-ji|SSKJr SSKrSSKJr SSKrSSKJr SSKJ r SSK J r "SS\\R5r g) ) annotationsN)Any) BaseSampler) FrozenTrial) TrialStatec^\rSrSrSrSrSrSU4Sjjr\SSj5r \SSj5r SSjr \ SS j5r \ RSS j5r \R SS j5rSS jrSS jrSSjrSrU=r$) BaseGASampler aFBase class for Genetic Algorithm (GA) samplers. Genetic Algorithm samplers generate new trials by mimicking natural selection, using generations and populations to iteratively improve solutions. This base class defines the interface for GA samplers in Optuna and provides utility methods for managing generations and populations. The selection process is handled by :meth:`~BaseGASampler.select_parent`, which must be implemented by subclasses to define the parent selection strategy. Generation and population management is facilitated by methods like :meth:`~BaseGASampler.get_generation` and :meth:`~BaseGASampler.get_population`, ensuring consistent tracking and selection. Note: This class should be extended by subclasses that define specific GA sampling strategies, including parent selection and crossover operations. zBaseGASampler:generationzBaseGASampler:parent:c v>[TU]"S0UD6 URS3UlURS3Ulg)Nz :generationz:parent:)super__init_subclass____name___GENERATION_KEY_PARENT_CACHE_KEY_PREFIX)clskwargs __class__s S/home/james-whalen/.local/lib/python3.13/site-packages/optuna/samplers/_ga/_base.pyrBaseGASampler.__init_subclass__$s9 !+F+!$k:*-,,x'@$cUR$N)rrs r_get_generation_key!BaseGASampler._get_generation_key)s"""rcUR$r)rrs r_get_parent_cache_key_prefix*BaseGASampler._get_parent_cache_key_prefix-s+++rcXlgr_population_size)selfpopulation_sizes r__init__BaseGASampler.__init__1s /rcUR$rr!)r#s rr$BaseGASampler.population_size4s$$$rcXlgrr!)r#values rr$r(8s %rc[e)aSelect parent trials from the population for the given generation. This method is called once per generation to select parents from the population of the current generation. Output of this function is cached in the study system attributes. This method must be implemented in a subclass to define the specific selection strategy. Args: study: Target study object. generation: Target generation number. Returns: List of parent frozen trials. )NotImplementedError)r#study generations r select_parentBaseGASampler.select_parent<s ("!rcURRUR5S5nUbU$URS[R /SS9nSupV[ U5HDnURRUR5S5nX5:aM4X5:aUnSnM?US- nMF URcS5eX`R:aUnOUS-nURRURUR5U5 U$) aGet the generation number of the given trial. This method returns the generation number of the specified trial. If the generation number is not set in the trial's system attributes, it will calculate and set the generation number. The current generation number depends on the maximum generation number of all completed trials. Args: study: Study object which trial belongs to. trial: Trial object to get the generation number. Returns: Generation number of the given trial. NFTdeepcopystates use_cache)rrzPopulation size must be set.) system_attrsgetr _get_trialsrCOMPLETEreversedr"_storageset_trial_system_attr _trial_id)r#r-trialr.trialsmax_generationmax_generation_countts rget_trial_generation"BaseGASampler.get_trial_generationRs&''++D,D,D,FM  ! ""E:;N;N:O[_"`/3,&!A++D,D,D,FKJ*,!+'($$)$"$$0P2PP0 "7"7 7'J'!+J ,, OOT557 rcURS[R/SS9Vs/sH5nURR UR 5S5U:XdM3UPM7 sn$s snf)zGet the population of the given generation. Args: study: Target study object. generation: Target generation number. Returns: List of frozen trials in the given generation. FTr2N)r:rr;r8r9r)r#r-r.r@s rget_populationBaseGASampler.get_populationss** (;(;'<+ !!%%d&>&>&@$G:U    s 2A"A"c&US:Xa/$URRUR5nURUR 5[ U5-S5nUb>UR SS9n[U5nUVs/sHowRU;dMUPM sn$URX5nURRURUR 5[ U5-UVs/sHowRPM sn5 U$s snfs snf)a9Get the parent population of the given generation. This method caches the parent population in the study's system attributes. Args: study: Target study object. generation: Target generation number. Returns: List of parent frozen trials. If `generation == 0`, returns an empty list. rNF)r3) r=get_study_system_attrs _study_idr9rstrr:setr?r/set_study_system_attr) r#r-r.study_system_attrscached_parent_population_idsrAparent_population_idsr@parent_populations rget_parent_population#BaseGASampler.get_parent_populations ?I"^^BB5??S'9'='=  - - /#j/ A4( $ ( 3&&&6F$'(D$E !'-ZveDY1YEvZ Z $ 2 25 E  NN 0 0113c*oE.?@.?U.?@  % $[ As;D D +D r!)rr)returnrM)r$ int | None)rVrW)r*intrVNone)r- optuna.Studyr.rXrVzlist[FrozenTrial])r-rZr@rrVrX)r __module__ __qualname____firstlineno____doc__rrr classmethodrrr%propertyr$setterabcabstractmethodr/rErHrT__static_attributes__ __classcell__)rs@rr r s&1O6A ##,,0%%&& ""*.` (!%!%rr ) __future__rrbtypingroptunaoptuna.samplers._baseroptuna.trial._frozenroptuna.trial._staterABCr r rrrms."  -,*j%Kj%r