ncalab.uncertainty.uncertainty_estimator
========================================

.. py:module:: ncalab.uncertainty.uncertainty_estimator


Classes
-------

.. autoapisummary::

   ncalab.uncertainty.uncertainty_estimator.UncertaintyEstimator
   ncalab.uncertainty.uncertainty_estimator.NQM
   ncalab.uncertainty.uncertainty_estimator.MCMC


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

.. py:class:: UncertaintyEstimator(nca: ncalab.models.AbstractNCAModel)

   
   :param nca: Trained NCA model
   :type nca: AbstractNCAModel


   .. py:attribute:: nca


   .. py:method:: _estimate(image: torch.Tensor) -> Tuple[torch.Tensor, List[ncalab.prediction.Prediction]]

      Internal uncertainty estimation method.

      :param image: Input image sample
      :type image: torch.Tensor

      :return: Float tensor of uncertainty heatmap (BCWH), predictions, reduced uncertainty score
      :rtype: Tuple[torch.Tensor, List[Prediction], float]



   .. py:method:: estimate(image: torch.Tensor, reduce: str = 'mean') -> Tuple[torch.Tensor, List[ncalab.prediction.Prediction], torch.Tensor]

      Estimate predictive uncertainty.

      :param image: Input image sample
      :type image: torch.Tensor
      :param reduce: Reduction strategy, defaults to "mean".
      :type reduce: str

      :return: Float tensor of uncertainty heatmap (BCWH), final prediction, reduced uncertainty score for batch (BC)
      :rtype: Tuple[torch.Tensor, List[Prediction], torch.Tensor]



   .. py:method:: __call__(*args, **kwargs)


.. py:class:: NQM(nca: ncalab.models.AbstractNCAModel, N: int = 10, normalize=False)

   Bases: :py:obj:`UncertaintyEstimator`


   Variance over multiple predictions.

   :param nca: Trained NCA model
   :type nca: AbstractNCAModel


   .. py:attribute:: N
      :value: 10



   .. py:attribute:: normalize
      :value: False



   .. py:method:: _estimate(image: torch.Tensor) -> Tuple[torch.Tensor, List[ncalab.prediction.Prediction]]

      Internal uncertainty estimation method.

      :param image: Input image sample
      :type image: torch.Tensor

      :return: Float tensor of uncertainty heatmap (BCWH), predictions, reduced uncertainty score
      :rtype: Tuple[torch.Tensor, List[Prediction], float]



.. py:class:: MCMC(nca: ncalab.models.AbstractNCAModel, N_last: int = 10, normalize=False)

   Bases: :py:obj:`UncertaintyEstimator`


   Markov-Chain Monte Carlo

   :param nca: Trained NCA model
   :type nca: AbstractNCAModel


   .. py:attribute:: N_last
      :value: 10



   .. py:attribute:: normalize
      :value: False



   .. py:method:: _estimate(image: torch.Tensor) -> Tuple[torch.Tensor, List[ncalab.prediction.Prediction]]

      Internal uncertainty estimation method.

      :param image: Input image sample
      :type image: torch.Tensor

      :return: Float tensor of uncertainty heatmap (BCWH), predictions, reduced uncertainty score
      :rtype: Tuple[torch.Tensor, List[Prediction], float]