[SciPy-Dev] ANN: SciPy 0.8.0 beta 1

Vincent Davis vincent@vincentdavis....
Sat Jun 12 15:26:58 CDT 2010


On Sat, Jun 12, 2010 at 2:13 PM,  <josef.pktd@gmail.com> wrote:
> On Sat, Jun 12, 2010 at 4:04 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
>> On Sat, Jun 12, 2010 at 2:00 PM,  <josef.pktd@gmail.com> wrote:
>>> On Sat, Jun 12, 2010 at 3:50 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
>>>> On Sat, Jun 12, 2010 at 1:47 PM,  <josef.pktd@gmail.com> wrote:
>>>>> On Sat, Jun 12, 2010 at 3:41 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
>>>>>> On Sat, Jun 12, 2010 at 1:37 PM,  <josef.pktd@gmail.com> wrote:
>>>>>>> On Sat, Jun 12, 2010 at 3:28 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
>>>>>>>> On Sat, Jun 12, 2010 at 1:22 PM,  <josef.pktd@gmail.com> wrote:
>>>>>>>>> On Sat, Jun 12, 2010 at 3:02 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
>>>>>>>>>> On Fri, Jun 11, 2010 at 6:41 PM,  <josef.pktd@gmail.com> wrote:
>>>>>>>>>>> On Fri, Jun 11, 2010 at 7:54 PM, Derek Homeier
>>>>>>>>>>> <derek@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? 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)

Vincent



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


>
> 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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