390 lines
18 KiB
Python
390 lines
18 KiB
Python
import argparse
|
|
import os
|
|
import sys
|
|
import torch
|
|
|
|
|
|
|
|
import random
|
|
import numpy as np
|
|
|
|
# from utils.str2bool import str2bool
|
|
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.')
|
|
|
|
def cal_accuracy(y_pred, y_true):
|
|
return np.mean(y_pred == y_true)
|
|
|
|
|
|
def cal_precision_recall(predictions, trues):
|
|
TP = np.sum((predictions == 1) & (trues == 1))
|
|
FP = np.sum((predictions == 1) & (trues == 0))
|
|
FN = np.sum((predictions == 0) & (trues == 1))
|
|
|
|
precision = TP / (TP + FP)
|
|
recall = TP / (TP + FN)
|
|
f1 = 2 * (precision * recall) / (precision + recall)
|
|
|
|
return precision, recall, f1
|
|
|
|
|
|
def infer_ltf(self, setting, test=0):
|
|
test_data, test_loader = self._get_data(flag='test')
|
|
|
|
if test:
|
|
print('loading model')
|
|
self.model.load_state_dict(torch.load(os.path.join('../weights/' + setting, 'checkpoint.pth')))
|
|
|
|
preds = []
|
|
trues = []
|
|
inputx = []
|
|
folder_path = './test_results/' + setting + '/'
|
|
if not os.path.exists(folder_path):
|
|
os.makedirs(folder_path)
|
|
|
|
self.model.eval()
|
|
if self.args.call_structural_reparam and hasattr(self.model, 'structural_reparam'):
|
|
self.model.structural_reparam()
|
|
|
|
with torch.no_grad():
|
|
for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
|
|
batch_x = batch_x.float().to(self.device)
|
|
batch_y = batch_y.float().to(self.device)
|
|
|
|
batch_x_mark = batch_x_mark.float().to(self.device)
|
|
batch_y_mark = batch_y_mark.float().to(self.device)
|
|
|
|
# decoder input
|
|
dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
|
|
dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
|
|
# encoder - decoder
|
|
if self.args.use_amp:
|
|
with torch.cuda.amp.autocast():
|
|
if 'Linear' in self.args.model or 'TST' in self.args.model:
|
|
outputs = self.model(batch_x)
|
|
elif 'TCN' in self.args.model:
|
|
outputs = self.model(batch_x, batch_x_mark)
|
|
# outputs = self.model(batch_x) #if decide not to use time stamp, use this code
|
|
else:
|
|
if self.args.output_attention:
|
|
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
|
|
else:
|
|
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
|
|
else:
|
|
if 'Linear' in self.args.model or 'TST' in self.args.model:
|
|
outputs = self.model(batch_x)
|
|
elif 'TCN' in self.args.model:
|
|
|
|
# import copy
|
|
x,y = batch_x.clone(), batch_y.clone()
|
|
|
|
outputs = self.model(batch_x, batch_x_mark)
|
|
|
|
|
|
|
|
# outputs_ = self.model(batch_x) #if decide not to use time stamp, use this code
|
|
else:
|
|
if self.args.output_attention:
|
|
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
|
|
else:
|
|
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
|
|
|
|
f_dim = -1 if self.args.features == 'MS' else 0
|
|
|
|
outputs = outputs[:, -self.args.pred_len:, f_dim:]
|
|
batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
|
|
outputs = outputs.detach().cpu().numpy()
|
|
batch_y = batch_y.detach().cpu().numpy()
|
|
|
|
pred = outputs # outputs.detach().cpu().numpy() # .squeeze()
|
|
true = batch_y # batch_y.detach().cpu().numpy() # .squeeze()
|
|
|
|
preds.append(pred)
|
|
trues.append(true)
|
|
inputx.append(batch_x.detach().cpu().numpy())
|
|
if i % 20 == 0:
|
|
input = batch_x.detach().cpu().numpy()
|
|
gt = np.concatenate((input[0, :, -1], true[0, :, -1]), axis=0)
|
|
pd = np.concatenate((input[0, :, -1], pred[0, :, -1]), axis=0)
|
|
visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
|
|
|
|
if self.args.test_flop:
|
|
test_params_flop((batch_x.shape[1], batch_x.shape[2]))
|
|
exit()
|
|
preds = np.array(preds)
|
|
trues = np.array(trues)
|
|
inputx = np.array(inputx)
|
|
|
|
preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
|
|
trues = trues.reshape(-1, trues.shape[-2], trues.shape[-1])
|
|
inputx = inputx.reshape(-1, inputx.shape[-2], inputx.shape[-1])
|
|
|
|
# result save
|
|
folder_path = './results/' + setting + '/'
|
|
if not os.path.exists(folder_path):
|
|
os.makedirs(folder_path)
|
|
|
|
mae, mse, rmse, mape, mspe, rse, corr = metric(preds, trues)
|
|
print('mse:{}, mae:{}, rse:{}'.format(mse, mae, rse))
|
|
f = open("result.txt", 'a')
|
|
f.write(setting + " \n")
|
|
f.write('mse:{}, mae:{}, rse:{}'.format(mse, mae, rse))
|
|
f.write('\n')
|
|
f.write('\n')
|
|
f.close()
|
|
|
|
|
|
return
|
|
|
|
def infer_cls(self, setting, test=0):
|
|
test_data, test_loader = self._get_data(flag='TEST')
|
|
if test:
|
|
print('loading model')
|
|
self.model.load_state_dict(torch.load(os.path.join('../weights/' + setting, 'checkpoint.pth')))
|
|
|
|
preds = []
|
|
trues = []
|
|
folder_path = './test_results/' + setting + '/'
|
|
if not os.path.exists(folder_path):
|
|
os.makedirs(folder_path)
|
|
|
|
self.model.eval()
|
|
with torch.no_grad():
|
|
for i, (batch_x, label, padding_mask) in enumerate(test_loader):
|
|
batch_x = batch_x.float().to(self.device)
|
|
padding_mask = padding_mask.float().to(self.device)
|
|
label = label.to(self.device)
|
|
|
|
outputs = self.model(batch_x, padding_mask, None, None)
|
|
|
|
|
|
preds.append(outputs.detach())
|
|
trues.append(label)
|
|
|
|
preds = torch.cat(preds, 0)
|
|
trues = torch.cat(trues, 0)
|
|
print('test shape:', preds.shape, trues.shape)
|
|
|
|
probs = torch.nn.functional.softmax(preds) # (total_samples, num_classes) est. prob. for each class and sample
|
|
predictions = torch.argmax(probs, dim=1).cpu().numpy() # (total_samples,) int class index for each sample
|
|
trues = trues.flatten().cpu().numpy()
|
|
accuracy = cal_accuracy(predictions, trues)
|
|
p,r,f1 = cal_precision_recall(predictions, trues)
|
|
|
|
|
|
# result save
|
|
folder_path = './results/' + setting + '/'
|
|
if not os.path.exists(folder_path):
|
|
os.makedirs(folder_path)
|
|
|
|
print('accuracy:{}'.format(accuracy))
|
|
print('p:{}'.format(p))
|
|
print('r:{}'.format(r))
|
|
print('f1:{}'.format(f1))
|
|
|
|
f = open("result_classification.txt", 'a')
|
|
f.write(setting + " \n")
|
|
f.write('accuracy:{}'.format(accuracy))
|
|
f.write('\n')
|
|
f.write('\n')
|
|
f.close()
|
|
return
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='ModernTCN')
|
|
|
|
# random seed
|
|
parser.add_argument('--random_seed', type=int, default=2021, help='random seed')
|
|
|
|
# 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='ModernTCN',
|
|
help='model name, options: [ModernTCN]')
|
|
|
|
# data loader
|
|
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('--embed', type=str, default='timeF',
|
|
help='time features encoding, options:[timeF, fixed, learned]')
|
|
|
|
|
|
# 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')
|
|
|
|
|
|
|
|
#ModernTCN
|
|
parser.add_argument('--stem_ratio', type=int, default=6, help='stem ratio')
|
|
parser.add_argument('--downsample_ratio', type=int, default=2, help='downsample_ratio')
|
|
parser.add_argument('--ffn_ratio', type=int, default=2, help='ffn_ratio')
|
|
parser.add_argument('--patch_size', type=int, default=16, help='the patch size')
|
|
parser.add_argument('--patch_stride', type=int, default=8, help='the patch stride')
|
|
|
|
parser.add_argument('--num_blocks', nargs='+',type=int, default=[1,1,1,1], help='num_blocks in each stage')
|
|
parser.add_argument('--large_size', nargs='+',type=int, default=[31,29,27,13], help='big kernel size')
|
|
parser.add_argument('--small_size', nargs='+',type=int, default=[5,5,5,5], help='small kernel size for structral reparam')
|
|
parser.add_argument('--dims', nargs='+',type=int, default=[256,256,256,256], help='dmodels in each stage')
|
|
parser.add_argument('--dw_dims', nargs='+',type=int, default=[256,256,256,256], help='dw dims in dw conv in each stage')
|
|
|
|
parser.add_argument('--small_kernel_merged', type=str2bool, default=False, help='small_kernel has already merged or not')
|
|
parser.add_argument('--call_structural_reparam', type=bool, default=False, help='structural_reparam after training')
|
|
parser.add_argument('--use_multi_scale', type=str2bool, default=True, help='use_multi_scale fusion')
|
|
|
|
|
|
# PatchTST
|
|
parser.add_argument('--fc_dropout', type=float, default=0.05, help='fully connected dropout')
|
|
parser.add_argument('--head_dropout', type=float, default=0.0, help='head dropout')
|
|
parser.add_argument('--patch_len', type=int, default=16, help='patch length')
|
|
parser.add_argument('--stride', type=int, default=8, help='stride')
|
|
parser.add_argument('--padding_patch', default='end', help='None: None; end: padding on the end')
|
|
parser.add_argument('--revin', type=int, default=1, help='RevIN; True 1 False 0')
|
|
parser.add_argument('--affine', type=int, default=0, help='RevIN-affine; True 1 False 0')
|
|
parser.add_argument('--subtract_last', type=int, default=0, help='0: subtract mean; 1: subtract last')
|
|
parser.add_argument('--decomposition', type=int, default=0, help='decomposition; True 1 False 0')
|
|
parser.add_argument('--kernel_size', type=int, default=25, help='decomposition-kernel')
|
|
parser.add_argument('--individual', type=int, default=0, help='individual head; True 1 False 0')
|
|
|
|
# Formers
|
|
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')
|
|
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('--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')
|
|
|
|
# optimization
|
|
parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
|
|
parser.add_argument('--itr', type=int, default=2, help='experiments times')
|
|
parser.add_argument('--train_epochs', type=int, default=100, help='train epochs')
|
|
parser.add_argument('--batch_size', type=int, default=128, help='batch size of train input data')
|
|
parser.add_argument('--patience', type=int, default=100, 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('--pct_start', type=float, default=0.3, help='pct_start')
|
|
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=False, 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')
|
|
|
|
#multi task
|
|
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]')
|
|
|
|
# inputation task
|
|
parser.add_argument('--mask_rate', type=float, default=0.25, help='mask ratio')
|
|
|
|
# anomaly detection task
|
|
parser.add_argument('--anomaly_ratio', type=float, default=0.25, help='prior anomaly ratio (%)')
|
|
|
|
# classfication task
|
|
parser.add_argument('--class_dropout', type=float, default=0.05, help='classfication dropout')
|
|
|
|
args = parser.parse_args()
|
|
|
|
# random seed
|
|
fix_seed = args.random_seed
|
|
random.seed(fix_seed)
|
|
torch.manual_seed(fix_seed)
|
|
np.random.seed(fix_seed)
|
|
# Exp = Exp_Main
|
|
if args.task_name == 'long_term_forecast':
|
|
sys.path.append(R"../0_moderntcn/ModernTCN-Long-term-forecasting/")
|
|
from exp.exp_ModernTCN import Exp_Main
|
|
Exp_Main.infer = infer_ltf
|
|
Exp = Exp_Main
|
|
from data_provider.data_factory import data_provider
|
|
|
|
from exp.exp_basic import Exp_Basic
|
|
from models import ModernTCN
|
|
from utils.tools import EarlyStopping, adjust_learning_rate, visual, test_params_flop
|
|
from utils.metrics import metric
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
import os
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
elif args.task_name == 'classification':
|
|
sys.path.append(R"../0_moderntcn/ModernTCN-classification/")
|
|
from exp.exp_classification import Exp_Classification
|
|
Exp_Classification.infer = infer_cls
|
|
Exp = Exp_Classification
|
|
|
|
from data_provider.data_factory import data_provider
|
|
from exp.exp_basic import Exp_Basic
|
|
# from utils.tools import cal_accuracy, cal_precision_recall
|
|
import torch
|
|
import os
|
|
import numpy as np
|
|
from models.ModernTCN import Model,ModernTCN
|
|
|
|
if args.is_training:
|
|
pass
|
|
else:
|
|
ii = 0
|
|
exp = Exp(args) # set experiments
|
|
setting = '{}_{}_{}_ft{}_sl{}_pl{}_dim{}_nb{}_lk{}_sk{}_ffr{}_ps{}_str{}_multi{}_merged{}_{}_{}'.format(
|
|
args.model_id,
|
|
args.model,
|
|
args.data,
|
|
args.features,
|
|
args.seq_len,
|
|
args.pred_len,
|
|
args.dims[0],
|
|
args.num_blocks[0],
|
|
args.large_size[0],
|
|
args.small_size[0],
|
|
args.ffn_ratio,
|
|
args.patch_size,
|
|
args.patch_stride,
|
|
args.use_multi_scale,
|
|
args.small_kernel_merged,
|
|
args.des,
|
|
ii)
|
|
|
|
|
|
|
|
print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
|
|
exp.infer(setting, test=1)
|
|
torch.cuda.empty_cache()
|