timesead.data.transforms.target_transforms
==========================================

.. py:module:: timesead.data.transforms.target_transforms


Classes
-------

.. autoapisummary::

   timesead.data.transforms.target_transforms.ReconstructionTargetTransform
   timesead.data.transforms.target_transforms.OneVsRestTargetTransform
   timesead.data.transforms.target_transforms.PredictionTargetTransform
   timesead.data.transforms.target_transforms.OverlapPredictionTargetTransform


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

.. py:class:: ReconstructionTargetTransform(parent: timesead.data.transforms.transform_base.Transform, replace_labels: bool = False)

   Bases: :py:obj:`timesead.data.transforms.transform_base.Transform`


   Adds the current inputs as targets for reconstruction objectives.

   :param parent: Another :class:`~timesead.data.transforms.Transform` which is used as the data source for this
       transform.
   :param replace_labels: Whether the original labels should be replaced by the reconstruction target.
      If `False`, the reconstruction target will be added to the tuple of original labels.


   .. py:attribute:: replace_labels
      :value: False



.. py:class:: OneVsRestTargetTransform(parent: timesead.data.transforms.transform_base.Transform, normal_class: Optional[Any] = None, anomalous_class: Optional[Any] = None, replace_labels: bool = False)

   Bases: :py:obj:`timesead.data.transforms.transform_base.Transform`


   Transforms multi-class labels into binary labels for anomaly detection.
   "Normal" data points will have label 0, others will have label 1.

   :param parent: Another :class:`~timesead.data.transforms.Transform` which is used as the data source for this
       :class:`~timesead.data.transforms.Transform`.
   :param normal_class: The input class label that should be considered normal and will have label 0 in the output.
   :param anomalous_class: You can also specify an anomalous class that will have label 1.
       All other labels will be transformed to 0. Note that you cannot specify both `normal_class` and
       `anomalous_class`.
   :param replace_labels: Whether the original labels should be replaced by the
       :class:`~timesead.data.transforms.Transform`.
       If `False`, the additional labels will be added to the tuple of original labels.


   .. py:attribute:: replace_labels
      :value: False



   .. py:attribute:: normal_class
      :value: None



   .. py:attribute:: anomalous_class
      :value: None



.. py:class:: PredictionTargetTransform(parent: timesead.data.transforms.transform_base.Transform, window_size: int, prediction_horizon: int, replace_labels: bool = False, step_size: int = 1, reverse: bool = False)

   Bases: :py:obj:`timesead.data.transforms.window_transform.WindowTransform`


   Adds the last `prediction_window` points from the current inputs as targets for prediction objectives.

   :param parent: Another :class:`~timesead.data.transforms.Transform` which is used as the data source for this
       :class:`~timesead.data.transforms.Transform`.
   :param prediction_horizon: Number of datapoints that should be predicted.
   :param replace_labels: Whether the original labels should be replaced by the prediction target.
      If `False`, the prediction target will be added to the tuple of original labels.


   .. py:attribute:: input_window_size


   .. py:attribute:: prediction_horizon


   .. py:attribute:: replace_labels
      :value: False



   .. py:property:: seq_len
      :type: Union[int, List[int]]


      This should return the length of each time series. If the time series have different lengths, the return
      value should be a list that contains the length of each sequence. If all sequences are of equal length,
      this should return an `int`.


.. py:class:: OverlapPredictionTargetTransform(parent: timesead.data.transforms.transform_base.Transform, offset: int, replace_labels: bool = False)

   Bases: :py:obj:`timesead.data.transforms.transform_base.Transform`


   Adds the sequence shifted by offset as the target.

   :param parent: Another :class:`~timesead.data.transforms.Transform` which is used as the data source for this
       :class:`~timesead.data.transforms.Transform`.
   :param offset: Number of steps ahead that should be predicted.
   :param replace_labels: Whether the original labels should be replaced by the prediction target.
      If `False`, the prediction target will be added to the tuple of original labels.


   .. py:attribute:: offset


   .. py:attribute:: replace_labels
      :value: False



   .. py:property:: seq_len
      :type: Union[int, List[int]]


      This should return the length of each time series. If the time series have different lengths, the return
      value should be a list that contains the length of each sequence. If all sequences are of equal length,
      this should return an `int`.