You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
290 lines
11 KiB
290 lines
11 KiB
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
|
|
import yaml
|
|
from models import SymTime_finetune as SymTime
|
|
from sys import exit
|
|
|
|
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]
|
|
with open("./configs/SymTime_base.yaml", "r", encoding="utf-8") as file:
|
|
configs = yaml.safe_load(file)
|
|
model = SymTime(args=self.args, configs=configs).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")
|
|
|
|
# for (batch_x, batch_y, batch_x_mark, batch_y_mark) in train_loader:
|
|
# print(batch_x.size(), batch_y.size(), batch_x_mark.size(), batch_y_mark.size())
|
|
# break
|
|
|
|
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)
|
|
|
|
outputs = self.model(batch_x)
|
|
|
|
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]]).detach().cpu()
|
|
)
|
|
f_dim = -1 if self.args.features == "MS" else 0
|
|
outputs = outputs[:, -self.args.pred_len :, f_dim:]
|
|
pred = outputs
|
|
y = np.array(y)
|
|
if y.dtype == np.object_:
|
|
y = np.array([np.array(v).astype(float) for v in y])
|
|
|
|
true = torch.from_numpy(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_pred = self.model(x[id_list[i] : id_list[i + 1]])
|
|
outputs[id_list[i] : id_list[i + 1], :, :] = outputs_pred[
|
|
:, -self.args.pred_len :, :
|
|
]
|
|
|
|
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
|