fix int_pctl.tune_results() with PSOCK

[simonpcouch]

Closes #885 using a trick that @topepo showed me some years back to work around this PSOCK/foreach oddity. With this PR:

library(tidymodels)

set.seed(991)
delivery_split <- initial_validation_split(deliveries, prop = c(0.6, 0.2), 
                                           strata = time_to_delivery)
delivery_rs <- validation_set(delivery_split)

lm_res <- 
  linear_reg() %>% 
  fit_resamples(
    time_to_delivery ~ .,
    resamples = delivery_rs,
    control = control_resamples(save_pred = TRUE)
  )

res_seq <- int_pctl(lm_res)

library(doMC)
#> Loading required package: foreach
#> 
#> Attaching package: 'foreach'
#> The following objects are masked from 'package:purrr':
#> 
#>     accumulate, when
#> Loading required package: iterators
#> Loading required package: parallel

registerDoMC(cores = 5)

res_mc <- int_pctl(lm_res)
#> Warning: ! tune detected a parallel backend registered with foreach but no backend
#>   registered with future.
#> ℹ Support for parallel processing with foreach was soft-deprecated in tune
#>   1.2.1.
#> ℹ See ?parallelism (`?tune::parallelism()`) to learn more.

library(doParallel)
cl <- makePSOCKcluster(parallel::detectCores(logical = FALSE))
registerDoParallel(cl)

res_psoc <- int_pctl(lm_res)
#> Warning: ! tune detected a parallel backend registered with foreach but no backend
#>   registered with future.
#> ℹ Support for parallel processing with foreach was soft-deprecated in tune
#>   1.2.1.
#> ℹ See ?parallelism (`?tune::parallelism()`) to learn more.

res_psoc
#> # A tibble: 2 × 6
#>   .metric .estimator .lower .estimate .upper .config             
#>   <chr>   <chr>       <dbl>     <dbl>  <dbl> <chr>               
#> 1 rmse    bootstrap   3.59      3.76   3.96  Preprocessor1_Model1
#> 2 rsq     bootstrap   0.689     0.713  0.735 Preprocessor1_Model1

^{Created on 2024-04-17 with reprex v2.1.0}

Since we've discussed writing out the internal comp_metrics() function before—and since it's named so similarly to compute_metrics()—this seems preferable to me to newly exporting it.

[simonpcouch]

Wanted to make sure the usual suspects with future do okay as well:

library(tidymodels)
library(future)
library(future.mirai)

set.seed(991)
delivery_split <- initial_validation_split(deliveries, prop = c(0.6, 0.2), 
                                           strata = time_to_delivery)
delivery_rs <- validation_set(delivery_split)

lm_res <- 
  linear_reg() %>% 
  fit_resamples(
    time_to_delivery ~ .,
    resamples = delivery_rs,
    control = control_resamples(save_pred = TRUE)
  )

plan(multisession, workers = 4)

int_pctl(lm_res)
#> # A tibble: 2 × 6
#>   .metric .estimator .lower .estimate .upper .config             
#>   <chr>   <chr>       <dbl>     <dbl>  <dbl> <chr>               
#> 1 rmse    bootstrap   3.59      3.76   3.96  Preprocessor1_Model1
#> 2 rsq     bootstrap   0.689     0.713  0.734 Preprocessor1_Model1

plan(cluster, workers = 4)

int_pctl(lm_res)
#> # A tibble: 2 × 6
#>   .metric .estimator .lower .estimate .upper .config             
#>   <chr>   <chr>       <dbl>     <dbl>  <dbl> <chr>               
#> 1 rmse    bootstrap   3.59      3.76   3.96  Preprocessor1_Model1
#> 2 rsq     bootstrap   0.689     0.713  0.734 Preprocessor1_Model1

plan(mirai_multisession, workers = 4)

int_pctl(lm_res)
#> # A tibble: 2 × 6
#>   .metric .estimator .lower .estimate .upper .config             
#>   <chr>   <chr>       <dbl>     <dbl>  <dbl> <chr>               
#> 1 rmse    bootstrap   3.59      3.76   3.96  Preprocessor1_Model1
#> 2 rsq     bootstrap   0.689     0.713  0.734 Preprocessor1_Model1

^{Created on 2024-04-17 with reprex v2.1.0}

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