summarize_results
=================

.. py:module:: summarize_results

.. autoapi-nested-parse::

   Module for creating tabular summaries from ML results data.



Classes
-------

.. autoapisummary::

   summarize_results.ResultsSummarizer


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

.. py:class:: ResultsSummarizer(data: pandas.DataFrame)

   Initializes the summarizer.

   :param data: Aggregated results DataFrame.
   :type data: pd.DataFrame

   :raises ValueError: If the input data is not a non-empty pandas DataFrame.


   .. py:attribute:: data


   .. py:attribute:: clean_data


   .. py:method:: get_best_model_per_outcome(metric: str = 'auc') -> pandas.DataFrame

      Finds the best model for each outcome and expands the feature list.

      This method identifies the single best-performing model run for each
      outcome variable based on the specified metric. It then transforms the
      'decoded_features' list into a set of boolean columns, where each new
      column represents a feature and its value indicates whether that feature
      was used in the best model run.

      :param metric: The performance metric to use for determining
                     the "best" model. Defaults to 'auc'.
      :type metric: str, optional

      :returns: A DataFrame containing the best model run for each outcome, with
                additional boolean columns for each feature.
      :rtype: pd.DataFrame