first commit

exp/exp_zero_shot_forecasting.py

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+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
+from utils.dtw_metric import dtw, accelerated_dtw
+from utils.augmentation import run_augmentation, run_augmentation_single
+
+warnings.filterwarnings('ignore')
+
+
+class Exp_Zero_Shot_Forecast(Exp_Basic):
+    def __init__(self, args):
+        super(Exp_Zero_Shot_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 test(self, setting, test=0):
+        test_data, test_loader = self._get_data(flag='test')
+
+        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):
+                # start_time = time.time()
+                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():
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                else:
+                    outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                # print("Test cost time: {}".format(time.time() - start_time))
+                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 = batch_y.shape
+                    if outputs.shape[-1] != batch_y.shape[-1]:
+                        outputs = np.tile(outputs, [1, 1, int(batch_y.shape[-1] / outputs.shape[-1])])
+                    outputs = test_data.inverse_transform(outputs.reshape(shape[0] * shape[1], -1)).reshape(shape)
+                    batch_y = test_data.inverse_transform(batch_y.reshape(shape[0] * shape[1], -1)).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.reshape(shape[0] * shape[1], -1)).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.concatenate(preds, axis=0)
+        trues = np.concatenate(trues, axis=0)
+        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)
+
+        # dtw calculation
+        if self.args.use_dtw:
+            dtw_list = []
+            manhattan_distance = lambda x, y: np.abs(x - y)
+            for i in range(preds.shape[0]):
+                x = preds[i].reshape(-1, 1)
+                y = trues[i].reshape(-1, 1)
+                if i % 100 == 0:
+                    print("calculating dtw iter:", i)
+                d, _, _, _ = accelerated_dtw(x, y, dist=manhattan_distance)
+                dtw_list.append(d)
+            dtw = np.array(dtw_list).mean()
+        else:
+            dtw = 'Not calculated'
+
+        mae, mse, rmse, mape, mspe = metric(preds, trues)
+        print('mse:{}, mae:{}, dtw:{}'.format(mse, mae, dtw))
+        f = open("result_zero_shot_forecast_search.txt", 'a')
+        f.write(setting + "  \n")
+        f.write('mse:{}, mae:{}, dtw:{}'.format(mse, mae, dtw))
+        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