"""Phase 2: 7개 방법 통합 매트릭스 (3 카테고리) - 키워드 (3): TF-IDF, Char n-gram, Kiwi+BM25 - 구조 (1): 구조 메타데이터 - 의미 (3): Sentence-BERT 문장 / 단어평균 / IDF가중""" import sys import json from pathlib import Path sys.path.insert(0, str(Path(__file__).parent)) from common import load_figma_texts from extract_units import extract_units from methods import ( method_tfidf, method_char_ngram, method_kiwi_bm25, method_structural, method_sbert_sentence, method_sbert_summary, method_sbert_chunk, ) ROOT = Path(r"d:\ad-hoc\kei\design_agent") PREVIEW_DIR = ROOT / "data" / "figma_previews" UNIT_ORDER = [ ("MDX01-intro", "중목차 앞 본문"), ("MDX01-intro-details", "팝업"), ("MDX01-1", "중목차"), ("MDX01-2", "중목차"), ("MDX01-2-image", "이미지"), ("MDX01-2-details", "팝업+표"), ("MDX02-1", "중목차"), ("MDX02-1-image", "이미지"), ("MDX02-2", "중목차(컨테이너)"), ("MDX02-2.1", "소목차"), ("MDX02-2.2", "소목차"), ("MDX02-2.2-table", "표"), ("MDX03-1", "중목차"), ("MDX03-2", "중목차(컨테이너)"), ("MDX03-2.1", "소목차"), ("MDX03-2.1-table", "표"), ("MDX03-2.2", "소목차"), ] def fmt_score(score, method_name): if method_name in ("TF-IDF", "Char-ngram", "Structural", "SBERT-원본", "SBERT-축약", "SBERT-청크"): return f"{score * 100:.0f}%" else: return f"{score:.1f}" def main(): units = extract_units() figma = load_figma_texts() 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()} # 7개 방법 methods_def = [ ("TF-IDF", method_tfidf), ("Char-ngram", method_char_ngram), ("Kiwi+BM25", method_kiwi_bm25), ("Structural", method_structural), ("SBERT-원본", method_sbert_sentence), ("SBERT-축약", method_sbert_summary), ("SBERT-청크", method_sbert_chunk), ] print("[Phase 2] 7개 방법 × 17 유닛 실행 시작...") results = {} for i, (uid, unit_text) in enumerate(units.items(), 1): print(f" [{i}/{len(units)}] {uid}") results[uid] = {} for mname, fn in methods_def: if not unit_text.strip(): results[uid][mname] = [] continue try: ranked = fn(unit_text, figma) results[uid][mname] = ranked[:3] except Exception as e: print(f" ERROR {mname}: {e}") results[uid][mname] = [] # 리포트 png_rel = "../../data/figma_previews/" lines = [] lines.append("# Phase 2 — 7개 방법 매트릭스") lines.append("") lines.append("3 카테고리 비교: **키워드 매칭 (3) + 구조 매칭 (1) + 의미 매칭 (3)**") lines.append("") lines.append("의미 매칭 3가지 — 같은 모델(ko-sroberta, 420MB) × 다른 전략:") lines.append("- **SBERT-원본**: 전체 텍스트를 한 벡터로 (그대로)") lines.append("- **SBERT-축약**: 제목+볼드 라벨만 뽑아서 짧게 축약 후 임베딩") lines.append("- **SBERT-청크**: 블릿/섹션 단위로 쪼개 각 chunk별 최대 유사도 평균") lines.append("") for i, (uid, kind) in enumerate(UNIT_ORDER, 1): lines.append(f"---") lines.append("") lines.append(f"## {i}. {uid} ({kind})") lines.append("") lines.append("| 방법 | 1순위 | 2순위 | 3순위 |") lines.append("|------|-------|-------|-------|") for mname, _ in methods_def: cells = [mname] top3 = results[uid].get(mname, []) if not top3: cells += ["-", "-", "-"] else: for fid, score in top3: short = frame_to_short.get(str(fid), "?") info = idx_data.get(short, {}) png_file = info.get("png", "") title = info.get("title_text", "").strip().replace("\n", " ") or "" if len(title) > 15: title = title[:15] + "…" cell = f"![{short}]({png_rel}{png_file})
**{short}** ({fmt_score(score, mname)})
{title}" cells.append(cell) while len(cells) < 4: cells.append("-") lines.append("| " + " | ".join(cells) + " |") lines.append("") out_path = Path(__file__).parent / "MATRIX_PHASE2.md" out_path.write_text("\n".join(lines), encoding="utf-8") print(f"\n완료: {out_path}") if __name__ == "__main__": main()