def __init__(self, filename, scalefactortxtfilename, treename, lumi,
channel, savelabel):
self.tmpplot = TreePlot()
self.batch_ = False
self.filedict_ = filename
self.scalefactortxtfilename_ = scalefactortxtfilename
self.treename_ = treename
self.histograms_ = {}
self.multiplicitylabel_ = {}
self.lumi_ = lumi
self.channel_ = channel
self.savelabel_ = savelabel
self.constrains_ = []
self.fit_ = None
self.qcdfraction_ = 0.0
self.qcdfractionerror_ = 0.0
def __init__(self,CHANNEL):#CHANNEL = 'mu', or 'el'
self.thstack = THStack("MC","MC")
self.samples = Samples(CHANNEL)
self.drawhistograms = {}
self.tmpplot = TreePlot()
self.bin_ = 0
self.min_ = 0.
self.max_ = 0.
self.cutname_ = ""
self.weightname_ = ""
self.plotdir_ = ""
self.setlogy_ = False
self.channel = CHANNEL
self.multiplicitylabel = {}
self.c1 = TCanvas()
'''
读取序列化对象
'''
def grabTree(filename):
fr = open(filename, "rb")
return pickle.load(fr)
if __name__ == '__main__':
# 创建示例数据集
dataSet, labels = createDataSet()
lebelsCopy = labels[:]
# 学习构建决策树
tree = createTree(dataSet, labels)
print(tree)
# 画决策树
TreePlot.createPlot(tree)
# 序列化存储树结构
storeTree(tree, "object.txt")
# 文件中读取数结构
myTree = grabTree("object.txt")
print(myTree)
print(classifyDecisionTree(myTree, lebelsCopy, [1, 1]))
firstLabelIndex = labels.index(firstLabel) # 属性对应的index
secondDict = tree[firstLabel]
value = testData[firstLabelIndex] # 属性值
if type(secondDict[value]).__name__ == "dict": # 假如还是一棵树则递归
classLabel = classifyDecisionTree(secondDict[value], labels, testData)
else:
classLabel = secondDict[value]
return classLabel
if __name__ == '__main__':
# 读取数据
lensesData, lensesLable = loadDataSet("lenses.txt")
# 复制属性标签，
# createTree()操作会影响传入的类别标签
lensesLableCopy = lensesLable[:]
# 创建树
decisionTree = createTree(lensesData, lensesLableCopy)
print(decisionTree)
# 对树进行绘图
TreePlot.createPlot(decisionTree)
# 进行预测
classLabel = classifyDecisionTree(decisionTree, lensesLable,
["young", "hyper", "yes", "normal"])
print(classLabel)
classCount[vote]=0
classCount[vote]+=1
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]
def createTree(dataSet,labels):
classList=[example[-1] for example in dataSet] # 类别：男或女
if classList.count(classList[0])==len(classList):
return classList[0]
if len(dataSet[0])==1:
return majorityCnt(classList)
bestFeat=chooseBestFeatureToSplit(dataSet) #选择最优特征
bestFeatLabel=labels[bestFeat]
myTree={bestFeatLabel:{}} #分类结果以字典形式保存
del(labels[bestFeat])
featValues=[example[bestFeat] for example in dataSet]
uniqueVals=set(featValues)
for value in uniqueVals:
subLabels=labels[:]
myTree[bestFeatLabel][value]=createTree(splitDataSet\
(dataSet,bestFeat,value),subLabels)
return myTree
if __name__=='__main__':
dataSet, labels=createDataSet1() # 创造示列数据
mytree=createTree(dataSet, labels) # 输出决策树模型结果
print mytree
TreePlot.createPlot(mytree)
__author__ = 'wanghao'
"""
the glasses examples
"""
import ID3Tree
import TreePlot
fr = open('./lenses.txt')
DataList = fr.readlines()
DataSet = []
for data in DataList:
DataSet.append(data.strip().split('\t'))
print "The dateSet is ", DataSet
Labels = ['age', 'prescript', 'astigmatic', 'tearRate']
LenseTree = ID3Tree.createTree(DataSet, Labels)
print "the result ID3 Tree is ", LenseTree
TreePlot.createPlot(LenseTree)
class ProducePlot:
def __init__(self,CHANNEL):#CHANNEL = 'mu', or 'el'
self.thstack = THStack("MC","MC")
self.samples = Samples(CHANNEL)
self.drawhistograms = {}
self.tmpplot = TreePlot()
self.bin_ = 0
self.min_ = 0.
self.max_ = 0.
self.cutname_ = ""
self.weightname_ = ""
self.plotdir_ = ""
self.setlogy_ = False
self.channel = CHANNEL
self.multiplicitylabel = {}
self.c1 = TCanvas()
def SetBinMinMax(self,bin,min,max):
self.bin_ = int(bin)
self.min_ = float(min)
self.max_ = float(max)
def SetCutWeightName(self,cutname,weightname):
self.cutname_ = cutname
self.weightname_ = weightname
def SetFilePath(self,filepath):
self.samples.SetFilePath(filepath)
def SetLumiTree(self,lumi,treename):
self.samples.SetLumi(lumi)
self.samples.SetTreeName(treename)
def SetPlotDir(self,plotdir):
self.plotdir_ = plotdir
def SetLogy(self,setlogy):
if(setlogy == "False"):
self.setlogy_ = False
if(setlogy == "True"):
self.setlogy_ = True
def SetChannel(self,Channel):
self.channel = Channel
def DrawTHStack(self,drawname,index,xtitle,ytitle,scalefactortxtfilename,n,ymin,latexcuttitle):
self.samples.SetFileNames()
print "We are ploting the variable: " + drawname
print "We are using the cut: " + self.cutname_
print "We are using the weight: " + self.weightname_
ttbarregionsignal = ""
for tmpfiletype in self.samples.GetFileNames().keys():
#print tmpfiletype
tmpcutname = self.cutname_
if self.plotdir_ == "controlPlots_ttbar" and (str(tmpfiletype).find("H") != -1): tmpcutname = "&&".join(self.cutname_.split("&&")[:-1])
self.tmpplot.SetBinMinMax(self.bin_,self.min_,self.max_)
self.tmpplot.SetCutWeightName(tmpcutname,self.weightname_)
tmphistogram = TH1D(tmpfiletype,tmpfiletype,self.bin_,self.min_,self.max_)
if (tmpfiletype != "data"):
self.tmpplot.DrawTrees(self.samples.GetFileNames()[tmpfiletype],self.samples.GetTreeName(),drawname,index,xtitle,ytitle,"%s:%s"%(scalefactortxtfilename,tmpfiletype),self.samples.GetLumi(),tmpfiletype,self.drawhistograms,self.multiplicitylabel,ttbarregionsignal)
else:
self.tmpplot.DrawTrees(self.samples.GetFileNames()[tmpfiletype],self.samples.GetTreeName(),drawname,index,xtitle,ytitle,1.0,1.0,tmpfiletype,self.drawhistograms,self.multiplicitylabel,ttbarregionsignal)
self.drawhistograms[tmpfiletype].SetStats(kFALSE)
################################Data############################
self.drawhistograms["data"].SetMarkerStyle(20)
self.drawhistograms["data"].SetMarkerSize(1)
self.drawhistograms["data"].Sumw2()
################################Data############################
##########TTbar###########################################
tt1D = TH1D("tt1D","tt1D",self.bin_,self.min_,self.max_)
self.drawhistograms["TTbar"].SetFillColor(kRed+1)
tt1D = self.drawhistograms["TTbar"]
tt1D.SetFillColor(kRed+1)
##########################################################
#############WJet########################################
wjet1D = TH1D("wjet1D","wjet1D",self.bin_,self.min_,self.max_)
wjet1D.Sumw2()
wjet1D.SetStats(kFALSE)
if(self.samples.GetFileNames().has_key("Wbb") and self.samples.GetFileNames().has_key("Wcc") and self.samples.GetFileNames().has_key("Wlight")):
wjet1D = self.drawhistograms["Wbb"] + self.drawhistograms["Wcc"] + self.drawhistograms["Wlight"]
else:
wjet1D = self.drawhistograms["WJets_Pythia"]
wjet1D.SetFillColor(kGreen-3)
#############WJet########################################
###########QCD############################################
qcd1D = TH1D("qcd1D","qcd1D",self.bin_,self.min_,self.max_)
qcd1D.Sumw2()
qcd1D.SetStats(kFALSE)
if (self.samples.GetFileNames().has_key("QCDBCtoE3080") and self.samples.GetFileNames().has_key("QCDBCtoE80170") and self.samples.GetFileNames().has_key("QCDEmEn3080") and self.samples.GetFileNames().has_key("QCDEmEn80170")):
qcd1D = self.drawhistograms["QCDBCtoE3080"] + self.drawhistograms["QCDBCtoE80170"] + self.drawhistograms["QCDEmEn3080"] + self.drawhistograms["QCDEmEn80170"]
qcd1D.SetFillColor(kYellow)
###########QCD############################################
############################DiBoson#######################################
diboson1D = TH1D("diboson1D","diboson1D",self.bin_,self.min_,self.max_)
diboson1D.Sumw2()
if (self.samples.GetFileNames().has_key("WW") and self.samples.GetFileNames().has_key("WZ") and self.samples.GetFileNames().has_key("ZZ")):
diboson1D = self.drawhistograms["WW"]+ self.drawhistograms["WZ"] + self.drawhistograms["ZZ"]
else:
if(self.samples.GetFileNames().has_key("WW") and self.samples.GetFileNames().has_key("WZ")):
diboson1D = self.drawhistograms["WW"]+ self.drawhistograms["WZ"]
diboson1D.SetStats(kFALSE)
diboson1D.SetFillColor(kBlue)
############################DiBoson#######################################
#####################################SumSingleTop###########################
sumsttop1D = TH1D("sumsttop1D","sumsttop1D",self.bin_,self.min_,self.max_)
sumsttop1D.Sumw2()
if (self.samples.GetFileNames().has_key("tch") and self.samples.GetFileNames().has_key("tWch") and self.samples.GetFileNames().has_key("sch") and self.samples.GetFileNames().has_key("tch_bar") and self.samples.GetFileNames().has_key("tWch_bar") and self.samples.GetFileNames().has_key("sch_bar")):
sumsttop1D = self.drawhistograms["tch"] + self.drawhistograms["tWch"] + self.drawhistograms["sch"] + self.drawhistograms["tch_bar"] + self.drawhistograms["tWch_bar"] + self.drawhistograms["sch_bar"]
sumsttop1D.SetStats(kFALSE)
sumsttop1D.SetFillColor(kMagenta)
#####################################SumSingleTop###########################
#####################################ZJet###################################
zjet1D = TH1D("zjet1D","zjet1D",self.bin_,self.min_,self.max_)
zjet1D.Sumw2()
if (self.samples.GetFileNames().has_key("ZJets")):
zjet1D = self.drawhistograms["ZJets"]
zjet1D.SetStats(kFALSE)
zjet1D.SetFillColor(kAzure-3)
#####################################ZJet###################################
mcbackground = [self.drawhistograms["TTbar"], wjet1D, qcd1D, diboson1D, sumsttop1D, zjet1D]
#####################################Data-MC/MC############################
allmc1D = TH1D("allmc1D","allmc1D",self.bin_,self.min_,self.max_)
allmc1D.Sumw2()
allmc1D = tt1D + wjet1D + diboson1D + sumsttop1D + zjet1D
if(qcd1D.GetEntries() > 0):
allmc1D = tt1D + wjet1D + diboson1D + sumsttop1D + zjet1D + qcd1D
allmc1D.SetStats(kFALSE)
dataminusmc1D = TH1D("dataminusmc1D","dataminusmc1D",self.bin_,self.min_,self.max_)
dataminusmc1D.Sumw2()
dataminusmc1D.Add(self.drawhistograms["data"],allmc1D,1.,-1.)
dataminusmc1D.SetStats(kFALSE)
dataovermc1D = TH1D("dataovermc1D","dataovermc1D",self.bin_,self.min_,self.max_)
dataovermc1D.Sumw2()
dataovermc1D.Divide(dataminusmc1D,allmc1D,1,1,"B")
dataminusmc1D.SetStats(kFALSE)
dataovermc1D.SetXTitle(xtitle)
dataovermc1D.SetYTitle("(Data - MC) / MC")
dataovermc1D.SetMarkerStyle(20)
dataovermc1D.SetMarkerSize(1)
dataovermc1D.SetTitle("")
dataovermc1D.SetTitleSize(0.05,"X")
dataovermc1D.SetTitleSize(0.05,"Y")
dataovermc1D.GetYaxis().SetRangeUser(-1,1)
#####################################Data-MC/MC############################
datamcksresult_ = self.drawhistograms["data"].KolmogorovTest(allmc1D)
#####################################Error_Band###########################
xvalue = array('d')
yvalue = array('d')
xlefterror = array('d')
xrighterror = array('d')
ylowerror = array('d')
yhigherror = array('d')
xlist = []
ylist = []
xleftlist = []
xrightlist = []
ylowlist = []
yhighlist = []
for ibin in range(1,allmc1D.GetNbinsX() + 1):
xlist.append(allmc1D.GetBinCenter(ibin))
ylist.append(allmc1D.GetBinContent(ibin))
xleftlist.append(0.5 * allmc1D.GetBinWidth(ibin))
xrightlist.append(0.5 * allmc1D.GetBinWidth(ibin))
ttbarerror = self.drawhistograms["TTbar"].GetBinContent(ibin) * 0.15
wjeterror = wjet1D.GetBinContent(ibin) * 0.3
lumierror = allmc1D.GetBinContent(ibin) * 0.022
statisticerror = allmc1D.GetBinError(ibin)
allerror = sqrt(pow(ttbarerror,2) + pow(wjeterror,2) + pow(lumierror,2) + pow(statisticerror,2))
allerror = statisticerror
ylowlist.append(allerror)
yhighlist.append(allerror)
xvalue.fromlist(xlist)
yvalue.fromlist(ylist)
xlefterror.fromlist(xleftlist)
xrighterror.fromlist(xrightlist)
ylowerror.fromlist(ylowlist)
yhigherror.fromlist(yhighlist)
mc1Derror = TGraphAsymmErrors(allmc1D.GetNbinsX(),xvalue,yvalue,xlefterror,xrighterror,ylowerror,yhigherror)
mc1Derror.SetName("MC Uncerntainty")
mc1Derror.SetFillColor(920+3)
mc1Derror.SetFillStyle(3008)
#####################################Error_Band###########################
self.thstack.SetHistogram(self.drawhistograms["TTbar"])
if(self.drawhistograms["TTbar"].GetEntries() > 0):
self.thstack.Add(self.drawhistograms["TTbar"])
if(wjet1D.GetEntries() > 0):
self.thstack.Add(wjet1D)
if(sumsttop1D.GetEntries() > 0):
self.thstack.Add(sumsttop1D)
if(self.samples.GetFileNames().has_key("ZJets")):
if(zjet1D.GetEntries() > 0):
self.thstack.Add(zjet1D)
if(diboson1D.GetEntries() > 0):
self.thstack.Add(diboson1D)
if(qcd1D.GetEntries() > 0):
self.thstack.Add(qcd1D)
self.thstack.SetMaximum(float(n) * self.thstack.GetMaximum())
self.thstack.SetMinimum(float(ymin))
#L = TLegend(0.66,0.65,0.93,0.93)
L = TLegend(0.7270115,0.6510417,0.9971264,0.9300595)
L.SetBorderSize(0)
L.SetLineStyle(0)
L.SetTextFont(42)
L.SetFillStyle(0)
L.SetMargin(0.12)
L.SetTextSize(0.025)
L.SetFillColor(10)
L.SetBorderSize(0)
if(self.drawhistograms["data"].GetEntries() > 0):
L.AddEntry(self.drawhistograms["data"],"Data", "lp")
if(self.drawhistograms["TTbar"].GetEntries() > 0):
L.AddEntry(self.drawhistograms["TTbar"],"t#bar{t}", "f")
if(wjet1D.GetEntries() > 0):
L.AddEntry(wjet1D,"W#rightarrowl#nu", "f")
if(self.samples.GetFileNames().has_key("ZJets")):
if(zjet1D.GetEntries() > 0):
L.AddEntry(zjet1D,"Z/#gamma^{\*}#rightarrowl^{+}l^{-}", "f")
if(qcd1D.GetEntries() > 0):
L.AddEntry(qcd1D,"QCD", "f")
if(diboson1D.GetEntries() > 0):
L.AddEntry(diboson1D,"Dibosons", "f")
if(sumsttop1D.GetEntries() > 0):
L.AddEntry(sumsttop1D,"Single-Top","f")
if(self.multiplicitylabel["ggH600"] != 1.0):
L.AddEntry(self.drawhistograms["ggH600"],"ggHWW 600 #times %d"%self.multiplicitylabel["ggH600"],"f")
else:
L.AddEntry(self.drawhistograms["ggH600"],"ggHWW 600","f")
banner = TLatex(0.25,0.88,("#splitline{CMS Preliminary}{%.1f fb^{-1} at #sqrt{s}=8TeV %s+jets}"%(self.samples.GetLumi(),self.channel)))
banner.SetNDC()
banner.SetTextSize(0.035)
tl = TLatex(0.25,0.85,("#splitline{KS=%.4f}{%s}"%(datamcksresult_,latexcuttitle)))
tl.SetNDC()
tl.SetTextSize(0.06)
self.c1 = TCanvas(drawname + self.cutname_,drawname + self.cutname_,10,10,700,700)
self.c1.cd()
pad1 = TPad("pad1","pad1",0.00,0.25,1.00,0.97)
pad2 = TPad("pad2","pad2",0.00,0.00,1.00,0.25)
pad1.SetFillColor(0)
pad2.SetFillColor(0)
pad1.Draw()
pad2.Draw()
pad1.SetTicks(1,1)
pad2.SetTicks(1,1)
pad2.SetGridx()
pad2.SetGridy()
pad1.cd()
if(self.setlogy_):
pad1.SetLogy()
self.thstack.Draw("hist")
mc1Derror.Draw("2 same")
self.drawhistograms["data"].Draw("Esame")
self.drawhistograms["ggH600"].SetLineStyle(2)
self.drawhistograms["ggH600"].Draw("histsame")
L.Draw()
banner.Draw()
pad2.cd()
dataovermc1D.Draw()
tl.Draw()
PWD = os.getcwd() + "/"
if os.path.isdir(PWD + self.plotdir_):
print "%s direcotry has aleady been created"%(self.plotdir_)
else:
os.system("mkdir %s"%(PWD + self.plotdir_))
tmplatexcuttitle = latexcuttitle
tmplatexcuttitle = "_".join(tmplatexcuttitle.split(" "))
tmplatexcuttitle = "".join(tmplatexcuttitle.split("&&"))
tmplatexcuttitle = "_".join(tmplatexcuttitle.split("__"))
tmplatexcuttitle = string.replace(tmplatexcuttitle,">","large")
tmplatexcuttitle = string.replace(tmplatexcuttitle,"=","euqal")
tmplatexcuttitle = string.replace(tmplatexcuttitle,"<","small")
tmplatexcuttitle = string.replace(tmplatexcuttitle,"#","")
self.c1.Print( PWD + self.plotdir_ + "/" + self.channel + "_" + drawname + "_" + tmplatexcuttitle + ".png")
self.c1.Print( PWD + self.plotdir_ + "/" + self.channel + "_" + drawname + "_" + tmplatexcuttitle + ".eps")
self.c1.Print( PWD + self.plotdir_ + "/" + self.channel + "_" + drawname + "_" + tmplatexcuttitle + ".pdf")
self.c1.Update()
class QCDFractionFitter:
def __init__(self, filename, scalefactortxtfilename, treename, lumi,
channel, savelabel):
self.tmpplot = TreePlot()
self.batch_ = False
self.filedict_ = filename
self.scalefactortxtfilename_ = scalefactortxtfilename
self.treename_ = treename
self.histograms_ = {}
self.multiplicitylabel_ = {}
self.lumi_ = lumi
self.channel_ = channel
self.savelabel_ = savelabel
self.constrains_ = []
self.fit_ = None
self.qcdfraction_ = 0.0
self.qcdfractionerror_ = 0.0
def SetBatch(self):
self.batch_ = True
def SetVariableCutWeightName(self, variable, cutname, weightname):
self.variable_ = variable
self.cutname_ = cutname
self.weightname_ = weightname
def SetXtitleYtitle(self, xtitle, ytitle):
self.xtitle_ = xtitle
self.ytitle_ = ytitle
def SetBinMinMax(self, bin, min, max):
self.bin_ = bin
self.min_ = min
self.max_ = max
def SetConstrainPartLabel(self, constrains):
self.constrains_ = constrains
def SetAnalysisCut(self, metcut):
self.metcut_ = metcut
def GetQCDFractionResult(self):
return [self.qcdfraction_, self.qcdfractionerror_]
def FitAndPlot(self, fitpattern):
#####Prepare the Histogram to the TFractionFitter####################
print "We are Fitting the variable: " + self.variable_
print "We are using the cut: " + self.cutname_
print "We are using the weight: " + self.weightname_
ttbarregionsignal = ""
for tmpfiletype in self.filedict_.keys():
tmpcutname = self.cutname_
self.tmpplot.SetBinMinMax(self.bin_, self.min_, self.max_)
self.tmpplot.SetCutWeightName(tmpcutname, self.weightname_)
if (tmpfiletype != "data" and tmpfiletype != "QCD"
and tmpfiletype != "WJets"):
self.tmpplot.DrawTrees(
self.filedict_[tmpfiletype], self.treename_,
self.variable_, 0, self.xtitle_, self.ytitle_,
"%s:%s" % (self.scalefactortxtfilename_, tmpfiletype),
self.lumi_, tmpfiletype, self.histograms_,
self.multiplicitylabel_, ttbarregionsignal)
elif (tmpfiletype == "WJets"):
self.tmpplot.SetCutWeightName(
tmpcutname, self.weightname_ + "*W_nParton_weight")
self.tmpplot.DrawTrees(
self.filedict_[tmpfiletype], self.treename_,
self.variable_, 0, self.xtitle_, self.ytitle_,
"%s:%s" % (self.scalefactortxtfilename_, tmpfiletype),
self.lumi_, tmpfiletype, self.histograms_,
self.multiplicitylabel_, ttbarregionsignal)
elif (tmpfiletype == "data"):
self.tmpplot.DrawTrees(self.filedict_[tmpfiletype],
self.treename_, self.variable_, 0,
self.xtitle_, self.ytitle_, 1.0, 1.0,
tmpfiletype, self.histograms_,
self.multiplicitylabel_,
ttbarregionsignal)
elif (tmpfiletype == "QCD"):
self.tmpplot.SetCutWeightName(
tmpcutname +
"&&((EWK_W_2jets_l_tagjet2_deltaR<EWK_W_2jets_l_tagjet1_deltaR?EWK_W_2jets_l_tagjet2_deltaR:EWK_W_2jets_l_tagjet1_deltaR)>1.0)",
self.weightname_)
self.tmpplot.DrawTrees(self.filedict_[tmpfiletype],
self.treename_, self.variable_, 0,
self.xtitle_, self.ytitle_, 1.0, 1.0,
tmpfiletype, self.histograms_,
self.multiplicitylabel_,
ttbarregionsignal)
if fitpattern == "QCDfraction":
wjet1D = TH1D("wjet1D", "wjet1D", self.bin_, self.min_, self.max_)
firstbin = 1
lastbin = self.histograms_["data"].GetNbinsX()
dataentries = self.histograms_["data"].Integral(firstbin, lastbin)
print "data Entries: " + str(dataentries)
self.histograms_["QCD"].Scale(
dataentries /
self.histograms_["QCD"].Integral(firstbin, lastbin + 1))
wjet1D = self.histograms_["WJets"] + self.histograms_[
"W1Jets"] + self.histograms_["W2Jets"] + self.histograms_[
"W3Jets"] + self.histograms_["W4Jets"]
wjet1D.Scale(dataentries / wjet1D.Integral(firstbin, lastbin + 1))
constrainfaction = 0.
tconstrain1D = TH1D("tconstrain", "tconstrain", self.bin_,
self.min_, self.max_)
if len(self.constrains_) > 0:
for icons in self.constrains_:
tconstrain1D.Add(self.histograms_[icons])
#tconstrain1D.Scale(dataentries/tconstrain1D.Integral(firstbin,lastbin))
constrainfaction = tconstrain1D.Integral(firstbin,
lastbin) / dataentries
print "TTbar constrainfaction: " + str(constrainfaction)
tconstrain1D.Scale(
dataentries / tconstrain1D.Integral(firstbin, lastbin + 1))
mc = TObjArray()
mc.Add(self.histograms_["QCD"])
mc.Add(wjet1D)
if len(self.constrains_) > 0:
mc.Add(tconstrain1D)
self.fit_ = TFractionFitter(self.histograms_["data"], mc)
self.fit_.Constrain(1, 0.0, 1.0)
self.fit_.Constrain(2, 0.0, 1.0)
if len(self.constrains_) > 0:
#self.fit_.Constrain(3,constrainfaction - 0.0001*constrainfaction,constrainfaction + 0.0001*constrainfaction)
self.fit_.Constrain(3, 0.0, 1.0)
self.fit_.SetRangeX(firstbin, lastbin)
status = self.fit_.Fit()
print "fit status: " + str(status)
self.SetUpPlotEnviroment()
if (self.batch_):
gROOT.SetBatch()
canvas = TCanvas("c1", "c1", 10, 10, 700, 700)
lgnd = TLegend(0.6, 0.6, 0.90, 0.9)
lgnd.SetFillColor(kWhite)
result = self.fit_.GetPlot()
result.SetFillColor(kYellow)
self.histograms_["data"].SetMinimum(0)
self.histograms_["data"].Draw("Ep")
result.Draw("histsame")
frac_value = array('d', [0.])
frac_error = array('d', [0.])
self.fit_.GetResult(0, frac_value, frac_error)
integralanalysisbin = self.histograms_["data"].FindBin(
self.metcut_)
dataentriesanalysisbin = self.histograms_["data"].Integral(
integralanalysisbin, lastbin)
qcdintegralanalysisbin = self.histograms_["QCD"].Integral(
integralanalysisbin, lastbin)
wjetsintegralanalysisbin = wjet1D.Integral(integralanalysisbin,
lastbin)
qcdfrac_valuemetcut = frac_value[
0] * qcdintegralanalysisbin / dataentriesanalysisbin
print "QCD: frac_value= " + str(qcdfrac_valuemetcut) + "+/-" + str(
frac_error[0]) + " NEvts=" + str(
dataentries * qcdfrac_valuemetcut) + "+/-" + str(
dataentries * frac_error[0])
self.qcdfraction_ = qcdfrac_valuemetcut
#self.qcdfraction_ = frac_value[0]
self.qcdfractionerror_ = frac_error[0]
self.histograms_["QCD"].Scale(frac_value[0])
self.histograms_["QCD"].SetLineWidth(2)
self.histograms_["QCD"].SetLineColor(kBlue)
self.histograms_["QCD"].Draw("histesame")
self.fit_.GetResult(1, frac_value, frac_error)
wjetsfrac_valuemetcut = frac_value[
0] * wjetsintegralanalysisbin / dataentriesanalysisbin
print "WJets: frac_value= " + str(
wjetsfrac_valuemetcut) + "+/-" + str(
frac_error[0]) + " NEvts=" + str(
dataentries * wjetsfrac_valuemetcut) + "+/-" + str(
dataentries * frac_error[0])
#self.histograms_["WJets"].Scale(frac_value[0])
#self.histograms_["WJets"].SetLineWidth(2)
#self.histograms_["WJets"].SetLineColor(kRed)
#self.histograms_["WJets"].Draw("histesame")
wjet1D.Scale(frac_value[0])
wjet1D.SetLineWidth(2)
wjet1D.SetLineColor(kRed)
wjet1D.Draw("histesame")
if len(self.constrains_) > 0:
self.fit_.GetResult(2, frac_value, frac_error)
tconstrain1D.Scale(frac_value[0])
#tconstrain1D.Scale(frac_value[0]*dataentries/tconstrain1D.Integral(firstbin,lastbin+1))
tconstrain1D.SetLineWidth(2)
tconstrain1D.SetLineColor(kGreen)
tconstrain1D.Draw("histsame")
lgnd.AddEntry(result, "TemplatePrediction", "f")
lgnd.AddEntry(self.histograms_["data"], "data", "p")
lgnd.AddEntry(self.histograms_["QCD"], "QCD", "l")
#lgnd.AddEntry(self.histograms_["WJets"], "WJetsMC","l")
lgnd.AddEntry(wjet1D, "WJetsMC", "l")
if len(self.constrains_) > 0:
#lgnd.AddEntry(tconstrain1D, "Fixed Processes", "l")
lgnd.AddEntry(tconstrain1D, "Other Processes", "l")
chi2ndof = -999.0
if len(self.constrains_) > 0:
print "FitChiSquare/ndof= " + str(self.fit_.GetChisquare() /
(self.bin_ - 3))
chi2ndof = self.fit_.GetChisquare() / (self.bin_ - 3)
else:
print "FitChiSquare/ndof= " + str(self.fit_.GetChisquare() /
(self.bin_ - 2))
chi2ndof = self.fit_.GetChisquare() / (self.bin_ - 2)
print "FitProbatility= " + str(self.fit_.GetProb())
latex = TLatex(0.60, 0.50, "#chi2/ndof=%s" % (round(chi2ndof, 2)))
latex.SetNDC()
latex.SetTextSize(0.035)
self.histograms_["data"].SetMarkerStyle(20)
self.histograms_["data"].SetMarkerSize(1)
self.histograms_["data"].Draw("Esame")
lgnd.Draw()
latex.Draw()
if len(self.constrains_) > 0:
self.channel_ = self.channel_ + "constrainotherprocess"
self.variable_ = self.variable_.replace(".", "_")
self.savelabel_ = self.savelabel_.replace(".", "_")
canvas.SaveAs("QCDFractionFit/" + self.channel_ + "_" +
self.variable_ + self.savelabel_ +
"_qcdfraction.png")
canvas.SaveAs("QCDFractionFit/" + self.channel_ + "_" +
self.variable_ + self.savelabel_ +
"_qcdfraction.pdf")
canvas.SaveAs("QCDFractionFit/" + self.channel_ + "_" +
self.variable_ + self.savelabel_ +
"_qcdfraction.eps")
def SetUpPlotEnviroment(self):
if os.path.isfile('tdrstyle.C'):
gROOT.ProcessLine('.L tdrstyle.C')
ROOT.setTDRStyle()
print "Found tdrstyle.C file, using this style."
HasCMSStyle = "True"
if os.path.isfile('CMSTopStyle.cc'):
gROOT.ProcessLine('.L CMSTopStyle.cc+')
style = CMSTopStyle()
style.setupICHEPv1()
print "Found CMSTopStyle.cc file, use TOP style if requested in xml file."
print "---------------------------------------\n" # Get the leaf Node LeafNodeNum = ID3Tree.getNumLeafs(myTree) print "This ID3 Tree leaf node num is ", LeafNodeNum print "---------------------------------------\n" # Get the depth of the ID3 MaxDepth = ID3Tree.getTreeDepth(myTree) print "This ID3 tree max depth is ", MaxDepth print "---------------------------------------\n" # Plot the tree TreePlot.createPlot(myTree) print "---------------------------------------\n"
from TestTrees import calcShannonEnt
from TestTrees import chooseBestFeatureToSplit
from TestTrees import createTree
import TestTrees
import TreePlot
def createDataSet():
dataSet = [[1, 1, 'yes'], [1, 1, 'yes'], [1, 0, 'no'], [0, 1, 'no'],
[0, 1, 'no']]
labels = ['no surfacing', 'flippers']
return dataSet, labels
fr = open('matpilb\lenses.txt')
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
lensesLabels = ['ages', 'prescript', 'astigmatic', 'tearRate']
print(lenses)
lensesTree = TestTrees.createTree(lenses, lensesLabels)
TreePlot.createPlot(lensesTree)
'>=' + str(splite))] = CreateTree(
son_data, label_lisan, label_lianxu)
elif j[feature] < splite and i == 1:
son_data.append(j)
if len(son_data) == 0:
return leaf(data)
else:
Tree[labels_word[feature]][str(
'<' + str(splite))] = CreateTree(
son_data, label_lisan, label_lianxu)
else:
for j in data:
if j[feature] == label_value[feature][i]: son_data.append(j)
if len(son_data) == 0:
return leaf(data)
else:
Tree[labels_word[feature]][
label_value[feature][i]] = CreateTree(
son_data, label_lisan, labels_lianxu)
return Tree
if __name__ == '__main__':
Tree = CreateTree(dataSet, labels_lisan, labels_lianxu)
TreePlot.createPlot(Tree)
