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DataClawEval / tasks /offline-compute /MySQL /mysql_003 /gt /grade.py
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def grade(workspace_path, **kwargs):
 """
 mysql_003 rule-based grading: 离线推理任务特征宽表构建
 总分结构 (100分, 归一化到0~1):
 产物评分 (70%): A~E 维度原始满分100分 x 0.7 = 70分
 A. 可执行性 (15分)
 B. Schema一致性 (15分)
 C. 行集一致性 (20分)
 D. 数值正确性 (40分)
 E. 主键/标签列正确性 (10分)
 过程评分 (30%): G~I 维度原始满分100分 x 0.3 = 30分
 G. 探索充分性 (35分)
 H. 执行效率 (40分)
 I. 自验证行为 (25分)
 Architecture: pymysql direct connection, no Spark/Hive dependency.
 """
 import os
 import re
 import sys
 import subprocess
 import time
 import json
DB_NAME = "internal_platform_db"
 INPUT_TABLE_SHORT = "aide_offline_inference_info_fixed_mysql_003"
 OUTPUT_TABLE = "dwd_aide_offline_inference_feature_cand_mysql_003"
 OUTPUT_TABLE_SHORT = "dwd_aide_offline_inference_feature_cand_mysql_003"
 FULL_OUTPUT = f"{DB_NAME}.{OUTPUT_TABLE_SHORT}"
 FULL_INPUT = f"{DB_NAME}.{INPUT_TABLE_SHORT}"
KEY_COLUMNS = ["id", "servingName", "submit_type", "mould_name"]
 EXPECTED_COL_COUNT = 23
 EXPECTED_ROW_COUNT = 58
MYSQL_CONFIG = {
 "host": "localhost",
 "port": 3306,
 "user": "root",
 "password": "root123",
 "charset": "utf8mb4",
 }
# Expected output (from query_engine_003 output/expected.csv)
 EXPECTED_ROWS = [{'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'WEB', 'mould_id': '501', 'mould_name': 'model_x', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"batch":10}', 'submit_operator': 'user_zhang', 'submit_time': '2026-05-06 23:00:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_5002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 200, 'dataset_fin_num': 200, 'dataset_err_num': 0, 'max_instance_first_wait_time': 150.0, 'avg_instance_first_wait_time': 150.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_3002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 200, 'dataset_fin_num': 200, 'dataset_err_num': 0, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 120.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_302_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 200, 'dataset_fin_num': 200, 'dataset_err_num': 0, 'max_instance_first_wait_time': 80.0, 'avg_instance_first_wait_time': 80.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_5002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 480, 'dataset_err_num': 20, 'max_instance_first_wait_time': 150.0, 'avg_instance_first_wait_time': 150.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_3002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 480, 'dataset_err_num': 20, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 120.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_302_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 480, 'dataset_err_num': 20, 'max_instance_first_wait_time': 80.0, 'avg_instance_first_wait_time': 80.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_5002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 500, 'dataset_err_num': 0, 'max_instance_first_wait_time': 150.0, 'avg_instance_first_wait_time': 150.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_3002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 500, 'dataset_err_num': 0, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 120.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-06 23:50:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_302_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'API', 'mould_id': '502', 'mould_name': 'model_y', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 500, 'dataset_err_num': 0, 'max_instance_first_wait_time': 80.0, 'avg_instance_first_wait_time': 80.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"batch":20}', 'submit_operator': 'user_li', 'submit_time': '2026-05-06 22:00:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 100, 'dataset_fin_num': 80, 'dataset_err_num': 5, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 900, 'dataset_err_num': 50, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_5001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 300.0, 'avg_instance_first_wait_time': 300.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_3001_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 60.0, 'avg_instance_first_wait_time': 60.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'v1.0-alpha'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:10:00', 'ct': 1, 'id': 1001, 'name': 'task_alpha', 'servingName': 'task_alpha_301_OFFLINE', 'base': 'JOB', 'status': 'RUNNING', 'submit_type': 'API', 'mould_id': '501', 'mould_name': 'model_a', 'time_cost_in_second': 1200, 'dataset_act_num': 1000, 'dataset_fin_num': 950, 'dataset_err_num': 10, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 90.0, 'task_dispatch_time': 120, 'offline_inference_time': 1080, 'inference_config': '{"key":"val"}', 'submit_operator': 'submitter_A', 'submit_time': '2026-05-07 06:25:00', 'image_tag': 'img_tag_A'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_5002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 200, 'dataset_fin_num': 200, 'dataset_err_num': 0, 'max_instance_first_wait_time': 150.0, 'avg_instance_first_wait_time': 150.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_3002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 200, 'dataset_fin_num': 200, 'dataset_err_num': 0, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 120.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_302_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 200, 'dataset_fin_num': 200, 'dataset_err_num': 0, 'max_instance_first_wait_time': 80.0, 'avg_instance_first_wait_time': 80.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_5002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 480, 'dataset_err_num': 20, 'max_instance_first_wait_time': 150.0, 'avg_instance_first_wait_time': 150.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_3002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 480, 'dataset_err_num': 20, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 120.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_302_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 480, 'dataset_err_num': 20, 'max_instance_first_wait_time': 80.0, 'avg_instance_first_wait_time': 80.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_5002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 500, 'dataset_err_num': 0, 'max_instance_first_wait_time': 150.0, 'avg_instance_first_wait_time': 150.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_3002_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 500, 'dataset_err_num': 0, 'max_instance_first_wait_time': 120.0, 'avg_instance_first_wait_time': 120.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:15:00', 'ct': 1, 'id': 1002, 'name': 'task_beta', 'servingName': 'task_beta_302_OFFLINE', 'base': 'JOB', 'status': 'FINISH', 'submit_type': 'WEB', 'mould_id': '502', 'mould_name': 'model_b', 'time_cost_in_second': 5400, 'dataset_act_num': 500, 'dataset_fin_num': 500, 'dataset_err_num': 0, 'max_instance_first_wait_time': 80.0, 'avg_instance_first_wait_time': 80.0, 'task_dispatch_time': 60, 'offline_inference_time': 5340, 'inference_config': '{"key2":"val2"}', 'submit_operator': 'submitter_B', 'submit_time': '2026-05-07 04:55:00', 'image_tag': 'img_tag_B'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:20:00', 'ct': 1, 'id': 1003, 'name': 'task_gamma', 'servingName': 'task_gamma_5003_OFFLINE', 'base': 'JOB', 'status': 'FAILED', 'submit_type': 'SDK', 'mould_id': '503', 'mould_name': 'model_c', 'time_cost_in_second': 60, 'dataset_act_num': 150, 'dataset_fin_num': 50, 'dataset_err_num': 30, 'max_instance_first_wait_time': 200.0, 'avg_instance_first_wait_time': 200.0, 'task_dispatch_time': 60, 'offline_inference_time': 0, 'inference_config': '{"key3":"val3"}', 'submit_operator': 'submitter_C', 'submit_time': '2026-05-07 06:05:00', 'image_tag': 'img_tag_C'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:20:00', 'ct': 1, 'id': 1003, 'name': 'task_gamma', 'servingName': 'task_gamma_303_OFFLINE', 'base': 'JOB', 'status': 'FAILED', 'submit_type': 'SDK', 'mould_id': '503', 'mould_name': 'model_c', 'time_cost_in_second': 60, 'dataset_act_num': 150, 'dataset_fin_num': 50, 'dataset_err_num': 30, 'max_instance_first_wait_time': None, 'avg_instance_first_wait_time': None, 'task_dispatch_time': 60, 'offline_inference_time': 0, 'inference_config': '{"key3":"val3"}', 'submit_operator': 'submitter_C', 'submit_time': '2026-05-07 06:05:00', 'image_tag': 'img_tag_C'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:20:00', 'ct': 1, 'id': 1003, 'name': 'task_gamma', 'servingName': 'task_gamma_5003_OFFLINE', 'base': 'JOB', 'status': 'FAILED', 'submit_type': 'SDK', 'mould_id': '503', 'mould_name': 'model_c', 'time_cost_in_second': 60, 'dataset_act_num': 200, 'dataset_fin_num': 100, 'dataset_err_num': 50, 'max_instance_first_wait_time': 200.0, 'avg_instance_first_wait_time': 200.0, 'task_dispatch_time': 60, 'offline_inference_time': 0, 'inference_config': '{"key3":"val3"}', 'submit_operator': 'submitter_C', 'submit_time': '2026-05-07 06:05:00', 'image_tag': 'img_tag_C'}, {'dt': '2026050700', 'create_time': '2026-05-07 00:20:00', 'ct': 1, 'id': 1003, 'name': 'task_gamma', 'servingName': 'task_gamma_303_OFFLINE', 'base': 'JOB', 'status': 'FAILED', 'submit_type': 'SDK', 'mould_id': '503', 'mould_name': 'model_c', 'time_cost_in_second': 60, 'dataset_act_num': 200, 'dataset_fin_num': 100, 'dataset_err_num': 50, 'max_instance_first_wait_time': None, 'avg_instance_first_wait_time': None, 'task_dispatch_time': 60, 'offline_inference_time': 0, 'inference_config': '{"key3":"val3"}', 'submit_operator': 'submitter_C', 'submit_time': '2026-05-07 06:05:00', 'image_tag': 'img_tag_C'}]
result = {
 "overall_score": 0.0,
 "total_points": 0,
 "grade": "",
 "details": {},
 "diagnostics": [],
 "anti_cheat": {"passed": True},
 }
# ========== Helper: MySQL connection ==========
 def _mysql_fetch_all(sql):
 import pymysql
 conn = pymysql.connect(**MYSQL_CONFIG)
 try:
 with conn.cursor() as cur:
 cur.execute(sql)
 return cur.fetchall()
 finally:
 conn.close()
def _mysql_execute(sql):
 import pymysql
 conn = pymysql.connect(**MYSQL_CONFIG)
 try:
 with conn.cursor() as cur:
 cur.execute(sql)
 conn.commit()
 finally:
 conn.close()
def _mysql_fetch_dicts(sql):
 import pymysql
 conn = pymysql.connect(**MYSQL_CONFIG)
 try:
 with conn.cursor(pymysql.cursors.DictCursor) as cur:
 cur.execute(sql)
 return cur.fetchall()
 finally:
 conn.close()
def _mysql_columns(table_full):
 import pymysql
 conn = pymysql.connect(**MYSQL_CONFIG)
 try:
 with conn.cursor(pymysql.cursors.DictCursor) as cur:
 cur.execute(f"DESCRIBE {table_full}")
 return cur.fetchall()
 finally:
 conn.close()
def coverage_to_ratio(rate):
 if rate >= 0.995:
 return 1.0
 elif rate >= 0.90:
 return 0.8
 elif rate >= 0.70:
 return 0.5
 else:
 return 0.0
def values_match(pred_val, gt_val):
 if pred_val is None and gt_val is None:
 return True
 if pred_val is None or gt_val is None:
 return False
 try:
 pv = int(pred_val)
 gv = int(gt_val)
 return pv == gv
 except (ValueError, TypeError):
 return str(pred_val).strip() == str(gt_val).strip()
def finalize(result):
 ALPHA = 0.3
product_dims = ["A_executability", "B_schema", "C_row_alignment",
 "D_numerical_accuracy", "E_labels"]
 product_raw = sum(result["details"].get(d, {}).get("score", 0) for d in product_dims)
 product_score = round(product_raw * (1 - ALPHA), 2)
product_ratio = product_raw / 100.0
 for dim in ["H_efficiency"]:
 if dim in result["details"]:
 raw = result["details"][dim].get("score", 0)
 result["details"][dim]["score_before_scaling"] = raw
 result["details"][dim]["score"] = round(raw * product_ratio, 2)
 result["details"][dim]["product_ratio"] = round(product_ratio, 4)
process_dims = ["G_exploration", "H_efficiency", "I_self_verification"]
 process_raw = sum(result["details"].get(d, {}).get("score", 0) for d in process_dims)
 process_score = round(process_raw * ALPHA, 2)
total = round(product_score + process_score, 2)
 result["total_points"] = total
 result["product_points"] = product_score
 result["process_points"] = round(process_score, 2)
 result["overall_score"] = round(total / 100.0, 4)
 if total >= 90:
 result["grade"] = "优秀"
 elif total >= 75:
 result["grade"] = "良好"
 elif total >= 60:
 result["grade"] = "合格"
 elif total >= 40:
 result["grade"] = "偏弱"
 else:
 result["grade"] = "不合格"
 return result
# ========== Product grading ==========
 def _product_grade():
 # ===== A. 可执行性 (15分) =====
 a_items = {"A1_no_error": 0, "A2_table_produced": 0, "A3_no_manual_fix": 0}
agent_code = os.path.join(workspace_path, "result.py")
 if not os.path.exists(agent_code):
 result["details"]["A_executability"] = {"score": 0, "max": 15, "items": a_items}
 result["error"] = "no_result_file"
 return
# Anti-cheat: check if agent references ground_truth.py
 with open(agent_code, "r", encoding="utf-8", errors="ignore") as f:
 source_code = f.read()
if re.search(r'ground_truth\.py', source_code):
 result["anti_cheat"] = {
 "passed": False,
 "reason": "Directly references ground_truth.py file",
 }
# Execute agent code
 agent_exec_success = False
 agent_stderr = ""
 try:
 r = subprocess.run(
 ["python3", agent_code],
 capture_output=True, text=True, timeout=300,
 cwd=workspace_path,
 )
 if r.returncode == 0:
 agent_exec_success = True
 else:
 agent_stderr = r.stderr or r.stdout or ""
 except subprocess.TimeoutExpired:
 agent_stderr = "agent code execution timeout (300s)"
 except Exception as e:
 agent_stderr = str(e)
if not agent_exec_success:
 a_items["A1_no_error"] = 0
 result["details"]["A_executability"] = {"score": 0, "max": 15, "items": a_items}
 result["error"] = agent_stderr[-2000:]
 return
a_items["A1_no_error"] = 5
 a_items["A3_no_manual_fix"] = 3
# Check if output table has data
 try:
 row_count = _mysql_fetch_all(f"SELECT COUNT(*) FROM {FULL_OUTPUT}")[0][0]
 except Exception as e:
 row_count = 0
 result["diagnostics"].append(f"mysql_count_pred_failed: {e}")
if row_count > 0:
 a_items["A2_table_produced"] = 7
 else:
 a_items["A2_table_produced"] = 0
a_score = sum(a_items.values())
 result["details"]["A_executability"] = {"score": a_score, "max": 15, "items": a_items}
# Gate: A2=0 => B/C/D/E all 0
 if a_items["A2_table_produced"] == 0:
 result["details"]["B_schema"] = {"score": 0, "max": 15, "items": {}}
 result["details"]["C_row_alignment"] = {"score": 0, "max": 20, "items": {}}
 result["details"]["D_numerical_accuracy"] = {"score": 0, "max": 40, "items": {}}
 result["details"]["E_labels"] = {"score": 0, "max": 10, "items": {}}
 result["error"] = "target table empty or not produced"
 return
# Read agent output from MySQL and save to temp table (before GT overwrites it)
 try:
 pred_rows = _mysql_fetch_dicts(f"SELECT * FROM {FULL_OUTPUT}")
 except Exception as e:
 result["diagnostics"].append(f"mysql_read_pred_failed: {e}")
 pred_rows = []
# Save agent output to a temporary table so GT overwrite doesn't destroy it
 _PRED_TMP_TABLE = f"{DB_NAME}.{OUTPUT_TABLE_SHORT}_pred_tmp"
 try:
 _mysql_execute(f"DROP TABLE IF EXISTS {_PRED_TMP_TABLE}")
 _mysql_execute(f"CREATE TABLE {_PRED_TMP_TABLE} AS SELECT * FROM {FULL_OUTPUT}")
 except Exception as e:
 result["diagnostics"].append(f"save_pred_tmp_failed: {e}")
# Read columns info (from original agent output)
 try:
 cols_info = _mysql_columns(FULL_OUTPUT)
 pred_columns = [c["Field"] for c in cols_info]
 except Exception:
 pred_columns = list(pred_rows[0].keys()) if pred_rows else []
# ----- Run ground truth -----
 gt_code = os.path.join(workspace_path, "gt", "ground_truth.py")
 gt_success = False
 for _ in range(3):
 try:
 r = subprocess.run(
 ["python3", gt_code],
 capture_output=True, text=True, timeout=300,
 cwd=workspace_path,
 )
 if r.returncode == 0:
 gt_success = True
 break
 except subprocess.TimeoutExpired:
 result["error"] = "ground_truth execution timeout"
 break
 except Exception as e:
 result["error"] = f"ground_truth_failed: {e}"
 break
if not gt_success:
 if "error" not in result:
 result["error"] = "ground_truth_failed after retries"
 result["details"]["B_schema"] = {"score": 0, "max": 15, "items": {}}
 result["details"]["C_row_alignment"] = {"score": 0, "max": 20, "items": {}}
 result["details"]["D_numerical_accuracy"] = {"score": 0, "max": 40, "items": {}}
 result["details"]["E_labels"] = {"score": 0, "max": 10, "items": {}}
 # Cleanup temp table
 try:
 _mysql_execute(f"DROP TABLE IF EXISTS {_PRED_TMP_TABLE}")
 except Exception:
 pass
 return
# Read GT from MySQL (now overwritten by ground_truth.py)
 try:
 gt_rows = _mysql_fetch_dicts(f"SELECT * FROM {FULL_OUTPUT}")
 except Exception as e:
 result["error"] = f"gt_result_read_failed: {e}"
 result["details"]["B_schema"] = {"score": 0, "max": 15, "items": {}}
 result["details"]["C_row_alignment"] = {"score": 0, "max": 20, "items": {}}
 result["details"]["D_numerical_accuracy"] = {"score": 0, "max": 40, "items": {}}
 result["details"]["E_labels"] = {"score": 0, "max": 10, "items": {}}
 try:
 _mysql_execute(f"DROP TABLE IF EXISTS {_PRED_TMP_TABLE}")
 except Exception:
 pass
 return
# Re-read pred rows from temp table (original agent output, not overwritten)
 try:
 pred_rows = _mysql_fetch_dicts(f"SELECT * FROM {_PRED_TMP_TABLE}")
 except Exception as e:
 result["diagnostics"].append(f"read_pred_tmp_failed: {e}")
 # pred_rows already has the data we read earlier, use that
# Cleanup temp table
 try:
 _mysql_execute(f"DROP TABLE IF EXISTS {_PRED_TMP_TABLE}")
 except Exception:
 pass
# ===== B. Schema一致性 (15分) =====
 b_items = {}
 b_items["B1_table_name"] = 2
 b_items["B2_col_count"] = 3 if len(pred_columns) == EXPECTED_COL_COUNT else 0
gt_col_set = set(c.lower() for c in ["dt", "create_time", "ct", "id", "name",
 "servingName", "base", "status", "submit_type",
 "mould_id", "mould_name", "time_cost_in_second",
 "dataset_act_num", "dataset_fin_num", "dataset_err_num",
 "max_instance_first_wait_time", "avg_instance_first_wait_time",
 "task_dispatch_time", "offline_inference_time",
 "inference_config", "submit_operator", "submit_time", "image_tag"])
 pred_col_set = set(c.lower() for c in pred_columns)
 if gt_col_set == pred_col_set:
 b_items["B3_col_names"] = 5
 elif len(gt_col_set & pred_col_set) / len(gt_col_set) >= 0.9:
 b_items["B3_col_names"] = 3
 else:
 b_items["B3_col_names"] = 0
# B4: column type alignment
 type_match_count = 0
 try:
 gt_cols_info = _mysql_columns(FULL_OUTPUT)
 gt_types = {c["Field"].lower(): c["Type"].lower() for c in gt_cols_info}
 pred_types = {c["Field"].lower(): c["Type"].lower() for c in cols_info}
 common_cols = gt_col_set & pred_col_set
 if common_cols:
 for col in common_cols:
 if pred_types.get(col, "") == gt_types.get(col, ""):
 type_match_count += 1
 except Exception:
 type_match_count = len(gt_col_set & pred_col_set)
common_count = len(gt_col_set & pred_col_set)
 if common_count > 0 and type_match_count / common_count >= 0.9:
 b_items["B4_col_types"] = 3
 elif common_count > 0 and type_match_count / common_count >= 0.7:
 b_items["B4_col_types"] = 2
 else:
 b_items["B4_col_types"] = 0
b_items["B5_engine_format"] = 2 # MySQL InnoDB
b_score = sum(b_items.values())
 result["details"]["B_schema"] = {"score": b_score, "max": 15, "items": b_items}
# ===== Anti-cheat lock =====
 if not result["anti_cheat"]["passed"]:
 result["details"]["C_row_alignment"] = {"score": 0, "max": 20, "items": {"anti_cheat_failed": True}}
 result["details"]["D_numerical_accuracy"] = {"score": 0, "max": 40, "items": {"anti_cheat_failed": True}}
 result["details"]["E_labels"] = {"score": 0, "max": 10, "items": {}}
 result["diagnostics"].append("Anti-cheat failed: C/D dimensions scored 0")
 return
# ===== C. 行集一致性 (20分) =====
 c_items = {}
def make_key(row):
 return tuple(str(row.get(k, "")).strip() for k in KEY_COLUMNS)
gt_keys = set()
 gt_keys_list = []
 for row in gt_rows:
 k = make_key(row)
 gt_keys.add(k)
 gt_keys_list.append(k)
pred_keys = []
 pred_key_set = set()
 for row in pred_rows:
 k = make_key(row)
 pred_keys.append(k)
 pred_key_set.add(k)
# C1: row count
 n_gt = len(gt_keys)
 n_pred = len(pred_key_set)
 n_pred_rows = len(pred_rows)
 if n_pred_rows == EXPECTED_ROW_COUNT:
 c_items["C1_row_count"] = 6
 elif n_pred_rows > 0 and n_pred_rows <= EXPECTED_ROW_COUNT + 2:
 c_items["C1_row_count"] = 3
 else:
 c_items["C1_row_count"] = 0
# C2: no duplicate keys (check if pred has same duplicate pattern as gt)
 from collections import Counter
 key_counter = Counter(pred_keys)
 gt_key_counter = Counter(gt_keys_list)
 # Compare duplicate patterns: if both have similar duplicate counts, it's OK
 pred_dup_count = sum(1 for v in key_counter.values() if v > 1)
 gt_dup_count = sum(1 for v in gt_key_counter.values() if v > 1)
 if pred_dup_count == gt_dup_count:
 c_items["C2_no_duplicates"] = 5
 elif abs(pred_dup_count - gt_dup_count) <= 2:
 c_items["C2_no_duplicates"] = 2
 else:
 c_items["C2_no_duplicates"] = 0
# C3: coverage (GT key coverage)
 hit_keys = gt_keys & pred_key_set
 hit_count = len(hit_keys)
 coverage = hit_count / n_gt if n_gt > 0 else 0
 c_items["C3_coverage"] = round(5 * coverage_to_ratio(coverage), 2)
 c_items["C3_coverage_rate"] = round(coverage, 4)
# C4: no extra rows
 extra_keys = pred_key_set - gt_keys
 extra_count = len(extra_keys)
 no_extra_rate = 1 - (extra_count / n_pred) if n_pred > 0 else 0
 c_items["C4_no_extra"] = round(4 * coverage_to_ratio(no_extra_rate), 2)
 c_items["C4_no_extra_rate"] = round(no_extra_rate, 4)
c_score = sum(v for k, v in c_items.items() if not k.endswith("_rate"))
 result["details"]["C_row_alignment"] = {"score": round(c_score, 2), "max": 20, "items": c_items}
# ===== D. 数值正确性 (40分) =====
 d_items = {}
# For D dimension, compare all rows after sorting (not dedup by key)
 # because same key can have multiple rows with different dataset values
 def row_sort_key(row):
 return tuple(str(row.get(k, '')).strip() for k in
 ["id", "servingName", "submit_type", "mould_name",
 "dataset_act_num", "dataset_fin_num", "dataset_err_num",
 "image_tag", "inference_config"])
sorted_pred = sorted(pred_rows, key=row_sort_key)
 sorted_gt = sorted(gt_rows, key=row_sort_key)
# Numeric columns to check (BIGINT/INT)
 numeric_cols = ["id", "time_cost_in_second", "dataset_act_num", "dataset_fin_num",
 "dataset_err_num", "task_dispatch_time", "offline_inference_time"]
 per_col_max = 4 # 7 cols x 4 = 28 points
 n_compare = min(len(sorted_pred), len(sorted_gt))
 for col in numeric_cols:
 if n_compare == 0:
 d_items[col] = {"pass_rate": 0.0, "score": 0, "max": per_col_max}
 continue
 passed = 0
 first_mismatch = None
 for i in range(n_compare):
 gt_val = sorted_gt[i].get(col)
 pred_val = sorted_pred[i].get(col)
 if values_match(pred_val, gt_val):
 passed += 1
 elif first_mismatch is None:
 first_mismatch = {
 "key": dict(zip(KEY_COLUMNS, make_key(sorted_pred[i]))),
 "column": col,
 "pred": pred_val,
 "gt": gt_val,
 }
 pass_rate = passed / n_compare
 col_score = round(per_col_max * pass_rate, 2)
 d_items[col] = {"pass_rate": round(pass_rate, 4), "score": col_score, "max": per_col_max}
 if first_mismatch and pass_rate < 0.95:
 result["diagnostics"].append(first_mismatch)
# Double/float columns
 double_cols = ["max_instance_first_wait_time", "avg_instance_first_wait_time"]
 for col in double_cols:
 if n_compare == 0:
 d_items[col] = {"pass_rate": 0.0, "score": 0, "max": 3}
 continue
 passed = 0
 for i in range(n_compare):
 gt_val = sorted_gt[i].get(col)
 pred_val = sorted_pred[i].get(col)
 # Both None is a match
 if pred_val is None and gt_val is None:
 passed += 1
 elif pred_val is not None and gt_val is not None:
 try:
 if abs(float(pred_val) - float(gt_val)) < 0.01:
 passed += 1
 except (ValueError, TypeError):
 if str(pred_val).strip() == str(gt_val).strip():
 passed += 1
 pass_rate = passed / n_compare
 d_items[col] = {"pass_rate": round(pass_rate, 4), "score": round(3 * pass_rate, 2), "max": 3}
# String/datetime columns
 string_cols = ["create_time", "submit_time", "mould_id", "mould_name"]
 string_col_max = 1.5 # 4 cols x 1.5 = 6 points
 for col in string_cols:
 if n_compare == 0:
 d_items[col] = {"pass_rate": 0.0, "score": 0, "max": string_col_max}
 continue
 passed = 0
 for i in range(n_compare):
 gt_val = str(sorted_gt[i].get(col, "")).strip()
 pred_val = str(sorted_pred[i].get(col, "")).strip()
 if pred_val == gt_val:
 passed += 1
 pass_rate = passed / n_compare
 d_items[col] = {"pass_rate": round(pass_rate, 4), "score": round(string_col_max * pass_rate, 2), "max": string_col_max}
d_score = sum(item["score"] for item in d_items.values())
 result["details"]["D_numerical_accuracy"] = {"score": round(d_score, 2), "max": 40, "items": d_items}
# ===== E. 主键/标签列正确性 (10分) =====
 e_items = {}
# E1: all expected task ids present
 expected_ids = {1001, 1002, 1003}
 pred_ids = set()
 for row in pred_rows:
 try:
 pred_ids.add(int(row.get("id", 0)))
 except (ValueError, TypeError):
 pass
 e1_pass = len(expected_ids & pred_ids)
 e_items["E1_task_ids"] = round(5 * (e1_pass / len(expected_ids)), 2)
# E2: expected servingName patterns present
 expected_serving_patterns = {"task_alpha_301_OFFLINE", "task_alpha_3001_OFFLINE", "task_alpha_5001_OFFLINE",
 "task_beta_302_OFFLINE", "task_beta_3002_OFFLINE", "task_beta_5002_OFFLINE",
 "task_gamma_303_OFFLINE", "task_gamma_5003_OFFLINE"}
 pred_servings = set(str(row.get("servingName", "")).strip() for row in pred_rows)
 e2_pass = len(expected_serving_patterns & pred_servings)
 e_items["E2_servingName"] = round(5 * (e2_pass / len(expected_serving_patterns)), 2)
e_score = sum(e_items.values())
 result["details"]["E_labels"] = {"score": round(e_score, 2), "max": 10, "items": e_items}
_product_grade()
# ========== G~I 过程性评分 (30分) ==========
 TRANSCRIPT_PATH = "/tmp/dataclaw_chat.jsonl"
transcript_entries = []
 has_transcript = False
 try:
 if os.path.exists(TRANSCRIPT_PATH):
 with open(TRANSCRIPT_PATH, "r", encoding="utf-8", errors="ignore") as f:
 for line in f:
 line = line.strip()
 if line:
 try:
 transcript_entries.append(json.loads(line))
 except json.JSONDecodeError:
 continue
 if len(transcript_entries) > 2:
 has_transcript = True
 except Exception:
 pass
if not has_transcript:
 result["details"]["G_exploration"] = {"score": 0, "max": 35, "items": {"no_transcript": True}}
 result["details"]["H_efficiency"] = {"score": 0, "max": 40, "items": {"no_transcript": True}}
 result["details"]["I_self_verification"] = {"score": 0, "max": 25, "items": {"no_transcript": True}}
 return finalize(result)
# Parse transcript
 tool_uses = []
 first_write_result_idx = None
 last_spark_submit_success_idx = None
 write_result_count = 0
 logic_error_retries = 0
 output_tokens = 0
for idx, entry in enumerate(transcript_entries):
 content = entry.get("content", [])
 if isinstance(content, str):
 content = [content]
usage_str = entry.get("usage", "")
 if isinstance(usage_str, str) and "output_tokens=" in usage_str:
 try:
 ot_match = re.search(r"output_tokens=(\d+)", usage_str)
 if ot_match:
 output_tokens = int(ot_match.group(1))
 except Exception:
 pass
for block_str in content:
 if not isinstance(block_str, str):
 continue
if "ToolUseBlock" in block_str:
 name_match = re.search(r"name='([^']+)'", block_str)
 input_match = re.search(r"input=(\{.*\})", block_str)
 if name_match:
 tool_name = name_match.group(1)
 tool_input = input_match.group(1) if input_match else ""
 tool_uses.append((idx, tool_name, tool_input))
if tool_name == "Write" and "result" in tool_input and ".py" in tool_input:
 write_result_count += 1
 if first_write_result_idx is None:
 first_write_result_idx = idx
if "ToolResultBlock" in block_str:
 if "Traceback" in block_str or "Exception" in block_str:
 if any(t[1] == "Bash" and "python" in t[2] and "result.py" in t[2]
 for t in tool_uses):
 logic_error_retries += 1
if "python" in block_str and "result.py" in block_str:
 if "Exit Code: 0" in block_str and "Traceback" not in block_str:
 last_spark_submit_success_idx = idx
before_first_write = first_write_result_idx if first_write_result_idx is not None else len(transcript_entries)
# ===== G. 探索充分性 (35分) =====
 g_items = {}
# G1: Checked source table schema
 g1_pass = False
 for idx, name, inp in tool_uses:
 if idx >= before_first_write:
 break
 if name == "Read" and "schema" in inp.lower():
 g1_pass = True
 break
 if name == "Bash" and ("DESCRIBE" in inp.upper() or "SHOW CREATE" in inp.upper()) and INPUT_TABLE_SHORT in inp:
 g1_pass = True
 break
 g_items["G1_source_schema"] = 9 if g1_pass else 0
# G2: Checked source table sample data
 g2_pass = False
 for idx, name, inp in tool_uses:
 if idx >= before_first_write:
 break
 if name == "Bash" and INPUT_TABLE_SHORT in inp:
 if "SELECT" in inp.upper() and ("LIMIT" in inp.upper() or "SELECT *" in inp.upper()):
 g2_pass = True
 break
 g_items["G2_source_sample"] = 9 if g2_pass else 0
# G3: Checked dt / partition values
 g3_pass = False
 for idx, name, inp in tool_uses:
 if idx >= before_first_write:
 break
 if name == "Bash" and ("dt" in inp or "2026050700" in inp):
 if "DISTINCT" in inp.upper() or "COUNT" in inp.upper() or "GROUP BY" in inp.upper():
 g3_pass = True
 break
 g_items["G3_date_range"] = 9 if g3_pass else 0
# G4: Checked target table structure
 g4_pass = False
 for idx, name, inp in tool_uses:
 if idx >= before_first_write:
 break
 if name == "Bash" and ("DESCRIBE" in inp.upper() or "SHOW CREATE" in inp.upper()) and OUTPUT_TABLE_SHORT in inp:
 g4_pass = True
 break
 g_items["G4_target_schema"] = 8 if g4_pass else 0
g_score = sum(g_items.values())
 result["details"]["G_exploration"] = {"score": g_score, "max": 35, "items": g_items}
# ===== H. 执行效率 (40分) =====
 h_items = {}
if write_result_count <= 2:
 h_items["H1_few_submissions"] = 14
 h_items["H2_moderate_submissions"] = 7
 elif write_result_count <= 4:
 h_items["H1_few_submissions"] = 0
 h_items["H2_moderate_submissions"] = 7
 else:
 h_items["H1_few_submissions"] = 0
 h_items["H2_moderate_submissions"] = 0
if logic_error_retries == 0:
 h_items["H3_no_logic_errors"] = 13
 elif logic_error_retries <= 1:
 h_items["H3_no_logic_errors"] = 7
 else:
 h_items["H3_no_logic_errors"] = 0
extra_writes = sum(1 for _, name, inp in tool_uses
 if name == "Write" and "result.py" not in inp
 and (".py" in inp or ".sql" in inp))
 h_items["H4_no_redundant_ops"] = 6 if extra_writes <= 1 else 0
h_score = sum(h_items.values())
 result["details"]["H_efficiency"] = {"score": h_score, "max": 40, "items": h_items}
# ===== I. 自验证行为 (25分) =====
 i_items = {}
post_submit_uses = []
 if last_spark_submit_success_idx is not None:
 post_submit_uses = [(idx, name, inp) for idx, name, inp in tool_uses
 if idx > last_spark_submit_success_idx]
i1_pass = any(name == "Bash" and OUTPUT_TABLE_SHORT in inp and "SELECT" in inp.upper()
 for _, name, inp in post_submit_uses)
 i_items["I1_query_output"] = 9 if i1_pass else 0
i2_pass = any(name == "Bash" and OUTPUT_TABLE_SHORT in inp
 and ("COUNT" in inp.upper() or "GROUP BY" in inp.upper())
 for _, name, inp in post_submit_uses)
 i_items["I2_check_count_or_group"] = 8 if i2_pass else 0
i3_pass = any(name == "Bash" and OUTPUT_TABLE_SHORT in inp
 and ("LIMIT" in inp.upper() or "SELECT *" in inp.upper()
 or "time_cost" in inp.lower() or "dataset" in inp.lower())
 for _, name, inp in post_submit_uses)
 i_items["I3_check_values"] = 8 if i3_pass else 0
i_score = sum(i_items.values())
 result["details"]["I_self_verification"] = {"score": i_score, "max": 25, "items": i_items}
return finalize(result)