transition from add_tailor(prop) and method

DESCRIPTION

@@ -59,7 +59,7 @@ Suggests:
 Remotes:
     tidymodels/rsample,
     tidymodels/tailor, 
-    tidymodels/workflows
+    tidymodels/workflows#262
 Config/Needs/website: pkgdown, tidymodels, kknn, doParallel, doFuture,
     tidyverse/tidytemplate
 Config/testthat/edition: 3

R/grid_code_paths.R

@@ -384,7 +384,7 @@ tune_grid_loop_iter <- function(split,
   assessment_rows <- as.integer(split, data = "assessment")
   assessment <- vctrs::vec_slice(split$data, assessment_rows)
 
-  if (workflows::.should_inner_split(workflow)) {
+  if (workflows::.workflow_includes_calibration(workflow)) {
     # if the workflow has a postprocessor that needs training (i.e. calibration),
     # further split the analysis data into an "inner" analysis and
     # assessment set.
@@ -397,11 +397,6 @@ tune_grid_loop_iter <- function(split,
     #   calibration set
     # * the model (including the post-processor) generates predictions on the
     #   assessment set and those predictions are assessed with performance metrics
-    # todo: check if workflow's `method` is incompatible with `class(split)`?
-    # todo: workflow's `method` is currently ignored in favor of the one
-    # automatically dispatched to from `split`. consider this is combination
-    # with above todo.
-    split_args <- c(split_args, list(prop = workflow$post$actions$tailor$prop))
     split <- rsample::inner_split(split, split_args = split_args)
     analysis <- rsample::analysis(split)
 

tests/testthat/test-last-fit.R

@@ -246,9 +246,7 @@ test_that("can use `last_fit()` with a workflow - postprocessor (requires traini
       parsnip::linear_reg()
     ) %>%
     workflows::add_tailor(
-      tailor::tailor("regression") %>% tailor::adjust_numeric_calibration("linear"),
-      prop = 2/3,
-      method = class(split)
+      tailor::tailor() %>% tailor::adjust_numeric_calibration("linear")
     )
 
   set.seed(1)
@@ -261,13 +259,21 @@ test_that("can use `last_fit()` with a workflow - postprocessor (requires traini
   last_fit_preds <- collect_predictions(last_fit_res)
 
   set.seed(1)
-  wflow_res <- generics::fit(wflow, rsample::analysis(split))
+  inner_split <- rsample::inner_split(split, split_args = list())
+
+  set.seed(1)
+  wflow_res <-
+    generics::fit(
+      wflow,
+      rsample::analysis(inner_split),
+      calibration = rsample::assessment(inner_split)
+    )
   wflow_preds <- predict(wflow_res, rsample::assessment(split))
 
   expect_equal(last_fit_preds[".pred"], wflow_preds)
 })
 
-test_that("can use `last_fit()` with a workflow - postprocessor (requires training)", {
+test_that("can use `last_fit()` with a workflow - postprocessor (does not require training)", {
   skip_if_not_installed("tailor")
 
   y <- seq(0, 7, .001)
@@ -284,9 +290,7 @@ test_that("can use `last_fit()` with a workflow - postprocessor (requires traini
       parsnip::linear_reg()
     ) %>%
     workflows::add_tailor(
-      tailor::tailor("regression") %>% tailor::adjust_numeric_range(lower_limit = 1),
-      prop = 2/3,
-      method = class(split)
+      tailor::tailor() %>% tailor::adjust_numeric_range(lower_limit = 1)
     )
 
   set.seed(1)

tests/testthat/test-resample.R

@@ -151,9 +151,7 @@ test_that("can use `fit_resamples()` with a workflow - postprocessor (requires t
       parsnip::linear_reg()
     ) %>%
     workflows::add_tailor(
-      tailor::tailor("regression") %>% tailor::adjust_numeric_calibration("linear"),
-      prop = 2/3,
-      method = class(folds$splits[[1]])
+      tailor::tailor() %>% tailor::adjust_numeric_calibration("linear")
     )
 
   set.seed(1)
@@ -178,8 +176,20 @@ test_that("can use `fit_resamples()` with a workflow - postprocessor (requires t
   seed <- generate_seeds(TRUE, 1)[[1]]
   old_kind <- RNGkind()[[1]]
   assign(".Random.seed", seed, envir = globalenv())
+  withr::defer(RNGkind(kind = old_kind))
 
-  wflow_res <- generics::fit(wflow, rsample::analysis(folds$splits[[1]]))
+  inner_split_1 <-
+    rsample::inner_split(
+      folds$splits[[1]],
+      split_args = list(v = 2, repeats = 1, breaks = 4, pool = 0.1)
+    )
+
+  wflow_res <-
+    generics::fit(
+      wflow,
+      rsample::analysis(inner_split_1),
+      calibration = rsample::assessment(inner_split_1)
+    )
   wflow_preds <- predict(wflow_res, rsample::assessment(folds$splits[[1]]))
 
   tune_wflow$fit$fit$elapsed$elapsed <- wflow_res$fit$fit$elapsed$elapsed
@@ -201,7 +211,7 @@ test_that("can use `fit_resamples()` with a workflow - postprocessor (no trainin
       parsnip::linear_reg()
     ) %>%
     workflows::add_tailor(
-      tailor::tailor("regression") %>% tailor::adjust_numeric_range(lower_limit = 1)
+      tailor::tailor() %>% tailor::adjust_numeric_range(lower_limit = 1)
     )
 
   set.seed(1)