def test_simulateAuction_straightMUa(self):
agentType = "msStraightMUa"
pricePrediction = jointGMM(n_components=2)
pricePrediction.means_ = numpy.asarray([[10,5], [0,5]],dtype='float')
pricePrediction.weights_ = numpy.asarray([0.5,0.5],dtype = 'float')
bids = simulateAuction(agentType = agentType, nAgents = 5, nGames = 10, nProc = 5, m = 2, pricePrediction = pricePrediction, ret = 'bids')
def main():
desc = "Simulates a homogenous auction randomizing over valuations."
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-i','--input', action = 'store', dest = 'input', required = True,
help = "Full path to price prediction pickle file.")
parser.add_argument('-a','--agentType', action = 'store', dest = 'agentType', required = True,
help = "The participating agent's strategy type.")
# parser.add_argument('-m', action = 'store', dest = 'm', required = False, default = 5,
# help = "The number of goods at auction.")
parser.add_argument('-na', '--nAgents', action = 'store', dest = 'n', required = False,
type = int, default = 5,
help = "Number of participating agents")
parser.add_argument('-ng', '--nGames', action = 'store', dest = 'ng', required = False, type=int,
default = 10000, help = "Number of Simulations (games) to run.")
# parser.add_argument('-o', '--output', action = 'store', dest = 'output', required = False,
# help = "Basename (or path) for output files.")
parser.add_argument('-p', '--parallel', action = 'store', dest = 'parallel', required = False,
type = bool, default = True,
help = "Run simulations in parallel or serially.")
parser.add_argument('-np', '--nProc', action = 'store', dest = 'nProc', required = False,
default = multiprocessing.cpu_count() - 1, type = int,
help = "Number of processes to spawn if running in parallel mode.")
parser.add_argument('-vmin', '--minValuation', action = 'store', dest = 'vmin', required = False,
default = 0, type = float, help = "Minimum Valuation.")
parser.add_argument('-vmax', '--maxValuation', action = 'store', dest = 'vmax', required = False,
default = 50, type = float, help = "Maximum Valuation.")
parser.add_argument('-v', '--verbose', action = 'store', dest = 'verbose', type = bool,
required = False, default = True, help = "Output runtime info to stdout.")
parser.add_argument('-nc','--normCorr', action = 'store', dest = 'nc', type = bool,
required = False, default = True, help = "Comput Sample normalized correlation matrix.")
parser.add_argument('-si', '--selfIdx', action = 'store', dest = 'si', type = int,
required = False, default = 0, help = "Compute highest other agent bid wrt this agents' index.")
args = parser.parse_args()
with open(args.input,'r') as f:
pp = pickle.load(f)
if isinstance(pp,jointGMM):
m = pp.m()
else:
raise ValueError("Unknown Price Prediction Type.")
start = time.time()
bids = simulateAuction(pricePrediction = pp,
nGames = args.ng,
agentType = args.agentType,
nAgents = args.n,
m = m,
minValuation = args.vmin,
maxValuation = args.vmax,
parallel = args.parallel,
nProc = args.nProc,
verbose = args.verbose)
end = time.time()
if args.verbose:
print 'Finished {0} simulations in {1} seconds.'.format(args.ng, end-start)
oDir = os.path.dirname(os.path.realpath(args.input))
basename = os.path.basename(args.input).split('.')[0]
basename = basename + "_{0}_{1}".format(args.agentType,args.ng)
obids = os.path.join(oDir,basename + "_bids.npz")
ohob = os.path.join(oDir,basename + "_hob.txt")
with open(obids,'w') as f:
numpy.save(f, bids)
idx2keep = numpy.arange(args.n)
idx2keep = numpy.delete(idx2keep,args.si)
hob = numpy.max(bids[:,idx2keep,:],1)
with open(ohob,'w') as f:
numpy.savetxt(f, hob)
if args.nc:
print 'Plotting Absolute Value of Normalized Correlation Matrix'
nc = numpy.corrcoef(hob.T)
ocorr = os.path.join(oDir, basename + "_normcorr.txt")
numpy.savetxt(ocorr, nc)
cmap = plt.get_cmap('jet')
ofig = os.path.join(oDir,basename + "_normcorr.pdf")
plt.matshow(numpy.abs(nc),vmin=0,vmax=1,cmap=cmap)
plt.title('Absolute Normalized Correlation Matrix')
plt.colorbar()
plt.savefig(ofig)
print 'Normalized Correlation Matrix:'
print nc
def yw2Hob(**kwargs):
oDir = kwargs.get('oDir')
if oDir == None:
raise ValueError("Must specify oDir")
if not os.path.exists(oDir):
os.makedirs(oDir)
agentType = kwargs.get('agentType')
if agentType == None:
raise ValueError("Must specify agentType.")
nAgents = kwargs.get('nAgents',8)
selfIdx = kwargs.get('selfIdx',numpy.random.randint(nAgents))
nGames = kwargs.get('nGames',100)
m = kwargs.get('m',5)
minPrice = int(kwargs.get('minPrice',0))
maxPrice = int(kwargs.get('maxPrice',50))
minValuation = kwargs.get('minValuation',0)
maxValuation = kwargs.get('maxValuation',50)
maxItr = kwargs.get('maxItr', 100)
tol = kwargs.get('tol', 0.01)
dampen = kwargs.get('dampen', True)
parallel = kwargs.get('parallel',False)
nProc = kwargs.get('nProc',multiprocessing.cpu_count()-1)
savePkl = kwargs.get('savePkl',True)
saveTime = kwargs.get('saveTime',True)
pltMarg = kwargs.get('pltMarg',True)
pltKld = kwargs.get('pltKld',True)
pltKs = kwargs.get('pltKs',True)
verbose = kwargs.get('verbose',True)
if verbose:
print 'oDir = {0}'.format(oDir)
print 'agentType = {0}'.format(agentType)
print 'nAgents = {0}'.format(nAgents)
print 'nGames = {0}'.format(nGames)
print 'm = {0}'.format(m)
print 'minPrice = {0}'.format(minPrice)
print 'maxPrice = {0}'.format(maxPrice)
print 'minValuation = {0}'.format(minValuation)
print 'maxValuation = {0}'.format(maxValuation)
print 'selfIdx = {0}'.format(selfIdx)
print 'maxItr = {0}'.format(maxItr)
print 'tol = {0}'.format(tol)
print 'parallel = {0}'.format(parallel)
print 'nProc = {0}'.format(nProc)
print 'savePkl = {0}'.format(savePkl)
print 'saveTime = {0}'.format(saveTime)
print 'pltMarg = {0}'.format(pltMarg)
print 'pltKld = {0}'.format(pltKld)
print 'pltKs = {0}'.format(pltKs)
if savePkl:
pklDir = os.path.join(oDir, 'gmmPkl')
if not os.path.exists(pklDir):
os.makedirs(pklDir)
else:
[os.remove(f) for f in glob.glob(os.path.join(pklDir,'*.pkl'))]
if pltMarg:
margDir = os.path.join(oDir,'pltMarg')
if not os.path.exists(margDir):
os.makedirs(margDir)
else:
[os.remove(f) for f in glob.glob(os.path.join(margDir,'*.png'))]
[os.remove(f) for f in glob.glob(os.path.join(margDir,'*.pdf'))]
if saveTime:
timeFile = os.path.join(oDir,'time.txt')
if os.path.exists(timeFile):
os.remove(timeFile)
klFile = os.path.join(oDir,'kld.txt')
if os.path.exists(klFile):
os.remove(klFile)
ksFile = os.path.join(oDir,'ks.txt')
if os.path.exists(ksFile):
os.remove(ksFile)
#initial uniform distribution
tempDist = []
p = float(1)/round(maxPrice - minPrice)
a = [p]*(maxPrice - minPrice)
binEdges = numpy.arange(minPrice,maxPrice+1,1)
for i in xrange(m):
tempDist.append((numpy.atleast_1d(a),numpy.atleast_1d(binEdges)))
currentDist = margDistSCPP(tempDist)
del tempDist, p, a
if pltMarg:
of = os.path.join(margDir,'yw2SccpHob_{0}_m{1}_n{2}_init.pdf'.format(agentType,m,nAgents))
title = 'Marginal SCPP {0}'.format(agentType)
xlabel = r'$q$'
ylabel = r'$p(q)$'
currentDist.graphPdfToFile(fname = of, title = title, xlabel = xlabel, ylabel = ylabel)
klList = []
ksList = []
for t in xrange(maxItr):
if verbose:
print ''
print 'Iteration = {0}'.format(t)
if dampen:
kappa = numpy.float(maxItr - t) / maxItr
else:
kappa = 1
if verbose:
print 'kappa = {0}'.format(kappa)
if verbose or saveTime:
start = time.time()
hob = simulateAuction( agentType = agentType,
pricePrediction = currentDist,
m = m,
nAgents = nAgents,
nGames = nGames,
parallel = parallel,
nProc = nProc,
minValuation = minValuation,
maxValuation = maxValuation,
retType = 'hob',
selfIdx = selfIdx,
verbose = verbose)
if verbose or saveTime:
end = time.time()
if verbose:
print 'Simulated {0} auctions with {1} {2} agents in {3} seconds.'.format(nGames, nAgents, agentType, end-start)
if savePkl:
pklFile = os.path.join(pklDir, 'yw2ScppHob_{0}_m{1}_n{2}_{3:05d}.pkl'.format(agentType,m,nAgents,t))
with open(pklFile,'wb') as f:
pickle.dump(currentDist,f)
histData = []
for goodIdx in xrange(hob.shape[1]):
histData.append(numpy.histogram(hob[:,goodIdx],binEdges,density=True))
newDist = margDistSCPP(histData)
klList.append( klDiv(currentDist, newDist) )
if verbose:
print 'kld = {0}'.format(klList[-1])
with open(klFile,'a') as f:
f.write("{0}\n".format(klList[-1]))
ksList.append( ksStat(currentDist,newDist) )
if verbose:
print 'kls = {0}'.format(ksList[-1])
with open(ksFile,'a') as f:
f.write("{0}\n".format(ksList[-1]))
if saveTime:
with open(timeFile,'a') as f:
f.write("{0}\n".format(end-start))
if pltMarg:
of = os.path.join(margDir,'yw2SccpHob_{0}_m{1}_n{2}_{3:05d}.pdf'.format(agentType,m,nAgents,t))
if verbose:
print 'Plotting Marginal pdf to {0}'.format(of)
title = 'Marginal SCPP {0}'.format(agentType)
xlabel = r'$q$'
ylabel = r'$p(q)$'
newDist.graphPdfToFile(fname = of, title = title, xlabel = xlabel, ylabel = ylabel)
if klList[-1] < tol or t == (maxItr - 1):
if verbose:
print 'TERMINATED:'
if klList[-1] < tol:
print '\tkld = {0} < tol = {1}'.format(klList[-1],tol)
else:
print '\tt = {0}, maxItr = {1}'.format(t,maxItr)
if pltKld:
if verbose:
print 'Plotting K-L Divergence vs. Iteration.'
klPdf = os.path.join(oDir,'kld.pdf')
plt.figure()
plt.plot(range(len(klList)), klList)
plt.xlim((0,maxItr))
plt.ylabel('K-L Divergence')
plt.xlabel('Iteration')
plt.title('Marginal SCPP {0}'.format(agentType))
plt.savefig(klPdf)
plt.close()
if pltKs:
if verbose:
print 'Plotting K-S Stat vs. Iteration'
ksPdf = os.path.join(oDir,'ks.pdf')
plt.figure()
plt.plot(range(len(ksList)), ksList)
plt.xlim((0,maxItr))
plt.ylabel('K-S Statistic')
plt.xlabel('Iteration')
plt.title('Marginal SCPP {0}'.format(agentType))
plt.savefig(ksPdf)
plt.close()
if verbose:
print 'DONE!!!!!'
break
else:
if verbose:
print 'Updating distribution.'
currentDist = updateDist(currentDist,newDist,kappa)
del hob, newDist
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))
