CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Added

• allow_missing and allow_extra keyword arguments to Objective.transform

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.

[0.11.2] - 2024-10-11

Added

• n_restarts and n_raw_samples keywords to configure continuous optimization behavior for BotorchRecommender
• User guide for utilities
• mypy rule expecting explicit override markers for method overrides

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

[0.11.1] - 2024-10-01

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

Deprecations

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

[0.11.0] - 2024-09-09

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

[0.10.0] - 2024-08-02

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
• Documentation building now uses a lockfile to fix the exact environment

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

[0.9.1] - 2024-06-04

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

[0.9.0] - 2024-05-21

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

[0.8.2] - 2024-03-27

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
• Use tox-uv in pipelines

Fixed

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

[0.8.1] - 2024-03-11

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

[0.8.0] - 2024-02-29

Changed

• BoTorch dependency bumped to >=0.9.3

Removed

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

[0.7.4] - 2024-02-28

Added

• Subpackages for the available recommender types
• Multi-style plotting capabilities for generated example plots
• JSON file for plotting themes
• Smoke testing in relevant tox environments
• ContinuousParameter base class
• New environment variable BAYBE_CACHE_DIR that can customize the disk cache directory or turn off disk caching entirely
• Options to control the number of nonzero parameters in SubspaceDiscrete.from_simplex
• Temporarily ignore ONNX vulnerabilities
• Better human readable __str__ representation of search spaces
• pretty_print_df function for printing shortened versions of dataframes
• Basic Transfer Learning example
• Repo now has reminders (https://github.com/marketplace/actions/issue-reminder) enabled
• mypy for recommenders

Changed

• Recommenders now share their core logic via their base class
• Remove progress bars in examples
• Strategies are now called MetaRecommender's and part of the recommenders.meta module
• Recommender's are now called PureRecommender's and part of the recommenders.pure module
• strategy keyword of Campaign renamed to recommender
• NaiveHybridRecommender renamed to NaiveHybridSpaceRecommender

Fixed

• Unhandled exception in telemetry when username could not be inferred on Windows
• Metadata is now correctly updated for hybrid spaces
• Unintended deactivation of telemetry due to import problem
• Line wrapping in examples

Deprecations

• TwoPhaseStrategy, SequentialStrategy and StreamingSequentialStrategy have been replaced with their new MetaRecommender versions

[0.7.3] - 2024-02-09

Added

• Copy button for code blocks in documentation
• mypy for campaign, constraints and telemetry
• Top-level example summaries
• RecommenderProtocol as common interface for Strategy and Recommender
• SubspaceDiscrete.from_simplex convenience constructor

Changed

• Order of README sections
• Imports from top level baybe.utils no longer possible
• Renamed utils.numeric to utils.numerical
• Optional chem dependencies are lazily imported, improving startup time

Fixed

• Several minor issues in documentation
• Visibility and constructor exposure of Campaign attributes that should be private
• TaskParameters no longer disappear from computational representation when the search space contains only one task parameter value
• Failing baybe import from environments containing only core dependencies caused by eagerly loading chem dependencies
• tox coretest now uses correct environment and skips unavailable tests
• Basic serialization example no longer requires optional chem dependencies

Removed

• Detailed headings in table of contents of examples

Deprecations

• Passing numerical_measurements_must_be_within_tolerance to the Campaign constructor is no longer supported. Instead, Campaign.add_measurements now takes an additional parameter to control the behavior.
• batch_quantity replaced with batch_size
• allow_repeated_recommendations and allow_recommending_already_measured are now attributes of Recommender and no longer attributes of Strategy

[0.7.2] - 2024-01-24

Added

• Target enums
• mypy for targets and intervals
• Tests for code blocks in README and user guides
• hypothesis strategies and roundtrip tests for targets, intervals, and dataframes
• De-/serialization of target subclasses via base class
• Docs building check now part of CI
• Automatic formatting checks for code examples in documentation
• Deserialization of classes with classmethod constructors can now be customized by providing an optional constructor field
• SearchSpace.from_dataframe convenience constructor

Changed

• Renamed bounds_transform_func target attribute to transformation
• Interval.is_bounded now implements the mathematical definition of boundedness
• Moved and renamed target transform utility functions
• Examples have two levels of headings in the table of content
• Fix orders of examples in table of content
• DiscreteCustomConstraint validator now expects dataframe instead of series
• ignore_example flag builds but does not execute examples when building documentation
• New user guide versions for campaigns, targets and objectives
• Binarization of dataframes now happens via pickling

Fixed

• Wrong use of tolerance argument in constraints user guide
• Errors with generics and type aliases in documentation
• Deduplication bug in substance_data hypothesis strategy
• Use pydoclint as flake8 plugin and not as a stand-alone linter
• Margins in documentation for desktop and mobile version
• Intervals can now also be deserialized from a bounds iterable
• SubspaceDiscrete and SubspaceContinuous now have de-/serialization methods

Removed

• Conda install instructions and version badge
• Early fail for different Python versions in regular pipeline

Deprecations

• Interval.is_finite replaced with Interval.is_bounded
• Specifying target configs without explicit type information is deprecated
• Specifying parameters/constraints at the top level of a campaign configuration JSON is deprecated. Instead, an explicit searchspace field must be provided with an optional constructor entry

[0.7.1] - 2023-12-07

Added

• Release pipeline now also publishes source distributions
• hypothesis strategies and tests for parameters package

Changed

• Reworked validation tests for parameters package
• SubstanceParameter now collects inconsistent user input in an ExceptionGroup

Fixed

• Link handling in documentation

[0.7.0] - 2023-12-04

Added

• GitHub CI pipelines
• GitHub documentation pipeline
• Optional --force option for building the documentation despite errors
• Enabled passing optional arguments to tox -e docs calls
• Logo and banner images
• Project metadata for pyproject.toml
• PyPI release pipeline
• Favicon for homepage
• More literature references
• First drafts of first user guides

Changed

• Reworked README for GitHub landing page
• Now has concise contribution guidelines
• Use Furo theme for documentation

Removed

• --debug flag for documentation building

[0.6.1] - 2023-11-27

Added

• Script for building HTML documentation and corresponding tox environment
• Linter typos for spellchecking
• Parameter encoding enums
• mypy for parameters package
• tox environments for mypy

Changed

• Replacing pylint, flake8, µfmt and usort with ruff
• Markdown based documentation replaced with HTML based documentation

Fixed

• encoding is no longer a class variable
• Now installed with correct pandas dependency flag
• comp_df column names for CustomDiscreteParameter are now safe

[0.6.0] - 2023-11-17

Added

• Raises section for validators and corresponding contributing guideline
• Bring your own model: surrogate classes for custom model architectures and pre-trained ONNX models
• Test module for deprecation warnings
• Option to control the switching point of TwoPhaseStrategy (former Strategy)
• SequentialStrategy and StreamingSequentialStrategy classes
• Telemetry env variable BAYBE_TELEMETRY_VPN_CHECK turning the initial connectivity check on/off
• Telemetry env variable BAYBE_TELEMETRY_VPN_CHECK_TIMEOUT for setting the connectivity check timeout

Changed

• Reorganized modules into subpackages
• Serialization no longer relies on cattrs' global converter
• Refined (un-)structuring logic
• Telemetry env variable BAYBE_TELEMETRY_HOST renamed to BAYBE_TELEMETRY_ENDPOINT
• Telemetry env variable BAYBE_DEBUG_FAKE_USERHASH renamed to BAYBE_TELEMETRY_USERNAME
• Telemetry env variable BAYBE_DEBUG_FAKE_HOSTHASH renamed to BAYBE_TELEMETRY_HOSTNAME
• Bumped cattrs version

Fixed

• Now supports Python 3.11
• Removed pyarrow version pin
• TaskParameter added to serialization test
• Deserialization (e.g. from config) no longer silently drops unknown arguments

Deprecations

• BayBE class replaced with Campaign
• baybe.surrogate replaced with baybe.surrogates
• baybe.targets.Objective replaced with baybe.objective.Objective
• baybe.strategies.Strategy replaced with baybe.strategies.TwoPhaseStrategy

[0.5.1] - 2023-10-19

Added

• Linear in-/equality constraints over continuous parameters
• Constrained optimization for SequentialGreedyRecommender
• RandomRecommender now supports linear in-/equality constraints via polytope sampling

Changed

• Include linting for all functions
• Rewrite functions to distinguish between private and public ones
• Unreachable telemetry endpoints now automatically disables telemetry and no longer cause any data submission loops
• add_fake_results utility now considers potential target bounds
• Constraint names have been refactored to indicate whether they operate on discrete or continuous parameters

Fixed

• Random recommendation failing for small discrete (sub-)spaces
• Deserialization issue with TaskParameter

[0.5.0] - 2023-09-15

Added

• TaskParameter for multitask modelling
• Basic transfer learning capability using multitask kernels
• Advanced simulation mechanisms for transfer learning and search space partitioning
• Extensive docstring documentation in all files
• Autodoc using sphinx
• Script for automatic code documentation
• New tox environments for a full and a core-only pytest run

Changed

• Discrete subspaces require unique indices
• Simulation function signatures are redesigned (but largely backwards compatible)
• Docstring contents and style (numpy -> google)
• Regrouped additional dependencies

[0.4.2] - 2023-08-29

Added

• Test environments for multiple Python versions via tox

Changed

• Removed environment.yml
• Telemetry host endpoint is now flexible via the environment variable BAYBE_TELEMETRY_HOST

Fixed

• Inference for __version__

[0.4.1] - 2023-08-23

Added

• Vulnerability check via pip-audit
• tests dependency group

Changed

• Removed no longer required fsspec dependency

Fixed

• Scipy vulnerability by bumping version to 1.10.1
• Missing pyarrow dependency

[0.4.0] - 2023-08-16

Added

• from_dataframe convenience constructors for discrete and continuous subspaces
• from_bounds convenience constructor for continuous subspaces
• empty convenience constructors discrete and continuous subspaces
• baybe, strategies and utils namespace for convenient imports
• Simple test for config validation
• VarUCB and qVarUCB acquisition functions emulating maximum variance for active learning
• Surrogate model serialization
• Surrogate model parameter passing

Changed

• Renamed create constructors to from_product
• Renamed empty checks for subspaces to is_empty
• Fixed inconsistent class names in surrogate.py
• Fixed inconsistent class names in parameters.py
• Cached recommendations are now private
• Parameters, targets and objectives are now immutable
• Adjusted comments in example files
• Accelerated the slowest tests
• Removed try blocks from config examples
• Upgraded numpy requirement to >= 1.24.1
• Requires protobuf<=3.20.3
• SearchSpace parameters in surrogate models are now handled in fit
• Dataframes are encoded in binary for serialization
• comp_rep is loaded directly from the serialization string

Fixed

• Include scaling in FPS recommender
• Support for pandas>=2.0.0

[0.3.2] - 2023-07-24

Added

• Constraints serialization

Changed

• A maximum of one DependenciesConstraint is allowed
• Bumped numpy and matplotlib versions

[0.3.1] - 2023-07-17

Added

• Code coverage check with pytest-cov
• Hybrid mode for SequentialGreedyRecommender

Changed

• Removed support for infinite parameter bounds
• Removed not yet implemented MULTI objective mode

Fixed

• Changelog assert in Azure pipeline
• Bug: telemetry could not be fully deactivated

[0.3.0] - 2023-06-27

Added

• Interval class for representing parameter/target bounds
• Activated mypy for the first few modules and fixed their type issues
• Automatic (de-)serialization and SerialMixin class
• Basic serialization example, demo and tests
• Mechanisms for loading and validating config files
• Telemetry via OpenTelemetry
• More detailed package installation info
• Fallback mechanism for NonPredictiveRecommender
• Introduce naive hybrid recommender

Changed

• Switched from pydantic to attrs in all modules except constraints.py
• Removed subclass initialization hooks and type attribute
• Refactored class attributes and their conversion/validation/initialization
• Removed no longer needed HashableDict class
• Refactored strategy and recommendation module structures
• Replaced dict-based configuration logic with object-based logic
• Overall versioning scheme and version inference for telemetry
• No longer using private telemetry imports
• Fixed package versions for dev tools
• Revised "Getting Started" section in README.md
• Revised examples

Fixed

• Telemetry no longer crashing when package was not installed

[0.2.4] - 2023-03-24

Added

• Tests for different search space types and their compatible recommenders

Changed

• Initial strategies converted to recommenders
• Config keyword initial_strategy replaced by initial_recommender_cls
• Config keywords for the clustering recommenders changed from x to CLUSTERING_x
• skicit-learn-extra is now optional dependency in the [extra] group
• Type identifiers of greedy recommenders changed to 'SEQUENTIAL_GREEDY_x'

Fixed

• Parameter bounds now only contain dimensions that actually appear in the search space

[0.2.3] - 2023-03-14

Added

• Parsing for continuous parameters
• Caching of recommendations to avoid unnecessary computations
• Strategy support for hybrid spaces
• Custom discrete constraint with user-provided validator

Changed

• Parameter class hierarchy
• SearchSpace has now a discrete and continuous subspace
• Model fit now done upon requesting recommendations

Fixed

• Updated BoTorch and GPyTorch versions are also used in pyproject.toml

[0.2.2] - 2023-01-13

Added

• SearchSpace class
• Code testing with pytest
• Option to specify initial data for backtesting simulations
• SequentialGreedyRecommender class

Changed

• Switched from miniconda to micromamba in Azure pipeline

Fixed

• BoTorch version upgrade to fix critical bug (pytorch/botorch#1454)

[0.2.1] - 2022-12-01

Fixed

• Parameters cannot be initialized with duplicate values

[0.2.0] - 2022-11-10

Added

• Initial strategy: Farthest Point Sampling
• Initial strategy: Partitioning Around Medoids
• Initial strategy: K-means
• Initial strategy: Gaussian Mixture Model
• Constraints and conditions for discrete parameters
• Data scaling functionality
• Decorator for automatic model scaling
• Decorator for handling constant targets
• Decorator for handling batched model input
• Surrogate model: Mean prediction
• Surrogate model: Random forrest
• Surrogate model: NGBoost
• Surrogate model: Bayesian linear
• Save/load functionality for BayBE objects

Fixed

• UCB now usable as acquisition function, hard-set beta parameter to 1.0
• Temporary GP priors now exactly reproduce EDBO setting

[0.1.0] - 2022-10-01

Added

• Code skeleton with a central object to access functionality
• Basic parser for categorical parameters with one-hot encoding
• Basic parser for discrete numerical parameters
• Azure pipeline for code formatting and linting
• Single-task Gaussian process strategy
• Streamlit dashboard for comparing single-task strategies
• Input functionality to read measurements including automatic matching to search space
• Integer encoding for categorical parameters
• Parser for numerical discrete parameters
• Single numerical target with Min and Max mode
• Recommendation functionality
• Parameter scaling depending on parameter types and user-chosen scalers
• Noise and fake-measurement utilities
• Internal metadata storing various info about datapoints in the search space
• BayBE options controlling recommendation and data addition behavior
• Config parsing and validation using pydantic
• Global random seed control
• Strategy connection with BayBE object
• Custom parameters as labels with user-provided encodings
• Substance parameters which are encoded via cheminformatics descriptors
• Data cleaning utilities useful for descriptors
• Simulation capabilities for testing the package on existing data
• Parsing and preprocessing for multiple targets / desirability ansatz
• Basic README file
• Automatic publishing of tagged versions
• Caching of experimental parameters and chemical descriptors
• Choices for acquisition functions and their usage with arbitrary surrogate models
• Temporary logic for selecting GP priors