TrackLwtnnClassifier.cc

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#include "RecoTracker/FinalTrackSelectors/interface/TrackMVAClassifier.h"

#include "FWCore/Framework/interface/EventSetup.h"
#include "FWCore/Framework/interface/ESHandle.h"

#include "DataFormats/TrackReco/interface/Track.h"
#include "DataFormats/VertexReco/interface/Vertex.h"

#include "getBestVertex.h"

//from lwtnn
#include "TrackingTools/Records/interface/TrackingComponentsRecord.h"
#include "lwtnn/LightweightNeuralNetwork.hh"

namespace {
  struct lwtnn {
    lwtnn(const edm::ParameterSet& cfg) : lwtnnLabel_(cfg.getParameter<std::string>("lwtnnLabel")) {}

    static const char* name() { return "TrackLwtnnClassifier"; }

    static void fillDescriptions(edm::ParameterSetDescription& desc) {
      desc.add<std::string>("lwtnnLabel", "trackSelectionLwtnn");
    }

    void beginStream() {}
    void initEvent(const edm::EventSetup& es) {
      edm::ESHandle<lwt::LightweightNeuralNetwork> lwtnnHandle;
      es.get<TrackingComponentsRecord>().get(lwtnnLabel_, lwtnnHandle);
      neuralNetwork_ = lwtnnHandle.product();
    }

    std::pair<float, bool> operator()(reco::Track const& trk,
                                      reco::BeamSpot const& beamSpot,
                                      reco::VertexCollection const& vertices,
                                      lwt::ValueMap& inputs) const {
      // lwt::ValueMap is typedef for std::map<std::string, double>
      //
      // It is cached per event to avoid constructing the map for each
      // track while keeping the operator() interface const.

      Point bestVertex = getBestVertex(trk, vertices);

      inputs["trk_pt"] = trk.pt();
      inputs["trk_eta"] = trk.eta();
      inputs["trk_lambda"] = trk.lambda();
      inputs["trk_dxy"] = trk.dxy(beamSpot.position());  // Training done without taking absolute value
      inputs["trk_dz"] = trk.dz(beamSpot.position());    // Training done without taking absolute value
      inputs["trk_dxyClosestPV"] = trk.dxy(bestVertex);  // Training done without taking absolute value
      // Training done without taking absolute value
      inputs["trk_dzClosestPVNorm"] = std::max(-0.2, std::min(trk.dz(bestVertex), 0.2));
      inputs["trk_ptErr"] = trk.ptError();
      inputs["trk_etaErr"] = trk.etaError();
      inputs["trk_lambdaErr"] = trk.lambdaError();
      inputs["trk_dxyErr"] = trk.dxyError();
      inputs["trk_dzErr"] = trk.dzError();
      inputs["trk_nChi2"] = trk.normalizedChi2();
      inputs["trk_ndof"] = trk.ndof();
      inputs["trk_nInvalid"] = trk.hitPattern().numberOfLostHits(reco::HitPattern::TRACK_HITS);
      inputs["trk_nPixel"] = trk.hitPattern().numberOfValidPixelHits();
      inputs["trk_nStrip"] = trk.hitPattern().numberOfValidStripHits();
      inputs["trk_nPixelLay"] = trk.hitPattern().pixelLayersWithMeasurement();
      inputs["trk_nStripLay"] = trk.hitPattern().stripLayersWithMeasurement();
      inputs["trk_n3DLay"] = (trk.hitPattern().numberOfValidStripLayersWithMonoAndStereo() +
                              trk.hitPattern().pixelLayersWithMeasurement());
      inputs["trk_nLostLay"] = trk.hitPattern().trackerLayersWithoutMeasurement(reco::HitPattern::TRACK_HITS);
      inputs["trk_algo"] = trk.algo();

      auto out = neuralNetwork_->compute(inputs);
      // there should only one output
      if (out.size() != 1)
        throw cms::Exception("LogicError") << "Expecting exactly one output from NN, got " << out.size();

      float output = 2.0 * out.begin()->second - 1.0;

      //Check if the network is known to be unreliable in that part of phase space. Hard cut values
      //correspond to rare tracks known to be difficult for the Deep Neural Network classifier

      bool isReliable = true;
      //T1qqqq
      if (std::abs(inputs["trk_dxy"]) >= 0.1 && inputs["trk_etaErr"] < 0.003 && inputs["trk_dxyErr"] < 0.03 &&
          inputs["trk_ndof"] > 3) {
        isReliable = false;
      }
      //T5qqqqLL
      if ((inputs["trk_pt"] > 100.0) && (inputs["trk_nChi2"] < 4.0) && (inputs["trk_etaErr"] < 0.001)) {
        isReliable = false;
      }

      std::pair<float, bool> return_(output, isReliable);
      return return_;
    }

    std::string lwtnnLabel_;
    const lwt::LightweightNeuralNetwork* neuralNetwork_;
  };

  using TrackLwtnnClassifier = TrackMVAClassifier<lwtnn, lwt::ValueMap>;
}  // namespace

#include "FWCore/PluginManager/interface/ModuleDef.h"
#include "FWCore/Framework/interface/MakerMacros.h"

DEFINE_FWK_MODULE(TrackLwtnnClassifier);