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

2026-05-21 22:07:41 +09:00
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+"""단순 매칭 매트릭스: MDX 섹션 × 8 방법 = 매칭된 프레임 번호 + 점수"""
+import sys
+import json
+import yaml
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+from common import load_ground_truth, load_figma_texts, load_mdx_sections
+from methods import (
+    method_tfidf, method_bm25, method_char_ngram,
+    method_kiwi_bm25, method_structural,
+    method_ai_metadata_matcher, method_weighted, method_hard_filter,
+)
+
+ROOT = Path(r"d:\ad-hoc\kei\design_agent")
+PREVIEW_DIR = ROOT / "data" / "figma_previews"
+
+
+def normalize_score(score, method_name):
+    """방법별 점수 스케일이 달라서 0~100% 범위로 정규화.
+    - TF-IDF, Char-ngram, AI-Meta, Weighted: 이미 0~1 → ×100
+    - Structural: 0~1 → ×100
+    - BM25, Kiwi+BM25, HardFilter: raw 점수 (0~200+) → top1을 100%로 상대화
+    """
+    return score  # 원래 점수 그대로 반환 (아래에서 별도 처리)
+
+
+def fmt_score(score, method_name, max_score=None):
+    """점수를 읽기 좋게 포매팅."""
+    if method_name in ("TF-IDF", "Char-ngram", "AI-Meta", "Weighted", "Structural"):
+        # 0~1 스케일 → %
+        return f"{score * 100:.0f}%"
+    else:
+        # BM25 계열: raw 점수. max 대비 상대값
+        if max_score and max_score > 0:
+            rel = score / max_score * 100
+            return f"{rel:.0f}% ({score:.1f})"
+        return f"{score:.1f}"
+
+
+def main():
+    gt_list = load_ground_truth()
+    figma = load_figma_texts()
+    mdx = load_mdx_sections()
+
+    with open(PREVIEW_DIR / "index.json", encoding="utf-8") as f:
+        idx_data = json.load(f)
+    frame_to_short = {info["frame_id"]: sid for sid, info in idx_data.items()}
+
+    with open(Path(__file__).parent / "metadata_db.yaml", encoding="utf-8") as f:
+        metadata_db = yaml.safe_load(f)
+
+    methods_def = [
+        ("TF-IDF", method_tfidf),
+        ("BM25", method_bm25),
+        ("Char-ngram", method_char_ngram),
+        ("Kiwi+BM25", method_kiwi_bm25),
+        ("Structural", method_structural),
+        ("AI-Meta", None),
+        ("Weighted", method_weighted),
+        ("HardFilter", method_hard_filter),
+    ]
+
+    # 각 섹션/방법별 Top-3 (score 포함)
+    top3_data = {}
+    for sec_id, sec_text in mdx.items():
+        top3_data[sec_id] = {}
+        for name, fn in methods_def:
+            if name == "AI-Meta":
+                ranked = method_ai_metadata_matcher(
+                    sec_id, metadata_db["figma_frames"], metadata_db["mdx_sections"]
+                )
+            else:
+                ranked = fn(sec_text, figma)
+            top3_data[sec_id][name] = ranked[:3]
+
+    # ──────────────────────
+    # 리포트 생성
+    # ──────────────────────
+    lines = []
+    lines.append("# MDX ↔ Figma 매칭 매트릭스 (점수 포함)")
+    lines.append("")
+    lines.append("각 셀 형식: `프레임번호(점수)`")
+    lines.append("")
+    lines.append("- TF-IDF / Char-ngram / AI-Meta / Weighted / Structural: 0~100% (코사인 or Jaccard)")
+    lines.append("- BM25 / Kiwi+BM25 / HardFilter: raw 점수 (상대 비교용, 절대값은 문서길이/어휘량에 따라 달라짐)")
+    lines.append("")
+
+    # ══ Top-1 매트릭스 (점수 포함) ══
+    lines.append("## Top-1 (1순위)")
+    lines.append("")
+    method_names = [m[0] for m in methods_def]
+    header = "| MDX 섹션 | " + " | ".join(method_names) + " |"
+    lines.append(header)
+    lines.append("|" + "---|" * (len(method_names) + 1))
+    for gt in gt_list:
+        sid = gt["id"]
+        cells = [sid]
+        for mname in method_names:
+            top = top3_data[sid][mname][:1]
+            if not top:
+                cells.append("-")
+                continue
+            fid, score = top[0]
+            short = frame_to_short.get(str(fid), "?")
+            cells.append(f"**{short}** ({fmt_score(score, mname)})")
+        lines.append("| " + " | ".join(cells) + " |")
+    lines.append("")
+
+    # ══ Top-3 매트릭스 (점수 포함) ══
+    lines.append("## Top-3 (1/2/3순위)")
+    lines.append("")
+    for gt in gt_list:
+        sid = gt["id"]
+        lines.append(f"### {sid}")
+        lines.append("")
+        lines.append("| 방법 | 1순위 | 2순위 | 3순위 |")
+        lines.append("|------|-------|-------|-------|")
+        for mname in method_names:
+            cells = [mname]
+            for fid, score in top3_data[sid][mname][:3]:
+                short = frame_to_short.get(str(fid), "?")
+                cells.append(f"**{short}** ({fmt_score(score, mname)})")
+            while len(cells) < 4:
+                cells.append("-")
+            lines.append("| " + " | ".join(cells) + " |")
+        lines.append("")
+
+    # ══ 프레임 번호 참고 ══
+    lines.append("## 프레임 번호 참고")
+    lines.append("")
+    lines.append("| # | 제목 | 미리보기 |")
+    lines.append("|---|------|---------|")
+    for sid in sorted(idx_data.keys()):
+        info = idx_data[sid]
+        title = info.get("title_text", "").strip().replace("\n", " ") or "_(제목 없음)_"
+        if len(title) > 50:
+            title = title[:50] + "…"
+        png_rel = f"../../data/figma_previews/{info['png']}"
+        lines.append(f"| **{sid}** | {title} | ![{sid}]({png_rel}) |")
+    lines.append("")
+
+    out_path = Path(__file__).parent / "MATRIX.md"
+    out_path.write_text("\n".join(lines), encoding="utf-8")
+    print(f"완료: {out_path}")
+
+
+if __name__ == "__main__":
+    main()