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run_longExp.py
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run_longExp.py
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import argparse
import os
import time
from multiprocessing import freeze_support
import torch
from exp.exp_main import Exp_Main
import random
import numpy as np
fix_seed = 2021
random.seed(fix_seed)
torch.manual_seed(fix_seed)
np.random.seed(fix_seed)
parser = argparse.ArgumentParser(description='MSGNet for Time Series Forecasting')
# basic config
parser.add_argument('--task_name', type=str, required=False, default='long_term_forecast',
help='task name, options:[long_term_forecast, mask, short_term_forecast, imputation, classification, anomaly_detection]')
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='Autoformer',
help='model name, options: [Autoformer, Informer, Transformer]')
# data loader
parser.add_argument('--data', type=str, required=True, default='ETTm1', 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='ETTh1.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=96, help='prediction sequence length')
parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
parser.add_argument('--top_k', type=int, default=5, help='for TimesBlock/ScaleGraphBlock')
parser.add_argument('--num_kernels', type=int, default=6, help='for Inception')
parser.add_argument('--num_nodes', type=int, default=7, help='to create Graph')
parser.add_argument('--subgraph_size', type=int, default=3, help='neighbors number')
parser.add_argument('--tanhalpha', type=float, default=3, help='')
#GCN
parser.add_argument('--node_dim', type=int, default=10, help='each node embbed to dim dimentions')
parser.add_argument('--gcn_depth', type=int, default=2, help='')
parser.add_argument('--gcn_dropout', type=float, default=0.3, help='')
parser.add_argument('--propalpha', type=float, default=0.3, help='')
parser.add_argument('--conv_channel', type=int, default=32, help='')
parser.add_argument('--skip_channel', type=int, default=32, help='')
# DLinear
parser.add_argument('--individual', action='store_true', default=False, help='DLinear: a linear layer for each variate(channel) individually')
# 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('--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('--d_model', type=int, default=512, help='dimension of model')
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('--d_ff', type=int, default=2048, help='dimension of fcn')
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)
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('--activation', type=str, default='gelu', help='activation')
parser.add_argument('--output_attention', action='store_true', help='whether to output attention in encoder')
parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
# optimization
parser.add_argument('--num_workers', type=int, default=8, 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('--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('--test_flop', action='store_true', default=False, help='See utils/tools for usage')
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.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]
print('Args in experiment:')
print(args)
Exp = Exp_Main
if args.is_training:
start = time.time()
for ii in range(args.itr):
# setting record of experiments
setting = '{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(
args.model_id,
args.model,
args.data,
args.features,
args.seq_len,
args.label_len,
args.pred_len,
args.d_model,
args.n_heads,
args.e_layers,
args.d_layers,
args.d_ff,
args.factor,
args.embed,
args.distil,
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()
end = time.time()
used_time = end -start
print("time:",used_time)
f = open("result.txt", 'a')
f.write('time:{}'.format(used_time))
f.write('\n')
f.write('\n')
f.close()
else:
ii = 0
setting = '{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(args.model_id,
args.model,
args.data,
args.features,
args.seq_len,
args.label_len,
args.pred_len,
args.d_model,
args.n_heads,
args.e_layers,
args.d_layers,
args.d_ff,
args.factor,
args.embed,
args.distil,
args.des, ii)
exp = Exp(args) # set experiments
print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
exp.test(setting, test=1)
torch.cuda.empty_cache()