Large-scale sparse linear classification and regression in Python/Cython.

lightning follows the scikit-learn API conventions.

The required dependencies to build the software are Python >= 2.6, setuptools, Numpy >= 1.3, SciPy >= 0.7, scikit-learn (git version), Cython >= 0.15.1 and a working C/C++ compiler.

To run the tests you will also need nose >= 0.10.

First run:

make cython

Then to install in your home directory, use:

python setup.py install --home

To install for all users on Unix/Linux:

python setup.py build
sudo python setup.py install

Classification:

• CDClassifier Learning linear classifiers by coordinate descent in the primal. Supports different losses and penalties.
• SGDClassifier Learning linear classifiers by stochastic (sub)gradient descent. Supports different losses and penalties.
• LinearSVC Learning linear SVM by coordinate descent in the dual. Supports optimizing for accuracy or AUC.
• KernelSVC Learning kernel SVM by Newton's method in the primal. Nice if kernel matrix fits is memory.

Regression:

• CDRegressor Learning linear regressors by coordinate descent in the primal. Supports different losses and penalties.
• SGDRegressor Learning linear regressors by stochastic (sub)gradient descent. Supports different losses and penalties.

If you used lightning in your research, please consider citing the following paper:

For more information, see http://www.mblondel.org/code/mlj2013/