timesead.models.baselines.threshold_ads

Classes

`Base_ThresholdAD`	Base class for all neural network modules.
`OOSAnomalyDetector`	Base class for all neural network modules.
`IQRAnomalyDetector`	Base class for all neural network modules.

Module Contents

class timesead.models.baselines.threshold_ads.Base_ThresholdAD(first_diffs: bool, cum_method: str, feature_index: int | 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:

first_diffs (bool)
cum_method (str)
feature_index (Optional[int])
input_shape (str)

A Basis Threshold Anomaly Detector: A simlpe anomaly detector that is equals zero for all data within the given Threshold and else equals the distance to the given lower/upper thr of training value.

Parameters:

first_diffs[bool] – Flag, if instead of raw values first difference should be used.
cum_method[str] – One of {‘mean’, ‘max’}. Accumulation method over feature dimension. Note that when feature_index is not None. Then accumulation method will be ignored.
feature_index[optional[int]] – Take scores for specific feature. If none, above accumulation rule will be used.
first_diffs (bool)
cum_method (str)
feature_index (Optional[int])
input_shape (str)

first_diffs

cum_method

feature

input_shape = 'btf'

lower_thresh = None

upper_thresh = 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

class timesead.models.baselines.threshold_ads.OOSAnomalyDetector(first_diffs: bool = False, cum_method: str = 'mean', feature_index: int | None = None, *args, **kwargs)

Bases: Base_ThresholdAD

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:

first_diffs (bool)
cum_method (str)
feature_index (Optional[int])

Out of Support Anomaly Detector: A simlpe anomaly detector that is equals zero for all data within the data support of the training data and else equals the distance to the min/max of training value.

Parameters:

first_diffs[bool] – Flag, if instead of raw values first difference should be used. Default is False.
cum_method[str] – One of {‘mean’, ‘max’}. Accumulation method over feature dimension. Note that when feature_index is not None. Then accumulation method will be ignored. Default is ‘mean’.
feature_index[optional[int]] – Take scores for specific feature. If none, above accumulation rule will be used. Default is None.
first_diffs (bool)
cum_method (str)
feature_index (Optional[int])

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

class timesead.models.baselines.threshold_ads.IQRAnomalyDetector(std_factor: float = 2.58, first_diffs: bool = False, cum_method: str = 'mean', feature_index: int | None = None, *args, **kwargs)

Bases: Base_ThresholdAD

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:

std_factor (float)
first_diffs (bool)
cum_method (str)
feature_index (Optional[int])

Interquantile Range Anomaly Detector: A simlpe anomaly detector that is equals zero for all data within some interquantile range of the normal training data and else equals the distance to nearest quantile border.

Parameters:

std_factor[float] – float, that gives the width of the IQR. Default is 2.58 (=99.5% normal quantile).
first_diffs[bool] – Flag, if instead of raw values first difference should be used. Default is False.
cum_method[str] – One of {‘mean’, ‘max’}. Accumulation method over feature dimension. Note that when feature_index is not None. Then accumulation method will be ignored. Default is ‘mean’.
feature_index[optional[int]] – Take scores for specific feature. If none, above accumulation rule will be used. Default is None.
std_factor (float)
first_diffs (bool)
cum_method (str)
feature_index (Optional[int])

std_factor = 2.58

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