How to use the eli5.explain_weights function in eli5

def test_explain_linear_tuple_top(newsgroups_train):
    docs, y, target_names = newsgroups_train
    vec = TfidfVectorizer()
    clf = LogisticRegression(random_state=42)

    X = vec.fit_transform(docs)
    clf.fit(X, y)

    res_neg = explain_weights(clf, vec=vec, target_names=target_names, top=(0, 10))
    expl_neg, _ = format_as_all(res_neg, clf)

    for target in res_neg.targets:
        assert len(target.feature_weights.pos) == 0
        assert len(target.feature_weights.neg) == 10

    assert "+0." not in expl_neg

    res_pos = explain_weights(clf, vec=vec, target_names=target_names, top=(10, 2))
    format_as_all(res_pos, clf)

    for target in res_pos.targets:
        assert len(target.feature_weights.pos) == 10
        assert len(target.feature_weights.neg) == 2

def assert_explained_weights_linear_regressor(boston_train, reg, has_bias=True):
    X, y, feature_names = boston_train
    reg.fit(X, y)
    res = explain_weights(reg)
    expl_text, expl_html = format_as_all(res, reg)

    for expl in [expl_text, expl_html]:
        assert 'x12' in expl
        assert 'x5' in expl

    if has_bias:
        assert '' in expl_text
        assert '<BIAS>' in expl_html

    pos, neg = top_pos_neg(res, 'y')
    assert 'x12' in pos or 'x12' in neg
    assert 'x5' in neg or 'x5' in pos

    if has_bias:
        assert '' in neg or '' in pos

def test_feature_importances_no_remaining(clf):
    """ Check that number of remaining features is not shown if it is zero,
    and that features with zero importance are not shown either.
    """
    n = 100
    clf.fit(np.array([[i % 2 + 0.1 * np.random.random(), 0] for i in range(n)]),
            np.array([i % 2 for i in range(n)]))
    res = explain_weights(clf)
    for expl in format_as_all(res, clf):
        assert 'more features' not in expl and 'more …' not in expl
        assert 'x1' not in expl  # it has zero importance

docs, y, target_names = newsgroups_train
    X = vec.fit_transform(docs)
    clf.fit(X, y)
    if isinstance(vec, HashingVectorizer):
        vec = InvertableHashingVectorizer(vec)
        vec.fit(docs)

    res = explain_weights(clf, vec=vec, feature_re='^ath')
    text_expl, _ = expls = format_as_all(res, clf)
    for expl in expls:
        assert 'atheists' in expl
        assert 'atheism' in expl
        assert 'space' not in expl
        assert 'BIAS' not in expl

    res = explain_weights(
        clf, vec=vec,
        feature_filter=lambda name: name.startswith('ath') or name == '')
    text_expl, _ = expls = format_as_all(res, clf)
    for expl in expls:
        assert 'atheists' in expl
        assert 'atheism' in expl
        assert 'space' not in expl
        assert 'BIAS' in expl
    assert '' in text_expl

for expl in [expl_text, expl_html]:
        assert 'x12' in expl
        assert 'x5' in expl

    if has_bias:
        assert '' in expl_text
        assert '<BIAS>' in expl_html

    pos, neg = top_pos_neg(res, 'y')
    assert 'x12' in pos or 'x12' in neg
    assert 'x5' in neg or 'x5' in pos

    if has_bias:
        assert '' in neg or '' in pos

    assert res == explain_weights(reg)

def explain_weights_dfs(estimator, **kwargs):
    # type: (...) -> Dict[str, pd.DataFrame]
    """ Explain weights and export them to a dict with ``pandas.DataFrame``
    values (as :func:`eli5.formatters.as_dataframe.format_as_dataframes` does).
    All keyword arguments are passed to :func:`eli5.explain_weights`.
    Weights of all features are exported by default.
    """
    kwargs = _set_defaults(kwargs)
    return format_as_dataframes(
        eli5.explain_weights(estimator, **kwargs))

def __explain_model(dataset, round_id, pipe_model, model, feature_names):
    """
    explain the weights and the prediction of the model

    :param dataset: dataset
    :param round_id: round if
    :param pipe_model: the pipeline including the model
    :param model: the model only
    :param feature_names: feature names
    :return:
    """
    try:
        exp = eli5.explain_weights(model, feature_names=list(feature_names))
        with open(get_dataset_folder(dataset.dataset_id) + '/predict/eli5_model_%s.html' % round_id, 'w') as f:
            f.write(eli5.format_as_html(exp))
    except:
        return

def explain_weights_df(estimator, **kwargs):
    # type: (...) -> pd.DataFrame
    """ Explain weights and export them to ``pandas.DataFrame``.
    All keyword arguments are passed to :func:`eli5.explain_weights`.
    Weights of all features are exported by default.
    """
    kwargs = _set_defaults(kwargs)
    return format_as_dataframe(
        eli5.explain_weights(estimator, **kwargs))

def explain_weights(self, **kwargs):
        """
        Call :func:`eli5.show_weights` for the locally-fit
        classification pipeline. Keyword arguments are passed
        to :func:`eli5.show_weights`.

        :func:`fit` must be called before using this method.
        """
        self._fix_target_names(kwargs)
        return eli5.explain_weights(self.clf_, vec=self.vec_, **kwargs)