def testcIntegerOutrankingDigraph():
print('==>> Testing IntegerBipolarOutrankingDigraph instantiation')
tc = cR3ObjPT(seed=1)
print(tc)
gi = IntegerBipolarOutrankingDigraph(tc, Threading=True, nbrCores=4)
print(gi)
gi.showRelationTable()
tcstd = tc.convert2Standard()
g = BipolarOutrankingDigraph(tcstd)
print(g)
g.showRelationTable()
def testCBPerformanceTableau():
print('*==>> random CB Performance Tableaux ------------*')
t = RandomCBPerformanceTableau(numberOfActions=10,\
commonPercentiles={'ind':5,'pref':10,'veto':95},\
weightDistribution="random",\
weightScale=[1,2],\
IntegerWeights=True,\
commonMode=["normal",50.0,25.0])
t.showCriteria(Debug=False)
g = BipolarOutrankingDigraph(t)
g.exportGraphViz()
#t.showPerformanceTableau()
g.showRelationTable()
def testFullRandomOutrankingDigraph():
print('*==>> testing full random outranking Digraphs ----*')
t = FullRandomPerformanceTableau()
#t = RandomCBPerformanceTableau()
t.showAll()
g = BipolarOutrankingDigraph(t)
g.showCriteria()
g.showPerformanceTableau()
g.showEvaluationStatistics()
## g.showStatistics()
g.showVetos(realVetosOnly=True)
print('criteria significance concentration: ',
g.computeWeightsConcentrationIndex())
def testExportPrincipalImage():
print('*------- test exportRelationPCAImage --------*')
t = RandomCBPerformanceTableau(numberOfActions=10,
weightDistribution="equiobjectives")
g = BipolarOutrankingDigraph(t)
g.save('test')
g = Digraph('test')
g.showRelationTable()
g.exportPrincipalImage('bipolar', bgcolor='lightblue')
g.recodeValuation(0, 2)
g.exportPrincipalImage('monopolar',
pictureFormat='xfig',
fontcolor='black',
fontsize='1.2')
def testSparseOutrankingDigraph():
print('==>> Testing SparseOutrankingDigraph instantiation')
MP = True
t0 = time()
tp = Random3ObjectivesPerformanceTableau(numberOfActions=100, BigData=True)
print(time() - t0)
print(total_size(tp.evaluation))
bg1 = PreRankedOutrankingDigraph(tp,
quantiles=10,
quantilesOrderingStrategy='average',
LowerClosed=True,
minimalComponentSize=1,
Threading=MP,
Debug=False)
print(bg1.computeDecompositionSummaryStatistics())
bg1.showDecomposition()
print(bg1)
t0 = time()
g = BipolarOutrankingDigraph(tp, Normalized=True, Threading=MP)
print(time() - t0)
print(total_size(g))
t0 = time()
print(
"Big outranking digraph's correlation with standard outranking digraph"
)
print(bg1.computeOrdinalCorrelation(g, Debug=False))
print(time() - t0)
nf = bg1.computeBoostedOrdering(orderingRule="NetFlows")
preordering1 = bg1.ordering2Preorder(nf)
print(nf, preordering1)
print(
'Boosted Netflows ranking correlation with complete outranking relation'
)
print(g.computeOrdinalCorrelation(g.computePreorderRelation(preordering1)))
ko = bg1.computeBoostedOrdering(orderingRule="Kohler")
preordering2 = bg1.ordering2Preorder(ko)
print(ko, preordering2)
print(
'Boosted Kohler ranking correlation with complete outranking relation')
print(g.computeOrdinalCorrelation(g.computePreorderRelation(preordering2)))
def testEstimateRankingCorrelation():
print('====> Testing estimateRankingCorrelation')
MP = True
nbrActions = 1000
tp = Random3ObjectivesPerformanceTableau(numberOfActions=nbrActions,
seed=100)
bg1 = PreRankedOutrankingDigraph(
tp,
CopyPerfTab=True,
quantiles=4,
quantilesOrderingStrategy='optimal',
componentRankingRule='NetFlows',
LowerClosed=True,
minimalComponentSize=1,
Threading=MP,
nbrOfCPUs=8,
#tempDir='.',
nbrOfThreads=8,
Comments=True,
Debug=False,
save2File='testbgMP')
seed = 1
sampleSize = 100
import random
random.seed(seed)
actionKeys = [x for x in bg1.actions]
sample = random.sample(actionKeys, sampleSize)
print(sample)
print(bg1.boostedRanking)
preRankedSample = []
for x in bg1.boostedRanking:
if x in sample:
preRankedSample.append(x)
print(preRankedSample)
ptp = PartialPerformanceTableau(tp, sample)
from outrankingDigraphs import BipolarOutrankingDigraph
pg = BipolarOutrankingDigraph(ptp, Normalized=True)
print(pg.computeRankingCorrelation(preRankedSample))
print(bg1.estimateRankingCorrelation(sampleSize, seed))
def testCompleteness():
print('*------- test (Weakly) Completeness ------*')
g = RandomValuationDigraph()
g.showRelationTable()
print('Relation %s is complete ? %s' %
(g.name, str(g.isComplete(Debug=True))))
print('Relation %s is weakly complete ? %s' %
(g.name, str(g.isWeaklyComplete(Debug=True))))
t = RandomCBPerformanceTableau(numberOfActions=9,
numberOfCriteria=5,
weightDistribution='equiobjectives')
g = BipolarOutrankingDigraph(t, Normalized=True)
g.showRelationTable()
print('Relation %s is complete ? %s' %
(g.name, str(g.isComplete(Debug=True))))
print('Relation %s is weakly complete ? %s' %
(g.name, str(g.isWeaklyComplete(Debug=True))))
gcd = CoDualDigraph(g)
gcd.showRelationTable()
print('Relation %s is complete ? %s' %
(gcd.name, str(gcd.isComplete(Debug=True))))
print('Relation %s is weakly complete ? %s' %
(gcd.name, str(gcd.isWeaklyComplete(Debug=True))))
def testLinearVotingBallots():
print("*==>> testing linear voting profiles ----*")
lvp = RandomLinearVotingProfile(numberOfVoters=100,
numberOfCandidates=10,
WithPolls=True,
partyRepartition=0.6,
other=0.1,
seed=None)
lvp.save()
lvp = LinearVotingProfile('templinearprofile')
lvp.showLinearBallots()
print(lvp.computeRankAnalysis())
lvp.showRankAnalysisTable(Debug=True)
print(lvp.computeBordaScores())
print(lvp.computeBordaWinners())
c = MajorityMarginsDigraph(lvp)
c.exportGraphViz()
print(c.computeChordlessCircuits())
lvp.save2PerfTab()
t = PerformanceTableau('votingPerfTab')
from outrankingDigraphs import BipolarOutrankingDigraph
g = BipolarOutrankingDigraph(t)
lvp.showHTMLVotingHeatmap()
print(c.computeCopelandRanking())
pt.showCriteria()
print('')
printline()
print('Actions')
printline()
pt.showActions()
print('')
printline()
print('Statistics')
printline()
pt.showStatistics()
print('')
full_digraph = BipolarOutrankingDigraph(pt)
html_heatmap = pt.htmlPerformanceHeatmap(RankingRule='Kohler')
fwrite(html_heatmap, make_path("full_heatmap.html"))
html_relationtable = full_digraph.htmlRelationTable(isColored=True)
fwrite(html_relationtable, make_path("full_bipolar_adj_matrix.html"))
weak_condorcet_winners = full_digraph.weakCondorcetWinners()
printline()
print("Weak Condorcet winners from a multi-objectives point of view -> {}".
format(weak_condorcet_winners))
printline()
condorcet_winners = full_digraph.condorcetWinners()
printline()
def fwrite(string, file):
with open(file, 'w') as f:
f.write(string)
pt = XMCDA2PerformanceTableau('project_2')
for ct in ['Eco', 'Soc', 'Env']:
printline()
print('RUBIS Best Choice Recommendation from {} point of view'.format(ct))
printline()
criterias = [c for c, val in pt.criteria.items() if ct in val['name']]
ppt = PartialPerformanceTableau(pt,criteriaSubset=criterias)
partial_digraph = BipolarOutrankingDigraph(ppt)
html_heatmap = ppt.htmlPerformanceHeatmap(colorLevels=5)
fwrite(html_heatmap, make_path(ct+"_heatmap.html"))
html_relationtable = partial_digraph.htmlRelationTable(isColored=True)
fwrite(html_relationtable, make_path(ct+"_bipolar_adj_matrix.html"))
condorcet_winners = partial_digraph.condorcetWinners()
printline()
print("Condorcet winners for {} -> {}".format(ct, condorcet_winners))
printline()
weak_condorcet_winners = partial_digraph.weakCondorcetWinners()
printline()
print("Weak Condorcet winners for {} -> {}".format(ct, weak_condorcet_winners))
