o ciu@sddlmZmZmZmZddlZddlZddlZ ddl m Z ddl Z ddl m Z ddlmZddlmZddlmZddlmZmZerJdd lmZd Zdd dZdefddZddZedkroe jddeZ ee Z!dSdS)) TYPE_CHECKINGAnyDictOptionalN)train_test_split)tune)PlacementGroupFactory)Trial)TuneReportCheckpointCallback) ASHASchedulerResourceChangingScheduler)TuneControllerzbooster-checkpoint.json best_resultray.train.ResultcCsBtj|jtd}d|jd}td|jtd|d|S)Nfilenameg? eval-loglosszBest model parameters: zBest model total accuracy: z.4f)r get_model checkpointCHECKPOINT_FILENAMEmetricsprintconfig)rbest_bstaccuracyrg/home/ubuntu/.local/lib/python3.10/site-packages/ray/tune/examples/xgboost_dynamic_resources_example.pyget_best_model_checkpointsrrc Cstjjdd\}}t||dd\}}}}tj||d}tj||d}d} t} | r1tj | t d} t t j|d<td|dd | tj|||d fgd | td t d gddS)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'rrrrtrain_breast_cancer"s0 r<c Csdddgddtddgtdd gtdd gdd d }td ddd }dddtdtttfdddttf dd}t ||d}tj tj t tdddgdtj ddd|d|d}|}|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_controllerr trialresult schedulerr returncSsl|j}|ddkr dS|durtdddg}|jdd}|j}t||t|}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#NrCPUGPUrV) _base_trial_resourcesrrequired_resourcesget_resource_updater get_num_cpusmaxlenget_live_trials)rOrPrQrRbase_trial_resourcemin_cputotal_available_cpus cpu_to_userrr%example_resources_allocation_function]s  z;tune_xgboost..example_resources_allocation_function)base_schedulerresources_allocation_functionrrU) resourcesrmin)metricmode num_samplesrR) tune_config param_space)r grid_searchr r rstrrrrr Tunerwith_resourcesr< TuneConfigfitget_best_result) search_spacererdrRtunerresultsrrr tune_xgboostKsT      < rx__main__)num_cpus)rr)"typingrrrrsklearn.datasetsr)sklearn.metricsxgboostr,sklearn.model_selectionrrayr#ray.tune.execution.placement_groupsrray.tune.experimentr ray.tune.integration.xgboostr ray.tune.schedulersr r "ray.tune.execution.tune_controllerr rrdictr<rx__name__initrrrrrrs,        )e