def test_min_value_entropy_search_chooses_same_as_probability_of_improvement(
) -> None:
"""
When based on a single max-value sample, MES should choose the same point that probability of
improvement would when calcualted with the max-value as its threshold (See :cite:`wang2017max`).
"""
kernel = tfp.math.psd_kernels.MaternFiveHalves()
model = GaussianProcess([branin], [kernel])
x_range = tf.linspace(0.0, 1.0, 11)
x_range = tf.cast(x_range, dtype=tf.float64)
xs = tf.reshape(
tf.stack(tf.meshgrid(x_range, x_range, indexing="ij"), axis=-1),
(-1, 2))
gumbel_sample = tf.constant([1.0], dtype=tf.float64)
mes_evals = min_value_entropy_search(model, gumbel_sample, xs)
mean, variance = model.predict(xs)
gamma = (tf.cast(gumbel_sample, dtype=mean.dtype) -
mean) / tf.sqrt(variance)
norm = tfp.distributions.Normal(tf.cast(0, dtype=mean.dtype),
tf.cast(1, dtype=mean.dtype))
pi_evals = norm.cdf(gamma)
npt.assert_array_equal(tf.argmax(mes_evals), tf.argmax(pi_evals))
def test_min_value_entropy_search_raises_for_invalid_batch_size(
at: TensorType) -> None:
mes = min_value_entropy_search(QuadraticMeanAndRBFKernel(),
tf.constant([[1.0], [2.0]]))
with pytest.raises(TF_DEBUGGING_ERROR_TYPES):
mes(at)
def test_min_value_entropy_search_returns_correct_shape() -> None:
model = QuadraticMeanAndRBFKernel()
gumbel_samples = tf.constant([1.0])
query_at = tf.linspace([[-10.0]], [[10.0]], 5)
evals = min_value_entropy_search(model, gumbel_samples)(query_at)
npt.assert_array_equal(evals.shape, tf.constant([5, 1]))
def test_min_value_entropy_search_raises_for_gumbel_samples_with_invalid_shape(
samples: TensorType,
) -> None:
with pytest.raises(ValueError):
min_value_entropy_search(QuadraticMeanAndRBFKernel(), samples)