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CHANGELOG.md

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Version 0.4

Go forth!

New features

  • Support early stopping of optimization loop.
  • Benchmarking scripts to evaluate performance of different surrogate models.
  • Support for parallel evaluations of the objective function via several constant liar stategies.
  • BayesSearchCV as a drop in replacement for scikit-learn's GridSearchCV.
  • New acquisition functions "EIps" and "PIps" that takes into account function compute time.

Bug fixes

  • Fixed inference of dimensions of type Real.

API changes

  • Change interface of GradientBoostingQuantileRegressor's predict method to match return type of other regressors
  • Dimensions of type Real are now inclusive of upper bound.

Version 0.3

Third time's a charm.

New features

  • Accuracy improvements of the optimization of the acquisition function by pre-selecting good candidates as starting points when using acq_optimizer='lbfgs'.
  • Support a ask-and-tell interface. Check out the Optimizer class if you need fine grained control over the iterations.
  • Parallelize L-BFGS minimization runs over the acquisition function.
  • Implement weighted hamming distance kernel for problems with only categorical dimensions.
  • New acquisition function gp_hedge that probabilistically chooses one of EI, PI or LCB at every iteration depending upon the cumulative gain.

Bug fixes

  • Warnings are now raised if a point is chosen as the candidate optimum multiple times.
  • Infinite gradients that were raised in the kernel gradient computation are now fixed.
  • Integer dimensions are now normalized to [0, 1] internally in gp_minimize.

API Changes.

  • The default acq_optimizer function has changed from "auto" to "lbfgs" in gp_minimize.

Version 0.2

New features

  • Speed improvements when using gp_minimize with acq_optimizer='lbfgs' and acq_optimizer='auto' when all the search-space dimensions are Real.
  • Persistence of minimization results using skopt.dump and skopt.load.
  • Support for using arbitrary estimators that implement a return_std argument in their predict method by means of base_minimize from skopt.optimizer.
  • Support for tuning noise in gp_minimize using the noise argument.
  • TimerCallback in skopt.callbacks to log the time between iterations of the minimization loop.

Version 0.1

First light!

New features

  • Bayesian optimization via gp_minimize.
  • Tree-based sequential model-based optimization via forest_minimize and gbrt_minimize, with support for multi-threading.
  • Support of LCB, EI and PI as acquisition functions.
  • Plotting functions for inspecting convergence, evaluations and the objective function.
  • API for specifying and sampling from a parameter space.

Contributors

See AUTHORS.md.