R/count.R

In recipes: Preprocessing and Feature Engineering Steps for Modeling

#' Create counts of patterns using regular expressions
#'
#' `step_count()` creates a *specification* of a recipe step that will create a
#' variable that counts instances of a regular expression pattern in text.
#'
#' @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 normalize A logical; should the integer counts be divided by the total
#'   number of characters in the string?.
#' @param options A list of options to [gregexpr()] 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, the new column names}
#'   \item{id}{character, id of this step}
#' }
#'
#' @template sparse-creation
#'
#' @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_count(description, pattern = "(rock|stony)", result = "rocks") |>
#'   step_count(description, pattern = "famil", normalize = TRUE)
#'
#' rec2 <- prep(rec, training = covers)
#' rec2
#'
#' count_values <- bake(rec2, new_data = covers)
#' count_values
#'
#' tidy(rec, number = 1)
#' tidy(rec2, number = 1)
step_count <- function(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  pattern = ".",
  normalize = FALSE,
  options = list(),
  result = make.names(pattern),
  input = NULL,
  sparse = "auto",
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("count")
) {
  check_string(pattern)

  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_count_new(
      terms = terms,
      role = role,
      trained = trained,
      pattern = pattern,
      normalize = normalize,
      options = options,
      result = result,
      input = input,
      sparse = sparse,
      keep_original_cols = keep_original_cols,
      skip = skip,
      id = id
    )
  )
}

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

#' @export
prep.step_count <- 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, allow_empty = TRUE, arg = "pattern")
  check_string(x$result, allow_empty = FALSE, arg = "result")
  check_bool(x$normalize, arg = "normalize")
  check_sparse_arg(x$sparse)
  check_options(x$options, exclude = c("x", "pattern"))

  step_count_new(
    terms = x$terms,
    role = x$role,
    trained = TRUE,
    pattern = x$pattern,
    normalize = x$normalize,
    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_count <- function(object, new_data, ...) {
  col_name <- names(object$input)
  check_new_data(col_name, object, new_data)

  if (length(col_name) == 0L) {
    return(new_data)
  }

  ## sub in options
  regex <- expr(
    gregexpr(
      text = 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 := vapply(eval(regex), counter, integer(1))
  )

  if (object$normalize) {
    totals <- nchar(as.character(new_data[[col_name]]))
    new_values[[object$result]] <- new_values[[object$result]] / totals
  }

  if (sparse_is_yes(object$sparse)) {
    if (object$normalize) {
      new_values[[object$result]] <- sparsevctrs::as_sparse_double(
        new_values[[object$result]]
      )
    } else {
      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, .name_repair = "minimal")
  new_data <- remove_original_cols(new_data, object, col_name)
  new_data
}

counter <- function(x) length(x[x > 0])

#' @export
print.step_count <-
  function(x, width = max(20, options()$width - 30), ...) {
    title <- "Regular expression counts using "
    print_step(x$input, x$terms, x$trained, title, width)
    invisible(x)
  }

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

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