:py:mod:`bluecast.evaluation.shap_values`
=========================================

.. py:module:: bluecast.evaluation.shap_values

.. autoapi-nested-parse::

   Module to calculate SHAP values for a trained ML model.

   The implementation is flexible and can be used for almost any ML model. The implementation is based on the SHAP library.



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


Functions
~~~~~~~~~

.. autoapisummary::

   bluecast.evaluation.shap_values.shap_explanations
   bluecast.evaluation.shap_values.shap_waterfall_plot
   bluecast.evaluation.shap_values.get_most_important_features_by_shap_values
   bluecast.evaluation.shap_values.shap_dependence_plots



.. py:function:: shap_explanations(model, df: pandas.DataFrame) -> Tuple[numpy.ndarray, shap.Explainer]

   See explanations under:
   https://medium.com/rapids-ai/gpu-accelerated-shap-values-with-xgboost-1-3-and-rapids-587fad6822
   :param model: Trained ML model
   :param df: Test data to predict on.
   :return: Shap values


.. py:function:: shap_waterfall_plot(explainer: shap.Explainer, indices: List[int], class_problem: Literal[binary, multiclass, regression]) -> None

   Plot the SHAP waterfall plot.
   :param explainer: SHAP Explainer instance
   :param indices: List of sample indices to plot. Each index represents a sample.
   :param class_problem: Class problem type (i.e.: binary, multiclass, regression)
   :return: None


.. py:function:: get_most_important_features_by_shap_values(shap_values: numpy.ndarray, df: pandas.DataFrame) -> pandas.DataFrame

   Get the most important features by SHAP values.
   :param shap_values: Numpy ndarray holding Shap values
   :param df: Pandas DataFrame
   :return: Pandas DataFrame with columns 'col_name' and 'feature_importance_vals'


.. py:function:: shap_dependence_plots(shap_values: numpy.ndarray, df: pandas.DataFrame, show_dependence_plots_of_top_n_features: int) -> None

   Plot the SHAP dependence plots for each column in the dataframe.
   :param shap_values: Numpy ndarray holding Shap values
   :param df: Pandas DataFrame
   :param show_dependence_plots_of_top_n_features: Number of features to show the dependence plots for.