[R] resolve brace_linter warnings

R-package/R/callbacks.R

@@ -615,9 +615,11 @@ cb.gblinear.history <- function(sparse=FALSE) {
   coefs <- NULL
 
   init <- function(env) {
-    if (!is.null(env$bst)) { # xgb.train:
-    } else if (!is.null(env$bst_folds)) { # xgb.cv:
-    } else stop("Parent frame has neither 'bst' nor 'bst_folds'")
+    # xgb.train(): bst will be present
+    # xgb.cv(): bst_folds will be present
+    if (is.null(env$bst) && is.null(env$bst_folds)) {
+        stop("Parent frame has neither 'bst' nor 'bst_folds'")
+    }
   }
 
   # convert from list to (sparse) matrix

R-package/R/utils.R

@@ -82,7 +82,7 @@ check.booster.params <- function(params, ...) {
 
   # interaction constraints parser (convert from list of column indices to string)
   if (!is.null(params[['interaction_constraints']]) &&
-      typeof(params[['interaction_constraints']]) != "character"){
+      typeof(params[['interaction_constraints']]) != "character") {
     # check input class
     if (!identical(class(params[['interaction_constraints']]), 'list')) stop('interaction_constraints should be class list')
     if (!all(unique(sapply(params[['interaction_constraints']], class)) %in% c('numeric', 'integer'))) {
@@ -251,8 +251,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
 # Creates CV folds stratified by the values of y.
 # It was borrowed from caret::createFolds and simplified
 # by always returning an unnamed list of fold indices.
-xgb.createFolds <- function(y, k = 10)
-{
+xgb.createFolds <- function(y, k = 10) {
   if (is.numeric(y)) {
     ## Group the numeric data based on their magnitudes
     ## and sample within those groups.

R-package/R/xgb.DMatrix.R

@@ -76,7 +76,7 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL, nth
       stop("label must be provided when data is a matrix")
     }
     dtrain <- xgb.DMatrix(data, label = label, missing = missing, nthread = nthread)
-    if (!is.null(weight)){
+    if (!is.null(weight)) {
       setinfo(dtrain, "weight", weight)
     }
   } else {
@@ -218,7 +218,7 @@ getinfo.xgb.DMatrix <- function(object, name, ...) {
   }
   if (name == "feature_name" || name == "feature_type") {
     ret <- .Call(XGDMatrixGetStrFeatureInfo_R, object, name)
-  } else if (name != "nrow"){
+  } else if (name != "nrow") {
     ret <- .Call(XGDMatrixGetInfo_R, object, name)
   } else {
     ret <- nrow(object)

R-package/R/xgb.importance.R

@@ -82,7 +82,7 @@
 #'
 #' @export
 xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
-                           data = NULL, label = NULL, target = NULL){
+                           data = NULL, label = NULL, target = NULL) {
 
   if (!(is.null(data) && is.null(label) && is.null(target)))
     warning("xgb.importance: parameters 'data', 'label' and 'target' are deprecated")
@@ -104,7 +104,11 @@ xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
       XGBoosterFeatureScore_R, model$handle, jsonlite::toJSON(args, auto_unbox = TRUE, null = "null")
     )
     names(results) <- c("features", "shape", "weight")
-    n_classes <-  if (length(results$shape) == 2) { results$shape[2] } else { 0 }
+    if (length(results$shape) == 2) {
+        n_classes <- results$shape[2]
+    } else {
+        n_classes <- 0
+    }
     importance <- if (n_classes == 0) {
       data.table(Feature = results$features, Weight = results$weight)[order(-abs(Weight))]
     } else {

R-package/R/xgb.model.dt.tree.R

@@ -62,7 +62,7 @@
 #'
 #' @export
 xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
-                              trees = NULL, use_int_id = FALSE, ...){
+                              trees = NULL, use_int_id = FALSE, ...) {
   check.deprecation(...)
 
   if (!inherits(model, "xgb.Booster") && !is.character(text)) {
@@ -82,7 +82,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
     stop("trees: must be a vector of integers.")
   }
 
-  if (is.null(text)){
+  if (is.null(text)) {
     text <- xgb.dump(model = model, with_stats = TRUE)
   }
 

R-package/R/xgb.plot.importance.R

@@ -102,7 +102,9 @@ xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure
     original_mar <- par()$mar
 
     # reset margins so this function doesn't have side effects
-    on.exit({par(mar = original_mar)})
+    on.exit({
+        par(mar = original_mar)
+    })
 
     mar <- original_mar
     if (!is.null(left_margin))

R-package/R/xgb.plot.multi.trees.R

@@ -61,7 +61,7 @@
 #'
 #' @export
 xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5, plot_width = NULL, plot_height = NULL,
-                                 render = TRUE, ...){
+                                 render = TRUE, ...) {
   if (!requireNamespace("DiagrammeR", quietly = TRUE)) {
     stop("DiagrammeR is required for xgb.plot.multi.trees")
   }

R-package/R/xgb.plot.tree.R

@@ -68,7 +68,7 @@
 #'
 #' @export
 xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot_width = NULL, plot_height = NULL,
-                          render = TRUE, show_node_id = FALSE, ...){
+                          render = TRUE, show_node_id = FALSE, ...) {
   check.deprecation(...)
   if (!inherits(model, "xgb.Booster")) {
     stop("model: Has to be an object of class xgb.Booster")

R-package/demo/cross_validation.R

@@ -12,7 +12,7 @@ cat('running cross validation\n')
 # do cross validation, this will print result out as
 # [iteration]  metric_name:mean_value+std_value
 # std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold = 5, metrics = {'error'})
+xgb.cv(param, dtrain, nrounds, nfold = 5, metrics = 'error')
 
 cat('running cross validation, disable standard deviation display\n')
 # do cross validation, this will print result out as

R-package/demo/predict_leaf_indices.R

@@ -24,7 +24,7 @@ accuracy.before <- (sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.te
 pred_with_leaf <- predict(bst, dtest, predleaf = TRUE)
 head(pred_with_leaf)
 
-create.new.tree.features <- function(model, original.features){
+create.new.tree.features <- function(model, original.features) {
   pred_with_leaf <- predict(model, original.features, predleaf = TRUE)
   cols <- list()
   for (i in 1:model$niter) {

R-package/tests/testthat/test_helpers.R

@@ -170,8 +170,9 @@ test_that("SHAPs sum to predictions, with or without DART", {
       label = y,
       nrounds = nrounds)
 
-    pr <- function(...)
+    pr <- function(...) {
       predict(fit, newdata = d, ...)
+    }
     pred <- pr()
     shap <- pr(predcontrib = TRUE)
     shapi <- pr(predinteraction = TRUE)

R-package/tests/testthat/test_interaction_constraints.R

@@ -17,7 +17,7 @@ test_that("interaction constraints for regression", {
 
   # Set all observations to have the same x3 values then increment
   #  by the same amount
-  preds <- lapply(c(1, 2, 3), function(x){
+  preds <- lapply(c(1, 2, 3), function(x) {
     tmat <- matrix(c(x1, x2, rep(x, 1000)), ncol = 3)
     return(predict(bst, tmat))
   })

demo/kaggle-otto/understandingXGBoostModel.Rmd

@@ -86,7 +86,10 @@ For that purpose, we will:
 
 ```{r classToIntegers}
 # Convert from classes to numbers
-y <- train[, nameLastCol, with = FALSE][[1]] %>% gsub('Class_','',.) %>% {as.integer(.) -1}
+y <- train[, nameLastCol, with = FALSE][[1]] %>%
+    gsub('Class_','',.) %>%
+    as.integer %>%
+    subtract(., 1)
 
 # Display the first 5 levels
 y[1:5]