def _test_spatial(path, subjects, conf_file, type_, **kwargs):
warnings.warn("Deprecated use test_spatial.", DeprecationWarning)
conf = read_configuration(path, conf_file, type_)
conf['analysis_type'] = 'spatial'
conf['analysis_task'] = type_
for arg in kwargs:
conf[arg] = kwargs[arg]
total_results = dict()
data_path = conf['data_path']
summarizers = [rs.DecodingSummarizer()]
savers = [rs.DecodingSaver()]
result = rs.ResultsCollection(conf, path, summarizers)
for subj in subjects:
print '------'
try:
ds = load_dataset(data_path, subj, type_, **conf)
except Exception as err:
print err
continue
ds = detrend_dataset(ds, type_, **conf)
balancer = balance_dataset(**conf)
for i, ds_ in enumerate(balancer.generate(ds)):
logger.info("Balanced dataset n. %d" % (i + 1))
subj_ = "%s_%03d" % (subj, i + 1)
ds_ = normalize_dataset(ds_, **conf)
logger.info(ds_.summary())
r = spatial(ds_, **conf)
total_results[subj_] = r
subj_result = rs.SubjectResult(subj_, r, savers)
result.add(subj_result)
#result.save()
result.summarize()
conf['classes'] = np.unique(ds.targets)
#save_results()
#save_results(path, total_results, conf)
return total_results, subj_result
def test_spatial(path, subjects, conf_file, type_, **kwargs):
#conf = read_configuration(path, conf_file, type_)
conf = read_json_configuration(path, conf_file, type_)
for arg in kwargs:
conf[arg] = kwargs[arg]
total_results = dict()
#data_path = conf['data_path']
data_path = conf['path']['data_path']
conf['analysis_type'] = 'spatial'
conf['analysis_task'] = type_
summarizers = [rs.DecodingSummarizer()]
savers = [rs.DecodingSaver()]
result = rs.ResultsCollection(conf, path, summarizers)
for subj in subjects:
print '------'
try:
ds = load_dataset(data_path, subj, type_, **conf)
except Exception:
continue
#ds = preprocess_dataset(ds, type_, **conf)
ds = get_preprocessing(**conf).transform(ds)
r = spatial(ds, **conf)
total_results[subj] = r
subj_result = rs.SubjectResult(subj, r, savers)
result.add(subj_result)
#result.save()
result.summarize()
conf['classes'] = np.unique(ds.targets)
#save_results()
#save_results(path, total_results, conf)
return total_results, subj_result
def test_searchlight(path, subjects, conf_file, type_, **kwargs):
conf = read_configuration(path, conf_file, type_)
for arg in kwargs:
conf[arg] = kwargs[arg]
conf['analysis_type'] = 'searchlight'
conf['analysis_task'] = type_
total_results = dict()
data_path = conf['data_path']
#
summarizers = [rs.SearchlightSummarizer()]
savers = [rs.SearchlightSaver()]
result = rs.ResultsCollection(conf, path, summarizers)
#
for subj in subjects:
ds = load_dataset(data_path, subj, type_, **conf)
ds = detrend_dataset(ds, type_, **conf)
r = searchlight(ds, **kwargs)
subj_result = rs.SubjectResult(subj, r, savers)
total_results[subj] = r
result.add(subj_result)
result.summarize()
conf['classes'] = np.unique(ds.targets)
#save_results()
#save_results(path, total_results, conf)
return result, r, subj_result
ds_roi = ds_[:, ds_.fa[roi] == label]
ds_roi = normalize_dataset(ds_roi, normalization='both', chunk_number=7, **conf)
r = spatial(ds_roi, **conf)
r = searchlight
subj_ = "%s_roi_%s_ev_%s_ds_%s_tf_%s" %(subj,
str(int(label)),
str(ev),
str(i+1),
str(slice_condition))
#print selected_variables
print subj_
subj_result = rs.SubjectResult(subj_, r, savers)
result.add(subj_result)
"""
r = searchlight(ds_)
subj_ = "%s_roi_%s_ev_%s_ds_%s_tf_%s" % (subj, str(
int(0)), str(ev), str(i + 1), str(slice_condition))
subj_result = rs.SubjectResult(subj_, r, savers)
result.add(subj_result)
# Run Analysis
result.summarize()
print '-------- '+str(p_+1)+' of 100 ------------'
y_perm = permutation(range(len(ds.targets)))
ds.targets = ds.targets[y_perm]
sl_map = sl(ds)
sl_map.samples *= -1
sl_map.samples += 1
map_ = map2nifti(sl_map, imghdr=ds.a.imghdr)
ni.save(map_, os.path.join(path, subj+'_permutation_'+str(p_+1)+'.nii.gz'))
permut_.append(map_.get_data())
permut_ = np.array(permut_).mean(4)
permut_ = np.rollaxis(permut_, 0, 4)
perm_map = ni.Nifti1Image(permut_, map_.get_affine())
ni.save(perm_map,
os.path.join(path, subj+'_permutation_'+str(i)+'_'+task_+'_'+ev+'.nii.gz'))
maps.append(permut_)
name = "%s_%s_%s_evidence_%s_balance_ds_%s" %(subj, task_, data_type, str(ev), str(i+1))
result_dict['radius'] = 3
result_dict['map'] = perm_map
subj_result = rs.SubjectResult(name, result_dict, savers)
collection.add(subj_result)
def test_transfer_learning(path, subjects, analysis, conf_file, source='task', \
analysis_type='single', calculateSimilarity='True', **kwargs):
if source == 'task':
target = 'rest'
else:
if source == 'rest':
target = 'task'
if source == 'saccade':
target = 'face'
else:
if source == 'face':
target = 'saccade'
p = kwargs['p']
##############################################
##############################################
## conf_src['label_included'] = 'all' ##
## conf_src['label_dropped'] = 'none' ##
## conf_src['mean_samples'] = 'False' ##
##############################################
##############################################
if analysis_type == 'group':
if path.__class__ == conf_file.__class__ == list:
ds_src, _, conf_src = sources_merged_ds(path, subjects, conf_file,
source, **kwargs)
ds_tar, subjects, conf_tar = sources_merged_ds(
path, subjects, conf_file, target, **kwargs)
conf_src['permutations'] = 0
conf_tar['permutations'] = 0
else:
print 'In group analysis path, subjects and conf_file must be lists: \
Check configuration file and/or parameters!!'
return 0
else:
conf_src = read_configuration(path, conf_file, source)
conf_tar = read_configuration(path, conf_file, target)
for arg in kwargs:
conf_src[arg] = kwargs[arg]
conf_tar[arg] = kwargs[arg]
data_path = conf_src['data_path']
conf_src['analysis_type'] = 'transfer_learning'
conf_src['analysis_task'] = source
conf_src['analysis_func'] = analysis.func_name
for arg in conf_src:
if arg == 'map_list':
map_list = conf_src[arg].split(',')
if arg == 'p_dist':
p = float(conf_src[arg])
print p
total_results = dict()
summarizers = [
rs.CrossDecodingSummarizer(),
rs.SimilaritySummarizer(),
rs.DecodingSummarizer(),
rs.SignalDetectionSummarizer(),
]
savers = [
rs.CrossDecodingSaver(),
rs.SimilaritySaver(),
rs.DecodingSaver(),
rs.SignalDetectionSaver(),
]
collection = rs.ResultsCollection(conf_src, path, summarizers)
for subj in subjects:
print '-------------------'
if (len(subjects) > 1) or (subj != 'group'):
try:
ds_src = load_dataset(data_path, subj, source, **conf_src)
ds_tar = load_dataset(data_path, subj, target, **conf_tar)
except Exception, err:
print err
continue
# Evaluate if is correct to do further normalization after merging two ds.
ds_src = detrend_dataset(ds_src, source, **conf_src)
ds_tar = detrend_dataset(ds_tar, target, **conf_tar)
if conf_src['label_included'] == 'all' and \
conf_src['label_dropped'] != 'fixation':
print 'Balancing dataset...'
ds_src = balance_dataset_timewise(ds_src, 'fixation')
# Make cross-decoding
r = transfer_learning(ds_src, ds_tar, analysis, **conf_src)
# Now we have cross-decoding results we could process it
pred = np.array(r['classifier'].ca.predictions)
targets = r['targets']
c_m = ConfusionMatrix(predictions=pred, targets=targets)
c_m.compute()
r['confusion_target'] = c_m
c_new = cross_decoding_confusion(pred, targets, map_list)
r['confusion_total'] = c_new
print c_new
# Similarity Analysis
if calculateSimilarity == 'True':
if 'p' not in locals():
print 'Ciao!'
mahala_data = similarity_measure(r['ds_tar'],
r['ds_src'],
r,
p_value=p,
method='mahalanobis')
#r['mahalanobis_similarity'] = mahala_data
for k_, v_ in mahala_data.items():
r[k_] = v_
r['confusion_mahala'] = mahala_data['confusion_mahalanobis']
else:
#r['mahalanobis_similarity'] = []
r['confusion_mahala'] = 'Null'
# Signal Detection Theory Analysis
sdt_res = signal_detection_measures(c_new)
for k_, v_ in sdt_res.items():
r[k_] = v_
'''
Same code of:
r['d_prime'] = d_prime
r['beta'] = beta
r['c'] = c
'''
total_results[subj] = r
subj_result = rs.SubjectResult(subj, r, savers=savers)
collection.add(subj_result)