timesead.models.generative.sis_vae
==================================

.. py:module:: timesead.models.generative.sis_vae


Classes
-------

.. autoapisummary::

   timesead.models.generative.sis_vae.SISVAE
   timesead.models.generative.sis_vae.SISVAELossWithGeneratedPrior
   timesead.models.generative.sis_vae.SISVAEAnomalyDetector


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

.. py:class:: SISVAE(input_dim: int, rnn_hidden_dim: int = 200, latent_dim: int = 40, x_hidden_dims: List[int] = [100], z_hidden_dims: List[int] = [100], enc_hidden_dims: List[int] = [100], dec_hidden_dims: List[int] = [100], prior_hidden_dims: List[int] = [100])

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


   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

   Li2021, ist aber im Prinzip nur Chung2015 mit einem extra loss term

   :param input_dim:
   :param lstm_hidden_dims:
   :param latent_dim:


   .. py:attribute:: latent_dim
      :value: 40



   .. py:attribute:: rnn_hidden_dim
      :value: 200



   .. py:attribute:: x_embed


   .. py:attribute:: z_embed


   .. py:attribute:: encoder


   .. py:attribute:: decoder


   .. py:attribute:: prior_decoder


   .. py:attribute:: rnn_cell


   .. py:attribute:: softplus


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


.. py:class:: SISVAELossWithGeneratedPrior(smooth_weight: float = 0.5)

   Bases: :py:obj:`timesead.models.common.VAELoss`


   .. py:attribute:: smooth_weight
      :value: 0.5



   .. py:method:: forward(predictions: Tuple[torch.Tensor, Ellipsis], targets: Tuple[torch.Tensor, Ellipsis], *args, **kwargs) -> torch.Tensor


.. py:class:: SISVAEAnomalyDetector(model: SISVAE, num_mc_samples: int = 128)

   Bases: :py:obj:`timesead.models.common.AnomalyDetector`


   
   We decided not to include the reconstruction step from the paper here, since we don't have missing data.

   :param model:
   :param num_mc_samples:


   .. py:attribute:: model


   .. py:attribute:: num_mc_samples
      :value: 128



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


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


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


   .. py:method:: format_online_targets(targets: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor