textrecipes (version 0.0.2)

step_textfeature: Generate the basic 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", skip = FALSE, id = rand_id("textfeature"))
# S3 method for step_textfeature
tidy(x, ...)

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 variables. For `step_textfeature`, this indicates the variables to be encoded into a list column. See [recipes::selections()] for more details. For the `tidy` method, these are not currently used.

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 recipe has been baked.

columns

A list of tibble results that define the encoding. This is `NULL` until the step is trained by [recipes::prep.recipe()].

extract_functions

A named list of feature extracting functions. default to [count_functions] from the textfeatures package. See details for more information.

prefix

A prefix for generated column names, default to "textfeature".

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 = TRUE` as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it

x

A `step_textfeature` object.

Value

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

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. The default is a list of functions from the textfeatures package.

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.

Examples

# NOT RUN {
library(recipes)

data(okc_text)

okc_rec <- recipe(~ ., data = okc_text) %>%
  step_textfeature(essay0) 
  
okc_obj <- okc_rec %>%
  prep(training = okc_text, retain = TRUE)

juice(okc_obj) %>%
  slice(1:2)

juice(okc_obj) %>%
  pull(textfeature_essay0_n_words)
  
tidy(okc_rec, number = 1)
tidy(okc_obj, number = 1)

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

recipe(~ ., data = okc_text) %>%
  step_textfeature(essay0, 
                   extract_functions = list(nchar10 = nchar_round_10)) %>%
  prep(training = okc_text) %>%
  juice()

# }

Run the code above in your browser using DataCamp Workspace