comments

R-package/R/lightgbm.R

@@ -92,13 +92,13 @@ NULL
 #' @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 save_name File name to use when writing the trained model to disk. Should end in ".model".
+#'                  If passing `NULL`, will not save the trained model to disk.
 #' @param init_score initial score is the base prediction lightgbm will boost from
 #' @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}{
 #'                  the "Parameters" section of the documentation}.
-#' @param save_name File name to use when writing the trained model to disk. Should end in ".model".
-#'                  If passing `NULL`, will not save the trained model to disk.
 #' @param ... Additional arguments passed to \code{\link{lgb.train}}. For example
 #'     \itemize{
 #'        \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation}
@@ -146,16 +146,6 @@ lightgbm <- function(data,
     dtrain <- lgb.Dataset(data = data, label = label, weight = weight, init_score = init_score)
   }
 
-  if (length(intersect(names(params), .PARAMETER_ALIASES()[["objective"]]))) {
-    obj_names <- .PARAMETER_ALIASES()[["objective"]]
-    for (obj_name in obj_names) {
-      if (!is.null(params[[obj_name]])) {
-        objective <- params[[obj_name]]
-        break
-      }
-    }
-  }
-
   train_args <- list(
     "params" = params
     , "data" = dtrain