"""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}** ({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()