sklearn.model_selection.PredefinedSplit — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.model_selection. PredefinedSplit(test_fold)[source]¶
Predefined split cross-validator
Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the test_fold parameter.
Read more in the User Guide.
| Parameters: | test_fold : array-like, shape (n_samples,) The entry test_fold[i] represents the index of the test set that sample i belongs to. It is possible to exclude sample i from any test set (i.e. include sample i in every training set) by setting test_fold[i] equal to -1. |
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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
| get_n_splits([X, y, groups]) | Returns the number of splitting iterations in the cross-validator |
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| split([X, y, groups]) | Generate indices to split data into training and test set. |
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. |
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| 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. |
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| Yields: | train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split. |