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#' Smoothing Transformation using Loess
#'
#' `step_smooth` creates a a *specification* of a recipe
#' step that will apply local polynomial regression
#' to one or more a Numeric column(s). The effect is smoothing the time series
#' __similar to a moving average without creating missing values or using partial smoothing.__
#'
#' @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 smoothing.
#' Similar to window size for a moving average.
#' See details for an explanation `period` vs `span` specification.
#' @param span The span is a percentage of data to be included
#' in the smoothing window. Period is preferred for shorter windows
#' to fix the window size.
#' See details for an explanation `period` vs `span` specification.
#' @param degree The degree of the polynomials to be used.
#' Set to 2 by default for 2nd order polynomial.
#' @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 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_smooth`, 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
#'
#' __Smoother Algorithm__
#' This function is a `recipe` specification that wraps the `stats::loess()`
#' with a modification to set a fixed `period` rather than a percentage of
#' data points via a `span`.
#'
#' __Why Period vs Span?__
#' The `period` is fixed whereas the `span` changes as the number of observations change.
#'
#' __When to use Period?__
#' The effect of using a `period` is similar to a Moving Average where the Window Size
#' is the ___Fixed Period___. This helps when you are trying to smooth local trends.
#' If you want a 30-day moving average, specify `period = 30`.
#'
#' __When to use Span?__
#' Span is easier to specify when you want a ___Long-Term Trendline___ where the
#' window size is unknown. You can specify `span = 0.75` to locally regress
#' using a window of 75% of the data.
#'
#' __Warning - Using Span with New Data__
#' When using span on New Data, the number of observations is likely different than
#' what you trained with. This means the trendline / smoother can be vastly different
#' than the smoother you trained with.
#'
#' __Solution to Span with New Data__
#' Don't use `span`. Rather, use `period` to fix the window size.
#' This ensures that new data includes the same number of observations in the local
#' polynomial regression (loess) as the training data.
#'
#' @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))
#'
#' # ---- PERIOD ----
#'
#' # Create a recipe object with a step_smooth()
#' rec_smooth_period <- recipe(adjusted ~ ., data = FB_tbl) %>%
#' step_smooth(adjusted, period = 30)
#'
#' # Bake the recipe object - Applies the Loess Transformation
#' training_data_baked <- bake(prep(rec_smooth_period), FB_tbl)
#'
#' # "Period" Effect on New Data
#' new_data_baked <- bake(prep(rec_smooth_period), new_data)
#'
#' # Smoother's fit on new data is very similar because
#' # 30 days are used in the new data regardless of the new data being 90 days
#' training_data_baked %>%
#' ggplot(aes(date, adjusted)) +
#' geom_line() +
#' geom_line(color = "red", data = new_data_baked)
#'
#' # ---- SPAN ----
#'
#' # Create a recipe object with a step_smooth
#' rec_smooth_span <- recipe(adjusted ~ ., data = FB_tbl) %>%
#' step_smooth(adjusted, span = 0.03)
#'
#' # Bake the recipe object - Applies the Loess Transformation
#' training_data_baked <- bake(prep(rec_smooth_span), FB_tbl)
#'
#' # "Period" Effect on New Data
#' new_data_baked <- bake(prep(rec_smooth_span), new_data)
#'
#' # Smoother's fit is not the same using span because new data is only 90 days
#' # and 0.03 x 90 = 2.7 days
#' 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_smooth_names <- recipe(adjusted ~ ., data = FB_tbl) %>%
#' step_smooth(adjusted, period = 30, names = "adjusted_smooth_30") %>%
#' step_smooth(adjusted, period = 180, names = "adjusted_smooth_180") %>%
#' step_smooth(adjusted, span = 0.75, names = "long_term_trend")
#'
#' bake(prep(rec_smooth_names), FB_tbl) %>%
#' ggplot(aes(date, adjusted)) +
#' geom_line(alpha = 0.5) +
#' geom_line(aes(y = adjusted_smooth_30), color = "red", size = 1) +
#' geom_line(aes(y = adjusted_smooth_180), color = "blue", size = 1) +
#' geom_line(aes(y = long_term_trend), color = "orange", size = 1)
#'
#'
#'
#' @importFrom recipes rand_id
#' @export
step_smooth <-
function(recipe,
...,
period = 30,
span = NULL,
degree = 2,
names = NULL,
role = "predictor",
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("smooth")) {
recipes::add_step(
recipe,
step_smooth_new(
terms = recipes::ellipse_check(...),
period = period,
span = span,
degree = degree,
names = names,
trained = trained,
role = role,
columns = columns,
skip = skip,
id = id
)
)
}
step_smooth_new <-
function(terms, role, trained, columns, period, span, degree, names, skip, id) {
step(
subclass = "smooth",
terms = terms,
role = role,
names = names,
trained = trained,
columns = columns,
period = period,
span = span,
degree = degree,
skip = skip,
id = id
)
}
#' @export
prep.step_smooth <- 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_smooth_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
period = x$period,
span = x$span,
degree = x$degree,
names = x$names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_smooth <- function(object, new_data, ...) {
# Loop through and create variables
col_names <- object$columns
# Span Calc
if (is.null(object$span)) {
span <- object$period / nrow(new_data)
} else {
span <- object$span
}
# Degree
degree <- object$degree
if (!is.null(object$names)) {
# New columns provided
for (i in seq_along(object$names)) {
new_data[,object$names[i]] <- new_data %>% dplyr::pull(col_names[i]) %>% smooth_vec(span = span, degree = degree)
}
} 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]) %>% smooth_vec(span = span, degree = degree)
}
}
new_data
}
#' @export
print.step_smooth <-
function(x, width = max(20, options()$width - 35), ...) {
title <- "Smoother: Local Polynomial Regression Fitting (Loess) on "
recipes::print_step(x$columns, x$terms, x$trained, width = width, title = title)
invisible(x)
}
#' @rdname step_smooth
#' @param x A `step_smooth` object.
#' @export
tidy.step_smooth <- function(x, ...) {
out <- simple_terms(x, ...)
if (is.null(x$span)) {
out$period <- x$period
} else {
out$span <- x$span
}
out$degree <- x$degree
out$id <- x$id
out
}
simple_terms <- function(x, ...) {
if (recipes::is_trained(x)) {
res <- tibble::tibble(terms = x$columns)
}
else {
term_names <- recipes::sel2char(x$terms)
res <- tibble::tibble(terms = term_names)
}
res
}
#' @rdname required_pkgs.timetk
#' @export
required_pkgs.step_smooth <- function(x, ...) {
c("timetk")
}
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