boliqq07 / multiego

This is a Global optimization algorithm named EGO.

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() #pre-trained 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()

Introduction

multiego.ego.Ego

For sklean-type single model.

multiego.base_ego.BaseEgo

1. For any user-defined single model, just need offer mean and std of search space.
2. For big search space out of memory , just need offer mean and std of search space.

multiego.multiplyego.MultiEgo

For sklean-type models.

multiego.base_multiplyego.BaseMultiEgo

1. For any user-defined models, just need offer predict_y of search space.
2. For big search space out of memory, just need offer predict_y of search space.

link

More examples can be found in test.

More powerful can be found mipego