Hi, Juan,
It's been some time, but I remember that I had similar issues. I think it has
to do with the numpy arrays that specifically cause problems in pickle.
(http://bugs.python.org/issue6784)
You could try to use joblib (which should also be more efficient):
>>> from sklearn.externals import joblib
>>> joblib.dump(clf, 'filename.pkl')
>>> clf = joblib.load('filename.pkl')
(http://scikit-learn.org/stable/modules/model_persistence.html)
Best,
Sebastian
> On Jan 22, 2015, at 8:50 AM, [email protected] wrote:
>
> Hi all,
>
> I'm working on a project that depends on sklearn. I've been up test coverage
> (which includes saving a RandomForest, so far using joblib serialization),
> and now I wanted to make the project Python 3-compatible. However, the final
> roadblock is the sharing of RF objects: I can't load the Python 2-serialized
> RFs with Python 3 tests. Of course, the test outcome depends on the exact RF
> that was created a while back. Is there any way around this?
>
> Thanks!
>
> Juan.
>
>
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GigeNET is offering a free month of service with a new server in Ashburn.
Choose from 2 high performing configs, both with 100TB of bandwidth.
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