pdstools.explanations.Plots
===========================

.. py:module:: pdstools.explanations.Plots


Classes
-------

.. autoapisummary::

   pdstools.explanations.Plots.Plots


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

.. py:class:: Plots(explanations: pdstools.explanations.Explanations.Explanations)

   Bases: :py:obj:`pdstools.utils.namespaces.LazyNamespace`


   .. py:attribute:: dependencies
      :value: ['numpy', 'plotly']



   .. py:attribute:: dependency_group
      :value: 'explanations'



   .. py:attribute:: X_AXIS_TITLE_DEFAULT
      :value: 'Contribution'



   .. py:attribute:: Y_AXIS_TITLE_DEFAULT
      :value: 'Predictor'



   .. py:attribute:: explanations


   .. py:attribute:: aggregate


   .. py:method:: contributions(top_n: int = defaults.top_n, top_k: int = defaults.top_k, **filter_kwargs)

      Plots contributions for the overall model or a selected context.

      Args:
          top_n (int):
              Number of top predictors to display.
          top_k (int):
              Number of top unique values for each categorical predictor to display.
          **filter_kwargs:
              Optional filtering, sorting, and display controls. Valid keys:

              - ``sort_by`` (str): Column to rank/select top predictors.
                Options: ``contribution``, ``contribution_abs``,
                ``contribution_weighted``, ``contribution_weighted_abs``.
                Default: ``"contribution_abs"``.
              - ``display_by`` (str): Column to use for the chart axis values.
                Default: ``"contribution"``.
              - ``descending`` (bool): Sort most- or least-impactful first.
                Default: ``True``.
              - ``missing`` (bool): Include missing-value bins. Default: ``True``.
              - ``remaining`` (bool): Include an aggregated "remaining" row.
                Default: ``True``.
              - ``include_numeric_single_bin`` (bool): Include numeric predictors
                that have only a single bin. Default: ``False``.
      Returns:
          tuple[go.Figure, list[go.Figure]]:
              - left: context header if context is selected, otherwise None
              - right: overall contributions plot and a list of predictor contribution plots.




   .. py:method:: plot_contributions_for_overall(top_n: int = defaults.top_n, top_k: int = defaults.top_k, **filter_kwargs) -> tuple[plotly.graph_objects.Figure, list[plotly.graph_objects.Figure]]


   .. py:method:: plot_contributions_by_context(context: dict[str, str], top_n: int = defaults.top_n, top_k: int = defaults.top_k, **filter_kwargs) -> tuple[plotly.graph_objects.Figure, plotly.graph_objects.Figure, list[plotly.graph_objects.Figure]]