def test_stable_marriage_produces_symmetric_matchings(caplog, build_func,
searches):
caplog.set_level(logging.INFO)
ranks = build_func(searches)
matches = algorithm.StableMarriage().find(ranks)
for search in matches:
opponent = matches[search]
assert matches[opponent] == search
def test_stable_marriage_unmatch(player_factory):
s1 = Search([player_factory(503, 64, name="p1")])
s2 = Search([player_factory(504, 64, name="p2")])
s3 = Search([player_factory(504, 64, name="p3")])
s4 = Search([player_factory(505, 64, name="p4")])
searches = [s1, s2, s3, s4]
ranks = algorithm._MatchingGraph.build_full(searches)
matches = algorithm.StableMarriage().find(ranks)
assert matches[s1] == s4 # quality: 0.96622
assert matches[s2] == s3 # quality: 0.96623
def test_stable_marriage_unmatch(p):
s1 = Search([p(503, 64, name='p1')])
s2 = Search([p(504, 64, name='p2')])
s3 = Search([p(504, 64, name='p3')])
s4 = Search([p(505, 64, name='p4')])
searches = [s1, s2, s3, s4]
ranks = algorithm._MatchingGraph.build_full(searches)
matches = algorithm.StableMarriage().find(ranks)
assert matches[s1] == s4 # quality: 0.96622
assert matches[s2] == s3 # quality: 0.96623
def test_stable_marriage_matches_new_players_with_new_and_old_with_old_if_different_mean(
player_factory):
new1 = Search([player_factory(1500, 500, name="new1", ladder_games=1)])
new2 = Search([player_factory(1400, 500, name="new2", ladder_games=2)])
old1 = Search([player_factory(2300, 75, name="old1", ladder_games=100)])
old2 = Search([player_factory(2350, 75, name="old2", ladder_games=200)])
searches = [new1, new2, old1, old2]
ranks = algorithm._MatchingGraph.build_full(searches)
matches = algorithm.StableMarriage().find(ranks)
assert matches[new1] == new2
assert matches[old1] == old2
def test_stable_marriage_matches_new_players_with_new_and_old_with_old_if_same_mean(
player_factory):
# Assumes that both new players initialized with mean 1500 will be matched
# as if they had mean 500
new1 = Search([player_factory(1500, 500, name="new1", ladder_games=0)])
new2 = Search([player_factory(1500, 500, name="new2", ladder_games=0)])
old1 = Search([player_factory(500, 75, name="old1", ladder_games=100)])
old2 = Search([player_factory(500, 75, name="old2", ladder_games=100)])
searches = [new1, new2, old1, old2]
ranks = algorithm._MatchingGraph.build_full(searches)
matches = algorithm.StableMarriage().find(ranks)
assert matches[new1] == new2
assert matches[old1] == old2
def test_stable_marriage(player_factory):
s1 = Search([player_factory(2300, 64, name="p1")])
s2 = Search([player_factory(1200, 72, name="p2")])
s3 = Search([player_factory(1300, 175, name="p3")])
s4 = Search([player_factory(2350, 125, name="p4")])
s5 = Search([player_factory(1200, 175, name="p5")])
s6 = Search([player_factory(1250, 175, name="p6")])
searches = [s1, s2, s3, s4, s5, s6]
ranks = algorithm._MatchingGraph.build_full(searches)
matches = algorithm.StableMarriage().find(ranks)
assert matches[s1] == s4
assert matches[s2] == s5
assert matches[s3] == s6
def test_stable_marriage(p):
s1 = Search([p(2300, 64, name='p1')])
s2 = Search([p(1200, 72, name='p2')])
s3 = Search([p(1300, 175, name='p3')])
s4 = Search([p(2350, 125, name='p4')])
s5 = Search([p(1200, 175, name='p5')])
s6 = Search([p(1250, 175, name='p6')])
searches = [s1, s2, s3, s4, s5, s6]
ranks = algorithm._MatchingGraph.build_full(searches)
matches = algorithm.StableMarriage().find(ranks)
assert matches[s1] == s4
assert matches[s2] == s5
assert matches[s3] == s6
def test_stable_marriage_better_than_greedy(player_factory):
s1 = Search([player_factory(2300, 64, name="p1")])
s2 = Search([player_factory(2000, 64, name="p2")])
s3 = Search([player_factory(2100, 64, name="p3")])
s4 = Search([player_factory(2200, 64, name="p4")])
s5 = Search([player_factory(2300, 64, name="p5")])
s6 = Search([player_factory(2400, 64, name="p6")])
searches = [s1, s2, s3, s4, s5, s6]
ranks = algorithm._MatchingGraph.build_full(searches)
matches = algorithm.StableMarriage().find(ranks)
# Note that the most balanced configuration would be
# (s1, s6) quality: 0.93
# (s2, s3) quality: 0.93
# (s4, s5) quality: 0.93
# However, because s1 is first in the list and gets top choice, we end with
# the following stable configuration
assert matches[s1] == s5 # quality: 0.97
assert matches[s2] == s3 # quality: 0.93
assert matches[s4] == s6 # quality: 0.82