Check sklearn version before loading model using joblib

Question

I've followed this guide to save a machine learning model for later use. The model was dumped in one machine:

from sklearn.externals import joblib
joblib.dump(clf, 'model.pkl')

And when I loaded it joblib.load('model.pkl') in another machine, I got this warning:

UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version pre-0.18 when using version 0.18.1. This might lead to breaking code or invalid results. Use at your own risk.

So is there any way to know the sklearn version of the saved model to compare it with the current version?

Answer 1

Versioning of pickled estimators was added in scikit-learn 0.18. Starting from v0.18, you can get the version of scikit-learn used to create the estimator with,

estimator.__getstate__()['_sklearn_version']

The warning you get is produced by the __setstate__ method of the estimator which is automatically called upon unpickling. It doesn't look like there is a straightforward way of getting this version without loading the estimator from disk. You can filter out the warning, with,

import warnings

with warnings.catch_warnings():
      warnings.simplefilter("ignore", category=UserWarning)
      estimator = joblib.load('model.pkl')

For pre-0.18 versions, there is no such mechanism, but I imagine you could, for instance, use not hasattr(estimator, '__getstate') as a test to detect to, at least, pre-0.18 versions.