untracked files on main: dceb101 feat(#63): IMP-34 R1 donor capacity measured bound (u1+u2)

2026-05-21 22:07:41 +09:00
commit 8f085a28d3
3220 changed files with 985495 additions and 0 deletions
@@ -0,0 +1,121 @@
+"""Pipeline Step 15 (BM25) — 정식 BM25 로 랭킹 계산해서 현재 가중치 방식과 비교.
+
+BM25 공식 (표준):
+  score(D, Q) = Σ_{t∈Q ∩ D}  IDF(t) × (tf(t,D) × (k1+1)) / (tf(t,D) + k1 × (1 - b + b × |D|/avg_dl))
+  IDF(t) = log((N - n(t) + 0.5) / (n(t) + 0.5) + 1)
+
+설정:
+  D = 각 Figma 프레임 (키워드 멀티셋)
+  Q = MDX 섹션 토큰 집합
+  N = 32 프레임
+  n(t) = t 를 포함한 프레임 수 (frame_df)
+  tf(t, D) = 프레임 D 에서 t 의 local_count
+  |D| = 프레임 D 의 총 토큰 수
+  avg_dl = 모든 프레임 평균 길이
+  k1 = 1.5, b = 0.75 (표준값)
+
+비교: 현재 0.30/0.50/0.20 가중치 결과 vs BM25 결과.
+"""
+from collections import defaultdict
+from math import log
+from pathlib import Path
+import yaml
+
+HERE = Path(__file__).parent
+N_FRAMES = 32
+K1 = 1.5
+B = 0.75
+
+
+def main():
+    auto = yaml.safe_load((HERE / 'auto_anchor_candidates.yaml').read_text(encoding='utf-8'))
+    v1 = yaml.safe_load((HERE / 'mdx_matching_result.yaml').read_text(encoding='utf-8'))
+    normalized = yaml.safe_load((HERE / 'normalized_text_tokens.yaml').read_text(encoding='utf-8'))
+
+    # ─── 프레임별 토큰 multiset (tf = 등장 횟수) ───
+    frame_tokens = defaultdict(lambda: defaultdict(int))
+    for set_id, s in auto['source_text_sets'].items():
+        fid = s['frame_id']
+        for t in s.get('terms', []):
+            tok = t['token']
+            frame_tokens[fid][tok] += t.get('local_count_in_frame', 1)
+
+    # 프레임 길이 |D| (= 모든 토큰 local_count 합)
+    frame_len = {fid: sum(c.values()) for fid, c in frame_tokens.items()}
+    avg_dl = sum(frame_len.values()) / max(len(frame_len), 1)
+
+    # n(t): t 를 포함하는 프레임 수
+    frame_df_map = defaultdict(int)
+    for fid, counts in frame_tokens.items():
+        for tok in counts:
+            frame_df_map[tok] += 1
+
+    def idf_bm25(t):
+        n = frame_df_map.get(t, 0)
+        return log((N_FRAMES - n + 0.5) / (n + 0.5) + 1)
+
+    def bm25_score(frame_id, mdx_tokens):
+        counts = frame_tokens[frame_id]
+        dl = frame_len[frame_id]
+        if dl == 0:
+            return 0.0
+        score = 0.0
+        for t in mdx_tokens:
+            if t in counts:
+                tf = counts[t]
+                idf_val = idf_bm25(t)
+                denom = tf + K1 * (1 - B + B * dl / avg_dl)
+                tf_norm = tf * (K1 + 1) / denom
+                score += idf_val * tf_norm
+        return score
+
+    # frame_id → frame_number
+    frame_num_map = {fid: info['frame_number'] for fid, info in auto['frame_stats'].items()}
+
+    # ─── 각 MDX 섹션별 계산 ───
+    print('=' * 90)
+    print(f'{"섹션":<8} | {"순위":<3} | {"현재 (0.30/0.50/0.20)":<28} | {"BM25 (k1=1.5, b=0.75)":<28}')
+    print('=' * 90)
+
+    top1_agree = 0
+    top3_agree = 0
+    total = 0
+
+    for sid, sec in v1['mdx_sections'].items():
+        mdx_tokens = set(normalized['mdx'][sid].get('unique_tokens', []))
+        bm25_scores = {
+            fid: (bm25_score(fid, mdx_tokens), frame_num_map.get(fid))
+            for fid in frame_tokens
+        }
+        bm25_ranking = sorted(bm25_scores.items(), key=lambda x: -x[1][0])
+
+        current_ranking = sec['rank_by_matching_score']
+        total += 1
+        if current_ranking[0]['frame_id'] == bm25_ranking[0][0]:
+            top1_agree += 1
+        cur_top3 = {r['frame_id'] for r in current_ranking[:3]}
+        bm25_top3 = {x[0] for x in bm25_ranking[:3]}
+        if cur_top3 == bm25_top3:
+            top3_agree += 1
+
+        for i in range(5):
+            cur = current_ranking[i]
+            bm = bm25_ranking[i]
+            cur_s = f'Frame {cur["frame_number"]:>2}  score {cur["matching_score"]:.3f}'
+            bm_s = f'Frame {bm[1][1]:>2}  score {bm[1][0]:>6.2f}'
+            prefix = sid if i == 0 else ''
+            print(f'{prefix:<8} | {i+1:<3} | {cur_s:<28} | {bm_s:<28}')
+        print('-' * 90)
+
+    print()
+    print('=' * 90)
+    print(f'Top-1 일치: {top1_agree}/{total}')
+    print(f'Top-3 집합 일치: {top3_agree}/{total}')
+    print('=' * 90)
+    print()
+    print(f'N = {N_FRAMES} 프레임, avg_dl = {avg_dl:.1f} (평균 프레임 토큰 수)')
+    print(f'k1 = {K1}, b = {B} (표준 BM25 파라미터)')
+
+
+if __name__ == '__main__':
+    main()