:j~n UddlmZddlZddlZddlZddlmZddlmZddlmZddl Z ddl Z ddl m Z ddl m Z ddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&erHddl'm(Z(ddlm)Z)ddl*Z*ddlm+Z+ddl%m,Z,e*jZe*j\ze*j^zZ0de1d<nedZ*e jde3Z4dZ5dZ6GddeZ7 d!d Z8y)") annotationsN)Any)cast) TYPE_CHECKING) _deprecated)logging)convert_positional_args)warn_experimental_argument) _LazyImport)_SearchSpaceTransform) optuna_warn)FloatDistribution)IntDistribution) BaseSampler)&_INDEPENDENT_SAMPLING_WARNING_TEMPLATE)LazyRandomState)IntersectionSearchSpace)StudyDirection) TrialState)Sequence) TypeAlias)BaseDistribution) FrozenTrialrCmaClasscmaesg|=iceZdZdZegdddddddddd ddd d d d dd  dd Zdd Z ddZ ddZe d dZ e d dZ d!dZ d"dZ d#dZ d$dZ d%dZd&dZd'dZ d(dZd)dZ d*dZy)+ CmaEsSampleru(A sampler using `cmaes `__ as the backend. Example: Optimize a simple quadratic function by using :class:`~optuna.samplers.CmaEsSampler`. .. code-block:: console $ pip install cmaes .. testcode:: import optuna def objective(trial): x = trial.suggest_float("x", -1, 1) y = trial.suggest_int("y", -1, 1) return x**2 + y sampler = optuna.samplers.CmaEsSampler() study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=20) Please note that this sampler does not support CategoricalDistribution. If your search space includes categorical parameters, it is recommended to use `CatCmawmSampler `__ available on `OptunaHub `__. Furthermore, there is room for performance improvements in parallel optimization settings. This sampler cannot use some trials for updating the parameters of multivariate normal distribution. For further information about CMA-ES algorithm, please refer to the following papers: - `N. Hansen, The CMA Evolution Strategy: A Tutorial. arXiv:1604.00772, 2016. `__ - `A. Auger and N. Hansen. A restart CMA evolution strategy with increasing population size. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), pages 1769–1776. IEEE Press, 2005. `__ - `N. Hansen. Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed. GECCO Workshop, 2009. `__ - `Raymond Ros, Nikolaus Hansen. A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. 10th International Conference on Parallel Problem Solving From Nature, Sep 2008, Dortmund, Germany. inria-00287367. `__ - `Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto, Yoshihiko Ozaki, Masaki Onishi. Warm Starting CMA-ES for Hyperparameter Optimization, AAAI. 2021. `__ - `R. Hamano, S. Saito, M. Nomura, S. Shirakawa. CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization, GECCO. 2022. `__ - `M. Nomura, Y. Akimoto, I. Ono. CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?, GECCO. 2023. `__ .. seealso:: You can also use `optuna_integration.PyCmaSampler `__ which is a sampler using cma library as the backend. Args: x0: A dictionary of an initial parameter values for CMA-ES. By default, the mean of ``low`` and ``high`` for each distribution is used. Note that ``x0`` is sampled uniformly within the search space domain for each restart if you specify ``restart_strategy`` argument. .. warning:: Deprecated in v4.9.0. ``x0`` argument will be removed in the future. The removal of this feature is currently scheduled for v6.0.0, but this schedule is subject to change. sigma0: Initial standard deviation of CMA-ES. By default, ``sigma0`` is set to ``min_range / 6``, where ``min_range`` denotes the minimum range of the distributions in the search space. .. warning:: Deprecated in v4.9.0. ``sigma0`` argument will be removed in the future. The removal of this feature is currently scheduled for v6.0.0, but this schedule is subject to change. seed: A random seed for CMA-ES. n_startup_trials: The independent sampling is used instead of the CMA-ES algorithm until the given number of trials finish in the same study. independent_sampler: A :class:`~optuna.samplers.BaseSampler` instance that is used for independent sampling. The parameters not contained in the relative search space are sampled by this sampler. The search space for :class:`~optuna.samplers.CmaEsSampler` is determined by :func:`~optuna.search_space.intersection_search_space()`. If :obj:`None` is specified, :class:`~optuna.samplers.RandomSampler` is used as the default. .. seealso:: :class:`optuna.samplers` module provides built-in independent samplers such as :class:`~optuna.samplers.RandomSampler` and :class:`~optuna.samplers.TPESampler`. warn_independent_sampling: If this is :obj:`True`, a warning message is emitted when the value of a parameter is sampled by using an independent sampler. Note that the parameters of the first trial in a study are always sampled via an independent sampler, so no warning messages are emitted in this case. restart_strategy: Strategy for restarting CMA-ES optimization when converges to a local minimum. If :obj:`None` is given, CMA-ES will not restart (default). If 'ipop' is given, CMA-ES will restart with increasing population size. if 'bipop' is given, CMA-ES will restart with the population size increased or decreased. Please see also ``inc_popsize`` parameter. .. warning:: Deprecated in v4.4.0. ``restart_strategy`` argument will be removed in the future. The removal of this feature is currently scheduled for v6.0.0, but this schedule is subject to change. From v4.4.0 onward, ``restart_strategy`` automatically falls back to ``None``, and ``restart_strategy`` will be supported in OptunaHub. See https://github.com/optuna/optuna/releases/tag/v4.4.0. popsize: A population size of CMA-ES. inc_popsize: Multiplier for increasing population size before each restart. This argument will be used when ``restart_strategy = 'ipop'`` or ``restart_strategy = 'bipop'`` is specified. .. warning:: Deprecated in v4.4.0. ``inc_popsize`` argument will be removed in the future. The removal of this feature is currently scheduled for v6.0.0, but this schedule is subject to change. From v4.4.0 onward, ``inc_popsize`` is no longer utilized within Optuna, and ``inc_popsize`` will be supported in OptunaHub. See https://github.com/optuna/optuna/releases/tag/v4.4.0. consider_pruned_trials: If this is :obj:`True`, the PRUNED trials are considered for sampling. .. note:: Added in v2.0.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.0.0. .. note:: It is suggested to set this flag :obj:`False` when the :class:`~optuna.pruners.MedianPruner` is used. On the other hand, it is suggested to set this flag :obj:`True` when the :class:`~optuna.pruners.HyperbandPruner` is used. Please see `the benchmark result `__ for the details. use_separable_cma: If this is :obj:`True`, the covariance matrix is constrained to be diagonal. Due to reduce the model complexity, the learning rate for the covariance matrix is increased. Consequently, this algorithm outperforms CMA-ES on separable functions. .. note:: Added in v2.6.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.6.0. with_margin: If this is :obj:`True`, CMA-ES with margin is used. This algorithm prevents samples in each discrete distribution (:class:`~optuna.distributions.FloatDistribution` with ``step`` and :class:`~optuna.distributions.IntDistribution`) from being fixed to a single point. Currently, this option cannot be used with ``use_separable_cma=True``. .. note:: Added in v3.1.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.1.0. lr_adapt: If this is :obj:`True`, CMA-ES with learning rate adaptation is used. This algorithm focuses on working well on multimodal and/or noisy problems with default settings. Currently, this option cannot be used with ``use_separable_cma=True`` or ``with_margin=True``. .. note:: Added in v3.3.0 or later, 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. source_trials: This option is for Warm Starting CMA-ES, a method to transfer prior knowledge on similar HPO tasks through the initialization of CMA-ES. This method estimates a promising distribution from ``source_trials`` and generates the parameter of multivariate gaussian distribution. Please note that it is prohibited to use ``use_separable_cma`` argument together. .. note:: Added in v2.6.0 as an experimental feature. The interface may change in newer versions without prior notice. 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