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rtidymodels
Christian
Christian

Reputation: 477

No tidy method for objects of class LiblineaR


I have fitted text data based on regression and LiblineaR engine. And I want to `tidy()` my results. I have also installed the dev version of `broom`. But I always get an error. `ERROR: No tidy method for objects of class LiblineaR` 

> svm_fit %>%
+   pull_workflow_fit() %>%
+   tidy()
ERROR: No tidy method for objects of class LiblineaR

Upvotes: 0

Views: 131

Answers (1)

Julia Silge
Julia Silge

Reputation: 11663

We just merged in support for the tidy() method for parsnip models fitted with the LiblineaR engine, so if you install from GitHub, you should be able to have this feature now:

devtools::install_github("tidymodels/parsnip")

Here is a demo of how it works:

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        0.361
#> 2 B       -0.966
#> 3 Bias     0.113

wf %>%
  add_model(spec2) %>% 
  fit(example_train) %>%
  tidy()
#> # A tibble: 3 x 2
#>   term  estimate
#>   <chr>    <dbl>
#> 1 A        1.06 
#> 2 B       -2.76 
#> 3 Bias     0.329

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 2021-04-22 by the reprex package (v2.0.0)

Upvotes: 1

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