step_untokenize: Untokenization of Token Variables

In textrecipes: Extra 'Recipes' for Text Processing

Untokenization of Token Variables

Description

step_untokenize() creates a specification of a recipe step that will convert a token variable into a character predictor.

Usage

step_untokenize(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  sep = " ",
  skip = FALSE,
  id = rand_id("untokenize")
)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose which variables are affected by the step. See recipes::selections() for more details.
role	Not used by this step since no new variables are created.
trained	A logical to indicate if the quantities for preprocessing have been estimated.
columns	A character string of variable names that will be populated (eventually) by the terms argument. This is NULL until the step is trained by recipes::prep.recipe().
sep	a character to determine how the tokens should be separated when pasted together. Defaults to " ".
skip	A logical. Should the step be skipped when the recipe is baked by recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = FALSE.
id	A character string that is unique to this step to identify it.

Details

This steps will turn a token vector back into a character vector. This step is calling paste internally to put the tokens back together to a character.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and value (seperator used for collapsing).

Case weights

The underlying operation does not allow for case weights.

See Also

step_tokenize() to turn characters into tokens

Examples

library(recipes)
library(modeldata)
data(tate_text)

tate_rec <- recipe(~., data = tate_text) %>%
  step_tokenize(medium) %>%
  step_untokenize(medium)

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)

tidy(tate_rec, number = 2)
tidy(tate_obj, number = 2)

textrecipes documentation built on Nov. 16, 2023, 5:06 p.m.