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gameloader
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exp/exp_short_term_forecasting.py
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exp/exp_short_term_forecasting.py
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from data_provider.data_factory import data_provider
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from data_provider.m4 import M4Meta
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from exp.exp_basic import Exp_Basic
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from utils.tools import EarlyStopping, adjust_learning_rate, visual
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from utils.losses import mape_loss, mase_loss, smape_loss
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from utils.m4_summary import M4Summary
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import torch
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import torch.nn as nn
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from torch import optim
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import os
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import time
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import warnings
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import numpy as np
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import pandas
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warnings.filterwarnings('ignore')
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class Exp_Short_Term_Forecast(Exp_Basic):
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def __init__(self, args):
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super(Exp_Short_Term_Forecast, self).__init__(args)
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def _build_model(self):
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if self.args.data == 'm4':
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self.args.pred_len = M4Meta.horizons_map[self.args.seasonal_patterns] # Up to M4 config
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self.args.seq_len = 2 * self.args.pred_len # input_len = 2*pred_len
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self.args.label_len = self.args.pred_len
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self.args.frequency_map = M4Meta.frequency_map[self.args.seasonal_patterns]
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model = self.model_dict[self.args.model].Model(self.args).float()
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if self.args.use_multi_gpu and self.args.use_gpu:
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model = nn.DataParallel(model, device_ids=self.args.device_ids)
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return model
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def _get_data(self, flag):
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data_set, data_loader = data_provider(self.args, flag)
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return data_set, data_loader
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def _select_optimizer(self):
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model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
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return model_optim
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def _select_criterion(self, loss_name='MSE'):
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if loss_name == 'MSE':
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return nn.MSELoss()
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elif loss_name == 'MAPE':
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return mape_loss()
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elif loss_name == 'MASE':
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return mase_loss()
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elif loss_name == 'SMAPE':
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return smape_loss()
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def train(self, setting):
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train_data, train_loader = self._get_data(flag='train')
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vali_data, vali_loader = self._get_data(flag='val')
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path = os.path.join(self.args.checkpoints, setting)
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if not os.path.exists(path):
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os.makedirs(path)
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time_now = time.time()
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train_steps = len(train_loader)
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early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
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model_optim = self._select_optimizer()
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criterion = self._select_criterion(self.args.loss)
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mse = nn.MSELoss()
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for epoch in range(self.args.train_epochs):
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iter_count = 0
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train_loss = []
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self.model.train()
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epoch_time = time.time()
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
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iter_count += 1
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model_optim.zero_grad()
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batch_x = batch_x.float().to(self.device)
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batch_y = batch_y.float().to(self.device)
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batch_y_mark = batch_y_mark.float().to(self.device)
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# decoder input
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dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
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dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
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outputs = self.model(batch_x, None, dec_inp, None)
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, -self.args.pred_len:, f_dim:]
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batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
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batch_y_mark = batch_y_mark[:, -self.args.pred_len:, f_dim:].to(self.device)
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loss_value = criterion(batch_x, self.args.frequency_map, outputs, batch_y, batch_y_mark)
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loss_sharpness = mse((outputs[:, 1:, :] - outputs[:, :-1, :]), (batch_y[:, 1:, :] - batch_y[:, :-1, :]))
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loss = loss_value # + loss_sharpness * 1e-5
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train_loss.append(loss.item())
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if (i + 1) % 100 == 0:
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print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
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speed = (time.time() - time_now) / iter_count
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left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
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print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
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iter_count = 0
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time_now = time.time()
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loss.backward()
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model_optim.step()
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print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
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train_loss = np.average(train_loss)
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vali_loss = self.vali(train_loader, vali_loader, criterion)
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test_loss = vali_loss
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print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
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epoch + 1, train_steps, train_loss, vali_loss, test_loss))
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early_stopping(vali_loss, self.model, path)
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if early_stopping.early_stop:
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print("Early stopping")
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break
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adjust_learning_rate(model_optim, epoch + 1, self.args)
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best_model_path = path + '/' + 'checkpoint.pth'
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self.model.load_state_dict(torch.load(best_model_path))
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return self.model
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def vali(self, train_loader, vali_loader, criterion):
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x, _ = train_loader.dataset.last_insample_window()
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y = vali_loader.dataset.timeseries
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x = torch.tensor(x, dtype=torch.float32).to(self.device)
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x = x.unsqueeze(-1)
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self.model.eval()
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with torch.no_grad():
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# decoder input
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B, _, C = x.shape
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dec_inp = torch.zeros((B, self.args.pred_len, C)).float().to(self.device)
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dec_inp = torch.cat([x[:, -self.args.label_len:, :], dec_inp], dim=1).float()
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# encoder - decoder
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outputs = torch.zeros((B, self.args.pred_len, C)).float() # .to(self.device)
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id_list = np.arange(0, B, 500) # validation set size
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id_list = np.append(id_list, B)
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for i in range(len(id_list) - 1):
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outputs[id_list[i]:id_list[i + 1], :, :] = self.model(x[id_list[i]:id_list[i + 1]], None,
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dec_inp[id_list[i]:id_list[i + 1]],
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None).detach().cpu()
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, -self.args.pred_len:, f_dim:]
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pred = outputs
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true = torch.from_numpy(np.array(y))
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batch_y_mark = torch.ones(true.shape)
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loss = criterion(x.detach().cpu()[:, :, 0], self.args.frequency_map, pred[:, :, 0], true, batch_y_mark)
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self.model.train()
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return loss
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def test(self, setting, test=0):
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_, train_loader = self._get_data(flag='train')
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_, test_loader = self._get_data(flag='test')
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x, _ = train_loader.dataset.last_insample_window()
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y = test_loader.dataset.timeseries
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x = torch.tensor(x, dtype=torch.float32).to(self.device)
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x = x.unsqueeze(-1)
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if test:
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print('loading model')
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self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
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folder_path = './test_results/' + setting + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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self.model.eval()
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with torch.no_grad():
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B, _, C = x.shape
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dec_inp = torch.zeros((B, self.args.pred_len, C)).float().to(self.device)
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dec_inp = torch.cat([x[:, -self.args.label_len:, :], dec_inp], dim=1).float()
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# encoder - decoder
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outputs = torch.zeros((B, self.args.pred_len, C)).float().to(self.device)
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id_list = np.arange(0, B, 1)
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id_list = np.append(id_list, B)
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for i in range(len(id_list) - 1):
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outputs[id_list[i]:id_list[i + 1], :, :] = self.model(x[id_list[i]:id_list[i + 1]], None,
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dec_inp[id_list[i]:id_list[i + 1]], None)
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if id_list[i] % 1000 == 0:
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print(id_list[i])
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, -self.args.pred_len:, f_dim:]
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outputs = outputs.detach().cpu().numpy()
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preds = outputs
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trues = y
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x = x.detach().cpu().numpy()
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for i in range(0, preds.shape[0], preds.shape[0] // 10):
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gt = np.concatenate((x[i, :, 0], trues[i]), axis=0)
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pd = np.concatenate((x[i, :, 0], preds[i, :, 0]), axis=0)
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visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
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print('test shape:', preds.shape)
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# result save
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folder_path = './m4_results/' + self.args.model + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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forecasts_df = pandas.DataFrame(preds[:, :, 0], columns=[f'V{i + 1}' for i in range(self.args.pred_len)])
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forecasts_df.index = test_loader.dataset.ids[:preds.shape[0]]
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forecasts_df.index.name = 'id'
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forecasts_df.set_index(forecasts_df.columns[0], inplace=True)
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forecasts_df.to_csv(folder_path + self.args.seasonal_patterns + '_forecast.csv')
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print(self.args.model)
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file_path = './m4_results/' + self.args.model + '/'
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if 'Weekly_forecast.csv' in os.listdir(file_path) \
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and 'Monthly_forecast.csv' in os.listdir(file_path) \
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and 'Yearly_forecast.csv' in os.listdir(file_path) \
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and 'Daily_forecast.csv' in os.listdir(file_path) \
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and 'Hourly_forecast.csv' in os.listdir(file_path) \
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and 'Quarterly_forecast.csv' in os.listdir(file_path):
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m4_summary = M4Summary(file_path, self.args.root_path)
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# m4_forecast.set_index(m4_winner_forecast.columns[0], inplace=True)
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smape_results, owa_results, mape, mase = m4_summary.evaluate()
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print('smape:', smape_results)
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print('mape:', mape)
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print('mase:', mase)
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print('owa:', owa_results)
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else:
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print('After all 6 tasks are finished, you can calculate the averaged index')
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return
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