521 lines
20 KiB
Python
521 lines
20 KiB
Python
"""
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药店销售预测系统 - mLSTM模型训练函数
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"""
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import os
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import time
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import pandas as pd
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from sklearn.preprocessing import MinMaxScaler
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from models.mlstm_model import MLSTMTransformer as MatrixLSTM
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from utils.data_utils import create_dataset, PharmacyDataset
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from utils.multi_store_data_utils import get_store_product_sales_data, aggregate_multi_store_data
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from utils.visualization import plot_loss_curve
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from analysis.metrics import evaluate_model
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from core.config import (
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DEVICE, DEFAULT_MODEL_DIR, LOOK_BACK, FORECAST_HORIZON
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)
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from utils.training_progress import progress_manager
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from utils.model_manager import model_manager
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def train_product_model_with_mlstm(
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product_id,
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model_identifier,
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product_df,
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store_id=None,
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training_mode='product',
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aggregation_method='sum',
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epochs=50,
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sequence_length=LOOK_BACK,
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forecast_horizon=FORECAST_HORIZON,
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model_dir=DEFAULT_MODEL_DIR,
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version=None,
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socketio=None,
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task_id=None,
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continue_training=False,
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progress_callback=None,
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patience=10,
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learning_rate=0.001,
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clip_norm=1.0
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):
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"""
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使用mLSTM训练产品销售预测模型
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参数:
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product_id: 产品ID
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store_id: 店铺ID，为None时使用全局数据
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training_mode: 训练模式 ('product', 'store', 'global')
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aggregation_method: 聚合方法 ('sum', 'mean', 'weighted')
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epochs: 训练轮次
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model_dir: 模型保存目录
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version: 模型版本，如果为None则自动生成
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socketio: Socket.IO实例，用于实时进度推送
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task_id: 任务ID
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continue_training: 是否继续训练
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progress_callback: 进度回调函数，用于多进程训练
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"""
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# 创建WebSocket进度反馈函数，支持多进程
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def emit_progress(message, progress=None, metrics=None):
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"""发送训练进度到前端"""
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progress_data = {
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'task_id': task_id,
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'message': message,
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'timestamp': time.time()
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}
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if progress is not None:
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progress_data['progress'] = progress
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if metrics is not None:
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progress_data['metrics'] = metrics
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# 在多进程环境中使用progress_callback
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if progress_callback:
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try:
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progress_callback(progress_data)
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except Exception as e:
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print(f"[mLSTM] 进度回调失败: {e}")
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# 在单进程环境中使用socketio
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if socketio and task_id:
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try:
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socketio.emit('training_progress', progress_data, namespace='/training')
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except Exception as e:
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print(f"[mLSTM] WebSocket发送失败: {e}")
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print(f"[mLSTM] {message}", flush=True)
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# 强制刷新输出缓冲区
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import sys
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sys.stdout.flush()
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sys.stderr.flush()
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emit_progress("开始mLSTM模型训练...")
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# 确定版本号
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emit_progress(f"开始训练 mLSTM 模型")
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if version:
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emit_progress(f"使用指定版本: {version}")
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# 初始化训练进度管理器（如果还未初始化）
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if socketio and task_id:
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print(f"[mLSTM] 任务 {task_id}: 开始mLSTM训练器", flush=True)
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try:
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# 初始化进度管理器
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if not hasattr(progress_manager, 'training_id') or progress_manager.training_id != task_id:
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progress_manager.start_training(
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training_id=task_id,
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product_id=product_id,
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model_type='mlstm',
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training_mode=training_mode,
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total_epochs=epochs,
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total_batches=0, # 将在后面设置
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batch_size=32, # 默认值
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total_samples=0 # 将在后面设置
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)
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print(f"[mLSTM] 任务 {task_id}: 进度管理器已初始化", flush=True)
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else:
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print(f"[mLSTM] 任务 {task_id}: 使用现有进度管理器", flush=True)
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except Exception as e:
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print(f"[mLSTM] 任务 {task_id}: 进度管理器初始化失败: {e}", flush=True)
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# 数据现在由调用方传入，不再在此处加载
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if training_mode == 'store' and store_id:
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training_scope = f"店铺 {store_id}"
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elif training_mode == 'global':
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training_scope = f"全局聚合({aggregation_method})"
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else:
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training_scope = "所有店铺"
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# 数据量检查
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min_required_samples = sequence_length + forecast_horizon
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if len(product_df) < min_required_samples:
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error_msg = (
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f"❌ 训练数据不足错误\n"
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f"当前配置需要: {min_required_samples} 天数据 (LOOK_BACK={sequence_length} + FORECAST_HORIZON={forecast_horizon})\n"
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f"实际数据量: {len(product_df)} 天\n"
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f"产品ID: {product_id}, 训练模式: {training_mode}\n"
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f"建议解决方案:\n"
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f"1. 生成更多数据: uv run generate_multi_store_data.py\n"
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f"2. 调整配置参数: 减小 LOOK_BACK 或 FORECAST_HORIZON\n"
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f"3. 使用全局训练模式聚合更多数据"
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)
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print(error_msg)
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emit_progress(f"训练失败：数据不足 ({len(product_df)}/{min_required_samples} 天)")
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raise ValueError(error_msg)
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product_name = product_df['product_name'].iloc[0]
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print(f"[mLSTM] 使用mLSTM模型训练产品 '{product_name}' (ID: {product_id}) 的销售预测模型", flush=True)
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print(f"[mLSTM] 训练范围: {training_scope}", flush=True)
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# print(f"[mLSTM] 版本: {version}", flush=True) # Version is now handled by model_manager
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print(f"[mLSTM] 使用设备: {DEVICE}", flush=True)
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print(f"[mLSTM] 模型将保存到目录: {model_dir}", flush=True)
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print(f"[mLSTM] 数据量: {len(product_df)} 条记录", flush=True)
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emit_progress(f"训练产品: {product_name} (ID: {product_id}) - {training_scope}")
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# 创建特征和目标变量
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features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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print(f"[mLSTM] 开始数据预处理，特征: {features}", flush=True)
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# 预处理数据
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X = product_df[features].values
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y = product_df[['sales']].values # 保持为二维数组
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print(f"[mLSTM] 特征矩阵形状: {X.shape}, 目标矩阵形状: {y.shape}", flush=True)
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emit_progress("数据预处理中...")
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# 归一化数据
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scaler_X = MinMaxScaler(feature_range=(0, 1))
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scaler_y = MinMaxScaler(feature_range=(0, 1))
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X_scaled = scaler_X.fit_transform(X)
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y_scaled = scaler_y.fit_transform(y)
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print(f"[mLSTM] 数据归一化完成", flush=True)
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# 划分训练集和测试集（80% 训练，20% 测试）
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train_size = int(len(X_scaled) * 0.8)
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X_train, X_test = X_scaled[:train_size], X_scaled[train_size:]
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y_train, y_test = y_scaled[:train_size], y_scaled[train_size:]
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# 创建时间序列数据
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trainX, trainY = create_dataset(X_train, y_train, sequence_length, forecast_horizon)
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testX, testY = create_dataset(X_test, y_test, sequence_length, forecast_horizon)
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# 转换为PyTorch的Tensor
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trainX_tensor = torch.Tensor(trainX)
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trainY_tensor = torch.Tensor(trainY)
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testX_tensor = torch.Tensor(testX)
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testY_tensor = torch.Tensor(testY)
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# 创建数据加载器
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train_dataset = PharmacyDataset(trainX_tensor, trainY_tensor)
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test_dataset = PharmacyDataset(testX_tensor, testY_tensor)
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batch_size = 32
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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# 更新进度管理器的批次信息
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total_batches = len(train_loader)
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total_samples = len(train_dataset)
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print(f"[mLSTM] 数据加载器创建完成 - 批次数: {total_batches}, 样本数: {total_samples}", flush=True)
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emit_progress(f"数据加载器准备完成 - 批次数: {total_batches}, 样本数: {total_samples}")
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# 初始化mLSTM结合Transformer模型
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input_dim = X_train.shape[1]
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output_dim = forecast_horizon
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hidden_size = 128
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num_heads = 4
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dropout_rate = 0.1
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num_blocks = 3
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embed_dim = 32
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dense_dim = 32
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print(f"[mLSTM] 初始化模型 - 输入维度: {input_dim}, 输出维度: {output_dim}", flush=True)
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print(f"[mLSTM] 模型参数 - 隐藏层: {hidden_size}, 注意力头: {num_heads}", flush=True)
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emit_progress(f"初始化mLSTM模型 - 输入维度: {input_dim}, 隐藏层: {hidden_size}")
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model = MatrixLSTM(
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num_features=input_dim,
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hidden_size=hidden_size,
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mlstm_layers=2,
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embed_dim=embed_dim,
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dense_dim=dense_dim,
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num_heads=num_heads,
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dropout_rate=dropout_rate,
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num_blocks=num_blocks,
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output_sequence_length=output_dim
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)
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print(f"[mLSTM] 模型创建完成", flush=True)
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emit_progress("mLSTM模型初始化完成")
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# 如果是继续训练，加载现有模型
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if continue_training and version != 'v1':
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# TODO: Implement continue_training logic with the new model_manager
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pass
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# 将模型移动到设备上
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model = model.to(DEVICE)
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=patience // 2, factor=0.5)
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emit_progress("数据预处理完成，开始模型训练...", progress=10)
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# 训练模型
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train_losses = []
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test_losses = []
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start_time = time.time()
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# 配置检查点保存
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checkpoint_interval = max(1, epochs // 10) # 每10%进度保存一次，最少每1个epoch
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best_loss = float('inf')
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epochs_no_improve = 0
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emit_progress(f"开始训练 - 总epoch: {epochs}, 检查点间隔: {checkpoint_interval}, 耐心值: {patience}")
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for epoch in range(epochs):
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emit_progress(f"开始训练 Epoch {epoch+1}/{epochs}")
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model.train()
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epoch_loss = 0
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for batch_idx, (X_batch, y_batch) in enumerate(train_loader):
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X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
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# 确保目标张量有正确的形状
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# 前向传播
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outputs = model(X_batch)
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loss = criterion(outputs, y_batch)
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# 反向传播和优化
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optimizer.zero_grad()
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loss.backward()
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if clip_norm:
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip_norm)
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optimizer.step()
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epoch_loss += loss.item()
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# 计算训练损失
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train_loss = epoch_loss / len(train_loader)
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train_losses.append(train_loss)
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# 在测试集上评估
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model.eval()
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test_loss = 0
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with torch.no_grad():
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for batch_idx, (X_batch, y_batch) in enumerate(test_loader):
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X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
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outputs = model(X_batch)
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loss = criterion(outputs, y_batch)
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test_loss += loss.item()
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test_loss = test_loss / len(test_loader)
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test_losses.append(test_loss)
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# 更新学习率
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scheduler.step(test_loss)
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# 计算总体训练进度
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epoch_progress = ((epoch + 1) / epochs) * 90 + 10 # 10-100% 范围
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# 发送训练进度
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current_metrics = {
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'train_loss': train_loss,
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'test_loss': test_loss,
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'epoch': epoch + 1,
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'total_epochs': epochs,
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'learning_rate': optimizer.param_groups[0]['lr']
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}
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emit_progress(f"Epoch {epoch+1}/{epochs} 完成 - Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}",
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progress=epoch_progress, metrics=current_metrics)
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# 定期保存检查点
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if (epoch + 1) % checkpoint_interval == 0 or epoch == epochs - 1:
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checkpoint_data = {
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'epoch': epoch + 1,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'train_loss': train_loss,
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'test_loss': test_loss,
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'train_losses': train_losses,
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'test_losses': test_losses,
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'scaler_X': scaler_X,
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'scaler_y': scaler_y,
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'config': {
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'input_dim': input_dim,
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'output_dim': output_dim,
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'hidden_size': hidden_size,
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'num_heads': num_heads,
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'dropout_rate': dropout_rate,
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'num_blocks': num_blocks,
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'embed_dim': embed_dim,
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'dense_dim': dense_dim,
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'mlstm_layers': 2, # 确保这个参数被保存
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'sequence_length': sequence_length,
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'forecast_horizon': forecast_horizon,
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'model_type': 'mlstm'
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},
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'training_info': {
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'product_id': product_id,
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'product_name': product_name,
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'training_mode': training_mode,
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'store_id': store_id,
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'aggregation_method': aggregation_method,
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'training_scope': training_scope,
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'timestamp': time.time()
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}
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}
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# 如果是最佳模型，额外保存一份
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if test_loss < best_loss:
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best_loss = test_loss
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model_manager.save_model(
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model_data=checkpoint_data,
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product_id=model_identifier, # 修正：使用唯一的标识符
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model_type='mlstm',
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store_id=store_id,
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training_mode=training_mode,
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aggregation_method=aggregation_method,
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product_name=product_name,
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version='best'
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)
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emit_progress(f"💾 保存最佳模型检查点 (epoch {epoch+1}, test_loss: {test_loss:.4f})")
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epochs_no_improve = 0
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else:
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epochs_no_improve += 1
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if (epoch + 1) % 10 == 0:
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print(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}", flush=True)
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# 提前停止逻辑
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if epochs_no_improve >= patience:
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emit_progress(f"连续 {patience} 个epoch测试损失未改善，提前停止训练。")
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break
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# 计算训练时间
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training_time = time.time() - start_time
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emit_progress("生成损失曲线...", progress=95)
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# 确定模型保存目录（支持多店铺）
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if store_id:
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# 为特定店铺创建子目录
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store_model_dir = os.path.join(model_dir, 'mlstm', store_id)
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os.makedirs(store_model_dir, exist_ok=True)
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loss_curve_filename = f"{product_id}_mlstm_{version}_loss_curve.png"
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loss_curve_path = os.path.join(store_model_dir, loss_curve_filename)
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else:
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# 全局模型保存在global目录
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global_model_dir = os.path.join(model_dir, 'mlstm', 'global')
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os.makedirs(global_model_dir, exist_ok=True)
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loss_curve_filename = f"{product_id}_mlstm_{version}_global_loss_curve.png"
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loss_curve_path = os.path.join(global_model_dir, loss_curve_filename)
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# 绘制损失曲线并保存到模型目录
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plt.figure(figsize=(10, 6))
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plt.plot(train_losses, label='Training Loss')
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plt.plot(test_losses, label='Test Loss')
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title_suffix = f" - {training_scope}" if store_id else " - 全局模型"
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plt.title(f'mLSTM 模型训练损失曲线 - {product_name} ({version}){title_suffix}')
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plt.xlabel('Epoch')
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plt.ylabel('Loss')
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plt.legend()
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plt.grid(True)
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plt.savefig(loss_curve_path, dpi=300, bbox_inches='tight')
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plt.close()
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print(f"损失曲线已保存到: {loss_curve_path}")
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emit_progress("模型评估中...", progress=98)
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# 评估模型
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model.eval()
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with torch.no_grad():
|
||
test_pred = model(testX_tensor.to(DEVICE)).cpu().numpy()
|
||
test_true = testY
|
||
|
||
# 反归一化预测结果和真实值
|
||
test_pred_inv = scaler_y.inverse_transform(test_pred)
|
||
test_true_inv = scaler_y.inverse_transform(test_true)
|
||
|
||
# 计算评估指标
|
||
metrics = evaluate_model(test_true_inv, test_pred_inv)
|
||
metrics['training_time'] = training_time
|
||
|
||
# 打印评估指标
|
||
print("\n模型评估指标:")
|
||
print(f"MSE: {metrics['mse']:.4f}")
|
||
print(f"RMSE: {metrics['rmse']:.4f}")
|
||
print(f"MAE: {metrics['mae']:.4f}")
|
||
print(f"R²: {metrics['r2']:.4f}")
|
||
print(f"MAPE: {metrics['mape']:.2f}%")
|
||
print(f"训练时间: {training_time:.2f}秒")
|
||
|
||
emit_progress("保存最终模型...", progress=99)
|
||
|
||
# 保存最终训练完成的模型（基于最终epoch）
|
||
final_model_data = {
|
||
'epoch': epochs, # 最终epoch
|
||
'model_state_dict': model.state_dict(),
|
||
'optimizer_state_dict': optimizer.state_dict(),
|
||
'train_loss': train_losses[-1],
|
||
'test_loss': test_losses[-1],
|
||
'train_losses': train_losses,
|
||
'test_losses': test_losses,
|
||
'scaler_X': scaler_X,
|
||
'scaler_y': scaler_y,
|
||
'config': {
|
||
'input_dim': input_dim,
|
||
'output_dim': output_dim,
|
||
'hidden_size': hidden_size,
|
||
'num_heads': num_heads,
|
||
'dropout_rate': dropout_rate,
|
||
'num_blocks': num_blocks,
|
||
'embed_dim': embed_dim,
|
||
'dense_dim': dense_dim,
|
||
'mlstm_layers': 2, # 确保这个参数被保存
|
||
'sequence_length': sequence_length,
|
||
'forecast_horizon': forecast_horizon,
|
||
'model_type': 'mlstm'
|
||
},
|
||
'metrics': metrics,
|
||
'loss_curve_path': loss_curve_path,
|
||
'training_info': {
|
||
'product_id': product_id,
|
||
'product_name': product_name,
|
||
'training_mode': training_mode,
|
||
'store_id': store_id,
|
||
'aggregation_method': aggregation_method,
|
||
'training_scope': training_scope,
|
||
'timestamp': time.time(),
|
||
'training_completed': True
|
||
}
|
||
}
|
||
|
||
# 保存最终模型，让 model_manager 自动处理版本号
|
||
final_model_path, final_version = model_manager.save_model(
|
||
model_data=final_model_data,
|
||
product_id=model_identifier, # 修正：使用唯一的标识符
|
||
model_type='mlstm',
|
||
store_id=store_id,
|
||
training_mode=training_mode,
|
||
aggregation_method=aggregation_method,
|
||
product_name=product_name
|
||
)
|
||
|
||
# 发送训练完成消息
|
||
final_metrics = {
|
||
'mse': metrics['mse'],
|
||
'rmse': metrics['rmse'],
|
||
'mae': metrics['mae'],
|
||
'r2': metrics['r2'],
|
||
'mape': metrics['mape'],
|
||
'training_time': training_time,
|
||
'final_epoch': epochs,
|
||
'model_path': final_model_path,
|
||
'version': final_version
|
||
}
|
||
|
||
emit_progress(f"✅ mLSTM模型训练完成！版本 {final_version} 已保存", progress=100, metrics=final_metrics)
|
||
|
||
return model, metrics, epochs, final_model_path
|
||
|
||
# --- 将此训练器注册到系统中 ---
|
||
from models.model_registry import register_trainer
|
||
register_trainer('mlstm', train_product_model_with_mlstm) |