_default_config = {
'loader':
DataLoader,
'loader__configuration_file':
conf_file,
'loader__loader':
'bids-meg',
'loader__bids_win':
'700',
'loader__task':
'reftep',
'loader__load_fx':
'reftep-iplv',
'fetch__subject_names': ['sub-1'],
'fetch__prepro': [Transformer()],
'prepro': ['sample_slicer', 'target_transformer'],
'target_transformer__fx':
lambda x: np.log(x),
'balancer__attr':
'all',
'estimator': [ #('fsel', SelectKBest(k=50, score_func=f_regression)),
('clf', SVR(C=1, kernel='linear'))
],
'cv':
ShuffleSplit,
'cv__n_splits':
2,
#'cv__test_size': 0.25,
'analysis__scoring': ['r2', 'explained_variance'],
'analysis':
cross_val_multiscore, LinearModel, get_coef,
Vectorizer, CSP)
from sklearn.linear_model import LogisticRegression
import warnings
warnings.filterwarnings("ignore")
conf_file = "/media/robbis/DATA/meg/c2b/meeting-december-data/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids-meg',
bids_window='300',
bids_ses='01',
task='power')
ds = loader.fetch(subject_names=['sub-109123'], prepro=[Transformer()])
_default_options = {
'loader__bids_ses': ['01', '02'],
'sample_slicer__targets' : [
['LH', 'RH'],
['LF', 'RF'],
#['LH', 'RH', 'LF', 'RF']
],
'estimator__clf': [
LinearModel(LogisticRegression(C=1, solver='liblinear')),
SVC(C=1, kernel='linear', probability=True),
SVC(C=1, gamma=1, kernel='rbf', probability=True),
from joblib import Parallel, delayed
path = "/media/robbis/Seagate_Pt1/data/working_memory/"
conf_file = "%s/data/working_memory.conf" % (path)
task = 'PSI'
task = 'PSICORR'
loader = DataLoader(configuration_file=conf_file,
loader='mat',
task=task,
data_path="%s/data/" % (path),
subjects="%s/data/participants.csv" % (path))
prepro = PreprocessingPipeline(nodes=[
Transformer(),
#SampleZNormalizer()
])
ds = loader.fetch(prepro=prepro)
_default_options = {
'sample_slicer__targets': [['0back', '2back']],
'sample_slicer__band': [[c] for c in np.unique(ds.sa.band)],
'estimator__fsel__k': np.arange(1, 1200, 50),
}
_default_config = {
'prepro': ['sample_slicer'],
#'ds_normalizer__ds_fx': np.std,
'sample_slicer__band': ['gamma'],
import warnings
warnings.filterwarnings("ignore")
from pyitab.utils import make_analysis
path = "/media/robbis/DATA/fmri/"
analysis = 'bunch-of-things'
conf_file = make_analysis(path, analysis)
loader = DataLoader(configuration_file=conf_file,
loader='simulations',
task='simulations')
ds = loader.fetch(prepro=Transformer())
_default_options = {
'sample_slicer__targets': [
['LH', 'RH'],
#['LF', 'RF'],
#['LH', 'RH', 'LF', 'RF']
],
'estimator__clf': [
SVC(C=1, kernel='linear', probability=True),
SVC(C=1, gamma=1, kernel='rbf', probability=True),
LinearDiscriminantAnalysis(),
QuadraticDiscriminantAnalysis(),
GaussianProcessClassifier(1 * RBF(1.))
],
#'estimator__fsel__k':np.arange(50, 100, 5),