def bayesMargDistSCPP(self):
"""
Return a MargDistSCPP with distribution
according to dirichlet formulation where alpha = [1 ... 1]
"""
tempDist = []
for c in self.counts:
tempDist.append( (numpy.atleast_1d((1 + c)/(len(c) + numpy.sum(c))),
numpy.atleast_1d(self.binEdges)) )
return margDistSCPP(tempDist)
def yw2SCPP(**kwargs):
oDir = kwargs.get('oDir')
if not oDir:
raise ValueError("Must provide oDir")
oDir = os.path.realpath(oDir)
agentType = kwargs.get('agentType')
nAgents = kwargs.get('nAgents',8)
m = kwargs.get('m', 5)
L = kwargs.get('L',100)
d = kwargs.get('d', 0.01)
g = kwargs.get('g', 1000000)
minPrice = kwargs.get('minPrice',0)
maxPrice = kwargs.get('maxPrice',50)
serial = kwargs.get('serial', False)
dampen = kwargs.get('dampen', True)
nProc = kwargs.get('nProc', multiprocessing.cpu_count() - 1)
verbose = kwargs.get('verbose',True)
pltItr = kwargs.get('pltItr', True)
if verbose:
print'Computing Symmetric Self Confirming Point Price Prediction.'
print'Agent Type = {0}'.format(agentType)
print 'Termination Threshold (d) = {0}'.format(d)
print'Number of Iterations = {0}'.format(L)
print'Number of Games = {0}'.format(g)
print'Number of Items per Auction = {0}'.format(m)
print 'Using Dampining = {0}'.format(dampen)
if serial:
print 'Using serial implementation'
else:
print'Number of Parallel Cores = {0}'.format(nProc)
if not serial:
nGamesList = [g/nProc]*nProc
nGamesList[-1] += (g%nProc)
#initial uniform distribution
tempDist = []
p = float(1)/round(maxPrice - minPrice)
a = [p]*(maxPrice - minPrice)
# binEdges = [bin for bin in xrange( int(minPrice - maxPrice)+1 ) ]
binEdges = numpy.arange(minPrice,maxPrice+1,1)
for i in xrange(m):
tempDist.append((numpy.atleast_1d(a),numpy.atleast_1d(binEdges)))
currentDist = margDistSCPP(tempDist)
#clean up
# keep the binEdges for later histograms
del p,a,tempDist
ksList = []
klList = []
for t in xrange(0,L):
if pltItr:
cs = cs = ['y--p', 'm-*', 'r-o','y-^','y-*']
graphname = os.path.join(oDir,'ywSCPP_itr_{0}.png'.format(t))
if not ksList:
title = "yw2SCPP, {0}, \n itr = {1}".format(agentType,t)
else:
title = "ywSCPP, {0} \n kld = {1}, ks = {2} \nitr = {3}".format(agentType,klList[-1],ksList[-1],t)
currentDist.graphPdfToFile(fname = graphname,
colorStyles = cs,
title = title)
if verbose:
print ""
print 'Iteration = {0}'.format(t)
if dampen:
kappa = float(L - t) / L
else:
kappa = 1
if serial:
result = numpy.zeros(g,m)
for i in xrange(g):
result[i,:] = simulateAuction(agentType = agentType, nAgents = nAgents, margDist = currentDist)
else:
tasks = multiprocessing.JoinableQueue()
results = multiprocessing.Queue()
consumers = [Consumer(tasks,results) for i in xrange(nProc)]
#start the consumers
[w.start() for w in consumers]
[tasks.put( yw2Task(agentType = agentType, nAgents = nAgents, margDist = currentDist, nGames = nGames) ) for nGames in nGamesList]
[tasks.put(None) for i in xrange(nProc)]
if verbose:
start = time.time()
print "Waiting for {0} game simulation Simulation...".format(g)
tasks.join()
if verbose:
print ""
print "Finished {0} simulations in {1} seconds.".format(g,time.time()-start)
if verbose:
print ""
print "Collecting Results"
start = time.time()
rList = []
while not results.empty():
rList.append(results.get())
result = numpy.vstack(rList)
if verbose:
print ""
print "Done collecting results in {0} seconds".format(time.time() - start)
[w.terminate() for w in consumers]
del results,tasks,consumers
histData = []
histCount = []
for m in xrange(result.shape[1]):
histData.append(numpy.histogram(result[:,m],binEdges,density=True))
# histCount.append(numpy.histogram(result[:,m],binEdges,density=False))
newDist = margDistSCPP(histData)
ksList.append(ksStat(currentDist, newDist))
klList.append(klDiv(currentDist, newDist))
if ksList[-1] < d or t == (L-1):
postfix = '{0}_{1}_{2}_{3}_{4}_{5}'.format(agentType, g, m, d,minPrice,maxPrice)
pklName = 'distPricePrediction_' + postfix + '.pkl'
txtName = 'distPricePrediction_' + postfix + '.txt'
pricePredictionPklFilename = os.path.join(oDir, pklName)
currentDist.savePickle(pricePredictionPklFilename)
pricePredictionTxtFilename = os.path.join(oDir, txtName)
#this section could be improved....
testdata = []
for m in xrange(currentDist.m):
if m == 0:
textdata = numpy.vstack([currentDist.data[m][0],currentDist.data[m][1][:-1]])
else:
textdata = numpy.vstack([textdata, numpy.vstack([currentDist.data[m][0],currentDist.data[m][1][:-1]])])
numpy.savetxt(pricePredictionTxtFilename,textdata)
print ''
print'Terminated after {0} Iterations'.format(t)
print'Final Expected Price Vector = {0}'.format(currentDist.expectedPrices())
ksListName = os.path.join(oDir,'ksList.json')
with open(ksListName,'w') as f:
json.dump(ksList,f)
klListName = os.path.join(oDir,'klList.json')
with open(klListName,'w') as f:
json.dump(klList,f)
break
else:
currentDist = updateDist(currentDist, newDist, kappa)
del result, newDist
def yw2Hob(**kwargs):
oDir = kwargs.get('oDir')
if oDir == None:
raise ValueError("Must specify oDir")
if not os.path.exists(oDir):
os.makedirs(oDir)
agentType = kwargs.get('agentType')
if agentType == None:
raise ValueError("Must specify agentType.")
nAgents = kwargs.get('nAgents',8)
selfIdx = kwargs.get('selfIdx',numpy.random.randint(nAgents))
nGames = kwargs.get('nGames',100)
m = kwargs.get('m',5)
minPrice = int(kwargs.get('minPrice',0))
maxPrice = int(kwargs.get('maxPrice',50))
minValuation = kwargs.get('minValuation',0)
maxValuation = kwargs.get('maxValuation',50)
maxItr = kwargs.get('maxItr', 100)
tol = kwargs.get('tol', 0.01)
dampen = kwargs.get('dampen', True)
parallel = kwargs.get('parallel',False)
nProc = kwargs.get('nProc',multiprocessing.cpu_count()-1)
savePkl = kwargs.get('savePkl',True)
saveTime = kwargs.get('saveTime',True)
pltMarg = kwargs.get('pltMarg',True)
pltKld = kwargs.get('pltKld',True)
pltKs = kwargs.get('pltKs',True)
verbose = kwargs.get('verbose',True)
if verbose:
print 'oDir = {0}'.format(oDir)
print 'agentType = {0}'.format(agentType)
print 'nAgents = {0}'.format(nAgents)
print 'nGames = {0}'.format(nGames)
print 'm = {0}'.format(m)
print 'minPrice = {0}'.format(minPrice)
print 'maxPrice = {0}'.format(maxPrice)
print 'minValuation = {0}'.format(minValuation)
print 'maxValuation = {0}'.format(maxValuation)
print 'selfIdx = {0}'.format(selfIdx)
print 'maxItr = {0}'.format(maxItr)
print 'tol = {0}'.format(tol)
print 'parallel = {0}'.format(parallel)
print 'nProc = {0}'.format(nProc)
print 'savePkl = {0}'.format(savePkl)
print 'saveTime = {0}'.format(saveTime)
print 'pltMarg = {0}'.format(pltMarg)
print 'pltKld = {0}'.format(pltKld)
print 'pltKs = {0}'.format(pltKs)
if savePkl:
pklDir = os.path.join(oDir, 'gmmPkl')
if not os.path.exists(pklDir):
os.makedirs(pklDir)
else:
[os.remove(f) for f in glob.glob(os.path.join(pklDir,'*.pkl'))]
if pltMarg:
margDir = os.path.join(oDir,'pltMarg')
if not os.path.exists(margDir):
os.makedirs(margDir)
else:
[os.remove(f) for f in glob.glob(os.path.join(margDir,'*.png'))]
[os.remove(f) for f in glob.glob(os.path.join(margDir,'*.pdf'))]
if saveTime:
timeFile = os.path.join(oDir,'time.txt')
if os.path.exists(timeFile):
os.remove(timeFile)
klFile = os.path.join(oDir,'kld.txt')
if os.path.exists(klFile):
os.remove(klFile)
ksFile = os.path.join(oDir,'ks.txt')
if os.path.exists(ksFile):
os.remove(ksFile)
#initial uniform distribution
tempDist = []
p = float(1)/round(maxPrice - minPrice)
a = [p]*(maxPrice - minPrice)
binEdges = numpy.arange(minPrice,maxPrice+1,1)
for i in xrange(m):
tempDist.append((numpy.atleast_1d(a),numpy.atleast_1d(binEdges)))
currentDist = margDistSCPP(tempDist)
del tempDist, p, a
if pltMarg:
of = os.path.join(margDir,'yw2SccpHob_{0}_m{1}_n{2}_init.pdf'.format(agentType,m,nAgents))
title = 'Marginal SCPP {0}'.format(agentType)
xlabel = r'$q$'
ylabel = r'$p(q)$'
currentDist.graphPdfToFile(fname = of, title = title, xlabel = xlabel, ylabel = ylabel)
klList = []
ksList = []
for t in xrange(maxItr):
if verbose:
print ''
print 'Iteration = {0}'.format(t)
if dampen:
kappa = numpy.float(maxItr - t) / maxItr
else:
kappa = 1
if verbose:
print 'kappa = {0}'.format(kappa)
if verbose or saveTime:
start = time.time()
hob = simulateAuction( agentType = agentType,
pricePrediction = currentDist,
m = m,
nAgents = nAgents,
nGames = nGames,
parallel = parallel,
nProc = nProc,
minValuation = minValuation,
maxValuation = maxValuation,
retType = 'hob',
selfIdx = selfIdx,
verbose = verbose)
if verbose or saveTime:
end = time.time()
if verbose:
print 'Simulated {0} auctions with {1} {2} agents in {3} seconds.'.format(nGames, nAgents, agentType, end-start)
if savePkl:
pklFile = os.path.join(pklDir, 'yw2ScppHob_{0}_m{1}_n{2}_{3:05d}.pkl'.format(agentType,m,nAgents,t))
with open(pklFile,'wb') as f:
pickle.dump(currentDist,f)
histData = []
for goodIdx in xrange(hob.shape[1]):
histData.append(numpy.histogram(hob[:,goodIdx],binEdges,density=True))
newDist = margDistSCPP(histData)
klList.append( klDiv(currentDist, newDist) )
if verbose:
print 'kld = {0}'.format(klList[-1])
with open(klFile,'a') as f:
f.write("{0}\n".format(klList[-1]))
ksList.append( ksStat(currentDist,newDist) )
if verbose:
print 'kls = {0}'.format(ksList[-1])
with open(ksFile,'a') as f:
f.write("{0}\n".format(ksList[-1]))
if saveTime:
with open(timeFile,'a') as f:
f.write("{0}\n".format(end-start))
if pltMarg:
of = os.path.join(margDir,'yw2SccpHob_{0}_m{1}_n{2}_{3:05d}.pdf'.format(agentType,m,nAgents,t))
if verbose:
print 'Plotting Marginal pdf to {0}'.format(of)
title = 'Marginal SCPP {0}'.format(agentType)
xlabel = r'$q$'
ylabel = r'$p(q)$'
newDist.graphPdfToFile(fname = of, title = title, xlabel = xlabel, ylabel = ylabel)
if klList[-1] < tol or t == (maxItr - 1):
if verbose:
print 'TERMINATED:'
if klList[-1] < tol:
print '\tkld = {0} < tol = {1}'.format(klList[-1],tol)
else:
print '\tt = {0}, maxItr = {1}'.format(t,maxItr)
if pltKld:
if verbose:
print 'Plotting K-L Divergence vs. Iteration.'
klPdf = os.path.join(oDir,'kld.pdf')
plt.figure()
plt.plot(range(len(klList)), klList)
plt.xlim((0,maxItr))
plt.ylabel('K-L Divergence')
plt.xlabel('Iteration')
plt.title('Marginal SCPP {0}'.format(agentType))
plt.savefig(klPdf)
plt.close()
if pltKs:
if verbose:
print 'Plotting K-S Stat vs. Iteration'
ksPdf = os.path.join(oDir,'ks.pdf')
plt.figure()
plt.plot(range(len(ksList)), ksList)
plt.xlim((0,maxItr))
plt.ylabel('K-S Statistic')
plt.xlabel('Iteration')
plt.title('Marginal SCPP {0}'.format(agentType))
plt.savefig(ksPdf)
plt.close()
if verbose:
print 'DONE!!!!!'
break
else:
if verbose:
print 'Updating distribution.'
currentDist = updateDist(currentDist,newDist,kappa)
del hob, newDist
if __name__ == "__main__":
oDir = "C:/auctionResearch/experiments/yw2/debug"
agentType = "targetMU8"
minPrice = 0
maxPrice = 50
m = 5
L = 100
d = 0.01
g = 5
tempDist = []
p = float(1)/round(maxPrice - minPrice)
a = [p]*(maxPrice - minPrice)
# binEdges = [bin for bin in xrange( int(minPrice - maxPrice)+1 ) ]
binEdges = numpy.arange(minPrice,maxPrice+1,1)
for i in xrange(m):
tempDist.append((numpy.atleast_1d(a),numpy.atleast_1d(binEdges)))
margDist = margDistSCPP(tempDist)
task = yw2Task(agentType = "straightMU8", nAgents = 8, margDist = margDist, nGames = 1)
winningBids = task()
# yw2SCPP(oDir = oDir, agentType = agentType, minPrice = minPrice, maxPrice = maxPrice, m = m, L = L, d = d, g = g)
pass
agentTypeList = ["straightMU8"]*8
selfIdx = 0
nGames = 20
parallel = False
m=5
tempDist = []
p = float(1)/round(maxPrice - minPrice)
a = [p]*(maxPrice - minPrice)
# binEdges = [bin for bin in xrange( int(minPrice - maxPrice)+1 ) ]
binEdges = numpy.arange(minPrice,maxPrice+1,1)
for i in xrange(m):
tempDist.append((numpy.atleast_1d(a),numpy.atleast_1d(binEdges)))
pricePrediction = margDistSCPP(tempDist)
print hobSim(pricePrediction = pricePrediction,
agentTypeList = agentTypeList,
selfIdx = selfIdx,
nGames = nGames,
parallel = parallel,
m=m)