R/autoplot.R

In workflowsets: Create a Collection of 'tidymodels' Workflows

#' Plot the results of a workflow set
#'
#' This `autoplot()` method plots performance metrics that have been ranked using
#' a metric. It can also run `autoplot()` on the individual results (per
#' `wflow_id`).
#'
#' @param object A `workflow_set` whose elements have results.
#' @param rank_metric A character string for which metric should be used to rank
#' the results. If none is given, the first metric in the metric set is used
#' (after filtering by the `metric` option).
#' @param id A character string for what to plot. If a value of
#' `"workflow_set"` is used, the results of each model (and sub-model) are ordered
#' and plotted. Alternatively, a value of the workflow set's `wflow_id` can be
#' given and the `autoplot()` method is executed on that workflow's results.
#' @param select_best A logical; should the results only contain the numerically
#' best submodel per workflow?
#' @param metric A character vector for which metrics (apart from `rank_metric`)
#' to be included in the visualization.
#' @param std_errs The number of standard errors to plot (if the standard error
#' exists).
#' @param ... Other options to pass to `autoplot()`.
#' @details
#' This function is intended to produce a default plot to visualize helpful
#'  information across all possible applications of a workflow set. A more
#'  appropriate plot for your specific analysis can be created by
#'  calling [rank_results()] and using standard `ggplot2` code for plotting.
#'
#' The x-axis is the workflow rank in the set (a value of one being the best)
#' versus the performance metric(s) on the y-axis. With multiple metrics, there
#' will be facets for each metric.
#'
#' If multiple resamples are used, confidence bounds are shown for each result
#' (90% confidence, by default).
#' @return A ggplot object.
#'
#' @includeRmd man-roxygen/example_data.Rmd note
#'
#' @examples
#' autoplot(two_class_res)
#' autoplot(two_class_res, select_best = TRUE)
#' autoplot(two_class_res, id = "yj_trans_cart", metric = "roc_auc")
#' @export
autoplot.workflow_set <- function(object, rank_metric = NULL, metric = NULL,
                                  id = "workflow_set",
                                  select_best = FALSE,
                                  std_errs = qnorm(0.95), ...) {
  if (id == "workflow_set") {
    p <- rank_plot(object,
      rank_metric = rank_metric, metric = metric,
      select_best = select_best, std_errs = std_errs
    )
  } else {
    p <- autoplot(object$result[[which(object$wflow_id == id)]], metric = metric, ...)
  }
  p
}

rank_plot <- function(object, rank_metric = NULL, metric = NULL,
                      select_best = FALSE, std_errs = 1, ...) {
  metric_info <- pick_metric(object, rank_metric, metric)
  metrics <- collate_metrics(object)
  res <- rank_results(object, rank_metric = metric_info$metric, select_best = select_best)

  if (!is.null(metric)) {
    keep_metrics <- unique(c(rank_metric, metric))
    res <- dplyr::filter(res, .metric %in% keep_metrics)
  }

  num_metrics <- length(unique(res$.metric))
  has_std_error <- !all(is.na(res$std_err))

  p <-
    ggplot(res, aes(x = rank, y = mean, col = model)) +
    geom_point(aes(shape = preprocessor))

  if (num_metrics > 1) {
    res$.metric <- factor(as.character(res$.metric), levels = metrics$metric)
    p <-
      p +
      facet_wrap(~.metric, scales = "free_y", as.table = FALSE) +
      labs(x = "Workflow Rank", y = "Metric")
  } else {
    p <- p + labs(x = "Workflow Rank", y = metric_info$metric)
  }

  if (has_std_error) {
    p <-
      p +
      geom_errorbar(
        aes(
          ymin = mean - std_errs * std_err,
          ymax = mean + std_errs * std_err
        ),
        width = diff(range(res$rank)) / 75
      )
  }

  p
}