def crossval():
"""
perform a crossvalidation on the data (beh + precomputed brain) of all subjects
"""
predscores = []
actualscores = []
clust_disj = np.zeros(imgshape)
for trainidx, testidx in cv.LeaveOneOut(subject_num):
# n-p training files
trainconfiles = [cf for i, cf in enumerate(confiles) if trainidx[i]]
# left out subjects to test with
testconfiles = [cf for i, cf in enumerate(confiles) if testidx[i]]
### get all the files from a leave2out crossval and get clusters
_, name = os.path.split(testconfiles[0])
sid = name.split('con')[0][:-1]
# sidx is the row# of the sid in our pdata variable
sidx = np.nonzero(pdata.subject == sid)[0][0]
analysisdirs = []
for idx in range(subject_num):
if not idx == sidx:
left_out = [sidx, idx]
left_out.sort()
analysisdirs.append(os.path.join(spmdir,'analysis_lo_%02d_%02d'%(left_out[0],left_out[1]),'thresh_h01_f05'))
# get labels and clustermeans
labels, nlabels = get_labels(analysisdirs)
clustermeans_train = get_clustermeans(labels, nlabels, trainconfiles)
clustermeans_test = get_clustermeans(labels, nlabels, testconfiles)
# make desmats
X_train = np.hstack((desmat[trainidx], clustermeans_train))
X_test = np.hstack((desmat[testidx], clustermeans_test))
# fit the model (by determining the best model first)
varsidx, model = determine_model_all(X_train, responsevar[trainidx])
# save location of _selected_ clusters
for clust in range(nlabels):
if varsidx[behvars+clust]:
idx = np.where(labels == clust+1)
clust_disj[idx] += 1
# and save scores
prediction = model.predict(X_test[:,varsidx])
predscores.append(prediction)
actualscores.append(responsevar[testidx][0])
# rearrange vectors for error computation
actualscores = np.array(actualscores)
predscores_beta = []
for y in xrange(len(predscores)):
[predscores_beta.append(x) for x in predscores[y]]
predscores_alpha = np.array(predscores_beta)
# compute errors
prederrors = predscores_alpha - actualscores
meanerr = np.mean(np.abs(prederrors))
rmsqerr = np.sqrt(np.mean(prederrors**2))
# save + plot cluster distribution in brain
brainplot(clust_disj, os.path.join(outdir,"cluster_disj_crossval.png"))
outimg = os.path.join(outdir,'clusterdisj_crossval.nii')
nib.Nifti1Image(clust_disj,imgaff,imghead).to_filename(outimg)
return predscores_alpha, actualscores, meanerr, rmsqerr
def _fit(X, y, behav_data=None):
# run the SPM analysis (external workflow) in a LOO crossval
# and save the directories in which the threshold images are located
print "Fitting"
print X
print y
print "doing spm cv"
analdir = setup_spm(X, y)
# get labels & clustermeans
labels, nlabels = get_labels(analdir)
# delete all the workflow directories again
shutil.rmtree(os.path.realpath(os.path.join(analdir, '..')))
clustermeans = get_clustermeans(X, labels, nlabels)
#print "finding model"
# make new design matrix (first behvars, then clustermeans)
if behav_data is not None:
X_new = np.hstack((behav_data, clustermeans))
varidx, model = determine_model_all(X_new, y, behav_data.shape[1])
else:
X_new = clustermeans
varidx, model = determine_model_all(X_new, y, 0)
return model, varidx, labels, nlabels
