Source code for whalrus.rules.rule_maximin

# -*- coding: utf-8 -*-
Copyright Sylvain Bouveret, Yann Chevaleyre and Fran├žois Durand,,

This file is part of Whalrus.

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

Whalrus is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with Whalrus.  If not, see <>.
from whalrus.rules.rule_score_num import RuleScoreNum
from whalrus.converters_ballot.converter_ballot_to_order import ConverterBallotToOrder
from whalrus.utils.utils import cached_property, NiceDict
from whalrus.converters_ballot.converter_ballot import ConverterBallot
from whalrus.matrices.matrix import Matrix
from whalrus.matrices.matrix_weighted_majority import MatrixWeightedMajority

[docs]class RuleMaximin(RuleScoreNum): """ Maximin rule. Also known as Simpson-Kramer rule. The score of a candidate is the minimal non-diagonal coefficient on its raw of the matrix. Parameters ---------- args Cf. parent class. converter : ConverterBallot Default: :class:`ConverterBallotToOrder`. matrix_weighted_majority : Matrix Default: :class:`MatrixWeightedMajority`. kwargs Cf. parent class. Examples -------- >>> rule = RuleMaximin(ballots=['a > b > c', 'b > c > a', 'c > a > b'], weights=[4, 3, 3]) >>> rule.matrix_weighted_majority_.as_array_of_floats_ array([[0. , 0.7, 0.4], [0.3, 0. , 0.7], [0.6, 0.3, 0. ]]) >>> rule.scores_as_floats_ {'a': 0.4, 'b': 0.3, 'c': 0.3} >>> rule.winner_ 'a' """ def __init__(self, *args, converter: ConverterBallot = None, matrix_weighted_majority: Matrix = None, **kwargs): if converter is None: converter = ConverterBallotToOrder() if matrix_weighted_majority is None: matrix_weighted_majority = MatrixWeightedMajority() self.matrix_weighted_majority = matrix_weighted_majority super().__init__(*args, converter=converter, **kwargs) @cached_property def matrix_weighted_majority_(self): """Matrix: The weighted majority matrix (once computed with the given profile). """ return self.matrix_weighted_majority(self.profile_converted_) @cached_property def scores_(self) -> NiceDict: matrix = self.matrix_weighted_majority_ return NiceDict({c: min({v for (i, j), v in matrix.as_dict_.items() if i == c and j != c}) for c in matrix.candidates_})