Пример #1
0
def outputDir(**kwargs):
    ss = paramString(**kwargs)
    
    ss = ss + '_' + timestamp_()
    
    oDir = os.path.join(kwargs['oDir'],ss)
    if not os.path.exists(oDir):
        os.makedirs(oDir)
        
    return oDir
Пример #2
0
def compareMargJointLocal(rootDir=None, scppFile=None, nItr=10000, n_expectedSurplus_samples=10000):
    if rootDir == None:
        raise ValueError("Must specify output directory - oDir")

    if scppFile == None:
        raise ValueError("Must specify scpp file (pkl)")

    rootDir = os.path.realpath(rootDir)

    oDir = os.path.join(rootDir, "compareMargLocal_{0}".format(timestamp_()))
    if not os.path.exists(oDir):
        os.makedirs(oDir)

    for itr in xrange(nItr):
        pass
Пример #3
0
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'))
Пример #4
0
def jointGmmScpp(**kwargs):
    """
    NOTE: EXTRA MODEL IS FIT TO FULL COVAR GMM - regardless of
    covariance_type kwarg.
    """
    
    kwargs['oDir']         = kwargs.get('oDir')
    if kwargs['oDir'] == None:
        raise ValueError("Must specify output directory - oDir.")
    
    kwargs['agentType']    = kwargs.get('agentType')
    
    if kwargs['agentType'] == None:
        raise ValueError('Must specify agent type - agentType.')
    kwargs['nAgents']      = kwargs.get('nAgents',5)

    kwargs['selfIdx']      = kwargs.get('selfIdx',numpy.random.randint(kwargs['nAgents']))
    
    kwargs['nGames']       = kwargs.get('nGames',10000)
    kwargs['nklsamples']   = kwargs.get('nklsamples',1000)
    
    kwargs['maxItr']       = kwargs.get('maxItr',100)

    kwargs['tol']          = kwargs.get('tol',0.01)
    
    kwargs['aicCompMin']   = kwargs.get('aicCompMin',5)

    kwargs['aicCompMax']   = kwargs.get('aicCompMax',21)

    kwargs['aicMinCovar']  = kwargs.get('aicMinCovar',0.1)

    kwargs['minPrice']     = kwargs.get('minPrice',0)
    
    kwargs['maxPrice']     = kwargs.get('maxPrice',numpy.float('inf'))
    
    kwargs['covariance_type']   = kwargs.get('covariance_type','full')
    
    kwargs['m']            = kwargs.get('m',5)

    kwargs['minValuation'] = kwargs.get('vmin',0)
    kwargs['maxValuation'] = kwargs.get('vmax',50)

    kwargs['parallel']     = kwargs.get('parallel',True)
    
    kwargs['nProc']        = kwargs.get('nProc', multiprocessing.cpu_count())
    
    kwargs['verbose']      = kwargs.get('verbose', True)
    
    kwargs['pltMarg']      = kwargs.get('pltMarg', True)
    
    kwargs['l']            = kwargs.get('l')
    
    kwargs['timeStamp']    = timestamp_()

    ps = paramString(**kwargs)
            
    kwargs['oDir'] = os.path.join(kwargs['oDir'],ps)
    if not os.path.exists(kwargs['oDir']):
        os.makedirs(kwargs['oDir'])
    
    models = numpy.arange(kwargs['aicCompMin'], kwargs['aicCompMax'])
            
    if kwargs['verbose']:    
        table = []
        
        for k,v in kwargs.iteritems():
            table.append([k,str(v)])
        
        pprint_table(sys.stdout, table)
    
    with open(os.path.join(kwargs['oDir'],'params.txt'),'w') as f:
        pprint_table(f, table)
        
    with open(os.path.join(kwargs['oDir'],'params.json'),'w') as f:
        json.dump(kwargs, f)
        
    kwargs['pricePrediction'] = uniformpp(kwargs['m'],kwargs['minValuation'],kwargs['maxValuation'])
    
    idx2keep = numpy.arange(kwargs['nAgents'])
    idx2keep = numpy.delete(idx2keep, kwargs['selfIdx'])
    if kwargs['verbose']:
        print 'indicies to keep = {0}'.format(idx2keep)
    
    filePostfix = fileNamePostfix(**kwargs)
    
    
    for itr in xrange(kwargs['maxItr']):
        itrStart = time.time()
        if kwargs['verbose']:
            print 'Iteration {0}'.format(itr+1)
        
        simStart = time.time()
        bids = simulateAuction(**kwargs)
        simEnd = time.time()
#        simFile = os.path.realpath(os.path.join(kwargs['oDir'],"simulationTime_{0}.txt".format(ps)))
        simFile = os.path.join(kwargs['oDir'],'simTime_0.01.txt')
#        if not simFile:
#            with open(os.path.realpath(simFile),'w+') as f:
#                numpy.savetxt(f, numpy.atleast_1d(simEnd-simStart))
#        else:
#        with open(os.path.join(kwargs['oDir'],"simulationTime_{0}.txt".format(ps)),'a+') as f:
        with open(simFile,'a+') as f:
            numpy.savetxt(f, numpy.atleast_1d(simEnd-simStart)) 
            
        if kwargs['verbose']:
            print 'Simulated {0} auctions in {1} seconds'.format(kwargs['nGames'],simEnd-simStart)
            
        del simStart, simEnd
        
        bidsFile = 'bids_{0:04}_{1}.npy'.format(itr,filePostfix)
        with open(os.path.join(kwargs['oDir'], bidsFile),'w') as f:
            numpy.save(f, bids)
            
        hob = numpy.max(bids[:,idx2keep,:],1)
        hobFile = os.path.join(kwargs['oDir'],'hob_{0:04}_{1}.txt'.format(itr,filePostfix))
        with open(hobFile,'w') as f:
            numpy.savetxt(f,hob)
        
        del bids
                    
        nextpp = jointGMM(covariance_type = kwargs.get('covariance_type'))
        temppp, aicValues, compRange = nextpp.aicFit(X=hob, compRange = models, min_covar = kwargs['aicMinCovar'], verbose = kwargs['verbose'])
        
        aicFile = os.path.join(kwargs['oDir'],'aic_{0:03}_{1}.pdf'.format(itr+1,filePostfix))
        
        pltAic(compRange,aicValues,itr,aicFile)
        
        del hob,temppp,compRange
        
        ppFile = os.path.join(kwargs['oDir'], 'gmmScpp_{0:04}_{1}.pkl'.format(itr,filePostfix))
        with open(ppFile,'w') as f:
            pickle.dump(nextpp,f)
            
        if kwargs['pltMarg']:
            oFile = os.path.join(kwargs['oDir'],'marg_{0:04}_{1}.pdf'.format(itr,filePostfix))
            nextpp.pltMarg(oFile = oFile)
        
        with open(os.path.join(kwargs['oDir'],'aic_{0:04}_{1}.txt'.format(itr,filePostfix)),'a') as f:
            numpy.savetxt(f,numpy.atleast_1d(aicValues).T)
        
        if kwargs['verbose']:
            print 'AIC Fit: number of components = {0}'.format(nextpp.n_components)
            
        with open(os.path.join(kwargs['oDir'],'n_components_{0}.txt'.format(filePostfix)), 'a') as f:
            numpy.savetxt(f,numpy.atleast_1d(nextpp.n_components))
            
        if itr > 0:
            kld = numpy.abs(apprxJointGmmKL(kwargs['pricePrediction'], nextpp, 
                            nSamples = kwargs['nklsamples'], verbose = kwargs['verbose']))
            
            with open(os.path.join(kwargs['oDir'],'kld_{0}.txt'.format(filePostfix)),'a') as f:
                numpy.savetxt(f,numpy.atleast_1d(kld))
                
            if kwargs['verbose']:
                print 'Symmetric KL Distance = {0}'.format(kld)
        
        itrEnd = time.time()
        with open(os.path.join(kwargs['oDir'], "itrTime_{0}.txt".format(filePostfix)),'a') as f:
            numpy.savetxt(f, numpy.atleast_1d(itrEnd-itrStart))
            
        kwargs['pricePrediction'] = nextpp
        
        if itr > 0:
            if kld < kwargs['tol']:
                if kwargs['verbose']:
                    print 'kld = {0} < tol = {1}'.format(kld, kwargs['tol'])
                    print 'CONVERGED!'
                    
                break
        else:
            print ''
            
    with open(os.path.join(kwargs['oDir'],'kld_{0}.txt'.format(filePostfix)),'r') as f:
        kld = numpy.loadtxt(f, 'float')
     
    f, ax = plt.subplots()
    plt.plot(kld,'r-',linewidth=3)
    plt.title("Absolute Symmetric K-L Divergence")
    plt.xlabel("Iteration")
    plt.ylabel(r"|kld|")
    plt.savefig(os.path.join(kwargs['oDir'],'kld_{0}.pdf'.format(ps)))
    
    del kld
    
    with open(os.path.join(kwargs['oDir'],'n_components_{0}.txt'.format(filePostfix)),'r') as f:
        comp = numpy.loadtxt(f)
        
    f,ax = plt.subplots()
    colors = ['#0A0A2A']*len(aicValues)
    ax.bar(range(len(comp)), comp, color=colors, align = 'center')
    ax.set_ylabel('GMM Model (Number of Components)')
    ax.set_xlabel('Iteration')
    ax.set_title('Model Selection')
    plt.ylim([0,numpy.max(comp) + 0.5])
    plt.savefig(os.path.join(kwargs['oDir'],'n_components_{0}.pdf'.format(ps)))
    
    del comp
    
    if kwargs['verbose']:
        print 'Simulating {0} auctions after scpp converged.'.format(kwargs['nGames'])
    
    # To check if distribution is SCPP, after convergence simulate
    # more bids then evaluate measures of similarity between the resulting 
    # bids and the scpp candidate.
    start = time.time()    
    extraBids = simulateAuction(**kwargs)
    end = time.time()
    
    with open(os.path.join(kwargs['oDir'],'extraBids_{0}.npy'.format(filePostfix)), 'w') as f:
        numpy.save(f, extraBids)
    
    if kwargs['verbose']:
        print 'Simulated {0} holdout auctions in {1} seconds'.format(kwargs['nGames'],end-start)
        
    extraHob = numpy.max(extraBids[:,idx2keep,:],1)
    with open(os.path.join(kwargs['oDir'],'extraHob_{0}.txt'.format(filePostfix)),'w') as f:
        numpy.savetxt(f, extraHob)
    
    ll = numpy.sum(kwargs['pricePrediction'].eval(extraHob)[0])
    
    if kwargs['verbose']:
        print 'log-likelihood hold out = {0}'.format(ll)
        
    with open(os.path.join(kwargs['oDir'],'extraHobLL_{0}.txt'.format(filePostfix)),'w') as f:
        numpy.savetxt(f,numpy.atleast_1d(ll))
        
    # fit another model to held out data and
    # compute the last skl between the scpp and 
    # the extra model
    
    extraModel = jointGMM()
    gmm, aicValues, compRange = extraModel.aicFit(X=extraHob, compRange = models, min_covar = kwargs['aicMinCovar'], verbose = kwargs['verbose'])
    kld = numpy.abs(apprxJointGmmKL(kwargs['pricePrediction'], extraModel, nSamples = kwargs['nklsamples'], verbose = kwargs['verbose']))
    
    f,ax = plt.subplots()
    colors = ['#777777']*len(aicValues)
    colors[numpy.argmin(aicValues)] = 'r'
    ax.bar(compRange, aicValues, color=colors, align = 'center')
    ax.set_ylabel('AIC score')
    ax.set_xlabel('GMM Model (Number of Components)')
    ax.set_title('Iteration {0}'.format(itr+1))
    plt.ylim([0,numpy.max(aicValues) + 0.5])
    extraAicFile = "extraAic_{0:04}_{1}.pdf".format(itr+1,ps)
    plt.savefig(os.path.join(kwargs['oDir'],extraAicFile))
    
    del gmm,aicValues, compRange
    
    if kwargs['verbose']:
        print 'SKL-D between SCPP proposal and extra model = {0}'.format(kld)
        
    extraSkdFile = os.path.join(kwargs['oDir'],'extraHobSkl_{0}.txt'.format(filePostfix))
    with open(extraSkdFile,'w') as f:
        numpy.savetxt(f, numpy.atleast_1d(kld))