o $iw@sddlZddlmZddlmZddlmZddlmZm Z m Z m Z m Z ddl mZddlmZddlZddlmZdd lmZdd lmZGd d d Zed dGdddeZdS)N)abstractmethod)contextmanager)Path)CallableDictListOptionalUnion)Booster) CallbackEnv) Checkpoint) flatten_dict) PublicAPIc@s$eZdZdZdedddfdeeeeeeeeffdede de d ee eeee ee ffgeee fff d d Z eefd eded efddZdeeeeeffd efddZded efddZeded eefddZededefddZedefddZded dfddZdS) RayReportCallbackz model.txtNrTmetricsfilename frequencycheckpoint_at_endresults_postprocessing_fncCs2t|tr|g}||_||_||_||_||_dSN) isinstancestr_metrics _filename _frequency_checkpoint_at_end_results_postprocessing_fn)selfrrrrrr_/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/ray/train/lightgbm/_lightgbm_utils.py__init__s  zRayReportCallback.__init__ checkpointreturncCs@|}tt||dWdS1swYdS)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_pathrrr get_model%s $zRayReportCallback.get_model evals_logcCsdt|dd}|js |}ni}|jD]}t|jtr|j|}n|}||||<q|jr0||}|S)N-) delimiter)r rrdictr)rr) result_dict report_dictkeymetricrrr_get_report_dict6s     z"RayReportCallback._get_report_dictenvc Csi}|jD]8}|dd\}}}t|dkr|d}d}nd}d}||vr)i||<|||||<|dur=||||d<q|S)Nrz-meanz-stdv)evaluation_result_listlen) rr2 eval_resultentry data_name eval_nameresultstdvsuffixrrr_get_eval_resultFs  z"RayReportCallback._get_eval_resultmodelcCt)z_Get checkpoint from model. This method needs to be implemented by subclasses. NotImplementedError)rr@rrr_get_checkpointWz!RayReportCallback._get_checkpointr.cCrA)zSave checkpoint and report metrics corresonding to this checkpoint. This method needs to be implemented by subclasses. rB)rr.r@rrr_save_and_report_checkpoint_rEz-RayReportCallback._save_and_report_checkpointcCrA)zTReport Metrics. This method needs to be implemented by subclasses. rBrr.rrr_report_metricsgrEz!RayReportCallback._report_metricscCsx||}||}|j|jdk}|o|j}|jdko%|jd|jdk}|p)|}|r5|||jdS||dS)Nr) r?r1 iteration end_iterationrrrFr@rH)rr2r8r. on_last_itershould_checkpoint_at_end should_checkpoint_with_frequencyshould_checkpointrrr__call__os   zRayReportCallback.__call__)__name__ __module__ __qualname__CHECKPOINT_NAMErr rrrintboolrfloatr classmethodr r r(listr,r1r r?rrDrFrHrPrrrrrsL(  "rbeta) stabilityc@sJeZdZdZededeefddZde defddZ de fd d Z d S) 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) r@r"ccsntjdvr2t}|t||j t |VWddS1s+wYdSdVdS)N)rN) raytrain get_contextget_world_ranktempfileTemporaryDirectory save_modelrrr%r from_directory)rr@temp_checkpoint_dirrrrrDs " z&RayTrainReportCallback._get_checkpointr.cCsB|j|d}tjj||dWddS1swYdS)N)r@)r!)rDr]r^report)rr.r@r!rrrrFs"z2RayTrainReportCallback._save_and_report_checkpointcCstj|dSr)r]r^rfrGrrrrHsz&RayTrainReportCallback._report_metricsN) rQrRrS__doc__rr rr rDrrFrHrrrrr\s :r\)raabcr contextlibrpathlibrtypingrrrrr lightgbm.basicr lightgbm.callbackr ray.trainr]r ray.tune.utilsr ray.util.annotationsrrr\rrrrs        s