Module bn2graph

A module to graphically display Bayesian networks using pydotplus (and then graphviz).

bn2graph_test.png 
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import pyAgrum as gum
from pyAgrum.lib.bn2graph import BN2dot

bn = gum.fastBN("a->b->d;a->c->d[3]->e;f->b")
g = BN2dot(bn,
           nodeColor={'a': 1,
                      'b': 0.3,
                      'c': 0.4,
                      'd': 0.1,
                      'e': 0.2,
                      'f': 0.5},
           arcColor={(0, 1): 0.2,
                     (1, 2): 0.5},
           arcWidth={(0, 3): 0.4,
                     (3, 2): 0.5,
                     (2,4) :0.6})

g.write("bn2graph_test.png", format='png')

Visualization of Potentials

pyAgrum.lib.bn2graph.proba2histo(p, scale=1.0, util=None, txtcolor='Black')

compute the representation of an histogram for a mono-dim Potential

Parameters:
  • p (pyAgrum.Potential) – the mono-dimensional Potential
  • util (pyAgrum.Potential) – an (optional) secondary Potential (values in labels)
  • txtcolor (str) – color for text
Returns:

a matplotlib histogram for a Potential p.
Return type:

matplotlib.Figure

Visualization of Bayesian networks

pyAgrum.lib.bn2graph.BN2dot(bn, size=None, nodeColor=None, arcWidth=None, arcColor=None, cmapNode=None, cmapArc=None, showMsg=None)

create a pydotplus representation of the BN

Parameters:
  • bn (pyAgrum.BayesNet) – the Bayesian network
  • size (str) – size of the rendered graph
  • nodeColor (dict) – a nodeMap of values to be shown as color nodes (with special color for 0 and 1)
  • arcWidth (dict) – a arcMap of values to be shown as bold arcs
  • arcColor (dict) – a arcMap of values (between 0 and 1) to be shown as color of arcs
  • cmapNode (ColorMap) – color map to show the vals of Nodes
  • cmapArc (ColorMap) – color map to show the vals of Arcs
  • dag (pyAgrum.DAG) – only shows nodes that have their id in the dag (and not in the whole BN)
  • showMsg (dict) – a nodeMap of values to be shown as tooltip
Returns:
Return type:

the desired representation of the Bayesian network
pyAgrum.lib.bn2graph.BNinference2dot(bn, size=None, engine=None, evs={}, targets={}, nodeColor=None, arcWidth=None, arcColor=None, cmapNode=None, cmapArc=None, dag=None)

create a pydotplus representation of an inference in a BN

Parameters:
  • bn (pyAgrum.BayesNet) – the Bayesian network
  • size (str) – size of the rendered graph
  • engine (pyAgrum.Inference) – inference algorithm used. If None, LazyPropagation will be used
  • evs (dict) – map of evidence
  • targets (set) – set of targets. If targets={} then each node is a target
  • nodeColor (dict) – a nodeMap of values to be shown as color nodes (with special color for 0 and 1)
  • arcWidth (dict) – a arcMap of values to be shown as bold arcs
  • arcColor (dict) – a arcMap of values (between 0 and 1) to be shown as color of arcs
  • cmapNode (ColorMap) – color map to show the vals of Nodes
  • cmapArc (ColorMap) – color map to show the vals of Arcs
  • dag (pyAgrum.DAG) – only shows nodes that have their id in the dag (and not in the whole BN)
Returns:
Return type:

the desired representation of the inference

Hi-level functions

pyAgrum.lib.bn2graph.dotize(aBN, name, format='pdf')

From a bn, creates an image of the BN

Parameters:
  • bn (pyAgrum.BayesNet) – the bayes net to show
  • name (string) – the filename (without extension) for the image
  • format (string) – format in [‘pdf’,’png’,’fig’,’jpg’,’svg’]
pyAgrum.lib.bn2graph.pngize(aBN, name)

From a bn, creates a png of the BN

Parameters:
  • bn (pyAgrum.BayesNet) – the bayes net to show
  • name (string) – the filename (without extension) for the image
pyAgrum.lib.bn2graph.pdfize(aBN, name)

From a bn, creates a pdf of the BN

Parameters:
  • bn (pyAgrum.BayesNet) – the bayes net to show
  • name (string) – the filename (without extension) for the image