On 24 July 2012 19:32, Gael Varoquaux <[email protected]> wrote:
> On Sat, Jul 21, 2012 at 12:18:46AM +0200, Olivier Grisel wrote:
> > Recent merges have slowly decreased the test coverage ratio of the
> > code base (from nearly 90% down to 85% now):
>
> This email discusses how to do something about this.
>
> I am not sure that everybody knows the efficient workflow to improve test
> coverage. It's easy:
>
> 1. Run 'make test-coverage'. The output lists for each file the line
> numbers that are not tested.
>
> 2. Find a low hanging fruit, look at which lines are not tested, write or
> adapt a test specifically for these lines.
>
> 3. Loop.
>
> Its also a great way to improve your knowledge of the scikit-learn
> codebase.
>
> >
> https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/
> As a side note, I get different covering numbers when running test-coverage
> on my box than on jenkins. No big deal.
>
>
> Non-algorithmic code that needs to be tested
> =============================================
>
> There is a lot of misc code that is not tested, such as setup.py. This is
> actually something that I don't believe that we can ignore for two
> reasons: i) the broken window effect and ii) the fact that this is code
> that indeed needs to be supported and maintained in the long run.
>
> * Setup.py
>
> Reason ii) does not really applied to the setup.py, as they are tested
> during the build. Actually, I am wondering: is it possible to rig up
> jenkins so that it is an install that is performed and tested, and not a
> build inplace? The install would then be tested, which is an additional
> benefit.
>
> * Datasets: a lot of dataset downloading code does not get tested. I am a
> bit uneasy about this. It would be great to write some testing code,
> for instance based on mocking urllib.
>
> * I have checked in a refactor and tests for __check_build:
>
> https://github.com/scikit-learn/scikit-learn/commit/f29363092c456d87e9d344f018938f8f0bb6ae23
> this is an example of how code can be engineered to be tested as much
> as possible.
>
> * The joblib tests do not seem to be running. I'll see what I can do about
> it.
>
> Algorithmic code that needs more tests
> ========================================
>
> Here are what I believe are low-hanging fruit that could use some love.
> It's probably not a huge amount of work to improve testing, and I
> wouldn't be surprised that once we do this, we uncover bugs or
> inconsistent behaviors:
>
> * Gaussian processes:
>
> https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/902/cobertura/sklearn_gaussian_process
>
> * Spectral clustering:
>
> https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/902/cobertura/sklearn_cluster/spectral_py/
>
> * Some feature selection code:
>
> https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/902/cobertura/sklearn_feature_selection/
>
> * sklearn.utils.bench
>
> Now its just a simple matter of programming :)
>
> Gael
>
>
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Hi Gael,
Thanks for the workflow -- I was not aware of it before!
I'll do some tests for the Spectral clustering now.
Thanks,
Robert
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