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rf606_nllerrorhandling.C File Reference

Detailed Description

View in nbviewer Open in SWAN Likelihood and minimization: understanding and customizing error handling in likelihood evaluations

␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
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 **SET PRINT 1
**********
**********
** 2 **SET NOGRAD
**********
PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 k -3.00000e+01 4.00000e+00 -5.00000e+01 -1.00000e+01
2 m0 5.29100e+00 4.50000e-03 5.20000e+00 5.30000e+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 1000 1
**********
FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
START MIGRAD MINIMIZATION. STRATEGY 1. CONVERGENCE WHEN EDM .LT. 1.00e-03
FCN=-2417.48 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 k -3.00000e+01 4.00000e+00 2.01358e-01 6.43007e+00
2 m0 5.29100e+00 4.50000e-03 1.62481e-01 6.64912e+01
ERR DEF= 0.5
RooMinimizerFcn: Minimized function has error status.
Returning maximum FCN so far (-2417.21) to force MIGRAD to back out of this region. Error log follows.
Parameter values: k=-33.8074 m0=5.29014
RooArgusBG::argus[ m=m m0=m0 c=k p=0.5 ]
getLogVal() top-level p.d.f evaluates to zero @ m=m=5.29019, m0=m0=5.29014, c=k=-33.8074, p=0.5=0.5
RooNLLVar::nll_argus_argusData[ paramSet=(k,m0) ]
function value is NAN @ paramSet=(k = -33.8074,m0 = 5.29014)
RooMinimizerFcn: Minimized function has error status.
Returning maximum FCN so far (-2417.21) to force MIGRAD to back out of this region. Error log follows.
Parameter values: k=-36.6878 m0=5.29012
RooArgusBG::argus[ m=m m0=m0 c=k p=0.5 ]
getLogVal() top-level p.d.f evaluates to zero @ m=m=5.29019, m0=m0=5.29012, c=k=-36.6878, p=0.5=0.5
RooNLLVar::nll_argus_argusData[ paramSet=(k,m0) ]
function value is NAN @ paramSet=(k = -36.6878,m0 = 5.29012)
MIGRAD MINIMIZATION HAS CONVERGED.
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=-2419.31 FROM MIGRAD STATUS=CONVERGED 44 CALLS 45 TOTAL
EDM=1.16119e-05 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 k -3.53724e+01 3.51965e+00 6.12703e-03 -1.66396e-02
2 m0 5.29040e+00 2.62256e-04 3.09448e-04 2.47503e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
1.253e+01 1.785e-04
1.785e-04 6.878e-08
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.19224 1.000 0.192
2 0.19224 0.192 1.000
**********
** 7 **SET ERR 0.5
**********
**********
** 8 **SET PRINT 1
**********
**********
** 9 **HESSE 1000
**********
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=-2419.31 FROM HESSE STATUS=OK 10 CALLS 55 TOTAL
EDM=1.16219e-05 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 k -3.53724e+01 3.51966e+00 1.22541e-03 -2.71960e-01
2 m0 5.29040e+00 2.62349e-04 1.23779e-05 9.40810e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
1.253e+01 1.786e-04
1.786e-04 6.883e-08
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.19229 1.000 0.192
2 0.19229 0.192 1.000
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
**********
** 10 **SET PRINT 1
**********
**********
** 11 **SET NOGRAD
**********
PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 k -3.53724e+01 3.51966e+00 -5.00000e+01 -1.00000e+01
2 m0 5.29040e+00 1.00000e-01 5.20000e+00 5.30000e+00
MINUIT WARNING IN PARAMETR
============== VARIABLE2 BROUGHT BACK INSIDE LIMITS.
MINUIT WARNING IN PARAMETR
============== VARIABLE2 BROUGHT BACK INSIDE LIMITS.
**********
** 12 **SET ERR 0.5
**********
**********
** 13 **SET PRINT 1
**********
**********
** 14 **SET STR 1
**********
NOW USING STRATEGY 1: TRY TO BALANCE SPEED AGAINST RELIABILITY
**********
** 15 **MIGRAD 1000 1
**********
FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
START MIGRAD MINIMIZATION. STRATEGY 1. CONVERGENCE WHEN EDM .LT. 1.00e-03
RooMinimizerFcn: Minimized function has error status but is ignored.
Parameter values: k=-35.3724 m0=5.28878
RooNLLVar::nll_argus_argusData[ paramSet=(k,m0) ] has 1 errors
RooArgusBG::argus[ m=m m0=m0 c=k p=0.5 ] has 4 errors
FCN=-2419.31 FROM MIGRAD STATUS=INITIATE 8 CALLS 9 TOTAL
EDM= unknown STRATEGY= 1 NO ERROR MATRIX
EXT PARAMETER CURRENT GUESS STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 k -3.53724e+01 3.51966e+00 1.83981e-01 -1.64778e-02
2 m0 5.29040e+00 1.00000e-01 1.56884e+00 1.11846e-01
ERR DEF= 0.5
MIGRAD MINIMIZATION HAS CONVERGED.
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=-2419.31 FROM MIGRAD STATUS=CONVERGED 28 CALLS 29 TOTAL
EDM=4.57045e-07 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 k -3.53621e+01 3.51925e+00 6.12459e-03 8.76390e-04
2 m0 5.29040e+00 2.62011e-04 2.96587e-04 7.04254e-02
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
1.253e+01 1.781e-04
1.781e-04 6.865e-08
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.19204 1.000 0.192
2 0.19204 0.192 1.000
**********
** 16 **SET ERR 0.5
**********
**********
** 17 **SET PRINT 1
**********
**********
** 18 **HESSE 1000
**********
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=-2419.31 FROM HESSE STATUS=OK 10 CALLS 39 TOTAL
EDM=4.58038e-07 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 k -3.53621e+01 3.51925e+00 2.44984e-04 -2.71425e-01
2 m0 5.29040e+00 2.62096e-04 1.18635e-05 9.40801e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
1.253e+01 1.782e-04
1.782e-04 6.870e-08
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.19207 1.000 0.192
2 0.19207 0.192 1.000
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooArgusBG.h"
#include "RooNLLVar.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit;
void rf606_nllerrorhandling()
{
// C r e a t e m o d e l a n d d a t a s e t
// ----------------------------------------------
// Observable
RooRealVar m("m", "m", 5.20, 5.30);
// Parameters
RooRealVar m0("m0", "m0", 5.291, 5.20, 5.30);
RooRealVar k("k", "k", -30, -50, -10);
// Pdf
RooArgusBG argus("argus", "argus", m, m0, k);
// Sample 1000 events in m from argus
RooDataSet *data = argus.generate(m, 1000);
// P l o t m o d e l a n d d a t a
// --------------------------------------
RooPlot *frame1 = m.frame(Bins(40), Title("Argus model and data"));
data->plotOn(frame1);
argus.plotOn(frame1);
// F i t m o d e l t o d a t a
// ---------------------------------
// The ARGUS background shape has a sharp kinematic cutoff at m=m0
// and is prone to evaluation errors if the cutoff parameter m0
// is floated: when the pdf cutoff value is lower than that in data
// events with m>m0 will have zero probability
// Perform unbinned ML fit. Print detailed error messages for up to
// 10 events per likelihood evaluation. The default error handling strategy
// is to return a very high value of the likelihood to MINUIT if errors occur,
// which will force MINUIT to retreat from the problematic area
argus.fitTo(*data, PrintEvalErrors(10));
// Perform another fit. In this configuration only the number of errors per
// likelihood evaluation is shown, if it is greater than zero. The
// EvalErrorWall(kFALSE) arguments disables the default error handling strategy
// and will cause the actual (problematic) value of the likelihood to be passed
// to MINUIT.
//
// NB: Use of this option is NOT recommended as default strategy as broken -log(L) values
// can often be lower than 'good' ones because offending events are removed.
// This may effectively create a false minimum in problem areas. This is clearly
// illustrated in the second plot
m0.setError(0.1);
argus.fitTo(*data, PrintEvalErrors(0), EvalErrorWall(kFALSE));
// P l o t l i k e l i h o o d a s f u n c t i o n o f m 0
// ------------------------------------------------------------------
// Construct likelihood function of model and data
RooNLLVar nll("nll", "nll", argus, *data);
// Plot likelihood in m0 in range that includes problematic values
// In this configuration no messages are printed for likelihood evaluation errors,
// but if an likelihood value evaluates with error, the corresponding value
// on the curve will be set to the value given in EvalErrorValue().
RooPlot *frame2 = m0.frame(Range(5.288, 5.293), Title("-log(L) scan vs m0, problematic regions masked"));
nll.plotOn(frame2, PrintEvalErrors(-1), ShiftToZero(), EvalErrorValue(nll.getVal() + 10), LineColor(kRed));
frame2->SetMaximum(15);
frame2->SetMinimum(0);
TCanvas *c = new TCanvas("rf606_nllerrorhandling", "rf606_nllerrorhandling", 1200, 400);
c->Divide(2);
c->cd(1);
gPad->SetLeftMargin(0.15);
frame1->GetYaxis()->SetTitleOffset(1.4);
frame1->Draw();
c->cd(2);
gPad->SetLeftMargin(0.15);
frame2->GetYaxis()->SetTitleOffset(1.4);
frame2->Draw();
}
#define c(i)
Definition RSha256.hxx:101
const Bool_t kFALSE
Definition RtypesCore.h:92
@ kRed
Definition Rtypes.h:66
#define gPad
virtual RooPlot * plotOn(RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
RooArgusBG is a RooAbsPdf implementation describing the ARGUS background shape.
Definition RooArgusBG.h:22
RooDataSet is a container class to hold unbinned data.
Definition RooDataSet.h:33
Class RooNLLVar implements a -log(likelihood) calculation from a dataset and a PDF.
Definition RooNLLVar.h:30
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition RooPlot.h:44
virtual void SetMinimum(Double_t minimum=-1111)
Set minimum value of Y axis.
Definition RooPlot.cxx:1091
TAxis * GetYaxis() const
Definition RooPlot.cxx:1263
static RooPlot * frame(const RooAbsRealLValue &var, Double_t xmin, Double_t xmax, Int_t nBins)
Create a new frame for a given variable in x.
Definition RooPlot.cxx:249
virtual void SetMaximum(Double_t maximum=-1111)
Set maximum value of Y axis.
Definition RooPlot.cxx:1081
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition RooPlot.cxx:691
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:39
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition TAttAxis.cxx:293
The Canvas class.
Definition TCanvas.h:23
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Ta Range(0, 0, 1, 1)
auto * m
Definition textangle.C:8
Date
July 2008
Author
Wouter Verkerke

Definition in file rf606_nllerrorhandling.C.