.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/linear_model/plot_lasso_coordinate_descent_path.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_lasso_coordinate_descent_path.py>`
        to download the full example code. or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py:


=====================
Lasso and Elastic Net
=====================

Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.

The coefficients can be forced to be positive.

.. GENERATED FROM PYTHON SOURCE LINES 12-85



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_001.png
         :alt: Lasso and Elastic-Net Paths
         :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_002.png
         :alt: Lasso and positive Lasso
         :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_003.png
         :alt: Elastic-Net and positive Elastic-Net
         :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_003.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    Computing regularization path using the lasso...
    Computing regularization path using the positive lasso...
    Computing regularization path using the elastic net...
    Computing regularization path using the positive elastic net...






|

.. code-block:: Python


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

    from itertools import cycle

    import matplotlib.pyplot as plt

    from sklearn import datasets
    from sklearn.linear_model import enet_path, lasso_path

    X, y = datasets.load_diabetes(return_X_y=True)


    X /= X.std(axis=0)  # Standardize data (easier to set the l1_ratio parameter)

    # Compute paths

    eps = 5e-3  # the smaller it is the longer is the path

    print("Computing regularization path using the lasso...")
    alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)

    print("Computing regularization path using the positive lasso...")
    alphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(
        X, y, eps=eps, positive=True
    )
    print("Computing regularization path using the elastic net...")
    alphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)

    print("Computing regularization path using the positive elastic net...")
    alphas_positive_enet, coefs_positive_enet, _ = enet_path(
        X, y, eps=eps, l1_ratio=0.8, positive=True
    )

    # Display results

    plt.figure(1)
    colors = cycle(["b", "r", "g", "c", "k"])
    for coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):
        l1 = plt.semilogx(alphas_lasso, coef_l, c=c)
        l2 = plt.semilogx(alphas_enet, coef_e, linestyle="--", c=c)

    plt.xlabel("alpha")
    plt.ylabel("coefficients")
    plt.title("Lasso and Elastic-Net Paths")
    plt.legend((l1[-1], l2[-1]), ("Lasso", "Elastic-Net"), loc="lower right")
    plt.axis("tight")


    plt.figure(2)
    for coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):
        l1 = plt.semilogy(alphas_lasso, coef_l, c=c)
        l2 = plt.semilogy(alphas_positive_lasso, coef_pl, linestyle="--", c=c)

    plt.xlabel("alpha")
    plt.ylabel("coefficients")
    plt.title("Lasso and positive Lasso")
    plt.legend((l1[-1], l2[-1]), ("Lasso", "positive Lasso"), loc="lower right")
    plt.axis("tight")


    plt.figure(3)
    for coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):
        l1 = plt.semilogx(alphas_enet, coef_e, c=c)
        l2 = plt.semilogx(alphas_positive_enet, coef_pe, linestyle="--", c=c)

    plt.xlabel("alpha")
    plt.ylabel("coefficients")
    plt.title("Elastic-Net and positive Elastic-Net")
    plt.legend((l1[-1], l2[-1]), ("Elastic-Net", "positive Elastic-Net"), loc="lower right")
    plt.axis("tight")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.445 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_lasso_coordinate_descent_path.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/main?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_lasso_coordinate_descent_path.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_lasso_coordinate_descent_path.recommendations


.. only:: html

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

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