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interactive notebooks

Creative Commons LicenseaGrUMinteractive 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.

import pyagrum as gum
import pyagrum.lib.notebook as gnb
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 name in ["res/alarm.dsl", "res/asia.bgum", "res/Diabetes.bgum"]:
progbar = TqdmProgressBarLoadListener(name)
bns[os.path.basename(name)] = gum.loadBN(name, listeners=[lambda progress: progbar.update(progress)])
res/alarm.dsl: 0%| |
res/asia.bgum: 0%| |
res/Diabetes.bgum: 0%| |

Which should give you something like

progess bars

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:

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=7.940532588672842e-06 ,max=0.8850119269966233
G LVFAILURE LVFAILURE LVEDVOLUME LVEDVOLUME HISTORY HISTORY STROKEVOLUME STROKEVOLUME MINVOL MINVOL HR HR HRSAT HRSAT HR->HRSAT CO CO HREKG HREKG HRBP HRBP HR->HRBP PCWP PCWP MINVOLSET MINVOLSET VENTMACH VENTMACH MINVOLSET->VENTMACH VENTLUNG VENTLUNG VENTALV VENTALV VENTLUNG->VENTALV EXPCO2 EXPCO2 VENTLUNG->EXPCO2 CVP CVP PRESS PRESS BP BP CO->BP SHUNT SHUNT SAO2 SAO2 ANAPHYLAXIS ANAPHYLAXIS TPR TPR ERRLOWOUTPUT ERRLOWOUTPUT VENTTUBE VENTTUBE CATECHOL CATECHOL ARTCO2 ARTCO2 PULMEMBOLUS PULMEMBOLUS PAP PAP PVSAT PVSAT INTUBATION INTUBATION VENTALV->ARTCO2 VENTALV->PVSAT INSUFFANESTH INSUFFANESTH LVEDVOLUME->PCWP LVEDVOLUME->CVP HYPOVOLEMIA HYPOVOLEMIA HYPOVOLEMIA->LVEDVOLUME HYPOVOLEMIA->STROKEVOLUME VENTMACH->VENTTUBE ERRCAUTER ERRCAUTER ERRCAUTER->HRSAT FIO2 FIO2 KINKEDTUBE KINKEDTUBE DISCONNECT DISCONNECT STROKEVOLUME->CO
BN filtered by $MI>0.3$
G LVFAILURE LVFAILURE LVEDVOLUME LVEDVOLUME HISTORY HISTORY STROKEVOLUME STROKEVOLUME MINVOL MINVOL HR HR HRSAT HRSAT CO CO HREKG HREKG HRBP HRBP PCWP PCWP MINVOLSET MINVOLSET VENTMACH VENTMACH VENTLUNG VENTLUNG VENTALV VENTALV VENTLUNG->VENTALV EXPCO2 EXPCO2 VENTLUNG->EXPCO2 CVP CVP PRESS PRESS BP BP SHUNT SHUNT SAO2 SAO2 ANAPHYLAXIS ANAPHYLAXIS TPR TPR ERRLOWOUTPUT ERRLOWOUTPUT VENTTUBE VENTTUBE CATECHOL CATECHOL ARTCO2 ARTCO2 PULMEMBOLUS PULMEMBOLUS PAP PAP PVSAT PVSAT INTUBATION INTUBATION INSUFFANESTH INSUFFANESTH LVEDVOLUME->PCWP LVEDVOLUME->CVP HYPOVOLEMIA HYPOVOLEMIA VENTMACH->VENTTUBE ERRCAUTER ERRCAUTER FIO2 FIO2 KINKEDTUBE KINKEDTUBE DISCONNECT DISCONNECT STROKEVOLUME->CO
BN filtered by $MI>0.5$

Now we can use this class for animation :

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));
interactive(children=(FloatSlider(value=0.44250993376460596, description='threshold', max=0.8850119269966233, …

Which should give you something like

informationVisualisation

from ipywidgets import interact, fixed
bn = bns["asia.bgum"]
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)
interactive(children=(Dropdown(description='visit_to_Asia', options=('0', '1'), value='0'), Dropdown(descripti…