197 lines
7.9 KiB
Markdown
197 lines
7.9 KiB
Markdown
# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [1.2.1] - 2025-07-19
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### Internal
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- Update Libtorch to version 2.7.1
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- Update libraries versions:
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- mdlp: 2.1.1
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- Folding: 1.1.2
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- ArffFiles: 1.2.1
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## [1.2.0] - 2025-07-08
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### Internal
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- Add docs generation to CMakeLists.txt.
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- Add new hyperparameters to the Ld classifiers:
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- *ld_algorithm*: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU".
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- *ld_proposed_cuts*: number of cut points to return.
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- *mdlp_min_length*: minimum length of a partition in MDLP algorithm to be evaluated for partition.
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- *mdlp_max_depth*: maximum level of recursion in MDLP algorithm.
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- *max_iterations*: maximum number of iterations of discretization-build model loop.
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- *verbose_convergence*: display status messages during the convergence process.
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- Remove vcpkg as a dependency manager, now the library is built with Conan package manager and CMake.
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- Add `build_type` option to the sample target in the Makefile to allow building in *Debug* or *Release* mode. Default is *Debug*.
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## [1.1.1] - 2025-05-20
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### Internal
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- Fix CFS metric expression in the FeatureSelection class.
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- Fix the vcpkg configuration in building the library.
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- Fix the sample app to use the vcpkg configuration.
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- Refactor the computeCPT method in the Node class with libtorch vectorized operations.
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- Refactor the sample to use local discretization models.
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### Added
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- Add predict_proba method to all Ld classifiers.
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- Add L1FS feature selection methods to the FeatureSelection class.
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## [1.1.0] - 2025-04-27
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### Internal
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- Add changes to .clang-format to adjust to vscode format style thanks to <https://clang-format-configurator.site/>
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- Remove all the dependencies as git submodules and add them as vcpkg dependencies.
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- Fix the dependencies versions for this specific BayesNet version.
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## [1.0.7] 2025-03-16
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### Added
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- A new hyperparameter to the BoostAODE class, *alphablock*, to control the way α is computed, with the last model or with the ensmble built so far. Default value is *false*.
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- A new hyperparameter to the SPODE class, *parent*, to set the root node of the model. If no value is set the root parameter of the constructor is used.
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- A new hyperparameter to the TAN class, *parent*, to set the root node of the model. If not set the first feature is used as root.
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- A new model named XSPODE, an optimized for speed averaged one dependence estimator.
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- A new model named XSP2DE, an optimized for speed averaged two dependence estimator.
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- A new model named XBAODE, an optimized for speed BoostAODE model.
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- A new model named XBA2DE, an optimized for speed BoostA2DE model.
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### Internal
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- Optimize ComputeCPT method in the Node class.
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- Add methods getCount and getMaxCount to the CountingSemaphore class, returning the current count and the maximum count of threads respectively.
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### Changed
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- Hyperparameter *maxTolerance* in the BoostAODE class is now in [1, 6] range (it was in [1, 4] range before).
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## [1.0.6] 2024-11-23
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### Fixed
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- Prevent existing edges to be added to the network in the `add_edge` method.
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- Don't allow to add nodes or edges on already fiited networks.
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- Number of threads spawned
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- Network class tests
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### Added
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- Library logo generated with <https://openart.ai> to README.md
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- Link to the coverage report in the README.md coverage label.
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- *convergence_best* hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is *false*.
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- SPnDE model.
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- A2DE model.
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- BoostA2DE model.
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- A2DE & SPnDE tests.
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- Add tests to reach 99% of coverage.
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- Add tests to check the correct version of the mdlp, folding and json libraries.
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- Library documentation generated with Doxygen.
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- Link to documentation in the README.md.
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- Three types of smoothing the Bayesian Network ORIGINAL, LAPLACE and CESTNIK.
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### Internal
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- Fixed doxygen optional dependency
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- Add env parallel variable to Makefile
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- Add CountingSemaphore class to manage the number of threads spawned.
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- Ignore CUDA language in CMake CodeCoverage module.
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- Update mdlp library as a git submodule.
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- Create library ShuffleArffFile to limit the number of samples with a parameter and shuffle them.
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- Refactor catch2 library location to test/lib
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- Refactor loadDataset function in tests.
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- Remove conditionalEdgeWeights method in BayesMetrics.
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- Refactor Coverage Report generation.
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- Add devcontainer to work on apple silicon.
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- Change build cmake folder names to Debug & Release.
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- Add a Makefile target (doc) to generate the documentation.
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- Add a Makefile target (doc-install) to install the documentation.
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### Libraries versions
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- mdlp: 2.0.1
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- Folding: 1.1.0
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- json: 3.11
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- ArffFiles: 1.1.0
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## [1.0.5] 2024-04-20
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### Added
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- Install command and instructions in README.md
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- Prefix to install command to install the package in the any location.
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- The 'block_update' hyperparameter to the BoostAODE class, to control the way weights/significances are updated. Default value is false.
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- Html report of coverage in the coverage folder. It is created with *make viewcoverage*
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- Badges of coverage and code quality (codacy) in README.md. Coverage badge is updated with *make viewcoverage*
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- Tests to reach 97% of coverage.
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- Copyright header to source files.
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- Diagrams to README.md: UML class diagram & dependency diagram
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- Action to create diagrams to Makefile: *make diagrams*
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### Changed
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- Sample app now is a separate target in the Makefile and shows how to use the library with a sample dataset
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- The worse model count in BoostAODE is reset to 0 every time a new model produces better accuracy, so the tolerance of the model is meant to be the number of **consecutive** models that produce worse accuracy.
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- Default hyperparameter values in BoostAODE: bisection is true, maxTolerance is 3, convergence is true
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### Removed
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- The 'predict_single' hyperparameter from the BoostAODE class.
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- The 'repeatSparent' hyperparameter from the BoostAODE class.
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## [1.0.4] 2024-03-06
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### Added
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- Change *ascending* hyperparameter to *order* with these possible values *{"asc", "desc", "rand"}*, Default is *"desc"*.
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- Add the *predict_single* hyperparameter to control if only the last model created is used to predict in boost training or the whole ensemble (all the models built so far). Default is true.
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- sample app to show how to use the library (make sample)
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### Changed
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- Change the library structure adding folders for each group of classes (classifiers, ensembles, etc).
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- The significances of the models generated under the feature selection algorithm are now computed after all the models have been generated and an α<sub>t</sub> value is computed and assigned to each model.
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## [1.0.3] 2024-02-25
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### Added
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- Voting / probability aggregation in Ensemble classes
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- predict_proba method in Classifier
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- predict_proba method in BoostAODE
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- predict_voting parameter in BoostAODE constructor to use voting or probability to predict (default is voting)
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- hyperparameter predict_voting to AODE, AODELd and BoostAODE (Ensemble child classes)
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- tests to check predict & predict_proba coherence
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## [1.0.2] - 2024-02-20
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### Fixed
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- Fix bug in BoostAODE: do not include the model if epsilon sub t is greater than 0.5
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- Fix bug in BoostAODE: compare accuracy with previous accuracy instead of the first of the ensemble if convergence true
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## [1.0.1] - 2024-02-12
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### Added
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- Notes in Classifier class
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- BoostAODE: Add note with used features in initialization with feature selection
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- BoostAODE: Add note with the number of models
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- BoostAODE: Add note with the number of features used to create models if not all features are used
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- Test version number in TestBayesModels
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- Add tests with feature_select and notes on BoostAODE
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### Fixed
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- Network predict test
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- Network predict_proba test
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- Network score test
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