docs

Author: felixleopoldo

docs/source/structure_learning_algorithms/pyagrum.rst

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+:og:description: Based on the C++ aGrUM library, it provides a high-level interface to the C++ part of aGrUM allowing to create, manage and perform efficient computations with Bayesian networks and others probabilistic graphical models : Markov random fields (MRF), influence diagrams (ID) and LIMIDs, credal networks (CN), dynamic BN (dBN), probabilistic relational models (PRM).
+:og:image:alt: Benchpress logo
+:og:sitename: Benchpress causal discovery platform
+:og:title: PyAgrum (pyagrum)
+ 
+.. meta::
+    :title: PyAgrum (pyagrum)
+    :description: Based on the C++ aGrUM library, it provides a high-level interface to the C++ part of aGrUM allowing to create, manage and perform efficient computations with Bayesian networks and others probabilistic graphical models : Markov random fields (MRF), influence diagrams (ID) and LIMIDs, credal networks (CN), dynamic BN (dBN), probabilistic relational models (PRM).
+
+
+.. _pyagrum: 
+
+PyAgrum (pyagrum) 
+******************
+
+
+
+.. list-table:: 
+
+   * - Module name
+     - `pyagrum <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/pyagrum>`__
+   * - Package
+     - `pyagrum <https://pyagrum.readthedocs.io/en/latest/>`__
+   * - Version
+     - 1.14.0
+   * - Language
+     - `Python <https://www.python.org/>`__
+   * - Docs
+     - `here <https://pyagrum.readthedocs.io/en/latest/notebooks/31-Learning_structuralLearning.html>`__
+   * - Paper
+     - :footcite:t:`10.1371/journal.pcbi.1005662`
+   * - Graph type
+     - `DAG <https://en.wikipedia.org/wiki/Directed_acyclic_graph>`__
+   * - MCMC
+     - No
+   * - Edge constraints
+     - No
+   * - Data type
+     - B
+   * - Data missingness
+     - 
+   * - Intervention type
+     - 
+   * - Docker 
+     - `bpimages/pyagrum:1.14.0 <https://hub.docker.com/r/bpimages/pyagrum/tags>`__
+
+
+
+
+PyAgrum 
+-----------
+
+
+pyAgrum is a scientific C++ and Python library dedicated to Bayesian networks (BN) and other Probabilistic Graphical Models. Based on the C++ aGrUM library, it provides a high-level interface to the C++ part of aGrUM allowing to create, manage and perform efficient computations with Bayesian networks and others probabilistic graphical models : Markov random fields (MRF), influence diagrams (ID) and LIMIDs, credal networks (CN), dynamic BN (dBN), probabilistic relational models (PRM).
+
+
+.. rubric:: Example 
+
+Config file: `config.json <https://github.com/felixleopoldo/benchpress/blob/master/workflow/rules/structure_learning_algorithms/pyagrum/config.json>`_
+
+Command:
+
+.. code:: bash
+
+    snakemake --cores all --use-singularity --configfile workflow/rules/structure_learning_algorithms/pyagrum/config.json
+
+The following figure shows FP/P vs. TP/P for pattern graphs based on 5 datsets corresponding to 5 realisations of a 80-variables random binary Bayesian network, with an average indegree of 4.
+
+
+.. _pyagrumplot:
+
+.. figure:: ../../../workflow/rules/structure_learning_algorithms/pyagrum/pattern.png
+    :width: 320 
+    :alt: pyAgrum FP/P vs. TP/P example
+    :align: center
+
+    FP/P vs. TP/P. for pattern graphs
+
+
+
+
+
+.. rubric:: Example JSON
+
+
+.. code-block:: json
+
+
+    [
+      {
+        "id": "pyagrum",
+        "useMDLCorrection": true,
+        "useSmoothingPrior": [
+          true,
+          false
+        ],
+        "timeout": null
+      }
+    ]
+
+.. footbibliography::
+