ProcLikelihood.cc

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// -*- C++ -*-
//
// Package:     MVAComputer
// Class  :     ProcLikelihood
// 

// Implementation:
//     A likelihood estimator variable processor. Reads in 0..n values for
//     m variables and calculates the total signal/background likelihood
//     using calibration PDFs for signal and background for each variable.
//     The output variable is set to s/(s+b) (or log(s/b) for logOutput).
//
// Author:      Christophe Saout
// Created:     Sat Apr 24 15:18 CEST 2007
//

#include <vector>
#include <memory>
#include <cmath>

#include "CondFormats/PhysicsToolsObjects/interface/Histogram.h"  
#include "PhysicsTools/MVAComputer/interface/VarProcessor.h"
#include "PhysicsTools/MVAComputer/interface/Calibration.h"
#include "PhysicsTools/MVAComputer/interface/Spline.h"
#include "FWCore/Utilities/interface/isFinite.h"

using namespace PhysicsTools;

namespace { // anonymous

class ProcLikelihood : public VarProcessor {
    public:
    typedef VarProcessor::Registry::Registry<ProcLikelihood,
                    Calibration::ProcLikelihood> Registry;

    ProcLikelihood(const char *name,
                   const Calibration::ProcLikelihood *calib,
                   const MVAComputer *computer);
    virtual ~ProcLikelihood() {}

    virtual void configure(ConfIterator iter, unsigned int n) override;
    virtual void eval(ValueIterator iter, unsigned int n) const override;
    virtual std::vector<double> deriv(
                ValueIterator iter, unsigned int n) const override;

    private:
    struct PDF {
        virtual ~PDF() {}
        virtual double eval(double value) const = 0;
        virtual double deriv(double value) const = 0;

        double  norm;
    };

    struct SplinePDF : public PDF {
        SplinePDF(const Calibration::HistogramF *calib) :
            min(calib->range().min),
            width(calib->range().width())
        {
            std::vector<double> values(
                    calib->values().begin() + 1,
                    calib->values().end() - 1);
            spline.set(values.size(), &values.front());
        }

        virtual double eval(double value) const override;
        virtual double deriv(double value) const override;

        double      min, width;
        Spline      spline;
    };

    struct HistogramPDF : public PDF {
        HistogramPDF(const Calibration::HistogramF *calib) :
            histo(calib) {}

        virtual double eval(double value) const override;
        virtual double deriv(double value) const override;

        const Calibration::HistogramF   *histo;
    };

    struct SigBkg {
        SigBkg(const Calibration::ProcLikelihood::SigBkg &calib)
        {
            if (calib.useSplines) {
                signal = std::auto_ptr<PDF>(
                    new SplinePDF(&calib.signal));
                background = std::auto_ptr<PDF>(
                    new SplinePDF(&calib.background));
            } else {
                signal = std::auto_ptr<PDF>(
                    new HistogramPDF(&calib.signal));
                background = std::auto_ptr<PDF>(
                    new HistogramPDF(&calib.background));
            }
            double norm = (calib.signal.numberOfBins() +
                           calib.background.numberOfBins()) / 2.0;
            signal->norm = norm;
            background->norm = norm;
        }

        std::auto_ptr<PDF>  signal;
        std::auto_ptr<PDF>  background;
    };

    int findPDFs(ValueIterator iter, unsigned int n,
                 std::vector<SigBkg>::const_iterator &begin,
                 std::vector<SigBkg>::const_iterator &end) const;

    std::vector<SigBkg> pdfs;
    std::vector<double> bias;
    int         categoryIdx;
    bool            logOutput;
    bool            individual;
    bool            neverUndefined;
    bool            keepEmpty;
    unsigned int        nCategories;
};

static ProcLikelihood::Registry registry("ProcLikelihood");

double ProcLikelihood::SplinePDF::eval(double value) const
{
    value = (value - min) / width;
    return spline.eval(value) * norm / spline.getArea();
}

double ProcLikelihood::SplinePDF::deriv(double value) const
{
    value = (value - min) / width;
    return spline.deriv(value) * norm / spline.getArea();
}

double ProcLikelihood::HistogramPDF::eval(double value) const
{
    return histo->normalizedValue(value) * norm;
}

double ProcLikelihood::HistogramPDF::deriv(double value) const
{
    return 0;
}

ProcLikelihood::ProcLikelihood(const char *name,
                               const Calibration::ProcLikelihood *calib,
                               const MVAComputer *computer) :
    VarProcessor(name, calib, computer),
    pdfs(calib->pdfs.begin(), calib->pdfs.end()),
    bias(calib->bias),
    categoryIdx(calib->categoryIdx), logOutput(calib->logOutput),
    individual(calib->individual), neverUndefined(calib->neverUndefined),
    keepEmpty(calib->keepEmpty), nCategories(1)
{
}

void ProcLikelihood::configure(ConfIterator iter, unsigned int n)
{
    if (categoryIdx >= 0) {
        if ((int)n < categoryIdx + 1)
            return;
        nCategories = pdfs.size() / (n - 1);
        if (nCategories * (n - 1) != pdfs.size())
            return;
        if (!bias.empty() && bias.size() != nCategories)
            return;
    } else if (n != pdfs.size() || bias.size() > 1)
        return;

    int i = 0;
    while(iter) {
        if (categoryIdx == i++)
            iter++(Variable::FLAG_NONE);
        else
            iter++(Variable::FLAG_ALL);
    }

    if (individual) {
        for(unsigned int i = 0; i < pdfs.size(); i += nCategories)
            iter << (neverUndefined ? Variable::FLAG_NONE
                                    : Variable::FLAG_OPTIONAL);
    } else
        iter << (neverUndefined ? Variable::FLAG_NONE
                                : Variable::FLAG_OPTIONAL);
}

int ProcLikelihood::findPDFs(ValueIterator iter, unsigned int n,
                             std::vector<SigBkg>::const_iterator &begin,
                             std::vector<SigBkg>::const_iterator &end) const
{
    int cat;
    if (categoryIdx >= 0) {
        ValueIterator iter2 = iter;
        for(int i = 0; i < categoryIdx; i++)
            ++iter2;

        cat = (int)*iter2;
        if (cat < 0 || (unsigned int)cat >= nCategories)
            return -1;

        begin = pdfs.begin() + cat * (n - 1);
        end = begin + (n - 1);
    } else {
        cat = 0;
        begin = pdfs.begin();
        end = pdfs.end();
    }       

    return cat;
}

void ProcLikelihood::eval(ValueIterator iter, unsigned int n) const
{
    std::vector<SigBkg>::const_iterator pdf, last;
    int cat = findPDFs(iter, n, pdf, last);
    int vars = 0;
    long double signal = bias.empty() ? 1.0 : bias[cat];
    long double background = 1.0;

    if (cat < 0) {
        if (individual)
            for(unsigned int i = 0; i < n; i++)
                iter();
        else
            iter();
        return;
    }

    for(int i = 0; pdf != last; ++iter, i++) {
        if (i == categoryIdx)
            continue;

        for(double *value = iter.begin();
            value < iter.end(); value++) {
            double signalProb =
                std::max(0.0, pdf->signal->eval(*value));
            double backgroundProb =
                std::max(0.0, pdf->background->eval(*value));
            if (!keepEmpty && !individual &&
                signalProb + backgroundProb < 1.0e-20)
                continue;
            vars++;

            if (individual) {
                signalProb *= signal;
                backgroundProb *= background;
                if (logOutput) {
                    if (signalProb < 1.0e-9 &&
                        backgroundProb < 1.0e-9) {
                        if (!neverUndefined)
                            continue;
                        iter << 0.0;
                    } else if (signalProb < 1.0e-9)
                        iter << -99999.0;
                    else if (backgroundProb < 1.0e-9)
                        iter << +99999.0;
                    else
                        iter << (std::log(signalProb) -
                                 std::log(backgroundProb));
                } else {
                    double sum =
                        signalProb + backgroundProb;
                    if (sum > 1.0e-9)
                        iter << (signalProb / sum);
                    else if (neverUndefined)
                        iter << 0.5;
                }
            } else {
                signal *= signalProb;
                background *= backgroundProb;
            }
        }

        ++pdf;
        if (individual)
            iter();
    }

    if (!individual) {
        if (!vars || signal + background < std::exp(-7 * vars - 3)) {
            if (neverUndefined)
                iter(logOutput ? 0.0 : 0.5);
            else
                iter();
        } else if (logOutput) {
            if (signal < 1.0e-9 && background < 1.0e-9) {
                if (neverUndefined)
                    iter(0.0);
                else
                    iter();
            } else if (signal < 1.0e-9)
                iter(-99999.0);
            else if (background < 1.0e-9)
                iter(+99999.0);
            else
                iter(std::log(signal) - std::log(background));
        } else
            iter(signal / (signal + background));
    }
}

std::vector<double> ProcLikelihood::deriv(ValueIterator iter,
                                          unsigned int n) const
{
    std::vector<SigBkg>::const_iterator pdf, last;
    int cat = findPDFs(iter, n, pdf, last);
    int vars = 0;
    long double signal = bias.empty() ? 1.0 : bias[cat];
    long double background = 1.0;

    std::vector<double> result;
    if (cat < 0)
        return result;

    unsigned int size = 0;
    for(ValueIterator iter2 = iter; iter2; ++iter2)
        size += iter2.size();

    // The logic whether a variable is used or net depends on the
    // evaluation, so FFS copy the whole ****

    if (!individual)
        result.resize(size);

    unsigned int j = 0;
    for(int i = 0; pdf != last; ++iter, i++) {
        if (i == categoryIdx) {
            j += iter.size();
            continue;
        }

        for(double *value = iter.begin();
            value < iter.end(); value++, j++) {
            double signalProb = pdf->signal->eval(*value);
            double signalDiff = pdf->signal->deriv(*value);
            if (signalProb < 0.0)
                signalProb = signalDiff = 0.0;

            double backgroundProb = pdf->background->eval(*value);
            double backgroundDiff = pdf->background->deriv(*value);
            if (backgroundProb < 0.0)
                backgroundProb = backgroundDiff = 0.0;

            if (!keepEmpty && !individual &&
                signalProb + backgroundProb < 1.0e-20)
                continue;
            vars++;

            if (individual) {
                signalProb *= signal;
                signalDiff *= signal;
                backgroundProb *= background;
                backgroundDiff *= background;
                if (logOutput) {
                    if (signalProb < 1.0e-9 &&
                        backgroundProb < 1.0e-9) {
                        if (!neverUndefined)
                            continue;
                        result.resize(result.size() +
                                      size);
                    } else if (signalProb < 1.0e-9 ||
                               backgroundProb < 1.0e-9)
                        result.resize(result.size() +
                                      size);
                    else {
                        result.resize(result.size() +
                                      size);
                        result[result.size() -
                               size + j] =
                            signalDiff /
                                signalProb -
                            backgroundDiff /
                                backgroundProb;
                    }
                } else {
                    double sum =
                        signalProb + backgroundProb;
                    if (sum > 1.0e-9) {
                        result.resize(result.size() +
                                      size);
                        result[result.size() -
                               size + j] =
                            (signalDiff *
                             backgroundProb -
                             signalProb *
                             backgroundDiff) /
                            (sum * sum);
                    } else if (neverUndefined)
                        result.resize(result.size() +
                                      size);
                }
            } else {
                signal *= signalProb;
                background *= backgroundProb;
                double s = signalDiff / signalProb;
                if (edm::isNotFinite(s))
                    s = 0.0;
                double b = backgroundDiff / backgroundProb;
                if (edm::isNotFinite(b))
                    b = 0.0;

                result[j] = s - b;
            }
        }

        ++pdf;
    }

    if (!individual) {
        if (!vars || signal + background < std::exp(-7 * vars - 3)) {
            if (neverUndefined)
                std::fill(result.begin(), result.end(), 0.0);
            else
                result.clear();
        } else if (logOutput) {
            if (signal < 1.0e-9 && background < 1.0e-9) {
                if (neverUndefined)
                    std::fill(result.begin(),
                              result.end(), 0.0);
                else
                    result.clear();
            } else if (signal < 1.0e-9 ||
                       background < 1.0e-9)
                std::fill(result.begin(), result.end(), 0.0);
            else {
                // should be ok
            }
        } else {
            double factor = signal * background /
                            ((signal + background) *
                             (signal + background));
            for(std::vector<double>::iterator p = result.begin();
                p != result.end(); ++p)
                *p *= factor;
        }
    }

    return result;
}

} // anonymous namespace