import argparse

import os

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='Autoformer & Transformer family for Time Series Forecasting')

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='Transformer',

help='model name, options: [Autoformer, Informer, Transformer]')

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')

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('--individual', action='store_true', default=False, help='DLinear: a linear layer for each variate(channel) individually') #not share

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') # DLinear with --individual, use this hyperparameter as the number of channels

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 ecoder')

parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')

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('--use_amp', action='store_true', help='use automatic mixed precision training', 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')

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:

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()

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()