.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/10_classification_kriging2d.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_10_classification_kriging2d.py>`
        to download the full example code.

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

.. _sphx_glr_examples_10_classification_kriging2d.py:


Classification kriging
----------------------

An example of classification kriging

.. GENERATED FROM PYTHON SOURCE LINES 7-51




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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/pykrige/envs/latest/lib/python3.11/site-packages/sklearn/preprocessing/_discretization.py:296: FutureWarning: The current default behavior, quantile_method='linear', will be changed to quantile_method='averaged_inverted_cdf' in scikit-learn version 1.9 to naturally support sample weight equivalence properties by default. Pass quantile_method='averaged_inverted_cdf' explicitly to silence this warning.
      warnings.warn(
    ========================================
    classification model: SVC
    Finished learning classification model
    Finished kriging residuals
    Classification Score:  0.212
    CK score:  0.6566666666666666
    ========================================
    classification model: RandomForestClassifier
    Finished learning classification model
    Finished kriging residuals
    Classification Score:  0.5766666666666667
    CK score:  0.5946666666666667
    ========================================
    classification model: LogisticRegression
    /home/docs/checkouts/readthedocs.org/user_builds/pykrige/envs/latest/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 10000 iteration(s) (status=1):
    STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT

    Increase the number of iterations to improve the convergence (max_iter=10000).
    You might also want to scale the data as shown in:
        https://scikit-learn.org/stable/modules/preprocessing.html
    Please also refer to the documentation for alternative solver options:
        https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
      n_iter_i = _check_optimize_result(
    Finished learning classification model
    Finished kriging residuals
    Classification Score:  0.524
    CK score:  0.6553333333333333






|

.. code-block:: Python


    import sys

    from sklearn.datasets import fetch_california_housing
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import KBinsDiscretizer
    from sklearn.svm import SVC

    from pykrige.ck import ClassificationKriging

    svc_model = SVC(C=0.1, gamma="auto", probability=True)
    rf_model = RandomForestClassifier(n_estimators=100)
    lr_model = LogisticRegression(max_iter=10000)

    models = [svc_model, rf_model, lr_model]

    try:
        housing = fetch_california_housing()
    except PermissionError:
        # this dataset can occasionally fail to download on Windows
        sys.exit(0)

    # take the first 5000 as Kriging is memory intensive
    p = housing["data"][:5000, :-2]
    x = housing["data"][:5000, -2:]
    target = housing["target"][:5000]
    discretizer = KBinsDiscretizer(encode="ordinal")
    target = discretizer.fit_transform(target.reshape(-1, 1))

    p_train, p_test, x_train, x_test, target_train, target_test = train_test_split(
        p, x, target, test_size=0.3, random_state=42
    )

    for m in models:
        print("=" * 40)
        print("classification model:", m.__class__.__name__)
        m_ck = ClassificationKriging(classification_model=m, n_closest_points=10)
        m_ck.fit(p_train, x_train, target_train)
        print(
            "Classification Score: ", m_ck.classification_model.score(p_test, target_test)
        )
        print("CK score: ", m_ck.score(p_test, x_test, target_test))


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

   **Total running time of the script:** (0 minutes 12.715 seconds)


.. _sphx_glr_download_examples_10_classification_kriging2d.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 10_classification_kriging2d.ipynb <10_classification_kriging2d.ipynb>`

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      :download:`Download Python source code: 10_classification_kriging2d.py <10_classification_kriging2d.py>`

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

      :download:`Download zipped: 10_classification_kriging2d.zip <10_classification_kriging2d.zip>`


.. only:: html

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

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