Release 0.11.0 · emdgroup/baybe

What's Changed

The public methods of Surrogate models now operate on dataframes in experimental
representation instead of tensors in computational representation
Surrogate.posterior models now returns a Posterior object
param_bounds_comp of SearchSpace, SubspaceDiscrete and SubspaceContinuous has
been replaced with comp_rep_bounds, which returns a dataframe

py.typed file to enable the use of type checkers on the user side
IndependentGaussianSurrogate base class for surrogate models providing independent
Gaussian posteriors for all candidates (cannot be used for batch prediction)
comp_rep_columns property for Parameter, SearchSpace, SubspaceDiscrete
and SubspaceContinuous classes
New mechanisms for surrogate input/output scaling configurable per class
SurrogateProtocol as an interface for user-defined surrogate architectures
Support for binary targets via BinaryTarget class
Support for bandit optimization via BetaBernoulliMultiArmedBanditSurrogate class
Bandit optimization example
qThompsonSampling acquisition function
BetaPrior class
recommend now accepts the pending_experiments argument, informing the algorithm
about points that were already selected for evaluation
Pure recommenders now have the allow_recommending_pending_experiments flag,
controlling whether pending experiments are excluded from candidates in purely
discrete search spaces
get_surrogate and posterior methods to Campaign
tenacity test dependency
Multi-version documentation

The transition from experimental to computational representation no longer happens
in the recommender but in the surrogate
Fallback models created by catch_constant_targets are stored outside the surrogate
to_tensor now also handles numpy arrays
MIN mode of NumericalTarget is now implemented via the acquisition function
instead of negating the computational representation
Search spaces now store their parameters in alphabetical order by name
Improvement-based acquisition functions now consider the maximum posterior mean
instead of the maximum noisy measurement as reference value
Iteration tests now attempt up to 5 repeated executions if they fail due to numerical
reasons

CategoricalParameter and TaskParameter no longer incorrectly coerce a single
string input to categories/tasks
farthest_point_sampling no longer depends on the provided point order
Batch predictions for RandomForestSurrogate
Surrogates providing only marginal posterior information can no longer be used for
batch recommendation
SearchSpace.from_dataframe now creates a proper empty discrete subspace without
index when called with continuous parameters only
Metadata updates are now only triggered when a discrete subspace is present
Unintended reordering of discrete search space parts for recommendations obtained
with BotorchRecommender

The role of register_custom_architecture has been taken over by
baybe.surrogates.base.SurrogateProtocol
BayesianRecommender.surrogate_model has been replaced with get_surrogate

Full Changelog: 0.10.0...0.11.0