timesead.models.baselines.pcas
==============================

.. py:module:: timesead.models.baselines.pcas


Classes
-------

.. autoapisummary::

   timesead.models.baselines.pcas.PCAAnomalyDetector
   timesead.models.baselines.pcas.KernelPCAAnomalyDetector


Module Contents
---------------

.. py:class:: PCAAnomalyDetector(n_components: Union[int, float], pca_method: str = 'standard', first_diffs: bool = False, cum_method: str = 'mean', input_shape: str = 'btf', **kwargs)

   Bases: :py:obj:`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 :meth:`to`, etc.

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

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

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

   :param 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.
   :param 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.
   :param pca_method[str: {'standard','kernel'}]: Which PCA method should be used. Default is standard.
   :param first_diffs[bool]: Flag, if instead of raw values first difference should be used. Default is False.
   :param 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'.


   .. py:attribute:: first_diffs
      :value: False



   .. py:attribute:: cum_method
      :value: ''



   .. py:attribute:: input_shape
      :value: 'btf'



   .. py:method:: fit(dataset: torch.utils.data.DataLoader) -> None

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

      :param dataset: A dataset



   .. py:method:: 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.

      :param inputs: tuple of input tensors
      :return: Tensor of shape (B,) that contains the anomaly scores for this batch



   .. py:method:: compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor
      :abstractmethod:


      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.

      :param inputs: tuple of input tensors
      :return: Tensor of shape (N,) that contains the anomaly scores for this batch



   .. py:method:: 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.

      :param targets: tuple of target tensors
      :return: Tensor of shape (B,) that contains the ground truth labels for this batch



.. py:class:: KernelPCAAnomalyDetector(n_components: int, kernel: str = 'rbf', gamma: Optional[float] = None, first_diffs: bool = False, cum_method: str = 'mean', input_shape: str = 'btf', **kwargs)

   Bases: :py:obj:`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 :meth:`to`, etc.

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

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

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

   :param 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.
   :param 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.
   :param first_diffs[bool]: Flag, if instead of raw values first difference should be used. Default is False.
   :param 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'.


   .. py:attribute:: model