from Workspace.HEPHYPythonTools.nnAnalysisHelpers import getObjFromFile
# ifile = "/afs/hephy.at/user/s/schoefbeck/www/pngNN/nnValidation_RA4_test_T1tttt-madgraph_4j_met100_mt2w200_NormDeco_10000_sigmoid_BP_S03_SE08_mD_600_800_mN_500_600_mT_type1phiMet_mt2w_nbtags_njets_minDeltaPhi_htRatio_deltaPhi.root"
# ifile = "/afs/hephy.at/user/s/schoefbeck/www/pngNN/nnValidation_RA4_4j_met100_mt2w200_NormDeco_5000_sigmoid_mD_200_400_mN_500_600_mT_type1phiMet_mt2w_nbtags_njets_minDeltaPhi_htRatio_deltaPhi.root"
# ifile = "/afs/hephy.at/user/s/schoefbeck/www/pngNN/nnValidation_RA4_test_refWeight200_T1tttt-madgraph_4j_met100_mt2w200_NormDeco_10000_sigmoid_BP_S03_SE08_mgl_1400_mN_800_mT_type1phiMet_mt2w_nbtags_njets_minDeltaPhi_htRatio_deltaPhi.root"
# ifile ="/afs/hephy.at/user/s/schoefbeck/www/pngNN/nnValidation_RA4_test_refWeight200_T1tttt_4j_met100_mt2w200_NormDeco_10000_sigmoid_BP_S03_SE08_mgl_1200_mN_750_mT_type1phiMet_mt2w_nbtags_njets_minDeltaPhi_htRatio_deltaPhi.root"
ifile = "/afs/hephy.at/user/s/schoefbeck/www/pngNN/nnValidation_RA4_test_refWeight1000_T1tttt_4j_bt1_met100_mt2w0_NormDeco_10000_sigmoid_BP_S03_SE08_mgl_1300_mN_850_mT_type1phiMet_mt2w_nbtags_njets_minDeltaPhi_htRatio_deltaPhi.root"
def delErr(g):
for i in range(g.GetN()):
g.GetEX()[i] = 0
g.GetEY()[i] = 0
canv = getObjFromFile(ifile, "mlpa_canvas")
myCut = canv.GetPrimitive("mlpa_canvas_1").GetListOfPrimitives().At(2)
pad = canv.GetPrimitive("mlpa_canvas_2")
l = pad.GetListOfPrimitives().At(1).Clone("l")
lp = pad.GetListOfPrimitives().At(2).Clone("lp")
lm = pad.GetListOfPrimitives().At(3).Clone("lm")
# lmT = pad.GetListOfPrimitives().At(5).Clone("lmT")
# lmet = pad.GetListOfPrimitives().At(8).Clone("lmet")
c = ROOT.TCanvas()
# res = smoother.Approx(l, "constant" )
if doSmooth:
ls = smoother.SmoothLowess(l, "", 0.01, 30, 0).Clone()
lsp = smoother.SmoothLowess(lp, "", 0.01, 30, 0).Clone()
lsm = smoother.SmoothLowess(lm, "", 0.01, 3, 0).Clone()
import ROOT
#c = ROOT.TChain("MonteCarlo")
#c.Add("/data/schoef/MonoJetNNAnalysis/datasets/MonoJet_BDTvsMLP_stop300lsp270FastSim_refsel_None_softIsolatedMT_type1phiMet_deltaPhi_isrJetPt_htRatio.root")
#c.SetScanField(10**7)
#
#c.Scan("type:weight:weightForMVA:type1phiMet:softIsolatedMuPt:isrJetPt:softIsolatedMT:deltaPhi:htRatio:softIsolatedMuCharge", "","@colsize=20")
import ROOT, os, sys
from Workspace.HEPHYPythonTools.nnAnalysisHelpers import getObjFromFile
from array import array
if len(sys.argv)>2:
ifile = sys.argv[2]
if len(sys.argv)>1:
if sys.argv[1].lower()=="test":
tree = getObjFromFile(ifile, "TestTree")
if sys.argv[1].lower()=="train":
tree = getObjFromFile(ifile, "TrainTree")
tree.SetScanField(10**7)
tree.Scan("classID:weight:type1phiMet:softIsolatedMT:deltaPhi:MLP33_nCyc500", "","@colsize=20")
setup=pickle.load(file('/data/'+nfsUser+'/MonoJetNNAnalysis/MVA_Analyzer/'+prefix+'.pkl'))
reader = ROOT.TMVA.Reader()
for var in setup['mvaInputVars']:
var_i = array('f',[0])
# vars[var] = var_i
reader.AddVariable(var,var_i)
allMethods = {}
for m in setup['methodConfigs']:
if m['type']!=ROOT.TMVA.Types.kCuts:
allMethods[m['name']] = {'reader':reader.BookMVA(m['name'],setup['weightDir']+'/TMVAClassification_'+m['name']+'.weights.xml'), 'config':m}
else:
allMethods[m['name']] = {'reader':reader.BookMVA(m['name'],setup['weightDir']+'/TMVAClassification_'+m['name']+'.weights.xml'), 'config':m}
for mD in [1,2,3,4,10]:
trainTree = getObjFromFile(setup['TMVAOutputFile'], 'TrainTree')
varListBkgTrain = getVarValList(trainTree, 'BDT_maxDepth'+str(mD), "classID==0")
varListSigTrain = getVarValList(trainTree, 'BDT_maxDepth'+str(mD), "classID==1")
testTree = getObjFromFile(setup['TMVAOutputFile'], 'TestTree')
varListBkgTest = getVarValList(testTree, 'BDT_maxDepth'+str(mD), "classID==0")
varListSigTest = getVarValList(testTree, 'BDT_maxDepth'+str(mD), "classID==1")
#Binned version
plotBkgTest = getPlot(testTree,"classID==0", 'BDT_maxDepth'+str(mD))
plotSigTest = getPlot(testTree,"classID==1", 'BDT_maxDepth'+str(mD))
plotBkgTrain = getPlot(trainTree,"classID==0", 'BDT_maxDepth'+str(mD))
plotSigTrain = getPlot(trainTree,"classID==1", 'BDT_maxDepth'+str(mD))
print "maxDepth",mD,"test/train",len(varListBkgTest),len(varListBkgTrain),"unbinned, Bkg", KolmogorovProbability(varListBkgTrain, varListBkgTest), 'binned', plotBkgTest.KolmogorovTest(plotBkgTrain),"X", plotBkgTest.KolmogorovTest(plotBkgTrain, "X")
print "maxDepth",mD,"test/train",len(varListSigTest),len(varListSigTrain),"unbinned, Sig", KolmogorovProbability(varListSigTrain, varListSigTest), 'binned', plotSigTest.KolmogorovTest(plotSigTrain),"X", plotSigTest.KolmogorovTest(plotSigTrain, "X")
{
"name": "MLP21, 10000",
"color": ROOT.kBlue,
"fname": "/afs/hephy.at/user/s/schoefbeck/www/pngMJNN/nnValidation_MonoJet_MLP21_stop300lsp270FastSim_refsel_NormDeco_10000_sigmoid_BP_S03_SE08_softIsolatedMT_type1phiMet_deltaPhi.root",
},
{
"name": "MLP21, 20000",
"color": ROOT.kMagenta,
"fname": "/afs/hephy.at/user/s/schoefbeck/www/pngMJNN/nnValidation_MonoJet_MLP21_stop300lsp270FastSim_refsel_NormDeco_20000_sigmoid_BP_S03_SE08_softIsolatedMT_type1phiMet_deltaPhi.root",
},
]
xRange = [0.10, 1.0]
yRange = [0.1, 0.30]
for f in foms:
canv = getObjFromFile(f["fname"], "mlpa_canvas")
pad = canv.GetPrimitive("mlpa_canvas_2")
l = pad.GetListOfPrimitives().At(1).Clone("l")
lp = pad.GetListOfPrimitives().At(2).Clone("lp")
lm = pad.GetListOfPrimitives().At(3).Clone("lm")
if doSmooth:
ls = smoother.SmoothLowess(l, "", 0.01, 30, 0).Clone()
lsp = smoother.SmoothLowess(lp, "", 0.01, 30, 0).Clone()
lsm = smoother.SmoothLowess(lm, "", 0.01, 3, 0).Clone()
delErr(ls)
delErr(lsp)
delErr(lsm)
else:
ls = l
lsp = lp
lsm = lm
{"name":"MLP21, 100", "color":ROOT.kBlack, "fname":"/afs/hephy.at/user/s/schoefbeck/www/pngMJNN/nnValidation_MonoJet_MLP21_stop300lsp270FastSim_refsel_NormDeco_100_sigmoid_BP_S03_SE08_softIsolatedMT_type1phiMet_deltaPhi.root"},
{"name":"MLP21, 1000", "color":ROOT.kGreen, "fname":"/afs/hephy.at/user/s/schoefbeck/www/pngMJNN/nnValidation_MonoJet_MLP21_stop300lsp270FastSim_refsel_NormDeco_1000_sigmoid_BP_S03_SE08_softIsolatedMT_type1phiMet_deltaPhi.root"},
{"name":"MLP21, 5000", "color":ROOT.kRed, "fname":"/afs/hephy.at/user/s/schoefbeck/www/pngMJNN/nnValidation_MonoJet_MLP21_stop300lsp270FastSim_refsel_NormDeco_5000_sigmoid_BP_S03_SE08_softIsolatedMT_type1phiMet_deltaPhi.root"},
{"name":"MLP21, 10000", "color":ROOT.kBlue, "fname":"/afs/hephy.at/user/s/schoefbeck/www/pngMJNN/nnValidation_MonoJet_MLP21_stop300lsp270FastSim_refsel_NormDeco_10000_sigmoid_BP_S03_SE08_softIsolatedMT_type1phiMet_deltaPhi.root"},
{"name":"MLP21, 20000", "color":ROOT.kMagenta, "fname":"/afs/hephy.at/user/s/schoefbeck/www/pngMJNN/nnValidation_MonoJet_MLP21_stop300lsp270FastSim_refsel_NormDeco_20000_sigmoid_BP_S03_SE08_softIsolatedMT_type1phiMet_deltaPhi.root"},
]
xRange = [0.10, 1.0]
yRange = [0.1, 0.30]
for f in foms:
canv = getObjFromFile(f['fname'], "mlpa_canvas")
pad = canv.GetPrimitive("mlpa_canvas_2")
l = pad.GetListOfPrimitives().At(1).Clone("l")
lp = pad.GetListOfPrimitives().At(2).Clone("lp")
lm = pad.GetListOfPrimitives().At(3).Clone("lm")
if doSmooth:
ls = smoother.SmoothLowess(l, "", 0.01, 30, 0).Clone()
lsp = smoother.SmoothLowess(lp, "", 0.01, 30, 0).Clone()
lsm = smoother.SmoothLowess(lm, "", 0.01, 3, 0).Clone()
delErr(ls)
delErr(lsp)
delErr(lsm)
else:
ls = l
lsp = lp
lsm = lm
m['name'], setup['weightDir'] + '/TMVAClassification_' +
m['name'] + '.weights.xml'),
'config':
m
}
else:
allMethods[m['name']] = {
'reader':
reader.BookMVA(
m['name'], setup['weightDir'] + '/TMVAClassification_' +
m['name'] + '.weights.xml'),
'config':
m
}
for mD in [1, 2, 3, 4, 10]:
trainTree = getObjFromFile(setup['TMVAOutputFile'], 'TrainTree')
varListBkgTrain = getVarValList(trainTree, 'BDT_maxDepth' + str(mD),
"classID==0")
varListSigTrain = getVarValList(trainTree, 'BDT_maxDepth' + str(mD),
"classID==1")
testTree = getObjFromFile(setup['TMVAOutputFile'], 'TestTree')
varListBkgTest = getVarValList(testTree, 'BDT_maxDepth' + str(mD),
"classID==0")
varListSigTest = getVarValList(testTree, 'BDT_maxDepth' + str(mD),
"classID==1")
#Binned version
plotBkgTest = getPlot(testTree, "classID==0", 'BDT_maxDepth' + str(mD))
plotSigTest = getPlot(testTree, "classID==1", 'BDT_maxDepth' + str(mD))
plotBkgTrain = getPlot(trainTree, "classID==0",
if not path in sys.path:
sys.path.insert(1, path)
from Workspace.HEPHYPythonTools.nnAnalysisHelpers import getObjFromFile
ifile = "/afs/hephy.at/user/s/schoefbeck/www/pngNN/nnValidation_RA4_test_refWeight1000_T1tttt_4j_bt1_met100_mt2w0_NormDeco_10000_sigmoid_BP_S03_SE08_mgl_1300_mN_850_mT_type1phiMet_mt2w_nbtags_njets_minDeltaPhi_htRatio_deltaPhi.root"
ROOT.TH1F().SetDefaultSumw2()
def delErr(g):
for i in range(g.GetN()):
g.GetEX()[i] = 0
g.GetEY()[i] = 0
canv = getObjFromFile(ifile, "mlpa_canvas")
#myCut = canv.GetPrimitive("mlpa_canvas_1").GetListOfPrimitives().At(2)
pad = canv.GetPrimitive("mlpa_canvas_3")
hbgTest = canv.GetPrimitive("mlpa_canvas_3").GetListOfPrimitives().FindObject(
"hbgTest")
hsigTest = canv.GetPrimitive("mlpa_canvas_3").GetListOfPrimitives().FindObject(
"hsigTest")
hbgTrain = canv.GetPrimitive("mlpa_canvas_4").GetListOfPrimitives().FindObject(
"hbgTrain")
hsigTrain = canv.GetPrimitive(
"mlpa_canvas_4").GetListOfPrimitives().FindObject("hsigTrain")
hbgTest.SetLineColor(ROOT.kBlue)
hbgTrain.SetLineColor(ROOT.kBlue)
hsigTest.SetLineColor(ROOT.kRed)
hsigTrain.SetLineColor(ROOT.kRed)
