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'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #601

Interactive minimization with MINUIT

import ROOT
# Setup pdf and likelihood
# -----------------------------------------------
# Observable
x = ROOT.RooRealVar("x", "x", -20, 20)
# Model (intentional strong correlations)
mean = ROOT.RooRealVar("mean", "mean of g1 and g2", 0)
sigma_g1 = ROOT.RooRealVar("sigma_g1", "width of g1", 3)
g1 = ROOT.RooGaussian("g1", "g1", x, mean, sigma_g1)
sigma_g2 = ROOT.RooRealVar("sigma_g2", "width of g2", 4, 3.0, 6.0)
g2 = ROOT.RooGaussian("g2", "g2", x, mean, sigma_g2)
frac = ROOT.RooRealVar("frac", "frac", 0.5, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "model", [g1, g2], [frac])
# Generate 1000 events
data = model.generate({x}, 1000)
# Construct unbinned likelihood of model w.r.t. data
nll = model.createNLL(data)
# Interactive minimization, error analysis
# -------------------------------------------------------------------------------
# Create MINUIT interface object
m = ROOT.RooMinimizer(nll)
# Activate verbose logging of MINUIT parameter space stepping
m.setVerbose(True)
# Call MIGRAD to minimize the likelihood
m.migrad()
# Print values of all parameters, reflect values (and error estimates)
# that are back propagated from MINUIT
model.getParameters({x}).Print("s")
# Disable verbose logging
m.setVerbose(False)
# Run HESSE to calculate errors from d2L/dp2
m.hesse()
# Print value (and error) of sigma_g2 parameter, reflects
# value and error back propagated from MINUIT
sigma_g2.Print()
# Run MINOS on sigma_g2 parameter only
m.minos({sigma_g2})
# Print value (and error) of sigma_g2 parameter, reflects
# value and error back propagated from MINUIT
sigma_g2.Print()
# Saving results, contour plots
# ---------------------------------------------------------
# Save a snapshot of the fit result. ROOT.This object contains the initial
# fit parameters, final fit parameters, complete correlation
# matrix, EDM, minimized FCN , last MINUIT status code and
# the number of times the ROOT.RooFit function object has indicated evaluation
# problems (e.g. zero probabilities during likelihood evaluation)
r = m.save()
# Make contour plot of mx vs sx at 1,2, sigma
frame = m.contour(frac, sigma_g2, 1, 2, 3)
frame.SetTitle("Contour plot")
# Print the fit result snapshot
r.Print("v")
# Change parameter values, plotting
# -----------------------------------------------------------------
# At any moment you can manually change the value of a (constant)
# parameter
mean.setVal(0.3)
# Rerun MIGRAD,HESSE
m.migrad()
m.hesse()
frac.Print()
# Now fix sigma_g2
sigma_g2.setConstant(True)
# Rerun MIGRAD,HESSE
m.migrad()
m.hesse()
frac.Print()
c = ROOT.TCanvas("rf601_intminuit", "rf601_intminuit", 600, 600)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()
c.SaveAs("rf601_intminuit.png")
void Print(std::ostream &os, const OptionType &opt)
Date
February 2018
Author
Clemens Lange

Definition in file rf601_intminuit.py.