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 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))
lightning requires Python >= 3.6, setuptools, Joblib, Numpy >= 1.12, SciPy >= 0.19 and scikit-learn >= 0.19. Building from source also requires Cython and a working C/C++ compiler. To run the tests you will also need pytest.
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 install
http://contrib.scikit-learn.org/lightning/
https://github.com/scikit-learn-contrib/lightning
If you use this software, please cite it. Here is a BibTex snippet that you can use:
@misc{lightning_2016, 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 = {https://doi.org/10.5281/zenodo.200504} }
Other citing formats are available in its Zenodo entry.
- Mathieu Blondel
- Manoj Kumar
- Arnaud Rachez
- Fabian Pedregosa
- Nikita Titov