o /wÖi˜ã@s^ddlmZddlZddlmZddlZddlmZddlm Z dd d „Z Gd d „d eƒZ dS)é)Ú annotationsN)ÚAny)Ú BasePruner)Ú_is_first_in_interval_stepÚvaluerÚreturnÚfloatc Cs<zt|ƒ}W|Sttfyd t|ƒj¡}t|ƒd‚w)Nz@The `value` argument is of type '{}' but supposed to be a float.)rÚ TypeErrorÚ ValueErrorÚformatÚtypeÚ__name__)rÚmessage©rúV/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/optuna/pruners/_threshold.pyÚ _check_value s úÿ ürc@s.eZdZdZ    ddd d„Zddd„ZdS)ÚThresholdPruneraÎPruner to detect outlying metrics of the trials. Prune if a metric exceeds upper threshold, falls behind lower threshold or reaches ``nan``. Example: .. testcode:: from optuna import create_study from optuna.pruners import ThresholdPruner from optuna import TrialPruned def objective_for_upper(trial): for step, y in enumerate(ys_for_upper): trial.report(y, step) if trial.should_prune(): raise TrialPruned() return ys_for_upper[-1] def objective_for_lower(trial): for step, y in enumerate(ys_for_lower): trial.report(y, step) if trial.should_prune(): raise TrialPruned() return ys_for_lower[-1] ys_for_upper = [0.0, 0.1, 0.2, 0.5, 1.2] ys_for_lower = [100.0, 90.0, 0.1, 0.0, -1] study = create_study(pruner=ThresholdPruner(upper=1.0)) study.optimize(objective_for_upper, n_trials=10) study = create_study(pruner=ThresholdPruner(lower=0.0)) study.optimize(objective_for_lower, n_trials=10) Args: lower: A minimum value which determines whether pruner prunes or not. If an intermediate value is smaller than lower, it prunes. upper: A maximum value which determines whether pruner prunes or not. If an intermediate value is larger than upper, it prunes. n_warmup_steps: Pruning is disabled if the step is less than the given number of warmup steps. interval_steps: Interval in number of steps between the pruning checks, offset by the warmup steps. If no value has been reported at the time of a pruning check, that particular check will be postponed until a value is reported. Value must be at least 1. NréÚlowerú float | NoneÚupperÚn_warmup_stepsÚintÚinterval_stepsrÚNonecCsº|dur |dur tdƒ‚|durt|ƒ}|durt|ƒ}|dur"|ntdƒ }|dur-|ntdƒ}||kr9tdƒ‚|dkrDtd |¡ƒ‚|dkrOtd |¡ƒ‚||_||_||_||_dS)Nz(Either lower or upper must be specified.Úinfz#lower should be smaller than upper.rz5Number of warmup steps cannot be negative but got {}.rz5Pruning interval steps must be at least 1 but got {}.) r rrr r Ú_lowerÚ_upperÚ_n_warmup_stepsÚ_interval_steps)ÚselfrrrrrrrÚ__init__Qs,ÿÿ zThresholdPruner.__init__Ústudyú'optuna.study.Study'Útrialú'optuna.trial.FrozenTrial'ÚboolcCsv|j}|dur dS|j}||krdSt||j ¡||jƒsdS|j|}t |¡r+dS||jkr2dS||j kr9dSdS)NFT) Ú last_steprrÚintermediate_valuesÚkeysrÚmathÚisnanrr)r r"r$ÚsteprÚ latest_valuerrrÚpruners$ÿ    zThresholdPruner.prune)NNrr) rrrrrrrrrr)r"r#r$r%rr&)r Ú __module__Ú __qualname__Ú__doc__r!r.rrrrrs: û!r)rrrr) Ú __future__rr*ÚtypingrÚoptunaÚoptuna.prunersrÚoptuna.pruners._percentilerrrrrrrÚs