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Filter Tokens Based on Term Frequency

Source: R/tokenfilter.R
step_tokenfilter.Rd

step_tokenfilter() creates a specification of a recipe step that will convert a token variable to be filtered based on frequency.

Usage

step_tokenfilter(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  max_times = Inf,
  min_times = 0,
  percentage = FALSE,
  max_tokens = 100,
  filter_fun = NULL,
  res = NULL,
  skip = FALSE,
  id = rand_id("tokenfilter")
)

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().

max_times

An integer. Maximal number of times a word can appear before getting removed.

min_times

An integer. Minimum number of times a word can appear before getting removed.

percentage

A logical. Should max_times and min_times be interpreted as a percentage instead of count.

max_tokens

An integer. Will only keep the top max_tokens tokens after filtering done by max_times and min_times. Defaults to 100.

filter_fun

A function. This function should take a vector of characters, and return a logical vector of the same length. This function will be applied to each observation of the data set. Defaults to NULL. All other arguments will be ignored if this argument is used.

res

The words that will be keep will be stored here once this preprocessing step has be trained by prep.recipe().

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

This step allow you to limit the tokens you are looking at by filtering on their occurrence in the corpus. You are able to exclude tokens if they appear too many times or too few times in the data. It can be specified as counts using max_times and min_times or as percentages by setting percentage as TRUE. In addition one can filter to only use the top max_tokens used tokens. If max_tokens is set to Inf then all the tokens will be used. This will generally lead to very large data sets when then tokens are words or trigrams. A good strategy is to start with a low token count and go up according to how much RAM you want to use.

It is strongly advised to filter before using step_tf or step_tfidf to limit the number of variables created.

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and value (number of unique tokens).

Tuning Parameters

This step has 3 tuning parameters:

  • max_times: Maximum Token Frequency (type: integer, default: Inf)

  • min_times: Minimum Token Frequency (type: integer, default: 0)

  • max_tokens: # Retained Tokens (type: integer, default: 100)

Case weights

The underlying operation does not allow for case weights.

See also

step_tokenize() to turn characters into tokens

Other Steps for Token Modification: step_lemma(), step_ngram(), step_pos_filter(), step_stem(), step_stopwords(), step_tokenmerge()

Examples

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

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

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>       medium
#>    <tknlist>
#> 1 [8 tokens]
#> 2 [3 tokens]

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [3 tokens]
#> # Unique Tokens: 3

tidy(tate_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms  value id               
#>   <chr>  <int> <chr>            
#> 1 medium    NA tokenfilter_wSraL
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 3
#>   terms  value id               
#>   <chr>  <int> <chr>            
#> 1 medium   952 tokenfilter_wSraL