nvflare.app_opt.xgboost.histogram_based.executor module

class FedXGBHistogramExecutor(num_rounds, early_stopping_rounds, xgb_params: dict, data_loader_id: str, verbose_eval=False, use_gpus=False, metrics_writer_id: str | None = None, model_file_name='test.model.json')[source]

Bases: Executor

Federated XGBoost Executor Spec for histogram-base collaboration.

This class implements a basic xgb_train logic, feel free to overwrite the function for custom behavior.

Federated XGBoost Executor for histogram-base collaboration.

This class sets up the training environment for Federated XGBoost. This is the executor running on each NVFlare client, which starts XGBoost training.

Parameters:

  • num_rounds – number of boosting rounds

  • early_stopping_rounds – early stopping rounds

  • xgb_params – This dict is passed to xgboost.train() as the first argument params. It contains all the Booster parameters. Please refer to XGBoost documentation for details: https://xgboost.readthedocs.io/en/stable/parameter.html

  • data_loader_id – the ID points to XGBDataLoader.

  • verbose_eval – verbose_eval in xgboost.train

  • use_gpus (bool) – A convenient flag to enable gpu training, if gpu device is specified in the xgb_params then this flag can be ignored.

  • metrics_writer_id – the ID points to a LogWriter, if provided, a MetricsCallback will be added. Users can then use the receivers from nvflare.app_opt.tracking.

  • model_file_name (str) – where to save the model.

execute(task_name: str, shareable: Shareable, fl_ctx: FLContext, abort_signal: Signal) → Shareable[source]

Executes a task.

Parameters:

  • task_name (str) – task name.

  • shareable (Shareable) – input shareable.

  • fl_ctx (FLContext) – fl context.

  • abort_signal (Signal) – signal to check during execution to determine whether this task is aborted.

Returns:

An output shareable.

handle_event(event_type: str, fl_ctx: FLContext)[source]

Handles events.

Parameters:

  • event_type (str) – event type fired by workflow.

  • fl_ctx (FLContext) – FLContext information.

initialize(fl_ctx)[source]
train(shareable: Shareable, fl_ctx: FLContext, abort_signal: Signal) → Shareable[source]

XGBoost training task pipeline which handles NVFlare specific tasks

xgb_train(params: XGBoostParams) → Booster[source]

XGBoost training logic.

Parameters:

params (XGBoostParams) – xgboost parameters.

Returns:

A xgboost booster.

class XGBoostParams(xgb_params: dict, num_rounds: int = 10, early_stopping_rounds: int = 2, verbose_eval: bool = False)[source]

Bases: object

Container for all XGBoost parameters.

Parameters:

xgb_params – This dict is passed to xgboost.train() as the first argument params. It contains all the Booster parameters. Please refer to XGBoost documentation for details: https://xgboost.readthedocs.io/en/stable/parameter.html