update pay ui
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808
ml/train.py
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808
ml/train.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Transformer Autoencoder 训练脚本 (修复版)
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修复问题：
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1. 按概念分组构建序列，避免跨概念切片
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2. 按时间（日期）切分数据集，避免数据泄露
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3. 使用 RobustScaler + Clipping 处理非平稳性
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4. 使用验证集计算阈值
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训练流程：
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1. 加载预处理好的特征数据（parquet 文件）
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2. 按概念分组，在每个概念内部构建序列
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3. 按日期划分训练/验证/测试集
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4. 训练 Autoencoder（最小化重构误差）
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5. 保存模型和阈值
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使用方法：
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python train.py --data_dir ml/data --epochs 100 --batch_size 256
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"""
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import os
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import sys
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import argparse
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import json
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from datetime import datetime
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from pathlib import Path
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from typing import List, Tuple, Dict
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import numpy as np
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import pandas as pd
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import torch
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import torch.nn as nn
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from torch.utils.data import Dataset, DataLoader
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from torch.optim import AdamW
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from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
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from tqdm import tqdm
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from model import TransformerAutoencoder, AnomalyDetectionLoss, count_parameters
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# 性能优化：启用 cuDNN benchmark（对固定输入尺寸自动选择最快算法）
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torch.backends.cudnn.benchmark = True
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# 启用 TF32（RTX 30/40 系列特有，提速约 3 倍）
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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# 可视化（可选）
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try:
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import matplotlib
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matplotlib.use('Agg') # 无头模式，不需要显示器
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import matplotlib.pyplot as plt
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HAS_MATPLOTLIB = True
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except ImportError:
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HAS_MATPLOTLIB = False
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# ==================== 配置 ====================
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TRAIN_CONFIG = {
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# 数据配置
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'seq_len': 30, # 输入序列长度（30分钟）
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'stride': 5, # 滑动窗口步长
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# 时间切分（按日期）
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'train_end_date': '2024-06-30', # 训练集截止日期
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'val_end_date': '2024-09-30', # 验证集截止日期（之后为测试集）
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# 特征配置
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'features': [
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'alpha', # 超额收益
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'alpha_delta', # Alpha 变化率
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'amt_ratio', # 成交额比率
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'amt_delta', # 成交额变化率
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'rank_pct', # Alpha 排名百分位
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'limit_up_ratio', # 涨停比例
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],
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# 训练配置（针对 4x RTX 4090 优化）
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'batch_size': 4096, # 256 -> 4096（大幅增加，充分利用显存）
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'epochs': 100,
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'learning_rate': 3e-4, # 1e-4 -> 3e-4（大 batch 需要更大学习率）
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'weight_decay': 1e-5,
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'gradient_clip': 1.0,
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# 早停配置
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'patience': 10,
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'min_delta': 1e-6,
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# 模型配置（LSTM Autoencoder，简洁有效）
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'model': {
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'n_features': 6,
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'hidden_dim': 32, # LSTM 隐藏维度（小）
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'latent_dim': 4, # 瓶颈维度（非常小！关键）
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'num_layers': 1, # LSTM 层数
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'dropout': 0.2,
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'bidirectional': True, # 双向编码器
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},
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# 标准化配置
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'use_instance_norm': True, # 模型内部使用 Instance Norm（推荐）
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'clip_value': 10.0, # 简单截断极端值
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# 阈值配置
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'threshold_percentiles': [90, 95, 99],
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}
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# ==================== 数据加载（修复版）====================
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def load_data_by_date(data_dir: str, features: List[str]) -> Dict[str, pd.DataFrame]:
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"""
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按日期加载数据，返回 {date: DataFrame} 字典
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每个 DataFrame 包含该日所有概念的所有时间点数据
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"""
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data_path = Path(data_dir)
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parquet_files = sorted(data_path.glob("features_*.parquet"))
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if not parquet_files:
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raise FileNotFoundError(f"未找到 parquet 文件: {data_dir}")
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print(f"找到 {len(parquet_files)} 个数据文件")
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date_data = {}
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for pf in tqdm(parquet_files, desc="加载数据"):
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# 提取日期
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date = pf.stem.replace('features_', '')
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df = pd.read_parquet(pf)
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# 检查必要列
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required_cols = features + ['concept_id', 'timestamp']
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missing_cols = [c for c in required_cols if c not in df.columns]
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if missing_cols:
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print(f"警告: {date} 缺少列: {missing_cols}, 跳过")
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continue
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date_data[date] = df
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print(f"成功加载 {len(date_data)} 天的数据")
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return date_data
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def split_data_by_date(
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date_data: Dict[str, pd.DataFrame],
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train_end: str,
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val_end: str
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) -> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame], Dict[str, pd.DataFrame]]:
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"""
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按日期严格划分数据集
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- 训练集: <= train_end
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- 验证集: train_end < date <= val_end
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- 测试集: > val_end
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"""
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train_data = {}
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val_data = {}
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test_data = {}
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for date, df in date_data.items():
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if date <= train_end:
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train_data[date] = df
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elif date <= val_end:
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val_data[date] = df
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else:
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test_data[date] = df
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print(f"数据集划分（按日期）:")
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print(f" 训练集: {len(train_data)} 天 (<= {train_end})")
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print(f" 验证集: {len(val_data)} 天 ({train_end} ~ {val_end})")
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print(f" 测试集: {len(test_data)} 天 (> {val_end})")
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return train_data, val_data, test_data
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def build_sequences_by_concept(
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date_data: Dict[str, pd.DataFrame],
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features: List[str],
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seq_len: int,
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stride: int
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) -> np.ndarray:
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"""
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按概念分组构建序列（性能优化版）
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使用 groupby 一次性分组，避免重复扫描大数组
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1. 将所有日期的数据合并
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2. 使用 groupby 按 concept_id 分组
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3. 在每个概念内部，按时间排序并滑动窗口
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4. 合并所有序列
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"""
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# 合并所有日期的数据
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all_dfs = []
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for date, df in sorted(date_data.items()):
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df = df.copy()
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df['date'] = date
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all_dfs.append(df)
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if not all_dfs:
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return np.array([])
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combined = pd.concat(all_dfs, ignore_index=True)
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# 预先排序（按概念、日期、时间），这样 groupby 会更快
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combined = combined.sort_values(['concept_id', 'date', 'timestamp'])
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# 使用 groupby 一次性分组（性能关键！）
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all_sequences = []
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grouped = combined.groupby('concept_id', sort=False)
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n_concepts = len(grouped)
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for concept_id, concept_df in tqdm(grouped, desc="构建序列", total=n_concepts, leave=False):
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# 已经排序过了，直接提取特征
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feature_data = concept_df[features].values
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# 处理缺失值
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feature_data = np.nan_to_num(feature_data, nan=0.0, posinf=0.0, neginf=0.0)
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# 在该概念内部滑动窗口
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n_points = len(feature_data)
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for start in range(0, n_points - seq_len + 1, stride):
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seq = feature_data[start:start + seq_len]
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all_sequences.append(seq)
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if not all_sequences:
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return np.array([])
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sequences = np.array(all_sequences)
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print(f" 构建序列: {len(sequences):,} 条 (来自 {n_concepts} 个概念)")
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return sequences
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# ==================== 数据集 ====================
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class SequenceDataset(Dataset):
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"""序列数据集（已经构建好的序列）"""
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def __init__(self, sequences: np.ndarray):
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self.sequences = torch.FloatTensor(sequences)
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def __len__(self) -> int:
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return len(self.sequences)
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def __getitem__(self, idx: int) -> torch.Tensor:
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return self.sequences[idx]
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# ==================== 训练器 ====================
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class EarlyStopping:
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"""早停机制"""
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def __init__(self, patience: int = 10, min_delta: float = 1e-6):
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self.patience = patience
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self.min_delta = min_delta
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self.counter = 0
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self.best_loss = float('inf')
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self.early_stop = False
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def __call__(self, val_loss: float) -> bool:
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if val_loss < self.best_loss - self.min_delta:
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self.best_loss = val_loss
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self.counter = 0
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else:
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self.counter += 1
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if self.counter >= self.patience:
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self.early_stop = True
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return self.early_stop
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class Trainer:
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"""模型训练器（支持 AMP 混合精度加速）"""
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def __init__(
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self,
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model: nn.Module,
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train_loader: DataLoader,
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val_loader: DataLoader,
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config: Dict,
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device: torch.device,
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save_dir: str = 'ml/checkpoints'
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):
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self.model = model.to(device)
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self.train_loader = train_loader
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self.val_loader = val_loader
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self.config = config
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self.device = device
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self.save_dir = Path(save_dir)
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self.save_dir.mkdir(parents=True, exist_ok=True)
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# 优化器
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self.optimizer = AdamW(
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model.parameters(),
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lr=config['learning_rate'],
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weight_decay=config['weight_decay']
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)
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# 学习率调度器
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self.scheduler = CosineAnnealingWarmRestarts(
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self.optimizer,
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T_0=10,
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T_mult=2,
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eta_min=1e-6
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)
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# 损失函数（简化版，只用 MSE）
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self.criterion = AnomalyDetectionLoss()
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# 早停
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self.early_stopping = EarlyStopping(
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patience=config['patience'],
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min_delta=config['min_delta']
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)
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# AMP 混合精度训练（大幅提速 + 省显存）
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self.use_amp = torch.cuda.is_available()
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self.scaler = torch.cuda.amp.GradScaler() if self.use_amp else None
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if self.use_amp:
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print(" ✓ 启用 AMP 混合精度训练")
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# 训练历史
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self.history = {
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'train_loss': [],
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'val_loss': [],
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'learning_rate': [],
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}
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self.best_val_loss = float('inf')
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def train_epoch(self) -> float:
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"""训练一个 epoch（使用 AMP 混合精度）"""
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self.model.train()
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total_loss = 0.0
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n_batches = 0
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pbar = tqdm(self.train_loader, desc="Training", leave=False)
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for batch in pbar:
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batch = batch.to(self.device, non_blocking=True) # 异步传输
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self.optimizer.zero_grad(set_to_none=True) # 更快的梯度清零
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# AMP 混合精度前向传播
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if self.use_amp:
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with torch.cuda.amp.autocast():
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output, latent = self.model(batch)
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loss, loss_dict = self.criterion(output, batch, latent)
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# AMP 反向传播
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self.scaler.scale(loss).backward()
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# 梯度裁剪（需要 unscale）
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self.scaler.unscale_(self.optimizer)
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torch.nn.utils.clip_grad_norm_(
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self.model.parameters(),
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self.config['gradient_clip']
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)
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self.scaler.step(self.optimizer)
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self.scaler.update()
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else:
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# 非 AMP 模式
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output, latent = self.model(batch)
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loss, loss_dict = self.criterion(output, batch, latent)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(
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self.model.parameters(),
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self.config['gradient_clip']
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)
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self.optimizer.step()
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total_loss += loss.item()
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n_batches += 1
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pbar.set_postfix({'loss': f"{loss.item():.4f}"})
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return total_loss / n_batches
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@torch.no_grad()
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def validate(self) -> float:
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"""验证（使用 AMP）"""
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self.model.eval()
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total_loss = 0.0
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n_batches = 0
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for batch in self.val_loader:
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batch = batch.to(self.device, non_blocking=True)
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if self.use_amp:
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with torch.cuda.amp.autocast():
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output, latent = self.model(batch)
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loss, _ = self.criterion(output, batch, latent)
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else:
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output, latent = self.model(batch)
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loss, _ = self.criterion(output, batch, latent)
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total_loss += loss.item()
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n_batches += 1
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return total_loss / n_batches
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def save_checkpoint(self, epoch: int, val_loss: float, is_best: bool = False):
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"""保存检查点"""
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# 处理 DataParallel 包装
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model_to_save = self.model.module if hasattr(self.model, 'module') else self.model
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checkpoint = {
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'epoch': epoch,
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'model_state_dict': model_to_save.state_dict(),
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'optimizer_state_dict': self.optimizer.state_dict(),
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'scheduler_state_dict': self.scheduler.state_dict(),
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'val_loss': val_loss,
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'config': self.config,
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}
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# 保存最新检查点
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torch.save(checkpoint, self.save_dir / 'last_checkpoint.pt')
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# 保存最佳模型
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if is_best:
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torch.save(checkpoint, self.save_dir / 'best_model.pt')
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print(f" ✓ 保存最佳模型 (val_loss: {val_loss:.6f})")
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def train(self, epochs: int):
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"""完整训练流程"""
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print(f"\n开始训练 ({epochs} epochs)...")
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print(f"设备: {self.device}")
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print(f"模型参数量: {count_parameters(self.model):,}")
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for epoch in range(1, epochs + 1):
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print(f"\nEpoch {epoch}/{epochs}")
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# 训练
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train_loss = self.train_epoch()
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# 验证
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val_loss = self.validate()
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# 更新学习率
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self.scheduler.step()
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current_lr = self.optimizer.param_groups[0]['lr']
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# 记录历史
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self.history['train_loss'].append(train_loss)
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self.history['val_loss'].append(val_loss)
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self.history['learning_rate'].append(current_lr)
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# 打印进度
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print(f" Train Loss: {train_loss:.6f}")
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print(f" Val Loss: {val_loss:.6f}")
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print(f" LR: {current_lr:.2e}")
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# 保存检查点
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is_best = val_loss < self.best_val_loss
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||||
if is_best:
|
||||
self.best_val_loss = val_loss
|
||||
self.save_checkpoint(epoch, val_loss, is_best)
|
||||
|
||||
# 早停检查
|
||||
if self.early_stopping(val_loss):
|
||||
print(f"\n早停触发！验证损失已 {self.early_stopping.patience} 个 epoch 未改善")
|
||||
break
|
||||
|
||||
print(f"\n训练完成！最佳验证损失: {self.best_val_loss:.6f}")
|
||||
|
||||
# 保存训练历史
|
||||
self.save_history()
|
||||
|
||||
return self.history
|
||||
|
||||
def save_history(self):
|
||||
"""保存训练历史"""
|
||||
history_path = self.save_dir / 'training_history.json'
|
||||
with open(history_path, 'w') as f:
|
||||
json.dump(self.history, f, indent=2)
|
||||
print(f"训练历史已保存: {history_path}")
|
||||
|
||||
# 绘制训练曲线
|
||||
self.plot_training_curves()
|
||||
|
||||
def plot_training_curves(self):
|
||||
"""绘制训练曲线"""
|
||||
if not HAS_MATPLOTLIB:
|
||||
print("matplotlib 未安装，跳过绘图")
|
||||
return
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
|
||||
|
||||
epochs = range(1, len(self.history['train_loss']) + 1)
|
||||
|
||||
# 1. Loss 曲线
|
||||
ax1 = axes[0]
|
||||
ax1.plot(epochs, self.history['train_loss'], 'b-', label='Train Loss', linewidth=2)
|
||||
ax1.plot(epochs, self.history['val_loss'], 'r-', label='Val Loss', linewidth=2)
|
||||
ax1.set_xlabel('Epoch', fontsize=12)
|
||||
ax1.set_ylabel('Loss', fontsize=12)
|
||||
ax1.set_title('Training & Validation Loss', fontsize=14)
|
||||
ax1.legend(fontsize=11)
|
||||
ax1.grid(True, alpha=0.3)
|
||||
|
||||
# 标记最佳点
|
||||
best_epoch = np.argmin(self.history['val_loss']) + 1
|
||||
best_val_loss = min(self.history['val_loss'])
|
||||
ax1.axvline(x=best_epoch, color='g', linestyle='--', alpha=0.7, label=f'Best Epoch: {best_epoch}')
|
||||
ax1.scatter([best_epoch], [best_val_loss], color='g', s=100, zorder=5)
|
||||
ax1.annotate(f'Best: {best_val_loss:.6f}', xy=(best_epoch, best_val_loss),
|
||||
xytext=(best_epoch + 2, best_val_loss + 0.0005),
|
||||
fontsize=10, color='green')
|
||||
|
||||
# 2. 学习率曲线
|
||||
ax2 = axes[1]
|
||||
ax2.plot(epochs, self.history['learning_rate'], 'g-', linewidth=2)
|
||||
ax2.set_xlabel('Epoch', fontsize=12)
|
||||
ax2.set_ylabel('Learning Rate', fontsize=12)
|
||||
ax2.set_title('Learning Rate Schedule', fontsize=14)
|
||||
ax2.set_yscale('log')
|
||||
ax2.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
# 保存图片
|
||||
plot_path = self.save_dir / 'training_curves.png'
|
||||
plt.savefig(plot_path, dpi=150, bbox_inches='tight')
|
||||
plt.close()
|
||||
print(f"训练曲线已保存: {plot_path}")
|
||||
|
||||
|
||||
# ==================== 阈值计算（使用验证集）====================
|
||||
|
||||
@torch.no_grad()
|
||||
def compute_thresholds(
|
||||
model: nn.Module,
|
||||
data_loader: DataLoader,
|
||||
device: torch.device,
|
||||
percentiles: List[float] = [90, 95, 99]
|
||||
) -> Dict[str, float]:
|
||||
"""
|
||||
在验证集上计算重构误差的百分位数阈值
|
||||
|
||||
注：使用验证集而非测试集，避免数据泄露
|
||||
"""
|
||||
model.eval()
|
||||
all_errors = []
|
||||
|
||||
print("计算异动阈值（使用验证集）...")
|
||||
for batch in tqdm(data_loader, desc="Computing thresholds"):
|
||||
batch = batch.to(device)
|
||||
errors = model.compute_reconstruction_error(batch, reduction='none')
|
||||
|
||||
# 取每个序列的最后一个时刻误差（预测当前时刻）
|
||||
seq_errors = errors[:, -1] # (batch,)
|
||||
all_errors.append(seq_errors.cpu().numpy())
|
||||
|
||||
all_errors = np.concatenate(all_errors)
|
||||
|
||||
thresholds = {}
|
||||
for p in percentiles:
|
||||
threshold = np.percentile(all_errors, p)
|
||||
thresholds[f'p{p}'] = float(threshold)
|
||||
print(f" P{p}: {threshold:.6f}")
|
||||
|
||||
# 额外统计
|
||||
thresholds['mean'] = float(np.mean(all_errors))
|
||||
thresholds['std'] = float(np.std(all_errors))
|
||||
thresholds['median'] = float(np.median(all_errors))
|
||||
|
||||
print(f" Mean: {thresholds['mean']:.6f}")
|
||||
print(f" Median: {thresholds['median']:.6f}")
|
||||
print(f" Std: {thresholds['std']:.6f}")
|
||||
|
||||
return thresholds
|
||||
|
||||
|
||||
# ==================== 主函数 ====================
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description='训练概念异动检测模型')
|
||||
parser.add_argument('--data_dir', type=str, default='ml/data',
|
||||
help='数据目录路径')
|
||||
parser.add_argument('--epochs', type=int, default=100,
|
||||
help='训练轮数')
|
||||
parser.add_argument('--batch_size', type=int, default=4096,
|
||||
help='批次大小（4x RTX 4090 推荐 4096~8192）')
|
||||
parser.add_argument('--lr', type=float, default=3e-4,
|
||||
help='学习率（大 batch 推荐 3e-4）')
|
||||
parser.add_argument('--device', type=str, default='auto',
|
||||
help='设备 (auto/cuda/cpu)')
|
||||
parser.add_argument('--save_dir', type=str, default='ml/checkpoints',
|
||||
help='模型保存目录')
|
||||
parser.add_argument('--train_end', type=str, default='2024-06-30',
|
||||
help='训练集截止日期')
|
||||
parser.add_argument('--val_end', type=str, default='2024-09-30',
|
||||
help='验证集截止日期')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 更新配置
|
||||
config = TRAIN_CONFIG.copy()
|
||||
config['batch_size'] = args.batch_size
|
||||
config['epochs'] = args.epochs
|
||||
config['learning_rate'] = args.lr
|
||||
config['train_end_date'] = args.train_end
|
||||
config['val_end_date'] = args.val_end
|
||||
|
||||
# 设备选择
|
||||
if args.device == 'auto':
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
else:
|
||||
device = torch.device(args.device)
|
||||
|
||||
print("=" * 60)
|
||||
print("概念异动检测模型训练（修复版）")
|
||||
print("=" * 60)
|
||||
print(f"配置:")
|
||||
print(f" 数据目录: {args.data_dir}")
|
||||
print(f" 设备: {device}")
|
||||
print(f" 批次大小: {config['batch_size']}")
|
||||
print(f" 学习率: {config['learning_rate']}")
|
||||
print(f" 训练轮数: {config['epochs']}")
|
||||
print(f" 训练集截止: {config['train_end_date']}")
|
||||
print(f" 验证集截止: {config['val_end_date']}")
|
||||
print("=" * 60)
|
||||
|
||||
# 1. 按日期加载数据
|
||||
print("\n[1/6] 加载数据...")
|
||||
date_data = load_data_by_date(args.data_dir, config['features'])
|
||||
|
||||
# 2. 按日期划分
|
||||
print("\n[2/6] 按日期划分数据集...")
|
||||
train_data, val_data, test_data = split_data_by_date(
|
||||
date_data,
|
||||
config['train_end_date'],
|
||||
config['val_end_date']
|
||||
)
|
||||
|
||||
# 3. 按概念构建序列
|
||||
print("\n[3/6] 按概念构建序列...")
|
||||
print("训练集:")
|
||||
train_sequences = build_sequences_by_concept(
|
||||
train_data, config['features'], config['seq_len'], config['stride']
|
||||
)
|
||||
print("验证集:")
|
||||
val_sequences = build_sequences_by_concept(
|
||||
val_data, config['features'], config['seq_len'], config['stride']
|
||||
)
|
||||
print("测试集:")
|
||||
test_sequences = build_sequences_by_concept(
|
||||
test_data, config['features'], config['seq_len'], config['stride']
|
||||
)
|
||||
|
||||
if len(train_sequences) == 0:
|
||||
print("错误: 训练集为空！请检查数据和日期范围")
|
||||
return
|
||||
|
||||
# 4. 数据预处理（简单截断极端值，标准化在模型内部通过 Instance Norm 完成）
|
||||
print("\n[4/6] 数据预处理...")
|
||||
print(" 注意: 使用 Instance Norm，每个序列在模型内部单独标准化")
|
||||
print(" 这样可以处理不同概念波动率差异（银行 vs 半导体）")
|
||||
|
||||
clip_value = config['clip_value']
|
||||
print(f" 截断极端值: ±{clip_value}")
|
||||
|
||||
# 简单截断极端值（防止异常数据影响训练）
|
||||
train_sequences = np.clip(train_sequences, -clip_value, clip_value)
|
||||
if len(val_sequences) > 0:
|
||||
val_sequences = np.clip(val_sequences, -clip_value, clip_value)
|
||||
if len(test_sequences) > 0:
|
||||
test_sequences = np.clip(test_sequences, -clip_value, clip_value)
|
||||
|
||||
# 保存配置
|
||||
save_dir = Path(args.save_dir)
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
preprocess_params = {
|
||||
'features': config['features'],
|
||||
'normalization': 'instance_norm', # 在模型内部完成
|
||||
'clip_value': clip_value,
|
||||
'note': '标准化在模型内部通过 InstanceNorm1d 完成，无需外部 Scaler'
|
||||
}
|
||||
|
||||
with open(save_dir / 'normalization_stats.json', 'w') as f:
|
||||
json.dump(preprocess_params, f, indent=2)
|
||||
print(f" 预处理参数已保存")
|
||||
|
||||
# 5. 创建数据集和加载器
|
||||
print("\n[5/6] 创建数据加载器...")
|
||||
train_dataset = SequenceDataset(train_sequences)
|
||||
val_dataset = SequenceDataset(val_sequences) if len(val_sequences) > 0 else None
|
||||
test_dataset = SequenceDataset(test_sequences) if len(test_sequences) > 0 else None
|
||||
|
||||
print(f" 训练序列: {len(train_dataset):,}")
|
||||
print(f" 验证序列: {len(val_dataset) if val_dataset else 0:,}")
|
||||
print(f" 测试序列: {len(test_dataset) if test_dataset else 0:,}")
|
||||
|
||||
# 多卡时增加 num_workers（Linux 上可以用更多）
|
||||
n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 1
|
||||
num_workers = min(32, 8 * n_gpus) if sys.platform != 'win32' else 0
|
||||
print(f" DataLoader workers: {num_workers}")
|
||||
print(f" Batch size: {config['batch_size']}")
|
||||
|
||||
# 大 batch + 多 worker + prefetch 提速
|
||||
train_loader = DataLoader(
|
||||
train_dataset,
|
||||
batch_size=config['batch_size'],
|
||||
shuffle=True,
|
||||
num_workers=num_workers,
|
||||
pin_memory=True,
|
||||
prefetch_factor=4 if num_workers > 0 else None, # 预取更多 batch
|
||||
persistent_workers=True if num_workers > 0 else False, # 保持 worker 存活
|
||||
drop_last=True # 丢弃不完整的最后一批，避免 batch 大小不一致
|
||||
)
|
||||
|
||||
val_loader = DataLoader(
|
||||
val_dataset,
|
||||
batch_size=config['batch_size'] * 2, # 验证时可以用更大 batch（无梯度）
|
||||
shuffle=False,
|
||||
num_workers=num_workers,
|
||||
pin_memory=True,
|
||||
prefetch_factor=4 if num_workers > 0 else None,
|
||||
persistent_workers=True if num_workers > 0 else False,
|
||||
) if val_dataset else None
|
||||
|
||||
test_loader = DataLoader(
|
||||
test_dataset,
|
||||
batch_size=config['batch_size'] * 2,
|
||||
shuffle=False,
|
||||
num_workers=num_workers,
|
||||
pin_memory=True,
|
||||
prefetch_factor=4 if num_workers > 0 else None,
|
||||
persistent_workers=True if num_workers > 0 else False,
|
||||
) if test_dataset else None
|
||||
|
||||
# 6. 训练
|
||||
print("\n[6/6] 训练模型...")
|
||||
model_config = config['model'].copy()
|
||||
model = TransformerAutoencoder(**model_config)
|
||||
|
||||
# 多卡并行
|
||||
if torch.cuda.device_count() > 1:
|
||||
print(f" 使用 {torch.cuda.device_count()} 张 GPU 并行训练")
|
||||
model = nn.DataParallel(model)
|
||||
|
||||
if val_loader is None:
|
||||
print("警告: 验证集为空，将使用训练集的一部分作为验证")
|
||||
# 简单处理：用训练集的后 10% 作为验证
|
||||
split_idx = int(len(train_dataset) * 0.9)
|
||||
train_subset = torch.utils.data.Subset(train_dataset, range(split_idx))
|
||||
val_subset = torch.utils.data.Subset(train_dataset, range(split_idx, len(train_dataset)))
|
||||
|
||||
train_loader = DataLoader(train_subset, batch_size=config['batch_size'], shuffle=True, num_workers=num_workers, pin_memory=True)
|
||||
val_loader = DataLoader(val_subset, batch_size=config['batch_size'], shuffle=False, num_workers=num_workers, pin_memory=True)
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
train_loader=train_loader,
|
||||
val_loader=val_loader,
|
||||
config=config,
|
||||
device=device,
|
||||
save_dir=args.save_dir
|
||||
)
|
||||
|
||||
history = trainer.train(config['epochs'])
|
||||
|
||||
# 7. 计算阈值（使用验证集）
|
||||
print("\n[额外] 计算异动阈值...")
|
||||
|
||||
# 加载最佳模型
|
||||
best_checkpoint = torch.load(
|
||||
save_dir / 'best_model.pt',
|
||||
map_location=device
|
||||
)
|
||||
model.load_state_dict(best_checkpoint['model_state_dict'])
|
||||
model.to(device)
|
||||
|
||||
# 使用验证集计算阈值（避免数据泄露）
|
||||
thresholds = compute_thresholds(
|
||||
model,
|
||||
val_loader,
|
||||
device,
|
||||
config['threshold_percentiles']
|
||||
)
|
||||
|
||||
# 保存阈值
|
||||
with open(save_dir / 'thresholds.json', 'w') as f:
|
||||
json.dump(thresholds, f, indent=2)
|
||||
print(f"阈值已保存")
|
||||
|
||||
# 保存完整配置
|
||||
with open(save_dir / 'config.json', 'w') as f:
|
||||
json.dump(config, f, indent=2)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("训练完成！")
|
||||
print("=" * 60)
|
||||
print(f"模型保存位置: {args.save_dir}")
|
||||
print(f" - best_model.pt: 最佳模型权重")
|
||||
print(f" - thresholds.json: 异动阈值")
|
||||
print(f" - normalization_stats.json: 标准化参数")
|
||||
print(f" - config.json: 训练配置")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user