9.13.5. sklearn.cross_validation.LeaveOneLabelOut

class sklearn.cross_validation.LeaveOneLabelOut(labels, indices=False)

Leave-One-Label_Out cross-validation iterator

Provides train/test indices to split data according to a third-party provided label. This label information can be used to encode arbitrary domain specific stratifications of the samples as integers.

For instance the labels could be the year of collection of the samples and thus allow for cross-validation against time-based splits.

Parameters :

labels : array-like of int with shape (n_samples,)

Arbitrary domain-specific stratification of the data to be used to draw the splits.

indices: boolean, optional (default False) :

Return train/test split with integer indices or boolean mask. Integer indices are useful when dealing with sparse matrices that cannot be indexed by boolean masks.

Examples

>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 1, 2])
>>> labels = np.array([1, 1, 2, 2])
>>> lol = cross_validation.LeaveOneLabelOut(labels)
>>> len(lol)
2
>>> print lol
sklearn.cross_validation.LeaveOneLabelOut(labels=[1 1 2 2])
>>> for train_index, test_index in lol:
...    print "TRAIN:", train_index, "TEST:", test_index
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
...    print X_train, X_test, y_train, y_test
TRAIN: [False False  True  True] TEST: [ True  True False False]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [1 2] [1 2]
TRAIN: [ True  True False False] TEST: [False False  True  True]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [1 2]

__init__(labels, indices=False)