[R-package] Fix misdetected objective when passing `lgb.Dataset` instance to `lightgbm()` (#6005)

R-package/R/lightgbm.R

@@ -116,7 +116,7 @@ NULL
 #'                  \item If passing a factor with more than two variables, will use objective \code{"multiclass"}
 #'                  (note that parameter \code{num_class} in this case will also be determined automatically from
 #'                  \code{label}).
-#'                  \item Otherwise, will use objective \code{"regression"}.
+#'                  \item Otherwise (or if passing \code{lgb.Dataset} as input), will use objective \code{"regression"}.
 #'                  }
 #'
 #'                  \emph{New in version 4.0.0}
@@ -211,6 +211,9 @@ lightgbm <- function(data,
     rm(temp)
   } else {
     data_processor <- NULL
+    if (objective == "auto") {
+      objective <- "regression"
+    }
   }
 
   # Set data to a temporary variable

R-package/man/lightgbm.Rd

@@ -68,7 +68,7 @@ set to the iteration number of the best iteration.}
                  \item If passing a factor with more than two variables, will use objective \code{"multiclass"}
                  (note that parameter \code{num_class} in this case will also be determined automatically from
                  \code{label}).
-                 \item Otherwise, will use objective \code{"regression"}.
+                 \item Otherwise (or if passing \code{lgb.Dataset} as input), will use objective \code{"regression"}.
                  }
 
                  \emph{New in version 4.0.0}}

R-package/tests/testthat/test_basic.R

@@ -3790,3 +3790,18 @@ test_that("lightgbm() accepts named categorical_features", {
   )
   expect_true(length(model$params$categorical_feature) > 0L)
 })
+
+test_that("lightgbm() correctly sets objective when passing lgb.Dataset as input", {
+  data(mtcars)
+  y <- mtcars$mpg
+  x <- as.matrix(mtcars[, -1L])
+  ds <- lgb.Dataset(x, label = y)
+  model <- lightgbm(
+    ds
+    , objective = "auto"
+    , verbose = .LGB_VERBOSITY
+    , nrounds = 5L
+    , num_threads = .LGB_MAX_THREADS
+  )
+  expect_equal(model$params$objective, "regression")
+})