CollaborativeCoding.metrics.precision
=====================================

.. py:module:: CollaborativeCoding.metrics.precision


Classes
-------

.. autoapisummary::

   CollaborativeCoding.metrics.precision.Precision


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

.. py:class:: Precision(num_classes: int, macro_averaging: bool = False)

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


   Metric module for precision. Can calculate both the micro- and macro-averaged precision.

   Parameters
   ----------
   num_classes : int
       Number of classes in the dataset.
   macro_averaging : bool
       Performs macro-averaging if True, otherwise micro-averaging.


   .. 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: torch.tensor, logits: torch.tensor) -> torch.tensor

      Add true and predicted values to the class-global lists.

      Parameters
      ----------
      y_true : torch.tensor
          True labels
      logits : torch.tensor
          Predicted labels




   .. py:method:: _micro_avg_precision(y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor

      Compute micro-average precision by first calculating true/false positive across all classes and then find the precision.

      Parameters
      ----------
      y_true : torch.tensor
          True labels
      y_pred : torch.tensor
          Predicted labels

      Returns
      -------
      torch.tensor
          Micro-averaged precision



   .. py:method:: _macro_avg_precision(y_true: torch.tensor, y_pred: torch.tensor) -> torch.tensor

      Compute macro-average precision by finding true/false positives of each class separately then averaging across all classes.

      Parameters
      ----------
      y_true : torch.tensor
          True labels
      y_pred : torch.tensor
          Predicted labels

      Returns
      -------
      torch.tensor
          Macro-averaged precision



   .. py:method:: __returnmetric__()

      Return the micro- or macro-averaged precision.

      Returns
      -------
      torch.tensor
          Micro- or macro-averaged precision



   .. py:method:: __reset__()

      Resets the class-global lists of true and predicted values to empty lists.

      Returns
      -------
      None
          Returns None