DJSW.evaluate
=============

.. py:module:: DJSW.evaluate


Functions
---------

.. autoapisummary::

   DJSW.evaluate.recall
   DJSW.evaluate.precision
   DJSW.evaluate.accuracy
   DJSW.evaluate.balanced_accuracy
   DJSW.evaluate.f1_score
   DJSW.evaluate.evaluate_model
   DJSW.evaluate.eval_model


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

.. py:function:: recall(y_true, y_pred, average='macro')

   Calculate recall for multi-class classification.
       
   Ratio of relevant retrieved instances to number of relevant instances.

   Args:
       y_true: Ground truth labels (1D array-like)
       y_pred: Predicted labels (1D array-like)
       average: Averaging mode, macro or micro (str)
       
   Returns:
       float: Recall score between 0 and 1
       
   Raises:
       AssertionError: If inputs are invalid
       ValueError: If computation fails


.. py:function:: precision(y_true, y_pred, average='macro')

   Calculate precision for multi-class classification
       
   Ratio of relevant retrieved instances to number of retrieved instances

   Args:
       y_true: Ground truth labels (1D array-like)
       y_pred: Predicted labels (1D array-like)
       average: Averaging mode, macro or micro (str)
       
   Returns:
       float: Accuracy score between 0 and 1
       
   Raises:
       AssertionError: If inputs are invalid
       ValueError: If computation fails


.. py:function:: accuracy(y_true, y_pred)

   Calculate accuracy for multi-class classification
       
   Ratio of correct classifications to number of classifications

   Args:
       y_true: Ground truth labels (1D array-like)
       y_pred: Predicted labels (1D array-like)
       
   Returns:
       float: Accuracy score between 0 and 1
       
   Raises:
       AssertionError: If inputs are invalid
       ValueError: If computation fails


.. py:function:: balanced_accuracy(y_true, y_pred)

   Calculate balanced accuracy for multi-class classification.

   Balanced accuracy is the average of recall obtained on each class.
   It's particularly useful for imbalanced datasets.

   Args:
       y_true: Ground truth labels (1D array-like)
       y_pred: Predicted labels (1D array-like)

   Returns:
       float: Balanced accuracy score between 0 and 1

   Raises:
       AssertionError: If inputs are invalid
       ValueError: If computation fails


.. py:function:: f1_score(y_true, y_pred)

   Calculate f1 for multi-class classification.
       
   Combines precision and recall using the harmonic mean

   Args:
       y_true: Ground truth labels (1D array-like)
       y_pred: Predicted labels (1D array-like)
       
   Returns:
       float: F1 score between 0 and 1
       
   Raises:
       AssertionError: If inputs are invalid
       ValueError: If computation fails


.. py:function:: evaluate_model(args)

   Evaluate a users model on their test data.


.. py:function:: eval_model(args)