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#
# 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 Compute performance across resamples.
#'
#' @description Computes the performance across resamples when class
#' probabilities can be computed.
#'
#' @seealso \code{\link{SummaryFunction}}
#'
#' @keywords misc
#'
#' @import R6
#'
#' @export UseProbability
UseProbability <- R6::R6Class(
classname = "UseProbability",
inherit = SummaryFunction,
portable = TRUE,
public = list(
#'
#' @description The function defined during runtime the usage of seven
#' measures: 'ROC', 'Sens', 'Kappa', 'Accuracy', 'TCR_9', 'MCC' and 'PPV'.
#'
initialize = function() {
super$initialize(c("ROC", "Sens", "Spec", "Kappa", "Accuracy", "TCR_9", "MCC", "PPV"))
},
#'
#' @description The function computes the performance across resamples using
#' the previously defined measures.
#'
#' @param data A \link{data.frame} containing the data used to compute the
#' performance.
#' @param lev An optional value used to define the levels of the target
#' class.
#' @param model An optional value used to define the M.L. model used.
#'
#' @return A vector of performance estimates.
#'
#' @import caret
#' @importFrom mltools mcc
#' @importFrom ModelMetrics auc
#'
execute = function(data, lev = NULL, model = NULL) {
lvls <- levels(data$obs)
if (length(lvls) > 2)
stop("[", class(self)[1], "][FATAL] Your outcome has ", length(lvls),
" levels. The 'UseProbability' function is not appropriate. Aborting...")
if (!all(levels(data[, "pred"]) == lvls))
stop("[", class(self)[1], "][FATAL] Levels of observed and predicted data ",
"do not match. Aborting...")
data$y = as.numeric(data$obs == lvls[2])
data$z = as.numeric(data$pred == lvls[2])
rocAUC <- ModelMetrics::auc(ifelse(data$obs == lev[2], 0, 1), data[, lvls[1]])
confMat <- caret::confusionMatrix(table(data$z, data$y), positive = "1")
mcc <- mltools::mcc(TP = confMat$table[1, 1], FP = confMat$table[1, 2], TN = confMat$table[2, 2], FN = confMat$table[2, 1])
ppv <- (confMat$table[1, 1] / (confMat$table[1, 1] + confMat$table[1, 2]))
fn_tcr_9 <- (9 * confMat$table[1, 2] + confMat$table[2, 1]) / (9 * (confMat$table[1, 2] + confMat$table[2, 2]) +
confMat$table[2, 1] + confMat$table[1, 1])
out <- c(rocAUC,
caret::sensitivity(data[, "pred"], data[, "obs"], lev[1]),
caret::specificity(data[, "pred"], data[, "obs"], lev[2]),
confMat$overall['Kappa'],
confMat$overall['Accuracy'],
fn_tcr_9, mcc, ppv)
names(out) <- c("ROC", "Sens", "Spec", "Kappa", "Accuracy", "TCR_9", "MCC", "PPV")
out
}
)
)
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