R/score_specificity.R

In auditor: Model Audit - Verification, Validation, and Error Analysis

#' @title Specificity
#'
#' @param object An object of class \code{explainer} created with function
#'  \code{\link[DALEX]{explain}} from the DALEX package.
#' @param cutoff Threshold value, which divides model predicted values (y_hat) to calculate confusion matrix.
#'  By default it's \code{0.5}.
#' @param data New data that will be used to calculate the score.
#'  Pass \code{NULL} if you want to use \code{data} from \code{object}.
#' @param y New y parameter will be used to calculate score.
#' @param ... Other arguments dependent on the type of score.
#'
#' @return An object of class \code{auditor_score}.
#'
#' @examples
#' data(titanic_imputed, package = "DALEX")
#'
#' # fit a model
#' model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)
#'
#' exp_glm <- audit(model_glm,
#'                  data = titanic_imputed,
#'                  y = titanic_imputed$survived)
#'
#' # calculate score
#' score_specificity(exp_glm)
#'
#'
#' @export


score_specificity <- function(object, cutoff = 0.5, data = NULL, y = NULL, ...) {
  if(!("explainer" %in% class(object))) stop("The function requires an object created with explain() function from the DALEX package.")


  # inject new data to the explainer
  if (!is.null(data)){
    object$data <- data
    object$y <- y
    object$y_hat <- object$predict_function(object$model, data)
  }

  conf <- confusionmatrix(object, cutoff)
  ret <- conf$TN / (conf$TN + conf$FP)

  specificity_results <- list(
    name = "specificity",
    score = ret
  )

  class(specificity_results) <- "auditor_score"
  return(specificity_results)
}


#' @title One minus specificity
#'
#' @param object An object of class \code{explainer} created with function
#'  \code{\link[DALEX]{explain}} from the DALEX package.
#' @param cutoff Threshold value, which divides model predicted values (y_hat) to calculate confusion matrix.
#'  By default it's \code{0.5}.
#' @param data New data that will be used to calculate the score.
#'  Pass \code{NULL} if you want to use \code{data} from \code{object}.
#' @param y New y parameter will be used to calculate score.
#' @param ... Other arguments dependent on the type of score.
#'
#' @return An object of class \code{auditor_score}.
#'
#' @examples
#' data(titanic_imputed, package = "DALEX")
#'
#' # fit a model
#' model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)
#'
#' # create an explainer
#' glm_audit <- audit(model_glm,
#'                    data = titanic_imputed,
#'                    y = titanic_imputed$survived)
#'
#' # calculate score
#' score_one_minus_specificity(glm_audit)
#'
#'
#' @export


score_one_minus_specificity <- function(object, cutoff = 0.5, data = NULL, y = NULL, ...) {
  if(!("explainer" %in% class(object))) stop("The function requires an object created with explain() function from the DALEX package.")


  # inject new data to the explainer
  if (!is.null(data)){
    object$data <- data
    object$y <- y
    object$y_hat <- object$predict_function(object$model, data)
  }

  conf <- confusionmatrix(object, cutoff)
  ret <- 1 - conf$TN / (conf$TN + conf$FP)

  specificity_results <- list(
    name = "one_minus_specificity",
    score = ret
  )

  class(specificity_results) <- "auditor_score"
  return(specificity_results)
}