import os, sys # exit
import time # time accounting
import getopt # command line parser
import ROOT as r
from ROOT import TMVA
#outfname = "/user_data/jlee/TTTT/CMSSW_9_4_6_patch1/src/TMVA/dataset/weights/TMVA_BDT_BigComb_3vars_mDepth2.root"
outfname = "/home/mhadley/testBDTFromJangbae/CMSSW_9_4_6_patch1/src/TTTT/TMVA/dataset/weights/TMVA_BDT_BigComb_3vars_mDepth2.root"
TMVA.mvaeffs('dataset', outfname) #Classifier Cut Efficiencies
TMVA.efficiencies('dataset', outfname, 1)
TMVA.correlations('dataset',
outfname) #Input Variable Linear Correlation Coefficients
TMVA.variables('dataset', outfname) #Input variables (training sample)
TMVA.mvas('dataset', outfname, 0)
TMVA.mvas('dataset', outfname, 3)
def main():
try:
# retrive command line options
shortopts = "m:i:n:d:k:l:t:o:vh?"
longopts = [
"methods=", "inputfile=", "nTrees=", "maxDepth=", "mass=",
"varListKey=", "inputtrees=", "outputfile=", "verbose", "help",
"usage"
]
opts, args = getopt.getopt(sys.argv[1:], shortopts, longopts)
except getopt.GetoptError:
# print help information and exit:
print "ERROR: unknown options in argument %s" % sys.argv[1:]
usage()
sys.exit(1)
infname = DEFAULT_INFNAME
treeNameSig = DEFAULT_TREESIG
treeNameBkg = DEFAULT_TREEBKG
outfname = DEFAULT_OUTFNAME
methods = DEFAULT_METHODS
nTrees = DEFAULT_NTREES
mDepth = DEFAULT_MDEPTH
mass = DEFAULT_MASS
varListKey = DEFAULT_VARLISTKEY
verbose = True
for o, a in opts:
if o in ("-?", "-h", "--help", "--usage"):
usage()
sys.exit(0)
elif o in ("-m", "--methods"):
methods = a
elif o in ("-d", "--maxDepth"):
mDepth = a
elif o in ("-k", "--mass"):
mass = a
elif o in ("-l", "--varListKey"):
varListKey = a
elif o in ("-i", "--inputfile"):
infname = a
elif o in ("-n", "--nTrees"):
nTrees = a
elif o in ("-o", "--outputfile"):
outfname = a
elif o in ("-t", "--inputtrees"):
a.strip()
trees = a.rsplit(' ')
trees.sort()
trees.reverse()
if len(trees) - trees.count('') != 2:
print "ERROR: need to give two trees (each one for signal and background)"
print trees
sys.exit(1)
treeNameSig = trees[0]
treeNameBkg = trees[1]
elif o in ("-v", "--verbose"):
verbose = True
varList = varsList.varList[varListKey]
nVars = str(len(varList)) + 'vars'
Note = methods + '_' + varListKey + '_' + nVars + '_mDepth' + mDepth + '_M' + mass
outfname = "weights/TMVA_" + Note + ".root"
# Print methods
mlist = methods.replace(' ', ',').split(',')
print "=== TMVAClassification: use method(s)..."
for m in mlist:
if m.strip() != '':
print "=== - <%s>" % m.strip()
# Import ROOT classes
from ROOT import gSystem, gROOT, gApplication, TFile, TTree, TCut
# check ROOT version, give alarm if 5.18
if gROOT.GetVersionCode() >= 332288 and gROOT.GetVersionCode() < 332544:
print "*** You are running ROOT version 5.18, which has problems in PyROOT such that TMVA"
print "*** does not run properly (function calls with enums in the argument are ignored)."
print "*** Solution: either use CINT or a C++ compiled version (see TMVA/macros or TMVA/examples),"
print "*** or use another ROOT version (e.g., ROOT 5.19)."
sys.exit(1)
# Logon not automatically loaded through PyROOT (logon loads TMVA library) load also GUI
# gROOT.SetMacroPath( "./" )
# gROOT.Macro ( "./TMVAlogon.C" )
# gROOT.LoadMacro ( "./TMVAGui.C" )
# Import TMVA classes from ROOT
from ROOT import TMVA
# Output file
outputFile = TFile(outfname, 'RECREATE')
# Create instance of TMVA factory (see TMVA/macros/TMVAClassification.C for more factory options)
# All TMVA output can be suppressed by removing the "!" (not) in
# front of the "Silent" argument in the option string
# factory = TMVA.Factory( "TMVAClassification", outputFile,
# "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" )
factory = TMVA.Factory(
"TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;:AnalysisType=Classification"
)
# Set verbosity
factory.SetVerbose(verbose)
# If you wish to modify default settings
# (please check "src/Config.h" to see all available global options)
# gConfig().GetVariablePlotting()).fTimesRMS = 8.0
(TMVA.gConfig().GetIONames()).fWeightFileDir = "weights/" + Note
# Define the input variables that shall be used for the classifier training
# note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
# [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
for iVar in varList:
if iVar[0] == 'NJets_singleLepCalc':
factory.AddVariable(iVar[0], iVar[1], iVar[2], 'I')
else:
factory.AddVariable(iVar[0], iVar[1], iVar[2], 'F')
# You can add so-called "Spectator variables", which are not used in the MVA training,
# but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
# input variables, the response values of all trained MVAs, and the spectator variables
inputDir = varsList.inputDir
print 'mass point ' + mass
infname = "ChargedHiggs_HplusTB_HplusToTB_M-%s_13TeV_amcatnlo_pythia8_hadd.root" % (
mass)
iFileSig = TFile.Open(inputDir + infname)
sigChain = iFileSig.Get("ljmet")
# os.exits(1)
#BDT machinary
factory.AddSignalTree(sigChain)
bkg_list = []
bkg_trees_list = []
hist_list = []
weightsList = []
for i in range(len(varsList.bkg)):
bkg_list.append(TFile.Open(inputDir + varsList.bkg[i]))
print inputDir + varsList.bkg[i]
bkg_trees_list.append(bkg_list[i].Get("ljmet"))
bkg_trees_list[i].GetEntry(0)
if bkg_trees_list[i].GetEntries() == 0:
continue
factory.AddBackgroundTree(bkg_trees_list[i], 1)
signalWeight = 1 #0.0159/sigChain.GetEntries() #xs (pb)
# ====== register trees ====================================================
# To give different trees for training and testing, do as follows:
# factory.AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" )
# factory.AddSignalTree( signalTestTree, signalTestWeight, "Test" )
# Use the following code instead of the above two or four lines to add signal and background
# training and test events "by hand"
# NOTE that in this case one should not give expressions (such as "var1+var2") in the input
# variable definition, but simply compute the expression before adding the event
#
# # --- begin ----------------------------------------------------------
#
# ... *** please lookup code in TMVA/macros/TMVAClassification.C ***
#
# # --- end ------------------------------------------------------------
#
# ====== end of register trees ==============================================
# Set individual event weights (the variables must exist in the original TTree)
# for signal : factory.SetSignalWeightExpression ("weight1*weight2");
# for background: factory.SetBackgroundWeightExpression("weight1*weight2");
#factory.SetBackgroundWeightExpression( "weight" )
factory.SetSignalWeightExpression(weightStrS)
factory.SetBackgroundWeightExpression(weightStrB)
# Apply additional cuts on the signal and background sample.
# example for cut: mycut = TCut( "abs(var1)<0.5 && abs(var2-0.5)<1" )
mycutSig = TCut(cutStrS)
mycutBkg = TCut(cutStrB)
# Here, the relevant variables are copied over in new, slim trees that are
# used for TMVA training and testing
# "SplitMode=Random" means that the input events are randomly shuffled before
# splitting them into training and test samples
factory.PrepareTrainingAndTestTree(
mycutSig,
mycutBkg,
# "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=10:nTest_Background=100:SplitMode=Random:NormMode=NumEvents:!V" )
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V"
)
# --------------------------------------------------------------------------------------------------
# ---- Book MVA methods
#
# please lookup the various method configuration options in the corresponding cxx files, eg:
# src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
# it is possible to preset ranges in the option string in which the cut optimisation should be done:
# "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
# Cut optimisation
# bdtSetting for "BDT"
bdtSetting = '!H:!V:NTrees=%s:MaxDepth=%s' % (nTrees, mDepth)
bdtSetting += ':MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20'
bdtSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTMitFisher"
bdtFSetting = '!H:!V:NTrees=%s' % nTrees
bdtFSetting += ':MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20'
bdtFSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTG"
bdtGSetting = '!H:!V:NTrees=%s:MaxDepth=%s' % (nTrees, mDepth)
bdtGSetting += ':MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20'
bdtGSetting += ':Pray' #Pray takes into account the effect of negative bins in BDTG
#bdtGSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTB"
bdtBSetting = '!H:!V:NTrees=%s' % nTrees
bdtBSetting += ':MinNodeSize=2.5%:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20'
bdtBSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTD"
bdtDSetting = '!H:!V:NTrees=%s' % nTrees
bdtDSetting += ':MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate'
bdtDSetting += ':IgnoreNegWeightsInTraining=True'
#Note also that explicitly setting *nEventsMin* so far OVERWRITES the option recomeded ^[[0m
#BOOKING AN ALGORITHM
if methods == "BDT": factory.BookMethod(TMVA.Types.kBDT, "BDT", bdtSetting)
if methods == "BDTG":
factory.BookMethod(TMVA.Types.kBDT, "BDTG", bdtGSetting)
if methods == "BDTMitFisher":
factory.BookMethod(TMVA.Types.kBDT, "BDTMitFisher", bdtFSetting)
if methods == "BDTB":
factory.BookMethod(TMVA.Types.kBDT, "BDTB", bdtBSetting)
if methods == "BDTD":
factory.BookMethod(TMVA.Types.kBDT, "BDTD", bdtDSetting)
# --------------------------------------------------------------------------------------------------
# ---- Now you can tell the factory to train, test, and evaluate the MVAs.
# Train MVAs
factory.TrainAllMethods()
# Test MVAs
factory.TestAllMethods()
# Evaluate MVAs
factory.EvaluateAllMethods()
# Save the output.
outputFile.Close()
#
# print "=== wrote root file %s\n" % outfname
# print "=== TMVAClassification is done!\n"
# save plots:
os.chdir('weights/' + Note)
#TMVA.mvaeffs( "../../"+outfname ) #Classifier Cut Efficiencies
gROOT.SetBatch(1)
TMVA.efficiencies(
"../../" + outfname
) #Classifier Background Rejection vs Signal Efficiency (ROC curve)
#TMVA.efficiencies( "weights/TMVA_BDTG_APR9_33vars_mDepth3_MLow.root", 3 ) #Classifier 1/(Backgr. Efficiency) vs Signal Efficiency (ROC curve)
TMVA.mvas("../../" + outfname,
0) #Classifier Output Distributions (test sample)
TMVA.correlations(
"../../" + outfname) #Input Variable Linear Correlation Coefficients
TMVA.variables("../../" + outfname) #Input variables (training sample)
#TMVA.mvas( "../../"+outfname ) #Classifier Output Distributions (test and training samples superimposed)
#gROOT.ProcessLine( ".x ../../mvas.C(\"%s\",3)" % ("../../"+outfname) ) #Classifier Output Distributions (test and training samples superimposed)
if not gROOT.IsBatch(): TMVA.TMVAGui("../../" + outfname)
# os.chdir('plots')
# try: os.system('convert CorrelationMatrixS.eps CorrelationMatrixS_'+Note+'.png')
# except: pass
# try: os.system('convert CorrelationMatrixB.eps CorrelationMatrixB_'+Note+'.png')
# except: pass
# #try: os.system('convert invBeffvsSeff.eps invBeffvsSeff_'+Note+'.png')
# #except: pass
# try: os.system('convert mva_'+Note.split('_')[0]+'.eps mva_'+Note+'.png')
# except: pass
# try: os.system('convert mvaeffs_'+Note.split('_')[0]+'.eps mvaeffs_'+Note+'.png')
# except: pass
# try: os.system('convert overtrain_'+Note.split('_')[0]+'.eps overtrain_'+Note+'.png')
# except: pass
# try: os.system('convert rejBvsS.eps rejBvsS_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c1.eps variables_id_c1_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c2.eps variables_id_c2_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c3.eps variables_id_c3_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c4.eps variables_id_c4_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c5.eps variables_id_c5_'+Note+'.png')
# except: pass
# try: os.system('convert variables_id_c6.eps variables_id_c6_'+Note+'.png')
# except: pass
# os.system('rm *.eps')
print "DONE"