Expose random seed for MaxValueSearch

[kiudee]

bayes-skopt/bask/acquisition.py

Lines 215 to 257 in 1f77d51

	class MaxValueSearch(UncertaintyAcquisition):
	"""Select points based on their mutual information with the optimum value.
	Parameters
	----------
	n_min_samples : int, default=1000
	Number of samples for the optimum distribution
	References
	----------
	[1] Wang, Z. & Jegelka, S.. (2017). Max-value Entropy Search for Efficient
	Bayesian Optimization. Proceedings of the 34th International Conference
	on Machine Learning, in PMLR 70:3627-3635
	"""
	def __call__(self, mu, std, *args, n_min_samples=1000, **kwargs):
	def probf(x):
	return np.exp(np.sum(st.norm.logcdf((x - mean) / std), axis=0))
	# Negative sign, since the original algorithm is defined in terms of the maximum
	mean = -mu
	left = np.min(mean - 3 * std)
	right = np.max(mean + 5 * std)
	# Binary search for 3 percentiles
	q1, med, q2 = [
	brentq(lambda x: probf(x) - val, left, right,) for val in [0.25, 0.5, 0.75]
	]
	beta = (q1 - q2) / (np.log(np.log(4.0 / 3.0)) - np.log(np.log(4.0)))
	alpha = med + beta * np.log(np.log(2.0))
	max_values = (
	-np.log(-np.log(np.random.rand(n_min_samples).astype(np.float32))) * beta
	+ alpha
	)
	gamma = (max_values[None, :] - mean[:, None]) / std[:, None]
	return (
	np.sum(
	gamma * st.norm().pdf(gamma) / (2.0 * st.norm().cdf(gamma))
	- st.norm().logcdf(gamma),
	axis=1,
	)
	/ n_min_samples
	)