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#' Slidify Rolling Window Transformation
#'
#' `step_slidify` creates a a *specification* of a recipe
#' step that will apply a function
#' to one or more a Numeric column(s).
#'
#' @param recipe A recipe object. The step will be added to the
#' sequence of operations for this recipe.
#' @param ... One or more numeric columns to be smoothed.
#' See [recipes::selections()] for more details.
#' For the `tidy` method, these are not currently used.
#' @param period The number of periods to include in the local rolling window.
#' This is effectively the "window size".
#' @param .f A summary __formula__ in one of the following formats:
#' - `mean` with no arguments
#' - `function(x) mean(x, na.rm = TRUE)`
#' - `~ mean(.x, na.rm = TRUE)`, it is converted to a function.
#'
#' @param align 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.
#' @param partial Should the moving window be allowed to return partial (incomplete) windows instead of NA values.
#' Set to FALSE by default, but can be switched to TRUE to remove NA's.
#' @param names An optional character string that is the same
#' length of the number of terms selected by `terms`. These will be
#' the names of the __new columns__ created by the step.
#'
#' - If `NULL`, existing columns are transformed.
#' - If not `NULL`, new columns will be created.
#' @param trained A logical to indicate if the quantities for
#' preprocessing have been estimated.
#' @param role For model terms created by this step, what analysis
#' role should they be assigned?. By default, the function assumes
#' that the new variable columns created by the original variables
#' will be used as predictors in a model.
#' @param columns A character string of variables that will be
#' used as inputs. This field is a placeholder and will be
#' populated once `recipes::prep()` is used.
#' @param f_name A character string for the function being applied.
#' This field is a placeholder and will be populated during the `tidy()` step.
#' @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 For `step_slidify`, 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(recipes)
#' library(dplyr)
#' library(ggplot2)
#'
#' # Training Data
#' FB_tbl <- FANG %>%
#' filter(symbol == "FB") %>%
#' select(symbol, date, adjusted)
#'
#' # New Data - Make some fake new data next 90 time stamps
#' new_data <- FB_tbl %>%
#' tail(90) %>%
#' mutate(date = date %>% tk_make_future_timeseries(length_out = 90))
#'
#' # OVERWRITE EXISTING COLUMNS -----
#'
#' # Create a recipe object with a step_slidify
#' rec_ma_50 <- recipe(adjusted ~ ., data = FB_tbl) %>%
#' step_slidify(adjusted, period = 50, .f = ~ mean(.x))
#'
#' # Bake the recipe object - Applies the Moving Average Transformation
#' training_data_baked <- bake(prep(rec_ma_50), FB_tbl)
#'
#' # Apply to New Data
#' new_data_baked <- bake(prep(rec_ma_50), new_data)
#'
#' # Visualize effect
#' training_data_baked %>%
#' ggplot(aes(date, adjusted)) +
#' geom_line() +
#' geom_line(color = "red", data = new_data_baked)
#'
#' # ---- NEW COLUMNS ----
#' # Use the `names` argument to create new columns instead of overwriting existing
#'
#' rec_ma_30_names <- recipe(adjusted ~ ., data = FB_tbl) %>%
#' step_slidify(adjusted, period = 30, .f = mean, names = "adjusted_ma_30")
#'
#' bake(prep(rec_ma_30_names), FB_tbl) %>%
#' ggplot(aes(date, adjusted)) +
#' geom_line(alpha = 0.5) +
#' geom_line(aes(y = adjusted_ma_30), color = "red", size = 1)
#'
#'
#'
#' @importFrom recipes rand_id
#' @export
step_slidify <-
function(recipe,
...,
period,
.f,
align = c("center", "left", "right"),
partial = FALSE,
names = NULL,
role = "predictor",
trained = FALSE,
columns = NULL,
f_name = NULL,
skip = FALSE,
id = rand_id("slidify")) {
if (rlang::quo(.f) %>% rlang::quo_is_missing()) stop(call. = FALSE, "step_slidify(.f) is missing.")
if (rlang::is_missing(period)) stop(call. = FALSE, "step_slidify(period) is missing.")
f_name <- rlang::enquo(.f) %>% rlang::expr_text()
if (length(period) > 1) rlang::abort("step_slidify() can only use one 'period'. For multiple periods, use step_slidify_augment().")
recipes::add_step(
recipe,
step_slidify_new(
terms = recipes::ellipse_check(...),
period = period,
.f = .f,
align = align,
partial = partial,
names = names,
trained = trained,
role = role,
columns = columns,
f_name = f_name,
skip = skip,
id = id
)
)
}
step_slidify_new <-
function(terms, role, trained, columns, period, .f, align, partial, names, f_name, skip, id) {
step(
subclass = "slidify",
terms = terms,
role = role,
names = names,
trained = trained,
columns = columns,
period = period,
.f = .f,
align = align,
partial = partial,
f_name = f_name,
skip = skip,
id = id
)
}
#' @export
prep.step_slidify <- function(x, training, info = NULL, ...) {
col_names <- recipes::recipes_eval_select(x$terms, data = training, info = info)
check_type(training[, col_names], types = c("double", "integer"))
if (!is.null(x$names)) {
if (length(x$names) != length(col_names))
rlang::abort(
paste0("There were ", length(col_names), " term(s) selected but ",
length(x$names), " values for the new features ",
"were passed to `names`."
)
)
}
step_slidify_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
period = x$period,
.f = x$.f,
align = x$align,
partial = x$partial,
names = x$names,
f_name = x$f_name,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_slidify <- function(object, new_data, ...) {
col_names <- object$columns
align <- object$align[1]
partial <- object$partial[1]
if (!is.null(object$names)) {
# New columns provided
for (i in seq_along(object$names)) {
new_data[,object$names[i]] <- new_data %>%
dplyr::pull(col_names[i]) %>%
slidify_vec(
.period = object$period,
.f = object$.f,
.align = align,
.partial = partial)
}
} else {
# No new columns - overwrite existing
for (i in seq_along(col_names)) {
new_data[,col_names[i]] <- new_data %>%
dplyr::pull(col_names[i]) %>%
slidify_vec(
.period = object$period,
.f = object$.f,
.align = align,
.partial = partial)
}
}
new_data
}
#' @export
print.step_slidify <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Slidify Function Applied on "
recipes::print_step(x$columns, x$terms, x$trained, width = width, title = title)
invisible(x)
}
#' @rdname step_slidify
#' @param x A `step_slidify` object.
#' @export
tidy.step_slidify <- function(x, ...) {
out <- simple_terms(x, ...)
out$period <- x$period
out$.f <- x$f_name
out$align <- x$align[1]
out$partial <- x$partial
out$id <- x$id
out
}
#' @rdname required_pkgs.timetk
#' @export
required_pkgs.step_slidify <- function(x, ...) {
c("timetk")
}
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