o 8wi3@sddlZddlZddlmZddlmZmZmZmZm Z m Z ddl Z ddl ZddlZddlZddlmZddlmZddlmZmZddlmZmZddlmZmZmZdd lmZddlmZmZmZd d lmZd d lm Z ej!"ej!j#Gd ddZ$dS)N)Path)IterableOptionalCallable GeneratorUnionDict)ExperimentalWarning) BasePruner) BaseSampler TPESampler)Trial FixedTrial) RDBStorageJournalStorageJournalFileStorage)tqdm)Pipeline) PipelineInputc@s6eZdZdZ      d dedeedeedeeee fdeeee fd ee d e fd d Z ed efddZed efddZed efddZ d!deedee efd eegeffddZ   d"deede dedee efd ef ddZ  d#deededee efd eeddffddZdS)$ OptimizeraMPipeline optimizer Parameters ---------- pipeline : `Pipeline` Pipeline. db : `Path`, optional Path to trial database on disk. Use ".sqlite" extension for SQLite backend, and ".journal" for Journal backend (prefered for parallel optimization). study_name : `str`, optional Name of study. In case it already exists, study will continue from there. # TODO -- generate this automatically sampler : `str` or sampler instance, optional Algorithm for value suggestion. Must be one of "RandomSampler" or "TPESampler", or a sampler instance. Defaults to "TPESampler". pruner : `str` or pruner instance, optional Algorithm for early pruning of trials. Must be one of "MedianPruner" or "SuccessiveHalvingPruner", or a pruner instance. Defaults to no pruning. seed : `int`, optional Seed value for the random number generator of the sampler. Defaults to no seed. average_case : `bool`, optional Optimise for average case. Defaults to False (i.e. worst case). NTpipelinedb study_namesamplerprunerseed average_casec Cs||_||_|durd|_n4t|jj}|dkr'tdtd|j|_n|dkr5td|j|_n |dkrBtt |j|_||_ t |t rN||_ n.t |trrz ttj||d|_ Wntyq} zd} t| d} ~ ww|dur|t|d|_ t |tr||_n%t |trz ttj||_Wnty} zd} t| d} ~ wwd|_tj|j d |j|j |j|jd |_||_dS) Nz.dbzHStorage with '.db' extension has been deprecated. Use '.sqlite' instead.z sqlite:///z.sqlitez.journal)rz8`sampler` must be one of "RandomSampler" or "TPESampler"zC`pruner` must be one of "MedianPruner" or "SuccessiveHalvingPruner"T)rload_if_existsstoragerr direction)rrstorage_rsuffixwarningswarnrrrr isinstancer rstrgetattroptunasamplersAttributeError ValueErrorr r rpruners create_study get_directionstudy_r) selfrrrrrrr extensionemsgr4X/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/pyannote/pipeline/optimizer.py__init__Qs^        zOptimizer.__init__returncCs|jjS)zReturn best loss so far)r/ best_valuer0r4r4r5 best_lossszOptimizer.best_losscCst|jj}|jj|dS)zReturn best parameters so fartrial)rr/ best_paramsr parameters)r0r<r4r4r5r=s zOptimizer.best_paramscCs|j|jS)z8Return pipeline instantiated with best parameters so far)r instantiater=r9r4r4r5 best_pipelineszOptimizer.best_pipelineFinputs show_progresscsBtt}dkrdddddtdtffdd }|S) a Create objective function used by optuna Parameters ---------- inputs : `iterable` List of inputs to process. show_progress : bool or dict Show within-trial progress bar using tqdm progress bar. Can also be a **kwarg dict passed to tqdm. Returns ------- objective : `callable` Callable that takes trial as input and returns correspond loss. Tz Current trialFr)descleavepositionr<r7c s0zj}Wnty}z d}g}WYd}~nd}~wwg}g}jjj|d}dkrAtddti}|dtD]q\}} t } || } t } | | | t } |durp| | | }| |nddl m }|| d| || d}t }| || dkr|d jdurqE||durt|nt||||rtjqEdkr||d t||d t||durtt|d kr|d}}}nt|d d \\}\}}}}n |jd d \}\}}jr |dur|St|Sjdkr|S|S)zCompute objective value Parameter --------- trial : `Trial` Current trial Returns ------- loss : `float` Loss Nr;Ftotalr) get_annotated annotation)uemrprocessing_timeevaluation_timeg?)alphaminimizer4)r get_metricNotImplementedErrorr?r>rlenupdate enumeratetimeappendlosspyannote.databaserGrreportnpmeanabs should_pruner(structs TrialPrunedclose set_user_attrsumunique bayes_mvsconfidence_intervalrr.)r<metricr2lossesrJrKr progress_bariinputbefore_processingoutputafter_processingbefore_evaluationrUrG_after_evaluationrY lower_bound upper_boundrAr0rBr4r5 objectivesl      "   z*Optimizer.get_objective..objective)listrPr float)r0rArBn_inputsrrr4rqr5 get_objectives  azOptimizer.get_objective n_iterations warm_startcCsd|j_|j||d}|r4|j|}ttjdtd|j |Wdn1s/wY|jj ||dddd|j_|j |j d S) aTune pipeline Parameters ---------- inputs : iterable List of inputs processed by the pipeline at each iteration. n_iterations : int, optional Number of iterations. Defaults to 10. warm_start : dict, optional Nested dictionary of initial parameters used to bootstrap tuning. Returns ------- result : dict ['loss'] ['params'] nested dictionary of optimal parameters TrBignorecategoryNrn_trialstimeoutn_jobsFrUparams) rtrainingrv_flattenr#catch_warningsfilterwarningsr r/ enqueue_trialoptimizer:r=)r0rArxryrBrrflattened_paramsr4r4r5tune"s  zOptimizer.tunec cs|j||d}z|j}Wnty"}z tj}WYd}~nd}~ww|rL|j|}ttj dt d|j |Wdn1sGwY d|j_ |j j|ddddz|j}|j}Wntyu}zWYd}~qLd}~wwd|j_ ||d VqM) a Parameters ---------- inputs : iterable List of inputs processed by the pipeline at each iteration. warm_start : dict, optional Nested dictionary of initial parameters used to bootstrap tuning. Yields ------ result : dict ['loss'] ['params'] nested dictionary of optimal parameters rzNr{r|Trr~Fr)rvr:r+rXinfrrr#rrr r/rrrr=) r0rAryrBrrr:r2rr=r4r4r5 tune_iterNs6      zOptimizer.tune_iter)NNNNNT)F)rwNT)NT)__name__ __module__ __qualname____doc__rrrr&rr r intboolr6propertyrtr:dictr=r@rrrrr rvrrrr4r4r4r5r5s~ A     /  r)%rSr#pathlibrtypingrrrrrrnumpyrXoptuna.loggingr(optuna.prunersoptuna.samplersoptuna.exceptionsr r r r optuna.trialr roptuna.storagesrrrrrrrlogging set_verbosityWARNINGrr4r4r4r5s&