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Reference Guide
rf603_multicpu.C File Reference

Detailed Description

View in nbviewer Open in SWAN 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #603

Setting up a multi-core parallelized unbinned maximum likelihood fit



Processing /mnt/build/workspace/root-makedoc-v608/rootspi/rdoc/src/v6-08-00-patches/tutorials/roofit/rf603_multicpu.C...
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooPolynomial.h"
#include "RooAddPdf.h"
#include "RooProdPdf.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;
void rf603_multicpu()
{
// C r e a t e 3 D p d f a n d d a t a
// -------------------------------------------
// Create observables
RooRealVar x("x","x",-5,5) ;
RooRealVar y("y","y",-5,5) ;
RooRealVar z("z","z",-5,5) ;
// Create signal pdf gauss(x)*gauss(y)*gauss(z)
RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
// Create background pdf poly(x)*poly(y)*poly(z)
RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
RooPolynomial pz("pz","pz",z) ;
RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
// Create composite pdf sig+bkg
RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
// Generate large dataset
RooDataSet* data = model.generate(RooArgSet(x,y,z),200000) ;
// P a r a l l e l f i t t i n g
// -------------------------------
// 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
model.fitTo(*data,NumCPU(4),Timer(kTRUE)) ;
// P a r a l l e l M C p r o j e c t i o n s
// ----------------------------------------------
// Construct signal, total likelihood projection on (y,z) observables and likelihood ratio
RooAbsPdf* sigyz = sig.createProjection(x) ;
RooAbsPdf* totyz = model.createProjection(x) ;
RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;
// Calculate likelihood ratio for each event, define subset of events with high signal likelihood
data->addColumn(llratio_func) ;
RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;
// Make plot frame and plot data
RooPlot* frame = x.frame(Title("Projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
dataSel->plotOn(frame) ;
// Perform parallel projection using MC integration of pdf using given input dataSet.
// In this 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
model.plotOn(frame,ProjWData(*dataSel),NumCPU(4)) ;
new TCanvas("rf603_multicpu","rf603_multicpu",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
}
Author
07/2008 - Wouter Verkerke

Definition in file rf603_multicpu.C.