GeneticPy

GeneticPy是一个使用遗传算法快速搜索自定义参数空间以找到最优解的优化器。

安装

GeneticPy需要Python 3.7+

pip install geneticpy

优化示例

以下包含一个简短的示例，以帮助您开始。

import geneticpy

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)}

results = geneticpy.optimize(loss_function, param_space, size=200, generation_count=500, verbose=True)
best_params = results['top_params']
loss = results['top_score']
total_time = results['total_time']

GeneticSearchCV 示例

您可以使用GeneticSearchCV类作为Scikit-Learn的GridSearchCV的替代品。这允许在使用Scikit-Learn估计器和/或管道时更快、更完整地优化超参数。

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

from geneticpy import GeneticSearchCV, ChoiceDistribution, LogNormalDistribution, UniformDistribution


# Define a pipeline to search for the best combination of PCA truncation
# and classifier regularization.
pca = PCA()
# set the tolerance to a large value to make the example faster
logistic = LogisticRegression(max_iter=10000, tol=0.1, solver='saga')
pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])

X_digits, y_digits = datasets.load_digits(return_X_y=True)

# Parameters of pipelines can be set using ‘__’ separated parameter names:
param_grid = {
    'pca__n_components': UniformDistribution(low=5, high=64, q=1),
    'logistic__C': LogNormalDistribution(mean=1, sigma=0.5, low=0.001, high=2),
    'logistic__penalty': ChoiceDistribution(choice_list=['l1', 'l2'])
}
search = GeneticSearchCV(pipe, param_grid)
search.fit(X_digits, y_digits)
print("Best parameter (CV score=%0.3f):" % search.best_score_)
print(search.best_params_)

PyPi项目

https://pypi.ac.cn/project/geneticpy/

联系方式

请随时通过brandonschabell@gmail.com给我发邮件，提出任何问题或反馈。