One of the benefits of working in R is the ease with which you can implement complex models and implement challenging data analysis pipelines. Take, for example, the parsnip package; with the installation of a few associated libraries and a few lines of code, you can fit something as sophisticated as a boosted tree:
fitted_model <- boost_tree(mode = "regression") %>%
fit(mpg ~ ., data = mtcars)Yet, while this code is compact, the underlying fitted result may not
be. Since parsnip works as a wrapper for many modeling packages, its
fitted model objects inherit the same properties as those that arise
from the original modeling package. A straightforward example is the
lm() function from the base stats package.
Whether you leverage parsnip or not, you get the same result:
parsnip_lm <- linear_reg() %>%
fit(mpg ~ ., data = mtcars)
parsnip_lm
#> parsnip model object
#>
#>
#> Call:
#> stats::lm(formula = mpg ~ ., data = data)
#>
#> Coefficients:
#> (Intercept) cyl disp hp drat
#> 12.30337 -0.11144 0.01334 -0.02148 0.78711
#> wt qsec vs am gear
#> -3.71530 0.82104 0.31776 2.52023 0.65541
#> carb
#> -0.19942Using just lm():
old_lm <- lm(mpg ~ ., data = mtcars)
old_lm
#>
#> Call:
#> lm(formula = mpg ~ ., data = mtcars)
#>
#> Coefficients:
#> (Intercept) cyl disp hp drat
#> 12.30337 -0.11144 0.01334 -0.02148 0.78711
#> wt qsec vs am gear
#> -3.71530 0.82104 0.31776 2.52023 0.65541
#> carb
#> -0.19942Let’s say we take this familiar old_lm approach in
building a custom in-house modeling pipeline. Such a pipeline might
entail wrapping lm() in other function, but in doing so, we
may end up carrying around some unnecessary junk.
in_house_model <- function() {
some_junk_in_the_environment <- runif(1e6) # we didn't know about
lm(mpg ~ ., data = mtcars)
}The linear model fit that exists in our custom modeling pipeline is then:
But it is functionally the same as our old_lm, which
only takes up:
obj_size(old_lm)
#> 22.22 kBIdeally, we want to avoid saving this new
in_house_model() on disk, when we could have something like
old_lm that takes up less memory. But what the heck is
going on here? We can examine possible issues with a fitted model object
using the butcher package:
big_lm <- in_house_model()
weigh(big_lm, threshold = 0, units = "MB")
#> # A tibble: 25 × 2
#> object size
#> <chr> <dbl>
#> 1 terms 8.01
#> 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 rowsThe problem here is in the terms component of
big_lm. Because of how lm() is implemented in
the base stats package (relying on intermediate forms of
the data from model.frame and model.matrix)
the environment in which the linear fit was created is
carried along in the model output.
We can see this with the env_print() function from the
rlang package:
library(rlang)
env_print(big_lm$terms)
#> <environment: 0x55b4c3b64920>
#> Parent: <environment: global>
#> Bindings:
#> • some_junk_in_the_environment: <dbl>To avoid carrying possible junk around in our production pipeline,
whether it be associated with an lm() model (or something
more complex), we can leverage axe_env() from the butcher
package:
cleaned_lm <- axe_env(big_lm, verbose = TRUE)
#> ✔ Memory released: 8.00 MBComparing it against our old_lm, we find:
weigh(cleaned_lm, threshold = 0, units = "MB")
#> # A tibble: 25 × 2
#> object size
#> <chr> <dbl>
#> 1 terms 0.00771
#> 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 rowsAnd now it takes the same memory on disk:
weigh(old_lm, threshold = 0, units = "MB")
#> # A tibble: 25 × 2
#> object size
#> <chr> <dbl>
#> 1 terms 0.00763
#> 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 rowsAxing the environment, however, is not the only functionality of butcher. This package provides five S3 generics that include:
-
axe_call(): Remove the call object. -
axe_ctrl(): Remove the controls fixed for training. -
axe_data(): Remove the original data. -
axe_env(): Replace inherited environments with empty environments. -
axe_fitted(): Remove fitted values.
In our case here with lm(), if we are only interested in
prediction as the end product of our modeling pipeline, we could free up
a lot of memory if we execute all the possible axe functions at once. To
do so, we simply run butcher():
butchered_lm <- butcher(big_lm)
predict(butchered_lm, mtcars[, 2:11])
#> Mazda RX4 Mazda RX4 Wag Datsun 710
#> 22.59951 22.11189 26.25064
#> Hornet 4 Drive Hornet Sportabout Valiant
#> 21.23740 17.69343 20.38304
#> Duster 360 Merc 240D Merc 230
#> 14.38626 22.49601 24.41909
#> Merc 280 Merc 280C Merc 450SE
#> 18.69903 19.19165 14.17216
#> Merc 450SL Merc 450SLC Cadillac Fleetwood
#> 15.59957 15.74222 12.03401
#> Lincoln Continental Chrysler Imperial Fiat 128
#> 10.93644 10.49363 27.77291
#> Honda Civic Toyota Corolla Toyota Corona
#> 29.89674 29.51237 23.64310
#> Dodge Challenger AMC Javelin Camaro Z28
#> 16.94305 17.73218 13.30602
#> Pontiac Firebird Fiat X1-9 Porsche 914-2
#> 16.69168 28.29347 26.15295
#> Lotus Europa Ford Pantera L Ferrari Dino
#> 27.63627 18.87004 19.69383
#> Maserati Bora Volvo 142E
#> 13.94112 24.36827Alternatively, we can pick and choose specific axe functions, removing only those parts of the model object that we are no longer interested in characterizing.
butchered_lm <- big_lm %>%
axe_env() %>%
axe_fitted()
predict(butchered_lm, mtcars[, 2:11])
#> Mazda RX4 Mazda RX4 Wag Datsun 710
#> 22.59951 22.11189 26.25064
#> Hornet 4 Drive Hornet Sportabout Valiant
#> 21.23740 17.69343 20.38304
#> Duster 360 Merc 240D Merc 230
#> 14.38626 22.49601 24.41909
#> Merc 280 Merc 280C Merc 450SE
#> 18.69903 19.19165 14.17216
#> Merc 450SL Merc 450SLC Cadillac Fleetwood
#> 15.59957 15.74222 12.03401
#> Lincoln Continental Chrysler Imperial Fiat 128
#> 10.93644 10.49363 27.77291
#> Honda Civic Toyota Corolla Toyota Corona
#> 29.89674 29.51237 23.64310
#> Dodge Challenger AMC Javelin Camaro Z28
#> 16.94305 17.73218 13.30602
#> Pontiac Firebird Fiat X1-9 Porsche 914-2
#> 16.69168 28.29347 26.15295
#> Lotus Europa Ford Pantera L Ferrari Dino
#> 27.63627 18.87004 19.69383
#> Maserati Bora Volvo 142E
#> 13.94112 24.36827The butcher package provides tooling to axe parts of the fitted output that are no longer needed, without sacrificing much functionality from the original model object.