BayesGPR.sample_y(...)applying input warping twice. This also fixes incorrect behavior by
Fix a bug in the predictive variance reduction search (PVRS) acquisition function, where the inputs were not warped correctly.
Fix a bug where the output
ywas not correctly normalized when passed to
Fix not adjusting
Fix divide by zero encountered in log when evaluating acquisition functions without noise.
Bump minimum arviz version to 0.10.0.
Add new initialization using the Steinerberger sequence. This works better in high-dimensional problems than the R2 sequence.
Fix exception when a categorical parameter is Iterable.
Make default priors for input warping more focused on the identity transform. This fixes issues with overfitting in high noise environments.
Fix incorrect recomputation of y mean when using
Fix calculation of max-value entropy search and make it more robust.
Add support for automatic input warping. It can be activated by passing
Optimizer.optimum_intervalswhich computes the highest density intervals for the optimal parameters.
normalize_ynow set to
Add option to normalize the optimality gap when computing
Optimizer.probability_of_optimality(activated by default).
Optimizer.runnow accepts target functions that also return a noise estimate.
Optimizer.runaccepts the same arguments as
guess_priorsnot correctly adding the prior for the
WhiteKernel. It is now called directly in
Restrict length scale bounds of the default kernel to a tighter interval. This should help start the MCMC walkers in a region with higher likelihood.
Replace the default inverse gamma distribution prior for the lengthscales by the round-flat distribution.
guess_priorsto correctly add kernels with multiple lengthscales.
Optimizer.expected_optimality_gapwhich estimates the expected optimality gap of the current global optimum to random optima sampled from the Gaussian process.
Check that the list of priors has the correct length.
Require emcee to be at least version 3.0.
Optimizer.probability_of_optimalitywhich estimates the probability that the current global optimum is optimal within a certain tolerance. This can be used to make stopping rules.
Update and fix dependencies.
BayesSearchCV. Allows the user to choose between returning the best observed configuration (in a noise-less setting) or the best predicted configuration (for noisy targets).
Fix error occuring when an unknown argument was passed to
Add predictive variance reduction search criterion. This is the new default acquisition function.
BayesSearchCVfor use with scikit-learn estimators and pipelines. This is an easy to use drop-in replacement for GridSearchCV or RandomSearchCV. It is implemented as a wrapper around skopt.BayesSearchCV.
Determine default kernels and priors to use, if the user provides none.
Add example notebooks on how to use the library.
Add API documentation of the library.
Allow user to pass a vector of noise variances to
sample. This can be used to warm start the optimization process.
tellmethod of the optimizer not updating
_n_initial_pointscorrectly, when using replace.
First release on PyPI.