def test_local_search_acquisition_optimizer_neighbours():
np.random.seed(0)
space = ParameterSpace([
CategoricalParameter('a', OneHotEncoding([1, 2, 3])),
CategoricalParameter('b', OrdinalEncoding([0.1, 1, 2])),
CategoricalParameter('c', OrdinalEncoding([0.1, 1, 2])),
DiscreteParameter('d', [0.1, 1.2, 2.3]),
ContinuousParameter('e', 0, 100),
DiscreteParameter('no_neighbours', [1]),
DiscreteParameter('f', [0.1, 1.2, 2.3]),
])
x = np.array([1, 0, 0, 1.6, 2.9, 0.1, 50, 1.2, 1.])
optimizer = LocalSearchAcquisitionOptimizer(space, 1000, 3, num_continuous=1)
neighbourhood = optimizer._neighbours_per_parameter(x, space.parameters)
assert_equal(np.array([[0, 1, 0], [0, 0, 1]]), neighbourhood[0])
assert_equal(np.array([[1], [3]]), neighbourhood[1])
assert_equal(np.array([[2]]), neighbourhood[2])
assert_equal(np.array([[1.2]]), neighbourhood[3])
assert_almost_equal(np.array([[53.5281047]]), neighbourhood[4])
assert_equal(np.empty((0, 1)), neighbourhood[5])
assert_equal(np.array([[0.1], [2.3]]), neighbourhood[6])
neighbours = optimizer._neighbours(x, space.parameters)
assert_almost_equal(np.array([
[0, 1, 0, 2., 3., 0.1, 50., 1., 1.2],
[0, 0, 1, 2., 3., 0.1, 50., 1., 1.2],
[1, 0, 0, 1., 3., 0.1, 50., 1., 1.2],
[1, 0, 0, 3., 3., 0.1, 50., 1., 1.2],
[1, 0, 0, 2., 2., 0.1, 50., 1., 1.2],
[1, 0, 0, 2., 3., 1.2, 50., 1., 1.2],
[1, 0, 0, 2., 3., 0.1, 50.80031442, 1., 1.2],
[1, 0, 0, 2., 3., 0.1, 50., 1., 0.1],
[1, 0, 0, 2., 3., 0.1, 50., 1., 2.3],
]), space.round(neighbours))
def test_local_search_acquisition_optimizer(simple_square_acquisition):
space = ParameterSpace(
[CategoricalParameter("x", OrdinalEncoding(np.arange(0, 100)))])
optimizer = LocalSearchAcquisitionOptimizer(space, 1000, 3)
opt_x, opt_val = optimizer.optimize(simple_square_acquisition)
# ordinal encoding is as integers 1, 2, ...
np.testing.assert_array_equal(opt_x, np.array([[1.0]]))
np.testing.assert_array_equal(opt_val, np.array([[0.0]]))
class UnknownParameter(Parameter):
def __init__(self, name: str):
self.name = name
def sample_uniform(num_points):
return np.random.randint(0, 1, (num_points, 1))
space.parameters.append(UnknownParameter("y"))
with pytest.raises(TypeError):
optimizer.optimize(simple_square_acquisition)
space.parameters.pop()
class UnknownEncoding(Encoding):
def __init__(self):
super().__init__([1], [[1]])
space.parameters.append(CategoricalParameter("y", UnknownEncoding()))
with pytest.raises(TypeError):
optimizer.optimize(simple_square_acquisition)
space.parameters.pop()
def test_random_search_acquisition_optimizer(simple_square_acquisition):
space = ParameterSpace([CategoricalParameter('x', OrdinalEncoding(np.arange(0, 100)))])
optimizer = RandomSearchAcquisitionOptimizer(space, 1000)
opt_x, opt_val = optimizer.optimize(simple_square_acquisition)
# ordinal encoding is as integers 1, 2, ...
assert_array_equal(opt_x, np.array([[1.]]))
assert_array_equal(opt_val, np.array([[0.]]))
def test_random_search_acquisition_optimizer_with_context(simple_square_acquisition):
space = ParameterSpace([CategoricalParameter('x', OrdinalEncoding(np.arange(0, 100))),
InformationSourceParameter(10)])
optimizer = RandomSearchAcquisitionOptimizer(space, 1000)
source_encoding = 1
opt_x, opt_val = optimizer.optimize(simple_square_acquisition, {'source': source_encoding})
assert_array_equal(opt_x, np.array([[1., source_encoding]]))
assert_array_equal(opt_val, np.array([[0. + source_encoding]]))
def test_local_search_acquisition_optimizer_with_context(
simple_square_acquisition):
space = ParameterSpace([
CategoricalParameter("x", OrdinalEncoding(np.arange(0, 100))),
InformationSourceParameter(10)
])
optimizer = LocalSearchAcquisitionOptimizer(space, 1000, 3)
source_encoding = 1
opt_x, opt_val = optimizer.optimize(simple_square_acquisition,
{"source": source_encoding})
np.testing.assert_array_equal(opt_x, np.array([[1.0, source_encoding]]))
np.testing.assert_array_equal(opt_val, np.array([[0.0 + source_encoding]]))
def test_ordinal_encoding_rounding(categories):
encoding = OrdinalEncoding(categories)
check_rounding(encoding, np.array([[1]]), np.array([[1]]))
check_rounding(encoding, np.array([[0.5]]), np.array([[1]]))
check_rounding(encoding, np.array([[2]]), np.array([[2]]))
check_rounding(encoding, np.array([[5]]), np.array([[3]]))
with pytest.raises(ValueError):
encoding.round(np.array([1]))
with pytest.raises(ValueError):
encoding.round(np.array([[1, 1]]))
def test_ordinal_encoding(categories):
encoding = OrdinalEncoding(categories)
assert encoding.dimension == 1
assert encoding.get_category([1]) == 'one'
assert encoding.get_encoding('three') == [3]
def test_ordinal_encoding(categories):
encoding = OrdinalEncoding(categories)
assert encoding.dimension == 1
assert encoding.get_category([1]) == "one"
assert encoding.get_encoding("three") == [3]