11 if __name__ ==
"__main__":
12 from basf2
import conditions
14 conditions.testing_payloads = [
15 'localdb/database.txt'
18 variables = [
'M',
'p',
'pt',
'pz',
19 'daughter(0, p)',
'daughter(0, pz)',
'daughter(0, pt)',
20 'daughter(1, p)',
'daughter(1, pz)',
'daughter(1, pt)',
21 'daughter(2, p)',
'daughter(2, pz)',
'daughter(2, pt)',
22 'chiProb',
'dr',
'dz',
23 'daughter(0, dr)',
'daughter(1, dr)',
24 'daughter(0, dz)',
'daughter(1, dz)',
25 'daughter(0, chiProb)',
'daughter(1, chiProb)',
'daughter(2, chiProb)',
26 'daughter(0, kaonID)',
'daughter(0, pionID)',
27 '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 =
"XGBoost"
33 general_options.m_variables = basf2_mva.vector(*variables)
34 general_options.m_target_variable =
"isSignal"
36 specific_options = basf2_mva.PythonOptions()
37 specific_options.m_steering_file =
'mva/examples/python/xgboost_default.py'
38 specific_options.m_framework =
"xgboost"
39 param = (
'{"max_depth": 3, "eta": 0.1, "silent": 1, "objective": "binary:logistic",'
40 '"subsample": 0.5, "nthread": 1, "nTrees": 100}')
41 specific_options.m_config = param
43 test_data = [
"test.root"] * 10
44 training_start = time.time()
45 basf2_mva.teacher(general_options, specific_options)
46 training_stop = time.time()
47 training_time = training_stop - training_start
49 inference_start = time.time()
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
54 print(
"XGBoost", training_time, inference_time, auc)