feature names from sklearn pipeline: not fitted error

I'm working with scikit learn on a text classification experiment. Now I would like to get the names of the best performing, selected features. I tried some of the answers to similar questions, but nothing works. The last lines of code are an example of what I tried. For example when I print feature_names, I get this error: sklearn.exceptions.NotFittedError: This SelectKBest instance is not fitted yet. Call 'fit' with appropriate arguments before using this method. Any solutions?

scaler = StandardScaler(with_mean=False) 

enc = LabelEncoder()
y = enc.fit_transform(labels)

feat_sel = SelectKBest(mutual_info_classif, k=200)  
clf = linear_model.LogisticRegression()

pipe = Pipeline([('vectorizer', DictVectorizer()),
                 ('scaler', StandardScaler(with_mean=False)),
                 ('mutual_info', feat_sel),
                 ('logistregress', clf)])

feature_names = pipe.named_steps['mutual_info']
X.columns[features.transform(np.arange(len(X.columns)))]