async def test_nash_failure():
"""With regret thresh of zero, nash will fail"""
game = gamegen.sym_2p2s_known_eq(1 / 3)
sched = gamesched.gamesched(game)
eqa = await innerloop.inner_loop(schedgame.schedgame(sched),
regret_thresh=0)
assert not eqa.size
def generate_games(num): # pylint: disable=too-many-branches
"""Produce num random games per type"""
np.random.seed(0)
with open(path.join(_DIR, 'example_games', 'hard_nash.json')) as fil:
yield 'hard', gamereader.load(fil).normalize()
with open(path.join(_DIR, 'example_games', '2x2x2.nfg')) as fil:
yield 'gambit', gamereader.load(fil).normalize()
for _ in range(num):
yield 'random', gamegen.game(*random_small()).normalize()
for _ in range(num):
strats = np.random.randint(2, 5, np.random.randint(2, 4))
yield 'covariant', gamegen.covariant_game(strats).normalize()
for _ in range(num):
strats = np.random.randint(2, 5, 2)
yield 'zero sum', gamegen.two_player_zero_sum_game(strats).normalize()
for _ in range(num):
yield 'prisoners', gamegen.prisoners_dilemma().normalize()
for _ in range(num):
yield 'chicken', gamegen.sym_2p2s_game(0, 3, 1, 2).normalize()
for _ in range(num):
prob = np.random.random()
yield 'mix', gamegen.sym_2p2s_known_eq(prob).normalize()
for _ in range(num):
strats = np.random.randint(2, 4)
plays = np.random.randint(2, 4)
yield 'polymatrix', gamegen.polymatrix_game(plays, strats).normalize()
for _ in range(num):
wins = np.random.random(3) + .5
loss = -np.random.random(3) - .5
yield 'roshambo', gamegen.rock_paper_scissors(wins, loss).normalize()
yield 'shapley easy', gamegen.rock_paper_scissors(win=2).normalize()
yield 'shapley normal', gamegen.rock_paper_scissors(win=1).normalize()
yield 'shapley hard', gamegen.rock_paper_scissors(win=0.5).normalize()
for _ in range(num):
yield 'normagg small', gamegen.normal_aggfn(*random_agg_small())
for _ in range(num):
yield 'polyagg small', gamegen.poly_aggfn(*random_agg_small())
for _ in range(num):
yield 'sineagg small', gamegen.sine_aggfn(*random_agg_small())
for _ in range(num):
facs = np.random.randint(2, 6)
req = np.random.randint(1, facs)
players = np.random.randint(2, 11)
yield 'congestion', gamegen.congestion(players, facs, req)
for _ in range(num):
strats = np.random.randint(2, 6)
players = np.random.randint(2, 11)
yield 'local effect', gamegen.local_effect(players, strats)
for _ in range(num):
yield 'normagg large', gamegen.normal_aggfn(*random_agg_large())
for _ in range(num):
yield 'polyagg large', gamegen.poly_aggfn(*random_agg_large())
for _ in range(num):
yield 'sineagg large', gamegen.sine_aggfn(*random_agg_large())
for _ in range(num):
agg = gamegen.sine_aggfn(*random_agg_small())
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
yield 'rbf', learning.rbfgame_train(agg)
def test_old_nash():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game, processes=2)
assert eqa.shape == (1, 2)
eqm, = eqa
assert np.allclose(eqm, [prob, 1 - prob], atol=1e-3)
def test_mixed_equilibria():
"""Test that mixed equilibria works for easy case"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_equilibria(game, processes=1)
assert eqa.shape == (1, 2)
eqm, = eqa
assert np.allclose(eqm, [prob, 1 - prob], atol=1e-3)
def test_mixed_known_eq(methods, eq_prob):
game = gamegen.sym_2p2s_known_eq(eq_prob)
eqa = nash.mixed_nash(game, processes=1, **methods)
assert eqa.shape[0] >= 1, "didn't find equilibrium"
expected = [eq_prob, 1 - eq_prob]
assert np.isclose(eqa, expected, atol=1e-3, rtol=1e-3).all(1).any(), \
"didn't find correct equilibrium {} instead of {}".format(
eqa, expected)
def test_optimization_stable_point(eq_prob):
game = gamegen.sym_2p2s_known_eq(eq_prob)
opt = nash.RegretOptimizer(game)
val, grad = opt.grad(np.array([eq_prob, 1 - eq_prob]), 1)
assert np.isclose(val, 0), \
"value at equilibrium was not close to zero: {}".format(val)
assert np.allclose(grad, 0), \
"grad at equilibrium was not close to zero: {}".format(grad)
def test_old_nash_at_least_one():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game, replicator=dict(max_iters=0), at_least_one=True)
assert eqa.shape == (1, 2)
eqm, = eqa
assert np.allclose(eqm, [prob, 1 - prob], atol=1e-3)
def test_old_nash():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game, processes=2)
assert eqa.shape == (1, 2)
eqm, = eqa
assert np.allclose(eqm, [prob, 1 - prob], atol=1e-3)
def test_mixed_equilibria():
"""Test that mixed equilibria works for easy case"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_equilibria(game, processes=1)
assert eqa.shape == (1, 2)
eqm, = eqa
assert np.allclose(eqm, [prob, 1 - prob], atol=1e-3)
def test_old_nash_min_reg():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game, replicator=dict(max_iters=0), min_reg=True)
assert eqa.shape == (1, 2)
eqm, = eqa
reg = regret.mixture_regret(game, eqm)
assert reg > 1e-3
def test_old_nash_min_reg():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game, replicator=dict(max_iters=0), min_reg=True)
assert eqa.shape == (1, 2)
eqm, = eqa
reg = regret.mixture_regret(game, eqm)
assert reg > 1e-3
def test_old_nash_at_least_one():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game,
replicator=dict(max_iters=0),
at_least_one=True)
assert eqa.shape == (1, 2)
eqm, = eqa
assert np.allclose(eqm, [prob, 1 - prob], atol=1e-3)
async def test_innerloop_known_eq(eq_prob):
"""Test that inner loop finds known equilibria"""
game = gamegen.sym_2p2s_known_eq(eq_prob)
sched = gamesched.gamesched(game)
eqa = await innerloop.inner_loop(schedgame.schedgame(sched),
devs_by_role=True)
assert eqa.size, "didn't find equilibrium"
expected = [eq_prob, 1 - eq_prob]
assert np.isclose(eqa, expected, atol=1e-3, rtol=1e-3).all(-1).any()
verify_dist_thresh(eqa)
def test_at_least_one():
# Equilibrium of game is not at a starting point for equilibria finding
game = gamegen.sym_2p2s_known_eq(1/math.sqrt(2))
# Don't converge
opts = {'max_iters': 0}
eqa = nash.mixed_nash(game, processes=1, replicator=opts)
assert eqa.size == 0, "found an equilibrium normally"
eqa = nash.mixed_nash(game, replicator=opts, processes=1,
at_least_one=True)
assert eqa.shape[0] == 1, "at_least_one didn't return anything"
async def test_backups_used():
"""Test that outerloop uses backups
Since restricted game size is 1, but the only equilibria has support two,
this must use backups to find an equilibrium."""
sgame = gamegen.sym_2p2s_known_eq(0.5)
sched = gamesched.gamesched(sgame)
eqa = await innerloop.inner_loop(schedgame.schedgame(sched),
restricted_game_size=1)
assert eqa.size, "didn't find equilibrium"
expected = [0.5, 0.5]
assert np.isclose(eqa, expected, atol=1e-3, rtol=1e-3).all(-1).any()
verify_dist_thresh(eqa)
def random_pairs():
"""Generate random pairs of games"""
mix = gamegen.sym_2p2s_known_eq(.5)
dom1 = paygame.game(
2, 2, [[2, 0], [1, 1], [0, 2]], [[.1, 0], [.1, 0], [0, 0]])
dom2 = paygame.game(
2, 2, [[2, 0], [1, 1], [0, 2]], [[0, 0], [0, .1], [0, .1]])
yield mix, dom1
yield dom1, mix
yield mix, dom2
yield dom2, mix
for base in tu.edge_games():
play, strats = base.num_role_players, base.num_role_strats
yield (gamegen.poly_aggfn(play, strats, 6),
gamegen.poly_aggfn(play, strats, 6))
def test_sym_2p2s_known_eq(eq_prob):
game = gamegen.sym_2p2s_known_eq(eq_prob)
assert game.is_complete(), "didn't generate a full game"
assert game.is_symmetric(), \
"didn't generate a symmetric game"
assert np.all(2 == game.num_players), \
"didn't generate correct number of strategies"
assert np.all(2 == game.num_strategies), \
"didn't generate correct number of strategies"
eqm = np.array([eq_prob, 1 - eq_prob])
reg = regret.mixture_regret(game, eqm)
assert np.isclose(reg, 0), \
"expected equilibrium wasn't an equilibrium, reg: {}".format(reg)
for non_eqm in game.pure_mixtures():
reg = regret.mixture_regret(game, non_eqm)
# If eq_prob is 0 or 1, then pure is the desired mixture
assert non_eqm[0] == eq_prob or not np.isclose(reg, 0), \
"pure mixtures was equilibrium, {} {}".format(non_eqm, reg)
def test_sym_2p2s_known_eq(eq_prob):
"""Test known equilibrium game"""
game = gamegen.sym_2p2s_known_eq(eq_prob)
assert game.is_complete(), "didn't generate a full game"
assert game.is_symmetric(), \
"didn't generate a symmetric game"
assert np.all(game.num_role_players == 2), \
"didn't generate correct number of strategies"
assert np.all(game.num_role_strats == 2), \
"didn't generate correct number of strategies"
eqm = np.array([eq_prob, 1 - eq_prob])
reg = regret.mixture_regret(game, eqm)
assert np.isclose(reg, 0), \
"expected equilibrium wasn't an equilibrium, reg: {}".format(reg)
for non_eqm in game.pure_mixtures():
reg = regret.mixture_regret(game, non_eqm)
# If eq_prob is 0 or 1, then pure is the desired mixture
assert non_eqm[0] == eq_prob or not np.isclose(reg, 0), \
'pure mixtures was equilibrium, {} {}'.format(non_eqm, reg)
def test_replicator_dynamics_noop():
"""Test that max_iters stops replicator dynamics"""
game = gamegen.sym_2p2s_known_eq(1 / np.sqrt(2))
eqm = nash.replicator_dynamics(game, [1 / 2, 1 / 2], max_iters=0) # pylint: disable=unexpected-keyword-arg
assert np.allclose(eqm, [1 / 2, 1 / 2])
def test_old_nash_failure():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game, replicator=dict(max_iters=0))
assert not eqa.size
def test_replicator_dynamics_noop():
"""Test that max_iters stops replicator dynamics"""
game = gamegen.sym_2p2s_known_eq(1 / np.sqrt(2))
eqm = nash.replicator_dynamics(game, [1/2, 1/2], max_iters=0) # pylint: disable=unexpected-keyword-arg
assert np.allclose(eqm, [1/2, 1/2])
def test_replicator_dynamics():
"""Test that it works for games we know it works for"""
game = gamegen.sym_2p2s_known_eq(1 / np.sqrt(2))
eqm = nash.replicator_dynamics(game, [1/2, 1/2])
assert np.allclose(eqm, [1 / np.sqrt(2), 1 - 1 / np.sqrt(2)])
def test_regret_matching_failure():
"""Test that it works for games we know it works for"""
game = gamegen.sym_2p2s_known_eq(1 / np.sqrt(2))
eqm = nash.regret_matching(game, [0, 2])
assert np.allclose(eqm, [1/2, 1/2], atol=1e-2)
def test_regret_matching_failure():
"""Test that it works for games we know it works for"""
game = gamegen.sym_2p2s_known_eq(1 / np.sqrt(2))
eqm = nash.regret_matching(game, [0, 2])
assert np.allclose(eqm, [1 / 2, 1 / 2], atol=1e-2)
def test_replicator_dynamics():
"""Test that it works for games we know it works for"""
game = gamegen.sym_2p2s_known_eq(1 / np.sqrt(2))
eqm = nash.replicator_dynamics(game, [1 / 2, 1 / 2])
assert np.allclose(eqm, [1 / np.sqrt(2), 1 - 1 / np.sqrt(2)])
def test_old_nash_failure():
"""Test old nash functions appropriately"""
prob = 1 / np.sqrt(2)
game = gamegen.sym_2p2s_known_eq(prob)
eqa = nash.mixed_nash(game, replicator=dict(max_iters=0))
assert not eqa.size
