def predic_unscore_api(inputtext):
charstop = True # True means label attributes to previous char
features = 3 # 1=discrete; 2=vectors; 3=both
dictfile = 'vector/24scbow50.txt'
modelname = 'datalunyu5001.m'
vdict = util.readvec(dictfile)
inputtext = inputtext
#li = [line for line in util.text_to_lines(inputtext)]
li = util.text_to_lines(inputtext)
print(li)
data = []
for line in li:
x, y = util.line_toseq(line, charstop)
print(x)
if features == 1:
d = crf.x_seq_to_features_discrete(x, charstop), y
elif features == 2:
d = crf.x_seq_to_features_vector(x, vdict, charstop), y
elif features == 3:
d = crf.x_seq_to_features_both(x, vdict, charstop), y
data.append(d)
tagger = pycrfsuite.Tagger()
tagger.open(modelname)
print("Start testing...")
results = []
lines = []
Spp = []
Npp = []
out = []
#while data:
for index in range(len(data)):
print(len(data))
xseq, yref = data.pop(0)
yout = tagger.tag(xseq)
sp = 0
np = 0
for i in range(len(yout)):
sp = tagger.marginal('S', i)
Spp.append(sp) #S標記的機率
print(sp)
np = tagger.marginal('N', i)
Npp.append(np) #Nㄅ標記的機率
print(np)
results.append(util.eval(yref, yout, "S"))
lines.append(
util.seq_to_line([x['gs0'] for x in xseq], yout, charstop, Spp,
Npp))
#print(util.seq_to_line([x['gs0'] for x in xseq], (str(sp) +'/'+ str(np)),charstop))
out.append(yout)
tp, fp, fn, tn = zip(*results)
tp, fp, fn, tn = sum(tp), sum(fp), sum(fn), sum(tn)
p, r = tp / (tp + fp), tp / (tp + fn)
score = ''
score = score + '<br>' + "Total tokens in Test Set:" + repr(tp + fp + fn +
tn)
score = score + '<br>' + "Total S in REF:" + repr(tp + fn)
score = score + '<br>' + "Total S in OUT:" + repr(tp + fp)
score = score + '<br>' + "Presicion:" + repr(p)
score = score + '<br>' + "Recall:" + repr(r)
score = score + '<br>' + "*******************F1-score:" + repr(2 * p * r /
(p + r))
output = ''
print(lines)
for line in lines:
#line = unquote(line)
print("output:")
print(line.encode('utf8'))
#output = output + '<br>' + line
output += line
print(line)
output = score + '<br>' + output
#output = jsonify({'str': output})
return (out)
def predic():
charstop = True # True means label attributes to previous char
features = 3 # 1=discrete; 2=vectors; 3=both
dictfile = 'vector/24scbow50.txt'
modelname = 'datalunyu5001.m'
vdict = util.readvec(dictfile)
inputtext = request.form.get('input_text', '')
#li = [line for line in util.text_to_lines(inputtext)]
li = util.text_to_lines(inputtext)
print(li)
data = []
for line in li:
x, y = util.line_toseq(line, charstop)
print(x)
if features == 1:
d = crf.x_seq_to_features_discrete(x, charstop), y
elif features == 2:
d = crf.x_seq_to_features_vector(x, vdict, charstop), y
elif features == 3:
d = crf.x_seq_to_features_both(x, vdict, charstop), y
data.append(d)
tagger = pycrfsuite.Tagger()
tagger.open(modelname)
print("Start testing...")
results = []
lines = []
Spp = []
Npp = []
#while data:
for index in range(len(data)):
print(len(data))
xseq, yref = data.pop(0)
yout = tagger.tag(xseq)
sp = 0
np = 0
for i in range(len(yout)):
sp = tagger.marginal('S', i)
Spp.append(sp) #S標記的機率
print(sp)
np = tagger.marginal('N', i)
Npp.append(np) #Nㄅ標記的機率
print(np)
results.append(util.eval(yref, yout, "S"))
lines.append(
util.seq_to_line([x['gs0'] for x in xseq], yout, charstop, Spp,
Npp))
#print(util.seq_to_line([x['gs0'] for x in xseq], (str(sp) +'/'+ str(np)),charstop))
U_score = 0
p_Scount = 0
p_Ncount = 0
for i in range(len(Spp)):
_s = 0
if Spp[i] > Npp[i]:
_s = Spp[i]
else:
_s = Npp[i]
_s = (_s - 0.5) * 10
U_score = U_score + _s
p_Scount = p_Scount + Spp[i]
p_Ncount = p_Ncount + Npp[i]
tp, fp, fn, tn = zip(*results)
tp, fp, fn, tn = sum(tp), sum(fp), sum(fn), sum(tn)
p, r = tp / (tp + fp), tp / (tp + fn)
score = ''
score = score + '<br>' + "Total tokens in Test Set:" + repr(tp + fp + fn +
tn)
score = score + '<br>' + "Total S in REF:" + repr(tp + fn)
score = score + '<br>' + "Total S in OUT:" + repr(tp + fp)
score = score + '<br>' + "Presicion:" + repr(p)
score = score + '<br>' + "Recall:" + repr(r)
score = score + '<br>' + "*******************F1-score:" + repr(2 * p * r /
(p + r))
score = score + '<br>' + "======================="
score = score + '<br>' + "character count:" + str(len(Spp))
score = score + '<br>' + "block uncertain rate:" + str(
(U_score / len(Spp)))
output = ''
key = 0
for line in lines:
#print (line.encode('utf8'))
output = output + '<br>' + line
#print (line)
key = key + 1
#for index_m in ypp:
# output = output + '<br>' + line
output = score + '<br>' + output
return (output)
xseq, yref = testdata.pop()
#print(xseq)
yout = tagger.tag(xseq)
all_len += len(yout)
#print(len(xseq),len(yout),all_len)
sp = 0
np = 0
for i in range(len(yout)):
sp = tagger.marginal('S',i)
Spp.append(sp) #S標記的機率
#print(sp)
np = tagger.marginal('N',i)
Npp.append(np)#N標記的機率
#print(np)
results.append(util.eval(yref, yout, "S"))
lines.append(util.seq_to_line([x['gs0'] for x in xseq],yout,charstop,Spp,Npp))
#print(util.seq_to_line([x['gs0'] for x in xseq], (str(sp) +'/'+ str(np)),charstop))
score_array = []
All_u_score = 0
for i in range(len(Spp)):
_s = 0
if Spp[i] > Npp[i]: _s = Spp[i]
else :_s = Npp[i]
#_s = (_s - 0.5) * 10
_s = (1 - _s)
#U_score = U_score + _s
score_array.append(_s)
for i in range(len(testidx)):
U_score = 0 #文本區塊的不確定值
text_count = 0 #字數
elif features == 3:
d = crf.x_seq_to_features_both(x, vdict, charstop), y
data.append(d)
tagger = pycrfsuite.Tagger()
tagger.open(modelname)
print datetime.datetime.now()
print "Start testing..."
results = []
lines = []
while data:
xseq, yref = data.pop()
yout = tagger.tag(xseq)
results.append(util.eval(yref, yout, "S"))
lines.append(util.seq_to_line([x['gs0'] for x in xseq],yout,charstop))
tp, fp, fn, tn = zip(*results)
tp, fp, fn, tn = sum(tp), sum(fp), sum(fn), sum(tn)
p, r = tp/(tp+fp), tp/(tp+fn)
print "Total tokens in Test Set:", tp+fp+fn+tn
print "Total S in REF:", tp+fn
print "Total S in OUT:", tp+fp
print "Presicion:", p
print "Recall:", r
print "*******************F1-score:", 2*p*r/(p+r)
for line in lines:
print line.encode('utf8')
stt = datetime.datetime.now()
print "Start training...", stt
hmmtagger = nt.hmm.HiddenMarkovModelTagger.train(traindata)
print "################# Training took:", datetime.datetime.now()-stt
results = []
lines = []
testdata = [line for line in util.file_to_lines(glob.glob(hu))]
for line in testdata:
x, yref = util.line_toseq(line, charstop)
out = hmmtagger.tag(x)
_, yout = zip(*out)
yout = list(yout)
results.append(util.eval(yref, yout, "S"))
lines.append(util.seq_to_line(x,yout,charstop))
tp, fp, fn, tn = zip(*results)
tp, fp, fn, tn = sum(tp), sum(fp), sum(fn), sum(tn)
p, r = tp/(tp+fp), tp/(tp+fn)
print "Total tokens in Test Set:", tp+fp+fn+tn
print "Total S in REF:", tp+fn
print "Total S in OUT:", tp+fp
print "Presicion:", p
print "Recall:", r
print "F1-score:", 2*p*r/(p+r)
while lines:
print lines.pop().encode('utf8')
stt = datetime.datetime.now()
print (traindata[0])
print ("Start training...", stt)
hmmtagger = nt.hmm.HiddenMarkovModelTagger.train(traindata)
#hmmtagger = nt.HiddenMarkovModelTagger.train(traindata)
print ("################# Training took:", datetime.datetime.now()-stt)
results = []
lines = []
testdata = [line for line in util.file_to_lines(glob.glob(hu))]
for line in testdata:
x, yref = util.line_toseq(line, charstop)
out = hmmtagger.tag(x)
_, yout = zip(*out)
yout = list(yout)
results.append(util.eval(yref, yout, "S"))
lines.append(util.seq_to_line(x,yout,charstop))
tp, fp, fn, tn = zip(*results)
tp, fp, fn, tn = sum(tp), sum(fp), sum(fn), sum(tn)
p, r = tp/(tp+fp), tp/(tp+fn)
print ("Total tokens in Test Set:", tp+fp+fn+tn)
print ("Total S in REF:", tp+fn)
print ("Total S in OUT:", tp+fp)
print ("Presicion:", p)
print ("Recall:", r)
print ("F1-score:", 2*p*r/(p+r))
while lines:
print (lines.pop().encode('utf8'))