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#' Slidify Rolling Window Transformation (Augmented Version)
#'
#' `step_slidify_augment` creates a a *specification* of a recipe
#' step that will "augment" (add multiple new columns) that have had a sliding function applied.
#'
#' @inheritParams step_slidify
#' @param prefix A prefix for generated column names, default to "slidify_".
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @param id A character string that is unique to this step to identify it.
#' @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
#'
#' @return For `step_slidify_augment`, 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), `value` (the feature
#' names).
#'
#' @keywords datagen
#' @concept preprocessing
#' @concept moving_windows
#'
#'
#' @details
#'
#' __Alignment__
#'
#' Rolling functions generate `period - 1` fewer values than the incoming vector.
#' Thus, the vector needs to be aligned. Alignment of the vector follows 3 types:
#'
#' - __Center:__ `NA` or `partial` values are divided and added to the beginning and
#' end of the series to "Center" the moving average.
#' This is common for de-noising operations. See also `[smooth_vec()]` for LOESS without NA values.
#' - __Left:__ `NA` or `partial` values are added to the end to shift the series to the Left.
#' - __Right:__ `NA` or `partial` values are added to the beginning to shif the series to the Right. This is common in
#' Financial Applications such as moving average cross-overs.
#'
#' __Partial Values__
#'
#' - The advantage to using `partial` values vs `NA` padding is that
#' the series can be filled (good for time-series de-noising operations).
#' - The downside to partial values is that the partials can become less stable
#' at the regions where incomplete windows are used.
#'
#' If instability is not desirable for de-noising operations, a suitable alternative
#' is [`step_smooth()`], which implements local polynomial regression.
#'
#' @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()]
#'
#' Main Recipe Functions:
#' - `recipes::recipe()`
#' - `recipes::prep()`
#' - `recipes::bake()`
#'
#' @examples
#' # library(tidymodels)
#' library(dplyr)
#' library(recipes)
#' library(parsnip)
#'
#' m750 <- m4_monthly %>%
#' filter(id == "M750") %>%
#' mutate(value_2 = value / 2)
#'
#' m750_splits <- time_series_split(m750, assess = "2 years", cumulative = TRUE)
#'
#' # Make a recipe
#' recipe_spec <- recipe(value ~ date + value_2, rsample::training(m750_splits)) %>%
#' step_slidify_augment(
#' value, value_2,
#' period = c(6, 12, 24),
#' .f = ~ mean(.x),
#' align = "center",
#' partial = FALSE
#' )
#'
#' recipe_spec %>% prep() %>% juice()
#'
#' bake(prep(recipe_spec), rsample::testing(m750_splits))
#'
#'
#' @importFrom recipes rand_id
#' @export
step_slidify_augment <-
function(recipe,
...,
period,
.f,
align = c("center", "left", "right"),
partial = FALSE,
prefix = "slidify_",
role = "predictor",
trained = FALSE,
columns = NULL,
f_name = NULL,
skip = FALSE,
id = rand_id("slidify_augment")) {
if (rlang::quo(.f) %>% rlang::quo_is_missing()) stop(call. = FALSE, "step_slidify_augment(.f) is missing.")
if (rlang::is_missing(period)) stop(call. = FALSE, "step_slidify_augment(period) is missing.")
f_name <- rlang::enquo(.f) %>% rlang::expr_text()
recipes::add_step(
recipe,
step_slidify_augment_new(
terms = recipes::ellipse_check(...),
period = period,
.f = .f,
align = align,
partial = partial,
prefix = prefix,
trained = trained,
role = role,
columns = columns,
f_name = f_name,
skip = skip,
id = id
)
)
}
step_slidify_augment_new <-
function(terms, role, trained, columns, period, .f, align, partial, prefix, f_name, skip, id) {
step(
subclass = "slidify_augment",
terms = terms,
role = role,
prefix = prefix,
trained = trained,
columns = columns,
period = period,
.f = .f,
align = align,
partial = partial,
f_name = f_name,
skip = skip,
id = id
)
}
#' @export
prep.step_slidify_augment <- function(x, training, info = NULL, ...) {
col_names <- recipes::recipes_eval_select(x$terms, data = training, info = info)
check_type(training[, col_names], types = c("double", "integer"))
step_slidify_augment_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
period = x$period,
.f = x$.f,
align = tolower(x$align[1]),
partial = x$partial,
prefix = x$prefix,
f_name = x$f_name,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_slidify_augment <- function(object, new_data, ...) {
if (!all(object$period == as.integer(object$lag)))
rlang::abort("step_slidify_augment() requires 'period' argument(s) to be integer valued.")
make_call <- function(col, period_val) {
rlang::call2(
.fn = "slidify_vec",
.x = rlang::sym(col),
.f = object$.f,
.period = period_val,
.align = tolower(object$align[1]),
.partial = object$partial[1],
.ns = "timetk"
)
}
grid <- expand.grid(
col = object$columns,
period_val = object$period,
stringsAsFactors = FALSE
)
calls <- purrr::pmap(.l = list(grid$col, grid$period_val), make_call)
newname <- paste0(object$prefix, grid$period_val, "_", grid$col)
calls <- recipes::check_name(calls, new_data, object, newname, TRUE)
# print(calls)
tibble::as_tibble(dplyr::mutate(new_data, !!!calls))
}
#' @export
print.step_slidify_augment <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Sliding Augmentation applied to: "
recipes::print_step(x$columns, x$terms, x$trained, width = width, title = title)
invisible(x)
}
#' @rdname step_slidify_augment
#' @param x A `step_slidify_augment` object.
#' @export
tidy.step_slidify_augment <- function(x, ...) {
res <- expand.grid(
terms = x$columns,
period = x$period,
stringsAsFactors = FALSE)
res$id <- x$id
res$terms <- paste0(x$prefix, res$period, "_", res$terms)
tibble::as_tibble(res)
}
#' @rdname required_pkgs.timetk
#' @export
required_pkgs.step_slidify_augment <- function(x, ...) {
c("timetk")
}
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