[SciPy-dev] Test Design Question for Stats.Models Code
Bruce Southey
bsouthey@gmail....
Fri Jul 17 12:42:47 CDT 2009
On 07/17/2009 12:26 PM, Robert Kern wrote:
> On Fri, Jul 17, 2009 at 12:18, Skipper Seabold<jsseabold@gmail.com> wrote:
>
>> Hello all,
>>
>> I am polishing up the generalized linear models right now for the
>> stats.models project and I have a question about using decorators with
>> my tests. The GLM framework has a central model with shared
>> properties and then several variations on this model, so to test I
>> have just as a simplified example:
>>
>> from numpy.testing import *
>> DECIMAL = 4
>> class check_glm(object):
>> '''
>> res2 results will be obtained from R or the RModelwrap
>> '''
>>
>> def test_params(self):
>> assert_almost_equal(self.res1.params, self.res2.params, DECIMAL)
>>
>> def test_resids(self):
>> assert_almost_equal(self.res1.resids, self.res2.resids, DECIMAL)
>>
>> class test_glm_gamma(check_glm):
>> def __init__(self):
>> # Preprocessing to setup results
>> self.res1 = ResultsFromGLM
>> self.res2 = R_Results
>>
>> if __name__=="__main__":
>> run_module_suite()
>>
>> My question is whether I can skip, for arguments sake, test_resids
>> depending, for example, on the class of self.res2 or because I defined
>> the test condition as True in the test_<> class. I tried putting in
>> the check_glm class
>>
>> @dec.skipif(TestCondition, "Skipping this test because of ...")
>> def test_resids(self):
>> ...
>>
>> TestCondition should be None by default, but how can I get the value
>> of TestCondition to evaluate to True if appropriate? I have tried a
>> few different ways, but I am a little stumped. Does this make sense/is
>> it possible? I'm sure I'm missing something obvious, but any insights
>> would be appreciated.
>>
>
> I don't think you can do that with the decorator. Just do the test in
> code inside the method and raise nose.SkipTest explicitly:
>
> def test_resids(self):
> if not isinstance(self.res1, GLMResults):
> raise nose.SkipTest("Not a GLM test")
>
>
I think you should follow (or do something similar to) the approach
given under the section 'Creating many similar tests' in the Numpy
guidelines under Tips & Tricks:
http://projects.scipy.org/numpy/wiki/TestingGuidelines
This gives the option to group tests from related models together.
Bruce
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