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 =
"MVADatabaseIdentifier"
33 general_options.m_variables = basf2_mva.vector(*variables)
34 general_options.m_target_variable =
"isSignal"
36 fastbdt_options = basf2_mva.FastBDTOptions()
37 fastbdt_options.m_nTrees = 100
38 fastbdt_options.m_nCuts = 10
39 fastbdt_options.m_nLevels = 3
40 fastbdt_options.m_shrinkage = 0.2
41 fastbdt_options.m_randRatio = 0.5
43 fastbdt_pt_options = basf2_mva.FastBDTOptions()
44 fastbdt_pt_options.m_nTrees = 100
45 fastbdt_pt_options.m_nCuts = 10
46 fastbdt_pt_options.m_nLevels = 3
47 fastbdt_pt_options.m_shrinkage = 0.2
48 fastbdt_pt_options.m_randRatio = 0.5
49 fastbdt_pt_options.m_purityTransformation =
True
52 test_data = [
"validation.root"]
53 for label, options
in [(
"FastBDT", fastbdt_options), (
"FastBDT_PT", fastbdt_pt_options)]:
54 training_start = time.time()
55 general_options.m_identifier = label
56 basf2_mva.teacher(general_options, options)
57 training_stop = time.time()
58 training_time = training_stop - training_start
60 inference_start = time.time()
61 p, t = method.apply_expert(basf2_mva.vector(*test_data), general_options.m_treename)
62 inference_stop = time.time()
63 inference_time = inference_stop - inference_start
65 print(label, training_time, inference_time, auc)
66 stats.append((label, training_time, inference_time, auc))