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)))
