R/regression_metrics.R

Defines functions regression_metrics

Documented in regression_metrics

#' Regression metrics
#'
#' @param explainer object created with \code{\link[DALEX]{explain}}
#' @param protected factor, protected variable (also called sensitive attribute), containing privileged and unprivileged groups
#' @param privileged factor/character, one value of \code{protected}, denoting subgroup suspected of the most privilege
#'
#'
#' @return \code{data.frame}
#' @export
#'
#'


regression_metrics <- function(explainer, protected, privileged) {
  stopifnot(explainer$model_info$type == "regression")

  y <- explainer$y
  y_hat <- explainer$y_hat

  protected_levels <- unique(as.character(protected))

  unprivileged_levels <- protected_levels[protected_levels != privileged]

  privileged_indices <- which(privileged == protected)

  data <- data.frame()
  for (unprivileged in unprivileged_levels) {
    unprivileged_indices <- which(unprivileged == protected)
    relevant_indices <- c(privileged_indices, unprivileged_indices)
    new_protected <- as.character(protected[relevant_indices])

    a <- rep(0, length(new_protected))
    a[new_protected == privileged] <- 1

    y_u <- scale(y[relevant_indices])
    s_u <- scale(y_hat[relevant_indices])

    p_y_data <- data.frame(a = a, y = y_u)
    p_s_data <- data.frame(a = a, s = s_u)
    p_ys_data <- data.frame(a = a, s = s_u, y = y_u)


    glm_without_warnings <- function(data) {
      r <-
        tryCatch(
          withCallingHandlers(
            {
              warnings_raised <- NULL
              list(
                value = stats::glm(a ~ .,
                  data = data,
                  family = stats::binomial(link = "logit")
                ),
                warnings_raised = warnings_raised
              )
            },
            warning = function(e) {
              warnings_raised <<- trimws(paste0("WARNING: ", e))
              invokeRestart("muffleWarning")
            }
          )
        )
      return(r)
    }

    p_y_obj <- glm_without_warnings(p_y_data)
    p_s_obj <- glm_without_warnings(p_s_data)
    p_ys_obj <- glm_without_warnings(p_ys_data)

    p_y <- p_y_obj$value
    p_s <- p_s_obj$value
    p_ys <- p_ys_obj$value

    warnings_p_y <- p_y_obj$warnings_raised
    warnings_p_s <- p_s_obj$warnings_raised
    warnings_p_ys <- p_ys_obj$warnings_raised

    warnings_raised <- c(warnings_p_y, warnings_p_s, warnings_p_ys)

    pred_p_y <- p_y$fitted.values
    pred_p_s <- p_s$fitted.values
    pred_p_ys <- p_ys$fitted.values

    n <- length(a)

    r_ind <- (n - sum(a)) / sum(a) * mean(pred_p_s / (1 - pred_p_s))
    r_sep <- mean((pred_p_ys / (1 - pred_p_ys) * (1 - pred_p_y) / pred_p_y))
    r_suf <- mean((pred_p_ys / (1 - pred_p_ys)) * ((1 - pred_p_s) / pred_p_s))

    data_ind <- data.frame(
      subgroup = unprivileged,
      score = r_ind,
      metric = "independence"
    )

    data_sep <- data.frame(
      subgroup = unprivileged,
      score = r_sep,
      metric = "separation"
    )

    data_suf <- data.frame(
      subgroup = unprivileged,
      score = r_suf,
      metric = "sufficiency"
    )


    data <- rbind(data, data_ind, data_sep, data_suf)
  }

  return(list(data, warnings_raised))
}

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