bestscore = score
besttau = tau
bestwfa = copy.deepcopy(wfa)
print "Tau: ",tau, " Score: ", score
avruntime += wfa.buildtime
nummodelsmade += 1
if metric == "WER":
bestscore = bestwfa.get_WER(testdata)
iohelpers.write_results(RESULTS_DIR+"co-"+wfatype+"-"+str(maxbasissize)+"-pautomac="+problem+"-"+metric+".txt", problem, wfatype+", "+"tau= "+str(besttau)+", basis size="+str(len(basisdict)), metric, bestscore, avruntime/float(nummodelsmade))
if probabilistic:
iohelpers.write_pnfa_model(MODEL_DIR+"co-"+wfatype+"-"+str(maxbasissize)+"-pautomac="+problem+"-"+metric+".fsm", bestwfa)
else:
RESULTS_DIR = "/home/williamleif/Dropbox/icml2014-experiments/results/real/"
if problem == "tree":
traindata = iohelpers.parse_file("/home/williamleif/Dropbox/icml2014-experiments/datasets/treebankdata.obs")
validdata = traindata[0:5000]
testdata = traindata[5000:10000]
traindata = traindata[10000:len(traindata)]
maxK = 500
if not success:
break
if metric == "WER":
score = wfa.get_WER(validdata)
else:
score = wfa.get_perplexity(validdata)
if bestsize == 0:
bestscore = score
bestsize = i
bestwfa = copy.deepcopy(wfa)
elif score < bestscore and abs(score-1000) > 0.1:
bestscore = score
bestsize = i
bestwfa = copy.deepcopy(wfa)
print "Model size: ", i, " Score: ", score
avruntime += wfa.buildtime
nummodelsmade += 1
if metric == "WER":
bestscore = bestwfa.get_WER(testdata)
else:
bestscore = bestwfa.get_perplexity(testdata)
iohelpers.write_results(RESULTS_DIR+"tensor-"+metric+".txt", problem,"size= "+str(bestsize)+", basis size="+str(basislength), metric, bestscore, 0)
iohelpers.write_pnfa_model(MODEL_DIR+"tensor-"+str(bestsize)+"-pautomac="+problem+"-"+metric+".fsm", bestwfa)