Modeling or machine learning in R can result in fitted model objects that take up too much memory. There are two main culprits:
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:
new_model_butcher(model_class = "your_object", package_name = "your_package")
weigh()
and locate()
to decide what
to axeR/your_object.R
and
tests/testthat/test-your_object.R
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