def test_sample_from_prior(self):
prior = TophatPrior(lower_bound=np.exp(-10), upper_bound=np.exp(2), rng=np.random.RandomState(1))
samples = prior.sample_from_prior(10)
np.testing.assert_array_equal(samples >= -10, True)
np.testing.assert_array_equal(samples <= 2, True)
# Test that the rng is set
self.assertAlmostEqual(samples[0], -4.995735943569112)
class DefaultPrior(BasePrior):
def __init__(self, n_dims: int, rng: np.random.RandomState = None):
"""
This class is a verbatim copy of the implementation of RoBO:
Klein, A. and Falkner, S. and Mansur, N. and Hutter, F.
RoBO: A Flexible and Robust Bayesian Optimization Framework in Python
In: NIPS 2017 Bayesian Optimization Workshop
"""
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng
# The number of hyperparameters
self.n_dims = n_dims
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-10, 2, rng=self.rng)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=0.0, sigma=1.0, rng=self.rng)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.1, rng=self.rng)
def lnprob(self, theta: np.ndarray):
lp = 0
# Covariance amplitude
lp += self.ln_prior.lnprob(theta[0])
# Lengthscales
lp += self.tophat.lnprob(theta[1:-1])
# Noise
lp += self.horseshoe.lnprob(theta[-1])
return lp
def sample_from_prior(self, n_samples: int):
p0 = np.zeros([n_samples, self.n_dims])
# Covariance amplitude
p0[:, 0] = self.ln_prior.sample_from_prior(n_samples)[:, 0]
# Lengthscales
ls_sample = np.array([
self.tophat.sample_from_prior(n_samples)[:, 0]
for _ in range(1, (self.n_dims - 1))
]).T
p0[:, 1:(self.n_dims - 1)] = ls_sample
# Noise
p0[:, -1] = self.horseshoe.sample_from_prior(n_samples)[:, 0]
return p0
def gradient(self, theta: np.ndarray):
# TODO: Implement real gradient here
return np.zeros([theta.shape[0]])
def test_sample_from_prior_shapes(self):
rng = np.random.RandomState(1)
lower_bound = 2 + rng.random_sample() * 50
upper_bound = lower_bound + rng.random_sample() * 50
prior = TophatPrior(lower_bound=lower_bound, upper_bound=upper_bound)
sample = prior.sample_from_prior(1)
self.assertEqual(sample.shape, (1, ))
sample = prior.sample_from_prior(2)
self.assertEqual(sample.shape, (2, ))
sample = prior.sample_from_prior(10)
self.assertEqual(sample.shape, (10, ))
with self.assertRaises(ValueError):
prior.sample_from_prior(0)
with self.assertRaises(ValueError):
prior.sample_from_prior((2, ))
def __init__(self, n_dims: int, rng: np.random.RandomState = None):
"""
This class is a verbatim copy of the implementation of RoBO:
Klein, A. and Falkner, S. and Mansur, N. and Hutter, F.
RoBO: A Flexible and Robust Bayesian Optimization Framework in Python
In: NIPS 2017 Bayesian Optimization Workshop
"""
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng
# The number of hyperparameters
self.n_dims = n_dims
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-10, 2, rng=self.rng)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=0.0, sigma=1.0, rng=self.rng)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.1, rng=self.rng)
def test_lnprob_and_grad_scalar(self):
prior = TophatPrior(lower_bound=np.exp(-10), upper_bound=np.exp(2))
# Legal scalar
for val in (-1, 0, 1):
self.assertEqual(prior.lnprob(val), 0, msg=str(val))
self.assertEqual(prior.gradient(val), 0, msg=str(val))
# Boundary
for val in (-10, 2):
self.assertEqual(prior.lnprob(val), 0)
self.assertEqual(prior.gradient(val), 0)
# Values outside the boundary
for val in (-10 - VERY_SMALL_NUMBER, 2 + VERY_SMALL_NUMBER, -50, 50):
self.assertTrue(np.isinf(prior.lnprob(val)))
self.assertEqual(prior.gradient(val), 0)