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('-n','--name',dest='name',
required=False,type=str,
default='local',
help='output file name prefix.')
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')
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,:], converged, nItr, d = \
jointLocal(bundles,revenue,initBid,jointSamples,
args.maxitr,args.tol,args.verbose,'all')
brd = {}
for b,r in zip(bundles,revenue):
brd[tuple(b)] = r
es[itr] = expectedSurplus_(brd, bids[itr,:], evalSamples)
print '\t bid = {0}'.format(bids[itr,:])
print '\t converged = {0}'.format(converged)
print '\t Num. Iterations = {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)
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 test_jointLocal1(self):
"""
Updates computed by hand given:
v = [45,20], l = 1
p(q_1 = 20) = 0.5
p(q_1 = 30) = 0.5
p(q_2 = 15) = 0.2
p(q_2 = 20) = 0.8
Under independent prices p(q_1,q_2) = p(q_1)p(q_2)
Hence:
p(q_1 = 20, q_2 = 15) = 0.1
p(q_1 = 20, q_2 = 20) = 0.4
p(q_1 = 30, q_2 = 15) = 0.1
p(q_1 = 30, q_2 = 20) = 0.4
The joint pdf is represented with 1000 samples.
100 samples of [20,15]
400 samples of [20,20]
100 samples of [30,15]
400 samples of [30,20]
Ground Truth Anwers:
Starting at [25,25]
1.1) b1 <- 25
1.2) b2 <- 10
2.1) b1 <- 45
2.2) b2 <- 0
3.1) b1 <- 25
3.2) b2 <- 0
Therefore, starting at [25,25] converges to [45,0] after 3 iterations
"""
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.])
bids[0] = jointLocalUpdate(bundles,revenue,bids,0,samples,True)
numpy.testing.assert_equal(bids[0], 25, "Update 1.1 Failed", True)
bids[1] = jointLocalUpdate(bundles,revenue,bids,1,samples,True)
numpy.testing.assert_equal(bids[1], 10, "Update 1.2 Failed", True )
bids[0] = jointLocalUpdate(bundles,revenue,bids,0,samples,True)
numpy.testing.assert_equal(bids[0], 45, "Update 2.1 Failed", True)
bids[1] = jointLocalUpdate(bundles,revenue,bids,1,samples,True)
numpy.testing.assert_equal(bids[1], 0, "Update 2.2 Failed", True)
bids[0] = jointLocalUpdate(bundles,revenue,bids,0,samples,True)
numpy.testing.assert_equal(bids[0], 45, "Update 3.1 Failed", True)
bids[1] = jointLocalUpdate(bundles,revenue,bids,1,samples,True)
numpy.testing.assert_equal(bids[1], 0, "Update 3.2 Failed", True)
#should converge after 3 iterations
bids = numpy.asarray([25.,25.])
bids, converged, itr, tol = jointLocal(bundles, revenue, bids, samples, ret = 'all')
numpy.testing.assert_array_equal(bids, numpy.asarray([45,0]), "margLocal bids test failed", True)
numpy.testing.assert_equal(converged, True, "margLocal converged failed", True)
numpy.testing.assert_equal(itr,3,"margLocal number of iterations failed.", True)
numpy.testing.assert_almost_equal(tol, 0., 8, "margLocal tol failed", True)