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

Large-scale linear classification, regression and ranking in Python
https://github.com/scikit-learn-contrib/lightning

machine-learning

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Large-scale linear classification, regression and ranking in Python

• Host: GitHub
• URL: https://github.com/scikit-learn-contrib/lightning
• Owner: scikit-learn-contrib
• Archived: true
• Created: 2012-01-11T13:53:52.000Z (about 13 years ago)
• Default Branch: master
• Last Pushed: 2023-07-18T11:41:11.000Z (over 1 year ago)
• Last Synced: 2024-04-24T16:46:43.526Z (9 months ago)
• Topics: machine-learning
• Language: Python
• Homepage: https://contrib.scikit-learn.org/lightning/
• Size: 14 MB
• Stars: 1,708
• Watchers: 38
• Forks: 212
• Open Issues: 56
• Metadata Files:
  • Readme: README.rst

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• awesome-python-machine-learning-resources - GitHub - 52% open · ⏱️ 30.01.2022): (Sklearn实用程序)

README

        
.. -*- mode: rst -*-

.. image:: https://github.com/scikit-learn-contrib/lightning/actions/workflows/main.yml/badge.svg?branch=master

    :target: https://github.com/scikit-learn-contrib/lightning/actions/workflows/main.yml

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.200504.svg

    :target: https://doi.org/10.5281/zenodo.200504

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

`_):

.. code-block:: python

    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:

.. code-block:: sh

    pip install sklearn-contrib-lightning

or conda:

.. code-block:: sh

    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:

.. code-block:: sh

  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:

.. code-block::

  @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