small testing updates

Author: EmilHvitfeldt

tests/testthat/_snaps/bayes.md

@@ -169,7 +169,7 @@
         control = control_bayes(verbose_iter = TRUE))
     Message
       Optimizing rmse using the expected improvement
-            
+      
       -- Iteration 1 -----------------------------------------------------------------
       
       i Current best:		rmse=2.418 (@iter 0)
@@ -523,68 +523,28 @@
         Bldg_Type + Latitude + Longitude, resamples = folds, initial = 3, metrics = yardstick::metric_set(
         rsq), param_info = parameters(dials::cost_complexity(c(-2, 0))))
     Message
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9998726`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 1 missing value was found and removed before fitting
         the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9995734`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 2 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9995`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 3 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9992439`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 4 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9996399`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 5 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9994867`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 6 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9997809`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! For the rsq estimates, 7 missing values were found and removed before
         fitting the Gaussian process model.
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 0.9999164`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
 
 ---
 
@@ -595,36 +555,11 @@
       metrics = yardstick::metric_set(rsq), param_info = parameters(dials::cost_complexity(
         c(0.5, 0))))
     Message
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 1.95966`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 2.500534`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 1.263317`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 2.792327`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
-      ! validation: internal:
-        There was 1 warning in `dplyr::summarise()`.
-        i In argument: `.estimate = metric_fn(truth = Sale_Price, estimate = ....
-        i In group 1: `cost_complexity = 1.224671`.
-        Caused by warning:
-        ! A correlation computation is required, but `estimate` is constant an...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
+      ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
       ! All of the rsq estimates were missing. The Gaussian process model cannot be
         fit to the data.
       ! Gaussian process model: no non-missing arguments to min; returning Inf, no non-missing arguments...

tests/testthat/_snaps/resample.md

@@ -39,10 +39,10 @@
     Message
       x Fold1: preprocessor 1/1, model 1/1:
         Error in `check_outcome()`:
-        ! For a classification model, the outcome should be a factor.
+        ! For a classification model, the outcome should be a `factor`, not a ...
       x Fold2: preprocessor 1/1, model 1/1:
         Error in `check_outcome()`:
-        ! For a classification model, the outcome should be a factor.
+        ! For a classification model, the outcome should be a `factor`, not a ...
     Condition
       Warning:
       All models failed. Run `show_notes(.Last.tune.result)` for more information.

tests/testthat/test-resample.R

@@ -221,13 +221,12 @@ test_that("classification models generate correct error message", {
   expect_length(notes, 2L)
 
   # Known failure in the recipe
-  expect_true(all(grepl("outcome should be a factor", note)))
+  expect_true(all(grepl("outcome should be a `factor`", note)))
 
   expect_equal(extract, list(NULL, NULL))
   expect_equal(predictions, list(NULL, NULL))
 })
 
-
 # tune_grid() fallback ---------------------------------------------------------
 
 test_that("`tune_grid()` falls back to `fit_resamples()` - formula", {