R/plot_performance_and_fairness.R

In ModelOriented/FairModels: Flexible Tool for Bias Detection, Visualization, and Mitigation

#' Plot fairness and performance
#'
#' @description visualize fairness and model metric at the same time. Note that fairness metric parity scale is reversed so that the best models are in top right corner.
#'
#' @param x \code{performance_and_fairness} object
#' @param ... other plot parameters
#'
#' @import ggplot2
#'
#' @return \code{ggplot} object
#' @export
#'
#'
#' @rdname plot_performance_and_fairness
#'
#' @examples
#'
#' data("german")
#'
#' y_numeric <- as.numeric(german$Risk) - 1
#'
#' lm_model <- glm(Risk ~ .,
#'   data = german,
#'   family = binomial(link = "logit")
#' )
#'
#'
#' explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric)
#'
#' fobject <- fairness_check(explainer_lm,
#'   protected = german$Sex,
#'   privileged = "male"
#' )
#'
#' paf <- performance_and_fairness(fobject)
#' plot(paf)
#' \donttest{
#'
#' rf_model <- ranger::ranger(Risk ~ .,
#'   data = german,
#'   probability = TRUE,
#'   num.trees = 200
#' )
#'
#' explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric)
#'
#' fobject <- fairness_check(explainer_rf, fobject)
#'
#' # same explainers with different cutoffs for female
#' fobject <- fairness_check(explainer_lm, explainer_rf, fobject,
#'   protected = german$Sex,
#'   privileged = "male",
#'   cutoff = list(female = 0.4),
#'   label = c("lm_2", "rf_2")
#' )
#'
#' paf <- performance_and_fairness(fobject)
#'
#' plot(paf)
#' }
#'
plot.performance_and_fairness <- function(x, ...) {
  if (!requireNamespace("ggrepel", quietly = TRUE)) {
    stop("Package \"ggrepel\" needed for this function to work. Please install it.",
      call. = FALSE
    )
  }
  data <- x$paf_data

  performance_metric <- x$performance_metric
  fairness_metric <- x$fairness_metric

  n <- length(unique(data$labels))

  inversed_fairness_metric <- paste("inversed", fairness_metric, "parity loss", collapse = " ")

  ggplot(data, aes(x = performance_metric, y = fairness_metric)) +
    ggrepel::geom_text_repel(aes(label = labels),
      segment.size = 0.2,
      segment.color = "grey50",
      size = 4
    ) +
    geom_point(aes(color = labels)) +
    DALEX::theme_drwhy() +
    scale_color_manual(values = colors_fairmodels(n)) +
    scale_y_reverse() +
    ggtitle("Performance and fairness plot") +
    xlab(performance_metric) +
    ylab(inversed_fairness_metric)
}