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Max weighted average selector

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/.

max_weighted_average_selector(scores, k, n_parents=1, rng=None)

Select the best individual according to Max Weighted Average Selection, k times.

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

Returns:

Type Description
A array of indices of selected individuals of shape (k, n_parents).
Source code in tpot/selectors/max_weighted_average_selector.py 
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def max_weighted_average_selector(scores,k, n_parents=1, rng=None):
    """
    Select the best individual according to Max Weighted Average Selection, *k* times.

    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.

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

    """
    ave_scores = [np.nanmean(s ) for s in scores ] #TODO make this more efficient
    chosen = np.argsort(ave_scores)[::-1][0:k] #TODO check this behavior with nans
    return np.reshape(chosen, (k, n_parents))