[R-package] fix warnings in examples (#4568)

* [R-package] fix warnings in examples

* fix silently-ignored parameter

R-package/R/lgb.Booster.R

@@ -743,20 +743,23 @@ Booster <- R6::R6Class(
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 5L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #' )
 #' preds <- predict(model, test$data)
 #'
 #' # pass other prediction parameters
-#' predict(
+#' preds <- predict(
 #'     model,
 #'     test$data,
 #'     params = list(
@@ -824,15 +827,18 @@ predict.lgb.Booster <- function(object,
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 5L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 3L
 #' )
 #' model_file <- tempfile(fileext = ".txt")
@@ -885,15 +891,18 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 10L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 5L
 #' )
 #' lgb.save(model, tempfile(fileext = ".txt"))
@@ -936,15 +945,18 @@ lgb.save <- function(booster, filename, num_iteration = NULL) {
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 10L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 5L
 #' )
 #' json_model <- lgb.dump(model)
@@ -983,15 +995,18 @@ lgb.dump <- function(booster, num_iteration = NULL) {
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 5L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #' )
 #'
 #' # Examine valid data_name values

R-package/R/lgb.Dataset.R

@@ -1145,12 +1145,15 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
 #'
 #' @examples
 #' \donttest{
+#' # create training Dataset
 #' data(agaricus.train, package ="lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
+#'
+#' # create a validation Dataset, using dtrain as a reference
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
-#' dtest <- lgb.Dataset(test$data, test = train$label)
+#' dtest <- lgb.Dataset(test$data, label = test$label)
 #' lgb.Dataset.set.reference(dtest, dtrain)
 #' }
 #' @rdname lgb.Dataset.set.reference

R-package/R/lgb.cv.R

@@ -63,14 +63,17 @@ CVBooster <- R6::R6Class(
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' model <- lgb.cv(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 5L
 #'   , nfold = 3L
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #' )
 #' }
 #' @importFrom data.table data.table setorderv

R-package/R/lgb.train.R

@@ -36,15 +36,18 @@
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 5L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 3L
 #' )
 #' }

R-package/R/lgb.unloader.R

@@ -21,15 +21,18 @@
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 5L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #' )
 #'
 #' lgb.unloader(restore = FALSE, wipe = FALSE, envir = .GlobalEnv)

R-package/R/readRDS.lgb.Booster.R

@@ -15,15 +15,18 @@
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'   params = params
 #'   , data = dtrain
 #'   , nrounds = 10L
 #'   , valids = valids
-#'   , min_data = 1L
-#'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 5L
 #' )
 #' model_file <- tempfile(fileext = ".rds")

R-package/R/saveRDS.lgb.Booster.R

@@ -26,15 +26,18 @@
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' params <- list(objective = "regression", metric = "l2")
+#' params <- list(
+#'   objective = "regression"
+#'   , metric = "l2"
+#'   , min_data = 1L
+#'   , learning_rate = 1.0
+#' )
 #' valids <- list(test = dtest)
 #' model <- lgb.train(
 #'     params = params
 #'     , data = dtrain
 #'     , nrounds = 10L
 #'     , valids = valids
-#'     , min_data = 1L
-#'     , learning_rate = 1.0
 #'     , early_stopping_rounds = 5L
 #' )
 #' model_file <- tempfile(fileext = ".rds")

R-package/man/lgb.Dataset.set.reference.Rd

@@ -19,12 +19,15 @@ If you want to use validation data, you should set reference to training data
 }
 \examples{
 \donttest{
+# create training Dataset
 data(agaricus.train, package ="lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
+
+# create a validation Dataset, using dtrain as a reference
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
-dtest <- lgb.Dataset(test$data, test = train$label)
+dtest <- lgb.Dataset(test$data, label = test$label)
 lgb.Dataset.set.reference(dtest, dtrain)
 }
 }

R-package/man/lgb.cv.Rd

@@ -159,14 +159,17 @@ Cross validation logic used by LightGBM
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 model <- lgb.cv(
   params = params
   , data = dtrain
   , nrounds = 5L
   , nfold = 3L
-  , min_data = 1L
-  , learning_rate = 1.0
 )
 }
 }

R-package/man/lgb.dump.Rd

@@ -26,15 +26,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 10L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
   , early_stopping_rounds = 5L
 )
 json_model <- lgb.dump(model)

R-package/man/lgb.get.eval.result.Rd

@@ -40,15 +40,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 5L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
 )
 
 # Examine valid data_name values

R-package/man/lgb.load.Rd

@@ -26,15 +26,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 5L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
   , early_stopping_rounds = 3L
 )
 model_file <- tempfile(fileext = ".txt")

R-package/man/lgb.save.Rd

@@ -28,15 +28,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 10L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
   , early_stopping_rounds = 5L
 )
 lgb.save(model, tempfile(fileext = ".txt"))

R-package/man/lgb.train.Rd

@@ -144,15 +144,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 5L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
   , early_stopping_rounds = 3L
 )
 }

R-package/man/lgb.unloader.Rd

@@ -33,15 +33,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 5L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
 )
 
 lgb.unloader(restore = FALSE, wipe = FALSE, envir = .GlobalEnv)

R-package/man/predict.lgb.Booster.Rd

@@ -74,20 +74,23 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 5L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
 )
 preds <- predict(model, test$data)
 
 # pass other prediction parameters
-predict(
+preds <- predict(
     model,
     test$data,
     params = list(

R-package/man/readRDS.lgb.Booster.Rd

@@ -26,15 +26,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
   , nrounds = 10L
   , valids = valids
-  , min_data = 1L
-  , learning_rate = 1.0
   , early_stopping_rounds = 5L
 )
 model_file <- tempfile(fileext = ".rds")

R-package/man/saveRDS.lgb.Booster.Rd

@@ -50,15 +50,18 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-params <- list(objective = "regression", metric = "l2")
+params <- list(
+  objective = "regression"
+  , metric = "l2"
+  , min_data = 1L
+  , learning_rate = 1.0
+)
 valids <- list(test = dtest)
 model <- lgb.train(
     params = params
     , data = dtrain
     , nrounds = 10L
     , valids = valids
-    , min_data = 1L
-    , learning_rate = 1.0
     , early_stopping_rounds = 5L
 )
 model_file <- tempfile(fileext = ".rds")