{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92bb1791",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving outputs to: /home/v-menggao/code/VLM2Vec/exps/vlm2vec_train_2.5_3b_multilayer_distill_only_image_ret_11_18_h100/checkpoint-5000/VLM2Vec-V2.0-Qwen2VL-2B/image_retrival/gating_model_analysis_mid2last_perds\n"
     ]
    }
   ],
   "source": [
    "# Cell 1: Imports & Config\n",
    "import os, json, glob, re, warnings, random\n",
    "from collections import OrderedDict, defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import GroupKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, GradientBoostingRegressor\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
    "\n",
    "sns.set(style=\"whitegrid\", font_scale=1.05)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# ========== 你的路径/数据集配置 ==========\n",
    "BASE_DIR = \"/home/v-menggao/code/VLM2Vec/exps/vlm2vec_train_2.5_3b_multilayer_distill_only_image_ret_11_18_h100/checkpoint-5000/VLM2Vec-V2.0-Qwen2VL-2B/image_retrival\"\n",
    "DATASETS = [\"CIRR\",\"EDIS\",\"FashionIQ\",\"NIGHTS\", \"OVEN\",\"VisDial\",\"MSCOCO_i2t\",\"MSCOCO_t2i\",\"VisualNews_i2t\",\"VisualNews_t2i\",\"WebQA\",\"Wiki-SS-NQ\"]\n",
    "#\n",
    "# 温度&TopK（用于概率近似和统计）\n",
    "TEMP = 1.0\n",
    "TOPK = 200\n",
    "\n",
    "# 输出目录\n",
    "OUT_DIR = os.path.join(BASE_DIR, \"gating_model_analysis_mid2last_perds\")\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "print(\"Saving outputs to:\", OUT_DIR)\n",
    "\n",
    "# 关键：选择使用哪条中间层路线的分数\n",
    "# - \"mid\"     表示 q_mid × c_mid（你原始的中间层检索）\n",
    "# - \"mid2last\"表示 q_mid × c_last（本次新增）\n",
    "MID_KEY = \"mid2last\"  # 若尚未生成 mid2last，可改为 \"mid\" 先跑通\n",
    "\n",
    "# 是否生成 mid2last 分数（需要 q_mid 与 c_last 的向量路径）\n",
    "GENERATE_MID2LAST = False  # True 时会尝试在 JSONL 里增补 \"mid2last\"\n",
    "\n",
    "# 可选：控制随机种子\n",
    "SEED = 2025\n",
    "np.random.seed(SEED); random.seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e5c2ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 2: Utils\n",
    "\n",
    "def load_rows(base_dir, dataset):\n",
    "    path = os.path.join(base_dir, f\"{dataset}_score_details_both_layers.jsonl\")\n",
    "    if not os.path.exists(path):\n",
    "        raise FileNotFoundError(f\"Not found: {path}\")\n",
    "    rows = []\n",
    "    with open(path, \"r\", encoding=\"utf-8\") as f:\n",
    "        for line in f:\n",
    "            if line.strip():\n",
    "                rows.append(json.loads(line))\n",
    "    return rows\n",
    "\n",
    "def softmax(x, T=1.0):\n",
    "    x = np.asarray(x, dtype=np.float64)\n",
    "    x = x / max(T, 1e-8)\n",
    "    x -= x.max()\n",
    "    e = np.exp(x)\n",
    "    s = e.sum()\n",
    "    if s <= 0.0: return np.zeros_like(x)\n",
    "    return e / s\n",
    "\n",
    "def entropy(p):\n",
    "    p = np.clip(p, 1e-12, 1.0)\n",
    "    return float(-(p * np.log(p)).sum())\n",
    "\n",
    "def topk_items(d: dict, k: int):\n",
    "    import heapq\n",
    "    if k <= 0 or k >= len(d):\n",
    "        return sorted(d.items(), key=lambda x: -x[1])\n",
    "    return heapq.nlargest(k, d.items(), key=lambda x: x[1])\n",
    "\n",
    "def s1_s2_margin(score_dict):\n",
    "    if not score_dict:\n",
    "        return 0.0, 0.0, 0.0\n",
    "    vals = np.array(list(score_dict.values()), dtype=np.float64)\n",
    "    if vals.size == 1:\n",
    "        return float(vals[0]), 0.0, float(\"inf\")\n",
    "    s1 = float(vals.max())\n",
    "    s2 = float(np.partition(vals, -2)[-2])\n",
    "    return s1, s2, s1 - s2\n",
    "\n",
    "def p1_p2_H(score_dict, T=1.0, topk=200):\n",
    "    top = topk_items(score_dict, topk)\n",
    "    vals = np.array([s for _, s in top], dtype=np.float64)\n",
    "    if vals.size == 0:\n",
    "        return 0.0, 0.0, 0.0, np.array([])\n",
    "    p = softmax(vals, T=T)\n",
    "    p_sorted = np.sort(p)\n",
    "    p1 = float(p_sorted[-1])\n",
    "    p2 = float(p_sorted[-2]) if p_sorted.size >= 2 else 0.0\n",
    "    H = entropy(p)\n",
    "    return p1, p2, H, p\n",
    "\n",
    "def gini_from_probs(p):\n",
    "    return float((p**2).sum()) if p.size > 0 else 0.0\n",
    "\n",
    "def pick_mid_tag(base_dir, dataset):\n",
    "    paths = glob.glob(os.path.join(base_dir, f\"{dataset}_qry_layer*\"))\n",
    "    tags = []\n",
    "    for p in paths:\n",
    "        tag = os.path.basename(p).split(f\"{dataset}_qry_\")[-1]\n",
    "        if tag == \"layerlast\": continue\n",
    "        if tag.startswith(\"layer\"):\n",
    "            try:\n",
    "                n = int(tag.replace(\"layer\",\"\"))\n",
    "                tags.append((n, tag))\n",
    "            except: pass\n",
    "    if not tags: return None\n",
    "    tags.sort(key=lambda x: x[0])\n",
    "    return tags[0][1]\n",
    "\n",
    "def get_timing(base_dir, dataset, mid_tag=None):\n",
    "    last_file = os.path.join(base_dir, f\"{dataset}_qry_inference_stats_layerlast.json\")\n",
    "    mid_tag = mid_tag or pick_mid_tag(base_dir, dataset)\n",
    "    mid_file  = os.path.join(base_dir, f\"{dataset}_qry_inference_stats_{mid_tag}.json\") if mid_tag else None\n",
    "    def read_avg(path):\n",
    "        if path and os.path.exists(path):\n",
    "            d = json.load(open(path,\"r\"))\n",
    "            return d.get(\"inference_times\",{}).get(\"avg_inference_time_per_item_seconds\", None)\n",
    "        return None\n",
    "    t_last = read_avg(last_file) or 1.0\n",
    "    t_mid  = read_avg(mid_file)  or 0.5\n",
    "    return float(t_mid), float(t_last)\n",
    "\n",
    "def score_dict_to_topk(d, k=200):\n",
    "    items = sorted(d.items(), key=lambda x: -x[1])\n",
    "    return items if k >= len(items) else items[:k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b273e5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# === 配置：早停层（默认统一用 layer12；也支持按数据集覆盖）===\n",
    "MID_LAYER_TAG_DEFAULT = \"layer12\"  # 你的示例就是 layer12\n",
    "MID_LAYER_TAG_MAP = {\n",
    "    # \"CIRR\": \"layer12\",\n",
    "    # \"MSCOCO_i2t\": \"layer12\",\n",
    "}\n",
    "\n",
    "# 可能存在的“最后层”统计文件后缀候选\n",
    "LAST_TAG_CANDIDATES = (\"layerlast\", \"layer-1\", \"last\")\n",
    "\n",
    "import os, re, json, glob\n",
    "import numpy as np\n",
    "\n",
    "def _read_avg_time(fp):\n",
    "    # 解析 avg_inference_time_per_item_seconds；若无则 total / count\n",
    "    try:\n",
    "        d = json.load(open(fp, \"r\"))\n",
    "        inf = d.get(\"inference_times\", {})\n",
    "        avg = inf.get(\"avg_inference_time_per_item_seconds\", None)\n",
    "        if avg is None:\n",
    "            tot = inf.get(\"total_inference_time_seconds\", None)\n",
    "            cnt = d.get(\"data_point_count\", None)\n",
    "            if tot is not None and cnt not in (None, 0):\n",
    "                avg = float(tot) / float(cnt)\n",
    "        return float(avg) if avg is not None else None\n",
    "    except Exception:\n",
    "        return None\n",
    "\n",
    "_layer_pat = re.compile(r\"_qry_inference_stats_(layer(?P<tag>last|-?\\d+))\\.json$\")\n",
    "\n",
    "def _discover_layer_times(base_dir, dataset):\n",
    "    # 扫描所有该数据集的 layer 统计文件，返回 { \"layer12\": 0.0124, \"layerlast\": 0.023, ... }\n",
    "    times = {}\n",
    "    for fp in glob.glob(os.path.join(base_dir, f\"{dataset}_qry_inference_stats_layer*.json\")):\n",
    "        m = _layer_pat.search(fp)\n",
    "        if not m: \n",
    "            continue\n",
    "        tag = m.group(1)  # 例如 \"layer12\" / \"layer-1\" / \"layerlast\"\n",
    "        t = _read_avg_time(fp)\n",
    "        if t is not None:\n",
    "            times[tag] = t\n",
    "    return times\n",
    "\n",
    "def get_timing_v2(base_dir, dataset, mid_tag_default=MID_LAYER_TAG_DEFAULT, mid_tag_map=MID_LAYER_TAG_MAP, verbose=False):\n",
    "    \"\"\"\n",
    "    返回 (t_mid, t_last)，单位秒/样本。\n",
    "    - mid：优先用 MID_LAYER_TAG_MAP[dataset]，否则用 MID_LAYER_TAG_DEFAULT；\n",
    "            若没有该文件则回退到“最小的正整数层号”。\n",
    "    - last：优先找 layerlast / layer-1；否则回退到“最大的层号”。\n",
    "    \"\"\"\n",
    "    times = _discover_layer_times(base_dir, dataset)\n",
    "\n",
    "    def pick_mid():\n",
    "        # 1) 指定 mid 层\n",
    "        tag = mid_tag_map.get(dataset, mid_tag_default)\n",
    "        if tag and f\"{tag}\" in times:\n",
    "            return f\"{tag}\", times[f\"{tag}\"]\n",
    "        # 2) 退化：选最小正整数层号\n",
    "        nums = []\n",
    "        for k in times.keys():\n",
    "            if k.startswith(\"layer\"):\n",
    "                s = k.replace(\"layer\", \"\")\n",
    "                if s.isdigit():\n",
    "                    nums.append((int(s), k))\n",
    "        if nums:\n",
    "            k = sorted(nums, key=lambda x: x[0])[0][1]\n",
    "            return k, times[k]\n",
    "        # 实在找不到，返回 None\n",
    "        return None, None\n",
    "\n",
    "    def pick_last():\n",
    "        # 1) 先看熟知后缀\n",
    "        for tag in LAST_TAG_CANDIDATES:\n",
    "            key = f\"layer{tag}\" if not tag.startswith(\"layer\") else tag\n",
    "            if key in times:\n",
    "                return key, times[key]\n",
    "        # 2) 退化：有 layer-1 就用 layer-1\n",
    "        if \"layer-1\" in times:\n",
    "            return \"layer-1\", times[\"layer-1\"]\n",
    "        # 3) 退化：选最大的层号当 last\n",
    "        nums = []\n",
    "        for k in times.keys():\n",
    "            if k.startswith(\"layer\"):\n",
    "                s = k.replace(\"layer\", \"\")\n",
    "                try:\n",
    "                    v = int(s)\n",
    "                    nums.append((v, k))\n",
    "                except:\n",
    "                    pass\n",
    "        if nums:\n",
    "            k = sorted(nums, key=lambda x: x[0])[-1][1]\n",
    "            return k, times[k]\n",
    "        return None, None\n",
    "\n",
    "    mid_tag, t_mid = pick_mid()\n",
    "    last_tag, t_last = pick_last()\n",
    "\n",
    "    # 最后兜底，避免 None\n",
    "    if t_mid is None:\n",
    "        t_mid = 0.5\n",
    "    if t_last is None:\n",
    "        t_last = 1.0\n",
    "\n",
    "    if verbose:\n",
    "        print(f\"[{dataset}] t_mid({mid_tag})={t_mid:.6f}s, t_last({last_tag})={t_last:.6f}s; files in dir:\", list(times.keys()))\n",
    "    return float(t_mid), float(t_last)\n",
    "\n",
    "# 用 v2 替换之前的 get_timing（后续评估代码会自动用新时间）\n",
    "def get_timing(base_dir, dataset):\n",
    "    return get_timing_v2(base_dir, dataset, mid_tag_default=MID_LAYER_TAG_DEFAULT, mid_tag_map=MID_LAYER_TAG_MAP, verbose=False)\n",
    "\n",
    "# 可快速 sanity check 一下\n",
    "# print(get_timing(BASE_DIR, \"CIRR\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8376abd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize_speedup_once(df_all, use_last_bool, base_dir):\n",
    "    ds_names = df_all[\"dataset\"].values\n",
    "    out = []\n",
    "    for ds in np.unique(ds_names):\n",
    "        mask = (ds_names == ds)\n",
    "        t_mid, t_last = get_timing(base_dir, ds)\n",
    "        frac_last = float(np.mean(use_last_bool[mask]))\n",
    "        exp_t = (1.0 - frac_last) * t_mid + frac_last * t_last\n",
    "        spd = t_last / exp_t if exp_t > 1e-12 else 1.0\n",
    "        out.append({\"dataset\": ds, \"t_mid\": t_mid, \"t_last\": t_last, \"frac_last\": frac_last, \"speedup\": spd})\n",
    "    return pd.DataFrame(out)\n",
    "\n",
    "# 例：用某个模型 + 阈值 0.5 的决策，快速看每个数据集 speedup\n",
    "# use_last = (oof[best_name] >= 0.5)\n",
    "# summarize_speedup_once(df, use_last, BASE_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "173a4116",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 3A: mid2last 相关的 I/O（可选）\n",
    "\n",
    "def l2norm(x):\n",
    "    n = np.linalg.norm(x, axis=1, keepdims=True) + 1e-12\n",
    "    return x / n\n",
    "\n",
    "def build_faiss_index(vecs, use_gpu=False):\n",
    "    try:\n",
    "        import faiss\n",
    "    except Exception as e:\n",
    "        raise RuntimeError(\"faiss 未安装。请 pip install faiss-gpu 或 faiss-cpu\") from e\n",
    "    d = vecs.shape[1]\n",
    "    index = faiss.IndexFlatIP(d)\n",
    "    if use_gpu:\n",
    "        res = faiss.StandardGpuResources()\n",
    "        index = faiss.index_cpu_to_gpu(res, 0, index)\n",
    "    index.add(vecs.astype(np.float32))\n",
    "    return index\n",
    "\n",
    "def compute_mid2last_for_dataset(\n",
    "    ds_name, base_dir, q_mid_path, c_last_path, topk=200,\n",
    "    input_jsonl=None, out_jsonl=None, use_gpu=True\n",
    "):\n",
    "    # 读取并 L2 归一化\n",
    "    Q = np.load(q_mid_path)     # shape [Nq, d]\n",
    "    C = np.load(c_last_path)    # shape [Nc, d]\n",
    "    Q = l2norm(Q); C = l2norm(C)\n",
    "\n",
    "    # 建 index\n",
    "    try:\n",
    "        index = build_faiss_index(C, use_gpu=use_gpu)\n",
    "        D, I = index.search(Q.astype(np.float32), topk)\n",
    "    except Exception as e:\n",
    "        # 退化实现（小数据可用，大数据建议 faiss）\n",
    "        print(\"FAISS 不可用，退化为 numpy 计算（仅限小规模）\")\n",
    "        D = (Q @ C.T)  # [Nq, Nc]\n",
    "        I = np.argsort(-D, axis=1)[:, :topk]\n",
    "        D = np.take_along_axis(D, I, axis=1)\n",
    "\n",
    "    # 读取旧 jsonl，增补 mid2last\n",
    "    rows = []\n",
    "    if input_jsonl and os.path.exists(input_jsonl):\n",
    "        with open(input_jsonl, \"r\", encoding=\"utf-8\") as f:\n",
    "            rows = [json.loads(l) for l in f if l.strip()]\n",
    "        assert len(rows) == Q.shape[0], f\"qid数不一致: {len(rows)} vs {Q.shape[0]}\"\n",
    "\n",
    "    out = []\n",
    "    for qid in tqdm(range(Q.shape[0]), desc=f\"{ds_name}: mid2last\"):\n",
    "        cand_scores = {int(I[qid, j]): float(D[qid, j]) for j in range(topk)}\n",
    "        top1 = int(I[qid, 0]) if topk > 0 else None\n",
    "        rec = rows[qid] if rows else {\"qid\": qid, \"dataset\": ds_name}\n",
    "        rec[\"mid2last\"] = {\"top1\": top1, \"cand_scores\": cand_scores}\n",
    "        out.append(rec)\n",
    "\n",
    "    out_jsonl = out_jsonl or input_jsonl or os.path.join(base_dir, f\"{ds_name}_score_details_both_layers.jsonl\")\n",
    "    with open(out_jsonl, \"w\", encoding=\"utf-8\") as f:\n",
    "        for r in out:\n",
    "            f.write(json.dumps(r, ensure_ascii=False) + \"\\n\")\n",
    "    print(\"Wrote:\", out_jsonl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "675f7eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 3B: 生成 mid2last 分数（可选）\n",
    "# 使用前先把下面两个映射 dict 补上路径（npy），并设 GENERATE_MID2LAST=True\n",
    "\n",
    "Q_MID_PATHS = {\n",
    "    # \"MSCOCO_i2t\": \"/path/to/MSCOCO_i2t_qry_layer12.npy\",\n",
    "}\n",
    "C_LAST_PATHS = {\n",
    "    # \"MSCOCO_i2t\": \"/path/to/MSCOCO_i2t_cand_layerlast.npy\",\n",
    "}\n",
    "\n",
    "if GENERATE_MID2LAST:\n",
    "    for ds in DATASETS:\n",
    "        if ds not in Q_MID_PATHS or ds not in C_LAST_PATHS:\n",
    "            print(f\"[skip] {ds} 缺少向量路径\")\n",
    "            continue\n",
    "        in_jsonl = os.path.join(BASE_DIR, f\"{ds}_score_details_both_layers.jsonl\")\n",
    "        compute_mid2last_for_dataset(\n",
    "            ds_name=ds, base_dir=BASE_DIR,\n",
    "            q_mid_path=Q_MID_PATHS[ds], c_last_path=C_LAST_PATHS[ds],\n",
    "            topk=TOPK, input_jsonl=in_jsonl, out_jsonl=in_jsonl, use_gpu=True\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c5aa812",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 4: 特征构建（支持 mid_key）\n",
    "\n",
    "EXTRA_TOPK = 200  # Δmax 计算时的 mid/last topK 联合集合\n",
    "\n",
    "def delta_metrics_from_dicts(s_mid: dict, s_last: dict, topk=EXTRA_TOPK):\n",
    "    top_mid = score_dict_to_topk(s_mid, topk)\n",
    "    top_last = score_dict_to_topk(s_last, topk)\n",
    "    cands = OrderedDict()\n",
    "    for cid, _ in top_mid:  cands[cid] = True\n",
    "    for cid, _ in top_last: cands[cid] = True\n",
    "    deltas = []\n",
    "    for cid in cands.keys():\n",
    "        a = s_mid.get(cid, None)\n",
    "        b = s_last.get(cid, None)\n",
    "        if a is None or b is None:\n",
    "            continue\n",
    "        deltas.append(abs(b - a))\n",
    "    if len(deltas) == 0:\n",
    "        return 0.0, 0.0, 0.0\n",
    "    arr = np.asarray(deltas, dtype=np.float64)\n",
    "    return float(arr.max()), float(arr.mean()), float(np.percentile(arr, 95))\n",
    "\n",
    "def score_shape_features(s_mid: dict, T=1.0, topk=200):\n",
    "    top = score_dict_to_topk(s_mid, topk)\n",
    "    vals = np.array([s for _, s in top], dtype=np.float64)\n",
    "    if vals.size == 0:\n",
    "        return dict(p1=0.0, p2=0.0, H=0.0, gini=0.0, slope=0.0, z1=0.0,\n",
    "                    s1_over_sk=0.0, tail_mean=0.0, head5_mean=0.0)\n",
    "    p = softmax(vals, T=T)\n",
    "    H = entropy(p)\n",
    "    gini = float((p**2).sum())\n",
    "    ranks = np.arange(1, len(p)+1)\n",
    "    slope = float(np.polyfit(ranks, np.log(p + 1e-12), 1)[0])\n",
    "    K = len(vals)\n",
    "    head5 = vals[:min(5,K)]\n",
    "    tail = vals[max(K-20,0):]\n",
    "    head5_mean = float(head5.mean()) if head5.size>0 else 0.0\n",
    "    tail_mean  = float(tail.mean()) if tail.size>0 else 0.0\n",
    "    mu, sd = float(vals.mean()), float(vals.std()+1e-12)\n",
    "    z1 = (float(vals[0]) - mu) / sd if sd>0 else 0.0\n",
    "    s1_over_sk = float(vals[0] / (vals[min(49, K-1)] + 1e-8)) if K > 50 else float(vals[0] / (vals[-1] + 1e-8))\n",
    "    p_sorted = np.sort(p)\n",
    "    p1 = float(p_sorted[-1])\n",
    "    p2 = float(p_sorted[-2]) if p_sorted.size>=2 else 0.0\n",
    "    return dict(p1=p1, p2=p2, H=H, gini=gini, slope=slope, z1=z1,\n",
    "                s1_over_sk=s1_over_sk, tail_mean=tail_mean, head5_mean=head5_mean)\n",
    "\n",
    "def build_feature_table_with_delta(base_dir, datasets, T=1.0, topk=200, mid_key=\"mid\"):\n",
    "    rows_all = []\n",
    "    for ds_name in datasets:\n",
    "        rows = load_rows(base_dir, ds_name)\n",
    "        for i, r in enumerate(rows):\n",
    "            labels = r.get(\"label\", r.get(\"labels\", []))\n",
    "            labels = set(labels if isinstance(labels, list) else [labels])\n",
    "\n",
    "            mid = r.get(mid_key, r.get(\"mid\", {}))  # 兼容无 mid2last 时回退到 mid\n",
    "            last = r[\"last\"]\n",
    "            s_mid = mid.get(\"cand_scores\", {})\n",
    "            s_last= last.get(\"cand_scores\", {})\n",
    "\n",
    "            mid_top1  = mid.get(\"top1\", None)\n",
    "            last_top1 = last.get(\"top1\", None)\n",
    "            mid_correct  = 1 if (mid_top1 in labels) else 0\n",
    "            last_correct = 1 if (last_top1 in labels) else 0\n",
    "\n",
    "            s1_mid, s2_mid, margin_mid = s1_s2_margin(s_mid)\n",
    "            p1_mid, p2_mid, H_mid, p_mid = p1_p2_H(s_mid, T=T, topk=topk)\n",
    "            gini_mid = gini_from_probs(p_mid)\n",
    "            top_vals = np.array([s for _, s in topk_items(s_mid, topk)], dtype=np.float64)\n",
    "            if top_vals.size == 0:\n",
    "                m_mean=m_std=m_cv=m_kurt=m_med=0.0\n",
    "                z_margin_mid=0.0; mad_margin_mid=0.0\n",
    "                s1_over_mean=0.0; s1_over_med=0.0\n",
    "            else:\n",
    "                m_mean = float(top_vals.mean())\n",
    "                m_std  = float(top_vals.std()) if top_vals.size>1 else 1.0\n",
    "                m_cv   = float(m_std / (abs(m_mean)+1e-8))\n",
    "                m_kurt = float(((top_vals - top_vals.mean())**4).mean() / (top_vals.var()+1e-8)**2 - 3.0) if top_vals.size>3 else 0.0\n",
    "                m_med  = float(np.median(top_vals))\n",
    "                z_margin_mid   = margin_mid / (m_std + 1e-8)\n",
    "                mad = float(np.median(np.abs(top_vals - np.median(top_vals)))) if top_vals.size>1 else 1.0\n",
    "                mad_margin_mid = margin_mid / (mad + 1e-8)\n",
    "                s1_over_mean = s1_mid / (m_mean + 1e-8)\n",
    "                s1_over_med  = s1_mid / (m_med  + 1e-8)\n",
    "\n",
    "            dmax, dmean, d95 = delta_metrics_from_dicts(s_mid, s_last, topk=EXTRA_TOPK)\n",
    "            shp = score_shape_features(s_mid, T=T, topk=topk)\n",
    "\n",
    "            rows_all.append({\n",
    "                \"dataset\": ds_name,\n",
    "                \"qid\": int(r.get(\"qid\", i)),\n",
    "                \"mid_correct\": mid_correct,\n",
    "                \"last_correct\": last_correct,\n",
    "                \"s1_mid\": s1_mid, \"s2_mid\": s2_mid, \"margin_mid\": margin_mid,\n",
    "                \"z_margin_mid\": z_margin_mid, \"mad_margin_mid\": mad_margin_mid,\n",
    "                \"p1_mid\": p1_mid, \"p2_mid\": p2_mid, \"H_mid\": H_mid, \"gini_mid\": gini_mid,\n",
    "                \"topk_mean\": m_mean, \"topk_std\": m_std, \"topk_cv\": m_cv, \"topk_kurt\": m_kurt, \"topk_med\": m_med,\n",
    "                \"s1_over_mean\": s1_over_mean, \"s1_over_med\": s1_over_med,\n",
    "                \"dmax\": dmax, \"dmean\": dmean, \"d95\": d95,\n",
    "                \"p1\": shp[\"p1\"], \"p2\": shp[\"p2\"], \"shape_H\": shp[\"H\"], \"shape_gini\": shp[\"gini\"],\n",
    "                \"slope\": shp[\"slope\"], \"z1\": shp[\"z1\"], \"s1_over_sk\": shp[\"s1_over_sk\"],\n",
    "                \"tail_mean\": shp[\"tail_mean\"], \"head5_mean\": shp[\"head5_mean\"],\n",
    "            })\n",
    "    df = pd.DataFrame(rows_all)\n",
    "    df[\"y_need_last\"] = ((df[\"mid_correct\"]==0) & (df[\"last_correct\"]==1)).astype(int)\n",
    "    return df\n",
    "\n",
    "def add_mask_features(df, base_dir, datasets, tag=None):\n",
    "    def pick_mid_tag_from_dir(out_dir, dataset):\n",
    "        paths = glob.glob(os.path.join(out_dir, f\"{dataset}_qry_layer*\"))\n",
    "        pairs = []\n",
    "        for p in paths:\n",
    "            t = os.path.basename(p).split(f\"{dataset}_qry_\")[-1]\n",
    "            if t==\"layerlast\": continue\n",
    "            if t.startswith(\"layer\"):\n",
    "                try: pairs.append((int(t.replace(\"layer\",\"\")), t))\n",
    "                except: pass\n",
    "        if not pairs: return None\n",
    "        return sorted(pairs, key=lambda x: x[0])[0][1]\n",
    "\n",
    "    feat_cols = {\"mask_ratio\": [], \"mask_len\": [], \"mask_runs\": []}\n",
    "    cache = {}\n",
    "    for ds in datasets:\n",
    "        mid_tag = tag or pick_mid_tag_from_dir(base_dir, ds)\n",
    "        mask_path = os.path.join(base_dir, f\"{ds}_qry_img_token_masks_{mid_tag}.jsonl\")\n",
    "        local = {}\n",
    "        if os.path.exists(mask_path):\n",
    "            with open(mask_path, \"r\", encoding=\"utf-8\") as f:\n",
    "                for line in f:\n",
    "                    o = json.loads(line)\n",
    "                    q = int(o.get(\"index\", len(local)))\n",
    "                    m = o.get(\"mask\", None)\n",
    "                    if isinstance(m, list):\n",
    "                        local[q] = [bool(x) for x in m]\n",
    "        cache[ds] = local\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        ds = row[\"dataset\"]; qid = int(row[\"qid\"])\n",
    "        m = cache.get(ds, {}).get(qid, None)\n",
    "        if not m:\n",
    "            feat_cols[\"mask_ratio\"].append(0.0)\n",
    "            feat_cols[\"mask_len\"].append(0)\n",
    "            feat_cols[\"mask_runs\"].append(0)\n",
    "            continue\n",
    "        arr = np.array(m, dtype=np.int8)\n",
    "        ratio = float(arr.mean())\n",
    "        runs = int(np.sum((arr[1:]==1) & (arr[:-1]==0))) + (1 if arr[0]==1 else 0)\n",
    "        feat_cols[\"mask_ratio\"].append(ratio)\n",
    "        feat_cols[\"mask_len\"].append(int(arr.size))\n",
    "        feat_cols[\"mask_runs\"].append(runs)\n",
    "\n",
    "    for k, v in feat_cols.items():\n",
    "        df[k] = v\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cddb01bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 5: 单阶段门控（全局 τ & 按数据集 τds）\n",
    "def train_gate_models(df, feat_cols, group_by=\"dataset\"):\n",
    "    X = df[feat_cols].values\n",
    "    y = df[\"y_need_last\"].values\n",
    "    groups = df[group_by].values\n",
    "\n",
    "    models = {\n",
    "        \"logreg\": make_pipeline(StandardScaler(), LogisticRegression(max_iter=2000, class_weight=\"balanced\")),\n",
    "        \"gbdt\": GradientBoostingClassifier(),\n",
    "        \"rf\": RandomForestClassifier(n_estimators=600, n_jobs=-1, class_weight=\"balanced\"),\n",
    "        \"svm_rbf\": make_pipeline(StandardScaler(), SVC(kernel=\"rbf\", C=2.0, gamma=\"scale\", probability=True, class_weight=\"balanced\")),\n",
    "    }\n",
    "    gkf = GroupKFold(n_splits=min(5, len(np.unique(groups))))\n",
    "    res, oof = {}, {k: np.zeros_like(y, dtype=np.float64) for k in models}\n",
    "    for name, clf in models.items():\n",
    "        aucs, aps = [], []\n",
    "        for tr, te in gkf.split(X, y, groups):\n",
    "            clf.fit(X[tr], y[tr])\n",
    "            p = clf.predict_proba(X[te])[:,1]\n",
    "            oof[name][te] = p\n",
    "            aucs.append(roc_auc_score(y[te], p))\n",
    "            aps.append(average_precision_score(y[te], p))\n",
    "        res[name] = {\"AUC\": float(np.mean(aucs)), \"AP\": float(np.mean(aps))}\n",
    "    return res, oof\n",
    "\n",
    "def eval_curve_global(df_, proba, base_dir, metric_key=\"hit@1\"):\n",
    "    ds = df_[\"dataset\"].values\n",
    "    mid_corr = df_[\"mid_correct\"].values\n",
    "    last_corr= df_[\"last_correct\"].values\n",
    "    base_hit = float(np.mean(last_corr))\n",
    "    taus = np.linspace(0.0,1.0,51)\n",
    "    rows=[]\n",
    "    for tau in taus:\n",
    "        use_last = (proba >= tau)\n",
    "        acc = float(np.mean(np.where(use_last, last_corr, mid_corr)))\n",
    "        coverage = float(np.mean(~use_last))\n",
    "        total=0.0\n",
    "        for dsu in np.unique(ds):\n",
    "            m = (ds==dsu)\n",
    "            t_mid, t_last = get_timing(base_dir, dsu)\n",
    "            frac_last = float(np.mean(use_last[m]))\n",
    "            total += ((1-frac_last)*t_mid + frac_last*t_last) * m.sum()\n",
    "        avg_time = total/len(ds)\n",
    "        base_time = np.mean([get_timing(base_dir, dsi)[1] for dsi in ds])\n",
    "        speed = base_time / avg_time if avg_time>1e-9 else 1.0\n",
    "        rows.append({\"tau\":tau, \"coverage\":coverage, metric_key:acc, \"drop\":base_hit-acc, \"speedup\":speed})\n",
    "    return pd.DataFrame(rows), base_hit\n",
    "\n",
    "def pick_tau_per_dataset(df_all, proba, base_dir, max_drop=0.01, grid=None):\n",
    "    grid = grid or np.linspace(0.0, 1.0, 101)\n",
    "    ds_names = df_all[\"dataset\"].values\n",
    "    mid_corr = df_all[\"mid_correct\"].values\n",
    "    last_corr= df_all[\"last_correct\"].values\n",
    "    time_map = {ds: get_timing(base_dir, ds) for ds in np.unique(ds_names)}\n",
    "    tau_map = {}; rows = []\n",
    "    for ds in np.unique(ds_names):\n",
    "        m = (ds_names == ds)\n",
    "        base_hit = float(np.mean(last_corr[m]))\n",
    "        best = None\n",
    "        for tau in grid:\n",
    "            use_last = (proba[m] >= tau)\n",
    "            acc = float(np.mean(np.where(use_last, last_corr[m], mid_corr[m])))\n",
    "            drop = base_hit - acc\n",
    "            t_mid, t_last = time_map[ds]\n",
    "            frac_last = float(np.mean(use_last))\n",
    "            avg_time = (1.0 - frac_last)*t_mid + frac_last*t_last\n",
    "            base_time = t_last\n",
    "            speedup = base_time / avg_time if avg_time>1e-9 else 1.0\n",
    "            if drop <= max_drop:\n",
    "                if (best is None) or (speedup > best[\"speedup\"]):\n",
    "                    best = dict(tau=float(tau), speedup=float(speedup), acc=float(acc), drop=float(drop))\n",
    "        if best is None:\n",
    "            # 退而求其次（无可行 τ）：尽量准确\n",
    "            best = {\"tau\": 1.0, \"speedup\": 1.0, \"acc\": float(np.mean(mid_corr[m])),\n",
    "                    \"drop\": base_hit - float(np.mean(mid_corr[m]))}\n",
    "        tau_map[ds] = best[\"tau\"]\n",
    "        rows.append({\"dataset\": ds, **best})\n",
    "    return tau_map, pd.DataFrame(rows)\n",
    "\n",
    "def eval_gating_per_dataset(df_all, proba, tau_map, base_dir):\n",
    "    ds_names = df_all[\"dataset\"].values\n",
    "    mid_corr = df_all[\"mid_correct\"].values\n",
    "    last_corr= df_all[\"last_correct\"].values\n",
    "    base_hit = float(np.mean(last_corr))\n",
    "    time_map = {ds: get_timing(base_dir, ds) for ds in np.unique(ds_names)}\n",
    "    use_last = np.array([proba[i] >= tau_map.get(ds_names[i], 0.5) for i in range(len(proba))], dtype=bool)\n",
    "    chosen_corr = np.where(use_last, last_corr, mid_corr)\n",
    "    acc = float(np.mean(chosen_corr))\n",
    "    coverage = float(np.mean(~use_last))\n",
    "    total_time = 0.0\n",
    "    for ds in np.unique(ds_names):\n",
    "        mask = (ds_names == ds)\n",
    "        t_mid, t_last = time_map[ds]\n",
    "        frac_last = float(np.mean(use_last[mask]))\n",
    "        exp_t = (1.0 - frac_last)*t_mid + frac_last*t_last\n",
    "        total_time += exp_t * mask.sum()\n",
    "    avg_time = total_time / len(ds_names)\n",
    "    base_time = np.mean([time_map[ds][1] for ds in ds_names])\n",
    "    speedup = base_time / avg_time if avg_time > 1e-9 else 1.0\n",
    "    return dict(coverage=coverage, hit_at_1=acc, drop=(base_hit-acc), speedup=speedup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "012d5e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 6: 两阶段（CQR 上界 + 学习门控；含 per-dataset τds）\n",
    "\n",
    "def cqr_upper_oof(df, feat_cols_eps, target_col=\"dmax\", alpha=0.05, group_col=\"dataset\"):\n",
    "    X_eps = df[feat_cols_eps].values\n",
    "    y_eps = df[target_col].values\n",
    "    groups = df[group_col].values\n",
    "    gkf = GroupKFold(n_splits=min(5, len(np.unique(groups))))\n",
    "    oof_upper = np.zeros_like(y_eps, dtype=np.float64)\n",
    "    group_shifts = {}\n",
    "    for tr_idx, te_idx in gkf.split(X_eps, y_eps, groups):\n",
    "        tr_full_X, tr_full_y, tr_full_g = X_eps[tr_idx], y_eps[tr_idx], groups[tr_idx]\n",
    "        n = len(tr_full_X)\n",
    "        idx = np.arange(n); np.random.shuffle(idx)\n",
    "        n_cal = max(64, int(0.2*n))\n",
    "        cal_idx = idx[:n_cal]; fit_idx = idx[n_cal:]\n",
    "        X_fit, y_fit, g_fit = tr_full_X[fit_idx], tr_full_y[fit_idx], tr_full_g[fit_idx]\n",
    "        X_cal, y_cal, g_cal = tr_full_X[cal_idx], tr_full_y[cal_idx], tr_full_g[cal_idx]\n",
    "        upper = GradientBoostingRegressor(loss=\"quantile\", alpha=1-alpha, random_state=SEED)\n",
    "        upper.fit(X_fit, y_fit)\n",
    "        pred_cal = upper.predict(X_cal)\n",
    "        for ds in np.unique(g_cal):\n",
    "            mask = (g_cal == ds)\n",
    "            resid = y_cal[mask] - pred_cal[mask]\n",
    "            shift = float(np.quantile(resid, 1 - alpha)) if mask.sum() > 0 else 0.0\n",
    "            group_shifts[ds] = shift\n",
    "        pred_te = upper.predict(X_eps[te_idx])\n",
    "        ds_te = groups[te_idx]\n",
    "        shift_te = np.array([group_shifts.get(ds, 0.0) for ds in ds_te], dtype=np.float64)\n",
    "        oof_upper[te_idx] = pred_te + shift_te\n",
    "    covered = (y_eps <= oof_upper).mean()\n",
    "    return oof_upper, covered\n",
    "\n",
    "def eval_phaseA_safe(df_all, upper_oof, base_dir, factor=2.0):\n",
    "    ds_names = df_all[\"dataset\"].values\n",
    "    mid_corr = df_all[\"mid_correct\"].values\n",
    "    last_corr = df_all[\"last_correct\"].values\n",
    "    margin_mid = df_all[\"margin_mid\"].values\n",
    "    time_map = {ds: get_timing(base_dir, ds) for ds in np.unique(ds_names)}\n",
    "    safe = margin_mid > factor * upper_oof\n",
    "    chosen_corr = np.where(safe, mid_corr, last_corr)\n",
    "    acc = float(np.mean(chosen_corr))\n",
    "    coverage = float(np.mean(safe))\n",
    "    total_time = 0.0\n",
    "    for ds in np.unique(ds_names):\n",
    "        mask = (ds_names == ds)\n",
    "        t_mid, t_last = time_map[ds]\n",
    "        frac_last = float(np.mean(~safe[mask]))\n",
    "        exp_t = (1.0 - frac_last) * t_mid + frac_last * t_last\n",
    "        total_time += exp_t * mask.sum()\n",
    "    avg_time = total_time / len(ds_names)\n",
    "    base_time = np.mean([time_map[ds][1] for ds in ds_names])\n",
    "    speedup = base_time / avg_time if avg_time > 1e-9 else 1.0\n",
    "    return dict(coverage=coverage, hit_at_1=acc, speedup=speedup), safe\n",
    "\n",
    "def train_gate_on_hard(df_hard, feat_cols_gate, group_col=\"dataset\"):\n",
    "    Xg = df_hard[feat_cols_gate].values\n",
    "    y_need = df_hard[\"y_need_last\"].values\n",
    "    groups = df_hard[group_col].values\n",
    "    # 可以把“安全归一化 margin”拼进去（若已在 feat_cols_gate 里则跳过）\n",
    "    models_gate = {\n",
    "        \"logreg\": make_pipeline(StandardScaler(), LogisticRegression(max_iter=2000, class_weight=\"balanced\")),\n",
    "        \"gbdt\": GradientBoostingClassifier(),\n",
    "        \"rf\": RandomForestClassifier(n_estimators=400, n_jobs=-1, class_weight=\"balanced\")\n",
    "    }\n",
    "    gkf = GroupKFold(n_splits=min(5, len(np.unique(groups))))\n",
    "    res_gate = {}; oof_gate = {name: np.zeros_like(y_need, dtype=np.float64) for name in models_gate}\n",
    "    for name, clf in models_gate.items():\n",
    "        aucs, aps = [], []\n",
    "        for tr, te in gkf.split(Xg, y_need, groups):\n",
    "            clf.fit(Xg[tr], y_need[tr])\n",
    "            proba = clf.predict_proba(Xg[te])[:,1]\n",
    "            oof_gate[name][te] = proba\n",
    "            aucs.append(roc_auc_score(y_need[te], proba))\n",
    "            aps.append(average_precision_score(y_need[te], proba))\n",
    "        res_gate[name] = {\"AUC\": float(np.mean(aucs)), \"AP\": float(np.mean(aps))}\n",
    "    return res_gate, oof_gate\n",
    "\n",
    "def eval_two_stage_global(df_all, oof_upper, proba_gate_full, base_dir, taus):\n",
    "    ds_names = df_all[\"dataset\"].values\n",
    "    mid_corr = df_all[\"mid_correct\"].values\n",
    "    last_corr = df_all[\"last_correct\"].values\n",
    "    margin_mid = df_all[\"margin_mid\"].values\n",
    "    time_map = {ds: get_timing(base_dir, ds) for ds in np.unique(ds_names)}\n",
    "    safe = margin_mid > 2.0 * oof_upper\n",
    "    out=[]\n",
    "    for tau in taus:\n",
    "        use_last = np.zeros(len(df_all), dtype=bool)\n",
    "        hard_idx = np.where(~safe)[0]\n",
    "        need_last = (proba_gate_full[hard_idx] >= tau)\n",
    "        use_last[hard_idx] = need_last\n",
    "        chosen_corr = np.where(safe | ~use_last, mid_corr, last_corr)\n",
    "        acc = float(np.mean(chosen_corr))\n",
    "        coverage = float(np.mean(safe | ~use_last))\n",
    "        total_time=0.0\n",
    "        for ds in np.unique(ds_names):\n",
    "            mask = (ds_names == ds)\n",
    "            t_mid, t_last = time_map[ds]\n",
    "            frac_last = float(np.mean(use_last[mask]))\n",
    "            exp_t = (1.0 - frac_last) * t_mid + frac_last * t_last\n",
    "            total_time += exp_t * mask.sum()\n",
    "        avg_time = total_time / len(ds_names)\n",
    "        base_time = np.mean([time_map[ds][1] for ds in ds_names])\n",
    "        speedup = base_time / avg_time if avg_time>1e-9 else 1.0\n",
    "        out.append({\"tau\": float(tau), \"coverage\": coverage, \"hit@1\": acc, \"speedup\": speedup})\n",
    "    return pd.DataFrame(out)\n",
    "\n",
    "def eval_two_stage_per_dataset(df_all, oof_upper, proba_gate_full, tau_map, base_dir, safe_factor=2.0):\n",
    "    ds_names = df_all[\"dataset\"].values\n",
    "    mid_corr = df_all[\"mid_correct\"].values\n",
    "    last_corr= df_all[\"last_correct\"].values\n",
    "    margin_mid = df_all[\"margin_mid\"].values\n",
    "    time_map = {ds: get_timing(base_dir, ds) for ds in np.unique(ds_names)}\n",
    "    safe = margin_mid > safe_factor * oof_upper\n",
    "    use_last = np.zeros(len(df_all), dtype=bool)\n",
    "    hard_idx = np.where(~safe)[0]\n",
    "    for i in hard_idx:\n",
    "        ds = ds_names[i]\n",
    "        tau_ds = tau_map.get(ds, 0.5)\n",
    "        use_last[i] = (proba_gate_full[i] >= tau_ds)\n",
    "    chosen_corr = np.where(safe | ~use_last, mid_corr, last_corr)\n",
    "    acc = float(np.mean(chosen_corr))\n",
    "    coverage = float(np.mean(safe | ~use_last))\n",
    "    total_time = 0.0\n",
    "    for ds in np.unique(ds_names):\n",
    "        mask = (ds_names == ds)\n",
    "        t_mid, t_last = time_map[ds]\n",
    "        frac_last = float(np.mean(use_last[mask]))\n",
    "        exp_t = (1.0 - frac_last)*t_mid + frac_last*t_last\n",
    "        total_time += exp_t * mask.sum()\n",
    "    avg_time = total_time / len(ds_names)\n",
    "    base_time = np.mean([time_map[ds][1] for ds in ds_names])\n",
    "    speedup = base_time / avg_time if avg_time > 1e-9 else 1.0\n",
    "    base_hit = float(np.mean(last_corr))\n",
    "    return dict(coverage=coverage, hit_at_1=acc, drop=base_hit-acc, speedup=speedup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "afa62192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 7: 按数据集的一致性阈值（可选）\n",
    "\n",
    "def conformal_threshold_per_dataset(df_all, proba, delta=0.05):\n",
    "    ds = df_all[\"dataset\"].values\n",
    "    mid_corr = df_all[\"mid_correct\"].values.astype(int)\n",
    "    tau_map = {}\n",
    "    for dsu in np.unique(ds):\n",
    "        m = (ds == dsu)\n",
    "        qid = df_all.loc[m, \"qid\"].values\n",
    "        calib = (qid % 5 == 0)  # 简易校准划分\n",
    "        p = proba[m]\n",
    "        y_mid = mid_corr[m]\n",
    "        taus = np.linspace(0, 1, 501)\n",
    "        best = None\n",
    "        for t in taus:\n",
    "            sel = calib\n",
    "            mask_mid = (p[sel] < t)\n",
    "            if mask_mid.sum() == 0:\n",
    "                risk = 0.0\n",
    "            else:\n",
    "                risk = float(np.mean(1 - y_mid[sel][mask_mid]))  # mid_corr=0 比例\n",
    "            if risk <= delta:\n",
    "                best = float(t); break\n",
    "        tau_map[dsu] = best if best is not None else 0.0\n",
    "    return tau_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "260d2e34",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "Not found: /home/v-menggao/code/VLM2Vec/exps/vlm2vec_train_2.5_3b_multilayer_distill_only_image_ret_11_18_h100/checkpoint-5000/VLM2Vec-V2.0-Qwen2VL-2B/image_retrival/CIRR_score_details_both_layers.jsonl",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Cell 8: 主流程（mid_key 可选 \"mid\" 或 \"mid2last\"）\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# 1) 构建特征（从 JSONL 读取 mid_key 与 last 的分数）\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_feature_table_with_delta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mBASE_DIR\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDATASETS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mTEMP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtopk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mTOPK\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmid_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mMID_KEY\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m df \u001b[38;5;241m=\u001b[39m add_mask_features(df, BASE_DIR, DATASETS)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# 2) 单阶段门控 —— 训练\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[8], line 51\u001b[0m, in \u001b[0;36mbuild_feature_table_with_delta\u001b[0;34m(base_dir, datasets, T, topk, mid_key)\u001b[0m\n\u001b[1;32m     49\u001b[0m rows_all \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ds_name \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[0;32m---> 51\u001b[0m     rows \u001b[38;5;241m=\u001b[39m \u001b[43mload_rows\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mds_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, r \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(rows):\n\u001b[1;32m     53\u001b[0m         labels \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m, r\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m, []))\n",
      "Cell \u001b[0;32mIn[3], line 6\u001b[0m, in \u001b[0;36mload_rows\u001b[0;34m(base_dir, dataset)\u001b[0m\n\u001b[1;32m      4\u001b[0m path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(base_dir, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_score_details_both_layers.jsonl\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(path):\n\u001b[0;32m----> 6\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      7\u001b[0m rows \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: Not found: /home/v-menggao/code/VLM2Vec/exps/vlm2vec_train_2.5_3b_multilayer_distill_only_image_ret_11_18_h100/checkpoint-5000/VLM2Vec-V2.0-Qwen2VL-2B/image_retrival/CIRR_score_details_both_layers.jsonl"
     ]
    }
   ],
   "source": [
    "# Cell 8: 主流程（mid_key 可选 \"mid\" 或 \"mid2last\"）\n",
    "\n",
    "# 1) 构建特征（从 JSONL 读取 mid_key 与 last 的分数）\n",
    "df = build_feature_table_with_delta(BASE_DIR, DATASETS, T=TEMP, topk=TOPK, mid_key=MID_KEY)\n",
    "df = add_mask_features(df, BASE_DIR, DATASETS)\n",
    "\n",
    "# 2) 单阶段门控 —— 训练\n",
    "feat_cols_single = [\n",
    "    \"s1_mid\",\"s2_mid\",\"margin_mid\",\"z_margin_mid\",\"mad_margin_mid\",\n",
    "    \"p1_mid\",\"H_mid\",\"gini_mid\",\n",
    "    \"topk_mean\",\"topk_std\",\"topk_cv\",\"topk_kurt\",\"topk_med\",\n",
    "    \"s1_over_mean\",\"s1_over_med\",\n",
    "    # 形状特征\n",
    "    \"p1\",\"p2\",\"shape_H\",\"shape_gini\",\"slope\",\"z1\",\"s1_over_sk\",\"tail_mean\",\"head5_mean\",\n",
    "    # mask 特征（若没有不会报错，因为上面 add_mask_features 已加）\n",
    "    \"mask_ratio\",\"mask_len\",\"mask_runs\",\n",
    "]\n",
    "feat_cols_single = [c for c in feat_cols_single if c in df.columns]\n",
    "\n",
    "res, oof = train_gate_models(df, feat_cols_single, group_by=\"dataset\")\n",
    "best_name = max(res, key=lambda k: res[k][\"AUC\"])\n",
    "print(\"Single-stage best model:\", best_name, res[best_name])\n",
    "\n",
    "# 3) 单阶段 —— 全局 τ 曲线\n",
    "curve_global, base_hit = eval_curve_global(df, oof[best_name], BASE_DIR)\n",
    "plt.figure(figsize=(7,5)); plt.plot(curve_global[\"coverage\"], curve_global[\"hit@1\"], marker=\"o\")\n",
    "plt.xlabel(\"coverage\"); plt.ylabel(\"hit@1\"); plt.title(f\"Gating tradeoff (global τ) [{MID_KEY}]\")\n",
    "plt.grid(alpha=0.3); plt.savefig(os.path.join(OUT_DIR, f\"global_tradeoff_{MID_KEY}.png\"), dpi=150); plt.show()\n",
    "\n",
    "plt.figure(figsize=(7,5)); plt.plot(curve_global[\"coverage\"], curve_global[\"speedup\"], marker=\"o\")\n",
    "plt.xlabel(\"coverage\"); plt.ylabel(\"speedup\"); plt.title(f\"Speedup vs coverage (global τ) [{MID_KEY}]\")\n",
    "plt.grid(alpha=0.3); plt.savefig(os.path.join(OUT_DIR, f\"global_speedup_{MID_KEY}.png\"), dpi=150); plt.show()\n",
    "\n",
    "# 4) 单阶段 —— 按数据集 τds\n",
    "tau_map, per_ds_table = pick_tau_per_dataset(df, oof[best_name], BASE_DIR, max_drop=0.01)\n",
    "res_per_ds = eval_gating_per_dataset(df, oof[best_name], tau_map, BASE_DIR)\n",
    "print(\"Per-dataset τ summary:\"); display(per_ds_table)\n",
    "print(\"Overall (per-dataset τ):\", res_per_ds)\n",
    "\n",
    "# 5) 两阶段 —— CQR 上界（Δmax 的上分位）\n",
    "feat_eps = [\n",
    "    \"s1_mid\",\"margin_mid\",\"z_margin_mid\",\"mad_margin_mid\",\n",
    "    \"p1_mid\",\"H_mid\",\"gini_mid\",\"topk_mean\",\"topk_std\",\"topk_cv\",\"topk_kurt\",\n",
    "    \"s1_over_mean\",\"s1_over_med\",\n",
    "    \"p1\",\"p2\",\"shape_H\",\"shape_gini\",\"slope\",\"z1\",\"s1_over_sk\",\"tail_mean\",\"head5_mean\",\n",
    "]\n",
    "feat_eps = [c for c in feat_eps if c in df.columns]\n",
    "oof_upper, cov_cqr = cqr_upper_oof(df, feat_eps, target_col=\"dmax\", alpha=0.05, group_col=\"dataset\")\n",
    "print(f\"CQR empirical coverage (target 0.95): {cov_cqr:.4f}\")\n",
    "\n",
    "# 6) 两阶段 —— Phase A 安全早停评估\n",
    "phaseA_res, safe_mask = eval_phaseA_safe(df, oof_upper, BASE_DIR, factor=2.0)\n",
    "print(\"Phase-A safe early-stop:\", phaseA_res)\n",
    "\n",
    "# 7) 两阶段 —— 对困难样本训练门控\n",
    "df_hard = df[~safe_mask].reset_index(drop=True)\n",
    "\n",
    "feat_cols_gate = [\n",
    "    \"s1_mid\",\"margin_mid\",\"z_margin_mid\",\"mad_margin_mid\",\n",
    "    \"p1_mid\",\"H_mid\",\"gini_mid\",\"topk_mean\",\"topk_std\",\"topk_cv\",\"topk_kurt\",\n",
    "    \"s1_over_mean\",\"s1_over_med\",\n",
    "    \"p1\",\"p2\",\"shape_H\",\"shape_gini\",\"slope\",\"z1\",\"s1_over_sk\",\"tail_mean\",\"head5_mean\",\n",
    "    \"mask_ratio\",\"mask_len\",\"mask_runs\",\n",
    "]\n",
    "feat_cols_gate = [c for c in feat_cols_gate if c in df_hard.columns]\n",
    "res_gate, oof_gate = train_gate_on_hard(df_hard, feat_cols_gate, group_col=\"dataset\")\n",
    "gate_name = max(res_gate, key=lambda k: res_gate[k][\"AUC\"])\n",
    "print(\"Two-stage gate best:\", gate_name, res_gate[gate_name])\n",
    "\n",
    "# 把困难样本的 oof 拼回全量数组（其它位置填 1.0，表示默认“需要 last”，后续通过 safe_mask 控制）\n",
    "proba_gate_full = np.ones(len(df), dtype=np.float64)\n",
    "hard_idx = np.where(~safe_mask)[0]\n",
    "proba_gate_full[hard_idx] = oof_gate[gate_name]\n",
    "\n",
    "# 8) 两阶段 —— 全局 τ 曲线\n",
    "taus = np.linspace(0,1,51)\n",
    "curve_2stage = eval_two_stage_global(df, oof_upper, proba_gate_full, BASE_DIR, taus)\n",
    "plt.figure(figsize=(7,5)); plt.plot(curve_2stage[\"coverage\"], curve_2stage[\"hit@1\"], marker=\"o\")\n",
    "plt.xlabel(\"coverage\"); plt.ylabel(\"hit@1\"); plt.title(f\"Two-stage tradeoff (global τ) [{MID_KEY}]\")\n",
    "plt.grid(alpha=0.3); plt.savefig(os.path.join(OUT_DIR, f\"2stage_global_tradeoff_{MID_KEY}.png\"), dpi=150); plt.show()\n",
    "plt.figure(figsize=(7,5)); plt.plot(curve_2stage[\"coverage\"], curve_2stage[\"speedup\"], marker=\"o\")\n",
    "plt.xlabel(\"coverage\"); plt.ylabel(\"speedup\"); plt.title(f\"Speedup vs coverage (2-stage, global τ) [{MID_KEY}]\")\n",
    "plt.grid(alpha=0.3); plt.savefig(os.path.join(OUT_DIR, f\"2stage_global_speedup_{MID_KEY}.png\"), dpi=150); plt.show()\n",
    "\n",
    "# 9) 两阶段 —— 按数据集 τds（在 hard 样本上选择 τds）\n",
    "# 仅对 hard 子集做 τds 搜索\n",
    "df_hard_full = df.copy()\n",
    "df_hard_full[\"proba_gate_full\"] = proba_gate_full\n",
    "df_hard_only = df_hard_full[~safe_mask].reset_index(drop=True)\n",
    "\n",
    "tau_map_hard, per_ds_hard = pick_tau_per_dataset(df_hard_only, df_hard_only[\"proba_gate_full\"].values, BASE_DIR, max_drop=0.01)\n",
    "res_2stage_perds = eval_two_stage_per_dataset(df, oof_upper, proba_gate_full, tau_map_hard, BASE_DIR, safe_factor=2.0)\n",
    "print(\"Two-stage per-dataset τ (on hard set):\", res_2stage_perds)\n",
    "display(per_ds_hard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2eb1febc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts by group4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "group4\n",
       "mid✓ last✓                6353\n",
       "mid✗ last✗                3144\n",
       "mid✗ last✓ (need last)    1962\n",
       "mid✓ last✗ (overkill)      541\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4.5) 可视化 —— 四类别散点图（mid/last 正误的 4 类）\n",
    "# 配置：选择两个要画的特征\n",
    "x_feat = \"s1_mid\"\n",
    "y_feat = \"margin_mid\"\n",
    "\n",
    "# 可选：只看某个数据集（设为 None 表示不过滤）\n",
    "dataset_filter = None  # 例如 \"MSCOCO_i2t\"\n",
    "\n",
    "# 1) 生成四类标记\n",
    "def build_group4(row):\n",
    "    if row[\"mid_correct\"] == 1 and row[\"last_correct\"] == 1:\n",
    "        return \"mid✓ last✓\"\n",
    "    elif row[\"mid_correct\"] == 0 and row[\"last_correct\"] == 0:\n",
    "        return \"mid✗ last✗\"\n",
    "    elif row[\"mid_correct\"] == 0 and row[\"last_correct\"] == 1:\n",
    "        return \"mid✗ last✓ (need last)\"\n",
    "    else:\n",
    "        return \"mid✓ last✗ (overkill)\"\n",
    "\n",
    "df_vis = df.copy()\n",
    "df_vis[\"group4\"] = df_vis.apply(build_group4, axis=1)\n",
    "\n",
    "# 2) 可选：按数据集过滤\n",
    "if dataset_filter is not None:\n",
    "    df_vis = df_vis[df_vis[\"dataset\"] == dataset_filter].copy()\n",
    "\n",
    "# 3) 下采样（避免点数过多，按类均匀采样）\n",
    "max_per_class = 4000  # 可调\n",
    "df_plot = []\n",
    "for g, gdf in df_vis.groupby(\"group4\"):\n",
    "    if len(gdf) > max_per_class:\n",
    "        df_plot.append(gdf.sample(n=max_per_class, random_state=SEED))\n",
    "    else:\n",
    "        df_plot.append(gdf)\n",
    "df_plot = pd.concat(df_plot, ignore_index=True)\n",
    "\n",
    "# 4) 只保留需要的列，去掉缺失\n",
    "needed_cols = [x_feat, y_feat, \"group4\"]\n",
    "df_plot = df_plot.dropna(subset=needed_cols)\n",
    "\n",
    "# 5) 固定类别顺序与调色板\n",
    "cat_order = [\"mid✓ last✓\", \"mid✗ last✗\", \"mid✗ last✓ (need last)\", \"mid✓ last✗ (overkill)\"]\n",
    "palette = {\n",
    "    \"mid✓ last✓\": \"#2ca02c\",             # 绿\n",
    "    \"mid✗ last✗\": \"#7f7f7f\",             # 灰\n",
    "    \"mid✗ last✓ (need last)\": \"#d62728\", # 红（关键类：需要跑 last）\n",
    "    \"mid✓ last✗ (overkill)\": \"#1f77b4\",  # 蓝（跑 last 可能有害）\n",
    "}\n",
    "df_plot[\"group4\"] = pd.Categorical(df_plot[\"group4\"], categories=cat_order, ordered=True)\n",
    "\n",
    "# 6) 画散点图\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(\n",
    "    data=df_plot, x=x_feat, y=y_feat,\n",
    "    hue=\"group4\", palette=palette, hue_order=cat_order,\n",
    "    s=16, alpha=0.45, edgecolor=None\n",
    ")\n",
    "ttl_ds = f\" [{dataset_filter}]\" if dataset_filter else \"\"\n",
    "plt.title(f\"Scatter by mid/last correctness{ttl_ds} on ({x_feat}, {y_feat})\")\n",
    "plt.xlabel(x_feat); plt.ylabel(y_feat)\n",
    "plt.legend(title=\"Group\", markerscale=1.2, frameon=True)\n",
    "plt.grid(alpha=0.25)\n",
    "\n",
    "# 7) 保存图\n",
    "fname = f\"scatter_group4_{x_feat}_{y_feat}_{MID_KEY}\"\n",
    "if dataset_filter: fname += f\"_{dataset_filter}\"\n",
    "plt.savefig(os.path.join(OUT_DIR, f\"{fname}.png\"), dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "\n",
    "# 8) 打印每类样本数（用于对照）\n",
    "print(\"Counts by group4:\")\n",
    "display(df_vis[\"group4\"].value_counts().reindex(cat_order))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d46b8f3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 25\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     23\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmid✓ last✗ (overkill)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup4\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m     26\u001b[0m     df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup4\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(_build_group4, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# 2) 过滤数据集（可选）\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "# 4.6) 可视化 —— 四个类分别单独画图（四张图）\n",
    "\n",
    "# 如果前面没设置，可在这里指定要看的两个特征\n",
    "x_feat = globals().get(\"x_feat\", \"s1_mid\")        # 横轴\n",
    "y_feat = globals().get(\"y_feat\", \"margin_mid\")    # 纵轴\n",
    "\n",
    "# 可选：仅看一个数据集；None 表示不过滤\n",
    "dataset_filter = globals().get(\"dataset_filter\", None)  # 例如 \"MSCOCO_i2t\"\n",
    "\n",
    "# 每个类别最多采样多少点，避免过多点导致绘图很慢\n",
    "max_per_class = 4000\n",
    "lock_axes = True  # 是否四张图共用相同的 x/y 轴范围，便于对比\n",
    "\n",
    "# 1) 生成四类标签（若之前已有 group4 列，会直接复用）\n",
    "def _build_group4(row):\n",
    "    if row[\"mid_correct\"] == 1 and row[\"last_correct\"] == 1:\n",
    "        return \"mid✓ last✓\"\n",
    "    elif row[\"mid_correct\"] == 0 and row[\"last_correct\"] == 0:\n",
    "        return \"mid✗ last✗\"\n",
    "    elif row[\"mid_correct\"] == 0 and row[\"last_correct\"] == 1:\n",
    "        return \"mid✗ last✓ (need last)\"\n",
    "    else:\n",
    "        return \"mid✓ last✗ (overkill)\"\n",
    "\n",
    "if \"group4\" not in df.columns:\n",
    "    df[\"group4\"] = df.apply(_build_group4, axis=1)\n",
    "\n",
    "# 2) 过滤数据集（可选）\n",
    "df_vis = df if dataset_filter is None else df[df[\"dataset\"] == dataset_filter].copy()\n",
    "\n",
    "# 3) 仅保留需要的列，并去掉缺失\n",
    "needed_cols = [x_feat, y_feat, \"group4\"]\n",
    "df_vis = df_vis.dropna(subset=needed_cols)\n",
    "\n",
    "# 4) 固定类别顺序与配色\n",
    "cat_order = [\"mid✓ last✓\", \"mid✗ last✗\", \"mid✗ last✓ (need last)\", \"mid✓ last✗ (overkill)\"]\n",
    "palette = {\n",
    "    \"mid✓ last✓\": \"#2ca02c\",             # 绿\n",
    "    \"mid✗ last✗\": \"#7f7f7f\",             # 灰\n",
    "    \"mid✗ last✓ (need last)\": \"#d62728\", # 红（需要跑 last）\n",
    "    \"mid✓ last✗ (overkill)\": \"#1f77b4\",  # 蓝（跑 last 可能有害）\n",
    "}\n",
    "df_vis[\"group4\"] = pd.Categorical(df_vis[\"group4\"], categories=cat_order, ordered=True)\n",
    "\n",
    "# 5) 计算统一坐标范围（如果 lock_axes=True）\n",
    "if lock_axes and len(df_vis) > 0:\n",
    "    x_all = df_vis[x_feat].values\n",
    "    y_all = df_vis[y_feat].values\n",
    "    # 稍微加一点边距\n",
    "    x_pad = (np.nanmax(x_all) - np.nanmin(x_all)) * 0.05 + 1e-12\n",
    "    y_pad = (np.nanmax(y_all) - np.nanmin(y_all)) * 0.05 + 1e-12\n",
    "    xlim_all = (float(np.nanmin(x_all) - x_pad), float(np.nanmax(x_all) + x_pad))\n",
    "    ylim_all = (float(np.nanmin(y_all) - y_pad), float(np.nanmax(y_all) + y_pad))\n",
    "else:\n",
    "    xlim_all = ylim_all = None\n",
    "\n",
    "# 6) 逐类绘图（四张图）\n",
    "for g in cat_order:\n",
    "    sub = df_vis[df_vis[\"group4\"] == g]\n",
    "    if len(sub) == 0:\n",
    "        print(f\"[skip] {g}: 无样本\")\n",
    "        continue\n",
    "    # 下采样\n",
    "    if len(sub) > max_per_class:\n",
    "        sub = sub.sample(n=max_per_class, random_state=SEED)\n",
    "\n",
    "    plt.figure(figsize=(7.2, 5.6))\n",
    "    sns.scatterplot(\n",
    "        data=sub, x=x_feat, y=y_feat,\n",
    "        color=palette[g], s=18, alpha=0.5, edgecolor=None\n",
    "    )\n",
    "    ttl_ds = f\" [{dataset_filter}]\" if dataset_filter else \"\"\n",
    "    plt.title(f\"{g}{ttl_ds} on ({x_feat}, {y_feat})  (n={len(sub)})\")\n",
    "    plt.xlabel(x_feat); plt.ylabel(y_feat)\n",
    "    plt.grid(alpha=0.25)\n",
    "    if xlim_all: plt.xlim(xlim_all)\n",
    "    if ylim_all: plt.ylim(ylim_all)\n",
    "\n",
    "    # 保存\n",
    "    def _slugify(s):\n",
    "        return s.replace(\" \", \"_\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"✓\",\"ok\").replace(\"✗\",\"x\").replace(\"/\",\"-\")\n",
    "    fname = f\"scatter_group4_split_{_slugify(g)}_{x_feat}_{y_feat}_{MID_KEY}\"\n",
    "    if dataset_filter:\n",
    "        fname += f\"_{dataset_filter}\"\n",
    "    plt.savefig(os.path.join(OUT_DIR, f\"{fname}.png\"), dpi=150, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "# 7) 打印各类样本数\n",
    "print(\"Counts by group4 (after dataset filter):\")\n",
    "display(df_vis[\"group4\"].value_counts().reindex(cat_order))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b0513f22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按单特征 AUC 排序（前 30 个）:\n"
     ]
    },
    {
     "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>feature</th>\n",
       "      <th>auc_uni</th>\n",
       "      <th>pearson</th>\n",
       "      <th>spearman</th>\n",
       "      <th>mean_pos</th>\n",
       "      <th>mean_neg</th>\n",
       "      <th>cohen_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>margin_mid</td>\n",
       "      <td>0.739990</td>\n",
       "      <td>-0.271141</td>\n",
       "      <td>-0.307451</td>\n",
       "      <td>0.018332</td>\n",
       "      <td>0.059362</td>\n",
       "      <td>-0.761678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mad_margin_mid</td>\n",
       "      <td>0.729667</td>\n",
       "      <td>-0.243163</td>\n",
       "      <td>-0.294226</td>\n",
       "      <td>0.818685</td>\n",
       "      <td>2.555696</td>\n",
       "      <td>-0.677841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>z_margin_mid</td>\n",
       "      <td>0.726563</td>\n",
       "      <td>-0.251161</td>\n",
       "      <td>-0.290250</td>\n",
       "      <td>0.433631</td>\n",
       "      <td>1.265678</td>\n",
       "      <td>-0.701614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>z1</td>\n",
       "      <td>0.685548</td>\n",
       "      <td>-0.230489</td>\n",
       "      <td>-0.237705</td>\n",
       "      <td>4.076918</td>\n",
       "      <td>4.928089</td>\n",
       "      <td>-0.640479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p1_mid</td>\n",
       "      <td>0.663703</td>\n",
       "      <td>-0.204682</td>\n",
       "      <td>-0.209719</td>\n",
       "      <td>0.006062</td>\n",
       "      <td>0.006390</td>\n",
       "      <td>-0.565427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>p1</td>\n",
       "      <td>0.663703</td>\n",
       "      <td>-0.204682</td>\n",
       "      <td>-0.209719</td>\n",
       "      <td>0.006062</td>\n",
       "      <td>0.006390</td>\n",
       "      <td>-0.565427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1_mid</td>\n",
       "      <td>0.660803</td>\n",
       "      <td>-0.207581</td>\n",
       "      <td>-0.206004</td>\n",
       "      <td>0.530143</td>\n",
       "      <td>0.588623</td>\n",
       "      <td>-0.573798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>topk_kurt</td>\n",
       "      <td>0.641182</td>\n",
       "      <td>-0.154070</td>\n",
       "      <td>-0.180868</td>\n",
       "      <td>3.003629</td>\n",
       "      <td>5.561933</td>\n",
       "      <td>-0.421633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>s1_over_sk</td>\n",
       "      <td>0.638828</td>\n",
       "      <td>-0.172306</td>\n",
       "      <td>-0.177852</td>\n",
       "      <td>1.498768</td>\n",
       "      <td>1.646021</td>\n",
       "      <td>-0.472988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s1_over_mean</td>\n",
       "      <td>0.632836</td>\n",
       "      <td>-0.165757</td>\n",
       "      <td>-0.170176</td>\n",
       "      <td>1.599362</td>\n",
       "      <td>1.757178</td>\n",
       "      <td>-0.454491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>s1_over_med</td>\n",
       "      <td>0.623013</td>\n",
       "      <td>-0.152878</td>\n",
       "      <td>-0.157592</td>\n",
       "      <td>1.679214</td>\n",
       "      <td>1.846530</td>\n",
       "      <td>-0.418299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>head5_mean</td>\n",
       "      <td>0.563765</td>\n",
       "      <td>-0.090946</td>\n",
       "      <td>-0.081689</td>\n",
       "      <td>0.496219</td>\n",
       "      <td>0.518604</td>\n",
       "      <td>-0.246941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>shape_gini</td>\n",
       "      <td>0.554879</td>\n",
       "      <td>-0.040297</td>\n",
       "      <td>-0.070305</td>\n",
       "      <td>0.005014</td>\n",
       "      <td>0.005016</td>\n",
       "      <td>-0.108628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gini_mid</td>\n",
       "      <td>0.554879</td>\n",
       "      <td>-0.040297</td>\n",
       "      <td>-0.070305</td>\n",
       "      <td>0.005014</td>\n",
       "      <td>0.005016</td>\n",
       "      <td>-0.108628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>shape_H</td>\n",
       "      <td>0.552960</td>\n",
       "      <td>0.038470</td>\n",
       "      <td>0.067847</td>\n",
       "      <td>5.296934</td>\n",
       "      <td>5.296822</td>\n",
       "      <td>0.104102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H_mid</td>\n",
       "      <td>0.552960</td>\n",
       "      <td>0.038470</td>\n",
       "      <td>0.067847</td>\n",
       "      <td>5.296934</td>\n",
       "      <td>5.296822</td>\n",
       "      <td>0.104102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>topk_std</td>\n",
       "      <td>0.549572</td>\n",
       "      <td>-0.056864</td>\n",
       "      <td>-0.063507</td>\n",
       "      <td>0.046836</td>\n",
       "      <td>0.049618</td>\n",
       "      <td>-0.154011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2_mid</td>\n",
       "      <td>0.542490</td>\n",
       "      <td>-0.064705</td>\n",
       "      <td>-0.054434</td>\n",
       "      <td>0.511810</td>\n",
       "      <td>0.529261</td>\n",
       "      <td>-0.175331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p2</td>\n",
       "      <td>0.540679</td>\n",
       "      <td>-0.050086</td>\n",
       "      <td>-0.052113</td>\n",
       "      <td>0.005950</td>\n",
       "      <td>0.006013</td>\n",
       "      <td>-0.135604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>slope</td>\n",
       "      <td>0.537351</td>\n",
       "      <td>0.041036</td>\n",
       "      <td>0.047850</td>\n",
       "      <td>-0.000722</td>\n",
       "      <td>-0.000753</td>\n",
       "      <td>0.111055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>topk_cv</td>\n",
       "      <td>0.534706</td>\n",
       "      <td>-0.039172</td>\n",
       "      <td>-0.044462</td>\n",
       "      <td>0.146275</td>\n",
       "      <td>0.153336</td>\n",
       "      <td>-0.106002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>topk_mean</td>\n",
       "      <td>0.515912</td>\n",
       "      <td>-0.028548</td>\n",
       "      <td>-0.020385</td>\n",
       "      <td>0.339646</td>\n",
       "      <td>0.346186</td>\n",
       "      <td>-0.077225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>topk_med</td>\n",
       "      <td>0.513201</td>\n",
       "      <td>-0.024639</td>\n",
       "      <td>-0.016912</td>\n",
       "      <td>0.326189</td>\n",
       "      <td>0.331973</td>\n",
       "      <td>-0.066645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tail_mean</td>\n",
       "      <td>0.509749</td>\n",
       "      <td>-0.019985</td>\n",
       "      <td>-0.012489</td>\n",
       "      <td>0.292144</td>\n",
       "      <td>0.296962</td>\n",
       "      <td>-0.054050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature   auc_uni   pearson  spearman  mean_pos  mean_neg   cohen_d\n",
       "2       margin_mid  0.739990 -0.271141 -0.307451  0.018332  0.059362 -0.761678\n",
       "4   mad_margin_mid  0.729667 -0.243163 -0.294226  0.818685  2.555696 -0.677841\n",
       "3     z_margin_mid  0.726563 -0.251161 -0.290250  0.433631  1.265678 -0.701614\n",
       "20              z1  0.685548 -0.230489 -0.237705  4.076918  4.928089 -0.640479\n",
       "5           p1_mid  0.663703 -0.204682 -0.209719  0.006062  0.006390 -0.565427\n",
       "15              p1  0.663703 -0.204682 -0.209719  0.006062  0.006390 -0.565427\n",
       "0           s1_mid  0.660803 -0.207581 -0.206004  0.530143  0.588623 -0.573798\n",
       "11       topk_kurt  0.641182 -0.154070 -0.180868  3.003629  5.561933 -0.421633\n",
       "21      s1_over_sk  0.638828 -0.172306 -0.177852  1.498768  1.646021 -0.472988\n",
       "13    s1_over_mean  0.632836 -0.165757 -0.170176  1.599362  1.757178 -0.454491\n",
       "14     s1_over_med  0.623013 -0.152878 -0.157592  1.679214  1.846530 -0.418299\n",
       "23      head5_mean  0.563765 -0.090946 -0.081689  0.496219  0.518604 -0.246941\n",
       "18      shape_gini  0.554879 -0.040297 -0.070305  0.005014  0.005016 -0.108628\n",
       "7         gini_mid  0.554879 -0.040297 -0.070305  0.005014  0.005016 -0.108628\n",
       "17         shape_H  0.552960  0.038470  0.067847  5.296934  5.296822  0.104102\n",
       "6            H_mid  0.552960  0.038470  0.067847  5.296934  5.296822  0.104102\n",
       "9         topk_std  0.549572 -0.056864 -0.063507  0.046836  0.049618 -0.154011\n",
       "1           s2_mid  0.542490 -0.064705 -0.054434  0.511810  0.529261 -0.175331\n",
       "16              p2  0.540679 -0.050086 -0.052113  0.005950  0.006013 -0.135604\n",
       "19           slope  0.537351  0.041036  0.047850 -0.000722 -0.000753  0.111055\n",
       "10         topk_cv  0.534706 -0.039172 -0.044462  0.146275  0.153336 -0.106002\n",
       "8        topk_mean  0.515912 -0.028548 -0.020385  0.339646  0.346186 -0.077225\n",
       "12        topk_med  0.513201 -0.024639 -0.016912  0.326189  0.331973 -0.066645\n",
       "22       tail_mean  0.509749 -0.019985 -0.012489  0.292144  0.296962 -0.054050"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 10: 单特征效果分析（相关性 & AUC）\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def univariate_feature_analysis(\n",
    "    df, \n",
    "    feat_cols, \n",
    "    target_col=\"y_need_last\",\n",
    "    group_col=\"dataset\"\n",
    "):\n",
    "    \"\"\"\n",
    "    对每个特征，计算：\n",
    "      - 全局 AUC（把该特征当成 score）\n",
    "      - Pearson 相关系数\n",
    "      - Spearman 相关系数\n",
    "      - 正样本/负样本均值、效应大小（Cohen's d）\n",
    "    \"\"\"\n",
    "    y = df[target_col].values.astype(float)\n",
    "    results = []\n",
    "\n",
    "    for col in feat_cols:\n",
    "        x = df[col].values.astype(float)\n",
    "        # 去掉 NaN / Inf\n",
    "        mask = np.isfinite(x) & np.isfinite(y)\n",
    "        x = x[mask]; y_ = y[mask]\n",
    "        if x.size < 10 or np.nanstd(x) < 1e-12:\n",
    "            continue  # 全是常数或有效样本太少\n",
    "\n",
    "        # AUC（注意方向不固定，取 max(auc, 1-auc)）\n",
    "        try:\n",
    "            auc = roc_auc_score(y_, x)\n",
    "            auc = max(auc, 1.0 - auc)\n",
    "        except Exception:\n",
    "            auc = np.nan\n",
    "\n",
    "        # Pearson / Spearman 相关\n",
    "        s_x = pd.Series(x)\n",
    "        s_y = pd.Series(y_)\n",
    "        pear = s_x.corr(s_y, method=\"pearson\")\n",
    "        spear = s_x.corr(s_y, method=\"spearman\")\n",
    "\n",
    "        # 正负样本分布差异（Cohen's d）\n",
    "        pos = x[y_ == 1]\n",
    "        neg = x[y_ == 0]\n",
    "        if len(pos) > 1 and len(neg) > 1:\n",
    "            m1, m0 = pos.mean(), neg.mean()\n",
    "            s1, s0 = pos.std(), neg.std()\n",
    "            # pooled std\n",
    "            sp = np.sqrt(((len(pos)-1)*s1**2 + (len(neg)-1)*s0**2) / (len(pos)+len(neg)-2) + 1e-12)\n",
    "            d = (m1 - m0) / sp\n",
    "        else:\n",
    "            m1 = pos.mean() if len(pos)>0 else np.nan\n",
    "            m0 = neg.mean() if len(neg)>0 else np.nan\n",
    "            d = np.nan\n",
    "\n",
    "        results.append({\n",
    "            \"feature\": col,\n",
    "            \"auc_uni\": auc,\n",
    "            \"pearson\": pear,\n",
    "            \"spearman\": spear,\n",
    "            \"mean_pos\": m1,\n",
    "            \"mean_neg\": m0,\n",
    "            \"cohen_d\": d,\n",
    "        })\n",
    "\n",
    "    df_res = pd.DataFrame(results)\n",
    "    df_res = df_res.sort_values(\"auc_uni\", ascending=False)\n",
    "    return df_res\n",
    "\n",
    "# 在你当前的特征集合上跑\n",
    "feat_cols_used = [c for c in feat_cols_single if c in df.columns]\n",
    "uni_stats = univariate_feature_analysis(df, feat_cols_used, target_col=\"y_need_last\")\n",
    "\n",
    "print(\"按单特征 AUC 排序（前 30 个）:\")\n",
    "display(uni_stats.head(30))\n",
    "\n",
    "# 存一份到文件\n",
    "uni_stats.to_csv(os.path.join(OUT_DIR, f\"univariate_feature_stats_{MID_KEY}.csv\"), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df3ac116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Permutation importance（前 30 个）:\n"
     ]
    },
    {
     "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>feature</th>\n",
       "      <th>perm_importance_mean</th>\n",
       "      <th>perm_importance_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>margin_mid</td>\n",
       "      <td>0.043662</td>\n",
       "      <td>0.016809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>z_margin_mid</td>\n",
       "      <td>0.023303</td>\n",
       "      <td>0.011218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>p1</td>\n",
       "      <td>0.012869</td>\n",
       "      <td>0.003551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mad_margin_mid</td>\n",
       "      <td>0.012142</td>\n",
       "      <td>0.003014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p1_mid</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.002077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1_mid</td>\n",
       "      <td>0.005592</td>\n",
       "      <td>0.005012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>z1</td>\n",
       "      <td>0.005450</td>\n",
       "      <td>0.003538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2_mid</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.004178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p2</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>0.005232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>topk_med</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>0.002560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>shape_gini</td>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.002767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tail_mean</td>\n",
       "      <td>0.001309</td>\n",
       "      <td>0.003193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gini_mid</td>\n",
       "      <td>0.001193</td>\n",
       "      <td>0.003300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H_mid</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.002418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>slope</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.002782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>topk_mean</td>\n",
       "      <td>0.000899</td>\n",
       "      <td>0.003367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>head5_mean</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>0.003509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>s1_over_med</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.003945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s1_over_mean</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.004546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>topk_kurt</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.001790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mask_ratio</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>mask_len</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>mask_runs</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>topk_cv</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.003617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>topk_std</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>0.001938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>shape_H</td>\n",
       "      <td>-0.000893</td>\n",
       "      <td>0.002346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>s1_over_sk</td>\n",
       "      <td>-0.001253</td>\n",
       "      <td>0.003470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature  perm_importance_mean  perm_importance_std\n",
       "2       margin_mid              0.043662             0.016809\n",
       "3     z_margin_mid              0.023303             0.011218\n",
       "15              p1              0.012869             0.003551\n",
       "4   mad_margin_mid              0.012142             0.003014\n",
       "5           p1_mid              0.008198             0.002077\n",
       "0           s1_mid              0.005592             0.005012\n",
       "20              z1              0.005450             0.003538\n",
       "1           s2_mid              0.002681             0.004178\n",
       "16              p2              0.002143             0.005232\n",
       "12        topk_med              0.002073             0.002560\n",
       "18      shape_gini              0.001419             0.002767\n",
       "22       tail_mean              0.001309             0.003193\n",
       "7         gini_mid              0.001193             0.003300\n",
       "6            H_mid              0.001087             0.002418\n",
       "19           slope              0.000949             0.002782\n",
       "8        topk_mean              0.000899             0.003367\n",
       "23      head5_mean              0.000770             0.003509\n",
       "14     s1_over_med              0.000586             0.003945\n",
       "13    s1_over_mean              0.000578             0.004546\n",
       "11       topk_kurt              0.000518             0.001790\n",
       "24      mask_ratio              0.000000             0.000000\n",
       "25        mask_len              0.000000             0.000000\n",
       "26       mask_runs              0.000000             0.000000\n",
       "10         topk_cv             -0.000101             0.003617\n",
       "9         topk_std             -0.000502             0.001938\n",
       "17         shape_H             -0.000893             0.002346\n",
       "21      s1_over_sk             -0.001253             0.003470"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 12: Permutation Importance（基于 ROC-AUC 的掉分）\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "def cv_permutation_importance(\n",
    "    df,\n",
    "    feat_cols,\n",
    "    model_ctor,\n",
    "    target_col=\"y_need_last\",\n",
    "    group_col=\"dataset\",\n",
    "    n_splits=5,\n",
    "    n_repeats=5,\n",
    "    random_state=SEED\n",
    "):\n",
    "    X = df[feat_cols].values\n",
    "    y = df[target_col].values.astype(int)\n",
    "    groups = df[group_col].values\n",
    "\n",
    "    gkf = GroupKFold(n_splits=min(n_splits, len(np.unique(groups))))\n",
    "    all_perm = []\n",
    "\n",
    "    for fold, (tr, te) in enumerate(gkf.split(X, y, groups)):\n",
    "        clf = model_ctor()\n",
    "        clf.fit(X[tr], y[tr])\n",
    "\n",
    "        r = permutation_importance(\n",
    "            clf,\n",
    "            X[te], y[te],\n",
    "            n_repeats=n_repeats,\n",
    "            random_state=random_state,\n",
    "            scoring=\"roc_auc\",\n",
    "            n_jobs=-1,\n",
    "        )\n",
    "        all_perm.append(r.importances_mean)\n",
    "\n",
    "    all_perm = np.vstack(all_perm)\n",
    "    mean_imp = all_perm.mean(axis=0)\n",
    "    std_imp  = all_perm.std(axis=0)\n",
    "\n",
    "    df_perm = pd.DataFrame({\n",
    "        \"feature\": feat_cols,\n",
    "        \"perm_importance_mean\": mean_imp,\n",
    "        \"perm_importance_std\": std_imp,\n",
    "    }).sort_values(\"perm_importance_mean\", ascending=False)\n",
    "    return df_perm\n",
    "\n",
    "# 建议用 RF 或 GBDT 其中一个，这里示例 RF\n",
    "rf_ctor_for_perm = lambda: RandomForestClassifier(\n",
    "    n_estimators=400,\n",
    "    n_jobs=-1,\n",
    "    class_weight=\"balanced\",\n",
    "    random_state=SEED,\n",
    ")\n",
    "\n",
    "perm_imp = cv_permutation_importance(\n",
    "    df,\n",
    "    feat_cols_used,\n",
    "    rf_ctor_for_perm,\n",
    "    target_col=\"y_need_last\",\n",
    "    group_col=\"dataset\",\n",
    "    n_splits=5,\n",
    "    n_repeats=5,\n",
    "    random_state=SEED,\n",
    ")\n",
    "\n",
    "print(\"Permutation importance（前 30 个）:\")\n",
    "display(perm_imp.head(30))\n",
    "perm_imp.to_csv(os.path.join(OUT_DIR, f\"perm_feature_importance_{MID_KEY}.csv\"), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e4c1ffce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 额外：看特征之间的相关性 Heatmap\n",
    "\n",
    "corr_mat = df[feat_cols_used].corr(method=\"pearson\")  # 也可以换成 \"spearman\"\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_mat, cmap=\"coolwarm\", center=0, vmin=-1, vmax=1)\n",
    "plt.title(\"Feature-Feature Correlation (Pearson)\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(OUT_DIR, f\"feature_corr_heatmap_{MID_KEY}.png\"), dpi=150)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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