351 lines
13 KiB
Python
351 lines
13 KiB
Python
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import os
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import pandas as pd
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import torch
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import argparse
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from models import ModelManager
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def main():
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"""模型管理工具主函数"""
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parser = argparse.ArgumentParser(description='药店销售预测系统 - 模型管理工具')
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parser.add_argument('--action', type=str, required=True,
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choices=['list', 'details', 'predict', 'compare', 'delete', 'export', 'import'],
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help='执行的操作类型')
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parser.add_argument('--product_id', type=str, help='产品ID，例如P001')
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parser.add_argument('--model_type', type=str, help='模型类型 (mlstm/transformer/kan)')
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parser.add_argument('--version', type=str, help='模型版本')
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parser.add_argument('--file_path', type=str, help='导入/导出的文件路径')
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parser.add_argument('--export_dir', type=str, default='exported_models', help='导出目录')
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parser.add_argument('--compare_models', type=str, help='要比较的模型类型，用逗号分隔')
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args = parser.parse_args()
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# 初始化模型管理器
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model_manager = ModelManager()
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# 根据操作类型执行相应的功能
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if args.action == 'list':
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# 列出模型
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models = model_manager.list_models(
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product_id=args.product_id,
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model_type=args.model_type
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)
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if not models:
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print("没有找到匹配的模型。")
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return
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print(f"\n找到 {len(models)} 个模型:")
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for i, model in enumerate(models, 1):
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print(f"\n{i}. {model['product_id']} - {model['model_type'].upper()} (版本: {model['version']})")
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print(f" 创建时间: {model['created_at']}")
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print(f" 文件大小: {model['file_size']}")
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# 如果有评估指标，显示它们
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if 'metrics' in model:
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print(" 评估指标:")
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for metric, value in model['metrics'].items():
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print(f" {metric}: {value}")
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elif args.action == 'details':
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# 显示模型详情
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if not args.product_id or not args.model_type:
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print("错误: 需要指定product_id和model_type")
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return
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details = model_manager.get_model_details(
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product_id=args.product_id,
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model_type=args.model_type,
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version=args.version
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)
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if not details:
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print("未找到模型详情。")
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return
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print(f"\n{args.product_id} - {args.model_type.upper()} 模型详情:")
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for key, value in details.items():
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if key == 'metrics':
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print(f"\n评估指标:")
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for metric, metric_value in value.items():
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print(f" {metric}: {metric_value}")
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else:
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print(f"{key}: {value}")
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elif args.action == 'predict':
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# 使用模型进行预测
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if not args.product_id:
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print("错误: 需要指定product_id")
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return
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# 使用指定的模型类型，如果未指定则使用mlstm
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model_type = args.model_type if args.model_type else 'mlstm'
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# 执行预测
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predictions = model_manager.predict_with_model(
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product_id=args.product_id,
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model_type=model_type,
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version=args.version
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)
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if predictions is None:
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print("预测失败。")
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elif args.action == 'compare':
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# 比较不同模型的预测结果
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if not args.product_id:
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print("错误: 需要指定product_id")
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return
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# 确定要比较的模型
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if args.compare_models:
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model_types = args.compare_models.split(',')
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else:
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model_types = ['mlstm', 'transformer', 'kan']
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# 执行比较
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comparison = model_manager.compare_models(
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product_id=args.product_id,
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model_types=model_types
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)
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if comparison is None:
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print("比较失败。")
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elif args.action == 'delete':
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# 删除模型
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if not args.product_id or not args.model_type:
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print("错误: 需要指定product_id和model_type")
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return
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success = model_manager.delete_model(
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product_id=args.product_id,
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model_type=args.model_type,
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version=args.version
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)
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if success:
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print("模型删除成功。")
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else:
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print("模型删除失败。")
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elif args.action == 'export':
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# 导出模型
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if not args.product_id or not args.model_type:
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print("错误: 需要指定product_id和model_type")
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return
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export_path = model_manager.export_model(
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product_id=args.product_id,
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model_type=args.model_type,
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version=args.version,
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export_dir=args.export_dir
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)
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if export_path:
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print(f"模型已成功导出到: {export_path}")
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else:
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print("模型导出失败。")
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elif args.action == 'import':
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# 导入模型
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if not args.file_path:
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print("错误: 需要指定file_path")
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return
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import_path = model_manager.import_model(
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import_file=args.file_path,
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overwrite=True
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)
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if import_path:
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print(f"模型已成功导入到: {import_path}")
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else:
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print("模型导入失败。")
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def interactive_mode():
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"""交互模式，通过菜单与用户交互"""
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model_manager = ModelManager()
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while True:
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print("\n" + "="*50)
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print("📊 药店销售预测系统 - 模型管理工具 📊")
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print("="*50)
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print("1. 查看所有模型")
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print("2. 查看特定产品的模型")
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print("3. 查看特定模型的详细信息")
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print("4. 使用模型进行预测")
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print("5. 比较不同模型的预测结果")
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print("6. 删除模型")
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print("7. 导出模型")
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print("8. 导入模型")
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print("0. 退出")
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print("="*50)
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choice = input("请输入选项 (0-8): ")
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if choice == '0':
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print("感谢使用模型管理工具！再见！")
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break
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elif choice == '1':
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# 查看所有模型
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models = model_manager.list_models()
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if not models:
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print("没有找到任何模型。")
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continue
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print(f"\n找到 {len(models)} 个模型:")
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for i, model in enumerate(models, 1):
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print(f"\n{i}. {model['product_id']} - {model['model_type'].upper()} (版本: {model['version']})")
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print(f" 创建时间: {model['created_at']}")
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print(f" 文件大小: {model['file_size']}")
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elif choice == '2':
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# 查看特定产品的模型
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product_id = input("请输入产品ID (例如P001): ")
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models = model_manager.list_models(product_id=product_id)
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if not models:
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print(f"没有找到产品 {product_id} 的模型。")
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continue
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print(f"\n找到 {len(models)} 个 {product_id} 的模型:")
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for i, model in enumerate(models, 1):
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print(f"\n{i}. {model['model_type'].upper()} (版本: {model['version']})")
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print(f" 创建时间: {model['created_at']}")
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print(f" 文件大小: {model['file_size']}")
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elif choice == '3':
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# 查看模型详情
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product_id = input("请输入产品ID (例如P001): ")
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model_type = input("请输入模型类型 (mlstm/transformer/kan): ")
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version = input("请输入版本 (如不指定则使用最新版本): ") or None
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details = model_manager.get_model_details(
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product_id=product_id,
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model_type=model_type,
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version=version
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)
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if not details:
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print("未找到模型详情。")
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continue
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print(f"\n{product_id} - {model_type.upper()} 模型详情:")
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for key, value in details.items():
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if key == 'metrics':
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print(f"\n评估指标:")
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for metric, metric_value in value.items():
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print(f" {metric}: {metric_value}")
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else:
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print(f"{key}: {value}")
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elif choice == '4':
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# 使用模型预测
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product_id = input("请输入产品ID (例如P001): ")
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model_type = input("请输入模型类型 (mlstm/transformer/kan): ") or 'mlstm'
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version = input("请输入版本 (如不指定则使用最新版本): ") or None
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# 执行预测
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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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version=version
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)
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if predictions is None:
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print("预测失败。")
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elif choice == '5':
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# 比较模型
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product_id = input("请输入产品ID (例如P001): ")
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compare_models = input("请输入要比较的模型类型，用逗号分隔 (如不指定则比较所有模型): ") or None
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# 确定要比较的模型
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if compare_models:
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model_types = compare_models.split(',')
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else:
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model_types = ['mlstm', 'transformer', 'kan']
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# 执行比较
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comparison = model_manager.compare_models(
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product_id=product_id,
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model_types=model_types
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)
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if comparison is None:
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print("比较失败。")
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elif choice == '6':
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# 删除模型
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product_id = input("请输入产品ID (例如P001): ")
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model_type = input("请输入模型类型 (mlstm/transformer/kan): ")
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version = input("请输入版本 (如不指定则删除所有版本): ") or None
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confirm = input(f"确定要删除 {product_id} 的 {model_type} 模型吗？(y/n): ")
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if confirm.lower() != 'y':
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print("已取消删除操作。")
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continue
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success = model_manager.delete_model(
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product_id=product_id,
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model_type=model_type,
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version=version
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)
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if success:
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print("模型删除成功。")
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else:
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print("模型删除失败。")
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elif choice == '7':
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# 导出模型
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product_id = input("请输入产品ID (例如P001): ")
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model_type = input("请输入模型类型 (mlstm/transformer/kan): ")
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version = input("请输入版本 (如不指定则使用最新版本): ") or None
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export_dir = input("请输入导出目录 (默认为exported_models): ") or 'exported_models'
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export_path = model_manager.export_model(
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product_id=product_id,
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model_type=model_type,
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version=version,
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export_dir=export_dir
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)
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if export_path:
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print(f"模型已成功导出到: {export_path}")
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else:
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print("模型导出失败。")
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elif choice == '8':
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# 导入模型
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file_path = input("请输入要导入的模型文件路径: ")
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if not os.path.exists(file_path):
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print(f"错误: 文件 {file_path} 不存在")
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continue
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import_path = model_manager.import_model(
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import_file=file_path,
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overwrite=True
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)
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if import_path:
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print(f"模型已成功导入到: {import_path}")
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else:
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print("模型导入失败。")
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else:
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print("无效的选项，请重新输入！")
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input("\n按回车键继续...")
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
# 检查命令行参数，如果没有参数则启动交互模式
|
|||
|
|
import sys
|
|||
|
|
if len(sys.argv) == 1:
|
|||
|
|
interactive_mode()
|
|||
|
|
else:
|
|||
|
|
main()
|