Nothing
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_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.
grid
columns are caughtCode
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'.
grid
columns are caughtCode
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'.
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.
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`.
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()].
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.
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()`
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.
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()]
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.
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 model parameters require finalization but there are recipe parameters that require tuning. Please use `extract_parameter_set_dials()` to set parameter ranges manually and supply the output to the `param_info` argument.
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