sklearn-contrib-lightning 0.1.1

Example

Example that shows how to learn a multiclass classifier with group lasso penalty on the News20 dataset (c.f., Blondel et al. 2013):

from sklearn.datasets import fetch_20newsgroups_vectorized
from lightning.classification import CDClassifier

# Load News20 dataset from scikit-learn.
bunch = fetch_20newsgroups_vectorized(subset="all")
X = bunch.data
y = bunch.target

# Set classifier options.
clf = CDClassifier(penalty="l1/l2",
                   loss="squared_hinge",
                   multiclass=True,
                   max_iter=20,
                   alpha=1e-4,
                   C=1.0 / X.shape[0],
                   tol=1e-3)

# Train the model.
clf.fit(X, y)

# Accuracy
print(clf.score(X, y))

# Percentage of selected features
print(clf.n_nonzero(percentage=True))

Dependencies

lightning requires Python >= 2.7, setuptools, Numpy >= 1.3, SciPy >= 0.7 and scikit-learn >= 0.15. Building from source also requires Cython and a working C/C++ compiler. To run the tests you will also need nose >= 0.10.

Installation

Precompiled binaries for the stable version of lightning are available for the main platforms and can be installed using pip:

pip install sklearn-contrib-lightning

or conda:

conda install -c https://conda.anaconda.org/scikit-learn-contrib lightning

The development version of lightning can be installed from its git repository. In this case it is assumed that you have the git version control system, a working C++ compiler, Cython and the numpy development libraries. In order to install this verion, type:

git clone https://github.com/scikit-learn-contrib/lightning.git
cd lightning
python setup.py build
sudo python setup.py install

Documentation

http://contrib.scikit-learn.org/lightning/

On Github

https://github.com/scikit-learn-contrib/lightning

Authors

• Mathieu Blondel, 2012-present

• Manoj Kumar, 2015-present

• Arnaud Rachez, 2016-present

• Fabian Pedregosa, 2016-present