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#' Inverse Transformation
#'
#' `step_inverse` creates a *specification* of a recipe
#' step that will inverse transform the data.
#'
#' @inheritParams step_center
#' @param offset An optional value to add to the data prior to
#' logging (to avoid `1/0`).
#' @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) is returned.
#'
#' @template case-weights-not-supported
#'
#' @examples
#' set.seed(313)
#' examples <- matrix(runif(40), ncol = 2)
#' examples <- data.frame(examples)
#'
#' rec <- recipe(~ X1 + X2, data = examples)
#'
#' inverse_trans <- rec %>%
#' step_inverse(all_numeric_predictors())
#'
#' inverse_obj <- prep(inverse_trans, training = examples)
#'
#' transformed_te <- bake(inverse_obj, examples)
#' plot(examples$X1, transformed_te$X1)
#'
#' tidy(inverse_trans, number = 1)
#' tidy(inverse_obj, number = 1)
step_inverse <-
function(recipe,
...,
role = NA,
offset = 0,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("inverse")) {
add_step(
recipe,
step_inverse_new(
terms = enquos(...),
role = role,
offset = offset,
trained = trained,
columns = columns,
skip = skip,
id = id
)
)
}
step_inverse_new <-
function(terms, role, offset, trained, columns, skip, id) {
step(
subclass = "inverse",
terms = terms,
role = role,
offset = offset,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_inverse <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
step_inverse_new(
terms = x$terms,
role = x$role,
offset = x$offset,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_inverse <- function(object, new_data, ...) {
check_new_data(names(object$columns), object, new_data)
for (i in seq_along(object$columns)) {
new_data[, object$columns[i]] <-
1 / (new_data[[object$columns[i]]] + object$offset)
}
new_data
}
print.step_inverse <-
function(x, width = max(20, options()$width - 33), ...) {
title <- "Inverse transformation on "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_inverse <- function(x, ...) {
res <- simple_terms(x, ...)
res$id <- x$id
res
}
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