order of features importance after make_column_transformer and pipeline

I have a data preparation and model fitting pipeline that takes a dataframe (X_trn) and uses the ‘make_column_transformer’ and ‘Pipeline’ functions in sklearn to prepare the data and fit XGBRegressor. The code looks something like this

 xgb = XGBRegressor()

preprocessor = make_column_transformer(
( Fun1(),List1),
( Fun2(),List2),
remainder='passthrough',
)

model_pipeline = Pipeline([
('preprocessing', preprocessor),
('classifier', xgb )
])

model_pipeline.fit(X_trn, Y_trn)


Therefore, the training data which inputted into the XGBRegressor have no labels and resorted due to the make_column_transformer function. Given this, how do I extract the features importance using XGBRegressor.get_booster().get_score() method?

Currently, the output of get_score() is a dictinary that looks like this: {‘f0’: 123 , ‘f10’: 222, ‘f100’: 334, ‘f101’: 34, … ‘f99’:12}

Can I assume that the order of the features provided by get_score() is identical to the order of features after make_column_transformer function (aka, I have to incorporate the feature sorting) such that 'f0' == 1st feature after make_column_transformer, 'f1' ==2nd feature after make_column_transformer, etc.?

from eli5 import show_weights,show_prediction