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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_linear_model_plot_logistic_path.py:


==============================================
Regularization path of L1- Logistic Regression
==============================================


Train l1-penalized logistic regression models on a binary classification
problem derived from the Iris dataset.

The models are ordered from strongest regularized to least regularized. The 4
coefficients of the models are collected and plotted as a "regularization
path": on the left-hand side of the figure (strong regularizers), all the
coefficients are exactly 0. When regularization gets progressively looser,
coefficients can get non-zero values one after the other.

Here we choose the liblinear solver because it can efficiently optimize for the
Logistic Regression loss with a non-smooth, sparsity inducing l1 penalty.

Also note that we set a low value for the tolerance to make sure that the model
has converged before collecting the coefficients.

We also use warm_start=True which means that the coefficients of the models are
reused to initialize the next model fit to speed-up the computation of the
full-path.

.. GENERATED FROM PYTHON SOURCE LINES 28-77



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png
   :alt: Logistic Regression Path
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png
   :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    Computing regularization path ...
    This took 0.035s






|

.. code-block:: default

    print(__doc__)

    # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
    # License: BSD 3 clause

    from time import time
    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn import linear_model
    from sklearn import datasets
    from sklearn.svm import l1_min_c

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    X = X[y != 2]
    y = y[y != 2]

    X /= X.max()  # Normalize X to speed-up convergence

    # #############################################################################
    # Demo path functions

    cs = l1_min_c(X, y, loss='log') * np.logspace(0, 7, 16)


    print("Computing regularization path ...")
    start = time()
    clf = linear_model.LogisticRegression(penalty='l1', solver='liblinear',
                                          tol=1e-6, max_iter=int(1e6),
                                          warm_start=True,
                                          intercept_scaling=10000.)
    coefs_ = []
    for c in cs:
        clf.set_params(C=c)
        clf.fit(X, y)
        coefs_.append(clf.coef_.ravel().copy())
    print("This took %0.3fs" % (time() - start))

    coefs_ = np.array(coefs_)
    plt.plot(np.log10(cs), coefs_, marker='o')
    ymin, ymax = plt.ylim()
    plt.xlabel('log(C)')
    plt.ylabel('Coefficients')
    plt.title('Logistic Regression Path')
    plt.axis('tight')
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.093 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_path.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_logistic_path.py <plot_logistic_path.py>`



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

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


.. only:: html

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

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