Hi,
I'd like to welcome Loic Esteve (@lesteve) as a new core contributor to
the scikit-learn team.
Loic has been reviewing very seriously a number of PR, beyond his own
contributions. It's great to have him on board!
Cheers,
Gaël
___
scikit-learn mai
Sorry, I've been off review duty for a while, should be back later this
summer ;)
On 06/21/2016 12:09 AM, olologin wrote:
Hi guys, I know scikit-learn may not be your main project, and you all
are very busy at work so you don't have free time to review all pull
requests, I understand it.
Is
Actually, I wonder if there is a difference between our implementation
and Matlab's behavior. We seem to reset the seed to a hard-coded value
when calling predict and predict_proba:
In predict() and predict_proba() in here, we call set_predict_params():
https://github.com/scikit-learn/scikit-
Have you tried comparing the fit support vectors prior to comparing
predicted values? You might need to set SaveSupportVectors in Matlab first.
Thanks,
Michael J. Bommarito II, CEO
Bommarito Consulting, LLC
*Web:* http://www.bommaritollc.com
*Mobile:* +1 (646) 450-3387
On Wed, Jun 22, 2016 at
Many thanks for the responses thus far!
*Did you fix the random seeds across implementations as well?
Differencesin seeds or generators might explain this.*
The implementation of libsvm used by Matlab always has a seed of 1. I tried
setting the seed for SKL SVM to 1 (and 0, 2, 3, and 4) as well,
import numpy as np
import nose.tools as nt
from sklearn.isotonic import isotonic_regression
def test_isotonic_ymin_ymax():
X = np.array([1.26, 1.31,-0.57, 0.30, -0.70,
-0.17, -1.59, 1.05, 1.39, 1.90,
0.20, 0.03, -0.08, 0.44, 0.01,
-0
A few quick thoughts:
1. What does the `isoreg` method in the `isotone` R library do with this
data? We have seen multiple situations where differences between our
implementation/behavior and the R implementation was not
expected/communicated for users, so it would be good to know and
potentially
Did you try using the Python API to libsvm directly instead of through SKL?
I'm guessing you have it on your computer since you have the Matlab API.
That would at least let you test whether it's the fake data or whether it's
SKL.
Also are you loading the fake data from a .mat file into Python (e.g.
Did you fix the random seeds across implementations as well? Differences
in seeds or generators might explain this.
Thanks,
Michael J. Bommarito II, CEO
Bommarito Consulting, LLC
*Web:* http://www.bommaritollc.com
*Mobile:* +1 (646) 450-3387
On Wed, Jun 22, 2016 at 1:15 PM, Taylor, Johnmark <
jo
Hello,
I am moving much of my neuroimaging coding over to Python from Matlab and
so I am switching from using libsvm in Matlab to using Scikit-learn SVM in
Python. Just to make sure I am not changing anything substantive about my
analyses, I am experimenting with the two implementations and trying
I've submitted a ticket:
https://github.com/scikit-learn/scikit-learn/issues/6921
with the small example Jonathan wrote up in the email.
Cheers,
N
On 22 June 2016 at 00:27, Gael Varoquaux
wrote:
> Looks like a bug indeed. Could you please put a small code snippet to
> enable us to reproduce.
>
Looks like a bug indeed. Could you please put a small code snippet to
enable us to reproduce.
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