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

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