Determine arguments tagged for tuning

tune_args() takes a model specification or a recipe and returns a tibble of information on all possible tunable arguments and whether or not they are actually tunable.

tune_args(object, ...)

# S3 method for model_spec
tune_args(object, full = FALSE, ...)

# S3 method for recipe
tune_args(object, full = FALSE, ...)

# S3 method for step
tune_args(object, full = FALSE, ...)

# S3 method for check
tune_args(object, full = FALSE, ...)

# S3 method for workflow
tune_args(object, ...)

Arguments

object	A model_spec, recipe, workflow or other object.
...	Not currently used.
full	A single logical. Should all possible tunable parameters be returned? If FALSE, then only the parameters that are actually tunable are returned.

Value

A tibble with columns for the parameter name (name), whether it contains any tunable value (tune), the id for the parameter (id), and the information on where the parameter was located (source).

Details

The source column is determined differently depending on whether a model_spec or a recipe is used (with additional detail on the type).

The id field has any identifier that was passed to tune() (e.g. tune("some note")). If not additional detail was used in that function, the id field reverts to the name of the parameters.

Examples

# \donttest{
library(recipes)

recipe(mpg ~ ., data = mtcars) %>%
  step_impute_knn(all_predictors(), neighbors = tune()) %>%
  step_pca(all_predictors(), num_comp = tune()) %>%
  tune_args()
#> # A tibble: 2 x 6
#>   name      tunable id        source component       component_id    
#>   <chr>     <lgl>   <chr>     <chr>  <chr>           <chr>           
#> 1 neighbors TRUE    neighbors recipe step_impute_knn impute_knn_4Psoc
#> 2 num_comp  TRUE    num_comp  recipe step_pca        pca_J8Cfd       

recipe(mpg ~ ., data = mtcars) %>%
  step_ns(disp, deg_free = tune("disp df")) %>%
  step_ns(wt, deg_free = tune("wt df")) %>%
  tune_args()
#> # A tibble: 2 x 6
#>   name     tunable id      source component component_id
#>   <chr>    <lgl>   <chr>   <chr>  <chr>     <chr>       
#> 1 deg_free TRUE    disp df recipe step_ns   ns_t9HFq    
#> 2 deg_free TRUE    wt df   recipe step_ns   ns_nSH9j    

recipe(mpg ~ ., data = mtcars) %>%
  step_normalize(all_predictors()) %>%
  tune_args()
#> # A tibble: 0 x 6
#> # … with 6 variables: name <chr>, tunable <lgl>, id <chr>, source <chr>,
#> #   component <chr>, component_id <chr>

library(parsnip)

boost_tree(trees = tune(), min_n = tune()) %>%
  set_engine("xgboost") %>%
  tune_args()
#> # A tibble: 2 x 6
#>   name  tunable id    source     component  component_id
#>   <chr> <lgl>   <chr> <chr>      <chr>      <chr>       
#> 1 trees TRUE    trees model_spec boost_tree NA          
#> 2 min_n TRUE    min_n model_spec boost_tree NA          

boost_tree(trees = tune(), min_n = tune()) %>%
  set_engine("C5.0", rules = TRUE) %>%
  tune_args()
#> # A tibble: 2 x 6
#>   name  tunable id    source     component  component_id
#>   <chr> <lgl>   <chr> <chr>      <chr>      <chr>       
#> 1 trees TRUE    trees model_spec boost_tree NA          
#> 2 min_n TRUE    min_n model_spec boost_tree NA          
# }