# -*- coding: utf-8 -*-
"""
Deep anomaly detection with deviation networks.
PyTorch's implementation
@Author: Hongzuo Xu <hongzuoxu@126.com, xuhongzuo13@nudt.edu.cn>
"""
from deepod.core.base_model import BaseDeepAD
from deepod.core.networks.base_networks import get_network
from deepod.models.tabular.devnet import DevLoss
from torch.utils.data import DataLoader, TensorDataset
from torch.utils.data.sampler import WeightedRandomSampler
import torch
import numpy as np
[docs]class DevNetTS(BaseDeepAD):
"""
Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19)
:cite:`pang2019deep`
Deviation Networks (DevNet) designed for weakly-supervised anomaly detection.
This implementation is based on the architecture presented in the KDD'19 paper:
"Deviation Networks for Weakly-supervised Anomaly Detection" by Pang et al.
Args:
hidden_dims (Union[list, str, int], optional):
The dimensions for the hidden layers. Can be a list of integers, a string of comma-separated integers, or a single integer.
- If list, each item is a layer
- If str, neural units of hidden layers are split by comma
- If int, number of neural units of single hidden layer
- Defaults to '100,50'.
act (str, optional):
Activation function to use. Choices include 'ReLU', 'LeakyReLU', 'Sigmoid', 'Tanh'. Default is 'ReLU'.
bias (bool, optional):
Whether to include a bias term in the linear layers. Default is False.
n_heads (int, optional):
Number of heads in multi-head attention. Only used when network is 'transformer'. Default is 8.
pos_encoding (str, optional):
The type of positional encoding to use. Only relevant when network is 'transformer'. Choices are 'fixed' or 'learnable'. Default is 'fixed'.
norm (str, optional):
Normalization method in the Transformer. Only relevant when network is 'transformer'. Choices are 'LayerNorm' or 'BatchNorm'. Default is 'LayerNorm'.
epochs (int, optional):
Number of training epochs. Default is 100.
batch_size (int, optional):
Batch size for training. Default is 64.
lr (float, optional):
Learning rate for the optimizer. Default is 1e-3.
network (str, optional):
Type of network architecture to use. Default is 'Transformer'.
seq_len (int, optional):
Length of input sequences for models that require it. Default is 100.
stride (int, optional):
Stride of the convolutional layers. Default is 1.
rep_dim (int, optional):
The representation dimension. Unused in this model but kept for consistency. Default is 128.
d_model (int, optional):
The number of expected features in the transformer model. Only used when network is 'transformer'. Default is 512.
attn (str, optional):
Type of attention to use. Only used when network is 'transformer'. Default is 'self_attn'.
margin (float, optional):
Margin for the deviation loss function. Default is 5.
l (int, optional):
The size of the sample for the Gaussian distribution in the deviation loss function. Default is 5000.
epoch_steps (int, optional):
Maximum number of steps per epoch. If -1, all batches will be processed. Default is -1.
prt_steps (int, optional):
Number of epoch intervals for printing during training. Default is 10.
device (str, optional):
The device to use for training ('cuda' or 'cpu'). Default is 'cuda'.
verbose (int, optional):
Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Default is 2.
random_state (int, optional):
Seed for the random number generator for reproducibility. Default is 42.
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
network='Transformer', seq_len=100, stride=1,
rep_dim=128, hidden_dims='100,50', act='ReLU', bias=False,
n_heads=8, d_model=512, attn='self_attn', pos_encoding='fixed', norm='LayerNorm',
margin=5., l=5000,
epoch_steps=-1, prt_steps=10, device='cuda',
verbose=2, random_state=42):
"""
Initialize the DevNetTS.
"""
super(DevNetTS, self).__init__(
data_type='ts', model_name='DevNet', epochs=epochs, batch_size=batch_size, lr=lr,
network=network, seq_len=seq_len, stride=stride,
epoch_steps=epoch_steps, prt_steps=prt_steps, device=device,
verbose=verbose, random_state=random_state
)
self.margin = margin
self.l = l
self.hidden_dims = hidden_dims
self.act = act
self.bias = bias
# parameters for Transformer
self.n_heads = n_heads
self.d_model = d_model
self.attn = attn
self.pos_encoding = pos_encoding
self.norm = norm
return
[docs] def training_prepare(self, X, y):
"""
Prepares the data and model for training by creating a balanced data loader,
initializing the network, and setting up the loss criterion.
Args:
X (np.ndarray):
The input features for training.
y (np.ndarray):
The target labels for training, where 1 indicates an anomaly.
Returns:
train_loader (DataLoader):
A DataLoader with balanced mini-batches for training.
net (nn.Module):
The initialized neural network model.
criterion (Loss):
The loss function used during training.
"""
# loader: balanced loader, a mini-batch contains a half of normal data and a half of anomalies
n_anom = np.where(y == 1)[0].shape[0]
n_norm = self.n_samples - n_anom
weight_map = {0: 1. / n_norm, 1: 1. / n_anom}
dataset = TensorDataset(torch.from_numpy(X).float(), torch.from_numpy(y).long())
sampler = WeightedRandomSampler(weights=[weight_map[label.item()] for data, label in dataset],
num_samples=len(dataset), replacement=True)
train_loader = DataLoader(dataset, batch_size=self.batch_size, sampler=sampler)
network_params = {
'n_features': self.n_features,
'n_hidden': self.hidden_dims,
'n_output': 1,
'activation': self.act,
'bias': self.bias
}
if self.network == 'Transformer':
network_params['n_heads'] = self.n_heads
network_params['d_model'] = self.d_model
network_params['pos_encoding'] = self.pos_encoding
network_params['norm'] = self.norm
network_params['attn'] = self.attn
network_params['seq_len'] = self.seq_len
elif self.network == 'ConvSeq':
network_params['seq_len'] = self.seq_len
network_class = get_network(self.network)
net = network_class(**network_params).to(self.device)
criterion = DevLoss(margin=self.margin, l=self.l)
if self.verbose >= 2:
print(net)
return train_loader, net, criterion
[docs] def inference_prepare(self, X):
"""
Prepares the data for inference.
Args:
X (Tensor):
The input features for inference.
Returns:
test_loader (DataLoader):
A DataLoader for inference.
"""
test_loader = DataLoader(X, batch_size=self.batch_size,
drop_last=False, shuffle=False)
self.criterion.reduction = 'none'
return test_loader
[docs] def training_forward(self, batch_x, net, criterion):
"""
Performs a forward pass during training.
Args:
batch_x (tuple):
A batch of input features and target labels.
net (nn.Module):
The neural network model.
criterion (Loss):
The loss function used during training.
Returns:
loss (Tensor):
The computed loss for the batch.
"""
batch_x, batch_y = batch_x
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.to(self.device)
pred = net(batch_x)
loss = criterion(batch_y, pred)
return loss
[docs] def inference_forward(self, batch_x, net, criterion):
"""
Performs a forward pass during inference.
Args:
batch_x (Tensor):
A batch of input features.
net (nn.Module):
The neural network model.
criterion (Loss):
The loss function used during training. Not used.
Returns:
batch_z (Tensor):
The batch of input features (unmodified).
s (Tensor):
The computed scores for the batch.
"""
batch_x = batch_x.float().to(self.device)
s = net(batch_x)
s = s.view(-1)
batch_z = batch_x
return batch_z, s