class RegressionTrainer():
def __init__(self, config):
vhbb_name_space = config.get('VHbbNameSpace','library')
ROOT.gSystem.Load(vhbb_name_space)
self.__weight = config.get("TrainRegression","weight")
self.__vars = config.get("TrainRegression","vars").split()
self.__target = config.get("TrainRegression","target")
self.__cut = config.get("TrainRegression","cut")
self.__title = config.get("TrainRegression","name")
self.__signals = config.get("TrainRegression","signals")
self.__regOptions = config.get("TrainRegression","options")
path = config.get('Directories','PREPout')
samplesinfo=config.get('Directories','samplesinfo')
self.__info = ParseInfo(samplesinfo,path)
self.__samples = self.__info.get_samples(self.__signals)
self.__tc = TreeCache([self.__cut],self.__samples,path,config)
self.__trainCut = config.get("TrainRegression","trainCut")
self.__testCut = config.get("TrainRegression","testCut")
self.__config = config
def train(self):
signals = []
signalsTest = []
for job in self.__samples:
print '\tREADING IN %s AS SIG'%job.name
signals.append(self.__tc.get_tree(job,'%s & %s' %(self.__cut,self.__trainCut)))
signalsTest.append(self.__tc.get_tree(job,'%s & %s'%(self.__cut,self.__testCut)))
# Get the tree from signal
tree = signals[0]
sWeight = 1.
fnameOutput='training_Reg_'+self.__title+'.root'
output = ROOT.TFile.Open('/exports/uftrig01a/dcurry/data/bbar/13TeV/heppy/files/regr_out/'+fnameOutput, "RECREATE")
print '\n----- Saving output to ', output
factory = ROOT.TMVA.Factory('MVA', output, '!V:!Silent:!Color:!DrawProgressBar:Transformations=I:AnalysisType=Regression')
for i, signal in enumerate(signals):
factory.AddRegressionTree( signal, sWeight, ROOT.TMVA.Types.kTraining)
factory.AddRegressionTree( signalsTest[i], sWeight, ROOT.TMVA.Types.kTesting)
for var in self.__vars:
factory.AddVariable(var,'D')
factory.AddTarget(self.__target )
mycut = ROOT.TCut( self.__cut )
factory.BookMethod(ROOT.TMVA.Types.kBDT,'BDT_REG_%s'%(self.__title),self.__regOptions)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Write()
'''
class RegressionTrainer():
def __init__(self, config):
vhbb_name_space = config.get('VHbbNameSpace','library')
ROOT.gSystem.Load(vhbb_name_space)
self.__weight = config.get("TrainRegression","weight")
self.__vars = config.get("TrainRegression","vars").split()
self.__target = config.get("TrainRegression","target")
self.__cut = config.get("TrainRegression","cut")
self.__title = config.get("TrainRegression","name")
self.__signals = config.get("TrainRegression","signals")
self.__regOptions = config.get("TrainRegression","options")
path = config.get('Directories','PREPout')
samplesinfo=config.get('Directories','samplesinfo')
self.__info = ParseInfo(samplesinfo,path)
self.__samples = self.__info.get_samples(self.__signals)
self.__tc = TreeCache([self.__cut],self.__samples,path,config)
self.__trainCut = config.get("TrainRegression","trainCut")
self.__testCut = config.get("TrainRegression","testCut")
self.__config = config
def train(self):
signals = []
signalsTest = []
for job in self.__samples:
print '\tREADING IN %s AS SIG'%job.name
signals.append(self.__tc.get_tree(job,'%s & %s' %(self.__cut,self.__trainCut)))
signalsTest.append(self.__tc.get_tree(job,'%s & %s'%(self.__cut,self.__testCut)))
sWeight = 1.
fnameOutput='training_Reg_%s.root' %(self.__title)
output = ROOT.TFile.Open(fnameOutput, "RECREATE")
factory = ROOT.TMVA.Factory('MVA', output, '!V:!Silent:!Color:!DrawProgressBar:Transformations=I:AnalysisType=Regression')
#factory.SetSignalWeightExpression( self.__weight )
#set input trees
for i, signal in enumerate(signals):
factory.AddRegressionTree( signal, sWeight, ROOT.TMVA.Types.kTraining)
factory.AddRegressionTree( signalsTest[i], sWeight, ROOT.TMVA.Types.kTesting)
self.__apply = []
p = re.compile(r'hJet_\w+')
for var in self.__vars:
factory.AddVariable(var,'D') # add the variables
self.__apply.append(p.sub(r'\g<0>[0]', var))
print (self.__apply)
factory.AddTarget( self.__target )
mycut = ROOT.TCut( self.__cut )
factory.BookMethod(ROOT.TMVA.Types.kBDT,'BDT_REG_%s'%(self.__title),self.__regOptions) # book an MVA method
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Write()
regDict = dict(zip(self.__vars, self.__apply))
self.__config.set('Regression', 'regWeight', '../data/MVA_BDT_REG_%s.weights.xml' %self.__title)
self.__config.set('Regression', 'regDict', '%s' %regDict)
self.__config.set('Regression', 'regVars', '%s' %self.__vars)
for section in self.__config.sections():
if not section == 'Regression':
self.__config.remove_section(section)
with open('8TeVconfig/appReg', 'w') as configfile:
self.__config.write(configfile)
with open('8TeVconfig/appReg', 'r') as configfile:
for line in configfile:
print line.strip()
class RegressionTrainer():
def __init__(self, config):
vhbb_name_space = config.get('VHbbNameSpace', 'library')
ROOT.gSystem.Load(vhbb_name_space)
self.__weight = config.get("TrainRegression", "weight")
self.__vars = config.get("TrainRegression", "vars").split()
self.__target = config.get("TrainRegression", "target")
self.__cut = config.get("TrainRegression", "cut")
self.__title = config.get("TrainRegression", "name")
self.__signals = config.get("TrainRegression", "signals")
self.__regOptions = config.get("TrainRegression", "options")
path = config.get('Directories', 'PREPout')
samplesinfo = config.get('Directories', 'samplesinfo')
self.__info = ParseInfo(samplesinfo, path)
self.__samples = self.__info.get_samples(self.__signals)
self.__tc = TreeCache([self.__cut], self.__samples, path, config)
self.__trainCut = config.get("TrainRegression", "trainCut")
self.__testCut = config.get("TrainRegression", "testCut")
self.__config = config
def train(self):
signals = []
signalsTest = []
for job in self.__samples:
print '\tREADING IN %s AS SIG' % job.name
signals.append(
self.__tc.get_tree(job,
'%s & %s' % (self.__cut, self.__trainCut)))
signalsTest.append(
self.__tc.get_tree(job,
'%s & %s' % (self.__cut, self.__testCut)))
sWeight = 1.
fnameOutput = 'training_Reg_%s.root' % (self.__title)
output = ROOT.TFile.Open(fnameOutput, "RECREATE")
factory = ROOT.TMVA.Factory(
'MVA', output,
'!V:!Silent:!Color:!DrawProgressBar:Transformations=I:AnalysisType=Regression'
)
#factory.SetSignalWeightExpression( self.__weight )
#set input trees
for i, signal in enumerate(signals):
factory.AddRegressionTree(signal, sWeight,
ROOT.TMVA.Types.kTraining)
factory.AddRegressionTree(signalsTest[i], sWeight,
ROOT.TMVA.Types.kTesting)
self.__apply = []
p = re.compile(r'hJet_\w+')
for var in self.__vars:
factory.AddVariable(var, 'D') # add the variables
self.__apply.append(p.sub(r'\g<0>[0]', var))
factory.AddTarget(self.__target)
mycut = ROOT.TCut(self.__cut)
factory.BookMethod(ROOT.TMVA.Types.kBDT, 'BDT_REG_%s' % (self.__title),
self.__regOptions) # book an MVA method
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Write()
regDict = dict(zip(self.__vars, self.__apply))
self.__config.set('Regression', 'regWeight',
'../data/MVA_BDT_REG_%s.weights.xml' % self.__title)
self.__config.set('Regression', 'regDict', '%s' % regDict)
self.__config.set('Regression', 'regVars', '%s' % self.__vars)
for section in self.__config.sections():
if not section == 'Regression':
self.__config.remove_section(section)
with open('8TeVconfig/appReg', 'w') as configfile:
self.__config.write(configfile)
with open('8TeVconfig/appReg', 'r') as configfile:
for line in configfile:
print line.strip()
class RegressionTrainer():
def __init__(self, config):
vhbb_name_space = config.get('VHbbNameSpace', 'library')
ROOT.gSystem.Load(vhbb_name_space)
self.__weight = config.get("TrainRegression", "weight")
self.__vars = config.get("TrainRegression", "vars").split()
self.__target = config.get("TrainRegression", "target")
self.__cut = config.get("TrainRegression", "cut")
self.__title = config.get("TrainRegression", "name")
self.__signals = config.get("TrainRegression", "signals")
self.__regOptions = config.get("TrainRegression", "options")
path = config.get('Directories', 'PREPout')
samplesinfo = config.get('Directories', 'samplesinfo')
self.__info = ParseInfo(samplesinfo, path)
self.__samples = self.__info.get_samples(self.__signals)
self.__tc = TreeCache([self.__cut], self.__samples, path, config)
self.__trainCut = config.get("TrainRegression", "trainCut")
self.__testCut = config.get("TrainRegression", "testCut")
self.__config = config
def train(self):
signals = []
signalsTest = []
for job in self.__samples:
print '\tREADING IN %s AS SIG' % job.name
signals.append(
self.__tc.get_tree(job,
'%s & %s' % (self.__cut, self.__trainCut)))
signalsTest.append(
self.__tc.get_tree(job,
'%s & %s' % (self.__cut, self.__testCut)))
# Perform regression for the two Higgs jets independently
for iJet in range(0, 2):
print '\n========== Performing Regression on Jet', iJet, '==========\n'
sWeight = 1.
#fnameOutput='training_Reg_%s_Jet'+str(iJet)+'.root'%(self.__title)
fnameOutput = 'training_Reg_' + self.__title + '_Jet' + str(
iJet) + '.root'
output = ROOT.TFile.Open(
'/exports/uftrig01a/dcurry/data/bbar/13TeV/heppy/files/regr_out/'
+ fnameOutput, "RECREATE")
print '\n----- Saving output to ', output
factory = ROOT.TMVA.Factory(
'MVA', output,
'!V:!Silent:!Color:!DrawProgressBar:Transformations=I:AnalysisType=Regression'
)
#factory.SetSignalWeightExpression( self.__weight )
#set input trees
for i, signal in enumerate(signals):
#print 'signal, sWeight, ROOT.TMVA.Types.kTesting', signal, sWeight, ROOT.TMVA.Types.kTraining
#print 'tree entries', signal.GetEntries()
factory.AddRegressionTree(signal, sWeight,
ROOT.TMVA.Types.kTraining)
factory.AddRegressionTree(signalsTest[i], sWeight,
ROOT.TMVA.Types.kTesting)
self.__apply = []
p = re.compile(r'hJet_\w+')
for var in self.__vars:
if var == 'rho' or var == 'met_pt':
factory.AddVariable(var, 'D')
continue
if iJet == 0:
if 'max' in var:
var = var.replace(')', '[hJidx[0]])')
factory.AddVariable(var, 'D')
else:
var = var + '[hJidx[0]]'
factory.AddVariable(var, 'D')
else:
if 'max' in var:
var = var.replace(')', '[hJidx[1]])')
factory.AddVariable(var, 'D')
else:
var = var + '[hJidx[1]]'
factory.AddVariable(var, 'D')
self.__apply.append(p.sub(r'\g<0>[0]', var))
print(self.__apply)
factory.AddTarget(self.__target + '%s' % ('[' + str(iJet) + ']'))
mycut = ROOT.TCut(self.__cut)
#factory.BookMethod(ROOT.TMVA.Types.kBDT,'BDT_REG_%s'%(self.__title),self.__regOptions)
factory.BookMethod(ROOT.TMVA.Types.kBDT,
'BDT_REG_' + self.__title + '_Jet' + str(iJet),
self.__regOptions)
factory.TrainAllMethods()
#factory.TestAllMethods()
#factory.EvaluateAllMethods()
output.Write()
'''
