Use rsample::manual_rset()

NAMESPACE

@@ -41,7 +41,6 @@ S3method(parameters,workflow)
 S3method(predict,conf_bound)
 S3method(predict,exp_improve)
 S3method(predict,prob_improve)
-S3method(pretty,manual_rset)
 S3method(print,control_grid)
 S3method(print,prob_improve)
 S3method(print,tune_results)

R/last_fit.R

@@ -126,10 +126,12 @@ last_fit_workflow <- function(object, split, metrics) {
   extr <- function(x)
     x
   ctrl <- control_resamples(save_pred = TRUE, extract = extr)
+  splits <- list(split)
+  resamples <- rsample::manual_rset(splits, ids = "train/test split")
   res <-
     fit_resamples(
       object,
-      resamples = split_to_rset(split),
+      resamples = resamples,
       metrics = metrics,
       control = ctrl
     )
@@ -139,19 +141,3 @@ last_fit_workflow <- function(object, split, metrics) {
   class(res) <- unique(class(res))
   res
 }
-
-# Fake an rset for `fit_resamples()`
-split_to_rset <- function(x) {
-  splits <- list(x)
-  ids <- "train/test split"
-  new_manual_rset(splits, ids)
-}
-
-new_manual_rset <- function(splits, ids) {
-  rsample::new_rset(splits, ids, subclass = c("manual_rset", "rset"))
-}
-
-#' @export
-pretty.manual_rset <- function(x, ...) {
-  "Manual resampling"
-}

tests/testthat/test-last-fit.R

@@ -29,20 +29,6 @@ test_that("recipe method", {
   expect_equal(res$.predictions[[1]]$.pred, unname(test_pred))
 })
 
-test_that("split_to_rset", {
-
-  res <- tune:::split_to_rset(split)
-  expect_true(inherits(res, "manual_rset"))
-  expect_true(nrow(res) == 1)
-  expect_true(nrow(res) == 1)
-
-  res <- linear_reg() %>% set_engine("lm") %>% last_fit(f, split)
-  expect_true(is.list(res$.workflow))
-  expect_true(inherits(res$.workflow[[1]], "workflow"))
-  expect_true(is.list(res$.predictions))
-  expect_true(inherits(res$.predictions[[1]], "tbl_df"))
-})
-
 test_that("collect metrics of last fit", {
 
   res <- linear_reg() %>% set_engine("lm") %>% last_fit(f, split)