.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/approximators/plot_kernelshap.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_approximators_plot_kernelshap.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_approximators_plot_kernelshap.py:


KernelSHAP
==========

Regression-based Shapley value approximation using
:class:`~shapiq.KernelSHAP` :footcite:t:`Lundberg.2017`.

.. GENERATED FROM PYTHON SOURCE LINES 8-27

.. code-block:: Python


    from __future__ import annotations

    import numpy as np

    import shapiq

    N_PLAYERS = 8
    BUDGET = 200
    feature_names = [f"x{i}" for i in range(N_PLAYERS)]

    weights = np.array([0.4, 0.3, 0.2, 0.1, 0.05, -0.1, -0.2, -0.3])


    def game_fun(coalitions: np.ndarray) -> np.ndarray:
        coalitions = np.atleast_2d(coalitions)
        return (coalitions @ weights) + 0.5 * coalitions[:, 0] * coalitions[:, 1]









.. GENERATED FROM PYTHON SOURCE LINES 28-30

Approximate Shapley values
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 30-35

.. code-block:: Python


    approximator = shapiq.KernelSHAP(n=N_PLAYERS, random_state=42)
    iv = approximator.approximate(BUDGET, game_fun)
    print(iv)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    InteractionValues(
        index=SV, max_order=1, min_order=0, estimated=True, estimation_budget=200,
        n_players=8, baseline_value=0.0,
        Top 10 interactions:
            (0,): 0.6455994977788215
            (1,): 0.5572185206852545
            (2,): 0.18232714794264368
            (3,): 0.09776947317044818
            (4,): 0.03651114828303627
            (): 0.0
            (5,): -0.09454436225720392
            (6,): -0.1858563003185488
            (7,): -0.28902512788822027
    )




.. GENERATED FROM PYTHON SOURCE LINES 36-38

Visualize the result
--------------------

.. GENERATED FROM PYTHON SOURCE LINES 38-41

.. code-block:: Python


    iv.plot_force(feature_names=feature_names)




.. image-sg:: /auto_examples/approximators/images/sphx_glr_plot_kernelshap_001.png
   :alt: plot kernelshap
   :srcset: /auto_examples/approximators/images/sphx_glr_plot_kernelshap_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 42-45

References
----------
.. footbibliography::


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.204 seconds)


.. _sphx_glr_download_auto_examples_approximators_plot_kernelshap.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_kernelshap.ipynb <plot_kernelshap.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_kernelshap.py <plot_kernelshap.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_kernelshap.zip <plot_kernelshap.zip>`