CollaborativeCoding.metrics.recall
==================================

.. py:module:: CollaborativeCoding.metrics.recall


Classes
-------

.. autoapisummary::

   CollaborativeCoding.metrics.recall.Recall


Functions
---------

.. autoapisummary::

   CollaborativeCoding.metrics.recall.one_hot_encode


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

.. py:function:: one_hot_encode(vec, num_classes)

   One-hot encode the target tensor.

   Args
   ----
   vec : torch.Tensor
       Target tensor.
   num_classes : int
       Number of classes in the dataset.

   Returns
   -------
   torch.Tensor
       One-hot encoded tensor.


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

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


   Recall metric.

   Args
   ----
   num_classes : int
       Number of classes in the dataset.
   macro_averaging : bool
       If True, calculate the recall for each class and return the average.
       If False, calculate the recall for the entire dataset.

   Methods
   -------
   forward(y_true, y_pred)
       Compute the recall metric.

   Examples
   --------
   >>> y_true = torch.tensor([0, 1, 2, 3, 4])
   >>> y_pred = torch.randn(5, 5).argmax(dim=-1)
   >>> recall = Recall(num_classes=5)
   >>> recall(y_true, y_pred)
   0.2
   >>> recall = Recall(num_classes=5, macro_averaging=True)
   >>> recall(y_true, y_pred)
   0.2


   .. py:attribute:: num_classes


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



   .. py:attribute:: __y_true
      :value: []



   .. py:attribute:: __y_pred
      :value: []



   .. py:method:: forward(true, logits)


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


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


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


   .. py:method:: __returnmetric__()


   .. py:method:: __reset__()