208 lines
7.6 KiB
Python
208 lines
7.6 KiB
Python
from data_provider.data_factory import data_provider
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from exp.exp_basic import Exp_Basic
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from utils.tools import EarlyStopping, adjust_learning_rate, adjustment
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from sklearn.metrics import precision_recall_fscore_support
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from sklearn.metrics import accuracy_score
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import torch.multiprocessing
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torch.multiprocessing.set_sharing_strategy('file_system')
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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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warnings.filterwarnings('ignore')
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class Exp_Anomaly_Detection(Exp_Basic):
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def __init__(self, args):
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super(Exp_Anomaly_Detection, self).__init__(args)
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def _build_model(self):
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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):
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criterion = nn.MSELoss()
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return criterion
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def vali(self, vali_data, vali_loader, criterion):
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total_loss = []
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self.model.eval()
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with torch.no_grad():
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for i, (batch_x, _) in enumerate(vali_loader):
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batch_x = batch_x.float().to(self.device)
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outputs = self.model(batch_x, None, None, None)
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, :, f_dim:]
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pred = outputs.detach()
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true = batch_x.detach()
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loss = criterion(pred, true)
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total_loss.append(loss.item())
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total_loss = np.average(total_loss)
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self.model.train()
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return total_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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test_data, test_loader = self._get_data(flag='test')
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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()
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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) 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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outputs = self.model(batch_x, None, None, None)
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, :, f_dim:]
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loss = criterion(outputs, batch_x)
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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(vali_data, vali_loader, criterion)
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test_loss = self.vali(test_data, test_loader, criterion)
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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 test(self, setting, test=0):
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test_data, test_loader = self._get_data(flag='test')
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train_data, train_loader = self._get_data(flag='train')
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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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attens_energy = []
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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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self.anomaly_criterion = nn.MSELoss(reduce=False)
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# (1) stastic on the train set
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with torch.no_grad():
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for i, (batch_x, batch_y) in enumerate(train_loader):
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batch_x = batch_x.float().to(self.device)
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# reconstruction
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outputs = self.model(batch_x, None, None, None)
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# criterion
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score = torch.mean(self.anomaly_criterion(batch_x, outputs), dim=-1)
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score = score.detach().cpu().numpy()
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attens_energy.append(score)
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attens_energy = np.concatenate(attens_energy, axis=0).reshape(-1)
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train_energy = np.array(attens_energy)
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# (2) find the threshold
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attens_energy = []
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test_labels = []
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for i, (batch_x, batch_y) in enumerate(test_loader):
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batch_x = batch_x.float().to(self.device)
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# reconstruction
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outputs = self.model(batch_x, None, None, None)
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# criterion
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score = torch.mean(self.anomaly_criterion(batch_x, outputs), dim=-1)
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score = score.detach().cpu().numpy()
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attens_energy.append(score)
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test_labels.append(batch_y)
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attens_energy = np.concatenate(attens_energy, axis=0).reshape(-1)
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test_energy = np.array(attens_energy)
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combined_energy = np.concatenate([train_energy, test_energy], axis=0)
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threshold = np.percentile(combined_energy, 100 - self.args.anomaly_ratio)
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print("Threshold :", threshold)
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# (3) evaluation on the test set
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pred = (test_energy > threshold).astype(int)
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test_labels = np.concatenate(test_labels, axis=0).reshape(-1)
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test_labels = np.array(test_labels)
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gt = test_labels.astype(int)
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print("pred: ", pred.shape)
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print("gt: ", gt.shape)
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# (4) detection adjustment
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gt, pred = adjustment(gt, pred)
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pred = np.array(pred)
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gt = np.array(gt)
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print("pred: ", pred.shape)
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print("gt: ", gt.shape)
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accuracy = accuracy_score(gt, pred)
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precision, recall, f_score, support = precision_recall_fscore_support(gt, pred, average='binary')
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print("Accuracy : {:0.4f}, Precision : {:0.4f}, Recall : {:0.4f}, F-score : {:0.4f} ".format(
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accuracy, precision,
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recall, f_score))
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f = open("result_anomaly_detection.txt", 'a')
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f.write(setting + " \n")
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f.write("Accuracy : {:0.4f}, Precision : {:0.4f}, Recall : {:0.4f}, F-score : {:0.4f} ".format(
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accuracy, precision,
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recall, f_score))
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f.write('\n')
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f.write('\n')
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f.close()
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return
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