6.10.2.2.3. statsmodels.sandbox.tools.cross_val.LeaveOneLabelOut¶

class statsmodels.sandbox.tools.cross_val.LeaveOneLabelOut(labels)[source]¶

Leave-One-Label_Out cross-validation iterator: Provides train/test indexes to split data in train test sets

Leave-One-Label_Out cross validation: Provides train/test indexes to split data in train test sets

Parameters:	labels : list List of labels

Examples

>>> from scikits.learn import cross_val
>>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]
>>> y = [1, 2, 1, 2]
>>> labels = [1, 1, 2, 2]
>>> lol = cross_val.LeaveOneLabelOut(labels)
>>> for train_index, test_index in lol:
...    print "TRAIN:", train_index, "TEST:", test_index
...    X_train, X_test, y_train, y_test = cross_val.split(train_index,             test_index, X, y)
...    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)[source]¶

Leave-One-Label_Out cross validation: Provides train/test indexes to split data in train test sets

Parameters:	labels : list List of labels

Examples

>>> from scikits.learn import cross_val
>>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]
>>> y = [1, 2, 1, 2]
>>> labels = [1, 1, 2, 2]
>>> lol = cross_val.LeaveOneLabelOut(labels)
>>> for train_index, test_index in lol:
...    print "TRAIN:", train_index, "TEST:", test_index
...    X_train, X_test, y_train, y_test = cross_val.split(train_index,             test_index, X, y)
...    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]