def testEquivDigraph():
print('*----- test EquivDigraph class ----*')
t = RandomCBPerformanceTableau(numberOfActions=10)
g = BipolarOutrankingDigraph(t)
t1 = RandomCBPerformanceTableau(numberOfActions=10)
g1 = BipolarOutrankingDigraph(t1)
equivg = EquivalenceDigraph(g, g1)
print(equivg.computeDeterminateness())
print(equivg.computeDeterminateness())
print(g.computeBipolarCorrelation(equivg))
print(g.computeOrdinalCorrelation(equivg))
def testCoceDigraph():
print('*----- test rxperimental CoceDigraph class ----*')
from digraphs import _CoceDigraph
t = RandomCBPerformanceTableau(numberOfActions=10)
g = BipolarOutrankingDigraph(t)
coceg = _CoceDigraph(g, Comments=True)
coceg.computeChordlessCircuits()
print(coceg.computeDeterminateness())
print(coceg.computeDeterminateness())
print(g.computeBipolarCorrelation(coceg))
print(g.computeOrdinalCorrelation(coceg))
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)))
printline()
print('Transitivity degree {}'.format(
full_digraph.computeTransitivityDegree()))
printline()
print('Chorless circuits')
full_digraph.computeChordlessCircuits()
full_digraph.showChordlessCircuits()
printline()
print('')
from linearOrders import CopelandOrder
cop = CopelandOrder(full_digraph)
print('The Copeland ranking')
cop.showRanking()
printline()
cop_corr = full_digraph.computeOrdinalCorrelation(cop)
print("Fitness of Copeland's ranking: %.3f" % cop_corr['correlation'])
printline()
print('')
from linearOrders import NetFlowsOrder
nf = NetFlowsOrder(full_digraph)
print('The Net-Flows ranking')
printline()
print('Net flow values for each alternative')
for n, va in enumerate(nf.netFlows):
print("{0} {2} -> {1:.2f}".format(n + 1, *va))
printline()
nf.showRanking()
nf_corr = full_digraph.computeOrdinalCorrelation(nf)
print("Fitness of Net-flows ranking: %.3f" % nf_corr['correlation'])
print('-' * 10, 'Question2', '-' * 10)
from outrankingDigraphs import BipolarOutrankingDigraph as BipolarOD
# bipolar outranking digraph
bipolar = BipolarOD(tab)
# compute chordless circuits
bipolar.computeChordlessCircuits()
# show computed chordless circuits
bipolar.showChordlessCircuits()
input("Press Enter to continue...")
###Q3###
print('-' * 10, 'Question3', '-' * 10)
#Copeland Ranking
cop = CopelandOrder(bipolar)
cop_corr = bipolar.computeOrdinalCorrelation(cop).get('correlation')
print(f'Correlation of Copeland order {cop_corr}')
#Kohler Ranking
koh = KohlerOrder(bipolar)
koh_corr = bipolar.computeOrdinalCorrelation(koh).get('correlation')
print(f'Correlation of Kohler order {koh_corr}')
#Netflows Ranking
netflow = NetFlowsOrder(bipolar)
netflow_corr = bipolar.computeOrdinalCorrelation(netflow).get('correlation')
print(f'Correlation of NetFlows order {netflow_corr}')
if cop_corr >= netflow_corr and cop_corr >= koh_corr:
print('According to the Copeland order,the ranking is:')
cop.showRanking()
elif netflow_corr >= cop_corr and netflow_corr >= koh_corr:
print('According to the NetFlows order,the ranking is:')
