def load_normal_data(self, feature_list, label_list, sen_list=None):
sens = loading_data.load_sentence()
num_sen = len(sens)
print "Loading total %s normal sentence." % num_sen
for i in xrange(num_sen - 1):
sen = sens[i]
spliter_id = len(sen) - 1
#for single sentence
feature, _ = self.feature_model.gen_feature_vector(sen, spliter_id)
feature_list.append(feature)
label_list.append(1)
if sen_list != None:
sen_list.append(sen)
#for merge sentence
sen_merge = " ".join([sens[i], sens[i + 1]])
feature, _ = self.feature_model.gen_feature_vector(
sen_merge, spliter_id)
feature_list.append(feature)
label_list.append(1)
if sen_list != None:
sen_list.append(sen)
idx = 0
for c in sen[:-1]:
if Feature.is_spliter_candidate(c):
feature, _ = self.feature_model.gen_feature_vector(
sen, idx)
feature_list.append(feature)
label_list.append(0)
if sen_list != None:
sen_list.append(sen)
idx += 1
def loading_none_spliter_rule(self,
feature_list,
label_list,
sen_list=None):
rules = loading_data.load_spliter_rules()
print "Loading rules."
for rule in rules:
if rule[0] == "#":
continue
if rule[0] == "r":
rule = rule[1:]
print "Add a soft regex: %s" % rule
self.feature_model.add_none_spliter_regrex(rule)
continue
elif rule[0] == "h":
rule = rule[1:]
print "Add a hard rule regex: %s" % rule
self.feature_model.add_none_spliter_regrex(rule, True)
continue
idx = 0
for c in rule:
if Feature.is_spliter_candidate(c):
feature, _ = self.feature_model.gen_feature_vector(
rule, idx, is_forced=True)
feature_list.append(feature)
label_list.append(0)
if sen_list != None:
sen_list.append(rule)
idx += 1
def __split_par(self, par, is_debug=False):
list_sens = []
list_features = []
list_candidates = []
list_hard_rule_none_spliter_idx = []
list_hard_rule_forcing_spliter_idx = []
idx = 0
for c in par:
if Feature.is_spliter_candidate(c):
list_candidates.append(idx)
feature, is_hard = self.feature_model.gen_feature_vector(
par, idx)
if is_hard > 0:
list_hard_rule_none_spliter_idx.append(
len(list_candidates) - 1)
elif is_hard < 0:
list_hard_rule_forcing_spliter_idx.append(
len(list_candidates) - 1)
if is_debug:
print feature
list_features.append(feature)
idx += 1
if is_debug:
print list_candidates
if len(list_candidates) == 0:
list_sens.append(par)
return list_sens
#print list_features
#list_features = np.array(list_features)
#print "Shape: ",list_features.shape
labels = self.classifier.predict(list_features)
for l in list_hard_rule_none_spliter_idx:
labels[l] = 0
for l in list_hard_rule_forcing_spliter_idx:
labels[l] = 1
list_true_spliters = [-1]
for i in xrange(len(labels)):
if labels[i] == 1:
list_true_spliters.append(list_candidates[i])
if list_candidates[-1] != len(par) - 1:
list_true_spliters.append(len(par) - 1)
if is_debug:
print list_true_spliters
if len(list_true_spliters) > 1:
for i in xrange(len(list_true_spliters) - 1):
list_sens.append(par[list_true_spliters[i] +
1:list_true_spliters[i + 1] + 1].strip())
else:
list_sens.append(par)
return list_sens
