[SciPy-Dev] ANN: SciPy 0.8.0 beta 1

josef.pktd at gmail.com josef.pktd at gmail.com
Sat Jun 12 16:47:27 EDT 2010 

On Sat, Jun 12, 2010 at 4:26 PM, Vincent Davis <vincent at vincentdavis.net> wrote:
> On Sat, Jun 12, 2010 at 2:13 PM,  <josef.pktd at gmail.com> wrote:
>> On Sat, Jun 12, 2010 at 4:04 PM, Vincent Davis <vincent at vincentdavis.net> wrote:
>>> On Sat, Jun 12, 2010 at 2:00 PM,  <josef.pktd at gmail.com> wrote:
>>>> On Sat, Jun 12, 2010 at 3:50 PM, Vincent Davis <vincent at vincentdavis.net> wrote:
>>>>> On Sat, Jun 12, 2010 at 1:47 PM,  <josef.pktd at gmail.com> wrote:
>>>>>> On Sat, Jun 12, 2010 at 3:41 PM, Vincent Davis <vincent at vincentdavis.net> wrote:
>>>>>>> On Sat, Jun 12, 2010 at 1:37 PM,  <josef.pktd at gmail.com> wrote:
>>>>>>>> On Sat, Jun 12, 2010 at 3:28 PM, Vincent Davis <vincent at vincentdavis.net> wrote:
>>>>>>>>> On Sat, Jun 12, 2010 at 1:22 PM,  <josef.pktd at gmail.com> wrote:
>>>>>>>>>> On Sat, Jun 12, 2010 at 3:02 PM, Vincent Davis <vincent at vincentdavis.net> wrote:
>>>>>>>>>>> On Fri, Jun 11, 2010 at 6:41 PM,  <josef.pktd at gmail.com> wrote:
>>>>>>>>>>>> On Fri, Jun 11, 2010 at 7:54 PM, Derek Homeier
>>>>>>>>>>>> <derek at astro.physik.uni-goettingen.de> wrote:
>>>>>>>>>>>>> Hi Josef,
>>>>>>>>>>>>>
>>>>>>>>>>>>>>> FAIL: test_stats.test_kstest
>>>>>>>>>>>>>>> ----------------------------------------------------------------------
>>>>>>>>>>>>>>> Traceback (most recent call last):
>>>>>>>>>>>>>>>  File "/sw/lib/python2.6/site-packages/nose/case.py", line 186, in runTest
>>>>>>>>>>>>>>>    self.test(*self.arg)
>>>>>>>>>>>>>>>  File "/sw/lib/python2.6/site-packages/scipy/stats/tests/test_stats.py", line 1078, in test_kstest
>>>>>>>>>>>>>>>    np.array((0.0072115233216310994, 0.98531158590396228)), 14)
>>>>>>>>>>>>>>>  File "/sw/lib/python2.6/site-packages/numpy/testing/utils.py", line 441, in assert_almost_equal
>>>>>>>>>>>>>>>    return assert_array_almost_equal(actual, desired, decimal, err_msg)
>>>>>>>>>>>>>>>  File "/sw/lib/python2.6/site-packages/numpy/testing/utils.py", line 765, in assert_array_almost_equal
>>>>>>>>>>>>>>>    header='Arrays are not almost equal')
>>>>>>>>>>>>>>>  File "/sw/lib/python2.6/site-packages/numpy/testing/utils.py", line 609, in assert_array_compare
>>>>>>>>>>>>>>>    raise AssertionError(msg)
>>>>>>>>>>>>>>> AssertionError:
>>>>>>>>>>>>>>> Arrays are not almost equal
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> (mismatch 100.0%)
>>>>>>>>>>>>>>>  x: array([ 0.007,  0.985])
>>>>>>>>>>>>>>>  y: array([ 0.007,  0.985])
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> maybe the precision (decimal 14) is too high for this test across platforms
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Could you check how large the difference is ?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> np.random.seed(987654321)
>>>>>>>>>>>>>> x = stats.norm.rvs(loc=0.2, size=100)
>>>>>>>>>>>>>> np.array(stats.kstest(x,'norm', alternative = 'greater')) -
>>>>>>>>>>>>>>                np.array((0.0072115233216310994, 0.98531158590396228))
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> (my line numbers differ, but this should be the right test given your numbers)
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> yes, just a decimal or two too high, if I got the numbers right:
>>>>>>>>>>>>> # OS X 10.5 i386 / 10.6 x86_64:
>>>>>>>>>>>>> array([  8.67361738e-18,   1.66533454e-15])
>>>>>>>>>>>>>
>>>>>>>>>>>>> # OS X 10.5 ppc:
>>>>>>>>>>>>> array([  2.05955045e-13,  -7.16759985e-13])
>>>>>>>>>>>>
>>>>>>>>>>>> interesting that there are differences in the calculations, but for
>>>>>>>>>>>> the test we can just reduce the precision to decimal=12 to avoid the
>>>>>>>>>>>> test failure.
>>>>>>>>>>>
>>>>>>>>>>> I must be doing something wrong here becuase I don't get anything
>>>>>>>>>>> close that what you have above.
>>>>>>>>>
>>>>>>>>>>> In [4]: np.random.seed(987654321)
>>>>>>>>>>>
>>>>>>>>>>> In [5]: x = stats.norm.rvs(loc=0.2, size=100)
>>>>>>>>>>>
>>>>>>>>>>> In [6]: r1 = np.array(stats.kstest(x,'norm', alternative = 'greater'))
>>>>>>>>>>>
>>>>>>>>>>> In [7]: r2 = np.array((0.0072115233216310994, 0.98531158590396228))
>>>>>>>>>>>
>>>>>>>>>>> In [8]: r1-r2
>>>>>>>>>>> Out[8]: array([ 0.03704986, -0.32866092])
>>>>>>>>>>
>>>>>>>>>>>>> np.random.seed(987654321)
>>>>>>>>>>>>> xrvs = stats.norm.rvs(loc=0.2, size=100)
>>>>>>>>>>>>> r1 = np.array(stats.kstest(xrvs,'norm', alternative = 'greater'))
>>>>>>>>>>>>> r2 = np.array((0.0072115233216310994, 0.98531158590396228))
>>>>>>>>>>>>> r1-r2
>>>>>>>>>> array([  8.67361738e-18,   1.66533454e-15])
>>>>>>>>>>
>>>>>>>>>> Can you check mean and var to see if you have the same random  numbers?
>>>>>>>>>>
>>>>>>>>>>>>> xrvs.mean()
>>>>>>>>>> 0.20830662128271851
>>>>>>>>>>>>> xrvs.var()
>>>>>>>>>> 1.1210385272356511
>>>>>>>>>
>>>>>>>>> In [11]: x.mean()
>>>>>>>>> Out[11]: 0.054996065027031464
>>>>>>>>>
>>>>>>>>> In [12]: x.var()
>>>>>>>>> Out[12]: 0.92731406990162746
>>>>>>>>
>>>>>>>> looks like you have different random numbers
>>>>>>>>
>>>>>>>>>
>>>>>>>>> I am cheating and using the enthought distribution, I just click install.
>>>>>>>>> How do I run all of the tests for scipy or numpy when they are already
>>>>>>>>> installed?
>>>>>>>>
>>>>>>>> scipy.stats.test()
>>>>>>>> .test() works for scipy and every subpackage
>>>>>>>>
>>>>>>>> is ipython messing with the RandomState ?
>>>>>>>
>>>>>>> In [19]: np.random.seed(987654321)
>>>>>>>
>>>>>>> In [20]: np.random.rand(3)
>>>>>>> Out[20]: array([ 0.07298833,  0.2160365 ,  0.46475349])
>>>>>>>
>>>>>>> In [21]: np.random.rand(3)
>>>>>>> Out[21]: array([ 0.62258994,  0.61838812,  0.42737911])
>>>>>>
>>>>>> same here
>>>>>>
>>>>>>>>> np.random.seed(987654321)
>>>>>>>>> np.random.rand(3)
>>>>>> array([ 0.07298833,  0.2160365 ,  0.46475349])
>>>>>>>>> np.random.rand(3)
>>>>>> array([ 0.62258994,  0.61838812,  0.42737911])
>>>>>>
>>>>>> ??
>>>>>
>>>>> Gets better, I just ran the test, I need to look above to see how this relates.
>>>>>
>>>>> FAIL: test_stats.test_kstest
>>>>> ----------------------------------------------------------------------
>>>>> Traceback (most recent call last):
>>>>>  File "/Library/Frameworks/EPD64.framework/Versions/6.2/lib/python2.6/site-packages/nose/case.py",
>>>>> line 186, in runTest
>>>>>    self.test(*self.arg)
>>>>>  File "/Library/Frameworks/EPD64.framework/Versions/6.2/lib/python2.6/site-packages/scipy/stats/tests/test_stats.py",
>>>>> line 1228, in test_kstest
>>>>>    assert_almost_equal( D, 0.12464329735846891, 15)
>>>>>  File "/Library/Frameworks/EPD64.framework/Versions/6.2/lib/python2.6/site-packages/numpy/testing/utils.py",
>>>>> line 459, in assert_almost_equal
>>>>>    raise AssertionError(msg)
>>>>> AssertionError:
>>>>> Arrays are not almost equal
>>>>>  ACTUAL: 0.093893737596468518
>>>>>  DESIRED: 0.12464329735846891
>>>>
>>>>
>>>> can you check stats random numbers
>>>>
>>>>>>> np.random.seed(987654321)
>>>>>>> stats.norm.rvs(size=3)
>>>> array([ 2.24655081, -0.64591822, -1.18357699])
>>>>>>> np.random.seed(987654321)
>>>>>>> np.random.randn(3)
>>>> array([ 2.24655081, -0.64591822, -1.18357699])
>>>>
>>>> which numpy, scipy versions?
>>>
>>>>>> np.random.seed(987654321)
>>>>>> stats.norm.rvs(size=3)
>>> array([-2.35810307,  0.97313103, -0.52004087])
>>>>>> np.random.seed(987654321)
>>>>>> np.random.randn(3)
>>> array([-2.35810307,  0.97313103, -0.52004087])
>>
>> looks like the np.random.randn implementation differs, but I also have
>> numpy 1.4.0
>>
>> maybe a different seed
>>>>> np.random.seed(0)
>>>>> np.random.randn(3)
>> array([ 1.76405235,  0.40015721,  0.97873798])
>
>>>> np.random.seed(0)
>>>> np.random.randn(3)
> array([ 0.06897149,  1.32078057,  1.5997924 ])
>
> I thought you where getting the same rand earlier?

same rand, but different randn

> And just to be sure
> I tried it in ipython and got the same.
>
> For numpy tests I get
> Ran 2510 tests in 12.698s
> OK (KNOWNFAIL=3, SKIP=1)

I don't think numpy tests the (values of the) random numbers .

>
> Vincent
>
>
>
> Thats a strange link, or was that random?
> http://www.ruthannzaroff.com/wonderland/curiouser.htm

with kids, some things are not so strange

Josef

>
>
>>
>> np.random.rand was the same
>>
>> I've never seen this and no idea what might be going on, but we won't
>> be able to use seeded random numbers in tests if this is "real"
>>
>> Josef
>> http://www.ruthannzaroff.com/wonderland/curiouser.htm
>>
>>
>>>
>>> Obviously different. Not sure how to get the build number from the
>>> scipy and numpy version.
>>>
>>> Vincent
>>>
>>>>>> scipy.__version__
>>> '0.8.0b1'
>>>>>> import numpy
>>>>>> numpy.__version__
>>> '1.4.0'
>>>
>>> Vincent
>>>
>>>>
>>>> Josef
>>>>
>>>>>
>>>>> Vincent
>>>>>
>>>>>>
>>>>>> Josef
>>>>>>>
>>>>>>> In [22]: np.random.seed(987654321)
>>>>>>>
>>>>>>> In [23]: np.random.rand(3)
>>>>>>> Out[23]: array([ 0.07298833,  0.2160365 ,  0.46475349])
>>>>>>>
>>>>>>> Vincent
>>>>>>>
>>>>>>>>
>>>>>>>> Josef
>>>>>>>>
>>>>>>>>>
>>>>>>>>> Vincent
>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> otherwise I have no clue, (but I guess your scipy.stats tests pass)
>>>>>>>>>>
>>>>>>>>>> Josef
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Vincent
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Josef
>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>                                                Derek
>>>>>>>>>>>>>
>>>>>>>>>>>>> _______________________________________________
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