timesead.models.reconstruction.tcn_ae
=====================================

.. py:module:: timesead.models.reconstruction.tcn_ae


Classes
-------

.. autoapisummary::

   timesead.models.reconstruction.tcn_ae.TCNAE
   timesead.models.reconstruction.tcn_ae.TCNAEAnomalyDetector


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

.. py:class:: TCNAE(input_dimension: int, dilations: List[int] = (1, 2, 4, 8, 16), nb_filters: Union[int, List[int]] = 20, kernel_size: int = 20, nb_stacks: int = 1, padding: str = 'same', dropout_rate: float = 0.0, filters_conv1d: int = 8, activation_conv1d: Union[str, Callable] = 'linear', latent_sample_rate: int = 42, pooler: Type[torch.nn.Module] = torch.nn.AvgPool1d)

   Bases: :py:obj:`timesead.models.BaseModel`


   A class used to represent the Temporal Convolutional Autoencoder (TCN-AE).
   Loss for this is logcosh

   :param ts_dimension: The dimension of the time series (default is 1)
   :type ts_dimension: int
   :param dilations: The dilation rates used in the TCN-AE model (default is (1, 2, 4, 8, 16))
   :type dilations: tuple
   :param nb_filters: The number of filters used in the dilated convolutional layers. All dilated conv. layers use the same number of filters (default is 20)
   :type nb_filters: int


   .. py:attribute:: tcn_enc


   .. py:attribute:: conv1d


   .. py:attribute:: activation
      :value: 'linear'



   .. py:attribute:: pooler


   .. py:attribute:: tcn_dec


   .. py:attribute:: linear


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

      :param inputs: Tuple with single Tensor of shape (B, T, D)
      :return:



.. py:class:: TCNAEAnomalyDetector(model: TCNAE, offline_window_size: int = 128)

   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

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: model


   .. py:attribute:: offline_window_size
      :value: 128



   .. 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

      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