R/measures.NPV.R

In D2MCS: Data Driving Multiple Classifier System

#
# D2MCS provides a novel framework to able to automatically develop and deploy
# an accurate Multiple Classifier System (MCS) based on the feature-clustering
# distribution achieved from an input dataset. D2MCS was developed focused on
# four main aspects: (i) the ability to determine an effective method to
# evaluate the independence of features, (ii) the identification of the optimal
# number of feature clusters, (iii) the training and tuning of ML models and
# (iv) the execution of voting schemes to combine the outputs of each classifier
# comprising the MCS.
#
# Copyright (C) 2021 Sing Group (University of Vigo)
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <https://www.gnu.org/licenses/gpl-3.0.html>

#' @title Computes the Negative Predictive Value.
#'
#' @description Negative Predictive Values are the proportions of negative
#' results in statistics and diagnostic tests that are true negative results.
#'
#' @details \deqn{NPV = TN / (TN + FN)}
#'
#' @seealso \code{\link{MeasureFunction}}, \code{\link{ClassificationOutput}},
#' \code{\link{ConfMatrix}}
#'
#' @keywords classif math
#'
#' @import R6
#'
#' @export NPV

NPV <- R6::R6Class(
  classname = "NPV",
  inherit = MeasureFunction,
  portable = TRUE,
  public = list(
    #'
    #' @description Method for initializing the object arguments during runtime.
    #'
    #' @param performance.output An optional \code{\link{ConfMatrix}} parameter
    #' to define the type of object used as basis to compute the \code{NPV}
    #' measure.
    #'
    initialize = function(performance.output = NULL) {
      super$initialize(performance.output)
    },
    #'
    #' @description The function computes the \strong{NPV} achieved by the M.L.
    #' model.
    #'
    #' @param performance.output An optional \code{\link{ConfMatrix}}  parameter
    #' to define the type of object used as basis to compute the \strong{NPV}
    #' measure.
    #'
    #' @details This function is automatically invoke by the
    #' \link{ClassificationOutput} object.
    #'
    #' @return A \link{numeric} vector of size 1 or \link{NULL} if an error
    #' occurred.
    #'
    compute = function(performance.output = NULL) {
      if (is.null(private$performance) && !inherits(performance.output, c("MinResult", "ConfMatrix")))
        stop("[", class(self)[1], "][FATAL] Performance output parameter must be ",
             "defined as 'MinResult' or 'ConfMatrix' type. Aborting...")

      if (!is.null(performance.output) && inherits(performance.output, c("MinResult", "ConfMatrix")))
        performance.output$getConfusionMatrix()$byClass["Neg Pred Value"]
      else private$performance$getConfusionMatrix()$byClass["Neg Pred Value"]
    }
  )
)