Helper function to extract fitted workflow from last_fit() output

[juliasilge]

In tidymodels/tidymodels#58, @hfrick pointed out that it is pretty awkward to get out the fitted workflow from the output of last_fit(); it's all $.workflow[[1]]. 😣 This is what you have to do to get a fitted workflow you can use for prediction.

library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip

set.seed(6735)
car_split <- initial_split(mtcars)

spline_rec <- recipe(mpg ~ ., data = mtcars) %>%
  step_ns(disp)

lin_mod <- linear_reg() %>%
  set_engine("lm")

spline_wfl <-
  workflow() %>%
  add_recipe(spline_rec) %>%
  add_model(lin_mod)

final_fitted <- last_fit(spline_wfl, car_split)

collect_metrics(final_fitted)
#> # A tibble: 2 x 4
#>   .metric .estimator .estimate .config             
#>   <chr>   <chr>          <dbl> <chr>               
#> 1 rmse    standard       5.74  Preprocessor1_Model1
#> 2 rsq     standard       0.503 Preprocessor1_Model1
collect_predictions(final_fitted)
#> # A tibble: 8 x 5
#>   id               .pred  .row   mpg .config             
#>   <chr>            <dbl> <int> <dbl> <chr>               
#> 1 train/test split 18.1      5  18.7 Preprocessor1_Model1
#> 2 train/test split 31.1      9  22.8 Preprocessor1_Model1
#> 3 train/test split 16.9     14  15.2 Preprocessor1_Model1
#> 4 train/test split 20.4     15  10.4 Preprocessor1_Model1
#> 5 train/test split 18.0     16  10.4 Preprocessor1_Model1
#> 6 train/test split 28.3     19  30.4 Preprocessor1_Model1
#> 7 train/test split  9.04    24  13.3 Preprocessor1_Model1
#> 8 train/test split 23.0     30  19.7 Preprocessor1_Model1


final_fitted$.workflow[[1]]
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 1 Recipe Step
#> 
#> • step_ns()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl           hp         drat           wt         qsec  
#>   -19.69774      1.09769     -0.02326     -3.05865     -4.28722      2.80367  
#>          vs           am         gear         carb    disp_ns_1    disp_ns_2  
#>    -3.37155      0.18869      4.33774      0.52292    -12.82725      4.55395

^{Created on 2021-05-03 by the reprex package (v2.0.0)}

What do we think about a little helper function to take care of this, like we have the helper functions for the metrics and predictions? We have collect_metrics(), collect_predictions() -- maybe collect_workflow()? It's nice and parallel from the same structure. I feel like pull_xx is another obvious choice but it is getting overloaded quickly for us.

[topepo]

We should review this at the next group meeting. I feel that we should make the pull_* functions generics (but there are other options).

[topepo]

Closed by #388

[github-actions]

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