| from ...smp import * | |
| import os | |
| def report_acc_hrbench(df): | |
| cycle_group = df.groupby('cycle_category') | |
| result_dic = defaultdict(list) | |
| avg_dic = defaultdict(int) | |
| count = 0 | |
| for key, data_value in cycle_group: | |
| count += 1 | |
| _, resp_dic = hrbench_score(data_value) | |
| for task_type, accuracy in resp_dic.items(): | |
| result_dic['cycle'].append(key) | |
| result_dic['type'].append(task_type) | |
| result_dic['accuracy'].append(accuracy) | |
| avg_dic[task_type] += accuracy | |
| for task_type, accuracy in avg_dic.items(): | |
| result_dic['cycle'].append('Average') | |
| result_dic['type'].append(task_type) | |
| result_dic['accuracy'].append(accuracy / count) | |
| result_pd = pd.DataFrame(result_dic) | |
| return result_pd | |
| def hrbench_score(data): | |
| ret = defaultdict(list) | |
| resp_dic = {} | |
| category_list = set(data['category']) | |
| score_dict = defaultdict(list) | |
| for i in range(len(data)): | |
| d = data.iloc[i] | |
| category = d['category'] | |
| gpt_score = d['hit'] | |
| score_dict[category].append(gpt_score) | |
| score_dict['all'].append(gpt_score) | |
| all_acc = np.mean(score_dict['all']) | |
| ret['type'].append('all') | |
| ret['acc'].append(all_acc) | |
| resp_dic['all'] = all_acc | |
| for cate in category_list: | |
| acc = np.mean(score_dict[cate]) | |
| ret['type'].append(cate) | |
| ret['acc'].append(acc) | |
| resp_dic[cate] = acc | |
| return pd.DataFrame(ret), resp_dic | |