tests/testthat/_snaps/checks.md

In tune: Tidy Tuning Tools

rsample objects

Code
  tune:::check_rset(obj_loo)
Condition
  Error in `tune:::check_rset()`:
  ! Leave-one-out cross-validation is not currently supported with tune.

Code
  tune:::check_rset(obj_nst)
Condition
  Error in `tune:::check_rset()`:
  ! Nested resampling is not currently supported with tune.

grid objects

Code
  tune:::check_grid(rbind(grid_1, grid_1), chi_wflow)
Condition
  Warning:
  Duplicate rows in grid of tuning combinations found and removed.
Output
  # A tibble: 10 x 6
     penalty mixture imputation threshold deg_free degree
       <int>   <int>      <int>     <int>    <int>  <int>
   1       1       1          1         1        1      1
   2       2       2          2         2        2      2
   3       3       3          3         3        3      3
   4       4       4          4         4        4      4
   5       5       5          5         5        5      5
   6       6       6          6         6        6      6
   7       7       7          7         7        7      7
   8       8       8          8         8        8      8
   9       9       9          9         9        9      9
  10      10      10         10        10       10     10

Code
  tune:::check_grid(chi_wflow, chi_wflow)
Condition
  Error in `tune:::check_grid()`:
  ! `grid` should be a positive integer or a data frame.

Unknown grid columns are caught

Code
  tune:::check_grid(grid, workflow)
Condition
  Error in `tune:::check_grid()`:
  ! The provided `grid` has the following parameter columns that have not been marked for tuning by `tune()`: 'other1', 'other2'.

Missing required grid columns are caught

Code
  tune:::check_grid(grid, workflow)
Condition
  Error in `tune:::check_grid()`:
  ! The provided `grid` is missing the following parameter columns that have been marked for tuning by `tune()`: 'deg_free'.

workflow objects

Code
  tune:::check_workflow(x = wflow_2, check_dials = TRUE)
Condition
  Error in `tune:::check_workflow()`:
  ! The workflow has arguments whose ranges are not finalized: 'mtry'

Code
  tune:::check_workflow(wflow_3)
Condition
  Error in `tune:::check_workflow()`:
  ! A formula, recipe, or variables preprocessor is required.

Code
  tune:::check_workflow(wflow_4)
Condition
  Error in `tune:::check_workflow()`:
  ! A parsnip model is required.

workflow objects (will not tune, tidymodels/tune#548)

Code
  tune_grid(lr_lm_1, rec_bare, rsample::bootstraps(Chicago, 2))
Condition
  Error in `tune_grid()`:
  ! The parameter `penalty` was marked with `tune()`, though will not be tuned.
  i This usually means that the current modeling engine `lm` does not support tuning `penalty`.

Code
  tune_bayes(lr_lm_2, rec_tune, rsample::bootstraps(Chicago, 2))
Condition
  Error in `tune_bayes()`:
  ! The parameters `penalty` and `mixture` were marked with `tune()`, though will not be tuned.
  i This usually means that the current modeling engine `lm` does not support tuning `penalty` and `mixture`.

yardstick objects

Code
  tune:::check_metrics(yardstick::rmse, chi_wflow)
Condition
  Error in `tune:::check_metrics()`:
  ! The `metrics` argument should be the results of [yardstick::metric_set()].

metrics must match the parsnip engine

Code
  tune:::check_metrics(metric_set1, workflow1)
Condition
  Error in `tune:::check_metrics()`:
  ! The parsnip model has `mode = 'regression'`, but `metrics` is a metric set for class / probability metrics.

Code
  tune:::check_metrics(metric_set2, workflow2)
Condition
  Error in `tune:::check_metrics()`:
  ! The parsnip model has `mode = 'classification'`, but `metrics` is a metric set for regression metrics.

grid control objects

Code
  control_grid(tomato = 1)
Condition
  Error in `control_grid()`:
  ! unused argument (tomato = 1)

Code
  control_grid(verbose = 1)
Condition
  Error in `val_class_and_single()`:
  ! Argument 'verbose' should be a single logical value in `control_grid()`

Code
  control_grid(verbose = rep(TRUE, 2))
Condition
  Error in `val_class_and_single()`:
  ! Argument 'verbose' should be a single logical value in `control_grid()`

Code
  control_grid(allow_par = 1)
Condition
  Error in `val_class_and_single()`:
  ! Argument 'allow_par' should be a single logical value in `control_grid()`

Code
  control_grid(save_pred = "no")
Condition
  Error in `val_class_and_single()`:
  ! Argument 'save_pred' should be a single logical value in `control_grid()`

Code
  control_grid(extract = Inf)
Condition
  Error in `val_class_or_null()`:
  ! Argument 'extract' should be a function or NULL in `control_grid()`

Code
  control_grid(pkgs = Inf)
Condition
  Error in `val_class_or_null()`:
  ! Argument 'pkgs' should be a character or NULL in `control_grid()`

Bayes control objects

Code
  control_bayes(tomato = 1)
Condition
  Error in `control_bayes()`:
  ! unused argument (tomato = 1)

Code
  control_bayes(verbose = 1)
Condition
  Error in `val_class_and_single()`:
  ! Argument 'verbose' should be a single logical value in `control_bayes()`

Code
  control_bayes(verbose = rep(TRUE, 2))
Condition
  Error in `val_class_and_single()`:
  ! Argument 'verbose' should be a single logical value in `control_bayes()`

Code
  control_bayes(no_improve = FALSE)
Condition
  Error in `val_class_and_single()`:
  ! Argument 'no_improve' should be a single numeric or integer value in `control_bayes()`

Code
  control_bayes(uncertain = FALSE)
Condition
  Error in `val_class_and_single()`:
  ! Argument 'uncertain' should be a single numeric or integer value in `control_bayes()`

Code
  control_bayes(seed = FALSE)
Condition
  Error in `val_class_and_single()`:
  ! Argument 'seed' should be a single numeric or integer value in `control_bayes()`

Code
  control_bayes(save_pred = "no")
Condition
  Error in `val_class_and_single()`:
  ! Argument 'save_pred' should be a single logical value in `control_bayes()`

Code
  control_bayes(extract = Inf)
Condition
  Error in `val_class_or_null()`:
  ! Argument 'extract' should be a function or NULL in `control_bayes()`

Code
  control_bayes(pkgs = Inf)
Condition
  Error in `val_class_or_null()`:
  ! Argument 'pkgs' should be a character or NULL in `control_bayes()`

Code
  control_bayes(time_limit = "a")
Condition
  Error in `val_class_and_single()`:
  ! Argument 'time_limit' should be a single logical or numeric value in `control_bayes()`

Code
  tmp <- control_bayes(no_improve = 2, uncertain = 5)
Message
  ! Uncertainty sample scheduled after 5 poor iterations but the search will stop after 2.

initial values

Code
  tune:::check_initial(data.frame(), extract_parameter_set_dials(wflow_1),
  wflow_1, mtfolds, yardstick::metric_set(yardstick::rsq), control_bayes())
Condition
  Error in `tune:::check_initial()`:
  ! `initial` should be a positive integer or the results of [tune_grid()]

Acquisition function objects

Code
  tune:::check_direction(1)
Condition
  Error in `tune:::check_direction()`:
  ! `maximize` should be a single logical.

Code
  tune:::check_direction(rep(TRUE, 2))
Condition
  Error in `tune:::check_direction()`:
  ! `maximize` should be a single logical.

Code
  tune:::check_best(FALSE)
Condition
  Error in `tune:::check_best()`:
  ! `best` should be a single, non-missing numeric.

Code
  tune:::check_best(rep(2, 2))
Condition
  Error in `tune:::check_best()`:
  ! `best` should be a single, non-missing numeric.

Code
  tune:::check_best(NA)
Condition
  Error in `tune:::check_best()`:
  ! `best` should be a single, non-missing numeric.

check parameter finalization

Code
  expect_error(p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = character(
    0)), regex = NA)
Message
  i Creating pre-processing data to finalize unknown parameter: mtry

Code
  expect_error(p2 <- tune:::check_parameters(w2, data = mtcars), regex = NA)
Message
  i Creating pre-processing data to finalize unknown parameter: mtry

Code
  expect_error(p3_a <- tune:::check_parameters(w3, data = mtcars), regex = NA)
Message
  i Creating pre-processing data to finalize unknown parameter: mtry

Code
  tune:::check_parameters(w4, data = mtcars)
Condition
  Error in `tune:::check_parameters()`:
  ! Some tuning parameters require finalization but there are recipe parameters that require tuning. Please use `parameters()` to finalize the parameter ranges.

tune documentation built on Aug. 24, 2023, 1:09 a.m.