0.13.2

What's Changed

Changed

• Lockfiles are now generated using uv lock and consumed using uv sync

Fixed

• The Python version specifier now also allows patch versions of Python 3.13

Merged Pull Requests

• Fix Python version specifier by @AdrianSosic in #587
• Uv lock by @AdrianSosic in #581
• Link badges to main branch by @AdrianSosic in #585
• xfail polars tests by @AdrianSosic in #586
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #588
• Release 0.13.2 by @AdrianSosic in #591

New Contributors

• @pre-commit-ci made their first contribution in #588

Full Changelog: 0.13.1...0.13.2

0.13.1

What's Changed

Added

• Support for Python 3.13
• random_tie_break flag to farthest_point_sampling to toggle between
  random or deterministic sampling for equidistant cases
• random_tie_break and initialization attributes to FPSRecommender to
  control sampling in farthest_point_sampling
• Flag for toggling parallel computation in simulate_scenarios
• Additional transfer learning and synthetic benchmarks
• Utility normalize_input_dtypes for ensuring all input dataframe columns have the
  expected dtypes
• CompositeSurrogate now has a _posterior_comp method similar to Surrogate
• SHAPInsight.explain_target method for computing explanations for only a specific
  target

Changed

• CategoricalParameter and TaskParameter now also allow Boolean entries as
  values and active_values
• SubspaceDiscrete.from_dataframe now handles purely Boolean columns differently,
  inferring a CategoricalParameter with INT encoding for them
• SHAPInsight.explain now returns a tuple of explanations that contains one
  explanation for each surrogate model used by the (possibly multi-output) objective
• SHAPInsight.plot now has the optional target_index argument, enabling users to
  select for which target they want to plot the shap assessment (default is the first
  target)
• add_measurements, update_measurements, fuzzy_row_match and some .recommend
  calls now operate on dtype-normalized copies of the input if it contained unexpected
  dtypes for a parameter or target
• scikit-learn and scipy are now lazy-loaded
• Validity of optional model_params attribute of RandomForestSurrogate,
  NGBoostSurrogate and BayesianLinearSurrogate is now checked via a hardcoded
  TypedDict instead of dynamically retrieved specifications, required for
  lazy-loading related packages
• CustomONNXSurrogate.onnx_str is no longer validated before being used

Fixed

• Using PosteriorStandardDeviation with MIN targets no longer results in
  minimization of the acquisition function
• Added missing garbage collection call to pareto.py, potentially solving
  serialization issues in certain cases
• catch_constant_targets decorator is now properly typed
• Incorrect normalization of explanation shapes for SHAPInsight

Removed

• SHAPInsight.uses_shap_explainer

Merged Pull Requests

• Tie breaking for FPS by @StefanPSchmid in #519
• Enable parallel execution of scenarios by @monken in #543
• Add missing garbage collection call by @AdrianSosic in #551
• Fix guard clause by @AdrianSosic in #554
• Make import of deprecated ONNX function robust by @AVHopp in #555
• Add boolean flag for toggling parallel execution of scenarios by @AVHopp in #548
• Enable Python 3.13 by @AdrianSosic in #414
• Add missing decorator return type by @AdrianSosic in #559
• Add benchmarks by @Hrovatin in #491
• Adhere to new mypy rules by @AdrianSosic in #563
• Remove ONNX Workarounds by @Scienfitz in #562
• Lazy-load sklearn and scipy + adjust required model validations by @Scienfitz in #556
• Allow Booleans for CategoricalParameter by @Scienfitz in #552
• Amend SHAPInsight for CompositeSurrogate by @Scienfitz in #558
• Release 0.13.1 by @Scienfitz in #565
• Fix required Python version by @AdrianSosic in #566

New Contributors

• @StefanPSchmid made their first contribution in #519
• @monken made their first contribution in #543

Full Changelog: 0.13.0...0.13.1

0.13.0

What's Changed

Added

• extras group for installing all dependencies required for optional features
• Support for NumPy 2.0+
• ParetoObjective class for Pareto optimization of multiple targets and corresponding
  qNoisyExpectedHypervolumeImprovement / qLogNoisyExpectedHypervolumeImprovement /
  qLogNParEGO acquisition functions
• Composite surrogates now drop rows containing NaNs (separately for each target),
  effectively enabling partial measurements
• SubstanceParameter, CustomDiscreteParameter and CategoricalParameter now also
  support restricting the search space via active_values, while values continue to
  identify allowed measurement inputs
• Campaign.posterior_stats and Surrogate.posterior_stats as convenience methods for
  providing statistical measures about the target predictions of a given set of
  candidates
• acquisition_values and joint_acquisition_value convenience methods to
  Campaign and BayesianRecommender for computing acquisition values
• Campaign.get_acquisition_function and BayesianRecommender.get_acquisition_function
  convenience methods for retrieving the underlying acquisition function
• AcquisitionFunction.evaluate convenience method for computing acquisition values
  from candidates in experimental representation
• qPSTD acquisition function
• BCUT2D encoding for SubstanceParameter
• SHAPInsight now supports the waterfall plot type
• Cardinality constraints sections to the user guide
• ContinuousCardinalityConstraint is now compatible with BotorchRecommender
• Attribute max_n_subspaces to BotorchRecommender, allowing to control
  optimization behavior in the presence of cardinality constraints
• Surrogate.replicate method for making single-target surrogate models multi-target
  compatible
• CompositeSurrogate class for composing multi-target surrogates from single-target
  surrogates
• is_multi_output attribute to Objective
• supports_multi_output attribute/property to Surrogate/AcquisitionFunction
• n_outputs property to Objective
• Attribute relative_threshold and method get_absolute_thresholds to
  ContinuousCardinalityConstraint for handling inactivity ranges
• Utilities inactive_parameter_combinations andn_inactive_parameter_combinations
  to both ContinuousCardinalityConstraintand SubspaceContinuous for iterating
  over cardinality-constrained parameter sets
• Utilities activate_parameter and is_cardinality_fulfilled for enforcing and
  validating cardinality constraints
• Utility is_inactive for determining if parameters are inactive
• A MinimumCardinalityViolatedWarning is triggered when minimum cardinality
  constraints are violated
• Stored benchmarking results now include the Python environment and version

Changed

• Targets are now allowed to contain NaN, deferring potential failure to attempted
  recommendation instead of data ingestion
• For label-like parameters, SubspaceDiscrete now only includes parameter values
  that are in active_values
• The default value for sequential_continuous in BotorchRecommender has been
  changed to True
• SHAPInsight now allows explanation input that has additional columns compared to
  the background data (will be ignored)
• Acquisition function indicator is_mc has been removed in favor of new indicators
  supports_batching and supports_pending_experiments
• fuzzy_row_match now uses vectorized operations, resulting in a speedup of matching
  measurements to the search space between 4x and 40x
• Model scaling now uses the parameter bounds instead of the search space bounds
• benchmarks module now accepts a list of domains to be executed
• Construction of BoTorch acquisition functions has been redesigned from ground up
• ngboost is now an optional dependency
• setuptools-scm is now an optional dependency, used for improved version inference
• create_example_plots, to_string and indent have been relocated within utils

Fixed

• Incorrect optimization direction with PSTD with a single minimization target
• Provide version fallback in case scm fails to infer version during installation

Removed

• fuzzy_row_match will no longer warn about entries not matching to the search space
• funcy dependency
• scikit-learn-extra dependency by integrating relevant code parts into baybe

Expired Deprecations (from 0.9.*)

• baybe.objective namespace
• acquisition_function_cls constructor parameter for BayesianRecommender
• VarUCB and qVarUCB acquisition functions

Merged Pull Requests

• Fix Substance Encodings by @Scienfitz in #474
• Add Python environment and version metadata to benchmark results by @fabianliebig in #476
• Serialize python env and version data by @fabianliebig in #478
• Refactor benchmark architecture by @AdrianSosic in #477
• Refactor ACQF indicators by @Scienfitz in #479
• XFail bugged test case by @Scienfitz in #481
• Fix SHAP Plot Tests by @Scienfitz in #486
• Rework validation for measurements and pending_experiments by @Scienfitz in #456
• Negative beta values by @AdrianSosic in #488
• Minor SHAP improvements by @Scienfitz in #494
• Increase timeout for benchmark test to 1440 minutes (24 hours) by @fabianliebig in #498
• Add logging to indicate currently running benchmark and its runtime by @fabianliebig in #499
• Vectorize fuzzy_row_match by @Scienfitz in #489
• Disable pre-commit.ci autofixing by @AdrianSosic in #501
• Pareto optimization by @AdrianSosic in #475
• Botorch with cardinality constraint via sampling by @Waschenbacher in #301
• Fix Pareto example plot export by @AdrianSosic in #505
• Fix serialization errors on 3.12 by @AdrianSosic in #506
• User guide for cardinality constraints by @AdrianSosic in #496
• Generalize active_values by @Scienfitz in #495
• Pipeline benchmark parallelization by @fabianliebig in #502
• Xfail bugged SHAP plot tests by @Scienfitz in #510
• Fix converter typing by @AdrianSosic in #511
• Acquisition function builder by @AdrianSosic in #490
• Add Campaign.posterior_stats by @Scienfitz in #504
• Provide version fallback in case scm fails to infer version during installation by @dasmy in #524
• Fix lockfile creation for uv>=0.6.0 by @AdrianSosic in #527
• Drop funcy dependency by @AdrianSosic in #528
• Add qLogNParEGO by @Scienfitz in #532
• More hypervolume acquisition functions by @AdrianSosic in #507
• Make ngboost optional by @AdrianSosic in #529
• Posterior Stats: Catch No Measurements Case by @Scienfitz in #536
• Make scikit-learn-extra optional by @AdrianSosic in #530
• Make setuptools-scm optional by @AdrianSosic in #526
• Enable Pareto and MATCH by @Scienfitz in #537
• Acquisition utility method by @AdrianSosic in #509
• Remove xfail for fixed SHAP case by @Scienfitz in #540
• Expire 0.9.* deprecations by @AdrianSosic in #497
• Enable Partial Measurements by @Scienfitz in #514
• Integrate KMedoids by @Scienfitz in #538
• Bump dev tools versions by @AdrianSosic in #503
• Release 0.13.0 by @AdrianSosic in #541

New Contributors

• @dasmy made their first contribution in #524

Full Changelog: 0.12.2...0.13.0

0.12.2

What's Changed

Changed

• More robust settings for the GP fitting

Merged Pull Requests

• Improve GP Fit by @Scienfitz in #472

Full Changelog: 0.12.1...0.12.2

0.12.1

Hotfix Release

• Reset flag default value by @AdrianSosic in #471

Full Changelog: 0.12.0...0.12.1

0.12.0

What's Changed

Breaking Changes

• Lookup callables for simulation are now expected to accept/return dataframes with
  the corresponding parameter/target column labels

Added

• SHAP explanations via the new SHAPInsight class
• Optional insights dependency group
• Insights user guide
• Example for a traditional mixture
• allow_missing and allow_extra keyword arguments to Objective.transform
• add_noise_to_perturb_degenerate_rows utility
• benchmarks subpackage for defining and running performance tests
• Campaign.toggle_discrete_candidates to dynamically in-/exclude discrete candidates
• filter_df utility for filtering dataframe content
• arrays_to_dataframes decorator to create lookups from array-based callables
• DiscreteConstraint.get_valid to conveniently access valid candidates
• Functionality for persisting benchmarking results on S3 from a manual pipeline run
• remain_switched option to TwoPhaseMetaRecommender
• is_stateful class variable to MetaRecommender
• get_non_meta_recommender method to MetaRecommender

Changed

• SubstanceParameter encodings are now computed exclusively with the
  scikit-fingerprints package, granting access to all fingerprints available therein
• Example for slot-based mixtures has been revised and grouped together with the new
  traditional mixture example
• Memory caching is now non-verbose
• CustomDiscreteParameter does not allow duplicated rows in data anymore
• De-/activating Polars via BAYBE_DEACTIVATE_POLARS now requires passing values
  compatible with strtobool
• All arguments to MetaRecommender.select_recommender are now optional
• MetaRecommenders can now be composed of other MetaRecommenders
• For performance reasons, search space manipulation using polars is no longer
  guaranteed to produce the same row order as the corresponding pandas operations
• allow_repeated_recommendations has been renamed to
  allow_recommending_already_recommended and is now True by default

Fixed

• Rare bug arising from degenerate SubstanceParameter.comp_df rows that caused
  wrong number of recommendations being returned
• ContinuousConstraints can now be used in single point precision
• Search spaces are now stateless, preventing unintended side effects that could lead to
  incorrect candidate sets when reused in different optimization contexts
• qNIPV not working with single MIN targets
• Passing a TargetTransformation without passing bounds when creating a
  NumericalTarget now raises an error
• Crash when using ContinuousCardinalityConstraint caused by an unintended interplay
  between constraints and dropped parameters yielding empty parameter sets
• Minimizing a single NumericalTarget with specified bounds/transformation via
  SingleTargetObjective no longer erroneously maximizes it
• allow_* flags are now context-aware, i.e. setting them in a context where they are
  irrelevant now raises an error instead of passing silently

Removed

• botorch_function_wrapper utility for creating lookup callables

Deprecations

• Passing a dataframe via the data argument to Objective.transform is no longer
  possible. The dataframe must now be passed as positional argument.
• The new allow_extra flag is automatically set to True in Objective.transform
  when left unspecified
• get_transform_parameters has been replaced with get_transform_objects
• Passing a dataframe via the data argument to Target.transform is no longer
  possible. The data must now be passed as a series as first positional argument.
• SubstanceEncoding value MORGAN_FP. As a replacement, ECFP with 1024 bits and
  radius of 4 can be used.
• SubstanceEncoding value RDKIT. As a replacement, RDKIT2DDESCRIPTORS can be used.
• The metadata attribute of SubspaceDiscrete no longer exists. Metadata is now
  exclusively handled by the Campaign class.
• get_current_recommender and get_next_recommender of MetaRecommender have become
  obsolete and calling them is no longer possible
• Passing allow_* flags to recommenders is no longer supported since the necessary
  metadata required for the flags is no longer available at that level. The
  functionality has been taken over by Campaign.

Merged Pull Requests

• Fix math in CardinalityConstraint docstring by @AVHopp in #405
• Typing overrides by @AdrianSosic in #400
• Bump Streamlit Requirement by @Scienfitz in #407
• Refactor objective transform interface by @AdrianSosic in #398
• Add Polars installation issues to known_issues by @AVHopp in #404
• Refactor target transform interface by @AdrianSosic in #406
• Drop batch_quantity keyword by @AdrianSosic in #411
• Add downloads badge by @AdrianSosic in #418
• Unpin protobuf by @Scienfitz in #421
• Fix some typos by @AdrianSosic in #426
• Fix Asserts in Dependency Constraint Example by @Scienfitz in #422
• Rework Mixture Examples by @Scienfitz in #394
• Use correct precision in botorch wrapper by @AVHopp in #417
• Enable scikit-fingerprints by @Hrovatin in #364
• Fix rare degenerate comp_df bug by @Scienfitz in #399
• Basic benchmarking structure by @fabianliebig in #413
• Require truthy/falsy values for BAYBE_DEACTIVATE_POLARS by @AdrianSosic in #427
• Fix dtype in continuous comp rep by @AVHopp in #415
• Drop CI audit dependence by @AdrianSosic in #434
• Immutable searchspace by @AdrianSosic in #412
• Fix: qNIPV not working with single MIN target by @Scienfitz in #435
• Add manual benchmark workflow and S3 result persistence by @fabianliebig in #429
• Raise error when specifying transformation without bounds by @AVHopp in #451
• Fix empty constraints after dropping parameters by @AVHopp in #448
• Include robust check for extracting column names for fingerprints by @AVHopp in #447
• Correct typo in docs on acquisition functions by @Hrovatin in #442
• Refactor callable lookup mechanism by @AdrianSosic in #441
• Pin version of scipy to <1.15 by @AVHopp in #458
• Hotfix: Minimization with bounds properly minimize targets by @AVHopp in #462
• Insights module and SHAPInsight by @Alex6022 in #391
• Simplify and extend meta recommender logic by @AdrianSosic in #457
• Fix minimization of improvement-based MC acquisition functions by @AdrianSosic in #465
• Maintain row order after cross join by @AdrianSosic in #463
• Move allow_* flags to Campaign by @AdrianSosic in #423
• Insights User Guide by @Scienfitz in #466
• Include 0.11.4 Hotfix Changes in main by @Scienfitz in #469
• Release 0.12.0 by @Scienfitz in #470

New Contributors

• @Hrovatin made their first contribution in #364
• @fabianliebig made their first contribution in #413
• @Alex6022 made their first contribution in #391

Full Changelog: 0.11.2...0.12.0

0.11.4

Hotfix Release

Changed

• Polars lazy streaming has been deactivated due to instabilities

Fixed

• Improvement-based Monte Carlo acquisition functions now use the correct
  reference value for single-target minimization

Full Changelog: 0.11.3...0.11.4

0.11.3

Hotfix Release

Fixed

• protobuf dependency issue, version pin was removed

Full Changelog: 0.11.2...0.11.3

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