tests/testthat/_snaps/resample.md

In tidymodels/tune: Tidy Tuning Tools

failure in recipe is caught elegantly

Code
  result <- fit_resamples(lin_mod, rec, folds, control = control)
Message
  x Fold1: preprocessor 1/1:
    Error in `step_spline_natural()`:
    Caused by error in `prep()`:
    ! `deg_free` must be a whole number, not a numeric `NA`.
  x Fold2: preprocessor 1/1:
    Error in `step_spline_natural()`:
    Caused by error in `prep()`:
    ! `deg_free` must be a whole number, not a numeric `NA`.
Condition
  Warning:
  All models failed. Run `show_notes(.Last.tune.result)` for more information.

Code
  note
Output
  [1] "Error in `step_spline_natural()`:\nCaused by error in `prep()`:\n! `deg_free` must be a whole number, not a numeric `NA`."

failure in variables tidyselect specification is caught elegantly

Code
  result <- fit_resamples(workflow, folds, control = control)
Message
  x Fold1: preprocessor 1/1:
    Error in `fit()`:
    ! Can't select columns that don't exist.
    x Column `foobar` doesn't exist.
  x Fold2: preprocessor 1/1:
    Error in `fit()`:
    ! Can't select columns that don't exist.
    x Column `foobar` doesn't exist.
Condition
  Warning:
  All models failed. Run `show_notes(.Last.tune.result)` for more information.

Code
  note
Output
  [1] "Error in `fit()`:\n! Can't select columns that don't exist.\nx Column `foobar` doesn't exist."

classification models generate correct error message

Code
  result <- fit_resamples(log_mod, rec, folds, control = control)
Message
  x Fold1: preprocessor 1/1, model 1/1:
    Error in `check_outcome()`:
    ! 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>, not a ...
Condition
  Warning:
  All models failed. Run `show_notes(.Last.tune.result)` for more information.

Code
  note
Output
  [1] "Error in `check_outcome()`:\n! For a classification model, the outcome should be a <factor>, not a double vector."

tune_grid() falls back to fit_resamples() - formula

Code
  result <- tune_grid(lin_mod, mpg ~ ., folds)
Condition
  Warning:
  No tuning parameters have been detected, performance will be evaluated using the resamples with no tuning.
  Did you want to assign any parameters with a value of `tune()`?

tune_grid() falls back to fit_resamples() - workflow variables

Code
  result <- tune_grid(wf, folds)
Condition
  Warning:
  No tuning parameters have been detected, performance will be evaluated using the resamples with no tuning.
  Did you want to assign any parameters with a value of `tune()`?

tune_grid() ignores grid if there are no tuning parameters

Code
  result <- lin_mod %>% tune_grid(mpg ~ ., grid = data.frame(x = 1), folds)
Condition
  Warning:
  No tuning parameters have been detected, performance will be evaluated using the resamples with no tuning.
  Did you want to assign any parameters with a value of `tune()`?

cannot autoplot fit_resamples() results

Code
  autoplot(result)
Condition
  Error in `autoplot()`:
  ! There is no `autoplot()` implementation for <resample_results>.

ellipses with fit_resamples

Code
  lin_mod %>% fit_resamples(mpg ~ ., folds, something = "wrong")
Condition
  Warning:
  The `...` are not used in this function but 1 object was passed: "something"
Output
  # Resampling results
  # 2-fold cross-validation 
  # A tibble: 2 x 4
    splits          id    .metrics         .notes          
    <list>          <chr> <list>           <list>          
  1 <split [16/16]> Fold1 <tibble [2 x 4]> <tibble [0 x 4]>
  2 <split [16/16]> Fold2 <tibble [2 x 4]> <tibble [0 x 4]>

argument order gives errors for recipe/formula

Code
  fit_resamples(rec, lin_mod, folds)
Condition
  Error in `fit_resamples()`:
  ! The first argument to `fit_resamples()` should be either a model or workflow, not a <recipe> object.

Code
  fit_resamples(mpg ~ ., lin_mod, folds)
Condition
  Error in `fit_resamples()`:
  ! The first argument to `fit_resamples()` should be either a model or workflow, not a <formula> object.

retain extra attributes

Code
  fit_resamples(lin_mod, recipes::recipe(mpg ~ ., mtcars[rep(1:32, 3000), ]),
  folds, control = control_resamples(save_workflow = TRUE))
Message
  i The workflow being saved contains a recipe, which is 8.07 Mb in i memory. If
  this was not intentional, please set the control setting i `save_workflow =
  FALSE`.
Output
  # Resampling results
  # 2-fold cross-validation 
  # A tibble: 2 x 4
    splits          id    .metrics         .notes          
    <list>          <chr> <list>           <list>          
  1 <split [16/16]> Fold1 <tibble [2 x 4]> <tibble [0 x 4]>
  2 <split [16/16]> Fold2 <tibble [2 x 4]> <tibble [0 x 4]>

fit_resamples() when objects need tuning

2 arguments have been tagged for tuning in these components: model_spec and recipe.
i Please use one of the tuning functions (e.g. `tune_grid()`) to optimize them.

1 argument has been tagged for tuning in this component: model_spec.
i Please use one of the tuning functions (e.g. `tune_grid()`) to optimize them.

1 argument has been tagged for tuning in this component: recipe.
i Please use one of the tuning functions (e.g. `tune_grid()`) to optimize them.

tidymodels/tune documentation built on April 12, 2025, 9:40 p.m.