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)
