12 if __name__ ==
"__main__":
13 from basf2
import conditions
15 conditions.testing_payloads = [
16 'localdb/database.txt'
19 variables = [
'p',
'pt',
'pz',
20 'daughter(0, p)',
'daughter(0, pz)',
'daughter(0, pt)',
21 'daughter(1, p)',
'daughter(1, pz)',
'daughter(1, pt)',
22 'daughter(2, p)',
'daughter(2, pz)',
'daughter(2, pt)',
23 'chiProb',
'dr',
'dz',
24 'daughter(0, dr)',
'daughter(1, dr)',
25 'daughter(0, dz)',
'daughter(1, dz)',
26 'daughter(0, chiProb)',
'daughter(1, chiProb)',
'daughter(2, chiProb)',
27 'daughter(0, kaonID)',
'daughter(0, pionID)',
28 'daughterInvariantMass(0, 1)',
'daughterInvariantMass(0, 2)',
'daughterInvariantMass(1, 2)']
30 general_options = basf2_mva.GeneralOptions()
31 general_options.m_datafiles = basf2_mva.vector(
"train.root")
32 general_options.m_treename =
"tree"
33 general_options.m_variables = basf2_mva.vector(*variables)
35 general_options.m_spectators = basf2_mva.vector(
'M')
36 general_options.m_target_variable =
"isSignal"
37 general_options.m_identifier =
"HepMLUBoost"
39 specific_options = basf2_mva.PythonOptions()
40 specific_options.m_steering_file =
'mva/examples/python/hep_ml_uboost.py'
44 specific_options.m_config = json.dumps({
'n_neighbors': 5,
'n_estimators': 5})
45 specific_options.m_framework =
'hep_ml'
47 training_start = time.time()
48 basf2_mva.teacher(general_options, specific_options)
49 training_stop = time.time()
50 training_time = training_stop - training_start
52 inference_start = time.time()
53 p, t = method.apply_expert(basf2_mva.vector(
"test.root"), general_options.m_treename)
54 inference_stop = time.time()
55 inference_time = inference_stop - inference_start
57 print(
"HepML", training_time, inference_time, auc)
59 subprocess.call(
'basf2_mva_evaluate.py -o latex.pdf -train train.root -data test.root -i HepMLUBoost', shell=
True)