R/regex.R

In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

#' Detect a regular expression
#'
#' `step_regex()` creates a *specification* of a recipe step that will create a
#' new dummy variable based on a regular expression.
#'
#' @inheritParams step_classdist
#' @inheritParams step_pca
#' @inheritParams step_center
#' @inheritParams step_dummy
#' @param ... A single selector function to choose which variable
#'  will be searched for the regex pattern. The selector should resolve
#'  to a single variable. See [selections()] for more details.
#' @param pattern A character string containing a regular
#'  expression (or character string for `fixed = TRUE`) to be
#'  matched in the given character vector. Coerced by
#'  `as.character` to a character string if possible.
#' @param options A list of options to [grepl()] that
#'  should not include `x` or `pattern`.
#' @param result A single character value for the name of the new
#'  variable. It should be a valid column name.
#' @param input A single character value for the name of the
#'  variable being searched. This is `NULL` until computed by
#'  [prep()].
#' @template step-return
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `result` , and `id`:
#'
#' \describe{
#'   \item{terms}{character, the selectors or variables selected}
#'   \item{result}{character, new column name}
#'   \item{id}{character, id of this step}
#' }
#'
#' @template case-weights-not-supported
#'
#' @family dummy variable and encoding steps
#' @export
#' @examplesIf rlang::is_installed("modeldata")
#' data(covers, package = "modeldata")
#'
#' rec <- recipe(~description, covers) %>%
#'   step_regex(description, pattern = "(rock|stony)", result = "rocks") %>%
#'   step_regex(description, pattern = "ratake families")
#'
#' rec2 <- prep(rec, training = covers)
#' rec2
#'
#' with_dummies <- bake(rec2, new_data = covers)
#' with_dummies
#' tidy(rec, number = 1)
#' tidy(rec2, number = 1)
step_regex <- function(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  pattern = ".",
  options = list(),
  result = make.names(pattern),
  input = NULL,
  sparse = "auto",
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("regex")
) {
  valid_args <- names(formals(grepl))[-(1:2)]
  if (any(!(names(options) %in% valid_args))) {
    cli::cli_abort(
      c(
        "x" = "The following elements of {.arg options} are not allowed:",
        "*" = "{.val {setdiff(names(options), valid_args)}}.",
        "i" = "Valid options are: {.val {valid_args}}."
      )
    )
  }

  terms <- enquos(...)
  if (length(terms) > 1) {
    cli::cli_abort(
      c(
        x = "For this step, only a single selector can be used.",
        i = "The following {length(terms)} selectors were used: \\
          {.var {as.character(terms)}}."
      )
    )
  }

  add_step(
    recipe,
    step_regex_new(
      terms = terms,
      role = role,
      trained = trained,
      pattern = pattern,
      options = options,
      result = result,
      input = input,
      sparse = sparse,
      keep_original_cols = keep_original_cols,
      skip = skip,
      id = id
    )
  )
}

step_regex_new <-
  function(
    terms,
    role,
    trained,
    pattern,
    options,
    result,
    input,
    sparse,
    keep_original_cols,
    skip,
    id
  ) {
    step(
      subclass = "regex",
      terms = terms,
      role = role,
      trained = trained,
      pattern = pattern,
      options = options,
      result = result,
      input = input,
      sparse = sparse,
      keep_original_cols = keep_original_cols,
      skip = skip,
      id = id
    )
  }

#' @export
prep.step_regex <- function(x, training, info = NULL, ...) {
  col_name <- recipes_eval_select(x$terms, training, info)
  check_type(training[, col_name], types = c("string", "factor", "ordered"))
  check_string(x$pattern, arg = "pattern", allow_empty = FALSE)
  check_sparse_arg(x$sparse)

  step_regex_new(
    terms = x$terms,
    role = x$role,
    trained = TRUE,
    pattern = x$pattern,
    options = x$options,
    input = col_name,
    result = x$result,
    sparse = x$sparse,
    keep_original_cols = get_keep_original_cols(x),
    skip = x$skip,
    id = x$id
  )
}

#' @export
bake.step_regex <- function(object, new_data, ...) {
  col_name <- names(object$input)
  if (length(col_name) == 0) {
    return(new_data)
  }

  check_new_data(col_name, object, new_data)

  ## sub in options
  regex <- expr(
    grepl(
      x = new_data[[col_name]],
      pattern = object$pattern,
      ignore.case = FALSE,
      perl = FALSE,
      fixed = FALSE,
      useBytes = FALSE
    )
  )
  if (length(object$options) > 0) {
    regex <- rlang::call_modify(regex, !!!object$options)
  }

  new_values <- tibble::tibble(!!object$result := ifelse(eval(regex), 1L, 0L))

  if (sparse_is_yes(object$sparse)) {
    new_values[[object$result]] <- sparsevctrs::as_sparse_integer(
      new_values[[object$result]]
    )
  }

  new_values <- check_name(new_values, new_data, object, object$result)
  new_data <- vec_cbind(new_data, new_values)
  new_data <- remove_original_cols(new_data, object, col_name)
  new_data
}

#' @export
print.step_regex <-
  function(x, width = max(20, options()$width - 30), ...) {
    title <- "Regular expression dummy variable using "
    pattern <- glue("\"{x$pattern}\"")
    untrained_terms <- rlang::parse_quos(pattern, rlang::current_env())
    print_step(pattern, untrained_terms, x$trained, title, width)
    invisible(x)
  }

#' @rdname tidy.recipe
#' @export
tidy.step_regex <- function(x, ...) {
  term_names <- sel2char(x$terms)
  p <- length(term_names)
  if (is_trained(x)) {
    res <- tibble(
      terms = term_names,
      result = rep(unname(x$result), p)
    )
  } else {
    res <- tibble(
      terms = term_names,
      result = rep(na_chr, p)
    )
  }
  res$id <- x$id
  res
}

#' @export
.recipes_estimate_sparsity.step_regex <- function(x, data, ...) {
  lapply(1, function(n_lvl) {
    c(
      n_cols = n_lvl,
      sparsity = 0.5
    )
  })
}