py-hyperpy 0.0.5
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pyhyperpy 0.0.5
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
👶 Our current version:
Installation
You can install hyperpy with pip:
# pip install py-hyperpy
Example
Import the library:
import hyperpy as hy
from hyperpy import ExampleConfig # Just for example
Reading data:
data=ExampleConfig()
train, test, sub = data.readData()
Extract features:
feat_X = train.filter(['Pclass','Age', 'SibSp', 'Parch','Fare']).values
Y = train.Survived.values
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
Documentation
Documentation is available at hyperpy
Working on tutorial, meanwhile explore documentation.
Development
Source code is available at hyperpy
Contact
License:
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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