Add tidy method for LiblineaR parsnip models

[juliasilge]

This PR adds a tidy method for LiblineaR models trained with parsnip:

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

data(two_class_dat, package = "modeldata")
example_split <- initial_split(two_class_dat, prop = 0.99)
example_train <- training(example_split)
example_test  <-  testing(example_split)

rec <- recipe(Class ~ ., data = example_train) %>%
  step_normalize(all_numeric_predictors())

spec1 <- svm_linear() %>%
  set_engine("LiblineaR") %>%
  set_mode("classification")

spec2 <- logistic_reg(penalty = 0.1, mixture = 1) %>%
  set_engine("LiblineaR") %>%
  set_mode("classification")

wf <- workflow() %>%
  add_recipe(rec)

wf %>%
  add_model(spec1) %>% 
  fit(example_train) %>%
  tidy()
#> # A tibble: 3 x 2
#>   term  estimate
#>   <chr>    <dbl>
#> 1 A        0.367
#> 2 B       -0.964
#> 3 Bias     0.118

wf %>%
  add_model(spec2) %>% 
  fit(example_train) %>%
  tidy()
#> # A tibble: 3 x 2
#>   term  estimate
#>   <chr>    <dbl>
#> 1 A        1.11 
#> 2 B       -2.78 
#> 3 Bias     0.349

svm_linear() %>%
  set_engine("LiblineaR") %>%
  set_mode("regression") %>% 
  fit(mpg ~ ., data = mtcars) %>% 
  tidy()
#> # A tibble: 11 x 2
#>    term  estimate
#>    <chr>    <dbl>
#>  1 cyl    0.141  
#>  2 disp  -0.0380 
#>  3 hp     0.0415 
#>  4 drat   0.226  
#>  5 wt     0.0757 
#>  6 qsec   1.06   
#>  7 vs     0.0648 
#>  8 am     0.0479 
#>  9 gear   0.219  
#> 10 carb   0.00861
#> 11 Bias   0.0525

^{Created on 2021-04-21 by the reprex package (v2.0.0)}

[juliasilge]

This PR will let us move a function out of _common.R for https://smltar.com/, which it looks like it is about the right time to do.

[github-actions]

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