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#' Box-Cox Transformation using Forecast Methods
#'
#' `step_box_cox` creates a *specification* of a recipe
#' step that will transform data using a Box-Cox
#' transformation. This function differs from
#' `recipes::step_BoxCox` by adding multiple methods
#' including Guerrero lambda optimization and handling for
#' negative data used in the Forecast R Package.
#'
#' @param recipe A `recipe` object. The step will be added to the sequence of operations for this recipe.
#' @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 method One of "guerrero" or "loglik"
#' @param limits A length 2 numeric vector defining the range to
#' compute the transformation parameter lambda.
#' @param role Not used by this step since no new variables are
#' created.
#' @param trained A logical to indicate if the quantities for preprocessing have been estimated.
#' @param lambdas_trained A numeric vector of transformation values. This
#' is `NULL` until computed by `prep()`.
#' @param skip A logical. Should the step be skipped when the recipe
#' is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` is run,
#' some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)).
#' Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations.
#' @param id A character string that is unique to this step to identify it.
#'
#' @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` (the
#' selectors or variables selected) and `value` (the
#' lambda estimate).
#'
#' @details
#'
#' The `step_box_cox()` function is designed specifically to handle time series
#' using methods implemented in the Forecast R Package.
#'
#' __Negative Data__
#'
#' This function can be applied to Negative Data.
#'
#' __Lambda Optimization Methods__
#'
#' This function uses 2 methods for optimizing the lambda selection
#' from the Forecast R Package:
#'
#' 1. `method = "guerrero"`: Guerrero's (1993) method is used, where lambda minimizes
#' the coefficient of variation for subseries of x.
#'
#' 2. `method = loglik`: the value of lambda is chosen to maximize the profile
#' log likelihood of a linear model fitted to x. For non-seasonal data, a
#' linear time trend is fitted while for seasonal data, a linear time trend
#' with seasonal dummy variables is used.
#'
#'
#' @references
#' 1. Guerrero, V.M. (1993) Time-series analysis supported by power transformations. _Journal of Forecasting_, __12__, 37–48.
#' 2. Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations. _JRSS_ B __26__ 211–246.
#'
#' @examples
#' library(dplyr)
#' library(recipes)
#'
#' FANG_wide <- FANG %>%
#' select(symbol, date, adjusted) %>%
#' tidyr::pivot_wider(names_from = symbol, values_from = adjusted)
#'
#' recipe_box_cox <- recipe(~ ., data = FANG_wide) %>%
#' step_box_cox(FB, AMZN, NFLX, GOOG) %>%
#' prep()
#'
#' recipe_box_cox %>% bake(FANG_wide)
#'
#' recipe_box_cox %>% tidy(1)
#'
#' @seealso
#'
#' Time Series Analysis:
#' - Engineered Features: [step_timeseries_signature()], [step_holiday_signature()], [step_fourier()]
#' - Diffs & Lags [step_diff()], `recipes::step_lag()`
#' - Smoothing: [step_slidify()], [step_smooth()]
#' - Variance Reduction: [step_box_cox()]
#' - Imputation: [step_ts_impute()], [step_ts_clean()]
#' - Padding: [step_ts_pad()]
#'
#' Transformations to reduce variance:
#' - `recipes::step_log()` - Log transformation
#' - `recipes::step_sqrt()` - Square-Root Power Transformation
#'
#' Recipe Setup and Application:
#' - `recipes::recipe()`
#' - `recipes::prep()`
#' - `recipes::bake()`
#'
#' @export
step_box_cox <-
function(recipe,
...,
method = c("guerrero", "loglik"),
limits = c(-1, 2),
role = NA,
trained = FALSE,
lambdas_trained = NULL,
skip = FALSE,
id = rand_id("box_cox")) {
recipes::add_step(
recipe,
step_box_cox_new(
terms = recipes::ellipse_check(...),
role = role,
trained = trained,
lambdas_trained = lambdas_trained,
limits = sort(limits)[1:2],
method = tolower(method)[1],
skip = skip,
id = id
)
)
}
step_box_cox_new <-
function(terms, role, trained, lambdas_trained, limits, method, skip, id) {
recipes::step(
subclass = "box_cox",
terms = terms,
role = role,
trained = trained,
lambdas_trained = lambdas_trained,
method = method,
limits = limits,
skip = skip,
id = id
)
}
#' @export
prep.step_box_cox <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, data = training, info = info)
recipes::check_type(training[, col_names], types = c("double", "integer"))
lambda_values <- training[, col_names] %>%
purrr::map(.f = function(vals) {
auto_lambda(
vals,
method = x$method,
lambda_lower = x$limits[1],
lambda_upper = x$limits[2]
)
})
step_box_cox_new(
terms = x$terms,
role = x$role,
trained = TRUE,
lambdas_trained = lambda_values,
limits = x$limits,
method = x$method,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_box_cox <- function(object, new_data, ...) {
# object$lambdas_trained
param <- names(object$lambdas_trained)
for (i in seq_along(object$lambdas_trained)) {
new_data[, param[i]] <- box_cox_vec(
x = new_data %>% purrr::pluck(param[i]),
lambda = as.numeric(object$lambdas_trained[i])
)
}
tibble::as_tibble(new_data)
}
#' @export
print.step_box_cox <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Box-Cox transformation on "
recipes::print_step(names(x$lambdas_trained), x$terms, x$trained, width = width, title = title)
invisible(x)
}
#' @rdname step_box_cox
#' @param x A `step_box_cox` object.
#' @export
tidy.step_box_cox <- function(x, ...) {
if (is_trained(x)) {
res <- tibble::tibble(
terms = names(x$lambdas_trained),
lambda = as.numeric(x$lambdas_trained)
)
} else {
term_names <- recipes::sel2char(x$terms)
res <- tibble::tibble(
terms = term_names,
lambda = rlang::na_dbl
)
}
res$id <- x$id
res
}
#' S3 methods for tracking which additional packages are needed for steps.
#'
#' Recipe-adjacent packages always list themselves as a required package so that
#' the steps can function properly within parallel processing schemes.
#' @param x A recipe step
#' @return A character vector
#' @rdname required_pkgs.timetk
#' @keywords internal
#' @export
required_pkgs.step_box_cox <- function(x, ...) {
c("timetk")
}
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