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diff --git a/python-celer.spec b/python-celer.spec new file mode 100644 index 0000000..c2a93e5 --- /dev/null +++ b/python-celer.spec @@ -0,0 +1,474 @@ +%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 + + + + + + + + +``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 + + + + + + + + +``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 + + + + + + + + +``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 <Python_Bot@openeuler.org> - 0.7.2-1 +- Package Spec generated |