.. _sphx_glr_auto_examples_linear_model_plot_lasso_lars.py:


=====================
Lasso path using LARS
=====================

Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regularization parameter.




.. image:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_lars_001.png
    :align: center


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

 Out::

      Computing regularization path using the LARS ...
    .




|


.. code-block:: python

    print(__doc__)

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

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn import linear_model
    from sklearn import datasets

    diabetes = datasets.load_diabetes()
    X = diabetes.data
    y = diabetes.target

    print("Computing regularization path using the LARS ...")
    alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)

    xx = np.sum(np.abs(coefs.T), axis=1)
    xx /= xx[-1]

    plt.plot(xx, coefs.T)
    ymin, ymax = plt.ylim()
    plt.vlines(xx, ymin, ymax, linestyle='dashed')
    plt.xlabel('|coef| / max|coef|')
    plt.ylabel('Coefficients')
    plt.title('LASSO Path')
    plt.axis('tight')
    plt.show()

**Total running time of the script:**
(0 minutes 0.110 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_lasso_lars.py <plot_lasso_lars.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_lasso_lars.ipynb <plot_lasso_lars.ipynb>`