def CheckViterbiVsSoft(jobpath, taskid):
taskpath = PU.MakePath(os.path.join(jobpath, str(taskid)))
hmodel = bnpy.load_model(taskpath)
LPsoft = hmodel.calc_local_params(Data, limitMemoryLP=1)
SSsoft = hmodel.get_global_suff_stats(Data, LPsoft)
#zHatBySeq = AlignModelAndCalcDistance(Data, hmodel)
from bnpy.allocmodel.hmm.HMMUtil import runViterbiAlg
initPi = hmodel.allocModel.get_init_prob_vector()
transPi = hmodel.allocModel.get_trans_prob_matrix()
LP = hmodel.obsModel.calc_local_params(Data)
Lik = LP['E_log_soft_ev']
# Loop over each sequence in the collection
zHatBySeq = list()
for n in range(Data.nDoc):
start = Data.doc_range[n]
stop = Data.doc_range[n+1]
zHat = runViterbiAlg(Lik[start:stop], initPi, transPi)
zHatBySeq.append(zHat)
zHatFlat = StateSeqUtil.convertStateSeq_list2flat(zHatBySeq, Data)
LP = dict(Z=zHatFlat)
LP = bnpy.init.FromTruth.convertLPFromHardToSoft(LP, Data,
startIDsAt0=True,
Kmax=SSsoft.K)
LP = hmodel.allocModel.initLPFromResp(Data, LP)
SShard = hmodel.get_global_suff_stats(Data, LP)
return SShard, SSsoft, LPsoft
def loadModelWithLPSSandLscore(Data, path, label=''):
M = bnpy.load_model(os.path.expandvars(path))
LP = M.calc_local_params(Data)
SS = M.get_global_suff_stats(Data, LP, doPrecompEntropy=1)
M.update_global_params(SS)
Lscore = M.calc_evidence(SS=SS)
print "%15s K=%d L=%.5f Ntotal=%.2f" % (label, SS.K, Lscore, SS.N.sum())
return M, LP, SS, Lscore
def RunManualIntervention(jobpath, taskid,
zBySeq=None, Kmax=None):
taskpath = PU.MakePath(os.path.join(jobpath, str(taskid)))
hmodel = bnpy.load_model(taskpath)
print '------------ FROM CURRENT'
RunForwardAndPrint(Data, hmodel.copy(), LP=None)
Zflat = bnpy.util.StateSeqUtil.convertStateSeq_list2flat(zBySeq, Data)
LP = dict(Z=Zflat)
LP = bnpy.init.FromTruth.convertLPFromHardToSoft(LP, Data,
startIDsAt0=True,
Kmax=Kmax)
LP = hmodel.allocModel.initLPFromResp(Data, LP)
print '------------ FROM NEW'
bestModel = RunForwardAndPrint(Data, hmodel.copy(), LP)
return AlignModelAndCalcDistance(Data, bestModel)
