def load_hmd_model(self, model_name):
model_path = '{0}/{1}__0.hmdmodel'.format(
self.conf.get('brain-ta.hmd.model.dir'), model_name)
if not os.path.exists(model_path):
raise Exception(
'[ERROR] Not existed HMD model. [{0}]'.format(model_path))
try:
in_file = open(model_path, 'rb')
hm = hmd_pb2.HmdModel()
hm.ParseFromString(in_file.read())
in_file.close()
return hm
except Exception:
raise Exception(traceback.format_exc())
def load_model(model_path):
"""
Load Model
:param model_path: Model Path
:return: Model Value
"""
if not model_path.endswith('.hmdmodel'):
raise Exception('model extension is not .hmdmodel : {0}'.format(model_path))
try:
in_file = open(model_path, 'rb')
hm = hmd_pb2.HmdModel()
hm.ParseFromString(in_file.read())
in_file.close()
return hm
except Exception:
raise Exception(traceback.format_exc())
def make_hmd_model(self, temp_list, model_name):
model = hmd_pb2.HmdModel()
model.lang = lang_pb2.kor
model.model = model_name
rules = list()
for item in temp_list:
category_list = item[0]
dtc_rule = item[1]
rule = hmd_pb2.HmdRule()
rule.rule = dtc_rule
rule.categories.extend(category_list)
rules.append(rule)
model.rules.extend(rules)
self.stub.SetModel(model)
model_key = hmd_pb2.ModelKey()
model_key.lang = lang_pb2.kor
model_key.model = model_name
self.stub.GetModel(model_key)
def set_model(self, model_name, target_list):
model = hmd_pb2.HmdModel()
model.lang = lang_pb2.kor
model.model = model_name
rules_list = list()
for item_dict in target_list:
category = item_dict['category']
rule = item_dict['rule']
category_list = category.split('!@#$')
hmd_client = hmd_pb2.HmdRule()
hmd_client.rule = rule
hmd_client.categories.extend(category_list)
rules_list.append(hmd_client)
model.rules.extend(rules_list)
self.stub.SetModel(model)
model_key = hmd_pb2.ModelKey()
model_key.lang = lang_pb2.kor
model_key.model = model_name
def load_hmd_model(category_delimiter, model_name):
"""
Load HMD model
:param category_delimiter: HMD category delimiter
:param model_name: Model name
:return: HMD dictionary
"""
conf = Config()
conf.init('brain-ta.conf')
model_path = '{0}/{1}__0.hmdmodel'.format(
conf.get('brain-ta.hmd.model.dir'), model_name)
if not os.path.exists(model_path):
raise Exception(
'[ERROR] Not existed HMD model [{0}]'.format(model_path))
try:
in_file = open(model_path, 'rb')
hm = hmd_pb2.HmdModel()
hm.ParseFromString(in_file.read())
in_file.close()
# Make HMD matrix list
matrix_list = list()
for rules in hm.rules:
dtc_keyword_list = list()
rule_list = split_hmd_rule(rules.rule)
for idx in range(len(rule_list)):
dtc_keyword = rule_list[idx].split('|')
dtc_keyword_list.append(dtc_keyword)
tmp_list = list()
category = category_delimiter.join(rules.categories)
matrix_list += vec_word_combine(tmp_list, category, '',
dtc_keyword_list, 0, rules.rule)
# Make HMD matrix dictionary
hmd_dict = dict()
for category, dtc_keyword, hmd_rule in matrix_list:
if len(category) < 1 or category.startswith(
'#') or len(dtc_keyword) < 1:
continue
if dtc_keyword not in hmd_dict:
hmd_dict[dtc_keyword] = [[category, hmd_rule]]
else:
hmd_dict[dtc_keyword].append([category, hmd_rule])
return hmd_dict
except Exception:
raise Exception(traceback.format_exc())
def set_model(self, model_name, target_file_path):
model = hmd_pb2.HmdModel()
model.lang = lang_pb2.kor
model.model = model_name
rules_list = list()
with open(target_file_path) as target_file:
for line in target_file:
line = line.strip()
line_list = line.split(self.args.file_delimiter)
if len(line_list) < 2:
print '[ERROR] Line field count at least two [{0}]'.format(
line)
continue
hmd_client = hmd_pb2.HmdRule()
hmd_client.rule = line_list[-1]
hmd_client.categories.extend(line_list[:-1])
rules_list.append(hmd_client)
model.rules.extend(rules_list)
self.stub.SetModel(model)
model_key = hmd_pb2.ModelKey()
model_key.lang = lang_pb2.kor
model_key.model = model_name
def set_model(self):
model = hmd_pb2.HmdModel()
model.lang = lang_pb2.kor
model.model = 'news'
rules = list()
rule1 = hmd_pb2.HmdRule()
# 형태소 분석 결과를 바탕으로 원형 단어를 이용
# ex) 안녕하세요. -> 안녕하/pa 시/ep 어/ec 요/jx ./s (ETRI 기준)
# 안녕하, 시어, 요 를 이용 rule을 제작
rule1.rule = '(안녕하)'
rule1.categories.extend(['level1', 'level2'])
rules.append(rule1)
rule2 = hmd_pb2.HmdRule()
rule2.rule = '(자연)'
rule2.categories.extend(['level1', 'level3'])
rules.append(rule2)
model.rules.extend(rules)
self.stub.SetModel(model)
model_key = hmd_pb2.ModelKey()
model_key.lang = lang_pb2.kor
model_key.model = 'news'
ret_model = self.stub.GetModel(model_key)
print unicode(ret_model)
def set_model2(self, file_name, model_name):
model = hmd_pb2.HmdModel()
model.lang = lang_pb2.kor
model.model = model_name
rules = list()
f = open(file_name)
for line in f.readlines():
tokens = line.strip().split('\t')
# level1, level2, level3...
levels = tokens[:-1]
# Last element of tokens
keyword = tokens[-1]
print levels, keyword
rule = hmd_pb2.HmdRule()
rule.rule = keyword
rule.categories.extend(levels)
rules.append(rule)
model.rules.extend(rules)
self.stub.SetModel(model)
model_key = hmd_pb2.ModelKey()
model_key.lang = lang_pb2.kor
model_key.model = model_name
ret_model = self.stub.GetModel(model_key)
print unicode(ret_model)
