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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 ... One or more selector functions to choose which
#' variables are affected by the step. See [selections()]
#' for more details. For the `tidy` method, these are not
#' currently used.
#' @param role Not used by this step since no new variables are
#' created.
#' @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.
#' @return An updated version of `recipe` with the new step
#' added to the sequence of existing steps (if any). For the
#' `tidy` method, a tibble with columns `terms` which
#' is the columns that will be affected.
#' @keywords datagen
#' @concept preprocessing
#' @concept transformation_methods
#' @export
#' @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_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)
#' @seealso [step_log()]
#' [step_sqrt()] [step_hyperbolic()] [recipe()]
#' [prep.recipe()] [bake.recipe()]
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 = ellipse_check(...),
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 <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
step_inverse_new(
terms = x$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, ...) {
for (i in seq_along(object$columns))
new_data[, object$columns[i]] <-
1 / (new_data [[ object$columns[i] ]] + object$offset)
as_tibble(new_data)
}
print.step_inverse <-
function(x, width = max(20, options()$width - 33), ...) {
cat("Inverse transformation on ", sep = "")
printer(x$columns, x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_inverse
#' @param x A `step_inverse` object.
#' @export
tidy.step_inverse <- function(x, ...) {
res <-simple_terms(x, ...)
res$id <- x$id
res
}
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