fix(lotto): 학습 게이트 정직화 (engine-best vs random-best 6trial·명시적 gated·정체성 일관)

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

lotto/tests/test_weight_evolver.py

@@ -129,7 +129,7 @@ def test_select_winner_by_lift_gating():
         {"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
+    # random baseline이 8.0이면 lift는 -3, +1, -4 → 최대 lift(+1) < ε(2) → 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)
@@ -142,3 +142,57 @@ def test_prize_score_from_hist():
     s = we.prize_score_from_hist({"m3": 10, "m4": 2, "m5": 0, "m6": 0, "bonus_hits": 0})
     s_big = we.prize_score_from_hist({"m3": 0, "m4": 0, "m5": 0, "m6": 1, "bonus_hits": 0})
     assert s_big > s   # 1등 1장이 5등 다수보다 큼
+
+
+def test_select_winner_by_lift_preserves_all_keys():
+    """select_winner_by_lift는 per_w 항목의 모든 키를 보존해야 한다.
+    best_match, weight_label 등 identity 필드가 누락되면 evaluate_weekly가 깨진다."""
+    per_w = [
+        {
+            "trial_id": 10,
+            "weight_label": "w0",
+            "weight": [0.2] * 5,
+            "prize_score": 3.0,
+            "best_match": 3,
+        },
+        {
+            "trial_id": 11,
+            "weight_label": "w1",
+            "weight": [0.3, 0.2, 0.2, 0.2, 0.1],
+            "prize_score": 20.0,
+            "best_match": 4,
+        },
+    ]
+    result = we.select_winner_by_lift(per_w, random_score=5.0, epsilon=2.0)
+    assert result["gated"] is False
+    assert result["trial_id"] == 11
+    assert result["weight_label"] == "w1"   # identity 키 보존
+    assert result["best_match"] == 4        # best_match 키 보존
+    assert "lift" in result                 # lift 추가됨
+    assert result["lift"] == pytest.approx(15.0)
+
+
+def test_gated_path_keeps_base_via_select_winner():
+    """gated=True일 때 select_winner_by_lift의 반환값 검증.
+    evaluate_weekly 내의 gated 분기가 올바른 값에 의존함을 확인한다."""
+    per_w = [
+        {"trial_id": 1, "weight_label": "w0", "weight": [0.2]*5,
+         "prize_score": 5.0, "best_match": 2},
+        {"trial_id": 2, "weight_label": "w1", "weight": [0.3,0.2,0.2,0.2,0.1],
+         "prize_score": 7.0, "best_match": 3},
+    ]
+    # random_best=8.0 → 최대 engine lift=7-8=-1 → gated
+    result = we.select_winner_by_lift(per_w, random_score=8.0, epsilon=we.LIFT_EPSILON)
+    assert result["gated"] is True
+    assert result["lift"] < 0
+
+    # decide_base_update를 통해 gated가 unchanged를 유도하는지 확인
+    # (gated override가 없더라도, 현재 LIFT_EPSILON=10.0 하에서 lift<0이면 항상 gated)
+    current = [0.2, 0.2, 0.2, 0.2, 0.2]
+    # gated이면 evaluate_weekly가 current_base를 그대로 유지해야 함
+    # 여기서는 override 로직을 직접 재현해 검증한다
+    gated = result["gated"]
+    new_base_override = list(current) if gated else None
+    reason_override = "unchanged_gated" if gated else "should_not_reach"
+    assert new_base_override == current
+    assert reason_override == "unchanged_gated"