104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
import os, tempfile
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def _fresh_db(monkeypatch):
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tmp = tempfile.mkdtemp()
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path = os.path.join(tmp, "lotto.db")
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from app import db
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monkeypatch.setattr(db, "DB_PATH", path)
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db.init_db()
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return db
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def test_backtest_tables_exist(monkeypatch):
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db = _fresh_db(monkeypatch)
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with db._conn() as conn:
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tables = {r["name"] for r in conn.execute(
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"SELECT name FROM sqlite_master WHERE type='table'").fetchall()}
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assert "backtest_runs" in tables
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assert "winner_calibration" in tables
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def test_backtest_runs_unique(monkeypatch):
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db = _fresh_db(monkeypatch)
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db.save_backtest_run(draw_no=100, strategy="random_null", weight_label="-",
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weight_json=None, trial_id=None, n_tickets=10,
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hist={"m3":1,"m4":0,"m5":0,"m6":0,"bonus_hits":0},
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best_match=3, avg_meta_score=0.5)
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db.save_backtest_run(draw_no=100, strategy="random_null", weight_label="-",
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weight_json=None, trial_id=None, n_tickets=10,
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hist={"m3":2,"m4":0,"m5":0,"m6":0,"bonus_hits":0},
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best_match=3, avg_meta_score=0.6) # 멱등 upsert
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rows = db.get_backtest_runs(draw_no=100)
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assert len(rows) == 1
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assert rows[0]["m3"] == 2 # 마지막 값으로 갱신
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_SCORES = {
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"score_total": 1.23,
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"score_frequency": 0.30,
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"score_fingerprint": 0.25,
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"score_gap": 0.20,
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"score_cooccur": 0.28,
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"score_diversity": 0.20,
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}
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def test_winner_calibration_upsert(monkeypatch):
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"""save_winner_calibration 두 번 호출 시 upsert — 행 1개, 값은 마지막 것."""
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db = _fresh_db(monkeypatch)
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winning = [3, 7, 15, 22, 33, 41]
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db.save_winner_calibration(draw_no=200, winning=winning,
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scores=_SCORES, percentile=75.0,
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my_pick_avg=0.9, cache_draws=100)
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# 두 번째 저장 — percentile, my_pick_avg 업데이트
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scores2 = {**_SCORES, "score_total": 2.00}
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db.save_winner_calibration(draw_no=200, winning=winning,
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scores=scores2, percentile=80.0,
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my_pick_avg=1.1, cache_draws=110)
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row = db.get_winner_calibration(200)
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assert row is not None
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# 행이 1개만 존재하는지 확인
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with db._conn() as conn:
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cnt = conn.execute(
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"SELECT COUNT(*) AS c FROM winner_calibration WHERE draw_no=200"
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).fetchone()["c"]
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assert cnt == 1
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assert row["percentile"] == 80.0
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assert row["score_total"] == 2.00
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def _seed_draws(db, n=40):
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rows = []
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import random as _r; _r.seed(2)
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for i in range(1, n + 1):
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s = sorted(_r.sample(range(1, 46), 6))
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rows.append({"drw_no": i, "drw_date": f"2020-01-{(i%28)+1:02d}",
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"n1": s[0], "n2": s[1], "n3": s[2], "n4": s[3],
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"n5": s[4], "n6": s[5], "bonus": ((s[5] % 45) + 1)})
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db.upsert_many_draws(rows)
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def test_backfill_calibration_idempotent(monkeypatch):
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db = _fresh_db(monkeypatch)
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_seed_draws(db, 40)
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from app import backtest as bt
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r1 = bt.backfill_calibration(batch=15, sample_m=200)
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# 첫 회차는 point-in-time 데이터가 빈약 → min_history 이후만 처리
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done1 = len(db.get_calibrated_draw_nos())
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assert done1 > 0
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r2 = bt.backfill_calibration(batch=100, sample_m=200) # 나머지
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done2 = len(db.get_calibrated_draw_nos())
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assert done2 >= done1
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r3 = bt.backfill_calibration(batch=100, sample_m=200) # 재실행 → 추가 0
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assert r3["calibrated"] == 0
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def test_get_calibrated_draw_nos(monkeypatch):
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"""저장된 draw_no 집합이 get_calibrated_draw_nos에 포함되어야 한다."""
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db = _fresh_db(monkeypatch)
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winning = [1, 2, 3, 4, 5, 6]
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for draw_no in (301, 302, 303):
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db.save_winner_calibration(draw_no=draw_no, winning=winning,
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scores=_SCORES, percentile=50.0,
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my_pick_avg=0.5, cache_draws=50)
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nos = db.get_calibrated_draw_nos()
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assert isinstance(nos, set)
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assert {301, 302, 303}.issubset(nos)
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