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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 Feature-clustering based on Kendall Correlation Test.
#'
#' @description Performs the feature-clustering using Kendall correlation tests.
#'
#' @details The method estimate the association between paired samples and
#' compute a test of the value being zero. They use different measures of
#' association, all in the range [-1, 1] with 0 indicating no association.
#' Method valid only for bi-class problems.
#'
#' @seealso \code{\link{Dataset}}, \code{\link[stats]{cor.test}}
#'
#' @keywords cluster manip
#'
#' @import R6
#'
#' @export KendallHeuristic
KendallHeuristic <- R6::R6Class(
classname = "KendallHeuristic",
inherit = GenericHeuristic,
portable = TRUE,
public = list(
#'
#' @description Empty function used to initialize the object arguments in
#' runtime.
#'
initialize = function() { },
# Heuristic valid for continuous variables
#'
#' @description Test for association between paired samples using Kendall's
#' tau value.
#'
#' @param col1 A \link{numeric} vector or matrix required to perform the
#' clustering operation.
#' @param col2 A \link{numeric} vector or matrix to perform the clustering
#' operation.
#' @param column.names An optional \link{character} vector with the names of
#' both columns.
#'
#' @return a \link{numeric} vector of length 1 or \link{NA} if an error
#' occurs.
#'
#' @importFrom stats cor.test
#'
heuristic = function(col1, col2, column.names = NULL) {
if (private$isBinary(col1) || !private$isBinary(col2)) {
message("[", class(self)[1], "][WARNING] Columns must be real. ",
"Returning NA")
NA
} else {
tryCatch(
unname(stats::cor.test(col1, col2, method = "kendall")$estimate, force = TRUE),
error = function(e) {
message("[", class(self)[1], "][ERROR] Error occurred calculating ",
"kendall heuristic: '", e, "' . Returning NA")
NA
})
}
}
)
)
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