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#' Log Interval Transformation for Constrained Interval Forecasting
#'
#' `step_log_interval` creates a *specification* of a recipe
#' step that will transform data using a Log-Inerval
#' transformation. This function provides a `recipes` interface
#' for the `log_interval_vec()` transformation function.
#'
#' @inheritParams log_interval_vec
#' @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 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 limit_lower_trained A numeric vector of transformation values. This
#' is `NULL` until computed by `prep()`.
#' @param limit_upper_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_log_interval()` function is designed specifically to handle time series
#' using methods implemented in the Forecast R Package.
#'
#' __Positive Data__
#'
#' If data includes values of zero, use `offset` to adjust the series to make the values positive.
#'
#' __Implementation__
#'
#' Refer to the [log_interval_vec()] function for the transformation implementation details.
#'
#'
#'
#' @examples
#' library(dplyr)
#' library(recipes)
#'
#' FANG_wide <- FANG %>%
#' select(symbol, date, adjusted) %>%
#' tidyr::pivot_wider(names_from = symbol, values_from = adjusted)
#'
#' recipe_log_interval <- recipe(~ ., data = FANG_wide) %>%
#' step_log_interval(FB, AMZN, NFLX, GOOG, offset = 1) %>%
#' prep()
#'
#' recipe_log_interval %>%
#' bake(FANG_wide) %>%
#' tidyr::pivot_longer(-date) %>%
#' plot_time_series(date, value, name, .smooth = FALSE, .interactive = FALSE)
#'
#' recipe_log_interval %>% 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_log_interval()]
#' - 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_log_interval <-
function(recipe,
...,
limit_lower = "auto",
limit_upper = "auto",
offset = 0,
role = NA,
trained = FALSE,
limit_lower_trained = NULL,
limit_upper_trained = NULL,
skip = FALSE,
id = rand_id("log_interval")) {
recipes::add_step(
recipe,
step_log_interval_new(
terms = recipes::ellipse_check(...),
role = role,
trained = trained,
limit_lower_trained = limit_lower_trained,
limit_upper_trained = limit_upper_trained,
limit_lower = limit_lower[1],
limit_upper = limit_upper[1],
offset = offset[1],
skip = skip,
id = id
)
)
}
step_log_interval_new <-
function(terms, role, trained, limit_lower_trained, limit_upper_trained,
limit_lower, limit_upper, offset, method, skip, id) {
recipes::step(
subclass = "log_interval",
terms = terms,
role = role,
trained = trained,
limit_lower_trained = limit_lower_trained,
limit_upper_trained = limit_upper_trained,
limit_lower = limit_lower,
limit_upper = limit_upper,
offset = offset,
skip = skip,
id = id
)
}
#' @export
prep.step_log_interval <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, data = training, info = info)
recipes::check_type(training[, col_names], types = c("double", "integer"))
limit_lower_trained <- training[, col_names] %>%
purrr::map(.f = function(vals) {
vals <- vals + x$offset
max_x <- max(vals)
min_x <- min(vals)
range_x <- abs(max_x - min_x)
auto_limit_lower(x$limit_lower, min_x, range_x)
})
limit_upper_trained <- training[, col_names] %>%
purrr::map(.f = function(vals) {
vals <- vals + x$offset
max_x <- max(vals)
min_x <- min(vals)
range_x <- abs(max_x - min_x)
auto_limit_upper(x$limit_upper, max_x, range_x)
})
step_log_interval_new(
terms = x$terms,
role = x$role,
trained = TRUE,
limit_lower_trained = limit_lower_trained,
limit_upper_trained = limit_upper_trained,
limit_lower = x$limit_lower,
limit_upper = x$limit_upper,
offset = x$offset,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_log_interval <- function(object, new_data, ...) {
# Column names to transform
param <- names(object$limit_lower_trained)
for (i in seq_along(object$limit_lower_trained)) {
print(object$limit_lower_trained[i])
print(object$limit_upper_trained[i])
new_data[, param[i]] <- log_interval_vec(
x = new_data %>% purrr::pluck(param[i]),
limit_lower = as.numeric(object$limit_lower_trained[i]),
limit_upper = as.numeric(object$limit_upper_trained[i]),
offset = object$offset,
silent = TRUE
)
}
tibble::as_tibble(new_data)
}
#' @export
print.step_log_interval <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Log-interval transformation on "
recipes::print_step(names(x$limit_lower_trained), x$terms, x$trained, width = width, title = title)
invisible(x)
}
#' @rdname step_log_interval
#' @param x A `step_log_interval` object.
#' @export
tidy.step_log_interval <- function(x, ...) {
if (is_trained(x)) {
res <- tibble::tibble(
terms = names(x$limit_lower_trained),
limit_lower = as.numeric(x$limit_lower_trained),
limit_upper = as.numeric(x$limit_upper_trained),
offset = x$offset
)
} else {
term_names <- recipes::sel2char(x$terms)
res <- tibble::tibble(
terms = term_names,
limit_lower = x$limit_lower,
limit_upper = x$limit_upper,
offset = x$offset
)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.timetk
#' @export
required_pkgs.step_log_interval <- function(x, ...) {
c("timetk")
}
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