[SciPy-Dev] scipy.stats: algorithm to for ticket 1493

[nicky van foreest]

>> Nice example. The answer is negative, while it should be positive, but
>> the answer is within numerical accuracy I would say.
>
> oops, didn't we have a case with negative sign already ?
> maybe a check self.a <= p <= self.b  ?

I included this. I also think that a check on whether left and right
stay within  self.a and self.b should be included, perhaps just for
safety reasons.

>
>>
>>> I don't see anything yet to criticize in your latest version :(
>>
>> Ok. I just checked the tests in scipy/stats/tests.
>
> If you are curious, you could temporarily go closer to q=0 and q=1 in
> the tests for ppf, and see whether it breaks for any distribution.

Good idea. Just to see what would happen I changed the following code
in test_continuous_basic.py:

@_silence_fp_errors
def check_cdf_ppf(distfn,arg,msg):
    values = [-1.e-5, 0.,0.001,0.5,0.999,1.]
    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
                            values, decimal=DECIMAL, err_msg= msg + \
                            ' - cdf-ppf roundtrip')


Thus, I changed the values into an array. It should fail on the first
value, as it is negative, but I get a pass. Specifically, I ran:

nicky at chuck:~/prog/scipy/scipy/stats/tests$ python test_continuous_basic.py
..............................................................................................................................
----------------------------------------------------------------------
Ran 126 tests in 93.990s

OK

>

Weird result. If I add a q  = 1.0000001 I get a fail on the fourth
test, as expected.

>> - repair for the cases q =  0 and q = 1 by means of an explicit test.
>
> isn't ppf (generic part) taking care of this, if not then it should, I think

Actually, from the code in lines:

https://github.com/scipy/scipy/blob/master/scipy/stats/distributions.py#L1529

I am inclined to believe you. However, in view of the above test ...
Might it be that the conditions on L1529 have been added quite
recently, and did not yet make it to my machine? I'll check this right
now....As a matter of fact, my distributions.py contains the same
check, i.e.,         cond1 = (q > 0) & (q < 1) . Hmmm.

Now I admit that I do not understand in all nitty-gritty detail the
entire implementation of ppf(), but I suspect that this is a bug.

>
> ppf(0) = self.a
> ppf(1) = self.b

Good idea.

I'll implement the code in my branch, and do a pull request.