o /wi,@sddlmZddlmZddlZddlZddlmZddl m Z ddl m Z ddl m Z  d9d:ddZ d9d;ddZddddd(d)Zdd*d?d+d,Zd@d0d1ZdAd7d8ZdS)B) annotations)SequenceN)_get_feasible_trials)StudyDirection) FrozenTrial) TrialStateFtrialsSequence[FrozenTrial] directionsSequence[StudyDirection]consider_constraintboolreturnlist[FrozenTrial]csdd|D}|r t|}t|dkrgStfdd|Dr$tdtfdd|D}t|dd }d dt||DS) NcSsg|] }|jtjkr|qS)staterCOMPLETE.0trrZ/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/optuna/study/_multi_objective.py sz6_get_pareto_front_trials_by_trials..rc3s"|] }t|jtkVqdSN)lenvaluesrr rr s z5_get_pareto_front_trials_by_trials..zLThe number of the values and the number of the objectives must be identical.cs"g|] }ddt|jDqS)cSg|] \}}t||qSr_normalize_valuervdrrrr zA_get_pareto_front_trials_by_trials...)ziprrrrrr s"Fassume_unique_lexsortedcSsg|]\}}|r|qSrr)rr is_paretorrrr#s)rrany ValueErrornpasarray_is_pareto_frontr$)rr r loss_valueson_frontrrr"_get_pareto_front_trials_by_trialss  r/study'optuna.study.Study'cCst|j|j|Sr)r/rr )r0r rrr_get_pareto_front_trials&sr2)penaltyn_belowr- np.ndarrayr3np.ndarray | Noner4 int | Nonec Csxt|dkr tjgtdS|pt|}|dksJd|dur%t||dSt|t|kr`__, but the handling of the case when penalty values are None is different. The algorithm assigns the rank according to the following rules: 1. Feasible trials: First, the algorithm assigns the rank to feasible trials, whose penalty values are less than or equal to 0, according to unconstrained version of fast non- dominated sort. 2. Infeasible trials: Next, the algorithm assigns the rank from the minimum penalty value of to the maximum penalty value. 3. Trials with no penalty information (constraints value is None): Finally, The algorithm assigns the rank to trials with no penalty information according to unconstrained version of fast non-dominated sort. Note that only this step is different from the original constrained NSGA-II algorithm. Plus, the algorithm terminates whenever the number of sorted trials reaches n_below. Args: loss_values: Objective values, which is better when it is lower, of each trials. penalty: Constraints values of each trials. Defaults to None. n_below: The minimum number of top trials required to be sorted. The algorithm will terminate when the number of sorted trials reaches n_below. Defaults to None. Returns: An ndarray in the shape of (n_trials,), where each element is the non-domination rank of each trial. The rank is 0-indexed. This function guarantees the correctness of the ranks only up to the top-``n_below`` solutions. If a solution's rank is worse than the top-``n_below`` solution, its rank will be guaranteed to be greater than the rank of the top-``n_below`` solution. rdtypez#n_below must be a positive integer.Nr4zIThe length of penalty and loss_values must be same, but got len(penalty)=z and len(loss_values)=.)initialzAll the rank must be updated.) rr*arrayint_calculate_nondomination_rankr)fullisnan logical_and count_nonzeromaxnewaxisall) r-r3r4ranksis_penalty_nan is_feasible is_infeasibletop_rank_infeasibletop_rank_penalty_nanrrr_fast_non_domination_rank,s> &     rOunique_lexsorted_loss_valuescCs||ddddf}|jd}tj|td}t|}t|rrr8)axisT)shaper*zerosr arangerr()rPr-n_trialsr.nondominated_indicesnondominated_and_not_toprrr_is_pareto_front_ndzs   rXcCsT|jd}tj|dddf}tj|td}|dd|ddk|dd<|S)Nrr>r8r<)rRr*minimum accumulateonesr )rPrU cummin_value1r.rrr_is_pareto_front_2ds  r]cCsH|j\}}|dkrtjt|td}d|d<|S|dkr t|St|S)Nr>r8Tr)rRr*rSrr r]rX)rPrU n_objectivesr.rrr"_is_pareto_front_for_unique_sorteds r`r&cCs6|rt|Stj|ddd\}}t|}||dS)NrT)rQreturn_inverser<)r`r*uniquereshape)r-r&rP order_invr.rrrr,s r,r:c Cst|dks|dur|dkrtjt|tdS|j\}}|dkr1tj|dddfdd\}}|Stj|ddd\}}|jd}t|pFt|t|}tj|td}d} t|} || j|krt |dd} | || | <| | } || }| d7} || j|ks`| || <|| dS) Nrr8r>T)ra)rarQr%r<) rr*rSr@rRrbminrTsizer,rc) r-r4rUr__rIrPrdn_uniquerankindicesr.rrrrAs*       rAtrial0rtrial1cCs|j}|j}|jtjkrdS|jtjkrdS|dusJ|dus"Jt|t|kr.tdt|t|kr:tdddt||D}ddt||D}||krTdStdd t||DS) NFTz?Trials with different numbers of objectives cannot be compared.zIThe number of the values and the number of the objectives are mismatched.cSrrrr rrrrr#z_dominates..cSrrrr rrrrr#css|] \}}||kVqdSrr)rv0v1rrrrsz_dominates..)rrrrrr)r$rH)rkrlr values0values1normalized_values0normalized_values1rrr _dominatess&    rsvalue float | None directionrfloatcCs$|durtdS|tjur| }|S)Ninf)rwrMAXIMIZE)rtrvrrrrs  r)F)rr r r r r rr)r0r1r r rr)r-r5r3r6r4r7rr5)rPr5rr5)r-r5r&r rr5)r-r5r4r7rr5)rkrrlrr r rr )rtrurvrrrw) __future__rcollections.abcrnumpyr*optuna&optuna.study._constrained_optimizationroptuna.study._study_directionr optuna.trialrrr/r2rOrXr]r`r,rArsrrrrrs,        N    #