<div align="center">
<img src="https://github.com/rvandewater/ReciPies/blob/development/docs/figures/recipies_logo.svg?raw=true"
alt="recipies logo" height="300">
</div>
# ReciPies 🥧
[](https://github.com/rvandewater/ReciPies/actions/workflows/ci.yml)
[](LICENSE)
[](https://pypi.python.org/pypi/recipies/)
[](https://pypi.python.org/pypi/recipies/)
[](https://pepy.tech/project/recipies)
[](http://arxiv.org/abs/2306.05109)
The ReciPies 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.
It operates on modified Dataframe objects from the established data science package Pandas.
## Installation
You can install ReciPies from pip using:
```
pip install recipies
```
> Note that the package is called `recipies` on pip.
>
You can install ReciPies from source to ensure you have the latest version:
```
conda env update -f environment.yml
conda activate ReciPies
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.
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
[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 which uses ReciPys extensively to create a
customisable preprocessing pipeline (a standalone paper is in preparation):
```
@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
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