How to map the features at the end of the pipeline back to the initial features

initial num of features 581 but feature importance of the final pipeline has 587 features.
It looks like that at each of the 3 steps of the pipeline, the # of features increased from 581 -> 584 -> 587

**Is there a way to map the 578 features at the end of the pipeline back to the original 581 features?**

from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import make_pipeline, make_union
from tpot.builtins import StackingEstimator
from xgboost import XGBClassifier

exported_pipeline = make_pipeline(
StackingEstimator(estimator=XGBClassifier(learning_rate=0.01, max_depth=4, min_child_weight=6, n_estimators=100, n_jobs=1, subsample=0.15000000000000002, verbosity=0)),
StackingEstimator(estimator=GaussianNB()),
XGBClassifier(learning_rate=0.5, max_depth=2, min_child_weight=20, n_estimators=100, n_jobs=1, subsample=0.9000000000000001, verbosity=0)
)

exported_pipeline.fit(x_v, y_v)

trans_x_t = exported_pipeline[0].transform(x_t)
trans_x_t1 = exported_pipeline[1].transform(trans_x_t)

print(x_t.shape)
(677279, 581)
print(trans_x_t.shape)
(677279, 584)
print(trans_x_t1.shape)
(677279, 587)
exported_pipeline[-1].feature_importances_.shape
(587,)