tests/testthat/_snaps/bayes.md

In tidymodels/tune: Tidy Tuning Tools

tune recipe only

Code
  tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1, iter = iter2,
    control = control_bayes(verbose = TRUE))
Message

  >  Generating a set of 2 initial parameter results
  v Initialization complete

  i Gaussian process model
  ! The Gaussian process model is being fit using 1 features but only has 2
    data points to do so. This may cause errors or a poor model fit.
  v Gaussian process model
  i Generating 3 candidates
  i Predicted candidates
  i Estimating performance
  i Fold01: preprocessor 1/1
  v Fold01: preprocessor 1/1
  i Fold01: preprocessor 1/1, model 1/1
  v Fold01: preprocessor 1/1, model 1/1
  i Fold01: preprocessor 1/1, model 1/1 (extracts)
  i Fold01: preprocessor 1/1, model 1/1 (predictions)
  i Fold02: preprocessor 1/1
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  v Fold02: preprocessor 1/1, model 1/1
  i Fold02: preprocessor 1/1, model 1/1 (extracts)
  i Fold02: preprocessor 1/1, model 1/1 (predictions)
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  i Fold03: preprocessor 1/1, model 1/1 (extracts)
  i Fold03: preprocessor 1/1, model 1/1 (predictions)
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  i Fold07: preprocessor 1/1, model 1/1 (predictions)
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  v Fold08: preprocessor 1/1, model 1/1
  i Fold08: preprocessor 1/1, model 1/1 (extracts)
  i Fold08: preprocessor 1/1, model 1/1 (predictions)
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  i Fold09: preprocessor 1/1, model 1/1
  v Fold09: preprocessor 1/1, model 1/1
  i Fold09: preprocessor 1/1, model 1/1 (extracts)
  i Fold09: preprocessor 1/1, model 1/1 (predictions)
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  v Fold10: preprocessor 1/1, model 1/1
  i Fold10: preprocessor 1/1, model 1/1 (extracts)
  i Fold10: preprocessor 1/1, model 1/1 (predictions)
  v Estimating performance
  i Gaussian process model
  v Gaussian process model
  i Generating 2 candidates
  i Predicted candidates
  i Estimating performance
  i Fold01: preprocessor 1/1
  v Fold01: preprocessor 1/1
  i Fold01: preprocessor 1/1, model 1/1
  v Fold01: preprocessor 1/1, model 1/1
  i Fold01: preprocessor 1/1, model 1/1 (extracts)
  i Fold01: preprocessor 1/1, model 1/1 (predictions)
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  i Fold02: preprocessor 1/1, model 1/1
  v Fold02: preprocessor 1/1, model 1/1
  i Fold02: preprocessor 1/1, model 1/1 (extracts)
  i Fold02: preprocessor 1/1, model 1/1 (predictions)
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  v Fold03: preprocessor 1/1, model 1/1
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  i Fold03: preprocessor 1/1, model 1/1 (predictions)
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  i Fold10: preprocessor 1/1, model 1/1 (extracts)
  i Fold10: preprocessor 1/1, model 1/1 (predictions)
  v Estimating performance
Output
  # Tuning results
  # 10-fold cross-validation 
  # A tibble: 30 x 5
     splits         id     .metrics         .notes           .iter
     <list>         <chr>  <list>           <list>           <int>
   1 <split [28/4]> Fold01 <tibble [4 x 5]> <tibble [0 x 4]>     0
   2 <split [28/4]> Fold02 <tibble [4 x 5]> <tibble [0 x 4]>     0
   3 <split [29/3]> Fold03 <tibble [4 x 5]> <tibble [0 x 4]>     0
   4 <split [29/3]> Fold04 <tibble [4 x 5]> <tibble [0 x 4]>     0
   5 <split [29/3]> Fold05 <tibble [4 x 5]> <tibble [0 x 4]>     0
   6 <split [29/3]> Fold06 <tibble [4 x 5]> <tibble [0 x 4]>     0
   7 <split [29/3]> Fold07 <tibble [4 x 5]> <tibble [0 x 4]>     0
   8 <split [29/3]> Fold08 <tibble [4 x 5]> <tibble [0 x 4]>     0
   9 <split [29/3]> Fold09 <tibble [4 x 5]> <tibble [0 x 4]>     0
  10 <split [29/3]> Fold10 <tibble [4 x 5]> <tibble [0 x 4]>     0
  # i 20 more rows

Code
  tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1, iter = iter2,
    control = control_bayes(verbose_iter = TRUE))
Message
  Optimizing rmse using the expected improvement

  -- Iteration 1 -----------------------------------------------------------------

  i Current best:       rmse=2.461 (@iter 0)
  i Gaussian process model
  ! The Gaussian process model is being fit using 1 features but only has 2
    data points to do so. This may cause errors or a poor model fit.
  v Gaussian process model
  i Generating 3 candidates
  i Predicted candidates
  i num_comp=5
  i Estimating performance
  v Estimating performance
  <3 Newest results:    rmse=2.453 (+/-0.381)

  -- Iteration 2 -----------------------------------------------------------------

  i Current best:       rmse=2.453 (@iter 1)
  i Gaussian process model
  v Gaussian process model
  i Generating 2 candidates
  i Predicted candidates
  i num_comp=1
  i Estimating performance
  v Estimating performance
  (x) Newest results:   rmse=2.646 (+/-0.286)
Output
  # Tuning results
  # 10-fold cross-validation 
  # A tibble: 30 x 5
     splits         id     .metrics         .notes           .iter
     <list>         <chr>  <list>           <list>           <int>
   1 <split [28/4]> Fold01 <tibble [4 x 5]> <tibble [0 x 4]>     0
   2 <split [28/4]> Fold02 <tibble [4 x 5]> <tibble [0 x 4]>     0
   3 <split [29/3]> Fold03 <tibble [4 x 5]> <tibble [0 x 4]>     0
   4 <split [29/3]> Fold04 <tibble [4 x 5]> <tibble [0 x 4]>     0
   5 <split [29/3]> Fold05 <tibble [4 x 5]> <tibble [0 x 4]>     0
   6 <split [29/3]> Fold06 <tibble [4 x 5]> <tibble [0 x 4]>     0
   7 <split [29/3]> Fold07 <tibble [4 x 5]> <tibble [0 x 4]>     0
   8 <split [29/3]> Fold08 <tibble [4 x 5]> <tibble [0 x 4]>     0
   9 <split [29/3]> Fold09 <tibble [4 x 5]> <tibble [0 x 4]>     0
  10 <split [29/3]> Fold10 <tibble [4 x 5]> <tibble [0 x 4]>     0
  # i 20 more rows

Code
  tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1, iter = iter2,
    control = control_bayes(verbose_iter = TRUE, verbose = TRUE))
Message

  >  Generating a set of 2 initial parameter results
  v Initialization complete

  Optimizing rmse using the expected improvement

  -- Iteration 1 -----------------------------------------------------------------

  i Current best:       rmse=2.461 (@iter 0)
  i Gaussian process model
  ! The Gaussian process model is being fit using 1 features but only has 2
    data points to do so. This may cause errors or a poor model fit.
  v Gaussian process model
  i Generating 3 candidates
  i Predicted candidates
  i num_comp=5
  i Estimating performance
  i Fold01: preprocessor 1/1
  v Fold01: preprocessor 1/1
  i Fold01: preprocessor 1/1, model 1/1
  v Fold01: preprocessor 1/1, model 1/1
  i Fold01: preprocessor 1/1, model 1/1 (extracts)
  i Fold01: preprocessor 1/1, model 1/1 (predictions)
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  i Fold10: preprocessor 1/1, model 1/1 (extracts)
  i Fold10: preprocessor 1/1, model 1/1 (predictions)
  v Estimating performance
  <3 Newest results:    rmse=2.453 (+/-0.381)

  -- Iteration 2 -----------------------------------------------------------------

  i Current best:       rmse=2.453 (@iter 1)
  i Gaussian process model
  v Gaussian process model
  i Generating 2 candidates
  i Predicted candidates
  i num_comp=1
  i Estimating performance
  i Fold01: preprocessor 1/1
  v Fold01: preprocessor 1/1
  i Fold01: preprocessor 1/1, model 1/1
  v Fold01: preprocessor 1/1, model 1/1
  i Fold01: preprocessor 1/1, model 1/1 (extracts)
  i Fold01: preprocessor 1/1, model 1/1 (predictions)
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  v Fold10: preprocessor 1/1, model 1/1
  i Fold10: preprocessor 1/1, model 1/1 (extracts)
  i Fold10: preprocessor 1/1, model 1/1 (predictions)
  v Estimating performance
  (x) Newest results:   rmse=2.646 (+/-0.286)
Output
  # Tuning results
  # 10-fold cross-validation 
  # A tibble: 30 x 5
     splits         id     .metrics         .notes           .iter
     <list>         <chr>  <list>           <list>           <int>
   1 <split [28/4]> Fold01 <tibble [4 x 5]> <tibble [0 x 4]>     0
   2 <split [28/4]> Fold02 <tibble [4 x 5]> <tibble [0 x 4]>     0
   3 <split [29/3]> Fold03 <tibble [4 x 5]> <tibble [0 x 4]>     0
   4 <split [29/3]> Fold04 <tibble [4 x 5]> <tibble [0 x 4]>     0
   5 <split [29/3]> Fold05 <tibble [4 x 5]> <tibble [0 x 4]>     0
   6 <split [29/3]> Fold06 <tibble [4 x 5]> <tibble [0 x 4]>     0
   7 <split [29/3]> Fold07 <tibble [4 x 5]> <tibble [0 x 4]>     0
   8 <split [29/3]> Fold08 <tibble [4 x 5]> <tibble [0 x 4]>     0
   9 <split [29/3]> Fold09 <tibble [4 x 5]> <tibble [0 x 4]>     0
  10 <split [29/3]> Fold10 <tibble [4 x 5]> <tibble [0 x 4]>     0
  # i 20 more rows

tune model only - failure in recipe is caught elegantly

Code
  cars_res <- tune_bayes(svm_mod, preprocessor = rec, resamples = data_folds)
Message
  x Fold1: preprocessor 1/1:
    Error in `step_spline_b()`:
    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_b()`:
    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.
Message
  x Optimization stopped prematurely; returning current results.

tune model only - failure in formula is caught elegantly

Code
  cars_res <- tune_bayes(wflow, resamples = data_folds, control = control_bayes(
    extract = function(x) {
      1
    }, save_pred = TRUE))
Message
  x Fold1: preprocessor 1/1:
    Error in `hardhat::mold()`:
    ! The following predictor was not found in `data`: "z".
  x Fold2: preprocessor 1/1:
    Error in `hardhat::mold()`:
    ! The following predictor was not found in `data`: "z".
Condition
  Warning:
  All models failed. Run `show_notes(.Last.tune.result)` for more information.
Message
  x Optimization stopped prematurely; returning current results.

argument order gives an error for recipes

Code
  tune_bayes(rec_tune_1, model = lm_mod, resamples = rsample::vfold_cv(mtcars, v = 2),
  param_info = extract_parameter_set_dials(rec_tune_1), iter = iter1, initial = iter2)
Condition
  Error in `tune_bayes()`:
  ! The first argument to `tune_bayes()` should be either a model or workflow, not a <recipe> object.

argument order gives an error for formula

Code
  tune_bayes(mpg ~ ., svm_mod, resamples = rsample::vfold_cv(mtcars, v = 2),
  param_info = extract_parameter_set_dials(svm_mod), initial = iter1, iter = iter2)
Condition
  Error in `tune_bayes()`:
  ! The first argument to `tune_bayes()` should be either a model or workflow, not a <formula> object.

retain extra attributes and saved GP candidates

Code
  setdiff(new_obj, current_objs)
Output
  [1] "candidates" "gp_fit"     "i"          "score_card"

Code
  res2 <- tune_bayes(wflow, resamples = folds, param_info = pset, initial = iter1,
    iter = iter2, control = control_bayes(save_workflow = TRUE))
Message
  ! The Gaussian process model is being fit using 1 features but only has 2
    data points to do so. This may cause errors or a poor model fit.

too few starting values

Code
  tune:::check_bayes_initial_size(5, 3, FALSE)
Message
  ! There are 5 tuning parameters and 3 grid points were requested.
  * There are as many tuning parameters as there are initial points. This is
    likely to cause numerical issues in the first few search iterations.

Code
  tune:::check_bayes_initial_size(5, 3, TRUE)
Message
  ! There are 5 tuning parameters and 3 grid points were requested.
  * There are as many tuning parameters as there are initial points. This is
    likely to cause numerical issues in the first few search iterations.
  * With racing, only completely resampled parameters are used.

Code
  tune:::check_bayes_initial_size(2, 2, FALSE)
Message
  ! There are 2 tuning parameters and 2 grid points were requested.
  * There are as many tuning parameters as there are initial points. This is
    likely to cause numerical issues in the first few search iterations.

Code
  tune:::check_bayes_initial_size(5, 1, FALSE)
Condition
  Error in `tune:::check_bayes_initial_size()`:
  ! There are 5 tuning parameters and 1 grid point was requested.
  i The GP model requires 2+ initial points. For best performance, supply more initial points than there are tuning parameters.

Code
  tune:::check_bayes_initial_size(5, 1, TRUE)
Condition
  Error in `tune:::check_bayes_initial_size()`:
  ! There are 5 tuning parameters and 1 grid point was requested.
  i The GP model requires 2+ initial points. For best performance, supply more initial points than there are tuning parameters.
  i With racing, only completely resampled parameters are used.

Code
  tune:::check_bayes_initial_size(1, 1, FALSE)
Condition
  Error in `tune:::check_bayes_initial_size()`:
  ! There is 1 tuning parameter and 1 grid point was requested.
  i The GP model requires 2+ initial points. For best performance, supply more initial points than there are tuning parameters.

missing performance values

Code
  set.seed(1)
  res <- mod %>% tune_bayes(Sale_Price ~ Neighborhood + Gr_Liv_Area + Year_Built +
    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: 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.
  ! The Gaussian process model is being fit using 1 features but only has 2
    data points to do so. This may cause errors or a poor model fit.
  ! For the rsq estimates, 1 missing value was found and removed before fitting
    the Gaussian process model.
  ! 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: 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: 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: 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: 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: 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: A correlation computation is required, but `estimate` is constant and ha...
  ! For the rsq estimates, 8 missing values were found and removed before
    fitting the Gaussian process model.
  ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
  ! For the rsq estimates, 9 missing values were found and removed before
    fitting the Gaussian process model.
  ! validation: internal: A correlation computation is required, but `estimate` is constant and ha...
  ! No improvement for 10 iterations; returning current results.

Code
  set.seed(2)
  res_fail <- mod %>% tune_bayes(Sale_Price ~ Neighborhood + Gr_Liv_Area +
    Year_Built + Bldg_Type + Latitude + Longitude, resamples = folds, initial = 5,
  metrics = yardstick::metric_set(rsq), param_info = parameters(dials::cost_complexity(
    c(0.5, 0))))
Message
  ! 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...
  x Gaussian process model: Error in seq_len(n - 1L): argument must be coercible to non-negative int...
Condition
  Error in `check_gp_failure()`:
  ! Gaussian process model was not fit.
Message
  x Optimization stopped prematurely; returning current results.

tune_bayes() output for iter edge cases (#721)

Code
  tune_bayes(wf, boots, iter = -1)
Condition
  Error in `tune_bayes()`:
  ! The `iter` argument must be a non-negative integer.

Code
  tune_bayes(wf, boots, iter = c(-1, 0, 1))
Condition
  Error in `tune_bayes()`:
  ! The `iter` argument must be a non-negative integer.

Code
  tune_bayes(wf, boots, iter = c(0, 1, 2))
Condition
  Error in `tune_bayes()`:
  ! The `iter` argument must be a non-negative integer.

Code
  tune_bayes(wf, boots, iter = NA)
Condition
  Error in `tune_bayes()`:
  ! The `iter` argument must be a non-negative integer.

Code
  tune_bayes(wf, boots, iter = NULL)
Condition
  Error in `tune_bayes()`:
  ! The `iter` argument must be a non-negative integer.

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