def main_process(input_file): sentence_count = 0 gen_questions = [] final_questions = [] with open(input_file, "r") as f: for line in f: sentences = ss.sentence_segment(line, tri_gram=False) sentence_count += 1 for i in range(len(sentences)): questions = qg.generate(sentences[i].pos) for tp in questions: (question, answer, answer_index) = tp ans_eng, ans_index = wnth.get_general_info(answer) answer_item = _wi.word_item(answer, ans_eng, ans_index, None) question_item = _qui.question_item(sentences[i], sentence_count, question, answer_item, answer_index) gen_questions.append(question_item) ranked_questions, qrank_scores = qr.rank_question(gen_questions) for question in ranked_questions: choices = chg.generate_choices(question) if choices != []: question.add_choices(choices) final_questions.append(question) return final_questions
def addQuestion(self, question_body, lang, sortorder, REQUEST=None):
""" """
id = 'q%s' % self.utGenRandomId(4)
try: sortorder = int(sortorder)
except: sortorder = 100
if not self.questions.has_key(id): id = id
else: id = id + self.utGenRandomId()
self.questions[id] = question_item(question_body, lang, sortorder)
self._p_changed = 1
return REQUEST.RESPONSE.redirect('%s/manage_questions_html' % (self.absolute_url(), ))
def addQuestion(self, question_body, lang, sortorder, REQUEST=None):
""" """
id = 'q%s' % self.utGenRandomId(4)
try:
sortorder = int(sortorder)
except:
sortorder = 100
if not self.questions.has_key(id): id = id
else: id = id + self.utGenRandomId()
self.questions[id] = question_item(question_body, lang, sortorder)
self._p_changed = 1
return REQUEST.RESPONSE.redirect('%s/manage_questions_html' %
(self.absolute_url(), ))
def get_question_item(question_file, pos_file):
sentences = []
sentence_with_pos = []
with open(pos_file) as f:
for line in f:
read_list = ast.literal_eval(line.strip())
for a_sentence in read_list:
sentences.append("".join([word for (word, _) in a_sentence]))
sentence_with_pos.append(a_sentence)
sentence_count = 0
all_question_items = []
with open(question_file) as f:
for line in f:
read_list = ast.literal_eval(line.strip())
for a_sentence in read_list:
sentence_item = sentence.sentence(sentences[sentence_count], sentence_with_pos[sentence_count])
for a_question in a_sentence:
(question_sentence, answer, choices) = a_question
answer_item = word_item.word_item(answer)
answer_index = find_blank_index([tp[0] for tp in sentence_with_pos[sentence_count]], question_sentence)
all_generated_choices = _cg.choice_generate(answer_item)
choice_items = []
for a_choice in choices:
if str(a_choice) == answer:
choice_items.append(answer_item)
else:
for gen_choice in all_generated_choices:
if str(gen_choice) == a_choice:
choice_items.append(gen_choice)
break
# print([str(choice) for choice in choice_items])
question = question_item.question_item(sentence_item, sentence_count, question_sentence, answer_item, answer_index)
question.add_choices(choice_items)
all_question_items.append(question)
sentence_count += 1
return all_question_items
for sentence in sentences:
print(str(sentence))
print("======================")
for sentence in sentences:
for word, pos in sentence.pos:
print(word, pos)
print("--------", end="\n\n")
if __name__ == "__main__":
# initialize - train model
all_questions = []
with open("ranking/test-globalwarming.out", "r") as f:
for line in f:
a_question_item = _qui.question_item(from_str=line.strip())
all_questions.append(a_question_item)
# print_sentence_cut_results(all_questions)
_ev.read_eval_file("ranking/evaluation/sheet1.csv", all_questions)
_ev.read_eval_file("ranking/evaluation/sheet2.csv", all_questions)
_ev.read_eval_file("ranking/evaluation/sheet3.csv", all_questions)
qr = qrank.question_ranker(question_set=all_questions)
# qr.test_kfolds(all_questions)
choice_rank = chrank.choice_ranker(training_set=all_questions, wordnet=wnth)
chg = _chg.choice_generator(wnth, choice_rank)
# ranked_question, ranked_scores = qr.rank_question(generated_questions)
args = sys.argv
custom_dict = dict()
if len(args) >= 2:
