[SciPy-Dev] ANN: SciPy 0.8.0 beta 1

Vincent Davis vincent at vincentdavis.net
Sat Jun 12 16:04:40 EDT 2010 

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])

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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