135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
"""Phase 3: 9개 방법 × 17 유닛 매트릭스 (의미 매칭 5개 family 비교)
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- 키워드 (3): TF-IDF, Char n-gram, Kiwi+BM25
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- 구조 (1): 구조 메타데이터
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- 의미 (5): SBERT / KoSimCSE / E5 / Cross-encoder / Word-avg
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"""
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import sys
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import json
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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from common import load_figma_texts
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from extract_units import extract_units
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from methods import (
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method_tfidf, method_char_ngram, method_kiwi_bm25,
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method_structural,
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method_sbert_sentence,
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method_kosimcse, method_e5, method_cross_encoder, method_wordavg,
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)
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ROOT = Path(r"d:\ad-hoc\kei\design_agent")
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PREVIEW_DIR = ROOT / "data" / "figma_previews"
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UNIT_ORDER = [
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("MDX01-intro", "중목차 앞 본문"),
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("MDX01-intro-details", "팝업"),
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("MDX01-1", "중목차"),
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("MDX01-2", "중목차"),
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("MDX01-2-image", "이미지"),
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("MDX01-2-details", "팝업+표"),
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("MDX02-1", "중목차"),
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("MDX02-1-image", "이미지"),
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("MDX02-2", "중목차(컨테이너)"),
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("MDX02-2.1", "소목차"),
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("MDX02-2.2", "소목차"),
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("MDX02-2.2-table", "표"),
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("MDX03-1", "중목차"),
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("MDX03-2", "중목차(컨테이너)"),
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("MDX03-2.1", "소목차"),
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("MDX03-2.1-table", "표"),
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("MDX03-2.2", "소목차"),
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]
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def fmt_score(score, method_name):
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# 모두 [0,1] 범위이므로 % 표기
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return f"{score * 100:.0f}%"
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def main():
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units = extract_units()
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figma = load_figma_texts()
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with open(PREVIEW_DIR / "index.json", encoding="utf-8") as f:
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idx_data = json.load(f)
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frame_to_short = {info["frame_id"]: sid for sid, info in idx_data.items()}
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methods_def = [
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("TF-IDF", method_tfidf),
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("Char-ngram", method_char_ngram),
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("Kiwi+BM25", method_kiwi_bm25),
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("Structural", method_structural),
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("SBERT", method_sbert_sentence),
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("KoSimCSE", method_kosimcse),
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("E5", method_e5),
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("Cross-encoder", method_cross_encoder),
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("Word-avg", method_wordavg),
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]
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print("[Phase 3] 9개 방법 × 17 유닛 실행 시작...")
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results = {}
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for i, (uid, unit_text) in enumerate(units.items(), 1):
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print(f" [{i}/{len(units)}] {uid}")
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results[uid] = {}
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for mname, fn in methods_def:
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if not unit_text.strip():
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results[uid][mname] = []
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continue
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try:
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ranked = fn(unit_text, figma)
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results[uid][mname] = ranked[:3]
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except Exception as e:
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print(f" ERROR {mname}: {e}")
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results[uid][mname] = []
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# 리포트
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png_rel = "../../data/figma_previews/"
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lines = []
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lines.append("# Phase 3 — 9개 방법 매트릭스 (의미 매칭 5개 family 비교)")
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lines.append("")
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lines.append("3 카테고리: **키워드 (3) + 구조 (1) + 의미 (5 family)**")
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lines.append("")
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lines.append("### 의미 매칭 5 family")
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lines.append("- **SBERT** (jhgan/ko-sroberta-multitask) — NLI+STS 학습, 한국어 기본")
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lines.append("- **KoSimCSE** (BM-K/KoSimCSE-roberta-multitask) — 대조학습 기반 sentence embedding")
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lines.append("- **E5** (intfloat/multilingual-e5-base) — 다국어 retrieval, query/passage 비대칭 prefix")
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lines.append("- **Cross-encoder** (Dongjin-kr/ko-reranker) — 임베딩 X, 질의-문서 쌍 직접 채점")
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lines.append("- **Word-avg** — 단어 임베딩 평균 (FastText 계열 baseline)")
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lines.append("")
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for i, (uid, kind) in enumerate(UNIT_ORDER, 1):
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lines.append(f"---")
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lines.append("")
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lines.append(f"## {i}. {uid} ({kind})")
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lines.append("")
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lines.append("| 방법 | 1순위 | 2순위 | 3순위 |")
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lines.append("|------|-------|-------|-------|")
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for mname, _ in methods_def:
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cells = [mname]
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top3 = results[uid].get(mname, [])
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if not top3:
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cells += ["-", "-", "-"]
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else:
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for fid, score in top3:
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short = frame_to_short.get(str(fid), "?")
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info = idx_data.get(short, {})
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png_file = info.get("png", "")
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title = info.get("title_text", "").strip().replace("\n", " ") or ""
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if len(title) > 15:
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title = title[:15] + "…"
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cell = f"<br>**{short}** ({fmt_score(score, mname)})<br>{title}"
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cells.append(cell)
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while len(cells) < 4:
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cells.append("-")
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lines.append("| " + " | ".join(cells) + " |")
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lines.append("")
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out_path = Path(__file__).parent / "MATRIX_PHASE3.md"
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out_path.write_text("\n".join(lines), encoding="utf-8")
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print(f"\n완료: {out_path}")
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if __name__ == "__main__":
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main()
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