l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.

l1l2py makes use of numpy http://numpy.scipy.org to provide fast N-dimensional array manipulation and it is the Python implementation of the method proposed and applied in [DeMol09].

l1l2py is fully compatible with scikit-learn Python toolbox for machine learning http://scikit-learn.org/stable/.

l1l2py is a project of SLIPGURU - Statistical Learning and Image Processing Genoa University Research Group - Via Dodecaneso, 35 - 16146 Genova, ITALY http://slipguru.unige.it/.

l1l2py is free software. It is licensed under the GNU General Public License (GPL) version 3 http://www.gnu.org/licenses/gpl.html.

Installation

l1l2py supports Python 2.7

Pip installation

$ pip install L1L2py

Installing from sources

$ git clone https://github.com/slipguru/l1l2py
$ cd l1l2py
$ python setup.py install

Try L1L2py

l1l2py can be used to solve both regression and classification problems.

Regression

>>> from l1l2py.linear_model import L1L2
>>> from sklearn.datasets import load_boston
>>> X, y = load_boston(return_X_y=True)
>>> l1l2 = L1L2(tau=1, mu=0.5).fit(X, y)
>>> l1l2
L1L2(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5, max_iter=10000,
   mu=0.5, normalize=False, positive=False, precompute=False,
   random_state=None, selection='cyclic', tau=1, tol=0.0001, use_gpu=False,
   warm_start=False)
>>> l1l2.coef_
array([-0.07871197,  0.05147779, -0.00579328,  0.        , -0.        ,
        1.03468713,  0.01992332, -0.74193449,  0.30231875, -0.01640304,
       -0.77808095,  0.00851397, -0.75325201])

Classification

>>> from l1l2py.classification import L1L2Classifier
>>> from sklearn.datasets import load_breast_cancer
>>> X, y = load_breast_cancer(return_X_y=True)
>>> l1l2 = L1L2Classifier(tau=1, mu=0.5).fit(X, y)
>>> l1l2
L1L2Classifier(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,
   max_iter=10000, mu=0.5, normalize=False, positive=False,
   precompute=False, random_state=None, selection='cyclic', tau=1,
   threshold=1e-16, tol=0.0001, use_gpu=False, warm_start=False)
>>> l1l2.coef_
array([[-0.        , -0.        ,  0.00013437,  0.00046001, -0.        ,
        -0.        , -0.        , -0.        , -0.        , -0.        ,
        -0.        , -0.        , -0.        ,  0.        , -0.        ,
        -0.        , -0.        , -0.        , -0.        , -0.        ,
        -0.        , -0.01317881, -0.03120279,  0.00032235, -0.        ,
        -0.        , -0.        , -0.        , -0.        , -0.        ]])

Help and support

HTML documentation L1L2py documentation.

Reference

[DeMol09] C. De Mol, S. Mosci, M. Traskine, A. Verri, "A Regularized Method for Selecting Nested Group of Genes from Microarray Data" Journal of Computational Biology, vol. 16, pp. 677-690, 2009.