363 lines
16 KiB
Python
363 lines
16 KiB
Python
import os
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from datetime import datetime
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import sys
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# 导入模块
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from pharmacy_predictor import train_product_model, train_product_model_with_mlstm, train_product_model_with_kan, train_product_model_with_transformer
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from pharmacy_predictor import load_model_and_predict, load_kan_model_and_predict
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from models import ModelManager
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def print_header():
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print("\n" + "="*60)
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print("📊 药店单品销售预测系统 📊")
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print("="*60)
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def main():
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# 初始化模型管理器
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model_manager = ModelManager()
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# 首先检查数据文件是否存在
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try:
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df = pd.read_excel('pharmacy_sales.xlsx')
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print("✅ 检测到现有数据文件 'pharmacy_sales.xlsx'")
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# 获取所有产品
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products = df[['product_id', 'product_name']].drop_duplicates().sort_values('product_id')
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print(f"\n📋 发现 {len(products)} 种药品:")
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for i, (_, row) in enumerate(products.iterrows(), 1):
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print(f" {i}. {row['product_name']} (ID: {row['product_id']})")
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except:
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print("❌ 未找到数据文件。正在生成模拟数据...")
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import generate_pharmacy_data
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df = pd.read_excel('pharmacy_sales.xlsx')
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print("✅ 数据生成完成！")
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# 获取所有产品
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products = df[['product_id', 'product_name']].drop_duplicates().sort_values('product_id')
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while True:
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print("\n" + "="*60)
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print("📋 请选择操作:")
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print(" 1. 训练所有药品的销售预测模型")
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print(" 2. 训练单个药品的销售预测模型")
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print(" 3. 使用mLSTM模型训练单个药品的销售预测模型")
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print(" 4. 使用KAN模型训练单个药品的销售预测模型")
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print(" 5. 使用Transformer模型训练单个药品的销售预测模型")
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print(" 6. 查看已有预测结果")
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print(" 7. 使用已训练的模型进行预测")
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print(" 8. 比较不同模型的预测结果")
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print(" 9. 模型管理")
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print(" 0. 退出")
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print("="*60)
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choice = input("\n请输入选项 (0-9): ")
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if choice == '0':
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print("感谢使用药店销售预测系统！再见！")
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break
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elif choice == '1':
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# 训练所有药品的预测模型
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print("\n开始训练所有药品的销售预测模型...")
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all_metrics = {}
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for _, row in products.iterrows():
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product_id = row['product_id']
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print(f"\n{'='*50}")
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print(f"开始训练产品 {row['product_name']} (ID: {product_id}) 的模型")
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print(f"{'='*50}")
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_, metrics = train_product_model(product_id)
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all_metrics[product_id] = metrics
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# 输出所有产品的评估指标
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print("\n所有产品模型评估结果汇总:")
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for product_id, metrics in all_metrics.items():
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product_name = df[df['product_id'] == product_id]['product_name'].iloc[0]
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print(f"\n{product_name} (ID: {product_id}):")
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for metric, value in metrics.items():
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print(f" {metric}: {value:.4f}")
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print("\n模型训练和评估完成!")
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elif choice == '2':
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# 训练单个药品的预测模型
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print("\n请选择要训练的药品:")
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for i, (_, row) in enumerate(products.iterrows(), 1):
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print(f" {i}. {row['product_name']} (ID: {row['product_id']})")
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product_idx = int(input("\n请输入药品编号 (1-{}): ".format(len(products))))
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if 1 <= product_idx <= len(products):
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product_id = products.iloc[product_idx-1]['product_id']
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product_name = products.iloc[product_idx-1]['product_name']
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print(f"\n开始训练 {product_name} (ID: {product_id}) 的销售预测模型...")
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_, metrics = train_product_model(product_id)
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print(f"\n{product_name} 模型评估指标:")
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for metric, value in metrics.items():
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print(f" {metric}: {value:.4f}")
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print(f"\n模型训练和评估完成!")
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else:
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print("\n❌ 无效的选择！")
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elif choice == '3':
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# 使用mLSTM模型训练单个药品
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print("\n请选择要训练的药品:")
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for i, (_, row) in enumerate(products.iterrows(), 1):
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print(f" {i}. {row['product_name']} (ID: {row['product_id']})")
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product_idx = int(input("\n请输入药品编号 (1-{}): ".format(len(products))))
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if 1 <= product_idx <= len(products):
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product_id = products.iloc[product_idx-1]['product_id']
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product_name = products.iloc[product_idx-1]['product_name']
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print(f"\n开始使用mLSTM模型训练 {product_name} (ID: {product_id}) 的销售预测模型...")
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model, metrics = train_product_model_with_mlstm(product_id)
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# 保存模型到模型管理器
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try:
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model_manager.save_model(
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model=model,
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model_type='mlstm',
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product_id=product_id,
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metrics=metrics,
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features=['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'],
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look_back=14,
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T=7
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)
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print("✅ 模型已保存到模型管理器")
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except Exception as e:
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print(f"❌ 保存模型到管理器时出错: {str(e)}")
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print(f"\n{product_name} mLSTM模型评估指标:")
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for metric, value in metrics.items():
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print(f" {metric}: {value:.4f}")
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print(f"\n模型训练和评估完成!")
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else:
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print("\n❌ 无效的选择！")
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elif choice == '4':
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# 使用KAN模型训练单个药品
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print("\n请选择要训练的药品:")
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for i, (_, row) in enumerate(products.iterrows(), 1):
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print(f" {i}. {row['product_name']} (ID: {row['product_id']})")
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product_idx = int(input("\n请输入药品编号 (1-{}): ".format(len(products))))
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if 1 <= product_idx <= len(products):
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product_id = products.iloc[product_idx-1]['product_id']
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product_name = products.iloc[product_idx-1]['product_name']
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print(f"\n开始使用KAN模型训练 {product_name} (ID: {product_id}) 的销售预测模型...")
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model, metrics = train_product_model_with_kan(product_id)
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# 保存模型到模型管理器
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try:
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model_manager.save_model(
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model=model,
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model_type='kan',
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product_id=product_id,
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metrics=metrics,
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features=['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'],
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look_back=14,
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T=7
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)
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print("✅ 模型已保存到模型管理器")
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except Exception as e:
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print(f"❌ 保存模型到管理器时出错: {str(e)}")
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print(f"\n{product_name} KAN模型评估指标:")
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for metric, value in metrics.items():
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print(f" {metric}: {value:.4f}")
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print(f"\n模型训练和评估完成!")
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else:
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print("\n❌ 无效的选择！")
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elif choice == '5':
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# 使用Transformer模型训练单个药品
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print("\n请选择要训练的药品:")
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for i, (_, row) in enumerate(products.iterrows(), 1):
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print(f" {i}. {row['product_name']} (ID: {row['product_id']})")
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product_idx = int(input("\n请输入药品编号 (1-{}): ".format(len(products))))
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if 1 <= product_idx <= len(products):
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product_id = products.iloc[product_idx-1]['product_id']
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product_name = products.iloc[product_idx-1]['product_name']
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print(f"\n开始使用Transformer模型训练 {product_name} (ID: {product_id}) 的销售预测模型...")
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model, metrics = train_product_model_with_transformer(product_id)
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# 保存模型到模型管理器
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try:
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model_manager.save_model(
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model=model,
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model_type='transformer',
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product_id=product_id,
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metrics=metrics,
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features=['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'],
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look_back=14,
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T=7
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)
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print("✅ 模型已保存到模型管理器")
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except Exception as e:
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print(f"❌ 保存模型到管理器时出错: {str(e)}")
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print(f"\n{product_name} Transformer模型评估指标:")
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for metric, value in metrics.items():
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print(f" {metric}: {value:.4f}")
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print(f"\n模型训练和评估完成!")
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else:
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print("\n❌ 无效的选择！")
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elif choice == '6':
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# 查看已有预测结果
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print("\n正在搜索预测结果文件...")
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prediction_files = [f for f in os.listdir() if f.endswith('_prediction_results.csv')]
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if not prediction_files:
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print("❌ 未找到任何预测结果文件。请先训练模型生成预测结果。")
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continue
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print(f"\n找到 {len(prediction_files)} 个预测结果文件:")
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for i, file in enumerate(prediction_files, 1):
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print(f" {i}. {file}")
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file_idx = int(input("\n请选择要查看的文件 (1-{}): ".format(len(prediction_files))))
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if 1 <= file_idx <= len(prediction_files):
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file_path = prediction_files[file_idx-1]
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try:
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results_df = pd.read_csv(file_path)
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print(f"\n{file_path} 内容:")
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print(results_df)
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# 可视化结果
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plt.figure(figsize=(12, 6))
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plt.plot(results_df['date'], results_df['actual_sales'], 'b-', label='实际销量')
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plt.plot(results_df['date'], results_df['predicted_sales'], 'r--', label='预测销量')
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plt.title('销量预测结果')
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plt.xlabel('日期')
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plt.ylabel('销量')
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plt.legend()
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plt.grid(True)
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plt.xticks(rotation=45)
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plt.tight_layout()
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plt.show()
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except Exception as e:
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print(f"❌ 读取文件时出错: {str(e)}")
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else:
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print("\n❌ 无效的选择！")
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elif choice == '7':
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# 使用已训练的模型进行预测
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print("\n请选择要使用的模型类型:")
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print(" 1. mLSTM模型")
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print(" 2. KAN模型")
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print(" 3. Transformer模型")
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model_choice = input("\n请输入选项 (1-3): ")
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if model_choice not in ['1', '2', '3']:
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print("\n❌ 无效的选择！")
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continue
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# 选择产品
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print("\n请选择要预测的药品:")
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for i, (_, row) in enumerate(products.iterrows(), 1):
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print(f" {i}. {row['product_name']} (ID: {row['product_id']})")
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product_idx = int(input("\n请输入药品编号 (1-{}): ".format(len(products))))
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if 1 <= product_idx <= len(products):
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product_id = products.iloc[product_idx-1]['product_id']
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product_name = products.iloc[product_idx-1]['product_name']
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# 使用模型管理器进行预测
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try:
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if model_choice == '1':
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# mLSTM模型
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model_type = 'mlstm'
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elif model_choice == '2':
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# KAN模型
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model_type = 'kan'
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else:
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# Transformer模型
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model_type = 'transformer'
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print(f"\n使用{model_type.upper()}模型预测 {product_name} (ID: {product_id}) 的未来销量...")
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# 使用模型管理器进行预测
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product_df = df[df['product_id'] == product_id].sort_values('date')
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predictions = model_manager.predict_with_model(
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product_id=product_id,
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model_type=model_type,
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product_df=product_df
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)
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if predictions is not None:
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print("\n✅ 预测完成！")
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except Exception as e:
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print(f"\n❌ 预测时出错: {str(e)}")
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print("请确保已经训练并保存了对应的模型。")
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else:
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print("\n❌ 无效的选择！")
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elif choice == '8':
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# 比较不同模型的预测结果
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print("\n请选择要比较的药品:")
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for i, (_, row) in enumerate(products.iterrows(), 1):
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print(f" {i}. {row['product_name']} (ID: {row['product_id']})")
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product_idx = int(input("\n请输入药品编号 (1-{}): ".format(len(products))))
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if 1 <= product_idx <= len(products):
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product_id = products.iloc[product_idx-1]['product_id']
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product_name = products.iloc[product_idx-1]['product_name']
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print(f"\n比较 {product_name} (ID: {product_id}) 的不同模型预测结果...")
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# 使用模型管理器进行比较
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try:
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product_df = df[df['product_id'] == product_id].sort_values('date')
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comparison = model_manager.compare_models(
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product_id=product_id,
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product_df=product_df
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)
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if comparison is not None:
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print("\n✅ 比较完成！")
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except Exception as e:
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print(f"\n❌ 比较时出错: {str(e)}")
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print("请确保已经训练并保存了对应的模型。")
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else:
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print("\n❌ 无效的选择！")
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elif choice == '9':
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# 模型管理
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print("\n启动模型管理工具...")
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import model_management
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model_management.interactive_mode()
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else:
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print("\n❌ 无效的选项！请重新输入。")
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if __name__ == "__main__":
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print_header()
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main() |