def __init__(self, labels):
"""
Leave Half a Label Out cross-validation iterator:
Provides train/test indexes to split data in train test sets
Parameters
----------
labels : list
List of labels
Examples
----------
>>> from parietal.learn.covariance.cov_estimator_l1 import LeaveHalfALabelOut
>>> N_SUBJECTS = 2
>>> N_SAMPLES = 2
>>> labels = np.repeat(np.arange(N_SUBJECTS), N_SAMPLES)
>>> labels
array([0, 0, 1, 1])
>>> cv = cross_val.LeaveHalfALabelOut(labels)
>>> len(cv)
4
>>> for train_mask, test_mask in cv:
... print "TRAIN:", train_mask, "TEST:", test_mask
TRAIN: [ True False True True] TEST: [False True False False]
TRAIN: [False True True True] TEST: [ True False False False]
TRAIN: [ True True True False] TEST: [False False False True]
TRAIN: [ True True False True] TEST: [False False True False]
"""
LeaveOneLabelOut.__init__(self, labels)
def __iter__(self):
for train_mask, test_mask in LeaveOneLabelOut.__iter__(self):
n_test = test_mask.sum()
sub_mask = np.ones(n_test, dtype=np.bool)
sub_mask[: n_test / 2] = False
new_test_mask = test_mask.copy()
new_test_mask[test_mask] = sub_mask
train_mask[test_mask] = np.logical_not(sub_mask)
yield train_mask, new_test_mask
new_test_mask[test_mask] = np.logical_not(sub_mask)
train_mask[test_mask] = sub_mask
yield train_mask, new_test_mask
