rename potato set to calibration (closes #260)

R/fit.R

@@ -58,22 +58,22 @@ fit.workflow <- function(object, data, ..., control = control_workflow()) {
     cli_abort("{.arg data} must be provided to fit a workflow.")
   }
 
-  # If `potato` is not overwritten in the following `if` statement, then the
+  # If `calibration` is not overwritten in the following `if` statement, then the
   # the postprocessor doesn't actually require training and the dataset
   # passed to `.fit_post()` will have no effect.
-  potato <- data
+  calibration <- data
   if (.should_inner_split(object)) {
     inner_split <- make_inner_split(object, data)
 
     data <- rsample::analysis(inner_split)
-    potato <- rsample::assessment(inner_split)
+    calibration <- rsample::assessment(inner_split)
   }
 
   workflow <- object
   workflow <- .fit_pre(workflow, data)
   workflow <- .fit_model(workflow, control)
   if (has_postprocessor(workflow)) {
-    workflow <- .fit_post(workflow, potato)
+    workflow <- .fit_post(workflow, calibration)
   }
   workflow <- .fit_finalize(workflow)
 

R/post-action-tailor.R

@@ -45,9 +45,9 @@
 #' predictions. Predictions on data that a model was trained on likely follow
 #' different distributions than predictions on unseen data; thus, workflows must
 #' split up the supplied `data` into two training sets, where the first is used to
-#' train the preprocessor and model and the second is passed to that trained
-#' processor and model to generate predictions, which then form the training data
-#' for the post-processor.
+#' train the preprocessor and model and the second, called the "calibration set,"
+#' is passed to that trained postprocessor and model to generate predictions,
+#' which then form the training data for the postprocessor.
 #'
 #' The arguments `prop` and `method` parameterize how that data is split up.
 #' `prop` determines the proportion of rows in `fit.workflow(data)` that are

tests/testthat/test-fit.R

@@ -230,7 +230,7 @@ test_that("`make_inner_split()` works", {
     add_model(parsnip::linear_reg()) %>%
     add_tailor(tlr)
 
-  # defaults to 1/3 allotted to potato via `mc_split`s
+  # defaults to 1/3 allotted to calibration via `mc_split`s
   inner_splt <- make_inner_split(wflow, data.frame(x = 1:36))
   expect_s3_class(inner_splt, c("mc_split_inner", "mc_split"))
   expect_equal(nrow(rsample::analysis(inner_splt)), 24)

tests/testthat/test-post-action-tailor.R

@@ -142,7 +142,7 @@ test_that("postprocessor fit aligns with manually fitted version (with calibrati
   wf_post_fit <- fit(wflow_post, dat)
 
   # ...verify predictions are the same as training the post-proc separately.
-  # note that this test naughtily re-predicts on the potato set.
+  # note that this test naughtily re-predicts on the calibration set.
   wflow_simple_preds <- augment(wf_simple_fit, rsample::assessment(inner_split))
   post_trained <- fit(post, wflow_simple_preds, y, .pred)
   wflow_manual_preds <- predict(post_trained, wflow_simple_preds)
@@ -188,7 +188,7 @@ test_that("postprocessor fit uses correct data (with calibration, non-default `p
   wf_post_fit <- fit(wflow_post, dat)
 
   # ...verify predictions are the same as training the post-proc separately.
-  # note that this test naughtily re-predicts on the potato set.
+  # note that this test naughtily re-predicts on the calibration set.
   wflow_simple_preds <- augment(wf_simple_fit, rsample::assessment(inner_split))
   post_trained <- fit(post, wflow_simple_preds, y, .pred)
   wflow_manual_preds <- predict(post_trained, wflow_simple_preds)