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Tree

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/.

TreePipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/tree.py 
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class TreePipeline(SearchSpace):
    def __init__(self, root_search_space : SearchSpace, 
                        leaf_search_space : SearchSpace = None, 
                        inner_search_space : SearchSpace =None, 
                        min_size: int = 2, 
                        max_size: int = 10,
                        crossover_same_depth=False) -> None:

        """
        Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1.

        """

        self.search_space = root_search_space
        self.leaf_search_space = leaf_search_space
        self.inner_search_space = inner_search_space
        self.min_size = min_size
        self.max_size = max_size
        self.crossover_same_depth = crossover_same_depth

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return TreePipelineIndividual(self.search_space, self.leaf_search_space, self.inner_search_space, self.min_size, self.max_size, self.crossover_same_depth, rng=rng)

__init__(root_search_space, leaf_search_space=None, inner_search_space=None, min_size=2, max_size=10, crossover_same_depth=False)

Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1.

Source code in tpot2/search_spaces/pipelines/tree.py 
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def __init__(self, root_search_space : SearchSpace, 
                    leaf_search_space : SearchSpace = None, 
                    inner_search_space : SearchSpace =None, 
                    min_size: int = 2, 
                    max_size: int = 10,
                    crossover_same_depth=False) -> None:

    """
    Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1.

    """

    self.search_space = root_search_space
    self.leaf_search_space = leaf_search_space
    self.inner_search_space = inner_search_space
    self.min_size = min_size
    self.max_size = max_size
    self.crossover_same_depth = crossover_same_depth