Dear PyMVPA experts, I'm trying to use Hyperalignment() procedure to align different subjects' brain.
I am mainly referring to the example described in this webpage - http://dev.pymvpa.org/examples/hyperalignment.html However, when I try to compute the common space on the training set (10 participant, single - participant matrix: 301 time-point x 1000 voxels ) I get the following message: LinAlgError, 'SVD did not converge' To the best of my knowledge, singular value decomposition could fail for several reasons, here is what I checked: - Matrices have no Nan's. - Standard deviation for each voxel over time is > 0, meaning that there are no zero or constant voxels. - By aligning each pair of participants independently, I couldn't find any specifically 'compromised' participant, as far as no clear pattern emerged (depending on the pairs the SVD could-couldn't converge). Further informations: - Data come from Human Connectome Project (HCP)- Working memory task - We tried also with another task (motor) that comes from HCP using the same subjects but we get a same error - The ds_all container with participants' information (samples and features): http://nilab.cimec.unitn.it/people/vittorioiacovella/ds_all.pickle - Please, find below the code I am using for this task. import numpy as np from mvpa2.suite import * import pickle as pk import sys #Insert all the part of loading ds_all path=sys.argv[1] ds_all = pk.load(open(path)) ### DEFINE THE CLASSIFIER ## use same linear support vector machine clf = LinearCSVMC() # STARTING THE HYPERALIGNMENT verbose(2, "between-subject (hyperaligned)...", cr=False, lf=False) hyper_start_time = time.time() bsc_hyper_results = [] #cross-validation over subjects cv = CrossValidation(clf, NFoldPartitioner(attr='subject'), errorfx=mean_match_accuracy) # HYPERALIGNMENT # - Leave-one-run-out for hyperalignment training nruns = 1,2 for test_run in nruns: #Split in training and testing set: # For leave one run out, use this: 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] #Defining hyper-function and computing hyperalignment parameters. hyper = Hyperalignment() hypmaps = hyper(ds_train) #Applying hyperalignment parameters on the test set (the run left out) ds_hyper = [ hypmaps[i].forward(sd) for i, sd in enumerate(ds_test)] # zscore each subject individually after transformation for optimal performance zscore(ds_hyper, chunks_attr='subject') #Encoding by simple avaraging each block after all subjects data are in common space. averager = mean_group_sample(['targets', 'block', 'chunks']) ds_hyper_encoded = [sd.get_mapped(averager) for sd in ds_hyper] ds_hyper = vstack(ds_hyper_encoded) #Store results of classification for different partitions. res_cv = cv(ds_hyper) #Append results on a list (each element comes from one run) bsc_hyper_results.append(res_cv) #Final results bsc_hyper_results = hstack(bsc_hyper_results) #Showing final results. verbose(2, "done in %.1f seconds" , (time.time() - hyper_start_time,)) verbose(2, "between-subject (hyperaligned): %.2f +/-%.3f" \ , (np.mean(bsc_hyper_results), np.std(np.mean(bsc_hyper_results, axis=1)) / np.sqrt(nsubjs - 1))) Thank you. Best,
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