Module bn2graph

pyAgrum.lib.bn2graph.BN2dot(bn, size='4', nodeColor=None, arcWidth=None, arcColor=None, cmapNode=None, cmapArc=None, showMsg=None)

create a pydotplus representation of the BN

Parameters:
  • bn (pyAgrum.BayesNet) –
  • size (string) – size of the rendered graph
  • nodeColor – a nodeMap of values (between 0 and 1) to be shown as color of nodes (with special colors for 0 and 1)
  • arcWidth – a arcMap of values to be shown as width of arcs
  • arcColor – a arcMap of values (between 0 and 1) to be shown as color of arcs
  • cmapNode – color map to show the vals of Nodes
  • cmapArc – color map to show the vals of Arcs.
  • showMsg – a nodeMap of values to be shown as tooltip
Returns:

the desired representation of the BN as a dot graph

pyAgrum.lib.bn2graph.BNinference2dot(bn, size='4', engine=None, evs={}, targets={}, format='png', nodeColor=None, arcWidth=None, cmap=None)

create a pydotplus representation of an inference in a BN

Parameters:
  • bn (pyAgrum.BayesNet) –
  • size (string) – size of the rendered graph
  • Inference engine (pyAgrum) – inference algorithm used. If None, LazyPropagation will be used
  • evs (dictionnary) – map of evidence
  • targets (set) – set of targets. If targets={} then each node is a target
  • format (string) – render as “png” or “svg”
  • nodeColor – a nodeMap of values to be shown as color nodes (with special color for 0 and 1)
  • arcWidth – a arcMap of values to be shown as bold arcs
  • cmap – color map to show the vals
Returns:

the desired representation of the inference

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.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
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.proba2histo(p)

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

Parameters:p (pyAgrum.Potential) – the mono-dim Potential
Returns:a matplotlib histogram for a Potential p.
test.png
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bn = gum.fastBN("a->b->d;a->c->d->e;f->b")
g = BNinference2dot(bn,
                    targets=['f', 'd'],
                    vals={'a': 1,
                          'b': 0.3,
                          'c': 0.3,
                          'd': 0.1,
                          'e': 0.1,
                          'f': 0.3},
                    arcvals={(0, 1): 2,
                             (0, 2): 0.5})
g.write("test.png", format='png')

Visualization of Potentials

pyAgrum.lib.bn2graph.proba2histo(p)

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

Parameters:p (pyAgrum.Potential) – the mono-dim Potential
Returns:a matplotlib histogram for a Potential p.

Visualization of Bayesian Networks

pyAgrum.lib.bn2graph.BN2dot(bn, size='4', nodeColor=None, arcWidth=None, arcColor=None, cmapNode=None, cmapArc=None, showMsg=None)

create a pydotplus representation of the BN

Parameters:
  • bn (pyAgrum.BayesNet) –
  • size (string) – size of the rendered graph
  • nodeColor – a nodeMap of values (between 0 and 1) to be shown as color of nodes (with special colors for 0 and 1)
  • arcWidth – a arcMap of values to be shown as width of arcs
  • arcColor – a arcMap of values (between 0 and 1) to be shown as color of arcs
  • cmapNode – color map to show the vals of Nodes
  • cmapArc – color map to show the vals of Arcs.
  • showMsg – a nodeMap of values to be shown as tooltip
Returns:

the desired representation of the BN as a dot graph

pyAgrum.lib.bn2graph.BNinference2dot(bn, size='4', engine=None, evs={}, targets={}, format='png', nodeColor=None, arcWidth=None, cmap=None)

create a pydotplus representation of an inference in a BN

Parameters:
  • bn (pyAgrum.BayesNet) –
  • size (string) – size of the rendered graph
  • Inference engine (pyAgrum) – inference algorithm used. If None, LazyPropagation will be used
  • evs (dictionnary) – map of evidence
  • targets (set) – set of targets. If targets={} then each node is a target
  • format (string) – render as “png” or “svg”
  • nodeColor – a nodeMap of values to be shown as color nodes (with special color for 0 and 1)
  • arcWidth – a arcMap of values to be shown as bold arcs
  • cmap – color map to show the vals
Returns:

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