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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

  • Target enums
  • mypy for targets package

Changed

  • Renamed bounds_transform_func target attribute to transformation
  • Moved and renamed target transform utility functions

Removed

  • Conda install instructions and version badge

[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

Fixed

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

Deprecations

  • Markdown based documentation replaced with HTML based documentation

[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

[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 searchspace
  • 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 searchspace
  • 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