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Wrapper

This file is part of the TPOT library.

The current version of TPOT was developed at Cedars-Sinai by: - Pedro Henrique Ribeiro (https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/) - Anil Saini (anil.saini@cshs.org) - Jose Hernandez (jgh9094@gmail.com) - Jay Moran (jay.moran@cshs.org) - Nicholas Matsumoto (nicholas.matsumoto@cshs.org) - Hyunjun Choi (hyunjun.choi@cshs.org) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

The original version of TPOT was primarily developed at the University of Pennsylvania by: - Randal S. Olson (rso@randalolson.com) - Weixuan Fu (weixuanf@upenn.edu) - Daniel Angell (dpa34@drexel.edu) - Jason Moore (moorejh28@gmail.com) - and many more generous open-source contributors

TPOT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

TPOT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with TPOT. If not, see http://www.gnu.org/licenses/.

WrapperPipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/wrapper.py 
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class WrapperPipeline(SearchSpace):
    def __init__(
            self, 
            method: type, 
            space: ConfigurationSpace,
            estimator_search_space: SearchSpace,
            hyperparameter_parser: callable = None, 
            wrapped_param_name: str = None
            ) -> None:

        """
        This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments.
        For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.

        """


        self.estimator_search_space = estimator_search_space
        self.method = method
        self.space = space
        self.hyperparameter_parser=hyperparameter_parser
        self.wrapped_param_name = wrapped_param_name

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return WrapperPipelineIndividual(method=self.method, space=self.space, estimator_search_space=self.estimator_search_space, hyperparameter_parser=self.hyperparameter_parser, wrapped_param_name=self.wrapped_param_name,  rng=rng)

__init__(method, space, estimator_search_space, hyperparameter_parser=None, wrapped_param_name=None)

This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments. For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.

Source code in tpot2/search_spaces/pipelines/wrapper.py 
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def __init__(
        self, 
        method: type, 
        space: ConfigurationSpace,
        estimator_search_space: SearchSpace,
        hyperparameter_parser: callable = None, 
        wrapped_param_name: str = None
        ) -> None:

    """
    This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments.
    For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.

    """


    self.estimator_search_space = estimator_search_space
    self.method = method
    self.space = space
    self.hyperparameter_parser=hyperparameter_parser
    self.wrapped_param_name = wrapped_param_name