fix(lotto): curator_helpers 시그니처 정합 (recommender/analyzer/strategy_evolver 실제 시그니처에 맞춤)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-04-15 08:22:56 +09:00
parent 4a8b0092d7
commit d1fec71bdc
2 changed files with 87 additions and 65 deletions

View File

@@ -1,18 +1,19 @@
"""큐레이터용 후보 가공 — 여러 엔진 결과를 하나로 병합, 중복 제거, 피처 계산."""
from typing import Dict, List, Any
from typing import Dict, List, Any, Set
from . import db
from .recommender import recommend_numbers, recommend_with_heatmap
from .analyzer import get_statistical_report
from .strategy_evolver import generate_smart_recommendation
LOW_HIGH_CUT = 22 # 1~22 저구간, 23~45 고구간
LOW_HIGH_CUT = 22
def compute_features(numbers: List[int], hot: set, cold: set) -> Dict[str, Any]:
def compute_features(numbers: List[int], hot: Set[int], cold: Set[int]) -> Dict[str, Any]:
nums = sorted(numbers)
odd = sum(1 for n in nums if n % 2 == 1)
low = sum(1 for n in nums if n <= LOW_HIGH_CUT)
buckets = [0, 0, 0, 0, 0] # 1-10, 11-20, 21-30, 31-40, 41-45
buckets = [0, 0, 0, 0, 0]
for n in nums:
if n <= 10: buckets[0] += 1
elif n <= 20: buckets[1] += 1
@@ -37,86 +38,105 @@ def _key(numbers: List[int]) -> str:
return ",".join(str(n) for n in sorted(numbers))
def collect_candidates(n: int, hot: set, cold: set) -> List[Dict[str, Any]]:
"""여러 엔진에서 후보를 모으고 중복 제거. 최대 n세트 반환.
def collect_candidates(n: int, hot: Set[int], cold: Set[int]) -> List[Dict[str, Any]]:
"""우선순위: simulation best_picks → meta → heatmap → statistics. 중복 제거 최대 n세트."""
seen: Dict[str, Dict[str, Any]] = {}
order: List[str] = []
우선순위: simulation best_picks → meta → heatmap → statistics
"""
seen = {}
sources_order = []
def _add(numbers: List[int], source: str) -> None:
if not numbers:
return
k = _key(numbers)
if k in seen:
return
seen[k] = {"numbers": sorted(numbers), "source": source}
order.append(k)
# 1. simulation best_picks
for row in db.get_best_picks(limit=n):
numbers = row.get("numbers") or []
if not numbers:
continue
k = _key(numbers)
if k not in seen:
seen[k] = {"numbers": sorted(numbers), "source": "simulation"}
sources_order.append(k)
try:
for row in db.get_best_picks(limit=n):
_add(row.get("numbers") or [], "simulation")
except Exception:
pass
# draws는 한 번만 로드
draws = []
try:
draws = db.get_all_draw_numbers()
except Exception:
pass
# 2. meta-strategy (smart)
try:
from .generator import generate_smart_recommendation
meta = generate_smart_recommendation(sets=n)
for s in meta.get("sets", []):
numbers = s.get("numbers") or []
k = _key(numbers)
if k not in seen and numbers:
seen[k] = {"numbers": sorted(numbers), "source": "meta"}
sources_order.append(k)
_add(s.get("numbers") or [], "meta")
except Exception:
pass
# 3. heatmap
try:
hm = recommend_with_heatmap(count=n)
for numbers in hm:
k = _key(numbers)
if k not in seen and numbers:
seen[k] = {"numbers": sorted(numbers), "source": "heatmap"}
sources_order.append(k)
except Exception:
pass
# 3. heatmap (n번 호출, 중복 회피)
if draws:
try:
for _ in range(n * 2):
if len(order) >= n * 2:
break
r = recommend_with_heatmap(draws, [])
_add(r.get("numbers") or [], "heatmap")
except Exception:
pass
# 4. statistics
try:
st = recommend_numbers(count=n)
for numbers in st:
k = _key(numbers)
if k not in seen and numbers:
seen[k] = {"numbers": sorted(numbers), "source": "statistics"}
sources_order.append(k)
except Exception:
pass
if draws:
try:
for _ in range(n * 2):
if len(order) >= n * 2:
break
r = recommend_numbers(draws)
_add(r.get("numbers") or [], "statistics")
except Exception:
pass
out = []
for k in sources_order[:n]:
for k in order[:n]:
item = seen[k]
item["features"] = compute_features(item["numbers"], hot, cold)
out.append(item)
return out
def build_context(hot_limit: int = 3, cold_limit: int = 3) -> Dict[str, Any]:
"""주간 맥락 패키지."""
report = get_statistical_report()
latest = db.get_latest_draw()
freq = report.get("frequency", {})
sorted_freq = sorted(freq.items(), key=lambda x: -x[1])
hot = [int(k) for k, _ in sorted_freq[:hot_limit]]
sorted_cold = sorted(freq.items(), key=lambda x: x[1])
cold = [int(k) for k, _ in sorted_cold[:cold_limit]]
def build_context(hot_limit: int = 10, cold_limit: int = 10) -> Dict[str, Any]:
"""주간 맥락 패키지 — get_statistical_report가 이미 hot/cold를 제공."""
hot: List[int] = []
cold: List[int] = []
last_summary = ""
if latest:
nums = [latest.get(f"drwtNo{i}") for i in range(1, 7)]
odd = sum(1 for n in nums if n and n % 2 == 1)
low = sum(1 for n in nums if n and n <= LOW_HIGH_CUT)
last_summary = f"{latest['drwNo']}회: {', '.join(str(n) for n in nums)} (홀{odd}{6-odd}, 저{low}{6-low})"
my_perf = []
try:
draws = db.get_all_draw_numbers()
except Exception:
draws = []
if draws:
try:
report = get_statistical_report(draws)
hot = list(report.get("hot_numbers", []))[:hot_limit]
cold = list(report.get("cold_numbers", []))[:cold_limit]
except Exception:
pass
try:
latest = db.get_latest_draw()
except Exception:
latest = None
if latest:
nums = [latest.get(f"n{i}") for i in range(1, 7)]
nums = [n for n in nums if n is not None]
if nums:
odd = sum(1 for n in nums if n % 2 == 1)
low = sum(1 for n in nums if n <= LOW_HIGH_CUT)
last_summary = f"{latest.get('drw_no')}회: {', '.join(str(n) for n in nums)} (홀{odd}{6-odd}, 저{low}{6-low})"
my_perf: List[Dict[str, Any]] = []
try:
from .purchase_manager import get_recent_performance
my_perf = get_recent_performance(limit=3)

View File

@@ -102,13 +102,15 @@ def check_purchases_for_draw(drw_no: int) -> int:
def get_recent_performance(limit: int = 3) -> list:
"""최근 N회차 내 구매 성과 요약. 없으면 빈 리스트."""
from . import db
purchases = db.get_purchases(days=None) or []
purchases = db.get_purchases() or []
by_draw: dict = {}
for p in purchases:
d = p.get("draw_no")
if not d:
continue
by_draw.setdefault(d, {"draw_no": d, "purchased_sets": 0, "best_match": 0})
by_draw[d]["purchased_sets"] += int(p.get("sets") or 1)
by_draw[d]["best_match"] = max(by_draw[d]["best_match"], int(p.get("correct_count") or 0))
results = p.get("results") or []
max_correct = max((int(r.get("correct") or 0) for r in results), default=0)
slot = by_draw.setdefault(d, {"draw_no": d, "purchased_sets": 0, "best_match": 0})
slot["purchased_sets"] += int(p.get("sets") or 1)
slot["best_match"] = max(slot["best_match"], max_correct)
return sorted(by_draw.values(), key=lambda x: -x["draw_no"])[:limit]