R/boilerplate-metrics-collection.R

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

#' Collect Metrics from Boilerplat Workflows
#'
#' @family Metric_Collection
#' @family Boiler_Plate
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function will extract the metrics from the `hai_auto_` boilerplate
#' functions. This function looks for a specific attribute from the `hai_auto_`
#' functions so that it will extract the `tuned_results` from the tuning process
#' if it has indeed been tuned.
#'
#' @description This function will extract the metrics from the `hai_auto_` boilerplate
#' functions.
#'
#' @param .data The output of the `hai_auto_` boilerplate function in it's entirety.
#'
#' @examples
#' \dontrun{
#' data <- iris
#'
#' rec_obj <- hai_knn_data_prepper(data, Species ~ .)
#'
#' auto_knn <- hai_auto_knn(
#'   .data = data,
#'   .rec_obj = rec_obj,
#'   .best_metric = "f_meas",
#'   .model_type = "classification",
#'   .grid_size = 2,
#'   .num_cores = 4
#' )
#'
#' hai_auto_wflw_metrics(auto_knn)
#' }
#'
#' @return
#' A tibble
#'
#' @export
#'

hai_auto_wflw_metrics <- function(.data) {
  input_data <- .data
  atb <- attributes(input_data)
  tuned <- atb$.tune

  # Checks
  if (!atb$function_type == "boilerplate") {
    rlang::abort(
      message = "You need to pass in a list object from an 'hai_auto_' function.",
      use_cli_format = TRUE
    )
  }

  if (!tuned) {
    rlang::abort(
      message = "The object you passed was not tuned, so there are no metrics
      for this function to collect.",
      use_cli_format = TRUE
    )
  }

  # Data
  df <- tune::collect_metrics(input_data$tuned_info$tuned_results)

  # Return
  return(df)
}