49ClassImp(RooFit::Detail::RooNLLVarNew);
55constexpr const char *RooNLLVarNew::weightVarName;
56constexpr const char *RooNLLVarNew::weightVarNameSumW2;
63 return std::make_unique<RooConstVar>(
name,
name, 1.0);
71 _observables(
"!observables",
"List of observables",
this),
75 _observables.add(*obs);
80 _observables(
"!servers",
this,
other._observables),
89 std::size_t nEvents =
output.size();
99 for (std::size_t i = 0; i < nEvents; ++i) {
101 var->setVal(ctx.
at(var)[i]);
103 dataHist.
add(_observables, weights[weights.size() == 1 ? 0 : i]);
107 RooHistPdf pdf{
"offsetPdf",
"offsetPdf", _observables, dataHist};
108 for (std::size_t i = 0; i < nEvents; ++i) {
110 var->setVal(ctx.
at(var)[i]);
112 output[i] = pdf.getVal(_observables);
117 double evaluate()
const override {
return 0.0; }
132RooNLLVarNew::RooNLLVarNew(
const char *
name,
const char *title,
RooAbsPdf &pdf,
RooArgSet const &observables,
135 _pdf{
"pdf",
"pdf",
this, pdf},
138 _binnedL{pdf.getAttribute(
"BinnedLikelihoodActive")}
152 if (expectedEvents) {
154 std::make_unique<RooTemplateProxy<RooAbsReal>>(
"expectedEvents",
"expectedEvents",
this, *expectedEvents);
155 addOwnedComponents(std::move(expectedEvents));
166 auto offsetPdf = std::make_unique<RooOffsetPdf>(
"_offset_pdf",
"_offset_pdf", obs, *
_weightVar);
167 _offsetPdf = std::make_unique<RooTemplateProxy<RooAbsPdf>>(
"offsetPdf",
"offsetPdf",
this, *
offsetPdf);
168 addOwnedComponents(std::move(
offsetPdf));
179 _doOffset{
other._doOffset},
180 _simCount{
other._simCount},
181 _prefix{
other._prefix},
184 if (
other._expectedEvents) {
185 _expectedEvents = std::make_unique<RooTemplateProxy<RooAbsReal>>(
"expectedEvents",
this, *
other._expectedEvents);
189void RooNLLVarNew::fillBinWidthsFromPdfBoundaries(
RooAbsReal const &pdf,
RooArgSet const &observables)
192 if (!_binw.empty()) {
196 if (observables.
size() != 1) {
197 throw std::runtime_error(
"BinnedPdf optimization only works with a 1D pdf.");
200 std::list<double> *boundaries = pdf.
binBoundaries(*var, var->getMin(), var->getMax());
201 std::list<double>::iterator
biter = boundaries->begin();
202 _binw.resize(boundaries->size() - 1);
206 while (
biter != boundaries->end()) {
216 std::span<const double> weights)
const
223 for (std::size_t i = 0; i <
preds.size(); ++i) {
226 double N = weights[i];
227 double mu =
preds[i];
234 logEvalError(
Form(
"Observed %f events in bin %lu with zero event yield",
N, (
unsigned long)i));
253 auto config = ctx.
config(
this);
257 _sumWeight = weights.size() == 1 ? weights[0] *
probas.size()
265 _doBinOffset ? ctx.at(*_offsetPdf) : std::span<const
double>{});
267 if (
nllOut.nInfiniteValues > 0) {
268 oocoutW(&*_pdf, Eval) <<
"RooAbsPdf::getLogVal(" << _pdf->GetName()
269 <<
") WARNING: top-level pdf has some infinite values" << std::endl;
271 for (std::size_t i = 0; i <
nllOut.nNonPositiveValues; ++i) {
272 _pdf->logEvalError(
"getLogVal() top-level p.d.f not greater than zero");
274 for (std::size_t i = 0; i <
nllOut.nNaNValues; ++i) {
275 _pdf->logEvalError(
"getLogVal() top-level p.d.f evaluates to NaN");
278 if (_expectedEvents) {
279 std::span<const double>
expected = ctx.
at(*_expectedEvents);
290void RooNLLVarNew::setPrefix(std::string
const &prefix)
297void RooNLLVarNew::resetWeightVarNames()
299 _weightVar->SetName((_prefix + weightVarName).c_str());
302 (*_offsetPdf)->SetName((_prefix +
"_offset_pdf").c_str());
308void RooNLLVarNew::applyWeightSquared(
bool flag)
313void RooNLLVarNew::enableOffsetting(
bool flag)
324 if (!_doBinOffset && _simCount > 1) {
332 if (_offset.Sum() == 0 && _offset.Carry() == 0 && (
result.Sum() != 0 ||
result.Carry() != 0)) {
341 if (
_binnedL && !_pdf->getAttribute(
"BinnedLikelihoodActiveYields")) {
343 errorMsg <<
"RooNLLVarNew::translate(): binned likelihood optimization is only supported when raw pdf "
344 "values can be interpreted as yields."
345 <<
" This is not the case for HistFactory models written with ROOT versions before 6.26.00";
347 throw std::runtime_error(
errorMsg.str());
363 std::string
simCountStr = std::to_string(
static_cast<double>(_simCount));
375 if (_expectedEvents) {
ROOT::RRangeCast< T, false, Range_t > static_range_cast(Range_t &&coll)
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t result
char * Form(const char *fmt,...)
Formats a string in a circular formatting buffer.
The Kahan summation is a compensated summation algorithm, which significantly reduces numerical error...
Common abstract base class for objects that represent a value and a "shape" in RooFit.
RooFit::OwningPtr< RooArgSet > getObservables(const RooArgSet &set, bool valueOnly=true) const
Given a set of possible observables, return the observables that this PDF depends on.
bool getAttribute(const Text_t *name) const
Check if a named attribute is set. By default, all attributes are unset.
Storage_t::size_type size() const
RooAbsArg * first() const
Abstract interface for all probability density functions.
virtual std::unique_ptr< RooAbsReal > createExpectedEventsFunc(const RooArgSet *nset) const
Returns an object that represents the expected number of events for a given normalization set,...
Abstract base class for objects that represent a real value and implements functionality common to al...
virtual std::list< double > * binBoundaries(RooAbsRealLValue &obs, double xlo, double xhi) const
Retrieve bin boundaries if this distribution is binned in obs.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Container class to hold N-dimensional binned data.
void add(const RooArgSet &row, double wgt=1.0) override
Add wgt to the bin content enclosed by the coordinates passed in row.
A class to maintain the context for squashing of RooFit models into code.
std::string buildCall(std::string const &funcname, Args_t const &...args)
Build the code to call the function with name funcname, passing some arguments.
void addResult(RooAbsArg const *key, std::string const &value)
A function to save an expression that includes/depends on the result of the input node.
void addToCodeBody(RooAbsArg const *klass, std::string const &in)
Adds the input string to the squashed code body.
std::string const & getResult(RooAbsArg const &arg)
Gets the result for the given node using the node name.
void addToGlobalScope(std::string const &str)
Adds the given string to the string block that will be emitted at the top of the squashed function.
std::unique_ptr< LoopScope > beginLoop(RooAbsArg const *in)
Create a RAII scope for iterating over vector observables.
std::span< const double > at(RooAbsArg const *arg, RooAbsArg const *caller=nullptr)
std::span< double > output()
RooBatchCompute::Config config(RooAbsArg const *arg) const
void setOutputWithOffset(RooAbsArg const *arg, ROOT::Math::KahanSum< double > val, ROOT::Math::KahanSum< double > const &offset)
Sets the output value with an offset.
A propability density function sampled from a multidimensional histogram.
Variable that can be changed from the outside.
Mother of all ROOT objects.
double reduceSum(Config cfg, InputArr input, size_t n)
ReduceNLLOutput reduceNLL(Config cfg, std::span< const double > probas, std::span< const double > weights, std::span< const double > offsetProbas)
double nll(double pdf, double weight, int binnedL, int doBinOffset)
std::string makeValidVarName(std::string const &in)
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
OffsetMode
For setting the offset mode with the Offset() command argument to RooAbsPdf::fitTo()
void evaluate(typename Architecture_t::Tensor_t &A, EActivationFunction f)
Apply the given activation function to each value in the given tensor A.
void probas(TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)