RSampler.hxx

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// Author: Martin Føll, University of Oslo (UiO) & CERN 01/2026
 
/*************************************************************************
 * Copyright (C) 1995-2026, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/
 
#ifndef ROOT_INTERNAL_ML_RSAMPLER
#define ROOT_INTERNAL_ML_RSAMPLER
 
#include <memory>
#include <string>
#include <vector>
 
#include "ROOT/ML/RFlat2DMatrix.hxx"
 
// Forward decls
namespace ROOT::Experimental::Internal::ML {
class RFlat2DMatrixOperators;
}
 
namespace ROOT::Experimental::Internal::ML {
/**
\class ROOT::Experimental::Internal::ML::RSampler
 
\brief Implementation of different sampling strategies.
*/
 
class RSampler {
private:
   const RFlat2DMatrix &fDataset;
   // rows of fDataset in each class
   std::vector<std::vector<std::size_t>> &fRows;
   std::string fSampleType;
   float fSampleRatio;
   bool fReplacement;
   bool fShuffle;
   std::size_t fSetSeed;
   std::size_t fNumEntries;
 
   std::size_t fMajor;
   std::size_t fMinor;
   std::size_t fNumMajor;
   std::size_t fNumMinor;
   std::size_t fNumResampledMajor;
   std::size_t fNumResampledMinor;
 
   std::vector<std::size_t> fSamples;
 
   std::unique_ptr<RFlat2DMatrixOperators> fTensorOperators;
 
public:
   RSampler(const RFlat2DMatrix &dataset, std::vector<std::vector<std::size_t>> &rows, const std::string &sampleType,
            float sampleRatio, bool replacement = false, bool shuffle = true, std::size_t setSeed = 0);
 
   ~RSampler();
 
   void SetupSampler();
 
   void Sampler(RFlat2DMatrix &SampledTensor);
 
   void SetupRandomUndersampler();
 
   void SetupRandomOversampler();
 
   void RandomUndersampler(RFlat2DMatrix &ShuffledTensor);
 
   void RandomOversampler(RFlat2DMatrix &ShuffledTensor);
 
   void GatherSamples(RFlat2DMatrix &ShuffledTensor, const std::vector<std::size_t> &ResampledRows,
                      std::size_t numResampled, const std::vector<std::size_t> &OtherRows);
 
   void SampleWithReplacement(std::size_t n_samples, std::size_t max);
 
   void SampleWithoutReplacement(std::size_t n_samples, std::size_t max);
 
   std::size_t GetNumEntries() { return fNumEntries; }
};
 
} // namespace ROOT::Experimental::Internal::ML
#endif // ROOT_INTERNAL_ML_RSAMPLER