collection = rs.ResultsCollection(conf, path, summarizers)
for i, partitioner in enumerate(partitioners):
ds = load_dataset(path, subj, task_, **conf)
ds.sa['memory_evidence'] = np.ones_like(ds.targets, dtype=np.int)
ds.sa.memory_evidence[ds.sa.stim == 'N'] = -1
ds.sa.memory_evidence = ds.sa.memory_evidence * ds.sa.evidence
ds.targets = [str(ii) for ii in ds.sa.memory_evidence]
conf['label_dropped'] = '0'
conf['label_included'] = ','.join([str(n) for n in np.array([-5,-3,-1,1,3,5])])
ds = preprocess_dataset(ds, task_, **conf)
ds.targets = np.float_(ds.targets)
ds.targets = (ds.targets - np.mean(ds.targets))/np.std(ds.targets)
cv = CrossValidation(slsim.RegressionMeasure(),
partitioner,
#NFoldPartitioner(cvtype=1),
errorfx=None
)
kwa = dict(voxel_indices=Sphere(3))
queryengine = IndexQueryEngine(**kwa)
sl = Searchlight(cv, queryengine=queryengine)
map_ = sl(ds)
count_ = 1
else: # decision
field_ = 'decision'
conf['label_dropped'] = 'FIX0'
conf['label_included'] = 'NEW'+ev+','+'OLD'+ev
count_ = 5
ds.targets = np.core.defchararray.add(np.array(ds.sa[field_].value, dtype=np.str),
np.array(ds.sa.evidence,dtype= np.str))
'''
ds.targets = ds.sa.memory_status
conf['label_dropped'] = 'None'
conf['label_included'] = 'all'
ds = preprocess_dataset(ds, data_type, **conf)
count_ = 1
field_ = 'memory'
balanc = Balancer(count=count_, apply_selection=True, limit=None)
gen = balanc.generate(ds)
cv_storage = StoreResults()
clf = LinearCSVMC(C=1)
# This is used for the sklearn crossvalidation
y = np.zeros_like(ds.targets, dtype=np.int_)
y[ds.targets == ds.uniquetargets[0]] = 1
# We needs to modify the chunks in order to use sklearn
ds.chunks = np.arange(len(ds.chunks))