Source code for optuna.samplers.nsgaii._crossovers._base

import abc

import numpy as np

from import Study

[docs] class BaseCrossover(abc.ABC): """Base class for crossovers. A crossover operation is used by :class:`~optuna.samplers.NSGAIISampler` to create new parameter combination from parameters of ``n`` parent individuals. .. note:: Concrete implementations of this class are expected to only accept parameters from numerical distributions. At the moment, only crossover operation for categorical parameters (uniform crossover) is built-in into :class:`~optuna.samplers.NSGAIISampler`. """ def __str__(self) -> str: return self.__class__.__name__ @property @abc.abstractmethod def n_parents(self) -> int: """Number of parent individuals required to perform crossover.""" raise NotImplementedError
[docs] @abc.abstractmethod def crossover( self, parents_params: np.ndarray, rng: np.random.RandomState, study: Study, search_space_bounds: np.ndarray, ) -> np.ndarray: """Perform crossover of selected parent individuals. This method is called in :func:`~optuna.samplers.NSGAIISampler.sample_relative`. Args: parents_params: A ``numpy.ndarray`` with dimensions ``num_parents x num_parameters``. Represents a parameter space for each parent individual. This space is continuous for numerical parameters. rng: An instance of ``numpy.random.RandomState``. study: Target study object. search_space_bounds: A ``numpy.ndarray`` with dimensions ``len_search_space x 2`` representing numerical distribution bounds constructed from transformed search space. Returns: A 1-dimensional ``numpy.ndarray`` containing new parameter combination. """ raise NotImplementedError