-
Notifications
You must be signed in to change notification settings - Fork 1.3k
/
Copy pathexp_long_term_forecasting.py
268 lines (219 loc) · 11.3 KB
/
exp_long_term_forecasting.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
from data_provider.data_factory import data_provider
from exp.exp_basic import Exp_Basic
from utils.tools import EarlyStopping, adjust_learning_rate, visual
from utils.metrics import metric
import torch
import torch.nn as nn
from torch import optim
import os
import time
import warnings
import numpy as np
warnings.filterwarnings('ignore')
class Exp_Long_Term_Forecast(Exp_Basic):
def __init__(self, args):
super(Exp_Long_Term_Forecast, self).__init__(args)
def _build_model(self):
model = self.model_dict[self.args.model].Model(self.args).float()
if self.args.use_multi_gpu and self.args.use_gpu:
model = nn.DataParallel(model, device_ids=self.args.device_ids)
return model
def _get_data(self, flag):
data_set, data_loader = data_provider(self.args, flag)
return data_set, data_loader
def _select_optimizer(self):
model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
return model_optim
def _select_criterion(self):
criterion = nn.MSELoss()
return criterion
def vali(self, vali_data, vali_loader, criterion):
total_loss = []
self.model.eval()
with torch.no_grad():
for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader):
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.float()
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 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 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)
pred = outputs.detach().cpu()
true = batch_y.detach().cpu()
loss = criterion(pred, true)
total_loss.append(loss)
total_loss = np.average(total_loss)
self.model.train()
return total_loss
def train(self, setting):
train_data, train_loader = self._get_data(flag='train')
vali_data, vali_loader = self._get_data(flag='val')
test_data, test_loader = self._get_data(flag='test')
path = os.path.join(self.args.checkpoints, setting)
if not os.path.exists(path):
os.makedirs(path)
time_now = time.time()
train_steps = len(train_loader)
early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
model_optim = self._select_optimizer()
criterion = self._select_criterion()
if self.args.use_amp:
scaler = torch.cuda.amp.GradScaler()
for epoch in range(self.args.train_epochs):
iter_count = 0
train_loss = []
self.model.train()
epoch_time = time.time()
for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
iter_count += 1
model_optim.zero_grad()
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 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)
loss = criterion(outputs, batch_y)
train_loss.append(loss.item())
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)
loss = criterion(outputs, batch_y)
train_loss.append(loss.item())
if (i + 1) % 100 == 0:
print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
speed = (time.time() - time_now) / iter_count
left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
iter_count = 0
time_now = time.time()
if self.args.use_amp:
scaler.scale(loss).backward()
scaler.step(model_optim)
scaler.update()
else:
loss.backward()
model_optim.step()
print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
train_loss = np.average(train_loss)
vali_loss = self.vali(vali_data, vali_loader, criterion)
test_loss = self.vali(test_data, test_loader, criterion)
print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
epoch + 1, train_steps, train_loss, vali_loss, test_loss))
early_stopping(vali_loss, self.model, path)
if early_stopping.early_stop:
print("Early stopping")
break
adjust_learning_rate(model_optim, epoch + 1, self.args)
best_model_path = path + '/' + 'checkpoint.pth'
self.model.load_state_dict(torch.load(best_model_path))
return self.model
def test(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('./checkpoints/' + 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, 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 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 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:, :]
batch_y = batch_y[:, -self.args.pred_len:, :].to(self.device)
outputs = outputs.detach().cpu().numpy()
batch_y = batch_y.detach().cpu().numpy()
if test_data.scale and self.args.inverse:
shape = outputs.shape
outputs = test_data.inverse_transform(outputs.squeeze(0)).reshape(shape)
batch_y = test_data.inverse_transform(batch_y.squeeze(0)).reshape(shape)
outputs = outputs[:, :, f_dim:]
batch_y = batch_y[:, :, f_dim:]
pred = outputs
true = batch_y
preds.append(pred)
trues.append(true)
if i % 20 == 0:
input = batch_x.detach().cpu().numpy()
if test_data.scale and self.args.inverse:
shape = input.shape
input = test_data.inverse_transform(input.squeeze(0)).reshape(shape)
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'))
preds = np.array(preds)
trues = np.array(trues)
print('test shape:', preds.shape, trues.shape)
preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
trues = trues.reshape(-1, trues.shape[-2], trues.shape[-1])
print('test shape:', preds.shape, trues.shape)
# result save
folder_path = './results/' + setting + '/'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
mae, mse, rmse, mape, mspe = metric(preds, trues)
print('mse:{}, mae:{}'.format(mse, mae))
f = open("result_long_term_forecast.txt", 'a')
f.write(setting + " \n")
f.write('mse:{}, mae:{}'.format(mse, mae))
f.write('\n')
f.write('\n')
f.close()
np.save(folder_path + 'metrics.npy', np.array([mae, mse, rmse, mape, mspe]))
np.save(folder_path + 'pred.npy', preds)
np.save(folder_path + 'true.npy', trues)
return