I am not quite sure what to test here. I just tweaked the test for SVR.

The results of SVR with linear kernel and LinearSVR are different. There 
is randomness in liblinear solution. I guess SVR is deterministic.

I have checked that the results are the same with original liblinear for 
some examples. Maybe I should check this in test.

On 04/18/2013 02:28 PM, jnothman wrote:
>
> In sklearn/svm/tests/test_svm.py:
>
> > @@ -175,6 +175,21 @@ def test_svr():
> >          clf.fit(diabetes.data, diabetes.target)
> >          assert_greater(clf.score(diabetes.data, diabetes.target), 0.02)
> >
> > +
> > +def test_liblinear_svr():
> > +    """
> > +    Test Liblinear SVR
> > +    """
> > +
> > +    diabetes = datasets.load_diabetes()
> > +    for clf in (svm.LinearSVR(C=1.0,epsilon=0.1),
> > +                svm.LinearSVR(C=10.,epsilon=0.1),
> > +                svm.LinearSVR(loss="l1",C=10.,epsilon=0.1),
> > +                svm.LinearSVR(loss="l1",C=100.,epsilon=0.1),
>
> Is it worth having a test to ensure epsilon is transmitted to 
> |liblinear| by varying it?
> Also, is it appropriate to check where appropriate that the 
> solutions/parameters found are close to those produced by |SVR| with a 
> linear kernel? (or is that not guaranteed? or is that liblinear's 
> problem, not ours?)
> (There should also be spaces after commas here.)
>
> —
> Reply to this email directly or view it on GitHub 
> <https://github.com/scikit-learn/scikit-learn/pull/1867/files#r3847554>.
>


-- 
Qiang Luo
Department of Mathematical Sciences
Tsinghua University
Beijing 100084
China