Organization and simultaneous fits: working with named parameter sets and parameter snapshots in workspaces
Author: Wouter Verkerke
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, March 19, 2024 at 07:16 PM.
%%cpp -d
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooWorkspace.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TFile.h"
#include "TH1.h"
using namespace RooFit;
void fillWorkspace(RooWorkspace &w);
Definition of a helper function:
%%cpp -d
void fillWorkspace(RooWorkspace &w)
{
// C r e a t e m o d e l
// -----------------------
// Declare observable x
RooRealVar x("x", "x", 0, 10);
// Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
RooRealVar mean("mean", "mean of gaussians", 5, 0, 10);
RooRealVar sigma1("sigma1", "width of gaussians", 0.5);
RooRealVar sigma2("sigma2", "width of gaussians", 1);
RooGaussian sig1("sig1", "Signal component 1", x, mean, sigma1);
RooGaussian sig2("sig2", "Signal component 2", x, mean, sigma2);
// Build Chebychev polynomial pdf
RooRealVar a0("a0", "a0", 0.5, 0., 1.);
RooRealVar a1("a1", "a1", 0.2, 0., 1.);
RooChebychev bkg("bkg", "Background", x, RooArgSet(a0, a1));
// Sum the signal components into a composite signal pdf
RooRealVar sig1frac("sig1frac", "fraction of component 1 in signal", 0.8, 0., 1.);
RooAddPdf sig("sig", "Signal", RooArgList(sig1, sig2), sig1frac);
// Sum the composite signal and background
RooRealVar bkgfrac("bkgfrac", "fraction of background", 0.5, 0., 1.);
RooAddPdf model("model", "g1+g2+a", RooArgList(bkg, sig), bkgfrac);
// Import model into pdf
w.import(model);
// E n c o d e d e f i n i t i o n o f p a r a m e t e r s i n w o r k s p a c e
// ---------------------------------------------------------------------------------------
// Define named sets "parameters" and "observables", which list which variables should be considered
// parameters and observables by the users convention
//
// Variables appearing in sets _must_ live in the workspace already, or the autoImport flag
// of defineSet must be set to import them on the fly. Named sets contain only references
// to the original variables, therefore the value of observables in named sets already
// reflect their 'current' value
std::unique_ptr<RooArgSet> params{model.getParameters(x)};
w.defineSet("parameters", *params);
w.defineSet("observables", x);
// E n c o d e r e f e r e n c e v a l u e f o r p a r a m e t e r s i n w o r k s p a c e
// ---------------------------------------------------------------------------------------------------
// Define a parameter 'snapshot' in the pdf
// Unlike a named set, a parameter snapshot stores an independent set of values for
// a given set of variables in the workspace. The values can be stored and reloaded
// into the workspace variable objects using the loadSnapshot() and saveSnapshot()
// methods. A snapshot saves the value of each variable, any errors that are stored
// with it as well as the 'Constant' flag that is used in fits to determine if a
// parameter is kept fixed or not.
// Do a dummy fit to a (supposedly) reference dataset here and store the results
// of that fit into a snapshot
std::unique_ptr<RooDataSet> refData{model.generate(x, 10000)};
model.fitTo(*refData, PrintLevel(-1));
// The true flag imports the values of the objects in (*params) into the workspace
// If not set, the present values of the workspace parameters objects are stored
w.saveSnapshot("reference_fit", *params, true);
// Make another fit with the signal component forced to zero
// and save those parameters too
bkgfrac.setVal(1);
bkgfrac.setConstant(true);
bkgfrac.removeError();
model.fitTo(*refData, PrintLevel(-1));
w.saveSnapshot("reference_fit_bkgonly", *params, true);
}
RooWorkspace *w = new RooWorkspace("w");
fillWorkspace(*w);
[#0] WARNING:InputArguments -- The parameter 'sigma1' with range [-inf, inf] of the RooGaussian 'sig1' exceeds the safe range of (0, inf). Advise to limit its range. [#0] WARNING:InputArguments -- The parameter 'sigma2' with range [-inf, inf] of the RooGaussian 'sig2' exceeds the safe range of (0, inf). Advise to limit its range. [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooAddPdf::model [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooChebychev::bkg [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::x [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::a0 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::a1 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::bkgfrac [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooAddPdf::sig [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooGaussian::sig1 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::mean [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::sigma1 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::sig1frac [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooGaussian::sig2 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::sigma2 [#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data [#1] INFO:Fitting -- using CPU computation library compiled with -mavx2 [#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level [#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization [#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization [#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data [#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level [#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization [#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
Warning in <ROOT::Math::Fitter::CalculateHessErrors>: Error when calculating Hessian
Exploit convention encoded in named set "parameters" and "observables" to use workspace contents w/o need for introspected
RooAbsPdf *model = w->pdf("model");
Generate data from pdf in given observables
std::unique_ptr<RooDataSet> data{model->generate(*w->set("observables"), 1000)};
input_line_70:2:2: warning: 'data' shadows a declaration with the same name in the 'std' namespace; use '::data' to reference this declaration std::unique_ptr<RooDataSet> data{model->generate(*w->set("observables"), 1000)}; ^
Fit model to data
model->fitTo(*data, PrintLevel(-1));
input_line_71:2:16: error: reference to 'data' is ambiguous model->fitTo(*data, PrintLevel(-1)); ^ input_line_70:2:30: note: candidate found by name lookup is 'data' std::unique_ptr<RooDataSet> data{model->generate(*w->set("observables"), 1000)}; ^ /usr/include/c++/9/bits/range_access.h:318:5: note: candidate found by name lookup is 'std::data' data(initializer_list<_Tp> __il) noexcept ^ /usr/include/c++/9/bits/range_access.h:289:5: note: candidate found by name lookup is 'std::data' data(_Container& __cont) noexcept(noexcept(__cont.data())) ^ /usr/include/c++/9/bits/range_access.h:299:5: note: candidate found by name lookup is 'std::data' data(const _Container& __cont) noexcept(noexcept(__cont.data())) ^ /usr/include/c++/9/bits/range_access.h:309:5: note: candidate found by name lookup is 'std::data' data(_Tp (&__array)[_Nm]) noexcept ^
Plot fitted model and data on frame of first (only) observable
RooPlot *frame = ((RooRealVar *)w->set("observables")->first())->frame();
data->plotOn(frame);
model->plotOn(frame);
input_line_72:3:1: error: reference to 'data' is ambiguous data->plotOn(frame); ^ input_line_70:2:30: note: candidate found by name lookup is 'data' std::unique_ptr<RooDataSet> data{model->generate(*w->set("observables"), 1000)}; ^ /usr/include/c++/9/bits/range_access.h:318:5: note: candidate found by name lookup is 'std::data' data(initializer_list<_Tp> __il) noexcept ^ /usr/include/c++/9/bits/range_access.h:289:5: note: candidate found by name lookup is 'std::data' data(_Container& __cont) noexcept(noexcept(__cont.data())) ^ /usr/include/c++/9/bits/range_access.h:299:5: note: candidate found by name lookup is 'std::data' data(const _Container& __cont) noexcept(noexcept(__cont.data())) ^ /usr/include/c++/9/bits/range_access.h:309:5: note: candidate found by name lookup is 'std::data' data(_Tp (&__array)[_Nm]) noexcept ^
Overlay plot with model with reference parameters as stored in snapshots
w->loadSnapshot("reference_fit");
model->plotOn(frame, LineColor(kRed));
w->loadSnapshot("reference_fit_bkgonly");
model->plotOn(frame, LineColor(kRed), LineStyle(kDashed));
input_line_73:3:32: error: cannot take the address of an rvalue of type 'EColor' model->plotOn(frame, LineColor(kRed)); ^~~~ Error while creating dynamic expression for: model->plotOn(frame, LineColor(kRed)) input_line_73:5:32: error: cannot take the address of an rvalue of type 'EColor' model->plotOn(frame, LineColor(kRed), LineStyle(kDashed)); ^~~~ Error while creating dynamic expression for: model->plotOn(frame, LineColor(kRed), LineStyle(kDashed))
Draw the frame on the canvas
new TCanvas("rf510_wsnamedsets", "rf503_wsnamedsets", 600, 600);
gPad->SetLeftMargin(0.15);
frame->GetYaxis()->SetTitleOffset(1.4);
frame->Draw();
IncrementalExecutor::executeFunction: symbol '_ZN5cling7runtime8internal9EvaluateTIvEET_PNS1_15DynamicExprInfoEPN5clang11DeclContextE' unresolved while linking [cling interface function]! You are probably missing the definition of void cling::runtime::internal::EvaluateT<void>(cling::runtime::internal::DynamicExprInfo*, clang::DeclContext*) Maybe you need to load the corresponding shared library?
Print workspace contents
w->Print();
RooWorkspace(w) w contents variables --------- (a0,a1,bkgfrac,mean,sig1frac,sigma1,sigma2,x) p.d.f.s ------- RooChebychev::bkg[ x=x coefList=(a0,a1) ] = 0.8 RooAddPdf::model[ bkgfrac * bkg + [%] * sig ] = 0.9/1 RooAddPdf::sig[ sig1frac * sig1 + [%] * sig2 ] = 1/1 RooGaussian::sig1[ x=x mean=mean sigma=sigma1 ] = 1 RooGaussian::sig2[ x=x mean=mean sigma=sigma2 ] = 1 parameter snapshots ------------------- reference_fit = (a0=0.500613 +/- 0.023199,a1=0.160315 +/- 0.0373121,bkgfrac=0.504699 +/- 0.0113933,mean=5.01883 +/- 0.0101222,sigma1=0.5[C],sig1frac=0.8179 +/- 0.0374037,sigma2=1[C]) reference_fit_bkgonly = (a0=0.474264 +/- 0,a1=6.8252e-12 +/- 0,bkgfrac=1[C],mean=5.01883 +/- 0,sigma1=0.5[C],sig1frac=0.8179 +/- 0,sigma2=1[C]) named sets ---------- observables:(x) parameters:(a0,a1,bkgfrac,mean,sig1frac,sigma1,sigma2)
Workspace will remain in memory after macro finishes
gDirectory->Add(w);
Draw all canvases
%jsroot on
gROOT->GetListOfCanvases()->Draw()