def init_opt():
parser = optparse.OptionParser('usage: ./%prog [options] \n'
'Example:\n'
' ./scan.py -f rule.json')
parser.add_option('-f',
'--file',
dest='rfile',
default='rule.json',
type='string',
help='rules config file.')
parser.add_option('-m',
'--mode',
dest='mode',
default='0',
type='string',
help='scan mode, 0:code, 1:repos')
(options, args) = parser.parse_args()
rules = common.read_data(options.rfile)
if len(rules) == 0:
print("no rule find, exit.")
sys.exit(1)
rules['mode'] = options.mode
os.system('mkdir -p report')
os.system("rm -f report/*")
return rules
def tag(self):
output = open(self.output_path, 'w')
for sentence in read_data(self.test_data_path):
word_seq = [part[0] for part in sentence]
predict_label = self.featureModel.viterbi(word_seq)
for (word, predict_tag) in zip(word_seq, predict_label):
output.write(word + '\t' + predict_tag + '\n')
output.write('\n')
def tag(self):
output = open(self.output_path, 'w')
for sentence in read_data(self.test_data_path):
word_seq = [part[0] for part in sentence]
predict_label = self.featureModel.viterbi(word_seq)
for (word, predict_tag) in zip(word_seq, predict_label):
output.write(word + '\t' + predict_tag + '\n')
output.write('\n')
def train(self, iteration):
for i in xrange(iteration):
print 'iteration: ', i + 1
for sentence in read_data(self.train_data_path):
observe_data = [pair[0] for pair in sentence]
ideal_label = [pair[1] for pair in sentence]
predict_label = self.featureModel.viterbi(observe_data)
#update feature vector
self.featureModel.update(observe_data, ideal_label, predict_label)
def train(self, iteration):
for i in xrange(iteration):
print 'iteration: ', i + 1
for sentence in read_data(self.train_data_path):
observe_data = [pair[0] for pair in sentence]
ideal_label = [pair[1] for pair in sentence]
predict_label = self.featureModel.viterbi(observe_data)
#update feature vector
self.featureModel.update(observe_data, ideal_label,
predict_label)
# -*- coding: utf-8 -*-
'''
Created on Dec 27, 2015
@author: jielin
'''
import sys
from GlobalLinearModel.PerceptronOnlineLearning import Perceptron
from GlobalLinearModel.PosTagger import PosTagger
from utils.common import read_data, PARAM_PATH, TEST_DATA_PATH, OUTPUT_PATH
from GlobalLinearModel.gen_features import features
if __name__ == '__main__':
params = PARAM_PATH
test_data_path = TEST_DATA_PATH
output_path = OUTPUT_PATH
#count all tags
for x in read_data(test_data_path):
pass
TaggerTool = PosTagger(test_data_path, features, params, output_path)
TaggerTool.tag()
# -*- coding: utf-8 -*-
'''
Created on Dec 27, 2015
@author: myue
'''
import sys
from GlobalLinearModel.PerceptronOnlineLearning import Perceptron
from utils.common import read_data, TRAIN_DATA_PATH, PARAM_PATH, ITER_NUM, BATCH_NUM
from GlobalLinearModel.gen_features import features
if __name__ == '__main__':
params = PARAM_PATH
train_data_path = TRAIN_DATA_PATH
iter_num = ITER_NUM
# load all label
sample_num = 0
for l in read_data(train_data_path):
sample_num += 1
TrainModel = Perceptron(train_data_path, features, params)
TrainModel.train(iter_num)
TrainModel.save_params_to_file()
#TrainModel.save_params_to_pickle()
