def test_filter():
cs = (RealSpace([1e-10, 1e-1], "x", 0.01, scale="log") +
IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z"))
cs *= 2
assert cs.filter(["x1"]).var_name == ["x1"]
assert "x1" not in cs.filter(["x1"], invert=True).var_name
def test_SearchSpace_sub():
for _ in range(3):
cs = RealSpace([0, 5], "x") + IntegerSpace(
[-10, 10], "y") + DiscreteSpace(["A", "B", "C"], "z")
_cs = DiscreteSpace(["A", "B", "C"], "z") + IntegerSpace([10, 20], "p")
cs2 = cs - cs[1:]
assert cs2.var_name[0] == "x"
assert cs.dim == 3
assert set((cs - _cs).var_name) == set(["x", "y"])
cs -= _cs
assert set(cs.var_name) == set(["x", "y"])
cs -= cs[1:]
assert cs.var_name[0] == "x"
def test_fixed_var():
search_space = (BoolSpace(var_name="bool") +
IntegerSpace([5, 15], var_name="ordinal") +
RealSpace([-5, 5], var_name="continuous", precision=2) +
DiscreteSpace(["OK", "A", "B", "C", "D", "E", "F", "G"],
var_name="nominal"))
opt = MOBO(
search_space=search_space,
obj_fun=(f1, f2),
model=RandomForest(levels=search_space.levels),
max_FEs=100,
DoE_size=1, # the initial DoE size
eval_type="dict",
n_job=1, # number of processes
verbose=True, # turn this off, if you prefer no output
)
X = opt.ask(3, fixed={"ordinal": 5, "continuous": 3.2})
assert all([x["ordinal"] == 5 and x["continuous"] == 3.2 for x in X])
opt.tell(X, [(f1(x), f2(x)) for x in X])
X = opt.ask(1, fixed={"nominal": "OK", "bool": False})
assert all([x["nominal"] == "OK" and not x["bool"] for x in X])
opt.recommend()
with pytest.raises(NotImplementedError):
X = opt.ask(3)
def test_fixed_var():
def obj_fun(x):
return (x["continuous"]**2 + abs(x["ordinal"] - 10) / 123.0 +
(x["nominal"] != "OK") * 2 + int(x["bool"]) * 3 +
(x["subset"] == ["A"]) * 5)
search_space = (BoolSpace(var_name="bool") +
IntegerSpace([5, 15], var_name="ordinal") +
RealSpace([-5, 5], var_name="continuous") +
DiscreteSpace(["OK", "A", "B", "C", "D", "E", "F", "G"],
var_name="nominal") +
SubsetSpace(["A", "B", "C"], var_name="subset"))
opt = ParallelBO(
search_space=search_space,
obj_fun=obj_fun,
model=RandomForest(levels=search_space.levels),
max_FEs=100,
DoE_size=3, # the initial DoE size
eval_type="dict",
acquisition_fun="MGFI",
acquisition_par={"t": 2},
n_job=1, # number of processes
n_point=3, # number of the candidate solution proposed in each iteration
verbose=True, # turn this off, if you prefer no output
)
X = opt.ask(3, fixed={"ordinal": 5, "continuous": 3.2})
assert all([x["ordinal"] == 5 and x["continuous"] == 3.2 for x in X])
opt.tell(X, [obj_fun(x) for x in X])
X = opt.ask(3, fixed={"nominal": "OK", "bool": False})
assert all([x["nominal"] == "OK" and not x["bool"] for x in X])
def test_iter():
cs = (RealSpace([1e-10, 1e-1], "x", 1e-3, scale="log") +
IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z"))
cs *= 2
for var in iter(cs):
assert isinstance(var, Variable)
def test_sample():
cs = (RealSpace([1e-10, 1e-1], "x", 1e-3, scale="log") +
IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z"))
X = cs.sample(10)
assert np.asarray(X).shape == (10, 3)
cs.sample(5, method="LHS")
X = cs.sample(5, method="uniform")
cs.to_linear_scale(X)
def test_SearchSpace_mul():
cs = RealSpace([0, 5], "x") + IntegerSpace([-10, 10], "y") + DiscreteSpace(
["A", "B", "C"], "z")
__ = ["x0", "y0", "z0", "x1", "y1", "z1"]
assert (cs * 2).dim == 6
assert all(np.array((2 * cs).var_name) == np.asarray(__))
cs *= 2
assert cs.dim == 6
def test_in():
cs = (IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z") +
RealSpace([1e-10, 1e-1], "x", 0.01, scale="log"))
x = Solution(cs.sample(1)[0], var_name=cs.var_name)
assert RealSpace([1e-10, 1e-1], "x", 0.01, scale="log") in cs
assert "x" in cs
assert "xx" not in cs
assert x.tolist() in cs
assert x.to_dict() in cs
def test_SearchSpace_slice():
cs = (RealSpace([1, 5], "x", 2, scale="log") +
IntegerSpace([-10, 10], "y") + DiscreteSpace(["A", "B", "C"], "z"))
assert isinstance(cs[[0]], RealSpace)
assert isinstance(cs[[1]], IntegerSpace)
assert isinstance(cs[[2]], DiscreteSpace)
assert isinstance(cs[0], Real)
assert isinstance(cs[1], Integer)
assert isinstance(cs[2], Discrete)
assert isinstance(cs["z"], Discrete)
cs = (RealSpace([1, 5], "x", 2, scale="log") * 2 +
IntegerSpace([-10, 10], "y") + DiscreteSpace(["A", "B", "C"], "z"))
assert isinstance(cs[:2], RealSpace)
assert isinstance(cs[["x0", "x1"]], RealSpace)
assert isinstance(cs[[False, False, True, False]], IntegerSpace)
assert isinstance(cs.filter(["x0", "x1"]), RealSpace)
def test_SearchSpace_var_name():
cs = (RealSpace([1e-10, 1e-1], "x", 1e-3, scale="log") +
IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z"))
cs.var_name = "x"
assert all(np.asarray(cs.var_name) == np.asarray(["x0", "x1", "x2"]))
cs.var_name = ["y0", "y1", "y2"]
assert all(np.asarray(cs.var_name) == np.asarray(["y0", "y1", "y2"]))
with pytest.raises(AssertionError):
cs.var_name = ["y0", "y1"]
def test_SearchSpace_remove():
cs = (RealSpace([1e-10, 1e-1], "x", 1e-3, scale="log") +
IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z"))
cs.remove(0)
assert isinstance(cs[[0]], IntegerSpace)
cs.remove("z")
assert isinstance(cs, IntegerSpace)
with pytest.raises(KeyError):
cs.remove("aaa")
def test_SearchSpace_concat():
cs_list = [RealSpace([0, 5], "x") for _ in range(3)]
cs = SearchSpace.concat(*cs_list)
assert all(np.array(cs.var_name) == np.array(["x0", "x1", "x2"]))
assert isinstance(cs, RealSpace)
cs_list = [
RealSpace([0, 5], "x"),
DiscreteSpace([1, 2, 3]),
IntegerSpace([-5, 5])
]
cs = SearchSpace.concat(*cs_list)
assert isinstance(cs, SearchSpace)
def test_homogenous(var_type):
dim = 5
def fitness(_):
return np.random.rand()
if var_type == "r":
lb, ub = -1, 5
space = RealSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="squared_exponential",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer="BFGS",
wait_iter=3,
random_start=dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
else:
if var_type == "b":
space = BoolSpace() * dim
elif var_type == "i":
space = IntegerSpace([0, 10], step=1) * dim
elif var_type == "c":
space = DiscreteSpace(list(range(10))) * dim
elif var_type == "o":
space = OrdinalSpace(list(string.ascii_lowercase))
elif var_type == "s":
space = SubsetSpace(list(string.ascii_lowercase)[:5])
model = RandomForest(levels=space.levels)
opt = BO(
search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=10,
verbose=True,
n_point=1,
)
print(opt.run())
def test_mix_space(eval_type):
dim_r = 2 # dimension of the real values
if eval_type == "dict":
def obj_fun(x):
# Do explicit type-casting since dataframe rows might be strings otherwise
x_r = np.array(
[float(x["continuous%d" % i]) for i in range(dim_r)])
x_i = int(x["ordinal"])
x_d = x["nominal"]
_ = 0 if x_d == "OK" else 1
return np.sum(x_r**2) + abs(x_i - 10) / 123.0 + _ * 2
elif eval_type == "list":
def obj_fun(x):
x_r = np.array([x[i] for i in range(dim_r)])
x_i = x[dim_r]
x_d = x[dim_r + 1]
_ = 0 if x_d == "OK" else 1
return np.sum(x_r**2) + abs(x_i - 10) / 123.0 + _ * 2
else:
raise NotImplementedError
search_space = (RealSpace([-5, 5], var_name="continuous") * dim_r +
IntegerSpace([5, 15], var_name="ordinal") +
DiscreteSpace(["OK", "A", "B", "C", "D", "E", "F", "G"],
var_name="nominal") +
SubsetSpace(["A", "B", "C"], var_name="subset"))
model = RandomForest(levels=search_space.levels)
opt = ParallelBO(
search_space=search_space,
obj_fun=obj_fun,
model=model,
max_FEs=6,
DoE_size=3, # the initial DoE size
eval_type=eval_type,
acquisition_fun="MGFI",
acquisition_par={"t": 2},
n_job=1, # number of processes
n_point=3, # number of the candidate solution proposed in each iteration
verbose=True, # turn this off, if you prefer no output
)
xopt, fopt, stop_dict = opt.run()
print("xopt: {}".format(xopt))
print("fopt: {}".format(fopt))
print("stop criteria: {}".format(stop_dict))
def test_recommend():
search_space = (RealSpace([10, 30], var_name="p1", precision=2) +
IntegerSpace([20, 40], var_name="p2") +
DiscreteSpace([128, 256, 512], var_name="p3") +
BoolSpace(var_name="p4"))
opt = MOBO(
search_space=search_space,
obj_fun=(f1, f2),
model=RandomForest(levels=search_space.levels),
max_FEs=100,
DoE_size=3, # the initial DoE size
eval_type="dict",
n_job=1, # number of processes
verbose=True, # turn this off, if you prefer no output
)
with pytest.raises(RecommendationUnavailableError):
opt.recommend()
def test_constraint():
search_space = (BoolSpace(var_name="bool") +
IntegerSpace([5, 15], var_name="ordinal") +
RealSpace([-5, 5], var_name="continuous") +
DiscreteSpace(["OK", "A", "B", "C", "D", "E", "F", "G"],
var_name="nominal"))
opt = MOBO(
search_space=search_space,
obj_fun=(f1, f2),
model=RandomForest(levels=search_space.levels),
ineq_fun=lambda x: x["continuous"],
max_FEs=10,
DoE_size=3, # the initial DoE size
eval_type="dict",
n_job=1, # number of processes
verbose=True, # turn this off, if you prefer no output
)
opt.run()
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_BO_bad_constraints():
search_space = (
DiscreteSpace(["1", "2", "3"], var_name="lambda")
+ RealSpace([0, 1], var_name="pc")
+ RealSpace([0.005, 0.5], var_name="p")
)
model = RandomForest(levels=search_space.levels)
with pytest.raises(ConstraintEvaluationError):
BO(
search_space=search_space,
obj_fun=lambda x: 10 * (x[0] == "3") + x[1] * x[2],
ineq_fun=lambda x: sum(np.array(x) ** 2),
model=model,
max_FEs=10,
DoE_size=3,
eval_type="list",
acquisition_fun="MGFI",
acquisition_par={"t": 2},
n_job=1,
n_point=1,
verbose=True,
random_seed=42,
).run()
def test_contains():
cs = (IntegerSpace([-10, 10], "y") +
DiscreteSpace(["A", "B", "C", "D", "E"], "z") +
RealSpace([1e-10, 1e-1], "x", 0.01, scale="log"))
assert RealSpace([1e-10, 1e-1], "x", 0.01, scale="log") in cs