c0fe213b70
1. 修复前端图表日期排序问题: - 改进 PredictionView.vue 和 HistoryView.vue 中的图表渲染逻辑 - 确保历史数据和预测数据按照正确的日期顺序显示 2. 修复后端API处理: - 解决 optimized_kan 模型类型的路径映射问题 - 添加 JSON 序列化器处理 Pandas Timestamp 对象 - 改进预测数据与历史数据的衔接处理 3. 优化图表样式和用户体验
279 lines
11 KiB
Python
279 lines
11 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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from pharmacy_predictor import PharmacyPredictor
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from generate_pharmacy_data import generate_pharmacy_data
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def clear_screen():
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"""清空控制台屏幕"""
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os.system('cls' if os.name == 'nt' else 'clear')
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def print_header():
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"""打印应用程序标题"""
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print("=" * 80)
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print(" " * 25 + "药店销售预测系统" + " " * 25)
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print("=" * 80)
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def main_menu():
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"""显示主菜单并获取用户选择"""
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print("\n主菜单:")
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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 (Transformer)")
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print("3. 训练单个药品的销售预测模型 (mLSTM)")
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print("4. 训练单个药品的销售预测模型 (KAN)")
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print("5. 训练单个药品的销售预测模型 (优化版KAN)") # 新增选项
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print("6. 比较原始KAN和优化版KAN模型性能") # 新增选项
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print("7. 查看已有预测结果")
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print("8. 使用已训练的模型进行预测")
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print("9. 比较不同模型的预测结果")
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print("10. 模型管理")
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print("0. 退出")
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choice = input("\n请输入您的选择 (0-10): ")
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return choice
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def train_all_products_menu(predictor):
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"""训练所有药品的菜单"""
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clear_screen()
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print_header()
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print("\n选择模型类型:")
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print("1. Transformer")
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print("2. mLSTM")
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print("3. KAN")
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print("4. 优化版KAN") # 新增选项
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print("0. 返回主菜单")
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choice = input("\n请输入您的选择 (0-4): ")
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if choice == '0':
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return
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model_types = {
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'1': 'transformer',
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'2': 'mlstm',
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'3': 'kan',
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'4': 'kan' # 使用相同的模型类型，但在训练时指定use_optimized=True
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}
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if choice in model_types:
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model_type = model_types[choice]
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use_optimized = (choice == '4') # 如果选择4，则使用优化版KAN
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# 获取所有产品ID
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product_ids = predictor.data['product_id'].unique()
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for product_id in product_ids:
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print(f"\n正在训练产品 {product_id} 的模型...")
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if model_type == 'kan' and use_optimized:
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predictor.train_optimized_kan_model(product_id)
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else:
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predictor.train_model(product_id, model_type=model_type)
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print("\n所有产品的模型训练完成!")
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input("\n按Enter键继续...")
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else:
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print("\n无效的选择!")
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input("\n按Enter键继续...")
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def train_single_product_menu(predictor, model_type, use_optimized=False):
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"""训练单个药品的菜单"""
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clear_screen()
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print_header()
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model_name = model_type
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if model_type == 'kan' and use_optimized:
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model_name = "优化版KAN"
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print(f"\n训练单个药品的{model_name}模型")
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# 获取所有产品ID
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if predictor.data is not None:
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product_ids = predictor.data['product_id'].unique()
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print("\n可用的产品ID:")
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for i, product_id in enumerate(product_ids):
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print(f"{i+1}. {product_id}")
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choice = input("\n请选择产品ID的编号 (或输入0返回): ")
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if choice == '0':
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return
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try:
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idx = int(choice) - 1
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if 0 <= idx < len(product_ids):
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product_id = product_ids[idx]
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# 获取训练参数
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epochs = int(input("\n请输入训练轮数 (默认100): ") or "100")
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batch_size = int(input("请输入批次大小 (默认32): ") or "32")
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learning_rate = float(input("请输入学习率 (默认0.001): ") or "0.001")
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sequence_length = int(input("请输入输入序列长度 (默认30): ") or "30")
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forecast_horizon = int(input("请输入预测天数 (默认7): ") or "7")
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hidden_size = int(input("请输入隐藏层大小 (默认64): ") or "64")
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num_layers = int(input("请输入层数 (默认2): ") or "2")
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dropout = float(input("请输入Dropout比例 (默认0.1): ") or "0.1")
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print(f"\n开始训练产品 {product_id} 的{model_name}模型...")
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if model_type == 'kan' and use_optimized:
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# 使用优化版KAN模型
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metrics = predictor.train_optimized_kan_model(
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product_id=product_id,
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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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)
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else:
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# 使用普通模型
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metrics = predictor.train_model(
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product_id=product_id,
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model_type=model_type,
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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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)
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print("\n模型训练完成!")
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print(f"MSE: {metrics['mse']:.4f}")
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print(f"RMSE: {metrics['rmse']:.4f}")
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print(f"MAE: {metrics['mae']:.4f}")
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print(f"R²: {metrics['r2']:.4f}")
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print(f"MAPE: {metrics['mape']:.2f}%")
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print(f"训练时间: {metrics['training_time']:.2f}秒")
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else:
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print("\n无效的选择!")
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except (ValueError, IndexError):
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print("\n无效的输入!")
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else:
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print("\n没有可用的数据。请先生成或加载数据。")
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input("\n按Enter键继续...")
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def compare_kan_models_menu(predictor):
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"""比较原始KAN和优化版KAN模型性能的菜单"""
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clear_screen()
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print_header()
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print("\n比较原始KAN和优化版KAN模型性能")
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# 获取所有产品ID
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if predictor.data is not None:
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product_ids = predictor.data['product_id'].unique()
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print("\n可用的产品ID:")
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for i, product_id in enumerate(product_ids):
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print(f"{i+1}. {product_id}")
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choice = input("\n请选择产品ID的编号 (或输入0返回): ")
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if choice == '0':
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return
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try:
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idx = int(choice) - 1
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if 0 <= idx < len(product_ids):
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product_id = product_ids[idx]
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# 获取训练参数
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epochs = int(input("\n请输入训练轮数 (默认100): ") or "100")
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batch_size = int(input("请输入批次大小 (默认32): ") or "32")
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learning_rate = float(input("请输入学习率 (默认0.001): ") or "0.001")
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sequence_length = int(input("请输入输入序列长度 (默认30): ") or "30")
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forecast_horizon = int(input("请输入预测天数 (默认7): ") or "7")
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hidden_size = int(input("请输入隐藏层大小 (默认64): ") or "64")
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num_layers = int(input("请输入层数 (默认2): ") or "2")
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dropout = float(input("请输入Dropout比例 (默认0.1): ") or "0.1")
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print(f"\n开始比较产品 {product_id} 的原始KAN和优化版KAN模型...")
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# 使用比较方法
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comparison = predictor.compare_kan_models(
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product_id=product_id,
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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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)
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print("\n比较完成!")
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else:
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print("\n无效的选择!")
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except (ValueError, IndexError):
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print("\n无效的输入!")
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else:
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print("\n没有可用的数据。请先生成或加载数据。")
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input("\n按Enter键继续...")
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def main():
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"""主函数"""
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# 检查数据文件是否存在，如果不存在则生成
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data_path = 'pharmacy_sales.xlsx'
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if not os.path.exists(data_path):
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print("生成药店销售数据...")
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generate_pharmacy_data(num_products=10, days=365, output_file=data_path)
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print(f"数据已生成并保存到 {data_path}")
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# 创建预测器实例
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predictor = PharmacyPredictor(data_path=data_path)
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while True:
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clear_screen()
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print_header()
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choice = main_menu()
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if choice == '0':
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print("\n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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train_all_products_menu(predictor)
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elif choice == '2':
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train_single_product_menu(predictor, model_type='transformer')
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elif choice == '3':
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train_single_product_menu(predictor, model_type='mlstm')
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elif choice == '4':
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train_single_product_menu(predictor, model_type='kan')
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elif choice == '5':
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# 使用优化版KAN模型
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train_single_product_menu(predictor, model_type='kan', use_optimized=True)
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elif choice == '6':
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# 比较原始KAN和优化版KAN模型性能
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compare_kan_models_menu(predictor)
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elif choice == '7':
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# 查看已有预测结果
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pass
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elif choice == '8':
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# 使用已训练的模型进行预测
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pass
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elif choice == '9':
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# 比较不同模型的预测结果
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pass
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elif choice == '10':
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# 模型管理
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pass
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else:
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print("\n无效的选择，请重试!")
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input("\n按Enter键继续...")
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if __name__ == "__main__":
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main() |