tighten integration with workflowsets

.Rbuildignore

@@ -17,3 +17,4 @@ pkgdown/
 tests/testthat/helper_data\.Rda$
 cran-comments.md
 ^CRAN-RELEASE$
+^vignettes/articles$

.gitignore

@@ -6,3 +6,4 @@
 notes/
 inst/doc
 data-raw/paper/paper.tex
+vignettes/articles/wfsets-candidates_cache*

R/add_candidates.R

@@ -19,15 +19,23 @@
 #' evaluated using the [blend_predictions()] function.
 #'
 #' @param data_stack A `data_stack` object.
-#' @param candidates A model definition: either a `tune_results` 
-#' or `resample_results` object outputted from
-#' [tune::tune_grid()], [tune::tune_bayes()], or [tune::fit_resamples()].
-#' These results must have been fitted with the `control` settings
+#' @param candidates A (set of) model definition(s) defining candidate model 
+#' stack members. Should inherit from `tune_results` or `workflow_set`.
+#' 
+#' - `tune_results`: An object outputted from [tune::tune_grid()], 
+#' [tune::tune_bayes()], or [tune::fit_resamples()].
+#' - `workflow_set`: An object outputted from [workflowsets::workflow_map()].
+#' This approach allows for supplying multiple sets of candidate members 
+#' with only one call to `add_candidates`. See the "Stacking With Workflow Sets"
+#' article on the [package website](https://stacks.tidymodels.org/) for example code!
+#' 
+#' Regardless, these results must have been fitted with the `control` settings
 #' `save_pred = TRUE, save_workflow = TRUE`—see the [control_stack_grid()],
 #' [control_stack_bayes()], and [control_stack_resamples()]
 #' documentation for helper functions.
 #' @param name The label for the model definition---defaults to the name
-#' of the `candidates` object.
+#' of the `candidates` object. Ignored if `candidates` inherits from 
+#' `workflow_set`.
 #' @inheritParams stacks
 #' 
 #' @return A `data_stack` object--see [stacks()] for more details! 

tests/testthat/test_add_candidates.R

@@ -293,3 +293,45 @@ test_that("stacks can handle columns and levels named 'class'", {
     "data_stack"
   )
 })
+
+test_that("stacks can add candidates via workflow sets", {
+  skip_on_cran()
+
+  wf_set <-
+    workflowsets::workflow_set(
+      preproc = list(tree_frogs_reg_rec, tree_frogs_reg_rec, spline_rec),
+      models = list(lin_reg_spec, svm_spec, lin_reg_spec),
+      cross = FALSE
+    )
+
+  wf_set_trained <-
+    workflowsets::workflow_map(
+      wf_set,
+      fn = "tune_grid",
+      seed = 1,
+      resamples = reg_folds,
+      metrics = yardstick::metric_set(yardstick::rmse),
+      control = control_stack_grid()
+    )
+
+  set.seed(1)
+  wf_set_stack <- 
+    stacks() %>%
+    add_candidates(wf_set_trained) %>%
+    blend_predictions() %>%
+    fit_members()
+
+  set.seed(1)
+  wf_stack <- 
+    stacks() %>%
+    add_candidates(wf_set_trained$result[[1]], name = wf_set_trained$wflow_id[[1]]) %>%
+    add_candidates(wf_set_trained$result[[2]], name = wf_set_trained$wflow_id[[2]]) %>%
+    add_candidates(wf_set_trained$result[[3]], name = wf_set_trained$wflow_id[[3]]) %>%
+    blend_predictions() %>%
+    fit_members()
+
+  expect_equal(
+    predict(wf_set_stack, tree_frogs),
+    predict(wf_stack, tree_frogs)
+  )
+})