def test_monroe_indivisible(algorithm):
profile = Profile(4)
profile.add_voters([[0], [0], [0], [1, 2], [1, 2], [1], [3]])
committeesize = 3
assert abcrules.compute_monroe(
profile, committeesize, algorithm=algorithm, resolute=False
) == [{0, 1, 2}, {0, 1, 3}, {0, 2, 3}]
def test_monroe_indivisible(algorithm):
if algorithm == "gurobi":
pytest.importorskip("gurobipy")
profile = Profile(4)
profile.add_preferences([[0], [0], [0], [1, 2], [1, 2], [1], [3]])
committeesize = 3
assert (abcrules.compute_monroe(profile,
committeesize,
algorithm=algorithm,
resolute=False) == [[0, 1, 2], [0, 1, 3],
[0, 2, 3]])
def test_pareto_optimality_methods(algorithm):
# profile with 4 candidates: a, b, c, d
profile = Profile(4)
# add voters in the profile
profile.add_voters([[0]] * 2 + [[0, 2]] + [[0, 3]] + [[1, 2]] * 10 +
[[1, 3]] * 10)
# compute output committee from Monroe's Rule
monroe_output = abcrules.compute_monroe(profile, 2)
# Monroe's Rule should output winning committee {2, 3} for this input
# It is not Pareto optimal because it is dominated by committee {0, 1}
# Check using the methods
is_pareto_optimal = properties.check_pareto_optimality(profile,
monroe_output[0],
algorithm=algorithm)
assert monroe_output == [{2, 3}]
assert abcvoting.misc.dominate(profile, {0, 1}, {2, 3})
assert not is_pareto_optimal
assert properties.check_pareto_optimality(profile, {0, 1},
algorithm=algorithm)
{a, b},
{a, c},
{a, c},
{a, c},
{a, d},
{a, d},
{b, c, f},
{e},
{f},
{g},
]
profile = Profile(num_cand, cand_names="abcdefgh")
profile.add_voters(approval_sets)
committeesize = 4
#
print(misc.header("Example 7", "*"))
print(misc.header("Input (election instance from Example 1):"))
print(profile.str_compact())
committees = abcrules.compute_monroe(profile, 4)
# verify correctness
a, b, c, d, e, f, g = range(7) # a = 0, b = 1, c = 2, ...
assert len(committees) == 6
# Monroe-score of all committees is the same
score = monroescore(profile, committees[0])
for committee in committees:
assert score == monroescore(profile, committee)