Axing a recipe object.

recipe objects are created from the recipes package, which is leveraged for its set of data pre-processing tools. These recipes work by sequentially defining each pre-processing step. The implementation of each step, however, results its own class so we bundle all the axe methods related to recipe objects in general here. Note that the butchered class is only added to the recipe as a whole, and not to each pre-processing step.

Usage

# S3 method for recipe
axe_env(x, verbose = FALSE, ...)

# S3 method for step
axe_env(x, ...)

# S3 method for step_arrange
axe_env(x, ...)

# S3 method for step_filter
axe_env(x, ...)

# S3 method for step_mutate
axe_env(x, ...)

# S3 method for step_slice
axe_env(x, ...)

# S3 method for step_impute_bag
axe_env(x, ...)

# S3 method for step_bagimpute
axe_env(x, ...)

# S3 method for step_impute_knn
axe_env(x, ...)

# S3 method for step_knnimpute
axe_env(x, ...)

# S3 method for step_geodist
axe_env(x, ...)

# S3 method for step_interact
axe_env(x, ...)

# S3 method for step_ratio
axe_env(x, ...)

# S3 method for quosure
axe_env(x, ...)

# S3 method for recipe
axe_fitted(x, verbose = FALSE, ...)

Arguments

x

A model object.

verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.

...

Any additional arguments related to axing.

Value

Axed recipe object.

Examples

library(recipes)
#> Loading required package: dplyr
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:randomForest’:
#> 
#>     combine
#> The following object is masked from ‘package:MASS’:
#> 
#>     select
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
#> 
#> Attaching package: ‘recipes’
#> The following object is masked from ‘package:stats’:
#> 
#>     step
data(biomass, package = "modeldata")

biomass_tr <- biomass[biomass$dataset == "Training",]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
              data = biomass_tr) %>%
  step_center(all_predictors()) %>%
  step_scale(all_predictors()) %>%
  step_spatialsign(all_predictors())

out <- butcher(rec, verbose = TRUE)
#> ✔ Memory released: 68.15 kB

# Another recipe object
wrapped_recipes <- function() {
  some_junk_in_environment <- runif(1e6)
  return(
    recipe(mpg ~ cyl, data = mtcars) %>%
      step_center(all_predictors()) %>%
      step_scale(all_predictors()) %>%
      prep()
  )
}

# Remove junk in environment
cleaned1 <- axe_env(wrapped_recipes(), verbose = TRUE)
#> ✔ Memory released: 8.11 MB
# Replace prepared training data with zero-row slice
cleaned2 <- axe_fitted(wrapped_recipes(), verbose = TRUE)
#> ✔ Memory released: 296 B

# Check size
lobstr::obj_size(cleaned1)
#> 13.09 kB
lobstr::obj_size(cleaned2)
#> 8.02 MB