refactor(digest): extract clustering helpers to clustering_common

Phase 4 Global Digest 의 클러스터링 핵심 알고리즘 (time-decay weight,
adaptive threshold, greedy cosine assign + EMA centroid, importance
normalize) 을 `app/services/clustering_common.py` 로 추출. country
축은 caller 책임 — Phase 4 cluster_country 는 그대로 country 별 호출,
신규 morning briefing 모듈이 country 없이 cluster_global 로 호출 예정.

selection.py 의 중복 _normalize 도 공통 util 로 통일.

동작 변경 0:
- LAMBDA / threshold / EMA alpha / MIN_ARTICLES 모두 Phase 4 기본값 유지
- docs.sort (in-place) → sorted (copy) 변경했으나 caller 가 정렬된
  docs 를 재사용하지 않으므로 무관 (dict element 의 weight 부여는
  reference 라 그대로 반영)

다음 commit 에서 Phase 4 회귀 검증 (digest regenerate diff 0).

app/services/clustering_common.py

@@ -0,0 +1,124 @@
+"""Cluster 알고리즘 공통 util — digest(country×topic) / briefing(topic×country) 양쪽이 import.
+
+추출 원칙:
+- digest.clustering.cluster_country / briefing.clustering.cluster_global 의 country 축은 caller 책임.
+- 본 모듈은 docs list (이미 분류된 슬라이스 또는 전체) 에 대한 순수 greedy assign + normalize.
+- LAMBDA / threshold / EMA alpha / MIN_ARTICLES 는 caller 가 주입 (Phase 4 = 3일 / Briefing = 2시간 등).
+"""
+
+import math
+from datetime import datetime, timezone
+
+import numpy as np
+
+
+SCORE_FLOOR = 0.01
+
+
+def normalize_vector(v: np.ndarray) -> np.ndarray:
+    norm = float(np.linalg.norm(v))
+    if norm == 0.0:
+        return v
+    return v / norm
+
+
+def time_decay_weight(now: datetime, created_at: datetime, lambda_val: float) -> float:
+    """exp(-λ · days_ago). created_at naive → UTC 가정."""
+    if created_at.tzinfo is None:
+        created_at = created_at.replace(tzinfo=timezone.utc)
+    days = (now - created_at).total_seconds() / 86400.0
+    if days < 0:
+        days = 0.0
+    return math.exp(-lambda_val * days)
+
+
+def adaptive_threshold_by_density(
+    n_docs: int,
+    *,
+    low_n: int = 50,
+    high_n: int = 200,
+    t_low: float = 0.75,
+    t_mid: float = 0.78,
+    t_high: float = 0.80,
+) -> float:
+    """문서 밀도 기반 동적 threshold — fragmentation / blob 동시 방어."""
+    if n_docs > high_n:
+        return t_high
+    if n_docs < low_n:
+        return t_low
+    return t_mid
+
+
+def greedy_assign_cluster(
+    docs: list[dict],
+    *,
+    threshold: float,
+    centroid_alpha: float = 0.7,
+    min_articles: int = 3,
+    max_topics: int = 10,
+    now: datetime | None = None,
+    lambda_val: float,
+) -> tuple[list[dict], int]:
+    """time-decay weight 적용 + greedy cosine assign + EMA centroid + MIN drop.
+
+    Args:
+        docs: [{embedding: np.ndarray, created_at: datetime, ...}]. 함수가 in-place 로 `weight` 키 추가.
+        threshold: cosine 유사도 cluster 병합 임계.
+        centroid_alpha: EMA 계수 (0.7 = 기존 70% 유지).
+        min_articles: cluster 당 최소 article 수 (미만 시 drop).
+        max_topics: 상위 cluster 보존 개수.
+        now: 기준 시각 (default = datetime.now(UTC)).
+        lambda_val: time-decay λ (caller 가 윈도우 폭에 맞게 주입).
+
+    Returns:
+        (clusters, raw_cluster_count_before_drop)
+        clusters = [{centroid, members, weight_sum, raw_weight_sum, importance_score}, ...]
+    """
+    if not docs:
+        return [], 0
+
+    now = now or datetime.now(timezone.utc)
+
+    for d in docs:
+        d["weight"] = time_decay_weight(now, d["created_at"], lambda_val)
+    docs_sorted = sorted(docs, key=lambda d: -d["weight"])
+
+    clusters: list[dict] = []
+    for d in docs_sorted:
+        v = normalize_vector(d["embedding"])
+        best_idx, best_sim = -1, 0.0
+        for i, c in enumerate(clusters):
+            sim = float(np.dot(c["centroid"], v))
+            if sim > best_sim and sim >= threshold:
+                best_sim, best_idx = sim, i
+        if best_idx >= 0:
+            c = clusters[best_idx]
+            c["centroid"] = centroid_alpha * c["centroid"] + (1.0 - centroid_alpha) * v
+            c["centroid"] = normalize_vector(c["centroid"])
+            c["members"].append(d)
+            c["weight_sum"] += d["weight"]
+        else:
+            clusters.append({
+                "centroid": v,
+                "members": [d],
+                "weight_sum": d["weight"],
+            })
+
+    raw_count = len(clusters)
+    clusters = [c for c in clusters if len(c["members"]) >= min_articles]
+    clusters.sort(key=lambda c: -c["weight_sum"])
+    clusters = clusters[:max_topics]
+
+    normalize_importance_scores(clusters)
+    return clusters, raw_count
+
+
+def normalize_importance_scores(clusters: list[dict], *, floor: float = SCORE_FLOOR) -> None:
+    """cluster.weight_sum 을 0~1 로 정규화 + floor. in-place. raw_weight_sum 보존."""
+    if not clusters:
+        return
+    max_w = max(c["weight_sum"] for c in clusters)
+    for c in clusters:
+        normalized = (c["weight_sum"] / max_w) if max_w > 0 else 0.0
+        c["raw_weight_sum"] = c["weight_sum"]
+        c["importance_score"] = max(normalized, floor)

app/services/digest/clustering.py

@@ -1,20 +1,16 @@
-"""Time-decay weight + adaptive threshold + EMA centroid greedy clustering.
+"""Phase 4 Global Digest — country 내 topic cluster (time-decay + EMA + adaptive threshold).
 
-플랜의 핵심 결정:
-- λ = ln(2)/3 (3일 반감기)
-- threshold: 0.75 / 0.78 / 0.80 (밀도 기반 adaptive)
-- centroid: EMA α=0.7 (단순 평균의 seed bias / drift 방어)
-- min_articles_per_topic = 3, max_topics_per_country = 10
-- importance_score: country 내 0~1 normalize + max(score, 0.01) floor
-- raw_weight_sum 별도 보존 (cross-day 트렌드 분석용)
+알고리즘 코어는 `app/services/clustering_common.py` 로 추출되어 briefing 모듈과 공유.
+본 파일은 Phase 4 고유 파라미터 (LAMBDA = ln(2)/3 일, MIN 3, MAX 10) 와 country 축 호출만 담당.
 """
 
 import math
-from datetime import datetime, timezone
-
-import numpy as np
 
 from core.utils import setup_logger
+from services.clustering_common import (
+    adaptive_threshold_by_density,
+    greedy_assign_cluster,
+)
 
 logger = setup_logger("digest_clustering")
 
@@ -22,94 +18,32 @@ LAMBDA = math.log(2) / 3        # 3일 반감기 — 사용자 확정값
 CENTROID_ALPHA = 0.7            # EMA: 기존 중심 70% 유지, 새 멤버 30% 반영
 MIN_ARTICLES_PER_TOPIC = 3
 MAX_TOPICS_PER_COUNTRY = 10
-SCORE_FLOOR = 0.01              # UI 0 표시 문제 사전 차단
 
 
 def adaptive_threshold(n_docs: int) -> float:
-    """문서 밀도 기반 동적 threshold — fragmentation/blob 동시 방어."""
-    if n_docs > 200:
-        return 0.80
-    if n_docs < 50:
-        return 0.75
-    return 0.78
-
-
-def _normalize(v: np.ndarray) -> np.ndarray:
-    norm = float(np.linalg.norm(v))
-    if norm == 0.0:
-        return v
-    return v / norm
-
-
-def _decay_weight(now: datetime, created_at: datetime) -> float:
-    """exp(-λ * days_ago). created_at 이 naive 면 UTC 가정."""
-    if created_at.tzinfo is None:
-        created_at = created_at.replace(tzinfo=timezone.utc)
-    days = (now - created_at).total_seconds() / 86400.0
-    if days < 0:
-        days = 0.0
-    return math.exp(-LAMBDA * days)
+    """Phase 4 임계 (0.75 / 0.78 / 0.80). 외부 import 호환용 alias."""
+    return adaptive_threshold_by_density(n_docs)
 
 
 def cluster_country(country: str, docs: list[dict]) -> list[dict]:
     """단일 country 의 docs 를 cluster 로 묶어 정렬 + normalize 후 반환.
 
-    Args:
-        country: 국가 코드 (KR, US, ...)
-        docs: loader.load_news_window 의 출력 (단일 country 슬라이스)
-
-    Returns:
-        [{centroid, members, weight_sum, raw_weight_sum, importance_score}, ...]
-        - members 는 weight 가 채워진 doc dict 리스트
-        - 정렬: importance_score 내림차순, 최대 MAX_TOPICS_PER_COUNTRY 개
+    공통 util `greedy_assign_cluster` 위에 country 라벨 로깅만 추가.
     """
     if not docs:
         logger.info(f"[{country}] docs=0 → skip")
         return []
 
     threshold = adaptive_threshold(len(docs))
-    now = datetime.now(timezone.utc)
-
-    # time-decay weight 계산 + 가중치 높은 순으로 seed 우선
-    for d in docs:
-        d["weight"] = _decay_weight(now, d["created_at"])
-    docs.sort(key=lambda d: -d["weight"])
-
-    clusters: list[dict] = []
-    for d in docs:
-        v = _normalize(d["embedding"])
-        best_idx, best_sim = -1, 0.0
-        for i, c in enumerate(clusters):
-            sim = float(np.dot(c["centroid"], v))
-            if sim > best_sim and sim >= threshold:
-                best_sim, best_idx = sim, i
-        if best_idx >= 0:
-            c = clusters[best_idx]
-            # EMA centroid update — drift 방지
-            c["centroid"] = CENTROID_ALPHA * c["centroid"] + (1.0 - CENTROID_ALPHA) * v
-            c["centroid"] = _normalize(c["centroid"])
-            c["members"].append(d)
-            c["weight_sum"] += d["weight"]
-        else:
-            clusters.append({
-                "centroid": v,
-                "members": [d],
-                "weight_sum": d["weight"],
-            })
-
-    raw_count = len(clusters)
-    clusters = [c for c in clusters if len(c["members"]) >= MIN_ARTICLES_PER_TOPIC]
+    clusters, raw_count = greedy_assign_cluster(
+        docs,
+        threshold=threshold,
+        centroid_alpha=CENTROID_ALPHA,
+        min_articles=MIN_ARTICLES_PER_TOPIC,
+        max_topics=MAX_TOPICS_PER_COUNTRY,
+        lambda_val=LAMBDA,
+    )
     dropped = raw_count - len(clusters)
-    clusters.sort(key=lambda c: -c["weight_sum"])
-    clusters = clusters[:MAX_TOPICS_PER_COUNTRY]
-
-    # country 내 normalize (0~1) + floor
-    if clusters:
-        max_w = max(c["weight_sum"] for c in clusters)
-        for c in clusters:
-            normalized = (c["weight_sum"] / max_w) if max_w > 0 else 0.0
-            c["raw_weight_sum"] = c["weight_sum"]
-            c["importance_score"] = max(normalized, SCORE_FLOOR)
 
     logger.info(
         f"[{country}] docs={len(docs)} threshold={threshold} "

app/services/digest/selection.py

@@ -6,18 +6,13 @@ ai_summary 길이는 LLM 토큰 보호를 위해 SUMMARY_TRUNCATE 로 제한.
 
 import numpy as np
 
+from services.clustering_common import normalize_vector as _normalize
+
 K_PER_CLUSTER = 5
 LAMBDA_MMR = 0.7         # relevance 70% / diversity 30%
 SUMMARY_TRUNCATE = 300   # long tail ai_summary 방어
 
 
-def _normalize(v: np.ndarray) -> np.ndarray:
-    norm = float(np.linalg.norm(v))
-    if norm == 0.0:
-        return v
-    return v / norm
-
-
 def select_for_llm(cluster: dict, k: int = K_PER_CLUSTER) -> list[dict]:
     """cluster 내 LLM 호출용 대표 article 들 선정.