Belle II Software  release-05-01-25
purity_transformation.py
1 #!/usr/bin/env python3
2 # -*- coding: utf-8 -*-
3 
4 # Thomas Keck 2016
5 
6 import basf2_mva
7 import basf2_mva_util
8 import time
9 
10 if __name__ == "__main__":
11  from basf2 import conditions
12  # NOTE: do not use testing payloads in production! Any results obtained like this WILL NOT BE PUBLISHED
13  conditions.testing_payloads = [
14  'localdb/database.txt'
15  ]
16 
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)']
27 
28  # Train a MVA method and directly upload it to the database
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"
35 
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
42 
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
50 
51  stats = []
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
59  method = basf2_mva_util.Method(general_options.m_identifier)
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))
67 
68  for l in stats:
69  print(*l)
basf2_mva_util.calculate_roc_auc
def calculate_roc_auc(p, t)
Definition: basf2_mva_util.py:39
basf2_mva_util.Method
Definition: basf2_mva_util.py:81