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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().cpu()
true = batch_x.detach().cpu()
loss = criterion(pred, true)
total_loss.append(loss)
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
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