plot_master
===========

.. py:module:: plot_master

.. autoapi-nested-parse::

   Master plotting module that provides a single entry point to generate a
   comprehensive set of visualizations for ML results analysis.



Classes
-------

.. autoapisummary::

   plot_master.MasterPlotter


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

.. py:class:: MasterPlotter(data: pandas.DataFrame, output_dir: str = '.')

   Initializes the MasterPlotter with aggregated results data.

   This class acts as a facade, instantiating various specialized plotters
   to generate a comprehensive suite of analysis visualizations from the
   provided results DataFrame.

   :param data: A DataFrame containing the aggregated ML
                experiment results. Must be non-empty.
   :type data: pd.DataFrame
   :param output_dir: The directory where output files (like
                      CSVs) will be saved. Defaults to '.'.
   :type output_dir: str, optional

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


   .. py:attribute:: data


   .. py:attribute:: output_dir
      :value: '.'



   .. py:attribute:: algo_plotter


   .. py:attribute:: dist_plotter


   .. py:attribute:: timeline_plotter


   .. py:attribute:: interaction_plotter


   .. py:method:: plot_all(metric: str = 'auc_m', stratify_by_outcome: bool = True, top_n_features: int = 20, top_n_algorithms: int = 10, save_best_results: bool = True) -> None

      Generates a comprehensive set of standard plots from all plotters.

      This method calls the main plotting functions from each specialized
      plotter to provide a full overview of the results, including algorithm
      comparisons, metric distributions, timeline trends, and feature
      importance. It also handles saving a summary of the best models.

      :param metric: The primary performance metric to use for
                     plotting (e.g., 'auc', 'f1'). Defaults to 'auc_m'.
      :type metric: str, optional
      :param stratify_by_outcome: If True, creates plots
                                  stratified by the 'outcome_variable' column. Defaults to True.
      :type stratify_by_outcome: bool, optional
      :param top_n_features: The number of top features to show
                             in feature-related plots. Defaults to 20.
      :type top_n_features: int, optional
      :param top_n_algorithms: The number of top algorithms to
                               show in ranking plots. Defaults to 10.
      :type top_n_algorithms: int, optional
      :param save_best_results: If True, saves a CSV summary of
                                the best model per outcome. Defaults to True.
      :type save_best_results: bool, optional