One of the beauties of working with R is the ease with which you can implement intricate models and make challenging data analysis pipelines seem almost trivial. 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 complex as a boosted tree:
library(rpart)
fitted_model <- boost_tree(trees = 15) %>%
set_engine("C5.0") %>%
fit(as.factor(am) ~ disp + hp, data = mtcars)Or, let’s say you’re working on petabytes of data, in which data are distributed across many nodes, just switch out the parsnip engine:
library(sparklyr)
sc <- spark_connect(master = "local")
mtcars_tbls <- sdf_copy_to(sc, mtcars[, c("am", "disp", "hp")])
fitted_model <- boost_tree(trees = 15) %>%
set_engine("spark") %>%
fit(am ~ disp + hp, data = mtcars_tbls)Yet, while our code may appear 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 popular lm function from the base stats package. Whether you leverage parsnip or not, you arrive at the same result:
parsnip_lm <- linear_reg() %>%
set_engine("lm") %>%
fit(mpg ~ ., data = mtcars)
parsnip_lm
#> parsnip model object
#>
#>
#> Call:
#> stats::lm(formula = formula, 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 we take this familiar old_lm approach in building our in-house modeling pipeline. Such a pipeline might entail wrapping lm() in other function, but in doing so, we may end up carrying some 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 pipeline is:
When it is fundamentally the same as our old_lm, which only takes up:
Ideally, 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. So, 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()
butcher::weigh(big_lm, threshold = 0, units = "MB")
#> # A tibble: 25 x 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
#> # … with 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 the model.frame and model.matrix output, the environment in which the linear fit was created was 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: 0x7fe9e8b00070>
#> parent: <environment: global>
#> bindings:
#> * some_junk_in_the_environment: <dbl>To avoid carrying possible junk in our production pipeline, whether it be associated with an lm model (or something more complex), we can leverage axe_env() within the butcher package. In other words,
Comparing it against our old_lm, we find:
butcher::weigh(cleaned_lm, threshold = 0, units = "MB")
#> # A tibble: 25 x 2
#> object size
#> <chr> <dbl>
#> 1 terms 0.00789
#> 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
#> # … with 15 more rows…it now takes the same memory on disk:
butcher::weigh(old_lm, threshold = 0, units = "MB")
#> # A tibble: 25 x 2
#> object size
#> <chr> <dbl>
#> 1 terms 0.00781
#> 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
#> # … with 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::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 %>%
butcher::axe_env() %>%
butcher::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 24.36827butcher makes it easy to axe parts of the fitted output that are no longer needed, without sacrificing much functionality from the original model object.