classifierpromax.ResultHandler
==============================

.. py:module:: classifierpromax.ResultHandler


Functions
---------

.. autoapisummary::

   classifierpromax.ResultHandler.ResultHandler


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

.. py:function:: ResultHandler(scoring_dict_trainer, scoring_dict_optimizer=None, std=False)

   Processes and combines scoring results from model training and optimization.

   Parameters:
   -----------
   scoring_dict_trainer : dict
       A dictionary where keys are model names and values are DataFrames containing scoring metrics
       (e.g., mean and standard deviation) for the baseline (non-optimized) models.

   scoring_dict_optimizer : dict, optional
       A dictionary where keys are model names and values are DataFrames containing scoring metrics
       (e.g., mean and standard deviation) for the optimized models. Default is None.

   std : bool, optional
       If `True`, returns both the mean and standard deviation of the scores. If `False`, filters the
       results to only include the mean scores. Default is `False`.

   Returns:
   --------
   pandas.DataFrame
       A DataFrame containing the combined scoring metrics:
       - If `scoring_dict_optimizer` is provided, the result includes both baseline and optimized scores,
         with column names indicating the source (e.g., `model_baseline` and `model_optimized`).
       - If `std` is `False`, the result includes only the mean scores. Otherwise, both mean and standard
         deviation scores are included.
       - If `scoring_dict_optimizer` is not provided, only the baseline scores are returned.

   Raises:
   -------
   ValueError
       If `scoring_dict_trainer` is not a dictionary.
       If `scoring_dict_optimizer` is provided but not a dictionary.
       If any value in `scoring_dict_trainer` or `scoring_dict_optimizer` is not a pandas DataFrame.
       If `std` is not a boolean value.

   Example:
   --------
   >>> scoring_dict_trainer = {
   >>>     "model1": pd.DataFrame({"mean": [0.85], "std": [0.03]}),
   >>>     "model2": pd.DataFrame({"mean": [0.80], "std": [0.04]})
   >>> }
   >>> scoring_dict_optimizer = {
   >>>     "model1": pd.DataFrame({"mean": [0.88], "std": [0.02]}),
   >>>     "model2": pd.DataFrame({"mean": [0.83], "std": [0.03]})
   >>> }
   >>> ResultHandler(scoring_dict_trainer, scoring_dict_optimizer, std=False)