# -*- coding: utf-8 -*-
"""
Copyright Sylvain Bouveret, Yann Chevaleyre and François Durand
sylvain.bouveret@imag.fr, yann.chevaleyre@dauphine.fr, fradurand@gmail.com
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
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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 <http://www.gnu.org/licenses/>.
"""
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_})