def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
a = optimizer.suggest(1)[0]
assert not numpy.allclose(a, optimizer.suggest(1)[0])
optimizer.seed_rng(1)
assert numpy.allclose(a, optimizer.suggest(1)[0])
def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
bayesian_optimizer = PrimaryAlgo(space, 'bayesianoptimizer')
bayesian_optimizer.seed_rng(1)
a = bayesian_optimizer.suggest(1)[0]
assert not numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
bayesian_optimizer.seed_rng(1)
assert numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
def test_seeding(space, algo):
"""Verify that seeding after init have no effects"""
optimizer = PrimaryAlgo(space, algo)
optimizer.seed_rng(1)
a = optimizer.suggest(1)[0]
assert not numpy.allclose(a, optimizer.suggest(1)[0])
optimizer.seed_rng(1)
assert not numpy.allclose(a, optimizer.suggest(1)[0])
def test_seed_rng(space):
"""Test that algo is seeded properly"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
a = optimizer.suggest(1)
# Hyperband will always return the full first rung
assert numpy.allclose(a, optimizer.suggest(1))
optimizer.seed_rng(2)
assert not numpy.allclose(a, optimizer.suggest(1))
def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
tpe_optimizer = PrimaryAlgo(space, 'tpeoptimizer')
tpe_optimizer.seed_rng(1)
a = tpe_optimizer.suggest(1)[0]
assert not numpy.allclose(a, tpe_optimizer.suggest(1)[0])
tpe_optimizer.seed_rng(1)
assert numpy.allclose(a, tpe_optimizer.suggest(1)[0])
def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
optimizer = PrimaryAlgo(space, "meshadaptivedirectsearch")
optimizer.seed_rng(1)
a = optimizer.suggest(1)[0]
with pytest.raises(AssertionError):
numpy.testing.assert_equal(a, optimizer.suggest(1)[0])
optimizer.seed_rng(1)
numpy.testing.assert_equal(a, optimizer.suggest(1)[0])
def test_set_state(space):
"""Verify that resetting state makes sampling deterministic"""
bayesian_optimizer = PrimaryAlgo(space, 'bayesianoptimizer')
bayesian_optimizer.seed_rng(1)
state = bayesian_optimizer.state_dict
a = bayesian_optimizer.suggest(1)[0]
assert not numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
bayesian_optimizer.set_state(state)
assert numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
def test_set_state(space):
"""Verify that resetting state makes sampling deterministic"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
state = optimizer.state_dict
a = optimizer.suggest(1)[0]
assert not numpy.allclose(a, optimizer.suggest(1)[0])
optimizer.set_state(state)
assert numpy.allclose(a, optimizer.suggest(1)[0])
def test_set_state(space):
"""Test that state is reset properly"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
state = optimizer.state_dict
points = optimizer.suggest(1)
# Hyperband will always return the full first rung
assert numpy.allclose(points, optimizer.suggest(1))
optimizer.seed_rng(2)
assert not numpy.allclose(points, optimizer.suggest(1))
optimizer.set_state(state)
assert numpy.allclose(points, optimizer.suggest(1))