def random(evals):
"""Use random search over a CGP representation to solve the XOR function."""
ea = random_search(
evals,
representation=cgp_representation,
# Our fitness function will be to solve the XOR problem
problem=xor_problem,
pipeline=[probe.FitnessStatsCSVProbe(stream=sys.stdout)] +
cgp_visual_probes(modulo=10))
list(ea)
def get_probes(genomes_file, environment, rep):
"""Set up probes for writings results to file and terminal and
displaying live metric plots."""
assert (genomes_file is not None)
assert (environment is not None)
assert (rep is not None)
assert (rep in ['neural', 'pitt'])
num_inputs = int(np.prod(environment.observation_space.shape))
num_outputs = int(np.prod(environment.action_space.shape))
probes = []
# Print fitness stats to stdout
probes.append(probe.FitnessStatsCSVProbe(context, stream=sys.stdout))
# Save genome of the best individual to a file
probes.append(
probe.AttributesCSVProbe(context,
stream=genomes_file,
best_only=True,
do_fitness=True,
do_genome=True))
# Open a figure to plot a fitness curve to
plt.figure()
plt.ylabel("Fitness")
plt.xlabel("Generations")
plt.title("Best-of-Generation Fitness")
probes.append(
probe.PopulationPlotProbe(context,
ylim=(0, 1),
xlim=(0, 1),
modulo=1,
ax=plt.gca()))
# Open a figure to plot a projection of the best Pitt rules to
if rep == 'pitt':
plt.figure()
plt.ylabel("Sensor 1")
plt.xlabel("Sensor 0")
plt.title("Rule Coverage")
probes.append(
rules.PlotPittRuleProbe(context,
num_inputs,
num_outputs, (0, 1),
ax=plt.gca()))
return probes
def do_cgp(gens):
pop_size = 5
ea = generational_ea(
gens,
pop_size,
representation=cgp_representation,
# Our fitness function will be to solve the XOR problem
problem=xor_problem,
pipeline=[
ops.tournament_selection, ops.clone,
cgp.cgp_mutate(cgp_decoder, expected_num_mutations=1),
ops.evaluate,
ops.pool(size=pop_size),
probe.FitnessStatsCSVProbe(stream=sys.stdout)
] + cgp_visual_probes(modulo=10))
list(ea)
def build_probes(genomes_file, decoder):
"""Set up probes for writings results to file and terminal and
displaying live metric plots."""
assert (genomes_file is not None)
probes = []
# Print fitness stats to stdout
probes.append(probe.FitnessStatsCSVProbe(stream=sys.stdout))
# Save genome of the best individual to a file
probes.append(
probe.AttributesCSVProbe(stream=genomes_file,
best_only=True,
do_fitness=True,
do_genome=True))
# Open a figure to plot a fitness curve to
plt.figure()
plt.ylabel("Fitness")
plt.xlabel("Generations")
plt.title("Best-of-Generation Fitness")
probes.append(
probe.FitnessPlotProbe(ylim=(0, 1),
xlim=(0, 1),
modulo=1,
ax=plt.gca()))
# Visualize the first two conditions of every rule
plt.figure()
plt.ylabel("Sensor 1")
plt.xlabel("Sensor 0")
plt.title("Rule Coverage")
probes.append(
rules.PlotPittRuleProbe(decoder,
xlim=(-1, 1),
ylim=(-1, 1),
ax=plt.gca()))
return probes
def cgp_cmd(gens):
"""Use an evolutionary CGP approach to solve the XOR function."""
pop_size = 5
ea = generational_ea(gens, pop_size,
representation=cgp_representation,
# Our fitness function will be to solve the XOR problem
problem=xor_problem,
pipeline=[
ops.tournament_selection,
ops.clone,
cgp.cgp_mutate(cgp_decoder),
ops.evaluate,
ops.pool(size=pop_size),
probe.FitnessStatsCSVProbe(context.context, stream=sys.stdout)
] + cgp_visual_probes(modulo=10)
)
list(ea)
def build_probes(genomes_file):
"""Set up probes for writings results to file and terminal and
displaying live metric plots."""
assert (genomes_file is not None)
probes = []
# Print fitness stats to stdout
probes.append(probe.FitnessStatsCSVProbe(stream=sys.stdout))
# Save genome of the best individual to a file
probes.append(
probe.AttributesCSVProbe(stream=genomes_file,
best_only=True,
do_fitness=True,
do_genome=True))
# Open a figure to plot a fitness curve to
plt.figure()
plt.ylabel("Fitness")
plt.xlabel("Generations")
plt.title("Best-of-Generation Fitness")
probes.append(
probe.FitnessPlotProbe(ylim=(0, 1),
xlim=(0, 1),
modulo=1,
ax=plt.gca()))
# Open a figure to plot the best-of-gen network graph to
plt.figure()
probes.append(
neural_network.GraphPhenotypeProbe(modulo=1,
ax=plt.gca(),
weights=True,
weight_multiplier=3.0))
return probes
),
# Operator pipeline
pipeline=[
ops.tournament_selection(k=2),
ops.clone,
# Apply binomial mutation: this is a lot like
# additive Gaussian mutation, but adds an integer
# value to each gene
mutate_gaussian(std=0.2, hard_bounds=[problem.bounds]*l,
expected_num_mutations=1),
ops.evaluate,
ops.pool(size=pop_size),
# Some visualization probes so we can watch what happens
probe.CartesianPhenotypePlotProbe(
xlim=problem.bounds,
ylim=problem.bounds,
contours=problem),
probe.FitnessPlotProbe(),
probe.PopulationMetricsPlotProbe(
metrics=[ probe.pairwise_squared_distance_metric ],
title='Population Diversity'),
probe.FitnessStatsCSVProbe(stream=sys.stdout)
]
)
list(ea)
# Representation
representation=Representation(
individual_cls=Individual,
initialize=create_real_vector(
bounds=[bounds] * l),
decoder=IdentityDecoder()
),
# Operator pipeline
shared_pipeline=[
ops.tournament_selection,
ops.clone,
mutate_gaussian(
std=0.03, hard_bounds=bounds),
ops.evaluate,
ops.pool(size=pop_size),
ops.migrate(context,
topology=topology,
emigrant_selector=ops.tournament_selection,
replacement_selector=ops.random_selection,
migration_gap=5,
customs_stamp=problem_stamp(problems)),
probe.FitnessStatsCSVProbe(context, stream=sys.stdout,
extra_columns={ 'island': get_island(context) })
],
subpop_pipelines=subpop_probes)
list(ea)
plt.show()
pop_size = 10
ea = multi_population_ea(
max_generations=generations,
num_populations=topology.number_of_nodes(),
pop_size=pop_size,
problem=problem, # Fitness function
# Representation
representation=Representation(
individual_cls=Individual,
initialize=create_real_vector(bounds=[problem.bounds] * l)),
# Operator pipeline
shared_pipeline=[
ops.tournament_selection, ops.clone,
mutate_gaussian(std=30,
expected_num_mutations=1,
hard_bounds=problem.bounds), ops.evaluate,
ops.pool(size=pop_size),
ops.migrate(topology=topology,
emigrant_selector=ops.tournament_selection,
replacement_selector=ops.random_selection,
migration_gap=50),
probe.FitnessStatsCSVProbe(
stream=sys.stdout,
extra_metrics={'island': get_island(context)})
],
subpop_pipelines=subpop_probes)
list(ea)
# Operator pipeline
pipeline=[
ops.tournament_selection(k=2),
ops.clone,
# Apply Gaussian mutation
mutate_gaussian(std=1.5, hard_bounds=[problem.bounds]*l,
expected_num_mutations=1),
ops.evaluate,
ops.pool(size=pop_size),
# Some visualization probes so we can watch what happens
probe.CartesianPhenotypePlotProbe(
xlim=problem.bounds,
ylim=problem.bounds,
contours=problem),
probe.FitnessPlotProbe(),
# Collect diversity metrics along with the standard CSV columns
probe.FitnessStatsCSVProbe(stream=sys.stdout,
extra_metrics={
'diversity_pairwise_dist': probe.pairwise_squared_distance_metric,
'diversity_sum_variance': probe.sum_of_variances_metric,
'diversity_num_fixated': probe.num_fixated_metric
})
]
)
list(ea)