ENH - New skglm README (#112)

Author: Badr-MOUFAD

README.md

@@ -0,0 +1,122 @@
+<section align="center">
+
+# ``skglm``
+
+## A fast :zap: and modular :hammer_and_pick: scikit-learn replacement for sparse GLMs
+
+</section>
+
+![build](https://github.com/scikit-learn-contrib/skglm/workflows/pytest/badge.svg)
+![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)
+![Downloads](https://pepy.tech/badge/skglm/month)
+![PyPI version](https://badge.fury.io/py/skglm.svg)
+
+
+``skglm`` is a Python package that offers **fast estimators** for sparse Generalized Linear Models (GLMs) that are **100% compatible with ``scikit-learn``**. It is **highly flexible** and supports a wide range of GLMs. You get to choose from ``skglm``'s already-made estimators or **customize your own** by combining the available datafits and penalty.
+
+Excited to have a tour on ``skglm`` [documentation](https://contrib.scikit-learn.org/skglm/) :memo:?
+
+# Why ``skglm``?
+
+``skglm`` is specifically conceived to solve sparse GLMs.
+It supports many missing models in ``scikit-learn`` and ensures high performance.
+There are several reasons to opt for ``skglm`` among which:
+
+|  |  |
+| ----- | -------------- |
+| **Speed** :zap: | Fast solvers able to tackle large datasets, either dense or sparse, with millions of features **up to 100 times faster** than ``scikit-learn``|
+| **Modularity** :hammer_and_pick: | User-friendly API than enables **composing custom estimators** with any combination of its existing datafits and penalties |
+| **Extensibility** :arrow_up_down: | Flexible design that makes it **simple and easy to implement new datafits and penalties**, a matter of few lines of code
+| **Compatibility** :electric_plug: | Estimators **fully compatible with the ``scikit-learn`` API** and drop-in replacements of its GLM estimators
+|  |  |
+
+
+# Get started with ``skglm``
+
+## Installing ``skglm``
+
+``skglm`` is available on PyPi. Run the following command to get the latest version of the package
+
+```shell
+pip install -U skglm
+```
+
+It is also available on Conda _(not yet, but very soon...)_ and can be installed via the command
+
+```shell
+conda install skglm
+```
+
+## First steps with ``skglm``
+
+Once you installed ``skglm``, you can run the following code snippet to fit a MCP Regression model on a toy dataset
+
+```python
+# import model to fit
+from skglm.estimators import MCPRegression
+# import util to create a toy dataset
+from skglm.utils import make_correlated_data
+
+# generate a toy dataset
+X, y, _ = make_correlated_data(n_samples=10, n_features=100)
+
+# init and fit estimator
+estimator = MCPRegression()
+estimator.fit(X, y)
+
+# print R²
+print(estimator.score(X, y))
+```
+You can refer to the documentation to explore the list of ``skglm``'s already-made estimators.
+
+Didn't find one that suits you :monocle_face:, you can still compose your own.
+Here is a code snippet that fits a MCP-regularized problem with Huber loss.
+
+```python
+# import datafit, penalty and GLM estimator
+from skglm.datafits import Huber
+from skglm.penalties import MCPenalty
+from skglm.estimators import GeneralizedLinearEstimator
+
+from skglm.utils import make_correlated_data
+
+X, y, _ = make_correlated_data(n_samples=10, n_features=100)
+# create and fit GLM estimator with Huber loss and MCP penalty
+estimator = GeneralizedLinearEstimator(
+    datafit=Huber(delta=1.),
+    penalty=MCPenalty(alpha=1e-2, gamma=3),
+)
+estimator.fit(X, y)
+```
+
+You will find detailed description on the supported datafits and penalties and how to combine them in the API section of the documentation.
+You can also take our tutorial to learn how to create your own datafit and penalty.
+
+
+# Contribute to ``skglm``
+
+``skglm`` is a continuous endeavour that relies on the community efforts to last and evolve. Your contribution is welcome and highly valuable. It can be
+
+- **bug report**: you may encounter a bug while using ``skglm``. Don't hesitate to report it on the [issue section](https://github.com/scikit-learn-contrib/skglm/issues).
+- **feature request**: you may want to extend/add new features to ``skglm``. You can use [the issue section](https://github.com/scikit-learn-contrib/skglm/issues) to make suggestions.
+- **pull request**: you may have fixed a bug, added a features, or even fixed a small typo in the documentation, ... you can submit a [pull request](https://github.com/scikit-learn-contrib/skglm/pulls) and we will reach out to you asap.
+
+
+# Cite
+
+``skglm`` is the result of perseverant research. It is licensed under [BSD 3-Clause](https://github.com/scikit-learn-contrib/skglm/blob/main/LICENSE). You are free to use it and if you do so, please cite
+
+```bibtex
+@inproceedings{skglm,
+    title     = {Beyond L1: Faster and better sparse models with skglm},
+    author    = {Q. Bertrand and Q. Klopfenstein and P.-A. Bannier and G. Gidel and M. Massias},
+    booktitle = {NeurIPS},
+    year      = {2022},
+}
+```
+
+
+# Useful links
+
+- link to documentation: https://contrib.scikit-learn.org/skglm/
+- link to ``skglm`` arXiv article: https://arxiv.org/pdf/2204.07826.pdf

README.rst

@@ -13,7 +13,7 @@
 
 DISTNAME = 'skglm'
 DESCRIPTION = 'A fast and modular scikit-learn replacement for generalized linear models'
-with open('README.rst', 'r') as f:
+with open('README.md', 'r', encoding='utf-8') as f:
     LONG_DESCRIPTION = f.read()
 MAINTAINER = 'Mathurin Massias'
 MAINTAINER_EMAIL = '[email protected]'
@@ -26,6 +26,7 @@
       version=version,
       description=DESCRIPTION,
       long_description=LONG_DESCRIPTION,
+      long_description_content_type='text/markdown',
       maintainer=MAINTAINER,
       maintainer_email=MAINTAINER_EMAIL,
       url=URL,