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run.py
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import argparse
import os
import torch
from exp.exp_main import Exp_Main
import random
import numpy as np
parser = argparse.ArgumentParser(description='Koopa for Time Series Forecasting')
# basic config
parser.add_argument('--is_training', type=int, required=True, default=1, help='status')
parser.add_argument('--model_id', type=str, required=True, default='test', help='model id')
parser.add_argument('--model', type=str, required=True, default='Koopa',
help='model name, options: [Koopa]')
# data loader
parser.add_argument('--data', type=str, required=True, default='ETTh2', help='dataset type')
parser.add_argument('--root_path', type=str, default='./data/ETT/', help='root path of the data file')
parser.add_argument('--data_path', type=str, default='ETTh2.csv', help='data file')
parser.add_argument('--features', type=str, default='M',
help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
parser.add_argument('--freq', type=str, default='h',
help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
# forecasting task
parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
parser.add_argument('--label_len', type=int, default=48, help='start token length')
parser.add_argument('--pred_len', type=int, default=48, help='prediction sequence length')
# model define
parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
parser.add_argument('--c_out', type=int, default=7, help='output size')
parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
parser.add_argument('--embed', type=str, default='timeF',
help='time features encoding, options:[timeF, fixed, learned]')
parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
# optimization
parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
parser.add_argument('--itr', type=int, default=1, help='experiments times')
parser.add_argument('--train_epochs', type=int, default=10, help='train epochs')
parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
parser.add_argument('--learning_rate', type=float, default=0.001, help='optimizer learning rate')
parser.add_argument('--des', type=str, default='test', help='exp description')
parser.add_argument('--loss', type=str, default='mse', help='loss function')
parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate')
parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
# GPU
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')
parser.add_argument('--seed', type=int, default=2023, help='random seed')
# Koopa
parser.add_argument('--dynamic_dim', type=int, default=128, help='latent dimension of koopman embedding')
parser.add_argument('--hidden_dim', type=int, default=64, help='hidden dimension of en/decoder')
parser.add_argument('--hidden_layers', type=int, default=2, help='number of hidden layers of en/decoder')
parser.add_argument('--seg_len', type=int, default=48, help='segment length of time series')
parser.add_argument('--num_blocks', type=int, default=3, help='number of Koopa blocks')
parser.add_argument('--alpha', type=float, default=0.2, help='spectrum filter ratio')
parser.add_argument('--multistep', action='store_true', help='whether to use approximation for multistep K', default=False)
args = parser.parse_args()
args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
fix_seed = args.seed
random.seed(fix_seed)
torch.manual_seed(fix_seed)
np.random.seed(fix_seed)
if args.use_gpu:
if 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]
else:
torch.cuda.set_device(args.gpu)
print('Args in experiment:')
print(args)
Exp = Exp_Main
if args.is_training:
for ii in range(args.itr):
# setting record of experiments
setting = '{}_{}_{}_ft{}_sl{}_pl{}_segl{}_dyna{}_h{}_l{}_nb{}_a{}_{}_{}'.format(
args.model_id,
args.model,
args.data,
args.features,
args.seq_len,
args.pred_len,
args.seg_len,
args.dynamic_dim,
args.hidden_dim,
args.hidden_layers,
args.num_blocks,
args.alpha,
args.des, ii)
exp = Exp(args) # set experiments
print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
exp.train(setting)
print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
exp.test(setting)
if args.do_predict:
print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
exp.predict(setting, True)
torch.cuda.empty_cache()
else:
ii = 0
setting = '{}_{}_{}_ft{}_sl{}_pl{}_segl{}_dyna{}_h{}_l{}_nb{}_a{}_{}_{}'.format(
args.model_id,
args.model,
args.data,
args.features,
args.seq_len,
args.pred_len,
args.seg_len,
args.dynamic_dim,
args.hidden_dim,
args.hidden_layers,
args.num_blocks,
args.alpha,
args.des, ii)
exp = Exp(args) # set experiments
print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
exp.test(setting, test=1)
torch.cuda.empty_cache()