path: root/python-celer.spec

%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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.2-1
- Package Spec generated