Hyperparameter optimization based on a genetic algorithm.
Project description
GeneticPy
GeneticPy is an optimizer that uses a genetic algorithm to quickly search through custom parameter spaces for optimal solutions.
Installation
GeneticPy requires Python 3.4+
pip install geneticpy
Example Usage:
A brief example to get you started is included below:
def loss_function(params):
if params['type'] == 'add':
return params['x'] + params['y']
elif params['type'] == 'multiply':
return params['x'] * params['y']
param_space = {'type': geneticpy.ChoiceDistribution(choice_list=['add', 'multiply']),
'x': geneticpy.UniformDistribution(low=5, high=10, q=1),
'y': geneticpy.GaussianDistribution(mean=0, standard_deviation=1)}
best_params, loss = geneticpy.optimize(loss_function, param_space, size=200, generation_count=500, verbose=False)
PyPi Project
https://pypi.org/project/geneticpy/
Contact
Please feel free to email me at brandonschabell@gmail.com with any questions or feedback.
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