DualFlow/models/causal-inference/args.py

import torch
import argparse

def get_args():
    parser = argparse.ArgumentParser(description='RL-Optimized GAT for time series prediction')
    
    # 数据参数
    parser.add_argument('--data_dir', type=str, default='../datasets_xishan', 
                       help='Directory for data files')
    parser.add_argument('--num_files', type=int, default=50, 
                       help='Number of data files (1 to num_files)')
    parser.add_argument('--test_ratio', type=float, default=0.2, 
                       help='Ratio of test data')
    parser.add_argument('--val_ratio', type=float, default=0.1, 
                       help='Ratio of validation data')
    
    # 模型参数
    parser.add_argument('--num_features', type=int, default=145, 
                       help='Number of feature variables')
    parser.add_argument('--num_targets', type=int, default=47, 
                       help='Number of target variables')
    parser.add_argument('--hidden_dim', type=int, default=64, 
                       help='Default hidden dimension of GAT')
    parser.add_argument('--num_heads', type=int, default=4, 
                       help='Default number of attention heads')
    parser.add_argument('--dropout', type=float, default=0.3, 
                       help='Default dropout rate')
    
    # 训练参数
    parser.add_argument('--batch_size', type=int, default=128, 
                       help='Batch size')
    parser.add_argument('--lr', type=float, default=0.001, 
                       help='Default learning rate')
    parser.add_argument('--epochs', type=int, default=100, 
                       help='Number of epochs for final training')
    parser.add_argument('--weight_decay', type=float, default=1e-4, 
                       help='Weight decay')
    parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu',
                       help='Device to use for training')
    parser.add_argument('--grad_clip', type=float, default=1.0,
                       help='Gradient clipping threshold')
    parser.add_argument('--patience', type=int, default=20,
                       help='Patience for early stopping')
    
    # 强化学习参数
    parser.add_argument('--rl_timesteps', type=int, default=5000, 
                       help='Total timesteps for RL training')
    parser.add_argument('--rl_max_steps', type=int, default=20, 
                       help='Max steps per RL episode')
    parser.add_argument('--rl_eval_episodes', type=int, default=10, 
                       help='Number of episodes for RL evaluation')
    
    # 小波去噪参数
    parser.add_argument('--wavelet', type=str, default='db4',
                       help='Wavelet type for denoising')
    parser.add_argument('--wavelet_level', type=int, default=1,
                       help='Wavelet decomposition level')
    
    args = parser.parse_args()
    return args