sklearn.cross_validation.PredefinedSplit

class sklearn.cross_validation.PredefinedSplit(test_fold) [source]

Predefined split cross validation iterator

Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.PredefinedSplit instead.

Splits the data into training/test set folds according to a predefined scheme. Each sample can be assigned to at most one test set fold, as specified by the user through the test_fold parameter.

Read more in the User Guide.

Parameters:	test_fold : “array-like, shape (n_samples,) test_fold[i] gives the test set fold of sample i. A value of -1 indicates that the corresponding sample is not part of any test set folds, but will instead always be put into the training fold.

Examples

>>> from sklearn.cross_validation import PredefinedSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> ps = PredefinedSplit(test_fold=[0, 1, -1, 1])
>>> len(ps)
2
>>> print(ps)       
sklearn.cross_validation.PredefinedSplit(test_fold=[ 0  1 -1  1])
>>> for train_index, test_index in ps:
...    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]
TRAIN: [1 2 3] TEST: [0]
TRAIN: [0 2] TEST: [1 3]
.. automethod:: __init__