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

.. only:: html

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

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

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

.. _sphx_glr_python_examples_gpu_tree_shap.py:


Use GPU to speedup SHAP value computation
=========================================

Demonstrates using GPU acceleration to compute SHAP values for feature importance.

.. GENERATED FROM PYTHON SOURCE LINES 8-64

.. code-block:: Python

    from urllib.error import HTTPError

    import shap
    from sklearn.datasets import fetch_california_housing, make_regression

    import xgboost as xgb

    # Fetch dataset using sklearn
    try:
        _data = fetch_california_housing(return_X_y=True)
        X = _data.data
        y = _data.target
        feature_names = _data.feature_names
        print(_data.DESCR)
    except HTTPError:
        # Use a synthetic dataset instead if we couldn't
        X, y = make_regression(n_samples=20640, n_features=8, random_state=1234)
        feature_names = [f"f{i}" for i in range(8)]

    num_round = 500

    param = {
        "eta": 0.05,
        "max_depth": 10,
        "tree_method": "hist",
        "device": "cuda",
    }

    # GPU accelerated training
    dtrain = xgb.DMatrix(X, label=y, feature_names=feature_names)
    model = xgb.train(param, dtrain, num_round)

    # Compute shap values using GPU with xgboost
    model.set_param({"device": "cuda"})
    shap_values = model.predict(dtrain, pred_contribs=True)

    # Compute shap interaction values using GPU
    shap_interaction_values = model.predict(dtrain, pred_interactions=True)


    # shap will call the GPU accelerated version as long as the device parameter is set to
    # "cuda"
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)

    # visualize the first prediction's explanation
    shap.force_plot(
        explainer.expected_value,
        shap_values[0, :],
        X[0, :],
        feature_names=feature_names,
        matplotlib=True,
    )

    # Show a summary of feature importance
    shap.summary_plot(shap_values, X, plot_type="bar", feature_names=feature_names)


.. _sphx_glr_download_python_examples_gpu_tree_shap.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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