Return an explanation of an XGBoost estimator (via scikit-learn wrapper
    XGBClassifier or XGBRegressor, or via xgboost.Booster)
    as feature importances.

    See :func:`eli5.explain_weights` for description of
    ``top``, ``feature_names``,
    ``feature_re`` and ``feature_filter`` parameters.

    ``target_names`` and ``targets`` parameters are ignored.

    Parameters
    ----------
    importance_type : str, optional
        A way to get feature importance. Possible values are:

        - 'gain' - the average gain of the feature when it is used in trees
          (default)
        - 'weight' - the number of times a feature is used to split the data
          across all trees
        - 'cover' - the average coverage of the feature when it is used in trees

def explain_weights_xgboost(xgb,
                            vec=None,
                            top=20,
                            target_names=None,  # ignored
                            targets=None,  # ignored
                            feature_names=None,
                            feature_re=None,  # type: Pattern[str]
                            feature_filter=None,
                            importance_type='gain',
                            ):
    """
    Return an explanation of an XGBoost estimator (via scikit-learn wrapper
    XGBClassifier or XGBRegressor, or via xgboost.Booster)
    as feature importances.

    See :func:`eli5.explain_weights` for description of
    ``top``, ``feature_names``,
    ``feature_re`` and ``feature_filter`` parameters.

    ``target_names`` and ``targets`` parameters are ignored.

    Parameters
    ----------
    importance_type : str, optional
        A way to get feature importance. Possible values are:

        - 'gain' - the average gain of the feature when it is used in trees
          (default)
        - 'weight' - the number of times a feature is used to split the data
          across all trees
        - 'cover' - the average coverage of the feature when it is used in trees
    """
    booster, is_regression = _check_booster_args(xgb)
    xgb_feature_names = booster.feature_names
    coef = _xgb_feature_importances(booster, importance_type=importance_type)
    return get_feature_importance_explanation(
        xgb, vec, coef,
        feature_names=feature_names,
        estimator_feature_names=xgb_feature_names,
        feature_filter=feature_filter,
        feature_re=feature_re,
        top=top,
        description=DESCRIPTION_XGBOOST,
        is_regression=is_regression,
        num_features=coef.shape[-1],
    )