529 lines
21 KiB
Python
529 lines
21 KiB
Python
"""
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药店销售预测系统 - 核心预测器类
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支持多店铺销售预测功能
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"""
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import os
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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 time
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import matplotlib.pyplot as plt
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from datetime import datetime
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# from trainers import (
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# train_product_model_with_mlstm,
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# train_product_model_with_kan,
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# train_product_model_with_tcn,
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# train_product_model_with_transformer
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# )
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# 上述导入已不再需要，因为我们现在通过模型注册表动态获取训练器
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from predictors.model_predictor import load_model_and_predict
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from utils.data_utils import prepare_data, prepare_sequences
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from utils.multi_store_data_utils import (
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load_multi_store_data,
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get_store_product_sales_data,
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aggregate_multi_store_data
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)
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from analysis.metrics import evaluate_model
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from core.config import DEVICE, DEFAULT_MODEL_DIR, DEFAULT_DATA_PATH
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class PharmacyPredictor:
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"""
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药店销售预测系统核心类，用于训练模型和进行预测
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"""
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def __init__(self, data_path=None, model_dir=DEFAULT_MODEL_DIR):
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"""
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初始化预测器
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参数:
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data_path: 数据文件路径，默认使用多店铺CSV文件
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model_dir: 模型保存目录
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"""
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# 设置默认数据路径为多店铺CSV文件
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if data_path is None:
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data_path = DEFAULT_DATA_PATH
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self.data_path = data_path
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self.model_dir = model_dir
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self.device = DEVICE
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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print(f"使用设备: {self.device}")
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# 尝试加载多店铺数据
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try:
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self.data = load_multi_store_data(data_path)
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print(f"已加载多店铺数据，来源: {data_path}")
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except Exception as e:
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print(f"加载数据失败: {e}")
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self.data = None
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def train_model(self, product_id, model_type='transformer', epochs=100, batch_size=32,
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learning_rate=0.001, sequence_length=30, forecast_horizon=7,
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hidden_size=64, num_layers=2, dropout=0.1, use_optimized=False,
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store_id=None, training_mode='product', aggregation_method='sum',
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socketio=None, task_id=None, version=None, continue_training=False,
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progress_callback=None):
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"""
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训练预测模型 - 支持多店铺训练
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参数:
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product_id: 产品ID
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model_type: 模型类型 ('transformer', 'mlstm', 'kan', 'tcn', 'optimized_kan')
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epochs: 训练轮次
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batch_size: 批次大小
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learning_rate: 学习率
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sequence_length: 输入序列长度
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forecast_horizon: 预测天数
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hidden_size: 隐藏层大小
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num_layers: 层数
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dropout: Dropout比例
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use_optimized: 是否使用优化版KAN（仅当model_type为'kan'时有效）
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store_id: 店铺ID（仅当training_mode为'store'时使用）
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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', 'median') - 仅用于全局训练
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返回:
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metrics: 模型评估指标
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"""
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# 创建统一的输出函数
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def log_message(message, log_type='info'):
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"""统一的日志输出函数"""
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print(message, flush=True) # 始终输出到控制台
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# 如果有进度回调，也发送到回调
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if progress_callback:
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try:
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progress_callback({
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'log_type': log_type,
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'message': message
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})
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except Exception as e:
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print(f"进度回调失败: {e}", flush=True)
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if self.data is None:
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log_message("没有可用的数据，请先加载或生成数据", 'error')
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return None
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# 根据训练模式准备数据
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if training_mode == 'product':
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# 按产品训练：使用所有店铺的该产品数据
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product_data = self.data[self.data['product_id'] == product_id].copy()
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if product_data.empty:
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log_message(f"找不到产品 {product_id} 的数据", 'error')
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return None
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log_message(f"按产品训练模式: 产品 {product_id}, 数据量: {len(product_data)}")
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elif training_mode == 'store':
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# 按店铺训练
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if not store_id:
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log_message("店铺训练模式需要指定 store_id", 'error')
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return None
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# 如果product_id是'unknown'，则表示为店铺所有商品训练一个聚合模型
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if product_id == 'unknown':
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try:
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# 使用新的聚合函数，按店铺聚合
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product_data = aggregate_multi_store_data(
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store_id=store_id,
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aggregation_method=aggregation_method,
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file_path=self.data_path
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)
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log_message(f"按店铺聚合训练: 店铺 {store_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
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# 将product_id设置为'store_{store_id}'，与API查找逻辑保持一致
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product_id = f"store_{store_id}"
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except Exception as e:
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log_message(f"聚合店铺 {store_id} 数据失败: {e}", 'error')
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return None
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else:
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# 为店铺的单个特定产品训练
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try:
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product_data = get_store_product_sales_data(
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store_id=store_id,
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product_id=product_id,
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file_path=self.data_path
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)
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log_message(f"按店铺-产品训练: 店铺 {store_id}, 产品 {product_id}, 数据量: {len(product_data)}")
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except Exception as e:
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log_message(f"获取店铺产品数据失败: {e}", 'error')
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return None
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elif training_mode == 'global':
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# 全局训练：聚合所有店铺的产品数据
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try:
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# 如果product_id是'unknown'，则表示为全局所有商品训练一个聚合模型
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if product_id == 'unknown':
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product_data = aggregate_multi_store_data(
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product_id=None, # 传递None以触发真正的全局聚合
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aggregation_method=aggregation_method,
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file_path=self.data_path
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)
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log_message(f"全局训练模式: 所有产品, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
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# 将product_id设置为一个有意义的标识符
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product_id = 'all_products'
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else:
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product_data = aggregate_multi_store_data(
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product_id=product_id,
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aggregation_method=aggregation_method,
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file_path=self.data_path
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)
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log_message(f"全局训练模式: 产品 {product_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
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except Exception as e:
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log_message(f"聚合全局数据失败: {e}", 'error')
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return None
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else:
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log_message(f"不支持的训练模式: {training_mode}", 'error')
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return None
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# 根据训练模式构建模型标识符 (v2 修正)
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if training_mode == 'store':
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# 店铺模型的标识符只应基于店铺ID
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model_identifier = f"store_{store_id}"
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elif training_mode == 'global':
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# 全局模型的标识符不应依赖于单个product_id
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model_identifier = f"global_{aggregation_method}"
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else: # product mode
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model_identifier = product_id
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# 调用相应的训练函数 (重构为使用注册表)
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try:
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from models.model_registry import get_trainer
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log_message(f"🤖 正在从注册表获取 '{model_type}' 训练器...")
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trainer_function = get_trainer(model_type)
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log_message(f"✅ 成功获取训练器: {trainer_function.__name__}")
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# 准备通用参数
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trainer_args = {
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'product_id': product_id,
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'model_identifier': model_identifier,
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'product_df': product_data,
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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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'epochs': epochs,
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'sequence_length': sequence_length,
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'forecast_horizon': forecast_horizon,
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'model_dir': self.model_dir,
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'socketio': socketio,
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'task_id': task_id,
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'progress_callback': progress_callback,
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'version': version,
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'continue_training': continue_training,
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'use_optimized': use_optimized # KAN模型需要
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}
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# 动态调用训练函数 (v2 - 智能参数过滤)
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import inspect
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sig = inspect.signature(trainer_function)
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valid_args = {k: v for k, v in trainer_args.items() if k in sig.parameters}
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log_message(f"🔍 准备调用 {trainer_function.__name__}，有效参数: {list(valid_args.keys())}")
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result = trainer_function(**valid_args)
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# 根据返回值的数量解析metrics
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if isinstance(result, tuple) and len(result) >= 2:
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metrics = result[1] # 通常第二个返回值是metrics
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else:
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log_message(f"⚠️ 训练器返回格式未知，无法直接提取metrics: {type(result)}", 'warning')
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metrics = None
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# 检查和打印返回的metrics
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log_message(f"📊 训练完成，检查返回的metrics: {metrics}")
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# 在返回的metrics中添加训练信息
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if metrics:
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log_message(f"✅ metrics不为空，添加训练信息")
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metrics.update({
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'training_mode': training_mode,
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'store_id': store_id,
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'product_id': product_id,
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'model_identifier': model_identifier,
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'aggregation_method': aggregation_method if training_mode == 'global' else None
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})
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log_message(f"📈 最终返回的metrics: {metrics}", 'success')
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else:
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log_message(f"⚠️ metrics为空或None", 'warning')
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return metrics
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except Exception as e:
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log_message(f"模型训练失败: {e}", 'error')
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return None
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def predict(self, product_id, model_type, future_days=7, start_date=None, analyze_result=False, version=None,
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store_id=None, training_mode='product', aggregation_method='sum'):
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"""
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使用已训练的模型进行预测 - 支持多店铺预测
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参数:
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product_id: 产品ID
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model_type: 模型类型
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future_days: 预测未来天数
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start_date: 预测起始日期
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analyze_result: 是否分析预测结果
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version: 模型版本，如果为None则使用最新版本
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store_id: 店铺ID（仅当training_mode为'store'时使用）
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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', 'median') - 仅用于全局预测
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返回:
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预测结果和分析（如果analyze_result为True）
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"""
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# 根据训练模式构建模型标识符 (v2 修正)
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if training_mode == 'store' and store_id:
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model_identifier = f"store_{store_id}"
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elif training_mode == 'global':
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# 全局模型的标识符不应依赖于单个product_id
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model_identifier = f"global_{aggregation_method}"
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else: # product mode
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model_identifier = product_id
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return load_model_and_predict(
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model_identifier,
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model_type,
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store_id=store_id,
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future_days=future_days,
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start_date=start_date,
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analyze_result=analyze_result,
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version=version,
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training_mode=training_mode
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)
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def train_optimized_kan_model(self, product_id, epochs=100, batch_size=32,
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learning_rate=0.001, sequence_length=30, forecast_horizon=7,
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hidden_size=64, num_layers=2, dropout=0.1):
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"""
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训练优化版KAN模型（便捷方法）
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参数与train_model相同，但固定model_type为'kan'且use_optimized为True
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"""
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return self.train_model(
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product_id=product_id,
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model_type='kan',
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epochs=epochs,
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batch_size=batch_size,
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learning_rate=learning_rate,
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sequence_length=sequence_length,
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forecast_horizon=forecast_horizon,
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hidden_size=hidden_size,
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num_layers=num_layers,
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dropout=dropout,
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use_optimized=True
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)
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def compare_kan_models(self, product_id, epochs=100, batch_size=32,
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learning_rate=0.001, sequence_length=30, forecast_horizon=7,
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hidden_size=64, num_layers=2, dropout=0.1):
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"""
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比较原始KAN和优化版KAN模型性能
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参数与train_model相同
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返回:
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比较结果字典
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"""
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print(f"开始比较产品 {product_id} 的原始KAN和优化版KAN模型性能...")
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# 训练原始KAN模型
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print("\n训练原始KAN模型...")
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kan_metrics = self.train_model(
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product_id=product_id,
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model_type='kan',
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epochs=epochs,
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batch_size=batch_size,
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learning_rate=learning_rate,
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sequence_length=sequence_length,
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forecast_horizon=forecast_horizon,
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hidden_size=hidden_size,
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num_layers=num_layers,
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dropout=dropout,
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use_optimized=False
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)
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# 训练优化版KAN模型
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print("\n训练优化版KAN模型...")
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optimized_kan_metrics = self.train_model(
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product_id=product_id,
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model_type='kan',
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epochs=epochs,
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batch_size=batch_size,
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learning_rate=learning_rate,
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sequence_length=sequence_length,
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forecast_horizon=forecast_horizon,
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hidden_size=hidden_size,
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num_layers=num_layers,
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dropout=dropout,
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use_optimized=True
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)
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# 比较结果
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comparison = {
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'kan': kan_metrics,
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'optimized_kan': optimized_kan_metrics
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}
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# 打印比较结果
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print("\n模型性能比较:")
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print(f"{'指标':<10} {'原始KAN':<15} {'优化版KAN':<15} {'改进百分比':<15}")
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print("-" * 55)
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for metric in ['mse', 'rmse', 'mae', 'mape']:
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if metric in kan_metrics and metric in optimized_kan_metrics:
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kan_value = kan_metrics[metric]
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opt_value = optimized_kan_metrics[metric]
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improvement = (kan_value - opt_value) / kan_value * 100 if kan_value != 0 else 0
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print(f"{metric.upper():<10} {kan_value:<15.4f} {opt_value:<15.4f} {improvement:<15.2f}%")
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# R²值越高越好，所以计算改进的方式不同
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if 'r2' in kan_metrics and 'r2' in optimized_kan_metrics:
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kan_r2 = kan_metrics['r2']
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opt_r2 = optimized_kan_metrics['r2']
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improvement = (opt_r2 - kan_r2) / (1 - kan_r2) * 100 if kan_r2 != 1 else 0
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print(f"{'R²':<10} {kan_r2:<15.4f} {opt_r2:<15.4f} {improvement:<15.2f}%")
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# 训练时间
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if 'training_time' in kan_metrics and 'training_time' in optimized_kan_metrics:
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kan_time = kan_metrics['training_time']
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opt_time = optimized_kan_metrics['training_time']
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time_diff = (opt_time - kan_time) / kan_time * 100 if kan_time != 0 else 0
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print(f"{'时间(秒)':<10} {kan_time:<15.2f} {opt_time:<15.2f} {time_diff:<15.2f}%")
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return comparison
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def list_available_models(self, product_id=None, store_id=None, training_mode=None):
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"""
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列出可用的已训练模型 - 支持多店铺模型
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参数:
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product_id: 产品ID，如果为None则列出所有模型
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store_id: 店铺ID，用于筛选店铺专属模型
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training_mode: 训练模式筛选 ('product', 'store', 'global')
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返回:
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可用模型列表
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"""
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if not os.path.exists(self.model_dir):
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print(f"模型目录 {self.model_dir} 不存在")
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return []
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model_files = os.listdir(self.model_dir)
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models = []
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for file in model_files:
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if file.endswith('.pth'):
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try:
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# 解析模型文件名
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model_info = self._parse_model_filename(file)
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if model_info:
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# 应用过滤条件
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if product_id and model_info.get('product_id') != product_id:
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continue
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if store_id and model_info.get('store_id') != store_id:
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continue
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if training_mode and model_info.get('training_mode') != training_mode:
|
||
continue
|
||
|
||
model_info['file_name'] = file
|
||
model_info['file_path'] = os.path.join(self.model_dir, file)
|
||
models.append(model_info)
|
||
except Exception as e:
|
||
print(f"解析模型文件名失败: {file}, 错误: {e}")
|
||
continue
|
||
|
||
return models
|
||
|
||
def _parse_model_filename(self, filename):
|
||
"""
|
||
解析模型文件名，提取模型信息
|
||
|
||
参数:
|
||
filename: 模型文件名
|
||
|
||
返回:
|
||
dict: 模型信息字典
|
||
"""
|
||
# 移除文件扩展名
|
||
name = filename.replace('.pth', '')
|
||
|
||
# 解析新的多店铺模型命名格式
|
||
if '_model_product_' in name:
|
||
parts = name.split('_model_product_')
|
||
model_type = parts[0]
|
||
product_part = parts[1]
|
||
|
||
# 检查是否是店铺模型 (格式: model_type_model_product_store_id_product_id)
|
||
if len(product_part.split('_')) > 1:
|
||
store_id = product_part.split('_')[0]
|
||
product_id = '_'.join(product_part.split('_')[1:])
|
||
training_mode = 'store'
|
||
# 检查是否是全局模型 (格式: model_type_model_product_global_product_id_method)
|
||
elif product_part.startswith('global_'):
|
||
parts = product_part.split('_')
|
||
if len(parts) >= 3:
|
||
product_id = '_'.join(parts[1:-1])
|
||
aggregation_method = parts[-1]
|
||
store_id = None
|
||
training_mode = 'global'
|
||
else:
|
||
product_id = product_part
|
||
store_id = None
|
||
training_mode = 'product'
|
||
else:
|
||
# 常规产品模型
|
||
product_id = product_part
|
||
store_id = None
|
||
training_mode = 'product'
|
||
|
||
# 处理优化版KAN模型
|
||
if 'optimized' in model_type:
|
||
model_type = 'optimized_kan'
|
||
|
||
return {
|
||
'model_type': model_type,
|
||
'product_id': product_id,
|
||
'store_id': store_id,
|
||
'training_mode': training_mode,
|
||
'aggregation_method': aggregation_method if training_mode == 'global' and 'aggregation_method' in locals() else None
|
||
}
|
||
|
||
# 处理旧格式的向后兼容性
|
||
elif "kan_optimized_model" in name:
|
||
model_type = "optimized_kan"
|
||
product_id = name.split('_product_')[1] if '_product_' in name else 'unknown'
|
||
return {
|
||
'model_type': model_type,
|
||
'product_id': product_id,
|
||
'store_id': None,
|
||
'training_mode': 'product',
|
||
'aggregation_method': None
|
||
}
|
||
|
||
return None
|
||
|
||
def delete_model(self, product_id, model_type):
|
||
"""
|
||
删除已训练的模型
|
||
|
||
参数:
|
||
product_id: 产品ID
|
||
model_type: 模型类型
|
||
|
||
返回:
|
||
是否成功删除
|
||
"""
|
||
model_suffix = '_optimized' if model_type == 'optimized_kan' else ''
|
||
model_name = f"{model_type}{model_suffix}_model_product_{product_id}.pth"
|
||
model_path = os.path.join(self.model_dir, model_name)
|
||
|
||
if os.path.exists(model_path):
|
||
os.remove(model_path)
|
||
print(f"已删除模型: {model_path}")
|
||
return True
|
||
else:
|
||
print(f"模型文件 {model_path} 不存在")
|
||
return False |