Metadata-Version: 2.1
Name: OptGBM
Version: 0.2.0
Summary: Optuna + LightGBM \= OptGBM
Home-page: UNKNOWN
Author: Kon
License: MIT License
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: joblib (>=0.14)
Requires-Dist: lightgbm
Requires-Dist: numpy
Requires-Dist: optuna
Requires-Dist: pandas
Requires-Dist: scikit-learn

# OptGBM

[![CircleCI](https://img.shields.io/circleci/build/github/Y-oHr-N/OptGBM)](https://circleci.com/gh/Y-oHr-N/OptGBM)
[![PyPI](https://img.shields.io/pypi/v/OptGBM)](https://pypi.org/project/OptGBM/)
[![PyPI - License](https://img.shields.io/pypi/l/OptGBM)](https://pypi.org/project/OptGBM/)
[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Y-oHr-N/OptGBM/master)

OptGBM (= Optuna + LightGBM) provides a scikit-learn compatible estimator that tunes hyperparameters in LightGBM with Optuna.

## Examples

```python
from optgbm.sklearn import OGBMRegressor
from sklearn.datasets import load_boston

reg = OGBMRegressor(random_state=0)
X, y = load_boston(return_X_y=True)

reg.fit(X, y)

score = reg.score(X, y)
```

## Installation

```
pip install optgbm
```

## Testing

```
python setup.py test
```


