Metadata-Version: 2.1
Name: celer
Version: 0.7.2
Summary: A fast algorithm with dual extrapolation for sparse problems
Home-page: https://mathurinm.github.io/celer
Maintainer: Mathurin Massias
Maintainer-email: mathurin.massias@gmail.com
License: BSD (3-clause)
Download-URL: https://github.com/mathurinm/celer.git
Description: # celer
        
        ![build](https://github.com/mathurinm/celer/workflows/build/badge.svg)
        ![coverage](https://codecov.io/gh/mathurinm/celer/branch/main/graphs/badge.svg?branch=main)
        ![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)
        ![Downloads](https://pepy.tech/badge/celer/month)
        ![PyPI version](https://badge.fury.io/py/celer.svg)
        
        
        ``celer`` is a Python package that solves Lasso-like problems and provides estimators that follow the ``scikit-learn`` API. Thanks to a tailored implementation, ``celer`` provides a fast solver that tackles large-scale datasets with millions of features **up to 100 times faster than ``scikit-learn``**.
        
        Currently, the package handles the following problems:
        
        
        | Problem                       | Support Weights | Native cross-validation
        | -----------                   | -----------     |----------------
        | Lasso                         | ✓               | ✓
        | ElasticNet                    | ✓               | ✓
        | Group Lasso                   | ✓               | ✓
        | Multitask Lasso               | ✕               | ✓
        | Sparse Logistic regression    | ✕               | ✕
        
        
        
        ## Why ``celer``?
        
        ``celer`` is specially designed to handle Lasso-like problems which makes it a fast solver of such problems.
        ``celer`` comes particularly with
        
        - automated parallel cross-validation
        - support of sparse and dense data
        - optional feature centering and normalization
        - unpenalized intercept fitting
        
        ``celer`` also provides easy-to-use estimators as it is designed under the ``scikit-learn`` API.
        
        
        
        ## Get started
        
        To get stared, install ``celer`` via pip
        
        ```shell
        pip install -U celer
        ```
        
        On your python console,
        run the following commands to fit a Lasso estimator on a toy dataset.
        
        ```python
        >>> from celer import Lasso
        >>> from celer.datasets import make_correlated_data
        >>> X, y, _ = make_correlated_data(n_samples=100, n_features=1000)
        >>> estimator = Lasso()
        >>> estimator.fit(X, y)
        ```
        
        This is just a starter examples.
        Make sure to browse [``celer`` documentation ](https://mathurinm.github.io/celer/) to learn more about its features.
        To get familiar with [``celer`` API](https://mathurinm.github.io/celer/api.html), you can also explore the gallery of examples
        which includes examples on real-life datasets as well as timing comparison with other solvers.
        
        
        
        ## Contribute to celer
        
        ``celer`` is an open source project and hence rely on community efforts to evolve.
        Your contribution is highly valuable and can come in three forms
        
        - **bug report:** you may encounter a bug while using ``celer``. Don't hesitate to report it on the [issue section](https://github.com/mathurinm/celer/issues).
        - **feature request:** you may want to extend/add new features to ``celer``. You can use the [issue section](https://github.com/mathurinm/celer/issues) to make suggestions.
        - **pull request:** you may have fixed a bug, enhanced the documentation, ... you can submit a [pull request](https://github.com/mathurinm/celer/pulls) and we will reach out to you asap.
        
        For the last mean of contribution, here are the steps to help you setup ``celer`` on your local machine:
        
        1. Fork the repository and afterwards run the following command to clone it on your local machine
        
        ```shell
        git clone https://github.com/{YOUR_GITHUB_USERNAME}/celer.git
        ```
        
        2. ``cd`` to ``celer`` directory and install it in edit mode by running
        
        ```shell
        cd celer
        pip install -e .
        ```
        
        3. To run the gallery examples and build the documentation, run the followings
        
        ```shell
        cd doc
        pip install -r doc-requirements.txt
        make html
        ```
        
        
        ## Cite
        
        ``celer`` is licensed under the [BSD 3-Clause](https://github.com/mathurinm/celer/blob/main/LICENSE). Hence, you are free to use it.
        If you do so, please cite:
        
        
        ```bibtex
        @InProceedings{pmlr-v80-massias18a,
          title     = {Celer: a Fast Solver for the Lasso with Dual Extrapolation},
          author    = {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},
          booktitle = {Proceedings of the 35th International Conference on Machine Learning},
          pages     = {3321--3330},
          year      = {2018},
          volume    = {80},
        }
        
        @article{massias2020dual,
          author  = {Mathurin Massias and Samuel Vaiter and Alexandre Gramfort and Joseph Salmon},
          title   = {Dual Extrapolation for Sparse GLMs},
          journal = {Journal of Machine Learning Research},
          year    = {2020},
          volume  = {21},
          number  = {234},
          pages   = {1-33},
          url     = {http://jmlr.org/papers/v21/19-587.html}
        }
        ```
        
        ## Further links
        
        - https://mathurinm.github.io/celer/
        - https://arxiv.org/abs/1802.07481
        - https://arxiv.org/abs/1907.05830
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