def product_distribution_sym_kl_dist(p1, p2):
"""
Returns the symmetric KL distance of two ProductDistribution objects, defined as sum of the
the component-wise KL distances
"""
d = 0.0
for j in range(p1.dist_nr):
d += sym_kl_dist(p1[j], p2[j])
return d
def product_distribution_sym_kl_dist(p1,p2):
"""
Returns the symmetric KL distance of two ProductDistribution objects, defined as sum of the
the component-wise KL distances
"""
d = 0.0
for j in range(p1.dist_nr):
d += sym_kl_dist(p1[j],p2[j])
return d
def scoreStructureLearning(N, gen, delta, seed=None, silent=False, skipAfterRNGcalls = False):
"""
If skipAfterRNGcalls is True, the function terminates after all calls to RNGs have been done.
"""
#print 'start scoring'
# if seed != None:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** given seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,999999999)
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** random seed=',seed
data = gen.sampleDataSet(N)
# XXX update NormalGammaPrior hyperparameters
for j in range(gen.dist_nr):
if isinstance(gen.prior.compPrior[j], NormalGammaPrior):
gen.prior.compPrior[j].setParams(data.getInternalFeature(j), gen.G)
gen.prior.structPriorHeuristic(delta, data.N)
print '\nupdating generating model structure:'
print 'vorher:'
print gen.leaders
print gen.groups
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(gen, data, silent=1)
print '\nnachher:'
print gen.leaders
print gen.groups
if silent == False:
printModel(gen,'generating model')
m = copy.copy(gen)
# reset structure
m.initStructure()
# training parameters
nr_rep = 40 # XXX
#nr_rep = 4 # XXX
nr_steps = 400
em_delta = 0.6
print 'start training'
print 'EM repeats:',nr_rep
m.randMaxTraining(data,nr_rep, nr_steps,em_delta,silent=1,rtype=0)
print 'finished training'
if skipAfterRNGcalls == True:
print '*** Skipping !'
return np.zeros(4)
# # check for consistency of component indices (identifiability issues)
# bad = 0
if silent == False:
cmap = {}
for j in range(gen.dist_nr):
print '\nfeature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j], gen.components[i2][j])
print i1,'->', kldists.argmin(), map(lambda x:'%.2f' % float(x),kldists) # kldists.min()
# for i1 in range(m.G):
# print
# cdists = np.zeros(m.G)
# for i2 in range(m.G):
# cdists[i2] = product_distribution_sym_kl_dist(m.components[i1], gen.components[i2])
# #print i1,i2,product_distribution_sym_kl_dist(m.components[i1], gen.components[i2])
#
# print i1,'maps to', np.argmin(cdists), cdists.tolist()
# amin = np.argmin(cdists)
# if not amin == i1: # minimal KL distance should occur at equal indices in gen and m
# bad = 1
# cmap[i1] = amin
# if bad:
#
#
#
# # XXX check whether cmap defines new unambiguous ordering
#
# # check whether components have switched positions
# reorder = 0
# order = range(m.G)
# try:
#
# #print cmap
#
# for i1 in cmap.keys():
# order[i1] = cmap[i1]
#
# #print order
# #print set(order)
# #print list(set(order))
#
# if len(set(order)) == m.G:
# reorder = 1
# except KeyError:
# pass
# except AssertionError:
# pass
#
# if reorder:
# print '** new order', order
#
# m.reorderComponents(order)
#
# else:
#
#
# #print cdists
# print i1,'maps to', np.argmin(cdists)
#
# print 'Failed matching.'
#
# print 'generating model gen:'
# print gen
#
# print 'trained model m:'
# print m
#
# raise ValueError
# mtest =copy.copy(gen)
# ch = mtest.updateStructureBayesian(data,silent=1)
# print '\nTEST:',ch
# for j in range(m.dist_nr):
# print j,mtest.leaders[j], mtest.groups[j]
#print m.prior
print '-----------------------------------------------------------------------'
print '\n True structure:'
print 'True model post:',mixture.get_loglikelihood(gen, data) + gen.prior.pdf(gen)
#for j in range(m.dist_nr):
# print j,gen.leaders[j], gen.groups[j]
print gen.leaders
print gen.groups
if silent == False:
printModel(m,'trained model')
m1 = copy.copy(m)
t0 = time.time()
#print '\n\n################# TOPDOWN #####################'
m1.updateStructureBayesian(data,silent=1)
t1 = time.time()
time2 = t1-t0
#m1.mapEM(data,40,0.1)
print '\nTop down (',str(time2),'s ):'
print m1.leaders
print m1.groups
print 'Top down model post:',mixture.get_loglikelihood(m1, data) + m1.prior.pdf(m1)
# print 'Accuracy:',mixture.structureAccuracy(gen,m1) # structureEditDistance(gen,m1)
if silent == False:
printModel(m1,'top down model')
#print '#############################'
#print '\n\n################# FULL FixedOrder #####################'
m2 = copy.copy(m)
t0 = time.time()
m2.updateStructureBayesianFullEnumerationFixedOrder(data,silent=1)
t1 = time.time()
time2 = t1-t0
#m2.mapEM(data,40,0.1)
# print
# for j in range(m2.dist_nr):
# print j,m2.leaders[j], m2.groups[j]
print '\nFull enumeration Fixed Order (',str(time2),'s ):'
print m2.leaders
print m2.groups
print 'Full fixed order model post:',mixture.get_loglikelihood(m2, data) + m2.prior.pdf(m2)
# print 'Accuracy:',mixture.structureAccuracy(gen,m2) # structureEditDistance(gen,m1)
if silent == False:
printModel(m2,'full fixed model')
#print '\n\n################# BOTTUMUP #####################'
m3 = copy.copy(m)
t0 = time.time()
m3.updateStructureBayesianBottomUp(data,silent=1)
t1 = time.time()
time2 = t1-t0
#m3.mapEM(data,40,0.1)
# print
# for j in range(m3.dist_nr):
# print j,m3.leaders[j], m3.groups[j]
print '\nBottom up: (',str(time2),'s ):'
print m3.leaders
print m3.groups
print 'Bottom up model post:',mixture.get_loglikelihood(m3, data) + m3.prior.pdf(m3)
# print 'Accuracy:',mixture.structureAccuracy(gen,m3) # structureEditDistance(gen,m1)
if silent == False:
printModel(m3,'bottom up model')
#print '\n\n################# FULL enumeration #####################'
m4 = copy.copy(m)
t0 = time.time()
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(m4, data, silent=0)
t1 = time.time()
time2 = t1-t0
# m4.mapEM(data,40,0.1)
# print
# for j in range(m4.dist_nr):
# print j,m4.leaders[j], m4.groups[j]
print '\nFull enumeration: (',str(time2),'s )'
print m4.leaders
print m4.groups
print 'Full enumeration model post:',mixture.get_loglikelihood(m4, data) + m4.prior.pdf(m4)
# print 'Accuracy:',mixture.structureAccuracy(gen,m4)
if silent == False:
printModel(m4,'full enumeration model')
print '-----------------------------------------------------------------------'
# dtop = structureAccuracy(gen,m1)
# dfull_fixed = structureAccuracy(gen,m2)
# dfull = structureAccuracy(gen,m4)
# dbottom = structureAccuracy(gen,m3)
logp_top = get_loglikelihood(m1, data) + m1.prior.pdf(m1)
logp_full_fixed = get_loglikelihood(m2, data) + m2.prior.pdf(m2)
logp_full = get_loglikelihood(m4, data) + m4.prior.pdf(m4)
logp_bottom = get_loglikelihood(m3, data) + m3.prior.pdf(m3)
if (not (round(logp_top,3) <= round(logp_full,3) ) or not (round(logp_full_fixed,3) <= round(logp_full,3))
or not (round(logp_bottom,3) <= round(logp_full,3)) ):
raise ValueError
return np.array([ logp_top, logp_full_fixed, logp_full, logp_bottom ])
def matchModelStructures(gen, m):
"""
Checks whether m1 and m2 are consistent in the sense that for each
leader in m1, there is a number of distributions in m2 which take minimum
distance to the leader as the number of distributions in the group in m1.
Used to check whether a the parameteric EM has obviously captured the CSI structure of
the generating model.
"""
#print '**** matchModelStructures'
gen_csi = []
for j in range(gen.dist_nr):
gen_csi.append({})
for l in gen.leaders[j]:
gen_csi[j][ tuple( [l] + gen.groups[j][l] ) ] = []
#print gen_csi
for j in range(gen.dist_nr):
#print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j], gen.components[i2][j])
cg = np.where( kldists == kldists.min() )[0]
gen_csi[j][tuple(cg)].append(i1)
#print gen_csi
# check easy case: all components match in gen and m
match = 1
for j in range(gen.dist_nr):
for cg in gen_csi[j]:
if cg != tuple(gen_csi[j][cg]):
match = 0
if match:
#print 'Simple match !'
return 1
# check whether component indices have changed but the structures are consistent otherwise
cmaps = []
for j in range(gen.dist_nr):
cmaps.append({})
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j], gen.components[i2][j])
cg = np.where( kldists == kldists.min() )[0]
cmaps[j][i1]=cg
#print cmaps
gen_compred = checkComponentRedundancy(gen.leaders, gen.groups)
#print 'gen_compred', gen_compred
if len(gen_compred) > 1:
return 0 # XXX case not covered yet
match = 1
m_to_gen = {}
for i in range(m.G):
m_to_gen[i] = -1
for j in range(gen.dist_nr):
for i in cmaps[j]:
if len(cmaps[j][i]) == 1:
if m_to_gen[i] == -1:
m_to_gen[i] = cmaps[j][i][0]
else:
if m_to_gen[i] == cmaps[j][i][0]:
continue
else:
match = 0
break
#print m_to_gen
if len(gen_compred) == 0:
for k in m_to_gen:
#print m_to_gen[k]
if m_to_gen[k] == -1:
return 0
return 1
for k in m_to_gen:
if m_to_gen[k] == -1: # no assignment so far
for j in range(gen.dist_nr):
#print gen_compred
#print k, cmaps[j][k].tolist(),gen_compred[0]
if not cmaps[j][k].tolist() == gen_compred[0]:
match = 0
#print '*** match=', match
return match
def getRandomCSIMixture_conditionalDists(G, p, KL_lower, KL_upper, M=8, dtypes='discgauss', seed = None, fullstruct=False, disc_sampling_dist=None):
# if seed:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# #print '*** seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,9999999)
# mixextend.set_gsl_rng_seed(seed)
# random.seed(seed)
# #print '*** seed=',seed
if disc_sampling_dist == None:
discSamp = DirichletPrior(M,[1.0] * M ) # uniform sampling
else:
discSamp = disc_sampling_dist
min_sigma = 0.3 # minimal std for Normal
max_sigma = 5.0 # maximal std for Normal
min_mu = -25.0 # minimal mean
max_mu = 25.0 # maximal mean
assert dtypes in ['disc','gauss','discgauss']
if dtypes == 'disc':
featureTypes = [0] * p
elif dtypes == 'gauss':
featureTypes = [1] * p
elif dtypes == 'discgauss':
# discrete or Normal features for now, chosen uniformly
# 0 discrete, 1 Normal
featureTypes = [ random.choice( (0, 1) ) for i in range(p) ]
else:
raise TypeError
#print featureTypes
# generate random CSI structures
if G < 15:
P = setPartitions.generate_all_partitions(G) # XXX too slow for large G
#print P
C = []
leaders = []
groups = []
for j in range(p):
c_j = {}
leaders_j = []
groups_j = {}
if fullstruct == True:
struct_j = [(i,) for i in range(G)]
elif G < 15:
struct_j = random.choice(P)
else:
print 'WARNING: improper structure sampling !'
struct_j = setPartitions.get_random_partition(G)
#print '\nstruct',j,struct_j
for i,grp in enumerate(struct_j):
lg = list(grp)
#print lg
lgj = lg.pop(0)
#print lgj
leaders_j.append(lgj)
groups_j[lgj] = lg
max_tries = 100000
tries = 0
if featureTypes[j] == 0:
acc = 0
while acc == 0:
cand = discSamp.sample()
#print 'Cand:', cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(c_j[d],cand)
#print c_j[d],cand, KL_dist
if KL_dist > KL_upper or KL_dist < KL_lower:
acc = 0
tries += 1
break
if tries >= max_tries:
raise RuntimeError, 'Failed to find separated parameters !'
for cind in grp:
c_j[cind] = cand
elif featureTypes[j] == 1:
acc = 0
while acc == 0:
mu = random.uniform(min_mu, max_mu)
sigma = random.uniform(min_sigma, max_sigma)
cand = NormalDistribution(mu, sigma )
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(c_j[d],cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
acc = 0
tries += 1
break
if tries >= max_tries:
raise RuntimeError
# print '.',
#print
for cind in grp:
c_j[cind] = cand
else:
RuntimeError
leaders.append(leaders_j)
groups.append(groups_j)
C.append(c_j)
comps = []
for i in range(G):
comps.append( ProductDistribution( [ C[j][i] for j in range(p) ] ) )
pi = get_random_pi(G, 0.3 / G)
#print '** pi =',pi
# create prior
piprior = DirichletPrior(G,[2.0]*G)
cprior = []
for j in range(p):
if featureTypes[j] == 0:
cprior.append( DirichletPrior(M,[1.02]*M))
elif featureTypes[j] == 1:
cprior.append( NormalGammaPrior(0,0,0,0)) # dummy parameters, to be set later
else:
RuntimeError
mprior = MixtureModelPrior(0.1,0.1, piprior, cprior)
m = BayesMixtureModel(G,pi, comps, mprior, struct =1)
m.leaders = leaders
m.groups = groups
m.identifiable()
m.updateFreeParams()
#print m
return m
def scoreStructureLearning_diffFullVsTopdown(N, gen, delta, seed=None, silent=False, skipAfterRNGcalls = False):
"""
If skipAfterRNGcalls is True, the function terminates after all calls to RNGs have been done.
"""
#print 'start scoring'
# if seed != None:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** given seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,999999999)
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** random seed=',seed
data = gen.sampleDataSet(N)
# XXX update NormalGammaPrior hyperparameters
for j in range(gen.dist_nr):
if isinstance(gen.prior.compPrior[j], NormalGammaPrior):
gen.prior.compPrior[j].setParams(data.getInternalFeature(j), gen.G)
gen.prior.structPriorHeuristic(delta, data.N)
# print '\nupdating generating model structure:'
# print 'vorher:'
# print gen.leaders
# print gen.groups
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(gen, data, silent=1)
# print '\nnachher:'
# print gen.leaders
# print gen.groups
m = copy.copy(gen)
# reset structure
m.initStructure()
# training parameters
nr_rep = 40 # XXX
#nr_rep = 4 # XXX
nr_steps = 400
em_delta = 0.6
# print 'start training'
# print 'EM repeats:',nr_rep
m.randMaxTraining(data,nr_rep, nr_steps,em_delta,silent=1,rtype=0)
# print 'finished training'
if skipAfterRNGcalls == True:
print '*** Skipping !'
return np.zeros(4)
m1 = copy.copy(m)
t0 = time.time()
#print '\n\n################# TOPDOWN #####################'
m1.updateStructureBayesian(data,silent=1)
t1 = time.time()
time2 = t1-t0
#m1.mapEM(data,40,0.1)
# print 'Accuracy:',mixture.structureAccuracy(gen,m1) # structureEditDistance(gen,m1)
#print '#############################'
#print '\n\n################# FULL FixedOrder #####################'
m2 = copy.copy(m)
t0 = time.time()
m2.updateStructureBayesianFullEnumerationFixedOrder(data,silent=1)
t1 = time.time()
time2 = t1-t0
#m2.mapEM(data,40,0.1)
# print
# for j in range(m2.dist_nr):
# print j,m2.leaders[j], m2.groups[j]
# print 'Accuracy:',mixture.structureAccuracy(gen,m2) # structureEditDistance(gen,m1)
#print '\n\n################# BOTTUMUP #####################'
m3 = copy.copy(m)
t0 = time.time()
m3.updateStructureBayesianBottomUp(data,silent=1)
t1 = time.time()
time2 = t1-t0
#m3.mapEM(data,40,0.1)
# print
# for j in range(m3.dist_nr):
# print j,m3.leaders[j], m3.groups[j]
# print 'Accuracy:',mixture.structureAccuracy(gen,m3) # structureEditDistance(gen,m1)
#print '\n\n################# FULL enumeration #####################'
m4 = copy.copy(m)
t0 = time.time()
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(m4, data, silent=1)
t1 = time.time()
time2 = t1-t0
# m4.mapEM(data,40,0.1)
# print
# for j in range(m4.dist_nr):
# print j,m4.leaders[j], m4.groups[j]
# print 'Accuracy:',mixture.structureAccuracy(gen,m4)
logp_top = get_loglikelihood(m1, data) + m1.prior.pdf(m1)
logp_full_fixed = get_loglikelihood(m2, data) + m2.prior.pdf(m2)
logp_full = get_loglikelihood(m4, data) + m4.prior.pdf(m4)
logp_bottom = get_loglikelihood(m3, data) + m3.prior.pdf(m3)
if (not (round(logp_top,3) <= round(logp_full,3) ) or not (round(logp_full_fixed,3) <= round(logp_full,3))
or not (round(logp_bottom,3) <= round(logp_full,3)) ):
print 'ERROR:'
print 'top:',logp_top
print 'full fixed:',logp_full_fixed
print 'full:',logp_full
print 'bottom:',logp_bottom,'\n'
printModel(gen,'generating model')
printStructure(gen)
print
printModel(m4,'full enumeration model')
printStructure(m4)
print
printModel(m2,'fixed full model')
printStructure(m2)
raise ValueError
# # as a measure of separation of the component in the trained model, sum up
# # sym. KL divergence of all components and features
# train_diff = 0
# for j in range(gen.dist_nr):
# for i1 in range(m.G):
# for i2 in range(m.G):
# train_diff += sym_kl_dist(m.components[i1][j], m.components[i2][j])
mix_dist1 = mixtureKLdistance(gen,m)
mix_dist2 = mixtureKLdistance(m, gen)
max_dist1 = mixtureMaxKLdistance(gen,m)
max_dist2 = mixtureMaxKLdistance(m, gen)
# number of leaders in the full enumeration model
nr_full_lead = 0
for ll in m4.leaders:
nr_full_lead += len(ll)
match = matchModelStructures(gen, m)
compred = checkComponentRedundancy(gen.leaders, gen.groups)
if not(str(logp_top) == str(logp_full_fixed) == str(logp_full) ):
print '-----------------------------------------------------------------------'
print 'Different:'
print 'top:',logp_top
print 'full fixed:',logp_full_fixed
print 'full:',logp_full
print 'bottom:',logp_bottom,'\n'
explain = 0
if str(compred) != '[]':
print '*** redundant components',compred
explain = 1
if gen.pi.min() < 0.05:
print '*** vanishing component in generating model'
explain = 1
if m.pi.min() < 0.05:
print '*** vanishing component in trained model'
explain = 1
if explain == 0:
print '*** UNEXPLAINED !'
printModel(gen,'generating model')
printModel(m,'trained model')
#print 'Trained model diff (simplistic):',train_diff
print 'D: Mixture distance gen/trained:',mix_dist1
print 'D: Mixture distance trained/gen:',mix_dist2
print 'D: Mixture Max-distance gen/trained:',max_dist1
print 'D: Mixture Max-distance trained/gen:',max_dist2
print '\nGenerating distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(gen.components[i1][j], gen.components[i2][j])
print map(lambda x:'%.2f' % float(x),kldists) # kldists.min()
print '\nTrained distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j], m.components[i2][j])
print map(lambda x:'%.2f' % float(x),kldists) # kldists.min()
print '\nTrained distances to generating:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j], gen.components[i2][j])
print i1,'->', kldists.argmin(), map(lambda x:'%.2f' % float(x),kldists) # kldists.min()
print '\n True structure:'
print 'True model post:',mixture.get_loglikelihood(gen, data) + gen.prior.pdf(gen)
#for j in range(m.dist_nr):
# print j,gen.leaders[j], gen.groups[j]
printStructure(gen)
print '\nTop down:'
printStructure(m1)
print 'Top down model post:',mixture.get_loglikelihood(m1, data) + m1.prior.pdf(m1)
printModel(m1,'top down model')
print '\nFull enumeration Fixed Order:'
printStructure(m2)
print 'Full fixed order model post:',mixture.get_loglikelihood(m2, data) + m2.prior.pdf(m2)
printModel(m2,'full fixed model')
print '\nBottom up:'
printStructure(m3)
print 'Bottom up model post:',mixture.get_loglikelihood(m3, data) + m3.prior.pdf(m3)
printModel(m3,'bottom up model')
print '\nFull enumeration:'
printStructure(m4)
print 'Full enumeration model post:',mixture.get_loglikelihood(m4, data) + m4.prior.pdf(m4)
printModel(m4,'full enumeration model')
print '-----------------------------------------------------------------------'
elif str(compred) != '[]' and nr_full_lead > m4.p and match != 1: # redundant components and not fully merged
print '-----------------------------------------------------------------------'
print 'Same but redundant components:', compred
printModel(gen,'generating model')
printModel(m,'trained model')
#print 'Trained model diff:',train_diff
print 'S: Mixture distance gen/trained:',mix_dist1
print 'S: Mixture distance trained/gen:',mix_dist2
print 'S: Mixture Max-distance gen/trained:',max_dist1
print 'S: Mixture Max-distance trained/gen:',max_dist2
print '\nGenerating distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(gen.components[i1][j], gen.components[i2][j])
print i1,':', map(lambda x:'%.2f' % float(x),kldists) # kldists.min()
print '\nTrained distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j], m.components[i2][j])
print i1,':', map(lambda x:'%.2f' % float(x),kldists) # kldists.min()
print '\nTrained distances to generating:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j], gen.components[i2][j])
print i1,'->', kldists.argmin(), map(lambda x:'%.2f' % float(x),kldists) # kldists.min()
print '\n True structure:'
print 'True model post:',mixture.get_loglikelihood(gen, data) + gen.prior.pdf(gen)
#for j in range(m.dist_nr):
# print j,gen.leaders[j], gen.groups[j]
printStructure(gen)
print '\nTop down:'
printStructure(m1)
print 'Top down model post:',mixture.get_loglikelihood(m1, data) + m1.prior.pdf(m1)
print '\nFull enumeration Fixed Order:'
printStructure(m2)
print 'Full fixed order model post:',mixture.get_loglikelihood(m2, data) + m2.prior.pdf(m2)
print '\nBottom up:'
printStructure(m3)
print 'Bottom up model post:',mixture.get_loglikelihood(m3, data) + m3.prior.pdf(m3)
print '\nFull enumeration:'
printStructure(m4)
print 'Full enumeration model post:',mixture.get_loglikelihood(m4, data) + m4.prior.pdf(m4)
print '-----------------------------------------------------------------------'
# else:
# print '-----------------------------------------------------------------------'
# print 'S: Mixture distance gen/trained:',mix_dist1
# print 'S: Mixture distance trained/gen:',mix_dist2
# print '-----------------------------------------------------------------------'
# else:
# print '** all equal.'
# dtop = structureAccuracy(gen,m1)
# dfull_fixed = structureAccuracy(gen,m2)
# dfull = structureAccuracy(gen,m4)
# dbottom = structureAccuracy(gen,m3)
return np.array([ logp_top, logp_full_fixed, logp_full, logp_bottom ])
def getRandomMixture(G, p, KL_lower, KL_upper, dtypes='discgauss', M=4,seed = None):
# if seed:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# #print '*** seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,9000000)
# mixextend.set_gsl_rng_seed(seed)
# random.seed(seed)
# #print '*** seed=',seed
#M = 4 # Alphabet size for discrete distributions
min_sigma = 0.1 # minimal std for Normal
max_sigma = 1.0 # maximal std for Normal
min_mu = -5.0 # minimal mean
max_mu = 8.0 # maximal mean
if dtypes == 'disc':
featureTypes = [0] * p
elif dtypes == 'gauss':
featureTypes = [1] * p
elif dtypes == 'discgauss':
# discrete or Normal features for now, chosen uniformly
# 0 discrete, 1 Normal
featureTypes = [ random.choice( (0, 1) ) for i in range(p) ]
else:
raise TypeError
#print featureTypes
C = []
for j in range(p):
c_j = []
for i in range(G):
#print i,j
if featureTypes[j] == 0:
acc = 0
while acc == 0:
cand = DiscreteDistribution(M, random_vector(M) )
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d,cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
break
c_j.append(cand)
elif featureTypes[j] == 1:
acc = 0
while acc == 0:
mu = random.uniform(min_mu, max_mu)
sigma = random.uniform(min_sigma, max_sigma)
cand = NormalDistribution(mu, sigma )
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d,cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
c_j.append(cand)
else:
RuntimeError
C.append(c_j)
# print '\n'
# for cc in C:
# print cc
comps = []
for i in range(G):
comps.append( ProductDistribution( [ C[j][i] for j in range(p) ] ) )
pi = get_random_pi(G,0.1)
m = MixtureModel(G,pi, comps,struct=1)
m.updateFreeParams()
return m
def scoreStructureLearning(N,
gen,
delta,
seed=None,
silent=False,
skipAfterRNGcalls=False):
"""
If skipAfterRNGcalls is True, the function terminates after all calls to RNGs have been done.
"""
#print 'start scoring'
# if seed != None:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** given seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,999999999)
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** random seed=',seed
data = gen.sampleDataSet(N)
# XXX update NormalGammaPrior hyperparameters
for j in range(gen.dist_nr):
if isinstance(gen.prior.compPrior[j], NormalGammaPrior):
gen.prior.compPrior[j].setParams(data.getInternalFeature(j), gen.G)
gen.prior.structPriorHeuristic(delta, data.N)
print '\nupdating generating model structure:'
print 'vorher:'
print gen.leaders
print gen.groups
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(gen,
data,
silent=1)
print '\nnachher:'
print gen.leaders
print gen.groups
if silent == False:
printModel(gen, 'generating model')
m = copy.copy(gen)
# reset structure
m.initStructure()
# training parameters
nr_rep = 40 # XXX
#nr_rep = 4 # XXX
nr_steps = 400
em_delta = 0.6
print 'start training'
print 'EM repeats:', nr_rep
m.randMaxTraining(data, nr_rep, nr_steps, em_delta, silent=1, rtype=0)
print 'finished training'
if skipAfterRNGcalls == True:
print '*** Skipping !'
return np.zeros(4)
# # check for consistency of component indices (identifiability issues)
# bad = 0
if silent == False:
cmap = {}
for j in range(gen.dist_nr):
print '\nfeature:', j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j],
gen.components[i2][j])
print i1, '->', kldists.argmin(), map(
lambda x: '%.2f' % float(x), kldists) # kldists.min()
# for i1 in range(m.G):
# print
# cdists = np.zeros(m.G)
# for i2 in range(m.G):
# cdists[i2] = product_distribution_sym_kl_dist(m.components[i1], gen.components[i2])
# #print i1,i2,product_distribution_sym_kl_dist(m.components[i1], gen.components[i2])
#
# print i1,'maps to', np.argmin(cdists), cdists.tolist()
# amin = np.argmin(cdists)
# if not amin == i1: # minimal KL distance should occur at equal indices in gen and m
# bad = 1
# cmap[i1] = amin
# if bad:
#
#
#
# # XXX check whether cmap defines new unambiguous ordering
#
# # check whether components have switched positions
# reorder = 0
# order = range(m.G)
# try:
#
# #print cmap
#
# for i1 in cmap.keys():
# order[i1] = cmap[i1]
#
# #print order
# #print set(order)
# #print list(set(order))
#
# if len(set(order)) == m.G:
# reorder = 1
# except KeyError:
# pass
# except AssertionError:
# pass
#
# if reorder:
# print '** new order', order
#
# m.reorderComponents(order)
#
# else:
#
#
# #print cdists
# print i1,'maps to', np.argmin(cdists)
#
# print 'Failed matching.'
#
# print 'generating model gen:'
# print gen
#
# print 'trained model m:'
# print m
#
# raise ValueError
# mtest =copy.copy(gen)
# ch = mtest.updateStructureBayesian(data,silent=1)
# print '\nTEST:',ch
# for j in range(m.dist_nr):
# print j,mtest.leaders[j], mtest.groups[j]
#print m.prior
print '-----------------------------------------------------------------------'
print '\n True structure:'
print 'True model post:', mixture.get_loglikelihood(
gen, data) + gen.prior.pdf(gen)
#for j in range(m.dist_nr):
# print j,gen.leaders[j], gen.groups[j]
print gen.leaders
print gen.groups
if silent == False:
printModel(m, 'trained model')
m1 = copy.copy(m)
t0 = time.time()
#print '\n\n################# TOPDOWN #####################'
m1.updateStructureBayesian(data, silent=1)
t1 = time.time()
time2 = t1 - t0
#m1.mapEM(data,40,0.1)
print '\nTop down (', str(time2), 's ):'
print m1.leaders
print m1.groups
print 'Top down model post:', mixture.get_loglikelihood(
m1, data) + m1.prior.pdf(m1)
# print 'Accuracy:',mixture.structureAccuracy(gen,m1) # structureEditDistance(gen,m1)
if silent == False:
printModel(m1, 'top down model')
#print '#############################'
#print '\n\n################# FULL FixedOrder #####################'
m2 = copy.copy(m)
t0 = time.time()
m2.updateStructureBayesianFullEnumerationFixedOrder(data, silent=1)
t1 = time.time()
time2 = t1 - t0
#m2.mapEM(data,40,0.1)
# print
# for j in range(m2.dist_nr):
# print j,m2.leaders[j], m2.groups[j]
print '\nFull enumeration Fixed Order (', str(time2), 's ):'
print m2.leaders
print m2.groups
print 'Full fixed order model post:', mixture.get_loglikelihood(
m2, data) + m2.prior.pdf(m2)
# print 'Accuracy:',mixture.structureAccuracy(gen,m2) # structureEditDistance(gen,m1)
if silent == False:
printModel(m2, 'full fixed model')
#print '\n\n################# BOTTUMUP #####################'
m3 = copy.copy(m)
t0 = time.time()
m3.updateStructureBayesianBottomUp(data, silent=1)
t1 = time.time()
time2 = t1 - t0
#m3.mapEM(data,40,0.1)
# print
# for j in range(m3.dist_nr):
# print j,m3.leaders[j], m3.groups[j]
print '\nBottom up: (', str(time2), 's ):'
print m3.leaders
print m3.groups
print 'Bottom up model post:', mixture.get_loglikelihood(
m3, data) + m3.prior.pdf(m3)
# print 'Accuracy:',mixture.structureAccuracy(gen,m3) # structureEditDistance(gen,m1)
if silent == False:
printModel(m3, 'bottom up model')
#print '\n\n################# FULL enumeration #####################'
m4 = copy.copy(m)
t0 = time.time()
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(m4,
data,
silent=0)
t1 = time.time()
time2 = t1 - t0
# m4.mapEM(data,40,0.1)
# print
# for j in range(m4.dist_nr):
# print j,m4.leaders[j], m4.groups[j]
print '\nFull enumeration: (', str(time2), 's )'
print m4.leaders
print m4.groups
print 'Full enumeration model post:', mixture.get_loglikelihood(
m4, data) + m4.prior.pdf(m4)
# print 'Accuracy:',mixture.structureAccuracy(gen,m4)
if silent == False:
printModel(m4, 'full enumeration model')
print '-----------------------------------------------------------------------'
# dtop = structureAccuracy(gen,m1)
# dfull_fixed = structureAccuracy(gen,m2)
# dfull = structureAccuracy(gen,m4)
# dbottom = structureAccuracy(gen,m3)
logp_top = get_loglikelihood(m1, data) + m1.prior.pdf(m1)
logp_full_fixed = get_loglikelihood(m2, data) + m2.prior.pdf(m2)
logp_full = get_loglikelihood(m4, data) + m4.prior.pdf(m4)
logp_bottom = get_loglikelihood(m3, data) + m3.prior.pdf(m3)
if (not (round(logp_top, 3) <= round(logp_full, 3))
or not (round(logp_full_fixed, 3) <= round(logp_full, 3))
or not (round(logp_bottom, 3) <= round(logp_full, 3))):
raise ValueError
return np.array([logp_top, logp_full_fixed, logp_full, logp_bottom])
def matchModelStructures(gen, m):
"""
Checks whether m1 and m2 are consistent in the sense that for each
leader in m1, there is a number of distributions in m2 which take minimum
distance to the leader as the number of distributions in the group in m1.
Used to check whether a the parameteric EM has obviously captured the CSI structure of
the generating model.
"""
#print '**** matchModelStructures'
gen_csi = []
for j in range(gen.dist_nr):
gen_csi.append({})
for l in gen.leaders[j]:
gen_csi[j][tuple([l] + gen.groups[j][l])] = []
#print gen_csi
for j in range(gen.dist_nr):
#print 'feature:',j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j],
gen.components[i2][j])
cg = np.where(kldists == kldists.min())[0]
gen_csi[j][tuple(cg)].append(i1)
#print gen_csi
# check easy case: all components match in gen and m
match = 1
for j in range(gen.dist_nr):
for cg in gen_csi[j]:
if cg != tuple(gen_csi[j][cg]):
match = 0
if match:
#print 'Simple match !'
return 1
# check whether component indices have changed but the structures are consistent otherwise
cmaps = []
for j in range(gen.dist_nr):
cmaps.append({})
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j],
gen.components[i2][j])
cg = np.where(kldists == kldists.min())[0]
cmaps[j][i1] = cg
#print cmaps
gen_compred = checkComponentRedundancy(gen.leaders, gen.groups)
#print 'gen_compred', gen_compred
if len(gen_compred) > 1:
return 0 # XXX case not covered yet
match = 1
m_to_gen = {}
for i in range(m.G):
m_to_gen[i] = -1
for j in range(gen.dist_nr):
for i in cmaps[j]:
if len(cmaps[j][i]) == 1:
if m_to_gen[i] == -1:
m_to_gen[i] = cmaps[j][i][0]
else:
if m_to_gen[i] == cmaps[j][i][0]:
continue
else:
match = 0
break
#print m_to_gen
if len(gen_compred) == 0:
for k in m_to_gen:
#print m_to_gen[k]
if m_to_gen[k] == -1:
return 0
return 1
for k in m_to_gen:
if m_to_gen[k] == -1: # no assignment so far
for j in range(gen.dist_nr):
#print gen_compred
#print k, cmaps[j][k].tolist(),gen_compred[0]
if not cmaps[j][k].tolist() == gen_compred[0]:
match = 0
#print '*** match=', match
return match
def getRandomCSIMixture_conditionalDists(G,
p,
KL_lower,
KL_upper,
M=8,
dtypes='discgauss',
seed=None,
fullstruct=False,
disc_sampling_dist=None):
# if seed:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# #print '*** seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,9999999)
# mixextend.set_gsl_rng_seed(seed)
# random.seed(seed)
# #print '*** seed=',seed
if disc_sampling_dist == None:
discSamp = DirichletPrior(M, [1.0] * M) # uniform sampling
else:
discSamp = disc_sampling_dist
min_sigma = 0.3 # minimal std for Normal
max_sigma = 5.0 # maximal std for Normal
min_mu = -25.0 # minimal mean
max_mu = 25.0 # maximal mean
assert dtypes in ['disc', 'gauss', 'discgauss']
if dtypes == 'disc':
featureTypes = [0] * p
elif dtypes == 'gauss':
featureTypes = [1] * p
elif dtypes == 'discgauss':
# discrete or Normal features for now, chosen uniformly
# 0 discrete, 1 Normal
featureTypes = [random.choice((0, 1)) for i in range(p)]
else:
raise TypeError
#print featureTypes
# generate random CSI structures
if G < 15:
P = setPartitions.generate_all_partitions(
G) # XXX too slow for large G
#print P
C = []
leaders = []
groups = []
for j in range(p):
c_j = {}
leaders_j = []
groups_j = {}
if fullstruct == True:
struct_j = [(i, ) for i in range(G)]
elif G < 15:
struct_j = random.choice(P)
else:
print 'WARNING: improper structure sampling !'
struct_j = setPartitions.get_random_partition(G)
#print '\nstruct',j,struct_j
for i, grp in enumerate(struct_j):
lg = list(grp)
#print lg
lgj = lg.pop(0)
#print lgj
leaders_j.append(lgj)
groups_j[lgj] = lg
max_tries = 100000
tries = 0
if featureTypes[j] == 0:
acc = 0
while acc == 0:
cand = discSamp.sample()
#print 'Cand:', cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(c_j[d], cand)
#print c_j[d],cand, KL_dist
if KL_dist > KL_upper or KL_dist < KL_lower:
acc = 0
tries += 1
break
if tries >= max_tries:
raise RuntimeError, 'Failed to find separated parameters !'
for cind in grp:
c_j[cind] = cand
elif featureTypes[j] == 1:
acc = 0
while acc == 0:
mu = random.uniform(min_mu, max_mu)
sigma = random.uniform(min_sigma, max_sigma)
cand = NormalDistribution(mu, sigma)
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(c_j[d], cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
acc = 0
tries += 1
break
if tries >= max_tries:
raise RuntimeError
# print '.',
#print
for cind in grp:
c_j[cind] = cand
else:
RuntimeError
leaders.append(leaders_j)
groups.append(groups_j)
C.append(c_j)
comps = []
for i in range(G):
comps.append(ProductDistribution([C[j][i] for j in range(p)]))
pi = get_random_pi(G, 0.3 / G)
#print '** pi =',pi
# create prior
piprior = DirichletPrior(G, [2.0] * G)
cprior = []
for j in range(p):
if featureTypes[j] == 0:
cprior.append(DirichletPrior(M, [1.02] * M))
elif featureTypes[j] == 1:
cprior.append(NormalGammaPrior(
0, 0, 0, 0)) # dummy parameters, to be set later
else:
RuntimeError
mprior = MixtureModelPrior(0.1, 0.1, piprior, cprior)
m = BayesMixtureModel(G, pi, comps, mprior, struct=1)
m.leaders = leaders
m.groups = groups
m.identifiable()
m.updateFreeParams()
#print m
return m
def scoreStructureLearning_diffFullVsTopdown(N,
gen,
delta,
seed=None,
silent=False,
skipAfterRNGcalls=False):
"""
If skipAfterRNGcalls is True, the function terminates after all calls to RNGs have been done.
"""
#print 'start scoring'
# if seed != None:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** given seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,999999999)
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# print '*** random seed=',seed
data = gen.sampleDataSet(N)
# XXX update NormalGammaPrior hyperparameters
for j in range(gen.dist_nr):
if isinstance(gen.prior.compPrior[j], NormalGammaPrior):
gen.prior.compPrior[j].setParams(data.getInternalFeature(j), gen.G)
gen.prior.structPriorHeuristic(delta, data.N)
# print '\nupdating generating model structure:'
# print 'vorher:'
# print gen.leaders
# print gen.groups
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(gen,
data,
silent=1)
# print '\nnachher:'
# print gen.leaders
# print gen.groups
m = copy.copy(gen)
# reset structure
m.initStructure()
# training parameters
nr_rep = 40 # XXX
#nr_rep = 4 # XXX
nr_steps = 400
em_delta = 0.6
# print 'start training'
# print 'EM repeats:',nr_rep
m.randMaxTraining(data, nr_rep, nr_steps, em_delta, silent=1, rtype=0)
# print 'finished training'
if skipAfterRNGcalls == True:
print '*** Skipping !'
return np.zeros(4)
m1 = copy.copy(m)
t0 = time.time()
#print '\n\n################# TOPDOWN #####################'
m1.updateStructureBayesian(data, silent=1)
t1 = time.time()
time2 = t1 - t0
#m1.mapEM(data,40,0.1)
# print 'Accuracy:',mixture.structureAccuracy(gen,m1) # structureEditDistance(gen,m1)
#print '#############################'
#print '\n\n################# FULL FixedOrder #####################'
m2 = copy.copy(m)
t0 = time.time()
m2.updateStructureBayesianFullEnumerationFixedOrder(data, silent=1)
t1 = time.time()
time2 = t1 - t0
#m2.mapEM(data,40,0.1)
# print
# for j in range(m2.dist_nr):
# print j,m2.leaders[j], m2.groups[j]
# print 'Accuracy:',mixture.structureAccuracy(gen,m2) # structureEditDistance(gen,m1)
#print '\n\n################# BOTTUMUP #####################'
m3 = copy.copy(m)
t0 = time.time()
m3.updateStructureBayesianBottomUp(data, silent=1)
t1 = time.time()
time2 = t1 - t0
#m3.mapEM(data,40,0.1)
# print
# for j in range(m3.dist_nr):
# print j,m3.leaders[j], m3.groups[j]
# print 'Accuracy:',mixture.structureAccuracy(gen,m3) # structureEditDistance(gen,m1)
#print '\n\n################# FULL enumeration #####################'
m4 = copy.copy(m)
t0 = time.time()
fullEnumerationExhaustive.updateStructureBayesianFullEnumeration(m4,
data,
silent=1)
t1 = time.time()
time2 = t1 - t0
# m4.mapEM(data,40,0.1)
# print
# for j in range(m4.dist_nr):
# print j,m4.leaders[j], m4.groups[j]
# print 'Accuracy:',mixture.structureAccuracy(gen,m4)
logp_top = get_loglikelihood(m1, data) + m1.prior.pdf(m1)
logp_full_fixed = get_loglikelihood(m2, data) + m2.prior.pdf(m2)
logp_full = get_loglikelihood(m4, data) + m4.prior.pdf(m4)
logp_bottom = get_loglikelihood(m3, data) + m3.prior.pdf(m3)
if (not (round(logp_top, 3) <= round(logp_full, 3))
or not (round(logp_full_fixed, 3) <= round(logp_full, 3))
or not (round(logp_bottom, 3) <= round(logp_full, 3))):
print 'ERROR:'
print 'top:', logp_top
print 'full fixed:', logp_full_fixed
print 'full:', logp_full
print 'bottom:', logp_bottom, '\n'
printModel(gen, 'generating model')
printStructure(gen)
print
printModel(m4, 'full enumeration model')
printStructure(m4)
print
printModel(m2, 'fixed full model')
printStructure(m2)
raise ValueError
# # as a measure of separation of the component in the trained model, sum up
# # sym. KL divergence of all components and features
# train_diff = 0
# for j in range(gen.dist_nr):
# for i1 in range(m.G):
# for i2 in range(m.G):
# train_diff += sym_kl_dist(m.components[i1][j], m.components[i2][j])
mix_dist1 = mixtureKLdistance(gen, m)
mix_dist2 = mixtureKLdistance(m, gen)
max_dist1 = mixtureMaxKLdistance(gen, m)
max_dist2 = mixtureMaxKLdistance(m, gen)
# number of leaders in the full enumeration model
nr_full_lead = 0
for ll in m4.leaders:
nr_full_lead += len(ll)
match = matchModelStructures(gen, m)
compred = checkComponentRedundancy(gen.leaders, gen.groups)
if not (str(logp_top) == str(logp_full_fixed) == str(logp_full)):
print '-----------------------------------------------------------------------'
print 'Different:'
print 'top:', logp_top
print 'full fixed:', logp_full_fixed
print 'full:', logp_full
print 'bottom:', logp_bottom, '\n'
explain = 0
if str(compred) != '[]':
print '*** redundant components', compred
explain = 1
if gen.pi.min() < 0.05:
print '*** vanishing component in generating model'
explain = 1
if m.pi.min() < 0.05:
print '*** vanishing component in trained model'
explain = 1
if explain == 0:
print '*** UNEXPLAINED !'
printModel(gen, 'generating model')
printModel(m, 'trained model')
#print 'Trained model diff (simplistic):',train_diff
print 'D: Mixture distance gen/trained:', mix_dist1
print 'D: Mixture distance trained/gen:', mix_dist2
print 'D: Mixture Max-distance gen/trained:', max_dist1
print 'D: Mixture Max-distance trained/gen:', max_dist2
print '\nGenerating distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:', j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(gen.components[i1][j],
gen.components[i2][j])
print map(lambda x: '%.2f' % float(x),
kldists) # kldists.min()
print '\nTrained distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:', j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j],
m.components[i2][j])
print map(lambda x: '%.2f' % float(x),
kldists) # kldists.min()
print '\nTrained distances to generating:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:', j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j],
gen.components[i2][j])
print i1, '->', kldists.argmin(), map(
lambda x: '%.2f' % float(x), kldists) # kldists.min()
print '\n True structure:'
print 'True model post:', mixture.get_loglikelihood(
gen, data) + gen.prior.pdf(gen)
#for j in range(m.dist_nr):
# print j,gen.leaders[j], gen.groups[j]
printStructure(gen)
print '\nTop down:'
printStructure(m1)
print 'Top down model post:', mixture.get_loglikelihood(
m1, data) + m1.prior.pdf(m1)
printModel(m1, 'top down model')
print '\nFull enumeration Fixed Order:'
printStructure(m2)
print 'Full fixed order model post:', mixture.get_loglikelihood(
m2, data) + m2.prior.pdf(m2)
printModel(m2, 'full fixed model')
print '\nBottom up:'
printStructure(m3)
print 'Bottom up model post:', mixture.get_loglikelihood(
m3, data) + m3.prior.pdf(m3)
printModel(m3, 'bottom up model')
print '\nFull enumeration:'
printStructure(m4)
print 'Full enumeration model post:', mixture.get_loglikelihood(
m4, data) + m4.prior.pdf(m4)
printModel(m4, 'full enumeration model')
print '-----------------------------------------------------------------------'
elif str(
compred
) != '[]' and nr_full_lead > m4.p and match != 1: # redundant components and not fully merged
print '-----------------------------------------------------------------------'
print 'Same but redundant components:', compred
printModel(gen, 'generating model')
printModel(m, 'trained model')
#print 'Trained model diff:',train_diff
print 'S: Mixture distance gen/trained:', mix_dist1
print 'S: Mixture distance trained/gen:', mix_dist2
print 'S: Mixture Max-distance gen/trained:', max_dist1
print 'S: Mixture Max-distance trained/gen:', max_dist2
print '\nGenerating distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:', j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(gen.components[i1][j],
gen.components[i2][j])
print i1, ':', map(lambda x: '%.2f' % float(x),
kldists) # kldists.min()
print '\nTrained distances to self:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:', j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j],
m.components[i2][j])
print i1, ':', map(lambda x: '%.2f' % float(x),
kldists) # kldists.min()
print '\nTrained distances to generating:'
cmap = {}
for j in range(gen.dist_nr):
print 'feature:', j
for i1 in range(m.G):
kldists = np.zeros(m.G)
for i2 in range(m.G):
kldists[i2] = sym_kl_dist(m.components[i1][j],
gen.components[i2][j])
print i1, '->', kldists.argmin(), map(
lambda x: '%.2f' % float(x), kldists) # kldists.min()
print '\n True structure:'
print 'True model post:', mixture.get_loglikelihood(
gen, data) + gen.prior.pdf(gen)
#for j in range(m.dist_nr):
# print j,gen.leaders[j], gen.groups[j]
printStructure(gen)
print '\nTop down:'
printStructure(m1)
print 'Top down model post:', mixture.get_loglikelihood(
m1, data) + m1.prior.pdf(m1)
print '\nFull enumeration Fixed Order:'
printStructure(m2)
print 'Full fixed order model post:', mixture.get_loglikelihood(
m2, data) + m2.prior.pdf(m2)
print '\nBottom up:'
printStructure(m3)
print 'Bottom up model post:', mixture.get_loglikelihood(
m3, data) + m3.prior.pdf(m3)
print '\nFull enumeration:'
printStructure(m4)
print 'Full enumeration model post:', mixture.get_loglikelihood(
m4, data) + m4.prior.pdf(m4)
print '-----------------------------------------------------------------------'
# else:
# print '-----------------------------------------------------------------------'
# print 'S: Mixture distance gen/trained:',mix_dist1
# print 'S: Mixture distance trained/gen:',mix_dist2
# print '-----------------------------------------------------------------------'
# else:
# print '** all equal.'
# dtop = structureAccuracy(gen,m1)
# dfull_fixed = structureAccuracy(gen,m2)
# dfull = structureAccuracy(gen,m4)
# dbottom = structureAccuracy(gen,m3)
return np.array([logp_top, logp_full_fixed, logp_full, logp_bottom])
def getRandomMixture(G,
p,
KL_lower,
KL_upper,
dtypes='discgauss',
M=4,
seed=None):
# if seed:
# random.seed(seed)
# mixextend.set_gsl_rng_seed(seed)
# #print '*** seed=',seed
#
# else: # XXX debug
# seed = random.randint(1,9000000)
# mixextend.set_gsl_rng_seed(seed)
# random.seed(seed)
# #print '*** seed=',seed
#M = 4 # Alphabet size for discrete distributions
min_sigma = 0.1 # minimal std for Normal
max_sigma = 1.0 # maximal std for Normal
min_mu = -5.0 # minimal mean
max_mu = 8.0 # maximal mean
if dtypes == 'disc':
featureTypes = [0] * p
elif dtypes == 'gauss':
featureTypes = [1] * p
elif dtypes == 'discgauss':
# discrete or Normal features for now, chosen uniformly
# 0 discrete, 1 Normal
featureTypes = [random.choice((0, 1)) for i in range(p)]
else:
raise TypeError
#print featureTypes
C = []
for j in range(p):
c_j = []
for i in range(G):
#print i,j
if featureTypes[j] == 0:
acc = 0
while acc == 0:
cand = DiscreteDistribution(M, random_vector(M))
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d, cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
break
c_j.append(cand)
elif featureTypes[j] == 1:
acc = 0
while acc == 0:
mu = random.uniform(min_mu, max_mu)
sigma = random.uniform(min_sigma, max_sigma)
cand = NormalDistribution(mu, sigma)
#print 'cand:',cand
acc = 1
for d in c_j:
KL_dist = sym_kl_dist(d, cand)
if KL_dist > KL_upper or KL_dist < KL_lower:
#print ' *', cand, 'rejected:', d , KL_dist
acc = 0
c_j.append(cand)
else:
RuntimeError
C.append(c_j)
# print '\n'
# for cc in C:
# print cc
comps = []
for i in range(G):
comps.append(ProductDistribution([C[j][i] for j in range(p)]))
pi = get_random_pi(G, 0.1)
m = MixtureModel(G, pi, comps, struct=1)
m.updateFreeParams()
return m
