def __call__(self, population, signalYield, signalSample, backgroundYield,
backgroundSample):
# Loop over the chromosomes in the population
for genome in population:
# Get a nn describe by the chromosome (feed foreward)
net = nn.create_ffphenotype(genome)
error = 0.0
# Loop over the events
for event in signalSample:
# Extract variables and weight
variables = event[1:]
weight = event[0] / signalYield
# Not strictly necessary in feedforward nets
net.flush()
# Computing the error
error = error + weight * (
1 - net.sactivate(variables)[0])**self.norm
# Loop over the events
for event in backgroundSample:
# Extract variables and weight
variables = event[1:]
weight = event[0] / backgroundYield
# Not strictly necessary in feedforward nets
net.flush()
# Computing the error
error = error + weight * (
net.sactivate(variables)[0])**self.norm
# Set the fitness value to the chomosome
genome.fitness = 1 - Math.pow(error / 2, 1. / self.norm)
def __call__(self, population, signalYield, signalSample, backgroundYield, backgroundSample):
# Loop over the chromosomes in the population
for genome in population:
# Get a nn describe by the chromosome (feed foreward)
net = nn.create_ffphenotype(genome)
error = 0.0
# Loop over the events
for event in signalSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]/signalYield
# Not strictly necessary in feedforward nets
net.flush()
# Computing the error
error = error + weight*(1 - net.sactivate(variables)[0])**self.norm
# Loop over the events
for event in backgroundSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]/backgroundYield
# Not strictly necessary in feedforward nets
net.flush()
# Computing the error
error = error + weight*(net.sactivate(variables)[0])**self.norm
# Set the fitness value to the chomosome
genome.fitness = 1 - Math.pow(error/2, 1./self.norm)
def __call__(self, population, signalYield, signalSample, backgroundYield,
backgroundSample):
# Histogram holders
signalHistogram = TH1F('signalHistogram', 'signal', self.nbins, 0, 1)
backgroundHistogram = TH1F('backgroundHistogram', 'background',
self.nbins, 0, 1)
# Loop over the chromosomes in the population
for genome in population:
# Reset histograms
signalHistogram.Reset()
backgroundHistogram.Reset()
# Get a nn describe by the chromosome (feed foreward)
net = nn.create_ffphenotype(genome)
# Loop over the events creating signal histogram
for event in signalSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
signalHistogram.Fill(output, weight)
# Loop over the events creating background histogram
for event in backgroundSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
backgroundHistogram.Fill(output, weight)
fitness = 0
# Computing fitness
for bin in xrange(1, signalHistogram.GetNbinsX() + 1):
signal = signalHistogram.GetBinContent(bin)
background = backgroundHistogram.GetBinContent(bin)
total = signal + background
if total > 0:
fitness = fitness + signal**2 / (signal + background)
# Adding fitness to genome
genome.fitness = Math.sqrt(fitness)
def __call__(self, population, signalYield, signalSample, backgroundYield, backgroundSample):
# Histogram holders
signalHistogram = TH1F('signalHistogram', 'signal', self.nbins, 0, 1)
backgroundHistogram = TH1F('backgroundHistogram', 'background', self.nbins, 0, 1)
# Loop over the chromosomes in the population
for genome in population:
# Reset histograms
signalHistogram.Reset()
backgroundHistogram.Reset()
# Get a nn describe by the chromosome (feed foreward)
net = nn.create_ffphenotype(genome)
# Loop over the events creating signal histogram
for event in signalSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
signalHistogram.Fill(output, weight)
# Loop over the events creating background histogram
for event in backgroundSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
backgroundHistogram.Fill(output, weight)
fitness = 0
# Computing fitness
for bin in xrange(1,signalHistogram.GetNbinsX()+1):
signal = signalHistogram.GetBinContent(bin)
background = backgroundHistogram.GetBinContent(bin)
total = signal + background
if total > 0:
fitness = fitness + signal**2/(signal+background)
# Adding fitness to genome
genome.fitness = Math.sqrt(fitness)
def eval_fitness(population):
for chromo in population:
net = nn.create_ffphenotype(chromo)
error = 0.0
#error_stanley = 0.0
for i, inputs in enumerate(INPUTS):
net.flush() # not strictly necessary in feedforward nets
output = net.sactivate(inputs) # serial activation
error += (output[0] - OUTPUTS[i])**2
#error_stanley += math.fabs(output[0] - OUTPUTS[i])
#chromo.fitness = (4.0 - error_stanley)**2 # (Stanley p. 43)
chromo.fitness = 1 - math.sqrt(error/len(OUTPUTS))
def eval_fitness(population):
for chromo in population:
net = nn.create_ffphenotype(chromo)
error = 0.0
#error_stanley = 0.0
for i, inputs in enumerate(INPUTS):
net.flush() # not strictly necessary in feedforward nets
output = net.sactivate(inputs) # serial activation
error += (output[0] - OUTPUTS[i])**2
#error_stanley += math.fabs(output[0] - OUTPUTS[i])
#chromo.fitness = (4.0 - error_stanley)**2 # (Stanley p. 43)
chromo.fitness = 1 - math.sqrt(error/len(OUTPUTS))
#chromo.fitness = (4.0 - error_stanley)**2 # (Stanley p. 43)
chromo.fitness = 1 - math.sqrt(error/len(OUTPUTS))
population.Population.evaluate = eval_fitness
pop = population.Population()
pop.epoch(300, report=True, save_best=False)
winner = pop.stats[0][-1]
print 'Number of evaluations: %d' %winner.id
# Visualize the winner network (requires PyDot)
visualize.draw_net(winner) # best chromosome
# Plots the evolution of the best/average fitness (requires Biggles)
visualize.plot_stats(pop.stats)
# Visualizes speciation
visualize.plot_species(pop.species_log)
# Let's check if it's really solved the problem
print '\nBest network output:'
brain = nn.create_ffphenotype(winner)
for i, inputs in enumerate(INPUTS):
output = brain.sactivate(inputs) # serial activation
print "%1.5f \t %1.5f" %(OUTPUTS[i], output[0])
# saves the winner
#file = open('winner_chromosome', 'w')
#pickle.dump(winner, file)
#file.close()
def __call__(self, population, signalYield, signalSample, backgroundYield,
backgroundSample):
# Histogram holders
signalHistogram = TH1F('signalHistogram', 'signal', self.nbins, 0, 1)
backgroundHistogram = TH1F('backgroundHistogram', 'background',
self.nbins, 0, 1)
# Loop over the chromosomes in the population
for genome in population:
# Reset histograms
signalHistogram.Reset()
backgroundHistogram.Reset()
# Get a nn describe by the chromosome (feed foreward)
net = nn.create_ffphenotype(genome)
# Loop over the events creating signal histogram
for event in signalSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
signalHistogram.Fill(output, weight)
# Signal overall normalization scale
signalScale = signalYield / len(signalSample)
# Loop over the events creating background histogram
for event in backgroundSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
backgroundHistogram.Fill(output, weight)
# Background overall normalization scale
backgroundScale = backgroundYield / len(backgroundSample)
# Random generators
rcount = TRandom3(int(random.uniform(0, 65535)))
rsignal = TRandom3(int(random.uniform(0, 65535)))
rbackground = TRandom3(int(random.uniform(0, 65535)))
zscore = 0.
sqweight = 0.
# Computing weighted z-score
for bin in xrange(1, signalHistogram.GetNbinsX() + 1):
newz = 0.
oldz = 0.
# Computing the zvalue per bin
for point in xrange(1, self.mpoints + 1):
# Sample background
background = Math.gamma_quantile(
rbackground.Uniform(),
(backgroundHistogram.GetBinContent(bin) /
backgroundScale) + 1., backgroundScale)
# Background larger that zero
if background > 0.:
# Sampling signal
signal = Math.gamma_quantile(
rsignal.Uniform(),
(signalHistogram.GetBinContent(bin) / signalScale)
+ 1., signalScale)
# Sampling count
count = rcount.Poisson(signal + background)
# Computing pvalue
pvalue = Math.poisson_cdf_c(
count, background) + Math.poisson_pdf(
count, background)
# Computing zvalue
zvalue = self.minz
if pvalue < 1.0:
zvalue = Math.normal_quantile_c(pvalue, 1)
# zvalue iterative average
newz = (zvalue + (point - 1) * oldz) / point
# Computing relative difference
error = math.fabs((newz - oldz) / newz)
# Convergency criteria
if error < self.error: break
# Updating oldz
oldz = newz
if point == self.mpoints:
self.message(
'Warning reach maximum number of integration %s points.'
% point)
weight = self.weight(signalHistogram.GetBinCenter(bin))
zscore = zscore + weight * newz
sqweight = sqweight + weight**2
# Fitness function is zscore transform back to 1 - pvalue
# Set the fitness value to the chomosome
genome.fitness = 1. - Math.normal_cdf_c(
zscore / Math.sqrt(sqweight))
def __call__(self, population, signalYield, signalSample, backgroundYield, backgroundSample):
# Histogram holders
signalHistogram = TH1F('signalHistogram', 'signal', self.nbins, 0, 1)
backgroundHistogram = TH1F('backgroundHistogram', 'background', self.nbins, 0, 1)
# Loop over the chromosomes in the population
for genome in population:
# Reset histograms
signalHistogram.Reset()
backgroundHistogram.Reset()
# Get a nn describe by the chromosome (feed foreward)
net = nn.create_ffphenotype(genome)
# Loop over the events creating signal histogram
for event in signalSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
signalHistogram.Fill(output, weight)
# Signal overall normalization scale
signalScale = signalYield/len(signalSample)
# Loop over the events creating background histogram
for event in backgroundSample:
# Extract variables and weight
variables = event[1:]
weight = event[0]
# Not strictly necessary in feedforward nets
net.flush()
# Net output
output = net.sactivate(variables)[0]
# Filling histograms
backgroundHistogram.Fill(output, weight)
# Background overall normalization scale
backgroundScale = backgroundYield/len(backgroundSample)
# Random generators
rcount = TRandom3(int(random.uniform(0,65535)))
rsignal = TRandom3(int(random.uniform(0,65535)))
rbackground = TRandom3(int(random.uniform(0,65535)))
zscore = 0.; sqweight = 0.
# Computing weighted z-score
for bin in xrange(1,signalHistogram.GetNbinsX()+1):
newz = 0.; oldz = 0.
# Computing the zvalue per bin
for point in xrange(1,self.mpoints+1):
# Sample background
background = Math.gamma_quantile(rbackground.Uniform(),
(backgroundHistogram.GetBinContent(bin)/backgroundScale)+1., backgroundScale
)
# Background larger that zero
if background > 0.:
# Sampling signal
signal = Math.gamma_quantile(rsignal.Uniform(),
(signalHistogram.GetBinContent(bin)/signalScale)+1., signalScale
)
# Sampling count
count = rcount.Poisson(signal+background)
# Computing pvalue
pvalue = Math.poisson_cdf_c(count,background) + Math.poisson_pdf(count,background)
# Computing zvalue
zvalue = self.minz
if pvalue < 1.0: zvalue = Math.normal_quantile_c(pvalue,1)
# zvalue iterative average
newz = (zvalue + (point - 1)*oldz)/point
# Computing relative difference
error = math.fabs((newz - oldz)/newz)
# Convergency criteria
if error < self.error: break
# Updating oldz
oldz = newz
if point == self.mpoints:
self.message('Warning reach maximum number of integration %s points.' % point)
weight = self.weight(signalHistogram.GetBinCenter(bin))
zscore = zscore + weight * newz
sqweight = sqweight + weight**2
# Fitness function is zscore transform back to 1 - pvalue
# Set the fitness value to the chomosome
genome.fitness = 1. - Math.normal_cdf_c(zscore/Math.sqrt(sqweight))
#chromo.fitness = (4.0 - error_stanley)**2 # (Stanley p. 43)
chromo.fitness = 1 - math.sqrt(error/len(OUTPUTS))
population.Population.evaluate = eval_fitness
pop = population.Population()
pop.epoch(300, report=True, save_best=False)
winner = pop.stats[0][-1]
print('Number of evaluations: %d' %winner.id)
# Visualize the winner network (requires PyDot)
#visualize.draw_net(winner) # best chromosome
# Plots the evolution of the best/average fitness (requires Biggles)
#visualize.plot_stats(pop.stats)
# Visualizes speciation
#visualize.plot_species(pop.species_log)
# Let's check if it's really solved the problem
print('\nBest network output:')
brain = nn.create_ffphenotype(winner)
for i, inputs in enumerate(INPUTS):
output = brain.sactivate(inputs) # serial activation
print("%1.5f \t %1.5f" %(OUTPUTS[i], output[0]))
# saves the winner
#file = open('winner_chromosome', 'w')
#pickle.dump(winner, file)
#file.close()
# Setting the indir directory
indir = '%s/scratch/%s' % (
Common.NeatDirectory, input
)
# Get channel winner network
winner = pickle.load(open('%s/winner.info' % indir))
# Collect the list of variables
variables[key] = winner['variables']
# Collect the net
net = pickle.load(open('%s/%s/winner.dat' % (indir, winner['training'])))
# Create phenotype from genome
nets[key] = nn.create_ffphenotype(net)
# Creating a variable normalizers
normalizers[key] = VariableNormalizer(winner['variables'], winner['aves'], winner['stds'])
# Create a topowriter for adding neat outputs
topovars = None
if len(variables) > 1:
topovars = TopovarWriter(options.output, [], options.input)
else:
topovars = TopovarWriter(options.output, variables.itervalues().next(), options.input)
# Add neat outputs
for key in nets:
topovars.addVariable(key, 'double')
