tasks/offline-compute/MySQL/mysql_005_en/gt/grade.py

def grade(workspace_path, **kwargs):
    """
    mysql_005 rule-based grading: GPU推理平台P90流量预估

    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_FEATURE = "dwm_gputj_platform_gpu_base_feature_agg_v2_mysql_005"
    INPUT_TABLE_SERVICE = "dwd_aide_inferencev2_done_service_info_h_mysql_005"
    INPUT_TABLE_HOLIDAY = "dim_holiday_list_mysql_005"
    OUTPUT_TABLE = "dws_gputj_platform_model_prediction_long_p90_mysql_005"
    FULL_OUTPUT = f"{DB_NAME}.{OUTPUT_TABLE}"
    FULL_INPUT_FEATURE = f"{DB_NAME}.{INPUT_TABLE_FEATURE}"

    KEY_COLUMNS = ["service_name", "agg_time", "agg_type"]
    EXPECTED_COL_COUNT = 28
    EXPECTED_ROW_COUNT = 56

    MYSQL_CONFIG = {
        "host": "localhost",
        "port": 3306,
        "user": "root",
        "password": "root123",
        "charset": "utf8mb4",
    }

    # Expected output (from query_engine_009 output/expected.csv)
    EXPECTED_ROWS = [{'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-07 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 4, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-07 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 4, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-08 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 5, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-08 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 5, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-09 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 6, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-09 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 6, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-10 08:00:00', 'agg_type': 1, 'is_holiday': 1, 'day_of_week': 7, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 106.0, 'statistic_time_count': 2, 'nv_inference_request_duration_ms_model_avg': 20.799999999999997, 'nv_inference_queue_duration_ms_model_avg': 5.26, 'num_queued_reqs_model_avg': 2.12, 'nv_inference_request_success_model_avg': 145.20000000000002, 'nv_inference_request_failure_model_avg': 1.06, 'nv_inference_request_duration_ms_perreq_avg': 18.599999999999998, 'nv_inference_queue_duration_ms_perreq_avg': 4.16, 'nv_inference_request_duration_ms_perreq_p95': 22.76, 'nv_inference_queue_duration_ms_perreq_p95': 5.18, 'nv_inference_request_success_model_max': 166.39999999999998, 'nv_inference_request_failure_model_max': 2.12, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 46.699999999999996, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.386, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.48800000000000004, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 31.0, 'k8s_container_vgpu_gpu_util_pod_avg': 41.44}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-10 10:00:00', 'agg_type': 2, 'is_holiday': 1, 'day_of_week': 7, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 106.0, 'statistic_time_count': 2, 'nv_inference_request_duration_ms_model_avg': 20.799999999999997, 'nv_inference_queue_duration_ms_model_avg': 5.26, 'num_queued_reqs_model_avg': 2.12, 'nv_inference_request_success_model_avg': 145.20000000000002, 'nv_inference_request_failure_model_avg': 1.06, 'nv_inference_request_duration_ms_perreq_avg': 18.599999999999998, 'nv_inference_queue_duration_ms_perreq_avg': 4.16, 'nv_inference_request_duration_ms_perreq_p95': 22.76, 'nv_inference_queue_duration_ms_perreq_p95': 5.18, 'nv_inference_request_success_model_max': 166.39999999999998, 'nv_inference_request_failure_model_max': 2.12, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 46.699999999999996, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.386, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.48800000000000004, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 31.0, 'k8s_container_vgpu_gpu_util_pod_avg': 41.44}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-11 08:00:00', 'agg_type': 1, 'is_holiday': 1, 'day_of_week': 1, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 106.0, 'statistic_time_count': 2, 'nv_inference_request_duration_ms_model_avg': 20.799999999999997, 'nv_inference_queue_duration_ms_model_avg': 5.26, 'num_queued_reqs_model_avg': 2.12, 'nv_inference_request_success_model_avg': 145.20000000000002, 'nv_inference_request_failure_model_avg': 1.06, 'nv_inference_request_duration_ms_perreq_avg': 18.599999999999998, 'nv_inference_queue_duration_ms_perreq_avg': 4.16, 'nv_inference_request_duration_ms_perreq_p95': 22.76, 'nv_inference_queue_duration_ms_perreq_p95': 5.18, 'nv_inference_request_success_model_max': 166.39999999999998, 'nv_inference_request_failure_model_max': 2.12, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 46.699999999999996, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.386, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.48800000000000004, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 31.0, 'k8s_container_vgpu_gpu_util_pod_avg': 41.44}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-11 10:00:00', 'agg_type': 2, 'is_holiday': 1, 'day_of_week': 1, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 106.0, 'statistic_time_count': 2, 'nv_inference_request_duration_ms_model_avg': 20.799999999999997, 'nv_inference_queue_duration_ms_model_avg': 5.26, 'num_queued_reqs_model_avg': 2.12, 'nv_inference_request_success_model_avg': 145.20000000000002, 'nv_inference_request_failure_model_avg': 1.06, 'nv_inference_request_duration_ms_perreq_avg': 18.599999999999998, 'nv_inference_queue_duration_ms_perreq_avg': 4.16, 'nv_inference_request_duration_ms_perreq_p95': 22.76, 'nv_inference_queue_duration_ms_perreq_p95': 5.18, 'nv_inference_request_success_model_max': 166.39999999999998, 'nv_inference_request_failure_model_max': 2.12, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 46.699999999999996, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.386, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.48800000000000004, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 31.0, 'k8s_container_vgpu_gpu_util_pod_avg': 41.44}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-12 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 2, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-12 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 2, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 'namespace': 'ns1', 'agg_time': '2026-05-13 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 3, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 127.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 25.150000000000002, 'nv_inference_queue_duration_ms_model_avg': 6.34, 'num_queued_reqs_model_avg': 2.55, 'nv_inference_request_success_model_avg': 175.7, 'nv_inference_request_failure_model_avg': 1.275, 'nv_inference_request_duration_ms_perreq_avg': 22.53, 'nv_inference_queue_duration_ms_perreq_avg': 5.03, 'nv_inference_request_duration_ms_perreq_p95': 27.560000000000002, 'nv_inference_queue_duration_ms_perreq_p95': 6.2700000000000005, 'nv_inference_request_success_model_max': 201.20000000000002, 'nv_inference_request_failure_model_max': 2.55, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 56.5, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.442, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.5660000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 37.550000000000004, 'k8s_container_vgpu_gpu_util_pod_avg': 50.160000000000004}, {'dt': '20260507', 'instance_uuid': 'uuid-001', 'service_name': 'svc_alpha', 'workload_name': 'wl_alpha', 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0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-15 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 5, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-16 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 6, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-16 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 6, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-17 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 7, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-17 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 7, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-18 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 1, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-18 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 1, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-19 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 2, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-19 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 2, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-20 08:00:00', 'agg_type': 1, 'is_holiday': 0, 'day_of_week': 3, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}, {'dt': '20260507', 'instance_uuid': 'uuid-002', 'service_name': 'svc_beta', 'workload_name': 'wl_beta', 'namespace': 'ns2', 'agg_time': '2026-05-20 10:00:00', 'agg_type': 2, 'is_holiday': 0, 'day_of_week': 3, 'prediction_type': 'request_model_count', 'nv_inference_count_model_avg_p90': 251.5, 'statistic_time_count': 13, 'nv_inference_request_duration_ms_model_avg': 43.75, 'nv_inference_queue_duration_ms_model_avg': 10.06, 'num_queued_reqs_model_avg': 5.03, 'nv_inference_request_success_model_avg': 349.3, 'nv_inference_request_failure_model_avg': 2.515, 'nv_inference_request_duration_ms_perreq_avg': 37.410000000000004, 'nv_inference_queue_duration_ms_perreq_avg': 8.75, 'nv_inference_request_duration_ms_perreq_p95': 47.4, 'nv_inference_queue_duration_ms_perreq_p95': 11.23, 'nv_inference_request_success_model_max': 399.6, 'nv_inference_request_failure_model_max': 5.03, 'DCGM_FI_DEV_GPU_UTIL_pod_avg': 81.3, 'k8s_container_bs_rate_cpu_core_used_request_pod_avg': 0.69, 'k8s_container_rate_mem_working_set_request_pod_avg': 0.8140000000000001, 'k8s_dcgm_fi_dev_fb_util_pod_avg': 62.35, 'k8s_container_vgpu_gpu_util_pod_avg': 74.96000000000001}]

    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 numeric_match(pred_val, gt_val, tol=0.01):
        """Match numeric values with tolerance for floating point."""
        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 = float(pred_val)
            gv = float(gt_val)
            if gv == 0:
                return abs(pv) < tol
            return abs(pv - gv) / max(abs(gv), tol) < tol
        except (ValueError, TypeError):
            return str(pred_val).strip() == str(gt_val).strip()

    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)
        PRED_TEMP_TABLE = f"{DB_NAME}.{OUTPUT_TABLE}_pred_temp"
        try:
            pred_rows = _mysql_fetch_dicts(f"SELECT * FROM {FULL_OUTPUT}")
            # Save pred to temp table so GT doesn't overwrite it
            _mysql_execute(f"DROP TABLE IF EXISTS {PRED_TEMP_TABLE}")
            _mysql_execute(f"CREATE TABLE {PRED_TEMP_TABLE} AS SELECT * FROM {FULL_OUTPUT}")
        except Exception as e:
            result["diagnostics"].append(f"mysql_read_pred_failed: {e}")
            pred_rows = []

        # Read columns info
        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": {}}
            return

        # Read pred from temp table (saved before GT ran)
        try:
            pred_rows = _mysql_fetch_dicts(f"SELECT * FROM {PRED_TEMP_TABLE}")
        except Exception as e:
            result["diagnostics"].append(f"mysql_read_pred_temp_failed: {e}")
            pred_rows = []

        # Read GT from MySQL (written 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": {}}
            return

        # ===== 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", "instance_uuid", "service_name", "workload_name", "namespace",
            "agg_time", "agg_type", "is_holiday", "day_of_week", "prediction_type",
            "nv_inference_count_model_avg_p90", "statistic_time_count",
            "nv_inference_request_duration_ms_model_avg", "nv_inference_queue_duration_ms_model_avg",
            "num_queued_reqs_model_avg", "nv_inference_request_success_model_avg",
            "nv_inference_request_failure_model_avg", "nv_inference_request_duration_ms_perreq_avg",
            "nv_inference_queue_duration_ms_perreq_avg", "nv_inference_request_duration_ms_perreq_p95",
            "nv_inference_queue_duration_ms_perreq_p95", "nv_inference_request_success_model_max",
            "nv_inference_request_failure_model_max", "DCGM_FI_DEV_GPU_UTIL_pod_avg",
            "k8s_container_bs_rate_cpu_core_used_request_pod_avg",
            "k8s_container_rate_mem_working_set_request_pod_avg",
            "k8s_dcgm_fi_dev_fb_util_pod_avg", "k8s_container_vgpu_gpu_util_pod_avg"
        ])
        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()
        for row in gt_rows:
            gt_keys.add(make_key(row))

        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)
        if n_pred == EXPECTED_ROW_COUNT:
            c_items["C1_row_count"] = 6
        elif n_pred > 0 and n_pred <= EXPECTED_ROW_COUNT + 1:
            c_items["C1_row_count"] = 3
        else:
            c_items["C1_row_count"] = 0

        # C2: no duplicate keys
        from collections import Counter
        key_counter = Counter(pred_keys)
        duplicates = sum(1 for v in key_counter.values() if v > 1)
        if duplicates == 0:
            c_items["C2_no_duplicates"] = 5
        elif duplicates <= 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 = {}

        gt_index = {make_key(row): row for row in gt_rows}
        pred_index = {}
        for row in pred_rows:
            k = make_key(row)
            if k not in pred_index:
                pred_index[k] = row

        hit_row_keys = list(hit_keys)

        # Numeric columns to check (key prediction metrics)
        numeric_cols = [
            "nv_inference_count_model_avg_p90", "statistic_time_count",
            "nv_inference_request_duration_ms_model_avg", "nv_inference_queue_duration_ms_model_avg",
            "num_queued_reqs_model_avg", "nv_inference_request_success_model_avg",
            "nv_inference_request_failure_model_avg",
            "nv_inference_request_duration_ms_perreq_avg", "nv_inference_queue_duration_ms_perreq_avg",
            "nv_inference_request_duration_ms_perreq_p95", "nv_inference_queue_duration_ms_perreq_p95",
            "nv_inference_request_success_model_max", "nv_inference_request_failure_model_max",
            "DCGM_FI_DEV_GPU_UTIL_pod_avg",
            "k8s_container_bs_rate_cpu_core_used_request_pod_avg",
            "k8s_container_rate_mem_working_set_request_pod_avg",
            "k8s_dcgm_fi_dev_fb_util_pod_avg", "k8s_container_vgpu_gpu_util_pod_avg"
        ]

        # Distribute 40 points across numeric cols
        per_numeric = 40.0 / len(numeric_cols)
        d_score_raw = 0.0

        for col in numeric_cols:
            if not hit_row_keys:
                d_items[col] = {"pass_rate": 0.0, "score": 0, "max": round(per_numeric, 2)}
                continue
            passed = 0
            first_mismatch = None
            for key in hit_row_keys:
                gt_val = gt_index[key].get(col)
                pred_val = pred_index.get(key, {}).get(col)
                if numeric_match(pred_val, gt_val):
                    passed += 1
                elif first_mismatch is None:
                    first_mismatch = {
                        "key": dict(zip(KEY_COLUMNS, key)),
                        "column": col,
                        "pred": pred_val,
                        "gt": gt_val,
                    }
            pass_rate = passed / len(hit_row_keys)
            col_score_raw = per_numeric * pass_rate
            d_score_raw += col_score_raw
            d_items[col] = {"pass_rate": round(pass_rate, 4), "score": round(col_score_raw, 2), "max": round(per_numeric, 2)}
            if first_mismatch and pass_rate < 0.95:
                result["diagnostics"].append(first_mismatch)

        d_score = round(d_score_raw, 2)
        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 service_name+agg_time combinations present
        expected_keys = set(make_key(row) for row in EXPECTED_ROWS)
        pred_key_strs = set(make_key(row) for row in pred_rows)
        e1_pass = len(expected_keys & pred_key_strs)
        e_items["E1_key_coverage"] = round(10 * (e1_pass / len(expected_keys)), 2)

        e_score = sum(e_items.values())
        result["details"]["E_labels"] = {"score": round(e_score, 2), "max": 10, "items": e_items}

        # Cleanup temp table
        try:
            _mysql_execute(f"DROP TABLE IF EXISTS {PRED_TEMP_TABLE}")
        except Exception:
            pass

    _product_grade()

    # ========== G~J 过程性评分 (30分) ==========
    TRANSCRIPT_PATH = "/tmp/dataclaw_chat.jsonl"
    INPUT_TABLE_SHORT = INPUT_TABLE_FEATURE
    OUTPUT_TABLE_SHORT = OUTPUT_TABLE

    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_exec_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_exec_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 agg_type / date distribution
    g3_pass = False
    for idx, name, inp in tool_uses:
        if idx >= before_first_write:
            break
        if name == "Bash" and ("agg_type" in inp or "dt" 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"] = 13
        h_items["H2_moderate_submissions"] = 0
    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"] = 7 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_exec_success_idx is not None:
        post_submit_uses = [(idx, name, inp) for idx, name, inp in tool_uses
                            if idx > last_exec_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"] = 8 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 "nv_inference" in inp.lower() or "is_holiday" in inp.lower())
                  for _, name, inp in post_submit_uses)
    i_items["I3_check_values"] = 9 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)