:jJddlmZddlZddlmZddlmZddlZddlm Z ddl m Z ddl m Z ddlmZdd lmZer dd lmZdd lmZd Ze j,d ddZGddej0Ze dGddeZddZe dGddeZy)) annotationsN)cast) TYPE_CHECKING)_DEPRECATION_WARNING_TEMPLATE)experimental_class) optuna_warn)StudyDirection) TrialState) FrozenTrial)Trialzterminator:cv_scoresz`optuna.terminator` modulez4.9.0z6.0.0)named_verr_vercBeZdZdZej ddZy)BaseErrorEvaluatorz Base class for error evaluators.cyNselftrialsstudy_directions p/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/optuna/terminator/erroreval.pyevaluatezBaseErrorEvaluator.evaluate s Nrzlist[FrozenTrial]rr returnfloat)__name__ __module__ __qualname____doc__abcabstractmethodrrrrrrs9* ! (     rr) metaclassz3.2.0c$eZdZdZ ddZy)CrossValidationErrorEvaluatoraAn error evaluator for objective functions based on cross-validation. This evaluator evaluates the objective function's statistical error, which comes from the randomness of dataset. This evaluator assumes that the objective function is the average of the cross-validation and uses the scaled variance of the cross-validation scores in the best trial at the moment as the statistical error. c|Dcgc]"}|jtjk(s!|$}}t|dkDsJ|tj k(rt |d}nt|d}|j}t|vr |t}n tdt|}|dkDsJdd|z d|dz z z}|tj|z} tj| } t| Scc}w)aEvaluate the statistical error of the objective function based on cross-validation. Args: trials: A list of trials to consider. The best trial in ``trials`` is used to compute the statistical error. study_direction: The direction of the study. Returns: A float representing the statistical error of the objective function. rc.td|jSNrrvaluets rz8CrossValidationErrorEvaluator.evaluate..K43Ir)keyc.td|jSr*r+r-s rr/z8CrossValidationErrorEvaluator.evaluate..Mr0rzCross-validation scores have not been reported. Please call `report_cross_validation_scores(trial, scores)` during a trial and pass the list of scores as `scores`.z9Should be guaranteed by `report_cross_validation_scores`.)stater COMPLETElenr MAXIMIZEmaxmin system_attrs_CROSS_VALIDATION_SCORES_KEY ValueErrornpvarsqrtr) rrrtrial best_trialbest_trial_attrs cv_scoreskscaler>stds rrz&CrossValidationErrorEvaluator.evaluate4s&&,REu{{j>Q>Q/Q%RR6{Q n55 5V)IJJV)IJJ%22 '+; ;()EFI.   N1uQQQuAQU #bffY''ggclSz3Ss "C4C4Nr)rr r!r"rrrrr'r')s(,!,(,  ,rr'ctttt|dkr t d|j j |jt|y)aA function to report cross-validation scores of a trial. This function should be called within the objective function to report the cross-validation scores. The reported scores are used to evaluate the statistical error for termination judgement. Args: trial: A :class:`~optuna.trial.Trial` object to report the cross-validation scores. scores: The cross-validation scores of the trial. r3z:The length of `scores` is expected to be greater than one.N) r_DEPRECATION_WARNING_MESSAGE FutureWarningr6r<storageset_trial_system_attr _trial_idr;)r@scoress rreport_cross_validation_scoresrNcsC,m< 6{aUVV MM''9UW]^rc,eZdZdZddZ ddZy)StaticErrorEvaluatora7An error evaluator that always returns a constant value. This evaluator can be used to terminate the optimization when the evaluated improvement potential is below the fixed threshold. Args: constant: A user-specified constant value to always return as an error estimate. c||_yr _constant)rconstants r__init__zStaticErrorEvaluator.__init__s !rc|jSrrRrs rrzStaticErrorEvaluator.evaluates ~~rN)rTrrNoner)rr r!r"rUrrrrrPrPxs- "!(  rrP)r@r rMz list[float]rrW) __future__rr#typingrrnumpyr=optuna._deprecatedroptuna._experimentalroptuna._warningsr optuna.studyr optuna.trial._stater optuna.trialr r r;formatrHABCMetarr'rNrPrrrrcs"  <3('*(" 6C<CC %     3;;  G6$666r_*G-r