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Tournament selection

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) - Gabriel Ketron (gabriel.ketron@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/.

tournament_selection(scores, k, n_parents=1, rng=None, tournament_size=2, score_index=0)

Select the best individual among tournsize randomly chosen individuals, k times. The returned list contains the indices of the chosen individuals.

Parameters:

Name Type Description Default
scores ndarray

The score matrix, where rows the individuals and the columns are the corresponds to scores on different objectives.

 required
k int

The number of individuals to select.

 required
n_parents int

The number of parents to select per individual. The default is 1.

 1
rng (int, Generator)

The random number generator. The default is None.

 None
tournament_size int

The number of individuals participating in each tournament.

 2
score_index (int, str)

The index of the score to use for selection. If "average" is passed, the average score is used. The default is 0 (only the first score is used).

 0

Returns:

Type Description
A array of indices of selected individuals of shape (k, n_parents).
Source code in tpot/selectors/tournament_selection.py 
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def tournament_selection(scores, k, n_parents=1, rng=None, tournament_size=2, score_index=0):
    """
    Select the best individual among *tournsize* randomly chosen
    individuals, *k* times. The returned list contains the indices of the chosen *individuals*.

    Parameters
    ----------
    scores : np.ndarray
        The score matrix, where rows the individuals and the columns are the corresponds to scores on different objectives.
    k : int
        The number of individuals to select.
    n_parents : int, optional
        The number of parents to select per individual. The default is 1.
    rng : int, np.random.Generator, optional
        The random number generator. The default is None.
    tournament_size : int, optional
        The number of individuals participating in each tournament.
    score_index : int, str, optional
        The index of the score to use for selection. If "average" is passed, the average score is used. The default is 0 (only the first score is used).

    Returns
    -------
        A array of indices of selected individuals of shape (k, n_parents).
    """

    rng = np.random.default_rng(rng)

    if isinstance(score_index,int):
        key=lambda x:x[1][score_index]
    elif score_index == "average":
        key=lambda x:np.mean(x[1])

    chosen = []
    for i in range(k*n_parents):
        aspirants_idx =[rng.choice(len(scores)) for i in range(tournament_size)]
        aspirants  = list(zip(aspirants_idx, scores[aspirants_idx])) # Zip indices and elements together
        chosen.append(max(aspirants, key=key)[0]) # Retrun the index of the maximum element

    return np.reshape(chosen, (k, n_parents))