# -*- 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 MLPnet
from torch.utils.data import DataLoader, TensorDataset
from torch.utils.data.sampler import WeightedRandomSampler
import torch
import numpy as np
[docs]class DevNet(BaseDeepAD):
"""
Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19)
:cite:`pang2019deep`
Args:
epochs (int, optional):
number of training epochs (default: 100).
batch_size (int, optional):
number of samples in a mini-batch (default: 64)
lr (float, optional):
learning rate (default: 1e-3)
rep_dim (int, optional):
it is for consistency, unused in this model.
hidden_dims (list, str or int, optional):
number of neural units in hidden layers,
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
(default: '100,50')
act (str, optional):
activation layer name,
choice = ['ReLU', 'LeakyReLU', 'Sigmoid', 'Tanh']
(default='ReLU')
bias (bool, optional):
Additive bias in linear layer (default=False)
margin (float, optional):
margin value used in the deviation loss function (default=5.)
l (int, optional):
the size of samples of the Gaussian distribution
used in the deviation loss function (default=5000.)
epoch_steps (int, optional):
Maximum steps in an epoch.
If -1, all the batches will be processed
(default=-1)
prt_steps (int, optional):
Number of epoch intervals per printing (default=10)
device (str, optional):
torch device (default='cuda').
verbose (int, optional):
Verbosity mode (default=1)
random_state (int, optional):
the seed used by the random (default=42)
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
network='MLP',
rep_dim=128, hidden_dims='100,50', act='ReLU', bias=False,
margin=5., l=5000,
epoch_steps=-1, prt_steps=10, device='cuda',
verbose=2, random_state=42):
super(DevNet, self).__init__(
data_type='tabular', model_name='DevNet', epochs=epochs, batch_size=batch_size, lr=lr,
network=network,
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
return
[docs] def training_prepare(self, X, y):
"""
Args:
X (np.array): input data array
y (np.array): input data label
Returns:
train_loader (torch.DataLoader): data loader of training data
net (torch.nn.Module): neural network
criterion (torch.nn.Module): loss function
"""
# 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
}
net = MLPnet(**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):
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):
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):
batch_x = batch_x.float().to(self.device)
s = net(batch_x)
s = s.view(-1)
batch_z = batch_x
return batch_z, s
class DevLoss(torch.nn.Module):
"""
Deviation Loss
Parameters
----------
margin: float, optional (default=5.)
Center of the pre-defined hyper-sphere in the representation space
l: int, optional (default=5000.)
the size of samples of the Gaussian distribution used in the deviation loss function
reduction: str, optional (default='mean')
choice = [``'none'`` | ``'mean'`` | ``'sum'``]
- If ``'none'``: no reduction will be applied;
- If ``'mean'``: the sum of the output will be divided by the number of
elements in the output;
- If ``'sum'``: the output will be summed
"""
def __init__(self, margin=5., l=5000, reduction='mean'):
super(DevLoss, self).__init__()
self.margin = margin
self.loss_l = l
self.reduction = reduction
return
def forward(self, y_true, y_pred):
ref = torch.randn(self.loss_l) # from the normal dataset
dev = (y_pred - torch.mean(ref)) / torch.std(ref)
inlier_loss = torch.abs(dev)
outlier_loss = torch.abs(torch.max(self.margin - dev, torch.zeros_like(dev)))
loss = (1 - y_true) * inlier_loss + y_true * outlier_loss
if self.reduction == 'mean':
return torch.mean(loss)
elif self.reduction == 'sum':
return torch.sum(loss)
elif self.reduction == 'none':
return loss
return loss