Q-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. See https://github.com/optuna/optuna/releases/tag/v2.6.0. )selfx0sigma0n_startup_trialsindependent_samplerwarn_independent_samplingseed4.9.06.0.0)previous_positional_arg_namesdeprecated_versionremoved_versionNTF)rr r!r"r#r$consider_pruned_trialsrestart_strategypopsize inc_popsizeuse_separable_cma with_marginlr_adapt source_trialscV|| dk7r5tjjddd}t|dt|2tjjddd}t|t|2tjjd dd}t|t||_||_|xs tjj| |_ ||_ ||_ t||_t|_||_| |_| |_| |_| |_||_|j&rd |_n|j(rd |_nd |_|j"r t1d|j&r t1d|j, t1d|j(r t1d|j*r t1d||| t3d| | r t3d| r| s| r t3d|j&r|j(r t3dyy)Nr+z`restart_strategy`z4.4.0r&)named_verr_verz~ From v4.4.0 onward, `restart_strategy` automatically falls back to `None`. `restart_strategy` will be supported in OptunaHub.z`x0`r%z`sigma0`)r$zsepcma:zcmawm:zcma:r,r0r3r1r2zQIt is prohibited to pass `source_trials` argument when x0 or sigma0 is specified.zNIt is prohibited to pass `source_trials` argument when using separable CMA-ES.z]It is prohibited to pass `use_separable_cma` or `with_margin` argument when using `lr_adapt`.zMCurrently, we do not support `use_separable_cma=True` and `with_margin=True`.)r_DEPRECATION_WARNING_TEMPLATEformatr FutureWarning_x0_sigma0optunasamplers RandomSampler_independent_sampler_n_startup_trials_warn_independent_samplingr_cma_rngr _search_space_consider_pruned_trials_popsize_use_separable_cma _with_margin _lr_adapt_source_trials _attr_prefixr ValueError)rrr r!r"r#r$r,r-r.r/r0r1r2r3msgs d/Users/jameslopez/projects/TradingBot25/.venv/lib/python3.12/site-packages/optuna/samplers/_cmaes.py__init__zCmaEsSampler.__init__sC>  ';"+<;;BB)CC %MM  >;;BB7'CC ] +  ;;BBwgCC ] + $7$c6??;X;X^b;X;c!!1*C''- 46'=$ "3'!+  " " )D     (D  &D   ' ' &'? @  " " &': ;    * & 7    &} 5 >> &z 2  $".F">u"E"K"K"M D,""$l->,PQ!-L #NrPc|j|t|dk(riS|j|}t||jkriSt ||j d}|j |}||j||j}|jt|jk7rN|jr@|jjj}tj!d|dd|_ iS|j#||j$}t||j&k\r@g} |d|j&D]} | j(Jdt+|t,j.r#t1j2| j4d} n|j7| j8} |jt:j<k(r | j(n | j( } | j?| | f|jA| tCjD|jG} |jI| }|D],}|jJjM|jN|||.|jPjRjUd d |jVz}|jXj[|t+|t,j.rI|j]\}}|jJjM|jNd|j_n|j]}|j`}|jJjM|jN||j$|jc|}|S) NrT)transform_step transform_0_1z7`CmaEsSampler` does not support dynamic search space. `z$` is used instead of `CmaEsSampler`.Fz"completed trials must have a value x_for_tellr*i)2_raise_error_if_multi_objectivelen _get_trialsrAr rH_restore_optimizer_init_optimizer directiondimboundsrBr@ __class____name___loggerwarning_get_solution_trials generationpopulation_sizevaluerYrCMAwMnparray system_attrs transformparamsrMINIMIZEappendtellpickledumpshex_split_optimizer_str_storageset_trial_system_attr _trial_idrCrngrandintnumber_rngr$asktolist_attr_key_generation untransform)rrZr[r\completed_trialstrans optimizerind_sampler_namesolution_trials solutionstxy optimizer_stroptimizer_attrskeyr$rxrbgeneration_attr_keyexternal_valuess rNsample_relativezCmaEsSampler.sample_relatives ,,U3 |  !I++E2  4#9#9 9I& T->->)>d ++,<=  ,,UEOODI ==C - -..#'#<#<#F#F#O#O ())MO38/I334DiFZFZ[  9#<#< <8:I$%@y'@'@Aww*P,PP*i5 !=>A1A$.2I2IIAGGPQPWPWx  !Q(B NN9 %#LL3779M"77 FO&44U__c?[^K_`'}}  ((E2U\\AD! i -!* FJ NN 0 0z/@/@/B ]]_F"77 ,, OO0)2F2F  ++F3rPc |jdzS)NrprKrTs rNrz!CmaEsSampler._attr_key_generations  <//rPc |jdzS)NrrrTs rN_attr_key_optimizerz CmaEsSampler._attr_key_optimizers  ;..rPc`djfdttDS)Nc3FK|]}jd|yw):N)r).0irrs rN z7CmaEsSampler._concat_optimizer_attrs..s- Hc1Ot778!= >Hcs!)joinrangerd)rrs``rN_concat_optimizer_attrsz$CmaEsSampler._concat_optimizer_attrss,ww HMcRaNbHc   rPct|}i}ttj|tz D]8}|tz}t |dztz|}|||||j d|<:|S)Nr*r)rdrmathceil_SYSTEM_ATTR_MAX_LENGTHminr)rr optimizer_lenattrsrstartends rNrz!CmaEsSampler._split_optimizer_strs{M* tyy1H!HIJA//Eq1u 77GC7DU37OET--.as3 4K rPcRt|D]}|jjDcic]#\}}|j|jr||%}}}t |dk(rZ|j |}tjtj|cSycc}}w)Nr) reversedrvrW startswithrrdrr|loadsbytesfromhex)rrr[rrrrrs rNrfzCmaEsSampler._restore_optimizers ./E#("4"4":":"<">$":":;U "<  ?#q( 88IM<< m <= =0s(B#c |jdddf}|jdddf}t|j}|jj|j |||z dz z}n|j |j}|j t j||z dz }n |j }d}ntjg} |jr| jtj|tjk(rdnd} |jD cgc]Z} | j| vrJt!|| j"r4|j | j$| t'd| j(zf\} } t| dk(r t+dt-j.| \}}}t1|t2}|j4rt|jdk(rt7dt8nVt-j:|||j|j<j>jAdd d |z|jB S|jDr/t jFt|jHtJ } tM|jHjOD]\}}tQ|tRtTfsJ|jV |jXrd | |<<|jZ|j\k(rd| |<[|jV|j\|jZz z | |<t-j^|||j| ||j<j>jAdd d |z|jBSt-j`||||j|j<j>jAdd d |z|jB|jbScc} w)Nrr*r+floatzNo compatible source_trialszSeparable CMA-ES does not operate meaningfully on single-dimensional search spaces. The setting `use_separable_cma=True` will be ignored.i )meansigmarjr$n_max_resamplingrq)dtypegg?)rrrjstepscovr$rrq)rrrrjr$rrqr2)2rjrdrJr;rwr<rtrrCOMPLETErErzPRUNEDrrystate_is_compatible_search_space distributionsrxrrrrLrget_warm_start_mgdmax_EPSrGr UserWarningSepCMArCrrrFrHemptyrDr enumeratevaluesrYrrsteploglowhighrsCMArI)rrrh lower_bounds upper_bounds n_dimensionrr rexpected_statessignrsource_solutionsrrdists rNrgzCmaEsSampler._init_optimizersY ||AqD) ||AqD) %,,'    &xx#|l'Ba&GGtxx0||# !< ABC)223O++&&z'8'89"^%<%<<1"D,, ,A77o-/qG*D43I,IJ,   #$) !>??!& 8 89I J D&#VT"  " "5<< A%[ ||  <<**221i@%'+%5$(MM    HHS!4!45UCE$U%8%8%?%?%AB4!$:K(LMMM99$"E!HXX*"E!H#yyDII,@AE!HC;;||]]&&..q)>3E3EFY[]3^%^6___s$==c<|jj||yrR)r@ before_trial)rrZr[s rNrzCmaEsSampler.before_trials !!..uers"   C;'3(20'H>78#( 5())ell2U[[@Hi@  E '  X & k K;k K\N N0KN NrP