256 lines
7.4 KiB
Python
256 lines
7.4 KiB
Python
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import matplotlib.pyplot as plt
|
|
from tqdm import tqdm
|
|
|
|
from datetime import datetime
|
|
from distutils.util import strtobool
|
|
import pandas as pd
|
|
|
|
from utils.metrics import metric
|
|
|
|
plt.switch_backend('agg')
|
|
|
|
def adjust_learning_rate(optimizer, epoch, args):
|
|
# lr = args.learning_rate * (0.2 ** (epoch // 2))
|
|
# if args.decay_fac is None:
|
|
# args.decay_fac = 0.5
|
|
# if args.lradj == 'type1':
|
|
# lr_adjust = {epoch: args.learning_rate * (args.decay_fac ** ((epoch - 1) // 1))}
|
|
# elif args.lradj == 'type2':
|
|
# lr_adjust = {
|
|
# 2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6,
|
|
# 10: 5e-7, 15: 1e-7, 20: 5e-8
|
|
# }
|
|
if args.lradj =='type1':
|
|
lr_adjust = {epoch: args.learning_rate if epoch < 3 else args.learning_rate * (0.9 ** ((epoch - 3) // 1))}
|
|
elif args.lradj =='type2':
|
|
lr_adjust = {epoch: args.learning_rate * (args.decay_fac ** ((epoch - 1) // 1))}
|
|
elif args.lradj =='type4':
|
|
lr_adjust = {epoch: args.learning_rate * (args.decay_fac ** ((epoch) // 1))}
|
|
else:
|
|
args.learning_rate = 1e-4
|
|
lr_adjust = {epoch: args.learning_rate if epoch < 3 else args.learning_rate * (0.9 ** ((epoch - 3) // 1))}
|
|
print("lr_adjust = {}".format(lr_adjust))
|
|
if epoch in lr_adjust.keys():
|
|
lr = lr_adjust[epoch]
|
|
for param_group in optimizer.param_groups:
|
|
param_group['lr'] = lr
|
|
print('Updating learning rate to {}'.format(lr))
|
|
|
|
|
|
class EarlyStopping:
|
|
def __init__(self, patience=7, verbose=False, delta=0):
|
|
self.patience = patience
|
|
self.verbose = verbose
|
|
self.counter = 0
|
|
self.best_score = None
|
|
self.early_stop = False
|
|
self.val_loss_min = np.inf
|
|
self.delta = delta
|
|
|
|
def __call__(self, val_loss, model, path):
|
|
score = -val_loss
|
|
if self.best_score is None:
|
|
self.best_score = score
|
|
self.save_checkpoint(val_loss, model, path)
|
|
elif score < self.best_score + self.delta:
|
|
self.counter += 1
|
|
print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
|
|
if self.counter >= self.patience:
|
|
self.early_stop = True
|
|
else:
|
|
self.best_score = score
|
|
self.save_checkpoint(val_loss, model, path)
|
|
self.counter = 0
|
|
|
|
def save_checkpoint(self, val_loss, model, path):
|
|
if self.verbose:
|
|
print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
|
|
torch.save(model.state_dict(), path + '/' + 'checkpoint.pth')
|
|
self.val_loss_min = val_loss
|
|
|
|
|
|
class dotdict(dict):
|
|
"""dot.notation access to dictionary attributes"""
|
|
__getattr__ = dict.get
|
|
__setattr__ = dict.__setitem__
|
|
__delattr__ = dict.__delitem__
|
|
|
|
|
|
class StandardScaler():
|
|
def __init__(self, mean, std):
|
|
self.mean = mean
|
|
self.std = std
|
|
|
|
def transform(self, data):
|
|
return (data - self.mean) / self.std
|
|
|
|
def inverse_transform(self, data):
|
|
return (data * self.std) + self.mean
|
|
|
|
|
|
def vali(model, vali_loader, criterion, args, device):
|
|
total_loss = []
|
|
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
for i, (batch_x, batch_y) in enumerate(vali_loader.get_iterator()):
|
|
# batch_x = torch.squeeze(batch_x)
|
|
# batch_y = torch.squeeze(batch_y)
|
|
outputs = model(batch_x)
|
|
|
|
# encoder - decoder
|
|
outputs = outputs[..., 0]
|
|
batch_y = batch_y[..., 0]
|
|
|
|
# pred = outputs.detach().cpu()
|
|
# true = batch_y.detach().cpu()
|
|
pred = outputs
|
|
true = batch_y
|
|
|
|
# loss = criterion(pred, true)
|
|
loss = masked_mae(pred, true, 0.0)
|
|
|
|
total_loss.append(loss)
|
|
# total_loss = np.average(total_loss)
|
|
total_loss = torch.mean(torch.tensor(total_loss))
|
|
|
|
|
|
model.train()
|
|
|
|
return total_loss
|
|
|
|
def MASE(x, freq, pred, true):
|
|
masep = np.mean(np.abs(x[:, freq:] - x[:, :-freq]))
|
|
return np.mean(np.abs(pred - true) / (masep + 1e-8))
|
|
|
|
def test(model, test_loader, args, device):
|
|
preds = []
|
|
trues = []
|
|
# mases = []
|
|
|
|
model.eval()
|
|
with torch.no_grad():
|
|
for i, (batch_x, batch_y) in enumerate(test_loader.get_iterator()):
|
|
|
|
outputs = model(batch_x)
|
|
|
|
# encoder - decoder
|
|
outputs = outputs[... , 0]
|
|
batch_y = batch_y[... , 0]
|
|
|
|
# pred = outputs.detach().cpu().numpy()
|
|
# true = batch_y.detach().cpu().numpy()
|
|
pred = outputs
|
|
true = batch_y
|
|
|
|
preds.append(pred)
|
|
trues.append(true)
|
|
|
|
|
|
# preds = torch.Tensor(preds)
|
|
# trues = torch.Tensor(trues)
|
|
preds = torch.stack(preds[:-1])
|
|
trues = torch.stack(trues[:-1])
|
|
|
|
amae = []
|
|
amape = []
|
|
armse = []
|
|
for i in range(args.pred_len):
|
|
pred = preds[..., i]
|
|
real = trues[..., i]
|
|
|
|
metric = metrics(pred,real)
|
|
|
|
log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
|
|
print(log.format(i+1, metric[0], metric[1], metric[2]))
|
|
amae.append(metric[0])
|
|
amape.append(metric[1])
|
|
armse.append(metric[2])
|
|
|
|
|
|
# return np.mean(amae),np.mean(amape),np.mean(armse)
|
|
return torch.mean(torch.tensor(amae)), torch.mean(torch.tensor(amape)), torch.mean(torch.tensor(armse))
|
|
|
|
|
|
|
|
def masked_mse(preds, labels, null_val=np.nan):
|
|
if np.isnan(null_val):
|
|
mask = ~torch.isnan(labels)
|
|
else:
|
|
mask = (labels!=null_val)
|
|
mask = mask.float()
|
|
mask /= torch.mean((mask))
|
|
mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
|
|
loss = (preds-labels)**2
|
|
loss = loss * mask
|
|
loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
|
|
loss = (preds-labels)**2
|
|
return torch.mean(loss)
|
|
|
|
def masked_rmse(preds, labels, null_val=np.nan):
|
|
return torch.sqrt(masked_mse(preds=preds, labels=labels, null_val=null_val))
|
|
|
|
|
|
def masked_mae(preds, labels, null_val=np.nan):
|
|
if np.isnan(null_val):
|
|
mask = ~torch.isnan(labels)
|
|
else:
|
|
mask = (labels!=null_val)
|
|
mask = mask.float()
|
|
mask /= torch.mean((mask))
|
|
mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
|
|
loss = torch.abs(preds-labels)
|
|
loss = loss * mask
|
|
loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
|
|
loss = torch.abs(preds-labels)
|
|
return torch.mean(loss)
|
|
|
|
|
|
def masked_mape(preds, labels, null_val=np.nan):
|
|
if np.isnan(null_val):
|
|
mask = ~torch.isnan(labels)
|
|
else:
|
|
mask = (labels!=null_val)
|
|
mask = mask.float()
|
|
mask /= torch.mean((mask))
|
|
mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
|
|
loss = torch.abs(preds-labels)/labels
|
|
loss = loss * mask
|
|
loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
|
|
loss = torch.abs(preds-labels)/labels
|
|
return torch.mean(loss)
|
|
|
|
|
|
def metrics(pred, real):
|
|
mae = masked_mae(pred,real,0.0).item()
|
|
mape = masked_mape(pred,real,0.0).item()
|
|
rmse = masked_rmse(pred,real,0.0).item()
|
|
return mae,mape,rmse
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# # import numpy as np
|
|
# def cal_metrics(y_true, y_pred):
|
|
# mse = torch.square(y_pred - y_true)
|
|
# mse = torch.mean(mse)
|
|
|
|
|
|
# # rmse = torch.square(np.abs(y_pred - y_true))
|
|
# rmse = torch.sqrt(mse)
|
|
|
|
|
|
|
|
# mae = torch.abs(y_pred - y_true)
|
|
# mae = torch.mean(mae)
|
|
# return rmse, 0, mae
|
|
|
|
|
|
|