Baybe

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

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)

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`
- `SearchSpace`s now use `ndarray` instead of `Tensor`

Fixed
- `from_simplex` now efficiently validated in `Campaign.validate_config`

Changed
- BoTorch dependency bumped to `>=0.9.3`

Removed
- Workaround for BoTorch hybrid recommender data type
- Support for Python 3.8

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
- `Recommender`s 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

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
- `TaskParameter`s 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`