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 = _DEFAULT.TOP_N.value, top_k: int = _DEFAULT.TOP_K.value, descending: bool = _DEFAULT.DESCENDING.value, missing: bool = _DEFAULT.MISSING.value, remaining: bool = _DEFAULT.REMAINING.value, contribution_calculation: str = _CONTRIBUTION_TYPE.CONTRIBUTION.value)

      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.
          descending (bool):
              Whether to sort the predictors by most or least contribution.
          missing (bool):
              Whether to include missing values in the plot.
          remaining (bool):
              predictors/predictor values not included in the top_n/top_k
              will be grouped into a "remaining" category.
          contribution_calculation (str):
              Type of contribution calculation to use.
      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 = _DEFAULT.TOP_N.value, top_k: int = _DEFAULT.TOP_K.value, descending: bool = _DEFAULT.DESCENDING.value, missing: bool = _DEFAULT.MISSING.value, remaining: bool = _DEFAULT.REMAINING.value, contribution_calculation: str = _CONTRIBUTION_TYPE.CONTRIBUTION.value) -> tuple[plotly.graph_objects.Figure, List[plotly.graph_objects.Figure]]


   .. py:method:: plot_contributions_by_context(context: dict[str, str], top_n: int = _DEFAULT.TOP_N.value, top_k: int = _DEFAULT.TOP_K.value, descending: bool = _DEFAULT.DESCENDING.value, missing: bool = _DEFAULT.MISSING.value, remaining: bool = _DEFAULT.REMAINING.value, contribution_calculation: str = _CONTRIBUTION_TYPE.CONTRIBUTION.value) -> tuple[plotly.graph_objects.Figure, plotly.graph_objects.Figure, List[plotly.graph_objects.Figure]]


   .. py:method:: _plot_overall_contributions(df: polars.DataFrame, x_col: str, y_col: str, x_title: str = X_AXIS_TITLE_DEFAULT, y_title: str = Y_AXIS_TITLE_DEFAULT, context: Optional[pdstools.explanations.ExplanationsUtils.ContextInfo] = None) -> plotly.graph_objects.Figure
      :staticmethod:



   .. py:method:: _plot_predictor_contributions(df: polars.DataFrame, x_col: str, y_col: str, x_title: str = X_AXIS_TITLE_DEFAULT, y_title: str = Y_AXIS_TITLE_DEFAULT) -> list[plotly.graph_objects.Figure]
      :staticmethod:



   .. py:method:: _plot_context_table(context_info: pdstools.explanations.ExplanationsUtils.ContextInfo) -> plotly.graph_objects.Figure
      :staticmethod: