tmva100_DataPreparation.py File Reference

Namespaces
namespace	tmva100_DataPreparation

Detailed Description

This tutorial illustrates how to prepare ROOT datasets to be nicely readable by most machine learning methods.

This requires filtering the initial complex datasets and writing the data in a flat format.

 
import ROOT
 
 
def filter_events(df):
    """
    Reduce initial dataset to only events which shall be used for training
    """
    return df.Filter("nElectron>=2 && nMuon>=2", "At least two electrons and two muons")
 
 
def define_variables(df):
    """
    Define the variables which shall be used for training
    """
    return df.Define("Muon_pt_1", "Muon_pt[0]")\
             .Define("Muon_pt_2", "Muon_pt[1]")\
             .Define("Electron_pt_1", "Electron_pt[0]")\
             .Define("Electron_pt_2", "Electron_pt[1]")
 
 
variables = ["Muon_pt_1", "Muon_pt_2", "Electron_pt_1", "Electron_pt_2"]
 
 
if __name__ == "__main__":
    for filename, label in [["SMHiggsToZZTo4L.root", "signal"], ["ZZTo2e2mu.root", "background"]]:
        print(">>> Extract the training and testing events for {} from the {} dataset.".format(
            label, filename))
 
        # Load dataset, filter the required events and define the training variables
        filepath = "root://eospublic.cern.ch//eos/root-eos/cms_opendata_2012_nanoaod/" + filename
        df = ROOT.RDataFrame("Events", filepath)
        df = filter_events(df)
        df = define_variables(df)
 
        # Book cutflow report
        report = df.Report()
 
        # Split dataset by event number for training and testing
        columns = ROOT.std.vector["string"](variables)
        df.Filter("event % 2 == 0", "Select events with even event number for training")\
          .Snapshot("Events", "train_" + label + ".root", columns)
        df.Filter("event % 2 == 1", "Select events with odd event number for training")\
          .Snapshot("Events", "test_" + label + ".root", columns)
 
        # Print cutflow report
        report.Print()

At least two electrons and two muons: pass=45352      all=299973     -- eff=15.12 % cumulative eff=15.12 %
At least two electrons and two muons: pass=262776     all=1497445    -- eff=17.55 % cumulative eff=17.55 %
>>> Extract the training and testing events for signal from the SMHiggsToZZTo4L.root dataset.
>>> Extract the training and testing events for background from the ZZTo2e2mu.root dataset.

Date

August 2019

Author

Stefan Wunsch

Definition in file tmva100_DataPreparation.py.