def setUp(self):
X = np.random.randn(100, 3)
self.Data = XData(X=X)
aPDict = dict(alpha0=1.0)
oPDict = dict(min_covar=1e-9)
self.hmodel = HModel.CreateEntireModel('EM', 'MixModel', 'ZMGauss',
aPDict, oPDict, self.Data)
def loadTopicModelFromMEDLDA(filepath, prefix=None, returnTPA=0):
''' Load topic model saved in medlda format.
'''
# Avoid circular import
import bnpy.HModel as HModel
assert prefix is not None
alphafilepath = os.path.join(filepath, prefix + '.alpha')
etafilepath = os.path.join(filepath, prefix + '.eta')
topicfilepath = os.path.join(filepath, prefix + '.log_prob_w')
alpha = float(np.loadtxt(alphafilepath))
eta = np.loadtxt(etafilepath)
logtopics = np.loadtxt(topicfilepath)
topics = np.exp(logtopics)
topics += 1e-9
topics /= topics.sum(axis=1)[:, np.newaxis]
assert np.all(np.isfinite(topics))
if returnTPA:
K = topics.shape[0]
probs = 1.0 / K * np.ones(K)
return topics, probs, alpha, eta
infAlg = 'VB'
aPriorDict = dict(alpha=alpha)
amodel = AllocModelConstructorsByName['FiniteTopicModel'](infAlg,
aPriorDict)
omodel = ObsModelConstructorsByName['Mult'](infAlg,
lam=0.001,
D=topics.shape[1])
hmodel = HModel(amodel, omodel)
hmodel.obsModel.set_global_params(topics=topics, nTotalTokens=1000)
return hmodel
def setUp(self):
X = np.random.randn(100, 3)
self.Data = XData(X=X)
aPDict = dict(alpha0=1.0)
oPDict = dict(dF=5, ECovMat='eye', sF=1.0)
self.hmodel = HModel.CreateEntireModel('VB', 'MixModel', 'ZMGauss',
aPDict, oPDict, self.Data)
def load_model( matfilepath, prefix='Best'): ''' Load model stored to disk by ModelWriter ''' # avoids circular import import bnpy.HModel as HModel obsModel = load_obs_model(matfilepath, prefix) allocModel = load_alloc_model(matfilepath, prefix) return HModel(allocModel, obsModel)
def setUp(self):
PRNG = np.random.RandomState(867)
X = PRNG.randn(100,2)
self.Data = XData(X=X)
aPDict = dict(alpha0=1.0)
oPDict = dict(min_covar=1e-9)
self.hmodel = HModel.CreateEntireModel('EM','MixModel','ZMGauss', aPDict, oPDict, self.Data)
initParams = dict(initname='randexamples', seed=0, K=5)
self.hmodel.init_global_params(self.Data, **initParams)
def MakeModelWithTrueComps(self):
''' Create DPMix-Gauss model of self.Data, with K=3 *true* components.
Store result as "hmodel" in self.
'''
aDict = dict(alpha0=1.0, truncType='z')
oDict = dict(kappa=1e-7, dF=1, ECovMat='eye', sF=1e-3)
self.hmodel = HModel.CreateEntireModel('VB', 'DPMixModel', 'Gauss',
aDict, oDict, self.Data)
LP = dict(resp=self.TrueResp)
SS = self.hmodel.get_global_suff_stats(self.Data, LP)
self.hmodel.update_global_params(SS)
def MakeModelWithTrueComps(self):
''' Create hmodel with "true" components as self.Data, add to self
Also create representative suff stats SS, add to self
'''
aDict = dict(alpha0=1.0, truncType='z')
oDict = dict(kappa=1e-5, dF=1, ECovMat='eye', sF=1e-3)
self.hmodel = HModel.CreateEntireModel('VB', 'DPMixModel', 'Gauss',
aDict, oDict, self.Data)
LP = dict(resp=self.TrueResp)
SS = self.hmodel.get_global_suff_stats(self.Data, LP)
self.hmodel.update_global_params(SS)
LP = self.hmodel.calc_local_params(self.Data)
flagDict = dict(doPrecompEntropy=True, doPrecompMergeEntropy=True)
self.SS = self.hmodel.get_global_suff_stats(self.Data, LP, **flagDict)
def MakeModelWithOneComp(self):
''' Create DPMix-Gauss model of self.Data, with K=1 components.
Store result as "oneModel" in self,
with updated suff stats field "oneSS"
'''
aDict = dict(alpha0=1.0, truncType='z')
oDict = dict(kappa=1e-7, dF=1, ECovMat='eye', sF=1e-3)
self.oneModel = HModel.CreateEntireModel('VB', 'DPMixModel', 'Gauss',
aDict, oDict, self.Data)
LP = dict(resp=np.ones((self.Data.nObs, 1)))
SS = self.oneModel.get_global_suff_stats(self.Data, LP)
assert SS.x.size == self.Data.dim
assert self.oneModel.obsModel.obsPrior.D == self.Data.dim
self.oneModel.update_global_params(SS)
LP = self.oneModel.calc_local_params(self.Data)
self.oneSS = self.oneModel.get_global_suff_stats(self.Data, LP)
def setUp(self, K=7):
''' Create random data, and a K component MixModel to go with it
Call this original model "hmodel".
We copy hmodel into "modelB", and then save to file via save_model()
'''
self.K = K
PRNG = np.random.RandomState(867)
X = PRNG.randn(100,2)
self.Data = XData(X=X)
aPDict = dict(alpha0=1.0)
oPDict = dict(min_covar=1e-9)
self.hmodel = HModel.CreateEntireModel('EM','MixModel','ZMGauss',
aPDict, oPDict, self.Data)
modelB = self.hmodel.copy()
initParams = dict(initname='randexamples', seed=0, K=self.K)
modelB.init_global_params(self.Data, **initParams)
ModelWriter.save_model(modelB, '/tmp/', 'Test')
self.modelB = modelB
def MakeModelWithDuplicatedComps(self):
''' Create model with "duplicated" components"
For each true component k, we have "two" versions of it, k1 and k2.
Half of the data generated by k is assigned to k1
and the other half is assigned to k2.
These assignments are done via "dupResp" local parameter,
and so by feeding that LP into SS and update_global_params.
'''
aDict = dict(alpha0=1.0, truncType='z')
oDict = dict(kappa=1e-5, dF=1, ECovMat='eye', sF=1e-3)
self.dupModel = HModel.CreateEntireModel('VB', 'DPMixModel', 'Gauss',
aDict, oDict, self.Data)
LP = dict(resp=self.DupResp)
SS = self.hmodel.get_global_suff_stats(self.Data, LP)
self.dupModel.update_global_params(SS)
LP = self.dupModel.calc_local_params(self.Data)
flagDict = dict(doPrecompEntropy=True, doPrecompMergeEntropy=True)
self.dupSS = self.dupModel.get_global_suff_stats(
self.Data, LP, **flagDict)
def load_model_at_prefix(matfilepath, prefix='Best', lap=None):
''' Load model stored to disk by ModelWriter
Returns
------
model : bnpy.HModel
Model object for saved at checkpoint indicated by prefix or lap.
'''
# Avoids circular import
import bnpy.HModel as HModel
if lap is not None:
prefix, _ = getPrefixForLapQuery(matfilepath, lap)
try:
obsModel = load_obs_model(matfilepath, prefix)
allocModel = load_alloc_model(matfilepath, prefix)
model = HModel.HModel(allocModel, obsModel)
except IOError as e:
print(str(e))
'''
if prefix == 'Best':
matList = glob.glob(os.path.join(matfilepath, '*TopicModel.mat'))
lpwList = glob.glob(os.path.join(matfilepath, '*.log_prob_w'))
if len(matList) > 0:
matList.sort() # ascending order, so most recent is last
prefix = matList[-1].split(os.path.sep)[-1][:11]
model = loadTopicModel(matfilepath, prefix=prefix)
elif len(lpwList) > 0:
lpwList.sort() # ascenting order
prefix = lpwList[-1].split(os.path.sep)[-1][:7]
else:
raise e
'''
try:
model = loadTopicModel(matfilepath, prefix=prefix)
except IOError as e:
model = loadTopicModelFromMEDLDA(matfilepath, prefix=prefix)
return model
def loadTopicModelFromTxtFiles(snapshotPath,
returnTPA=False,
returnWordCounts=False,
normalizeProbs=True,
normalizeTopics=True,
**kwargs):
''' Load from snapshot text files.
Returns
-------
hmodel
'''
Mdict = dict()
possibleKeys = [
'K', 'probs', 'alpha', 'beta', 'lam', 'gamma', 'nTopics', 'nTypes',
'vocab_size'
]
keyMap = dict(beta='lam', nTopics='K', nTypes='vocab_size')
for key in possibleKeys:
try:
arr = np.loadtxt(snapshotPath + "/%s.txt" % (key))
if key in keyMap:
Mdict[keyMap[key]] = arr
else:
Mdict[key] = arr
except Exception:
pass
assert 'K' in Mdict
assert 'lam' in Mdict
K = int(Mdict['K'])
V = int(Mdict['vocab_size'])
if os.path.exists(snapshotPath + "/topics.txt"):
Mdict['topics'] = np.loadtxt(snapshotPath + "/topics.txt")
Mdict['topics'] = as2D(toCArray(Mdict['topics'], dtype=np.float64))
assert Mdict['topics'].ndim == 2
assert Mdict['topics'].shape == (K, V)
else:
TWC_data = np.loadtxt(snapshotPath + "/TopicWordCount_data.txt")
TWC_inds = np.loadtxt(snapshotPath + "/TopicWordCount_indices.txt",
dtype=np.int32)
if os.path.exists(snapshotPath + "/TopicWordCount_cscindptr.txt"):
TWC_cscindptr = np.loadtxt(snapshotPath +
"/TopicWordCount_cscindptr.txt",
dtype=np.int32)
TWC = scipy.sparse.csc_matrix((TWC_data, TWC_inds, TWC_cscindptr),
shape=(K, V))
else:
TWC_csrindptr = np.loadtxt(snapshotPath +
"/TopicWordCount_indptr.txt",
dtype=np.int32)
TWC = scipy.sparse.csr_matrix((TWC_data, TWC_inds, TWC_csrindptr),
shape=(K, V))
Mdict['WordCounts'] = TWC.toarray()
if returnTPA:
# Load topics : 2D array, K x vocab_size
if 'WordCounts' in Mdict:
topics = Mdict['WordCounts'] + Mdict['lam']
else:
topics = Mdict['topics']
topics = as2D(toCArray(topics, dtype=np.float64))
assert topics.ndim == 2
K = topics.shape[0]
if normalizeTopics:
topics /= topics.sum(axis=1)[:, np.newaxis]
# Load probs : 1D array, size K
try:
probs = Mdict['probs']
except KeyError:
probs = (1.0 / K) * np.ones(K)
probs = as1D(toCArray(probs, dtype=np.float64))
assert probs.ndim == 1
assert probs.size == K
if normalizeProbs:
probs = probs / np.sum(probs)
# Load alpha : scalar float > 0
try:
alpha = float(Mdict['alpha'])
except KeyError:
if 'alpha' in os.environ:
alpha = float(os.environ['alpha'])
else:
raise ValueError('Unknown parameter alpha')
return topics, probs, alpha
# BUILD HMODEL FROM LOADED TXT
infAlg = 'VB'
# avoids circular import
from bnpy.HModel import HModel
if 'gamma' in Mdict:
aPriorDict = dict(alpha=Mdict['alpha'], gamma=Mdict['gamma'])
HDPTopicModel = AllocModelConstructorsByName['HDPTopicModel']
amodel = HDPTopicModel(infAlg, aPriorDict)
else:
FiniteTopicModel = AllocModelConstructorsByName['FiniteTopicModel']
amodel = FiniteTopicModel(infAlg, dict(alpha=Mdict['alpha']))
omodel = ObsModelConstructorsByName['Mult'](infAlg, **Mdict)
hmodel = HModel(amodel, omodel)
hmodel.set_global_params(**Mdict)
if returnWordCounts:
return hmodel, Mdict['WordCounts']
return hmodel
def loadTopicModel(matfilepath,
queryLap=None,
prefix=None,
returnWordCounts=0,
returnTPA=0,
normalizeTopics=0,
normalizeProbs=0,
**kwargs):
''' Load saved topic model
Returns
-------
topics : 2D array, K x vocab_size (if returnTPA)
probs : 1D array, size K (if returnTPA)
alpha : scalar (if returnTPA)
hmodel : HModel
WordCounts : 2D array, size K x vocab_size (if returnWordCounts)
'''
if prefix is None:
prefix, lapQuery = getPrefixForLapQuery(matfilepath, queryLap)
# avoids circular import
from bnpy.HModel import HModel
if len(glob.glob(os.path.join(matfilepath, "*.log_prob_w"))) > 0:
return loadTopicModelFromMEDLDA(matfilepath,
prefix,
returnTPA=returnTPA)
snapshotList = glob.glob(os.path.join(matfilepath, 'Lap*TopicSnapshot'))
matfileList = glob.glob(os.path.join(matfilepath, 'Lap*TopicModel.mat'))
if len(snapshotList) > 0:
if prefix is None:
snapshotList.sort()
snapshotPath = snapshotList[-1]
else:
snapshotPath = None
for curPath in snapshotList:
if curPath.count(prefix):
snapshotPath = curPath
return loadTopicModelFromTxtFiles(snapshotPath,
normalizeTopics=normalizeTopics,
normalizeProbs=normalizeProbs,
returnWordCounts=returnWordCounts,
returnTPA=returnTPA)
if prefix is not None:
matfilepath = os.path.join(matfilepath, prefix + 'TopicModel.mat')
Mdict = loadDictFromMatfile(matfilepath)
if 'SparseWordCount_data' in Mdict:
data = np.asarray(Mdict['SparseWordCount_data'], dtype=np.float64)
K = int(Mdict['K'])
vocab_size = int(Mdict['vocab_size'])
try:
indices = Mdict['SparseWordCount_indices']
indptr = Mdict['SparseWordCount_indptr']
WordCounts = scipy.sparse.csr_matrix((data, indices, indptr),
shape=(K, vocab_size))
except KeyError:
rowIDs = Mdict['SparseWordCount_i'] - 1
colIDs = Mdict['SparseWordCount_j'] - 1
WordCounts = scipy.sparse.csr_matrix((data, (rowIDs, colIDs)),
shape=(K, vocab_size))
Mdict['WordCounts'] = WordCounts.toarray()
if returnTPA:
# Load topics : 2D array, K x vocab_size
if 'WordCounts' in Mdict:
topics = Mdict['WordCounts'] + Mdict['lam']
else:
topics = Mdict['topics']
topics = as2D(toCArray(topics, dtype=np.float64))
assert topics.ndim == 2
K = topics.shape[0]
if normalizeTopics:
topics /= topics.sum(axis=1)[:, np.newaxis]
# Load probs : 1D array, size K
try:
probs = Mdict['probs']
except KeyError:
probs = (1.0 / K) * np.ones(K)
probs = as1D(toCArray(probs, dtype=np.float64))
assert probs.ndim == 1
assert probs.size == K
if normalizeProbs:
probs = probs / np.sum(probs)
# Load alpha : scalar float > 0
try:
alpha = float(Mdict['alpha'])
except KeyError:
if 'alpha' in os.environ:
alpha = float(os.environ['alpha'])
else:
raise ValueError('Unknown parameter alpha')
if 'eta' in Mdict:
return topics, probs, alpha, as1D(toCArray(Mdict['eta']))
return topics, probs, alpha
infAlg = 'VB'
if 'gamma' in Mdict:
aPriorDict = dict(alpha=Mdict['alpha'], gamma=Mdict['gamma'])
HDPTopicModel = AllocModelConstructorsByName['HDPTopicModel']
amodel = HDPTopicModel(infAlg, aPriorDict)
else:
FiniteTopicModel = AllocModelConstructorsByName['FiniteTopicModel']
amodel = FiniteTopicModel(infAlg, dict(alpha=Mdict['alpha']))
omodel = ObsModelConstructorsByName['Mult'](infAlg, **Mdict)
hmodel = HModel(amodel, omodel)
hmodel.set_global_params(**Mdict)
if returnWordCounts:
return hmodel, Mdict['WordCounts']
return hmodel
def proposeNewResp_dpmixture(Z_n,
propResp,
tempModel=None,
tempSS=None,
Data_n=None,
origK=0,
Kfresh=3,
nVBIters=3,
PRNG=None,
PastAttemptLog=dict(),
**kwargs):
''' Create new resp matrix by DP mixture clustering of subsampled data.
Returns
-------
propResp : 2D array, N x K'
'''
# Avoid circular imports
from bnpy.allocmodel import DPMixtureModel
from bnpy import HModel
from bnpy.mergemove import MergePlanner, MergeMove
# Select ktarget
if 'strategy' not in PastAttemptLog:
PastAttemptLog['strategy'] = 'byState'
if PastAttemptLog['strategy'] == 'byState':
Kcur = tempModel.obsModel.K
Kextra = Kcur - PastAttemptLog['uIDs'].size
if Kextra > 0:
maxUID = PastAttemptLog['maxUID']
uIDs = PastAttemptLog['uIDs']
for extraPos in range(Kextra):
maxUID += 1
uIDs = np.append(uIDs, maxUID)
PastAttemptLog['maxUID'] = maxUID
PastAttemptLog['uIDs'] = uIDs
candidateStateUIDs = set()
for state in np.unique(Z_n):
uid = PastAttemptLog['uIDs'][state]
candidateStateUIDs.add(uid)
allAvailableUIDs = [x for x in candidateStateUIDs]
if 'nTryByStateUID' not in PastAttemptLog:
PastAttemptLog['nTryByStateUID'] = dict()
minTry = np.inf
for badState, nTry in list(PastAttemptLog['nTryByStateUID'].items()):
if badState in candidateStateUIDs:
if nTry < minTry:
minTry = nTry
untriedList = [
x for x in candidateStateUIDs
if x not in PastAttemptLog['nTryByStateUID']
or PastAttemptLog['nTryByStateUID'][x] == 0
]
if len(untriedList) > 0:
candidateStateUIDs = untriedList
else:
# Keep only candidates that have been tried the least
for badState, nTry in list(
PastAttemptLog['nTryByStateUID'].items()):
# Remove bad State from candidateStateUIDs
if badState in candidateStateUIDs:
if nTry > minTry:
candidateStateUIDs.remove(badState)
candidateStateUIDs = np.asarray([x for x in candidateStateUIDs])
# Pick a state that we haven't tried yet,
# uniformly at random
if len(candidateStateUIDs) > 0:
chosenStateUID = PRNG.choice(np.asarray(candidateStateUIDs))
ktarget = np.flatnonzero(
chosenStateUID == PastAttemptLog['uIDs'])[0]
else:
# Just pick a target at random
chosenStateUID = PRNG.choice(np.asarray(allAvailableUIDs))
ktarget = np.flatnonzero(chosenStateUID == PastAttemptLog['uIDs'])[0]
relDataIDs = np.flatnonzero(Z_n == ktarget)
# If the selected state is too small, just make a new state for all relIDs
if relDataIDs.size < Kfresh:
if chosenStateUID in PastAttemptLog['nTryByStateUID']:
PastAttemptLog['nTryByStateUID'][chosenStateUID] += 1
else:
PastAttemptLog['nTryByStateUID'][chosenStateUID] = 1
propResp[relDataIDs, :] = 0
propResp[relDataIDs, origK + 1] = 1
return propResp, origK + 1
if hasattr(Data_n, 'Xprev'):
Xprev = Data_n.Xprev[relDataIDs]
else:
Xprev = None
targetData = XData(X=Data_n.X[relDataIDs], Xprev=Xprev)
myDPModel = DPMixtureModel('VB', gamma0=10)
myObsModel = copy.deepcopy(tempModel.obsModel)
delattr(myObsModel, 'Post')
myObsModel.ClearCache()
myHModel = HModel(myDPModel, myObsModel)
initname = PRNG.choice(['randexamplesbydist', 'randcontigblocks'])
myHModel.init_global_params(targetData,
K=Kfresh,
initname=initname,
**kwargs)
Kfresh = myHModel.obsModel.K
mergeIsPromising = True
while Kfresh > 1 and mergeIsPromising:
for vbiter in range(nVBIters):
targetLP = myHModel.calc_local_params(targetData)
targetSS = myHModel.get_global_suff_stats(targetData, targetLP)
# Delete unnecessarily small comps
if vbiter == nVBIters - 1:
smallIDs = np.flatnonzero(targetSS.getCountVec() <= 1)
for kdel in reversed(smallIDs):
if targetSS.K > 1:
targetSS.removeComp(kdel)
# Global step
myHModel.update_global_params(targetSS)
# Do merges
mPairIDs, MM = MergePlanner.preselect_candidate_pairs(
myHModel,
targetSS,
preselect_routine='wholeELBO',
doLimitNumPairs=0,
returnScoreMatrix=1,
**kwargs)
targetLP = myHModel.calc_local_params(targetData,
mPairIDs=mPairIDs,
limitMemoryLP=1)
targetSS = myHModel.get_global_suff_stats(targetData,
targetLP,
mPairIDs=mPairIDs,
doPrecompEntropy=1,
doPrecompMergeEntropy=1)
myHModel.update_global_params(targetSS)
curELBO = myHModel.calc_evidence(SS=targetSS)
myHModel, targetSS, curELBO, Info = MergeMove.run_many_merge_moves(
myHModel, targetSS, curELBO, mPairIDs, M=MM, isBirthCleanup=1)
mergeIsPromising = len(Info['AcceptedPairs']) > 0
Kfresh = targetSS.K
if mergeIsPromising:
targetLP = myHModel.calc_local_params(targetData)
propResp[relDataIDs, :] = 0
propResp[relDataIDs, origK:origK + Kfresh] = targetLP['resp']
# Test if we added at least 2 states with mass > 1
didAddNonEmptyNewStates = np.sum(targetSS.N > 1.0) >= 2
print('dpmixture proposal: targetUID %d didAddNonEmptyNewStates %d' %
(chosenStateUID, didAddNonEmptyNewStates))
if didAddNonEmptyNewStates:
print('NEW STATE MASSES:', end=' ')
print(' '.join(['%5.1f' % (x) for x in targetSS.N]))
PastAttemptLog['nTryByStateUID'][chosenStateUID] = 0 # success!
else:
if chosenStateUID in PastAttemptLog['nTryByStateUID']:
PastAttemptLog['nTryByStateUID'][chosenStateUID] += 1
else:
PastAttemptLog['nTryByStateUID'][chosenStateUID] = 1
return propResp, origK + Kfresh
def MakeHModel(self):
aPDict = dict(alpha0=1.0)
oPDict = dict(min_covar=1e-9)
self.hmodel = HModel.CreateEntireModel('EM', 'MixModel', 'ZMGauss',
aPDict, oPDict, self.Data)
def MakeHModel(self):
aPDict = dict(alpha0=1.0)
oPDict = dict(dF=5, ECovMat='eye', sF=1.0)
self.hmodel = HModel.CreateEntireModel('VB', 'MixModel', 'ZMGauss',
aPDict, oPDict, self.Data)
