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215 lines
6.9 KiB
215 lines
6.9 KiB
# -*- coding: utf-8 -*-
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"""
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Created on 2024/10/19 21:20
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@author: Whenxuan Wang
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@email: wwhenxuan@gmail.com
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@url: https://github.com/wwhenxuan/SymTime
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"""
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import argparse
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import torch
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from exp import Exp_Long_Term_Forecast
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from utils.print_args import print_args
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import random
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import numpy as np
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parser = argparse.ArgumentParser(description="SymTime-Long_Term_forecast")
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# basic config
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parser.add_argument("--task_name", type=str, default="long_term_forecast")
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parser.add_argument("--is_training", type=int, default=1, help="status")
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parser.add_argument("--dataset_name", type=str, default="Exchange", help="model id")
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parser.add_argument("--model", type=str, default="SymTime")
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# load pre-trained model
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parser.add_argument(
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"--pretrain_path", type=str, default="./models/params/finetuning.pth"
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)
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parser.add_argument("--pretrain_id", type=str, default="zero")
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# data loader
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parser.add_argument("--data", type=str, default="custom", help="datasets type")
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parser.add_argument(
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"--root_path",
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type=str,
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default="./datasets/exchange_rate/",
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help="root path of the data file",
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)
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parser.add_argument(
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"--data_path", type=str, default="exchange_rate.csv", help="data file"
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)
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parser.add_argument(
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"--features",
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type=str,
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default="M",
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help="forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate",
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)
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parser.add_argument(
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"--target", type=str, default="OT", help="target feature in S or MS task"
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)
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parser.add_argument(
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"--freq",
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type=str,
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default="h",
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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",
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)
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parser.add_argument(
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"--checkpoints",
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type=str,
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default="./checkpoints/",
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help="location of model checkpoints",
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)
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# do patching
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parser.add_argument(
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"--forward_layers", type=int, default=3, help="the feed forward layers numbers"
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)
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parser.add_argument(
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"--patch_len", type=int, default=16, help="Divide the length of the patch"
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)
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parser.add_argument("--stride", type=int, default=8, help="Patch division step size")
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parser.add_argument(
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"--padding_patch", type=bool, default=True, help="Whether to fill the last Patch"
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)
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parser.add_argument(
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"--out_dropout",
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type=float,
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default=0.1,
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help="Dropout of the final output of the model",
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)
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parser.add_argument(
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"--use_avg", type=bool, default=True, help="use moving average decomposition"
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)
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parser.add_argument(
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"--moving_avg", type=int, default=25, help="window size of moving average"
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)
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parser.add_argument(
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"--individual",
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type=bool,
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default=False,
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help="whether to forecast the final output individually",
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)
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# forecasting task
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parser.add_argument("--seq_len", type=int, default=96, help="input sequence length")
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parser.add_argument("--label_len", type=int, default=48, help="start token length")
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parser.add_argument(
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"--pred_len", type=int, default=96, help="prediction sequence length"
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)
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parser.add_argument(
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"--seasonal_patterns", type=str, default="Monthly", help="subset for M4"
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)
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parser.add_argument(
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"--inverse", action="store_true", help="inverse output data", default=False
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)
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# model define
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parser.add_argument("--dropout", type=float, default=0.1, help="dropout")
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parser.add_argument(
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"--embed",
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type=str,
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default="timeF",
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help="time features encoding, options:[timeF, fixed, learned]",
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)
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parser.add_argument("--activation", type=str, default="gelu", help="activation")
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parser.add_argument(
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"--channel_independence",
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type=int,
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default=1,
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help="0: channel dependence 1: channel independence for FreTS model",
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)
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parser.add_argument(
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"--use_norm", type=int, default=1, help="whether to use normalize; True 1 False 0"
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)
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# optimization
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parser.add_argument("--enc_in", type=int, default=8, help="encoder input size")
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parser.add_argument(
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"--num_workers", type=int, default=5, help="data loader num workers"
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)
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parser.add_argument("--itr", type=int, default=1, help="experiments times")
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parser.add_argument("--train_epochs", type=int, default=16, help="train epochs")
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parser.add_argument(
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"--batch_size", type=int, default=8, help="batch size of train input data"
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)
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parser.add_argument("--patience", type=int, default=3, help="early stopping patience")
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parser.add_argument(
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"--learning_rate", type=float, default=0.0001, help="optimizer learning rate"
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)
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parser.add_argument("--des", type=str, default="test", help="exp description")
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parser.add_argument("--loss", type=str, default="MSE", help="loss function")
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parser.add_argument("--lradj", type=str, default="type1", help="adjust learning rate")
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parser.add_argument(
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"--use_amp",
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action="store_true",
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help="use automatic mixed precision training",
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default=False,
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)
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# GPU
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parser.add_argument("--use_gpu", type=bool, default=True, help="use gpu")
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parser.add_argument("--gpu", type=int, default=0, help="gpu")
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parser.add_argument(
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"--use_multi_gpu", action="store_true", help="use multiple gpus", default=False
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)
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parser.add_argument(
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"--devices", type=str, default="0,1,2,3", help="device ids of multile gpus"
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)
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# metrics (dtw)
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parser.add_argument(
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"--use_dtw", type=bool, default=False, help="the controller of using dtw metric"
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)
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# Augmentation
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parser.add_argument(
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"--augmentation_ratio", type=int, default=0, help="How many times to augment"
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)
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parser.add_argument("--seed", type=int, default=2025, help="Randomization seed")
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args = parser.parse_args()
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# args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
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args.use_gpu = True if torch.cuda.is_available() else False
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# Set the random seed for reproducibility
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random.seed(args.seed)
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torch.manual_seed(args.seed)
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np.random.seed(args.seed)
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if __name__ == "__main__":
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if args.use_gpu and args.use_multi_gpu:
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args.devices = args.devices.replace(" ", "")
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device_ids = args.devices.split(",")
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args.device_ids = [int(id_) for id_ in device_ids]
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args.gpu = args.device_ids[0]
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torch.cuda.set_device(args.gpu)
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print("Args in experiment:")
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print_args(args)
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exp = Exp_Long_Term_Forecast(args) # set experiments
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setting = "{}_{}_{}_{}_seq_len{}_pred_len{}_moving_avg{}_individual{}_forward_layers{}_patch_len{}_stride{}_batch_size{}_learning_rate{}_lradj{}_seed{}_{}".format(
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args.task_name,
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args.dataset_name,
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args.model,
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args.data,
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args.seq_len,
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args.pred_len,
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args.use_avg,
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args.individual,
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args.forward_layers,
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args.patch_len,
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args.stride,
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args.batch_size,
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args.learning_rate,
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args.lradj,
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args.seed,
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args.pretrain_id,
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)
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print(">>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>".format(setting))
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exp.train(setting)
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print(">>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<".format(setting))
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exp.test(setting)
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torch.cuda.empty_cache()
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