Rf 6 0 3_Multicpu

Likelihood and minimization: setting up a multi-core parallelized unbinned maximum likelihood fit

Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Wednesday, January 19, 2022 at 10:27 AM.

In [ ]:
import ROOT

Create 3D pdf and data

Create observables

In [ ]:
x = ROOT.RooRealVar("x", "x", -5, 5)
y = ROOT.RooRealVar("y", "y", -5, 5)
z = ROOT.RooRealVar("z", "z", -5, 5)

Create signal pdf gauss(x)gauss(y)gauss(z)

In [ ]:
gx = ROOT.RooGaussian("gx", "gx", x, ROOT.RooFit.RooConst(0), ROOT.RooFit.RooConst(1))
gy = ROOT.RooGaussian("gy", "gy", y, ROOT.RooFit.RooConst(0), ROOT.RooFit.RooConst(1))
gz = ROOT.RooGaussian("gz", "gz", z, ROOT.RooFit.RooConst(0), ROOT.RooFit.RooConst(1))
sig = ROOT.RooProdPdf("sig", "sig", [gx, gy, gz])

Create background pdf poly(x)poly(y)poly(z)

In [ ]:
px = ROOT.RooPolynomial("px", "px", x, [-0.1, 0.004])
py = ROOT.RooPolynomial("py", "py", y, [0.1, -0.004])
pz = ROOT.RooPolynomial("pz", "pz", z)
bkg = ROOT.RooProdPdf("bkg", "bkg", [px, py, pz])

Create composite pdf sig+bkg

In [ ]:
fsig = ROOT.RooRealVar("fsig", "signal fraction", 0.1, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "model", [sig, bkg], [fsig])

Generate large dataset

In [ ]:
data = model.generate({x, y, z}, 200000)

Parallel fitting

In parallel mode the likelihood calculation is split in N pieces, that are calculated in parallel and added a posteriori before passing it back to MINUIT.

Use four processes and time results both in wall time and CPU time

In [ ]:
model.fitTo(data, NumCPU=4, Timer=True)

Parallel MC projections

Construct signal, likelihood projection on (y,z) observables and likelihood ratio

In [ ]:
sigyz = sig.createProjection({x})
totyz = model.createProjection({x})
llratio_func = ROOT.RooFormulaVar("llratio", "log10(@0)-log10(@1)", [sigyz, totyz])

Calculate likelihood ratio for each event, subset of events with high signal likelihood

In [ ]:
dataSel = data.reduce(Cut="llratio>0.7")

Make plot frame and plot data

In [ ]:
frame = x.frame(Title="Projection on X with LLratio(y,z)>0.7", Bins=40)

Perform parallel projection using MC integration of pdf using given input dataSet. In self mode the data-weighted average of the pdf is calculated by splitting the input dataset in N equal pieces and calculating in parallel the weighted average one each subset. The N results of those calculations are then weighted into the final result

Use four processes

In [ ]:
model.plotOn(frame, ProjWData=dataSel, NumCPU=4)

c = ROOT.TCanvas("rf603_multicpu", "rf603_multicpu", 600, 600)


Draw all canvases

In [ ]:
from ROOT import gROOT