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CHANGELOG.md

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Changelog

v0.5.1

  • Fixes vcpkg bad hashes (vcpkg/#38974).
  • Updates arrow to 17.0.0.

v0.5.0

  • Changed the build process to statically link Apache Arrow. With this change and using the PyCapsule interface, PyBNesian can interoperate with different versions of pyarrow>=14.0.0. You can now upgrade pyarrow (pip install --upgrade pyarrow) without breaking PyBNesian. The dependencies are also managed by vcpkg, so the build process is simpler and orchestrated by scikit-build-core and a CMakeLists.txt.

  • Some tests failed because pandas and scipy were updated. These issues have been fixed.

  • A bug in the DiscreteFactor.sample() function has been fixed. The previous implementation sampled equally from the first and last category of the DiscreteFactor.

v0.4.3

  • Fixed a bug in DiscreteFactor and others hybrid factors, such as CLinearGaussianCPD and HCKDE, where categorical data would not be correctly validated. This could lead to erroneous results or undefined behavior (often leading to segmentation fault). Thanks to Carlos Li for reporting this bug.

  • Support for Python 3.10 and pyarrow>=9.0 has been added. Support for Python 3.6 has been deprecated, as pyarrow no longer supports it.

  • manylinux2014 wheels are now used instead of manylinux2010, since pyarrow no longer provides manylinux2010 wheels.

v0.4.2

v0.4.1

  • Added support for Apache Arrow 7.0.0.

v0.4.0

  • Added method ConditionalBayesianNetworkBase.interface_arcs().

  • GreedyHillClimbing and MMHC now accepts a blacklist of FactorType.

  • BayesianNetworkType.data_default_node_type() now returns a list of FactorType indicating the priority of each FactorType for each data type.

  • BayesianNetworkBase.set_unknown_node_types() now accepts an argument of FactorType blacklist.

  • Change HeterogeneousBN constructor and HeterogeneousBNType.default_node_types() to accept lists of default FactorType.

  • Adds constructors for HeterogeneousBN and CLGNetwork that can set the FactorType for each node.

  • Bug Fixes:

    • An overflow error in ChiSquare hypothesis test was raised when the statistic were close to 0.
    • Arc blacklists/whitelists with repeated arcs were not correctly processed.
    • Fixed an error in the use of the patience parameter. Previously, the algorithm was executed as with a patience - 1 value.
    • Improve the validation of objects returned from Python class extensions, so it errors when the extensions are not correctly implemented.
    • Fixed many serialization bugs. In particular, there were multiple bugs related with the serialization of models with Python extensions.
    • Included a fix for the Windows build (by setting a correct __cplusplus value).
    • Fixed a bug in LinearGaussianCPD.fit() with 2 parents. In some cases, it was detecting a linear dependence between the parents that did not exist.
    • Fixes a bug which causes that the Python-class extension functionality is removed. Related to: pybind/pybind11#1333.

v0.3.4

  • Improvements on the code that checks that a matrix is positive definite.
  • A bug affecting the learning of conditional Bayesian networks with MMHC has been fixed. This bug also affected DMMHC.
  • Fixed a bug that affected the type of the parameter bn_type of MMHC.estimate(), MMHC.estimate_conditional() and DMMHC.estimate().

v0.3.3

  • Adds support for pyarrow 5.0.0 in the PyPi wheels.
  • Added Arguments.args() to access the args and kwargs for a node.
  • Added BayesianNetworkBase.underlying_node_type() to get the underlying node type of a node given some data.
  • Improves the fitting of hybrid factors. Now, an specific discrete configuration can be left unfitted if the base continuous factor raises SingularCovarianceData.
  • Improves the LinearGaussianCPD fit when the covariance matrix of the data is singular.
  • Improves the NormalReferenceRule, ScottsBandwidth, and UCV estimation when the covariance of the data is singular.
  • Fixes a bug loading an heterogeneous Bayesian network from a file.
  • Introduces a check that a needed category exists in discrete data.
  • Assignment now supports integer numbers converting them automatically to float.
  • Fix a bug in GreedyHillClimbing that caused the return of Bayesian networks with UnknownFactorType.
  • Reduces memory usage when fitting and printing an hybrid Factor.
  • Fixes a precision bug in GreedyHillClimbing.
  • Improves CrossValidation parameter checking.

v0.3.2

  • Fixed a bug in the UCV bandwidth selector that may cause segmentation fault.
  • Added some checks to ensure that the categorical data is of type string.
  • Fixed the GreedyHillClimbing iteration counter, which was begin increased twice per iteration.
  • Added a default parameter value for include_cpd in BayesianNetworkBase:save() and DynamicBayesianNetworkBase::save().
  • Added more checks to detect ill-conditioned regression problems. The BIC score returns -infinity for ill-conditioned regression problems.

v0.3.1

  • Fixed the build process to support CMake versions older than 3.13.
  • Fixed a bug that might raise an error with a call to FactorType::new_factor() with *args and **kwargs arguments . This bug was only reproducible if the library was compiled with gcc.
  • Added CMake as prerequisite to compile the library in the docs.

v0.3.0

  • Removed all the submodules to simplify the imports. Now, all the classes are accessible directly from the pybnesian root module.
  • Added a ProductKDE class that implements KDE with diagonal bandwidth matrix.
  • Added an abstract class BandwidthSelector to implement bandwidth selection for KDE and ProductKDE. Three concrete implementations of bandwidth selection are included: ScottsBandwidth, NormalReferenceRule and UCV.
  • Added Arguments, Args and Kwargs to store a set of arguments to be used to create new factors through FactorType::new_factor(). The Arguments are accepted by BayesianNetworkBase::fit() and the constructors of CVLikelihood, HoldoutLikelihood and ValidatedLikelihood.

v0.2.1

  • An error related to the processing of categorical data with too many categories has been corrected.
  • Removed -march=native flag in the build script to avoid the use of instruction sets not available on some CPUs.

v0.2.0

  • Added conditional linear Gaussian networks (CLGNetworkType, CLGNetwork, ConditionalCLGNetwork and DynamicCLGNetwork).
  • Implemented ChiSquare (and DynamicChiSquare) indepencence test.
  • Implemented MutualInformation (and DynamicMutualInformation) indepencence test. This is valid for hybrid data.
  • Implemented BDe (Bayesian Dirichlet equivalent) score (and DynamicBDe).
  • Added UnknownFactorType as default FactorType for Bayesian networks when the node type could not be deduced.
  • Added Assignment class to represent the assignment of values to variables.

API changes:

  • Added method Score::data().
  • Added BayesianNetworkType::data_default_node_type() for non-homogeneous BayesianNetworkType.
  • Added constructor for HeterogeneousBN to specify a default FactorType for each data type. Also, it adds HeterogeneousBN::default_node_types() and HeterogeneousBN::single_default().
  • Added BayesianNetworkBase::has_unknown_node_types() and BayesianNetworkBase::set_unknown_node_types().
  • Changed signature of BayesianNetworkType::compatible_node_type() to include the new node type as argument.
  • Removed FactorType::opposite_semiparametric(). This functionality has been replaced by BayesianNetworkType::alternative_node_type().
  • Included model as parameter of Operator::opposite().
  • Added method OperatorSet::set_type_blacklist(). Added a type blacklist argument to ChangeNodeTypeSet constructor.

v0.1.0

  • First release! =).