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Use existing objects to create a workflow set. A list of objects that are either simple workflows or objects that have class "tune_results" can be converted into a workflow set.





One or more named objects. Names should be unique and the objects should have at least one of the following classes: workflow, iteration_results, tune_results, resample_results, or tune_race. Each tune_results element should also contain the original workflow (accomplished using the save_workflow option in the control function).


A workflow set. Note that the option column will not reflect the options that were used to create each object.


The package supplies two pre-generated workflow sets, two_class_set and chi_features_set, and associated sets of model fits two_class_res and chi_features_res.

The two_class_* objects are based on a binary classification problem using the two_class_dat data from the modeldata package. The six models utilize either a bare formula or a basic recipe utilizing recipes::step_YeoJohnson() as a preprocessor, and a decision tree, logistic regression, or MARS model specification. See ?two_class_set for source code.

The chi_features_* objects are based on a regression problem using the Chicago data from the modeldata package. Each of the three models utilize a linear regression model specification, with three different recipes of varying complexity. The objects are meant to approximate the sequence of models built in Section 1.3 of Kuhn and Johnson (2019). See ?chi_features_set for source code.


# ------------------------------------------------------------------------------
# Existing results

# Use the already worked example to show how to add tuned
# objects to a workflow set
#> # A workflow set/tibble: 6 × 4
#>   wflow_id      info             option    result   
#>   <chr>         <list>           <list>    <list>   
#> 1 none_cart     <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 2 none_glm      <tibble [1 × 4]> <opts[3]> <rsmp[+]>
#> 3 none_mars     <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 4 yj_trans_cart <tibble [1 × 4]> <opts[3]> <tune[+]>
#> 5 yj_trans_glm  <tibble [1 × 4]> <opts[3]> <rsmp[+]>
#> 6 yj_trans_mars <tibble [1 × 4]> <opts[3]> <tune[+]>

results <- two_class_res %>% purrr::pluck("result")
names(results) <- two_class_res$wflow_id

# These are all objects that have been resampled or tuned:
purrr::map_chr(results, ~ class(.x)[1])
#>          none_cart           none_glm          none_mars 
#>     "tune_results" "resample_results"     "tune_results" 
#>      yj_trans_cart       yj_trans_glm      yj_trans_mars 
#>     "tune_results" "resample_results"     "tune_results" 

# Use rlang's !!! operator to splice in the elements of the list
new_set <- as_workflow_set(!!!results)

# ------------------------------------------------------------------------------
# Make a set from unfit workflows


lr_spec <- logistic_reg()

main_effects <-
  workflow() %>%
  add_model(lr_spec) %>%
  add_formula(Class ~ .)

interactions <-
  workflow() %>%
  add_model(lr_spec) %>%
  add_formula(Class ~ (.)^2)

as_workflow_set(main = main_effects, int = interactions)
#> # A workflow set/tibble: 2 × 4
#>   wflow_id info             option    result
#>   <chr>    <list>           <list>    <list>
#> 1 main     <tibble [1 × 4]> <opts[0]> <NULL>
#> 2 int      <tibble [1 × 4]> <opts[0]> <NULL>