BayesNet/CHANGELOG.md

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.

[1.2.1] - 2025-07-19

Internal

• Update Libtorch to version 2.7.1
• Update libraries versions:
  • mdlp: 2.1.1
  • Folding: 1.1.2
  • ArffFiles: 1.2.1

[1.2.0] - 2025-07-08

Internal

• Add docs generation to CMakeLists.txt.
• Add new hyperparameters to the Ld classifiers:
  • ld_algorithm: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU".
  • ld_proposed_cuts: number of cut points to return.
  • mdlp_min_length: minimum length of a partition in MDLP algorithm to be evaluated for partition.
  • mdlp_max_depth: maximum level of recursion in MDLP algorithm.
  • max_iterations: maximum number of iterations of discretization-build model loop.
  • verbose_convergence: display status messages during the convergence process.
• Remove vcpkg as a dependency manager, now the library is built with Conan package manager and CMake.
• Add build_type option to the sample target in the Makefile to allow building in Debug or Release mode. Default is Debug.

[1.1.1] - 2025-05-20

Internal

• Fix CFS metric expression in the FeatureSelection class.
• Fix the vcpkg configuration in building the library.
• Fix the sample app to use the vcpkg configuration.
• Refactor the computeCPT method in the Node class with libtorch vectorized operations.
• Refactor the sample to use local discretization models.

Added

• Add predict_proba method to all Ld classifiers.
• Add L1FS feature selection methods to the FeatureSelection class.

[1.1.0] - 2025-04-27

Internal

• Add changes to .clang-format to adjust to vscode format style thanks to https://clang-format-configurator.site/
• Remove all the dependencies as git submodules and add them as vcpkg dependencies.
• Fix the dependencies versions for this specific BayesNet version.

[1.0.7] 2025-03-16

Added

• 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.
• 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.
• 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.
• A new model named XSPODE, an optimized for speed averaged one dependence estimator.
• A new model named XSP2DE, an optimized for speed averaged two dependence estimator.
• A new model named XBAODE, an optimized for speed BoostAODE model.
• A new model named XBA2DE, an optimized for speed BoostA2DE model.

Internal

• Optimize ComputeCPT method in the Node class.
• Add methods getCount and getMaxCount to the CountingSemaphore class, returning the current count and the maximum count of threads respectively.

Changed

• Hyperparameter maxTolerance in the BoostAODE class is now in [1, 6] range (it was in [1, 4] range before).

[1.0.6] 2024-11-23

Fixed

• Prevent existing edges to be added to the network in the add_edge method.
• Don't allow to add nodes or edges on already fiited networks.
• Number of threads spawned
• Network class tests

Added

• Library logo generated with https://openart.ai to README.md
• Link to the coverage report in the README.md coverage label.
• convergence_best hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is false.
• SPnDE model.
• A2DE model.
• BoostA2DE model.
• A2DE & SPnDE tests.
• Add tests to reach 99% of coverage.
• Add tests to check the correct version of the mdlp, folding and json libraries.
• Library documentation generated with Doxygen.
• Link to documentation in the README.md.
• Three types of smoothing the Bayesian Network ORIGINAL, LAPLACE and CESTNIK.

Internal

• Fixed doxygen optional dependency
• Add env parallel variable to Makefile
• Add CountingSemaphore class to manage the number of threads spawned.
• Ignore CUDA language in CMake CodeCoverage module.
• Update mdlp library as a git submodule.
• Create library ShuffleArffFile to limit the number of samples with a parameter and shuffle them.
• Refactor catch2 library location to test/lib
• Refactor loadDataset function in tests.
• Remove conditionalEdgeWeights method in BayesMetrics.
• Refactor Coverage Report generation.
• Add devcontainer to work on apple silicon.
• Change build cmake folder names to Debug & Release.
• Add a Makefile target (doc) to generate the documentation.
• Add a Makefile target (doc-install) to install the documentation.

Libraries versions

• mdlp: 2.0.1
• Folding: 1.1.0
• json: 3.11
• ArffFiles: 1.1.0

[1.0.5] 2024-04-20

Added

• Install command and instructions in README.md
• Prefix to install command to install the package in the any location.
• The 'block_update' hyperparameter to the BoostAODE class, to control the way weights/significances are updated. Default value is false.
• Html report of coverage in the coverage folder. It is created with make viewcoverage
• Badges of coverage and code quality (codacy) in README.md. Coverage badge is updated with make viewcoverage
• Tests to reach 97% of coverage.
• Copyright header to source files.
• Diagrams to README.md: UML class diagram & dependency diagram
• Action to create diagrams to Makefile: make diagrams

Changed

• Sample app now is a separate target in the Makefile and shows how to use the library with a sample dataset
• 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.
• Default hyperparameter values in BoostAODE: bisection is true, maxTolerance is 3, convergence is true

Removed

• The 'predict_single' hyperparameter from the BoostAODE class.
• The 'repeatSparent' hyperparameter from the BoostAODE class.

[1.0.4] 2024-03-06

Added

• Change ascending hyperparameter to order with these possible values {"asc", "desc", "rand"}, Default is "desc".
• 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.
• sample app to show how to use the library (make sample)

Changed

• Change the library structure adding folders for each group of classes (classifiers, ensembles, etc).
• The significances of the models generated under the feature selection algorithm are now computed after all the models have been generated and an α_t value is computed and assigned to each model.

[1.0.3] 2024-02-25

Added

• Voting / probability aggregation in Ensemble classes
• predict_proba method in Classifier
• predict_proba method in BoostAODE
• predict_voting parameter in BoostAODE constructor to use voting or probability to predict (default is voting)
• hyperparameter predict_voting to AODE, AODELd and BoostAODE (Ensemble child classes)
• tests to check predict & predict_proba coherence

[1.0.2] - 2024-02-20

Fixed

• Fix bug in BoostAODE: do not include the model if epsilon sub t is greater than 0.5
• Fix bug in BoostAODE: compare accuracy with previous accuracy instead of the first of the ensemble if convergence true

[1.0.1] - 2024-02-12

Added

• Notes in Classifier class
• BoostAODE: Add note with used features in initialization with feature selection
• BoostAODE: Add note with the number of models
• BoostAODE: Add note with the number of features used to create models if not all features are used
• Test version number in TestBayesModels
• Add tests with feature_select and notes on BoostAODE

Fixed

• Network predict test
• Network predict_proba test
• Network score test