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celer-0.7.2
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مشاهده بیشترتوضیحات
A fast algorithm with dual extrapolation for sparse problems
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | celer-0.7.2 |
| نام | celer |
| نسخه کتابخانه | 0.7.2 |
| نگهدارنده | ['Mathurin Massias'] |
| ایمیل نگهدارنده | ['mathurin.massias@gmail.com'] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://mathurinm.github.io/celer |
| آدرس اینترنتی | https://pypi.org/project/celer/ |
| مجوز | BSD (3-clause) |
# 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
نیازمندی
| مقدار | نام |
|---|---|
| >=0.7 | seaborn |
| >=1.12 | numpy |
| >=0.18.0 | scipy |
| >=2.0.0 | matplotlib |
| >=0.26 | Cython |
| >=0.3 | libsvmdata |
| >=1.0 | scikit-learn |
| - | xarray |
| - | download |
| - | tqdm |
نحوه نصب
نصب پکیج whl celer-0.7.2:
pip install celer-0.7.2.whl
نصب پکیج tar.gz celer-0.7.2:
pip install celer-0.7.2.tar.gz