def cons_train_sample_for_cla(filename, indexs, local_fun, dic_path,
sample_save_path, delete, str_splitTag,
tc_splitTag):
'''根据提供的词典，将指定文件中的指定位置上的内容构造成SVM所需的问题格式，并进行保存'''
dic_list, global_weight = fileutil.read_dic_ex(dic_path, dtype=str)
if type(local_fun) == types.StringType:
local_fun = measure.local_f(local_fun)
label = set()
#对原训练样本进行词干化处理
print "-----------------正在对源文本进行词干化处理-------------------"
stem.stemFile(filename, str_splitTag, tc_splitTag)
f = file(filename, 'r')
fs = file(sample_save_path, 'w')
for line in f.readlines():
text = line.strip().split(tc_splitTag)
text_temp = ""
if len(text) < indexs[len(indexs) - 1] + 1:
continue
for i in indexs:
text_temp += str_splitTag + text[i]
y, x = ctmutil.cons_pro_for_svm(text[0],
text_temp.strip().split(str_splitTag),
dic_list, local_fun, global_weight)
if delete == True and len(x[0]) == 0:
continue
save_dic_train_sample(fs, y, x)
label.add(y[0])
f.close()
fs.close()
return label
def cons_train_sample_for_cla(filename,indexs,local_fun,dic_path,sample_save_path,delete,str_splitTag,tc_splitTag):
'''根据提供的词典，将指定文件中的指定位置上的内容构造成SVM所需的问题格式，并进行保存'''
dic_list,global_weight = fileutil.read_dic_ex(dic_path,dtype=str)
if type(local_fun)==types.StringType:
local_fun = measure.local_f(local_fun)
label = set()
#对原训练样本进行词干化处理
print "-----------------正在对源文本进行词干化处理-------------------"
stem.stemFile(filename,str_splitTag,tc_splitTag)
f= file(filename,'r')
fs = file(sample_save_path,'w')
for line in f.readlines():
text = line.strip().split(tc_splitTag)
text_temp=""
if len(text)<indexs[len(indexs)-1]+1:
continue
for i in indexs:
text_temp+=str_splitTag+text[i]
y,x = ctmutil.cons_pro_for_svm(text[0],text_temp.strip().split(str_splitTag),dic_list,local_fun,global_weight)
if delete == True and len(x[0])==0:
continue
save_dic_train_sample(fs,y,x)
label.add(y[0])
f.close()
fs.close()
return label
def load_tms_model(config_file):
'''通过模型配置文件加载词典、全局因子、局部因子、SVM模型'''
model_main_path = os.path.dirname(config_file)
f = file(config_file,'r')
for line in f.readlines():
text = line.split(":")
if text[0].strip()=="DicName":
dic,global_weight = fileutil.read_dic_ex(os.path.join(model_main_path,text[1].strip()),dtype=str)
if text[0].strip()=="ModelName":
tms_svm.set_svm_type(tms_svm.detect_svm_type(os.path.join(model_main_path,text[1].strip())))
model= tms_svm.load_model(os.path.join(model_main_path,text[1].strip()))
if text[0].strip()=="LocalFun":
local_fun = measure.local_f(text[1].strip())
if text[0].strip()=="WordSeg":
seg = int(float(text[1]))
return local_fun,dic,global_weight,model,seg
