def ctm_predict(filename,config_file,indexes,result_save_path,result_indexes,str_splitTag,tc_splitTag,seg,delete=False,change_decode=False,in_decode="UTF-8",out_encode="GBK"):
'''一般形式的下得模型预测，即单个模型。'''
local_fun,dic,global_weight,model,seg_ori = load_tms_model(config_file)
if seg!=0:
if seg_ori !=seg:
print u"预测分词工具与原训练时分词工具不一样"
print "-----------------正在对源文本进行分词-------------------"
segment_file = os.path.dirname(filename)+"/segmented"
segment.file_seg(filename,indexes,segment_file,str_splitTag,tc_splitTag,seg)
filename = segment_file
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(result_save_path,'w')
print "-----------------正在对样本进行预测-------------------"
for line in f.readlines():
if change_decode ==True:
line = line.decode(in_decode).encode(out_encode,'ignore')
text = line.strip().split(tc_splitTag)
if len(text)<indexes[len(indexes)-1]+1 or len(text)<result_indexes[len(result_indexes)-1]+1:
continue
text_temp=""
for i in indexes:
text_temp+=str_splitTag+text[i]
label,sc=cal_sc_optim(1,model,text_temp,dic,local_fun,global_weight,str_splitTag)
fs.write(str(label)+"\t"+str(sc)+"\t")
for index in result_indexes:
fs.write(text[index]+"\t")
fs.write("\n")
f.close()
fs.close()
print u"-----------------预测完毕-------------------"
def file_seg(filename,
indexes=[1],
out_filename="",
str_splitTag="^",
tc_splitTag="\t",
seg=1):
if out_filename == "":
out_filename = os.path.dirname(filename) + "/segmented"
segment.file_seg(filename, indexes, out_filename, str_splitTag,
tc_splitTag, type)
def ctm_predict_multi(filename,
config_files,
indexes_lists,
result_save_path,
result_indexes,
str_splitTag,
tc_splitTag,
seg,
delete=False,
change_decode=False,
in_decode="UTF-8",
out_encode="GBK"):
'''多个模型的预测，如一个文本有多个模型需要预测
其中title_indexes，dic_path ，model_path为二维度的。
'''
if seg != 0:
print "-----------------正在对源文本进行分词-------------------"
all_index = list()
for index in indexes_lists:
all_index.extend(index)
segment_file = os.path.dirname(filename) + "/segmented"
segment.file_seg(filename, all_index, segment_file, str_splitTag,
tc_splitTag, seg)
filename = segment_file
k = len(config_files) #得到预测模型的个数
dic_list = []
local_fun_list = []
model_list = []
global_weight_list = []
for i in range(k):
local_fun, dic, global_weight, model, seg_ori = load_tms_model(
config_files[i])
dic_list.append(dic)
local_fun_list.append(local_fun)
model_list.append(model)
global_weight_list.append(global_weight)
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(result_save_path, 'w')
print "-----------------正在对样本进行预测-------------------"
for line in f.readlines():
if len(line.strip()) < 1:
continue
if change_decode == True:
line = line.decode(in_decode).encode(out_encode, 'ignore')
text = line.strip().split(tc_splitTag)
for j in range(k):
indexes = indexes_lists[j]
model = model_list[j]
dic = dic_list[j]
local_fun = local_fun_list[j]
if len(text) < indexes[len(indexes) - 1] + 1 or len(
text) < result_indexes[len(result_indexes) - 1] + 1:
label = 0
sc = 0
else:
text_temp = ""
for index in indexes:
text_temp += str_splitTag + text[index]
if dir(model).count("get_svm_type") == 1:
tms_svm.set_svm_type("libsvm")
if dir(model).count("get_nr_feature") == 1:
tms_svm.set_svm_type("liblinear")
label, sc = cal_sc_optim(1, model, text_temp, dic, local_fun,
global_weight, str_splitTag)
fs.write(str(label) + "\t" + str(sc) + "\t")
for index in result_indexes:
if index > len(text) - 1:
break
fs.write(text[index] + "\t")
fs.write("\n")
f.close()
fs.close()
print "-----------------预测完毕-------------------"
def ctm_predict_multi(filename,config_files,indexes_lists,result_save_path,result_indexes,str_splitTag,tc_splitTag,seg,delete=False,change_decode=False,in_decode="UTF-8",out_encode="GBK"):
'''多个模型的预测，如一个文本有多个模型需要预测
其中title_indexes，dic_path ，model_path为二维度的。
'''
if seg!=0:
print "-----------------正在对源文本进行分词-------------------"
all_index = list()
for index in indexes_lists:
all_index.extend(index)
segment_file = os.path.dirname(filename)+"/segmented"
segment.file_seg(filename,all_index,segment_file,str_splitTag,tc_splitTag,seg)
filename = segment_file
k = len(config_files) #得到预测模型的个数
dic_list=[]
local_fun_list=[]
model_list=[]
global_weight_list = []
for i in range(k):
local_fun,dic,global_weight,model,seg_ori = load_tms_model(config_files[i])
dic_list.append(dic)
local_fun_list.append(local_fun)
model_list.append(model)
global_weight_list .append(global_weight)
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(result_save_path,'w')
print "-----------------正在对样本进行预测-------------------"
for line in f.readlines():
if len(line.strip())<1:
continue
if change_decode ==True:
line = line.decode(in_decode).encode(out_encode,'ignore')
text = line.strip().split(tc_splitTag)
for j in range(k):
indexes = indexes_lists[j]
model = model_list[j]
dic = dic_list[j]
local_fun = local_fun_list[j]
if len(text)<indexes[len(indexes)-1]+1 or len(text)<result_indexes[len(result_indexes)-1]+1:
label =0
sc=0
else:
text_temp=""
for index in indexes:
text_temp+=str_splitTag+text[index]
if dir(model).count("get_svm_type")==1:
tms_svm.set_svm_type("libsvm")
if dir(model).count("get_nr_feature")==1:
tms_svm.set_svm_type("liblinear")
label,sc=cal_sc_optim(1,model,text_temp,dic,local_fun,global_weight,str_splitTag)
fs.write(str(label)+"\t"+str(sc)+"\t")
for index in result_indexes:
if index>len(text)-1:
break
fs.write(text[index]+"\t")
fs.write("\n")
f.close()
fs.close()
print u"-----------------预测完毕-------------------"
def ctm_train(filename, indexes, main_save_path, stopword_filename, svm_param,
config_name, dic_name, model_name, train_name, svm_type,
param_name, ratio, delete, str_splitTag, tc_splitTag, seg,
param_select, global_fun, local_fun, label_file):
'''训练的自动化程序，分词,先进行特征选择，重新定义词典，根据新的词典，自动选择SVM最优的参数。
然后使用最优的参数进行SVM分类，最后生成训练后的模型。
需要保存的文件：（需定义一个主保存路径）
模型文件：词典.key+模型.model
临时文件 ：svm分类数据文件.train
filename 训练文本所在的文件名
indexs需要训练的指标项
main_save_path 模型保存的路径
stopword_filename 停用词的名称以及路径 ;默认不适用停用词
svm_type :svm类型：libsvm 或liblinear
svm_param 用户自己设定的svm的参数,这个要区分libsvm与liblinear参数的限制；例如"-s 0 -t 2 -c 0.2 "
dic_name 用户自定义词典名称;例如“dic.key”
model_name用户自定义模型名称 ;例如"svm.model"
train_name用户自定义训练样本名称 ；例如“svm.train”
param_name用户自定义参数文件名称 ；例如"svm.param"
ratio 特征选择保留词的比例 ；例如 0.4
delete对于所有特征值为0的样本是否删除,True or False
str_splitTag 分词所用的分割符号 例如"^"
tc_splitTag训练样本中各个字段分割所用的符号 ，例如"\t"
seg 分词的选择：0为不进行分词；1为使用mmseg分词；2为使用aliws分词
param_select ;是否进行SVM模型参数的搜索。True即为使用SVM模型grid.搜索，False即为不使用参数搜索。
local_fun：即对特征向量计算特征权重时需要设定的计算方式:x(i,j) = local(i,j)*global(i).可选的有tf,logtf
global_fun :全局权重的计算方式：有"one","idf","rf"
label_file:类标签的解释说明文件。
'''
print "-----------------创建模型文件保存的路径-----------------"
if os.path.exists(main_save_path):
if os.path.exists(os.path.join(main_save_path, "model")) is False:
os.makedirs(os.path.join(main_save_path, "model"))
if os.path.exists(main_save_path):
if os.path.exists(os.path.join(main_save_path, "temp")) is False:
os.makedirs(os.path.join(main_save_path, "temp"))
#设定SVM模型的类型。
tms_svm.set_svm_type(svm_type)
#如果没有给出停用词的文件名，则默认不使用停用词
if stopword_filename == "":
stop_words_dic = dict()
else:
stop_words_dic = fileutil.read_dic(stopword_filename)
#如果需要分词，则对原文件进行分词
if seg != 0:
print "-----------------正在对源文本进行分词-------------------"
segment_file = os.path.dirname(filename) + "/segmented"
segment.file_seg(filename, indexes, segment_file, str_splitTag,
tc_splitTag, seg)
filename = segment_file
#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)
print "-----------------现在正在进行特征选择---------------"
dic_path = os.path.join(main_save_path, "model", dic_name)
feature_select(filename,
indexes,
global_fun,
dic_path,
ratio,
stop_words_dic,
str_splitTag=str_splitTag,
tc_splitTag=tc_splitTag)
print "-----------------再根据特征选择后的词典构造新的SVM分类所需的训练样本------------------- "
problem_save_path = os.path.join(main_save_path, "temp", train_name)
local_fun_str = local_fun
local_fun = measure.local_f(local_fun)
label = cons_train_sample_for_cla(filename, indexes, local_fun, dic_path,
problem_save_path, delete, str_splitTag,
tc_splitTag)
if param_select == True:
print "--------------------选择最优的c,g------------------------------"
search_result_save_path = main_save_path + "temp/" + param_name
if svm_type == "libsvm":
coarse_c_range = (-5, 7, 2)
coarse_g_range = (3, -10, -2)
fine_c_step = 0.5
fine_g_step = 0.5
c, g = grid_search_param.grid(problem_save_path,
search_result_save_path, svm_type,
coarse_c_range, coarse_g_range,
fine_c_step, fine_g_step)
svm_param = svm_param + " -c " + str(c) + " -g " + str(g)
if svm_type == "liblinear" or (svm_type == "libsvm" and
is_linear_kernal(svm_param) is True):
coarse_c_range = (-5, 7, 2)
coarse_g_range = (1, 1, 1)
fine_c_step = 0.5
fine_g_step = 0
c, g = grid_search_param.grid(problem_save_path,
search_result_save_path, svm_type,
coarse_c_range, coarse_g_range,
fine_c_step, fine_g_step)
svm_param = svm_param + " -c " + str(c)
print "-----------------训练模型，并将模型进行保存----------"
model_save_path = main_save_path + "model/" + model_name
ctm_train_model(problem_save_path, svm_type, svm_param, model_save_path)
print "-----------------保存模型配置-----------------"
f_config = file(os.path.join(main_save_path, "model", config_name), 'w')
save_config(f_config, dic_name, model_name, local_fun_str, global_fun, seg,
svm_type, svm_param, label_file, label)
f_config.close()
def ctm_train(filename,indexes,main_save_path,stopword_filename,svm_param,config_name,dic_name,model_name,train_name,svm_type,param_name,ratio,delete,str_splitTag,tc_splitTag,seg,param_select,global_fun,local_fun,label_file):
'''训练的自动化程序，分词,先进行特征选择，重新定义词典，根据新的词典，自动选择SVM最优的参数。
然后使用最优的参数进行SVM分类，最后生成训练后的模型。
需要保存的文件：（需定义一个主保存路径）
模型文件：词典.key+模型.model
临时文件 ：svm分类数据文件.train
filename 训练文本所在的文件名
indexs需要训练的指标项
main_save_path 模型保存的路径
stopword_filename 停用词的名称以及路径 ;默认不适用停用词
svm_type :svm类型：libsvm 或liblinear
svm_param 用户自己设定的svm的参数,这个要区分libsvm与liblinear参数的限制；例如"-s 0 -t 2 -c 0.2 "
dic_name 用户自定义词典名称;例如“dic.key”
model_name用户自定义模型名称 ;例如"svm.model"
train_name用户自定义训练样本名称 ；例如“svm.train”
param_name用户自定义参数文件名称 ；例如"svm.param"
ratio 特征选择保留词的比例 ；例如 0.4
delete对于所有特征值为0的样本是否删除,True or False
str_splitTag 分词所用的分割符号 例如"^"
tc_splitTag训练样本中各个字段分割所用的符号 ，例如"\t"
seg 分词的选择：0为不进行分词；1为使用mmseg分词；2为使用aliws分词
param_select ;是否进行SVM模型参数的搜索。True即为使用SVM模型grid.搜索，False即为不使用参数搜索。
local_fun：即对特征向量计算特征权重时需要设定的计算方式:x(i,j) = local(i,j)*global(i).可选的有tf,logtf
global_fun :全局权重的计算方式：有"one","idf","rf"
label_file:类标签的解释说明文件。
'''
print "-----------------创建模型文件保存的路径-----------------"
if os.path.exists(main_save_path):
if os.path.exists(os.path.join(main_save_path,"model")) is False:
os.makedirs(os.path.join(main_save_path,"model"))
if os.path.exists(main_save_path):
if os.path.exists(os.path.join(main_save_path,"temp")) is False:
os.makedirs(os.path.join(main_save_path,"temp"))
#设定SVM模型的类型。
tms_svm.set_svm_type(svm_type)
#如果没有给出停用词的文件名，则默认不使用停用词
if stopword_filename =="":
stop_words_dic=dict()
else:
stop_words_dic = fileutil.read_dic(stopword_filename)
#如果需要分词，则对原文件进行分词
if seg!=0:
print "-----------------正在对源文本进行分词-------------------"
segment_file = os.path.dirname(filename)+"/segmented"
segment.file_seg(filename,indexes,segment_file,str_splitTag,tc_splitTag,seg)
filename = segment_file
#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)
print "-----------------现在正在进行特征选择---------------"
dic_path= os.path.join(main_save_path,"model",dic_name)
feature_select(filename,indexes,global_fun,dic_path,ratio,stop_words_dic,str_splitTag=str_splitTag,tc_splitTag=tc_splitTag)
print "-----------------再根据特征选择后的词典构造新的SVM分类所需的训练样本------------------- "
problem_save_path = os.path.join(main_save_path,"temp",train_name)
local_fun_str = local_fun
local_fun = measure.local_f(local_fun)
label = cons_train_sample_for_cla(filename,indexes,local_fun,dic_path,problem_save_path,delete,str_splitTag,tc_splitTag)
if param_select ==True:
print"--------------------选择最优的c,g------------------------------"
search_result_save_path = main_save_path +"temp/"+param_name
if svm_type=="libsvm":
coarse_c_range=(-5,7,2)
coarse_g_range=(3,-10,-2)
fine_c_step=0.5
fine_g_step=0.5
c,g=grid_search_param.grid(problem_save_path,search_result_save_path,svm_type,coarse_c_range,coarse_g_range,fine_c_step,fine_g_step)
svm_param = svm_param + " -c "+str(c)+" -g "+str(g)
if svm_type=="liblinear" or (svm_type=="libsvm" and is_linear_kernal(svm_param) is True):
coarse_c_range=(-5,7,2)
coarse_g_range=(1,1,1)
fine_c_step=0.5
fine_g_step=0
c,g=grid_search_param.grid(problem_save_path,search_result_save_path,svm_type,coarse_c_range,coarse_g_range,fine_c_step,fine_g_step)
svm_param = svm_param + " -c "+str(c)
print "-----------------训练模型，并将模型进行保存----------"
model_save_path = main_save_path+"model/"+model_name
ctm_train_model(problem_save_path,svm_type,svm_param,model_save_path)
print "-----------------保存模型配置-----------------"
f_config = file(os.path.join(main_save_path,"model",config_name),'w')
save_config(f_config,dic_name,model_name,local_fun_str,global_fun,seg,svm_type,svm_param,label_file,label)
f_config.close()
def file_seg(filename,indexes=[1],out_filename="",str_splitTag="^",tc_splitTag="\t",seg=1):
if out_filename=="":
out_filename = os.path.dirname(filename)+"/segmented"
segment.file_seg(filename, indexes, out_filename, str_splitTag, tc_splitTag, type)