506 lines
19 KiB
Python
506 lines
19 KiB
Python
"""
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药店销售预测系统 - TCN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.tcn_model import TCNForecaster
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from utils.data_utils import create_dataset, PharmacyDataset
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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 DEVICE, DEFAULT_MODEL_DIR, LOOK_BACK, FORECAST_HORIZON
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from utils.training_progress import progress_manager
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def save_checkpoint(checkpoint_data: dict, epoch_or_label, product_id: str,
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model_type: str, model_dir: str, store_id=None,
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training_mode: str = 'product', aggregation_method=None):
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"""
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保存训练检查点
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Args:
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checkpoint_data: 检查点数据
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epoch_or_label: epoch编号或标签（如'best'）
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product_id: 产品ID
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model_type: 模型类型
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model_dir: 模型保存目录
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store_id: 店铺ID
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training_mode: 训练模式
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aggregation_method: 聚合方法
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"""
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# 创建检查点目录
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checkpoint_dir = os.path.join(model_dir, 'checkpoints')
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os.makedirs(checkpoint_dir, exist_ok=True)
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# 生成检查点文件名
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if training_mode == 'store' and store_id:
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filename = f"{model_type}_store_{store_id}_{product_id}_epoch_{epoch_or_label}.pth"
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elif training_mode == 'global' and aggregation_method:
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filename = f"{model_type}_global_{product_id}_{aggregation_method}_epoch_{epoch_or_label}.pth"
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else:
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filename = f"{model_type}_product_{product_id}_epoch_{epoch_or_label}.pth"
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checkpoint_path = os.path.join(checkpoint_dir, filename)
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# 保存检查点
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torch.save(checkpoint_data, checkpoint_path)
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print(f"[TCN] 检查点已保存: {checkpoint_path}", flush=True)
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return checkpoint_path
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def train_product_model_with_tcn(
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product_id,
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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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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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):
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"""
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使用TCN模型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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epochs: 训练轮次
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model_dir: 模型保存目录，默认使用配置中的DEFAULT_MODEL_DIR
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version: 指定版本号，如果为None则自动生成
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socketio: WebSocket对象，用于实时反馈
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task_id: 训练任务ID
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continue_training: 是否继续训练现有模型
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返回:
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model: 训练好的模型
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metrics: 模型评估指标
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version: 实际使用的版本号
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model_path: 模型文件路径
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"""
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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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if socketio and task_id:
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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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data['progress'] = progress
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if metrics is not None:
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data['metrics'] = metrics
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socketio.emit('training_progress', data, namespace='/training')
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# 确定版本号
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if version is None:
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from core.config import get_latest_model_version, get_next_model_version
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if continue_training:
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version = get_latest_model_version(product_id, 'tcn')
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if version is None:
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version = get_next_model_version(product_id, 'tcn')
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else:
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version = get_next_model_version(product_id, 'tcn')
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emit_progress(f"开始训练 TCN 模型版本 {version}")
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# 根据训练模式加载数据
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from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
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try:
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if training_mode == 'store' and store_id:
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# 加载特定店铺的数据
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product_df = get_store_product_sales_data(
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store_id,
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product_id,
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'pharmacy_sales_multi_store.csv'
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)
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training_scope = f"店铺 {store_id}"
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elif training_mode == 'global':
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# 聚合所有店铺的数据
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product_df = aggregate_multi_store_data(
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product_id,
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aggregation_method=aggregation_method,
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file_path='pharmacy_sales_multi_store.csv'
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)
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training_scope = f"全局聚合({aggregation_method})"
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else:
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# 默认：加载所有店铺的产品数据
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product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
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training_scope = "所有店铺"
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except Exception as e:
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print(f"多店铺数据加载失败: {e}")
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# 后备方案：尝试原始数据
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df = pd.read_excel('pharmacy_sales.xlsx')
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product_df = df[df['product_id'] == product_id].sort_values('date')
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training_scope = "原始数据"
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if product_df.empty:
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raise ValueError(f"产品 {product_id} 没有可用的销售数据")
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# 数据量检查
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min_required_samples = LOOK_BACK + 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={LOOK_BACK} + 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_df = product_df.sort_values('date')
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product_name = product_df['product_name'].iloc[0]
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print(f"使用TCN模型训练产品 '{product_name}' (ID: {product_id}) 的销售预测模型")
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print(f"训练范围: {training_scope}")
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print(f"版本: {version}")
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print(f"使用设备: {DEVICE}")
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print(f"模型将保存到目录: {model_dir}")
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emit_progress(f"训练产品: {product_name} (ID: {product_id})")
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# 创建特征和目标变量
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features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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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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# 设置数据预处理阶段
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progress_manager.set_stage("data_preprocessing", 0)
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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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# 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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progress_manager.set_stage("data_preprocessing", 50)
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# 创建时间序列数据
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trainX, trainY = create_dataset(X_train, y_train, LOOK_BACK, FORECAST_HORIZON)
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testX, testY = create_dataset(X_test, y_test, LOOK_BACK, 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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progress_manager.total_batches_per_epoch = total_batches
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progress_manager.batch_size = batch_size
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progress_manager.total_samples = total_samples
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progress_manager.set_stage("data_preprocessing", 100)
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# 初始化TCN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 = 64
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num_layers = 3
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kernel_size = 3
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dropout_rate = 0.2
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model = TCNForecaster(
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num_features=input_dim,
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output_sequence_length=output_dim,
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num_channels=[hidden_size] * num_layers,
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kernel_size=kernel_size,
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dropout=dropout_rate
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)
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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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try:
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from core.config import get_model_file_path
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existing_model_path = get_model_file_path(product_id, 'tcn', version)
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if os.path.exists(existing_model_path):
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checkpoint = torch.load(existing_model_path, map_location=DEVICE)
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model.load_state_dict(checkpoint['model_state_dict'])
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print(f"加载现有模型: {existing_model_path}")
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emit_progress(f"加载现有模型版本 {version} 进行继续训练")
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except Exception as e:
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print(f"无法加载现有模型，将重新开始训练: {e}")
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emit_progress("无法加载现有模型，重新开始训练")
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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=0.001)
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emit_progress("开始模型训练...")
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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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progress_manager.set_stage("model_training", 0)
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emit_progress(f"开始训练 - 总epoch: {epochs}, 检查点间隔: {checkpoint_interval}")
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for epoch in range(epochs):
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# 开始新的轮次
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progress_manager.start_epoch(epoch)
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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 (batch_size, forecast_horizon, 1)
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if y_batch.dim() == 2:
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y_batch = y_batch.unsqueeze(-1)
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# 前向传播
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outputs = model(X_batch)
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# 确保输出和目标形状匹配
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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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optimizer.step()
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epoch_loss += loss.item()
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# 更新批次进度（每10个批次更新一次）
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if batch_idx % 10 == 0 or batch_idx == len(train_loader) - 1:
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current_lr = optimizer.param_groups[0]['lr']
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progress_manager.update_batch(batch_idx, loss.item(), current_lr)
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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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# 设置验证阶段
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progress_manager.set_stage("validation", 0)
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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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# 确保目标张量有正确的形状
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if y_batch.dim() == 2:
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y_batch = y_batch.unsqueeze(-1)
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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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# 更新验证进度
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if batch_idx % 5 == 0 or batch_idx == len(test_loader) - 1:
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val_progress = (batch_idx / len(test_loader)) * 100
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progress_manager.set_stage("validation", val_progress)
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test_loss = test_loss / len(test_loader)
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test_losses.append(test_loss)
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# 完成当前轮次
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progress_manager.finish_epoch(train_loss, test_loss)
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# 发送训练进度（保持与旧系统的兼容性）
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if (epoch + 1) % 5 == 0 or epoch == epochs - 1:
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progress = ((epoch + 1) / epochs) * 100
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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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}
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emit_progress(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}",
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progress=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_layers': num_layers,
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'dropout': dropout_rate,
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'kernel_size': kernel_size,
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'sequence_length': LOOK_BACK,
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'forecast_horizon': FORECAST_HORIZON,
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'model_type': 'tcn'
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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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'timestamp': time.time()
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}
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}
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# 保存检查点
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save_checkpoint(checkpoint_data, epoch + 1, product_id, 'tcn',
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model_dir, store_id, training_mode, aggregation_method)
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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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save_checkpoint(checkpoint_data, 'best', product_id, 'tcn',
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model_dir, store_id, training_mode, aggregation_method)
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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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emit_progress(f"💾 保存训练检查点 epoch_{epoch+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}")
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# 计算训练时间
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training_time = time.time() - start_time
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# 设置模型保存阶段
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progress_manager.set_stage("model_saving", 0)
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emit_progress("训练完成，正在保存模型...")
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# 绘制损失曲线并保存到模型目录
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loss_curve_path = plot_loss_curve(
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train_losses,
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test_losses,
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product_name,
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'TCN',
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model_dir=model_dir
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)
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print(f"损失曲线已保存到: {loss_curve_path}")
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# 评估模型
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model.eval()
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with torch.no_grad():
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# 确保测试数据的形状正确
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test_pred = model(testX_tensor.to(DEVICE))
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# 将输出转换为二维数组 [samples, forecast_horizon]
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test_pred = test_pred.squeeze(-1).cpu().numpy()
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# 反归一化预测结果和真实值
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test_pred_inv = scaler_y.inverse_transform(test_pred.reshape(-1, 1)).flatten()
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test_true_inv = scaler_y.inverse_transform(testY.reshape(-1, 1)).flatten()
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# 计算评估指标
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metrics = evaluate_model(test_true_inv, test_pred_inv)
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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")
|
||
|
||
# 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_layers': num_layers,
|
||
'dropout': dropout_rate,
|
||
'kernel_size': kernel_size,
|
||
'sequence_length': LOOK_BACK,
|
||
'forecast_horizon': FORECAST_HORIZON,
|
||
'model_type': 'tcn'
|
||
},
|
||
'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,
|
||
'timestamp': time.time(),
|
||
'training_completed': True
|
||
}
|
||
}
|
||
|
||
progress_manager.set_stage("model_saving", 50)
|
||
|
||
# 保存最终模型（使用epoch标识）
|
||
final_model_path = save_checkpoint(
|
||
final_model_data, f"final_epoch_{epochs}", product_id, 'tcn',
|
||
model_dir, store_id, training_mode, aggregation_method
|
||
)
|
||
|
||
progress_manager.set_stage("model_saving", 100)
|
||
|
||
final_metrics = {
|
||
'mse': metrics['mse'],
|
||
'rmse': metrics['rmse'],
|
||
'mae': metrics['mae'],
|
||
'r2': metrics['r2'],
|
||
'mape': metrics['mape'],
|
||
'training_time': training_time,
|
||
'final_epoch': epochs
|
||
}
|
||
|
||
emit_progress(f"模型训练完成！最终epoch: {epochs}", progress=100, metrics=final_metrics)
|
||
|
||
return model, metrics, epochs, final_model_path |