o $i"@sddlZddlmZddlmZddlmZddlmZddl m Z m Z m Z m Z mZddlmZddlZddlmZdd lmZdd lmZzdd lmZWney^Gd d d ZYnwGdddeZeddGdddeZdS)N)abstractmethod) OrderedDict)contextmanager)Path)CallableDictListOptionalUnion)Booster) Checkpoint) flatten_dict) PublicAPI)TrainingCallbackc@s eZdZdS)rN)__name__ __module__ __qualname__rr]/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/ray/train/xgboost/_xgboost_utils.pyrsrc@seZdZdZdedddfdeeeeeeeeffdede de d ee eeee ee ffgeee fff d d Z eefd eded efddZddZeded eefddZededefddZedefddZdede defddZded efddZdS) RayReportCallbackz model.ubjNrTmetricsfilename frequencycheckpoint_at_endresults_postprocessing_fncCs>t|tr|g}||_||_||_||_||_d|_d|_dSN) isinstancestr_metrics _filename _frequency_checkpoint_at_end_results_postprocessing_fn _evals_log_last_checkpoint_iterationselfrrrrrrrr__init__s  zRayReportCallback.__init__ checkpointreturncCsJ|}t}|t|||WdS1swYdS)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. N) as_directoryr load_modelras_posix)clsr(rcheckpoint_pathboosterrrr get_model4s $zRayReportCallback.get_modelcCst|trt|dd}t|D] }||d||<qnt|}|js%|}ni}|jD]}t|jtr8|j|}n|}||||<q*|jrI||}|S)N-) delimiter)rrr listdictrr")r& evals_log result_dictk report_dictkeymetricrrr_get_report_dictJs"       z"RayReportCallback._get_report_dictmodelcCt)z_Get checkpoint from model. This method needs to be implemented by subclasses. NotImplementedError)r&r=rrr_get_checkpointcz!RayReportCallback._get_checkpointr9cCr>)zSave checkpoint and report metrics corresonding to this checkpoint. This method needs to be implemented by subclasses. r?)r&r9r=rrr_save_and_report_checkpointkrBz-RayReportCallback._save_and_report_checkpointcCr>)zTReport Metrics. This method needs to be implemented by subclasses. r?r&r9rrr_report_metricssrBz!RayReportCallback._report_metricsepochr6cCsZ||_|jdk}| o|d|jdk}||}|r&||_|||dS||dS)Nr)r#r r<r$rCrE)r&r=rFr6checkpointing_disabledshould_checkpointr9rrrafter_iteration{s  z!RayReportCallback.after_iterationcCsP|js|S|jdur|d|jkr|S|jr||jni}||||S)NrG)r!r$num_boosted_roundsr#r<rC)r&r=r9rrrafter_trainings  z RayReportCallback.after_training)rrrCHECKPOINT_NAMEr r rrrintboolrfloatr' classmethodr r r0r<rrArCrErJrLrrrrrsL(  rbeta) stabilitycseZdZdZdejdddfdeeee ee eeffdede de d ee e eeee effge eefff fd d Zed ed eefddZde d efddZde fddZZS)RayTrainReportCallbackaaXGBoost callback to save checkpoints and report metrics. Args: metrics: Metrics to report. If this is a list, each item describes the metric key reported to XGBoost, and it will be reported under the same name. This can also be a dict of {: }, which can be used to rename xgboost default metrics. filename: Customize the saved checkpoint file type by passing a filename. Defaults to "model.ubj". 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. For example, this can be used to average results across CV fold when using ``xgboost.cv``. Examples -------- Reporting checkpoints and metrics to Ray Tune when running many independent xgboost trials (without data parallelism within a trial). .. testcode:: :skipif: True import xgboost from ray.tune import Tuner from ray.train.xgboost import RayTrainReportCallback def train_fn(config): # Report log loss to Ray Tune after each validation epoch. bst = xgboost.train( ..., callbacks=[ RayTrainReportCallback( metrics={"loss": "eval-logloss"}, frequency=1 ) ], ) tuner = Tuner(train_fn) results = tuner.fit() Loading a model from a checkpoint reported by this callback. .. testcode:: :skipif: True from ray.train.xgboost import RayTrainReportCallback # Get a `Checkpoint` object that is saved by the callback during training. result = trainer.fit() booster = RayTrainReportCallback.get_model(result.checkpoint) NrTrrrrrcstj|||||ddS)N)rrrrr)superr'r% __class__rrr's  zRayTrainReportCallback.__init__r=r)ccsltjdvr1t}|t||j t |VWddS1s*wYdSdVdS)N)rN) raytrain get_contextget_world_ranktempfileTemporaryDirectory save_modelrrr,r )r&r=temp_checkpoint_dirrrrrAs  " z&RayTrainReportCallback._get_checkpointr9cCsB|j|d}tjj||dWddS1swYdS)N)r=)r()rArXrYreport)r&r9r=r(rrrrCs"z2RayTrainReportCallback._save_and_report_checkpointcCstj|dSr)rXrYr`rDrrrrEsz&RayTrainReportCallback._report_metrics)rrr__doc__rrMr r rrrrNrOrrPr'rr r rArCrE __classcell__rrrVrrTs0=( rT)r\abcr collectionsr contextlibrpathlibrtypingrrrr r xgboost.corer ray.trainrXr ray.tune.utilsr ray.util.annotationsrxgboost.callbackr ImportErrorrrTrrrrs(