timesead.models.baselines.eif
=============================

.. py:module:: timesead.models.baselines.eif


Classes
-------

.. autoapisummary::

   timesead.models.baselines.eif.EIFAD


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

.. py:class:: EIFAD(n_trees: int = 200, sample_size: int = 256, extension_level: Optional[int] = None, input_shape: str = 'btf')

   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

   Extended Isolation Forest Anomaly Detector
       An implementation of the Extended Isolation Forest (EIF) for anomaly detection
       as described in [Hariri2019]_.

       Implementation derived from https://github.com/HPI-Information-Systems/TimeEval-algorithms

       .. [Hariri2019] S. Hariri, M. C. Kind and R. J. Brunner,
           "Extended Isolation Forest,"
           in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 4, pp. 1479-1489,
           1 April 2021, doi: 10.1109/TKDE.2019.2947676.

   :param n_trees[int]: The number of trees in the forest.
   :param sample_size[int]: The size of the subsample to be used in creation of each tree. Must be smaller than data size.
   :param extension_level[Optional[int]]: Specifies degree of freedom in choosing the hyperplanes for dividing up data. Must be smaller than the dimension n of the dataset.
                                          Value of 0 is identical to standard Isolation Forest, and None is equivalent to N-1 or fully extended


   .. py:attribute:: n_trees
      :value: 200



   .. py:attribute:: sample_size
      :value: 256



   .. py:attribute:: extension_level
      :value: None



   .. 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