Dataset.save <- function(dataset, fname) {

Expand Down
5 changes: 2 additions & 3 deletions R-package/R/lgb.convert.R
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Expand Up @@ -12,26 +12,25 @@
#' for input in \code{lgb.Dataset}.
#'
#' @examples
#' \dontrun{
#' data(iris)
#'
#' str(iris)
#'
#' # Convert all factors/chars to integer
#' str(lgb.convert(data = iris))
#'
#' \dontrun{
#' # When lightgbm package is installed, and you do not want to load it
#' # You can still use the function!
#' lgb.unloader()
#' str(lightgbm::lgb.convert(data = iris))
#' str(lgb.convert(data = iris))
#' # 'data.frame': 150 obs. of 5 variables:
#' # $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#' # $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#' # $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#' # $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#' # $ Species : int 1 1 1 1 1 1 1 1 1 1 ...
#' }
#'
#' @export
lgb.convert <- function(data) {

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3 changes: 2 additions & 1 deletion R-package/R/lgb.convert_with_rules.R
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Expand Up @@ -13,6 +13,7 @@
#' \code{lgb.Dataset}.
#'
#' @examples
#' \dontrun{
#' data(iris)
#'
#' str(iris)
Expand Down Expand Up @@ -48,7 +49,7 @@
#' )
#' newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules)
#' str(newest_iris$data) # SUCCESS!
#'
#' }
#' @importFrom data.table set
#' @export
lgb.convert_with_rules <- function(data, rules = NULL) {
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2 changes: 2 additions & 0 deletions R-package/R/lgb.cv.R
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Expand Up @@ -56,6 +56,7 @@ CVBooster <- R6::R6Class(
#' @return a trained model \code{lgb.CVBooster}.
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -68,6 +69,7 @@ CVBooster <- R6::R6Class(
#' , min_data = 1L
#' , learning_rate = 1.0
#' )
#' }
#' @importFrom data.table data.table setorderv
#' @export
lgb.cv <- function(params = list()
Expand Down
3 changes: 2 additions & 1 deletion R-package/R/lgb.importance.R
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Expand Up @@ -13,6 +13,7 @@
#' }
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -32,7 +33,7 @@
#'
#' tree_imp1 <- lgb.importance(model, percentage = TRUE)
#' tree_imp2 <- lgb.importance(model, percentage = FALSE)
#'
#' }
#' @importFrom data.table := setnames setorderv
#' @export
lgb.importance <- function(model, percentage = TRUE) {
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3 changes: 2 additions & 1 deletion R-package/R/lgb.interprete.R
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Expand Up @@ -16,6 +16,7 @@
#' Contribution columns to each class.
#'
#' @examples
#' \dontrun{
#' Logit <- function(x) log(x / (1.0 - x))
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
Expand All @@ -38,7 +39,7 @@
#' )
#'
#' tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
#'
#' }
#' @importFrom data.table as.data.table
#' @export
lgb.interprete <- function(model,
Expand Down
4 changes: 2 additions & 2 deletions R-package/R/lgb.model.dt.tree.R
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Expand Up @@ -28,7 +28,7 @@
#' }
#'
#' @examples
#'
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -44,7 +44,7 @@
#' model <- lgb.train(params, dtrain, 10L)
#'
#' tree_dt <- lgb.model.dt.tree(model)
#'
#' }
#' @importFrom data.table := rbindlist
#' @importFrom jsonlite fromJSON
#' @export
Expand Down
2 changes: 2 additions & 0 deletions R-package/R/lgb.plot.importance.R
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Expand Up @@ -18,6 +18,7 @@
#' and silently returns a processed data.table with \code{top_n} features sorted by defined importance.
#'
#' @examples
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
Expand All @@ -37,6 +38,7 @@
#'
#' tree_imp <- lgb.importance(model, percentage = TRUE)
#' lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
#' }
#' @importFrom graphics barplot par
#' @export
lgb.plot.importance <- function(tree_imp,
Expand Down
2 changes: 1 addition & 1 deletion R-package/R/lgb.plot.interpretation.R
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Expand Up @@ -15,7 +15,7 @@
#' The \code{lgb.plot.interpretation} function creates a \code{barplot}.
#'
#' @examples
#' \donttest{
#' \dontrun{
#' Logit <- function(x) {
#' log(x / (1.0 - x))
#' }
Expand Down
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