Baybe

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)

Fixed
- Parameters cannot be initialized with duplicate values

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

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