feat(insta-lab): selection.py 순수 선별 점수(4신호)

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

insta-lab/app/selection.py

@@ -0,0 +1,81 @@
+"""발행 가치 자율 선별 — 순수 점수 함수 (외부 IO 없음, 단위테스트 대상).
+
+신호: dedup(게이트), freshness, account_fit, claude(선택).
+final = 가중합(존재하는 신호만 정규화). eligible = dedup통과 and final>=threshold.
+"""
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+DEFAULT_WEIGHTS = {"freshness": 0.3, "account_fit": 0.3, "claude": 0.4}
+FRESH_WINDOW_HOURS = 168.0  # 7일 → 0
+
+
+def _parse_iso(s: str) -> datetime:
+    return datetime.fromisoformat(s.replace("Z", "+00:00")).astimezone(timezone.utc)
+
+
+def _norm(kw: str) -> str:
+    return (kw or "").strip().lower()
+
+
+def _is_duplicate(keyword: str, category: str, issued: List[Dict[str, Any]]) -> bool:
+    n = _norm(keyword)
+    if not n:
+        return False
+    for it in issued:
+        if it.get("category") != category:
+            continue
+        m = _norm(it.get("keyword", ""))
+        if not m:
+            continue
+        if n == m or n in m or m in n:
+            return True
+    return False
+
+
+def _freshness(suggested_at: str, now: datetime) -> float:
+    try:
+        hours = (now - _parse_iso(suggested_at)).total_seconds() / 3600.0
+    except Exception:
+        return 0.0
+    return max(0.0, min(1.0, 1.0 - hours / FRESH_WINDOW_HOURS))
+
+
+def score_candidates(
+    candidates: List[Dict[str, Any]],
+    issued_topics: List[Dict[str, Any]],
+    prefs: Dict[str, float],
+    claude_scores: Optional[Dict[int, float]] = None,
+    weights: Optional[Dict[str, float]] = None,
+    threshold: float = 0.6,
+    now_iso: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    w = weights or DEFAULT_WEIGHTS
+    now = _parse_iso(now_iso) if now_iso else datetime.now(timezone.utc)
+    max_w = max(prefs.values()) if prefs else 1.0
+    out: List[Dict[str, Any]] = []
+    for c in candidates:
+        cat = c.get("category", "")
+        dup = _is_duplicate(c.get("keyword", ""), cat, issued_topics)
+        freshness = _freshness(c.get("suggested_at", ""), now)
+        weight = prefs.get(cat, 1.0)
+        account_fit = max(0.0, min(1.0, (weight / max_w) * float(c.get("score", 0.0))))
+        claude = None
+        if claude_scores is not None and c["id"] in claude_scores:
+            claude = max(0.0, min(1.0, float(claude_scores[c["id"]])))
+        parts = [("freshness", freshness), ("account_fit", account_fit)]
+        if claude is not None:
+            parts.append(("claude", claude))
+        total_w = sum(w[name] for name, _ in parts)
+        final = sum(w[name] * val for name, val in parts) / total_w if total_w else 0.0
+        eligible = (not dup) and (final >= threshold)
+        out.append({
+            "id": c["id"], "keyword": c.get("keyword"), "category": cat,
+            "final_score": round(final, 4), "eligible": eligible,
+            "breakdown": {"dedup_excluded": dup, "freshness": round(freshness, 4),
+                          "account_fit": round(account_fit, 4), "claude": claude},
+        })
+    out.sort(key=lambda x: (-x["eligible"], -x["final_score"]))
+    return out