def execute(self, ws, debug=0):
print self.legend, 'Markov chain MC calculation started...'
# time the action
t = TStopwatch()
t.Start()
# model config is guaranteed to be here
# the action won't configure without it
# and this code will never run unless valid model config is found
mconf = ws.obj(self._model_config_name)
_poi = mconf.GetParametersOfInterest()
mcInt = self._mc.GetInterval()
# stop watch and print how long it took to get the interval
t.Print()
# iterate over all parameters of interest and print out the intervals
# (could there be more than one?)
_iter = _poi.createIterator()
while True:
_poi_name = _iter().GetTitle()
lower_limit = mcInt.LowerLimit(_poi[_poi_name])
upper_limit = mcInt.UpperLimit(_poi[_poi_name])
print self.legend, 'MCMC interval for', _poi_name, 'is ['+ \
str(lower_limit) + ', ' + \
str(upper_limit) + ']'
if _iter.Next() == None:
break
if self._posterior:
# draw posterior plot
print self.legend, 'making the posterior plot'
_plot_name = _poi_name + '_mcmc_posterior_exost.' + self._plot_format
c1 = TCanvas("c1", "c1", 600, 600)
mcPlot = RooStats.MCMCIntervalPlot(mcInt)
gROOT.SetStyle("Plain")
mcPlot.Draw()
c1.SaveAs(_plot_name)
return (lower_limit, upper_limit)
def GetBayesianInterval(filename="workspace.root",
wsname='myWS',
interactive=False):
pInFile = ROOT.TFile(filename, "read")
# load workspace
pWs = pInFile.Get("myWS")
if not pWs:
print "workspace ", wsname, " not found"
return -1
# printout workspace content
pWs.Print()
# load and print data from workspace
data = pWs.data("data")
data.Print()
# load and print S+B Model Config
pSbHypo = pWs.obj("SbHypo")
pSbHypo.Print()
# create RooStats Bayesian calculator and set parameters
# Metropolis-Hastings algorithm needs a proposal function
sp = RooStats.SequentialProposal(10.0)
mcmc = RooStats.MCMCCalculator(data, pSbHypo)
mcmc.SetConfidenceLevel(0.95)
mcmc.SetNumIters(100000) # Metropolis-Hastings algorithm iterations
mcmc.SetProposalFunction(sp)
mcmc.SetNumBurnInSteps(500) # first N steps to be ignored as burn-in
mcmc.SetLeftSideTailFraction(0.0)
mcmc.SetNumBins(
40) # for plotting only - does not affect limit calculation
# estimate credible interval
# NOTE: unfortunate notation: the UpperLimit() name refers
# to the upper boundary of an interval,
# NOT to the upper limit on the parameter of interest
# (it just happens to be the same for the one-sided
# interval starting at 0)
pMcmcInt = mcmc.GetInterval()
upper_bound = pMcmcInt.UpperLimit(pWs.var("xsec"))
lower_bound = pMcmcInt.LowerLimit(pWs.var("xsec"))
print "one-sided 95%.C.L. bayesian credible interval for xsec: ", "[", lower_bound, ", ", upper_bound, "]"
# make posterior PDF plot for POI
c1 = ROOT.TCanvas("posterior", "posterior")
plot = RooStats.MCMCIntervalPlot(pMcmcInt)
plot.Draw()
c1.SaveAs("bayesian_mcmc_posterior.pdf")
# make scatter plots to visualise the Markov chain
c2 = ROOT.TCanvas("xsec_vs_beta_lumi", "xsec vs lumi_beta")
plot.DrawChainScatter(pWs.var("xsec"), pWs.var("lumi_beta"))
c2.SaveAs("scatter_mcmc_xsec_vs_beta_lumi.pdf")
c3 = ROOT.TCanvas("xsec_vs_beta_efficiency", "xsec vs eff_beta")
plot.DrawChainScatter(pWs.var("xsec"), pWs.var("eff_beta"))
c3.SaveAs("scatter_mcmc_xsec_vs_beta_efficiency.pdf")
c4 = ROOT.TCanvas("xsec_vs_beta_nbkg", "xsec vs nbkg_beta")
plot.DrawChainScatter(pWs.var("xsec"), pWs.var("nbkg_beta"))
c4.SaveAs("scatter_mcmc_xsec_vs_beta_nbkg.pdf")
ROOT.gPad.Update()
if interactive:
raw_input("\npress <enter> to continue")
