optuna.samplers._nsga2 源代码

from collections import defaultdict
import hashlib
import itertools
from typing import Any
from typing import Callable
from typing import cast
from typing import DefaultDict
from typing import Dict
from typing import List
from typing import Optional
from typing import Sequence
from typing import Tuple
import warnings

import numpy as np

import optuna
from optuna._experimental import ExperimentalWarning
from optuna._multi_objective import _dominates
from optuna.distributions import BaseDistribution
from optuna.samplers._base import BaseSampler
from optuna.samplers._random import RandomSampler
from optuna.study import Study
from optuna.study import StudyDirection
from optuna.trial import FrozenTrial
from optuna.trial import TrialState

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

[文档]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://ieeexplore.ieee.org/document/996017>`_ Args: population_size: Number of individuals (trials) in a generation. 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_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 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. """ def __init__( self, *, population_size: int = 50, mutation_prob: Optional[float] = None, crossover_prob: float = 0.9, swapping_prob: float = 0.5, seed: Optional[int] = None, constraints_func: Optional[Callable[[FrozenTrial], Sequence[float]]] = None, ) -> None: # TODO(ohta): Reconsider the default value of each parameter. if not isinstance(population_size, int): raise TypeError("`population_size` must be an integer value.") if population_size < 2: raise ValueError("`population_size` must be greater than or equal to 2.") if not (mutation_prob is None or 0.0 <= mutation_prob <= 1.0): raise ValueError( "`mutation_prob` must be None or a float value within the range [0.0, 1.0]." ) if not (0.0 <= crossover_prob <= 1.0): raise ValueError("`crossover_prob` must be a float value within the range [0.0, 1.0].") if not (0.0 <= swapping_prob <= 1.0): raise ValueError("`swapping_prob` must be a float value within the range [0.0, 1.0].") if constraints_func is not None: warnings.warn( "The constraints_func option is an experimental feature." " The interface can change in the future.", ExperimentalWarning, ) self._population_size = population_size self._mutation_prob = mutation_prob self._crossover_prob = crossover_prob self._swapping_prob = swapping_prob self._random_sampler = RandomSampler(seed=seed) self._rng = np.random.RandomState(seed) self._constraints_func = constraints_func
[文档] def reseed_rng(self) -> None: self._random_sampler.reseed_rng() self._rng = np.random.RandomState()
[文档] def infer_relative_search_space( self, study: Study, trial: FrozenTrial ) -> Dict[str, BaseDistribution]: return {}
[文档] 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) trial_id = trial._trial_id generation = parent_generation + 1 study._storage.set_trial_system_attr(trial_id, _GENERATION_KEY, generation) if parent_generation >= 0: p0 = self._select_parent(study, parent_population) if self._rng.rand() < self._crossover_prob: p1 = self._select_parent( study, [t for t in parent_population if t._trial_id != p0._trial_id] ) else: p1 = p0 study._storage.set_trial_system_attr( trial_id, _PARENTS_KEY, [p0._trial_id, p1._trial_id] ) return {}
[文档] def sample_independent( self, study: Study, trial: FrozenTrial, param_name: str, param_distribution: BaseDistribution, ) -> Any: if _PARENTS_KEY not in trial.system_attrs: return self._random_sampler.sample_independent( study, trial, param_name, param_distribution ) p0_id, p1_id = trial.system_attrs[_PARENTS_KEY] p0 = study._storage.get_trial(p0_id) p1 = study._storage.get_trial(p1_id) param = p0.params.get(param_name, None) parent_params_len = len(p0.params) if param is None or self._rng.rand() < self._swapping_prob: param = p1.params.get(param_name, None) parent_params_len = len(p1.params) mutation_prob = self._mutation_prob if mutation_prob is None: mutation_prob = 1.0 / max(1.0, parent_params_len) if param is None or self._rng.rand() < mutation_prob: return self._random_sampler.sample_independent( study, trial, param_name, param_distribution ) return param
def _collect_parent_population(self, study: Study) -> Tuple[int, List[FrozenTrial]]: trials = study.get_trials(deepcopy=False) 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 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()) 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._select_elite_population(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.set_system_attr(cache_key, (generation, population_numbers)) parent_generation = generation parent_population = population return parent_generation, parent_population def _select_elite_population( self, study: Study, population: List[FrozenTrial] ) -> List[FrozenTrial]: elite_population: List[FrozenTrial] = [] population_per_rank = self._fast_non_dominated_sort(population, study.directions) for population in population_per_rank: if len(elite_population) + len(population) < self._population_size: elite_population.extend(population) else: n = self._population_size - len(elite_population) _crowding_distance_sort(population) elite_population.extend(population[:n]) break return elite_population def _select_parent(self, study: Study, population: List[FrozenTrial]) -> FrozenTrial: # TODO(ohta): Consider to allow users to specify the number of parent candidates. candidate0 = self._rng.choice(population) candidate1 = self._rng.choice(population) dominates = _dominates if self._constraints_func is None else _constrained_dominates # TODO(ohta): Consider crowding distance. if dominates(candidate0, candidate1, study.directions): return candidate0 else: return candidate1 def _fast_non_dominated_sort( self, population: List[FrozenTrial], directions: List[optuna.study.StudyDirection], ) -> List[List[FrozenTrial]]: dominated_count: DefaultDict[int, int] = defaultdict(int) dominates_list = defaultdict(list) dominates = _dominates if self._constraints_func is None else _constrained_dominates for p, q in itertools.combinations(population, 2): if dominates(p, q, directions): dominates_list[p.number].append(q.number) dominated_count[q.number] += 1 elif dominates(q, p, directions): dominates_list[q.number].append(p.number) dominated_count[p.number] += 1 population_per_rank = [] while population: non_dominated_population = [] i = 0 while i < len(population): if dominated_count[population[i].number] == 0: individual = population[i] if i == len(population) - 1: population.pop() else: population[i] = population.pop() non_dominated_population.append(individual) else: i += 1 for x in non_dominated_population: for y in dominates_list[x.number]: dominated_count[y] -= 1 assert non_dominated_population population_per_rank.append(non_dominated_population) return population_per_rank
[文档] def after_trial( self, study: Study, trial: FrozenTrial, state: TrialState, values: Optional[Sequence[float]], ) -> None: if self._constraints_func is not None: constraints = None try: con = self._constraints_func(trial) if not isinstance(con, (tuple, list)): warnings.warn( f"Constraints should be a sequence of floats but got {type(con).__name__}." ) constraints = tuple(con) except Exception: raise finally: assert constraints is None or isinstance(constraints, tuple) study._storage.set_trial_system_attr( trial._trial_id, _CONSTRAINTS_KEY, constraints, ) self._random_sampler.after_trial(study, trial, state, values)
def _crowding_distance_sort(population: List[FrozenTrial]) -> None: manhattan_distances = defaultdict(float) for i in range(len(population[0].values)): population.sort(key=lambda x: cast(float, x.values[i])) v_min = population[0].values[i] v_max = population[-1].values[i] assert v_min is not None assert v_max is not None width = v_max - v_min if width == 0: continue manhattan_distances[population[0].number] = float("inf") manhattan_distances[population[-1].number] = float("inf") for j in range(1, len(population) - 1): v_high = population[j + 1].values[i] v_low = population[j - 1].values[i] assert v_high is not None assert v_low is not None manhattan_distances[population[j].number] += (v_high - v_low) / width population.sort(key=lambda x: manhattan_distances[x.number]) population.reverse() def _constrained_dominates( trial0: FrozenTrial, trial1: FrozenTrial, directions: Sequence[StudyDirection] ) -> bool: """Checks 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 solution x has a smaller overall constraint violation. 3) Trial x and y are feasible and trial x dominates trial y. """ constraints0 = trial0.system_attrs.get(_CONSTRAINTS_KEY) constraints1 = trial1.system_attrs.get(_CONSTRAINTS_KEY) if constraints0 is None: warnings.warn( f"Trial {trial0.number} does not have constraint values." " It will be dominated by the other trials." ) if constraints1 is None: warnings.warn( f"Trial {trial1.number} does not have constraint values." " It will be dominated by the other trials." ) if constraints0 is None and constraints1 is None: # Neither Trial x nor y has constraints values return _dominates(trial0, trial1, directions) if constraints0 is not None and constraints1 is None: # Trial x has constraint values, but y doesn't. return True if constraints0 is None and constraints1 is not None: # If Trial y has constraint values, but x doesn't. return False assert isinstance(constraints0, (list, tuple)) assert isinstance(constraints1, (list, tuple)) if len(constraints0) != len(constraints1): raise ValueError("Trials with different numbers of constraints cannot be compared.") if trial0.state != TrialState.COMPLETE: return False if trial1.state != TrialState.COMPLETE: return True satisfy_constraints0 = all(v <= 0 for v in constraints0) satisfy_constraints1 = all(v <= 0 for v in constraints1) if satisfy_constraints0 and satisfy_constraints1: # Both trials satisfy the constraints. return _dominates(trial0, trial1, directions) if satisfy_constraints0: # trial0 satisfies the constraints, but trial1 violates them. return True if satisfy_constraints1: # trial1 satisfies the constraints, but trial0 violates them. return False # Both trials violate the constraints. violation0 = sum(v for v in constraints0 if v > 0) violation1 = sum(v for v in constraints1 if v > 0) return violation0 < violation1