interactive notebooks

Creative Commons License

aGrUM

interactive online version

pyAgrum can easily interact with other applications. In this notebook, we propose for example some application tracks with notebook ipywidgets to make the exploration of graphical models and their inferences more interactive.

In [1]:
import pyAgrum as gum
import pyAgrum.lib.notebook as gnb

Listeners and progress bars

In [2]:
import glob
import os.path
from tqdm.auto import tqdm

class TqdmProgressBarLoadListener:
    def __init__(self,filename:str):
        self.pbar=tqdm(total=100,
                      desc=filename,
                      bar_format='{desc}: {percentage:3.0f}%|{bar}|')
    def update(self,progress):
        if progress==200:
            self.pbar.close()
        else:
            self.pbar.update()
            self.pbar.display()


bns={}
for ext in ['dsl','bif']:
    for name in glob.glob(f"res/*.{ext}"):
        progbar=TqdmProgressBarLoadListener(name)
        bns[os.path.basename(name)]=gum.loadBN(name,listeners=[lambda progress:progbar.update(progress)])

Which should give you something like

progess bars

Animated graphs

ipywidget can be used with different types of objects. Let’s say that you have a class that show the arcs of a Bayesian network only the mutual information of this arc is above a certain threshold:

In [3]:
import pydot as dot

class InformationViewer:
    def __init__(self,bn:gum.BayesNet):
        self.bn=bn

        ie=gum.LazyPropagation(bn)
        self._min=float("inf")
        self._max=float("-inf")
        self._arcs={}
        for x,y in bn.arcs():
            nameX=bn.variable(x).name()
            nameY=bn.variable(y).name()
            ie.addJointTarget({nameX,nameY})
            info=gum.InformationTheory(ie,[nameX],[nameY])
            m=info.mutualInformationXY()
            if self._min>m: self._min=m
            if self._max<m: self._max=m
            self._arcs[x,y]=m

    def min(self):
        return self._min

    def max(self):
        return self._max

    def showBN(self,minVal:float=0):
        graph=dot.Dot(graph_type="digraph",bgcolor="transparent")
        bgcol = gum.config["notebook", "default_node_bgcolor"]
        fgcol = gum.config["notebook", "default_node_fgcolor"]
        for n in self.bn.names():
            graph.add_node(dot.Node('"' + n + '"', style="filled",
                                    fillcolor=bgcol,
                                    fontcolor=fgcol))
        for x,y in self.bn.arcs():
            graph.add_edge(dot.Edge('"' + self.bn.variable(x).name() + '"',
                                    '"' + self.bn.variable(y).name() + '"',
                                    style="invis" if self._arcs[x,y]<minVal else ""))


        size = gum.config["notebook", "default_graph_size"]
        graph.set_size(size)
        return graph

view=InformationViewer(bns['alarm.dsl'])
print(f"min={view.min()} ,max={view.max()}")
gnb.sideBySide(view.showBN(0.3),view.showBN(0.5),
              captions=["BN filtered by $MI>0.3$","BN filtered by $MI>0.5$"])
min=7.940532588368974e-06 ,max=0.8850119269966232
G VENTALV VENTALV ARTCO2 ARTCO2 VENTALV->ARTCO2 PVSAT PVSAT VENTALV->PVSAT VENTTUBE VENTTUBE PRESS PRESS VENTLUNG VENTLUNG HREKG HREKG CO CO BP BP CO->BP LVFAILURE LVFAILURE LVEDVOLUME LVEDVOLUME HISTORY HISTORY STROKEVOLUME STROKEVOLUME PAP PAP HR HR HRBP HRBP HR->HRBP HRSAT HRSAT HR->HRSAT CVP CVP LVEDVOLUME->CVP PCWP PCWP LVEDVOLUME->PCWP ERRCAUTER ERRCAUTER ERRCAUTER->HRSAT SAO2 SAO2 CATECHOL CATECHOL MINVOL MINVOL EXPCO2 EXPCO2 ERRLOWOUTPUT ERRLOWOUTPUT SHUNT SHUNT ANAPHYLAXIS ANAPHYLAXIS TPR TPR INSUFFANESTH INSUFFANESTH STROKEVOLUME->CO INTUBATION INTUBATION FIO2 FIO2 KINKEDTUBE KINKEDTUBE PULMEMBOLUS PULMEMBOLUS DISCONNECT DISCONNECT VENTMACH VENTMACH VENTMACH->VENTTUBE HYPOVOLEMIA HYPOVOLEMIA HYPOVOLEMIA->LVEDVOLUME HYPOVOLEMIA->STROKEVOLUME MINVOLSET MINVOLSET MINVOLSET->VENTMACH VENTLUNG->VENTALV VENTLUNG->EXPCO2
BN filtered by $MI>0.3$
G VENTALV VENTALV ARTCO2 ARTCO2 PVSAT PVSAT VENTTUBE VENTTUBE PRESS PRESS VENTLUNG VENTLUNG HREKG HREKG CO CO BP BP LVFAILURE LVFAILURE LVEDVOLUME LVEDVOLUME HISTORY HISTORY STROKEVOLUME STROKEVOLUME PAP PAP HR HR HRBP HRBP HRSAT HRSAT CVP CVP LVEDVOLUME->CVP PCWP PCWP LVEDVOLUME->PCWP ERRCAUTER ERRCAUTER SAO2 SAO2 CATECHOL CATECHOL MINVOL MINVOL EXPCO2 EXPCO2 ERRLOWOUTPUT ERRLOWOUTPUT SHUNT SHUNT ANAPHYLAXIS ANAPHYLAXIS TPR TPR INSUFFANESTH INSUFFANESTH STROKEVOLUME->CO INTUBATION INTUBATION FIO2 FIO2 KINKEDTUBE KINKEDTUBE PULMEMBOLUS PULMEMBOLUS DISCONNECT DISCONNECT VENTMACH VENTMACH VENTMACH->VENTTUBE HYPOVOLEMIA HYPOVOLEMIA MINVOLSET MINVOLSET VENTLUNG->VENTALV VENTLUNG->EXPCO2
BN filtered by $MI>0.5$

Now we can use this class for animation :

In [4]:
import ipywidgets as widgets
def interactive_view(threshold:float):
    return view.showBN(threshold)
widgets.interact(interactive_view,threshold=(view.min(),
                                             view.max(),
                                             (view.max()-view.min())/100.0));

Which should give you something like

informationVisualisation

Vizualizing evidence impact

In [5]:
from ipywidgets import interact, fixed

bn = bns['asia.bif']

asia = list(bn["visit_to_Asia"].labels())
smoking = list(bn["smoking"].labels())
XraY = list(bn["positive_XraY"].labels())
cig_ped_day = gum.RangeVariable("cigarettes_per_day","cigarettes_per_day in [0, 10]?",0,10)
bn.add(cig_ped_day)

@interact(bn=fixed(bn), visit_to_Asia=asia, smoking=smoking, positive_XraY=XraY, smoked_cigarettes=(cig_ped_day.minVal(), cig_ped_day.maxVal(), 1))
def evidence_impact(bn, visit_to_Asia, smoking, positive_XraY, smoked_cigarettes):
    evs = {"visit_to_Asia":visit_to_Asia, "smoking":smoking, "positive_XraY":positive_XraY, "cigarettes_per_day":smoked_cigarettes}
    gnb.showInference(bn, evs=evs)