liaozhaorun
0e9ea5d533
- 将因子定义、日期配置、股票池筛选等提取到 common.py - 重构 learn_to_rank 和 regression 脚本，统一使用公共配置 - 简化代码结构，消除重复定义
817 lines
28 KiB
Plaintext
817 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. 导入依赖"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"import os\n",
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"from datetime import datetime\n",
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"\n",
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"import polars as pl\n",
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"\n",
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"from src.factors import FactorEngine\n",
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"from src.training import (\n",
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" DateSplitter,\n",
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" LightGBMModel,\n",
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" STFilter,\n",
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" StandardScaler,\n",
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" StockPoolManager,\n",
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" Trainer,\n",
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" Winsorizer,\n",
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" NullFiller,\n",
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" check_data_quality,\n",
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")\n",
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"from src.training.config import TrainingConfig\n",
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"\n",
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"# 从 common 模块导入共用配置和函数\n",
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"from src.experiment.common import (\n",
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" SELECTED_FACTORS,\n",
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" FACTOR_DEFINITIONS,\n",
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" get_label_factor,\n",
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" register_factors,\n",
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" prepare_data,\n",
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" TRAIN_START,\n",
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" TRAIN_END,\n",
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" VAL_START,\n",
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" VAL_END,\n",
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" TEST_START,\n",
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" TEST_END,\n",
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" stock_pool_filter,\n",
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" STOCK_FILTER_REQUIRED_COLUMNS,\n",
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" OUTPUT_DIR,\n",
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" SAVE_PREDICTIONS,\n",
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" PERSIST_MODEL,\n",
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" TOP_N,\n",
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")\n",
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"\n"
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## 2. 配置参数\n",
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"#\n",
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"### 2.1 标签定义"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# Label 名称（回归任务使用连续收益率）\n",
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"LABEL_NAME = \"future_return_5\"\n",
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"\n",
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"# 获取 Label 因子定义\n",
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"LABEL_FACTOR = get_label_factor(LABEL_NAME)\n",
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"\n",
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"# 模型参数配置\n",
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"MODEL_PARAMS = {\n",
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" \"objective\": \"regression\",\n",
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" \"metric\": \"mae\", # 改为 MAE，对异常值更稳健\n",
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" # 树结构控制（防过拟合核心）\n",
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" # \"num_leaves\": 20, # 从31降为20，降低模型复杂度\n",
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" # \"max_depth\": 16, # 显式限制深度，防止过度拟合噪声\n",
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" # \"min_child_samples\": 50, # 叶子最小样本数，防止学习极端样本\n",
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" # \"min_child_weight\": 0.001,\n",
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" # 学习参数\n",
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" \"learning_rate\": 0.01, # 降低学习率，配合更多树\n",
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" \"n_estimators\": 1000, # 增加树数量，配合早停\n",
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" # 采样策略（关键防过拟合）\n",
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" \"subsample\": 0.8, # 每棵树随机采样80%数据（行采样）\n",
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" \"subsample_freq\": 5, # 每5轮迭代进行一次 subsample\n",
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" \"colsample_bytree\": 0.8, # 每棵树随机选择80%特征（列采样）\n",
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" # 正则化\n",
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" \"reg_alpha\": 0.1, # L1正则，增加稀疏性\n",
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" \"reg_lambda\": 1.0, # L2正则，平滑权重\n",
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" # 数值稳定性\n",
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" \"verbose\": -1,\n",
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" \"random_state\": 42,\n",
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"}"
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## 4. 训练流程\n",
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"#\n",
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"### 4.1 初始化组件"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"print(\"\\n\" + \"=\" * 80)\n",
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"print(\"LightGBM 回归模型训练\")\n",
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"print(\"=\" * 80)\n",
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"\n",
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"# 1. 创建 FactorEngine（启用 metadata 功能）\n",
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"print(\"\\n[1] 创建 FactorEngine\")\n",
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"engine = FactorEngine(metadata_path=\"data/factors.jsonl\")\n",
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"\n",
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"# 2. 使用 metadata 定义因子\n",
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"print(\"\\n[2] 定义因子（从 metadata 注册）\")\n",
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"feature_cols = register_factors(\n",
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" engine, SELECTED_FACTORS, FACTOR_DEFINITIONS, LABEL_FACTOR\n",
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")\n",
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"target_col = LABEL_NAME\n",
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"\n",
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"# 3. 准备数据（使用模块级别的日期配置）\n",
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"print(\"\\n[3] 准备数据\")\n",
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"\n",
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"data = prepare_data(\n",
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" engine=engine,\n",
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" feature_cols=feature_cols,\n",
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" start_date=TRAIN_START,\n",
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" end_date=TEST_END,\n",
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" label_name=LABEL_NAME,\n",
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")\n",
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"\n",
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"# 4. 打印配置信息\n",
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"print(f\"\\n[配置] 训练期: {TRAIN_START} - {TRAIN_END}\")\n",
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"print(f\"[配置] 验证期: {VAL_START} - {VAL_END}\")\n",
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"print(f\"[配置] 测试期: {TEST_START} - {TEST_END}\")\n",
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"print(f\"[配置] 特征数: {len(feature_cols)}\")\n",
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"print(f\"[配置] 目标变量: {target_col}\")\n",
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"\n",
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"# 5. 创建模型\n",
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"model = LightGBMModel(params=MODEL_PARAMS)\n",
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"\n",
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"# 6. 创建数据处理器（使用函数返回的完整特征列表）\n",
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"processors = [\n",
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" NullFiller(feature_cols=feature_cols, strategy=\"mean\"),\n",
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" Winsorizer(feature_cols=feature_cols, lower=0.01, upper=0.99),\n",
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" StandardScaler(feature_cols=feature_cols),\n",
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"]\n",
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"\n",
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"# 7. 创建数据划分器（正确的 train/val/test 三分法）\n",
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"# Train: 训练模型参数 | Val: 验证/早停 | Test: 最终评估\n",
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"splitter = DateSplitter(\n",
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" train_start=TRAIN_START,\n",
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" train_end=TRAIN_END,\n",
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" val_start=VAL_START,\n",
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" val_end=VAL_END,\n",
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" test_start=TEST_START,\n",
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" test_end=TEST_END,\n",
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")\n",
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"\n",
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"# 8. 创建股票池管理器\n",
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"# 使用新的 API：传入自定义筛选函数和所需列\n",
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"pool_manager = StockPoolManager(\n",
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" filter_func=stock_pool_filter,\n",
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" required_columns=STOCK_FILTER_REQUIRED_COLUMNS, # 筛选所需的额外列\n",
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" # required_factors=STOCK_FILTER_REQUIRED_FACTORS, # 可选：筛选所需的因子\n",
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" data_router=engine.router,\n",
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")\n",
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"print(\"[股票池筛选] 使用自定义函数进行股票池筛选\")\n",
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"print(f\"[股票池筛选] 所需基础列: {STOCK_FILTER_REQUIRED_COLUMNS}\")\n",
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"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",
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"# print(f\"[股票池筛选] 所需因子: {list(STOCK_FILTER_REQUIRED_FACTORS.keys())}\")\n",
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"\n",
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"# 9. 创建 ST 股票过滤器\n",
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"st_filter = STFilter(\n",
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" data_router=engine.router,\n",
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")\n",
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"\n",
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"# 10. 创建训练器\n",
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"trainer = Trainer(\n",
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" model=model,\n",
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" pool_manager=pool_manager,\n",
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" processors=processors,\n",
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" filters=[st_filter], # 使用STFilter过滤ST股票\n",
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" splitter=splitter,\n",
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" target_col=target_col,\n",
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" feature_cols=feature_cols,\n",
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" persist_model=PERSIST_MODEL,\n",
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")"
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### 4.2 执行训练"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"print(\"\\n\" + \"=\" * 80)\n",
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"print(\"开始训练\")\n",
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"print(\"=\" * 80)\n",
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"\n",
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"# 步骤 1: 股票池筛选\n",
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"print(\"\\n[步骤 1/6] 股票池筛选\")\n",
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"print(\"-\" * 60)\n",
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"if pool_manager:\n",
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" print(\" 执行每日独立筛选股票池...\")\n",
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" filtered_data = pool_manager.filter_and_select_daily(data)\n",
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" print(f\" 筛选前数据规模: {data.shape}\")\n",
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" print(f\" 筛选后数据规模: {filtered_data.shape}\")\n",
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" print(f\" 筛选前股票数: {data['ts_code'].n_unique()}\")\n",
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" print(f\" 筛选后股票数: {filtered_data['ts_code'].n_unique()}\")\n",
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" print(f\" 删除记录数: {len(data) - len(filtered_data)}\")\n",
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"else:\n",
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" filtered_data = data\n",
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" print(\" 未配置股票池管理器，跳过筛选\")"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# 步骤 2: 划分训练/验证/测试集（正确的三分法）\n",
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"print(\"\\n[步骤 2/6] 划分训练集、验证集和测试集\")\n",
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"print(\"-\" * 60)\n",
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"if splitter:\n",
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" # 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",
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" train_data, val_data, test_data = splitter.split(filtered_data)\n",
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" print(f\" 训练集数据规模: {train_data.shape}\")\n",
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" print(f\" 验证集数据规模: {val_data.shape}\")\n",
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" print(f\" 测试集数据规模: {test_data.shape}\")\n",
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" print(f\" 训练集股票数: {train_data['ts_code'].n_unique()}\")\n",
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" print(f\" 验证集股票数: {val_data['ts_code'].n_unique()}\")\n",
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" print(f\" 测试集股票数: {test_data['ts_code'].n_unique()}\")\n",
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" print(\n",
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" f\" 训练集日期范围: {train_data['trade_date'].min()} - {train_data['trade_date'].max()}\"\n",
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" )\n",
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" print(\n",
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" f\" 验证集日期范围: {val_data['trade_date'].min()} - {val_data['trade_date'].max()}\"\n",
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" )\n",
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" print(\n",
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" f\" 测试集日期范围: {test_data['trade_date'].min()} - {test_data['trade_date'].max()}\"\n",
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" )\n",
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"\n",
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" print(\"\\n 训练集前5行预览:\")\n",
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" print(train_data.head())\n",
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" print(\"\\n 验证集前5行预览:\")\n",
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" print(val_data.head())\n",
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" print(\"\\n 测试集前5行预览:\")\n",
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" print(test_data.head())\n",
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"else:\n",
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" train_data = filtered_data\n",
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" test_data = filtered_data\n",
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" print(\" 未配置划分器，全部作为训练集\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 步骤 3: 数据质量检查（必须在预处理之前）\n",
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"print(\"\\n[步骤 3/7] 数据质量检查\")\n",
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"print(\"-\" * 60)\n",
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"print(\" [说明] 此检查在 fillna 等处理之前执行，用于发现数据问题\")\n",
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"\n",
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"print(\"\\n 检查训练集...\")\n",
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"check_data_quality(train_data, feature_cols, raise_on_error=True)\n",
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"\n",
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"if \"val_data\" in locals() and val_data is not None:\n",
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" print(\"\\n 检查验证集...\")\n",
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" check_data_quality(val_data, feature_cols, raise_on_error=True)\n",
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"\n",
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"print(\"\\n 检查测试集...\")\n",
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"check_data_quality(test_data, feature_cols, raise_on_error=True)\n",
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"\n",
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"print(\" [成功] 数据质量检查通过，未发现异常\")\n"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# 步骤 4: 训练集数据处理\n",
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"print(\"\\n[步骤 4/7] 训练集数据处理\")\n",
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"print(\"-\" * 60)\n",
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"fitted_processors = []\n",
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"if processors:\n",
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" for i, processor in enumerate(processors, 1):\n",
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" print(f\" [{i}/{len(processors)}] 应用处理器: {processor.__class__.__name__}\")\n",
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" train_data_before = len(train_data)\n",
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" train_data = processor.fit_transform(train_data)\n",
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" train_data_after = len(train_data)\n",
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" fitted_processors.append(processor)\n",
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" print(f\" 处理前记录数: {train_data_before}\")\n",
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" print(f\" 处理后记录数: {train_data_after}\")\n",
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" if train_data_before != train_data_after:\n",
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" print(f\" 删除记录数: {train_data_before - train_data_after}\")\n",
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"\n",
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"print(\"\\n 训练集处理后前5行预览:\")\n",
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"print(train_data.head())\n",
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"print(f\"\\n 训练集特征统计:\")\n",
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"print(f\" 特征数: {len(feature_cols)}\")\n",
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"print(f\" 样本数: {len(train_data)}\")\n",
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"print(f\" 缺失值统计:\")\n",
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"for col in feature_cols[:5]: # 只显示前5个特征的缺失值\n",
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" null_count = train_data[col].null_count()\n",
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" if null_count > 0:\n",
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" print(f\" {col}: {null_count} ({null_count / len(train_data) * 100:.2f}%)\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 步骤 4: 训练模型\n",
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"print(\"\\n[步骤 5/7] 训练模型\")\n",
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"print(\"-\" * 60)\n",
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"print(f\" 模型类型: LightGBM\")\n",
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"print(f\" 训练样本数: {len(train_data)}\")\n",
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"print(f\" 特征数: {len(feature_cols)}\")\n",
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"print(f\" 目标变量: {target_col}\")\n",
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"\n",
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"X_train = train_data.select(feature_cols)\n",
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"y_train = train_data.select(target_col).to_series()\n",
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"\n",
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"print(f\"\\n 目标变量统计:\")\n",
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"print(f\" 均值: {y_train.mean():.6f}\")\n",
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"print(f\" 标准差: {y_train.std():.6f}\")\n",
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"print(f\" 最小值: {y_train.min():.6f}\")\n",
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"print(f\" 最大值: {y_train.max():.6f}\")\n",
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"print(f\" 缺失值: {y_train.null_count()}\")\n",
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"\n",
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"print(\"\\n 开始训练...\")\n",
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"model.fit(X_train, y_train)\n",
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"print(\" 训练完成！\")"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 步骤 5: 测试集数据处理\n",
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"print(\"\\n[步骤 6/7] 测试集数据处理\")\n",
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"print(\"-\" * 60)\n",
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"if processors and test_data is not train_data:\n",
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" for i, processor in enumerate(fitted_processors, 1):\n",
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" print(\n",
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" f\" [{i}/{len(fitted_processors)}] 应用处理器: {processor.__class__.__name__}\"\n",
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" )\n",
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" 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",
|
||
"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\"]))"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": null
|
||
},
|
||
{
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"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": "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"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|