def main():
file_name = 'input/BioASQ-task6bPhaseB-testset3.json'
file_name = 'input/BioASQ-trainingDataset6b.json'
file_name = 'input/BioASQ-trainingDataset5b.json'
file_name = 'input/phaseB_5b_05.json'
save_model_file_name = 'weights_2'
ranker = SVMRank(save_model_file_name)
data = DataLoader(file_name)
data.load_ner_entities()
ans_file = 'output/factoid_list_%s.json' % data.name
questions = data.get_questions_of_type(C.FACTOID_TYPE)
for i, question in enumerate(tqdm(questions)):
ranked_sentences = question.ranked_sentences()
X, candidates = get_only_features(question, ranked_sentences)
top_answers = ranker.classify_from_feed(X, candidates, i)
question.exact_answer = [[answer] for answer in top_answers[:5]]
# question.exact_answer = [answer for answer in top_answers]
# print question.exact_answer_ref
# print '\n'
# print top5
# print '\n'
# print '\n\n\n'
questions = data.get_questions_of_type(C.LIST_TYPE)
for i, question in enumerate(tqdm(questions)):
ranked_sentences = question.ranked_sentences()
X, candidates = get_only_features(question, ranked_sentences)
top_answers = ranker.classify_from_feed(X, candidates, i)
question.exact_answer = [[answer] for answer in top_answers[:10]]
data.save_factoid_list_answers(ans_file)
def main():
ranker = SVMRank()
file_name = 'input/BioASQ-trainingDataset6b.json'
data = DataLoader(file_name)
data.load_ner_entities()
questions = data.get_questions_of_type(C.FACTOID_TYPE)[:419]
for i, question in enumerate(questions):
ranked_sentences = question.ranked_sentences()
X, y = get_features(question, ranked_sentences)
ranker.feed(X, y, i)
ranker.train_from_feed()
ranker.save('weights_2')
