def one_run(evaluator, config, frequencies):
'''
Performs a single run of the given configuration. Returns a dictionary
containing results.
Parameters:
- ``evaluator``: An object with the function get_fitness that takes an
individual and returns its fitness value
- ``config``: A dictionary containing all of the configuration information
required to perform a experimental run, including:
- All information required to initialize an individual.
- All information required to run ``evolution.generate``.
- ``verbose``: Boolean value for if extra runtime information should
be displayed.
- ``max_evals``: The maximum number of evaluations allowed before
termination.
- ``max_fitness``: The fitness required to cause a "successful"
termination.
- ``frequencies``: Dictionary used to return information about how often
individuals of different lengths are evolved.
'''
best = Individual(**config)
last_improved = -1
generator = enumerate(generate(config, frequencies))
for evals, individual in generator:
individual.fitness = evaluator.get_fitness(individual)
if best < individual:
best = individual
last_improved = evals
if config['verbose']:
print '\t', last_improved, best.fitness, len(best.active)
if (evals >= config['max_evals']
or best.fitness >= config['max_fitness']):
break
if config['verbose']:
print "Best Found"
best.show_active()
return {
'fitness': best.fitness,
'evals': evals,
'success': best.fitness >= config['max_fitness'],
'phenotype': len(best.active)
}
def one_run(evaluator, config, frequencies):
'''
Performs a single run of the given configuration. Returns a dictionary
containing results.
Parameters:
- ``evaluator``: An object with the function get_fitness that takes an
individual and returns its fitness value
- ``config``: A dictionary containing all of the configuration information
required to perform a experimental run, including:
- All information required to initialize an individual.
- All information required to run ``evolution.generate``.
- ``verbose``: Boolean value for if extra runtime information should
be displayed.
- ``max_evals``: The maximum number of evaluations allowed before
termination.
- ``max_fitness``: The fitness required to cause a "successful"
termination.
- ``frequencies``: Dictionary used to return information about how often
individuals of different lengths are evolved.
'''
best = Individual(**config)
last_improved = -1
generator = enumerate(generate(config, frequencies))
for evals, individual in generator:
individual.fitness = evaluator.get_fitness(individual)
if best < individual:
best = individual
last_improved = evals
if config['verbose']:
print '\t', last_improved, best.fitness, len(best.active)
if (evals >= config['max_evals'] or
best.fitness >= config['max_fitness']):
break
if config['verbose']:
print "Best Found"
best.show_active()
return {'fitness': best.fitness, 'evals': evals,
'success': best.fitness >= config['max_fitness'],
'phenotype': len(best.active)}
def one_run(evaluator, config):
'''
Performs a single run of the given configuration. Returns a dictionary
containing results.
Parameters:
- ``evaluator``: An object with the function get_fitness that takes an
individual and returns its fitness value
- ``config``: A dictionary containing all of the configuration information
required to perform a experimental run, including:
- All information required to initialize an individual.
- All information required to run ``evolution.generate``.
- ``verbose``: Boolean value for if extra runtime information should
be displayed.
- ``max_evals``: The maximum number of evaluations allowed before
termination.
- ``max_fitness``: The fitness required to cause a "successful"
termination.
'''
best = Individual(**config)
output = {}
for evals, individual in enumerate(generate(config, output)):
individual.fitness = evaluator.get_fitness(individual)
if best < individual:
best = individual
if config['verbose']:
print '\t', evals, best.fitness, len(best.active)
if (evals >= config['max_evals'] or
best.fitness >= config['max_fitness']):
break
if config['verbose']:
print "Best Found"
best.show_active()
output.update({'fitness': best.fitness, 'evals': evals,
'success': best.fitness >= config['max_fitness'],
'phenotype': len(best.active),
# When running 'Skip' this will correctly give the number
# of evaluations required by 'Normal'. Otherwise its junk.
'normal': output['skipped'] + evals})
return output
control_group = None
for filename in sys.argv[1:]:
base = path.basename(filename)
try:
# Determine the settings from the filename
problem, dup, ordering, nodes, mut, seed = base.split('_')
with open_file_method(filename)(filename, 'r') as f:
data = json.load(f)
version = dup, ordering, nodes, mut
if (dup, ordering) == ('skip', 'normal'):
control_group = version
statify[version].append(data[1]['evals'])
active[version].append(data[1]['phenotype'])
best = data[1]['bests'][-1]
test = data[1]['test_inputs']
individual = Individual.reconstruct_individual(best, test)
simplified = individual.new(Individual.simplify)
reduced[version].append(len(simplified.active))
filecount += 1
except ValueError:
print filename, "FAILED"
# Kruskal's requires a rectangular matrix
rect = make_rectangular(statify.values(), 10000001)
print 'Files Successfully Loaded', filecount
print 'Kruskal Wallis', kruskalwallis(rect)
for version, data in statify.iteritems():
print '--------- %s ---------' % str(version)
print "MES, MAD", median_deviation(data)
print 'Active', median_deviation(active[version])
control_group = None
for filename in sys.argv[1:]:
base = path.basename(filename)
try:
# Determine the settings from the filename
problem, dup, ordering, nodes, mut, seed = base.split('_')
with open_file_method(filename)(filename, 'r') as f:
data = json.load(f)
version = dup, ordering, nodes, mut
if (dup, ordering) == ('skip', 'normal'):
control_group = version
statify[version].append(data[1]['evals'])
active[version].append(data[1]['phenotype'])
best = data[1]['bests'][-1]
test = data[1]['test_inputs']
individual = Individual.reconstruct_individual(best, test)
simplified = individual.new(Individual.simplify)
reduced[version].append(len(simplified.active))
filecount += 1
except ValueError:
print(filename, "FAILED")
# Kruskal's requires a rectangular matrix
rect = make_rectangular(list(statify.values()), 10000001)
print('Files Successfully Loaded', filecount)
print('Kruskal Wallis', kruskalwallis(rect))
for version, data in statify.items():
print('--------- %s ---------' % str(version))
print("MES, MAD", median_deviation(data))
print('Active', median_deviation(active[version]))
