sklearn-contrib-lightning 0.6.2.post0

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 >= 3.7, 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.

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 install

Documentation

http://contrib.scikit-learn.org/lightning/

On GitHub

https://github.com/scikit-learn-contrib/lightning

Citing

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.

Authors

• Mathieu Blondel

• Manoj Kumar

• Arnaud Rachez

• Fabian Pedregosa

• Nikita Titov