def svm_rank_classify(features, model, predictions):
logging.info(('==' * 10 + '%s' + '==' * 10) % ('START SVM CLASSIFING'))
svm_rank_classify_format = '%s %s %s %s'
cmd_text = svm_rank_classify_format % (
SVM_RANK.svm_rank_classify_command, features, model, predictions)
logging.debug(cmd_text)
os.system(cmd_text)
def svm_rank_learn(features, output_model, args=''):
logging.info(('==' * 10 + '%s' + '==' * 10) % ('START SVM LEARNING'))
svm_rank_learn_format = '%s %s %s %s'
cmd_text = svm_rank_learn_format % (SVM_RANK.svm_rank_learn_command,
args, features, output_model)
logging.debug(cmd_text)
os.system(cmd_text)
def __get_question_id_group(self):
"""
:return: tuple(question1_id, question2_id, ...)
"""
question_id_group = re.findall(find_question_regex, self.page_source_code)
if question_id_group:
logging.debug("Parse Question Success！")
return tuple(zip(*question_id_group)[0])
else:
logging.debug("find_question_regex:%s" % find_question_regex)
logging.error("Can not find question on page source code.")
self.error_message = u"无法解析问题"
return ()
def _load_question_bank_unicode(online_question_bank_url=online_question_bank_url, callback=None):
"""
:return: List [item1, item2, ...]
item = {
"id": "1010",
"answer": "A",
"options": [
{ "text": "OptionsA",
"name": "A"
},
{ "text": ""OptionsB,
"name": "B"
},
{ "text": "OptionsC",
"name": "C"
},
{ "text": "OptionsD",
"name": "D"
}
],
"title": "qustion_text"
}
"""
try:
r = requests.get(url=online_question_bank_url, timeout=15)
if r.status_code != 200:
logging.debug("online_bank_status_code：%s" % r.status_code)
raise ConnectionError
else:
logging.debug("Load online bank success!")
question_bank_temp = r.content
except Exception, e:
logging.error("Load online bank fail;Error:%s" % e.message)
logging.debug("Load local bank...")
question_bank_temp = local_question_bank.get()
def __get_answer_choice_group(self):
"""
:return: list [answer1, answer2, ...]
"""
if not self.question_id_group:
return []
logging.debug(self.question_id_group)
__answer_item_group = map(self.__find_answer, self.question_id_group)
logging.debug("answer_item_group:{}".format(__answer_item_group))
__answer_choice_group = map(lambda x: x["answer"] if x else "X", __answer_item_group)
logging.debug("answer_choice_group:{}".format(__answer_choice_group))
return __answer_choice_group
def __set_question_bank(question_bank):
cls.question_bank = question_bank
logging.debug("use online question bank instead of local bank!")
def get_auto_select_answer_script(self):
if not self.parse_page_status:
return ""
logging.debug(
select_answer_script_template % (",".join(self.question_id_group), ",".join(self.answer_choice_group)))
return select_answer_script_template % (",".join(self.question_id_group), ",".join(self.answer_choice_group))
def svm_rank_learn(features, output_model, args = '') :
logging.info(('=='*10 + '%s' + '=='*10) % ( 'START SVM LEARNING'))
svm_rank_learn_format = '%s %s %s %s'
cmd_text = svm_rank_learn_format % (SVM_RANK.svm_rank_learn_command, args, features, output_model)
logging.debug(cmd_text)
os.system(cmd_text)
def svm_rank_classify(features, model, predictions):
logging.info(('=='*10 + '%s' + '=='*10) % ( 'START SVM CLASSIFING'))
svm_rank_classify_format = '%s %s %s %s'
cmd_text = svm_rank_classify_format % (SVM_RANK.svm_rank_classify_command, features, model, predictions)
logging.debug(cmd_text)
os.system(cmd_text)