&`i".ddlZddlmZddlmZddlmZddlmZddl m Z m Z m Z m Z mZddlmZddlZddlmZdd lmZdd lmZ dd lmZn#e$rGd d ZYnwxYwGddeZedGddeZdS)N)abstractmethod) OrderedDict)contextmanager)Path)CallableDictListOptionalUnion)Booster) Checkpoint) flatten_dict) PublicAPI)TrainingCallbackceZdZdS)rN)__name__ __module__ __qualname__t/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/train/xgboost/_xgboost_utils.pyrrs rrceZdZdZdedddfdeeeeeeeeffdede de d ee eeee ee ffgeee fff d Z eefd eded efd ZdZeded eefdZededefdZedefdZdede defdZded efdZdS)RayReportCallbackz model.ubjNrTmetricsfilename frequencycheckpoint_at_endresults_postprocessing_fnct|tr|g}||_||_||_||_||_d|_d|_dSN) isinstancestr_metrics _filename _frequency_checkpoint_at_end_results_postprocessing_fn _evals_log_last_checkpoint_iteration)selfrrrrrs r__init__zRayReportCallback.__init__s[ gs # # iG !#"3*C' +/'''r checkpointreturnc|5}t}|t|||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. N) as_directoryr load_modelras_posix)clsr,rcheckpoint_pathboosters r get_modelzRayReportCallback.get_model4s" $ $ & & /iiG   tOX>>GGII J J J                  sAA''A+.A+ct|tr5t|d}t|D]}||d||<nt |}|js|}nAi}|jD]7}t|jtr|j|}n|}||||<8|jr||}|S)N-) delimiter)r!rrlistdictr#r')r* evals_log result_dictk report_dictkeymetrics r_get_report_dictz"RayReportCallback._get_report_dictJs i - - *&yC@@@K+&& 4 4!,Q!3 A 4y//K} 7%KKK} 7 7dmT22!!]3/FF F#.v#6 C   * G99+FFKrmodelct)z_Get checkpoint from model. This method needs to be implemented by subclasses. NotImplementedError)r*rCs r_get_checkpointz!RayReportCallback._get_checkpointc "!rr?ct)zSave checkpoint and report metrics corresonding to this checkpoint. This method needs to be implemented by subclasses. rE)r*r?rCs r_save_and_report_checkpointz-RayReportCallback._save_and_report_checkpointkrHrct)zTReport Metrics. This method needs to be implemented by subclasses. rEr*r?s r_report_metricsz!RayReportCallback._report_metricssrHrepochr<c||_|jdk}| o|dz|jzdk}||}|r||_|||dS||dS)Nr)r(r%rBr)rJrM)r*rCrNr<checkpointing_disabledshould_checkpointr?s rafter_iterationz!RayReportCallback.after_iteration{s#!%A!5' & MEAI+HA+M ++I66  ..3D +  , ,[% @ @ @ @ @   - - - - -rc|js|S|j"|dz |jkr|S|jr||jni}||||S)NrP)r&r)num_boosted_roundsr(rBrJ)r*rCr?s rafter_trainingz RayReportCallback.after_trainings& L  + 7((**Q.$2QQQL@DWd++DO<<?@// /  / $, d3eT%[&8 99:;T#u*=MM N$ ////4(  [*2"W"*1E"""^""t"G"""^""4"""^".W.S.T...."Grrbeta) stabilityc*eZdZdZdejdddfdeeee ee eeffdede de d ee e eeee effge eefff fd Zed ed eefd Zde d efdZde fdZxZS)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) NrTrrrrrcTt|||||dS)N)rrrrr)superr+)r*rrrrr __class__s rr+zRayTrainReportCallback.__init__s? /&?      rrCr-c#hKtjdvryt j5}|t||j t|VddddS#1swxYwYdSdVdS)N)rN) raytrain get_contextget_world_ranktempfileTemporaryDirectory save_modelrr$r1r )r*rCtemp_checkpoint_dirs rrGz&RayTrainReportCallback._get_checkpoints 9 " " 1 1 3 3y @ @,.. 62E  &94>!J!J!S!S!U!UVVV !455555 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6JJJJJsA B!!B%(B%r?c||5}tj||ddddS#1swxYwYdS)N)rC)r,)rGrdrereport)r*r?rCr,s rrJz2RayTrainReportCallback._save_and_report_checkpoints  ! ! ! . . 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