timesead.models.generative.donut
================================

.. py:module:: timesead.models.generative.donut


Classes
-------

.. autoapisummary::

   timesead.models.generative.donut.Donut
   timesead.models.generative.donut.MaskedVAELoss
   timesead.models.generative.donut.DonutAnomalyDetector


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

.. py:class:: Donut(input_dim: int, hidden_dims: List[int] = [100, 100], latent_dim: int = 20, mask_prob: float = 0.01)

   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

   Xu2018

   :param input_dim: Should be window_size * features
   :param hidden_dims:
   :param latent_dim:


   .. py:attribute:: latent_dim
      :value: 20



   .. py:attribute:: mask_prob
      :value: 0.01



   .. py:attribute:: vae


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


.. py:class:: MaskedVAELoss(size_average=None, reduce=None, reduction: str = 'mean')

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


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


.. py:class:: DonutAnomalyDetector(model: Donut, num_mc_samples: int = 1024)

   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: 1024



   .. 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