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exp/exp_anomaly_detection.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, adjustment
+from sklearn.metrics import precision_recall_fscore_support
+from sklearn.metrics import accuracy_score
+import torch.multiprocessing
+
+torch.multiprocessing.set_sharing_strategy('file_system')
+import torch
+import torch.nn as nn
+from torch import optim
+import os
+import time
+import warnings
+import numpy as np
+
+warnings.filterwarnings('ignore')
+
+
+class Exp_Anomaly_Detection(Exp_Basic):
+    def __init__(self, args):
+        super(Exp_Anomaly_Detection, 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 vali(self, vali_data, vali_loader, criterion):
+        total_loss = []
+        self.model.eval()
+        with torch.no_grad():
+            for i, (batch_x, _) in enumerate(vali_loader):
+                batch_x = batch_x.float().to(self.device)
+
+                outputs = self.model(batch_x, None, None, None)
+
+                f_dim = -1 if self.args.features == 'MS' else 0
+                outputs = outputs[:, :, f_dim:]
+                pred = outputs.detach()
+                true = batch_x.detach()
+
+                loss = criterion(pred, true)
+                total_loss.append(loss.item())
+        total_loss = np.average(total_loss)
+        self.model.train()
+        return total_loss
+
+    def train(self, setting):
+        train_data, train_loader = self._get_data(flag='train')
+        vali_data, vali_loader = self._get_data(flag='val')
+        test_data, test_loader = self._get_data(flag='test')
+
+        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()
+
+        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) in enumerate(train_loader):
+                iter_count += 1
+                model_optim.zero_grad()
+
+                batch_x = batch_x.float().to(self.device)
+
+                outputs = self.model(batch_x, None, None, None)
+
+                f_dim = -1 if self.args.features == 'MS' else 0
+                outputs = outputs[:, :, f_dim:]
+                loss = criterion(outputs, batch_x)
+                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(vali_data, vali_loader, criterion)
+            test_loss = self.vali(test_data, test_loader, criterion)
+
+            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 test(self, setting, test=0):
+        test_data, test_loader = self._get_data(flag='test')
+        train_data, train_loader = self._get_data(flag='train')
+        if test:
+            print('loading model')
+            self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
+
+        attens_energy = []
+        folder_path = './test_results/' + setting + '/'
+        if not os.path.exists(folder_path):
+            os.makedirs(folder_path)
+
+        self.model.eval()
+        self.anomaly_criterion = nn.MSELoss(reduce=False)
+
+        # (1) stastic on the train set
+        with torch.no_grad():
+            for i, (batch_x, batch_y) in enumerate(train_loader):
+                batch_x = batch_x.float().to(self.device)
+                # reconstruction
+                outputs = self.model(batch_x, None, None, None)
+                # criterion
+                score = torch.mean(self.anomaly_criterion(batch_x, outputs), dim=-1)
+                score = score.detach().cpu().numpy()
+                attens_energy.append(score)
+
+        attens_energy = np.concatenate(attens_energy, axis=0).reshape(-1)
+        train_energy = np.array(attens_energy)
+
+        # (2) find the threshold
+        attens_energy = []
+        test_labels = []
+        for i, (batch_x, batch_y) in enumerate(test_loader):
+            batch_x = batch_x.float().to(self.device)
+            # reconstruction
+            outputs = self.model(batch_x, None, None, None)
+            # criterion
+            score = torch.mean(self.anomaly_criterion(batch_x, outputs), dim=-1)
+            score = score.detach().cpu().numpy()
+            attens_energy.append(score)
+            test_labels.append(batch_y)
+
+        attens_energy = np.concatenate(attens_energy, axis=0).reshape(-1)
+        test_energy = np.array(attens_energy)
+        combined_energy = np.concatenate([train_energy, test_energy], axis=0)
+        threshold = np.percentile(combined_energy, 100 - self.args.anomaly_ratio)
+        print("Threshold :", threshold)
+
+        # (3) evaluation on the test set
+        pred = (test_energy > threshold).astype(int)
+        test_labels = np.concatenate(test_labels, axis=0).reshape(-1)
+        test_labels = np.array(test_labels)
+        gt = test_labels.astype(int)
+
+        print("pred:   ", pred.shape)
+        print("gt:     ", gt.shape)
+
+        # (4) detection adjustment
+        gt, pred = adjustment(gt, pred)
+
+        pred = np.array(pred)
+        gt = np.array(gt)
+        print("pred: ", pred.shape)
+        print("gt:   ", gt.shape)
+
+        accuracy = accuracy_score(gt, pred)
+        precision, recall, f_score, support = precision_recall_fscore_support(gt, pred, average='binary')
+        print("Accuracy : {:0.4f}, Precision : {:0.4f}, Recall : {:0.4f}, F-score : {:0.4f} ".format(
+            accuracy, precision,
+            recall, f_score))
+
+        f = open("result_anomaly_detection.txt", 'a')
+        f.write(setting + "  \n")
+        f.write("Accuracy : {:0.4f}, Precision : {:0.4f}, Recall : {:0.4f}, F-score : {:0.4f} ".format(
+            accuracy, precision,
+            recall, f_score))
+        f.write('\n')
+        f.write('\n')
+        f.close()
+        return