R/plot_fairness_regression_object.R

Defines functions plot.fairness_regression_object

Documented in plot.fairness_regression_object

#' Plot fairness regression object
#'
#' @description Please note that this is experimental approach. Plot fairness check regression enables to look how big differences are between base subgroup (privileged) and unprivileged ones.
#' If bar plot reaches red zone it means that for this subgroup fairness goal is not satisfied. Multiple subgroups and models can be plotted.
#' Red and green zone boundary can be moved through epsilon parameter, that needs to be passed through \code{fairness_check}.
#'
#' @param x \code{fairness_regression_object} object
#' @param ... other plot parameters
#'
#' @import ggplot2
#'
#' @return \code{ggplot2} object
#' @rdname plot_fairness_regression_object
#' @export
#'
#' @examples
#'
#' set.seed(123)
#' data <- data.frame(
#'   x = c(rnorm(500, 500, 100), rnorm(500, 400, 200)),
#'   pop = c(rep("A", 500), rep("B", 500))
#' )
#'
#' data$y <- rnorm(length(data$x), 1.5 * data$x, 100)
#'
#' # create model
#' model <- lm(y ~ ., data = data)
#'
#' # create explainer
#' exp <- DALEX::explain(model, data = data, y = data$y)
#'
#' # create fobject
#' fobject <- fairness_check_regression(exp, protected = data$pop, privileged = "A")
#'
#' # results
#'
#' fobject
#' plot(fobject)
#' \donttest{
#'
#' model_ranger <- ranger::ranger(y ~ ., data = data, seed = 123)
#' exp2 <- DALEX::explain(model_ranger, data = data, y = data$y)
#'
#' fobject <- fairness_check_regression(exp2, fobject)
#'
#' # results
#' fobject
#'
#' plot(fobject)
#' }
#'
plot.fairness_regression_object <- function(x, ...) {
  n_exp <- length(x$explainers)
  data <- x$fairness_check_data
  metrics <- unique(data$metric)
  n_met <- length(metrics)
  epsilon <- x$epsilon

  if (any(is.na(data$score))) {
    warning(
      "Omiting NA for models: ",
      paste(unique(data[is.na(data$score), "model"]),
        collapse = ", "
      ),
      "\nInformation about passed metrics may be inaccurate due to NA present, it is advisable to check metric_scores plot.\n"
    )
  }

  #### first the visible values and breaks ####

  upper_bound <- max(na.omit(data$score), 1 / epsilon - 1) * 1.05
  if (upper_bound < 1.3) upper_bound <- 1.3

  lower_bound <- min(na.omit(data$score), epsilon) * 1.1
  if (lower_bound > 0.75) lower_bound <- 0.75

  green <- "#c7f5bf"
  red <- "#f05a71"

  ticks <- get_nice_ticks(lower_bound, upper_bound)

  breaks <- seq(ticks$min, ticks$max, ticks$spacing)

  if (!1 %in% breaks) breaks <- c(breaks, 1)

  breaks <- breaks[breaks >= lower_bound & breaks <= upper_bound]

  #### now the 'backend' values for plots ####

  # bars should start at 0
  data$score <- data$score - 1

  upper_bound <- max(na.omit(data$score), 1 / epsilon - 1) * 1.05
  if (upper_bound < 0.3) upper_bound <- 0.3

  lower_bound <- min(na.omit(data$score), epsilon - 1) * 1.1
  if (lower_bound > -0.25) lower_bound <- -0.25

  #### plotting ####

  subgroup <- score <- model <- metric <- NULL
  plt <- ggplot(data = data, aes(x = subgroup, y = score, fill = model)) +

    # middle (green)
    annotate("rect",
      xmin  = -Inf,
      xmax  = Inf,
      ymin  =  epsilon - 1,
      ymax  =  1 / epsilon - 1,
      fill  = green,
      alpha = 0.1
    ) +
    # left (red)
    annotate("rect",
      xmin  = -Inf,
      xmax  = Inf,
      ymin  =  -Inf,
      ymax  =  epsilon - 1,
      fill  = red,
      alpha = 0.1
    ) +

    # right (red)
    annotate("rect",
      xmin  = -Inf,
      xmax  = Inf,
      ymin  =  1 / epsilon - 1,
      ymax  =  Inf,
      fill  = red,
      alpha = 0.1
    ) +
    geom_bar(stat = "identity", position = "dodge") +
    geom_hline(yintercept = 0) +
    coord_flip() +
    facet_wrap(vars(metric), ncol = 1) +
    DALEX::theme_drwhy_vertical() +
    scale_y_continuous(
      limits = c(lower_bound, upper_bound),
      breaks = breaks - 1,
      labels = breaks,
      expand = c(0, 0),
      minor_breaks = NULL
    ) +
    theme(
      panel.grid.major.y = element_blank(),
      panel.grid.minor.y = element_blank()
    ) +
    scale_fill_manual(values = colors_fairmodels(n_exp)) +
    ggtitle("Fairness check regression", subtitle = paste("Created with", paste(
      as.character(unique(data$model)),
      collapse = ", "
    )))
  ####
  plt
}

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fairmodels documentation built on Oct. 8, 2021, 5:06 p.m.