R/prediction.SimpleVoting.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 Abtract class to define simple voting schemes.
#'
#' @description Abstract class used as a template to define new customized
#' simple voting schemes.
#'
#' @seealso \code{\link{D2MCS}}, \code{\link{ClassMajorityVoting}},
#' \code{\link{ClassWeightedVoting}}, \code{\link{ProbAverageVoting}},
#' \code{\link{ProbAverageWeightedVoting}}, \code{\link{ProbBasedMethodology}},
#' \code{\link{CombinedVoting}}
#'
#' @keywords models methods math
#'
#' @import R6
#'
#' @export SimpleVoting

SimpleVoting <- R6::R6Class(
  classname = "SimpleVoting",
  portable = TRUE,
  public = list(
    #'
    #' @description Method for initializing the object arguments during runtime.
    #'
    #' @param cutoff A \link{character} vector defining the minimum probability
    #' used to perform a positive classification. If is not defined, 0.5 will be
    #' used as default value.
    #'
    initialize = function(cutoff = NULL) {
      if (!is.null(cutoff) && !is.numeric(cutoff)) {
        stop("[", class(self)[1], "][FATAL] Invalid values of cutoff. Aborting...")
      }

      if (is.null(cutoff) || !is.numeric(cutoff) || !(dplyr::between(cutoff, 0, 1))) {
        private$cutoff <- 0.5
      } else private$cutoff <- cutoff

      private$final.pred <- FinalPred$new()
    },
    #'
    #' @description The function obtains the minimum probabilistic value used to
    #' perform a positive classification.
    #'
    #' @return A \link{numeric} value.
    #'
    getCutoff = function() { private$cutoff },
    #'
    #' @description The function is used to return the prediction values
    #' computed by a voting strategy.
    #'
    #' @param type A \link{character} to define which type of predictions
    #' should be returned. If not defined all type of probabilities will be
    #' returned. Conversely if 'prob' or 'raw' is defined then computed
    #' 'probabilistic' or 'class' values are returned.
    #' @param target A \link{character} defining the value of the positive
    #' class.
    #' @param filter A \link{logical} value used to specify if only predictions
    #' matching the target value should be returned or not. If \link{TRUE} the
    #' function returns only the predictions matching the target value.
    #' Conversely if \link{FALSE} (by default) the function returns all the
    #' predictions.
    #'
    #' @return A \link{FinalPred} object.
    #'
    getFinalPred = function(type = NULL, target = NULL, filter = NULL) {
      if (any(is.null(type), !(type %in% c("raw", "prob")))) {
        private$final.pred
      } else {
        if (!is.logical(filter)) {
          message("[", class(self)[1], "][WARNING] Filter parameter must be ",
                  "defined as 'logical' type. Aborting...")
          filter <- FALSE
        }
        class.values <- private$final.pred$getClassValues()

        switch(type,
               "prob" = {
                 if (is.null(target) || !(target %in% class.values)) {
                   message("[", class(self)[1], "][WARNING] Target not ",
                           "specified or invalid. Using '",
                           paste0(class.values, collapse = ", "), "'")
                   target <- class.values
                 }
                 if (filter) {
                   private$final.pred$getProb()[private$final.pred$getRaw() == target,
                                                as.character(target), drop = FALSE]
                 } else {
                   private$final.pred$getProb()[, as.character(target), drop = FALSE]
                 }
               },
               "raw" = {
                 if (filter) {
                   private$final.pred$getRaw()[private$final.pred$getRaw() == target,
                                               , drop = FALSE]
                 } else { private$final.pred$getRaw() }
               }
        )
      }
    },
    #'
    #' @description Abstract function used to implement the operation of the
    #' voting scheme.
    #'
    #' @param predictions A \code{\link{ClusterPredictions}} object containing
    #' all the predictions achieved for each cluster.
    #' @param verbose A \link{logical} value to specify if more verbosity is
    #' needed.
    #'
    execute = function(predictions, verbose = FALSE) {
      stop("[", class(self)[1], "][FATAL] Class is abstract. ",
           "Method should be defined in inherited class. Aborting...")
    }
  ),
  private = list(
    cutoff = NULL,
    final.pred = NULL
  )
)