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#' @importFrom stats update
#' @export
stats::update
#' Update a recipe step
#'
#' This `step` method for `update()` takes named arguments as `...` who's values
#' will replace the elements of the same name in the actual step.
#'
#' For a step to be updated, it must not already have been trained. Otherwise,
#' conflicting information can arise between the data returned from
#' `bake(object, new_data = NULL)` and the information in the step.
#'
#'
#' @param object A recipe `step`.
#' @param ... Key-value pairs where the keys match up with names of elements
#' in the step, and the values are the new values to update the step with.
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(biomass, package = "modeldata")
#'
#' biomass_tr <- biomass[biomass$dataset == "Training", ]
#' biomass_te <- biomass[biomass$dataset == "Testing", ]
#'
#' # Create a recipe using step_bs() with degree = 3
#' rec <- recipe(
#' HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
#' data = biomass_tr
#' ) %>%
#' step_bs(carbon, hydrogen, degree = 3)
#'
#' # Update the step to use degree = 4
#' rec2 <- rec
#' rec2$steps[[1]] <- update(rec2$steps[[1]], degree = 4)
#'
#' # Prep both recipes
#' rec_prepped <- prep(rec, training = biomass_tr)
#' rec2_prepped <- prep(rec2, training = biomass_tr)
#'
#' # To see what changed
#' bake(rec_prepped, new_data = NULL)
#' bake(rec2_prepped, new_data = NULL)
#'
#' # Cannot update a recipe step that has been trained!
#' \dontrun{
#' update(rec_prepped$steps[[1]], degree = 4)
#' }
#'
#' @export
update.step <- function(object, ...) {
changes <- list(...)
validate_not_trained(object)
# Replace the appropriate values in object with the changes
object <- update_fields(object, changes)
# Call step() to construct a new step to ensure all new changes are validated
reconstruct_step(object)
}
update_fields <- function(object, changes) {
validate_has_unique_names(changes)
new_nms <- names(changes)
old_nms <- names(object)
step_type <- class(object)[1]
for (nm in new_nms) {
if (!(nm %in% old_nms)) {
rlang::abort(glue::glue(
"The step you are trying to update, ",
"'{step_type}', does not have the '{nm}' field."
))
}
object[[nm]] <- changes[[nm]]
}
object
}
reconstruct_step <- function(x) {
# Collect the subclass of the step to use
# when recreating it
subclass <- setdiff(class(x), "step")
# A step is just a list of its arguments
args <- unclass(x)
# Construct the call and splice in the args
# no prefix is needed because we know the full subclass
call_step <- rlang::call2(
.fn = "step",
subclass = subclass,
!!!args,
.prefix = "",
.ns = "recipes"
)
rlang::eval_tidy(call_step)
}
has_unique_names <- function(x) {
nms <- names(x)
if (length(nms) != length(x)) {
return(FALSE)
}
if (any(is.na(nms) | nms == "")) {
return(FALSE)
}
!anyDuplicated(nms)
}
validate_has_unique_names <- function(x) {
if (!has_unique_names(x)) {
rlang::abort("All of the changes supplied in `...` must be uniquely named.")
}
invisible(x)
}
validate_not_trained <- function(x) {
if (is_trained(x)) {
step_type <- class(x)[1]
rlang::abort(glue::glue(
"To update '{step_type}', it must not be trained."
))
}
invisible(x)
}
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