def discr_ROC_maker(rootFileName):
sig_chain = TChain("varTree")
sig_chain.Add("../"+rootFileName+".root")
sig_Full = sig_chain.AsMatrix()
sig_Full = np.transpose(sig_Full)
sig_Full = sig_Full[2:11]
sig_Full = np.transpose(sig_Full)
sig_scaled = scaler.transform(sig_Full)
sig_predict = loaded_model.predict(sig_scaled)
sig_sigprob = np.array(sig_predict)[:,0]
tpr = []
fpr = []
sigProb = np.arange(0, 1.01, 0.01)
for x in sigProb:
sig_class = sig_sigprob>=x
bkg_class = bkg_sigprob>=x
tp1 = sig_class.sum()
fn1 = (1-sig_class).sum()
tn1 = (1-bkg_class).sum()
fp1 = bkg_class.sum()
tpr.append(tp1/(tp1+fn1))
fpr.append(fp1/(fp1+tn1))
print(rootFileName+" completed.")
return [sig_predict, tpr, fpr]
brNameList = []
for br in sigChan.GetListOfBranches():
brNameList.append(br.GetName())
print(brNameList)
print(len(brNameList))
# In[6]:
# Read input data from root files
sigSampleSize = sigChan.GetEntries()
bkgSampleSize = bkgChan.GetEntries()
# Convert the input data to matrices
sigFull = sigChan.AsMatrix()
bkgFull = bkgChan.AsMatrix()
print(sigFull.shape)
print(bkgFull.shape)
# In[7]:
# Load the input data scaler
scaler = joblib.load("../scaler.save")
# Load the model
loaded_model = m.load_model("../simplePer.h5")
loaded_model.summary()
# In[9]:
sigChan3.Add("signal_SR3.root")
bkgChan3 = TChain("varTree")
bkgChan3.Add("background_SR3.root")
print("Data read from the trees. Printing out the contents.")
# In[3]:
sigChan3.Print()
bkgChan3.Print()
# In[4]:
sig3SampleSize = sigChan3.GetEntries()
bkg3SampleSize = bkgChan3.GetEntries()
sig3Full = sigChan3.AsMatrix()
bkg3Full = bkgChan3.AsMatrix()
# In[5]:
# Load the input data scaler
scaler = joblib.load("../scaler.save")
# Load the model
loaded_model = m.load_model("../simplePer.h5")
loaded_model.summary()
# In[6]:
sig3FullScaled = scaler.transform(sig3Full)
bkg3FullScaled = scaler.transform(bkg3Full)
def yieldCalc(rootFileName, crossSec, nSimu):
# Signal Significance Calculation
luminosity = 2.6 # in fb^{-1}
nSimu = 20 * 100000
nEvent = luminosity * crossSec
wt = nEvent / nSimu
# Read from corresponding signal and background chain
signal_Chain = TChain("varTree")
signal_SR1_Chain = TChain("varTree_SR1")
signal_SR2_Chain = TChain("varTree_SR2")
signal_SR3_Chain = TChain("varTree_SR3")
signal_Chain.Add("../" + rootFileName + ".root")
signal_SR1_Chain.Add("../" + rootFileName + ".root")
signal_SR2_Chain.Add("../" + rootFileName + ".root")
signal_SR3_Chain.Add("../" + rootFileName + ".root")
background_Chain = TChain("varTree")
background_Chain.Add("../background.root")
# Get the sample size
signal_SR1_SampleSize = signal_SR1_Chain.GetEntries()
signal_SR2_SampleSize = signal_SR2_Chain.GetEntries()
signal_SR3_SampleSize = signal_SR3_Chain.GetEntries()
background_SampleSize = background_Chain.GetEntries()
# Include 2d D0 plots
bins2D = np.array([0.01, 0.1, 0.2, 0.5, 1.0, 100.0, 1000.0])
nbins2D = 6
d02d = TH2D("d02d", "d02d", nbins2D, bins2D, nbins2D, bins2D)
d02d_SR1 = TH2D("d02d_SR1", "d02d_SR1", nbins2D, bins2D, nbins2D, bins2D)
d02d_SR2 = TH2D("d02d_SR2", "d02d_SR2", nbins2D, bins2D, nbins2D, bins2D)
d02d_SR3 = TH2D("d02d_SR3", "d02d_SR3", nbins2D, bins2D, nbins2D, bins2D)
signal_Chain.Draw("D0El:D0Mu>>d02d")
signal_SR1_Chain.Draw("D0El_SR1:D0Mu_SR1>>d02d_SR1")
signal_SR2_Chain.Draw("D0El_SR2:D0Mu_SR2>>d02d_SR2")
signal_SR3_Chain.Draw("D0El_SR3:D0Mu_SR3>>d02d_SR3")
d02d = gDirectory.Get("d02d")
d02d_SR1 = gDirectory.Get("d02d_SR1")
d02d_SR2 = gDirectory.Get("d02d_SR2")
d02d_SR3 = gDirectory.Get("d02d_SR3")
# Extract the variables to a numpy array
signal_SR1_Full = signal_SR1_Chain.AsMatrix()
signal_SR1_Full = np.transpose(signal_SR1_Full)
weight_SR1 = signal_SR1_Full[11:19]
signal_SR1_Full = signal_SR1_Full[2:11]
signal_SR1_Full = np.transpose(signal_SR1_Full)
#print(signal_SR1_Full.shape)
signal_SR2_Full = signal_SR2_Chain.AsMatrix()
signal_SR2_Full = np.transpose(signal_SR2_Full)
weight_SR2 = signal_SR2_Full[11:19]
signal_SR2_Full = signal_SR2_Full[2:11]
signal_SR2_Full = np.transpose(signal_SR2_Full)
#print(signal_SR2_Full.shape)
signal_SR3_Full = signal_SR3_Chain.AsMatrix()
signal_SR3_Full = np.transpose(signal_SR3_Full)
weight_SR3 = signal_SR3_Full[11:19]
signal_SR3_Full = signal_SR3_Full[2:11]
signal_SR3_Full = np.transpose(signal_SR3_Full)
#print(signal_SR3_Full.shape)
background_Full = background_Chain.AsMatrix()
#print(background_Full.shape)
# Decide the weight scheme
#wt_SR1 = weight_SR1[0]*weight_SR1[1] # CMS HEP Scheme
#wt_SR1 = weight_SR1[2]*weight_SR1[3] # Freya Scheme
#wt_SR1 = weight_SR1[4]*weight_SR1[5] # Nishita Scheme
wt_SR1 = weight_SR1[6] * weight_SR1[7] # Kamal Scheme
#wt_SR2 = weight_SR2[0]*weight_SR2[1]
#wt_SR2 = weight_SR2[2]*weight_SR2[3]
#wt_SR2 = weight_SR2[4]*weight_SR2[5]
wt_SR2 = weight_SR2[6] * weight_SR2[7]
#wt_SR3 = weight_SR3[0]*weight_SR3[1]
#wt_SR3 = weight_SR3[2]*weight_SR3[3]
#wt_SR3 = weight_SR3[4]*weight_SR3[5]
wt_SR3 = weight_SR3[6] * weight_SR3[7]
# Load the input data scaler
scaler = joblib.load("../Classifier/scaler.save")
# Load the model
loaded_model = m.load_model("../Classifier/simplePer.h5")
#loaded_model.summary()
# Scale the variables
signal_SR1_Scaled = scaler.transform(signal_SR1_Full)
signal_SR2_Scaled = scaler.transform(signal_SR2_Full)
signal_SR3_Scaled = scaler.transform(signal_SR3_Full)
background_Scaled = scaler.transform(background_Full)
# Prdict on the variables
signal_SR1_Predict = loaded_model.predict(signal_SR1_Scaled)
signal_SR2_Predict = loaded_model.predict(signal_SR2_Scaled)
signal_SR3_Predict = loaded_model.predict(signal_SR3_Scaled)
background_Predict = loaded_model.predict(background_Scaled)
# Obtain the signal probability
signal_SR1_SigProb = np.array(signal_SR1_Predict)[:, 0]
signal_SR2_SigProb = np.array(signal_SR2_Predict)[:, 0]
signal_SR3_SigProb = np.array(signal_SR3_Predict)[:, 0]
# Cut on the discriminator to calculate the yield
discCut = 0.0
signalYield_SR1 = wt * (signal_SR1_SigProb >= discCut).sum()
signalYield_SR2 = wt * (signal_SR2_SigProb >= discCut).sum()
signalYield_SR3 = wt * (signal_SR3_SigProb >= discCut).sum()
#print(rootFileName,"\t",round(signalYield_SR1,5),"\t",round(signalYield_SR2,5),"\t",round(signalYield_SR3,5))
# Discriminator Shape in ROOT plotting with proper weight
discFile = TFile("../" + rootFileName + "_wtK_Disc.root", "RECREATE")
nBins = 101
signal_SR1_histo = TH1D("SR1", "", nBins, 0, 1.01)
signal_SR2_histo = TH1D("SR2", "", nBins, 0, 1.01)
signal_SR3_histo = TH1D("SR3", "", nBins, 0, 1.01)
signal_SR1_wt_histo = TH1D("SR1_wt", "", nBins, 0, 1.01)
signal_SR2_wt_histo = TH1D("SR2_wt", "", nBins, 0, 1.01)
signal_SR3_wt_histo = TH1D("SR3_wt", "", nBins, 0, 1.01)
background_histo = TH1D("background", "", nBins, 0, 1.01)
signal_SR1_histo.Sumw2()
signal_SR2_histo.Sumw2()
signal_SR3_histo.Sumw2()
signal_SR1_wt_histo.Sumw2()
signal_SR2_wt_histo.Sumw2()
signal_SR3_wt_histo.Sumw2()
signal_SR1_histo.FillN(signal_SR1_Predict.shape[0],
(signal_SR1_Predict[:, 0]).astype(float),
np.ones(signal_SR1_Predict.shape[0]))
signal_SR2_histo.FillN(signal_SR2_Predict.shape[0],
(signal_SR2_Predict[:, 0]).astype(float),
np.ones(signal_SR2_Predict.shape[0]))
signal_SR3_histo.FillN(signal_SR3_Predict.shape[0],
(signal_SR3_Predict[:, 0]).astype(float),
np.ones(signal_SR3_Predict.shape[0]))
signal_SR1_wt_histo.FillN(signal_SR1_Predict.shape[0],
(signal_SR1_Predict[:, 0]).astype(float),
wt_SR1.astype(float))
signal_SR2_wt_histo.FillN(signal_SR2_Predict.shape[0],
(signal_SR2_Predict[:, 0]).astype(float),
wt_SR2.astype(float))
signal_SR3_wt_histo.FillN(signal_SR3_Predict.shape[0],
(signal_SR3_Predict[:, 0]).astype(float),
wt_SR3.astype(float))
background_histo.FillN(background_Predict.shape[0],
(background_Predict[:, 0]).astype(float),
np.ones(background_Predict.shape[0]))
# Print the yield
#print(rootFileName,
# "\t",round(signal_SR1_histo.Integral(),5),"\t",round(signal_SR1_wt_histo.Integral(),5),
# "\t",round(signal_SR2_histo.Integral(),5),"\t",round(signal_SR2_wt_histo.Integral(),5),
# "\t",round(signal_SR3_histo.Integral(),5),"\t",round(signal_SR3_wt_histo.Integral(),5))
signal_SR1_wt_histo.Scale(signalYield_SR1 / signal_SR1_histo.Integral())
signal_SR2_wt_histo.Scale(signalYield_SR2 / signal_SR2_histo.Integral())
signal_SR3_wt_histo.Scale(signalYield_SR3 / signal_SR3_histo.Integral())
signal_SR1_histo.Scale(signalYield_SR1 / signal_SR1_histo.Integral())
signal_SR2_histo.Scale(signalYield_SR2 / signal_SR2_histo.Integral())
signal_SR3_histo.Scale(signalYield_SR3 / signal_SR3_histo.Integral())
background_histo.Scale(1.0 / background_histo.Integral())
# Print the yield
#print(rootFileName,
# "\t",round(signal_SR1_histo.Integral(),5),"\t",round(signal_SR1_wt_histo.Integral(),5),
# "\t",round(signal_SR2_histo.Integral(),5),"\t",round(signal_SR2_wt_histo.Integral(),5),
# "\t",round(signal_SR3_histo.Integral(),5),"\t",round(signal_SR3_wt_histo.Integral(),5))
print(rootFileName, "\t", round(signal_SR1_wt_histo.Integral(), 5), "\t",
round(signal_SR2_wt_histo.Integral(), 5), "\t",
round(signal_SR3_wt_histo.Integral(), 5))
signal_SR1_histo.Write()
signal_SR2_histo.Write()
signal_SR3_histo.Write()
signal_SR1_wt_histo.Write()
signal_SR2_wt_histo.Write()
signal_SR3_wt_histo.Write()
background_histo.Write()
d02d.Write()
d02d_SR1.Write()
d02d_SR2.Write()
d02d_SR3.Write()
discFile.Close()
from ROOT import TH1D, TH2D, TCanvas
from ROOT import gDirectory, TGraph, TMultiGraph
import ROOT as rt
import os
import sys
sys.path.insert(0, os.path.abspath('/home/arsahasransu/Documents/SoftDisplacedLeptons/Classifier/'))
print("All classes initialized successfully!!!")
import plotBeautifier as pB
pB.trial_func("AR")
bkg_chain = TChain("varTree")
bkg_chain.Add("../background.root")
bkg_Full = bkg_chain.AsMatrix()
# Load the input data scaler
scaler = joblib.load("../Classifier/scaler.save")
# Load the model
loaded_model = m.load_model("../Classifier/simplePer.h5")
bkg_scaled = scaler.transform(bkg_Full)
bkg_predict = loaded_model.predict(bkg_scaled)
bkg_sigprob = np.array(bkg_predict)[:,0]
def discr_modify(discr, val):
modDiscr = discr
