Metadata-Version: 2.3
Name: bokbokbok
Version: 1.0.2
Summary: Custom Losses and Metrics for XGBoost, LightGBM
License: MIT
Author: Daniel Timbrell
Author-email: dantimbrell@gmail.com
Requires-Python: >=3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: numpy (>=2.2.4,<3.0.0)
Requires-Dist: scikit-learn (>=1.6.1,<2.0.0)
Requires-Dist: scipy (>=1.15.2,<2.0.0)
Project-URL: Documentation, https://orchardbirds.github.io/bokbokbok/
Project-URL: Homepage, https://github.com/orchardbirds/bokbokbok
Description-Content-Type: text/markdown

<img src="https://github.com/orchardbirds/bokbokbok/raw/main/docs/img/bokbokbok.png" width="120" align="right">

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# bokbokbok

## Overview

**bokbokbok** is a python package that enables you to use Custom Loss Functions and Evaluation Metrics for XGBoost and LightGBM.
Main features:

- [Weighted Cross Entropy](https://orchardbirds.github.io/bokbokbok/tutorials/weighted_cross_entropy.html)
- [Weighted Focal Loss](https://orchardbirds.github.io/bokbokbok/tutorials/focal_loss.html)
- [Log Cosh Loss](https://orchardbirds.github.io/bokbokbok/tutorials/log_cosh_loss.html)
- [Root Mean Squared Percentage Error](https://orchardbirds.github.io/bokbokbok/tutorials/RMSPE.html)
- [F1 score](https://orchardbirds.github.io/bokbokbok/tutorials/F1_score.html)
- [Quadratic Weighted Kappa](https://orchardbirds.github.io/bokbokbok/tutorials/quadratic_weighted_kappa.html)

## Installation

```bash
pip install bokbokbok
```

## Documentation

The documentation can [be found here.](https://orchardbirds.github.io/bokbokbok/)

## Contributing

To learn more about making a contribution to bokbokbok, please see [CONTRIBUTING.md.](https://github.com/orchardbirds/bokbokbok/blob/main/CONTRIBUTING.md)

