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Base

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/.

SklearnIndividual

Bases: BaseIndividual

Source code in tpot2/search_spaces/base.py 
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class SklearnIndividual(tpot2.BaseIndividual):

    def __init_subclass__(cls):
        cls.crossover = cls.validate_same_type(cls.crossover)


    def __init__(self,) -> None:
        super().__init__()

    def mutate(self, rng=None):
        return

    def crossover(self, other, rng=None, **kwargs):
        return 

    @final
    def validate_same_type(func):

        def wrapper(self, other, rng=None, **kwargs):
            if not isinstance(other, type(self)):
                return False
            return func(self, other, rng=rng, **kwargs)

        return wrapper

    def export_pipeline(self, **kwargs) -> BaseEstimator:
        return

    def unique_id(self):
        """
        Returns a unique identifier for the individual. Used for preventing duplicate individuals from being evaluated.
        """
        return self

    #TODO currently TPOT2 population class manually uses the unique_id to generate the index for the population data frame.
    #alternatively, the index could be the individual itself, with the __eq__ and __hash__ methods implemented.

    # Though this breaks the graphpipeline. When a mutation is called, it changes the __eq__ and __hash__ outputs.
    # Since networkx uses the hash and eq to determine if a node is already in the graph, this causes the graph thing that 
    # This is a new node not in the graph. But this could be changed if when the graphpipeline mutates nodes, 
    # it "replaces" the existing node with the mutated node. This would require a change in the graphpipeline class.

    # def __eq__(self, other):
    #     return self.unique_id() == other.unique_id()

    # def __hash__(self):
    #     return hash(self.unique_id())

    #number of components in the pipeline
    def get_size(self):
        return 1

    @final
    def export_flattened_graphpipeline(self, **graphpipeline_kwargs) -> tpot2.GraphPipeline:
        return flatten_to_graphpipeline(self.export_pipeline(), **graphpipeline_kwargs)

unique_id()

Returns a unique identifier for the individual. Used for preventing duplicate individuals from being evaluated.

Source code in tpot2/search_spaces/base.py 
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def unique_id(self):
    """
    Returns a unique identifier for the individual. Used for preventing duplicate individuals from being evaluated.
    """
    return self