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_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_design_with_mixed_domain(encoding):
p1 = ContinuousParameter("p1", 1.0, 5.0)
p2 = CategoricalParameter("p2", encoding)
p3 = DiscreteParameter("p3", [1, 2, 5, 6])
space = ParameterSpace([p1, p2, p3])
points_count = 5
designs = create_model_free_designs(space)
for design in designs:
points = design.get_samples(points_count)
assert points_count == len(points)
# columns count is 1 for continuous plus 1 for discrete plus number of categories
columns_count = 1 + 1 + len(encoding.categories)
assert all([len(p) == columns_count for p in points])
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 test_single_value_in_domain_discrete_parameter():
param = DiscreteParameter('x', [0, 1, 2])
assert param.check_in_domain(0) is True
assert param.check_in_domain(3) is False
def test_discrete_parameter_str_repr():
param = DiscreteParameter('x', [0, 1, 2])
_ = str(param)
_ = repr(param)
def test_discrete_parameter():
param = DiscreteParameter('x', [0, 1, 2])
assert param.name == 'x'
assert param.check_in_domain(np.array([0, 1])) is True
assert param.check_in_domain(np.array([3])) is False
assert param.check_in_domain(np.array([[1], [0]])) is True
assert param.check_in_domain([1, 0]) is True
assert param.check_in_domain(1) is True
assert param.check_in_domain(0.5) is False
with pytest.raises(ValueError): # too many columns
param.check_in_domain(np.array([[1, 0], [0, 2]]))
with pytest.raises(ValueError): # not a 1d/2d array
param.check_in_domain(np.array([[[1]]]))
# Parameter Space Options
__C.PARAMETERS_OPTS = edict()
__C.PARAMETERS_OPTS.GRID_SIZE = [
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
]
__C.PARAMETERS_OPTS.EDGE_MAX_SPEED = [8, 25] # 28.8 km/h to 90 km/h
__C.PARAMETERS_OPTS.MAX_SPEED = [5, 27] # 18 km/h to 144 km/h
__C.PARAMETERS_OPTS.EDGE_LENGTH = [30, 70] # 30 meters to 150 meters
__C.PARAMETERS_OPTS.NUM_LANES = [1, 2, 3]
__C.PARAMETERS_OPTS.ACCEL = [1.5, 5] # 1.5 m/s^2 to 5 m/s^2
# Parameters
__C.PARAMETERS = edict()
__C.PARAMETERS.GRID_SIZE = DiscreteParameter(
'gridSize', domain=__C.PARAMETERS_OPTS.GRID_SIZE)
__C.PARAMETERS.EDGE_MAX_SPEED = ContinuousParameter(
'edgeMaxSpeed',
min_value=min(__C.PARAMETERS_OPTS.EDGE_MAX_SPEED),
max_value=max(__C.PARAMETERS_OPTS.EDGE_MAX_SPEED))
__C.PARAMETERS.MAX_SPEED = ContinuousParameter(
'maxSpeed',
min_value=min(__C.PARAMETERS_OPTS.MAX_SPEED),
max_value=max(__C.PARAMETERS_OPTS.MAX_SPEED))
__C.PARAMETERS.EDGE_LENGTH = ContinuousParameter(
'edgeLength',
min_value=min(__C.PARAMETERS_OPTS.EDGE_LENGTH),
max_value=max(__C.PARAMETERS_OPTS.EDGE_LENGTH))
__C.PARAMETERS.NUM_LANES = DiscreteParameter(
'numberOfLanes', domain=__C.PARAMETERS_OPTS.NUM_LANES)
__C.PARAMETERS.ACCEL = ContinuousParameter(
def get_parameter(name: str, mode: str, idx: int):
if mode == 'locked':
return DiscreteParameter(name, domain=[df.iloc[0][idx]])
else:
return __C['PARAMETERS'][name]
def test_single_value_in_domain_discrete_parameter():
param = DiscreteParameter('x', [0, 1, 2])
assert param.check_in_domain(0) is True
assert param.check_in_domain(3) is False
def test_discrete_parameter():
param = DiscreteParameter('x', [0, 1, 2])
assert param.name == 'x'
assert param.check_in_domain(np.array([0, 1])) is True
assert param.check_in_domain(np.array([3])) is False
assert param.check_in_domain(np.array([[1], [0]])) is True
assert param.check_in_domain([1, 0]) is True
assert param.check_in_domain(1) is True
assert param.check_in_domain(0.5) is False
with pytest.raises(ValueError): # too many columns
param.check_in_domain(np.array([[1, 0], [0, 2]]))
with pytest.raises(ValueError): # not a 1d/2d array
param.check_in_domain(np.array([[[1]]]))
# configs which survive to the next generation (and sorted)
T = [ T[i] for i in np.argsort(val_losses)[0:int( n_i/eta )]]
# the loss of the incumbent is simply the min of val_losses.
incumbent_loss = np.min(val_losses)
# return the first element of the survived config (which is the incumbent) and its loss
return T[0], incumbent_loss
if __name__ == '__main__':
# define optimized hyperparameters
list_params = []
list_params.append(ContinuousParameter("brightnessFactor", 0.6,1))
list_params.append(ContinuousParameter("colorFactor", 0, 1))
list_params.append(ContinuousParameter("sharpnessFactor", 0, 2))
list_params.append(DiscreteParameter("gaussianFactor", [0, 5]))
list_params.append(ContinuousParameter("saltPepperFactor", 0,0.05))
list_params.append(DiscreteParameter("cutoutWidth", [0, 20]))
list_params.append(DiscreteParameter("cutoutHeight", [0, 20]))
list_params.append(DiscreteParameter("cutoutAreas", [0, 10]))
# list_params.append(ContinuousParameter("minCrop", 0, 1))
# list_params.append(ContinuousParameter("maxCrop", 0, 1))
# list_params.append(ContinuousParameter("stayFactor", 0, 1))
list_params.append(DiscreteParameter("cropLeft", [0, 300]))
list_params.append(DiscreteParameter("cropTop", [0, 300]))
list_params.append(DiscreteParameter("cropRight", [0, 300]))
list_params.append(DiscreteParameter("cropBottom", [0, 300]))
# list_params.append(DiscreteParameter("rotateFactor", [0, 360]))
list_params.append(DiscreteParameter("rotateFactor", [0, 25]))
list_params.append(ContinuousParameter("scaleFactor", 0, 1))
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])
def test_discrete_parameter():
param = DiscreteParameter('x', [0, 1, 2])
assert param.name == 'x'
assert param.check_in_domain(np.array([0, 1])) is True
assert param.check_in_domain(np.array([3])) is False
