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
119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
"""极简基线预测:主客场场均进球估计(不调用 LLM,不产生费用)。
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用于与 LLM 预测做 eval 对比。这是最简单的统计基线,仅供研究参考,
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文档与 reasoning 均明确标注「非投注建议」。
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"""
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from __future__ import annotations
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import logging
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from datetime import datetime
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from sqlalchemy import case, func, select
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from src.db.base import AsyncSession, AsyncSessionLocal
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from src.db.models import Match
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from src.llm.predict import PredictResult
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logger = logging.getLogger(__name__)
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async def _avg_goals(
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db: AsyncSession,
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*,
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team_id: int,
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side: str,
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league_id: int,
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before: datetime | None,
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) -> float:
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"""某队在该联赛已完赛场次的场均进球(side=home/away)。"""
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if side == "home":
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goals_col = Match.home_goals
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team_col = Match.home_team_id
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else:
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goals_col = Match.away_goals
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team_col = Match.away_team_id
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stmt = (
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select(func.avg(goals_col).label("avg_goals"), func.count().label("cnt"))
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.where(
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Match.match_status == "finished",
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team_col == team_id,
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Match.league_id == league_id,
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goals_col.is_not(None),
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)
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)
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if before is not None:
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stmt = stmt.where(Match.match_date < before)
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row = (await db.execute(stmt)).one()
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return float(row.avg_goals) if row.avg_goals is not None and row.cnt > 0 else 0.0
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async def predict_baseline(
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match_id: int,
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*,
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backtest: bool = False,
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cutoff_at: datetime | None = None,
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) -> PredictResult:
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"""极简基线预测:主场场均进球 vs 客场场均进球。
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返回 PredictResult(D2 统一结果类型):
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provider=model="baseline", 不调用 LLM,latency_ms≈0。
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prediction_id 为占位 0 —— baseline 不在服务层落库,
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由路由层 _persist_baseline 落库后取得真实 id。
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"""
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async with AsyncSessionLocal() as db:
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match = await db.get(Match, match_id)
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if match is None:
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raise ValueError(f"match {match_id} not found")
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before = None
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if backtest and match.match_dt:
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from datetime import timedelta
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before = match.match_dt - timedelta(days=1)
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elif cutoff_at is not None:
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before = cutoff_at
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home_avg = await _avg_goals(
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db, team_id=match.home_team_id, side="home",
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league_id=match.league_id, before=before,
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)
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away_avg = await _avg_goals(
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db, team_id=match.away_team_id, side="away",
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league_id=match.league_id, before=before,
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)
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pred_home = max(0, min(10, round(home_avg)))
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pred_away = max(0, min(10, round(away_avg)))
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# 主场轻微加成(可选,这里保持极简不额外加权)
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if pred_home > pred_away:
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pred_1x2 = "1"
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elif pred_home < pred_away:
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pred_1x2 = "2"
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else:
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pred_1x2 = "X"
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return PredictResult(
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prediction_id=0, # 占位:真实 id 由路由层 _persist_baseline 落库后返回
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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=float(pred_home),
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pred_away_goals=float(pred_away),
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alt_pred_home_goals=None,
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alt_pred_away_goals=None,
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pred_1x2=pred_1x2,
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subjective_confidence=0.5,
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reasoning=(
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f"基线估计(非投注建议): 主队主场场均进球 {home_avg:.2f} → 预测 {pred_home}; "
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f"客队客场场均进球 {away_avg:.2f} → 预测 {pred_away}。"
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),
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context="", # baseline 不构建 LLM 上下文
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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": round(home_avg, 2), "away_avg": round(away_avg, 2)},
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)
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