[R-package] Rename weight -> weights (#4975)

* rename weight -> weights * add test for 'weights' argument * Update R-package/R/lightgbm.R * update docs Co-authored-by: James Lamb <[email protected]>
microsoft · Mar 26, 2022 · c991b2b · c991b2b
1 parent 6b56a90
commit c991b2b
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Showing 3 changed files with 33 additions and 5 deletions.
diff --git a/R-package/R/lightgbm.R b/R-package/R/lightgbm.R
@@ -91,7 +91,8 @@ NULL
 #' @description Simple interface for training a LightGBM model.
 #' @inheritParams lgb_shared_params
 #' @param label Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}}
-#' @param weight vector of response values. If not NULL, will set to dataset
+#' @param weights Sample / observation weights for rows in the input data. If \code{NULL}, will assume that all
+#' observations / rows have the same importance / weight.
 #' @param objective Optimization objective (e.g. `"regression"`, `"binary"`, etc.).
 #' For a list of accepted objectives, see
 #' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{
@@ -117,7 +118,7 @@ NULL
 #' @export
 lightgbm <- function(data,
  label = NULL,
- weight = NULL,
+ weights = NULL,
  params = list(),
  nrounds = 100L,
  verbose = 1L,
@@ -140,7 +141,7 @@ lightgbm <- function(data,
 
  # Check whether data is lgb.Dataset, if not then create lgb.Dataset manually
  if (!lgb.is.Dataset(x = dtrain)) {
- dtrain <- lgb.Dataset(data = data, label = label, weight = weight, init_score = init_score)
+ dtrain <- lgb.Dataset(data = data, label = label, weight = weights, init_score = init_score)
  }
 
  train_args <- list(

diff --git a/R-package/man/lightgbm.Rd b/R-package/man/lightgbm.Rd
diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R
@@ -2927,3 +2927,29 @@ test_that("lightgbm() defaults to 'regression' objective if objective not otherw
  expect_true(any(model_txt_lines == "objective=regression"))
  expect_false(any(model_txt_lines == "objective=regression_l1"))
 })
+
+test_that("lightgbm() accepts 'weight' and 'weights'", {
+ data(mtcars)
+ X <- as.matrix(mtcars[, -1L])
+ y <- as.numeric(mtcars[, 1L])
+ w <- rep(1.0, nrow(X))
+ model <- lightgbm(
+ X
+ , y
+ , weights = w
+ , obj = "regression"
+ , nrounds = 5L
+ , verbose = -1L
+ )
+
+ # Avoid a bad CRAN check due to partial argument matches
+ lgb_args <- list(
+ X
+ , y
+ , weight = w
+ , obj = "regression"
+ , nrounds = 5L
+ , verbose = -1L
+ )
+ model <- do.call(lightgbm, lgb_args)
+})