CollaborativeCoding.metrics.accuracy
====================================

.. py:module:: CollaborativeCoding.metrics.accuracy


Classes
-------

.. autoapisummary::

   CollaborativeCoding.metrics.accuracy.Accuracy


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

.. py:class:: Accuracy(num_classes, macro_averaging=False)

   Bases: :py:obj:`torch.nn.Module`


   Computes the accuracy of a model's predictions.

   Args
   ----------
   num_classes : int
       The number of classes in the classification task.
   macro_averaging : bool, optional
       If True, computes macro-average accuracy. Otherwise, computes micro-average accuracy. Default is False.


   Methods
   -------
   forward(y_true, y_pred)
       Stores the true and predicted labels. Typically called for each batch during the forward pass of a model.
   _macro_acc()
       Computes the macro-average accuracy.
   _micro_acc()
       Computes the micro-average accuracy.
   __returnmetric__()
       Returns the computed accuracy based on the averaging method for all stored predictions.
   __reset__()
       Resets the stored true and predicted labels.

   Examples
   --------
   >>> y_true = torch.tensor([0, 1, 2, 3, 3])
   >>> y_pred = torch.tensor([0, 1, 2, 3, 0])
   >>> accuracy = Accuracy(num_classes=4)
   >>> accuracy(y_true, y_pred)
   >>> accuracy.__returnmetric__()
   0.8
   >>> accuracy.__reset__()
   >>> accuracy.macro_averaging = True
   >>> accuracy(y_true, y_pred)
   >>> accuracy.__returnmetric__()
   0.875


   .. py:attribute:: num_classes


   .. py:attribute:: macro_averaging
      :value: False



   .. py:attribute:: y_true
      :value: []



   .. py:attribute:: y_pred
      :value: []



   .. py:method:: forward(y_true, y_pred)

      Store the true and predicted labels.

      Parameters
      ----------
      y_true : torch.Tensor
          True labels.
      y_pred : torch.Tensor
          Predicted labels. Either a 1D tensor of shape (batch_size,) or a 2D tensor of shape (batch_size, num_classes).



   .. py:method:: _macro_acc()

      Compute the macro-average accuracy on the stored predictions.

      Returns
      -------
      float
          Macro-average accuracy score.



   .. py:method:: _micro_acc()

      Compute the micro-average accuracy on the stored predictions.

      Returns
      -------
      float
          Micro-average accuracy score.



   .. py:method:: __returnmetric__()

      Return the computed accuracy based on the averaging method for all stored predictions.

      Returns
      -------
      float
          Computed accuracy score.



   .. py:method:: __reset__()

      Reset the stored true and predicted labels.