Metadata-Version: 2.4
Name: mqboost
Version: 1.0.0
Summary: Monotonic composite quantile gradient boost regressor
Author-email: RektPunk <rektpunk@gmail.com>
Project-URL: repository, https://github.com/RektPunk/MQBoost
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: lightgbm>=4.6.0
Requires-Dist: numpy>=2.0.2
Requires-Dist: pandas>=2.3.3
Requires-Dist: xgboost>=2.1.4
Dynamic: license-file

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**MQBoost** is a gradient boosting-based framework for simultaneous multi-quantile regression with monotonicity constraints (non-crossing quantiles).
It is built on top of  [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks, enabling efficient and scalable training while ensuring valid quantile estimates.

Standard quantile regression models often suffer from quantile crossing (e.g., 90% quantile < 50% quantile) and independent training per quantile → inconsistent predictions. We solve this by:
- Learning multiple quantiles jointly
- Enforcing monotonicity across quantiles
- Leveraging efficient boosting frameworks

# Usage
## Installation
Install using pip:
```bash
pip install mqboost
```

## Features
- **MQDataset**: Encapsulates the dataset used for MQRegressor.
- **MQRegressor**: Custom multiple quantile estimator with preserving monotonicity among quantiles.

## Example
Please refer to the [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RektPunk/MQBoost/blob/main/examples/mqregressor.ipynb) or [**Examples**](https://github.com/RektPunk/MQBoost/tree/main/examples/mqregressor.py) provided for further clarification.

# Citation
If you use MQBoost in your research or project, please cite it as follows:
```bibtex
@article{Moon2026,
  title={Monotone Composite Quantile Regression via Second-Order Gradient Boosting Framework},
  author={Moon, Sangjun and Hong, Sungchul and Park, Beomjin},
  journal={Machine Learning},
  volume={115},
  number={6},
  pages={127},
  year={2026},
  publisher={Springer}
}
```
