def create_ftset_ex():
allwsdict = '/users/wenny/nju/task/法条文书分析/故意杀人罪/2014'
allwsdict = '/盗窃罪'
testwsdict = '../data/testjtzs1w'
testdata = '../data/1w篇_事实到法条'
dir = os.listdir(allwsdict)
print(len(dir))
resultList = random.sample(range(0, 40000), 10000)
for i in resultList:
wsname = dir[i]
src = allwsdict + '/' + wsname
target = testwsdict + '/' + wsname
shutil.copy(src, target)
dir = os.listdir(testwsdict)
for wsname in dir:
print(wsname)
wspath = testwsdict + '/' + wsname
# wstestpath = testdata + '/' + wsname.split('.')[0] + '.txt'
if wsname.endswith('.xml'):
ftmcls, ftnrls = getFTList(wspath)
zipob = zip(ftmcls, ftnrls)
cols = []
for ftmc, ftnr in zipob:
cols.append(str(ftmc) + ':' + str(ftnr))
wsStrls = cutcontent(getSSMatchObject(wspath))
createx(wsname, wsStrls, cols, [], testdata)
def txttestset(dictpath, output):
dir = os.listdir(dictpath)
content = ''
for i in range(len(dir)):
ws = dir[i]
wspath = dictpath + '/' + ws
ftls = getFTfromQW(wspath)
ssls = cutcontent(getSSMatchObject(wspath))
jlls = cutcontent(getJLMatchObject(wspath))
content += ws + 'end!fact:'
content += '!@#'.join(ssls)
content += 'end!ft:'
content += '!@#'.join(ftls)
content += 'end!jl:'
content += '!@#'.join(jlls)
content += '\n'
with open(output, 'w', encoding='utf-8') as f:
f.write(content)
def predictjlws(wspath):
fj_model = jlCnn()
jlStrls = cutcontent(getJLMatchObject(wspath))
ftmcls, ftnrls = getFTList(wspath)
# 法条到结论
for jl in jlStrls:
print(jl)
for ft in ftnrls:
print(ft)
print(fj_model.predict(jl, ft))
def usekeys(wspath, model):
outputdata_ss = []
outputdata_jl = []
ftmcls, ftnrls = getFTList(wspath)
print('wsft..', ftmcls)
ssStrls = cutcontent(getSSMatchObject(wspath))
# 预先将结论所有句子的关键词提取好
jlStrls = cutcontent(getJLMatchObject(wspath))
# 获取关键词json文件
keys = readkeysjson('../data/交通肇事罪.json')
with open('../data/2014corpus.txt', 'r', encoding='utf-8') as f:
vocab = list(f.read().split(' '))
for ssStr in ssStrls:
line = []
mk = mapkey(ssStr, keys, model, vocab)
for ft in ftnrls:
f = 0
for key in mk:
if ft.find(key) > 0:
line.append(1)
f = 1
break
if f == 0:
line.append(0)
outputdata_ss.append(line)
# for jlStr in jlStrls:
# mk = mapkey(jlStr,keys,model,vocab)
# line = []
# for ft in ftnrls:
# f = 0
# for key in mk:
# if ft.find(key) > 0:
# line.append(1)
# f = 1
# break
# if f == 0:
# line.append(0)
# outputdata_jl.append(line)
return numpy.array(outputdata_ss).T
def predictssws(wspath):
sf_model = ssCnn()
ssStrls = cutcontent(getSSMatchObject(wspath))
ftmcls, ftnrls = getFTList(wspath)
outputss = []
#事实到法条
for ss in ssStrls:
print(ss)
for ft in ftnrls:
print(ft)
print(sf_model.predict(ss, ft))
def create_zjset_ex(testwsdict, expath):
dir = os.listdir(testwsdict)
index = 0
for wsname in dir:
print(index)
index += 1
wspath = testwsdict + '/' + wsname
if str(wsname).endswith('.xml'):
zjlist = getZJ(wspath)
wsStrls = cutcontent(getSSMatchObject(wspath))
createx(wsname, wsStrls, zjlist, [], expath)
def createtest(wspath, wstestpath):
ftmcls, ftnrls = getFTList(wspath)
wsStrls = cutcontent(getJLMatchObject(wspath))
with open(wstestpath, 'w', encoding='utf-8') as f:
for i in range(len(ftmcls)):
for wsStr in wsStrls:
line = []
line.append(ftmcls[i])
line.append(ftnrls[i])
line.append(wsStr)
f.write('!@#'.join(line))
f.write('\n')
def traversews(wspath, model):
# 加入data
# outputdata_dict = {}
outputdata_ss = []
outputdata_jl = []
ftmcls, ftnrls = getFTList(wspath)
print('wsft..', ftmcls)
wsStrls = cutcontent(getSSMatchObject(wspath))
wsStrkeys = []
# 预先将文书所有句子的关键词提取好
for wsStr in wsStrls:
wsStrkeys.append(getnormalizeweigth(wsStr, False)) # h获取文书关键词，[[]]
print('set ws sp length....', len(wsStrkeys))
# 预先将结论所有句子的关键词提取好
jlStrkeys = []
jlStrls = cutcontent(getJLMatchObject(wspath))
for jlStr in jlStrls:
jlStrkeys.append(getnormalizeweigth(jlStr, False))
print('set jl sp length....', len(jlStrkeys))
# 遍历法条
for i in range(len(ftnrls)):
# 加入法条data
ftdata_ss = []
ftdata_jl = []
ftnr = ftnrls[i]
ftmc = ftmcls[i]
print('ftnr', ftnr)
ftnrArra = cutcontent(ftnr)
print('ftnrArra lenght..', len(ftnrArra))
# 优化，将nr对应的keys先算好，存进dict中
nrkeysdict = {}
for nr in ftnrArra:
keys, weights = getnormalizeweigth(nr, True)
nrkeysdict[nr] = [keys, weights]
print(keys, weights)
# 对于每个事实句子都把法条sp遍历比较一遍
for i in range(len(wsStrkeys)):
wsStrkey = wsStrkeys[i]
wsStr = wsStrls[i]
smaxsum = distance(wsStrkey, ftnrArra, nrkeysdict, wsStr, model)
if smaxsum > 0.25:
ftdata_ss.append(1)
else:
ftdata_ss.append(0)
print('ws nr',wsStr)
print('ws keys',wsStrkey)
print('ws ft max distance',smaxsum)
# 对于每个结论句子都把法条sp遍历比较一遍
for i in range(len(jlStrkeys)):
jlStrkey = jlStrkeys[i]
jlStr = jlStrls[i]
smaxsum = distance(jlStrkey, ftnrArra, nrkeysdict, jlStr, model)
if smaxsum > 0.45:
ftdata_jl.append(1) # error1
else:
ftdata_jl.append(0)
# outputdata_dict[ftmc] = [ftdata_ss,ftdata_jl]
outputdata_ss.append(ftdata_ss)
outputdata_jl.append(ftdata_jl)
# 输出到法条与事实的映射list，以及法条到结论映射list
return outputdata_ss, outputdata_jl
def traversews(expath, model):
# 加入data
outputdata_ss = []
outputdata_jl = []
ftmcls, ftnrls = getFTList(expath)
print('wsft..', ftmcls)
# wsStrls = cutcontent(getSSMatchObject(wspath))
# wsStrkeys = []
# 预先将文书所有句子的关键词提取好
# for wsStr in wsStrls:
# wsStrkeys.append(getnormalizeweigth(wsStr, False)) # h获取文书关键词，[[]]
# print('set ws keys length....', len(wsStrkeys))
# 预先将结论所有句子的关键词提取好
jlStrkeys = []
jlStrls = cutcontent(getJLMatchObject(wspath))
for jlStr in jlStrls:
jlStrkeys.append(getnormalizeweigth(jlStr, False))
# print('set jl keys length....', len(jlStrkeys))
# 遍历法条
for i in range(len(ftnrls)):
# 加入法条data
ftdata_ss = []
ftdata_jl = []
ftnr = ftnrls[i]
ftmc = ftmcls[i]
print('ftnr', ftnr)
#加载该法条的lda模型
# ft_corpus = '../LDAmodel/source/corpus/'+ftmc+'.txt'
# ft_model = '../LDAmodel/source/model/'+ftmc+'_lda.model'
# if os.path.exists(ft_model):
# dictionary,lda = getLDAmodel(ft_corpus,ft_model)
ftnrArra = cutcontent(ftnr)
this_ftkeys = getftkeys(ftmc)
# 优化，将nr对应的keys先算好，存进dict中
nrkeysdict = {}
allftkeys = []
for nr in ftnrArra:
keys, weights = getnormalizeweigth(nr, True)
print(keys)
nrkeysdict[nr] = [keys, weights]
#
# if len(keys) >=4:
# allftkeys.extend([keys[3]])
if len(keys) >= 3:
allftkeys.extend([keys[2]])
if len(keys) >= 2:
allftkeys.extend([keys[1]])
elif len(keys) == 1:
allftkeys.extend([keys[0]])
# 对于每个事实句子都把法条sp遍历比较一遍
# for i in range(len(wsStrkeys)):
# wsStrkey = wsStrkeys[i]
# wsStr = wsStrls[i]
# print('wsStr',wsStr)
# print('wsStrkeys',wsStrkey)
#使用法条使用lda得出的keys=========================================
# ftkeyflag = 0
# for _ in allftkeys:
# if wsStr.count(_) > 0:
# ftkeyflag += 1
#
# if ftkeyflag >= 1:
# ftdata_ss.append(1)
# else:
#
# count = 0
# if len(this_ftkeys) > 0:
# for this_key in this_ftkeys:
# if wsStr.count(this_key) > 0:
# count += 1
# if count > 0:
# if count > 5:
# ftdata_ss.append(1)
# else:
# smaxsum = distance(wsStrkey, ftnrArra, nrkeysdict, wsStr, model)
# print('samxsum', smaxsum)
# if smaxsum > 0.3:
# ftdata_ss.append(1)
# else:
# ftdata_ss.append(0)
# else:
# ftdata_ss.append(0)
# else:
# smaxsum = distance(wsStrkey, ftnrArra, nrkeysdict, wsStr, model)
# print('samxsum', smaxsum)
# if smaxsum > 0.3:
# ftdata_ss.append(1)
# else:
# ftdata_ss.append(0)
# 使用法条使用lda得出的keys=========================================
# if os.path.exists(ft_model) == True:
# tp = predict(dictionary, lda, wsStr)
# print('tp',tp)
# if tp > 0.95:
# ftdata_ss.append(1)
# # else:
# # smaxsum = distance(wsStrkey, ftnrArra, nrkeysdict, wsStr, model)
# # if smaxsum > 0.2:
# # ftdata_ss.append(1)
# # else:
# # ftdata_ss.append(0)
#
# elif tp > 0.85:
# smaxsum = distance(wsStrkey, ftnrArra, nrkeysdict, wsStr, model)
# print('samxsum',smaxsum)
# if smaxsum > 0.2:
# ftdata_ss.append(1)
# else:
# ftdata_ss.append(0)
# else:
# ftdata_ss.append(0)
#
# else:
# smaxsum = distance(wsStrkey, ftnrArra, nrkeysdict, wsStr, model)
# print('samxsum', smaxsum)
# if smaxsum > 0.2:
# ftdata_ss.append(1)
# else:
# ftdata_ss.append(0)
# smaxsum = distance(wsStrkey, ftnrArra, nrkeysdict, wsStr, model)
# if smaxsum > 0.3:
# ftdata_ss.append(1)
# else:
# ftdata_ss.append(0)
# 对于每个结论句子都把法条sp遍历比较一遍
for i in range(len(jlStrkeys)):
jlStrkey = jlStrkeys[i]
jlStr = jlStrls[i]
smaxsum = distance(jlStrkey, ftnrArra, nrkeysdict, jlStr, model)
if smaxsum > 0.5:
ftdata_jl.append(1) # error1
else:
ftdata_jl.append(0)
# outputdata_ss.append(ftdata_ss)
outputdata_jl.append(ftdata_jl)
# 输出到法条与事实的映射list，以及法条到结论映射list
return outputdata_jl
