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.

[0.11.3] - 2024-11-06

Fixed

• protobuf dependency issue, version pin was removed

[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

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 (https://github.com/pytorch/botorch/pull/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