import sys import json import argparse import sqlite3 import multiprocessing as mp from func_timeout import func_timeout, FunctionTimedOut def load_json(dir): with open(dir, 'r') as j: contents = json.loads(j.read()) return contents def result_callback(result): exec_result.append(result) def execute_sql(predicted_sql,ground_truth, db_path): if predicted_sql.lower().startswith('insert into') or predicted_sql.lower().startswith('update') or predicted_sql.lower().startswith('delete') or predicted_sql.lower().startswith('truncate'): return 0 conn = sqlite3.connect(db_path) # Connect to the database cursor = conn.cursor() cursor.execute(predicted_sql) predicted_res = cursor.fetchall() cursor.execute(ground_truth) ground_truth_res = cursor.fetchall() res = 0 if set(predicted_res) == set(ground_truth_res): res = 1 # if res == 0 and len(str(predicted_res)) == len(str(ground_truth_res)): # print(predicted_sql) # print(ground_truth) # print(predicted_res) # print(ground_truth_res) # print("-------------------") return res def execute_model(predicted_sql,ground_truth, db_place, idx, meta_time_out): try: res = func_timeout(meta_time_out, execute_sql, args=(predicted_sql, ground_truth, db_place)) except KeyboardInterrupt: sys.exit(0) except FunctionTimedOut: result = [(f'timeout',)] res = 0 except Exception as e: result = [(f'error',)] # possibly len(query) > 512 or not executable res = 0 # print(result) # result = str(set([ret[0] for ret in result])) result = {'sql_idx': idx, 'res': res} # if res == 0: # print("predicted_sql:", predicted_sql) # print("ground_truth:", ground_truth) # print("-"*20) # print(result) return result def package_sqls(sql_path, db_root_path, mode='gpt', data_mode='dev'): clean_sqls = [] db_path_list = [] if mode == 'gpt': sql_data = json.load(open(sql_path, 'r')) for idx, sql_str in sql_data.items(): if type(sql_str) == str: sql, db_name = sql_str.split('\t----- bird -----\t') else: sql, db_name = " ", "financial" clean_sqls.append(sql) db_path_list.append(db_root_path + db_name + '/' + db_name + '.sqlite') #with open(sql_path, 'r', encoding='utf8') as f: # for idx, line in enumerate(f): # row = json.loads(line) # db_name = row['db_id'] # clean_sqls.append(row['sql_query']) # db_path_list.append(db_root_path + db_name + '/' + db_name + '.sqlite') elif mode == 'gt': sqls = open(sql_path + data_mode + '_gold.sql') sql_txt = sqls.readlines() # sql_txt = [sql.split('\t')[0] for sql in sql_txt] for idx, sql_str in enumerate(sql_txt): sql, db_name = sql_str.strip().split('\t') clean_sqls.append(sql) db_path_list.append(db_root_path + db_name + '/' + db_name + '.sqlite') return clean_sqls, db_path_list def run_sqls_parallel(sqls, db_places, num_cpus=1, meta_time_out=30.0): pool = mp.Pool(processes=num_cpus) for i,sql_pair in enumerate(sqls): predicted_sql, ground_truth = sql_pair pool.apply_async(execute_model, args=(predicted_sql, ground_truth, db_places[i], i, meta_time_out), callback=result_callback) pool.close() pool.join() def sort_results(list_of_dicts): return sorted(list_of_dicts, key=lambda x: x['sql_idx']) def compute_acc_by_diff(exec_results,diff_json_path): num_queries = len(exec_results) results = [res['res'] for res in exec_results] contents = load_json(diff_json_path) simple_results, moderate_results, challenging_results = [], [], [] for i,content in enumerate(contents): if 'difficulty' not in content: content['difficulty'] = 'simple' if content['difficulty'] == 'simple': simple_results.append(exec_results[i]) if content['difficulty'] == 'moderate': moderate_results.append(exec_results[i]) if content['difficulty'] == 'challenging': challenging_results.append(exec_results[i]) simple_acc = sum([res['res'] for res in simple_results])/(len(simple_results) + 1e-9) moderate_acc = sum([res['res'] for res in moderate_results])/(len(moderate_results)+1e-9) challenging_acc = sum([res['res'] for res in challenging_results])/(len(challenging_results) + 1e-9) all_acc = sum(results)/num_queries count_lists = [len(simple_results), len(moderate_results), len(challenging_results), num_queries] return simple_acc * 100, moderate_acc * 100, challenging_acc * 100, all_acc * 100, count_lists def print_data(score_lists,count_lists): levels = ['simple', 'moderate', 'challenging', 'total'] print("{:20} {:20} {:20} {:20} {:20}".format("", *levels)) print("{:20} {:<20} {:<20} {:<20} {:<20}".format('count', *count_lists)) print('====================================== ACCURACY =====================================') print("{:20} {:<20.2f} {:<20.2f} {:<20.2f} {:<20.2f}".format('accuracy', *score_lists)) if __name__ == '__main__': args_parser = argparse.ArgumentParser() args_parser.add_argument('--predicted_sql_path', type=str, required=True, default='') args_parser.add_argument('--ground_truth_path', type=str, required=True, default='') args_parser.add_argument('--data_mode', type=str, required=True, default='dev') args_parser.add_argument('--db_root_path', type=str, required=True, default='') args_parser.add_argument('--num_cpus', type=int, default=1) args_parser.add_argument('--meta_time_out', type=float, default=30.0) args_parser.add_argument('--mode_gt', type=str, default='gt') args_parser.add_argument('--mode_predict', type=str, default='gpt') args_parser.add_argument('--difficulty',type=str,default='simple') args_parser.add_argument('--diff_json_path',type=str,default='') args = args_parser.parse_args() exec_result = [] pred_queries, db_paths = package_sqls(args.predicted_sql_path, args.db_root_path, mode=args.mode_predict, data_mode=args.data_mode) # generate gt sqls: gt_queries, db_paths_gt = package_sqls(args.ground_truth_path, args.db_root_path, mode='gt', data_mode=args.data_mode) query_pairs = list(zip(pred_queries,gt_queries)) run_sqls_parallel(query_pairs, db_places=db_paths, num_cpus=args.num_cpus, meta_time_out=args.meta_time_out) exec_result = sort_results(exec_result) # with open("exec_result.json", "w", encoding="utf-8") as f: # f.write(json.dumps(exec_result, indent=2, ensure_ascii=False)) print('start calculate') simple_acc, moderate_acc, challenging_acc, acc, count_lists = \ compute_acc_by_diff(exec_result,args.diff_json_path) score_lists = [simple_acc, moderate_acc, challenging_acc, acc] print_data(score_lists,count_lists) print('===========================================================================================') print("Finished evaluation")