def __init__(self,
signature: ProgramSignature,
evaluator: Evaluator,
spawner: GeneSpawner,
selection: Union[Selector, DiscreteProbDistrib,
str] = "lexicase",
variation: Union[VariationOperator, DiscreteProbDistrib,
str] = "umad",
population_size: int = 500,
max_generations: int = 100,
error_threshold: float = 0.0,
initial_genome_size: Tuple[int, int] = (10, 50),
simplification_steps: int = 2000,
parallelism: Union[int, bool] = True,
verbose: int = 0,
**kwargs):
self.signature = signature
self.evaluator = evaluator
self.spawner = spawner
self.population_size = population_size
self.max_generations = max_generations
self.error_threshold = error_threshold
self.initial_genome_size = initial_genome_size
self.simplification_steps = simplification_steps
self.verbose = verbose
self.ext = kwargs
set_verbosity(self.verbose)
self.parallel_context = None
if isinstance(parallelism, bool):
if parallelism:
self.parallel_context = ParallelContext(spawner, evaluator)
elif parallelism > 1:
self.parallel_context = ParallelContext(spawner, evaluator,
parallelism)
if isinstance(selection, Selector):
self.selection = DiscreteProbDistrib().add(selection, 1.0)
elif isinstance(selection, DiscreteProbDistrib):
self.selection = selection
else:
selector = get_selector(selection, **self.ext)
self.selection = DiscreteProbDistrib().add(selector, 1.0)
if isinstance(variation, VariationOperator):
self.variation = DiscreteProbDistrib().add(variation, 1.0)
elif isinstance(variation, DiscreteProbDistrib):
self.variation = variation
else:
variation_op = get_variation_operator(variation, **self.ext)
self.variation = DiscreteProbDistrib().add(variation_op, 1.0)
def __init__(self,
evaluator: Evaluator,
spawner: GeneSpawner,
selection: Union[Selector, DiscreteProbDistrib,
str] = "lexicase",
variation: Union[VariationOperator, DiscreteProbDistrib,
str] = "umad",
population_size: int = 500,
max_generations: int = 100,
error_threshold: float = 0.0,
initial_genome_size: Tuple[int, int] = (10, 50),
simplification_steps: int = 2000,
verbosity_config: Union[VerbosityConfig, str] = "default",
**kwargs):
self.evaluator = evaluator
self.spawner = spawner
self.population_size = population_size
self.max_generations = max_generations
self.error_threshold = error_threshold
self.initial_genome_size = initial_genome_size
self.simplification_steps = simplification_steps
self.ext = kwargs
if isinstance(selection, Selector):
self._selection = DiscreteProbDistrib().add(selection, 1.0)
elif isinstance(selection, DiscreteProbDistrib):
self._selection = selection
else:
selector = get_selector(selection, **self.ext)
self._selection = DiscreteProbDistrib().add(selector, 1.0)
if isinstance(variation, VariationOperator):
self._variation = DiscreteProbDistrib().add(variation, 1.0)
elif isinstance(variation, DiscreteProbDistrib):
self._variation = variation
else:
variationOp = get_variation_operator(variation, **self.ext)
self._variation = DiscreteProbDistrib().add(variationOp, 1.0)
if verbosity_config == "default":
self.verbosity_config = DEFAULT_VERBOSITY_LEVELS[0]
else:
self.verbosity_config = verbosity_config
def __init__(self,
evaluator: Evaluator,
spawner: GeneSpawner,
selection: Union[Selector, DiscreteProbDistrib, str] = "lexicase",
variation: Union[VariationOperator, DiscreteProbDistrib, str] = "umad",
population_size: int = 500,
max_generations: int = 100,
error_threshold: float = 0.0,
initial_genome_size: Tuple[int, int] = (10, 50),
simplification_steps: int = 2000,
verbosity_config: Union[VerbosityConfig, str] = "default",
**kwargs):
self.evaluator = evaluator
self.spawner = spawner
self.population_size = population_size
self.max_generations = max_generations
self.error_threshold = error_threshold
self.initial_genome_size = initial_genome_size
self.simplification_steps = simplification_steps
self.ext = kwargs
if isinstance(selection, Selector):
self._selection = DiscreteProbDistrib().add(selection, 1.0)
elif isinstance(selection, DiscreteProbDistrib):
self._selection = selection
else:
selector = get_selector(selection, **self.ext)
self._selection = DiscreteProbDistrib().add(selector, 1.0)
if isinstance(variation, VariationOperator):
self._variation = DiscreteProbDistrib().add(variation, 1.0)
elif isinstance(variation, DiscreteProbDistrib):
self._variation = variation
else:
variationOp = get_variation_operator(variation, **self.ext)
self._variation = DiscreteProbDistrib().add(variationOp, 1.0)
if verbosity_config == "default":
self.verbosity_config = DEFAULT_VERBOSITY_LEVELS[0]
else:
self.verbosity_config = verbosity_config
self.verbosity_config._update_log_level()
def _build_search_algo(self):
if isinstance(self.variation_strategy, dict):
var_strat = vr.VariationStrategy()
for op_name, prob in self.variation_strategy.items():
var_op = vr.get_variation_operator(op_name)
if not isinstance(var_op, vr.VariationOperator):
var_op = self._build_component(var_op)
var_strat.add(var_op, prob)
self.variation_strategy = var_strat
search_config = sr.SearchConfiguration(
spawner=self.spawner,
evaluator=self.evaluator,
selection=self.selector,
variation=self.variation_strategy,
population_size=self.population_size,
max_generations=self.max_generations,
initial_genome_size=self.initial_genome_size,
simplification_steps=self.simplification_steps,
verbosity_config=self.verbosity_config
)
self.search = sr.get_search_algo(self._search_name, config=search_config, **self.ext)
def _build_search_algo(self):
if isinstance(self.variation_strategy, dict):
var_strat = vr.VariationStrategy()
for op_name, prob in self.variation_strategy.items():
var_op = vr.get_variation_operator(op_name)
var_strat.add(var_op, prob)
self.variation_strategy = var_strat
search_config = sr.SearchConfiguration(
signature=self.signature,
spawner=self.spawner,
evaluator=self.evaluator,
selection=self.selector,
variation=self.variation_strategy,
population_size=self.population_size,
max_generations=self.max_generations,
initial_genome_size=self.initial_genome_size,
simplification_steps=self.simplification_steps,
parallelism=self.parallelism,
push_config=self.push_config)
self.search = sr.get_search_algo(self._search_name,
config=search_config,
**self.ext)
def _build_search_algo(self):
if isinstance(self.variation_strategy, dict):
var_strat = vr.VariationStrategy()
for op_name, prob in self.variation_strategy.items():
var_op = vr.get_variation_operator(op_name)
if not isinstance(var_op, vr.VariationOperator):
var_op = self._build_component(var_op)
var_strat.add(var_op, prob)
self.variation_strategy = var_strat
search_config = sr.SearchConfiguration(
spawner=self.spawner,
evaluator=self.evaluator,
selection=self.selector,
variation=self.variation_strategy,
population_size=self.population_size,
max_generations=self.max_generations,
initial_genome_size=self.initial_genome_size,
simplification_steps=self.simplification_steps,
verbosity_config=DEFAULT_VERBOSITY_LEVELS[self.verbose])
self.search = sr.get_search_algo(self._search_name,
config=search_config,
**self.ext)