[Mne_analysis] test_filter.py value mismatch error

Alexandre Gramfort alexandre.gramfort at inria.fr
Thu Sep 28 15:13:04 EDT 2017
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Dear Kyuhwa,

this is really strange. All continuous integration systems we use
don't report this issue.

DId you try with latest master ?

Alex


On Wed, Sep 27, 2017 at 7:30 PM, Lee, KyuHwa <lee.kyuh at gmail.com> wrote:

> Hi Alex, here's the result:
>
> Platform:      Windows-10-10.0.15063-SP0
> Python:        3.6.1 |Anaconda custom (64-bit)| (default, May 11 2017,
> 13:25:24) [MSC v.1900 64 bit (AMD64)]
> Executable:    C:\Anaconda3\python.exe
> CPU:           Intel64 Family 6 Model 78 Stepping 3, GenuineIntel: 4 cores
> Memory:        7.9 GB
>
> mne:           0.15.dev0
> numpy:         1.13.1 {blas=mkl_core_dll, lapack=mkl_core_dll}
> scipy:         0.19.1
> matplotlib:    2.0.2
>
> sklearn:       0.19.0
> nibabel:       Not found
> mayavi:        Not found
> pycuda:        2017.1.1
> skcuda:        0.5.1
> pandas:        0.20.3
>
> Best,
> Kyuhwa
>
> On Sun, Sep 24, 2017 at 1:38 PM, Alexandre Gramfort <
> alexandre.gramfort at inria.fr> wrote:
>
>> hi Kyuhwa,
>>
>> can you tell me what this gives for you
>>
>> import mne
>> mne.sys_info()
>>
>> Alex
>>
>>
>> On Sat, Sep 23, 2017 at 7:22 PM, Lee, KyuHwa <lee.kyuh at gmail.com> wrote:
>>
>>> Hello,
>>>
>>> I installed MNE 0.15-dev from git with CUDA 8.0 and Anaconda 4.4 (Python
>>> 3.6), following this manual:
>>> http://martinos.org/mne/dev/advanced_setup.html?highlight=dev%20install
>>>
>>> When I run:
>>> mne.utils.set_config('MNE_USE_CUDA', 'true'); mne.cuda.init_cuda()
>>> I get the correct result:
>>> Enabling CUDA with 761.0 MB available memory
>>>
>>> When I run test_filter.py, I get the following error:
>>>
>>> AssertionError:
>>> Not equal to tolerance rtol=0.001, atol=0.001
>>>
>>> (mismatch 0.9333333333333371%)
>>>  x: array([[ -2.558717e-16,  -1.525432e-01,  -2.835838e-01, ...,
>>>           4.634404e-01,   2.543413e-01,  -6.938894e-17],
>>>        [ -2.532696e-16,   1.745948e-01,   3.111300e-01, ...,
>>>           6.255135e-01,   3.478937e-01,  -2.775558e-17]])
>>>  y: array([[  8.933826e-17,  -1.527561e-01,  -2.836241e-01, ...,
>>>           4.634049e-01,   2.543312e-01,  -2.428613e-16],
>>>        [  1.565588e-16,   1.746173e-01,   3.108850e-01, ...,
>>>           6.256731e-01,   3.481433e-01,  -3.053113e-16]])
>>>
>>> It was caused by line 332 of test_filter.py:
>>> assert_allclose(hp, bp, rtol=1e-3, atol=1e-3)
>>>
>>> I tried increasing the tolerence to 1e-2, and now it throws another
>>> error caused by line 336 (FFT calculation):
>>> AssertionError:
>>> Not equal to tolerance rtol=1e-07, atol=0.02
>>>
>>> (mismatch 100.0%)
>>>  x: array(1.0259702184926076)
>>>  y: array(1.0)
>>>
>>> Seems like there's a serious numerical problem with the computation.
>>> What should I do in this case?
>>> Weird thing is, even if I set MNE_USE_CUDA to 'false', it still gives me
>>> exactly the same error, which might suggest it's independent of CUDA
>>> setting?
>>>
>>> Best wishes,
>>> Kyuhwa
>>>
>>>
>>>
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