o mi6 @sdZddlmZddlmZddlmZmZddlm Z m Z ddl Z ddl m ZGdd d Zd ee e fd eed dfd dZd ee e fd dfddZ   dd ee e fdededed df ddZdS)zcatboost init.)Path)SimpleNamespace)ListUnion)CatBoostClassifierCatBoostRegressorN) telemetryc@s2eZdZdZd defddZdedefdd Zd S) WandbCallbackaV`WandbCallback` automatically integrates CatBoost with wandb. Args: - metric_period: (int) if you are passing `metric_period` to your CatBoost model please pass the same value here (default=1). Passing `WandbCallback` to CatBoost will: - log training and validation metrics at every `metric_period` - log iteration at every `metric_period` Example: ``` train_pool = Pool(train[features], label=train["label"], cat_features=cat_features) test_pool = Pool(test[features], label=test["label"], cat_features=cat_features) model = CatBoostRegressor( iterations=100, loss_function="Cox", eval_metric="Cox", ) model.fit( train_pool, eval_set=test_pool, callbacks=[WandbCallback()], ) ```  metric_periodcCsNtjdur tdt }d|j_Wdn1swY||_dS)Nz5You must call `wandb.init()` before `WandbCallback()`T)wandbrunError wb_telemetrycontextfeaturecatboost_wandb_callbackr )selfr telra/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/wandb/integration/catboost/catboost.py__init__*s     zWandbCallback.__init__inforeturncCsr|j|jdkr7|jD]\}}|D]\}}tj|d||diddqq td|j|jidS)Nr-Fcommitziteration@metric-period-T) iterationr metricsitemsr log)rrdatametric metric_namer!rrrafter_iteration3s"zWandbCallback.after_iterationN)r ) __name__ __module__ __qualname____doc__intrrboolr%rrrrr s r modelaliasesrcCsjtjdur tddtjj}ttjjd}||tj|dd}|t |tj ||ddS)z(Upload model checkpoint as W&B artifact.N^sz+_log_feature_importance..z Feature Id ImportancesFeature Importance)r"columnszFeature Importance)titleFr) r r rget_feature_importancezipTabler!plotbar)r,feat_dffi_datatablerrr_log_feature_importanceSs   rPTFlog_all_paramssave_model_checkpointlog_feature_importancecCstjdur tdt|ttfstdt }d|j_ Wdn1s)wY| }|r:tj || tjjd<|tjjd<|r\|drSdgnd g}t||d |rdt|dSdS) a.`log_summary` logs useful metrics about catboost model after training is done. Args: model: it can be CatBoostClassifier or CatBoostRegressor. log_all_params: (boolean) if True (default) log the model hyperparameters as W&B config. save_model_checkpoint: (boolean) if True saves the model upload as W&B artifacts. log_feature_importance: (boolean) if True (default) logs feature importance as W&B bar chart using the default setting of `get_feature_importance`. Using this along with `wandb_callback` will: - save the hyperparameters as W&B config, - log `best_iteration` and `best_score` as `wandb.summary`, - save and upload your trained model to Weights & Biases Artifacts (when `save_model_checkpoint = True`) - log feature importance plot. Example: ```python train_pool = Pool(train[features], label=train["label"], cat_features=cat_features) test_pool = Pool(test[features], label=test["label"], cat_features=cat_features) model = CatBoostRegressor( iterations=100, loss_function="Cox", eval_metric="Cox", ) model.fit( train_pool, eval_set=test_pool, callbacks=[WandbCallback()], ) log_summary(model) ``` Nz3You must call `wandb.init()` before `log_summary()`zFModel should be an instance of CatBoostClassifier or CatBoostRegressorTbest_iteration best_scoreuse_best_modelbestlastr2)r r r isinstancerrrrrcatboost_log_summaryget_all_paramsconfigupdateget_best_iterationsummaryget_best_scorer=rP)r,rQrRrSrparamsr-rrr log_summaryns( )      rb)TFT)r)pathlibrtypesrtypingrrcatboostrrr wandb.sdk.librrr r8r=rPr+rbrrrrsD   2