def test_bounded_bq_raises_exception(base_gp_wrong_kernel):
# wrong kernel embedding
with pytest.raises(ValueError):
BoundedBayesianQuadrature(
base_gp=base_gp_wrong_kernel,
X=base_gp_wrong_kernel.X,
Y=base_gp_wrong_kernel.Y,
lower_bound=np.min(base_gp_wrong_kernel.Y) - 0.5,
)
# both upper and lower bound are given
with pytest.raises(ValueError):
BoundedBayesianQuadrature(
base_gp=base_gp_wrong_kernel,
X=base_gp_wrong_kernel.X,
Y=base_gp_wrong_kernel.Y,
lower_bound=np.min(base_gp_wrong_kernel.Y) - 0.5,
upper_bound=np.min(base_gp_wrong_kernel.Y) - 0.5,
)
# no bound is given
with pytest.raises(ValueError):
BoundedBayesianQuadrature(base_gp=base_gp_wrong_kernel,
X=base_gp_wrong_kernel.X,
Y=base_gp_wrong_kernel.Y)
def test_bounded_bq_raises(gpy_model):
gpy_model, _ = gpy_model
measure = LebesgueMeasure.from_bounds(gpy_model.X.shape[1] * [(0, 1)],
normalized=False)
qrbf = QuadratureRBFLebesgueMeasure(RBFGPy(gpy_model.kern),
measure=measure)
base_gp_wrong_kernel = BaseGaussianProcessGPy(kern=qrbf,
gpy_model=gpy_model)
# wrong kernel embedding
with pytest.raises(ValueError):
BoundedBayesianQuadrature(
base_gp=base_gp_wrong_kernel,
X=base_gp_wrong_kernel.X,
Y=base_gp_wrong_kernel.Y,
lower_bound=np.min(base_gp_wrong_kernel.Y) - 0.5,
)
# both upper and lower bound are given
with pytest.raises(ValueError):
BoundedBayesianQuadrature(
base_gp=base_gp_wrong_kernel,
X=base_gp_wrong_kernel.X,
Y=base_gp_wrong_kernel.Y,
lower_bound=np.min(base_gp_wrong_kernel.Y) - 0.5,
upper_bound=np.min(base_gp_wrong_kernel.Y) - 0.5,
)
# no bound is given
with pytest.raises(ValueError):
BoundedBayesianQuadrature(base_gp=base_gp_wrong_kernel,
X=base_gp_wrong_kernel.X,
Y=base_gp_wrong_kernel.Y)
def bounded_bq_upper(base_gp):
bound = np.max(
base_gp.Y) + 0.5 # make sure bound is larger than the Y values
bounded_bq = BoundedBayesianQuadrature(base_gp=base_gp,
X=base_gp.X,
Y=base_gp.Y,
upper_bound=bound)
return bounded_bq, bound
def bounded_bq_lower(base_gp):
bound = np.min(
base_gp.Y) - 0.5 # make sure bound is lower than the Y values
bounded_bq = BoundedBayesianQuadrature(base_gp=base_gp,
X=base_gp.X,
Y=base_gp.Y,
lower_bound=bound)
return bounded_bq, bound
def integral_mean_from_measure_samples(num_samples: int,
model: BoundedBayesianQuadrature):
samples = model.measure.get_samples(num_samples=num_samples)
mean_at_samples, _ = model.predict(samples)
return np.mean(mean_at_samples)
kernel=GPy.kern.RBF(input_dim=D))
# the measure
measure = IsotropicGaussianMeasure(mean=np.array([0.1, 1.8]), variance=0.8)
# the emukit base GP
qrbf = QuadratureRBFIsoGaussMeasure(RBFGPy(gpy_model.kern),
measure=measure)
base_gp = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)
# the emukit bounded BQ model
if METHOD == "Bounded BQ lower":
bound = np.min(base_gp.Y) - 0.5
model = BoundedBayesianQuadrature(base_gp=base_gp,
X=X,
Y=Y,
bound=bound,
is_lower_bounded=True)
elif METHOD == "Bounded BQ upper":
bound = np.max(base_gp.Y) + 0.5
model = BoundedBayesianQuadrature(base_gp=base_gp,
X=X,
Y=Y,
bound=bound,
is_lower_bounded=False)
elif METHOD == "WSABI-l adapt":
model = WSABIL(base_gp=base_gp,
X=base_gp.X,
Y=base_gp.Y,
adapt_alpha=True)
def get_bounded_bq_upper():
base_gp, dat = get_base_gp()
return BoundedBayesianQuadrature(base_gp=base_gp,
X=dat.X,
Y=dat.Y,
upper_bound=dat.bound_upper)