mirror of
https://github.com/pezkuwichain/consensus.git
synced 2026-04-28 12:07:59 +00:00
Added factor 2 by binary search method
This commit is contained in:
A S
+118
-35
@@ -175,13 +175,14 @@ def calculateScores(a,cutoff):
|
||||
|
||||
|
||||
def calculateMaxScore(a):
|
||||
supportList=[a.cansupport[i] for i in range(len(a.candidates))]
|
||||
supportList=[a.cansupport[can.index] for can in a.electedcandidates]
|
||||
supportList.append(0.0)
|
||||
supportList.sort()
|
||||
lowerindex=0
|
||||
upperindex=len(a.candidates)+1
|
||||
upperindex=len(a.electedcandidates)+1
|
||||
currentindex=0
|
||||
while(True):
|
||||
#print(len(supportList), currentindex, len(a.electedcandidates),upperindex,lowerindex)
|
||||
cutoff=supportList[currentindex]
|
||||
calculateScores(a,cutoff)
|
||||
scores=[(can, a.canscore[can.index]) for can in a.candidates if not a.canelected[can.index]]
|
||||
@@ -189,6 +190,8 @@ def calculateMaxScore(a):
|
||||
if score > cutoff:
|
||||
# In this case both score and cutoff are lower bounds to the max score
|
||||
lowerindex=len([s for s in supportList if s <= score]) - 1
|
||||
#print("lowerindex ",lowerindex, " upperindex ",upperindex, " cutoff ", cutoff, "score ",score, " candidate ",bestcandidate.canid)
|
||||
#print("bottom support ",supportList[0], " real lowest support ",supportList[1], " highest support ",supportList[-1])
|
||||
if currentindex == upperindex-1 or currentindex==lowerindex:
|
||||
return bestcandidate,score
|
||||
elif score < cutoff:
|
||||
@@ -241,7 +244,6 @@ def printresult(a,listvoters=True,listelectedcandidates=True):
|
||||
print(edge.canid," with stake ",a.edgeweight[edge.index], end=" ")
|
||||
print()
|
||||
print("Minimum support ",min([a.cansupport[candidate.index] for candidate in a.electedcandidates]))
|
||||
print()
|
||||
|
||||
def equalise(a, nom, tolerance):
|
||||
# Attempts to redistribute the nominators budget between elected validators
|
||||
@@ -346,7 +348,94 @@ def SFFB18(votelist, numtoelect,tolerance=0.1):
|
||||
if bestvalue > 0:
|
||||
a=bestassignment
|
||||
return a
|
||||
|
||||
|
||||
def binarysearchfeasible(votelist,numtoelect,tolerance=0.1):
|
||||
nomlist,candidates=setuplists(votelist)
|
||||
a=assignment(nomlist,candidates)
|
||||
|
||||
#First do factor 3.15
|
||||
#but keep track of the order we elect people and the value then
|
||||
orderelectedwithvalue=[]
|
||||
for round in range(numtoelect):
|
||||
bestcandidate,score=calculateMaxScore(a)
|
||||
insertWithScore(a,bestcandidate, score)
|
||||
equaliseall(a,1000000,tolerance/numtoelect)
|
||||
currentvalue=min([a.cansupport[candidate.index] for candidate in a.electedcandidates])
|
||||
orderelectedwithvalue.append((bestcandidate,currentvalue))
|
||||
if len(a.candidates)==numtoelect:
|
||||
return a
|
||||
bestknownvalue=currentvalue
|
||||
bestassignment=assignment(a.voterlist,a.candidates,a)
|
||||
bestorderelected=orderelectedwithvalue.copy()
|
||||
maxunelectedapproval = max([a.canapproval[i] for i in range(len(a.candidates)) if not a.canelected[i]])
|
||||
totalvotes=sum([nom.budget for nom in a.voterlist])
|
||||
maxvalue=min(3.15*currentvalue, max(currentvalue,maxunelectedapproval), totalvotes/numtoelect)
|
||||
while(maxvalue - bestknownvalue > tolerance):
|
||||
targetvalue=math.sqrt(maxvalue*bestknownvalue)
|
||||
lastgoodindex=len([x for x in orderelectedwithvalue if x[1] >= targetvalue])-1
|
||||
#print(orderelectedwithvalue, targetvalue,lastgoodindex, maxvalue,bestknownvalue,currentvalue,targetvalue)
|
||||
assert(lastgoodindex >= 0)
|
||||
assert(orderelectedwithvalue[lastgoodindex][1] >= targetvalue)
|
||||
for x in orderelectedwithvalue[lastgoodindex+1:]:
|
||||
a.unelect(x[0])
|
||||
del orderelectedwithvalue[lastgoodindex+1:]
|
||||
for nom in a.voterlist:
|
||||
for edge in nom.edges:
|
||||
if not a.canelected[edge.canindex]:
|
||||
a.setweight(edge,0)
|
||||
equaliseall(a,1000000,tolerance/numtoelect)
|
||||
currentvalue=min([a.cansupport[candidate.index] for candidate in a.electedcandidates])
|
||||
if currentvalue < targetvalue:
|
||||
if targetvalue >= sqrt(currentvalue, maxvalue):
|
||||
#At this point we are getting so much error from tolerance in equaliseall
|
||||
#that we should give up
|
||||
print("Giving up with error at most ",maxvalue-bestknownvalue)
|
||||
return
|
||||
else:
|
||||
targetvalue=currentvalue
|
||||
#print(targetvalue,lastgoodindex, maxvalue,bestknownvalue,currentvalue)
|
||||
|
||||
|
||||
for round in range(lastgoodindex+1,numtoelect):
|
||||
# First try maxscore candidate, which will help with PJR
|
||||
bestcandidate,score=calculateMaxScore(a)
|
||||
if score >= targetvalue:
|
||||
insertWithScore(a,bestcandidate, score)
|
||||
equaliseall(a,1000000,tolerance/numtoelect)
|
||||
currentvalue=min([a.cansupport[candidate.index] for candidate in a.electedcandidates])
|
||||
assert(currentvalue >= targetvalue)
|
||||
orderelectedwithvalue.append((bestcandidate,currentvalue))
|
||||
continue
|
||||
else:
|
||||
b,newvalue = maybecandidate(a,bestcandidate, False, tolerance)
|
||||
if newvalue >= targetvalue:
|
||||
a=b
|
||||
orderelectedwithvalue.append((bestcandidate,newvalue))
|
||||
currentvalue=newvalue
|
||||
continue
|
||||
#Then try some candidates in which we are guaranteed that one is feasible if threshold >= d*/2
|
||||
calculateScores(a,targetvalue/2)
|
||||
scores=[(can, a.canscore[can.index]) for can in a.candidates if not a.canelected[can.index] and a.canapproval[can.index] >= targetvalue and a.canscore[can.index] >= targetvalue/2]
|
||||
scores.sort(reverse=True,key=lambda x: x[1])
|
||||
for can,score in scores:
|
||||
b,newvalue = maybecandidate(a,can, False, tolerance)
|
||||
if newvalue >= targetvalue:
|
||||
a=b
|
||||
orderelectedwithvalue.append((can,newvalue))
|
||||
currentvalue=newvalue
|
||||
break
|
||||
else:
|
||||
break
|
||||
# print("here",currentvalue,targetvalue)
|
||||
if len(a.electedcandidates) < numtoelect:
|
||||
maxvalue = targetvalue
|
||||
a=bestassignment
|
||||
orderelectedwithvalue=bestorderelected
|
||||
else:
|
||||
bestknownvalue=currentvalue
|
||||
bestassignment=assignment(a.voterlist,a.candidates,a)
|
||||
bestorderelected=orderelectedwithvalue.copy()
|
||||
return a
|
||||
|
||||
import unittest
|
||||
class electiontests(unittest.TestCase):
|
||||
@@ -369,36 +458,17 @@ import time
|
||||
def doall(votelist, numtoelect, listvoters=True, listcans=True):
|
||||
if listvoters:
|
||||
print("Votes ",votelist)
|
||||
st=time.perf_counter()
|
||||
a = approvalvoting(votelist,numtoelect)
|
||||
et=time.perf_counter()
|
||||
print("Approval voting gives")
|
||||
printresult(a,listvoters,listcans)
|
||||
print(" in ",et-st," seconds.")
|
||||
st=time.perf_counter()
|
||||
a = seqPhragmén(votelist,numtoelect)
|
||||
et=time.perf_counter()
|
||||
print("Sequential Phragmén gives")
|
||||
printresult(a,listvoters,listcans)
|
||||
print(" in ",et-st," seconds.")
|
||||
st=time.perf_counter()
|
||||
a = seqPhragménwithpostprocessing(votelist,numtoelect)
|
||||
et=time.perf_counter()
|
||||
print("Sequential Phragmén with post processing gives")
|
||||
printresult(a,listvoters,listcans)
|
||||
print(" in ",et-st," seconds.")
|
||||
st=time.perf_counter()
|
||||
a = factor3point15(votelist,numtoelect)
|
||||
et=time.perf_counter()
|
||||
print("The factor 3.15 thing gives")
|
||||
printresult(a,listvoters,listcans)
|
||||
print(" in ",et-st," seconds.")
|
||||
st=time.perf_counter()
|
||||
a = SFFB18(votelist,numtoelect)
|
||||
et=time.perf_counter()
|
||||
print("SFFB18 gives")
|
||||
printresult(a,listvoters,listcans)
|
||||
print(" in ",et-st," seconds.")
|
||||
alglist=[(approvalvoting,"Approval voting"), (seqPhragmén, "Sequential Phragmén"),
|
||||
(seqPhragménwithpostprocessing, "Sequential Phragmén with post processing"),
|
||||
(factor3point15, "The factor 3.15 thing"), (binarysearchfeasible,"Factor 2 by binary search"), (SFFB18, "SFFB18")]
|
||||
for alg,name in alglist:
|
||||
st=time.perf_counter()
|
||||
a = alg(votelist,numtoelect)
|
||||
et=time.perf_counter()
|
||||
print(name, " gives")
|
||||
printresult(a,listvoters,listcans)
|
||||
print(" in ",et-st," seconds.")
|
||||
print()
|
||||
|
||||
|
||||
def example1():
|
||||
@@ -466,16 +536,29 @@ def ri(vals=20,noms=2000, votesize=10):
|
||||
votelist=[("Nom"+str(i), 100, random.sample(candidates,votesize)) for i in range(noms)]
|
||||
doall(votelist, vals // 2, False, False)
|
||||
|
||||
def riparty(vals=200,noms=2000, votesize=10):
|
||||
def ripartylist(vals=200,noms=2000, votesize=10,seed=1):
|
||||
#Half the validators are in a party which 1/4 of the nominators vote for.
|
||||
# Approval voting does worse now
|
||||
# and this is probably more realistic than the pure random instance.
|
||||
random.seed(seed)
|
||||
candidates=["Val"+str(i) for i in range(vals//2)]
|
||||
partycandidates=["PartyVal"+str(i) for i in range(vals- vals // 2)]
|
||||
partynoms = noms // 4
|
||||
votelist=[("Nom"+str(i), 100, random.sample(candidates,votesize)) for i in range(noms - partynoms)]
|
||||
votelist += [("Nom"+str(i), 100, partycandidates) for i in range(partynoms)]
|
||||
return votelist
|
||||
|
||||
|
||||
def riparty(vals=200,noms=2000, votesize=10,seed=1):
|
||||
#Half the validators are in a party which 1/4 of the nominators vote for.
|
||||
# Approval voting does worse now
|
||||
# and this is probably more realistic than the pure random instance.
|
||||
votelist=ripartylist(vals,noms,seed)
|
||||
doall(votelist, vals // 4, False, False)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user