[scikit-learn] Model trained in 0.17 gives entirely different results in 0.15

Shi Yu shee.yu at gmail.com
Wed Aug 3 15:02:46 EDT 2016

Hi Andy,

Thanks for the feedback.  Indeed we think it would be a good idea to
enforce version persistence something like in serialVersionUID Java here.

We deployed models trained on our laptop onto our clusters, and ran into
this issue and paid a serious lesson for that.



---------- Forwarded message ----------
From: Andreas Mueller <t3kcit at gmail.com>
Date: Wed, Aug 3, 2016 at 1:29 PM
Subject: Re: [scikit-learn] Model trained in 0.17 gives entirely different
results in 0.15
To: Scikit-learn user and developer mailing list <scikit-learn at python.org>

Hi Shi.
In general, there is no guarantee that models built with one version will
work in a different version.
In particular, loading in an older version when built in a newer version
seems something that's tricky to achieve.

We might want to warn the user when doing this. The docs are not very
explicit about this.

Opened an issue:


On 08/02/2016 05:02 PM, Shi Yu wrote:


We trained SVM models in scikit-learn 0.17 and saved it as pickle files.
When loading the models back in a lower version of scikit-learn 0.15, the
outputs are entirely different.  Basically for binary classification
problem, for the same test data,  it swapped the probabilities and gave an
opposite prediction.  In 0.17 the probability is [0.02668825,  0.97331175]
and the prediction is 1.  In 0.15 the probability is [0.97331175,
0.02668825] and the prediction is 0.

I wonder is anyone seeing the same issue, or it has been notified.  I could
provide more details for error replication if required.



scikit-learn mailing
listscikit-learn at python.orghttps://mail.python.org/mailman/listinfo/scikit-learn

scikit-learn mailing list
scikit-learn at python.org
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20160803/369e7c3a/attachment.html>

More information about the scikit-learn mailing list