tests/testthat/_snaps/checks.md

In tidymodels/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.

Code
  tune:::check_rset(obj_permut)
Condition
  Error in `tune:::check_rset()`:
  ! Permutation samples are not suitable for tuning.

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 2 parameter columns (`other1` and `other2`) that have not been marked for tuning by `tune()`.

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 1 parameter column that has 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.

errors informatively when needed package isn't installed

Code
  check_workflow(stan_wflow)
Condition
  Error:
  ! Package install is required for rstanarm.

Code
  fit_resamples(stan_wflow, rsample::bootstraps(mtcars))
Condition
  Error in `fit_resamples()`:
  ! Package install is required for rstanarm.

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 a different model mode.

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 a different model mode.

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 `control_grid()`:
  ! `verbose` must be `TRUE` or `FALSE`, not the number 1.

Code
  control_grid(verbose = rep(TRUE, 2))
Condition
  Error in `control_grid()`:
  ! `verbose` must be `TRUE` or `FALSE`, not a logical vector.

Code
  control_grid(allow_par = 1)
Condition
  Error in `control_grid()`:
  ! `allow_par` must be `TRUE` or `FALSE`, not the number 1.

Code
  control_grid(save_pred = "no")
Condition
  Error in `control_grid()`:
  ! `save_pred` must be `TRUE` or `FALSE`, not the string "no".

Code
  control_grid(extract = Inf)
Condition
  Error in `control_grid()`:
  ! `extract` must be a function or `NULL`, not `Inf`.

Code
  control_grid(pkgs = Inf)
Condition
  Error in `control_grid()`:
  ! `pkgs` must be a character vector or `NULL`, not `Inf`.

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 `control_bayes()`:
  ! `verbose` must be `TRUE` or `FALSE`, not the number 1.

Code
  control_bayes(verbose = rep(TRUE, 2))
Condition
  Error in `control_bayes()`:
  ! `verbose` must be `TRUE` or `FALSE`, not a logical vector.

Code
  control_bayes(no_improve = FALSE)
Condition
  Error in `control_bayes()`:
  ! `no_improve` must be a whole number, not `FALSE`.

Code
  control_bayes(uncertain = FALSE)
Condition
  Error in `control_bayes()`:
  ! `uncertain` must be a whole number, not `FALSE`.

Code
  control_bayes(seed = FALSE)
Condition
  Error in `control_bayes()`:
  ! `seed` must be a whole number, not `FALSE`.

Code
  control_bayes(save_pred = "no")
Condition
  Error in `control_bayes()`:
  ! `save_pred` must be `TRUE` or `FALSE`, not the string "no".

Code
  control_bayes(extract = Inf)
Condition
  Error in `control_bayes()`:
  ! `extract` must be a function or `NULL`, not `Inf`.

Code
  control_bayes(pkgs = Inf)
Condition
  Error in `control_bayes()`:
  ! `pkgs` must be a character vector or `NULL`, not `Inf`.

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

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

initial values

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

check parameter finalization

Code
  expect_no_error(p1 <- tune:::check_parameters(w1, data = mtcars, grid_names = character(
    0)))
Message
  i Creating pre-processing data to finalize 1 unknown parameter: "mtry"

Code
  expect_no_error(p2 <- tune:::check_parameters(w2, data = mtcars))
Message
  i Creating pre-processing data to finalize 1 unknown parameter: "mtry"

Code
  expect_no_error(p3_a <- tune:::check_parameters(w3, data = mtcars))
Message
  i Creating pre-processing data to finalize 1 unknown parameter: "mtry"

Code
  tune:::check_parameters(w4, data = mtcars)
Condition
  Error in `tune:::check_parameters()`:
  ! Some model parameters require finalization but there are recipe parameters that require tuning.
  i Please use `extract_parameter_set_dials()` to set parameter ranges manually and supply the output to the `param_info` argument.

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