HyperPy: An automatic hyperparameter optimization framework

👶current version:

Description

HyperPy: 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

Installation

You can install hyperpy with pip:

# pip install py-hyperpy

Example

Import the library:

Read library

import hyperpy as hy

Run the optimization:

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

See the results:

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

NOTE

• The function hy.run() return a Study object. And only needs: Features, target. In the example: best test accuracy = 0.7407407164573669
• feat_X: features in dataset
• Y: target in dataset

IMPORTANT

At moment only solves binary clasification problems.

Documentation

Documentation is available at hyperpy

Working on tutorial, meanwhile explore documentation.

Development

Source code is available at hyperpy

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