[LNT] r207898 - Use Mann-Whitney U test to identify changes

[Chris Matthews]

I get a “malformed patch” warning with patch and git.   Can you resend the patch?

On May 8, 2014, at 2:01 AM, Yi Kong <Yi.Kong at arm.com> wrote:

> Updated again. If SciPy is installed, it will use normal approximation
> to compute MWU. Otherwise it will show an error page in large problem
> sizes.
> 
> On Wed, 2014-05-07 at 17:16 +0100, Hal Finkel wrote:
>> ----- Original Message -----
>>> From: "Yi Kong" <Yi.Kong at arm.com>
>>> To: "Hal Finkel" <hfinkel at anl.gov>
>>> Cc: "LLVM Commits" <llvm-commits at cs.uiuc.edu>, "Renato Golin" <renato.golin at linaro.org>, "Anton Korobeynikov"
>>> <anton at korobeynikov.info>, "Chris Matthews" <chris.matthews at apple.com>
>>> Sent: Wednesday, May 7, 2014 11:06:36 AM
>>> Subject: Re: [LNT] r207898 - Use Mann-Whitney U test to identify changes
>>> 
>>> Updated. If the sample size if greater than 20, Mann-Whitney U test
>>> won't be performed.
>> 
>> Sorry, I was not clear...
>> 
>> +        # Use Mann-Whitney U test to test null hypothesis that result is
>> 
>> +        # unchanged.
>> 
>> +        if len(self.samples) >= 4 and len(self.samples) <= 20 and\
>> 
>> +        len(self.prev_samples) >= 4 and len(self.prev_samples) <= 20:
>> 
>> +            same = stats.mannwhitneyu(self.samples, self.prev_samples, self.confidence_lv)
>> 
>> +            if same:
>> 
>> +                return UNCHANGED_PASS
>> 
>> This is going to be very confusing; in theory, this means that increasing the number of samples will give you better results until you hit 20, and then suddenly you'll get meaningless answers. I had meant that it should produce an *error* if you even try to run such a configuration.
>> 
>> -Hal
>> 
>>> 
>>> On Wed, 2014-05-07 at 16:12 +0100, Hal Finkel wrote:
>>>> ----- Original Message -----
>>>>> From: "Yi Kong" <Yi.Kong at arm.com>
>>>>> To: "Anton Korobeynikov" <anton at korobeynikov.info>, "Chris
>>>>> Matthews" <chris.matthews at apple.com>
>>>>> Cc: "Hal Finkel" <hfinkel at anl.gov>, "LLVM Commits"
>>>>> <llvm-commits at cs.uiuc.edu>, "Renato Golin"
>>>>> <renato.golin at linaro.org>
>>>>> Sent: Wednesday, May 7, 2014 10:02:11 AM
>>>>> Subject: Re: [LNT] r207898 - Use Mann-Whitney U test to identify
>>>>> changes
>>>>> 
>>>>> I've updated the patch to perform Mann-Whitney U test using
>>>>> significance
>>>>> table. We still need SciPy when sample size is large(> 20).
>>>> 
>>>> Why do you still require SciPy? That is a large package to pull in
>>>> for one rarely-used function. Are there other parts of SciPy that
>>>> you anticipate us using in the future? If not, then there are two
>>>> options (which I think are both reasonable):
>>>> 
>>>> 1. Limit the number of repeat samples taken to 20 (running the
>>>> test suite more than 20 times per revision seems unlikely in
>>>> practice).
>>>> 2. Implement the normal approximation to the U-value calculation
>>>> in the code. From the description on the wikipedia page, the
>>>> algorithm seems pretty simple.
>>>> 
>>>> -Hal
>>>> 
>>>>> 
>>>>> On Wed, 2014-05-07 at 09:12 +0100, Anton Korobeynikov wrote:
>>>>>>> If we are using significance table, we can no longer
>>>>>>> calculate p
>>>>>>> values,
>>>>>>> right? Is there any algorithm to calculate p value for all
>>>>>>> sample
>>>>>>> sizes?
>>>>>> We would just need to fix the threshold (say, 0.05 or 0.1 or
>>>>>> 0.01).
>>>>>> Also, we can have like 2 or 3 tables for various thresholds.
>>>>>> This
>>>>>> should be enough for all the practical purposes.
>>>>>> 
>>>>> 
>>>>> 
>>>>> 
>>>> 
>>> 
>> 
> 
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