ray.tune.integration.xgboost.TuneReportCallback#

class ray.tune.integration.xgboost.TuneReportCallback(metrics: Optional[Union[str, List[str], Dict[str, str]]] = None, results_postprocessing_fn: Optional[Callable[[Dict[str, Union[float, List[float]]]], Dict[str, float]]] = None)[source]#

Bases: ray.tune.integration.xgboost.TuneCallback

XGBoost to Ray Tune reporting callback

Reports metrics to Ray Tune.

Parameters

  • metrics – Metrics to report to Tune. If this is a list, each item describes the metric key reported to XGBoost, and it will reported under the same name to Tune. If this is a dict, each key will be the name reported to Tune and the respective value will be the metric key reported to XGBoost. If this is None, all metrics will be reported to Tune under their default names as obtained from XGBoost.

  • results_postprocessing_fn – An optional Callable that takes in the dict that will be reported to Tune (after it has been flattened) and returns a modified dict that will be reported instead. Can be used to eg. average results across CV fold when using xgboost.cv.

Example:

import xgboost
from ray.tune.integration.xgboost import TuneReportCallback

config = {
    # ...
    "eval_metric": ["auc", "logloss"]
}

# Report only log loss to Tune after each validation epoch:
bst = xgb.train(
    config,
    train_set,
    evals=[(test_set, "eval")],
    verbose_eval=False,
    callbacks=[TuneReportCallback({"loss": "eval-logloss"})])

Methods

after_training(model)

Run after training is finished.

before_iteration(model, epoch, evals_log)

Run before each iteration.

before_training(model)

Run before training starts.

Attributes

EvalsLog

alias of Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]]