178 lines
7.5 KiB
Python
178 lines
7.5 KiB
Python
"""Phase 21b — ontology 기반 구조축 (tie-breaker 가중치)
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공식: 점수 = 0.5 × 키워드 + 0.3 × 내용 + 0.1 × 구조(v3 ontology)
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(구조는 tie-breaker 수준)
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Phase 21(원본, old structure) vs Phase 21b(new ontology) 비교 의도.
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"""
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import sys
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import json
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import math
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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 phase_common import (
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TARGET_UNITS, load_synonyms, load_32_frames, compute_df_idf_tier,
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extract_mdx_keywords, keyword_score, content_scores_batch,
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load_target_units, load_frame_index, normalize_with_synonyms,
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)
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from structure_v3 import load_ontology, detect_mdx_structure_v3, structural_match_v3
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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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def main():
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synonyms = load_synonyms()
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frames = load_32_frames()
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df, idf, tier, N = compute_df_idf_tier(frames)
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vocab = set()
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for v in frames.values(): vocab.update(v["keywords"])
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units_full, units_title = load_target_units()
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idx_data, frame_to_short = load_frame_index()
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ontology = load_ontology()
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W_KW, W_CONTENT, W_STRUCT = 0.5, 0.3, 0.1
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reports = []
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hits = 0
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for uid, display, correct_sid, *_ in TARGET_UNITS:
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text_orig = units_full[uid]
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text = normalize_with_synonyms(text_orig, synonyms) # synonym 정규화 공통 적용
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title = units_title[uid]
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mdx_kws = extract_mdx_keywords(text, vocab, synonyms=None) # 이미 정규화됨
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mdx_schema = detect_mdx_structure_v3(text, title) # 정규화된 text 사용
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c_scores = content_scores_batch(title, frames)
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ranked = []
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for fid, v in frames.items():
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fig_kws = set(v["keywords"])
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k_s, inter = keyword_score(mdx_kws, fig_kws, idf, tier)
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c_s = c_scores.get(fid, 0.0)
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s_s, aligns = structural_match_v3(mdx_schema, ontology.get(fid, {}))
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final = W_KW * k_s + W_CONTENT * c_s + W_STRUCT * s_s
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ranked.append((fid, final, {
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"kw": k_s, "content": c_s, "struct": s_s,
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"aligns": aligns, "inter": sorted(inter),
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}))
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ranked.sort(key=lambda x: -x[1])
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top3 = ranked[:3]
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top_sid = frame_to_short.get(top3[0][0], "?")
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if top_sid == correct_sid:
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hits += 1
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reports.append({
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"uid": uid, "display": display, "correct_sid": correct_sid,
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"mdx_kws": mdx_kws, "mdx_title": title, "mdx_schema": mdx_schema,
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"top3": top3,
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})
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print(f"{uid}(정답={correct_sid}): "
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f"Top1={top_sid} ({top3[0][1]:.3f}) "
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f"[{'✓' if top_sid == correct_sid else '✗'}] "
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f"struct={top3[0][2]['struct']:.2f}")
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print(f"\n=== Phase 21b 결과: {hits}/4 ===")
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# MD 리포트
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png_rel = "../../data/figma_previews/"
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lines = []
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lines.append(f"# Phase 21b — Ontology 기반 구조축 (tie-breaker)")
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lines.append("")
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lines.append(f"**공식**: `점수 = {W_KW} × 키워드 + {W_CONTENT} × 내용 + {W_STRUCT} × 구조(ontology)`")
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lines.append(f"**결과: {hits}/4**")
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lines.append("")
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lines.append("**Phase 21 원본 대비 차이:**")
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lines.append("- 구조 매칭 로직: `structural_match_v2` (layout 문자열) → `structural_match_v3` (속성 교집합)")
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lines.append("- 구조 가중치: 0.2 → 0.1 (tie-breaker 수준)")
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lines.append("- 구조 ontology: layout 문자열 → `structure_ontology.yaml` 속성 schema")
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lines.append("- MDX 구조 감지: ### 서브섹션/표헤더/블릿 기반 schema emit")
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lines.append("- confidence 체크: low면 구조 점수 0")
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lines.append("")
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# 통합 매트릭스
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lines.append("## 테스트 결과")
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lines.append("")
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lines.append("| 콘텐츠 | 1순위 | 2순위 | 3순위 |")
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lines.append("|--------|-------|-------|-------|")
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for r in reports:
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row = [f"**{r['display']}**<br>정답 Frame **{r['correct_sid']}**"]
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for rank_idx in range(3):
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fid, score, bd = r["top3"][rank_idx]
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sid = frame_to_short.get(fid, "?")
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info = idx_data.get(sid, {})
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png = info.get("png", "")
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title = info.get("title_text", "").strip().replace("\n", " ")[:15] + "…"
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inner = f"<br>**{sid}** ({score:.3f})<br>{title}"
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if sid == r["correct_sid"]:
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cell = f"<div style='background:#fff3cd;border:3px solid #dc2626;padding:8px;border-radius:6px'>🎯 <b>정답</b><br>{inner}</div>"
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else:
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cell = inner
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row.append(cell)
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lines.append("| " + " | ".join(row) + " |")
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lines.append("")
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# 세부 결과
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lines.append("## 테스트 결과 세부")
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lines.append("")
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for r in reports:
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lines.append(f"### {r['display']} — 정답 Frame **{r['correct_sid']}**")
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lines.append("")
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lines.append(f"**MDX 구조 (v3 emit)**")
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lines.append("```yaml")
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for k, vv in r["mdx_schema"].items():
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lines.append(f"{k}: {vv}")
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lines.append("```")
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lines.append("")
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lines.append(f"**MDX 키워드 ({len(r['mdx_kws'])}개)**: {', '.join(sorted(r['mdx_kws']))}")
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lines.append("")
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for rank, (fid, score, bd) in enumerate(r["top3"], 1):
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sid = frame_to_short.get(fid, "?")
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fig_schema = ontology.get(fid, {})
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info = idx_data.get(sid, {})
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png = info.get("png", "")
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fig_content = frames[fid].get("content", "")
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common = ", ".join(bd["inter"]) if bd["inter"] else "(없음)"
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align_str = ", ".join(bd["aligns"]) if bd["aligns"] else "-"
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is_correct = sid == r["correct_sid"]
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if is_correct:
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lines.append(f"**<span style='background:#fff3cd;border:2px solid #dc2626;padding:2px 8px;border-radius:3px'>🎯 {rank}위 Frame {sid} (정답)</span>**")
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else:
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lines.append(f"**{rank}위 Frame {sid}**")
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lines.append("")
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lines.append(f"")
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lines.append("")
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lines.append(f"- **매칭 내용**: {fig_content}")
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lines.append(f"- **매칭 키워드**: {common}")
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lines.append(f"- **Figma schema**: family=`{fig_schema.get('family')}`, "
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f"columns={fig_schema.get('columns')}, "
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f"role=`{fig_schema.get('semantic_role')}`, "
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f"conf=`{fig_schema.get('confidence')}`")
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lines.append(f"- **구조 속성 정렬**: {align_str}")
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lines.append(f"- **축별 점수**: 키워드 {bd['kw']:.3f} / 내용 {bd['content']:.3f} / 구조 {bd['struct']:.2f}")
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lines.append(f"- **최종**: **{score:.3f}**")
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lines.append("")
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out = Path(__file__).parent / "MATRIX_PHASE21b.md"
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out.write_text("\n".join(lines), encoding="utf-8")
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print(f"완료: {out}")
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# 별도 결과 저장 (phase22용)
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import pickle
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serializable = {
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"phase": "21b",
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"desc": "synonym + 키워드 + 내용 + 구조(ontology v3)",
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"weights": (W_KW, W_CONTENT, W_STRUCT),
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"hits": hits,
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"reports": [{
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"uid": r["uid"], "display": r["display"], "correct_sid": r["correct_sid"],
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"mdx_kws_count": len(r["mdx_kws"]),
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"top3": [(fid, score, frame_to_short.get(fid, "?"), bd) for fid, score, bd in r["top3"]],
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} for r in reports]
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}
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with open(Path(__file__).parent / "_phase21b_results.pkl", "wb") as f:
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pickle.dump(serializable, f)
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if __name__ == "__main__":
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main()
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