# 🥧ReciPys🐍
[](https://github.com/rvandewater/recipys/actions/workflows/ci.yml)
[](https://github.com/psf/black)
[](LICENSE)
[](https://pypi.python.org/pypi/recipies/)
[](http://arxiv.org/abs/2306.05109)
The ReciPys package is a preprocessing framework operating on Pandas dataframes.
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 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.
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:
```
@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},
}
```
This paper can also be found on arxiv: https://arxiv.org/pdf/2306.05109.pdf
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"description": "\r\n# \ud83e\udd67ReciPys\ud83d\udc0d\r\n[](https://github.com/rvandewater/recipys/actions/workflows/ci.yml)\r\n[](https://github.com/psf/black)\r\n\r\n[](LICENSE)\r\n[](https://pypi.python.org/pypi/recipies/)\r\n[](http://arxiv.org/abs/2306.05109)\r\n\r\nThe ReciPys package is a preprocessing framework operating on Pandas dataframes. \r\nThe operation of this package is inspired by the R-package [recipes](https://recipes.tidymodels.org/).\r\nThis package allows the user to apply a number of extensible operations for imputation, feature generation/extraction, \r\nscaling, and encoding. \r\nIt operates on modified Dataframe objects from the established data science package Pandas.\r\n## Installation \r\nYou can install ReciPys from pip using:\r\n```\r\npip install recipies\r\n```\r\n> Note that the package is called `recipies` and not `recipys` on pip due to a name clash with an existing package.\r\n> \r\nYou can install ReciPys from source to ensure you have the latest version:\r\n```\r\nconda env update -f environment.yml\r\nconda activate recipys\r\npip install -e .\r\n```\r\n> Note that the last command installs the package called `recipies`.\r\n\r\n## Usage\r\nTo define preprocessing operations, one has to supply _roles_ to the different columns of the Dataframe. \r\nThis allows the user to create groups of columns which have a particular function.\r\nThen, we provide several \"steps\" that can be applied to the datasets, among which: Historical accumulation, \r\nResampling the time resolution, A number of imputation methods, and a wrapper for any \r\n[Scikit-learn](https://github.com/scikit-learn/scikit-learn) preprocessing step.\r\nWe believe to have covered any basic preprocessing needs for prepared datasets.\r\nAny missing step can be added by following the step interface.\r\n\r\n# \ud83d\udcc4Paper\r\n\r\nIf you use this code in your research, please cite the following publication:\r\n\r\n```\r\n@article{vandewaterYetAnotherICUBenchmark2023,\r\n\ttitle = {Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML},\r\n\tshorttitle = {Yet Another ICU Benchmark},\r\n\turl = {http://arxiv.org/abs/2306.05109},\r\n\tlanguage = {en},\r\n\turldate = {2023-06-09},\r\n\tpublisher = {arXiv},\r\n\tauthor = {van de Water, Robin and Schmidt, Hendrik and Elbers, Paul and Thoral, Patrick and Arnrich, Bert and Rockenschaub, Patrick},\r\n\tmonth = jun,\r\n\tyear = {2023},\r\n\tnote = {arXiv:2306.05109 [cs]},\r\n\tkeywords = {Computer Science - Machine Learning},\r\n}\r\n```\r\nThis paper can also be found on arxiv: https://arxiv.org/pdf/2306.05109.pdf\r\n\r\n\r\n\r\n\r\n",
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