all_qual_num = all_qual_num + 1
# pdb.set_trace()
result_file.write(
"true_count={},predict_count={},all_qual_num={}\n".format(
true_tags_count, predic_tags_count, all_qual_num))
# pdb.set_trace()
outf_path = '../output/'
out_filename = "{}_output.json".format(task_type_name)
outf_file = os.path.join(outf_path, out_filename)
inf_path = os.path.join(labor_data_path, data_filename)
generate_pred_file(labor_tags_list, labor_preds, inf_path, outf_file)
# 对结果进行评估
judger_labor = Judger(tag_path=labor_tag_file)
reslt_labor = judger_labor.test(truth_path=inf_path, output_path=outf_file)
score_labor = judger_labor.gen_score(reslt_labor)
result_file.write('score_{}={}\n\n'.format(model_filename, score_labor))
exit()
# 生成divorce领域的预测文件
print('predict_divorce...')
tags_list = []
with open('../../data/divorce/tags.txt', 'r', encoding='utf-8') as tagf:
for line in tagf.readlines():
tags_list.append(line.strip())
prd = Predictor('model_divorce/')
inf_path = '../../data/divorce/data_small_selected.json'
outf_path = '../../output/divorce_output.json'
generate_pred_file(tags_list, prd, inf_path, outf_path)
