484 lines
19 KiB
Python
484 lines
19 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 load_multi_store_data, aggregate_multi_store_data
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# 导入新的特征选择模块
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from utils.feature_selection import get_feature_list_for_model
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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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# --- 新数据管道 ---
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# 1. 加载完整的、经过基础标准化的数据
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# 注意：此时的load_multi_store_data已经过改造，不再创造特征
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full_data = load_multi_store_data(self.data_path)
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if full_data.empty:
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log_message("错误：加载数据后得到空的DataFrame。", 'error')
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return None, None
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# 2. 根据训练模式，筛选出本次训练所需的数据子集
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if training_mode == 'product':
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training_df = full_data[full_data['product_id'] == product_id].copy()
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model_identifier = product_id
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elif training_mode == 'store':
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training_df = full_data[full_data['store_id'] == store_id].copy()
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model_identifier = f"store_{store_id}"
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elif training_mode == 'global':
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# 全局模型使用所有数据
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training_df = full_data.copy()
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model_identifier = f"global_{aggregation_method}"
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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, None
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if training_df.empty:
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log_message(f"错误：根据训练模式 '{training_mode}' 和标识 '{product_id or store_id}' 筛选后，没有剩余数据。", 'error')
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return None, None
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log_message(f"数据筛选完成，用于训练的记录数: {len(training_df)}")
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# 3. 根据模型类型，获取专属的特征列表
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all_columns = training_df.columns.tolist()
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feature_list = get_feature_list_for_model(model_type, all_columns)
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if not feature_list:
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log_message(f"错误：未能为模型 '{model_type}' 获取任何特征。", 'error')
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return None, None
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log_message(f"为模型 '{model_type}' 选择了 {len(feature_list)} 个特征: {feature_list[:5]}...")
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# 4. 调用相应的训练器，并传入数据和特征列表
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try:
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from models.model_registry import get_trainer
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trainer_function = get_trainer(model_type)
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# --- 临时添加：用于快速测试的调试模式 ---
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debug_fast_mode = True
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if debug_fast_mode:
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print("🚀 快速测试模式已激活，截取前100条数据进行训练。")
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training_df = training_df.head(66)
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# ------------------------------------
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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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'training_df': training_df, # 传入筛选后的数据
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'feature_list': feature_list, # 传入选择好的特征
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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
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}
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# 动态过滤不兼容的参数
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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__}...")
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result = trainer_function(**valid_args)
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# 解析返回结果
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if isinstance(result, tuple) and len(result) >= 2:
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metrics, artifacts = result[0], result[1]
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else:
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log_message(f"训练器返回格式未知: {type(result)}", 'warning')
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return None, None
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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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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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return metrics, artifacts
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except Exception as e:
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import traceback
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log_message(f"模型训练失败: {e}\n{traceback.format_exc()}", 'error')
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return None, 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:
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continue
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model_info['file_name'] = file
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model_info['file_path'] = os.path.join(self.model_dir, file)
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models.append(model_info)
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except Exception as e:
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print(f"解析模型文件名失败: {file}, 错误: {e}")
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continue
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return models
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def _parse_model_filename(self, filename):
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"""
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解析模型文件名，提取模型信息
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参数:
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filename: 模型文件名
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返回:
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dict: 模型信息字典
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"""
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# 移除文件扩展名
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name = filename.replace('.pth', '')
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# 解析新的多店铺模型命名格式
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if '_model_product_' in name:
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parts = name.split('_model_product_')
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model_type = parts[0]
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product_part = parts[1]
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# 检查是否是店铺模型 (格式: model_type_model_product_store_id_product_id)
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if len(product_part.split('_')) > 1:
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store_id = product_part.split('_')[0]
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product_id = '_'.join(product_part.split('_')[1:])
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training_mode = 'store'
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# 检查是否是全局模型 (格式: model_type_model_product_global_product_id_method)
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elif product_part.startswith('global_'):
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parts = product_part.split('_')
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if len(parts) >= 3:
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product_id = '_'.join(parts[1:-1])
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aggregation_method = parts[-1]
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store_id = None
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training_mode = 'global'
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else:
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product_id = product_part
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store_id = None
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training_mode = 'product'
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else:
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# 常规产品模型
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product_id = product_part
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store_id = None
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training_mode = 'product'
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# 处理优化版KAN模型
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if 'optimized' in model_type:
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model_type = 'optimized_kan'
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return {
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'model_type': model_type,
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'product_id': product_id,
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'store_id': store_id,
|
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'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 |