timesead.models.baselines.pcas

Classes

`PCAAnomalyDetector`	Base class for all neural network modules.
`KernelPCAAnomalyDetector`	Base class for all neural network modules.

Module Contents

class timesead.models.baselines.pcas.PCAAnomalyDetector(n_components: int | float, pca_method: str = 'standard', first_diffs: bool = False, cum_method: str = 'mean', input_shape: str = 'btf', **kwargs)

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_components (Union[int, float])
pca_method (str)
first_diffs (bool)
cum_method (str)
input_shape (str)

PCA Anomaly Detector: A simlpe anomaly detector that uses PCA as reconstruction method.

Parameters:

n_components[int – number of principle components to keep. If float with 0<n_comp<=1.,the number of components will equal the explained variance, note, that this only works for standard pca. Defaults equals 0.95.
float] – number of principle components to keep. If float with 0<n_comp<=1.,the number of components will equal the explained variance, note, that this only works for standard pca. Defaults equals 0.95.
pca_method[str – {‘standard’,’kernel’}]: Which PCA method should be used. Default is standard.
first_diffs[bool] – Flag, if instead of raw values first difference should be used. Default is False.
cum_method[str – {‘mean’,’max’}]: Accumulation method over feature dimension. Note that when feature_index is not None. Then accumulation method will be ignored. Default is ‘mean’.
n_components (Union[int, float])
pca_method (str)
first_diffs (bool)
cum_method (str)
input_shape (str)

first_diffs = False

cum_method = ''

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

class timesead.models.baselines.pcas.KernelPCAAnomalyDetector(n_components: int, kernel: str = 'rbf', gamma: float | None = None, first_diffs: bool = False, cum_method: str = 'mean', input_shape: str = 'btf', **kwargs)

Bases: PCAAnomalyDetector

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_components (int)
kernel (str)
gamma (Optional[float])
first_diffs (bool)
cum_method (str)
input_shape (str)

KernelPCA Anomaly Detector: A simlpe anomaly detector that uses Kernel PCA as reconstruction method.

Parameters:

n_components[int – number of principle components to keep. If float with 0<n_comp<=1.,the number of components will equal the explained variance. Defaults equals 0.95.
float] – number of principle components to keep. If float with 0<n_comp<=1.,the number of components will equal the explained variance. Defaults equals 0.95.
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’.
n_components (int)
kernel (str)
gamma (Optional[float])
first_diffs (bool)
cum_method (str)
input_shape (str)

model