0.10.5 (2021-03-11)

  • Fix BayesGPR.sample_y(...) applying input warping twice. This also fixes incorrect behavior by PVRS, ThompsonSampling and VarianceReduction.

0.10.4 (2021-02-11)

  • Fix a bug in the predictive variance reduction search (PVRS) acquisition function, where the inputs were not warped correctly.

0.10.3 (2021-02-07)

  • Fix a bug where the output y was not correctly normalized when passed to BayesGPR.sample(...).

  • Fix not adjusting noise_vector when normalize_y=True.

0.10.2 (2020-09-28)

  • Fix divide by zero encountered in log when evaluating acquisition functions without noise.

0.10.1 (2020-09-26)

  • Bump minimum arviz version to 0.10.0.

0.10.0 (2020-09-20)

  • 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.

0.9.3 (2020-09-14)

  • Make default priors for input warping more focused on the identity transform. This fixes issues with overfitting in high noise environments.

0.9.2 (2020-09-04)

  • Fix incorrect recomputation of y mean when using normalize_y=True.

0.9.1 (2020-09-02)

  • Fix calculation of max-value entropy search and make it more robust.

0.9.0 (2020-08-31)

  • Add support for automatic input warping. It can be activated by passing warp_inputs=True to BayesGPR.

0.8.0 (2020-08-09)

  • Add Optimizer.optimum_intervals which computes the highest density intervals for the optimal parameters.

  • BayesGPR has normalize_y now set to True by default.

  • Add option to normalize the optimality gap when computing Optimizer.expected_optimality_gap or Optimizer.probability_of_optimality (activated by default).

  • Optimizer.run now accepts target functions that also return a noise estimate.

  • Optimizer.run accepts the same arguments as Optimizer.tell.

0.7.2 (2020-08-01)

  • Fix guess_priors not correctly adding the prior for the WhiteKernel. It is now called directly in BayesGPR.sample.

0.7.1 (2020-07-28)

  • 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.

0.7.0 (2020-07-26)

  • Replace the default inverse gamma distribution prior for the lengthscales by the round-flat distribution.

  • Fix guess_priors to correctly add kernels with multiple lengthscales.

0.6.0 (2020-05-21)

  • Add Optimizer.expected_optimality_gap which 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.

0.5.0 (2020-05-21)

  • Add Optimizer.probability_of_optimality which estimates the probability that the current global optimum is optimal within a certain tolerance. This can be used to make stopping rules.

0.4.1 (2020-05-19)

  • Update and fix dependencies.

0.4.0 (2020-04-27)

  • Add return_policy parameter to 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).

0.3.3 (2020-03-16)

  • Fix error occuring when an unknown argument was passed to Optimizer.

0.3.0 (2020-03-12)

  • Add predictive variance reduction search criterion. This is the new default acquisition function.

  • Implement BayesSearchCV for 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.

0.2.0 (2020-03-01)

  • Allow user to pass a vector of noise variances to tell, fit and sample. This can be used to warm start the optimization process.

0.1.2 (2020-02-16)

  • Fix the tell method of the optimizer not updating _n_initial_points correctly, when using replace.

0.1.0 (2020-02-01)

  • First release on PyPI.