152 lines
4.5 KiB
Python
152 lines
4.5 KiB
Python
"""큐레이터용 후보 가공 — 여러 엔진 결과를 하나로 병합, 중복 제거, 피처 계산."""
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from typing import Dict, List, Any, Set
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from . import db
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from .recommender import recommend_numbers, recommend_with_heatmap
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from .analyzer import get_statistical_report
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from .strategy_evolver import generate_smart_recommendation
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LOW_HIGH_CUT = 22
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def compute_features(numbers: List[int], hot: Set[int], cold: Set[int]) -> Dict[str, Any]:
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nums = sorted(numbers)
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odd = sum(1 for n in nums if n % 2 == 1)
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low = sum(1 for n in nums if n <= LOW_HIGH_CUT)
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buckets = [0, 0, 0, 0, 0]
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for n in nums:
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if n <= 10: buckets[0] += 1
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elif n <= 20: buckets[1] += 1
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elif n <= 30: buckets[2] += 1
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elif n <= 40: buckets[3] += 1
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else: buckets[4] += 1
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consecutive = any(nums[i+1] - nums[i] == 1 for i in range(len(nums) - 1))
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return {
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"odd_count": odd,
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"even_count": 6 - odd,
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"low_count": low,
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"high_count": 6 - low,
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"range_distribution": buckets,
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"has_consecutive": consecutive,
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"hot_number_count": len(set(nums) & hot),
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"cold_number_count": len(set(nums) & cold),
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"sum": sum(nums),
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}
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def _key(numbers: List[int]) -> str:
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return ",".join(str(n) for n in sorted(numbers))
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def collect_candidates(n: int, hot: Set[int], cold: Set[int]) -> List[Dict[str, Any]]:
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"""우선순위: simulation best_picks → meta → heatmap → statistics. 중복 제거 후 최대 n세트."""
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seen: Dict[str, Dict[str, Any]] = {}
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order: List[str] = []
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def _add(numbers: List[int], source: str) -> None:
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if not numbers:
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return
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k = _key(numbers)
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if k in seen:
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return
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seen[k] = {"numbers": sorted(numbers), "source": source}
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order.append(k)
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# 1. simulation best_picks
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try:
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for row in db.get_best_picks(limit=n):
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_add(row.get("numbers") or [], "simulation")
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except Exception:
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pass
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# draws는 한 번만 로드
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draws = []
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try:
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draws = db.get_all_draw_numbers()
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except Exception:
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pass
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# 2. meta-strategy (smart)
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try:
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meta = generate_smart_recommendation(sets=n)
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for s in meta.get("sets", []):
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_add(s.get("numbers") or [], "meta")
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except Exception:
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pass
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# 3. heatmap (n번 호출, 중복 회피)
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if draws:
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try:
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for _ in range(n * 2):
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if len(order) >= n * 2:
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break
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r = recommend_with_heatmap(draws, [])
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_add(r.get("numbers") or [], "heatmap")
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except Exception:
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pass
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# 4. statistics
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if draws:
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try:
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for _ in range(n * 2):
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if len(order) >= n * 2:
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break
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r = recommend_numbers(draws)
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_add(r.get("numbers") or [], "statistics")
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except Exception:
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pass
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out = []
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for k in order[:n]:
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item = seen[k]
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item["features"] = compute_features(item["numbers"], hot, cold)
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out.append(item)
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return out
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def build_context(hot_limit: int = 10, cold_limit: int = 10) -> Dict[str, Any]:
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"""주간 맥락 패키지 — get_statistical_report가 이미 hot/cold를 제공."""
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hot: List[int] = []
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cold: List[int] = []
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last_summary = ""
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try:
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draws = db.get_all_draw_numbers()
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except Exception:
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draws = []
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if draws:
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try:
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report = get_statistical_report(draws)
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hot = list(report.get("hot_numbers", []))[:hot_limit]
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cold = list(report.get("cold_numbers", []))[:cold_limit]
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except Exception:
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pass
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try:
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latest = db.get_latest_draw()
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except Exception:
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latest = None
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if latest:
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nums = [latest.get(f"n{i}") for i in range(1, 7)]
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nums = [n for n in nums if n is not None]
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if nums:
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odd = sum(1 for n in nums if n % 2 == 1)
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low = sum(1 for n in nums if n <= LOW_HIGH_CUT)
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last_summary = f"{latest.get('drw_no')}회: {', '.join(str(n) for n in nums)} (홀{odd}짝{6-odd}, 저{low}고{6-low})"
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my_perf: List[Dict[str, Any]] = []
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try:
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from .purchase_manager import get_recent_performance
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my_perf = get_recent_performance(limit=3)
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except Exception:
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my_perf = []
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return {
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"hot_numbers": hot,
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"cold_numbers": cold,
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"last_draw_summary": last_summary,
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"my_recent_performance": my_perf,
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}
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