Modeling or machine learning in R can result in fitted model objects that take up too much memory. There are two main culprits:

1. Heavy usage of formulas and closures that capture the enclosing environment in model training
2. Lack of selectivity in the construction of the model object itself

As a result, fitted model objects contain components that are often redundant and not required for post-fit estimation activities. The butcher package provides tooling to “axe” parts of the fitted output that are no longer needed, without sacrificing prediction functionality from the original model object.

Install the released version from CRAN:

install.packages("butcher")

Or install the development version from GitHub:

# install.packages("pak")
pak::pak("tidymodels/butcher")

As an example, let’s wrap an lm model so it contains a lot of unnecessary stuff:

library(butcher)
our_model <- function() {
  some_junk_in_the_environment <- runif(1e6) # we didn't know about
  lm(mpg ~ ., data = mtcars) 
}

This object is unnecessarily large:

library(lobstr)
obj_size(our_model())
#> 8.02 MB

When, in fact, it should only be:

small_lm <- lm(mpg ~ ., data = mtcars) 
obj_size(small_lm)
#> 22.22 kB

To understand which part of our original model object is taking up the most memory, we leverage the weigh() function:

big_lm <- our_model()
weigh(big_lm)
#> # A tibble: 25 × 2
#>    object            size
#>    <chr>            <dbl>
#>  1 terms         8.05    
#>  2 qr.qr         0.00666 
#>  3 residuals     0.00286 
#>  4 fitted.values 0.00286 
#>  5 effects       0.0014  
#>  6 coefficients  0.00109 
#>  7 call          0.000728
#>  8 model.mpg     0.000304
#>  9 model.cyl     0.000304
#> 10 model.disp    0.000304
#> # ℹ 15 more rows

The problem here is in the terms component of our big_lm. Because of how lm() is implemented in the stats package, the environment in which our model was made is carried along in the fitted output. To remove the (mostly) extraneous component, we can use butcher():

cleaned_lm <- butcher(big_lm, verbose = TRUE)
#> ✔ Memory released: 8.03 MB
#> ✖ Disabled: `print()`, `summary()`, and `fitted()`

Comparing it against our small_lm, we find:

weigh(cleaned_lm)
#> # A tibble: 25 × 2
#>    object           size
#>    <chr>           <dbl>
#>  1 terms        0.00771 
#>  2 qr.qr        0.00666 
#>  3 residuals    0.00286 
#>  4 effects      0.0014  
#>  5 coefficients 0.00109 
#>  6 model.mpg    0.000304
#>  7 model.cyl    0.000304
#>  8 model.disp   0.000304
#>  9 model.hp     0.000304
#> 10 model.drat   0.000304
#> # ℹ 15 more rows

And now it will take up about the same memory on disk as small_lm:

weigh(small_lm)
#> # A tibble: 25 × 2
#>    object            size
#>    <chr>            <dbl>
#>  1 terms         8.06    
#>  2 qr.qr         0.00666 
#>  3 residuals     0.00286 
#>  4 fitted.values 0.00286 
#>  5 effects       0.0014  
#>  6 coefficients  0.00109 
#>  7 call          0.000728
#>  8 model.mpg     0.000304
#>  9 model.cyl     0.000304
#> 10 model.disp    0.000304
#> # ℹ 15 more rows

To make the most of your memory available, this package provides five S3 generics for you to remove parts of a model object:

• axe_call(): To remove the call object.
• axe_ctrl(): To remove controls associated with training.
• axe_data(): To remove the original training data.
• axe_env(): To remove environments.
• axe_fitted(): To remove fitted values.

When you run butcher(), you execute all of these axing functions at once. Any kind of axing on the object will append a butchered class to the current model object class(es) as well as a new attribute named butcher_disabled that lists any post-fit estimation functions that are disabled as a result.

Check out the vignette("available-axe-methods") to see butcher’s current coverage. If you are working with a new model object that could benefit from any kind of axing, we would love for you to make a pull request! You can visit the vignette("adding-models-to-butcher") for more guidelines, but in short, to contribute a set of axe methods:

1. Run new_model_butcher(model_class = "your_object", package_name = "your_package")
2. Use butcher helper functions weigh() and locate() to decide what to axe
3. Finalize edits to R/your_object.R and tests/testthat/test-your_object.R
4. Make a pull request!

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

• For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.

• If you think you have encountered a bug, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.