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rf603_multicpu.C File Reference
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View in nbviewer Open in SWAN 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #603

Setting up a multi-core parallelized unbinned maximum likelihood fit

pict1_rf603_multicpu.C.png
Processing /mnt/build/workspace/root-makedoc-v614/rootspi/rdoc/src/v6-14-00-patches/tutorials/roofit/rf603_multicpu.C...
RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
All rights reserved, please read http://roofit.sourceforge.net/license.txt
[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
[#1] INFO:Eval -- RooAbsTestStatistic::initMPMode: started 4 remote server process.
**********
** 1 **SET PRINT 1
**********
**********
** 2 **SET NOGRAD
**********
PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 fsig 1.00000e-01 5.00000e-02 0.00000e+00 1.00000e+00
**********
** 3 **SET ERR 0.5
**********
**********
** 4 **SET PRINT 1
**********
**********
** 5 **SET STR 1
**********
NOW USING STRATEGY 1: TRY TO BALANCE SPEED AGAINST RELIABILITY
**********
** 6 **MIGRAD 500 1
**********
FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (sig,bkg)
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (sig,bkg)
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (sig,bkg)
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (sig,bkg)
START MIGRAD MINIMIZATION. STRATEGY 1. CONVERGENCE WHEN EDM .LT. 1.00e-03
FCN=1.35338e+06 FROM MIGRAD STATUS=INITIATE 6 CALLS 7 TOTAL
EDM= unknown STRATEGY= 1 NO ERROR MATRIX
EXT PARAMETER CURRENT GUESS STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 fsig 1.00000e-01 5.00000e-02 1.72186e-01 -1.02596e+02
ERR DEF= 0.5
MIGRAD MINIMIZATION HAS CONVERGED.
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=1.35338e+06 FROM MIGRAD STATUS=CONVERGED 16 CALLS 17 TOTAL
EDM=1.10509e-08 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 fsig 1.00271e-01 8.91444e-04 2.38484e-03 -3.54200e-02
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 1 ERR DEF=0.5
7.947e-07
[#1] INFO:Minization -- Command timer: Real time 0:00:00, CP time 0.040
[#1] INFO:Minization -- Session timer: Real time 0:00:00, CP time 0.040
**********
** 7 **SET ERR 0.5
**********
**********
** 8 **SET PRINT 1
**********
**********
** 9 **HESSE 500
**********
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=1.35338e+06 FROM HESSE STATUS=OK 5 CALLS 22 TOTAL
EDM=1.11088e-08 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 fsig 1.00271e-01 8.91444e-04 9.53935e-05 -9.26391e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 1 ERR DEF=0.5
7.947e-07
[#1] INFO:Minization -- Command timer: Real time 0:00:00, CP time 0.000
[#1] INFO:Minization -- Session timer: Real time 0:00:00, CP time 0.040, 2 slices
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x averages using data variables (y,z)
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) only the following components of the projection data will be used: (y,z)
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(modelDataWgtAvg) constructing data weighted average of function model_Norm[x] over 14497 data points of (y,z) with a total weight of 14497
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(modelDataWgtAvg) constructing data weighted average of function model_Norm[x] over 14497 data points of (y,z) with a total weight of 14497
[#1] INFO:Eval -- RooAbsTestStatistic::initMPMode: started 4 remote server process.
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (gy,gz,py,pz)
.[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (gy,gz,py,pz)
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (gy,gz,py,pz)
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (gy,gz,py,pz)
................................................................................................................................................
#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.