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

.. only:: html

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

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

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

.. _sphx_glr_generated_gallery_examples_plot_msage.py:


Shapley Additive Global Importance (SAGE) example
=================================================

In this example, we demonstrate how to measure feature importance using
SAGE :footcite:t:`Covert2020` on the diabetes dataset.
Read more in the :ref:`User Guide <shapley_additive_global_explanation>`.
For this example, we use
the marginal version of SAGE, which limits the computational cost. To further
reduce the computational cost, Shapley values are estimated using a Monte Carlo
approximation. Only a subset of all possible feature coalitions is sampled.
This is controlled by the `n_subsets` parameter. Finally, the expectation over
the marginal distribution is also approximated using `n_permutations`.

.. GENERATED FROM PYTHON SOURCE LINES 18-23

LightGBM example on the bike sharing dataset
--------------------------------------------
We demonstrate how to use SAGE on the bike sharing
dataset. We fit a LightGBM model and compute its :math:`R^2` score on a
held-out test set.

.. GENERATED FROM PYTHON SOURCE LINES 23-45

.. code-block:: Python


    import numpy as np
    from sklearn.datasets import fetch_openml
    from sklearn.ensemble import HistGradientBoostingRegressor
    from sklearn.metrics import r2_score
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import OrdinalEncoder

    bike_sharing = fetch_openml("Bike_Sharing_Demand", version=2, as_frame=True)
    df = bike_sharing.frame
    df = df[df["year"] == 0].drop(columns=["year"])

    X = df.drop(columns=["count"]).to_numpy()
    X = OrdinalEncoder().fit_transform(X)
    y = df["count"].to_numpy()
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    model = HistGradientBoostingRegressor(random_state=0, max_depth=5)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    print("R2 score:", r2_score(y_test, y_pred))






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

 .. code-block:: none

    R2 score: 0.9283833661677658




.. GENERATED FROM PYTHON SOURCE LINES 46-52

SAGE feature importance
-----------------------
We compute the SAGE feature importance for the fitted model. To keep the
computational cost tractable, we use a subset of the test set to compute the
SAGE values. Finally, the SAGE values are plotted using the `plot_importance`
function.

.. GENERATED FROM PYTHON SOURCE LINES 52-78

.. code-block:: Python


    import matplotlib.pyplot as plt

    from hidimstat import SAGE

    sage = SAGE(
        model,
        n_subsets=512,
        n_permutations=10,
        random_state=0,
        n_jobs=8,
    )
    sage.fit(X_train)
    subsample_size = 1024
    rng = np.random.default_rng(0)
    subsample_ids = rng.choice(len(X_test), size=subsample_size, replace=False)
    sage.importance(X_test[subsample_ids], y_test[subsample_ids])
    ax = sage.plot_importance(
        feature_names=df.drop(columns=["count"]).columns.tolist(),
        color="tab:purple",
    )
    ax.semilogx()
    plt.tight_layout()
    plt.show()





.. image-sg:: /generated/gallery/examples/images/sphx_glr_plot_msage_001.png
   :alt: plot msage
   :srcset: /generated/gallery/examples/images/sphx_glr_plot_msage_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 79-88

This analysis reveals that the hour of the day is the most important feature
for predicting bike sharing demand. This example also illustrates a specific
property of SAGE: it tends to distribute importance over correlated features.
This can be seen for the temperature and "feel temperature" features,
which have a near-perfect correlation, as seen in the correlation matrix below,
and are both assigned similar importance.
This property is a consequence of the axioms of Shapley values, and contrasts
with other feature importance methods, such as
:class:`~hidimstat.LOCO` or :class:`~hidimstat.CFI`.

.. GENERATED FROM PYTHON SOURCE LINES 88-104

.. code-block:: Python


    corr_mat = np.corrcoef(X_train, rowvar=False)
    fig, ax = plt.subplots()
    ax.imshow(corr_mat, cmap="coolwarm", vmin=-1, vmax=1)
    ax.set_xticks(np.arange(corr_mat.shape[0]))
    ax.set_yticks(np.arange(corr_mat.shape[0]))
    ax.set_xticklabels(
        df.drop(columns=["count"]).columns.tolist(), rotation=45, ha="right"
    )
    ax.set_yticklabels(df.drop(columns=["count"]).columns.tolist())
    cbar = ax.figure.colorbar(
        ax.imshow(corr_mat, cmap="coolwarm", vmin=-1, vmax=1), label="correlation"
    )
    plt.tight_layout()
    plt.show()




.. image-sg:: /generated/gallery/examples/images/sphx_glr_plot_msage_002.png
   :alt: plot msage
   :srcset: /generated/gallery/examples/images/sphx_glr_plot_msage_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 105-108

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


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

   **Total running time of the script:** (1 minutes 4.070 seconds)

**Estimated memory usage:**  221 MB


.. _sphx_glr_download_generated_gallery_examples_plot_msage.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_