# Rf 4 0 3_Weightedevts¶

'DATA AND CATEGORIES' RooFit tutorial macro #403

Using weights in unbinned datasets

Author: Wouter Verkerke (C version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, January 17, 2022 at 10:02 AM.

In [ ]:
from __future__ import print_function
import ROOT


## Create observable and unweighted dataset¶

Declare observable

In [ ]:
x = ROOT.RooRealVar("x", "x", -10, 10)
x.setBins(40)


Construction a uniform pdf

In [ ]:
p0 = ROOT.RooPolynomial("px", "px", x)


Sample 1000 events from pdf

In [ ]:
data = p0.generate({x}, 1000)


## Calculate weight and make dataset weighted¶

Construct formula to calculate (fake) weight for events

In [ ]:
wFunc = ROOT.RooFormulaVar("w", "event weight", "(x*x+10)", [x])


Add column with variable w to previously generated dataset

In [ ]:
w = data.addColumn(wFunc)


Dataset d is now a dataset with two observable (x,w) with 1000 entries

In [ ]:
data.Print()


Instruct dataset wdata in interpret w as event weight rather than as observable

In [ ]:
wdata = ROOT.RooDataSet(data.GetName(), data.GetTitle(), data, data.get(), "", w.GetName())


Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430K

In [ ]:
wdata.Print()


## Unbinned ML fit to weighted data¶

Construction quadratic polynomial pdf for fitting

In [ ]:
a0 = ROOT.RooRealVar("a0", "a0", 1)
a1 = ROOT.RooRealVar("a1", "a1", 0, -1, 1)
a2 = ROOT.RooRealVar("a2", "a2", 1, 0, 10)
p2 = ROOT.RooPolynomial("p2", "p2", x, [a0, a1, a2], 0)


Fit quadratic polynomial to weighted data

NOTE: A plain Maximum likelihood fit to weighted data does in general NOT result in correct error estimates, individual event weights represent Poisson statistics themselves.

Fit with 'wrong' errors

In [ ]:
r_ml_wgt = p2.fitTo(wdata, Save=True)


A first order correction to estimated parameter errors in an (unbinned) ML fit can be obtained by calculating the covariance matrix as

V' = V C-1 V

where V is the covariance matrix calculated from a fit to -logL = - sum [ w_i log f(x_i) ] and C is the covariance matrix calculated from -logL' = -sum [ w_i^2 log f(x_i) ] (i.e. the weights are applied squared)

A fit in self mode can be performed as follows:

In [ ]:
r_ml_wgt_corr = p2.fitTo(wdata, Save=True, SumW2Error=True)


## Plot weighted data and fit result¶

Construct plot frame

In [ ]:
frame = x.frame(ROOT.RooFit.Title("Unbinned ML fit, chi^2 fit to weighted data"))


Plot data using sum-of-weights-squared error rather than Poisson errors

In [ ]:
wdata.plotOn(frame, ROOT.RooFit.DataError(ROOT.RooAbsData.SumW2))


Overlay result of 2nd order polynomial fit to weighted data

In [ ]:
p2.plotOn(frame)


## ML fit of pdf to equivalent unweighted dataset¶

Construct a pdf with the same shape as p0 after weighting

In [ ]:
genPdf = ROOT.RooGenericPdf("genPdf", "x*x+10", [x])


Sample a dataset with the same number of events as data

In [ ]:
data2 = genPdf.generate({x}, 1000)


Sample a dataset with the same number of weights as data

In [ ]:
data3 = genPdf.generate({x}, 43000)


Fit the 2nd order polynomial to both unweighted datasets and save the results for comparison

In [ ]:
r_ml_unw10 = p2.fitTo(data2, Save=True)
r_ml_unw43 = p2.fitTo(data3, Save=True)


## Chis2 fit of pdf to binned weighted dataset¶

Construct binned clone of unbinned weighted dataset

In [ ]:
binnedData = wdata.binnedClone()
binnedData.Print("v")


Perform chi2 fit to binned weighted dataset using sum-of-weights errors

NB: Within the usual approximations of a chi2 fit, chi2 fit to weighted data using sum-of-weights-squared errors does give correct error estimates

In [ ]:
chi2 = ROOT.RooChi2Var("chi2", "chi2", p2, binnedData, ROOT.RooFit.DataError(ROOT.RooAbsData.SumW2))
m = ROOT.RooMinimizer(chi2)
m.hesse()


Plot chi^2 fit result on frame as well

In [ ]:
r_chi2_wgt = m.save()
p2.plotOn(frame, LineStyle="--", LineColor="r")


## Compare fit results of chi2, L fits to (un)weighted data¶

Note that ML fit on 1Kevt of weighted data is closer to result of ML fit on 43Kevt of unweighted data than to 1Kevt of unweighted data, the reference chi^2 fit with SumW2 error gives a result closer to that of an unbinned ML fit to 1Kevt of unweighted data.

In [ ]:
print("==> ML Fit results on 1K unweighted events")
r_ml_unw10.Print()
print("==> ML Fit results on 43K unweighted events")
r_ml_unw43.Print()
print("==> ML Fit results on 1K weighted events with a summed weight of 43K")
r_ml_wgt.Print()
print("==> Corrected ML Fit results on 1K weighted events with a summed weight of 43K")
r_ml_wgt_corr.Print()
print("==> Chi2 Fit results on 1K weighted events with a summed weight of 43K")
r_chi2_wgt.Print()

c = ROOT.TCanvas("rf403_weightedevts", "rf403_weightedevts", 600, 600)

from ROOT import gROOT