# rf612_recoverFromInvalidParameters¶

Likelihood and minimization: Recover from regions where the function is not defined.

We demonstrate improved recovery from disallowed parameters. For this, we use a polynomial PDF of the form \mathrm{Pol2} = \mathcal{N} \left( c + a_1 \cdot x + a_2 \cdot x^2 + 0.01 \cdot x^3 \right), where \f$\mathcal{N} \f$ is a normalisation factor. Unless the parameters are chosen carefully, this function can be negative, and hence, it cannot be used as a PDF. In this case, RooFit passes an error to the minimiser, which might try to recover.

Author: Harshal Shende, Stephan Hageboeck (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Wednesday, November 30, 2022 at 11:24 AM.

In [1]:
import ROOT

Welcome to JupyROOT 6.27/01


Create a fit model: The polynomial is notoriously unstable, because it can quickly go negative. Since PDFs need to be positive, one often ends up with an unstable fit model.

In [2]:
x = ROOT.RooRealVar("x", "x", -15, 15)
a1 = ROOT.RooRealVar("a1", "a1", -0.5, -10.0, 20.0)
a2 = ROOT.RooRealVar("a2", "a2", 0.2, -10.0, 20.0)
a3 = ROOT.RooRealVar("a3", "a3", 0.01)
pdf = ROOT.RooPolynomial("pol3", "c + a1 * x + a2 * x*x + 0.01 * x*x*x", x, [a1, a2, a3])


Create toy data with all-positive coefficients:

In [3]:
data = pdf.generate(x, 10000)


For plotting. We create pointers to the plotted objects. We want these objects to leak out of the function, so we can still see them after it returns.

In [4]:
c = ROOT.TCanvas()
frame = x.frame()
data.plotOn(frame, Name="data")

Out[4]:
<cppyy.gbl.RooPlot object at 0x8e25ef0>

Plotting a PDF with disallowed parameters doesn't work. We would get a lot of error messages. Therefore, we disable plotting messages in RooFit's message streams:

In [5]:
ROOT.RooMsgService.instance().getStream(0).removeTopic(ROOT.RooFit.Plotting)
ROOT.RooMsgService.instance().getStream(1).removeTopic(ROOT.RooFit.Plotting)


## RooFit before ROOT 6.24¶

Before 6.24, RooFit wasn't able to recover from invalid parameters. The minimiser just errs around the starting values of the parameters without finding any improvement.

Set up the parameters such that the PDF would come out negative. The PDF is now undefined.

In [6]:
a1.setVal(10.0)
a2.setVal(-1.0)


Perform a fit:

In [7]:
fitWithoutRecovery = pdf.fitTo(
data,
Save=True,
RecoverFromUndefinedRegions=0.0,  # This is how RooFit behaved prior to ROOT 6.24
PrintEvalErrors=-1,  # We are expecting a lot of evaluation errors. -1 switches off printing.
PrintLevel=-1,
)

pdf.plotOn(frame, LineColor="r", Name="noRecovery")

Out[7]:
<cppyy.gbl.RooPlot object at 0x8e25ef0>
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#0] ERROR:Eval -- RooAbsReal::logEvalError(pol3) evaluation error,
origin       : RooPolynomial::pol3[ x=x coefList=(a1,a2,a3) ]
message      : p.d.f normalization integral is zero or negative: -2220.000000
server values: x=x=0, coefList=(a1 = 2.60781 +/- 11.9002,a2 = -1 +/- 11.5683,a3 = 0.01)


## RooFit since ROOT 6.24¶

The minimiser gets information about the "badness" of the violation of the function definition. It uses this to find its way out of the disallowed parameter regions.

In [8]:
print("\n\n\n-------------- Starting second fit ---------------\n\n")



-------------- Starting second fit ---------------



Reset the parameters such that the PDF is again undefined.

In [9]:
a1.setVal(10.0)
a2.setVal(-1.0)


Fit again, but pass recovery information to the minimiser:

In [10]:
fitWithRecovery = pdf.fitTo(
data,
Save=True,
RecoverFromUndefinedRegions=1.0,  # The magnitude of the recovery information can be chosen here.
# Higher values mean more aggressive recovery.
PrintEvalErrors=-1,  # We are still expecting a few evaluation errors.
PrintLevel=0,
)

pdf.plotOn(frame, LineColor="b", Name="recovery")

Out[10]:
<cppyy.gbl.RooPlot object at 0x8e25ef0>
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
**********
**********
PARAMETER DEFINITIONS:
NO.   NAME         VALUE      STEP SIZE      LIMITS
1 a1           1.00000e+01  1.19002e+01   -1.00000e+01  2.00000e+01
2 a2          -1.00000e+00  1.15683e+01   -1.00000e+01  2.00000e+01
**********
**   16 **SET ERR         0.5
**********
**********
**   17 **SET PRINT           0
**********
**********
**   18 **SET STR           1
**********
**********
**********
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
FCN=-858.564 FROM MIGRAD    STATUS=CONVERGED     243 CALLS         244 TOTAL
EDM=7.33131e-05    STRATEGY= 1      ERROR MATRIX ACCURATE
EXT PARAMETER                                   STEP         FIRST
NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE
1  a1          -4.98209e-01   2.27025e-02   2.77075e-05  -2.18163e+00
2  a2           1.98271e-01   5.64128e-03   1.48249e-05  -2.54212e+01
ERR DEF= 0.5
**********
**   20 **SET ERR         0.5
**********
**********
**   21 **SET PRINT           0
**********
**********
**   22 **HESSE        1000
**********
FCN=-858.564 FROM HESSE     STATUS=OK             10 CALLS         254 TOTAL
EDM=7.3377e-05    STRATEGY= 1      ERROR MATRIX ACCURATE
EXT PARAMETER                                INTERNAL      INTERNAL
NO.   NAME      VALUE            ERROR       STEP SIZE       VALUE
1  a1          -4.98209e-01   2.27254e-02   8.14968e-06  -3.41822e+01
2  a2           1.98271e-01   5.64697e-03   7.41245e-06   3.10901e+01
ERR DEF= 0.5
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization


## Collect results and plot.¶

We print the two fit results, and plot the fitted curves. The curve of the fit without recovery cannot be plotted, because the PDF is undefined if a2 < 0.

In [11]:
fitWithoutRecovery.Print()
print(
"Without recovery, the fitter encountered {}".format(fitWithoutRecovery.numInvalidNLL())
+ " invalid function values. The parameters are unchanged.\n"
)

fitWithRecovery.Print()
print(
"With recovery, the fitter encountered {}".format(fitWithoutRecovery.numInvalidNLL())
+ " invalid function values, but the parameters are fitted.\n"
)

legend = ROOT.TLegend(0.5, 0.7, 0.9, 0.9)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
legend.AddEntry("noRecovery", "Without recovery (cannot be plotted)", "L")
frame.Draw()
legend.Draw()
c.Draw()

c.SaveAs("rf612_recoverFromInvalidParameters.png")

Without recovery, the fitter encountered 66 invalid function values. The parameters are unchanged.

With recovery, the fitter encountered 66 invalid function values, but the parameters are fitted.

RooFitResult: minimized FCN value: 0, estimated distance to minimum: 0
covariance matrix quality: Approximation only, not accurate
Status : MINIMIZE=0 HESSE=0

Floating Parameter    FinalValue +/-  Error
--------------------  --------------------------
a1    2.6078e+00 +/-  1.19e+01
a2   -1.0000e+00 +/-  1.16e+01

RooFitResult: minimized FCN value: 29650.9, estimated distance to minimum: 7.3377e-05
covariance matrix quality: Full, accurate covariance matrix
Status : MINIMIZE=0 HESSE=0

Floating Parameter    FinalValue +/-  Error
--------------------  --------------------------
a1   -4.9821e-01 +/-  2.27e-02
a2    1.9827e-01 +/-  5.65e-03


Info in <TCanvas::Print>: png file rf612_recoverFromInvalidParameters.png has been created


Draw all canvases

In [12]:
from ROOT import gROOT
gROOT.GetListOfCanvases().Draw()