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        "description": "# Surrogate Search CV\n[![CircleCI](https://circleci.com/gh/bettercallshao/sklearn_surrogatesearchcv.svg?style=shield)](https://circleci.com/gh/bettercallshao/sklearn_surrogatesearchcv)\n[![PyPi](https://badge.fury.io/py/sklearn-surrogatesearchcv.svg)](https://badge.fury.io/py/sklearn-surrogatesearchcv)\n\nThis 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.\n\n## Usage\n\n```\npip install sklearn-surrogatesearchcv\n```\n\nThe interface is unimaginative, stylistically similar to `RandomizedSearchCV`.\n\n```\nclass SurrogateSearchCV(object):\n    \"\"\"Surrogate search with cross validation for hyper parameter tuning.\n    \"\"\"\n\n    def __init__(self, estimator, n_iter=10, param_def=None, refit=False,\n                 **kwargs):\n        \"\"\"\n        :param estimator: estimator\n        :param n_iter: number of iterations to run (default 10)\n        :param param_def: list of dictionaries, e.g.\n            [\n                {\n                    'name': 'alpha',\n                    'integer': False,\n                    'lb': 0.1,\n                    'ub': 0.9,\n                },\n                {\n                    'name': 'max_depth',\n                    'integer': True,\n                    'lb': 3,\n                    'ub': 12,\n                }\n            ]\n        :param **: every other parameter is the same as GridSearchCV\n        \"\"\"\n```\n\nThe result can be found in the following properties of the class instance after running.\n\n```\nparams_history_\nscore_history_\nbest_params_\nbest_score_\n```\n\nFor a complete example, please refer to `src/test/test_basic.py`.\n\n## Resources\n\nA slide about role of surrogate optimization in ml. [link](https://www.slideshare.net/TimTan2/machine-learning-vs-traditional-optimization)",
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