[R-package] removed horizontal whitespace (fixes #1642) (#1651)

* [R-package] removed horizontal whitespace (fixes #1642)

* [R-package] fixed missing newline in test file

R-package/man/lgb.train.Rd

@@ -19,7 +19,7 @@ lgb.train(params = list(), data, nrounds = 10, valids = list(),
 
 \item{valids}{a list of \code{lgb.Dataset} objects, used for validation}
 
-\item{obj}{objective function, can be character or custom objective function. Examples include 
+\item{obj}{objective function, can be character or custom objective function. Examples include
 \code{regression}, \code{regression_l1}, \code{huber},
 \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}}
 
@@ -55,10 +55,10 @@ List of callback functions that are applied at each iteration.}
 \itemize{
     \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
     \item{num_leaves}{number of leaves in one tree. defaults to 127}
-    \item{max_depth}{Limit the max depth for tree model. This is used to deal with 
+    \item{max_depth}{Limit the max depth for tree model. This is used to deal with
                      overfit when #data is small. Tree still grow by leaf-wise.}
     \item{num_threads}{Number of threads for LightGBM. For the best speed, set this to
-                       the number of real CPU cores, not the number of threads (most 
+                       the number of real CPU cores, not the number of threads (most
                        CPU using hyper-threading to generate 2 threads per CPU core).}
 }}
 }

R-package/man/lightgbm.Rd

@@ -41,21 +41,21 @@ List of callback functions that are applied at each iteration.}
 \item{...}{Additional arguments passed to \code{\link{lgb.train}}. For example
 \itemize{
    \item{valids}{a list of \code{lgb.Dataset} objects, used for validation}
-   \item{obj}{objective function, can be character or custom objective function. Examples include 
+   \item{obj}{objective function, can be character or custom objective function. Examples include
               \code{regression}, \code{regression_l1}, \code{huber},
                \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}}
    \item{eval}{evaluation function, can be (a list of) character or custom eval function}
    \item{record}{Boolean, TRUE will record iteration message to \code{booster$record_evals}}
    \item{colnames}{feature names, if not null, will use this to overwrite the names in dataset}
    \item{categorical_feature}{list of str or int. type int represents index, type str represents feature names}
-   \item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model 
+   \item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model
                      into a predictor model which frees up memory and the original datasets}
     \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
     \item{num_leaves}{number of leaves in one tree. defaults to 127}
-    \item{max_depth}{Limit the max depth for tree model. This is used to deal with 
+    \item{max_depth}{Limit the max depth for tree model. This is used to deal with
                      overfit when #data is small. Tree still grow by leaf-wise.}
      \item{num_threads}{Number of threads for LightGBM. For the best speed, set this to
-                        the number of real CPU cores, not the number of threads (most 
+                        the number of real CPU cores, not the number of threads (most
                         CPU using hyper-threading to generate 2 threads per CPU core).}
 }}
 }

R-package/src/install.libs.R

@@ -10,9 +10,9 @@ if (.Machine$sizeof.pointer != 8){
 R_int_UUID <- .Internal(internalsID())
 R_ver <- as.double(R.Version()$major) + as.double(R.Version()$minor)/10
 
-if (!(R_int_UUID == "0310d4b8-ccb1-4bb8-ba94-d36a55f60262" 
+if (!(R_int_UUID == "0310d4b8-ccb1-4bb8-ba94-d36a55f60262"
     || R_int_UUID == "2fdf6c18-697a-4ba7-b8ef-11c0d92f1327")){
-  print("Warning: unmatched R_INTERNALS_UUID, may cannot run normally.")   
+  print("Warning: unmatched R_INTERNALS_UUID, may cannot run normally.")
 }
 
 # Move in CMakeLists.txt
@@ -26,17 +26,17 @@ if (!use_precompile) {
   # Check repository content
   source_dir <- file.path(R_PACKAGE_SOURCE, "src", fsep = "/")
   setwd(source_dir)
-  
+
   # Prepare building package
   build_dir <- file.path(source_dir, "build", fsep = "/")
   dir.create(build_dir, recursive = TRUE, showWarnings = FALSE)
   setwd(build_dir)
-  
+
   # Prepare installation steps
   cmake_cmd <- "cmake "
   build_cmd <- "make _lightgbm"
   lib_folder <- file.path(R_PACKAGE_SOURCE, "src", fsep = "/")
-  
+
   if (use_gpu) {
     cmake_cmd <- paste0(cmake_cmd, " -DUSE_GPU=ON ")
   }
@@ -76,12 +76,12 @@ if (!use_precompile) {
       }
     }
   }
-  
+
   # Install
   system(paste0(cmake_cmd, " .."))
   system(build_cmd)
   src <- file.path(lib_folder, paste0("lib_lightgbm", SHLIB_EXT), fsep = "/")
-  
+
 } else {
 
   # Has precompiled package
@@ -89,11 +89,11 @@ if (!use_precompile) {
   if (file.exists(file.path(lib_folder, paste0("lib_lightgbm", SHLIB_EXT), fsep = "/"))) {
     src <- file.path(lib_folder, paste0("lib_lightgbm", SHLIB_EXT), fsep = "/")
   } else if (file.exists(file.path(lib_folder, paste0("Release/lib_lightgbm", SHLIB_EXT), fsep = "/"))) {
-    src <- file.path(lib_folder, paste0("Release/lib_lightgbm", SHLIB_EXT), fsep = "/") 
+    src <- file.path(lib_folder, paste0("Release/lib_lightgbm", SHLIB_EXT), fsep = "/")
   } else {
     src <- file.path(lib_folder, paste0("/windows/x64/DLL/lib_lightgbm", SHLIB_EXT), fsep = "/") # Expected result: installation will fail if it is not here or any other
   }
-  
+
 }
 
 # Check installation correctness

R-package/tests/testthat/test_dataset.R

@@ -10,10 +10,10 @@ test_label <- agaricus.test$label[1:100]
 test_that("lgb.Dataset: basic construction, saving, loading", {
   # from sparse matrix
   dtest1 <- lgb.Dataset(test_data, label=test_label)
-  # from dense matrix 
+  # from dense matrix
   dtest2 <- lgb.Dataset(as.matrix(test_data), label=test_label)
   expect_equal(getinfo(dtest1, 'label'), getinfo(dtest2, 'label'))
-  
+
   # save to a local file
   tmp_file <- tempfile('lgb.Dataset_')
   lgb.Dataset.save(dtest1, tmp_file)
@@ -27,14 +27,14 @@ test_that("lgb.Dataset: basic construction, saving, loading", {
 test_that("lgb.Dataset: getinfo & setinfo", {
   dtest <- lgb.Dataset(test_data)
   dtest$construct()
-  
+
   setinfo(dtest, 'label', test_label)
   labels <- getinfo(dtest, 'label')
   expect_equal(test_label, getinfo(dtest, 'label'))
-  
+
   expect_true(length(getinfo(dtest, 'weight')) == 0)
   expect_true(length(getinfo(dtest, 'init_score')) == 0)
-  
+
   # any other label should error
   expect_error(setinfo(dtest, 'asdf', test_label))
 })

R-package/tests/testthat/test_parameters.R

@@ -7,7 +7,7 @@ test_that("Feature penalties work properly", {
   # Fit a series of models with varying penalty on most important variable
   var_name <- "odor=none"
   var_index <- which(train$data@Dimnames[[2]] == var_name)
-  
+
   bst <- lapply(seq(1, 0, by = -0.1), function(x) {
     feature_penalties <- rep(1, ncol(train$data))
     feature_penalties[var_index] <- x
@@ -16,27 +16,27 @@ test_that("Feature penalties work properly", {
       label = train$label,
       num_leaves = 5,
       learning_rate = 0.05,
-      nrounds = 20, 
-      objective = "binary", 
+      nrounds = 20,
+      objective = "binary",
       feature_penalty = paste0(feature_penalties, collapse = ","),
       metric="binary_error",
       verbose = -1
     )
   })
-  
+
   var_gain <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Gain])
   var_cover <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Cover])
   var_freq <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Frequency])
-  
+
   # Ensure that feature gain, cover, and frequency decreases with stronger penalties
   expect_true(all(diff(unlist(var_gain)) <= 0))
   expect_true(all(diff(unlist(var_cover)) <= 0))
   expect_true(all(diff(unlist(var_freq)) <= 0))
-  
+
   expect_lt(min(diff(unlist(var_gain))), 0)
   expect_lt(min(diff(unlist(var_cover))), 0)
   expect_lt(min(diff(unlist(var_freq))), 0)
-  
+
   # Ensure that feature is not used when feature_penalty = 0
   expect_length(var_gain[[length(var_gain)]], 0)
-})
\ No newline at end of file
+})