0.11.3

Hotfix Release

Fixed

• protobuf dependency issue, version pin was removed

0.11.2

What's Changed

Added

• n_restarts and n_raw_samples keywords to configure continuous optimization
  behavior for BotorchRecommender
• User guide for utilities

Changed

• Utility add_fake_results renamed to add_fake_measurements
• Utilities add_fake_measurements and add_parameter_noise now also return the
  dataframe they modified in-place

Fixed

• Leftover attrs-decorated classes are garbage collected before the subclass tree is
  traversed, avoiding sporadic serialization problems

Merged Pull Requests

• Subclass garbage collection by @AdrianSosic in #397
• Update known_issues.md by @Scienfitz in #390
• Update CONTRIBUTING.md by @Scienfitz in #395
• Add Continuous Optimization Controls to BotorchRecommender by @Scienfitz in #389
• Add Explanation of some Utilities to User Guide by @Scienfitz in #392
• Release 0.11.2 by @Scienfitz in #401

Full Changelog: 0.11.1...0.11.2

0.11.1

What's Changed

Added

• Continuous linear constraints have been consolidated in the new
  ContinuousLinearConstraint class

Changed

• get_surrogate now also returns the model for transformed single targets or
  desirability objectives

Fixed

• Unsafe name-based matching of columns in get_comp_rep_parameter_indices

Deprecated

• ContinuousLinearEqualityConstraint and ContinuousLinearInequalityConstraint
  replaced by ContinuousLinearConstraint with the corresponding operator keyword

Merged Pull Requests

• Speedup Bernoulli Multi-Armed Bandit Example by @julianStreibel in #380
• Fix intersphinx link for torch by @AVHopp in #382
• Fix Name Matching for Retrieving Comp Rep Indices by @Scienfitz in #379
• Remove get_surrogate Restrictions by @Scienfitz in #386
• Consolidate Continuous Linear Constraints by @Scienfitz in #384
• Release 0.11.1 by @Scienfitz in #388

Full Changelog: 0.11.0...0.11.1

0.11.0

What's Changed

Breaking Changes

• 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

Added

• 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

Changed

• 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

Fixed

• 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

Removed

• register_custom_architecture decorator
• Scalar and DefaultScaler classes

Deprecations

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

Merged Pull Requests

• Comp Rep Transition Point by @AdrianSosic in #278
• Surrogate Posteriors by @AdrianSosic in #309
• Surrogate scaling by @AdrianSosic in #315
• Surrogate interface by @AdrianSosic in #325
• Mypy for surrogates by @AdrianSosic in #337
• Add py.typed by @AdrianSosic in #342
• Lockfile for doc generation by @AdrianSosic in #323
• Fix parameter value conversion by @AdrianSosic in #259
• Improve FPS by @AdrianSosic in #336
• Refactor Surrogates by @AdrianSosic in #338
• Silence mypy type-abstract errors by @AdrianSosic in #354
• Hotfix: Remove python version in tox docs calls by @AVHopp in #351
• Fix local doc building by @AVHopp in #339
• MIN mode via acquisition function by @AdrianSosic in #340
• Fix batch recommendation by @AdrianSosic in #348
• Remove token from publishing pipelines by @Scienfitz in #361
• Parameter permutation by @AdrianSosic in #341
• Add workflow permissions by @Scienfitz in #362
• Best_f from posterior means by @AdrianSosic in #350
• Fix SubspaceDiscrete.from_dataframe edge case by @AdrianSosic in #360
• Multi-Armed Bandit by @julianStreibel in #343
• Fix visuals in examples by @AVHopp in #347
• Enable Pending Points by @Scienfitz in #319
• Fix Recommendation Order for BotorchRecommender by @Scienfitz in #370
• Expose surrogate by @AdrianSosic in #355
• Add iteration test retries by @Scienfitz in #374
• Multi-version documentation by @AVHopp in #317
• Fix broken links to stable by @AVHopp in #377
• Release 0.11.0 by @Scienfitz in #376

New Contributors

• @julianStreibel made their first contribution in #343

Full Changelog: 0.10.0...0.11.0

0.10.0

What's Changed

Breaking Changes

• Providing an explicit batch_size is now mandatory when asking for recommendations
• RecommenderProtocol.recommend now accepts an optional Objective
• RecommenderProtocol.recommend now expects training data to be provided as a single
  dataframe in experimental representation instead of two separate dataframes in
  computational representation
• Parameter.is_numeric has been replaced with Parameter.is_numerical
• DiscreteParameter.transform_rep_exp2comp has been replaced with
  DiscreteParameter.transform
• filter_attributes has been replaced with match_attributes

Added

• Surrogate base class now exposes a to_botorch method
• SubspaceDiscrete.to_searchspace and SubspaceContinuous.to_searchspace
  convenience constructor
• Validators for Campaign attributes
• _optional subpackage for managing optional dependencies
• New acquisition functions for active learning: qNIPV (negative integrated posterior
  variance) and PSTD (posterior standard deviation)
• Acquisition function: qKG (knowledge gradient)
• Abstract ContinuousNonlinearConstraint class
• Abstract CardinalityConstraint class and
  DiscreteCardinalityConstraint/ContinuousCardinalityConstraint subclasses
• Uniform sampling mechanism for continuous spaces with cardinality constraints
• register_hooks utility enabling user-defined augmentation of arbitrary callables
• transform methods of SearchSpace, SubspaceDiscrete and SubspaceContinuous
  now take additional allow_missing and allow_extra keyword arguments
• More details to the transfer learning user guide
• Activated doctests
• SubspaceDiscrete.from_parameter, SubspaceContinuous.from_parameter,
  SubspaceContinuous.from_product and SearchSpace.from_parameter
  convenience constructors
• DiscreteParameter.to_subspace, ContinuousParameter.to_subspace and
  Parameter.to_searchspace convenience constructors
• Utilities for permutation and dependency data augmentation
• Validation and translation tests for kernels
• BasicKernel and CompositeKernel base classes
• Activated pre-commit.ci with auto-update
• User guide for active learning
• Polars expressions for DiscreteSumConstraint, DiscreteProductConstraint,
  DiscreteExcludeConstraint, DiscreteLinkedParametersConstraint and
  DiscreteNoLabelDuplicatesConstraint
• Discrete search space Cartesian product can be created lazily via Polars
• Examples demonstrating the register_hooks utility: basic registration mechanism,
  monitoring the probability of improvement, and automatic campaign stopping

Changed

• Passing an Objective to Campaign is now optional
• GaussianProcessSurrogate models are no longer wrapped when cast to BoTorch
• Restrict upper versions of main dependencies, motivated by major numpy release
• Sampling methods in qNIPV and BotorchRecommender are now specified via
  DiscreteSamplingMethod enum
• Interval class now supports degenerate intervals containing only one element
• add_fake_results now directly processes Target objects instead of a Campaign
• path argument in plotting utility is now optional and defaults to Path(".")
• UnusedObjectWarning by non-predictive recommenders is now ignored during simulations
• The default kernel factory now avoids strong jumps by linearly interpolating between
  two fixed low and high dimensional prior regimes
• The previous default kernel factory has been renamed to EDBOKernelFactory and now
  fully reflects the original logic
• The default acquisition function has been changed from qEI to qLogEI for improved
  numerical stability

Removed

• Support for Python 3.9 removed due to new BoTorch requirements
  and guidelines from Scientific Python
• Linter typos for spellchecking

Fixed

• sequential flag of SequentialGreedyRecommender is now set to True
• Serialization bug related to class layout of SKLearnClusteringRecommender
• MetaRecommenders no longer trigger warnings about non-empty objectives or
  measurements when calling a NonPredictiveRecommender
• Bug introduced in 0.9.0 (PR #221, commit 3078f3), where arguments to to_gpytorch
  are not passed on to the GPyTorch kernels
• Positive-valued kernel attributes are now correctly handled by validators
  and hypothesis strategies
• As a temporary workaround to compensate for missing IndexKernel priors,
  fit_gpytorch_mll_torch is used instead of fit_gpytorch_mll when a TaskParameter
  is present, which acts as regularization via early stopping during model fitting

Deprecations

• SequentialGreedyRecommender class replaced with BotorchRecommender
• SubspaceContinuous.samples_random has been replaced with
  SubspaceContinuous.sample_uniform
• SubspaceContinuous.samples_full_factorial has been replaced with
  SubspaceContinuous.sample_from_full_factorial
• Passing a dataframe via the data argument to the transform methods of
  SearchSpace, SubspaceDiscrete and SubspaceContinuous is no longer possible.
  The dataframe must now be passed as positional argument.
• The new allow_extra flag is automatically set to True in transform methods
  of search space classes when left unspecified

Expired Deprecations (from 0.7.*)

• Interval.is_finite property
• Specifying target configs without type information
• Specifying parameters/constraints at the top level of a campaign configs
• Passing numerical_measurements_must_be_within_tolerance to Campaign
• batch_quantity argument
• Passing allow_repeated_recommendations or allow_recommending_already_measured
  to MetaRecommender (or former Strategy)
• *Strategy classes and baybe.strategies subpackage
• Specifying MetaRecommender (or former Strategy) configs without type information

Merged Pull Requests

• Drop Python 3.9 by @AdrianSosic in #253
• Refactor recommender signature by @AdrianSosic in #220
• Python 3.10 syntax upgrade by @AdrianSosic in #255
• Fix changelog by @AdrianSosic in #261
• Refactor adapter model by @AdrianSosic in #260
• Deprecate SequentialGreedyRecommender by @AdrianSosic in #256
• Campaign validators by @AdrianSosic in #262
• Refactor optional imports by @AdrianSosic in #266
• Add upper version limits by @AdrianSosic in #271
• Add qNIPV acqf by @Scienfitz in #264
• Fix optional dependency flags by @AdrianSosic in #272
• Cardinality constraints for random recommender by @Waschenbacher in #243
• Register hook with the same signature by @RimRihana in #275
• register_hooks utility by @AdrianSosic in #276
• Remove core test caching by @Scienfitz in #277
• Add CI dev branch triggers by @Scienfitz in #279
• Fix attrs incompatibility of pydoclint by @AdrianSosic in #282
• Replace is_numeric class attribute with is_numerical by @AdrianSosic in #281
• Add discrete cardinality constraint by @Waschenbacher in #270
• Remove tox caching in Regular by @Scienfitz in #293
• Fix serialization and caching by @AdrianSosic in #298
• Refactor search space transform methods by @AdrianSosic in #289
• Fix MetaRecommender Warnings by @Scienfitz in #292
• Fix Deprecated Calls by @Scienfitz in #302
• Polars pipeline for discrete sum, product and exclusion constraints by @rjavadi in #252
• Update TL User Guide by @Scienfitz in #299
• Reorganize simulation lookup by @AdrianSosic in #265
• Change path from sys.path[0] to . by @AVHopp in #288
• Polars: Translate linked parameter and no label duplicate to polars by @rjavadi in #305
• Searchspace convenience constructors by @AdrianSosic in #303
• Add KnowledgeGradient ACQF by @Scienfitz in #308
• Augmentation Utilities by @Scienfitz in #290
• Fix kernel translation by @AdrianSosic in #311
• Upgrade dev tools by @AdrianSosic in #316
• Polars: from_product() implementation by @rjavadi in #306
• Add Active Learning Userguide by @Scienfitz in #273
• Ignore UnusedObjectWarning caused by nonpred recommenders in simulation by @Scienfitz in #313
• Temp Fix: Avoid Prior Jumps by @Scienfitz in #310
• Restore old MLL fit by @Scienfitz in #320
• Fix broken docstring links by @AVHopp in #324
• Custom Hooks: PI Example by @RimRihana in #287
• S...

0.9.1

What's Changed

Changed

• Discrete searchspace memory estimate is now natively represented in bytes

Fixed

• Non-GP surrogates not working with deepcopy and the simulation package due to
  slotted base class
• Datatype inconsistencies for various parameters' values and comp_df and
  SubSelectionCondition's selection related to floating point precision

Merged Pull Requests

• Fix wrong picture in README by @AVHopp in #248
• Fix surrogate slots by @Scienfitz in #251
• Refine MemorySize class by @Scienfitz in #249
• Separate tox commands for doc building by @AVHopp in #234
• Fix dtypes related to floating point precision by @Scienfitz in #254
• Release 0.9.1 by @AdrianSosic in #258

Full Changelog: 0.9.0...0.9.1

0.9.0

What's Changed

Added

• Class hierarchy for objectives
• AdditiveKernel, LinearKernel, MaternKernel, PeriodicKernel,
  PiecewisePolynomialKernel, PolynomialKernel, ProductKernel, RBFKernel,
  RFFKernel, RQKernel, ScaleKernel classes
• KernelFactory protocol enabling context-dependent construction of kernels
• Preset mechanism for GaussianProcessSurrogate
• hypothesis strategies and roundtrip test for kernels, constraints, objectives,
  priors and acquisition functions
• New acquisition functions: qSR, qNEI, LogEI, qLogEI, qLogNEI
• GammaPrior, HalfCauchyPrior, NormalPrior, HalfNormalPrior, LogNormalPrior
  and SmoothedBoxPrior classes
• Possibility to deserialize classes from optional class name abbreviations
• Basic deserialization tests using different class type specifiers
• Serialization user guide
• Environment variables user guide
• Utility for estimating memory requirements of discrete product search space
• mypy for search space and objectives

Changed

• Reorganized acquisition.py into acquisition subpackage
• Reorganized simulation.py into simulation subpackage
• Reorganized gaussian_process.py into gaussian_process subpackage
• Acquisition functions are now their own objects
• acquisition_function_cls constructor parameter renamed to acquisition_function
• User guide now explains the new objective classes
• Telemetry deactivation warning is only shown to developers
• torch, gpytorch and botorch are lazy-loaded for improved startup time
• If an exception is encountered during simulation, incomplete results are returned
  with a warning instead of passing through the uncaught exception
• Environment variables BAYBE_NUMPY_USE_SINGLE_PRECISION and
  BAYBE_TORCH_USE_SINGLE_PRECISION to enforce single point precision usage

Removed

• model_params attribute from Surrogate base class, GaussianProcessSurrogate and
  CustomONNXSurrogate
• Dependency on requests package

Fixed

• n_task_params now evaluates to 1 if task_idx == 0
• Simulation no longer fails in ignore mode when lookup dataframe contains duplicate
  parameter configurations
• Simulation no longer fails for targets in MATCH mode
• closest_element now works for array-like input of all kinds
• Structuring concrete subclasses no longer requires providing an explicit type field
• _target(s) attributes of Objectives are now de-/serialized without leading
  underscore to support user-friendly serialization strings
• Telemetry does not execute any code if it was disabled
• Running simulations no longer alters the states of the global random number generators

Deprecations

• The former baybe.objective.Objective class has been replaced with
  SingleTargetObjective and DesirabilityObjective
• acquisition_function_cls constructor parameter for BayesianRecommender
• VarUCB and qVarUCB acquisition functions

Expired Deprecations (from 0.6.*)

• BayBE class
• baybe.surrogate module
• baybe.targets.Objective class
• baybe.strategies.Strategy class

Merged Pull Requests

• Emojification by @AdrianSosic in #189
• Lazy load DTypeFloatTorch by @rjavadi in #173
• Fix: n_task_params now evaluates to 1 if task_idx == 0 by @tobiasploetz in #187
• Acquisition package by @AdrianSosic in #190
• Mypy for search spaces by @AdrianSosic in #177
• Lazy load torch in surrogates by @rjavadi in #165
• Objective classes by @AdrianSosic in #188
• Correct simulate_scenarios to simulate_experiment in docs by @brandon-holt in #199
• Acquisition function classes by @AdrianSosic in #191
• Skeleton for kernels by @AVHopp in #196
• Hypothesis for objectives by @AdrianSosic in #197
• Fix lookup bug in simulation by @AdrianSosic in #202
• Hypothesis for constraints by @AdrianSosic in #178
• Drop model_params where unnecessary by @AdrianSosic in #204
• Hotfix for match mode simulation by @AdrianSosic in #208
• Update objective user guide by @AdrianSosic in #201
• Telemetry changes by @Scienfitz in #205
• Use tox-uv by @AdrianSosic in #182
• torch lazy loading by @AdrianSosic in #206
• Create simulation subpackage by @AdrianSosic in #210
• New acquisition functions by @Scienfitz in #203
• Custom kernel priors by @AVHopp in #219
• Serialization user guide by @AdrianSosic in #216
• Refactor surrogates and decorators by @AdrianSosic in #209
• Enable force docs and tag selection by @Scienfitz in #230
• Simplification of doc building script by @AVHopp in #224
• More Priors by @Scienfitz in #225
• Fix telemetry execution by @Scienfitz in #237
• Enable returning incomplete simulation results by @AdrianSosic in #213
• Include custom ScaleKernel by @AVHopp in #221
• Relocate priors by @AdrianSosic in #238
• Enable single point precision via env vars by @Scienfitz in #226
• Refactor GP Surrogates by @AdrianSosic in #235
• Arithmetic kernels by @AdrianSosic in #241
• Ditch requests by @Scienfitz in #240
• Increate hypothesis deadline by @Scienfitz in #245
• Avoid altering RNG states by @AdrianSosic in #239
• User guide for environment variables by @Scienfitz in #227
• Minor README fixes by @AdrianSosic in #242
• Memory estimation utilities by @Scienfitz in #228
• Add more kernels by @Scienfitz in #244
• Release 0.9.0 by @AdrianSosic in #217

New Contributors

• @tobiasploetz made their first contribution in #187
• @brandon-holt made their first contribution in #199

Full Changelog: 0.8.2...0.9.0

0.8.2

What's Changed

Added

• Simulation user guide
• Example for transfer learning backtesting utility
• pyupgrade pre-commit hook
• Better human readable __str__ representation of objective and targets
• Alternative dataframe deserialization from pd.DataFrame constructors

Changed

• More detailed and sophisticated search space user guide
• Support for Python 3.12
• Upgraded syntax to Python 3.9
• Bumped onnx version to fix vulnerability
• Increased threshold for low-dimensional GP priors
• Replaced fit_gpytorch_mll_torch with fit_gpytorch_mll

Fixed

• telemetry dependency is no longer a group (enables Poetry installation)

Merged Pull Requests

• New search space userguide by @AVHopp in #170
• Simulation userguide by @AVHopp in #172
• Enable Python 3.12 by @Scienfitz in #153
• Python syntax upgrade by @AdrianSosic in #176
• Feature/pretty print objective by @RimRihana in #175
• Enable flexible dataframe de-serialization by @Scienfitz in #174
• Change order in sidebar by @AVHopp in #180
• Remove optional telemetry group by @AVHopp in #181
• Fix ONNX vulnerability by @AdrianSosic in #185
• Botorch settings by @AdrianSosic in #183
• Release 0.8.2 by @AdrianSosic in #186

Full Changelog: 0.8.1...0.8.2

0.8.1

What's Changed

Added

• Better human readable __str__ representation of campaign
• README now contains an example on substance encoding results
• Transfer learning user guide
• from_simplex constructor now also takes and applies optional constraints

Changed

• Full lookup backtesting example now tests different substance encodings
• Replaced unmaintained mordred dependency by mordredcommunity
• SearchSpaces now use ndarray instead of Tensor

Fixed

• from_simplex now efficiently validated in Campaign.validate_config

Merged Pull Requests

• Lazy loading torch - part 1 by @rjavadi in #119
• HOTFIX: Downgrade upload-pages-artifact action to v1 by @AVHopp in #158
• Feature/pretty print campaign by @RimRihana in #152
• Chemical Encoding Docs Update by @Scienfitz in #157
• First draft of Transfer Learning User Guide by @AVHopp in #140
• Extend from_simplex with constraints by @Scienfitz in #159
• Mordred dependency update by @Scienfitz in #163
• Migrate tensor to ndarray in searchspace by @rjavadi in #135
• Fix Regular Action by @Scienfitz in #167
• Add efficient from_simplex validation by @Scienfitz in #166
• Release 0.8.1 by @Scienfitz in #168

Full Changelog: 0.8.0...0.8.1

0.8.0

What's Changed

Changed

• BoTorch dependency bumped to >=0.9.3

Removed

• Workaround for BoTorch hybrid recommender data type
• Support for Python 3.8

Merged Pull Requests

• Remove BoTorch Workaround (new) by @Scienfitz in #151
• Release 0.8.0 by @Scienfitz in #155

Full Changelog: 0.7.4...0.8.0