{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3e553bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未检测到已安装的中文字体，正在下载 NotoSansSC-Regular ...\n",
      "正在从: https://fonts.gstatic.com/ea/notosanssc/v1/NotoSansSC-Regular.otf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下载完成： /home/v-menggao/code/VLM2Vec/_fonts/NotoSansSC-Regular.otf\n",
      "已启用中文字体： Noto Sans SC\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 768x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "绘图测试成功！\n"
     ]
    }
   ],
   "source": [
    "# 中文字体设置（Matplotlib/Seaborn 通用）\n",
    "import os, urllib.request\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False # 负号正常显示\n",
    "\n",
    "# 1) 先在系统中找常见中文字体\n",
    "candidates = [\n",
    "    \"Noto Sans CJK SC\", \"Noto Sans SC\",\n",
    "    \"SimHei\", \"Microsoft YaHei\", \"PingFang SC\", \"Hiragino Sans GB\",\n",
    "    \"Source Han Sans SC\", \"WenQuanYi Micro Hei\", \"STHeiti\", \"Arial Unicode MS\"\n",
    "]\n",
    "installed = {f.name for f in fm.fontManager.ttflist}\n",
    "chosen = next((name for name in candidates if name in installed), None)\n",
    "\n",
    "# 2) 如果系统里没有中文字体，就下载一个开源中文字体并临时注册（无需管理员权限）\n",
    "if chosen is None:\n",
    "    print(\"未检测到已安装的中文字体，正在下载 NotoSansSC-Regular ...\")\n",
    "    \n",
    "    # 使用 gstatic.com 的稳定链接\n",
    "    font_url = \"https://fonts.gstatic.com/ea/notosanssc/v1/NotoSansSC-Regular.otf\"\n",
    "    \n",
    "    font_dir = os.path.join(os.getcwd(), \"_fonts\")\n",
    "    os.makedirs(font_dir, exist_ok=True)\n",
    "    font_path = os.path.join(font_dir, \"NotoSansSC-Regular.otf\")\n",
    "    \n",
    "    if not os.path.exists(font_path):\n",
    "        print(\"正在从:\", font_url)\n",
    "        try:\n",
    "            urllib.request.urlretrieve(font_url, font_path)\n",
    "            print(\"下载完成：\", font_path)\n",
    "        except Exception as e:\n",
    "            print(\"字体下载失败：\", e)\n",
    "    \n",
    "    # 注册并获取名称\n",
    "    if os.path.exists(font_path):\n",
    "        fm.fontManager.addfont(font_path)\n",
    "        chosen = fm.FontProperties(fname=font_path).get_name()\n",
    "\n",
    "# 3) 应用到 Matplotlib\n",
    "if chosen:\n",
    "    plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "    plt.rcParams[\"font.sans-serif\"] = [chosen]\n",
    "    print(\"已启用中文字体：\", chosen)\n",
    "else:\n",
    "    print(\"未能启用中文字体。请安装中文字体（如 Noto Sans CJK/思源黑体）后重试。\")\n",
    "\n",
    "# --- 测试绘图 ---\n",
    "try:\n",
    "    plt.figure()\n",
    "    plt.title(\"你好，Matplotlib！\")\n",
    "    plt.xlabel(\"X 轴\")\n",
    "    plt.ylabel(\"Y 轴\")\n",
    "    plt.plot([1, 2, 3], [1, 4, 9])\n",
    "    plt.show()\n",
    "    print(\"绘图测试成功！\")\n",
    "except Exception as e:\n",
    "    print(\"绘图失败，请检查字体设置：\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "716b7d1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip -q install numpy pandas matplotlib seaborn scipy ipywidgets\n",
    "\n",
    "import warnings, os, json, gzip\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from dataclasses import dataclass, asdict\n",
    "from typing import Dict, List, Tuple, Optional, Set\n",
    "\n",
    "try:\n",
    "    from scipy.stats import spearmanr\n",
    "    SCIPY_OK = True\n",
    "except Exception:\n",
    "    SCIPY_OK = False\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "plt.rcParams[\"figure.dpi\"] = 120\n",
    "sns.set(style=\"whitegrid\")\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "EPS = 1e-9\n",
    "\n",
    "def smart_open(path: str):\n",
    "    return gzip.open(path, \"rt\", encoding=\"utf-8\") if str(path).endswith(\".gz\") else open(path, \"r\", encoding=\"utf-8\")\n",
    "\n",
    "def normalize_id(s: str) -> str:\n",
    "    return str(s).strip().rstrip(\":\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "938db624",
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class QueryRunData:\n",
    "    qid: str\n",
    "    labels: Set[str]               # 正样本 id 集合（通常 1 个）\n",
    "    cand_scores: Dict[str, float]  # cand_id -> score（id 已 normalize）\n",
    "    sorted_items: List[Tuple[str, float]]  # 降序 (id, score)\n",
    "\n",
    "@dataclass\n",
    "class QueryStats:\n",
    "    qid: str\n",
    "    pos_id: Optional[str]\n",
    "    pos_rank: Optional[int]    # 1-based\n",
    "    pos_score: Optional[float]\n",
    "    top1_id: Optional[str]\n",
    "    top1_score: Optional[float]\n",
    "    top2_score: Optional[float]\n",
    "    top1_gap: Optional[float]\n",
    "    topneg_id: Optional[str]\n",
    "    topneg_score: Optional[float]\n",
    "    mean_neg: Optional[float]\n",
    "    median_neg: Optional[float]\n",
    "    std_neg: Optional[float]\n",
    "    n_cands: int\n",
    "    score_range: Optional[float]\n",
    "    mean_all: Optional[float]\n",
    "    std_all: Optional[float]\n",
    "    margin_max: Optional[float]    # pos - hardest negative\n",
    "    margin_mean: Optional[float]   # pos - mean(neg)\n",
    "    z_pos: Optional[float]         # (pos - mean(neg))/std(neg)\n",
    "\n",
    "class RetrievalRun:\n",
    "    def __init__(self, name: str, path: str, topk_analyze: int = 1000):\n",
    "        self.name = name\n",
    "        self.path = path\n",
    "        self.topk_analyze = topk_analyze\n",
    "        self.by_qid: Dict[str, QueryRunData] = {}\n",
    "\n",
    "    def load(self):\n",
    "        with smart_open(self.path) as f:\n",
    "            for line in f:\n",
    "                if not line.strip():\n",
    "                    continue\n",
    "                obj = json.loads(line)\n",
    "                qid = str(obj.get(\"qid\"))\n",
    "                cand_scores = {}\n",
    "                for cs in obj.get(\"cand_scores\", []):\n",
    "                    cid = normalize_id(cs.get(\"cand_id\"))\n",
    "                    cand_scores[cid] = float(cs.get(\"score\"))\n",
    "                sorted_items = sorted(cand_scores.items(), key=lambda x: x[1], reverse=True)\n",
    "                if self.topk_analyze and self.topk_analyze > 0:\n",
    "                    sorted_items = sorted_items[:self.topk_analyze]\n",
    "                    cand_scores = dict(sorted_items)\n",
    "                # 先不设置 labels，后续从 info 注入\n",
    "                self.by_qid[qid] = QueryRunData(qid=qid, labels=set(), cand_scores=cand_scores, sorted_items=sorted_items)\n",
    "\n",
    "    def stats_for_qid(self, qid: str) -> QueryStats:\n",
    "        q = self.by_qid[qid]\n",
    "        items = q.sorted_items\n",
    "        n = len(items)\n",
    "\n",
    "        top1_id, top1_score = (items[0] if n > 0 else (None, None))\n",
    "        top2_score = items[1][1] if n > 1 else None\n",
    "        top1_gap = (top1_score - top2_score) if (top1_score is not None and top2_score is not None) else None\n",
    "        last_score = items[-1][1] if n > 0 else None\n",
    "        score_range = (top1_score - last_score) if (top1_score is not None and last_score is not None) else None\n",
    "\n",
    "        # 正样本（取最高排名的一个）\n",
    "        pos_id, pos_rank, pos_score = None, None, None\n",
    "        for i, (cid, sc) in enumerate(items):\n",
    "            if cid in q.labels:\n",
    "                pos_id, pos_rank, pos_score = cid, i + 1, sc\n",
    "                break\n",
    "\n",
    "        neg_scores = [sc for cid, sc in items if cid not in q.labels]\n",
    "        mean_neg = np.mean(neg_scores) if len(neg_scores) > 0 else None\n",
    "        median_neg = np.median(neg_scores) if len(neg_scores) > 0 else None\n",
    "        std_neg = np.std(neg_scores) if len(neg_scores) > 1 else None\n",
    "\n",
    "        # 最高分负样本\n",
    "        topneg_id, topneg_score = None, None\n",
    "        for cid, sc in items:\n",
    "            if cid not in q.labels:\n",
    "                topneg_id, topneg_score = cid, sc\n",
    "                break\n",
    "\n",
    "        margin_max = (pos_score - topneg_score) if (pos_score is not None and topneg_score is not None) else None\n",
    "        margin_mean = (pos_score - mean_neg) if (pos_score is not None and mean_neg is not None) else None\n",
    "        z_pos = ((pos_score - mean_neg) / (std_neg + EPS)) if (pos_score is not None and mean_neg is not None and std_neg and std_neg > 0) else None\n",
    "\n",
    "        all_scores = [sc for _, sc in items]\n",
    "        mean_all = np.mean(all_scores) if len(all_scores) > 0 else None\n",
    "        std_all = np.std(all_scores) if len(all_scores) > 1 else None\n",
    "\n",
    "        return QueryStats(\n",
    "            qid=qid,\n",
    "            pos_id=pos_id, pos_rank=pos_rank, pos_score=pos_score,\n",
    "            top1_id=top1_id, top1_score=top1_score, top2_score=top2_score, top1_gap=top1_gap,\n",
    "            topneg_id=topneg_id, topneg_score=topneg_score,\n",
    "            mean_neg=mean_neg, median_neg=median_neg, std_neg=std_neg,\n",
    "            n_cands=n, score_range=score_range, mean_all=mean_all, std_all=std_all,\n",
    "            margin_max=margin_max, margin_mean=margin_mean, z_pos=z_pos\n",
    "        )\n",
    "\n",
    "def safe_recall_at_k(stats: Dict[str, QueryStats], k: int) -> float:\n",
    "    flags = [1 for st in stats.values() if (st.pos_rank is not None and st.pos_rank <= k)]\n",
    "    return float(np.mean(flags)) if len(flags) > 0 else 0.0\n",
    "\n",
    "def topk_jaccard(a_ids: List[str], b_ids: List[str], k: int) -> float:\n",
    "    a, b = set(a_ids[:k]), set(b_ids[:k])\n",
    "    if not a and not b: return 1.0\n",
    "    inter = len(a & b); union = len(a | b)\n",
    "    return inter / (union + EPS)\n",
    "\n",
    "def spearman_topM(items_a: List[Tuple[str, float]], items_b: List[Tuple[str, float]], M: int = 100) -> Optional[float]:\n",
    "    dict_a = {cid: i for i, (cid, _) in enumerate(items_a[:M])}\n",
    "    dict_b = {cid: i for i, (cid, _) in enumerate(items_b[:M])}\n",
    "    common = list(set(dict_a.keys()) & set(dict_b.keys()))\n",
    "    if len(common) < 3: return None\n",
    "    ranks_a = np.array([dict_a[cid] + 1 for cid in common], dtype=float)\n",
    "    ranks_b = np.array([dict_b[cid] + 1 for cid in common], dtype=float)\n",
    "    if SCIPY_OK:\n",
    "        corr, _ = spearmanr(ranks_a, ranks_b)\n",
    "    else:\n",
    "        corr = np.corrcoef(ranks_a, ranks_b)[0, 1]\n",
    "    return float(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61a700f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_label_map_from_info(info_jsonl_path: str) -> Dict[str, Set[str]]:\n",
    "    \"\"\"\n",
    "    从 CIRR_info.jsonl 读取 qid -> {label} 映射。\n",
    "    - 若行内包含 qid，则用该 qid；\n",
    "    - 否则按行号（从 0 开始）作为 qid。\n",
    "    \"\"\"\n",
    "    label_map: Dict[str, Set[str]] = {}\n",
    "    with smart_open(info_jsonl_path) as f:\n",
    "        for idx, line in enumerate(f):\n",
    "            if not line.strip():\n",
    "                continue\n",
    "            obj = json.loads(line)\n",
    "            qid = str(obj.get(\"qid\", idx))  # 兼容无 qid 的情况\n",
    "            label_name = obj.get(\"label_name\", None)\n",
    "            if isinstance(label_name, str) and label_name.strip():\n",
    "                label_map[qid] = {normalize_id(label_name)}\n",
    "    return label_map\n",
    "\n",
    "def match_label_to_candidates(label: str, cand_ids: List[str]) -> Optional[str]:\n",
    "    \"\"\"\n",
    "    尝试把 label 对齐到候选集的具体 id：\n",
    "    1) exact match\n",
    "    2) basename/suffix match\n",
    "    \"\"\"\n",
    "    label_n = normalize_id(label)\n",
    "    if label_n in cand_ids:\n",
    "        return label_n\n",
    "    base = os.path.basename(label_n)\n",
    "    if base:\n",
    "        for cid in cand_ids:\n",
    "            if cid.endswith(base):\n",
    "                return cid\n",
    "    return None\n",
    "\n",
    "def apply_label_map(run: RetrievalRun, label_map: Dict[str, Set[str]]):\n",
    "    miss, ok = 0, 0\n",
    "    for qid, q in run.by_qid.items():\n",
    "        cand_ids = list(q.cand_scores.keys())\n",
    "        labs = label_map.get(qid, None)\n",
    "        if not labs:\n",
    "            miss += 1\n",
    "            continue\n",
    "        # 对齐到候选的 id 值\n",
    "        aligned = set()\n",
    "        for lab in labs:\n",
    "            m = match_label_to_candidates(lab, cand_ids)\n",
    "            if m:\n",
    "                aligned.add(m)\n",
    "        if not aligned and labs:\n",
    "            # 兜底：用原始 normalize 后的 label（即便不在 cand 中，后续 pos_rank 可能是 None）\n",
    "            aligned = {normalize_id(list(labs)[0])}\n",
    "        q.labels = aligned\n",
    "        ok += int(len(aligned) > 0)\n",
    "    print(f\"[{run.name}] labels applied for {ok}/{len(run.by_qid)} queries; not found: {miss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e40ed88",
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class QueryComparison:\n",
    "    qid: str\n",
    "    label: str\n",
    "    base_pos_rank: Optional[int]\n",
    "    base_pos_score: Optional[float]\n",
    "    base_topneg_score: Optional[float]\n",
    "    base_margin_max: Optional[float]\n",
    "    base_margin_mean: Optional[float]\n",
    "    base_z_pos: Optional[float]\n",
    "    base_top1_gap: Optional[float]\n",
    "    base_std_neg: Optional[float]\n",
    "    base_score_range: Optional[float]\n",
    "    comp_pos_rank: Optional[int]\n",
    "    comp_pos_score: Optional[float]\n",
    "    comp_topneg_score: Optional[float]\n",
    "    comp_margin_max: Optional[float]\n",
    "    comp_margin_mean: Optional[float]\n",
    "    comp_z_pos: Optional[float]\n",
    "    comp_top1_gap: Optional[float]\n",
    "    comp_std_neg: Optional[float]\n",
    "    comp_score_range: Optional[float]\n",
    "    d_pos_rank: Optional[float]\n",
    "    d_pos_score: Optional[float]\n",
    "    d_topneg_score: Optional[float]\n",
    "    d_margin_max: Optional[float]\n",
    "    d_margin_mean: Optional[float]\n",
    "    d_z_pos: Optional[float]\n",
    "    d_top1_gap: Optional[float]\n",
    "    d_std_neg: Optional[float]\n",
    "    d_score_range: Optional[float]\n",
    "    spearman_topM: Optional[float]\n",
    "    jaccard_topK: Optional[float]\n",
    "\n",
    "def build_comparisons(run_base: RetrievalRun, run_comp: RetrievalRun,\n",
    "                      topk_j=10, topM_spear=100) -> Tuple[List[QueryComparison], Dict[str, QueryStats], Dict[str, QueryStats]]:\n",
    "    qids = sorted(set(run_base.by_qid.keys()) & set(run_comp.by_qid.keys()))\n",
    "    base_stats = {qid: run_base.stats_for_qid(qid) for qid in qids}\n",
    "    comp_stats = {qid: run_comp.stats_for_qid(qid) for qid in qids}\n",
    "\n",
    "    comps: List[QueryComparison] = []\n",
    "    for qid in qids:\n",
    "        bs = base_stats[qid]; cs = comp_stats[qid]\n",
    "        def _d(a, b): return None if (a is None or b is None) else (b - a)\n",
    "\n",
    "        jacc = topk_jaccard(\n",
    "            [cid for cid, _ in run_base.by_qid[qid].sorted_items],\n",
    "            [cid for cid, _ in run_comp.by_qid[qid].sorted_items],\n",
    "            k=topk_j\n",
    "        )\n",
    "        spear = spearman_topM(run_base.by_qid[qid].sorted_items,\n",
    "                              run_comp.by_qid[qid].sorted_items, M=topM_spear)\n",
    "        label_show = next(iter(run_base.by_qid[qid].labels)) if run_base.by_qid[qid].labels else \"\"\n",
    "\n",
    "        comps.append(QueryComparison(\n",
    "            qid=qid, label=label_show,\n",
    "            base_pos_rank=bs.pos_rank, base_pos_score=bs.pos_score,\n",
    "            base_topneg_score=bs.topneg_score, base_margin_max=bs.margin_max,\n",
    "            base_margin_mean=bs.margin_mean, base_z_pos=bs.z_pos,\n",
    "            base_top1_gap=bs.top1_gap, base_std_neg=bs.std_neg, base_score_range=bs.score_range,\n",
    "            comp_pos_rank=cs.pos_rank, comp_pos_score=cs.pos_score,\n",
    "            comp_topneg_score=cs.topneg_score, comp_margin_max=cs.margin_max,\n",
    "            comp_margin_mean=cs.margin_mean, comp_z_pos=cs.z_pos,\n",
    "            comp_top1_gap=cs.top1_gap, comp_std_neg=cs.std_neg, comp_score_range=cs.score_range,\n",
    "            d_pos_rank=_d(bs.pos_rank, cs.pos_rank),\n",
    "            d_pos_score=_d(bs.pos_score, cs.pos_score),\n",
    "            d_topneg_score=_d(bs.topneg_score, cs.topneg_score),\n",
    "            d_margin_max=_d(bs.margin_max, cs.margin_max),\n",
    "            d_margin_mean=_d(bs.margin_mean, cs.margin_mean),\n",
    "            d_z_pos=_d(bs.z_pos, cs.z_pos),\n",
    "            d_top1_gap=_d(bs.top1_gap, cs.top1_gap),\n",
    "            d_std_neg=_d(bs.std_neg, cs.std_neg),\n",
    "            d_score_range=_d(bs.score_range, cs.score_range),\n",
    "            spearman_topM=spear, jaccard_topK=jacc\n",
    "        ))\n",
    "    return comps, base_stats, comp_stats\n",
    "\n",
    "def summarize_and_plot(df: pd.DataFrame, base_stats: Dict[str, QueryStats],\n",
    "                       comp_stats: Dict[str, QueryStats], jaccard_k=10, out_dir=None):\n",
    "    # 将数值列强制为 numeric，避免 None/字符串导致比较报错\n",
    "    numeric_cols = [\n",
    "        \"base_pos_rank\",\"base_pos_score\",\"base_topneg_score\",\"base_margin_max\",\"base_margin_mean\",\n",
    "        \"base_z_pos\",\"base_top1_gap\",\"base_std_neg\",\"base_score_range\",\n",
    "        \"comp_pos_rank\",\"comp_pos_score\",\"comp_topneg_score\",\"comp_margin_max\",\"comp_margin_mean\",\n",
    "        \"comp_z_pos\",\"comp_top1_gap\",\"comp_std_neg\",\"comp_score_range\",\n",
    "        \"d_pos_rank\",\"d_pos_score\",\"d_topneg_score\",\"d_margin_max\",\"d_margin_mean\",\"d_z_pos\",\n",
    "        \"d_top1_gap\",\"d_std_neg\",\"d_score_range\",\n",
    "        \"spearman_topM\",\"jaccard_topK\",\n",
    "    ]\n",
    "    for col in numeric_cols:\n",
    "        if col in df.columns:\n",
    "            df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
    "\n",
    "    base_recall1  = safe_recall_at_k(base_stats, 1);  comp_recall1  = safe_recall_at_k(comp_stats, 1)\n",
    "    base_recall5  = safe_recall_at_k(base_stats, 5);  comp_recall5  = safe_recall_at_k(comp_stats, 5)\n",
    "    base_recall10 = safe_recall_at_k(base_stats, 10); comp_recall10 = safe_recall_at_k(comp_stats, 10)\n",
    "\n",
    "    def _mean(col):\n",
    "        x = df[col].dropna().values\n",
    "        return float(np.mean(x)) if len(x) else None\n",
    "    def _ratio(cond):\n",
    "        try:\n",
    "            return (df[cond].shape[0] / df.shape[0]) if df.shape[0] else 0.0\n",
    "        except Exception:\n",
    "            return 0.0\n",
    "\n",
    "    summary = {\n",
    "        \"num_queries\": df.shape[0],\n",
    "        \"recall@1_base\": base_recall1, \"recall@1_comp\": comp_recall1, \"d_recall@1\": comp_recall1 - base_recall1,\n",
    "        \"recall@5_base\": base_recall5, \"recall@5_comp\": comp_recall5, \"d_recall@5\": comp_recall5 - base_recall5,\n",
    "        \"recall@10_base\": base_recall10, \"recall@10_comp\": comp_recall10, \"d_recall@10\": comp_recall10 - base_recall10,\n",
    "        \"ratio_margin_mean_decrease\": _ratio(df[\"d_margin_mean\"] < 0),\n",
    "        \"ratio_margin_max_decrease\":  _ratio(df[\"d_margin_max\"]  < 0),\n",
    "        \"mean_d_margin_mean\": _mean(\"d_margin_mean\"),\n",
    "        \"mean_d_margin_max\":  _mean(\"d_margin_max\"),\n",
    "        \"mean_d_z_pos\":       _mean(\"d_z_pos\"),\n",
    "        \"mean_d_top1_gap\":    _mean(\"d_top1_gap\"),\n",
    "        \"mean_d_std_neg\":     _mean(\"d_std_neg\"),\n",
    "        \"mean_d_score_range\": _mean(\"d_score_range\"),\n",
    "        \"mean_spearman_topM\": _mean(\"spearman_topM\"),\n",
    "        \"mean_jaccard_topK\":  _mean(\"jaccard_topK\"),\n",
    "        \"ratio_pos_rank_worsen\": _ratio(df[\"d_pos_rank\"] > 0),\n",
    "        \"ratio_pos_rank_improve\": _ratio(df[\"d_pos_rank\"] < 0),\n",
    "    }\n",
    "    display(pd.DataFrame([summary]).T.rename(columns={0: \"value\"}))\n",
    "\n",
    "    # 关键直方图\n",
    "    plots = [\n",
    "        (\"d_margin_mean\", \"Δ margin_mean (comp - base): 正-负均值差变化\"),\n",
    "        (\"d_margin_max\",  \"Δ margin_max (comp - base): 正 vs 最难负 差距变化\"),\n",
    "        (\"d_z_pos\",       \"Δ z_pos (comp - base): 标准化区分度变化\"),\n",
    "        (\"d_top1_gap\",    \"Δ top1_gap: 排名峰度变化\"),\n",
    "        (\"d_std_neg\",     \"Δ std_neg: 负样本分布标准差变化\"),\n",
    "        (\"d_score_range\", \"Δ score_range: 分数动态范围变化\"),\n",
    "        (\"spearman_topM\", \"Spearman(topM) 排名稳定性\"),\n",
    "        (\"jaccard_topK\",  f\"Top-{jaccard_k} 候选集合 Jaccard\"),\n",
    "    ]\n",
    "    ncol = 2\n",
    "    nrow = int(np.ceil(len(plots) / ncol))\n",
    "    plt.figure(figsize=(12, 3.5*nrow))\n",
    "    for i, (col, title) in enumerate(plots, 1):\n",
    "        ax = plt.subplot(nrow, ncol, i)\n",
    "        x = df[col].dropna().values\n",
    "        if len(x) == 0:\n",
    "            ax.set_title(title + \" (no data)\"); continue\n",
    "        ax.hist(x, bins=60, alpha=0.85, color=\"#1f77b4\")\n",
    "        ax.axvline(0, color=\"r\", linestyle=\"--\", linewidth=1)\n",
    "        ax.set_title(title); ax.set_xlabel(col); ax.set_ylabel(\"count\")\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "    # 易/难样本 vs 退化\n",
    "    if \"base_z_pos\" in df.columns and \"d_margin_mean\" in df.columns:\n",
    "        base_z = df[\"base_z_pos\"].values\n",
    "        d_margin = df[\"d_margin_mean\"].values\n",
    "        mask = ~np.isnan(base_z) & ~np.isnan(d_margin)\n",
    "        if np.sum(mask) > 10:\n",
    "            plt.figure(figsize=(5,4))\n",
    "            plt.scatter(base_z[mask], d_margin[mask], s=9, alpha=0.4)\n",
    "            plt.axhline(0, color=\"r\", linestyle=\"--\", linewidth=1)\n",
    "            plt.xlabel(\"base z_pos (越大越容易)\")\n",
    "            plt.ylabel(\"Δ margin_mean\")\n",
    "            plt.title(\"易/难样本与退化关系\")\n",
    "            plt.tight_layout(); plt.show()\n",
    "\n",
    "    # # 导出恶化最多的 Top-30\n",
    "    # if \"d_pos_rank\" in df.columns:\n",
    "    #     worst = df.sort_values(\"d_pos_rank\", ascending=False).head(30)\n",
    "    #     display(HTML(\"<b>正样本排名恶化最多的 Top-30</b>\"))\n",
    "    #     cols = [\"qid\",\"label\",\"base_pos_rank\",\"comp_pos_rank\",\"d_pos_rank\",\n",
    "    #             \"base_margin_mean\",\"comp_margin_mean\",\"d_margin_mean\",\"spearman_topM\",\"jaccard_topK\"]\n",
    "    #     display(worst[[c for c in cols if c in worst.columns]])\n",
    "    \n",
    "    if out_dir:\n",
    "        os.makedirs(out_dir, exist_ok=True)\n",
    "        df.to_csv(os.path.join(out_dir, \"per_query_deltas.csv\"), index=False)\n",
    "        pd.DataFrame([summary]).to_json(os.path.join(out_dir, \"summary.json\"), force_ascii=False, indent=2)\n",
    "        print(\"结果已导出到：\", out_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5719e1e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_auc(run: RetrievalRun, qid: str) -> Optional[float]:\n",
    "    q = run.by_qid[qid]\n",
    "    items = q.sorted_items\n",
    "    pos_scores = [sc for cid, sc in items if cid in q.labels]\n",
    "    neg_scores = [sc for cid, sc in items if cid not in q.labels]\n",
    "    if len(pos_scores) == 0 or len(neg_scores) == 0:\n",
    "        return None\n",
    "    neg_scores_np = np.array(neg_scores)\n",
    "    aucs = [float(np.mean(ps > neg_scores_np)) for ps in pos_scores]\n",
    "    return float(np.mean(aucs)) if aucs else None\n",
    "\n",
    "def mean_auc(run: RetrievalRun, qids: Optional[List[str]] = None) -> float:\n",
    "    qids = qids or list(run.by_qid.keys())\n",
    "    aucs = [query_auc(run, qid) for qid in qids]\n",
    "    aucs = [x for x in aucs if x is not None]\n",
    "    return float(np.mean(aucs)) if aucs else np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd78ebe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def inspect_query(runA: RetrievalRun, runB: RetrievalRun, qid: str, topn: int = 10):\n",
    "    if qid not in runA.by_qid or qid not in runB.by_qid:\n",
    "        print(\"qid 不在两份结果的交集中\"); return\n",
    "    \n",
    "    bs, cs = runA.stats_for_qid(qid), runB.stats_for_qid(qid)\n",
    "    def _fmt(x): \n",
    "        return \"None\" if x is None else (f\"{x:.4f}\" if isinstance(x, (int, float, np.floating)) else str(x))\n",
    "    \n",
    "    print(\"Query:\", qid, \"| label:\", next(iter(runA.by_qid[qid].labels), \"\"))\n",
    "    print(f\"Pos rank (A -> B): {bs.pos_rank} -> {cs.pos_rank}  | Δ = {None if (bs.pos_rank is None or cs.pos_rank is None) else (cs.pos_rank - bs.pos_rank)}\")\n",
    "    print(f\"Pos score (A -> B): {_fmt(bs.pos_score)} -> {_fmt(cs.pos_score)}\")\n",
    "    print(f\"Margin_mean (A -> B): {_fmt(bs.margin_mean)} -> {_fmt(cs.margin_mean)} | Δ={_fmt(cs.margin_mean - bs.margin_mean if bs.margin_mean is not None and cs.margin_mean is not None else None)}\")\n",
    "    print(f\"Margin_max  (A -> B): {_fmt(bs.margin_max)}  -> {_fmt(cs.margin_max)}  | Δ={_fmt(cs.margin_max - bs.margin_max if bs.margin_max is not None and cs.margin_max is not None else None)}\")\n",
    "    print(f\"z_pos       (A -> B): {_fmt(bs.z_pos)}       -> {_fmt(cs.z_pos)}       | Δ={_fmt(cs.z_pos - bs.z_pos if bs.z_pos is not None and cs.z_pos is not None else None)}\")\n",
    "    print(f\"std_neg     (A -> B): {_fmt(bs.std_neg)}     -> {_fmt(cs.std_neg)}     | Δ={_fmt(cs.std_neg - bs.std_neg if bs.std_neg is not None and cs.std_neg is not None else None)}\")\n",
    "    print(f\"score_range (A -> B): {_fmt(bs.score_range)} -> {_fmt(cs.score_range)} | Δ={_fmt(cs.score_range - bs.score_range if bs.score_range is not None and cs.score_range is not None else None)}\")\n",
    "    \n",
    "    b_items = runA.by_qid[qid].sorted_items[:topn]\n",
    "    c_items = runB.by_qid[qid].sorted_items[:topn]\n",
    "    b_df = pd.DataFrame(b_items, columns=[\"cand_id\",\"A_score\"]); b_df[\"A_rank\"] = np.arange(1, len(b_df)+1)\n",
    "    c_df = pd.DataFrame(c_items, columns=[\"cand_id\",\"B_score\"]); c_df[\"B_rank\"] = np.arange(1, len(c_df)+1)\n",
    "    merged = pd.merge(b_df, c_df, on=\"cand_id\", how=\"outer\")\n",
    "    labels = runA.by_qid[qid].labels\n",
    "    merged[\"label\"] = merged[\"cand_id\"].apply(lambda x: \"POS\" if x in labels else \"NEG\")\n",
    "    merged = merged.sort_values(\"A_rank\", na_position=\"last\")\n",
    "    display(merged[[\"cand_id\",\"label\",\"A_rank\",\"A_score\",\"B_rank\",\"B_score\"]].head(2*topn))\n",
    "    \n",
    "    b_neg = [sc for cid, sc in runA.by_qid[qid].sorted_items if cid not in labels]\n",
    "    c_neg = [sc for cid, sc in runB.by_qid[qid].sorted_items if cid not in labels]\n",
    "    plt.figure(figsize=(6,4))\n",
    "    plt.hist(b_neg, bins=40, alpha=0.5, label=\"A neg\", color=\"#1f77b4\", density=True)\n",
    "    plt.hist(c_neg, bins=40, alpha=0.5, label=\"B neg\", color=\"#ff7f0e\", density=True)\n",
    "    if bs.pos_score is not None:\n",
    "        plt.axvline(bs.pos_score, color=\"#1f77b4\", linestyle=\"--\", label=\"A pos\")\n",
    "    if cs.pos_score is not None:\n",
    "        plt.axvline(cs.pos_score, color=\"#ff7f0e\", linestyle=\"--\", label=\"B pos\")\n",
    "    plt.title(f\"Neg 分布与 Pos 分数 (qid={qid})\"); plt.legend(); plt.tight_layout(); plt.show()\n",
    "\n",
    "def make_inspector(runA, runB, limit=5000):\n",
    "    qid_list = sorted(set(runA.by_qid.keys()) & set(runB.by_qid.keys()))\n",
    "    qid_dropdown = widgets.Dropdown(options=qid_list[:limit], description=\"qid\", layout=widgets.Layout(width='50%'))\n",
    "    topn_slider = widgets.IntSlider(value=10, min=5, max=50, step=5, description=\"TopN\")\n",
    "    ui = widgets.HBox([qid_dropdown, topn_slider])\n",
    "    out = widgets.interactive_output(lambda qid, topn: inspect_query(runA, runB, qid, topn),\n",
    "                                     {\"qid\": qid_dropdown, \"topn\": topn_slider})\n",
    "    display(ui, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7bbed8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded runs: 1000 1000\n",
      "intersection size: 1000\n",
      "labels in info: 1000\n",
      "[base] labels applied for 1000/1000 queries; not found: 0\n",
      "[comp] labels applied for 1000/1000 queries; not found: 0\n",
      "pos 覆盖率 base: 1.0  | comp: 1.0\n"
     ]
    }
   ],
   "source": [
    "# 路径配置（请替换为你的实际路径）\n",
    "# A：baseline（无压缩）结果\n",
    "baseline_path = \"/home/v-menggao/code/VLM2Vec/~/exps/Qwen2vl_2B_single_node_image_ret_10_30_h100/VisionZip-checkpoint-1000-1-0/VLM2Vec-V2.0-Qwen2VL-2B/image_retrival/OVEN_score_details_layerlast.jsonl\"\n",
    "# B：压缩（例如 VisionZip/FastV）结果\n",
    "compressed_path = \"/home/v-menggao/code/VLM2Vec/~/exps/Qwen2vl_2B_single_node_image_ret_10_30_h100/VisionZip-checkpoint-1000-0.5-0.05/VLM2Vec-V2.0-Qwen2VL-2B/image_retrival/OVEN_score_details_layerlast.jsonl\"\n",
    "# GT 信息文件（含 cand_names + label_name）\n",
    "info_path = \"/home/v-menggao/code/VLM2Vec/~/exps/train_on_qwen2vl/VLM2Vec-V2.0-Qwen2VL-2B/image_retrival/OVEN_info.jsonl\"\n",
    "\n",
    "# 如果担心正样本不在前TOPK中，建议设置为 0（不截断）\n",
    "TOPK_ANALYZE = 1000     # 0 或 None 表示不截断\n",
    "JACCARD_K    = 10\n",
    "SPEAR_M      = 100\n",
    "OUT_DIR      = \"./analysis_out\"\n",
    "\n",
    "# 加载两份 run\n",
    "base = RetrievalRun(\"base\", baseline_path, topk_analyze=TOPK_ANALYZE); base.load()\n",
    "comp = RetrievalRun(\"comp\", compressed_path, topk_analyze=TOPK_ANALYZE); comp.load()\n",
    "\n",
    "print(\"Loaded runs:\", len(base.by_qid), len(comp.by_qid))\n",
    "qids_inter = sorted(set(base.by_qid.keys()) & set(comp.by_qid.keys()))\n",
    "print(\"intersection size:\", len(qids_inter))\n",
    "\n",
    "# 从 info 文件抽取 label map 并注入到两个 run\n",
    "label_map = extract_label_map_from_info(info_path)\n",
    "print(\"labels in info:\", len(label_map))\n",
    "\n",
    "apply_label_map(base, label_map)\n",
    "apply_label_map(comp, label_map)\n",
    "\n",
    "# 查看正样本覆盖率\n",
    "def pos_coverage(run, inter=None):\n",
    "    inter = inter or (set(base.by_qid.keys()) & set(comp.by_qid.keys()))\n",
    "    hit = 0\n",
    "    for qid in inter:\n",
    "        st = run.stats_for_qid(qid)\n",
    "        hit += int(st.pos_rank is not None)\n",
    "    return hit / len(inter) if len(inter) else 0.0\n",
    "\n",
    "print(\"pos 覆盖率 base:\", pos_coverage(base), \" | comp:\", pos_coverage(comp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4399cd47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>总体统计与变化（使用 CIRR_info.jsonl 注入 label）</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_queries</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall@1_base</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall@1_comp</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_recall@1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall@5_base</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall@5_comp</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_recall@5</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall@10_base</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall@10_comp</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_recall@10</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_margin_mean_decrease</th>\n",
       "      <td>0.864000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_margin_max_decrease</th>\n",
       "      <td>0.662000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_d_margin_mean</th>\n",
       "      <td>-0.023550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_d_margin_max</th>\n",
       "      <td>-0.009136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_d_z_pos</th>\n",
       "      <td>-0.205424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_d_top1_gap</th>\n",
       "      <td>-0.011179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_d_std_neg</th>\n",
       "      <td>-0.002386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_d_score_range</th>\n",
       "      <td>-0.027457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_spearman_topM</th>\n",
       "      <td>0.886182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_jaccard_topK</th>\n",
       "      <td>0.758382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_pos_rank_worsen</th>\n",
       "      <td>0.148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_pos_rank_improve</th>\n",
       "      <td>0.127000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  value\n",
       "num_queries                 1000.000000\n",
       "recall@1_base                  1.000000\n",
       "recall@1_comp                  1.000000\n",
       "d_recall@1                     0.000000\n",
       "recall@5_base                  1.000000\n",
       "recall@5_comp                  1.000000\n",
       "d_recall@5                     0.000000\n",
       "recall@10_base                 1.000000\n",
       "recall@10_comp                 1.000000\n",
       "d_recall@10                    0.000000\n",
       "ratio_margin_mean_decrease     0.864000\n",
       "ratio_margin_max_decrease      0.662000\n",
       "mean_d_margin_mean            -0.023550\n",
       "mean_d_margin_max             -0.009136\n",
       "mean_d_z_pos                  -0.205424\n",
       "mean_d_top1_gap               -0.011179\n",
       "mean_d_std_neg                -0.002386\n",
       "mean_d_score_range            -0.027457\n",
       "mean_spearman_topM             0.886182\n",
       "mean_jaccard_topK              0.758382\n",
       "ratio_pos_rank_worsen          0.148000\n",
       "ratio_pos_rank_improve         0.127000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x1680 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<b>正样本排名恶化最多的 Top-30</b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>label</th>\n",
       "      <th>base_pos_rank</th>\n",
       "      <th>comp_pos_rank</th>\n",
       "      <th>d_pos_rank</th>\n",
       "      <th>base_margin_mean</th>\n",
       "      <th>comp_margin_mean</th>\n",
       "      <th>d_margin_mean</th>\n",
       "      <th>spearman_topM</th>\n",
       "      <th>jaccard_topK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>93</td>\n",
       "      <td>OVEN/Q34932.jpg:Brisbane. Brisbane ( ) is the ...</td>\n",
       "      <td>33</td>\n",
       "      <td>260</td>\n",
       "      <td>227</td>\n",
       "      <td>0.091341</td>\n",
       "      <td>0.035656</td>\n",
       "      <td>-0.055684</td>\n",
       "      <td>0.599737</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>51</td>\n",
       "      <td>OVEN/Q66.jpg:Boeing. The Boeing Company () is ...</td>\n",
       "      <td>147</td>\n",
       "      <td>349</td>\n",
       "      <td>202</td>\n",
       "      <td>0.043730</td>\n",
       "      <td>0.013086</td>\n",
       "      <td>-0.030643</td>\n",
       "      <td>0.856900</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>925</td>\n",
       "      <td>OVEN/Q220971.jpg:Marimba. The marimba () is a ...</td>\n",
       "      <td>101</td>\n",
       "      <td>293</td>\n",
       "      <td>192</td>\n",
       "      <td>0.071735</td>\n",
       "      <td>0.024779</td>\n",
       "      <td>-0.046956</td>\n",
       "      <td>0.794209</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>276</td>\n",
       "      <td>OVEN/Q742168.jpg:Serpent (instrument). The ser...</td>\n",
       "      <td>198</td>\n",
       "      <td>368</td>\n",
       "      <td>170</td>\n",
       "      <td>0.044489</td>\n",
       "      <td>0.011538</td>\n",
       "      <td>-0.032951</td>\n",
       "      <td>0.597288</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>447</td>\n",
       "      <td>OVEN/Q3364054.jpg:Grayson Highlands State Park...</td>\n",
       "      <td>72</td>\n",
       "      <td>236</td>\n",
       "      <td>164</td>\n",
       "      <td>0.090573</td>\n",
       "      <td>0.043746</td>\n",
       "      <td>-0.046828</td>\n",
       "      <td>0.736569</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>760</td>\n",
       "      <td>OVEN/Q1142586.jpg:Ford Field. Ford Field is a ...</td>\n",
       "      <td>229</td>\n",
       "      <td>346</td>\n",
       "      <td>117</td>\n",
       "      <td>0.030020</td>\n",
       "      <td>0.014368</td>\n",
       "      <td>-0.015653</td>\n",
       "      <td>0.766015</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>533</td>\n",
       "      <td>OVEN/Q13450283.jpg:Wetsuit. A wetsuit is a gar...</td>\n",
       "      <td>11</td>\n",
       "      <td>119</td>\n",
       "      <td>108</td>\n",
       "      <td>0.133417</td>\n",
       "      <td>0.051087</td>\n",
       "      <td>-0.082330</td>\n",
       "      <td>0.781500</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>420</td>\n",
       "      <td>OVEN/Q177.jpg:Pizza. Pizza (, ) is a dish of I...</td>\n",
       "      <td>65</td>\n",
       "      <td>169</td>\n",
       "      <td>104</td>\n",
       "      <td>0.071197</td>\n",
       "      <td>0.038060</td>\n",
       "      <td>-0.033137</td>\n",
       "      <td>0.852140</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>651</td>\n",
       "      <td>OVEN/Q4582964.jpg:Mexican Mint. La Casa de Mon...</td>\n",
       "      <td>186</td>\n",
       "      <td>274</td>\n",
       "      <td>88</td>\n",
       "      <td>0.039238</td>\n",
       "      <td>0.027482</td>\n",
       "      <td>-0.011755</td>\n",
       "      <td>0.762155</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>416</td>\n",
       "      <td>OVEN/Q388.jpg:Linux. Linux ( or ) is an open-s...</td>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "      <td>55</td>\n",
       "      <td>0.085471</td>\n",
       "      <td>0.061502</td>\n",
       "      <td>-0.023969</td>\n",
       "      <td>0.555497</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>720</td>\n",
       "      <td>OVEN/Q41301.jpg:Epithelium. Epithelium is one ...</td>\n",
       "      <td>56</td>\n",
       "      <td>102</td>\n",
       "      <td>46</td>\n",
       "      <td>0.071529</td>\n",
       "      <td>0.053901</td>\n",
       "      <td>-0.017628</td>\n",
       "      <td>0.828484</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>965</td>\n",
       "      <td>OVEN/Q133279.jpg:Human back. The human back, a...</td>\n",
       "      <td>52</td>\n",
       "      <td>97</td>\n",
       "      <td>45</td>\n",
       "      <td>0.077935</td>\n",
       "      <td>0.056751</td>\n",
       "      <td>-0.021185</td>\n",
       "      <td>0.598542</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>789</td>\n",
       "      <td>OVEN/Q183670.jpg:Chalk. Chalk is a soft, white...</td>\n",
       "      <td>85</td>\n",
       "      <td>123</td>\n",
       "      <td>38</td>\n",
       "      <td>0.058858</td>\n",
       "      <td>0.049167</td>\n",
       "      <td>-0.009691</td>\n",
       "      <td>0.717221</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>499</td>\n",
       "      <td>OVEN/Q244347.jpg:Gorée. (; \"Gorée Island\"; Wol...</td>\n",
       "      <td>100</td>\n",
       "      <td>134</td>\n",
       "      <td>34</td>\n",
       "      <td>0.098915</td>\n",
       "      <td>0.080872</td>\n",
       "      <td>-0.018043</td>\n",
       "      <td>0.934034</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>380</td>\n",
       "      <td>OVEN/Q10285.jpg:Colosseum. The Colosseum ( ; )...</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>28</td>\n",
       "      <td>0.104239</td>\n",
       "      <td>0.091217</td>\n",
       "      <td>-0.013022</td>\n",
       "      <td>0.786834</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>370</td>\n",
       "      <td>OVEN/Q3063375.jpg:Theatro da Paz. The Theatro ...</td>\n",
       "      <td>76</td>\n",
       "      <td>103</td>\n",
       "      <td>27</td>\n",
       "      <td>0.119831</td>\n",
       "      <td>0.103599</td>\n",
       "      <td>-0.016232</td>\n",
       "      <td>0.869378</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>145</td>\n",
       "      <td>OVEN/Q127060.jpg:Folate. Folate, also known as...</td>\n",
       "      <td>46</td>\n",
       "      <td>71</td>\n",
       "      <td>25</td>\n",
       "      <td>0.082193</td>\n",
       "      <td>0.066621</td>\n",
       "      <td>-0.015571</td>\n",
       "      <td>0.846961</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>582</td>\n",
       "      <td>OVEN/Q128652.jpg:Palace of Justice, Nuremberg....</td>\n",
       "      <td>114</td>\n",
       "      <td>139</td>\n",
       "      <td>25</td>\n",
       "      <td>0.099507</td>\n",
       "      <td>0.081647</td>\n",
       "      <td>-0.017860</td>\n",
       "      <td>0.890282</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>833</td>\n",
       "      <td>OVEN/Q1589941.jpg:Kongens Nytorv. Kongens Nyto...</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>24</td>\n",
       "      <td>0.151871</td>\n",
       "      <td>0.118533</td>\n",
       "      <td>-0.033338</td>\n",
       "      <td>0.620988</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>757</td>\n",
       "      <td>OVEN/Q2436387.jpg:Museum de Fundatie. Museum d...</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>24</td>\n",
       "      <td>0.155041</td>\n",
       "      <td>0.129581</td>\n",
       "      <td>-0.025460</td>\n",
       "      <td>0.946520</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>415</td>\n",
       "      <td>OVEN/Q1049301.jpg:Groot Begijnhof, Leuven. The...</td>\n",
       "      <td>15</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>0.201441</td>\n",
       "      <td>0.173300</td>\n",
       "      <td>-0.028141</td>\n",
       "      <td>0.863910</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>392</td>\n",
       "      <td>OVEN/Q3580520.jpg:Église Notre-Dame de Calais....</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>0.245959</td>\n",
       "      <td>0.197047</td>\n",
       "      <td>-0.048912</td>\n",
       "      <td>0.917942</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>502</td>\n",
       "      <td>OVEN/Q43471.jpg:Arm. In human anatomy, the arm...</td>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>18</td>\n",
       "      <td>0.057915</td>\n",
       "      <td>0.054154</td>\n",
       "      <td>-0.003761</td>\n",
       "      <td>0.789325</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>445</td>\n",
       "      <td>OVEN/Q66.jpg:Boeing. The Boeing Company () is ...</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>0.128634</td>\n",
       "      <td>0.097723</td>\n",
       "      <td>-0.030911</td>\n",
       "      <td>0.752129</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>237</td>\n",
       "      <td>OVEN/Q843607.jpg:Beige. Beige is variously des...</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>14</td>\n",
       "      <td>0.075126</td>\n",
       "      <td>0.069993</td>\n",
       "      <td>-0.005134</td>\n",
       "      <td>0.737376</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>570</td>\n",
       "      <td>OVEN/Q191189.jpg:Zentrum Paul Klee. The Zentru...</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>0.193371</td>\n",
       "      <td>0.148562</td>\n",
       "      <td>-0.044809</td>\n",
       "      <td>0.773301</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>904</td>\n",
       "      <td>OVEN/Q252417.jpg:Beamish Museum. Beamish Museu...</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>0.204208</td>\n",
       "      <td>0.137734</td>\n",
       "      <td>-0.066474</td>\n",
       "      <td>0.789679</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>135</td>\n",
       "      <td>OVEN/Q21212911.jpg:Church of the Holy Archange...</td>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>0.200071</td>\n",
       "      <td>0.180517</td>\n",
       "      <td>-0.019554</td>\n",
       "      <td>0.934574</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>592</td>\n",
       "      <td>OVEN/Q1049301.jpg:Groot Begijnhof, Leuven. The...</td>\n",
       "      <td>32</td>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "      <td>0.143868</td>\n",
       "      <td>0.129753</td>\n",
       "      <td>-0.014115</td>\n",
       "      <td>0.887028</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>386</td>\n",
       "      <td>OVEN/Q127060.jpg:Folate. Folate, also known as...</td>\n",
       "      <td>33</td>\n",
       "      <td>44</td>\n",
       "      <td>11</td>\n",
       "      <td>0.099399</td>\n",
       "      <td>0.093767</td>\n",
       "      <td>-0.005632</td>\n",
       "      <td>0.908862</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     qid                                              label  base_pos_rank  \\\n",
       "923   93  OVEN/Q34932.jpg:Brisbane. Brisbane ( ) is the ...             33   \n",
       "457   51  OVEN/Q66.jpg:Boeing. The Boeing Company () is ...            147   \n",
       "918  925  OVEN/Q220971.jpg:Marimba. The marimba () is a ...            101   \n",
       "197  276  OVEN/Q742168.jpg:Serpent (instrument). The ser...            198   \n",
       "387  447  OVEN/Q3364054.jpg:Grayson Highlands State Park...             72   \n",
       "735  760  OVEN/Q1142586.jpg:Ford Field. Ford Field is a ...            229   \n",
       "483  533  OVEN/Q13450283.jpg:Wetsuit. A wetsuit is a gar...             11   \n",
       "358  420  OVEN/Q177.jpg:Pizza. Pizza (, ) is a dish of I...             65   \n",
       "614  651  OVEN/Q4582964.jpg:Mexican Mint. La Casa de Mon...            186   \n",
       "353  416  OVEN/Q388.jpg:Linux. Linux ( or ) is an open-s...             20   \n",
       "691  720  OVEN/Q41301.jpg:Epithelium. Epithelium is one ...             56   \n",
       "962  965  OVEN/Q133279.jpg:Human back. The human back, a...             52   \n",
       "766  789  OVEN/Q183670.jpg:Chalk. Chalk is a soft, white...             85   \n",
       "444  499  OVEN/Q244347.jpg:Gorée. (; \"Gorée Island\"; Wol...            100   \n",
       "313  380  OVEN/Q10285.jpg:Colosseum. The Colosseum ( ; )...             22   \n",
       "302  370  OVEN/Q3063375.jpg:Theatro da Paz. The Theatro ...             76   \n",
       "52   145  OVEN/Q127060.jpg:Folate. Folate, also known as...             46   \n",
       "537  582  OVEN/Q128652.jpg:Palace of Justice, Nuremberg....            114   \n",
       "816  833  OVEN/Q1589941.jpg:Kongens Nytorv. Kongens Nyto...             17   \n",
       "731  757  OVEN/Q2436387.jpg:Museum de Fundatie. Museum d...             47   \n",
       "352  415  OVEN/Q1049301.jpg:Groot Begijnhof, Leuven. The...             15   \n",
       "326  392  OVEN/Q3580520.jpg:Église Notre-Dame de Calais....              7   \n",
       "449  502  OVEN/Q43471.jpg:Arm. In human anatomy, the arm...             80   \n",
       "385  445  OVEN/Q66.jpg:Boeing. The Boeing Company () is ...              9   \n",
       "154  237  OVEN/Q843607.jpg:Beige. Beige is variously des...             50   \n",
       "524  570  OVEN/Q191189.jpg:Zentrum Paul Klee. The Zentru...              1   \n",
       "895  904  OVEN/Q252417.jpg:Beamish Museum. Beamish Museu...              1   \n",
       "41   135  OVEN/Q21212911.jpg:Church of the Holy Archange...             29   \n",
       "548  592  OVEN/Q1049301.jpg:Groot Begijnhof, Leuven. The...             32   \n",
       "319  386  OVEN/Q127060.jpg:Folate. Folate, also known as...             33   \n",
       "\n",
       "     comp_pos_rank  d_pos_rank  base_margin_mean  comp_margin_mean  \\\n",
       "923            260         227          0.091341          0.035656   \n",
       "457            349         202          0.043730          0.013086   \n",
       "918            293         192          0.071735          0.024779   \n",
       "197            368         170          0.044489          0.011538   \n",
       "387            236         164          0.090573          0.043746   \n",
       "735            346         117          0.030020          0.014368   \n",
       "483            119         108          0.133417          0.051087   \n",
       "358            169         104          0.071197          0.038060   \n",
       "614            274          88          0.039238          0.027482   \n",
       "353             75          55          0.085471          0.061502   \n",
       "691            102          46          0.071529          0.053901   \n",
       "962             97          45          0.077935          0.056751   \n",
       "766            123          38          0.058858          0.049167   \n",
       "444            134          34          0.098915          0.080872   \n",
       "313             50          28          0.104239          0.091217   \n",
       "302            103          27          0.119831          0.103599   \n",
       "52              71          25          0.082193          0.066621   \n",
       "537            139          25          0.099507          0.081647   \n",
       "816             41          24          0.151871          0.118533   \n",
       "731             71          24          0.155041          0.129581   \n",
       "352             36          21          0.201441          0.173300   \n",
       "326             28          21          0.245959          0.197047   \n",
       "449             98          18          0.057915          0.054154   \n",
       "385             23          14          0.128634          0.097723   \n",
       "154             64          14          0.075126          0.069993   \n",
       "524             14          13          0.193371          0.148562   \n",
       "895             14          13          0.204208          0.137734   \n",
       "41              41          12          0.200071          0.180517   \n",
       "548             44          12          0.143868          0.129753   \n",
       "319             44          11          0.099399          0.093767   \n",
       "\n",
       "     d_margin_mean  spearman_topM  jaccard_topK  \n",
       "923      -0.055684       0.599737      0.333333  \n",
       "457      -0.030643       0.856900      0.818182  \n",
       "918      -0.046956       0.794209      0.538462  \n",
       "197      -0.032951       0.597288      0.538462  \n",
       "387      -0.046828       0.736569      0.538462  \n",
       "735      -0.015653       0.766015      0.818182  \n",
       "483      -0.082330       0.781500      0.538462  \n",
       "358      -0.033137       0.852140      0.818182  \n",
       "614      -0.011755       0.762155      0.538462  \n",
       "353      -0.023969       0.555497      0.666667  \n",
       "691      -0.017628       0.828484      0.818182  \n",
       "962      -0.021185       0.598542      0.250000  \n",
       "766      -0.009691       0.717221      0.666667  \n",
       "444      -0.018043       0.934034      0.666667  \n",
       "313      -0.013022       0.786834      0.538462  \n",
       "302      -0.016232       0.869378      0.538462  \n",
       "52       -0.015571       0.846961      0.666667  \n",
       "537      -0.017860       0.890282      0.666667  \n",
       "816      -0.033338       0.620988      0.333333  \n",
       "731      -0.025460       0.946520      0.818182  \n",
       "352      -0.028141       0.863910      0.818182  \n",
       "326      -0.048912       0.917942      0.333333  \n",
       "449      -0.003761       0.789325      0.666667  \n",
       "385      -0.030911       0.752129      0.538462  \n",
       "154      -0.005134       0.737376      0.428571  \n",
       "524      -0.044809       0.773301      0.428571  \n",
       "895      -0.066474       0.789679      0.538462  \n",
       "41       -0.019554       0.934574      0.666667  \n",
       "548      -0.014115       0.887028      0.818182  \n",
       "319      -0.005632       0.908862      0.666667  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "结果已导出到： ./analysis_out\n",
      "AUC_base = 0.9922 | AUC_comp = 0.9916 | ΔAUC = -0.0007\n"
     ]
    }
   ],
   "source": [
    "comps, base_stats, comp_stats = build_comparisons(base, comp, topk_j=JACCARD_K, topM_spear=SPEAR_M)\n",
    "df = pd.DataFrame([asdict(c) for c in comps])\n",
    "\n",
    "display(HTML(\"<h3>总体统计与变化（使用 CIRR_info.jsonl 注入 label）</h3>\"))\n",
    "summarize_and_plot(df, base_stats, comp_stats, jaccard_k=JACCARD_K, out_dir=OUT_DIR)\n",
    "\n",
    "base_auc = mean_auc(base, qids_inter)\n",
    "comp_auc = mean_auc(comp, qids_inter)\n",
    "print(f\"AUC_base = {base_auc:.4f} | AUC_comp = {comp_auc:.4f} | ΔAUC = {comp_auc - base_auc:+.4f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "VLMtoVec",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}