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

.. only:: html

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

        Click :ref:`here <sphx_glr_download__auto_examples_kernel_ridge_plot_kernel_ridge.py>`
        to download the full example code

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

.. _sphx_glr__auto_examples_kernel_ridge_plot_kernel_ridge.py:


Kernel ridge
============

This example demonstrates how to solve kernel ridge regression, using
himalaya's estimator ``KernelRidge`` compatible with scikit-learn's API.

.. GENERATED FROM PYTHON SOURCE LINES 10-12

Create a random dataset
-----------------------

.. GENERATED FROM PYTHON SOURCE LINES 12-17

.. code-block:: default

    import numpy as np
    n_samples, n_features, n_targets = 10, 20, 4
    X = np.random.randn(n_samples, n_features)
    Y = np.random.randn(n_samples, n_targets)








.. GENERATED FROM PYTHON SOURCE LINES 18-22

Scikit-learn API
----------------
Himalaya implements a ``KernelRidge`` estimator, similar to the corresponding
scikit-learn estimator, with similar parameters and methods.

.. GENERATED FROM PYTHON SOURCE LINES 22-39

.. code-block:: default

    import sklearn.kernel_ridge
    import himalaya.kernel_ridge

    # Fit a scikit-learn model
    model_skl = sklearn.kernel_ridge.KernelRidge(kernel="linear", alpha=0.1)
    model_skl.fit(X, Y)

    # Fit a himalaya model
    model_him = himalaya.kernel_ridge.KernelRidge(kernel="linear", alpha=0.1)
    model_him.fit(X, Y)

    Y_pred_skl = model_skl.predict(X)
    Y_pred_him = model_him.predict(X)

    # The predictions are virtually identical.
    print(np.max(np.abs(Y_pred_skl - Y_pred_him)))





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

 Out:

 .. code-block:: none

    7.105427357601002e-15




.. GENERATED FROM PYTHON SOURCE LINES 40-45

Small API difference
--------------------
Since himalaya focuses on fitting multiple targets, the ``score`` method
returns the score on each target separately, while scikit-learn returns the
average score over targets.

.. GENERATED FROM PYTHON SOURCE LINES 45-49

.. code-block:: default


    print(model_skl.score(X, Y))
    print(model_him.score(X, Y))
    print(model_him.score(X, Y).mean())




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

 Out:

 .. code-block:: none

    0.9999104439221304
    [0.99990451 0.99985375 0.99994683 0.99993669]
    0.9999104439221304





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

   **Total running time of the script:** ( 0 minutes  0.017 seconds)


.. _sphx_glr_download__auto_examples_kernel_ridge_plot_kernel_ridge.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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