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"""
This function is adapted from [TranAD] by [imperial-qore]
Original source: [https://github.com/imperial-qore/TranAD]
"""
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.nn import TransformerEncoder
from torch.nn import TransformerDecoder
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 PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model).float() * (-math.log(10000.0) / d_model)
)
pe += torch.sin(position * div_term)
pe += torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer("pe", pe)
def forward(self, x, pos=0):
x = x + self.pe[pos : pos + x.size(0), :]
return self.dropout(x)
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=16, dropout=0):
super(TransformerEncoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = nn.LeakyReLU(True)
def forward(self, src, *args, **kwargs):
src2 = self.self_attn(src, src, src)[0]
src = src + self.dropout1(src2)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = src + self.dropout2(src2)
return src
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=16, dropout=0):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.activation = nn.LeakyReLU(True)
def forward(self, tgt, memory, *args, **kwargs):
tgt2 = self.self_attn(tgt, tgt, tgt)[0]
tgt = tgt + self.dropout1(tgt2)
tgt2 = self.multihead_attn(tgt, memory, memory)[0]
tgt = tgt + self.dropout2(tgt2)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout3(tgt2)
return tgt
class TranADModel(nn.Module):
def __init__(self, batch_size, feats, win_size):
super(TranADModel, self).__init__()
self.name = "TranAD"
self.batch = batch_size
self.n_feats = feats
self.n_window = win_size
self.n = self.n_feats * self.n_window
self.pos_encoder = PositionalEncoding(2 * feats, 0.1, self.n_window)
encoder_layers = TransformerEncoderLayer(
d_model=2 * feats, nhead=feats, dim_feedforward=16, dropout=0.1
)
self.transformer_encoder = TransformerEncoder(encoder_layers, 1)
decoder_layers1 = TransformerDecoderLayer(
d_model=2 * feats, nhead=feats, dim_feedforward=16, dropout=0.1
)
self.transformer_decoder1 = TransformerDecoder(decoder_layers1, 1)
decoder_layers2 = TransformerDecoderLayer(
d_model=2 * feats, nhead=feats, dim_feedforward=16, dropout=0.1
)
self.transformer_decoder2 = TransformerDecoder(decoder_layers2, 1)
self.fcn = nn.Sequential(nn.Linear(2 * feats, feats), nn.Sigmoid())
def encode(self, src, c, tgt):
src = torch.cat((src, c), dim=2)
src = src * math.sqrt(self.n_feats)
src = self.pos_encoder(src)
memory = self.transformer_encoder(src)
tgt = tgt.repeat(1, 1, 2)
return tgt, memory
def forward(self, src, tgt):
# Phase 1 - Without anomaly scores
c = torch.zeros_like(src)
x1 = self.fcn(self.transformer_decoder1(*self.encode(src, c, tgt)))
# Phase 2 - With anomaly scores
c = (x1 - src) ** 2
x2 = self.fcn(self.transformer_decoder2(*self.encode(src, c, tgt)))
return x1, x2
class TranAD(BaseDetector):
def __init__(self,
win_size = 100,
feats = 1,
batch_size = 128,
epochs = 50,
patience = 3,
lr = 1e-4,
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 = TranADModel(batch_size=self.batch_size, feats=self.feats, win_size=self.win_size).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()
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
)
for epoch in range(1, self.epochs + 1):
self.model.train(mode=True)
avg_loss = 0
loop = tqdm.tqdm(
enumerate(train_loader), total=len(train_loader), leave=True
)
for idx, (x, _) in loop:
if torch.isnan(x).any() or torch.isinf(x).any():
print("Input data contains nan or inf")
x = torch.nan_to_num(x)
x = x.to(self.device)
bs = x.shape[0]
x = x.permute(1, 0, 2)
elem = x[-1, :, :].view(1, bs, self.feats)
self.optimizer.zero_grad()
z = self.model(x, elem)
loss = (1 / epoch) * self.criterion(z[0], elem) + (1 - 1 / epoch) * self.criterion(z[1], elem)
loss.backward(retain_graph=True)
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 torch.isnan(loss):
print(f"Loss is nan at epoch {epoch}")
break
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, (x, _) in loop:
if torch.isnan(x).any() or torch.isinf(x).any():
print("Input data contains nan or inf")
x = torch.nan_to_num(x)
x = x.to(self.device)
# x = x.unsqueeze(-1)
bs = x.shape[0]
x = x.permute(1, 0, 2)
elem = x[-1, :, :].view(1, bs, self.feats)
self.optimizer.zero_grad()
z = self.model(x, elem)
loss = (1 / epoch) * self.criterion(z[0], elem) + (
1 - 1 / epoch
) * self.criterion(z[1], elem)
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 = []
loop = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), leave=True)
with torch.no_grad():
for idx, (x, _) in loop:
x = x.to(self.device)
bs = x.shape[0]
x = x.permute(1, 0, 2)
elem = x[-1, :, :].view(1, bs, self.feats)
# breakpoint()
_, z = self.model(x, elem)
loss = torch.mean(F.mse_loss(z, elem, reduction="none")[0], axis=-1)
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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