Blame for scripts/17_eval_comparison.py

"""
Step 17: bge-m3 vs bge-m3+HyDE vs +HyDE+Rerank 비교 평가 (Top5)

세 검색 방식의 Top5 결과를 나란히 비교하는 Excel 출력.

실행:
    python scripts/17_eval_comparison.py

출력:
    data/generated/Menusearch_100_answer.xlsx (비교 시트로 교체)

Excel 컬럼 구조:
    A: No | B: 쿼리 | C: 정답 메뉴
    D~H : [bge-m3]            Top1~5
    I   : [bge-m3]            평가결과
    J   : [bge-m3]            Top1 유사도
    K~O : [+HyDE]             Top1~5
    P   : [+HyDE]             평가결과
    Q   : [+HyDE]             Top1 유사도
    R~V : [+HyDE+Rerank]      Top1~5
    W   : [+HyDE+Rerank]      평가결과
    X   : [+HyDE+Rerank]      Top1 유사도
    Y   : 변화 (+HyDE vs +HyDE+Rerank)
"""
import sys
import re
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent.parent))

import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Font, Alignment, Border, Side

from core.search_engine import MenuSearchEngine

EXCEL_PATH = Path(__file__).parent.parent / "data" / "generated" / "Menusearch_100_answer.xlsx"

# 결과 우선순위 (높을수록 좋음)
RANK_MAP = {"Top1 정답": 3, "Top3 정답": 2, "Top5 정답": 1, "오답": 0, "평가제외": -1}


# ---------------------------------------------------------------------------
# 정답 비교 유틸 (16_eval_with_answer.py 와 동일)
# ---------------------------------------------------------------------------

def normalize_path(path: str) -> str:
    if not path:
        return ""
    path = re.sub(r'\s*>\s*', '>', path)
    path = re.sub(r'\(.*?\)', '', path)
    return path.strip().lower()


def matches(pred: str, ans: str) -> bool:
    if not pred or not ans:
        return False
    if pred == ans:
        return True
    pred_end = pred.split(">")[-1].strip()
    ans_end  = ans.split(">")[-1].strip()
    if pred_end and ans_end and pred_end == ans_end:
        pred_parts = pred.split(">")
        ans_parts  = ans.split(">")
        overlap = sum(1 for p in pred_parts if p in ans_parts)
        return overlap >= min(2, len(ans_parts))
    return False


def evaluate(answer_cell, top_paths: list) -> tuple:
    if not answer_cell or pd.isna(answer_cell):
        return None, None, None
    ans_lower = str(answer_cell).lower()
    first_line = ans_lower.split("\n")[0]
    if ">" not in first_line and any(
        first_line.startswith(m) for m in ["hts", "계좌개설", "마이페이지"]
    ):
        return None, None, None
    valid_answers = [normalize_path(a) for a in str(answer_cell).split("\n") if a.strip()]
    norm_top = [normalize_path(p) for p in top_paths if p]
    def hit_at(k):
        return any(matches(p, a) for p in norm_top[:k] for a in valid_answers)
    return hit_at(1), hit_at(3), hit_at(5)


def label_from_eval(a1, a3, a5) -> str:
    if a1 is None:
        return "평가제외"
    if a1:
        return "Top1 정답"
    if a3:
        return "Top3 정답"
    if a5:
        return "Top5 정답"
    return "오답"


# ---------------------------------------------------------------------------
# 검색 실행 (단일 모드)
# ---------------------------------------------------------------------------

def run_search(engine, df_src: pd.DataFrame, use_hyde: bool, use_reranker: bool = False) -> list:
    if use_reranker:
        mode = "+HyDE+Rerank" if use_hyde else "+Rerank"
    else:
        mode = "+HyDE" if use_hyde else "bge-m3"
    print(f"\n[검색] {mode} 모드 시작...")
    results = []
    for _, row in df_src.iterrows():
        query      = str(row["쿼리"]).strip()
        answer_raw = row["정답 메뉴"] if pd.notna(row.get("정답 메뉴")) else None

        hits = engine.search(query, top_n=5, threshold=0.0, use_hyde=use_hyde, use_reranker=use_reranker)
        paths = [h.get("menu_path", "") for h in hits]
        while len(paths) < 5:
            paths.append("")
        sims = [round(h["similarity"], 4) for h in hits]
        while len(sims) < 5:
            sims.append(0.0)

        a1, a3, a5 = evaluate(answer_raw, paths)
        lbl = label_from_eval(a1, a3, a5)

        status = "[O]" if a1 else ("[  ]" if a1 is None else "[X]")
        print(f"  {status} [{int(row['No']):3d}] {query[:28]:<28} -> {paths[0][:40]}")

        results.append({
            "paths": paths,
            "sims":  sims,
            "label": lbl,
            "a1": a1, "a3": a3, "a5": a5,
        })
    return results


# ---------------------------------------------------------------------------
# 메인
# ---------------------------------------------------------------------------

def main():
    print("[17_eval] bge-m3 vs bge-m3+HyDE 비교 평가 시작")

    # ── 1. 원본 쿼리/정답 로드 ──────────────────────────────────────────────
    df_src = pd.read_excel(EXCEL_PATH, sheet_name="시트1", usecols=["No", "쿼리", "정답 메뉴"])
    df_src = df_src[pd.to_numeric(df_src["No"], errors="coerce").notna()].reset_index(drop=True)
    print(f"[17_eval] {len(df_src)}개 쿼리 로드 완료")

    # ── 2. 검색엔진 초기화 ──────────────────────────────────────────────────
    print("[17_eval] 검색엔진 초기화 중...")
    engine = MenuSearchEngine.get_instance()
    print("[17_eval] 검색엔진 준비 완료")

    # ── 3. 세 모드 검색 실행 ────────────────────────────────────────────────
    base_results  = run_search(engine, df_src, use_hyde=False, use_reranker=False)
    hyde_results  = run_search(engine, df_src, use_hyde=True,  use_reranker=False)
    rerank_results = run_search(engine, df_src, use_hyde=True, use_reranker=True)

    # ── 4. 통계 계산 ────────────────────────────────────────────────────────
    def calc_stats(results):
        a1_list = [r["a1"] for r in results]
        a3_list = [r["a3"] for r in results]
        a5_list = [r["a5"] for r in results]
        valid = sum(a is not None for a in a1_list)
        skip  = len(results) - valid
        n1 = sum(a for a in a1_list if a is not None)
        n3 = sum(a for a in a3_list if a is not None)
        n5 = sum(a for a in a5_list if a is not None)
        r1 = n1 / valid if valid else 0
        r3 = n3 / valid if valid else 0
        r5 = n5 / valid if valid else 0
        return valid, skip, n1, n3, n5, r1, r3, r5

    b_valid,  b_skip,  b_n1,  b_n3,  b_n5,  b_r1,  b_r3,  b_r5  = calc_stats(base_results)
    h_valid,  h_skip,  h_n1,  h_n3,  h_n5,  h_r1,  h_r3,  h_r5  = calc_stats(hyde_results)
    rr_valid, rr_skip, rr_n1, rr_n3, rr_n5, rr_r1, rr_r3, rr_r5 = calc_stats(rerank_results)

    print("\n" + "=" * 80)
    print("== 비교 결과 요약 ==")
    print("=" * 80)
    print(f"{'':20s} {'bge-m3':>12s}   {'+HyDE':>12s}   {'+HyDE+Rerank':>14s}")
    print(f"  {'Acc@1':<18s} {b_n1}/{b_valid}={b_r1:.1%}   {h_n1}/{h_valid}={h_r1:.1%}   {rr_n1}/{rr_valid}={rr_r1:.1%}")
    print(f"  {'Acc@3':<18s} {b_n3}/{b_valid}={b_r3:.1%}   {h_n3}/{h_valid}={h_r3:.1%}   {rr_n3}/{rr_valid}={rr_r3:.1%}")
    print(f"  {'Acc@5':<18s} {b_n5}/{b_valid}={b_r5:.1%}   {h_n5}/{h_valid}={h_r5:.1%}   {rr_n5}/{rr_valid}={rr_r5:.1%}")
    print("=" * 80)

    # ── 5. DataFrame 구성 ───────────────────────────────────────────────────
    rows = []
    for i, (_, src_row) in enumerate(df_src.iterrows()):
        br  = base_results[i]
        hr  = hyde_results[i]
        rr  = rerank_results[i]

        hyde_rank   = RANK_MAP.get(hr["label"],  -1)
        rerank_rank = RANK_MAP.get(rr["label"],  -1)
        if hr["label"] == "평가제외":
            change = "제외"
        elif rerank_rank > hyde_rank:
            change = "개선"
        elif rerank_rank < hyde_rank:
            change = "하락"
        else:
            change = "동일"

        rows.append({
            "No":              src_row["No"],
            "쿼리":            str(src_row["쿼리"]).strip(),
            "정답 메뉴":       src_row["정답 메뉴"] if pd.notna(src_row.get("정답 메뉴")) else "",
            # bge-m3
            "bge Top1":  br["paths"][0], "bge Top2":  br["paths"][1], "bge Top3":  br["paths"][2],
            "bge Top4":  br["paths"][3], "bge Top5":  br["paths"][4],
            "bge 결과":  br["label"],    "bge 유사도": br["sims"][0],
            # +HyDE
            "hyde Top1": hr["paths"][0], "hyde Top2": hr["paths"][1], "hyde Top3": hr["paths"][2],
            "hyde Top4": hr["paths"][3], "hyde Top5": hr["paths"][4],
            "hyde 결과": hr["label"],    "hyde 유사도": hr["sims"][0],
            # +HyDE+Rerank
            "rr Top1":   rr["paths"][0], "rr Top2":   rr["paths"][1], "rr Top3":   rr["paths"][2],
            "rr Top4":   rr["paths"][3], "rr Top5":   rr["paths"][4],
            "rr 결과":   rr["label"],    "rr 유사도":  rr["sims"][0],
            # 변화 (+HyDE vs +HyDE+Rerank)
            "변화":      change,
        })

    df_out = pd.DataFrame(rows)
    df_out.columns = [
        "No", "쿼리", "정답 메뉴",
        "[bge-m3] Top1", "[bge-m3] Top2", "[bge-m3] Top3", "[bge-m3] Top4", "[bge-m3] Top5",
        "[bge-m3] 결과", "[bge-m3] 유사도",
        "[+HyDE] Top1",  "[+HyDE] Top2",  "[+HyDE] Top3",  "[+HyDE] Top4",  "[+HyDE] Top5",
        "[+HyDE] 결과",  "[+HyDE] 유사도",
        "[+HyDE+Rerank] Top1", "[+HyDE+Rerank] Top2", "[+HyDE+Rerank] Top3",
        "[+HyDE+Rerank] Top4", "[+HyDE+Rerank] Top5",
        "[+HyDE+Rerank] 결과", "[+HyDE+Rerank] 유사도",
        "변화",
    ]

    df_out.to_excel(EXCEL_PATH, sheet_name="시트1", index=False)

    # ── 6. Excel 스타일링 ────────────────────────────────────────────────────
    wb = load_workbook(EXCEL_PATH)
    ws = wb["시트1"]

    # 열 너비 (A~Y, 25컬럼)
    col_widths = {
        "A": 5,  "B": 34, "C": 34,
        "D": 28, "E": 28, "F": 28, "G": 28, "H": 28, "I": 12, "J": 11,
        "K": 28, "L": 28, "M": 28, "N": 28, "O": 28, "P": 12, "Q": 11,
        "R": 28, "S": 28, "T": 28, "U": 28, "V": 28, "W": 12, "X": 11,
        "Y": 9,
    }
    for col, w in col_widths.items():
        ws.column_dimensions[col].width = w
    ws.row_dimensions[1].height = 28

    HDR_BASE   = "1F4E79"   # bge-m3 (진파랑)
    HDR_HYDE   = "1A5276"   # +HyDE (중간파랑)
    HDR_RERANK = "145A32"   # +Rerank (진초록)
    HDR_ETC    = "2C3E50"   # 공통 (차콜)

    FILL_MAP = {
        "Top1 정답": PatternFill("solid", start_color="C6EFCE"),
        "Top3 정답": PatternFill("solid", start_color="FFEB9C"),
        "Top5 정답": PatternFill("solid", start_color="FCE4D6"),
        "오답":      PatternFill("solid", start_color="FFC7CE"),
        "평가제외":  PatternFill("solid", start_color="F2F2F2"),
    }
    CHANGE_FILL = {
        "개선": PatternFill("solid", start_color="ABEBC6"),
        "하락": PatternFill("solid", start_color="F1948A"),
        "동일": PatternFill("solid", start_color="EBF5FB"),
        "제외": PatternFill("solid", start_color="F2F2F2"),
    }
    CHANGE_FC = {"개선": "1E8449", "하락": "922B21", "동일": "1A5276", "제외": "7F7F7F"}

    # 헤더
    for cell in ws[1]:
        cl = cell.column_letter
        if cl in ("A", "B", "C", "Y"):
            bg = HDR_ETC
        elif cl in ("D", "E", "F", "G", "H", "I", "J"):
            bg = HDR_BASE
        elif cl in ("K", "L", "M", "N", "O", "P", "Q"):
            bg = HDR_HYDE
        else:
            bg = HDR_RERANK
        cell.fill = PatternFill("solid", start_color=bg)
        cell.font = Font(bold=True, color="FFFFFF", name="맑은 고딕", size=9)
        cell.alignment = Alignment(horizontal="center", vertical="center", wrap_text=True)

    # 데이터 행
    for row in ws.iter_rows(min_row=2, max_row=ws.max_row):
        rc = {cell.column_letter: cell for cell in row}
        bge_lbl    = rc.get("I").value if rc.get("I") else ""
        hyde_lbl   = rc.get("P").value if rc.get("P") else ""
        rerank_lbl = rc.get("W").value if rc.get("W") else ""
        change_val = rc.get("Y").value if rc.get("Y") else ""

        for cell in row:
            cell.font = Font(name="맑은 고딕", size=8)
            cell.alignment = Alignment(wrap_text=True, vertical="center")

        for col_letter, lbl in [("I", bge_lbl), ("P", hyde_lbl), ("W", rerank_lbl)]:
            if rc.get(col_letter) and lbl:
                rc[col_letter].fill = FILL_MAP.get(lbl, FILL_MAP["평가제외"])
                rc[col_letter].font = Font(name="맑은 고딕", size=8, bold=True)
                rc[col_letter].alignment = Alignment(horizontal="center", vertical="center")

        for col_letter in ("J", "Q", "X"):
            if rc.get(col_letter):
                rc[col_letter].alignment = Alignment(horizontal="center", vertical="center")

        if rc.get("Y") and change_val:
            rc["Y"].fill = CHANGE_FILL.get(change_val, CHANGE_FILL["동일"])
            rc["Y"].font = Font(name="맑은 고딕", size=8, bold=True,
                                color=CHANGE_FC.get(change_val, "000000"))
            rc["Y"].alignment = Alignment(horizontal="center", vertical="center")

        if rc.get("A"):
            rc["A"].alignment = Alignment(horizontal="center", vertical="center")

    # ── 요약 행 ─────────────────────────────────────────────────────────────
    ws.append([])
    ws.append([
        "", "【요약】",
        f"유효 {b_valid}개 / 제외 {b_skip}개",
        f"Acc@1: {b_r1:.1%}", f"Acc@3: {b_r3:.1%}", f"Acc@5: {b_r5:.1%}",
        "", "", "", "",
        f"Acc@1: {h_r1:.1%}", f"Acc@3: {h_r3:.1%}", f"Acc@5: {h_r5:.1%}",
        "", "", "", "",
        f"Acc@1: {rr_r1:.1%}", f"Acc@3: {rr_r3:.1%}", f"Acc@5: {rr_r5:.1%}",
        "", "", "", "",
        "",
    ])
    sum_fill = PatternFill("solid", start_color="D9E1F2")
    for cell in ws[ws.max_row]:
        cell.fill = sum_fill
        cell.font = Font(bold=True, name="맑은 고딕", size=10)
        cell.alignment = Alignment(horizontal="center", vertical="center")

    # 개선 델타 행 (vs bge-m3 baseline)
    ws.append([
        "", "【개선폭 vs bge-m3】", "",
        "-", "-", "-", "", "", "", "",
        f"+{h_r1 - b_r1:.1%}", f"+{h_r3 - b_r3:.1%}", f"+{h_r5 - b_r5:.1%}",
        "", "", "", "",
        f"+{rr_r1 - b_r1:.1%}", f"+{rr_r3 - b_r3:.1%}", f"+{rr_r5 - b_r5:.1%}",
        "", "", "", "",
        "",
    ])
    delta_fill = PatternFill("solid", start_color="E2EFDA")
    for cell in ws[ws.max_row]:
        cell.fill = delta_fill
        cell.font = Font(bold=True, name="맑은 고딕", size=10, color="375623")
        cell.alignment = Alignment(horizontal="center", vertical="center")

    wb.save(EXCEL_PATH)
    print(f"\n[17_eval] 저장 완료: {EXCEL_PATH}")
    print(f"  bge-m3:         Acc@1={b_r1:.1%}  Acc@3={b_r3:.1%}  Acc@5={b_r5:.1%}")
    print(f"  +HyDE:          Acc@1={h_r1:.1%}  Acc@3={h_r3:.1%}  Acc@5={h_r5:.1%}")
    print(f"  +HyDE+Rerank:   Acc@1={rr_r1:.1%}  Acc@3={rr_r3:.1%}  Acc@5={rr_r5:.1%}")
    print(f"  최종 개선폭:    Acc@1 +{rr_r1-b_r1:.1%}  Acc@3 +{rr_r3-b_r3:.1%}  Acc@5 +{rr_r5-b_r5:.1%}")


if __name__ == "__main__":
    main()