diff --git a/PrefpyInput-ElectionDataFormat.png b/PrefpyInput-ElectionDataFormat.png new file mode 100644 index 0000000..c6e3a5d Binary files /dev/null and b/PrefpyInput-ElectionDataFormat.png differ diff --git a/generateInputProfile.py b/generateInputProfile.py new file mode 100644 index 0000000..d061ec4 --- /dev/null +++ b/generateInputProfile.py @@ -0,0 +1,152 @@ +import sys +import random +import math +import itertools +import prefpy +from prefpy import preference +from prefpy import profile +from prefpy import io + +from prefpy.profile import Profile +from prefpy.preference import Preference +from outputFormatting import * + +#===================================================================================== + +def main(argv): + filename, numCands, numUniqueRankings, maxVotesPerRanking = handleInput(argv) + + candOptions = "0abcdefghijklmnopqrstuvwxyz" + candMap = dict() + for i in range(1, numCands+1): + candMap[i] = candOptions[i] + + preferencesList = [] + + ranksWithNums = pickRankings(candMap, numUniqueRankings) + + for ranking in ranksWithNums.values(): + wmgMap = genWmgMapFromRankMap( convertRankingToRankMap(ranking) ) + voteCount = random.randint(0, maxVotesPerRanking) + + newPref = Preference(wmgMap,voteCount) + preferencesList.append(newPref) + + generatedProfile = Profile(candMap, preferencesList) + generatedProfile.exportPreflibFile(filename) + print("Generated file '" + filename + "'") + +#===================================================================================== + +def pickRankings(candMap, numUniqueRankings): + + # ranksWithNums = dict() + # i = 0 + # while len(ranksWithNums.keys()) < numUniqueRankings and i < 6: + # ranking = [] + # candsCopy = list(candMap.keys()) + # while len(candsCopy) > 0: + # rankNum = random.randint(0, len(candsCopy)-1) + # ranking.append(candsCopy[rankNum]) + # del candsCopy[rankNum] + # print("ranking: ", ranking) + # print("ranksWithNums: ", ranksWithNums) + # if tuple(ranking) not in ranksWithNums.keys(): + # ranksWithNums[tuple(ranking)] = i + # i += 1 + + + numPosRankings = math.factorial(len(candMap.keys())) + # pick the random indices for the rankings to be chosen + while len(ranksWithNums.keys()) < numUniqueRankings: + rankNum = random.randint(0, numPosRankings) + ranksWithNums[rankNum] = 0 + + numFound = 0 + rankNum = 0 + for ranking in itertools.permutations(candMap.keys()): + if rankNum in ranksWithNums.keys(): + ranksWithNums[rankNum] = ranking + numFound += 1 + if numFound >= numUniqueRankings: + break + rankNum += 1 + + return ranksWithNums + +#===================================================================================== + +def handleInput(argv): + if len(argv) >= 2: + filename = argv[1].lower() + else: + filename = input("Enter name of file to generate: ").lower() + if len(argv) >= 3: + numCands = int(argv[2]) + else: + numCands = int(input("Enter the number of candidates: ")) + if len(argv) >= 4: + numUniqueRankings = int(argv[3]) + else: + numUniqueRankings = int(input("Enter the number of unique votes/rankings: ")) + if len(argv) >= 5: + maxVotesPerRanking = int(argv[4]) + else: + maxVotesPerRanking = int(input("Enter the maximum number of votes for each ranking: ")) + + return filename, numCands, numUniqueRankings, maxVotesPerRanking + +#===================================================================================== + +def convertRankingToRankMap(ranking): + rankMap = dict() + for i, cand in enumerate(ranking): + rankMap[cand] = i + return rankMap + +#===================================================================================== + +def genWmgMapFromRankMap(rankMap): + """ + Converts a single rankMap into a weighted majorty graph (wmg). We return the wmg as a + two-dimensional dictionary that associates integer representations of each pair of candidates, + cand1 and cand2, with the number of times cand1 is ranked above cand2 minus the number of times + cand2 is ranked above cand1. + + :ivar dict rankMap: Associates integer representations of each candidate with its + ranking in a single vote. + """ + + wmgMap = dict() + for cand1, cand2 in itertools.combinations(rankMap.keys(), 2): + + # Check whether or not the candidates are already present in the dictionary. + if cand1 not in wmgMap.keys(): + wmgMap[cand1] = dict() + if cand2 not in wmgMap.keys(): + wmgMap[cand2] = dict() + + # Check which candidate is ranked above the other. Then assign 1 or -1 as appropriate. + if rankMap[cand1] < rankMap[cand2]: + wmgMap[cand1][cand2] = 1 + wmgMap[cand2][cand1] = -1 + elif rankMap[cand1] > rankMap[cand2]: + wmgMap[cand1][cand2] = -1 + wmgMap[cand2][cand1] = 1 + + # If the two candidates are tied, We make 0 the number of edges between them. + elif rankMap[cand1] == rankMap[cand2]: + wmgMap[cand1][cand2] = 0 + wmgMap[cand2][cand1] = 0 + + return wmgMap + +#===================================================================================== + +if __name__ == '__main__': + main(sys.argv) + + + + + diff --git a/input-10c-1000r b/input-10c-1000r new file mode 100644 index 0000000..199f87c --- /dev/null +++ b/input-10c-1000r @@ -0,0 +1,1012 @@ +10 +1,a +2,b +3,c +4,d +5,e +6,f +7,g +8,h +9,i +10,j +150263,150263,1000 +84,8,9,5,7,6,4,2,3,10,1 +224,3,6,5,8,2,7,4,10,1,9 +147,8,6,5,7,1,2,4,3,9,10 +1,5,7,4,8,1,6,3,2,10,9 +174,6,3,10,2,4,9,7,8,5,1 +15,8,9,10,2,7,1,6,3,4,5 +195,10,3,5,7,4,8,1,6,9,2 +17,5,8,10,7,4,9,2,1,6,3 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+1,1,2,3 +1,1,3,2 +1,2,3,1 +1,2,1,3 +1,3,1,2 +1,3,2,1 \ No newline at end of file diff --git a/input-4c-4r-hw1 b/input-4c-4r-hw1 new file mode 100644 index 0000000..369d082 --- /dev/null +++ b/input-4c-4r-hw1 @@ -0,0 +1,10 @@ +4 +1,a +2,b +3,c +4,d +26,26,4 +10,1,2,3,4 +7,4,1,2,3 +6,3,4,1,2 +3,2,3,4,1 \ No newline at end of file diff --git a/input-4c-4r-midterm b/input-4c-4r-midterm new file mode 100644 index 0000000..ca93a4a --- /dev/null +++ b/input-4c-4r-midterm @@ -0,0 +1,10 @@ +4 +1,a +2,b +3,c +4,d +8,8,4 +1,1,2,3,4 +2,4,3,2,1 +3,3,2,1,4 +2,1,4,2,3 \ No newline at end of file diff --git a/input-4c-5r b/input-4c-5r new file mode 100644 index 0000000..ece362b --- /dev/null +++ b/input-4c-5r @@ -0,0 +1,11 @@ +4 +1,a +2,b +3,c +4,d +23,23,5 +3,2,4,3,1 +7,3,2,4,1 +5,2,1,3,4 +2,1,3,4,2 +6,4,1,3,2 \ No newline at end of file diff --git a/input-6c-20r b/input-6c-20r new file mode 100644 index 0000000..bcf7356 --- /dev/null +++ b/input-6c-20r @@ -0,0 +1,28 @@ +6 +1,a +2,b +3,c +4,d +5,e 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+#===================================================================================== + +def convertCandIntsToNames(candsToConvert, candMap): + convertedCands = [] + for i, candInt in enumerate(candsToConvert): + if isinstance(candInt, int): + candStr = candMap[candInt] + else: + candStr = convertCandIntsToNames(candInt, candMap) + convertedCands.append(candStr) + if isinstance(candsToConvert, tuple): + convertedCands = tuple(convertedCands) + # print("ToConvert: ", str(candsToConvert)) + # print("Converted: ", str(convertedCands)) + return convertedCands + +#===================================================================================== + +def convertDictionaryCandIntsToNames(candDictToConvert, candMap): + convertedCands = dict() + for candInt, val in candDictToConvert.items(): + candStr = candMap[candInt] + if isinstance(val, dict): + val = convertDictionaryCandIntsToNames(val, candMap) + convertedCands[candStr] = val + # print("ToConvert: ", getDictString(candDictToConvert)) + # print("Converted: ", getDictString(convertedCands)) + return convertedCands + +#===================================================================================== + +def getRankingString(ranking): + rankStr = "" + for i, cand in enumerate(ranking): + if i != 0: + rankStr += " > " + rankStr += str(cand) + return rankStr + +#===================================================================================== + +def getDictString(dictionary): + dictStr = "" + line = "{s}{key}: {val}{e}" + for i, k in enumerate(dictionary.keys()): + start = "\n{ " if (i == 0) else " " + end = " }" if (i == len(dictionary)-1) else ",\n" + dictStr += line.format(s=start, key=k, val=dictionary[k], e=end) + return dictStr + +#===================================================================================== + + + + diff --git a/prefpy/kemeny.py b/prefpy/kemeny.py new file mode 100644 index 0000000..6a53405 --- /dev/null +++ b/prefpy/kemeny.py @@ -0,0 +1,106 @@ +''' +Authors: Tobe Ezekwenna, Sam Saks-Fithian, Aman Zargarpur +''' + +import itertools +from prefpy.mechanism import Mechanism + + +#===================================================================================== +#===================================================================================== + +class MechanismKemeny(Mechanism): + """ + The Kemeny mechanism. Calculates winning ranking(s)/candidate(s) based on the sums of + the weights of edges of a given profile's WMG that are inconsistent with those of the + WMG for each possible ranking. + """ + #===================================================================================== + + def __init__(self): + self.maximizeCandScore = False + self.winningRankings = [] + + #===================================================================================== + + def getCandScoresMap(self, profile): + """ + Returns a dictonary that associates the integer representation of each candidate with + their place in the winning ranking. + + :ivar Profile profile: A Profile object that represents an election profile. + """ + + # Currently, we expect the profile to contain complete ordering over candidates. + elecType = profile.getElecType() + if elecType != "soc": + print("ERROR: unsupported election type") + exit() + + rankWeights = dict() + wmgMap = profile.getWmg() + for ranking in itertools.permutations(wmgMap.keys()): + # Initialize inconsistent weight to 0 + rankWeights[ranking] = 0.0 + + # For each pair of candidates in ranking, determine if edge/order in ranking + # is inconsistent with corresponding edge/order in the WMG of the profile + # Sum the weights of all such inconsistent edges + for cand1, cand2 in itertools.combinations(ranking, 2): + # cand1 > cand2 in wmg + wmgOrd = 1 if (wmgMap[cand1][cand2] > 0) else 0 + # cand1 > cand2 in ranking + rankOrd = 1 if (ranking.index(cand1) < ranking.index(cand2)) else 0 + if wmgOrd != rankOrd: + rankWeights[ranking] += abs(wmgMap[cand1][cand2]) + + bestScore = min(rankWeights.values()) + self.winningRankings = [] + for ranking in rankWeights.keys(): + if rankWeights[ranking] == bestScore: + self.winningRankings.append(ranking) + + # handle tie/multiple winning rankings + if len(self.winningRankings) > 1: + winRank = self.tiebreakRankings(self.winningRankings) + else: + winRank = self.winningRankings[0] + + return self.convertRankingToCandMap(winRank) + + #===================================================================================== + + def convertRankingToCandMap(self, ranking): + """ + Returns a dictonary that associates the integer representation of each candidate with + their place in the winning ranking. + + :ivar Tuple ranking: A tuple representing the winning order ranking of the canditates. + """ + candScoresMap = dict() + for place, cand in enumerate(ranking): + candScoresMap[cand] = place + + return candScoresMap + + #===================================================================================== + + def tiebreakRankings(self, wRankings): + """ + Returns a tuple that is the single winning ranking. + + :ivar List wRankings: A list of tuples that represent preference rankings. + """ + return wRankings[0] + +#===================================================================================== +#===================================================================================== + + + + + + + + + diff --git a/prefpy/kemenyILP.py b/prefpy/kemenyILP.py new file mode 100644 index 0000000..ff4665d --- /dev/null +++ b/prefpy/kemenyILP.py @@ -0,0 +1,150 @@ +''' +Authors: Tobe Ezekwenna, Sam Saks-Fithian, Aman Zargarpur +''' + +from prefpy.mechanism import Mechanism +from gurobipy import * +import numpy as np + +#===================================================================================== +#===================================================================================== + +def precedenceMatrix(preferences, counts): + n, m = sum(counts), len(preferences[0]) # n preferences, m candidates + #print("m is", m) + Q = np.zeros((m,m)) + for k in range(len(preferences)): + vote = preferences[k] + for i in range(len(vote) - 1): + for j in range(i + 1, len(vote)): + Q[vote[i][0]-1][vote[j][0]-1] += 1*counts[k] #Q[vote[i]][vote[j]] += 1 + return Q / n + +#===================================================================================== +#===================================================================================== + +class MechanismKemenyILP(Mechanism): + """ + The Kemeny mechanism. Calculates winning ranking(s)/candidate(s) based on Gurobi + optimization of ILP formula: + + Goal: minimize SUMMATION_a,b( Q_ab * X_ba + Q_ba * X_ab ) + where Q is the precedence matrix formed from profile + i.e. Q_ab is the fraction of times a>b across all rankings in profile + Constraints: + X_ab in {0, 1} + X_ab + X_ba = 1, for ALL a,b + X_ab + X_bc + X_ca >= 1, for ALL a,b,c + + """ + #===================================================================================== + + def __init__(self): + self.maximizeCandScore = True + self.winningRankings = [] + self.precMtx = [] + self.gModel = None + + #===================================================================================== + + def getCandScoresMap(self, profile): + """ + Returns a dictonary that associates the integer representation of each candidate + with their score from the ILP optimization. The score for each candidate is the + sum of all the binary variables that represent preference with respect to another + candidate after optimization. + Sets/saves data variables for later use (self.winningRanking, self.precMtx, self.gModel) + + :ivar Profile profile: A Profile object that represents an election profile. + """ + # Currently, we expect the profile to contain complete ordering over candidates. + elecType = profile.getElecType() + if elecType != "soc": + print("ERROR: unsupported election type") + exit() + + try: + # Create a new model + m = Model("kemeny") + + binaryMap = {} + candMap = profile.candMap + keys = candMap.keys() + # print(keys) + + precedence = precedenceMatrix(profile.getOrderVectors(), profile.getPreferenceCounts()) + + # Begin constructing the objective + obj = LinExpr() + for i in range(len(keys)-1): + for j in range(i+1, len(keys)): + + # Create variables (2, 3) + binaryMap[(i,j)] = m.addVar(vtype=GRB.BINARY, name=candMap[i+1]) + binaryMap[(j,i)] = m.addVar(vtype=GRB.BINARY, name=candMap[j+1]) + # Integrate new variables + m.update() + + # Add constraint: X_ab + X_ba = 1 (4) + m.addConstr(binaryMap[(i,j)] + binaryMap[(j,i)] == 1) + obj += precedence[i][j]*binaryMap[(j,i)] + precedence[j][i]*binaryMap[(i,j)] + obj += precedence[j][i]*binaryMap[(i,j)] + precedence[i][j]*binaryMap[(j,i)] + + # Add transitivity constraint: X_ab + X_bc + X_ca >= 1 (5) + for i in range(len(keys)): + for j in range(len(keys)): + for k in range(len(keys)): + if(i == j or j == k or i == k): + continue + m.addConstr(binaryMap[(i,j)] + binaryMap[(j,k)] + binaryMap[k, i] >= 1) + + # Set objective + m.setObjective(obj, GRB.MINIMIZE) + m.optimize() + + # print(precedence) + # for v in m.getVars(): + # print(v.varName, v.x) + # print('Obj:', m.objVal) + + candScoresMap = self.convertBinVarsToCandMap(m.getVars()) + + self.winningRankings = sorted(candScoresMap, key=candScoresMap.get, reverse=True) + self.precMtx = precedence + self.gModel = m + + return candScoresMap + + except GurobiError: + print('Gurobi Error reported') + + #===================================================================================== + + def convertBinVarsToCandMap(self, varList): + """ + Returns a dictonary that associates the integer representation of each candidate + with their score from the ILP optimization. The score for each candidate is the + sum of all the binary variables that represent preference with respect to another + candidate after optimization. + + :ivar List varList: A list of the binary variables set by the optimization of a Gurobi model. + """ + candScoresMap = dict() + for v in varList: + if v.varName in candScoresMap.keys(): + candScoresMap[v.varName] += v.x + else: + candScoresMap[v.varName] = v.x + return candScoresMap + +#===================================================================================== +#===================================================================================== + + + + + + + + + diff --git a/prefpy/profile.py b/prefpy/profile.py index 34951f5..b4b1d8b 100644 --- a/prefpy/profile.py +++ b/prefpy/profile.py @@ -2,7 +2,7 @@ Author: Kevin J. Hwang """ import copy -import io +from . import io import itertools import math import json diff --git a/runKemeny.py b/runKemeny.py new file mode 100644 index 0000000..1a9410d --- /dev/null +++ b/runKemeny.py @@ -0,0 +1,117 @@ +import sys +import prefpy +from prefpy import preference +from prefpy import profile +from prefpy import io + +from prefpy.kemenyILP import MechanismKemenyILP +from prefpy.profile import Profile +from prefpy.preference import Preference + +from gurobipy import * +import numpy as np +#===================================================================================== + +def preferenceMatrix(preferences, counts): + n, m = sum(counts), len(preferences[0]) # n preferences, m candidates + #print("m is", m) + Q = np.zeros((m,m)) + for k in range(len(preferences)): + vote = preferences[k] + for i in range(len(vote) - 1): + for j in range(i + 1, len(vote)): + Q[vote[i][0]-1][vote[j][0]-1] += 1*counts[k] #Q[vote[i]][vote[j]] += 1 + return Q / n + + +def main(): + + data = Profile({},[]) + + + # filename = "input1" + filename = input("Enter name of election data file: ").lower() + data.importPreflibFile(filename) + print("Imported file") + + kemenyMech = MechanismKemenyILP() + #print("Created KemenyMechanism obj") + + kemWinner1 = kemenyMech.getWinners(data) + kemWinRank1 = kemenyMech.getWinningRankings() + print("W* = " + str(kemWinRank1) + ", winner = " + str(kemWinner1)) + + + #print(data.getPreferenceCounts()) + #print(data.getOrderVectors()) + #print(preferenceMatrix(data.getOrderVectors(), data.getPreferenceCounts())) + #print(data.candMap) + """ + try: + # Create a new model + m = Model("kemeny") + + binaryMap = {} + candMap = data.candMap + keys = candMap.keys() + print(keys) + precedence = preferenceMatrix(data.getOrderVectors(), data.getPreferenceCounts()) + obj = LinExpr() + for i in range(len(keys)-1): + for j in range(i+1, len(keys)): + + # Create variables + binaryMap[(i,j)] = m.addVar(vtype=GRB.BINARY, name=candMap[i+1]) + binaryMap[(j,i)] = m.addVar(vtype=GRB.BINARY, name=candMap[j+1]) + # Integrate new variables + m.update() + + # Add constraint: X_ab + X_ba = 1 + m.addConstr(binaryMap[(i,j)] + binaryMap[(j,i)] == 1) + obj += precedence[i][j]*binaryMap[(j,i)] + precedence[j][i]*binaryMap[(i,j)] + obj += precedence[j][i]*binaryMap[(i,j)] + precedence[i][j]*binaryMap[(j,i)] + + + for i in range(len(keys)): + for j in range(len(keys)): + for k in range(len(keys)): + if(i == j or j == k or i == k): + continue + m.addConstr(binaryMap[(i,j)] + binaryMap[(j,k)] + binaryMap[k, i] >= 1) + + # Set objective + m.setObjective(obj, GRB.MINIMIZE) + + m.optimize() + + print(precedence) + + for v in m.getVars(): + print(v.varName, v.x) + + print('Obj:', m.objVal) + + except GurobiError: + print('Error reported') + + """ + + + # data.exportPreflibFile(filename + "-output") + # print("Created output file") + + # myList = [(4, 'a', 'b'), (3, 'b', 'c'), (3, 'c', 'd'), (2, 'd', 'b'), (2, 'c', 'a'), (4, 'd', 'a')] + # #print(rankpairMech.getWinners(edges = myList)) + # print(rankpairMech.getWinners(prof = data)) + # print(rankpairMech.getOneWinner(data)) + # edgeList=rankpairMech.getSortedEdges(data) + # rankpairMech.createNXGraph(edgeList) + + + +#===================================================================================== + +if __name__ == '__main__': + main() + + diff --git a/runKemenyBasic.py b/runKemenyBasic.py new file mode 100644 index 0000000..1e06c6b --- /dev/null +++ b/runKemenyBasic.py @@ -0,0 +1,60 @@ +import sys +import prefpy +from prefpy import preference +from prefpy import profile +from prefpy import io + +from prefpy.kemeny import MechanismKemeny +from prefpy.profile import Profile +from prefpy.preference import Preference +from outputFormatting import * + +#===================================================================================== + +def main(argv): + if len(argv) == 1: + filename = input("Enter name of election data file: ").lower() + solveFile(filename) + else: + for i in range(1, len(argv)): + print("============================{}============================".format(argv[i])) + solveFile(argv[i]) + print("------------------------end of {}------------------------\n".format(argv[i])) + +#===================================================================================== + +def solveFile(filename): + data = Profile({},[]) + data.importPreflibFile(filename) + print("Imported file '" + filename + "'") + + print("Candidates: ", data.candMap) + # print("WMG: ", getDictString(data.getWmg()), "\n") + print("WMG w/ names:", getDictString(convertDictionaryCandIntsToNames(data.getWmg(), data.candMap)), "\n" ) + + + kemenyMech = MechanismKemeny() + # print("Created KemenyMechanism obj") + + # individual winner + kemWinners = kemenyMech.getWinners(data) + kemWinnersNames = convertCandIntsToNames(kemWinners, data.candMap) + + # winning ranking(s) + kemWinRanksBase = kemenyMech.winningRankings + kemWinRanksNames = convertCandIntsToNames(kemWinRanksBase, data.candMap) + + # winning ranking(s) formatted as strings in the form "a > b > c" + kemWinRankStrs = [] + for ranking in kemWinRanksNames: + kemWinRankStrs.append( getRankingString(ranking) ) + + winnerPrint = "W* = {ranking}\nWinner(s) = {w}" + print( winnerPrint.format(ranking=kemWinRankStrs, w=kemWinnersNames) ) + +#===================================================================================== + +if __name__ == '__main__': + main(sys.argv) + + diff --git a/runKemenyILP.py b/runKemenyILP.py new file mode 100644 index 0000000..0e7efc5 --- /dev/null +++ b/runKemenyILP.py @@ -0,0 +1,95 @@ +import sys +import prefpy +from prefpy import preference +from prefpy import profile +from prefpy import io + +from prefpy.kemenyILP import MechanismKemenyILP +from prefpy.profile import Profile +from prefpy.preference import Preference +from outputFormatting import * + +#===================================================================================== + +def main(argv): + if len(argv) == 1: + filename = input("Enter name of election data file: ").lower() + solveFile(filename) + else: + for i in range(1, len(argv)): + print("============================{}============================".format(argv[i])) + solveFile(argv[i]) + print("------------------------end of {}------------------------\n".format(argv[i])) + +#===================================================================================== + +def solveFile(filename): + data = Profile({},[]) + data.importPreflibFile(filename) + print("Imported file '" + filename + "'") + + print("Candidates: ", data.candMap) + print("WMG: ", getDictString(data.getWmg()), "\n") + + kemenyMech = MechanismKemenyILP() + print("Created KemenyMechanismILP obj") + + kemWinners = kemenyMech.getWinners(data) + print("Calculated ILP Winner(s)") + + # print precedence matrix + print("Precedence matrix: ", kemenyMech.precMtx) + + # print variables + print("Optimized Binary Variables: ") + for v in kemenyMech.gModel.getVars(): + print(v.varName, v.x) + + print('Obj: ', kemenyMech.gModel.objVal) + + # individual winner + kemWinners = kemenyMech.getWinners(data) + kemWinnersNames = convertCandIntsToNames(kemWinners, data.candMap) + + # winning ranking(s) + kemWinRanksBase = kemenyMech.winningRankings + kemWinRanksNames = convertCandIntsToNames(kemWinRanksBase, data.candMap) + + # winning ranking(s) formatted as strings in the form "a > b > c" + kemWinRankStrs = [] + for ranking in kemWinRanksNames: + kemWinRankStrs.append( getRankingString(ranking) ) + + winnerPrint = "W* = {ranking}\nWinner(s) = {w}" + print( winnerPrint.format(ranking=kemWinRankStrs, w=kemWinnersNames) ) + +#===================================================================================== + +def testVarSumScoreAndSortRanking(): + varList = [('a', -0.0),('b', 1.0), ('a', -0.0),('c', 1.0), ('a', 1.0),('d', 0.0), ('b', -0.0),('c', 1.0), ('b', 1.0),('d', 0.0), ('c', 1.0),('d', 0.0)] + candMap = [(1,"a"), (2,"b"), (3,"c"), (4,"d")] + + # sum binary variables + candScoresMap = dict() + for v,x in varList: + if(v in candScoresMap.keys()): + candScoresMap[v] += x + else: + candScoresMap[v] = x + print("candScoresMap: ", candScoresMap) + + # sort solution scores to get ranking + sortedDataWithScores = sorted(candScoresMap.items(), key=lambda tup: tup[1], reverse=True) + print("sortedDataWithScores: ", sortedDataWithScores) + + sortedDataRanking = sorted(candScoresMap, key=candScoresMap.get, reverse=True) + print("sortedDataRanking: ", sortedDataRanking) + + print("Winning Ranking: ", getRankingString(sortedDataRanking)) + +#===================================================================================== + +if __name__ == '__main__': + main(sys.argv) + +