def test_expectedSurplus(self):
"""
[20,15] -> (45 - 35)*.1
[20,20] -> (45 -40)*.4
[30,15] -> (20 - 5)*.1
[30,20] -> (20 - 20)*.4
Expected Surplus = 3.5
"""
samples = numpy.zeros((1000,2))
samples[:100,:] = numpy.asarray([20,15])
samples[100:500,:] = numpy.asarray([20,20])
samples[500:600,:] = numpy.asarray([30,15])
samples[600:,:] = numpy.asarray([30,20])
m=2
l = 1
v = [45,20]
bundles = listBundles(m)
revenue = msListRevenue(bundles, v, l)
bids = numpy.asarray([25.,25.])
bundleRevenueDict = {}
for b,r in zip(bundles,revenue):
bundleRevenueDict[tuple(b)] = r
numpy.testing.assert_equal(expectedSurplus_(bundleRevenueDict, bids, samples),
3.5,'test_expetedSurplus failed.',True)
def bruteForceS(bundleRevenueDict, evalSamples=None, min=0.0, max=50.0, step=1.0, ret="bid"):
m = evalSamples.shape[1]
xx = numpy.arange(min, max + 1.0, step)
maxSurplus = -numpy.float("inf")
bid = None
for c in itertools.product(xx, repeat=m):
es = expectedSurplus_(bundleRevenueDict, numpy.atleast_1d(c), evalSamples)
if es > maxSurplus:
bid = numpy.atleast_1d(c)
maxSurplus = es
print bid
print maxSurplus
if ret == "bid":
return bid
elif ret == "all":
return bid, maxSurplus
def bidEvalS(bundleRevenueDict, candidateSamples, evalSamples, ret='bid'):
maxSurplus = 0.0
bid = numpy.zeros(candidateSamples.shape[1])
for candidateSample in candidateSamples:
es = expectedSurplus_(bundleRevenueDict,
numpy.atleast_1d(candidateSample),
evalSamples)
if es > maxSurplus:
bid = numpy.atleast_1d(candidateSample)
maxSurplus = es
if ret == 'bid':
return bid
elif ret == 'all':
return bid, maxSurplus
def main():
desc="Compute jointLocal bids given v's, l's initial bids and parameters"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-s','--samplesFile',dest='samplesFile',
required=True,type=str,
help='Samples (text file)')
parser.add_argument('-v','--vfile',dest='vfile',
required=True,type=str,
help='Valuation v vector txt file.')
parser.add_argument('-l','--lfile',dest='lfile',
required=True,type=str,
help='Valuation lambda txt file.')
parser.add_argument('-ib','--initBidFile',dest='initBidFile',
required=True,type=str,
help='Initial Bids txt file.')
parser.add_argument('-es','--evalSamples',dest='evalFile',
required=True,type=str,
help='Evaluation samples file.')
parser.add_argument('-o','--odir',dest='odir',
required=True,type=str,
help='Output directory.')
parser.add_argument('-eps','--eps',dest='eps',
required=False,default=1,type=float,
help='Eps condLocal parameter')
parser.add_argument('-mi','--maxitr', dest='maxitr',
required=False, default=100, type=int,
help='Maximum Update Iterations')
parser.add_argument('-t','--tol',dest='tol',
required=False, default=1e-5,
type=float,help='L2 Update Stopping Tolerance')
parser.add_argument('--verbose',dest='verbose',
required=False,default=False,
type=bool,help='Output debugg information')
args = parser.parse_args()
args.odir = os.path.realpath(args.odir)
jointSamples = numpy.loadtxt(os.path.realpath(args.samplesFile))
initBids = numpy.loadtxt(os.path.realpath(args.initBidFile))
vmat = numpy.loadtxt(os.path.realpath(args.vfile))
lmat = numpy.loadtxt(os.path.realpath(args.lfile))
evalSamples = numpy.loadtxt(os.path.realpath(args.evalFile))
m = initBids.shape[1]
bundles = listBundles(m)
bids = numpy.zeros(initBids.shape)
es = numpy.zeros(initBids.shape[0])
for itr, initBid, v, l in zip(xrange(vmat.shape[0]),initBids,vmat,lmat):
print 'iteration {0}'.format(itr)
revenue = msListRevenue(bundles,v,l)
bids[itr,:] = \
condLocal(bundles,revenue,
initBid,jointSamples,
maxItr = args.maxitr,
tol = args.tol,
verbose = args.verbose,
ret = 'bids',
eps = args.eps)
brd = {}
for b,r in zip(bundles,revenue):
brd[tuple(b)] = r
es[itr] = expectedSurplus_(brd, bids[itr,:], evalSamples)
numpy.savetxt(os.path.join(args.odir,'condLocalBids.txt'), bids)
numpy.savetxt(os.path.join(args.odir,'condLocalExpectedSurplus.txt'),es)
with open(os.path.join(args.odir,'condLocalStats.txt'),'w') as f:
print >> f, numpy.mean(es)
print >> f, numpy.var(es)
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 main():
desc="Compute jointLocal bids given v's, l's initial bids and parameters"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-s','--samplesFile',dest='samplesFile',
required=True,type=str,
help='Samples (text file)')
parser.add_argument('-v','--vfile',dest='vfile',
required=True,type=str,
help='Valuation v vector txt file.')
parser.add_argument('-l','--lfile',dest='lfile',
required=True,type=str,
help='Valuation lambda txt file.')
parser.add_argument('-es','--evalSamples',dest='evalFile',
required=True,type=str,
help='Evaluation samples file.')
parser.add_argument('-ib','--initBidFile',dest='initBidFile',
required=True,type=str,
help='Initial Bids txt file.')
parser.add_argument('-o','--odir',dest='odir',
required=True,type=str,
help='Output directory.')
parser.add_argument('-mcs','--maxCandidateSamples', dest='maxCandidateSamples',
required=False, default=-1, type=int,
help='Limit the number of samples used as candidates.')
parser.add_argument('-mes','--maxEvalSamples',dest='maxEvalSamples',
required=False, default=-1,type=int,
help='Limit the number of evaluation samples used.')
parser.add_argument('--verbose',dest='verbose',
required=False,default=False,
type=bool,help='Output debugg information')
args = parser.parse_args()
args.odir = os.path.realpath(args.odir)
jointSamples = numpy.loadtxt(os.path.realpath(args.samplesFile))
initBids = numpy.loadtxt(os.path.realpath(args.initBidFile))
vmat = numpy.loadtxt(os.path.realpath(args.vfile))
lmat = numpy.loadtxt(os.path.realpath(args.lfile))
evalSamples = numpy.loadtxt(os.path.realpath(args.evalFile))
numSamples = jointSamples.shape[0]
m = jointSamples.shape[1]
if args.maxCandidateSamples > 0:
if args.maxCandidateSamples < jointSamples.shape[0]:
test = jointSamples[:args.maxCandidateSamples,:]
print jointSamples.shape
bids = numpy.zeros(vmat.shape)
es = numpy.zeros(vmat.shape[0])
for itr, initBid, v, l in zip(xrange(vmat.shape[0]),initBids,vmat,lmat):
print 'iteration {0}'.format(itr)
bundleRevenueDict = msDictRevenue(v,l)
bid = downHillSS(bundleRevenueDict,
initBid = initBid,
evalSamples = jointSamples,
ret = 1)
print '\t bid = {0}'.format(bid)
bids[itr,:] = bid
es[itr] = expectedSurplus_(bundleRevenueDict, bid, evalSamples)
print '\t Expected Surplus = {0}'.format(es[itr])
numpy.savetxt(os.path.join(args.odir,'downHillBids.txt'), bids)
numpy.savetxt(os.path.join(args.odir,'downHillExpectedSurplus.txt'),es)
with open(os.path.join(args.odir,'downHillStats.txt'),'w') as f:
print >> f, numpy.mean(es)
print >> f, numpy.var(es)
def main():
desc="Compute jointLocal bids given v's, l's initial bids and parameters"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-s','--samplesFile',dest='samplesFile',
required=True,type=str,
help='Samples (text file)')
parser.add_argument('-ms','--maxBidSamples',dest='maxBidSamples',
required=False,type=int, default=-1,
help='Maximum samples used to compute bids.')
parser.add_argument('-v','--vfile',dest='vfile',
required=True,type=str,
help='Valuation v vector txt file.')
parser.add_argument('-l','--lfile',dest='lfile',
required=True,type=str,
help='Valuation lambda txt file.')
parser.add_argument('-ib','--initBidFile',dest='initBidFile',
required=True,type=str,
help='Initial Bids txt file.')
parser.add_argument('-es','--evalSamples',dest='evalFile',
required=True,type=str,
help='Evaluation samples file.')
parser.add_argument('-o','--odir',dest='odir',
required=True,type=str,
help='Output directory.')
parser.add_argument('-mi','--maxitr', dest='maxitr',
required=False, default=100, type=int,
help='Maximum Update Iterations')
parser.add_argument('-t','--tol',dest='tol',
required=False, default=1e-5,
type=float,help='L2 Update Stopping Tolerance')
parser.add_argument('--verbose',dest='verbose',
required=False,default=False,
type=bool,help='Output debugg information')
parser.add_argument('-n','--name',dest='name',
required=False, default='margLocalMc',
type=str,help='Output filename.')
args = parser.parse_args()
args.odir = os.path.realpath(args.odir)
samples = numpy.loadtxt(os.path.realpath(args.samplesFile))
initBids = numpy.loadtxt(os.path.realpath(args.initBidFile))
vmat = numpy.loadtxt(os.path.realpath(args.vfile))
lmat = numpy.loadtxt(os.path.realpath(args.lfile))
evalSamples = numpy.loadtxt(os.path.realpath(args.evalFile))
m = initBids.shape[1]
# bundles = listBundles(m)
if samples.shape[0] > args.maxBidSamples:
samples = samples[:args.maxBidSamples,:]
bids = numpy.zeros(initBids.shape)
es = numpy.zeros(initBids.shape[0])
bidsFile = os.path.join(args.odir, args.name + 'Bids.txt')
if os.path.exists(bidsFile):
os.remove(bidsFile)
esFile = os.path.join(args.odir, args.name + 'ExpectedSurplus.txt')
if os.path.exists(esFile):
os.remove(esFile)
statsFile = os.path.join(args.odir, args.name + 'Stats.txt')
if os.path.exists(statsFile):
os.remove(statsFile)
for itr, initBid, v, l in zip(xrange(vmat.shape[0]),initBids,vmat,lmat):
print 'iteration {0}'.format(itr)
brd = msDictRevenue(v,l)
bids[itr,:], converged, nitr, d = \
margLocalMc(brd,
initBid,samples,
maxItr = args.maxitr,
tol = args.tol,
verbose = args.verbose,
ret = 'all')
es[itr] = expectedSurplus_(brd, bids[itr,:], evalSamples)
with open(bidsFile,'a+') as f:
numpy.savetxt(f, bids[itr,:][None])
with open(esFile,'a+') as f:
numpy.savetxt(f,es[itr][None])
# numpy.savetxt(f,es[itr])
print '\t bid = {0}'.format(bids[itr,:])
print '\t converged = {0}'.format(converged)
print '\t nItr = {0}'.format(nitr)
print '\t d = {0}'.format(d)
print '\t Expected Surplus = {0}'.format(es[itr])
# numpy.savetxt(os.path.join(args.odir, args.name +'Bids.txt'), bids)
# numpy.savetxt(os.path.join(args.odir, args.name +'ExpectedSurplus.txt'),es)
with open(os.path.join(args.odir,args.name + 'Stats.txt'),'w') as f:
print >> f, numpy.mean(es)
print >> f, numpy.var(es)
print numpy.mean(es)
print numpy.var(es)
