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

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James Lamb
2023-10-07 20:42:09 -05:00
committed by GitHub
parent 4d7a187cb0
commit 799f8485e2
14 changed files with 33 additions and 32 deletions
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2
R-package/demo/cross_validation.R
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@@ -25,7 +25,7 @@ xgb.cv(param, dtrain, nrounds, nfold = 5,
# you can also do cross validation with customized loss function
# See custom_objective.R
##
print ('running cross validation, with customized loss function')
print('running cross validation, with customized loss function')
logregobj <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")

4
R-package/demo/custom_objective.R
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@@ -35,7 +35,7 @@ evalerror <- function(preds, dtrain) {
param <- list(max_depth = 2, eta = 1, nthread = 2, verbosity = 0,
objective = logregobj, eval_metric = evalerror)
print ('start training with user customized objective')
print('start training with user customized objective')
# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst <- xgb.train(param, dtrain, num_round, watchlist)
@@ -59,7 +59,7 @@ logregobjattr <- function(preds, dtrain) {
}
param <- list(max_depth = 2, eta = 1, nthread = 2, verbosity = 0,
objective = logregobjattr, eval_metric = evalerror)
print ('start training with user customized objective, with additional attributes in DMatrix')
print('start training with user customized objective, with additional attributes in DMatrix')
# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst <- xgb.train(param, dtrain, num_round, watchlist)

2
R-package/demo/early_stopping.R
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@@ -30,7 +30,7 @@ evalerror <- function(preds, dtrain) {
err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
return(list(metric = "error", value = err))
}
print ('start training with early Stopping setting')
print('start training with early Stopping setting')
bst <- xgb.train(param, dtrain, num_round, watchlist,
objective = logregobj, eval_metric = evalerror, maximize = FALSE,