def test_perturb_int_no_duplicate_below(self):
explore = PerturbExplore(factor=0.75)
rng = RNGStub()
rng.random = lambda: 1.0
assert explore.perturb_int(rng, 1, (0, 10)) == 0
def test_perturb_int_duplicate_equal(self):
explore = PerturbExplore(factor=1.0)
rng = RNGStub()
rng.random = lambda: 1.0
assert explore.perturb_int(rng, 1, (0, 10)) == 1
def test_perturb_real_volatility_above(self, volatility):
explore = PerturbExplore(factor=1.0, volatility=volatility)
rng = RNGStub()
rng.random = lambda: 1.0
rng.normal = lambda mean, variance: variance
assert explore.perturb_real(rng, 3.0, (1.0, 2.0)) == 2.0 - volatility
def test_perturb_cat(self):
explore = PerturbExplore()
rng = RNGStub()
rng.randint = lambda low, high, size: [1]
rng.choice = lambda choices: choices[0]
dim = Categorical("name", ["one", "two", 3, 4.0])
assert explore.perturb_cat(rng, "whatever", dim) in dim
def test_perturb_real_factor(self, factor):
explore = PerturbExplore(factor=factor)
rng = RNGStub()
rng.random = lambda: 1.0
assert explore.perturb_real(rng, 1.0, (0.1, 2.0)) == factor
rng.random = lambda: 0.0
assert explore.perturb_real(rng, 1.0, (0.1, 2.0)) == 1.0 / factor
def test_perturb_real_above_interval_cap(self):
explore = PerturbExplore(factor=1.0, volatility=0)
rng = RNGStub()
rng.random = lambda: 1.0
rng.normal = lambda mean, variance: variance
assert explore.perturb_real(rng, 3.0, (1.0, 2.0)) == 2.0
explore.volatility = 1000
assert explore.perturb_real(rng, 3.0, (1.0, 2.0)) == 1.0
def test_perturb_int_factor(self, factor):
explore = PerturbExplore(factor=factor)
rng = RNGStub()
rng.random = lambda: 1.0
assert explore.perturb_int(rng, 5,
(0, 10)) == int(numpy.round(5 * factor))
rng.random = lambda: 0.0
assert explore.perturb_int(rng, 5,
(0, 10)) == int(numpy.round(5 / factor))
def test_perturb_int_no_out_of_bounds(self):
explore = PerturbExplore(factor=0.75, volatility=0)
rng = RNGStub()
rng.random = lambda: 1.0
rng.normal = lambda mean, variance: variance
assert explore.perturb_int(rng, 0, (0, 10)) == 0
rng.random = lambda: 0.0
rng.normal = lambda mean, variance: variance
assert explore.perturb_int(rng, 10, (0, 10)) == 10
def test_configuration(self):
explore = PerturbExplore(factor=2.0, volatility=10.0)
assert explore.configuration == dict(of_type="perturbexplore",
factor=2.0,
volatility=10.0)
def test_perturb_with_invalid_dim(self, space, monkeypatch):
explore = PerturbExplore()
monkeypatch.setattr(Dimension, "type", "type_that_dont_exist")
with pytest.raises(
ValueError,
match="Unsupported dimension type type_that_dont_exist"):
explore(RNGStub(), space, {"x": 1.0, "y": 2, "z": 0, "f": 10})
def test_perturb(self, space):
explore = PerturbExplore()
rng = RNGStub()
rng.randint = lambda low, high, size: [1]
rng.random = lambda: 1.0
rng.normal = lambda mean, variance: 0.0
rng.choice = lambda choices: choices[0]
params = {"x": 1.0, "y": 2, "z": 0, "f": 10}
new_params = explore(rng, space, params)
for key in space.keys():
assert new_params[key] in space[key]
def test_perturb_hierarchical_params(self, hspace):
explore = PerturbExplore()
rng = RNGStub()
rng.randint = lambda low, high, size: [1]
rng.random = lambda: 1.0
rng.normal = lambda mean, variance: 0.0
rng.choice = lambda choices: choices[0]
params = {"numerical": {"x": 1.0, "y": 2, "f": 10}, "z": 0}
new_params = explore(rng, hspace, params)
assert "numerical" in new_params
assert "x" in new_params["numerical"]
for key in hspace.keys():
assert flatten(new_params)[key] in hspace[key]