.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/07_regression_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_07_regression_kriging2d.py>`
        to download the full example code.

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

.. _sphx_glr_examples_07_regression_kriging2d.py:


Regression kriging
------------------

An example of regression kriging

.. GENERATED FROM PYTHON SOURCE LINES 7-46




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

 .. code-block:: none

    ========================================
    regression model: SVR
    Finished learning regression model
    Finished kriging residuals
    Regression Score:  -0.03405385545698292
    RK score:  0.6706182225388981
    ========================================
    regression model: RandomForestRegressor
    Finished learning regression model
    Finished kriging residuals
    Regression Score:  0.7041419269689924
    RK score:  0.7415694880137507
    ========================================
    regression model: LinearRegression
    Finished learning regression model
    Finished kriging residuals
    Regression Score:  0.5277968398381674
    RK score:  0.6036605153133718






|

.. code-block:: Python


    import sys

    from sklearn.datasets import fetch_california_housing
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVR

    from pykrige.rk import RegressionKriging

    svr_model = SVR(C=0.1, gamma="auto")
    rf_model = RandomForestRegressor(n_estimators=100)
    lr_model = LinearRegression(copy_X=True, fit_intercept=False)

    models = [svr_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]

    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("regression model:", m.__class__.__name__)
        m_rk = RegressionKriging(regression_model=m, n_closest_points=10)
        m_rk.fit(p_train, x_train, target_train)
        print("Regression Score: ", m_rk.regression_model.score(p_test, target_test))
        print("RK score: ", m_rk.score(p_test, x_test, target_test))


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

   **Total running time of the script:** (0 minutes 4.571 seconds)


.. _sphx_glr_download_examples_07_regression_kriging2d.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 07_regression_kriging2d.ipynb <07_regression_kriging2d.ipynb>`

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

      :download:`Download Python source code: 07_regression_kriging2d.py <07_regression_kriging2d.py>`

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

      :download:`Download zipped: 07_regression_kriging2d.zip <07_regression_kriging2d.zip>`


.. only:: html

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

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