%global _empty_manifest_terminate_build 0 Name: python-celer Version: 0.7.2 Release: 1 Summary: A fast algorithm with dual extrapolation for sparse problems License: BSD (3-clause) URL: https://mathurinm.github.io/celer Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7e/77/868d7e4bf533c8f91ea0568fad993f4c6cc38b55edf62a8b9b4a86f76d54/celer-0.7.2.tar.gz Requires: python3-seaborn Requires: python3-numpy Requires: python3-scipy Requires: python3-matplotlib Requires: python3-Cython Requires: python3-libsvmdata Requires: python3-scikit-learn Requires: python3-xarray Requires: python3-download Requires: python3-tqdm %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 %package -n python3-celer Summary: A fast algorithm with dual extrapolation for sparse problems Provides: python-celer BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-celer # 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 %package help Summary: Development documents and examples for celer Provides: python3-celer-doc %description help # 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 %prep %autosetup -n celer-0.7.2 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-celer -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 0.7.2-1 - Package Spec generated