10 if __name__ ==
"__main__":
11 from basf2
import conditions
13 conditions.testing_payloads = [
14 'localdb/database.txt'
17 variables = [
'M',
'p',
'pt',
'pz',
18 'daughter(0, p)',
'daughter(0, pz)',
'daughter(0, pt)',
19 'daughter(1, p)',
'daughter(1, pz)',
'daughter(1, pt)',
20 'daughter(2, p)',
'daughter(2, pz)',
'daughter(2, pt)',
21 'chiProb',
'dr',
'dz',
22 'daughter(0, dr)',
'daughter(1, dr)',
23 'daughter(0, dz)',
'daughter(1, dz)',
24 'daughter(0, chiProb)',
'daughter(1, chiProb)',
'daughter(2, chiProb)',
25 'daughter(0, kaonID)',
'daughter(0, pionID)',
26 'daughterInvariantMass(0, 1)',
'daughterInvariantMass(0, 2)',
'daughterInvariantMass(1, 2)']
29 general_options = basf2_mva.GeneralOptions()
30 general_options.m_datafiles = basf2_mva.vector(
"train.root")
31 general_options.m_treename =
"tree"
32 general_options.m_identifier =
"TMVA"
33 general_options.m_variables = basf2_mva.vector(*variables)
34 general_options.m_target_variable =
"isSignal"
36 tmva_nn_options = basf2_mva.TMVAOptionsClassification()
37 tmva_nn_options.m_type =
"MLP"
38 tmva_nn_options.m_method =
"MLP"
39 tmva_nn_options.m_config = (
"H:!V:CreateMVAPdfs:VarTransform=N:NCycles=10:HiddenLayers=N+1:TrainingMethod=BFGS")
41 training_start = time.time()
42 basf2_mva.teacher(general_options, tmva_nn_options)
43 training_stop = time.time()
45 training_time = training_stop - training_start
48 inference_start = time.time()
49 test_data = [
"test.root"] * 10
50 p, t = method.apply_expert(basf2_mva.vector(*test_data), general_options.m_treename)
51 inference_stop = time.time()
52 inference_time = inference_stop - inference_start
55 print(
"TMVA", training_time, inference_time, auc)