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[RF] Fix data span alignment in batch-mode RooUnbinnedL #23267

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[RF] Fix data span alignment in batch-mode RooUnbinnedL #23267

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6 changes: 5 additions & 1 deletion roofit/roofitcore/src/TestStatistics/RooUnbinnedL.cxx
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Original file line number Diff line number Diff line change
Expand Up @@ -62,8 +62,12 @@ RooUnbinnedL::RooUnbinnedL(RooAbsPdf *pdf, RooAbsData *data, RooAbsL::Extended e
if (evalBackend.value() != RooFit::EvalBackend::Value::Legacy) {
evaluator_ = std::make_unique<RooFit::Evaluator>(*pdf_, evalBackend.value() == RooFit::EvalBackend::Value::Cuda);
std::stack<std::vector<double>>{}.swap(_vectorBuffers);
// Zero-weight events must not be skipped here: the probabilities from
// the evaluator are indexed by the original event indices, aligned with
// the weights obtained from RooAbsData::getWeightBatch(). Events with
// zero weight are skipped in the summation instead.
auto dataSpans =
RooFit::BatchModeDataHelpers::getDataSpans(*data, "", nullptr, /*skipZeroWeights=*/true,
RooFit::BatchModeDataHelpers::getDataSpans(*data, "", nullptr, /*skipZeroWeights=*/false,
/*takeGlobalObservablesFromData=*/false, _vectorBuffers);
for (auto const &item : dataSpans) {
evaluator_->setInput(item.first->GetName(), item.second, false);
Expand Down
42 changes: 42 additions & 0 deletions roofit/roofitcore/test/TestStatistics/testRooAbsL.cxx
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Expand Up @@ -28,6 +28,7 @@

#include "Math/Util.h" // KahanSum

#include <cmath> // isnan
#include <stdexcept> // runtime_error

#include "gtest/gtest.h"
Expand Down Expand Up @@ -304,6 +305,47 @@ TEST_F(SimBinnedConstrainedTest, SubEventSections)
EXPECT_DOUBLE_EQ(whole.Sum(), thrice_N_events_total_parts.Sum());
}

TEST_F(RooAbsLTest, ZeroWeightsWithBatchEvaluator)
{
// The probabilities returned by the RooFit::Evaluator are indexed by the
// original event indices, aligned with the weights that are obtained from
// RooAbsData::getWeightBatch(). This test checks that zero-weight events
// (e.g. empty bins of a binned dataset) don't spoil this alignment, which
// resulted in NaN likelihood values in the past.

w.factory("Gaussian::g(x[-10,10],mu[0,-5,5],sigma[1,0.1,10])");
w.var("x")->setBins(40);
pdf = w.pdf("g");
data = std::unique_ptr<RooDataHist>{pdf->generateBinned(*w.var("x"), 100)};

// The whole point of this test is to have zero-weight events in the
// dataset, so make sure the empty tail bins are really there.
std::size_t nZeroWeight = 0;
for (int i = 0; i < data->numEntries(); ++i) {
data->get(i);
nZeroWeight += (data->weight() == 0.);
}
ASSERT_GT(nZeroWeight, 0);

// Compute the reference NLL value by hand, skipping zero-weight events.
RooArgSet normSet{*w.var("x")};
ROOT::Math::KahanSum<double> refNll;
for (int i = 0; i < data->numEntries(); ++i) {
w.var("x")->setVal(data->get(i)->getRealValue("x"));
if (data->weight() == 0.)
continue;
refNll += -data->weight() * std::log(pdf->getVal(normSet));
}

likelihood = std::make_shared<RooFit::TestStatistics::RooUnbinnedL>(
pdf, data.get(), RooFit::TestStatistics::RooAbsL::Extended::Auto, RooFit::EvalBackend::Cpu());

const double nllVal = likelihood->evaluatePartition({0, 1}, 0, 0).Sum();

EXPECT_FALSE(std::isnan(nllVal));
EXPECT_NEAR(nllVal, refNll.Sum(), 1e-8 * std::abs(refNll.Sum()));
}

TEST_F(RooAbsLTest, VSRooNLLVar)
{
// compare the value of the likelihood to that generated by a similarly configured RooNLLVar
Expand Down
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