I am following the instructions from this past topic, I paste below the relevant part of my code:
ROOT.gInterpreter.ProcessLine(f'''
TMVA::Experimental::RReader BDT1("{training_dir}/BDT1/weights/BDT1_{cuts}_BDT1.weights.xml");
TMVA::Experimental::RReader BDT2("{training_dir}/BDT2/weights/BDT2_{cuts}_BDT2.weights.xml");
TMVA::Experimental::RReader BDT3("{training_dir}/BDT3/weights/BDT3_{cuts}_BDT3.weights.xml");
computeBDT1 = TMVA::Experimental::Compute<13, float>(BDT1);
computeBDT2 = TMVA::Experimental::Compute<13, float>(BDT2);
computeBDT3 = TMVA::Experimental::Compute<13, float>(BDT3);
''')
df = df.Define('BDT1v', ROOT.computeBDT1, ROOT.BDT1.GetVariableNames())
df = df.Define('BDT2v', ROOT.computeBDT2, ROOT.BDT2.GetVariableNames())
df = df.Define('BDT3v', ROOT.computeBDT3, ROOT.BDT3.GetVariableNames())
df = df.Define('BDT1', 'BDT1v[0]')
df = df.Define('BDT2', 'BDT2v[0]')
df = df.Define('BDT3', 'BDT3v[0]')
df = df.Define('id1', 'idBDT==1')
df = df.Define('id2', 'idBDT==2')
df = df.Define('id3', 'idBDT==3')
df = df.Define('BDT', 'id1*BDT3 + id2*BDT1 + id3*BDT2')
df = df.Filter('BDT > -0.2', 'BDT')
What I see is that when running with multithreading the efficiency of the BDT cut increases, meaning that the script processes some events more than once.
Can you point me to the available thread-safe variants of the BDT classifier?
Is it possible to use it to read already existing weight files or should I rerun the training with this new model?
Thanks
Andrea