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#' Hyperbolic Transformations
#'
#' `step_hyperbolic` creates a *specification* of a
#' recipe step that will transform data using a hyperbolic
#' function.
#'
#' @inheritParams step_center
#' @param func A character value for the function. Valid values
#' are "sinh", "cosh", or "tanh".
#' @param inverse A logical: should the inverse function be used?
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @template step-return
#' @family individual transformation steps
#' @export
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the columns that will be affected), `inverse`, and `func` is
#' returned.
#'
#' @template case-weights-not-supported
#'
#' @examples
#' set.seed(313)
#' examples <- matrix(rnorm(40), ncol = 2)
#' examples <- as.data.frame(examples)
#'
#' rec <- recipe(~ V1 + V2, data = examples)
#'
#' cos_trans <- rec %>%
#' step_hyperbolic(
#' all_numeric_predictors(),
#' func = "cosh", inverse = FALSE
#' )
#'
#' cos_obj <- prep(cos_trans, training = examples)
#'
#' transformed_te <- bake(cos_obj, examples)
#' plot(examples$V1, transformed_te$V1)
#'
#' tidy(cos_trans, number = 1)
#' tidy(cos_obj, number = 1)
step_hyperbolic <-
function(recipe,
...,
role = NA,
trained = FALSE,
func = c("sinh", "cosh", "tanh"),
inverse = TRUE,
columns = NULL,
skip = FALSE,
id = rand_id("hyperbolic")) {
func <- rlang::arg_match(func)
add_step(
recipe,
step_hyperbolic_new(
terms = enquos(...),
role = role,
trained = trained,
func = func,
inverse = inverse,
columns = columns,
skip = skip,
id = id
)
)
}
step_hyperbolic_new <-
function(terms, role, trained, func, inverse, columns, skip, id) {
step(
subclass = "hyperbolic",
terms = terms,
role = role,
trained = trained,
func = func,
inverse = inverse,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_hyperbolic <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
step_hyperbolic_new(
terms = x$terms,
role = x$role,
trained = TRUE,
func = x$func,
inverse = x$inverse,
columns = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_hyperbolic <- function(object, new_data, ...) {
check_new_data(names(object$columns), object, new_data)
func <- if (object$inverse) {
get(paste0("a", object$func))
} else {
get(object$func)
}
col_names <- object$columns
for (i in seq_along(col_names)) {
new_data[, col_names[i]] <-
func(getElement(new_data, col_names[i]))
}
new_data
}
print.step_hyperbolic <-
function(x, width = max(20, options()$width - 32), ...) {
ttl <- paste("Hyperbolic", substr(x$func, 1, 3))
if (x$inverse) {
ttl <- paste(ttl, "(inv)")
}
title <- glue::glue("{ttl} transformation on ")
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_hyperbolic <- function(x, ...) {
out <- simple_terms(x, ...)
out$inverse <- x$inverse
out$func <- x$func
out$id <- x$id
out
}
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