hyperpy
HyperPy: An automatic hyperparameter optimization framework
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 aStudy
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