timesead.models.baselines.hbos

Classes

HBOSAD

Base class for all neural network modules.

Module Contents

class timesead.models.baselines.hbos.HBOSAD(n_bins: int | None = 10, alpha: float = 0.1, bin_tol: float = 0.5, input_shape: str = 'btf')

Bases: timesead.models.common.AnomalyDetector

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:
Histogram Based Outlier Score

The method assumes feature independence and calculates the degree of outlyingness by building histograms. See [Goldstein2012] for details.

Implementation derived from https://github.com/HPI-Information-Systems/TimeEval-algorithms

[Goldstein2012]

Markus Goldstein and Andreas Dengel. 2012. Histogrambased Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm. In Proceedings of the German Conference on Artificial Intelligence Poster and Demo Track (KI), 59-63.

Parameters:

  • n_bins[Optional[int]] – The number of bins. Set to None for automatic selection.

  • alpha[float] – The regularizer for preventing overflow.

  • bin_tol[float] – The parameter to decide the flexibility while dealing with the samples falling outside the bins.

  • n_bins (Optional[int])

  • alpha (float)

  • bin_tol (float)

  • input_shape (str)

n_bins = 10
alpha = 0.1
bin_tol = 0.5
input_shape = 'btf'
model
fit(dataset: torch.utils.data.DataLoader) → None

Fit this anomaly detector on a dataset. Note that we assume only normal data here.

Parameters:

dataset (torch.utils.data.DataLoader) – A dataset

Return type:

None

compute_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Compute the online anomaly score for a batch of inputs. The output tensor must have the same shape as the output of format_targets when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window.

Parameters:

inputs (Tuple[torch.Tensor, Ellipsis]) – tuple of input tensors

Returns:

Tensor of shape (B,) that contains the anomaly scores for this batch

Return type:

torch.Tensor

abstract compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Compute the offline anomaly score for a batch of inputs. The output tensor must have the same shape as the output of format_targets when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window.

Parameters:

inputs (Tuple[torch.Tensor, Ellipsis]) – tuple of input tensors

Returns:

Tensor of shape (N,) that contains the anomaly scores for this batch

Return type:

torch.Tensor

format_online_targets(targets: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Format the labels for a batch of targets. The output tensor must have the same shape as the output of compute_online_anomaly_score when called with the corresponding inputs for this batch.

Parameters:

targets (Tuple[torch.Tensor, Ellipsis]) – tuple of target tensors

Returns:

Tensor of shape (B,) that contains the ground truth labels for this batch

Return type:

torch.Tensor