Source code for optuna.samplers.nsgaii._sampler

from __future__ import annotations

from collections import defaultdict
from collections.abc import Callable
from collections.abc import Sequence
import hashlib
from typing import Any
import warnings

import optuna
from optuna.distributions import BaseDistribution
from optuna.exceptions import ExperimentalWarning
from optuna.samplers._base import BaseSampler
from optuna.samplers._lazy_random_state import LazyRandomState
from optuna.samplers._random import RandomSampler
from optuna.samplers.nsgaii._after_trial_strategy import NSGAIIAfterTrialStrategy
from optuna.samplers.nsgaii._child_generation_strategy import NSGAIIChildGenerationStrategy
from optuna.samplers.nsgaii._crossovers._base import BaseCrossover
from optuna.samplers.nsgaii._crossovers._uniform import UniformCrossover
from optuna.samplers.nsgaii._elite_population_selection_strategy import (
    NSGAIIElitePopulationSelectionStrategy,
)
from optuna.search_space import IntersectionSearchSpace
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState


# Define key names of `Trial.system_attrs`.
_GENERATION_KEY = "nsga2:generation"
_POPULATION_CACHE_KEY_PREFIX = "nsga2:population"


[docs] class NSGAIISampler(BaseSampler): """Multi-objective sampler using the NSGA-II algorithm. NSGA-II stands for "Nondominated Sorting Genetic Algorithm II", which is a well known, fast and elitist multi-objective genetic algorithm. For further information about NSGA-II, please refer to the following paper: - `A fast and elitist multiobjective genetic algorithm: NSGA-II <https://doi.org/10.1109/4235.996017>`_ Args: population_size: Number of individuals (trials) in a generation. ``population_size`` must be greater than or equal to ``crossover.n_parents``. For :class:`~optuna.samplers.nsgaii.UNDXCrossover` and :class:`~optuna.samplers.nsgaii.SPXCrossover`, ``n_parents=3``, and for the other algorithms, ``n_parents=2``. mutation_prob: Probability of mutating each parameter when creating a new individual. If :obj:`None` is specified, the value ``1.0 / len(parent_trial.params)`` is used where ``parent_trial`` is the parent trial of the target individual. crossover: Crossover to be applied when creating child individuals. The available crossovers are listed here: https://optuna.readthedocs.io/en/stable/reference/samplers/nsgaii.html. :class:`~optuna.samplers.nsgaii.UniformCrossover` is always applied to parameters sampled from :class:`~optuna.distributions.CategoricalDistribution`, and by default for parameters sampled from other distributions unless this argument is specified. For more information on each of the crossover method, please refer to specific crossover documentation. crossover_prob: Probability that a crossover (parameters swapping between parents) will occur when creating a new individual. swapping_prob: Probability of swapping each parameter of the parents during crossover. seed: Seed for random number generator. constraints_func: An optional function that computes the objective constraints. It must take a :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must be a sequence of :obj:`float` s. A value strictly larger than 0 means that a constraints is violated. A value equal to or smaller than 0 is considered feasible. If ``constraints_func`` returns more than one value for a trial, that trial is considered feasible if and only if all values are equal to 0 or smaller. The ``constraints_func`` will be evaluated after each successful trial. The function won't be called when trials fail or they are pruned, but this behavior is subject to change in the future releases. The constraints are handled by the constrained domination. A trial x is said to constrained-dominate a trial y, if any of the following conditions is true: 1. Trial x is feasible and trial y is not. 2. Trial x and y are both infeasible, but trial x has a smaller overall violation. 3. Trial x and y are feasible and trial x dominates trial y. .. note:: Added in v2.5.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.5.0. elite_population_selection_strategy: The selection strategy for determining the individuals to survive from the current population pool. Default to :obj:`None`. .. note:: The arguments ``elite_population_selection_strategy`` was added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0. child_generation_strategy: The strategy for generating child parameters from parent trials. Defaults to :obj:`None`. .. note:: The arguments ``child_generation_strategy`` was added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0. after_trial_strategy: A set of procedure to be conducted after each trial. Defaults to :obj:`None`. .. note:: The arguments ``after_trial_strategy`` was added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0. """ def __init__( self, *, population_size: int = 50, mutation_prob: float | None = None, crossover: BaseCrossover | None = None, crossover_prob: float = 0.9, swapping_prob: float = 0.5, seed: int | None = None, constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, elite_population_selection_strategy: ( Callable[[Study, list[FrozenTrial]], list[FrozenTrial]] | None ) = None, child_generation_strategy: ( Callable[[Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]] | None ) = None, after_trial_strategy: ( Callable[[Study, FrozenTrial, TrialState, Sequence[float] | None], None] | None ) = None, ) -> None: # TODO(ohta): Reconsider the default value of each parameter. if population_size < 2: raise ValueError("`population_size` must be greater than or equal to 2.") if constraints_func is not None: warnings.warn( "The constraints_func option is an experimental feature." " The interface can change in the future.", ExperimentalWarning, ) if after_trial_strategy is not None: warnings.warn( "The after_trial_strategy option is an experimental feature." " The interface can change in the future.", ExperimentalWarning, ) if child_generation_strategy is not None: warnings.warn( "The child_generation_strategy option is an experimental feature." " The interface can change in the future.", ExperimentalWarning, ) if elite_population_selection_strategy is not None: warnings.warn( "The elite_population_selection_strategy option is an experimental feature." " The interface can change in the future.", ExperimentalWarning, ) if crossover is None: crossover = UniformCrossover(swapping_prob) if not isinstance(crossover, BaseCrossover): raise ValueError( f"'{crossover}' is not a valid crossover." " For valid crossovers see" " https://optuna.readthedocs.io/en/stable/reference/samplers.html." ) if population_size < crossover.n_parents: raise ValueError( f"Using {crossover}," f" the population size should be greater than or equal to {crossover.n_parents}." f" The specified `population_size` is {population_size}." ) self._population_size = population_size self._random_sampler = RandomSampler(seed=seed) self._rng = LazyRandomState(seed) self._constraints_func = constraints_func self._search_space = IntersectionSearchSpace() self._elite_population_selection_strategy = ( elite_population_selection_strategy or NSGAIIElitePopulationSelectionStrategy( population_size=population_size, constraints_func=constraints_func ) ) self._child_generation_strategy = ( child_generation_strategy or NSGAIIChildGenerationStrategy( crossover_prob=crossover_prob, mutation_prob=mutation_prob, swapping_prob=swapping_prob, crossover=crossover, constraints_func=constraints_func, rng=self._rng, ) ) self._after_trial_strategy = after_trial_strategy or NSGAIIAfterTrialStrategy( constraints_func=constraints_func )
[docs] def reseed_rng(self) -> None: self._random_sampler.reseed_rng() self._rng.rng.seed()
[docs] def infer_relative_search_space( self, study: Study, trial: FrozenTrial ) -> dict[str, BaseDistribution]: search_space: dict[str, BaseDistribution] = {} for name, distribution in self._search_space.calculate(study).items(): if distribution.single(): # The `untransform` method of `optuna._transform._SearchSpaceTransform` # does not assume a single value, # so single value objects are not sampled with the `sample_relative` method, # but with the `sample_independent` method. continue search_space[name] = distribution return search_space
[docs] def sample_relative( self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution], ) -> dict[str, Any]: parent_generation, parent_population = self._collect_parent_population(study) generation = parent_generation + 1 study._storage.set_trial_system_attr(trial._trial_id, _GENERATION_KEY, generation) if parent_generation < 0: return {} return self._child_generation_strategy(study, search_space, parent_population)
[docs] def sample_independent( self, study: Study, trial: FrozenTrial, param_name: str, param_distribution: BaseDistribution, ) -> Any: # Following parameters are randomly sampled here. # 1. A parameter in the initial population/first generation. # 2. A parameter to mutate. # 3. A parameter excluded from the intersection search space. return self._random_sampler.sample_independent( study, trial, param_name, param_distribution )
def _collect_parent_population(self, study: Study) -> tuple[int, list[FrozenTrial]]: trials = study._get_trials(deepcopy=False, use_cache=True) generation_to_runnings = defaultdict(list) generation_to_population = defaultdict(list) for trial in trials: if _GENERATION_KEY not in trial.system_attrs: continue generation = trial.system_attrs[_GENERATION_KEY] if trial.state != optuna.trial.TrialState.COMPLETE: if trial.state == optuna.trial.TrialState.RUNNING: generation_to_runnings[generation].append(trial) continue # Do not use trials whose states are not COMPLETE, or `constraint` will be unavailable. generation_to_population[generation].append(trial) hasher = hashlib.sha256() parent_population: list[FrozenTrial] = [] parent_generation = -1 while True: generation = parent_generation + 1 population = generation_to_population[generation] # Under multi-worker settings, the population size might become larger than # `self._population_size`. if len(population) < self._population_size: break # [NOTE] # It's generally safe to assume that once the above condition is satisfied, # there are no additional individuals added to the generation (i.e., the members of # the generation have been fixed). # If the number of parallel workers is huge, this assumption can be broken, but # this is a very rare case and doesn't significantly impact optimization performance. # So we can ignore the case. # The cache key is calculated based on the key of the previous generation and # the remaining running trials in the current population. # If there are no running trials, the new cache key becomes exactly the same as # the previous one, and the cached content will be overwritten. This allows us to # skip redundant cache key calculations when this method is called for the subsequent # trials. for trial in generation_to_runnings[generation]: hasher.update(bytes(str(trial.number), "utf-8")) cache_key = "{}:{}".format(_POPULATION_CACHE_KEY_PREFIX, hasher.hexdigest()) study_system_attrs = study._storage.get_study_system_attrs(study._study_id) cached_generation, cached_population_numbers = study_system_attrs.get( cache_key, (-1, []) ) if cached_generation >= generation: generation = cached_generation population = [trials[n] for n in cached_population_numbers] else: population.extend(parent_population) population = self._elite_population_selection_strategy(study, population) # To reduce the number of system attribute entries, # we cache the population information only if there are no running trials # (i.e., the information of the population has been fixed). # Usually, if there are no too delayed running trials, the single entry # will be used. if len(generation_to_runnings[generation]) == 0: population_numbers = [t.number for t in population] study._storage.set_study_system_attr( study._study_id, cache_key, (generation, population_numbers) ) parent_generation = generation parent_population = population return parent_generation, parent_population
[docs] def before_trial(self, study: Study, trial: FrozenTrial) -> None: self._random_sampler.before_trial(study, trial)
[docs] def after_trial( self, study: Study, trial: FrozenTrial, state: TrialState, values: Sequence[float] | None, ) -> None: assert state in [TrialState.COMPLETE, TrialState.FAIL, TrialState.PRUNED] self._after_trial_strategy(study, trial, state, values) self._random_sampler.after_trial(study, trial, state, values)