&`itddlmZmZmZmZddlZddlZddlZ ddl m Z ddl Z ddl m Z ddlmZddlmZddlmZddlmZmZerdd lmZd Zdd ZdefdZdZedkr(e jdeZ ee Z!dSdS)) TYPE_CHECKINGAnyDictOptionalN)train_test_split)tune)PlacementGroupFactory)Trial)TuneReportCheckpointCallback) ASHASchedulerResourceChangingScheduler)TuneControllerzbooster-checkpoint.json best_resultray.tune.Resultctj|jt}d|jdz }t d|jt d|d|S)Nfilenameg? eval-loglosszBest model parameters: zBest model total accuracy: z.4f)r get_model checkpointCHECKPOINT_FILENAMEmetricsprintconfig)rbest_bstaccuracys /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/tune/examples/xgboost_dynamic_resources_example.pyget_best_model_checkpointrso+5)<H[(88H 8K$6 8 8999 6 6 6 6777 Orc Dtjd\}}t||d\}}}}t j||}t j||}d} t j} | rtj | t} tt j j|d<td|dd | t j|||d fgd | td t gdS)NT) return_X_yg?) test_size)labelrnthreadz nthreads: z xgb_model: evalF) frequencyr)evals verbose_eval xgb_model callbacks)sklearndatasetsload_breast_cancerrxgbDMatrixrget_checkpointr rrint get_contextget_trial_resources head_cpusrtrain) rdatalabelstrain_xtest_xtrain_ytest_y train_settest_setr*rs rtrain_breast_cancerr?"sJ#66$6GGLD&'7fPT'U'U'U$GVWf G7333I{6000HI$&&J 0: !4   D,..BBDDNOOF9 Avi( A Ai A ABBBI&!" (,     rc Ndddgddtjddgtjdd gtjdd gdd d }td dd }dddtdtt t fdddttf d}t||}tj tj ttdddgtj ddd||}|}|S)Nzbinary:logisticloglosserror r&g?g?i) objective eval_metric max_depth learning_ratemin_child_weight subsamplecolsample_bynode random_statenum_parallel_tree)max_t grace_periodreduction_factortune_controllerrtrialresult schedulerr returncH|j}|ddkrdS|tdddg}|jdd}|j}t ||t|z}t|ddgS)aOThis is a basic example of a resource allocating function. The function naively balances available CPUs over live trials. This function returns a new ``PlacementGroupFactory`` with updated resource requirements, or None. If the returned ``PlacementGroupFactory`` is equal by value to the one the trial has currently, the scheduler will skip the update process internally (same with None). See :class:`DistributeResources` for a more complex, robust approach. Args: tune_controller: Trial runner for this Tune run. Can be used to obtain information about other trials. trial: The trial to allocate new resources to. result: The latest results of trial. scheduler: The scheduler calling the function. training_iterationr&NrCPUGPUr[) _base_trial_resourcesr required_resourcesget_resource_updater get_num_cpusmaxlenget_live_trials)rSrTrUrVbase_trial_resourcemin_cputotal_available_cpus cpu_to_uses r%example_resources_allocation_functionz;tune_xgboost..example_resources_allocation_function]s:(= & '! + +4  &"719M9M8N"O"O &8<rsu555555555555444444 EEEEEE%%%%%%EEEEEEHHHHHHHHBAAAAAA/&&&&&Rb%b%b%J z CHa,..K((55HHH r