Metadata-Version: 2.1
Name: multiego
Version: 0.0.11
Summary: 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
Home-page: UNKNOWN
Author: wangchangxin
Author-email: 986798607@qq.com
Maintainer: wangchangxin
License: UNKNOWN
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)
        
        [![Python Versions](https://img.shields.io/pypi/pyversions/multiego.svg)](https://pypi.org/project/multiego/)
        [![Version](https://img.shields.io/github/tag/MGEdata/multiego.svg)](https://github.com/MGEdata/multiego/releases/latest)
        ![pypi Versions](https://badge.fury.io/py/multiego.svg)
        
        # Install
        
        ```bash
        pip install multiego
        ```
        
        # Usage
        
        ```bash
        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**](https://github.com/MGEdata/multiego/blob/master/multiego/ego.py) 
        
        For `sklean-type` single model.
        
        [**multiego.base_ego.BaseEgo**](https://github.com/MGEdata/multiego/blob/master/multiego/base_ego.py)
        
        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**](https://github.com/MGEdata/multiego/blob/master/multiego/multiplyego.py)
        
        For `sklean-type` models.
        
        [**multiego.base_multiplyego.BaseMultiEgo**](https://github.com/MGEdata/multiego/blob/master/multiego/base_multiplyego.py) 
        
        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](https://github.com/MGEdata/multiego/tree/master/test).
        
        More powerful can be found  [mipego](https://github.com/wangronin/MIP-EGO)
Keywords: ego,multiplyego
Platform: Windows
Platform: Unix
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
