Hi Juan.
For up to floating point precision, that is pretty hard as Gael
mentioned. 1e-5 on sigma seems pretty low, though.
Can you post data to reproduce?
I would expect most classifiers to go to around 1e-8.
Cheers,
Andreas
On 02/02/2015 10:46 AM, Juan Nunez-Iglesias wrote:
Hi all,
*TL;DR version:*
I'm looking for a classifier that will get the *exact same model* for
shuffled versions of the training data. I thought GaussianNB would do
the trick but either I don't understand it, or some kind of numerical
instability prevents it from achieving the same model on subsequent
shuffling of the data — I get about 1e-18 absolute tolerance on theta_
but only 1e-5 on sigma_. Thoughts?
*Longer version with cute lesson learned:*
I hit another snag with testing for the Py2-3 transition on my
sklearn-dependent library. This was a fun one to debug. Essentially, I
was getting some training data, learning a random forest, and then
checking the predict_proba() outcome on a test set. This was failing,
so I assumed that somehow the seeding wasn't giving the same outcome
in Py2 and 3. I checked up and down and sure enough, random seeding
was working fine.
The random change that *did* happen was because I was learning edges
from a networkx graph. Fun fact: networkx.Graph.edges() is actually an
iterator over dictionary keys, whose ordering is thus not guaranteed,
/though it is perfectly reproducible across most implementations of
Py2.7/. So, although my tests had been happily chugging along for a
long time, this ordering changed in Py3.4, thus changing the order of
the training data and the outcome of RandomForestClassifier().fit().
I tried using GaussianNB() as the classifier but that still doesn't
have reproducible behaviour between Python versions. Any other
suggestions?
Thanks!
Juan.
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