feat(lotto): select_winner_by_lift + ε-게이팅

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

lotto/app/weight_evolver.py

@@ -4,6 +4,7 @@
 순수 함수 (clamp/perturb/Dirichlet/score/base-rule) + DB 진입점은 별도 섹션.
 """
 from __future__ import annotations
+import json
 import math
 import random
 from datetime import datetime, timedelta, timezone
@@ -18,6 +19,30 @@ DEFAULT_UNIFORM = [0.2] * N_METRICS  # cold start
 RANK_BY_CORRECT = {6: 1, 5: 3, 4: 4, 3: 5}
 RANK_BONUS = {1: 1.0, 2: 0.8, 3: 0.6, 4: 0.3, 5: 0.1}
 
+LIFT_EPSILON = 0.5  # 등수점수 노이즈 게이팅 임계 (튜닝 가능)
+
+PRIZE_WEIGHTS = {"m6": 1000.0, "bonus_hits": 50.0, "m5": 30.0, "m4": 4.0, "m3": 1.0}
+
+
+def select_winner_by_lift(per_w: List[Dict[str, Any]], random_score: float,
+                          epsilon: float = LIFT_EPSILON) -> Dict[str, Any]:
+    """engine_w 후보들 중 random 대비 lift 최대 선택.
+    최대 lift가 epsilon 미만이면 gated=True (노이즈 → base 유지 권고)."""
+    scored = [{**w, "lift": w["prize_score"] - random_score} for w in per_w]
+    best = max(scored, key=lambda w: w["lift"])
+    return {**best, "gated": best["lift"] < epsilon}
+
+
+def prize_score_from_hist(hist: Dict[str, int]) -> float:
+    """매칭 히스토그램 → 등수 가중 합산 점수.
+    1등=m6, 2등=bonus_hits, 3등=m5−bonus_hits, 4등=m4, 5등=m3."""
+    third = max(0, hist.get("m5", 0) - hist.get("bonus_hits", 0))
+    return (hist.get("m6", 0) * PRIZE_WEIGHTS["m6"]
+            + hist.get("bonus_hits", 0) * PRIZE_WEIGHTS["bonus_hits"]
+            + third * PRIZE_WEIGHTS["m5"]
+            + hist.get("m4", 0) * PRIZE_WEIGHTS["m4"]
+            + hist.get("m3", 0) * PRIZE_WEIGHTS["m3"])
+
 
 def clamp_and_normalize(W: List[float], min_w: float = MIN_WEIGHT) -> List[float]:
     """각 값 ≥ min_w + 합=1.0. 보장 안 되면 raise."""