285 lines
10 KiB
Python
285 lines
10 KiB
Python
"""
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药店销售预测系统 - 核心预测器类
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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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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 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=DEFAULT_DATA_PATH, 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
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model_dir: 模型保存目录
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"""
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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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if os.path.exists(data_path):
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self.data = pd.read_excel(data_path)
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print(f"已加载数据，来源: {data_path}")
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else:
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print(f"数据文件 {data_path} 不存在，请先生成数据")
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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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"""
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训练预测模型
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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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返回:
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metrics: 模型评估指标
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"""
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if self.data is None:
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print("没有可用的数据，请先加载或生成数据")
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return None
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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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print(f"找不到产品 {product_id} 的数据")
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return None
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if model_type == 'transformer':
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_, metrics = train_product_model_with_transformer(product_id, epochs=epochs, model_dir=self.model_dir)
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elif model_type == 'mlstm':
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_, metrics = train_product_model_with_mlstm(product_id, epochs=epochs, model_dir=self.model_dir)
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elif model_type == 'kan':
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_, metrics = train_product_model_with_kan(product_id, epochs=epochs, use_optimized=use_optimized, model_dir=self.model_dir)
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elif model_type == 'optimized_kan':
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_, metrics = train_product_model_with_kan(product_id, epochs=epochs, use_optimized=True, model_dir=self.model_dir)
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elif model_type == 'tcn':
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_, metrics = train_product_model_with_tcn(product_id, epochs=epochs, model_dir=self.model_dir)
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else:
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print(f"不支持的模型类型: {model_type}")
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return None
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return metrics
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def predict(self, product_id, model_type, future_days=7, start_date=None, analyze_result=False):
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"""
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使用已训练的模型进行预测
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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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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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return load_model_and_predict(
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product_id,
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model_type,
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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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)
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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):
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"""
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列出可用的已训练模型
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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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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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if product_id:
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model_files = [f for f in model_files if f"product_{product_id}" in f]
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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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# 处理不同的模型文件命名格式
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if "kan_optimized_model" in file:
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model_type = "optimized_kan"
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product_id = file.split('_product_')[1].split('.pth')[0]
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elif "_optimized_model" in file:
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model_type = "optimized_kan"
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product_id = file.split('_product_')[1].split('.pth')[0]
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else:
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model_type = file.split('_model_product_')[0]
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product_id = file.split('_product_')[1].split('.pth')[0]
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models.append({
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'model_type': model_type,
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'product_id': product_id,
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'file_name': file,
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'file_path': os.path.join(self.model_dir, file)
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})
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return models
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def delete_model(self, product_id, model_type):
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"""
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删除已训练的模型
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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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返回:
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是否成功删除
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"""
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model_suffix = '_optimized' if model_type == 'optimized_kan' else ''
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model_name = f"{model_type}{model_suffix}_model_product_{product_id}.pth"
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model_path = os.path.join(self.model_dir, model_name)
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if os.path.exists(model_path):
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os.remove(model_path)
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print(f"已删除模型: {model_path}")
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return True
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else:
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print(f"模型文件 {model_path} 不存在")
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return False |