WorkBuddy
5c7fdce0a3
- PredictResult 扩展可选字段 mode/agent_outputs/agent_weights/prompt_tokens/completion_tokens - MultiPredictResult 变为 PredictResult 别名(保留 R4 守卫标记与 re-export) - baseline 改返回 PredictResult(修复 backtest 对 baseline AttributeError 的潜伏 bug) - 预测路由单一属性映射,删除全部 isinstance(result, dict) 分支 - _persist_baseline 属性化,baseline upsert 语义不变(prompt_version/token/latency 同前) - TDD: 5 新测试 + test_baseline.py 属性化;全量 257 passed
217 lines
7.3 KiB
Python
217 lines
7.3 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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with patch("src.llm.baseline._avg_goals", fake_avg), \
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patch("src.llm.baseline.AsyncSessionLocal") as SLC:
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SLC.return_value = FakeCM()
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result = await predict_baseline(1)
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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. _persist_baseline 属性映射(baseline 落库语义不变)
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# ============================================================
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class _FakeUoW:
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"""替代 get_uow 的最小上下文管理器。"""
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def __init__(self):
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self.session = SimpleNamespace()
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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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@pytest.mark.asyncio
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async def test_persist_baseline_maps_attributes(monkeypatch):
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captured = {}
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async def fake_upsert(session, **kwargs):
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captured.update(kwargs)
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return SimpleNamespace(id=77)
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monkeypatch.setattr("src.db.unit_of_work.get_uow", lambda: _FakeUoW())
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monkeypatch.setattr("src.llm.predict._upsert_prediction", fake_upsert)
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from src.api.routes.predict import _persist_baseline
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baseline = PredictResult(
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prediction_id=0, # baseline 不在服务层落库,由 _persist_baseline 落库后取得真实 id
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provider="baseline",
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model="baseline",
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prompt_version="baseline_v1",
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mode="baseline",
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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=0.5,
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reasoning="r",
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context="",
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status="success",
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latency_ms=0,
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prompt_tokens=0,
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completion_tokens=0,
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raw={"home_avg": 2.1, "away_avg": 1.4},
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)
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pid = await _persist_baseline(1, baseline)
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assert pid == 77
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assert captured["match_id"] == 1
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assert captured["provider_name"] == "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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v = captured["values"]
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assert v["prompt_version"] == "baseline_v1"
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assert v["pred_home_goals"] == 2.0
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assert v["pred_away_goals"] == 1.0
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assert v["pred_1x2"] == "1"
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assert v["subjective_confidence"] == 0.5
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assert v["prompt_tokens"] == 0
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assert v["completion_tokens"] == 0
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assert v["latency_ms"] == 0
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assert v["raw_response"] == {"home_avg": 2.1, "away_avg": 1.4}
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assert v["status"] == "success"
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