plot_global_importance
======================

.. py:module:: plot_global_importance

.. autoapi-nested-parse::

   Global importance analysis plotting module for ML results analysis.
   This module trains a meta-model on the experimental parameters to determine
   which settings have the most significant impact on the target metric.



Classes
-------

.. autoapisummary::

   plot_global_importance.GlobalImportancePlotter


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

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

   Initialize the plotter.

   :param data: Results DataFrame, must contain columns for experimental parameters and performance metrics.


   .. py:attribute:: data


   .. py:attribute:: clean_data


   .. py:attribute:: feature_categories
      :value: ['age', 'sex', 'bmi', 'ethnicity', 'bloods', 'diagnostic_order', 'drug_order', 'annotation_n',...



   .. py:attribute:: pipeline_categorical_params
      :value: ['resample', 'scale', 'param_space_size', 'percent_missing']



   .. py:attribute:: pipeline_continuous_params
      :value: ['nb_size', 'X_train_size', 'X_test_orig_size', 'X_test_size', 'n_fits', 't_fits', 'run_time']



   .. py:attribute:: algorithm_col
      :value: 'method_name'



   .. py:method:: plot_global_importance(metric: str = 'auc', top_n: int = 30, figsize: Tuple[int, int] = (12, 10)) -> None

      Trains a model to predict a metric from experimental parameters and plots importances.

      This method trains a RandomForestRegressor on the various pipeline and
      algorithm parameters to predict the outcome of a given performance metric.
      parameters and plots the resulting feature importances.

      :param metric: The target metric to predict. Defaults to 'auc'.
      :type metric: str, optional
      :param top_n: The number of top important features to plot.
                    Defaults to 30.
      :type top_n: int, optional
      :param figsize: The figure size for the plot.
                      Defaults to (12, 10).
      :type figsize: Tuple[int, int], optional