def test_all_points_different(strategy, surrogate):
"""
Tests whether the parallel optimizer always generates
different points to evaluate.
Parameters
----------
* `strategy` [string]:
Name of the strategy to use during optimization.
* `surrogate` [scikit-optimize surrogate class]:
A class of the scikit-optimize surrogate used in Optimizer.
"""
optimizer = Optimizer(
base_estimator=surrogate(),
dimensions=[Real(-5.0, 10.0), Real(0.0, 15.0)],
acq_optimizer="sampling",
random_state=1,
)
tolerance = 1e-3 # distance above which points are assumed same
for i in range(n_steps):
x = optimizer.ask(n_points, strategy)
optimizer.tell(x, [branin(v) for v in x])
distances = pdist(x)
assert all(distances > tolerance)
def test_constant_liar_runs(strategy, surrogate, acq_func):
"""
Tests whether the optimizer runs properly during the random
initialization phase and beyond
Parameters
----------
* `strategy` [string]:
Name of the strategy to use during optimization.
* `surrogate` [scikit-optimize surrogate class]:
A class of the scikit-optimize surrogate used in Optimizer.
"""
optimizer = Optimizer(
base_estimator=surrogate(),
dimensions=[Real(-5.0, 10.0), Real(0.0, 15.0)],
acq_func=acq_func,
acq_optimizer="sampling",
random_state=0,
)
# test arguments check
assert_raises(ValueError, optimizer.ask, {"strategy": "cl_maen"})
assert_raises(ValueError, optimizer.ask, {"n_points": "0"})
assert_raises(ValueError, optimizer.ask, {"n_points": 0})
for i in range(n_steps):
x = optimizer.ask(n_points=n_points, strategy=strategy)
# check if actually n_points was generated
assert_equal(len(x), n_points)
if "ps" in acq_func:
optimizer.tell(x, [[branin(v), 1.1] for v in x])
else:
optimizer.tell(x, [branin(v) for v in x])
def test_same_set_of_points_ask(strategy, surrogate):
"""
For n_points not None, tests whether two consecutive calls to ask
return the same sets of points.
Parameters
----------
* `strategy` [string]:
Name of the strategy to use during optimization.
* `surrogate` [scikit-optimize surrogate class]:
A class of the scikit-optimize surrogate used in Optimizer.
"""
optimizer = Optimizer(
base_estimator=surrogate(),
dimensions=[Real(-5.0, 10.0), Real(0.0, 15.0)],
acq_optimizer="sampling",
random_state=2,
)
for i in range(n_steps):
xa = optimizer.ask(n_points, strategy)
xb = optimizer.ask(n_points, strategy)
optimizer.tell(xa, [branin(v) for v in xa])
assert_equal(xa, xb) # check if the sets of points generated are equal
def test_reproducible_runs(strategy, surrogate):
# two runs of the optimizer should yield exactly the same results
optimizer = Optimizer(
base_estimator=surrogate(random_state=1),
dimensions=[Real(-5.0, 10.0), Real(0.0, 15.0)],
acq_optimizer="sampling",
random_state=1,
)
points = []
for i in range(n_steps):
x = optimizer.ask(n_points, strategy)
points.append(x)
optimizer.tell(x, [branin(v) for v in x])
# the x's should be exaclty as they are in `points`
optimizer = Optimizer(
base_estimator=surrogate(random_state=1),
dimensions=[Real(-5.0, 10.0), Real(0.0, 15.0)],
acq_optimizer="sampling",
random_state=1,
)
for i in range(n_steps):
x = optimizer.ask(n_points, strategy)
assert points[i] == x
optimizer.tell(x, [branin(v) for v in x])
def test_purely_categorical_space():
# Test reproduces the bug in #908, make sure it doesn't come back
dims = [Categorical(["a", "b", "c"]), Categorical(["A", "B", "C"])]
optimizer = Optimizer(dims, n_initial_points=2, random_state=3)
for _ in range(2):
x = optimizer.ask()
# before the fix this call raised an exception
optimizer.tell(x, np.random.uniform())