o mi- @s4dZddlmZddlmZmZddlZddlmZddlZddl m Z gdZ gdZ erYdd lmZmZmZmZmZmZeeeeeefeeeeeeffZGd d d eZd ed eddfddZdddeddddfddZd&ddZGdddZd'dededefdd Z !d(ded"ed#eddfd$d%ZdS))aCW&B callback for lightgbm. Really simple callback to get logging for each tree Example usage: param_list = [("eta", 0.08), ("max_depth", 6), ("subsample", 0.8), ("colsample_bytree", 0.8), ("alpha", 8), ("num_class", 10)] config.update(dict(param_list)) lgb = lgb.train(param_list, d_train, callbacks=[wandb_callback()]) )Path) TYPE_CHECKINGCallableN)Booster) telemetry) l1l2rmsemapehuberfairpoissongammabinary_logloss)mapaucaverage_precision)AnyDictList NamedTupleTupleUnionc@sBeZdZUeed<eed<eed<eed<eed<eeed<dS) CallbackEnvmodelparams iterationbegin_interation end_iterationevaluation_result_listN) __name__ __module__ __qualname__r__annotations__rintr_EvalResultTupler&r&a/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/wandb/integration/lightgbm/__init__.pyr,s rdata metric_namereturncCsdt|vrtj|d|dddSt|tvr*tj|d|dddSt|tvr?tj|d|dddSdS)zsCapture model performance at the best step. instead of the last step, of training in your `wandb.summary` loss_min)summarymaxN)strlowerwandb define_metricMINIMIZE_METRICSMAXIMIZE_METRICS)r(r)r&r&r'_define_metric5sr6rrraliasesz List[str]cCsbdtjj}ttjjd|d}|j||dtj|dd}|t|tj ||ddS) z(Upload model checkpoint as W&B artifact.model_ model_ckpt_z.txt) num_iterationr)nametyper7N) r2runidrdir save_modelArtifactadd_filer0 log_artifact)rrr7 model_name model_pathmodel_artifactr&r&r'_checkpoint_artifactBs rHcCs\|}|}ddt||D}tj|ddgd}tjdtjj|ddddidd d S) zLog feature importance.cSsg|]\}}||gqSr&r&).0featfeat_impr&r&r' Usz+_log_feature_importance..Feature Importance)r(columnszFeature Importance)titleFcommitN)feature_importance feature_namezipr2Tablelogplotbar)r feat_impsfeatsfi_datatabler&r&r'_log_feature_importanceQs r^c@s<eZdZdZddededdfddZdd d Zdd dZdS)_WandbCallbackzInternal class to handle `wandb_callback` logic. This callback is adapted form the LightGBM's `_RecordEvaluationCallback`. T log_paramsr3r*NcCsd|_d|_||_||_dS)NF)orderbefore_iterationr`define_metric_bool)selfr`r3r&r&r'__init__gs z_WandbCallback.__init__envrcCst }d|j_Wdn1swY|jr"tj|j|j D]1}|j rVt |dkr>|dd\}}t ||q%|d \}}t ||dt ||dq%dS)NT-mean-stdv) wb_telemetrycontextfeaturelightgbm_wandb_callbackr`r2configupdaterrrdlenr6split)rergtelitem data_name eval_namer&r&r'_initms     z_WandbCallback._initcCs|j|jkr |||jD]F}t|dkr-|dd\}}}tj|d||iddq|d\}}|d}|d}tj|d|d||d|d |iddqtjd |jid ddS) Nrhr,FrQrjrirkrlrT)rbegin_iterationryrrsr2rWrt)rergrvrwrxresultres_meanres_stdvr&r&r'__call__s&     z_WandbCallback.__call__TT)rgrr*N)r r!r"__doc__boolrfryrr&r&r&r'r_as  r_Tr`r3cCs t||S)aAutomatically integrates LightGBM with wandb. Args: log_params: (boolean) if True (default) logs params passed to lightgbm.train as W&B config define_metric: (boolean) if True (default) capture model performance at the best step, instead of the last step, of training in your `wandb.summary` Passing `wandb_callback` to LightGBM will: - log params passed to lightgbm.train as W&B config (default). - log evaluation metrics collected by LightGBM, such as rmse, accuracy etc to Weights & Biases - Capture the best metric in `wandb.summary` when `define_metric=True` (default). Use `log_summary` as an extension of this callback. Example: ```python params = { "boosting_type": "gbdt", "objective": "regression", } gbm = lgb.train( params, lgb_train, num_boost_round=10, valid_sets=lgb_eval, valid_names=("validation"), callbacks=[wandb_callback()], ) ``` )r_)r`r3r&r&r'wandb_callbacks rFrSsave_model_checkpointcCstjdur tdt|tstd|jtjjd<|jtjjd<|r(t||r3t ||jdgdt }d|j _ WddS1sGwYdS) aLog useful metrics about lightgbm model after training is done. Args: model: (Booster) is an instance of lightgbm.basic.Booster. feature_importance: (boolean) if True (default), logs the feature importance plot. save_model_checkpoint: (boolean) if True saves the best model and upload as W&B artifacts. Using this along with `wandb_callback` will: - log `best_iteration` and `best_score` as `wandb.summary`. - log feature importance plot. - save and upload your best trained model to Weights & Biases Artifacts (when `save_model_checkpoint = True`) Example: ```python params = { "boosting_type": "gbdt", "objective": "regression", } gbm = lgb.train( params, lgb_train, num_boost_round=10, valid_sets=lgb_eval, valid_names=("validation"), callbacks=[wandb_callback()], ) log_summary(gbm) ``` Nz1You must call wandb.init() before WandbCallback()z5Model should be an instance of lightgbm.basic.Boosterbest_iteration best_scorebestr=T)r2r>Error isinstancerrr.rr^rHrmrnrolightgbm_log_summary)rrSrrur&r&r' log_summarys "     "r)rrr*Nr)TF) rpathlibrtypingrrlightgbmrr2 wandb.sdk.librrmr4r5rrrrrrr0floatrr%rr6r$rHr^r_rrr&r&r&r'sL         :"