def __init__(self):
'''
This class is meant as a base class for the FatJet studies
You will not need to edit it for trying out things
'''
TrainData.__init__(self)
#define truth:
self.treename = "tree"
self.undefTruth = ['']
self.truthclasses = ['sig', 'bkg']
self.referenceclass = 'sig' ## used for pt reshaping
self.registerBranches([])
self.weightbranchX = 'H_pt'
self.weightbranchY = 'H_mass'
self.weight_binX = numpy.array([0, 3000], dtype=float)
self.weight_binY = numpy.array([0, 3000], dtype=float)
#this is only needed because the truth definitions are different from deepFlavour
self.allbranchestoberead = []
self.registerBranches(self.undefTruth)
self.registerBranches(self.truthclasses)
print("Branches read:", self.allbranchestoberead)
def __init__(self):
TrainData.__init__(self)
self.treename = "clusters" #input root tree name
self.truthclasses = [
'electron', 'muon', 'photon', 'pi0', 'neutral', 'charged'
] #truth classes for classification
self.weightbranchX = 'pt' #needs to be specified
self.weightbranchY = 'eta' #needs to be specified
self.referenceclass = 'flatten'
self.weight_binX = numpy.array([0, 40000], dtype=float)
self.weight_binY = numpy.array([-40000, 40000], dtype=float)
self.addBranches(['pt', 'eta']) #list of branches to be used
self.channels = [
'bin_x_1', 'bin_y_1', 'bin_z_1', 'bin_energy_1', 'bin_x_2',
'bin_y_2', 'bin_z_2', 'bin_energy_2'
]
self.addBranches(self.channels, 950)
self.registerBranches(self.truthclasses)
#call this at the end
self.reduceTruth(None)
def __init__(self):
TrainData.__init__(self)
self.treename="tree" #input root tree name
self.truthclasses=[]#['isA','isB','isC'] #truth classes for classification
self.regressiontargetclasses=['sigsum']
self.weightbranchX='isA' #needs to be specified if weighter is used
self.weightbranchY='isB' #needs to be specified if weighter is used
#there is no need to resample/reweight
self.weight=False
self.remove=False
#does not do anything in this configuration
self.referenceclass='flatten'
self.weight_binX = numpy.array([0,40000],dtype=float)
self.weight_binY = numpy.array([0,40000],dtype=float)
self.registerBranches(self.regressiontargetclasses) #list of branches to be used
self.registerBranches(self.truthclasses)
#call this at the end
self.reduceTruth(None)
def __init__(self):
import numpy
TrainData.__init__(self)
self.treename = "tree"
self.undefTruth = ['']
self.truthclasses = [
'isGamma',
'isElectron',
'isPionCharged',
'isNeutralPion',
]
self.weightbranchX = 'true_energy'
self.weightbranchY = 'seed_eta'
#is already flat
self.referenceclass = 'flatten'
self.weight_binX = numpy.array([0, 0.1, 40000], dtype=float)
self.weight_binY = numpy.array([-40000, 40000], dtype=float)
self.registerBranches([
'rechit_energy', 'rechit_eta', 'rechit_phi', 'rechit_layer',
'nrechits', 'seed_eta', 'seed_phi', 'true_energy'
])
self.regtruth = 'true_energy'
self.regressiontargetclasses = ['E']
self.registerBranches(self.truthclasses)
self.reduceTruth(None)
def __init__(self):
import numpy
TrainData.__init__(self)
#setting DeepJet specific defaults
self.treename = "deepntuplizer/tree"
self.truthclasses = ['class1', 'class2']
self.treename = "deepntuplizer/tree"
self.referenceclass = 'class1'
self.registerBranches(self.truthclasses)
self.registerBranches(['x'])
self.weightbranchX = 'x'
self.weightbranchY = 'x'
self.weight_binX = numpy.array([-1, 0.9, 2.0], dtype=float)
self.weight_binY = numpy.array([-1, 0.9, 2.0], dtype=float)
def reduceTruth(self, tuple_in):
self.reducedtruthclasses = [
'isB', 'isBB', 'isLeptB', 'isC', 'isUDS', 'isG'
]
if tuple_in is not None:
class1 = tuple_in['class1'].view(numpy.ndarray)
class2 = tuple_in['class2'].view(numpy.ndarray)
return numpy.vstack((class1, class2)).transpose()
def __init__(self):
TrainData.__init__(self)
self.nPU = 200
self.nfilespremix = 20
self.eventsperround = 200
self.always_use_test_minbias = False
def __init__(self):
TrainData.__init__(self)
self.treename = "clusters" #input root tree name
self.truthclasses = [
'electron', 'muon', 'photon', 'pi0', 'neutral', 'charged'
] #truth classes for classification
self.weightbranchX = 'cluster_pt' #needs to be specified
self.weightbranchY = 'cluster_eta' #needs to be specified
self.referenceclass = 'electron'
self.weight_binX = np.array(
[0, 3, 5, 10, 20, 40, 70, 100, 150, 200, 300, 500, 40000],
dtype=float)
self.weight_binY = np.array(
[1.3, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5, 2.7, 3.0], dtype=float)
self.registerBranches(['cluster_pt',
'cluster_eta']) #list of branches to be used
self.registerBranches(self.truthclasses)
#call this at the end
self.reducedtruthclasses = ['egamma', 'muon', 'pi0', 'hadron']
def __init__(self):
import numpy
TrainData.__init__(self)
#setting DeepJet specific defaults
self.treename="tree"
self.undefTruth=[]
self.referenceclass='isB'
self.truthclasses=['isB','isC','isUDSG']
#standard branches
self.registerBranches(self.undefTruth)
self.registerBranches(self.truthclasses)
self.registerBranches(['jet_pt','jet_eta'])
self.weightbranchX='jet_pt'
self.weightbranchY='jet_eta'
self.weight_binX = numpy.array([
10,25,30,35,40,45,50,60,75,100,
125,150,175,200,250,300,400,500,
600,2000],dtype=float)
self.weight_binY = numpy.array(
[-2.5,-2.,-1.5,-1.,-0.5,0.5,1,1.5,2.,2.5],
dtype=float
)
self.reduceTruth(None)
def __init__(self):
import numpy
TrainData.__init__(self)
self.treename="events"
self.undefTruth=['']
self.truthclasses=[]
self.remove=False
self.weight=False
self.weightbranchX='true_energy'
self.weightbranchY='true_x'
#is already flat
self.referenceclass='flatten'
self.weight_binX = numpy.array([0,0.1,40000],dtype=float)
self.weight_binY = numpy.array([-40000,40000],dtype=float)
self.regtruth='true_energy'
self.regressiontargetclasses=['E']
self.registerBranches([self.regtruth])
self.reduceTruth(None)
self.rebinx = 1
self.rebiny = 1
self.rebinz = 1
def __init__(self):
import numpy
TrainData.__init__(self)
#setting DeepJet specific defaults
self.treename="tree"
#self.undefTruth=['isUndefined']
self.referenceclass='lep_isPromptId_Training'
self.truthclasses=['lep_isPromptId_Training','lep_isNonPromptId_Training','lep_isFakeId_Training']
#standard branches
#self.registerBranches(self.undefTruth)
self.registerBranches(self.truthclasses)
self.registerBranches(['lep_pt','lep_eta'])
self.weightbranchX='lep_pt'
self.weightbranchY='lep_eta'
self.weight_binX = numpy.array([
5,7.5,10,12.5,15,17.5,20,25,30,35,40,45,50,60,75,100,
125,150,175,200,250,300,400,500,
600,2000],dtype=float)
self.weight_binY = numpy.array(
[-2.5,-2.,-1.5,-1.,-0.5,0.5,1,1.5,2.,2.5],
dtype=float
)
self.reduceTruth(None)
def __init__(self):
'''
This class is meant as a base class for the FatJet studies
You will not need to edit it for trying out things
'''
TrainData.__init__(self)
#define truth:
self.treename = "deepntuplizer/tree"
self.undefTruth=['isUndefined']
self.truthclasses=['fj_isNonCC', 'fj_isCC', 'fj_isNonBB']
self.referenceclass='fj_isNonCC' ## used for pt reshaping
self.registerBranches(['fj_pt','fj_sdmass'])
self.weightbranchX='fj_pt'
self.weightbranchY='fj_sdmass'
#self.weight_binX = numpy.array([
# 300,2500],dtype=float)
self.weight_binX = numpy.array([
10,25,30,35,40,45,50,60,75,100,
125,150,175,200,250,300,400,500,
600,700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000],dtype=float)
self.weight_binY = numpy.array(
[40,200],
dtype=float
)
#this is only needed because the truth definitions are different from deepFlavour
self.allbranchestoberead=[]
self.registerBranches(self.undefTruth)
self.registerBranches(self.truthclasses)
self.registerBranches(['fj_isBB', 'fj_isNonBB'])
print("Branches read:", self.allbranchestoberead)
def __init__(self):
TrainData.__init__(self)
self.description = "DeepLepton training datastructure"
self.truth_branches = ['lep_isPromptId_Training','lep_isNonPromptId_Training','lep_isFakeId_Training']
self.undefTruth = []
self.weightbranchX = 'lep_pt'
self.weightbranchY = 'lep_eta'
self.remove = True
self.referenceclass = 'lep_isPromptId_Training' #'lep_isNonPromptId_Training'
#setting DeepLepton specific defaults
self.treename = "tree"
#self.undefTruth=['isUndefined']
#self.red_classes = ['cat_P', 'cat_NP', 'cat_F']
#self.reduce_truth = ['lep_isPromptId_Training', 'lep_isNonPromptId_Training', 'lep_isFakeId_Training']
#self.class_weights = [1.00, 1.00, 1.00]
#self.weight_binX = np.array([
# 5,7.5,10,12.5,15,17.5,20,25,30,35,40,45,50,60,75,100,
# 125,150,175,200,250,300,400,500,
# 600,2000],dtype=float)
self.weight_binX = np.geomspace(3.5, 2000, 30)
self.weight_binY = np.array(
[-2.5,-2.,-1.5,-1.,-0.5,0.5,1,1.5,2.,2.5],
dtype=float
)
self.global_branches = [
'lep_pt', 'lep_eta', 'lep_phi',
'lep_mediumId',
'lep_miniPFRelIso_all',
'lep_sip3d', 'lep_dxy', 'lep_dz',
'lep_charge',
'lep_dxyErr', 'lep_dzErr', 'lep_ip3d',
'lep_jetPtRelv2', 'lep_jetRelIso',
'lep_miniPFRelIso_chg', 'lep_mvaLowPt', 'lep_nStations', 'lep_nTrackerLayers', 'lep_pfRelIso03_all', 'lep_pfRelIso03_chg', 'lep_pfRelIso04_all', 'lep_ptErr',
'lep_segmentComp', 'lep_tkRelIso', 'lep_tunepRelPt',
]
self.pfCand_neutral_branches = ['pfCand_neutral_eta', 'pfCand_neutral_phi', 'pfCand_neutral_pt', 'pfCand_neutral_puppiWeight', 'pfCand_neutral_puppiWeightNoLep', 'pfCand_neutral_ptRel', 'pfCand_neutral_deltaR',]
self.npfCand_neutral = 10
self.pfCand_charged_branches = ['pfCand_charged_d0', 'pfCand_charged_d0Err', 'pfCand_charged_dz', 'pfCand_charged_dzErr', 'pfCand_charged_eta', 'pfCand_charged_mass', 'pfCand_charged_phi', 'pfCand_charged_pt', 'pfCand_charged_puppiWeight', 'pfCand_charged_puppiWeightNoLep', 'pfCand_charged_trkChi2', 'pfCand_charged_vtxChi2', 'pfCand_charged_charge', 'pfCand_charged_lostInnerHits', 'pfCand_charged_pvAssocQuality', 'pfCand_charged_trkQuality', 'pfCand_charged_ptRel', 'pfCand_charged_deltaR',]
self.npfCand_charged = 80
self.pfCand_photon_branches = ['pfCand_photon_eta', 'pfCand_photon_phi', 'pfCand_photon_pt', 'pfCand_photon_puppiWeight', 'pfCand_photon_puppiWeightNoLep', 'pfCand_photon_ptRel', 'pfCand_photon_deltaR',]
self.npfCand_photon = 50
self.pfCand_electron_branches = ['pfCand_electron_d0', 'pfCand_electron_d0Err', 'pfCand_electron_dz', 'pfCand_electron_dzErr', 'pfCand_electron_eta', 'pfCand_electron_mass', 'pfCand_electron_phi', 'pfCand_electron_pt', 'pfCand_electron_puppiWeight', 'pfCand_electron_puppiWeightNoLep', 'pfCand_electron_trkChi2', 'pfCand_electron_vtxChi2', 'pfCand_electron_charge', 'pfCand_electron_lostInnerHits', 'pfCand_electron_pvAssocQuality', 'pfCand_electron_trkQuality', 'pfCand_electron_ptRel', 'pfCand_electron_deltaR',]
self.npfCand_electron = 4
self.pfCand_muon_branches = ['pfCand_muon_d0', 'pfCand_muon_d0Err', 'pfCand_muon_dz', 'pfCand_muon_dzErr', 'pfCand_muon_eta', 'pfCand_muon_mass', 'pfCand_muon_phi', 'pfCand_muon_pt', 'pfCand_muon_puppiWeight', 'pfCand_muon_puppiWeightNoLep', 'pfCand_muon_trkChi2', 'pfCand_muon_vtxChi2', 'pfCand_muon_charge', 'pfCand_muon_lostInnerHits', 'pfCand_muon_pvAssocQuality', 'pfCand_muon_trkQuality', 'pfCand_muon_ptRel', 'pfCand_muon_deltaR']
self.npfCand_muon = 6
self.SV_branches = ['SV_dlen', 'SV_dlenSig', 'SV_dxy', 'SV_dxySig', 'SV_pAngle', 'SV_chi2', 'SV_eta', 'SV_mass', 'SV_ndof', 'SV_phi', 'SV_pt', 'SV_x', 'SV_y', 'SV_z', 'SV_ptRel', 'SV_deltaR',]
self.nSV = 10
def __init__(self):
import numpy
TrainData.__init__(self)
#setting DeepJet specific defaults
self.treename="deepntuplizer/tree"
self.truthclasses=['class1','class2']
self.treename="deepntuplizer/tree"
self.referenceclass='class1'
def __init__(self):
TrainData.__init__(self)
self.description = "DeepCSV training datastructure"
self.truth_branches = [
'isB', 'isBB', 'isGBB', 'isLeptonicB', 'isLeptonicB_C', 'isC',
'isGCC', 'isCC', 'isUD', 'isS', 'isG'
]
self.undefTruth = ['isUndefined']
self.weightbranchX = 'jet_pt'
self.weightbranchY = 'jet_eta'
self.remove = True
self.referenceclass = 'isB'
self.weight_binX = np.array([
10, 25, 30, 35, 40, 45, 50, 60, 75, 100, 125, 150, 175, 200, 250,
300, 400, 500, 600, 2000
],
dtype=float)
self.weight_binY = np.array(
[-2.5, -2., -1.5, -1., -0.5, 0.5, 1, 1.5, 2., 2.5], dtype=float)
self.global_branches = [
'jet_pt', 'jet_eta', 'TagVarCSV_jetNSecondaryVertices',
'TagVarCSV_trackSumJetEtRatio', 'TagVarCSV_trackSumJetDeltaR',
'TagVarCSV_vertexCategory', 'TagVarCSV_trackSip2dValAboveCharm',
'TagVarCSV_trackSip2dSigAboveCharm',
'TagVarCSV_trackSip3dValAboveCharm',
'TagVarCSV_trackSip3dSigAboveCharm',
'TagVarCSV_jetNSelectedTracks', 'TagVarCSV_jetNTracksEtaRel'
]
self.track_branches = [
'TagVarCSVTrk_trackJetDistVal', 'TagVarCSVTrk_trackPtRel',
'TagVarCSVTrk_trackDeltaR', 'TagVarCSVTrk_trackPtRatio',
'TagVarCSVTrk_trackSip3dSig', 'TagVarCSVTrk_trackSip2dSig',
'TagVarCSVTrk_trackDecayLenVal'
]
self.n_track = 6
self.eta_rel_branches = ['TagVarCSV_trackEtaRel']
self.n_eta_rel = 4
self.vtx_branches = [
'TagVarCSV_vertexMass', 'TagVarCSV_vertexNTracks',
'TagVarCSV_vertexEnergyRatio', 'TagVarCSV_vertexJetDeltaR',
'TagVarCSV_flightDistance2dVal', 'TagVarCSV_flightDistance2dSig',
'TagVarCSV_flightDistance3dVal', 'TagVarCSV_flightDistance3dSig'
]
self.n_vtx = 1
self.reduced_truth = ['isB', 'isBB', 'isC', 'isUDSG']
def __init__(self):
TrainData.__init__(self)
#define truth:
self.treename = "deepntuplizerCA8/tree"
self.undefTruth = ['isUndefined']
self.truthclasses = [
'isB',
'isBB', #'isGBB',
#'isLeptonicB','isLeptonicB_C',
'isC',
'isCC', #'isGCC',
'isUD',
'isS',
'isG',
'isTauHTauH',
'isTauHTauM',
'isTauHTauE',
#'isTauMTauM','isTauMTauE','isTauETauE',
#'isTauH','isTauM','isTauE',
]
self.registerBranches(self.truthclasses)
self.registerBranches(self.undefTruth)
self.referenceclass = 'isTauHTauH' # 'flatten' or class name
#self.referenceclass='flatten'
#self.referenceclass='lowest'
self.weightbranchX = 'jet_pt'
self.weightbranchY = 'jet_eta'
#self.weightbranchY='jet_mass'
self.registerBranches([self.weightbranchX, self.weightbranchY])
self.weight_binX = np.array([
10, 25, 30, 35, 40, 45, 50, 60, 75, 100, 125, 150, 175, 200, 250,
300, 400, 500, 600, 2000
],
dtype=float)
self.weight_binY = np.array(
[-2.5, -2., -1.5, -1., -0.5, 0.5, 1, 1.5, 2., 2.5], dtype=float)
#self.weight_binY = np.array([
# 10,30,40,50,75,100,
# 125,150,175,200,250,300,400,500,
# 600,800,1000,1500,2000],dtype=float)
self.weight = False
self.remove = True
def __init__(self):
import numpy
TrainData.__init__(self)
#setting DeepJet specific defaults
self.treename = "deepntuplizer/tree"
self.undefTruth = ['isUndefined']
self.referenceclass = 'isB'
self.truthclasses = [
'isB', 'isBB', 'isGBB', 'isLeptonicB', 'isLeptonicB_C', 'isC',
'isCC', 'isGCC', 'isUD', 'isS', 'isG', 'isUndefined'
]
self.branches = []
self.reduceTruth(None)
def __init__(self):
'''
This class is meant as a base class for the FatJet studies
You will not need to edit it for trying out things
'''
TrainData.__init__(self)
#define truth:
self.treename = "deepntuplizer/tree"
self.undefTruth = ['isUndefined']
#self.truthclasses=['fj_isNonCC', 'fj_isCC', 'fj_isNonBB', 'fj_isQCD', 'fj_isH']
#self.truthclasses=['fj_isCC', 'fj_isBB', 'fj_isQCD' ]
#self.truthclasses=["label_H_bb", "label_H_cc", "label_QCD_bb", "label_QCD_cc", "label_QCD_others"]
self.truthclasses = ['fj_isNonCC', 'fj_isCC']
#self.truthclasses=["fj_isCC", "fj_isNonCC"]
#self.referenceclass='label_H_cc' ## used for pt reshaping
self.referenceclass = 'lowest' ## used for pt reshaping
#self.referenceclass='flatten' ## used for pt reshaping
self.registerBranches(['fj_pt', 'fj_sdmass'])
self.weightbranchX = 'fj_pt'
self.weightbranchY = 'fj_sdmass'
#self.weight_binX = numpy.array([
# 300,1000,2500],dtype=float)
self.weight_binX = numpy.array(
#[300,400,500,600,700, 800, 900, 1000,
#1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000]
range(300, 1000, 50) + range(1000, 2600, 200),
dtype=float)
self.weight_binY = numpy.array(
[40, 200],
#range(40,200,40),
dtype=float)
self.weight = True
self.remove = False
#this is only needed because the truth definitions are different from deepFlavour
self.allbranchestoberead = []
self.registerBranches(self.undefTruth)
self.registerBranches(self.truthclasses)
self.registerBranches([
'fj_isBB', 'fj_isNonBB', 'fj_isNonCC', 'fj_isCC', 'fj_isQCD',
'fj_isH', "label_H_bb", "label_H_cc", "label_QCD_bb",
"label_QCD_cc", "label_QCD_others"
])
print("Branches read:", self.allbranchestoberead)
def __init__(self):
'''
This class is meant as a base class for the FatJet studies
You will not need to edit it for trying out things
'''
TrainData.__init__(self)
#define truth:
self.treename = "deepntuplizer/tree"
self.undefTruth = ['isUndefined']
self.truthclasses = [
"fj_isH", "fj_isCC", "fj_isBB", "fj_isNonCC", "fj_isNonBB",
"fj_isZ", "fj_isQCD", "sample_isQCD"
]
self.referenceclass = 'lowest' ## used for pt reshaping options=['lowest', 'flatten', '<class_name>']
self.weightbranchX = 'fj_pt'
self.weightbranchY = 'fj_sdmass'
self.weight_binX = numpy.array(range(300, 1000, 50) +
range(1000, 2600, 200),
dtype=float)
self.weight_binY = numpy.array([40, 200], dtype=float)
self.weight = True
self.remove = False
self.removeUnderOverflow = True
#this is only needed because the truth definitions are different from deepFlavour
self.allbranchestoberead = []
self.registerBranches(self.undefTruth)
self.registerBranches(self.truthclasses)
self.registerBranches(['fj_pt', 'fj_sdmass'])
self.registerBranches([
"label_H_bb",
"label_H_cc",
"label_QCD_bb",
"label_QCD_cc",
"label_QCD_others",
"label_Z_bb",
"label_Z_cc",
])
print("Branches read:", self.allbranchestoberead)
def __init__(self):
TrainData.__init__(self)
self.treename = "Delphes" #input root tree name
self.feat_branch = "rechit_features"
self.truth_branch = "rechit_simcluster_fractions"
# this needs to be adapted!!
self.max_rechits = 3500
#this should be fine
self.n_features = 10
self.n_simcluster = 20
self.regressiontargetclasses = [
str(i) for i in range(self.n_simcluster)
]
self.other_useless_inits()
def __init__(self):
import numpy
TrainData.__init__(self)
self.treename = "deepntuplizer/tree"
self.weightbranchX = 'true_energy'
self.weightbranchY = 'true_eta'
self.referenceclass = 'flatten'
self.weight_binX = numpy.array([
0, 1, 2, 3, 4, 5, 7.5, 10, 20, 30, 40, 50, 60, 80, 100, 120, 140,
160, 200, 240, 300, 400
],
dtype=float)
self.registerBranches([
'rechit_energy', 'rechit_eta', 'rechit_phi', 'rechit_time',
'rechit_layer', 'nrechits', 'seed_eta', 'seed_phi', 'true_energy',
'true_eta', 'true_energyfraction'
])
def __init__(self):
TrainData.__init__(self)
self.description = "DeepLepton training datastructure"
# Define the four truth labels, should be consistent with
# Network architecture, i.e. the output layer.
self.truth_branches = [
'lep_isPromptId_Training', 'lep_isNonPromptId_Training',
'lep_isFakeId_Training', 'lep_isFromSUSYandHF_Training'
]
# 'lep_isFromSUSY_Training',
# 'lep_isFromSUSYHF_Training',]
self.undefTruth = []
# The Weighter can "weight" two lepton features.
# Here we only "weight" the dxy feature by making only one bin in eta
# "weight" means to throw away leptons from the non reference class s.t.
# weightbranchX/Y gets similarly distributed as the reference class
self.weightbranchX = 'lep_eta'
self.weightbranchY = 'lep_dxy'
# enables or disables (False) Weighter
self.remove = True
# Define the reference class
self.referenceclass = 'lep_isFromSUSYandHF_Training'
# setting DeepLepton specific defaults
self.treename = "tree"
# make one huge bin for eta
self.weight_binX = np.array([-30, 30])
# make the extension large enough to cover the whole feature range of the
# leptons in the non-reference class
self.weight_binY = np.linspace(start=-50,
stop=50,
num=50,
endpoint=True,
dtype=float)
# if training not flat in pt, eta one could remove the
# feature from the training as well
self.global_branches = [
'lep_pt', # 0
'lep_eta', # 1
'lep_phi', # 2
'lep_mediumId', # 3
'lep_miniPFRelIso_all', # 4
'lep_sip3d', # 5
'lep_dxy', # 6
'lep_dz',
'lep_charge',
'lep_dxyErr',
'lep_dzErr',
'lep_ip3d',
'lep_jetPtRelv2',
'lep_jetRelIso',
'lep_miniPFRelIso_chg',
'lep_mvaLowPt',
'lep_nStations',
'lep_nTrackerLayers',
'lep_pfRelIso03_all',
'lep_pfRelIso03_chg',
'lep_pfRelIso04_all',
'lep_ptErr',
'lep_segmentComp',
'lep_tkRelIso',
'lep_tunepRelPt',
]
self.pfCand_neutral_branches = [
'pfCand_neutral_eta',
'pfCand_neutral_phi',
'pfCand_neutral_pt',
'pfCand_neutral_puppiWeight',
'pfCand_neutral_puppiWeightNoLep',
'pfCand_neutral_ptRel',
'pfCand_neutral_deltaR',
]
self.npfCand_neutral = 10
self.pfCand_charged_branches = [
'pfCand_charged_d0',
'pfCand_charged_d0Err',
'pfCand_charged_dz',
'pfCand_charged_dzErr',
'pfCand_charged_eta',
'pfCand_charged_mass',
'pfCand_charged_phi',
'pfCand_charged_pt',
'pfCand_charged_puppiWeight',
'pfCand_charged_puppiWeightNoLep',
'pfCand_charged_trkChi2',
'pfCand_charged_vtxChi2',
'pfCand_charged_charge',
'pfCand_charged_lostInnerHits',
'pfCand_charged_pvAssocQuality',
'pfCand_charged_trkQuality',
'pfCand_charged_ptRel',
'pfCand_charged_deltaR',
]
self.npfCand_charged = 80
self.pfCand_photon_branches = [
'pfCand_photon_eta',
'pfCand_photon_phi',
'pfCand_photon_pt',
'pfCand_photon_puppiWeight',
'pfCand_photon_puppiWeightNoLep',
'pfCand_photon_ptRel',
'pfCand_photon_deltaR',
]
self.npfCand_photon = 50
self.pfCand_electron_branches = [
'pfCand_electron_d0',
'pfCand_electron_d0Err',
'pfCand_electron_dz',
'pfCand_electron_dzErr',
'pfCand_electron_eta',
'pfCand_electron_mass',
'pfCand_electron_phi',
'pfCand_electron_pt',
'pfCand_electron_puppiWeight',
'pfCand_electron_puppiWeightNoLep',
'pfCand_electron_trkChi2',
'pfCand_electron_vtxChi2',
'pfCand_electron_charge',
'pfCand_electron_lostInnerHits',
'pfCand_electron_pvAssocQuality',
'pfCand_electron_trkQuality',
'pfCand_electron_ptRel',
'pfCand_electron_deltaR',
]
self.npfCand_electron = 4
self.pfCand_muon_branches = [
'pfCand_muon_d0', 'pfCand_muon_d0Err', 'pfCand_muon_dz',
'pfCand_muon_dzErr', 'pfCand_muon_eta', 'pfCand_muon_mass',
'pfCand_muon_phi', 'pfCand_muon_pt', 'pfCand_muon_puppiWeight',
'pfCand_muon_puppiWeightNoLep', 'pfCand_muon_trkChi2',
'pfCand_muon_vtxChi2', 'pfCand_muon_charge',
'pfCand_muon_lostInnerHits', 'pfCand_muon_pvAssocQuality',
'pfCand_muon_trkQuality', 'pfCand_muon_ptRel', 'pfCand_muon_deltaR'
]
self.npfCand_muon = 6
self.SV_branches = [
'SV_dlen',
'SV_dlenSig',
'SV_dxy',
'SV_dxySig',
'SV_pAngle',
'SV_chi2',
'SV_eta',
'SV_mass',
'SV_ndof',
'SV_phi',
'SV_pt',
'SV_x',
'SV_y',
'SV_z',
'SV_ptRel',
'SV_deltaR',
]
self.nSV = 10
def __init__(self):
self.splitIdx = None
TrainData.__init__(self)
def __init__(self):
TrainData.__init__(self)
def __init__(self):
TrainData.__init__(self)
# no class member is mandatory
self.description = "This is a TrainData example file. Having a description string is not a bad idea (but not mandatory), e.g. for describing the array structure."
#define any other (configuration) members that seem useful
self.someusefulemember = "something you might need later"
def __init__(self):
TrainData.__init__(self)
self.npart = 9
def __init__(self):
TrainData.__init__(self)
self.truth_branches = [
'isB', 'isBB', 'isGBB', 'isLeptonicB', 'isLeptonicB_C', 'isC',
'isGCC', 'isCC', 'isUD', 'isS', 'isG'
]
self.undefTruth = ['isUndefined']
self.weightbranchX = 'jet_pt'
self.weightbranchY = 'jet_eta'
self.remove = True
self.referenceclass = 'isB' #Choose 'flatten' for flat or one of the truth branch for ref
self.red_classes = ['cat_B', 'cat_C', 'cat_UDS',
'cat_G'] #Reduced classes (flat only)
self.truth_red_fusion = [
('isB', 'isBB', 'isGBB', 'isLeptonicB', 'isLeptonicB_C'),
('isC', 'isGCC', 'isCC'), ('isUD', 'isS'), ('isG')
] #Indicate here how you are making the fusion of your truth branches to the reduced classes for the flat reweighting
self.class_weights = [1.00, 1.00, 2.50, 5.00
] #Ratio between our reduced classes (flat only)
self.weight_binX = np.array([
10, 25, 30, 35, 40, 45, 50, 60, 75, 100, 125, 150, 175, 200, 250,
300, 400, 500, 600, 2000
],
dtype=float)
self.weight_binY = np.array(
[-2.5, -2., -1.5, -1., -0.5, 0.5, 1, 1.5, 2., 2.5], dtype=float)
self.global_branches = [
'jet_pt', 'jet_eta', 'nCpfcand', 'nNpfcand', 'nsv', 'npv',
'TagVarCSV_trackSumJetEtRatio', 'TagVarCSV_trackSumJetDeltaR',
'TagVarCSV_vertexCategory', 'TagVarCSV_trackSip2dValAboveCharm',
'TagVarCSV_trackSip2dSigAboveCharm',
'TagVarCSV_trackSip3dValAboveCharm',
'TagVarCSV_trackSip3dSigAboveCharm',
'TagVarCSV_jetNSelectedTracks', 'TagVarCSV_jetNTracksEtaRel'
]
self.cpf_branches = [
'Cpfcan_BtagPf_trackEtaRel', 'Cpfcan_BtagPf_trackPtRel',
'Cpfcan_BtagPf_trackPPar', 'Cpfcan_BtagPf_trackDeltaR',
'Cpfcan_BtagPf_trackPParRatio', 'Cpfcan_BtagPf_trackSip2dVal',
'Cpfcan_BtagPf_trackSip2dSig', 'Cpfcan_BtagPf_trackSip3dVal',
'Cpfcan_BtagPf_trackSip3dSig', 'Cpfcan_BtagPf_trackJetDistVal',
'Cpfcan_ptrel', 'Cpfcan_drminsv', 'Cpfcan_VTX_ass',
'Cpfcan_puppiw', 'Cpfcan_chi2', 'Cpfcan_quality'
]
self.n_cpf = 25
self.npf_branches = [
'Npfcan_ptrel', 'Npfcan_deltaR', 'Npfcan_isGamma',
'Npfcan_HadFrac', 'Npfcan_drminsv', 'Npfcan_puppiw'
]
self.n_npf = 25
self.vtx_branches = [
'sv_pt',
'sv_deltaR',
'sv_mass',
'sv_ntracks',
'sv_chi2',
'sv_normchi2',
'sv_dxy',
'sv_dxysig',
'sv_d3d',
'sv_d3dsig',
'sv_costhetasvpv',
'sv_enratio',
]
self.n_vtx = 4
self.reduced_truth = [
'isB', 'isBB', 'isLeptonicB', 'isC', 'isUDS', 'isG'
]
def __init__(self):
TrainData.__init__(self)
self.description = "DeepCSV training datastructure"
self.truth_branches = [
'isB', 'isBB', 'isGBB', 'isLeptonicB', 'isLeptonicB_C', 'isC',
'isGCC', 'isCC', 'isUD', 'isS', 'isG'
]
self.undefTruth = ['isUndefined']
self.weightbranchX = 'jet_pt'
self.weightbranchY = 'jet_eta'
self.remove = True
self.referenceclass = 'isB' #Choose 'flatten' for flat or one of the truth branch for ref
self.red_classes = ['cat_B', 'cat_C', 'cat_UDS',
'cat_G'] #Reduced classes (flat only)
self.truth_red_fusion = [
('isB', 'isBB', 'isGBB', 'isLeptonicB', 'isLeptonicB_C'),
('isC', 'isGCC', 'isCC'), ('isUD', 'isS'), ('isG')
] #Indicate here how you are making the fusion of your truth branches to the reduced classes for the flat reweighting
self.class_weights = [1.00, 1.00, 2.50, 5.00
] #Ratio between our reduced classes (flat only)
self.weight_binX = np.array([
10, 25, 30, 35, 40, 45, 50, 60, 75, 100, 125, 150, 175, 200, 250,
300, 400, 500, 600, 2000
],
dtype=float)
self.weight_binY = np.array(
[-2.5, -2., -1.5, -1., -0.5, 0.5, 1, 1.5, 2., 2.5], dtype=float)
self.global_branches = [
'jet_pt', 'jet_eta', 'TagVarCSV_jetNSecondaryVertices',
'TagVarCSV_trackSumJetEtRatio', 'TagVarCSV_trackSumJetDeltaR',
'TagVarCSV_vertexCategory', 'TagVarCSV_trackSip2dValAboveCharm',
'TagVarCSV_trackSip2dSigAboveCharm',
'TagVarCSV_trackSip3dValAboveCharm',
'TagVarCSV_trackSip3dSigAboveCharm',
'TagVarCSV_jetNSelectedTracks', 'TagVarCSV_jetNTracksEtaRel'
]
self.track_branches = [
'TagVarCSVTrk_trackJetDistVal', 'TagVarCSVTrk_trackPtRel',
'TagVarCSVTrk_trackDeltaR', 'TagVarCSVTrk_trackPtRatio',
'TagVarCSVTrk_trackSip3dSig', 'TagVarCSVTrk_trackSip2dSig',
'TagVarCSVTrk_trackDecayLenVal'
]
self.n_track = 6
self.eta_rel_branches = ['TagVarCSV_trackEtaRel']
self.n_eta_rel = 4
self.vtx_branches = [
'TagVarCSV_vertexMass', 'TagVarCSV_vertexNTracks',
'TagVarCSV_vertexEnergyRatio', 'TagVarCSV_vertexJetDeltaR',
'TagVarCSV_flightDistance2dVal', 'TagVarCSV_flightDistance2dSig',
'TagVarCSV_flightDistance3dVal', 'TagVarCSV_flightDistance3dSig'
]
self.n_vtx = 1
self.reduced_truth = ['isB', 'isBB', 'isC', 'isUDSG']
def __init__(self):
TrainData.__init__(self)
self.treename = "tree" #input root tree name
self.truthclasses = ['isTau',
'isNoTau'] #truth classes for classification
self.weightbranchX = 'recTau_pt' #needs to be specified
self.weightbranchY = 'recTau_eta' #needs to be specified
self.referenceclass = 'isTau'
#this removes everything that is not a recTau, which has pt>0, and eta!=0
self.weight_binX = numpy.array([
0, 5, 20, 22.5, 25, 27.5, 30, 35, 40, 45, 50, 60, 70, 80, 100, 150,
200, 250, 500, 1e5
],
dtype=float)
self.weight_binY = numpy.array(
[-4, -3, -2, -1, -1e-4, 0, 1e-4, 1, 2, 3, 4], dtype=float)
#globals
self.addBranches([
#'isRecTau',
'numPileUp',
'recTauDecayMode',
'recTau_pt',
'recTau_eta',
'recTau_phi',
'recTau_M',
'recTauVtxZ',
'recImpactParamPCA_x',
'recImpactParamPCA_y',
'recImpactParamPCA_z',
'recImpactParam',
'recImpactParamSign',
'recImpactParam3D',
'recImpactParamSign3D',
'hasRecDecayVertex',
'recDecayDist_x',
'recDecayDist_y',
'recDecayDist_z',
'recDecayDistSign',
'recTauPtWeightedDetaStrip',
'recTauPtWeightedDphiStrip',
'recTauPtWeightedDrSignal',
'recTauPtWeightedDrIsolation',
'recTauNphoton',
'recTauEratio',
'recTauLeadingTrackChi2',
'recTauNphotonSignal',
'recTauNphotonIso',
'recJet_pt',
'recJet_eta',
'recJet_mass',
'recJetLooseId',
'nCpfcan',
'nNpfcand',
])
self.addBranches([
'Cpfcan_pt',
'Cpfcan_eta',
'Cpfcan_ptrel',
'Cpfcan_erel',
'Cpfcan_deltaR',
'Cpfcan_puppiw',
'Cpfcan_VTX_ass',
'Cpfcan_fromPV',
'Cpfcan_vertex_rho',
'Cpfcan_vertex_phirel',
'Cpfcan_vertex_etarel',
'Cpfcan_dz',
'Cpfcan_dxy',
'Cpfcan_dxyerrinv',
'Cpfcan_dxysig',
'Cpfcan_BtagPf_trackMomentum',
'Cpfcan_BtagPf_trackEtaRel',
'Cpfcan_BtagPf_trackPtRel',
'Cpfcan_BtagPf_trackPPar',
'Cpfcan_BtagPf_trackDeltaR',
'Cpfcan_BtagPf_trackPtRatio',
'Cpfcan_BtagPf_trackPParRatio',
'Cpfcan_BtagPf_trackSip3dVal',
'Cpfcan_BtagPf_trackSip3dSig',
'Cpfcan_BtagPf_trackSip2dVal',
'Cpfcan_BtagPf_trackSip2dSig',
'Cpfcan_BtagPf_trackJetDistVal',
'Cpfcan_isMu',
'Cpfcan_isEl',
'Cpfcan_pdgID',
'Cpfcan_charge',
'Cpfcan_lostInnerHits',
'Cpfcan_numberOfPixelHits',
'Cpfcan_chi2',
'Cpfcan_quality',
], 20)
self.addBranches([
'Npfcan_pt',
'Npfcan_eta',
'Npfcan_ptrel',
'Npfcan_erel',
'Npfcan_puppiw',
'Npfcan_phirel',
'Npfcan_etarel',
'Npfcan_deltaR',
'Npfcan_isGamma',
'Npfcan_HadFrac',
], 40)
self.addBranches(['recTau_pt', 'recJet_pt'])
self.regtruth = 'genTau_pt'
self.regressiontargetclasses = ['pt']
self.registerBranches(['genTau_pt']) #list of branches to be used
self.registerBranches(self.truthclasses)
#call this at the end
self.reduceTruth(None)
def __init__(self):
import numpy
TrainData.__init__(self)
def __init__(self):
TrainData.__init__(self)
self.rebinx = 1
self.rebiny = 1
self.rebinz = 1