Baybe

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

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

Added
- Constraints serialization

Changed
- A maximum of one `DependenciesConstraint` is allowed
- Bumped numpy and matplotlib versions

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

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

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