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#' Identifies all sets of assignment examples which are incompatible with the
#' MRSort sorting method extended to handle large performance differences.
#'
#' MRSort is a simplified ElectreTRI method that uses the pessimistic
#' assignment rule, without indifference or preference thresholds attached to
#' criteria. LPDMRSort considers both a binary discordance and a binary
#' concordance conditions including several interactions between them. This
#' function outputs all (or a fixed number of) sets of incompatible assignment
#' examples ranging in size from the minimal size and up to a given threshold.
#' The retrieved sets are also not contained in each other.
#'
#'
#' @param performanceTable Matrix or data frame containing the performance
#' table. Each row corresponds to an alternative, and each column to a
#' criterion. Rows (resp. columns) must be named according to the IDs of the
#' alternatives (resp. criteria).
#' @param assignments Vector containing the assignments (IDs of the categories)
#' of the alternatives to the categories. The elements are named according to
#' the alternatives.
#' @param categoriesRanks Vector containing the ranks of the categories. The
#' elements are named according to the IDs of the categories.
#' @param criteriaMinMax Vector containing the preference direction on each of
#' the criteria. "min" (resp. "max") indicates that the criterion has to be
#' minimized (maximized). The elements are named according to the IDs of the
#' criteria.
#' @param majorityRule String denoting how the vetoes and dictators are
#' combined in order to form the assignment rule. The values to choose from
#' are "M", "V", "D", "v", "d", "dV", "Dv", "dv". "M" corresponds to using
#' only the majority rule without vetoes or dictators, "V" considers only the
#' vetoes, "D" only the dictators, "v" is like "V" only that a dictator may
#' invalidate a veto, "d" is like "D" only that a veto may invalidate a
#' dictator, "dV" is like "V" only that if there is no veto we may then
#' consider the dictator, "Dv" is like "D" only that when there is no dictator
#' we may consider the vetoes, while finally "dv" is identical to using both
#' dictator and vetoes only that when both are active they invalidate each
#' other, so the majority rule is considered in that case.
#' @param incompatibleSetsLimit Pozitive integer denoting the upper limit of
#' the number of sets to be retrieved.
#' @param largerIncompatibleSetsMargin Pozitive integer denoting whether sets
#' larger than the minimal size should be retrieved, and by what margin. For
#' example, if this is 0 then only sets of the minimal size will be retrieved,
#' if this is 1 then sets also larger by 1 element will be retrieved.
#' @param alternativesIDs Vector containing IDs of alternatives, according to
#' which the datashould be filtered.
#' @param criteriaIDs Vector containing IDs of criteria, according to which the
#' data should be filtered.
#' @return The function returns NULL if there is a problem, or a list
#' containing a list of incompatible sets of alternatives as vectors and the
#' status of the execution.
#' @references Bouyssou, D. and Marchant, T. An axiomatic approach to
#' noncompen-satory sorting methods in MCDM, II: more than two categories.
#' European Journal of Operational Research, 178(1): 246--276, 2007.
#'
#' Meyer, P. and Olteanu, A-L. Integrating large positive and negative
#' performance differences in majority-rule sorting models. European Journal of
#' Operational Research, submitted , 2015.
#' @keywords methods
#' @examples
#'
#' # the performance table
#'
#' performanceTable <- rbind(c(10,10,9), c(10,9,10), c(9,10,10), c(9,9,10),
#' c(9,10,9), c(10,9,9), c(10,10,7), c(10,7,10),
#' c(7,10,10), c(9,9,17), c(9,17,9), c(17,9,9),
#' c(7,10,17), c(10,17,7), c(17,7,10), c(7,17,10),
#' c(17,10,7), c(10,7,17), c(7,9,17), c(9,17,7),
#' c(17,7,9), c(7,17,9), c(17,9,7), c(9,7,17),
#' c(7,7,7))
#'
#' rownames(performanceTable) <- c("a1", "a2", "a3", "a4", "a5", "a6", "a7",
#' "a8", "a9", "a10", "a11", "a12", "a13",
#' "a14", "a15", "a16", "a17", "a18", "a19",
#' "a20", "a21", "a22", "a23", "a24", "a25")
#'
#' colnames(performanceTable) <- c("c1","c2","c3")
#'
#' assignments <-rbind(c("P","P","P","F","F","F","F","F","F","F","F","F",
#' "F","F","F","F","F","F","F","F","F","F","F","F","P"),
#' c("P","P","P","F","F","F","P","P","P","P","P","P",
#' "P","P","P","P","P","P","P","P","P","P","P","P","P"),
#' c("P","P","P","F","F","F","F","F","F","F","F","F",
#' "P","P","P","P","P","P","F","F","F","F","F","F","P"),
#' c("P","P","P","F","F","F","P","P","P","P","P","P",
#' "P","P","P","P","P","P","F","F","F","F","F","F","P"),
#' c("P","P","P","F","F","F","F","F","F","P","P","P",
#' "F","F","F","F","F","F","F","F","F","F","F","F","P"),
#' c("P","P","P","F","F","F","F","F","F","P","P","P",
#' "P","P","P","P","P","P","P","P","P","P","P","P","P"),
#' c("P","P","P","F","F","F","F","F","F","P","P","P",
#' "P","P","P","P","P","P","F","F","F","F","F","F","P"))
#'
#' colnames(assignments) <- rownames(performanceTable)
#'
#' categoriesRanks <-c(1,2)
#'
#' names(categoriesRanks) <- c("P","F")
#'
#' criteriaMinMax <- c("max","max","max")
#'
#' names(criteriaMinMax) <- colnames(performanceTable)
#'
#' majorityRules <- c("V","D","v","d","dV","Dv","dv")
#'
#' for(i in 1:1)# change to 7 in order to perform all tests
#' {
#' incompatibleAssignmentsSets<-LPDMRSortIdentifyIncompatibleAssignments(
#' performanceTable, assignments[i,],
#' categoriesRanks, criteriaMinMax,
#' majorityRule = majorityRules[i])
#'
#' filteredAlternativesIDs <- setdiff(rownames(performanceTable),
#' incompatibleAssignmentsSets[[1]][1])
#'
#' x<-LPDMRSortInferenceExact(performanceTable, assignments[i,],
#' categoriesRanks, criteriaMinMax,
#' majorityRule = majorityRules[i],
#' readableWeights = TRUE,
#' readableProfiles = TRUE,
#' minmaxLPD = TRUE,
#' alternativesIDs = filteredAlternativesIDs)
#'
#' ElectreAssignments<-LPDMRSort(performanceTable, x$profilesPerformances,
#' categoriesRanks,
#' x$weights, criteriaMinMax, x$lambda,
#' criteriaVetos=x$vetoPerformances,
#' criteriaDictators=x$dictatorPerformances,
#' majorityRule = majorityRules[i],
#' alternativesIDs = filteredAlternativesIDs)
#'
#' print(all(ElectreAssignments == assignments[i,filteredAlternativesIDs]))
#' }
#'
#' @export LPDMRSortIdentifyIncompatibleAssignments
LPDMRSortIdentifyIncompatibleAssignments <- function(performanceTable, assignments, categoriesRanks, criteriaMinMax, majorityRule = "M", incompatibleSetsLimit = 100, largerIncompatibleSetsMargin = 0, alternativesIDs = NULL, criteriaIDs = NULL){
## check the input data
if (!((is.matrix(performanceTable) || (is.data.frame(performanceTable)))))
stop("wrong performanceTable, should be a matrix or a data frame")
if (!(is.vector(assignments)))
stop("assignments should be a vector")
if (!(is.vector(categoriesRanks)))
stop("categoriesRanks should be a vector")
if (!(is.vector(criteriaMinMax)))
stop("criteriaMinMax should be a vector")
if (!is.character(majorityRule))
stop("majorityRule should be a string")
else if (!(majorityRule %in% c("M","V","D","v","d","dV","Dv","dv")))
stop("majorityRule needs to take values in {'M','V','D','v','d','dV','Dv','dv'}")
if (!is.numeric(incompatibleSetsLimit))
stop("incompatibleSetsLimit should be numeric")
else if (incompatibleSetsLimit%%1!=0)
stop("incompatibleSetsLimit should be an integer")
else if (incompatibleSetsLimit<=0)
stop("incompatibleSetsLimit should be strictly pozitive")
if (!is.numeric(largerIncompatibleSetsMargin))
stop("largerIncompatibleSetsMargin should be numeric")
else if (largerIncompatibleSetsMargin%%1!=0)
stop("largerIncompatibleSetsMargin should be an integer")
else if (largerIncompatibleSetsMargin<0)
stop("largerIncompatibleSetsMargin should be pozitive")
if (!(is.null(alternativesIDs) || is.vector(alternativesIDs)))
stop("alternativesIDs should be a vector")
if (!(is.null(criteriaIDs) || is.vector(criteriaIDs)))
stop("criteriaIDs should be a vector")
## filter the data according to the given alternatives and criteria
if (!is.null(alternativesIDs)){
performanceTable <- performanceTable[alternativesIDs,]
assignments <- assignments[alternativesIDs]
}
else
alternativesIDs = rownames(performanceTable)
if (!is.null(criteriaIDs)){
performanceTable <- performanceTable[,criteriaIDs]
criteriaMinMax <- criteriaMinMax[criteriaIDs]
}
else
criteriaIDs = colnames(performanceTable)
# data is filtered, check for some data consistency
# if there are less than 2 criteria or 2 alternatives, there is no MCDA problem
if (is.null(dim(performanceTable)))
stop("less than 2 criteria or 2 alternatives")
# -------------------------------------------------------
numCrit <- dim(performanceTable)[2]
numAlt <- dim(performanceTable)[1]
numCat <- length(categoriesRanks)
tempPath <- tempdir()
# get data content that remains the same for all following linear program executions
datacontent <- paste("data;\nparam X := ", numAlt, ";\n\nparam F := ", numCrit, ";\n\nparam Fdir := \n", sep = "")
for (i in 1:numCrit){
datacontent <- paste(datacontent, i, "\t", sep = "")
if (criteriaMinMax[i]=="min")
datacontent <- paste(datacontent, "-1", sep = "")
else
datacontent <- paste(datacontent, "1", sep = "")
if (i!=numCrit)
datacontent <- paste(datacontent, "\n", sep = "")
else
datacontent <- paste(datacontent, ";\n\n", sep = "")
}
datacontent <- paste(datacontent, "param Fmin :=\n", sep = "")
for (i in 1:numCrit){
datacontent <- paste(datacontent, i, "\t", apply(performanceTable, 2, min)[i], sep = "")
if (i!=numCrit)
datacontent <- paste(datacontent, "\n", sep = "")
else
datacontent <- paste(datacontent, ";\n\n", sep = "")
}
datacontent <- paste(datacontent, "param Fmax :=\n", sep = "")
for (i in 1:numCrit){
datacontent <- paste(datacontent, i, "\t", apply(performanceTable, 2, max)[i], sep = "")
if (i!=numCrit)
datacontent <- paste(datacontent, "\n", sep = "")
else
datacontent <- paste(datacontent, ";\n\n", sep = "")
}
datacontent <- paste(datacontent, "param K :=", numCat, ";\n\n", sep = "")
datacontent <- paste(datacontent, "param A:=\n", sep = "")
for (i in 1:numAlt){
datacontent <- paste(datacontent, i, "\t", categoriesRanks[assignments[i]], sep = "")
if (i!=numAlt)
datacontent <- paste(datacontent, "\n", sep = "")
else
datacontent <- paste(datacontent, ";\n\n", sep = "")
}
datacontent <- paste(datacontent, "param PTx : ", sep = "")
for(i in 1:numCrit)
datacontent <- paste(datacontent, i, sep = " ")
datacontent <- paste(datacontent, ":= \n", sep = "")
for (i in 1:numAlt){
datacontent <- paste(datacontent, i, "\t", sep = "")
for (j in 1:numCrit){
datacontent <- paste(datacontent, performanceTable[i,j], sep = "")
if (j!=numCrit)
datacontent <- paste(datacontent, " ", sep = "")
}
if (i!=numAlt)
datacontent <- paste(datacontent, "\n", sep = "")
else
datacontent <- paste(datacontent, ";\n\n", sep = "")
}
datacontent <- paste(datacontent, "param gamma:=0.0001;\n", sep = "")
# get first model file
modelfilename <- paste("MRSort", c("","V","D","DV1","DV2","DV3","DV4","DV5")[match(majorityRule,c("M","V","D","v","d","dV","Dv","dv"))], "IdentifyMinimalInvalidAssignmentsSet.gmpl", sep = "")
modelFile <- system.file("extdata",modelfilename, package="MCDA")
# write data file
dataFile <- tempfile()
file.copy(modelFile, dataFile)
sink(dataFile, append=TRUE)
cat(datacontent)
cat("end;\n")
sink()
# init and run linear program
lp<-initProbGLPK()
tran<-mplAllocWkspGLPK()
setMIPParmGLPK(PRESOLVE, GLP_ON)
termOutGLPK(GLP_OFF)
out<-mplReadModelGLPK(tran, dataFile, skip=0)
if (is.null(out))
out <- mplGenerateGLPK(tran)
else
stop(return_codeGLPK(out))
if (is.null(out))
mplBuildProbGLPK(tran,lp)
else
stop(return_codeGLPK(out))
solveMIPGLPK(lp)
if(mipStatusGLPK(lp)==5){
mplPostsolveGLPK(tran, lp, sol = GLP_MIP)
solution <- mipColsValGLPK(lp)
varnames <- c()
for (i in 1:length(solution))
varnames <- c(varnames,getColNameGLPK(lp,i))
paro <- "["
parc <- "]"
error <- FALSE
}
if (!error){
# get size of minimal incompatible assignments set and one such set
minIncompatibleSetsSize <- 0
incompatibleSet <- c()
for (i in 1:numAlt)
{
if(solution[varnames==paste("OnOff",paro,i,parc,sep="")] == 1)
{
incompatibleSet <- c(incompatibleSet,alternativesIDs[i])
minIncompatibleSetsSize <- minIncompatibleSetsSize + 1
}
}
incompatibleSets <- list(incompatibleSet)
# if there are no incompatible sets return the empty set
if(minIncompatibleSetsSize == 0)
return(incompatibleSets)
# get second model file
modelfilename <- paste("MRSort", c("","V","D","DV1","DV2","DV3","DV4","DV5")[match(majorityRule,c("M","V","D","v","d","dV","Dv","dv"))], "IdentifyInvalidAssignmentsSet.gmpl", sep = "")
modelFile <- system.file("extdata",modelfilename, package="MCDA")
# create new data content
datacontent2a <- "param PrevOnOff : "
for(i in 1:numAlt)
datacontent2a <- paste(datacontent2a, i, sep = " ")
datacontent2a <- paste(datacontent2a, ":= \n1\t", sep = "")
for(i in 1:numAlt)
datacontent2a <- paste(datacontent2a, solution[varnames==paste("OnOff",paro,i,parc,sep="")], sep = " ")
datacontent2b <- paste("param PrevOnOffLimit := \n1\t ", minIncompatibleSetsSize, sep ="")
# iterate through acceptes sizes for incompatible assignment sets
incompatibleSetSize <- minIncompatibleSetsSize
while(incompatibleSetSize <= minIncompatibleSetsSize + largerIncompatibleSetsMargin)
{
# break if we've retrieved the desired number of incompatible sets
if(length(incompatibleSets) >= incompatibleSetsLimit)
break
repeat{
# write data file
dataFile <- tempfile()
file.copy(modelFile, dataFile)
sink(dataFile, append=TRUE)
cat(datacontent)
cat("param invalid:=")
cat(incompatibleSetSize)
cat(";\n")
cat("param Y:=")
cat(length(incompatibleSets))
cat(";\n")
cat(datacontent2a)
cat(";\n\n")
cat(datacontent2b)
cat(";\n\n")
cat("end;\n")
sink()
# init and run linear program
lp<-initProbGLPK()
tran<-mplAllocWkspGLPK()
setMIPParmGLPK(PRESOLVE, GLP_ON)
termOutGLPK(GLP_OFF)
out<-mplReadModelGLPK(tran, dataFile, skip=0)
if (is.null(out))
out <- mplGenerateGLPK(tran)
else
stop(return_codeGLPK(out))
if (is.null(out))
mplBuildProbGLPK(tran,lp)
else
stop(return_codeGLPK(out))
solveMIPGLPK(lp)
error <- TRUE
if(mipStatusGLPK(lp)==5){
mplPostsolveGLPK(tran, lp, sol = GLP_MIP)
solution <- mipColsValGLPK(lp)
varnames <- c()
for (i in 1:length(solution))
varnames <- c(varnames,getColNameGLPK(lp,i))
paro <- "["
parc <- "]"
error <- FALSE
}
if (!error){
# get incompatible assignments set
incompatibleSet <- c()
for (i in 1:numAlt)
if(solution[varnames==paste("OnOff",paro,i,parc,sep="")] == 1)
incompatibleSet <- c(incompatibleSet,alternativesIDs[i])
# add set
incompatibleSets <- c(incompatibleSets, list(incompatibleSet))
# update data content
datacontent2a <- paste(datacontent2a, "\n", length(incompatibleSets), "\t", sep = "")
for(i in 1:numAlt)
datacontent2a <- paste(datacontent2a, solution[varnames==paste("OnOff",paro,i,parc,sep="")], sep = " ")
datacontent2b <- paste(datacontent2b, "\n", length(incompatibleSets), "\t", incompatibleSetSize, sep ="")
}
else
break
}
# increase size of incompatible sets
incompatibleSetSize <- incompatibleSetSize + 1
}
return(list(incompatibleSets = incompatibleSets, solverStatus = 'Success'))
}
else
return(list(solverStatus = 'Failed'))
}
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