"""Pipeline Step 15 — IDF 기반 vs 현재 0.30/0.50/0.20 가중치 랭킹 비교.

질문: 현재 0.30/0.50/0.20 고정 가중치 대신 TF-IDF 원리로 계산하면
     프레임 랭킹이 같게 나오는가?

방법:
  1. 각 프레임의 개별 키워드 (핵심+연관) + 복합 키워드(세트) 모으기
  2. 개별 토큰 IDF = log(32 / frame_df(token))
  3. 복합 토큰 IDF = log(32 / "그 모든 키워드를 다 가진 프레임 수")
  4. 매칭 점수 = Σ IDF × match / Σ IDF
     - 개별 토큰: match = 1 if 등장 else 0
     - 복합 토큰: match = coverage (부분 일치 허용)
  5. 기존 0.30/0.50/0.20 랭킹과 비교
"""
from collections import defaultdict
from math import log
from pathlib import Path
import yaml

HERE = Path(__file__).parent
N_FRAMES = 32


def main():
    auto = yaml.safe_load((HERE / 'auto_anchor_candidates.yaml').read_text(encoding='utf-8'))
    v1 = yaml.safe_load((HERE / 'mdx_matching_result.yaml').read_text(encoding='utf-8'))

    # ─── 1. 프레임별 개별 토큰 집합 ───
    frame_tokens = defaultdict(set)
    token_frame_df = {}
    for set_id, s in auto['source_text_sets'].items():
        fid = s['frame_id']
        for t in s.get('terms', []):
            tok = t['token']
            frame_tokens[fid].add(tok)
            if tok not in token_frame_df:
                token_frame_df[tok] = t.get('frame_df', 1)

    # ─── 2. 프레임별 복합 토큰 (세트) ───
    frame_compounds = defaultdict(list)
    for set_id, s in auto['source_text_sets'].items():
        fid = s['frame_id']
        comp = tuple(sorted(set(s['term_values'])))
        if comp:
            frame_compounds[fid].append(comp)

    # ─── 3. 복합 토큰 df (모든 키워드를 포함하는 프레임 수) ───
    all_compounds = set()
    for comps in frame_compounds.values():
        all_compounds.update(comps)
    compound_df = {}
    for comp in all_compounds:
        df = sum(1 for fid, tokens in frame_tokens.items() if all(k in tokens for k in comp))
        compound_df[comp] = max(df, 1)

    # ─── 4. IDF 계산 ───
    idf_individual = {t: log(N_FRAMES / df) for t, df in token_frame_df.items() if df}
    idf_compound = {c: log(N_FRAMES / df) for c, df in compound_df.items()}

    # ─── 5. 각 MDX 섹션 × 프레임 IDF-based 점수 ───
    print('=' * 80)
    print(f'{"섹션":<8} | {"순위":<4} | {"현재 (0.30/0.50/0.20)":<30} | {"IDF-based":<30}')
    print('=' * 80)

    agreement_top1 = 0
    agreement_top3 = 0
    total_sections = 0

    for sid, sec in v1['mdx_sections'].items():
        idf_scores = {}
        for frame_id, detail in sec['per_frame_detail'].items():
            # 개별 토큰 (핵심 + 연관)
            individual_tokens = frame_tokens.get(frame_id, set())
            standalone_hit = set(detail['standalone']['hit'])
            related_hit = set(detail['related']['hit'])
            all_hits = standalone_hit | related_hit

            total_idf = 0.0
            matched_idf = 0.0

            for tok in individual_tokens:
                idf = idf_individual.get(tok, 0)
                total_idf += idf
                if tok in all_hits:
                    matched_idf += idf

            # 복합 토큰 (세트)
            for comp in frame_compounds.get(frame_id, []):
                idf = idf_compound.get(comp, 0)
                total_idf += idf
                # coverage 를 per_frame_detail.keyword_group.groups 에서 찾기
                coverage = 0
                for g in detail.get('keyword_group', {}).get('groups', []):
                    g_comp = tuple(sorted(set(g['keywords'])))
                    if g_comp == comp:
                        coverage = g.get('coverage', 0)
                        break
                matched_idf += idf * coverage

            score = matched_idf / total_idf if total_idf > 0 else 0
            idf_scores[frame_id] = (score, detail['frame_number'])

        idf_ranking = sorted(idf_scores.items(), key=lambda x: -x[1][0])
        current_ranking = sec['rank_by_matching_score']

        total_sections += 1
        if current_ranking[0]['frame_id'] == idf_ranking[0][0]:
            agreement_top1 += 1
        current_top3 = {r['frame_id'] for r in current_ranking[:3]}
        idf_top3 = {x[0] for x in idf_ranking[:3]}
        if current_top3 == idf_top3:
            agreement_top3 += 1

        for i in range(5):
            cur = current_ranking[i]
            idf_r = idf_ranking[i]
            cur_s = f'Frame {cur["frame_number"]:>2}  {cur["matching_score"]:.3f}'
            idf_s = f'Frame {idf_r[1][1]:>2}  {idf_r[1][0]:.3f}'
            if i == 0:
                print(f'{sid:<8} | {i+1:<4} | {cur_s:<30} | {idf_s:<30}')
            else:
                print(f'{"":<8} | {i+1:<4} | {cur_s:<30} | {idf_s:<30}')
        print('-' * 80)

    print()
    print('=' * 80)
    print(f'Top-1 일치: {agreement_top1}/{total_sections}')
    print(f'Top-3 집합 일치: {agreement_top3}/{total_sections}')
    print('=' * 80)


if __name__ == '__main__':
    main()