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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.

../../_images/sphx_glr_plot_lasso_lars_001.png

Out:

  Computing regularization path using the LARS ...
.
print(__doc__)

# Author: Fabian Pedregosa <[email protected]>
#         Alexandre Gramfort <[email protected]>
# 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 0.106 seconds)

Download Python source code: plot_lasso_lars.py
Download IPython notebook: plot_lasso_lars.ipynb

© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html