[R-package] fix R CMD check NOTE about leftover files (#3181)

* [R-package] fix R CMD check NOTE about leftover files

* update number of allowed notes

.ci/test_r_package_windows.ps1

@@ -124,7 +124,7 @@ if ($env:COMPILER -ne "MSVC") {
     .\miktex\download\miktexsetup.exe --remote-package-repository="$env:CTAN_PACKAGE_ARCHIVE" --portable="$env:R_LIB_PATH/miktex" --quiet install ; Check-Output $?
     Write-Output "Done installing MiKTeX"
 
-    Run-R-Code-Redirect-Stderr "processx::run(command = 'initexmf', args = c('--set-config-value', '[MPM]AutoInstall=1'), windows_verbatim_args = TRUE)" ; Check-Output $?
+    Run-R-Code-Redirect-Stderr "result <- processx::run(command = 'initexmf', args = c('--set-config-value', '[MPM]AutoInstall=1'), echo = TRUE, windows_verbatim_args = TRUE)" ; Check-Output $?
     conda install -q -y --no-deps pandoc
 }
 
@@ -140,7 +140,7 @@ if ($env:COMPILER -ne "MSVC") {
 
   $env:_R_CHECK_FORCE_SUGGESTS_ = 0
   Write-Output "Running R CMD check as CRAN"
-  Run-R-Code-Redirect-Stderr "processx::run(command = 'R.exe', args = c('CMD', 'check', '--no-multiarch', '--as-cran', '$PKG_FILE_NAME'), windows_verbatim_args = FALSE)" ; $check_succeeded = $?
+  Run-R-Code-Redirect-Stderr "result <- processx::run(command = 'R.exe', args = c('CMD', 'check', '--no-multiarch', '--as-cran', '$PKG_FILE_NAME'), echo = TRUE, windows_verbatim_args = FALSE)" ; $check_succeeded = $?
 
   Write-Output "R CMD check build logs:"
   $INSTALL_LOG_FILE_NAME = "$env:BUILD_SOURCESDIRECTORY\lightgbm.Rcheck\00install.out"
@@ -157,7 +157,7 @@ if ($env:COMPILER -ne "MSVC") {
   $note_str = Get-Content -Path "${LOG_FILE_NAME}" | Select-String -Pattern '.*Status.* NOTE' | Out-String ; Check-Output $?
   $relevant_line = $note_str -match '(\d+) NOTE'
   $NUM_CHECK_NOTES = $matches[1]
-  $ALLOWED_CHECK_NOTES = 4
+  $ALLOWED_CHECK_NOTES = 3
   if ([int]$NUM_CHECK_NOTES -gt $ALLOWED_CHECK_NOTES) {
       Write-Output "Found ${NUM_CHECK_NOTES} NOTEs from R CMD check. Only ${ALLOWED_CHECK_NOTES} are allowed"
       Check-Output $False

R-package/R/lgb.Booster.R

@@ -789,8 +789,9 @@ predict.lgb.Booster <- function(object,
 #'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 3L
 #' )
-#' lgb.save(model, "model.txt")
-#' load_booster <- lgb.load(filename = "model.txt")
+#' model_file <- tempfile(fileext = ".txt")
+#' lgb.save(model, model_file)
+#' load_booster <- lgb.load(filename = model_file)
 #' model_string <- model$save_model_to_string(NULL) # saves best iteration
 #' load_booster_from_str <- lgb.load(model_str = model_string)
 #' }
@@ -849,7 +850,7 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
 #'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 5L
 #' )
-#' lgb.save(model, "lgb-model.txt")
+#' lgb.save(model, tempfile(fileext = ".txt"))
 #' }
 #' @export
 lgb.save <- function(booster, filename, num_iteration = NULL) {

R-package/R/lgb.Dataset.R

@@ -728,8 +728,9 @@ Dataset <- R6::R6Class(
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
-#' lgb.Dataset.save(dtrain, "lgb.Dataset.data")
-#' dtrain <- lgb.Dataset("lgb.Dataset.data")
+#' data_file <- tempfile(fileext = ".data")
+#' lgb.Dataset.save(dtrain, data_file)
+#' dtrain <- lgb.Dataset(data_file)
 #' lgb.Dataset.construct(dtrain)
 #'
 #' @export
@@ -1073,8 +1074,9 @@ setinfo.lgb.Dataset <- function(dataset, name, info, ...) {
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
-#' lgb.Dataset.save(dtrain, "lgb-Dataset.data")
-#' dtrain <- lgb.Dataset("lgb-Dataset.data")
+#' 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
@@ -1134,7 +1136,7 @@ lgb.Dataset.set.reference <- function(dataset, reference) {
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
-#' lgb.Dataset.save(dtrain, "data.bin")
+#' lgb.Dataset.save(dtrain, tempfile(fileext = ".bin"))
 #' @export
 lgb.Dataset.save <- function(dataset, fname) {
 

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

@@ -26,8 +26,9 @@
 #'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 5L
 #' )
-#' saveRDS.lgb.Booster(model, "model.rds")
-#' new_model <- readRDS.lgb.Booster("model.rds")
+#' model_file <- tempfile(fileext = ".rds")
+#' saveRDS.lgb.Booster(model, model_file)
+#' new_model <- readRDS.lgb.Booster(model_file)
 #' }
 #' @export
 readRDS.lgb.Booster <- function(file = "", refhook = NULL) {

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

@@ -37,7 +37,8 @@
 #'     , learning_rate = 1.0
 #'     , early_stopping_rounds = 5L
 #' )
-#' saveRDS.lgb.Booster(model, "lgb-model.rds")
+#' model_file <- tempfile(fileext = ".rds")
+#' saveRDS.lgb.Booster(model, model_file)
 #' }
 #' @export
 saveRDS.lgb.Booster <- function(object,

R-package/man/lgb.Dataset.Rd

@@ -43,8 +43,9 @@ Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
-lgb.Dataset.save(dtrain, "lgb.Dataset.data")
-dtrain <- lgb.Dataset("lgb.Dataset.data")
+data_file <- tempfile(fileext = ".data")
+lgb.Dataset.save(dtrain, data_file)
+dtrain <- lgb.Dataset(data_file)
 lgb.Dataset.construct(dtrain)
 
 }

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

@@ -22,5 +22,5 @@ Please note that \code{init_score} is not saved in binary file.
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
-lgb.Dataset.save(dtrain, "data.bin")
+lgb.Dataset.save(dtrain, tempfile(fileext = ".bin"))
 }

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

@@ -24,8 +24,9 @@ Set the categorical features of an \code{lgb.Dataset} object. Use this function
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
-lgb.Dataset.save(dtrain, "lgb-Dataset.data")
-dtrain <- lgb.Dataset("lgb-Dataset.data")
+data_file <- tempfile(fileext = ".data")
+lgb.Dataset.save(dtrain, data_file)
+dtrain <- lgb.Dataset(data_file)
 lgb.Dataset.set.categorical(dtrain, 1L:2L)
 
 }

R-package/man/lgb.load.Rd

@@ -37,8 +37,9 @@ model <- lgb.train(
   , learning_rate = 1.0
   , early_stopping_rounds = 3L
 )
-lgb.save(model, "model.txt")
-load_booster <- lgb.load(filename = "model.txt")
+model_file <- tempfile(fileext = ".txt")
+lgb.save(model, model_file)
+load_booster <- lgb.load(filename = model_file)
 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.save.Rd

@@ -39,6 +39,6 @@ model <- lgb.train(
   , learning_rate = 1.0
   , early_stopping_rounds = 5L
 )
-lgb.save(model, "lgb-model.txt")
+lgb.save(model, tempfile(fileext = ".txt"))
 }
 }

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

@@ -37,7 +37,8 @@ model <- lgb.train(
   , learning_rate = 1.0
   , early_stopping_rounds = 5L
 )
-saveRDS.lgb.Booster(model, "model.rds")
-new_model <- readRDS.lgb.Booster("model.rds")
+model_file <- tempfile(fileext = ".rds")
+saveRDS.lgb.Booster(model, model_file)
+new_model <- readRDS.lgb.Booster(model_file)
 }
 }

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

@@ -61,6 +61,7 @@ model <- lgb.train(
     , learning_rate = 1.0
     , early_stopping_rounds = 5L
 )
-saveRDS.lgb.Booster(model, "lgb-model.rds")
+model_file <- tempfile(fileext = ".rds")
+saveRDS.lgb.Booster(model, model_file)
 }
 }

R-package/tests/testthat/test_basic.R

@@ -16,6 +16,7 @@ test_that("train and predict binary classification", {
     , nrounds = nrounds
     , objective = "binary"
     , metric = "binary_error"
+    , save_name = tempfile(fileext = ".model")
   )
   expect_false(is.null(bst$record_evals))
   record_results <- lgb.get.eval.result(bst, "train", "binary_error")
@@ -47,6 +48,7 @@ test_that("train and predict softmax", {
     , objective = "multiclass"
     , metric = "multi_error"
     , num_class = 3L
+    , save_name = tempfile(fileext = ".model")
   )
 
   expect_false(is.null(bst$record_evals))
@@ -68,6 +70,7 @@ test_that("use of multiple eval metrics works", {
     , nrounds = 10L
     , objective = "binary"
     , metric = metrics
+    , save_name = tempfile(fileext = ".model")
   )
   expect_false(is.null(bst$record_evals))
   expect_named(
@@ -88,6 +91,7 @@ test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expec
     , nrounds = nrounds
     , objective = "binary"
     , metric = "binary_error"
+    , save_name = tempfile(fileext = ".model")
   )
   expect_true(abs(bst$lower_bound() - -1.590853) < TOLERANCE)
   expect_true(abs(bst$upper_bound() - 1.871015) <  TOLERANCE)
@@ -103,6 +107,7 @@ test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expec
     , nrounds = nrounds
     , objective = "regression"
     , metric = "l2"
+    , save_name = tempfile(fileext = ".model")
   )
   expect_true(abs(bst$lower_bound() - 0.1513859) < TOLERANCE)
   expect_true(abs(bst$upper_bound() - 0.9080349) < TOLERANCE)
@@ -117,6 +122,7 @@ test_that("lightgbm() rejects negative or 0 value passed to nrounds", {
         data = dtrain
         , params = params
         , nrounds = nround_value
+        , save_name = tempfile(fileext = ".model")
       )
     }, "nrounds should be greater than zero")
   }
@@ -147,6 +153,7 @@ test_that("lightgbm() performs evaluation on validation sets if they are provide
       "valid1" = dvalid1
       , "valid2" = dvalid2
     )
+    , save_name = tempfile(fileext = ".model")
   )
 
   expect_named(
@@ -188,13 +195,14 @@ test_that("training continuation works", {
   # first 5 iterations:
   bst1 <- lgb.train(param, dtrain, nrounds = 5L, watchlist)
   # test continuing from a model in file
-  lgb.save(bst1, "lightgbm.model")
+  model_file <- tempfile(fileext = ".model")
+  lgb.save(bst1, model_file)
   # continue for 5 more:
   bst2 <- lgb.train(param, dtrain, nrounds = 5L, watchlist, init_model = bst1)
   err_bst2 <- lgb.get.eval.result(bst2, "train", "binary_logloss", 10L)
   expect_lt(abs(err_bst - err_bst2), 0.01)
 
-  bst2 <- lgb.train(param, dtrain, nrounds = 5L, watchlist, init_model = "lightgbm.model")
+  bst2 <- lgb.train(param, dtrain, nrounds = 5L, watchlist, init_model = model_file)
   err_bst2 <- lgb.get.eval.result(bst2, "train", "binary_logloss", 10L)
   expect_lt(abs(err_bst - err_bst2), 0.01)
 })
@@ -1007,6 +1015,7 @@ test_that("using lightgbm() without early stopping, best_iter and best_score com
       , learning_rate = 1.5
     )
     , verbose = -7L
+    , save_name = tempfile(fileext = ".model")
   )
   # when verbose <= 0 is passed to lightgbm(), 'valids' is passed through to lgb.train()
   # untouched. If you set verbose to > 0, the training data will still be first but called "train"

R-package/tests/testthat/test_lgb.Booster.R

@@ -104,6 +104,7 @@ test_that("lgb.load() gives the expected error messages given different incorrec
         , learning_rate = 1.0
         , nrounds = 2L
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
 
     # you have to give model_str or filename
@@ -115,9 +116,9 @@ test_that("lgb.load() gives the expected error messages given different incorrec
     }, regexp = "either filename or model_str must be given")
 
     # if given, filename should be a string that points to an existing file
-    out_file <- "lightgbm.model"
+    model_file <- tempfile(fileext = ".model")
     expect_error({
-        lgb.load(filename = list(out_file))
+        lgb.load(filename = list(model_file))
     }, regexp = "filename should be character")
     file_to_check <- paste0("a.model")
     while (file.exists(file_to_check)) {
@@ -147,11 +148,13 @@ test_that("Loading a Booster from a file works", {
         , learning_rate = 1.0
         , nrounds = 2L
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     expect_true(lgb.is.Booster(bst))
 
     pred <- predict(bst, test$data)
-    lgb.save(bst, "lightgbm.model")
+    model_file <- tempfile(fileext = ".model")
+    lgb.save(bst, model_file)
 
     # finalize the booster and destroy it so you know we aren't cheating
     bst$finalize()
@@ -159,7 +162,7 @@ test_that("Loading a Booster from a file works", {
     rm(bst)
 
     bst2 <- lgb.load(
-        filename = "lightgbm.model"
+        filename = model_file
     )
     pred2 <- predict(bst2, test$data)
     expect_identical(pred, pred2)
@@ -178,6 +181,7 @@ test_that("Loading a Booster from a string works", {
         , learning_rate = 1.0
         , nrounds = 2L
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     expect_true(lgb.is.Booster(bst))
 
@@ -209,11 +213,13 @@ test_that("If a string and a file are both passed to lgb.load() the file is used
         , learning_rate = 1.0
         , nrounds = 2L
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     expect_true(lgb.is.Booster(bst))
 
     pred <- predict(bst, test$data)
-    lgb.save(bst, "lightgbm.model")
+    model_file <- tempfile(fileext = ".model")
+    lgb.save(bst, model_file)
 
     # finalize the booster and destroy it so you know we aren't cheating
     bst$finalize()
@@ -221,7 +227,7 @@ test_that("If a string and a file are both passed to lgb.load() the file is used
     rm(bst)
 
     bst2 <- lgb.load(
-        filename = "lightgbm.model"
+        filename = model_file
         , model_str = 4.0
     )
     pred2 <- predict(bst2, test$data)
@@ -261,6 +267,7 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
         , learning_rate = 1.0
         , nrounds = nrounds
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     data(agaricus.test, package = "lightgbm")
     dtest <- Dataset$new(
@@ -294,6 +301,7 @@ test_that("Booster$rollback_one_iter() should work as expected", {
         , learning_rate = 1.0
         , nrounds = nrounds
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     expect_equal(bst$current_iter(), nrounds)
     expect_true(lgb.is.Booster(bst))
@@ -325,6 +333,7 @@ test_that("Booster$update() passing a train_set works as expected", {
         , learning_rate = 1.0
         , nrounds = nrounds
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     expect_true(lgb.is.Booster(bst))
     expect_equal(bst$current_iter(), nrounds)
@@ -345,6 +354,7 @@ test_that("Booster$update() passing a train_set works as expected", {
         , learning_rate = 1.0
         , nrounds = nrounds +  1L
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     expect_true(lgb.is.Booster(bst2))
     expect_equal(bst2$current_iter(), nrounds +  1L)
@@ -367,6 +377,7 @@ test_that("Booster$update() throws an informative error if you provide a non-Dat
         , learning_rate = 1.0
         , nrounds = nrounds
         , objective = "binary"
+        , save_name = tempfile(fileext = ".model")
     )
     expect_error({
         bst$update(

R-package/tests/testthat/test_parameters.R

@@ -24,6 +24,7 @@ test_that("Feature penalties work properly", {
       , feature_penalty = paste0(feature_penalties, collapse = ",")
       , metric = "binary_error"
       , verbose = -1L
+      , save_name = tempfile(fileext = ".model")
     )
   })
 
@@ -75,6 +76,7 @@ test_that("training should warn if you use 'dart' boosting, specified with 'boos
           object = "dart"
           , nm = boosting_param
         )
+        , save_name = tempfile(fileext = ".model")
       )
     }, regexp = "Early stopping is not available in 'dart' mode")
   }