def bid(self,**kwargs):
pricePrediction = kwargs.get('pricePrediction', self.pricePrediction)
bundles = kwargs.get('bundles', self.listBundles())
revenue = kwargs.get('revenue', listRevenue(bundles, self.v, self.l))
verbose = kwargs.get('verbose', False)
initStrategy = kwargs.get('initStrategy', self.initStrategy)
maxItr = kwargs.get('maxItr', self.maxItr)
tol = kwargs.get('tol', self.tol)
verbose = kwargs.get('verbose', self.verbose)
ret = kwargs.get('ret', self.ret)
nsamples = kwargs.get('nsamples', self.nsamples)
initBids = kwargs.get('initBids',initStrategy(bundles, revenue, pricePrediction))
samples = pricePrediction.sample(n_samples = nsamples)
return jointLocalStrategy(bundles, revenue, initBids, samples, maxItr, tol, verbose, ret)
def bid(self, **kwargs):
pricePrediction = kwargs.get('pricePrediction',self.pricePrediction)
bundles = kwargs.get('bundles', self.listBundles())
revenue = kwargs.get('revenue',
listRevenue(bundles, self.v, self.l))
return straightMV_(bundles = bundles,
revenue = revenue,
pricePrediction = pricePrediction)
def localSearchDominance(nbids = 100, n_samples = 1000, rootDir = ".",
scppFile = None, vfile = None, lfile = None,
njs = 1000, nms = 1000):
scppFile = os.path.realpath(scppFile)
with open(scppFile,'r') as f:
scpp = pickle.load(f)
m = scpp.m()
bundles = listBundles(m)
rootDir = os.path.realpath(rootDir)
oDir = os.path.join(rootDir,timestamp_())
if not os.path.exists(oDir):
os.makedirs(oDir)
table = [["parameter", "value"]]
table.append(["nbids", nbids])
table.append(["n_samples",n_samples])
table.append(["m",m])
table.append(["njs",njs])
table.append(["nms",nms])
table.append(["vfile",vfile])
table.append(["lfile",lfile])
table.append(["sccpFile", scppFile])
pprint_table(sys.stdout,table)
with open(os.path.join(oDir,'params.txt'),'a') as f:
pprint_table(f,table)
jointBidFile = os.path.join(oDir,"jointLocalBids.txt")
condBidFile = os.path.join(oDir,"condLocalBids.txt")
margBidFile = os.path.join(oDir,"margLocalBids.txt")
useExternalValuations = False
if vfile == None and lfile == None:
vfile = os.path.join(oDir,'v.txt')
lfile = os.path.join(oDir,'l.txt')
else:
vmat = numpy.loadtxt(vfile)
lmat = numpy.loadtxt(lfile)
nbids = lmat.shape[0]
useExternalValuations = True
surplusSamples = scpp.sample(n_samples = n_samples)
numpy.savetxt(os.path.join(oDir,'ppSamples.txt'),surplusSamples)
jsdir = os.path.join(oDir,'jointSamples')
if not os.path.exists(jsdir):
os.makedirs(jsdir)
msdir = os.path.join(oDir,'margSamples')
if not os.path.exists(msdir):
os.makedirs(msdir)
es = numpy.zeros((nbids,3))
for i in xrange(nbids):
print 'Bid number {0}'.format(i)
if useExternalValuations:
v = vmat[i,:]
l = lmat[i]
else:
print 'randomizing valuation'
v,l = randomValueVector(m=m)
with open(vfile,'a') as f:
numpy.savetxt(f, v.reshape(1,v.shape[0]))
# print >> f, v
with open(lfile,'a') as f:
numpy.savetxt(f,numpy.atleast_1d(l))
print 'v = {0}'.format(v)
print 'l = {0}'.format(l)
revenue = listRevenue(bundles, v, l)
bundleRevenueDict = {}
for b, r in zip(bundles,revenue):
bundleRevenueDict[tuple(b)] = r
jointSamples = scpp.sample(n_samples = njs)
margSamples = scpp.sampleMarg(n_samples = nms)
numpy.savetxt(
os.path.join(jsdir,'jointSamples_{0:04}.txt'.format(i)),
jointSamples)
numpy.savetxt(
os.path.join(msdir,'margSamples_{0:04}.txt'.format(i)),
margSamples)
initBid = straightMUa(bundles, revenue, scpp)
jbid = jointLocal(bundles,revenue,initBid,jointSamples)
cbid = condLocal(bundles, revenue, initBid, jointSamples)
mbid = margLocal(bundles, revenue, initBid, margSamples)
with open(jointBidFile,'a') as f:
numpy.savetxt(f, jbid.T)
with open(condBidFile,'a') as f:
numpy.savetxt(f, cbid.T)
with open(margBidFile,'a') as f:
numpy.savetxt(f, mbid.T)
es[i,0] = expectedSurplus_(bundleRevenueDict, jbid, surplusSamples)
es[i,1] = expectedSurplus_(bundleRevenueDict, cbid, surplusSamples)
es[i,2] = expectedSurplus_(bundleRevenueDict, mbid, surplusSamples)
with open(os.path.join(oDir,"jointLocalExpectedSurplus.txt"),'a') as f:
numpy.savetxt(f,numpy.atleast_1d(es[i,0]))
with open(os.path.join(oDir,"condLocalExpectedSurplus.txt"),'a') as f:
numpy.savetxt(f,numpy.atleast_1d(es[i,1]))
with open(os.path.join(oDir,"margLocalExpectedSurplus.txt"),'a') as f:
numpy.savetxt(f,numpy.atleast_1d(es[i,2]))
jmean = numpy.mean(es[:,0])
jvar = numpy.var(es[:,0])
cmean = numpy.mean(es[:,1])
cvar = numpy.var(es[:,1])
mmean = numpy.mean(es[:,2])
mvar = numpy.var(es[:,2])
print 'jointLocal Expected Surplus Mean {0}'.format(jmean)
print 'jointLocal Expected Surplus Variance {0}'.format(jvar)
print 'condLocal Expected Surplus Mean {0}'.format(cmean)
print 'condLocal Expected Surplus Variance {0}'.format(cvar)
print 'margLocal Expected Surplus Mean {0}'.format(mmean)
print 'margLocal Expected Surplus Variance {0}'.format(mvar)
with open(os.path.join(oDir,'jointLocalStats.txt'),'a') as f:
print >> f, 'mean ', jmean
print >> f, 'var ', jvar
with open(os.path.join(oDir,'condLocalStats.txt'),'a') as f:
print >> f, 'mean ', cmean
print >> f, 'var ', cvar
with open(os.path.join(oDir,'margLocalStats.txt'),'a') as f:
print >> f, 'mean ', mmean
print >> f, 'var ', mvar
with open(os.path.join(oDir,'stats.txt'),'a') as f:
print >> f, 'jointLocal expected surplus mean: {0}'.format(jmean)
print >> f, 'jointLocal expected surplus variation: {0}'.format(jvar)
print >> f, 'condLocal expected surplus mean: {0}'.format(cmean)
print >> f, 'condLocal expected surplus variation: {0}'.format(cvar)
print >> f, 'margLocal expected surplus mean: {0}'.format(mmean)
print >> f, 'margLocal expected surplus variation: {0}'.format(mvar)
bins = range(int(es.min())-1, int(es.max())+1)
jhist, jbins = numpy.histogram(es[:,0], bins, normed = True)
chist, cbins = numpy.histogram(es[:,1], bins, normed = True)
mhist, mbins = numpy.histogram(es[:,2], bins, normed = True)
f,ax = plt.subplots(3,1, sharex = True)
ax[0].bar( (jbins[:-1]+jbins[1:])/2, jhist, align = 'center')
ax[0].set_title('jointLocal expected surplus')
ax[1].bar( (cbins[:-1]+cbins[1:])/2, chist, align = 'center' )
ax[1].set_title('condLocal expected surplus')
ax[2].bar( (mbins[:-1]+mbins[1:])/2, mhist, align = 'center')
ax[2].set_title('margLocal expected surplus')
plt.savefig(os.path.join(oDir,'expectedSurplus.pdf'))
def targetPrice64(bundles, revenue, pricePrediction, verbose = False):
samples = pricePrediction.sample(n_samples = 64)
expectedPrices = numpy.mean(samples,0)
return targetPrice(bundles, revenue, expectedPrices, verbose)
def targetPrice256(bundles, revenue, pricePrediction, verbose = False):
samples = pricePrediction.sample(n_samples = 256)
expectedPrices = numpy.mean(samples,0)
return targetPrice(bundles, revenue, expectedPrices, verbose)
if __name__ == "__main__":
from ssapy.util import listBundles
from ssapy.agents.marketSchedule import listRevenue
pp = [5,5]
l = 1
v = [20,10]
bundles = listBundles(2)
rev = listRevenue(bundles, v, l)
print bundles
print rev
bid = targetPrice(bundles,rev,pp,True)
n_goods = bundles.shape[1]
bid = numpy.zeros(n_goods, dtype = numpy.float)
for goodIdx, good in enumerate(optBundle):
if good:
bid[goodIdx] = marginalUtility(bundles, valuation,
pricePrediction, goodIdx)
if verbose:
print "bid = {0}".format(bid)
return bid
if __name__ == "__main__":
from ssapy.agents.marketSchedule import listRevenue
pp = [5,5]
bundles = listBundles(2)
l = 1
v = [20, 10]
valuation = listRevenue(bundles, v, l)
print bundles
print valuation
bid = targetMV(bundles, valuation, pp, True)