refactor(lotto): Phase 1 코드리뷰 반영 (로컬 RNG·write-once·가드·테스트 보강)

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

lotto/tests/test_backtest.py

@@ -1,5 +1,5 @@
 from app import backtest as bt
-from app.analyzer import build_analysis_cache, score_combination
+from app.analyzer import build_analysis_cache, build_number_weights, score_combination
 
 
 def _toy_draws(n=120):
@@ -38,16 +38,17 @@ def test_grade_tickets_histogram_and_prizes():
 
 def test_purchase_tickets_distinct_and_count():
     draws = _toy_draws()
-    cache = bt.build_analysis_cache(draws)
-    nw = bt.build_number_weights(cache)
+    cache = build_analysis_cache(draws)
+    nw = build_number_weights(cache)
     pool = bt.generate_pool(cache, nw, n=2000, seed=7)
     W = [0.25, 0.30, 0.20, 0.15, 0.10]
     bought = bt.purchase_tickets(pool, cache, W, k=50)
     assert len(bought) == 50
     assert len({tuple(t) for t in bought}) == 50           # distinct
-    # W로 랭킹된 상위 → 평균 분석치가 풀 평균보다 높아야
+    # W로 랭킹된 상위 k → 평균 점수가 풀 전체 평균 이상이어야
     avg_bought = sum(score_combination(t, cache, W)["score_total"] for t in bought) / 50
-    assert avg_bought > 0
+    avg_pool = sum(score_combination(t, cache, W)["score_total"] for t in pool) / len(pool)
+    assert avg_bought >= avg_pool
 
 
 def test_random_null_and_coverage_distinct():
@@ -57,3 +58,24 @@ def test_random_null_and_coverage_distinct():
     flat = {n for t in cov for n in t}
     assert len(cov) == 9 and len({tuple(t) for t in cov}) == 9
     assert len(flat) >= 40   # 커버리지 전략은 번호를 넓게 퍼뜨림
+
+
+def test_generate_pool_partial_fill(monkeypatch):
+    """weighted_sample_6이 항상 같은 조합만 반환하도록 패치 → cap에 먼저 걸려 len < n — 예외 없이 반환."""
+    import random as _r
+    _r.seed(42)
+    tiny_draws = [(i, sorted(_r.sample(range(1, 46), 6))) for i in range(1, 10)]
+    cache = build_analysis_cache(tiny_draws)
+    nw = build_number_weights(cache)
+
+    # weighted_sample_6을 항상 동일한 하나의 조합만 반환하도록 패치
+    # → 두 번째 시도부터 seen에 막혀 n개를 채울 수 없고 cap=n*4 이후 종료
+    import app.backtest as _bt_mod
+    monkeypatch.setattr(_bt_mod, "weighted_sample_6", lambda _w: [1, 2, 3, 4, 5, 6])
+
+    n = 50
+    pool = bt.generate_pool(cache, nw, n=n, seed=0)
+    # 예외 없이 반환해야 하고, 결과는 n 미만이어야 하며 모두 distinct
+    assert isinstance(pool, list)
+    assert len(pool) < n
+    assert len({tuple(t) for t in pool}) == len(pool)

lotto/tests/test_backtest_db.py

@@ -29,3 +29,50 @@ def test_backtest_runs_unique(monkeypatch):
     rows = db.get_backtest_runs(draw_no=100)
     assert len(rows) == 1
     assert rows[0]["m3"] == 2  # 마지막 값으로 갱신
+
+
+_SCORES = {
+    "score_total": 1.23,
+    "score_frequency": 0.30,
+    "score_fingerprint": 0.25,
+    "score_gap": 0.20,
+    "score_cooccur": 0.28,
+    "score_diversity": 0.20,
+}
+
+
+def test_winner_calibration_upsert(monkeypatch):
+    """save_winner_calibration 두 번 호출 시 upsert — 행 1개, 값은 마지막 것."""
+    db = _fresh_db(monkeypatch)
+    winning = [3, 7, 15, 22, 33, 41]
+    db.save_winner_calibration(draw_no=200, winning=winning,
+                               scores=_SCORES, percentile=75.0,
+                               my_pick_avg=0.9, cache_draws=100)
+    # 두 번째 저장 — percentile, my_pick_avg 업데이트
+    scores2 = {**_SCORES, "score_total": 2.00}
+    db.save_winner_calibration(draw_no=200, winning=winning,
+                               scores=scores2, percentile=80.0,
+                               my_pick_avg=1.1, cache_draws=110)
+    row = db.get_winner_calibration(200)
+    assert row is not None
+    # 행이 1개만 존재하는지 확인
+    with db._conn() as conn:
+        cnt = conn.execute(
+            "SELECT COUNT(*) AS c FROM winner_calibration WHERE draw_no=200"
+        ).fetchone()["c"]
+    assert cnt == 1
+    assert row["percentile"] == 80.0
+    assert row["score_total"] == 2.00
+
+
+def test_get_calibrated_draw_nos(monkeypatch):
+    """저장된 draw_no 집합이 get_calibrated_draw_nos에 포함되어야 한다."""
+    db = _fresh_db(monkeypatch)
+    winning = [1, 2, 3, 4, 5, 6]
+    for draw_no in (301, 302, 303):
+        db.save_winner_calibration(draw_no=draw_no, winning=winning,
+                                   scores=_SCORES, percentile=50.0,
+                                   my_pick_avg=0.5, cache_draws=50)
+    nos = db.get_calibrated_draw_nos()
+    assert isinstance(nos, set)
+    assert {301, 302, 303}.issubset(nos)