File size: 7,697 Bytes
778d47d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | import os
import pdb
import sys
import json
import numpy as np
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
import time
import math
def result_callback(result):
exec_result.append(result)
def clean_abnormal(input):
input = np.asarray(input)
processed_list = []
mean = np.mean(input,axis=0)
std = np.std(input,axis=0)
for x in input:
if x < mean + 3 * std and x > mean - 3 * std:
processed_list.append(x)
return processed_list
def execute_sql(sql, db_path):
# Connect to the database
conn = sqlite3.connect(db_path)
# Create a cursor object
cursor = conn.cursor()
start_time = time.time()
cursor.execute(sql)
exec_time = time.time() - start_time
return exec_time
def iterated_execute_sql(predicted_sql,ground_truth,db_path,iterate_num):
conn = sqlite3.connect(db_path)
diff_list = []
cursor = conn.cursor()
cursor.execute(predicted_sql)
predicted_res = cursor.fetchall()
cursor.execute(ground_truth)
ground_truth_res = cursor.fetchall()
time_ratio = 0
if set(predicted_res) == set(ground_truth_res):
for i in range(iterate_num):
predicted_time = execute_sql(predicted_sql, db_path)
ground_truth_time = execute_sql(ground_truth, db_path)
diff_list.append(ground_truth_time / predicted_time)
processed_diff_list = clean_abnormal(diff_list)
time_ratio = sum(processed_diff_list) / len(processed_diff_list)
return time_ratio
def execute_model(predicted_sql,ground_truth, db_place, idx, iterate_num, meta_time_out):
try:
# you can personalize the total timeout number
# larger timeout leads to more stable ves
# while it needs more your patience....
time_ratio = func_timeout(meta_time_out * iterate_num, iterated_execute_sql,
args=(predicted_sql, ground_truth, db_place, iterate_num))
# print([idx, math.sqrt(time_ratio)])
except KeyboardInterrupt:
sys.exit(0)
except FunctionTimedOut:
result = [(f'timeout',)]
time_ratio = 0
except Exception as e:
result = [(f'error',)] # possibly len(query) > 512 or not executable
time_ratio = 0
result = {'sql_idx': idx, 'time_ratio': time_ratio}
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()
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, iterate_num=100, 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, iterate_num, 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_ves(exec_results):
num_queries = len(exec_results)
total_ratio = 0
count = 0
for i, result in enumerate(exec_results):
if result['time_ratio'] != 0:
count += 1
total_ratio += math.sqrt(result['time_ratio']) * 100
ves = (total_ratio/num_queries)
return ves
def load_json(dir):
with open(dir, 'r') as j:
contents = json.loads(j.read())
return contents
def compute_ves_by_diff(exec_results,diff_json_path):
num_queries = len(exec_results)
contents = load_json(diff_json_path)
simple_results, moderate_results, challenging_results = [], [], []
for i,content in enumerate(contents):
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_ves = compute_ves(simple_results)
moderate_ves = compute_ves(moderate_results)
challenging_ves = compute_ves(challenging_results)
all_ves = compute_ves(exec_results)
count_lists = [len(simple_results), len(moderate_results), len(challenging_results), num_queries]
return simple_ves, moderate_ves, challenging_ves, all_ves, 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('========================================= VES ========================================')
print("{:20} {:<20.2f} {:<20.2f} {:<20.2f} {:<20.2f}".format('ves', *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('--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)
print('start calculate')
simple_ves, moderate_ves, challenging_ves, ves, count_lists = \
compute_ves_by_diff(exec_result, args.diff_json_path)
score_lists = [simple_ves, moderate_ves, challenging_ves, ves]
print_data(score_lists, count_lists)
print('===========================================================================================')
print("Finished evaluation")
|