# -*- coding: utf-8 -*-
"""
One-class classification
this is partially adapted from https://github.com/lukasruff/Deep-SAD-PyTorch (MIT license)
@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.dsad import DSADLoss
from torch.utils.data import DataLoader, TensorDataset
from torch.utils.data.sampler import WeightedRandomSampler
import torch
import numpy as np
from collections import Counter
[docs]class DeepSADTS(BaseDeepAD):
""" Deep Semi-supervised Anomaly Detection (ICLR'20)
:cite:`ruff2020dsad`
This model extends the semi-supervised anomaly detection framework to time-series datasets, aiming
to detect anomalies by learning a representation of the data in a lower-dimensional hypersphere.
Args:
data_type (str, optional):
The type of data, here it's defaulted to 'ts' (time-series).
epochs (int, optional):
The number of epochs for training, default is 100.
batch_size (int, optional):
The size of the mini-batch for training, default is 64.
lr (float, optional):
The learning rate for the optimizer, default is 1e-3.
network (str, optional):
The type of network architecture to use, default is 'TCN'.
rep_dim (int, optional):
The size of the representation dimension, default is 128.
hidden_dims (Union[list, str, int], optional):
The dimensions for hidden layers. It can be a list, a comma-separated string, or a single integer. Default is '100,50'.
- 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
act (str, optional):
The activation function to use. Possible values are 'ReLU', 'LeakyReLU', 'Sigmoid', 'Tanh', default is 'ReLU'.
bias (bool, optional):
Whether to include a bias term in the layers, default is False.
n_heads (int, optional):
The number of heads in a multi-head attention mechanism, default is 8.
d_model (int, optional):
The number of dimensions in the transformer model, default is 512.
attn (str, optional):
The type of attention mechanism used, default is 'self_attn'.
pos_encoding (str, optional):
The type of positional encoding used in the transformer model, default is 'fixed'.
norm (str, optional):
The type of normalization used in the transformer model, default is 'LayerNorm'.
epoch_steps (int, optional):
The maximum number of steps per epoch, default is -1, indicating that all batches will be processed.
prt_steps (int, optional):
The number of epoch intervals for printing progress, default is 10.
device (str, optional):
The device to use for training and inference, default is 'cuda'.
verbose (int, optional):
The verbosity mode, default is 2.
random_state (int, optional):
The seed for the random number generator, default is 42.
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
network='TCN', 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',
epoch_steps=-1, prt_steps=10, device='cuda',
verbose=2, random_state=42):
"""
Initializes the DeepSADTS model with the provided parameters.
"""
super(DeepSADTS, self).__init__(
data_type='ts', model_name='DeepSAD', 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.hidden_dims = hidden_dims
self.rep_dim = rep_dim
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
self.c = None
return
[docs] def training_prepare(self, X, y):
"""
Prepares the model for training by setting up data loaders, initializing the network, and defining the loss criterion.
Args:
X (np.ndarray):
The input feature matrix for training.
y (np.ndarray):
The target labels where 1 indicates known anomalies.
Returns:
train_loader (DataLoader):
The data loader for training.
net (nn.Module):
The neural network for feature extraction.
criterion (Loss):
The loss function used for training.
"""
# By following the original paper,
# use -1 to denote known anomalies, and 1 to denote known inliers
known_anom_id = np.where(y == 1)
y = np.zeros_like(y)
y[known_anom_id] = -1
counter = Counter(y)
if self.verbose >= 2:
print(f'training data counter: {counter}')
dataset = TensorDataset(torch.from_numpy(X).float(),
torch.from_numpy(y).long())
weight_map = {0: 1. / counter[0], -1: 1. / counter[-1]}
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,
shuffle=True if sampler is None else False)
network_params = {
'n_features': self.n_features,
'n_hidden': self.hidden_dims,
'n_output': self.rep_dim,
'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)
self.c = self._set_c(net, train_loader)
criterion = DSADLoss(c=self.c)
if self.verbose >= 2:
print(net)
return train_loader, net, criterion
[docs] def inference_prepare(self, X):
"""
Prepares the model for inference by setting up data loaders.
Args:
X (np.ndarray):
The input feature matrix for inference.
Returns:
test_loader (DataLoader):
The data loader 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 training pass.
Args:
batch_x (tuple):
A batch of input data and labels.
net (nn.Module):
The neural network model.
criterion (Loss):
The loss function.
Returns:
loss (torch.Tensor):
The computed loss for the batch.
"""
batch_x, batch_y = batch_x
# from collections import Counter
# tmp = batch_y.data.cpu().numpy()
# print(Counter(tmp))
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.long().to(self.device)
z = net(batch_x)
loss = criterion(z, batch_y)
return loss
[docs] def inference_forward(self, batch_x, net, criterion):
"""
Performs a forward inference pass.
Args:
batch_x (torch.Tensor):
A batch of input data.
net (nn.Module):
The neural network model.
criterion (Loss):
The loss function used to calculate the anomaly score.
Returns:
batch_z (torch.Tensor):
The encoded batch of data in the feature space.
s (torch.Tensor):
The anomaly scores for the batch.
"""
batch_x = batch_x.float().to(self.device)
batch_z = net(batch_x)
s = criterion(batch_z)
return batch_z, s
def _set_c(self, net, dataloader, eps=0.1):
"""
Initializes the center 'c' for the hypersphere.
Args:
net (nn.Module):
The neural network model.
dataloader (DataLoader):
The data loader to compute the center from.
eps (float):
A small value to ensure 'c' is away from zero, default is 0.1.
Returns:
c (torch.Tensor):
The initialized center of the hypersphere.
"""
net.eval()
z_ = []
with torch.no_grad():
for x, _ in dataloader:
x = x.float().to(self.device)
z = net(x)
z_.append(z.detach())
z_ = torch.cat(z_)
c = torch.mean(z_, dim=0)
# if c is too close to zero, set to +- eps
# a zero unit can be trivially matched with zero weights
c[(abs(c) < eps) & (c < 0)] = -eps
c[(abs(c) < eps) & (c > 0)] = eps
return c