This is ego method.Some of code are non-originality, just copy for use. All the referenced code are marked,details can be shown in their sources
Project description
Multiply EGO
EGO (Efficient global optimization) and multiply target EGO method.
References: Jones, D. R., Schonlau, M. & Welch, W. J. Efficient global optimization of expensive black-box functions. J. Global Optim. 13, 455–492 (1998)
Install
pip install multiego
usage
if __name__ == "__main__":
from sklearn.datasets import load_boston
import numpy as np
from multiego.ego import search_space, Ego
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVR
#####model1#####
model = SVR() #good model with optimized prarmeters for special features
###
X, y = load_boston(return_X_y=True)
X = X[:, :5]
searchspace_list = [
np.arange(0.01, 1, 0.1),
np.array([0, 20, 30, 50, 70, 90]),
np.arange(1, 10, 1),
np.array([0, 1]),
np.arange(0.4, 0.6, 0.02),
]
searchspace = search_space(*searchspace_list)
#
me = Ego(searchspace, X, y, 500, model, n_jobs=6)
re = me.egosearch()
link
More examples can be found in test. More powerful can be found mipego
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