&`iwddlZddlmZddlmZddlmZddlmZm Z m Z m Z m Z ddl mZddlmZddlZddlmZdd lmZdd lmZGd d Zed GddeZdS)N)abstractmethod)contextmanager)Path)CallableDictListOptionalUnion)Booster) CallbackEnv) Checkpoint) flatten_dict) PublicAPIceZdZdZdedddfdeeeeeeeeffdede de d ee eeee ee ffgeee fff d Z eefd eded efd Zdeeeeeffd efdZded efdZeded eefdZededefdZedefdZded dfdZdS)RayReportCallbackz model.txtNrTmetricsfilename frequencycheckpoint_at_endresults_postprocessing_fnc|t|tr|g}||_||_||_||_||_dSN) isinstancestr_metrics _filename _frequency_checkpoint_at_end_results_postprocessing_fn)selfrrrrrs v/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/train/lightgbm/_lightgbm_utils.py__init__zRayReportCallback.__init__sH gs # # iG !#"3*C''' checkpointreturnc|5}tt||cdddS#1swxYwYdS)aRetrieve the model stored in a checkpoint reported by this callback. Args: checkpoint: The checkpoint object returned by a training run. The checkpoint should be saved by an instance of this callback. filename: The filename to load the model from, which should match the filename used when creating the callback. Returns: The model loaded from the checkpoint. ) model_fileN) as_directoryr ras_posix)clsr$rcheckpoint_paths r! get_modelzRayReportCallback.get_model%s $ $ & & R/d?H&E&E&N&N&P&PQQQ R R R R R R R R R R R R R R R R R Rs0AAA evals_logct|d}|js|}nAi}|jD]7}t|jtr|j|}n|}||||<8|jr||}|S)N-) delimiter)rrrdictr)r r- result_dict report_dictkeymetrics r!_get_report_dictz"RayReportCallback._get_report_dict6s"9<<< } 7%KKK} 7 7dmT22!!]3/FF F#.v#6 C   * G99+FFKr#envci}|jD]Y}|dd\}}}t|dkr |d}d}nd}d}||vri||<|||||z<|||||dz<Z|S)Nrz-meanz-stdv)evaluation_result_listlen) r r7 eval_resultentry data_name eval_nameresultstdvsuffixs r!_get_eval_resultz"RayReportCallback._get_eval_resultFs / C CE+01: (Iy&5zzA~~Qx  ++)+ I&9?K "9v#5 6>B I&y7':;r#modelct)z_Get checkpoint from model. This method needs to be implemented by subclasses. NotImplementedError)r rFs r!_get_checkpointz!RayReportCallback._get_checkpointW "!r#r3ct)zSave checkpoint and report metrics corresonding to this checkpoint. This method needs to be implemented by subclasses. rH)r r3rFs r!_save_and_report_checkpointz-RayReportCallback._save_and_report_checkpoint_rKr#ct)zTReport Metrics. This method needs to be implemented by subclasses. rHr r3s r!_report_metricsz!RayReportCallback._report_metricsgrKr#cD||}||}|j|jdz k}|o|j}|jdko|jdz|jzdk}|p|}|r|||jdS||dS)Nr) rEr6 iteration end_iterationrrrMrFrP)r r7r>r3 on_last_itershould_checkpoint_at_end should_checkpoint_with_frequencyshould_checkpoints r!__call__zRayReportCallback.__call__os++C00 ++K88 }(9A(== #/#KD4K Oq Ocma&74?%Ja%O )5X8X  .  , ,[#) D D D D D   - - - - -r#)__name__ __module__ __qualname__CHECKPOINT_NAMEr r rrrintboolrfloatr" classmethodr r r,listr1r6r rErrJrMrPrYr#r!rrs!ODH'"& DD%T#YS#X >?@DD D  D $, d3eT%[&8 99:;T#u*=MM N$ DDDD$5DRR#R/2R RRR[R $sDdO/C*D KD""W"*1E"""^""t"G"""^""4"""^".K.D......r#rbeta) stabilitycZeZdZdZededeefdZde defdZ de fdZ dS) RayTrainReportCallbackaCreates a callback that reports metrics and checkpoints model. Args: metrics: Metrics to report. If this is a list, each item should be a metric key reported by LightGBM, and it will be reported to Ray Train/Tune under the same name. This can also be a dict of {: }, which can be used to rename LightGBM default metrics. filename: Customize the saved checkpoint file type by passing a filename. Defaults to "model.txt". frequency: How often to save checkpoints, in terms of iterations. Defaults to 0 (no checkpoints are saved during training). checkpoint_at_end: Whether or not to save a checkpoint at the end of training. results_postprocessing_fn: An optional Callable that takes in the metrics dict that will be reported (after it has been flattened) and returns a modified dict. Examples -------- Reporting checkpoints and metrics to Ray Tune when running many independent LightGBM trials (without data parallelism within a trial). .. testcode:: :skipif: True import lightgbm from ray.train.lightgbm import RayTrainReportCallback config = { # ... "metric": ["binary_logloss", "binary_error"], } # Report only log loss to Tune after each validation epoch. bst = lightgbm.train( ..., callbacks=[ RayTrainReportCallback( metrics={"loss": "eval-binary_logloss"}, frequency=1 ) ], ) Loading a model from a checkpoint reported by this callback. .. testcode:: :skipif: True from ray.train.lightgbm import RayTrainReportCallback # Get a `Checkpoint` object that is saved by the callback during training. result = trainer.fit() booster = RayTrainReportCallback.get_model(result.checkpoint) rFr%c#rKtjdvr~t j5}|t||j tj |VddddS#1swxYwYdSdVdS)N)rN) raytrain get_contextget_world_ranktempfileTemporaryDirectory save_modelrrr)r from_directory)r rFtemp_checkpoint_dirs r!rJz&RayTrainReportCallback._get_checkpoints 9 " " 1 1 3 3y @ @,.. E2E  &94>!J!J!S!S!U!UVVV /0CDDDDD E E E E E E E E E E E E E E E E E EJJJJJsAB&&B*-B*r3c||5}tj||ddddS#1swxYwYdS)N)rF)r$)rJrirjreport)r r3rFr$s r!rMz2RayTrainReportCallback._save_and_report_checkpoints  ! ! ! . . A* I  [Z  @ @ @ A A A A A A A A A A A A A A A A A As"AA  A cDtj|dSr)rirjrsrOs r!rPz&RayTrainReportCallback._report_metricss %%%%%r#N) rZr[r\__doc__rr r r rJrrMrPrcr#r!rgrgs88tW*1E^AtAGAAAA&4&&&&&&r#rg)rmabcr contextlibrpathlibrtypingrrrr r lightgbm.basicr lightgbm.callbackr ray.trainrir ray.tune.utilsrray.util.annotationsrrrgrcr#r!rs^%%%%%%88888888888888"""""")))))) ''''''******p.p.p.p.p.p.p.p.f VI&I&I&I&I&.I&I&I&I&I&r#