CollaborativeCoding.load_metric
===============================

.. py:module:: CollaborativeCoding.load_metric


Attributes
----------

.. autoapisummary::

   CollaborativeCoding.load_metric.metrics


Classes
-------

.. autoapisummary::

   CollaborativeCoding.load_metric.MetricWrapper


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

.. py:class:: MetricWrapper(*metrics, num_classes, macro_averaging=False, **kwargs)

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


   A wrapper class for evaluating multiple metrics on the same dataset.
   This class allows you to compute several metrics simultaneously on given
   true and predicted labels. It supports a variety of common metrics and
   provides methods to accumulate results and reset the state.

   Args
   ----
       num_classes : int
           The number of classes in the classification task.
       metrics : list[str]
           A list of metric names to be evaluated.
       macro_averaging : bool
           Whether to compute macro-averaged metrics for multi-class classification.

   Attributes
   ----------
       metrics : dict
           A dictionary mapping metric names to their corresponding functions.
       num_classes : int
           The number of classes for the classification task.

   Methods
   -------
       __call__(y_true, y_pred)
           Passes the true and predicted logits to the metric functions.
       getmetrics(str_prefix: str = None)
           Retrieves the dictionary of computed metrics, optionally all keys can be prefixed with a string.
       resetmetric()
           Resets the state of all metric computations.

   Examples
   --------
   >>> from CollaborativeCoding import MetricWrapperProposed
   >>> metrics = MetricWrapperProposed(2, "entropy", "f1", "precision")
   >>> y_true = [0, 1, 0, 1]
   >>> y_pred = [[0.8, -1.9],
                [0.1,   9.0],
                [-1.9, -0.1],
                [1.9,   1.8]]
   >>> metrics(y_true, y_pred)
   >>> metrics.getmetrics()
   {'entropy': 0.3292665, 'f1': 0.5, 'precision': 0.5}
   >>> metrics.resetmetric()
   >>> metrics.getmetrics()
   {'entropy': [], 'f1': [], 'precision': []}


   .. py:attribute:: metrics


   .. py:attribute:: params


   .. py:method:: _get_metric(key)

      Retrieves the metric function based on the provided key.
      Args
      ----
          key (str): The name of the metric.
      Returns
      -------
          metric (callable): The function that computes the metric.



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


   .. py:method:: getmetrics(str_prefix: str = None)


   .. py:method:: resetmetric()


.. py:data:: metrics
   :value: ['entropy', 'f1', 'recall', 'precision', 'accuracy']