py-hyperpy 0.0.1

HyperPy: An automatic hyperparameter optimization framework

hyperpy

HyperPy: An automatic hyperparameter optimization framework

Description

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 py-hyperpy with pip:

# pip install hyperpy

Example

Import the library:

import hyperpy.core as hy
from hyperpy.util 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 best test accuracy -> 'Adam': 0.7407407164573669

Documentation

Documentation is available at hyperpy

Working on tutorial, meanwhile explore documentation.

Development

Source code is available at https://github.com/sergiomora03/hyperpy

https://packaging.python.org/tutorials/creating-documentation/

Welcome, I've been expecting you.

Contact

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