updated setup and readme.

README.md

@@ -1,60 +1,69 @@
 ![logo](https://github.com/rvandewater/ReciPys/blob/development/docs/figures/recipys_logo.png?raw=true)
+
 # 🥧ReciPys🐍
+
 [![CI](https://github.com/rvandewater/recipys/actions/workflows/ci.yml/badge.svg)](https://github.com/rvandewater/recipys/actions/workflows/ci.yml)
 [![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 ![Platform](https://img.shields.io/badge/platform-linux--64%20|%20win--64%20|%20osx--64-lightgrey)
 [![License](https://img.shields.io/badge/license-MIT-green)](LICENSE)
 [![PyPI version shields.io](https://img.shields.io/pypi/v/recipies.svg)](https://pypi.python.org/pypi/recipies/)
 [![arXiv](https://img.shields.io/badge/arXiv-2306.05109-b31b1b.svg)](http://arxiv.org/abs/2306.05109)
 
-The ReciPys package is a preprocessing framework operating on Pandas dataframes. 
+The ReciPys package is a preprocessing framework operating on [Polars](https://github.com/pola-rs/polars)
+and [Pandas](https://github.com/pandas-dev/pandas) dataframes. The backend can be chosen by the user.
 The operation of this package is inspired by the R-package [recipes](https://recipes.tidymodels.org/).
-This package allows the user to apply a number of extensible operations for imputation, feature generation/extraction, 
-scaling, and encoding. 
+This package allows the user to apply a number of extensible operations for imputation, feature generation/extraction,
+scaling, and encoding.
 It operates on modified Dataframe objects from the established data science package Pandas.
-## Installation 
+
+## Installation
+
 You can install ReciPys from pip using:
+
 ```
 pip install recipies
 ```
+
 > Note that the package is called `recipies` and not `recipys` on pip due to a name clash with an existing package.
-> 
+>
 You can install ReciPys from source to ensure you have the latest version:
+
 ```
 conda env update -f environment.yml
 conda activate recipys
 pip install -e .
 ```
+
 > Note that the last command installs the package called `recipies`.
 
 ## Usage
-To define preprocessing operations, one has to supply _roles_ to the different columns of the Dataframe. 
+
+To define preprocessing operations, one has to supply _roles_ to the different columns of the Dataframe.
 This allows the user to create groups of columns which have a particular function.
-Then, we provide several "steps" that can be applied to the datasets, among which: Historical accumulation, 
-Resampling the time resolution, A number of imputation methods, and a wrapper for any 
+Then, we provide several "steps" that can be applied to the datasets, among which: Historical accumulation,
+Resampling the time resolution, A number of imputation methods, and a wrapper for any
 [Scikit-learn](https://github.com/scikit-learn/scikit-learn) preprocessing step.
 We believe to have covered any basic preprocessing needs for prepared datasets.
 Any missing step can be added by following the step interface.
 
 # 📄Paper
 
-If you use this code in your research, please cite the following publication:
+If you use this code in your research, please cite the following publication (a standalone paper is in preparation):
 
 ```
-@article{vandewaterYetAnotherICUBenchmark2023,
-	title = {Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML},
-	shorttitle = {Yet Another ICU Benchmark},
-	url = {http://arxiv.org/abs/2306.05109},
-	language = {en},
-	urldate = {2023-06-09},
-	publisher = {arXiv},
-	author = {van de Water, Robin and Schmidt, Hendrik and Elbers, Paul and Thoral, Patrick and Arnrich, Bert and Rockenschaub, Patrick},
-	month = jun,
-	year = {2023},
-	note = {arXiv:2306.05109 [cs]},
-	keywords = {Computer Science - Machine Learning},
+@inproceedings{vandewaterYetAnotherICUBenchmark2024,
+  title = {Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML},
+  shorttitle = {Yet Another ICU Benchmark},
+  booktitle = {The Twelfth International Conference on Learning Representations},
+  author = {van de Water, Robin and Schmidt, Hendrik Nils Aurel and Elbers, Paul and Thoral, Patrick and Arnrich, Bert and Rockenschaub, Patrick},
+  year = {2024},
+  month = oct,
+  urldate = {2024-02-19},
+  langid = {english},
 }
+
 ```
+
 This paper can also be found on arxiv: https://arxiv.org/pdf/2306.05109.pdf
 
 

pyproject.toml

@@ -0,0 +1,34 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "recipies"
+version = "1.0"
+description = "A modular preprocessing package for Pandas Dataframe"
+readme = "README.md"
+license = { text = "MIT license" }
+authors = [
+    { name = "Robin van de Water", email = "[email protected]" }
+]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: MIT License",
+    "Natural Language :: English",
+    "Programming Language :: Python :: 3.12"
+]
+keywords = ["recipies", "pandas", "dataframe", "polars", "preprocessing", "recipys"]
+dependencies = [
+    "coverage>=7.0.0",
+    "numpy>=1.24.0",
+    "pandas>=2.0.0",
+    "polars[all]>=1.0.0",
+    "scikit-learn>=1.4.0"
+]
+
+[project.scripts]
+recipys = "recipys.recipe:Recipe"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]