问题
I've fit a Pipeline
object with RandomizedSearchCV
pipe_sgd = Pipeline([('scl', StandardScaler()),
('clf', SGDClassifier(n_jobs=-1))])
param_dist_sgd = {'clf__loss': ['log'],
'clf__penalty': [None, 'l1', 'l2', 'elasticnet'],
'clf__alpha': np.linspace(0.15, 0.35),
'clf__n_iter': [3, 5, 7]}
sgd_randomized_pipe = RandomizedSearchCV(estimator = pipe_sgd,
param_distributions=param_dist_sgd,
cv=3, n_iter=30, n_jobs=-1)
sgd_randomized_pipe.fit(X_train, y_train)
I want to access the coef_
attribute of the best_estimator_
but I'm unable to do that. I've tried accessing coef_
with the code below.
sgd_randomized_pipe.best_estimator_.coef_
However I get the following AttributeError...
AttributeError: 'Pipeline' object has no attribute 'coef_'
The scikit-learn docs say that coef_
is an attribute of SGDClassifier
, which is the class of my base_estimator_
.
What am I doing wrong?
回答1:
You can always use the names you assigned to them while making the pipeline by using the named_steps
dict.
scaler = sgd_randomized_pipe.best_estimator_.named_steps['scl']
classifier = sgd_randomized_pipe.best_estimator_.named_steps['clf']
and then access all the attributes like coef_
, intercept_
etc. which are available to corresponding fitted estimator.
This is the formal attribute exposed by the Pipeline as specified in the documentation:
named_steps : dict
Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.
回答2:
I've found one way to do this is by chained indexing with the steps
attribute...
sgd_randomized_pipe.best_estimator_.steps[1][1].coef_
Is this best practice, or is there another way?
回答3:
I think this should work:
sgd_randomized_pipe.named_steps['clf'].coef_
来源:https://stackoverflow.com/questions/43856280/return-coefficients-from-pipeline-object-in-sklearn