ProStock/src/experiment/regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 导入依赖"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "from typing import List\n",
    "\n",
    "import polars as pl\n",
    "\n",
    "from src.factors import FactorEngine\n",
    "from src.training import (\n",
    "    DateSplitter,\n",
    "    LightGBMModel,\n",
    "    STFilter,\n",
    "    StandardScaler,\n",
    "    # StockFilterConfig,  # 已删除，使用 StockPoolManager + filter_func 替代\n",
    "    StockPoolManager,\n",
    "    Trainer,\n",
    "    Winsorizer,\n",
    "    NullFiller,\n",
    "    check_data_quality,\n",
    ")\n",
    "from src.training.config import TrainingConfig\n",
    "\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 2. 定义辅助函数"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def register_factors(\n",
    "    engine: FactorEngine,\n",
    "    selected_factors: List[str],\n",
    "    factor_definitions: dict,\n",
    "    label_factor: dict,\n",
    ") -> List[str]:\n",
    "    \"\"\"注册因子（selected_factors 从 metadata 查询，factor_definitions 用 DSL 表达式注册）\"\"\"\n",
    "    print(\"=\" * 80)\n",
    "    print(\"注册因子\")\n",
    "    print(\"=\" * 80)\n",
    "\n",
    "    # 注册 SELECTED_FACTORS 中的因子（已在 metadata 中）\n",
    "    print(\"\\n注册特征因子（从 metadata）:\")\n",
    "    for name in selected_factors:\n",
    "        engine.add_factor(name)\n",
    "        print(f\"  - {name}\")\n",
    "\n",
    "    # 注册 FACTOR_DEFINITIONS 中的因子（通过表达式，尚未在 metadata 中）\n",
    "    print(\"\\n注册特征因子（表达式）:\")\n",
    "    for name, expr in factor_definitions.items():\n",
    "        engine.add_factor(name, expr)\n",
    "        print(f\"  - {name}: {expr}\")\n",
    "\n",
    "    # 注册 label 因子（通过表达式）\n",
    "    print(\"\\n注册 Label 因子（表达式）:\")\n",
    "    for name, expr in label_factor.items():\n",
    "        engine.add_factor(name, expr)\n",
    "        print(f\"  - {name}: {expr}\")\n",
    "\n",
    "    # 特征列 = SELECTED_FACTORS + FACTOR_DEFINITIONS 的 keys\n",
    "    feature_cols = selected_factors + list(factor_definitions.keys())\n",
    "\n",
    "    print(f\"\\n特征因子数: {len(feature_cols)}\")\n",
    "    print(f\"  - 来自 metadata: {len(selected_factors)}\")\n",
    "    print(f\"  - 来自表达式: {len(factor_definitions)}\")\n",
    "    print(f\"Label: {list(label_factor.keys())[0]}\")\n",
    "    print(f\"已注册因子总数: {len(engine.list_registered())}\")\n",
    "\n",
    "    return feature_cols\n",
    "\n",
    "\n",
    "def prepare_data(\n",
    "    engine: FactorEngine,\n",
    "    feature_cols: List[str],\n",
    "    start_date: str,\n",
    "    end_date: str,\n",
    ") -> pl.DataFrame:\n",
    "    print(\"\\n\" + \"=\" * 80)\n",
    "    print(\"准备数据\")\n",
    "    print(\"=\" * 80)\n",
    "\n",
    "    # 计算因子（全市场数据）\n",
    "    print(f\"\\n计算因子: {start_date} - {end_date}\")\n",
    "    factor_names = feature_cols + [LABEL_NAME]  # 包含 label\n",
    "\n",
    "    data = engine.compute(\n",
    "        factor_names=factor_names,\n",
    "        start_date=start_date,\n",
    "        end_date=end_date,\n",
    "    )\n",
    "\n",
    "    print(f\"数据形状: {data.shape}\")\n",
    "    print(f\"数据列: {data.columns}\")\n",
    "    print(f\"\\n前5行预览:\")\n",
    "    print(data.head())\n",
    "\n",
    "    return data\n",
    "\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 3. 配置参数\n",
    "#\n",
    "### 3.1 因子定义"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 特征因子定义字典：新增因子只需在此处添加一行\n",
    "LABEL_NAME = \"future_return_5\"\n",
    "\n",
    "# 当前选择的因子列表（从 FACTOR_DEFINITIONS 中选择要使用的因子）\n",
    "SELECTED_FACTORS = [\n",
    "    # ================= 1. 价格、趋势与路径依赖 =================\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",
    "    # ================= 2. 波动率、风险调整与高阶矩 =================\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",
    "    # ================= 3. 日内微观结构与异象 =================\n",
    "    \"overnight_intraday_diff\",\n",
    "    \"upper_shadow_ratio\",\n",
    "    \"capital_retention_20\",\n",
    "    \"max_ret_20\",\n",
    "    # ================= 4. 量能、流动性与量价背离 =================\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",
    "    # ================= 5. 基本面财务特征 =================\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",
    "    # ================= 6. 基本面估值与截面动量共振 =================\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",
    "\n",
    "# 因子定义字典（完整因子库）\n",
    "FACTOR_DEFINITIONS = {\n",
    "    # ================= 1. 价格、趋势与路径依赖 (Trend, Momentum & Path Dependency) =================\n",
    "    \"ma_5\": \"ts_mean(close, 5)\",\n",
    "    \"ma_20\": \"ts_mean(close, 20)\",\n",
    "    \"ma_ratio_5_20\": \"ts_mean(close, 5) / (ts_mean(close, 20) + 1e-8) - 1\",  # 均线发散度\n",
    "    \"bias_10\": \"close / (ts_mean(close, 10) + 1e-8) - 1\",  # 10日乖离率\n",
    "    \"high_low_ratio\": \"(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\",  # 威廉指标变形\n",
    "    \"bbi_ratio\": \"(ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / (4 * close + 1e-8)\",  # 多空指标比率\n",
    "    \"return_5\": \"(close / (ts_delay(close, 5) + 1e-8)) - 1\",  # 5日动量\n",
    "    \"return_20\": \"(close / (ts_delay(close, 20) + 1e-8)) - 1\",  # 20日动量\n",
    "    # [高阶] Kaufman 趋势效率 (极高价值) - 衡量趋势流畅度，剔除无序震荡\n",
    "    \"kaufman_ER_20\": \"abs(close - ts_delay(close, 20)) / (ts_sum(abs(close - ts_delay(close, 1)), 20) + 1e-8)\",\n",
    "    # [高阶] 动量加速度 - 寻找二阶导数大于0，正在加速爆发的股票\n",
    "    \"mom_acceleration_10_20\": \"(close / (ts_delay(close, 10) + 1e-8) - 1) - (ts_delay(close, 10) / (ts_delay(close, 20) + 1e-8) - 1)\",\n",
    "    # [高阶] 高点距离衰减 - 衡量套牢盘压力\n",
    "    \"drawdown_from_high_60\": \"close / (ts_max(high, 60) + 1e-8) - 1\",\n",
    "    # [高阶] 趋势一致性 - 过去20天内收红的天数比例\n",
    "    \"up_days_ratio_20\": \"ts_sum(close > ts_delay(close, 1), 20) / 20\",\n",
    "    # ================= 2. 波动率、风险调整与高阶矩 (Volatility & Risk-Adjusted Returns) =================\n",
    "    \"volatility_5\": \"ts_std(close, 5)\",\n",
    "    \"volatility_20\": \"ts_std(close, 20)\",\n",
    "    \"volatility_ratio\": \"ts_std(close, 5) / (ts_std(close, 20) + 1e-8)\",  # 波动率期限结构\n",
    "    \"std_return_20\": \"ts_std((close / (ts_delay(close, 1) + 1e-8)) - 1, 20)\",  # 真实收益率波动率\n",
    "    # [高阶] 夏普趋势比率 - 惩罚暴涨暴跌，奖励稳健爬坡\n",
    "    \"sharpe_ratio_20\": \"ts_mean(close / (ts_delay(close, 1) + 1e-8) - 1, 20) / (ts_std(close / (ts_delay(close, 1) + 1e-8) - 1, 20) + 1e-8)\",\n",
    "    # [高阶] 尾部崩盘风险 - 过去一个月最大单日跌幅\n",
    "    \"min_ret_20\": \"ts_min(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\",\n",
    "    # [高阶] 波动率挤压比 - 寻找盘整到极致面临变盘的股票 (布林带收口)\n",
    "    \"volatility_squeeze_5_60\": \"ts_std(close, 5) / (ts_std(close, 60) + 1e-8)\",\n",
    "    # ================= 3. 日内微观结构与异象 (Intraday Microstructure & Anomalies) =================\n",
    "    # [高阶] 隔夜与日内背离 - 差值越小说明主力越喜欢在盘中吸筹\n",
    "    \"overnight_intraday_diff\": \"(open / (ts_delay(close, 1) + 1e-8) - 1) - (close / (open + 1e-8) - 1)\",\n",
    "    # [高阶] 上影线抛压极值 - 冲高回落被套牢的概率\n",
    "    \"upper_shadow_ratio\": \"(high - ((open + close + abs(open - close)) / 2)) / (high - low + 1e-8)\",\n",
    "    # [高阶] 资金沉淀率 - 衡量主力日内高抛低吸洗盘的剧烈程度\n",
    "    \"capital_retention_20\": \"ts_sum(abs(close - open), 20) / (ts_sum(high - low, 20) + 1e-8)\",\n",
    "    # [高阶] MAX 彩票效应 - 反转因子，剔除近期有过妖股连板特征的标的\n",
    "    \"max_ret_20\": \"ts_max(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\",\n",
    "    # ================= 4. 量能、流动性与量价背离 (Volume, Liquidity & Divergence) =================\n",
    "    \"volume_ratio_5_20\": \"ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\",  # 相对放量比\n",
    "    \"turnover_rate_mean_5\": \"ts_mean(turnover_rate, 5)\",  # 活跃度\n",
    "    \"turnover_deviation\": \"(turnover_rate - ts_mean(turnover_rate, 10)) / (ts_std(turnover_rate, 10) + 1e-8)\",  # 换手率偏离度\n",
    "    # [高阶] Amihud 非流动性异象 (绝对核心) - 衡量砸盘/拉升的摩擦成本\n",
    "    \"amihud_illiq_20\": \"ts_mean(abs(close / (ts_delay(close, 1) + 1e-8) - 1) / (amount + 1e-8), 20)\",\n",
    "    # [高阶] 换手率惩罚因子 - 换手率忽高忽低说明游资接力，行情极不稳定\n",
    "    \"turnover_cv_20\": \"ts_std(turnover_rate, 20) / (ts_mean(turnover_rate, 20) + 1e-8)\",\n",
    "    # [高阶] 纯粹量价相关性 - 检验是否是\"放量上涨，缩量下跌\"的良性多头\n",
    "    \"pv_corr_20\": \"ts_corr(close / (ts_delay(close, 1) + 1e-8) - 1, vol, 20)\",\n",
    "    # [高阶] 收盘价与均价背离 - 专门抓尾盘突袭拉升骗线的股票\n",
    "    \"close_vwap_deviation\": \"close / (amount / (vol * 100 + 1e-8) + 1e-8) - 1\",\n",
    "    # ================= 5. 基本面财务特征 (Fundamental Quality & Structure) =================\n",
    "    \"roe\": \"n_income / (total_hldr_eqy_exc_min_int + 1e-8)\",  # 净资产收益率\n",
    "    \"roa\": \"n_income / (total_assets + 1e-8)\",  # 总资产收益率\n",
    "    \"profit_margin\": \"n_income / (revenue + 1e-8)\",  # 销售净利率\n",
    "    \"debt_to_equity\": \"total_liab / (total_hldr_eqy_exc_min_int + 1e-8)\",  # 杠杆率\n",
    "    \"current_ratio\": \"total_cur_assets / (total_cur_liab + 1e-8)\",  # 短期偿债安全垫\n",
    "    # [高阶] 利润同比增速 (日频延后252天等于去年同期)\n",
    "    \"net_profit_yoy\": \"(n_income / (ts_delay(n_income, 252) + 1e-8)) - 1\",\n",
    "    # [高阶] 营收同比增速\n",
    "    \"revenue_yoy\": \"(revenue / (ts_delay(revenue, 252) + 1e-8)) - 1\",\n",
    "    # [高阶] 资产负债表扩张斜率 - 剔除单纯靠举债扩张的公司\n",
    "    \"healthy_expansion_velocity\": \"(total_assets / (ts_delay(total_assets, 252) + 1e-8) - 1) - (total_liab / (ts_delay(total_liab, 252) + 1e-8) - 1)\",\n",
    "    # ================= 6. 基本面估值与截面动量共振 (Valuation & Cross-Sectional Ranking) =================\n",
    "    # 估值水平绝对值 (Tushare 市值单位需要 * 10000 转换为元)\n",
    "    \"EP\": \"n_income / (total_mv * 10000 + 1e-8)\",  # 盈利收益率 (1/PE)\n",
    "    \"BP\": \"total_hldr_eqy_exc_min_int / (total_mv * 10000 + 1e-8)\",  # 账面市值比 (1/PB)\n",
    "    \"CP\": \"n_cashflow_act / (total_mv * 10000 + 1e-8)\",  # 经营现金流收益率 (1/PCF)\n",
    "    # 全市场截面排名因子\n",
    "    \"market_cap_rank\": \"cs_rank(total_mv)\",  # 规模因子 (Size)\n",
    "    \"turnover_rank\": \"cs_rank(turnover_rate)\",\n",
    "    \"return_5_rank\": \"cs_rank((close / (ts_delay(close, 5) + 1e-8)) - 1)\",\n",
    "    \"EP_rank\": \"cs_rank(n_income / (total_mv + 1e-8))\",  # 谁最便宜\n",
    "    # [高阶] 戴维斯双击动量 - 估值相对上一年是否在扩张\n",
    "    \"pe_expansion_trend\": \"(total_mv / (n_income + 1e-8)) / (ts_delay(total_mv, 60) / (ts_delay(n_income, 60) + 1e-8) + 1e-8) - 1\",\n",
    "    # [高阶] 业绩与价格背离度 - 截面做差：利润排名全市场第一，但近20日价格排名倒数第一，捕捉被错杀的潜伏股\n",
    "    \"value_price_divergence\": \"cs_rank((n_income - ts_delay(n_income, 252)) / (abs(ts_delay(n_income, 252)) + 1e-8)) - cs_rank(close / (ts_delay(close, 20) + 1e-8))\",\n",
    "    # [高阶] 流动性溢价调整后市值 - 识别僵尸大盘股和极度活跃的小微盘\n",
    "    \"active_market_cap\": \"total_mv * ts_mean(turnover_rate, 20)\",\n",
    "    \"ebit_rank\": \"cs_rank(ebit)\",\n",
    "}\n",
    "\n",
    "# Label 因子定义（不参与训练，用于计算目标）\n",
    "LABEL_FACTOR = {\n",
    "    LABEL_NAME: \"(ts_delay(close, -5) / ts_delay(open, -1)) - 1\",  # 未来5日收益率\n",
    "}"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 3.2 训练参数配置"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 日期范围配置（正确的 train/val/test 三分法）\n",
    "# Train: 用于训练模型参数\n",
    "# Val: 用于验证/早停/调参（位于 train 之后，test 之前）\n",
    "# Test: 仅用于最终评估，完全独立于训练过程\n",
    "TRAIN_START = \"20200101\"\n",
    "TRAIN_END = \"20231231\"\n",
    "VAL_START = \"20240101\"\n",
    "VAL_END = \"20241231\"\n",
    "TEST_START = \"20250101\"\n",
    "TEST_END = \"20261231\"\n",
    "\n",
    "# 模型参数配置\n",
    "MODEL_PARAMS = {\n",
    "    \"objective\": \"regression\",\n",
    "    \"metric\": \"mae\",  # 改为 MAE，对异常值更稳健\n",
    "    # 树结构控制（防过拟合核心）\n",
    "    # \"num_leaves\": 20,  # 从31降为20，降低模型复杂度\n",
    "    # \"max_depth\": 16,  # 显式限制深度，防止过度拟合噪声\n",
    "    # \"min_child_samples\": 50,  # 叶子最小样本数，防止学习极端样本\n",
    "    # \"min_child_weight\": 0.001,\n",
    "    # 学习参数\n",
    "    \"learning_rate\": 0.01,  # 降低学习率，配合更多树\n",
    "    \"n_estimators\": 1000,  # 增加树数量，配合早停\n",
    "    # 采样策略（关键防过拟合）\n",
    "    \"subsample\": 0.8,  # 每棵树随机采样80%数据（行采样）\n",
    "    \"subsample_freq\": 5,  # 每5轮迭代进行一次 subsample\n",
    "    \"colsample_bytree\": 0.8,  # 每棵树随机选择80%特征（列采样）\n",
    "    # 正则化\n",
    "    \"reg_alpha\": 0.1,  # L1正则，增加稀疏性\n",
    "    \"reg_lambda\": 1.0,  # L2正则，平滑权重\n",
    "    # 数值稳定性\n",
    "    \"verbose\": -1,\n",
    "    \"random_state\": 42,\n",
    "}\n",
    "\n",
    "\n",
    "# 股票池筛选函数\n",
    "# 使用新的 StockPoolManager API：传入自定义筛选函数和所需列/因子\n",
    "# 筛选函数接收单日 DataFrame，返回布尔 Series\n",
    "#\n",
    "# 筛选逻辑（针对单日数据）：\n",
    "# 1. 先排除创业板、科创板、北交所（ST过滤由STFilter组件处理）\n",
    "# 2. 然后选取市值最小的500只股票\n",
    "def stock_pool_filter(df: pl.DataFrame) -> pl.Series:\n",
    "    \"\"\"股票池筛选函数（单日数据）\n",
    "\n",
    "    筛选条件：\n",
    "    1. 排除创业板（代码以 300 开头）\n",
    "    2. 排除科创板（代码以 688 开头）\n",
    "    3. 排除北交所（代码以 8、9 或 4 开头）\n",
    "    4. 选取当日市值最小的500只股票\n",
    "    \"\"\"\n",
    "    # 代码筛选（排除创业板、科创板、北交所）\n",
    "    code_filter = (\n",
    "        ~df[\"ts_code\"].str.starts_with(\"30\")  # 排除创业板\n",
    "        & ~df[\"ts_code\"].str.starts_with(\"68\")  # 排除科创板\n",
    "        & ~df[\"ts_code\"].str.starts_with(\"8\")  # 排除北交所\n",
    "        & ~df[\"ts_code\"].str.starts_with(\"9\")  # 排除北交所\n",
    "        & ~df[\"ts_code\"].str.starts_with(\"4\")  # 排除北交所\n",
    "    )\n",
    "\n",
    "    # 在已筛选的股票中，选取市值最小的500只\n",
    "    # 按市值升序排序，取前500\n",
    "    valid_df = df.filter(code_filter)\n",
    "    n = min(1000, len(valid_df))\n",
    "    small_cap_codes = valid_df.sort(\"total_mv\").head(n)[\"ts_code\"]\n",
    "\n",
    "    # 返回布尔 Series：是否在被选中的股票中\n",
    "    return df[\"ts_code\"].is_in(small_cap_codes)\n",
    "\n",
    "\n",
    "# 定义筛选所需的基础列\n",
    "STOCK_FILTER_REQUIRED_COLUMNS = [\"total_mv\"]  # ST过滤由STFilter组件处理\n",
    "\n",
    "# 可选：定义筛选所需的因子（如果需要用因子进行筛选）\n",
    "# STOCK_FILTER_REQUIRED_FACTORS = {\n",
    "#     \"market_cap_rank\": \"cs_rank(total_mv)\",\n",
    "# }\n",
    "\n",
    "\n",
    "# 输出配置（相对于本文件所在目录）\n",
    "OUTPUT_DIR = \"output\"\n",
    "SAVE_PREDICTIONS = True\n",
    "PERSIST_MODEL = False\n",
    "\n",
    "# Top N 配置：每日推荐股票数量\n",
    "TOP_N = 5  # 可调整为 10, 20 等"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 4. 训练流程\n",
    "#\n",
    "### 4.1 初始化组件"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"LightGBM 回归模型训练\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 1. 创建 FactorEngine（启用 metadata 功能）\n",
    "print(\"\\n[1] 创建 FactorEngine\")\n",
    "engine = FactorEngine(metadata_path=\"data/factors.jsonl\")\n",
    "\n",
    "# 2. 使用 metadata 定义因子\n",
    "print(\"\\n[2] 定义因子（从 metadata 注册）\")\n",
    "feature_cols = register_factors(\n",
    "    engine, SELECTED_FACTORS, FACTOR_DEFINITIONS, LABEL_FACTOR\n",
    ")\n",
    "target_col = LABEL_NAME\n",
    "\n",
    "# 3. 准备数据（使用模块级别的日期配置）\n",
    "print(\"\\n[3] 准备数据\")\n",
    "\n",
    "data = prepare_data(\n",
    "    engine=engine,\n",
    "    feature_cols=feature_cols,\n",
    "    start_date=TRAIN_START,\n",
    "    end_date=TEST_END,\n",
    ")\n",
    "\n",
    "# 4. 打印配置信息\n",
    "print(f\"\\n[配置] 训练期: {TRAIN_START} - {TRAIN_END}\")\n",
    "print(f\"[配置] 验证期: {VAL_START} - {VAL_END}\")\n",
    "print(f\"[配置] 测试期: {TEST_START} - {TEST_END}\")\n",
    "print(f\"[配置] 特征数: {len(feature_cols)}\")\n",
    "print(f\"[配置] 目标变量: {target_col}\")\n",
    "\n",
    "# 5. 创建模型\n",
    "model = LightGBMModel(params=MODEL_PARAMS)\n",
    "\n",
    "# 6. 创建数据处理器（使用函数返回的完整特征列表）\n",
    "processors = [\n",
    "    NullFiller(feature_cols=feature_cols, strategy=\"mean\"),\n",
    "    Winsorizer(feature_cols=feature_cols, lower=0.01, upper=0.99),\n",
    "    StandardScaler(feature_cols=feature_cols),\n",
    "]\n",
    "\n",
    "# 7. 创建数据划分器（正确的 train/val/test 三分法）\n",
    "# Train: 训练模型参数 | Val: 验证/早停 | Test: 最终评估\n",
    "splitter = DateSplitter(\n",
    "    train_start=TRAIN_START,\n",
    "    train_end=TRAIN_END,\n",
    "    val_start=VAL_START,\n",
    "    val_end=VAL_END,\n",
    "    test_start=TEST_START,\n",
    "    test_end=TEST_END,\n",
    ")\n",
    "\n",
    "# 8. 创建股票池管理器\n",
    "# 使用新的 API：传入自定义筛选函数和所需列\n",
    "pool_manager = StockPoolManager(\n",
    "    filter_func=stock_pool_filter,\n",
    "    required_columns=STOCK_FILTER_REQUIRED_COLUMNS,  # 筛选所需的额外列\n",
    "    # required_factors=STOCK_FILTER_REQUIRED_FACTORS,  # 可选：筛选所需的因子\n",
    "    data_router=engine.router,\n",
    ")\n",
    "print(\"[股票池筛选] 使用自定义函数进行股票池筛选\")\n",
    "print(f\"[股票池筛选] 所需基础列: {STOCK_FILTER_REQUIRED_COLUMNS}\")\n",
    "print(\"[股票池筛选] 筛选逻辑: 排除创业板/科创板/北交所后，每日选市值最小的500只\")\n",
    "# print(f\"[股票池筛选] 所需因子: {list(STOCK_FILTER_REQUIRED_FACTORS.keys())}\")\n",
    "\n",
    "# 9. 创建 ST 股票过滤器\n",
    "st_filter = STFilter(\n",
    "    data_router=engine.router,\n",
    ")\n",
    "\n",
    "# 10. 创建训练器\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    pool_manager=pool_manager,\n",
    "    processors=processors,\n",
    "    filters=[st_filter],  # 使用STFilter过滤ST股票\n",
    "    splitter=splitter,\n",
    "    target_col=target_col,\n",
    "    feature_cols=feature_cols,\n",
    "    persist_model=PERSIST_MODEL,\n",
    ")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 4.2 执行训练"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"开始训练\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 步骤 1: 股票池筛选\n",
    "print(\"\\n[步骤 1/6] 股票池筛选\")\n",
    "print(\"-\" * 60)\n",
    "if pool_manager:\n",
    "    print(\"  执行每日独立筛选股票池...\")\n",
    "    filtered_data = pool_manager.filter_and_select_daily(data)\n",
    "    print(f\"  筛选前数据规模: {data.shape}\")\n",
    "    print(f\"  筛选后数据规模: {filtered_data.shape}\")\n",
    "    print(f\"  筛选前股票数: {data['ts_code'].n_unique()}\")\n",
    "    print(f\"  筛选后股票数: {filtered_data['ts_code'].n_unique()}\")\n",
    "    print(f\"  删除记录数: {len(data) - len(filtered_data)}\")\n",
    "else:\n",
    "    filtered_data = data\n",
    "    print(\"  未配置股票池管理器，跳过筛选\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 步骤 2: 划分训练/验证/测试集（正确的三分法）\n",
    "print(\"\\n[步骤 2/6] 划分训练集、验证集和测试集\")\n",
    "print(\"-\" * 60)\n",
    "if splitter:\n",
    "    # 正确的三分法：train用于训练，val用于验证/早停，test仅用于最终评估\n",
    "    train_data, val_data, test_data = splitter.split(filtered_data)\n",
    "    print(f\"  训练集数据规模: {train_data.shape}\")\n",
    "    print(f\"  验证集数据规模: {val_data.shape}\")\n",
    "    print(f\"  测试集数据规模: {test_data.shape}\")\n",
    "    print(f\"  训练集股票数: {train_data['ts_code'].n_unique()}\")\n",
    "    print(f\"  验证集股票数: {val_data['ts_code'].n_unique()}\")\n",
    "    print(f\"  测试集股票数: {test_data['ts_code'].n_unique()}\")\n",
    "    print(\n",
    "        f\"  训练集日期范围: {train_data['trade_date'].min()} - {train_data['trade_date'].max()}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"  验证集日期范围: {val_data['trade_date'].min()} - {val_data['trade_date'].max()}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"  测试集日期范围: {test_data['trade_date'].min()} - {test_data['trade_date'].max()}\"\n",
    "    )\n",
    "\n",
    "    print(\"\\n  训练集前5行预览:\")\n",
    "    print(train_data.head())\n",
    "    print(\"\\n  验证集前5行预览:\")\n",
    "    print(val_data.head())\n",
    "    print(\"\\n  测试集前5行预览:\")\n",
    "    print(test_data.head())\n",
    "else:\n",
    "    train_data = filtered_data\n",
    "    test_data = filtered_data\n",
    "    print(\"  未配置划分器，全部作为训练集\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 步骤 3: 数据质量检查（必须在预处理之前）\n",
    "print(\"\\n[步骤 3/7] 数据质量检查\")\n",
    "print(\"-\" * 60)\n",
    "print(\"  [说明] 此检查在 fillna 等处理之前执行，用于发现数据问题\")\n",
    "\n",
    "print(\"\\n  检查训练集...\")\n",
    "check_data_quality(train_data, feature_cols, raise_on_error=True)\n",
    "\n",
    "if \"val_data\" in locals() and val_data is not None:\n",
    "    print(\"\\n  检查验证集...\")\n",
    "    check_data_quality(val_data, feature_cols, raise_on_error=True)\n",
    "\n",
    "print(\"\\n  检查测试集...\")\n",
    "check_data_quality(test_data, feature_cols, raise_on_error=True)\n",
    "\n",
    "print(\"  [成功] 数据质量检查通过，未发现异常\")\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 步骤 4: 训练集数据处理\n",
    "print(\"\\n[步骤 4/7] 训练集数据处理\")\n",
    "print(\"-\" * 60)\n",
    "fitted_processors = []\n",
    "if processors:\n",
    "    for i, processor in enumerate(processors, 1):\n",
    "        print(f\"  [{i}/{len(processors)}] 应用处理器: {processor.__class__.__name__}\")\n",
    "        train_data_before = len(train_data)\n",
    "        train_data = processor.fit_transform(train_data)\n",
    "        train_data_after = len(train_data)\n",
    "        fitted_processors.append(processor)\n",
    "        print(f\"      处理前记录数: {train_data_before}\")\n",
    "        print(f\"      处理后记录数: {train_data_after}\")\n",
    "        if train_data_before != train_data_after:\n",
    "            print(f\"      删除记录数: {train_data_before - train_data_after}\")\n",
    "\n",
    "print(\"\\n  训练集处理后前5行预览:\")\n",
    "print(train_data.head())\n",
    "print(f\"\\n  训练集特征统计:\")\n",
    "print(f\"    特征数: {len(feature_cols)}\")\n",
    "print(f\"    样本数: {len(train_data)}\")\n",
    "print(f\"    缺失值统计:\")\n",
    "for col in feature_cols[:5]:  # 只显示前5个特征的缺失值\n",
    "    null_count = train_data[col].null_count()\n",
    "    if null_count > 0:\n",
    "        print(f\"      {col}: {null_count} ({null_count / len(train_data) * 100:.2f}%)\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 步骤 4: 训练模型\n",
    "print(\"\\n[步骤 5/7] 训练模型\")\n",
    "print(\"-\" * 60)\n",
    "print(f\"  模型类型: LightGBM\")\n",
    "print(f\"  训练样本数: {len(train_data)}\")\n",
    "print(f\"  特征数: {len(feature_cols)}\")\n",
    "print(f\"  目标变量: {target_col}\")\n",
    "\n",
    "X_train = train_data.select(feature_cols)\n",
    "y_train = train_data.select(target_col).to_series()\n",
    "\n",
    "print(f\"\\n  目标变量统计:\")\n",
    "print(f\"    均值: {y_train.mean():.6f}\")\n",
    "print(f\"    标准差: {y_train.std():.6f}\")\n",
    "print(f\"    最小值: {y_train.min():.6f}\")\n",
    "print(f\"    最大值: {y_train.max():.6f}\")\n",
    "print(f\"    缺失值: {y_train.null_count()}\")\n",
    "\n",
    "print(\"\\n  开始训练...\")\n",
    "model.fit(X_train, y_train)\n",
    "print(\"  训练完成！\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 步骤 5: 测试集数据处理\n",
    "print(\"\\n[步骤 6/7] 测试集数据处理\")\n",
    "print(\"-\" * 60)\n",
    "if processors and test_data is not train_data:\n",
    "    for i, processor in enumerate(fitted_processors, 1):\n",
    "        print(\n",
    "            f\"  [{i}/{len(fitted_processors)}] 应用处理器: {processor.__class__.__name__}\"\n",
    "        )\n",
    "        test_data_before = len(test_data)\n",
    "        test_data = processor.transform(test_data)\n",
    "        test_data_after = len(test_data)\n",
    "        print(f\"      处理前记录数: {test_data_before}\")\n",
    "        print(f\"      处理后记录数: {test_data_after}\")\n",
    "else:\n",
    "    print(\"  跳过测试集处理\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 步骤 6: 生成预测\n",
    "print(\"\\n[步骤 7/7] 生成预测\")\n",
    "print(\"-\" * 60)\n",
    "X_test = test_data.select(feature_cols)\n",
    "print(f\"  测试样本数: {len(X_test)}\")\n",
    "print(\"  预测中...\")\n",
    "predictions = model.predict(X_test)\n",
    "print(f\"  预测完成！\")\n",
    "\n",
    "print(f\"\\n  预测结果统计:\")\n",
    "print(f\"    均值: {predictions.mean():.6f}\")\n",
    "print(f\"    标准差: {predictions.std():.6f}\")\n",
    "print(f\"    最小值: {predictions.min():.6f}\")\n",
    "print(f\"    最大值: {predictions.max():.6f}\")\n",
    "\n",
    "# 保存结果到 trainer\n",
    "trainer.results = test_data.with_columns([pl.Series(\"prediction\", predictions)])"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 4.3 训练指标曲线"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"训练指标曲线\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 重新训练以收集指标（因为之前的训练没有保存评估结果）\n",
    "print(\"\\n重新训练模型以收集训练指标...\")\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "# 准备数据（使用 val 做验证，test 不参与训练过程）\n",
    "X_train_np = X_train.to_numpy()\n",
    "y_train_np = y_train.to_numpy()\n",
    "X_val_np = val_data.select(feature_cols).to_numpy()\n",
    "y_val_np = val_data.select(target_col).to_series().to_numpy()\n",
    "\n",
    "# 创建数据集\n",
    "train_dataset = lgb.Dataset(X_train_np, label=y_train_np)\n",
    "val_dataset = lgb.Dataset(X_val_np, label=y_val_np, reference=train_dataset)\n",
    "\n",
    "# 用于存储评估结果\n",
    "evals_result = {}\n",
    "\n",
    "# 使用与原模型相同的参数重新训练\n",
    "# 正确的三分法：train用于训练，val用于验证，test不参与训练过程\n",
    "# 添加早停：如果验证指标连续100轮没有改善则停止训练\n",
    "booster_with_eval = lgb.train(\n",
    "    MODEL_PARAMS,\n",
    "    train_dataset,\n",
    "    num_boost_round=MODEL_PARAMS.get(\"n_estimators\", 100),\n",
    "    valid_sets=[train_dataset, val_dataset],\n",
    "    valid_names=[\"train\", \"val\"],\n",
    "    callbacks=[\n",
    "        lgb.record_evaluation(evals_result),\n",
    "        lgb.early_stopping(stopping_rounds=100, verbose=True),\n",
    "    ],\n",
    ")\n",
    "\n",
    "print(\"训练完成，指标已收集\")\n",
    "\n",
    "# 获取指标名称\n",
    "metric_name = list(evals_result[\"train\"].keys())[0]\n",
    "print(f\"\\n评估指标: {metric_name}\")\n",
    "\n",
    "# 提取训练和验证指标\n",
    "train_metric = evals_result[\"train\"][metric_name]\n",
    "val_metric = evals_result[\"val\"][metric_name]\n",
    "\n",
    "# 显示早停信息\n",
    "actual_rounds = len(train_metric)\n",
    "expected_rounds = MODEL_PARAMS.get(\"n_estimators\", 100)\n",
    "print(f\"\\n[早停信息]\")\n",
    "print(f\"  配置的最大轮数: {expected_rounds}\")\n",
    "print(f\"  实际训练轮数: {actual_rounds}\")\n",
    "if actual_rounds < expected_rounds:\n",
    "    print(f\"  早停状态: 已触发（连续100轮验证指标未改善）\")\n",
    "else:\n",
    "    print(f\"  早停状态: 未触发（达到最大轮数）\")\n",
    "\n",
    "print(f\"\\n最终指标:\")\n",
    "print(f\"  训练 {metric_name}: {train_metric[-1]:.6f}\")\n",
    "print(f\"  验证 {metric_name}: {val_metric[-1]:.6f}\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 绘制训练指标曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# 绘制训练集和验证集的指标曲线（注意：val用于验证，test不参与训练）\n",
    "iterations = range(1, len(train_metric) + 1)\n",
    "ax.plot(\n",
    "    iterations, train_metric, label=f\"Train {metric_name}\", linewidth=2, color=\"blue\"\n",
    ")\n",
    "ax.plot(\n",
    "    iterations, val_metric, label=f\"Validation {metric_name}\", linewidth=2, color=\"red\"\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Iteration\", fontsize=12)\n",
    "ax.set_ylabel(metric_name.upper(), fontsize=12)\n",
    "ax.set_title(\n",
    "    f\"Training and Validation {metric_name.upper()} Curve\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "ax.legend(fontsize=10)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# 标记最佳验证指标点（用于早停决策）\n",
    "best_iter = val_metric.index(min(val_metric))\n",
    "best_metric = min(val_metric)\n",
    "ax.axvline(\n",
    "    x=best_iter + 1,\n",
    "    color=\"green\",\n",
    "    linestyle=\"--\",\n",
    "    alpha=0.7,\n",
    "    label=f\"Best Iteration ({best_iter + 1})\",\n",
    ")\n",
    "ax.scatter([best_iter + 1], [best_metric], color=\"green\", s=100, zorder=5)\n",
    "ax.annotate(\n",
    "    f\"Best: {best_metric:.6f}\\nIter: {best_iter + 1}\",\n",
    "    xy=(best_iter + 1, best_metric),\n",
    "    xytext=(best_iter + 1 + len(iterations) * 0.1, best_metric),\n",
    "    fontsize=9,\n",
    "    arrowprops=dict(arrowstyle=\"->\", color=\"green\", alpha=0.7),\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n[指标分析]\")\n",
    "print(f\"  最佳验证 {metric_name}: {best_metric:.6f}\")\n",
    "print(f\"  最佳迭代轮数: {best_iter + 1}\")\n",
    "print(f\"  早停建议: 如果验证指标连续10轮不下降，建议在第 {best_iter + 1} 轮停止训练\")\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！\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 4.4 查看结果"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"训练结果\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "results = trainer.results\n",
    "\n",
    "print(f\"\\n结果数据形状: {results.shape}\")\n",
    "print(f\"结果列: {results.columns}\")\n",
    "print(f\"\\n结果前10行预览:\")\n",
    "print(results.head(10))\n",
    "print(f\"\\n结果后5行预览:\")\n",
    "print(results.tail())\n",
    "\n",
    "print(f\"\\n每日预测样本数统计:\")\n",
    "daily_counts = results.group_by(\"trade_date\").agg(pl.len()).sort(\"trade_date\")\n",
    "print(f\"  最小: {daily_counts['len'].min()}\")\n",
    "print(f\"  最大: {daily_counts['len'].max()}\")\n",
    "print(f\"  平均: {daily_counts['len'].mean():.2f}\")\n",
    "\n",
    "# 展示某一天的前10个预测结果\n",
    "sample_date = results[\"trade_date\"][0]\n",
    "sample_data = results.filter(results[\"trade_date\"] == sample_date).head(10)\n",
    "print(f\"\\n示例日期 {sample_date} 的前10条预测:\")\n",
    "print(sample_data.select([\"ts_code\", \"trade_date\", target_col, \"prediction\"]))"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 4.4 保存结果"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"保存预测结果\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 确保输出目录存在\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "\n",
    "# 生成时间戳\n",
    "start_dt = datetime.strptime(TEST_START, \"%Y%m%d\")\n",
    "end_dt = datetime.strptime(TEST_END, \"%Y%m%d\")\n",
    "date_str = f\"{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}\"\n",
    "\n",
    "# 保存每日 Top N\n",
    "print(f\"\\n[1/1] 保存每日 Top {TOP_N} 股票...\")\n",
    "topn_output_path = os.path.join(OUTPUT_DIR, f\"regression_output.csv\")\n",
    "\n",
    "# 按日期分组，取每日 top N\n",
    "topn_by_date = []\n",
    "unique_dates = results[\"trade_date\"].unique().sort()\n",
    "for date in unique_dates:\n",
    "    day_data = results.filter(results[\"trade_date\"] == date)\n",
    "    # 按 prediction 降序排序，取前 N\n",
    "    topn = day_data.sort(\"prediction\", descending=True).head(TOP_N)\n",
    "    topn_by_date.append(topn)\n",
    "\n",
    "# 合并所有日期的 top N\n",
    "topn_results = pl.concat(topn_by_date)\n",
    "\n",
    "# 格式化日期并调整列顺序：日期、分数、股票\n",
    "topn_to_save = topn_results.select(\n",
    "    [\n",
    "        pl.col(\"trade_date\").str.slice(0, 4)\n",
    "        + \"-\"\n",
    "        + pl.col(\"trade_date\").str.slice(4, 2)\n",
    "        + \"-\"\n",
    "        + pl.col(\"trade_date\").str.slice(6, 2).alias(\"date\"),\n",
    "        pl.col(\"prediction\").alias(\"score\"),\n",
    "        pl.col(\"ts_code\"),\n",
    "    ]\n",
    ")\n",
    "topn_to_save.write_csv(topn_output_path, include_header=True)\n",
    "print(f\"  保存路径: {topn_output_path}\")\n",
    "print(\n",
    "    f\"  保存行数: {len(topn_to_save)}（{len(unique_dates)}个交易日 × 每日top{TOP_N}）\"\n",
    ")\n",
    "print(f\"\\n  预览（前15行）:\")\n",
    "print(topn_to_save.head(15))"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 4.5 特征重要性"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "importance = model.feature_importance()\n",
    "if importance is not None:\n",
    "    print(\"\\n特征重要性:\")\n",
    "    print(importance.sort_values(ascending=False))\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"训练完成！\")\n",
    "print(\"=\" * 80)"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 5. 可视化分析\n",
    "#\n",
    "使用训练好的模型直接绘图。\n",
    "- **特征重要性图**：辅助特征选择\n",
    "- **决策树图**：理解决策逻辑"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 导入可视化库\n",
    "import matplotlib.pyplot as plt\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "\n",
    "# 从封装的model中取出底层Booster\n",
    "booster = model.model\n",
    "print(f\"模型类型: {type(booster)}\")\n",
    "print(f\"特征数量: {len(feature_cols)}\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### 5.1 绘制特征重要性（辅助特征选择）\n",
    "#\n",
    "**解读**：\n",
    "- 重要性高的特征对模型贡献大\n",
    "- 重要性为0的特征可以考虑删除\n",
    "- 可以帮助理解哪些因子最有效"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "print(\"绘制特征重要性...\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "lgb.plot_importance(\n",
    "    booster,\n",
    "    max_num_features=20,\n",
    "    importance_type=\"gain\",\n",
    "    title=\"Feature Importance (Gain)\",\n",
    "    ax=ax,\n",
    ")\n",
    "ax.set_xlabel(\"Importance (Gain)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 打印重要性排名\n",
    "importance_gain = pd.Series(\n",
    "    booster.feature_importance(importance_type=\"gain\"), index=feature_cols\n",
    ").sort_values(ascending=False)\n",
    "\n",
    "print(\"\\n[特征重要性排名 - Gain]\")\n",
    "print(importance_gain)\n",
    "\n",
    "# 识别低重要性特征\n",
    "zero_importance = importance_gain[importance_gain == 0].index.tolist()\n",
    "if zero_importance:\n",
    "    print(f\"\\n[低重要性特征] 以下{len(zero_importance)}个特征重要性为0，可考虑删除:\")\n",
    "    for feat in zero_importance:\n",
    "        print(f\"  - {feat}\")\n",
    "else:\n",
    "    print(\"\\n所有特征都有一定重要性\")\n"
   ]
  }
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