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from data_provider.data_factory import data_provider
from data_provider.m4 import M4Meta
from exp.exp_basic import Exp_Basic
from utils.tools import EarlyStopping, adjust_learning_rate, visual
from utils.losses import mape_loss, mase_loss, smape_loss
from utils.m4_summary import M4Summary
import torch
import torch.nn as nn
from torch import optim
import os
import time
import warnings
import numpy as np
import pandas
warnings.filterwarnings('ignore')
class Exp_Short_Term_Forecast(Exp_Basic):
def __init__(self, args):
super(Exp_Short_Term_Forecast, self).__init__(args)
def _build_model(self):
if self.args.data == 'm4':
self.args.pred_len = M4Meta.horizons_map[self.args.seasonal_patterns] # Up to M4 config
self.args.seq_len = 2 * self.args.pred_len # input_len = 2*pred_len
self.args.label_len = self.args.pred_len
self.args.frequency_map = M4Meta.frequency_map[self.args.seasonal_patterns]
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, loss_name='MSE'):
if loss_name == 'MSE':
return nn.MSELoss()
elif loss_name == 'MAPE':
return mape_loss()
elif loss_name == 'MASE':
return mase_loss()
elif loss_name == 'SMAPE':
return smape_loss()
def train(self, setting):
train_data, train_loader = self._get_data(flag='train')
vali_data, vali_loader = self._get_data(flag='val')
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(self.args.loss)
mse = nn.MSELoss()
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_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)
outputs = self.model(batch_x, None, dec_inp, None)
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)
batch_y_mark = batch_y_mark[:, -self.args.pred_len:, f_dim:].to(self.device)
loss_value = criterion(batch_x, self.args.frequency_map, outputs, batch_y, batch_y_mark)
loss_sharpness = mse((outputs[:, 1:, :] - outputs[:, :-1, :]), (batch_y[:, 1:, :] - batch_y[:, :-1, :]))
loss = loss_value # + loss_sharpness * 1e-5
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()
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(train_loader, vali_loader, criterion)
test_loss = vali_loss
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 vali(self, train_loader, vali_loader, criterion):
x, _ = train_loader.dataset.last_insample_window()
y = vali_loader.dataset.timeseries
x = torch.tensor(x, dtype=torch.float32).to(self.device)
x = x.unsqueeze(-1)
self.model.eval()
with torch.no_grad():
# decoder input
B, _, C = x.shape
dec_inp = torch.zeros((B, self.args.pred_len, C)).float().to(self.device)
dec_inp = torch.cat([x[:, -self.args.label_len:, :], dec_inp], dim=1).float()
# encoder - decoder
outputs = torch.zeros((B, self.args.pred_len, C)).float() # .to(self.device)
id_list = np.arange(0, B, 500) # validation set size
id_list = np.append(id_list, B)
for i in range(len(id_list) - 1):
outputs[id_list[i]:id_list[i + 1], :, :] = self.model(x[id_list[i]:id_list[i + 1]], None,
dec_inp[id_list[i]:id_list[i + 1]],
None).detach().cpu()
f_dim = -1 if self.args.features == 'MS' else 0
outputs = outputs[:, -self.args.pred_len:, f_dim:]
pred = outputs
true = torch.from_numpy(np.array(y))
batch_y_mark = torch.ones(true.shape)
loss = criterion(x.detach().cpu()[:, :, 0], self.args.frequency_map, pred[:, :, 0], true, batch_y_mark)
self.model.train()
return loss
def test(self, setting, test=0):
_, train_loader = self._get_data(flag='train')
_, test_loader = self._get_data(flag='test')
x, _ = train_loader.dataset.last_insample_window()
y = test_loader.dataset.timeseries
x = torch.tensor(x, dtype=torch.float32).to(self.device)
x = x.unsqueeze(-1)
if test:
print('loading model')
self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
folder_path = './test_results/' + setting + '/'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
self.model.eval()
with torch.no_grad():
B, _, C = x.shape
dec_inp = torch.zeros((B, self.args.pred_len, C)).float().to(self.device)
dec_inp = torch.cat([x[:, -self.args.label_len:, :], dec_inp], dim=1).float()
# encoder - decoder
outputs = torch.zeros((B, self.args.pred_len, C)).float().to(self.device)
id_list = np.arange(0, B, 1)
id_list = np.append(id_list, B)
for i in range(len(id_list) - 1):
outputs[id_list[i]:id_list[i + 1], :, :] = self.model(x[id_list[i]:id_list[i + 1]], None,
dec_inp[id_list[i]:id_list[i + 1]], None)
if id_list[i] % 1000 == 0:
print(id_list[i])
f_dim = -1 if self.args.features == 'MS' else 0
outputs = outputs[:, -self.args.pred_len:, f_dim:]
outputs = outputs.detach().cpu().numpy()
preds = outputs
trues = y
x = x.detach().cpu().numpy()
for i in range(0, preds.shape[0], preds.shape[0] // 10):
gt = np.concatenate((x[i, :, 0], trues[i]), axis=0)
pd = np.concatenate((x[i, :, 0], preds[i, :, 0]), axis=0)
visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
print('test shape:', preds.shape)
# result save
folder_path = './m4_results/' + self.args.model + '/'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
forecasts_df = pandas.DataFrame(preds[:, :, 0], columns=[f'V{i + 1}' for i in range(self.args.pred_len)])
forecasts_df.index = test_loader.dataset.ids[:preds.shape[0]]
forecasts_df.index.name = 'id'
forecasts_df.set_index(forecasts_df.columns[0], inplace=True)
forecasts_df.to_csv(folder_path + self.args.seasonal_patterns + '_forecast.csv')
print(self.args.model)
file_path = './m4_results/' + self.args.model + '/'
if 'Weekly_forecast.csv' in os.listdir(file_path) \
and 'Monthly_forecast.csv' in os.listdir(file_path) \
and 'Yearly_forecast.csv' in os.listdir(file_path) \
and 'Daily_forecast.csv' in os.listdir(file_path) \
and 'Hourly_forecast.csv' in os.listdir(file_path) \
and 'Quarterly_forecast.csv' in os.listdir(file_path):
m4_summary = M4Summary(file_path, self.args.root_path)
# m4_forecast.set_index(m4_winner_forecast.columns[0], inplace=True)
smape_results, owa_results, mape, mase = m4_summary.evaluate()
print('smape:', smape_results)
print('mape:', mape)
print('mase:', mase)
print('owa:', owa_results)
else:
print('After all 6 tasks are finished, you can calculate the averaged index')
return
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