Export and visualize Graphical models and Tensors (png, pdf)

pyagrum.lib.image aims to graphically export models and inference using pydot (and then graphviz).

import pyagrum as gum
from pyagrum.lib.image as gumimage

bn = gum.fastBN("a->b->d;a->c->d[3]->e;f->b")
gumimage.export(bn,"out/test_export.png",
                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})

Visualization of models and inference

pyagrum.lib.image.export(obj, filename=None, **kwargs)

export a graphical model or an inference or an obj with _repr_html as an image

• Parameters:
  • obj (Any) – the model to export (pyagrum.BayesNet, pyagrum.MarkovRandomField, pyagrum.InfluenceDiagram or pyagrum.Tensor) or a string in dot format or an object with a method _repr_html_()
  • filename (str | None) – the name of the resulting file (suffix in [‘pdf’, ‘png’, ‘fig’, ‘jpg’, ‘svg’, ‘ps’]). If filename is None, the result is a np.array ready to be used with imshow().
• Return type: Any

Note

Model can also just possess a method toDot() or even be a simple string in dot syntax.

Note

When exporting in pdf, you can adjust the margin with the config parameters export_pdf_margin_x and export_pdf_margin_y (in pixels, default 40 and 37 resp.). You can also adjust the size of the exported graph with the parameter size (default “8,5!”).

pdf export with custom margin : pyagrum.config.typed["notebook", "export_pdf_margin_x"] = 40 pyagrum.config.typed["notebook", "export_pdf_margin_y"] = 37

pyagrum.lib.image.exportInference(model, filename=None, **kwargs)

the graphical representation of an inference in a notebook

• Parameters:
  • model (Any) – the model in which to infer (pyagrum.BayesNet, pyagrum.MarkovRandomField or pyagrum.InfluenceDiagram)
  • filename (str | None) – the name of the resulting file (suffix in [‘pdf’, ‘png’, ‘ps’]). If filename is None, the result is a np.array ready to be used with imshow().
  • engine (pyagrum.BNInference | pyagrum.MRFInference | pyagrum.CNInference | pyagrum.IDInference) – inference algorithm used. If None, pyagrum.LazyPropagation will be used for BayesNet,pyagrum.ShaferShenoy for pyagrum.MarkovRandomField and pyagrum.ShaferShenoyLIMIDInference for pyagrum.InfluenceDiagram.
  • evs (dict *[*str *,*str *|*int ]) – map of evidence
  • targets (Set *[*str *|*int ]) – set of targets
  • size (str) – size of the rendered graph
  • nodeColor (dict *[*int *,*float ]) – a nodeMap of values (between 0 and 1) to be shown as color of nodes (with special colors for 0 and 1)
  • factorColor (dict *[*int *,*float ]) – a nodeMap of values (between 0 and 1) to be shown as color of factors (in MarkovRandomField representation)
  • arcWidth (dict [ *(*int *,*int ) *,*float ]) – a arcMap of values to be shown as width of arcs
  • arcColor (dict [ *(*int *,*int ) *,*float ]) – a arcMap of values (between 0 and 1) to be shown as color of arcs
  • cmap (matplotlib.colors.ColorMap) – color map to show the color of nodes and arcs
  • cmapArc (matplotlib.colors.ColorMap) – color map to show the vals of Arcs.
  • graph (pyagrum.Graph) – only shows nodes that have their id in the graph (and not in the whole BN)
  • view (str) – graph | factorgraph | None (default) for Markov random field
• Returns: the desired representation of the inference
• Return type: Any