[pymvpa] Hyperalignment: "For now do not handle invariant in time datasets"

[Swaroop Guntupalli]

Hi,

I am glad it is resolved. Yes, we had this issue before with numpy's
svd, which is why we included dgesvd option :)
Sorry, the error message in PyMVPA misled me.

Thanks,
Swarooop

On Thu, Nov 23, 2017 at 2:41 AM, Müller, K. (Katja)
<K.Muller at psych.ru.nl> wrote:
> Hi Swaroop,
>
> Thanks for your response, but I had already checked for 0 variance in multiple variants and could not find voxels with 0 variance. I also checked the variables used in this call (means and sum of squares ssqs), and could not find something suspicious.
>
> This turned out to be an issue of the numpy SVD, which is used as a default by the ProcrusteanMapper. For large matrices it apparently may result in this error:
>
>>> init_dgesdd failed init
>
>
> Initializing Hyperalignment() as
>
> Hyperalignment(alignment=ProcrusteanMapper(svd='dgesvd'))
>
> (i.e. using the LAPACK dgesvd) solves it. There might be documentation about this that I'm not aware of.
>
>
> Best regards,
> Katja
>
>> Am 17.11.29 Heisei um 20:11 schrieb Swaroop Guntupalli <swaroopgj at gmail.com>:
>>
>> Hi Katja,
>>
>> It looks the code is checking if variance/sum of squares of time
>> series in your data is practically zero, and raising that error.
>> Check if all your input datasets have non-zero variance in all voxels.
>>
>> Best,
>> Swaroop
>>
>> On Mon, Nov 13, 2017 at 4:41 AM, Müller, K. (Katja)
>> <K.Muller at psych.ru.nl> wrote:
>>> Dear all,
>>>
>>> It would be great if somebody has any kind of information about what causes this issue, and ways to solve it.
>>>
>>> I am running hyperalignment ROI-by-ROI on 3 subjects. It works on every ROI except the largest one with ~28k voxels (the dataset has 53k voxels in total), where it fails with the error message:
>>> "For now do not handle invariant in time datasets"
>>>
>>> It was suggested earlier (similar mailing list question from 2013) to remove invariant voxels to solve this issue. I tried both remove_invariant_features() and my own code for this, but the behaviour and error message did not change.
>>>
>>> My datasets are inside a standard Python list.
>>>
>>>       train_pymv_datasets = [mvpa2.datasets.Dataset(dataset) for dataset in roi0_train_data]
>>>       hyperalign_fit = mvpa2.algorithms.hyperalignment.Hyperalignment()(train_pymv_datasets)
>>>
>>> This is what I get when calling the second line:
>>>
>>> init_dgesdd failed init
>>> init_dgesdd failed init
>>> init_dgesdd failed init
>>> init_dgesdd failed init
>>> Traceback (most recent call last):
>>> File "save_hyperaligned_subject.py", line 77, in <module>
>>>   hyperalign_fit = mvpa2.algorithms.hyperalignment.Hyperalignment()(train_pymv_datasets)
>>> File ".../anaconda2/lib/python2.7/site-packages/mvpa2/algorithms/hyperalignment.py", line 339, in __call__
>>>   self.train(datasets)
>>> File ".../anaconda2/lib/python2.7/site-packages/mvpa2/algorithms/hyperalignment.py", line 319, in train
>>>   residuals)
>>> File ".../anaconda2/lib/python2.7/site-packages/mvpa2/algorithms/hyperalignment.py", line 483, in _level2
>>>   m.train(ds_new)
>>> File ".../anaconda2/lib/python2.7/site-packages/mvpa2/base/learner.py", line 137, in train
>>>   self._train(ds)
>>> File ".../anaconda2/lib/python2.7/site-packages/mvpa2/mappers/procrustean.py", line 123, in _train
>>>   raise ValueError, "For now do not handle invariant in time datasets"
>>> ValueError: For now do not handle invariant in time datasets
>>>
>>>
>>> I looked up the respective code section in procrustean.py, but am not sure what the code is checking for there.
>>>
>>>
>>> Best regards from the Netherlands,
>>> Katja Müller
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