[pymvpa] hyperalignment inquiry
Swaroop Guntupalli
swaroopgj at gmail.com
Thu Aug 4 15:09:22 UTC 2016
Hi David,
You need to change this line
qe.append(testqe.query_byid)
to this
qe.append(testqe)
Intersection mask might work. I usually do it by computing a binary mask in
each subject with non-invariant features (use std(axis=0)==0) and then do
an "and" of these masks.
Does that help?
Best,
Swaroop
On Thu, Aug 4, 2016 at 3:50 AM, David Soto <d.soto.b at gmail.com> wrote:
> Thanks very much Swaroop, the code still gives an error, please see below
> -also final comment at the bottom of the email
>
> ---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-52-8bb0dc44f9a7> in <module>() 63 # Searchlight Hyperalignment returns a list of mappers corresponding to 64 # subjects in the same order as the list of datasets we passed in.---> 65 slhypmaps = slhyper(ds_train) 66 67 # Applying hyperalignment parameters is similar to applying any mapper in
> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc in __call__(self, datasets) 554 # alignment, i.e. the SL ROIs cover roughly the same area 555 queryengines = self._get_trained_queryengines(--> 556 datasets, params.queryengine, params.radius, params.ref_ds) 557 # For surface nodes to voxels queryengines, roi_seed hardly makes sense 558 if isinstance(queryengines[params.ref_ds], SurfaceVerticesQueryEngine):
> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc in _get_trained_queryengines(self, datasets, queryengine, radius, ref_ds) 666 _shpaldebug("Training provided query engines") 667 for qe, ds in zip(queryengines, datasets):--> 668 qe.train(ds) 669 else: 670 queryengine.train(datasets[ref_ds])
> AttributeError: 'function' object has no attribute 'train'
>
>
> _____
> %reset
> from mvpa2.suite import *
> path0='/Users/dsoto/Dropbox/4hyperal'
> attr = SampleAttributes(os.path.join(path0, 'attrs_cv_singlsubj2chunk.txt'
> ))
> d=[]
> qe=[]
> num=-1
> for subj in range(1,5):
> num=num+1
> print(subj)
> datapath='/Users/dsoto/Dropbox/4hyperal/subj%d' %(int(subj))
> d.append(fmri_dataset(samples=os.path.join(datapath,
> 'reg4mm.nii.gz'),
> targets=attr.targets, chunks=attr.chunks,
> mask=os.path.join(path0, 'mni4mm.nii.gz')))
>
> d[num]=remove_invariant_features(d[num])
> testqe=IndexQueryEngine(voxel_indices=Sphere(3))
> testqe.train(d[num])
> qe.append(testqe.query_byid)
>
> qe_merged = [qe[0],qe[1],qe[2],qe[3]]
> ds_merged = [d[0],d[1],d[2],d[3]]
>
> _ = [zscore(ds) for ds in ds_merged]
>
> for i, sd in enumerate(ds_merged):
> sd.sa['subject'] = np.repeat(i, len(sd))
>
> nsubjs = 4
> ncats = 2
> nruns = 2
>
>
> bsc_slhyper_results = []
> clf = LinearCSVMC()
>
> cv = CrossValidation(clf, NFoldPartitioner(attr='subject'),
> errorfx=mean_match_accuracy)
>
> for test_run in range(nruns):
> ds_train = [sd[sd.sa.chunks != test_run, :] for sd in ds_merged]
> ds_test = [sd[sd.sa.chunks == test_run, :] for sd in ds_merged]
>
> slhyper = SearchlightHyperalignment(queryengine=qe_merged)
> slhypmaps = slhyper(ds_train)
>
> ds_hyper = [h.forward(sd) for h, sd in zip(slhypmaps, ds_test)]
>
> ds_hyper = vstack(ds_hyper)
> zscore(ds_hyper, chunks_attr='subject')
> res_cv = cv(ds_hyper)
> bsc_slhyper_results.append(res_cv)
>
> bsc_slhyper_results = hstack(bsc_slhyper_results)
>
> However, as you know it is likely that searchlighhyperaligment wont work
> well if each dataset has different number of features....so perhaps would
> be worth to compare any result with the second analyses option you
> suggested, that is once I remove invariant features from each subjects
> dataset, I could derive an intersection mask with valid features across all
> subjects and then use this mask when I load the data with
> fmri_dataset......however could you let me know how can this intersection
> mask be derived?
>
> best
> david
>
> [image: ][image: ][image: ]
> ___________
>
> On 3 August 2016 at 18:33, Swaroop Guntupalli <swaroopgj at gmail.com> wrote:
>
>> Hi David,
>>
>> You have to train the queryengine, here is a little modified version of
>> what you pasted.
>> However, remember that the datasets should be approximately similar in
>> coverage. I noticed that your first subject has ~80,000 non-invariant
>> features and some other subjects have ~30,000. That's a huge margin.
>>
>> I am not sure how hyperalignment performs with so many missing regions.
>> Swaroop
>>
>> d[num]=remove_invariant_features(d[num])
>>
>> # You don't need this line below
>> #voxel_indices=d[num].fa.voxel_indices
>>
>> testqe=IndexQueryEngine(voxel_indices=Sphere(3))
>> # Train the testqe with that dataset
>> testqe.train(d[num])
>>
>> qe.append(testqe.query_byid)
>>
>> On Tue, Aug 2, 2016 at 6:30 AM, David Soto <d.soto.b at gmail.com> wrote:
>>
>>> Hi Swaroop, thanks for looking into it...
>>>
>>> There does not seem be a clear documentation on how to get the query
>>> engines ..from what I have seen this is what I did:
>>>
>>> d=[]
>>> qe=[]
>>> num=-1
>>> for subj in range(1,5):
>>> num=num+1
>>> print(subj)
>>> datapath='/Users/dsoto/Dropbox/4hyperal/subj%d' %(int(subj))
>>> d.append(fmri_dataset(samples=os.path.join(datapath,
>>> 'reg4mm.nii.gz'),
>>> targets=attr.targets, chunks=attr.chunks,
>>> mask=os.path.join(path0, 'mni4mm.nii.gz')))
>>>
>>> d[num]=remove_invariant_features(d[num])
>>>
>>> voxel_indices=d[num].fa.voxel_indices
>>>
>>> testqe=IndexQueryEngine(voxel_indices=Sphere(3))
>>>
>>> qe.append(testqe.query_byid)
>>>
>>> qe_merged = [qe[0],qe[1],qe[2],qe[3]]
>>>
>>> ds_merged = [d[0],d[1],d[2],d[3]]
>>>
>>> which I then was hoping to pass later onto
>>> slhyper = SearchlightHyperalignment(queryengine=qe_merged)
>>>
>>> however there does not appear to be an output for each of the 'qe'
>>> above, even though I can see the voxel_ids...
>>>
>>> any tips would be very useful!
>>>
>>> thanks!
>>>
>>>
>>> [image: ][image: ][image: ]
>>> -------------------
>>>
>>> On 1 August 2016 at 19:02, Swaroop Guntupalli <swaroopgj at gmail.com>
>>> wrote:
>>>
>>>> Hi David,
>>>>
>>>> Thanks for the data. The issue is that when using searchlight
>>>> hyperalignment without supplying queryengines but providing radius (as in
>>>> your case), we compute a single spherical volume queryengine on the
>>>> reference subject, which means all the datasets need to be aligned and have
>>>> exact same number of features. The issue in your script is that you remove
>>>> invariance features separately for each subject, which results in different
>>>> numbers of features for each subject.
>>>>
>>>> There are two ways to resolve it:
>>>> 1) after removing invariant features, supply list of spherical volume
>>>> queryengines corresponding to subjects, or
>>>> 2) find the union of invariant features in all subjects, and then
>>>> remove the same set of features from all subjects, and proceed with
>>>> hyperalignment as you have currently.
>>>>
>>>> Hope that helps.
>>>> Best,
>>>> Swaroop
>>>>
>>>> On Mon, Aug 1, 2016 at 6:30 AM, David Soto <d.soto.b at gmail.com> wrote:
>>>>
>>>>> Hi Swaroop, I have run the analyses using the individual copes that
>>>>> were co-registered in FSL to the individual T1 (using BBR) and then to
>>>>> MNI2mm template using a 12 DOF. The problem persists even after doing
>>>>> remove_invariant_features().
>>>>> I get a similar 'Indexerror: index 226822 is out of bounds for axis 1
>>>>> with size 198603'
>>>>>
>>>>> I have uploaded all the datafiles which you can find in the link
>>>>> below, just in case you could have a look. If you open the zip then you
>>>>> will find all the relevant files, including the pymvpa script that I use to
>>>>> run the analyses ('hyperalscript.py'), the individual data within a folder
>>>>> and also the attributes file and the MNI brain mask
>>>>> https://drive.google.com/open?id=0B7-qbWx3g9KfUU1jOFVpQ05oRWM
>>>>>
>>>>> any help would be much appreciated...
>>>>>
>>>>> thanks!
>>>>> david
>>>>>
>>>>> On 29 July 2016 at 18:28, Swaroop Guntupalli <swaroopgj at gmail.com>
>>>>> wrote:
>>>>>
>>>>>> That is correct. But the searchlight centers across subjects are
>>>>>> assumed to be aligned.
>>>>>>
>>>>>>
>>>>>> On Fri, Jul 29, 2016 at 8:24 AM, David Soto <d.soto.b at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi, thanks I will try that, I understand therefore that the number
>>>>>>> of features per subject need not be equal across subjects for searchlight
>>>>>>> hyperalignment - but please correct me if am wrong.
>>>>>>> best
>>>>>>> david
>>>>>>>
>>>>>>> On 29 July 2016 at 16:17, Swaroop Guntupalli <swaroopgj at gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi David,
>>>>>>>>
>>>>>>>> If you are using searchlight hyperalignment, it is advisable to
>>>>>>>> align the data across subjects using anatomy first. Simplest would be to be
>>>>>>>> align them to an MNI template and then run the searchlight hyperalignment.
>>>>>>>> Our tutorial dataset is affine aligned to MNI template.
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Swaroop
>>>>>>>>
>>>>>>>> On Thu, Jul 28, 2016 at 10:51 AM, David Soto <d.soto.b at gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Thanks Swaroop, I managed to get the dataset in the right format
>>>>>>>>> as per the hyperaligmentsearchlight tutorial
>>>>>>>>> however when I run the hyperaligment I get the following error (IndexError:
>>>>>>>>> index 46268 is out of bounds for axis 1 with size 43506, see
>>>>>>>>> further below)...to recap the dataset is a concatenation of each subject
>>>>>>>>> data, each in individual native space, so number of features are different
>>>>>>>>> across subjects...
>>>>>>>>> The code I use is the same as in the tutorial, namely, any
>>>>>>>>> feedback would be great, thanks, david
>>>>>>>>> cv = CrossValidation(clf, NFoldPartitioner(attr='subject'),
>>>>>>>>> errorfx=mean_match_accuracy)
>>>>>>>>>
>>>>>>>>> for test_run in range(nruns):
>>>>>>>>> ds_train = [sd[sd.sa.chunks != test_run, :] for sd in ds_all]
>>>>>>>>> ds_test = [sd[sd.sa.chunks == test_run, :] for sd in ds_all]
>>>>>>>>>
>>>>>>>>> slhyper = SearchlightHyperalignment(radius=3, featsel=0.4,
>>>>>>>>> sparse_radius=3)
>>>>>>>>> slhypmaps = slhyper(ds_train)
>>>>>>>>> ds_hyper = [h.forward(sd) for h, sd in zip(slhypmaps, ds_test)]
>>>>>>>>>
>>>>>>>>> ds_hyper = vstack(ds_hyper)
>>>>>>>>> zscore(ds_hyper, chunks_attr='subject')
>>>>>>>>> res_cv = cv(ds_hyper)
>>>>>>>>> bsc_slhyper_results.append(res_cv)
>>>>>>>>>
>>>>>>>>> OUTPUT MESSAGE.........
>>>>>>>>> Performing classification analyses...
>>>>>>>>> between-subject (searchlight hyperaligned)...
>>>>>>>>>
>>>>>>>>> ------------------------------------------------------------
>>>>>>>>> ---------------
>>>>>>>>> IndexError Traceback (most recent
>>>>>>>>> call last)
>>>>>>>>> <ipython-input-191-85bdb873d4f1> in <module>()
>>>>>>>>> 24 # Searchlight Hyperalignment returns a list of mappers
>>>>>>>>> corresponding to
>>>>>>>>> 25 # subjects in the same order as the list of datasets
>>>>>>>>> we passed in.
>>>>>>>>> ---> 26 slhypmaps = slhyper(ds_train)
>>>>>>>>> 27
>>>>>>>>> 28 # Applying hyperalignment parameters is similar to
>>>>>>>>> applying any mapper in
>>>>>>>>>
>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc
>>>>>>>>> in __call__(self, datasets)
>>>>>>>>> 626 node_blocks = np.array_split(roi_ids,
>>>>>>>>> params.nblocks)
>>>>>>>>> 627 p_results = [self._proc_block(block, datasets,
>>>>>>>>> hmeasure, queryengines)
>>>>>>>>> --> 628 for block in node_blocks]
>>>>>>>>> 629 results_ds = self.__handle_all_results(p_results)
>>>>>>>>> 630 # Dummy iterator for, you know, iteration
>>>>>>>>>
>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc
>>>>>>>>> in _proc_block(self, block, datasets, featselhyper, queryengines, seed,
>>>>>>>>> iblock)
>>>>>>>>> 387 continue
>>>>>>>>> 388 # selecting neighborhood for all subject for
>>>>>>>>> hyperalignment
>>>>>>>>> --> 389 ds_temp = [sd[:, ids] for sd, ids in
>>>>>>>>> zip(datasets, roi_feature_ids_all)]
>>>>>>>>> 390 if self.force_roi_seed:
>>>>>>>>> 391 roi_seed = np.array(roi_feature_ids_all[self.params.ref_ds])
>>>>>>>>> == node_id
>>>>>>>>>
>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/datasets/base.pyc in
>>>>>>>>> __getitem__(self, args)
>>>>>>>>> 139
>>>>>>>>> 140 # let the base do the work
>>>>>>>>> --> 141 ds = super(Dataset, self).__getitem__(args)
>>>>>>>>> 142
>>>>>>>>> 143 # and adjusting the mapper (if any)
>>>>>>>>>
>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/base/dataset.pyc in
>>>>>>>>> __getitem__(self, args)
>>>>>>>>> 445 if isinstance(self.samples, np.ndarray):
>>>>>>>>> 446 if np.any([isinstance(a, slice) for a in
>>>>>>>>> args]):
>>>>>>>>> --> 447 samples = self.samples[args[0], args[1]]
>>>>>>>>> 448 else:
>>>>>>>>> 449 # works even with bool masks (although
>>>>>>>>> without
>>>>>>>>>
>>>>>>>>> IndexError: index 46268 is out of bounds for axis 1 with size 43506
>>>>>>>>>
>>>>>>>>> On 28 July 2016 at 00:25, Swaroop Guntupalli <swaroopgj at gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi David,
>>>>>>>>>>
>>>>>>>>>> If you have limited data, you can use a part of it (however you
>>>>>>>>>> split the data for training and testing)
>>>>>>>>>> to train hyperalignment, and also use the same part to train the
>>>>>>>>>> classifier and then apply hyperalignment and test classifier on the
>>>>>>>>>> left-out part. Yes, you can artificially create 2 chunks (or more if you
>>>>>>>>>> prefer).
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Wed, Jul 27, 2016 at 3:17 PM, David Soto <d.soto.b at gmail.com>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> sounds great thanks, a further thing is that I have seen that in
>>>>>>>>>>> order to preclude circularity issues, hyperalinment is implemented on a
>>>>>>>>>>> subset of training chunks and then the transformation is applied to the
>>>>>>>>>>> full datasets prior to classification analyses. Given that I have no
>>>>>>>>>>> proper chunks/runs here, but only 56 betas across trials, would it be okay
>>>>>>>>>>> to train hyperaligment just on half of the 56 betas, eg artificially split
>>>>>>>>>>> the data set in 2 chunks each containing 14 betas of class A and 14 of
>>>>>>>>>>> class B? Or would it be just OK to train hyperaligment on the 56 betas in
>>>>>>>>>>> the first instance?
>>>>>>>>>>> thanks!
>>>>>>>>>>> david
>>>>>>>>>>>
>>>>>>>>>>> On 28 July 2016 at 00:00, Swaroop Guntupalli <
>>>>>>>>>>> swaroopgj at gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> The hyperalignment example on PyMVPA uses one beta map for each
>>>>>>>>>>>> category per run.
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Jul 27, 2016 at 2:57 PM, Swaroop Guntupalli <
>>>>>>>>>>>> swaroopgj at gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi David,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Beta maps should work fine for hyperalignment. The more maps
>>>>>>>>>>>>> (or TRs) there are, better the estimate.
>>>>>>>>>>>>> We used within-subject hyperalignment in Haxby et al. 2011,
>>>>>>>>>>>>> which uses maps from 6 categories (we used 3 successive betas per condition
>>>>>>>>>>>>> I think).
>>>>>>>>>>>>>
>>>>>>>>>>>>> vstack() merges multiple datasets into a single dataset, and
>>>>>>>>>>>>> if there is any voxel count (nfeatures) mismatch across subjects, it won't
>>>>>>>>>>>>> work (as evidenced by the error).
>>>>>>>>>>>>> Hyperalignment takes in a list of datasets, one per each
>>>>>>>>>>>>> subject.
>>>>>>>>>>>>> So, you can make that a list as
>>>>>>>>>>>>> ds_all =[ds1, ds2, ...., ds16]
>>>>>>>>>>>>> and use for Hyperalignment()
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>> Swaroop
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Wed, Jul 27, 2016 at 2:28 PM, David Soto <
>>>>>>>>>>>>> d.soto.b at gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> hi,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> in my experiment I have 28 betas in condition A and 28
>>>>>>>>>>>>>> parameter estimate images and 28 in condition B for each subject (N=16 in
>>>>>>>>>>>>>> total).
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> i have performed across-subjects SVM-based searchlight
>>>>>>>>>>>>>> classification using MNI-registered individual beta images and I would like
>>>>>>>>>>>>>> to repeat and confirm my results using searchlight based on hyperaligned
>>>>>>>>>>>>>> data.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> i am not aware of any paper using hyperaligment on beta
>>>>>>>>>>>>>> images but I think this should be possible, any advise please would be nice
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> i've created individual datasets concatenating the 28 betas
>>>>>>>>>>>>>> in condition A and the 28 in condition (in the actual experiment condition
>>>>>>>>>>>>>> A and B can appear randomly on each trial). I have 16 nifti datasets, one
>>>>>>>>>>>>>> per subject, with each in individual native anatomical space. In trying to
>>>>>>>>>>>>>> get a dataset in the same format as in the hyperlignment tutorial I use
>>>>>>>>>>>>>> fmri_dataset on each individual wholebrain 48 betas and then try to merged
>>>>>>>>>>>>>> then all i.e. ds_merged = vstack((d1, d2, d3, d4, d5, d6,
>>>>>>>>>>>>>> d7, d8, d9, d10, d11, d12, d13, d14, d15,d16)) but this gives the following
>>>>>>>>>>>>>> error pasted at the end,
>>>>>>>>>>>>>> which I think it is becos the number of voxels is different
>>>>>>>>>>>>>> across subjects. This is one issue.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Another is that the function vstack does appear to produce
>>>>>>>>>>>>>> the list of individual datasets that is in the hyperligment tutorial
>>>>>>>>>>>>>> dataset, but a list of individual betas, I would be grateful to receive
>>>>>>>>>>>>>> some tips.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> thanks!
>>>>>>>>>>>>>> david
>>>>>>>>>>>>>> ------------------------------------------------------------
>>>>>>>>>>>>>> ---------------
>>>>>>>>>>>>>> ValueError Traceback (most
>>>>>>>>>>>>>> recent call last)
>>>>>>>>>>>>>> <ipython-input-64-2fef46542bfc> in <module>()
>>>>>>>>>>>>>> 19 h5save('/home/dsoto/dsoto/fmri/wmlearning/h5.hdf5',
>>>>>>>>>>>>>> [d1,d2])
>>>>>>>>>>>>>> 20 #ds_merged = vstack((d1, d2, d3, d4, d5, d6,
>>>>>>>>>>>>>> d7,d8,d9, d10, d11, d12, d13, d14, d15, d16))
>>>>>>>>>>>>>> ---> 21 ds_merged = vstack((d1, d2))
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/base/dataset.pyc
>>>>>>>>>>>>>> in vstack(datasets, a)
>>>>>>>>>>>>>> 687 "datasets have varying
>>>>>>>>>>>>>> attributes.")
>>>>>>>>>>>>>> 688 # will puke if not equal number of features
>>>>>>>>>>>>>> --> 689 stacked_samp = np.concatenate([ds.samples for ds
>>>>>>>>>>>>>> in datasets], axis=0)
>>>>>>>>>>>>>> 690
>>>>>>>>>>>>>> 691 stacked_sa = {}
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> ValueError: all the input array dimensions except for the
>>>>>>>>>>>>>> concatenation axis must match exactly
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>> Pkg-ExpPsy-PyMVPA mailing list
>>>>>>>>>>>>>> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
>>>>>>>>>>>>>> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-
>>>>>>>>>>>>>> exppsy-pymvpa
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
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>>>>>>>>>>>>
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