def tms_feature_select(filename,
indexes=[1],
global_fun="one",
main_save_path="../",
dic_name="dic.key",
ratio=0.4,
stopword_filename="",
str_splitTag="^",
tc_splitTag="\t"):
'''特征选择的主程序，输入指定的文件，会自动生成词典，并根据卡方公式进行特征选择。
必须参数：
filename 训练文本所在的文件名，默认情况下，已经分好词。
结果文件：
词典。
可选参数：
indexs需要训练的指标项 ，默认为[1]
main_save_path 模型保存的路径.默认为"../"
stopword_filename 停用词的名称以及路径 ;默认不适用停用词
dic_name 用户自定义词典名称;默认“dic.key”
ratio 特征选择保留词的比例 ；默认 0.4
str_splitTag 分词所用的分割符号 ，默认"^"
tc_splitTag训练样本中各个字段分割所用的符号 ，默认"\t"
global_fun :全局权重的计算方式：有"one","idf","rf" ,默认为"one"
'''
train_model.ctm_feature_select(filename, indexes, global_fun,
main_save_path, dic_name, ratio,
stopword_filename, str_splitTag,
tc_splitTag)
def tms_feature_select(filename,indexes=[1],global_fun="one",main_save_path="../",dic_name="dic.key",ratio=0.4,stopword_filename="",str_splitTag="^",tc_splitTag="\t"):
'''特征选择的主程序，输入指定的文件，会自动生成词典，并根据卡方公式进行特征选择。
必须参数：
filename 训练文本所在的文件名，默认情况下，已经分好词。
结果文件：
词典。
可选参数：
indexs需要训练的指标项 ，默认为[1]
main_save_path 模型保存的路径.默认为"../"
stopword_filename 停用词的名称以及路径 ;默认不适用停用词
dic_name 用户自定义词典名称;默认“dic.key”
ratio 特征选择保留词的比例 ；默认 0.4
str_splitTag 分词所用的分割符号 ，默认"^"
tc_splitTag训练样本中各个字段分割所用的符号 ，默认"\t"
global_fun :全局权重的计算方式：有"one","idf","rf" ,默认为"one"
'''
train_model.ctm_feature_select(filename, indexes, global_fun, main_save_path, dic_name, ratio, stopword_filename, str_splitTag, tc_splitTag)
def main():
usage = "usage:%prog [options] version=%prog 1.0"
parser = OptionParser(usage=usage)
parser.add_option("-s",
"--step",
type="choice",
choices=["1", "2", "3", "4", "5"],
dest="step",
help="step1 is auto training the svm model")
parser.add_option("-p", "--path", dest="save_main_path")
parser.add_option("-P", "--problem_path", dest="problem_save_path")
parser.add_option("-i",
"--indexes",
dest="indexes",
action="callback",
type="string",
default=[1],
callback=list_callback)
parser.add_option("-w",
"--stopword",
action="store_false",
dest="stopword",
default=True)
parser.add_option("-n",
"--config_name",
dest="config_name",
default="tms.config")
parser.add_option("-d", "--dic_name", dest="dic_name", default="dic.key")
parser.add_option("-D", "--dic_path", dest="dic_path")
parser.add_option("-m",
"--model_name",
dest="model_name",
default="tms.model")
parser.add_option("-t",
"--train_name",
dest="train_name",
default="tms.train")
parser.add_option("-a",
"--param_name",
dest="param_name",
default="tms.param")
parser.add_option("-r", "--ratio", dest="ratio", type="float", default=0.4)
parser.add_option("-A",
"--svm_param",
dest="svm_param",
default="'-s 0 -t 2 -c 1.0 -g 0.25'")
parser.add_option("-T",
"--tc_splitTag",
dest="tc_splitTag",
type="string",
default="\t")
parser.add_option("-S",
"--str_splitTag",
dest="str_splitTag",
type="string",
default="^")
parser.add_option("-v",
"--svm_type",
dest="svm_type",
default="libsvm",
type="choice",
choices=["libsvm", "liblinear"])
parser.add_option("-e",
"--segment",
type="choice",
dest="segment",
default=0,
choices=[0, 1, 2])
parser.add_option("-c",
"--param_select",
action="store_false",
dest="param_select",
default=True)
parser.add_option("-g",
"--global_fun",
dest="global_fun",
default="one",
type="choice",
choices=["one", "idf", "rf"])
parser.add_option("-l",
"--local_fun",
dest="local_fun",
default="tf",
type="choice",
choices=["tf"])
parser.add_option("-b",
"--label_file",
dest="label_file",
type="string",
default="")
options, args = parser.parse_args()
if options.indexes:
indexes = [int(i) for i in options.indexes]
if options.step:
step = int(options.step)
if options.stopword == False:
stopword_filename = ""
else:
stopword_filename = os.path.dirname(args[0]) + "/stopwords.txt"
if options.svm_param:
svm_param = options.svm_param.replace("'", "")
if step == 1:
train_model.ctm_train(args[0],
indexes,
options.save_main_path,
stopword_filename,
config_name=options.config_name,
svm_type=options.svm_type,
segment=options.segment,
param_select=options.param_select,
global_fun=options.global_fun,
local_fun=options.local_fun,
svm_param=svm_param,
dic_name=options.dic_name,
model_name=options.model_name,
train_name=options.train_name,
param_name=options.param_name,
ratio=options.ratio,
delete=True,
str_splitTag=options.str_splitTag,
tc_splitTag=options.tc_splitTag,
label_file=options.label_file)
if step == 2:
train_model.ctm_feature_select(args[0],
indexes,
options.global_fun,
options.save_main_path,
options.dic_name,
options.ratio,
stopword_filename,
str_splitTag=options.str_splitTag,
tc_splitTag=options.tc_splitTag)
if step == 3:
if os.path.exists(options.save_main_path):
if os.path.exists(options.save_main_path + "temp/") is False:
os.makedirs(options.save_main_path + "temp/")
sample_save_path = options.save_main_path + "temp/svm.train"
train_model.cons_train_sample_for_cla(
args[0],
indexes,
options.local_fun,
options.dic_path,
sample_save_path,
delete=True,
str_splitTag=options.str_splitTag,
tc_splitTag=options.tc_splitTag)
if step == 4:
search_result_save_path = options.save_main_path + "temp/" + "svm.param"
tms_svm.set_svm_type(options.svm_type)
if options.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
if options.svm_type == "liblinear":
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(args[0], search_result_save_path,
options.svm_type, coarse_c_range,
coarse_g_range, fine_c_step, fine_g_step)
print "best c = %s\t g = %s\n" % (c, g)
if step == 5:
model_save_path = options.save_main_path + "model/" + options.model_name
train_model.ctm_train_model(options.problem_save_path, svm_param,
model_save_path)
def main():
usage ="usage:%prog [options] version=%prog 1.0"
parser = OptionParser(usage=usage)
parser.add_option("-s","--step",type="choice",choices=["1","2","3","4","5"],dest="step",help="step1 is auto training the svm model")
parser.add_option("-p","--path",dest="save_main_path")
parser.add_option("-P","--problem_path",dest="problem_save_path")
parser.add_option("-i","--indexes",dest="indexes",action="callback",type="string",default=[1],callback=list_callback)
parser.add_option("-w","--stopword",action="store_false",dest="stopword",default=True)
parser.add_option("-n","--config_name",dest="config_name",default="tms.config")
parser.add_option("-d","--dic_name",dest="dic_name",default="dic.key")
parser.add_option("-D","--dic_path",dest="dic_path")
parser.add_option("-m","--model_name",dest="model_name",default="tms.model")
parser.add_option("-t","--train_name",dest="train_name",default="tms.train")
parser.add_option("-a","--param_name",dest="param_name",default="tms.param")
parser.add_option("-r","--ratio",dest="ratio",type="float",default=0.4)
parser.add_option("-A","--svm_param",dest="svm_param",default="'-s 0 -t 2 -c 1.0 -g 0.25'")
parser.add_option("-T","--tc_splitTag",dest="tc_splitTag",type="string",default="\t")
parser.add_option("-S","--str_splitTag",dest="str_splitTag",type="string",default="^")
parser.add_option("-v","--svm_type",dest="svm_type",default="libsvm",type="choice",choices=["libsvm","liblinear"])
parser.add_option("-e","--segment",type="choice",dest="segment",default=0,choices=[0,1,2])
parser.add_option("-c","--param_select",action="store_false",dest="param_select",default=True)
parser.add_option("-g","--global_fun",dest="global_fun",default="one",type="choice",choices=["one","idf","rf"])
parser.add_option("-l","--local_fun",dest="local_fun",default="tf",type="choice",choices=["tf"])
parser.add_option("-b","--label_file",dest="label_file",type="string",default="")
options, args = parser.parse_args()
if options.indexes:
indexes = [int(i) for i in options.indexes]
if options.step:
step = int(options.step)
if options.stopword ==False:
stopword_filename=""
else:
stopword_filename = os.path.dirname(args[0])+"/stopwords.txt"
if options.svm_param:
svm_param = options.svm_param.replace("'","")
if step==1:
train_model.ctm_train(args[0],indexes,options.save_main_path,stopword_filename,config_name=options.config_name,svm_type =options.svm_type,segment=options.segment,param_select=options.param_select,global_fun=options.global_fun,local_fun=options.local_fun,svm_param=svm_param,dic_name=options.dic_name,model_name=options.model_name,train_name=options.train_name,param_name=options.param_name,ratio=options.ratio,delete=True,str_splitTag=options.str_splitTag,tc_splitTag=options.tc_splitTag,label_file=options.label_file)
if step==2:
train_model.ctm_feature_select(args[0],indexes,options.global_fun,options.save_main_path,options.dic_name,options.ratio,stopword_filename,str_splitTag=options.str_splitTag,tc_splitTag=options.tc_splitTag)
if step==3:
if os.path.exists(options.save_main_path):
if os.path.exists(options.save_main_path+"temp/") is False:
os.makedirs(options.save_main_path+"temp/")
sample_save_path = options.save_main_path +"temp/svm.train"
train_model.cons_train_sample_for_cla(args[0],indexes,options.local_fun,options.dic_path,sample_save_path,delete=True,str_splitTag=options.str_splitTag,tc_splitTag=options.tc_splitTag)
if step==4:
search_result_save_path = options.save_main_path +"temp/"+"svm.param"
tms_svm.set_svm_type(options.svm_type)
if options.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
if options.svm_type =="liblinear":
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(args[0],search_result_save_path,options.svm_type,coarse_c_range,coarse_g_range,fine_c_step,fine_g_step)
print "best c = %s\t g = %s\n"%(c,g)
if step==5:
model_save_path = options.save_main_path+"model/"+options.model_name
train_model.ctm_train_model(options.problem_save_path,svm_param,model_save_path)
