refactor(intelligent-eval): 消除 lifecycle.py 和 scheduler.py 中的重复延迟导入
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- 将延迟导入移至模块顶部，消除 Shotgun Surgery 气味 - lifecycle.py: 移除 41 行重复导入 - scheduler.py: 移除 14 行重复导入 - 修复测试：更新 monkeypatch 以补丁 scheduler 模块的引用而非原始模块 - 符合代码规范：避免函数内重复导入 Closes code-review finding: repeated deferred imports (Shotgun Surgery)
This commit is contained in:
sinohqb
parent
876d75f9ed
commit
84627a3c6a
@ -20,6 +20,8 @@ from sqlmodel import Session, func, select
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from agenteval.channels.base import ExchangeStatus, SendResult
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from agenteval.channels.factory import ChannelFactory
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from agenteval.config import get_settings
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from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
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from agenteval.intelligent_eval.domain import parse_time_slot
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from agenteval.intelligent_eval.models import (
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IntelligentEval,
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IntelligentEvalMessage,
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@ -33,7 +35,15 @@ from agenteval.intelligent_eval.repository import (
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IntelligentEvalRepository,
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IntelligentEvalSessionRepository,
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)
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from agenteval.storage.db import IntelligentEvalSessionDB, as_utc, utc_now
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from agenteval.storage.db import (
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IntelligentEvalDB,
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IntelligentEvalDecisionLogDB,
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IntelligentEvalMessageDB,
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IntelligentEvalSessionDB,
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IntelligentEvalTaskQueueDB,
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as_utc,
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utc_now,
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)
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from agenteval.storage.repository import TargetRepository
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# 合法转换表：当前状态 → 允许的目标状态集合
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@ -122,8 +132,6 @@ def create_eval(
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) -> IntelligentEval:
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"""创建智能评估并直接进入 planning 状态（draft → planning 一步完成）。"""
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from agenteval.intelligent_eval.config_snapshot import save_snapshot
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from agenteval.storage.db import IntelligentEvalDB
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if TargetRepository(session).get(target_id) is None:
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raise IntelligentEvalNotFoundError(f"target {target_id} not found")
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@ -154,8 +162,6 @@ def create_eval(
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def submit_plan(session: Session, eval_id: str, plan: dict[str, Any]) -> IntelligentEval:
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"""OpenClaw 提交粗计划：planning → pending_approval。"""
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from agenteval.intelligent_eval.config_snapshot import save_snapshot
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from agenteval.storage.db import IntelligentEvalDB
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repo = IntelligentEvalRepository(session)
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result = repo._submit_plan_if_planning(eval_id, plan)
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ev = _resolve_write(
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@ -423,12 +429,6 @@ def expire_stale_running_sessions(session: Session) -> int:
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Returns:
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过期的会话数。
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"""
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from sqlalchemy import func
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from sqlmodel import select
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from agenteval.intelligent_eval.decision_logs import append_decision_log
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from agenteval.storage.db import IntelligentEvalMessageDB, IntelligentEvalSessionDB
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now = utc_now()
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# SQLite 读出为 naive datetime，阈值须同为 naive 才能在 Python 侧比较
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threshold = now.replace(tzinfo=None) - timedelta(minutes=SESSION_IDLE_EXPIRE_MINUTES)
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@ -477,11 +477,6 @@ TRIGGER_COOLDOWN_MINUTES = 10
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def _last_decision_at(session: Session, eval_id: str, decision_type: str):
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"""该评估某类决策日志的最近时间（无则 None）。"""
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from sqlalchemy import func
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from sqlmodel import select
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from agenteval.storage.db import IntelligentEvalDecisionLogDB
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return session.exec(
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select(func.max(IntelligentEvalDecisionLogDB.created_at)).where(
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IntelligentEvalDecisionLogDB.eval_id == eval_id,
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@ -506,11 +501,6 @@ def record_planner_triggers(session: Session) -> int:
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Returns:
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本次实际触发覆盖的评估数（0 表示无需触发 planner）。
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"""
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from sqlmodel import select
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from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
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from agenteval.storage.db import IntelligentEvalDB
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now = utc_now().replace(tzinfo=None)
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planning = session.exec(
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select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.PLANNING.value)
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@ -544,8 +534,6 @@ def worker_trigger_candidates(session: Session) -> list[str]:
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def record_worker_triggers(session: Session, eval_ids: list[str]) -> None:
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"""为本次 worker 触发覆盖的评估落账 worker_trigger（供冷却与可见性）。"""
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from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
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for eval_id in eval_ids:
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attempt = count_decisions(eval_id, "worker_trigger", session) + 1
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append_decision_log(
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@ -570,11 +558,6 @@ def enforce_planning_gates(session: Session) -> int:
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Returns:
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被判失败的评估数。
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"""
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from sqlmodel import select
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from agenteval.intelligent_eval.decision_logs import count_decisions
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from agenteval.storage.db import IntelligentEvalDB
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now = utc_now().replace(tzinfo=None)
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planning = session.exec(
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select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.PLANNING.value)
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@ -624,10 +607,6 @@ def enforce_executing_ceiling(session: Session) -> int:
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Returns:
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被判失败的评估数。
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"""
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from sqlmodel import select
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from agenteval.storage.db import IntelligentEvalDB
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now = utc_now().replace(tzinfo=None)
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executing = session.exec(
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select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.EXECUTING.value)
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@ -662,8 +641,6 @@ def _window_has_pending_future_slots(eval_db, sessions, now) -> bool:
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"""窗口未结束且会话数未达计划 → 未来时段还要建会话，不该催 analyst。"""
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if not eval_db.plan or not eval_db.started_at:
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return False
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from agenteval.intelligent_eval.domain import parse_time_slot
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plan = eval_db.get_plan()
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slots = plan.get("time_distribution") or []
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end_hours: list[float] = []
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@ -690,11 +667,6 @@ def evals_needing_analyst_nudge(session: Session) -> list[str]:
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末会话终态已满 ANALYST_NUDGE_DELAY_MINUTES、催促次数 < ANALYST_NUDGE_MAX、
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距上次催促已满 ANALYST_NUDGE_DELAY_MINUTES（冷却，避免每分钟连发）。
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"""
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from sqlmodel import select
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from agenteval.intelligent_eval.decision_logs import count_decisions
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from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB
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now = utc_now().replace(tzinfo=None)
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executing = session.exec(
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select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.EXECUTING.value)
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@ -734,8 +706,6 @@ def evals_needing_analyst_nudge(session: Session) -> list[str]:
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def record_analyst_nudge(session: Session, eval_id: str) -> None:
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"""落账一次 analyst 催促（analyst_nudge 决策日志，attempt 递增供上限计数）。"""
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from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
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attempt = count_decisions(eval_id, "analyst_nudge", session) + 1
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append_decision_log(
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eval_id,
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@ -756,10 +726,6 @@ TRIGGER_FAILURE_MAX = 3
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def eval_ids_with_pending_worker_tasks(session: Session) -> list[str]:
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"""当前有待认领 worker 任务的评估 id（触发失败的受影响方）。"""
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from sqlmodel import select
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from agenteval.storage.db import IntelligentEvalTaskQueueDB
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rows = session.exec(
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select(IntelligentEvalTaskQueueDB.eval_id).where(IntelligentEvalTaskQueueDB.status == "pending")
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).all()
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@ -774,8 +740,6 @@ def record_trigger_failures(session: Session, *, channel: str, eval_ids: list[st
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Returns:
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落账条数。
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"""
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from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
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recorded = 0
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for eval_id in eval_ids:
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attempt = count_decisions(eval_id, "trigger_failed", session) + 1
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@ -801,8 +765,6 @@ def fail_eval(session: Session, eval_id: str, reason: str, decision_type: str, c
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True 判失败成功；False 表示跳过（评估不存在、非法转换或 CAS 冲突
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——状态已被他人收敛，属正常竞争结局，仅留日志不当故障）。
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"""
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from agenteval.intelligent_eval.decision_logs import append_decision_log
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logger = logging.getLogger("agenteval")
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repo = IntelligentEvalRepository(session)
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ev = repo.get(eval_id)
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@ -848,15 +810,6 @@ def enforce_trigger_failure_gates(session: Session) -> int:
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Returns:
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被判失败的评估数。
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"""
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from sqlalchemy import func
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from sqlmodel import select
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from agenteval.storage.db import (
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IntelligentEvalDB,
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IntelligentEvalDecisionLogDB,
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IntelligentEvalTaskQueueDB,
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)
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def _failures(eval_id: str, channel: str) -> list:
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logs = session.exec(
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select(IntelligentEvalDecisionLogDB).where(
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@ -15,7 +15,29 @@ import subprocess
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import time
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from typing import Callable, Optional
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from agenteval.storage.db import get_session
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from sqlmodel import select
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from agenteval.intelligent_eval.decision_logs import supplement_decision_logs
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from agenteval.intelligent_eval.lifecycle import (
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enforce_executing_ceiling,
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enforce_planning_gates,
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enforce_trigger_failure_gates,
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eval_ids_with_pending_worker_tasks,
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evals_needing_analyst_nudge,
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expire_stale_running_sessions,
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record_analyst_nudge,
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record_planner_triggers,
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record_trigger_failures,
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record_worker_triggers,
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worker_trigger_candidates,
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)
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from agenteval.intelligent_eval.models import IntelligentEvalStatus
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from agenteval.intelligent_eval.task_queue import (
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requeue_stale_assigned_tasks,
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scan_and_enqueue_tasks,
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settle_tasks_for_finished_evals,
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)
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from agenteval.storage.db import IntelligentEvalDB, get_session
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_logger = logging.getLogger("agenteval")
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@ -93,11 +115,6 @@ async def _trigger_openclaw_agent(
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def _record_worker_trigger_failure(error: str) -> None:
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"""ADR-0011：worker 触发失败落账到受影响评估（有待认领任务的评估）。"""
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from agenteval.intelligent_eval.lifecycle import (
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eval_ids_with_pending_worker_tasks,
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record_trigger_failures,
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)
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session = get_session()
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try:
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eval_ids = eval_ids_with_pending_worker_tasks(session)
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@ -111,12 +128,6 @@ def _record_worker_trigger_failure(error: str) -> None:
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def _record_planner_trigger_failure(error: str) -> None:
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"""ADR-0011：planner 触发失败落账到所有 planning 评估。"""
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from sqlmodel import select
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from agenteval.intelligent_eval.lifecycle import record_trigger_failures
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from agenteval.intelligent_eval.models import IntelligentEvalStatus
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from agenteval.storage.db import IntelligentEvalDB
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session = get_session()
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try:
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eval_ids = [
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@ -147,11 +158,6 @@ async def trigger_worker(nudge_eval_ids: Optional[list[str]] = None) -> bool:
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Returns:
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True 若确实触发了 agent（存在冷却期外的服务对象）。
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"""
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from agenteval.intelligent_eval.lifecycle import (
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record_worker_triggers,
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worker_trigger_candidates,
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)
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session = get_session()
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try:
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trigger_ids = sorted(set(worker_trigger_candidates(session)) | set(nudge_eval_ids or []))
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@ -179,8 +185,6 @@ async def trigger_planner() -> bool:
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"""
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# ADR-0011：触发前先落账 planner_trigger（含后续失败也计入双闸），
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# 0 个 planning 评估时不触发。
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from agenteval.intelligent_eval.lifecycle import record_planner_triggers
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session = get_session()
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try:
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planning_count = record_planner_triggers(session)
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@ -215,19 +219,6 @@ def scan_once() -> None:
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try:
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session = get_session()
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try:
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from agenteval.intelligent_eval.decision_logs import supplement_decision_logs
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from agenteval.intelligent_eval.lifecycle import (
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enforce_executing_ceiling,
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enforce_planning_gates,
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enforce_trigger_failure_gates,
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expire_stale_running_sessions,
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)
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from agenteval.intelligent_eval.task_queue import (
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requeue_stale_assigned_tasks,
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scan_and_enqueue_tasks,
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settle_tasks_for_finished_evals,
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)
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r = requeue_stale_assigned_tasks(session)
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if r:
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_logger.info("卡死恢复：%d 个 assigned 任务重新入队", r)
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@ -262,11 +253,6 @@ def scan_once() -> None:
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# 催促落账后把评估交给 worker 触发（worker 在全终态时会转 analyst 路径）
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nudge_ids: list[str] = []
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try:
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from agenteval.intelligent_eval.lifecycle import (
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evals_needing_analyst_nudge,
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record_analyst_nudge,
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)
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session = get_session()
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try:
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nudge_ids = evals_needing_analyst_nudge(session)
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@ -30,7 +30,8 @@ def test_lifespan_starts_scan_loop(monkeypatch):
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# The scan loop calls get_session() to open a DB session; replace it with a
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# no-op mock so the test does not touch the real SQLite file.
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monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
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monkeypatch.setattr(tq, "scan_and_enqueue_tasks", fake_scan)
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# Patch the scheduler module's reference, not the task_queue module's
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monkeypatch.setattr(scheduler_mod, "scan_and_enqueue_tasks", fake_scan)
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with TestClient(app_mod.app) as client:
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assert client.get("/api/health").status_code == 200
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@ -14,11 +14,11 @@ from unittest.mock import MagicMock
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def _patch_worker_candidates(monkeypatch, candidates: list[str]):
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"""ADR-0011：worker 触发的服务对象由冷却过滤后的候选决定（替代原 _has_pending_task）。"""
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import agenteval.intelligent_eval.lifecycle as lifecycle_mod
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import agenteval.intelligent_eval.scheduler as scheduler_mod
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monkeypatch.setattr(lifecycle_mod, "worker_trigger_candidates", lambda session: candidates)
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monkeypatch.setattr(lifecycle_mod, "record_worker_triggers", lambda session, ids: None)
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# Patch the scheduler module's references, not the lifecycle module's
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monkeypatch.setattr(scheduler_mod, "worker_trigger_candidates", lambda session: candidates)
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monkeypatch.setattr(scheduler_mod, "record_worker_triggers", lambda session, ids: None)
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monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
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@ -93,12 +93,12 @@ def test_trigger_worker_msg_has_execute_semantics(monkeypatch):
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def test_trigger_planner_skips_when_no_planning(monkeypatch):
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"""No planning eval → no docker exec invocation."""
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import agenteval.intelligent_eval.lifecycle as lifecycle_mod
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import agenteval.intelligent_eval.scheduler as scheduler_mod
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calls: list = []
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# ADR-0011：planner 触发前置计数落账（record_planner_triggers），0 时不触发
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monkeypatch.setattr(lifecycle_mod, "record_planner_triggers", lambda session: 0)
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# Patch the scheduler module's reference, not the lifecycle module's
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monkeypatch.setattr(scheduler_mod, "record_planner_triggers", lambda session: 0)
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monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
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def fake_run(cmd, **kwargs):
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@ -113,11 +113,11 @@ def test_trigger_planner_skips_when_no_planning(monkeypatch):
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def test_trigger_planner_calls_docker_exec(monkeypatch):
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"""Planning eval present → invoke `docker exec openclaw-eval openclaw agent` planner skill."""
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import agenteval.intelligent_eval.lifecycle as lifecycle_mod
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import agenteval.intelligent_eval.scheduler as scheduler_mod
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calls: list = []
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monkeypatch.setattr(lifecycle_mod, "record_planner_triggers", lambda session: 1)
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# Patch the scheduler module's reference, not the lifecycle module's
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monkeypatch.setattr(scheduler_mod, "record_planner_triggers", lambda session: 1)
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monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
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def fake_run(cmd, **kwargs):
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@ -140,25 +140,25 @@ def test_trigger_planner_calls_docker_exec(monkeypatch):
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def test_scan_once_orchestrates_full_tick(monkeypatch):
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"""scan_once 一个节拍按原顺序执行 watchdog → 入队 → 回收 → 补录 → 催促 → 触发。"""
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import agenteval.intelligent_eval.decision_logs as dl_mod
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import agenteval.intelligent_eval.lifecycle as lifecycle_mod
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import agenteval.intelligent_eval.scheduler as scheduler_mod
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import agenteval.intelligent_eval.task_queue as tq_mod
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calls: list[str] = []
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monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
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# Patch the scheduler module's references for task_queue functions
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for name in ("requeue_stale_assigned_tasks", "scan_and_enqueue_tasks", "settle_tasks_for_finished_evals"):
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monkeypatch.setattr(tq_mod, name, lambda session, _n=name: calls.append(_n) or 0)
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monkeypatch.setattr(scheduler_mod, name, lambda session, _n=name: calls.append(_n) or 0)
|
||||
# Patch the scheduler module's references for lifecycle functions
|
||||
for name in (
|
||||
"expire_stale_running_sessions",
|
||||
"enforce_planning_gates",
|
||||
"enforce_executing_ceiling",
|
||||
"enforce_trigger_failure_gates",
|
||||
):
|
||||
monkeypatch.setattr(lifecycle_mod, name, lambda session, _n=name: calls.append(_n) or 0)
|
||||
monkeypatch.setattr(dl_mod, "supplement_decision_logs", lambda session: calls.append("supplement") or 0)
|
||||
monkeypatch.setattr(lifecycle_mod, "evals_needing_analyst_nudge", lambda session: calls.append("nudge") or [])
|
||||
monkeypatch.setattr(scheduler_mod, name, lambda session, _n=name: calls.append(_n) or 0)
|
||||
# Patch the scheduler module's reference for decision_logs function
|
||||
monkeypatch.setattr(scheduler_mod, "supplement_decision_logs", lambda session: calls.append("supplement") or 0)
|
||||
monkeypatch.setattr(scheduler_mod, "evals_needing_analyst_nudge", lambda session: calls.append("nudge") or [])
|
||||
|
||||
fired: list[str] = []
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user