[tests][R-package] use one `#` symbol for comments (#4940)

* Update lgb.cv.R

* Update test_learning_to_rank.R

* Update test_basic.R

R-package/R/lgb.cv.R

@@ -528,14 +528,14 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
 #' @importFrom stats quantile
 lgb.stratified.folds <- function(y, k) {
 
-  ## Group the numeric data based on their magnitudes
-  ## and sample within those groups.
-  ## When the number of samples is low, we may have
-  ## issues further slicing the numeric data into
-  ## groups. The number of groups will depend on the
-  ## ratio of the number of folds to the sample size.
-  ## At most, we will use quantiles. If the sample
-  ## is too small, we just do regular unstratified CV
+  # Group the numeric data based on their magnitudes
+  # and sample within those groups.
+  # When the number of samples is low, we may have
+  # issues further slicing the numeric data into
+  # groups. The number of groups will depend on the
+  # ratio of the number of folds to the sample size.
+  # At most, we will use quantiles. If the sample
+  # is too small, we just do regular unstratified CV
   if (is.numeric(y)) {
 
     cuts <- length(y) %/% k
@@ -555,29 +555,28 @@ lgb.stratified.folds <- function(y, k) {
 
   if (k < length(y)) {
 
-    ## Reset levels so that the possible levels and
-    ## the levels in the vector are the same
+    # Reset levels so that the possible levels and
+    # the levels in the vector are the same
     y <- as.factor(as.character(y))
     numInClass <- table(y)
     foldVector <- vector(mode = "integer", length(y))
 
-    ## For each class, balance the fold allocation as far
-    ## as possible, then resample the remainder.
-    ## The final assignment of folds is also randomized.
-
+    # For each class, balance the fold allocation as far
+    # as possible, then resample the remainder.
+    # The final assignment of folds is also randomized.
     for (i in seq_along(numInClass)) {
 
-      ## Create a vector of integers from 1:k as many times as possible without
-      ## going over the number of samples in the class. Note that if the number
-      ## of samples in a class is less than k, nothing is produced here.
+      # Create a vector of integers from 1:k as many times as possible without
+      # going over the number of samples in the class. Note that if the number
+      # of samples in a class is less than k, nothing is produced here.
       seqVector <- rep(seq_len(k), numInClass[i] %/% k)
 
-      ## Add enough random integers to get length(seqVector) == numInClass[i]
+      # Add enough random integers to get length(seqVector) == numInClass[i]
       if (numInClass[i] %% k > 0L) {
         seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
       }
 
-      ## Shuffle the integers for fold assignment and assign to this classes's data
+      # Shuffle the integers for fold assignment and assign to this classes's data
       foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)
 
     }

R-package/tests/testthat/test_basic.R

@@ -244,8 +244,8 @@ test_that("lightgbm() accepts nrounds as either a top-level argument or paramete
   )
 
   top_level_l2 <- top_level_bst$eval_train()[[1L]][["value"]]
-  params_l2    <- param_bst$eval_train()[[1L]][["value"]]
-  both_l2      <- both_customized$eval_train()[[1L]][["value"]]
+  params_l2 <- param_bst$eval_train()[[1L]][["value"]]
+  both_l2 <- both_customized$eval_train()[[1L]][["value"]]
 
   # check type just to be sure the subsetting didn't return a NULL
   expect_true(is.numeric(top_level_l2))
@@ -781,8 +781,8 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
   )
 
   top_level_l2 <- top_level_bst$eval_train()[[1L]][["value"]]
-  params_l2    <- param_bst$eval_train()[[1L]][["value"]]
-  both_l2      <- both_customized$eval_train()[[1L]][["value"]]
+  params_l2 <- param_bst$eval_train()[[1L]][["value"]]
+  both_l2 <- both_customized$eval_train()[[1L]][["value"]]
 
   # check type just to be sure the subsetting didn't return a NULL
   expect_true(is.numeric(top_level_l2))
@@ -1003,7 +1003,7 @@ test_that("lgb.train() works with early stopping for classification", {
   # train with early stopping #
   #############################
   early_stopping_rounds <- 5L
-  bst  <- lgb.train(
+  bst <- lgb.train(
     params = list(
       objective = "binary"
       , metric = "binary_error"
@@ -1115,7 +1115,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
   #############################
   early_stopping_rounds <- 5L
   # the harsh max_depth guarantees that AUC improves over at least the first few iterations
-  bst_auc  <- lgb.train(
+  bst_auc <- lgb.train(
     params = list(
       objective = "binary"
       , metric = "auc"
@@ -1129,7 +1129,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
       "valid1" = dvalid
     )
   )
-  bst_binary_error  <- lgb.train(
+  bst_binary_error <- lgb.train(
     params = list(
       objective = "binary"
       , metric = "binary_error"
@@ -1213,7 +1213,7 @@ test_that("lgb.train() works with early stopping for regression", {
   # train with early stopping #
   #############################
   early_stopping_rounds <- 5L
-  bst  <- lgb.train(
+  bst <- lgb.train(
     params = list(
       objective = "regression"
       , metric = "rmse"
@@ -1577,7 +1577,7 @@ test_that("lgb.train() works with early stopping for regression with a metric th
   # train with early stopping #
   #############################
   early_stopping_rounds <- 5L
-  bst  <- lgb.train(
+  bst <- lgb.train(
     params = list(
       objective = "regression"
       , metric = c(

R-package/tests/testthat/test_learning_to_rank.R

@@ -20,7 +20,7 @@ test_that("learning-to-rank with lgb.train() works as expected", {
         , group = rep(150L, 40L)
     )
     ndcg_at <- "1,2,3"
-    eval_names <-  paste0("ndcg@", strsplit(ndcg_at, ",")[[1L]])
+    eval_names <- paste0("ndcg@", strsplit(ndcg_at, ",")[[1L]])
     params <- list(
         objective = "lambdarank"
         , metric = "ndcg"
@@ -74,7 +74,7 @@ test_that("learning-to-rank with lgb.cv() works as expected", {
         , group = rep(150L, 40L)
     )
     ndcg_at <- "1,2,3"
-    eval_names <-  paste0("ndcg@", strsplit(ndcg_at, ",")[[1L]])
+    eval_names <- paste0("ndcg@", strsplit(ndcg_at, ",")[[1L]])
     params <- list(
         objective = "lambdarank"
         , metric = "ndcg"
@@ -97,7 +97,7 @@ test_that("learning-to-rank with lgb.cv() works as expected", {
 
     # "valid" should contain results for each metric
     eval_results <- cv_bst$record_evals[["valid"]]
-    eval_names <-  c("ndcg@1", "ndcg@2", "ndcg@3")
+    eval_names <- c("ndcg@1", "ndcg@2", "ndcg@3")
     expect_identical(names(eval_results), eval_names)
 
     # check that best score and iter make sense (0.0 < nDCG < 1.0)