run.py

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import argparse
import torch
import torch.nn as nn
from exp.exp_main import Exp_Main
import random
from utils.tools import StandardScaler
import torch.nn.functional as F
from torch import Tensor

if __name__ == '__main__':
    fix_seed = 2021
    random.seed(fix_seed)
    torch.manual_seed(fix_seed)
    np.random.seed(fix_seed)

    parser = argparse.ArgumentParser(description='CATS')

    # 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='CATS',
                        help='model name, options: [Autoformer, Transformer, TimesNet]')

    # data loader
    parser.add_argument('--data', type=str, required=True, default='ETTm1', help='dataset type')
    parser.add_argument('--root_path', type=str, default='./dataset/ETT-small/', 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('--inverse',type=bool, default=False,help='use inverse transform')
    parser.add_argument('--ratios', type=str, default='0.7,0.1,0.2', help='train,validation,test ratios (comma-separated, must sum to 1)')

    # 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')

    # model define
    parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
    parser.add_argument('--d_model', type=int, default=128, help='dimension of model')
    parser.add_argument('--n_heads', type=int, default=16, help='num of heads')
    parser.add_argument('--e_layers', type=int, default=0, help='num of encoder layers')
    parser.add_argument('--d_layers', type=int, default=3, help='num of decoder layers')
    parser.add_argument('--d_ff', type=int, default=256, help='dimension of fcn')
    parser.add_argument('--dropout', type=float, default=0.2, help='dropout')
    parser.add_argument('--embed', type=str, default='timeF',
                        help='time features encoding, options:[timeF, fixed, learned]')
    parser.add_argument('--use_norm', type=int, default=1, help='whether to use normalize; True 1 False 0')

    # 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=30, 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=10, 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='type2', 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')

    # CATS
    parser.add_argument('--QAM_start', type=float, default=0.1, help='masking start probability')
    parser.add_argument('--QAM_end', type=float, default=0.3, help='masking end probability')
    parser.add_argument('--patch_len', type=int, default=24, help='patch length')
    parser.add_argument('--stride', type=int, default=24, help='stride')
    parser.add_argument('--pct_start', type=float, default=0.3, help='pct_start')
    parser.add_argument('--padding_patch', default='end', help='None: None; end: padding on the end')
    parser.add_argument('--query_independence', action='store_true', default=False, help='sharing query across dimension')
    parser.add_argument('--store_attn', action='store_true', default=False, help='store attention score')

    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 in experiment:')
    print(args)

    Exp = Exp_Main
    print('check')
    if args.is_training:
        for ii in range(args.itr):
            # setting record of experiments
            
            exp = Exp(args)  # set experiments
            setting = '{}_{}_{}_sl{}_pl{}_dm{}_nh{}_dl{}_df{}_qi{}_{}'.format(
                args.model_id,
                args.model,
                args.data,
                args.seq_len,
                args.pred_len,
                args.d_model,
                args.n_heads,
                args.d_layers,
                args.d_ff,
                args.query_independence, ii)

            print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
            exp.train(setting)

            print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
            exp.test(setting)
            torch.cuda.empty_cache()
    else:
        ii = 0
        setting = '{}_{}_{}_sl{}_pl{}_dm{}_nh{}_dl{}_df{}_qi{}_{}'.format(
            args.model_id,
            args.model,
            args.data,
            args.seq_len,
            args.pred_len,
            args.d_model,
            args.n_heads,
            args.d_layers,
            args.d_ff,
            args.query_independence, ii)

        exp = Exp(args)  # set experiments
        print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
        exp.test(setting, test=1)
        torch.cuda.empty_cache()