From bdf937086f175dab2daab623e76585797b9a4528 Mon Sep 17 00:00:00 2001
From: liaozhaorun <1300336796@qq.com>
Date: Sat, 14 Mar 2026 02:41:24 +0800
Subject: [PATCH] =?UTF-8?q?refactor(training):=20=E7=AE=80=E5=8C=96=20Ligh?=
 =?UTF-8?q?tGBM=20=E6=A8=A1=E5=9E=8B=E5=8F=82=E6=95=B0=E5=A4=84=E7=90=86?=
 =?UTF-8?q?=20-=20=E9=87=8D=E6=9E=84=20LightGBM=20=E5=92=8C=20LambdaRank?=
 =?UTF-8?q?=20=E6=A8=A1=E5=9E=8B=EF=BC=8C=E7=A7=BB=E9=99=A4=E5=8F=82?=
 =?UTF-8?q?=E6=95=B0=E6=8F=90=E5=8F=96=E9=80=BB=E8=BE=91=20-=20=E6=A8=A1?=
 =?UTF-8?q?=E5=9E=8B=E7=B1=BB=E5=8F=AA=E4=BF=9D=E7=95=99=20params=20?=
 =?UTF-8?q?=E5=B1=9E=E6=80=A7=EF=BC=8C=E7=AC=A6=E5=90=88=20LightGBM=20?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/experiment/learn_to_rank.ipynb            | 661 +-----------------
 src/experiment/learn_to_rank.py               |   8 +-
 src/training/components/models/lightgbm.py    |  71 +-
 .../components/models/lightgbm_lambdarank.py  | 229 ++----
 4 files changed, 105 insertions(+), 864 deletions(-)

diff --git a/src/experiment/learn_to_rank.ipynb b/src/experiment/learn_to_rank.ipynb
index cc29291..a7c7616 100644
--- a/src/experiment/learn_to_rank.ipynb
+++ b/src/experiment/learn_to_rank.ipynb
@@ -22,12 +22,7 @@
    "source": "## 1. 导入依赖"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:00.178020Z",
-     "start_time": "2026-03-13T18:09:58.791253Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import os\n",
@@ -56,7 +51,7 @@
     "\n"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -64,12 +59,7 @@
    "source": "## 2. 辅助函数"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:00.209449Z",
-     "start_time": "2026-03-13T18:10:00.197996Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def register_factors(\n",
@@ -273,7 +263,7 @@
     "\n"
    ],
    "outputs": [],
-   "execution_count": 2
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -285,12 +275,7 @@
    ]
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:00.241454Z",
-     "start_time": "2026-03-13T18:10:00.237620Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# 特征因子定义字典（复用 regression.ipynb 的因子定义）\n",
@@ -341,6 +326,7 @@
     "    \"net_profit_yoy\",\n",
     "    \"revenue_yoy\",\n",
     "    \"healthy_expansion_velocity\",\n",
+    "    \"ebit_rank\",\n",
     "    # ================= 6. 基本面估值与截面动量共振 =================\n",
     "    \"EP\",\n",
     "    \"BP\",\n",
@@ -352,7 +338,6 @@
     "    \"pe_expansion_trend\",\n",
     "    \"value_price_divergence\",\n",
     "    \"active_market_cap\",\n",
-    "    \"ebit_rank\",\n",
     "]\n",
     "\n",
     "# 因子定义字典（完整因子库）\n",
@@ -366,7 +351,7 @@
     "}"
    ],
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -374,12 +359,7 @@
    "source": "### 3.2 训练参数配置"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:00.257506Z",
-     "start_time": "2026-03-13T18:10:00.253934Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# 日期范围配置（正确的 train/val/test 三分法）\n",
@@ -404,7 +384,7 @@
     "    \"max_depth\": 4,\n",
     "    \"min_data_in_leaf\": 20,\n",
     "    \"n_estimators\": 2000,\n",
-    "    \"early_stopping_round\": 300,\n",
+    "    \"early_stopping_round\": 100,\n",
     "    \"subsample\": 0.8,\n",
     "    \"colsample_bytree\": 0.8,\n",
     "    \"reg_alpha\": 0.1,\n",
@@ -435,7 +415,7 @@
     "    )\n",
     "\n",
     "    valid_df = df.filter(code_filter)\n",
-    "    n = min(1000, len(valid_df))\n",
+    "    n = min(500, len(valid_df))\n",
     "    small_cap_codes = valid_df.sort(\"total_mv\").head(n)[\"ts_code\"]\n",
     "\n",
     "    return df[\"ts_code\"].is_in(small_cap_codes)\n",
@@ -452,7 +432,7 @@
     "TOP_N = 5  # 可调整为 10, 20 等"
    ],
    "outputs": [],
-   "execution_count": 4
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -460,12 +440,7 @@
    "source": "## 4. 训练流程"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:11.844363Z",
-     "start_time": "2026-03-13T18:10:00.265162Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -548,224 +523,8 @@
     "    persist_model=PERSIST_MODEL,\n",
     ")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "LightGBM LambdaRank 排序学习训练\n",
-      "================================================================================\n",
-      "\n",
-      "[1] 创建 FactorEngine\n",
-      "\n",
-      "[2] 定义因子（从 metadata 注册）\n",
-      "================================================================================\n",
-      "注册因子\n",
-      "================================================================================\n",
-      "\n",
-      "注册特征因子（从 metadata）:\n",
-      "  - ma_5\n",
-      "  - ma_20\n",
-      "  - ma_ratio_5_20\n",
-      "  - bias_10\n",
-      "  - high_low_ratio\n",
-      "  - bbi_ratio\n",
-      "  - return_5\n",
-      "  - return_20\n",
-      "  - kaufman_ER_20\n",
-      "  - mom_acceleration_10_20\n",
-      "  - drawdown_from_high_60\n",
-      "  - up_days_ratio_20\n",
-      "  - volatility_5\n",
-      "  - volatility_20\n",
-      "  - volatility_ratio\n",
-      "  - std_return_20\n",
-      "  - sharpe_ratio_20\n",
-      "  - min_ret_20\n",
-      "  - volatility_squeeze_5_60\n",
-      "  - overnight_intraday_diff\n",
-      "  - upper_shadow_ratio\n",
-      "  - capital_retention_20\n",
-      "  - max_ret_20\n",
-      "  - volume_ratio_5_20\n",
-      "  - turnover_rate_mean_5\n",
-      "  - turnover_deviation\n",
-      "  - amihud_illiq_20\n",
-      "  - turnover_cv_20\n",
-      "  - pv_corr_20\n",
-      "  - close_vwap_deviation\n",
-      "  - roe\n",
-      "  - roa\n",
-      "  - profit_margin\n",
-      "  - debt_to_equity\n",
-      "  - current_ratio\n",
-      "  - net_profit_yoy\n",
-      "  - revenue_yoy\n",
-      "  - healthy_expansion_velocity\n",
-      "  - EP\n",
-      "  - BP\n",
-      "  - CP\n",
-      "  - market_cap_rank\n",
-      "  - turnover_rank\n",
-      "  - return_5_rank\n",
-      "  - EP_rank\n",
-      "  - pe_expansion_trend\n",
-      "  - value_price_divergence\n",
-      "  - active_market_cap\n",
-      "  - ebit_rank\n",
-      "\n",
-      "注册特征因子（表达式）:\n",
-      "  - turnover_rate_volatility: ts_std(log(turnover_rate), 20)\n",
-      "\n",
-      "注册 Label 因子（表达式）:\n",
-      "  - future_return_5_rank: (ts_delay(close, -5) / ts_delay(open, -1)) - 1\n",
-      "\n",
-      "特征因子数: 50\n",
-      "  - 来自 metadata: 49\n",
-      "  - 来自表达式: 1\n",
-      "Label: future_return_5_rank\n",
-      "已注册因子总数: 64\n",
-      "\n",
-      "[3] 准备数据\n",
-      "\n",
-      "================================================================================\n",
-      "准备数据\n",
-      "================================================================================\n",
-      "\n",
-      "计算因子: 20200101 - 20251231\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\PyProject\\ProStock\\src\\data\\financial_loader.py:148: UserWarning: Sortedness of columns cannot be checked when 'by' groups provided\n",
-      "  merged = df_price.join_asof(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "数据形状: (7044952, 71)\n",
-      "数据列: ['ts_code', 'trade_date', 'open', 'high', 'turnover_rate', 'low', 'vol', 'amount', 'close', 'total_assets', 'total_mv', 'f_ann_date', 'ebit', 'revenue', 'n_income', 'total_liab', 'total_hldr_eqy_exc_min_int', 'total_cur_assets', 'total_cur_liab', 'n_cashflow_act', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'value_price_divergence', 'active_market_cap', 'ebit_rank', 'turnover_rate_volatility', 'future_return_5_rank']\n",
-      "\n",
-      "前5行预览:\n",
-      "shape: (5, 71)\n",
-      "┌───────────┬────────────┬─────────┬─────────┬───┬────────────┬───────────┬────────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ open    ┆ high    ┆ … ┆ active_mar ┆ ebit_rank ┆ turnover_r ┆ future_re │\n",
-      "│ ---       ┆ ---        ┆ ---     ┆ ---     ┆   ┆ ket_cap    ┆ ---       ┆ ate_volati ┆ turn_5_ra │\n",
-      "│ str       ┆ str        ┆ f64     ┆ f64     ┆   ┆ ---        ┆ f64       ┆ lity       ┆ nk        │\n",
-      "│           ┆            ┆         ┆         ┆   ┆ f64        ┆           ┆ ---        ┆ ---       │\n",
-      "│           ┆            ┆         ┆         ┆   ┆            ┆           ┆ f64        ┆ f64       │\n",
-      "╞═══════════╪════════════╪═════════╪═════════╪═══╪════════════╪═══════════╪════════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20200102   ┆ 1817.67 ┆ 1850.42 ┆ … ┆ null       ┆ null      ┆ null       ┆ -0.008857 │\n",
-      "│ 000001.SZ ┆ 20200103   ┆ 1849.33 ┆ 1889.72 ┆ … ┆ null       ┆ null      ┆ null       ┆ -0.01881  │\n",
-      "│ 000001.SZ ┆ 20200106   ┆ 1856.97 ┆ 1893.0  ┆ … ┆ null       ┆ null      ┆ null       ┆ -0.008171 │\n",
-      "│ 000001.SZ ┆ 20200107   ┆ 1870.07 ┆ 1886.45 ┆ … ┆ null       ┆ null      ┆ null       ┆ -0.014117 │\n",
-      "│ 000001.SZ ┆ 20200108   ┆ 1855.88 ┆ 1861.34 ┆ … ┆ null       ┆ null      ┆ null       ┆ -0.017252 │\n",
-      "└───────────┴────────────┴─────────┴─────────┴───┴────────────┴───────────┴────────────┴───────────┘\n",
-      "\n",
-      "[4] 转换为排序学习格式\n",
-      "\n",
-      "================================================================================\n",
-      "准备排序学习数据（将 future_return_5_rank 转换为 20 分位数标签）\n",
-      "================================================================================\n",
-      "\n",
-      "原始 future_return_5_rank 统计:\n",
-      "shape: (9, 2)\n",
-      "┌────────────┬───────────┐\n",
-      "│ statistic  ┆ value     │\n",
-      "│ ---        ┆ ---       │\n",
-      "│ str        ┆ f64       │\n",
-      "╞════════════╪═══════════╡\n",
-      "│ count      ┆ 7.01659e6 │\n",
-      "│ null_count ┆ 28362.0   │\n",
-      "│ mean       ┆ 0.003779  │\n",
-      "│ std        ┆ 0.073221  │\n",
-      "│ min        ┆ -0.969459 │\n",
-      "│ 25%        ┆ -0.033163 │\n",
-      "│ 50%        ┆ -0.001483 │\n",
-      "│ 75%        ┆ 0.032547  │\n",
-      "│ max        ┆ 10.361925 │\n",
-      "└────────────┴───────────┘\n",
-      "\n",
-      "转换后 future_return_5_rank_rank 统计:\n",
-      "shape: (9, 2)\n",
-      "┌────────────┬───────────┐\n",
-      "│ statistic  ┆ value     │\n",
-      "│ ---        ┆ ---       │\n",
-      "│ str        ┆ f64       │\n",
-      "╞════════════╪═══════════╡\n",
-      "│ count      ┆ 7.01659e6 │\n",
-      "│ null_count ┆ 28362.0   │\n",
-      "│ mean       ┆ 9.495412  │\n",
-      "│ std        ┆ 5.765668  │\n",
-      "│ min        ┆ 0.0       │\n",
-      "│ 25%        ┆ 4.0       │\n",
-      "│ 50%        ┆ 9.0       │\n",
-      "│ 75%        ┆ 14.0      │\n",
-      "│ max        ┆ 19.0      │\n",
-      "└────────────┴───────────┘\n",
-      "\n",
-      "每日样本数统计:\n",
-      "shape: (9, 2)\n",
-      "┌────────────┬─────────────┐\n",
-      "│ statistic  ┆ value       │\n",
-      "│ ---        ┆ ---         │\n",
-      "│ str        ┆ f64         │\n",
-      "╞════════════╪═════════════╡\n",
-      "│ count      ┆ 1455.0      │\n",
-      "│ null_count ┆ 0.0         │\n",
-      "│ mean       ┆ 4841.891409 │\n",
-      "│ std        ┆ 560.948186  │\n",
-      "│ min        ┆ 3740.0      │\n",
-      "│ 25%        ┆ 4369.0      │\n",
-      "│ 50%        ┆ 5060.0      │\n",
-      "│ 75%        ┆ 5344.0      │\n",
-      "│ max        ┆ 5458.0      │\n",
-      "└────────────┴─────────────┘\n",
-      "\n",
-      "分位数标签分布:\n",
-      "shape: (21, 2)\n",
-      "┌───────────────────────────┬────────┐\n",
-      "│ future_return_5_rank_rank ┆ count  │\n",
-      "│ ---                       ┆ ---    │\n",
-      "│ i64                       ┆ u32    │\n",
-      "╞═══════════════════════════╪════════╡\n",
-      "│ null                      ┆ 28362  │\n",
-      "│ 0                         ┆ 351599 │\n",
-      "│ 1                         ┆ 350894 │\n",
-      "│ 2                         ┆ 350944 │\n",
-      "│ 3                         ┆ 351077 │\n",
-      "│ …                         ┆ …      │\n",
-      "│ 15                        ┆ 350910 │\n",
-      "│ 16                        ┆ 350835 │\n",
-      "│ 17                        ┆ 350848 │\n",
-      "│ 18                        ┆ 350871 │\n",
-      "│ 19                        ┆ 349137 │\n",
-      "└───────────────────────────┴────────┘\n",
-      "\n",
-      "[配置] 训练期: 20200101 - 20231231\n",
-      "[配置] 验证期: 20240101 - 20241231\n",
-      "[配置] 测试期: 20250101 - 20251231\n",
-      "[配置] 特征数: 50\n",
-      "[配置] 目标变量: future_return_5_rank_rank（20分位数）\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_26384\\2929956284.py:125: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n",
-      "(Deprecated in version 0.20.5)\n",
-      "  daily_counts = df_ranked.group_by(date_col).agg(pl.count().alias(\"count\"))\n"
-     ]
-    }
-   ],
-   "execution_count": 5
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -773,12 +532,7 @@
    "source": "### 4.1 股票池筛选"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:34.925345Z",
-     "start_time": "2026-03-13T18:10:11.853318Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -803,46 +557,8 @@
     "    filtered_data = data\n",
     "    print(\"  未配置股票池管理器，跳过筛选\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "股票池筛选\n",
-      "================================================================================\n",
-      "\n",
-      "[过滤] 应用 ST 过滤器...\n",
-      "  ST 过滤后数据规模: (6823808, 72)\n",
-      "\n",
-      "执行每日独立筛选股票池...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_26384\\4061767669.py:57: DeprecationWarning: `is_in` with a collection of the same datatype is ambiguous and deprecated.\n",
-      "Please use `implode` to return to previous behavior.\n",
-      "\n",
-      "See https://github.com/pola-rs/polars/issues/22149 for more information.\n",
-      "  return df[\"ts_code\"].is_in(small_cap_codes)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  筛选前数据规模: (6823808, 72)\n",
-      "  筛选后数据规模: (1455000, 72)\n",
-      "  筛选前股票数: 5678\n",
-      "  筛选后股票数: 1934\n",
-      "  删除记录数: 5368808\n"
-     ]
-    }
-   ],
-   "execution_count": 6
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -850,12 +566,7 @@
    "source": "### 4.2 数据划分"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:34.997214Z",
-     "start_time": "2026-03-13T18:10:34.932346Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -882,39 +593,8 @@
     "else:\n",
     "    raise ValueError(\"必须配置数据划分器\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "数据划分\n",
-      "================================================================================\n",
-      "\n",
-      "训练集数据规模: (970000, 72)\n",
-      "验证集数据规模: (242000, 72)\n",
-      "测试集数据规模: (243000, 72)\n",
-      "\n",
-      "训练集 group 数量: 970\n",
-      "验证集 group 数量: 242\n",
-      "测试集 group 数量: 243\n",
-      "训练集日均样本数: 1000.0\n",
-      "验证集日均样本数: 1000.0\n",
-      "测试集日均样本数: 1000.0\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_26384\\2929956284.py:149: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n",
-      "(Deprecated in version 0.20.5)\n",
-      "  pl.count().alias(\"count\")\n"
-     ]
-    }
-   ],
-   "execution_count": 7
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -922,12 +602,7 @@
    "source": "### 4.3 数据质量检查"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:37.417614Z",
-     "start_time": "2026-03-13T18:10:35.002702Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -945,52 +620,8 @@
     "\n",
     "print(\"[成功] 数据质量检查通过，未发现异常\")\n"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "数据质量检查（必须在预处理之前）\n",
-      "================================================================================\n",
-      "\n",
-      "检查训练集...\n",
-      "\n",
-      "================================================================================\n",
-      "数据质量检查报告\n",
-      "================================================================================\n",
-      "\n",
-      "[严重] 发现 1657 个全空因子:\n",
-      "  (某天的某个因子所有值都是 null，可能是数据缺失或计算错误)\n",
-      "    - 日期 20200217: drawdown_from_high_60 (样本数: 1000)\n",
-      "    - 日期 20200217: volatility_squeeze_5_60 (样本数: 1000)\n",
-      "    - 日期 20200217: net_profit_yoy (样本数: 1000)\n",
-      "    - 日期 20200217: revenue_yoy (样本数: 1000)\n",
-      "    - 日期 20200217: healthy_expansion_velocity (样本数: 1000)\n",
-      "    - 日期 20200217: pe_expansion_trend (样本数: 1000)\n",
-      "    - 日期 20200217: value_price_divergence (样本数: 1000)\n",
-      "    - 日期 20200213: drawdown_from_high_60 (样本数: 1000)\n",
-      "    - 日期 20200213: volatility_squeeze_5_60 (样本数: 1000)\n",
-      "    - 日期 20200213: net_profit_yoy (样本数: 1000)\n",
-      "    ... 还有 1647 个\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "建议处理方式:\n",
-      "  1. 检查因子定义和数据源，确认计算逻辑是否正确\n",
-      "  2. 如果是预期内的缺失（如新股无历史数据），考虑调整因子计算窗口\n",
-      "  3. 如果是数据同步问题，重新同步相关数据\n",
-      "  4. 可以使用 filter 排除问题日期或因子\n",
-      "================================================================================\n",
-      "\n",
-      "检查验证集...\n",
-      "\n",
-      "检查测试集...\n",
-      "[成功] 数据质量检查通过，未发现异常\n"
-     ]
-    }
-   ],
-   "execution_count": 8
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -998,12 +629,7 @@
    "source": "### 4.4 数据预处理"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:37.997346Z",
-     "start_time": "2026-03-13T18:10:37.438005Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -1030,32 +656,8 @@
     "print(f\"处理后验证集形状: {val_data.shape}\")\n",
     "print(f\"处理后测试集形状: {test_data.shape}\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "数据预处理\n",
-      "================================================================================\n",
-      "\n",
-      "训练集处理...\n",
-      "  [1/3] NullFiller\n",
-      "  [2/3] Winsorizer\n",
-      "  [3/3] StandardScaler\n",
-      "\n",
-      "验证集处理...\n",
-      "\n",
-      "测试集处理...\n",
-      "\n",
-      "处理后训练集形状: (970000, 72)\n",
-      "处理后验证集形状: (242000, 72)\n",
-      "处理后测试集形状: (243000, 72)\n"
-     ]
-    }
-   ],
-   "execution_count": 9
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -1063,12 +665,7 @@
    "source": "### 4.4 训练 LambdaRank 模型"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:39.279997Z",
-     "start_time": "2026-03-13T18:10:38.028508Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -1099,48 +696,8 @@
     ")\n",
     "print(\"训练完成！\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "训练 LambdaRank 模型\n",
-      "================================================================================\n",
-      "\n",
-      "训练样本数: 970000\n",
-      "验证样本数: 242000\n",
-      "特征数: 50\n",
-      "目标变量: future_return_5_rank_rank\n",
-      "\n",
-      "目标变量统计（训练集）:\n",
-      "shape: (9, 2)\n",
-      "┌────────────┬──────────┐\n",
-      "│ statistic  ┆ value    │\n",
-      "│ ---        ┆ ---      │\n",
-      "│ str        ┆ f64      │\n",
-      "╞════════════╪══════════╡\n",
-      "│ count      ┆ 969665.0 │\n",
-      "│ null_count ┆ 335.0    │\n",
-      "│ mean       ┆ 9.810091 │\n",
-      "│ std        ┆ 5.346526 │\n",
-      "│ min        ┆ 0.0      │\n",
-      "│ 25%        ┆ 6.0      │\n",
-      "│ 50%        ┆ 10.0     │\n",
-      "│ 75%        ┆ 14.0     │\n",
-      "│ max        ┆ 19.0     │\n",
-      "└────────────┴──────────┘\n",
-      "\n",
-      "开始训练...\n",
-      "Training until validation scores don't improve for 50 rounds\n",
-      "Early stopping, best iteration is:\n",
-      "[5]\ttrain's ndcg@10: 0.59368\tval's ndcg@10: 0.546167\n",
-      "训练完成！\n"
-     ]
-    }
-   ],
-   "execution_count": 10
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -1148,12 +705,7 @@
    "source": "### 4.5 训练指标曲线"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:39.394848Z",
-     "start_time": "2026-03-13T18:10:39.285414Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -1206,57 +758,8 @@
     "        print(f\"    {metric}: {best_val:.4f} (迭代 {best_iter_metric + 1})\")\n",
     "    print(f\"\\n[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "训练指标曲线\n",
-      "================================================================================\n",
-      "[成功] 已从模型获取训练评估结果\n",
-      "\n",
-      "评估的 NDCG 指标: ['ndcg@10']\n",
-      "\n",
-      "[早停信息]\n",
-      "  配置的最大轮数: 2000\n",
-      "  实际训练轮数: 55\n",
-      "  早停状态: 已触发（最佳迭代: 5）\n",
-      "\n",
-      "最终 NDCG 指标:\n",
-      "  ndcg@10: 训练集=0.6331, 验证集=0.5393\n",
-      "\n",
-      "[绘图] 使用 LightGBM 原生接口绘制训练曲线...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1400x1000 with 1 Axes>"
-      ],
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[指标分析]\n",
-      "  各NDCG指标在验证集上的最佳值:\n",
-      "    ndcg@10: 0.5462 (迭代 5)\n",
-      "\n",
-      "[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\n"
-     ]
-    }
-   ],
-   "execution_count": 11
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -1264,12 +767,7 @@
    "source": "### 4.6 模型评估"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:39.655028Z",
-     "start_time": "2026-03-13T18:10:39.403487Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(\"\\n\" + \"=\" * 80)\n",
@@ -1310,61 +808,11 @@
     "    for i, (feature, score) in enumerate(top_features.items(), 1):\n",
     "        print(f\"  {i:2d}. {feature:30s} {score:10.2f}\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "模型评估\n",
-      "================================================================================\n",
-      "\n",
-      "生成预测...\n",
-      "\n",
-      "计算 NDCG 指标...\n",
-      "\n",
-      "NDCG 评估结果:\n",
-      "----------------------------------------\n",
-      "  ndcg@1: 0.4710\n",
-      "  ndcg@5: 0.4927\n",
-      "  ndcg@10: 0.5003\n",
-      "  ndcg@20: 0.5075\n",
-      "\n",
-      "特征重要性（Top 20）:\n",
-      "----------------------------------------\n",
-      "   1. roe                                345.77\n",
-      "   2. ma_20                              244.73\n",
-      "   3. profit_margin                      223.10\n",
-      "   4. revenue_yoy                        216.85\n",
-      "   5. active_market_cap                  214.10\n",
-      "   6. std_return_20                      149.44\n",
-      "   7. close_vwap_deviation               136.42\n",
-      "   8. max_ret_20                         110.61\n",
-      "   9. debt_to_equity                     105.78\n",
-      "  10. bbi_ratio                          104.63\n",
-      "  11. market_cap_rank                    104.10\n",
-      "  12. up_days_ratio_20                    99.44\n",
-      "  13. turnover_rank                       80.60\n",
-      "  14. min_ret_20                          72.72\n",
-      "  15. ebit_rank                           64.03\n",
-      "  16. drawdown_from_high_60               61.28\n",
-      "  17. turnover_rate_mean_5                57.16\n",
-      "  18. healthy_expansion_velocity          54.10\n",
-      "  19. BP                                  51.36\n",
-      "  20. EP                                  51.29\n"
-     ]
-    }
-   ],
-   "execution_count": 12
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-13T18:10:39.872615Z",
-     "start_time": "2026-03-13T18:10:39.663834Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# 确保输出目录存在\n",
@@ -1413,41 +861,8 @@
     "\n",
     "print(\"\\n训练流程完成！\")"
    ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "[1/1] 保存每日 Top 5 股票...\n",
-      "  保存路径: output\\rank_output.csv\n",
-      "  保存行数: 1215（243个交易日 x 每日top5）\n",
-      "\n",
-      "  预览（前15行）:\n",
-      "shape: (15, 3)\n",
-      "┌────────────┬──────────┬───────────┐\n",
-      "│ trade_date ┆ score    ┆ ts_code   │\n",
-      "│ ---        ┆ ---      ┆ ---       │\n",
-      "│ str        ┆ f64      ┆ str       │\n",
-      "╞════════════╪══════════╪═══════════╡\n",
-      "│ 2025-01-02 ┆ 0.022198 ┆ 002480.SZ │\n",
-      "│ 2025-01-02 ┆ 0.018886 ┆ 000826.SZ │\n",
-      "│ 2025-01-02 ┆ 0.018845 ┆ 600683.SH │\n",
-      "│ 2025-01-02 ┆ 0.016044 ┆ 000632.SZ │\n",
-      "│ 2025-01-02 ┆ 0.014577 ┆ 002072.SZ │\n",
-      "│ …          ┆ …        ┆ …         │\n",
-      "│ 2025-01-06 ┆ 0.03183  ┆ 600683.SH │\n",
-      "│ 2025-01-06 ┆ 0.023015 ┆ 000701.SZ │\n",
-      "│ 2025-01-06 ┆ 0.018886 ┆ 000826.SZ │\n",
-      "│ 2025-01-06 ┆ 0.016044 ┆ 000632.SZ │\n",
-      "│ 2025-01-06 ┆ 0.014577 ┆ 002072.SZ │\n",
-      "└────────────┴──────────┴───────────┘\n",
-      "\n",
-      "训练流程完成！\n"
-     ]
-    }
-   ],
-   "execution_count": 13
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
diff --git a/src/experiment/learn_to_rank.py b/src/experiment/learn_to_rank.py
index aa3a268..bf85985 100644
--- a/src/experiment/learn_to_rank.py
+++ b/src/experiment/learn_to_rank.py
@@ -293,6 +293,7 @@ SELECTED_FACTORS = [
     "net_profit_yoy",
     "revenue_yoy",
     "healthy_expansion_velocity",
+    "ebit_rank",
     # ================= 6. 基本面估值与截面动量共振 =================
     "EP",
     "BP",
@@ -304,12 +305,11 @@ SELECTED_FACTORS = [
     "pe_expansion_trend",
     "value_price_divergence",
     "active_market_cap",
-    "ebit_rank",
 ]
 
 # 因子定义字典（完整因子库）
 FACTOR_DEFINITIONS = {
-    "turnover_rate_volatility": "ts_std(log(turnover_rate), 20)"
+    # "turnover_rate_volatility": "ts_std(log(turnover_rate), 20)"
 }
 
 # Label 因子定义（不参与训练，用于计算目标）
@@ -341,7 +341,7 @@ MODEL_PARAMS = {
     "max_depth": 4,
     "min_data_in_leaf": 20,
     "n_estimators": 2000,
-    "early_stopping_round": 300,
+    "early_stopping_round": 100,
     "subsample": 0.8,
     "colsample_bytree": 0.8,
     "reg_alpha": 0.1,
@@ -372,7 +372,7 @@ def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
     )
 
     valid_df = df.filter(code_filter)
-    n = min(1000, len(valid_df))
+    n = min(500, len(valid_df))
     small_cap_codes = valid_df.sort("total_mv").head(n)["ts_code"]
 
     return df["ts_code"].is_in(small_cap_codes)
diff --git a/src/training/components/models/lightgbm.py b/src/training/components/models/lightgbm.py
index 56638df..d3cf82e 100644
--- a/src/training/components/models/lightgbm.py
+++ b/src/training/components/models/lightgbm.py
@@ -29,33 +29,14 @@ class LightGBMModel(BaseModel):
 
     name = "lightgbm"
 
-    def __init__(
-        self,
-        params: Optional[dict] = None,
-        objective: str = "regression",
-        metric: str = "rmse",
-        num_leaves: int = 31,
-        learning_rate: float = 0.05,
-        n_estimators: int = 100,
-        **kwargs,
-    ):
+    def __init__(self, params: Optional[dict] = None):
         """初始化 LightGBM 模型
 
-        支持两种方式传入参数：
-        1. 通过 params 字典传入所有参数（推荐方式）
-        2. 通过独立参数传入（向后兼容）
-
         Args:
-            params: LightGBM 参数字典，如果提供则直接使用此字典
-            objective: 目标函数，默认 "regression"
-            metric: 评估指标，默认 "rmse"
-            num_leaves: 叶子节点数，默认 31
-            learning_rate: 学习率，默认 0.05
-            n_estimators: 迭代次数，默认 100
-            **kwargs: 其他 LightGBM 参数
+            params: LightGBM 参数字典，直接传递给 lgb.train()。
+                   包含所有模型参数和训练控制参数（如 n_estimators）。
 
         Examples:
-            >>> # 方式1：通过 params 字典（推荐）
             >>> model = LightGBMModel(params={
             ...     "objective": "regression",
             ...     "metric": "rmse",
@@ -63,32 +44,8 @@ class LightGBMModel(BaseModel):
             ...     "learning_rate": 0.05,
             ...     "n_estimators": 100,
             ... })
-            >>>
-            >>> # 方式2：通过独立参数（向后兼容）
-            >>> model = LightGBMModel(
-            ...     objective="regression",
-            ...     num_leaves=31,
-            ...     learning_rate=0.05,
-            ... )
         """
-        if params is not None:
-            # 方式1：直接使用 params 字典
-            self.params = dict(params)  # 复制一份，避免修改原始字典
-            self.params.setdefault("verbose", -1)  # 默认抑制训练输出
-            # n_estimators 可能存在于 params 中
-            self.n_estimators = self.params.pop("n_estimators", n_estimators)
-        else:
-            # 方式2：通过独立参数构建 params
-            self.params = {
-                "objective": objective,
-                "metric": metric,
-                "num_leaves": num_leaves,
-                "learning_rate": learning_rate,
-                "verbose": -1,  # 抑制训练输出
-                **kwargs,
-            }
-            self.n_estimators = n_estimators
-
+        self.params = dict(params) if params is not None else {}
         self.model = None
         self.feature_names_: Optional[list] = None
 
@@ -113,21 +70,19 @@ class LightGBMModel(BaseModel):
                 "使用 LightGBMModel 需要安装 lightgbm: pip install lightgbm"
             )
 
-        # 保存特征名称
         self.feature_names_ = X.columns
-
-        # 转换为 numpy
         X_np = X.to_numpy()
         y_np = y.to_numpy()
 
-        # 创建数据集
         train_data = lgb.Dataset(X_np, label=y_np)
 
-        # 训练
+        # 从 params 中提取 num_boost_round，默认 100
+        num_boost_round = self.params.pop("n_estimators", 100)
+
         self.model = lgb.train(
             self.params,
             train_data,
-            num_boost_round=self.n_estimators,
+            num_boost_round=num_boost_round,
         )
 
         return self
@@ -148,7 +103,8 @@ class LightGBMModel(BaseModel):
             raise RuntimeError("模型尚未训练，请先调用 fit()")
 
         X_np = X.to_numpy()
-        return self.model.predict(X_np)
+        result = self.model.predict(X_np)
+        return np.asarray(result)
 
     def feature_importance(self) -> Optional[pd.Series]:
         """返回特征重要性
@@ -179,7 +135,6 @@ class LightGBMModel(BaseModel):
 
         self.model.save_model(path)
 
-        # 同时保存特征名称（LightGBM 原生格式不保存这个）
         import json
 
         meta_path = path + ".meta.json"
@@ -188,7 +143,6 @@ class LightGBMModel(BaseModel):
                 {
                     "feature_names": self.feature_names_,
                     "params": self.params,
-                    "n_estimators": self.n_estimators,
                 },
                 f,
             )
@@ -211,16 +165,13 @@ class LightGBMModel(BaseModel):
         instance = cls()
         instance.model = lgb.Booster(model_file=path)
 
-        # 加载元数据
         meta_path = path + ".meta.json"
         try:
             with open(meta_path, "r") as f:
                 meta = json.load(f)
                 instance.feature_names_ = meta.get("feature_names")
-                instance.params = meta.get("params", instance.params)
-                instance.n_estimators = meta.get("n_estimators", instance.n_estimators)
+                instance.params = meta.get("params", {})
         except FileNotFoundError:
-            # 如果没有元数据文件，继续运行（feature_names_ 为 None）
             pass
 
         return instance
diff --git a/src/training/components/models/lightgbm_lambdarank.py b/src/training/components/models/lightgbm_lambdarank.py
index 01fa5d1..38f6851 100644
--- a/src/training/components/models/lightgbm_lambdarank.py
+++ b/src/training/components/models/lightgbm_lambdarank.py
@@ -30,35 +30,14 @@ class LightGBMLambdaRankModel(BaseModel):
 
     name = "lightgbm_lambdarank"
 
-    def __init__(
-        self,
-        params: Optional[dict] = None,
-        learning_rate: float = 0.05,
-        num_leaves: int = 31,
-        n_estimators: int = 100,
-        min_data_in_leaf: int = 20,
-        ndcg_at: Optional[List[int]] = None,
-        early_stopping_rounds: int = 50,
-        **kwargs,
-    ):
+    def __init__(self, params: Optional[dict] = None):
         """初始化 LambdaRank 模型
 
-        支持两种方式传入参数：
-        1. 通过 params 字典传入所有参数（推荐方式）
-        2. 通过独立参数传入（向后兼容）
-
         Args:
-            params: LightGBM 参数字典，如果提供则直接使用此字典
-            learning_rate: 学习率，默认 0.05
-            num_leaves: 叶子节点数，默认 31
-            n_estimators: 迭代次数，默认 100
-            min_data_in_leaf: 叶子最小样本数，默认 20
-            ndcg_at: NDCG 评估的 k 值列表，默认 [1, 5, 10, 20]
-            early_stopping_rounds: 早停轮数，默认 50
-            **kwargs: 其他 LightGBM 参数
+            params: LightGBM 参数字典，直接传递给 lgb.train()。
+                   包含所有模型参数和训练控制参数。
 
         Examples:
-            >>> # 方式1：通过 params 字典（推荐）
             >>> model = LightGBMLambdaRankModel(params={
             ...     "objective": "lambdarank",
             ...     "metric": "ndcg",
@@ -66,39 +45,13 @@ class LightGBMLambdaRankModel(BaseModel):
             ...     "num_leaves": 31,
             ...     "learning_rate": 0.05,
             ...     "n_estimators": 1000,
+            ...     "early_stopping_round": 50,
             ... })
         """
-        if ndcg_at is None:
-            ndcg_at = [1, 5, 10, 20]
-
-        if params is not None:
-            # 方式1：直接使用 params 字典
-            self.params = dict(params)  # 复制一份，避免修改原始字典
-            self.params.setdefault("objective", "lambdarank")
-            self.params.setdefault("metric", "ndcg")
-            self.params.setdefault("verbose", -1)
-            self.n_estimators = self.params.pop("n_estimators", n_estimators)
-            self.early_stopping_rounds = self.params.pop(
-                "early_stopping_rounds", early_stopping_rounds
-            )
-        else:
-            # 方式2：通过独立参数构建 params
-            self.params = {
-                "objective": "lambdarank",
-                "metric": "ndcg",
-                "ndcg_at": ndcg_at,
-                "num_leaves": num_leaves,
-                "learning_rate": learning_rate,
-                "min_data_in_leaf": min_data_in_leaf,
-                "verbose": -1,
-                **kwargs,
-            }
-            self.n_estimators = n_estimators
-            self.early_stopping_rounds = early_stopping_rounds
-
+        self.params = dict(params) if params is not None else {}
         self.model = None
         self.feature_names_: Optional[list] = None
-        self.evals_result_: Optional[dict] = None  # 存储训练评估结果
+        self.evals_result_: Optional[dict] = None
 
     def fit(
         self,
@@ -112,10 +65,7 @@ class LightGBMLambdaRankModel(BaseModel):
         Args:
             X: 特征矩阵 (Polars DataFrame)
             y: 目标变量 (Polars Series)，应为分位数标签 (0, 1, 2, ...)
-            group: 分组数组，表示每个 query 的样本数。
-                   例如 [10, 15, 20] 表示第一个 query 有 10 个样本，
-                   第二个 query 有 15 个样本，第三个 query 有 20 个样本。
-                   如果为 None，则假设所有样本属于同一个 query。
+            group: 分组数组，表示每个 query 的样本数
             eval_set: 验证集元组 (X_val, y_val, group_val)，用于早停
 
         Returns:
@@ -133,19 +83,13 @@ class LightGBMLambdaRankModel(BaseModel):
                 "使用 LightGBMLambdaRankModel 需要安装 lightgbm: pip install lightgbm"
             )
 
-        # 保存特征名称
         self.feature_names_ = X.columns
-
-        # 转换为 numpy
         X_np = X.to_numpy()
         y_np = y.to_numpy()
 
-        # 处理 group 参数
         if group is None:
-            # 如果未提供 group，假设所有样本属于同一个 query
             group = np.array([len(y_np)])
 
-        # 验证 group 参数
         if not isinstance(group, np.ndarray):
             group = np.array(group)
         if group.sum() != len(y_np):
@@ -153,10 +97,8 @@ class LightGBMLambdaRankModel(BaseModel):
                 f"group 数组的和 ({group.sum()}) 必须等于样本数 ({len(y_np)})"
             )
 
-        # 创建训练数据集
         train_data = lgb.Dataset(X_np, label=y_np, group=group)
 
-        # 准备验证集和验证集名称
         valid_sets = [train_data]
         valid_names = ["train"]
         if eval_set is not None:
@@ -173,19 +115,22 @@ class LightGBMLambdaRankModel(BaseModel):
             valid_sets.append(val_data)
             valid_names.append("val")
 
-        # 初始化评估结果存储
         self.evals_result_ = {}
 
-        # 训练
+        # 从 params 提取训练控制参数
+        params_copy = dict(self.params)
+        num_boost_round = params_copy.pop("n_estimators", 100)
+        early_stopping_round = params_copy.pop("early_stopping_round", 50)
+
         callbacks = [
-            lgb.early_stopping(stopping_rounds=self.early_stopping_rounds),
+            lgb.early_stopping(stopping_rounds=early_stopping_round),
             lgb.record_evaluation(self.evals_result_),
         ]
 
         self.model = lgb.train(
-            self.params,
+            params_copy,
             train_data,
-            num_boost_round=self.n_estimators,
+            num_boost_round=num_boost_round,
             valid_sets=valid_sets,
             valid_names=valid_names,
             callbacks=callbacks,
@@ -200,7 +145,7 @@ class LightGBMLambdaRankModel(BaseModel):
             X: 特征矩阵 (Polars DataFrame)
 
         Returns:
-            预测分数 (numpy ndarray)，分数越高表示排序越靠前
+            预测分数 (numpy ndarray)
 
         Raises:
             RuntimeError: 模型未训练时调用
@@ -209,18 +154,37 @@ class LightGBMLambdaRankModel(BaseModel):
             raise RuntimeError("模型尚未训练，请先调用 fit()")
 
         X_np = X.to_numpy()
-        return self.model.predict(X_np)
+        result = self.model.predict(X_np)
+        return np.asarray(result)
 
     def get_evals_result(self) -> Optional[dict]:
         """获取训练评估结果
 
         Returns:
-            评估结果字典，包含训练集和验证集的指标历史
-            格式: {'train': {'metric_name': [...]}, 'val': {'metric_name': [...]}}
-            如果模型尚未训练，返回 None
+            评估结果字典，如果模型尚未训练返回 None
         """
         return self.evals_result_
 
+    def get_best_iteration(self) -> Optional[int]:
+        """获取最佳迭代轮数（考虑早停）
+
+        Returns:
+            最佳迭代轮数，如果模型未训练返回 None
+        """
+        if self.model is None:
+            return None
+        return self.model.best_iteration
+
+    def get_best_score(self) -> Optional[dict]:
+        """获取最佳评分
+
+        Returns:
+            最佳评分字典，如果模型未训练返回 None
+        """
+        if self.model is None:
+            return None
+        return self.model.best_score
+
     def plot_metric(
         self,
         metric: Optional[str] = None,
@@ -230,25 +194,14 @@ class LightGBMLambdaRankModel(BaseModel):
     ):
         """绘制训练指标曲线
 
-        使用 LightGBM 原生的 plot_metric 接口绘制训练曲线。
-
         Args:
-            metric: 要绘制的指标名称，如 'ndcg@5'、'ndcg@10' 等。
-                   如果为 None，则自动选择第一个可用的 NDCG 指标。
+            metric: 要绘制的指标名称，如 'ndcg@5'
             figsize: 图大小，默认 (10, 6)
-            title: 图表标题，如果为 None 则自动生成
-            ax: matplotlib Axes 对象，如果为 None 则创建新图
+            title: 图表标题
+            ax: matplotlib Axes 对象
 
         Returns:
             matplotlib Axes 对象
-
-        Raises:
-            RuntimeError: 模型尚未训练
-            ValueError: 指定的指标不存在
-
-        Examples:
-            >>> model.plot_metric('ndcg@20')  # 绘制 ndcg@20 曲线
-            >>> model.plot_metric()  # 自动选择指标
         """
         if self.model is None:
             raise RuntimeError("模型尚未训练，请先调用 fit()")
@@ -259,7 +212,6 @@ class LightGBMLambdaRankModel(BaseModel):
         import lightgbm as lgb
         import matplotlib.pyplot as plt
 
-        # 如果没有指定指标，自动选择第一个 NDCG 指标
         if metric is None:
             available_metrics = list(self.evals_result_.get("train", {}).keys())
             ndcg_metrics = [m for m in available_metrics if "ndcg" in m.lower()]
@@ -270,20 +222,17 @@ class LightGBMLambdaRankModel(BaseModel):
             else:
                 raise ValueError("没有可用的评估指标")
 
-        # 检查指标是否存在
         if metric not in self.evals_result_.get("train", {}):
             available = list(self.evals_result_.get("train", {}).keys())
             raise ValueError(f"指标 '{metric}' 不存在。可用的指标: {available}")
 
-        # 创建图表
         if ax is None:
-            fig, ax = plt.subplots(figsize=figsize)
+            _, ax = plt.subplots(figsize=figsize)
 
-        # 使用 LightGBM 原生接口绘制
         lgb.plot_metric(self.evals_result_, metric=metric, ax=ax)
 
-        # 设置标题
         if title is None:
+            assert metric is not None
             title = f"Training Metric ({metric.upper()}) over Iterations"
         ax.set_title(title, fontsize=12, fontweight="bold")
 
@@ -297,18 +246,13 @@ class LightGBMLambdaRankModel(BaseModel):
     ):
         """绘制所有训练指标曲线
 
-        在一个图表中绘制多个指标的训练曲线。
-
         Args:
-            metrics: 要绘制的指标列表，如果为 None 则绘制所有 NDCG 指标
+            metrics: 要绘制的指标列表
             figsize: 图大小，默认 (14, 10)
             max_cols: 每行最多显示的子图数，默认 2
 
         Returns:
             matplotlib Figure 对象
-
-        Raises:
-            RuntimeError: 模型尚未训练
         """
         if self.model is None:
             raise RuntimeError("模型尚未训练，请先调用 fit()")
@@ -321,7 +265,6 @@ class LightGBMLambdaRankModel(BaseModel):
 
         available_metrics = list(self.evals_result_.get("train", {}).keys())
 
-        # 如果没有指定指标，使用所有 NDCG 指标（最多 4 个）
         if metrics is None:
             ndcg_metrics = [m for m in available_metrics if "ndcg" in m.lower()]
             metrics = ndcg_metrics[:4] if ndcg_metrics else available_metrics[:4]
@@ -329,7 +272,6 @@ class LightGBMLambdaRankModel(BaseModel):
         if not metrics:
             raise ValueError("没有可用的评估指标")
 
-        # 计算子图布局
         n_metrics = len(metrics)
         n_cols = min(max_cols, n_metrics)
         n_rows = (n_metrics + n_cols - 1) // n_cols
@@ -362,34 +304,12 @@ class LightGBMLambdaRankModel(BaseModel):
                         transform=ax.transAxes,
                     )
 
-        # 隐藏多余的子图
         for idx in range(n_metrics, len(axes)):
             axes[idx].axis("off")
 
         plt.tight_layout()
         return fig
 
-    def get_best_iteration(self) -> Optional[int]:
-        """获取最佳迭代轮数（考虑早停）
-
-        Returns:
-            最佳迭代轮数，如果模型未训练返回 None
-        """
-        if self.model is None:
-            return None
-        return self.model.best_iteration
-
-    def get_best_score(self) -> Optional[dict]:
-        """获取最佳评分
-
-        Returns:
-            最佳评分字典，格式: {'valid_0': {'metric': value}, 'valid_1': {...}}
-            如果模型未训练返回 None
-        """
-        if self.model is None:
-            return None
-        return self.model.best_score
-
     def feature_importance(self) -> Optional[pd.Series]:
         """返回特征重要性
 
@@ -405,9 +325,6 @@ class LightGBMLambdaRankModel(BaseModel):
     def save(self, path: str) -> None:
         """保存模型（使用 LightGBM 原生格式）
 
-        使用 LightGBM 的原生格式保存，不依赖 pickle，
-        可以在不同环境中加载。
-
         Args:
             path: 保存路径
 
@@ -419,7 +336,6 @@ class LightGBMLambdaRankModel(BaseModel):
 
         self.model.save_model(path)
 
-        # 同时保存特征名称和其他元数据
         import json
 
         meta_path = path + ".meta.json"
@@ -428,8 +344,6 @@ class LightGBMLambdaRankModel(BaseModel):
                 {
                     "feature_names": self.feature_names_,
                     "params": self.params,
-                    "n_estimators": self.n_estimators,
-                    "early_stopping_rounds": self.early_stopping_rounds,
                 },
                 f,
             )
@@ -438,8 +352,6 @@ class LightGBMLambdaRankModel(BaseModel):
     def load(cls, path: str) -> "LightGBMLambdaRankModel":
         """加载模型
 
-        从 LightGBM 原生格式加载模型。
-
         Args:
             path: 模型文件路径
 
@@ -452,19 +364,13 @@ class LightGBMLambdaRankModel(BaseModel):
         instance = cls()
         instance.model = lgb.Booster(model_file=path)
 
-        # 加载元数据
         meta_path = path + ".meta.json"
         try:
             with open(meta_path, "r") as f:
                 meta = json.load(f)
                 instance.feature_names_ = meta.get("feature_names")
-                instance.params = meta.get("params", instance.params)
-                instance.n_estimators = meta.get("n_estimators", instance.n_estimators)
-                instance.early_stopping_rounds = meta.get(
-                    "early_stopping_rounds", instance.early_stopping_rounds
-                )
+                instance.params = meta.get("params", {})
         except FileNotFoundError:
-            # 如果没有元数据文件，继续运行（feature_names_ 为 None）
             pass
 
         return instance
@@ -476,24 +382,13 @@ class LightGBMLambdaRankModel(BaseModel):
     ) -> np.ndarray:
         """从日期列生成 group 数组
 
-        将数据按日期分组，每个日期作为一个 query。
-
         Args:
             df: 包含日期列的 DataFrame
             date_col: 日期列名，默认 "trade_date"
 
         Returns:
-            group 数组，表示每个日期的样本数
-
-        Example:
-            >>> df = pl.DataFrame({
-            ...     "trade_date": ["20240101", "20240101", "20240102", "20240102", "20240102"],
-            ...     "feature": [1, 2, 3, 4, 5]
-            ... })
-            >>> group = LightGBMLambdaRankModel.prepare_group_from_dates(df)
-            >>> print(group)  # array([2, 3])
+            group 数组
         """
-        # 按日期统计样本数
         group_counts = df.group_by(date_col, maintain_order=True).agg(
             pl.count().alias("count")
         )
@@ -509,35 +404,19 @@ class LightGBMLambdaRankModel(BaseModel):
     ) -> pl.DataFrame:
         """将连续标签转换为分位数标签
 
-        对每个日期的数据分别进行分位数划分，生成 0, 1, 2, ..., n_quantiles-1 的标签。
-        值越大表示原始值越大（排序越靠前）。
-
         Args:
             df: 输入 DataFrame
-            label_col: 原始标签列名（如 "future_return_5"）
+            label_col: 原始标签列名
             date_col: 日期列名，默认 "trade_date"
             n_quantiles: 分位数数量，默认 20
-            new_col_name: 新列名，默认在原始列名后加 "_rank"
+            new_col_name: 新列名
 
         Returns:
             添加了分位数标签列的 DataFrame
-
-        Example:
-            >>> df = pl.DataFrame({
-            ...     "trade_date": ["20240101"] * 5 + ["20240102"] * 5,
-            ...     "future_return_5": [0.01, 0.02, 0.03, 0.04, 0.05,
-            ...                         0.02, 0.03, 0.04, 0.05, 0.06]
-            ... })
-            >>> df = LightGBMLambdaRankModel.convert_to_quantile_labels(
-            ...     df, "future_return_5", n_quantiles=5
-            ... )
-            >>> print(df["future_return_5_rank"])  # [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
         """
         if new_col_name is None:
             new_col_name = f"{label_col}_rank"
 
-        # 使用 qcut 按日期分组进行分位数划分
-        # qcut 返回的是 Categorical，使用 to_physical() 转换为整数（0, 1, 2, ...）
         return df.with_columns(
             pl.col(label_col)
             .qcut(n_quantiles)
@@ -560,17 +439,15 @@ class LightGBMLambdaRankModel(BaseModel):
             X: 特征矩阵
             y: 真实标签
             group: 分组数组
-            k: NDCG@k 的 k 值，None 表示使用所有位置
+            k: NDCG@k 的 k 值
 
         Returns:
             NDCG 分数
         """
         from sklearn.metrics import ndcg_score
 
-        # 获取预测分数
         y_pred = self.predict(X)
 
-        # 将数据按 group 拆分
         y_true_list = []
         y_score_list = []
 
@@ -581,15 +458,13 @@ class LightGBMLambdaRankModel(BaseModel):
             y_score_list.append(y_pred[start_idx:end_idx])
             start_idx = end_idx
 
-        # 计算平均 NDCG
         ndcg_scores = []
         for y_true, y_score in zip(y_true_list, y_score_list):
-            if len(y_true) > 1:  # 至少要有 2 个样本才能计算 NDCG
+            if len(y_true) > 1:
                 try:
                     score = ndcg_score([y_true], [y_score], k=k)
                     ndcg_scores.append(score)
                 except ValueError:
-                    # 如果标签都相同，跳过
                     pass
 
-        return np.mean(ndcg_scores) if ndcg_scores else 0.0
+        return float(np.mean(ndcg_scores)) if ndcg_scores else 0.0