RDFActionHelpers.cxx

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// Author: Enrico Guiraud, Danilo Piparo CERN  12/2016
 
/*************************************************************************
 * Copyright (C) 1995-2018, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/
 
#include "ROOT/RDF/ActionHelpers.hxx"
 
#include "ROOT/RDF/Utils.hxx" // CacheLineStep
 
#include <RtypesCore.h>
#include <TStatistic.h>
 
namespace ROOT {
namespace Internal {
namespace RDF {
 
void ResetIfPossible(TStatistic *h)
{
   *h = TStatistic();
}
// cannot safely re-initialize variations of the result, hence error out
void ResetIfPossible(...)
{
   throw std::runtime_error(
      "A systematic variation was requested for a custom Fill action, but the type of the object to be filled does "
      "not implement a Reset method, so we cannot safely re-initialize variations of the result. Aborting.");
}
 
void UnsetDirectoryIfPossible(TH1 *h)
{
   h->SetDirectory(nullptr);
}
void UnsetDirectoryIfPossible(...) {}
 
CountHelper::CountHelper(const std::shared_ptr<ULong64_t> &resultCount, const unsigned int nSlots)
   : fResultCount(resultCount), fCounts(nSlots, 0)
{
}
 
void CountHelper::Exec(unsigned int slot)
{
   fCounts[slot]++;
}
 
void CountHelper::Finalize()
{
   *fResultCount = 0;
   for (auto &c : fCounts) {
      *fResultCount += c;
   }
}
 
ULong64_t &CountHelper::PartialUpdate(unsigned int slot)
{
   return fCounts[slot];
}
 
void BufferedFillHelper::UpdateMinMax(unsigned int slot, double v)
{
   auto &thisMin = fMin[slot * CacheLineStep<BufEl_t>()];
   auto &thisMax = fMax[slot * CacheLineStep<BufEl_t>()];
   thisMin = std::min(thisMin, v);
   thisMax = std::max(thisMax, v);
}
 
BufferedFillHelper::BufferedFillHelper(const std::shared_ptr<Hist_t> &h, const unsigned int nSlots)
   : fResultHist(h), fNSlots(nSlots), fBufSize(fgTotalBufSize / nSlots), fPartialHists(fNSlots),
     fMin(nSlots * CacheLineStep<BufEl_t>(), std::numeric_limits<BufEl_t>::max()),
     fMax(nSlots * CacheLineStep<BufEl_t>(), std::numeric_limits<BufEl_t>::lowest())
{
   fBuffers.reserve(fNSlots);
   fWBuffers.reserve(fNSlots);
   for (unsigned int i = 0; i < fNSlots; ++i) {
      Buf_t v;
      v.reserve(fBufSize);
      fBuffers.emplace_back(v);
      fWBuffers.emplace_back(v);
   }
}
 
void BufferedFillHelper::Exec(unsigned int slot, double v)
{
   UpdateMinMax(slot, v);
   fBuffers[slot].emplace_back(v);
}
 
void BufferedFillHelper::Exec(unsigned int slot, double v, double w)
{
   UpdateMinMax(slot, v);
   fBuffers[slot].emplace_back(v);
   fWBuffers[slot].emplace_back(w);
}
 
Hist_t &BufferedFillHelper::PartialUpdate(unsigned int slot)
{
   auto &partialHist = fPartialHists[slot];
   // TODO it is inefficient to re-create the partial histogram everytime the callback is called
   //      ideally we could incrementally fill it with the latest entries in the buffers
   partialHist = std::make_unique<Hist_t>(*fResultHist);
   auto weights = fWBuffers[slot].empty() ? nullptr : fWBuffers[slot].data();
   partialHist->FillN(fBuffers[slot].size(), fBuffers[slot].data(), weights);
   return *partialHist;
}
 
void BufferedFillHelper::Finalize()
{
   for (unsigned int i = 0; i < fNSlots; ++i) {
      if (!fWBuffers[i].empty() && fBuffers[i].size() != fWBuffers[i].size()) {
         throw std::runtime_error("Cannot fill weighted histogram with values in containers of different sizes.");
      }
   }
 
   BufEl_t globalMin = *std::min_element(fMin.begin(), fMin.end());
   BufEl_t globalMax = *std::max_element(fMax.begin(), fMax.end());
 
   if (fResultHist->CanExtendAllAxes() && globalMin != std::numeric_limits<BufEl_t>::max() &&
       globalMax != std::numeric_limits<BufEl_t>::lowest()) {
      fResultHist->SetBins(fResultHist->GetNbinsX(), globalMin, globalMax);
   }
 
   for (unsigned int i = 0; i < fNSlots; ++i) {
      auto weights = fWBuffers[i].empty() ? nullptr : fWBuffers[i].data();
      fResultHist->FillN(fBuffers[i].size(), fBuffers[i].data(), weights);
   }
}
 
MeanHelper::MeanHelper(const std::shared_ptr<double> &meanVPtr, const unsigned int nSlots)
   : fResultMean(meanVPtr), fCounts(nSlots, 0), fSums(nSlots, 0), fPartialMeans(nSlots), fCompensations(nSlots)
{
}
 
void MeanHelper::Exec(unsigned int slot, double v)
{
   fCounts[slot]++;
   // Kahan Sum:
   double y = v - fCompensations[slot];
   double t = fSums[slot] + y;
   fCompensations[slot] = (t - fSums[slot]) - y;
   fSums[slot] = t;
}
 
void MeanHelper::Finalize()
{
   double sumOfSums = 0;
   // Kahan Sum:
   double compensation(0);
   double y(0);
   double t(0);
   for (auto &m : fSums) {
      y = m - compensation;
      t = sumOfSums + y;
      compensation = (t - sumOfSums) - y;
      sumOfSums = t;
   }
   ULong64_t sumOfCounts = 0;
   for (auto &c : fCounts)
      sumOfCounts += c;
   *fResultMean = sumOfSums / (sumOfCounts > 0 ? sumOfCounts : 1);
}
 
double &MeanHelper::PartialUpdate(unsigned int slot)
{
   fPartialMeans[slot] = fSums[slot] / fCounts[slot];
   return fPartialMeans[slot];
}
 
StdDevHelper::StdDevHelper(const std::shared_ptr<double> &meanVPtr, const unsigned int nSlots)
   : fNSlots(nSlots), fResultStdDev(meanVPtr), fCounts(nSlots, 0), fMeans(nSlots, 0), fDistancesfromMean(nSlots, 0)
{
}
 
void StdDevHelper::Exec(unsigned int slot, double v)
{
   // Applies the Welford's algorithm to the stream of values received by the thread
   auto count = ++fCounts[slot];
   auto delta = v - fMeans[slot];
   auto mean = fMeans[slot] + delta / count;
   auto delta2 = v - mean;
   auto distance = fDistancesfromMean[slot] + delta * delta2;
 
   fCounts[slot] = count;
   fMeans[slot] = mean;
   fDistancesfromMean[slot] = distance;
}
 
void StdDevHelper::Finalize()
{
   // Evaluates and merges the partial result of each set of data to get the overall standard deviation.
   double totalElements = 0;
   for (auto c : fCounts) {
      totalElements += c;
   }
   if (totalElements == 0 || totalElements == 1) {
      // Std deviation is not defined for 1 element.
      *fResultStdDev = 0;
      return;
   }
 
   double overallMean = 0;
   for (unsigned int i = 0; i < fNSlots; ++i) {
      overallMean += fCounts[i] * fMeans[i];
   }
   overallMean = overallMean / totalElements;
 
   double variance = 0;
   for (unsigned int i = 0; i < fNSlots; ++i) {
      if (fCounts[i] == 0) {
         continue;
      }
      auto setVariance = fDistancesfromMean[i] / (fCounts[i]);
      variance += (fCounts[i]) * (setVariance + std::pow((fMeans[i] - overallMean), 2));
   }
 
   variance = variance / (totalElements - 1);
   *fResultStdDev = std::sqrt(variance);
}
 
// External templates are disabled for gcc5 since this version wrongly omits the C++11 ABI attribute
#if __GNUC__ > 5
template class TakeHelper<bool, bool, std::vector<bool>>;
template class TakeHelper<unsigned int, unsigned int, std::vector<unsigned int>>;
template class TakeHelper<unsigned long, unsigned long, std::vector<unsigned long>>;
template class TakeHelper<unsigned long long, unsigned long long, std::vector<unsigned long long>>;
template class TakeHelper<int, int, std::vector<int>>;
template class TakeHelper<long, long, std::vector<long>>;
template class TakeHelper<long long, long long, std::vector<long long>>;
template class TakeHelper<float, float, std::vector<float>>;
template class TakeHelper<double, double, std::vector<double>>;
#endif
 
} // namespace RDF
} // namespace Internal
} // namespace ROOT