How to use the eli5.explain.explain_weights function in eli5

def test_explain_weights_feature_names_pandas(boston_train):
    pd = pytest.importorskip('pandas')
    X, y, feature_names = boston_train
    df = pd.DataFrame(X, columns=feature_names)
    reg = XGBRegressor().fit(df, y)

    # it shoud pick up feature names from DataFrame columns
    res = explain_weights(reg)
    for expl in format_as_all(res, reg):
        assert 'PTRATIO' in expl

    # it is possible to override DataFrame feature names
    numeric_feature_names = ["zz%s" % idx for idx in range(len(feature_names))]
    res = explain_weights(reg, feature_names=numeric_feature_names)
    for expl in format_as_all(res, reg):
        assert 'zz12' in expl

def test_explain_xgboost_regressor(boston_train):
    xs, ys, feature_names = boston_train
    reg = XGBRegressor()
    reg.fit(xs, ys)
    res = explain_weights(reg)
    for expl in format_as_all(res, reg):
        assert 'f12' in expl
    res = explain_weights(reg, feature_names=feature_names)
    for expl in format_as_all(res, reg):
        assert 'LSTAT' in expl

def test_explain_xgboost_regressor(boston_train):
    xs, ys, feature_names = boston_train
    reg = XGBRegressor()
    reg.fit(xs, ys)
    res = explain_weights(reg)
    for expl in format_as_all(res, reg):
        assert 'f12' in expl
    res = explain_weights(reg, feature_names=feature_names)
    for expl in format_as_all(res, reg):
        assert 'LSTAT' in expl

def test_explain_xgboost_booster(boston_train):
    xs, ys, feature_names = boston_train
    booster = xgboost.train(
        params={'objective': 'reg:linear', 'silent': True},
        dtrain=xgboost.DMatrix(xs, label=ys),
    )
    res = explain_weights(booster)
    for expl in format_as_all(res, booster):
        assert 'f12' in expl
    res = explain_weights(booster, feature_names=feature_names)
    for expl in format_as_all(res, booster):
        assert 'LSTAT' in expl

def test_explain_xgboost_booster(boston_train):
    xs, ys, feature_names = boston_train
    booster = xgboost.train(
        params={'objective': 'reg:linear', 'silent': True},
        dtrain=xgboost.DMatrix(xs, label=ys),
    )
    res = explain_weights(booster)
    for expl in format_as_all(res, booster):
        assert 'f12' in expl
    res = explain_weights(booster, feature_names=feature_names)
    for expl in format_as_all(res, booster):
        assert 'LSTAT' in expl

@explain_weights.register(XGBRegressor)
@explain_weights.register(Booster)
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.

]

_REGRESSORS = [
    regression.AdaGradRegressor,
    regression.CDRegressor,
    regression.FistaRegressor,
    regression.LinearSVR,
    regression.SAGARegressor,
    regression.SAGRegressor,
    regression.SDCARegressor,
    regression.SGDRegressor,
    # regression.SVRGRegressor
]

for clf in _CLASSIFIERS:
    explain_weights.register(clf, explain_linear_classifier_weights)
    explain_weights_lightning.register(clf, explain_linear_classifier_weights)
    explain_prediction.register(clf, explain_prediction_linear_classifier)
    explain_prediction_lightning.register(clf, explain_prediction_linear_classifier)


for reg in _REGRESSORS:
    explain_weights.register(reg, explain_linear_regressor_weights)
    explain_weights_lightning.register(reg, explain_linear_regressor_weights)
    explain_prediction.register(reg, explain_prediction_linear_regressor)
    explain_prediction_lightning.register(reg, explain_prediction_linear_regressor)

@explain_weights.register(BaseEstimator)
def explain_weights_sklearn_not_supported(
        estimator, vec=None, top=_TOP,
        target_names=None,
        targets=None,
        feature_names=None, coef_scale=None,
        feature_re=None, feature_filter=None):
    return Explanation(
        estimator=repr(estimator),
        error="estimator %r is not supported" % estimator,
    )

@explain_weights.register(catboost.CatBoostClassifier)
@explain_weights.register(catboost.CatBoostRegressor)
def explain_weights_catboost(catb, 
                             vec=None,
                             top=20,
                             importance_type='PredictionValuesChange',
                             feature_names=None,
                             pool=None
                             ):
    """
    Return an explanation of an CatBoost estimator (CatBoostClassifier,
    CatBoost, CatBoostRegressor) as feature importances.

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

def explain_weights_ovr(ovr, **kwargs):
    estimator = ovr.estimator
    func = explain_weights.dispatch(estimator.__class__)
    return func(ovr, **kwargs)