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 test_straightMUa_jointGMM(self):
pricePrediction = jointGMM()
pricePrediction.means_ = numpy.asarray([[10,5], [0,5]],dtype='float')
pricePrediction.weights_ = numpy.asarray([0.5,0.5],dtype = 'float')
print pricePrediction.expectedValue()
agent = straightMUa(pricePrediction = pricePrediction, v = [20.,10.], l = 1)
numpy.testing.assert_equal(agent.bid(),[15.,0.], "test_straightMUa.test_straightMUa_jointGMM - failed.",True)
def test_collectBids(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')
nAgents = 5
v = [20.,10.]
l = 1
agentList = [agentFactory(agentType = agentType, pricePrediction = pricePrediction, v = v, l = l) for i in xrange(nAgents)]
bids = collectBids(agentList)
true_bids = numpy.asarray([[15.,0.],[15.,0.],[15.,0.],[15.,0.],[15.,0.]])
numpy.testing.assert_equal(bids,true_bids)
def test_cdf(self):
means = numpy.atleast_2d([[3,5] ,[18,10]])
covars = []
covars.append(numpy.eye(2,2))
covars.append(numpy.eye(2,2)*4)
covars = numpy.array(covars)
weights = [0.3,0.7]
weights = numpy.array(weights)
jgmm = jointGMM(n_components = 2)
jgmm.weights_ = weights
jgmm.means_ = means
jgmm.covars_ = covars
N = 100
xmin = 0
xmax = 30
xx, ds = numpy.linspace(xmin, xmax, N, retstep = True)
p = numpy.zeros((N,N))
c_fnc = numpy.zeros((N,N))
c_apprx = numpy.zeros((N,N))
for i, x1 in enumerate(xx):
for j, x2 in enumerate(xx):
p[i,j] = numpy.exp(jgmm.score(numpy.atleast_2d([x1,x2])))*ds*ds
c_apprx[i,j] = numpy.sum(p[:i,:j])
c_fnc[i,j] = jgmm.cdf(-numpy.inf,[x1,x2])
viz = True
if viz:
import matplotlib.pyplot as plt
plt.figure()
plt.pcolor(p.T)
plt.figure()
plt.pcolor(c_apprx.T)
plt.colorbar()
plt.figure()
plt.pcolor(c_fnc.T)
plt.colorbar()
plt.show()
numpy.testing.assert_array_almost_equal(c_fnc, c_apprx, err_msg = "approximate and analytical cdf don't match" )
# p1 = numpy.random.random(50)
# p1 /= sum(p1)
#
# p2 = numpy.random.random(50)
# p2 /= sum(p2)
v, l = simYW.randomValueVector(0, 50, m, l=1)
print "v = {0}".format(v)
print "l = {0}".format(l)
bundles = simYW.allBundles(nGoods=m)
valuation = simYW.valuation(bundles, v, l)
pricePrediction = jointGMM(n_components=3)
# x,y = make_blobs(n_samples=1000,centers = [[5,5],[15,20],[20,30]], n_features = 2)
# x,y = make_blobs(n_samples=1000, centers = [5,5], n_features = 2)
x, y = make_blobs(n_samples=1000, centers=[5, 5, 5], n_features=3)
pricePrediction.fit(x)
bids = localBid.SS(
bundles=bundles,
valuation=valuation,
pricePrediction=pricePrediction,
l=l,
verbose=True,
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))
