B2A712-DeepContinuumSuppression_MVATrain.py

#!/usr/bin/env python3
 

 

 
import basf2 as b2
import basf2_mva
import subprocess
import json
import os
 
 
def choose_input_features(use_vertex_features=True, use_charge_and_ROE_features=False, use_continuum_features=1):
    """
    Function to return all names of input features.
    :param use_vertex_features: If Vertex info should be included.
    :param use_charge_and_ROE_features: If charge and ROE should be included as extra features(information already
        included in group structure). This option is only nevessary when using Relation Layers.
    :param use_continuum_features: Use old Continuum Features (0: No, 1: Yes, 2: Use only the old features)
    :return: Array of feature names
    """
    contVar = [
        'R2',
        'thrustBm',
        'thrustOm',
        'cosTBTO',
        'cosTBz',
        'KSFWVariables(pt_sum)',
        'KSFWVariables(mm2)',
        'KSFWVariables(hso00)',
        'KSFWVariables(hso02)',
        'KSFWVariables(hso04)',
        'KSFWVariables(hso10)',
        'KSFWVariables(hso12)',
        'KSFWVariables(hso14)',
        'KSFWVariables(hso20)',
        'KSFWVariables(hso22)',
        'KSFWVariables(hso24)',
        'KSFWVariables(hoo0)',
        'KSFWVariables(hoo1)',
        'KSFWVariables(hoo2)',
        'KSFWVariables(hoo3)',
        'KSFWVariables(hoo4)',
        'CleoConeCS(1)',
        'CleoConeCS(2)',
        'CleoConeCS(3)',
        'CleoConeCS(4)',
        'CleoConeCS(5)',
        'CleoConeCS(6)',
        'CleoConeCS(7)',
        'CleoConeCS(8)',
        'CleoConeCS(9)']
 
    if use_continuum_features == 2:
        return contVar
 
    basic_variables = ['p', 'phi', 'cosTheta', 'pErr', 'phiErr', 'cosThetaErr']
    vertex_variables = ['distance', 'dphi', 'dcosTheta']
 
    cluster_specific_variables = ['clusterNHits', 'clusterTiming', 'clusterE9E25', 'clusterReg']
    track_specific_variables = ['kaonID', 'electronID', 'muonID', 'protonID', 'pValue', 'nCDCHits']
 
    if use_charge_and_ROE_features:
        cluster_specific_variables += ['isInRestOfEvent']
        track_specific_variables += ['isInRestOfEvent', 'charge']
 
    cluster_specific_variables += ['thrustsig' + var for var in basic_variables]
    track_specific_variables += ['thrustsig' + var for var in basic_variables]
 
    if use_vertex_features:
        track_specific_variables += ['thrustsig' + var for var in vertex_variables]
 
    cluster_lists = ['Csig', 'Croe']
    track_lists = ['TPsig', 'TMsig', 'TProe', 'TMroe']
 
    variables = []
    for plist in track_lists:
        for rank in range(5):
            for var in track_specific_variables:
                variables.append(f'{var}_{plist}{rank}')
 
    for plist in cluster_lists:
        for rank in range(10):
            for var in cluster_specific_variables:
                variables.append(f'{var}_{plist}{rank}')
 
    if use_continuum_features:
        variables += contVar
 
    return variables
 
 
if __name__ == "__main__":
 
    if not os.getenv('BELLE2_EXAMPLES_DATA_DIR'):
        b2.B2FATAL("You need the example data installed. Run `b2install-data example` in terminal for it.")
 
    path = os.getenv('BELLE2_EXAMPLES_DATA_DIR')+'/mva/'
 
    train_data = path + 'DNN_train.root'
    test_data = path + 'DNN_test.root'
 
    general_options = basf2_mva.GeneralOptions()
    general_options.m_datafiles = basf2_mva.vector(train_data)
    general_options.m_treename = "tree"
    general_options.m_identifier = "Deep_Feed_Forward.xml"
    general_options.m_variables = basf2_mva.vector(*choose_input_features(True, False, 1))
    general_options.m_spectators = basf2_mva.vector('Mbc', 'DeltaZ')
    general_options.m_target_variable = "isNotContinuumEvent"
 
    specific_options = basf2_mva.PythonOptions()
    specific_options.m_framework = "keras"
    specific_options.m_steering_file = 'analysis/examples/mva/B2A714-DeepContinuumSuppression_MVAModel.py'
    specific_options.m_training_fraction = 0.9
 
    # These options are custom made in B2A714. You can also add your own parameters.
    # Try different options and compare them by handing multiple weightfiles in basf2_mva_evaluation.py
    keras_dic = {
        # If Relation layer should be used instead of Feed Forward.
        # Only works with choose_input_features(True, True, 1)
        'use_relation_layers': False,
        # The following options are for using Adversaries. To disable them leave lambda to zero.
        # See mva/examples/keras/adversary_network.py for details
        'lambda': 0,  # Use 500 as starting point to try the Adversaries out
        'number_bins': 10,
        'adversary_steps': 5}
    specific_options.m_config = json.dumps(keras_dic)
 
    # Train a MVA method and store the weightfile (Deep_Feed_Forward.xml) locally.
    basf2_mva.teacher(general_options, specific_options)
 
    # Evaluate training.
    subprocess.call('basf2_mva_evaluate.py '
                    ' -train ' + train_data +
                    ' -data ' + test_data +
                    ' -id ' + 'Deep_Feed_Forward.xml' +
                    ' --output qqbarSuppressionEvaluation.pdf' +
                    ' --fillnan',
                    shell=True
                    )
 
    # If you're only interested in the network output distribution, then
    # comment these in to apply the trained methods on train and test sample
    #
    # basf2_mva.expert(basf2_mva.vector('Deep_Feed_Forward.xml'),
    #                                basf2_mva.vector(train_data), 'tree', 'MVAExpert_train.root')
    # basf2_mva.expert(basf2_mva.vector('Deep_Feed_Forward.xml'),
    #                                basf2_mva.vector(test_data), 'tree', 'MVAExpert_test.root')