o }oi+@sddlmZddlmZddlmZddlmZddlZddl Z ddl m Z ddl m Z ddlmZer:dd lmZ d;dd d!Zd?d#d$Zd?d%d&Zd?d'd(Zd@d*d+Zdd,dAd-d.ZdBd2d3ZdCd9d:ZdS)D) annotations)Sequence)cast) TYPE_CHECKINGN)_get_feasible_trials)StudyDirection) TrialState) FrozenTrialFtrialsSequence[FrozenTrial] directionsSequence[StudyDirection]consider_constraintboolreturnlist[FrozenTrial]csdd|D}|r t|}t|dkrgStfdd|Dr$tdtfdd|D}t|dd }d dt||DS) NcSsg|] }|jtjkr|qS)staterCOMPLETE.0trrQ/home/ubuntu/.local/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&)r r r loss_valueson_frontrrr"_get_pareto_front_trials_by_trialss  r1study'optuna.study.Study'cCst|j|j|Sr)r1r r )r2rrrr_get_pareto_front_trials+sr4)penaltyn_belowr/ np.ndarrayr5np.ndarray | Noner6 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.Nr6zIThe 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/r5r6ranksis_penalty_nan is_feasible is_infeasibletop_rank_infeasibletop_rank_penalty_nanrrr_fast_non_domination_rank1s> &     rQunique_lexsorted_loss_valuescCs|ddddf}|jd}tj|td}t|}t|rJd||d}<tj||||kdd}ttjt t tj tj f||}t|s|S)Nr@rr:T)axis) shaper,zerosrarangerr*rndarraytuplerBr; signedinteger)rRr/n_trialsr0remaining_indicesnew_nondominated_indexnondominated_and_not_toprrr_is_pareto_front_nds   r^cCsT|jd}tj|dddf}tj|td}|dd|ddk|dd<|S)Nrr@r:r>)rTr,minimum accumulateonesr)rRrZ cummin_value1r0rrr_is_pareto_front_2ds  rccCsH|j\}}|dkrtjt|td}d|d<|S|dkr t|St|S)Nr@r:Tr)rTr,rUrrrcr^)rRrZ n_objectivesr0rrr"_is_pareto_front_for_unique_sorteds rfr(cCs6|rt|Stj|ddd\}}t|}||dS)NrT)rSreturn_inverser>)rfr,uniquereshape)r/r(rR order_invr0rrrr.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) Nrr:r@T)rg)rgrSr'r>) rr,rUrBrTrhminrVsizer.ri) r/r6rZre_rKrRrjn_uniquerankindicesr0rrrrCs*       rCtrial0r trial1cCs|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.cSrrr r"rrrrr%z_dominates..cSrrr r"rrrrr%css|] \}}||kVqdSrr)rv0v1rrrrsz_dominates..)rrrrrr+r&rJ)rqrrr values0values1normalized_values0normalized_values1rrr _dominatess&    ryvalue float | None directionrfloatcCs$|durtdS|tjur| }|S)Ninf)r}rMAXIMIZE)rzr|rrrr!s  r!)F)r r r r rrrr)r2r3rrrr)r/r7r5r8r6r9rr7)rRr7rr7)r/r7r(rrr7)r/r7r6r9rr7)rqr rrr r r rr)rzr{r|rrr}) __future__rcollections.abcrtypingrrnumpyr,optuna&optuna.study._constrained_optimizationroptuna.study._study_directionr optuna.trialrr r1r4rQr^rcrfr.rCryr!rrrrs2          N    #