rf902_numgenconfig.py File Reference

Detailed Description

Numeric algorithm tuning: configuration and customization of how MC sampling algorithms on specific pdfs are executed

 
import ROOT
 
 
# Adjust global MC sampling strategy
# ------------------------------------------------------------------
 
# Example pdf for use below
x = ROOT.RooRealVar("x", "x", 0, 10)
model = ROOT.RooChebychev("model", "model", x, [0.0, 0.5, -0.1])
 
# Change global strategy for 1D sampling problems without conditional observable
# (1st kFALSE) and without discrete observable (2nd kFALSE) from ROOT.RooFoamGenerator,
# ( an interface to the ROOT.TFoam MC generator with adaptive subdivisioning strategy ) to ROOT.RooAcceptReject,
# a plain accept/reject sampling algorithm [ ROOT.RooFit default before
# ROOT 5.23/04 ]
ROOT.RooAbsPdf.defaultGeneratorConfig().method1D(False, False).setLabel("RooAcceptReject")
 
# Generate 10Kevt using ROOT.RooAcceptReject
data_ar = model.generate({x}, 10000, Verbose=True)
data_ar.Print()
 
# Adjusting default config for a specific pdf
# -------------------------------------------------------------------------------------
 
# Another possibility: associate custom MC sampling configuration as default for object 'model'
# The kTRUE argument will install a clone of the default configuration as specialized configuration
# for self model if none existed so far
model.specialGeneratorConfig(True).method1D(False, False).setLabel("RooFoamGenerator")
 
# Adjusting parameters of a specific technique
# ---------------------------------------------------------------------------------------
 
# Adjust maximum number of steps of ROOT.RooIntegrator1D in the global
# default configuration
ROOT.RooAbsPdf.defaultGeneratorConfig().getConfigSection("RooAcceptReject").setRealValue("nTrial1D", 2000)
 
# Example of how to change the parameters of a numeric integrator
# (Each config section is a ROOT.RooArgSet with ROOT.RooRealVars holding real-valued parameters
#  and ROOT.RooCategories holding parameters with a finite set of options)
model.specialGeneratorConfig().getConfigSection("RooFoamGenerator").setRealValue("chatLevel", 1)
 
# Generate 10Kevt using ROOT.RooFoamGenerator (FOAM verbosity increased
# with above chatLevel adjustment for illustration purposes)
data_foam = model.generate({x}, 10000, Verbose=True)
data_foam.Print()

[#0] ERROR:InputArguments -- Trying to set invalid state label 'RooAcceptReject' for category method1D
 --- RooGenContext --- 
Using PDF RooChebychev::model[ x=x coefList=(0,0.5,-0.1) ]
Use PDF generator for ()
Use MC sampling generator RooFoamGenerator for (x)
RooDataSet::modelData[x] = 10000 entries
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
F                                                                              F
F                   ****************************************                   F
F                    ******      TFoam::Initialize    ******                   F
F                   ****************************************                   F
F                                                      TFOAM                   F
F    Version =           1.02M   =                   Release date:  2005.04.10 F
F       kDim =          1 =  Dimension of the hyper-cubical space              F
F     nCells =         30 =  Requested number of Cells (half of them active)   F
F     nSampl =        200 =  No of MC events in exploration of a cell          F
F       nBin =          8 =  No of bins in histograms, MC exploration of cell  F
F   EvPerBin =         25 =  Maximum No effective_events/bin, MC exploration   F
F   OptDrive =          2 =  Type of Driver   =1,2 for Sigma,WtMax             F
F     OptRej =          1 =  MC rejection on/off for OptRej=0,1                F
F   MaxWtRej =             1.1   =       Maximum wt in rejection for wt=1 evts F
F                                                                              F
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
11
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
F                                                                              F
F                    ***  TFoam::Initialize FINISHED!!!  ***                   F
F     nCalls =       5800 =            Total number of function calls          F
F     XPrime =      0.10992972   =     Primary total integral                  F
F     XDiver =     0.010000374   =     Driver  total integral                  F
F   mcResult =     0.099929343   =     Estimate of the true MC Integral        F
F                                                                              F
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
 --- RooGenContext --- 
Using PDF RooChebychev::model[ x=x coefList=(0,0.5,-0.1) ]
Use PDF generator for ()
Use MC sampling generator RooFoamGenerator for (x)
RooDataSet::modelData[x] = 10000 entries

Date

February 2018

Authors

Clemens Lange, Wouter Verkerke (C++ version)

Definition in file rf902_numgenconfig.py.