TimeRecipe/run.py at main · AdityaLab/TimeRecipe · GitHub

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import argparse
import os
import torch
from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
from utils.print_args import print_args
import random
import numpy as np

def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

def str2bool(v):
    if isinstance(v, bool):
        return v
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')

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

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

    # basic config
    parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast',
                        help='task name, options:[long_term_forecast, 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='unitsf',
                        help='model name, options: [Autoformer, Transformer, TimesNet]')

    # data loader
    parser.add_argument('--data', type=str, required=True, default='ETTh1', 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='S',
                        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=192, help='prediction sequence length')
    parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
    parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)

    # model define
    parser.add_argument('--enc_in', type=int, default=1, help='encoder input size')
    parser.add_argument('--dec_in', type=int, default=1, help='decoder input size')
    parser.add_argument('--c_out', type=int, default=1, help='output size')
    parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
    parser.add_argument('--n_heads', type=int, default=4, 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('--dropout', type=float, default=0.1, 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('--decomp_method', type=str, default='moving_avg', help='method of series decompsition, only support moving_avg or dft_decomp')
    parser.add_argument('--patch_len', type=int, default=16, help='patch length')

    # 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=20, help='train epochs')
    parser.add_argument('--batch_size', type=int, default=64, 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=1e-4, 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='type3', 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')

    # unitsf config
    parser.add_argument('--seed', type=int, default=2021, help='random seed')
    parser.add_argument('--use_norm', type=str2bool, default=True, help='use or not use instance normalization')
    parser.add_argument('--use_decomp', type=str2bool, default=False, help='use or not use series decomposition')
    parser.add_argument('--fusion', type=str, default='temporal', help='temporal or feature fusion')
    parser.add_argument('--emb_type', type=str, default='token', help='token, patch, invert, freq, or no embedding')
    parser.add_argument('--ff_type', type=str, default='mlp', help='model feed-forward type of mlp, rnn, and transformer')

    parser.add_argument('--exp_name', type=str, default='test', help='setup name for saving')

    args = parser.parse_args()
    # args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
    args.use_gpu = True if torch.cuda.is_available() else False

    fix_seed = args.seed
    random.seed(fix_seed)
    torch.manual_seed(fix_seed)
    np.random.seed(fix_seed)

    print(torch.cuda.is_available())

    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(args)

    if args.task_name == 'long_term_forecast':
        Exp = Exp_Long_Term_Forecast
    # elif args.task_name == 'short_term_forecast':
    #     Exp = Exp_Short_Term_Forecast
    else:
        Exp = Exp_Long_Term_Forecast

    if args.is_training:
        for ii in range(args.itr):
            # setting record of experiments
            exp = Exp(args)  # set experiments
            print('Number of Model Parameters: {}'.format(count_parameters(exp.model.ff_model)))
            n_param = count_parameters(exp.model.ff_model)
            setting = '{}_pl{}_sd{}_un{}_ud{}_fu{}_eb{}_ff{}_ft{}'.format(
                args.data,
                args.pred_len,
                fix_seed,
                args.use_norm,
                args.use_decomp,
                args.fusion,
                args.emb_type,
                args.ff_type,
                args.features,)

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

            print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
            exp.test(setting)
            torch.cuda.empty_cache()
    else:
        ii = 0
        exp = Exp(args)  # set experiments
        print('Number of Model Parameters: {}'.format(count_parameters(exp.model.ff_model)))
        n_param = count_parameters(exp.model.ff_model)
        setting = '{}_pl{}_sd{}_un{}_ud{}_fu{}_eb{}_ff{}_ft{}'.format(
                args.data,
                args.pred_len,
                fix_seed,
                args.use_norm,
                args.use_decomp,
                args.ci,
                args.emb_type,
                args.ff_type,
                args.features,)

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