How to access attribute from a trained estimator in TransformedTargetRegressor pipeline in scikit-learn?

Question

I setup a small pipeline with scikit-Learn that I wrapped in a TransforedTargetRegressor object. After the training, I would like to access the attribute from my trained estimator (e.g. feature_importances_). Can anyone tell me how this can be done?

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import MinMaxScaler
from sklearn.compose import TransformedTargetRegressor

# setup the pipeline
Pipeline(steps = [('scale', StandardScaler(with_mean=True, with_std=True)),
                  ('estimator', RandomForestRegressor())])

# tranform target variable
model = TransformedTargetRegressor(regressor=pipeline, 
                                   transformer=MinMaxScaler())
           
# fit model
model.fit(X_train, y_train)

I tried the following:

# try to access the attribute of the fitted estimator
model.get_params()['regressor__estimator'].feature_importances_
model.regressor.named_steps['estimator'].feature_importances_

But this results in the following NotFittedError:

NotFittedError: This RandomForestRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

afsharov · Accepted Answer · 2020-06-24 08:07:36Z

3

When you look into the documentation of TransformedTargetRegressor it says that the attribute .regressor_ (note the trailing underscore) returns the fitted regressor. Hence, your call should look like:

model.regressor_.named_steps['estimator'].feature_importances_

Your previous calls were just returning an unfitted clone. That's were the error came from.

edited Jun 24, 2020 at 8:07

answered Jun 24, 2020 at 7:55

afsharov

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2 Comments

lux7 Over a year ago

Thanks for the hint! the code snippet you proposed returned a "TypeError: 'Pipeline' object is not subscriptable". However, based on your answer I figured out, that "model.regressor_.named_steps['estimator'].feature_importances_" works!

Abhishek Sourabh Over a year ago

Does anyone know why they used a trailing underscore here? Is there some convention about naming?

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