def catg_space():
return ParameterSpace([
ContinuousParameter('x1', 0, 15),
CategoricalParameter('x2', OneHotEncoding([0, 1, 2, 3])),
CategoricalParameter('x3', OneHotEncoding([1, 2, 3, 4, 5])),
ContinuousParameter('x4', -2, 3)
])
def catg_space():
return ParameterSpace([
ContinuousParameter("x1", 0, 15),
CategoricalParameter("x2", OneHotEncoding(["A", "B", "C", "D"])),
CategoricalParameter("x3", OneHotEncoding([1, 2, 3, 4, 5])),
ContinuousParameter("x4", -2, 3),
])
def test_categorical_variables():
np.random.seed(123)
def objective(x):
return np.array(np.sum(x, axis=1).reshape(-1, 1))
carol_spirits = ['past', 'present', 'yet to come']
encoding = OneHotEncoding(carol_spirits)
parameter_space = ParameterSpace([
ContinuousParameter('real_param', 0.0, 1.0),
CategoricalParameter('categorical_param', encoding)
])
random_design = RandomDesign(parameter_space)
x_init = random_design.get_samples(10)
assert x_init.shape == (10, 4)
assert np.all(np.logical_or(x_init[:, 1:3] == 0.0, x_init[:, 1:3] == 1.0))
y_init = objective(x_init)
gpy_model = GPy.models.GPRegression(x_init, y_init)
gpy_model.Gaussian_noise.fix(1)
model = GPyModelWrapper(gpy_model)
acquisition = ExpectedImprovement(model)
loop = BayesianOptimizationLoop(parameter_space, model, acquisition)
loop.run_loop(objective, 5)
assert len(loop.loop_state.Y) == 15
assert np.all(
np.logical_or(loop.loop_state.X[:, 1:3] == 0.0,
loop.loop_state.X[:, 1:3] == 1.0))
def test_categorical_variables():
np.random.seed(123)
def objective(x):
return np.array(np.sum(x, axis=1).reshape(-1, 1))
carol_spirits = ["past", "present", "yet to come"]
encoding = OneHotEncoding(carol_spirits)
parameter_space = ParameterSpace(
[ContinuousParameter("real_param", 0.0, 1.0), CategoricalParameter("categorical_param", encoding)]
)
random_design = LatinDesign(parameter_space)
x_init = random_design.get_samples(10)
assert x_init.shape == (10, 4)
assert np.all(np.logical_or(x_init[:, 1:3] == 0.0, x_init[:, 1:3] == 1.0))
y_init = objective(x_init)
gpy_model = GPy.models.GPRegression(x_init, y_init)
gpy_model.Gaussian_noise.fix(1)
model = GPyModelWrapper(gpy_model)
loop = ExperimentalDesignLoop(parameter_space, model)
loop.run_loop(objective, 5)
assert len(loop.loop_state.Y) == 15
assert np.all(np.logical_or(loop.loop_state.X[:, 1:3] == 0.0, loop.loop_state.X[:, 1:3] == 1.0))
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_check_in_domain_with_bandit_parameter():
mixed_space_with_bandit = ParameterSpace([
ContinuousParameter("c", 1.0, 5.0),
DiscreteParameter("d", [0, 1, 2]),
CategoricalParameter("cat", OneHotEncoding(["blue", "red"])),
BanditParameter("bandit", np.array([[0, 1], [1, 1], [1.0, 0]])),
])
x_test = np.array([[1.5, 0, 1.0, 0.0, 0, 1], [1.5, 0, 1.0, 0.0, 0.0, 0.0]])
in_domain = mixed_space_with_bandit.check_points_in_domain(x_test)
assert np.array_equal(in_domain, np.array([True, False]))
def test_one_hot_encoding(categories):
encoding = OneHotEncoding(categories)
assert encoding.dimension == 3
assert encoding.get_category([1, 0, 0]) == 'one'
assert encoding.get_encoding('three') == [0, 0, 1]
with pytest.raises(ValueError):
encoding.get_category([1, 1, 0])
with pytest.raises(ValueError):
encoding.get_encoding("four")
def test_gradient_acquisition_optimizer_categorical(simple_square_acquisition):
space = ParameterSpace([
ContinuousParameter("x", 0, 1),
CategoricalParameter("y", OneHotEncoding(["A", "B"]))
])
optimizer = GradientAcquisitionOptimizer(space)
context = {"y": "B"}
opt_x, opt_val = optimizer.optimize(simple_square_acquisition, context)
assert_array_equal(opt_x, np.array([[0.0, 0.0, 1.0]]))
assert_array_equal(opt_val, np.array([[2.0]]))
def test_gradient_acquisition_optimizer_categorical(simple_square_acquisition):
space = ParameterSpace([
ContinuousParameter('x', 0, 1),
CategoricalParameter('y', OneHotEncoding(['A', 'B']))
])
optimizer = GradientAcquisitionOptimizer(space)
context = {'y': 'B'}
opt_x, opt_val = optimizer.optimize(simple_square_acquisition, context)
assert_array_equal(opt_x, np.array([[0., 0., 1.]]))
assert_array_equal(opt_val, np.array([[2.]]))
def test_check_in_domain_with_bandit_parameter():
mixed_space_with_bandit = ParameterSpace([
ContinuousParameter('c', 1.0, 5.0),
DiscreteParameter('d', [0, 1, 2]),
CategoricalParameter('cat', OneHotEncoding(['blue', 'red'])),
BanditParameter('bandit', np.array([[0, 1], [1, 1], [1., 0]]))
])
x_test = np.array([[1.5, 0, 1., 0., 0, 1], [1.5, 0, 1., 0., 0., 0.]])
in_domain = mixed_space_with_bandit.check_points_in_domain(x_test)
assert np.array_equal(in_domain, np.array([True, False]))
def test_one_hot_encoding_rounding(categories):
encoding = OneHotEncoding(categories)
check_rounding(encoding, np.array([[1, 0, 0]]), np.array([[1, 0, 0]]))
check_rounding(encoding, np.array([[0.5, 0.1, 0.1]]), np.array([[1, 0, 0]]))
check_rounding(encoding, np.array([[2, 0, 1.5]]), np.array([[1, 0, 0]]))
check_rounding(encoding, np.array([[0.7, 0.75, 0.5]]), np.array([[0, 1, 0]]))
with pytest.raises(ValueError):
encoding.round(np.array([1, 0, 0]))
with pytest.raises(ValueError):
encoding.round(np.array([[1, 0]]))
def encoding():
# different types of volcanoes
return OneHotEncoding(["strato", "shield", "dome"])
def test_get_bounds():
p1 = ContinuousParameter('c', 1.0, 5.0)
p2 = DiscreteParameter('d', [1, 2, 3])
p3 = CategoricalParameter('cat', OneHotEncoding(['Maine Coon', 'Siamese']))
space = ParameterSpace([p1, p2, p3])
assert space.get_bounds() == [(1., 5.), (1., 3.), (0, 1), (0, 1)]
def encoding():
# different types of volcanoes
return OneHotEncoding(['strato', 'shield', 'dome'])
modelOptions.withScheduleCompression=True
#modelOptions.interface = "VAE"
model = generateModel(modelOptions)
'''define parameter space'''
actionSpace = model.getActionSpace()
action = ActionSpace.Action(actionSpace)
parameterList = []
encodingList = []
groupNumber = 0
for space in actionSpace.spaces:
if isinstance(space, spaces.MultiDiscrete):
for index, noVariants in enumerate(space.nvec):
options = range(noVariants)
encoding = OneHotEncoding(options)
encodingList.append(encoding)
categoricalParam = CategoricalParameter('Param_%d_%d' % (groupNumber,index), encoding)
parameterList.append(categoricalParam)
elif isinstance(space, spaces.Box):
if space.is_bounded():
for index, low, high in zip(range(space.shape[0]), space.low, space.high):
realParam = ContinuousParameter('Param_%d_%d' % (groupNumber, index), low, high)
parameterList.append(realParam)
else:
for index in range(space.shape[0]):
realParam = ContinuousParameter('Param_%d_%d' % (groupNumber, index),-5,5)#no unbounded param possible
parameterList.append(realParam)
else:
raise NotImplementedError
def space_3d_mixed():
p1 = ContinuousParameter('c', 1.0, 5.0)
p2 = DiscreteParameter('d', [1, 2, 3])
p3 = CategoricalParameter('cat', OneHotEncoding(['Maine Coon', 'Siamese']))
return ParameterSpace([p1, p2, p3])
def space_3d_mixed():
p1 = ContinuousParameter("c", 1.0, 5.0)
p2 = DiscreteParameter("d", [1, 2, 3])
p3 = CategoricalParameter("cat", OneHotEncoding(["Maine Coon", "Siamese"]))
return ParameterSpace([p1, p2, p3])