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Add code comments for documentation
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@yyysjz1997
yyysjz1997 authored Nov 21, 2023
1 parent e50de07 commit 96c34e7
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222 changes: 161 additions & 61 deletions deepod/models/time_series/devnet.py
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Expand Up @@ -19,78 +19,94 @@ class DevNetTS(BaseDeepAD):
Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19)
:cite:`pang2019deep`
Parameters
----------
epochs: int, optional (default=100)
Number of training epochs
batch_size: int, optional (default=64)
Number of samples in a mini-batch
lr: float, optional (default=1e-3)
Learning rate
rep_dim: int, optional (default=128)
it is for consistency, unused in this model
hidden_dims: list, str or int, optional (default='100,50')
Number of neural units in hidden layers
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
act: str, optional (default='ReLU')
activation layer name
choice = ['ReLU', 'LeakyReLU', 'Sigmoid', 'Tanh']
bias: bool, optional (default=False)
Additive bias in linear layer
n_heads: int, optional(default=8):
number of head in multi-head attention
used when network='transformer', deprecated in other networks
d_model: int, optional (default=64)
number of dimensions in Transformer
used when network='transformer', deprecated in other networks
pos_encoding: str, optional (default='fixed')
manner of positional encoding, deprecated in other networks
choice = ['fixed', 'learnable']
norm: str, optional (default='BatchNorm')
manner of norm in Transformer, deprecated in other networks
choice = ['LayerNorm', 'BatchNorm']
margin: float, optional (default=5.)
margin value used in the deviation loss function
l: int, optional (default=5000.)
the size of samples of the Gaussian distribution used in the deviation loss function
epoch_steps: int, optional (default=-1)
Maximum steps in an epoch
- If -1, all the batches will be processed
prt_steps: int, optional (default=10)
Number of epoch intervals per printing
device: str, optional (default='cuda')
torch device,
verbose: int, optional (default=1)
Verbosity mode
random_state： int, optional (default=42)
the seed used by the random
- 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,
Expand All @@ -115,6 +131,31 @@ def __init__(self, epochs=100, batch_size=64, lr=1e-3,
return

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
Expand Down Expand Up @@ -153,12 +194,47 @@ def training_prepare(self, X, y):
return train_loader, net, criterion

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

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)
Expand All @@ -167,6 +243,30 @@ def training_forward(self, batch_x, net, criterion):
return loss

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)
Expand Down
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