step_textfeature: Calculate Set of Text Features
In EmilHvitfeldt/textrecipes: Extra 'Recipes' for Text Processing

step_textfeature

R Documentation

Calculate Set of Text Features

Description

step_textfeature() creates a specification of a recipe step that will extract a number of numeric features of a text column.

Usage

step_textfeature(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  extract_functions = count_functions,
  prefix = "textfeature",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("textfeature")
)

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`	For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created by the original variables will be used as predictors in a model.
`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()`.
`extract_functions`	A named list of feature extracting functions. Defaults to `count_functions`. See details for more information.
`prefix`	A prefix for generated column names, defaults to "textfeature".
`keep_original_cols`	A logical to keep the original variables in the output. Defaults to `FALSE`.
`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 step will take a character column and returns a number of numeric columns equal to the number of functions in the list passed to the extract_functions argument.

All the functions passed to extract_functions must take a character vector as input and return a numeric vector of the same length, otherwise an error will be thrown.

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 is returned with columns terms, functions, and id:

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

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_textfeature(medium)

tate_obj <- tate_rec %>%
  prep()

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

bake(tate_obj, new_data = NULL) %>%
  pull(textfeature_medium_n_words)

tidy(tate_rec, number = 1)
tidy(tate_obj, number = 1)

# Using custom extraction functions
nchar_round_10 <- function(x) round(nchar(x) / 10) * 10

recipe(~., data = tate_text) %>%
  step_textfeature(medium,
    extract_functions = list(nchar10 = nchar_round_10)
  ) %>%
  prep() %>%
  bake(new_data = NULL)

EmilHvitfeldt/textrecipes documentation built on April 7, 2024, 5:02 a.m.