formulize
If you:
- like using formulas, recipes and data frames to specify design matrices
- develop nervous ticks when you come across modelling packages that only offer matrix/vector interfaces
- don’t have the time or motivation to write a formula wrapper around these interfaces
- like untested and hacky software written by amateurs
then formulize may be for you. Formulize is very new, but you can install it from CRAN if you have R 3.4+ with:
install.packages("formulize")You can get the development version (recommended) with:
# install.packages("devtools")
devtools::install_github("alexpghayes/formulize")Adding a formula or recipe interface
Suppose you want to add a formula interface to an existing modelling function, say cv.glmnet. Then you could do the following
library(recipes)
library(glmnet)
library(formulize)
glmnet_cv <- formulize(cv.glmnet)
glmnet_model <- glmnet_cv(mpg ~ drat + hp - 1, mtcars)
predict(glmnet_model, head(mtcars))
#> 1
#> Mazda RX4 22.44822
#> Mazda RX4 Wag 22.44822
#> Datsun 710 22.95815
#> Hornet 4 Drive 19.94218
#> Hornet Sportabout 17.62212
#> Valiant 19.15914Similarly glmnet_cv works with recipe objects like so
rec <- recipe(mpg ~ drat + hp, data = mtcars)
glmnet_model2 <- glmnet_cv(rec, mtcars)
predict(glmnet_model2, head(mtcars))
#> 1
#> [1,] 22.35392
#> [2,] 22.35392
#> [3,] 22.85062
#> [4,] 19.97897
#> [5,] 17.72884
#> [6,] 19.24084You may also be interested in the more dangerous exciting version genericize, which you should call for its side effects.
genericize(cv.glmnet)
form <- mpg ~ drat + hp - 1
X <- model.matrix(form, mtcars)
y <- mtcars$mpg
set.seed(27)
mat_model <- cv.glmnet(X, y, intercept = TRUE)
set.seed(27)
frm_model <- cv.glmnet(form, mtcars, intercept = TRUE)
set.seed(27)
rec_model <- cv.glmnet(rec, mtcars, intercept = TRUE)
predict(mat_model, head(X))
#> 1
#> Mazda RX4 22.25028
#> Mazda RX4 Wag 22.25028
#> Datsun 710 22.73249
#> Hornet 4 Drive 20.01959
#> Hornet Sportabout 17.84620
#> Valiant 19.33092
predict(frm_model, head(mtcars))
#> 1
#> Mazda RX4 22.25028
#> Mazda RX4 Wag 22.25028
#> Datsun 710 22.73249
#> Hornet 4 Drive 20.01959
#> Hornet Sportabout 17.84620
#> Valiant 19.33092
predict(rec_model, head(mtcars))
#> 1
#> [1,] 22.25035
#> [2,] 22.25035
#> [3,] 22.73255
#> [4,] 20.01946
#> [5,] 17.84608
#> [6,] 19.33070This creates a new S3 generic cv.glmnet, sets the provided function as the default method (cv.glmnet.default), and adds methods cv.glmnet.formula and cv.glmnet.recipe using formulize.
This will mask cv.glmnet and features no safety checks because safety isn’t fun.
Caveats
formulizedoesn’t do anything special with intercepts. This means that you need to careful with functions that require you to specify intercepts in non-standard ways, such ascv.glmnetabove.- If the original modelling function doesn’t return a list,
formulizewill probably break. - If you’re just looking for a formula interface to
glmnet, take a look at glmnetUtils.