Different sampling inference

%matplotlib inline
from pylab import *
import matplotlib.pyplot as plt

import warnings

warnings.simplefilter("error")

import pyagrum as gum

bn = gum.loadBN("res/Diabetes.bif")
# gnb.showBN(bn,size='8')
print(bn)

BN{nodes: 413, arcs: 602, domainSize: 10^406.108, dim: 429409, mem: 3Mo 530Ko 104o}

First, some helpers

import timeit


class Timer:
  def __enter__(self):
    self.start = timeit.default_timer()
    return self

  def __exit__(self, *args):
    self.end = timeit.default_timer()
    self.duration = self.end - self.start

def execute(ie):
  with Timer() as t:
    ie.makeInference()
    for i in bn.nodes():
      a = ie.posterior(i)
  return "duration : {:3.3f}s".format(t.duration)


def vals(bn, ie):
  exact = []
  appro = []
  for node in bn.nodes():
    # Tensor as float list
    exact += ie.posterior(node).tolist()

  return exact

Exact inference.

import matplotlib_inline

matplotlib_inline.backend_inline.set_matplotlib_formats("png")

plt.rcParams["figure.figsize"] = [30, 3]


def compareIE(bn, maxtime, epsilon, evs=None):
  ie = gum.LazyPropagation(bn)
  if evs is not None:
    ie.setEvidence(evs)
  x = vals(bn, ie)

  ie2 = gum.GibbsSampling(bn)
  if evs is not None:
    ie2.setEvidence(evs)
  ie2.setMaxTime(maxtime)
  ie2.setEpsilon(epsilon)
  txt = "Gibbs : " + execute(ie2) + "\n" + ie2.messageApproximationScheme()
  y = vals(bn, ie2)
  plt.subplot(181)
  plt.plot(x, y, "ro")
  plt.title(txt)

  ie3 = gum.MonteCarloSampling(bn)
  if evs is not None:
    ie3.setEvidence(evs)
  ie3.setMaxTime(maxtime)
  ie3.setEpsilon(epsilon)
  txt = "MonteCarlo : " + execute(ie3) + "\n" + ie3.messageApproximationScheme()
  y = vals(bn, ie3)
  plt.subplot(182)
  plt.plot(x, y, "ro")
  plt.title(txt)

  ie4 = gum.WeightedSampling(bn)
  if evs is not None:
    ie4.setEvidence(evs)
  ie4.setMaxTime(maxtime)
  ie4.setEpsilon(epsilon)
  txt = "Weighted : " + execute(ie4) + "\n" + ie4.messageApproximationScheme()
  y = vals(bn, ie4)
  plt.subplot(183)
  plt.plot(x, y, "ro")
  plt.title(txt)

  ie5 = gum.ImportanceSampling(bn)
  if evs is not None:
    ie5.setEvidence(evs)
  ie5.setMaxTime(maxtime)
  ie5.setEpsilon(epsilon)
  txt = "Importance: " + execute(ie5) + "\n" + ie5.messageApproximationScheme()
  y = vals(bn, ie5)
  plt.subplot(184)
  plt.plot(x, y, "ro")
  plt.title(txt)

  ie6 = gum.LoopyBeliefPropagation(bn)
  if evs is not None:
    ie6.setEvidence(evs)
  ie6.setMaxTime(maxtime)
  ie6.setEpsilon(epsilon)
  txt = "LBP: " + execute(ie6) + "\n" + ie6.messageApproximationScheme()
  y = vals(bn, ie6)
  plt.subplot(185)
  plt.plot(x, y, "ro")
  plt.title(txt)

  ie7 = gum.LoopyWeightedSampling(bn)
  if evs is not None:
    ie7.setEvidence(evs)
  ie7.setMaxTime(maxtime)
  ie7.setEpsilon(epsilon)
  txt = "LoopyWeighted: " + execute(ie7) + "\n" + ie7.messageApproximationScheme()
  y = vals(bn, ie7)
  plt.subplot(186)
  plt.plot(x, y, "ro")
  plt.title(txt)

  ie8 = gum.LoopyGibbsSampling(bn)
  if evs is not None:
    ie8.setEvidence(evs)
  ie8.setMaxTime(maxtime)
  ie8.setEpsilon(epsilon)
  txt = "LoopyGibbs: " + execute(ie8) + "\n" + ie8.messageApproximationScheme()
  y = vals(bn, ie8)
  plt.subplot(187)
  plt.plot(x, y, "ro")
  plt.title(txt)

  ie9 = gum.LoopyImportanceSampling(bn)
  if evs is not None:
    ie9.setEvidence(evs)
  ie9.setMaxTime(maxtime)
  ie9.setEpsilon(epsilon)
  txt = "LoopyImportance: " + execute(ie9) + "\n" + ie9.messageApproximationScheme()
  y = vals(bn, ie9)
  plt.subplot(188)
  plt.plot(x, y, "ro")
  plt.title(txt)

  plt.show()

compareIE(bn, 5, 1e-2)

compareIE(bn, 50, 1e-2)

compareIE(bn, 5, 1e-2, evs={"bg_24": 0, "ins_indep_util_23": 1, "renal_cl_14": 1})

compareIE(bn, 100, 1e-2, evs={"bg_24": 0, "ins_indep_util_23": 1, "renal_cl_14": 1})