11 def get_model(number_of_features, number_of_spectators, number_of_events, training_fraction, parameters):
13 Create SKLearn classifier and store it in a State object
15 from sklearn.neural_network
import MLPClassifier
17 if isinstance(parameters, collections.Mapping):
18 clf = MLPClassifier(**parameters)
19 elif isinstance(parameters, collections.Sequence):
20 clf = MLPClassifier(*parameters)
28 Merge received data together and fit estimator.
29 Neural network do not support weights at the moment (slearn 0.18.1).
30 So these are ignored here!
32 state.estimator = state.estimator.fit(np.vstack(state.X), np.hstack(state.y))
33 return state.estimator
36 if __name__ ==
"__main__":
37 from basf2
import conditions
39 conditions.testing_payloads = [
40 'localdb/database.txt'
43 variables = [
'M',
'p',
'pt',
'pz',
44 'daughter(0, p)',
'daughter(0, pz)',
'daughter(0, pt)',
45 'daughter(1, p)',
'daughter(1, pz)',
'daughter(1, pt)',
46 'daughter(2, p)',
'daughter(2, pz)',
'daughter(2, pt)',
47 'chiProb',
'dr',
'dz',
48 'daughter(0, dr)',
'daughter(1, dr)',
49 'daughter(0, dz)',
'daughter(1, dz)',
50 'daughter(0, chiProb)',
'daughter(1, chiProb)',
'daughter(2, chiProb)',
51 'daughter(0, kaonID)',
'daughter(0, pionID)',
52 'daughterInvariantMass(0, 1)',
'daughterInvariantMass(0, 2)',
'daughterInvariantMass(1, 2)']
55 general_options = basf2_mva.GeneralOptions()
56 general_options.m_datafiles = basf2_mva.vector(
"train.root")
57 general_options.m_treename =
"tree"
58 general_options.m_identifier =
"SKLearn-NN"
59 general_options.m_variables = basf2_mva.vector(*variables)
60 general_options.m_target_variable =
"isSignal"
62 sklearn_nn_options = basf2_mva.PythonOptions()
63 sklearn_nn_options.m_framework =
"sklearn"
64 sklearn_nn_options.m_steering_file =
'mva/examples/python/sklearn_mlpclassifier.py'
65 param =
'{"hidden_layer_sizes": [29], "activation": "logistic", "max_iter": 100, "solver": "adam", "batch_size": 100}'
66 sklearn_nn_options.m_config = param
67 sklearn_nn_options.m_normalize =
True
69 test_data = [
"test.root"] * 10
70 training_start = time.time()
71 basf2_mva.teacher(general_options, sklearn_nn_options)
72 training_stop = time.time()
73 training_time = training_stop - training_start
75 inference_start = time.time()
76 p, t = method.apply_expert(basf2_mva.vector(*test_data), general_options.m_treename)
77 inference_stop = time.time()
78 inference_time = inference_stop - inference_start
80 print(
"SKLearn", training_time, inference_time, auc)