input_geneticAlgorithm.py

#name of input root file, relative to the directory of this script
root_file_name = 'ttW_trainingData_new.root'

#names of trees that contain signal and background events 
signal_tree_name = 'signalTree'
background_tree_name = 'bkgTree'

#list of variables to be used in training (corresponding to branches in the tree)
list_of_branches = [
    '_lepPt1', '_lepEta1', '_lepPhi1',
    '_lepPt2', '_lepEta2', '_lepPhi2',
    '_jetPt1', '_jetEta1', '_jetPhi1', '_jetCSV1',
    '_jetPt2', '_jetEta2', '_jetPhi2', '_jetCSV2',
    '_jetPt3', '_jetEta3', '_jetPhi3', '_jetCSV3',
    '_jetPt4', '_jetEta4', '_jetPhi4', '_jetCSV4',
    '_jetPt5', '_jetEta5', '_jetPhi5', '_jetCSV5',
    '_jetPt6', '_jetEta6', '_jetPhi6', '_jetCSV6',
    '_metPt1', '_metPhi1'
    ]

#branch that indicates the event weights 
weight_branch = '_weight'

#use only positive weights in training or not 
only_positive_weights = True

#validation and test fractions
validation_fraction = 0.4
test_fraction = 0.2

#use genetic algorithm or grid-scan for optimization
use_genetic_algorithm = False

number_of_threads = 1
high_memory = False

if use_genetic_algorithm:

    population_size = 200

    #ranges of neural network parameters for the genetic algorithm to scan
    parameter_ranges = {
        'num_hidden_layers' : [2, 3, 4, 5, 6, 7, 8, 9, 10],
        'units_per_layer' : list( range(16, 1024) ),
        'optimizer' : ['RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam'],
        'learning_rate' : (0.01, 1),
        'learning_rate_decay' : (0.9, 1),
        'dropout_first' : (False, True),
        'dropout_all' : (False, True), 
        'dropout_rate' : (0, 0.5),

        #advice not to change these parameters
        'number_of_epochs' : [20], 
        'batch_size' : list( range( 32, 256 ) )
    }

else:
    parameter_values = {
        'num_hidden_layers' : [2, 3, 4, 5, 6, 7, 8, 9, 10],
        'units_per_layer' : [16, 32, 64, 128, 256, 512],
        'optimizer' : ['Nadam'],
        'learning_rate' : [0.1, 1, 0.01],
        'learning_rate_decay' : [1, 0.99, 0.95],
        'dropout_first' : [False, True],
        'dropout_all' : [False, True],
        'dropout_rate' : [0.5, 0.3],

        #advice not to change these parameters
        'number_of_epochs' : [5],
        'batch_size' : [128]
    }