shangfangjian
64ae8e663a
_upsert_prediction 改为 _insert_or_find_by_fingerprint: - 同 input_hash → 返回已有行(绝不 UPDATE pred_/reasoning/agent_outputs) - 不同 input_hash → INSERT 新行 input_hash 升级为规范 JSON SHA-256,捕获:match_id, cutoff, prompt_version, prompt_hash, system_prompt_hash, provider, model, mode, run_type, temperature, context_hash, agent_ids。移除旧 (match, provider, model, mode, run_type) 唯一约束, 改为 partial unique index(WHERE input_hash IS NOT NULL,兼容旧 NULL 数据)。 三条路径(single/multi/baseline)统一传足指纹字段。 迁移 0024 + 测试 test_p0_prediction_fingerprint(10/10);全量 295 通过。
300 lines
10 KiB
Python
300 lines
10 KiB
Python
"""D2 工程债回归测试: 统一预测结果类型。
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背景: predict_match 三条路径返回类型不一 —— single 返回 PredictResult,
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multi 返回字段重复定义的 MultiPredictResult dataclass,baseline 返回裸 dict。
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后果: (1) 预测路由 PredictOut 映射被迫写 isinstance(result, dict) 双分支;
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(2) backtest 对 baseline 模式直接 AttributeError(dict 没有 .prediction_id,
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潜伏 bug);(3) 字段清单在两处 dataclass 重复维护,加字段必漏一处。
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统一方案: 扩展 PredictResult(可选字段)承载全部模式;
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MultiPredictResult 变为其别名(保留 orchestrator 签名标记,兼容 re-export);
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baseline 返回 PredictResult;路由单一字段映射。
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本测试守护四件事:
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1. predict_baseline 返回 PredictResult(属性访问)
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2. MultiPredictResult 与 PredictResult 兼容(orchestrator 构造调用的
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全字段 kwargs 可直接构造别名)
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3. 预测路由不再有 isinstance(result, dict) 分支(源码守卫,仿 R4 范式)
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4. _persist_baseline 用属性访问构造 upsert values(baseline 落库语义不变)
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"""
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from __future__ import annotations
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from pathlib import Path
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from types import SimpleNamespace
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import pytest
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from src.llm.baseline import predict_baseline
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from src.llm.predict import PredictResult
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from src.llm.agents import MultiPredictResult
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ROUTE_PATH = Path(__file__).resolve().parents[1] / "src" / "api" / "routes" / "predict.py"
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# ============================================================
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# 1. baseline 返回 PredictResult
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# ============================================================
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@pytest.mark.asyncio
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async def test_predict_baseline_returns_predict_result():
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"""基线预测返回 PredictResult 实例,mode=baseline,token/延迟为 0。"""
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from unittest.mock import patch
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async def fake_avg(db, *, team_id, side, league_id, before):
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return 2.4 if side == "home" else 1.6
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class FakeMatch:
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id = 1
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home_team_id = 10
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away_team_id = 20
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league_id = 1
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match_status = "scheduled"
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class FakeSession:
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async def get(self, cls, mid):
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return FakeMatch()
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class FakeCM:
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async def __aenter__(self):
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return FakeSession()
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async def __aexit__(self, *a):
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return None
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# P3-2:baseline 在服务层落库(get_uow + _insert_or_find_by_fingerprint),需 mock 掉。
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class FakeUoW:
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async def __aenter__(self):
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return _make_session()
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async def __aexit__(self, *a):
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return None
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captured = {}
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async def fake_upsert(session, *, values):
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captured.update(values)
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return SimpleNamespace(id=77)
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# baseline.py 内部 from-import get_uow / _insert_or_find_by_fingerprint,需 patch 真实来源模块。
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with patch("src.llm.baseline._avg_goals", fake_avg), \
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patch("src.llm.baseline.AsyncSessionLocal") as SLC, \
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patch("src.db.unit_of_work.get_uow", FakeUoW), \
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patch("src.llm.baseline._insert_or_find_by_fingerprint", fake_upsert):
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SLC.return_value = FakeCM()
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result = await predict_baseline(1)
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# P3-2:验证服务层落库被调用且属性映射正确
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assert captured["match_id"] == 1
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assert captured["provider"] == "baseline"
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assert captured["run_type"] == "live"
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assert captured["pred_home_goals"] == 2.0
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assert isinstance(result, PredictResult)
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assert result.mode == "baseline"
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assert result.provider == "baseline"
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assert result.model == "baseline"
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assert result.prompt_version == "baseline_v1"
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assert result.pred_home_goals == 2.0
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assert result.pred_away_goals == 2.0
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assert result.pred_1x2 == "X"
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assert result.subjective_confidence == 0.5
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assert result.prompt_tokens == 0
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assert result.completion_tokens == 0
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assert result.latency_ms == 0
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assert result.status == "success"
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assert "非投注建议" in (result.reasoning or "")
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# baseline 不构建 LLM 上下文,但字段必须存在且可安全序列化
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assert result.context == ""
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# 原始统计快照保留在 raw 中
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assert result.raw is not None
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assert "home_avg" in result.raw
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# ============================================================
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# 2. MultiPredictResult 与扩展后的 PredictResult 兼容
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# ============================================================
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def test_multi_predict_result_is_predict_result_alias():
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"""multi 结果不再是重复定义的 dataclass,而是扩展 PredictResult 的别名。"""
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assert MultiPredictResult is PredictResult
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def test_multi_result_constructor_kwargs_still_supported():
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"""orchestrator 现有构造调用的全部字段 kwargs 必须仍可构造(别名完整性)。"""
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# 与 orchestrator.predict_match_multi 的 return MultiPredictResult(...) 逐一对应
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result = MultiPredictResult(
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prediction_id=1,
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provider="openai",
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model="gpt-x",
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prompt_version="multi_v1",
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mode="multi",
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pred_home_goals=2.0,
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pred_away_goals=1.0,
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alt_pred_home_goals=None,
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alt_pred_away_goals=None,
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pred_1x2="1",
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subjective_confidence=0.7,
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reasoning="r",
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status="success",
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agent_outputs=[{"agent": "form"}],
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agent_weights={"form": 0.2},
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context="ctx",
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latency_ms=100,
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prompt_tokens=10,
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completion_tokens=5,
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raw={"final": True},
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)
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assert result.mode == "multi"
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assert result.agent_outputs == [{"agent": "form"}]
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assert result.agent_weights == {"form": 0.2}
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assert result.prompt_tokens == 10
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assert result.completion_tokens == 5
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# ============================================================
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# 3. 路由去 dict 分支(源码守卫)
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# ============================================================
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def test_predict_route_has_no_dict_branch():
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"""PredictOut 映射必须统一走属性访问,禁止 isinstance(result, dict) 回潮。"""
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src = ROUTE_PATH.read_text(encoding="utf-8")
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assert "isinstance(result, dict)" not in src
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assert ".get(\"pred_home_goals\")" not in src
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# ============================================================
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# 4. P3-2:baseline 服务层落库属性映射(落库已从路由移到 baseline.py)
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# ============================================================
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class _FakeResult:
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"""支持 .scalar_one_or_none() 的最小假结果集。"""
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def __init__(self, items):
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self._items = list(items)
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def scalars(self):
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return self
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def all(self):
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return self._items
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def scalar_one_or_none(self):
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return self._items[0] if self._items else None
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class _FakeUoW:
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"""替代 get_uow 的最小上下文管理器(session.execute 是 async 的)。"""
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def __init__(self):
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self.session = _make_session()
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async def __aenter__(self):
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return self.session
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async def __aexit__(self, *a):
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return None
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def __call__(self):
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return self
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def _make_session(existing=None):
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"""构造带 async execute / add / flush 的假 session。"""
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sess = SimpleNamespace()
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async def execute(*a, **k):
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return _FakeResult(existing or [])
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sess.execute = execute
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sess.add = lambda *a, **k: None
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async def flush(*a, **k):
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return None
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sess.flush = flush
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return sess
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@pytest.mark.asyncio
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async def test_baseline_service_persists_with_correct_attributes(monkeypatch):
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"""P3-2:baseline 在服务层(predict_baseline)落库,属性映射与路由旧版一致。"""
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captured = {}
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async def fake_upsert(session, *, values):
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captured.update(values)
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return SimpleNamespace(id=77)
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class FakeMatch:
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id = 1
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home_team_id = 10
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away_team_id = 20
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league_id = 1
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match_status = "scheduled"
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class FakeSession:
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async def get(self, cls, mid):
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return FakeMatch()
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class FakeSLC:
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async def __aenter__(self):
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return FakeSession()
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async def __aexit__(self, *a):
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return None
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async def fake_avg(db, *, team_id, side, league_id, before):
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return 2.0 if side == "home" else 1.0
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monkeypatch.setattr("src.llm.baseline._avg_goals", fake_avg)
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monkeypatch.setattr("src.llm.baseline.AsyncSessionLocal", FakeSLC)
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monkeypatch.setattr("src.db.unit_of_work.get_uow", lambda: _FakeUoW())
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# baseline.py 模块级 import _insert_or_find_by_fingerprint(第 15 行),需 patch baseline 模块属性
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monkeypatch.setattr("src.llm.baseline._insert_or_find_by_fingerprint", fake_upsert)
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result = await predict_baseline(1)
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# 落库被调用且属性映射正确
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assert captured, f"predict_baseline 应调用 _insert_or_find_by_fingerprint 落库,但 captured 为空(result.prediction_id={result.prediction_id!r})"
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assert captured["match_id"] == 1
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assert captured["provider"] == "baseline"
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assert captured["model"] == "baseline"
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assert captured["mode"] == "baseline"
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assert captured["run_type"] == "live"
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assert captured["prompt_version"] == "baseline_v1"
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assert captured["pred_home_goals"] == 2.0
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assert captured["pred_away_goals"] == 1.0
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assert captured["pred_1x2"] == "1"
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assert captured["subjective_confidence"] == 0.5
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assert captured["prompt_tokens"] == 0
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assert captured["completion_tokens"] == 0
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assert captured["latency_ms"] == 0
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assert captured["raw_response"] == {"home_avg": 2.0, "away_avg": 1.0}
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assert captured["status"] == "success"
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# 回填真实 prediction_id(服务层落库后取得)
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assert result.prediction_id == 77
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assert result.pred_1x2 == "1"
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assert captured["match_id"] == 1
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assert captured["provider"] == "baseline"
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assert captured["model"] == "baseline"
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assert captured["mode"] == "baseline"
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assert captured["run_type"] == "live"
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assert captured["prompt_version"] == "baseline_v1"
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assert captured["pred_home_goals"] == 2.0
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assert captured["pred_away_goals"] == 1.0
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assert captured["pred_1x2"] == "1"
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assert captured["subjective_confidence"] == 0.5
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assert captured["prompt_tokens"] == 0
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assert captured["completion_tokens"] == 0
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assert captured["latency_ms"] == 0
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assert captured["raw_response"] == {"home_avg": 2.0, "away_avg": 1.0}
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assert captured["status"] == "success"
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# 回填真实 prediction_id(服务层落库后取得)
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assert result.prediction_id == 77
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