Metadata-Version: 2.1
Name: py-hyperpy
Version: 0.0.2
Summary: HyperPy: An automatic hyperparameter optimization framework
Home-page: https://github.com/sergiomora03/hyperpy
Author: Sergio A. Mora Pardo
Author-email: sergiomora823@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# hyperpy
HyperPy: An automatic hyperparameter optimization framework

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# Description

[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/sergiomora03/hyperpy/issues)

Library for automatic hyperparameter optimization. Build on top of Optuna to perform hyperparameter optimization with low code.

This library corresponds to part of the work of [Sergio A. Mora Pardo](https://sergiomora03.github.io/)

ðŸ‘¶ Our current version: [![PyPI version](https://badge.fury.io/py/py-hyperpy.svg)](https://badge.fury.io/py/py-hyperpy)

# Installation

![GitHub Release Date](https://img.shields.io/github/release-date/sergiomora03/hyperpy) ![GitHub last commit](https://img.shields.io/github/last-commit/sergiomora03/hyperpy)

You can install ```hyperpy``` with pip:

```
# pip install py-hyperpy
```

# Example

Import the library:

```py
import hyperpy.core as hy
from hyperpy.util import ExampleConfig # Just for example
```

Reading data:

```py
data=ExampleConfig()
train, test, sub = data.readData()
```

Extract features:

```py
feat_X = train.filter(['Pclass','Age', 'SibSp', 'Parch','Fare']).values
Y = train.Survived.values
```

Run the optimization:

```py
running=hy.run(feat_X, Y)
study = running.buildStudy()
```

See the results:

```py
print("best params: ", study.best_params)
print("best test accuracy: ", study.best_value)
best_params, best_value = hy.results.results(study)
```

**NOTE**
best test accuracy -> 'Adam':  0.7407407164573669


# Documentation

Documentation is available at [hyperpy](https://hyperapy.readthedocs.io/en/latest/)

Working on tutorial, meanwhile explore documentation.

# Development ![GitHub issues](https://img.shields.io/github/issues/sergiomora03/hyperpy) ![GitHub issues](https://img.shields.io/github/issues-raw/sergiomora03/hyperpy) 

Source code is available at [hyperpy](https://github.com/sergiomora03/hyperpy)


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