run.py

import argparse
import os
import torch

from code_exp.exp_stanhop import Exp_Stanhop
from utils.tools import string_split
import gc
import random
import json
import importlib

# random.seed(0)
# numpy.random.seed(0)
# torch.backends.cudnn.benchmark = False
# torch.use_deterministic_algorithms(True)

parser = argparse.ArgumentParser(description='STanHop-Net')

parser.add_argument('--data', type=str, required=True, default='ETTh1', help='data')
parser.add_argument('--root_path', type=str, default='./datasets/', help='root path of the data file')
parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')  
parser.add_argument('--data_split', type=str, default='0.7,0.1,0.2',help='train/val/test split, can be ratio or number')
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location to store model checkpoints')

parser.add_argument('--in_len', type=int, default=96, help='input MTS length')
parser.add_argument('--out_len', type=int, default=24, help='output MTS length')
parser.add_argument('--seg_len', type=int, default=6, help='Patch size')
parser.add_argument('--win_size', type=int, default=1, help='window size for Coarse graining')
parser.add_argument('--factor', type=int, default=10, help='num of GSHPooling queries')

parser.add_argument('--data_dim', type=int, default=7, help='Number of Series of the MTS data')
parser.add_argument('--d_model', type=int, default=256, help='dimension of hidden states (d_model)')
parser.add_argument('--d_ff', type=int, default=512, help='dimension of MLP in transformer')
parser.add_argument('--n_heads', type=int, default=4, help='num of heads')
parser.add_argument('--e_layers', type=int, default=3, help='num of encoder layers (N)')
parser.add_argument('--dropout', type=float, default=0.1, help='dropout')
parser.add_argument('--weight_decay', type=float, default=0.0, help='weight decay')

parser.add_argument('--baseline', action='store_true', help='whether to use mean of past series as baseline for prediction', default=False)

parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
parser.add_argument('--batch_size', type=int, default=512, help='batch size of train input data')
parser.add_argument('--train_epochs', type=int, default=50, help='train epochs')
parser.add_argument('--patience', type=int, default=20, help='early stopping patience')
parser.add_argument('--learning_rate', type=float, default=1e-4, help='optimizer initial learning rate')
parser.add_argument('--lradj', type=str, default='type1',help='adjust learning rate')
parser.add_argument('--itr', type=int, default=1, help='experiments times')

parser.add_argument('--save_pred', action='store_true', help='whether to save the predicted future MTS', default=False)

parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
parser.add_argument('--gpu', type=int, default=0, help='gpu')
parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
parser.add_argument('--devices', type=str, default='0,1,2,3',help='device ids of multile gpus')

args = parser.parse_args()

args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False

if args.use_gpu and args.use_multi_gpu:
    args.devices = args.devices.replace(' ','')
    device_ids = args.devices.split(',')
    args.device_ids = [int(id_) for id_ in device_ids]
    args.gpu = args.device_ids[0]
    print(args.gpu)

data_parser = {
    'ETTh1':{'data':'ETTh1.csv', 'data_dim':7, 'split':[12*30*24, 4*30*24, 4*30*24]},
    'ETTm1':{'data':'ETTm1.csv', 'data_dim':7, 'split':[4*12*30*24, 4*4*30*24, 4*4*30*24]},
    'WTH':{'data':'WTH.csv', 'data_dim':12, 'split':[28*30*24, 10*30*24, 10*30*24]},
    'ECL':{'data':'ECL.csv', 'data_dim':321, 'split':[15*30*24, 3*30*24, 4*30*24]},
    'ILI':{'data':'national_illness.csv', 'data_dim':7, 'split':[0.7, 0.1, 0.2]},
    'Traffic':{'data':'traffic.csv', 'data_dim':862, 'split':[0.7, 0.1, 0.2]},
    'Stocks':{'data': 'final.csv', 'data_dim':5, 'split':[0.7, 0.1, 0.2]},
    'Open':{'data': 'big_open.csv', 'data_dim':871, 'split':[0.7, 0.1, 0.2]},
    'Sim1':{'data': 'sim1.csv', 'data_dim':44, 'split':[0.7, 0.1, 0.2]}
}

if args.data in data_parser.keys():
    data_info = data_parser[args.data]
    args.data_path = data_info['data']
    args.data_dim = data_info['data_dim']
    args.data_split = data_info['split']
else:
    args.data_split = string_split(args.data_split)


def main():

    Exp = Exp_Stanhop
    with open(f"config/{args.data}_{args.out_len}.json", "r") as json_file:
        param = json.load(json_file)

    args.in_len = param['in_len']
    args.seg_len = param['seg_len']
    args.win_size = param['win_size']
    args.batch_size = param['batch_size']
    args.d_model = param['d_model']
    args.learning_rate = param['learning_rate']
    args.dropout = param['dropout']
    args.e_layers = param['e_layers']
    print('Args in experiment:')
    print(args)

    setting = 'stanhop_{}_il{}_ol{}_sl{}_win{}_fa{}_dm{}_nh{}_el{}_itr{}'.format(args.data, 
                args.in_len, args.out_len, args.seg_len, args.win_size, args.factor,
                args.d_model, args.n_heads, args.e_layers, 1)
    
    exp = Exp(args) # set experiments
    model, val_loss = exp.train(setting)
    test_mse, test_mae = exp.test(setting, args.save_pred)

    torch.cuda.empty_cache()
    del exp
    del model
    gc.collect()


main()