class SearchAlgorithm(ABC):
"""Base class for all search algorithms.
Parameters
----------
config : SearchConfiguration
The configuation of the search algorithm.
Attributes
----------
config : SearchConfiguration
The configuation of the search algorithm.
generation : int
The current generation, or iteration, of the search.
best_seen : Individual
The best Individual, with respect to total error, seen so far.
population : Population
The current Population of individuals.
"""
def __init__(self, config: SearchConfiguration):
self.config = config
self.generation = 0
self.best_seen = None
self.init_population()
def init_population(self):
"""Initialize the population."""
self.population = Population()
for i in range(self.config.population_size):
spawner = self.config.spawner
genome = spawner.spawn_genome(self.config.initial_genome_size)
self.population.add(Individual(genome))
@abstractmethod
def step(self) -> bool:
"""Perform one generation (step) of the search. Return if should continue.
The step method should assume an evaluated Population, and must only
perform parent selection and variation (producing children). The step
method should modify the search algorithms population in-place, or
assign a new Population to the population attribute.
"""
pass
def _full_step(self) -> bool:
self.generation += 1
self.population.evaluate(self.config.evaluator)
best_this_gen = self.population.best()
if self.best_seen is None or best_this_gen.total_error < self.best_seen.total_error:
self.best_seen = best_this_gen
if self.best_seen.total_error <= self.config.error_threshold:
return False
if self.config.verbosity_config.generation >= self.config.verbosity_config.log_level and \
self.generation % self.config.verbosity_config.every_n_generations == 0:
stat_logs = []
stat_logs.append("GENERATION: {g}".format(
g=self.generation
))
stat_logs.append("ERRORS: best={b}, median={m}, diversity={e_d}".format(
b=self.population.best().total_error,
m=self.population.median_error(),
e_d=self.population.error_diversity()
))
# stat_logs.append("Population: size={p_s}, genome_diversity={g_d}".format(
# p_s=len(self.population),
# g_d=self.population.genome_diversity()
# ))
log(self.config.verbosity_config.generation, " | ".join(stat_logs))
self.step()
return True
def _is_solved(self):
return self.best_seen.total_error <= self.config.error_threshold
def run(self):
"""Run the algorithm until termination."""
while self._full_step():
if self.generation >= self.config.max_generations:
break
verbose_solution = self.config.verbosity_config.solution_found
if verbose_solution >= self.config.verbosity_config.log_level:
if self._is_solved():
log(verbose_solution, "Unsimplified solution found.")
else:
log(verbose_solution, "No unsimplified solution found.")
# Simplify the best individual for a better generalization and interpreteation.
simplifier = GenomeSimplifier(self.config.evaluator, self.config.verbosity_config)
simp_genome, simp_error_vector = simplifier.simplify(
self.best_seen.genome,
self.best_seen.error_vector,
self.config.simplification_steps
)
simplified_best = Individual(simp_genome)
simplified_best.error_vector = simp_error_vector
if verbose_solution >= self.config.verbosity_config.log_level:
if self._is_solved():
log(verbose_solution, "Simplified solution found.")
else:
log(verbose_solution, "No simplified solution found.")
return simplified_best
class SearchAlgorithm(ABC):
"""Base class for all search algorithms.
Parameters
----------
config : SearchConfiguration
The configuation of the search algorithm.
Attributes
----------
config : SearchConfiguration
The configuration of the search algorithm.
generation : int
The current generation, or iteration, of the search.
best_seen : Individual
The best Individual, with respect to total error, seen so far.
population : Population
The current Population of individuals.
"""
def __init__(self, config: SearchConfiguration):
self.config = config
self._p_context = config.parallel_context
self.generation = 0
self.best_seen = None
self.population = None
self.init_population()
def init_population(self):
"""Initialize the population."""
spawner = self.config.spawner
init_gn_size = self.config.initial_genome_size
pop_size = self.config.population_size
signature = self.config.signature
self.population = Population()
if self._p_context is not None:
gen_func = partial(_spawn_individual, self._p_context.ns.spawner,
init_gn_size, signature)
for indiv in self._p_context.pool.imap_unordered(
gen_func, range(pop_size)):
self.population.add(indiv)
else:
for i in range(pop_size):
self.population.add(
_spawn_individual(spawner, init_gn_size, signature))
@tap
@abstractmethod
def step(self) -> bool:
"""Perform one generation (step) of the search.
The step method should assume an evaluated Population, and must only
perform parent selection and variation (producing children). The step
method should modify the search algorithms population in-place, or
assign a new Population to the population attribute.
"""
pass
def _full_step(self) -> bool:
self.generation += 1
if self._p_context is not None:
self.population.p_evaluate(self._p_context.ns.evaluator,
self._p_context.pool)
else:
self.population.evaluate(self.config.evaluator)
best_this_gen = self.population.best()
if self.best_seen is None or best_this_gen.total_error < self.best_seen.total_error:
self.best_seen = best_this_gen
if self.best_seen.total_error <= self.config.error_threshold:
return False
self.step()
return True
def is_solved(self) -> bool:
"""Return ``True`` if the search algorithm has found a solution or ``False`` otherwise."""
return self.best_seen.total_error <= self.config.error_threshold
@tap
def run(self) -> Individual:
"""Run the algorithm until termination."""
while self._full_step():
if self.generation >= self.config.max_generations:
break
# Simplify the best individual for a better generalization and interpretation.
simplifier = GenomeSimplifier(self.config.evaluator,
self.config.signature)
simp_genome, simp_error_vector = simplifier.simplify(
self.best_seen.genome, self.best_seen.error_vector,
self.config.simplification_steps)
simplified_best = Individual(simp_genome, self.config.signature)
simplified_best.error_vector = simp_error_vector
self.best_seen = simplified_best
return self.best_seen
class SearchAlgorithm(ABC):
"""Base class for all search algorithms.
Parameters
----------
config : SearchConfiguration
The configuation of the search algorithm.
Attributes
----------
config : SearchConfiguration
The configuation of the search algorithm.
generation : int
The current generation, or iteration, of the search.
best_seen : Individual
The best Individual, with respect to total error, seen so far.
population : Population
The current Population of individuals.
"""
def __init__(self, config: SearchConfiguration):
self.config = config
self.generation = 0
self.best_seen = None
self.init_population()
def init_population(self):
"""Initialize the population."""
self.population = Population()
for i in range(self.config.population_size):
spawner = self.config.spawner
genome = spawner.spawn_genome(self.config.initial_genome_size)
self.population.add(Individual(genome))
@abstractmethod
def step(self) -> bool:
"""Perform one generation (step) of the search. Return if should continue.
The step method should assume an evaluated Population, and must only
perform parent selection and variation (producing children). The step
method should modify the search algorithms population in-place, or
assign a new Population to the population attribute.
"""
pass
def _full_step(self) -> bool:
self.generation += 1
self.population.evaluate(self.config.evaluator)
best_this_gen = self.population.best()
if self.best_seen is None or best_this_gen.total_error < self.best_seen.total_error:
self.best_seen = best_this_gen
if self.best_seen.total_error <= self.config.error_threshold:
return False
if self.config.verbosity_config.generation and \
self.generation % self.config.verbosity_config.every_n_generations == 0:
stat_logs = []
stat_logs.append("GENERATION: {g}".format(g=self.generation))
stat_logs.append(
"ERRORS: best={b}, median={m}, diversity={e_d}".format(
b=self.population.best().total_error,
m=self.population.median_error(),
e_d=self.population.error_diversity()))
# stat_logs.append("Population: size={p_s}, genome_diversity={g_d}".format(
# p_s=len(self.population),
# g_d=self.population.genome_diversity()
# ))
self.config.verbosity_config.generation(" | ".join(stat_logs))
self.step()
return True
def _is_solved(self):
return self.best_seen.total_error <= self.config.error_threshold
def run(self):
"""Run the algorithm until termination."""
while self._full_step():
if self.generation >= self.config.max_generations:
break
verbose_solution = self.config.verbosity_config.solution_found
if verbose_solution:
if self._is_solved():
verbose_solution("Unsimplified solution found.")
else:
verbose_solution("No unsimplified solution found.")
# Simplify the best individual for a better generalization and interpreteation.
simplifier = GenomeSimplifier(self.config.evaluator,
self.config.verbosity_config)
simp_genome, simp_error_vector = simplifier.simplify(
self.best_seen.genome, self.best_seen.error_vector,
self.config.simplification_steps)
simplified_best = Individual(simp_genome)
simplified_best.error_vector = simp_error_vector
if verbose_solution:
if self._is_solved():
verbose_solution("Simplified solution found.")
else:
verbose_solution("No simplified solution found.")
return simplified_best