timesead.models.baselines.eif

Classes

EIFAD

Base class for all neural network modules.

Module Contents

class timesead.models.baselines.eif.EIFAD(n_trees: int = 200, sample_size: int = 256, extension_level: int | None = None, 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:

  • n_trees (int)

  • sample_size (int)

  • extension_level (Optional[int])

  • input_shape (str)

Extended Isolation Forest Anomaly Detector

An implementation of the Extended Isolation Forest (EIF) for anomaly detection as described in [Hariri2019].

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

[Hariri2019]

S. Hariri, M. C. Kind and R. J. Brunner, “Extended Isolation Forest,” in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 4, pp. 1479-1489, 1 April 2021, doi: 10.1109/TKDE.2019.2947676.

Parameters:

  • n_trees[int] – The number of trees in the forest.

  • sample_size[int] – The size of the subsample to be used in creation of each tree. Must be smaller than data size.

  • extension_level[Optional[int]] – Specifies degree of freedom in choosing the hyperplanes for dividing up data. Must be smaller than the dimension n of the dataset. Value of 0 is identical to standard Isolation Forest, and None is equivalent to N-1 or fully extended

  • n_trees (int)

  • sample_size (int)

  • extension_level (Optional[int])

  • input_shape (str)

n_trees = 200
sample_size = 256
extension_level = None
input_shape = 'btf'
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