def test_spatiotemporal(path, subjects, conf_file, type_, **kwargs):
conf = read_configuration(path, conf_file, type_)
for arg in kwargs:
conf[arg] = kwargs[arg]
if arg == 'balance':
balance = kwargs[arg]
total_results = dict()
data_path = conf['data_path']
conf['analysis_type'] = 'spatiotemporal'
conf['analysis_task'] = type_
for subj in subjects:
try:
ds = load_dataset(data_path, subj, type_, **conf)
except Exception, err:
print err
continue
ds = detrend_dataset(ds, type_, **conf)
if 'balance' in locals() and balance == True:
if conf['label_included'] == 'all' and \
conf['label_dropped'] == 'none':
ds = balance_dataset_timewise(ds, 'fixation')
r = spatiotemporal(ds, **conf)
total_results[subj] = r
def test_spatiotemporal(path, subjects, conf_file, type_, **kwargs):
conf = read_configuration(path, conf_file, type_)
for arg in kwargs:
conf[arg] = kwargs[arg]
if arg == 'balance':
balance = kwargs[arg]
total_results = dict()
data_path = conf['data_path']
conf['analysis_type'] = 'spatiotemporal'
conf['analysis_task'] = type_
for subj in subjects:
try:
ds = load_dataset(data_path, subj, type_, **conf)
except Exception, err:
print err
continue
ds = detrend_dataset(ds, type_, **conf)
if 'balance' in locals() and balance == True:
if conf['label_included'] == 'all' and \
conf['label_dropped'] == 'none':
ds = balance_dataset_timewise(ds, 'fixation')
r = spatiotemporal(ds, **conf)
total_results[subj] = r
def pre_operations(self, **options):
# dictionary of conditions used
self.conditions = {k: options[k] for k in self._condition_names}
logger.debug(self._default_conf.keys())
# On-fly change default options
# A little bit dangerous!!
for k, v in options.iteritems():
logger.debug(k)
if k in self._default_conf.keys():
setattr(self, "_"+k, v)
else:
setattr(self, k, v)
ds = self.ds_orig.copy()
ds = change_target(ds, options['task'])
ds = detrend_dataset(ds,
self._data_type,
**self._conf)
ds = slice_dataset(ds, options['condition'])
ds = normalize_dataset(ds, **self._conf)
return ds
def test_clustering(path,
subjects,
analysis,
conf_file,
source='task',
**kwargs):
if source == 'task':
target = 'rest'
else:
target = 'task'
conf_src = read_configuration(path, conf_file, source)
conf_tar = read_configuration(path, conf_file, target)
##############################################
conf_src['label_included'] = 'all'
conf_src['label_dropped'] = 'none'
conf_src['mean_samples'] = 'True'
##############################################
for arg in kwargs:
conf_src[arg] = kwargs[arg]
conf_tar[arg] = kwargs[arg]
total_results = dict()
data_path = conf_src['data_path']
for subj in subjects:
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
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':
ds_src = balance_dataset_timewise(ds_src, 'fixation')
r = clustering_analysis(ds_src, ds_tar, analysis, **kwargs)
total_results[subj] = r
def test_clustering(path, subjects, analysis, conf_file, source='task', **kwargs):
if source == 'task':
target = 'rest'
else:
target = 'task'
conf_src = read_configuration(path, conf_file, source)
conf_tar = read_configuration(path, conf_file, target)
##############################################
conf_src['label_included'] = 'all'
conf_src['label_dropped'] = 'none'
conf_src['mean_samples'] = 'True'
##############################################
for arg in kwargs:
conf_src[arg] = kwargs[arg]
conf_tar[arg] = kwargs[arg]
total_results = dict()
data_path = conf_src['data_path']
for subj in subjects:
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
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':
ds_src = balance_dataset_timewise(ds_src, 'fixation')
r = clustering_analysis(ds_src, ds_tar, analysis, **kwargs)
total_results[subj] = r
def load_subjectwise_ds(path,
subjects,
conf_file,
task,
extra_sa=None,
**kwargs):
"""
extra_sa: dict or None, sample attributes added to the final dataset, they should be
the same length as the subjects.
subject: either a list of subjects or a csv file
"""
conf = read_configuration(os.path.join(path, conf_file), task)
conf.update(kwargs)
logger.debug(conf)
data_path = conf['data_path']
if isinstance(subjects, str):
subjects, extra_sa = load_subject_file(subjects)
logger.info('Merging subjects from '+data_path)
for i, subj in enumerate(subjects):
ds = load_dataset(data_path, subj, task, **conf)
ds = detrend_dataset(ds, task, **conf)
ds = normalize_dataset(ds, **conf)
# add extra samples
if extra_sa != None:
for k, v in extra_sa.iteritems():
if len(v) == len(subjects):
ds.sa[k] = [v[i] for _ in range(ds.samples.shape[0])]
# First subject
if i == 0:
ds_merged = ds.copy()
else:
ds_merged = vstack((ds_merged, ds))
ds_merged.a.update(ds.a)
del ds
return ds_merged, ['group'], conf
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):
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, 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)
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
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
def _group_transfer_learning(path, subjects, analysis, conf_file, source='task', analysis_type='single', **kwargs):
if source == 'task':
target = 'rest'
else:
if source == 'rest':
target = 'task'
if source == 'saccade':
target = 'face'
else:
if source == 'face':
target = 'saccade'
##############################################
##############################################
## 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, s, conf_src = sources_merged_ds(path, subjects, conf_file, source, **kwargs)
conf_src['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)
for arg in conf_src:
if arg == 'map_list':
map_list = conf_src[arg].split(',')
r_group = spatial(ds_src, **conf_src)
total_results = dict()
total_results['group'] = r_group
clf = r_group['classifier']
for subj_, conf_, path_ in zip(subjects, conf_file, path):
for subj in subj_:
print '-----------'
r = dict()
if len(subj_) > 1:
conf_tar = read_configuration(path_, conf_, target)
for arg in kwargs:
conf_tar[arg] = kwargs[arg]
data_path = conf_tar['data_path']
try:
ds_tar = load_dataset(data_path, subj, target, **conf_tar)
except Exception, err:
print err
continue
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')
predictions = clf.predict(ds_tar)
pred = np.array(predictions)
targets = ds_tar.targets
for arg in r_group.keys():
r[arg] = copy.copy(r_group[arg])
r['targets'] = targets
r['predictions'] = predictions
r['fclf'] = clf
c_m = ConfusionMatrix(predictions=pred, targets=targets)
c_m.compute()
r['confusion_target'] = c_m
print c_m
tr_pred = similarity_measure_mahalanobis(ds_tar, ds_src, r)
r['mahalanobis_similarity'] = tr_pred
#print tr_pred
c_mat_mahala = ConfusionMatrix(predictions=tr_pred.T[1], targets=tr_pred.T[0])
c_mat_mahala.compute()
r['confusion_mahala'] = c_mat_mahala
d_prime, beta, c, c_new = signal_detection_measures(pred, targets, map_list)
r['d_prime'] = d_prime
print d_prime
r['beta'] = beta
r['c'] = c
r['confusion_total'] = c_new
'''
d_prime_maha, c_new_maha = d_prime_statistics(tr_pred.T[1], tr_pred.T[0], map_list)
r['d_prime_maha'] = d_prime_maha
r['confusion_tot_maha'] = c_new_maha
'''
total_results[subj] = r
def get_merged_ds(path, subjects, conf_file, source='task', dim=3, **kwargs):
#Mettere source e target nel conf!
if source == 'task':
target = 'rest'
else:
if source == 'rest':
target = 'task'
if source == 'saccade':
target = 'face'
else:
if source == 'face':
target = 'saccade'
ds_merged_list = []
conf_src = read_configuration(path, conf_file, source)
conf_tar = read_configuration(path, conf_file, target)
##############################################
##############################################
## conf_src['label_included'] = 'all' ##
## conf_src['label_dropped'] = 'none' ##
## conf_src['mean_samples'] = 'False' ##
##############################################
##############################################
for arg in kwargs:
conf_src[arg] = kwargs[arg]
conf_tar[arg] = kwargs[arg]
data_path = conf_src['data_path']
for subj in subjects:
print '--------'
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
ds_src = detrend_dataset(ds_src, source, **conf_src)
ds_tar = detrend_dataset(ds_tar, target, **conf_tar)
if dim == 4:
duration = np.min([e['duration'] for e in ds_src.a.events])
ds_tar = build_events_ds(ds_tar, duration, overlap=duration-1)
ds_src = load_spatiotemporal_dataset(ds_src, duration=duration)
print ds_src.samples.shape
print ds_tar.samples.shape
ds_src.sa['task'] = [source for s in range(ds_src.samples.shape[0])]
ds_tar.sa['task'] = [target for s in range(ds_tar.samples.shape[0])]
ds_merged = vstack((ds_src, ds_tar))
ds_merged.a.update(ds_src.a)
print ds_merged.sa.task
ds_merged_list.append(ds_merged)
'''
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)
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)
for subj in subjects[:1]:
conf = read_configuration(path, conf_file, task)
# Load dataset
ds_orig = load_dataset(path,
subj,
task,
roi_labels={'conjunction': mask},
**conf)
# Change the target
ds_orig = change_target(ds_orig, target)
# Process dataset
ds_orig = detrend_dataset(ds_orig, task, **conf)
# Balance dataset
balancer = balance_dataset(balancer__count=5, **conf)
for ev in evidences:
for slice_condition in frames:
selected_variables.update({
'frame': [1, 2, 3, 4, 5],
'evidence': [ev]
})
print selected_variables
ds = slice_dataset(ds_orig, selected_variables)
def get_merged_ds(path, subjects, conf_file, source='task', dim=3, **kwargs):
#Mettere source e target nel conf!
if source == 'task':
target = 'rest'
else:
if source == 'rest':
target = 'task'
if source == 'saccade':
target = 'face'
else:
if source == 'face':
target = 'saccade'
ds_merged_list = []
conf_src = read_configuration(path, conf_file, source)
conf_tar = read_configuration(path, conf_file, target)
##############################################
##############################################
## conf_src['label_included'] = 'all' ##
## conf_src['label_dropped'] = 'none' ##
## conf_src['mean_samples'] = 'False' ##
##############################################
##############################################
for arg in kwargs:
conf_src[arg] = kwargs[arg]
conf_tar[arg] = kwargs[arg]
data_path = conf_src['data_path']
for subj in subjects:
print '--------'
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
ds_src = detrend_dataset(ds_src, source, **conf_src)
ds_tar = detrend_dataset(ds_tar, target, **conf_tar)
if dim == 4:
duration = np.min([e['duration'] for e in ds_src.a.events])
ds_tar = build_events_ds(ds_tar, duration, overlap=duration - 1)
ds_src = load_spatiotemporal_dataset(ds_src, duration=duration)
print ds_src.samples.shape
print ds_tar.samples.shape
ds_src.sa['task'] = [source for s in range(ds_src.samples.shape[0])]
ds_tar.sa['task'] = [target for s in range(ds_tar.samples.shape[0])]
ds_merged = vstack((ds_src, ds_tar))
ds_merged.a.update(ds_src.a)
print ds_merged.sa.task
ds_merged_list.append(ds_merged)
'''
def _group_transfer_learning(path,
subjects,
analysis,
conf_file,
source='task',
analysis_type='single',
**kwargs):
if source == 'task':
target = 'rest'
else:
if source == 'rest':
target = 'task'
if source == 'saccade':
target = 'face'
else:
if source == 'face':
target = 'saccade'
##############################################
##############################################
## 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, s, conf_src = sources_merged_ds(path, subjects, conf_file,
source, **kwargs)
conf_src['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)
for arg in conf_src:
if arg == 'map_list':
map_list = conf_src[arg].split(',')
r_group = spatial(ds_src, **conf_src)
total_results = dict()
total_results['group'] = r_group
clf = r_group['classifier']
for subj_, conf_, path_ in zip(subjects, conf_file, path):
for subj in subj_:
print '-----------'
r = dict()
if len(subj_) > 1:
conf_tar = read_configuration(path_, conf_, target)
for arg in kwargs:
conf_tar[arg] = kwargs[arg]
data_path = conf_tar['data_path']
try:
ds_tar = load_dataset(data_path, subj, target, **conf_tar)
except Exception, err:
print err
continue
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')
predictions = clf.predict(ds_tar)
pred = np.array(predictions)
targets = ds_tar.targets
for arg in r_group.keys():
r[arg] = copy.copy(r_group[arg])
r['targets'] = targets
r['predictions'] = predictions
r['fclf'] = clf
c_m = ConfusionMatrix(predictions=pred, targets=targets)
c_m.compute()
r['confusion_target'] = c_m
print c_m
tr_pred = similarity_measure_mahalanobis(ds_tar, ds_src, r)
r['mahalanobis_similarity'] = tr_pred
#print tr_pred
c_mat_mahala = ConfusionMatrix(predictions=tr_pred.T[1],
targets=tr_pred.T[0])
c_mat_mahala.compute()
r['confusion_mahala'] = c_mat_mahala
d_prime, beta, c, c_new = signal_detection_measures(
pred, targets, map_list)
r['d_prime'] = d_prime
print d_prime
r['beta'] = beta
r['c'] = c
r['confusion_total'] = c_new
'''
d_prime_maha, c_new_maha = d_prime_statistics(tr_pred.T[1], tr_pred.T[0], map_list)
r['d_prime_maha'] = d_prime_maha
r['confusion_tot_maha'] = c_new_maha
'''
total_results[subj] = r