Skip to main content

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

Project description

https://travis-ci.org/scikit-learn-contrib/lightning.svg?branch=master https://ci.appveyor.com/api/projects/status/mmm0llccmvn5iooq?svg=true

lightning

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

Highlights:

  • 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

  • FISTA

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 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 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

Project details


Download files

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

Source Distribution

sklearn-contrib-lightning-0.3.0.tar.gz (701.6 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page