Metadata-Version: 2.1
Name: sklearn_surrogatesearchcv
Version: 0.1
Summary: Surrogate adaptive randomized search for hyper parametersin sklearn.
Home-page: https://github.com/timlyrics/sklearn_surrogatesearchcv
Author: Shaoqing Tan
Author-email: tansq7@gmail.com
License: MIT
Description: # Surrogate Search CV
        
        This package implements a randomized hyper parameter search for sklearn (similar to `RandomizedSearchCV`) but utilizes surrogate adaptive sampling from pySOT. Use this similarly to GridSearchCV with a few extra paramters.
        
        ## Usage
        
        The interface is unimaginative, stylistically similar to `RandomizedSearchCV`.
        
        ```
        class SurrogateSearchCV(object):
            """Surrogate search with cross validation for hyper parameter tuning.
            """
        
            def __init__(self, estimator, n_iter=10, param_def=None, refit=False,
                         **kwargs):
                """
                :param estimator: estimator
                :param n_iter: number of iterations to run (default 10)
                :param param_def: list of dictionaries, e.g.
                    [
                        {
                            'name': 'alpha',
                            'integer': False,
                            'lb': 0.1,
                            'ub': 0.9,
                        },
                        {
                            'name': 'max_depth',
                            'integer': True,
                            'lb': 3,
                            'ub': 12,
                        }
                    ]
                :param **: every other parameter is the same as GridSearchCV
                """
        ```
        
        The result can be found in the following properties of the class instance after running.
        
        ```
        params_history_
        score_history_
        best_params_
        best_score_
        ```
        
        For a complete example, please refer to `src/test/test_basic.py`.
        
        ## CI
        
        [![CircleCI](https://circleci.com/gh/timlyrics/sklearn_surrogatesearchcv.svg?style=svg)](https://circleci.com/gh/timlyrics/sklearn_surrogatesearchcv)
        
        ## Resources
        
        A slide about role of surrogate optimization in ml. [link](https://www.slideshare.net/TimTan2/machine-learning-vs-traditional-optimization)
        
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Description-Content-Type: text/markdown
