neuro_morpho.model.metrics
==========================

.. py:module:: neuro_morpho.model.metrics

.. autoapi-nested-parse::

   Evaluation metrics for model performance.



Attributes
----------

.. autoapisummary::

   neuro_morpho.model.metrics.PRED
   neuro_morpho.model.metrics.LBL
   neuro_morpho.model.metrics.METRIC_FN


Functions
---------

.. autoapisummary::

   neuro_morpho.model.metrics.accuracy
   neuro_morpho.model.metrics.class_accuracy


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

.. py:data:: PRED

.. py:data:: LBL

.. py:data:: METRIC_FN

.. py:function:: accuracy(pred: PRED, lbl: LBL, threshold: float) -> tuple[str, float]

   Calculate the accuracy of predictions.

   :param pred: The predicted labels.
   :type pred: np.ndarray
   :param lbl: The true labels.
   :type lbl: np.ndarray
   :param threshold: The threshold for binary classification.
   :type threshold: float

   :returns: A tuple containing the metric name and the accuracy value.
   :rtype: tuple[str, float]


.. py:function:: class_accuracy(pred: PRED, lbl: LBL, class_idx: int, threshold: float) -> tuple[str, float]

   Calculate the class-wise accuracy of predictions.

   :param pred: The predicted labels.
   :type pred: np.ndarray
   :param lbl: The true labels.
   :type lbl: np.ndarray
   :param class_idx: The class index to calculate accuracy for.
   :type class_idx: int
   :param threshold: The threshold for binary classification.
   :type threshold: float

   :returns: A tuple containing the metric name and the class-wise accuracy value.
   :rtype: tuple[str, float]