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Update chi2 tq bins (cms-sw#165)
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* change binning method used by track quality

* clean TrackQuality class to only create features used by default classifier

* ran scram b -j8 clang-format
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@cgsavard
cgsavard authored Jun 6, 2022
1 parent f661aa9 commit 26ff0dd
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Showing 3 changed files with 22 additions and 156 deletions.
1 change: 0 additions & 1 deletion L1Trigger/TrackTrigger/interface/TrackQuality.h
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Expand Up @@ -76,7 +76,6 @@ class TrackQuality {
float bendchi2Max_;
float minPt_;
int nStubsmin_;
float ONNXInvRScaling_;
const hph::Setup* setup_;
bool useHPH;
};
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2 changes: 0 additions & 2 deletions L1Trigger/TrackTrigger/python/TrackQualityParams_cfi.py
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Expand Up @@ -16,6 +16,4 @@
bendchi2Max = cms.double( 2.4 ),
minPt = cms.double( 2. ), # in GeV
nStubsmin = cms.int32( 4 ),

ONNXInvRScale = cms.double(500) # Scaling InvR to same order of magnitude as other variables for ML models
)
175 changes: 22 additions & 153 deletions L1Trigger/TrackTrigger/src/TrackQuality.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -29,52 +29,29 @@ TrackQuality::TrackQuality(const edm::ParameterSet& qualityParams) : setup_(), u
qualityParams.getParameter<edm::FileInPath>("ONNXmodel"),
qualityParams.getParameter<std::string>("ONNXInputName"),
qualityParams.getParameter<std::vector<std::string>>("featureNames"));
ONNXInvRScaling_ = qualityParams.getParameter<double>("ONNXInvRScale");
}
}

std::vector<float> TrackQuality::featureTransform(TTTrack<Ref_Phase2TrackerDigi_>& aTrack,
std::vector<std::string> const& featureNames) {
// List input features for MVA in proper order below, the features options are
// {"log_chi2","log_chi2rphi","log_chi2rz","log_bendchi2","nstubs","lay1_hits","lay2_hits",
// "lay3_hits","lay4_hits","lay5_hits","lay6_hits","disk1_hits","disk2_hits","disk3_hits",
// "disk4_hits","disk5_hits","rinv","tanl","z0","dtot","ltot","chi2","chi2rz","chi2rphi",
// "bendchi2","pt","eta","nlaymiss_interior","phi","bendchi2_bin","chi2rz_bin","chi2rphi_bin",
// "nlaymiss_PS","nlaymiss_2S"}
// List input features for MVA in proper order below, the current features options are
// {"phi", "eta", "z0", "bendchi2_bin", "nstub", "nlaymiss_interior", "chi2rphi_bin",
// "chi2rz_bin"}
//
// To use more features, they must be created here and added to feature_map below

std::vector<float> transformedFeatures;

// Define feature map, filled as features are generated
std::map<std::string, float> feature_map;

// The following converts the 7 bit hitmask in the TTTrackword to an expected
// 11 bit hitmask based on the eta of the track
std::vector<int> hitpattern_binary = {0, 0, 0, 0, 0, 0, 0};
std::vector<int> hitpattern_expanded_binary = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
std::vector<float> eta_bins = {0.0, 0.2, 0.41, 0.62, 0.9, 1.26, 1.68, 2.08, 2.4};

// Expected hitmap table, each row corresponds to an eta bin, each value corresponds to
// the expected layer in the expanded hit pattern. The expanded hit pattern should be
// 11 bits but contains a 12th element so this hitmap table is symmetric
int hitmap[8][7] = {{0, 1, 2, 3, 4, 5, 11},
{0, 1, 2, 3, 4, 5, 11},
{0, 1, 2, 3, 4, 5, 11},
{0, 1, 2, 3, 4, 5, 11},
{0, 1, 2, 3, 4, 5, 11},
{0, 1, 2, 6, 7, 8, 9},
{0, 1, 7, 8, 9, 10, 11},
{0, 6, 7, 8, 9, 10, 11}};

// iterate through bits of the hitpattern and compare to 1 filling the hitpattern binary vector
int tmp_trk_hitpattern = aTrack.hitPattern();
// -------- calculate feature variables --------

// calculate number of missed interior layers from hitpattern
int tmp_trk_hitpattern = aTrack.hitPattern();
int nbits = floor(log2(tmp_trk_hitpattern)) + 1;
int lay_i = 0;
int tmp_trk_nlaymiss_interior = 0;
int tmp_trk_nlaymiss_PS = 0;
int tmp_trk_nlaymiss_2S = 0;
double tmp_trk_tanL = aTrack.tanL();
double tmp_trk_z0 = aTrack.z0();
bool seq = false;
for (int i = 0; i < nbits; i++) {
lay_i = ((1 << i) & tmp_trk_hitpattern) >> i; //0 or 1 in ith bit (right to left)
Expand All @@ -85,140 +62,32 @@ std::vector<float> TrackQuality::featureTransform(TTTrack<Ref_Phase2TrackerDigi_
tmp_trk_nlaymiss_interior++;
}

if (useHPH) {
hph::HitPatternHelper hph(setup_, tmp_trk_hitpattern, tmp_trk_tanL, tmp_trk_z0);
hitpattern_expanded_binary = hph.binary();
tmp_trk_nlaymiss_PS = hph.numMissingPS();
tmp_trk_nlaymiss_2S = hph.numMissing2S();
} else {
for (int i = 6; i >= 0; i--) {
int k = tmp_trk_hitpattern >> i;
if (k & 1)
hitpattern_binary[i] = 1;
}
float eta = abs(aTrack.eta());
int eta_size = static_cast<int>(eta_bins.size());
// First iterate through eta bins
for (int j = 1; j < eta_size; j++) {
if (eta < eta_bins[j] && eta >= eta_bins[j - 1]) // if track in eta bin
{
// Iterate through hitpattern binary
for (int k = 0; k <= 6; k++)
// Fill expanded binary entries using the expected hitmap table positions
hitpattern_expanded_binary[hitmap[j - 1][k]] = hitpattern_binary[k];
break;
}
}
}

int tmp_trk_ltot = 0;
//calculate number of layer hits
for (int i = 0; i < 6; ++i) {
tmp_trk_ltot += hitpattern_expanded_binary[i];
}

int tmp_trk_dtot = 0;
//calculate number of disk hits
for (int i = 6; i < 11; ++i) {
tmp_trk_dtot += hitpattern_expanded_binary[i];
}

// bin bendchi2 variable (bins from https://twiki.cern.ch/twiki/bin/viewauth/CMS/HybridDataFormat#Fitted_Tracks_written_by_KalmanF)
float tmp_trk_bendchi2 = aTrack.stubPtConsistency();
std::array<float, 8> bendchi2_bins{{0, 0.75, 1.0, 1.5, 2.25, 3.5, 5.0, 20.0}};
int n_bendchi2 = static_cast<int>(bendchi2_bins.size());
float tmp_trk_bendchi2_bin = -1;
for (int i = 0; i < n_bendchi2; i++) {
if (tmp_trk_bendchi2 >= bendchi2_bins[i] && tmp_trk_bendchi2 < bendchi2_bins[i + 1]) {
tmp_trk_bendchi2_bin = i;
break;
}
}
if (tmp_trk_bendchi2_bin < 0)
tmp_trk_bendchi2_bin = n_bendchi2;

// bin chi2rphi variable (bins from https://twiki.cern.ch/twiki/bin/viewauth/CMS/HybridDataFormat#Fitted_Tracks_written_by_KalmanF)
float tmp_trk_chi2rphi = aTrack.chi2XYRed();
std::array<float, 16> chi2rphi_bins{
{0, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, 10.0, 15.0, 20.0, 35.0, 60.0, 200.0}};
int n_chi2rphi = static_cast<int>(chi2rphi_bins.size());
float tmp_trk_chi2rphi_bin = -1;
for (int i = 0; i < n_chi2rphi; i++) {
if (tmp_trk_chi2rphi >= chi2rphi_bins[i] && tmp_trk_chi2rphi < chi2rphi_bins[i + 1]) {
tmp_trk_chi2rphi_bin = i;
break;
}
}
if (tmp_trk_chi2rphi_bin < 0)
tmp_trk_chi2rphi_bin = n_chi2rphi;

// bin chi2rz variable (bins from https://twiki.cern.ch/twiki/bin/viewauth/CMS/HybridDataFormat#Fitted_Tracks_written_by_KalmanF)
float tmp_trk_chi2rz = aTrack.chi2ZRed();
std::array<float, 16> chi2rz_bins{{0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 6.0, 8.0, 10.0, 20.0, 50.0}};
int n_chi2rz = static_cast<int>(chi2rz_bins.size());
float tmp_trk_chi2rz_bin = -1;
for (int i = 0; i < n_chi2rz; i++) {
if (tmp_trk_chi2rz >= chi2rz_bins[i] && tmp_trk_chi2rz < chi2rz_bins[i + 1]) {
tmp_trk_chi2rz_bin = i;
break;
}
}
if (tmp_trk_chi2rz_bin < 0)
tmp_trk_chi2rz_bin = n_chi2rz;
// binned chi2 variables
int tmp_trk_bendchi2_bin = aTrack.getBendChi2Bits();
int tmp_trk_chi2rphi_bin = aTrack.getChi2RPhiBits();
int tmp_trk_chi2rz_bin = aTrack.getChi2RZBits();

// get the nstub
std::vector<edm::Ref<edmNew::DetSetVector<TTStub<Ref_Phase2TrackerDigi_>>, TTStub<Ref_Phase2TrackerDigi_>>> stubRefs =
aTrack.getStubRefs();
int tmp_trk_nstub = stubRefs.size();

// fill the feature map
feature_map["nstub"] = stubRefs.size();
feature_map["rinv"] = ONNXInvRScaling_ * abs(aTrack.rInv());
feature_map["tanl"] = abs(tmp_trk_tanL);
feature_map["z0"] = tmp_trk_z0;
feature_map["phi"] = aTrack.phi();
feature_map["pt"] = aTrack.momentum().perp();
feature_map["eta"] = aTrack.eta();

float tmp_trk_chi2 = aTrack.chi2();
feature_map["chi2"] = tmp_trk_chi2;
feature_map["log_chi2"] = log(tmp_trk_chi2);

feature_map["chi2rphi"] = tmp_trk_chi2rphi;
feature_map["log_chi2rphi"] = log(tmp_trk_chi2rphi);
// get other variables directly from TTTrack
float tmp_trk_z0 = aTrack.z0();
float tmp_trk_phi = aTrack.phi();
float tmp_trk_eta = aTrack.eta();

feature_map["chi2rz"] = tmp_trk_chi2rz;
feature_map["log_chi2rz"] = log(tmp_trk_chi2rz);

feature_map["chi2rz"] = tmp_trk_chi2rz;
feature_map["log_chi2rz"] = log(tmp_trk_chi2rz);

feature_map["bendchi2"] = tmp_trk_bendchi2;
feature_map["log_bendchi2"] = log(tmp_trk_bendchi2);

feature_map["lay1_hits"] = float(hitpattern_expanded_binary[0]);
feature_map["lay2_hits"] = float(hitpattern_expanded_binary[1]);
feature_map["lay3_hits"] = float(hitpattern_expanded_binary[2]);
feature_map["lay4_hits"] = float(hitpattern_expanded_binary[3]);
feature_map["lay5_hits"] = float(hitpattern_expanded_binary[4]);
feature_map["lay6_hits"] = float(hitpattern_expanded_binary[5]);
feature_map["disk1_hits"] = float(hitpattern_expanded_binary[6]);
feature_map["disk2_hits"] = float(hitpattern_expanded_binary[7]);
feature_map["disk3_hits"] = float(hitpattern_expanded_binary[8]);
feature_map["disk4_hits"] = float(hitpattern_expanded_binary[9]);
feature_map["disk5_hits"] = float(hitpattern_expanded_binary[10]);

feature_map["dtot"] = float(tmp_trk_dtot);
feature_map["ltot"] = float(tmp_trk_ltot);
// -------- fill the feature map ---------

feature_map["nstub"] = float(tmp_trk_nstub);
feature_map["z0"] = tmp_trk_z0;
feature_map["phi"] = tmp_trk_phi;
feature_map["eta"] = tmp_trk_eta;
feature_map["nlaymiss_interior"] = float(tmp_trk_nlaymiss_interior);
feature_map["bendchi2_bin"] = tmp_trk_bendchi2_bin;
feature_map["chi2rphi_bin"] = tmp_trk_chi2rphi_bin;
feature_map["chi2rz_bin"] = tmp_trk_chi2rz_bin;

//Bonus features from hitpattern
feature_map["nlaymiss_PS"] = float(tmp_trk_nlaymiss_PS);
feature_map["nlaymiss_2S"] = float(tmp_trk_nlaymiss_2S);

// fill tensor with track params
transformedFeatures.reserve(featureNames.size());
for (const std::string& feature : featureNames)
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