def Evaluate(set):
global signalYield, signalSample, backgroundYield, backgroundSample
# Reading variable list
variables = open('%s/inputvars.txt' % set['directory']).readlines()
variables = [variable.rstrip() for variable in variables]
# Setting the number of input and output in the neat config file
file = open ('%s/neat.config' % set['directory'])
template = string.Template(file.read())
file.close()
file = open ('%s/neat.config' % set['directory'], 'w')
file.write(template.safe_substitute(input_nodes = len(variables), output_nodes = 1))
file.close()
# Read neat configuration file
config.load('%s/neat.config' % set['directory'])
print('Training: Reading signal files')
# Signal topovars
signals = TopovarReader(variables)
# Adding signal files
for sample in Common.TrainingSignals:
signals.add('%s/%s/%s.root' % (
Common.SampleLocation, Common.TrainingSample, Common.filename(set, sample)
)
)
# Creating a variable normalizer
normalizer = VariableNormalizer(variables)
# Saving the population in buffer
signalSample = signals.sample(compress = True)
# Adding the sample to the normalizer
normalizer.add(signalSample)
# Compute the total weight
signalYield = normalizer.getTotalWeight()
print ('Training: Reading background files')
# Background topovars
backgrounds = TopovarReader(variables)
# Adding background files
for sample in Common.TrainingBackgrounds:
backgrounds.add('%s/%s/%s.root' % (
Common.SampleLocation, Common.TrainingSample, Common.filename(set, sample)
)
)
# Saving the population in buffer
backgroundSample = backgrounds.sample(compress = True)
# Adding the sample to the normalizer
normalizer.add(backgroundSample)
# Compute the total weight
backgroundYield = normalizer.getTotalWeight() - signalYield
# Reporting the normalization
normalizer.report()
# Normalization of the variables
normalizer.normalizeSample(signalSample)
normalizer.normalizeSample(backgroundSample)
# NEAT training
chromosome.node_gene_type = genome.NodeGene
population.Population.evaluate = FitnessFunctionWrapper
pop = population.Population()
pop.epoch(int(set['number_generations']), report=True, save_best=False, checkpoint_interval = None)
winner = pop.stats[0][-1]
print 'Training: Number of evaluations: %d' % winner.id
print 'Training: Best NN fitness: %0.2f' % winner.fitness
# Save the best network
file = open('%s/winner.dat' % set['directory'], 'w')
pickle.dump(winner, file)
file.close()
# Save the best network
file = open('%s/winner-fitness.txt' % set['directory'], 'w')
file.write('%f' % winner.fitness)
file.close()
def Evaluate(set):
global signalYield, signalSample, backgroundYield, backgroundSample
# Reading variable list
variables = open('%s/inputvars.txt' % set['directory']).readlines()
variables = [variable.rstrip() for variable in variables]
# Setting the number of input and output in the neat config file
file = open('%s/neat.config' % set['directory'])
template = string.Template(file.read())
file.close()
file = open('%s/neat.config' % set['directory'], 'w')
file.write(
template.safe_substitute(input_nodes=len(variables), output_nodes=1))
file.close()
# Read neat configuration file
config.load('%s/neat.config' % set['directory'])
print('Training: Reading signal files')
# Signal topovars
signals = TopovarReader(variables)
# Adding signal files
for sample in Common.TrainingSignals:
signals.add('%s/%s/%s.root' %
(Common.SampleLocation, Common.TrainingSample,
Common.filename(set, sample)))
# Creating a variable normalizer
normalizer = VariableNormalizer(variables)
# Saving the population in buffer
signalSample = signals.sample(compress=True)
# Adding the sample to the normalizer
normalizer.add(signalSample)
# Compute the total weight
signalYield = normalizer.getTotalWeight()
print('Training: Reading background files')
# Background topovars
backgrounds = TopovarReader(variables)
# Adding background files
for sample in Common.TrainingBackgrounds:
backgrounds.add('%s/%s/%s.root' %
(Common.SampleLocation, Common.TrainingSample,
Common.filename(set, sample)))
# Saving the population in buffer
backgroundSample = backgrounds.sample(compress=True)
# Adding the sample to the normalizer
normalizer.add(backgroundSample)
# Compute the total weight
backgroundYield = normalizer.getTotalWeight() - signalYield
# Reporting the normalization
normalizer.report()
# Normalization of the variables
normalizer.normalizeSample(signalSample)
normalizer.normalizeSample(backgroundSample)
# NEAT training
chromosome.node_gene_type = genome.NodeGene
population.Population.evaluate = FitnessFunctionWrapper
pop = population.Population()
pop.epoch(int(set['number_generations']),
report=True,
save_best=False,
checkpoint_interval=None)
winner = pop.stats[0][-1]
print 'Training: Number of evaluations: %d' % winner.id
print 'Training: Best NN fitness: %0.2f' % winner.fitness
# Save the best network
file = open('%s/winner.dat' % set['directory'], 'w')
pickle.dump(winner, file)
file.close()
# Save the best network
file = open('%s/winner-fitness.txt' % set['directory'], 'w')
file.write('%f' % winner.fitness)
file.close()
