"""数据治理：编码规范清洗 — 检查KPI编码前缀与维度一致性 + 修复误分类"""
import pymysql
import os
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("encoding-cleanup")

DB_USER = os.getenv("CMA_DB_USER", "cma_user")
DB_PASS = os.getenv("CMA_DB_PASS", "cma_pass_2026")
DB_HOST = os.getenv("CMA_DB_HOST", "127.0.0.1")
DB_PORT = int(os.getenv("CMA_DB_PORT", "3306"))
DB_NAME = os.getenv("CMA_DB_NAME", "cma")

conn = pymysql.connect(
    host=DB_HOST, port=DB_PORT, user=DB_USER, password=DB_PASS, database=DB_NAME,
    charset="utf8mb4", cursorclass=pymysql.cursors.DictCursor,
)
cur = conn.cursor()

# 编码前缀 → 正确维度映射
PREFIX_DIM_MAP = {
    "F_": "finance",
    "C_": "customer",
    "P_": "process",
    "L_": "learning",
}

# 已知误分类修复（编码 → 正确维度）
KNOWN_FIXES = {
    "F_QUALITY_RATE": "process",   # 产品合格率 → 流程层
    "F_REWORK_RATE": "process",    # 返工率 → 流程层
    "P_COST_CUT": "process",       # 招待费砍半 → 流程层
    "P_TRAIN_PASS": "process",     # Model C考核通过 → 流程层
}


def run():
    logger.info("=== 编码规范清洗 开始 ===")

    cur.execute("SELECT id, entity_id, kpi_code, kpi_name, dimension FROM kpi_definitions WHERE status = 'active'")
    kpis = cur.fetchall()

    issues = []
    fixes_applied = 0

    for kpi in kpis:
        kpi_code = kpi["kpi_code"]
        current_dim = kpi["dimension"]
        entity_id = kpi["entity_id"]

        # 检查前缀
        prefix = kpi_code[:2] if len(kpi_code) >= 2 else ""
        expected_dim = PREFIX_DIM_MAP.get(prefix)

        if expected_dim and current_dim != expected_dim:
            # 先检查是否在已知修复列表
            correct_dim = KNOWN_FIXES.get(kpi_code, expected_dim)
            issues.append({
                "kpi_code": kpi_code,
                "kpi_name": kpi["kpi_name"],
                "current_dim": current_dim,
                "expected_dim": correct_dim,
                "prefix": prefix,
                "entity_id": entity_id,
            })

            if kpi_code in KNOWN_FIXES:
                logger.info(f"  🔧 修复: {kpi_code} ({kpi['kpi_name']}) {current_dim} → {correct_dim} (entity={entity_id})")
                cur.execute(
                    "UPDATE kpi_definitions SET dimension = %s WHERE id = %s",
                    (correct_dim, kpi["id"]),
                )
                fixes_applied += 1

    conn.commit()

    # 输出报告
    logger.info(f"\n=== 清洗报告 ===")
    logger.info(f"  检查KPI总数: {len(kpis)}")
    logger.info(f"  编码-维度不一致: {len(issues)}")
    logger.info(f"  已自动修复: {fixes_applied}")

    if issues:
        logger.info(f"\n  不一致详情:")
        for i, iss in enumerate(issues, 1):
            status = "✅ 已修复" if iss["kpi_code"] in KNOWN_FIXES else "⚠️ 需人工确认"
            logger.info(f"    {i}. {iss['kpi_code']} ({iss['kpi_name']}) "
                        f"当前维度={iss['current_dim']}, 期望维度={iss['expected_dim']} [{status}]")

    # 检查未命名规范问题
    logger.info(f"\n  编码前缀统计:")
    for prefix, dim in PREFIX_DIM_MAP.items():
        cur.execute("SELECT COUNT(*) as cnt FROM kpi_definitions WHERE kpi_code LIKE %s AND status='active'", (f"{prefix}%",))
        row = cur.fetchone()
        cnt = row["cnt"] if row else 0
        logger.info(f"    {prefix} → {dim}: {cnt} 个KPI")

    # 检查前缀不匹配编码
    cur.execute("""
        SELECT kpi_code, dimension FROM kpi_definitions
        WHERE status='active'
        AND (
            (kpi_code LIKE 'F_%' AND dimension != 'finance')
            OR (kpi_code LIKE 'C_%' AND dimension != 'customer')
            OR (kpi_code LIKE 'P_%' AND dimension != 'process')
            OR (kpi_code LIKE 'L_%' AND dimension != 'learning')
        )
    """)
    remaining = cur.fetchall()
    if remaining:
        logger.warning(f"\n  ⚠️ 仍有 {len(remaining)} 个KPI编码前缀与维度不匹配:")
        for r in remaining:
            logger.warning(f"    {r['kpi_code']} → {r['dimension']}")
    else:
        logger.info(f"\n  ✅ 所有KPI编码前缀与维度一致！")

    logger.info("\n=== 编码规范清洗 完成 ===")


if __name__ == "__main__":
    try:
        run()
    finally:
        cur.close()
        conn.close()