timesead.models.baselines.iforest
=================================

.. py:module:: timesead.models.baselines.iforest


Classes
-------

.. autoapisummary::

   timesead.models.baselines.iforest.IForestAD


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

.. py:class:: IForestAD(n_trees: int = 100, max_samples: Optional[float] = None, max_features: float = 1.0, bootstrap: bool = False, 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

   Isolation Forest Anomaly Detector
       The Isolation Forest 'isolates' observations by randomly selecting a feature and then randomly
       selecting a split value between the maximum and minimum of the selected feature.
       See [Liu2008] for more details.

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

   .. [Liu2008] F. T. Liu, K. M. Ting and Z. -H. Zhou,
       "Isolation Forest,"
       2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy,
       2008, pp. 413-422, doi: 10.1109/ICDM.2008.17.

   :param n_trees[int]: Number of trees in the forest.
   :param max_samples[Optional[float]]: The number of samples to train each tree.
   :param max_features[float]: Percent of features to draw from to train each tree.
   :param bootstrap[bool]: If True, individual trees are fit on random subsets of the training data
                           sampled with replacement. Else sampling is without replacement.


   .. py:attribute:: n_trees
      :value: 100



   .. py:attribute:: max_samples
      :value: None



   .. py:attribute:: max_features
      :value: 1.0



   .. py:attribute:: bootstrap
      :value: False



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



   .. py:attribute:: model


   .. 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