model_selection.PredefinedSplit()

Predefined split cross-validator

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.

Examples

>>> from sklearn.model_selection import PredefinedSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> test_fold = [0, 1, -1, 1]
>>> ps = PredefinedSplit(test_fold)
>>> ps.get_n_splits()
2
>>> print(ps)       
PredefinedSplit(test_fold=array([ 0,  1, -1,  1]))
>>> for train_index, test_index in ps.split():
...    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]

Methods

__init__(test_fold) [source]

get_n_splits(X=None, y=None, groups=None) [source]

Returns the number of splitting iterations in the cross-validator

Parameters:	X : object Always ignored, exists for compatibility. y : object Always ignored, exists for compatibility. groups : object Always ignored, exists for compatibility.
Returns:	n_splits : int Returns the number of splitting iterations in the cross-validator.

split(X=None, y=None, groups=None) [source]

Generate indices to split data into training and test set.

Parameters:	X : object Always ignored, exists for compatibility. y : object Always ignored, exists for compatibility. groups : object Always ignored, exists for compatibility.
Returns:	train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split.