R/step-hai-scale-zscore.R

In healthyR.ai: The Machine Learning and AI Modeling Companion to 'healthyR'

#' Recipes Data Scale by Z-Score
#'
#' @family Recipes
#' @family Scale
#'
#' @description
#' `step_hai_scale_zscore` creates a a *specification* of a recipe
#'  step that will convert numeric data into from a time series into its
#'  velocity.
#'
#' @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 that will be used to create the new variables. The
#'  selected variables should have class `numeric`
#' @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_hai_scale_zscore`, an updated version of recipe with
#'  the new step added to the sequence of existing steps (if any).
#'
#'  Main Recipe Functions:
#'  - `recipes::recipe()`
#'  - `recipes::prep()`
#'  - `recipes::bake()`
#'
#'
#' @details
#'
#' __Numeric Variables__
#'  Unlike other steps, `step_hai_scale_zscore` does *not*
#'  remove the original numeric variables. [recipes::step_rm()] can be
#'  used for this purpose.
#'
#' @examples
#' suppressPackageStartupMessages(library(dplyr))
#' suppressPackageStartupMessages(library(recipes))
#'
#' data_tbl <- data.frame(
#'   a = mtcars$mpg,
#'   b = AirPassengers %>% as.vector() %>% head(32)
#' )
#'
#' # Create a recipe object
#' rec_obj <- recipe(a ~ ., data = data_tbl) %>%
#'   step_hai_scale_zscore(b)
#'
#' # View the recipe object
#' rec_obj
#'
#' # Prepare the recipe object
#' prep(rec_obj)
#'
#' # Bake the recipe object - Adds the Time Series Signature
#' bake(prep(rec_obj), data_tbl)
#'
#' rec_obj %>%
#'   prep() %>%
#'   juice()
#'
#' @export
#'
#' @importFrom recipes prep bake rand_id

step_hai_scale_zscore <- function(recipe,
                                  ...,
                                  role = "predictor",
                                  trained = FALSE,
                                  columns = NULL,
                                  skip = FALSE,
                                  id = rand_id("hai_scale_zscore")) {
  terms <- recipes::ellipse_check(...)

  recipes::add_step(
    recipe,
    step_hai_scale_zscore_new(
      terms = terms,
      role = role,
      trained = trained,
      columns = columns,
      skip = skip,
      id = id
    )
  )
}

step_hai_scale_zscore_new <- function(terms, role, trained, columns, skip, id) {
  recipes::step(
    subclass = "hai_scale_zscore",
    terms = terms,
    role = role,
    trained = trained,
    columns = columns,
    skip = skip,
    id = id
  )
}

#' @export
prep.step_hai_scale_zscore <- function(x, training, info = NULL, ...) {
  col_names <- recipes::recipes_eval_select(x$terms, training, info)
  recipes::check_type(training[, col_names])

  # value_data <- info[info$variable %in% col_names, ]
  #
  # if (any(value_data$type != "numeric")) {
  #   rlang::abort(
  #     paste0(
  #       "All variables for `step_hai_scale_zscore` must be `numeric`",
  #       "`integer`,`double` classes."
  #     )
  #   )
  # }

  step_hai_scale_zscore_new(
    terms   = x$terms,
    role    = x$role,
    trained = TRUE,
    columns = col_names,
    skip    = x$skip,
    id      = x$id
  )
}

#' @export
bake.step_hai_scale_zscore <- function(object, new_data, ...) {
  make_call <- function(col) {
    rlang::call2(
      "hai_scale_zscore_vec",
      .x = rlang::sym(col),
      .ns = "healthyR.ai"
    )
  }

  grid <- expand.grid(
    col = object$columns,
    stringsAsFactors = FALSE
  )

  calls <- purrr::pmap(.l = list(grid$col), make_call)

  # Columns Names
  newname <- paste0("hai_scale_zscore_", grid$col)
  calls <- recipes::check_name(calls, new_data, object, newname, TRUE)

  tibble::as_tibble(dplyr::mutate(new_data, !!!calls))
}

#' @export
print.step_hai_scale_zscore <- function(x, width = max(20, options()$width - 35), ...) {
  title <- "Zero-One Scale Transformation on "
  recipes::print_step(
    x$columns, x$terms, x$trained,
    width = width, title = title
  )
  invisible(x)
}

#' Required Packages
#' @rdname required_pkgs.healthyR.ai
#' @keywords internal
#' @return A character vector
#' @param x A recipe step
# @noRd
#' @export
required_pkgs.step_hai_scale_zscore <- function(x, ...) {
  c("healthyR.ai")
}