219 lines
7.9 KiB
Python
219 lines
7.9 KiB
Python
import os
|
|
import sys
|
|
|
|
# 获取当前脚本所在目录的绝对路径
|
|
current_dir = os.path.dirname(os.path.abspath(__file__))
|
|
|
|
# 将当前目录添加到系统路径
|
|
sys.path.append(current_dir)
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from datetime import datetime
|
|
|
|
# 直接从各个模块导入所需的函数和类
|
|
from core.predictor import PharmacyPredictor
|
|
from trainers import train_product_model
|
|
from trainers.mlstm_trainer import train_product_model_with_mlstm
|
|
from trainers.kan_trainer import train_product_model_with_kan
|
|
from trainers.tcn_trainer import train_product_model_with_tcn
|
|
from trainers.transformer_trainer import train_product_model_with_transformer
|
|
from predictors.model_predictor import load_model_and_predict
|
|
|
|
from generate_pharmacy_data import generate_pharmacy_data
|
|
|
|
def clear_screen():
|
|
"""清空控制台屏幕"""
|
|
os.system('cls' if os.name == 'nt' else 'clear')
|
|
|
|
def print_header():
|
|
"""打印应用程序标题"""
|
|
print("=" * 80)
|
|
print(" " * 25 + "药店销售预测系统" + " " * 25)
|
|
print("=" * 80)
|
|
|
|
def main_menu():
|
|
"""显示主菜单并获取用户选择"""
|
|
print("\n主菜单:")
|
|
print("1. 训练所有药品的销售预测模型")
|
|
print("2. 训练单个药品的销售预测模型 (Transformer)")
|
|
print("3. 训练单个药品的销售预测模型 (mLSTM)")
|
|
print("4. 训练单个药品的销售预测模型 (KAN)")
|
|
print("5. 训练单个药品的销售预测模型 (优化版KAN)")
|
|
print("6. 比较原始KAN和优化版KAN模型性能")
|
|
print("7. 训练单个药品的销售预测模型 (TCN)")
|
|
print("8. 查看已有预测结果")
|
|
print("9. 使用已训练的模型进行预测")
|
|
print("10. 比较不同模型的预测结果")
|
|
print("11. 模型管理")
|
|
print("0. 退出")
|
|
|
|
choice = input("\n请输入您的选择 (0-11): ")
|
|
return choice
|
|
|
|
def train_all_products_menu(predictor):
|
|
"""训练所有药品的菜单"""
|
|
clear_screen()
|
|
print_header()
|
|
print("\n请选择要使用的模型类型:")
|
|
print("1. Transformer")
|
|
print("2. mLSTM")
|
|
print("3. KAN")
|
|
print("4. TCN")
|
|
print("0. 返回主菜单")
|
|
|
|
choice = input("\n请输入您的选择 (0-4): ")
|
|
|
|
if choice == '0':
|
|
return
|
|
|
|
model_types = {
|
|
'1': 'transformer',
|
|
'2': 'mlstm',
|
|
'3': 'kan',
|
|
'4': 'tcn'
|
|
}
|
|
|
|
if choice in model_types:
|
|
model_type = model_types[choice]
|
|
|
|
# 获取所有产品ID
|
|
product_ids = predictor.data['product_id'].unique()
|
|
|
|
for product_id in product_ids:
|
|
print(f"\n正在训练产品 {product_id} 的模型...")
|
|
predictor.train_model(product_id, model_type=model_type)
|
|
|
|
print("\n所有产品的模型训练完成!")
|
|
input("\n按Enter键继续...")
|
|
else:
|
|
print("\n无效的选择!")
|
|
input("\n按Enter键继续...")
|
|
|
|
def train_single_product_menu(predictor, model_type, use_optimized=False):
|
|
"""训练单个药品的菜单"""
|
|
clear_screen()
|
|
print_header()
|
|
|
|
model_name = model_type
|
|
if model_type == 'kan' and use_optimized:
|
|
model_name = "优化版KAN"
|
|
|
|
print(f"\n训练单个药品的{model_name}模型")
|
|
|
|
# 获取所有产品ID
|
|
if predictor.data is not None:
|
|
product_ids = predictor.data['product_id'].unique()
|
|
print("\n可用的产品ID:")
|
|
for i, product_id in enumerate(product_ids):
|
|
print(f"{i+1}. {product_id}")
|
|
|
|
choice = input("\n请选择产品ID的编号 (或输入0返回): ")
|
|
|
|
if choice == '0':
|
|
return
|
|
|
|
try:
|
|
idx = int(choice) - 1
|
|
if 0 <= idx < len(product_ids):
|
|
product_id = product_ids[idx]
|
|
|
|
# 获取训练参数
|
|
epochs = int(input("\n请输入训练轮数 (默认100): ") or "100")
|
|
batch_size = int(input("请输入批次大小 (默认32): ") or "32")
|
|
learning_rate = float(input("请输入学习率 (默认0.001): ") or "0.001")
|
|
sequence_length = int(input("请输入输入序列长度 (默认30): ") or "30")
|
|
forecast_horizon = int(input("请输入预测天数 (默认7): ") or "7")
|
|
hidden_size = int(input("请输入隐藏层大小 (默认64): ") or "64")
|
|
num_layers = int(input("请输入层数 (默认2): ") or "2")
|
|
dropout = float(input("请输入Dropout比例 (默认0.1): ") or "0.1")
|
|
|
|
print(f"\n开始训练产品 {product_id} 的{model_name}模型...")
|
|
|
|
if model_type == 'kan' and use_optimized:
|
|
# 使用优化版KAN模型
|
|
metrics = predictor.train_model(
|
|
product_id=product_id,
|
|
model_type='optimized_kan',
|
|
epochs=epochs,
|
|
batch_size=batch_size,
|
|
learning_rate=learning_rate,
|
|
sequence_length=sequence_length,
|
|
forecast_horizon=forecast_horizon,
|
|
hidden_size=hidden_size,
|
|
num_layers=num_layers,
|
|
dropout=dropout
|
|
)
|
|
else:
|
|
# 使用普通模型
|
|
metrics = predictor.train_model(
|
|
product_id=product_id,
|
|
model_type=model_type,
|
|
epochs=epochs,
|
|
batch_size=batch_size,
|
|
learning_rate=learning_rate,
|
|
sequence_length=sequence_length,
|
|
forecast_horizon=forecast_horizon,
|
|
hidden_size=hidden_size,
|
|
num_layers=num_layers,
|
|
dropout=dropout
|
|
)
|
|
|
|
print("\n模型训练完成!")
|
|
print(f"MSE: {metrics['mse']:.4f}")
|
|
print(f"RMSE: {metrics['rmse']:.4f}")
|
|
print(f"MAE: {metrics['mae']:.4f}")
|
|
print(f"R²: {metrics['r2']:.4f}")
|
|
print(f"MAPE: {metrics['mape']:.2f}%")
|
|
print(f"训练时间: {metrics['training_time']:.2f}秒")
|
|
else:
|
|
print("\n无效的选择!")
|
|
except (ValueError, IndexError):
|
|
print("\n无效的输入!")
|
|
else:
|
|
print("\n没有可用的数据。请先生成或加载数据。")
|
|
|
|
input("\n按Enter键继续...")
|
|
|
|
def main():
|
|
"""主函数"""
|
|
# 创建预测器实例
|
|
predictor = PharmacyPredictor()
|
|
|
|
# 加载数据
|
|
try:
|
|
predictor.load_data('pharmacy_sales.xlsx')
|
|
print(f"已加载数据，共 {len(predictor.data)} 条记录")
|
|
except FileNotFoundError:
|
|
print("未找到数据文件，正在生成示例数据...")
|
|
generate_pharmacy_data()
|
|
predictor.load_data('pharmacy_sales.xlsx')
|
|
print(f"已生成并加载示例数据，共 {len(predictor.data)} 条记录")
|
|
|
|
while True:
|
|
clear_screen()
|
|
print_header()
|
|
choice = main_menu()
|
|
|
|
if choice == '1':
|
|
train_all_products_menu(predictor)
|
|
elif choice == '2':
|
|
train_single_product_menu(predictor, 'transformer')
|
|
elif choice == '3':
|
|
train_single_product_menu(predictor, 'mlstm')
|
|
elif choice == '4':
|
|
train_single_product_menu(predictor, 'kan')
|
|
elif choice == '5':
|
|
train_single_product_menu(predictor, 'kan', use_optimized=True)
|
|
elif choice == '7':
|
|
train_single_product_menu(predictor, 'tcn')
|
|
elif choice == '0':
|
|
print("\n感谢使用药店销售预测系统，再见!")
|
|
break
|
|
else:
|
|
print("\n无效的选择或功能尚未实现!")
|
|
input("\n按Enter键继续...")
|
|
|
|
if __name__ == "__main__":
|
|
main() |