From 8e7b4adabd52f49b8d44830e768e3b8ba81bae74 Mon Sep 17 00:00:00 2001
From: gahusb <bgg8988@gmail.com>
Date: Sun, 31 May 2026 17:32:37 +0900
Subject: [PATCH] =?UTF-8?q?feat(lotto):=20select=5Fwinner=5Fby=5Flift=20+?=
 =?UTF-8?q?=20=CE=B5-=EA=B2=8C=EC=9D=B4=ED=8C=85?=
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Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 lotto/app/weight_evolver.py        | 25 +++++++++++++++++++++++++
 lotto/tests/test_weight_evolver.py | 15 +++++++++++++++
 2 files changed, 40 insertions(+)

diff --git a/lotto/app/weight_evolver.py b/lotto/app/weight_evolver.py
index 6969748..c5bf308 100644
--- a/lotto/app/weight_evolver.py
+++ b/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."""
diff --git a/lotto/tests/test_weight_evolver.py b/lotto/tests/test_weight_evolver.py
index c7d9d0d..3ac3f0e 100644
--- a/lotto/tests/test_weight_evolver.py
+++ b/lotto/tests/test_weight_evolver.py
@@ -120,3 +120,18 @@ def test_decide_base_update_cold_start_returns_default():
     )
     assert new_base == winner_W
     assert reason == "winner_4plus"
+
+
+def test_select_winner_by_lift_gating():
+    # engine_w 3개 + random_null 기준. lift = engine 등수점수 − random 등수점수
+    per_w = [
+        {"trial_id": 1, "day_of_week": 0, "weight": [0.2]*5, "prize_score": 5.0},
+        {"trial_id": 2, "day_of_week": 1, "weight": [0.3,0.2,0.2,0.2,0.1], "prize_score": 9.0},
+        {"trial_id": 3, "day_of_week": 2, "weight": [0.1,0.3,0.2,0.2,0.2], "prize_score": 4.0},
+    ]
+    # random baseline이 8.0이면 lift는 +1, +1, -4 → 노이즈 ε=2 안에서 게이팅
+    winner = we.select_winner_by_lift(per_w, random_score=8.0, epsilon=2.0)
+    assert winner["gated"] is True       # 최대 lift(+1) < ε(2) → 게이팅
+    winner2 = we.select_winner_by_lift(per_w, random_score=3.0, epsilon=2.0)
+    assert winner2["gated"] is False
+    assert winner2["trial_id"] == 2      # prize 9 → lift +6