Skip to main content
Help us improve Python packaging – donate today!

Large-scale sparse linear classification, regression and ranking in Python

Project Description


lightning is a library for large-scale linear classification, regression and ranking in Python.


  • follows the scikit-learn API conventions
  • supports natively both dense and sparse data representations
  • computationally demanding parts implemented in Cython

Solvers supported:

  • primal coordinate descent
  • dual coordinate descent (SDCA, Prox-SDCA)
  • SGD, AdaGrad, SAG, SAGA, SVRG


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 =
y =

# Set classifier options.
clf = CDClassifier(penalty="l1/l2",
                   C=1.0 / X.shape[0],

# Train the model., y)

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

# Percentage of selected features


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.


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 conda-forge sklearn-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 the development version, type:

git clone
cd lightning
python build
sudo python install


If you use this software, please cite it. Here is a BibTex snippet that you can use:

  author       = {Blondel, Mathieu and
                  Pedregosa, Fabian},
  title        = {{Lightning: large-scale linear classification,
                 regression and ranking in Python}},
  year         = 2016,
  doi          = {10.5281/zenodo.200504},
  url          = {}

Other citing formats are available in its Zenodo entry .


  • Mathieu Blondel, 2012-present
  • Manoj Kumar, 2015-present
  • Arnaud Rachez, 2016-present
  • Fabian Pedregosa, 2016-present

Release history Release notifications

This version
History Node


History Node


History Node


History Node


History Node


History Node


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
sklearn-contrib-lightning-0.5.0.tar.gz (915.4 kB) Copy SHA256 hash SHA256 Source None Nov 4, 2017

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page