Welcome to HyperPy’s documentation!

HyperPy (py-hyperpy in PyPi) is a Python library for build an automatic hyperparameter optimization.

You can install hyperpy with pip:

# pip install py-hyperpy

Example

import 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

Warning

At moment only solves binary clasification problems.

Note

This project is active development.

Citing HyperPy:

If you’re citing HyperPy in research or scientific paper, please cite this page as the resource. HyperPy’s first stable release 0.0.5 was made publicly available in October 2021. py-hyperpy.readthedocs. HyperPy, October 2021. URL https://py-hyperpy.readthedocs.io/en/latest/. HyperPy version 0.0.5.

A formatted version of the citation would look like this:

@Manual{HyperPy,
  author  = {Mora, Sergio},
  title   = {HyperPy: An automatic hyperparameter optimization framework in Python},
  year    = {2021},
  month   = {October},
  note    = {HyperPy version 0.0.5},
  url     = {https://py-hyperpy.readthedocs.io/en/latest/}
}

We are appreciated that HyperPy has been increasingly referred and cited in scientific works. See all citations here: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=hyperpy&btnG=

Key Links and Resources: