Esempio n. 1
0
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)
Esempio n. 2
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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'))
Esempio n. 3
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    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)