https://github.com/rvandewater/recipys

🥧ReciPys: easily define and execute preprocessing and feature engineering steps on Pandas dataframes.
https://github.com/rvandewater/recipys

data-science pandas python scikit-learn tidymodels

Last synced: 27 days ago
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🥧ReciPys: easily define and execute preprocessing and feature engineering steps on Pandas dataframes.

• Host: GitHub
• URL: https://github.com/rvandewater/recipys
• Owner: rvandewater
• License: mit
• Created: 2022-11-25T11:56:34.000Z (almost 2 years ago)
• Default Branch: development
• Last Pushed: 2024-07-09T14:17:52.000Z (4 months ago)
• Last Synced: 2024-09-27T20:22:48.844Z (about 1 month ago)
• Topics: data-science, pandas, python, scikit-learn, tidymodels
• Language: Python
• Homepage:
• Size: 604 KB
• Stars: 3
• Watchers: 2
• Forks: 2
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
![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 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