Generate n-grams From Token Variables

step_ngram() creates a specification of a recipe step that will convert a token variable into a token variable of ngrams.

Usage

step_ngram(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  num_tokens = 3L,
  min_num_tokens = 3L,
  delim = "_",
  skip = FALSE,
  id = rand_id("ngram")
)

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().
num_tokens: The number of tokens in the n-gram. This must be an integer greater than or equal to 1. Defaults to 3.
min_num_tokens: The minimum number of tokens in the n-gram. This must be an integer greater than or equal to 1 and smaller than n. Defaults to 3.
delim: The separator between words in an n-gram. 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.

Value

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

Details

The use of this step will leave the ordering of the tokens meaningless. If min_num_tokens < num_tokens then the tokens will be ordered in increasing fashion with respect to the number of tokens in the n-gram. If min_num_tokens = 1 and num_tokens = 3 then the output will contain all the 1-grams followed by all the 2-grams followed by all the 3-grams.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms: character, the selectors or variables selected
id: character, id of this step

Tuning Parameters

This step has 1 tuning parameters:

num_tokens: Number of tokens (type: integer, default: 3)

Case weights

The underlying operation does not allow for case weights.

Examples

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

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

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>       medium
#>    <tknlist>
#> 1 [6 tokens]
#> 2 [1 tokens]

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [1 tokens]
#> # Unique Tokens: 1

tidy(tate_rec, number = 2)
#> # A tibble: 1 × 2
#>   terms  id         
#>   <chr>  <chr>      
#> 1 medium ngram_adK2u
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 2
#>   terms  id         
#>   <chr>  <chr>      
#> 1 medium ngram_adK2u