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mlr3mbo (development version)

mlr3mbo 0.2.8

  • docs: gracefully exit examples of OptimizerAsyncMbo, OptimizerADBO, TunerAsyncMbo, and TunerADBO if Redis is not available.
  • test: skip tests involving asynchronous logic if Redis is not available.

mlr3mbo 0.2.7

  • refactor: refactored SurrogateLearner and SurrogateLearnerCollection to allow updating on an asynchronous Archive.
  • feat: added experimental OptimizerAsyncMbo, OptimizerADBO, TunerAsyncMbo, and TunerADBO that allow for asynchronous optimization.
  • feat: added AcqFunctionStochasticCB and AcqFunctionStochasticEI that are useful for asynchronous optimization.
  • docs: minor changes to highlight differences between batch and asynchronous objects related to asynchronous support.
  • refactor: AcqFunctions and AcqOptimizer gained a reset() method.

mlr3mbo 0.2.6

  • refactor: Extract internal tuned values in instance.

mlr3mbo 0.2.5

  • docs: Move vignette to mlr3book.
  • feat: Add AcqFunctionMulti that can wrap multiple acquisition functions resulting in a multi-objective acquisition function problem.
  • feat: Support callbacks in AcqOptimizer.
  • feat: Allow AcqFunctionEI to be adjusted by epsilon to strengthen exploration.

mlr3mbo 0.2.4

  • fix: Improve runtime of AcqOptimizer by setting check_values = FALSE.

mlr3mbo 0.2.3

  • compatibility: Work with new bbotk and mlr3tuning version 1.0.0.

mlr3mbo 0.2.2

  • refactor: compatibility with upcoming paradox upgrade.
  • feat: OptimizerMbo and TunerMbo now update the Surrogate a final time after the optimization process finished to ensure that the Surrogate correctly reflects the state of being trained on all data seen during optimization.
  • fix: AcqFunction domain construction now respects Surrogate cols_x field.
  • feat: support more than one candidate point as a result of acquisition function optimization even for non-batch acquisition functions.
  • feat: added default_gp and default_rf helpers that allow for construction of a default Gaussian Process and random forest as for example used within default_surrogate.
  • refactor: changed Gaussian Process and random forest defaults (in default_gp and default_rf and therefore also in default_surrogate). Gaussian Process now uses a "matern5_2" kernel. Random forest now uses 100 trees. The number of trees used in the fallback random forest was reduced to 10.

mlr3mbo 0.2.1

  • docs: updated some references in vignette.
  • refactor: minor clean up of the internal structure of all loop functions.
  • perf: default initial design constructed based on a Sobol sequence in all loop functions.
  • refactor: no longer depend on mlr3tuning but import instead.
  • refactor: srlrn sugar function now can construct both a SurrogateLearner and SurrogateLearnerCollection; dropped srlrnc.
  • feat: added AcqFunctionSD, AcqFunctionEHVI and AcqFunctionEHVIGH, introduced bayesopt_emo loop function.
  • feat: AcqFunctions now include a $packages field stating required packages which are checked for whether their namespace can be loaded prior to optimization.
  • fix: fixed bug in fix_xdt_missing() helper function.
  • BREAKING CHANGE: renaming default_loopfun -> default_loop_function, default_acqfun -> default_acqfunction, default_acqopt -> default_acqoptimizer.
  • BREAKING CHANGE: result_functions now replaced by ResultAssigners.
  • BREAKING CHANGE: renamed $model field of all Surrogate classes to $learner.
  • BREAKING CHANGE: For all Surrogate and AcquisitionFunction classes fields *_cols renamed to cols_* (e.g., x_cols to cols_x).

mlr3mbo 0.1.2

  • refactor: adapt to mlr3tuning 0.18.0.
  • feat: Acquisition functions now assert whether surrogates match their required predict type.
  • fix: Unloading mlr3mbo removes optimizers and tuners from the dictionaries.
  • docs: faster examples.
  • feat: characters in surrogate regression tasks are no longer automatically converted to factors. default_surrogate now respects this and gained an appropriate pipeline step.
  • feat: AcqFunctionAEI added.
  • docs: fix of docs, README and bibentries.

mlr3mbo 0.1.1

  • Initial upload to CRAN.