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run.py
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import argparse
import os
import torch
from exp.exp_classification import Exp_Classification
import random
import numpy as np
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="TimesNet")
# basic config
parser.add_argument("--task_name", type=str, default="classification")
parser.add_argument("--is_training", type=int, default=1, help="status")
parser.add_argument("--model_id", type=str, default="APAVA-Subject", help="model id")
parser.add_argument("--model", type=str, default="MedGNN", help="[MedGNN, Medformer, iTransformer]")
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
parser.add_argument("--data", type=str, default="APAVA", help="dataset type")
parser.add_argument("--root_path", type=str, default="../dataset/APAVA", help="root path of the data file")
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("--d_model", type=int, default=256, help="dimension of model")
parser.add_argument("--d_ff", type=int, default=512, help="dimension of fcn")
parser.add_argument("--n_heads", type=int, default=8, help="num of heads")
parser.add_argument("--e_layers", type=int, default=4, help="num of encoder layers")
parser.add_argument("--d_layers", type=int, default=1, help="num of decoder layers")
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("--output_attention", action="store_true", help="whether to output attention in encoder")
parser.add_argument("--patch_len_list", type=str, default="2,2,2,4,4,4,16,16,16,16,32,32,32,32,32", help="a list of patch len used in Medformer")
parser.add_argument("--single_channel", action="store_true", default=False, help="whether to use single channel patching for Medformer")
parser.add_argument("--augmentations", type=str, default="none,drop0.35",
help="a comma-seperated list of augmentation types (none, jitter or scale). Append numbers to specify the strength of the augmentation, e.g., jitter0.1")
# MedGNN
parser.add_argument('--resolution_list', type=str, default="2,4,6,8")
parser.add_argument('--nodedim', type=int, default=10)
# 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=10, 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=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", default=False, help="use automatic mixed precision training")
parser.add_argument("--swa", action="store_true", default=False, help="use stochastic weight averaging")
# 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", help="use multiple gpus", default=False)
parser.add_argument("--devices", type=str, default="0, 1, 2, 3", help="device ids of multiple gpus")
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)
if args.task_name == "classification":
Exp = Exp_Classification
if args.is_training:
for ii in range(args.itr):
seed = 41 + ii
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# comment out the following lines if you are using dilated convolutions, e.g., TCN
# otherwise it will slow down the training extremely
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# setting record of experiments
args.seed = seed
setting = "{}_{}_{}_dm{}_df{}_nh{}_el{}_res{}_node{}_seed{}_bs{}_lr{}".format(
args.model_id,
args.model,
args.data,
args.d_model,
args.d_ff,
args.n_heads,
args.e_layers,
args.resolution_list,
args.nodedim,
args.seed,
args.batch_size,
args.learning_rate,
)
exp = Exp(args) # set experiments
print(
">>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>".format(setting)
)
exp.train(setting)
print(
">>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<".format(setting)
)
exp.test(setting)
torch.cuda.empty_cache()
else:
for ii in range(args.itr):
seed = 41 + ii
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# comment out the following lines if you are using dilated convolutions
# otherwise it will slow down the training extremely
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
args.seed = seed
setting = "{}_{}_{}_dm{}_df{}_nh{}_el{}_res{}_node{}_seed{}_bs{}_lr{}".format(
args.model_id,
args.model,
args.data,
args.d_model,
args.d_ff,
args.n_heads,
args.e_layers,
args.resolution_list,
args.nodedim,
args.seed,
args.batch_size,
args.learning_rate,
)
exp = Exp(args) # set experiments
print(
">>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<".format(setting)
)
exp.test(setting, test=1)
torch.cuda.empty_cache()