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"""
This function is adapted from [OmniAnomaly] by [TsingHuasuya et al.]
Original source: [https://github.com/NetManAIOps/OmniAnomaly]
"""
from __future__ import division
from __future__ import print_function
import numpy as np
import math
import torch
import torch.nn.functional as F
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from torch import nn
from torch.utils.data import DataLoader
from sklearn.preprocessing import MinMaxScaler
import tqdm
from .base import BaseDetector
from ..utils.dataset import ReconstructDataset
from ..utils.torch_utility import EarlyStoppingTorch, get_gpu
class OmniAnomalyModel(nn.Module):
def __init__(self, feats, device):
super(OmniAnomalyModel, self).__init__()
self.name = 'OmniAnomaly'
self.device = device
self.lr = 0.002
self.beta = 0.01
self.n_feats = feats
self.n_hidden = 32
self.n_latent = 8
self.lstm = nn.GRU(feats, self.n_hidden, 2)
self.encoder = nn.Sequential(
nn.Linear(self.n_hidden, self.n_hidden), nn.PReLU(),
nn.Linear(self.n_hidden, self.n_hidden), nn.PReLU(),
# nn.Flatten(),
nn.Linear(self.n_hidden, 2*self.n_latent)
)
self.decoder = nn.Sequential(
nn.Linear(self.n_latent, self.n_hidden), nn.PReLU(),
nn.Linear(self.n_hidden, self.n_hidden), nn.PReLU(),
nn.Linear(self.n_hidden, self.n_feats), nn.Sigmoid(),
)
def forward(self, x, hidden = None):
bs = x.shape[0]
win = x.shape[1]
# hidden = torch.rand(2, bs, self.n_hidden, dtype=torch.float64) if hidden is not None else hidden
hidden = torch.rand(2, bs, self.n_hidden).to(self.device) if hidden is not None else hidden
out, hidden = self.lstm(x.view(-1, bs, self.n_feats), hidden)
# print('out: ', out.shape) # (L, bs, n_hidden)
# print('hidden: ', hidden.shape) # (2, bs, n_hidden)
## Encode
x = self.encoder(out)
mu, logvar = torch.split(x, [self.n_latent, self.n_latent], dim=-1)
## Reparameterization trick
std = torch.exp(0.5*logvar)
eps = torch.randn_like(std)
x = mu + eps*std
## Decoder
x = self.decoder(x) # (L, bs, n_feats)
return x.reshape(bs, win*self.n_feats), mu.reshape(bs, win*self.n_latent), logvar.reshape(bs, win*self.n_latent), hidden
class OmniAnomaly(BaseDetector):
def __init__(self,
win_size = 5,
feats = 1,
batch_size = 128,
epochs = 50,
patience = 3,
lr = 0.002,
validation_size=0.2
):
super().__init__()
self.__anomaly_score = None
self.cuda = True
self.device = get_gpu(self.cuda)
self.win_size = win_size
self.batch_size = batch_size
self.epochs = epochs
self.feats = feats
self.validation_size = validation_size
self.model = OmniAnomalyModel(feats=self.feats, device=self.device).to(self.device)
self.optimizer = torch.optim.AdamW(
self.model.parameters(), lr=lr, weight_decay=1e-5
)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, 5, 0.9)
self.criterion = nn.MSELoss(reduction = 'none')
self.early_stopping = EarlyStoppingTorch(None, patience=patience)
def fit(self, data):
tsTrain = data[:int((1-self.validation_size)*len(data))]
tsValid = data[int((1-self.validation_size)*len(data)):]
train_loader = DataLoader(
dataset=ReconstructDataset(tsTrain, window_size=self.win_size),
batch_size=self.batch_size,
shuffle=True
)
valid_loader = DataLoader(
dataset=ReconstructDataset(tsValid, window_size=self.win_size),
batch_size=self.batch_size,
shuffle=False
)
mses, klds = [], []
for epoch in range(1, self.epochs + 1):
self.model.train(mode=True)
n = epoch + 1
avg_loss = 0
loop = tqdm.tqdm(
enumerate(train_loader), total=len(train_loader), leave=True
)
for idx, (d, _) in loop:
d = d.to(self.device)
# print('d: ', d.shape)
y_pred, mu, logvar, hidden = self.model(d, hidden if idx else None)
d = d.view(-1, self.feats*self.win_size)
MSE = torch.mean(self.criterion(y_pred, d), axis=-1)
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1)
loss = torch.mean(MSE + self.model.beta * KLD)
mses.append(torch.mean(MSE).item())
klds.append(self.model.beta * torch.mean(KLD).item())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
avg_loss += loss.cpu().item()
loop.set_description(f"Training Epoch [{epoch}/{self.epochs}]")
loop.set_postfix(loss=loss.item(), avg_loss=avg_loss / (idx + 1))
if len(valid_loader) > 0:
self.model.eval()
avg_loss_val = 0
loop = tqdm.tqdm(
enumerate(valid_loader), total=len(valid_loader), leave=True
)
with torch.no_grad():
for idx, (d, _) in loop:
d = d.to(self.device)
y_pred, mu, logvar, hidden = self.model(d, hidden if idx else None)
d = d.view(-1, self.feats*self.win_size)
MSE = torch.mean(self.criterion(y_pred, d), axis=-1)
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1)
loss = torch.mean(MSE + self.model.beta * KLD)
avg_loss_val += loss.cpu().item()
loop.set_description(
f"Validation Epoch [{epoch}/{self.epochs}]"
)
loop.set_postfix(loss=loss.item(), avg_loss_val=avg_loss_val / (idx + 1))
self.scheduler.step()
if len(valid_loader) > 0:
avg_loss = avg_loss_val / len(valid_loader)
else:
avg_loss = avg_loss / len(train_loader)
self.early_stopping(avg_loss, self.model)
if self.early_stopping.early_stop:
print(" Early stopping<<<")
break
def decision_function(self, data):
test_loader = DataLoader(
dataset=ReconstructDataset(data, window_size=self.win_size),
batch_size=self.batch_size,
shuffle=False
)
self.model.eval()
scores = []
y_preds = []
loop = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), leave=True)
with torch.no_grad():
for idx, (d, _) in loop:
d = d.to(self.device)
# print('d: ', d.shape)
y_pred, _, _, hidden = self.model(d, hidden if idx else None)
y_preds.append(y_pred)
d = d.view(-1, self.feats*self.win_size)
# print('y_pred: ', y_pred.shape)
# print('d: ', d.shape)
loss = torch.mean(self.criterion(y_pred, d), axis=-1)
# print('loss: ', loss.shape)
scores.append(loss.cpu())
scores = torch.cat(scores, dim=0)
scores = scores.numpy()
self.__anomaly_score = scores
if self.__anomaly_score.shape[0] < len(data):
self.__anomaly_score = np.array([self.__anomaly_score[0]]*math.ceil((self.win_size-1)/2) +
list(self.__anomaly_score) + [self.__anomaly_score[-1]]*((self.win_size-1)//2))
return self.__anomaly_score
def anomaly_score(self) -> np.ndarray:
return self.__anomaly_score
def param_statistic(self, save_file):
pass
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