builtin_sideband_substraction.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
 
# Thomas Keck 2016
 
# The mva package has a builtin sideband substraction mechanism.
# If your data and mc do not match, you can use the MetaOptions to do a meta-training.
# Using 'm_use_sideband_substraction = True' the mva package will train
#  * signal
#     - from a user-defined signal region with positive weight
#     - from a user-defined negative signal region with negative weight
#  * against background
#     - from a user-defined background region with positive weight
#
# You have to define the sideband in one of the branches in the provided datafiles
# 1 = signal region
# 2 = background region
# 3 = negative signal region
# otherwise = not used in the training
#
# In addition you have to provide a MC file which contains the same variables,
# from which the number of signal events in the different regions can be estimated.
# This is the only information which is used from MC during the training.
 
# For the sideband substraction it is important that the used features
# are independent of the variable used to define the sidebands.
# Otherwise the classification quality will be poor.
 
import basf2_mva
from basf2 import *
from modularAnalysis import *
 
if __name__ == "__main__":
    variables = ['p', 'pt', 'pz', 'phi',
                 # 'daughter(0, p)', 'daughter(0, pz)', 'daughter(0, pt)', 'daughter(0, phi)',
                 # 'daughter(1, p)', 'daughter(1, pz)', 'daughter(1, pt)', 'daughter(1, phi)',
                 # 'daughter(2, p)', 'daughter(2, pz)', 'daughter(2, pt)', 'daughter(2, phi)',
                 'chiProb', 'dr', 'dz', 'dphi',
                 'daughter(0, dr)', 'daughter(1, dr)', 'daughter(0, dz)', 'daughter(1, dz)',
                 'daughter(0, dphi)', 'daughter(1, dphi)',
                 'daughter(0, chiProb)', 'daughter(1, chiProb)', 'daughter(2, chiProb)', 'daughter(2, M)',
                 'daughter(0, atcPIDBelle(3,2))', 'daughter(1, atcPIDBelle(3,2))',
                 # 'daughterAngle(0, 1)', 'daughterAngle(0, 2)', 'daughterAngle(1, 2)',
                 'daughter(2, daughter(0, E))', 'daughter(2, daughter(1, E))',
                 'daughter(2, daughter(0, clusterLAT))', 'daughter(2, daughter(1, clusterLAT))',
                 'daughter(2, daughter(0, clusterHighestE))', 'daughter(2, daughter(1, clusterHighestE))',
                 'daughter(2, daughter(0, clusterNHits))', 'daughter(2, daughter(1, clusterNHits))',
                 'daughter(2, daughter(0, clusterE9E25))', 'daughter(2, daughter(1, clusterE9E25))',
                 'daughter(2, daughter(0, minC2HDist))', 'daughter(2, daughter(1, minC2HDist))',
                 # 'daughterInvariantMass(1, 2)', 'daughterInvariantMass(0, 1)', 'daughterInvariantMass(0, 2)'
                 ]
 
    general_options = basf2_mva.GeneralOptions()
    general_options.m_datafiles = basf2_mva.vector("train_data.root")
    general_options.m_identifier = "MVASideband"
    general_options.m_treename = "tree"
    general_options.m_variables = basf2_mva.vector(*variables)
    general_options.m_target_variable = "isSignal"
 
    fastbdt_options = basf2_mva.FastBDTOptions()
 
    meta_options = basf2_mva.MetaOptions()
    meta_options.m_use_sideband_substraction = True
    meta_options.m_sideband_variable = 'sideband'
    meta_options.m_sideband_mc_files = basf2_mva.vector("train_mc.root")
 
    basf2_mva.teacher(general_options, fastbdt_options, meta_options)