82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
"""발행 가치 자율 선별 — 순수 점수 함수 (외부 IO 없음, 단위테스트 대상).
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신호: dedup(게이트), freshness, account_fit, claude(선택).
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final = 가중합(존재하는 신호만 정규화). eligible = dedup통과 and final>=threshold.
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"""
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from __future__ import annotations
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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DEFAULT_WEIGHTS = {"freshness": 0.3, "account_fit": 0.3, "claude": 0.4}
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FRESH_WINDOW_HOURS = 168.0 # 7일 → 0
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def _parse_iso(s: str) -> datetime:
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return datetime.fromisoformat(s.replace("Z", "+00:00")).astimezone(timezone.utc)
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def _norm(kw: str) -> str:
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return (kw or "").strip().lower()
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def _is_duplicate(keyword: str, category: str, issued: List[Dict[str, Any]]) -> bool:
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n = _norm(keyword)
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if not n:
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return False
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for it in issued:
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if it.get("category") != category:
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continue
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m = _norm(it.get("keyword", ""))
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if not m:
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continue
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if n == m or n in m or m in n:
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return True
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return False
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def _freshness(suggested_at: str, now: datetime) -> float:
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try:
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hours = (now - _parse_iso(suggested_at)).total_seconds() / 3600.0
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except Exception:
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return 0.0
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return max(0.0, min(1.0, 1.0 - hours / FRESH_WINDOW_HOURS))
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def score_candidates(
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candidates: List[Dict[str, Any]],
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issued_topics: List[Dict[str, Any]],
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prefs: Dict[str, float],
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claude_scores: Optional[Dict[int, float]] = None,
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weights: Optional[Dict[str, float]] = None,
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threshold: float = 0.6,
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now_iso: Optional[str] = None,
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) -> List[Dict[str, Any]]:
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w = weights or DEFAULT_WEIGHTS
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now = _parse_iso(now_iso) if now_iso else datetime.now(timezone.utc)
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max_w = max(prefs.values()) if prefs else 1.0
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out: List[Dict[str, Any]] = []
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for c in candidates:
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cat = c.get("category", "")
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dup = _is_duplicate(c.get("keyword", ""), cat, issued_topics)
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freshness = _freshness(c.get("suggested_at", ""), now)
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weight = prefs.get(cat, 1.0)
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account_fit = max(0.0, min(1.0, (weight / max_w) * float(c.get("score", 0.0))))
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claude = None
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if claude_scores is not None and c["id"] in claude_scores:
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claude = max(0.0, min(1.0, float(claude_scores[c["id"]])))
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parts = [("freshness", freshness), ("account_fit", account_fit)]
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if claude is not None:
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parts.append(("claude", claude))
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total_w = sum(w[name] for name, _ in parts)
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final = sum(w[name] * val for name, val in parts) / total_w if total_w else 0.0
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eligible = (not dup) and (final >= threshold)
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out.append({
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"id": c["id"], "keyword": c.get("keyword"), "category": cat,
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"final_score": round(final, 4), "eligible": eligible,
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"breakdown": {"dedup_excluded": dup, "freshness": round(freshness, 4),
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"account_fit": round(account_fit, 4), "claude": claude},
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})
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out.sort(key=lambda x: (-x["eligible"], -x["final_score"]))
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return out
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