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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_linear_model_plot_sgd_penalties.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_linear_model_plot_sgd_penalties.py:


==============
SGD: Penalties
==============

Contours of where the penalty is equal to 1
for the three penalties L1, L2 and elastic-net.

All of the above are supported by :class:`~sklearn.linear_model.SGDClassifier`
and :class:`~sklearn.linear_model.SGDRegressor`.

.. GENERATED FROM PYTHON SOURCE LINES 13-58



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_sgd_penalties_001.png
   :alt: plot sgd penalties
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_sgd_penalties_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    l1_color = "navy"
    l2_color = "c"
    elastic_net_color = "darkorange"

    line = np.linspace(-1.5, 1.5, 1001)
    xx, yy = np.meshgrid(line, line)

    l2 = xx**2 + yy**2
    l1 = np.abs(xx) + np.abs(yy)
    rho = 0.5
    elastic_net = rho * l1 + (1 - rho) * l2

    plt.figure(figsize=(10, 10), dpi=100)
    ax = plt.gca()

    elastic_net_contour = plt.contour(
        xx, yy, elastic_net, levels=[1], colors=elastic_net_color
    )
    l2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)
    l1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)
    ax.set_aspect("equal")
    ax.spines["left"].set_position("center")
    ax.spines["right"].set_color("none")
    ax.spines["bottom"].set_position("center")
    ax.spines["top"].set_color("none")

    plt.clabel(
        elastic_net_contour,
        inline=1,
        fontsize=18,
        fmt={1.0: "elastic-net"},
        manual=[(-1, -1)],
    )
    plt.clabel(l2_contour, inline=1, fontsize=18, fmt={1.0: "L2"}, manual=[(-1, -1)])
    plt.clabel(l1_contour, inline=1, fontsize=18, fmt={1.0: "L1"}, manual=[(-1, -1)])

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.260 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_penalties.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_sgd_penalties.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/linear_model/plot_sgd_penalties.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_sgd_penalties.recommendations


.. only:: html

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

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