Metadata-Version: 2.1
Name: hyperparameter-tuning
Version: 0.1.0
Summary: A minimal framework for running hyperparameter tuning
Home-page: https://github.com/AndreFCruz/hpt
Author: AndreFCruz
License: MIT
Keywords: ml,optimization,hyperparameter,tuning,fairness
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: plotting
Provides-Extra: testing
Provides-Extra: all
License-File: LICENSE

# hpt

> This repository is under construction :construction:

![badge for tests status](https://github.com/AndreFCruz/hpt/actions/workflows/python-package.yml/badge.svg)
![badge for PyPI publishing status](https://github.com/AndreFCruz/hpt/actions/workflows/python-publish.yml/badge.svg)

A minimal hyperparameter tuning framework to help you train hundreds of models.

It's essentially a set of helpful wrappers over optuna.


## Install

`
pip install hpt
`

## Getting started

```py
from hpt.tuner import ObjectiveFunction, OptunaTuner

obj_func = ObjectiveFunction(
    X_train, y_train, X_test, y_test,
    hyperparameter_space=HYPERPARAM_SPACE_PATH,
    eval_metric='accuracy',
    s_train=s_train,
    s_val=s_test,
    threshold=0.50,
)

tuner = OptunaTuner(obj_func) # NOTE: can pass other useful study kwargs here (e.g. storage)
```

**_TODO:_** finish readme.


<!-- ## Defining a hyperparameter space -->
