Generate n-grams From Token VariablesSource:
step_ngram( recipe, ..., role = NA, trained = FALSE, columns = NULL, num_tokens = 3L, min_num_tokens = 3L, delim = "_", skip = FALSE, id = rand_id("ngram") )
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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of variable names that will be populated (eventually) by the
termsargument. This is
NULLuntil the step is trained by
The number of tokens in the n-gram. This must be an integer greater than or equal to 1. Defaults to 3.
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.
The separator between words in an n-gram. Defaults to "_".
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.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added
to the sequence of existing steps (if any).
The use of this step will leave the ordering of the tokens meaningless. If
min_num_tokens < num_tokens then the tokens order in increasing fashion
with respect to the number of tokens in the n-gram. If
min_num_tokens = 1
num_tokens = 3 then the output contains all the 1-grams followed by all
the 2-grams followed by all the 3-grams.
tidy() this step, a tibble with columns
(the selectors or variables selected).
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 tokens] #> # Unique Tokens: 1 tidy(tate_rec, number = 2) #> # A tibble: 1 × 3 #> terms value id #> <chr> <chr> <chr> #> 1 medium NA ngram_O30rG tidy(tate_obj, number = 2) #> # A tibble: 1 × 2 #> terms id #> <chr> <chr> #> 1 medium ngram_O30rG