Tags

* aGrUM
    * Learning algorithm `gum::learning::MIIC` can use the weighted databases.
    * Internal improvements for `act` tool, `cmake` and compilers (`clang`).
  
* pyAgrum
    * New visualisation for `gum::DiscretizedVariable` + new config to select this visualisation.
    * `pyAgrum.BNLearner` can use now the weighted databases for all learning algorithms.
    * Documentation improvements.
    * `pyAgrum.lib.bn2roc`
        * adding new functions `get{ROC|PR}points()`.
        * accepting `pandas.DataFrame` as data source (`datasrc`).
        * adding Fbeta (beta!=1) scores to bn2roc.
        * adding F-Beta threshold on ROC and PR curves.
        * `bn2roc` functions now force many parameters to be keyword-arguments in order to prevent the risk of mixing arguments.
        * adding new functions `anim{ROC|PR}`.
    * `pyAgrum.skbn.Discretizer` can propose a set of labels (that includes the labels from the database) when `"NoDiscretization"` is selected. (see tutorial `52-Classifier_Discretizer`).

* aGrUM
    * Removing GCC warnings about overloaded virtual methods.
    * Fixing a bug in jointree inference in rare cases (when an irrelevant soft evidence is added).
  
* pyAgrum
    * Fixing a bug in jointree inference in rare cases (when an irrelevant soft evidence is added).
    * Removing deprecated methods from pyAgrum<1.1.0.
    * Adding new syntax for set (conditional) marginal with dict : `p[{"A":0,"C":1}]={"no":0.3,"yes":0.7}`.
    * small updates for pyAgrum's documentation.

* aGrUM
  * Fixing a bug (still) in `gum::PDAG::cSeparation` (thanks to Keneth Lee).
  * Fixing a bug in `gum::BIFXMLIdWriter::save`.
  * typos in `gum::PDAG::toDot()`.

* pyAgrum
  * Fixing a bug in `pyAgrum/{load|save}ID`.
  * Wrapping `gum::PDAG::moralizedAncestralGraph()`.
  * Adding a warning when importing `pyAgrum.lib.notebook` if graphviz is not installed.

* aGrUM
  * Bugfixes in `gum::PDAG::cSeparation` (thanks to Keneth Lee).
  * Using 'table' instead of 'default' for marginal cpt in `gum::BIFWriter`.
  * Adding `std::cout<<(gum::Timer& t)`

* pyAgrum
  * Updating build image for rtd.
  * Improving documentations.
  * Documentation for pyAgrum.InformationTheory.
  * Uniformizing argume `cmap` to `cmapNode` in `pyAgrum.lib.notebook`.
  * Adding beeswarmplot for shapley values figures in `pyAgrum.lib.explain`.

* aGrUM
  * Adding new class `gum::InfomationTheory` that regroups every information theory concepts (entropies, conditional entropies and mutual information, ...) with only one inference, which allows also conditioned information theory concepts.  
  * Adding `gum::Potential::expectedValue()`
  * Adding the alternative *fast* syntax for NumericalDiscreteVariable : `{1.5:3.5:3}` meaning from 1.5 to 3.5 in 3 steps.
  * Updating `GraphicalModel::exists(const std::string&)` : return false instead of throwing an exception.
  * Adding `gum::graphicalModels::variables()` and a new alias for `gum::VariableSet`.
  * Fixing a bug in `gum::ShaferShenoyInference` for joint posterior when some nodes in the target received hard evidence.
  * Improving error message w.r.t `gum::*::jointPosterior`.
  * Adding mingw as a target for CI.
  * Fixing dangling pointers in testsuite, only detected by MSVC.
  * Optimized `gum::PDAG::cSeparation`
  * Renaming `gum::Set::isProper{Sub|Super}Set` to `gum::Set::isStrict{Sub|Super}Set`.

* pyAgrum
  * swig>4.1 is now needed.
  * Enabling installation with poetry (thanks to Lorenzo Conti)
  * Adding new class `pyAgrum.InfomationTheory` that regroups every information theory concepts (entropies, conditional entropies and mutual information, ...) with only one inference, which allows also conditioned information theory concepts.
  * Adding `pyAgrum.Potential.expectedValue()`.
  * Restructuring a bit the sphinx documentation.
  * Down-exporting the read-only API for every types of discrete Variable to `pyAgrum.DiscreteVariable`. (see `VariablesTestSuite.testExportDerivedReadOnlyAPIforDiscreteVariable`)
  * Updating notebooks to the new down-exported read-only API of `pyAgrum.DiscreteVariable`.
  * Adding the alternative *fast* syntax for NumericalDiscreteVariable : `{1.5:3.5:3}` meaning from 1.5 to 3.5 in 3 steps.
  * Updating docs for new *fast* syntax.
  * Updating `GraphicalModel::exists(const std::string&)` : return false instead of throw an exception.
  * Initiating a new notebook about the interaction with ipywidget
  * Optimized and correctly wrapped `gum::PDAG::cSeparation` (thanks to Kenneth Lee).
  * New functions `pyAgrum.lib.explain.nestedMarkovBlankets` and `pyAgrum.lib.explain.nestedMarkovBlanketsNames`.

* aGrUM
  * Adding `gum::{AllDiscreteVariable}.toFast()` to write the variable in *fast* syntax.
  * Fixing a bug in BN's random generators : `domain_size` incremented by 1 (thanks to Kenneth Lee).
  * Fixing a bug in *fast* syntax : `'A[n]' with n<2`.

* pyAgrum
  * Adding `pyAgrum.{AllDiscreteVariable}.toFast()` to write the variable in *fast* syntax.
  * Fixing a bug in BN's random generators (e.g. `pyAgrum.randomBN`) : `domain_size` incremented by 1 (thanks to Kenneth Lee).
  * Fixing a bug in *fast* syntax : `'A[n]' with n<2`.
  * Modifying (lightly) the structure for documentation.

* aGrUM
  * Add `gum::Potential::sign` and `gum::Potential::new_sign` 
  * Bug fix in causalImpact found by Musfiqur Rahman
  * Generalization and rationalization of access of aGrUM's generator of pseudo-random values
  * Adding `gum::Timer::toString()`.
  * Improving BN random generator.
  * get rid of `sprintf` as much as possible.
  * Fixing random seed.
  * Fixing `gum::Potential::random()` : not a distribution.
  * Updating *Fast* syntax : enumerating consecutive integers (e.g `{1|2|3|4}`) creates a `gum::RangeVariable[1,4]` rather than an `gum::IntegerVariable{1|2|3|4}`.
* pyAgrum
  * Add `pyAgrum.Potential.sign` and `pyAgrum.Potential.new_sign` 
  * Bug fix in causalImpact found by Musfiqur Rahman
  * Adding `pyAgrum.Timer.__str()__`.
  * Improving BN random generator.
  * Fixing random seed.
  * Fixing `pyAgrum.Potential.random()` : not a distribution.
  * Adding access to `gum.fastVariable()`.
  * Updating *Fast* syntax : enumerating consecutive integers (e.g `{1|2|3|4}`) creates a `pyAgrum.RangeVariable[1,4]` rather than an `pyAgrum.IntegerVariable{1|2|3|4}`.
  * add a new module to export in different formats (for now just GML).

Quick fix for the configuration of pyAgrum documentation generation by readthedoc.

The main points of this tag is the renaming of *Markov network* (`MarkovNet`, `MN`) for the better known *Markov Random Field* (`MarkovRandomField`, `MRF`) and a new reader/writer of XDSL format (Genie/Smile) for Bayesian networks. Other improvements and corrections have naturally also been made.

* aGrUM
  * Renaming `gum::MarkovNet` to `gum::MarkovRandomField`. Renaming `gum::*MN*` to `gum::*MRF*` when necessary.
  * Fixing glitches and bugs induced or revealed by `gum::MarkovNet`->`gum::MarkovRandomField`.
  * new `XDSL` Reader/writer for Bayesian network.
  * Renaming `gum::Learning::BNLearner::learnMixedStructure` to `gum::Learning::BNLearner::learnPDAG`
  * working on documentation : better rendering for doxygen pages.
  * Renaming `gum::dSeparation` to `gum::dSeparationAlgorithm`.

* pyAgrum
  * Renaming `pyAgrum.MarkovNet` to `pyAgrum.MarkovRandomField`. Renaming `pyAgrum.*MN*` to `pyAgrum.*MRF*` when necessary.
  * new `XDSL` Reader/writer for Bayesian network.
  * Renaming `pyAgrum.BNLearner.learnMixedStructure()` to `pyAgrum.BNLearner.learnPDAG()`.
  * For figure containing nodes drawn by matplotlib (e.g. inference), use the same font for all nodes (default from matplotlib) (thanks to Jonathon Blackford).
  * Working on documentation : better rendering for readthedocs pages, improved structuration, new thumbnail image for some notebooks/tutorials.
  * Significant improvement of the documentation coverage. 
  * Improving `gum.DiscreteVariable.to[typeOfVariable]`, renaming as `gum.DiscreteVariable.as[typeOfVariable]` and adding documentations.

Mainly documentations and new analytics for the different sites.

* pyAgrum
    * Adding a new and very easy to use `pyAgrum.randomBN` that creates a BN from a list of names.
    * Removing obsolete `pyAgrum.generateCSV` replaced by `pyAgrum.generateSample`. Fixing tests.
    * Fixing and improving pyAgrum documentation.

* aGrUM
  * Improving `gum::BNLearner::state()`.
  * (internals) new macro `GUM_TEST` and `GUM_INACTIVE_TEST` for CI
  
* pyAgrum
  * Improving `gum.BNLearner.state()`.
  * Improving style for readthedoc (pygments and no StickySideBar)
  * Improving documentation coverage (for methods) from 84.9% to 90.7% : more than 100 newly covered methods.

* aGrUM

  * (internals) Updating `act --stats`.
  * (internals) Reorganizing build folder : `build/{aGrUM|pyAgrum}/{debug|release}`.
  * (internals) Improving cmake & CIs.
  * Threadsafe graphs and Graphical Models.
  * Adding new graph `gum::PDAG` (Partially Directed Acyclic Graph).
  * Renaming `gum::MixedGraph::adjacents` with correct graph notion : `gum::MixedGraph::boundary`.
  * Initializing the majority of the end/rend iterators at compile time.
  * Adding AVL binary search trees: `gum::AVLTree`.
  * Adding priority queues that can be iterated in order: `gum::SortedPriorityQueue`.
  * Better messages for `gum::BayesNet::check()`.

* pyAgrum

  * (internals) New docker images with linux gcc 11 for wheels.
  * (internals) CMake: Use FindPython module.
  * (internals) Removing some (false positive) warning notifications from swig.
  * Fixing NaN bugs for new versions of Graphviz.
  * Improving gum.lib.notebooks.flow and light/dark theme compatibility.
  * Renaming `pyAgrum.MixedGraph.adjacents` with correct graph notion : `pyAgrum.MixedGraph.boundary`.
  * New graph class `gum.PDAG` (Partially Directed Acyclic Graph).
  * Fixing a small typo when displaying function as Potential.
  * better messages for `pyAgrum.BayesNet.check()`.
  * More robust `gum.explain.showInformation()` w.r.t. NaN.
  * Fixing typos in documentation.
  * Improving the organization of ReadTheDoc documentation.

* pyAgrum

  * bugfixes in `pyAgrum.lib.[causal.]notebook`.

* aGrUM

    * better `toString` for BN : show the memory used by the parameters.

* pyAgrum

    * **adding conda and pip packages for python 3.11**
    * better `__str__` for BN : show the memory used by the parameters.
    * bug fix in obsolete pyAgrum.`BNLearner.useNoAPriori()`.
    * bug fix when displaying a `pyAgrum.causal.CausalFormula` generated by do-Calculus : retrieving the original `doing` and `knowing` sets.