What's the best way to test whether an sklearn model has been fitted?

Question

What's the most elegant way to check whether an sklearn model has been fitted? i.e. whether its fit() function has been called after it was instantiated, or not.

Answer 1

You can do something like:

from sklearn.exceptions import NotFittedError  for model in models:     try:         model.predict(some_test_data)     except NotFittedError as e:         print(repr(e)) 

Ideally you would check the results of model.predict against expected results but if all you want to know if wether the model is fitted or not that should suffice.

Update:

Some commenters have suggested using check_is_fitted. I consider check_is_fitted an internal method. Most algorithms will call check_is_fitted inside their predict method which in turn might raise NotFittedError if needed. The problem with using check_is_fitted directly is that it is model specific, i.e. you need to know which members to check depending on your algorithm. For example:

╔════════════════╦════════════════════════════════════════════╗ ║ Tree models    ║ check_is_fitted(self, 'tree_')             ║ ║ Linear models  ║ check_is_fitted(self, 'coefs_')            ║ ║ KMeans         ║ check_is_fitted(self, 'cluster_centers_')  ║ ║ SVM            ║ check_is_fitted(self, 'support_')          ║ ╚════════════════╩════════════════════════════════════════════╝ 

and so on. So in general I would recommend calling model.predict() and letting the specific algorithm handle the best way to check whether it is already fitted or not.