cross_validation.LabelKFold()

K-fold iterator variant with non-overlapping labels.

The same label will not appear in two different folds (the number of distinct labels has to be at least equal to the number of folds).

The folds are approximately balanced in the sense that the number of distinct labels is approximately the same in each fold.

Examples

>>> from sklearn.cross_validation import LabelKFold
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> labels = np.array([0, 0, 2, 2])
>>> label_kfold = LabelKFold(labels, n_folds=2)
>>> len(label_kfold)
2
>>> print(label_kfold)
sklearn.cross_validation.LabelKFold(n_labels=4, n_folds=2)
>>> for train_index, test_index in label_kfold:
...     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: [0 1] TEST: [2 3]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [3 4]
TRAIN: [2 3] TEST: [0 1]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [3 4] [1 2]
.. automethod:: __init__