[R] [CI] enforce lintr::function_left_parentheses_linter check (#9631)

R-package/tests/helper_scripts/generate_models.R

@@ -19,15 +19,15 @@ w <- runif(metadata$kRows)
 version <- packageVersion('xgboost')
 target_dir <- 'models'
 
-save_booster <- function (booster, model_name) {
-  booster_bin <- function (model_name) {
-    return (file.path(target_dir, paste('xgboost-', version, '.', model_name, '.bin', sep = '')))
+save_booster <- function(booster, model_name) {
+  booster_bin <- function(model_name) {
+    return(file.path(target_dir, paste('xgboost-', version, '.', model_name, '.bin', sep = '')))
   }
-  booster_json <- function (model_name) {
-    return (file.path(target_dir, paste('xgboost-', version, '.', model_name, '.json', sep = '')))
+  booster_json <- function(model_name) {
+    return(file.path(target_dir, paste('xgboost-', version, '.', model_name, '.json', sep = '')))
   }
-  booster_rds <- function (model_name) {
-    return (file.path(target_dir, paste('xgboost-', version, '.', model_name, '.rds', sep = '')))
+  booster_rds <- function(model_name) {
+    return(file.path(target_dir, paste('xgboost-', version, '.', model_name, '.rds', sep = '')))
   }
   xgb.save(booster, booster_bin(model_name))
   saveRDS(booster, booster_rds(model_name))
@@ -36,7 +36,7 @@ save_booster <- function (booster, model_name) {
   }
 }
 
-generate_regression_model <- function () {
+generate_regression_model <- function() {
   print('Regression')
   y <- rnorm(metadata$kRows)
 
@@ -47,7 +47,7 @@ generate_regression_model <- function () {
   save_booster(booster, 'reg')
 }
 
-generate_logistic_model <- function () {
+generate_logistic_model <- function() {
   print('Binary classification with logistic loss')
   y <- sample(0:1, size = metadata$kRows, replace = TRUE)
   stopifnot(max(y) == 1, min(y) == 0)
@@ -64,7 +64,7 @@ generate_logistic_model <- function () {
   }
 }
 
-generate_classification_model <- function () {
+generate_classification_model <- function() {
   print('Multi-class classification')
   y <- sample(0:(metadata$kClasses - 1), size = metadata$kRows, replace = TRUE)
   stopifnot(max(y) == metadata$kClasses - 1, min(y) == 0)
@@ -77,7 +77,7 @@ generate_classification_model <- function () {
   save_booster(booster, 'cls')
 }
 
-generate_ranking_model <- function () {
+generate_ranking_model <- function() {
   print('Learning to rank')
   y <- sample(0:4, size = metadata$kRows, replace = TRUE)
   stopifnot(max(y) == 4, min(y) == 0)

R-package/tests/testthat/test_model_compatibility.R

@@ -9,20 +9,20 @@ metadata <- list(
   kClasses = 3
 )
 
-run_model_param_check <- function (config) {
+run_model_param_check <- function(config) {
   testthat::expect_equal(config$learner$learner_model_param$num_feature, '4')
   testthat::expect_equal(config$learner$learner_train_param$booster, 'gbtree')
 }
 
-get_num_tree <- function (booster) {
+get_num_tree <- function(booster) {
   dump <- xgb.dump(booster)
   m <- regexec('booster\\[[0-9]+\\]', dump, perl = TRUE)
   m <- regmatches(dump, m)
   num_tree <- Reduce('+', lapply(m, length))
-  return (num_tree)
+  return(num_tree)
 }
 
-run_booster_check <- function (booster, name) {
+run_booster_check <- function(booster, name) {
   # If given a handle, we need to call xgb.Booster.complete() prior to using xgb.config().
   if (inherits(booster, "xgb.Booster") && xgboost:::is.null.handle(booster$handle)) {
     booster <- xgb.Booster.complete(booster)
@@ -68,7 +68,7 @@ test_that("Models from previous versions of XGBoost can be loaded", {
 
   pred_data <- xgb.DMatrix(matrix(c(0, 0, 0, 0), nrow = 1, ncol = 4), nthread = 2)
 
-  lapply(list.files(model_dir), function (x) {
+  lapply(list.files(model_dir), function(x) {
     model_file <- file.path(model_dir, x)
     m <- regexec("xgboost-([0-9\\.]+)\\.([a-z]+)\\.[a-z]+", model_file, perl = TRUE)
     m <- regmatches(model_file, m)[[1]]

R-package/tests/testthat/test_ranking.R

@@ -47,7 +47,7 @@ test_that('Test ranking with weighted data', {
     pred <- predict(bst, newdata = dtrain, ntreelimit = i)
     # is_sorted[i]: is i-th group correctly sorted by the ranking predictor?
     is_sorted <- lapply(seq(1, 20, by = 5),
-      function (k) {
+      function(k) {
         ind <- order(-pred[k:(k + 4)])
         z <- y[ind + (k - 1)]
         all(diff(z) <= 0)  # Check if z is monotone decreasing