Source code for optuna.integration.xgboost

import optuna

try:
    import xgboost as xgb  # NOQA
    _available = True
except ImportError as e:
    _import_error = e
    # XGBoostPruningCallback is disabled because XGBoost is not available.
    _available = False


def _get_callback_context(env):
    # type: (xgb.core.CallbackEnv) -> str
    """Return whether the current callback context is cv or train.

    .. note::
        `Reference
        <https://github.com/dmlc/xgboost/blob/master/python-package/xgboost/callback.py>`_.
    """

    if env.model is None and env.cvfolds is not None:
        context = 'cv'
    else:
        context = 'train'
    return context


[docs]class XGBoostPruningCallback(object): """Callback for XGBoost to prune unpromising trials. Example: Add a pruning callback which observes validation errors to training of an XGBoost model. .. code:: pruning_callback = XGBoostPruningCallback(trial, 'validation-error') bst = xgb.train(param, dtrain, evals=[(dtest, 'validation')], callbacks=[pruning_callback]) Args: trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. observation_key: An evaluation metric for pruning, e.g., ``validation-error`` and ``validation-merror``. Please refer to ``eval_metric`` in `XGBoost reference <https://xgboost.readthedocs.io/en/latest/parameter.html>`_ for further details. """ def __init__(self, trial, observation_key): # type: (optuna.trial.Trial, str) -> None _check_xgboost_availability() self._trial = trial self._observation_key = observation_key def __call__(self, env): # type: (xgb.core.CallbackEnv) -> None context = _get_callback_context(env) evaluation_result_list = env.evaluation_result_list if context == 'cv': # Remove a third element: the stddev of the metric across the cross-valdation folds. evaluation_result_list = [ (key, metric) for key, metric, _ in evaluation_result_list ] current_score = dict(evaluation_result_list)[self._observation_key] self._trial.report(current_score, step=env.iteration) if self._trial.should_prune(): message = "Trial was pruned at iteration {}.".format(env.iteration) raise optuna.exceptions.TrialPruned(message)
def _check_xgboost_availability(): # type: () -> None if not _available: raise ImportError( 'XGBoost is not available. Please install XGBoost to use this feature. ' 'XGBoost can be installed by executing `$ pip install xgboost`. ' 'For further information, please refer to the installation guide of XGBoost. ' '(The actual import error is as follows: ' + str(_import_error) + ')')