lightning is a library for large-scale linear classification and regression 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:

• coordinate descent
• FISTA
• stochastic gradient descent
• SpaRSA

Loss functions supported:

• hinge
• squared hinge
• logistic
• multiclass logistic
• squared
• epsilon-insensitive
• modified-huber

Penalties supported:

• L2 (Ridge)
• L1 (Lasso)
• L1/L2 (Group Lasso)

Note: Not all solvers support all loss / penalty combinations. See the class references for more details.

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 needs Python >= 2.7, setuptools, Numpy >= 1.3, SciPy >= 0.7, scikit-learn >= 0.14 and a working C/C++ compiler.

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

To install lightning from pip, type:

pip install https://github.com/mblondel/lightning/archive/master.zip

To install lightning from source, type:

git clone https://github.com/mblondel/lightning.git
cd lightning
python setup.py build
sudo python setup.py install

http://www.mblondel.org/lightning/

https://github.com/mblondel/lightning

Mathieu Blondel, 2012-present