How to print feature names in conjunction with feature Importance using Imbalanced-learn library?

I used BalancedBaggingClassifier from imblearn library to do an unbalanced classification task. How can I get feature improtance of the estimator in conjunction with feature names especially when the max_features are less than the total number of features? For example, in the following code total number of features equal to 20 but max_features are 8.

from collections import Counter
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from imblearn.ensemble import BalancedBaggingClassifier
from xgboost.sklearn import XGBClassifier

X, y = make_classification(n_classes=2, class_sep=2,weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
print('Original dataset shape {}'.format(Counter(y)))

ln = X.shape
names = ["x%s" % i for i in range(1, ln[1] + 1)]

X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=0)
bbc = BalancedBaggingClassifier(base_estimator=XGBClassifier(n_estimators=10,min_child_weight=5,max_depth=3,learning_rate=0.02,colsample_bytree=1,subsample=1,scale_pos_weight=.26), ratio='all',random_state=0, max_features=8)
bbc.fit(X_train,y_train)
for estimator in bbc.estimators_:
    print(sorted(zip(map(lambda x: round(x, 4), estimator.steps[1][1].feature_importances_),names), reverse=True))

I think there is a problem with the above code because always printed features are named x1 to x8 while for example, feature x19 may be among the most important features.

Thanks.