def test_constraints():
cs = RealSpace([-5, 5], "x") * 2
g = lambda x: x[0] + x[1] - 5
X = cs.sample(10, g=g)
assert all([g(x) <= 0 for x in X])
X = cs.sample(10, g=lambda x: x[0] + 5.1)
assert len(X) == 0
def test_precision():
cs = RealSpace([0, 1], precision=2) * 3
X = cs.sample(1, method="LHS")
X = [re.sub(r"^-?\d+\.(\d+)$", r"\1", str(_)) for _ in X[0]]
assert all([len(x) <= 2 for x in X])
X = cs.round(np.random.randn(3))
X = [re.sub(r"^-?\d+\.(\d+)$", r"\1", str(_)) for _ in X[0]]
assert all([len(x) <= 2 for x in X])
X = np.random.rand(2, 3)
assert isinstance(cs.round(X), np.ndarray)
X = Solution(cs.sample(10, method="LHS"))
cs.round(X)
cs = (RealSpace([0, 1], "x", precision=2) + IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z"))
X = cs.sample(1, method="LHS")[0][0]
X = re.sub(r"^-?\d+\.(\d+)$", r"\1", str(X))
assert len(X) <= 2
def test_scale():
cs = RealSpace([1e-10, 1e-1], "x", scale="log", random_seed=42)
x = cs.sample(1)
assert np.isclose(x, 2.3488813e-07)
assert np.isclose(cs.to_linear_scale(-15.812834391811666), 1.35697948e-07)
assert np.isclose(cs.to_linear_scale([-15.812834391811666]),
1.35697948e-07)
assert np.isclose(cs.to_linear_scale((-15.812834391811666)),
1.35697948e-07)
C = RealSpace([1, 5], scale="log")
assert getattr(C.data[0], "_bounds_transformed")[0] == 0
C = RealSpace([0.5, 0.8], scale="logit")
assert getattr(C.data[0], "_bounds_transformed")[0] == 0
C = RealSpace([-1, 1], scale="bilog")
assert getattr(C.data[0], "_bounds_transformed")[0] == -np.log(2)
assert getattr(C.data[0], "_bounds_transformed")[1] == np.log(2)
C = RealSpace([-1, 1], scale="bilog") * 2
x = C.to_linear_scale([-np.log(2), np.log(2)])
assert np.all(x == np.array([-1, 1]))
def test_sample_with_constraints():
g = lambda x: x - 0.1
cs = RealSpace([1e-10, 1e-1], "x", 0.01, scale="log")
X = cs.sample(10, g=g)
assert all(list(map(lambda x: g(x) <= 0, X)))