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main.py
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main.py
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import argparse
import os
import torch
from exp_build import Exp_builder
import random
import numpy as np
from utils.utils import set_seed
def main():
set_seed(args.seed)
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.dvices = args.devices.replace(' ', '')
device_ids = args.devices.split(',')
args.device_ids = [int(id_) for id_ in device_ids]
args.gpu = args.device_ids[0]
Exp = Exp_builder
if args.is_training:
for ii in range(args.itr):
# setting record of experiments
setting = '{}_{}_{}_ft{}_sl{}_ll{}_pl{}_sc{}_{}'.format(
args.model_id,
args.model,
args.data,
args.features,
args.window_len,
args.label_len,
args.pred_len,
args.scaler,
ii)
exp = Exp(args) # set experiments
print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
exp.train(setting)
if not args.train_only:
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{}_ll{}_pl{}_sc{}_{}'.format(
args.model_id,
args.model,
args.data,
args.features,
args.window_len,
args.label_len,
args.pred_len,
args.scaler,
ii)
exp = Exp(args) # set experiments
if args.do_predict:
print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
exp.predict(setting, True)
else:
print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
exp.test(setting, test=1)
torch.cuda.empty_cache()
if __name__=='__main__':
parser = argparse.ArgumentParser(description='Time Series Forecasting')
# basic config
parser.add_argument('--is_training', type=int, required=True, default=1, help='status')
parser.add_argument('--train_only', type=bool, required=False, default=False, help='perform training on full input dataset without validation and testing')
parser.add_argument('--model_id', type=str, required=True, default='test', help='model id')
parser.add_argument('--model', type=str, required=True, default='Dlinear',
help='model name, options: [Dlinear, TimesNet]')
# data loader
parser.add_argument('--data_type', type=str, required=True, default = 'Forecasting', help='dataset type')
parser.add_argument('--data', type=str, required=True, help='dataset type')
parser.add_argument('--root_path', type=str, help='root path of the data file')
parser.add_argument('--data_path', type=str, 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')
#!
parser.add_argument('--train_ratio', type=float, default=0.7, help='train_ratio')
parser.add_argument('--test_ratio', type=float, default=0.2, help='test_ratio')
# forecasting task
parser.add_argument('--window_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=96, help='prediction sequence length')
# DLinear
parser.add_argument('--individual', action='store_true', default=False, help='DLinear: a linear layer for each variate(channel) individually')
# TimesNet
parser.add_argument('--top_k', type=int, default=5, help='for TimesBlock')
parser.add_argument('--num_kernels', type=int, default=6, help='for Inception')
parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
parser.add_argument('--c_out', type=int, default=7, help='output size')
parser.add_argument('--enc_in', type=int, default=7, help='encoder input size') # DLinear with --individual, use this hyperparameter as the number of channels
parser.add_argument('--embed', type=str, default='timeF',
help='time features encoding, options:[timeF, fixed, learned]')
# EXP Setting
parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
parser.add_argument('--activation', type=str, default='gelu', help='activation')
parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
parser.add_argument('--time_encode', type=int, default=0, help='time_encode')
# Formers
parser.add_argument('--embed_type', type=int, default=0, help='0: default 1: value embedding + temporal embedding + positional embedding 2: value embedding + temporal embedding 3: value embedding + positional embedding 4: value embedding')
parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average')
parser.add_argument('--factor', type=int, default=1, help='attn factor')
parser.add_argument('--distil', action='store_false',
help='whether to use distilling in encoder, using this argument means not using distilling',
default=True)
# optimization
parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
parser.add_argument('--itr', type=int, default=2, 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=0.0001, 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('--seed', type=int, default='72', help='Random seed')
parser.add_argument('--scaler', type=str, default=None, help='Use scaler')
parser.add_argument('--optim', type=str, default='adam', help='Optimizer')
# 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')
# RevIN
parser.add_argument('--RevIN', type=bool, default = False, help='Use RevIN')
args = parser.parse_args()
print('Args in experiment:')
print(args)
main()